diff --git a/.eslintrc.json b/.eslintrc.json index bccb07d..c430a49 100644 --- a/.eslintrc.json +++ b/.eslintrc.json @@ -17,7 +17,6 @@ "plugin:import/warnings", "plugin:node/recommended", "plugin:promise/recommended", - "plugin:json/recommended-with-comments", "plugin:@typescript-eslint/eslint-recommended", "plugin:@typescript-eslint/recommended", "airbnb-base" @@ -29,6 +28,7 @@ "@typescript-eslint/ban-ts-comment": "off", "@typescript-eslint/explicit-module-boundary-types": "off", "@typescript-eslint/no-var-requires": "off", + "@typescript-eslint/no-empty-object-type": "off", "camelcase": "off", "class-methods-use-this": "off", "default-param-last": "off", diff --git a/.npmrc b/.npmrc index 29879f1..a395252 100644 --- a/.npmrc +++ b/.npmrc @@ -2,3 +2,4 @@ force=true production=true legacy-peer-deps=true strict-peer-dependencies=false +node-options='--no-deprecation' diff --git a/CHANGELOG.md b/CHANGELOG.md index dbad7aa..3adaf26 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,6 +1,6 @@ # @vladmandic/face-api - Version: **1.7.13** + Version: **1.7.14** Description: **FaceAPI: AI-powered Face Detection & Rotation Tracking, Face Description & Recognition, Age & Gender & Emotion Prediction for Browser and NodeJS using TensorFlow/JS** Author: **Vladimir Mandic ** @@ -9,8 +9,12 @@ ## Changelog -### **HEAD -> master** 2024/01/20 mandic00@live.com +### **1.7.14** 2024/09/10 mandic00@live.com + +### **origin/master** 2024/01/20 mandic00@live.com + +- merge pull request #188 from rebser/master - fixing leaking eventhandlers when using htmlcanvaselement - rebuild types - rebuild diff --git a/dist/face-api.esm-nobundle.js b/dist/face-api.esm-nobundle.js index 5b56bc4..2141f73 100644 --- a/dist/face-api.esm-nobundle.js +++ b/dist/face-api.esm-nobundle.js @@ -4,4 +4,4 @@ author: ' */ -var ur=Object.defineProperty;var ho=Object.getOwnPropertyDescriptor;var bo=Object.getOwnPropertyNames;var go=Object.prototype.hasOwnProperty;var xo=(o=>typeof require!="undefined"?require:typeof 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i(m,p,u){let f=e(m,`${u}/depthwise_conv`),l=a(m,p,1,`${u}/pointwise_conv`);return{depthwise_conv:f,pointwise_conv:l}}function s(){let m=a(3,32,3,"mobilenetv1/conv_0"),p=i(32,64,"mobilenetv1/conv_1"),u=i(64,128,"mobilenetv1/conv_2"),f=i(128,128,"mobilenetv1/conv_3"),l=i(128,256,"mobilenetv1/conv_4"),d=i(256,256,"mobilenetv1/conv_5"),b=i(256,512,"mobilenetv1/conv_6"),y=i(512,512,"mobilenetv1/conv_7"),h=i(512,512,"mobilenetv1/conv_8"),g=i(512,512,"mobilenetv1/conv_9"),T=i(512,512,"mobilenetv1/conv_10"),x=i(512,512,"mobilenetv1/conv_11"),E=i(512,1024,"mobilenetv1/conv_12"),B=i(1024,1024,"mobilenetv1/conv_13");return{conv_0:m,conv_1:p,conv_2:u,conv_3:f,conv_4:l,conv_5:d,conv_6:b,conv_7:y,conv_8:h,conv_9:g,conv_10:T,conv_11:x,conv_12:E,conv_13:B}}function c(){let 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t=[],{extractMobilenetV1Params:e,extractPredictionLayerParams:r}=zo(o,t),a=o["Output/extra_dim"];if(t.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!K(a))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${a}`);let i={mobilenetv1:e(),prediction_layer:r(),output_layer:{extra_dim:a}};return N(o,t),{params:i,paramMappings:t}}function H(o,t,e){return n.tidy(()=>{let r=n.conv2d(o,t.filters,e,"same");return r=n.add(r,t.batch_norm_offset),n.clipByValue(r,0,6)})}var Yo=.0010000000474974513;function Vo(o,t,e){return n.tidy(()=>{let r=n.depthwiseConv2d(o,t.filters,e,"same");return r=n.batchNorm(r,t.batch_norm_mean,t.batch_norm_variance,t.batch_norm_offset,t.batch_norm_scale,Yo),n.clipByValue(r,0,6)})}function Go(o){return[2,4,6,12].some(t=>t===o)?[2,2]:[1,1]}function Jr(o,t){return n.tidy(()=>{let 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z=class{constructor({minConfidence:t,maxResults:e}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=t||.5,this._maxResults=e||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var Tt=class extends I{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("SsdMobilenetv1 - load model before inference");return n.tidy(()=>{let r=n.cast(t.toBatchTensor(512,!1),"float32"),a=n.sub(n.div(r,127.5),1),i=Jr(a,e.mobilenetv1),{boxPredictions:s,classPredictions:c}=Kr(i.out,i.conv11,e.prediction_layer);return Zr(s,c,e.output_layer)})}async forward(t){return this.forwardInput(await F(t))}async 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m=e(`${c}/sub`,1),p=e(`${c}/truediv`,1);return{sub:m,truediv:p}}function a(c){let m=e(`${c}/filters`,4),p=e(`${c}/bias`,1);return{filters:m,bias:p}}function i(c){let m=a(`${c}/conv`),p=r(`${c}/bn`);return{conv:m,bn:p}}let s=Ot(e);return{extractConvParams:a,extractConvWithBatchNormParams:i,extractSeparableConvParams:s}}function co(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:a,extractSeparableConvParams:i}=Zo(o,e),s;if(t.withSeparableConvs){let c=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):i("conv0"),conv1:i("conv1"),conv2:i("conv2"),conv3:i("conv3"),conv4:i("conv4"),conv5:i("conv5"),conv6:c>7?i("conv6"):void 0,conv7:c>8?i("conv7"):void 0,conv8:r("conv8")}}else s={conv0:a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:a("conv6"),conv7:a("conv7"),conv8:r("conv8")};return N(o,e),{params:s,paramMappings:e}}var q=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var Se=class Se extends I{constructor(t){super("TinyYolov2"),so(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,e){let r=et(t,e.conv0);return r=n.maxPool(r,[2,2],[2,2],"same"),r=et(r,e.conv1),r=n.maxPool(r,[2,2],[2,2],"same"),r=et(r,e.conv2),r=n.maxPool(r,[2,2],[2,2],"same"),r=et(r,e.conv3),r=n.maxPool(r,[2,2],[2,2],"same"),r=et(r,e.conv4),r=n.maxPool(r,[2,2],[2,2],"same"),r=et(r,e.conv5),r=n.maxPool(r,[2,2],[1,1],"same"),r=et(r,e.conv6),r=et(r,e.conv7),vt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?Ut(vt(t,e.conv0,"valid",!1)):rt(t,e.conv0);return r=n.maxPool(r,[2,2],[2,2],"same"),r=rt(r,e.conv1),r=n.maxPool(r,[2,2],[2,2],"same"),r=rt(r,e.conv2),r=n.maxPool(r,[2,2],[2,2],"same"),r=rt(r,e.conv3),r=n.maxPool(r,[2,2],[2,2],"same"),r=rt(r,e.conv4),r=n.maxPool(r,[2,2],[2,2],"same"),r=rt(r,e.conv5),r=n.maxPool(r,[2,2],[1,1],"same"),r=e.conv6?rt(r,e.conv6):r,r=e.conv7?rt(r,e.conv7):r,vt(r,e.conv8,"valid",!1)}forwardInput(t,e){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return n.tidy(()=>{let a=n.cast(t.toBatchTensor(e,!1),"float32");return a=this.config.meanRgb?J(a,this.config.meanRgb):a,a=a.div(255),this.config.withSeparableConvs?this.runMobilenet(a,r):this.runTinyYolov2(a,r)})}async forward(t,e){return this.forwardInput(await F(t),e)}async detect(t,e={}){let{inputSize:r,scoreThreshold:a}=new q(e),i=await F(t),s=await this.forwardInput(i,r),c=n.tidy(()=>n.unstack(s)[0].expandDims()),m={width:i.getInputWidth(0),height:i.getInputHeight(0)},p=await this.extractBoxes(c,i.getReshapedInputDimensions(0),a);s.dispose(),c.dispose();let u=p.map(h=>h.box),f=p.map(h=>h.score),l=p.map(h=>h.classScore),d=p.map(h=>this.config.classes[h.label]);return br(u.map(h=>h.rescale(r)),f,this.config.iouThreshold,!0).map(h=>new It(f[h],l[h],d[h],u[h],m))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return co(t,this.config)}extractParams(t){let e=this.config.filterSizes||Se.DEFAULT_FILTER_SIZES,r=e?e.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return io(t,this.config,this.boxEncodingSize,e)}async extractBoxes(t,e,r){let{width:a,height:i}=e,s=Math.max(a,i),c=s/a,m=s/i,p=t.shape[1],u=this.config.anchors.length,[f,l,d]=n.tidy(()=>{let g=t.reshape([p,p,u,this.boxEncodingSize]),T=g.slice([0,0,0,0],[p,p,u,4]),x=g.slice([0,0,0,4],[p,p,u,1]),E=this.withClassScores?n.softmax(g.slice([0,0,0,5],[p,p,u,this.config.classes.length]),3):n.scalar(0);return[T,x,E]}),b=[],y=await l.array(),h=await f.array();for(let g=0;gr){let B=(T+fe(h[g][T][x][0]))/p*c,V=(g+fe(h[g][T][x][1]))/p*m,U=Math.exp(h[g][T][x][2])*this.config.anchors[x].x/p*c,O=Math.exp(h[g][T][x][3])*this.config.anchors[x].y/p*m,ot=B-U/2,nt=V-O/2,at={row:g,col:T,anchor:x},{classScore:Et,label:mr}=this.withClassScores?await this.extractPredictedClass(d,at):{classScore:1,label:0};b.push({box:new Ct(ot,nt,ot+U,nt+O),score:E,classScore:E*Et,label:mr,...at})}}return f.dispose(),l.dispose(),d.dispose(),b}async extractPredictedClass(t,e){let{row:r,col:a,anchor:i}=e,s=await t.array();return Array(this.config.classes.length).fill(0).map((c,m)=>s[r][a][i][m]).map((c,m)=>({classScore:c,label:m})).reduce((c,m)=>c.classScore>m.classScore?c:m)}};Se.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Xt=Se;var Jt=class extends Xt{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:to,classes:["face"],...t?{anchors:ro,meanRgb:oo}:{anchors:eo,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(a=>new D(a.score,a.relativeBox,{width:a.imageWidth,height:a.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?ao:no}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function cd(o,t=!0){let e=new Jt(t);return e.extractWeights(o),e}var Ae=class extends q{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var Y=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function _t(o,t,e,r,a=({alignedRect:i})=>i){let i=o.map(m=>Yt(m)?a(m):m.detection),s=r||(t instanceof n.Tensor?await oe(t,i):await re(t,i)),c=await e(s);return s.forEach(m=>m instanceof n.Tensor&&m.dispose()),c}async function qt(o,t,e,r,a){return _t([o],t,async i=>e(i[0]),r,a)}var mo=.4,po=[new v(1.603231,2.094468),new v(6.041143,7.080126),new v(2.882459,3.518061),new v(4.266906,5.178857),new v(9.041765,10.66308)],uo=[117.001,114.697,97.404];var Zt=class extends Xt{constructor(){let t={withSeparableConvs:!0,iouThreshold:mo,classes:["face"],anchors:po,meanRgb:uo,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(a=>new D(a.score,a.relativeBox,{width:a.imageWidth,height:a.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var P={ssdMobilenetv1:new Tt,tinyFaceDetector:new Zt,tinyYolov2:new Jt,faceLandmark68Net:new Gt,faceLandmark68TinyNet:new Ie,faceRecognitionNet:new jt,faceExpressionNet:new we,ageGenderNet:new 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Ft(this,this.input)}},wt=class extends We{async run(){let t=await this.parentTask;if(!t)return;let e=await qt(t,this.input,r=>P.faceExpressionNet.predictExpressions(r),this.extractedFaces);return tr(t,e)}withAgeAndGender(){return new Dt(this,this.input)}},ct=class extends Pt{withAgeAndGender(){return new pt(this,this.input)}withFaceDescriptors(){return new ft(this,this.input)}},mt=class extends wt{withAgeAndGender(){return new ut(this,this.input)}withFaceDescriptor(){return new lt(this,this.input)}};var ke=class extends Y{constructor(e,r,a){super();this.parentTask=e;this.input=r;this.extractedFaces=a}},Ft=class extends ke{async run(){let t=await this.parentTask,e=await _t(t,this.input,async r=>Promise.all(r.map(a=>P.ageGenderNet.predictAgeAndGender(a))),this.extractedFaces);return t.map((r,a)=>{let{age:i,gender:s,genderProbability:c}=e[a];return sr(ir(r,s,c),i)})}withFaceExpressions(){return new Pt(this,this.input)}},Dt=class extends ke{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:a}=await qt(t,this.input,i=>P.ageGenderNet.predictAgeAndGender(i),this.extractedFaces);return sr(ir(t,r,a),e)}withFaceExpressions(){return new wt(this,this.input)}},pt=class extends Ft{withFaceExpressions(){return new ct(this,this.input)}withFaceDescriptors(){return new ft(this,this.input)}},ut=class extends Dt{withFaceExpressions(){return new mt(this,this.input)}withFaceDescriptor(){return new lt(this,this.input)}};var Be=class extends Y{constructor(e,r){super();this.parentTask=e;this.input=r}},ft=class extends Be{async run(){let t=await this.parentTask;return(await _t(t,this.input,r=>Promise.all(r.map(a=>P.faceRecognitionNet.computeFaceDescriptor(a))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,a)=>ar(t[a],r))}withFaceExpressions(){return new ct(this,this.input)}withAgeAndGender(){return new pt(this,this.input)}},lt=class extends Be{async run(){let t=await this.parentTask;if(!t)return;let e=await 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Promise((t,e)=>{this.run().then(r=>t(r.map(a=>St({},a)))).catch(r=>e(r))})}withFaceLandmarks(t=!1){return new $e(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Pt(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Ft(this.runAndExtendWithFaceDetections(),this.input)}},ze=class extends He{async run(){let t=await new me(this.input,this.options),e=t[0];return t.forEach(r=>{r.score>e.score&&(e=r)}),e}runAndExtendWithFaceDetection(){return new Promise(async t=>{let e=await this.run();t(e?St({},e):void 0)})}withFaceLandmarks(t=!1){return new Oe(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new wt(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Dt(this.runAndExtendWithFaceDetection(),this.input)}};function qh(o,t=new z){return new ze(o,t)}function cr(o,t=new z){return new me(o,t)}async function en(o,t){return cr(o,new 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this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>fo(r,t)).reduce((r,a)=>r+a,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new Kt(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distancet.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>gt.fromJSON(r));return new o(e,t.distanceThreshold)}};function Tb(o){let t=new Zt;return t.extractWeights(o),t}function rn(o,t){let{width:e,height:r}=new A(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(a=>rn(a,{width:e,height:r}));if(Yt(o)){let a=o.detection.forSize(e,r),i=o.unshiftedLandmarks.forSize(a.box.width,a.box.height);return ie(St(o,a),i)}return Q(o)?St(o,o.detection.forSize(e,r)):o instanceof $||o instanceof D?o.forSize(e,r):o}var Lb=Ar;export{Me as AgeGenderNet,Ct as BoundingBox,C as Box,Y as 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e,r=H(o,t.conv_0,[2,2]);if([t.conv_1,t.conv_2,t.conv_3,t.conv_4,t.conv_5,t.conv_6,t.conv_7,t.conv_8,t.conv_9,t.conv_10,t.conv_11,t.conv_12,t.conv_13].forEach((i,s)=>{let c=s+1,m=Go(c);r=Vo(r,i.depthwise_conv,m),r=H(r,i.pointwise_conv,[1,1]),c===11&&(e=r)}),e===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out:r,conv11:e}})}function jo(o,t,e){let r=o.arraySync(),a=Math.min(r[t][0],r[t][2]),i=Math.min(r[t][1],r[t][3]),s=Math.max(r[t][0],r[t][2]),c=Math.max(r[t][1],r[t][3]),m=Math.min(r[e][0],r[e][2]),p=Math.min(r[e][1],r[e][3]),u=Math.max(r[e][0],r[e][2]),f=Math.max(r[e][1],r[e][3]),l=(s-a)*(c-i),d=(u-m)*(f-p);if(l<=0||d<=0)return 0;let b=Math.max(a,m),y=Math.max(i,p),h=Math.min(s,u),g=Math.min(c,f),T=Math.max(h-b,0)*Math.max(g-y,0);return T/(l+d-T)}function qr(o,t,e,r,a){let i=o.shape[0],s=Math.min(e,i),c=t.map((u,f)=>({score:u,boxIndex:f})).filter(u=>u.score>a).sort((u,f)=>f.score-u.score),m=u=>u<=r?1:0,p=[];return c.forEach(u=>{if(p.length>=s)return;let f=u.score;for(let l=p.length-1;l>=0;--l){let d=jo(o,u.boxIndex,p[l]);if(d!==0&&(u.score*=m(d),u.score<=a))break}f===u.score&&p.push(u.boxIndex)}),p}function Uo(o){let t=n.unstack(n.transpose(o,[1,0])),e=[n.sub(t[2],t[0]),n.sub(t[3],t[1])],r=[n.add(t[0],n.div(e[0],2)),n.add(t[1],n.div(e[1],2))];return{sizes:e,centers:r}}function Xo(o,t){let{sizes:e,centers:r}=Uo(o),a=n.unstack(n.transpose(t,[1,0])),i=n.div(n.mul(n.exp(n.div(a[2],5)),e[0]),2),s=n.add(n.mul(n.div(a[0],10),e[0]),r[0]),c=n.div(n.mul(n.exp(n.div(a[3],5)),e[1]),2),m=n.add(n.mul(n.div(a[1],10),e[1]),r[1]);return n.transpose(n.stack([n.sub(s,i),n.sub(m,c),n.add(s,i),n.add(m,c)]),[1,0])}function Zr(o,t,e){return n.tidy(()=>{let r=o.shape[0],a=Xo(n.reshape(n.tile(e.extra_dim,[r,1,1]),[-1,4]),n.reshape(o,[-1,4]));a=n.reshape(a,[r,a.shape[0]/r,4]);let i=n.sigmoid(n.slice(t,[0,0,1],[-1,-1,-1])),s=n.slice(i,[0,0,0],[-1,-1,1]);s=n.reshape(s,[r,s.shape[1]]);let c=n.unstack(a),m=n.unstack(s);return{boxes:c,scores:m}})}function yt(o,t){return n.tidy(()=>{let e=o.shape[0],r=n.reshape(vt(o,t.box_encoding_predictor),[e,-1,1,4]),a=n.reshape(vt(o,t.class_predictor),[e,-1,3]);return{boxPredictionEncoding:r,classPrediction:a}})}function Kr(o,t,e){return n.tidy(()=>{let r=H(o,e.conv_0,[1,1]),a=H(r,e.conv_1,[2,2]),i=H(a,e.conv_2,[1,1]),s=H(i,e.conv_3,[2,2]),c=H(s,e.conv_4,[1,1]),m=H(c,e.conv_5,[2,2]),p=H(m,e.conv_6,[1,1]),u=H(p,e.conv_7,[2,2]),f=yt(t,e.box_predictor_0),l=yt(o,e.box_predictor_1),d=yt(a,e.box_predictor_2),b=yt(s,e.box_predictor_3),y=yt(m,e.box_predictor_4),h=yt(u,e.box_predictor_5),g=n.concat([f.boxPredictionEncoding,l.boxPredictionEncoding,d.boxPredictionEncoding,b.boxPredictionEncoding,y.boxPredictionEncoding,h.boxPredictionEncoding],1),T=n.concat([f.classPrediction,l.classPrediction,d.classPrediction,b.classPrediction,y.classPrediction,h.classPrediction],1);return{boxPredictions:g,classPredictions:T}})}var z=class{constructor({minConfidence:t,maxResults:e}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=t||.5,this._maxResults=e||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var Tt=class extends I{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("SsdMobilenetv1 - load model before inference");return n.tidy(()=>{let r=n.cast(t.toBatchTensor(512,!1),"float32"),a=n.sub(n.div(r,127.5),1),i=Jr(a,e.mobilenetv1),{boxPredictions:s,classPredictions:c}=Kr(i.out,i.conv11,e.prediction_layer);return Zr(s,c,e.output_layer)})}async forward(t){return this.forwardInput(await F(t))}async 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io(o,t,e,r){let{extractWeights:a,getRemainingWeights:i}=L(o),s=[],{extractConvParams:c,extractConvWithBatchNormParams:m,extractSeparableConvParams:p}=qo(a,s),u;if(t.withSeparableConvs){let[f,l,d,b,y,h,g,T,x]=r,E=t.isFirstLayerConv2d?c(f,l,3,"conv0"):p(f,l,"conv0"),B=p(l,d,"conv1"),V=p(d,b,"conv2"),U=p(b,y,"conv3"),O=p(y,h,"conv4"),ot=p(h,g,"conv5"),nt=T?p(g,T,"conv6"):void 0,at=x?p(T,x,"conv7"):void 0,Et=c(x||T||g,5*e,1,"conv8");u={conv0:E,conv1:B,conv2:V,conv3:U,conv4:O,conv5:ot,conv6:nt,conv7:at,conv8:Et}}else{let[f,l,d,b,y,h,g,T,x]=r,E=m(f,l,"conv0"),B=m(l,d,"conv1"),V=m(d,b,"conv2"),U=m(b,y,"conv3"),O=m(y,h,"conv4"),ot=m(h,g,"conv5"),nt=m(g,T,"conv6"),at=m(T,x,"conv7"),Et=c(x,5*e,1,"conv8");u={conv0:E,conv1:B,conv2:V,conv3:U,conv4:O,conv5:ot,conv6:nt,conv7:at,conv8:Et}}if(i().length!==0)throw new Error(`weights remaing after extract: ${i().length}`);return{params:u,paramMappings:s}}function Zo(o,t){let e=k(o,t);function r(c){let m=e(`${c}/sub`,1),p=e(`${c}/truediv`,1);return{sub:m,truediv:p}}function a(c){let m=e(`${c}/filters`,4),p=e(`${c}/bias`,1);return{filters:m,bias:p}}function i(c){let m=a(`${c}/conv`),p=r(`${c}/bn`);return{conv:m,bn:p}}let s=Ot(e);return{extractConvParams:a,extractConvWithBatchNormParams:i,extractSeparableConvParams:s}}function co(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:a,extractSeparableConvParams:i}=Zo(o,e),s;if(t.withSeparableConvs){let c=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):i("conv0"),conv1:i("conv1"),conv2:i("conv2"),conv3:i("conv3"),conv4:i("conv4"),conv5:i("conv5"),conv6:c>7?i("conv6"):void 0,conv7:c>8?i("conv7"):void 0,conv8:r("conv8")}}else s={conv0:a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:a("conv6"),conv7:a("conv7"),conv8:r("conv8")};return N(o,e),{params:s,paramMappings:e}}var q=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var Se=class Se extends I{constructor(t){super("TinyYolov2"),so(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,e){let r=et(t,e.conv0);return r=n.maxPool(r,[2,2],[2,2],"same"),r=et(r,e.conv1),r=n.maxPool(r,[2,2],[2,2],"same"),r=et(r,e.conv2),r=n.maxPool(r,[2,2],[2,2],"same"),r=et(r,e.conv3),r=n.maxPool(r,[2,2],[2,2],"same"),r=et(r,e.conv4),r=n.maxPool(r,[2,2],[2,2],"same"),r=et(r,e.conv5),r=n.maxPool(r,[2,2],[1,1],"same"),r=et(r,e.conv6),r=et(r,e.conv7),vt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?Ut(vt(t,e.conv0,"valid",!1)):rt(t,e.conv0);return r=n.maxPool(r,[2,2],[2,2],"same"),r=rt(r,e.conv1),r=n.maxPool(r,[2,2],[2,2],"same"),r=rt(r,e.conv2),r=n.maxPool(r,[2,2],[2,2],"same"),r=rt(r,e.conv3),r=n.maxPool(r,[2,2],[2,2],"same"),r=rt(r,e.conv4),r=n.maxPool(r,[2,2],[2,2],"same"),r=rt(r,e.conv5),r=n.maxPool(r,[2,2],[1,1],"same"),r=e.conv6?rt(r,e.conv6):r,r=e.conv7?rt(r,e.conv7):r,vt(r,e.conv8,"valid",!1)}forwardInput(t,e){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return n.tidy(()=>{let a=n.cast(t.toBatchTensor(e,!1),"float32");return a=this.config.meanRgb?J(a,this.config.meanRgb):a,a=a.div(255),this.config.withSeparableConvs?this.runMobilenet(a,r):this.runTinyYolov2(a,r)})}async forward(t,e){return this.forwardInput(await F(t),e)}async detect(t,e={}){let{inputSize:r,scoreThreshold:a}=new q(e),i=await F(t),s=await this.forwardInput(i,r),c=n.tidy(()=>n.unstack(s)[0].expandDims()),m={width:i.getInputWidth(0),height:i.getInputHeight(0)},p=await this.extractBoxes(c,i.getReshapedInputDimensions(0),a);s.dispose(),c.dispose();let u=p.map(h=>h.box),f=p.map(h=>h.score),l=p.map(h=>h.classScore),d=p.map(h=>this.config.classes[h.label]);return br(u.map(h=>h.rescale(r)),f,this.config.iouThreshold,!0).map(h=>new It(f[h],l[h],d[h],u[h],m))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return co(t,this.config)}extractParams(t){let e=this.config.filterSizes||Se.DEFAULT_FILTER_SIZES,r=e?e.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return io(t,this.config,this.boxEncodingSize,e)}async extractBoxes(t,e,r){let{width:a,height:i}=e,s=Math.max(a,i),c=s/a,m=s/i,p=t.shape[1],u=this.config.anchors.length,[f,l,d]=n.tidy(()=>{let g=t.reshape([p,p,u,this.boxEncodingSize]),T=g.slice([0,0,0,0],[p,p,u,4]),x=g.slice([0,0,0,4],[p,p,u,1]),E=this.withClassScores?n.softmax(g.slice([0,0,0,5],[p,p,u,this.config.classes.length]),3):n.scalar(0);return[T,x,E]}),b=[],y=await l.array(),h=await f.array();for(let g=0;gr){let B=(T+fe(h[g][T][x][0]))/p*c,V=(g+fe(h[g][T][x][1]))/p*m,U=Math.exp(h[g][T][x][2])*this.config.anchors[x].x/p*c,O=Math.exp(h[g][T][x][3])*this.config.anchors[x].y/p*m,ot=B-U/2,nt=V-O/2,at={row:g,col:T,anchor:x},{classScore:Et,label:mr}=this.withClassScores?await this.extractPredictedClass(d,at):{classScore:1,label:0};b.push({box:new Ct(ot,nt,ot+U,nt+O),score:E,classScore:E*Et,label:mr,...at})}}return f.dispose(),l.dispose(),d.dispose(),b}async extractPredictedClass(t,e){let{row:r,col:a,anchor:i}=e,s=await t.array();return Array(this.config.classes.length).fill(0).map((c,m)=>s[r][a][i][m]).map((c,m)=>({classScore:c,label:m})).reduce((c,m)=>c.classScore>m.classScore?c:m)}};Se.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Xt=Se;var Jt=class extends Xt{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:to,classes:["face"],...t?{anchors:ro,meanRgb:oo}:{anchors:eo,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(a=>new D(a.score,a.relativeBox,{width:a.imageWidth,height:a.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?ao:no}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function cd(o,t=!0){let e=new Jt(t);return e.extractWeights(o),e}var Ae=class extends q{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var Y=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function _t(o,t,e,r,a=({alignedRect:i})=>i){let i=o.map(m=>Yt(m)?a(m):m.detection),s=r||(t instanceof n.Tensor?await oe(t,i):await re(t,i)),c=await e(s);return s.forEach(m=>m instanceof n.Tensor&&m.dispose()),c}async function qt(o,t,e,r,a){return _t([o],t,async i=>e(i[0]),r,a)}var mo=.4,po=[new v(1.603231,2.094468),new v(6.041143,7.080126),new v(2.882459,3.518061),new v(4.266906,5.178857),new v(9.041765,10.66308)],uo=[117.001,114.697,97.404];var Zt=class extends Xt{constructor(){let t={withSeparableConvs:!0,iouThreshold:mo,classes:["face"],anchors:po,meanRgb:uo,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(a=>new D(a.score,a.relativeBox,{width:a.imageWidth,height:a.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var P={ssdMobilenetv1:new Tt,tinyFaceDetector:new Zt,tinyYolov2:new Jt,faceLandmark68Net:new Gt,faceLandmark68TinyNet:new Ie,faceRecognitionNet:new jt,faceExpressionNet:new we,ageGenderNet:new Me},Ko=(o,t)=>P.ssdMobilenetv1.locateFaces(o,t),Rd=(o,t)=>P.tinyFaceDetector.locateFaces(o,t),$d=(o,t)=>P.tinyYolov2.locateFaces(o,t),Qo=o=>P.faceLandmark68Net.detectLandmarks(o),Od=o=>P.faceLandmark68TinyNet.detectLandmarks(o),Hd=o=>P.faceRecognitionNet.computeFaceDescriptor(o),zd=o=>P.faceExpressionNet.predictExpressions(o),Yd=o=>P.ageGenderNet.predictAgeAndGender(o),tn=o=>P.ssdMobilenetv1.load(o),Vd=o=>P.tinyFaceDetector.load(o),Gd=o=>P.tinyYolov2.load(o),jd=o=>P.faceLandmark68Net.load(o),Ud=o=>P.faceLandmark68TinyNet.load(o),Xd=o=>P.faceRecognitionNet.load(o),Jd=o=>P.faceExpressionNet.load(o),qd=o=>P.ageGenderNet.load(o),Zd=tn,Kd=Ko,Qd=Qo;var We=class extends Y{constructor(e,r,a){super();this.parentTask=e;this.input=r;this.extractedFaces=a}},Pt=class extends We{async run(){let t=await this.parentTask,e=await _t(t,this.input,async r=>Promise.all(r.map(a=>P.faceExpressionNet.predictExpressions(a))),this.extractedFaces);return t.map((r,a)=>tr(r,e[a]))}withAgeAndGender(){return new Ft(this,this.input)}},wt=class extends We{async run(){let t=await this.parentTask;if(!t)return;let e=await qt(t,this.input,r=>P.faceExpressionNet.predictExpressions(r),this.extractedFaces);return tr(t,e)}withAgeAndGender(){return new Dt(this,this.input)}},ct=class extends Pt{withAgeAndGender(){return new pt(this,this.input)}withFaceDescriptors(){return new ft(this,this.input)}},mt=class extends wt{withAgeAndGender(){return new ut(this,this.input)}withFaceDescriptor(){return new lt(this,this.input)}};var ke=class extends Y{constructor(e,r,a){super();this.parentTask=e;this.input=r;this.extractedFaces=a}},Ft=class extends ke{async run(){let t=await this.parentTask,e=await _t(t,this.input,async r=>Promise.all(r.map(a=>P.ageGenderNet.predictAgeAndGender(a))),this.extractedFaces);return t.map((r,a)=>{let{age:i,gender:s,genderProbability:c}=e[a];return sr(ir(r,s,c),i)})}withFaceExpressions(){return new Pt(this,this.input)}},Dt=class extends ke{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:a}=await qt(t,this.input,i=>P.ageGenderNet.predictAgeAndGender(i),this.extractedFaces);return sr(ir(t,r,a),e)}withFaceExpressions(){return new wt(this,this.input)}},pt=class extends Ft{withFaceExpressions(){return new ct(this,this.input)}withFaceDescriptors(){return new ft(this,this.input)}},ut=class extends Dt{withFaceExpressions(){return new mt(this,this.input)}withFaceDescriptor(){return new lt(this,this.input)}};var Be=class extends Y{constructor(e,r){super();this.parentTask=e;this.input=r}},ft=class extends Be{async run(){let t=await this.parentTask;return(await _t(t,this.input,r=>Promise.all(r.map(a=>P.faceRecognitionNet.computeFaceDescriptor(a))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,a)=>ar(t[a],r))}withFaceExpressions(){return new ct(this,this.input)}withAgeAndGender(){return new pt(this,this.input)}},lt=class extends Be{async run(){let t=await this.parentTask;if(!t)return;let e=await qt(t,this.input,r=>P.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return ar(t,e)}withFaceExpressions(){return new mt(this,this.input)}withAgeAndGender(){return new ut(this,this.input)}};var Re=class extends Y{constructor(e,r,a){super();this.parentTask=e;this.input=r;this.useTinyLandmarkNet=a}get landmarkNet(){return this.useTinyLandmarkNet?P.faceLandmark68TinyNet:P.faceLandmark68Net}},$e=class extends Re{async run(){let t=await this.parentTask,e=t.map(s=>s.detection),r=this.input instanceof n.Tensor?await oe(this.input,e):await re(this.input,e),a=await Promise.all(r.map(s=>this.landmarkNet.detectLandmarks(s)));return r.forEach(s=>s instanceof n.Tensor&&s.dispose()),t.filter((s,c)=>a[c]).map((s,c)=>ie(s,a[c]))}withFaceExpressions(){return new ct(this,this.input)}withAgeAndGender(){return new pt(this,this.input)}withFaceDescriptors(){return new ft(this,this.input)}},Oe=class extends Re{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof n.Tensor?await oe(this.input,[e]):await re(this.input,[e]),a=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(i=>i instanceof n.Tensor&&i.dispose()),ie(t,a)}withFaceExpressions(){return new mt(this,this.input)}withAgeAndGender(){return new ut(this,this.input)}withFaceDescriptor(){return new lt(this,this.input)}};var He=class extends Y{constructor(e,r=new z){super();this.input=e;this.options=r}},me=class extends He{async run(){let{input:t,options:e}=this,r;if(e instanceof Ae)r=P.tinyFaceDetector.locateFaces(t,e);else if(e instanceof z)r=P.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof q)r=P.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise((t,e)=>{this.run().then(r=>t(r.map(a=>St({},a)))).catch(r=>e(r))})}withFaceLandmarks(t=!1){return new $e(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Pt(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Ft(this.runAndExtendWithFaceDetections(),this.input)}},ze=class extends He{async run(){let t=await new me(this.input,this.options),e=t[0];return t.forEach(r=>{r.score>e.score&&(e=r)}),e}runAndExtendWithFaceDetection(){return new Promise(async t=>{let e=await this.run();t(e?St({},e):void 0)})}withFaceLandmarks(t=!1){return new Oe(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new wt(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Dt(this.runAndExtendWithFaceDetection(),this.input)}};function qh(o,t=new z){return new ze(o,t)}function cr(o,t=new z){return new me(o,t)}async function en(o,t){return cr(o,new z(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function rb(o,t={}){return cr(o,new q(t)).withFaceLandmarks().withFaceDescriptors()}var ob=en;function fo(o,t){if(o.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let e=Array.from(o),r=Array.from(t);return Math.sqrt(e.map((a,i)=>a-r[i]).reduce((a,i)=>a+i*i,0))}var lo=class o{constructor(t,e=.6){this._distanceThreshold=e;let r=Array.isArray(t)?t:[t];if(!r.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let a=1,i=()=>`person ${a++}`;this._labeledDescriptors=r.map(s=>{if(s instanceof gt)return s;if(s instanceof Float32Array)return new gt(i(),[s]);if(s.descriptor&&s.descriptor instanceof Float32Array)return new gt(i(),[s.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>fo(r,t)).reduce((r,a)=>r+a,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new Kt(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distancet.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>gt.fromJSON(r));return new o(e,t.distanceThreshold)}};function Tb(o){let t=new Zt;return t.extractWeights(o),t}function rn(o,t){let{width:e,height:r}=new A(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(a=>rn(a,{width:e,height:r}));if(Yt(o)){let a=o.detection.forSize(e,r),i=o.unshiftedLandmarks.forSize(a.box.width,a.box.height);return ie(St(o,a),i)}return Q(o)?St(o,o.detection.forSize(e,r)):o instanceof $||o instanceof D?o.forSize(e,r):o}var Lb=Ar;export{Me as AgeGenderNet,Ct as BoundingBox,C as Box,Y as ComposableTask,ft as ComputeAllFaceDescriptorsTask,Be as ComputeFaceDescriptorsTaskBase,lt as ComputeSingleFaceDescriptorTask,$e as DetectAllFaceLandmarksTask,me as DetectAllFacesTask,Re as DetectFaceLandmarksTaskBase,He as DetectFacesTaskBase,Oe as DetectSingleFaceLandmarksTask,ze as DetectSingleFaceTask,A as Dimensions,Nr as FACE_EXPRESSION_LABELS,D as FaceDetection,Qr as FaceDetectionNet,we as FaceExpressionNet,it as FaceExpressions,Gt as FaceLandmark68Net,Ie as FaceLandmark68TinyNet,zr as FaceLandmarkNet,$ as FaceLandmarks,xr as FaceLandmarks5,Lt as FaceLandmarks68,Kt as FaceMatch,lo as FaceMatcher,jt as FaceRecognitionNet,rr as Gender,Qt as LabeledBox,gt as LabeledFaceDescriptors,tt as NetInput,I as NeuralNetwork,It as ObjectDetection,v as Point,vr as PredictedBox,Nt as Rect,Tt as SsdMobilenetv1,z as SsdMobilenetv1Options,Zt as TinyFaceDetector,Ae as TinyFaceDetectorOptions,Jt as TinyYolov2,q as TinyYolov2Options,ob as allFaces,en as allFacesSsdMobilenetv1,rb as allFacesTinyYolov2,yr as awaitMediaLoaded,Tr as bufferToImage,Hd as computeFaceDescriptor,Bt as createCanvas,be as createCanvasFromMedia,gl as createFaceDetectionNet,gf as createFaceRecognitionNet,Jo as createSsdMobilenetv1,Tb as createTinyFaceDetector,cd as createTinyYolov2,cr as detectAllFaces,Qo as detectFaceLandmarks,Od as detectFaceLandmarksTiny,Qd as detectLandmarks,qh as detectSingleFace,Sr as draw,_ as env,fo as euclideanDistance,sr as extendWithAge,ar as extendWithFaceDescriptor,St as extendWithFaceDetection,tr as extendWithFaceExpressions,ie as extendWithFaceLandmarks,ir as extendWithGender,oe as extractFaceTensors,re as extractFaces,Fi as fetchImage,wr as fetchJson,Ii as fetchNetWeights,st as fetchOrThrow,ki as fetchVideo,W as getContext2dOrThrow,kt as getMediaDimensions,_r as imageTensorToCanvas,Pr as imageToSquare,On as inverseSigmoid,dr as iou,Qe as isMediaElement,he as isMediaLoaded,Tf as isWithAge,Q as isWithFaceDetection,Lr as isWithFaceExpressions,Yt as isWithFaceLandmarks,Ff as isWithGender,qd as loadAgeGenderModel,Zd as loadFaceDetectionModel,Jd as loadFaceExpressionModel,jd as loadFaceLandmarkModel,Ud as loadFaceLandmarkTinyModel,Xd as loadFaceRecognitionModel,tn as loadSsdMobilenetv1Model,Vd as loadTinyFaceDetectorModel,Gd as loadTinyYolov2Model,Dr as loadWeightMap,Kd as locateFaces,Yi as matchDimensions,hr as minBbox,P as nets,br as nonMaxSuppression,J as normalize,gr as padToSquare,Yd as predictAgeAndGender,zd as recognizeFaceExpressions,rn as resizeResults,At as resolveInput,Rn as shuffleArray,fe as sigmoid,Ko as ssdMobilenetv1,n as tf,Rd as tinyFaceDetector,$d as tinyYolov2,F as toNetInput,lr as utils,so as validateConfig,Lb as version}; diff --git a/dist/face-api.esm.js b/dist/face-api.esm.js index 046b4df..8b8aad1 100644 --- a/dist/face-api.esm.js +++ b/dist/face-api.esm.js @@ -4,69 +4,69 @@ author: ' */ -var yR=Object.defineProperty;var xR=(e=>typeof require!="undefined"?require:typeof Proxy!="undefined"?new 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be rank 4 or rank 1 but got rank ${l.rank}.`),u!=null&&A(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),p!=null&&A(p.rank===4||p.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${p.rank}.`),Ns(i,o,l,p,u,s)}var Xv=L({batchNorm4d_:TP});function CP(e,t,n){let a=E(e,"x","bincount"),r=E(t,"weights","bincount");A(a.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${a.dtype}`),A(n>=0,()=>`size must be non-negative, but got ${n}.`),A(r.size===a.size||r.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${a.shape}, weights shape: ${r.shape}.`);let s={x:a,weights:r},i={size:n};return P.runKernel(au,s,i)}var Yv=L({bincount_:CP});function EP(e,t){let n=E(e,"x","bitwiseAnd"),a=E(t,"y","bitwiseAnd");if(!Ar(n.shape,a.shape))throw new Error(`BitwiseAnd: Tensors must have the same shape. x: ${n.shape}, y: 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Wz(e,t,n,a,r,s="NHWC",i){let o=e;e.rank===3&&(o=W(e,[1,e.shape[0],e.shape[1],e.shape[2]]));let l=t;l.rank===3&&(l=W(t,[1,t.shape[0],t.shape[1],t.shape[2]])),A(o.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${o.shape}.`),A(l.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${l.shape}.`),A(n.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${n}.`);let u=s==="NHWC"?o.shape[3]:o.shape[1],p=s==="NHWC"?l.shape[3]:l.shape[1];A(u===n[2],()=>`Error in conv2dDerFilter: depth of input ${u}) must match input depth in filter (${n[2]}.`),A(p===n[3],()=>`Error in conv2dDerFilter: depth of dy (${p}) must match output depth for filter (${n[3]}).`),Tn("conv2dDerFilter",r,i);let d={x:o,dy:l},c={strides:a,pad:r,dataFormat:s,dimRoundingMode:i,filterShape:n};return P.runKernel(km,d,c)}var zw=L({conv2DBackpropFilter_:Wz});function cf(e,t,n){if(n==null||n==="linear")return e;if(n==="relu")return z(e,jo(t));throw new 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className(){return"Adadelta"}constructor(e,t,n=null){super(),this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=P.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=P.registeredVariables[t],r=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${t}/accum_grad`,variable:O(()=>je(a).variable(r))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${t}/accum_var`,variable:O(()=>je(a).variable(r))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[n].variable,o=this.accumulatedUpdates[n].variable;O(()=>{let l=X(z(i,this.rho),z(pt(s),1-this.rho)),u=z(he(cn(X(o,this.epsilon)),cn(X(i,this.epsilon))),s),p=X(z(o,this.rho),z(pt(u),1-this.rho));i.assign(l),o.assign(p);let d=X(z(u,-this.learningRate),a);a.assign(d)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Ee(this.accumulatedGrads.map(e=>e.variable)),Ee(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}},Uw=class extends Rr{static get className(){return"Adagrad"}constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=P.registeredVariables[t];this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:O(()=>yn(a.shape,this.initialAccumulatorValue).variable(!1))});let r=Array.isArray(e)?e[n].tensor:e[t];if(r==null)return;let s=this.accumulatedGrads[n].variable;O(()=>{let i=X(s,pt(r));s.assign(i);let o=X(z(he(r,cn(X(i,P.backend.epsilon()))),-this.learningRate),a);a.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Ee(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}},Gw=class extends Rr{static get className(){return"Adam"}constructor(e,t,n,a=null){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=a,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],O(()=>{this.accBeta1=ve(t).variable(),this.accBeta2=ve(n).variable()}),a==null&&(this.epsilon=P.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);O(()=>{let n=pe(1,this.accBeta1),a=pe(1,this.accBeta2);t.forEach((r,s)=>{let i=P.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:O(()=>je(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${r}/v`,variable:O(()=>je(i).variable(o))});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,p=this.accumulatedSecondMoment[s].variable,d=X(z(u,this.beta1),z(l,1-this.beta1)),c=X(z(p,this.beta2),z(pt(l),1-this.beta2)),h=he(d,n),m=he(c,a);u.assign(d),p.assign(c);let f=X(z(he(h,X(cn(m),this.epsilon)),-this.learningRate),i);i.assign(f)}),this.accBeta1.assign(z(this.accBeta1,this.beta1)),this.accBeta2.assign(z(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Ee(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Ee(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),O(()=>{this.accBeta1.assign(_r(this.beta1,this.iterations_+1)),this.accBeta2.assign(_r(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)}},Hw=class extends Rr{static get className(){return"Adamax"}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=[],O(()=>{this.iteration=ve(0).variable(),this.accBeta1=ve(t).variable()}),a==null&&(this.epsilon=P.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);O(()=>{let n=pe(1,this.accBeta1),a=he(-this.learningRate,X(z(this.iteration,this.decay),1));t.forEach((r,s)=>{let 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Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}},mf=class extends Rr{static get className(){return"SGD"}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];O(()=>{let s=X(z(this.c,a),r);r.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Ht(ve(-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.model.stopTraining}getConfig(){let t=[];for(let n of this.layers){let a={};a.className=n.getClassName(),a.config=n.getConfig(),t.push(a)}return{name:this.name,layers:t}}};Cf.className="Sequential";ne.registerClass(Cf);function o6(e){return new Cr(e)}function l6(e){return new Cf(e)}function q2(e){return S2(e)}function u6(e,t){f0.registerCallbackConstructor(e,t)}var Vn=class extends ne.Serializable{getConfig(){return{}}},j2=class extends Vn{apply(e,t=1){return _G(e,t)}};j2.className="elu";ne.registerClass(j2);var K2=class extends Vn{apply(e){return Jm(e)}};K2.className="selu";ne.registerClass(K2);var X2=class extends Vn{apply(e){return Ke(e)}};X2.className="relu";ne.registerClass(X2);var Y2=class extends Vn{apply(e){return O(()=>cs(6,Ke(e)))}};Y2.className="relu6";ne.registerClass(Y2);var Z2=class extends Vn{apply(e){return e}};Z2.className="linear";ne.registerClass(Z2);var J2=class extends Vn{apply(e){return ha(e)}};J2.className="sigmoid";ne.registerClass(J2);var Q2=class extends Vn{apply(e){return FG(e)}};Q2.className="hardSigmoid";ne.registerClass(Q2);var eC=class extends Vn{apply(e){return Go(e)}};eC.className="softplus";ne.registerClass(eC);var tC=class extends Vn{apply(e){return AG(e)}};tC.className="softsign";ne.registerClass(tC);var nC=class extends Vn{apply(e){return hi(e)}};nC.className="tanh";ne.registerClass(nC);var w0=class extends Vn{apply(e,t=-1){return ja(e,t)}};w0.className="softmax";ne.registerClass(w0);var aC=class extends Vn{apply(e,t=-1){return Hm(e,t)}};aC.className="logSoftmax";ne.registerClass(aC);var rC=class extends Vn{apply(e,t=1){return O(()=>z(ha(z(e,t)),e))}};rC.className="swish";ne.registerClass(rC);var sC=class extends Vn{apply(e){return O(()=>z(e,hi(Go(e))))}};sC.className="mish";ne.registerClass(sC);function hs(e){return e.getClassName()}function mx(e,t={}){return vd(e,ne.SerializationMap.getMap().classNameMap,t,"activation")}function ms(e){if(e==null){let t={};return t.className="linear",t.config={},mx(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},mx(t)}else return e instanceof Vn?e:mx(e)}function k0(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 iC=class extends ne.Serializable{},Nd=class extends iC{constructor(e){super(),k0(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 O(()=>{let t=It([1]);return this.hasL1&&(t=X(t,fe(z(this.l1,Lt(e))))),this.hasL2&&(t=X(t,fe(z(this.l2,kd(e))))),W(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Nd.className="L1L2";ne.registerClass(Nd);function p6(e){return k0(e),new Nd({l1:e!=null?e.l1:null,l2:0})}function c6(e){return k0(e),new Nd({l2:e!=null?e.l2:null,l1:0})}var FI={l1l2:"L1L2"};function ft(e){return Qw(e)}function $I(e,t={}){return vd(e,ne.SerializationMap.getMap().classNameMap,t,"regularizer")}function Nt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in FI?FI[e]:e,config:{}};return $I(t)}else return e instanceof iC?e:$I(e)}var I0=class extends We{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Te(e);let n=Ke(e);return this.maxValue!=null&&(n=an(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};I0.className="ReLU";ne.registerClass(I0);var S0=class extends We{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=Te(e);return ud(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};S0.className="LeakyReLU";ne.registerClass(S0);var N0=class extends We{constructor(e){if(super(e==null?{}:e),this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=St(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Nt(e.alphaRegularizer),this.alphaConstraint=Yt(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 V(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=Je(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{let n=Te(e),a=t.mask;if(a!=null){let r=z(pe(On(n.shape),re(a,n.dtype)),ve(-1e9));n=X(n,r)}return this.axis instanceof 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V(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new V(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=_0(e,s);if(r==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Vl.conv2d({x:l,filter:t,strides:a,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=De(l,[0,3,1,2])),l})}function h6(e,t,n,a=[1,1,1],r="valid",s,i){return O(()=>{if(s==null&&(s=Ga()),Rt(s),e.rank!==4&&e.rank!==5)throw new V(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new V(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=oC(e,s);if(r==="causal")throw new ze("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=nw(o,t,a,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Ka(o,n)),s==="channelsFirst"&&(o=De(o,[0,4,1,2,3])),o})}var lC=class uC extends We{constructor(t,n){if(super(n),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",uC.verifyArgs(n),this.rank=t,tn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new ze(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Fl(n.kernelSize,t,"kernelSize"),this.strides=Fl(n.strides==null?1:n.strides,t,"strides"),this.padding=n.padding==null?"valid":n.padding,va(this.padding),this.dataFormat=n.dataFormat==null?"channelsLast":n.dataFormat,Rt(this.dataFormat),this.activation=ms(n.activation),this.useBias=n.useBias==null?!0:n.useBias,this.biasInitializer=St(n.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Yt(n.biasConstraint),this.biasRegularizer=Nt(n.biasRegularizer),this.activityRegularizer=Nt(n.activityRegularizer),this.dilationRate=Fl(n.dilationRate==null?1:n.dilationRate,t,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new V(`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 V(`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 V(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(t){if(tr("kernelSize"in t,"required key 'kernelSize' not in config"),typeof t.kernelSize!="number"&&!e0(t.kernelSize,"number",1,3))throw new V(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(t.kernelSize)}.`)}getConfig(){let t={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:hs(this.activation),useBias:this.useBias,biasInitializer:Et(this.biasInitializer),biasRegularizer:ft(this.biasRegularizer),activityRegularizer:ft(this.activityRegularizer),biasConstraint:Xt(this.biasConstraint)},n=super.getConfig();return Object.assign(t,n),t}},Ef=class pC extends lC{constructor(t,n){super(t,n),this.kernel=null,pC.verifyArgs(n),this.filters=n.filters,tn(this.filters,"filters"),this.kernelInitializer=St(n.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Yt(n.kernelConstraint),this.kernelRegularizer=Nt(n.kernelRegularizer)}build(t){t=Je(t);let n=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[n]==null)throw new V(`The channel dimension of the input should be defined. Found ${t[n]}`);let a=t[n],r=this.kernelSize.concat([a,this.filters]);this.kernel=this.addWeight("kernel",r,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:{[n]:a}}],this.built=!0}call(t,n){return O(()=>{t=Te(t);let a,r=this.bias==null?null:this.bias.read(),s=m2(this.activation.getClassName());if(s!=null&&this.rank===2)a=DI(t,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)a=d6(t,this.kernel.read(),r,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)a=DI(t,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)a=h6(t,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new ze("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(a=this.activation.apply(a))}return a})}computeOutputShape(t){t=Je(t);let n=[],a=this.dataFormat==="channelsLast"?t.slice(1,t.length-1):t.slice(2);for(let s=0;s 0 but got ${JSON.stringify(t.filters)}`)}},_f=class cC extends Ef{constructor(t){super(2,t),cC.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!e0(t.kernelSize,"number",1,2))throw new V(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(t.kernelSize)}.`)}};_f.className="Conv2D";ne.registerClass(_f);var Af=class dC extends Ef{constructor(t){super(3,t),dC.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!(Array.isArray(t.kernelSize)&&(t.kernelSize.length===1||t.kernelSize.length===3)))throw new V(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(t.kernelSize)}.`)}};Af.className="Conv3D";ne.registerClass(Af);var A0=class extends _f{constructor(e){if(super(e),this.inputSpec=[new zt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new V(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Je(e),e.length!==4)throw new V("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. 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Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new V(`SeparableConv${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=St(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Nt(t.depthwiseRegularizer),this.depthwiseConstraint=Yt(t.depthwiseConstraint),this.pointwiseInitializer=St(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Nt(t.pointwiseRegularizer),this.pointwiseConstraint=Yt(t.pointwiseConstraint)}build(e){if(e=Je(e),e.length{e=Te(e);let n;if(this.rank===1)throw new ze("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=De(e,[0,2,3,1])),n=Es(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Ka(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=De(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=Et(this.depthwiseInitializer),e.pointwiseInitializer=Et(this.pointwiseInitializer),e.depthwiseRegularizer=ft(this.depthwiseRegularizer),e.pointwiseRegularizer=ft(this.pointwiseRegularizer),e.depthwiseConstraint=Xt(this.depthwiseConstraint),e.pointwiseConstraint=Xt(this.pointwiseConstraint),e}};hC.className="SeparableConv";var $0=class extends hC{constructor(e){super(2,e)}};$0.className="SeparableConv2D";ne.registerClass($0);var D0=class mC extends Ef{constructor(t){super(1,t),mC.verifyArgs(t),this.inputSpec=[{ndim:3}]}getConfig(){let t=super.getConfig();return delete t.rank,delete t.dataFormat,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!e0(t.kernelSize,"number",1,1))throw new V(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(t.kernelSize)}.`)}};D0.className="Conv1D";ne.registerClass(D0);var R0=class extends We{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 O(()=>{if(e=Te(e),this.dataFormat==="channelsLast"){let n=Ih(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Ih(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Ih(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Ih(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}};R0.className="Cropping2D";ne.registerClass(R0);var M0=class extends We{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,Rt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,kG(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 O(()=>{let n=Te(e),a=n.shape;if(this.dataFormat==="channelsFirst"){n=De(n,[0,2,3,1]);let r=this.size[0]*a[2],s=this.size[1]*a[3],i=this.interpolation==="nearest"?Zn.resizeNearestNeighbor(n,[r,s]):Zn.resizeBilinear(n,[r,s]);return De(i,[0,3,1,2])}else{let r=this.size[0]*a[1],s=this.size[1]*a[2];return this.interpolation==="nearest"?Zn.resizeNearestNeighbor(n,[r,s]):Zn.resizeBilinear(n,[r,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},t=super.getConfig();return Object.assign(e,t),e}};M0.className="UpSampling2D";ne.registerClass(M0);function m6(e,t,n=[1,1],a="valid",r,s){return O(()=>{r==null&&(r=Ga()),Rt(r);let i=_0(e,r);if(e.rank!==4)throw new V(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new V(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=Ns(i,t,n,a==="same"?"same":"valid","NHWC",s),r==="channelsFirst"&&(i=De(i,[0,3,1,2])),i})}var O0=class extends lC{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=St(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Yt(e.depthwiseConstraint),this.depthwiseRegularizer=Nt(e.depthwiseRegularizer)}build(e){if(e=Je(e),e.length<4)throw new V(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new V(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${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 O(()=>{e=Te(e);let n=m6(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Ka(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=Je(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=Va(t,this.kernelSize[0],this.padding,this.strides[0]),s=Va(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=Et(this.depthwiseInitializer),e.depthwiseRegularizer=ft(this.depthwiseRegularizer),e.depthwiseConstraint=Xt(this.depthwiseRegularizer),e}};O0.className="DepthwiseConv2D";ne.registerClass(O0);function fC(e,t,n,a){if(Array.isArray(e)){if(t!=null||n!=null)throw new V("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 gC(e,t,n,a=!1,r,s,i=!1,o=!1){return O(()=>{let l=t.shape.length;if(l<3)throw new V(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(Ua(2,l));if(t=De(t,u),s!=null)throw new ze("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=re(re(r,"bool"),"float32"),r.rank===l-1&&(r=Gt(r,-1)),r=De(r,u)),a&&(t=ba(t,0),r!=null&&(r=ba(r,0)));let p=[],d,c=n,h=t.shape[0],m=dt(t),f;r!=null&&(f=dt(r));for(let b=0;be(y,c));if(r==null)d=x[0],c=x[1];else{let v=O(()=>{let I=f[b],N=pe(ea(I),I),C=X(z(x[0],I),z(c[0],N)),_=c.map((F,D)=>X(z(x[1][D],I),z(F,N)));return{output:C,newStates:_}});d=v.output,c=v.newStates}o&&p.push(d)}let g;return o&&(g=At(p,1)),[d,g,c]})}var Mr=class bC extends We{constructor(t){super(t);let n;if(t.cell==null)throw new V("cell property is missing for the constructor of RNN.");if(Array.isArray(t.cell)?n=new Df({cells:t.cell}):n=t.cell,n.stateSize==null)throw new V("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=n,this.returnSequences=t.returnSequences==null?!1:t.returnSequences,this.returnState=t.returnState==null?!1:t.returnState,this.goBackwards=t.goBackwards==null?!1:t.goBackwards,this._stateful=t.stateful==null?!1:t.stateful,this.unroll=t.unroll==null?!1:t.unroll,this.supportsMasking=!0,this.inputSpec=[new zt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Ua(0,t).map(n=>null)}else return this.states_}setStates(t){this.states_=t}computeOutputShape(t){Bx(t)&&(t=t[0]),t=t;let n=this.cell.stateSize;Array.isArray(n)||(n=[n]);let a=n[0],r;if(this.returnSequences?r=[t[0],t[1],a]:r=[t[0],a],this.returnState){let s=[];for(let i of n)s.push([t[0],i]);return[r].concat(s)}else return r}computeMask(t,n){return O(()=>{Array.isArray(n)&&(n=n[0]);let a=this.returnSequences?n:null;if(this.returnState){let r=this.states.map(s=>null);return[a].concat(r)}else return a})}get states(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,n=[];for(let a=0;ai.shape[i.shape.length-1]),s))throw new V(`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 zt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(t,n=!1){O(()=>{if(!this.stateful)throw new Xr("Cannot call resetStates() on an RNN Layer that is not stateful.");let a=this.inputSpec[0].shape[0];if(a==null)throw new V("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(r=>It([a,r])):this.states_=[It([a,this.cell.stateSize])];else if(t==null)Ee(this.states_),this.keptStates!=null&&(Ee(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>It([a,r])):this.states_[0]=It([a,this.cell.stateSize]);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new V(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);n===!0?this.keptStates.push(this.states_.slice()):Ee(this.states_);for(let r=0;rHt(r.clone()))})}apply(t,n){let a=n==null?null:n.initialState,r=n==null?null:n.constants;n==null&&(n={});let s=fC(t,a,r,this.numConstants);t=s.inputs,a=s.initialState,r=s.constants;let i=[],o=[];if(a!=null){n.initialState=a,i=i.concat(a),this.stateSpec=[];for(let l of a)this.stateSpec.push(new zt({shape:l.shape}));o=o.concat(this.stateSpec)}if(r!=null&&(n.constants=r,i=i.concat(r),this.numConstants=r.length),i[0]instanceof Ha){let l=[t].concat(i),u=this.inputSpec.concat(o),p=this.inputSpec;this.inputSpec=u;let d=super.apply(l,n);return this.inputSpec=p,d}else return super.apply(t,n)}call(t,n){return O(()=>{let a=n==null?null:n.mask,r=n==null?null:n.training,s=n==null?null:n.initialState;t=Te(t),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(t));let i=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==i)throw new V(`RNN Layer has ${i} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let o={training:r},l=gC((h,m)=>{let f=this.cell.call([h].concat(m),o);return[f[0],f.slice(1)]},t,s,this.goBackwards,a,null,this.unroll,this.returnSequences),u=l[0],p=l[1],d=l[2];this.stateful&&this.resetStates(d,r);let c=this.returnSequences?p:u;return this.returnState?[c].concat(d):c})}getInitialState(t){return O(()=>{let n=It(t.shape);return n=fe(n,[1,2]),n=wd(n),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(a=>a>1?zx(n,[1,a]):n):this.cell.stateSize>1?[zx(n,[1,this.cell.stateSize])]:[n]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(t)}getConfig(){let t=super.getConfig(),n={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(n.numConstants=this.numConstants);let a=this.cell.getConfig();return this.getClassName()===bC.className&&(n.cell={className:this.cell.getClassName(),config:a}),Object.assign(Object.assign(Object.assign({},a),t),n)}static fromConfig(t,n,a={}){let r=n.cell,s=Ba(r,a);return new t(Object.assign(n,{cell:s}))}};Mr.className="RNN";ne.registerClass(Mr);var Td=class extends We{},Ff=class extends Td{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,tn(this.units,"units"),this.activation=ms(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=Ul([1,ds([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ul([1,ds([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Je(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 O(()=>{if(e=e,e.length!==2)throw new V(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let a=t.training==null?!1:t.training;0ea(e),rate:this.dropout,training:a,dropoutFunc:this.dropoutFunc})),0ea(n),rate:this.recurrentDropout,training:a,dropoutFunc:this.dropoutFunc}));let r,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?r=or(z(e,s),this.kernel.read()):r=or(e,this.kernel.read()),this.bias!=null&&(r=Ka(r,this.bias.read())),i!=null&&(n=z(n,i));let o=X(r,or(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:hs(this.activation),useBias:this.useBias,kernelInitializer:Et(this.kernelInitializer),recurrentInitializer:Et(this.recurrentInitializer),biasInitializer:Et(this.biasInitializer),kernelRegularizer:ft(this.kernelRegularizer),recurrentRegularizer:ft(this.recurrentRegularizer),biasRegularizer:ft(this.biasRegularizer),activityRegularizer:ft(this.activityRegularizer),kernelConstraint:Xt(this.kernelConstraint),recurrentConstraint:Xt(this.recurrentConstraint),biasConstraint:Xt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},e),t)}};Ff.className="SimpleRNNCell";ne.registerClass(Ff);var P0=class extends Mr{constructor(e){e.cell=new Ff(e),super(e)}call(e,t){return O(()=>{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(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)}};P0.className="SimpleRNN";ne.registerClass(P0);var $f=class extends Td{constructor(e){if(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",e.resetAfter)throw new V("GRUCell does not support reset_after parameter set to true.");this.units=e.units,tn(this.units,"units"),this.activation=ms(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=ms(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=Ul([1,ds([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ul([1,ds([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Je(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 O(()=>{if(e=e,e.length!==2)throw new V(`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],0ea(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0ea(a),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(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)}};L0.className="GRU";ne.registerClass(L0);var Cd=class extends Td{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,tn(this.units,"units"),this.activation=ms(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=ms(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=Ul([1,ds([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ul([1,ds([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=Je(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 $a{apply(i,o){let l=r.apply([s]),u=new bf().apply([s]),p=r.apply([s*2]);return bI(bI(l,u),p)}},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 O(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new V(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let a=e[1],r=e[2];e=e[0],0ea(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0ea(a),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,p;0{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};z0.className="LSTM";ne.registerClass(z0);var Df=class extends Td{constructor(e){super(e),this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return O(()=>{e=e;let n=e.slice(1),a=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?a.push(n.splice(0,i.stateSize.length)):a.push(n.splice(0,1));a.reverse();let r=[],s;for(let i=0;i{ai(`RNNCell_${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(Object.assign({},e),n)}static fromConfig(e,t,n={}){let a=[];for(let r of t.cells)a.push(Ba(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 Vx(e)}setWeights(e){let t=[];for(let n of this.cells){let a=n.weights.length,r=e.splice(a);for(let s=0;ss!=null?s(t(),n):w2(t(),n),o=()=>Id(i,t,a);return!r||r<=1?Ht(o().clone()):Array(r).fill(void 0).map(o).map(l=>Ht(l.clone()))}var f6=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{if(this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new V("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 O(()=>{let{stateSize:t}=this.cell,n=e.shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)],s=It(r);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){O(()=>{if(!this.stateful)throw new Xr("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 V("If an RNN is stateful, it needs to know its batch size. 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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 l=this.biasInitializer,u=this.filters;o=new(t=class extends $a{apply(p,d){let c=l.apply([u]),h=On([u]),m=l.apply([u*2]);return t0([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 O(()=>{if(e.length!==3)throw new V(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0ea(a),rate:this.dropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,l=(Z,J,ee)=>!J||!J[ee]?Z:z(J[ee],Z),u=l(a,o,0),p=l(a,o,1),d=l(a,o,2),c=l(a,o,3);0ea(r),rate:this.recurrentDropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,m=l(r,h,0),f=l(r,h,1),g=l(r,h,2),b=l(r,h,3),y=3,[x,v,I,N]=Pn(this.kernel.read(),i,y),[C,_,F,D]=this.useBias?Pn(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,x,C,this.padding),p=this.inputConv(p,v,_,this.padding),d=this.inputConv(d,I,F,this.padding),c=this.inputConv(c,N,D,this.padding);let[$,S,M,B]=Pn(this.recurrentKernel.read(),i,y);m=this.recurrentConv(m,$),f=this.recurrentConv(f,S),g=this.recurrentConv(g,M),b=this.recurrentConv(b,B);let U=this.recurrentActivation.apply(X(u,m)),H=this.recurrentActivation.apply(X(p,f)),j=X(z(H,s),z(U,this.activation.apply(X(d,g)))),K=z(this.recurrentActivation.apply(X(c,b)),this.activation.apply(j));return[K,K,j]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=f6(e,["units"]),a={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),a)}inputConv(e,t,n,a){let r=$t(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Ka(r,n,this.dataFormat):r}recurrentConv(e,t){return $t(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Rf.className="ConvLSTM2DCell";ne.registerClass(Rf);var W0=class extends yC{constructor(e){let t=new Rf(e);super(Object.assign(Object.assign({},e),{cell:t}))}static fromConfig(e,t){return new e(t)}};W0.className="ConvLSTM2D";ne.registerClass(W0);var Mf=class extends We{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.invokeCallHook(e,t);let n=Te(e);if(0w2(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()}};Mf.className="Dropout";ne.registerClass(Mf);var B0=class extends Mf{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};B0.className="SpatialDropout1D";ne.registerClass(B0);var V0=class extends We{constructor(e){if(super(e),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,tn(this.units,"units"),this.activation=ms(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Yt(e.kernelConstraint),this.biasConstraint=Yt(e.biasConstraint),this.kernelRegularizer=Nt(e.kernelRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.activityRegularizer=Nt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=Je(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=Je(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return O(()=>{this.invokeCallHook(e,t);let n=Te(e),a=m2(this.activation.getClassName()),r;return a!=null?r=or(n,this.kernel.read(),a,this.bias?this.bias.read():null):(r=or(n,this.kernel.read()),this.bias!=null&&(r=Ka(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:hs(this.activation),useBias:this.useBias,kernelInitializer:Et(this.kernelInitializer),biasInitializer:Et(this.biasInitializer),kernelRegularizer:ft(this.kernelRegularizer),biasRegularizer:ft(this.biasRegularizer),activityRegularizer:ft(this.activityRegularizer),kernelConstraint:Xt(this.kernelConstraint),biasConstraint:Xt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};V0.className="Dense";ne.registerClass(V0);var U0=class extends We{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=Je(e);for(let t of e.slice(1))if(t==null)throw new V(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). 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this.model.fitDataset(t,n)}async trainOnBatch(t,n){return this.model.trainOnBatch(t,n)}static fromConfig(t,n,a={},r=!1){let s,i={};if(n instanceof Array){if(n[0].className==null||n[0].className==="Merge")throw new V("Legacy serialization format not supported yet.");s=n}else w.assert(n.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),s=n.layers,delete n.layers,i=n;let o=new t(i);if(!(o instanceof Kx))throw new ze(`Sequential.fromConfig called on non-Sequential input: ${o}`);for(let l of s){let u=Ba(l,void 0,r);r&&u.setFastWeightInitDuringBuild(!0),o.add(u)}return o}set stopTraining(t){if(this.model==null)throw new V("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=t}get stopTraining(){if(this.model==null)throw new V("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let t=[];for(let n of this.layers){let a={};a.className=n.getClassName(),a.config=n.getConfig(),t.push(a)}return{name:this.name,layers:t}}};Ef.className="Sequential";ne.registerClass(Ef);function pj(e){return new Er(e)}function cj(e){return new Ef(e)}function j2(e){return S2(e)}function dj(e,t){f0.registerCallbackConstructor(e,t)}var xn=class extends ne.Serializable{getConfig(){return{}}},q2=class extends xn{apply(e,t=1){return FG(e,t)}};q2.className="elu";ne.registerClass(q2);var K2=class extends xn{apply(e){return Qm(e)}};K2.className="selu";ne.registerClass(K2);var X2=class extends xn{apply(e){return Ke(e)}};X2.className="relu";ne.registerClass(X2);var Y2=class extends xn{apply(e){return O(()=>ds(6,Ke(e)))}};Y2.className="relu6";ne.registerClass(Y2);var Z2=class extends xn{apply(e){return e}};Z2.className="linear";ne.registerClass(Z2);var J2=class extends xn{apply(e){return ha(e)}};J2.className="sigmoid";ne.registerClass(J2);var Q2=class extends 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xn{apply(e,t=1){return O(()=>z(ha(z(e,t)),e))}};oC.className="swish";ne.registerClass(oC);function ms(e){return e.getClassName()}function fx(e,t={}){return vd(e,ne.SerializationMap.getMap().classNameMap,t,"activation")}function fs(e){if(e==null){let t={};return t.className="linear",t.config={},fx(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},fx(t)}else return e instanceof xn?e:fx(e)}function k0(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 lC=class extends ne.Serializable{},Nd=class extends lC{constructor(e){super(),k0(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 O(()=>{let t=It([1]);return this.hasL1&&(t=X(t,fe(z(this.l1,Lt(e))))),this.hasL2&&(t=X(t,fe(z(this.l2,kd(e))))),W(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Nd.className="L1L2";ne.registerClass(Nd);function hj(e){return k0(e),new Nd({l1:e!=null?e.l1:null,l2:0})}function mj(e){return k0(e),new Nd({l2:e!=null?e.l2:null,l1:0})}var FI={l1l2:"L1L2"};function ft(e){return Qw(e)}function $I(e,t={}){return vd(e,ne.SerializationMap.getMap().classNameMap,t,"regularizer")}function Nt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in FI?FI[e]:e,config:{}};return $I(t)}else return e instanceof lC?e:$I(e)}var I0=class extends We{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Te(e);let n=Ke(e);return this.maxValue!=null&&(n=an(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};I0.className="ReLU";ne.registerClass(I0);var S0=class extends We{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=Te(e);return ud(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};S0.className="LeakyReLU";ne.registerClass(S0);var N0=class extends We{constructor(e){if(super(e==null?{}:e),this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=St(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Nt(e.alphaRegularizer),this.alphaConstraint=Yt(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 V(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=Je(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{let n=Te(e),a=t.mask;if(a!=null){let r=z(pe(Pn(n.shape),re(a,n.dtype)),xe(-1e9));n=X(n,r)}return this.axis instanceof Array?this.axis.length>1?dn(pe(n,cd(n,this.axis,!0))):this.softmax(n,this.axis[0]):this.softmax(n,this.axis)})}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};E0.className="Softmax";ne.registerClass(E0);function Fl(e,t,n){if(typeof e=="number")return bi(e,t);if(e.length!==t)throw new V(`The ${n} argument must be an integer or tuple of ${t} integers. 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V(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new V(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=_0(e,s);if(r==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Vl.conv2d({x:l,filter:t,strides:a,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=De(l,[0,3,1,2])),l})}function gj(e,t,n,a=[1,1,1],r="valid",s,i){return O(()=>{if(s==null&&(s=Ga()),Rt(s),e.rank!==4&&e.rank!==5)throw new V(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new V(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=uC(e,s);if(r==="causal")throw new ze("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=aw(o,t,a,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Ka(o,n)),s==="channelsFirst"&&(o=De(o,[0,4,1,2,3])),o})}var pC=class cC extends We{constructor(t,n){if(super(n),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",cC.verifyArgs(n),this.rank=t,tn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new ze(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Fl(n.kernelSize,t,"kernelSize"),this.strides=Fl(n.strides==null?1:n.strides,t,"strides"),this.padding=n.padding==null?"valid":n.padding,va(this.padding),this.dataFormat=n.dataFormat==null?"channelsLast":n.dataFormat,Rt(this.dataFormat),this.activation=fs(n.activation),this.useBias=n.useBias==null?!0:n.useBias,this.biasInitializer=St(n.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Yt(n.biasConstraint),this.biasRegularizer=Nt(n.biasRegularizer),this.activityRegularizer=Nt(n.activityRegularizer),this.dilationRate=Fl(n.dilationRate==null?1:n.dilationRate,t,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new V(`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 V(`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 V(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(t){if(tr("kernelSize"in t,"required key 'kernelSize' not in config"),typeof t.kernelSize!="number"&&!e0(t.kernelSize,"number",1,3))throw new V(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(t.kernelSize)}.`)}getConfig(){let t={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:ms(this.activation),useBias:this.useBias,biasInitializer:Et(this.biasInitializer),biasRegularizer:ft(this.biasRegularizer),activityRegularizer:ft(this.activityRegularizer),biasConstraint:Xt(this.biasConstraint)},n=super.getConfig();return Object.assign(t,n),t}},_f=class dC extends pC{constructor(t,n){super(t,n),this.kernel=null,dC.verifyArgs(n),this.filters=n.filters,tn(this.filters,"filters"),this.kernelInitializer=St(n.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Yt(n.kernelConstraint),this.kernelRegularizer=Nt(n.kernelRegularizer)}build(t){t=Je(t);let n=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[n]==null)throw new V(`The channel dimension of the input should be defined. 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Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new V(`SeparableConv${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=St(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Nt(t.depthwiseRegularizer),this.depthwiseConstraint=Yt(t.depthwiseConstraint),this.pointwiseInitializer=St(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Nt(t.pointwiseRegularizer),this.pointwiseConstraint=Yt(t.pointwiseConstraint)}build(e){if(e=Je(e),e.length{e=Te(e);let n;if(this.rank===1)throw new ze("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=De(e,[0,2,3,1])),n=_s(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Ka(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=De(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=Et(this.depthwiseInitializer),e.pointwiseInitializer=Et(this.pointwiseInitializer),e.depthwiseRegularizer=ft(this.depthwiseRegularizer),e.pointwiseRegularizer=ft(this.pointwiseRegularizer),e.depthwiseConstraint=Xt(this.depthwiseConstraint),e.pointwiseConstraint=Xt(this.pointwiseConstraint),e}};fC.className="SeparableConv";var $0=class extends fC{constructor(e){super(2,e)}};$0.className="SeparableConv2D";ne.registerClass($0);var D0=class gC extends _f{constructor(t){super(1,t),gC.verifyArgs(t),this.inputSpec=[{ndim:3}]}getConfig(){let t=super.getConfig();return delete t.rank,delete t.dataFormat,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!e0(t.kernelSize,"number",1,1))throw new V(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(t.kernelSize)}.`)}};D0.className="Conv1D";ne.registerClass(D0);var R0=class extends We{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 O(()=>{if(e=Te(e),this.dataFormat==="channelsLast"){let n=Ih(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Ih(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Ih(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Ih(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}};R0.className="Cropping2D";ne.registerClass(R0);var M0=class extends We{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,Rt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,SG(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 O(()=>{let n=Te(e),a=n.shape;if(this.dataFormat==="channelsFirst"){n=De(n,[0,2,3,1]);let r=this.size[0]*a[2],s=this.size[1]*a[3],i=this.interpolation==="nearest"?Zn.resizeNearestNeighbor(n,[r,s]):Zn.resizeBilinear(n,[r,s]);return De(i,[0,3,1,2])}else{let r=this.size[0]*a[1],s=this.size[1]*a[2];return this.interpolation==="nearest"?Zn.resizeNearestNeighbor(n,[r,s]):Zn.resizeBilinear(n,[r,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},t=super.getConfig();return Object.assign(e,t),e}};M0.className="UpSampling2D";ne.registerClass(M0);function bj(e,t,n=[1,1],a="valid",r,s){return O(()=>{r==null&&(r=Ga()),Rt(r);let i=_0(e,r);if(e.rank!==4)throw new V(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new V(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=Ts(i,t,n,a==="same"?"same":"valid","NHWC",s),r==="channelsFirst"&&(i=De(i,[0,3,1,2])),i})}var O0=class extends pC{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=St(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Yt(e.depthwiseConstraint),this.depthwiseRegularizer=Nt(e.depthwiseRegularizer)}build(e){if(e=Je(e),e.length<4)throw new V(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new V(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${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 O(()=>{e=Te(e);let n=bj(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Ka(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=Je(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=Va(t,this.kernelSize[0],this.padding,this.strides[0]),s=Va(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=Et(this.depthwiseInitializer),e.depthwiseRegularizer=ft(this.depthwiseRegularizer),e.depthwiseConstraint=Xt(this.depthwiseRegularizer),e}};O0.className="DepthwiseConv2D";ne.registerClass(O0);function bC(e,t,n,a){if(Array.isArray(e)){if(t!=null||n!=null)throw new V("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 yC(e,t,n,a=!1,r,s,i=!1,o=!1){return O(()=>{let l=t.shape.length;if(l<3)throw new V(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(Ua(2,l));if(t=De(t,u),s!=null)throw new ze("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=re(re(r,"bool"),"float32"),r.rank===l-1&&(r=Gt(r,-1)),r=De(r,u)),a&&(t=ba(t,0),r!=null&&(r=ba(r,0)));let p=[],d,c=n,h=t.shape[0],m=dt(t),f;r!=null&&(f=dt(r));for(let b=0;be(y,c));if(r==null)d=x[0],c=x[1];else{let v=O(()=>{let I=f[b],N=pe(ea(I),I),C=X(z(x[0],I),z(c[0],N)),_=c.map((F,D)=>X(z(x[1][D],I),z(F,N)));return{output:C,newStates:_}});d=v.output,c=v.newStates}o&&p.push(d)}let g;return o&&(g=At(p,1)),[d,g,c]})}var Mr=class xC extends We{constructor(t){super(t);let n;if(t.cell==null)throw new V("cell property is missing for the constructor of RNN.");if(Array.isArray(t.cell)?n=new Rf({cells:t.cell}):n=t.cell,n.stateSize==null)throw new V("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=n,this.returnSequences=t.returnSequences==null?!1:t.returnSequences,this.returnState=t.returnState==null?!1:t.returnState,this.goBackwards=t.goBackwards==null?!1:t.goBackwards,this._stateful=t.stateful==null?!1:t.stateful,this.unroll=t.unroll==null?!1:t.unroll,this.supportsMasking=!0,this.inputSpec=[new zt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Ua(0,t).map(n=>null)}else return this.states_}setStates(t){this.states_=t}computeOutputShape(t){Vx(t)&&(t=t[0]),t=t;let n=this.cell.stateSize;Array.isArray(n)||(n=[n]);let a=n[0],r;if(this.returnSequences?r=[t[0],t[1],a]:r=[t[0],a],this.returnState){let s=[];for(let i of n)s.push([t[0],i]);return[r].concat(s)}else return r}computeMask(t,n){return O(()=>{Array.isArray(n)&&(n=n[0]);let a=this.returnSequences?n:null;if(this.returnState){let r=this.states.map(s=>null);return[a].concat(r)}else return a})}get states(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,n=[];for(let a=0;ai.shape[i.shape.length-1]),s))throw new V(`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 zt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(t,n=!1){O(()=>{if(!this.stateful)throw new Xr("Cannot call resetStates() on an RNN Layer that is not stateful.");let a=this.inputSpec[0].shape[0];if(a==null)throw new V("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(r=>It([a,r])):this.states_=[It([a,this.cell.stateSize])];else if(t==null)Ee(this.states_),this.keptStates!=null&&(Ee(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>It([a,r])):this.states_[0]=It([a,this.cell.stateSize]);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new V(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);n===!0?this.keptStates.push(this.states_.slice()):Ee(this.states_);for(let r=0;rHt(r.clone()))})}apply(t,n){let a=n==null?null:n.initialState,r=n==null?null:n.constants;n==null&&(n={});let s=bC(t,a,r,this.numConstants);t=s.inputs,a=s.initialState,r=s.constants;let i=[],o=[];if(a!=null){n.initialState=a,i=i.concat(a),this.stateSpec=[];for(let l of a)this.stateSpec.push(new zt({shape:l.shape}));o=o.concat(this.stateSpec)}if(r!=null&&(n.constants=r,i=i.concat(r),this.numConstants=r.length),i[0]instanceof Ha){let l=[t].concat(i),u=this.inputSpec.concat(o),p=this.inputSpec;this.inputSpec=u;let d=super.apply(l,n);return this.inputSpec=p,d}else return super.apply(t,n)}call(t,n){return O(()=>{let a=n==null?null:n.mask,r=n==null?null:n.training,s=n==null?null:n.initialState;t=Te(t),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(t));let i=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==i)throw new V(`RNN Layer has ${i} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let o={training:r},l=yC((h,m)=>{let f=this.cell.call([h].concat(m),o);return[f[0],f.slice(1)]},t,s,this.goBackwards,a,null,this.unroll,this.returnSequences),u=l[0],p=l[1],d=l[2];this.stateful&&this.resetStates(d,r);let c=this.returnSequences?p:u;return this.returnState?[c].concat(d):c})}getInitialState(t){return O(()=>{let n=It(t.shape);return n=fe(n,[1,2]),n=wd(n),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(a=>a>1?Wx(n,[1,a]):n):this.cell.stateSize>1?[Wx(n,[1,this.cell.stateSize])]:[n]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(t)}getConfig(){let t=super.getConfig(),n={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(n.numConstants=this.numConstants);let a=this.cell.getConfig();return this.getClassName()===xC.className&&(n.cell={className:this.cell.getClassName(),config:a}),Object.assign(Object.assign(Object.assign({},a),t),n)}static fromConfig(t,n,a={}){let r=n.cell,s=Ba(r,a);return new t(Object.assign(n,{cell:s}))}};Mr.className="RNN";ne.registerClass(Mr);var Td=class extends We{},$f=class extends Td{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,tn(this.units,"units"),this.activation=fs(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=Ul([1,hs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ul([1,hs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Je(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 O(()=>{if(e=e,e.length!==2)throw new V(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let a=t.training==null?!1:t.training;0ea(e),rate:this.dropout,training:a,dropoutFunc:this.dropoutFunc})),0ea(n),rate:this.recurrentDropout,training:a,dropoutFunc:this.dropoutFunc}));let r,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?r=or(z(e,s),this.kernel.read()):r=or(e,this.kernel.read()),this.bias!=null&&(r=Ka(r,this.bias.read())),i!=null&&(n=z(n,i));let o=X(r,or(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:ms(this.activation),useBias:this.useBias,kernelInitializer:Et(this.kernelInitializer),recurrentInitializer:Et(this.recurrentInitializer),biasInitializer:Et(this.biasInitializer),kernelRegularizer:ft(this.kernelRegularizer),recurrentRegularizer:ft(this.recurrentRegularizer),biasRegularizer:ft(this.biasRegularizer),activityRegularizer:ft(this.activityRegularizer),kernelConstraint:Xt(this.kernelConstraint),recurrentConstraint:Xt(this.recurrentConstraint),biasConstraint:Xt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},e),t)}};$f.className="SimpleRNNCell";ne.registerClass($f);var P0=class extends Mr{constructor(e){e.cell=new $f(e),super(e)}call(e,t){return O(()=>{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(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)}};P0.className="SimpleRNN";ne.registerClass(P0);var Df=class extends Td{constructor(e){if(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",e.resetAfter)throw new V("GRUCell does not support reset_after parameter set to true.");this.units=e.units,tn(this.units,"units"),this.activation=fs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=fs(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=Ul([1,hs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ul([1,hs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Je(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 O(()=>{if(e=e,e.length!==2)throw new V(`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],0ea(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0ea(a),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(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)}};L0.className="GRU";ne.registerClass(L0);var Cd=class extends Td{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,tn(this.units,"units"),this.activation=fs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=fs(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=Ul([1,hs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ul([1,hs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=Je(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 $a{apply(i,o){let l=r.apply([s]),u=new yf().apply([s]),p=r.apply([s*2]);return bI(bI(l,u),p)}},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 O(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new V(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let a=e[1],r=e[2];e=e[0],0ea(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0ea(a),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,p;0{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};z0.className="LSTM";ne.registerClass(z0);var Rf=class extends Td{constructor(e){super(e),this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return O(()=>{e=e;let n=e.slice(1),a=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?a.push(n.splice(0,i.stateSize.length)):a.push(n.splice(0,1));a.reverse();let r=[],s;for(let i=0;i{ri(`RNNCell_${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(Object.assign({},e),n)}static fromConfig(e,t,n={}){let a=[];for(let r of t.cells)a.push(Ba(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 Ux(e)}setWeights(e){let t=[];for(let n of this.cells){let a=n.weights.length,r=e.splice(a);for(let s=0;ss!=null?s(t(),n):w2(t(),n),o=()=>Id(i,t,a);return!r||r<=1?Ht(o().clone()):Array(r).fill(void 0).map(o).map(l=>Ht(l.clone()))}var yj=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{if(this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new V("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 O(()=>{let{stateSize:t}=this.cell,n=e.shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)],s=It(r);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){O(()=>{if(!this.stateful)throw new Xr("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 V("If an RNN is stateful, it needs to know its batch size. 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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 l=this.biasInitializer,u=this.filters;o=new(t=class extends $a{apply(p,d){let c=l.apply([u]),h=Pn([u]),m=l.apply([u*2]);return t0([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 O(()=>{if(e.length!==3)throw new V(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0ea(a),rate:this.dropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,l=(Z,J,ee)=>!J||!J[ee]?Z:z(J[ee],Z),u=l(a,o,0),p=l(a,o,1),d=l(a,o,2),c=l(a,o,3);0ea(r),rate:this.recurrentDropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,m=l(r,h,0),f=l(r,h,1),g=l(r,h,2),b=l(r,h,3),y=3,[x,v,I,N]=Ln(this.kernel.read(),i,y),[C,_,F,D]=this.useBias?Ln(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,x,C,this.padding),p=this.inputConv(p,v,_,this.padding),d=this.inputConv(d,I,F,this.padding),c=this.inputConv(c,N,D,this.padding);let[$,S,M,B]=Ln(this.recurrentKernel.read(),i,y);m=this.recurrentConv(m,$),f=this.recurrentConv(f,S),g=this.recurrentConv(g,M),b=this.recurrentConv(b,B);let U=this.recurrentActivation.apply(X(u,m)),H=this.recurrentActivation.apply(X(p,f)),q=X(z(H,s),z(U,this.activation.apply(X(d,g)))),K=z(this.recurrentActivation.apply(X(c,b)),this.activation.apply(q));return[K,K,q]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=yj(e,["units"]),a={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),a)}inputConv(e,t,n,a){let r=$t(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Ka(r,n,this.dataFormat):r}recurrentConv(e,t){return $t(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Mf.className="ConvLSTM2DCell";ne.registerClass(Mf);var W0=class extends vC{constructor(e){let t=new Mf(e);super(Object.assign(Object.assign({},e),{cell:t}))}static fromConfig(e,t){return new e(t)}};W0.className="ConvLSTM2D";ne.registerClass(W0);var Of=class extends We{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.invokeCallHook(e,t);let n=Te(e);if(0w2(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()}};Of.className="Dropout";ne.registerClass(Of);var B0=class extends Of{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};B0.className="SpatialDropout1D";ne.registerClass(B0);var V0=class extends We{constructor(e){if(super(e),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,tn(this.units,"units"),this.activation=fs(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Yt(e.kernelConstraint),this.biasConstraint=Yt(e.biasConstraint),this.kernelRegularizer=Nt(e.kernelRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.activityRegularizer=Nt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=Je(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=Je(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return O(()=>{this.invokeCallHook(e,t);let n=Te(e),a=m2(this.activation.getClassName()),r;return a!=null?r=or(n,this.kernel.read(),a,this.bias?this.bias.read():null):(r=or(n,this.kernel.read()),this.bias!=null&&(r=Ka(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:ms(this.activation),useBias:this.useBias,kernelInitializer:Et(this.kernelInitializer),biasInitializer:Et(this.biasInitializer),kernelRegularizer:ft(this.kernelRegularizer),biasRegularizer:ft(this.biasRegularizer),activityRegularizer:ft(this.activityRegularizer),kernelConstraint:Xt(this.kernelConstraint),biasConstraint:Xt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};V0.className="Dense";ne.registerClass(V0);var U0=class extends We{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=Je(e);for(let t of e.slice(1))if(t==null)throw new V(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). 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We{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 V(`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];tn(this.poolSize,"poolSize"),tn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),va(this.padding),this.inputSpec=[new zt({ndim:5})]}computeOutputShape(e){e=Je(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=Va(t,this.poolSize[0],this.padding,this.strides[0]),n=Va(n,this.poolSize[1],this.padding,this.strides[1]),a=Va(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 O(()=>(this.invokeCallHook(e,t),this.poolingFunction(Te(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}},h1=class extends SC{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),va(a),wC(e,t,n,a,r,"max")}};h1.className="MaxPooling3D";ne.registerClass(h1);var m1=class extends SC{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),va(a),wC(e,t,n,a,r,"avg")}};m1.className="AveragePooling3D";ne.registerClass(m1);var NC=class extends We{constructor(e){super(e),this.inputSpec=[new zt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new 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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=Ba(t.layer);if(delete t.layer,t.numConstants!=null)throw new ze("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let a=t;return a.layer=n,new e(a)}};v1.className="Bidirectional";ne.registerClass(v1);var w1=class extends We{constructor(e){super(e),this.scale=e.scale,e.offset?this.offset=e.offset:this.offset=0}getConfig(){let e={scale:this.scale,offset:this.offset},t=super.getConfig();return Object.assign(e,t),e}call(e,t){return 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e=Je(e),e==null?[this.numTokens]:this.outputMode==="oneHot"&&e[e.length-1]!==1?(e.push(this.numTokens),e):(e[e.length-1]=this.numTokens,e)}call(e,t){return O(()=>{e=Te(e),e.dtype!=="int32"&&(e=ir(e,"int32"));let n;if(typeof t.countWeights!="undefined"){if(this.outputMode!=="count")throw new V(`countWeights is not used when outputMode !== count. + Received countWeights=${t.countWeights}`);n=Te(t.countWeights)}let a=ma(e),r=Ol(e),s=Cn(this.numTokens,a).bufferSync().get(0),i=$r(r,0).bufferSync().get(0);if(!(s&&i))throw new V(`Input values must be between 0 < values <= numTokens with numTokens=${this.numTokens}`);return Ej(e,this.outputMode,this.numTokens,n)})}};I1.className="CategoryEncoding";ne.registerClass(I1);var _j=["bilinear","nearest"],RI=new Set(_j),S1=class extends We{constructor(e){if(super(e),this.height=e.height,this.width=e.width,e.interpolation)if(RI.has(e.interpolation))this.interpolation=e.interpolation;else throw new V(`Invalid interpolation parameter: ${e.interpolation} 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Sj=[{tfOpName:"EuclideanNorm",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool",defaultValue:!1}]},{tfOpName:"FusedBatchNorm",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"FusedBatchNormV2",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"FusedBatchNormV3",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"LRN",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"depth_radius",name:"radius",type:"number",defaultValue:5},{tfName:"bias",name:"bias",type:"number",defaultValue:1},{tfName:"alpha",name:"alpha",type:"number",defaultValue:1},{tfName:"beta",name:"beta",type:"number",defaultValue:.5}]},{tfOpName:"Softmax",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"LogSoftmax",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}]}],ZC={};_e(ZC,{json:()=>Nj});var 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TypeError(`Node type ${e.op} is not implemented`)}};function VI(e,t,n){let[a,r]=k("fusedOps",e,t,n),s=a==="biasadd",i=!s,o=r==="prelu",l=a==="fusedbatchnorm",u=k("numArgs",e,t,n);if(s){if(o&&u!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!o&&s&&u!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(l)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");let p=k("strides",e,t,n),d=Mh(e,t,n),c=k("dataFormat",e,t,n).toUpperCase(),h=k("dilations",e,t,n),[m,f]=k("args",e,t,n);i&&(f=m,m=void 0);let g=k("leakyreluAlpha",e,t,n);return{stride:p,pad:d,dataFormat:c,dilations:h,biasArg:m,preluArg:f,activationFunc:r,leakyreluAlpha:g}}var Bj=(e,t,n,a=sn)=>{switch(e.op){case"Conv1D":{let 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TypeError(`Node type ${e.op} is not implemented`)}},Zj=(e,t,n,a=sn)=>{switch(e.op){case"EuclideanNorm":return[a.euclideanNorm(k("x",e,t,n),k("axis",e,t,n),k("keepDims",e,t,n))];case"FusedBatchNorm":case"FusedBatchNormV2":return[a.batchNorm(k("x",e,t,n),k("mean",e,t,n),k("variance",e,t,n),k("offset",e,t,n),k("scale",e,t,n),k("epsilon",e,t,n))];case"FusedBatchNormV3":return[a.batchNorm(k("x",e,t,n),k("mean",e,t,n),k("variance",e,t,n),k("offset",e,t,n),k("scale",e,t,n),k("epsilon",e,t,n))];case"LRN":return[a.localResponseNormalization(k("x",e,t,n),k("radius",e,t,n),k("bias",e,t,n),k("alpha",e,t,n),k("beta",e,t,n))];case"Softmax":return[a.softmax(k("x",e,t,n))];case"LogSoftmax":return[a.logSoftmax(k("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Jj=(e,t,n,a=sn)=>{switch(e.op){case"RaggedGather":{let{outputNestedSplits:r,outputDenseValues:s}=a.raggedGather(k("paramsNestedSplits",e,t,n),k("paramsDenseValues",e,t,n),k("indices",e,t,n),k("outputRaggedRank",e,t,n));return r.concat(s)}case"RaggedRange":{let{rtNestedSplits:r,rtDenseValues:s}=a.raggedRange(k("starts",e,t,n),k("limits",e,t,n),k("splits",e,t,n));return[r,s]}case"RaggedTensorToTensor":return[a.raggedTensorToTensor(k("shape",e,t,n),k("values",e,t,n),k("defaultValue",e,t,n),k("rowPartitionTensors",e,t,n),k("rowPartitionTypes",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Qj=(e,t,n,a=sn)=>{switch(e.op){case"Max":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.max(k("x",e,t,n),o,l)]}case"Mean":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.mean(k("x",e,t,n),o,l)]}case"Min":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.min(k("x",e,t,n),o,l)]}case"Sum":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.sum(k("x",e,t,n),o,l)]}case"All":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.all(k("x",e,t,n),o,l)]}case"Any":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.any(k("x",e,t,n),o,l)]}case"ArgMax":{let o=k("axis",e,t,n);return[a.argMax(k("x",e,t,n),o)]}case"ArgMin":{let o=k("axis",e,t,n);return[a.argMin(k("x",e,t,n),o)]}case"Prod":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.prod(k("x",e,t,n),o,l)]}case"Cumprod":{let o=k("axis",e,t,n),l=k("exclusive",e,t,n),u=k("reverse",e,t,n);return[a.cumprod(k("x",e,t,n),o,l,u)]}case"Cumsum":{let o=k("axis",e,t,n),l=k("exclusive",e,t,n),u=k("reverse",e,t,n);return[a.cumsum(k("x",e,t,n),o,l,u)]}case"Bincount":let r=k("x",e,t,n),s=k("weights",e,t,n),i=k("size",e,t,n);return[a.bincount(r,s,i)];case"DenseBincount":{let o=k("x",e,t,n),l=k("weights",e,t,n),u=k("size",e,t,n),p=k("binaryOutput",e,t,n);return[a.denseBincount(o,l,u,p)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},e5=(e,t,n,a=sn)=>{switch(e.op){case"ConcatV2":case"Concat":{let r=k("n",e,t,n),s=k("axis",e,t,n),i=k("tensors",e,t,n);return i=i.slice(0,r),[a.concat(i,s)]}case"Gather":{let r=k("x",e,t,n),s=k("indices",e,t,n);return[a.gather(r,a.cast(s,"int32"),0)]}case"GatherV2":{let r=k("axis",e,t,n),s=k("batchDims",e,t,n),i=k("x",e,t,n),o=k("indices",e,t,n);return[a.gather(i,a.cast(o,"int32"),r,s)]}case"Reverse":{let r=k("dims",e,t,n),s=[];for(let o=0;o{let r=k("axis",e,t,n),s=k("tensors",e,t,n),i=s[0].shape,o=a.squeeze(s[0]).shape,l=s.map(u=>{let p=w.arraysEqual(u.shape,i);if(!p&&!w.arraysEqual(a.squeeze(u).shape,o))throw new Error("the input tensors shape does not match");return p?u:a.reshape(u,i)});return[a.stack(l,r)]});case"Unpack":{let r=k("axis",e,t,n),s=k("tensor",e,t,n);return a.unstack(s,r)}case"Tile":{let r=k("reps",e,t,n);return[a.tile(k("x",e,t,n),r)]}case"Split":case"SplitV":{let r=k("axis",e,t,n),s=k("numOrSizeSplits",e,t,n),i=k("x",e,t,n);return a.split(i,s,r)}case"ScatterNd":{let r=k("indices",e,t,n),s=k("values",e,t,n),i=k("shape",e,t,n);return[a.scatterND(r,s,i)]}case"GatherNd":{let r=k("x",e,t,n),s=k("indices",e,t,n);return[a.gatherND(r,s)]}case"SparseToDense":{let r=k("sparseIndices",e,t,n),s=k("outputShape",e,t,n),i=k("sparseValues",e,t,n),o=k("defaultValue",e,t,n);return[a.sparseToDense(r,i,s,i.dtype===o.dtype?o:a.cast(o,i.dtype))]}case"TensorScatterUpdate":{let r=k("indices",e,t,n),s=k("values",e,t,n),i=k("tensor",e,t,n);return[a.tensorScatterUpdate(i,r,s)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},t5=(e,t,n,a=sn)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:r,outputValues:s,emptyRowIndicator:i,reverseIndexMap:o}=a.sparse.sparseFillEmptyRows(k("indices",e,t,n),k("values",e,t,n),k("denseShape",e,t,n),k("defaultValue",e,t,n));return[r,s,i,o]}case"SparseReshape":{let{outputIndices:r,outputShape:s}=a.sparse.sparseReshape(k("inputIndices",e,t,n),k("inputShape",e,t,n),k("newShape",e,t,n));return[r,s]}case"SparseSegmentMean":return[a.sparse.sparseSegmentMean(k("data",e,t,n),k("indices",e,t,n),k("segmentIds",e,t,n))];case"SparseSegmentSum":return[a.sparse.sparseSegmentSum(k("data",e,t,n),k("indices",e,t,n),k("segmentIds",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},n5=(e,t,n,a=sn)=>{switch(e.op){case"FFT":return[a.fft(k("x",e,t,n))];case"IFFT":return[a.ifft(k("x",e,t,n))];case"RFFT":return[a.rfft(k("x",e,t,n))];case"IRFFT":return[a.irfft(k("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},a5=(e,t,n,a=sn)=>{switch(e.op){case"StaticRegexReplace":return[a.string.staticRegexReplace(k("input",e,t,n),k("pattern",e,t,n),k("rewrite",e,t,n),k("replaceGlobal",e,t,n))];case"StringNGrams":{let{nGrams:r,nGramsSplits:s}=a.string.stringNGrams(k("data",e,t,n),k("dataSplits",e,t,n),k("separator",e,t,n),k("nGramWidths",e,t,n),k("leftPad",e,t,n),k("rightPad",e,t,n),k("padWidth",e,t,n),k("preserveShortSequences",e,t,n));return[r,s]}case"StringSplit":{let{indices:r,values:s,shape:i}=a.string.stringSplit(k("input",e,t,n),k("delimiter",e,t,n),k("skipEmpty",e,t,n));return[r,s,i]}case"StringToHashBucketFast":return[a.string.stringToHashBucketFast(k("input",e,t,n),k("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},r5=(e,t,n,a=sn)=>{switch(e.op){case"Cast":return[a.cast(k("x",e,t,n),k("dtype",e,t,n))];case"ExpandDims":{let r=k("axis",e,t,n);return[a.expandDims(k("x",e,t,n),r)]}case"Squeeze":{let r=k("axis",e,t,n);return[a.squeeze(k("x",e,t,n),r)]}case"Reshape":return[a.reshape(k("x",e,t,n),k("shape",e,t,n))];case"EnsureShape":return[a.ensureShape(k("x",e,t,n),k("shape",e,t,n))];case"MirrorPad":return[a.mirrorPad(k("x",e,t,n),k("padding",e,t,n),k("mode",e,t,n))];case"PadV2":case"Pad":return[a.pad(k("x",e,t,n),k("padding",e,t,n),k("constantValue",e,t,n))];case"SpaceToBatchND":{let r=k("blockShape",e,t,n),s=k("paddings",e,t,n);return[a.spaceToBatchND(k("x",e,t,n),r,s)]}case"BatchToSpaceND":{let r=k("blockShape",e,t,n),s=k("crops",e,t,n);return[a.batchToSpaceND(k("x",e,t,n),r,s)]}case"DepthToSpace":{let r=k("blockSize",e,t,n),s=k("dataFormat",e,t,n).toUpperCase();return[a.depthToSpace(k("x",e,t,n),r,s)]}case"BroadcastTo":return[a.broadcastTo(k("x",e,t,n),k("shape",e,t,n))];case"BroadcastArgs":return[a.broadcastArgs(k("s0",e,t,n),k("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function UI(e,t,n,a,r=O){let s=((i,o,l)=>{switch(i.category){case"arithmetic":return r(()=>Dj(i,o,l));case"basic_math":return r(()=>Rj(i,o,l));case"control":return Wj(i,o,l);case"convolution":return r(()=>Bj(i,o,l));case"creation":return r(()=>Vj(i,o,l));case"dynamic":return Uj(i,o,l);case"evaluation":return r(()=>Gj(i,o,l));case"image":return r(()=>Kj(i,o,l));case"graph":return r(()=>Hj(i,o,l));case"logical":return r(()=>Xj(i,o,l));case"matrices":return r(()=>Yj(i,o,l));case"normalization":return r(()=>Zj(i,o,l));case"ragged":return r(()=>Jj(i,o,l));case"reduction":return r(()=>Qj(i,o,l));case"slice_join":return r(()=>e5(i,o,l));case"sparse":return r(()=>t5(i,o,l));case"spectral":return r(()=>n5(i,o,l));case"string":return r(()=>a5(i,o,l));case"transformation":return r(()=>r5(i,o,l));case"hash_table":return jj(i,o,l,a);case"custom":let u=PC(i.op);if(u&&u.customExecutor)return u.customExecutor(new $j(i,o,l));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.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 w.isPromise(s)?s.then(i=>[].concat(i)):[].concat(s)}var GI=class{constructor(e={},t={},n={},a={},r){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=a,this.parseNodeNameCache=r,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;tt.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 HI(e,t,n,a){let r=new Set,s=[],i=null,o=null,l=new Set,u=new Set(Object.keys(e).map(c=>Xn(c)[0]));a=a||[];let p=new Set(a.map(c=>Xn(c.name)[0])),d=[...t];for(;d.length>0;){let c=d.pop();if((Js(c)||d5(c)||h5(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&&!u.has(c.name)&&!p.has(c.name)){if(c.inputs.length===0){s.push(c.name);continue}c.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),d.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function s5(e,t){let{usedNodes:n,inputs:a}=t,r=Object.keys(a).map(g=>Xn(g)[0]).map(g=>e.nodes[g]),s=e.initNodes||[],i=g=>n.has(typeof g=="string"?g:g.name);function o(g){return[...new Map(g.map(b=>[b.name,b])).values()]}let l=o([...r,...e.weights,...s]).filter(i),u=o([...l,...Object.values(e.nodes)]).filter(i),p=new Map(u.map(g=>[g.name,g])),d={};for(let g of u){d[g.name]=d[g.name]||0;for(let b of g.children)i(b)||(d[b.name]=Number.POSITIVE_INFINITY),d[b.name]=(d[b.name]||0)+1}let c=Object.entries(d).filter(([,g])=>g===0).map(([g])=>g),h=[...c];for(;c.length>0;){let g=c.pop(),b=p.get(g);for(let y of b.children.filter(i))--d[y.name]===0&&(h.push(y.name),c.push(y.name))}let m=h.map(g=>p.get(g)),f=i5(m,l);return o5(f,l),f}function i5(e,t){let n=new Map(e.map(s=>[s.name,s])),a=t.map(s=>s.name),r=new Set(a);for(;a.length>0;){let s=a.pop(),i=n.get(s);for(let o of i.children)!n.has(o.name)||r.has(o.name)||(r.add(o.name),a.push(o.name))}return e.filter(s=>r.has(s.name))}var Th=class extends Error{constructor(e){super(`NodesExecutionOrderError: ${e}`)}};function o5(e,t){let n=new Map(e.map((o,l)=>[o.name,l])),a=new Set(t.map(o=>o.name)),r=o=>a.has(typeof o=="string"?o:o.name),s=new Set(e.map(o=>o.name)),i=o=>s.has(typeof o=="string"?o:o.name);for(let o of e){for(let l of o.children.filter(i)){if(!n.has(l.name))throw new Th(`Child ${l.name} of node ${o.name} is unreachable.`);if(n.get(o.name)>n.get(l.name))throw new Th(`Node ${o.name} is scheduled to run after its child ${l.name}.`)}if(!r(o))for(let l of o.inputs){if(!n.has(l.name))throw new Th(`Input ${l.name} of node ${o.name} is unreachable.`);if(n.get(l.name)>n.get(o.name))throw new Th(`Node ${o.name} is scheduled to run before its input ${l.name}.`)}}}function l5(e){let t=new Map(e.map((o,l)=>[o.name,l])),n=Number.MAX_SAFE_INTEGER,a=e.map((o,l)=>Js(o)?n:l),r=o=>{let l=a[t.get(o.name)];return l==null?-1:l},s=e.map((o,l)=>o.children.map(r).reduce((u,p)=>Math.max(u,p),a[l])),i=new Map;for(let o=0;ot[a].map(r=>r.id));this._weightIds=[].concat(...n),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let n=t.signatureKey||t.name;return t.defaultOutput?`${n}:${t.defaultOutput}`:n})}get functions(){return Object.keys(this._functions).reduce((t,n)=>(t[n]=this._functions[n].signature,t),{})}constructor(t,n){this.graph=t,this.parent=n,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(a=>{this._functionExecutorMap[a]=new sE(t.functions[a],this)})}getCompilationKey(t,n){let a=t.map(s=>s.name).sort(),r=n.map(s=>s.name).sort();return a.join(this.SEPARATOR)+"--"+r.join(this.SEPARATOR)}compile(t,n){let a=HI(t,n,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:s,syncInputs:i}=a;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${i}]`);if(r.length>0){let u=n.map(d=>d.name),p=Object.keys(t);throw new Error(`Cannot compute the outputs [${u}] from the provided inputs [${p}]. Missing the following inputs: [${r}]`)}let o=s5(this.graph,a),l=l5(o);return{orderedNodes:o,nodeLiveUntilMap:l}}cloneAndKeepTensor(t){if(t==null)return null;let n=t.clone();return Ht(n),n}cloneTensorList(t){return t?t.map(n=>this.cloneAndKeepTensor(n)):null}cloneTensorMap(t){return Object.fromEntries(Object.entries(t).map(([n,a])=>[n,this.cloneTensorList(a)]))}execute(t,n){this.disposeIntermediateTensors(),t=this.mapInputs(t);let a=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),n=this.mapOutputs(n),this.checkOutputs(n);let r=a.map(c=>this.graph.nodes[Xn(c)[0]]),s=n.map(c=>Xn(c)[0]),i=new Set(s),o=s.map(c=>this.graph.nodes[c]);o.length===0&&(o=this._outputs);let l=this.getCompilationKey(r,o),u=this.compiledMap.get(l);u==null&&(u=this.compile(t,o),this.compiledMap.set(l,u));try{this.keepIntermediateTensors=G().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(c){this.keepIntermediateTensors=!1,console.warn(c.message)}let p={},d={};return O(()=>{let c=new GI(this.weightMap,p,d,this.functionExecutorMap,this.parseNodeNameCache),h=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(t).forEach(b=>{let[y,x]=Xn(b,c),v=[];v[x]=t[b],h[y]=v,this.keepIntermediateTensors&&(this.clonedTensorsMap[y]=this.cloneTensorList(v))});let m=this.getFrozenTensorIds(h),{orderedNodes:f,nodeLiveUntilMap:g}=u;for(let b of f){if(h[b.name])continue;let y=UI(b,h,c,this._resourceManager);if(w.isPromise(y))throw new Error(`The execution of the op '${b.op}' returned a promise. Please use model.executeAsync() instead.`);h[b.name]=y,this.keepIntermediateTensors&&(this.clonedTensorsMap[b.name]=this.cloneTensorList(y)),this.checkTensorForDisposalWithNodeLiveUntilInfo(b,h,c,m,i,g.get(b.name))}return this.parent==null&&c.dispose(m),n.map(b=>un(b,h,c))})}getFrozenTensorIds(t){let n=[].concat.apply([],Object.keys(t).map(a=>t[a]).map(a=>a.map(r=>r.id)));return new Set(n)}checkTensorForDisposal(t,n,a,r,s,i,o){if(!(Js(n)||i.has(t))){for(let l of a[t])l!=null&&(o[l.id]=(o[l.id]||0)+n.children.length);for(let l of n.inputs){if(Js(l))continue;let u=LI(l.name,a,r);if(u!=null)for(let p of u){if(!p||p.kept||s.has(p.id))continue;let d=o[p.id];d===1?(p.dispose(),delete o[p.id]):d!=null&&o[p.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(t,n,a,r,s,i){function o(l){return Js(l)||s.has(l.name)}if(!(Js(t)||i==null))for(let l of i){if(o(l))continue;let u=LI(l.name,n,a);for(let p of u)!p||p.kept||r.has(p.id)||p.dispose()}}async executeAsync(t,n){return this._executeAsync(t,n)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(t=>{for(let n of t)n&&!n.isDisposed&&n.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(t,n,a=!1,r={},s={}){this.disposeIntermediateTensors(),a||(t=this.mapInputs(t),this.checkInputs(t),this.checkInputShapeAndType(t),n=this.mapOutputs(n),this.checkOutputs(n));try{this.keepIntermediateTensors=G().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(c){this.keepIntermediateTensors=!1,console.warn(c.message)}let i=new GI(this.weightMap,r,s,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let o=await this.executeWithControlFlow(t,i,n,a),l=n.map(c=>un(c,o,i)),u=l.map(c=>c.id),p=Object.keys(t).map(c=>t[c].id),d=new Set([...u,...p,...this.weightIds]);return Object.values(o).forEach(c=>{c.forEach(h=>{h&&!h.isDisposed&&!d.has(h.id)&&h.dispose()})}),this.parent==null&&i.dispose(d),l}async executeFunctionAsync(t,n,a){let r=t.reduce((s,i,o)=>(s[this.inputs[o].name]=i,s),{});return this._executeAsync(r,this.outputNodes,!0,n,a)}async executeWithControlFlow(t,n,a,r){let s=Object.keys(t),i=s.map(v=>this.graph.nodes[Xn(v)[0]]),o=a.map(v=>Xn(v)[0]),l=new Set(o),u=o.map(v=>this.graph.nodes[v]);u.length===0&&(u=this._outputs);let{usedNodes:p,missingInputs:d,dynamicNode:c,syncInputs:h}=HI(t,u,this.weightMap,this._initNodes),m=[...i,...this.graph.weights,...this._initNodes||[]].map(v=>({node:v,contexts:n.currentContext})),f=Object.assign({},this.weightMap);Object.keys(t).forEach(v=>{let[I,N]=Xn(v),C=[];C[N]=t[v],f[I]=C});let g={},b=this.getFrozenTensorIds(f),y={};for(;m.length>0;){let v=this.processStack(i,m,n,f,y,b,l,g,p);await Promise.all(v)}c==null&&!r&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let x=u.filter(v=>!Js(v)&&!un(v.name,f,n)).map(v=>v.name);if(x.length>0){let v="";throw c!=null&&(v=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${h}]`),new Error(`Cannot compute the outputs [${x}] from the provided inputs [${s}]. Consider providing the following inputs: [${d}]. ${v}`)}return f}processStack(t,n,a,r,s,i,o,l,u){let p=[];for(;n.length>0;){let d=n.pop();a.currentContext=d.contexts;let c="";if(d.node.op==="Enter"&&k("isConstant",d.node,r,a)&&([c]=Ir(d.node.name,a)),r[d.node.name]==null){let h=UI(d.node,r,a,this._resourceManager);c||([c]=Ir(d.node.name,a));let m=a.currentContext;w.isPromise(h)?p.push(h.then(f=>(r[c]=f,this.keepIntermediateTensors&&(this.clonedTensorsMap[c]=this.cloneTensorList(f)),a.currentContext=m,this.checkTensorForDisposal(c,d.node,r,a,i,o,l),this.processChildNodes(d.node,n,a,r,s,u),f))):(r[c]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[c]=this.cloneTensorList(h)),this.checkTensorForDisposal(c,d.node,r,a,i,o,l),this.processChildNodes(d.node,n,a,r,s,u))}else this.processChildNodes(d.node,n,a,r,s,u)}return p}processChildNodes(t,n,a,r,s,i){t.children.forEach(o=>{let[l]=Ir(o.name,a);s[l]||!i.has(o.name)||(o.op==="Merge"?o.inputNames.some(u=>!!un(u,r,a))&&(s[l]=!0,n.push({contexts:a.currentContext,node:o})):o.inputNames.every(u=>!!un(u,r,a))&&(s[l]=!0,n.push({contexts:a.currentContext,node:o})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(n=>n.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(n=>{let a=t[n],[r]=Xn(n),s=this.graph.nodes[r];if(s.attrParams.shape&&s.attrParams.shape.value){let i=s.attrParams.shape.value,o=i.length===a.shape.length&&a.shape.every((l,u)=>i[u]===-1||i[u]===l);w.assert(o,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${i}], but was [${a.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&w.assert(a.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${a.dtype}`)})}mapInputs(t){var n,a;let r={};for(let s in t){let i=(a=(n=this._signature)===null||n===void 0?void 0:n.inputs)===null||a===void 0?void 0:a[s];i!=null?r[i.name]=t[s]:r[s]=t[s]}return r}checkInputs(t){let n=Object.keys(t).filter(a=>{let[r]=Xn(a);return this.graph.nodes[r]==null});if(n.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${n}] that are not part of graph`)}mapOutputs(t){return t.map(n=>{var a,r;let s=(r=(a=this._signature)===null||a===void 0?void 0:a.outputs)===null||r===void 0?void 0:r[n];return s!=null?s.name:n},{})}checkOutputs(t){t.forEach(n=>{let[a]=Xn(n);if(!this.graph.nodes[a])throw new Error(`The output '${n}' is not found in the graph`)})}},m5=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in 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n=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+n!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-n} non-resource placeholders, while there are ${e.length} input tensors provided.`);let a=0;return this.inputNodes.reduce((r,s)=>{var i,o,l;let u=(l=(o=(i=this.signature)===null||i===void 0?void 0:i.inputs)===null||o===void 0?void 0:o[s])===null||l===void 0?void 0:l.resourceId;return u!=null?r[s]=this.resourceIdToCapturedInput[u]:r[s]=e[a++],r},{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(e){if(this.resourceIdToCapturedInput={},this.initializerSignature){let t=this.initializerSignature.outputs,n=Object.keys(t);for(let a=0;a1?n:n[0]}async executeAsync(e,t){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&(this.initializer.dispose(),this.resourceIdToCapturedInput&&Ee(this.resourceIdToCapturedInput)),this.resourceManager.dispose()}};async function b5(e,t={},n=qt){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. 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this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},D5=class extends rn{constructor(e){super(),this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},R5=class extends rn{constructor(e,t){super(),this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++ Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},O5=class extends rn{constructor(e,t,n=!0){super(),this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},P5=class extends rn{constructor(e,t){super(),this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Ee(e.value)}}},L5=class extends rn{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Wa.getTensorsInContainer(e.value),n=this.transform(e.value),a=Wa.getTensorsInContainer(n);for(let r of t)Wa.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},z5=class extends rn{constructor(e,t){super(),this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},jI=class extends rn{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Wa.getTensorsInContainer(e.value),n=await this.transform(e.value),a=Wa.getTensorsInContainer(n);for(let r of t)Wa.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},D1=class extends rn{constructor(){super(),this.outputQueue=new cE,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}}},W5=class extends D1{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=Wa.getTensorsInContainer(e.value),n=this.transform(e.value),a=Wa.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)Wa.isTensorInList(r,a)||r.dispose();return!0}},mE=class extends rn{constructor(e,t){super(),this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries 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uE(this.iterators,a);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case es.FAIL:throw new Error(`Zipped streams should have the same length. 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If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let a=this,r=w5.alea(t||w.now().toString());return Kn(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,Kn(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};ip.MAX_BUFFER_SIZE=1e4;function Kn(e,t=null){return new class extends ip{constructor(){super(...arguments),this.size=t}async iterator(){return e()}}}function U5(e){return Kn(async()=>hE(e),e.length)}function G5(e){if(!Hl(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{let n=await uE(e,a=>{if(a instanceof ip)return{value:a.iterator(),recurse:!1};if(Hl(a))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return A5(n,es.SHORTEST)},t)}function H5(e){if(e===null)return null;let t=e[0];return N5(t)?{value:q5(e),recurse:!1}:{value:null,recurse:!0}}function q5(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Ce?At(e):bn(e)}var gE=class extends ip{constructor(e){super(),this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(` -`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},Ch='"',Jp=Symbol("out"),KI=Symbol("field"),Eh=Symbol("quote"),gx=Symbol("quoteafterquote"),XI=Symbol("quoteinquote"),bE=class extends ip{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&&w.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(w.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}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 gE(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(w.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 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;r14||!Number.isInteger(n))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=t.numFramesPerSpectrogram||43,this.sampleRateHz=t.sampleRateHz,this.columnTruncateLength=t.columnTruncateLength||this.fftSize,this.audioTrackConstraints=t.audioTrackConstraints,this.smoothingTimeConstant=t.smoothingTimeConstant||0,this.includeSpectrogram=t.includeSpectrogram!==!1,this.includeWaveform=t.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. 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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(t){throw t.message=`Error thrown while initializing video stream: ${t.message}`,t}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(t){console.log(t),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(t=>{this.webcamVideoElement.onloadedmetadata=()=>{t()}})}async next(){if(this.isClosed)return{value:null,done:!0};let t;try{t=jo.fromPixels(this.webcamVideoElement)}catch(n){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(n)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(t),done:!1}}catch(n){throw new Error(`Error thrown cropping the video: ${n.message}`)}finally{t.dispose()}else return{value:t,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(t){return O(()=>{let n=Gt(re(t,"float32"),0),a;a=Zn.cropAndResize(n,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let r=a.shape;return W(a,r.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},vE=class{},wE=class extends rn{split(e){return new X5(this,e)}},X5=class extends wE{constructor(e,t){super(),this.upstream=e,this.impl=new Y5(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Y5=class extends D1{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},Z5=class extends rn{decodeUTF8(){return new J5(this)}},J5=class extends wE{constructor(e){super(),this.upstream=e,this.impl=new Q5(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Q5=class extends D1{constructor(e){if(super(),this.upstream=e,G().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=FS();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return G().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},kE=class extends Z5{constructor(e,t={}){super(),this.file=e,this.options=t,w.assert(e instanceof Uint8Array||(G().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 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implemented`)}},Qq=(e,t,n,a=on)=>{switch(e.op){case"Equal":return[a.equal(k("a",e,t,n),k("b",e,t,n))];case"NotEqual":return[a.notEqual(k("a",e,t,n),k("b",e,t,n))];case"Greater":return[a.greater(k("a",e,t,n),k("b",e,t,n))];case"GreaterEqual":return[a.greaterEqual(k("a",e,t,n),k("b",e,t,n))];case"Less":return[a.less(k("a",e,t,n),k("b",e,t,n))];case"LessEqual":return[a.lessEqual(k("a",e,t,n),k("b",e,t,n))];case"LogicalAnd":return[a.logicalAnd(k("a",e,t,n),k("b",e,t,n))];case"LogicalNot":return[a.logicalNot(k("a",e,t,n))];case"LogicalOr":return[a.logicalOr(k("a",e,t,n),k("b",e,t,n))];case"Select":case"SelectV2":return[a.where(k("condition",e,t,n),k("a",e,t,n),k("b",e,t,n))];case"BitwiseAnd":return[a.bitwiseAnd(k("a",e,t,n),k("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},e5=(e,t,n,a=on)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[a.matMul(k("a",e,t,n),k("b",e,t,n),k("transposeA",e,t,n),k("transposeB",e,t,n))];case"Einsum":return[a.einsum(k("equation",e,t,n),...k("tensors",e,t,n))];case"Transpose":return[a.transpose(k("x",e,t,n),k("perm",e,t,n))];case"_FusedMatMul":let[r,s]=k("fusedOps",e,t,n),i=r==="biasadd",o=s==="prelu",l=k("numArgs",e,t,n),u=k("leakyreluAlpha",e,t,n);if(i){if(o&&l!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!o&&l!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[p,d]=k("args",e,t,n);return[a.fused.matMul({a:k("a",e,t,n),b:k("b",e,t,n),transposeA:k("transposeA",e,t,n),transposeB:k("transposeB",e,t,n),bias:p,activation:s,preluActivationWeights:d,leakyreluAlpha:u})];case"MatrixBandPart":return[a.linalg.bandPart(k("a",e,t,n),k("numLower",e,t,n),k("numUpper",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},t5=(e,t,n,a=on)=>{switch(e.op){case"EuclideanNorm":return[a.euclideanNorm(k("x",e,t,n),k("axis",e,t,n),k("keepDims",e,t,n))];case"FusedBatchNorm":case"FusedBatchNormV2":return[a.batchNorm(k("x",e,t,n),k("mean",e,t,n),k("variance",e,t,n),k("offset",e,t,n),k("scale",e,t,n),k("epsilon",e,t,n))];case"FusedBatchNormV3":return[a.batchNorm(k("x",e,t,n),k("mean",e,t,n),k("variance",e,t,n),k("offset",e,t,n),k("scale",e,t,n),k("epsilon",e,t,n))];case"LRN":return[a.localResponseNormalization(k("x",e,t,n),k("radius",e,t,n),k("bias",e,t,n),k("alpha",e,t,n),k("beta",e,t,n))];case"Softmax":return[a.softmax(k("x",e,t,n))];case"LogSoftmax":return[a.logSoftmax(k("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},n5=(e,t,n,a=on)=>{switch(e.op){case"RaggedGather":{let{outputNestedSplits:r,outputDenseValues:s}=a.raggedGather(k("paramsNestedSplits",e,t,n),k("paramsDenseValues",e,t,n),k("indices",e,t,n),k("outputRaggedRank",e,t,n));return r.concat(s)}case"RaggedRange":{let{rtNestedSplits:r,rtDenseValues:s}=a.raggedRange(k("starts",e,t,n),k("limits",e,t,n),k("splits",e,t,n));return[r,s]}case"RaggedTensorToTensor":return[a.raggedTensorToTensor(k("shape",e,t,n),k("values",e,t,n),k("defaultValue",e,t,n),k("rowPartitionTensors",e,t,n),k("rowPartitionTypes",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},a5=(e,t,n,a=on)=>{switch(e.op){case"Max":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.max(k("x",e,t,n),o,l)]}case"Mean":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.mean(k("x",e,t,n),o,l)]}case"Min":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.min(k("x",e,t,n),o,l)]}case"Sum":{let 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implemented`)}},r5=(e,t,n,a=on)=>{switch(e.op){case"ConcatV2":case"Concat":{let r=k("n",e,t,n),s=k("axis",e,t,n),i=k("tensors",e,t,n);return i=i.slice(0,r),[a.concat(i,s)]}case"Gather":{let r=k("x",e,t,n),s=k("indices",e,t,n);return[a.gather(r,a.cast(s,"int32"),0)]}case"GatherV2":{let r=k("axis",e,t,n),s=k("batchDims",e,t,n),i=k("x",e,t,n),o=k("indices",e,t,n);return[a.gather(i,a.cast(o,"int32"),r,s)]}case"Reverse":{let r=k("dims",e,t,n),s=[];for(let o=0;o{let r=k("axis",e,t,n),s=k("tensors",e,t,n),i=s[0].shape,o=a.squeeze(s[0]).shape,l=s.map(u=>{let p=w.arraysEqual(u.shape,i);if(!p&&!w.arraysEqual(a.squeeze(u).shape,o))throw new Error("the input tensors shape does not match");return p?u:a.reshape(u,i)});return[a.stack(l,r)]});case"Unpack":{let r=k("axis",e,t,n),s=k("tensor",e,t,n);return a.unstack(s,r)}case"Tile":{let r=k("reps",e,t,n);return[a.tile(k("x",e,t,n),r)]}case"Split":case"SplitV":{let r=k("axis",e,t,n),s=k("numOrSizeSplits",e,t,n),i=k("x",e,t,n);return 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implemented`)}},s5=(e,t,n,a=on)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:r,outputValues:s,emptyRowIndicator:i,reverseIndexMap:o}=a.sparse.sparseFillEmptyRows(k("indices",e,t,n),k("values",e,t,n),k("denseShape",e,t,n),k("defaultValue",e,t,n));return[r,s,i,o]}case"SparseReshape":{let{outputIndices:r,outputShape:s}=a.sparse.sparseReshape(k("inputIndices",e,t,n),k("inputShape",e,t,n),k("newShape",e,t,n));return[r,s]}case"SparseSegmentMean":return[a.sparse.sparseSegmentMean(k("data",e,t,n),k("indices",e,t,n),k("segmentIds",e,t,n))];case"SparseSegmentSum":return[a.sparse.sparseSegmentSum(k("data",e,t,n),k("indices",e,t,n),k("segmentIds",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},i5=(e,t,n,a=on)=>{switch(e.op){case"FFT":return[a.fft(k("x",e,t,n))];case"IFFT":return[a.ifft(k("x",e,t,n))];case"RFFT":return[a.rfft(k("x",e,t,n))];case"IRFFT":return[a.irfft(k("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not 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s=((i,o,l)=>{switch(i.category){case"arithmetic":return r(()=>Pq(i,o,l));case"basic_math":return r(()=>Lq(i,o,l));case"control":return Gq(i,o,l);case"convolution":return r(()=>Hq(i,o,l));case"creation":return r(()=>jq(i,o,l));case"dynamic":return qq(i,o,l);case"evaluation":return r(()=>Kq(i,o,l));case"image":return r(()=>Jq(i,o,l));case"graph":return r(()=>Xq(i,o,l));case"logical":return r(()=>Qq(i,o,l));case"matrices":return r(()=>e5(i,o,l));case"normalization":return r(()=>t5(i,o,l));case"ragged":return r(()=>n5(i,o,l));case"reduction":return r(()=>a5(i,o,l));case"slice_join":return r(()=>r5(i,o,l));case"sparse":return r(()=>s5(i,o,l));case"spectral":return r(()=>i5(i,o,l));case"string":return r(()=>o5(i,o,l));case"transformation":return r(()=>l5(i,o,l));case"hash_table":return Zq(i,o,l,a);case"custom":let u=zC(i.op);if(u&&u.customExecutor)return u.customExecutor(new Oq(i,o,l));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.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 w.isPromise(s)?s.then(i=>[].concat(i)):[].concat(s)}var GI=class{constructor(e={},t={},n={},a={},r){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=a,this.parseNodeNameCache=r,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;tt.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 HI(e,t,n,a){let r=new Set,s=[],i=null,o=null,l=new Set,u=new Set(Object.keys(e).map(c=>Xn(c)[0]));a=a||[];let p=new Set(a.map(c=>Xn(c.name)[0])),d=[...t];for(;d.length>0;){let c=d.pop();if((Qs(c)||g5(c)||b5(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&&!u.has(c.name)&&!p.has(c.name)){if(c.inputs.length===0){s.push(c.name);continue}c.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),d.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function u5(e,t){let{usedNodes:n,inputs:a}=t,r=Object.keys(a).map(g=>Xn(g)[0]).map(g=>e.nodes[g]),s=e.initNodes||[],i=g=>n.has(typeof g=="string"?g:g.name);function o(g){return[...new Map(g.map(b=>[b.name,b])).values()]}let l=o([...r,...e.weights,...s]).filter(i),u=o([...l,...Object.values(e.nodes)]).filter(i),p=new Map(u.map(g=>[g.name,g])),d={};for(let g of u){d[g.name]=d[g.name]||0;for(let b of g.children)i(b)||(d[b.name]=Number.POSITIVE_INFINITY),d[b.name]=(d[b.name]||0)+1}let c=Object.entries(d).filter(([,g])=>g===0).map(([g])=>g),h=[...c];for(;c.length>0;){let g=c.pop(),b=p.get(g);for(let y of b.children.filter(i))--d[y.name]===0&&(h.push(y.name),c.push(y.name))}let m=h.map(g=>p.get(g)),f=p5(m,l);return c5(f,l),f}function p5(e,t){let n=new Map(e.map(s=>[s.name,s])),a=t.map(s=>s.name),r=new Set(a);for(;a.length>0;){let s=a.pop(),i=n.get(s);for(let o of i.children)!n.has(o.name)||r.has(o.name)||(r.add(o.name),a.push(o.name))}return e.filter(s=>r.has(s.name))}var Th=class extends Error{constructor(e){super(`NodesExecutionOrderError: ${e}`)}};function c5(e,t){let n=new Map(e.map((o,l)=>[o.name,l])),a=new Set(t.map(o=>o.name)),r=o=>a.has(typeof o=="string"?o:o.name),s=new Set(e.map(o=>o.name)),i=o=>s.has(typeof o=="string"?o:o.name);for(let o of e){for(let l of o.children.filter(i)){if(!n.has(l.name))throw new Th(`Child ${l.name} of node ${o.name} is unreachable.`);if(n.get(o.name)>n.get(l.name))throw new Th(`Node ${o.name} is scheduled to run after its child ${l.name}.`)}if(!r(o))for(let l of o.inputs){if(!n.has(l.name))throw new Th(`Input ${l.name} of node ${o.name} is unreachable.`);if(n.get(l.name)>n.get(o.name))throw new Th(`Node ${o.name} is scheduled to run before its input ${l.name}.`)}}}function d5(e){let t=new Map(e.map((o,l)=>[o.name,l])),n=Number.MAX_SAFE_INTEGER,a=e.map((o,l)=>Qs(o)?n:l),r=o=>{let l=a[t.get(o.name)];return l==null?-1:l},s=e.map((o,l)=>o.children.map(r).reduce((u,p)=>Math.max(u,p),a[l])),i=new Map;for(let o=0;ot[a].map(r=>r.id));this._weightIds=[].concat(...n),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let n=t.signatureKey||t.name;return t.defaultOutput?`${n}:${t.defaultOutput}`:n})}get functions(){return Object.keys(this._functions).reduce((t,n)=>(t[n]=this._functions[n].signature,t),{})}constructor(t,n){this.graph=t,this.parent=n,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(a=>{this._functionExecutorMap[a]=new oE(t.functions[a],this)})}getCompilationKey(t,n){let a=t.map(s=>s.name).sort(),r=n.map(s=>s.name).sort();return a.join(this.SEPARATOR)+"--"+r.join(this.SEPARATOR)}compile(t,n){let a=HI(t,n,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:s,syncInputs:i}=a;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. 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Missing the following inputs: [${r}]`)}let o=u5(this.graph,a),l=d5(o);return{orderedNodes:o,nodeLiveUntilMap:l}}cloneAndKeepTensor(t){if(t==null)return null;let n=t.clone();return Ht(n),n}cloneTensorList(t){return t?t.map(n=>this.cloneAndKeepTensor(n)):null}cloneTensorMap(t){return Object.fromEntries(Object.entries(t).map(([n,a])=>[n,this.cloneTensorList(a)]))}execute(t,n){this.disposeIntermediateTensors(),t=this.mapInputs(t);let a=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),n=this.mapOutputs(n),this.checkOutputs(n);let r=a.map(c=>this.graph.nodes[Xn(c)[0]]),s=n.map(c=>Xn(c)[0]),i=new Set(s),o=s.map(c=>this.graph.nodes[c]);o.length===0&&(o=this._outputs);let l=this.getCompilationKey(r,o),u=this.compiledMap.get(l);u==null&&(u=this.compile(t,o),this.compiledMap.set(l,u));try{this.keepIntermediateTensors=G().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(c){this.keepIntermediateTensors=!1,console.warn(c.message)}let p={},d={};return O(()=>{let c=new GI(this.weightMap,p,d,this.functionExecutorMap,this.parseNodeNameCache),h=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(t).forEach(b=>{let[y,x]=Xn(b,c),v=[];v[x]=t[b],h[y]=v,this.keepIntermediateTensors&&(this.clonedTensorsMap[y]=this.cloneTensorList(v))});let m=this.getFrozenTensorIds(h),{orderedNodes:f,nodeLiveUntilMap:g}=u;for(let b of f){if(h[b.name])continue;let y=UI(b,h,c,this._resourceManager);if(w.isPromise(y))throw new Error(`The execution of the op '${b.op}' returned a promise. 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You can use model.execute() instead.");let x=u.filter(v=>!Qs(v)&&!pn(v.name,f,n)).map(v=>v.name);if(x.length>0){let v="";throw c!=null&&(v=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${h}]`),new Error(`Cannot compute the outputs [${x}] from the provided inputs [${s}]. Consider providing the following inputs: [${d}]. ${v}`)}return f}processStack(t,n,a,r,s,i,o,l,u){let p=[];for(;n.length>0;){let d=n.pop();a.currentContext=d.contexts;let c="";if(d.node.op==="Enter"&&k("isConstant",d.node,r,a)&&([c]=Sr(d.node.name,a)),r[d.node.name]==null){let h=UI(d.node,r,a,this._resourceManager);c||([c]=Sr(d.node.name,a));let m=a.currentContext;w.isPromise(h)?p.push(h.then(f=>(r[c]=f,this.keepIntermediateTensors&&(this.clonedTensorsMap[c]=this.cloneTensorList(f)),a.currentContext=m,this.checkTensorForDisposal(c,d.node,r,a,i,o,l),this.processChildNodes(d.node,n,a,r,s,u),f))):(r[c]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[c]=this.cloneTensorList(h)),this.checkTensorForDisposal(c,d.node,r,a,i,o,l),this.processChildNodes(d.node,n,a,r,s,u))}else this.processChildNodes(d.node,n,a,r,s,u)}return p}processChildNodes(t,n,a,r,s,i){t.children.forEach(o=>{let[l]=Sr(o.name,a);s[l]||!i.has(o.name)||(o.op==="Merge"?o.inputNames.some(u=>!!pn(u,r,a))&&(s[l]=!0,n.push({contexts:a.currentContext,node:o})):o.inputNames.every(u=>!!pn(u,r,a))&&(s[l]=!0,n.push({contexts:a.currentContext,node:o})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(n=>n.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(n=>{let a=t[n],[r]=Xn(n),s=this.graph.nodes[r];if(s.attrParams.shape&&s.attrParams.shape.value){let i=s.attrParams.shape.value,o=i.length===a.shape.length&&a.shape.every((l,u)=>i[u]===-1||i[u]===l);w.assert(o,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${i}], but was [${a.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&w.assert(a.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${a.dtype}`)})}mapInputs(t){var n,a;let r={};for(let s in t){let i=(a=(n=this._signature)===null||n===void 0?void 0:n.inputs)===null||a===void 0?void 0:a[s];i!=null?r[i.name]=t[s]:r[s]=t[s]}return r}checkInputs(t){let n=Object.keys(t).filter(a=>{let[r]=Xn(a);return this.graph.nodes[r]==null});if(n.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${n}] that are not part of graph`)}mapOutputs(t){return t.map(n=>{var a,r;let s=(r=(a=this._signature)===null||a===void 0?void 0:a.outputs)===null||r===void 0?void 0:r[n];return s!=null?s.name:n},{})}checkOutputs(t){t.forEach(n=>{let[a]=Xn(n);if(!this.graph.nodes[a])throw new Error(`The output '${n}' is not found in the graph`)})}},y5=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]}},x5="?tfjs-format=file",v5="model.json",F1=class{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}get modelStructuredOutputKeys(){return this.structuredOutputKeys}constructor(e,t={},n=jt){this.modelUrl=e,this.loadOptions=t,this.version="n/a",this.io=n,t==null&&(this.loadOptions={}),this.resourceManager=new y5}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else 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n=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+n!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-n} non-resource placeholders, while there are ${e.length} input tensors provided.`);let a=0;return this.inputNodes.reduce((r,s)=>{var i,o,l;let u=(l=(o=(i=this.signature)===null||i===void 0?void 0:i.inputs)===null||o===void 0?void 0:o[s])===null||l===void 0?void 0:l.resourceId;return u!=null?r[s]=this.resourceIdToCapturedInput[u]:r[s]=e[a++],r},{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return 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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(Hl(a)){let s=Array.isArray(a)?[]:{};n.add(a);for(let i in a){let o=e.map(u=>u[i]),l=uE(o,t,n);s[i]=l}return n.delete(a),s}else throw new Error(`Can't recurse into non-iterable type: ${a}`);else return r.value}function pE(e){return e===null?null:Hl(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function cE(e,t){let n=new Map;om(e,t,n);for(let a of Array.from(n.keys())){let r=n.get(a);if(w.isPromise(r)){let s=await r;n.set(a,s)}}return om(e,t,n)}function Hl(e){let t=!1;if(G().get("IS_BROWSER"))t=e instanceof TextDecoder;else{let{StringDecoder:n}=FS();t=e instanceof n}return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Ce)&&!(e instanceof Promise)&&!t)}function _5(e){return e==null||A5(e)||Array.isArray(e)||typeof 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dE{constructor(){super(mE.INITIAL_CAPACITY)}isFull(){return!1}push(t){super.isFull()&&this.expand(),super.push(t)}unshift(t){super.isFull()&&this.expand(),super.unshift(t)}expand(){let t=this.capacity*2,n=new Array(t),a=this.length();for(let r=0;rt===!0)}rowMajorBatch(e,t=!0){return new W5(this,e,t)}columnMajorBatch(e,t=!0,n=pE){return this.rowMajorBatch(e,t).map(a=>E5(a,n))}concatenate(e,t){return new gE(fE([this,e]),t)}take(e){return e<0||e==null?this:new z5(this,e)}skip(e){return e<0||e==null?this:new L5(this,e)}prefetch(e){return new bE(this,e)}shuffle(e,t){return new j5(this,e,t)}serial(){return new P5(this)}},M5=class extends sn{constructor(e){super(),this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:F5(e),done:!1}}},O5=class extends sn{constructor(e){super(),this.nextFn=e}summary(){return"Function call"}async next(){try{return 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sn{constructor(e,t,n=!0){super(),this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},B5=class extends sn{constructor(e,t){super(),this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Ee(e.value)}}},V5=class extends sn{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Wa.getTensorsInContainer(e.value),n=this.transform(e.value),a=Wa.getTensorsInContainer(n);for(let r of t)Wa.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},U5=class extends sn{constructor(e,t){super(),this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},qI=class extends sn{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Wa.getTensorsInContainer(e.value),n=await this.transform(e.value),a=Wa.getTensorsInContainer(n);for(let r of t)Wa.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},D1=class extends sn{constructor(){super(),this.outputQueue=new hE,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}}},G5=class extends D1{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=Wa.getTensorsInContainer(e.value),n=this.transform(e.value),a=Wa.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)Wa.isTensorInList(r,a)||r.dispose();return!0}},gE=class extends sn{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}},es;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(es||(es={}));var H5=class extends sn{constructor(e,t=es.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 sn?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let r=await cE(this.iterators,a);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case es.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case es.SHORTEST:return{value:null,done:!0};case es.LONGEST:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},bE=class extends sn{constructor(e,t){super(),this.upstream=e,this.bufferSize=t,this.buffer=new dE(t)}summary(){return`${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()}},j5=class extends bE{constructor(e,t,n){super(e,t),this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=T5.alea(n||w.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}}},ip=class{constructor(){this.size=null}batch(e,t=!0){let n=this;w.assert(e>0,()=>`batchSize needs to be positive, but it is + ${e}`);let a;return this.size===1/0||this.size==null?a=this.size:t?a=Math.ceil(this.size/e):a=Math.floor(this.size/e),Kn(async()=>(await n.iterator()).columnMajorBatch(e,t,X5),a)}concatenate(e){let t=this,n;return this.size===1/0||e.size===1/0?n=1/0:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,Kn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===1/0?n=1/0:n=null,Kn(async()=>(await t.iterator()).filter(a=>O(()=>e(a))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return Kn(async()=>(await t.iterator()).map(n=>O(()=>e(n))),this.size)}mapAsync(e){let t=this;return Kn(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 Kn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=1/0:n=null,Kn(async()=>{let a=$1(async()=>({value:await t.iterator(),done:!1}));return D5(a.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let a=this,r=N5.alea(t||w.now().toString());return Kn(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,Kn(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};ip.MAX_BUFFER_SIZE=1e4;function Kn(e,t=null){return new class extends ip{constructor(){super(...arguments),this.size=t}async iterator(){return e()}}}function q5(e){return Kn(async()=>fE(e),e.length)}function K5(e){if(!Hl(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{let n=await cE(e,a=>{if(a instanceof ip)return{value:a.iterator(),recurse:!1};if(Hl(a))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return R5(n,es.SHORTEST)},t)}function X5(e){if(e===null)return null;let t=e[0];return _5(t)?{value:Y5(e),recurse:!1}:{value:null,recurse:!0}}function Y5(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Ce?At(e):bn(e)}var yE=class extends ip{constructor(e){super(),this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(` +`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},Ch='"',Jp=Symbol("out"),KI=Symbol("field"),Eh=Symbol("quote"),bx=Symbol("quoteafterquote"),XI=Symbol("quoteinquote"),xE=class extends ip{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&&w.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(w.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}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 yE(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(w.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 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;r14||!Number.isInteger(n))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=t.numFramesPerSpectrogram||43,this.sampleRateHz=t.sampleRateHz,this.columnTruncateLength=t.columnTruncateLength||this.fftSize,this.audioTrackConstraints=t.audioTrackConstraints,this.smoothingTimeConstant=t.smoothingTimeConstant||0,this.includeSpectrogram=t.includeSpectrogram!==!1,this.includeWaveform=t.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(t={}){if(!G().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let n=new vE(t);return await n.start(),n}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(a){throw new Error(`Error thrown while initializing video stream: ${a.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let t=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new t,!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 n=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,n.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 t,n,a=await this.getAudioData();if(this.includeSpectrogram){let r=this.flattenQueue(a.freqDataQueue);t=this.getTensorFromAudioDataArray(r,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let r=this.flattenQueue(a.timeDataQueue);n=this.getTensorFromAudioDataArray(r,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:t,waveform:n},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let t=[],n=[],a=0;return new Promise(r=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&r({freqDataQueue:t,timeDataQueue:n}),t.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),n.push(this.timeData.slice())),++a===this.numFrames&&(clearInterval(s),r({freqDataQueue:t,timeDataQueue:n}))},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(t){let n=t[0].length,a=new Float32Array(t.length*n);return t.forEach((r,s)=>a.set(r,s*n)),a}getTensorFromAudioDataArray(t,n){let a=new Float32Array(w.sizeFromShape(n));return a.set(t,a.length-t.length),bn(a,n)}},J5=class wE extends sn{constructor(t,n){if(super(),this.webcamVideoElement=t,this.webcamConfig=n,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=je([0],"int32"),this.webcamConfig.centerCrop){let a=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,r=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-a)/2,i=(1-r)/2,o=s+a,l=r+i;this.cropBox=Ea([i,s,l,o],[1,4])}else this.cropBox=Ea([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(t,n={}){if(!G().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!t){if(t=document.createElement("video"),!n.resizeWidth||!n.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");t.width=n.resizeWidth,t.height=n.resizeHeight}let a=new wE(t,n);return await a.start(),a}async start(){this.webcamConfig.facingMode&&w.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(t){throw t.message=`Error thrown while initializing video stream: ${t.message}`,t}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(t){console.log(t),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(t=>{this.webcamVideoElement.onloadedmetadata=()=>{t()}})}async next(){if(this.isClosed)return{value:null,done:!0};let t;try{t=qo.fromPixels(this.webcamVideoElement)}catch(n){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(n)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(t),done:!1}}catch(n){throw new Error(`Error thrown cropping the video: ${n.message}`)}finally{t.dispose()}else return{value:t,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(t){return O(()=>{let n=Gt(re(t,"float32"),0),a;a=Zn.cropAndResize(n,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let r=a.shape;return W(a,r.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},kE=class{},IE=class extends sn{split(e){return new Q5(this,e)}},Q5=class extends IE{constructor(e,t){super(),this.upstream=e,this.impl=new e8(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},e8=class extends D1{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},t8=class extends sn{decodeUTF8(){return new n8(this)}},n8=class extends IE{constructor(e){super(),this.upstream=e,this.impl=new a8(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},a8=class extends D1{constructor(e){if(super(),this.upstream=e,G().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=FS();this.decoder=new 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p=T.computePool3DInfo(s.shape,i,o,1,l,u),d=p.strideDepth,c=p.strideHeight,h=p.strideWidth,m=p.filterDepth,f=p.filterHeight,g=p.filterWidth,b=p.dilationDepth,y=p.dilationHeight,x=p.dilationWidth,v=p.effectiveFilterDepth,I=p.effectiveFilterHeight,N=p.effectiveFilterWidth,C=v-1-p.padInfo.front,_=N-1-p.padInfo.left,F=I-1-p.padInfo.top,D=Oe(s.shape,"float32"),$=1/(m*f*g),S=n.bufferSync(r);for(let M=0;M=p.outDepth||Math.floor(te)!==te))for(let se=0;se=p.outHeight||Math.floor(ie)!==ie))for(let xe=0;xe=p.outWidth||Math.floor(ue)!==ue)continue;let ye=S.get(M,te,ie,ue,B);ee+=ye}}}D.set(ee*$,M,U,H,j,B)}return n.makeTensorInfo(D.shape,D.dtype,D.values)}var uX={kernelName:Rc,backendName:"cpu",kernelFunc:lX};function pX(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;ge([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,p=T.computePool2DInfo(i.shape,o,l,1,u),d=p.strideHeight,c=p.strideWidth,h=p.filterHeight,m=p.filterWidth,f=p.dilationHeight,g=p.dilationWidth,b=p.effectiveFilterHeight,y=p.effectiveFilterWidth,x=y-1-p.padInfo.left,v=b-1-p.padInfo.top,I=Oe(i.shape,"float32"),N=1/(h*m),C=n.data.get(r.dataId).values,_=Oe(r.shape,"float32",C);for(let F=0;F=p.outHeight||Math.floor(j)!==j))for(let K=0;K=p.outWidth||Math.floor(Z)!==Z)continue;let J=_.get(F,j,Z,D);U+=J}}I.set(U*N,F,$,S,D)}return n.makeTensorInfo(I.shape,I.dtype,I.values)}var cX={kernelName:Dc,backendName:"cpu",kernelFunc:pX};function dX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,scale:s,offset:i,mean:o,variance:l}=t;w.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient 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o=s.reduce((b,y)=>b*y),l=T.getReshaped(r.shape,s,o),u=T.getPermuted(l.length,s.length),p=T.getReshapedPermuted(r.shape,s,o),d=T.getSliceBeginCoords(i,s.length),c=T.getSliceSize(p,i,s.length),h=xt({inputs:{x:r},backend:n,attrs:{shape:l}}),m=Bn({inputs:{x:h},backend:n,attrs:{perm:u}}),f=xt({inputs:{x:m},backend:n,attrs:{shape:p}}),g=xi({inputs:{x:f},backend:n,attrs:{begin:d,size:c}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),g}var fX={kernelName:nu,backendName:"cpu",kernelFunc:mX};function gX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.data.get(r.dataId).values,l=n.data.get(s.dataId).values,u=P1(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var bX={kernelName:au,backendName:"cpu",kernelFunc:gX};function yX(e){let{inputs:t,backend:n}=e,{s0:a,s1:r}=t,s=n.data.get(a.dataId).values,i=n.data.get(r.dataId).values,o=T.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var xX={kernelName:Mc,backendName:"cpu",kernelFunc:yX},vX=lt(vs,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e{let{x:t}=e.inputs,n=e.backend,a=new Float32Array(w.sizeFromShape(t.shape)),r=n.data.get(t.dataId),s=r.complexTensorInfos.real,i=r.complexTensorInfos.imag,o=n.data.get(s.dataId).values,l=n.data.get(i.dataId).values;for(let u=0;uf.shape);T.assertParamsConsistent(i,s);let o=T.computeOutShape(t.map(f=>f.shape),s);if(w.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(f=>w.sizeFromShape(f.shape)>0);if(l.length===1)return pr({inputs:{x:l[0]},backend:n});if(l[0].dtype==="complex64"){let f=l.map(v=>yi({inputs:{input:v},backend:n})),g=l.map(v=>jl({inputs:{input:v},backend:n})),b=Kl({inputs:f,backend:n,attrs:{axis:s}}),y=Kl({inputs:g,backend:n,attrs:{axis:s}}),x=Yn({inputs:{real:b,imag:y},backend:n});return f.forEach(v=>n.disposeIntermediateTensorInfo(v)),g.forEach(v=>n.disposeIntermediateTensorInfo(v)),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(y),x}let u=l.map(f=>{let g=[-1,w.sizeFromShape(f.shape.slice(s))];return xt({inputs:{x:f},backend:n,attrs:{shape:g}})}),p=u.map(f=>({vals:n.data.get(f.dataId).values,shape:f.shape}));o=T.computeOutShape(u.map(f=>f.shape),1);let d=u[0].shape[0]===1,c=L1(p,o,t[0].dtype,d),h=T.computeOutShape(l.map(f=>f.shape),s),m=n.makeTensorInfo(h,t[0].dtype,c);return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var NX={kernelName:su,backendName:"cpu",kernelFunc:Kl};function k_(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=a;ge([r,s],"conv2d");let d=T.convertConv2DDataFormat(l),c=T.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,d),h=c.filterHeight,m=c.filterWidth,f=c.dilationHeight,g=c.dilationWidth,b=c.padInfo.left,y=c.padInfo.top,x=c.dataFormat==="channelsLast",v=new Wt(c.outShape,r.dtype),I=w.computeStrides(r.shape),N=w.computeStrides(s.shape),C=I[0],_=x?I[1]:I[2],F=x?I[2]:1,D=x?1:I[1],$=v.strides[0],S=x?v.strides[1]:v.strides[2],M=x?v.strides[2]:1,B=x?1:v.strides[1],U=n.data.get(r.dataId).values,H=n.data.get(s.dataId).values,j=v.values;for(let K=0;K=c.inHeight)continue;let xe=se*N[0],ue=Z+ie*_;for(let ye=0;ye=c.inWidth)continue;let mt=xe+Le*N[1],st=ue+Ue*F,tt=mt;for(let nt=0;nt=u.inDepth)continue;let K=H*F[0],Z=$+j*_[1];for(let J=0;J=u.inHeight)continue;let ie=K+te*F[1],xe=Z+se*_[2];for(let ue=0;ue=u.inWidth)continue;let Ue=ie+Se*F[2],mt=xe+Le*u.inChannels,st=Ue;for(let tt=0;ttMath.cos(e)),LX={kernelName:Wi,backendName:"cpu",kernelFunc:PX},zX=lt(Bi,e=>Math.cosh(e)),WX={kernelName:Bi,backendName:"cpu",kernelFunc:zX};function BX(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,[p,d,c,h]=r.shape,m=s.shape[0],[f,g]=o,b=Oe([m,f,g,h],"float32"),y=n.data.get(s.dataId).values,x=n.data.get(i.dataId).values,v=n.data.get(r.dataId).values,I=w.computeStrides(r.shape),N=w.computeStrides(b.shape);for(let C=0;C=p)continue;let B=f>1?($-F)*(d-1)/(f-1):0,U=g>1?(S-D)*(c-1)/(g-1):0;for(let H=0;H1?F*(d-1)+H*B:.5*(F+$)*(d-1);if(j<0||j>d-1){for(let K=0;K1?D*(c-1)+ee*U:.5*(D+S)*(c-1);if(ae<0||ae>c-1){for(let xe=0;xe1?D*(c-1)+K*U:.5*(D+S)*(c-1);if(Z<0||Z>c-1){for(let ae=0;aeb+m-y-1:(b,y)=>b+y;for(let b=0;bb+m-y-1:(b,y)=>b+y;for(let b=0;b`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let o=r.shape[0],l=r.shape[1],u=r.shape[2],p=r.shape[3],d=l*s,c=u*s,h=p/(s*s),m=n.data.get(r.dataId).values,f=new Float32Array(o*d*c*h),g=0;for(let b=0;b`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let h=T.computeConv2DInfo(r.shape,s.shape,i,c,o,u,!0),{filterHeight:m,filterWidth:f,dilationHeight:g,dilationWidth:b,padInfo:y}=h,x=y.left,v=y.top,I=h.outChannels/h.inChannels,N=new Wt(h.outShape,r.dtype),C=n.data.get(r.dataId).values,_=n.data.get(s.dataId).values,F=N.values;for(let D=0;D=h.inHeight)continue;let K=H*d[0],Z=$+j*p[1];for(let J=0;J=h.inWidth)continue;let ie=K+te*d[1],xe=Z+se*h.inChannels,ue=ee,ye=ie;for(let ke=0;ke{let{x:a,filter:r}=e,{strides:s,pad:i,dilations:o}=n,l=t,u=l.data.get(a.dataId).values,p=a.shape.length,d=l.data.get(r.dataId).values,c=r.shape.length,{batchSize:h,inHeight:m,inWidth:f,inChannels:g,outHeight:b,outWidth:y,padInfo:x,strideHeight:v,strideWidth:I,filterHeight:N,filterWidth:C,dilationHeight:_,dilationWidth:F,outShape:D}=T.computeDilation2DInfo(a.shape,r.shape,s,i,"NHWC",o),$=w.sizeFromShape(D),S=D.length,M=w.getArrayFromDType(a.dtype,$);for(let B=0;B=0&&te=0&&ieJ&&(J=ye)}}}let ee=w.locToIndex([B,U,j,Z],S,w.computeStrides(D));M[ee]=J}}}return{dataId:l.write(w.toTypedArray(M,a.dtype),D,a.dtype),shape:D,dtype:a.dtype}}},sY={kernelName:Rl,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,p=w.toNestedArray(a.shape,u.data.get(a.dataId).values),d=w.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:b,padInfo:y,strideHeight:x,strideWidth:v,filterHeight:I,filterWidth:N,dilationHeight:C,dilationWidth:_,outShape:F}=T.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);w.assert(s.rank===F.length,()=>`Error in ${Rl}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let D=w.toNestedArray(F,u.data.get(s.dataId).values),$=w.makeZerosNestedTypedArray(r.shape,r.dtype);for(let S=0;S=0&&ae=0&&seK&&(K=ie,Z=ee,J=te)}}}$[Z][J][j]+=D[S][M][U][j]}}}return{dataId:u.write(w.toTypedArray($,a.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},iY={kernelName:Dl,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,p=w.toNestedArray(a.shape,u.data.get(a.dataId).values),d=w.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:b,padInfo:y,strideHeight:x,strideWidth:v,filterHeight:I,filterWidth:N,dilationHeight:C,dilationWidth:_,outShape:F}=T.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);w.assert(s.rank===F.length,()=>`Error in ${Dl}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let D=w.toNestedArray(F,u.data.get(s.dataId).values),$=w.makeZerosNestedTypedArray(a.shape,a.dtype);for(let S=0;S=0&&ae=0&&seK&&(K=ie,Z=ae,J=se)}}}$[S][Z][J][j]+=D[S][M][U][j]}}}return{dataId:u.write(w.toTypedArray($,a.dtype),a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}};function oY(e){let{inputs:t,backend:n,attrs:a}=e,{image:r}=t,{canvas:s,options:i}=a,{contextOptions:o,imageOptions:l}=i||{},u=(l==null?void 0:l.alpha)||1,p=(o==null?void 0:o.contextType)||"2d";if(p!=="2d")throw new Error(`Context type ${o.contextType} is not supported by the CPU backend.`);let d=s.getContext(p,(o==null?void 0:o.contextAttributes)||{});if(d==null)throw new Error(`Could not get the context with ${p} type.`);let[c,h]=r.shape.slice(0,2),m=r.shape.length===2?1:r.shape[2],f=n.data.get(r.dataId).values,g=r.dtype==="float32"?255:1,b=new Uint8ClampedArray(h*c*4);for(let x=0;x1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 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p=T.computePool3DInfo(s.shape,i,o,1,l,u),d=p.strideDepth,c=p.strideHeight,h=p.strideWidth,m=p.filterDepth,f=p.filterHeight,g=p.filterWidth,b=p.dilationDepth,y=p.dilationHeight,x=p.dilationWidth,v=p.effectiveFilterDepth,I=p.effectiveFilterHeight,N=p.effectiveFilterWidth,C=v-1-p.padInfo.front,_=N-1-p.padInfo.left,F=I-1-p.padInfo.top,D=Oe(s.shape,"float32"),$=1/(m*f*g),S=n.bufferSync(r);for(let M=0;M=p.outDepth||Math.floor(te)!==te))for(let se=0;se=p.outHeight||Math.floor(ie)!==ie))for(let ve=0;ve=p.outWidth||Math.floor(ue)!==ue)continue;let ye=S.get(M,te,ie,ue,B);ee+=ye}}}D.set(ee*$,M,U,H,q,B)}return n.makeTensorInfo(D.shape,D.dtype,D.values)}var hX={kernelName:Rc,backendName:"cpu",kernelFunc:dX};function 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o=s.reduce((b,y)=>b*y),l=T.getReshaped(r.shape,s,o),u=T.getPermuted(l.length,s.length),p=T.getReshapedPermuted(r.shape,s,o),d=T.getSliceBeginCoords(i,s.length),c=T.getSliceSize(p,i,s.length),h=xt({inputs:{x:r},backend:n,attrs:{shape:l}}),m=Vn({inputs:{x:h},backend:n,attrs:{perm:u}}),f=xt({inputs:{x:m},backend:n,attrs:{shape:p}}),g=xi({inputs:{x:f},backend:n,attrs:{begin:d,size:c}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),g}var xX={kernelName:nu,backendName:"cpu",kernelFunc:yX};function vX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.data.get(r.dataId).values,l=n.data.get(s.dataId).values,u=P1(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var wX={kernelName:au,backendName:"cpu",kernelFunc:vX};function kX(e){let{inputs:t,backend:n}=e,{s0:a,s1:r}=t,s=n.data.get(a.dataId).values,i=n.data.get(r.dataId).values,o=T.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var IX={kernelName:Mc,backendName:"cpu",kernelFunc:kX},SX=lt(ws,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e{let{x:t}=e.inputs,n=e.backend,a=new Float32Array(w.sizeFromShape(t.shape)),r=n.data.get(t.dataId),s=r.complexTensorInfos.real,i=r.complexTensorInfos.imag,o=n.data.get(s.dataId).values,l=n.data.get(i.dataId).values;for(let u=0;uf.shape);T.assertParamsConsistent(i,s);let o=T.computeOutShape(t.map(f=>f.shape),s);if(w.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(f=>w.sizeFromShape(f.shape)>0);if(l.length===1)return cr({inputs:{x:l[0]},backend:n});if(l[0].dtype==="complex64"){let f=l.map(v=>yi({inputs:{input:v},backend:n})),g=l.map(v=>ql({inputs:{input:v},backend:n})),b=Kl({inputs:f,backend:n,attrs:{axis:s}}),y=Kl({inputs:g,backend:n,attrs:{axis:s}}),x=Yn({inputs:{real:b,imag:y},backend:n});return f.forEach(v=>n.disposeIntermediateTensorInfo(v)),g.forEach(v=>n.disposeIntermediateTensorInfo(v)),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(y),x}let u=l.map(f=>{let g=[-1,w.sizeFromShape(f.shape.slice(s))];return xt({inputs:{x:f},backend:n,attrs:{shape:g}})}),p=u.map(f=>({vals:n.data.get(f.dataId).values,shape:f.shape}));o=T.computeOutShape(u.map(f=>f.shape),1);let d=u[0].shape[0]===1,c=L1(p,o,t[0].dtype,d),h=T.computeOutShape(l.map(f=>f.shape),s),m=n.makeTensorInfo(h,t[0].dtype,c);return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var _X={kernelName:su,backendName:"cpu",kernelFunc:Kl};function S_(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=a;ge([r,s],"conv2d");let d=T.convertConv2DDataFormat(l),c=T.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,d),h=c.filterHeight,m=c.filterWidth,f=c.dilationHeight,g=c.dilationWidth,b=c.padInfo.left,y=c.padInfo.top,x=c.dataFormat==="channelsLast",v=new Wt(c.outShape,r.dtype),I=w.computeStrides(r.shape),N=w.computeStrides(s.shape),C=I[0],_=x?I[1]:I[2],F=x?I[2]:1,D=x?1:I[1],$=v.strides[0],S=x?v.strides[1]:v.strides[2],M=x?v.strides[2]:1,B=x?1:v.strides[1],U=n.data.get(r.dataId).values,H=n.data.get(s.dataId).values,q=v.values;for(let K=0;K=c.inHeight)continue;let ve=se*N[0],ue=Z+ie*_;for(let ye=0;ye=c.inWidth)continue;let mt=ve+Le*N[1],st=ue+Ue*F,tt=mt;for(let nt=0;nt=u.inDepth)continue;let K=H*F[0],Z=$+q*_[1];for(let J=0;J=u.inHeight)continue;let ie=K+te*F[1],ve=Z+se*_[2];for(let ue=0;ue=u.inWidth)continue;let Ue=ie+Se*F[2],mt=ve+Le*u.inChannels,st=Ue;for(let tt=0;ttMath.cos(e)),VX={kernelName:Wi,backendName:"cpu",kernelFunc:BX},UX=lt(Bi,e=>Math.cosh(e)),GX={kernelName:Bi,backendName:"cpu",kernelFunc:UX};function HX(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,[p,d,c,h]=r.shape,m=s.shape[0],[f,g]=o,b=Oe([m,f,g,h],"float32"),y=n.data.get(s.dataId).values,x=n.data.get(i.dataId).values,v=n.data.get(r.dataId).values,I=w.computeStrides(r.shape),N=w.computeStrides(b.shape);for(let C=0;C=p)continue;let B=f>1?($-F)*(d-1)/(f-1):0,U=g>1?(S-D)*(c-1)/(g-1):0;for(let H=0;H1?F*(d-1)+H*B:.5*(F+$)*(d-1);if(q<0||q>d-1){for(let K=0;K1?D*(c-1)+ee*U:.5*(D+S)*(c-1);if(ae<0||ae>c-1){for(let ve=0;ve1?D*(c-1)+K*U:.5*(D+S)*(c-1);if(Z<0||Z>c-1){for(let ae=0;aeb+m-y-1:(b,y)=>b+y;for(let b=0;bb+m-y-1:(b,y)=>b+y;for(let b=0;b`Only NHWC dataFormat supported on CPU for depthToSpace. 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Got strides ${i} and dilations '${c}'`);let h=T.computeConv2DInfo(r.shape,s.shape,i,c,o,u,!0),{filterHeight:m,filterWidth:f,dilationHeight:g,dilationWidth:b,padInfo:y}=h,x=y.left,v=y.top,I=h.outChannels/h.inChannels,N=new Wt(h.outShape,r.dtype),C=n.data.get(r.dataId).values,_=n.data.get(s.dataId).values,F=N.values;for(let D=0;D=h.inHeight)continue;let K=H*d[0],Z=$+q*p[1];for(let J=0;J=h.inWidth)continue;let ie=K+te*d[1],ve=Z+se*h.inChannels,ue=ee,ye=ie;for(let ke=0;ke{let{x:a,filter:r}=e,{strides:s,pad:i,dilations:o}=n,l=t,u=l.data.get(a.dataId).values,p=a.shape.length,d=l.data.get(r.dataId).values,c=r.shape.length,{batchSize:h,inHeight:m,inWidth:f,inChannels:g,outHeight:b,outWidth:y,padInfo:x,strideHeight:v,strideWidth:I,filterHeight:N,filterWidth:C,dilationHeight:_,dilationWidth:F,outShape:D}=T.computeDilation2DInfo(a.shape,r.shape,s,i,"NHWC",o),$=w.sizeFromShape(D),S=D.length,M=w.getArrayFromDType(a.dtype,$);for(let B=0;B=0&&te=0&&ieJ&&(J=ye)}}}let ee=w.locToIndex([B,U,q,Z],S,w.computeStrides(D));M[ee]=J}}}return{dataId:l.write(w.toTypedArray(M,a.dtype),D,a.dtype),shape:D,dtype:a.dtype}}},uY={kernelName:Rl,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,p=w.toNestedArray(a.shape,u.data.get(a.dataId).values),d=w.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:b,padInfo:y,strideHeight:x,strideWidth:v,filterHeight:I,filterWidth:N,dilationHeight:C,dilationWidth:_,outShape:F}=T.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);w.assert(s.rank===F.length,()=>`Error in ${Rl}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let D=w.toNestedArray(F,u.data.get(s.dataId).values),$=w.makeZerosNestedTypedArray(r.shape,r.dtype);for(let S=0;S=0&&ae=0&&seK&&(K=ie,Z=ee,J=te)}}}$[Z][J][q]+=D[S][M][U][q]}}}return{dataId:u.write(w.toTypedArray($,a.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},pY={kernelName:Dl,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,p=w.toNestedArray(a.shape,u.data.get(a.dataId).values),d=w.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:b,padInfo:y,strideHeight:x,strideWidth:v,filterHeight:I,filterWidth:N,dilationHeight:C,dilationWidth:_,outShape:F}=T.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);w.assert(s.rank===F.length,()=>`Error in ${Dl}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let D=w.toNestedArray(F,u.data.get(s.dataId).values),$=w.makeZerosNestedTypedArray(a.shape,a.dtype);for(let S=0;S=0&&ae=0&&seK&&(K=ie,Z=ae,J=se)}}}$[S][Z][J][q]+=D[S][M][U][q]}}}return{dataId:u.write(w.toTypedArray($,a.dtype),a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}};function cY(e){let{inputs:t,backend:n,attrs:a}=e,{image:r}=t,{canvas:s,options:i}=a,{contextOptions:o,imageOptions:l}=i||{},u=(l==null?void 0:l.alpha)||1,p=(o==null?void 0:o.contextType)||"2d";if(p!=="2d")throw new Error(`Context type ${o.contextType} is not supported by the CPU backend.`);let d=s.getContext(p,(o==null?void 0:o.contextAttributes)||{});if(d==null)throw new Error(`Could not get the context with ${p} type.`);let[c,h]=r.shape.slice(0,2),m=r.shape.length===2?1:r.shape[2],f=n.data.get(r.dataId).values,g=r.dtype==="float32"?255:1,b=new Uint8ClampedArray(h*c*4);for(let x=0;x1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 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l=o?r:E_({inputs:{logits:r},backend:n,attrs:{dim:-1}}),u=l.shape[0],p=l.shape[1],d=n.data.get(l.dataId).values,c=[u,s],h=w.makeZerosTypedArray(w.sizeFromShape(c),"int32");for(let m=0;m=0&&d[c]{w.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let d=cm({inputs:{input:p},backend:n,attrs:{dim:r}});return o.push(d),d}),u=Kl({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var Z7={kernelName:Du,backendName:"cpu",kernelFunc:A_};function J7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a;ge(r,"pad");let o=s.map((b,y)=>b[0]+r.shape[y]+b[1]),l=s.map(b=>b[0]),u=n.data.get(r.dataId).values,p=w.sizeFromShape(r.shape),d=r.shape.length,c=w.computeStrides(r.shape),h=w.sizeFromShape(o),m=o.length,f=w.computeStrides(o),g=w.getTypedArrayFromDType(r.dtype,h);i!==0&&g.fill(i);for(let 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be=G();be.registerFlag("HAS_WEBGL",()=>be.getNumber("WEBGL_VERSION")>0);be.registerFlag("WEBGL_VERSION",()=>dv(2)?2:dv(1)?1:0);be.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);be.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>be.get("WEBGL_VERSION")===2);be.registerFlag("WEBGL_CPU_FORWARD",()=>!0);be.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);be.registerFlag("WEBGL_PACK",()=>be.getBool("HAS_WEBGL"));be.registerFlag("WEBGL_PACK_NORMALIZATION",()=>be.getBool("WEBGL_PACK"));be.registerFlag("WEBGL_PACK_CLIP",()=>be.getBool("WEBGL_PACK"));be.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>be.getBool("WEBGL_PACK"));be.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>be.getBool("WEBGL_PACK"));be.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>be.getBool("WEBGL_PACK"));be.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>be.getBool("WEBGL_PACK"));be.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>be.getBool("WEBGL_PACK"));be.registerFlag("WEBGL_PACK_REDUCE",()=>be.getBool("WEBGL_PACK"));be.registerFlag("WEBGL_LAZILY_UNPACK",()=>be.getBool("WEBGL_PACK"));be.registerFlag("WEBGL_CONV_IM2COL",()=>be.getBool("WEBGL_PACK"));be.registerFlag("WEBGL_PACK_CONV2DTRANSPOSE",()=>be.getBool("WEBGL_PACK"));be.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>J_(be.getNumber("WEBGL_VERSION")));be.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>Q_(be.getNumber("WEBGL_VERSION")));be.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=be.getNumber("WEBGL_VERSION");return e===0?0:eA(e)});be.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>be.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!ad.isMobile());be.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>tA(be.getNumber("WEBGL_VERSION")));be.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>be.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:be.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));be.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>nA(be.getNumber("WEBGL_VERSION")));be.registerFlag("WEBGL_FENCE_API_ENABLED",()=>aA(be.getNumber("WEBGL_VERSION")));be.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>be.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);be.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,e=>{if(typeof e!="number")throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be a number but got ${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}.`)});be.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>ad.isMobile()?1:-1,e=>{if(typeof e!="number")throw new Error(`WEBGL_FLUSH_THRESHOLD must be a number but got ${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}.`)});be.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD",()=>128);be.registerFlag("WEBGL_USE_SHAPES_UNIFORMS",()=>!1);be.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e5);be.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD",()=>128);be.registerFlag("WEBGL_EXP_CONV",()=>!1);be.registerFlag("SOFTWARE_WEBGL_ENABLED",()=>be.getBool("IS_TEST"));be.registerFlag("WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE",()=>1/0);be.registerFlag("WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE",()=>!1);be.registerFlag("WEBGL2_ISNAN_CUSTOM",()=>!1);be.registerFlag("ENGINE_COMPILE_ONLY",()=>!1);function En(){let e,t,n,a,r,s,i,o,l,u;return G().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",a="in",r="texture",s="outputColor",i="out vec4 outputColor;",o=G().getBool("WEBGL2_ISNAN_CUSTOM")?` bool isnan_custom(float val) { uint floatToUint = floatBitsToUint(val); return (floatToUint & 0x7fffffffu) > 0x7f800000u; @@ -112,7 +112,7 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram 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:l,defineRound:u}}function Zo(e,t,n="index"){let a=w.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 zf(e,t,n="index"){let a=w.computeStrides(t);return a.map((r,s)=>{let i=`int ${e[s]} = ${n} / outShapeStrides[${s}]`,o=s===a.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * outShapeStrides[${s}]`:`index -= ${e[s]} * outShapeStrides[${s}]`;return`${i}; ${o};`}).join("")}function QJ(e,t){let n=e.length,a=e.map(s=>`${t}[${s}]`),r=new Array(n-1);r[n-2]=a[n-1];for(let s=n-3;s>=0;--s)r[s]=`(${r[s+1]} * ${a[s+1]})`;return r}function e9(e,t,n="index"){let a=e.map((s,i)=>i),r=QJ(a,t);return r.map((s,i)=>{let o=`int ${e[i]} = ${n} / ${r[i]}`,l=i===r.length-1?`int ${e[i+1]} = ${n} - ${e[i]} * ${r[i]}`:`index -= ${e[i]} * ${r[i]}`;return`${o}; ${l};`}).join("")}function Q1(e){let t=w.computeStrides(e).map(n=>n.toString());return` + `),{version:e,attribute:t,varyingVs:n,varyingFs:a,texture2D:r,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:u}}function Zo(e,t,n="index"){let a=w.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 Wf(e,t,n="index"){let a=w.computeStrides(t);return a.map((r,s)=>{let i=`int ${e[s]} = ${n} / outShapeStrides[${s}]`,o=s===a.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * outShapeStrides[${s}]`:`index -= ${e[s]} * outShapeStrides[${s}]`;return`${i}; ${o};`}).join("")}function a9(e,t){let n=e.length,a=e.map(s=>`${t}[${s}]`),r=new Array(n-1);r[n-2]=a[n-1];for(let s=n-3;s>=0;--s)r[s]=`(${r[s+1]} * ${a[s+1]})`;return r}function r9(e,t,n="index"){let a=e.map((s,i)=>i),r=a9(a,t);return r.map((s,i)=>{let o=`int ${e[i]} = ${n} / ${r[i]}`,l=i===r.length-1?`int ${e[i+1]} = ${n} - ${e[i]} * ${r[i]}`:`index -= ${e[i]} * ${r[i]}`;return`${o}; ${l};`}).join("")}function Q1(e){let t=w.computeStrides(e).map(n=>n.toString());return` int getFlatIndex(ivec3 coords) { return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z; } @@ -120,7 +120,7 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram int getFlatIndex(ivec3 coords) { return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z; } -`}var nA=` +`}var rA=` const float FLOAT_MAX = 1.70141184e38; const float FLOAT_MIN = 1.17549435e-38; @@ -159,22 +159,22 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram return c / 255.0; } -`,{getBroadcastDims:aA}=T;function t9(e,t,n){let a=[];if(e.forEach(c=>{let h=w.sizeFromShape(c.shapeInfo.logicalShape);if(c.shapeInfo.isUniform?a.push(`uniform float ${c.name}${h>1?`[${h}]`:""};`):(a.push(`uniform sampler2D ${c.name};`),a.push(`uniform int offset${c.name};`)),n.enableShapeUniforms){let{uniformShape:m}=tk(n.packedInputs,c.shapeInfo.logicalShape,c.shapeInfo.texShape);switch(m.length){case 1:a.push(`uniform int ${c.name}Shape;`);break;case 2:a.push(`uniform ivec2 ${c.name}Shape;`);break;case 3:a.push(`uniform ivec3 ${c.name}Shape;`);break;case 4:a.push(`uniform ivec4 ${c.name}Shape;`);break;default:break}a.push(`uniform ivec2 ${c.name}TexShape;`)}}),n.enableShapeUniforms){switch(t.logicalShape.length){case 1:a.push("uniform int outShape;");break;case 2:a.push("uniform ivec2 outShape;"),a.push("uniform int outShapeStrides;");break;case 3:a.push("uniform ivec3 outShape;"),a.push("uniform ivec2 outShapeStrides;");break;case 4:a.push("uniform ivec4 outShape;"),a.push("uniform ivec3 outShapeStrides;");break;default:break}a.push("uniform ivec2 outTexShape;")}n.customUniforms&&n.customUniforms.forEach(c=>{a.push(`uniform ${c.type} ${c.name}${c.arrayIndex?`[${c.arrayIndex}]`:""};`)});let r=a.join(` -`),s=e.map(c=>n9(c,t,n.packedInputs,n.enableShapeUniforms)).join(` -`),i=t.texShape,o=Cn(),l=s9(o),u,p,d=l9(o);return t.isPacked?(u=a9(t.logicalShape,i,n.enableShapeUniforms),p=o9(o)):(u=r9(t.logicalShape,i,n.enableShapeUniforms),p=i9(o)),n.packedInputs&&(d+=d9),[d,l,p,r,u,s,n.userCode].join(` -`)}function up(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return S9(e,t);case 1:return T9(e,t);case 2:return E9(e,t);case 3:return A9(e,t);case 4:return $9(e,t);case 5:return D9(e);case 6:return R9(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function rA(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return I9(e);case 1:return N9(e,t);case 2:return C9(e,t);case 3:return _9(e,t);default:return F9(e,t)}}function n9(e,t,n=!1,a){let r="";n?r+=rA(e,a):r+=up(e,a);let s=e.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(n?r+=M9(e,t):r+=O9(e,t)),r}function a9(e,t,n){switch(e.length){case 0:return sA();case 1:return h9(e,t,n);case 2:return w9(e,t,n);case 3:return f9(e,t,n);default:return b9(e,t,n)}}function r9(e,t,n){switch(e.length){case 0:return sA();case 1:return m9(e,t,n);case 2:return k9(e,t,n);case 3:return g9(e,t,n);case 4:return y9(e,t,n);case 5:return x9(e,t);case 6:return v9(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function s9(e){return` +`,{getBroadcastDims:sA}=T;function s9(e,t,n){let a=[];if(e.forEach(c=>{let h=w.sizeFromShape(c.shapeInfo.logicalShape);if(c.shapeInfo.isUniform?a.push(`uniform float ${c.name}${h>1?`[${h}]`:""};`):(a.push(`uniform sampler2D ${c.name};`),a.push(`uniform int offset${c.name};`)),n.enableShapeUniforms){let{uniformShape:m}=tk(n.packedInputs,c.shapeInfo.logicalShape,c.shapeInfo.texShape);switch(m.length){case 1:a.push(`uniform int ${c.name}Shape;`);break;case 2:a.push(`uniform ivec2 ${c.name}Shape;`);break;case 3:a.push(`uniform ivec3 ${c.name}Shape;`);break;case 4:a.push(`uniform ivec4 ${c.name}Shape;`);break;default:break}a.push(`uniform ivec2 ${c.name}TexShape;`)}}),n.enableShapeUniforms){switch(t.logicalShape.length){case 1:a.push("uniform int outShape;");break;case 2:a.push("uniform ivec2 outShape;"),a.push("uniform int outShapeStrides;");break;case 3:a.push("uniform ivec3 outShape;"),a.push("uniform ivec2 outShapeStrides;");break;case 4:a.push("uniform ivec4 outShape;"),a.push("uniform ivec3 outShapeStrides;");break;default:break}a.push("uniform ivec2 outTexShape;")}n.customUniforms&&n.customUniforms.forEach(c=>{a.push(`uniform ${c.type} ${c.name}${c.arrayIndex?`[${c.arrayIndex}]`:""};`)});let r=a.join(` +`),s=e.map(c=>i9(c,t,n.packedInputs,n.enableShapeUniforms)).join(` +`),i=t.texShape,o=En(),l=u9(o),u,p,d=d9(o);return t.isPacked?(u=o9(t.logicalShape,i,n.enableShapeUniforms),p=c9(o)):(u=l9(t.logicalShape,i,n.enableShapeUniforms),p=p9(o)),n.packedInputs&&(d+=g9),[d,l,p,r,u,s,n.userCode].join(` +`)}function up(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return E9(e,t);case 1:return A9(e,t);case 2:return $9(e,t);case 3:return R9(e,t);case 4:return O9(e,t);case 5:return P9(e);case 6:return L9(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function iA(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return C9(e);case 1:return _9(e,t);case 2:return F9(e,t);case 3:return D9(e,t);default:return M9(e,t)}}function i9(e,t,n=!1,a){let r="";n?r+=iA(e,a):r+=up(e,a);let s=e.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(n?r+=z9(e,t):r+=W9(e,t)),r}function o9(e,t,n){switch(e.length){case 0:return oA();case 1:return b9(e,t,n);case 2:return N9(e,t,n);case 3:return x9(e,t,n);default:return w9(e,t,n)}}function l9(e,t,n){switch(e.length){case 0:return oA();case 1:return y9(e,t,n);case 2:return T9(e,t,n);case 3:return v9(e,t,n);case 4:return k9(e,t,n);case 5:return I9(e,t);case 6:return S9(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function u9(e){return` float sampleTexture(sampler2D textureSampler, vec2 uv) { return ${e.texture2D}(textureSampler, uv).r; } - `}function i9(e){return` + `}function p9(e){return` void setOutput(float val) { ${e.output} = vec4(val, 0, 0, 0); } - `}function o9(e){return` + `}function c9(e){return` void setOutput(vec4 val) { ${e.output} = val; } - `}function l9(e){return`${e.version} + `}function d9(e){return`${e.version} precision highp float; precision highp int; precision highp sampler2D; @@ -229,10 +229,10 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram return fract((p3.x + p3.y) * p3.z); } - ${u9} - ${p9} - ${c9} - `}var u9=` + ${h9} + ${m9} + ${f9} + `}var h9=` vec2 uvFromFlat(int texNumR, int texNumC, int index) { int texR = index / texNumC; int texC = index - texR * texNumC; @@ -244,7 +244,7 @@ vec2 packedUVfrom1D(int texNumR, int texNumC, int index) { int texC = texelIndex - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,p9=` +`,m9=` vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR, int texNumC, int row, int col) { int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2); @@ -252,7 +252,7 @@ vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR, int texC = texelIndex - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,c9=` +`,f9=` vec2 packedUVfrom3D(int texNumR, int texNumC, int texelsInBatch, int texelsInLogicalRow, int b, int row, int col) { @@ -261,7 +261,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, int texC = index - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,d9=` +`,g9=` float getChannel(vec4 frag, vec2 innerDims) { vec2 modCoord = mod(innerDims, 2.); return modCoord.x == 0. ? @@ -272,11 +272,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float modCoord = mod(float(dim), 2.); return modCoord == 0. ? frag.r : frag.g; } -`;function sA(){return` +`;function oA(){return` int getOutputCoords() { return 0; } - `}function h9(e,t,n){let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return a[0]===1?n?` + `}function b9(e,t,n){let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return a[0]===1?n?` int getOutputCoords() { return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0)); } @@ -305,7 +305,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2(${a[0]}, ${a[1]})); return 2 * (resTexRC.x * ${a[1]} + resTexRC.y); } - `}function m9(e,t,n){return t[0]===1?n?` + `}function y9(e,t,n){return t[0]===1?n?` int getOutputCoords() { return int(resultUV.x * float(outTexShape[1])); } @@ -333,7 +333,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2(${t[0]}, ${t[1]})); return resTexRC.x * ${t[1]} + resTexRC.y; } - `}function f9(e,t,n){if(n)return` + `}function x9(e,t,n){if(n)return` ivec3 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0)); @@ -364,12 +364,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return ivec3(b, r, c); } - `}function g9(e,t,n){if(n)return` + `}function v9(e,t,n){if(n)return` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); int index = resTexRC.x * outTexShape[1] + resTexRC.y; - ${zf(["r","c","d"],e)} + ${Wf(["r","c","d"],e)} return ivec3(r, c, d); } `;let a=Zo(["r","c","d"],e);return` @@ -380,7 +380,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${a} return ivec3(r, c, d); } - `}function b9(e,t,n){if(n)return` + `}function w9(e,t,n){if(n)return` ivec4 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); ivec2 resTexRC = ivec2(resultUV.yx * @@ -421,12 +421,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return ivec${e.length}(${l}); } - `}function y9(e,t,n){if(n)return` + `}function k9(e,t,n){if(n)return` ivec4 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); int index = resTexRC.x * outTexShape[1] + resTexRC.y; - ${zf(["r","c","d","d2"],e)} + ${Wf(["r","c","d","d2"],e)} return ivec4(r, c, d, d2); } `;let a=Zo(["r","c","d","d2"],e);return` @@ -437,7 +437,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${a} return ivec4(r, c, d, d2); } - `}function x9(e,t){let n=Zo(["r","c","d","d2","d3"],e);return` + `}function I9(e,t){let n=Zo(["r","c","d","d2","d3"],e);return` ivec5 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -449,7 +449,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ivec5 outShape = ivec5(r, c, d, d2, d3); return outShape; } - `}function v9(e,t){let n=Zo(["r","c","d","d2","d3","d4"],e);return` + `}function S9(e,t){let n=Zo(["r","c","d","d2","d3","d4"],e);return` ivec6 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -460,7 +460,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ivec6 result = ivec6(r, c, d, d2, d3, d4); return result; } - `}function w9(e,t,n){let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(w.arraysEqual(e,t))return n?` + `}function N9(e,t,n){let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(w.arraysEqual(e,t))return n?` ivec2 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1])); @@ -493,7 +493,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return ivec2(r, c); } - `}function k9(e,t,n){return w.arraysEqual(e,t)?n?` + `}function T9(e,t,n){return w.arraysEqual(e,t)?n?` ivec2 getOutputCoords() { return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); } @@ -547,11 +547,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, int c = index - r * ${e[1]}; return ivec2(r, c); } - `}function Jo(e){return`offset${e}`}function I9(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=Cn();return` + `}function Jo(e){return`offset${e}`}function C9(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=En();return` vec4 ${n}() { return ${a.texture2D}(${t}, halfCR); } - `}function S9(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${a}() {return ${n};}`;let[r,s]=e.shapeInfo.texShape;if(r===1&&s===1)return` + `}function E9(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${a}() {return ${n};}`;let[r,s]=e.shapeInfo.texShape;if(r===1&&s===1)return` float ${a}() { return sampleTexture(${n}, halfCR); } @@ -565,7 +565,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = uvFromFlat(${o}, ${l}, ${i}); return sampleTexture(${n}, uv); } - `}function N9(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,s=Cn();if(t)return` + `}function _9(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,s=En();if(t)return` vec4 ${a}(int index) { ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0)); vec2 uv = packedUVfrom1D( @@ -578,7 +578,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${i[0]}, ${i[1]}, index); return ${s.texture2D}(${n}, uv); } - `}function T9(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return` + `}function A9(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return` float ${a}(int index) { ${pp(e)} } @@ -616,7 +616,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = uvFromFlat(${s}, ${i}, index + ${o}); return sampleTexture(${n}, uv); } - `}function C9(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape,i=s[0],o=s[1],l=Cn();if(s!=null&&w.arraysEqual(n,s))return t?` + `}function F9(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape,i=s[0],o=s[1],l=En();if(s!=null&&w.arraysEqual(n,s))return t?` vec4 ${r}(int row, int col) { vec2 uv = (vec2(col, row) + halfCR) / vec2(${a}TexShape[1], ${a}TexShape[0]); @@ -640,7 +640,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = packedUVfrom2D(${p}, ${u[0]}, ${u[1]}, row, col); return ${l.texture2D}(${a}, uv); } - `}function E9(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape;if(s!=null&&w.arraysEqual(n,s)){if(t)return` + `}function $9(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape;if(s!=null&&w.arraysEqual(n,s)){if(t)return` float ${r}(int row, int col) { vec2 uv = (vec2(col, row) + halfCR) / vec2(${a}TexShape[1], ${a}TexShape[0]); return sampleTexture(${a}, uv); @@ -698,12 +698,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = uvFromFlat(${u}, ${p}, index); return sampleTexture(${a}, uv); } -`}function _9(e,t){let n=e.shapeInfo.logicalShape,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)];if(n[0]===1){let c=n.slice(1),h=[1,2],m=cp(e,c),f=["b","row","col"];return` - ${rA(m,t)} +`}function D9(e,t){let n=e.shapeInfo.logicalShape,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)];if(n[0]===1){let c=n.slice(1),h=[1,2],m=cp(e,c),f=["b","row","col"];return` + ${iA(m,t)} vec4 ${r}(int b, int row, int col) { return ${r}(${dp(f,h)}); } - `}let o=Cn();if(t)return` + `}let o=En();if(t)return` vec4 ${r}(int b, int row, int col) { ivec2 packedTexShape = ivec2(ceil(float(${a}TexShape[0]) / 2.0), ceil(float(${a}TexShape[1]) / 2.0)); int valuesPerRow = int(ceil(float(${a}Shape[2]) / 2.0)); @@ -718,7 +718,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${l}, ${u}, ${d}, ${p}, b, row, col); return ${o.texture2D}(${a}, uv); } - `}function A9(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=n[1]*n[2],i=n[2],{newShape:o,keptDims:l}=w.squeezeShape(n),u=o;if(u.length=1?p="coords = 0;":p=o.map(g=>`coords.${d[g+u]} = 0;`).join(` + `}function z9(e,t){let n=e.name,a=n.charAt(0).toUpperCase()+n.slice(1),r="get"+a+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=sA(e.shapeInfo.logicalShape,t.logicalShape),l=ht(i),u=i-s,p,d=["x","y","z","w","u","v"];s===0?p="":i<2&&o.length>=1?p="coords = 0;":p=o.map(g=>`coords.${d[g+u]} = 0;`).join(` `);let c="";i<2&&s>0?c="coords":c=e.shapeInfo.logicalShape.map((g,b)=>`coords.${d[b+u]}`).join(", ");let h="return outputValue;",m=w.sizeFromShape(e.shapeInfo.logicalShape)===1,f=w.sizeFromShape(t.logicalShape)===1;if(s===1&&!m&&!f)h=` return vec4(outputValue.xy, outputValue.xy); `;else if(m&&!f)i===1?h=` @@ -977,20 +977,20 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec4 outputValue = get${a}(${c}); ${h} } - `}function O9(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,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&e.shapeInfo.flatOffset==null&&w.arraysEqual(i,s))return` + `}function W9(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,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&e.shapeInfo.flatOffset==null&&w.arraysEqual(i,s))return` float ${r}() { return sampleTexture(${n}, resultUV); } - `;let u=ht(l),p=aA(e.shapeInfo.logicalShape,t.logicalShape),d=l-o,c,h=["x","y","z","w","u","v"];o===0?c="":l<2&&p.length>=1?c="coords = 0;":c=p.map(f=>`coords.${h[f+d]} = 0;`).join(` + `;let u=ht(l),p=sA(e.shapeInfo.logicalShape,t.logicalShape),d=l-o,c,h=["x","y","z","w","u","v"];o===0?c="":l<2&&p.length>=1?c="coords = 0;":c=p.map(f=>`coords.${h[f+d]} = 0;`).join(` `);let m="";return l<2&&o>0?m="coords":m=e.shapeInfo.logicalShape.map((f,g)=>`coords.${h[g+d]}`).join(", "),` float ${r}() { ${u} coords = getOutputCoords(); 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I=w.computeStrides(p);m=`${I[0]===l[1]}_${I[I.length-1]===l[1]}`}let f=i.shape.length,g=p.length===2&&w.arraysEqual(i.shape,l),b=w.sizeFromShape(i.shape)===1,y=T.getBroadcastDims(i.shape,n.shape),x=!e.packedInputs&&f===n.shape.length&&w.arraysEqual(l,n.texData.texShape),v=e.packedInputs||p.length>2?"":`${l[0]>1}_${l[1]>1}`;a+=`${f}_${x}_${u?d:""}_${p.length}_${b}_${y}_${g}_${c}_${h}_${m}_${v}_${o}`}else{let l=i.isUniform?"uniform":i.texData.texShape;a+=`${i.shape}_${l}_${o}`}});let r=e.userCode,s=e.constructor.name;return s+="_"+a+"_"+r+`${G().getNumber("WEBGL_VERSION")}`,s}function vn(e){return G().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var G9=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Nc.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=En();this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` ivec3 outCoordsFromFlatIndex(int index) { - ${this.enableShapeUniforms?zf(["r","c","d"],e):Zo(["r","c","d"],e)} + ${this.enableShapeUniforms?Wf(["r","c","d"],e):Zo(["r","c","d"],e)} return ivec3(r, c, d); } @@ -1008,9 +1008,9 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${t.output} = result; } - `}},B9=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Nc.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Cn();this.outputShape=e,this.enableShapeUniforms=xn(this.outputShape.length),this.userCode=` + `}},H9=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Nc.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=En();this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` ivec3 outCoordsFromFlatIndex(int index) { - ${this.enableShapeUniforms?zf(["r","c","d"],e):Zo(["r","c","d"],e)} + ${this.enableShapeUniforms?Wf(["r","c","d"],e):Zo(["r","c","d"],e)} return ivec3(r, c, d); } @@ -1028,24 +1028,24 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${t.output} = result; } - `}},V9=class{constructor(e){this.variableNames=["A"],this.outTexUsage=ca.DOWNLOAD;let t=Cn();this.outputShape=e,this.userCode=` - ${nA} + `}},j9=class{constructor(e){this.variableNames=["A"],this.outTexUsage=ca.DOWNLOAD;let t=En();this.outputShape=e,this.userCode=` + ${rA} void main() { float x = getAAtOutCoords(); ${t.output} = encode_float(x); } - `}},U9=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=ca.DOWNLOAD;let t=Cn();this.outputShape=e,this.userCode=` - ${nA} + `}},q9=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=ca.DOWNLOAD;let t=En();this.outputShape=e,this.userCode=` + ${rA} void main() { ivec3 coords = getOutputCoords(); float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z)); ${t.output} = encode_float(x); } - `}},G9={R:0,G:1,B:2,A:3},aS=class{constructor(e,t=!1,n="RGBA"){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let a=Cn();this.outputShape=e,this.enableShapeUniforms=xn(this.outputShape.length);let r="result";t&&(r="floor(result * 255. + 0.5)");let s="";for(let i=0;igA,createBufferFromOutputTexture:()=>xA,createFloat16MatrixTexture:()=>dA,createFloat16PackedMatrixTexture:()=>fA,createFloat32MatrixTexture:()=>cA,createIndexBuffer:()=>pA,createPackedMatrixTexture:()=>mA,createUnsignedBytesMatrixTexture:()=>hA,createVertexBuffer:()=>uA,createVertexShader:()=>lA,downloadByteEncodedFloatMatrixFromOutputTexture:()=>wA,downloadFloat32MatrixFromBuffer:()=>vA,downloadMatrixFromPackedOutputTexture:()=>IA,downloadPackedMatrixFromBuffer:()=>kA,getInternalFormatForFloat16MatrixTexture:()=>ak,getInternalFormatForFloat16PackedMatrixTexture:()=>ik,getInternalFormatForFloat32MatrixTexture:()=>nk,getInternalFormatForPackedMatrixTexture:()=>sk,getInternalFormatForUnsignedBytesMatrixTexture:()=>rk,uploadDenseMatrixToTexture:()=>bA,uploadPixelDataToTexture:()=>yA});function lA(e){let t=Cn(),n=`${t.version} + `}},uA={};_e(uA,{bindVertexProgramAttributeStreams:()=>yA,createBufferFromOutputTexture:()=>wA,createFloat16MatrixTexture:()=>mA,createFloat16PackedMatrixTexture:()=>bA,createFloat32MatrixTexture:()=>hA,createIndexBuffer:()=>dA,createPackedMatrixTexture:()=>gA,createUnsignedBytesMatrixTexture:()=>fA,createVertexBuffer:()=>cA,createVertexShader:()=>pA,downloadByteEncodedFloatMatrixFromOutputTexture:()=>IA,downloadFloat32MatrixFromBuffer:()=>kA,downloadMatrixFromPackedOutputTexture:()=>NA,downloadPackedMatrixFromBuffer:()=>SA,getInternalFormatForFloat16MatrixTexture:()=>ak,getInternalFormatForFloat16PackedMatrixTexture:()=>ik,getInternalFormatForFloat32MatrixTexture:()=>nk,getInternalFormatForPackedMatrixTexture:()=>sk,getInternalFormatForUnsignedBytesMatrixTexture:()=>rk,uploadDenseMatrixToTexture:()=>xA,uploadPixelDataToTexture:()=>vA});function pA(e){let t=En(),n=`${t.version} precision highp float; ${t.attribute} vec3 clipSpacePos; ${t.attribute} vec2 uv; @@ -1119,11 +1119,11 @@ vec2 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e.internalFormatHalfFloat}function dA(e,t,n,a){let[r,s]=_d(t,n);return Ad(e,r,s,ak(a),a.textureFormatFloat,a.textureTypeHalfFloat)}function rk(e){return e.downloadTextureFormat}function hA(e,t,n,a){let[r,s]=_d(t,n);return Ad(e,r,s,rk(a),e.RGBA,e.UNSIGNED_BYTE)}function sk(e){return e.internalFormatPackedFloat}function mA(e,t,n,a){let[r,s]=op(t,n);return Ad(e,r,s,sk(a),e.RGBA,e.FLOAT)}function ik(e){return e.internalFormatPackedHalfFloat}function fA(e,t,n,a){let[r,s]=op(t,n);return Ad(e,r,s,ik(a),e.RGBA,a.textureTypeHalfFloat)}function gA(e,t,n){return de(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),uv(e,t,"clipSpacePos",n,3,20,0)&&uv(e,t,"uv",n,2,20,12)}function bA(e,t,n,a,r,s){de(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,l;r instanceof Uint8Array?(i=new Uint8Array(n*a*4),o=e.UNSIGNED_BYTE,l=e.RGBA):(i=new 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a=0;ae.bindTexture(o,i)),de(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),de(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),de(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),de(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),G().getNumber("WEBGL_VERSION")===1?de(e,()=>e.texImage2D(o,0,a,t,n,0,r,s,null)):de(e,()=>e.texStorage2D(o,1,a,t,n)),de(e,()=>e.bindTexture(e.TEXTURE_2D,null)),{texture:i,texShape:[n,t]}}function nk(e){return e.internalFormatFloat}function hA(e,t,n,a){let[r,s]=_d(t,n);return Ad(e,r,s,nk(a),a.textureFormatFloat,e.FLOAT)}function ak(e){return e.internalFormatHalfFloat}function mA(e,t,n,a){let[r,s]=_d(t,n);return Ad(e,r,s,ak(a),a.textureFormatFloat,a.textureTypeHalfFloat)}function rk(e){return e.downloadTextureFormat}function fA(e,t,n,a){let[r,s]=_d(t,n);return Ad(e,r,s,rk(a),e.RGBA,e.UNSIGNED_BYTE)}function sk(e){return e.internalFormatPackedFloat}function gA(e,t,n,a){let[r,s]=op(t,n);return 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implementations are expected to offer OES_vertex_array_object.");this.createVertexArray=()=>de(e,()=>r.createVertexArrayOES()),this.bindVertexArray=s=>de(e,()=>r.bindVertexArrayOES(s)),this.deleteVertexArray=s=>de(e,()=>r.deleteVertexArrayOES(s)),this.getVertexArray=()=>de(e,()=>e.getParameter(r.VERTEX_ARRAY_BINDING_OES))}let n="WEBGL_color_buffer_float",a="EXT_color_buffer_half_float";if(this.parallelCompilationExtension=this.gl.getExtension("KHR_parallel_shader_compile"),G().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=rc(this.gl,r),da(this.gl,s))this.textureHalfFloatExtension=rc(this.gl,s);else if(G().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),da(this.gl,a))this.colorBufferHalfFloatExtension=rc(this.gl,a);else if(G().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",da(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(da(this.gl,a))this.colorBufferHalfFloatExtension=this.gl.getExtension(a);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=cA(this.gl),this.indexBuffer=dA(this.gl),this.framebuffer=G_(this.gl),this.textureConfig=Z1(this.gl,this.textureHalfFloatExtension)}get debug(){return G().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. 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this.throwIfDisposed(),bA(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),gA(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(cv(this.gl,this.framebuffer),this.outputTexture=null),de(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>IA(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,a,r,s){return SA(this.gl,e,t,n,a,r,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return kA(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let a=wA(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(G().getBool("WEBGL_FENCE_API_ENABLED")){let a=e,r=a.fenceSync(a.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let 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t=this.gl;de(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),yA(t,e,this.vertexBuffer)}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&(de(this.gl,()=>this.gl.deleteProgram(e)),this.deleteVertexArray(e.vao))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&Oh(this.gl,this.program),de(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?j_(this.gl,e,t):q_(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),de(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(),K_(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[a,r]=op(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&&Oh(this.gl,this.program),sc(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;if(this.debug){let t=this.getVertexArray();console.assert(t===this.program.vao,"VAO changed between setProgram and executeProgram!"),this.debugValidate()}de(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),de(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=rc(this.gl,G().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(G().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(G().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 w.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,G().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=Y9(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){if(this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1)return;let n;"setTimeoutCustom"in G().platform&&(n=G().platform.setTimeoutCustom.bind(G().platform)),w.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,n)}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Ph(this.gl,e,this.framebuffer),this.debug&&sc(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Ph(this.gl,this.outputTexture,this.framebuffer),this.debug&&sc(this.gl)):cv(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;Ph(a,e,this.framebuffer),this.debug&&sc(a),this.outputTexture=e,de(a,()=>a.viewport(0,0,t,n)),de(a,()=>a.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,a){this.throwIfDisposed(),de(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 Y9(e){let t=0;for(;t`${e}.${n}`)}function In(e,t){return t===1?[e]:_A(e,t)}function VQ(e,t){if(e===1)return"rc";let n="";for(let a=0;a= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}), cEdge ? 0. : getA(${t[1]}), rEdge ? 0. : getA(${t[2]}), - rEdge || cEdge ? 0. : getA(${t[3]})`}},EA=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=xn(this.outputShape.length);let n="";for(let a=0;a<4;a++){let r="thisRC = rc;";a%2===1&&(r+="thisRC.z += 1;"),a>1&&(r+="thisRC.y += 1;"),n+=` + rEdge || cEdge ? 0. : getA(${t[3]})`}},AA=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length);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); @@ -1158,7 +1158,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims); ${a>0?"}":""} `}this.userCode=` - ${WQ(t,this.enableShapeUniforms)} + ${GQ(t,this.enableShapeUniforms)} ${this.enableShapeUniforms?ek():Q1(e)} void main() { @@ -1174,12 +1174,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(result); } - `}};function WQ(e,t){return` + `}};function GQ(e,t){return` ivec3 inputCoordsFromReshapedOutCoords(int index) { - ${t?e9(["r","c","d"],"inputShape"):Zo(["r","c","d"],e)} + ${t?r9(["r","c","d"],"inputShape"):Zo(["r","c","d"],e)} return ivec3(r, c, d); } - `}var BQ=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(e,t,n){let a=sS(t,n),r=iS(e,a,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let s=rS(e,a,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[r].pop();return this.usedTextures[r].push(o),o}let i;return a===pn.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):a===pn.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):a===pn.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):a===pn.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):a===pn.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,a){if(this.freeTextures==null)return;let r=sS(n,a),s=iS(t,r,a);s in this.freeTextures||(this.freeTextures[s]=[]);let i=rS(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,a),o=G().getNumber("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l&&l.indexOf(e);if(u==null||u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l[u]=l[l.length-1],l.pop(),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function VQ(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;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function rS(e,t,n,a,r){let s=UQ(t,a),i;if(r){let[l,u]=op(e[0],e[1]);i=l*u}else{let[l,u]=_d(e[0],e[1]);i=l*u}let o=VQ(n,s);return i*o}function UQ(e,t){switch(e){case pn.PACKED_2X2_FLOAT32:return sk(t);case pn.PACKED_2X2_FLOAT16:return ik(t);case pn.UNPACKED_FLOAT32:return nk(t);case pn.UNPACKED_FLOAT16:return ak(t);case pn.PACKED_4X1_UNSIGNED_BYTE:return rk(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function GQ(e){return G().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?pn.PACKED_2X2_FLOAT32:pn.UNPACKED_FLOAT32:e?pn.PACKED_2X2_FLOAT16:pn.UNPACKED_FLOAT16}function sS(e,t){if(e===ca.UPLOAD)return pn.PACKED_2X2_FLOAT32;if(e===ca.RENDER||e==null)return GQ(t);if(e===ca.DOWNLOAD||e===ca.PIXELS)return pn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function iS(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var rr=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=xn(this.outputShape.length),this.userCode=` + `}var HQ=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(e,t,n){let a=sS(t,n),r=iS(e,a,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let s=rS(e,a,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[r].pop();return this.usedTextures[r].push(o),o}let i;return a===cn.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):a===cn.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):a===cn.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):a===cn.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):a===cn.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,a){if(this.freeTextures==null)return;let r=sS(n,a),s=iS(t,r,a);s in this.freeTextures||(this.freeTextures[s]=[]);let i=rS(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,a),o=G().getNumber("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l&&l.indexOf(e);if(u==null||u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l[u]=l[l.length-1],l.pop(),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function jQ(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;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function rS(e,t,n,a,r){let s=qQ(t,a),i;if(r){let[l,u]=op(e[0],e[1]);i=l*u}else{let[l,u]=_d(e[0],e[1]);i=l*u}let o=jQ(n,s);return i*o}function qQ(e,t){switch(e){case cn.PACKED_2X2_FLOAT32:return sk(t);case cn.PACKED_2X2_FLOAT16:return ik(t);case cn.UNPACKED_FLOAT32:return nk(t);case cn.UNPACKED_FLOAT16:return ak(t);case cn.PACKED_4X1_UNSIGNED_BYTE:return rk(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function KQ(e){return G().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?cn.PACKED_2X2_FLOAT32:cn.UNPACKED_FLOAT32:e?cn.PACKED_2X2_FLOAT16:cn.UNPACKED_FLOAT16}function sS(e,t){if(e===ca.UPLOAD)return cn.PACKED_2X2_FLOAT32;if(e===ca.RENDER||e==null)return KQ(t);if(e===ca.DOWNLOAD||e===ca.PIXELS)return cn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function iS(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var rr=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` float unaryOperation(float x) { ${t} } @@ -1190,11 +1190,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(y); } - `}},Da="if (isnan(x)) return x;",HQ="return x;",oS="return abs(x);",qQ="return (x >= 0.0) ? x : (exp(x) - 1.0);",jQ=Da+` + `}},Da="if (isnan(x)) return x;",XQ="return x;",oS="return abs(x);",YQ="return (x >= 0.0) ? x : (exp(x) - 1.0);",ZQ=Da+` return (x < 0.0) ? 0.0 : x; -`,KQ=Da+` +`,JQ=Da+` return (x < 0.0) ? 0.0 : min(6.0, x); -`,Yr="return x;",XQ="return 1.0 / (1.0 + exp(-1.0 * x));",YQ="return x;",ZQ=` +`,Yr="return x;",QQ="return 1.0 / (1.0 + exp(-1.0 * x));",eee="return x;",tee=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -1203,7 +1203,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; -`,JQ=` +`,nee=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -1213,7 +1213,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = isNaN.a ? x.a : result.a; return result; -`,QQ=` +`,aee=` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -1223,7 +1223,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = isNaN.a ? x.a : result.a; return result; -`,eee="return 1.0 / (1.0 + exp(-1.0 * x));",ts=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=xn(this.outputShape.length),this.userCode=` +`,ree="return 1.0 / (1.0 + exp(-1.0 * x));",ts=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` vec4 unaryOperation(vec4 x) { ${t} } @@ -1234,17 +1234,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(y); } - `}},tee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=xn(this.outputShape.length);let t=e.length,n=kn("rc",t),a=ht(t),r=LQ(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=` + `}},see=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length);let t=e.length,n=In("rc",t),a=ht(t),r=VQ(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})); } - `}},nee=hr.whereImpl,aee=1e-7,ree=1e-4,bx={};function see(e){return e in bx||(bx[e]={}),bx[e]}var iee=G().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),oee=600;function lee(){return G().global.screen==null?1024:G().global.screen.height*G().global.screen.width*window.devicePixelRatio*oee/1024/1024}var lk=class _A extends Fc{nextDataId(){return _A.nextDataId++}constructor(t){if(super(),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,!G().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let n;if(t!=null){if(t instanceof Wh)n=t;else{let a=qa(G().getNumber("WEBGL_VERSION"),t);n=new Wh(a)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let a=qa(G().getNumber("WEBGL_VERSION"));n=new Wh(a),this.binaryCache=see(G().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=n,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new BQ(this.gpgpu),this.numMBBeforeWarning=lee(),this.texData=new ym(this,Ta())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(t,n,a,r,s,i){let o=this.makeTensorInfo(n,a),l=this.texData.get(o.dataId);l.isPacked=!1,l.texture={texture:t,texShape:[r,s]},l.texShape=[r,s];let u=ic(n),p=new aS(u,!1,i),d=this.runWebGLProgram(p,[o],a,[[r,s]]);return d.shape=n,l.texture=null,this.disposeIntermediateTensorInfo(o),d.dataId}write(t,n,a){if((G().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||G().getBool("DEBUG"))&&this.checkNumericalProblems(t),a==="complex64"&&t!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let r={id:this.nextDataId()};return this.texData.set(r,{shape:n,dtype:a,values:t,usage:ca.UPLOAD,refCount:1}),r}refCount(t){return this.texData.has(t)?this.texData.get(t).refCount:0}incRef(t){let n=this.texData.get(t);n.refCount++}decRef(t){if(this.texData.has(t)){let n=this.texData.get(t);n.refCount--}}move(t,n,a,r,s){if(G().getBool("DEBUG")&&this.checkNumericalProblems(n),r==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(t,{shape:a,dtype:r,values:n,usage:ca.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(t){this.disposeData(t.dataId)}readSync(t){let n=this.texData.get(t),{values:a,dtype:r,complexTensorInfos:s,slice:i,shape:o,isPacked:l}=n;if(i!=null){let c;l?c=new ts(o,Yr):c=new rr(o,Yr);let h=this.runWebGLProgram(c,[{dataId:t,shape:o,dtype:r}],r),m=this.readSync(h.dataId);return this.disposeIntermediateTensorInfo(h),m}if(a!=null)return this.convertAndCacheOnCPU(t);if(r==="string")return a;let u=this.activeTimers!=null,p;u&&(p=w.now());let d;if(r==="complex64"){let c=this.readSync(s.real.dataId),h=this.readSync(s.imag.dataId);d=T.mergeRealAndImagArrays(c,h)}else d=this.getValuesFromTexture(t);return u&&(this.downloadWaitMs+=w.now()-p),this.convertAndCacheOnCPU(t,d)}async read(t){if(this.pendingRead.has(t)){let m=this.pendingRead.get(t);return new Promise(f=>m.push(f))}let n=this.texData.get(t),{values:a,shape:r,slice:s,dtype:i,complexTensorInfos:o,isPacked:l}=n;if(s!=null){let m;l?m=new ts(r,Yr):m=new rr(r,Yr);let f=this.runWebGLProgram(m,[{dataId:t,shape:r,dtype:i}],i),g=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),g}if(a!=null)return this.convertAndCacheOnCPU(t);if(G().getBool("DEBUG")&&!G().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&G().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,p;if(i!=="complex64"&&G().get("WEBGL_BUFFER_SUPPORTED")){p=this.decode(t);let m=this.texData.get(p.dataId);u=this.gpgpu.createBufferFromTexture(m.texture.texture,...Ah(r))}this.pendingRead.set(t,[]),i!=="complex64"&&await this.gpgpu.createAndWaitForFence();let d;if(i==="complex64"){let m=await Promise.all([this.read(o.real.dataId),this.read(o.imag.dataId)]),f=m[0],g=m[1];d=T.mergeRealAndImagArrays(f,g)}else if(u==null)d=this.getValuesFromTexture(t);else{let m=w.sizeFromShape(r);d=this.gpgpu.downloadFloat32MatrixFromBuffer(u,m)}if(p!=null&&this.disposeIntermediateTensorInfo(p),u!=null){let m=this.gpgpu.gl;de(m,()=>m.deleteBuffer(u))}let c=this.convertAndCacheOnCPU(t,d),h=this.pendingRead.get(t);return this.pendingRead.delete(t),h.forEach(m=>m(c)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t)&&Ta().removeDataId(t,this),this.pendingDeletes--),c}readToGPU(t,n={}){let a=this.texData.get(t),{values:r,shape:s,slice:i,dtype:o,isPacked:l,texture:u}=a;if(o==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(i!=null){let h;l?h=new ts(s,Yr):h=new rr(s,Yr);let m=this.runWebGLProgram(h,[{dataId:t,shape:s,dtype:o}],o),f=this.readToGPU(m,n);return this.disposeIntermediateTensorInfo(m),f}if(u==null)throw r!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let p=this.decode(t,n.customTexShape),d=Ta().makeTensorFromTensorInfo(p),c=this.texData.get(p.dataId);return Object.assign({tensorRef:d},c.texture)}bufferSync(t){let n=this.readSync(t.dataId);if(t.dtype==="string")try{let a=n.map(r=>w.decodeString(r));return Oe(t.shape,t.dtype,a)}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return Oe(t.shape,t.dtype,n)}checkNumericalProblems(t){if(t!=null)for(let n=0;n0}time(t){let n=this.activeTimers,a=[],r=!1;this.programTimersStack==null?(this.programTimersStack=a,r=!0):this.activeTimers.push(a),this.activeTimers=a,t();let s=w.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),i=w.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=n,r&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let l=await Promise.all(s);o.kernelMs=w.sum(l),o.getExtraProfileInfo=()=>l.map((u,p)=>({name:i[p],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else o.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,o})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(t){return G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),t):(t.endMs=w.now(),t)}async getQueryTime(t){if(G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(t);let n=t;return n.endMs-n.startMs}disposeData(t,n=!1){if(this.pendingDisposal.has(t))return!1;if(!this.texData.has(t))return!0;if(n?this.texData.get(t).refCount=0:this.texData.get(t).refCount--,!n&&this.texData.get(t).refCount>0)return!1;if(this.pendingRead.has(t))return this.pendingDisposal.add(t),this.pendingDeletes++,!1;this.releaseGPUData(t);let{complexTensorInfos:a}=this.texData.get(t);return a!=null&&(this.disposeData(a.real.dataId,n),this.disposeData(a.imag.dataId,n)),this.texData.delete(t),!0}releaseGPUData(t){let{texture:n,dtype:a,texShape:r,usage:s,isPacked:i,slice:o}=this.texData.get(t),l=o&&o.origDataId||t,u=this.dataRefCount.get(l);u>1?this.dataRefCount.set(l,u-1):(this.dataRefCount.delete(l),n!=null&&(this.numBytesInGPU-=this.computeBytes(r,a),this.textureManager.releaseTexture(n,r,s,i)));let p=this.texData.get(t);p.texture=null,p.texShape=null,p.isPacked=!1,p.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture.texture}getDataInfo(t){return this.texData.get(t)}shouldExecuteOnCPU(t,n=iee){return G().getBool("WEBGL_CPU_FORWARD")&&t.every(a=>this.texData.get(a.dataId).texture==null&&w.sizeFromShape(a.shape)0&&w.isString(a[0])){let s=a.map(i=>w.encodeString(i));r=this.write(s,t,n)}else r=this.write(a,t,n);return this.texData.get(r).usage=null,{dataId:r,shape:t,dtype:n}}makeOutput(t,n,a){return Ta().makeTensorFromTensorInfo(this.makeTensorInfo(t,n,a),this)}unpackTensor(t){let n=new tee(t.shape);return this.runWebGLProgram(n,[t],t.dtype)}packTensor(t){let n=new zQ(t.shape);return this.runWebGLProgram(n,[t],t.dtype,null,!0)}packedReshape(t,n){let a=[vi(t.shape),...wi(t.shape)],r={dtype:t.dtype,shape:a,dataId:t.dataId},s=[vi(n),...wi(n)],i=new EA(s,a),o=!0,l=[a],u=this.runWebGLProgram(i,[r],t.dtype,l,o);return{dataId:u.dataId,shape:n,dtype:u.dtype}}decode(t,n){let a=this.texData.get(t),{isPacked:r,shape:s,dtype:i}=a;if(n!=null){let c=w.sizeFromShape(s),h=n[0]*n[1]*4;w.assert(c<=h,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let o=ic(s),l;r?l=new B9(o):l=new W9(o);let u=!0,p=[n!=null?n:Ah(o)],d=this.runWebGLProgram(l,[{shape:o,dtype:i,dataId:t}],i,p,u,n);return{dtype:i,shape:s,dataId:d.dataId}}runWebGLProgram(t,n,a,r,s=!1,i){let o=this.makeTensorInfo(t.outputShape,a),l=this.texData.get(o.dataId);if(t.packedOutput&&(l.isPacked=!0),t.outPackingScheme===Nc.DENSE){let b=i!=null?i:Ah(t.outputShape);l.texShape=b.map(y=>y*2)}if(t.outTexUsage!=null&&(l.usage=t.outTexUsage),w.sizeFromShape(o.shape)===0)return l.values=w.getTypedArrayFromDType(o.dtype,0),o;let u=[],p=n.map(b=>{if(b.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let y=this.texData.get(b.dataId);if(y.texture==null){if(!t.packedInputs&&w.sizeFromShape(b.shape)<=G().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:b.shape,texData:null,isUniform:!0,uniformValues:y.values};t.packedInputs&&(y.isPacked=!0,y.shape=b.shape)}if(this.uploadToGPU(b.dataId),!!y.isPacked!=!!t.packedInputs)b=y.isPacked?this.unpackTensor(b):this.packTensor(b),u.push(b),y=this.texData.get(b.dataId);else if(y.isPacked&&!Tc(y.shape,b.shape)){let x=b,v=b.shape;b.shape=y.shape,b=this.packedReshape(b,v),u.push(b),y=this.texData.get(b.dataId),x.shape=v}return{shape:b.shape,texData:y,isUniform:!1}});this.uploadToGPU(o.dataId);let d={shape:o.shape,texData:l,isUniform:!1},c=z9(t,p,d),h=this.getAndSaveBinary(c,()=>P9(this.gpgpu,t,p,d)),m=this.activeTimers!=null,f;m&&(f=this.startTimer()),G().get("ENGINE_COMPILE_ONLY")||L9(this.gpgpu,h,p,d,r),u.forEach(b=>this.disposeIntermediateTensorInfo(b)),m&&(f=this.endTimer(f),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(f)}));let g=G().getNumber("WEBGL_FLUSH_THRESHOLD");if(g>0){let b=w.now();b-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=b)}if(!G().getBool("WEBGL_LAZILY_UNPACK")&&l.isPacked&&s===!1){let b=this.unpackTensor(o);return this.disposeIntermediateTensorInfo(o),b}return o}compileAndRun(t,n,a,r,s=!1){return a=a||n[0].dtype,this.runWebGLProgram(t,n,a,r,s)}getAndSaveBinary(t,n){return t in this.binaryCache||(this.binaryCache[t]=n()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(G().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=O(()=>{if(!G().get("WEBGL_RENDER_FLOAT32_ENABLED")){let t=G().getBool("DEBUG");G().set("DEBUG",!1);let n=this.abs(ve(1e-8)).dataSync()[0];if(G().set("DEBUG",t),n>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?aee:ree}uploadToGPU(t){let n=this.texData.get(t),{shape:a,dtype:r,values:s,texture:i,usage:o,isPacked:l}=n;if(i!=null)return;let u=this.activeTimers!=null,p;u&&(p=w.now());let d=n.texShape;if(d==null&&(d=X_(a,l),n.texShape=d),s!=null){let c=ic(a),h,m=d[1],f=d[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(l||!g)&&([m,f]=op(d[0],d[1])),l?h=new H9(c,g):h=new aS(c,g);let b=g?[f,m]:d,y=this.makeTensorInfo(b,r),x=this.texData.get(y.dataId);g?x.usage=ca.PIXELS:x.usage=ca.UPLOAD,x.texShape=b,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),m,f,s);let v=[[f,m]],I=this.runWebGLProgram(h,[y],r,v,!0),N=this.texData.get(I.dataId);n.texShape=N.texShape,n.isPacked=N.isPacked,n.usage=N.usage,G().get("ENGINE_COMPILE_ONLY")?this.disposeData(I.dataId):(n.texture=N.texture,n.values=null,this.texData.delete(I.dataId)),this.disposeIntermediateTensorInfo(y),u&&(this.uploadWaitMs+=w.now()-p)}else{let c=this.acquireTexture(d,o,r,l);n.texture=c}}convertAndCacheOnCPU(t,n){let a=this.texData.get(t),{dtype:r}=a;return n!=null&&(a.values=uee(n,r)),a.values}acquireTexture(t,n,a,r){if(this.numBytesInGPU+=this.computeBytes(t,a),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(t,n,r)}computeBytes(t,n){return t[0]*t[1]*w.bytesPerElement(n)}checkCompileCompletion(){for(let[,t]of Object.entries(this.binaryCache))this.checkCompletion_(t)}async checkCompileCompletionAsync(){let t=[];if(this.gpgpu.parallelCompilationExtension){for(let[,n]of Object.entries(this.binaryCache))t.push(this.checkCompletionAsync_(n));return Promise.all(t)}else{for(let[,n]of Object.entries(this.binaryCache)){let a=new Promise(r=>{try{this.checkCompletion_(n),r(!0)}catch(s){throw s}});t.push(a)}return Promise.all(t)}}async checkCompletionAsync_(t){return this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(t):(await Zw(),this.checkCompletionAsync_(t))}checkCompletion_(t){if(this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(t.webGLProgram)),this.gpgpu.gl.getShaderParameter(t.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(J1(t.source,this.gpgpu.gl.getShaderInfoLog(t.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let t of Object.values(this.binaryCache)){this.gpgpu.buildVao(t.webGLProgram);let{variablesLocations:n,customUniformLocations:a,infLoc:r,nanLoc:s,outShapeLocation:i,outShapeStridesLocation:o,outTexShapeLocation:l}=iA(this.gpgpu,t.program,t.webGLProgram);t.variablesLocations=n,t.customUniformLocations=a,t.infLoc=r,t.nanLoc=s,t.outShapeLocation=i,t.outShapeStridesLocation=o,t.outTexShapeLocation=l}}createTensorFromGPUData(t,n,a){t.channels=t.channels||"RGBA";let{texture:r,height:s,width:i,channels:o}=t,l=Ta().backend;if(!l.gpgpu.gl.isTexture(r))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let u=l.writeTexture(r,n,a,s,i,o);return Ta().makeTensorFromDataId(u,n,a,l)}};lk.nextDataId=0;function uee(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;anew lk,2);var cee={forceHalfFloat:AA},uk=` + `}},iee=mr.whereImpl,oee=1e-7,lee=1e-4,yx={};function uee(e){return e in yx||(yx[e]={}),yx[e]}var pee=G().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),cee=600;function dee(){return G().global.screen==null?1024:G().global.screen.height*G().global.screen.width*window.devicePixelRatio*cee/1024/1024}var lk=class FA extends Fc{nextDataId(){return FA.nextDataId++}constructor(t){if(super(),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,!G().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let n;if(t!=null){if(t instanceof Wh)n=t;else{let a=ja(G().getNumber("WEBGL_VERSION"),t);n=new Wh(a)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let a=ja(G().getNumber("WEBGL_VERSION"));n=new Wh(a),this.binaryCache=uee(G().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=n,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new HQ(this.gpgpu),this.numMBBeforeWarning=dee(),this.texData=new ym(this,Ta())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(t,n,a,r,s,i){let o=this.makeTensorInfo(n,a),l=this.texData.get(o.dataId);l.isPacked=!1,l.texture={texture:t,texShape:[r,s]},l.texShape=[r,s];let u=ic(n),p=new aS(u,!1,i),d=this.runWebGLProgram(p,[o],a,[[r,s]]);return d.shape=n,l.texture=null,this.disposeIntermediateTensorInfo(o),d.dataId}write(t,n,a){if((G().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||G().getBool("DEBUG"))&&this.checkNumericalProblems(t),a==="complex64"&&t!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let r={id:this.nextDataId()};return this.texData.set(r,{shape:n,dtype:a,values:t,usage:ca.UPLOAD,refCount:1}),r}refCount(t){return this.texData.has(t)?this.texData.get(t).refCount:0}incRef(t){let n=this.texData.get(t);n.refCount++}decRef(t){if(this.texData.has(t)){let n=this.texData.get(t);n.refCount--}}move(t,n,a,r,s){if(G().getBool("DEBUG")&&this.checkNumericalProblems(n),r==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(t,{shape:a,dtype:r,values:n,usage:ca.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(t){this.disposeData(t.dataId)}readSync(t){let n=this.texData.get(t),{values:a,dtype:r,complexTensorInfos:s,slice:i,shape:o,isPacked:l}=n;if(i!=null){let c;l?c=new ts(o,Yr):c=new rr(o,Yr);let h=this.runWebGLProgram(c,[{dataId:t,shape:o,dtype:r}],r),m=this.readSync(h.dataId);return this.disposeIntermediateTensorInfo(h),m}if(a!=null)return this.convertAndCacheOnCPU(t);if(r==="string")return a;let u=this.activeTimers!=null,p;u&&(p=w.now());let d;if(r==="complex64"){let c=this.readSync(s.real.dataId),h=this.readSync(s.imag.dataId);d=T.mergeRealAndImagArrays(c,h)}else d=this.getValuesFromTexture(t);return u&&(this.downloadWaitMs+=w.now()-p),this.convertAndCacheOnCPU(t,d)}async read(t){if(this.pendingRead.has(t)){let m=this.pendingRead.get(t);return new Promise(f=>m.push(f))}let n=this.texData.get(t),{values:a,shape:r,slice:s,dtype:i,complexTensorInfos:o,isPacked:l}=n;if(s!=null){let m;l?m=new ts(r,Yr):m=new rr(r,Yr);let f=this.runWebGLProgram(m,[{dataId:t,shape:r,dtype:i}],i),g=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),g}if(a!=null)return this.convertAndCacheOnCPU(t);if(G().getBool("DEBUG")&&!G().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&G().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,p;if(i!=="complex64"&&G().get("WEBGL_BUFFER_SUPPORTED")){p=this.decode(t);let m=this.texData.get(p.dataId);u=this.gpgpu.createBufferFromTexture(m.texture.texture,...Ah(r))}this.pendingRead.set(t,[]),i!=="complex64"&&await this.gpgpu.createAndWaitForFence();let d;if(i==="complex64"){let m=await Promise.all([this.read(o.real.dataId),this.read(o.imag.dataId)]),f=m[0],g=m[1];d=T.mergeRealAndImagArrays(f,g)}else if(u==null)d=this.getValuesFromTexture(t);else{let m=w.sizeFromShape(r);d=this.gpgpu.downloadFloat32MatrixFromBuffer(u,m)}if(p!=null&&this.disposeIntermediateTensorInfo(p),u!=null){let m=this.gpgpu.gl;de(m,()=>m.deleteBuffer(u))}let c=this.convertAndCacheOnCPU(t,d),h=this.pendingRead.get(t);return this.pendingRead.delete(t),h.forEach(m=>m(c)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t)&&Ta().removeDataId(t,this),this.pendingDeletes--),c}readToGPU(t,n={}){let a=this.texData.get(t),{values:r,shape:s,slice:i,dtype:o,isPacked:l,texture:u}=a;if(o==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(i!=null){let h;l?h=new ts(s,Yr):h=new rr(s,Yr);let m=this.runWebGLProgram(h,[{dataId:t,shape:s,dtype:o}],o),f=this.readToGPU(m,n);return this.disposeIntermediateTensorInfo(m),f}if(u==null)throw r!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let p=this.decode(t,n.customTexShape),d=Ta().makeTensorFromTensorInfo(p),c=this.texData.get(p.dataId);return Object.assign({tensorRef:d},c.texture)}bufferSync(t){let n=this.readSync(t.dataId);if(t.dtype==="string")try{let a=n.map(r=>w.decodeString(r));return Oe(t.shape,t.dtype,a)}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return Oe(t.shape,t.dtype,n)}checkNumericalProblems(t){if(t!=null)for(let n=0;n0}time(t){let n=this.activeTimers,a=[],r=!1;this.programTimersStack==null?(this.programTimersStack=a,r=!0):this.activeTimers.push(a),this.activeTimers=a,t();let s=w.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),i=w.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=n,r&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let l=await Promise.all(s);o.kernelMs=w.sum(l),o.getExtraProfileInfo=()=>l.map((u,p)=>({name:i[p],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else o.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,o})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(t){return G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),t):(t.endMs=w.now(),t)}async getQueryTime(t){if(G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(t);let n=t;return n.endMs-n.startMs}disposeData(t,n=!1){if(this.pendingDisposal.has(t))return!1;if(!this.texData.has(t))return!0;if(n?this.texData.get(t).refCount=0:this.texData.get(t).refCount--,!n&&this.texData.get(t).refCount>0)return!1;if(this.pendingRead.has(t))return this.pendingDisposal.add(t),this.pendingDeletes++,!1;this.releaseGPUData(t);let{complexTensorInfos:a}=this.texData.get(t);return a!=null&&(this.disposeData(a.real.dataId,n),this.disposeData(a.imag.dataId,n)),this.texData.delete(t),!0}releaseGPUData(t){let{texture:n,dtype:a,texShape:r,usage:s,isPacked:i,slice:o}=this.texData.get(t),l=o&&o.origDataId||t,u=this.dataRefCount.get(l);u>1?this.dataRefCount.set(l,u-1):(this.dataRefCount.delete(l),n!=null&&(this.numBytesInGPU-=this.computeBytes(r,a),this.textureManager.releaseTexture(n,r,s,i)));let p=this.texData.get(t);p.texture=null,p.texShape=null,p.isPacked=!1,p.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture.texture}getDataInfo(t){return this.texData.get(t)}shouldExecuteOnCPU(t,n=pee){return G().getBool("WEBGL_CPU_FORWARD")&&t.every(a=>this.texData.get(a.dataId).texture==null&&w.sizeFromShape(a.shape)0&&w.isString(a[0])){let s=a.map(i=>w.encodeString(i));r=this.write(s,t,n)}else r=this.write(a,t,n);return this.texData.get(r).usage=null,{dataId:r,shape:t,dtype:n}}makeOutput(t,n,a){return Ta().makeTensorFromTensorInfo(this.makeTensorInfo(t,n,a),this)}unpackTensor(t){let n=new see(t.shape);return this.runWebGLProgram(n,[t],t.dtype)}packTensor(t){let n=new UQ(t.shape);return this.runWebGLProgram(n,[t],t.dtype,null,!0)}packedReshape(t,n){let a=[vi(t.shape),...wi(t.shape)],r={dtype:t.dtype,shape:a,dataId:t.dataId},s=[vi(n),...wi(n)],i=new AA(s,a),o=!0,l=[a],u=this.runWebGLProgram(i,[r],t.dtype,l,o);return{dataId:u.dataId,shape:n,dtype:u.dtype}}decode(t,n){let a=this.texData.get(t),{isPacked:r,shape:s,dtype:i}=a;if(n!=null){let c=w.sizeFromShape(s),h=n[0]*n[1]*4;w.assert(c<=h,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let o=ic(s),l;r?l=new H9(o):l=new G9(o);let u=!0,p=[n!=null?n:Ah(o)],d=this.runWebGLProgram(l,[{shape:o,dtype:i,dataId:t}],i,p,u,n);return{dtype:i,shape:s,dataId:d.dataId}}runWebGLProgram(t,n,a,r,s=!1,i){let o=this.makeTensorInfo(t.outputShape,a),l=this.texData.get(o.dataId);if(t.packedOutput&&(l.isPacked=!0),t.outPackingScheme===Nc.DENSE){let b=i!=null?i:Ah(t.outputShape);l.texShape=b.map(y=>y*2)}if(t.outTexUsage!=null&&(l.usage=t.outTexUsage),w.sizeFromShape(o.shape)===0)return l.values=w.getTypedArrayFromDType(o.dtype,0),o;let u=[],p=n.map(b=>{if(b.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let y=this.texData.get(b.dataId);if(y.texture==null){if(!t.packedInputs&&w.sizeFromShape(b.shape)<=G().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:b.shape,texData:null,isUniform:!0,uniformValues:y.values};t.packedInputs&&(y.isPacked=!0,y.shape=b.shape)}if(this.uploadToGPU(b.dataId),!!y.isPacked!=!!t.packedInputs)b=y.isPacked?this.unpackTensor(b):this.packTensor(b),u.push(b),y=this.texData.get(b.dataId);else if(y.isPacked&&!Tc(y.shape,b.shape)){let x=b,v=b.shape;b.shape=y.shape,b=this.packedReshape(b,v),u.push(b),y=this.texData.get(b.dataId),x.shape=v}return{shape:b.shape,texData:y,isUniform:!1}});this.uploadToGPU(o.dataId);let d={shape:o.shape,texData:l,isUniform:!1},c=U9(t,p,d),h=this.getAndSaveBinary(c,()=>B9(this.gpgpu,t,p,d)),m=this.activeTimers!=null,f;m&&(f=this.startTimer()),G().get("ENGINE_COMPILE_ONLY")||V9(this.gpgpu,h,p,d,r),u.forEach(b=>this.disposeIntermediateTensorInfo(b)),m&&(f=this.endTimer(f),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(f)}));let g=G().getNumber("WEBGL_FLUSH_THRESHOLD");if(g>0){let b=w.now();b-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=b)}if(!G().getBool("WEBGL_LAZILY_UNPACK")&&l.isPacked&&s===!1){let b=this.unpackTensor(o);return this.disposeIntermediateTensorInfo(o),b}return o}compileAndRun(t,n,a,r,s=!1){return a=a||n[0].dtype,this.runWebGLProgram(t,n,a,r,s)}getAndSaveBinary(t,n){return t in this.binaryCache||(this.binaryCache[t]=n()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(G().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=O(()=>{if(!G().get("WEBGL_RENDER_FLOAT32_ENABLED")){let t=G().getBool("DEBUG");G().set("DEBUG",!1);let n=this.abs(xe(1e-8)).dataSync()[0];if(G().set("DEBUG",t),n>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?oee:lee}uploadToGPU(t){let n=this.texData.get(t),{shape:a,dtype:r,values:s,texture:i,usage:o,isPacked:l}=n;if(i!=null)return;let u=this.activeTimers!=null,p;u&&(p=w.now());let d=n.texShape;if(d==null&&(d=Z_(a,l),n.texShape=d),s!=null){let c=ic(a),h,m=d[1],f=d[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(l||!g)&&([m,f]=op(d[0],d[1])),l?h=new X9(c,g):h=new aS(c,g);let b=g?[f,m]:d,y=this.makeTensorInfo(b,r),x=this.texData.get(y.dataId);g?x.usage=ca.PIXELS:x.usage=ca.UPLOAD,x.texShape=b,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),m,f,s);let v=[[f,m]],I=this.runWebGLProgram(h,[y],r,v,!0),N=this.texData.get(I.dataId);n.texShape=N.texShape,n.isPacked=N.isPacked,n.usage=N.usage,G().get("ENGINE_COMPILE_ONLY")?this.disposeData(I.dataId):(n.texture=N.texture,n.values=null,this.texData.delete(I.dataId)),this.disposeIntermediateTensorInfo(y),u&&(this.uploadWaitMs+=w.now()-p)}else{let c=this.acquireTexture(d,o,r,l);n.texture=c}}convertAndCacheOnCPU(t,n){let a=this.texData.get(t),{dtype:r}=a;return n!=null&&(a.values=hee(n,r)),a.values}acquireTexture(t,n,a,r){if(this.numBytesInGPU+=this.computeBytes(t,a),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(t,n,r)}computeBytes(t,n){return t[0]*t[1]*w.bytesPerElement(n)}checkCompileCompletion(){for(let[,t]of Object.entries(this.binaryCache))this.checkCompletion_(t)}async checkCompileCompletionAsync(){let t=[];if(this.gpgpu.parallelCompilationExtension){for(let[,n]of Object.entries(this.binaryCache))t.push(this.checkCompletionAsync_(n));return Promise.all(t)}else{for(let[,n]of Object.entries(this.binaryCache)){let a=new Promise(r=>{try{this.checkCompletion_(n),r(!0)}catch(s){throw s}});t.push(a)}return Promise.all(t)}}async checkCompletionAsync_(t){return this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(t):(await Zw(),this.checkCompletionAsync_(t))}checkCompletion_(t){if(this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(t.webGLProgram)),this.gpgpu.gl.getShaderParameter(t.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(J1(t.source,this.gpgpu.gl.getShaderInfoLog(t.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let t of Object.values(this.binaryCache)){this.gpgpu.buildVao(t.webGLProgram);let{variablesLocations:n,customUniformLocations:a,infLoc:r,nanLoc:s,outShapeLocation:i,outShapeStridesLocation:o,outTexShapeLocation:l}=lA(this.gpgpu,t.program,t.webGLProgram);t.variablesLocations=n,t.customUniformLocations=a,t.infLoc=r,t.nanLoc=s,t.outShapeLocation=i,t.outShapeStridesLocation=o,t.outTexShapeLocation=l}}createTensorFromGPUData(t,n,a){t.channels=t.channels||"RGBA";let{texture:r,height:s,width:i,channels:o}=t,l=Ta().backend;if(!l.gpgpu.gl.isTexture(r))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let u=l.writeTexture(r,n,a,s,i,o);return Ta().makeTensorFromDataId(u,n,a,l)}};lk.nextDataId=0;function hee(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;anew lk,2);var fee={forceHalfFloat:$A},uk=` if (isnan(a)) return a; if (isnan(b)) return b; -`,ki=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=T.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=xn(this.outputShape.length),this.userCode=` +`,ki=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=T.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` float binaryOperation(float a, float b) { ${e} } @@ -1259,7 +1259,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.g = isNaN.g ? NAN : result.g; result.b = isNaN.b ? NAN : result.b; result.a = isNaN.a ? NAN : result.a; -`,hp=class{constructor(e,t,n,a=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=T.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=xn(r);let s="";if(a)if(r===0||w.sizeFromShape(this.outputShape)===1)s=` +`,hp=class{constructor(e,t,n,a=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=T.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=vn(r);let s="";if(a)if(r===0||w.sizeFromShape(this.outputShape)===1)s=` result.y = 0.; result.z = 0.; result.w = 0.; @@ -1273,7 +1273,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y; result.z = 0.; result.w = 0.; - `;else{let i=kn("coords",r);this.enableShapeUniforms?s+=` + `;else{let i=In("coords",r);this.enableShapeUniforms?s+=` bool nextRowOutOfBounds = (${i[r-2]} + 1) >= outShape[${r} - 2]; bool nextColOutOfBounds = @@ -1303,13 +1303,13 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(result); } - `}};function ta(e){let{inputs:t,backend:n}=e,{x:a}=t;return n.incRef(a.dataId),{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}var dee={kernelName:eo,backendName:"webgl",kernelFunc:ta};function Fs(e){let{inputs:t,backend:n}=e,{real:a,imag:r}=t,s=n.makeTensorInfo(a.shape,"complex64"),i=n.texData.get(s.dataId),o=ta({inputs:{x:a},backend:n}),l=ta({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var hee={kernelName:wm,backendName:"webgl",kernelFunc:Fs},FA="return (a < 0.) ? b * a : a;",$A=` + `}};function ta(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 gee={kernelName:eo,backendName:"webgl",kernelFunc:ta};function $s(e){let{inputs:t,backend:n}=e,{real:a,imag:r}=t,s=n.makeTensorInfo(a.shape,"complex64"),i=n.texData.get(s.dataId),o=ta({inputs:{x:a},backend:n}),l=ta({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var bee={kernelName:wm,backendName:"webgl",kernelFunc:$s},DA="return (a < 0.) ? b * a : a;",RA=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function mee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a,i=n.makeTensorInfo([],"float32",w.createScalarValue(s,"float32")),o=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new hp($A,r.shape,i.shape):new ki(FA,r.shape,i.shape),l=n.runWebGLProgram(o,[r,i],"float32");return n.disposeIntermediateTensorInfo(i),l}var fee={kernelName:ro,backendName:"webgl",kernelFunc:mee},DA="return (a < 0.) ? b * a : a;",RA=` +`;function yee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a,i=n.makeTensorInfo([],"float32",w.createScalarValue(s,"float32")),o=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new hp(RA,r.shape,i.shape):new ki(DA,r.shape,i.shape),l=n.runWebGLProgram(o,[r,i],"float32");return n.disposeIntermediateTensorInfo(i),l}var xee={kernelName:ro,backendName:"webgl",kernelFunc:yee},MA="return (a < 0.) ? b * a : a;",OA=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function gee(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new hp(RA,a.shape,r.shape):new ki(DA,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],"float32")}var bee={kernelName:wo,backendName:"webgl",kernelFunc:gee},mp="if (isnan(x)) return x;";function Ze({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:a}){return({inputs:r,backend:s})=>{let{x:i}=r,o=s,l=a||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let d=o.texData.get(i.dataId),c=n(d.values,l);return o.makeTensorInfo(i.shape,l,c)}let u=G().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,p;return u?p=new ts(i.shape,t):p=new rr(i.shape,e),o.runWebGLProgram(p,[i],l)}}function hn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:a=!1,cpuKernelImpl:r,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,p=o;if(a&&l.dtype==="complex64"){let m=p.texData.get(l.dataId),f=p.texData.get(u.dataId),[g,b]=[[m.complexTensorInfos.real,f.complexTensorInfos.real],[m.complexTensorInfos.imag,f.complexTensorInfos.imag]].map(x=>{let[v,I]=x,N={dataId:v.dataId,dtype:v.dtype,shape:l.shape},C={dataId:I.dataId,dtype:I.dtype,shape:u.shape},_=new ki(e,l.shape,u.shape);return p.runWebGLProgram(_,[N,C],fa(v.dtype,I.dtype))}),y=Fs({inputs:{real:g,imag:b},backend:p});return p.disposeIntermediateTensorInfo(g),p.disposeIntermediateTensorInfo(b),y}let d=s||fa(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||p.shouldExecuteOnCPU([l,u]))&&r!=null){let m=p.texData.get(l.dataId).values,f=p.texData.get(u.dataId).values,g=l.dtype==="string"?T.fromUint8ToStringArray(m):m,b=l.dtype==="string"?T.fromUint8ToStringArray(f):f,[y,x]=r(l.shape,u.shape,g,b,d),v=p.makeTensorInfo(x,d),I=p.texData.get(v.dataId);return I.values=y,v}let c=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return c?h=new hp(t,l.shape,u.shape,n):h=new ki(e,l.shape,u.shape),p.runWebGLProgram(h,[l,u],d)}}function Cc(e,t=!1){if(e==="linear")return t?YQ:HQ;if(e==="relu")return t?JQ:jQ;if(e==="elu")return t?ZQ:qQ;if(e==="relu6")return t?QQ:KQ;if(e==="prelu")return t?RA:DA;if(e==="leakyrelu")return t?$A:FA;if(e==="sigmoid")return t?eee:XQ;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var MA=class{constructor(e,t,n,a=!1,r=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=xn(this.outputShape.length);let u=a?e[1]:e[2],p=Math.ceil(u/2),d=a?"i * 2, rc.y":"rc.y, i * 2",c=r?"rc.z, i * 2":"i * 2, rc.z",h=a?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],m=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],f="",g="";i&&(o?f=`vec4 activation(vec4 a) { +`;function vee(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new hp(OA,a.shape,r.shape):new ki(MA,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],"float32")}var wee={kernelName:wo,backendName:"webgl",kernelFunc:vee},mp="if (isnan(x)) return x;";function Ze({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:a}){return({inputs:r,backend:s})=>{let{x:i}=r,o=s,l=a||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let d=o.texData.get(i.dataId),c=n(d.values,l);return o.makeTensorInfo(i.shape,l,c)}let u=G().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,p;return u?p=new ts(i.shape,t):p=new rr(i.shape,e),o.runWebGLProgram(p,[i],l)}}function hn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:a=!1,cpuKernelImpl:r,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,p=o;if(a&&l.dtype==="complex64"){let m=p.texData.get(l.dataId),f=p.texData.get(u.dataId),[g,b]=[[m.complexTensorInfos.real,f.complexTensorInfos.real],[m.complexTensorInfos.imag,f.complexTensorInfos.imag]].map(x=>{let[v,I]=x,N={dataId:v.dataId,dtype:v.dtype,shape:l.shape},C={dataId:I.dataId,dtype:I.dtype,shape:u.shape},_=new ki(e,l.shape,u.shape);return p.runWebGLProgram(_,[N,C],fa(v.dtype,I.dtype))}),y=$s({inputs:{real:g,imag:b},backend:p});return p.disposeIntermediateTensorInfo(g),p.disposeIntermediateTensorInfo(b),y}let d=s||fa(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||p.shouldExecuteOnCPU([l,u]))&&r!=null){let m=p.texData.get(l.dataId).values,f=p.texData.get(u.dataId).values,g=l.dtype==="string"?T.fromUint8ToStringArray(m):m,b=l.dtype==="string"?T.fromUint8ToStringArray(f):f,[y,x]=r(l.shape,u.shape,g,b,d),v=p.makeTensorInfo(x,d),I=p.texData.get(v.dataId);return I.values=y,v}let c=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return c?h=new hp(t,l.shape,u.shape,n):h=new ki(e,l.shape,u.shape),p.runWebGLProgram(h,[l,u],d)}}function Cc(e,t=!1){if(e==="linear")return t?eee:XQ;if(e==="relu")return t?nee:ZQ;if(e==="elu")return t?tee:YQ;if(e==="relu6")return t?aee:JQ;if(e==="prelu")return t?OA:MA;if(e==="leakyrelu")return t?RA:DA;if(e==="sigmoid")return t?ree:QQ;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var PA=class{constructor(e,t,n,a=!1,r=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=vn(this.outputShape.length);let u=a?e[1]:e[2],p=Math.ceil(u/2),d=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="",g="";i&&(o?f=`vec4 activation(vec4 a) { vec4 b = getPreluActivationWeightsAtOutCoords(); ${i} }`:l?f=`vec4 activation(vec4 a) { @@ -1361,7 +1361,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float bimag = getBImagAtOutCoords(); setOutput(binaryOpComplex(areal, aimag, breal, bimag)); } - `}},pS="return a * b;";function pk(e){let{inputs:t,backend:n}=e,{a,b:r}=t,s=T.upcastType(a.dtype,r.dtype);if(a.dtype==="complex64"){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),u=new uS(lS.REAL,a.shape,r.shape),p=new uS(lS.IMAG,a.shape,r.shape),d=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:a.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],c=n.runWebGLProgram(u,d,"float32"),h=n.runWebGLProgram(p,d,"float32"),m=Fs({inputs:{real:c,imag:h},backend:n});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}if(n.shouldExecuteOnCPU([a,r])){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),[u,p]=mQ(a.shape,r.shape,o.values,l.values,s),d=n.makeTensorInfo(p,s),c=n.texData.get(d.dataId);return c.values=u,d}let i;return G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new hp(pS,a.shape,r.shape):i=new ki(pS,a.shape,r.shape),n.runWebGLProgram(i,[a,r],s)}var yee={kernelName:bo,backendName:"webgl",kernelFunc:pk};function xee(e,t,n){let a=[vi(e.shape),...wi(e.shape)],r={dtype:e.dtype,shape:a,dataId:e.dataId},s=[vi(t),...wi(t)],i=new EA(s,a),o=!0,l=[a],u=n.runWebGLProgram(i,[r],e.dtype,l,o);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function ce(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{shape:s}=a,i=n,o=w.sizeFromShape(r.shape),l=w.inferFromImplicitShape(s,o),u=w.sizeFromShape(l);w.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let p=i.texData.get(r.dataId);return p.isPacked&&!Tc(r.shape,l)&&!(p.texture!==null&&Tc(p.shape,l))?xee(r,l,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var vee={kernelName:Ru,backendName:"webgl",kernelFunc:ce},cS=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,l="sumValue += dot(values, ones);";if(t!=null){let p=1/t;l=`sumValue += dot(values * ${w.isInt(p)?p.toPrecision(2):p}, ones);`}let u="";r%n>0&&(u=` + `}},pS="return a * b;";function pk(e){let{inputs:t,backend:n}=e,{a,b:r}=t,s=T.upcastType(a.dtype,r.dtype);if(a.dtype==="complex64"){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),u=new uS(lS.REAL,a.shape,r.shape),p=new uS(lS.IMAG,a.shape,r.shape),d=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:a.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],c=n.runWebGLProgram(u,d,"float32"),h=n.runWebGLProgram(p,d,"float32"),m=$s({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),l=n.texData.get(r.dataId),[u,p]=yQ(a.shape,r.shape,o.values,l.values,s),d=n.makeTensorInfo(p,s),c=n.texData.get(d.dataId);return c.values=u,d}let i;return G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new hp(pS,a.shape,r.shape):i=new ki(pS,a.shape,r.shape),n.runWebGLProgram(i,[a,r],s)}var kee={kernelName:bo,backendName:"webgl",kernelFunc:pk};function Iee(e,t,n){let a=[vi(e.shape),...wi(e.shape)],r={dtype:e.dtype,shape:a,dataId:e.dataId},s=[vi(t),...wi(t)],i=new AA(s,a),o=!0,l=[a],u=n.runWebGLProgram(i,[r],e.dtype,l,o);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function ce(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{shape:s}=a,i=n,o=w.sizeFromShape(r.shape),l=w.inferFromImplicitShape(s,o),u=w.sizeFromShape(l);w.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let p=i.texData.get(r.dataId);return p.isPacked&&!Tc(r.shape,l)&&!(p.texture!==null&&Tc(p.shape,l))?Iee(r,l,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var See={kernelName:Ru,backendName:"webgl",kernelFunc:ce},cS=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,l="sumValue += dot(values, ones);";if(t!=null){let p=1/t;l=`sumValue += dot(values * ${w.isInt(p)?p.toPrecision(2):p}, ones);`}let u="";r%n>0&&(u=` if (inIdx < 0 || inIdx >= ${r}) { return 0.0; } @@ -1414,7 +1414,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, } setOutput(sumValue); } - `}},wee=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let u=Math.floor(n/4)*4,p=n%4,d=` + `}},Nee=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 l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let u=Math.floor(n/4)*4,p=n%4,d=` if (${t==="sum"}) { sumValue += dot(values, ones); } else if (${t==="prod"}) { @@ -1506,12 +1506,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, } setOutput(${l}); } - `}};function kee(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=T.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:a,outSize:Math.ceil(n/a)})}return t}function el(e,t,n,a){let r=kee(e.shape),s=e;for(let i=0;i6)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;r6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let a=ht(this.rank),r=CA("rc",this.rank),s=new Array(this.rank);for(let u=0;u6)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;r6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let a=ht(this.rank),r=_A("rc",this.rank),s=new Array(this.rank);for(let u=0;u`Error in matMul: inner shapes (${d}) and (${c}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${a} must match.`);let v=n?[b,d,h]:[b,h,d],I=a?[y,m,c]:[y,c,m],N=ce({inputs:{x:e},backend:r,attrs:{shape:v}}),C=ce({inputs:{x:t},backend:r,attrs:{shape:I}}),_=[N,C],F=Math.max(b,y),D=n?N.shape[1]:N.shape[2],$=s!=null,S=i!=null,M=l==="leakyrelu",B=l!=null?Cc(l,!0):null,U=$||S||M||B!=null,H;if((h===1||m===1)&&D>OA&&U===!1){let K=N,Z=C;n&&(K=In({inputs:{x:N},backend:r,attrs:{perm:[0,2,1]}}),_.push(K)),a&&(Z=In({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),_.push(Z));let J=m!==1,ee=m===1,ae=K;J&&(ae=ce({inputs:{x:K},backend:r,attrs:{shape:[F,D,1]}}),_.push(ae));let te=m===1?2:1,se=Z;ee&&(se=ce({inputs:{x:Z},backend:r,attrs:{shape:[F,1,D]}}),_.push(se));let ie=pk({inputs:{a:ae,b:se},backend:r});H=Bf({inputs:{x:ie},backend:r,attrs:{axis:te,keepDims:!0}}),_.push(ie)}else{let K=fa(e.dtype,t.dtype),Z=new MA(v,I,[F,h,m],n,a,$,B,S,M),J=[N,C];if(s!=null&&J.push(s),S&&J.push(i),M){let ee=r.makeTensorInfo([],"float32",w.createScalarValue(o,"float32"));J.push(ee),_.push(ee)}H=r.runWebGLProgram(Z,J,K)}let j=ce({inputs:{x:H},backend:r,attrs:{shape:x}});_.push(H);for(let K of _)r.disposeIntermediateTensorInfo(K);return j}function _ee(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:d}=a;return hm({a:r,b:s,transposeA:l,transposeB:u,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:d,activation:p})}var Aee={kernelName:si,backendName:"webgl",kernelFunc:_ee},dS="return abs(x);";function Fee(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=NA(s.values);return n.makeTensorInfo(a.shape,a.dtype,i)}let r;return G().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new ts(a.shape,dS):r=new rr(a.shape,dS),n.runWebGLProgram(r,[a],a.dtype)}var $ee={kernelName:Yl,backendName:"webgl",kernelFunc:Fee},Dee=Da+` + `}};function Bf(e,t,n){let a=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new _ee(e.shape,t):new Cee(e.shape,t);return n.runWebGLProgram(a,[e],e.dtype)}function Aee(e,t,n,a){let r=t,s=e.shape.length,i=w.parseAxisParam(r,e.shape),o=i,l=T.getAxesPermutation(o,s),u=l!=null,p=e;u&&(p=Bf(e,l,a),o=T.getInnerMostAxes(o.length,s)),T.assertAxesAreInnerMostDims("sum",o,s);let[d,c]=T.computeOutAndReduceShapes(p.shape,o),h=d;n&&(h=T.expandShapeToKeepDim(d,i));let m=w.sizeFromShape(c),f=w.sizeFromShape(e.shape)/m,g=ce({inputs:{x:p},attrs:{shape:[f,m]},backend:a}),b=Mm(e.dtype),y=el(g,b,"sum",a),x=ce({inputs:{x:y},attrs:{shape:h},backend:a});return a.disposeIntermediateTensorInfo(g),a.disposeIntermediateTensorInfo(y),u&&a.disposeIntermediateTensorInfo(p),x}function Vf(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;return Aee(r,s,i,n)}var Fee={kernelName:Lo,backendName:"webgl",kernelFunc:Vf};function Sn(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{perm:s}=a,i=n,o=r.shape.length,l=new Array(o);for(let p=0;p`Error in matMul: inner shapes (${d}) and (${c}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${a} must match.`);let v=n?[b,d,h]:[b,h,d],I=a?[y,m,c]:[y,c,m],N=ce({inputs:{x:e},backend:r,attrs:{shape:v}}),C=ce({inputs:{x:t},backend:r,attrs:{shape:I}}),_=[N,C],F=Math.max(b,y),D=n?N.shape[1]:N.shape[2],$=s!=null,S=i!=null,M=l==="leakyrelu",B=l!=null?Cc(l,!0):null,U=$||S||M||B!=null,H;if((h===1||m===1)&&D>LA&&U===!1){let K=N,Z=C;n&&(K=Sn({inputs:{x:N},backend:r,attrs:{perm:[0,2,1]}}),_.push(K)),a&&(Z=Sn({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),_.push(Z));let J=m!==1,ee=m===1,ae=K;J&&(ae=ce({inputs:{x:K},backend:r,attrs:{shape:[F,D,1]}}),_.push(ae));let te=m===1?2:1,se=Z;ee&&(se=ce({inputs:{x:Z},backend:r,attrs:{shape:[F,1,D]}}),_.push(se));let ie=pk({inputs:{a:ae,b:se},backend:r});H=Vf({inputs:{x:ie},backend:r,attrs:{axis:te,keepDims:!0}}),_.push(ie)}else{let K=fa(e.dtype,t.dtype),Z=new PA(v,I,[F,h,m],n,a,$,B,S,M),J=[N,C];if(s!=null&&J.push(s),S&&J.push(i),M){let ee=r.makeTensorInfo([],"float32",w.createScalarValue(o,"float32"));J.push(ee),_.push(ee)}H=r.runWebGLProgram(Z,J,K)}let q=ce({inputs:{x:H},backend:r,attrs:{shape:x}});_.push(H);for(let K of _)r.disposeIntermediateTensorInfo(K);return q}function Dee(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:d}=a;return hm({a:r,b:s,transposeA:l,transposeB:u,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:d,activation:p})}var Ree={kernelName:ii,backendName:"webgl",kernelFunc:Dee},dS="return abs(x);";function Mee(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=CA(s.values);return n.makeTensorInfo(a.shape,a.dtype,i)}let r;return G().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new ts(a.shape,dS):r=new rr(a.shape,dS),n.runWebGLProgram(r,[a],a.dtype)}var Oee={kernelName:Yl,backendName:"webgl",kernelFunc:Mee},Pee=Da+` if (abs(x) > 1.) { return NAN; } return acos(x); -`,Ree=Ze({opSnippet:Dee}),Mee={kernelName:Ni,backendName:"webgl",kernelFunc:Ree},Oee=Da+` +`,Lee=Ze({opSnippet:Pee}),zee={kernelName:Ni,backendName:"webgl",kernelFunc:Lee},Wee=Da+` if (x < 1.0) return NAN; -return log(x + sqrt(x * x - 1.0));`,Pee=Ze({opSnippet:Oee}),Lee={kernelName:Ti,backendName:"webgl",kernelFunc:Pee},hS="return a + b;",zee=hn({opSnippet:hS,packedOpSnippet:hS,supportsComplex:!0,cpuKernelImpl:j9}),Wee={kernelName:xs,backendName:"webgl",kernelFunc:zee},Bee=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=` +return log(x + sqrt(x * x - 1.0));`,Bee=Ze({opSnippet:Wee}),Vee={kernelName:Ti,backendName:"webgl",kernelFunc:Bee},hS="return a + b;",Uee=hn({opSnippet:hS,packedOpSnippet:hS,supportsComplex:!0,cpuKernelImpl:Z9}),Gee={kernelName:vs,backendName:"webgl",kernelFunc:Uee},Hee=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(` `)} @@ -1543,7 +1543,7 @@ return log(x + sqrt(x * x - 1.0));`,Pee=Ze({opSnippet:Oee}),Lee={kernelName:Ti,b float result = ${a}; setOutput(result); } - `}},Vee=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let n=[];this.variableNames.forEach(r=>{n.push(`vec4 v${r} = get${r}AtOutCoords();`)});let a=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=` + `}},jee=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,s)=>`T${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(` `)} @@ -1551,7 +1551,7 @@ return log(x + sqrt(x * x - 1.0));`,Pee=Ze({opSnippet:Oee}),Lee={kernelName:Ti,b vec4 result = ${a}; setOutput(result); } - `}};function Bh(e){let{inputs:t,backend:n}=e,a=t;if(a.length===1)return ta({inputs:{x:a[0]},backend:n});if(a.length>G().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(a.length/2),l=Bh({inputs:a.slice(0,o),backend:n}),u=Bh({inputs:a.slice(o),backend:n});return Bh({inputs:[l,u],backend:n})}let r=a.map(o=>o.dtype).reduce((o,l)=>fa(o,l)),s=a.map(o=>o.shape),i=G().getBool("WEBGL_PACK")?new Vee(a[0].shape,s):new Bee(a[0].shape,s);return n.runWebGLProgram(i,a,r)}var Uee={kernelName:Ci,backendName:"webgl",kernelFunc:Bh};function Gee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=w.parseAxisParam(s,r.shape),u=l,p=T.getAxesPermutation(u,o),d=r;p!=null&&(d=In({inputs:{x:r},backend:n,attrs:{perm:p}}),u=T.getInnerMostAxes(u.length,o)),T.assertAxesAreInnerMostDims("all",u,o);let[c,h]=T.computeOutAndReduceShapes(d.shape,u),m=w.sizeFromShape(h),f=ce({inputs:{x:d},backend:n,attrs:{shape:[-1,m]}}),g=el(f,f.dtype,"all",n),b;if(i){let y=T.expandShapeToKeepDim(c,l);b=ce({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=ce({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),p!=null&&n.disposeIntermediateTensorInfo(d),b}var Hee={kernelName:Zl,backendName:"webgl",kernelFunc:Gee};function qee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=w.parseAxisParam(s,r.shape),u=l,p=T.getAxesPermutation(u,o),d=r;p!=null&&(d=In({inputs:{x:r},backend:n,attrs:{perm:p}}),u=T.getInnerMostAxes(u.length,o)),T.assertAxesAreInnerMostDims("any",u,o);let[c,h]=T.computeOutAndReduceShapes(d.shape,u),m=w.sizeFromShape(h),f=ce({inputs:{x:d},backend:n,attrs:{shape:[-1,m]}}),g=el(f,f.dtype,"any",n),b;if(i){let y=T.expandShapeToKeepDim(c,l);b=ce({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=ce({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),p!=null&&n.disposeIntermediateTensorInfo(d),b}var jee={kernelName:Jl,backendName:"webgl",kernelFunc:qee},Kee=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=` + `}};function Bh(e){let{inputs:t,backend:n}=e,a=t;if(a.length===1)return ta({inputs:{x:a[0]},backend:n});if(a.length>G().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(a.length/2),l=Bh({inputs:a.slice(0,o),backend:n}),u=Bh({inputs:a.slice(o),backend:n});return Bh({inputs:[l,u],backend:n})}let r=a.map(o=>o.dtype).reduce((o,l)=>fa(o,l)),s=a.map(o=>o.shape),i=G().getBool("WEBGL_PACK")?new jee(a[0].shape,s):new Hee(a[0].shape,s);return n.runWebGLProgram(i,a,r)}var qee={kernelName:Ci,backendName:"webgl",kernelFunc:Bh};function Kee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=w.parseAxisParam(s,r.shape),u=l,p=T.getAxesPermutation(u,o),d=r;p!=null&&(d=Sn({inputs:{x:r},backend:n,attrs:{perm:p}}),u=T.getInnerMostAxes(u.length,o)),T.assertAxesAreInnerMostDims("all",u,o);let[c,h]=T.computeOutAndReduceShapes(d.shape,u),m=w.sizeFromShape(h),f=ce({inputs:{x:d},backend:n,attrs:{shape:[-1,m]}}),g=el(f,f.dtype,"all",n),b;if(i){let y=T.expandShapeToKeepDim(c,l);b=ce({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=ce({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),p!=null&&n.disposeIntermediateTensorInfo(d),b}var Xee={kernelName:Zl,backendName:"webgl",kernelFunc:Kee};function 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const float avgMultiplier = float(${g}); @@ -2053,7 +2053,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,mte=Ze({opSnippet:hte}),fte={kernel } setOutput(dotProd); } - `}};function kte(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=a,d=[1,1,1],c=T.computePool3DInfo(i.shape,o,l,d,u,p),h=new wte(c);return n.runWebGLProgram(h,[r],i.dtype)}var Ite={kernelName:Rc,backendName:"webgl",kernelFunc:kte};function Ste(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;lp([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,p=T.computePool2DInfo(i.shape,o,l,1,u),d=new vte(p);return n.runWebGLProgram(d,[r],i.dtype)}var Nte={kernelName:Dc,backendName:"webgl",kernelFunc:Ste};function Tte(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;return hm({a:r,b:s,transposeA:i,transposeB:o,backend:n})}var Cte={kernelName:Ri,backendName:"webgl",kernelFunc:Tte},Ete=class{constructor(e,t,n,a,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],T.assertAndGetBroadcastShape(e,t),T.assertAndGetBroadcastShape(e,n);let i="0.0";a!=null&&(T.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(T.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` + `}};function Tte(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=a,d=[1,1,1],c=T.computePool3DInfo(i.shape,o,l,d,u,p),h=new Nte(c);return n.runWebGLProgram(h,[r],i.dtype)}var Cte={kernelName:Rc,backendName:"webgl",kernelFunc:Tte};function Ete(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;lp([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,p=T.computePool2DInfo(i.shape,o,l,1,u),d=new Ste(p);return n.runWebGLProgram(d,[r],i.dtype)}var _te={kernelName:Dc,backendName:"webgl",kernelFunc:Ete};function Ate(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;return hm({a:r,b:s,transposeA:i,transposeB:o,backend:n})}var Fte={kernelName:Ri,backendName:"webgl",kernelFunc:Ate},$te=class{constructor(e,t,n,a,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],T.assertAndGetBroadcastShape(e,t),T.assertAndGetBroadcastShape(e,n);let i="0.0";a!=null&&(T.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(T.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { float x = getXAtOutCoords(); float mean = getMeanAtOutCoords(); @@ -2063,7 +2063,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,mte=Ze({opSnippet:hte}),fte={kernel float inv = scale * inversesqrt(variance + float(${s})); setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1))); } - `}},_te=class{constructor(e,t,n,a,r,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],T.assertAndGetBroadcastShape(e,t),T.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";a!=null&&(T.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(T.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` + `}},Dte=class{constructor(e,t,n,a,r,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],T.assertAndGetBroadcastShape(e,t),T.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";a!=null&&(T.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(T.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { vec4 offset = ${i}; vec4 scale = ${o}; @@ -2076,7 +2076,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,mte=Ze({opSnippet:hte}),fte={kernel setOutput((x - mean) * inv + offset); } - `}},Ate=({inputs:e,backend:t,attrs:n})=>{let{x:a,mean:r,variance:s,offset:i,scale:o}=e;w.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let u=[a,r,s],p=null;i!=null&&(p=i.shape,u.push(i));let d=null;o!=null&&(d=o.shape,u.push(o));let c=G().getBool("WEBGL_PACK_NORMALIZATION")?new _te(a.shape,r.shape,s.shape,p,d,l):new Ete(a.shape,r.shape,s.shape,p,d,l);return t.runWebGLProgram(c,u,u[0].dtype)},Fte={kernelName:Ji,backendName:"webgl",kernelFunc:Ate},$te=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ht(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=Dte(this.rank),a,r=e.map((s,i)=>`sourceLoc.${hv[i]} = start[${i}] + coords.${hv[i]};`);a=` + `}},Rte=({inputs:e,backend:t,attrs:n})=>{let{x:a,mean:r,variance:s,offset:i,scale:o}=e;w.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let u=[a,r,s],p=null;i!=null&&(p=i.shape,u.push(i));let d=null;o!=null&&(d=o.shape,u.push(o));let c=G().getBool("WEBGL_PACK_NORMALIZATION")?new Dte(a.shape,r.shape,s.shape,p,d,l):new $te(a.shape,r.shape,s.shape,p,d,l);return t.runWebGLProgram(c,u,u[0].dtype)},Mte={kernelName:Ji,backendName:"webgl",kernelFunc:Rte},Ote=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ht(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=Pte(this.rank),a,r=e.map((s,i)=>`sourceLoc.${mv[i]} = start[${i}] + coords.${mv[i]};`);a=` ${t} sourceLoc; ${t} coords = getOutputCoords(); ${r.join(` @@ -2086,7 +2086,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,mte=Ze({opSnippet:hte}),fte={kernel ${a} setOutput(getSource(${n})); } - `}},hv=["x","y","z","w","u","v"];function Dte(e){if(e===1)return"sourceLoc";if(e<=6)return hv.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var Rte=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=ht(this.rank),n=kn("coords",this.rank),a=kn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${a.slice(-2).join()})`,s=`getChannel(getSource(${a.join()}), ${r})`,i=` + `}},mv=["x","y","z","w","u","v"];function Pte(e){if(e===1)return"sourceLoc";if(e<=6)return mv.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var Lte=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=ht(this.rank),n=In("coords",this.rank),a=In("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${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]}; @@ -2115,15 +2115,15 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,mte=Ze({opSnippet:hte}),fte={kernel ${o} setOutput(result); } - `}};function Mte(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=Kt.computeFlatOffset(t,w.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=a.dataRefCount.get(i.slice.origDataId)||1;return a.dataRefCount.set(i.slice.origDataId,l+1),s}function fp(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a,[o,l]=Kt.parseSliceParams(r,s,i);if(Kt.assertParamsValid(r,o,l),w.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.texData.get(r.dataId),c=NQ(d.values,o,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,c)}let{isPacked:u}=n.texData.get(r.dataId),p=Kt.isSliceContinous(r.shape,o,l);if(u||!p){let d=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Rte(l):new $te(l),c=[o];return n.runWebGLProgram(d,[r],r.dtype,c)}return n.uploadToGPU(r.dataId),Mte(r,o,l,n)}var Ote={kernelName:Bu,backendName:"webgl",kernelFunc:fp},Pte=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;w.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,x)=>y*x),l=T.getReshaped(r.shape,s,o),u=T.getPermuted(l.length,s.length),p=T.getReshapedPermuted(r.shape,s,o),d=T.getSliceBeginCoords(i,s.length),c=T.getSliceSize(p,i,s.length),h=[],m=ce({inputs:{x:r},backend:n,attrs:{shape:l}}),f=In({inputs:{x:m},backend:n,attrs:{perm:u}}),g=ce({inputs:{x:f},backend:n,attrs:{shape:p}}),b=fp({inputs:{x:g},backend:n,attrs:{begin:d,size:c}});return h.push(m),h.push(f),h.push(g),h.forEach(y=>n.disposeIntermediateTensorInfo(y)),b},Lte={kernelName:nu,backendName:"webgl",kernelFunc:Pte};function zte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.readSync(r.dataId),l=n.readSync(s.dataId),u=SA(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var Wte={kernelName:au,backendName:"webgl",kernelFunc:zte},Bte=` + `}};function zte(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=Kt.computeFlatOffset(t,w.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=a.dataRefCount.get(i.slice.origDataId)||1;return a.dataRefCount.set(i.slice.origDataId,l+1),s}function fp(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a,[o,l]=Kt.parseSliceParams(r,s,i);if(Kt.assertParamsValid(r,o,l),w.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.texData.get(r.dataId),c=_Q(d.values,o,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,c)}let{isPacked:u}=n.texData.get(r.dataId),p=Kt.isSliceContinous(r.shape,o,l);if(u||!p){let d=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Lte(l):new Ote(l),c=[o];return n.runWebGLProgram(d,[r],r.dtype,c)}return n.uploadToGPU(r.dataId),zte(r,o,l,n)}var Wte={kernelName:Bu,backendName:"webgl",kernelFunc:fp},Bte=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;w.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,x)=>y*x),l=T.getReshaped(r.shape,s,o),u=T.getPermuted(l.length,s.length),p=T.getReshapedPermuted(r.shape,s,o),d=T.getSliceBeginCoords(i,s.length),c=T.getSliceSize(p,i,s.length),h=[],m=ce({inputs:{x:r},backend:n,attrs:{shape:l}}),f=Sn({inputs:{x:m},backend:n,attrs:{perm:u}}),g=ce({inputs:{x:f},backend:n,attrs:{shape:p}}),b=fp({inputs:{x:g},backend:n,attrs:{begin:d,size:c}});return h.push(m),h.push(f),h.push(g),h.forEach(y=>n.disposeIntermediateTensorInfo(y)),b},Vte={kernelName:nu,backendName:"webgl",kernelFunc:Bte};function Ute(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.readSync(r.dataId),l=n.readSync(s.dataId),u=TA(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var Gte={kernelName:au,backendName:"webgl",kernelFunc:Ute},Hte=` int r = int(a.r) & int(b.r); int g = int(a.g) & int(b.g); int rb = int(a.b) & int(b.b); int ra = int(a.a) & int(b.a); return vec4(r, g, rb, ra); -`,Vte=` +`,jte=` return float(int(a.r) & int(b.r)); -`;function Ute(e){let{inputs:t,backend:n}=e,{a,b:r}=t,s=G().getBool("WEBGL_PACK_BINARY_OPERATIONS"),i=G().getNumber("WEBGL_VERSION");if(n.shouldExecuteOnCPU([a,r])||i===1){let l=n.texData.get(a.dataId).values,u=n.texData.get(r.dataId).values,[p,d]=X9(a.shape,r.shape,l,u,a.dtype),c=n.makeTensorInfo(d,a.dtype),h=n.texData.get(c.dataId);return h.values=p,c}let o;return s?o=new hp(Bte,a.shape,r.shape,!1):o=new ki(Vte,a.shape,r.shape),n.runWebGLProgram(o,[a,r],a.dtype)}var Gte={kernelName:ru,backendName:"webgl",kernelFunc:Ute};function Hte(e){let{inputs:t,backend:n}=e,{s0:a,s1:r}=t,s=n.readSync(a.dataId),i=n.readSync(r.dataId),o=T.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var qte={kernelName:Mc,backendName:"webgl",kernelFunc:Hte},jte="return float(a != b);",WA=hn({opSnippet:jte,cpuKernelImpl:gQ,dtype:"bool"}),Kte={kernelName:Eu,backendName:"webgl",kernelFunc:WA};function Fd(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return ta({inputs:{x:r.complexTensorInfos.real},backend:n})}var Xte={kernelName:Dm,backendName:"webgl",kernelFunc:Fd},Yte="return float(int(x));";function Zte(e,t){let n=new rr(e.shape,Yte),a=t.runWebGLProgram(n,[e],"int32");return{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}function mv(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dtype:s}=a;if(s==="complex64"){if(r.dtype==="complex64")return ta({inputs:{x:r},backend:n});let i=It(r.shape),o=mv({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=Fs({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=Fd({inputs:{input:r},backend:n}),o=mv({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!w.hasEncodingLoss(r.dtype,s)){let i=ta({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(n.shouldExecuteOnCPU([r])){let i=n.texData.get(r.dataId).values,[o,l,u]=Y9(i,r.shape,r.dtype,s);return n.makeTensorInfo(o,l,u)}if(s==="int32")return Zte(r,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),o=WA({inputs:{a:r,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var Jte={kernelName:Mi,backendName:"webgl",kernelFunc:mv},mS="return ceil(x);",Qte=Ze({opSnippet:mS,packedOpSnippet:mS,cpuKernelImpl:Z9}),ene={kernelName:Oi,backendName:"webgl",kernelFunc:Qte},tne=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` +`;function qte(e){let{inputs:t,backend:n}=e,{a,b:r}=t,s=G().getBool("WEBGL_PACK_BINARY_OPERATIONS"),i=G().getNumber("WEBGL_VERSION");if(n.shouldExecuteOnCPU([a,r])||i===1){let l=n.texData.get(a.dataId).values,u=n.texData.get(r.dataId).values,[p,d]=Q9(a.shape,r.shape,l,u,a.dtype),c=n.makeTensorInfo(d,a.dtype),h=n.texData.get(c.dataId);return h.values=p,c}let o;return s?o=new hp(Hte,a.shape,r.shape,!1):o=new ki(jte,a.shape,r.shape),n.runWebGLProgram(o,[a,r],a.dtype)}var Kte={kernelName:ru,backendName:"webgl",kernelFunc:qte};function Xte(e){let{inputs:t,backend:n}=e,{s0:a,s1:r}=t,s=n.readSync(a.dataId),i=n.readSync(r.dataId),o=T.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var Yte={kernelName:Mc,backendName:"webgl",kernelFunc:Xte},Zte="return float(a != b);",VA=hn({opSnippet:Zte,cpuKernelImpl:vQ,dtype:"bool"}),Jte={kernelName:Eu,backendName:"webgl",kernelFunc:VA};function Fd(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return ta({inputs:{x:r.complexTensorInfos.real},backend:n})}var Qte={kernelName:Dm,backendName:"webgl",kernelFunc:Fd},ene="return float(int(x));";function tne(e,t){let n=new rr(e.shape,ene),a=t.runWebGLProgram(n,[e],"int32");return{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}function fv(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dtype:s}=a;if(s==="complex64"){if(r.dtype==="complex64")return ta({inputs:{x:r},backend:n});let i=It(r.shape),o=fv({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=$s({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=Fd({inputs:{input:r},backend:n}),o=fv({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!w.hasEncodingLoss(r.dtype,s)){let i=ta({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(n.shouldExecuteOnCPU([r])){let i=n.texData.get(r.dataId).values,[o,l,u]=eQ(i,r.shape,r.dtype,s);return n.makeTensorInfo(o,l,u)}if(s==="int32")return tne(r,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),o=VA({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 nne={kernelName:Mi,backendName:"webgl",kernelFunc:fv},mS="return ceil(x);",ane=Ze({opSnippet:mS,packedOpSnippet:mS,cpuKernelImpl:tQ}),rne={kernelName:Oi,backendName:"webgl",kernelFunc:ane},sne=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` void main() { float value = getAAtOutCoords(); @@ -2134,7 +2134,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,mte=Ze({opSnippet:hte}),fte={kernel setOutput(clamp(value, minVal, maxVal)); } - `}},nne=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` + `}},ine=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` void main() { vec4 value = getAAtOutCoords(); @@ -2145,7 +2145,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,mte=Ze({opSnippet:hte}),fte={kernel setOutput(clamp(value, vec4(minVal), vec4(maxVal))); } - `}};function ane(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o;G().getBool("WEBGL_PACK_CLIP")?o=new nne(r.shape):o=new tne(r.shape);let l=[[s],[i]];return n.runWebGLProgram(o,[r],r.dtype,l)}var rne={kernelName:vs,backendName:"webgl",kernelFunc:ane},sne=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=` + `}};function one(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o;G().getBool("WEBGL_PACK_CLIP")?o=new ine(r.shape):o=new sne(r.shape);let l=[[s],[i]];return n.runWebGLProgram(o,[r],r.dtype,l)}var lne={kernelName:ws,backendName:"webgl",kernelFunc:one},une=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=` void main() { float re = abs(getRealAtOutCoords()); float im = abs(getImagAtOutCoords()); @@ -2158,7 +2158,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,mte=Ze({opSnippet:hte}),fte={kernel mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx)) ); } - `}};function fS(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function ine(e){let{inputs:t,backend:n}=e,{x:a}=t,r=n.texData.get(a.dataId),s=new sne(a.shape),i=[fS(a,r.complexTensorInfos.real),fS(a,r.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var one={kernelName:Oc,backendName:"webgl",kernelFunc:ine},lne=class{constructor(e){this.outputShape=[],this.outputShape=T.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${i}`);let t=new 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a=T.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>ce({inputs:{x:r},attrs:{shape:[-1,w.sizeFromShape(r.shape.slice(t))]},backend:n})),outShape:a}}function BA(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=w.parseAxisParam(r,t[0].shape)[0],i=t.map(u=>u.shape);T.assertParamsConsistent(i,s);let o=T.computeOutShape(t.map(u=>u.shape),s);if(w.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(u=>w.sizeFromShape(u.shape)>0);return l.length===1?ta({inputs:{x:l[0]},backend:n}):oc(l,s,n)}var dne={kernelName:su,backendName:"webgl",kernelFunc:BA},VA=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,l=e.strideWidth,u=e.dilationHeight,p=e.dilationWidth,d=e.filterHeight,c=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4,f=e.dataFormat==="channelsLast",g=f?1:2,b=f?2:3,y=f?3:1,x="",v="";n&&(a?x=`float activation(float a) { + `}};function 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a=T.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>ce({inputs:{x:r},attrs:{shape:[-1,w.sizeFromShape(r.shape.slice(t))]},backend:n})),outShape:a}}function UA(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=w.parseAxisParam(r,t[0].shape)[0],i=t.map(u=>u.shape);T.assertParamsConsistent(i,s);let o=T.computeOutShape(t.map(u=>u.shape),s);if(w.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(u=>w.sizeFromShape(u.shape)>0);return l.length===1?ta({inputs:{x:l[0]},backend:n}):oc(l,s,n)}var gne={kernelName:su,backendName:"webgl",kernelFunc:UA},GA=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,l=e.strideWidth,u=e.dilationHeight,p=e.dilationWidth,d=e.filterHeight,c=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4,f=e.dataFormat==="channelsLast",g=f?1:2,b=f?2:3,y=f?3:1,x="",v="";n&&(a?x=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${n} }`:r?x=`float activation(float a) { @@ -2338,7 +2338,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,mte=Ze({opSnippet:hte}),fte={kernel ${v} setOutput(result); } - `}},hne=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,a=e.padInfo.left,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,p=e.filterDepth,d=e.filterHeight,c=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=` + `}},bne=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,a=e.padInfo.left,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,p=e.filterDepth,d=e.filterHeight,c=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=` const ivec3 strides = ivec3(${r}, ${s}, ${i}); const ivec3 pads = 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s=e.padInfo.left,i=e.strideWidth,o=e.dilationWidth,l=e.filterHeight,u=e.filterWidth,p=u,d=` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`;for(let f=0;f=3?t?[...e.slice(0,-3),e[n-3]*e[n-2],e[n-1]]:[...e.slice(0,-3),e[n-3],e[n-2]*e[n-1]]:!t&&n===1&&e[0]>1?[e[0],1]:null}function GA({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=a.texData.get(e.dataId),p=n.inChannels,d=l[0]*l[1]*l[2],c=n.outChannels,h=n.dataFormat==="channelsLast",m=!1,f=!1,g,b=[];if(s!=null){let y=mm(s.shape,h);y!=null&&(s=ce({inputs:{x:s},backend:a,attrs:{shape:y}}),b.push(s))}if(r!=null){let y=mm(r.shape,h);y!=null&&(r=ce({inputs:{x:r},backend:a,attrs:{shape:y}}),b.push(r))}if(!((d===1||c===1)&&p>OA)&&u.isPacked&&h&&u.texture!=null&&l[2]%2!==0&&w.arraysEqual(u.shape.slice(-3),l.slice(-3))){let y=l[0]*l[1]*(l[2]+1),x={dataId:e.dataId,shape:[1,y,n.inChannels],dtype:e.dtype},v=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,w.assert(Tc(u.shape,x.shape),()=>`packed reshape ${u.shape} to ${x.shape} isn't free`);let I=ce({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}});b.push(I);let N=hm({a:x,b:I,backend:a,transposeA:m,transposeB:f,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),C=a.texData.get(N.dataId);w.assert(C.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=v,C.shape=n.outShape,g=ta({inputs:{x:N},backend:a}),g.shape=n.outShape,b.push(N)}else{let y=n.outHeight*n.outWidth,x=ce({inputs:{x:e},backend:a,attrs:{shape:h?[n.batchSize,y,n.inChannels]:[n.batchSize,n.inChannels,y]}}),v=ce({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}}),I=hm({a:h?x:v,b:h?v:x,transposeA:!h,transposeB:f,backend:a,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=ce({inputs:{x:I},backend:a,attrs:{shape:n.outShape}}),b.push(x),b.push(v),b.push(I)}for(let y of b)a.disposeIntermediateTensorInfo(y);return g}function HA({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:p,outWidth:d,outHeight:c,dataFormat:h}=n,m=h==="channelsLast",f=l*u*p,g=c*d,b=[n.batchSize,f,g],y=!0,x=!1,v=[];if(s!=null){let K=mm(s.shape,m);K!=null&&(s=ce({inputs:{x:s},backend:a,attrs:{shape:K}}),v.push(s))}if(r!=null){let K=mm(r.shape,m);K!=null&&(r=ce({inputs:{x:r},backend:a,attrs:{shape:K}}),v.push(r))}let I=ce({inputs:{x:t},backend:a,attrs:{shape:[1,f,w.sizeFromShape(t.shape)/f]}});v.push(I);let N=new mne(b,n),C=[e.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],_=a.runWebGLProgram(N,[e],"float32",C),F=ce({inputs:{x:_},backend:a,attrs:{shape:b}});v.push(_),v.push(F);let D=r!=null,$=s!=null,S=o==="leakyrelu",M=o?Cc(o,!0):null,B=new MA(m?F.shape:I.shape,m?I.shape:F.shape,m?[n.batchSize,g,n.outChannels]:[n.batchSize,n.outChannels,g],y,x,D,M,$,S),U=m?[F,I]:[I,F];if(r&&U.push(r),$&&U.push(s),S){let K=a.makeTensorInfo([],"float32",w.createScalarValue(i,"float32"));U.push(K),v.push(K)}let H=a.runWebGLProgram(B,U,"float32"),j=ce({inputs:{x:H},backend:a,attrs:{shape:n.outShape}});v.push(H);for(let K of v)a.disposeIntermediateTensorInfo(K);return j}function fne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=a,d=T.convertConv2DDataFormat(l),c=T.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,d),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=GA({x:r,filter:s,convInfo:c,backend:n});else if(c.strideWidth<=2&&d==="channelsLast"&&G().getBool("WEBGL_EXP_CONV")){let f=new UA(c),g=[[c.padInfo.top,c.padInfo.left],[c.strideHeight,c.strideWidth],[c.dilationHeight,c.dilationWidth],[c.inHeight,c.inWidth]];h=n.runWebGLProgram(f,[r,s],"float32",g)}else if(G().getBool("WEBGL_CONV_IM2COL"))h=HA({x:r,filter:s,convInfo:c,backend:n});else{let f=new VA(c);h=n.runWebGLProgram(f,[r,s],"float32")}let m=ce({inputs:{x:h},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(h),m}var gne={kernelName:Pi,backendName:"webgl",kernelFunc:fne},bne=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=` + `}};function mm(e,t){let n=e.length;return n>=3?t?[...e.slice(0,-3),e[n-3]*e[n-2],e[n-1]]:[...e.slice(0,-3),e[n-3],e[n-2]*e[n-1]]:!t&&n===1&&e[0]>1?[e[0],1]:null}function jA({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=a.texData.get(e.dataId),p=n.inChannels,d=l[0]*l[1]*l[2],c=n.outChannels,h=n.dataFormat==="channelsLast",m=!1,f=!1,g,b=[];if(s!=null){let y=mm(s.shape,h);y!=null&&(s=ce({inputs:{x:s},backend:a,attrs:{shape:y}}),b.push(s))}if(r!=null){let y=mm(r.shape,h);y!=null&&(r=ce({inputs:{x:r},backend:a,attrs:{shape:y}}),b.push(r))}if(!((d===1||c===1)&&p>LA)&&u.isPacked&&h&&u.texture!=null&&l[2]%2!==0&&w.arraysEqual(u.shape.slice(-3),l.slice(-3))){let y=l[0]*l[1]*(l[2]+1),x={dataId:e.dataId,shape:[1,y,n.inChannels],dtype:e.dtype},v=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,w.assert(Tc(u.shape,x.shape),()=>`packed reshape ${u.shape} to ${x.shape} isn't free`);let I=ce({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}});b.push(I);let N=hm({a:x,b:I,backend:a,transposeA:m,transposeB:f,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),C=a.texData.get(N.dataId);w.assert(C.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=v,C.shape=n.outShape,g=ta({inputs:{x:N},backend:a}),g.shape=n.outShape,b.push(N)}else{let y=n.outHeight*n.outWidth,x=ce({inputs:{x:e},backend:a,attrs:{shape:h?[n.batchSize,y,n.inChannels]:[n.batchSize,n.inChannels,y]}}),v=ce({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}}),I=hm({a:h?x:v,b:h?v:x,transposeA:!h,transposeB:f,backend:a,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=ce({inputs:{x:I},backend:a,attrs:{shape:n.outShape}}),b.push(x),b.push(v),b.push(I)}for(let y of b)a.disposeIntermediateTensorInfo(y);return g}function qA({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:p,outWidth:d,outHeight:c,dataFormat:h}=n,m=h==="channelsLast",f=l*u*p,g=c*d,b=[n.batchSize,f,g],y=!0,x=!1,v=[];if(s!=null){let K=mm(s.shape,m);K!=null&&(s=ce({inputs:{x:s},backend:a,attrs:{shape:K}}),v.push(s))}if(r!=null){let K=mm(r.shape,m);K!=null&&(r=ce({inputs:{x:r},backend:a,attrs:{shape:K}}),v.push(r))}let I=ce({inputs:{x:t},backend:a,attrs:{shape:[1,f,w.sizeFromShape(t.shape)/f]}});v.push(I);let N=new yne(b,n),C=[e.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],_=a.runWebGLProgram(N,[e],"float32",C),F=ce({inputs:{x:_},backend:a,attrs:{shape:b}});v.push(_),v.push(F);let D=r!=null,$=s!=null,S=o==="leakyrelu",M=o?Cc(o,!0):null,B=new PA(m?F.shape:I.shape,m?I.shape:F.shape,m?[n.batchSize,g,n.outChannels]:[n.batchSize,n.outChannels,g],y,x,D,M,$,S),U=m?[F,I]:[I,F];if(r&&U.push(r),$&&U.push(s),S){let K=a.makeTensorInfo([],"float32",w.createScalarValue(i,"float32"));U.push(K),v.push(K)}let H=a.runWebGLProgram(B,U,"float32"),q=ce({inputs:{x:H},backend:a,attrs:{shape:n.outShape}});v.push(H);for(let K of v)a.disposeIntermediateTensorInfo(K);return q}function xne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=a,d=T.convertConv2DDataFormat(l),c=T.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,d),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=jA({x:r,filter:s,convInfo:c,backend:n});else if(c.strideWidth<=2&&d==="channelsLast"&&G().getBool("WEBGL_EXP_CONV")){let f=new HA(c),g=[[c.padInfo.top,c.padInfo.left],[c.strideHeight,c.strideWidth],[c.dilationHeight,c.dilationWidth],[c.inHeight,c.inWidth]];h=n.runWebGLProgram(f,[r,s],"float32",g)}else if(G().getBool("WEBGL_CONV_IM2COL"))h=qA({x:r,filter:s,convInfo:c,backend:n});else{let f=new GA(c);h=n.runWebGLProgram(f,[r,s],"float32")}let m=ce({inputs:{x:h},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(h),m}var vne={kernelName:Pi,backendName:"webgl",kernelFunc:xne},wne=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; @@ -2706,7 +2706,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,mte=Ze({opSnippet:hte}),fte={kernel } setOutput(dotProd); } - `}},yne=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,l=s?1:2,u=s?2:3,p=s?3:1;this.userCode=` + `}},kne=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,l=s?1:2,u=s?2:3,p=s?3:1;this.userCode=` const ivec2 pads = ivec2(${i}, ${o}); void main() { @@ -2759,7 +2759,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,mte=Ze({opSnippet:hte}),fte={kernel } setOutput(dotProd); } - `}},xne=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=` + `}},Ine=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=` void main() { ivec5 coords = getOutputCoords(); int wF = coords.x; @@ -2801,7 +2801,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,mte=Ze({opSnippet:hte}),fte={kernel } setOutput(dotProd); } - `}},vne=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=a-1-e.padInfo.left;this.userCode=` + `}},Sne=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=a-1-e.padInfo.left;this.userCode=` const ivec3 pads = ivec3(${o}, ${l}, ${u}); void main() { @@ -2858,7 +2858,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,mte=Ze({opSnippet:hte}),fte={kernel } setOutput(dotProd); } - `}};function wne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=a,d=T.convertConv2DDataFormat(l),c=T.computeConv2DInfo(r.shape,p,i,1,o,u,!1,d),h=new bne(c);return n.runWebGLProgram(h,[r,s],"float32")}var kne={kernelName:km,backendName:"webgl",kernelFunc:wne},Ine=class{constructor(e){this.variableNames=["dy","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"strides",type:"vec2"}],this.outputShape=e.inShape,this.enableShapeUniforms=xn(this.outputShape.length);let t=e.filterHeight,n=e.filterWidth,a=t-1-e.padInfo.top,r=n-1-e.padInfo.left;this.userCode=` + `}};function Nne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=a,d=T.convertConv2DDataFormat(l),c=T.computeConv2DInfo(r.shape,p,i,1,o,u,!1,d),h=new wne(c);return n.runWebGLProgram(h,[r,s],"float32")}var Tne={kernelName:km,backendName:"webgl",kernelFunc:Nne},Cne=class{constructor(e){this.variableNames=["dy","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"strides",type:"vec2"}],this.outputShape=e.inShape,this.enableShapeUniforms=vn(this.outputShape.length);let t=e.filterHeight,n=e.filterWidth,a=t-1-e.padInfo.top,r=n-1-e.padInfo.left;this.userCode=` const ivec2 pads = ivec2(${a}, ${r}); void main() { @@ -2932,17 +2932,17 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,mte=Ze({opSnippet:hte}),fte={kernel } setOutput(result); } - `}};function Sne(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:p}=a,d=T.convertConv2DDataFormat(u),c=T.computeConv2DInfo(i,s.shape,o,1,l,p,!1,d);if(G().getBool("WEBGL_PACK_CONV2DTRANSPOSE")&&d==="channelsLast"){let h=[[c.strideHeight,c.strideWidth]],m=new Ine(c);return n.runWebGLProgram(m,[r,s],"float32",h)}else{let h=new yne(c);return n.runWebGLProgram(h,[r,s],"float32")}}var Nne={kernelName:Li,backendName:"webgl",kernelFunc:Sne};function Tne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=T.computeConv3DInfo(r.shape,s.shape,i,l,o),p=new hne(u);return n.runWebGLProgram(p,[r,s],"float32")}var Cne={kernelName:zi,backendName:"webgl",kernelFunc:Tne};function Ene(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=a,u=T.computeConv3DInfo(r.shape,l,i,1,o),p=new xne(u);return n.runWebGLProgram(p,[r,s],"float32")}var _ne={kernelName:iu,backendName:"webgl",kernelFunc:Ene};function Ane(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a,u=T.computeConv3DInfo(l,s.shape,o,1,i),p=new vne(u);return n.runWebGLProgram(p,[r,s],"float32")}var Fne={kernelName:ou,backendName:"webgl",kernelFunc:Ane},$ne=mp+` + `}};function Ene(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:p}=a,d=T.convertConv2DDataFormat(u),c=T.computeConv2DInfo(i,s.shape,o,1,l,p,!1,d);if(G().getBool("WEBGL_PACK_CONV2DTRANSPOSE")&&d==="channelsLast"){let h=[[c.strideHeight,c.strideWidth]],m=new Cne(c);return n.runWebGLProgram(m,[r,s],"float32",h)}else{let h=new kne(c);return n.runWebGLProgram(h,[r,s],"float32")}}var _ne={kernelName:Li,backendName:"webgl",kernelFunc:Ene};function Ane(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=T.computeConv3DInfo(r.shape,s.shape,i,l,o),p=new bne(u);return n.runWebGLProgram(p,[r,s],"float32")}var Fne={kernelName:zi,backendName:"webgl",kernelFunc:Ane};function $ne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=a,u=T.computeConv3DInfo(r.shape,l,i,1,o),p=new Ine(u);return n.runWebGLProgram(p,[r,s],"float32")}var Dne={kernelName:iu,backendName:"webgl",kernelFunc:$ne};function Rne(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a,u=T.computeConv3DInfo(l,s.shape,o,1,i),p=new Sne(u);return n.runWebGLProgram(p,[r,s],"float32")}var Mne={kernelName:ou,backendName:"webgl",kernelFunc:Rne},One=mp+` return cos(x); -`,Dne=` +`,Pne=` vec4 result = cos(x); bvec4 isNaN = isnan(x); ${Qo} return result; -`,Rne=Ze({opSnippet:$ne,packedOpSnippet:Dne}),Mne={kernelName:Wi,backendName:"webgl",kernelFunc:Rne},One=` +`,Lne=Ze({opSnippet:One,packedOpSnippet:Pne}),zne={kernelName:Wi,backendName:"webgl",kernelFunc:Lne},Wne=` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; -`,Pne=Ze({opSnippet:One}),Lne={kernelName:Bi,backendName:"webgl",kernelFunc:Pne},zne=class{constructor(e,t,n,a,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[p,d]=n;this.outputShape=[u,p,d,l];let c=a==="bilinear"?1:0,[h,m]=[`${i-1}.0`,`${o-1}.0`],[f,g,b]=p>1?[`${(i-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[y,x,v]=d>1?[`${(o-1)/(d-1)}`,"(x2-x1) * width_ratio",`x1*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=` +`,Bne=Ze({opSnippet:Wne}),Vne={kernelName:Bi,backendName:"webgl",kernelFunc:Bne},Une=class{constructor(e,t,n,a,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[p,d]=n;this.outputShape=[u,p,d,l];let c=a==="bilinear"?1:0,[h,m]=[`${i-1}.0`,`${o-1}.0`],[f,g,b]=p>1?[`${(i-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[y,x,v]=d>1?[`${(o-1)/(d-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(${y}); void main() { @@ -3003,7 +3003,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,mte=Ze({opSnippet:hte}),fte={kernel setOutput(newValue); } } - `}},Wne=e=>{let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,p=new zne(r.shape,s.shape,o,l,u);return n.runWebGLProgram(p,[r,s,i],"float32")},Bne={kernelName:uu,backendName:"webgl",kernelFunc:Wne},_c;(function(e){e.Prod="*",e.Sum="+"})(_c||(_c={}));var gS=class{constructor(e,t,n,a){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let r=this.outputShape.length,s=this.op===_c.Prod?"1.0":"0.0",i=n?s:`getX(${bS(r,"coords",this.op)})`,o=this.outputShape[this.outputShape.length-1],l="",u="";n?(l=a?`end != ${o-1}`:"end != 0",u=a?"end + 1":"end - 1"):(l=a?`end + pow2 < ${o}`:"end >= pow2",u=a?"end + pow2":"end - pow2"),this.userCode=` + `}},Gne=e=>{let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,p=new Une(r.shape,s.shape,o,l,u);return n.runWebGLProgram(p,[r,s,i],"float32")},Hne={kernelName:uu,backendName:"webgl",kernelFunc:Gne},_c;(function(e){e.Prod="*",e.Sum="+"})(_c||(_c={}));var gS=class{constructor(e,t,n,a){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let r=this.outputShape.length,s=this.op===_c.Prod?"1.0":"0.0",i=n?s:`getX(${bS(r,"coords",this.op)})`,o=this.outputShape[this.outputShape.length-1],l="",u="";n?(l=a?`end != ${o-1}`:"end != 0",u=a?"end + 1":"end - 1"):(l=a?`end + pow2 < ${o}`:"end >= pow2",u=a?"end + pow2":"end - pow2"),this.userCode=` void main() { ${ht(r)} coords = getOutputCoords(); int end = ${yS(r,"coords",this.op)}; @@ -3016,7 +3016,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,mte=Ze({opSnippet:hte}),fte={kernel } setOutput(val); } - `}};function bS(e,t,n){if(e===1)return`${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 new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function yS(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function qA(e,t,n,a,r,s){let i=t.shape.length,o=T.getAxesPermutation([a],i),l=t;o!=null&&(l=In({inputs:{x:t},backend:n,attrs:{perm:o}}));let u=T.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${a}`);let p=l.shape[u],d=ta({inputs:{x:l},backend:n});for(let c=0;c<=Math.ceil(Math.log2(p))-1;c++){let h=new gS(e,l.shape,!1,s),m=[[c]],f=d;d=n.runWebGLProgram(h,[d],d.dtype,m),n.disposeIntermediateTensorInfo(f)}if(r){let c=new gS(e,l.shape,r,s),h=d;d=n.runWebGLProgram(c,[d],d.dtype),n.disposeIntermediateTensorInfo(h)}if(o!=null){let c=T.getUndoAxesPermutation(o),h=In({inputs:{x:d},backend:n,attrs:{perm:c}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(l),h}return d}function Vne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;return qA(_c.Prod,r,n,s,i,o)}var Une={kernelName:lu,backendName:"webgl",kernelFunc:Vne};function Gne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;return qA(_c.Sum,r,n,s,i,o)}var Hne={kernelName:Vi,backendName:"webgl",kernelFunc:Gne};function qne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.readSync(r.dataId),u=n.readSync(s.dataId),p=SA(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,p)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(s),p=K9(l,u,i,o);return n.makeTensorInfo(p.shape,s.dtype,p.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var jne={kernelName:Pc,backendName:"webgl",kernelFunc:qne},Kne=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=` + `}};function bS(e,t,n){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 new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function yS(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function KA(e,t,n,a,r,s){let i=t.shape.length,o=T.getAxesPermutation([a],i),l=t;o!=null&&(l=Sn({inputs:{x:t},backend:n,attrs:{perm:o}}));let u=T.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${a}`);let p=l.shape[u],d=ta({inputs:{x:l},backend:n});for(let c=0;c<=Math.ceil(Math.log2(p))-1;c++){let h=new gS(e,l.shape,!1,s),m=[[c]],f=d;d=n.runWebGLProgram(h,[d],d.dtype,m),n.disposeIntermediateTensorInfo(f)}if(r){let c=new gS(e,l.shape,r,s),h=d;d=n.runWebGLProgram(c,[d],d.dtype),n.disposeIntermediateTensorInfo(h)}if(o!=null){let c=T.getUndoAxesPermutation(o),h=Sn({inputs:{x:d},backend:n,attrs:{perm:c}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(l),h}return d}function jne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;return KA(_c.Prod,r,n,s,i,o)}var qne={kernelName:lu,backendName:"webgl",kernelFunc:jne};function Kne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;return KA(_c.Sum,r,n,s,i,o)}var Xne={kernelName:Vi,backendName:"webgl",kernelFunc:Kne};function Yne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.readSync(r.dataId),u=n.readSync(s.dataId),p=TA(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,p)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(s),p=J9(l,u,i,o);return n.makeTensorInfo(p.shape,s.dtype,p.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var Zne={kernelName:Pc,backendName:"webgl",kernelFunc:Yne},Jne=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]; @@ -3035,7 +3035,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,mte=Ze({opSnippet:hte}),fte={kernel float result = ${this.getInputSamplingString()}; setOutput(result); } - `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function Xne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],d=l*s,c=u*s,h=p/(s*s),m=i==="NHWC"?[o,d,c,h]:[o,h,d,c],f=new Kne(m,s,i);return n.runWebGLProgram(f,[r],r.dtype)}var Yne={kernelName:pu,backendName:"webgl",kernelFunc:Xne},jA=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=xn(this.outputShape.length);let s=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,l="",u="";n&&(a?l=`float activation(float a) { + `}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 Qne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],d=l*s,c=u*s,h=p/(s*s),m=i==="NHWC"?[o,d,c,h]:[o,h,d,c],f=new Jne(m,s,i);return n.runWebGLProgram(f,[r],r.dtype)}var eae={kernelName:pu,backendName:"webgl",kernelFunc:Qne},XA=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=vn(this.outputShape.length);let s=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,l="",u="";n&&(a?l=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${n} }`:r?l=`float activation(float a) { @@ -3088,7 +3088,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,mte=Ze({opSnippet:hte}),fte={kernel ${u} setOutput(result); } - `}},KA=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=xn(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,p=e.filterWidth,d=p,c=` + `}},YA=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=vn(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,p=e.filterWidth,d=p,c=` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${p}'`);let d=T.computeConv2DInfo(r.shape,s.shape,i,p,o,u,!0),c;G().getBool("WEBGL_PACK_DEPTHWISECONV")&&d.strideWidth<=2&&d.outChannels/d.inChannels===1?c=new KA(d):c=new jA(d);let h=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];return n.runWebGLProgram(c,[r,s],"float32",h)}var Jne={kernelName:Ui,backendName:"webgl",kernelFunc:Zne},Qne=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=` + `}};function tae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=a,p=l;p==null&&(p=[1,1]),w.assert(T.eitherStridesOrDilationsAreOne(i,p),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${p}'`);let d=T.computeConv2DInfo(r.shape,s.shape,i,p,o,u,!0),c;G().getBool("WEBGL_PACK_DEPTHWISECONV")&&d.strideWidth<=2&&d.outChannels/d.inChannels===1?c=new YA(d):c=new XA(d);let h=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];return n.runWebGLProgram(c,[r,s],"float32",h)}var nae={kernelName:Ui,backendName:"webgl",kernelFunc:tae},aae=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; @@ -3312,7 +3312,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,mte=Ze({opSnippet:hte}),fte={kernel } setOutput(dotProd); } - `}},eae=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=` + `}},rae=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() { @@ -3357,13 +3357,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,mte=Ze({opSnippet:hte}),fte={kernel } setOutput(dotProd); } - `}};function tae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:p}=a,d=T.computeConv2DInfo(r.shape,p,i,o,l,u,!0),c=new Qne(d);return n.runWebGLProgram(c,[r,s],"float32")}var nae={kernelName:Im,backendName:"webgl",kernelFunc:tae};function aae(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:p}=a,d=T.computeConv2DInfo(p,s.shape,i,o,l,u,!0),c=new eae(d);return n.runWebGLProgram(c,[r,s],"float32")}var rae={kernelName:Sm,backendName:"webgl",kernelFunc:aae},sae=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=` + `}};function sae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:p}=a,d=T.computeConv2DInfo(r.shape,p,i,o,l,u,!0),c=new aae(d);return n.runWebGLProgram(c,[r,s],"float32")}var iae={kernelName:Im,backendName:"webgl",kernelFunc:sae};function oae(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:p}=a,d=T.computeConv2DInfo(p,s.shape,i,o,l,u,!0),c=new rae(d);return n.runWebGLProgram(c,[r,s],"float32")}var lae={kernelName:Sm,backendName:"webgl",kernelFunc:oae},uae=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 iae(e){let{inputs:t,backend:n}=e,{x:a}=t,r=[...a.shape,...a.shape],s=w.sizeFromShape(a.shape),i=ce({inputs:{x:a},backend:n,attrs:{shape:[s]}}),o=new sae(s),l=n.runWebGLProgram(o,[i],i.dtype),u=ce({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var oae={kernelName:Lc,backendName:"webgl",kernelFunc:iae},lae=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:l,dilationWidth:u}=e,{top:p,left:d}=a;this.userCode=` + `}};function pae(e){let{inputs:t,backend:n}=e,{x:a}=t,r=[...a.shape,...a.shape],s=w.sizeFromShape(a.shape),i=ce({inputs:{x:a},backend:n,attrs:{shape:[s]}}),o=new uae(s),l=n.runWebGLProgram(o,[i],i.dtype),u=ce({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var cae={kernelName:Lc,backendName:"webgl",kernelFunc:pae},dae=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:l,dilationWidth:u}=e,{top:p,left:d}=a;this.userCode=` const ivec2 strides = ivec2(${r}, ${s}); const ivec2 pads = ivec2(${p}, ${d}); const float neg_infinity = -3.4e38; @@ -3401,7 +3401,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,mte=Ze({opSnippet:hte}),fte={kernel float result = curVal; setOutput(result); } - `}};function uae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=T.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p,d=new lae(u);p=n.runWebGLProgram(d,[r,s],"float32");let c=ce({inputs:{x:p},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(p),c}var pae={kernelName:Gi,backendName:"webgl",kernelFunc:uae};function cae(e){let{inputs:t,backend:n,attrs:a}=e,{equation:r}=a,s=t,{allDims:i,summedDims:o,idDims:l}=T.decodeEinsumEquation(r,s.length);T.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:p}=T.getEinsumComputePath(o,l),d=p.length,c=null,h=i.length,m=[];for(let f=0;f=0&&(c=Bf({inputs:{x:c},backend:n,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(c)),h--)}for(let f of m)f!==c&&n.disposeIntermediateTensorInfo(f);return c}var dae={kernelName:Tm,backendName:"webgl",kernelFunc:cae},hae="return (x >= 0.0) ? x : (exp(x) - 1.0);",mae=` + `}};function hae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=T.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p,d=new dae(u);p=n.runWebGLProgram(d,[r,s],"float32");let c=ce({inputs:{x:p},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(p),c}var mae={kernelName:Gi,backendName:"webgl",kernelFunc:hae};function fae(e){let{inputs:t,backend:n,attrs:a}=e,{equation:r}=a,s=t,{allDims:i,summedDims:o,idDims:l}=T.decodeEinsumEquation(r,s.length);T.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:p}=T.getEinsumComputePath(o,l),d=p.length,c=null,h=i.length,m=[];for(let f=0;f=0&&(c=Vf({inputs:{x:c},backend:n,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(c)),h--)}for(let f of m)f!==c&&n.disposeIntermediateTensorInfo(f);return c}var gae={kernelName:Tm,backendName:"webgl",kernelFunc:fae},bae="return (x >= 0.0) ? x : (exp(x) - 1.0);",yae=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -3410,12 +3410,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,mte=Ze({opSnippet:hte}),fte={kernel result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; -`,fae=Ze({opSnippet:hae,packedOpSnippet:mae}),gae={kernelName:qi,backendName:"webgl",kernelFunc:fae},bae="return (b >= 0.0) ? a : a * (b + 1.0);",yae=` +`,xae=Ze({opSnippet:bae,packedOpSnippet:yae}),vae={kernelName:ji,backendName:"webgl",kernelFunc:xae},wae="return (b >= 0.0) ? a : a * (b + 1.0);",kae=` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); -`,xae=e=>{let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new hp(yae,a.shape,r.shape):new ki(bae,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)},vae={kernelName:cu,backendName:"webgl",kernelFunc:xae},wae=` +`,Iae=e=>{let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new hp(kae,a.shape,r.shape):new ki(wae,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)},Sae={kernelName:cu,backendName:"webgl",kernelFunc:Iae},Nae=` return vec4(equal(a, b)); -`,kae="return float(a == b);",Iae=hn({opSnippet:kae,packedOpSnippet:wae,dtype:"bool",cpuKernelImpl:Q9}),Sae={kernelName:du,backendName:"webgl",kernelFunc:Iae},Nae=` +`,Tae="return float(a == b);",Cae=hn({opSnippet:Tae,packedOpSnippet:Nae,dtype:"bool",cpuKernelImpl:aQ}),Eae={kernelName:du,backendName:"webgl",kernelFunc:Cae},_ae=` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. @@ -3430,9 +3430,9 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,mte=Ze({opSnippet:hte}),fte={kernel 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)); -`,Tae=Ze({opSnippet:Nae}),Cae={kernelName:ji,backendName:"webgl",kernelFunc:Tae},Eae=mp+` +`,Aae=Ze({opSnippet:_ae}),Fae={kernelName:qi,backendName:"webgl",kernelFunc:Aae},$ae=mp+` return exp(x); -`,_ae=` +`,Dae=` vec4 result = exp(x); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : result.r; @@ -3441,7 +3441,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,mte=Ze({opSnippet:hte}),fte={kernel result.a = isNaN.a ? x.a : result.a; return result; -`,XA=Ze({opSnippet:Eae,packedOpSnippet:_ae,cpuKernelImpl:eQ,dtype:"float32"}),Aae={kernelName:Ki,backendName:"webgl",kernelFunc:XA};function fv(e){let{inputs:t,attrs:n,backend:a}=e,{dim:r}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(w.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),ce({inputs:{x:s},backend:a,attrs:{shape:o}})}var Fae={kernelName:hu,backendName:"webgl",kernelFunc:fv},xS="return exp(x) - 1.0;",$ae=Ze({opSnippet:xS,packedOpSnippet:xS,cpuKernelImpl:tQ}),Dae={kernelName:Xi,backendName:"webgl",kernelFunc:$ae},vS=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 ${e}.`);this.userCode=` +`,ZA=Ze({opSnippet:$ae,packedOpSnippet:Dae,cpuKernelImpl:rQ,dtype:"float32"}),Rae={kernelName:Ki,backendName:"webgl",kernelFunc:ZA};function gv(e){let{inputs:t,attrs:n,backend:a}=e,{dim:r}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(w.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),ce({inputs:{x:s},backend:a,attrs:{shape:o}})}var Mae={kernelName:hu,backendName:"webgl",kernelFunc:gv},xS="return exp(x) - 1.0;",Oae=Ze({opSnippet:xS,packedOpSnippet:xS,cpuKernelImpl:sQ}),Pae={kernelName:Xi,backendName:"webgl",kernelFunc:Oae},vS=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 = ${r}; float unaryOpComplex(float real, float expR, float imag, float expI) { @@ -3474,12 +3474,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,mte=Ze({opSnippet:hte}),fte={kernel ivec2 coords = getOutputCoords(); setOutput(mulMatDFT(coords[0], coords[1])); } - `}};function YA(e,t,n){let a=n.texData.get(e.dataId),r=w.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=ce({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,u=new vS("real",l,t),p=new vS("imag",l,t),d=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:l},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:l}],c=n.runWebGLProgram(u,d,"float32"),h=n.runWebGLProgram(p,d,"float32"),m=Fs({inputs:{real:c,imag:h},backend:n});n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h);let f=ce({inputs:{x:m},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(m),f}function Rae(e){let{inputs:t,backend:n}=e,{input:a}=t;return YA(a,!1,n)}var Mae={kernelName:Cm,backendName:"webgl",kernelFunc:Rae},Oae=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=` + `}};function JA(e,t,n){let a=n.texData.get(e.dataId),r=w.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=ce({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,u=new vS("real",l,t),p=new vS("imag",l,t),d=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:l},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:l}],c=n.runWebGLProgram(u,d,"float32"),h=n.runWebGLProgram(p,d,"float32"),m=$s({inputs:{real:c,imag:h},backend:n});n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h);let f=ce({inputs:{x:m},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(m),f}function Lae(e){let{inputs:t,backend:n}=e,{input:a}=t;return JA(a,!1,n)}var zae={kernelName:Cm,backendName:"webgl",kernelFunc:Lae},Wae=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=` void main() { // Input can be obtained from uniform value. setOutput(value); } - `}};function $d(e){let{backend:t,attrs:n}=e,{shape:a,value:r}=n,{dtype:s}=n;if(s=s||w.inferDtype(r),s==="string"){let i=w.getArrayFromDType(s,w.sizeFromShape(a));return i.fill(r),t.makeTensorInfo(a,s,i)}else{let i=new Oae(a,r),o=[[r]];return t.runWebGLProgram(i,[],s,o)}}var Pae={kernelName:zc,backendName:"webgl",kernelFunc:$d},Lae=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=` + `}};function $d(e){let{backend:t,attrs:n}=e,{shape:a,value:r}=n,{dtype:s}=n;if(s=s||w.inferDtype(r),s==="string"){let i=w.getArrayFromDType(s,w.sizeFromShape(a));return i.fill(r),t.makeTensorInfo(a,s,i)}else{let i=new Wae(a,r),o=[[r]];return t.runWebGLProgram(i,[],s,o)}}var Bae={kernelName:zc,backendName:"webgl",kernelFunc:$d},Vae=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]; @@ -3493,7 +3493,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,mte=Ze({opSnippet:hte}),fte={kernel } setOutput(outputValue); } - `}},zae={kernelName:mu,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,a=t,r=new Lae(n.shape);return a.runWebGLProgram(r,[n],n.dtype)}},wS="return floor(x);",Wae=Ze({opSnippet:wS,packedOpSnippet:wS,cpuKernelImpl:nQ}),Bae={kernelName:Yi,backendName:"webgl",kernelFunc:Wae},Vae=` + `}},Uae={kernelName:mu,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,a=t,r=new Vae(n.shape);return a.runWebGLProgram(r,[n],n.dtype)}},wS="return floor(x);",Gae=Ze({opSnippet:wS,packedOpSnippet:wS,cpuKernelImpl:iQ}),Hae={kernelName:Yi,backendName:"webgl",kernelFunc:Gae},jae=` float s = sign(a) * sign(b); int ia = round(a); int ib = round(b); @@ -3503,7 +3503,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,mte=Ze({opSnippet:hte}),fte={kernel } else { return NAN; } -`,Uae=` +`,qae=` ivec4 ia = round(a); ivec4 ib = round(b); bvec4 cond = notEqual(ib, ivec4(0)); @@ -3524,7 +3524,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,mte=Ze({opSnippet:hte}),fte={kernel result[3] = idiv(ia[3], ib[3], s[3]); } return vec4(result); -`,Gae=hn({opSnippet:Vae,packedOpSnippet:Uae,dtype:"int32"}),Hae={kernelName:Zi,backendName:"webgl",kernelFunc:Gae},qae=class{constructor(e){this.variableNames=["A"];let t=Cn(),[n,a]=e;this.outputShape=e,this.userCode=` +`,Kae=hn({opSnippet:jae,packedOpSnippet:qae,dtype:"int32"}),Xae={kernelName:Zi,backendName:"webgl",kernelFunc:Kae},Yae=class{constructor(e){this.variableNames=["A"];let t=En(),[n,a]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; @@ -3546,7 +3546,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,mte=Ze({opSnippet:hte}),fte={kernel setOutput(floor(value * 255.0 + 0.5)); } - `}},jae=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Cn(),[n,a]=e;this.outputShape=e,this.userCode=` + `}},Zae=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=En(),[n,a]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); 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-`,Cre=hn({opSnippet:Nre,packedOpSnippet:Tre,cpuKernelImpl:oQ,dtype:"bool"}),Ere={kernelName:yu,backendName:"webgl",kernelFunc:Cre},_re="return float(a <= b);",Are=` +`,Fre=hn({opSnippet:_re,packedOpSnippet:Are,cpuKernelImpl:cQ,dtype:"bool"}),$re={kernelName:yu,backendName:"webgl",kernelFunc:Fre},Dre="return float(a <= b);",Rre=` return vec4(lessThanEqual(a, b)); -`,Fre=hn({opSnippet:_re,packedOpSnippet:Are,cpuKernelImpl:lQ,dtype:"bool"}),$re={kernelName:xu,backendName:"webgl",kernelFunc:Fre};function Dre(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=uQ(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var Rre={kernelName:vu,backendName:"webgl",kernelFunc:Dre},Mre=mp+` +`,Mre=hn({opSnippet:Dre,packedOpSnippet:Rre,cpuKernelImpl:dQ,dtype:"bool"}),Ore={kernelName:xu,backendName:"webgl",kernelFunc:Mre};function Pre(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=hQ(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var Lre={kernelName:vu,backendName:"webgl",kernelFunc:Pre},zre=mp+` return x < 0.0 ? 0./0. : log(x); 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-`,Zre=hn({opSnippet:Xre,packedOpSnippet:Yre,dtype:"bool"}),Jre={kernelName:Iu,backendName:"webgl",kernelFunc:Zre},Qre=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=` +`,tse=hn({opSnippet:Qre,packedOpSnippet:ese,dtype:"bool"}),nse={kernelName:Iu,backendName:"webgl",kernelFunc:tse},ase=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3650,7 +3650,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,mte=Ze({opSnippet:hte}),fte={kernel float val = x * ${o}; setOutput(val); } - `}},ese=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=` + `}},rse=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); 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int b = coords[0]; @@ -3767,16 +3767,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,mte=Ze({opSnippet:hte}),fte={kernel } setOutput(result); } - `}},rse=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=a,d=new ase(r.shape,o,l,u,p);return n.runWebGLProgram(d,[r,s,i],r.dtype)},sse={kernelName:Su,backendName:"webgl",kernelFunc:rse};function ise(e,t,n,a){let r=w.sizeFromShape(t),s=w.sizeFromShape(e.shape)/r,i=ce({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=el(i,e.dtype,"max",a),l=ce({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}function JA(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=r.shape.length,l=w.parseAxisParam(s,r.shape),u=l,p=T.getAxesPermutation(u,o),d=p!=null,c=n.shouldExecuteOnCPU([r]),h=r;if(d){if(c){let y=n.texData.get(h.dataId).values,x=new Array(o);for(let N=0;N{let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=a,d=new ose(r.shape,o,l,u,p);return n.runWebGLProgram(d,[r,s,i],r.dtype)},use={kernelName:Su,backendName:"webgl",kernelFunc:lse};function pse(e,t,n,a){let r=w.sizeFromShape(t),s=w.sizeFromShape(e.shape)/r,i=ce({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=el(i,e.dtype,"max",a),l=ce({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}function eF(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=r.shape.length,l=w.parseAxisParam(s,r.shape),u=l,p=T.getAxesPermutation(u,o),d=p!=null,c=n.shouldExecuteOnCPU([r]),h=r;if(d){if(c){let y=n.texData.get(h.dataId).values,x=new Array(o);for(let N=0;N`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=T.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&w.arraysEqual(p.inShape,p.outShape))return ta({inputs:{x:r},backend:n});let d=new Ec(p,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var hse={kernelName:po,backendName:"webgl",kernelFunc:dse};function mse(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=a,p=[1,1,1],d=T.computePool3DInfo(r.shape,s,i,p,o,u,l),c=new ck(d,"max",!1);return n.runWebGLProgram(c,[r],r.dtype)}var fse={kernelName:Nu,backendName:"webgl",kernelFunc:mse},gse=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,l=r*s-1;this.userCode=` +`,mse=hn({opSnippet:dse,packedOpSnippet:hse,cpuKernelImpl:gQ}),fse={kernelName:uo,backendName:"webgl",kernelFunc:mse};function gse(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;lp(r,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;w.assert(T.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=T.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&w.arraysEqual(p.inShape,p.outShape))return ta({inputs:{x:r},backend:n});let d=new Ec(p,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var bse={kernelName:po,backendName:"webgl",kernelFunc:gse};function yse(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=a,p=[1,1,1],d=T.computePool3DInfo(r.shape,s,i,p,o,u,l),c=new ck(d,"max",!1);return n.runWebGLProgram(c,[r],r.dtype)}var xse={kernelName:Nu,backendName:"webgl",kernelFunc:yse},vse=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,l=r*s-1;this.userCode=` const ivec2 pads = ivec2(${i}, ${o}); void main() { @@ -3822,7 +3822,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,mte=Ze({opSnippet:hte}),fte={kernel } setOutput(dotProd); } - `}},bse=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,p=o-1-e.padInfo.front,d=l-1-e.padInfo.top,c=u-1-e.padInfo.left,h=o*l*u-1;this.userCode=` + `}},wse=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,p=o-1-e.padInfo.front,d=l-1-e.padInfo.top,c=u-1-e.padInfo.left,h=o*l*u-1;this.userCode=` const ivec3 pads = ivec3(${p}, ${d}, ${c}); void main() { @@ -3886,16 +3886,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,mte=Ze({opSnippet:hte}),fte={kernel } setOutput(dotProd); } - `}};function yse(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=a,d=[1,1,1],c=T.computePool3DInfo(i.shape,o,l,d,u,p),h=new ck(c,"max",!0),m=n.runWebGLProgram(h,[i],i.dtype),f=new bse(c),g=n.runWebGLProgram(f,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),g}var xse={kernelName:Bc,backendName:"webgl",kernelFunc:yse};function vse(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;lp([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:p,dimRoundingMode:d}=a,c=T.computePool2DInfo(o.shape,l,u,1,p,d),h=!0,m=new Ec(c,"max",h),f=n.runWebGLProgram(m,[o],o.dtype),g=new gse(c),b=n.runWebGLProgram(g,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),b}var wse={kernelName:Wc,backendName:"webgl",kernelFunc:vse};function kse(e,t,n,a){let r=new Ec(n,"max",!1),s=a.runWebGLProgram(r,[e],"float32");r=new Ec(n,"max",!0,!0,t);let i=a.runWebGLProgram(r,[e],"float32");return[s,i]}var Ise={kernelName:Vc,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;w.assert(a.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${a.shape.length}.`);let u=[1,1];w.assert(T.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let p=T.computePool2DInfo(a.shape,r,s,u,i),[d,c]=kse(a,o,p,l);return[d,c]}};function Sse(e,t,n,a){let r=w.sizeFromShape(t),s=w.sizeFromShape(e.shape)/r,i=ce({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=el(i,"float32","mean",a),l=ce({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}var Nse={kernelName:co,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{keepDims:r,axis:s}=t,i=n,o=a.shape.length,l=w.parseAxisParam(s,a.shape),u=l,p=T.getAxesPermutation(u,o),d=p!=null,c=i.shouldExecuteOnCPU([a]),h=[],m=a;if(d){if(c){let x=i.texData.get(m.dataId).values,v=new Array(o);for(let C=0;C{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;w.assert(a.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${a.shape.length}.`);let u=[1,1];w.assert(T.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let p=T.computePool2DInfo(a.shape,r,s,u,i),[d,c]=Tse(a,o,p,l);return[d,c]}};function Ese(e,t,n,a){let r=w.sizeFromShape(t),s=w.sizeFromShape(e.shape)/r,i=ce({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=el(i,"float32","mean",a),l=ce({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}var _se={kernelName:co,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{keepDims:r,axis:s}=t,i=n,o=a.shape.length,l=w.parseAxisParam(s,a.shape),u=l,p=T.getAxesPermutation(u,o),d=p!=null,c=i.shouldExecuteOnCPU([a]),h=[],m=a;if(d){if(c){let x=i.texData.get(m.dataId).values,v=new Array(o);for(let C=0;Cu[0]+e[p]+u[1]);let a=e.length,r=ht(a),s=t.map(u=>u[0]).join(","),i=t.map((u,p)=>u[0]+e[p]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a),l=n==="reflect"?0:1;if(a===1){this.userCode=` +`,Rse=hn({opSnippet:$se,packedOpSnippet:Dse,cpuKernelImpl:bQ}),Mse={kernelName:mo,backendName:"webgl",kernelFunc:Rse},Ose=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((u,p)=>u[0]+e[p]+u[1]);let a=e.length,r=ht(a),s=t.map(u=>u[0]).join(","),i=t.map((u,p)=>u[0]+e[p]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a),l=n==="reflect"?0:1;if(a===1){this.userCode=` int start = ${s}; int end = ${i}; @@ -3924,7 +3924,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,mte=Ze({opSnippet:hte}),fte={kernel ${r} coords = outC - start; setOutput(getX(${o})); } - `}},Dse=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=ht(a),s=t.map(h=>h[0]).join(","),i=t.map((h,m)=>h[0]+e[m]).join(","),o=kn("rc",a),l=kn("source",a),u=`${o[a-1]} < ${this.outputShape[a-1]}`,p=a===1?"source":`vec2(${l.slice(-2).join()})`,d=n==="reflect"?0:1,c="";if(a===1){let h=` + `}},Pse=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=ht(a),s=t.map(h=>h[0]).join(","),i=t.map((h,m)=>h[0]+e[m]).join(","),o=In("rc",a),l=In("source",a),u=`${o[a-1]} < ${this.outputShape[a-1]}`,p=a===1?"source":`vec2(${l.slice(-2).join()})`,d=n==="reflect"?0:1,c="";if(a===1){let h=` ${r} source = rc; if (source < start) { source = start * 2 - source - ${d}; @@ -3980,13 +3980,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,mte=Ze({opSnippet:hte}),fte={kernel ${c} setOutput(result); } - `}},Rse=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{paddings:r,mode:s}=n,i=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Dse(a.shape,r,s):new $se(a.shape,r,s);return t.runWebGLProgram(i,[a],a.dtype)},Mse={kernelName:fo,backendName:"webgl",kernelFunc:Rse},Ose=`if (b == 0.0) return NAN; - return mod(a, b);`,Pse=` + `}},Lse=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{paddings:r,mode:s}=n,i=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Pse(a.shape,r,s):new Ose(a.shape,r,s);return t.runWebGLProgram(i,[a],a.dtype)},zse={kernelName:fo,backendName:"webgl",kernelFunc:Lse},Wse=`if (b == 0.0) return NAN; + return mod(a, b);`,Bse=` vec4 result = mod(a, b); bvec4 isNaN = equal(b, vec4(0.0)); `+Qo+` return result; -`,Lse=hn({opSnippet:Ose,packedOpSnippet:Pse}),zse={kernelName:go,backendName:"webgl",kernelFunc:Lse},Wse=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=` +`,Vse=hn({opSnippet:Wse,packedOpSnippet:Bse}),Use={kernelName:go,backendName:"webgl",kernelFunc:Vse},Gse=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -4006,11 +4006,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,mte=Ze({opSnippet:hte}),fte={kernel // If no other event happened, last event happened. setOutput(float(${t-1})); } - `}},Bse=` + `}},Hse=` if (a == b) { return 1.0; }; -return a / b;`,Vse=` +return a / b;`,jse=` // vec4 one = vec4(equal(a, b)); // return one + (vec4(1.0) - one) * a / b; vec4 result = a / b; @@ -4028,9 +4028,9 @@ return a / b;`,Vse=` } return result; -`,QA=hn({opSnippet:Bse,packedOpSnippet:Vse,checkOutOfBounds:!0}),Use={kernelName:Hi,backendName:"webgl",kernelFunc:QA},kS="return a - b;",eF=hn({opSnippet:kS,packedOpSnippet:kS,supportsComplex:!0,cpuKernelImpl:RQ}),Gse={kernelName:Bo,backendName:"webgl",kernelFunc:eF};function tF(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=w.parseAxisParam([s],r.shape),o=JA({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=T.expandShapeToKeepDim(o.shape,i),u=ce({inputs:{x:o},backend:n,attrs:{shape:l}}),p=eF({inputs:{a:r,b:u},backend:n}),d=XA({inputs:{x:p},backend:n}),c=Bf({inputs:{x:d},backend:n,attrs:{axis:i,keepDims:!1}}),h=ce({inputs:{x:c},backend:n,attrs:{shape:l}}),m=QA({inputs:{a:d,b:h},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}var Hse={kernelName:zo,backendName:"webgl",kernelFunc:tF};function qse(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a,l=o?r:tF({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),u=l.shape[0],p=l.shape[1],d=new Wse(u,p,s),c=[[i]],h=n.runWebGLProgram(d,[l],"int32",c);return o||n.disposeIntermediateTensorInfo(l),h}var jse={kernelName:Tu,backendName:"webgl",kernelFunc:qse},Kse=Da+` +`,tF=hn({opSnippet:Hse,packedOpSnippet:jse,checkOutOfBounds:!0}),qse={kernelName:Hi,backendName:"webgl",kernelFunc:tF},kS="return a - b;",nF=hn({opSnippet:kS,packedOpSnippet:kS,supportsComplex:!0,cpuKernelImpl:LQ}),Kse={kernelName:Bo,backendName:"webgl",kernelFunc:nF};function aF(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=w.parseAxisParam([s],r.shape),o=eF({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=T.expandShapeToKeepDim(o.shape,i),u=ce({inputs:{x:o},backend:n,attrs:{shape:l}}),p=nF({inputs:{a:r,b:u},backend:n}),d=ZA({inputs:{x:p},backend:n}),c=Vf({inputs:{x:d},backend:n,attrs:{axis:i,keepDims:!1}}),h=ce({inputs:{x:c},backend:n,attrs:{shape:l}}),m=tF({inputs:{a:d,b:h},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}var Xse={kernelName:zo,backendName:"webgl",kernelFunc:aF};function Yse(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a,l=o?r:aF({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),u=l.shape[0],p=l.shape[1],d=new Gse(u,p,s),c=[[i]],h=n.runWebGLProgram(d,[l],"int32",c);return o||n.disposeIntermediateTensorInfo(l),h}var Zse={kernelName:Tu,backendName:"webgl",kernelFunc:Yse},Jse=Da+` return -x; -`,Xse=` +`,Qse=` vec4 result = -x; bvec4 isNaN = isnan(x); @@ -4040,14 +4040,14 @@ return a / b;`,Vse=` result.a = isNaN.a ? x.a : result.a; return result; -`;function Yse(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])){let s=n.texData.get(a.dataId),[i,o]=fQ(s.values,a.shape,a.dtype);return n.makeTensorInfo(o,a.dtype,i)}let r;return G().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new ts(a.shape,Xse):r=new rr(a.shape,Kse),n.runWebGLProgram(r,[a],a.dtype)}var Zse={kernelName:Cu,backendName:"webgl",kernelFunc:Yse},Jse=hr.nonMaxSuppressionV3Impl;function Qse(e){T.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:l}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),{selectedIndices:d}=Jse(u,p,i,o,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var eie={kernelName:_u,backendName:"webgl",kernelFunc:Qse},tie=hr.nonMaxSuppressionV4Impl;function nie(e){T.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:l,padToMaxOutputSize:u}=a,p=n.readSync(r.dataId),d=n.readSync(s.dataId),{selectedIndices:c,validOutputs:h}=tie(p,d,i,o,l,u);return[n.makeTensorInfo([c.length],"int32",new Int32Array(c)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var aie={kernelName:Au,backendName:"webgl",kernelFunc:nie},rie=hr.nonMaxSuppressionV5Impl;function sie(e){T.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:l,softNmsSigma:u}=a,p=n.readSync(r.dataId),d=n.readSync(s.dataId),c=i,h=o,m=l,f=u,{selectedIndices:g,selectedScores:b}=rie(p,d,c,h,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([b.length],"float32",new Float32Array(b))]}var iie={kernelName:Fu,backendName:"webgl",kernelFunc:sie},oie=class{constructor(e,t,n,a){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=` +`;function eie(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])){let s=n.texData.get(a.dataId),[i,o]=xQ(s.values,a.shape,a.dtype);return n.makeTensorInfo(o,a.dtype,i)}let r;return G().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new ts(a.shape,Qse):r=new rr(a.shape,Jse),n.runWebGLProgram(r,[a],a.dtype)}var tie={kernelName:Cu,backendName:"webgl",kernelFunc:eie},nie=mr.nonMaxSuppressionV3Impl;function aie(e){T.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:l}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),{selectedIndices:d}=nie(u,p,i,o,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var rie={kernelName:_u,backendName:"webgl",kernelFunc:aie},sie=mr.nonMaxSuppressionV4Impl;function iie(e){T.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:l,padToMaxOutputSize:u}=a,p=n.readSync(r.dataId),d=n.readSync(s.dataId),{selectedIndices:c,validOutputs:h}=sie(p,d,i,o,l,u);return[n.makeTensorInfo([c.length],"int32",new Int32Array(c)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var oie={kernelName:Au,backendName:"webgl",kernelFunc:iie},lie=mr.nonMaxSuppressionV5Impl;function uie(e){T.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:l,softNmsSigma:u}=a,p=n.readSync(r.dataId),d=n.readSync(s.dataId),c=i,h=o,m=l,f=u,{selectedIndices:g,selectedScores:b}=lie(p,d,c,h,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([b.length],"float32",new Float32Array(b))]}var pie={kernelName:Fu,backendName:"webgl",kernelFunc:uie},cie=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))); } - `}},lie=e=>{let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=a,u=w.sizeFromShape(r.shape),p=new oie(u,i,o,l),d=ce({inputs:{x:r},backend:n,attrs:{shape:[u]}}),c=n.runWebGLProgram(p,[d],s);n.disposeIntermediateTensorInfo(d);let h=[...r.shape,i],m=ce({inputs:{x:c},backend:n,attrs:{shape:h}});return n.disposeIntermediateTensorInfo(c),m},uie={kernelName:yo,backendName:"webgl",kernelFunc:lie};function fm(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="complex64"){let r=Fd({inputs:{input:a},backend:n}),s=fm({inputs:{x:r},backend:n}),i=Vf({inputs:{input:a},backend:n}),o=fm({inputs:{x:i},backend:n}),l=Fs({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return $d({attrs:{shape:a.shape,dtype:a.dtype,value:a.dtype==="string"?"":0},backend:n})}var pie={kernelName:Yu,backendName:"webgl",kernelFunc:fm};function nF(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=Fd({inputs:{input:a},backend:n}),s=nF({inputs:{x:r},backend:n}),i=Vf({inputs:{input:a},backend:n}),o=fm({inputs:{x:i},backend:n}),l=Fs({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return $d({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:n})}var cie={kernelName:$u,backendName:"webgl",kernelFunc:nF};function die(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return fv({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{w.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let d=fv({inputs:{input:p},backend:n,attrs:{dim:r}});return o.push(d),d}),u=BA({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var hie={kernelName:Du,backendName:"webgl",kernelFunc:die},mie=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let a=e.length,r=ht(a),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a);if(a===1){this.userCode=` + `}},die=e=>{let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=a,u=w.sizeFromShape(r.shape),p=new cie(u,i,o,l),d=ce({inputs:{x:r},backend:n,attrs:{shape:[u]}}),c=n.runWebGLProgram(p,[d],s);n.disposeIntermediateTensorInfo(d);let h=[...r.shape,i],m=ce({inputs:{x:c},backend:n,attrs:{shape:h}});return n.disposeIntermediateTensorInfo(c),m},hie={kernelName:yo,backendName:"webgl",kernelFunc:die};function fm(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="complex64"){let r=Fd({inputs:{input:a},backend:n}),s=fm({inputs:{x:r},backend:n}),i=Uf({inputs:{input:a},backend:n}),o=fm({inputs:{x:i},backend:n}),l=$s({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return $d({attrs:{shape:a.shape,dtype:a.dtype,value:a.dtype==="string"?"":0},backend:n})}var mie={kernelName:Yu,backendName:"webgl",kernelFunc:fm};function rF(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(a.dtype==="complex64"){let r=Fd({inputs:{input:a},backend:n}),s=rF({inputs:{x:r},backend:n}),i=Uf({inputs:{input:a},backend:n}),o=fm({inputs:{x:i},backend:n}),l=$s({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return $d({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:n})}var fie={kernelName:$u,backendName:"webgl",kernelFunc:rF};function gie(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return gv({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{w.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let d=gv({inputs:{input:p},backend:n,attrs:{dim:r}});return o.push(d),d}),u=UA({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var bie={kernelName:Du,backendName:"webgl",kernelFunc:gie},yie=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let a=e.length,r=ht(a),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a);if(a===1){this.userCode=` int start = ${s}; int end = ${i}; @@ -4072,7 +4072,7 @@ return a / b;`,Vse=` setOutput(getX(${o})); } } - `}},fie=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((m,f)=>m[0]+e[f]+m[1]);let a=e.length,r=ht(a),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[0]+e[f]).join(","),o=kn("rc",a),l=kn("source",a),u=`${o[a-1]} < ${this.outputShape[a-1]}`,p=a===1?"source":`vec2(${l.slice(-2).join()})`,d=[`${r} rc = outputLoc;`,`${o[a-1]} += 1; + `}},xie=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((m,f)=>m[0]+e[f]+m[1]);let a=e.length,r=ht(a),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[0]+e[f]).join(","),o=In("rc",a),l=In("source",a),u=`${o[a-1]} < ${this.outputShape[a-1]}`,p=a===1?"source":`vec2(${l.slice(-2).join()})`,d=[`${r} rc = outputLoc;`,`${o[a-1]} += 1; if(${u}) { `,a===1?"":`} rc = outputLoc; @@ -4096,7 +4096,7 @@ return a / b;`,Vse=` ${h} setOutput(result); } - `}},aF=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a;if(w.sizeFromShape(r.shape)===0){let u=s.map((p,d)=>p[0]+r.shape[d]+p[1]);return $d({backend:n,attrs:{shape:u,value:i,dtype:r.dtype}})}let o=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new fie(r.shape,s,i):new mie(r.shape,s,i),l=[[i]];return n.runWebGLProgram(o,[r],r.dtype,l)},gie={kernelName:xo,backendName:"webgl",kernelFunc:aF},bie=` + `}},sF=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a;if(w.sizeFromShape(r.shape)===0){let u=s.map((p,d)=>p[0]+r.shape[d]+p[1]);return $d({backend:n,attrs:{shape:u,value:i,dtype:r.dtype}})}let o=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new xie(r.shape,s,i):new yie(r.shape,s,i),l=[[i]];return n.runWebGLProgram(o,[r],r.dtype,l)},vie={kernelName:xo,backendName:"webgl",kernelFunc:sF},wie=` if(a < 0.0 && floor(b) < b){ return NAN; } @@ -4105,7 +4105,7 @@ return a / b;`,Vse=` } return (round(mod(b, 2.0)) != 1) ? pow(abs(a), b) : sign(a) * pow(abs(a), b); -`,yie=` +`,kie=` // 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); @@ -4123,9 +4123,9 @@ return a / b;`,Vse=` bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w); `+Qo+` return result; -`,xie=hn({opSnippet:bie,packedOpSnippet:yie}),vie={kernelName:vo,backendName:"webgl",kernelFunc:xie};function wie(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=[],u=w.parseAxisParam(s,r.shape),p=u,d=T.getAxesPermutation(p,o),c=r;d!=null&&(c=In({inputs:{x:r},backend:n,attrs:{perm:d}}),p=T.getInnerMostAxes(p.length,o),l.push(c)),T.assertAxesAreInnerMostDims("prod",p,o);let h;if(n.shouldExecuteOnCPU([c])){let m=n.texData.get(c.dataId).values,{outVals:f,outShape:g,outDtype:b}=bQ(c.shape,c.dtype,m,p);h=n.makeTensorInfo(g,b,f)}else{let[m,f]=T.computeOutAndReduceShapes(c.shape,p),g=w.sizeFromShape(f),b=ce({inputs:{x:c},backend:n,attrs:{shape:[-1,g]}}),y=Mm(r.dtype),x=el(b,y,"prod",n);h=ce({inputs:{x},backend:n,attrs:{shape:m}}),l.push(b),l.push(x)}if(i){l.push(h);let m=T.expandShapeToKeepDim(h.shape,u);h=ce({inputs:{x:h},backend:n,attrs:{shape:m}})}return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var kie={kernelName:ko,backendName:"webgl",kernelFunc:wie};function Iie(e){let{inputs:t,backend:n,attrs:a}=e,{paramsNestedSplits:r,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:o}=a,l=r.map(b=>n.readSync(b.dataId)),u=r.map(b=>b.shape),p=n.readSync(s.dataId),d=n.readSync(i.dataId),[c,h,m]=yQ(l,u,p,s.shape,s.dtype,d,i.shape,o),f=c.map(b=>n.makeTensorInfo([b.length],"int32",b)),g=n.makeTensorInfo(m,s.dtype,h);return f.concat([g])}var Sie={kernelName:Am,backendName:"webgl",kernelFunc:Iie};function Nie(e){let{inputs:t,backend:n}=e,{starts:a,limits:r,deltas:s}=t,i=n.readSync(a.dataId),o=n.readSync(r.dataId),l=n.readSync(s.dataId),[u,p]=xQ(i,a.shape,a.dtype,o,r.shape,l,s.shape),d=n.makeTensorInfo([u.length],"int32",u),c=n.makeTensorInfo([p.length],a.dtype,p);return[d,c]}var Tie={kernelName:Fm,backendName:"webgl",kernelFunc:Nie};function Cie(e){let{inputs:t,backend:n,attrs:a}=e,{shape:r,values:s,defaultValue:i,rowPartitionTensors:o}=t,{rowPartitionTypes:l}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),d=n.readSync(i.dataId),c=o.map(g=>n.readSync(g.dataId)),h=o.map(g=>g.shape),[m,f]=vQ(u,r.shape,p,s.shape,s.dtype,d,i.shape,c,h,l);return n.makeTensorInfo(m,s.dtype,f)}var Eie={kernelName:$m,backendName:"webgl",kernelFunc:Cie},rF=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=wQ(a,r,s,i);return t.makeTensorInfo([o.length],i,o)},_ie={kernelName:Uc,backendName:"webgl",kernelFunc:rF},Aie="return 1.0 / x;",Fie=Ze({opSnippet:Aie}),$ie={kernelName:Io,backendName:"webgl",kernelFunc:Fie},Die=Da+` +`,Iie=hn({opSnippet:wie,packedOpSnippet:kie}),Sie={kernelName:vo,backendName:"webgl",kernelFunc:Iie};function Nie(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=[],u=w.parseAxisParam(s,r.shape),p=u,d=T.getAxesPermutation(p,o),c=r;d!=null&&(c=Sn({inputs:{x:r},backend:n,attrs:{perm:d}}),p=T.getInnerMostAxes(p.length,o),l.push(c)),T.assertAxesAreInnerMostDims("prod",p,o);let h;if(n.shouldExecuteOnCPU([c])){let m=n.texData.get(c.dataId).values,{outVals:f,outShape:g,outDtype:b}=wQ(c.shape,c.dtype,m,p);h=n.makeTensorInfo(g,b,f)}else{let[m,f]=T.computeOutAndReduceShapes(c.shape,p),g=w.sizeFromShape(f),b=ce({inputs:{x:c},backend:n,attrs:{shape:[-1,g]}}),y=Mm(r.dtype),x=el(b,y,"prod",n);h=ce({inputs:{x},backend:n,attrs:{shape:m}}),l.push(b),l.push(x)}if(i){l.push(h);let m=T.expandShapeToKeepDim(h.shape,u);h=ce({inputs:{x:h},backend:n,attrs:{shape:m}})}return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var Tie={kernelName:ko,backendName:"webgl",kernelFunc:Nie};function Cie(e){let{inputs:t,backend:n,attrs:a}=e,{paramsNestedSplits:r,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:o}=a,l=r.map(b=>n.readSync(b.dataId)),u=r.map(b=>b.shape),p=n.readSync(s.dataId),d=n.readSync(i.dataId),[c,h,m]=kQ(l,u,p,s.shape,s.dtype,d,i.shape,o),f=c.map(b=>n.makeTensorInfo([b.length],"int32",b)),g=n.makeTensorInfo(m,s.dtype,h);return f.concat([g])}var Eie={kernelName:Am,backendName:"webgl",kernelFunc:Cie};function _ie(e){let{inputs:t,backend:n}=e,{starts:a,limits:r,deltas:s}=t,i=n.readSync(a.dataId),o=n.readSync(r.dataId),l=n.readSync(s.dataId),[u,p]=IQ(i,a.shape,a.dtype,o,r.shape,l,s.shape),d=n.makeTensorInfo([u.length],"int32",u),c=n.makeTensorInfo([p.length],a.dtype,p);return[d,c]}var Aie={kernelName:Fm,backendName:"webgl",kernelFunc:_ie};function Fie(e){let{inputs:t,backend:n,attrs:a}=e,{shape:r,values:s,defaultValue:i,rowPartitionTensors:o}=t,{rowPartitionTypes:l}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),d=n.readSync(i.dataId),c=o.map(g=>n.readSync(g.dataId)),h=o.map(g=>g.shape),[m,f]=SQ(u,r.shape,p,s.shape,s.dtype,d,i.shape,c,h,l);return n.makeTensorInfo(m,s.dtype,f)}var $ie={kernelName:$m,backendName:"webgl",kernelFunc:Fie},iF=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=NQ(a,r,s,i);return t.makeTensorInfo([o.length],i,o)},Die={kernelName:Uc,backendName:"webgl",kernelFunc:iF},Rie="return 1.0 / x;",Mie=Ze({opSnippet:Rie}),Oie={kernelName:Io,backendName:"webgl",kernelFunc:Mie},Pie=Da+` return (x < 0.0) ? 0.0 : x; -`,Rie=` +`,Lie=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -4135,9 +4135,9 @@ return a / b;`,Vse=` result.a = isNaN.a ? x.a : result.a; return result; -`,Mie=Ze({opSnippet:Die,packedOpSnippet:Rie}),Oie={kernelName:So,backendName:"webgl",kernelFunc:Mie},Pie=Da+` +`,zie=Ze({opSnippet:Pie,packedOpSnippet:Lie}),Wie={kernelName:So,backendName:"webgl",kernelFunc:zie},Bie=Da+` return (x < 0.0) ? 0.0 : min(6.0, x); -`,Lie=` +`,Vie=` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -4147,7 +4147,7 @@ return a / b;`,Vse=` result.a = isNaN.a ? x.a : result.a; return result; -`,zie=Ze({opSnippet:Pie,packedOpSnippet:Lie}),Wie={kernelName:Co,backendName:"webgl",kernelFunc:zie},Bie=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d;r?d="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` +`,Uie=Ze({opSnippet:Bie,packedOpSnippet:Vie}),Gie={kernelName:Co,backendName:"webgl",kernelFunc:Uie},Hie=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d;r?d="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${u[0]/p[0]}, ${u[1]/p[1]}); @@ -4180,7 +4180,7 @@ return a / b;`,Vse=` setOutput(newValue); } - `}},Vie=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d;r?d="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}},jie=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d;r?d="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${u[0]/p[0]}, ${u[1]/p[1]}, @@ -4257,7 +4257,7 @@ return a / b;`,Vse=` setOutput(newValue); } - `}};function Uie(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,p=G().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Vie(r.shape,l,u,s,i):new Bie(r.shape,l,u,s,i);return n.runWebGLProgram(p,[r],"float32")}var Gie={kernelName:To,backendName:"webgl",kernelFunc:Uie},Hie=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],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],d=1/u,c=1/p,h=Math.ceil(d)*2+2,m=Math.ceil(c)*2+2;this.userCode=` + `}};function qie(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,p=G().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new jie(r.shape,l,u,s,i):new Hie(r.shape,l,u,s,i);return n.runWebGLProgram(p,[r],"float32")}var Kie={kernelName:To,backendName:"webgl",kernelFunc:qie},Xie=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],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],d=1/u,c=1/p,h=Math.ceil(d)*2+2,m=Math.ceil(c)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -4338,7 +4338,7 @@ return a / b;`,Vse=` setOutput(accumulator); } - `}};function qie(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new Hie(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var jie={kernelName:Ou,backendName:"webgl",kernelFunc:qie},Kie=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d=a?"0.5":"0.0",c;r?c="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":c="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}};function Yie(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new Xie(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var Zie={kernelName:Ou,backendName:"webgl",kernelFunc:Yie},Jie=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d=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( ${u[0]/p[0]}, ${u[1]/p[1]}); @@ -4360,7 +4360,7 @@ return a / b;`,Vse=` setOutput(newValue); } - `}},Xie=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d=a?"0.5":"0.0",c;r?c="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":c="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}},Qie=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d=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( ${u[0]/p[0]}, ${u[1]/p[1]}, @@ -4401,7 +4401,7 @@ return a / b;`,Vse=` setOutput(newValue); } - `}};function Yie(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,p=G().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Xie(r.shape,l,u,s,i):new Kie(r.shape,l,u,s,i);return n.runWebGLProgram(p,[r],r.dtype)}var Zie={kernelName:No,backendName:"webgl",kernelFunc:Yie},Jie=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],d=1/u,c=1/p,h=Math.ceil(d)*2+2,m=Math.ceil(c)*2+2;this.userCode=` + `}};function eoe(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,p=G().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Qie(r.shape,l,u,s,i):new Jie(r.shape,l,u,s,i);return n.runWebGLProgram(p,[r],r.dtype)}var toe={kernelName:No,backendName:"webgl",kernelFunc:eoe},noe=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],d=1/u,c=1/p,h=Math.ceil(d)*2+2,m=Math.ceil(c)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -4471,7 +4471,7 @@ return a / b;`,Vse=` setOutput(accumulator); } - `}};function Qie(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new Jie(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var eoe={kernelName:Mu,backendName:"webgl",kernelFunc:Qie},toe=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=` + `}};function aoe(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new noe(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var roe={kernelName:Mu,backendName:"webgl",kernelFunc:aoe},soe=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)); @@ -4481,7 +4481,7 @@ return a / b;`,Vse=` ${s} coords = getOutputCoords(); setOutput(getX(${r})); } - `}},noe=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-${n} tensor is not yet supported`);this.outputShape=e;let a=kn("rc",n),r=`${a[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${a[n-2]} + 1 < ${this.outputShape[n-2]}`,i=ht(n);n===1?this.userCode=` + `}},ioe=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let a=In("rc",n),r=`${a[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${a[n-2]} + 1 < ${this.outputShape[n-2]}`,i=ht(n);n===1?this.userCode=` void main(){ int rc = getOutputCoords(); vec4 result = vec4(0.); @@ -4509,7 +4509,7 @@ return a / b;`,Vse=` } setOutput(result); } - `;function o(h){return d(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",d(h)}function u(h){return h[n-2]="("+h[n-2]+" + 1)",d(h)}function p(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",d(h)}function d(h){let m=e.map((b,y)=>c(y,h)),f=m.join(","),g=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${g}))`}function c(h,m){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${m[h]} - 1`:`${m[h]}`}}};function aoe(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=r.shape.length,o=w.parseAxisParam(s,r.shape);if(i===0)return ta({inputs:{x:r},backend:n});let l=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new noe(r.shape,o):new toe(r.shape,o);return n.runWebGLProgram(l,[r],r.dtype)}var roe={kernelName:Eo,backendName:"webgl",kernelFunc:aoe},soe=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],a=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=` + `;function o(h){return d(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",d(h)}function u(h){return h[n-2]="("+h[n-2]+" + 1)",d(h)}function p(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",d(h)}function d(h){let m=e.map((b,y)=>c(y,h)),f=m.join(","),g=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${g}))`}function c(h,m){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${m[h]} - 1`:`${m[h]}`}}};function ooe(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=r.shape.length,o=w.parseAxisParam(s,r.shape);if(i===0)return ta({inputs:{x:r},backend:n});let l=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new ioe(r.shape,o):new soe(r.shape,o);return n.runWebGLProgram(l,[r],r.dtype)}var loe={kernelName:Eo,backendName:"webgl",kernelFunc:ooe},uoe=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];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=` void main() { @@ -4528,7 +4528,7 @@ return a / b;`,Vse=` } setOutput(outputValue); } - `}},ioe={kernelName:Zu,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,{radians:r,fillValue:s,center:i}=t,o=n,l=new soe(a.shape,s),[u,p]=T.getImageCenter(i,a.shape[1],a.shape[2]),d=[[u,p,Math.sin(r),Math.cos(r)]];return o.runWebGLProgram(l,[a],a.dtype,d)}},ooe=` + `}},poe={kernelName:Zu,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,{radians:r,fillValue:s,center:i}=t,o=n,l=new uoe(a.shape,s),[u,p]=T.getImageCenter(i,a.shape[1],a.shape[2]),d=[[u,p,Math.sin(r),Math.cos(r)]];return o.runWebGLProgram(l,[a],a.dtype,d)}},coe=` // OpenGL ES does not support round function. // The algorithm is based on banker's rounding. float base = floor(x); @@ -4543,7 +4543,7 @@ return a / b;`,Vse=` return base + 1.0; } } -`,loe=Ze({opSnippet:ooe}),uoe={kernelName:_o,backendName:"webgl",kernelFunc:loe},poe="return inversesqrt(x);",coe=Ze({opSnippet:poe,cpuKernelImpl:kQ}),doe={kernelName:Ao,backendName:"webgl",kernelFunc:coe},dk=class{constructor(e,t,n,a,r,s,i=!0,o=!1){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let l=ht(r.length),u=ht(s.length),p="";n===1?p="i":n===2&&(p="i, j");let d=`getIndices(${p})`,c="";a===1?c="i":a===2&&(c="i, coords[1]");let h=`getUpdates(${c})`,m="";o&&(m="coords[0], coords[1]");let f=`getDefaultValue(${m})`,g=t>1?"strides[j]":"strides";this.userCode=` +`,doe=Ze({opSnippet:coe}),hoe={kernelName:_o,backendName:"webgl",kernelFunc:doe},moe="return inversesqrt(x);",foe=Ze({opSnippet:moe,cpuKernelImpl:TQ}),goe={kernelName:Ao,backendName:"webgl",kernelFunc:foe},dk=class{constructor(e,t,n,a,r,s,i=!0,o=!1){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let l=ht(r.length),u=ht(s.length),p="";n===1?p="i":n===2&&(p="i, j");let d=`getIndices(${p})`,c="";a===1?c="i":a===2&&(c="i, coords[1]");let h=`getUpdates(${c})`,m="";o&&(m="coords[0], coords[1]");let f=`getDefaultValue(${m})`,g=t>1?"strides[j]":"strides";this.userCode=` ${l} strides = ${l}(${r}); void main() { @@ -4563,7 +4563,7 @@ return a / b;`,Vse=` } setOutput(mix(${f}, sum, float(found))); } - `}},hoe=class{constructor(e,t,n,a,r,s,i=!0,o=!1){this.variableNames=["updates","indices","defaultValue"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=s;let l=ht(r.length),u=ht(s.length),p="";n===1?p="i":n===2&&(p="i, j");let d=`getIndices(${p})`,c="";a===1?c="i":a===2&&(c="i, coords[1]");let h=`getUpdates(${c})`,m="";o&&(m="coords[0], coords[1]");let f=`getDefaultValue(${m})`,g=t>1?"strides[j]":"strides",b=t>1?"strides[j + 1]":"strides";this.userCode=` + `}},boe=class{constructor(e,t,n,a,r,s,i=!0,o=!1){this.variableNames=["updates","indices","defaultValue"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=s;let l=ht(r.length),u=ht(s.length),p="";n===1?p="i":n===2&&(p="i, j");let d=`getIndices(${p})`,c="";a===1?c="i":a===2&&(c="i, coords[1]");let h=`getUpdates(${c})`,m="";o&&(m="coords[0], coords[1]");let f=`getDefaultValue(${m})`,g=t>1?"strides[j]":"strides",b=t>1?"strides[j + 1]":"strides";this.userCode=` ${l} strides = ${l}(${r}); void main() { @@ -4600,7 +4600,7 @@ return a / b;`,Vse=` } setOutput(mix(${f}, sum, found)); } - `}};function moe(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r,updates:s}=t,{shape:i}=a,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:d}=T.calculateShapes(s,r,i),c=[d/u,u];if(d===0)return n.makeTensorInfo(i,r.dtype);let h=ce({inputs:{x:r},backend:n,attrs:{shape:[l,o]}}),m=ce({inputs:{x:s},backend:n,attrs:{shape:[l,u]}}),f=n.makeTensorInfo([],"float32",new Float32Array([0])),g;G().getBool("WEBGL_PACK")?g=new hoe(l,o,h.shape.length,m.shape.length,p,c):g=new dk(l,o,h.shape.length,m.shape.length,p,c);let b=n.runWebGLProgram(g,[m,h,f],m.dtype),y=ce({inputs:{x:b},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(f),y}var foe={kernelName:Pu,backendName:"webgl",kernelFunc:moe},goe=class{constructor(e,t,n,a){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,n];let r="while (left < right) {",s=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,i=G().getNumber("WEBGL_VERSION")===2?r:s,o=a==="left"?"<":"<=";this.userCode=` + `}};function yoe(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r,updates:s}=t,{shape:i}=a,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:d}=T.calculateShapes(s,r,i),c=[d/u,u];if(d===0)return n.makeTensorInfo(i,r.dtype);let h=ce({inputs:{x:r},backend:n,attrs:{shape:[l,o]}}),m=ce({inputs:{x:s},backend:n,attrs:{shape:[l,u]}}),f=n.makeTensorInfo([],"float32",new Float32Array([0])),g;G().getBool("WEBGL_PACK")?g=new boe(l,o,h.shape.length,m.shape.length,p,c):g=new dk(l,o,h.shape.length,m.shape.length,p,c);let b=n.runWebGLProgram(g,[m,h,f],m.dtype),y=ce({inputs:{x:b},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(f),y}var xoe={kernelName:Pu,backendName:"webgl",kernelFunc:yoe},voe=class{constructor(e,t,n,a){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,n];let r="while (left < right) {",s=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,i=G().getNumber("WEBGL_VERSION")===2?r:s,o=a==="left"?"<":"<=";this.userCode=` int findBound(int batch, float value) { int left = 0; int right = numInputs; @@ -4625,7 +4625,7 @@ return a / b;`,Vse=` setOutput(float(findBound(batch, value))); } - `}};function boe(e){let{inputs:t,backend:n,attrs:a}=e,{sortedSequence:r,values:s}=t,{side:i}=a,o=new goe(r.shape[0],r.shape[1],s.shape[1],i),l=[[r.shape[1]]];return n.runWebGLProgram(o,[r,s],"int32",l)}var yoe={kernelName:zu,backendName:"webgl",kernelFunc:boe},xoe=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=[],l=[];for(let u=0;u4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",a="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0); -`,Ioe=Ze({opSnippet:koe}),Soe={kernelName:Fo,backendName:"webgl",kernelFunc:Ioe},Noe=mp+` +`,Coe=Ze({opSnippet:Toe}),Eoe={kernelName:Fo,backendName:"webgl",kernelFunc:Coe},_oe=mp+` return 1.0 / (1.0 + exp(-1.0 * x)); -`,Toe=` +`,Aoe=` vec4 result = 1.0 / (1.0 + exp(-1.0 * x)); bvec4 isNaN = isnan(x); @@ -4653,20 +4653,20 @@ return a / b;`,Vse=` result.a = isNaN.a ? x.a : result.a; return result; -`,Coe=Ze({opSnippet:Noe,packedOpSnippet:Toe,cpuKernelImpl:SQ}),Eoe={kernelName:Mo,backendName:"webgl",kernelFunc:Coe},_oe=` +`,Foe=Ze({opSnippet:_oe,packedOpSnippet:Aoe,cpuKernelImpl:EQ}),$oe={kernelName:Mo,backendName:"webgl",kernelFunc:Foe},Doe=` if (isnan(x)) { return 0.0; } return sign(x); -`,Aoe=Ze({opSnippet:_oe}),Foe={kernelName:Ro,backendName:"webgl",kernelFunc:Aoe},$oe=mp+` +`,Roe=Ze({opSnippet:Doe}),Moe={kernelName:Ro,backendName:"webgl",kernelFunc:Roe},Ooe=mp+` return sin(x); -`,Doe=` +`,Poe=` vec4 result = sin(x); bvec4 isNaN = isnan(x); ${Qo} return result; -`,Roe=Ze({opSnippet:$oe,packedOpSnippet:Doe}),Moe={kernelName:$o,backendName:"webgl",kernelFunc:Roe},Ooe=` +`,Loe=Ze({opSnippet:Ooe,packedOpSnippet:Poe}),zoe={kernelName:$o,backendName:"webgl",kernelFunc:Loe},Woe=` float e2x = exp(x); return (e2x - 1.0 / e2x) / 2.0; -`,Poe=Ze({opSnippet:Ooe}),Loe={kernelName:Do,backendName:"webgl",kernelFunc:Poe},zoe=` 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Yoe(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),l=Array.from(n.readSync(s.dataId)),[u,p,d]=FQ(o,a.shape,a.dtype,i,l);return[n.makeTensorInfo(p,a.dtype,u),n.makeTensorInfo([d.length],s.dtype,new Int32Array(d))]}var Zoe={kernelName:Gu,backendName:"webgl",kernelFunc:Yoe};function Joe(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 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ele(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=a,o=w.parseAxisParam(i,r.shape)[0],l=T.prepareSplitSize(r,s,o),u=r.shape.length,p=new Array(u).fill(0),d=r.shape.slice();return l.map(c=>{let h=[...d];h[o]=c;let m=fp({inputs:{x:r},backend:n,attrs:{begin:p,size:h}});return p[o]+=c,m})}var tle={kernelName:Uu,backendName:"webgl",kernelFunc:ele},IS="return sqrt(x);",nle=Ze({opSnippet:IS,packedOpSnippet:IS,cpuKernelImpl:EQ}),ale={kernelName:Po,backendName:"webgl",kernelFunc:nle},rle="return x * x;",sle=Ze({opSnippet:rle}),ile={kernelName:jc,backendName:"webgl",kernelFunc:sle},SS="return (a - b) * (a - b);",ole=hn({opSnippet:SS,packedOpSnippet:SS}),lle={kernelName:Wo,backendName:"webgl",kernelFunc:ole};function ule(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;if(r.dtype!=="string")throw new Error("Input must be of datatype string");let s=n.readSync(r.dataId),i=T.fromUint8ToStringArray(s),o=_Q(i,"string",a);return n.makeTensorInfo(r.shape,"string",o)}var ple={kernelName:Kc,backendName:"webgl",kernelFunc:ule};function cle({inputs:e,attrs:t,backend:n}){let{x:a}=e,r=Da+` + ${s.shape}`);let i=n.readSync(a.dataId),o=n.readSync(r.dataId),l=n.readSync(s.dataId),[u,p]=EA(i,a.shape,a.dtype,o,l);return n.makeTensorInfo(p,a.dtype,u)}var tle={kernelName:jc,backendName:"webgl",kernelFunc:ele};function nle(e){let{inputs:t,backend:n,attrs:a}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=a,{sliceRank:l,numUpdates:u,sliceSize:p,strides:d,outputSize:c}=T.calculateShapes(s,r,o),h=!1;if(s.dtype==="string"){let b=n.bufferSync(r),y=n.bufferSync(s),x=w.decodeString(n.readSync(i.dataId)[0]),v=CQ(b,y,o,c,p,u,l,d,x,h);return n.makeTensorInfo(o,v.dtype,v.values)}let m=new dk(u,l,r.shape.length,s.shape.length,d,[c,1],h),f=n.runWebGLProgram(m,[s,r,i],s.dtype),g=ce({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(f),g}var ale={kernelName:Hu,backendName:"webgl",kernelFunc:nle};function rle(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=a,o=w.parseAxisParam(i,r.shape)[0],l=T.prepareSplitSize(r,s,o),u=r.shape.length,p=new Array(u).fill(0),d=r.shape.slice();return l.map(c=>{let h=[...d];h[o]=c;let m=fp({inputs:{x:r},backend:n,attrs:{begin:p,size:h}});return p[o]+=c,m})}var sle={kernelName:Uu,backendName:"webgl",kernelFunc:rle},IS="return sqrt(x);",ile=Ze({opSnippet:IS,packedOpSnippet:IS,cpuKernelImpl:$Q}),ole={kernelName:Po,backendName:"webgl",kernelFunc:ile},lle="return x * x;",ule=Ze({opSnippet:lle}),ple={kernelName:qc,backendName:"webgl",kernelFunc:ule},SS="return (a - b) * (a - b);",cle=hn({opSnippet:SS,packedOpSnippet:SS}),dle={kernelName:Wo,backendName:"webgl",kernelFunc:cle};function hle(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;if(r.dtype!=="string")throw new Error("Input must be of datatype string");let s=n.readSync(r.dataId),i=T.fromUint8ToStringArray(s),o=DQ(i,"string",a);return n.makeTensorInfo(r.shape,"string",o)}var mle={kernelName:Kc,backendName:"webgl",kernelFunc:hle};function fle({inputs:e,attrs:t,backend:n}){let{x:a}=e,r=Da+` return x > 0.0 ? 1.0 : float(${t.alpha}); - `,s=new rr(a.shape,r);return n.runWebGLProgram(s,[a],a.dtype)}var dle={kernelName:ks,backendName:"webgl",kernelFunc:cle},hle=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let a=n.length,r=ht(n.length),s=ht(n.length),i="";if(a===1)i="coords * strides + begin";else{let o=0;i=n.map((l,u)=>(o++,n.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=` + `,s=new rr(a.shape,r);return n.runWebGLProgram(s,[a],a.dtype)}var gle={kernelName:Is,backendName:"webgl",kernelFunc:fle},ble=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let a=n.length,r=ht(n.length),s=ht(n.length),i="";if(a===1)i="coords * strides + begin";else{let o=0;i=n.map((l,u)=>(o++,n.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=` ${r} begin = ${r}(${e}); ${r} strides = ${r}(${t}); @@ -4704,15 +4704,15 @@ return a / b;`,Vse=` ${s} coords = getOutputCoords(); setOutput(getX(${i})); } - `}};function mle(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:p,newAxisMask:d,shrinkAxisMask:c}=a,{finalShapeSparse:h,finalShape:m,isIdentity:f,sliceDim0:g,isSimpleSlice:b,begin:y,end:x,strides:v}=Kt.sliceInfo(r.shape,s,i,o,l,u,p,d,c),I;if(f)I=ce({inputs:{x:r},backend:n,attrs:{shape:m}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let C=Kt.computeOutShape(y,x,v),_=fp({inputs:{x:r},backend:n,attrs:{begin:y,size:C}});I=ce({inputs:{x:_},backend:n,attrs:{shape:m}}),n.disposeIntermediateTensorInfo(_)}else if(n.shouldExecuteOnCPU([r])){let C=n.readSync(r.dataId),_=Oe(r.shape,r.dtype,C),F=AQ(h,_,v,y);I=n.makeTensorInfo(m,r.dtype,F.values)}else{let C=new 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o=n.readSync(s.dataId),l=n.readSync(i.dataId)[0],[u,p,d]=$Q(o,l,r),c=p.length;return[n.makeTensorInfo([c,2],"int32",u),n.makeTensorInfo([c],"string",p),n.makeTensorInfo([2],"int32",new Int32Array(d))]}var xle={kernelName:Yc,backendName:"webgl",kernelFunc:yle};function vle(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=DQ(i,r);return n.makeTensorInfo(s.shape,"int32",o)}var wle={kernelName:Zc,backendName:"webgl",kernelFunc:vle},kle="return tan(x);",Ile=Ze({opSnippet:kle}),Sle={kernelName:Vo,backendName:"webgl",kernelFunc:Ile},Nle=` + `}};function yle(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:p,newAxisMask:d,shrinkAxisMask:c}=a,{finalShapeSparse:h,finalShape:m,isIdentity:f,sliceDim0:g,isSimpleSlice:b,begin:y,end:x,strides:v}=Kt.sliceInfo(r.shape,s,i,o,l,u,p,d,c),I;if(f)I=ce({inputs:{x:r},backend:n,attrs:{shape:m}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let C=Kt.computeOutShape(y,x,v),_=fp({inputs:{x:r},backend:n,attrs:{begin:y,size:C}});I=ce({inputs:{x:_},backend:n,attrs:{shape:m}}),n.disposeIntermediateTensorInfo(_)}else if(n.shouldExecuteOnCPU([r])){let C=n.readSync(r.dataId),_=Oe(r.shape,r.dtype,C),F=RQ(h,_,v,y);I=n.makeTensorInfo(m,r.dtype,F.values)}else{let C=new ble(y,v,h);I=n.runWebGLProgram(C,[r],r.dtype)}let N=ce({inputs:{x:I},backend:n,attrs:{shape:m}});return n.disposeIntermediateTensorInfo(I),N}var xle={kernelName:ju,backendName:"webgl",kernelFunc:yle};function 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Ole={kernelName:ks,backendName:"webgl",kernelFunc:oF},Ple=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -4752,7 +4752,7 @@ return a / b;`,Vse=` setOutput(float(i1)); } } - `}},Rle=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode=` + `}},Lle=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode=` void main() { // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ... ivec2 coords = getOutputCoords(); @@ -4786,7 +4786,7 @@ return a / b;`,Vse=` 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Gue={kernelName:nu,backendName:"wasm",kernelFunc:Uue},bF;function Hue(e){bF=e.wasm.cwrap(au,null,["number","number","boolean","number","number","number"])}function que(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,weights:s}=n,{size:i}=a,o=s.shape.reduce((d,c)=>d*c,1)!==0,l=r.shape.length===1?[i]:[r.shape[0],i],u=t.makeOutput(l,s.dtype);function p(d){return t.dataIdMap.get(d.dataId).id}return bF(p(r),i,o,p(s),Qe[s.dtype],p(u)),u}var jue={kernelName:au,backendName:"wasm",setupFunc:Hue,kernelFunc:que},Kue=!0,Xue=Ut(ru,Kue);function Yue(e){let{inputs:t,backend:n}=e,{s0:a,s1:r}=t,s=n.typedArrayFromHeap(a),i=n.typedArrayFromHeap(r),o=T.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeOutput([o.length],"int32",void 0,new Int32Array(o))}var Zue={kernelName:Mc,backendName:"wasm",kernelFunc:Yue};function Ds(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 Jue={kernelName:Mi,backendName:"wasm",kernelFunc:Ds},Que=Xe(Oi),yF;function epe(e){yF=e.wasm.cwrap(vs,null,["number","number","number","number"])}function tpe(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o=n.dataIdMap.get(r.dataId).id,l=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(l.dataId).id;return yF(o,s,i,u),l}var npe={kernelName:vs,backendName:"wasm",setupFunc:epe,kernelFunc:tpe};function xF(e){let{inputs:t,backend:n}=e,a=w.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=t.map(h=>h.shape);T.assertParamsConsistent(r,a);let s=T.computeOutShape(t.map(h=>h.shape),a),i=t.filter(h=>w.sizeFromShape(h.shape)>0);if(i.length===1)return Uf({inputs:{x:i[0]},backend:n});let o=n.makeOutput(s,t[0].dtype);if(w.sizeFromShape(s)===0)return o;if(i[0].dtype==="string"){let h=i.map(x=>{let v=[-1,w.sizeFromShape(x.shape.slice(a))];return 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b=T.getUndoAxesPermutation(u);g=bs({inputs:{x:c},attrs:{perm:b},backend:n}),n.disposeData(p.dataId),n.disposeData(c.dataId)}return g}var Tpe={kernelName:lu,backendName:"wasm",setupFunc:Spe,kernelFunc:Npe},CF;function Cpe(e){CF=e.wasm.cwrap(Vi,null,["number","number","number","number","number","number"])}function Epe(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length;w.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let u=T.getAxesPermutation([s],l),p=r;u!==null&&(p=bs({inputs:{x:r},attrs:{perm:u},backend:n}));let d=T.getInnerMostAxes(1,l)[0];T.assertAxesAreInnerMostDims("cumsum",[d],l);let c=n.makeOutput(p.shape,p.dtype),h=p.shape[d],m=n.dataIdMap.get(p.dataId).id,f=n.dataIdMap.get(c.dataId).id;CF(m,i?1:0,o?1:0,h,f,Qe[r.dtype]);let g=c;if(u!==null){let b=T.getUndoAxesPermutation(u);g=bs({inputs:{x:c},attrs:{perm:b},backend:n}),n.disposeData(p.dataId),n.disposeData(c.dataId)}return g}var _pe={kernelName:Vi,backendName:"wasm",setupFunc:Cpe,kernelFunc:Epe},EF;function Ape(e){EF=e.wasm.cwrap("DenseBincount",null,["number","array","number","number","boolean","number","number","boolean","number"])}function Fpe(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,weights:s}=n,{size:i,binaryOutput:o}=a,l=s.shape.reduce((c,h)=>c*h,1)!==0,u=r.shape.length===1?[i]:[r.shape[0],i],p=t.makeOutput(u,s.dtype);function d(c){return t.dataIdMap.get(c.dataId).id}return EF(d(r),new Uint8Array(new Int32Array(r.shape).buffer),r.shape.length,i,l,d(s),Qe[s.dtype],o,d(p)),p}var $pe={kernelName:Pc,backendName:"wasm",setupFunc:Ape,kernelFunc:Fpe},_F;function Dpe(e){_F=e.wasm.cwrap(pu,null,["number","number","number","array","number","array","array","number","number"])}function Rpe(e){let{backend:t,inputs:n,attrs:a}=e,{x:r}=n,{blockSize:s,dataFormat:i}=a,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],d=l*s,c=u*s,h=p/(s*s),m=i==="NHWC"?[o,d,c,h]:[o,h,d,c],f=t.makeOutput(m,"float32"),g=t.dataIdMap.get(r.dataId).id,b=new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer),y=new Uint8Array(new Int32Array(m).buffer),x=new Uint8Array(new Int32Array(w.computeStrides(m)).buffer),v=t.dataIdMap.get(f.dataId).id;return _F(g,s,i==="NHWC"?1:0,b,r.shape.length-1,y,x,m.length,v),f}var Mpe={kernelName:pu,backendName:"wasm",setupFunc:Dpe,kernelFunc:Rpe},AF;function Ope(e){AF=e.wasm.cwrap(Ui,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Ppe(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s}=t,i=a.dataIdMap.get(r.dataId).id,o=a.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:p,dimRoundingMode:d}=n,c=u==null?[1,1]:u,h=T.computeConv2DInfo(r.shape,s.shape,l,c,p,d,!0),m=h.filterHeight,f=h.filterWidth,g=h.padInfo.top,b=h.padInfo.right,y=h.padInfo.bottom,x=h.padInfo.left,v=h.dilationHeight,I=h.dilationWidth,N=h.strideHeight,C=h.strideWidth,_=h.inChannels,F=h.outChannels,D=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. 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Got ${r.dtype}, ${s.dtype}, and ${i.dtype}`);let p=T.computeDilation2DInfo(r.shape,s.shape,o,l,"NHWC",u),d=n.makeOutput(r.shape,r.dtype);return RF(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(i.dataId).id,n.dataIdMap.get(d.dataId).id,Qe[r.dtype],p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.filterHeight,p.filterWidth,p.padInfo.top,p.padInfo.left),d}var Ype={kernelName:Dl,backendName:"wasm",setupFunc:Kpe,kernelFunc:Xpe},Zpe=Xe(qi),MF;function Jpe(e){MF=e.wasm.cwrap(cu,null,["number","number","number"])}function Qpe(e){let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=n.makeOutput(r.shape,"float32"),i=o=>n.dataIdMap.get(o.dataId).id;return MF(i(r),i(a),i(s)),s}var ece={kernelName:cu,backendName:"wasm",setupFunc:Jpe,kernelFunc:Qpe},tce=!1,nce=Ut(du,tce,"bool"),ace=Xe(ji),rce=Xe(Ki,"float32");function bv(e){let{inputs:t,attrs:n,backend:a}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(w.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Ln({inputs:{x:r},backend:a,attrs:{shape:o}})}var sce={kernelName:hu,backendName:"wasm",kernelFunc:bv},ice=Xe(Xi,"float32");function OF(e){let{attrs:{shape:t,value:n},backend:a}=e,{attrs:{dtype:r}}=e;r=r||w.inferDtype(n);let s=a.makeOutput(t,r);return a.typedArrayFromHeap(s).fill(n),s}var oce={kernelName:zc,backendName:"wasm",kernelFunc:OF},PF;function lce(e){PF=e.wasm.cwrap(mu,null,["number","number","number","number","number","number"])}function uce(e){let{inputs:t,backend:n}=e,{image:a}=t,r=n.makeOutput(a.shape,a.dtype),s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,l,u,p]=a.shape;return PF(s,o,l,u,p,i),r}var pce={kernelName:mu,backendName:"wasm",kernelFunc:uce,setupFunc:lce},cce=Xe(Yi),dce=!1,hce=Ut(Zi,dce),LF;function mce(e){LF=e.wasm.cwrap(Ji,null,["number","number","number","number","number","number","number"])}function fce(e){let{backend:t,inputs:n,attrs:a}=e,{varianceEpsilon:r}=a,{x:s,mean:i,variance:o,offset:l,scale:u}=n,p=t.dataIdMap.get(s.dataId).id,d=t.dataIdMap.get(i.dataId).id,c=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=u!=null?t.dataIdMap.get(u.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(w.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return LF(p,d,c,h,m,r,g),f}var gce={kernelName:Ji,backendName:"wasm",setupFunc:mce,kernelFunc:fce},zF;function bce(e){zF=e.wasm.cwrap(ii,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function yce(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=T.computeConv2DInfo(r.shape,s.shape,l,p,u,c),g=Ac[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let b=a.dataIdMap.get(r.dataId).id,y=a.dataIdMap.get(s.dataId).id,x=f.outChannels,v=0;if(i!=null){let te=a.dataIdMap.get(i.dataId);if(te.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);if(te.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${te.shape}) does not match the number of output channels (${x})`);v=te.id}let I=f.filterHeight,N=f.filterWidth,C=f.padInfo.top,_=f.padInfo.right,F=f.padInfo.bottom,D=f.padInfo.left,$=f.dilationHeight,S=f.dilationWidth,M=f.strideHeight,B=f.strideWidth,U=f.inChannels,H=f.padInfo.type==="SAME"?1:0,j=f.batchSize,K=f.inHeight,Z=f.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let J=a.makeOutput(f.outShape,"float32"),ee=a.dataIdMap.get(J.dataId).id,ae=o==null?0:a.dataIdMap.get(o.dataId).id;return zF(b,j,K,Z,y,I,N,v,C,_,F,D,H,$,S,M,B,U,x,g,ae,m||0,ee),J}var xce={kernelName:ii,backendName:"wasm",setupFunc:bce,kernelFunc:yce},WF;function vce(e){WF=e.wasm.cwrap(oi,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 wce(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=T.computeConv2DInfo(r.shape,s.shape,l,p,u,c,!0),g=Ac[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let b=a.dataIdMap.get(r.dataId).id,y=a.dataIdMap.get(s.dataId).id,x=f.outChannels,v=0;if(i!=null){let te=a.dataIdMap.get(i.dataId);if(te.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);if(te.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${te.shape}) does not match the number of output channels (${x})`);v=te.id}let I=f.filterHeight,N=f.filterWidth,C=f.padInfo.top,_=f.padInfo.right,F=f.padInfo.bottom,D=f.padInfo.left,$=f.dilationHeight,S=f.dilationWidth,M=f.strideHeight,B=f.strideWidth,U=f.inChannels,H=f.padInfo.type==="SAME"?1:0,j=f.batchSize,K=f.inHeight,Z=f.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let J=a.makeOutput(f.outShape,"float32"),ee=a.dataIdMap.get(J.dataId).id,ae=o==null?0:a.dataIdMap.get(o.dataId).id;return WF(b,j,K,Z,y,I,N,v,C,_,F,D,H,$,S,M,B,U,x,g,ae,m||0,ee),J}var kce={kernelName:oi,backendName:"wasm",setupFunc:vce,kernelFunc:wce},BF;function Ice(e){BF=e.wasm.cwrap(gu,null,["number","number","number","number","number","number","array","number"])}function Sce(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=Yw.prepareAndValidate(a,r),u=t.makeOutput(s,a.dtype);if(i===0)return u;let p=r.shape,d=p[p.length-1],c=t.dataIdMap.get(a.dataId).id,h=t.dataIdMap.get(r.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(u.dataId).id;return BF(c,Qe[a.dtype],h,i,d,o,m,f),u}var Nce={kernelName:gu,backendName:"wasm",setupFunc:Ice,kernelFunc:Sce},VF;function Tce(e){VF=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function Cce(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=w.parseAxisParam(i,r.shape)[0],u=t.readSync(s.dataId),p=r.shape[l];for(let C=0;C=0,()=>`GatherV2: the index value ${_} is not in [0, ${p-1}]`)}let d=T.segment_util.collectGatherOpShapeInfo(r,s,l,o),c=Ln({inputs:{x:r},attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]},backend:t}),h=w.sizeFromShape(s.shape),m=Ln({inputs:{x:s},attrs:{shape:[d.batchSize,h/d.batchSize]},backend:t}),f=[d.batchSize,d.outerSize,h/d.batchSize,d.sliceSize],g=t.makeOutput(f,r.dtype);if(w.sizeFromShape(r.shape)===0)return g;let b=c.shape.length-1,y=t.dataIdMap.get(c.dataId).id,x=t.dataIdMap.get(m.dataId).id,v=t.dataIdMap.get(g.dataId).id,I=new Uint8Array(new Int32Array(w.computeStrides(c.shape)).buffer),N=new Uint8Array(new Int32Array(w.computeStrides(f)).buffer);return VF(y,Qe[r.dtype],I,b,x,d.batchSize,N,v),t.disposeData(c.dataId),t.disposeData(m.dataId),g.shape=d.outputShape,g}var Ece={kernelName:fu,backendName:"wasm",setupFunc:Tce,kernelFunc:Cce},_ce=!1,Ace=Ut(bu,_ce,"bool"),Fce=!1,$ce=Ut(Qi,Fce,"bool"),Dce=Xe(to,"bool"),Rce=Xe(no,"bool"),Mce=Xe(ao,"bool"),UF;function Oce(e){UF=e.wasm.cwrap(ro,null,["number","number","number","number"])}function Pce(e){let{inputs:{x:t},attrs:{alpha:n},backend:a}=e,r=a.dataIdMap.get(t.dataId).id,s=a.makeOutput(t.shape,"float32");if(w.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;UF(r,Qe[t.dtype],n,i)}return s}var Lce={kernelName:ro,backendName:"wasm",setupFunc:Oce,kernelFunc:Pce},zce=!1,Wce=Ut(yu,zce,"bool"),Bce=!1,Vce=Ut(xu,Bce,"bool"),GF;function Uce(e){GF=e.wasm.cwrap(vu,null,["number","number","number","number"])}function Gce(e){let{attrs:t,backend:n}=e,{start:a,stop:r,num:s}=t,i=Math.floor(s),o=n.makeOutput([i],"float32");return GF(n.dataIdMap.get(o.dataId).id,a,r,i),o}var Hce={kernelName:vu,backendName:"wasm",setupFunc:Uce,kernelFunc:Gce},qce=Xe(so),jce=Xe(io),Kce=!1,Xce=Ut(wu,Kce,"bool"),Yce=Xe(ku),Zce=!1,Jce=Ut(Iu,Zce,"bool"),Qce=!1,ede=Ut(qS,Qce,"bool"),HF;function tde(e){HF=e.wasm.cwrap(oo,null,["number","number","number","number","number","number","number"])}function nde(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a;if(r.dtype!=="float32")throw new Error("LRN error: x must have dtype float32");let u=n.makeOutput(r.shape,r.dtype);return HF(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(u.dataId).id,r.shape[3],s,i,o,l),u}var ade={kernelName:oo,backendName:"wasm",setupFunc:tde,kernelFunc:nde},qF;function rde(e){qF=e.wasm.cwrap(Su,null,["number","number","number","number","number","number","number","number","number"])}function sde(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=a;if(r.dtype!=="float32"||s.dtype!=="float32"||i.dtype!=="float32")throw new Error("LRNGrad error: x, y, and dy must have dtype float32");let d=n.makeOutput(r.shape,r.dtype);return qF(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(i.dataId).id,n.dataIdMap.get(d.dataId).id,i.shape[3],o,l,u,p),d}var ide={kernelName:Su,backendName:"wasm",setupFunc:rde,kernelFunc:sde},jF;function ode(e){jF=e.wasm.cwrap(lo,null,["number","number","number","number"])}function lde(e){let{backend:t,inputs:n,attrs:a}=e,{reductionIndices:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:c}=$s(i,r,t);if(c){let y=t.dataIdMap.get(u.dataId).id;l=u,o=y}let h=l.shape.length;T.assertAxesAreInnerMostDims("max",p,h);let[m,f]=T.computeOutAndReduceShapes(l.shape,p),g=w.sizeFromShape(f),b=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let y=t.dataIdMap.get(b.dataId).id;jF(o,Qe[i.dtype],g,y)}if(c&&t.disposeData(u.dataId),s){let y=T.expandShapeToKeepDim(b.shape,d);b.shape=y}return b}var ude={kernelName:lo,backendName:"wasm",setupFunc:ode,kernelFunc:lde},pde=!1,cde=Ut(uo,pde),KF;function dde(e){KF=e.wasm.cwrap(po,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function hde(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id;w.assert(r.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${r.dtype}.`);let{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,p=T.computePool2DInfo(r.shape,i,o,1,l,u),d=p.filterHeight,c=p.filterWidth,h=p.padInfo.top,m=p.padInfo.right,f=p.padInfo.bottom,g=p.padInfo.left,b=p.dilationHeight,y=p.dilationWidth,x=p.strideHeight,v=p.strideWidth,I=p.inChannels,N=p.outChannels;if(p.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${p.dataFormat}'. Please use 'channelsLast'.`);let C=a.makeOutput(p.outShape,"float32"),_=a.dataIdMap.get(C.dataId).id;return KF(s,r.shape[0],r.shape[1],r.shape[2],d,c,h,m,f,g,b,y,x,v,I,N,_),C}var mde={kernelName:po,backendName:"wasm",setupFunc:dde,kernelFunc:hde},XF;function fde(e){XF=e.wasm.cwrap("MaxPool3D",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function gde(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=a,p=T.computePool3DInfo(r.shape,s,i,1,o,l,u),d=n.makeOutput(p.outShape,r.dtype);return XF(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(d.dataId).id,p.batchSize,p.inChannels,p.inDepth,p.inHeight,p.inWidth,p.outDepth,p.outHeight,p.outWidth,p.strideDepth,p.strideHeight,p.strideWidth,p.dilationDepth,p.dilationHeight,p.dilationWidth,p.effectiveFilterDepth,p.effectiveFilterHeight,p.effectiveFilterWidth,p.padInfo.front,p.padInfo.top,p.padInfo.left),d}var bde={kernelName:Nu,backendName:"wasm",setupFunc:fde,kernelFunc:gde},YF;function yde(e){YF=e.wasm.cwrap("MaxPool3DGrad",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 xde(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=a,p=T.computePool3DInfo(s.shape,i,o,1,l,u),d=n.makeOutput(s.shape,s.dtype);return YF(n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(d.dataId).id,p.batchSize,p.inChannels,p.inDepth,p.inHeight,p.inWidth,p.outDepth,p.outHeight,p.outWidth,p.strideDepth,p.strideHeight,p.strideWidth,p.dilationDepth,p.dilationHeight,p.dilationWidth,p.effectiveFilterDepth,p.effectiveFilterHeight,p.effectiveFilterWidth,p.padInfo.front,p.padInfo.top,p.padInfo.left),d}var vde={kernelName:Bc,backendName:"wasm",setupFunc:yde,kernelFunc:xde},ZF;function wde(e){ZF=e.wasm.cwrap("MaxPoolGrad",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function kde(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=a,p=T.computePool2DInfo(s.shape,i,o,1,l,u),d=n.makeOutput(s.shape,s.dtype);return ZF(n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(d.dataId).id,p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.effectiveFilterHeight,p.effectiveFilterWidth,p.padInfo.top,p.padInfo.left),d}var Ide={kernelName:Wc,backendName:"wasm",setupFunc:wde,kernelFunc:kde},JF;function Sde(e){JF=e.wasm.cwrap("MaxPoolWithArgmax",null,["number","number","number","number","boolean","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Nde(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,includeBatchInIndex:l}=a;w.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let u=[1,1];w.assert(T.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=T.computePool2DInfo(r.shape,s,i,[1,1],o),d=n.makeOutput(p.outShape,r.dtype),c=n.makeOutput(p.outShape,"int32");return JF(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(d.dataId).id,n.dataIdMap.get(c.dataId).id,Qe[r.dtype],l,p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.effectiveFilterHeight,p.effectiveFilterWidth,p.padInfo.top,p.padInfo.left),[d,c]}var Tde={kernelName:Vc,backendName:"wasm",setupFunc:Sde,kernelFunc:Nde},QF;function Cde(e){QF=e.wasm.cwrap(co,null,["number, number, number"])}function Ede(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:p,axes:d,originalAxes:c,inputWasTransposed:h}=$s(i,r,t),m=d;if(h){let v=t.dataIdMap.get(p.dataId).id;v!==o&&(u=p,l=v,m=T.getInnerMostAxes(m.length,u.shape.length))}T.assertAxesAreInnerMostDims("mean",m,u.shape.length);let[f,g]=T.computeOutAndReduceShapes(u.shape,m),b=w.sizeFromShape(g),y=u;u.dtype!=="float32"&&(y=Ds({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(y.dataId).id);let x=t.makeOutput(f,"float32");if(w.sizeFromShape(u.shape)!==0){let v=t.dataIdMap.get(x.dataId).id;QF(l,b,v)}if(h&&t.disposeData(p.dataId),s){let v=T.expandShapeToKeepDim(x.shape,c);x.shape=v}return u.dtype!=="float32"&&t.disposeData(y.dataId),x}var _de={kernelName:co,backendName:"wasm",setupFunc:Cde,kernelFunc:Ede},e$;function Ade(e){e$=e.wasm.cwrap(ho,null,["number","number","number","number"])}function Fde(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:p,axes:d,originalAxes:c,inputWasTransposed:h}=$s(i,r,t);if(h){let x=t.dataIdMap.get(p.dataId).id;x!==o&&(u=p,l=x)}let m=u.shape.length;T.assertAxesAreInnerMostDims("min",d,m);let[f,g]=T.computeOutAndReduceShapes(u.shape,d),b=w.sizeFromShape(g),y=t.makeOutput(f,u.dtype);if(w.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(y.dataId).id;e$(l,Qe[i.dtype],b,x)}if(h&&t.disposeData(p.dataId),s){let x=T.expandShapeToKeepDim(y.shape,c);y.shape=x}return y}var $de={kernelName:ho,backendName:"wasm",setupFunc:Ade,kernelFunc:Fde},Dde=!1,Rde=Ut(mo,Dde),yv;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(yv||(yv={}));var t$;function Mde(e){t$=e.wasm.cwrap(fo,null,["number","array","number","number","array","array","number","number"])}function Ode(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),l=n.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),p=a.map(m=>m[0]),d=a.map(m=>m[1]),c=new Uint8Array(new Int32Array(p).buffer),h=new Uint8Array(new 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tue=Xe(Yl),nue=Xe(Ni),aue=Xe(Ti);function Ut(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:l}=i,{a:u,b:p}=l,d=o.dataIdMap.get(u.dataId).id,c=o.dataIdMap.get(p.dataId).id,h=n!=null?n:u.dtype,m=T.assertAndGetBroadcastShape(u.shape,p.shape),f=o.makeOutput(m,h);if(w.sizeFromShape(m)===0)return f;let g=new Uint8Array(new Int32Array(u.shape).buffer),b=new Uint8Array(new Int32Array(p.shape).buffer),y=o.dataIdMap.get(f.dataId).id;return a(d,g,u.shape.length,c,b,p.shape.length,Qe[u.dtype],y),f}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:s}}var rue=!0,sue=Ut(vs,rue),uF;function iue(e){uF=e.wasm.cwrap(Ci,null,["array","number","number","number"])}function oue(e){let{inputs:t,backend:n}=e,a=n.makeOutput(t[0].shape,t[0].dtype);if(w.sizeFromShape(a.shape)===0)return a;let r=t.map(o=>n.dataIdMap.get(o.dataId).id),s=new Uint8Array(new 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Kue={kernelName:nu,backendName:"wasm",kernelFunc:que},xF;function Xue(e){xF=e.wasm.cwrap(au,null,["number","number","boolean","number","number","number"])}function Yue(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,weights:s}=n,{size:i}=a,o=s.shape.reduce((d,c)=>d*c,1)!==0,l=r.shape.length===1?[i]:[r.shape[0],i],u=t.makeOutput(l,s.dtype);function p(d){return t.dataIdMap.get(d.dataId).id}return xF(p(r),i,o,p(s),Qe[s.dtype],p(u)),u}var Zue={kernelName:au,backendName:"wasm",setupFunc:Xue,kernelFunc:Yue},Jue=!0,Que=Ut(ru,Jue);function epe(e){let{inputs:t,backend:n}=e,{s0:a,s1:r}=t,s=n.typedArrayFromHeap(a),i=n.typedArrayFromHeap(r),o=T.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeOutput([o.length],"int32",void 0,new Int32Array(o))}var tpe={kernelName:Mc,backendName:"wasm",kernelFunc:epe};function Rs(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 npe={kernelName:Mi,backendName:"wasm",kernelFunc:Rs},ape=Xe(Oi),vF;function rpe(e){vF=e.wasm.cwrap(ws,null,["number","number","number","number"])}function spe(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o=n.dataIdMap.get(r.dataId).id,l=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(l.dataId).id;return vF(o,s,i,u),l}var ipe={kernelName:ws,backendName:"wasm",setupFunc:rpe,kernelFunc:spe};function wF(e){let{inputs:t,backend:n}=e,a=w.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=t.map(h=>h.shape);T.assertParamsConsistent(r,a);let s=T.computeOutShape(t.map(h=>h.shape),a),i=t.filter(h=>w.sizeFromShape(h.shape)>0);if(i.length===1)return Gf({inputs:{x:i[0]},backend:n});let o=n.makeOutput(s,t[0].dtype);if(w.sizeFromShape(s)===0)return o;if(i[0].dtype==="string"){let h=i.map(x=>{let v=[-1,w.sizeFromShape(x.shape.slice(a))];return 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b=T.getUndoAxesPermutation(u);g=ys({inputs:{x:c},attrs:{perm:b},backend:n}),n.disposeData(p.dataId),n.disposeData(c.dataId)}return g}var Dpe={kernelName:Vi,backendName:"wasm",setupFunc:Fpe,kernelFunc:$pe},AF;function Rpe(e){AF=e.wasm.cwrap("DenseBincount",null,["number","array","number","number","boolean","number","number","boolean","number"])}function Mpe(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,weights:s}=n,{size:i,binaryOutput:o}=a,l=s.shape.reduce((c,h)=>c*h,1)!==0,u=r.shape.length===1?[i]:[r.shape[0],i],p=t.makeOutput(u,s.dtype);function d(c){return t.dataIdMap.get(c.dataId).id}return AF(d(r),new Uint8Array(new Int32Array(r.shape).buffer),r.shape.length,i,l,d(s),Qe[s.dtype],o,d(p)),p}var Ope={kernelName:Pc,backendName:"wasm",setupFunc:Rpe,kernelFunc:Mpe},FF;function Ppe(e){FF=e.wasm.cwrap(pu,null,["number","number","number","array","number","array","array","number","number"])}function Lpe(e){let{backend:t,inputs:n,attrs:a}=e,{x:r}=n,{blockSize:s,dataFormat:i}=a,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],d=l*s,c=u*s,h=p/(s*s),m=i==="NHWC"?[o,d,c,h]:[o,h,d,c],f=t.makeOutput(m,"float32"),g=t.dataIdMap.get(r.dataId).id,b=new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer),y=new Uint8Array(new Int32Array(m).buffer),x=new Uint8Array(new Int32Array(w.computeStrides(m)).buffer),v=t.dataIdMap.get(f.dataId).id;return FF(g,s,i==="NHWC"?1:0,b,r.shape.length-1,y,x,m.length,v),f}var zpe={kernelName:pu,backendName:"wasm",setupFunc:Ppe,kernelFunc:Lpe},$F;function Wpe(e){$F=e.wasm.cwrap(Ui,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Bpe(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:l,dilations:u,pad:p,dimRoundingMode:d}=n,c=u==null?[1,1]:u,h=T.computeConv2DInfo(r.shape,s.shape,l,c,p,d,!0),m=h.filterHeight,f=h.filterWidth,g=h.padInfo.top,b=h.padInfo.right,y=h.padInfo.bottom,x=h.padInfo.left,v=h.dilationHeight,I=h.dilationWidth,N=h.strideHeight,C=h.strideWidth,_=h.inChannels,F=h.outChannels,D=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. 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Got ${r.dtype} and ${s.dtype}`);let u=T.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p=n.makeOutput(u.outShape,r.dtype);return RF(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(p.dataId).id,Qe[r.dtype],u.batchSize,u.inChannels,u.inHeight,u.inWidth,u.outHeight,u.outWidth,u.strideHeight,u.strideWidth,u.dilationHeight,u.dilationWidth,u.filterHeight,u.filterWidth,u.padInfo.top,u.padInfo.left),p}var Kpe={kernelName:Gi,backendName:"wasm",setupFunc:jpe,kernelFunc:qpe},MF;function Xpe(e){MF=e.wasm.cwrap(Rl,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Ype(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,dy:i}=t,{strides:o,pad:l,dilations:u}=a;if(r.dtype!==s.dtype||r.dtype!==i.dtype)throw new Error(`Dilation2DBackpropFilter error: x must have the same dtype as filter and dy. Got ${r.dtype}, ${s.dtype}, and ${i.dtype}`);let p=T.computeDilation2DInfo(r.shape,s.shape,o,l,"NHWC",u),d=n.makeOutput(s.shape,s.dtype);return MF(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(i.dataId).id,n.dataIdMap.get(d.dataId).id,Qe[r.dtype],p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.filterHeight,p.filterWidth,p.padInfo.top,p.padInfo.left),d}var Zpe={kernelName:Rl,backendName:"wasm",setupFunc:Xpe,kernelFunc:Ype},OF;function Jpe(e){OF=e.wasm.cwrap(Dl,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Qpe(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,dy:i}=t,{strides:o,pad:l,dilations:u}=a;if(r.dtype!==s.dtype||r.dtype!==i.dtype)throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${r.dtype}, ${s.dtype}, and ${i.dtype}`);let p=T.computeDilation2DInfo(r.shape,s.shape,o,l,"NHWC",u),d=n.makeOutput(r.shape,r.dtype);return OF(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(i.dataId).id,n.dataIdMap.get(d.dataId).id,Qe[r.dtype],p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.filterHeight,p.filterWidth,p.padInfo.top,p.padInfo.left),d}var ece={kernelName:Dl,backendName:"wasm",setupFunc:Jpe,kernelFunc:Qpe},tce=Xe(ji),PF;function nce(e){PF=e.wasm.cwrap(cu,null,["number","number","number"])}function ace(e){let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=n.makeOutput(r.shape,"float32"),i=o=>n.dataIdMap.get(o.dataId).id;return PF(i(r),i(a),i(s)),s}var rce={kernelName:cu,backendName:"wasm",setupFunc:nce,kernelFunc:ace},sce=!1,ice=Ut(du,sce,"bool"),oce=Xe(qi),lce=Xe(Ki,"float32");function yv(e){let{inputs:t,attrs:n,backend:a}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(w.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),zn({inputs:{x:r},backend:a,attrs:{shape:o}})}var uce={kernelName:hu,backendName:"wasm",kernelFunc:yv},pce=Xe(Xi,"float32");function LF(e){let{attrs:{shape:t,value:n},backend:a}=e,{attrs:{dtype:r}}=e;r=r||w.inferDtype(n);let s=a.makeOutput(t,r);return a.typedArrayFromHeap(s).fill(n),s}var cce={kernelName:zc,backendName:"wasm",kernelFunc:LF},zF;function dce(e){zF=e.wasm.cwrap(mu,null,["number","number","number","number","number","number"])}function hce(e){let{inputs:t,backend:n}=e,{image:a}=t,r=n.makeOutput(a.shape,a.dtype),s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,l,u,p]=a.shape;return zF(s,o,l,u,p,i),r}var mce={kernelName:mu,backendName:"wasm",kernelFunc:hce,setupFunc:dce},fce=Xe(Yi),gce=!1,bce=Ut(Zi,gce),WF;function yce(e){WF=e.wasm.cwrap(Ji,null,["number","number","number","number","number","number","number"])}function xce(e){let{backend:t,inputs:n,attrs:a}=e,{varianceEpsilon:r}=a,{x:s,mean:i,variance:o,offset:l,scale:u}=n,p=t.dataIdMap.get(s.dataId).id,d=t.dataIdMap.get(i.dataId).id,c=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=u!=null?t.dataIdMap.get(u.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(w.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return WF(p,d,c,h,m,r,g),f}var vce={kernelName:Ji,backendName:"wasm",setupFunc:yce,kernelFunc:xce},BF;function wce(e){BF=e.wasm.cwrap(oi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function kce(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=T.computeConv2DInfo(r.shape,s.shape,l,p,u,c),g=Ac[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let b=a.dataIdMap.get(r.dataId).id,y=a.dataIdMap.get(s.dataId).id,x=f.outChannels,v=0;if(i!=null){let te=a.dataIdMap.get(i.dataId);if(te.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);if(te.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${te.shape}) does not match the number of output channels (${x})`);v=te.id}let I=f.filterHeight,N=f.filterWidth,C=f.padInfo.top,_=f.padInfo.right,F=f.padInfo.bottom,D=f.padInfo.left,$=f.dilationHeight,S=f.dilationWidth,M=f.strideHeight,B=f.strideWidth,U=f.inChannels,H=f.padInfo.type==="SAME"?1:0,q=f.batchSize,K=f.inHeight,Z=f.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let J=a.makeOutput(f.outShape,"float32"),ee=a.dataIdMap.get(J.dataId).id,ae=o==null?0:a.dataIdMap.get(o.dataId).id;return BF(b,q,K,Z,y,I,N,v,C,_,F,D,H,$,S,M,B,U,x,g,ae,m||0,ee),J}var Ice={kernelName:oi,backendName:"wasm",setupFunc:wce,kernelFunc:kce},VF;function Sce(e){VF=e.wasm.cwrap(li,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 Nce(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=T.computeConv2DInfo(r.shape,s.shape,l,p,u,c,!0),g=Ac[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let b=a.dataIdMap.get(r.dataId).id,y=a.dataIdMap.get(s.dataId).id,x=f.outChannels,v=0;if(i!=null){let te=a.dataIdMap.get(i.dataId);if(te.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);if(te.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${te.shape}) does not match the number of output channels (${x})`);v=te.id}let I=f.filterHeight,N=f.filterWidth,C=f.padInfo.top,_=f.padInfo.right,F=f.padInfo.bottom,D=f.padInfo.left,$=f.dilationHeight,S=f.dilationWidth,M=f.strideHeight,B=f.strideWidth,U=f.inChannels,H=f.padInfo.type==="SAME"?1:0,q=f.batchSize,K=f.inHeight,Z=f.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let J=a.makeOutput(f.outShape,"float32"),ee=a.dataIdMap.get(J.dataId).id,ae=o==null?0:a.dataIdMap.get(o.dataId).id;return VF(b,q,K,Z,y,I,N,v,C,_,F,D,H,$,S,M,B,U,x,g,ae,m||0,ee),J}var Tce={kernelName:li,backendName:"wasm",setupFunc:Sce,kernelFunc:Nce},UF;function Cce(e){UF=e.wasm.cwrap(gu,null,["number","number","number","number","number","number","array","number"])}function Ece(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=Yw.prepareAndValidate(a,r),u=t.makeOutput(s,a.dtype);if(i===0)return u;let p=r.shape,d=p[p.length-1],c=t.dataIdMap.get(a.dataId).id,h=t.dataIdMap.get(r.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(u.dataId).id;return UF(c,Qe[a.dtype],h,i,d,o,m,f),u}var _ce={kernelName:gu,backendName:"wasm",setupFunc:Cce,kernelFunc:Ece},GF;function Ace(e){GF=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function Fce(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=w.parseAxisParam(i,r.shape)[0],u=t.readSync(s.dataId),p=r.shape[l];for(let C=0;C=0,()=>`GatherV2: the index value ${_} is not in [0, ${p-1}]`)}let d=T.segment_util.collectGatherOpShapeInfo(r,s,l,o),c=zn({inputs:{x:r},attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]},backend:t}),h=w.sizeFromShape(s.shape),m=zn({inputs:{x:s},attrs:{shape:[d.batchSize,h/d.batchSize]},backend:t}),f=[d.batchSize,d.outerSize,h/d.batchSize,d.sliceSize],g=t.makeOutput(f,r.dtype);if(w.sizeFromShape(r.shape)===0)return g;let b=c.shape.length-1,y=t.dataIdMap.get(c.dataId).id,x=t.dataIdMap.get(m.dataId).id,v=t.dataIdMap.get(g.dataId).id,I=new Uint8Array(new Int32Array(w.computeStrides(c.shape)).buffer),N=new Uint8Array(new Int32Array(w.computeStrides(f)).buffer);return GF(y,Qe[r.dtype],I,b,x,d.batchSize,N,v),t.disposeData(c.dataId),t.disposeData(m.dataId),g.shape=d.outputShape,g}var $ce={kernelName:fu,backendName:"wasm",setupFunc:Ace,kernelFunc:Fce},Dce=!1,Rce=Ut(bu,Dce,"bool"),Mce=!1,Oce=Ut(Qi,Mce,"bool"),Pce=Xe(to,"bool"),Lce=Xe(no,"bool"),zce=Xe(ao,"bool"),HF;function Wce(e){HF=e.wasm.cwrap(ro,null,["number","number","number","number"])}function Bce(e){let{inputs:{x:t},attrs:{alpha:n},backend:a}=e,r=a.dataIdMap.get(t.dataId).id,s=a.makeOutput(t.shape,"float32");if(w.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;HF(r,Qe[t.dtype],n,i)}return s}var Vce={kernelName:ro,backendName:"wasm",setupFunc:Wce,kernelFunc:Bce},Uce=!1,Gce=Ut(yu,Uce,"bool"),Hce=!1,jce=Ut(xu,Hce,"bool"),jF;function qce(e){jF=e.wasm.cwrap(vu,null,["number","number","number","number"])}function Kce(e){let{attrs:t,backend:n}=e,{start:a,stop:r,num:s}=t,i=Math.floor(s),o=n.makeOutput([i],"float32");return jF(n.dataIdMap.get(o.dataId).id,a,r,i),o}var Xce={kernelName:vu,backendName:"wasm",setupFunc:qce,kernelFunc:Kce},Yce=Xe(so),Zce=Xe(io),Jce=!1,Qce=Ut(wu,Jce,"bool"),ede=Xe(ku),tde=!1,nde=Ut(Iu,tde,"bool"),ade=!1,rde=Ut(jS,ade,"bool"),qF;function sde(e){qF=e.wasm.cwrap(oo,null,["number","number","number","number","number","number","number"])}function ide(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a;if(r.dtype!=="float32")throw new Error("LRN error: x must have dtype float32");let u=n.makeOutput(r.shape,r.dtype);return qF(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(u.dataId).id,r.shape[3],s,i,o,l),u}var ode={kernelName:oo,backendName:"wasm",setupFunc:sde,kernelFunc:ide},KF;function lde(e){KF=e.wasm.cwrap(Su,null,["number","number","number","number","number","number","number","number","number"])}function ude(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=a;if(r.dtype!=="float32"||s.dtype!=="float32"||i.dtype!=="float32")throw new Error("LRNGrad error: x, y, and dy must have dtype float32");let d=n.makeOutput(r.shape,r.dtype);return KF(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(i.dataId).id,n.dataIdMap.get(d.dataId).id,i.shape[3],o,l,u,p),d}var pde={kernelName:Su,backendName:"wasm",setupFunc:lde,kernelFunc:ude},XF;function cde(e){XF=e.wasm.cwrap(lo,null,["number","number","number","number"])}function dde(e){let{backend:t,inputs:n,attrs:a}=e,{reductionIndices:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:c}=Ds(i,r,t);if(c){let y=t.dataIdMap.get(u.dataId).id;l=u,o=y}let h=l.shape.length;T.assertAxesAreInnerMostDims("max",p,h);let[m,f]=T.computeOutAndReduceShapes(l.shape,p),g=w.sizeFromShape(f),b=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let y=t.dataIdMap.get(b.dataId).id;XF(o,Qe[i.dtype],g,y)}if(c&&t.disposeData(u.dataId),s){let y=T.expandShapeToKeepDim(b.shape,d);b.shape=y}return b}var hde={kernelName:lo,backendName:"wasm",setupFunc:cde,kernelFunc:dde},mde=!1,fde=Ut(uo,mde),YF;function gde(e){YF=e.wasm.cwrap(po,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function bde(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id;w.assert(r.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${r.dtype}.`);let{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,p=T.computePool2DInfo(r.shape,i,o,1,l,u),d=p.filterHeight,c=p.filterWidth,h=p.padInfo.top,m=p.padInfo.right,f=p.padInfo.bottom,g=p.padInfo.left,b=p.dilationHeight,y=p.dilationWidth,x=p.strideHeight,v=p.strideWidth,I=p.inChannels,N=p.outChannels;if(p.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${p.dataFormat}'. Please use 'channelsLast'.`);let C=a.makeOutput(p.outShape,"float32"),_=a.dataIdMap.get(C.dataId).id;return YF(s,r.shape[0],r.shape[1],r.shape[2],d,c,h,m,f,g,b,y,x,v,I,N,_),C}var yde={kernelName:po,backendName:"wasm",setupFunc:gde,kernelFunc:bde},ZF;function xde(e){ZF=e.wasm.cwrap("MaxPool3D",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function vde(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=a,p=T.computePool3DInfo(r.shape,s,i,1,o,l,u),d=n.makeOutput(p.outShape,r.dtype);return ZF(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(d.dataId).id,p.batchSize,p.inChannels,p.inDepth,p.inHeight,p.inWidth,p.outDepth,p.outHeight,p.outWidth,p.strideDepth,p.strideHeight,p.strideWidth,p.dilationDepth,p.dilationHeight,p.dilationWidth,p.effectiveFilterDepth,p.effectiveFilterHeight,p.effectiveFilterWidth,p.padInfo.front,p.padInfo.top,p.padInfo.left),d}var wde={kernelName:Nu,backendName:"wasm",setupFunc:xde,kernelFunc:vde},JF;function kde(e){JF=e.wasm.cwrap("MaxPool3DGrad",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 Ide(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=a,p=T.computePool3DInfo(s.shape,i,o,1,l,u),d=n.makeOutput(s.shape,s.dtype);return JF(n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(d.dataId).id,p.batchSize,p.inChannels,p.inDepth,p.inHeight,p.inWidth,p.outDepth,p.outHeight,p.outWidth,p.strideDepth,p.strideHeight,p.strideWidth,p.dilationDepth,p.dilationHeight,p.dilationWidth,p.effectiveFilterDepth,p.effectiveFilterHeight,p.effectiveFilterWidth,p.padInfo.front,p.padInfo.top,p.padInfo.left),d}var Sde={kernelName:Bc,backendName:"wasm",setupFunc:kde,kernelFunc:Ide},QF;function Nde(e){QF=e.wasm.cwrap("MaxPoolGrad",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Tde(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=a,p=T.computePool2DInfo(s.shape,i,o,1,l,u),d=n.makeOutput(s.shape,s.dtype);return QF(n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(d.dataId).id,p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.effectiveFilterHeight,p.effectiveFilterWidth,p.padInfo.top,p.padInfo.left),d}var Cde={kernelName:Wc,backendName:"wasm",setupFunc:Nde,kernelFunc:Tde},e$;function Ede(e){e$=e.wasm.cwrap("MaxPoolWithArgmax",null,["number","number","number","number","boolean","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function _de(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,includeBatchInIndex:l}=a;w.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let u=[1,1];w.assert(T.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. 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v=t.dataIdMap.get(p.dataId).id;v!==o&&(u=p,l=v,m=T.getInnerMostAxes(m.length,u.shape.length))}T.assertAxesAreInnerMostDims("mean",m,u.shape.length);let[f,g]=T.computeOutAndReduceShapes(u.shape,m),b=w.sizeFromShape(g),y=u;u.dtype!=="float32"&&(y=Rs({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(y.dataId).id);let x=t.makeOutput(f,"float32");if(w.sizeFromShape(u.shape)!==0){let v=t.dataIdMap.get(x.dataId).id;t$(l,b,v)}if(h&&t.disposeData(p.dataId),s){let v=T.expandShapeToKeepDim(x.shape,c);x.shape=v}return u.dtype!=="float32"&&t.disposeData(y.dataId),x}var Dde={kernelName:co,backendName:"wasm",setupFunc:Fde,kernelFunc:$de},n$;function Rde(e){n$=e.wasm.cwrap(ho,null,["number","number","number","number"])}function Mde(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:p,axes:d,originalAxes:c,inputWasTransposed:h}=Ds(i,r,t);if(h){let x=t.dataIdMap.get(p.dataId).id;x!==o&&(u=p,l=x)}let 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lg=class extends fn{constructor(t=new og(2)){super("AgeGenderNet"),this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return O(()=>{let a=t instanceof Wr?this.faceFeatureExtractor.forwardInput(t):t,r=ya(a,[7,7],[2,2],"valid").as2D(a.shape[0],-1),s=Bd(r,n.fc.age).as1D(),i=Bd(r,n.fc.gender);return{age:s,gender:i}})}forwardInput(t){return O(()=>{let{age:n,gender:a}=this.runNet(t);return{age:n,gender:ja(a)}})}async forward(t){return this.forwardInput(await vt(t))}async predictAgeAndGender(t){let n=await vt(t),a=await this.forwardInput(n),r=dt(a.age),s=dt(a.gender),i=r.map((l,u)=>({ageTensor:l,genderTensor:s[u]})),o=await Promise.all(i.map(async({ageTensor:l,genderTensor:u})=>{let p=l.dataSync()[0],d=u.dataSync()[0],c=d>.5,h=c?"male":"female",m=c?d:1-d;return l.dispose(),u.dispose(),{age:p,gender:h,genderProbability:m}}));return 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Ak(e,t){return{...e,...{descriptor:t}}}function K2e(e){return typeof e.age=="number"}function Fk(e,t){return{...e,...{age:t}}}function J2e(e){return(e.gender==="male"||e.gender==="female")&&gp(e.genderProbability)}function $k(e,t,n){return{...e,...{gender:t,genderProbability:n}}}function Yfe(e,t){function n(l,u){let p=Fa(e(9*l),[3,3,l,1]),d=qe(e(l)),c=qe(e(l)),h=qe(e(l)),m=qe(e(l));return t.push({paramPath:`${u}/filters`},{paramPath:`${u}/batch_norm_scale`},{paramPath:`${u}/batch_norm_offset`},{paramPath:`${u}/batch_norm_mean`},{paramPath:`${u}/batch_norm_variance`}),{filters:p,batch_norm_scale:d,batch_norm_offset:c,batch_norm_mean:h,batch_norm_variance:m}}function a(l,u,p,d,c){let h=Fa(e(l*u*p*p),[p,p,l,u]),m=qe(e(u));return t.push({paramPath:`${d}/filters`},{paramPath:`${d}/${c?"batch_norm_offset":"bias"}`}),{filters:h,bias:m}}function r(l,u,p,d){let{filters:c,bias:h}=a(l,u,p,d,!0);return{filters:c,batch_norm_offset:h}}function s(l,u,p){let d=n(l,`${p}/depthwise_conv`),c=r(l,u,1,`${p}/pointwise_conv`);return{depthwise_conv:d,pointwise_conv:c}}function i(){let l=r(3,32,3,"mobilenetv1/conv_0"),u=s(32,64,"mobilenetv1/conv_1"),p=s(64,128,"mobilenetv1/conv_2"),d=s(128,128,"mobilenetv1/conv_3"),c=s(128,256,"mobilenetv1/conv_4"),h=s(256,256,"mobilenetv1/conv_5"),m=s(256,512,"mobilenetv1/conv_6"),f=s(512,512,"mobilenetv1/conv_7"),g=s(512,512,"mobilenetv1/conv_8"),b=s(512,512,"mobilenetv1/conv_9"),y=s(512,512,"mobilenetv1/conv_10"),x=s(512,512,"mobilenetv1/conv_11"),v=s(512,1024,"mobilenetv1/conv_12"),I=s(1024,1024,"mobilenetv1/conv_13");return{conv_0:l,conv_1:u,conv_2:p,conv_3:d,conv_4:c,conv_5:h,conv_6:m,conv_7:f,conv_8:g,conv_9:b,conv_10:y,conv_11:x,conv_12:v,conv_13:I}}function o(){let 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l.dispose(),u.dispose(),y}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return xD(t)}extractParams(t){return yD(t)}};function rge(e){let t=new ll;return t.extractWeights(e),t}function GCe(e){return rge(e)}var SD=class extends ll{};var ND=.4,TD=[new He(.738768,.874946),new He(2.42204,2.65704),new He(4.30971,7.04493),new He(10.246,4.59428),new He(12.6868,11.8741)],CD=[new He(1.603231,2.094468),new He(6.041143,7.080126),new He(2.882459,3.518061),new He(4.266906,5.178857),new He(9.041765,10.66308)],ED=[117.001,114.697,97.404],_D="tiny_yolov2_model",AD="tiny_yolov2_separable_conv_model";var dg=e=>typeof e=="number";function FD(e){if(!e)throw new Error(`invalid config: ${e}`);if(typeof e.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${e.withSeparableConvs}`);if(!dg(e.iouThreshold)||e.iouThreshold<0||e.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: 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n=Es(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=X(n,t.bias),Mp(n)})}function sge(e,t){let n=Tp(e,t);function a(i,o){let l=qe(e(i)),u=qe(e(i));return t.push({paramPath:`${o}/sub`},{paramPath:`${o}/truediv`}),{sub:l,truediv:u}}function r(i,o,l){let u=n(i,o,3,`${l}/conv`),p=a(o,`${l}/bn`);return{conv:u,bn:p}}let s=Cp(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}}function $D(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=_n(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:u}=sge(r,i),p;if(t.withSeparableConvs){let[d,c,h,m,f,g,b,y,x]=a,v=t.isFirstLayerConv2d?o(d,c,3,"conv0"):u(d,c,"conv0"),I=u(c,h,"conv1"),N=u(h,m,"conv2"),C=u(m,f,"conv3"),_=u(f,g,"conv4"),F=u(g,b,"conv5"),D=y?u(b,y,"conv6"):void 0,$=x?u(y,x,"conv7"):void 0,S=o(x||y||b,5*n,1,"conv8");p={conv0:v,conv1:I,conv2:N,conv3:C,conv4:_,conv5:F,conv6:D,conv7:$,conv8:S}}else{let[d,c,h,m,f,g,b,y,x]=a,v=l(d,c,"conv0"),I=l(c,h,"conv1"),N=l(h,m,"conv2"),C=l(m,f,"conv3"),_=l(f,g,"conv4"),F=l(g,b,"conv5"),D=l(b,y,"conv6"),$=l(y,x,"conv7"),S=o(x,5*n,1,"conv8");p={conv0:v,conv1:I,conv2:N,conv3:C,conv4:_,conv5:F,conv6:D,conv7:$,conv8:S}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:p,paramMappings:i}}function ige(e,t){let n=sa(e,t);function a(o){let l=n(`${o}/sub`,1),u=n(`${o}/truediv`,1);return{sub:l,truediv:u}}function r(o){let l=n(`${o}/filters`,4),u=n(`${o}/bias`,1);return{filters:l,bias:u}}function s(o){let l=r(`${o}/conv`),u=a(`${o}/bn`);return{conv:l,bn:u}}let i=Ep(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function DD(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=ige(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return En(e,n),{params:i,paramMappings:n}}var br=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var hg=class hg extends fn{constructor(t){super("TinyYolov2"),FD(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,n){let a=Br(t,n.conv0);return a=Dt(a,[2,2],[2,2],"same"),a=Br(a,n.conv1),a=Dt(a,[2,2],[2,2],"same"),a=Br(a,n.conv2),a=Dt(a,[2,2],[2,2],"same"),a=Br(a,n.conv3),a=Dt(a,[2,2],[2,2],"same"),a=Br(a,n.conv4),a=Dt(a,[2,2],[2,2],"same"),a=Br(a,n.conv5),a=Dt(a,[2,2],[1,1],"same"),a=Br(a,n.conv6),a=Br(a,n.conv7),il(a,n.conv8,"valid",!1)}runMobilenet(t,n){let a=this.config.isFirstLayerConv2d?Mp(il(t,n.conv0,"valid",!1)):Vr(t,n.conv0);return a=Dt(a,[2,2],[2,2],"same"),a=Vr(a,n.conv1),a=Dt(a,[2,2],[2,2],"same"),a=Vr(a,n.conv2),a=Dt(a,[2,2],[2,2],"same"),a=Vr(a,n.conv3),a=Dt(a,[2,2],[2,2],"same"),a=Vr(a,n.conv4),a=Dt(a,[2,2],[2,2],"same"),a=Vr(a,n.conv5),a=Dt(a,[2,2],[1,1],"same"),a=n.conv6?Vr(a,n.conv6):a,a=n.conv7?Vr(a,n.conv7):a,il(a,n.conv8,"valid",!1)}forwardInput(t,n){let{params:a}=this;if(!a)throw new Error("TinyYolov2 - load model before inference");return O(()=>{let r=re(t.toBatchTensor(n,!1),"float32");return r=this.config.meanRgb?gr(r,this.config.meanRgb):r,r=r.div(255),this.config.withSeparableConvs?this.runMobilenet(r,a):this.runTinyYolov2(r,a)})}async forward(t,n){return this.forwardInput(await vt(t),n)}async detect(t,n={}){let{inputSize:a,scoreThreshold:r}=new br(n),s=await vt(t),i=await this.forwardInput(s,a),o=O(()=>dt(i)[0].expandDims()),l={width:s.getInputWidth(0),height:s.getInputHeight(0)},u=await this.extractBoxes(o,s.getReshapedInputDimensions(0),r);i.dispose(),o.dispose();let p=u.map(g=>g.box),d=u.map(g=>g.score),c=u.map(g=>g.classScore),h=u.map(g=>this.config.classes[g.label]);return B$(p.map(g=>g.rescale(a)),d,this.config.iouThreshold,!0).map(g=>new yp(d[g],c[g],h[g],p[g],l))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return DD(t,this.config)}extractParams(t){let n=this.config.filterSizes||hg.DEFAULT_FILTER_SIZES,a=n?n.length:void 0;if(a!==7&&a!==8&&a!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${a} filterSizes in config`);return $D(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,a){let{width:r,height:s}=n,i=Math.max(r,s),o=i/r,l=i/s,u=t.shape[1],p=this.config.anchors.length,[d,c,h]=O(()=>{let b=t.reshape([u,u,p,this.boxEncodingSize]),y=b.slice([0,0,0,0],[u,u,p,4]),x=b.slice([0,0,0,4],[u,u,p,1]),v=this.withClassScores?ja(b.slice([0,0,0,5],[u,u,p,this.config.classes.length]),3):ve(0);return[y,x,v]}),m=[],f=await c.array(),g=await d.array();for(let b=0;ba){let I=(y+qf(g[b][y][x][0]))/u*o,N=(b+qf(g[b][y][x][1]))/u*l,C=Math.exp(g[b][y][x][2])*this.config.anchors[x].x/u*o,_=Math.exp(g[b][y][x][3])*this.config.anchors[x].y/u*l,F=I-C/2,D=N-_/2,$={row:b,col:y,anchor:x},{classScore:S,label:M}=this.withClassScores?await this.extractPredictedClass(h,$):{classScore:1,label:0};m.push({box:new bp(F,D,F+C,D+_),score:v,classScore:v*S,label:M,...$})}}return d.dispose(),c.dispose(),h.dispose(),m}async extractPredictedClass(t,n){let{row:a,col:r,anchor:s}=n,i=await t.array();return Array(this.config.classes.length).fill(0).map((o,l)=>i[a][r][s][l]).map((o,l)=>({classScore:o,label:l})).reduce((o,l)=>o.classScore>l.classScore?o:l)}};hg.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Op=hg;var Pp=class extends Op{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:ND,classes:["face"],...t?{anchors:CD,meanRgb:ED}:{anchors:TD,withClassScores:!0}};super(n)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new Ft(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?AD:_D}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function MEe(e,t=!0){let n=new Pp(t);return n.extractWeights(e),n}var mg=class extends br{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var Oa=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function ul(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>Fp(l)?r(l):l.detection),i=a||(t instanceof Ce?await Ld(t,s):await Pd(t,s)),o=await n(i);return i.forEach(l=>l instanceof Ce&&l.dispose()),o}async function Lp(e,t,n,a,r){return ul([e],t,async s=>n(s[0]),a,r)}var RD=.4,MD=[new He(1.603231,2.094468),new He(6.041143,7.080126),new He(2.882459,3.518061),new He(4.266906,5.178857),new He(9.041765,10.66308)],OD=[117.001,114.697,97.404];var zp=class extends Op{constructor(){let t={withSeparableConvs:!0,iouThreshold:RD,classes:["face"],anchors:MD,meanRgb:OD,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new Ft(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var rt={ssdMobilenetv1:new ll,tinyFaceDetector:new zp,tinyYolov2:new Pp,faceLandmark68Net:new Dp,faceLandmark68TinyNet:new pg,faceRecognitionNet:new Rp,faceExpressionNet:new rg,ageGenderNet:new lg},oge=(e,t)=>rt.ssdMobilenetv1.locateFaces(e,t),c_e=(e,t)=>rt.tinyFaceDetector.locateFaces(e,t),d_e=(e,t)=>rt.tinyYolov2.locateFaces(e,t),lge=e=>rt.faceLandmark68Net.detectLandmarks(e),h_e=e=>rt.faceLandmark68TinyNet.detectLandmarks(e),m_e=e=>rt.faceRecognitionNet.computeFaceDescriptor(e),f_e=e=>rt.faceExpressionNet.predictExpressions(e),g_e=e=>rt.ageGenderNet.predictAgeAndGender(e),uge=e=>rt.ssdMobilenetv1.load(e),b_e=e=>rt.tinyFaceDetector.load(e),y_e=e=>rt.tinyYolov2.load(e),x_e=e=>rt.faceLandmark68Net.load(e),v_e=e=>rt.faceLandmark68TinyNet.load(e),w_e=e=>rt.faceRecognitionNet.load(e),k_e=e=>rt.faceExpressionNet.load(e),I_e=e=>rt.ageGenderNet.load(e),S_e=uge,N_e=oge,T_e=lge;var fg=class extends Oa{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.extractedFaces=r}},pl=class extends fg{async run(){let t=await this.parentTask,n=await ul(t,this.input,async a=>Promise.all(a.map(r=>rt.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>Nk(a,n[r]))}withAgeAndGender(){return new dl(this,this.input)}},cl=class extends fg{async run(){let t=await this.parentTask;if(!t)return;let n=await Lp(t,this.input,a=>rt.faceExpressionNet.predictExpressions(a),this.extractedFaces);return Nk(t,n)}withAgeAndGender(){return new hl(this,this.input)}},Os=class extends pl{withAgeAndGender(){return new Ls(this,this.input)}withFaceDescriptors(){return new Ws(this,this.input)}},Ps=class extends cl{withAgeAndGender(){return new zs(this,this.input)}withFaceDescriptor(){return new Bs(this,this.input)}};var gg=class extends Oa{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.extractedFaces=r}},dl=class extends gg{async run(){let t=await this.parentTask,n=await ul(t,this.input,async a=>Promise.all(a.map(r=>rt.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((a,r)=>{let{age:s,gender:i,genderProbability:o}=n[r];return Fk($k(a,i,o),s)})}withFaceExpressions(){return new pl(this,this.input)}},hl=class extends gg{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await Lp(t,this.input,s=>rt.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return Fk($k(t,a,r),n)}withFaceExpressions(){return new cl(this,this.input)}},Ls=class extends dl{withFaceExpressions(){return new Os(this,this.input)}withFaceDescriptors(){return new Ws(this,this.input)}},zs=class extends hl{withFaceExpressions(){return new Ps(this,this.input)}withFaceDescriptor(){return new Bs(this,this.input)}};var bg=class extends Oa{constructor(n,a){super();this.parentTask=n;this.input=a}},Ws=class extends bg{async run(){let t=await this.parentTask;return(await ul(t,this.input,a=>Promise.all(a.map(r=>rt.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>Ak(t[r],a))}withFaceExpressions(){return new Os(this,this.input)}withAgeAndGender(){return new Ls(this,this.input)}},Bs=class extends bg{async run(){let t=await 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t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof Ce?await Ld(this.input,[n]):await Pd(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof Ce&&s.dispose()),Vd(t,r)}withFaceExpressions(){return new Ps(this,this.input)}withAgeAndGender(){return new zs(this,this.input)}withFaceDescriptor(){return new Bs(this,this.input)}};var wg=class extends Oa{constructor(n,a=new Ma){super();this.input=n;this.options=a}},Gd=class extends wg{async run(){let{input:t,options:n}=this,a;if(n instanceof mg)a=rt.tinyFaceDetector.locateFaces(t,n);else if(n instanceof Ma)a=rt.ssdMobilenetv1.locateFaces(t,n);else if(n instanceof br)a=rt.tinyYolov2.locateFaces(t,n);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return a}runAndExtendWithFaceDetections(){return new Promise((t,n)=>{this.run().then(a=>t(a.map(r=>wp({},r)))).catch(a=>n(a))})}withFaceLandmarks(t=!1){return new xg(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new pl(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new dl(this.runAndExtendWithFaceDetections(),this.input)}},kg=class extends wg{async run(){let t=await new Gd(this.input,this.options),n=t[0];return t.forEach(a=>{a.score>n.score&&(n=a)}),n}runAndExtendWithFaceDetection(){return new Promise(async t=>{let n=await this.run();t(n?wp({},n):void 0)})}withFaceLandmarks(t=!1){return new vg(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new cl(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new hl(this.runAndExtendWithFaceDetection(),this.input)}};function IAe(e,t=new Ma){return new kg(e,t)}function Dk(e,t=new Ma){return new Gd(e,t)}async function pge(e,t){return Dk(e,new 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this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,n){return n.map(a=>PD(a,t)).reduce((a,r)=>a+r,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:a})=>new Dd(a,this.computeMeanDistance(t,n))).reduce((n,a)=>n.distancet.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(a=>rl.fromJSON(a));return new e(n,t.distanceThreshold)}};function KAe(e){let t=new zp;return t.extractWeights(e),t}function cge(e,t){let{width:n,height:a}=new aa(t.width,t.height);if(n<=0||a<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:a})}`);if(Array.isArray(e))return e.map(r=>cge(r,{width:n,height:a}));if(Fp(e)){let r=e.detection.forSize(n,a),s=e.unshiftedLandmarks.forSize(r.box.width,r.box.height);return Vd(wp(e,r),s)}return zr(e)?wp(e,e.detection.forSize(n,a)):e instanceof ka||e instanceof Ft?e.forSize(n,a):e}var sFe=sD;export{lg as AgeGenderNet,bp as BoundingBox,mn as Box,Oa as ComposableTask,Ws as ComputeAllFaceDescriptorsTask,bg as ComputeFaceDescriptorsTaskBase,Bs as ComputeSingleFaceDescriptorTask,xg as DetectAllFaceLandmarksTask,Gd as DetectAllFacesTask,yg as DetectFaceLandmarksTaskBase,wg as DetectFacesTaskBase,vg as DetectSingleFaceLandmarksTask,kg as DetectSingleFaceTask,aa as Dimensions,nD as FACE_EXPRESSION_LABELS,Ft as FaceDetection,SD as FaceDetectionNet,rg as FaceExpressionNet,Ms as FaceExpressions,Dp as FaceLandmark68Net,pg as FaceLandmark68TinyNet,hD as FaceLandmarkNet,ka as FaceLandmarks,U$ as FaceLandmarks5,vp as FaceLandmarks68,Dd as FaceMatch,LD as FaceMatcher,Rp as FaceRecognitionNet,Ck as Gender,Rd as LabeledBox,rl as LabeledFaceDescriptors,Wr as NetInput,fn as NeuralNetwork,yp as ObjectDetection,He as Point,G$ as PredictedBox,xp as Rect,ll as SsdMobilenetv1,Ma as SsdMobilenetv1Options,zp as TinyFaceDetector,mg as TinyFaceDetectorOptions,Pp as TinyYolov2,br as TinyYolov2Options,AAe as allFaces,pge as allFacesSsdMobilenetv1,_Ae as allFacesTinyYolov2,H$ as awaitMediaLoaded,q$ as bufferToImage,m_e as computeFaceDescriptor,Np as createCanvas,Yf as createCanvasFromMedia,GCe as createFaceDetectionNet,G2e as createFaceRecognitionNet,rge as createSsdMobilenetv1,KAe as createTinyFaceDetector,MEe as createTinyYolov2,Dk as detectAllFaces,lge as detectFaceLandmarks,h_e as detectFaceLandmarksTiny,T_e as detectLandmarks,IAe as detectSingleFace,rD as draw,at as env,PD as euclideanDistance,Fk as extendWithAge,Ak as extendWithFaceDescriptor,wp as extendWithFaceDetection,Nk as extendWithFaceExpressions,Vd as extendWithFaceLandmarks,$k as extendWithGender,Ld as extractFaceTensors,Pd as extractFaces,Qke as fetchImage,X$ as fetchJson,rIe as fetchNetWeights,Rs as fetchOrThrow,pIe as fetchVideo,ra as getContext2dOrThrow,Sp as getMediaDimensions,j$ as imageTensorToCanvas,K$ as imageToSquare,m0e as inverseSigmoid,z$ as iou,Sk as isMediaElement,Xf as isMediaLoaded,K2e as isWithAge,zr as isWithFaceDetection,aD as isWithFaceExpressions,Fp as isWithFaceLandmarks,J2e as isWithGender,I_e as loadAgeGenderModel,S_e as loadFaceDetectionModel,k_e as loadFaceExpressionModel,x_e as loadFaceLandmarkModel,v_e as loadFaceLandmarkTinyModel,w_e as loadFaceRecognitionModel,uge as loadSsdMobilenetv1Model,b_e as loadTinyFaceDetectorModel,y_e as loadTinyYolov2Model,Z$ as loadWeightMap,N_e as locateFaces,bIe as matchDimensions,W$ as minBbox,rt as nets,B$ as nonMaxSuppression,gr as normalize,V$ as padToSquare,g_e as predictAgeAndGender,f_e as recognizeFaceExpressions,cge as resizeResults,kp as resolveInput,d0e as shuffleArray,qf as sigmoid,oge as ssdMobilenetv1,Pe as tf,c_e as tinyFaceDetector,d_e as tinyYolov2,vt as toNetInput,L$ as utils,FD as validateConfig,sFe as version}; + ${r.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let 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c=s("exit_flow/reduction_block"),h=r("exit_flow/separable_conv"),m={reduction_block:c,separable_conv:h};return _n(e,n),{params:{entry_flow:p,middle_flow:d,exit_flow:m},paramMappings:n}}function pD(e,t,n){return X($t(e,t.filters,n,"same"),t.bias)}function Tk(e,t,n=!0){let a=n?Ke(e):e;return a=Un(a,t.separable_conv0,[1,1]),a=Un(Ke(a),t.separable_conv1,[1,1]),a=Dt(a,[3,3],[2,2],"same"),a=X(a,pD(e,t.expansion_conv,[2,2])),a}function Zfe(e,t){let n=Un(Ke(e),t.separable_conv0,[1,1]);return n=Un(Ke(n),t.separable_conv1,[1,1]),n=Un(Ke(n),t.separable_conv2,[1,1]),n=X(n,e),n}var lg=class extends fn{constructor(t){super("TinyXception"),this._numMainBlocks=t}forwardInput(t){let{params:n}=this;if(!n)throw new Error("TinyXception - load model before inference");return O(()=>{let a=re(t.toBatchTensor(112,!0),"float32"),s=br(a,[122.782,117.001,104.298]).div(255),i=Ke(pD(s,n.entry_flow.conv_in,[2,2]));return i=Tk(i,n.entry_flow.reduction_block_0,!1),i=Tk(i,n.entry_flow.reduction_block_1),gr(this._numMainBlocks,0,1).forEach(o=>{i=Zfe(i,n.middle_flow[`main_block_${o}`])}),i=Tk(i,n.exit_flow.reduction_block),i=Ke(Un(i,n.exit_flow.separable_conv,[1,1])),i})}async forward(t){return this.forwardInput(await vt(t))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(t){return uD(t,this._numMainBlocks)}extractParams(t){return lD(t,this._numMainBlocks)}};function cD(e){let t=[],{extractWeights:n,getRemainingWeights:a}=An(e),r=eg(n,t),s=r(512,1,"fc/age"),i=r(512,2,"fc/gender");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:t,params:{fc:{age:s,gender:i}}}}function dD(e){let t=[],n=sa(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:{age:a("fc/age"),gender:a("fc/gender")}};return _n(e,t),{params:r,paramMappings:t}}var Ck=(n=>(n.FEMALE="female",n.MALE="male",n))(Ck||{});var ug=class extends fn{constructor(t=new lg(2)){super("AgeGenderNet"),this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return O(()=>{let a=t instanceof Wr?this.faceFeatureExtractor.forwardInput(t):t,r=ya(a,[7,7],[2,2],"valid").as2D(a.shape[0],-1),s=Bd(r,n.fc.age).as1D(),i=Bd(r,n.fc.gender);return{age:s,gender:i}})}forwardInput(t){return O(()=>{let{age:n,gender:a}=this.runNet(t);return{age:n,gender:qa(a)}})}async forward(t){return this.forwardInput(await vt(t))}async predictAgeAndGender(t){let n=await vt(t),a=await this.forwardInput(n),r=dt(a.age),s=dt(a.gender),i=r.map((l,u)=>({ageTensor:l,genderTensor:s[u]})),o=await Promise.all(i.map(async({ageTensor:l,genderTensor:u})=>{let p=l.dataSync()[0],d=u.dataSync()[0],c=d>.5,h=c?"male":"female",m=c?d:1-d;return l.dispose(),u.dispose(),{age:p,gender:h,genderProbability:m}}));return a.age.dispose(),a.gender.dispose(),n.isBatchInput?o:o[0]}getDefaultModelName(){return"age_gender_model"}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:n,paramMappings:a}=this.extractClassifierParams(t);this._params=n,this._paramMappings=a}extractClassifierParams(t){return cD(t)}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:a}=rg(t);return this.faceFeatureExtractor.loadFromWeightMap(n),dD(a)}extractParams(t){let a=t.slice(0,t.length-1539),r=t.slice(t.length-1539);return this.faceFeatureExtractor.extractWeights(a),this.extractClassifierParams(r)}};var $p=class extends Ap{postProcess(t,n,a){let r=a.map(({width:i,height:o})=>{let l=n/Math.max(o,i);return{width:i*l,height:o*l}}),s=r.length;return O(()=>{let i=(d,c)=>At([yn([68],d,"float32"),yn([68],c,"float32")],1).as2D(1,136).as1D(),o=(d,c)=>{let{width:h,height:m}=r[d];return 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a=Ra(e,n.conv_0,[1,1]),r=Ra(a,n.conv_1,[2,2]),s=Ra(r,n.conv_2,[1,1]),i=Ra(s,n.conv_3,[2,2]),o=Ra(i,n.conv_4,[1,1]),l=Ra(o,n.conv_5,[2,2]),u=Ra(l,n.conv_6,[1,1]),p=Ra(u,n.conv_7,[2,2]),d=ol(t,n.box_predictor_0),c=ol(e,n.box_predictor_1),h=ol(r,n.box_predictor_2),m=ol(i,n.box_predictor_3),f=ol(l,n.box_predictor_4),g=ol(p,n.box_predictor_5),b=et([d.boxPredictionEncoding,c.boxPredictionEncoding,h.boxPredictionEncoding,m.boxPredictionEncoding,f.boxPredictionEncoding,g.boxPredictionEncoding],1),y=et([d.classPrediction,c.classPrediction,h.classPrediction,m.classPrediction,f.classPrediction,g.classPrediction],1);return{boxPredictions:b,classPredictions:y}})}var Ma=class{constructor({minConfidence:t,maxResults:n}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=t||.5,this._maxResults=n||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof 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l.dispose(),u.dispose(),y}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return wD(t)}extractParams(t){return vD(t)}};function lge(e){let t=new ll;return t.extractWeights(e),t}function YCe(e){return lge(e)}var TD=class extends ll{};var CD=.4,ED=[new He(.738768,.874946),new He(2.42204,2.65704),new He(4.30971,7.04493),new He(10.246,4.59428),new He(12.6868,11.8741)],_D=[new He(1.603231,2.094468),new He(6.041143,7.080126),new He(2.882459,3.518061),new He(4.266906,5.178857),new He(9.041765,10.66308)],AD=[117.001,114.697,97.404],FD="tiny_yolov2_model",$D="tiny_yolov2_separable_conv_model";var hg=e=>typeof e=="number";function DD(e){if(!e)throw new Error(`invalid config: ${e}`);if(typeof e.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${e.withSeparableConvs}`);if(!hg(e.iouThreshold)||e.iouThreshold<0||e.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${e.iouThreshold}`);if(!Array.isArray(e.classes)||!e.classes.length||!e.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(e.classes)}`);if(!Array.isArray(e.anchors)||!e.anchors.length||!e.anchors.map(t=>t||{}).every(t=>hg(t.x)&&hg(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(e.anchors)}`);if(e.meanRgb&&(!Array.isArray(e.meanRgb)||e.meanRgb.length!==3||!e.meanRgb.every(hg)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(e.meanRgb)}`)}function Mp(e){return O(()=>{let t=z(e,xe(.10000000149011612));return X(Ke(pe(e,t)),t)})}function Br(e,t){return O(()=>{let n=xa(e,[[0,0],[1,1],[1,1],[0,0]]);return n=$t(n,t.conv.filters,[1,1],"valid"),n=pe(n,t.bn.sub),n=z(n,t.bn.truediv),n=X(n,t.conv.bias),Mp(n)})}function Vr(e,t){return O(()=>{let n=xa(e,[[0,0],[1,1],[1,1],[0,0]]);return n=_s(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=X(n,t.bias),Mp(n)})}function uge(e,t){let n=Tp(e,t);function a(i,o){let l=je(e(i)),u=je(e(i));return t.push({paramPath:`${o}/sub`},{paramPath:`${o}/truediv`}),{sub:l,truediv:u}}function r(i,o,l){let u=n(i,o,3,`${l}/conv`),p=a(o,`${l}/bn`);return{conv:u,bn:p}}let s=Cp(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}}function RD(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=An(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:u}=uge(r,i),p;if(t.withSeparableConvs){let[d,c,h,m,f,g,b,y,x]=a,v=t.isFirstLayerConv2d?o(d,c,3,"conv0"):u(d,c,"conv0"),I=u(c,h,"conv1"),N=u(h,m,"conv2"),C=u(m,f,"conv3"),_=u(f,g,"conv4"),F=u(g,b,"conv5"),D=y?u(b,y,"conv6"):void 0,$=x?u(y,x,"conv7"):void 0,S=o(x||y||b,5*n,1,"conv8");p={conv0:v,conv1:I,conv2:N,conv3:C,conv4:_,conv5:F,conv6:D,conv7:$,conv8:S}}else{let[d,c,h,m,f,g,b,y,x]=a,v=l(d,c,"conv0"),I=l(c,h,"conv1"),N=l(h,m,"conv2"),C=l(m,f,"conv3"),_=l(f,g,"conv4"),F=l(g,b,"conv5"),D=l(b,y,"conv6"),$=l(y,x,"conv7"),S=o(x,5*n,1,"conv8");p={conv0:v,conv1:I,conv2:N,conv3:C,conv4:_,conv5:F,conv6:D,conv7:$,conv8:S}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:p,paramMappings:i}}function pge(e,t){let n=sa(e,t);function a(o){let l=n(`${o}/sub`,1),u=n(`${o}/truediv`,1);return{sub:l,truediv:u}}function r(o){let l=n(`${o}/filters`,4),u=n(`${o}/bias`,1);return{filters:l,bias:u}}function s(o){let l=r(`${o}/conv`),u=a(`${o}/bn`);return{conv:l,bn:u}}let i=Ep(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function MD(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=pge(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return _n(e,n),{params:i,paramMappings:n}}var yr=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var mg=class mg extends fn{constructor(t){super("TinyYolov2"),DD(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,n){let a=Br(t,n.conv0);return a=Dt(a,[2,2],[2,2],"same"),a=Br(a,n.conv1),a=Dt(a,[2,2],[2,2],"same"),a=Br(a,n.conv2),a=Dt(a,[2,2],[2,2],"same"),a=Br(a,n.conv3),a=Dt(a,[2,2],[2,2],"same"),a=Br(a,n.conv4),a=Dt(a,[2,2],[2,2],"same"),a=Br(a,n.conv5),a=Dt(a,[2,2],[1,1],"same"),a=Br(a,n.conv6),a=Br(a,n.conv7),il(a,n.conv8,"valid",!1)}runMobilenet(t,n){let a=this.config.isFirstLayerConv2d?Mp(il(t,n.conv0,"valid",!1)):Vr(t,n.conv0);return a=Dt(a,[2,2],[2,2],"same"),a=Vr(a,n.conv1),a=Dt(a,[2,2],[2,2],"same"),a=Vr(a,n.conv2),a=Dt(a,[2,2],[2,2],"same"),a=Vr(a,n.conv3),a=Dt(a,[2,2],[2,2],"same"),a=Vr(a,n.conv4),a=Dt(a,[2,2],[2,2],"same"),a=Vr(a,n.conv5),a=Dt(a,[2,2],[1,1],"same"),a=n.conv6?Vr(a,n.conv6):a,a=n.conv7?Vr(a,n.conv7):a,il(a,n.conv8,"valid",!1)}forwardInput(t,n){let{params:a}=this;if(!a)throw new Error("TinyYolov2 - load model before inference");return O(()=>{let r=re(t.toBatchTensor(n,!1),"float32");return r=this.config.meanRgb?br(r,this.config.meanRgb):r,r=r.div(255),this.config.withSeparableConvs?this.runMobilenet(r,a):this.runTinyYolov2(r,a)})}async forward(t,n){return this.forwardInput(await vt(t),n)}async detect(t,n={}){let{inputSize:a,scoreThreshold:r}=new yr(n),s=await vt(t),i=await this.forwardInput(s,a),o=O(()=>dt(i)[0].expandDims()),l={width:s.getInputWidth(0),height:s.getInputHeight(0)},u=await this.extractBoxes(o,s.getReshapedInputDimensions(0),r);i.dispose(),o.dispose();let p=u.map(g=>g.box),d=u.map(g=>g.score),c=u.map(g=>g.classScore),h=u.map(g=>this.config.classes[g.label]);return U$(p.map(g=>g.rescale(a)),d,this.config.iouThreshold,!0).map(g=>new yp(d[g],c[g],h[g],p[g],l))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return MD(t,this.config)}extractParams(t){let n=this.config.filterSizes||mg.DEFAULT_FILTER_SIZES,a=n?n.length:void 0;if(a!==7&&a!==8&&a!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${a} filterSizes in config`);return RD(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,a){let{width:r,height:s}=n,i=Math.max(r,s),o=i/r,l=i/s,u=t.shape[1],p=this.config.anchors.length,[d,c,h]=O(()=>{let b=t.reshape([u,u,p,this.boxEncodingSize]),y=b.slice([0,0,0,0],[u,u,p,4]),x=b.slice([0,0,0,4],[u,u,p,1]),v=this.withClassScores?qa(b.slice([0,0,0,5],[u,u,p,this.config.classes.length]),3):xe(0);return[y,x,v]}),m=[],f=await c.array(),g=await d.array();for(let b=0;ba){let I=(y+qf(g[b][y][x][0]))/u*o,N=(b+qf(g[b][y][x][1]))/u*l,C=Math.exp(g[b][y][x][2])*this.config.anchors[x].x/u*o,_=Math.exp(g[b][y][x][3])*this.config.anchors[x].y/u*l,F=I-C/2,D=N-_/2,$={row:b,col:y,anchor:x},{classScore:S,label:M}=this.withClassScores?await this.extractPredictedClass(h,$):{classScore:1,label:0};m.push({box:new bp(F,D,F+C,D+_),score:v,classScore:v*S,label:M,...$})}}return d.dispose(),c.dispose(),h.dispose(),m}async extractPredictedClass(t,n){let{row:a,col:r,anchor:s}=n,i=await t.array();return Array(this.config.classes.length).fill(0).map((o,l)=>i[a][r][s][l]).map((o,l)=>({classScore:o,label:l})).reduce((o,l)=>o.classScore>l.classScore?o:l)}};mg.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Op=mg;var Pp=class extends Op{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:CD,classes:["face"],...t?{anchors:_D,meanRgb:AD}:{anchors:ED,withClassScores:!0}};super(n)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new Ft(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?$D:FD}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function BEe(e,t=!0){let n=new Pp(t);return n.extractWeights(e),n}var fg=class extends yr{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var Oa=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function ul(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>Fp(l)?r(l):l.detection),i=a||(t instanceof Ce?await Ld(t,s):await Pd(t,s)),o=await n(i);return i.forEach(l=>l instanceof Ce&&l.dispose()),o}async function Lp(e,t,n,a,r){return ul([e],t,async s=>n(s[0]),a,r)}var OD=.4,PD=[new He(1.603231,2.094468),new He(6.041143,7.080126),new He(2.882459,3.518061),new He(4.266906,5.178857),new He(9.041765,10.66308)],LD=[117.001,114.697,97.404];var zp=class extends Op{constructor(){let t={withSeparableConvs:!0,iouThreshold:OD,classes:["face"],anchors:PD,meanRgb:LD,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new Ft(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var rt={ssdMobilenetv1:new ll,tinyFaceDetector:new zp,tinyYolov2:new Pp,faceLandmark68Net:new Dp,faceLandmark68TinyNet:new cg,faceRecognitionNet:new Rp,faceExpressionNet:new sg,ageGenderNet:new ug},cge=(e,t)=>rt.ssdMobilenetv1.locateFaces(e,t),b_e=(e,t)=>rt.tinyFaceDetector.locateFaces(e,t),y_e=(e,t)=>rt.tinyYolov2.locateFaces(e,t),dge=e=>rt.faceLandmark68Net.detectLandmarks(e),x_e=e=>rt.faceLandmark68TinyNet.detectLandmarks(e),v_e=e=>rt.faceRecognitionNet.computeFaceDescriptor(e),w_e=e=>rt.faceExpressionNet.predictExpressions(e),k_e=e=>rt.ageGenderNet.predictAgeAndGender(e),hge=e=>rt.ssdMobilenetv1.load(e),I_e=e=>rt.tinyFaceDetector.load(e),S_e=e=>rt.tinyYolov2.load(e),N_e=e=>rt.faceLandmark68Net.load(e),T_e=e=>rt.faceLandmark68TinyNet.load(e),C_e=e=>rt.faceRecognitionNet.load(e),E_e=e=>rt.faceExpressionNet.load(e),__e=e=>rt.ageGenderNet.load(e),A_e=hge,F_e=cge,$_e=dge;var gg=class extends Oa{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.extractedFaces=r}},pl=class extends gg{async run(){let t=await this.parentTask,n=await ul(t,this.input,async a=>Promise.all(a.map(r=>rt.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>Nk(a,n[r]))}withAgeAndGender(){return new dl(this,this.input)}},cl=class extends gg{async run(){let t=await this.parentTask;if(!t)return;let n=await Lp(t,this.input,a=>rt.faceExpressionNet.predictExpressions(a),this.extractedFaces);return Nk(t,n)}withAgeAndGender(){return new hl(this,this.input)}},Ps=class extends pl{withAgeAndGender(){return new zs(this,this.input)}withFaceDescriptors(){return new Bs(this,this.input)}},Ls=class extends cl{withAgeAndGender(){return new Ws(this,this.input)}withFaceDescriptor(){return new Vs(this,this.input)}};var bg=class extends Oa{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.extractedFaces=r}},dl=class extends bg{async run(){let t=await this.parentTask,n=await ul(t,this.input,async a=>Promise.all(a.map(r=>rt.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((a,r)=>{let{age:s,gender:i,genderProbability:o}=n[r];return Fk($k(a,i,o),s)})}withFaceExpressions(){return new pl(this,this.input)}},hl=class extends bg{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await Lp(t,this.input,s=>rt.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return Fk($k(t,a,r),n)}withFaceExpressions(){return new cl(this,this.input)}},zs=class extends dl{withFaceExpressions(){return new Ps(this,this.input)}withFaceDescriptors(){return new Bs(this,this.input)}},Ws=class extends hl{withFaceExpressions(){return new Ls(this,this.input)}withFaceDescriptor(){return new Vs(this,this.input)}};var yg=class extends Oa{constructor(n,a){super();this.parentTask=n;this.input=a}},Bs=class extends yg{async run(){let t=await this.parentTask;return(await ul(t,this.input,a=>Promise.all(a.map(r=>rt.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>Ak(t[r],a))}withFaceExpressions(){return new Ps(this,this.input)}withAgeAndGender(){return new zs(this,this.input)}},Vs=class extends yg{async run(){let t=await this.parentTask;if(!t)return;let n=await Lp(t,this.input,a=>rt.faceRecognitionNet.computeFaceDescriptor(a),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}));return Ak(t,n)}withFaceExpressions(){return new Ls(this,this.input)}withAgeAndGender(){return new Ws(this,this.input)}};var xg=class extends Oa{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?rt.faceLandmark68TinyNet:rt.faceLandmark68Net}},vg=class extends xg{async run(){let t=await this.parentTask,n=t.map(i=>i.detection),a=this.input instanceof Ce?await Ld(this.input,n):await Pd(this.input,n),r=await Promise.all(a.map(i=>this.landmarkNet.detectLandmarks(i)));return a.forEach(i=>i instanceof Ce&&i.dispose()),t.filter((i,o)=>r[o]).map((i,o)=>Vd(i,r[o]))}withFaceExpressions(){return new Ps(this,this.input)}withAgeAndGender(){return new zs(this,this.input)}withFaceDescriptors(){return new Bs(this,this.input)}},wg=class extends xg{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof Ce?await Ld(this.input,[n]):await Pd(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof Ce&&s.dispose()),Vd(t,r)}withFaceExpressions(){return new Ls(this,this.input)}withAgeAndGender(){return new Ws(this,this.input)}withFaceDescriptor(){return new Vs(this,this.input)}};var kg=class extends Oa{constructor(n,a=new Ma){super();this.input=n;this.options=a}},Gd=class extends kg{async run(){let{input:t,options:n}=this,a;if(n instanceof fg)a=rt.tinyFaceDetector.locateFaces(t,n);else if(n instanceof Ma)a=rt.ssdMobilenetv1.locateFaces(t,n);else if(n instanceof yr)a=rt.tinyYolov2.locateFaces(t,n);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return a}runAndExtendWithFaceDetections(){return new Promise((t,n)=>{this.run().then(a=>t(a.map(r=>wp({},r)))).catch(a=>n(a))})}withFaceLandmarks(t=!1){return new vg(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new pl(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new dl(this.runAndExtendWithFaceDetections(),this.input)}},Ig=class extends kg{async run(){let t=await new Gd(this.input,this.options),n=t[0];return t.forEach(a=>{a.score>n.score&&(n=a)}),n}runAndExtendWithFaceDetection(){return new Promise(async t=>{let n=await this.run();t(n?wp({},n):void 0)})}withFaceLandmarks(t=!1){return new wg(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new cl(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new hl(this.runAndExtendWithFaceDetection(),this.input)}};function _Ae(e,t=new Ma){return new Ig(e,t)}function Dk(e,t=new Ma){return new Gd(e,t)}async function mge(e,t){return Dk(e,new Ma(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function MAe(e,t={}){return Dk(e,new yr(t)).withFaceLandmarks().withFaceDescriptors()}var OAe=mge;function zD(e,t){if(e.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let n=Array.from(e),a=Array.from(t);return Math.sqrt(n.map((r,s)=>r-a[s]).reduce((r,s)=>r+s*s,0))}var WD=class e{constructor(t,n=.6){this._distanceThreshold=n;let a=Array.isArray(t)?t:[t];if(!a.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let r=1,s=()=>`person ${r++}`;this._labeledDescriptors=a.map(i=>{if(i instanceof rl)return i;if(i instanceof Float32Array)return new rl(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new rl(s(),[i.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,n){return n.map(a=>zD(a,t)).reduce((a,r)=>a+r,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:a})=>new Dd(a,this.computeMeanDistance(t,n))).reduce((n,a)=>n.distancet.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(a=>rl.fromJSON(a));return new e(n,t.distanceThreshold)}};function eFe(e){let t=new zp;return t.extractWeights(e),t}function fge(e,t){let{width:n,height:a}=new aa(t.width,t.height);if(n<=0||a<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:a})}`);if(Array.isArray(e))return e.map(r=>fge(r,{width:n,height:a}));if(Fp(e)){let r=e.detection.forSize(n,a),s=e.unshiftedLandmarks.forSize(r.box.width,r.box.height);return Vd(wp(e,r),s)}return zr(e)?wp(e,e.detection.forSize(n,a)):e instanceof ka||e instanceof Ft?e.forSize(n,a):e}var cFe=oD;export{ug as AgeGenderNet,bp as BoundingBox,mn as Box,Oa as ComposableTask,Bs as ComputeAllFaceDescriptorsTask,yg as ComputeFaceDescriptorsTaskBase,Vs as ComputeSingleFaceDescriptorTask,vg as DetectAllFaceLandmarksTask,Gd as DetectAllFacesTask,xg as DetectFaceLandmarksTaskBase,kg as DetectFacesTaskBase,wg as DetectSingleFaceLandmarksTask,Ig as DetectSingleFaceTask,aa as Dimensions,rD as FACE_EXPRESSION_LABELS,Ft as FaceDetection,TD as FaceDetectionNet,sg as FaceExpressionNet,Os as FaceExpressions,Dp as FaceLandmark68Net,cg as FaceLandmark68TinyNet,fD as FaceLandmarkNet,ka as FaceLandmarks,H$ as FaceLandmarks5,vp as FaceLandmarks68,Dd as FaceMatch,WD as FaceMatcher,Rp as FaceRecognitionNet,Ck as Gender,Rd as LabeledBox,rl as LabeledFaceDescriptors,Wr as NetInput,fn as NeuralNetwork,yp as ObjectDetection,He as Point,j$ as PredictedBox,xp as Rect,ll as SsdMobilenetv1,Ma as SsdMobilenetv1Options,zp as TinyFaceDetector,fg as TinyFaceDetectorOptions,Pp as TinyYolov2,yr as TinyYolov2Options,OAe as allFaces,mge as allFacesSsdMobilenetv1,MAe as allFacesTinyYolov2,q$ as awaitMediaLoaded,K$ as bufferToImage,v_e as computeFaceDescriptor,Np as createCanvas,Zf as createCanvasFromMedia,YCe as createFaceDetectionNet,Y2e as createFaceRecognitionNet,lge as createSsdMobilenetv1,eFe as createTinyFaceDetector,BEe as createTinyYolov2,Dk as detectAllFaces,dge as detectFaceLandmarks,x_e as detectFaceLandmarksTiny,$_e as detectLandmarks,_Ae as detectSingleFace,iD as draw,at as env,zD as euclideanDistance,Fk as extendWithAge,Ak as extendWithFaceDescriptor,wp as extendWithFaceDetection,Nk as extendWithFaceExpressions,Vd as extendWithFaceLandmarks,$k as extendWithGender,Ld as extractFaceTensors,Pd as extractFaces,sIe as fetchImage,Z$ as fetchJson,pIe as fetchNetWeights,Ms as fetchOrThrow,gIe as fetchVideo,ra as getContext2dOrThrow,Sp as getMediaDimensions,X$ as imageTensorToCanvas,Y$ as imageToSquare,v0e as inverseSigmoid,B$ as iou,Sk as isMediaElement,Yf as isMediaLoaded,eCe as isWithAge,zr as isWithFaceDetection,sD as isWithFaceExpressions,Fp as isWithFaceLandmarks,rCe as isWithGender,__e as loadAgeGenderModel,A_e as loadFaceDetectionModel,E_e as loadFaceExpressionModel,N_e as loadFaceLandmarkModel,T_e as loadFaceLandmarkTinyModel,C_e as loadFaceRecognitionModel,hge as loadSsdMobilenetv1Model,I_e as loadTinyFaceDetectorModel,S_e as loadTinyYolov2Model,Q$ as loadWeightMap,F_e as locateFaces,IIe as matchDimensions,V$ as minBbox,rt as nets,U$ as nonMaxSuppression,br as normalize,G$ as padToSquare,k_e as predictAgeAndGender,w_e as recognizeFaceExpressions,fge as resizeResults,kp as resolveInput,y0e as shuffleArray,qf as sigmoid,cge as ssdMobilenetv1,Pe as tf,b_e as tinyFaceDetector,y_e as tinyYolov2,vt as toNetInput,W$ as utils,DD as validateConfig,cFe as version}; //# sourceMappingURL=face-api.esm.js.map diff --git a/dist/face-api.esm.js.map b/dist/face-api.esm.js.map index 3fedcf3..1b2a327 100644 --- a/dist/face-api.esm.js.map +++ b/dist/face-api.esm.js.map @@ -1,7 +1,7 @@ { "version": 3, "sources": ["tfjs.esm.js", "../src/draw/index.ts", "../src/draw/drawContour.ts", "../src/utils/index.ts", "../src/classes/Dimensions.ts", "../src/classes/Point.ts", "../src/classes/Box.ts", "../src/classes/BoundingBox.ts", "../src/classes/ObjectDetection.ts", "../src/classes/FaceDetection.ts", "../src/ops/iou.ts", "../src/ops/minBbox.ts", "../src/ops/nonMaxSuppression.ts", "../src/ops/normalize.ts", "../src/ops/padToSquare.ts", "../src/ops/shuffleArray.ts", "../src/ops/index.ts", "../src/classes/Rect.ts", "../src/classes/FaceLandmarks.ts", "../src/classes/FaceLandmarks5.ts", "../src/classes/FaceLandmarks68.ts", "../src/classes/FaceMatch.ts", "../src/classes/LabeledBox.ts", "../src/classes/LabeledFaceDescriptors.ts", "../src/classes/PredictedBox.ts", "../src/factories/WithFaceDetection.ts", "../src/env/createBrowserEnv.ts", "../src/env/isNodejs.ts", "../src/env/createFileSystem.ts", "../src/env/createNodejsEnv.ts", "../src/env/isBrowser.ts", "../src/env/index.ts", "../src/dom/resolveInput.ts", "../src/dom/getContext2dOrThrow.ts", "../src/draw/DrawTextField.ts", "../src/draw/DrawBox.ts", "../src/draw/drawDetections.ts", "../src/dom/isMediaLoaded.ts", "../src/dom/awaitMediaLoaded.ts", "../src/dom/bufferToImage.ts", "../src/dom/getMediaDimensions.ts", "../src/dom/createCanvas.ts", "../src/dom/imageTensorToCanvas.ts", "../src/dom/isMediaElement.ts", "../src/dom/imageToSquare.ts", "../src/dom/NetInput.ts", "../src/dom/toNetInput.ts", "../src/dom/extractFaces.ts", "../src/dom/extractFaceTensors.ts", "../src/dom/fetchOrThrow.ts", "../src/dom/fetchImage.ts", "../src/dom/fetchJson.ts", "../src/dom/fetchNetWeights.ts", "../src/dom/bufferToVideo.ts", "../src/dom/fetchVideo.ts", "../src/common/getModelUris.ts", "../src/dom/loadWeightMap.ts", "../src/dom/matchDimensions.ts", "../src/NeuralNetwork.ts", "../src/common/depthwiseSeparableConv.ts", "../src/faceFeatureExtractor/denseBlock.ts", "../src/common/convLayer.ts", "../src/common/disposeUnusedWeightTensors.ts", "../src/common/extractConvParamsFactory.ts", "../src/common/extractFCParamsFactory.ts", "../src/common/types.ts", "../src/common/extractSeparableConvParamsFactory.ts", "../src/common/extractWeightEntryFactory.ts", "../src/common/extractWeightsFactory.ts", "../src/faceFeatureExtractor/extractorsFactory.ts", "../src/faceFeatureExtractor/extractParams.ts", "../src/common/loadConvParamsFactory.ts", "../src/faceFeatureExtractor/loadParamsFactory.ts", "../src/faceFeatureExtractor/extractParamsFromWeightMap.ts", "../src/faceFeatureExtractor/FaceFeatureExtractor.ts", "../src/common/fullyConnectedLayer.ts", "../src/faceProcessor/extractParams.ts", "../src/faceProcessor/extractParamsFromWeightMap.ts", "../src/faceProcessor/util.ts", "../src/faceProcessor/FaceProcessor.ts", "../src/faceExpressionNet/FaceExpressions.ts", "../src/faceExpressionNet/FaceExpressionNet.ts", "../src/factories/WithFaceExpressions.ts", "../src/draw/drawFaceExpressions.ts", "../src/factories/WithFaceLandmarks.ts", "../src/draw/DrawFaceLandmarks.ts", "../package.json", "../src/xception/extractParams.ts", "../src/xception/extractParamsFromWeightMap.ts", "../src/xception/TinyXception.ts", "../src/ageGenderNet/extractParams.ts", "../src/ageGenderNet/extractParamsFromWeightMap.ts", "../src/ageGenderNet/types.ts", "../src/ageGenderNet/AgeGenderNet.ts", "../src/faceLandmarkNet/FaceLandmark68NetBase.ts", "../src/faceLandmarkNet/FaceLandmark68Net.ts", "../src/faceFeatureExtractor/extractParamsFromWeightMapTiny.ts", "../src/faceFeatureExtractor/extractParamsTiny.ts", "../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts", "../src/faceLandmarkNet/FaceLandmark68TinyNet.ts", "../src/faceLandmarkNet/index.ts", "../src/faceRecognitionNet/scaleLayer.ts", "../src/faceRecognitionNet/convLayer.ts", "../src/faceRecognitionNet/extractParams.ts", "../src/faceRecognitionNet/extractParamsFromWeightMap.ts", "../src/faceRecognitionNet/residualLayer.ts", "../src/faceRecognitionNet/FaceRecognitionNet.ts", "../src/faceRecognitionNet/index.ts", "../src/factories/WithFaceDescriptor.ts", "../src/factories/WithAge.ts", "../src/factories/WithGender.ts", "../src/ssdMobilenetv1/extractParams.ts", "../src/ssdMobilenetv1/extractParamsFromWeightMap.ts", "../src/ssdMobilenetv1/pointwiseConvLayer.ts", "../src/ssdMobilenetv1/mobileNetV1.ts", "../src/ssdMobilenetv1/nonMaxSuppression.ts", "../src/ssdMobilenetv1/outputLayer.ts", "../src/ssdMobilenetv1/boxPredictionLayer.ts", "../src/ssdMobilenetv1/predictionLayer.ts", "../src/ssdMobilenetv1/SsdMobilenetv1Options.ts", "../src/ssdMobilenetv1/SsdMobilenetv1.ts", "../src/ssdMobilenetv1/index.ts", "../src/tinyYolov2/const.ts", "../src/tinyYolov2/config.ts", "../src/tinyYolov2/leaky.ts", "../src/tinyYolov2/convWithBatchNorm.ts", "../src/tinyYolov2/depthwiseSeparableConv.ts", "../src/tinyYolov2/extractParams.ts", "../src/tinyYolov2/extractParamsFromWeightMap.ts", "../src/tinyYolov2/TinyYolov2Options.ts", "../src/tinyYolov2/TinyYolov2Base.ts", 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D0;(function(r){r.float32=\"float32\",r.int32=\"int32\",r.bool=\"bool\",r.complex64=\"complex64\"})(D0||(D0={}));var $0;(function(r){r.float32=\"float32\",r.int32=\"float32\",r.bool=\"float32\",r.complex64=\"complex64\"})($0||($0={}));var R0;(function(r){r.float32=\"complex64\",r.int32=\"complex64\",r.bool=\"complex64\",r.complex64=\"complex64\"})(R0||(R0={}));var CK={float32:$0,int32:A0,bool:D0,complex64:R0};function ur(r,t){if(r===\"string\"||t===\"string\"){if(r===\"string\"&&t===\"string\")return\"string\";throw new Error(`Can not upcast ${r} with ${t}`)}return CK[r][t]}function lc(r){return ur(r,\"int32\")}function ex(r){return r!=null&&typeof r==\"object\"&&\"texture\"in r&&r.texture instanceof WebGLTexture}function rx(r){return typeof GPUBuffer!=\"undefined\"&&r!=null&&typeof r==\"object\"&&\"buffer\"in r&&r.buffer instanceof GPUBuffer}function Xt(r,t){if(r.dtype===t.dtype)return[r,t];let e=ur(r.dtype,t.dtype);return[r.cast(e),t.cast(e)]}function F0(r,t){_(r.dtype===t.dtype,()=>`The dtypes of the first(${r.dtype}) and second(${t.dtype}) input must match`)}function vK(r,t){return t.some(e=>e.id===r.id)}function sh(r){let t=[];return Q_(r,t,new Set),t}function Q_(r,t,e){if(r==null)return;if(r instanceof Lt){t.push(r);return}if(!SK(r))return;let n=r;for(let o in n){let s=n[o];e.has(s)||(e.add(s),Q_(s,t,e))}}function SK(r){return Array.isArray(r)||typeof r==\"object\"}function O0(r){return r.kernelName!=null}var nx=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(t=>t.name)))}}}dispose(){for(let t in this.registeredVariables)this.registeredVariables[t].dispose()}},ih=class r{constructor(t){this.ENV=t,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new nx}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let t=this.getSortedBackends();for(let e=0;e{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(t){Xg(t).forEach(n=>{n.disposeFunc!=null&&n.disposeFunc(this.registry[t])})}initializeBackend(t){let e=this.registryFactory[t];if(e==null)throw new Error(`Cannot initialize backend ${t}, no registration found.`);try{let n=e.factory();if(n&&!(n instanceof Bo)&&typeof n.then==\"function\"){let o=++this.pendingBackendInitId,s=n.then(i=>o(othis.registryFactory[e].priority-this.registryFactory[t].priority)}initializeBackendsAndReturnBest(){let t=this.getSortedBackends();for(let e=0;ethis.startScope(n),()=>this.endScope(o),()=>(o=e(),o instanceof Promise&&console.error(\"Cannot return a Promise inside of tidy.\"),o))}scopedRun(t,e,n){t();try{let o=n();return e(),o}catch(o){throw e(),o}}nextTensorId(){return r.nextTensorId++}nextVariableId(){return r.nextVariableId++}clone(t){let e=T.runKernel(go,{x:t}),n={x:t},o=i=>({x:()=>{let a=\"float32\",u={x:i},l={dtype:a};return T.runKernel(fo,u,l)}}),s=[];return this.addTapeNode(this.state.activeScope.name,n,[e],o,s,{}),e}runKernel(t,e,n){if(this.backendName==null&&this.backend,!(Wp(t,this.backendName)!=null))throw new Error(`Kernel '${t}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:t,inputs:e,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool(\"IS_TEST\")}checkKernelForMemLeak(t,e,n){let o=this.backend.numDataIds(),s=0;n.forEach(u=>{s+=u.dtype===\"complex64\"?3:1});let i=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],a=o-e-s-i;if(a>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${a} data ids) after running '${t}'`)}runKernelFunc(t){let e,n=[],o=this.isTapeOn(),s=this.state.numBytes,i=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let a;this.backendName==null&&this.backend;let u,l=O0(t)?t.kernelName:this.state.activeScope!=null?this.state.activeScope.name:\"\";if(O0(t)){let{kernelName:d,inputs:h,attrs:g}=t;this.backendName==null&&this.backend;let x=Wp(d,this.backendName);_(x!=null,()=>`Cannot find registered kernel '${d}' for backend '${this.backendName}'`),a=()=>{let b=this.backend.numDataIds();u=x.kernelFunc({inputs:h,attrs:g,backend:this.backend});let w=Array.isArray(u)?u:[u];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(d,b,w);let I=w.map(N=>N.rank!=null?N:this.makeTensorFromTensorInfo(N));if(o){let N=this.getTensorsForGradient(d,h,I);n=this.saveTensorsForBackwardMode(N)}return I}}else{let{forwardFunc:d}=t,h=g=>{o&&(n=g.map(x=>this.keep(this.clone(x))))};a=()=>{let g=this.backend.numDataIds();u=this.tidy(()=>d(this.backend,h));let x=Array.isArray(u)?u:[u];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,g,x),x}}let{inputs:c,attrs:p}=t,m=O0(t)?null:t.backwardsFunc,f;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool(\"DEBUG\")&&!this.state.profiling?e=a():(f=this.profiler.profileKernel(l,c,()=>a()),this.ENV.getBool(\"DEBUG\")&&this.profiler.logKernelProfile(f),e=f.outputs)}),o&&this.addTapeNode(l,c,e,m,n,p),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-i,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(d=>c[d]!=null?c[d].shape:null),outputShapes:e.map(d=>d.shape),kernelTimeMs:f.timeMs,extraInfo:f.extraInfo}),Array.isArray(u)?e:e[0]}saveTensorsForBackwardMode(t){return t.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(t,e,n){let o=I0(t);if(o!=null){let s=o.inputsToSave||[],i=o.outputsToSave||[],a;o.saveAllInputs?(_(Array.isArray(e),()=>\"saveAllInputs is true, expected inputs to be an array.\"),a=Object.keys(e).map(l=>e[l])):a=s.map(l=>e[l]);let u=n.filter((l,c)=>i[c]);return a.concat(u)}return[]}makeTensor(t,e,n,o){if(t==null)throw new Error(\"Values passed to engine.makeTensor() are null\");n=n||\"float32\",o=o||this.backend;let s=t;n===\"string\"&&Vo(t[0])&&(s=t.map(u=>fu(u)));let i=o.write(s,e,n),a=new Lt(e,n,i,this.nextTensorId());if(this.trackTensor(a,o),n===\"string\"){let u=this.state.tensorInfo.get(i),l=x0(s);this.state.numBytes+=l-u.bytes,u.bytes=l}return a}makeTensorFromDataId(t,e,n,o){n=n||\"float32\";let s={dataId:t,shape:e,dtype:n};return this.makeTensorFromTensorInfo(s,o)}makeTensorFromTensorInfo(t,e){let{dataId:n,shape:o,dtype:s}=t,i=new Lt(o,s,n,this.nextTensorId());return this.trackTensor(i,e),i}makeVariable(t,e=!0,n,o){n=n||this.nextVariableId().toString(),o!=null&&o!==t.dtype&&(t=t.cast(o));let s=new ml(t,e,n,this.nextTensorId());if(this.state.registeredVariables[s.name]!=null)throw new Error(`Variable with name ${s.name} was already registered`);return this.state.registeredVariables[s.name]=s,this.incRef(s,this.backend),s}trackTensor(t,e){this.state.numTensors++,t.dtype===\"string\"&&this.state.numStringTensors++;let n=0;t.dtype!==\"complex64\"&&t.dtype!==\"string\"&&(n=t.size*Tp(t.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(t.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(t.dataId,{backend:e||this.backend,dtype:t.dtype,shape:t.shape,bytes:n})),t instanceof ml||this.track(t)}incRef(t,e){this.trackTensor(t,e),this.backend.incRef(t.dataId)}removeDataId(t,e){this.state.tensorInfo.has(t)&&this.state.tensorInfo.get(t).backend===e&&(this.state.tensorInfo.delete(t),this.state.numDataBuffers--)}disposeTensor(t){if(!this.state.tensorInfo.has(t.dataId))return;let e=this.state.tensorInfo.get(t.dataId);if(this.state.numTensors--,t.dtype===\"string\"&&(this.state.numStringTensors--,this.state.numBytes-=e.bytes),t.dtype!==\"complex64\"&&t.dtype!==\"string\"){let n=t.size*Tp(t.dtype);this.state.numBytes-=n}e.backend.disposeData(t.dataId)&&this.removeDataId(t.dataId,e.backend)}disposeVariables(){for(let t in this.state.registeredVariables){let e=this.state.registeredVariables[t];this.disposeVariable(e)}}disposeVariable(t){this.disposeTensor(t),this.state.registeredVariables[t.name]!=null&&delete this.state.registeredVariables[t.name]}memory(){let t=this.backend.memory();return t.numTensors=this.state.numTensors,t.numDataBuffers=this.state.numDataBuffers,t.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(t.unreliable=!0,t.reasons==null&&(t.reasons=[]),t.reasons.push(\"Memory usage by string tensors is approximate (2 bytes per character)\")),t}async profile(t){this.state.profiling=!0;let e=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await t(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(o=>o.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-e,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let o of this.state.activeProfile.kernels)o.kernelTimeMs=await o.kernelTimeMs,o.extraInfo=await o.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(t,e,n,o,s,i){let a={id:this.state.nextTapeNodeId++,kernelName:t,inputs:e,outputs:n,saved:s},u=I0(t);u!=null&&(o=u.gradFunc),o!=null&&(a.gradient=l=>(l=l.map((c,p)=>{if(c==null){let m=n[p],f=Ep(m.size,m.dtype);return this.makeTensor(f,m.shape,m.dtype)}return c}),o(l.length>1?l:l[0],s,i))),this.state.activeTape.push(a)}keep(t){return t.kept=!0,t}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(t){let e={track:[],name:\"unnamed scope\",id:this.state.nextScopeId++};t&&(e.name=t),this.state.scopeStack.push(e),this.state.activeScope=e}endScope(t){let e=sh(t),n=new Set(e.map(s=>s.id));for(let s=0;s{!s.kept&&s.scopeId===o.id&&this.track(s)})}gradients(t,e,n,o=!1){if(_(e.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 s=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy(\"forward\",t));_(s instanceof Lt,()=>\"The result y returned by f() must be a tensor.\");let i=H_(this.state.activeTape,e,s);if(!o&&i.length===0&&e.length>0)throw new Error(\"Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.\");return this.tidy(\"backward\",()=>{let a={};a[s.id]=n==null?NK(s.shape):n,q_(a,i,l=>this.tidy(l),kK);let u=e.map(l=>a[l.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(l=>{for(let c of l.saved)c.dispose()}),this.state.activeTape=null),{value:s,grads:u}})}customGrad(t){return _(Ti(t),()=>\"The f passed in customGrad(f) must be a function.\"),(...e)=>{_(e.every(a=>a instanceof Lt),()=>\"The args passed in customGrad(f)(x1, x2,...) must all be tensors\");let n,o={};e.forEach((a,u)=>{o[u]=a});let s=(a,u)=>(n=t(...e,u),_(n.value instanceof Lt,()=>\"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor\"),_(Ti(n.gradFunc),()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function.\"),n.value),i=(a,u)=>{let l=n.gradFunc(a,u),c=Array.isArray(l)?l:[l];_(c.length===e.length,()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...).\"),_(c.every(m=>m instanceof Lt),()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.\");let p={};return c.forEach((m,f)=>{p[f]=()=>m}),p};return this.runKernelFunc({forwardFunc:s,backwardsFunc:i,inputs:o})}}readSync(t){return this.state.tensorInfo.get(t).backend.readSync(t)}read(t){return this.state.tensorInfo.get(t).backend.read(t)}readToGPU(t,e){return this.state.tensorInfo.get(t).backend.readToGPU(t,e)}async time(t){let e=ac(),n=await this.backend.time(t);return n.wallMs=ac()-e,n}track(t){return this.state.activeScope!=null&&(t.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(t)),t}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new nx;for(let t in this.registry)this.disposeRegisteredKernels(t),this.registry[t].dispose(),delete this.registry[t];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}};ih.nextTensorId=0;ih.nextVariableId=0;function NK(r){let t=Xd(jt(r),\"float32\");return T.makeTensor(t,r,\"float32\")}function M0(){let r=w0();if(r._tfengine==null){let t=new Yd(r);r._tfengine=new ih(t)}return N_(r._tfengine.ENV),Y_(()=>r._tfengine),r._tfengine}var T=M0();function kK(r,t){let e={a:r,b:t};return T.runKernel(no,e)}var du={};Kt(du,{isBrowser:()=>L0,isMobile:()=>EK,mockIsMobile:()=>_K});function TK(){return typeof navigator!=\"undefined\"&&navigator!=null}var P0;function _K(r){P0=r}function EK(r){if(P0!==void 0)return P0;if(r||TK()){if(r||(r=navigator),r.product===\"ReactNative\")return!0;let t=r.userAgent||r.vendor||(typeof window!=\"undefined\"?window.opera:\"\");if(!t){let e=r;return 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e=C(r,\"x\",\"bitwiseAnd\"),n=C(t,\"y\",\"bitwiseAnd\");if(!on(e.shape,n.shape))throw new Error(`BitwiseAnd: Tensors must have the same shape. x: ${e.shape}, y: ${n.shape}`);if(e.dtype!==\"int32\"||n.dtype!==\"int32\")throw new Error(`BitwiseAnd: Only supports 'int32' values in tensor, found type of x: ${e.dtype} and type of y: ${n.dtype}`);let o={a:e,b:n};return T.runKernel($a,o)}var $E=k({bitwiseAnd_:Pj});function Lj(r,t){let e=C(r,\"s0\",\"broadcastArgs\",\"int32\"),n=C(t,\"s1\",\"broadcastArgs\",\"int32\");if(e.rank!==1)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). 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Has rank ${n.rank}`);let o={s0:e,s1:n};return T.runKernel(ql,o)}var RE=k({broadcastArgs_:Lj});function zj(r,t){let e=C(r,\"broadcastTo\",\"x\"),n=e.shape;if(Le(t),t.lengthe.rank){let l=e.shape.slice();for(;l.length=0;l--)if(o[l]===t[l])s[l]=1;else if(e.shape[l]!==1)throw new Error(`broadcastTo(): [${n}] cannot be broadcast to [${t}].`);if(s.map((l,c)=>l>1?c:-1).filter(l=>l>=0).length===0)return ln(e);let a={x:e},u={reps:s};return T.runKernel(oo,a,u)}var sa=k({broadcastTo_:zj});function Bj(r){let e={x:C(r,\"x\",\"ceil\",\"float32\")};return T.runKernel(Jo,e)}var Nx=k({ceil_:Bj});function Co(r,t,e){Le(r),e=e||Wl(t);let n={shape:r,value:t,dtype:e};return T.runKernel(Jl,{},n)}function Vj(r,t,e){let n=C(r,\"x\",\"clipByValue\");if(_(t<=e,()=>`Error in clip: min (${t}) must be less than or equal to max (${e}).`),t===e)return Co(n.shape,t,n.dtype);let o={x:n},s={clipValueMin:t,clipValueMax:e};return T.runKernel(ho,o,s)}var vr=k({clipByValue_:Vj});function Gj(r){return ie(r,0)}var kx=k({concat1d_:Gj});function Wj(r,t){return ie(r,t)}var Tx=k({concat2d_:Wj});function Uj(r,t){return ie(r,t)}var _x=k({concat3d_:Uj});function Hj(r,t){return ie(r,t)}var Ex=k({concat4d_:Hj});function qj(r,t,e,n,o=\"NHWC\",s=[1,1],i){let a=C(r,\"x\",\"conv2d\",\"float32\"),u=C(t,\"filter\",\"conv2d\",\"float32\"),l=a,c=!1;a.rank===3&&(c=!0,l=R(a,[1,a.shape[0],a.shape[1],a.shape[2]])),_(l.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${l.rank}.`),_(u.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${u.rank}.`),ve(\"conv2d\",n,i);let p=o===\"NHWC\"?l.shape[3]:l.shape[1];_(p===u.shape[2],()=>`Error in conv2d: depth of input (${p}) must match input depth for filter ${u.shape[2]}.`),_(Dr(e,s),()=>`Error in conv2D: Either strides or dilations must be 1. 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s={x:C(r,\"x\",\"cumsum\")},i={axis:t,exclusive:e,reverse:n};return T.runKernel(os,s,i)}var im=k({cumsum_:r6});function n6(r,t,e,n=!1){let o=C(r,\"x\",\"denseBincount\"),s=C(t,\"weights\",\"denseBincount\");_(o.dtype===\"int32\",()=>`Error in denseBincount: input dtype must be int32, but got ${o.dtype}`),_(o.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${o.rank}.`),_(e>=0,()=>`size must be non-negative, but got ${e}.`),_(s.size===o.size||s.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${o.shape}, weights shape: ${s.shape}.`);let i={x:o,weights:s},a={size:e,binaryOutput:n};return T.runKernel(jl,i,a)}var ph=k({denseBincount_:n6});function o6(r,t,e=\"NHWC\"){let n=C(r,\"x\",\"depthToSpace\",\"float32\"),o=e===\"NHWC\"?n.shape[1]:n.shape[2],s=e===\"NHWC\"?n.shape[2]:n.shape[3],i=e===\"NHWC\"?n.shape[3]:n.shape[1];_(t>1,()=>`blockSize should be > 1 for depthToSpace, but was: 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Cm=k({unsortedSegmentSum_:z5});function B5(r,t=0){let e=C(r,\"x\",\"unstack\",\"string_or_numeric\");_(t>=-e.shape.length&&t`Axis = ${t} is not in [-${e.shape.length}, ${e.shape.length})`);let n={value:e},o={axis:t};return T.runKernel(qi,n,o)}var gr=k({unstack_:B5});function RA(r,t){return fh(r,t,\"right\")}function my(r,t=!0,e,n){return T.makeVariable(r,t,e,n)}function fy(r,t){let e=[];for(let s=0;s0,()=>\"mask cannot be scalar\"),$e(a.slice(s,s+i),o.shape,\"mask's shape must match the first K dimensions of tensor's shape,\");let u=1;for(let h=s;ha).reverse()),_(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of perm ${t}.`),t.forEach(i=>{_(i>=0&&i`All entries in 'perm' must be between 0 and ${n.rank-1} but got ${t}`)}),n.rank<=1)return n.clone();let o={x:n},s={perm:t};return n.dtype===\"complex64\"?B(()=>{let i=bl(n),a=Iu(n);return i=T.runKernel(so,{x:i},s),a=T.runKernel(so,{x:a},s),e&&(a=Ut(a)),Sn(i,a)}):T.runKernel(so,o,s)}var Vt=k({transpose_:U5});function H5(r,t,e,n,o=!0){let s=C(r,\"v\",\"movingAverage\"),i=C(t,\"x\",\"movingAverage\"),a=C(e,\"decay\",\"movingAverage\");F0(s,i),_(on(s.shape,i.shape),()=>\"Shape mismatch in v and x\");let u=pt(1),l=at(u,a),c=$(at(i,s),l);if(o){_(n!=null,()=>\"When using zeroDebias: true, step is required.\");let p=C(n,\"step\",\"movingAverage\");c=ct(c,at(u,un(a,p)))}return X(s,c)}var q5=k({movingAverage_:H5});function K5(r,t,e){Le(e);let n=C(r,\"indices\",\"scatterND\",\"int32\"),o=C(t,\"updates\",\"scatterND\");wm(o,n,e);let s={indices:n,updates:o},i={shape:e};return T.runKernel(nl,s,i)}var j5=k({scatterND_:K5});function FA(r,t,e,n){if(r.dtype!==\"int32\")throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${r.dtype}.`);if(r.rank>2)throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${r.shape}.`);let o=r.rank>0?r.shape[0]:1,s=r.rank>1?r.shape[1]:1;if(e.length!==s)throw new Error(`outputShape has 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o.data(),[u,l]=[i.length/s,s],c=d0(\"bool\",u);for(let p=0;pg.value-h.value),c[p]=0;for(let h=0;hMA,depthwiseConv2d:()=>PA,matMul:()=>LA});function n8(r,t,e,n,o,s=\"NHWC\",i){let a=r;r.rank===3&&(a=R(r,[1,r.shape[0],r.shape[1],r.shape[2]]));let u=t;u.rank===3&&(u=R(t,[1,t.shape[0],t.shape[1],t.shape[2]])),_(a.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${a.shape}.`),_(u.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${u.shape}.`),_(e.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${e}.`);let l=s===\"NHWC\"?a.shape[3]:a.shape[1],c=s===\"NHWC\"?u.shape[3]:u.shape[1];_(l===e[2],()=>`Error in conv2dDerFilter: depth of input ${l}) must match input depth in filter (${e[2]}.`),_(c===e[3],()=>`Error in conv2dDerFilter: depth of dy (${c}) must match output depth for filter (${e[3]}).`),ve(\"conv2dDerFilter\",o,i);let p={x:a,dy:u},m={strides:n,pad:o,dataFormat:s,dimRoundingMode:i,filterShape:e};return T.runKernel(Dp,p,m)}var vm=k({conv2DBackpropFilter_:n8});function bc(r,t,e){if(e==null||e===\"linear\")return r;if(e===\"relu\")return $(r,So(t));throw new Error(`Cannot compute gradient for fused activation ${e}.`)}function wc(r,t){let e=t,n=be(r.shape,t.shape);return n.length>0&&(e=mt(e,n)),R(e,r.shape)}function Ic(r,t,e,n){if(t===\"linear\")return r;if(t===\"relu\")return Or(r);if(t===\"elu\")return aa(r);if(t===\"relu6\")return um(r);if(t===\"prelu\")return _u(r,e);if(t===\"leakyrelu\")return Cu(r,n);if(t===\"sigmoid\")return Qr(r);throw new Error(`Unknown fused activation ${t}.`)}var Cc=(r,t)=>!(r>0)||t===\"linear\";function o8({x:r,filter:t,strides:e,pad:n,dataFormat:o=\"NHWC\",dilations:s=[1,1],dimRoundingMode:i,bias:a,activation:u=\"linear\",preluActivationWeights:l,leakyreluAlpha:c}){if(u=u||\"linear\",Cc(T.state.gradientDepth,u)===!1){_(o===\"NHWC\",()=>`Error in fused conv2d: got dataFormat of ${o} but only NHWC is currently supported for the case of gradient depth is 0 and the activation is not linear.`);let E=Nn(r,t,e,n,o,s,i);return a!=null&&(E=X(E,a)),Ic(E,u,l,c)}let p=C(r,\"x\",\"conv2d\",\"float32\"),m=C(t,\"filter\",\"conv2d\",\"float32\"),f=p,d=!1;p.rank===3&&(d=!0,f=R(p,[1,p.shape[0],p.shape[1],p.shape[2]])),_(f.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${f.rank}.`),_(m.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${m.rank}.`),ve(\"fused conv2d\",n,i);let h=o===\"NHWC\"?f.shape[3]:f.shape[1];_(m.shape[2]===h,()=>`Error in conv2d: depth of input (${h}) must match input depth for filter ${m.shape[2]}.`),_(Dr(e,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${e} and dilations '${s}'`);let g=pc(f.shape,m.shape,e,s,n,i),x;a!=null&&(x=C(a,\"bias\",\"fused conv2d\"),[x]=Xt(x,p),o===\"NHWC\"?Mt(g.outShape,x.shape):(_(x.shape.length<=1,()=>`Error in fused conv2d: only supports scalar or 1-D Tensor bias for NCHW format but got the bias of rank-${x.shape.length}.`),_(x.shape.length===0||x.shape[0]===g.outChannels||x.shape[0]===1,()=>`Error in fused conv2d: bias shape (${x.shape}) is not compatible with the number of output channels (${g.outChannels})`)));let b;if(l!=null){let E=l.shape;if(_(E.length<=1||E.length===3,()=>`Error in fused conv2d: only supports scalar, 1-D Tensor or 3-D Tensor PReLU activation weights but got a tensor of rank-${E.length}.`),E.length===1)_(E[0]===1||E[0]===g.outChannels,()=>`Error in fused conv2d: PReLU activation weights (${E}) is not compatible with the number of output channels (${g.outChannels}).`);else if(E.length===3)try{Mt(E,g.outShape)}catch(A){let D=`Error in fused conv2d: PReLU activation weights (${E}) is not compatible with the output shape of the conv2d (${g.outShape}).`;throw Error(D)}b=C(l,\"prelu weights\",\"fused conv2d\")}let w=(E,A)=>{_(o===\"NHWC\",()=>`Error in gradient of fused conv2D: got dataFormat of ${o} but only NHWC is currently supported.`);let[D,F,M,V]=A,G=bc(E,M,u);_(io(s),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`);let W=nm(F.shape,G,D,e,n),q=vm(F,G,D.shape,e,n),H=[W,q];if(V!=null){let K=wc(V,G);H.push(K)}return H},I={x:f,filter:m,bias:x,preluActivationWeights:b},N={strides:e,pad:n,dataFormat:o,dilations:s,dimRoundingMode:i,activation:u,leakyreluAlpha:c};return a==null?pn((A,D,F)=>{let M=T.runKernel(Xi,I,N);return F([D,A,M]),d&&(M=R(M,[M.shape[1],M.shape[2],M.shape[3]])),{value:M,gradFunc:w}})(f,m):pn((A,D,F,M)=>{let V=T.runKernel(Xi,I,N);return M([D,A,V,F]),d&&(V=R(V,[V.shape[1],V.shape[2],V.shape[3]])),{value:V,gradFunc:w}})(f,m,x)}var MA=k({fusedConv2d_:o8});function s8(r,t,e,n,o,s=[1,1],i){let a=r;r.rank===3&&(a=R(r,[1,r.shape[0],r.shape[1],r.shape[2]]));let u=t;u.rank===3&&(u=R(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let l={x:a,dy:u},c={strides:n,pad:o,dimRoundingMode:i,dilations:s,filterShape:e};return T.runKernel($p,l,c)}var hy=k({depthwiseConv2dNativeBackpropFilter_:s8});function i8(r,t,e,n,o,s=[1,1],i){let a=t,u=!1;t.rank===3&&(u=!0,a=R(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let l={dy:a,filter:e},c={strides:n,pad:o,dimRoundingMode:i,dilations:s,inputShape:r},p=T.runKernel(Rp,l,c);return u?R(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var gy=k({depthwiseConv2dNativeBackpropInput_:i8});function a8({x:r,filter:t,strides:e,pad:n,dataFormat:o=\"NHWC\",dilations:s=[1,1],dimRoundingMode:i,bias:a,activation:u=\"linear\",preluActivationWeights:l,leakyreluAlpha:c}){if(Cc(T.state.gradientDepth,u)===!1){let N=ia(r,t,e,n,o,s,i);return a!=null&&(N=X(N,a)),Ic(N,u,l,c)}let p=C(r,\"x\",\"depthwiseConv2d\",\"float32\"),m=C(t,\"filter\",\"depthwiseConv2d\",\"float32\"),f=p,d=!1;p.rank===3&&(d=!0,f=R(p,[1,p.shape[0],p.shape[1],p.shape[2]])),_(f.rank===4,()=>`Error in fused depthwiseConv2d: input must be rank 4, but got rank ${f.rank}.`),_(m.rank===4,()=>`Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${m.rank}.`),_(f.shape[3]===m.shape[2],()=>`Error in fused depthwiseConv2d: number of input channels (${f.shape[3]}) must match the inChannels dimension in filter ${m.shape[2]}.`),s==null&&(s=[1,1]),_(Dr(e,s),()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${e} and dilations '${s}'`),ve(\"fused depthwiseConv2d\",n,i);let h=pc(f.shape,m.shape,e,s,n,i,!0),g;a!=null&&(g=C(a,\"bias\",\"fused conv2d\"),[g]=Xt(g,p),Mt(h.outShape,g.shape));let x;l!=null&&(x=C(l,\"prelu weights\",\"fused depthwiseConv2d\"));let b=(N,E)=>{_(io(s),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${s}'`);let[A,D,F,M]=E,V=bc(N,F,u),G=gy(D.shape,V,A,e,n,s,i),W=hy(D,V,A.shape,e,n,s,i);if(M!=null){let q=wc(g,V);return[G,W,q]}return[G,W]},w={x:f,filter:m,bias:g,preluActivationWeights:x},I={strides:e,pad:n,dataFormat:o,dilations:s,dimRoundingMode:i,activation:u,leakyreluAlpha:c};return a==null?pn((E,A,D)=>{let F=T.runKernel(Yi,w,I);return D([A,E,F]),d&&(F=R(F,[F.shape[1],F.shape[2],F.shape[3]])),{value:F,gradFunc:b}})(f,m):pn((E,A,D,F)=>{let M=T.runKernel(Yi,w,I);return F([A,E,M,D]),d&&(M=R(M,[M.shape[1],M.shape[2],M.shape[3]])),{value:M,gradFunc:b}})(f,m,g)}var PA=k({fusedDepthwiseConv2d_:a8});function l8({a:r,b:t,transposeA:e=!1,transposeB:n=!1,bias:o,activation:s=\"linear\",preluActivationWeights:i,leakyreluAlpha:a=.2}){if(Cc(T.state.gradientDepth,s)===!1){let V=Bt(r,t,e,n);return o!=null&&(V=X(V,o)),Ic(V,s,i,a)}let u=C(r,\"a\",\"fused matMul\"),l=C(t,\"b\",\"fused matMul\");[u,l]=Xt(u,l);let c=e?u.shape[u.rank-2]:u.shape[u.rank-1],p=n?l.shape[l.rank-1]:l.shape[l.rank-2],m=e?u.shape[u.rank-1]:u.shape[u.rank-2],f=n?l.shape[l.rank-2]:l.shape[l.rank-1],d=u.shape.slice(0,-2),h=l.shape.slice(0,-2),g=jt(d),x=jt(h);_(c===p,()=>`Error in fused matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${u.shape} and ${l.shape} and transposeA=${e} and transposeB=${n} must match.`);let w=Mt(u.shape.slice(0,-2),l.shape.slice(0,-2)).concat([m,f]),I=e?R(u,[g,c,m]):R(u,[g,m,c]),N=n?R(l,[x,f,p]):R(l,[x,p,f]),E;o!=null&&(E=C(o,\"bias\",\"fused matMul\"),[E]=Xt(E,u),Mt(w,E.shape));let A;i!=null&&(A=C(i,\"prelu weights\",\"fused matMul\"));let D=(V,G)=>{let[W,q,H,K]=G,Y=bc(R(V,H.shape),H,s),Z,et;if(!e&&!n?(Z=Bt(Y,q,!1,!0),et=Bt(W,Y,!0,!1)):!e&&n?(Z=Bt(Y,q,!1,!1),et=Bt(Y,W,!0,!1)):e&&!n?(Z=Bt(q,Y,!1,!0),et=Bt(W,Y,!1,!1)):(Z=Bt(q,Y,!0,!0),et=Bt(Y,W,!0,!0)),o!=null){let nt=wc(K,Y);return[Z,et,nt]}else return[Z,et]},F={a:I,b:N,bias:E,preluActivationWeights:A},M={transposeA:e,transposeB:n,activation:s,leakyreluAlpha:a};return o==null?pn((G,W,q)=>{let H=T.runKernel(ji,F,M);return q([G,W,H]),{value:R(H,w),gradFunc:D}})(I,N):pn((G,W,q,H)=>{let K=T.runKernel(ji,F,M);return H([G,W,K,q]),{value:R(K,w),gradFunc:D}})(I,N,E)}var LA=k({fusedMatMul_:l8});function u8(r){return gh(r,.54,.46)}var zA=k({hammingWindow_:u8});function c8(r){return gh(r,.5,.5)}var xy=k({hannWindow_:c8});function p8(r,t,e,n=!1,o=0){let s=0,i=[];for(;s+t<=r.size;)i.push(Ot(r,s,t)),s+=e;if(n)for(;s`Error in cropAndResize: image must be rank 4,but got rank ${i.rank}.`),_(a.rank===2&&a.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${l},4] but had shape ${a.shape}.`),_(u.rank===1&&u.shape[0]===l,()=>`Error in cropAndResize: boxInd must be have size [${l}] but had shape ${a.shape}.`),_(n.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${n.length}.`),_(n[0]>=1&&n[1]>=1,()=>`cropSize must be atleast [1,1], but was ${n}`),_(o===\"bilinear\"||o===\"nearest\",()=>`method must be bilinear or nearest, but was ${o}`);let c={image:i,boxes:a,boxInd:u},p={method:o,extrapolationValue:s,cropSize:n};return T.runKernel(Ma,c,p)}var VA=k({cropAndResize_:f8});function d8(r){let t=C(r,\"image\",\"flipLeftRight\",\"float32\");_(t.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${t.rank}.`);let e={image:t};return T.runKernel(Ba,e,{})}var GA=k({flipLeftRight_:d8});function h8(r){let t=C(r,\"image\",\"grayscaleToRGB\"),e=t.rank-1,n=t.shape[e];_(t.rank>=2,()=>`Error in grayscaleToRGB: images must be at least rank 2, but got rank ${t.rank}.`),_(n===1,()=>`Error in grayscaleToRGB: last dimension of a grayscale image should be size 1, but got size ${n}.`);let o=new Array(t.rank);return o.fill(1,0,e),o[e]=3,Rr(t,o)}var WA=k({grayscaleToRGB_:h8});function g8(r){let t=C(r,\"image\",\"RGBToGrayscale\"),e=t.rank-1,n=t.shape[e];_(t.rank>=2,()=>`Error in RGBToGrayscale: images must be at least rank 2, but got rank ${t.rank}.`),_(n===3,()=>`Error in RGBToGrayscale: last dimension of an RGB image should be size 3, but got size ${n}.`);let o=t.dtype,s=J(t,\"float32\"),i=Oe([.2989,.587,.114]),a;switch(t.rank){case 2:a=wu(\"ij,j->i\",s,i);break;case 3:a=wu(\"ijk,k->ij\",s,i);break;case 4:a=wu(\"ijkl,l->ijk\",s,i);break;case 5:a=wu(\"ijklm,m->ijkl\",s,i);break;case 6:a=wu(\"ijklmn,n->ijklm\",s,i);break;default:throw new Error(\"Not a valid tensor rank.\")}return a=je(a,-1),J(a,o)}var UA=k({rgbToGrayscale_:g8});function x8(r,t,e=0,n=.5){let o=C(r,\"image\",\"rotateWithOffset\",\"float32\");_(o.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${o.rank}.`);let s={image:o},i={radians:t,fillValue:e,center:n};return T.runKernel(pl,s,i)}var HA=k({rotateWithOffset_:x8});function No(r,t,e,n,o,s){n==null&&(n=.5),o==null&&(o=Number.NEGATIVE_INFINITY),s==null&&(s=0);let i=r.shape[0];return e=Math.min(e,i),_(0<=n&&n<=1,()=>`iouThreshold must be in [0, 1], but was '${n}'`),_(r.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${r.rank}'`),_(r.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${r.shape[1]}`),_(t.rank===1,()=>\"scores must be a 1D tensor\"),_(t.shape[0]===i,()=>`scores has incompatible shape with boxes. 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Je;(function(r){r[r.NONE=0]=\"NONE\",r[r.MEAN=1]=\"MEAN\",r[r.SUM=2]=\"SUM\",r[r.SUM_BY_NONZERO_WEIGHTS=3]=\"SUM_BY_NONZERO_WEIGHTS\"})(Je||(Je={}));function P8(r,t,e=Je.SUM_BY_NONZERO_WEIGHTS){let n=C(r,\"losses\",\"computeWeightedLoss\"),o=null;t!=null&&(o=C(t,\"weights\",\"computeWeightedLoss\"));let s=o==null?n:$(n,o);if(e===Je.NONE)return s;if(e===Je.SUM)return mt(s);if(e===Je.MEAN){if(o==null)return Ne(s);{let i=n.size/o.size,a=ct(mt(s),mt(o));return i>1?ct(a,pt(i)):a}}if(e===Je.SUM_BY_NONZERO_WEIGHTS){if(o==null)return ct(mt(s),pt(n.size));{let i=$(o,ar(n.shape)),a=J(mt(ui(i,pt(0))),\"float32\");return ct(mt(s),a)}}throw Error(`Unknown reduction: ${e}`)}var Hr=k({computeWeightedLoss_:P8});function L8(r,t,e,n=Je.SUM_BY_NONZERO_WEIGHTS){let o=C(r,\"labels\",\"absoluteDifference\"),s=C(t,\"predictions\",\"absoluteDifference\"),i=null;e!=null&&(i=C(e,\"weights\",\"absoluteDifference\")),$e(o.shape,s.shape,\"Error in absoluteDifference: \");let a=_e(at(o,s));return Hr(a,i,n)}var i2=k({absoluteDifference_:L8});function z8(r,t,e,n,o=Je.SUM_BY_NONZERO_WEIGHTS){let s=C(r,\"labels\",\"cosineDistance\"),i=C(t,\"predictions\",\"cosineDistance\"),a=null;n!=null&&(a=C(n,\"weights\",\"cosineDistance\")),$e(s.shape,i.shape,\"Error in cosineDistance: \");let u=pt(1),l=at(u,mt($(s,i),e,!0));return Hr(l,a,o)}var a2=k({cosineDistance_:z8});function B8(r,t,e,n=Je.SUM_BY_NONZERO_WEIGHTS){let o=C(r,\"labels\",\"hingeLoss\"),s=C(t,\"predictions\",\"hingeLoss\"),i=null;e!=null&&(i=C(e,\"weights\",\"hingeLoss\")),$e(o.shape,s.shape,\"Error in hingeLoss: \");let a=pt(1);o=at($(pt(2),o),a);let u=Or(at(a,$(o,s)));return Hr(u,i,n)}var l2=k({hingeLoss_:B8});function V8(r,t,e,n=1,o=Je.SUM_BY_NONZERO_WEIGHTS){let s=C(r,\"labels\",\"huberLoss\"),i=C(t,\"predictions\",\"huberLoss\"),a=null;e!=null&&(a=C(e,\"weights\",\"huberLoss\")),$e(s.shape,i.shape,\"Error in huberLoss: \");let u=pt(n),l=_e(at(i,s)),c=lo(l,u),p=at(l,c),m=X($(pt(.5),Wt(c)),$(u,p));return Hr(m,a,o)}var u2=k({huberLoss_:V8});function G8(r,t,e,n=1e-7,o=Je.SUM_BY_NONZERO_WEIGHTS){let s=C(r,\"labels\",\"logLoss\"),i=C(t,\"predictions\",\"logLoss\"),a=null;e!=null&&(a=C(e,\"weights\",\"logLoss\")),$e(s.shape,i.shape,\"Error in logLoss: \");let u=pt(1),l=pt(n),c=Ut($(s,Nr(X(i,l)))),p=$(at(u,s),Nr(X(at(u,i),l))),m=at(c,p);return Hr(m,a,o)}var c2=k({logLoss_:G8});function W8(r,t,e,n=Je.SUM_BY_NONZERO_WEIGHTS){let o=C(r,\"labels\",\"meanSquaredError\"),s=C(t,\"predictions\",\"meanSquaredError\"),i=null;e!=null&&(i=C(e,\"weights\",\"meanSquaredError\")),$e(o.shape,s.shape,\"Error in meanSquaredError: \");let a=bm(o,s);return Hr(a,i,n)}var p2=k({meanSquaredError_:W8});function U8(r,t){let e=C(r,\"labels\",\"sigmoidCrossEntropyWithLogits\"),n=C(t,\"logits\",\"sigmoidCrossEntropyWithLogits\");$e(e.shape,n.shape,\"Error in sigmoidCrossEntropyWithLogits: \");let o=Or(n),s=$(n,e),i=vu(Ke(Ut(_e(n))));return X(at(o,s),i)}function H8(r,t,e,n=0,o=Je.SUM_BY_NONZERO_WEIGHTS){let s=C(r,\"multiClassLabels\",\"sigmoidCrossEntropy\"),i=C(t,\"logits\",\"sigmoidCrossEntropy\"),a=null;if(e!=null&&(a=C(e,\"weights\",\"sigmoidCrossEntropy\")),$e(s.shape,i.shape,\"Error in sigmoidCrossEntropy: \"),n>0){let l=pt(n),c=pt(1),p=pt(.5);s=X($(s,at(c,l)),$(p,l))}let u=U8(s,i);return Hr(u,a,o)}var m2=k({sigmoidCrossEntropy_:H8});function q8(r,t,e=-1){if(e===-1&&(e=t.rank-1),e!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. 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i={inputIndices:n,inputShape:o,newShape:s},a=T.runKernel(il,i);return{outputIndices:a[0],outputShape:a[1]}}var h2=k({sparseReshape_:X8});function Y8(r,t,e){let n=C(r,\"data\",\"sparseSegmentMean\"),o=C(t,\"indices\",\"sparseSegmentMean\",\"int32\"),s=C(e,\"segmentIds\",\"sparseSegmentMean\",\"int32\");if(n.rank<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(o.rank!==1)throw new Error(`Indices should be Tensor1D but received shape\n ${o.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape\n ${s.shape}`);let i={data:n,indices:o,segmentIds:s};return T.runKernel(ou,i)}var g2=k({sparseSegmentMean_:Y8});function Z8(r,t,e){let n=C(r,\"data\",\"sparseSegmentSum\"),o=C(t,\"indices\",\"sparseSegmentSum\",\"int32\"),s=C(e,\"segmentIds\",\"sparseSegmentSum\",\"int32\");if(n.rank<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(o.rank!==1)throw new Error(`Indices should be Tensor1D but 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className(){return\"Adagrad\"}constructor(t,e=.1){super(),this.learningRate=t,this.initialAccumulatorValue=e,this.accumulatedGrads=[]}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=T.registeredVariables[n];this.accumulatedGrads[o]==null&&(this.accumulatedGrads[o]={originalName:`${n}/accumulator`,variable:B(()=>Co(s.shape,this.initialAccumulatorValue).variable(!1))});let i=Array.isArray(t)?t[o].tensor:t[n];if(i==null)return;let a=this.accumulatedGrads[o].variable;B(()=>{let u=X(a,Wt(i));a.assign(u);let l=X($(ct(i,Se(X(u,T.backend.epsilon()))),-this.learningRate),s);s.assign(l)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Tt(this.accumulatedGrads.map(t=>t.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=!1;this.accumulatedGrads=t.map(n=>({originalName:n.name,variable:n.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(t,e){return new t(e.learningRate,e.initialAccumulatorValue)}};var Nc=class extends qr{static get className(){return\"Adam\"}constructor(t,e,n,o=null){super(),this.learningRate=t,this.beta1=e,this.beta2=n,this.epsilon=o,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],B(()=>{this.accBeta1=pt(e).variable(),this.accBeta2=pt(n).variable()}),o==null&&(this.epsilon=T.backend.epsilon())}applyGradients(t){let e=Array.isArray(t)?t.map(n=>n.name):Object.keys(t);B(()=>{let n=at(1,this.accBeta1),o=at(1,this.accBeta2);e.forEach((s,i)=>{let 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Error(\"getWeights() is not implemented for Adamax yet.\")}async setWeights(t){throw new Error(\"setWeights() is not implemented for Adamax yet.\")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon,e.decay)}};var Il=class extends qr{static get className(){return\"SGD\"}constructor(t){super(),this.learningRate=t,this.setLearningRate(t)}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=Array.isArray(t)?t[o].tensor:t[n];if(s==null)return;let i=T.registeredVariables[n];B(()=>{let a=X($(this.c,s),i);i.assign(a)})}),this.incrementIterations()}setLearningRate(t){this.learningRate=t,this.c!=null&&this.c.dispose(),this.c=De(pt(-t))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(t){if(t=await this.extractIterations(t),t.length!==0)throw new Error(\"SGD 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this.saveIterations()].concat(this.accumulations.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=!1;this.accumulations=t.map(n=>({originalName:n.name,variable:n.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(t,e){return new t(e.learningRate,e.momentum,e.useNesterov)}};var _c=class extends qr{static get className(){return\"RMSProp\"}constructor(t,e=.9,n=0,o=null,s=!1){if(super(),this.learningRate=t,this.decay=e,this.momentum=n,this.epsilon=o,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,o==null&&(this.epsilon=T.backend.epsilon()),t==null)throw new Error(\"learningRate for RMSPropOptimizer must be defined.\")}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=T.registeredVariables[n],i=!1;this.accumulatedMeanSquares[o]==null&&(this.accumulatedMeanSquares[o]={originalName:`${n}/rms`,variable:B(()=>vt(s).variable(i))}),this.accumulatedMoments[o]==null&&(this.accumulatedMoments[o]={originalName:`${n}/momentum`,variable:B(()=>vt(s).variable(i))}),this.accumulatedMeanGrads[o]==null&&this.centered&&(this.accumulatedMeanGrads[o]={originalName:`${n}/mg`,variable:B(()=>vt(s).variable(i))});let a=Array.isArray(t)?t[o].tensor:t[n];if(a==null)return;let u=this.accumulatedMeanSquares[o].variable,l=this.accumulatedMoments[o].variable;B(()=>{let c=X($(u,this.decay),$(Wt(a),1-this.decay));if(this.centered){let p=this.accumulatedMeanGrads[o].variable,m=X($(p,this.decay),$(a,1-this.decay)),f=ct($(a,this.learningRate),Se(at(c,X(Wt(m),this.epsilon)))),d=X($(l,this.momentum),f);u.assign(c),p.assign(m),l.assign(d);let h=at(s,d);s.assign(h)}else{let p=X($(u,this.decay),$(Wt(a),1-this.decay)),m=X($(l,this.momentum),ct($(a,this.learningRate),Se(X(p,this.epsilon))));u.assign(p),l.assign(m);let f=at(s,m);s.assign(f)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Tt(this.accumulatedMeanSquares.map(t=>t.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Tt(this.accumulatedMeanGrads.map(t=>t.variable)),this.accumulatedMoments!=null&&Tt(this.accumulatedMoments.map(t=>t.variable))}async getWeights(){let t=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&t.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=this.centered?t.length/3:t.length/2,n=!1;this.accumulatedMeanSquares=t.slice(0,e).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.accumulatedMoments=t.slice(e,e*2).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=t.slice(e*2,e*3).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(t,e){return new t(e.learningRate,e.decay,e.momentum,e.epsilon,e.centered)}};var uY=[vc,Sc,Nc,kc,Tc,_c,Il];function C2(){for(let r of uY)xN(r)}var 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used.\");return this.model.fitDataset(t,e)}async trainOnBatch(t,e){return this.model.trainOnBatch(t,e)}static fromConfig(t,e,n={},o=!1){let s,i={};if(e instanceof Array){if(e[0].className==null||e[0].className===\"Merge\")throw new z(\"Legacy serialization format not supported yet.\");s=e}else y.assert(e.layers!=null,()=>\"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field.\"),s=e.layers,delete e.layers,i=e;let a=new t(i);if(!(a instanceof r))throw new _t(`Sequential.fromConfig called on non-Sequential input: ${a}`);for(let u of s){let c=wn(u,void 0,o);o&&c.setFastWeightInitDuringBuild(!0),a.add(c)}return a}set stopTraining(t){if(this.model==null)throw new z(\"Cannot set the stopTraining property of a sequential model before it is compiled.\");this.model.stopTraining=t}get stopTraining(){if(this.model==null)throw new z(\"Cannot get the stopTraining property of a sequential model before it is compiled.\");return this.model.stopTraining}getConfig(){let t=[];for(let e of this.layers){let n={};n.className=e.getClassName(),n.config=e.getConfig(),t.push(n)}return{name:this.name,layers:t}}};Wc.className=\"Sequential\";Q.registerClass(Wc);function fJ(r){return new Un(r)}function dJ(r){return new Wc(r)}function YN(r){return Hy(r)}function hJ(r,t){Wm.registerCallbackConstructor(r,t)}var rn=class extends Q.Serializable{getConfig(){return{}}},fb=class extends rn{apply(t,e=1){return aR(t,e)}};fb.className=\"elu\";Q.registerClass(fb);var db=class extends rn{apply(t){return mm(t)}};db.className=\"selu\";Q.registerClass(db);var hb=class extends rn{apply(t){return Or(t)}};hb.className=\"relu\";Q.registerClass(hb);var gb=class extends rn{apply(t){return B(()=>lo(6,Or(t)))}};gb.className=\"relu6\";Q.registerClass(gb);var xb=class extends rn{apply(t){return t}};xb.className=\"linear\";Q.registerClass(xb);var yb=class extends rn{apply(t){return Qr(t)}};yb.className=\"sigmoid\";Q.registerClass(yb);var bb=class extends rn{apply(t){return uR(t)}};bb.className=\"hardSigmoid\";Q.registerClass(bb);var wb=class extends rn{apply(t){return li(t)}};wb.className=\"softplus\";Q.registerClass(wb);var Ib=class extends rn{apply(t){return lR(t)}};Ib.className=\"softsign\";Q.registerClass(Ib);var Cb=class extends rn{apply(t){return na(t)}};Cb.className=\"tanh\";Q.registerClass(Cb);var Xm=class extends rn{apply(t,e=-1){return Eu(t,e)}};Xm.className=\"softmax\";Q.registerClass(Xm);var vb=class extends rn{apply(t,e=-1){return am(t,e)}};vb.className=\"logSoftmax\";Q.registerClass(vb);var Sb=class extends rn{apply(t,e=1){return B(()=>$(Qr($(t,e)),t))}};Sb.className=\"swish\";Q.registerClass(Sb);var Nb=class extends rn{apply(t){return B(()=>$(t,na(li(t))))}};Nb.className=\"mish\";Q.registerClass(Nb);function hi(r){return r.getClassName()}function ZN(r,t={}){return fa(r,Q.SerializationMap.getMap().classNameMap,t,\"activation\")}function gi(r){if(r==null){let t={};return t.className=\"linear\",t.config={},ZN(t)}if(typeof r==\"string\"){let t={};return t.className=r,t.config={},ZN(t)}else return r instanceof rn?r:ZN(r)}function JN(r){if(r!=null&&typeof r!=\"object\")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${r}`)}var kb=class extends Q.Serializable{},Lu=class extends kb{constructor(t){super(),JN(t),this.l1=t==null||t.l1==null?.01:t.l1,this.l2=t==null||t.l2==null?.01:t.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(t){return B(()=>{let e=ke([1]);return this.hasL1&&(e=X(e,mt($(this.l1,_e(t))))),this.hasL2&&(e=X(e,mt($(this.l2,$c(t))))),R(e,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(t,e){return new t({l1:e.l1,l2:e.l2})}};Lu.className=\"L1L2\";Q.registerClass(Lu);function UR(r){return JN(r),new Lu({l1:r!=null?r.l1:null,l2:0})}function HR(r){return JN(r),new Lu({l2:r!=null?r.l2:null,l1:0})}var GR={l1l2:\"L1L2\"};function fe(r){return Nm(r)}function WR(r,t={}){return fa(r,Q.SerializationMap.getMap().classNameMap,t,\"regularizer\")}function Ce(r){if(r==null)return null;if(typeof r==\"string\"){let e={className:r in GR?GR[r]:r,config:{}};return WR(e)}else return r instanceof kb?r:WR(r)}var Ym=class extends Et{constructor(t){super(t==null?{}:t),this.supportsMasking=!0,t!=null&&(this.maxValue=t.maxValue)}call(t,e){t=St(t);let n=Or(t);return this.maxValue!=null&&(n=vr(n,0,this.maxValue)),n}computeOutputShape(t){return t}getConfig(){let t={maxValue:this.maxValue},e=super.getConfig();return Object.assign(t,e),t}};Ym.className=\"ReLU\";Q.registerClass(Ym);var Zm=class extends Et{constructor(t){super(t==null?{}:t),this.DEFAULT_ALPHA=.3,t==null&&(t={}),this.alpha=t.alpha==null?this.DEFAULT_ALPHA:t.alpha}call(t,e){let n=St(t);return Cu(n,this.alpha)}computeOutputShape(t){return t}getConfig(){let t={alpha:this.alpha},e=super.getConfig();return Object.assign(t,e),t}};Zm.className=\"LeakyReLU\";Q.registerClass(Zm);var Jm=class extends Et{constructor(t){if(super(t==null?{}:t),this.DEFAULT_ALPHA_INITIALIZER=\"zeros\",t==null&&(t={}),this.supportsMasking=!0,this.alphaInitializer=ge(t.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Ce(t.alphaRegularizer),this.alphaConstraint=Ge(t.alphaConstraint),t.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(t.sharedAxes))this.sharedAxes=t.sharedAxes;else if(typeof t.sharedAxes==\"number\")this.sharedAxes=[t.sharedAxes];else throw new z(`Expected sharedAxes to be a number or an array of numbers, but got ${t.sharedAxes}`)}build(t){t=Gt(t);let e=t.slice(1);if(this.sharedAxes!=null)for(let o of this.sharedAxes)e[o-1]=1;this.alpha=this.addWeight(\"alpha\",e,\"float32\",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let o=1;o{let n=St(t),o=e.mask;if(o!=null){let s=$(at(ar(n.shape),J(o,n.dtype)),pt(-1e9));n=X(n,s)}return this.axis instanceof Array?this.axis.length>1?Ke(at(n,Su(n,this.axis,!0))):this.softmax(n,this.axis[0]):this.softmax(n,this.axis)})}computeOutputShape(t){return t}getConfig(){let t={axis:this.axis},e=super.getConfig();return Object.assign(t,e),t}};ef.className=\"Softmax\";Q.registerClass(ef);function zu(r,t,e){if(typeof r==\"number\")return To(r,t);if(r.length!==t)throw new z(`The ${e} argument must be an integer or tuple of ${t} integers. Received: ${r.length} elements.`);for(let n=0;n(Me(t),t===\"channelsFirst\"?Vt(r,[0,2,3,1]):r))}function QN(r,t){return B(()=>(Me(t),t===\"channelsFirst\"?Vt(r,[0,2,3,4,1]):r))}function xJ(r,t,e,n=1,o=\"valid\",s,i=1){return B(()=>{if(s==null&&(s=xn()),Me(s),r.shape.length!==3)throw new z(`The input of a conv1dWithBias operation should be 3, but is ${r.shape.length} instead.`);if(t.shape.length!==3)throw new z(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(e!=null&&e.shape.length!==1)throw new z(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s===\"channelsFirst\"&&(r=Vt(r,[0,2,1])),o===\"causal\")throw new _t(\"The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.\");let a=rm(r,t,n,o===\"same\"?\"same\":\"valid\",\"NWC\",i);return e!=null&&(a=yn(a,e)),a})}function qR(r,t,e,n=[1,1],o=\"valid\",s,i,a=null){return B(()=>{if(s==null&&(s=xn()),Me(s),r.rank!==3&&r.rank!==4)throw new z(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${r.rank}.`);if(t.rank!==3&&t.rank!==4)throw new z(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${r.rank}.`);let u=Mh(r,s);if(o===\"causal\")throw new _t(\"The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.\");return u=Ru.conv2d({x:u,filter:t,strides:n,pad:o===\"same\"?\"same\":\"valid\",dilations:i,dataFormat:\"NHWC\",bias:e,activation:a}),s===\"channelsFirst\"&&(u=Vt(u,[0,3,1,2])),u})}function yJ(r,t,e,n=[1,1,1],o=\"valid\",s,i){return B(()=>{if(s==null&&(s=xn()),Me(s),r.rank!==4&&r.rank!==5)throw new z(`conv3dWithBias expects input to be of rank 4 or 5, but received ${r.rank}.`);if(t.rank!==4&&t.rank!==5)throw new z(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${r.rank}.`);let a=QN(r,s);if(o===\"causal\")throw new _t(\"The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.\");return a=Ax(a,t,n,o===\"same\"?\"same\":\"valid\",\"NDHWC\",i),e!=null&&(a=yn(a,e)),s===\"channelsFirst\"&&(a=Vt(a,[0,4,1,2,3])),a})}var Oh=class r extends Et{constructor(t,e){if(super(e),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",r.verifyArgs(e),this.rank=t,tr(this.rank,\"rank\"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new _t(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=zu(e.kernelSize,t,\"kernelSize\"),this.strides=zu(e.strides==null?1:e.strides,t,\"strides\"),this.padding=e.padding==null?\"valid\":e.padding,hn(this.padding),this.dataFormat=e.dataFormat==null?\"channelsLast\":e.dataFormat,Me(this.dataFormat),this.activation=gi(e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.biasInitializer=ge(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Ge(e.biasConstraint),this.biasRegularizer=Ce(e.biasRegularizer),this.activityRegularizer=Ce(e.activityRegularizer),this.dilationRate=zu(e.dilationRate==null?1:e.dilationRate,t,\"dilationRate\"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new z(`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 z(`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 z(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(t){if(co(\"kernelSize\"in t,\"required key 'kernelSize' not in config\"),typeof t.kernelSize!=\"number\"&&!Fy(t.kernelSize,\"number\",1,3))throw new z(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(t.kernelSize)}.`)}getConfig(){let t={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:hi(this.activation),useBias:this.useBias,biasInitializer:Te(this.biasInitializer),biasRegularizer:fe(this.biasRegularizer),activityRegularizer:fe(this.activityRegularizer),biasConstraint:Ve(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}},rf=class r extends Oh{constructor(t,e){super(t,e),this.kernel=null,r.verifyArgs(e),this.filters=e.filters,tr(this.filters,\"filters\"),this.kernelInitializer=ge(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Ge(e.kernelConstraint),this.kernelRegularizer=Ce(e.kernelRegularizer)}build(t){t=Gt(t);let e=this.dataFormat===\"channelsFirst\"?1:t.length-1;if(t[e]==null)throw new z(`The channel dimension of the input should be defined. Found ${t[e]}`);let n=t[e],o=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight(\"kernel\",o,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:{[e]:n}}],this.built=!0}call(t,e){return B(()=>{t=St(t);let n,o=this.bias==null?null:this.bias.read(),s=Oy(this.activation.getClassName());if(s!=null&&this.rank===2)n=qR(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)n=xJ(t,this.kernel.read(),o,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=qR(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=yJ(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new _t(\"convolutions greater than 3D are not implemented yet.\");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(t){t=Gt(t);let e=[],n=this.dataFormat===\"channelsLast\"?t.slice(1,t.length-1):t.slice(2);for(let s=0;s 0 but got ${JSON.stringify(t.filters)}`)}},Uc=class r extends rf{constructor(t){super(2,t),r.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!=\"number\"&&!Fy(t.kernelSize,\"number\",1,2))throw new z(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(t.kernelSize)}.`)}};Uc.className=\"Conv2D\";Q.registerClass(Uc);var Hc=class r extends rf{constructor(t){super(3,t),r.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!=\"number\"&&!(Array.isArray(t.kernelSize)&&(t.kernelSize.length===1||t.kernelSize.length===3)))throw new z(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(t.kernelSize)}.`)}};Hc.className=\"Conv3D\";Q.registerClass(Hc);var nf=class extends Uc{constructor(t){if(super(t),this.inputSpec=[new Ie({ndim:4})],this.padding!==\"same\"&&this.padding!==\"valid\")throw new z(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(t=Gt(t),t.length!==4)throw new z(\"Input should have rank 4; Received input shape: \"+JSON.stringify(t));let e=this.dataFormat===\"channelsFirst\"?1:t.length-1;if(t[e]==null)throw new z(\"The channel dimension of the inputs should be defined. 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Found `None`.\");let n=t[e],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight(\"kernel\",o,\"float32\",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],\"float32\",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ie({ndim:5,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{let n=St(t);if(n.shape.length!==5)throw new z(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],i,a,u;this.dataFormat===\"channelsFirst\"?(u=2,i=3,a=4):(u=1,i=2,a=3);let l=o[u],c=o[i],p=o[a],m=this.kernelSize[0],f=this.kernelSize[1],d=this.kernelSize[2],h=this.strides[0],g=this.strides[1],x=this.strides[2],b=xi(l,h,m,this.padding),w=xi(c,g,f,this.padding),I=xi(p,x,d,this.padding),N=[s,b,w,I,this.filters];this.dataFormat!==\"channelsLast\"&&(n=Vt(n,[0,2,3,4,1]));let E=$x(n,this.kernel.read(),N,this.strides,this.padding);return this.dataFormat!==\"channelsLast\"&&(E=Vt(E,[0,4,1,2,3])),this.bias!==null&&(E=yn(E,this.bias.read(),this.dataFormat)),this.activation!==null&&(E=this.activation.apply(E)),E})}computeOutputShape(t){t=Gt(t);let e=t.slice(),n,o,s,i;this.dataFormat===\"channelsFirst\"?(n=1,o=2,s=3,i=4):(n=4,o=1,s=2,i=3);let a=this.kernelSize[0],u=this.kernelSize[1],l=this.kernelSize[2],c=this.strides[0],p=this.strides[1],m=this.strides[2];return e[n]=this.filters,e[o]=xi(e[o],c,a,this.padding),e[s]=xi(e[s],p,u,this.padding),e[i]=xi(e[i],m,l,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};of.className=\"Conv3DTranspose\";Q.registerClass(of);var Tb=class extends rf{constructor(t,e){if(super(t,e),this.DEFAULT_DEPTHWISE_INITIALIZER=\"glorotUniform\",this.DEFAULT_POINTWISE_INITIALIZER=\"glorotUniform\",this.depthwiseKernel=null,this.pointwiseKernel=null,e.filters==null)throw new z(\"The `filters` configuration field is required by SeparableConv, but is unspecified.\");if(e.kernelInitializer!=null||e.kernelRegularizer!=null||e.kernelConstraint!=null)throw new z(\"Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.\");if(e.padding!=null&&e.padding!==\"same\"&&e.padding!==\"valid\")throw new z(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(e.padding)}`);this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=ge(e.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Ce(e.depthwiseRegularizer),this.depthwiseConstraint=Ge(e.depthwiseConstraint),this.pointwiseInitializer=ge(e.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Ce(e.pointwiseRegularizer),this.pointwiseConstraint=Ge(e.pointwiseConstraint)}build(t){if(t=Gt(t),t.length{t=St(t);let n;if(this.rank===1)throw new _t(\"1D separable convolution is not implemented yet.\");return this.rank===2&&(this.dataFormat===\"channelsFirst\"&&(t=Vt(t,[0,2,3,1])),n=fm(t,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,\"NHWC\")),this.useBias&&(n=yn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat===\"channelsFirst\"&&(n=Vt(n,[0,3,1,2])),n})}getConfig(){let t=super.getConfig();return delete t.rank,delete t.kernelInitializer,delete t.kernelRegularizer,delete t.kernelConstraint,t.depthwiseInitializer=Te(this.depthwiseInitializer),t.pointwiseInitializer=Te(this.pointwiseInitializer),t.depthwiseRegularizer=fe(this.depthwiseRegularizer),t.pointwiseRegularizer=fe(this.pointwiseRegularizer),t.depthwiseConstraint=Ve(this.depthwiseConstraint),t.pointwiseConstraint=Ve(this.pointwiseConstraint),t}};Tb.className=\"SeparableConv\";var sf=class extends Tb{constructor(t){super(2,t)}};sf.className=\"SeparableConv2D\";Q.registerClass(sf);var af=class r extends rf{constructor(t){super(1,t),r.verifyArgs(t),this.inputSpec=[{ndim:3}]}getConfig(){let t=super.getConfig();return delete t.rank,delete t.dataFormat,t}static verifyArgs(t){if(typeof t.kernelSize!=\"number\"&&!Fy(t.kernelSize,\"number\",1,1))throw new z(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(t.kernelSize)}.`)}};af.className=\"Conv1D\";Q.registerClass(af);var lf=class extends Et{constructor(t){super(t),typeof t.cropping==\"number\"?this.cropping=[[t.cropping,t.cropping],[t.cropping,t.cropping]]:typeof t.cropping[0]==\"number\"?this.cropping=[[t.cropping[0],t.cropping[0]],[t.cropping[1],t.cropping[1]]]:this.cropping=t.cropping,this.dataFormat=t.dataFormat===void 0?\"channelsLast\":t.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(t){return this.dataFormat===\"channelsFirst\"?[t[0],t[1],t[2]-this.cropping[0][0]-this.cropping[0][1],t[3]-this.cropping[1][0]-this.cropping[1][1]]:[t[0],t[1]-this.cropping[0][0]-this.cropping[0][1],t[2]-this.cropping[1][0]-this.cropping[1][1],t[3]]}call(t,e){return B(()=>{if(t=St(t),this.dataFormat===\"channelsLast\"){let n=Nh(t,this.cropping[0][0],t.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Nh(n,this.cropping[1][0],t.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Nh(t,this.cropping[0][0],t.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Nh(n,this.cropping[1][0],t.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let t={cropping:this.cropping,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}};lf.className=\"Cropping2D\";Q.registerClass(lf);var uf=class extends Et{constructor(t){super(t),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=t.size==null?this.DEFAULT_SIZE:t.size,this.dataFormat=t.dataFormat==null?\"channelsLast\":t.dataFormat,Me(this.dataFormat),this.interpolation=t.interpolation==null?\"nearest\":t.interpolation,eR(this.interpolation)}computeOutputShape(t){if(this.dataFormat===\"channelsFirst\"){let e=t[2]==null?null:this.size[0]*t[2],n=t[3]==null?null:this.size[1]*t[3];return[t[0],t[1],e,n]}else{let e=t[1]==null?null:this.size[0]*t[1],n=t[2]==null?null:this.size[1]*t[2];return[t[0],e,n,t[3]]}}call(t,e){return B(()=>{let n=St(t),o=n.shape;if(this.dataFormat===\"channelsFirst\"){n=Vt(n,[0,2,3,1]);let s=this.size[0]*o[2],i=this.size[1]*o[3],a=this.interpolation===\"nearest\"?fn.resizeNearestNeighbor(n,[s,i]):fn.resizeBilinear(n,[s,i]);return Vt(a,[0,3,1,2])}else{let s=this.size[0]*o[1],i=this.size[1]*o[2];return this.interpolation===\"nearest\"?fn.resizeNearestNeighbor(n,[s,i]):fn.resizeBilinear(n,[s,i])}})}getConfig(){let t={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},e=super.getConfig();return Object.assign(t,e),t}};uf.className=\"UpSampling2D\";Q.registerClass(uf);function bJ(r,t,e=[1,1],n=\"valid\",o,s){return B(()=>{o==null&&(o=xn()),Me(o);let i=Mh(r,o);if(r.rank!==4)throw new z(`Input for depthwiseConv2d is required to be 4-D, but is instead ${r.rank}-D`);if(t.rank!==4)throw new z(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=ia(i,t,e,n===\"same\"?\"same\":\"valid\",\"NHWC\",s),o===\"channelsFirst\"&&(i=Vt(i,[0,3,1,2])),i})}var cf=class extends Oh{constructor(t){super(2,t),this.depthwiseKernel=null,this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=ge(t.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Ge(t.depthwiseConstraint),this.depthwiseRegularizer=Ce(t.depthwiseRegularizer)}build(t){if(t=Gt(t),t.length<4)throw new z(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(t)}.`);let e=this.dataFormat===\"channelsFirst\"?1:3;if(t[e]==null||t[e]<0)throw new z(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${t[e]}).`);let n=t[e],o=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight(\"depthwise_kernel\",o,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(t,e){return B(()=>{t=St(t);let n=bJ(t,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=yn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(t){t=Gt(t);let e=this.dataFormat===\"channelsFirst\"?t[2]:t[1],n=this.dataFormat===\"channelsFirst\"?t[3]:t[2],o=this.dataFormat===\"channelsFirst\"?t[1]*this.depthMultiplier:t[3]*this.depthMultiplier,s=Tn(e,this.kernelSize[0],this.padding,this.strides[0]),i=Tn(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat===\"channelsFirst\"?[t[0],o,s,i]:[t[0],s,i,o]}getConfig(){let t=super.getConfig();return t.depthMultiplier=this.depthMultiplier,t.depthwiseInitializer=Te(this.depthwiseInitializer),t.depthwiseRegularizer=fe(this.depthwiseRegularizer),t.depthwiseConstraint=Ve(this.depthwiseRegularizer),t}};cf.className=\"DepthwiseConv2D\";Q.registerClass(cf);function tk(r,t,e,n){if(Array.isArray(r)){if(t!=null||e!=null)throw new z(\"When inputs is an array, neither initialState or constants should be provided\");n!=null&&(e=r.slice(r.length-n,r.length),r=r.slice(0,r.length-n)),r.length>1&&(t=r.slice(1,r.length)),r=r[0]}function o(s){return s==null||Array.isArray(s)?s:[s]}return t=o(t),e=o(e),{inputs:r,initialState:t,constants:e}}function ek(r,t,e,n=!1,o,s,i=!1,a=!1){return B(()=>{let u=t.shape.length;if(u<3)throw new z(`Input should be at least 3D, but is ${u}D.`);let l=[1,0].concat(gn(2,u));if(t=Vt(t,l),s!=null)throw new _t(\"The rnn() functoin of the deeplearn.js backend does not support constants yet.\");i&&console.warn(\"Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend.\"),o!=null&&(o=J(J(o,\"bool\"),\"float32\"),o.rank===u-1&&(o=je(o,-1)),o=Vt(o,l)),n&&(t=dr(t,0),o!=null&&(o=dr(o,0)));let c=[],p,m=e,f=t.shape[0],d=gr(t),h;o!=null&&(h=gr(o));for(let x=0;xr(b,m));if(o==null)p=w[0],m=w[1];else{let I=B(()=>{let N=h[x],E=at(wr(N),N),A=X($(w[0],N),$(m[0],E)),D=m.map((F,M)=>X($(w[1][M],N),$(F,E)));return{output:A,newStates:D}});p=I.output,m=I.newStates}a&&c.push(p)}let g;return a&&(g=Fe(c,1)),[p,g,m]})}var po=class r extends Et{constructor(t){super(t);let e;if(t.cell==null)throw new z(\"cell property is missing for the constructor of RNN.\");if(Array.isArray(t.cell)?e=new jc({cells:t.cell}):e=t.cell,e.stateSize==null)throw new z(\"The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).\");this.cell=e,this.returnSequences=t.returnSequences==null?!1:t.returnSequences,this.returnState=t.returnState==null?!1:t.returnState,this.goBackwards=t.goBackwards==null?!1:t.goBackwards,this._stateful=t.stateful==null?!1:t.stateful,this.unroll=t.unroll==null?!1:t.unroll,this.supportsMasking=!0,this.inputSpec=[new Ie({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return gn(0,t).map(e=>null)}else return this.states_}setStates(t){this.states_=t}computeOutputShape(t){Uy(t)&&(t=t[0]),t=t;let e=this.cell.stateSize;Array.isArray(e)||(e=[e]);let n=e[0],o;if(this.returnSequences?o=[t[0],t[1],n]:o=[t[0],n],this.returnState){let s=[];for(let i of e)s.push([t[0],i]);return[o].concat(s)}else return o}computeMask(t,e){return B(()=>{Array.isArray(e)&&(e=e[0]);let n=this.returnSequences?e:null;if(this.returnState){let o=this.states.map(s=>null);return[n].concat(o)}else return n})}get states(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,e=[];for(let n=0;na.shape[a.shape.length-1]),i))throw new z(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=i.map(a=>new Ie({shape:[null,a]}));this.stateful&&this.resetStates()}resetStates(t,e=!1){B(()=>{if(!this.stateful)throw new uo(\"Cannot call resetStates() on an RNN Layer that is not stateful.\");let n=this.inputSpec[0].shape[0];if(n==null)throw new z(\"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(o=>ke([n,o])):this.states_=[ke([n,this.cell.stateSize])];else if(t==null)Tt(this.states_),this.keptStates!=null&&(Tt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>ke([n,o])):this.states_[0]=ke([n,this.cell.stateSize]);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e===!0?this.keptStates.push(this.states_.slice()):Tt(this.states_);for(let o=0;oDe(o.clone()))})}apply(t,e){let n=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=tk(t,n,o,this.numConstants);t=s.inputs,n=s.initialState,o=s.constants;let i=[],a=[];if(n!=null){e.initialState=n,i=i.concat(n),this.stateSpec=[];for(let l of n)this.stateSpec.push(new Ie({shape:l.shape}));a=a.concat(this.stateSpec)}if(o!=null&&(e.constants=o,i=i.concat(o),this.numConstants=o.length),i[0]instanceof en){let l=[t].concat(i),c=this.inputSpec.concat(a),p=this.inputSpec;this.inputSpec=c;let m=super.apply(l,e);return this.inputSpec=p,m}else return super.apply(t,e)}call(t,e){return B(()=>{let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;t=St(t),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(t));let i=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==i)throw new z(`RNN Layer has ${i} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn(\"Ignoring unroll = true for RNN layer, due to imperative backend.\");let a={training:o},l=ek((d,h)=>{let g=this.cell.call([d].concat(h),a);return[g[0],g.slice(1)]},t,s,this.goBackwards,n,null,this.unroll,this.returnSequences),c=l[0],p=l[1],m=l[2];this.stateful&&this.resetStates(m,o);let f=this.returnSequences?p:c;return this.returnState?[f].concat(m):f})}getInitialState(t){return B(()=>{let e=ke(t.shape);return e=mt(e,[1,2]),e=Sl(e),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Vy(e,[1,n]):e):this.cell.stateSize>1?[Vy(e,[1,this.cell.stateSize])]:[e]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(t)}getConfig(){let t=super.getConfig(),e={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(e.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===r.className&&(e.cell={className:this.cell.getClassName(),config:n}),Object.assign(Object.assign(Object.assign({},n),t),e)}static fromConfig(t,e,n={}){let o=e.cell,s=wn(o,n);return new t(Object.assign(e,{cell:s}))}};po.className=\"RNN\";Q.registerClass(po);var Tl=class extends Et{},qc=class extends Tl{constructor(t){super(t),this.DEFAULT_ACTIVATION=\"tanh\",this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",this.units=t.units,tr(this.units,\"units\"),this.activation=gi(t.activation==null?this.DEFAULT_ACTIVATION:t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=ge(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ge(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ge(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ce(t.kernelRegularizer),this.recurrentRegularizer=Ce(t.recurrentRegularizer),this.biasRegularizer=Ce(t.biasRegularizer),this.kernelConstraint=Ge(t.kernelConstraint),this.recurrentConstraint=Ge(t.recurrentConstraint),this.biasConstraint=Ge(t.biasConstraint),this.dropout=Dc([1,fi([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Dc([1,fi([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Gt(t),this.kernel=this.addWeight(\"kernel\",[t[t.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(t,e){return B(()=>{if(t=t,t.length!==2)throw new z(`SimpleRNNCell expects 2 input Tensors, got ${t.length}.`);let n=t[1];t=t[0];let o=e.training==null?!1:e.training;0wr(t),rate:this.dropout,training:o,dropoutFunc:this.dropoutFunc})),0wr(n),rate:this.recurrentDropout,training:o,dropoutFunc:this.dropoutFunc}));let s,i=this.dropoutMask,a=this.recurrentDropoutMask;i!=null?s=Do($(t,i),this.kernel.read()):s=Do(t,this.kernel.read()),this.bias!=null&&(s=yn(s,this.bias.read())),a!=null&&(n=$(n,a));let u=X(s,Do(n,this.recurrentKernel.read()));return this.activation!=null&&(u=this.activation.apply(u)),[u,u]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:hi(this.activation),useBias:this.useBias,kernelInitializer:Te(this.kernelInitializer),recurrentInitializer:Te(this.recurrentInitializer),biasInitializer:Te(this.biasInitializer),kernelRegularizer:fe(this.kernelRegularizer),recurrentRegularizer:fe(this.recurrentRegularizer),biasRegularizer:fe(this.biasRegularizer),activityRegularizer:fe(this.activityRegularizer),kernelConstraint:Ve(this.kernelConstraint),recurrentConstraint:Ve(this.recurrentConstraint),biasConstraint:Ve(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},t),e)}};qc.className=\"SimpleRNNCell\";Q.registerClass(qc);var pf=class extends po{constructor(t){t.cell=new qc(t),super(t)}call(t,e){return B(()=>{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return new t(e)}};pf.className=\"SimpleRNN\";Q.registerClass(pf);var Kc=class extends Tl{constructor(t){if(super(t),this.DEFAULT_ACTIVATION=\"tanh\",this.DEFAULT_RECURRENT_ACTIVATION=\"hardSigmoid\",this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",t.resetAfter)throw new z(\"GRUCell does not support reset_after parameter set to true.\");this.units=t.units,tr(this.units,\"units\"),this.activation=gi(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=gi(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=ge(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ge(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ge(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ce(t.kernelRegularizer),this.recurrentRegularizer=Ce(t.recurrentRegularizer),this.biasRegularizer=Ce(t.biasRegularizer),this.kernelConstraint=Ge(t.kernelConstraint),this.recurrentConstraint=Ge(t.recurrentConstraint),this.biasConstraint=Ge(t.biasConstraint),this.dropout=Dc([1,fi([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Dc([1,fi([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Gt(t);let e=t[t.length-1];this.kernel=this.addWeight(\"kernel\",[e,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(t,e){return B(()=>{if(t=t,t.length!==2)throw new z(`GRUCell expects 2 input Tensors (inputs, h, c), got ${t.length}.`);let n=e.training==null?!1:e.training,o=t[1];t=t[0],0wr(t),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0wr(o),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,a,u,l;0{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};mf.className=\"GRU\";Q.registerClass(mf);var _l=class extends Tl{constructor(t){super(t),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=t.units,tr(this.units,\"units\"),this.activation=gi(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=gi(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=ge(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ge(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ge(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=t.unitForgetBias,this.kernelRegularizer=Ce(t.kernelRegularizer),this.recurrentRegularizer=Ce(t.recurrentRegularizer),this.biasRegularizer=Ce(t.biasRegularizer),this.kernelConstraint=Ge(t.kernelConstraint),this.recurrentConstraint=Ge(t.recurrentConstraint),this.biasConstraint=Ge(t.biasConstraint),this.dropout=Dc([1,fi([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Dc([1,fi([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){var e;t=Gt(t);let n=t[t.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 o;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,i=this.units;o=new(e=class extends bn{apply(u,l){let c=s.apply([i]),p=new Mu().apply([i]),m=s.apply([i*2]);return BN(BN(c,p),m)}},e.className=\"CustomInit\",e)}else o=this.biasInitializer;this.bias=this.addWeight(\"bias\",[this.units*4],null,o,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training;if(t=t,t.length!==3)throw new z(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let o=t[1],s=t[2];t=t[0],0wr(t),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0wr(o),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,a=this.recurrentDropoutMask,u,l,c,p;0{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};ff.className=\"LSTM\";Q.registerClass(ff);var jc=class extends Tl{constructor(t){super(t),this.cells=t.cells}get stateSize(){let t=[];for(let e of this.cells.slice().reverse())Array.isArray(e.stateSize)?t.push(...e.stateSize):t.push(e.stateSize);return t}call(t,e){return B(()=>{t=t;let n=t.slice(1),o=[];for(let a of this.cells.slice().reverse())Array.isArray(a.stateSize)?o.push(n.splice(0,a.stateSize.length)):o.push(n.splice(0,1));o.reverse();let s=[],i;for(let a=0;a{mi(`RNNCell_${o}`,()=>{n.build(t),Array.isArray(n.stateSize)?e=n.stateSize[0]:e=n.stateSize,t=[t[0],e]})}),this.built=!0}getConfig(){let t=super.getConfig(),e=s=>({className:s.getClassName(),config:s.getConfig()}),o={cells:this.cells.map(e)};return Object.assign(Object.assign({},t),o)}static fromConfig(t,e,n={}){let o=[];for(let s of e.cells)o.push(wn(s,n));return new t({cells:o})}get trainableWeights(){if(!this.trainable)return[];let t=[];for(let e of this.cells)t.push(...e.trainableWeights);return t}get nonTrainableWeights(){let t=[];for(let e of this.cells)t.push(...e.nonTrainableWeights);if(!this.trainable){let e=[];for(let n of this.cells)e.push(...n.trainableWeights);return e.concat(t)}return t}getWeights(){let t=[];for(let e of this.cells)t.push(...e.weights);return Th(t)}setWeights(t){let e=[];for(let n of this.cells){let o=n.weights.length,s=t.splice(o);for(let i=0;is!=null?s(t(),e):Wy(t(),e),a=()=>Ou(i,t,n);return!o||o<=1?De(a().clone()):Array(o).fill(void 0).map(a).map(l=>De(l.clone()))}var wJ=function(r,t){var e={};for(var n in r)Object.prototype.hasOwnProperty.call(r,n)&&t.indexOf(n)<0&&(e[n]=r[n]);if(r!=null&&typeof Object.getOwnPropertySymbols==\"function\")for(var o=0,n=Object.getOwnPropertySymbols(r);o{if(this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),e&&e.constants)throw new z(\"ConvRNN2D cell does not support constants\");let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}computeOutputShape(t){let e=this.computeSingleOutputShape(t);return this.returnSequences||(e=[e[0],...e.slice(2)]),this.returnState&&(e=[e,...Array(2).fill([t[0],...e.slice(-3)])]),e}getInitialState(t){return B(()=>{let{stateSize:e}=this.cell,n=t.shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)],i=ke(s);return Array.isArray(e)?Array(e.length).fill(i):[i]})}resetStates(t,e=!1){B(()=>{if(!this.stateful)throw new uo(\"Cannot call resetStates() on an RNN Layer that is not stateful.\");let n=this.inputSpec[0].shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)];if(n[0]==null)throw new z(\"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(()=>ke(s)):this.states_=[ke(s)];else if(t==null)Tt(this.states_),this.keptStates!=null&&(Tt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>ke(s)):this.states_[0]=ke(s);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e?this.keptStates.push(this.states_.slice()):Tt(this.states_);for(let a=0;aDe(a.clone()))})}computeSingleOutputShape(t){let{dataFormat:e,filters:n,kernelSize:o,padding:s,strides:i,dilationRate:a}=this.cell,u=e===\"channelsFirst\",l=t[u?3:2],c=t[u?4:3],p=Tn(l,o[0],s,i[0],a[0]),m=Tn(c,o[1],s,i[1],a[1]);return[...t.slice(0,2),...u?[n,p,m]:[p,m,n]]}};_b.className=\"ConvRNN2D\";var Xc=class extends _l{constructor(t){let{filters:e,kernelSize:n,strides:o,padding:s,dataFormat:i,dilationRate:a}=t;super(Object.assign(Object.assign({},t),{units:e})),this.filters=e,tr(this.filters,\"filters\"),this.kernelSize=zu(n,2,\"kernelSize\"),this.kernelSize.forEach(u=>tr(u,\"kernelSize\")),this.strides=zu(o||1,2,\"strides\"),this.strides.forEach(u=>tr(u,\"strides\")),this.padding=s||\"valid\",hn(this.padding),this.dataFormat=i||\"channelsLast\",Me(this.dataFormat),this.dilationRate=zu(a||1,2,\"dilationRate\"),this.dilationRate.forEach(u=>tr(u,\"dilationRate\"))}build(t){var e;t=Gt(t);let n=this.dataFormat===\"channelsFirst\"?1:t.length-1;if(t[n]==null)throw new z(`The channel dimension of the input should be defined. Found ${t[n]}`);let o=t[n],s=4,i=this.kernelSize.concat([o,this.filters*s]);this.kernel=this.addWeight(\"kernel\",i,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let a=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight(\"recurrent_kernel\",a,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let u;if(this.unitForgetBias){let l=this.biasInitializer,c=this.filters;u=new(e=class extends bn{apply(m,f){let d=l.apply([c]),h=ar([c]),g=l.apply([c*2]);return Tm([d,h,g])}},e.className=\"CustomInit\",e)}else u=this.biasInitializer;this.bias=this.addWeight(\"bias\",[this.filters*s],null,u,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(t,e){return B(()=>{if(t.length!==3)throw new z(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let n=e.training||!1,o=t[0],s=t[1],i=t[2],a=4;0wr(o),rate:this.dropout,training:n,count:a,dropoutFunc:this.dropoutFunc}));let u=this.dropoutMask,l=(nt,st,lt)=>!st||!st[lt]?nt:$(st[lt],nt),c=l(o,u,0),p=l(o,u,1),m=l(o,u,2),f=l(o,u,3);0wr(s),rate:this.recurrentDropout,training:n,count:a,dropoutFunc:this.dropoutFunc}));let d=this.recurrentDropoutMask,h=l(s,d,0),g=l(s,d,1),x=l(s,d,2),b=l(s,d,3),w=3,[I,N,E,A]=hr(this.kernel.read(),a,w),[D,F,M,V]=this.useBias?hr(this.bias.read(),a):[null,null,null,null];c=this.inputConv(c,I,D,this.padding),p=this.inputConv(p,N,F,this.padding),m=this.inputConv(m,E,M,this.padding),f=this.inputConv(f,A,V,this.padding);let[G,W,q,H]=hr(this.recurrentKernel.read(),a,w);h=this.recurrentConv(h,G),g=this.recurrentConv(g,W),x=this.recurrentConv(x,q),b=this.recurrentConv(b,H);let K=this.recurrentActivation.apply(X(c,h)),Y=this.recurrentActivation.apply(X(p,g)),Z=X($(Y,i),$(K,this.activation.apply(X(m,x)))),et=$(this.recurrentActivation.apply(X(f,b)),this.activation.apply(Z));return[et,et,Z]})}getConfig(){let t=super.getConfig(),{units:e}=t,n=wJ(t,[\"units\"]),o={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),o)}inputConv(t,e,n,o){let s=Nn(t,e,this.strides,o||\"valid\",this.dataFormat===\"channelsFirst\"?\"NCHW\":\"NHWC\",this.dilationRate);return n?yn(s,n,this.dataFormat):s}recurrentConv(t,e){return Nn(t,e,1,\"same\",this.dataFormat===\"channelsFirst\"?\"NCHW\":\"NHWC\")}};Xc.className=\"ConvLSTM2DCell\";Q.registerClass(Xc);var df=class extends _b{constructor(t){let e=new Xc(t);super(Object.assign(Object.assign({},t),{cell:e}))}static fromConfig(t,e){return new t(e)}};df.className=\"ConvLSTM2D\";Q.registerClass(df);var Yc=class extends Et{constructor(t){super(t),this.rate=Math.max(Math.min(t.rate,1),0),this.noiseShape=t.noiseShape,this.seed=t.seed,this.supportsMasking=!0}getNoiseShape(t){if(this.noiseShape==null)return this.noiseShape;let e=t.shape,n=[];for(let o=0;o{this.invokeCallHook(t,e);let n=St(t);if(0Wy(n,this.rate,s,this.seed),()=>n,o)}return t})}getConfig(){let t={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},e=super.getConfig();return Object.assign(t,e),t}dispose(){return super.dispose()}};Yc.className=\"Dropout\";Q.registerClass(Yc);var hf=class extends Yc{constructor(t){super(t),this.inputSpec=[{ndim:3}]}getNoiseShape(t){let e=t.shape;return[e[0],1,e[2]]}};hf.className=\"SpatialDropout1D\";Q.registerClass(hf);var gf=class extends Et{constructor(t){if(super(t),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",t.batchInputShape==null&&t.inputShape==null&&t.inputDim!=null){let e=null;t.batchSize!=null&&(e=t.batchSize),this.batchInputShape=[e,t.inputDim]}this.units=t.units,tr(this.units,\"units\"),this.activation=gi(t.activation),t.useBias!=null&&(this.useBias=t.useBias),this.kernelInitializer=ge(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=ge(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Ge(t.kernelConstraint),this.biasConstraint=Ge(t.biasConstraint),this.kernelRegularizer=Ce(t.kernelRegularizer),this.biasRegularizer=Ce(t.biasRegularizer),this.activityRegularizer=Ce(t.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(t){t=Gt(t);let e=t[t.length-1];this.kernel==null&&(this.kernel=this.addWeight(\"kernel\",[e,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]:e}}],this.built=!0}computeOutputShape(t){t=Gt(t);let e=t.slice();return e[e.length-1]=this.units,e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t),o=Oy(this.activation.getClassName()),s;return o!=null?s=Do(n,this.kernel.read(),o,this.bias?this.bias.read():null):(s=Do(n,this.kernel.read()),this.bias!=null&&(s=yn(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let t={units:this.units,activation:hi(this.activation),useBias:this.useBias,kernelInitializer:Te(this.kernelInitializer),biasInitializer:Te(this.biasInitializer),kernelRegularizer:fe(this.kernelRegularizer),biasRegularizer:fe(this.biasRegularizer),activityRegularizer:fe(this.activityRegularizer),kernelConstraint:Ve(this.kernelConstraint),biasConstraint:Ve(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}};gf.className=\"Dense\";Q.registerClass(gf);var xf=class extends Et{constructor(t){t=t||{},super(t),this.inputSpec=[{minNDim:3}],this.dataFormat=t.dataFormat}computeOutputShape(t){t=Gt(t);for(let e of t.slice(1))if(e==null)throw new z(`The shape of the input to \"Flatten\" is not fully defined (got ${t.slice(1)}). Make sure to pass a complete \"input_shape\" or \"batch_input_shape\" argument to the first layer in your model.`);return[t[0],Ao(t,1)]}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);if(this.dataFormat===\"channelsFirst\"&&n.rank>1){let o=[0];for(let s=2;s{this.invokeCallHook(t,e);let n=St(t);return this.activation.apply(n)})}getConfig(){let t={activation:hi(this.activation)},e=super.getConfig();return Object.assign(t,e),t}};yf.className=\"Activation\";Q.registerClass(yf);var bf=class extends Et{constructor(t){super(t),this.n=t.n,this.inputSpec=[{ndim:2}]}computeOutputShape(t){return[t[0],this.n,t[1]]}call(t,e){return B(()=>(t=St(t),oR(t,this.n)))}getConfig(){let t={n:this.n},e=super.getConfig();return Object.assign(t,e),t}};bf.className=\"RepeatVector\";Q.registerClass(bf);var wf=class extends Et{constructor(t){super(t),this.targetShape=t.targetShape;for(let e=0;e{this.invokeCallHook(t,e);let n=St(t),o=n.shape,s=o.slice(0,1).concat(this.fixUnknownDimension(o.slice(1),this.targetShape));return R(n,s)})}getConfig(){let t={targetShape:this.targetShape},e=super.getConfig();return Object.assign(t,e),t}};wf.className=\"Reshape\";Q.registerClass(wf);var If=class extends Et{constructor(t){if(super(t),t.dims==null)throw new Error(\"Required configuration field `dims` is missing during Permute constructor call.\");if(!Array.isArray(t.dims))throw new Error(`Permute constructor requires \\`dims\\` to be an Array, but received ${t.dims} instead.`);let e=gn(1,t.dims.length+1);if(!y.arraysEqual(t.dims.slice().sort(),e))throw new Error(\"Invalid permutation `dims`: \"+JSON.stringify(t.dims)+\" `dims` must contain consecutive integers starting from 1.\");this.dims=t.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Ie({ndim:this.dims.length+1})]}computeOutputShape(t){t=Gt(t);let e=t.slice();return this.dims.forEach((n,o)=>{e[o+1]=t[n]}),e}call(t,e){return Vt(St(t),this.dimsIncludingBatch)}getConfig(){let t={dims:this.dims},e=super.getConfig();return Object.assign(t,e),t}};If.className=\"Permute\";Q.registerClass(If);var Cf=class extends Et{constructor(t){super(t==null?{}:t),this.supportsMasking=!0,t!=null?this.maskValue=t.maskValue==null?0:t.maskValue:this.maskValue=0}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={maskValue:this.maskValue};return Object.assign(e,t),e}computeMask(t,e){let n=St(t);return cc(ui(n,this.maskValue),-1)}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t),i=cc(ui(n,this.maskValue),-1,!0);return $(n,J(i,n.dtype))})}};Cf.className=\"Masking\";Q.registerClass(Cf);var vf=class extends Et{constructor(t){if(super(t),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER=\"randomUniform\",t.batchInputShape==null&&t.inputShape==null){let e=null;t.batchSize!=null&&(e=t.batchSize),t.inputLength==null?this.batchInputShape=[e,null]:this.batchInputShape=[e].concat(ue(t.inputLength))}this.inputDim=t.inputDim,tr(this.inputDim,\"inputDim\"),this.outputDim=t.outputDim,tr(this.outputDim,\"outputDim\"),this.embeddingsInitializer=ge(t.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Ce(t.embeddingsRegularizer),this.activityRegularizer=Ce(t.activityRegularizer),this.embeddingsConstraint=Ge(t.embeddingsConstraint),this.maskZero=t.maskZero,this.supportsMasking=t.maskZero,this.inputLength=t.inputLength}build(t){this.embeddings=this.addWeight(\"embeddings\",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(t){}computeMask(t,e){return B(()=>this.maskZero?(t=St(t),ui(t,vt(t))):null)}computeOutputShape(t){if(t=Gt(t),this.inputLength==null)return[...t,this.outputDim];let e=ue(this.inputLength);if(e.length!==t.length-1)throw new z(`\"inputLength\" is ${this.inputLength}, but received input shape has shape ${t}`);{let n=0;for(let o=0;o{this.invokeCallHook(t,e);let n=St(t);n.dtype!==\"int32\"&&(n=tn(n,\"int32\"));let o=Gy(this.embeddings.read(),R(n,[n.size]));return R(o,Gt(this.computeOutputShape(n.shape)))})}getConfig(){let t={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Te(this.embeddingsInitializer),embeddingsRegularizer:fe(this.embeddingsRegularizer),activityRegularizer:fe(this.activityRegularizer),embeddingsConstraint:Ve(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},e=super.getConfig();return Object.assign(t,e),t}};vf.className=\"Embedding\";Q.registerClass(vf);var Al=class extends Et{constructor(t){super(t||{}),this.supportsMasking=!0}mergeFunction(t){throw new _t}computeElementwiseOpOutputShape(t,e){if(t==null||e==null)return null;if(t.length1)throw new z(`Can not merge tensors with different batch sizes. 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Array.isArray(this.axes)?o=this.axes.map((s,i)=>Ph(s,t[i].shape.length)):o=[Ph(this.axes,e.shape.length),Ph(this.axes,n.shape.length)],this.normalize&&(e=_h(e,o[0]),n=_h(n,o[1])),IJ(e,n,o)}interpretAxes(t,e){let n;return Array.isArray(this.axes)?n=this.axes:n=[Ph(this.axes,t.length),Ph(this.axes,e.length)],n}computeOutputShape(t){y.assert(Array.isArray(t)&&t.length===2&&Array.isArray(t[0])&&Array.isArray(t[1]),()=>\"A `Dot` layer should be called on a list of exactly 2 inputs.\");let e=t[0].slice(),n=t[1].slice();if(e.length>3||n.length>3)throw new _t(\"Dot layer does not support tensors of 4D or higher rank yet.\");let o=this.interpretAxes(e,n);e.splice(o[0],1),n.splice(o[1],1),n.splice(0,1);let s=e.concat(n);return s.length===1&&s.push(1),s}computeMask(t,e){return null}getConfig(){let t={axes:this.axes,normalize:this.normalize},e=super.getConfig();return Object.assign(t,e),t}};Af.className=\"Dot\";Q.registerClass(Af);var Df=class extends Et{constructor(t){super(t),this.supportsMasking=!0,this.stddev=t.stddev}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={stddev:this.stddev};return Object.assign(e,t),e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);return Ou(()=>X(_m(n.shape,0,this.stddev),n),()=>n,e.training||!1)})}};Df.className=\"GaussianNoise\";Q.registerClass(Df);var $f=class extends Et{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);return this.rate>0&&this.rate<1?Ou(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return $(n,_m(n.shape,1,s))},()=>n,e.training||!1):n})}};$f.className=\"GaussianDropout\";Q.registerClass($f);var Rf=class extends Et{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate,this.noiseShape=t.noiseShape}_getNoiseShape(t){return this.noiseShape||St(t).shape}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return B(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(t);return Ou(()=>{let s=St(t),u=-1.6732632423543772*1.0507009873554805,l=cn(Gn(n),this.rate);l=tn(l,\"float32\");let c=((1-this.rate)*(1+this.rate*u**2))**-.5,p=-c*u*this.rate,m=X($(s,l),$(X(l,-1),u));return X($(m,c),p)},()=>St(t),e.training||!1)}return t})}};Rf.className=\"AlphaDropout\";Q.registerClass(Rf);function Lh(r,t,e,n,o,s=.001){let i;if(r.rank===2)i=Ix(r,t,e,n,o,s);else if(r.rank===3)i=Cx(r,t,e,n,o,s);else if(r.rank===4)i=vx(r,t,e,n,o,s);else throw new _t(`batchNormalization is not implemented for array of rank ${r.rank} yet`);return i}function CJ(r,t,e,n,o=.001){return B(()=>{let s=dc(r,n),i=s.mean,a=s.variance;return[Lh(r,i,a,e,t,o),i,a]})}function vJ(r,t,e,n,o=.001){return B(()=>{let s=dc(r,n),i=s.mean,a=s.variance,u=[];for(let d of 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e=this.axis>=0?this.axis:this.axis+t.length,n=t[e];if(n==null)throw new z(`Axis ${e} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(t)}.`);this.inputSpec=[new Ie({ndim:t.length,axes:{[e]:n}})];let o=[n];this.scale&&(this.gamma=this.addWeight(\"gamma\",o,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight(\"beta\",o,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight(\"moving_mean\",o,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight(\"moving_variance\",o,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training,o=St(t),s=o.shape,i=s.length,a=gn(0,i),u=this.axis>=0?this.axis:this.axis+i;a.splice(u,1);let l=To(1,i);l[u]=s[u];let c=a.slice();c.sort();let p=!y.arraysEqual(c,gn(0,i).slice(0,i-1)),m=()=>{if(p){let 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t={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Te(this.betaInitializer),gammaInitializer:Te(this.gammaInitializer),movingMeanInitializer:Te(this.movingMeanInitializer),movingVarianceInitializer:Te(this.movingVarianceInitializer),betaRegularizer:fe(this.betaRegularizer),gammaRegularizer:fe(this.gammaRegularizer),betaConstraint:Ve(this.betaConstraint),gammaConstraint:Ve(this.gammaConstraint)},e=super.getConfig();return Object.assign(t,e),t}};Ff.className=\"BatchNormalization\";Q.registerClass(Ff);var Of=class extends Et{constructor(t){if(t==null&&(t={}),super(t),this.axis=t.axis==null?-1:t.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 e of this.axis)if(!Number.isInteger(e))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=t.epsilon==null?.001:t.epsilon,this.center=t.center==null?!0:t.center,this.scale=t.scale==null?!0:t.scale,this.betaInitializer=ge(t.betaInitializer||\"zeros\"),this.gammaInitializer=ge(t.gammaInitializer||\"ones\"),this.betaRegularizer=Ce(t.betaRegularizer),this.gammaRegularizer=Ce(t.gammaRegularizer),this.supportsMasking=!0}build(t){t=Gt(t);let e=t.length;typeof this.axis==\"number\"&&(this.axis=[this.axis]);for(let s=0;s=e)throw new Error(`Invalid axis: ${s}`);if(this.axis.length!==Eo(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(s=>t[s]),o=!0;this.scale?this.gamma=this.addWeight(\"gamma\",n,\"float32\",this.gammaInitializer,this.gammaRegularizer,o):this.gamma=null,this.center?this.beta=this.addWeight(\"beta\",n,\"float32\",this.betaInitializer,this.betaRegularizer,o):this.beta=null,this.built=!0}call(t,e){let n=St(t),o=n.shape,s=o.length;return B(()=>{let{mean:a,variance:u}=dc(n,this.axis,!0),l=To(1,s);for(let h of this.axis)l[h]=o[h];let c=h=>h!=null&&h.shape.length!==s?R(h,l):h,p=this.scale?c(this.gamma.read()):null,m=this.center?c(this.beta.read()):null,f=[],d=[];for(let h=0;h{if(r.rank!==4)throw new z(`temporalPadding expects input tensor to be 4-D, but received a ${r.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new z(\"spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.\");if(e==null&&(e=xn()),e!==\"channelsLast\"&&e!==\"channelsFirst\")throw new z(`Unknown data format: ${e}. 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length-${t.padding[1].length} array.`);n=t.padding[1]}this.padding=[e,n]}this.inputSpec=[new Ie({ndim:4})]}computeOutputShape(t){t=Gt(t);let e,n;return this.dataFormat===\"channelsFirst\"?(t[2]!=null&&t[2]>=0?e=t[2]+this.padding[0][0]+this.padding[0][1]:e=null,t[3]!=null&&t[3]>=0?n=t[3]+this.padding[1][0]+this.padding[1][1]:n=null,[t[0],t[1],e,n]):(t[1]!=null&&t[1]>=0?e=t[1]+this.padding[0][0]+this.padding[0][1]:e=null,t[2]!=null&&t[2]>=0?n=t[2]+this.padding[1][0]+this.padding[1][1]:n=null,[t[0],e,n,t[3]])}call(t,e){return B(()=>NJ(St(t),this.padding,this.dataFormat))}getConfig(){let t={padding:this.padding,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}};Mf.className=\"ZeroPadding2D\";Q.registerClass(Mf);function Fb(r,t,e,n,o,s){return B(()=>{Me(o),MN(s),hn(n),e==null&&(e=[1,1]),n==null&&(n=\"valid\"),o==null&&(o=xn()),s==null&&(s=\"max\"),r=Mh(r,o);let i,a=n===\"same\"?\"same\":\"valid\";return s===\"max\"?i=ku(r,t,e,a):i=xu(r,t,e,a),o===\"channelsFirst\"&&(i=Vt(i,[0,3,1,2])),i})}function KR(r,t,e,n,o,s){return B(()=>{Me(o),MN(s),hn(n),e==null&&(e=[1,1,1]),n==null&&(n=\"valid\"),o==null&&(o=xn()),s==null&&(s=\"max\"),r=QN(r,o);let i,a=n===\"same\"?\"same\":\"valid\";return s===\"max\"?i=Xx(r,t,e,a):i=wx(r,t,e,a),o===\"channelsFirst\"&&(i=Vt(i,[0,4,1,2,3])),i})}var Eb=class extends Et{constructor(t){if(t.poolSize==null&&(t.poolSize=2),super(t),typeof t.poolSize==\"number\")this.poolSize=[t.poolSize];else if(Array.isArray(t.poolSize)&&t.poolSize.length===1&&typeof t.poolSize[0]==\"number\")this.poolSize=t.poolSize;else throw new z(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.poolSize)}`);if(tr(this.poolSize,\"poolSize\"),t.strides==null)this.strides=this.poolSize;else if(typeof t.strides==\"number\")this.strides=[t.strides];else if(Array.isArray(t.strides)&&t.strides.length===1&&typeof t.strides[0]==\"number\")this.strides=t.strides;else throw new z(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.strides)}`);tr(this.strides,\"strides\"),this.padding=t.padding==null?\"valid\":t.padding,hn(this.padding),this.inputSpec=[new Ie({ndim:3})]}computeOutputShape(t){t=Gt(t);let e=Tn(t[1],this.poolSize[0],this.padding,this.strides[0]);return[t[0],e,t[2]]}call(t,e){return B(()=>{this.invokeCallHook(t,e),t=Sl(St(t),2);let n=this.poolingFunction(St(t),[this.poolSize[0],1],[this.strides[0],1],this.padding,\"channelsLast\");return Wn(n,[2])})}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides},e=super.getConfig();return Object.assign(t,e),t}},Pf=class extends Eb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Me(s),hn(o),Fb(t,e,n,o,s,\"max\")}};Pf.className=\"MaxPooling1D\";Q.registerClass(Pf);var Lf=class extends Eb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Me(s),hn(o),Fb(t,e,n,o,s,\"avg\")}};Lf.className=\"AveragePooling1D\";Q.registerClass(Lf);var Ab=class extends Et{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==2)throw new z(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides];tr(this.poolSize,\"poolSize\"),tr(this.strides,\"strides\"),this.padding=t.padding==null?\"valid\":t.padding,this.dataFormat=t.dataFormat==null?\"channelsLast\":t.dataFormat,Me(this.dataFormat),hn(this.padding),this.inputSpec=[new Ie({ndim:4})]}computeOutputShape(t){t=Gt(t);let e=this.dataFormat===\"channelsFirst\"?t[2]:t[1],n=this.dataFormat===\"channelsFirst\"?t[3]:t[2];return e=Tn(e,this.poolSize[0],this.padding,this.strides[0]),n=Tn(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat===\"channelsFirst\"?[t[0],t[1],e,n]:[t[0],e,n,t[3]]}call(t,e){return B(()=>(this.invokeCallHook(t,e),this.poolingFunction(St(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},zf=class extends Ab{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Me(s),hn(o),Fb(t,e,n,o,s,\"max\")}};zf.className=\"MaxPooling2D\";Q.registerClass(zf);var Bf=class extends Ab{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Me(s),hn(o),Fb(t,e,n,o,s,\"avg\")}};Bf.className=\"AveragePooling2D\";Q.registerClass(Bf);var Db=class extends Et{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==3)throw new z(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides,t.strides];tr(this.poolSize,\"poolSize\"),tr(this.strides,\"strides\"),this.padding=t.padding==null?\"valid\":t.padding,this.dataFormat=t.dataFormat==null?\"channelsLast\":t.dataFormat,Me(this.dataFormat),hn(this.padding),this.inputSpec=[new Ie({ndim:5})]}computeOutputShape(t){t=Gt(t);let e=this.dataFormat===\"channelsFirst\"?t[2]:t[1],n=this.dataFormat===\"channelsFirst\"?t[3]:t[2],o=this.dataFormat===\"channelsFirst\"?t[4]:t[3];return e=Tn(e,this.poolSize[0],this.padding,this.strides[0]),n=Tn(n,this.poolSize[1],this.padding,this.strides[1]),o=Tn(o,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat===\"channelsFirst\"?[t[0],t[1],e,n,o]:[t[0],e,n,o,t[4]]}call(t,e){return B(()=>(this.invokeCallHook(t,e),this.poolingFunction(St(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Vf=class extends Db{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Me(s),hn(o),KR(t,e,n,o,s,\"max\")}};Vf.className=\"MaxPooling3D\";Q.registerClass(Vf);var Gf=class extends Db{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Me(s),hn(o),KR(t,e,n,o,s,\"avg\")}};Gf.className=\"AveragePooling3D\";Q.registerClass(Gf);var $b=class extends Et{constructor(t){super(t),this.inputSpec=[new Ie({ndim:3})]}computeOutputShape(t){return[t[0],t[2]]}call(t,e){throw new _t}},Wf=class extends $b{constructor(t){super(t||{})}call(t,e){return B(()=>{let n=St(t);return Ne(n,1)})}};Wf.className=\"GlobalAveragePooling1D\";Q.registerClass(Wf);var Uf=class extends $b{constructor(t){super(t||{})}call(t,e){return B(()=>{let n=St(t);return Sr(n,1)})}};Uf.className=\"GlobalMaxPooling1D\";Q.registerClass(Uf);var Rb=class extends Et{constructor(t){super(t),this.dataFormat=t.dataFormat==null?\"channelsLast\":t.dataFormat,Me(this.dataFormat),this.inputSpec=[new Ie({ndim:4})]}computeOutputShape(t){return t=t,this.dataFormat===\"channelsLast\"?[t[0],t[3]]:[t[0],t[1]]}call(t,e){throw new _t}getConfig(){let t={dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Hf=class extends Rb{call(t,e){return B(()=>{let n=St(t);return this.dataFormat===\"channelsLast\"?Ne(n,[1,2]):Ne(n,[2,3])})}};Hf.className=\"GlobalAveragePooling2D\";Q.registerClass(Hf);var qf=class extends Rb{call(t,e){return B(()=>{let n=St(t);return this.dataFormat===\"channelsLast\"?Sr(n,[1,2]):Sr(n,[2,3])})}};qf.className=\"GlobalMaxPooling2D\";Q.registerClass(qf);var Ob=class extends Et{constructor(t){super(t),this.layer=t.layer}build(t){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(t){this.layer!=null&&(this.layer.trainable=t)}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(t){this.layer.setWeights(t)}getConfig(){let t={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},e=super.getConfig();return Object.assign(t,e),t}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(t)}static fromConfig(t,e,n={}){let o=e.layer,s=wn(o,n);delete e.layer;let i={layer:s};return Object.assign(i,e),new t(i)}},Kf=class extends Ob{constructor(t){super(t),this.supportsMasking=!0}build(t){if(t=Gt(t),t.length<3)throw new z(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(t)}`);this.inputSpec=[{shape:t}];let e=[t[0]].concat(t.slice(2));this.layer.built||(this.layer.build(e),this.layer.built=!0),super.build(t)}computeOutputShape(t){t=Gt(t);let e=[t[0]].concat(t.slice(2)),n=this.layer.computeOutputShape(e),o=t[1];return[n[0],o].concat(n.slice(1))}call(t,e){return B(()=>(t=St(t),ek((i,a)=>[St(this.layer.call(i,e)),[]],t,[],!1,null,null,!1,!0)[1]))}};Kf.className=\"TimeDistributed\";Q.registerClass(Kf);function kJ(r){da(Q$,\"BidirectionalMergeMode\",r)}var TJ=\"concat\",jf=class extends Ob{constructor(t){super(t);let e=t.layer.getConfig(),n={};n.className=t.layer.getClassName(),n.config=e,this.forwardLayer=wn(n),e.goBackwards=e.goBackwards!==!0;let o={};if(o.className=t.layer.getClassName(),o.config=e,this.backwardLayer=wn(o),this.forwardLayer.name=\"forward_\"+this.forwardLayer.name,this.backwardLayer.name=\"backward_\"+this.backwardLayer.name,this.mergeMode=t.mergeMode===void 0?TJ:t.mergeMode,kJ(this.mergeMode),t.weights)throw new _t(\"weights support is not implemented for Bidirectional layer yet.\");this._stateful=t.layer.stateful,this.returnSequences=t.layer.returnSequences,this.returnState=t.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=t.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(t){this._trainable=t,this.forwardLayer!=null&&(this.forwardLayer.trainable=t),this.backwardLayer!=null&&(this.backwardLayer.trainable=t)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(t){let e=t.length,n=Math.floor(e/2);this.forwardLayer.setWeights(t.slice(0,n)),this.backwardLayer.setWeights(t.slice(n))}computeOutputShape(t){let e=this.forwardLayer.computeOutputShape(t);Array.isArray(e)&&Array.isArray(e[0])||(e=[e]),e=e;let n,o,s;return this.returnState&&(s=e.slice(1)),n=e[0],n=n,this.mergeMode===\"concat\"?(n[n.length-1]*=2,o=[n]):this.mergeMode==null?o=[n,n.slice()]:o=[n],this.returnState?this.mergeMode==null?o.concat(s).concat(s.slice()):[n].concat(s).concat(s.slice()):kr(o)}apply(t,e){let n=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=tk(t,n,o,this.numConstants);if(t=s.inputs,n=s.initialState,o=s.constants,Array.isArray(t)&&(n=t.slice(1),t=t[0]),(n==null||n.length===0)&&o==null)return super.apply(t,e);let i=[],a=[];if(n!=null){let l=n.length;if(l%2>0)throw new z(\"When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.\");e.initialState=n,i.push(...n);let c=n.map(p=>new Ie({shape:p.shape}));this.forwardLayer.stateSpec=c.slice(0,l/2),this.backwardLayer.stateSpec=c.slice(l/2),a.push(...c)}if(o!=null)throw new _t(\"Support for constants in Bidirectional layers is not implemented yet.\");let u=i[0]instanceof en;for(let l of i)if(l instanceof en!==u)throw new z(\"The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors\");if(u){let l=[t].concat(i),c=this.inputSpec.concat(a),p=this.inputSpec;this.inputSpec=c;let m=super.apply(l,e);return this.inputSpec=p,m}else return super.apply(t,e)}call(t,e){return B(()=>{let n=e.initialState,o,s;if(n==null)o=this.forwardLayer.call(t,e),s=this.backwardLayer.call(t,e);else{let u=n.slice(0,n.length/2),l=n.slice(n.length/2);o=this.forwardLayer.call(t,Object.assign(e,{initialState:u})),s=this.backwardLayer.call(t,Object.assign(e,{initialState:l}))}let i;this.returnState&&(Array.isArray(o)&&(i=o.slice(1).concat(s.slice(1))),o=o[0],s=s[0]),this.returnSequences&&(s=dr(s,1));let a;return this.mergeMode===\"concat\"?a=Tm([o,s]):this.mergeMode===\"sum\"?a=X(o,s):this.mergeMode===\"ave\"?a=$(.5,X(o,s)):this.mergeMode===\"mul\"?a=$(o,s):this.mergeMode==null&&(a=[o,s]),this.returnState?this.mergeMode==null?a.concat(i):[a].concat(i):a})}resetStates(t){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(t){mi(this.forwardLayer.name,()=>{this.forwardLayer.build(t)}),mi(this.backwardLayer.name,()=>{this.backwardLayer.build(t)}),this.built=!0}computeMask(t,e){Array.isArray(e)&&(e=e[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[e,e]:n=e:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let s=this.forwardLayer.states.map(i=>null);return Array.isArray(n)?n.concat(s).concat(s):[n].concat(s).concat(s)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(t),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(t)}getConfig(){let t={mergeMode:this.mergeMode},e=super.getConfig();return Object.assign(t,e),t}static fromConfig(t,e){let n=wn(e.layer);if(delete e.layer,e.numConstants!=null)throw new _t(\"Deserialization of a Bidirectional layer with numConstants present is not supported yet.\");let o=e;return o.layer=n,new t(o)}};jf.className=\"Bidirectional\";Q.registerClass(jf);var Xf=class extends Et{constructor(t){super(t),this.scale=t.scale,t.offset?this.offset=t.offset:this.offset=0}getConfig(){let t={scale:this.scale,offset:this.offset},e=super.getConfig();return Object.assign(t,e),t}call(t,e){return B(()=>(t=St(t),t.dtype!==\"float32\"&&(t=tn(t,\"float32\")),X($(t,this.scale),this.offset)))}};Xf.className=\"Rescaling\";Q.registerClass(Xf);var{resizeBilinear:_J,cropAndResize:EJ}=fn,Yf=class extends Et{constructor(t){super(t),this.height=t.height,this.width=t.width}centerCrop(t,e,n,o,s,i,a,u){return B(()=>{let l,c=!1,p=e/i,m=n/a,f=(o+e)/i,d=(s+n)/a,h=[p,m,f,d],g=[];t.rank===3?(c=!0,l=Fe([t])):l=t;for(let N=0;N{let s=_J(t,[e,n]);return tn(s,o)})}call(t,e){return B(()=>{let n=St(t),o=n.dtype,s=n.shape,i=s[s.length-3],a=s[s.length-2],u=0;i!==this.height&&(u=Math.floor((i-this.height)/2));let l=0;return a!==this.width&&(l=Math.floor((a-this.width)/2),l===0&&(l=1)),u>=0&&l>=0?this.centerCrop(n,u,l,this.height,this.width,i,a,o):this.upsize(t,this.height,this.width,o)})}getConfig(){let t={height:this.height,width:this.width},e=super.getConfig();return Object.assign(t,e),t}computeOutputShape(t){t=Gt(t);let e=t.length-3,n=t.length-2;return t[e]=this.height,t[n]=this.width,t}};Yf.className=\"CenterCrop\";Q.registerClass(Yf);function jR(r,t,e,n){let o=St(r);if(o.dtype!==\"int32\"&&(o=tn(o,\"int32\")),t===\"int\")return o;let s=o.shape;if(o.rank===0&&(o=je(o,-1)),t===\"oneHot\"&&o.shape[o.shape.length-1]!==1&&(o=je(o,-1)),o.rank>2)throw new z(`When outputMode is not int, maximum output rank is 2 Received outputMode ${t} and input shape ${s} which would result in output rank ${o.rank}.`);let i=[\"multiHot\",\"oneHot\"].includes(t),a=o,u;if(typeof n!=\"undefined\"&&t===\"count\"?u=ph(a,n,e,i):u=ph(a,[],e,i),t!==\"tfIdf\")return u;if(n)return $(u,n);throw new z(\"When outputMode is 'tfIdf', weights must be provided.\")}var Zf=class extends Et{constructor(t){super(t),this.numTokens=t.numTokens,t.outputMode?this.outputMode=t.outputMode:this.outputMode=\"multiHot\"}getConfig(){let t={numTokens:this.numTokens,outputMode:this.outputMode},e=super.getConfig();return Object.assign(t,e),t}computeOutputShape(t){return t=Gt(t),t==null?[this.numTokens]:this.outputMode===\"oneHot\"&&t[t.length-1]!==1?(t.push(this.numTokens),t):(t[t.length-1]=this.numTokens,t)}call(t,e){return B(()=>{t=St(t),t.dtype!==\"int32\"&&(t=tn(t,\"int32\"));let n;if(typeof e.countWeights!=\"undefined\"){if(this.outputMode!==\"count\")throw new z(`countWeights is not used when outputMode !== count.\n Received countWeights=${e.countWeights}`);n=St(e.countWeights)}let o=Sr(t),s=gl(t),i=Re(this.numTokens,o).bufferSync().get(0),a=cn(s,0).bufferSync().get(0);if(!(i&&a))throw new z(`Input values must be between 0 < values <= numTokens with numTokens=${this.numTokens}`);return jR(t,this.outputMode,this.numTokens,n)})}};Zf.className=\"CategoryEncoding\";Q.registerClass(Zf);var DJ=[\"bilinear\",\"nearest\"],XR=new Set(DJ),Jf=class extends Et{constructor(t){if(super(t),this.height=t.height,this.width=t.width,t.interpolation)if(XR.has(t.interpolation))this.interpolation=t.interpolation;else throw new z(`Invalid interpolation parameter: ${t.interpolation} is not implemented`);else this.interpolation=\"bilinear\";this.cropToAspectRatio=!!t.cropToAspectRatio}computeOutputShape(t){t=Gt(t);let e=t[2];return[this.height,this.width,e]}getConfig(){let t={height:this.height,width:this.width,interpolation:this.interpolation,cropToAspectRatio:this.cropToAspectRatio},e=super.getConfig();return Object.assign(t,e),t}call(t,e){return B(()=>{let n=[this.height,this.width];if(this.interpolation===\"bilinear\")return fn.resizeBilinear(t,n,!this.cropToAspectRatio);if(this.interpolation===\"nearest\")return fn.resizeNearestNeighbor(t,n,!this.cropToAspectRatio);throw new Error(`Interpolation is ${this.interpolation} but only ${[...XR]} are supported`)})}};Jf.className=\"Resizing\";Q.registerClass(Jf);var zh=class{constructor(t){this.seed=t}next(){if(this.seed!==void 0)return this.seed++}};zh.className=\"RandomSeed\";var Bh=class extends Et{constructor(t){super(t),this.randomGenerator=new zh(t.seed)}getConfig(){let 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o=v(\"strides\",r,t,e),s=Vh(r,t,e),i=v(\"dataFormat\",r,t,e).toUpperCase(),a=v(\"dilations\",r,t,e);return[n.conv2d(v(\"x\",r,t,e),v(\"filter\",r,t,e),[o[1],o[2]],s,i,[a[1],a[2]])]}case\"_FusedConv2D\":{let{stride:o,pad:s,dataFormat:i,dilations:a,biasArg:u,preluArg:l,activationFunc:c,leakyreluAlpha:p}=yF(r,t,e);return[n.fused.conv2d({x:v(\"x\",r,t,e),filter:v(\"filter\",r,t,e),strides:[o[1],o[2]],pad:s,dataFormat:i,dilations:[a[1],a[2]],bias:u,activation:c,preluActivationWeights:l,leakyreluAlpha:p})]}case\"FusedDepthwiseConv2dNative\":{let{stride:o,pad:s,dataFormat:i,dilations:a,biasArg:u,preluArg:l,activationFunc:c,leakyreluAlpha:p}=yF(r,t,e);return[n.fused.depthwiseConv2d({x:v(\"x\",r,t,e),filter:v(\"filter\",r,t,e),strides:[o[1],o[2]],pad:s,dataFormat:i,dilations:[a[1],a[2]],bias:u,activation:c,preluActivationWeights:l,leakyreluAlpha:p})]}case\"Conv2DBackpropInput\":case\"Conv2dTranspose\":{let 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o=v(\"start\",r,t,e),s=v(\"stop\",r,t,e),i=v(\"num\",r,t,e);return[n.linspace(o,s,i)]}case\"Multinomial\":{let o=v(\"logits\",r,t,e),s=v(\"numSamples\",r,t,e),i=v(\"seed\",r,t,e);return[n.multinomial(o,s,i)]}case\"OneHot\":{let o=v(\"indices\",r,t,e),s=v(\"depth\",r,t,e),i=v(\"onValue\",r,t,e),a=v(\"offValue\",r,t,e),u=v(\"dtype\",r,t,e);return[n.oneHot(o,s,i,a,u)]}case\"Ones\":return[n.ones(v(\"shape\",r,t,e),v(\"dtype\",r,t,e))];case\"OnesLike\":return[n.onesLike(v(\"x\",r,t,e))];case\"RandomStandardNormal\":return[n.randomStandardNormal(v(\"shape\",r,t,e),v(\"dtype\",r,t,e),v(\"seed\",r,t,e))];case\"RandomUniform\":return[n.randomUniform(v(\"shape\",r,t,e),v(\"minval\",r,t,e),v(\"maxval\",r,t,e),v(\"dtype\",r,t,e))];case\"RandomUniformInt\":return[n.randomUniformInt(v(\"shape\",r,t,e),v(\"minval\",r,t,e),v(\"maxval\",r,t,e),v(\"seed\",r,t,e))];case\"Range\":{let o=v(\"start\",r,t,e),s=v(\"stop\",r,t,e),i=v(\"step\",r,t,e);return[n.range(o,s,i,v(\"dtype\",r,t,e))]}case\"TruncatedNormal\":{let o=v(\"shape\",r,t,e),s=v(\"mean\",r,t,e),i=v(\"stdDev\",r,t,e),a=v(\"seed\",r,t,e);return[n.truncatedNormal(o,s,i,v(\"dtype\",r,t,e),a)]}case\"Zeros\":return[n.zeros(v(\"shape\",r,t,e),v(\"dtype\",r,t,e))];case\"ZerosLike\":return[n.zerosLike(v(\"x\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function Tk(r,t,e){let n=v(\"boxes\",r,t,e),o=v(\"scores\",r,t,e),s=v(\"maxOutputSize\",r,t,e),i=v(\"iouThreshold\",r,t,e),a=v(\"scoreThreshold\",r,t,e),u=v(\"softNmsSigma\",r,t,e);return{boxes:n,scores:o,maxOutputSize:s,iouThreshold:i,scoreThreshold:a,softNmsSigma:u}}var IF=async(r,t,e,n,o=ae)=>{switch(r.op){case\"NonMaxSuppressionV5\":{let{boxes:s,scores:i,maxOutputSize:a,iouThreshold:u,scoreThreshold:l,softNmsSigma:c}=Tk(r,t,e),p=await 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o=v(\"x\",r,t,e),s=v(\"k\",r,t,e),i=v(\"sorted\",r,t,e),a=n.topk(o,s,i);return[a.values,a.indices]}case\"UpperBound\":{let o=v(\"sortedSequence\",r,t,e),s=v(\"values\",r,t,e);return[n.upperBound(o,s)]}case\"Unique\":{let o=v(\"x\",r,t,e),s=n.unique(o);return[s.values,s.indices]}case\"UniqueV2\":{let o=v(\"x\",r,t,e),s=v(\"axis\",r,t,e),i=n.unique(o,s);return[i.values,i.indices]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var vF=(r,t,e,n=ae)=>{switch(r.op){case\"Const\":return t[r.name];case\"PlaceholderWithDefault\":let o=v(\"default\",r,t,e);return[pr(r.name,t,e)||o];case\"Placeholder\":return[pr(r.name,t,e)];case\"Identity\":case\"StopGradient\":case\"FakeQuantWithMinMaxVars\":{let c=v(\"x\",r,t,e);return[bi(c)]}case\"IdentityN\":return v(\"x\",r,t,e).map(c=>bi(c));case\"Snapshot\":let s=v(\"x\",r,t,e);return[bi(s)];case\"Shape\":return[n.tensor1d(v(\"x\",r,t,e).shape,\"int32\")];case\"ShapeN\":return v(\"x\",r,t,e).map(c=>n.tensor1d(c.shape));case\"Size\":return[n.scalar(v(\"x\",r,t,e).size,\"int32\")];case\"Rank\":return[n.scalar(v(\"x\",r,t,e).rank,\"int32\")];case\"NoOp\":return[n.scalar(1)];case\"Print\":let i=v(\"x\",r,t,e),a=v(\"data\",r,t,e),u=v(\"message\",r,t,e),l=v(\"summarize\",r,t,e);console.warn(\"The graph has a tf.print() operation,usually used for debugging, which slows down performance.\"),console.log(u);for(let c=0;ct.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return pt(this.size(),\"int32\")}async import(t,e){this.checkKeyAndValueTensor(t,e);let n=await t.data();return this.tensorMap.forEach(o=>o.dispose()),this.tensorMap.clear(),B(()=>{let o=gr(e),s=n.length,i=o.length;y.assert(s===i,()=>`The number of elements doesn't match, keys has ${s} elements, the values has ${i} elements.`);for(let a=0;a{let o=[];for(let s=0;s{switch(r.op){case\"HashTable\":case\"HashTableV2\":{let o=n.getHashTableHandleByName(r.name);if(o!=null)return[o];{let s=v(\"keyDType\",r,t,e),i=v(\"valueDType\",r,t,e),a=new tw(s,i);return n.addHashTable(r.name,a),[a.handle]}}case\"InitializeTable\":case\"InitializeTableV2\":case\"LookupTableImport\":case\"LookupTableImportV2\":{let o=v(\"tableHandle\",r,t,e,n),s=v(\"keys\",r,t,e),i=v(\"values\",r,t,e);return[await n.getHashTableById(o.id).import(s,i)]}case\"LookupTableFind\":case\"LookupTableFindV2\":{let o=v(\"tableHandle\",r,t,e,n),s=v(\"keys\",r,t,e),i=v(\"defaultValue\",r,t,e);return[await n.getHashTableById(o.id).find(s,i)]}case\"LookupTableSize\":case\"LookupTableSizeV2\":{let o=v(\"tableHandle\",r,t,e,n);return[n.getHashTableById(o.id).tensorSize()]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var NF=(r,t,e,n=ae)=>{switch(r.op){case\"ResizeBilinear\":{let o=v(\"images\",r,t,e),s=v(\"size\",r,t,e),i=v(\"alignCorners\",r,t,e),a=v(\"halfPixelCenters\",r,t,e);return[n.image.resizeBilinear(o,[s[0],s[1]],i,a)]}case\"ResizeNearestNeighbor\":{let o=v(\"images\",r,t,e),s=v(\"size\",r,t,e),i=v(\"alignCorners\",r,t,e),a=v(\"halfPixelCenters\",r,t,e);return[n.image.resizeNearestNeighbor(o,[s[0],s[1]],i,a)]}case\"CropAndResize\":{let o=v(\"image\",r,t,e),s=v(\"boxes\",r,t,e),i=v(\"boxInd\",r,t,e),a=v(\"cropSize\",r,t,e),u=v(\"method\",r,t,e),l=v(\"extrapolationValue\",r,t,e);return[n.image.cropAndResize(o,s,i,a,u,l)]}case\"ImageProjectiveTransformV3\":{let o=v(\"images\",r,t,e),s=v(\"transforms\",r,t,e),i=v(\"outputShape\",r,t,e),a=v(\"fillValue\",r,t,e),u=v(\"interpolation\",r,t,e),l=v(\"fillMode\",r,t,e);return[n.image.transform(o,s,u.toLowerCase(),l.toLowerCase(),a,i)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var kF=(r,t,e,n=ae)=>{switch(r.op){case\"Equal\":return[n.equal(v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"NotEqual\":return[n.notEqual(v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"Greater\":return[n.greater(v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"GreaterEqual\":return[n.greaterEqual(v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"Less\":return[n.less(v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"LessEqual\":return[n.lessEqual(v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"LogicalAnd\":return[n.logicalAnd(v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"LogicalNot\":return[n.logicalNot(v(\"a\",r,t,e))];case\"LogicalOr\":return[n.logicalOr(v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"Select\":case\"SelectV2\":return[n.where(v(\"condition\",r,t,e),v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"BitwiseAnd\":return[n.bitwiseAnd(v(\"a\",r,t,e),v(\"b\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var TF=(r,t,e,n=ae)=>{switch(r.op){case\"BatchMatMul\":case\"BatchMatMulV2\":case\"MatMul\":return[n.matMul(v(\"a\",r,t,e),v(\"b\",r,t,e),v(\"transposeA\",r,t,e),v(\"transposeB\",r,t,e))];case\"Einsum\":return[n.einsum(v(\"equation\",r,t,e),...v(\"tensors\",r,t,e))];case\"Transpose\":return[n.transpose(v(\"x\",r,t,e),v(\"perm\",r,t,e))];case\"_FusedMatMul\":let[o,s]=v(\"fusedOps\",r,t,e),i=o===\"biasadd\",a=s===\"prelu\",u=v(\"numArgs\",r,t,e),l=v(\"leakyreluAlpha\",r,t,e);if(i){if(a&&u!==2)throw new Error(\"Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!a&&u!==1)throw new Error(\"Fused MatMul with BiasAdd must have one extra argument: bias.\")}let[c,p]=v(\"args\",r,t,e);return[n.fused.matMul({a:v(\"a\",r,t,e),b:v(\"b\",r,t,e),transposeA:v(\"transposeA\",r,t,e),transposeB:v(\"transposeB\",r,t,e),bias:c,activation:s,preluActivationWeights:p,leakyreluAlpha:l})];case\"MatrixBandPart\":return[n.linalg.bandPart(v(\"a\",r,t,e),v(\"numLower\",r,t,e),v(\"numUpper\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var _F=(r,t,e,n=ae)=>{switch(r.op){case\"EuclideanNorm\":return[n.euclideanNorm(v(\"x\",r,t,e),v(\"axis\",r,t,e),v(\"keepDims\",r,t,e))];case\"FusedBatchNorm\":case\"FusedBatchNormV2\":return[n.batchNorm(v(\"x\",r,t,e),v(\"mean\",r,t,e),v(\"variance\",r,t,e),v(\"offset\",r,t,e),v(\"scale\",r,t,e),v(\"epsilon\",r,t,e))];case\"FusedBatchNormV3\":return[n.batchNorm(v(\"x\",r,t,e),v(\"mean\",r,t,e),v(\"variance\",r,t,e),v(\"offset\",r,t,e),v(\"scale\",r,t,e),v(\"epsilon\",r,t,e))];case\"LRN\":return[n.localResponseNormalization(v(\"x\",r,t,e),v(\"radius\",r,t,e),v(\"bias\",r,t,e),v(\"alpha\",r,t,e),v(\"beta\",r,t,e))];case\"Softmax\":return[n.softmax(v(\"x\",r,t,e))];case\"LogSoftmax\":return[n.logSoftmax(v(\"x\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var EF=(r,t,e,n=ae)=>{switch(r.op){case\"RaggedGather\":{let{outputNestedSplits:o,outputDenseValues:s}=n.raggedGather(v(\"paramsNestedSplits\",r,t,e),v(\"paramsDenseValues\",r,t,e),v(\"indices\",r,t,e),v(\"outputRaggedRank\",r,t,e));return o.concat(s)}case\"RaggedRange\":{let{rtNestedSplits:o,rtDenseValues:s}=n.raggedRange(v(\"starts\",r,t,e),v(\"limits\",r,t,e),v(\"splits\",r,t,e));return[o,s]}case\"RaggedTensorToTensor\":return[n.raggedTensorToTensor(v(\"shape\",r,t,e),v(\"values\",r,t,e),v(\"defaultValue\",r,t,e),v(\"rowPartitionTensors\",r,t,e),v(\"rowPartitionTypes\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var AF=(r,t,e,n=ae)=>{switch(r.op){case\"Max\":{let a=v(\"axis\",r,t,e),u=v(\"keepDims\",r,t,e);return[n.max(v(\"x\",r,t,e),a,u)]}case\"Mean\":{let a=v(\"axis\",r,t,e),u=v(\"keepDims\",r,t,e);return[n.mean(v(\"x\",r,t,e),a,u)]}case\"Min\":{let a=v(\"axis\",r,t,e),u=v(\"keepDims\",r,t,e);return[n.min(v(\"x\",r,t,e),a,u)]}case\"Sum\":{let a=v(\"axis\",r,t,e),u=v(\"keepDims\",r,t,e);return[n.sum(v(\"x\",r,t,e),a,u)]}case\"All\":{let a=v(\"axis\",r,t,e),u=v(\"keepDims\",r,t,e);return[n.all(v(\"x\",r,t,e),a,u)]}case\"Any\":{let a=v(\"axis\",r,t,e),u=v(\"keepDims\",r,t,e);return[n.any(v(\"x\",r,t,e),a,u)]}case\"ArgMax\":{let a=v(\"axis\",r,t,e);return[n.argMax(v(\"x\",r,t,e),a)]}case\"ArgMin\":{let a=v(\"axis\",r,t,e);return[n.argMin(v(\"x\",r,t,e),a)]}case\"Prod\":{let a=v(\"axis\",r,t,e),u=v(\"keepDims\",r,t,e);return[n.prod(v(\"x\",r,t,e),a,u)]}case\"Cumprod\":{let a=v(\"axis\",r,t,e),u=v(\"exclusive\",r,t,e),l=v(\"reverse\",r,t,e);return[n.cumprod(v(\"x\",r,t,e),a,u,l)]}case\"Cumsum\":{let a=v(\"axis\",r,t,e),u=v(\"exclusive\",r,t,e),l=v(\"reverse\",r,t,e);return[n.cumsum(v(\"x\",r,t,e),a,u,l)]}case\"Bincount\":let o=v(\"x\",r,t,e),s=v(\"weights\",r,t,e),i=v(\"size\",r,t,e);return[n.bincount(o,s,i)];case\"DenseBincount\":{let a=v(\"x\",r,t,e),u=v(\"weights\",r,t,e),l=v(\"size\",r,t,e),c=v(\"binaryOutput\",r,t,e);return[n.denseBincount(a,u,l,c)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var DF=(r,t,e,n=ae)=>{switch(r.op){case\"ConcatV2\":case\"Concat\":{let o=v(\"n\",r,t,e),s=v(\"axis\",r,t,e),i=v(\"tensors\",r,t,e);return i=i.slice(0,o),[n.concat(i,s)]}case\"Gather\":{let o=v(\"x\",r,t,e),s=v(\"indices\",r,t,e);return[n.gather(o,n.cast(s,\"int32\"),0)]}case\"GatherV2\":{let o=v(\"axis\",r,t,e),s=v(\"batchDims\",r,t,e),i=v(\"x\",r,t,e),a=v(\"indices\",r,t,e);return[n.gather(i,n.cast(a,\"int32\"),o,s)]}case\"Reverse\":{let o=v(\"dims\",r,t,e),s=[];for(let a=0;a{let o=v(\"axis\",r,t,e),s=v(\"tensors\",r,t,e),i=s[0].shape,a=n.squeeze(s[0]).shape,u=s.map(l=>{let c=y.arraysEqual(l.shape,i);if(!c&&!y.arraysEqual(n.squeeze(l).shape,a))throw new Error(\"the input tensors shape does not match\");return c?l:n.reshape(l,i)});return[n.stack(u,o)]});case\"Unpack\":{let o=v(\"axis\",r,t,e),s=v(\"tensor\",r,t,e);return n.unstack(s,o)}case\"Tile\":{let o=v(\"reps\",r,t,e);return[n.tile(v(\"x\",r,t,e),o)]}case\"Split\":case\"SplitV\":{let o=v(\"axis\",r,t,e),s=v(\"numOrSizeSplits\",r,t,e),i=v(\"x\",r,t,e);return n.split(i,s,o)}case\"ScatterNd\":{let o=v(\"indices\",r,t,e),s=v(\"values\",r,t,e),i=v(\"shape\",r,t,e);return[n.scatterND(o,s,i)]}case\"GatherNd\":{let o=v(\"x\",r,t,e),s=v(\"indices\",r,t,e);return[n.gatherND(o,s)]}case\"SparseToDense\":{let o=v(\"sparseIndices\",r,t,e),s=v(\"outputShape\",r,t,e),i=v(\"sparseValues\",r,t,e),a=v(\"defaultValue\",r,t,e);return[n.sparseToDense(o,i,s,i.dtype===a.dtype?a:n.cast(a,i.dtype))]}case\"TensorScatterUpdate\":{let o=v(\"indices\",r,t,e),s=v(\"values\",r,t,e),i=v(\"tensor\",r,t,e);return[n.tensorScatterUpdate(i,o,s)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var $F=(r,t,e,n=ae)=>{switch(r.op){case\"SparseFillEmptyRows\":{let{outputIndices:o,outputValues:s,emptyRowIndicator:i,reverseIndexMap:a}=n.sparse.sparseFillEmptyRows(v(\"indices\",r,t,e),v(\"values\",r,t,e),v(\"denseShape\",r,t,e),v(\"defaultValue\",r,t,e));return[o,s,i,a]}case\"SparseReshape\":{let{outputIndices:o,outputShape:s}=n.sparse.sparseReshape(v(\"inputIndices\",r,t,e),v(\"inputShape\",r,t,e),v(\"newShape\",r,t,e));return[o,s]}case\"SparseSegmentMean\":return[n.sparse.sparseSegmentMean(v(\"data\",r,t,e),v(\"indices\",r,t,e),v(\"segmentIds\",r,t,e))];case\"SparseSegmentSum\":return[n.sparse.sparseSegmentSum(v(\"data\",r,t,e),v(\"indices\",r,t,e),v(\"segmentIds\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var RF=(r,t,e,n=ae)=>{switch(r.op){case\"FFT\":return[n.fft(v(\"x\",r,t,e))];case\"IFFT\":return[n.ifft(v(\"x\",r,t,e))];case\"RFFT\":return[n.rfft(v(\"x\",r,t,e))];case\"IRFFT\":return[n.irfft(v(\"x\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var FF=(r,t,e,n=ae)=>{switch(r.op){case\"StaticRegexReplace\":return[n.string.staticRegexReplace(v(\"input\",r,t,e),v(\"pattern\",r,t,e),v(\"rewrite\",r,t,e),v(\"replaceGlobal\",r,t,e))];case\"StringNGrams\":{let{nGrams:o,nGramsSplits:s}=n.string.stringNGrams(v(\"data\",r,t,e),v(\"dataSplits\",r,t,e),v(\"separator\",r,t,e),v(\"nGramWidths\",r,t,e),v(\"leftPad\",r,t,e),v(\"rightPad\",r,t,e),v(\"padWidth\",r,t,e),v(\"preserveShortSequences\",r,t,e));return[o,s]}case\"StringSplit\":{let{indices:o,values:s,shape:i}=n.string.stringSplit(v(\"input\",r,t,e),v(\"delimiter\",r,t,e),v(\"skipEmpty\",r,t,e));return[o,s,i]}case\"StringToHashBucketFast\":return[n.string.stringToHashBucketFast(v(\"input\",r,t,e),v(\"numBuckets\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var OF=(r,t,e,n=ae)=>{switch(r.op){case\"Cast\":return[n.cast(v(\"x\",r,t,e),v(\"dtype\",r,t,e))];case\"ExpandDims\":{let o=v(\"axis\",r,t,e);return[n.expandDims(v(\"x\",r,t,e),o)]}case\"Squeeze\":{let o=v(\"axis\",r,t,e);return[n.squeeze(v(\"x\",r,t,e),o)]}case\"Reshape\":return[n.reshape(v(\"x\",r,t,e),v(\"shape\",r,t,e))];case\"EnsureShape\":return[n.ensureShape(v(\"x\",r,t,e),v(\"shape\",r,t,e))];case\"MirrorPad\":return[n.mirrorPad(v(\"x\",r,t,e),v(\"padding\",r,t,e),v(\"mode\",r,t,e))];case\"PadV2\":case\"Pad\":return[n.pad(v(\"x\",r,t,e),v(\"padding\",r,t,e),v(\"constantValue\",r,t,e))];case\"SpaceToBatchND\":{let o=v(\"blockShape\",r,t,e),s=v(\"paddings\",r,t,e);return[n.spaceToBatchND(v(\"x\",r,t,e),o,s)]}case\"BatchToSpaceND\":{let o=v(\"blockShape\",r,t,e),s=v(\"crops\",r,t,e);return[n.batchToSpaceND(v(\"x\",r,t,e),o,s)]}case\"DepthToSpace\":{let o=v(\"blockSize\",r,t,e),s=v(\"dataFormat\",r,t,e).toUpperCase();return[n.depthToSpace(v(\"x\",r,t,e),o,s)]}case\"BroadcastTo\":return[n.broadcastTo(v(\"x\",r,t,e),v(\"shape\",r,t,e))];case\"BroadcastArgs\":return[n.broadcastArgs(v(\"s0\",r,t,e),v(\"s1\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function _k(r,t,e,n,o=B){let s=((i,a,u)=>{switch(i.category){case\"arithmetic\":return o(()=>cF(i,a,u));case\"basic_math\":return o(()=>pF(i,a,u));case\"control\":return xF(i,a,u);case\"convolution\":return o(()=>bF(i,a,u));case\"creation\":return o(()=>wF(i,a,u));case\"dynamic\":return IF(i,a,u);case\"evaluation\":return o(()=>CF(i,a,u));case\"image\":return o(()=>NF(i,a,u));case\"graph\":return o(()=>vF(i,a,u));case\"logical\":return o(()=>kF(i,a,u));case\"matrices\":return o(()=>TF(i,a,u));case\"normalization\":return o(()=>_F(i,a,u));case\"ragged\":return o(()=>EF(i,a,u));case\"reduction\":return o(()=>AF(i,a,u));case\"slice_join\":return o(()=>DF(i,a,u));case\"sparse\":return o(()=>$F(i,a,u));case\"spectral\":return o(()=>RF(i,a,u));case\"string\":return o(()=>FF(i,a,u));case\"transformation\":return o(()=>OF(i,a,u));case\"hash_table\":return SF(i,a,u,n);case\"custom\":let l=zb(i.op);if(l&&l.customExecutor)return l.customExecutor(new Zb(i,a,u));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(r,t,e);return y.isPromise(s)?s.then(i=>[].concat(i)):[].concat(s)}var Wh=class{constructor(t={},e={},n={},o={},s){this.weightMap=t,this.tensorArrayMap=e,this.tensorListMap=n,this.functionMap=o,this.parseNodeNameCache=s,this.rootContext={id:0,frameName:\"\",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(t,e){return{id:t,frameName:e,iterationId:0}}set currentContext(t){this.contexts!==t&&(this.contexts=t,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let t=[];for(let e=0;ee.id===0&&e.iterationId===0?\"\":`${e.frameName}-${e.iterationId}`).join(\"/\"):\"\"}enterFrame(t){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,t)),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 t=Object.assign({},this.contexts[this.contexts.length-1]);t.iterationId+=1,t.id=this.lastId,this.contexts.splice(-1,1,t),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error(\"Cannot increase frame iteration, the context is empty\")}getWeight(t){return this.weightMap[t]}addTensorArray(t){this.tensorArrayMap[t.id]=t}getTensorArray(t){return this.tensorArrayMap[t]}addTensorList(t){this.tensorListMap[t.id]=t}getTensorList(t){return this.tensorListMap[t]}dispose(t){for(let e in this.tensorArrayMap)this.tensorArrayMap[e].clearAndClose(t);for(let e in this.tensorListMap)this.tensorListMap[e].clearAndClose(t)}};function Ek(r,t,e,n){let o=new Set,s=[],i=null,a=null,u=new Set,l=new Set(Object.keys(r).map(m=>In(m)[0]));n=n||[];let c=new Set(n.map(m=>In(m.name)[0])),p=[...t];for(;p.length>0;){let m=p.pop();if((Bu(m)||utt(m)||ctt(m))&&i==null&&(i=m,a=i.children.map(f=>f.name).filter(f=>o.has(f))),o.add(m.name),e[m.name]==null&&!l.has(m.name)&&!c.has(m.name)){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(f=>{u.has(f.name)||(u.add(f.name),p.push(f))})}}return{inputs:r,outputs:t,usedNodes:o,missingInputs:s,dynamicNode:i,syncInputs:a}}function MF(r,t){let{usedNodes:e,inputs:n}=t,o=Object.keys(n).map(g=>In(g)[0]).map(g=>r.nodes[g]),s=r.initNodes||[],i=g=>e.has(typeof g==\"string\"?g:g.name);function a(g){return[...new Map(g.map(x=>[x.name,x])).values()]}let u=a([...o,...r.weights,...s]).filter(i),l=a([...u,...Object.values(r.nodes)]).filter(i),c=new Map(l.map(g=>[g.name,g])),p={};for(let g of l){p[g.name]=p[g.name]||0;for(let x of g.children)i(x)||(p[x.name]=Number.POSITIVE_INFINITY),p[x.name]=(p[x.name]||0)+1}let m=Object.entries(p).filter(([,g])=>g===0).map(([g])=>g),f=[...m];for(;m.length>0;){let g=m.pop(),x=c.get(g);for(let b of x.children.filter(i))--p[b.name]===0&&(f.push(b.name),m.push(b.name))}let d=f.map(g=>c.get(g)),h=ott(d,u);return stt(h,u),h}function ott(r,t){let e=new Map(r.map(i=>[i.name,i])),n=t.map(i=>i.name),o=new Set(n);for(;n.length>0;){let i=n.pop(),a=e.get(i);for(let u of a.children)!e.has(u.name)||o.has(u.name)||(o.add(u.name),n.push(u.name))}return r.filter(i=>o.has(i.name))}var rd=class extends Error{constructor(t){super(`NodesExecutionOrderError: ${t}`)}};function stt(r,t){let e=new Map(r.map((a,u)=>[a.name,u])),n=new Set(t.map(a=>a.name)),o=a=>n.has(typeof a==\"string\"?a:a.name),s=new Set(r.map(a=>a.name)),i=a=>s.has(typeof a==\"string\"?a:a.name);for(let a of r){for(let u of a.children.filter(i)){if(!e.has(u.name))throw new rd(`Child ${u.name} of node ${a.name} is unreachable.`);if(e.get(a.name)>e.get(u.name))throw new rd(`Node ${a.name} is scheduled to run after its child ${u.name}.`)}if(!o(a))for(let u of a.inputs){if(!e.has(u.name))throw new rd(`Input ${u.name} of node ${a.name} is unreachable.`);if(e.get(u.name)>e.get(a.name))throw new rd(`Node ${a.name} is scheduled to run before its input ${u.name}.`)}}}function PF(r){let t=new Map(r.map((a,u)=>[a.name,u])),e=Number.MAX_SAFE_INTEGER,n=r.map((a,u)=>Bu(a)?e:u),o=a=>{let u=n[t.get(a.name)];return u==null?-1:u},s=r.map((a,u)=>a.children.map(o).reduce((l,c)=>Math.max(l,c),n[u])),i=new Map;for(let a=0;at[n].map(o=>o.id));this._weightIds=[].concat(...e),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let e=t.signatureKey||t.name;return t.defaultOutput?`${e}:${t.defaultOutput}`:e})}get functions(){return Object.keys(this._functions).reduce((t,e)=>(t[e]=this._functions[e].signature,t),{})}constructor(t,e){this.graph=t,this.parent=e,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=\",\",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(n=>{this._functionExecutorMap[n]=new r(t.functions[n],this)})}getCompilationKey(t,e){let n=t.map(s=>s.name).sort(),o=e.map(s=>s.name).sort();return n.join(this.SEPARATOR)+\"--\"+o.join(this.SEPARATOR)}compile(t,e){let n=Ek(t,e,this.weightMap,this._initNodes),{missingInputs:o,dynamicNode:s,syncInputs:i}=n;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${i}]`);if(o.length>0){let l=e.map(p=>p.name),c=Object.keys(t);throw new Error(`Cannot compute the outputs [${l}] from the provided inputs [${c}]. Missing the following inputs: [${o}]`)}let a=MF(this.graph,n),u=PF(a);return{orderedNodes:a,nodeLiveUntilMap:u}}cloneAndKeepTensor(t){if(t==null)return null;let e=t.clone();return De(e),e}cloneTensorList(t){return t?t.map(n=>this.cloneAndKeepTensor(n)):null}cloneTensorMap(t){return Object.fromEntries(Object.entries(t).map(([e,n])=>[e,this.cloneTensorList(n)]))}execute(t,e){this.disposeIntermediateTensors(),t=this.mapInputs(t);let n=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e);let o=n.map(m=>this.graph.nodes[In(m)[0]]),s=e.map(m=>In(m)[0]),i=new Set(s),a=s.map(m=>this.graph.nodes[m]);a.length===0&&(a=this._outputs);let u=this.getCompilationKey(o,a),l=this.compiledMap.get(u);l==null&&(l=this.compile(t,a),this.compiledMap.set(u,l));try{this.keepIntermediateTensors=L().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let c={},p={};return B(()=>{let m=new Wh(this.weightMap,c,p,this.functionExecutorMap,this.parseNodeNameCache),f=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(t).forEach(x=>{let[b,w]=In(x,m),I=[];I[w]=t[x],f[b]=I,this.keepIntermediateTensors&&(this.clonedTensorsMap[b]=this.cloneTensorList(I))});let d=this.getFrozenTensorIds(f),{orderedNodes:h,nodeLiveUntilMap:g}=l;for(let x of h){if(f[x.name])continue;let b=_k(x,f,m,this._resourceManager);if(y.isPromise(b))throw new Error(`The execution of the op '${x.op}' returned a promise. Please use model.executeAsync() instead.`);f[x.name]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[x.name]=this.cloneTensorList(b)),this.checkTensorForDisposalWithNodeLiveUntilInfo(x,f,m,d,i,g.get(x.name))}return this.parent==null&&m.dispose(d),e.map(x=>pr(x,f,m))})}getFrozenTensorIds(t){let e=[].concat.apply([],Object.keys(t).map(n=>t[n]).map(n=>n.map(o=>o.id)));return new Set(e)}checkTensorForDisposal(t,e,n,o,s,i,a){if(!(Bu(e)||i.has(t))){for(let u of n[t])u!=null&&(a[u.id]=(a[u.id]||0)+e.children.length);for(let u of e.inputs){if(Bu(u))continue;let l=ik(u.name,n,o);if(l!=null)for(let c of l){if(!c||c.kept||s.has(c.id))continue;let p=a[c.id];p===1?(c.dispose(),delete a[c.id]):p!=null&&a[c.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(t,e,n,o,s,i){function a(u){return Bu(u)||s.has(u.name)}if(!(Bu(t)||i==null))for(let u of i){if(a(u))continue;let l=ik(u.name,e,n);for(let c of l)!c||c.kept||o.has(c.id)||c.dispose()}}async executeAsync(t,e){return this._executeAsync(t,e)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(t=>{for(let e of t)e&&!e.isDisposed&&e.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(t,e,n=!1,o={},s={}){this.disposeIntermediateTensors(),n||(t=this.mapInputs(t),this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e));try{this.keepIntermediateTensors=L().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let i=new Wh(this.weightMap,o,s,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let a=await this.executeWithControlFlow(t,i,e,n),u=e.map(m=>pr(m,a,i)),l=u.map(m=>m.id),c=Object.keys(t).map(m=>t[m].id),p=new Set([...l,...c,...this.weightIds]);return Object.values(a).forEach(m=>{m.forEach(f=>{f&&!f.isDisposed&&!p.has(f.id)&&f.dispose()})}),this.parent==null&&i.dispose(p),u}async executeFunctionAsync(t,e,n){let o=t.reduce((s,i,a)=>(s[this.inputs[a].name]=i,s),{});return this._executeAsync(o,this.outputNodes,!0,e,n)}async executeWithControlFlow(t,e,n,o){let s=Object.keys(t),i=s.map(I=>this.graph.nodes[In(I)[0]]),a=n.map(I=>In(I)[0]),u=new Set(a),l=a.map(I=>this.graph.nodes[I]);l.length===0&&(l=this._outputs);let{usedNodes:c,missingInputs:p,dynamicNode:m,syncInputs:f}=Ek(t,l,this.weightMap,this._initNodes),d=[...i,...this.graph.weights,...this._initNodes||[]].map(I=>({node:I,contexts:e.currentContext})),h=Object.assign({},this.weightMap);Object.keys(t).forEach(I=>{let[N,E]=In(I),A=[];A[E]=t[I],h[N]=A});let g={},x=this.getFrozenTensorIds(h),b={};for(;d.length>0;){let I=this.processStack(i,d,e,h,b,x,u,g,c);await Promise.all(I)}m==null&&!o&&console.warn(\"This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.\");let w=l.filter(I=>!Bu(I)&&!pr(I.name,h,e)).map(I=>I.name);if(w.length>0){let I=\"\";throw m!=null&&(I=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${f}]`),new Error(`Cannot compute the outputs [${w}] from the provided inputs [${s}]. Consider providing the following inputs: [${p}]. ${I}`)}return h}processStack(t,e,n,o,s,i,a,u,l){let c=[];for(;e.length>0;){let p=e.pop();n.currentContext=p.contexts;let m=\"\";if(p.node.op===\"Enter\"&&v(\"isConstant\",p.node,o,n)&&([m]=yi(p.node.name,n)),o[p.node.name]==null){let f=_k(p.node,o,n,this._resourceManager);m||([m]=yi(p.node.name,n));let d=n.currentContext;y.isPromise(f)?c.push(f.then(h=>(o[m]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(h)),n.currentContext=d,this.checkTensorForDisposal(m,p.node,o,n,i,a,u),this.processChildNodes(p.node,e,n,o,s,l),h))):(o[m]=f,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(f)),this.checkTensorForDisposal(m,p.node,o,n,i,a,u),this.processChildNodes(p.node,e,n,o,s,l))}else this.processChildNodes(p.node,e,n,o,s,l)}return c}processChildNodes(t,e,n,o,s,i){t.children.forEach(a=>{let[u]=yi(a.name,n);s[u]||!i.has(a.name)||(a.op===\"Merge\"?a.inputNames.some(l=>!!pr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})):a.inputNames.every(l=>!!pr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(e=>e.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(e=>{let n=t[e],[o]=In(e),s=this.graph.nodes[o];if(s.attrParams.shape&&s.attrParams.shape.value){let i=s.attrParams.shape.value,a=i.length===n.shape.length&&n.shape.every((u,l)=>i[l]===-1||i[l]===u);y.assert(a,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${i}], but was [${n.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(n.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(t){var e,n;let o={};for(let s in t){let i=(n=(e=this._signature)===null||e===void 0?void 0:e.inputs)===null||n===void 0?void 0:n[s];i!=null?o[i.name]=t[s]:o[s]=t[s]}return o}checkInputs(t){let e=Object.keys(t).filter(n=>{let[o]=In(n);return this.graph.nodes[o]==null});if(e.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${e}] that are not part of graph`)}mapOutputs(t){return t.map(e=>{var n,o;let s=(o=(n=this._signature)===null||n===void 0?void 0:n.outputs)===null||o===void 0?void 0:o[e];return s!=null?s.name:e},{})}checkOutputs(t){t.forEach(e=>{let[n]=In(e);if(!this.graph.nodes[n])throw new Error(`The output '${e}' is not found in the graph`)})}};var ew=class{constructor(t={},e={}){this.hashTableNameToHandle=t,this.hashTableMap=e}addHashTable(t,e){this.hashTableNameToHandle[t]=e.handle,this.hashTableMap[e.id]=e}getHashTableHandleByName(t){return this.hashTableNameToHandle[t]}getHashTableById(t){return this.hashTableMap[t]}dispose(){for(let t in this.hashTableMap)this.hashTableMap[t].clearAndClose(),delete this.hashTableMap[t];for(let t in this.hashTableNameToHandle)this.hashTableNameToHandle[t].dispose(),delete this.hashTableNameToHandle[t]}};var ptt=\"?tfjs-format=file\",mtt=\"model.json\",Hh=class{get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}constructor(t,e={},n=Mr){this.modelUrl=t,this.loadOptions=e,this.version=\"n/a\",this.io=n,e==null&&(this.loadOptions={}),this.resourceManager=new ew}findIOHandler(){let t=this.modelUrl;if(t.load!=null)this.handler=t;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(t,this.loadOptions);else{let e=this.io.getLoadHandlers(t,this.loadOptions);if(e.length===0)e.push(this.io.browserHTTPRequest(t,this.loadOptions));else if(e.length>1)throw new Error(`Found more than one (${e.length}) load handlers for URL '${[t]}'`);this.handler=e[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error(\"Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.\");let t=this.handler.load();return y.isPromise(t)?t.then(e=>e.getWeightStream==null?this.loadSync(e):this.loadStreaming(e)):this.loadSync(t)}loadSync(t){let e=this.io.decodeWeights(t.weightData,t.weightSpecs);return this.loadWithWeightMap(t,e)}async loadStreaming(t){if(t.getWeightStream==null)throw new Error(\"Model artifacts missing streamWeights function\");let e=await ix(t.getWeightStream(),t.weightSpecs);return this.loadWithWeightMap(t,e)}loadWithWeightMap(t,e){this.artifacts=t;let n=this.artifacts.modelTopology,o=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let s=this.artifacts.userDefinedMetadata;s.signature!=null&&(o=s.signature),s.structuredOutputKeys!=null&&(this.structuredOutputKeys=s.structuredOutputKeys)}if(this.signature=o,this.version=`${n.versions.producer}.${n.versions.minConsumer}`,this.executor=new Uh(Gh.Instance.transformGraph(n,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(e),this.executor.resourceManager=this.resourceManager,t.modelInitializer!=null&&t.modelInitializer.node!=null){let s=Gh.Instance.transformGraph(t.modelInitializer);this.initializer=new Uh(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=t.initializerSignature}return!0}async save(t,e){if(typeof t==\"string\"){let n=this.io.getSaveHandlers(t);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${t}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${t}'`);t=n[0]}if(t.save==null)throw new Error(\"GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.\");return t.save(this.artifacts)}addStructuredOutputNames(t){if(this.structuredOutputKeys){let e=t instanceof Lt?[t]:t,n={};return e.forEach((o,s)=>n[this.structuredOutputKeys[s]]=o),n}return t}predict(t,e){let n=this.execute(t,this.outputNodes);return this.addStructuredOutputNames(n)}async predictAsync(t,e){let n=await this.executeAsync(t,this.outputNodes);return this.addStructuredOutputNames(n)}normalizeInputs(t){var e;if(!(t instanceof Lt)&&!Array.isArray(t)){let s=(e=this.signature)===null||e===void 0?void 0:e.inputs;if(s!=null)for(let i in s){let a=s[i];a.resourceId!=null&&(t[i]=this.resourceIdToCapturedInput[a.resourceId])}return t}t=Array.isArray(t)?t:[t];let n=Object.keys(this.resourceIdToCapturedInput).length;if(t.length+n!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-n} non-resource placeholders, while there are ${t.length} input tensors provided.`);let o=0;return this.inputNodes.reduce((s,i)=>{var a,u,l;let c=(l=(u=(a=this.signature)===null||a===void 0?void 0:a.inputs)===null||u===void 0?void 0:u[i])===null||l===void 0?void 0:l.resourceId;return c!=null?s[i]=this.resourceIdToCapturedInput[c]:s[i]=t[o++],s},{})}normalizeOutputs(t){return t=t||this.outputNodes,Array.isArray(t)?t:[t]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(t){if(this.resourceIdToCapturedInput={},this.initializerSignature){let e=this.initializerSignature.outputs,n=Object.keys(e);for(let o=0;o1?n:n[0]}async executeAsync(t,e){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),t=this.normalizeInputs(t),e=this.normalizeOutputs(e);let n=await this.executor.executeAsync(t,e);return n.length>1?n:n[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(t){return Object.keys(t).reduce((e,n)=>(e[n]=[t[n]],e),{})}dispose(){this.executor.dispose(),this.initializer&&(this.initializer.dispose(),this.resourceIdToCapturedInput&&Tt(this.resourceIdToCapturedInput)),this.resourceManager.dispose()}};async function ftt(r,t={},e=Mr){if(r==null)throw new Error(\"modelUrl in loadGraphModel() cannot be null. 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er{constructor(t,e,n=!0){super(),this.upstream=t,this.batchSize=e,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let t=[];for(;t.length0?{value:t,done:!1}:{value:null,done:!0};t.push(e.value)}return{value:t,done:!1}}},Lk=class extends er{constructor(t,e){super(),this.upstream=t,this.predicate=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let t=await this.upstream.next();if(t.done||this.predicate(t.value))return t;Tt(t.value)}}},zk=class extends er{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Map`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=Io.getTensorsInContainer(t.value),n=this.transform(t.value),o=Io.getTensorsInContainer(n);for(let s of e)Io.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},Bk=class extends er{constructor(t,e){super(),this.upstream=t,this.handler=e,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(t){if(!this.handler(t))return{value:null,done:!0}}}},ow=class extends er{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=Io.getTensorsInContainer(t.value),n=await this.transform(t.value),o=Io.getTensorsInContainer(n);for(let s of e)Io.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},Zc=class extends er{constructor(){super(),this.outputQueue=new qh,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}}},Vk=class extends Zc{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let t=await this.upstream.next();if(t.done)return!1;let e=Io.getTensorsInContainer(t.value),n=this.transform(t.value),o=Io.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let s of e)Io.isTensorInList(s,o)||s.dispose();return!0}},sw=class extends er{constructor(t,e){super(),this.baseErrorHandler=e,this.lastRead=null,this.iterator=null,this.moreIterators=t}summary(){return\"TODO: fill in upstream of chained summaries 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At least one type of data should be returned.\")}summary(){return\"microphone\"}static async create(t={}){if(!L().get(\"IS_BROWSER\"))throw new Error(\"microphone API is only supported in browser environment.\");let e=new r(t);return await e.start(),e}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 t=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new t,!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 e=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,e.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 t,e,n=await this.getAudioData();if(this.includeSpectrogram){let o=this.flattenQueue(n.freqDataQueue);t=this.getTensorFromAudioDataArray(o,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let o=this.flattenQueue(n.timeDataQueue);e=this.getTensorFromAudioDataArray(o,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:t,waveform:e},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let t=[],e=[],n=0;return new Promise(o=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&o({freqDataQueue:t,timeDataQueue:e}),t.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),e.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(s),o({freqDataQueue:t,timeDataQueue:e}))},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(t){let e=t[0].length,n=new Float32Array(t.length*e);return t.forEach((o,s)=>n.set(o,s*e)),n}getTensorFromAudioDataArray(t,e){let n=new Float32Array(y.sizeFromShape(e));return n.set(t,n.length-t.length),ir(n,e)}};var cw=class r extends er{constructor(t,e){if(super(),this.webcamVideoElement=t,this.webcamConfig=e,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Oe([0],\"int32\"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,o=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-n)/2,i=(1-o)/2,a=s+n,u=o+i;this.cropBox=ci([i,s,u,a],[1,4])}else this.cropBox=ci([0,0,1,1],[1,4])}summary(){return\"webcam\"}static async create(t,e={}){if(!L().get(\"IS_BROWSER\"))throw new Error(\"tf.data.webcam is only supported in browser environment.\");if(!t){if(t=document.createElement(\"video\"),!e.resizeWidth||!e.resizeHeight)throw new Error(\"Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.\");t.width=e.resizeWidth,t.height=e.resizeHeight}let n=new r(t,e);return await n.start(),n}async start(){this.webcamConfig.facingMode&&y.assert(this.webcamConfig.facingMode===\"user\"||this.webcamConfig.facingMode===\"environment\",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. 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xet(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s;tt([o,s],\"avgPoolGrad\");let{filterSize:a,strides:u,pad:l}=n,c=S.computePool2DInfo(i.shape,a,u,1,l),p=c.strideHeight,m=c.strideWidth,f=c.filterHeight,d=c.filterWidth,h=c.dilationHeight,g=c.dilationWidth,x=c.effectiveFilterHeight,b=c.effectiveFilterWidth,w=b-1-c.padInfo.left,I=x-1-c.padInfo.top,N=wt(i.shape,\"float32\"),E=1/(f*d),A=e.data.get(o.dataId).values,D=wt(o.shape,\"float32\",A);for(let F=0;F=c.outHeight||Math.floor(Y)!==Y))for(let Z=0;Z=c.outWidth||Math.floor(et)!==et)continue;let nt=D.get(F,Y,et,M);H+=nt}}N.set(H*E,F,V,G,M)}return e.makeTensorInfo(N.shape,N.dtype,N.values)}var hM={kernelName:Ul,backendName:\"cpu\",kernelFunc:xet};function yet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,scale:s,offset:i,mean:a,variance:u}=t;y.assert(a.shape.length===u.shape.length,()=>\"Batch normalization gradient requires mean and variance to have equal ranks.\"),y.assert(i==null||a.shape.length===i.shape.length,()=>\"Batch 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a=s.reduce((x,b)=>x*b),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=te({inputs:{x:o},backend:e,attrs:{shape:u}}),d=We({inputs:{x:f},backend:e,attrs:{perm:l}}),h=te({inputs:{x:d},backend:e,attrs:{shape:c}}),g=Mo({inputs:{x:h},backend:e,attrs:{begin:p,size:m}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),g}var xM={kernelName:Ri,backendName:\"cpu\",kernelFunc:bet};function wet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i}=n,a=e.data.get(o.dataId).values,u=e.data.get(s.dataId).values,l=fd(a,u,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,l)}var yM={kernelName:Da,backendName:\"cpu\",kernelFunc:wet};function Iet(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.data.get(n.dataId).values,i=e.data.get(o.dataId).values,a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeTensorInfo([a.length],\"int32\",Int32Array.from(a))}var bM={kernelName:ql,backendName:\"cpu\",kernelFunc:Iet};var Cet=At(ho,(r,t)=>{let e=t;return r>e.clipValueMax?e.clipValueMax:r{let{x:t}=r.inputs,e=r.backend,n=new Float32Array(y.sizeFromShape(t.shape)),o=e.data.get(t.dataId),s=o.complexTensorInfos.real,i=o.complexTensorInfos.imag,a=e.data.get(s.dataId).values,u=e.data.get(i.dataId).values;for(let l=0;lh.shape);S.assertParamsConsistent(i,s);let a=S.computeOutShape(t.map(h=>h.shape),s);if(y.sizeFromShape(a)===0)return e.makeTensorInfo(a,t[0].dtype,[]);let u=t.filter(h=>y.sizeFromShape(h.shape)>0);if(u.length===1)return Xr({inputs:{x:u[0]},backend:e});if(u[0].dtype===\"complex64\"){let h=u.map(I=>Ro({inputs:{input:I},backend:e})),g=u.map(I=>ba({inputs:{input:I},backend:e})),x=Gu({inputs:h,backend:e,attrs:{axis:s}}),b=Gu({inputs:g,backend:e,attrs:{axis:s}}),w=Ir({inputs:{real:x,imag:b},backend:e});return h.forEach(I=>e.disposeIntermediateTensorInfo(I)),g.forEach(I=>e.disposeIntermediateTensorInfo(I)),e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(b),w}let l=u.map(h=>{let x=[-1,y.sizeFromShape(h.shape.slice(s))];return te({inputs:{x:h},backend:e,attrs:{shape:x}})}),c=l.map(h=>({vals:e.data.get(h.dataId).values,shape:h.shape}));a=S.computeOutShape(l.map(h=>h.shape),1);let p=l[0].shape[0]===1,m=Jc(c,a,t[0].dtype,p),f=S.computeOutShape(u.map(h=>h.shape),s),d=e.makeTensorInfo(f,t[0].dtype,m);return l.forEach(h=>e.disposeIntermediateTensorInfo(h)),d}var vM={kernelName:Fi,backendName:\"cpu\",kernelFunc:Gu};function $T(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dataFormat:u,dilations:l,dimRoundingMode:c}=n;tt([o,s],\"conv2d\");let p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,s.shape,i,l,a,c,!1,p),f=m.filterHeight,d=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,x=m.padInfo.left,b=m.padInfo.top,w=m.dataFormat===\"channelsLast\",I=new le(m.outShape,o.dtype),N=y.computeStrides(o.shape),E=y.computeStrides(s.shape),A=N[0],D=w?N[1]:N[2],F=w?N[2]:1,M=w?1:N[1],V=I.strides[0],G=w?I.strides[1]:I.strides[2],W=w?I.strides[2]:1,q=w?1:I.strides[1],H=e.data.get(o.dataId).values,K=e.data.get(s.dataId).values,Y=I.values;for(let Z=0;Z=m.inHeight)continue;let gt=it*E[0],Ct=et+ft*D;for(let Rt=0;Rt=m.inWidth)continue;let xe=gt+qt*E[1],re=Ct+pe*F,ye=xe;for(let de=0;de=l.inDepth)continue;let Z=K*F[0],et=V+Y*D[1];for(let nt=0;nt=l.inHeight)continue;let ft=Z+ot*F[1],gt=et+it*D[2];for(let Ct=0;Ct=l.inWidth)continue;let pe=ft+Ht*F[2],xe=gt+qt*l.inChannels,re=pe;for(let ye=0;yeMath.cos(r)),AM={kernelName:rs,backendName:\"cpu\",kernelFunc:Aet};var Det=At(ns,r=>Math.cosh(r)),DM={kernelName:ns,backendName:\"cpu\",kernelFunc:Det};function $et(r){let{inputs:t,backend:e,attrs:n}=r,{image:o,boxes:s,boxInd:i}=t,{cropSize:a,method:u,extrapolationValue:l}=n,[c,p,m,f]=o.shape,d=s.shape[0],[h,g]=a,x=wt([d,h,g,f],\"float32\"),b=e.data.get(s.dataId).values,w=e.data.get(i.dataId).values,I=e.data.get(o.dataId).values,N=y.computeStrides(o.shape),E=y.computeStrides(x.shape);for(let A=0;A=c)continue;let q=h>1?(V-F)*(p-1)/(h-1):0,H=g>1?(G-M)*(m-1)/(g-1):0;for(let K=0;K1?F*(p-1)+K*q:.5*(F+V)*(p-1);if(Y<0||Y>p-1){for(let Z=0;Z1?M*(m-1)+st*H:.5*(M+G)*(m-1);if(lt<0||lt>m-1){for(let gt=0;gt1?M*(m-1)+Z*H:.5*(M+G)*(m-1);if(et<0||et>m-1){for(let lt=0;ltx+d-b-1:(x,b)=>x+b;for(let x=0;xx+d-b-1:(x,b)=>x+b;for(let x=0;x`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let a=o.shape[0],u=o.shape[1],l=o.shape[2],c=o.shape[3],p=u*s,m=l*s,f=c/(s*s),d=e.data.get(o.dataId).values,h=new Float32Array(a*p*m*f),g=0;for(let x=0;x`Error in depthwiseConv2d: Either strides or dilations must be 1. 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1] but encountered ${A}.`)}else if(o.dtype===\"int32\"&&(A<0||A>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${A}.`);d===1?(I[0]=A*g,I[1]=A*g,I[2]=A*g):I[E]=A*g}let N=w*4;x[N+0]=Math.round(I[0]),x[N+1]=Math.round(I[1]),x[N+2]=Math.round(I[2]),x[N+3]=Math.round(I[3])}s.width=f,s.height=m;let b=new ImageData(x,f,m);return p.putImageData(b,0,0),o}var UM={kernelName:Jd,backendName:\"cpu\",kernelFunc:Bet};function $l(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n;tt(o,\"sum\");let a;o.dtype===\"bool\"?a=Fo({inputs:{x:o},backend:e,attrs:{dtype:\"int32\"}}):a=Xr({inputs:{x:o},backend:e});let u=a.shape.length,l=y.parseAxisParam(s,a.shape),c=S.getAxesPermutation(l,u),p=l,m=a;c!=null&&(m=We({inputs:{x:a},backend:e,attrs:{perm:c}}),p=S.getInnerMostAxes(p.length,u)),S.assertAxesAreInnerMostDims(\"sum\",p,m.shape.length);let[f,d]=S.computeOutAndReduceShapes(m.shape,p),h=S.upcastType(m.dtype,\"int32\"),g=pd(e,f,h),x=y.sizeFromShape(d),b=e.data.get(g.dataId).values,w=e.data.get(m.dataId).values;for(let I=0;I=0&&(m=$l({inputs:{x:m},backend:e,attrs:{axis:l[h]-(i.length-f),keepDims:!1}}),d.push(m)),f--)}for(let h of d)h!==m&&e.disposeIntermediateTensorInfo(h);return m}var qM={kernelName:Fp,backendName:\"cpu\",kernelFunc:Vet};function Get(r){let{inputs:t,backend:e}=r,{dy:n,y:o}=t;tt([n,o],\"eluGrad\");let s=new Float32Array(y.sizeFromShape(o.shape)),i=e.data.get(o.dataId).values,a=e.data.get(n.dataId).values;for(let u=0;u=0?s[u]=a[u]:s[u]=a[u]*(l+1)}return e.makeTensorInfo(o.shape,\"float32\",s)}var KM={kernelName:La,backendName:\"cpu\",kernelFunc:Get};var Wet=S.ERF_P,Uet=S.ERF_A1,Het=S.ERF_A2,qet=S.ERF_A3,Ket=S.ERF_A4,jet=S.ERF_A5,Xet=At(us,r=>{let t=Math.sign(r),e=Math.abs(r),n=1/(1+Wet*e);return t*(1-((((jet*n+Ket)*n+qet)*n+Het)*n+Uet)*n*Math.exp(-e*e))}),jM={kernelName:us,backendName:\"cpu\",kernelFunc:Xet};function xd(r){let{inputs:t,backend:e,attrs:n}=r,{input:o}=t,{dim:s}=n,i=o.shape.length,a=o.shape.slice(),u=s;return s<0&&(y.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+s+1),a.splice(u,0,1),te({inputs:{x:o},backend:e,attrs:{shape:a}})}var XM={kernelName:Oi,backendName:\"cpu\",kernelFunc:xd};var Yet=Qt((r,t)=>r/t),Qh=oe(as,Yet),tg={kernelName:as,backendName:\"cpu\",kernelFunc:Qh};function Aw(r,t,e){let n=r.shape,o=n[0],s=n[1],i=e.data.get(r.dataId),a=i.complexTensorInfos.real,u=i.complexTensorInfos.imag,l=[o,s],c=y.sizeFromShape(l),p=y.getTypedArrayFromDType(\"float32\",c),m=y.getTypedArrayFromDType(\"float32\",c);for(let g=0;g{let{image:n}=r,o=e,s=y.getTypedArrayFromDType(n.dtype,y.sizeFromShape(n.shape)),[i,a,u,l]=n.shape,c=o.data.get(n.dataId).values;for(let m=0;m=0&&w=0,()=>`GatherV2: the index value ${N} is not in [0, ${c-1}]`)}let p=a;a==null&&(p=0);let m=y.sizeFromShape(s.shape),f=S.segment_util.collectGatherOpShapeInfo(o,s,u,p),d=te({inputs:{x:o},backend:e,attrs:{shape:[f.batchSize,f.outerSize,f.dimSize,f.sliceSize]}}),h=te({inputs:{x:s},backend:e,attrs:{shape:[f.batchSize,m/f.batchSize]}}),g=[f.batchSize,f.outerSize,m/f.batchSize,f.sliceSize],x=e.bufferSync(h),b=e.bufferSync(d),w=gw(b,x,g);return e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),e.makeTensorInfo(f.outputShape,w.dtype,w.values)}var rP={kernelName:Mi,backendName:\"cpu\",kernelFunc:srt};function irt(r){let{inputs:t,backend:e}=r,{input:n}=t,o=y.sizeFromShape(n.shape),s=n.shape[n.shape.length-1],i=o/s,a=te({inputs:{x:n},backend:e,attrs:{shape:[i,s]}}),u=Aw(a,!0,e),l=te({inputs:{x:u},backend:e,attrs:{shape:n.shape}});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(u),l}var nP={kernelName:Mp,backendName:\"cpu\",kernelFunc:irt};var art=At(gs,r=>Number.isFinite(r)?1:0,\"bool\"),oP={kernelName:gs,backendName:\"cpu\",kernelFunc:art};var lrt=At(xs,r=>Math.abs(r)===1/0?1:0,\"bool\"),sP={kernelName:xs,backendName:\"cpu\",kernelFunc:lrt};var urt=At(ys,r=>Number.isNaN(r)?1:0,\"bool\"),iP={kernelName:ys,backendName:\"cpu\",kernelFunc:urt};function crt(r){let{backend:t,attrs:e}=r,{start:n,stop:o,num:s}=e,i=xw(n,o,s);return t.makeTensorInfo([i.length],\"float32\",i)}var aP={kernelName:Ha,backendName:\"cpu\",kernelFunc:crt};var prt=At(Is,r=>Math.log1p(r)),lP={kernelName:Is,backendName:\"cpu\",kernelFunc:prt};var mrt=Qt((r,t)=>r&&t),frt=oe(qa,mrt,null,\"bool\"),uP={kernelName:qa,backendName:\"cpu\",kernelFunc:frt};var drt=At(Ka,r=>r?0:1,\"bool\"),cP={kernelName:Ka,backendName:\"cpu\",kernelFunc:drt};var hrt=Qt((r,t)=>r||t),grt=oe(ja,hrt,null,\"bool\"),pP={kernelName:ja,backendName:\"cpu\",kernelFunc:grt};function xrt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n;tt(o,\"LRN\");let l=o.shape[3],c=l-1,p=e.data.get(o.dataId).values,m=y.sizeFromShape(o.shape),f=new Float32Array(m);function d(h){let g=h%l,x=h-g+Math.max(0,g-s),b=h-g+Math.min(g+s,c),w=0;for(;x<=b;x++){let I=p[x];w+=I*I}return w}for(let h=0;h`Error in maxPool: Either strides or dilations must be 1. 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kt=L();kt.registerFlag(\"HAS_WEBGL\",()=>kt.getNumber(\"WEBGL_VERSION\")>0);kt.registerFlag(\"WEBGL_VERSION\",()=>zw(2)?2:zw(1)?1:0);kt.registerFlag(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\",()=>!1);kt.registerFlag(\"WEBGL_BUFFER_SUPPORTED\",()=>kt.get(\"WEBGL_VERSION\")===2);kt.registerFlag(\"WEBGL_CPU_FORWARD\",()=>!0);kt.registerFlag(\"WEBGL_FORCE_F16_TEXTURES\",()=>!1);kt.registerFlag(\"WEBGL_PACK\",()=>kt.getBool(\"HAS_WEBGL\"));kt.registerFlag(\"WEBGL_PACK_NORMALIZATION\",()=>kt.getBool(\"WEBGL_PACK\"));kt.registerFlag(\"WEBGL_PACK_CLIP\",()=>kt.getBool(\"WEBGL_PACK\"));kt.registerFlag(\"WEBGL_PACK_DEPTHWISECONV\",()=>kt.getBool(\"WEBGL_PACK\"));kt.registerFlag(\"WEBGL_PACK_BINARY_OPERATIONS\",()=>kt.getBool(\"WEBGL_PACK\"));kt.registerFlag(\"WEBGL_PACK_UNARY_OPERATIONS\",()=>kt.getBool(\"WEBGL_PACK\"));kt.registerFlag(\"WEBGL_PACK_ARRAY_OPERATIONS\",()=>kt.getBool(\"WEBGL_PACK\"));kt.registerFlag(\"WEBGL_PACK_IMAGE_OPERATIONS\",()=>kt.getBool(\"WEBGL_PACK\"));kt.registerFlag(\"WEBGL_PACK_REDUCE\",()=>kt.getBool(\"WEBGL_PACK\"));kt.registerFlag(\"WEBGL_LAZILY_UNPACK\",()=>kt.getBool(\"WEBGL_PACK\"));kt.registerFlag(\"WEBGL_CONV_IM2COL\",()=>kt.getBool(\"WEBGL_PACK\"));kt.registerFlag(\"WEBGL_PACK_CONV2DTRANSPOSE\",()=>kt.getBool(\"WEBGL_PACK\"));kt.registerFlag(\"WEBGL_MAX_TEXTURE_SIZE\",()=>e1(kt.getNumber(\"WEBGL_VERSION\")));kt.registerFlag(\"WEBGL_MAX_TEXTURES_IN_SHADER\",()=>r1(kt.getNumber(\"WEBGL_VERSION\")));kt.registerFlag(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\",()=>{let r=kt.getNumber(\"WEBGL_VERSION\");return r===0?0:n1(r)});kt.registerFlag(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\",()=>kt.getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0&&!du.isMobile());kt.registerFlag(\"WEBGL_RENDER_FLOAT32_CAPABLE\",()=>o1(kt.getNumber(\"WEBGL_VERSION\")));kt.registerFlag(\"WEBGL_RENDER_FLOAT32_ENABLED\",()=>kt.getBool(\"WEBGL_FORCE_F16_TEXTURES\")?!1:kt.getBool(\"WEBGL_RENDER_FLOAT32_CAPABLE\"));kt.registerFlag(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\",()=>s1(kt.getNumber(\"WEBGL_VERSION\")));kt.registerFlag(\"WEBGL_FENCE_API_ENABLED\",()=>i1(kt.getNumber(\"WEBGL_VERSION\")));kt.registerFlag(\"WEBGL_SIZE_UPLOAD_UNIFORM\",()=>kt.getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?4:0);kt.registerFlag(\"WEBGL_DELETE_TEXTURE_THRESHOLD\",()=>-1,r=>{if(typeof r!=\"number\")throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be a number but got ${r}.`);if(r<0&&r!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${r}.`)});kt.registerFlag(\"WEBGL_FLUSH_THRESHOLD\",()=>du.isMobile()?1:-1,r=>{if(typeof r!=\"number\")throw new Error(`WEBGL_FLUSH_THRESHOLD must be a number but got ${r}.`);if(r<0&&r!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${r}.`)});kt.registerFlag(\"CPU_HANDOFF_SIZE_THRESHOLD\",()=>128);kt.registerFlag(\"WEBGL_USE_SHAPES_UNIFORMS\",()=>!1);kt.registerFlag(\"TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD\",()=>1e5);kt.registerFlag(\"TOPK_K_CPU_HANDOFF_THRESHOLD\",()=>128);kt.registerFlag(\"WEBGL_EXP_CONV\",()=>!1);kt.registerFlag(\"SOFTWARE_WEBGL_ENABLED\",()=>kt.getBool(\"IS_TEST\"));kt.registerFlag(\"WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE\",()=>1/0);kt.registerFlag(\"WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE\",()=>!1);kt.registerFlag(\"WEBGL2_ISNAN_CUSTOM\",()=>!1);kt.registerFlag(\"ENGINE_COMPILE_ONLY\",()=>!1);function Ue(){let r,t,e,n,o,s,i,a,u,l;return L().getNumber(\"WEBGL_VERSION\")===2?(r=\"#version 300 es\",t=\"in\",e=\"out\",n=\"in\",o=\"texture\",s=\"outputColor\",i=\"out vec4 outputColor;\",a=L().getBool(\"WEBGL2_ISNAN_CUSTOM\")?`\n bool isnan_custom(float val) {\n uint floatToUint = floatBitsToUint(val);\n return (floatToUint & 0x7fffffffu) > 0x7f800000u;\n }\n\n bvec4 isnan_custom(vec4 val) {\n return bvec4(isnan_custom(val.x),\n isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));\n }\n\n #define isnan(value) isnan_custom(value)\n `:\"\",u=\"\",l=`\n #define round(value) newRound(value)\n int newRound(float value) {\n return int(floor(value + 0.5));\n }\n\n ivec4 newRound(vec4 value) {\n return ivec4(floor(value + vec4(0.5)));\n }\n `):(r=\"\",t=\"attribute\",e=\"varying\",n=\"varying\",o=\"texture2D\",s=\"gl_FragColor\",i=\"\",a=`\n #define isnan(value) isnan_custom(value)\n bool isnan_custom(float val) {\n return (val > 0. || val < 1. || val == 0.) ? false : true;\n }\n bvec4 isnan_custom(vec4 val) {\n return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));\n }\n `,u=`\n uniform float INFINITY;\n\n bool isinf(float val) {\n return abs(val) == INFINITY;\n }\n bvec4 isinf(vec4 val) {\n return equal(abs(val), vec4(INFINITY));\n }\n `,l=`\n int round(float value) {\n return int(floor(value + 0.5));\n }\n\n ivec4 round(vec4 value) {\n return ivec4(floor(value + vec4(0.5)));\n }\n `),{version:r,attribute:t,varyingVs:e,varyingFs:n,texture2D:o,output:s,defineOutput:i,defineSpecialNaN:a,defineSpecialInf:u,defineRound:l}}function vi(r,t,e=\"index\"){let n=y.computeStrides(t);return n.map((o,s)=>{let i=`int ${r[s]} = ${e} / ${o}`,a=s===n.length-1?`int ${r[s+1]} = ${e} - ${r[s]} * ${o}`:`index -= ${r[s]} * ${o}`;return`${i}; ${a};`}).join(\"\")}function up(r,t,e=\"index\"){let n=y.computeStrides(t);return n.map((o,s)=>{let i=`int ${r[s]} = ${e} / outShapeStrides[${s}]`,a=s===n.length-1?`int ${r[s+1]} = ${e} - ${r[s]} * outShapeStrides[${s}]`:`index -= ${r[s]} * outShapeStrides[${s}]`;return`${i}; ${a};`}).join(\"\")}function Xnt(r,t){let e=r.length,n=r.map(s=>`${t}[${s}]`),o=new Array(e-1);o[e-2]=n[e-1];for(let s=e-3;s>=0;--s)o[s]=`(${o[s+1]} * ${n[s+1]})`;return o}function FL(r,t,e=\"index\"){let n=r.map((s,i)=>i),o=Xnt(n,t);return o.map((s,i)=>{let a=`int ${r[i]} = ${e} / ${o[i]}`,u=i===o.length-1?`int ${r[i+1]} = ${e} - ${r[i]} * ${o[i]}`:`index -= ${r[i]} * ${o[i]}`;return`${a}; ${u};`}).join(\"\")}function Cd(r){let t=y.computeStrides(r).map(e=>e.toString());return`\n int getFlatIndex(ivec3 coords) {\n return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;\n }\n`}function vd(){return`\n int getFlatIndex(ivec3 coords) {\n return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;\n }\n`}var Bw=`\n const float FLOAT_MAX = 1.70141184e38;\n const float FLOAT_MIN = 1.17549435e-38;\n\n lowp vec4 encode_float(highp float v) {\n if (isnan(v)) {\n return vec4(255, 255, 255, 255);\n }\n\n highp float av = abs(v);\n\n if(av < FLOAT_MIN) {\n return vec4(0.0, 0.0, 0.0, 0.0);\n } else if(v > FLOAT_MAX) {\n return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n } else if(v < -FLOAT_MAX) {\n return vec4(0.0, 0.0, 128.0, 255.0) / 255.0;\n }\n\n highp vec4 c = vec4(0,0,0,0);\n\n highp float e = floor(log2(av));\n highp float m = exp2(fract(log2(av))) - 1.0;\n\n c[2] = floor(128.0 * m);\n m -= c[2] / 128.0;\n c[1] = floor(32768.0 * m);\n m -= c[1] / 32768.0;\n c[0] = floor(8388608.0 * m);\n\n highp float ebias = e + 127.0;\n c[3] = floor(ebias / 2.0);\n ebias -= c[3] * 2.0;\n c[2] += floor(ebias) * 128.0;\n\n c[3] += 128.0 * step(0.0, -v);\n\n return c / 255.0;\n }\n`;var{getBroadcastDims:OL}=S;function ML(r,t,e){let n=[];if(r.forEach(f=>{let d=y.sizeFromShape(f.shapeInfo.logicalShape);if(f.shapeInfo.isUniform?n.push(`uniform float ${f.name}${d>1?`[${d}]`:\"\"};`):(n.push(`uniform sampler2D ${f.name};`),n.push(`uniform int offset${f.name};`)),e.enableShapeUniforms){let{uniformShape:h}=Vw(e.packedInputs,f.shapeInfo.logicalShape,f.shapeInfo.texShape);switch(h.length){case 1:n.push(`uniform int ${f.name}Shape;`);break;case 2:n.push(`uniform ivec2 ${f.name}Shape;`);break;case 3:n.push(`uniform ivec3 ${f.name}Shape;`);break;case 4:n.push(`uniform ivec4 ${f.name}Shape;`);break;default:break}n.push(`uniform ivec2 ${f.name}TexShape;`)}}),e.enableShapeUniforms){switch(t.logicalShape.length){case 1:n.push(\"uniform int outShape;\");break;case 2:n.push(\"uniform ivec2 outShape;\"),n.push(\"uniform int outShapeStrides;\");break;case 3:n.push(\"uniform ivec3 outShape;\"),n.push(\"uniform ivec2 outShapeStrides;\");break;case 4:n.push(\"uniform ivec4 outShape;\"),n.push(\"uniform ivec3 outShapeStrides;\");break;default:break}n.push(\"uniform ivec2 outTexShape;\")}e.customUniforms&&e.customUniforms.forEach(f=>{n.push(`uniform ${f.type} ${f.name}${f.arrayIndex?`[${f.arrayIndex}]`:\"\"};`)});let o=n.join(`\n`),s=r.map(f=>Ynt(f,t,e.packedInputs,e.enableShapeUniforms)).join(`\n`),i=t.texShape,a=Ue(),u=Qnt(a),l,c,p=rot(a);return t.isPacked?(l=Znt(t.logicalShape,i,e.enableShapeUniforms),c=eot(a)):(l=Jnt(t.logicalShape,i,e.enableShapeUniforms),c=tot(a)),e.packedInputs&&(p+=iot),[p,u,c,o,l,s,e.userCode].join(`\n`)}function Nd(r,t=!1){let e=r.shapeInfo.logicalShape;switch(e.length){case 0:return yot(r,t);case 1:return wot(r,t);case 2:return Cot(r,t);case 3:return Sot(r,t);case 4:return kot(r,t);case 5:return Tot(r);case 6:return _ot(r);default:throw new Error(`${e.length}-D input sampling is not yet supported`)}}function PL(r,t){switch(r.shapeInfo.logicalShape.length){case 0:return xot(r);case 1:return bot(r,t);case 2:return Iot(r,t);case 3:return vot(r,t);default:return Not(r,t)}}function Ynt(r,t,e=!1,n){let o=\"\";e?o+=PL(r,n):o+=Nd(r,n);let s=r.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(e?o+=Eot(r,t):o+=Aot(r,t)),o}function Znt(r,t,e){switch(r.length){case 0:return LL();case 1:return aot(r,t,e);case 2:return hot(r,t,e);case 3:return uot(r,t,e);default:return pot(r,t,e)}}function Jnt(r,t,e){switch(r.length){case 0:return LL();case 1:return lot(r,t,e);case 2:return got(r,t,e);case 3:return cot(r,t,e);case 4:return mot(r,t,e);case 5:return fot(r,t);case 6:return dot(r,t);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function Qnt(r){return`\n float sampleTexture(sampler2D textureSampler, vec2 uv) {\n return ${r.texture2D}(textureSampler, uv).r;\n }\n `}function tot(r){return`\n void setOutput(float val) {\n ${r.output} = vec4(val, 0, 0, 0);\n }\n `}function eot(r){return`\n void setOutput(vec4 val) {\n ${r.output} = val;\n }\n `}function rot(r){return`${r.version}\n precision highp float;\n precision highp int;\n precision highp sampler2D;\n ${r.varyingFs} vec2 resultUV;\n ${r.defineOutput}\n const vec2 halfCR = vec2(0.5, 0.5);\n\n struct ivec5\n {\n int x;\n int y;\n int z;\n int w;\n int u;\n };\n\n struct ivec6\n {\n int x;\n int y;\n int z;\n int w;\n int u;\n int v;\n };\n\n uniform float NAN;\n ${r.defineSpecialNaN}\n ${r.defineSpecialInf}\n ${r.defineRound}\n\n int imod(int x, int y) {\n return x - y * (x / y);\n }\n\n int idiv(int a, int b, float sign) {\n int res = a / b;\n int mod = imod(a, b);\n if (sign < 0. && mod != 0) {\n res -= 1;\n }\n return res;\n }\n\n //Based on the work of Dave Hoskins\n //https://www.shadertoy.com/view/4djSRW\n #define HASHSCALE1 443.8975\n float random(float seed){\n vec2 p = resultUV * seed;\n vec3 p3 = fract(vec3(p.xyx) * HASHSCALE1);\n p3 += dot(p3, p3.yzx + 19.19);\n return fract((p3.x + p3.y) * p3.z);\n }\n\n ${not}\n ${oot}\n ${sot}\n `}var not=`\nvec2 uvFromFlat(int texNumR, int texNumC, int index) {\n int texR = index / texNumC;\n int texC = index - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n int texelIndex = index / 2;\n int texR = texelIndex / texNumC;\n int texC = texelIndex - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,oot=`\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n int texNumC, int row, int col) {\n int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n int texR = texelIndex / texNumC;\n int texC = texelIndex - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,sot=`\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n int texelsInBatch, int texelsInLogicalRow, int b,\n int row, int col) {\n int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n int texR = index / texNumC;\n int texC = index - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,iot=`\n float getChannel(vec4 frag, vec2 innerDims) {\n vec2 modCoord = mod(innerDims, 2.);\n return modCoord.x == 0. ?\n (modCoord.y == 0. ? frag.r : frag.g) :\n (modCoord.y == 0. ? frag.b : frag.a);\n }\n float getChannel(vec4 frag, int dim) {\n float modCoord = mod(float(dim), 2.);\n return modCoord == 0. ? frag.r : frag.g;\n }\n`;function LL(){return`\n int getOutputCoords() {\n return 0;\n }\n `}function aot(r,t,e){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?e?`\n int getOutputCoords() {\n return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));\n }\n `:`\n int getOutputCoords() {\n return 2 * int(resultUV.x * ${n[1]}.0);\n }\n `:n[1]===1?e?`\n int getOutputCoords() {\n return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));\n }\n `:`\n int getOutputCoords() {\n return 2 * int(resultUV.y * ${n[0]}.0);\n }\n `:e?`\n int getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(packedTexShape[0], packedTexShape[1]));\n return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);\n }\n `:`\n int getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${n[0]}, ${n[1]}));\n return 2 * (resTexRC.x * ${n[1]} + resTexRC.y);\n }\n `}function lot(r,t,e){return t[0]===1?e?`\n int getOutputCoords() {\n return int(resultUV.x * float(outTexShape[1]));\n }\n `:`\n int getOutputCoords() {\n return int(resultUV.x * ${t[1]}.0);\n }\n `:t[1]===1?e?`\n int getOutputCoords() {\n return int(resultUV.y * float(outTexShape[0]));\n }\n `:`\n int getOutputCoords() {\n return int(resultUV.y * ${t[0]}.0);\n }\n `:e?`\n int getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(outTexShape[0], outTexShape[1]));\n return resTexRC.x * outTexShape[1] + resTexRC.y;\n }\n `:`\n int getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${t[0]}, ${t[1]}));\n return resTexRC.x * ${t[1]} + resTexRC.y;\n }\n `}function uot(r,t,e){if(e)return`\n ivec3 getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));\n int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(packedTexShape[0], packedTexShape[1]));\n int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n int b = index / texelsInBatch;\n index -= b * texelsInBatch;\n\n int r = 2 * (index / texelsInLogicalRow);\n int c = imod(index, texelsInLogicalRow) * 2;\n\n return ivec3(b, r, c);\n }\n `;let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],o=Math.ceil(r[2]/2),s=o*Math.ceil(r[1]/2);return`\n ivec3 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${n[0]}, ${n[1]}));\n int index = resTexRC.x * ${n[1]} + resTexRC.y;\n\n int b = index / ${s};\n index -= b * ${s};\n\n int r = 2 * (index / ${o});\n int c = imod(index, ${o}) * 2;\n\n return ivec3(b, r, c);\n }\n `}function cot(r,t,e){if(e)return`\n ivec3 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(outTexShape[0], outTexShape[1]));\n int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n ${up([\"r\",\"c\",\"d\"],r)}\n return ivec3(r, c, d);\n }\n`;let n=vi([\"r\",\"c\",\"d\"],r);return`\n ivec3 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${t[0]}, ${t[1]}));\n int index = resTexRC.x * ${t[1]} + resTexRC.y;\n ${n}\n return ivec3(r, c, d);\n }\n `}function pot(r,t,e){if(e)return`\n ivec4 getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(packedTexShape[0], packedTexShape[1]));\n int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));\n int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));\n int texelsInBatchN = texelsInBatch * outShape[1];\n\n int b2 = index / texelsInBatchN;\n index -= b2 * texelsInBatchN;\n\n int b = index / texelsInBatch;\n index -= b * texelsInBatch;\n\n int r = 2 * (index / texelsInLogicalRow);\n int c = imod(index, texelsInLogicalRow) * 2;\n\n return ivec4(b2, b, r, c);\n }\n `;let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],o=Math.ceil(r[r.length-1]/2),s=o*Math.ceil(r[r.length-2]/2),i=s,a=\"\",u=\"b, r, c\";for(let l=2;l=1?c=\"coords = 0;\":c=a.map(b=>`coords.${p[b+l]} = 0;`).join(`\n`);let m=\"\";i<2&&s>0?m=\"coords\":m=r.shapeInfo.logicalShape.map((b,w)=>`coords.${p[w+l]}`).join(\", \");let f=\"return outputValue;\",h=y.sizeFromShape(r.shapeInfo.logicalShape)===1,x=y.sizeFromShape(t.logicalShape)===1;if(s===1&&!h&&!x)f=`\n return vec4(outputValue.xy, outputValue.xy);\n `;else if(h&&!x)i===1?f=`\n return vec4(outputValue.x, outputValue.x, 0., 0.);\n `:f=`\n return vec4(outputValue.x);\n `;else if(a.length){let b=s-2,w=s-1;a.indexOf(b)>-1&&a.indexOf(w)>-1?f=\"return vec4(outputValue.x);\":a.indexOf(b)>-1?f=\"return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);\":a.indexOf(w)>-1&&(f=\"return vec4(outputValue.xx, outputValue.zz);\")}return`\n vec4 ${o}() {\n ${u} coords = getOutputCoords();\n ${c}\n vec4 outputValue = get${n}(${m});\n ${f}\n }\n 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Vw(r,t,e){let{newShape:n,keptDims:o}=y.squeezeShape(t),s=t.length,i=r&&s===3&&t[0]===1,a=i?t.slice(1):n,u=!r&&s>1&&!y.arraysEqual(t,e)&&n.lengthr[e]).join(\", \")}function BL(r,t,e,n){let o=e.map((c,p)=>{let m={logicalShape:c.shape,texShape:c.isUniform?null:c.texData.texShape,isUniform:c.isUniform,isPacked:c.isUniform?!1:c.texData.isPacked,flatOffset:null};return c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0&&(m.flatOffset=c.texData.slice.flatOffset),{name:t.variableNames[p],shapeInfo:m}}),s=o.map(c=>c.shapeInfo),i={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked,flatOffset:null},a=ML(o,i,t),u=WT(r.gl,a),l=r.createProgram(u);return L().get(\"ENGINE_COMPILE_ONLY\")?{program:t,fragmentShader:u,source:a,webGLProgram:l,inShapeInfos:s,outShapeInfo:i,variablesLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:(r.buildVao(l),Object.assign({program:t,fragmentShader:u,source:a,webGLProgram:l,inShapeInfos:s,outShapeInfo:i},a1(r,t,l)))}function a1(r,t,e){let n=[],o=[],s,i,a,u=null,l=null;l=r.getUniformLocation(e,\"NAN\",!1),L().getNumber(\"WEBGL_VERSION\")===1&&(u=r.getUniformLocation(e,\"INFINITY\",!1));let c=!1;for(let p of t.variableNames){let m={name:p,uniform:r.getUniformLocation(e,p,c),offset:r.getUniformLocation(e,`offset${p}`,c)};t.enableShapeUniforms&&(m.shape=r.getUniformLocation(e,`${p}Shape`,c),m.texShape=r.getUniformLocation(e,`${p}TexShape`,c)),n.push(m)}if(t.enableShapeUniforms&&(s=r.getUniformLocation(e,\"outShape\",c),a=r.getUniformLocation(e,\"outShapeStrides\",c),i=r.getUniformLocation(e,\"outTexShape\",c)),t.customUniforms)for(let p of t.customUniforms)o.push(r.getUniformLocation(e,p.name,c));return{variablesLocations:n,customUniformLocations:o,infLoc:u,nanLoc:l,outShapeLocation:s,outShapeStridesLocation:a,outTexShapeLocation:i}}function zL(r,t){if(r.length!==t.length)throw Error(`Binary was compiled with ${r.length} inputs, but was executed with ${t.length} inputs`);r.forEach((e,n)=>{let o=e.logicalShape,s=t[n],i=s.shape;if(!y.arraysEqual(o,i))throw Error(`Binary was compiled with different shapes than the current args. 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Shape ${a} and ${u} must match`)})}function VL(r,t,e,n,o){t.program.enableShapeUniforms||(zL(t.inShapeInfos,e),zL([t.outShapeInfo],[n]));let s=n.texData.texture,i=n.texData.texShape;n.texData.isPacked?r.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):r.setOutputMatrixTexture(s.texture,i[0],i[1]),r.setProgram(t.webGLProgram),r.bindVertexArray(t.webGLProgram.vao),L().getNumber(\"WEBGL_VERSION\")===1&&t.infLoc!==null&&r.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&r.gl.uniform1f(t.nanLoc,NaN);for(let u=0;u{let a=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(r.enableShapeUniforms&&!i.isUniform){let u=i.texData.texShape,{useSqueezeShape:l,uniformShape:c,keptDims:p}=Vw(r.packedInputs,i.shape,u),m=\"\",f=\"\",d=\"\";if(c.length===1&&r.packedInputs){let N=[Math.ceil(u[0]/2),Math.ceil(u[1]/2)];m=`${N[0]>1}_${N[1]>1}`}else if(c.length===2&&!r.packedInputs)f=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!r.packedInputs){let N=y.computeStrides(c);d=`${N[0]===u[1]}_${N[N.length-1]===u[1]}`}let h=i.shape.length,g=c.length===2&&y.arraysEqual(i.shape,u),x=y.sizeFromShape(i.shape)===1,b=S.getBroadcastDims(i.shape,e.shape),w=!r.packedInputs&&h===e.shape.length&&y.arraysEqual(u,e.texData.texShape),I=r.packedInputs||c.length>2?\"\":`${u[0]>1}_${u[1]>1}`;n+=`${h}_${w}_${l?p:\"\"}_${c.length}_${x}_${b}_${g}_${m}_${f}_${d}_${I}_${a}`}else{let u=i.isUniform?\"uniform\":i.texData.texShape;n+=`${i.shape}_${u}_${a}`}});let o=r.userCode,s=r.constructor.name;return s+=\"_\"+n+\"_\"+o+`${L().getNumber(\"WEBGL_VERSION\")}`,s}function he(r){return L().getBool(\"WEBGL_USE_SHAPES_UNIFORMS\")&&r<=4}var Gw=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Wu.DENSE,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let e=Ue();this.outputShape=t,this.enableShapeUniforms=he(this.outputShape.length),this.userCode=`\n ivec3 outCoordsFromFlatIndex(int index) {\n 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texShape[1]));\n int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n vec4 result = vec4(0.);\n\n for (int i=0; i<4; i++) {\n int flatIndex = index + i;\n ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n }\n\n ${e.output} = result;\n }\n `}};var Uw=class{constructor(t){this.variableNames=[\"A\"],this.outTexUsage=Yr.DOWNLOAD;let e=Ue();this.outputShape=t,this.userCode=`\n ${Bw}\n\n void main() {\n float x = getAAtOutCoords();\n ${e.output} = encode_float(x);\n }\n `}};var Hw=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Yr.DOWNLOAD;let e=Ue();this.outputShape=t,this.userCode=`\n ${Bw}\n\n void main() {\n ivec3 coords = getOutputCoords();\n float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));\n ${e.output} = encode_float(x);\n }\n `}};var 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n=\"WEBGL_color_buffer_float\",o=\"EXT_color_buffer_half_float\";if(this.parallelCompilationExtension=this.gl.getExtension(\"KHR_parallel_shader_compile\"),L().getNumber(\"WEBGL_VERSION\")===1){let s=\"OES_texture_float\",i=\"OES_texture_half_float\";if(this.textureFloatExtension=yd(this.gl,s),Kn(this.gl,i))this.textureHalfFloatExtension=yd(this.gl,i);else if(L().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),Kn(this.gl,o))this.colorBufferHalfFloatExtension=yd(this.gl,o);else if(L().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\",Kn(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Kn(this.gl,o))this.colorBufferHalfFloatExtension=this.gl.getExtension(o);else throw new Error(\"GL context does not support color renderable floats\");this.vertexBuffer=u1(this.gl),this.indexBuffer=c1(this.gl),this.framebuffer=YT(this.gl),this.textureConfig=sg(this.gl,this.textureHalfFloatExtension)}get debug(){return L().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 t=this.gl;ht(t,()=>t.finish()),ht(t,()=>t.bindFramebuffer(t.FRAMEBUFFER,null)),ht(t,()=>t.deleteFramebuffer(this.framebuffer)),ht(t,()=>t.bindBuffer(t.ARRAY_BUFFER,null)),ht(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,null)),ht(t,()=>t.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(t,e){return this.throwIfDisposed(),p1(this.gl,t,e,this.textureConfig)}createFloat16MatrixTexture(t,e){return this.throwIfDisposed(),m1(this.gl,t,e,this.textureConfig)}createUnsignedBytesMatrixTexture(t,e){return this.throwIfDisposed(),f1(this.gl,t,e,this.textureConfig)}uploadPixelDataToTexture(t,e){this.throwIfDisposed(),y1(this.gl,t,e)}uploadDenseMatrixToTexture(t,e,n,o){this.throwIfDisposed(),x1(this.gl,t,e,n,o,this.textureConfig)}createFloat16PackedMatrixTexture(t,e){return this.throwIfDisposed(),h1(this.gl,t,e,this.textureConfig)}createPackedMatrixTexture(t,e){return this.throwIfDisposed(),d1(this.gl,t,e,this.textureConfig)}deleteMatrixTexture(t){this.throwIfDisposed(),this.outputTexture===t&&(Lw(this.gl,this.framebuffer),this.outputTexture=null),ht(this.gl,()=>this.gl.deleteTexture(t))}downloadByteEncodedFloatMatrixFromOutputTexture(t,e,n){return this.downloadMatrixDriver(t,()=>I1(this.gl,e,n,this.textureConfig))}downloadPackedMatrixFromBuffer(t,e,n,o,s,i){return C1(this.gl,t,e,n,o,s,i,this.textureConfig)}downloadFloat32MatrixFromBuffer(t,e){return w1(this.gl,t,e)}createBufferFromTexture(t,e,n){this.bindTextureToFrameBuffer(t);let o=b1(this.gl,e,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),o}createAndWaitForFence(){let t=this.createFence(this.gl);return this.pollFence(t)}createFence(t){let e,n;if(L().getBool(\"WEBGL_FENCE_API_ENABLED\")){let o=t,s=o.fenceSync(o.SYNC_GPU_COMMANDS_COMPLETE,0);t.flush(),n=()=>{let i=o.clientWaitSync(s,0,0);return i===o.ALREADY_SIGNALED||i===o.CONDITION_SATISFIED},e=s}else L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0?(e=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(e,L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))):n=()=>!0;return{query:e,isFencePassed:n}}downloadMatrixFromPackedTexture(t,e,n){return this.downloadMatrixDriver(t,()=>v1(this.gl,e,n))}createProgram(t){this.throwIfDisposed();let e=this.gl;this.vertexShader==null&&(this.vertexShader=l1(e));let n=UT(e);ht(e,()=>e.attachShader(n,this.vertexShader)),ht(e,()=>e.attachShader(n,t)),HT(e,n);let o=Object.assign(n,{vao:this.createVertexArray()});return this.debug&&ig(e,o),o}buildVao(t){this.setProgram(t),this.bindVertexArray(t.vao);let e=this.gl;ht(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),g1(e,t,this.vertexBuffer)}deleteProgram(t){this.throwIfDisposed(),t===this.program&&(this.program=null),t!=null&&(ht(this.gl,()=>this.gl.deleteProgram(t)),this.deleteVertexArray(t.vao))}setProgram(t){this.throwIfDisposed(),this.program=t,this.program!=null&&this.debug&&ig(this.gl,this.program),ht(this.gl,()=>this.gl.useProgram(t))}getUniformLocation(t,e,n=!0){return this.throwIfDisposed(),n?ZT(this.gl,t,e):JT(this.gl,t,e)}getAttributeLocation(t,e){return this.throwIfDisposed(),ht(this.gl,()=>this.gl.getAttribLocation(t,e))}getUniformLocationNoThrow(t,e){return this.throwIfDisposed(),this.gl.getUniformLocation(t,e)}setInputMatrixTexture(t,e,n){this.throwIfDisposed(),this.throwIfNoProgram(),QT(this.gl,t,e,n)}setOutputMatrixTexture(t,e,n){this.setOutputMatrixTextureDriver(t,n,e)}setOutputPackedMatrixTexture(t,e,n){this.throwIfDisposed();let[o,s]=wa(e,n);this.setOutputMatrixTextureDriver(t,o,s)}setOutputMatrixWriteRegion(t,e,n,o){this.setOutputMatrixWriteRegionDriver(n,t,o,e)}setOutputPackedMatrixWriteRegion(t,e,n,o){throw new Error(\"setOutputPackedMatrixWriteRegion not implemented.\")}debugValidate(){this.program!=null&&ig(this.gl,this.program),bd(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let t=this.gl;if(this.debug){let e=this.getVertexArray();console.assert(e===this.program.vao,\"VAO changed between setProgram and executeProgram!\"),this.debugValidate()}ht(t,()=>t.drawElements(t.TRIANGLES,6,t.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),ht(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=yd(this.gl,L().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(L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2){let n=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=n.createQuery();return n.beginQuery(o.TIME_ELAPSED_EXT,s),s}let t=this.getQueryTimerExtensionWebGL1(),e=t.createQueryEXT();return t.beginQueryEXT(t.TIME_ELAPSED_EXT,e),e}endQuery(){if(L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2){let e=this.gl,n=this.getQueryTimerExtensionWebGL2();e.endQuery(n.TIME_ELAPSED_EXT);return}let t=this.getQueryTimerExtensionWebGL1();t.endQueryEXT(t.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(t){return await y.repeatedTry(()=>this.disposed||this.isQueryAvailable(t,L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))),this.getQueryTime(t,L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))}getQueryTime(t,e){if(e===0)return null;if(e===2){let n=this.gl;return n.getQueryParameter(t,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(t,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(t,e){if(e===0)return!0;if(e===2){let n=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=n.getQueryParameter(t,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(o.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),o=n.getQueryObjectEXT(t,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),o&&!this.disjoint}}pollFence(t){return new Promise(e=>{this.addItemToPoll(()=>t.isFencePassed(),()=>e())})}pollItems(){let t=Fot(this.itemsToPoll.map(e=>e.isDoneFn));for(let e=0;e<=t;++e){let{resolveFn:n}=this.itemsToPoll[e];n()}this.itemsToPoll=this.itemsToPoll.slice(t+1)}addItemToPoll(t,e){if(this.itemsToPoll.push({isDoneFn:t,resolveFn:e}),this.itemsToPoll.length>1)return;let n;\"setTimeoutCustom\"in L().platform&&(n=L().platform.setTimeoutCustom.bind(L().platform)),y.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,n)}bindTextureToFrameBuffer(t){this.throwIfDisposed(),ag(this.gl,t,this.framebuffer),this.debug&&bd(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(ag(this.gl,this.outputTexture,this.framebuffer),this.debug&&bd(this.gl)):Lw(this.gl,this.framebuffer)}downloadMatrixDriver(t,e){this.bindTextureToFrameBuffer(t);let n=e();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(t,e,n){this.throwIfDisposed();let o=this.gl;ag(o,t,this.framebuffer),this.debug&&bd(o),this.outputTexture=t,ht(o,()=>o.viewport(0,0,e,n)),ht(o,()=>o.scissor(0,0,e,n))}setOutputMatrixWriteRegionDriver(t,e,n,o){this.throwIfDisposed(),ht(this.gl,()=>this.gl.scissor(t,e,n,o))}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 Fot(r){let t=0;for(;t`${r}.${e}`)}function rr(r,t){return t===1?[r]:N1(r,t)}function Fz(r,t){if(r===1)return\"rc\";let e=\"\";for(let n=0;n ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]}`;let e=\"\";for(let n=this.rank-2;n= ${this.enableShapeUniforms?`outShape[${n}]`:this.outputShape[n]}`,n= ${n};\n bool rEdge = rp1 >= ${o};\n `}getOutput(t){let e=this.getSourceCoordsArr(t);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${e[0]}),\n cEdge ? 0. : getA(${e[1]}),\n rEdge ? 0. : getA(${e[2]}),\n rEdge || cEdge ? 0. : getA(${e[3]})`}};var Ed=class{constructor(t,e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"inputShape\",type:\"ivec3\"}],this.outputShape=t,this.enableShapeUniforms=he(this.outputShape.length);let n=\"\";for(let o=0;o<4;o++){let s=\"thisRC = rc;\";o%2===1&&(s+=\"thisRC.z += 1;\"),o>1&&(s+=\"thisRC.y += 1;\"),n+=`\n ${s}\n ${o>0?\"if(thisRC.y < rows && thisRC.z < cols){\":\"\"}\n int flatIndex = getFlatIndex(thisRC);\n\n ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n result[${o}] =\n getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n ${o>0?\"}\":\"\"}\n `}this.userCode=`\n ${Oot(e,this.enableShapeUniforms)}\n ${this.enableShapeUniforms?vd():Cd(t)}\n\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0.);\n\n ivec3 thisRC;\n int rows = ${this.enableShapeUniforms?\"outShape[1]\":t[1]};\n int cols = ${this.enableShapeUniforms?\"outShape[2]\":t[2]};\n\n ${n}\n\n setOutput(result);\n }\n `}};function Oot(r,t){return`\n ivec3 inputCoordsFromReshapedOutCoords(int index) {\n ${t?FL([\"r\",\"c\",\"d\"],\"inputShape\"):vi([\"r\",\"c\",\"d\"],r)}\n return ivec3(r, c, d);\n }\n `}var rI=class{constructor(t){this.gpgpu=t,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(t,e,n){let o=Mz(e,n),s=Pz(t,o,n);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let i=Oz(t,o,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=i,this.log();let u=this.freeTextures[s].pop();return this.usedTextures[s].push(u),u}let a;return o===Pr.PACKED_2X2_FLOAT32?a=this.gpgpu.createPackedMatrixTexture(t[0],t[1]):o===Pr.PACKED_2X2_FLOAT16?a=this.gpgpu.createFloat16PackedMatrixTexture(t[0],t[1]):o===Pr.UNPACKED_FLOAT32?a=this.gpgpu.createFloat32MatrixTexture(t[0],t[1]):o===Pr.UNPACKED_FLOAT16?a=this.gpgpu.createFloat16MatrixTexture(t[0],t[1]):o===Pr.PACKED_4X1_UNSIGNED_BYTE&&(a=this.gpgpu.createUnsignedBytesMatrixTexture(t[0],t[1])),this.usedTextures[s].push(a),this.numUsedTextures++,this._numBytesAllocated+=i,this.log(),a}releaseTexture(t,e,n,o){if(this.freeTextures==null)return;let s=Mz(n,o),i=Pz(e,s,o);i in this.freeTextures||(this.freeTextures[i]=[]);let a=Oz(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,o),u=L().getNumber(\"WEBGL_DELETE_TEXTURE_THRESHOLD\");u!==-1&&this._numBytesAllocated>u?(this.gpgpu.deleteMatrixTexture(t.texture),this._numBytesAllocated-=a):(this.freeTextures[i].push(t),this.numFreeTextures++,this._numBytesFree+=a),this.numUsedTextures--;let l=this.usedTextures[i],c=l&&l.indexOf(t);if(c==null||c<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");l[c]=l[l.length-1],l.pop(),this.log()}log(){if(!this.logEnabled)return;let t=this.numFreeTextures+this.numUsedTextures;console.log(\"Free/Used\",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${t})`);let e=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*e)}%)`)}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 t in this.freeTextures)this.freeTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});for(let t in this.usedTextures)this.usedTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function Mot(r,t){let e=r;if(t===e.R32F)return 4;if(t===e.R16F)return 2;if(t===e.RGBA32F)return 16;if(t===r.RGBA)return 16;if(t===e.RGBA16F)return 8;if(t===e.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function Oz(r,t,e,n,o){let s=Pot(t,n),i;if(o){let[u,l]=wa(r[0],r[1]);i=u*l}else{let[u,l]=lp(r[0],r[1]);i=u*l}let a=Mot(e,s);return i*a}function Pot(r,t){switch(r){case Pr.PACKED_2X2_FLOAT32:return Yw(t);case Pr.PACKED_2X2_FLOAT16:return Zw(t);case Pr.UNPACKED_FLOAT32:return Kw(t);case Pr.UNPACKED_FLOAT16:return jw(t);case Pr.PACKED_4X1_UNSIGNED_BYTE:return Xw(t);default:throw new Error(`Unknown physical texture type ${r}`)}}function Lot(r){return L().getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?r?Pr.PACKED_2X2_FLOAT32:Pr.UNPACKED_FLOAT32:r?Pr.PACKED_2X2_FLOAT16:Pr.UNPACKED_FLOAT16}function Mz(r,t){if(r===Yr.UPLOAD)return Pr.PACKED_2X2_FLOAT32;if(r===Yr.RENDER||r==null)return Lot(t);if(r===Yr.DOWNLOAD||r===Yr.PIXELS)return Pr.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function Pz(r,t,e){return`${r[0]}_${r[1]}_${t}_${e}`}var Lr=class{constructor(t,e){this.variableNames=[\"A\"],this.outputShape=t,this.enableShapeUniforms=he(this.outputShape.length),this.userCode=`\n float unaryOperation(float x) {\n ${e}\n }\n\n void main() {\n float x = getAAtOutCoords();\n float y = unaryOperation(x);\n\n setOutput(y);\n }\n `}},xr=\"if (isnan(x)) return x;\",Lz=\"return x;\",k1=\"return abs(x);\";var zz=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",Bz=xr+`\n return (x < 0.0) ? 0.0 : x;\n`,Vz=xr+`\n return (x < 0.0) ? 0.0 : min(6.0, x);\n`,Ia=\"return x;\",Gz=\"return 1.0 / (1.0 + exp(-1.0 * x));\";var Uz=\"return x;\",Hz=`\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n`,qz=`\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,Kz=`\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,jz=\"return 1.0 / (1.0 + exp(-1.0 * x));\",Dn=class{constructor(t,e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.enableShapeUniforms=he(this.outputShape.length),this.userCode=`\n vec4 unaryOperation(vec4 x) {\n ${e}\n }\n\n void main() {\n vec4 x = getAAtOutCoords();\n vec4 y = unaryOperation(x);\n\n setOutput(y);\n }\n `}};var nI=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=t,this.enableShapeUniforms=he(this.outputShape.length);let e=t.length,n=rr(\"rc\",e),o=zt(e),s=Fz(e,n),i=n.slice(-2),a=e<=1?\"rc\":`vec2(${i.join(\",\")})`;this.userCode=`\n void main() {\n ${o} rc = getOutputCoords();\n vec4 packedInput = getA(${s});\n\n setOutput(getChannel(packedInput, ${a}));\n }\n `}};var Bot=Kr.whereImpl,Vot=1e-7,Got=1e-4,oI={};function Wot(r){return r in oI||(oI[r]={}),oI[r]}var Uot=L().getNumber(\"CPU_HANDOFF_SIZE_THRESHOLD\"),Hot=600;function qot(){return L().global.screen==null?1024:L().global.screen.height*L().global.screen.width*window.devicePixelRatio*Hot/1024/1024}var Ad=class r extends Bo{nextDataId(){return r.nextDataId++}constructor(t){if(super(),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,!L().getBool(\"HAS_WEBGL\"))throw new Error(\"WebGL is not supported on this device\");let e;if(t!=null){if(t instanceof pp)e=t;else{let n=qn(L().getNumber(\"WEBGL_VERSION\"),t);e=new pp(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=qn(L().getNumber(\"WEBGL_VERSION\"));e=new pp(n),this.binaryCache=Wot(L().getNumber(\"WEBGL_VERSION\")),this.gpgpuCreatedLocally=!0}this.gpgpu=e,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new rI(this.gpgpu),this.numMBBeforeWarning=qot(),this.texData=new Ta(this,Bn())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(t,e,n,o,s,i){let a=this.makeTensorInfo(e,n),u=this.texData.get(a.dataId);u.isPacked=!1,u.texture={texture:t,texShape:[o,s]},u.texShape=[o,s];let l=wd(e),c=new lg(l,!1,i),p=this.runWebGLProgram(c,[a],n,[[o,s]]);return p.shape=e,u.texture=null,this.disposeIntermediateTensorInfo(a),p.dataId}write(t,e,n){if((L().getBool(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\")||L().getBool(\"DEBUG\"))&&this.checkNumericalProblems(t),n===\"complex64\"&&t!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let o={id:this.nextDataId()};return this.texData.set(o,{shape:e,dtype:n,values:t,usage:Yr.UPLOAD,refCount:1}),o}refCount(t){return this.texData.has(t)?this.texData.get(t).refCount:0}incRef(t){let e=this.texData.get(t);e.refCount++}decRef(t){if(this.texData.has(t)){let e=this.texData.get(t);e.refCount--}}move(t,e,n,o,s){if(L().getBool(\"DEBUG\")&&this.checkNumericalProblems(e),o===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");this.texData.set(t,{shape:n,dtype:o,values:e,usage:Yr.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(t){this.disposeData(t.dataId)}readSync(t){let e=this.texData.get(t),{values:n,dtype:o,complexTensorInfos:s,slice:i,shape:a,isPacked:u}=e;if(i!=null){let m;u?m=new Dn(a,Ia):m=new Lr(a,Ia);let f=this.runWebGLProgram(m,[{dataId:t,shape:a,dtype:o}],o),d=this.readSync(f.dataId);return this.disposeIntermediateTensorInfo(f),d}if(n!=null)return this.convertAndCacheOnCPU(t);if(o===\"string\")return n;let l=this.activeTimers!=null,c;l&&(c=y.now());let p;if(o===\"complex64\"){let m=this.readSync(s.real.dataId),f=this.readSync(s.imag.dataId);p=S.mergeRealAndImagArrays(m,f)}else p=this.getValuesFromTexture(t);return l&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(t,p)}async read(t){if(this.pendingRead.has(t)){let d=this.pendingRead.get(t);return new Promise(h=>d.push(h))}let e=this.texData.get(t),{values:n,shape:o,slice:s,dtype:i,complexTensorInfos:a,isPacked:u}=e;if(s!=null){let d;u?d=new Dn(o,Ia):d=new Lr(o,Ia);let h=this.runWebGLProgram(d,[{dataId:t,shape:o,dtype:i}],i),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(n!=null)return this.convertAndCacheOnCPU(t);if(L().getBool(\"DEBUG\")&&!L().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")&&L().getNumber(\"WEBGL_VERSION\")===2)throw new Error(\"tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.\");let l=null,c;if(i!==\"complex64\"&&L().get(\"WEBGL_BUFFER_SUPPORTED\")){c=this.decode(t);let d=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(d.texture.texture,...og(o))}this.pendingRead.set(t,[]),i!==\"complex64\"&&await this.gpgpu.createAndWaitForFence();let p;if(i===\"complex64\"){let d=await Promise.all([this.read(a.real.dataId),this.read(a.imag.dataId)]),h=d[0],g=d[1];p=S.mergeRealAndImagArrays(h,g)}else if(l==null)p=this.getValuesFromTexture(t);else{let d=y.sizeFromShape(o);p=this.gpgpu.downloadFloat32MatrixFromBuffer(l,d)}if(c!=null&&this.disposeIntermediateTensorInfo(c),l!=null){let d=this.gpgpu.gl;ht(d,()=>d.deleteBuffer(l))}let m=this.convertAndCacheOnCPU(t,p),f=this.pendingRead.get(t);return this.pendingRead.delete(t),f.forEach(d=>d(m)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t)&&Bn().removeDataId(t,this),this.pendingDeletes--),m}readToGPU(t,e={}){let n=this.texData.get(t),{values:o,shape:s,slice:i,dtype:a,isPacked:u,texture:l}=n;if(a===\"complex64\")throw new Error(\"Does not support reading texture for complex64 dtype.\");if(i!=null){let f;u?f=new Dn(s,Ia):f=new Lr(s,Ia);let d=this.runWebGLProgram(f,[{dataId:t,shape:s,dtype:a}],a),h=this.readToGPU(d,e);return this.disposeIntermediateTensorInfo(d),h}if(l==null)throw o!=null?new Error(\"Data is not on GPU but on CPU.\"):new Error(\"There is no data on GPU or CPU.\");let c=this.decode(t,e.customTexShape),p=Bn().makeTensorFromTensorInfo(c),m=this.texData.get(c.dataId);return Object.assign({tensorRef:p},m.texture)}bufferSync(t){let e=this.readSync(t.dataId);if(t.dtype===\"string\")try{let n=e.map(o=>y.decodeString(o));return wt(t.shape,t.dtype,n)}catch(n){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return wt(t.shape,t.dtype,e)}checkNumericalProblems(t){if(t!=null)for(let e=0;e0}time(t){let e=this.activeTimers,n=[],o=!1;this.programTimersStack==null?(this.programTimersStack=n,o=!0):this.activeTimers.push(n),this.activeTimers=n,t();let s=y.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),i=y.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=e,o&&(this.programTimersStack=null);let a={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0){let u=await Promise.all(s);a.kernelMs=y.sum(u),a.getExtraProfileInfo=()=>u.map((l,c)=>({name:i[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(\", \")}else a.kernelMs={error:\"WebGL query timers are not supported in this environment.\"};return this.uploadWaitMs=0,this.downloadWaitMs=0,a})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(t){return L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?(this.gpgpu.endQuery(),t):(t.endMs=y.now(),t)}async getQueryTime(t){if(L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0)return this.gpgpu.waitForQueryAndGetTime(t);let e=t;return e.endMs-e.startMs}disposeData(t,e=!1){if(this.pendingDisposal.has(t))return!1;if(!this.texData.has(t))return!0;if(e?this.texData.get(t).refCount=0:this.texData.get(t).refCount--,!e&&this.texData.get(t).refCount>0)return!1;if(this.pendingRead.has(t))return this.pendingDisposal.add(t),this.pendingDeletes++,!1;this.releaseGPUData(t);let{complexTensorInfos:n}=this.texData.get(t);return n!=null&&(this.disposeData(n.real.dataId,e),this.disposeData(n.imag.dataId,e)),this.texData.delete(t),!0}releaseGPUData(t){let{texture:e,dtype:n,texShape:o,usage:s,isPacked:i,slice:a}=this.texData.get(t),u=a&&a.origDataId||t,l=this.dataRefCount.get(u);l>1?this.dataRefCount.set(u,l-1):(this.dataRefCount.delete(u),e!=null&&(this.numBytesInGPU-=this.computeBytes(o,n),this.textureManager.releaseTexture(e,o,s,i)));let c=this.texData.get(t);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture.texture}getDataInfo(t){return this.texData.get(t)}shouldExecuteOnCPU(t,e=Uot){return L().getBool(\"WEBGL_CPU_FORWARD\")&&t.every(n=>this.texData.get(n.dataId).texture==null&&y.sizeFromShape(n.shape)0&&y.isString(n[0])){let s=n.map(i=>y.encodeString(i));o=this.write(s,t,e)}else o=this.write(n,t,e);return this.texData.get(o).usage=null,{dataId:o,shape:t,dtype:e}}makeOutput(t,e,n){return Bn().makeTensorFromTensorInfo(this.makeTensorInfo(t,e,n),this)}unpackTensor(t){let e=new nI(t.shape);return this.runWebGLProgram(e,[t],t.dtype)}packTensor(t){let e=new eI(t.shape);return this.runWebGLProgram(e,[t],t.dtype,null,!0)}packedReshape(t,e){let n=[Fl(t.shape),...Ol(t.shape)],o={dtype:t.dtype,shape:n,dataId:t.dataId},s=[Fl(e),...Ol(e)],i=new Ed(s,n),a=!0,u=[n],l=this.runWebGLProgram(i,[o],t.dtype,u,a);return{dataId:l.dataId,shape:e,dtype:l.dtype}}decode(t,e){let n=this.texData.get(t),{isPacked:o,shape:s,dtype:i}=n;if(e!=null){let m=y.sizeFromShape(s),f=e[0]*e[1]*4;y.assert(m<=f,()=>\"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.\")}let a=wd(s),u;o?u=new Ww(a):u=new Gw(a);let l=!0,c=[e!=null?e:og(a)],p=this.runWebGLProgram(u,[{shape:a,dtype:i,dataId:t}],i,c,l,e);return{dtype:i,shape:s,dataId:p.dataId}}runWebGLProgram(t,e,n,o,s=!1,i){let a=this.makeTensorInfo(t.outputShape,n),u=this.texData.get(a.dataId);if(t.packedOutput&&(u.isPacked=!0),t.outPackingScheme===Wu.DENSE){let x=i!=null?i:og(t.outputShape);u.texShape=x.map(b=>b*2)}if(t.outTexUsage!=null&&(u.usage=t.outTexUsage),y.sizeFromShape(a.shape)===0)return u.values=y.getTypedArrayFromDType(a.dtype,0),a;let l=[],c=e.map(x=>{if(x.dtype===\"complex64\")throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");let b=this.texData.get(x.dataId);if(b.texture==null){if(!t.packedInputs&&y.sizeFromShape(x.shape)<=L().getNumber(\"WEBGL_SIZE_UPLOAD_UNIFORM\"))return{shape:x.shape,texData:null,isUniform:!0,uniformValues:b.values};t.packedInputs&&(b.isPacked=!0,b.shape=x.shape)}if(this.uploadToGPU(x.dataId),!!b.isPacked!=!!t.packedInputs)x=b.isPacked?this.unpackTensor(x):this.packTensor(x),l.push(x),b=this.texData.get(x.dataId);else if(b.isPacked&&!Uu(b.shape,x.shape)){let w=x,I=x.shape;x.shape=b.shape,x=this.packedReshape(x,I),l.push(x),b=this.texData.get(x.dataId),w.shape=I}return{shape:x.shape,texData:b,isUniform:!1}});this.uploadToGPU(a.dataId);let p={shape:a.shape,texData:u,isUniform:!1},m=GL(t,c,p),f=this.getAndSaveBinary(m,()=>BL(this.gpgpu,t,c,p)),d=this.activeTimers!=null,h;d&&(h=this.startTimer()),L().get(\"ENGINE_COMPILE_ONLY\")||VL(this.gpgpu,f,c,p,o),l.forEach(x=>this.disposeIntermediateTensorInfo(x)),d&&(h=this.endTimer(h),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(h)}));let g=L().getNumber(\"WEBGL_FLUSH_THRESHOLD\");if(g>0){let x=y.now();x-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=x)}if(!L().getBool(\"WEBGL_LAZILY_UNPACK\")&&u.isPacked&&s===!1){let x=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),x}return a}compileAndRun(t,e,n,o,s=!1){return n=n||e[0].dtype,this.runWebGLProgram(t,e,n,o,s)}getAndSaveBinary(t,e){return t in this.binaryCache||(this.binaryCache[t]=e()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(L().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=B(()=>{if(!L().get(\"WEBGL_RENDER_FLOAT32_ENABLED\")){let t=L().getBool(\"DEBUG\");L().set(\"DEBUG\",!1);let e=this.abs(pt(1e-8)).dataSync()[0];if(L().set(\"DEBUG\",t),e>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?Vot:Got}uploadToGPU(t){let e=this.texData.get(t),{shape:n,dtype:o,values:s,texture:i,usage:a,isPacked:u}=e;if(i!=null)return;let l=this.activeTimers!=null,c;l&&(c=y.now());let p=e.texShape;if(p==null&&(p=t1(n,u),e.texShape=p),s!=null){let m=wd(n),f,d=p[1],h=p[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(u||!g)&&([d,h]=wa(p[0],p[1])),u?f=new qw(m,g):f=new lg(m,g);let x=g?[h,d]:p,b=this.makeTensorInfo(x,o),w=this.texData.get(b.dataId);g?w.usage=Yr.PIXELS:w.usage=Yr.UPLOAD,w.texShape=x,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),d,h,s);let I=[[h,d]],E=this.runWebGLProgram(f,[b],o,I,!0),A=this.texData.get(E.dataId);e.texShape=A.texShape,e.isPacked=A.isPacked,e.usage=A.usage,L().get(\"ENGINE_COMPILE_ONLY\")?this.disposeData(E.dataId):(e.texture=A.texture,e.values=null,this.texData.delete(E.dataId)),this.disposeIntermediateTensorInfo(b),l&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(p,a,o,u);e.texture=m}}convertAndCacheOnCPU(t,e){let n=this.texData.get(t),{dtype:o}=n;return e!=null&&(n.values=Kot(e,o)),n.values}acquireTexture(t,e,n,o){if(this.numBytesInGPU+=this.computeBytes(t,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(t,e,o)}computeBytes(t,e){return t[0]*t[1]*y.bytesPerElement(e)}checkCompileCompletion(){for(let[,t]of Object.entries(this.binaryCache))this.checkCompletion_(t)}async checkCompileCompletionAsync(){let t=[];if(this.gpgpu.parallelCompilationExtension){for(let[,e]of Object.entries(this.binaryCache))t.push(this.checkCompletionAsync_(e));return Promise.all(t)}else{for(let[,e]of Object.entries(this.binaryCache)){let n=new Promise(o=>{try{this.checkCompletion_(e),o(!0)}catch(s){throw s}});t.push(n)}return Promise.all(t)}}async checkCompletionAsync_(t){return this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(t):(await Ih(),this.checkCompletionAsync_(t))}checkCompletion_(t){if(this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(t.webGLProgram)),this.gpgpu.gl.getShaderParameter(t.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(Mw(t.source,this.gpgpu.gl.getShaderInfoLog(t.fragmentShader)),new Error(\"Failed to compile fragment shader.\")):new Error(\"Failed to link vertex and fragment shaders.\");return!0}getUniformLocations(){for(let t of Object.values(this.binaryCache)){this.gpgpu.buildVao(t.webGLProgram);let{variablesLocations:e,customUniformLocations:n,infLoc:o,nanLoc:s,outShapeLocation:i,outShapeStridesLocation:a,outTexShapeLocation:u}=a1(this.gpgpu,t.program,t.webGLProgram);t.variablesLocations=e,t.customUniformLocations=n,t.infLoc=o,t.nanLoc=s,t.outShapeLocation=i,t.outShapeStridesLocation=a,t.outTexShapeLocation=u}}createTensorFromGPUData(t,e,n){t.channels=t.channels||\"RGBA\";let{texture:o,height:s,width:i,channels:a}=t,u=Bn().backend;if(!u.gpgpu.gl.isTexture(o))throw new Error(\"The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.\");let l=u.writeTexture(o,e,n,s,i,a);return Bn().makeTensorFromDataId(l,e,n,u)}};Ad.nextDataId=0;function Kot(r,t){if(t===\"float32\"||t===\"complex64\")return r;if(t===\"int32\"||t===\"bool\"){let e=t===\"int32\"?new Int32Array(r.length):new Uint8Array(r.length);for(let n=0;nnew Ad,2);var N$e={forceHalfFloat:Yz};var Dd=`\n if (isnan(a)) return a;\n if (isnan(b)) return b;\n`;var $n=class{constructor(t,e,n){this.variableNames=[\"A\",\"B\"],this.outputShape=S.assertAndGetBroadcastShape(e,n),this.enableShapeUniforms=he(this.outputShape.length),this.userCode=`\n float binaryOperation(float a, float b) {\n ${t}\n }\n\n void main() {\n float a = getAAtOutCoords();\n float b = getBAtOutCoords();\n setOutput(binaryOperation(a, b));\n }\n `}};var Xn=`\n result.r = isNaN.r ? NAN : result.r;\n result.g = isNaN.g ? NAN : result.g;\n result.b = isNaN.b ? NAN : result.b;\n result.a = isNaN.a ? NAN : result.a;\n`;var jn=class{constructor(t,e,n,o=!1){this.variableNames=[\"A\",\"B\"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=S.assertAndGetBroadcastShape(e,n);let s=this.outputShape.length;this.enableShapeUniforms=he(s);let i=\"\";if(o)if(s===0||y.sizeFromShape(this.outputShape)===1)i=`\n result.y = 0.;\n result.z = 0.;\n result.w = 0.;\n `;else if(i=`\n ${zt(s)} coords = getOutputCoords();\n `,s===1)this.enableShapeUniforms?i+=`\n result.y = (coords + 1) >= outShape ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `:i+=`\n result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `;else{let u=rr(\"coords\",s);this.enableShapeUniforms?i+=`\n bool nextRowOutOfBounds =\n (${u[s-2]} + 1) >= outShape[${s} - 2];\n bool nextColOutOfBounds =\n (${u[s-1]} + 1) >= outShape[${s} - 1];\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `:i+=`\n bool nextRowOutOfBounds =\n (${u[s-2]} + 1) >= ${this.outputShape[s-2]};\n bool nextColOutOfBounds =\n (${u[s-1]} + 1) >= ${this.outputShape[s-1]};\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `}this.userCode=`\n vec4 binaryOperation(vec4 a, vec4 b) {\n ${t}\n }\n\n void main() {\n vec4 a = getAAtOutCoords();\n vec4 b = getBAtOutCoords();\n\n vec4 result = binaryOperation(a, b);\n ${i}\n\n setOutput(result);\n }\n `}};function nr(r){let{inputs:t,backend:e}=r,{x:n}=t;return e.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var Zz={kernelName:go,backendName:\"webgl\",kernelFunc:nr};function Rn(r){let{inputs:t,backend:e}=r,{real:n,imag:o}=t,s=e.makeTensorInfo(n.shape,\"complex64\"),i=e.texData.get(s.dataId),a=nr({inputs:{x:n},backend:e}),u=nr({inputs:{x:o},backend:e});return i.complexTensorInfos={real:a,imag:u},s}var Jz={kernelName:Ap,backendName:\"webgl\",kernelFunc:Rn};var T1=\"return (a < 0.) ? b * a : a;\",_1=`\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function jot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{alpha:s}=n,i=e.makeTensorInfo([],\"float32\",y.createScalarValue(s,\"float32\")),a=L().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new jn(_1,o.shape,i.shape):new $n(T1,o.shape,i.shape),u=e.runWebGLProgram(a,[o,i],\"float32\");return e.disposeIntermediateTensorInfo(i),u}var Qz={kernelName:bs,backendName:\"webgl\",kernelFunc:jot};var E1=\"return (a < 0.) ? b * a : a;\",A1=`\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function Xot(r){let{inputs:t,backend:e}=r,{x:n,alpha:o}=t,s=L().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new jn(A1,n.shape,o.shape):new $n(E1,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],\"float32\")}var t3={kernelName:Os,backendName:\"webgl\",kernelFunc:Xot};var Po=\"if (isnan(x)) return x;\";function It({opSnippet:r,packedOpSnippet:t,cpuKernelImpl:e,dtype:n}){return({inputs:o,backend:s})=>{let{x:i}=o,a=s,u=n||i.dtype;if(a.shouldExecuteOnCPU([i])&&e!=null){let p=a.texData.get(i.dataId),m=e(p.values,u);return a.makeTensorInfo(i.shape,u,m)}let l=L().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")&&t!=null,c;return l?c=new Dn(i.shape,t):c=new Lr(i.shape,r),a.runWebGLProgram(c,[i],u)}}function ce({opSnippet:r,packedOpSnippet:t,checkOutOfBounds:e=!1,supportsComplex:n=!1,cpuKernelImpl:o,dtype:s}){return({inputs:i,backend:a})=>{let{a:u,b:l}=i,c=a;if(n&&u.dtype===\"complex64\"){let d=c.texData.get(u.dataId),h=c.texData.get(l.dataId),[g,x]=[[d.complexTensorInfos.real,h.complexTensorInfos.real],[d.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(w=>{let[I,N]=w,E={dataId:I.dataId,dtype:I.dtype,shape:u.shape},A={dataId:N.dataId,dtype:N.dtype,shape:l.shape},D=new $n(r,u.shape,l.shape);return c.runWebGLProgram(D,[E,A],ur(I.dtype,N.dtype))}),b=Rn({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let p=s||ur(u.dtype,l.dtype);if((u.dtype===\"string\"||l.dtype===\"string\"||c.shouldExecuteOnCPU([u,l]))&&o!=null){let d=c.texData.get(u.dataId).values,h=c.texData.get(l.dataId).values,g=u.dtype===\"string\"?S.fromUint8ToStringArray(d):d,x=u.dtype===\"string\"?S.fromUint8ToStringArray(h):h,[b,w]=o(u.shape,l.shape,g,x,p),I=c.makeTensorInfo(w,p),N=c.texData.get(I.dataId);return N.values=b,I}let m=L().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")&&t!=null,f;return m?f=new jn(t,u.shape,l.shape,e):f=new $n(r,u.shape,l.shape),c.runWebGLProgram(f,[u,l],p)}}function Ml(r,t=!1){if(r===\"linear\")return t?Uz:Lz;if(r===\"relu\")return t?qz:Bz;if(r===\"elu\")return t?Hz:zz;if(r===\"relu6\")return t?Kz:Vz;if(r===\"prelu\")return t?A1:E1;if(r===\"leakyrelu\")return t?_1:T1;if(r===\"sigmoid\")return t?jz:Gz;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var $d=class{constructor(t,e,n,o=!1,s=!1,i=!1,a=null,u=!1,l=!1){this.variableNames=[\"matrixA\",\"matrixB\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=he(this.outputShape.length);let c=o?t[1]:t[2],p=Math.ceil(c/2),m=o?\"i * 2, rc.y\":\"rc.y, i * 2\",f=s?\"rc.z, i * 2\":\"i * 2, rc.z\",d=o?[\"a.xxyy\",\"a.zzww\"]:[\"a.xxzz\",\"a.yyww\"],h=s?[\"b.xzxz\",\"b.ywyw\"]:[\"b.xyxy\",\"b.zwzw\"],g=\"\",x=\"\";a&&(u?g=`vec4 activation(vec4 a) {\n vec4 b = getPreluActivationWeightsAtOutCoords();\n ${a}\n }`:l?g=`vec4 activation(vec4 a) {\n vec4 b = getLeakyreluAlphaAtOutCoords();\n ${a}\n }`:g=`vec4 activation(vec4 x) {\n ${a}\n }`,x=\"result = activation(result);\");let b=i?\"result += getBiasAtOutCoords();\":\"\";i&&this.variableNames.push(\"bias\"),u&&this.variableNames.push(\"preluActivationWeights\"),l&&this.variableNames.push(\"leakyreluAlpha\");let w=\"rc.x\",I=\"rc.x\";t[0]`The new shape (${u}) has ${l} elements and the old shape (${o.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`);let c=i.texData.get(o.dataId);return c.isPacked&&!Uu(o.shape,u)&&!(c.texture!==null&&Uu(c.shape,u))?n3(o,u,i):(i.incRef(o.dataId),{dataId:o.dataId,shape:u,dtype:o.dtype})}var o3={kernelName:Vi,backendName:\"webgl\",kernelFunc:rt};var mg=class{constructor(t,e){this.variableNames=[\"x\"];let{windowSize:n,batchSize:o,inSize:s,outSize:i}=t;this.outputShape=[o,i];let a=Math.floor(n/4)*4,u=n%4,l=\"sumValue += dot(values, ones);\";if(e!=null){let p=1/e;l=`sumValue += dot(values * ${y.isInt(p)?p.toPrecision(2):p}, ones);`}let c=\"\";s%n>0&&(c=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return 0.0;\n }\n `),this.userCode=`\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n ${c}\n return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${n};\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${a}; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n ${l}\n }\n\n int inIdx = inOffset + ${a};\n if (${u===1}) {\n vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n ${l}\n } else if (${u===2}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1), 0.0, 0.0);\n\n ${l}\n } else if (${u===3}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2), 0.0);\n\n ${l}\n }\n setOutput(sumValue);\n }\n `}};var sI=class{constructor(t,e){this.variableNames=[\"x\"];let{windowSize:n,batchSize:o,inSize:s,outSize:i}=t;this.outputShape=[o,i];let a=\"0.0\",u=\"\";e===\"prod\"?a=\"1.0\":e===\"min\"?(a=\"1.0 / 1e-20\",u=\"min\"):e===\"max\"&&(a=\"-1.0 / 1e-20\",u=\"max\");let l=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), 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);\n\n ${m}\n } else if (${p===2}) {\n ${f} values = ${f}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n ${m}\n } else if (${p===3}) {\n ${f} values = ${f}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n ${m}\n }\n setOutput(${l});\n }\n `}};function Zot(r){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let e=t.length?t[t.length-1].outSize:r[1],n=S.computeOptimalWindowSize(e);t.push({inSize:e,windowSize:n,outSize:Math.ceil(e/n)})}return t}function Yn(r,t,e,n){let o=Zot(r.shape),s=r;for(let i=0;i6)throw Error(`Transpose for rank ${t} is not yet supported`);let e=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\",\"resRC.v\"],n=new Array(t);for(let o=0;o6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let o=zt(this.rank),s=N1(\"rc\",this.rank),i=new Array(this.rank);for(let c=0;c`Error in matMul: inner shapes (${p}) and (${m}) of Tensors with shapes ${r.shape} and ${t.shape} and transposeA=${e} and transposeB=${n} must match.`);let N=e?[x,p,f]:[x,f,p],E=n?[b,d,m]:[b,m,d],A=rt({inputs:{x:r},backend:o,attrs:{shape:N}}),D=rt({inputs:{x:t},backend:o,attrs:{shape:E}}),F=[A,D],M=Math.max(x,b),V=e?A.shape[1]:A.shape[2],G=s!=null,W=i!=null,q=u===\"leakyrelu\",H=u!=null?Ml(u,!0):null,K=G||W||q||H!=null,Y;if((f===1||d===1)&&V>$1&&K===!1){let et=A,nt=D;e&&(et=Pe({inputs:{x:A},backend:o,attrs:{perm:[0,2,1]}}),F.push(et)),n&&(nt=Pe({inputs:{x:D},backend:o,attrs:{perm:[0,2,1]}}),F.push(nt));let st=d!==1,lt=d===1,ot=et;st&&(ot=rt({inputs:{x:et},backend:o,attrs:{shape:[M,V,1]}}),F.push(ot));let it=d===1?2:1,ft=nt;lt&&(ft=rt({inputs:{x:nt},backend:o,attrs:{shape:[M,1,V]}}),F.push(ft));let gt=pg({inputs:{a:ot,b:ft},backend:o});Y=fp({inputs:{x:gt},backend:o,attrs:{axis:it,keepDims:!0}}),F.push(gt)}else{let et=ur(r.dtype,t.dtype),nt=new $d(N,E,[M,f,d],e,n,G,H,W,q),st=[A,D];if(s!=null&&st.push(s),W&&st.push(i),q){let lt=o.makeTensorInfo([],\"float32\",y.createScalarValue(a,\"float32\"));st.push(lt),F.push(lt)}Y=o.runWebGLProgram(nt,st,et)}let Z=rt({inputs:{x:Y},backend:o,attrs:{shape:I}});F.push(Y);for(let et of F)o.disposeIntermediateTensorInfo(et);return Z}function Qot(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s,bias:i,preluActivationWeights:a}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=n;return dp({a:o,b:s,transposeA:u,transposeB:l,backend:e,bias:i,preluActivationWeights:a,leakyreluAlpha:p,activation:c})}var l3={kernelName:ji,backendName:\"webgl\",kernelFunc:Qot};var u3=\"return abs(x);\";function tst(r){let{inputs:t,backend:e}=r,{x:n}=t;if(e.shouldExecuteOnCPU([n])&&n.dtype!==\"complex64\"){let s=e.texData.get(n.dataId),i=Qw(s.values);return e.makeTensorInfo(n.shape,n.dtype,i)}let o;return L().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")?o=new Dn(n.shape,u3):o=new Lr(n.shape,u3),e.runWebGLProgram(o,[n],n.dtype)}var c3={kernelName:Ei,backendName:\"webgl\",kernelFunc:tst};var est=xr+`\n if (abs(x) > 1.) {\n return NAN;\n }\n return acos(x);\n`,rst=It({opSnippet:est}),p3={kernelName:Go,backendName:\"webgl\",kernelFunc:rst};var nst=xr+`\n if (x < 1.0) return NAN;\nreturn log(x + sqrt(x * x - 1.0));`,ost=It({opSnippet:nst}),m3={kernelName:Wo,backendName:\"webgl\",kernelFunc:ost};var f3=\"return a + b;\",sst=ce({opSnippet:f3,packedOpSnippet:f3,supportsComplex:!0,cpuKernelImpl:WL}),d3={kernelName:no,backendName:\"webgl\",kernelFunc:sst};var lI=class{constructor(t,e){this.outputShape=[],this.outputShape=t,this.variableNames=e.map((s,i)=>`T${i}`);let n=[];this.variableNames.forEach(s=>{n.push(`float v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(\" + \");this.userCode=`\n void main() {\n ${n.join(`\n `)}\n\n float result = ${o};\n setOutput(result);\n }\n `}};var uI=class{constructor(t,e){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.variableNames=e.map((s,i)=>`T${i}`);let n=[];this.variableNames.forEach(s=>{n.push(`vec4 v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(\" + \");this.userCode=`\n void main() {\n ${n.join(`\n `)}\n\n vec4 result = ${o};\n setOutput(result);\n }\n `}};function cI(r){let{inputs:t,backend:e}=r,n=t;if(n.length===1)return nr({inputs:{x:n[0]},backend:e});if(n.length>L().getNumber(\"WEBGL_MAX_TEXTURES_IN_SHADER\")){let u=Math.floor(n.length/2),l=cI({inputs:n.slice(0,u),backend:e}),c=cI({inputs:n.slice(u),backend:e});return cI({inputs:[l,c],backend:e})}let o=n.map(u=>u.dtype).reduce((u,l)=>ur(u,l)),s=n.map(u=>u.shape),a=L().getBool(\"WEBGL_PACK\")?new uI(n[0].shape,s):new lI(n[0].shape,s);return e.runWebGLProgram(a,n,o)}var h3={kernelName:Uo,backendName:\"webgl\",kernelFunc:cI};function ist(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=o;c!=null&&(p=Pe({inputs:{x:o},backend:e,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,a)),S.assertAxesAreInnerMostDims(\"all\",l,a);let[m,f]=S.computeOutAndReduceShapes(p.shape,l),d=y.sizeFromShape(f),h=rt({inputs:{x:p},backend:e,attrs:{shape:[-1,d]}}),g=Yn(h,h.dtype,\"all\",e),x;if(i){let b=S.expandShapeToKeepDim(m,u);x=rt({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=rt({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(p),x}var g3={kernelName:Ea,backendName:\"webgl\",kernelFunc:ist};function ast(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=o;c!=null&&(p=Pe({inputs:{x:o},backend:e,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,a)),S.assertAxesAreInnerMostDims(\"any\",l,a);let[m,f]=S.computeOutAndReduceShapes(p.shape,l),d=y.sizeFromShape(f),h=rt({inputs:{x:p},backend:e,attrs:{shape:[-1,d]}}),g=Yn(h,h.dtype,\"any\",e),x;if(i){let b=S.expandShapeToKeepDim(m,u);x=rt({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=rt({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(p),x}var x3={kernelName:Aa,backendName:\"webgl\",kernelFunc:ast};var pI=class{constructor(t,e,n){this.variableNames=[\"A\"];let{windowSize:o,batchSize:s,outSize:i}=t;n||this.variableNames.push(\"bestIndicesA\"),this.outputShape=[s,i];let a=e===\"max\"?\">\":\"<\",u=n?\"inOffset + i;\":\"round(getBestIndicesA(batch, inOffset + i));\";this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${o};\n\n int bestIndex = inOffset;\n float bestValue = getA(batch, bestIndex);\n\n for (int i = 0; i < ${o}; i++) {\n int inIdx = ${u};\n float candidate = getA(batch, inIdx);\n if (candidate ${a} bestValue) {\n bestValue = candidate;\n bestIndex = inIdx;\n }\n }\n setOutput(float(bestIndex));\n }\n `}};var mI=class{constructor(t,e,n,o){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,y.assert(t.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let s=t[t.length-1],i=Math.ceil(s/e);this.outputShape=t.slice(0,-1),i>1&&this.outputShape.push(i),o||this.variableNames.push(\"bestIndicesA\");let a=this.outputShape,u=a.length,l=zt(u),c=rr(\"coords\",u),p,m;if(i===1){m=u+1;let D=zt(m);p=`\n ${D} sourceLocR = ${D}(${c.join()}, 0);\n ++${c[u-1]};\n ${D} sourceLocG = ${D}(${c.join()}, 0);\n ++${c[u-2]};\n ${D} sourceLocA = ${D}(${c.join()}, 0);\n --${c[u-1]};\n ${D} sourceLocB = ${D}(${c.join()}, 0);\n --${c[u-2]};`}else m=u,p=`\n ${l} sourceLocR = coords;\n ++${c[u-1]};\n ${l} sourceLocG = coords;\n ++${c[u-2]};\n ${l} sourceLocA = coords;\n --${c[u-1]};\n ${l} sourceLocB = coords;\n --${c[u-2]};`;let f=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,m),d=\".\"+f[m-1],h=f.map(D=>\"int \"+D),g=rr(\"sourceLocR\",m-1).concat(\"inIdx.r\"),x=rr(\"sourceLocG\",m-1).concat(\"inIdx.g\"),b=rr(\"sourceLocB\",m-1).concat(\"inIdx.b\"),w=rr(\"sourceLocA\",m-1).concat(\"inIdx.a\"),I=n===\"max\"?\"greaterThan\":\"lessThan\",N=o?\"\":`\n inIdx = round(vec4(getBestIndicesAChannel(${g.join()}),\n getBestIndicesAChannel(${x.join()}),\n getBestIndicesAChannel(${b.join()}),\n getBestIndicesAChannel(${w.join()})));`,E=`vec4(\n getAChannel(${g.join()}),\n hasNextCol ? getAChannel(${x.join()}) : 0.,\n hasNextRow ? getAChannel(${b.join()}) : 0.,\n hasNextRow && hasNextCol ? getAChannel(${w.join()}) : 0.)`,A=o?\"\":`\n float getBestIndicesAChannel(${h.join()}) {\n return getChannel(getBestIndicesA(${f.join()}),\n vec2(${f.slice(-2).join()}));\n }`;this.userCode=`\n float getAChannel(${h.join()}) {\n return getChannel(getA(${f.join()}),\n vec2(${f.slice(-2).join()}));\n }\n ${A}\n void main() {\n ${l} coords = getOutputCoords();\n bool hasNextCol = ${c[u-1]} < ${a[u-1]-1};\n bool hasNextRow = ${c[u-2]} < ${a[u-2]-1};\n ${p}\n ivec4 srcIdx = ivec4(sourceLocR${d}, sourceLocG${d},\n sourceLocB${d}, sourceLocA${d}) * ${e};\n ivec4 inIdx = srcIdx;\n vec4 bestIndex = vec4(inIdx);\n vec4 bestValue = ${E};\n\n for (int i = 0; i < ${e}; i++) {\n inIdx = srcIdx;\n ${N}\n vec4 candidate = ${E};\n bvec4 nan = isnan(candidate);\n bvec4 replace = bvec4(\n vec4(${I}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n bestValue = vec4(replace.x ? candidate.x : bestValue.x,\n replace.y ? candidate.y : bestValue.y,\n replace.z ? candidate.z : bestValue.z,\n replace.w ? candidate.w : bestValue.w);\n bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n srcIdx++;\n }\n setOutput(bestIndex);\n }\n `}};function y3(r,t,e,n=null){let o=t.shape[0],s=t.shape[1];n!=null&&(o=n.shape[0],s=n.shape[1]);let i=S.computeOptimalWindowSize(s),a={windowSize:i,inSize:s,batchSize:o,outSize:Math.ceil(s/i)},u=new pI(a,e,n==null),l=[t];n!=null&&l.push(n);let c=r.runWebGLProgram(u,l,\"int32\");if(c.shape[1]===1)return c;let p=y3(r,t,e,c);return r.disposeIntermediateTensorInfo(c),p}function b3(r,t,e,n=null){let o=n!=null?n.shape:t.shape,s=o[o.length-1],i=S.computeOptimalWindowSize(s),a=new mI(o,i,e,n==null),u=n==null?[t]:[t,n],l=r.runWebGLProgram(a,u,\"int32\");if(l.shape.length===t.shape.length){let c=b3(r,t,e,l);return r.disposeIntermediateTensorInfo(l),c}return l}function fI(r,t,e,n){let o=[e];if(S.assertAxesAreInnerMostDims(\"arg\"+n.charAt(0).toUpperCase()+n.slice(1),o,t.shape.length),!L().getBool(\"WEBGL_PACK_REDUCE\")||t.shape.length<=2){let s=[],i=r.texData.get(t.dataId),a=i!==null&&i.isPacked,u=t;a&&(u=r.unpackTensor(t),s.push(u));let[l,c]=S.computeOutAndReduceShapes(u.shape,o),p=y.sizeFromShape(c),m=rt({inputs:{x:u},backend:r,attrs:{shape:[-1,p]}});s.push(m);let f=y3(r,m,n);s.push(f);let d=rt({inputs:{x:f},backend:r,attrs:{shape:l}});return s.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}return b3(r,t,n)}function lst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s}=n,i=y.parseAxisParam(s,o.shape),a=S.getAxesPermutation(i,o.shape.length),u=o,l=[];a!=null&&(u=Pe({inputs:{x:o},backend:e,attrs:{perm:a}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims(\"argMax\",[i[0]],u.shape.length);let c=fI(e,u,i[0],\"max\");return l.forEach(p=>e.disposeIntermediateTensorInfo(p)),c}var w3={kernelName:Ai,backendName:\"webgl\",kernelFunc:lst};function ust(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s}=n,i=y.parseAxisParam(s,o.shape),a=S.getAxesPermutation(i,o.shape.length),u=o,l=[];a!=null&&(u=Pe({inputs:{x:o},backend:e,attrs:{perm:a}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims(\"argMin\",[i[0]],u.shape.length);let c=fI(e,u,i[0],\"min\");return l.forEach(p=>e.disposeIntermediateTensorInfo(p)),c}var I3={kernelName:Di,backendName:\"webgl\",kernelFunc:ust};var cst=xr+`\n if (abs(x) > 1.) {\n return NAN;\n }\n return asin(x);\n`,pst=It({opSnippet:cst}),C3={kernelName:Ho,backendName:\"webgl\",kernelFunc:pst};var mst=xr+\"return log(x + sqrt(x * x + 1.0));\",fst=It({opSnippet:mst}),v3={kernelName:qo,backendName:\"webgl\",kernelFunc:fst};var dst=xr+`\n return atan(x);\n`,hst=It({opSnippet:dst}),S3={kernelName:Ko,backendName:\"webgl\",kernelFunc:hst};var gst=Dd+`\n return atan(a, b);\n`,xst=`\n vec4 result = atan(a, b);\n bvec4 isNaNA = isnan(a);\n bvec4 isNaNB = isnan(b);\n bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);\n `+Xn+`\n return result;\n`,yst=ce({opSnippet:gst,packedOpSnippet:xst}),N3={kernelName:Xo,backendName:\"webgl\",kernelFunc:yst};var bst=xr+`\n if ((x < -1.0) || (x > 1.0)) return NAN;\nreturn (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wst=It({opSnippet:bst}),k3={kernelName:jo,backendName:\"webgl\",kernelFunc:wst};var Si=class{constructor(t,e,n,o=!1,s=!1){if(this.variableNames=[\"x\"],e===\"avg\"&&n)throw new Error(\"Cannot compute positions for average pool.\");let i=t.filterWidth,a=t.strideHeight,u=t.strideWidth,l=t.dilationHeight,c=t.dilationWidth,p=t.effectiveFilterHeight,m=t.effectiveFilterWidth,f=t.padInfo.top,d=t.padInfo.left;this.outputShape=t.outShape;let h=e===\"avg\",g=`((batch * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + d`,x=`(xR * ${t.inWidth} + xC) * ${t.inChannels} + d`,b=\"0.0\";if(h||(b=\"-1.0 / 1e-20\"),n){let D=\">=\";this.userCode=`\n const ivec2 strides = ivec2(${a}, ${u});\n const ivec2 pads = ivec2(${f}, ${d});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n float avgValue = 0.0;\n\n for (int wR = 0; wR < ${p};\n wR += ${l}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${m};\n wC += ${c}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xR, xC, d);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${D} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${o?s?g:x:`wR * ${m} + wC`};\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `;return}let w=\"max\",I=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e===\"avg\"&&(I=\"avgValue / max(count, 1.0)\");let N=Math.floor(i/4)*4,E=i%4,A=`\n if (${h}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = ${w}(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec2 strides = ivec2(${a}, ${u});\n const ivec2 pads = ivec2(${f}, ${d});\n const float initializationValue = ${b};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xR, int xC, int d) {\n if (xC < 0 || xC >= ${t.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xR, xC, d);\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n vec4 minMaxValue = vec4(${b});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wR = 0; wR < ${p};\n wR += ${l}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${N}; wC += 4) {\n int xC = xCCorner + wC * ${c};\n\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n getValue(batch, xR, xC + 2 * ${c}, d),\n getValue(batch, xR, xC + 3 * ${c}, d)\n );\n\n ${A}\n }\n\n int xC = xCCorner + ${N};\n if (${E===1}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${A}\n } else if (${E===2}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n initializationValue,\n initializationValue\n );\n\n ${A}\n } else if (${E===3}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n getValue(batch, xR, xC + 2 * ${c}, d),\n initializationValue\n );\n\n ${A}\n }\n }\n setOutput(${I});\n }\n `}},qu=class{constructor(t,e,n,o=!1,s=!1){if(this.variableNames=[\"x\"],e===\"avg\"&&n)throw new Error(\"Cannot compute positions for average pool.\");let i=t.filterWidth,a=t.strideDepth,u=t.strideHeight,l=t.strideWidth,c=t.dilationDepth,p=t.dilationHeight,m=t.dilationWidth,f=t.effectiveFilterDepth,d=t.effectiveFilterHeight,h=t.effectiveFilterWidth,g=t.padInfo.front,x=t.padInfo.top,b=t.padInfo.left;this.outputShape=t.outShape;let w=e===\"avg\",I=\"0.0\";if(w||(I=\"-1.0 / 1e-20\"),n){let M=\">=\";this.userCode=`\n const ivec3 strides =\n ivec3(${a}, ${u}, ${l});\n const ivec3 pads = ivec3(${g}, ${x}, ${b});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n\n for (int wD = 0; wD < ${f};\n wD += ${c}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${t.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${d};\n wR += ${p}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${h};\n wC += ${m}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xD, xR, xC, ch);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${M} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${o?s?`(((batch * ${t.inDepth} + xD) * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + ch`:`((xD * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + ch`:`wD * ${d} * ${h} +\n wR * ${h} + wC`};\n }\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `;return}let N=\"max\",E=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e===\"avg\"&&(E=\"avgValue / max(count, 1.0)\");let A=Math.floor(i/4)*4,D=i%4,F=`\n if (${w}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = ${N}(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec3 strides =\n ivec3(${a}, ${u}, ${l});\n const ivec3 pads = ivec3(${g}, ${x}, ${b});\n const float initializationValue = ${I};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xD, int xR, int xC, int ch) {\n if (xC < 0 || xC >= ${t.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xD, xR, xC, ch);\n }\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n // ? = to be determined\n vec4 minMaxValue = vec4(${I});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wD = 0; wD < ${f};\n wD += ${c}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${t.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${d};\n wR += ${p}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${A}; wC += 4) {\n int xC = xCCorner + wC * ${m};\n\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n getValue(batch, xD, xR, xC + 2 * ${m}, ch),\n getValue(batch, xD, xR, xC + 3 * ${m}, ch)\n );\n\n ${F}\n }\n\n int xC = xCCorner + ${A};\n if (${D===1}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${F}\n } else if (${D===2}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n initializationValue,\n initializationValue\n );\n\n ${F}\n } else if (${D===3}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n getValue(batch, xD, xR, xC + 2 * ${m}, ch),\n initializationValue\n );\n\n ${F}\n }\n }\n }\n setOutput(${E});\n }\n `}};function Ist(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;Ci(o,\"avgPool\");let{filterSize:s,strides:i,pad:a,dimRoundingMode:u}=n,l=1;y.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,l,a,u);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return nr({inputs:{x:o},backend:e});let p=new Si(c,\"avg\",!1);return e.runWebGLProgram(p,[o],\"float32\")}var T3={kernelName:Yo,backendName:\"webgl\",kernelFunc:Ist};function Cst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dimRoundingMode:u,dataFormat:l}=n,c=[1,1,1],p=S.computePool3DInfo(o.shape,s,i,c,a,u,l),m=new qu(p,\"avg\",!1);return e.runWebGLProgram(m,[o],\"float32\")}var _3={kernelName:$i,backendName:\"webgl\",kernelFunc:Cst};var dI=class{constructor(t){this.variableNames=[\"dy\"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=t.dilationHeight,a=t.dilationWidth,u=t.effectiveFilterHeight,l=t.effectiveFilterWidth,c=u-1-t.padInfo.top,p=l-1-t.padInfo.left,m=1/(e*n);this.userCode=`\n const ivec2 pads = ivec2(${c}, ${p});\n const float avgMultiplier = float(${m});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${u};\n wR += ${i}) {\n float dyR = float(dyRCorner + wR) / ${o}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${l};\n wC+= ${a}) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n setOutput(dotProd);\n }\n `}},hI=class{constructor(t){this.variableNames=[\"dy\"],this.outputShape=t.inShape;let e=t.filterDepth,n=t.filterHeight,o=t.filterWidth,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,c=t.dilationWidth,p=t.effectiveFilterDepth,m=t.effectiveFilterHeight,f=t.effectiveFilterWidth,d=p-1-t.padInfo.front,h=m-1-t.padInfo.top,g=f-1-t.padInfo.left,x=1/(e*n*o);this.userCode=`\n const ivec3 pads = ivec3(${d}, ${h}, ${g});\n const float avgMultiplier = float(${x});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${p};\n wD += ${u}) {\n float dyD = float(dyDCorner + wD) / ${s}.0;\n\n if (dyD < 0.0 || dyD >= ${t.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${m};\n wR += ${l}) {\n float dyR = float(dyRCorner + wR) / ${i}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${f};\n wC += ${c}) {\n float dyC = float(dyCCorner + wC) / ${a}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function vst(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s,{filterSize:a,strides:u,pad:l,dimRoundingMode:c}=n,p=[1,1,1],m=S.computePool3DInfo(i.shape,a,u,p,l,c),f=new hI(m);return e.runWebGLProgram(f,[o],i.dtype)}var E3={kernelName:Hl,backendName:\"webgl\",kernelFunc:vst};function Sst(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s;Ci([o,s],\"avgPoolGrad\");let{filterSize:a,strides:u,pad:l}=n,c=S.computePool2DInfo(i.shape,a,u,1,l),p=new dI(c);return e.runWebGLProgram(p,[o],i.dtype)}var A3={kernelName:Ul,backendName:\"webgl\",kernelFunc:Sst};function Nst(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s}=t,{transposeA:i,transposeB:a}=n;return dp({a:o,b:s,transposeA:i,transposeB:a,backend:e})}var D3={kernelName:Zo,backendName:\"webgl\",kernelFunc:Nst};var gI=class{constructor(t,e,n,o,s,i){this.outputShape=[],this.variableNames=[\"x\",\"mean\",\"variance\"],S.assertAndGetBroadcastShape(t,e),S.assertAndGetBroadcastShape(t,n);let a=\"0.0\";o!=null&&(S.assertAndGetBroadcastShape(t,o),this.variableNames.push(\"offset\"),a=\"getOffsetAtOutCoords()\");let u=\"1.0\";s!=null&&(S.assertAndGetBroadcastShape(t,s),this.variableNames.push(\"scale\"),u=\"getScaleAtOutCoords()\"),this.outputShape=t,this.userCode=`\n void main() {\n float x = getXAtOutCoords();\n float mean = getMeanAtOutCoords();\n float variance = getVarianceAtOutCoords();\n float offset = ${a};\n float scale = ${u};\n float inv = scale * inversesqrt(variance + float(${i}));\n setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n }\n `}};var xI=class{constructor(t,e,n,o,s,i){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=[\"x\",\"mean\",\"variance\"],S.assertAndGetBroadcastShape(t,e),S.assertAndGetBroadcastShape(t,n);let a=\"vec4(0.0)\";o!=null&&(S.assertAndGetBroadcastShape(t,o),this.variableNames.push(\"offset\"),a=\"getOffsetAtOutCoords()\");let u=\"vec4(1.0)\";s!=null&&(S.assertAndGetBroadcastShape(t,s),this.variableNames.push(\"scale\"),u=\"getScaleAtOutCoords()\"),this.outputShape=t,this.userCode=`\n void main() {\n vec4 offset = ${a};\n vec4 scale = ${u};\n\n vec4 x = getXAtOutCoords();\n vec4 mean = getMeanAtOutCoords();\n vec4 variance = getVarianceAtOutCoords();\n\n vec4 inv = scale * inversesqrt(variance + vec4(${i}));\n\n setOutput((x - mean) * inv + offset);\n }\n `}};var kst=({inputs:r,backend:t,attrs:e})=>{let{x:n,mean:o,variance:s,offset:i,scale:a}=r;y.assert(o.shape.length===s.shape.length,()=>\"Batch normalization gradient requires mean and variance to have equal ranks.\"),y.assert(i==null||o.shape.length===i.shape.length,()=>\"Batch normalization gradient requires mean and offset to have equal ranks.\"),y.assert(a==null||o.shape.length===a.shape.length,()=>\"Batch normalization gradient requires mean and scale to have equal ranks.\");let{varianceEpsilon:u}=e;u==null&&(u=.001);let l=[n,o,s],c=null;i!=null&&(c=i.shape,l.push(i));let p=null;a!=null&&(p=a.shape,l.push(a));let m=L().getBool(\"WEBGL_PACK_NORMALIZATION\")?new xI(n.shape,o.shape,s.shape,c,p,u):new gI(n.shape,o.shape,s.shape,c,p,u);return t.runWebGLProgram(m,l,l[0].dtype)},$3={kernelName:ds,backendName:\"webgl\",kernelFunc:kst};var yI=class{constructor(t){this.variableNames=[\"source\"],this.outputShape=t,this.rank=t.length;let e=zt(this.rank);this.customUniforms=[{name:\"start\",arrayIndex:this.rank,type:\"int\"}];let n=Tst(this.rank),o,s=t.map((i,a)=>`sourceLoc.${R1[a]} = start[${a}] + coords.${R1[a]};`);o=`\n ${e} sourceLoc;\n ${e} coords = getOutputCoords();\n ${s.join(`\n`)}\n `,this.userCode=`\n void main() {\n ${o}\n setOutput(getSource(${n}));\n }\n `}},R1=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];function Tst(r){if(r===1)return\"sourceLoc\";if(r<=6)return R1.slice(0,r).map(t=>\"sourceLoc.\"+t).join(\",\");throw Error(`Slicing for rank ${r} is not yet supported`)}var bI=class{constructor(t){this.variableNames=[\"source\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.rank=t.length,this.customUniforms=[{name:\"start\",arrayIndex:this.rank,type:\"int\"}];let e=zt(this.rank),n=rr(\"coords\",this.rank),o=rr(\"sourceLoc\",this.rank),s=this.rank===1?\"sourceLoc\":`vec2(${o.slice(-2).join()})`,i=`getChannel(getSource(${o.join()}), ${s})`,a=`\n result.x = ${i};\n if (++${n[this.rank-1]} < ${t[this.rank-1]}) {\n ++${o[this.rank-1]};\n result.y = ${i};\n --${o[this.rank-1]};\n }\n `,u=this.rank===1?\"\":`\n --${n[this.rank-1]};\n if (++${n[this.rank-2]} < ${t[this.rank-2]}) {\n ++${o[this.rank-2]};\n result.z = ${i};\n if (++${n[this.rank-1]} < ${t[this.rank-1]}) {\n ++${o[this.rank-1]};\n result.w = ${i};\n }\n }\n `,l=this.rank<=4?`sourceLoc = coords +\n ${e}(${t.map((c,p)=>`start[${p}]`).join()});`:t.map((c,p)=>`${o[p]} = ${n[p]} + start[${p}];`).join(`\n`);this.userCode=`\n void main() {\n ${e} coords = getOutputCoords();\n ${e} sourceLoc;\n ${l}\n vec4 result = vec4(0.);\n ${a}\n ${u}\n setOutput(result);\n }\n `}};function _st(r,t,e,n){let o=n.texData.get(r.dataId),s=n.makeTensorInfo(e,r.dtype),i=n.texData.get(s.dataId);Object.assign(i,o),i.refCount=1,i.shape=e,i.dtype=r.dtype;let a=Be.computeFlatOffset(t,y.computeStrides(r.shape));o.slice&&(a+=o.slice.flatOffset),i.slice={flatOffset:a,origDataId:o.slice&&o.slice.origDataId||r.dataId};let u=n.dataRefCount.get(i.slice.origDataId)||1;return n.dataRefCount.set(i.slice.origDataId,u+1),s}function Ni(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{begin:s,size:i}=n,[a,u]=Be.parseSliceParams(o,s,i);if(Be.assertParamsValid(o,a,u),y.sizeFromShape(u)===0)return e.makeTensorInfo(u,o.dtype,[]);if(e.shouldExecuteOnCPU([o])||o.dtype===\"string\"){let p=e.texData.get(o.dataId),m=Iz(p.values,a,u,o.shape,o.dtype);return e.makeTensorInfo(u,o.dtype,m)}let{isPacked:l}=e.texData.get(o.dataId),c=Be.isSliceContinous(o.shape,a,u);if(l||!c){let p=L().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new bI(u):new yI(u),m=[a];return e.runWebGLProgram(p,[o],o.dtype,m)}return e.uploadToGPU(o.dataId),_st(o,a,u,e)}var R3={kernelName:Wi,backendName:\"webgl\",kernelFunc:Ni};var Est=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,crops:i}=n;y.assert(o.shape.length<=4,()=>\"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet\");let a=s.reduce((b,w)=>b*w),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=[],d=rt({inputs:{x:o},backend:e,attrs:{shape:u}}),h=Pe({inputs:{x:d},backend:e,attrs:{perm:l}}),g=rt({inputs:{x:h},backend:e,attrs:{shape:c}}),x=Ni({inputs:{x:g},backend:e,attrs:{begin:p,size:m}});return f.push(d),f.push(h),f.push(g),f.forEach(b=>e.disposeIntermediateTensorInfo(b)),x},F3={kernelName:Ri,backendName:\"webgl\",kernelFunc:Est};function Ast(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i}=n,a=e.readSync(o.dataId),u=e.readSync(s.dataId),l=Jw(a,u,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,l)}var O3={kernelName:Da,backendName:\"webgl\",kernelFunc:Ast};var Dst=`\n int r = int(a.r) & int(b.r);\n int g = int(a.g) & int(b.g);\n int rb = int(a.b) & int(b.b);\n int ra = int(a.a) & int(b.a);\n return vec4(r, g, rb, ra);\n`,$st=`\n return float(int(a.r) & int(b.r));\n`;function Rst(r){let{inputs:t,backend:e}=r,{a:n,b:o}=t,s=L().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\"),i=L().getNumber(\"WEBGL_VERSION\");if(e.shouldExecuteOnCPU([n,o])||i===1){let u=e.texData.get(n.dataId).values,l=e.texData.get(o.dataId).values,[c,p]=HL(n.shape,o.shape,u,l,n.dtype),m=e.makeTensorInfo(p,n.dtype),f=e.texData.get(m.dataId);return f.values=c,m}let a;return s?a=new jn(Dst,n.shape,o.shape,!1):a=new $n($st,n.shape,o.shape),e.runWebGLProgram(a,[n,o],n.dtype)}var M3={kernelName:$a,backendName:\"webgl\",kernelFunc:Rst};function Fst(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.readSync(n.dataId),i=e.readSync(o.dataId),a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeTensorInfo([a.length],\"int32\",Int32Array.from(a))}var P3={kernelName:ql,backendName:\"webgl\",kernelFunc:Fst};var Ost=\"return float(a != b);\",F1=ce({opSnippet:Ost,cpuKernelImpl:mz,dtype:\"bool\"}),L3={kernelName:Za,backendName:\"webgl\",kernelFunc:F1};function Pl(r){let{inputs:t,backend:e}=r,{input:n}=t,o=e.texData.get(n.dataId);return nr({inputs:{x:o.complexTensorInfos.real},backend:e})}var z3={kernelName:Vp,backendName:\"webgl\",kernelFunc:Pl};var Mst=\"return float(int(x));\";function B3(r,t){let e=new Lr(r.shape,Mst),n=t.runWebGLProgram(e,[r],\"int32\");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function O1(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{dtype:s}=n;if(s===\"complex64\"){if(o.dtype===\"complex64\")return nr({inputs:{x:o},backend:e});let i=ke(o.shape),a=O1({inputs:{x:o},backend:e,attrs:{dtype:\"float32\"}}),u=Rn({inputs:{real:a,imag:i},backend:e});return i.dispose(),e.disposeIntermediateTensorInfo(a),u}if(o.dtype===\"complex64\"){let i=Pl({inputs:{input:o},backend:e}),a=O1({inputs:{x:i},backend:e,attrs:{dtype:s}});return e.disposeIntermediateTensorInfo(i),a}if(!y.hasEncodingLoss(o.dtype,s)){let i=nr({inputs:{x:o},backend:e});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(e.shouldExecuteOnCPU([o])){let i=e.texData.get(o.dataId).values,[a,u,l]=qL(i,o.shape,o.dtype,s);return e.makeTensorInfo(a,u,l)}if(s===\"int32\")return B3(o,e);if(s===\"bool\"){let i=e.makeTensorInfo([],\"bool\",y.getTypedArrayFromDType(\"bool\",1)),u=F1({inputs:{a:o,b:i},backend:e});return e.disposeIntermediateTensorInfo(i),u}throw new Error(`Error in Cast: failed to cast ${o.dtype} to ${s}`)}var V3={kernelName:fo,backendName:\"webgl\",kernelFunc:O1};var G3=\"return ceil(x);\",Pst=It({opSnippet:G3,packedOpSnippet:G3,cpuKernelImpl:KL}),W3={kernelName:Jo,backendName:\"webgl\",kernelFunc:Pst};var wI=class{constructor(t){this.variableNames=[\"A\"],this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=t,this.userCode=`\n\n void main() {\n float value = getAAtOutCoords();\n if (isnan(value)) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, minVal, maxVal));\n }\n `}};var II=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=t,this.userCode=`\n void main() {\n vec4 value = getAAtOutCoords();\n\n if (any(isnan(value))) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n }\n `}};function Lst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{clipValueMin:s,clipValueMax:i}=n,a;L().getBool(\"WEBGL_PACK_CLIP\")?a=new II(o.shape):a=new wI(o.shape);let u=[[s],[i]];return e.runWebGLProgram(a,[o],o.dtype,u)}var U3={kernelName:ho,backendName:\"webgl\",kernelFunc:Lst};var CI=class{constructor(t){this.variableNames=[\"real\",\"imag\"],this.outputShape=t,this.userCode=`\n void main() {\n float re = abs(getRealAtOutCoords());\n float im = abs(getImagAtOutCoords());\n float mx = max(re, im);\n\n // sadly the length function in glsl is not underflow-safe\n // (at least not on Intel GPUs). So the safe solution is\n // to ensure underflow-safety in all cases.\n setOutput(\n mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n );\n }\n `}};function H3(r,t){return{dataId:t.dataId,dtype:t.dtype,shape:r.shape}}function zst(r){let{inputs:t,backend:e}=r,{x:n}=t,o=e.texData.get(n.dataId),s=new CI(n.shape),i=[H3(n,o.complexTensorInfos.real),H3(n,o.complexTensorInfos.imag)];return e.runWebGLProgram(s,i,i[0].dtype)}var q3={kernelName:Kl,backendName:\"webgl\",kernelFunc:zst};var vI=class{constructor(t){this.outputShape=[],this.outputShape=S.computeOutShape(t,1),this.variableNames=t.map((i,a)=>`T${a}`);let e=new Array(t.length-1);e[0]=t[0][1];for(let i=1;i`T${g}`);let u=new Array(t.length-1);u[0]=t[0][e];for(let h=1;h= ${u[h-1]}) {\n return getChannel(\n getT${h}(${SI(a,l,g)}),\n vec2(${SI(c,l,g)}));\n }`}let f=u.length,d=u[u.length-1];m+=`\n return getChannel(\n getT${f}(${SI(a,l,d)}),\n vec2(${SI(c,l,d)}));`,this.userCode=`\n float getValue(${a.map(h=>\"int \"+h)}) {\n ${m}\n }\n\n void main() {\n ${s} coords = getOutputCoords();\n vec4 result = vec4(getValue(${i}), 0., 0., 0.);\n\n ${i[o-1]} = ${i[o-1]} + 1;\n if (${i[o-1]} < ${n[o-1]}) {\n result.g = getValue(${i});\n }\n\n ${i[o-2]} = ${i[o-2]} + 1;\n if (${i[o-2]} < ${n[o-2]}) {\n result.a = getValue(${i});\n }\n\n ${i[o-1]} = ${i[o-1]} - 1;\n if (${i[o-2]} < ${n[o-2]} &&\n ${i[o-1]} < ${n[o-1]}) {\n result.b = getValue(${i});\n }\n setOutput(result);\n }\n `}};function SI(r,t,e){let n=r.indexOf(t);return r.map((s,i)=>i===n?`${s} - ${e}`:s).join()}function hp(r){let{inputs:t,backend:e}=r,{input:n}=t,o=e.texData.get(n.dataId);return nr({inputs:{x:o.complexTensorInfos.imag},backend:e})}var K3={kernelName:Pp,backendName:\"webgl\",kernelFunc:hp};function Rd(r,t,e){let n=r[0].dtype;if(n===\"complex64\"){let f=r.map(b=>Pl({inputs:{input:b},backend:e})),d=r.map(b=>hp({inputs:{input:b},backend:e})),h=Rd(f,t,e),g=Rd(d,t,e),x=Rn({inputs:{real:h,imag:g},backend:e});return f.forEach(b=>e.disposeIntermediateTensorInfo(b)),d.forEach(b=>e.disposeIntermediateTensorInfo(b)),e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),x}let o=e.shouldExecuteOnCPU(r);if(n===\"string\"&&(o=!0),o){let f=r.map(I=>{let E=[-1,y.sizeFromShape(I.shape.slice(t))];return rt({inputs:{x:I},backend:e,attrs:{shape:E}})}),d=f.map(I=>({vals:e.readSync(I.dataId),shape:I.shape})),h=S.computeOutShape(f.map(I=>I.shape),1),g=f[0].shape[0]===1,x=jL(d,h,n,g),b=S.computeOutShape(r.map(I=>I.shape),t),w=e.makeTensorInfo(b,n,x);return f.forEach(I=>e.disposeIntermediateTensorInfo(I)),w}let s=r.filter(f=>y.sizeFromShape(f.shape)>0),i=L().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")&&s[0].shape.length>1;if(s.length===1){let f=i?new Lr(r[0].shape,Ia):new Dn(r[0].shape,Ia);return e.runWebGLProgram(f,r,n)}let a=L().getNumber(\"WEBGL_MAX_TEXTURES_IN_SHADER\");if(s.length>a){let f=[];for(let h=0;hd.shape),t);return e.runWebGLProgram(f,s,n)}let{tensors2D:u,outShape:l}=Bst(s,t,e),c=new vI(u.map(f=>f.shape)),p=e.runWebGLProgram(c,u,n);u.forEach(f=>e.disposeIntermediateTensorInfo(f));let m=rt({inputs:{x:p},attrs:{shape:l},backend:e});return e.disposeIntermediateTensorInfo(p),m}function Bst(r,t,e){let n=S.computeOutShape(r.map(s=>s.shape),t);return{tensors2D:r.map(s=>rt({inputs:{x:s},attrs:{shape:[-1,y.sizeFromShape(s.shape.slice(t))]},backend:e})),outShape:n}}function M1(r){let{inputs:t,backend:e,attrs:n}=r,{axis:o}=n,s=y.parseAxisParam(o,t[0].shape)[0],i=t.map(l=>l.shape);S.assertParamsConsistent(i,s);let a=S.computeOutShape(t.map(l=>l.shape),s);if(y.sizeFromShape(a)===0)return e.makeTensorInfo(a,t[0].dtype,[]);let u=t.filter(l=>y.sizeFromShape(l.shape)>0);return u.length===1?nr({inputs:{x:u[0]},backend:e}):Rd(u,s,e)}var j3={kernelName:Fi,backendName:\"webgl\",kernelFunc:M1};var Fd=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.outputShape=t.outShape;let i=t.padInfo.top,a=t.padInfo.left,u=t.strideHeight,l=t.strideWidth,c=t.dilationHeight,p=t.dilationWidth,m=t.filterHeight,f=t.filterWidth,d=Math.floor(t.inChannels/4)*4,h=t.inChannels%4,g=t.dataFormat===\"channelsLast\",x=g?1:2,b=g?2:3,w=g?3:1,I=\"\",N=\"\";n&&(o?I=`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${n}\n }`:s?I=`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${n}\n }`:I=`\n float activation(float x) {\n ${n}\n }\n `,N=\"result = activation(result);\");let E=e?\"result += getBiasAtOutCoords();\":\"\";e&&this.variableNames.push(\"bias\"),o&&this.variableNames.push(\"preluActivationWeights\"),s&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n ${I}\n\n const ivec2 strides = ivec2(${u}, ${l});\n const ivec2 pads = ivec2(${i}, ${a});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d2 = coords[${w}];\n\n ivec2 xRCCorner =\n ivec2(coords[${x}], coords[${b}]) * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${m}; wR++) {\n int xR = xRCorner + wR * ${c};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${f}; wC++) {\n int xC = xCCorner + wC * ${p};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${d}; d1 += 4) {\n vec4 wValues = vec4(\n getW(wR, wC, d1, d2),\n getW(wR, wC, d1 + 1, d2),\n getW(wR, wC, d1 + 2, d2),\n getW(wR, wC, d1 + 3, d2)\n );\n\n if (${g}) {\n vec4 xValues = vec4(\n getX(batch, xR, xC, d1),\n getX(batch, xR, xC, d1 + 1),\n getX(batch, xR, xC, d1 + 2),\n getX(batch, xR, xC, d1 + 3)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec4 xValues = vec4(\n getX(batch, d1, xR, xC),\n getX(batch, d1 + 1, xR, xC),\n getX(batch, d1 + 2, xR, xC),\n getX(batch, d1 + 3, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n\n if (${h===1}) {\n\n if (${g}) {\n dotProd +=\n getX(batch, xR, xC, ${d}) *\n getW(wR, wC, ${d}, d2);\n } else {\n dotProd +=\n getX(batch, ${d}, xR, xC) *\n getW(wR, wC, ${d}, d2);\n }\n\n } else if (${h===2}) {\n vec2 wValues = vec2(\n getW(wR, wC, ${d}, d2),\n getW(wR, wC, ${d} + 1, d2)\n );\n\n if (${g}) {\n vec2 xValues = vec2(\n getX(batch, xR, xC, ${d}),\n getX(batch, xR, xC, ${d} + 1)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec2 xValues = vec2(\n getX(batch, ${d}, xR, xC),\n getX(batch, ${d} + 1, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n } else if (${h===3}) {\n vec3 wValues = vec3(\n getW(wR, wC, ${d}, d2),\n getW(wR, wC, ${d} + 1, d2),\n getW(wR, wC, ${d} + 2, d2)\n );\n\n if (${g}) {\n vec3 xValues = vec3(\n getX(batch, xR, xC, ${d}),\n getX(batch, xR, xC, ${d} + 1),\n getX(batch, xR, xC, ${d} + 2)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec3 xValues = vec3(\n getX(batch, ${d}, xR, xC),\n getX(batch, ${d} + 1, xR, xC),\n getX(batch, ${d} + 2, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n }\n }\n }\n\n float result = dotProd;\n ${E}\n ${N}\n setOutput(result);\n }\n `}},kI=class{constructor(t){this.variableNames=[\"x\",\"W\"],this.outputShape=t.outShape;let e=t.padInfo.front,n=t.padInfo.top,o=t.padInfo.left,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,c=t.dilationWidth,p=t.filterDepth,m=t.filterHeight,f=t.filterWidth,d=Math.floor(t.inChannels/4)*4,h=t.inChannels%4;this.userCode=`\n const ivec3 strides = ivec3(${s}, ${i}, ${a});\n const ivec3 pads = ivec3(${e}, ${n}, ${o});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d2 = coords.u;\n\n ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xFCorner = xFRCCorner.x;\n int xRCorner = xFRCCorner.y;\n int xCCorner = xFRCCorner.z;\n\n // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n // y(yF, yR, yC, d2). ? = to be determined. : = across all\n // values in that axis.\n float dotProd = 0.0;\n for (int wF = 0; wF < ${p}; wF++) {\n int xF = xFCorner + wF * ${u};\n\n if (xF < 0 || xF >= ${t.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${m}; wR++) {\n int xR = xRCorner + wR * ${l};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${f}; wC++) {\n int xC = xCCorner + wC * ${c};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${d}; d1 += 4) {\n vec4 xValues = vec4(\n getX(batch, xF, xR, xC, d1),\n getX(batch, xF, xR, xC, d1 + 1),\n getX(batch, xF, xR, xC, d1 + 2),\n getX(batch, xF, xR, xC, d1 + 3)\n );\n vec4 wValues = vec4(\n getW(wF, wR, wC, d1, d2),\n getW(wF, wR, wC, d1 + 1, d2),\n getW(wF, wR, wC, d1 + 2, d2),\n getW(wF, wR, wC, d1 + 3, d2)\n );\n\n dotProd += dot(xValues, wValues);\n }\n\n if (${h===1}) {\n dotProd +=\n getX(batch, xF, xR, xC, ${d}) *\n getW(wF, wR, wC, ${d}, d2);\n } else if (${h===2}) {\n vec2 xValues = vec2(\n getX(batch, xF, xR, xC, ${d}),\n getX(batch, xF, xR, xC, ${d} + 1)\n );\n vec2 wValues = vec2(\n getW(wF, wR, wC, ${d}, d2),\n getW(wF, wR, wC, ${d} + 1, d2)\n );\n dotProd += dot(xValues, wValues);\n } else if (${h===3}) {\n vec3 xValues = vec3(\n getX(batch, xF, xR, xC, ${d}),\n getX(batch, xF, xR, xC, ${d} + 1),\n getX(batch, xF, xR, xC, ${d} + 2)\n );\n vec3 wValues = vec3(\n getW(wF, wR, wC, ${d}, d2),\n getW(wF, wR, wC, ${d} + 1, d2),\n getW(wF, wR, wC, ${d} + 2, d2)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}};var Od=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=t.outShape,this.enableShapeUniforms=he(this.outputShape.length);let i=t.padInfo.left,a=t.strideWidth,u=t.dilationWidth,l=t.filterHeight,c=t.filterWidth,p=c,m=`\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g=0 && xR < inDims[0]) {\n `;for(let g=0;g<(p+1)/2;g++){let x=g*2;if(m+=`\n xC = xCCorner + ${x*u};\n `,a===1){if(x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n `,u===1&&x>0?m+=`\n xC${x} = vec4(xTexelC${x-2}.zw, xTexelC${x}.xy);\n `:m+=`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n previous.zw = vec2(0.0);\n }\n\n xC${x} = vec4(previous.zw, xTexelC${x}.xy);\n } else {\n xC${x} = vec4(0.0, 0.0, xTexelC${x}.xy);\n }\n `):m+=`\n if (xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n xC${x} = xTexelC${x};\n `,x+1= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n `,u>1?m+=`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n xC${x+1} = vec4(previous.zw, xTexelC${x+1}.xy);\n } else {\n xC${x+1} = vec4(0.0, 0.0, xTexelC${x+1}.xy);\n }\n `:m+=`\n xC${x+1} = vec4(xTexelC${x}.zw, xTexelC${x+1}.xy);\n `):b===1?m+=`\n xC${x+1} = xTexelC${x};\n `:m+=`\n xCOffset = xC + ${b};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x+1} = xTexelC${x+1};\n `}}else x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x} = vec4(xTexelC${x}.zw, xTexelC${x+1}.zw);\n `,x+1= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${x+1} = vec4(xTexelC${x+1}.xy, final.xy);\n `)):(m+=`\n if(xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x} = vec4(\n xTexelC${x}.xy, xTexelC${x+1}.xy);\n `,x+1= 0) {\n // Use custom imod instead mod. On Intel GPU, mod may generate\n // unexpected value.\n // https://github.com/tensorflow/tfjs/issues/5447\n offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];\n d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /\n inChannels);\n\n if(d1 < inputShape[${a}] && d1 >= 0) {\n\n ch = imod(pos, inChannels);\n\n if (${s}) {\n innerDims = vec2(d1, ch);\n result[${c*2+p}] = getChannel(\n getA(rc.x, d0, int(innerDims.x),\n int(innerDims.y)), innerDims);\n } else {\n innerDims = vec2(d0, d1);\n result[${c*2+p}] = getChannel(\n getA(rc.x, ch, int(innerDims.x),\n int(innerDims.y)), innerDims);\n }\n }\n }\n }\n `;this.userCode=`\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0);\n\n int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n vec2 innerDims;\n\n ${l}\n\n ${o.output} = result;\n }\n `}};function _I(r,t){let e=r.length;return e>=3?t?[...r.slice(0,-3),r[e-3]*r[e-2],r[e-1]]:[...r.slice(0,-3),r[e-3],r[e-2]*r[e-1]]:!t&&e===1&&r[0]>1?[r[0],1]:null}function EI({x:r,filter:t,convInfo:e,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:a=null}){let u=r.shape,l=n.texData.get(r.dataId),c=e.inChannels,p=u[0]*u[1]*u[2],m=e.outChannels,f=e.dataFormat===\"channelsLast\",d=!1,h=!1,g,x=[];if(s!=null){let I=_I(s.shape,f);I!=null&&(s=rt({inputs:{x:s},backend:n,attrs:{shape:I}}),x.push(s))}if(o!=null){let I=_I(o.shape,f);I!=null&&(o=rt({inputs:{x:o},backend:n,attrs:{shape:I}}),x.push(o))}if(!((p===1||m===1)&&c>$1)&&l.isPacked&&f&&l.texture!=null&&u[2]%2!==0&&y.arraysEqual(l.shape.slice(-3),u.slice(-3))){let I=u[0]*u[1]*(u[2]+1),N={dataId:r.dataId,shape:[1,I,e.inChannels],dtype:r.dtype},E=l.shape;l.shape=l.shape.slice(),l.shape[l.shape.length-2]++,y.assert(Uu(l.shape,N.shape),()=>`packed reshape ${l.shape} to ${N.shape} isn't free`);let A=rt({inputs:{x:t},backend:n,attrs:{shape:[1,e.inChannels,e.outChannels]}});x.push(A);let D=dp({a:N,b:A,backend:n,transposeA:d,transposeB:h,bias:o,activation:a,preluActivationWeights:s,leakyreluAlpha:i}),F=n.texData.get(D.dataId);y.assert(F.isPacked,()=>\"batchMatMul result is expected to be packed\"),l.shape=E,F.shape=e.outShape,g=nr({inputs:{x:D},backend:n}),g.shape=e.outShape,x.push(D)}else{let I=e.outHeight*e.outWidth,N=rt({inputs:{x:r},backend:n,attrs:{shape:f?[e.batchSize,I,e.inChannels]:[e.batchSize,e.inChannels,I]}}),E=rt({inputs:{x:t},backend:n,attrs:{shape:[1,e.inChannels,e.outChannels]}}),A=dp({a:f?N:E,b:f?E:N,transposeA:!f,transposeB:h,backend:n,bias:o,activation:a,preluActivationWeights:s,leakyreluAlpha:i});g=rt({inputs:{x:A},backend:n,attrs:{shape:e.outShape}}),x.push(N),x.push(E),x.push(A)}for(let I of x)n.disposeIntermediateTensorInfo(I);return g}function AI({x:r,filter:t,convInfo:e,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:a=null}){let{filterWidth:u,filterHeight:l,inChannels:c,outWidth:p,outHeight:m,dataFormat:f}=e,d=f===\"channelsLast\",h=u*l*c,g=m*p,x=[e.batchSize,h,g],b=!0,w=!1,I=[];if(s!=null){let Z=_I(s.shape,d);Z!=null&&(s=rt({inputs:{x:s},backend:n,attrs:{shape:Z}}),I.push(s))}if(o!=null){let Z=_I(o.shape,d);Z!=null&&(o=rt({inputs:{x:o},backend:n,attrs:{shape:Z}}),I.push(o))}let N=rt({inputs:{x:t},backend:n,attrs:{shape:[1,h,y.sizeFromShape(t.shape)/h]}});I.push(N);let E=new TI(x,e),A=[r.shape,[e.padInfo.top,e.padInfo.left],[e.strideHeight,e.strideWidth],[e.dilationHeight,e.dilationWidth],[e.inChannels],[e.filterWidth*e.inChannels],[e.outWidth]],D=n.runWebGLProgram(E,[r],\"float32\",A),F=rt({inputs:{x:D},backend:n,attrs:{shape:x}});I.push(D),I.push(F);let M=o!=null,V=s!=null,G=a===\"leakyrelu\",W=a?Ml(a,!0):null,q=new $d(d?F.shape:N.shape,d?N.shape:F.shape,d?[e.batchSize,g,e.outChannels]:[e.batchSize,e.outChannels,g],b,w,M,W,V,G),H=d?[F,N]:[N,F];if(o&&H.push(o),V&&H.push(s),G){let Z=n.makeTensorInfo([],\"float32\",y.createScalarValue(i,\"float32\"));H.push(Z),I.push(Z)}let K=n.runWebGLProgram(q,H,\"float32\"),Y=rt({inputs:{x:K},backend:n,attrs:{shape:e.outShape}});I.push(K);for(let Z of I)n.disposeIntermediateTensorInfo(Z);return Y}function Vst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dataFormat:u,dilations:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,s.shape,i,l,a,c,!1,p),f;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type===\"SAME\"||m.padInfo.type===\"VALID\"))f=EI({x:o,filter:s,convInfo:m,backend:e});else if(m.strideWidth<=2&&p===\"channelsLast\"&&L().getBool(\"WEBGL_EXP_CONV\")){let h=new Od(m),g=[[m.padInfo.top,m.padInfo.left],[m.strideHeight,m.strideWidth],[m.dilationHeight,m.dilationWidth],[m.inHeight,m.inWidth]];f=e.runWebGLProgram(h,[o,s],\"float32\",g)}else if(L().getBool(\"WEBGL_CONV_IM2COL\"))f=AI({x:o,filter:s,convInfo:m,backend:e});else{let h=new Fd(m);f=e.runWebGLProgram(h,[o,s],\"float32\")}let d=rt({inputs:{x:f},backend:e,attrs:{shape:m.outShape}});return e.disposeIntermediateTensorInfo(f),d}var X3={kernelName:Qo,backendName:\"webgl\",kernelFunc:Vst};var DI=class{constructor(t){this.variableNames=[\"x\",\"dy\"],this.outputShape=t.filterShape;let e=t.strideHeight,n=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,i=t.dataFormat===\"channelsLast\";this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int d2 = coords.w;\n\n // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int b = 0; b < ${t.batchSize}; b++) {\n for (int yR = 0; yR < ${t.outHeight}; yR++) {\n int xR = wR + yR * ${e} - ${o};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${t.outWidth}; yC++) {\n int xC = wC + yC * ${n} - ${s};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n ${i?`float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);`:`float dyValue = getDy(b, d2, yR, yC);\n float xValue = getX(b, d1, xR, xC);\n dotProd += (xValue * dyValue);`}\n }\n }\n }\n setOutput(dotProd);\n }\n `}},$I=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=t.dataFormat===\"channelsLast\",a=e-1-t.padInfo.top,u=n-1-t.padInfo.left,l=i?1:2,c=i?2:3,p=i?3:1;this.userCode=`\n const ivec2 pads = ivec2(${a}, ${u});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[${p}];\n\n ivec2 dyCorner = ivec2(coords[${l}], coords[${c}]) - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${e}; wR++) {\n float dyR = float(dyRCorner + wR) / ${o}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${e} - 1 - wR;\n\n for (int wC = 0; wC < ${n}; wC++) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${n} - 1 - wC;\n\n for (int d2 = 0; d2 < ${t.outChannels}; d2++) {\n\n if (${i}) {\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n } else {\n float xValue = getDy(batch, d2, idyR, idyC);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n\n }\n }\n }\n setOutput(dotProd);\n }\n `}},RI=class{constructor(t){this.variableNames=[\"x\",\"dy\"],this.outputShape=t.filterShape;let e=t.strideDepth,n=t.strideHeight,o=t.strideWidth,s=t.padInfo.front,i=t.padInfo.top,a=t.padInfo.left;this.userCode=`\n void main() {\n ivec5 coords = getOutputCoords();\n int wF = coords.x;\n int wR = coords.y;\n int wC = coords.z;\n int d1 = coords.w;\n int d2 = coords.u;\n\n float dotProd = 0.0;\n\n for (int b = 0; b < ${t.batchSize}; b++) {\n for (int yF = 0; yF < ${t.outDepth}; yF++) {\n int xF = wF + yF * ${e} - ${s};\n\n if (xF < 0 || xF >= ${t.inDepth}) {\n continue;\n }\n\n for (int yR = 0; yR < ${t.outHeight}; yR++) {\n int xR = wR + yR * ${n} - ${i};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${t.outWidth}; yC++) {\n int xC = wC + yC * ${o} - ${a};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yF, yR, yC, d2);\n float xValue = getX(b, xF, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}},FI=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.outputShape=t.inShape;let e=t.filterDepth,n=t.filterHeight,o=t.filterWidth,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=e-1-t.padInfo.front,l=n-1-t.padInfo.top,c=o-1-t.padInfo.left;this.userCode=`\n const ivec3 pads = ivec3(${u}, ${l}, ${c});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.u;\n\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyFCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n float dotProd = 0.0;\n for (int wF = 0; wF < ${e}; wF++) {\n float dyF = float(dyFCorner + wF) / ${s}.0;\n\n if (dyF < 0.0 || dyF >= ${t.outDepth}.0 || fract(dyF) > 0.0) {\n continue;\n }\n int idyF = int(dyF);\n\n int wFPerm = ${e} - 1 - wF;\n\n for (int wR = 0; wR < ${n}; wR++) {\n float dyR = float(dyRCorner + wR) / ${i}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${n} - 1 - wR;\n\n for (int wC = 0; wC < ${o}; wC++) {\n float dyC = float(dyCCorner + wC) / ${a}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${o} - 1 - wC;\n\n for (int d2 = 0; d2 < ${t.outChannels}; d2++) {\n float xValue = getDy(batch, idyF, idyR, idyC, d2);\n float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function Gst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,dataFormat:u,dimRoundingMode:l,filterShape:c}=n,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,c,i,1,a,l,!1,p),f=new DI(m);return e.runWebGLProgram(f,[o,s],\"float32\")}var Y3={kernelName:Dp,backendName:\"webgl\",kernelFunc:Gst};var OI=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"strides\",type:\"vec2\"}],this.outputShape=t.inShape,this.enableShapeUniforms=he(this.outputShape.length);let e=t.filterHeight,n=t.filterWidth,o=e-1-t.padInfo.top,s=n-1-t.padInfo.left;this.userCode=`\n const ivec2 pads = ivec2(${o}, ${s});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n\n ivec2 dyCorner = ivec2(coords[1], coords[2]) - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n vec4 result = vec4(0.);\n for (int wR = 0; wR < ${e}; wR++) {\n float dyR = float(dyRCorner + wR) / strides[0];\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n int wRPerm = ${e} - 1 - wR;\n\n for (int wC = 0; wC < ${n}; wC++) {\n int wCPerm = ${n} - 1 - wC;\n\n float dyC = float(dyCCorner + wC) / strides[1];\n bool idyCVal = (dyC >= 0.0) && (dyC < ${t.outWidth}.0)\n && (fract(dyC) == 0.0);\n int idyC = int(dyC);\n\n float dyC2 = float(dyCCorner + wC + 1) / strides[1];\n bool idyCVal2 = (dyC2 >= 0.0) && (dyC2 < ${t.outWidth}.0)\n && (fract(dyC2) == 0.0);\n int idyC2 = int(dyC2);\n\n if (idyCVal && idyCVal2) {\n for (int d2 = 0; d2 < ${t.outChannels}; d2 += 2) {\n vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n vec4 dySample = getDy(batch, idyR, idyC, d2);\n vec4 dySample2 = (idyC / 2 == idyC2 / 2) ?\n dySample : getDy(batch, idyR, idyC2, d2);\n\n vec2 dyValue = mod(float(idyC), 2.) == 0. ?\n dySample.xy : dySample.zw;\n result.xy += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n\n dyValue = mod(float(idyC2), 2.) == 0. ?\n dySample2.xy : dySample2.zw;\n result.zw += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n }\n } else if (idyCVal) {\n for (int d2 = 0; d2 < ${t.outChannels}; d2 += 2) {\n vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n vec4 dySample = getDy(batch, idyR, idyC, d2);\n vec2 dyValue = mod(float(idyC), 2.) == 0. ?\n dySample.xy : dySample.zw;\n result.xy += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n }\n } else if (idyCVal2) {\n for (int d2 = 0; d2 < ${t.outChannels}; d2 += 2) {\n vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n vec4 dySample = getDy(batch, idyR, idyC2, d2);\n vec2 dyValue = mod(float(idyC2), 2.) == 0. ?\n dySample.xy : dySample.zw;\n result.zw += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n }\n }\n }\n }\n setOutput(result);\n }\n `}};function Wst(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{inputShape:i,strides:a,pad:u,dataFormat:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(l),m=S.computeConv2DInfo(i,s.shape,a,1,u,c,!1,p);if(L().getBool(\"WEBGL_PACK_CONV2DTRANSPOSE\")&&p===\"channelsLast\"){let f=[[m.strideHeight,m.strideWidth]],d=new OI(m);return e.runWebGLProgram(d,[o,s],\"float32\",f)}else{let f=new $I(m);return e.runWebGLProgram(f,[o,s],\"float32\")}}var Z3={kernelName:ts,backendName:\"webgl\",kernelFunc:Wst};function Ust(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n,l=S.computeConv3DInfo(o.shape,s.shape,i,u,a),c=new kI(l);return e.runWebGLProgram(c,[o,s],\"float32\")}var J3={kernelName:es,backendName:\"webgl\",kernelFunc:Ust};function Hst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,filterShape:u}=n,l=S.computeConv3DInfo(o.shape,u,i,1,a),c=new RI(l);return e.runWebGLProgram(c,[o,s],\"float32\")}var Q3={kernelName:Ra,backendName:\"webgl\",kernelFunc:Hst};function qst(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{pad:i,strides:a,inputShape:u}=n,l=S.computeConv3DInfo(u,s.shape,a,1,i),c=new FI(l);return e.runWebGLProgram(c,[o,s],\"float32\")}var tB={kernelName:Fa,backendName:\"webgl\",kernelFunc:qst};var Kst=Po+`\n return cos(x);\n`,jst=`\n vec4 result = cos(x);\n bvec4 isNaN = isnan(x);\n ${Xn}\n return result;\n`,Xst=It({opSnippet:Kst,packedOpSnippet:jst}),eB={kernelName:rs,backendName:\"webgl\",kernelFunc:Xst};var Yst=`\n float e2x = exp(-x);\n return (e2x + 1.0 / e2x) / 2.0;\n`,Zst=It({opSnippet:Yst}),rB={kernelName:ns,backendName:\"webgl\",kernelFunc:Zst};var MI=class{constructor(t,e,n,o,s){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.outputShape=[];let[i,a,u,l]=t,[c]=e,[p,m]=n;this.outputShape=[c,p,m,l];let f=o===\"bilinear\"?1:0,[d,h]=[`${a-1}.0`,`${u-1}.0`],[g,x,b]=p>1?[`${(a-1)/(p-1)}`,\"(y2-y1) * height_ratio\",`y1*${d} + float(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${d}`],[w,I,N]=m>1?[`${(u-1)/(m-1)}`,\"(x2-x1) * width_ratio\",`x1*${h} + float(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${h}`];this.userCode=`\n const float height_ratio = float(${g});\n const float width_ratio = float(${w});\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int y = coords[1];\n int x = coords[2];\n int d = coords[3];\n\n // get box vals\n float y1 = getBoxes(b,0);\n float x1 = getBoxes(b,1);\n float y2 = getBoxes(b,2);\n float x2 = getBoxes(b,3);\n\n // get image in batch index\n int bInd = round(getBoxInd(b));\n if(bInd < 0 || bInd >= ${i}) {\n return;\n }\n\n float height_scale = ${x};\n float width_scale = ${I};\n\n float in_y = ${b};\n if( in_y < 0.0 || in_y > ${d} ) {\n setOutput(float(${s}));\n return;\n }\n float in_x = ${N};\n if( in_x < 0.0 || in_x > ${h} ) {\n setOutput(float(${s}));\n return;\n }\n\n vec2 sourceFracIndexCR = vec2(in_x,in_y);\n if(${f} == 1) {\n // Compute the four integer indices.\n ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n float top = topLeft + (topRight - topLeft) * fracCR.x;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n float newValue = top + (bottom - top) * fracCR.y;\n setOutput(newValue);\n } else {\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestCR = ivec2(floor(\n sourceFracIndexCR + vec2(0.5,0.5)));\n float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n setOutput(newValue);\n }\n }\n `}};var Jst=r=>{let{inputs:t,backend:e,attrs:n}=r,{image:o,boxes:s,boxInd:i}=t,{cropSize:a,method:u,extrapolationValue:l}=n,c=new MI(o.shape,s.shape,a,u,l);return e.runWebGLProgram(c,[o,s,i],\"float32\")},nB={kernelName:Ma,backendName:\"webgl\",kernelFunc:Jst};var gp;(function(r){r.Prod=\"*\",r.Sum=\"+\"})(gp||(gp={}));var fg=class{constructor(t,e,n,o){this.op=t,this.outputShape=e,this.variableNames=[\"x\"],this.customUniforms=[{name:\"index\",type:\"float\"}];let s=this.outputShape.length,i=this.op===gp.Prod?\"1.0\":\"0.0\",a=n?i:`getX(${oB(s,\"coords\",this.op)})`,u=this.outputShape[this.outputShape.length-1],l=\"\",c=\"\";n?(l=o?`end != ${u-1}`:\"end != 0\",c=o?\"end + 1\":\"end - 1\"):(l=o?`end + pow2 < ${u}`:\"end >= pow2\",c=o?\"end + pow2\":\"end - pow2\"),this.userCode=`\n void main() {\n ${zt(s)} coords = getOutputCoords();\n int end = ${sB(s,\"coords\",this.op)};\n float val = ${a};\n int pow2 = int(pow(2.0, index));\n if (${l}) {\n int idx = ${c};\n ${sB(s,\"coords\",this.op)} = idx;\n val ${this.op}= getX(${oB(s,\"coords\",this.op)});\n }\n setOutput(val);\n }\n `}};function oB(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.x, ${t}.y`;if(r===3)return`${t}.x, ${t}.y, ${t}.z`;if(r===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function sB(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.y`;if(r===3)return`${t}.z`;if(r===4)return`${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function PI(r,t,e,n,o,s){let i=t.shape.length,a=S.getAxesPermutation([n],i),u=t;a!=null&&(u=Pe({inputs:{x:t},backend:e,attrs:{perm:a}}));let l=S.getInnerMostAxes(1,i)[0];if(l!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let c=u.shape[l],p=nr({inputs:{x:u},backend:e});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let f=new fg(r,u.shape,!1,s),d=[[m]],h=p;p=e.runWebGLProgram(f,[p],p.dtype,d),e.disposeIntermediateTensorInfo(h)}if(o){let m=new fg(r,u.shape,o,s),f=p;p=e.runWebGLProgram(m,[p],p.dtype),e.disposeIntermediateTensorInfo(f)}if(a!=null){let m=S.getUndoAxesPermutation(a),f=Pe({inputs:{x:p},backend:e,attrs:{perm:m}});return e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(u),f}return p}function Qst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;return PI(gp.Prod,o,e,s,i,a)}var iB={kernelName:Oa,backendName:\"webgl\",kernelFunc:Qst};function tit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;return PI(gp.Sum,o,e,s,i,a)}var aB={kernelName:os,backendName:\"webgl\",kernelFunc:tit};function eit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i,binaryOutput:a}=n;if(o.shape.length===1){let u=e.readSync(o.dataId),l=e.readSync(s.dataId),c=Jw(u,l,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,c)}else if(o.shape.length===2){let u=e.bufferSync(o),l=e.bufferSync(s),c=UL(u,l,i,a);return e.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${o.shape.length}.`)}var lB={kernelName:jl,backendName:\"webgl\",kernelFunc:eit};var LI=class{constructor(t,e,n){this.variableNames=[\"x\"],this.outputShape=[],this.outputShape=t,this.blockSize=e,this.dataFormat=n,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int h = ${this.getHeightCoordString()};\n int w = ${this.getWidthCoordString()};\n int d = ${this.getDepthCoordString()};\n\n int in_h = h / ${e};\n int offset_h = imod(h, ${e});\n int in_w = w / ${e};\n int offset_w = imod(w, ${e});\n int offset_d = (offset_h * ${e} + offset_w) *\n ${this.getOutputDepthSize()};\n int in_d = d + offset_d;\n\n float result = ${this.getInputSamplingString()};\n setOutput(result);\n }\n `}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 rit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockSize:s,dataFormat:i}=n,a=o.shape[0],u=i===\"NHWC\"?o.shape[1]:o.shape[2],l=i===\"NHWC\"?o.shape[2]:o.shape[3],c=i===\"NHWC\"?o.shape[3]:o.shape[1],p=u*s,m=l*s,f=c/(s*s),d=i===\"NHWC\"?[a,p,m,f]:[a,f,p,m],h=new LI(d,s,i);return e.runWebGLProgram(h,[o],o.dtype)}var uB={kernelName:Pa,backendName:\"webgl\",kernelFunc:rit};var Md=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=t.outShape,this.enableShapeUniforms=he(this.outputShape.length);let i=t.filterHeight,a=t.filterWidth,u=t.outChannels/t.inChannels,l=\"\",c=\"\";n&&(o?l=`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${n}\n }`:s?l=`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${n}\n }`:l=`\n float activation(float x) {\n ${n}\n }\n `,c=\"result = activation(result);\");let p=e?\"result += getBiasAtOutCoords();\":\"\";e&&this.variableNames.push(\"bias\"),o&&this.variableNames.push(\"preluActivationWeights\"),s&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n ${l}\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n ivec2 xRCCorner = coords.yz * strides - pads;\n int d2 = coords.w;\n int d1 = d2 / ${u};\n int q = d2 - d1 * ${u};\n\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n for (int wR = 0; wR < ${i}; wR++) {\n int xR = xRCorner + wR * dilations[0];\n\n if (xR < 0 || xR >= inDims[0]) {\n continue;\n }\n\n for (int wC = 0; wC < ${a}; wC++) {\n int xC = xCCorner + wC * dilations[1];\n\n if (xC < 0 || xC >= inDims[1]) {\n continue;\n }\n\n float xVal = getX(batch, xR, xC, d1);\n float wVal = getW(wR, wC, d1, q);\n dotProd += xVal * wVal;\n }\n }\n\n float result = dotProd;\n ${p}\n ${c}\n setOutput(result);\n }\n `}};var Pd=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=t.outShape,this.enableShapeUniforms=he(this.outputShape.length);let i=t.outChannels/t.inChannels,a=t.padInfo.left,u=t.strideWidth,l=t.dilationWidth,c=t.filterHeight,p=t.filterWidth,m=p,f=`\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;`;for(let x=0;x=0 && xR < inDims[0]) {\n `;for(let x=0;x<(m+1)/2;x++){let b=x*2;if(f+=`\n xC = xCCorner + ${b*l};\n `,u===1){if(b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n `,l===1&&b>0?f+=`\n xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.xy);\n `:f+=`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n previous.zw = vec2(0.0);\n }\n\n xC${b} = vec4(previous.zw, xTexelC${b}.xy);\n } else {\n xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy);\n }\n `):f+=`\n if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n xC${b} = xTexelC${b};\n `,b+1= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n `,l>1?f+=`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n xC${b+1} = vec4(previous.zw, xTexelC${b+1}.xy);\n } else {\n xC${b+1} = vec4(0.0, 0.0, xTexelC${b+1}.xy);\n }\n `:f+=`\n xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy);\n `):w===1?f+=`\n xC${b+1} = xTexelC${b};\n `:f+=`\n xCOffset = xC + ${w};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b+1} = xTexelC${b+1};\n `}}else b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);\n `,b+1= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy);\n `)):(f+=`\n if(xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b} = vec4(\n xTexelC${b}.xy, xTexelC${b+1}.xy);\n `,b+1`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let p=S.computeConv2DInfo(o.shape,s.shape,i,c,a,l,!0),m;L().getBool(\"WEBGL_PACK_DEPTHWISECONV\")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?m=new Pd(p):m=new Md(p);let f=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return e.runWebGLProgram(m,[o,s],\"float32\",f)}var cB={kernelName:ss,backendName:\"webgl\",kernelFunc:nit};var zI=class{constructor(t){this.variableNames=[\"x\",\"dy\"],this.outputShape=t.filterShape;let e=t.strideHeight,n=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,i=t.outChannels/t.inChannels;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int dm = coords.w;\n int d2 = d1 * ${i} + dm;\n\n float dotProd = 0.0;\n\n // TO DO: Vec4 over the batch size\n for (int b = 0; b < ${t.batchSize}; b++) {\n for (int yR = 0; yR < ${t.outHeight}; yR++) {\n int xR = wR + yR * ${e} - ${o};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${t.outWidth}; yC++) {\n int xC = wC + yC * ${n} - ${s};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n setOutput(dotProd);\n }\n `}},BI=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=e-1-t.padInfo.top,a=n-1-t.padInfo.left,u=t.outChannels/t.inChannels;this.userCode=`\n const ivec2 pads = ivec2(${i}, ${a});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n ivec2 dyCorner = coords.yz - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n float dotProd = 0.0;\n\n for (int wR = 0; wR < ${e}; wR++) {\n float dyR = float(dyRCorner + wR) / ${o}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${e} - 1 - wR;\n\n for (int wC = 0; wC < ${n}; wC++) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${n} - 1 - wC;\n\n // TO DO: Vec4 over the channelMul\n for (int dm = 0; dm < ${u}; dm++) {\n int d2 = d1 * ${u} + dm;\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, dm);\n dotProd += xValue * wValue;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function oit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,filterShape:c}=n,p=S.computeConv2DInfo(o.shape,c,i,a,u,l,!0),m=new zI(p);return e.runWebGLProgram(m,[o,s],\"float32\")}var pB={kernelName:$p,backendName:\"webgl\",kernelFunc:oit};function sit(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,inputShape:c}=n,p=S.computeConv2DInfo(c,s.shape,i,a,u,l,!0),m=new BI(p);return e.runWebGLProgram(m,[o,s],\"float32\")}var mB={kernelName:Rp,backendName:\"webgl\",kernelFunc:sit};var VI=class{constructor(t){this.variableNames=[\"X\"],this.outputShape=[t,t],this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n setOutput(val);\n }\n `}};function iit(r){let{inputs:t,backend:e}=r,{x:n}=t,o=[...n.shape,...n.shape],s=y.sizeFromShape(n.shape),i=rt({inputs:{x:n},backend:e,attrs:{shape:[s]}}),a=new VI(s),u=e.runWebGLProgram(a,[i],i.dtype),l=rt({inputs:{x:u},backend:e,attrs:{shape:o}});return e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(u),l}var fB={kernelName:Xl,backendName:\"webgl\",kernelFunc:iit};var GI=class{constructor(t){this.variableNames=[\"x\",\"W\"],this.outputShape=t.outShape;let{inHeight:e,inWidth:n,padInfo:o,strideHeight:s,strideWidth:i,filterHeight:a,filterWidth:u,dilationHeight:l,dilationWidth:c}=t,{top:p,left:m}=o;this.userCode=`\n const ivec2 strides = ivec2(${s}, ${i});\n const ivec2 pads = ivec2(${p}, ${m});\n const float neg_infinity = -3.4e38;\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.w;\n ivec2 outTopLeftCorner =\n coords.yz * strides - pads;\n int hBeg = outTopLeftCorner.x;\n int wBeg = outTopLeftCorner.y;\n\n float curVal = neg_infinity;\n for (int h = 0; h < ${a}; h++) {\n int hIn = hBeg + h * ${l};\n\n if (hIn >= 0 && hIn < ${e}) {\n for (int w = 0; w < ${u}; w++) {\n int wIn = wBeg + w * ${c};\n\n if (wIn >= 0 && wIn < ${n}) {\n float xVal = getX(batch, hIn, wIn, d1);\n float wVal = getW(h, w, d1);\n\n float val = xVal + wVal;\n if (val > curVal) {\n curVal = val;\n }\n }\n }\n }\n }\n\n float result = curVal;\n setOutput(result);\n }\n `}};function ait(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n,l=S.computeDilation2DInfo(o.shape,s.shape,i,a,\"NHWC\",u),c,p=new GI(l);c=e.runWebGLProgram(p,[o,s],\"float32\");let m=rt({inputs:{x:c},backend:e,attrs:{shape:l.outShape}});return e.disposeIntermediateTensorInfo(c),m}var dB={kernelName:is,backendName:\"webgl\",kernelFunc:ait};function lit(r){let{inputs:t,backend:e,attrs:n}=r,{equation:o}=n,s=t,{allDims:i,summedDims:a,idDims:u}=S.decodeEinsumEquation(o,s.length);S.checkEinsumDimSizes(i.length,u,s);let{path:l,steps:c}=S.getEinsumComputePath(a,u),p=c.length,m=null,f=i.length,d=[];for(let h=0;h=0&&(m=fp({inputs:{x:m},backend:e,attrs:{axis:l[h]-(i.length-f),keepDims:!1}}),d.push(m)),f--)}for(let h of d)h!==m&&e.disposeIntermediateTensorInfo(h);return m}var hB={kernelName:Fp,backendName:\"webgl\",kernelFunc:lit};var uit=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",cit=`\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n`,pit=It({opSnippet:uit,packedOpSnippet:cit}),gB={kernelName:ls,backendName:\"webgl\",kernelFunc:pit};var mit=\"return (b >= 0.0) ? a : a * (b + 1.0);\",fit=`\n vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n`,dit=r=>{let{inputs:t,backend:e}=r,{dy:n,y:o}=t,s=L().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new jn(fit,n.shape,o.shape):new $n(mit,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],n.dtype)},xB={kernelName:La,backendName:\"webgl\",kernelFunc:dit};var hit=`\n return vec4(equal(a, b));\n`,git=\"return float(a == b);\",xit=ce({opSnippet:git,packedOpSnippet:hit,dtype:\"bool\",cpuKernelImpl:XL}),yB={kernelName:za,backendName:\"webgl\",kernelFunc:xit};var yit=`\n // Error function is calculated approximately with elementary function.\n // See \"Handbook of Mathematical Functions with Formulas,\n // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n float p = ${S.ERF_P};\n float a1 = ${S.ERF_A1};\n float a2 = ${S.ERF_A2};\n float a3 = ${S.ERF_A3};\n float a4 = ${S.ERF_A4};\n float a5 = ${S.ERF_A5};\n\n float sign = sign(x);\n x = abs(x);\n float t = 1.0 / (1.0 + p * x);\n return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n`,bit=It({opSnippet:yit}),bB={kernelName:us,backendName:\"webgl\",kernelFunc:bit};var wit=Po+`\n return exp(x);\n`,Iit=`\n vec4 result = exp(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,P1=It({opSnippet:wit,packedOpSnippet:Iit,cpuKernelImpl:YL,dtype:\"float32\"}),wB={kernelName:cs,backendName:\"webgl\",kernelFunc:P1};function WI(r){let{inputs:t,attrs:e,backend:n}=r,{dim:o}=e,{input:s}=t,i=s.shape.length,a=s.shape.slice(),u=o;return o<0&&(y.assert(-(i+1)<=o,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+o+1),a.splice(u,0,1),rt({inputs:{x:s},backend:n,attrs:{shape:a}})}var IB={kernelName:Oi,backendName:\"webgl\",kernelFunc:WI};var CB=\"return exp(x) - 1.0;\",Cit=It({opSnippet:CB,packedOpSnippet:CB,cpuKernelImpl:ZL}),vB={kernelName:ps,backendName:\"webgl\",kernelFunc:Cit};var dg=class{constructor(t,e,n){this.variableNames=[\"real\",\"imag\"];let o=e[1];this.outputShape=e;let s=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,i=n?`${o}.0`:\"1.0\",a;if(t===\"real\")a=\"return real * expR - imag * expI;\";else if(t===\"imag\")a=\"return real * expI + imag * expR;\";else throw new Error(`FFT component must be either \"real\" or \"imag\", got ${t}.`);this.userCode=`\n const float exponentMultiplier = ${s};\n\n float unaryOpComplex(float real, float expR, float imag, float expI) {\n ${a}\n }\n\n float mulMatDFT(int batch, int index) {\n float indexRatio = float(index) / float(${o});\n float exponentMultiplierTimesIndexRatio =\n exponentMultiplier * indexRatio;\n\n float result = 0.0;\n\n for (int i = 0; i < ${o}; i++) {\n // x = (-2|2 * PI / N) * index * i;\n float x = exponentMultiplierTimesIndexRatio * float(i);\n float expR = cos(x);\n float expI = sin(x);\n float real = getReal(batch, i);\n float imag = getImag(batch, i);\n\n result +=\n unaryOpComplex(real, expR, imag, expI) / ${i};\n }\n\n return result;\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n setOutput(mulMatDFT(coords[0], coords[1]));\n }\n `}};function UI(r,t,e){let n=e.texData.get(r.dataId),o=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],i=o/s,a=rt({inputs:{x:r},backend:e,attrs:{shape:[i,s]}}),u=a.shape,l=new dg(\"real\",u,t),c=new dg(\"imag\",u,t),p=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:u},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:u}],m=e.runWebGLProgram(l,p,\"float32\"),f=e.runWebGLProgram(c,p,\"float32\"),d=Rn({inputs:{real:m,imag:f},backend:e});e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f);let h=rt({inputs:{x:d},backend:e,attrs:{shape:r.shape}});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(d),h}function vit(r){let{inputs:t,backend:e}=r,{input:n}=t;return UI(n,!1,e)}var SB={kernelName:Op,backendName:\"webgl\",kernelFunc:vit};var HI=class{constructor(t,e){this.outputShape=[],this.customUniforms=[{name:\"value\",type:\"float\"}],this.variableNames=[\"x\"],this.outputShape=t,this.userCode=`\n void main() {\n // Input can be obtained from uniform value.\n setOutput(value);\n }\n `}};function Ll(r){let{backend:t,attrs:e}=r,{shape:n,value:o}=e,{dtype:s}=e;if(s=s||y.inferDtype(o),s===\"string\"){let i=y.getArrayFromDType(s,y.sizeFromShape(n));return i.fill(o),t.makeTensorInfo(n,s,i)}else{let i=new HI(n,o),a=[[o]];return t.runWebGLProgram(i,[],s,a)}}var NB={kernelName:Jl,backendName:\"webgl\",kernelFunc:Ll};var qI=class{constructor(t){this.variableNames=[\"Image\"],this.outputShape=[];let e=t[2];this.outputShape=t,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n\n int coordX = ${e} - x - 1;\n float outputValue;\n if(coordX >= 0 && coordX < ${e}) {\n outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n } else {\n outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n }\n setOutput(outputValue);\n }\n `}};var kB={kernelName:Ba,backendName:\"webgl\",kernelFunc:({inputs:r,backend:t})=>{let{image:e}=r,n=t,o=new qI(e.shape);return n.runWebGLProgram(o,[e],e.dtype)}};var TB=\"return floor(x);\",Sit=It({opSnippet:TB,packedOpSnippet:TB,cpuKernelImpl:JL}),_B={kernelName:ms,backendName:\"webgl\",kernelFunc:Sit};var Nit=`\n float s = sign(a) * sign(b);\n int ia = round(a);\n int ib = round(b);\n if (ib != 0) {\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n return float(idiv(ia, ib, s));\n } else {\n return NAN;\n }\n`,kit=`\n ivec4 ia = round(a);\n ivec4 ib = round(b);\n bvec4 cond = notEqual(ib, ivec4(0));\n ivec4 result = ivec4(0);\n vec4 s = sign(a) * sign(b);\n\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n if (cond[0]) {\n result[0] = idiv(ia[0], ib[0], s[0]);\n }\n if (cond[1]) {\n result[1] = idiv(ia[1], ib[1], s[1]);\n }\n if (cond[2]) {\n result[2] = idiv(ia[2], ib[2], s[2]);\n }\n if (cond[3]) {\n result[3] = idiv(ia[3], ib[3], s[3]);\n }\n return vec4(result);\n`,Tit=ce({opSnippet:Nit,packedOpSnippet:kit,dtype:\"int32\"}),EB={kernelName:fs,backendName:\"webgl\",kernelFunc:Tit};var KI=class{constructor(t){this.variableNames=[\"A\"];let e=Ue(),[n,o]=t;this.outputShape=t,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${o}.0, ${n}.0);\n\n vec4 values = ${e.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n setOutput(floor(value * 255.0 + 0.5));\n }\n `}};var jI=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0;let e=Ue(),[n,o]=t;this.outputShape=t,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n\n vec4 result = vec4(0.);\n\n for(int row=0; row<=1; row++) {\n for(int col=0; col<=1; col++) {\n texC = coords[1] + row;\n depth = coords[2] + col;\n\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2(${o}.0, ${n}.0);\n vec4 values = ${e.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n result[row * 2 + col] = floor(value * 255.0 + 0.5);\n }\n }\n\n ${e.output} = result;\n }\n `}};var AB={kernelName:Qd,backendName:\"webgl\",kernelFunc:_it},Ld,L1=L().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");function _it(r){let{inputs:t,backend:e,attrs:n}=r,{pixels:o}=t,{numChannels:s}=n,i=typeof HTMLVideoElement!=\"undefined\"&&o instanceof HTMLVideoElement,a=typeof HTMLImageElement!=\"undefined\"&&o instanceof HTMLImageElement,[u,l]=i?[o.videoWidth,o.videoHeight]:[o.width,o.height],c=[l,u],p=[l,u,s];if(a||i){let h=L().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");(Ld==null||h!==L1)&&(L1=h,Ld=document.createElement(\"canvas\").getContext(\"2d\",{willReadFrequently:L1})),Ld.canvas.width=u,Ld.canvas.height=l,Ld.drawImage(o,0,0,u,l),o=Ld.canvas}let m=e.makeTensorInfo(c,\"int32\");e.texData.get(m.dataId).usage=Yr.PIXELS,e.gpgpu.uploadPixelDataToTexture(e.getTexture(m.dataId),o);let f=L().getBool(\"WEBGL_PACK\")?new jI(p):new KI(p),d=e.runWebGLProgram(f,[m],\"int32\");return e.disposeData(m.dataId),d}function Eit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=n,h=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(o.shape,s.shape,u,p,l,m,!1,h),x,b=[],w=i!=null,I=a!=null,N=f===\"leakyrelu\",E=()=>{let D=[o,s],F=(M,V)=>{if(V===\"NCHW\"&&M.shape.length===1&&M.shape[0]!==1){let G=rt({inputs:{x:M},backend:e,attrs:{shape:[M.shape[0],1,1]}});return b.push(G),G}return M};if(w&&D.push(F(i,c)),I&&D.push(F(a,c)),N){let M=e.makeTensorInfo([],\"float32\",y.createScalarValue(d,\"float32\"));D.push(M),b.push(M)}return D};if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type===\"SAME\"||g.padInfo.type===\"VALID\"))x=EI({x:o,filter:s,convInfo:g,backend:e,bias:i,activation:f,preluActivationWeights:a,leakyreluAlpha:d});else if(g.strideWidth<=2&&h===\"channelsLast\"&&L().getBool(\"WEBGL_EXP_CONV\")){let D=f?Ml(f,!0):null,F=new Od(g,w,D,I,N),M=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],V=E();x=e.runWebGLProgram(F,V,\"float32\",M)}else if(L().getBool(\"WEBGL_CONV_IM2COL\"))x=AI({x:o,filter:s,convInfo:g,backend:e,bias:i,activation:f,preluActivationWeights:a,leakyreluAlpha:d});else{let D=f?Ml(f,!1):null,F=new Fd(g,w,D,I,N),M=E();x=e.runWebGLProgram(F,M,\"float32\")}let A=rt({inputs:{x},backend:e,attrs:{shape:g.outShape}});return b.push(x),b.forEach(D=>e.disposeIntermediateTensorInfo(D)),A}var DB={kernelName:Xi,backendName:\"webgl\",kernelFunc:Eit};function Ait(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:p,activation:m,leakyreluAlpha:f}=n,d=[],h=c;h==null&&(h=[1,1]),y.assert(S.eitherStridesOrDilationsAreOne(u,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${h}'`);let g=S.computeConv2DInfo(o.shape,s.shape,u,h,l,p,!0),x=L().getBool(\"WEBGL_PACK_DEPTHWISECONV\")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,b=m?Ml(m,x):null,w=[o,s],I=i!=null,N=a!=null,E=m===\"leakyrelu\";if(I&&w.push(i),N&&w.push(a),E){let M=e.makeTensorInfo([],\"float32\",y.createScalarValue(f,\"float32\"));w.push(M),d.push(M)}let A;x?A=new Pd(g,I,b,N,E):A=new Md(g,I,b,N,E);let D=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],F=e.runWebGLProgram(A,w,\"float32\",D);return d.forEach(M=>e.disposeIntermediateTensorInfo(M)),F}var $B={kernelName:Yi,backendName:\"webgl\",kernelFunc:Ait};var XI=class{constructor(t,e,n,o){this.sliceDim=t,this.strides=e,this.paramsShape=o,this.variableNames=[\"x\",\"indices\"],this.outputShape=n;let s=zt(n.length),i=`\n int index;`;for(let a=0;a= ${this.paramsShape[a]};\n flattenIndex += index * ${this.strides[a]};`;this.userCode=`\n void main() {\n ${s} coords = getOutputCoords();\n int flattenIndex = 0;\n bool out_of_bounds = false;\n\n ${i}\n\n setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));\n }\n `}};function Dit(r){let{inputs:t,backend:e}=r,{params:n,indices:o}=t,s=o.shape,i=s[s.length-1],a=y.sizeFromShape(n.shape),[u,l,c,p]=S.prepareAndValidate(n,o),m=rt({inputs:{x:o},backend:e,attrs:{shape:[l,i]}}),f=rt({inputs:{x:n},backend:e,attrs:{shape:[y.sizeFromShape(n.shape)/c,c]}});if(e.shouldExecuteOnCPU([n,o])||n.dtype===\"string\"){let x=e.readSync(o.dataId),b=e.bufferSync(n),w=QL(x,b,n.dtype,l,i,c,p,n.shape,a);return e.makeTensorInfo(u,n.dtype,w.values)}let d=new XI(i,p,[l,c],n.shape),h=e.runWebGLProgram(d,[f,m],f.dtype),g=rt({inputs:{x:h},backend:e,attrs:{shape:u}});return e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(h),g}var RB={kernelName:Va,backendName:\"webgl\",kernelFunc:Dit};var YI=class{constructor(t,e){this.variableNames=[\"A\",\"indices\"],this.outputShape=e,this.rank=e.length;let n=zt(this.rank),o=$it(t,2);this.userCode=`\n void main() {\n ${n} resRC = getOutputCoords();\n int index = int(getIndices(resRC.x, resRC.z));\n float inBounds = (index >= 0) && (index < ${t[2]}) ? 1.0 : 0.0;\n setOutput(inBounds * getA(${o}));\n }\n `}};function $it(r,t){let e=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],n=[];for(let o=0;o=0,()=>`GatherV2: the index value ${N} is not in [0, ${w-1}]`)}}let l=S.segment_util.collectGatherOpShapeInfo(o,s,u,a),c=y.sizeFromShape(s.shape),p=[],m=rt({inputs:{x:o},backend:e,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),f=rt({inputs:{x:s},backend:e,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(m),p.push(f);let d=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(e.shouldExecuteOnCPU([o,s])||o.dtype===\"string\"){let b=e.bufferSync(f),w=e.bufferSync(m),I=tz(w,b,d);return p.forEach(N=>e.disposeIntermediateTensorInfo(N)),e.makeTensorInfo(l.outputShape,I.dtype,I.values)}let h=new YI(m.shape,d),g=e.runWebGLProgram(h,[m,f],m.dtype);p.push(g);let x=rt({inputs:{x:g},backend:e,attrs:{shape:l.outputShape}});return p.forEach(b=>e.disposeIntermediateTensorInfo(b)),x}var FB={kernelName:Mi,backendName:\"webgl\",kernelFunc:z1};var Rit=\"return float(a > b);\",Fit=`\n return vec4(greaterThan(a, b));\n`,Oit=ce({opSnippet:Rit,packedOpSnippet:Fit,cpuKernelImpl:ez,dtype:\"bool\"}),OB={kernelName:Ga,backendName:\"webgl\",kernelFunc:Oit};var Mit=\"return float(a >= b);\",Pit=`\n return vec4(greaterThanEqual(a, b));\n`,Lit=ce({opSnippet:Mit,packedOpSnippet:Pit,dtype:\"bool\",cpuKernelImpl:rz}),MB={kernelName:hs,backendName:\"webgl\",kernelFunc:Lit};function zit(r){let{inputs:t,backend:e}=r,{input:n}=t;return UI(n,!0,e)}var PB={kernelName:Mp,backendName:\"webgl\",kernelFunc:zit};var Bit=\"return float(!isnan(x) && !isinf(x));\",Vit=It({opSnippet:Bit,dtype:\"bool\"}),LB={kernelName:gs,backendName:\"webgl\",kernelFunc:Vit};var Git=\"return float(isinf(x));\",Wit=It({opSnippet:Git,dtype:\"bool\"}),zB={kernelName:xs,backendName:\"webgl\",kernelFunc:Wit};var Uit=\"return float(isnan(x));\",Hit=It({opSnippet:Uit,dtype:\"bool\"}),BB={kernelName:ys,backendName:\"webgl\",kernelFunc:Hit};var qit=\"return float(a < b);\",Kit=`\n return vec4(lessThan(a, b));\n`,jit=ce({opSnippet:qit,packedOpSnippet:Kit,cpuKernelImpl:nz,dtype:\"bool\"}),VB={kernelName:Wa,backendName:\"webgl\",kernelFunc:jit};var Xit=\"return float(a <= b);\",Yit=`\n return vec4(lessThanEqual(a, b));\n`,Zit=ce({opSnippet:Xit,packedOpSnippet:Yit,cpuKernelImpl:oz,dtype:\"bool\"}),GB={kernelName:Ua,backendName:\"webgl\",kernelFunc:Zit};function Jit(r){let{backend:t,attrs:e}=r,{start:n,stop:o,num:s}=e,i=sz(n,o,s);return t.makeTensorInfo([i.length],\"float32\",i)}var WB={kernelName:Ha,backendName:\"webgl\",kernelFunc:Jit};var Qit=Po+`\n return x < 0.0 ? 0./0. : log(x);\n`,tat=`\n vec4 result = log(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);\n result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);\n result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);\n result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);\n return result;\n`,eat=It({opSnippet:Qit,packedOpSnippet:tat,cpuKernelImpl:iz}),UB={kernelName:ws,backendName:\"webgl\",kernelFunc:eat};var rat=Po+`\n return log(1.0 + x);\n`,nat=It({opSnippet:rat}),HB={kernelName:Is,backendName:\"webgl\",kernelFunc:nat};var oat=\"return float(a >= 1.0 && b >= 1.0);\",sat=`\n return vec4(\n vec4(greaterThanEqual(a, vec4(1.0))) *\n vec4(greaterThanEqual(b, vec4(1.0))));\n`,iat=ce({opSnippet:oat,packedOpSnippet:sat,dtype:\"bool\"}),qB={kernelName:qa,backendName:\"webgl\",kernelFunc:iat};var aat=\"return float(!(x >= 1.0));\",lat=It({opSnippet:aat}),KB={kernelName:Ka,backendName:\"webgl\",kernelFunc:lat};var uat=\"return float(a >= 1.0 || b >= 1.0);\",cat=`\n return min(\n vec4(greaterThanEqual(a, vec4(1.0))) +\n vec4(greaterThanEqual(b, vec4(1.0))),\n vec4(1.0));\n`,pat=ce({opSnippet:uat,packedOpSnippet:cat,dtype:\"bool\"}),jB={kernelName:ja,backendName:\"webgl\",kernelFunc:pat};var ZI=class{constructor(t,e,n,o,s){this.variableNames=[\"x\"],this.outputShape=[];let i=e,a=t[3]-1;this.outputShape=t;let u,l=`float(${n}) + float(${o}) * sum`;s===.5?u=`inversesqrt(${l})`:s===1?u=`1.0/(${l})`:u=`exp(log(${l}) * float(-${s}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n int d = coords[3];\n float x = getX(b, r, c, d);\n float sum = 0.0;\n for (int j = -${i}; j <= ${i}; j++) {\n int idx = d + j;\n if (idx >= 0 && idx <= ${a}) {\n float z = getX(b, r, c, idx);\n sum += z * z;\n }\n }\n float val = x * ${u};\n setOutput(val);\n }\n `}};var JI=class{constructor(t,e,n,o,s){this.variableNames=[\"x\"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let i=e,a=t[3]-1;this.outputShape=t;let u,l=`float(${n}) + float(${o}) * sum`;s===.5?u=`inversesqrt(${l})`:s===1?u=`1.0/(${l})`:u=`exp(log(${l}) * float(-${s}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords.x;\n int r = coords.y;\n int c = coords.z;\n int d = coords.w;\n\n bool hasNextCol = d < ${this.outputShape[3]};\n bool hasNextRow = c < ${this.outputShape[2]};\n\n vec4 sum = vec4(0.);\n vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n vec4 xAtOutputCoords = vec4(\n getChannel(xFragAtOutputCoords, vec2(c, d)),\n hasNextCol ?\n getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n hasNextRow ?\n getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n (hasNextRow && hasNextCol) ?\n getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n );\n\n int firstChannel = d - ${i};\n vec2 cache = vec2(0.);\n if(firstChannel >= 0){\n vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n if(hasNextRow){\n cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n }\n }\n\n ivec2 depth = ivec2(d, d + 1);\n for (int j = - ${i}; j <= ${i}; j++) {\n ivec2 idx = depth + j;\n bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${a}));\n\n bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n if(depthInRange || depthPlusOneInRange){\n vec4 z = vec4(0.);\n vec4 xFragAtCurrentDepth;\n z.xz = cache.xy;\n if(depthPlusOneInRange && hasNextCol){\n xFragAtCurrentDepth = idx.y != d ?\n getX(b, r, c, idx.y) : xFragAtOutputCoords;\n z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n if(hasNextRow){\n z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n }\n }\n cache.xy = z.yw;\n sum += z * z;\n }\n }\n vec4 result = xAtOutputCoords * ${u};\n setOutput(result);\n }\n `}};var mat=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n,l=L().getBool(\"WEBGL_PACK_NORMALIZATION\")?new JI(o.shape,s,i,a,u):new ZI(o.shape,s,i,a,u);return e.runWebGLProgram(l,[o],o.dtype)},XB={kernelName:Cs,backendName:\"webgl\",kernelFunc:mat};var QI=class{constructor(t,e,n,o,s){this.variableNames=[\"inputImage\",\"outputImage\",\"dy\"],this.outputShape=[],this.outputShape=t,this.depth=t[3],this.depthRadius=e,this.bias=n,this.alpha=o,this.beta=s,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n\n float result = 0.0;\n for (int d = 0; d < ${this.depth}; ++d) {\n int depthBegin = int(max(0.0, float(d - ${e})));\n int depthEnd = int(min(float(${this.depth}),\n float(d + ${e} + 1)));\n\n const int MIN_DEPTH_BEGIN = 0;\n const int MAX_DEPTH_END = ${this.depth};\n\n float norm = 0.0;\n for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd) {\n norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n }\n else {\n break;\n }\n }\n\n norm = float(${o}) * norm + float(${n});\n\n for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd){\n float dyi = -2.0 * float(${o})\n * float(${s})\n * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d)\n / norm;\n if (k == d) {\n dyi += pow(norm, -1.0 * ${s});\n }\n if (k == coords[3]) {\n dyi *= getDy(b, r, c, d);\n result += dyi;\n }\n }\n else {\n break;\n }\n }\n }\n setOutput(result);\n }\n `}};var fat=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o,y:s,dy:i}=t,{depthRadius:a,bias:u,alpha:l,beta:c}=n,p=new QI(o.shape,a,u,l,c);return e.runWebGLProgram(p,[o,s,i],o.dtype)},YB={kernelName:Xa,backendName:\"webgl\",kernelFunc:fat};function ZB(r,t,e,n){let o=y.sizeFromShape(t),i=y.sizeFromShape(r.shape)/o,a=rt({inputs:{x:r},attrs:{shape:[i,o]},backend:n}),u=Yn(a,r.dtype,\"max\",n),l=rt({inputs:{x:u},attrs:{shape:e},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(u),l}function B1(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{reductionIndices:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=c!=null,m=e.shouldExecuteOnCPU([o]),f=o;if(p){if(m){let w=e.texData.get(f.dataId).values,I=new Array(a);for(let A=0;A`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,l,a,u);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return nr({inputs:{x:o},backend:e});let p=new Si(c,\"max\",!1);return e.runWebGLProgram(p,[o],o.dtype)}var tV={kernelName:Ns,backendName:\"webgl\",kernelFunc:xat};function yat(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dataFormat:u,dimRoundingMode:l}=n,c=[1,1,1],p=S.computePool3DInfo(o.shape,s,i,c,a,l,u),m=new qu(p,\"max\",!1);return e.runWebGLProgram(m,[o],o.dtype)}var eV={kernelName:Pi,backendName:\"webgl\",kernelFunc:yat};var tC=class{constructor(t){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=t.inShape;let e=t.strideHeight,n=t.strideWidth,o=t.dilationHeight,s=t.effectiveFilterHeight,i=t.effectiveFilterWidth,a=s-1-t.padInfo.top,u=i-1-t.padInfo.left,l=s*i-1;this.userCode=`\n const ivec2 pads = ivec2(${a}, ${u});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${s};\n wR += ${o}) {\n float dyR = float(dyRCorner + wR) / ${e}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${i}; wC++) {\n float dyC = float(dyCCorner + wC) / ${n}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n int maxPosValue = ${l} - int(getMaxPos(b, idyR, idyC, d));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue = wR * ${i} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n setOutput(dotProd);\n }\n `}},eC=class{constructor(t){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=t.inShape;let e=t.strideDepth,n=t.strideHeight,o=t.strideWidth,s=t.dilationDepth,i=t.dilationHeight,a=t.dilationWidth,u=t.effectiveFilterDepth,l=t.effectiveFilterHeight,c=t.effectiveFilterWidth,p=u-1-t.padInfo.front,m=l-1-t.padInfo.top,f=c-1-t.padInfo.left,d=u*l*c-1;this.userCode=`\n const ivec3 pads = ivec3(${p}, ${m}, ${f});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${u};\n wD += ${s}) {\n float dyD = float(dyDCorner + wD) / ${e}.0;\n\n if (dyD < 0.0 || dyD >= ${t.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${l};\n wR += ${i}) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${c};\n wC += ${a}) {\n float dyC = float(dyCCorner + wC) / ${o}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n int maxPosValue = ${d} -\n int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue =\n wD * ${l} * ${c} +\n wR * ${c} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function bat(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s,{filterSize:a,strides:u,pad:l,dimRoundingMode:c}=n,p=[1,1,1],m=S.computePool3DInfo(i.shape,a,u,p,l,c),f=new qu(m,\"max\",!0),d=e.runWebGLProgram(f,[i],i.dtype),h=new eC(m),g=e.runWebGLProgram(h,[o,d],i.dtype);return e.disposeIntermediateTensorInfo(d),g}var rV={kernelName:tu,backendName:\"webgl\",kernelFunc:bat};function wat(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s,output:i}=t,a=s;Ci([s,i],\"maxPoolGrad\");let{filterSize:u,strides:l,pad:c,dimRoundingMode:p}=n,m=S.computePool2DInfo(a.shape,u,l,1,c,p),f=!0,d=new Si(m,\"max\",f),h=e.runWebGLProgram(d,[a],a.dtype),g=new tC(m),x=e.runWebGLProgram(g,[o,h],a.dtype);return e.disposeIntermediateTensorInfo(h),x}var nV={kernelName:Ql,backendName:\"webgl\",kernelFunc:wat};function oV(r,t,e,n){let o=new Si(e,\"max\",!1),s=n.runWebGLProgram(o,[r],\"float32\");o=new Si(e,\"max\",!0,!0,t);let i=n.runWebGLProgram(o,[r],\"float32\");return[s,i]}var sV={kernelName:eu,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:n}=r,{filterSize:o,strides:s,pad:i,includeBatchInIndex:a}=t,u=e;y.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let l=[1,1];y.assert(S.eitherStridesOrDilationsAreOne(s,l),()=>`Error in maxPool: Either strides or dilations must be 1. 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aC=class{constructor(t,e,n){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"value\",type:\"float\"}],this.outputShape=e.map((h,g)=>h[0]+t[g]+h[1]);let o=t.length,s=zt(o),i=e.map(h=>h[0]).join(\",\"),a=e.map((h,g)=>h[0]+t[g]).join(\",\"),u=rr(\"rc\",o),l=rr(\"source\",o),c=`${u[o-1]} < ${this.outputShape[o-1]}`,p=o===1?\"source\":`vec2(${l.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${u[o-1]} += 1;\n if(${c}) {\n `,o===1?\"\":`}\n rc = outputLoc;\n ${u[o-2]} += 1;\n if(${u[o-2]} < ${this.outputShape[o-2]}) {`,o===1?\"\":` ${u[o-1]} += 1;\n if(${c}) {`],f=o===1?\"rc < start || rc >= end\":\"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))\",d=\"\";for(let h=0,g=o===1?2:4;h{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{paddings:s,constantValue:i}=n;if(y.sizeFromShape(o.shape)===0){let l=s.map((c,p)=>c[0]+o.shape[p]+c[1]);return Ll({backend:e,attrs:{shape:l,value:i,dtype:o.dtype}})}let a=L().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new aC(o.shape,s,i):new iC(o.shape,s,i),u=[[i]];return e.runWebGLProgram(a,[o],o.dtype,u)},kV={kernelName:Rs,backendName:\"webgl\",kernelFunc:U1};var Wat=`\n if(a < 0.0 && floor(b) < b){\n return NAN;\n }\n if (b == 0.0) {\n return 1.0;\n }\n return (round(mod(b, 2.0)) != 1) ?\n pow(abs(a), b) : sign(a) * pow(abs(a), b);\n`,Uat=`\n // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n vec4 result = multiplier * pow(abs(a), b);\n\n // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n bvec4 isExpZero = equal(b, vec4(0.0));\n result.r = isExpZero.r ? 1.0 : result.r;\n result.g = isExpZero.g ? 1.0 : result.g;\n result.b = isExpZero.b ? 1.0 : result.b;\n result.a = isExpZero.a ? 1.0 : result.a;\n\n bvec4 isNaN1 = lessThan(a, vec4(0.0));\n bvec4 isNaN2 = lessThan(floor(b), b);\n bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w);\n `+Xn+`\n return result;\n`,Hat=ce({opSnippet:Wat,packedOpSnippet:Uat}),TV={kernelName:Fs,backendName:\"webgl\",kernelFunc:Hat};function qat(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=[],l=y.parseAxisParam(s,o.shape),c=l,p=S.getAxesPermutation(c,a),m=o;p!=null&&(m=Pe({inputs:{x:o},backend:e,attrs:{perm:p}}),c=S.getInnerMostAxes(c.length,a),u.push(m)),S.assertAxesAreInnerMostDims(\"prod\",c,a);let f;if(e.shouldExecuteOnCPU([m])){let d=e.texData.get(m.dataId).values,{outVals:h,outShape:g,outDtype:x}=fz(m.shape,m.dtype,d,c);f=e.makeTensorInfo(g,x,h)}else{let[d,h]=S.computeOutAndReduceShapes(m.shape,c),g=y.sizeFromShape(h),x=rt({inputs:{x:m},backend:e,attrs:{shape:[-1,g]}}),b=lc(o.dtype),w=Yn(x,b,\"prod\",e);f=rt({inputs:{x:w},backend:e,attrs:{shape:d}}),u.push(x),u.push(w)}if(i){u.push(f);let d=S.expandShapeToKeepDim(f.shape,l);f=rt({inputs:{x:f},backend:e,attrs:{shape:d}})}return u.forEach(d=>e.disposeIntermediateTensorInfo(d)),f}var _V={kernelName:Ms,backendName:\"webgl\",kernelFunc:qat};function Kat(r){let{inputs:t,backend:e,attrs:n}=r,{paramsNestedSplits:o,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:a}=n,u=o.map(x=>e.readSync(x.dataId)),l=o.map(x=>x.shape),c=e.readSync(s.dataId),p=e.readSync(i.dataId),[m,f,d]=dz(u,l,c,s.shape,s.dtype,p,i.shape,a),h=m.map(x=>e.makeTensorInfo([x.length],\"int32\",x)),g=e.makeTensorInfo(d,s.dtype,f);return h.concat([g])}var EV={kernelName:Lp,backendName:\"webgl\",kernelFunc:Kat};function jat(r){let{inputs:t,backend:e}=r,{starts:n,limits:o,deltas:s}=t,i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=hz(i,n.shape,n.dtype,a,o.shape,u,s.shape),p=e.makeTensorInfo([l.length],\"int32\",l),m=e.makeTensorInfo([c.length],n.dtype,c);return[p,m]}var AV={kernelName:zp,backendName:\"webgl\",kernelFunc:jat};function Xat(r){let{inputs:t,backend:e,attrs:n}=r,{shape:o,values:s,defaultValue:i,rowPartitionTensors:a}=t,{rowPartitionTypes:u}=n,l=e.readSync(o.dataId),c=e.readSync(s.dataId),p=e.readSync(i.dataId),m=a.map(g=>e.readSync(g.dataId)),f=a.map(g=>g.shape),[d,h]=gz(l,o.shape,c,s.shape,s.dtype,p,i.shape,m,f,u);return e.makeTensorInfo(d,s.dtype,h)}var DV={kernelName:Bp,backendName:\"webgl\",kernelFunc:Xat};var H1=r=>{let{backend:t,attrs:e}=r,{start:n,stop:o,step:s,dtype:i}=e,a=xz(n,o,s,i);return t.makeTensorInfo([a.length],i,a)},$V={kernelName:ru,backendName:\"webgl\",kernelFunc:H1};var Yat=\"return 1.0 / x;\",Zat=It({opSnippet:Yat}),RV={kernelName:Ps,backendName:\"webgl\",kernelFunc:Zat};var Jat=xr+`\n return (x < 0.0) ? 0.0 : x;\n`,Qat=`\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,tlt=It({opSnippet:Jat,packedOpSnippet:Qat}),FV={kernelName:Ls,backendName:\"webgl\",kernelFunc:tlt};var elt=xr+`\n return (x < 0.0) ? 0.0 : min(6.0, x);\n`,rlt=`\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,nlt=It({opSnippet:elt,packedOpSnippet:rlt}),OV={kernelName:Vs,backendName:\"webgl\",kernelFunc:nlt};var lC=class{constructor(t,e,n,o,s){this.variableNames=[\"A\"],this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m;s?m=\"(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)\":m=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${c[0]/p[0]},\n ${c[1]/p[1]});\n const vec2 inputShapeRC = vec2(${a}.0, ${u}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${m};\n\n // Compute the four integer indices.\n ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));\n ivec2 sourceCeilRC = ivec2(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n float top = topLeft + (topRight - topLeft) * fracRC.y;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n float newValue = top + (bottom - top) * fracRC.x;\n\n setOutput(newValue);\n }\n `}};var uC=class{constructor(t,e,n,o,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m;s?m=\"(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)\":m=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${c[0]/p[0]},\n ${c[1]/p[1]},\n ${c[1]/p[1]});\n const vec3 inputShapeRC = vec3(${a}.0, ${u}.0,\n ${u}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${m};\n\n // Compute the four integer indices.\n ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));\n ivec3 sourceCeilRC = ivec3(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${l-1};\n bool hasNextRow = coords.z < ${n-1};\n\n // In parallel, construct four corners for all four components in\n // packed 2x2 cell.\n vec4 topLeft = vec4(\n getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 bottomLeft = vec4(\n getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 topRight = vec4(\n getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec4 bottomRight = vec4(\n getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n vec4 newValue = mix(top, bottom, fracRC.x);\n\n setOutput(newValue);\n }\n `}};function olt(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n,[u,l]=a,c=L().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new uC(o.shape,u,l,s,i):new lC(o.shape,u,l,s,i);return e.runWebGLProgram(c,[o],\"float32\")}var MV={kernelName:Bs,backendName:\"webgl\",kernelFunc:olt};var cC=class{constructor(t,e,n){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=e;let[,o,s]=e,[,i,a]=t,u=[n&&i>1?o-1:o,n&&a>1?s-1:s],l=[n&&i>1?i-1:i,n&&a>1?a-1:a],c=u[0]/l[0],p=u[1]/l[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${c});\n const float widthScale = float(${p});\n\n const float invHeightScale = float(${m});\n const float invWidthScale = float(${f});\n\n const int winHeight = int(${d});\n const int winWidth = int(${h});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(startRLerp - float(winHeight / 2));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(startCLerp - float(winWidth / 2));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${i}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${a}) {\n continue;\n }\n\n float dxR = float(dyR) * heightScale;\n int topDxRIndex = int(floor(dxR));\n int bottomDxRIndex = int(min(ceil(dxR), ${o-1}.0));\n float dxRLerp = dxR - float(topDxRIndex);\n float inverseDxRLerp = 1.0 - dxRLerp;\n\n float dxC = float(dyC) * widthScale;\n int leftDxCIndex = int(floor(dxC));\n int rightDxCIndex = int(min(ceil(dxC), ${s-1}.0));\n float dxCLerp = dxC - float(leftDxCIndex);\n float inverseDxCLerp = 1.0 - dxCLerp;\n\n if (r == topDxRIndex && c == leftDxCIndex) {\n // topLeft\n accumulator +=\n getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n }\n\n if (r == topDxRIndex && c == rightDxCIndex) {\n // topRight\n accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n }\n\n if (r == bottomDxRIndex && c == leftDxCIndex) {\n // bottomLeft\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n }\n\n if (r == bottomDxRIndex && c == rightDxCIndex) {\n // bottomRight\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}};function slt(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=new cC(s.shape,o.shape,i);return e.runWebGLProgram(a,[s],s.dtype)}var PV={kernelName:rl,backendName:\"webgl\",kernelFunc:slt};var pC=class{constructor(t,e,n,o,s){this.variableNames=[\"A\"],this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m=o?\"0.5\":\"0.0\",f;s?f=\"max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))\":f=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${c[0]/p[0]},\n ${c[1]/p[1]});\n const vec2 inputShapeRC = vec2(${a}.0, ${u}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${f};\n\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestRC = ivec2(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));\n float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n setOutput(newValue);\n }\n `}};var mC=class{constructor(t,e,n,o,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m=o?\"0.5\":\"0.0\",f;s?f=\"max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))\":f=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${c[0]/p[0]},\n ${c[1]/p[1]},\n ${c[1]/p[1]});\n const vec3 inputShapeRC = vec3(${a}.0, ${u}.0,\n ${u}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${f};\n\n // Compute the coordinators of nearest neighbor point.\n ivec3 sourceNearestRC = ivec3(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${l-1};\n bool hasNextRow = coords.z < ${n-1};\n\n vec4 newValue = vec4(\n getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),\n hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);\n\n setOutput(newValue);\n }\n `}};function ilt(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n,[u,l]=a,c=L().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new mC(o.shape,u,l,s,i):new pC(o.shape,u,l,s,i);return e.runWebGLProgram(c,[o],o.dtype)}var LV={kernelName:zs,backendName:\"webgl\",kernelFunc:ilt};var fC=class{constructor(t,e,n){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=e;let[,o,s]=e,[,i,a]=t,u=[n&&i>1?o-1:o,n&&a>1?s-1:s],l=[n&&i>1?i-1:i,n&&a>1?a-1:a],c=u[0]/l[0],p=u[1]/l[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${c});\n const float widthScale = float(${p});\n\n const float invHeightScale = float(${m});\n const float invWidthScale = float(${f});\n\n const int winHeight = int(${d});\n const int winWidth = int(${h});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${i}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${a}) {\n continue;\n }\n\n float sourceFracRow =\n float(${u[0]}) *\n (float(dyR) / float(${l[0]}));\n\n float sourceFracCol =\n float(${u[1]}) *\n (float(dyC) / float(${l[1]}));\n\n int sourceNearestRow = int(min(\n float(int(${o}) - 1),\n ${n} ? float(round(sourceFracRow)) :\n float(floor(sourceFracRow))));\n\n int sourceNearestCol = int(min(\n float(int(${s}) - 1),\n ${n} ? float(round(sourceFracCol)) :\n float(floor(sourceFracCol))));\n\n if (r == sourceNearestRow && c == sourceNearestCol) {\n accumulator += getDy(b, dyR, dyC, d);\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}};function alt(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=new fC(s.shape,o.shape,i);return e.runWebGLProgram(a,[s],s.dtype)}var zV={kernelName:el,backendName:\"webgl\",kernelFunc:alt};var dC=class{constructor(t,e){this.variableNames=[\"x\"];let n=t.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=t,n===1){this.userCode=`\n void main() {\n int coord = getOutputCoords();\n setOutput(getX(${t[0]} - coord - 1));\n }\n `;return}let o=a=>e.indexOf(a)!==-1&&t[a]!==1?`${t[a]} - coords[${a}] - 1`:`coords[${a}]`,s=t.map((a,u)=>o(u)).join(\",\"),i=zt(n);this.userCode=`\n void main() {\n ${i} coords = getOutputCoords();\n setOutput(getX(${s}));\n }\n `}};var hC=class{constructor(t,e){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0;let n=t.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=t;let o=rr(\"rc\",n),s=`${o[n-1]} + 1 < ${this.outputShape[n-1]}`,i=`${o[n-2]} + 1 < ${this.outputShape[n-2]}`,a=zt(n);n===1?this.userCode=`\n void main(){\n int rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = getChannel(getX(${t[0]} - rc - 1),\n ${t[0]} - rc - 1);\n if(${s}){\n result.g = getChannel(getX(${t[0]} - (rc + 1) - 1),\n ${t[0]} - (rc + 1) - 1);\n }\n setOutput(result);\n }\n `:this.userCode=`\n void main() {\n ${a} rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = ${u(o.slice())};\n if(${s}){\n result.g = ${l(o.slice())};\n }\n if(${i}) {\n result.b = ${c(o.slice())};\n if(${s}) {\n result.a = ${p(o.slice())};\n }\n }\n setOutput(result);\n }\n `;function u(d){return m(d)}function l(d){return d[n-1]=\"(\"+d[n-1]+\" + 1)\",m(d)}function c(d){return d[n-2]=\"(\"+d[n-2]+\" + 1)\",m(d)}function p(d){return d[n-1]=\"(\"+d[n-1]+\" + 1)\",d[n-2]=\"(\"+d[n-2]+\" + 1)\",m(d)}function m(d){let h=t.map((b,w)=>f(w,d)),g=h.join(\",\"),x=h.slice(-2).join(\",\");return`getChannel(getX(${g}), vec2(${x}))`}function f(d,h){return e.indexOf(d)!==-1&&t[d]!==1?`${t[d]} - ${h[d]} - 1`:`${h[d]}`}}};function llt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{dims:s}=n,i=o.shape.length,a=y.parseAxisParam(s,o.shape);if(i===0)return nr({inputs:{x:o},backend:e});let u=L().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new hC(o.shape,a):new dC(o.shape,a);return e.runWebGLProgram(u,[o],o.dtype)}var BV={kernelName:Gs,backendName:\"webgl\",kernelFunc:llt};var gC=class{constructor(t,e){this.variableNames=[\"Image\"],this.outputShape=[],this.customUniforms=[{name:\"params\",type:\"vec4\"}];let n=t[1],o=t[2];this.outputShape=t;let s=\"\";typeof e==\"number\"?s=`float outputValue = ${e.toFixed(2)};`:s=`\n vec3 fill = vec3(${e.join(\",\")});\n float outputValue = fill[coords[3]];`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n int y = coords[1];\n float coordXFloat = (float(x) - params[0]) * params[3] -\n (float(y) - params[1]) * params[2];\n float coordYFloat = (float(x) - params[0]) * params[2] +\n (float(y) - params[1]) * params[3];\n int coordX = int(round(coordXFloat + params[0]));\n int coordY = int(round(coordYFloat + params[1]));\n ${s}\n if(coordX >= 0 && coordX < ${o} && coordY >= 0 && coordY < ${n}) {\n outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n }\n setOutput(outputValue);\n }\n `}};var VV={kernelName:pl,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{image:n}=r,{radians:o,fillValue:s,center:i}=t,a=e,u=new gC(n.shape,s),[l,c]=S.getImageCenter(i,n.shape[1],n.shape[2]),p=[[l,c,Math.sin(o),Math.cos(o)]];return a.runWebGLProgram(u,[n],n.dtype,p)}};var ult=`\n // OpenGL ES does not support round function.\n // The algorithm is based on banker's rounding.\n float base = floor(x);\n if ((x - base) < 0.5) {\n return floor(x);\n } else if ((x - base) > 0.5) {\n return ceil(x);\n } else {\n if (mod(base, 2.0) == 0.0) {\n return base;\n } else {\n return base + 1.0;\n }\n }\n`,clt=It({opSnippet:ult}),GV={kernelName:Ws,backendName:\"webgl\",kernelFunc:clt};var plt=\"return inversesqrt(x);\",mlt=It({opSnippet:plt,cpuKernelImpl:yz}),WV={kernelName:Us,backendName:\"webgl\",kernelFunc:mlt};var Ku=class{constructor(t,e,n,o,s,i,a=!0,u=!1){this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.outputShape=i;let l=zt(s.length),c=zt(i.length),p=\"\";n===1?p=\"i\":n===2&&(p=\"i, j\");let m=`getIndices(${p})`,f=\"\";o===1?f=\"i\":o===2&&(f=\"i, coords[1]\");let d=`getUpdates(${f})`,h=\"\";u&&(h=\"coords[0], coords[1]\");let g=`getDefaultValue(${h})`,x=e>1?\"strides[j]\":\"strides\";this.userCode=`\n ${l} strides = ${l}(${s});\n\n void main() {\n ${c} coords = getOutputCoords();\n float sum = 0.0;\n bool found = false;\n for (int i = 0; i < ${t}; i++) {\n int flattenedIndex = 0;\n for (int j = 0; j < ${e}; j++) {\n int index = round(${m});\n flattenedIndex += index * ${x};\n }\n if (flattenedIndex == coords[0]) {\n sum += ${d};\n found = true;\n }\n }\n setOutput(mix(${g}, sum, float(found)));\n }\n `}};var xC=class{constructor(t,e,n,o,s,i,a=!0,u=!1){this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=i;let l=zt(s.length),c=zt(i.length),p=\"\";n===1?p=\"i\":n===2&&(p=\"i, j\");let m=`getIndices(${p})`,f=\"\";o===1?f=\"i\":o===2&&(f=\"i, coords[1]\");let d=`getUpdates(${f})`,h=\"\";u&&(h=\"coords[0], coords[1]\");let g=`getDefaultValue(${h})`,x=e>1?\"strides[j]\":\"strides\",b=e>1?\"strides[j + 1]\":\"strides\";this.userCode=`\n ${l} strides = ${l}(${s});\n\n void main() {\n ${c} coords = getOutputCoords();\n vec4 sum = vec4(0.);\n vec4 found = vec4(0.);\n for (int i = 0; i < ${t}; i+=2) {\n ivec2 flattenedIndex = ivec2(0);\n for (int j = 0; j < ${e}; j+=2) {\n ivec4 index = round(${m});\n flattenedIndex += index.xz * ${x};\n if (j + 1 < ${e}) {\n flattenedIndex += index.yw * ${b};\n }\n }\n if (flattenedIndex[0] == coords[0] || flattenedIndex[1] == coords[0] ||\n flattenedIndex[0] == coords[0] + 1 || flattenedIndex[1] == coords[0] + 1) {\n vec4 updVals = ${d};\n if (flattenedIndex[0] == coords[0]) {\n sum.xy += updVals.xy;\n found.xy = vec2(1.);\n } else if (flattenedIndex[0] == coords[0] + 1) {\n sum.zw += updVals.xy;\n found.zw = vec2(1.);\n }\n if (flattenedIndex[1] == coords[0]) {\n sum.xy += updVals.zw;\n found.xy = vec2(1.);\n } else if (flattenedIndex[1] == coords[0] + 1) {\n sum.zw += updVals.zw;\n found.zw = vec2(1.);\n }\n }\n }\n setOutput(mix(${g}, sum, found));\n }\n `}};function flt(r){let{inputs:t,backend:e,attrs:n}=r,{indices:o,updates:s}=t,{shape:i}=n,{sliceRank:a,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=S.calculateShapes(s,o,i),m=[p/l,l];if(p===0)return e.makeTensorInfo(i,o.dtype);let f=rt({inputs:{x:o},backend:e,attrs:{shape:[u,a]}}),d=rt({inputs:{x:s},backend:e,attrs:{shape:[u,l]}}),h=e.makeTensorInfo([],\"float32\",new Float32Array([0])),g;L().getBool(\"WEBGL_PACK\")?g=new xC(u,a,f.shape.length,d.shape.length,c,m):g=new Ku(u,a,f.shape.length,d.shape.length,c,m);let x=e.runWebGLProgram(g,[d,f,h],d.dtype),b=rt({inputs:{x},backend:e,attrs:{shape:i}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(h),b}var UV={kernelName:nl,backendName:\"webgl\",kernelFunc:flt};var yC=class{constructor(t,e,n,o){this.variableNames=[\"sortedSequence\",\"values\"],this.customUniforms=[{name:\"numInputs\",type:\"int\"}],this.outputShape=[t,n];let s=\"while (left < right) {\",i=`for (int i = 0; i < ${Math.ceil(Math.log2(e+1))}; ++i) { if (left >= right) break;`,a=L().getNumber(\"WEBGL_VERSION\")===2?s:i,u=o===\"left\"?\"<\":\"<=\";this.userCode=`\n int findBound(int batch, float value) {\n int left = 0;\n int right = numInputs;\n int mid;\n ${a}\n mid = (left + right) / 2;\n if (getSortedSequence(batch, mid) ${u} value) {\n left = mid + 1;\n } else {\n right = mid;\n }\n }\n return right;\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int valueIndex = coords[1];\n\n float value = getValues(batch, valueIndex);\n\n setOutput(float(findBound(batch, value)));\n }\n `}};function dlt(r){let{inputs:t,backend:e,attrs:n}=r,{sortedSequence:o,values:s}=t,{side:i}=n,a=new yC(o.shape[0],o.shape[1],s.shape[1],i),u=[[o.shape[1]]];return e.runWebGLProgram(a,[o,s],\"int32\",u)}var HV={kernelName:sl,backendName:\"webgl\",kernelFunc:dlt};var bC=class{constructor(t,e,n){this.variableNames=[\"c\",\"a\",\"b\"],this.outputShape=e;let o,s;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)s=\"resRC\",o=\"resRC\";else{let a=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],u=[],l=[];for(let c=0;c= 1.0) {\n setOutput(getA(${s}));\n } else {\n setOutput(getB(${s}));\n }\n }\n `}};function hlt(r){let{inputs:t,backend:e}=r,{condition:n,t:o,e:s}=t,i=new bC(n.shape.length,o.shape,o.shape.length);return e.runWebGLProgram(i,[n,o,s],ur(o.dtype,s.dtype))}var qV={kernelName:Gi,backendName:\"webgl\",kernelFunc:hlt};var glt=`\n // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n // see: https://arxiv.org/abs/1706.02515\n float scaleAlpha = ${S.SELU_SCALEALPHA};\n float scale = ${S.SELU_SCALE};\n return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`,xlt=It({opSnippet:glt}),KV={kernelName:Hs,backendName:\"webgl\",kernelFunc:xlt};var ylt=Po+`\n return 1.0 / (1.0 + exp(-1.0 * x));\n`,blt=`\n vec4 result = 1.0 / (1.0 + exp(-1.0 * x));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,wlt=It({opSnippet:ylt,packedOpSnippet:blt,cpuKernelImpl:wz}),jV={kernelName:Xs,backendName:\"webgl\",kernelFunc:wlt};var Ilt=`\n if (isnan(x)) { return 0.0; }\n return sign(x);\n`,Clt=It({opSnippet:Ilt}),XV={kernelName:js,backendName:\"webgl\",kernelFunc:Clt};var vlt=Po+`\n return sin(x);\n`,Slt=`\n vec4 result = sin(x);\n bvec4 isNaN = isnan(x);\n ${Xn}\n return result;\n`,Nlt=It({opSnippet:vlt,packedOpSnippet:Slt}),YV={kernelName:qs,backendName:\"webgl\",kernelFunc:Nlt};var klt=`\n float e2x = exp(x);\n return (e2x - 1.0 / e2x) / 2.0;\n`,Tlt=It({opSnippet:klt}),ZV={kernelName:Ks,backendName:\"webgl\",kernelFunc:Tlt};var _lt=`\n float epsilon = 1.1920928955078125e-7;\n float threshold = log(epsilon) + 2.0;\n\n bool too_large = x > -threshold;\n bool too_small = x < threshold;\n\n float result;\n float exp_x = exp(x);\n\n if (too_large){\n result = x;\n }\n else if (too_small){\n result = exp_x;\n }\n else{\n result = log(exp_x + 1.0);\n }\n return result;\n`,Elt=It({opSnippet:_lt}),JV={kernelName:Ys,backendName:\"webgl\",kernelFunc:Elt};var Alt=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,paddings:i}=n;y.assert(o.shape.length<=4,()=>\"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet\");let a=s.reduce((x,b)=>x*b),u=[[0,0]];u.push(...i);for(let x=1+s.length;xe.disposeIntermediateTensorInfo(x)),g},QV={kernelName:Ui,backendName:\"webgl\",kernelFunc:Alt};function Dlt(r){let{inputs:t,backend:e}=r,{indices:n,values:o,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:\n ${s.shape}`);if(n.shape.length!==2)throw new Error(`Indices must be a matrix, saw:\n ${n.shape}`);if(o.shape.length!==1)throw new Error(`Values must be a vector, saw:\n ${o.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:\n ${i.shape}`);let a=e.readSync(n.dataId),u=e.readSync(o.dataId),l=e.readSync(s.dataId),c=e.readSync(i.dataId)[0],[p,m,f,d,h]=Cz(a,n.shape,n.dtype,u,o.dtype,l,c);return[e.makeTensorInfo(m,n.dtype,p),e.makeTensorInfo([m[0]],o.dtype,f),e.makeTensorInfo([d.length],\"bool\",new Uint8Array(d.map(g=>Number(g)))),e.makeTensorInfo([h.length],n.dtype,new Int32Array(h))]}var tG={kernelName:nu,backendName:\"webgl\",kernelFunc:Dlt};function $lt(r){let{inputs:t,backend:e}=r,{inputIndices:n,inputShape:o,newShape:s}=t;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${n.shape}`);if(o.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${o.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(e.readSync(o.dataId)),a=e.readSync(n.dataId),u=Array.from(e.readSync(s.dataId)),[l,c,p]=vz(a,n.shape,n.dtype,i,u);return[e.makeTensorInfo(c,n.dtype,l),e.makeTensorInfo([p.length],s.dtype,new Int32Array(p))]}var eG={kernelName:il,backendName:\"webgl\",kernelFunc:$lt};function Rlt(r){let{inputs:t,backend:e}=r,{data:n,indices:o,segmentIds:s}=t;if(n.shape.length<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(o.shape.length!==1)throw new Error(`Indices should be a vector but received shape\n ${o.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape\n ${s.shape}`);let i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=tI(i,n.shape,n.dtype,a,u,!0);return e.makeTensorInfo(c,n.dtype,l)}var rG={kernelName:ou,backendName:\"webgl\",kernelFunc:Rlt};function Flt(r){let{inputs:t,backend:e}=r,{data:n,indices:o,segmentIds:s}=t;if(n.shape.length<1)throw new Error(\"Data should be at 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e.disposeIntermediateTensorInfo(h),g}var oG={kernelName:al,backendName:\"webgl\",kernelFunc:Olt};function Mlt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{numOrSizeSplits:s,axis:i}=n,a=y.parseAxisParam(i,o.shape)[0],u=S.prepareSplitSize(o,s,a),l=o.shape.length,c=new Array(l).fill(0),p=o.shape.slice();return u.map(m=>{let f=[...p];f[a]=m;let d=Ni({inputs:{x:o},backend:e,attrs:{begin:c,size:f}});return c[a]+=m,d})}var sG={kernelName:Hi,backendName:\"webgl\",kernelFunc:Mlt};var iG=\"return sqrt(x);\",Plt=It({opSnippet:iG,packedOpSnippet:iG,cpuKernelImpl:Sz}),aG={kernelName:Zs,backendName:\"webgl\",kernelFunc:Plt};var Llt=\"return x * x;\",zlt=It({opSnippet:Llt}),lG={kernelName:iu,backendName:\"webgl\",kernelFunc:zlt};var uG=\"return (a - b) * (a - b);\",Blt=ce({opSnippet:uG,packedOpSnippet:uG}),cG={kernelName:ti,backendName:\"webgl\",kernelFunc:Blt};function Vlt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;if(o.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");let s=e.readSync(o.dataId),i=S.fromUint8ToStringArray(s),a=Nz(i,\"string\",n);return e.makeTensorInfo(o.shape,\"string\",a)}var pG={kernelName:ec,backendName:\"webgl\",kernelFunc:Vlt};function Glt({inputs:r,attrs:t,backend:e}){let{x:n}=r,o=xr+`\n return x > 0.0 ? 1.0 : float(${t.alpha});\n `,s=new Lr(n.shape,o);return e.runWebGLProgram(s,[n],n.dtype)}var mG={kernelName:xo,backendName:\"webgl\",kernelFunc:Glt};var wC=class{constructor(t,e,n){this.variableNames=[\"x\"],this.outputShape=n;let o=n.length,s=zt(n.length),i=zt(n.length),a=\"\";if(o===1)a=\"coords * strides + begin\";else{let u=0;a=n.map((l,c)=>(u++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${u-1}] * strides[${c}] + begin[${c}]`)).join(\",\")}this.userCode=`\n ${s} begin = ${s}(${t});\n ${s} strides = ${s}(${e});\n\n void main() {\n ${i} coords = getOutputCoords();\n setOutput(getX(${a}));\n }\n `}};function Wlt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{begin:s,end:i,strides:a,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:m}=n,{finalShapeSparse:f,finalShape:d,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:w,strides:I}=Be.sliceInfo(o.shape,s,i,a,u,l,c,p,m),N;if(h)N=rt({inputs:{x:o},backend:e,attrs:{shape:d}});else if(g||x){y.assert(o.shape.length>=1,()=>`Input must have rank at least 1, got: ${o.shape.length}`);let A=Be.computeOutShape(b,w,I),D=Ni({inputs:{x:o},backend:e,attrs:{begin:b,size:A}});N=rt({inputs:{x:D},backend:e,attrs:{shape:d}}),e.disposeIntermediateTensorInfo(D)}else if(e.shouldExecuteOnCPU([o])){let D=e.readSync(o.dataId),F=wt(o.shape,o.dtype,D),M=kz(f,F,I,b);N=e.makeTensorInfo(d,o.dtype,M.values)}else{let D=new wC(b,I,f);N=e.runWebGLProgram(D,[o],o.dtype)}let E=rt({inputs:{x:N},backend:e,attrs:{shape:d}});return e.disposeIntermediateTensorInfo(N),E}var fG={kernelName:ll,backendName:\"webgl\",kernelFunc:Wlt};function Ult(r){let{inputs:t,backend:e,attrs:n}=r,{separator:o,nGramWidths:s,leftPad:i,rightPad:a,padWidth:u,preserveShortSequences:l}=n,{data:c,dataSplits:p}=t,m=e.readSync(c.dataId),f=e.readSync(p.dataId),[d,h]=Tz(m,f,o,s,i,a,u,l);return[e.makeTensorInfo([d.length],\"string\",d),e.makeTensorInfo(p.shape,\"int32\",h)]}var dG={kernelName:au,backendName:\"webgl\",kernelFunc:Ult};function Hlt(r){let{inputs:t,backend:e,attrs:n}=r,{skipEmpty:o}=n,{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 a=e.readSync(s.dataId),u=e.readSync(i.dataId)[0],[l,c,p]=_z(a,u,o),m=c.length;return[e.makeTensorInfo([m,2],\"int32\",l),e.makeTensorInfo([m],\"string\",c),e.makeTensorInfo([2],\"int32\",new Int32Array(p))]}var hG={kernelName:lu,backendName:\"webgl\",kernelFunc:Hlt};function qlt(r){let{inputs:t,backend:e,attrs:n}=r,{numBuckets:o}=n,{input:s}=t;if(s.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");if(o<=0)throw new Error(\"Number of buckets must be at least 1\");let i=e.readSync(s.dataId),a=Ez(i,o);return e.makeTensorInfo(s.shape,\"int32\",a)}var gG={kernelName:uu,backendName:\"webgl\",kernelFunc:qlt};var Klt=\"return tan(x);\",jlt=It({opSnippet:Klt}),xG={kernelName:ri,backendName:\"webgl\",kernelFunc:jlt};var Xlt=`\n float e2x = exp(-2.0 * abs(x));\n return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n`,Ylt=It({opSnippet:Xlt}),yG={kernelName:ni,backendName:\"webgl\",kernelFunc:Ylt};function Zlt(r){let{inputs:t,backend:e,attrs:n}=r,{tensor:o,indices:s,updates:i}=t,{}=n,{sliceRank:a,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=S.calculateShapes(i,s,o.shape),m=[p/l,l];if(p===0)return e.makeTensorInfo(o.shape,s.dtype);let f=rt({inputs:{x:s},backend:e,attrs:{shape:[u,a]}}),d=rt({inputs:{x:i},backend:e,attrs:{shape:[u,l]}}),h=rt({inputs:{x:o},backend:e,attrs:{shape:m}}),g=new Ku(u,a,f.shape.length,d.shape.length,c,m,!1,!0),x=e.runWebGLProgram(g,[d,f,h],h.dtype),b=rt({inputs:{x},backend:e,attrs:{shape:o.shape}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(x),b}var bG={kernelName:ol,backendName:\"webgl\",kernelFunc:Zlt};var IC=class{constructor(t,e){this.variableNames=[\"A\"];let n=new Array(t.length);for(let i=0;i5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${r[0]})`;let e=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\"],n=[];for(let o=0;o5){let u=e.readSync(o.dataId),l=o.dtype===\"string\"?u.map(m=>y.decodeString(m)):u,c=wt(o.shape,o.dtype,l),p=Dz(c,s);return e.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new IC(o.shape,s);return e.runWebGLProgram(i,[o],o.dtype)}var wG={kernelName:oo,backendName:\"webgl\",kernelFunc:q1};var CC=class{constructor(t){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"negativeInf\",type:\"float\"},{name:\"dir\",type:\"int\"},{name:\"inc\",type:\"int\"}],this.outputShape=t,this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int elemIdx = coords[1];\n\n // We compare elements pair-wise within a group of size 2 * inc.\n // The comparing rule for each group alternates between ascending\n // and descending. Within each group, we compare each pair at\n // positions i and i+inc. To decide whether an element at position i\n // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n // inc, it is in the first half of the group, we denote it as x0,\n // otherwise we denote it as x1.\n // For example, as shown in the Bitonic top K paper referenced above,\n // Figure5(a) shows that element[1] is in the\n // second half of the group when group size is 2, but it is in the\n // first half of the group when group size is 4.\n\n bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;\n int i = isFirstInPair ? elemIdx : elemIdx - inc;\n\n int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));\n float x0 = i0 < n ? getX(batch, i0) : negativeInf;\n float x1 = i1 < n ? getX(batch, i1) : negativeInf;\n\n // Denotes which direction indices are in (ascending or descending).\n bool reverse = imod(elemIdx, 2 * dir) >= dir;\n bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n if (reverse == isGreater) { // Elements in opposite order of direction\n int iTemp = i0;\n i0 = i1;\n i1 = iTemp;\n }\n if (isFirstInPair) {\n setOutput(float(i0));\n } else {\n setOutput(float(i1));\n }\n }\n `}},vC=class{constructor(t){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"k\",type:\"int\"}],this.outputShape=t,this.userCode=`\n void main() {\n // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int elemIdx = coords[1];\n\n // The output size is half of the previous size.\n // If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ (k=4),\n // we only need to output the indices at positions |, the indices at\n // positions _ can be thrown away, see Figure5(b) After Phase 2\n // (Merge phase) in the Bitonic Top K paper referenced above.\n // For example, the paper shows we only need to output the orange bars.\n // The output sequence should look like this | | | | | | | |.\n // Because the sequence is halved, to map the output index back\n // to the previous sequence to find the corresponding value,\n // we need to double the index. When we double the index,\n // we basically interpolate a position, so 2i looks like\n // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position\n // of each 2k positions by - elemIdx % k. E.g. for output at\n // index 4,5,6,7, we want to get the corresponding element at\n // original index 8,9,10,11, for output at index 8,9,10,11,\n // we want to get the corresponding element at original index\n // 16,17,18,19, so on and so forth.\n\n int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));\n int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));\n\n float x0 = getX(batch, i0);\n float x1 = i1 < n ? getX(batch, i1) : x0;\n\n setOutput(x0 >= x1 ? float(i0) : float(i1));\n }\n `}};function xp(r,t){t!==null&&r.disposeIntermediateTensorInfo(t)}function IG(r){let t=1;for(;tu){let M=e.readSync(o.dataId),[V,G]=$z(M,l,o.dtype,s,i);return[e.makeTensorInfo(V.shape,V.dtype,V.values),e.makeTensorInfo(G.shape,G.dtype,G.values)]}if(s===0)return l[l.length-1]=0,[e.makeTensorInfo(l,o.dtype,[]),e.makeTensorInfo(l,\"int32\",[])];if(c===1)return[o,Ll({attrs:{shape:l,dtype:\"int32\",value:0},backend:e})];let p=e.texData.get(o.dataId),m=p!==null&&p.isPacked,f=m?e.unpackTensor(o):o,h=y.sizeFromShape(l)/c,g=rt({inputs:{x:f},attrs:{shape:[h,c]},backend:e});m&&xp(e,f);let x=IG(s),b=IG(c),w=null,I=()=>w===null?[g,g]:[g,w],N=(M,V,G)=>{let W=I(),q=new CC(G),K=[[c],[w===null?1:0],[Number.NEGATIVE_INFINITY],[M],[V]],Y=w;w=e.runWebGLProgram(q,W,\"int32\",K),xp(e,Y)};for(let M=1;M=1;G/=2)N(V,G,[h,b])}for(let M=b;M>x;M/=2){let V=I(),G=new vC([h,M/2]),q=[[c],[w===null?1:0],[x]],H=w;w=e.runWebGLProgram(G,V,\"int32\",q),xp(e,H);let K=x/2,Y=K*2;for(let Z=K;Z>=1;Z/=2)N(Y,Z,w.shape)}let E=w;w=Ni({inputs:{x:w},backend:e,attrs:{begin:0,size:[h,s]}}),xp(e,E);let A=z1({inputs:{x:g,indices:w},backend:e,attrs:{axis:1,batchDims:1}});xp(e,g);let D=l.slice(0,-1);D.push(s),E=w,w=rt({inputs:{x:w},attrs:{shape:D},backend:e}),xp(e,E);let F=A;return A=rt({inputs:{x:A},attrs:{shape:D},backend:e}),xp(e,F),[A,w]}var CG={kernelName:ul,backendName:\"webgl\",kernelFunc:Qlt};var SC=class{constructor(t,e,n,o,s,i){this.variableNames=[\"Image\",\"Transforms\"],this.outputShape=i;let a=n===\"nearest\"?1:2,u;switch(o){case\"constant\":u=1;break;case\"reflect\":u=2;break;case\"wrap\":u=3;break;case\"nearest\":u=4;break;default:u=1;break}this.userCode=`\n float mapCoord(float outCoord, float len) {\n float inCoord = outCoord;\n if(${u} == 2) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz2 = 2.0 * len;\n if (inCoord < sz2) {\n inCoord = sz2 * float(int(float(-inCoord / sz2))) +\n inCoord;\n }\n inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz2 = 2.0 * len;\n inCoord -= sz2 * float(int(float(inCoord / sz2)));\n if (inCoord >= len) {\n inCoord = sz2 - inCoord - 1.0;\n }\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (${u} == 3) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord -= len * float(int(float(inCoord / sz)));\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (${u} == 4) {\n return clamp(outCoord, 0.0, len - 1.0);\n } else {\n return outCoord;\n }\n }\n\n float readWithFillValue(int batch, int coordY, int coordX,\n int channel) {\n float outputValue;\n if (0 <= coordY && coordY < ${t} && 0 <= coordX && coordX < ${e}) {\n outputValue = getImage(batch, coordY, coordX, channel);\n } else {\n outputValue = float(${s});\n }\n return outputValue;\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n float outputValue;\n int batch = coords[0];\n int x = coords[2];\n int y = coords[1];\n int channel = coords[3];\n float xf = float(x);\n float yf = float(y);\n float a1 = getTransforms(batch, 0);\n float a2 = getTransforms(batch, 1);\n float a3 = getTransforms(batch, 2);\n float b1 = getTransforms(batch, 3);\n float b2 = getTransforms(batch, 4);\n float b3 = getTransforms(batch, 5);\n float c1 = getTransforms(batch, 6);\n float c2 = getTransforms(batch, 7);\n float projection = c1 * xf + c2 * yf + 1.0;\n if (projection == 0.0) {\n outputValue = float(${s});\n } else {\n float inX = (a1 * xf + a2 * yf + a3) / projection;\n float inY = (b1 * xf + b2 * yf + b3) / projection;\n float mapX = mapCoord(inX, float(${e}));\n float mapY = mapCoord(inY, float(${t}));\n\n if (${a} == 1) {\n int coordY = int(round(mapY));\n int coordX = int(round(mapX));\n outputValue = readWithFillValue(batch, coordY, coordX,\n channel);\n } else {\n float yFloor = floor(mapY);\n float xFloor = floor(mapX);\n float yCeil = yFloor + 1.0;\n float xCeil = xFloor + 1.0;\n float valueYFloor = (xCeil - mapX) *\n readWithFillValue(batch, int(yFloor), int(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, int(yFloor), int(xCeil), channel);\n float valueYCeil = (xCeil - mapX) *\n readWithFillValue(batch, int(yCeil), int(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, int(yCeil), int(xCeil), channel);\n outputValue = (yCeil - mapY) * valueYFloor +\n (mapY - yFloor) * valueYCeil;\n }\n }\n setOutput(outputValue);\n }\n `}};function tut(r){let{inputs:t,backend:e,attrs:n}=r,{image:o,transforms:s}=t,{interpolation:i,fillMode:a,fillValue:u,outputShape:l}=n,[c,p,m,f]=o.shape,[d,h]=l!=null?l:[p,m],g=[c,d,h,f],x=new SC(p,m,i,a,u,g);return e.runWebGLProgram(x,[o,s],\"float32\")}var vG={kernelName:cl,backendName:\"webgl\",kernelFunc:tut};function eut(r){let{inputs:t,attrs:e,backend:n}=r,{axis:o}=e,{x:s}=t;Ci(s,\"unique\"),console.warn(\"WARNING: \",\"UI might be locked temporarily as data is being downloaded\");let i=n.readSync(s.dataId),{outputValues:a,outputShape:u,indices:l}=Rz(i,o,s.shape,s.dtype);return[n.makeTensorInfo(u,s.dtype,a),n.makeTensorInfo([l.length],\"int32\",l)]}var SG={kernelName:cu,backendName:\"webgl\",kernelFunc:eut};function rut(r){let{inputs:t,backend:e,attrs:n}=r,{value:o}=t,{axis:s}=n;s<0&&(s+=o.shape.length);let i=o,a=i.shape.length,u=o.shape[s],l=new Array(a-1),c=0;for(let h=0;he.disposeIntermediateTensorInfo(h)),d}var NG={kernelName:qi,backendName:\"webgl\",kernelFunc:rut};var NC=class{constructor(t,e){this.variableNames=[\"x\",\"segmentIds\"];let n=t.windowSize,o=t.batchSize,s=t.inSize,i=t.numSegments,a=i*Math.ceil(s/n);this.outputShape=[o,a];let u=\"0.0\",l=\"sumValue\",c=Math.floor(n/4)*4,p=n%4,m=`\n sumValue += dot(values, segFilter);\n `,f=\"\";s%n>0&&(f=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return initializationValue;\n }\n `);let d=\"\";s%n>0&&(d=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return -1.0;\n }\n `),this.userCode=`\n const float initializationValue = ${u};\n\n float getValue(int batch, int inIdx) {\n ${f}\n return getX(batch, inIdx);\n }\n\n float getSegmentIdAtIndex(int inIdx) {\n ${d}\n return getSegmentIds(inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = int(floor(float(outIdx) / float(\n ${i})) * float(${n}));\n int currentSeg = int(mod(float(outIdx), float(${i})));\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${c}; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n );\n\n ${m}\n }\n\n int inIdx = inOffset + ${c};\n if (${p===1}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n 0,\n 0,\n 0\n );\n\n ${m}\n } else if (${p===2}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n 0,\n 0\n );\n\n ${m}\n } else if (${p===3}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n 0\n );\n\n ${m}\n }\n setOutput(${l});\n }\n `}};function nut(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,segmentIds:s}=t,{numSegments:i}=n,a=o.shape.length,u=[],l=0,c=S.getAxesPermutation([l],a),p=o;c!=null&&(p=Pe({inputs:{x:o},backend:e,attrs:{perm:c}}),u.push(p),l=S.getInnerMostAxes(1,a)[0]);let m=S.segment_util.computeOutShape(p.shape,l,i),f=y.sizeFromShape([p.shape[l]]),d=rt({inputs:{x:p},backend:e,attrs:{shape:[-1,f]}});u.push(d);let h=lc(o.dtype),g=(I,N,E,A,D)=>{let F=I.shape[0],M=I.shape[1],V=S.segment_util.segOpComputeOptimalWindowSize(M,D),G={windowSize:V,inSize:M,batchSize:F,numSegments:D},W=new NC(G,N),q=e.compileAndRun(W,[I,E],A);if(u.push(q),q.shape[1]===D)return q;let H=H1({backend:e,attrs:{start:0,stop:D,step:1,dtype:\"float32\"}}),K=q1({inputs:{x:H},backend:e,attrs:{reps:[M/V]}});return u.push(H),u.push(K),g(q,N,K,A,D)},x=g(d,\"unsortedSegmentSum\",s,h,i),b=rt({inputs:{x},backend:e,attrs:{shape:m}}),w=b;if(c!=null){u.push(b);let I=S.getUndoAxesPermutation(c);w=Pe({inputs:{x:w},backend:e,attrs:{perm:I}})}return u.forEach(I=>e.disposeIntermediateTensorInfo(I)),w}var kG={kernelName:pu,backendName:\"webgl\",kernelFunc:nut};var out=[l3,c3,p3,m3,d3,h3,g3,x3,w3,I3,C3,v3,S3,N3,k3,T3,_3,E3,A3,D3,$3,F3,O3,M3,P3,V3,W3,U3,Jz,q3,j3,X3,Y3,Z3,J3,Q3,tB,eB,rB,nB,iB,aB,lB,uB,cB,pB,mB,fB,dB,hB,gB,xB,yB,bB,wB,IB,vB,SB,NB,kB,_B,EB,AB,DB,$B,RB,FB,OB,MB,Zz,PB,K3,LB,zB,BB,Qz,VB,GB,WB,UB,HB,qB,KB,jB,XB,YB,JB,QB,tV,eV,rV,nV,sV,aV,lV,uV,cV,pV,gV,r3,xV,yV,bV,wV,L3,IV,SV,NV,kV,TV,t3,_V,EV,AV,DV,$V,z3,mV,RV,FV,OV,o3,MV,PV,LV,zV,BV,VV,GV,WV,UV,HV,qV,KV,jV,XV,YV,ZV,R3,hV,JV,QV,tG,eG,rG,nG,oG,sG,aG,lG,cG,pG,mG,fG,dG,hG,gG,dV,i3,xG,yG,bG,wG,CG,vG,a3,SG,NG,kG,CV];for(let r of out)rc(r);var Nt;(function(r){r[r.float32=0]=\"float32\",r[r.int32=1]=\"int32\",r[r.bool=2]=\"bool\",r[r.string=3]=\"string\",r[r.complex64=4]=\"complex64\"})(Nt||(Nt={}));var ju;(function(r){r[r.linear=0]=\"linear\",r[r.relu=1]=\"relu\",r[r.relu6=2]=\"relu6\",r[r.prelu=3]=\"prelu\",r[r.leakyrelu=4]=\"leakyrelu\",r[r.sigmoid=5]=\"sigmoid\",r[r.elu=6]=\"elu\"})(ju||(ju={}));var TG;function sut(r){TG=r.wasm.cwrap(ji,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function iut(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s,bias:i,preluActivationWeights:a}=t;if(o.dtype!==\"float32\"||s.dtype!==\"float32\")throw new Error(\"_FusedMatMul for non non-float32 tensors not yet supported.\");let{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=n,m=e.dataIdMap.get(o.dataId).id,f=e.dataIdMap.get(s.dataId).id,d=0;if(i!=null){let D=e.dataIdMap.get(i.dataId);if(D.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${D.shape.length}.`);d=D.id}let h=a==null?0:e.dataIdMap.get(a.dataId).id,g=ju[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let x=u?o.shape[2]:o.shape[1],b=l?s.shape[1]:s.shape[2],w=Ur.assertAndGetBroadcastShape(o.shape.slice(0,-2),s.shape.slice(0,-2)),I=e.makeOutput([...w,x,b],o.dtype),N=e.dataIdMap.get(I.dataId).id,E=new Uint8Array(new Int32Array(o.shape).buffer),A=new Uint8Array(new Int32Array(s.shape).buffer);return TG(m,E,o.shape.length,f,A,s.shape.length,u,l,g,d,h,p||0,N),I}var _G={kernelName:ji,backendName:\"wasm\",setupFunc:sut,kernelFunc:iut};function yt(r,t){let e;function n(s){e=s.wasm.cwrap(r,null,[\"number\",\"number\",\"number\"])}function o(s){let{backend:i,inputs:{x:a}}=s,u=i.dataIdMap.get(a.dataId).id,l=i.makeOutput(a.shape,t||a.dtype),c=i.dataIdMap.get(l.dataId).id;return y.sizeFromShape(l.shape)===0||e(u,Nt[a.dtype],c),l}return{kernelName:r,backendName:\"wasm\",setupFunc:n,kernelFunc:o}}var EG=yt(Ei);var AG=yt(Go);var DG=yt(Wo);function ee(r,t,e){let n;function o(i){n=i.wasm.cwrap(r,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\"])}function s(i){let{backend:a,inputs:u}=i,{a:l,b:c}=u,p=a.dataIdMap.get(l.dataId).id,m=a.dataIdMap.get(c.dataId).id,f=e!=null?e:l.dtype,d=S.assertAndGetBroadcastShape(l.shape,c.shape),h=a.makeOutput(d,f);if(y.sizeFromShape(d)===0)return h;let g=new Uint8Array(new Int32Array(l.shape).buffer),x=new Uint8Array(new Int32Array(c.shape).buffer),b=a.dataIdMap.get(h.dataId).id;return n(p,g,l.shape.length,m,x,c.shape.length,Nt[l.dtype],b),h}return{kernelName:r,backendName:\"wasm\",setupFunc:o,kernelFunc:s}}var aut=!0,$G=ee(no,aut);var RG;function lut(r){RG=r.wasm.cwrap(Uo,null,[\"array\",\"number\",\"number\",\"number\"])}function uut(r){let{inputs:t,backend:e}=r,n=e.makeOutput(t[0].shape,t[0].dtype);if(y.sizeFromShape(n.shape)===0)return n;let o=t.map(a=>e.dataIdMap.get(a.dataId).id),s=new Uint8Array(new Int32Array(o).buffer),i=e.dataIdMap.get(n.dataId).id;return RG(s,o.length,Nt[n.dtype],i),n}var FG={kernelName:Uo,backendName:\"wasm\",setupFunc:lut,kernelFunc:uut};function yp(r){let{inputs:{x:t},backend:e}=r;if(t.dtype===\"string\")return ir(e.readSync(t.dataId),t.shape,t.dtype);let n=e.makeOutput(t.shape,t.dtype),o=e.typedArrayFromHeap(t);return e.typedArrayFromHeap(n).set(o),n}var OG={kernelName:go,backendName:\"wasm\",kernelFunc:yp};var MG;function cut(r){MG=r.wasm.cwrap(so,null,[\"number\",\"array\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function mo(r){let{inputs:t,backend:e,attrs:n}=r,[o,s]=mut(t.x.shape,n.perm),i=!0;for(let d=0;d=o&&(s===-1||n[s]>n[i])&&(s=i);n[s]=o}return[e,n]}var PG={kernelName:so,backendName:\"wasm\",kernelFunc:mo,setupFunc:cut};function Cn(r,t,e){let n=r.shape,o=r.shape.length,s=y.parseAxisParam(t,n),i=s,a=S.getAxesPermutation(i,o),u=null,l=!1;if(a!=null){let c=new Array(o);for(let f=0;f`new shape: ${i}, old shape: ${n.shape}. 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aW={kernelName:Ri,backendName:\"wasm\",kernelFunc:Aut};var lW;function Dut(r){lW=r.wasm.cwrap(Da,null,[\"number\",\"number\",\"boolean\",\"number\",\"number\",\"number\"])}function $ut(r){let{backend:t,inputs:e,attrs:n}=r,{x:o,weights:s}=e,{size:i}=n,a=s.shape.reduce((p,m)=>p*m,1)!==0,u=o.shape.length===1?[i]:[o.shape[0],i],l=t.makeOutput(u,s.dtype);function c(p){return t.dataIdMap.get(p.dataId).id}return lW(c(o),i,a,c(s),Nt[s.dtype],c(l)),l}var uW={kernelName:Da,backendName:\"wasm\",setupFunc:Dut,kernelFunc:$ut};var Rut=!0,cW=ee($a,Rut);function Fut(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.typedArrayFromHeap(n),i=e.typedArrayFromHeap(o),a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeOutput([a.length],\"int32\",void 0,new Int32Array(a))}var pW={kernelName:ql,backendName:\"wasm\",kernelFunc:Fut};function Fn(r){let{inputs:{x:t},attrs:{dtype:e},backend:n}=r,o=n.makeOutput(t.shape,e),s=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(o).set(s),o}var mW={kernelName:fo,backendName:\"wasm\",kernelFunc:Fn};var fW=yt(Jo);var dW;function Out(r){dW=r.wasm.cwrap(ho,null,[\"number\",\"number\",\"number\",\"number\"])}function Mut(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{clipValueMin:s,clipValueMax:i}=n,a=e.dataIdMap.get(o.dataId).id,u=e.makeOutput(o.shape,o.dtype),l=e.dataIdMap.get(u.dataId).id;return dW(a,s,i,l),u}var hW={kernelName:ho,backendName:\"wasm\",setupFunc:Out,kernelFunc:Mut};function K1(r){let{inputs:t,backend:e}=r,n=y.parseAxisParam(r.attrs.axis,t[0].shape)[0],o=t.map(f=>f.shape);S.assertParamsConsistent(o,n);let s=S.computeOutShape(t.map(f=>f.shape),n),i=t.filter(f=>y.sizeFromShape(f.shape)>0);if(i.length===1)return yp({inputs:{x:i[0]},backend:e});let a=e.makeOutput(s,t[0].dtype);if(y.sizeFromShape(s)===0)return a;if(i[0].dtype===\"string\"){let f=i.map(w=>{let N=[-1,y.sizeFromShape(w.shape.slice(n))];return 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x=S.getUndoAxesPermutation(l);g=mo({inputs:{x:m},attrs:{perm:x},backend:e}),e.disposeData(c.dataId),e.disposeData(m.dataId)}return g}var FW={kernelName:os,backendName:\"wasm\",setupFunc:Zut,kernelFunc:Jut};var OW;function Qut(r){OW=r.wasm.cwrap(\"DenseBincount\",null,[\"number\",\"array\",\"number\",\"number\",\"boolean\",\"number\",\"number\",\"boolean\",\"number\"])}function tct(r){let{backend:t,inputs:e,attrs:n}=r,{x:o,weights:s}=e,{size:i,binaryOutput:a}=n,u=s.shape.reduce((m,f)=>m*f,1)!==0,l=o.shape.length===1?[i]:[o.shape[0],i],c=t.makeOutput(l,s.dtype);function p(m){return t.dataIdMap.get(m.dataId).id}return OW(p(o),new Uint8Array(new Int32Array(o.shape).buffer),o.shape.length,i,u,p(s),Nt[s.dtype],a,p(c)),c}var MW={kernelName:jl,backendName:\"wasm\",setupFunc:Qut,kernelFunc:tct};var PW;function ect(r){PW=r.wasm.cwrap(Pa,null,[\"number\",\"number\",\"number\",\"array\",\"number\",\"array\",\"array\",\"number\",\"number\"])}function rct(r){let{backend:t,inputs:e,attrs:n}=r,{x:o}=e,{blockSize:s,dataFormat:i}=n,a=o.shape[0],u=i===\"NHWC\"?o.shape[1]:o.shape[2],l=i===\"NHWC\"?o.shape[2]:o.shape[3],c=i===\"NHWC\"?o.shape[3]:o.shape[1],p=u*s,m=l*s,f=c/(s*s),d=i===\"NHWC\"?[a,p,m,f]:[a,f,p,m],h=t.makeOutput(d,\"float32\"),x=t.dataIdMap.get(o.dataId).id,b=new Uint8Array(new Int32Array(y.computeStrides(o.shape)).buffer),w=new Uint8Array(new Int32Array(d).buffer),I=new Uint8Array(new Int32Array(y.computeStrides(d)).buffer),N=t.dataIdMap.get(h.dataId).id;return PW(x,s,i===\"NHWC\"?1:0,b,o.shape.length-1,w,I,d.length,N),h}var LW={kernelName:Pa,backendName:\"wasm\",setupFunc:ect,kernelFunc:rct};var zW;function nct(r){zW=r.wasm.cwrap(ss,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function oct(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s}=t,i=n.dataIdMap.get(o.dataId).id,a=n.dataIdMap.get(s.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p}=e,m=l==null?[1,1]:l,f=S.computeConv2DInfo(o.shape,s.shape,u,m,c,p,!0),d=f.filterHeight,h=f.filterWidth,g=f.padInfo.top,x=f.padInfo.right,b=f.padInfo.bottom,w=f.padInfo.left,I=f.dilationHeight,N=f.dilationWidth,E=f.strideHeight,A=f.strideWidth,D=f.inChannels,F=f.outChannels,M=f.padInfo.type===\"SAME\"?1:0;if(f.dataFormat!==\"channelsLast\")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`);let V=n.makeOutput(f.outShape,\"float32\"),G=n.dataIdMap.get(V.dataId).id;return zW(i,o.shape[0],o.shape[1],o.shape[2],a,d,h,g,x,b,w,M,I,N,E,A,D,F,G),V}var BW={kernelName:ss,backendName:\"wasm\",setupFunc:nct,kernelFunc:oct};var VW;function sct(r){VW=r.wasm.cwrap(\"Diag\",null,[\"number\",\"number\",\"number\",\"number\"])}function ict(r){let{inputs:t,backend:e}=r,{x:n}=t,o=y.sizeFromShape(n.shape),s=e.makeOutput([...n.shape,...n.shape],n.dtype);return VW(e.dataIdMap.get(n.dataId).id,Nt[n.dtype],o,e.dataIdMap.get(s.dataId).id),s}var GW={kernelName:Xl,backendName:\"wasm\",setupFunc:sct,kernelFunc:ict};var WW;function act(r){WW=r.wasm.cwrap(is,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function lct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n;if(o.dtype!==s.dtype)throw new Error(`Dilation2D error: x must have the same dtype as filter. 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Got ${o.dtype}, ${s.dtype}, and ${i.dtype}`);let c=S.computeDilation2DInfo(o.shape,s.shape,a,u,\"NHWC\",l),p=e.makeOutput(s.shape,s.dtype);return HW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,e.dataIdMap.get(p.dataId).id,Nt[o.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),p}var qW={kernelName:Zl,backendName:\"wasm\",setupFunc:uct,kernelFunc:cct};var KW;function pct(r){KW=r.wasm.cwrap(Yl,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function mct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,dy:i}=t,{strides:a,pad:u,dilations:l}=n;if(o.dtype!==s.dtype||o.dtype!==i.dtype)throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${o.dtype}, ${s.dtype}, and ${i.dtype}`);let c=S.computeDilation2DInfo(o.shape,s.shape,a,u,\"NHWC\",l),p=e.makeOutput(o.shape,o.dtype);return KW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,e.dataIdMap.get(p.dataId).id,Nt[o.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),p}var jW={kernelName:Yl,backendName:\"wasm\",setupFunc:pct,kernelFunc:mct};var XW=yt(ls);var YW;function fct(r){YW=r.wasm.cwrap(La,null,[\"number\",\"number\",\"number\"])}function dct(r){let{inputs:t,backend:e}=r,{dy:n,y:o}=t,s=e.makeOutput(o.shape,\"float32\"),i=a=>e.dataIdMap.get(a.dataId).id;return YW(i(o),i(n),i(s)),s}var ZW={kernelName:La,backendName:\"wasm\",setupFunc:fct,kernelFunc:dct};var hct=!1,JW=ee(za,hct,\"bool\");var QW=yt(us);var tU=yt(cs,\"float32\");function TC(r){let{inputs:t,attrs:e,backend:n}=r,{input:o}=t,{dim:s}=e,i=o.shape.length,a=o.shape.slice(),u=s;return s<0&&(y.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+s+1),a.splice(u,0,1),mr({inputs:{x:o},backend:n,attrs:{shape:a}})}var eU={kernelName:Oi,backendName:\"wasm\",kernelFunc:TC};var rU=yt(ps,\"float32\");function X1(r){let{attrs:{shape:t,value:e},backend:n}=r,{attrs:{dtype:o}}=r;o=o||y.inferDtype(e);let s=n.makeOutput(t,o);return n.typedArrayFromHeap(s).fill(e),s}var nU={kernelName:Jl,backendName:\"wasm\",kernelFunc:X1};var oU;function gct(r){oU=r.wasm.cwrap(Ba,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function xct(r){let{inputs:t,backend:e}=r,{image:n}=t,o=e.makeOutput(n.shape,n.dtype),s=e.dataIdMap.get(n.dataId).id,i=e.dataIdMap.get(o.dataId).id,[a,u,l,c]=n.shape;return oU(s,a,u,l,c,i),o}var sU={kernelName:Ba,backendName:\"wasm\",kernelFunc:xct,setupFunc:gct};var iU=yt(ms);var yct=!1,aU=ee(fs,yct);var lU;function bct(r){lU=r.wasm.cwrap(ds,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function wct(r){let{backend:t,inputs:e,attrs:n}=r,{varianceEpsilon:o}=n,{x:s,mean:i,variance:a,offset:u,scale:l}=e,c=t.dataIdMap.get(s.dataId).id,p=t.dataIdMap.get(i.dataId).id,m=t.dataIdMap.get(a.dataId).id,f=u!=null?t.dataIdMap.get(u.dataId).id:0,d=l!=null?t.dataIdMap.get(l.dataId).id:0,h=t.makeOutput(s.shape,s.dtype);if(y.sizeFromShape(s.shape)===0)return h;let g=t.dataIdMap.get(h.dataId).id;return lU(c,p,m,f,d,o,g),h}var uU={kernelName:ds,backendName:\"wasm\",setupFunc:bct,kernelFunc:wct};var cU;function Ict(r){cU=r.wasm.cwrap(Xi,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 Cct(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=e,h=S.computeConv2DInfo(o.shape,s.shape,u,c,l,m),g=ju[f];if(g==null)throw new Error(`${f} activation not yet supported for FusedConv2D in the wasm backend.`);let x=n.dataIdMap.get(o.dataId).id,b=n.dataIdMap.get(s.dataId).id,w=h.outChannels,I=0;if(i!=null){let ot=n.dataIdMap.get(i.dataId);if(ot.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ot.shape.length}.`);if(ot.shape[0]!==w)throw new Error(`FusedConv2D bias shape (${ot.shape}) does not match the number of output channels (${w})`);I=ot.id}let N=h.filterHeight,E=h.filterWidth,A=h.padInfo.top,D=h.padInfo.right,F=h.padInfo.bottom,M=h.padInfo.left,V=h.dilationHeight,G=h.dilationWidth,W=h.strideHeight,q=h.strideWidth,H=h.inChannels,K=h.padInfo.type===\"SAME\"?1:0,Y=h.batchSize,Z=h.inHeight,et=h.inWidth;if(p!==\"NHWC\")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let nt=n.makeOutput(h.outShape,\"float32\"),st=n.dataIdMap.get(nt.dataId).id,lt=a==null?0:n.dataIdMap.get(a.dataId).id;return cU(x,Y,Z,et,b,N,E,I,A,D,F,M,K,V,G,W,q,H,w,g,lt,d||0,st),nt}var pU={kernelName:Xi,backendName:\"wasm\",setupFunc:Ict,kernelFunc:Cct};var mU;function vct(r){mU=r.wasm.cwrap(Yi,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 Sct(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=e,h=S.computeConv2DInfo(o.shape,s.shape,u,c,l,m,!0),g=ju[f];if(g==null)throw new Error(`${f} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let x=n.dataIdMap.get(o.dataId).id,b=n.dataIdMap.get(s.dataId).id,w=h.outChannels,I=0;if(i!=null){let ot=n.dataIdMap.get(i.dataId);if(ot.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ot.shape.length}.`);if(ot.shape[0]!==w)throw new Error(`FusedDepthwiseConv2D bias shape (${ot.shape}) does not match the number of output channels (${w})`);I=ot.id}let N=h.filterHeight,E=h.filterWidth,A=h.padInfo.top,D=h.padInfo.right,F=h.padInfo.bottom,M=h.padInfo.left,V=h.dilationHeight,G=h.dilationWidth,W=h.strideHeight,q=h.strideWidth,H=h.inChannels,K=h.padInfo.type===\"SAME\"?1:0,Y=h.batchSize,Z=h.inHeight,et=h.inWidth;if(p!==\"NHWC\")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let nt=n.makeOutput(h.outShape,\"float32\"),st=n.dataIdMap.get(nt.dataId).id,lt=a==null?0:n.dataIdMap.get(a.dataId).id;return mU(x,Y,Z,et,b,N,E,I,A,D,F,M,K,V,G,W,q,H,w,g,lt,d||0,st),nt}var fU={kernelName:Yi,backendName:\"wasm\",setupFunc:vct,kernelFunc:Sct};var dU;function Nct(r){dU=r.wasm.cwrap(Va,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function kct(r){let{backend:t,inputs:e}=r,{params:n,indices:o}=e,[s,i,a,u]=Ey.prepareAndValidate(n,o),l=t.makeOutput(s,n.dtype);if(i===0)return l;let c=o.shape,p=c[c.length-1],f=t.dataIdMap.get(n.dataId).id,h=t.dataIdMap.get(o.dataId).id,g=new Uint8Array(new Int32Array(u).buffer),x=t.dataIdMap.get(l.dataId).id;return dU(f,Nt[n.dtype],h,i,p,a,g,x),l}var hU={kernelName:Va,backendName:\"wasm\",setupFunc:Nct,kernelFunc:kct};var gU;function Tct(r){gU=r.wasm.cwrap(\"Gather\",null,[\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function _ct(r){let{backend:t,inputs:e,attrs:n}=r,{x:o,indices:s}=e,{axis:i,batchDims:a}=n,u=y.parseAxisParam(i,o.shape)[0],l=t.readSync(s.dataId),c=o.shape[u];for(let F=0;F=0,()=>`GatherV2: the index value ${M} is not in [0, ${c-1}]`)}let p=S.segment_util.collectGatherOpShapeInfo(o,s,u,a),m=mr({inputs:{x:o},attrs:{shape:[p.batchSize,p.outerSize,p.dimSize,p.sliceSize]},backend:t}),f=y.sizeFromShape(s.shape),d=mr({inputs:{x:s},attrs:{shape:[p.batchSize,f/p.batchSize]},backend:t}),h=[p.batchSize,p.outerSize,f/p.batchSize,p.sliceSize],g=t.makeOutput(h,o.dtype);if(y.sizeFromShape(o.shape)===0)return g;let x=m.shape.length-1,w=t.dataIdMap.get(m.dataId).id,N=t.dataIdMap.get(d.dataId).id,E=t.dataIdMap.get(g.dataId).id,A=new Uint8Array(new Int32Array(y.computeStrides(m.shape)).buffer),D=new Uint8Array(new Int32Array(y.computeStrides(h)).buffer);return gU(w,Nt[o.dtype],A,x,N,p.batchSize,D,E),t.disposeData(m.dataId),t.disposeData(d.dataId),g.shape=p.outputShape,g}var xU={kernelName:Mi,backendName:\"wasm\",setupFunc:Tct,kernelFunc:_ct};var Ect=!1,yU=ee(Ga,Ect,\"bool\");var Act=!1,bU=ee(hs,Act,\"bool\");var wU=yt(gs,\"bool\");var IU=yt(xs,\"bool\");var CU=yt(ys,\"bool\");var vU;function Dct(r){vU=r.wasm.cwrap(bs,null,[\"number\",\"number\",\"number\",\"number\"])}function $ct(r){let{inputs:{x:t},attrs:{alpha:e},backend:n}=r,o=n.dataIdMap.get(t.dataId).id,s=n.makeOutput(t.shape,\"float32\");if(y.sizeFromShape(t.shape)!==0){let i=n.dataIdMap.get(s.dataId).id;vU(o,Nt[t.dtype],e,i)}return s}var SU={kernelName:bs,backendName:\"wasm\",setupFunc:Dct,kernelFunc:$ct};var Rct=!1,NU=ee(Wa,Rct,\"bool\");var Fct=!1,kU=ee(Ua,Fct,\"bool\");var TU;function Oct(r){TU=r.wasm.cwrap(Ha,null,[\"number\",\"number\",\"number\",\"number\"])}function Mct(r){let{attrs:t,backend:e}=r,{start:n,stop:o,num:s}=t,i=Math.floor(s),a=e.makeOutput([i],\"float32\");return TU(e.dataIdMap.get(a.dataId).id,n,o,i),a}var _U={kernelName:Ha,backendName:\"wasm\",setupFunc:Oct,kernelFunc:Mct};var EU=yt(ws);var AU=yt(Is);var Pct=!1,DU=ee(qa,Pct,\"bool\");var $U=yt(Ka);var Lct=!1,RU=ee(ja,Lct,\"bool\");var zct=!1,FU=ee(T_,zct,\"bool\");var OU;function Bct(r){OU=r.wasm.cwrap(Cs,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Vct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n;if(o.dtype!==\"float32\")throw new Error(\"LRN error: x must have dtype float32\");let l=e.makeOutput(o.shape,o.dtype);return OU(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(l.dataId).id,o.shape[3],s,i,a,u),l}var MU={kernelName:Cs,backendName:\"wasm\",setupFunc:Bct,kernelFunc:Vct};var PU;function Gct(r){PU=r.wasm.cwrap(Xa,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Wct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,y:s,dy:i}=t,{depthRadius:a,bias:u,alpha:l,beta:c}=n;if(o.dtype!==\"float32\"||s.dtype!==\"float32\"||i.dtype!==\"float32\")throw new Error(\"LRNGrad error: x, y, and dy must have dtype float32\");let p=e.makeOutput(o.shape,o.dtype);return PU(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,e.dataIdMap.get(p.dataId).id,i.shape[3],a,u,l,c),p}var LU={kernelName:Xa,backendName:\"wasm\",setupFunc:Gct,kernelFunc:Wct};var zU;function Uct(r){zU=r.wasm.cwrap(vs,null,[\"number\",\"number\",\"number\",\"number\"])}function Hct(r){let{backend:t,inputs:e,attrs:n}=r,{reductionIndices:o,keepDims:s}=n,{x:i}=e,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=Cn(i,o,t);if(f){let w=t.dataIdMap.get(c.dataId).id;l=c,u=w}let d=l.shape.length;S.assertAxesAreInnerMostDims(\"max\",p,d);let[h,g]=S.computeOutAndReduceShapes(l.shape,p),x=y.sizeFromShape(g),b=t.makeOutput(h,i.dtype);if(y.sizeFromShape(l.shape)!==0){let w=t.dataIdMap.get(b.dataId).id;zU(u,Nt[i.dtype],x,w)}if(f&&t.disposeData(c.dataId),s){let w=S.expandShapeToKeepDim(b.shape,m);b.shape=w}return b}var BU={kernelName:vs,backendName:\"wasm\",setupFunc:Uct,kernelFunc:Hct};var qct=!1,VU=ee(Ss,qct);var GU;function Kct(r){GU=r.wasm.cwrap(Ns,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function jct(r){let{inputs:t,attrs:e,backend:n}=r,o=t.x,s=n.dataIdMap.get(o.dataId).id;y.assert(o.dtype===\"float32\",()=>`Error in MaxPool: only float32 input is supported. Got ${o.dtype}.`);let{filterSize:i,strides:a,pad:u,dimRoundingMode:l}=e,c=S.computePool2DInfo(o.shape,i,a,1,u,l),p=c.filterHeight,m=c.filterWidth,f=c.padInfo.top,d=c.padInfo.right,h=c.padInfo.bottom,g=c.padInfo.left,x=c.dilationHeight,b=c.dilationWidth,w=c.strideHeight,I=c.strideWidth,N=c.inChannels,E=c.outChannels;if(c.dataFormat!==\"channelsLast\")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let A=n.makeOutput(c.outShape,\"float32\"),D=n.dataIdMap.get(A.dataId).id;return GU(s,o.shape[0],o.shape[1],o.shape[2],p,m,f,d,h,g,x,b,w,I,N,E,D),A}var WU={kernelName:Ns,backendName:\"wasm\",setupFunc:Kct,kernelFunc:jct};var UU;function Xct(r){UU=r.wasm.cwrap(\"MaxPool3D\",null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Yct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dimRoundingMode:u,dataFormat:l}=n,c=S.computePool3DInfo(o.shape,s,i,1,a,u,l),p=e.makeOutput(c.outShape,o.dtype);return UU(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(p.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),p}var HU={kernelName:Pi,backendName:\"wasm\",setupFunc:Xct,kernelFunc:Yct};var qU;function Zct(r){qU=r.wasm.cwrap(\"MaxPool3DGrad\",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 Jct(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,{filterSize:i,strides:a,pad:u,dimRoundingMode:l}=n,c=S.computePool3DInfo(s.shape,i,a,1,u,l),p=e.makeOutput(s.shape,s.dtype);return qU(e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(p.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),p}var KU={kernelName:tu,backendName:\"wasm\",setupFunc:Zct,kernelFunc:Jct};var jU;function Qct(r){jU=r.wasm.cwrap(\"MaxPoolGrad\",null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function tpt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,{filterSize:i,strides:a,pad:u,dimRoundingMode:l}=n,c=S.computePool2DInfo(s.shape,i,a,1,u,l),p=e.makeOutput(s.shape,s.dtype);return jU(e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(p.dataId).id,c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.top,c.padInfo.left),p}var XU={kernelName:Ql,backendName:\"wasm\",setupFunc:Qct,kernelFunc:tpt};var YU;function ept(r){YU=r.wasm.cwrap(\"MaxPoolWithArgmax\",null,[\"number\",\"number\",\"number\",\"number\",\"boolean\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function rpt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,includeBatchInIndex:u}=n;y.assert(o.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.shape.length}.`);let l=[1,1];y.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,[1,1],a),p=e.makeOutput(c.outShape,o.dtype),m=e.makeOutput(c.outShape,\"int32\");return YU(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(p.dataId).id,e.dataIdMap.get(m.dataId).id,Nt[o.dtype],u,c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.top,c.padInfo.left),[p,m]}var ZU={kernelName:eu,backendName:\"wasm\",setupFunc:ept,kernelFunc:rpt};var JU;function npt(r){JU=r.wasm.cwrap(ks,null,[\"number, number, number\"])}function opt(r){let{backend:t,inputs:e,attrs:n}=r,{axis:o,keepDims:s}=n,{x:i}=e,a=t.dataIdMap.get(i.dataId).id,u=a,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=Cn(i,o,t),d=p;if(f){let I=t.dataIdMap.get(c.dataId).id;I!==a&&(l=c,u=I,d=S.getInnerMostAxes(d.length,l.shape.length))}S.assertAxesAreInnerMostDims(\"mean\",d,l.shape.length);let[h,g]=S.computeOutAndReduceShapes(l.shape,d),x=y.sizeFromShape(g),b=l;l.dtype!==\"float32\"&&(b=Fn({backend:t,inputs:{x:l},attrs:{dtype:\"float32\"}}),u=t.dataIdMap.get(b.dataId).id);let w=t.makeOutput(h,\"float32\");if(y.sizeFromShape(l.shape)!==0){let I=t.dataIdMap.get(w.dataId).id;JU(u,x,I)}if(f&&t.disposeData(c.dataId),s){let I=S.expandShapeToKeepDim(w.shape,m);w.shape=I}return l.dtype!==\"float32\"&&t.disposeData(b.dataId),w}var QU={kernelName:ks,backendName:\"wasm\",setupFunc:npt,kernelFunc:opt};var t4;function spt(r){t4=r.wasm.cwrap(Ts,null,[\"number\",\"number\",\"number\",\"number\"])}function ipt(r){let{backend:t,inputs:e,attrs:n}=r,{axis:o,keepDims:s}=n,{x:i}=e,a=t.dataIdMap.get(i.dataId).id,u=a,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=Cn(i,o,t);if(f){let 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Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}a_=t}var eq=-1,o_=-1;function zmt(r){eq=r}function Bmt(){if(o_===-1)throw new Error(\"WASM backend not initialized.\");return o_}var Vmt=\"4.16.0\";var Gmt=2;Xp(\"wasm\",async()=>{let{wasm:r}=await tq();return new bg(r)},Gmt);var rq=\"4.16.0\",Wmt=\"4.16.0\",Umt=\"4.16.0\",Hmt=\"4.16.0\",qmt=\"4.16.0\",Kmt={tfjs:rq,\"tfjs-core\":rq,\"tfjs-converter\":Wmt,\"tfjs-backend-cpu\":Umt,\"tfjs-backend-webgl\":Hmt,\"tfjs-backend-wasm\":qmt};export{Ei as Abs,Go as Acos,Wo as Acosh,vc as AdadeltaOptimizer,Sc as AdagradOptimizer,Nc as AdamOptimizer,kc as AdamaxOptimizer,no as Add,Uo as AddN,Ea as All,Aa as Any,Ai as ArgMax,Di as ArgMin,Ho as Asin,qo as Asinh,Ko as Atan,Xo as Atan2,jo as Atanh,Yo as AvgPool,$i as AvgPool3D,Hl as AvgPool3DGrad,Ul as AvgPoolGrad,bg as BackendWasm,Zo as BatchMatMul,Ri as BatchToSpaceND,Da as Bincount,$a as BitwiseAnd,ql as BroadcastArgs,k_ as BroadcastTo,Pb as Callback,Xy as CallbackList,fo as Cast,Jo as Ceil,ho as ClipByValue,Ap as Complex,Kl as ComplexAbs,Fi as Concat,Qo as Conv2D,Dp as Conv2DBackpropFilter,ts as Conv2DBackpropInput,es as Conv3D,Ra as Conv3DBackpropFilterV2,Fa as Conv3DBackpropInputV2,rs as Cos,ns as Cosh,Ma as CropAndResize,Oa as Cumprod,os as Cumsum,Zy as CustomCallback,Ta as DataStorage,jl as DenseBincount,Pa as DepthToSpace,ss as DepthwiseConv2dNative,$p as DepthwiseConv2dNativeBackpropFilter,Rp as DepthwiseConv2dNativeBackpropInput,Xl as Diag,is as Dilation2D,Zl as Dilation2DBackpropFilter,Yl as Dilation2DBackpropInput,Jd as Draw,y0 as ENV,Lb as EarlyStopping,Fp as Einsum,ls as Elu,La as EluGrad,Yd as Environment,za as Equal,us as Erf,cs as Exp,Oi as ExpandDims,ps as Expm1,Op as FFT,Jl as Fill,Ba as FlipLeftRight,ms as Floor,fs as FloorDiv,Qd as FromPixels,ds as FusedBatchNorm,Xi as FusedConv2D,Yi as FusedDepthwiseConv2D,pp as GPGPUContext,Va as GatherNd,Mi as GatherV2,Hh as GraphModel,Ga as Greater,hs as GreaterEqual,Yy as History,Mp as IFFT,go as Identity,Pp as Imag,Ie as InputSpec,gs as IsFinite,xs as IsInf,ys as IsNan,Bo as KernelBackend,Cs as LRN,Xa as LRNGrad,kh as LayerVariable,Un as LayersModel,bs as LeakyRelu,Wa as Less,Ua as LessEqual,Ha as LinSpace,ws as Log,Is as Log1p,__ as LogSoftmax,qa as LogicalAnd,Ka as LogicalNot,ja as LogicalOr,T_ as LogicalXor,Qmt as LowerBound,cd as MathBackendCPU,Ad as MathBackendWebGL,tft as MatrixBandPart,vs as Max,Ns as MaxPool,Pi as MaxPool3D,tu as MaxPool3DGrad,Ql as MaxPoolGrad,eu as MaxPoolWithArgmax,Ss as Maximum,ks as Mean,Ts as Min,_s as Minimum,Es as MirrorPad,As as Mod,Tc as MomentumOptimizer,Ya as Multinomial,Ds as Multiply,Li as Neg,Ja as NonMaxSuppressionV3,Qa as NonMaxSuppressionV4,tl as NonMaxSuppressionV5,Za as NotEqual,z0 as OP_SCOPE_SUFFIX,$s as OneHot,zi as OnesLike,qr as Optimizer,wh as OptimizerConstructors,Bi as Pack,Rs as PadV2,eft as Pool,Fs as Pow,Os as Prelu,Ms as Prod,_c as RMSPropOptimizer,po as RNN,Lp as RaggedGather,zp as RaggedRange,Bp as RaggedTensorToTensor,ru as Range,E0 as Rank,Vp as Real,as as RealDiv,Ps as Reciprocal,Je as Reduction,Ls as Relu,Vs as Relu6,Vi as Reshape,Bs as ResizeBilinear,rl as ResizeBilinearGrad,zs as ResizeNearestNeighbor,el as ResizeNearestNeighborGrad,Gs as Reverse,pl as RotateWithOffset,Ws as Round,Us as Rsqrt,Il as SGDOptimizer,nl as ScatterNd,sl as SearchSorted,Gi as Select,Hs as Selu,Wc as Sequential,Xs as Sigmoid,js as Sign,qs as Sin,Ks as Sinh,Wi as Slice,Qs as Softmax,Ys as Softplus,Ui as SpaceToBatchND,nu as SparseFillEmptyRows,il as SparseReshape,ou as SparseSegmentMean,su as SparseSegmentSum,al as SparseToDense,Hi as SplitV,Zs as Sqrt,iu as Square,ti as SquaredDifference,ec as StaticRegexReplace,xo as Step,ll as StridedSlice,au as StringNGrams,lu as StringSplit,uu as StringToHashBucketFast,ei as Sub,Js as Sum,en as SymbolicTensor,ri as Tan,ni as Tanh,Lt as Tensor,le as TensorBuffer,ol as TensorScatterUpdate,oo as Tile,ul as TopK,cl as Transform,so as Transpose,cu as Unique,qi as Unpack,pu as UnsortedSegmentSum,rft as UpperBound,ml as Variable,Ki as ZerosLike,ji as _FusedMatMul,_e as abs,mx as acos,fx as acosh,X as add,TE as addN,tm as all,cc as any,ea as argMax,dx as argMin,hx as asin,gx as asinh,xx as atan,yx as atan2,bx as atanh,xu as avgPool,wx as avgPool3d,ox as backend,S as backend_util,AE as basicLSTMCell,oa as batchNorm,Ix as batchNorm2d,Cx as batchNorm3d,vx as batchNorm4d,yu as batchToSpaceND,Sx as bincount,$E as bitwiseAnd,W5 as booleanMaskAsync,RE as broadcastArgs,sa as broadcastTo,Ur as broadcast_util,_y as browser,wt as buffer,fQ as callbacks,J as cast,Nx as ceil,vr as clipByValue,ln as clone,Sn as complex,ie as concat,kx as concat1d,Tx as concat2d,_x as concat3d,Ex as concat4d,wR as constraints,rm as conv1d,Nn as conv2d,om as conv2dTranspose,Ax as conv3d,$x as conv3dTranspose,uft as copyRegisteredKernels,bu as cos,sm as cosh,gh as cosineWindow,mc as cumprod,im as cumsum,pn as customGrad,oO as data,ph as denseBincount,B0 as deprecationWarn,Rx as depthToSpace,ia as depthwiseConv2d,xQ as deregisterOp,du as device_util,FE as diag,Fx as dilation2d,wdt as disableDeprecationWarnings,Tt as dispose,Idt as disposeVariables,ct as div,Ox as divNoNan,Mx as dot,mN as dropout,wu as einsum,aa as elu,bdt as enableDebugMode,ydt as enableProdMode,fN as enclosingPowerOfTwo,Bn as engine,ME as ensureShape,L as env,$r as equal,Px as erf,Lx as euclideanNorm,Ke as exp,je as expandDims,zx as expm1,fc as eye,Au as fft,Co as fill,kdt as findBackend,Tdt as findBackendFactory,la as floor,Qp as floorDiv,Yz as forceHalfFloat,Ru as fused,ua as gather,Q5 as gatherND,Ey as gather_util,rE as getBackend,I0 as getGradient,Wp as getKernel,Xg as getKernelsForBackend,Bmt as getThreadsCount,S1 as gpgpu_util,q6 as grad,K6 as grads,Re as greater,cn as greaterEqual,wl as ifft,Iu as imag,fn as image,r8 as inTopKAsync,IR as initializers,YN as input,Mr as io,ym as irfft,Bx as isFinite,Vx as isInf,Gx as isNaN,De as keep,Kr as kernel_impls,eF as layers,Cu as leakyRelu,yl as less,Vn as lessEqual,hN as linalg,zE as linspace,ftt as loadGraphModel,dtt as loadGraphModelSync,VR as loadLayersModel,Wx as localResponseNormalization,Nr as log,vu as log1p,qx as logSigmoid,am as logSoftmax,Su as logSumExp,Fr as logicalAnd,Nu as logicalNot,lm as logicalOr,Kx as logicalXor,oY as losses,BE as lowerBound,Bt as matMul,R2 as math,Sr as max,ku as maxPool,Xx as maxPool3d,VE as maxPoolWithArgmax,kn as maximum,Ne as mean,ah as memory,GE as meshgrid,rF as metrics,gl as min,lo as minimum,Yx as mirrorPad,Zx as mod,fJ as model,nF as models,dc as moments,q5 as movingAverage,$ as mul,WE as multiRNNCell,UE as multinomial,Ut as neg,Ih as nextFrame,xl as norm,ui as notEqual,ca as oneHot,ar as ones,wr as onesLike,k as op,HE as outerProduct,mn as pad,qE as pad1d,KE as pad2d,jE as pad3d,XE as pad4d,Jx as pool,un as pow,_u as prelu,px as print,Qx as prod,Cdt as profile,YE as raggedGather,ZE as raggedRange,JE as raggedTensorToTensor,QE as rand,wA as randomGamma,xc as randomNormal,IA as randomStandardNormal,Gn as randomUniform,CA as randomUniformInt,pa as range,Sdt as ready,bl as real,sy as reciprocal,Xp as registerBackend,hJ as registerCallbackConstructor,A_ as registerGradient,rc as registerKernel,gQ as registerOp,oF as regularizers,Or as relu,um as relu6,Ndt as removeBackend,R as reshape,dr as reverse,vA as reverse1d,SA as reverse2d,NA as reverse3d,kA as reverse4d,Du as rfft,cm as round,pm as rsqrt,pt as scalar,j5 as scatterND,$u as scatter_util,fh as searchSorted,mm as selu,fm as separableConv2d,dJ as sequential,Q as serialization,$K as setBackend,_dt as setPlatform,zmt as setThreadsCount,Pmt as setWasmPath,Lmt as setWasmPaths,LT as setWebGLContext,TA as setdiff1dAsync,Tw as shared,Qr as sigmoid,iy as sign,nY as signal,dm as sin,hm as sinh,Ot as slice,gm as slice1d,hh as slice2d,xm as slice3d,yc as slice4d,Be as slice_util,Eu as softmax,li as softplus,Tu as spaceToBatchND,sY as sparse,Z5 as sparseToDense,rY as spectral,hr as split,Se as sqrt,Wt as square,bm as squaredDifference,Wn as squeeze,Fe as stack,So as step,ay as stridedSlice,iY as string,at as sub,mt as sum,lc as sumOutType,ly as tan,na as tanh,ir as tensor,Oe as tensor1d,ci as tensor2d,uy as tensor3d,_A as tensor4d,EA as tensor5d,AA as tensor6d,$A as tensorScatterUpdate,Io as tensor_util,bA as test_util,B as tidy,Rr as tile,vdt as time,cy as topk,Ac as train,Vt as transpose,Im as truncatedNormal,py as unique,lft as unregisterGradient,aft as unregisterKernel,Cm as unsortedSegmentSum,gr as unstack,ur as upcastType,RA as upperBound,y as util,j6 as valueAndGrad,X6 as valueAndGrads,my as variable,Ux as variableGrads,Kmt as version,LF as version_converter,K2 as version_core,UO as version_cpu,Km as version_layers,Vmt as version_wasm,Xz as version_webgl,N$e as webgl,Id as webgl_util,we as where,dy as whereAsync,ke as zeros,vt as zerosLike};\n", "export * from './drawContour';\nexport * from './drawDetections';\nexport * from './drawFaceExpressions';\nexport * from './DrawBox';\nexport * from './DrawFaceLandmarks';\nexport * from './DrawTextField';\n", "import { Point } from '../classes/index';\n\nexport function drawContour(\n ctx: CanvasRenderingContext2D,\n points: Point[],\n isClosed = false,\n) {\n ctx.beginPath();\n\n points.slice(1).forEach(({ x, y }, prevIdx) => {\n const from = points[prevIdx];\n ctx.moveTo(from.x, from.y);\n ctx.lineTo(x, y);\n });\n\n if (isClosed) {\n const from = points[points.length - 1];\n const to = points[0];\n if (!from || !to) {\n return;\n }\n\n ctx.moveTo(from.x, from.y);\n ctx.lineTo(to.x, to.y);\n }\n\n ctx.stroke();\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Point } from '../classes/index';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n return tensor instanceof tf.Tensor && tensor.shape.length === dim;\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n return isTensor(tensor, 1);\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n return isTensor(tensor, 2);\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n return isTensor(tensor, 3);\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n return isTensor(tensor, 4);\n}\n\nexport function isFloat(num: number) {\n return num % 1 !== 0;\n}\n\nexport function isEven(num: number) {\n return num % 2 === 0;\n}\n\nexport function round(num: number, prec = 2) {\n const f = 10 ** prec;\n return Math.floor(num * f) / f;\n}\n\nexport function isDimensions(obj: any): boolean {\n return obj && obj.width && obj.height;\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n const scale = inputSize / Math.max(height, width);\n return new Dimensions(Math.round(width * scale), Math.round(height * scale));\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n .div(new Point(pts.length, pts.length));\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n return Array(num).fill(0).map((_, i) => start + (i * step));\n}\n\nexport function isValidNumber(num: any) {\n return !!num && (num !== Infinity) && (num !== -Infinity) && !Number.isNaN(num) || num === 0;\n}\n\nexport function isValidProbablitiy(num: any) {\n return isValidNumber(num) && num >= 0 && num <= 1.0;\n}\n", "import { isValidNumber } from '../utils/index';\n\nexport interface IDimensions {\n width: number\n height: number\n}\n\nexport class Dimensions implements IDimensions {\n private _width: number;\n\n private _height: number;\n\n constructor(width: number, height: number) {\n if (!isValidNumber(width) || !isValidNumber(height)) {\n throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`);\n }\n\n this._width = width;\n this._height = height;\n }\n\n public get width(): number { return this._width; }\n\n public get height(): number { return this._height; }\n\n public reverse(): Dimensions {\n return new Dimensions(1 / this.width, 1 / this.height);\n }\n}\n", "export interface IPoint {\n x: number\n y: number\n}\n\nexport class Point implements IPoint {\n private _x: number;\n\n private _y: number;\n\n constructor(x: number, y: number) {\n this._x = x;\n this._y = y;\n }\n\n get x(): number { return this._x; }\n\n get y(): number { return this._y; }\n\n public add(pt: IPoint): Point {\n return new Point(this.x + pt.x, this.y + pt.y);\n }\n\n public sub(pt: IPoint): Point {\n return new Point(this.x - pt.x, this.y - pt.y);\n }\n\n public mul(pt: IPoint): Point {\n return new Point(this.x * pt.x, this.y * pt.y);\n }\n\n public div(pt: IPoint): Point {\n return new Point(this.x / pt.x, this.y / pt.y);\n }\n\n public abs(): Point {\n return new Point(Math.abs(this.x), Math.abs(this.y));\n }\n\n public magnitude(): number {\n return Math.sqrt((this.x ** 2) + (this.y ** 2));\n }\n\n public floor(): Point {\n return new Point(Math.floor(this.x), Math.floor(this.y));\n }\n}\n", "import { isDimensions, isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box implements IBoundingBox, IRect {\n public static isRect(rect: any): boolean {\n return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber);\n }\n\n public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions = false) {\n if (!Box.isRect(box)) {\n throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`);\n }\n\n if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\n throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`);\n }\n }\n\n private _x: number;\n\n private _y: number;\n\n private _width: number;\n\n private _height: number;\n\n constructor(_box: IBoundingBox | IRect, allowNegativeDimensions = true) {\n const box = (_box || {}) as any;\n\n const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber);\n const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber);\n\n if (!isRect && !isBbox) {\n throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`);\n }\n\n const [x, y, width, height] = isRect\n ? [box.x, box.y, box.width, box.height]\n : [box.left, box.top, box.right - box.left, box.bottom - box.top];\n\n Box.assertIsValidBox({\n x, y, width, height,\n }, 'Box.constructor', allowNegativeDimensions);\n\n this._x = x;\n this._y = y;\n this._width = width;\n this._height = height;\n }\n\n public get x(): number { return this._x; }\n\n public get y(): number { return this._y; }\n\n public get width(): number { return this._width; }\n\n public get height(): number { return this._height; }\n\n public get left(): number { return this.x; }\n\n public get top(): number { return this.y; }\n\n public get right(): number { return this.x + this.width; }\n\n public get bottom(): number { return this.y + this.height; }\n\n public get area(): number { return this.width * this.height; }\n\n public get topLeft(): Point { return new Point(this.left, this.top); }\n\n public get topRight(): Point { return new Point(this.right, this.top); }\n\n public get bottomLeft(): Point { return new Point(this.left, this.bottom); }\n\n public get bottomRight(): Point { return new Point(this.right, this.bottom); }\n\n public round(): Box {\n const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n .map((val) => Math.round(val));\n return new Box({\n x, y, width, height,\n });\n }\n\n public floor(): Box {\n const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n .map((val) => Math.floor(val));\n return new Box({\n x, y, width, height,\n });\n }\n\n public toSquare(): Box {\n let {\n x, y, width, height,\n } = this;\n const diff = Math.abs(width - height);\n if (width < height) {\n x -= (diff / 2);\n width += diff;\n }\n if (height < width) {\n y -= (diff / 2);\n height += diff;\n }\n\n return new Box({ x, y, width, height });\n }\n\n public rescale(s: IDimensions | number): Box {\n const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number;\n const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number;\n return new Box({\n x: this.x * scaleX,\n y: this.y * scaleY,\n width: this.width * scaleX,\n height: this.height * scaleY,\n });\n }\n\n public pad(padX: number, padY: number): Box {\n const [x, y, width, height] = [\n this.x - (padX / 2),\n this.y - (padY / 2),\n this.width + padX,\n this.height + padY,\n ];\n return new Box({ x, y, width, height });\n }\n\n public clipAtImageBorders(imgWidth: number, imgHeight: number): Box {\n const { x, y, right, bottom } = this;\n const clippedX = Math.max(x, 0);\n const clippedY = Math.max(y, 0);\n\n const newWidth = right - clippedX;\n const newHeight = bottom - clippedY;\n const clippedWidth = Math.min(newWidth, imgWidth - clippedX);\n const clippedHeight = Math.min(newHeight, imgHeight - clippedY);\n\n return (new Box({ x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight })).floor();\n }\n\n public shift(sx: number, sy: number): Box {\n const { width, height } = this;\n const x = this.x + sx;\n const y = this.y + sy;\n\n return new Box({ x, y, width, height });\n }\n\n public padAtBorders(imageHeight: number, imageWidth: number) {\n const w = this.width + 1;\n const h = this.height + 1;\n\n const dx = 1;\n const dy = 1;\n let edx = w;\n let edy = h;\n\n let x = this.left;\n let y = this.top;\n let ex = this.right;\n let ey = this.bottom;\n\n if (ex > imageWidth) {\n edx = -ex + imageWidth + w;\n ex = imageWidth;\n }\n if (ey > imageHeight) {\n edy = -ey + imageHeight + h;\n ey = imageHeight;\n }\n if (x < 1) {\n edy = 2 - x;\n x = 1;\n }\n if (y < 1) {\n edy = 2 - y;\n y = 1;\n }\n\n return { dy, edy, dx, edx, y, ey, x, ex, w, h };\n }\n\n public calibrate(region: Box) {\n return new Box({\n left: this.left + (region.left * this.width),\n top: this.top + (region.top * this.height),\n right: this.right + (region.right * this.width),\n bottom: this.bottom + (region.bottom * this.height),\n }).toSquare().round();\n }\n}\n", "import { Box } from './Box';\n\nexport interface IBoundingBox {\n left: number\n top: number\n right: number\n bottom: number\n}\n\nexport class BoundingBox extends Box implements IBoundingBox {\n constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions = false) {\n super({ left, top, right, bottom }, allowNegativeDimensions);\n }\n}\n", "import { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { IRect, Rect } from './Rect';\n\nexport class ObjectDetection {\n private _score: number;\n\n private _classScore: number;\n\n private _className: string;\n\n private _box: Rect;\n\n private _imageDims: Dimensions;\n\n constructor(\n score: number,\n classScore: number,\n className: string,\n relativeBox: IRect,\n imageDims: IDimensions,\n ) {\n this._imageDims = new Dimensions(imageDims.width, imageDims.height);\n this._score = score;\n this._classScore = classScore;\n this._className = className;\n this._box = new Box(relativeBox).rescale(this._imageDims);\n }\n\n public get score(): number { return this._score; }\n\n public get classScore(): number { return this._classScore; }\n\n public get className(): string { return this._className; }\n\n public get box(): Box { return this._box; }\n\n public get imageDims(): Dimensions { return this._imageDims; }\n\n public get imageWidth(): number { return this.imageDims.width; }\n\n public get imageHeight(): number { return this.imageDims.height; }\n\n public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()); }\n\n public forSize(width: number, height: number): ObjectDetection {\n return new ObjectDetection(\n this.score,\n this.classScore,\n this.className,\n this.relativeBox,\n { width, height },\n );\n }\n}\n", "import { Box } from './Box';\nimport { IDimensions } from './Dimensions';\nimport { ObjectDetection } from './ObjectDetection';\nimport { Rect } from './Rect';\n\nexport interface IFaceDetecion {\n score: number\n box: Box\n}\n\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\n constructor(\n score: number,\n relativeBox: Rect,\n imageDims: IDimensions,\n ) {\n super(score, score, '', relativeBox, imageDims);\n }\n\n public override forSize(width: number, height: number): FaceDetection {\n const { score, relativeBox, imageDims } = super.forSize(width, height);\n return new FaceDetection(score, relativeBox, imageDims);\n }\n}\n", "import { Box } from '../classes/Box';\n\nexport function iou(box1: Box, box2: Box, isIOU = true) {\n const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left));\n const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top));\n const interSection = width * height;\n\n return isIOU\n ? interSection / (box1.area + box2.area - interSection)\n : interSection / Math.min(box1.area, box2.area);\n}\n", "import { BoundingBox, IPoint } from '../classes/index';\n\nexport function minBbox(pts: IPoint[]): BoundingBox {\n const xs = pts.map((pt) => pt.x);\n const ys = pts.map((pt) => pt.y);\n const minX = xs.reduce((min, x) => (x < min ? x : min), Infinity);\n const minY = ys.reduce((min, y) => (y < min ? y : min), Infinity);\n const maxX = xs.reduce((max, x) => (max < x ? x : max), 0);\n const maxY = ys.reduce((max, y) => (max < y ? y : max), 0);\n\n return new BoundingBox(minX, minY, maxX, maxY);\n}\n", "import { Box } from '../classes/Box';\nimport { iou } from './iou';\n\nexport function nonMaxSuppression(\n boxes: Box[],\n scores: number[],\n iouThreshold: number,\n isIOU = true,\n): number[] {\n let indicesSortedByScore = scores\n .map((score, boxIndex) => ({ score, boxIndex }))\n .sort((c1, c2) => c1.score - c2.score)\n .map((c) => c.boxIndex);\n\n const pick: number[] = [];\n\n while (indicesSortedByScore.length > 0) {\n const curr = indicesSortedByScore.pop() as number;\n pick.push(curr);\n\n const indices = indicesSortedByScore;\n\n const outputs: number[] = [];\n for (let i = 0; i < indices.length; i++) {\n const idx = indices[i];\n\n const currBox = boxes[curr];\n const idxBox = boxes[idx];\n\n outputs.push(iou(currBox, idxBox, isIOU));\n }\n\n indicesSortedByScore = indicesSortedByScore.filter(\n (_, j) => outputs[j] <= iouThreshold,\n );\n }\n\n return pick;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {\n return tf.tidy(() => {\n const [r, g, b] = meanRgb;\n const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32');\n const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32');\n const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32');\n const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3);\n\n return tf.sub(x, avg_rgb);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n/**\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\n *\n * @param imgTensor The image tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The padded tensor with width === height.\n */\nexport function padToSquare(imgTensor: tf.Tensor4D, isCenterImage = false): tf.Tensor4D {\n return tf.tidy(() => {\n const [height, width] = imgTensor.shape.slice(1);\n if (height === width) return imgTensor;\n const dimDiff = Math.abs(height - width);\n const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1));\n const paddingAxis = height > width ? 2 : 1;\n const createPaddingTensor = (paddingAmountLocal: number): tf.Tensor => {\n const paddingTensorShape = imgTensor.shape.slice();\n paddingTensorShape[paddingAxis] = paddingAmountLocal;\n return tf.fill(paddingTensorShape, 0, 'float32');\n };\n const paddingTensorAppend = createPaddingTensor(paddingAmount);\n const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number);\n const paddingTensorPrepend = isCenterImage && remainingPaddingAmount ? createPaddingTensor(remainingPaddingAmount) : null;\n const tensorsToStack = [paddingTensorPrepend, imgTensor, paddingTensorAppend]\n .filter((t) => !!t)\n .map((t) => tf.cast(t as tf.Tensor4D, 'float32')) as tf.Tensor4D[];\n return tf.concat(tensorsToStack, paddingAxis);\n });\n}\n", "export function shuffleArray(inputArray: any[]) {\n const array = inputArray.slice();\n for (let i = array.length - 1; i > 0; i--) {\n const j = Math.floor(Math.random() * (i + 1));\n const x = array[i];\n array[i] = array[j];\n array[j] = x;\n }\n return array;\n}\n", "export * from './iou';\nexport * from './minBbox';\nexport * from './nonMaxSuppression';\nexport * from './normalize';\nexport * from './padToSquare';\nexport * from './shuffleArray';\n\nexport function sigmoid(x: number) {\n return 1 / (1 + Math.exp(-x));\n}\n\nexport function inverseSigmoid(x: number) {\n return Math.log(x / (1 - x));\n}\n", "import { Box } from './Box';\n\nexport interface IRect {\n x: number\n y: number\n width: number\n height: number\n}\n\nexport class Rect extends Box implements IRect {\n constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions = false) {\n super({ x, y, width, height }, allowNegativeDimensions);\n }\n}\n", "import { minBbox } from '../ops/index';\nimport { getCenterPoint } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { FaceDetection } from './FaceDetection';\nimport { Point } from './Point';\nimport { IRect, Rect } from './Rect';\n\n// face alignment constants\nconst relX = 0.5;\nconst relY = 0.43;\nconst relScale = 0.45;\n\nexport interface IFaceLandmarks {\n positions: Point[]\n shift: Point\n}\n\nexport class FaceLandmarks implements IFaceLandmarks {\n protected _shift: Point;\n\n protected _positions: Point[];\n\n protected _imgDims: Dimensions;\n\n constructor(\n relativeFaceLandmarkPositions: Point[],\n imgDims: IDimensions,\n shift: Point = new Point(0, 0),\n ) {\n const { width, height } = imgDims;\n this._imgDims = new Dimensions(width, height);\n this._shift = shift;\n this._positions = relativeFaceLandmarkPositions.map(\n (pt) => pt.mul(new Point(width, height)).add(shift),\n );\n }\n\n public get shift(): Point { return new Point(this._shift.x, this._shift.y); }\n\n public get imageWidth(): number { return this._imgDims.width; }\n\n public get imageHeight(): number { return this._imgDims.height; }\n\n public get positions(): Point[] { return this._positions; }\n\n public get relativePositions(): Point[] {\n return this._positions.map(\n (pt) => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight)),\n );\n }\n\n public forSize(width: number, height: number): T {\n return new (this.constructor as any)(\n this.relativePositions,\n { width, height },\n );\n }\n\n public shiftBy(x: number, y: number): T {\n return new (this.constructor as any)(\n this.relativePositions,\n this._imgDims,\n new Point(x, y),\n );\n }\n\n public shiftByPoint(pt: Point): T {\n return this.shiftBy(pt.x, pt.y);\n }\n\n /**\n * Aligns the face landmarks after face detection from the relative positions of the faces\n * bounding box, or it's current shift. This function should be used to align the face images\n * after face detection has been performed, before they are passed to the face recognition net.\n * This will make the computed face descriptor more accurate.\n *\n * @param detection (optional) The bounding box of the face or the face detection result. If\n * no argument was passed the position of the face landmarks are assumed to be relative to\n * it's current shift.\n * @returns The bounding box of the aligned face.\n */\n public align(\n detection?: FaceDetection | IRect | IBoundingBox | null,\n options: { useDlibAlignment?: boolean, minBoxPadding?: number } = { },\n ): Box {\n if (detection) {\n const box = detection instanceof FaceDetection\n ? detection.box.floor()\n : new Box(detection);\n\n return this.shiftBy(box.x, box.y).align(null, options);\n }\n\n const { useDlibAlignment, minBoxPadding } = { useDlibAlignment: false, minBoxPadding: 0.2, ...options };\n\n if (useDlibAlignment) {\n return this.alignDlib();\n }\n\n return this.alignMinBbox(minBoxPadding);\n }\n\n private alignDlib(): Box {\n const centers = this.getRefPointsForAlignment();\n\n const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers;\n const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude();\n const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2;\n\n const size = Math.floor(eyeToMouthDist / relScale);\n\n const refPoint = getCenterPoint(centers);\n // TODO: pad in case rectangle is out of image bounds\n const x = Math.floor(Math.max(0, refPoint.x - (relX * size)));\n const y = Math.floor(Math.max(0, refPoint.y - (relY * size)));\n\n return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y));\n }\n\n private alignMinBbox(padding: number): Box {\n const box = minBbox(this.positions);\n return box.pad(box.width * padding, box.height * padding);\n }\n\n protected getRefPointsForAlignment(): Point[] {\n throw new Error('getRefPointsForAlignment not implemented by base class');\n }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks5 extends FaceLandmarks {\n protected override getRefPointsForAlignment(): Point[] {\n const pts = this.positions;\n return [\n pts[0],\n pts[1],\n getCenterPoint([pts[3], pts[4]]),\n ];\n }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks68 extends FaceLandmarks {\n public getJawOutline(): Point[] {\n return this.positions.slice(0, 17);\n }\n\n public getLeftEyeBrow(): Point[] {\n return this.positions.slice(17, 22);\n }\n\n public getRightEyeBrow(): Point[] {\n return this.positions.slice(22, 27);\n }\n\n public getNose(): Point[] {\n return this.positions.slice(27, 36);\n }\n\n public getLeftEye(): Point[] {\n return this.positions.slice(36, 42);\n }\n\n public getRightEye(): Point[] {\n return this.positions.slice(42, 48);\n }\n\n public getMouth(): Point[] {\n return this.positions.slice(48, 68);\n }\n\n protected override getRefPointsForAlignment(): Point[] {\n return [\n this.getLeftEye(),\n this.getRightEye(),\n this.getMouth(),\n ].map(getCenterPoint);\n }\n}\n", "import { round } from '../utils/index';\n\nexport interface IFaceMatch {\n label: string\n distance: number\n}\n\nexport class FaceMatch implements IFaceMatch {\n private _label: string;\n private _distance: number;\n\n constructor(label: string, distance: number) {\n this._label = label;\n this._distance = distance;\n }\n\n public get label(): string { return this._label; }\n\n public get distance(): number { return this._distance; }\n\n public toString(withDistance = true): string {\n return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`;\n }\n}\n", "import { isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { IRect } from './Rect';\n\nexport class LabeledBox extends Box {\n public static assertIsValidLabeledBox(box: any, callee: string) {\n Box.assertIsValidBox(box, callee);\n if (!isValidNumber(box.label)) {\n throw new Error(`${callee} - expected property label (${box.label}) to be a number`);\n }\n }\n\n private _label: number;\n\n constructor(box: IBoundingBox | IRect | any, label: number) {\n super(box);\n this._label = label;\n }\n\n public get label(): number { return this._label; }\n}\n", "export class LabeledFaceDescriptors {\n private _label: string;\n\n private _descriptors: Float32Array[];\n\n constructor(label: string, descriptors: Float32Array[]) {\n if (!(typeof label === 'string')) {\n throw new Error('LabeledFaceDescriptors - constructor expected label to be a string');\n }\n\n if (!Array.isArray(descriptors) || descriptors.some((desc) => !(desc instanceof Float32Array))) {\n throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array');\n }\n\n this._label = label;\n this._descriptors = descriptors;\n }\n\n public get label(): string { return this._label; }\n\n public get descriptors(): Float32Array[] { return this._descriptors; }\n\n public toJSON(): any {\n return {\n label: this.label,\n descriptors: this.descriptors.map((d) => Array.from(d)),\n };\n }\n\n public static fromJSON(json: any): LabeledFaceDescriptors {\n const descriptors = json.descriptors.map((d: any) => new Float32Array(d));\n return new LabeledFaceDescriptors(json.label, descriptors);\n }\n}\n", "import { isValidProbablitiy } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { LabeledBox } from './LabeledBox';\nimport { IRect } from './Rect';\n\nexport class PredictedBox extends LabeledBox {\n public static assertIsValidPredictedBox(box: any, callee: string) {\n LabeledBox.assertIsValidLabeledBox(box, callee);\n\n if (\n !isValidProbablitiy(box.score)\n || !isValidProbablitiy(box.classScore)\n ) {\n throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`);\n }\n }\n\n private _score: number;\n\n private _classScore: number;\n\n constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {\n super(box, label);\n this._score = score;\n this._classScore = classScore;\n }\n\n public get score(): number { return this._score; }\n\n public get classScore(): number { return this._classScore; }\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\n\nexport type WithFaceDetection = TSource & {\n detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n return obj.detection instanceof FaceDetection;\n}\n\nexport function extendWithFaceDetection(sourceObj: TSource, detection: FaceDetection): WithFaceDetection {\n const extension = { detection };\n return { ...sourceObj, ...extension };\n}\n", "import { Environment } from './types';\n\nexport function createBrowserEnv(): Environment {\n const fetch = window.fetch;\n if (!fetch) throw new Error('fetch - missing fetch implementation for browser environment');\n\n const readFile = () => {\n throw new Error('readFile - filesystem not available for browser environment');\n };\n\n return {\n Canvas: HTMLCanvasElement,\n CanvasRenderingContext2D,\n Image: HTMLImageElement,\n ImageData,\n Video: HTMLVideoElement,\n createCanvasElement: () => document.createElement('canvas'),\n createImageElement: () => document.createElement('img'),\n createVideoElement: () => document.createElement('video'),\n fetch,\n readFile,\n };\n}\n", "export function isNodejs(): boolean {\n return typeof global === 'object'\n && typeof process !== 'undefined'\n && process.versions != null\n && process.versions.node != null;\n}\n", "import { FileSystem } from './types';\nimport { isNodejs } from './isNodejs';\n\nexport function createFileSystem(fs?: any): FileSystem {\n let requireFsError = '';\n if (!fs && isNodejs()) {\n try {\n // eslint-disable-next-line global-require\n fs = require('fs');\n } catch (err) {\n requireFsError = (err as any).toString();\n }\n }\n\n const readFile = fs\n // eslint-disable-next-line no-undef\n ? (filePath: string) => new Promise((resolve, reject) => { fs.readFile(filePath, (err: NodeJS.ErrnoException | null, buffer: string | Buffer) => (err ? reject(err) : resolve(buffer))); })\n : () => { throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`); };\n return { readFile };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n const Canvas: (new () => HTMLCanvasElement) = (global as any)['Canvas'] || global.HTMLCanvasElement;\n const Image = global.Image || global.HTMLImageElement;\n const Video: (new () => HTMLVideoElement) = (global as any)['Video'] || global.HTMLVideoElement;\n\n const createCanvasElement = () => {\n if (Canvas) return new Canvas();\n throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment');\n };\n\n const createImageElement = () => {\n if (Image) return new Image();\n throw new Error('createImageElement - missing Image implementation for nodejs environment');\n };\n\n const createVideoElement = () => {\n if (Video) return new Video();\n throw new Error('createVideoElement - missing Video implementation for nodejs environment');\n };\n\n const fetch = global.fetch;\n // if (!fetch) throw new Error('fetch - missing fetch implementation for nodejs environment');\n\n const fileSystem = createFileSystem();\n\n return {\n Canvas: Canvas || class {},\n CanvasRenderingContext2D: global.CanvasRenderingContext2D || class {},\n Image: Image || class {},\n ImageData: global.ImageData || class {},\n Video: global.HTMLVideoElement || class {},\n createCanvasElement,\n createImageElement,\n createVideoElement,\n fetch,\n ...fileSystem,\n };\n}\n", "export function isBrowser(): boolean {\n return typeof window === 'object'\n && typeof document !== 'undefined'\n && typeof HTMLImageElement !== 'undefined'\n && typeof HTMLCanvasElement !== 'undefined'\n && typeof HTMLVideoElement !== 'undefined'\n && typeof ImageData !== 'undefined'\n && typeof CanvasRenderingContext2D !== 'undefined';\n}\n", "import { createBrowserEnv } from './createBrowserEnv';\nimport { createFileSystem } from './createFileSystem';\nimport { createNodejsEnv } from './createNodejsEnv';\nimport { isBrowser } from './isBrowser';\nimport { isNodejs } from './isNodejs';\nimport { Environment } from './types';\n\nlet environment: Environment | null;\n\nfunction getEnv(): Environment {\n if (!environment) {\n throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()');\n }\n return environment;\n}\n\nfunction setEnv(env: Environment) {\n environment = env;\n}\n\nfunction initialize() {\n // check for isBrowser() first to prevent electron renderer process\n // to be initialized with wrong environment due to isNodejs() returning true\n if (isBrowser()) return setEnv(createBrowserEnv());\n if (isNodejs()) return setEnv(createNodejsEnv());\n return null;\n}\n\nfunction monkeyPatch(env: Partial) {\n if (!environment) {\n initialize();\n }\n\n if (!environment) {\n throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()');\n }\n\n const { Canvas = environment.Canvas, Image = environment.Image } = env;\n environment.Canvas = Canvas;\n environment.Image = Image;\n environment.createCanvasElement = env.createCanvasElement || (() => new Canvas());\n environment.createImageElement = env.createImageElement || (() => new Image());\n\n environment.ImageData = env.ImageData || environment.ImageData;\n environment.Video = env.Video || environment.Video;\n environment.fetch = env.fetch || environment.fetch;\n environment.readFile = env.readFile || environment.readFile;\n}\n\nexport const env = {\n getEnv,\n setEnv,\n initialize,\n createBrowserEnv,\n createFileSystem,\n createNodejsEnv,\n monkeyPatch,\n isBrowser,\n isNodejs,\n};\n\ninitialize();\n\nexport * from './types';\n", "import { env } from '../env/index';\n\nexport function resolveInput(arg: string | any) {\n if (!env.isNodejs() && typeof arg === 'string') {\n return document.getElementById(arg);\n }\n return arg;\n}\n", "import { env } from '../env/index';\nimport { resolveInput } from './resolveInput';\n\nexport function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D {\n const { Canvas, CanvasRenderingContext2D } = env.getEnv();\n if (canvasArg instanceof CanvasRenderingContext2D) return canvasArg;\n const canvas = resolveInput(canvasArg);\n if (!(canvas instanceof Canvas)) throw new Error('resolveContext2d - expected canvas to be of instance of Canvas');\n const ctx = canvas.getContext('2d', { willReadFrequently: true });\n if (!ctx) throw new Error('resolveContext2d - canvas 2d context is null');\n return ctx;\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IDimensions, IPoint } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { resolveInput } from '../dom/resolveInput';\n\n// eslint-disable-next-line no-shadow\nexport enum AnchorPosition {\n // eslint-disable-next-line no-unused-vars\n TOP_LEFT = 'TOP_LEFT',\n // eslint-disable-next-line no-unused-vars\n TOP_RIGHT = 'TOP_RIGHT',\n // eslint-disable-next-line no-unused-vars\n BOTTOM_LEFT = 'BOTTOM_LEFT',\n // eslint-disable-next-line no-unused-vars\n BOTTOM_RIGHT = 'BOTTOM_RIGHT'\n}\n\nexport interface IDrawTextFieldOptions {\n anchorPosition?: AnchorPosition\n backgroundColor?: string\n fontColor?: string\n fontSize?: number\n fontStyle?: string\n padding?: number\n}\n\nexport class DrawTextFieldOptions implements IDrawTextFieldOptions {\n public anchorPosition: AnchorPosition;\n\n public backgroundColor: string;\n\n public fontColor: string;\n\n public fontSize: number;\n\n public fontStyle: string;\n\n public padding: number;\n\n constructor(options: IDrawTextFieldOptions = {}) {\n const {\n anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding,\n } = options;\n this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT;\n this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)';\n this.fontColor = fontColor || 'rgba(255, 255, 255, 1)';\n this.fontSize = fontSize || 14;\n this.fontStyle = fontStyle || 'Georgia';\n this.padding = padding || 4;\n }\n}\n\nexport class DrawTextField {\n public text: string[];\n\n public anchor : IPoint;\n\n public options: DrawTextFieldOptions;\n\n constructor(\n text: string | string[] | DrawTextField,\n anchor: IPoint,\n options: IDrawTextFieldOptions = {},\n ) {\n // eslint-disable-next-line no-nested-ternary\n this.text = typeof text === 'string'\n ? [text]\n : (text instanceof DrawTextField ? text.text : text);\n this.anchor = anchor;\n this.options = new DrawTextFieldOptions(options);\n }\n\n measureWidth(ctx: CanvasRenderingContext2D): number {\n const { padding } = this.options;\n return this.text.map((l) => ctx.measureText(l).width).reduce((w0, w1) => (w0 < w1 ? w1 : w0), 0) + (2 * padding);\n }\n\n measureHeight(): number {\n const { fontSize, padding } = this.options;\n return this.text.length * fontSize + (2 * padding);\n }\n\n getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint {\n const { anchorPosition } = this.options;\n const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT;\n const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT;\n\n const textFieldWidth = this.measureWidth(ctx);\n const textFieldHeight = this.measureHeight();\n const x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x);\n const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y;\n\n // adjust anchor if text box exceeds canvas borders\n if (canvasDims) {\n const { width, height } = canvasDims;\n const newX = Math.max(Math.min(x, width - textFieldWidth), 0);\n const newY = Math.max(Math.min(y, height - textFieldHeight), 0);\n return { x: newX, y: newY };\n }\n return { x, y };\n }\n\n draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n const canvas = resolveInput(canvasArg);\n const ctx = getContext2dOrThrow(canvas);\n\n const {\n backgroundColor, fontColor, fontSize, fontStyle, padding,\n } = this.options;\n\n ctx.font = `${fontSize}px ${fontStyle}`;\n const maxTextWidth = this.measureWidth(ctx);\n const textHeight = this.measureHeight();\n\n ctx.fillStyle = backgroundColor;\n const upperLeft = this.getUpperLeft(ctx, canvas);\n ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight);\n\n ctx.fillStyle = fontColor;\n this.text.forEach((textLine, i) => {\n const x = padding + upperLeft.x;\n const y = padding + upperLeft.y + ((i + 1) * fontSize);\n ctx.fillText(textLine, x, y);\n });\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { Box, IBoundingBox, IRect } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions } from './DrawTextField';\n\nexport interface IDrawBoxOptions {\n boxColor?: string\n lineWidth?: number\n drawLabelOptions?: IDrawTextFieldOptions\n label?: string\n}\n\nexport class DrawBoxOptions {\n public boxColor: string;\n\n public lineWidth: number;\n\n public drawLabelOptions: DrawTextFieldOptions;\n\n public label?: string;\n\n constructor(options: IDrawBoxOptions = {}) {\n const {\n boxColor, lineWidth, label, drawLabelOptions,\n } = options;\n this.boxColor = boxColor || 'rgba(0, 0, 255, 1)';\n this.lineWidth = lineWidth || 2;\n this.label = label;\n\n const defaultDrawLabelOptions = {\n anchorPosition: AnchorPosition.BOTTOM_LEFT,\n backgroundColor: this.boxColor,\n };\n this.drawLabelOptions = new DrawTextFieldOptions({ ...defaultDrawLabelOptions, ...drawLabelOptions });\n }\n}\n\nexport class DrawBox {\n public box: Box;\n\n public options: DrawBoxOptions;\n\n constructor(\n box: IBoundingBox | IRect,\n options: IDrawBoxOptions = {},\n ) {\n this.box = new Box(box);\n this.options = new DrawBoxOptions(options);\n }\n\n draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n const ctx = getContext2dOrThrow(canvasArg);\n\n const { boxColor, lineWidth } = this.options;\n\n const {\n x, y, width, height,\n } = this.box;\n ctx.strokeStyle = boxColor;\n ctx.lineWidth = lineWidth;\n ctx.strokeRect(x, y, width, height);\n\n const { label } = this.options;\n if (label) {\n new DrawTextField([label], { x: x - (lineWidth / 2), y }, this.options.drawLabelOptions).draw(canvasArg);\n }\n }\n}\n", "import { Box, IBoundingBox, IRect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { round } from '../utils/index';\nimport { DrawBox } from './DrawBox';\n\nexport type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>\n\nexport function drawDetections(\n canvasArg: string | HTMLCanvasElement,\n detections: TDrawDetectionsInput | Array,\n) {\n const detectionsArray = Array.isArray(detections) ? detections : [detections];\n\n detectionsArray.forEach((det) => {\n // eslint-disable-next-line no-nested-ternary\n const score = det instanceof FaceDetection\n ? det.score\n : (isWithFaceDetection(det) ? det.detection.score : undefined);\n\n // eslint-disable-next-line no-nested-ternary\n const box = det instanceof FaceDetection\n ? det.box\n : (isWithFaceDetection(det) ? det.detection.box : new Box(det));\n\n const label = score ? `${round(score)}` : undefined;\n new DrawBox(box, { label }).draw(canvasArg);\n });\n}\n", "import { env } from '../env/index';\n\nexport function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {\n const { Image, Video } = env.getEnv();\n\n return (media instanceof Image && media.complete)\n || (media instanceof Video && media.readyState >= 3);\n}\n", "import { env } from '../env/index';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {\n // eslint-disable-next-line consistent-return\n return new Promise((resolve, reject) => {\n if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {\n resolve(null);\n return;\n }\n\n function onError(e: Event) {\n if (!e.currentTarget) return;\n // eslint-disable-next-line no-use-before-define\n e.currentTarget.removeEventListener('load', onLoad);\n e.currentTarget.removeEventListener('error', onError);\n reject(e);\n }\n\n function onLoad(e: Event) {\n if (!e.currentTarget) return;\n e.currentTarget.removeEventListener('load', onLoad);\n e.currentTarget.removeEventListener('error', onError);\n resolve(e);\n }\n\n media.addEventListener('load', onLoad);\n media.addEventListener('error', onError);\n });\n}\n", "import { env } from '../env/index';\n\nexport function bufferToImage(buf: Blob): Promise {\n return new Promise((resolve, reject) => {\n if (!(buf instanceof Blob)) reject(new Error('bufferToImage - expected buf to be of type: Blob'));\n const reader = new FileReader();\n reader.onload = () => {\n if (typeof reader.result !== 'string') reject(new Error('bufferToImage - expected reader.result to be a string, in onload'));\n const img = env.getEnv().createImageElement();\n img.onload = () => resolve(img);\n img.onerror = reject;\n img.src = reader.result as string;\n };\n reader.onerror = reject;\n reader.readAsDataURL(buf);\n });\n}\n", "import { Dimensions, IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\n\nexport function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {\n const { Image, Video } = env.getEnv();\n\n if (input instanceof Image) {\n return new Dimensions(input.naturalWidth, input.naturalHeight);\n }\n if (input instanceof Video) {\n return new Dimensions(input.videoWidth, input.videoHeight);\n }\n return new Dimensions(input.width, input.height);\n}\n", "import { IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {\n const { createCanvasElement } = env.getEnv();\n const canvas = createCanvasElement();\n canvas.width = width;\n canvas.height = height;\n return canvas;\n}\n\nexport function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {\n const { ImageData } = env.getEnv();\n\n if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\n throw new Error('createCanvasFromMedia - media has not finished loading yet');\n }\n\n const { width, height } = dims || getMediaDimensions(media);\n const canvas = createCanvas({ width, height });\n\n if (media instanceof ImageData) {\n getContext2dOrThrow(canvas).putImageData(media, 0, 0);\n } else {\n getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);\n }\n return canvas;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { env } from '../env/index';\nimport { isTensor4D } from '../utils/index';\n\nexport async function imageTensorToCanvas(\n imgTensor: tf.Tensor,\n canvas?: HTMLCanvasElement,\n): Promise {\n const targetCanvas = canvas || env.getEnv().createCanvasElement();\n\n const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0);\n const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt());\n await tf['browser'].toPixels(imgTensor3D, targetCanvas);\n\n imgTensor3D.dispose();\n\n return targetCanvas;\n}\n", "import { env } from '../env/index';\n\nexport function isMediaElement(input: any) {\n const { Image, Canvas, Video } = env.getEnv();\n\n return input instanceof Image\n || input instanceof Canvas\n || input instanceof Video;\n}\n", "import { env } from '../env/index';\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage = false) {\n const { Image, Canvas } = env.getEnv();\n\n if (!(input instanceof Image || input instanceof Canvas)) {\n throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement');\n }\n\n if (inputSize <= 0) return createCanvas({ width: 1, height: 1 });\n const dims = getMediaDimensions(input);\n const scale = inputSize / Math.max(dims.height, dims.width);\n const width = scale * dims.width;\n const height = scale * dims.height;\n\n const targetCanvas = createCanvas({ width: inputSize, height: inputSize });\n const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input);\n\n const offset = Math.abs(width - height) / 2;\n const dx = centerImage && width < height ? offset : 0;\n const dy = centerImage && height < width ? offset : 0;\n if (inputCanvas.width > 0 && inputCanvas.height > 0) getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height);\n\n return targetCanvas;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { padToSquare } from '../ops/padToSquare';\nimport { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils/index';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n private _imageTensors: Array = [];\n\n private _canvases: HTMLCanvasElement[] = [];\n\n private _batchSize: number;\n\n private _treatAsBatchInput = false;\n\n private _inputDimensions: number[][] = [];\n\n private _inputSize = 0;\n\n constructor(inputs: Array, treatAsBatchInput = false) {\n if (!Array.isArray(inputs)) {\n throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`);\n }\n\n this._treatAsBatchInput = treatAsBatchInput;\n this._batchSize = inputs.length;\n\n inputs.forEach((input, idx) => {\n if (isTensor3D(input)) {\n this._imageTensors[idx] = input;\n this._inputDimensions[idx] = input.shape;\n return;\n }\n\n if (isTensor4D(input)) {\n const batchSize = (input as any).shape[0];\n if (batchSize !== 1) {\n throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n }\n\n this._imageTensors[idx] = input;\n this._inputDimensions[idx] = (input as any).shape.slice(1);\n return;\n }\n\n // @ts-ignore\n const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input);\n this._canvases[idx] = canvas as HTMLCanvasElement;\n this._inputDimensions[idx] = [canvas.height, canvas.width, 3];\n });\n }\n\n public get imageTensors(): Array {\n return this._imageTensors;\n }\n\n public get canvases(): HTMLCanvasElement[] {\n return this._canvases;\n }\n\n public get isBatchInput(): boolean {\n return this.batchSize > 1 || this._treatAsBatchInput;\n }\n\n public get batchSize(): number {\n return this._batchSize;\n }\n\n public get inputDimensions(): number[][] {\n return this._inputDimensions;\n }\n\n public get inputSize(): number | undefined {\n return this._inputSize;\n }\n\n public get reshapedInputDimensions(): Dimensions[] {\n return range(this.batchSize, 0, 1).map(\n (_, batchIdx) => this.getReshapedInputDimensions(batchIdx),\n );\n }\n\n public getInput(batchIdx: number): tf.Tensor3D | tf.Tensor4D | HTMLCanvasElement {\n return this.canvases[batchIdx] || this.imageTensors[batchIdx];\n }\n\n public getInputDimensions(batchIdx: number): number[] {\n return this._inputDimensions[batchIdx];\n }\n\n public getInputHeight(batchIdx: number): number {\n return this._inputDimensions[batchIdx][0];\n }\n\n public getInputWidth(batchIdx: number): number {\n return this._inputDimensions[batchIdx][1];\n }\n\n public getReshapedInputDimensions(batchIdx: number): Dimensions {\n if (typeof this.inputSize !== 'number') {\n throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet');\n }\n\n const width = this.getInputWidth(batchIdx);\n const height = this.getInputHeight(batchIdx);\n return computeReshapedDimensions({ width, height }, this.inputSize);\n }\n\n /**\n * Create a batch tensor from all input canvases and tensors\n * with size [batchSize, inputSize, inputSize, 3].\n *\n * @param inputSize Height and width of the tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The batch tensor.\n */\n public toBatchTensor(inputSize: number, isCenterInputs = true): tf.Tensor4D {\n this._inputSize = inputSize;\n\n return tf.tidy(() => {\n const inputTensors = range(this.batchSize, 0, 1).map((batchIdx) => {\n const input = this.getInput(batchIdx);\n\n if (input instanceof tf.Tensor) {\n let imgTensor = isTensor4D(input) ? input : tf.expandDims(input);\n imgTensor = padToSquare(imgTensor as tf.Tensor4D, isCenterInputs);\n\n if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n imgTensor = tf['image'].resizeBilinear(imgTensor as tf.Tensor4D, [inputSize, inputSize], false, false);\n }\n\n return imgTensor.as3D(inputSize, inputSize, 3);\n }\n\n if (input instanceof env.getEnv().Canvas) {\n return tf['browser'].fromPixels(imageToSquare(input, inputSize, isCenterInputs));\n }\n\n throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`);\n });\n\n const batchTensor = tf.stack(inputTensors.map((t) => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3);\n // const batchTensor = tf.stack(inputTensors.map((t) => tf.cast(t, 'float32'))) as tf.Tensor4D;\n\n return batchTensor;\n });\n }\n}\n", "import { isTensor3D, isTensor4D } from '../utils/index';\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\nimport { isMediaElement } from './isMediaElement';\nimport { NetInput } from './NetInput';\nimport { resolveInput } from './resolveInput';\nimport { TNetInput } from './types';\n\n/**\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\n * to be finished loading.\n *\n * @param input The input, which can be a media element or an array of different media elements.\n * @returns A NetInput instance, which can be passed into one of the neural networks.\n */\nexport async function toNetInput(inputs: TNetInput): Promise {\n if (inputs instanceof NetInput) return inputs;\n const inputArgArray = Array.isArray(inputs) ? inputs : [inputs];\n if (!inputArgArray.length) throw new Error('toNetInput - empty array passed as input');\n const getIdxHint = (idx: number) => (Array.isArray(inputs) ? ` at input index ${idx}:` : '');\n const inputArray = inputArgArray.map(resolveInput);\n inputArray.forEach((input, i) => {\n if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n if (typeof inputArgArray[i] === 'string') throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`);\n throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);\n }\n if (isTensor4D(input)) {\n // if tf.Tensor4D is passed in the input array, the batch size has to be 1\n const batchSize = input.shape[0];\n if (batchSize !== 1) throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n }\n });\n // wait for all media elements being loaded\n await Promise.all(inputArray.map((input) => isMediaElement(input) && awaitMediaLoaded(input)));\n return new NetInput(inputArray, Array.isArray(inputs));\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\nimport { Rect } from '../classes/Rect';\nimport { env } from '../env/index';\nimport { createCanvas } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\nimport { toNetInput } from './toNetInput';\nimport { TNetInput } from './types';\n\n/**\n * Extracts the image regions containing the detected faces.\n *\n * @param input The image that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns The Canvases of the corresponding image region for each detected face.\n */\nexport async function extractFaces(input: TNetInput, detections: Array): Promise {\n const { Canvas } = env.getEnv();\n let canvas = input as HTMLCanvasElement;\n if (!(input instanceof Canvas)) {\n const netInput = await toNetInput(input);\n if (netInput.batchSize > 1) throw new Error('extractFaces - batchSize > 1 not supported');\n const tensorOrCanvas = netInput.getInput(0);\n canvas = tensorOrCanvas instanceof Canvas ? tensorOrCanvas : await imageTensorToCanvas(tensorOrCanvas);\n }\n const ctx = getContext2dOrThrow(canvas);\n const boxes = detections\n .map((det) => (det instanceof FaceDetection ? det.forSize(canvas.width, canvas.height).box.floor() : det))\n .map((box) => box.clipAtImageBorders(canvas.width, canvas.height));\n return boxes.map(({ x, y, width, height }) => {\n const faceImg = createCanvas({ width, height });\n if (width > 0 && height > 0) getContext2dOrThrow(faceImg).putImageData(ctx.getImageData(x, y, width, height), 0, 0);\n return faceImg;\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils/index';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(imageTensor: tf.Tensor3D | tf.Tensor4D, detections: Array): Promise {\n if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D');\n }\n\n if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n throw new Error('extractFaceTensors - batchSize > 1 not supported');\n }\n\n return tf.tidy(() => {\n const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0);\n const boxes = detections.map((det) => (det instanceof FaceDetection ? det.forSize(imgWidth, imgHeight).box : det))\n .map((box) => box.clipAtImageBorders(imgWidth, imgHeight));\n const faceTensors = boxes\n .filter((box) => box.width > 0 && box.height > 0)\n .map(({ x, y, width, height }) => tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]));\n return faceTensors;\n });\n}\n", "import { env } from '../env/index';\n\nexport async function fetchOrThrow(\n url: string,\n // eslint-disable-next-line no-undef\n init?: RequestInit,\n): Promise {\n const { fetch } = env.getEnv();\n const res = await fetch(url, init);\n if (!(res.status < 400)) {\n throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);\n }\n return res;\n}\n", "import { bufferToImage } from './bufferToImage';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchImage(uri: string): Promise {\n const res = await fetchOrThrow(uri);\n const blob = await (res).blob();\n\n if (!blob.type.startsWith('image/')) {\n throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`);\n }\n return bufferToImage(blob);\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson(uri: string): Promise {\n return (await fetchOrThrow(uri)).json();\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise {\n return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer());\n}\n", "import { env } from '../env/index';\n\nexport function bufferToVideo(buf: Blob): Promise {\n return new Promise((resolve, reject) => {\n if (!(buf instanceof Blob)) reject(new Error('bufferToVideo - expected buf to be of type: Blob'));\n\n const video = env.getEnv().createVideoElement();\n video.oncanplay = () => resolve(video);\n video.onerror = reject;\n video.playsInline = true;\n video.muted = true;\n video.src = URL.createObjectURL(buf);\n video.play();\n });\n}\n", "import { bufferToVideo } from './bufferToVideo';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchVideo(uri: string): Promise {\n const res = await fetchOrThrow(uri);\n const blob = await (res).blob();\n\n if (!blob.type.startsWith('video/')) {\n throw new Error(`fetchVideo - expected blob type to be of type video/*, instead have: ${blob.type}, for url: ${res.url}`);\n }\n return bufferToVideo(blob);\n}\n", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`;\n\n if (!uri) {\n return {\n modelBaseUri: '',\n manifestUri: defaultManifestFilename,\n };\n }\n\n if (uri === '/') {\n return {\n modelBaseUri: '/',\n manifestUri: `/${defaultManifestFilename}`,\n };\n }\n // eslint-disable-next-line no-nested-ternary\n const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\n uri = uri.replace(protocol, '');\n\n const parts = uri.split('/').filter((s) => s);\n\n const manifestFile = uri.endsWith('.json')\n ? parts[parts.length - 1]\n : defaultManifestFilename;\n\n let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/');\n modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri;\n\n return {\n modelBaseUri,\n manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`,\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n uri: string | undefined,\n defaultModelName: string,\n): Promise {\n const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName);\n // @ts-ignore\n const manifest = await fetchJson(manifestUri);\n // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n return tf['io'].loadWeights(manifest, modelBaseUri);\n}\n", "import { IDimensions } from '../classes/index';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions = false) {\n const { width, height } = useMediaDimensions\n ? getMediaDimensions(reference)\n : reference;\n input.width = width;\n input.height = height;\n return { width, height };\n}\n", "import * as tf from '../dist/tfjs.esm';\n\nimport { ParamMapping } from './common/index';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom/index';\nimport { env } from './env/index';\n\nexport abstract class NeuralNetwork {\n constructor(name: string) {\n this._name = name;\n }\n\n protected _params: TNetParams | undefined = undefined;\n\n protected _paramMappings: ParamMapping[] = [];\n\n public _name: any;\n\n public get params(): TNetParams | undefined { return this._params; }\n\n public get paramMappings(): ParamMapping[] { return this._paramMappings; }\n\n public get isLoaded(): boolean { return !!this.params; }\n\n public getParamFromPath(paramPath: string): tf.Tensor {\n const { obj, objProp } = this.traversePropertyPath(paramPath);\n return obj[objProp];\n }\n\n public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n const { obj, objProp } = this.traversePropertyPath(paramPath);\n obj[objProp].dispose();\n obj[objProp] = tensor;\n }\n\n public getParamList() {\n return this._paramMappings.map(({ paramPath }) => ({\n path: paramPath,\n tensor: this.getParamFromPath(paramPath),\n }));\n }\n\n public getTrainableParams() {\n return this.getParamList().filter((param) => param.tensor instanceof tf.Variable);\n }\n\n public getFrozenParams() {\n return this.getParamList().filter((param) => !(param.tensor instanceof tf.Variable));\n }\n\n public variable() {\n this.getFrozenParams().forEach(({ path, tensor }) => {\n this.reassignParamFromPath(path, tensor.variable());\n });\n }\n\n public freeze() {\n this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n const tensor = tf.tensor(variable.dataSync());\n variable.dispose();\n this.reassignParamFromPath(path, tensor);\n });\n }\n\n public dispose(throwOnRedispose = true) {\n this.getParamList().forEach((param) => {\n if (throwOnRedispose && param.tensor.isDisposed) {\n throw new Error(`param tensor has already been disposed for path ${param.path}`);\n }\n param.tensor.dispose();\n });\n this._params = undefined;\n }\n\n public serializeParams(): Float32Array {\n return new Float32Array(\n this.getParamList()\n .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n .reduce((flat, arr) => flat.concat(arr)),\n );\n }\n\n public async load(weightsOrUrl: Float32Array | string | undefined): Promise {\n if (weightsOrUrl instanceof Float32Array) {\n this.extractWeights(weightsOrUrl);\n return;\n }\n await this.loadFromUri(weightsOrUrl);\n }\n\n public async loadFromUri(uri: string | undefined) {\n if (uri && typeof uri !== 'string') {\n throw new Error(`${this._name}.loadFromUri - expected model uri`);\n }\n const weightMap = await loadWeightMap(uri, this.getDefaultModelName());\n this.loadFromWeightMap(weightMap);\n }\n\n public async loadFromDisk(filePath: string | undefined) {\n if (filePath && typeof filePath !== 'string') {\n throw new Error(`${this._name}.loadFromDisk - expected model file path`);\n }\n const { readFile } = env.getEnv();\n const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName());\n const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(filePaths.map((fp) => readFile(fp).then((buf) => (typeof buf === 'string' ? Buffer.from(buf) : buf.buffer))));\n const loadWeights = tf['io'].weightsLoaderFactory(fetchWeightsFromDisk);\n const manifest = JSON.parse((await readFile(manifestUri)).toString());\n const weightMap = await loadWeights(manifest, modelBaseUri);\n this.loadFromWeightMap(weightMap);\n }\n\n public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n const { paramMappings, params } = this.extractParamsFromWeightMap(weightMap);\n this._paramMappings = paramMappings;\n this._params = params;\n }\n\n public extractWeights(weights: Float32Array) {\n const { paramMappings, params } = this.extractParams(weights);\n this._paramMappings = paramMappings;\n this._params = params;\n }\n\n private traversePropertyPath(paramPath: string) {\n if (!this.params) {\n throw new Error('traversePropertyPath - model has no loaded params');\n }\n\n const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n // eslint-disable-next-line no-prototype-builtins\n if (!res.nextObj.hasOwnProperty(objProp)) {\n throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`);\n }\n return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] };\n }, { nextObj: this.params });\n\n const { obj, objProp } = result;\n if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);\n }\n\n return { obj, objProp };\n }\n\n protected abstract getDefaultModelName(): string\n\n // eslint-disable-next-line no-unused-vars\n protected abstract extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n\n // eslint-disable-next-line no-unused-vars\n protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n x: tf.Tensor4D,\n params: SeparableConvParams,\n stride: [number, number],\n): tf.Tensor4D {\n return tf.tidy(() => {\n let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same');\n out = tf.add(out, params.bias);\n return out;\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, SeparableConvParams } from '../common/index';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n x: tf.Tensor4D,\n denseBlockParams: DenseBlock3Params,\n isFirstLayer = false,\n): tf.Tensor4D {\n return tf.tidy(() => {\n const out1 = tf.relu(\n isFirstLayer\n ? tf.add(\n tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n denseBlockParams.conv0.bias,\n )\n : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2]),\n ) as tf.Tensor4D;\n const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n });\n}\n\nexport function denseBlock4(\n x: tf.Tensor4D,\n denseBlockParams: DenseBlock4Params,\n isFirstLayer = false,\n isScaleDown = true,\n): tf.Tensor4D {\n return tf.tidy(() => {\n const out1 = tf.relu(\n isFirstLayer\n ? tf.add(\n tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n denseBlockParams.conv0.bias,\n )\n : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1]),\n ) as tf.Tensor4D;\n const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1]);\n\n return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D;\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\nexport function convLayer(\n x: tf.Tensor4D,\n params: ConvParams,\n padding: 'valid' | 'same' = 'same',\n withRelu = false,\n): tf.Tensor4D {\n return tf.tidy(() => {\n const out = tf.add(\n tf.conv2d(x, params.filters, [1, 1], padding),\n params.bias,\n ) as tf.Tensor4D;\n\n return withRelu ? tf.relu(out) : out;\n });\n}\n", "import { ParamMapping } from './types';\n\nexport function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {\n Object.keys(weightMap).forEach((path) => {\n if (!paramMappings.some((pm) => pm.originalPath === path)) {\n weightMap[path].dispose();\n }\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';\n\nexport function extractConvParamsFactory(\n extractWeights: ExtractWeightsFunction,\n paramMappings: ParamMapping[],\n) {\n return (\n channelsIn: number,\n channelsOut: number,\n filterSize: number,\n mappedPrefix: string,\n ): ConvParams => {\n const filters = tf.tensor4d(\n extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n [filterSize, filterSize, channelsIn, channelsOut],\n );\n const bias = tf.tensor1d(extractWeights(channelsOut));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/filters` },\n { paramPath: `${mappedPrefix}/bias` },\n );\n\n return { filters, bias };\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, FCParams, ParamMapping } from './types';\n\nexport function extractFCParamsFactory(\n extractWeights: ExtractWeightsFunction,\n paramMappings: ParamMapping[],\n) {\n return (\n channelsIn: number,\n channelsOut: number,\n mappedPrefix: string,\n ): FCParams => {\n const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);\n const fc_bias = tf.tensor1d(extractWeights(channelsOut));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/weights` },\n { paramPath: `${mappedPrefix}/bias` },\n );\n\n return {\n weights: fc_weights,\n bias: fc_bias,\n };\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n// eslint-disable-next-line no-unused-vars\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\n\nexport type ParamMapping = {\n originalPath?: string\n paramPath: string\n}\n\nexport type ConvParams = {\n filters: tf.Tensor4D\n bias: tf.Tensor1D\n}\n\nexport type FCParams = {\n weights: tf.Tensor2D\n bias: tf.Tensor1D\n}\n\nexport class SeparableConvParams {\n // eslint-disable-next-line no-useless-constructor\n constructor(\n // eslint-disable-next-line no-unused-vars\n public depthwise_filter: tf.Tensor4D,\n // eslint-disable-next-line no-unused-vars\n public pointwise_filter: tf.Tensor4D,\n // eslint-disable-next-line no-unused-vars\n public bias: tf.Tensor1D,\n // eslint-disable-next-line no-empty-function\n ) {}\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n extractWeights: ExtractWeightsFunction,\n paramMappings: ParamMapping[],\n) {\n return (channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams => {\n const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);\n const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);\n const bias = tf.tensor1d(extractWeights(channelsOut));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/depthwise_filter` },\n { paramPath: `${mappedPrefix}/pointwise_filter` },\n { paramPath: `${mappedPrefix}/bias` },\n );\n\n return new SeparableConvParams(\n depthwise_filter,\n pointwise_filter,\n bias,\n );\n };\n}\n\nexport function loadSeparableConvParamsFactory(\n // eslint-disable-next-line no-unused-vars\n extractWeightEntry: (originalPath: string, paramRank: number) => T,\n) {\n return (prefix: string): SeparableConvParams => {\n const depthwise_filter = extractWeightEntry(`${prefix}/depthwise_filter`, 4);\n const pointwise_filter = extractWeightEntry(`${prefix}/pointwise_filter`, 4);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n\n return new SeparableConvParams(\n depthwise_filter,\n pointwise_filter,\n bias,\n );\n };\n}\n", "import { isTensor } from '../utils/index';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n return (originalPath: string, paramRank: number, mappedPath?: string) => {\n const tensor = weightMap[originalPath];\n\n if (!isTensor(tensor, paramRank)) {\n throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`);\n }\n\n paramMappings.push(\n { originalPath, paramPath: mappedPath || originalPath },\n );\n\n return tensor;\n };\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n let remainingWeights = weights;\n\n function extractWeights(numWeights: number): Float32Array {\n const ret = remainingWeights.slice(0, numWeights);\n remainingWeights = remainingWeights.slice(numWeights);\n return ret;\n }\n\n function getRemainingWeights(): Float32Array {\n return remainingWeights;\n }\n\n return {\n extractWeights,\n getRemainingWeights,\n };\n}\n", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, ExtractWeightsFunction, ParamMapping } from '../common/index';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer = false): DenseBlock3Params {\n const conv0 = isFirstLayer\n ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`);\n const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`);\n const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`);\n\n return { conv0, conv1, conv2 };\n }\n\n function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer = false): DenseBlock4Params {\n const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer);\n const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`);\n\n return {\n conv0, conv1, conv2, conv3,\n };\n }\n\n return {\n extractDenseBlock3Params,\n extractDenseBlock4Params,\n };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const {\n extractDenseBlock4Params,\n } = extractorsFactory(extractWeights, paramMappings);\n\n const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true);\n const dense1 = extractDenseBlock4Params(32, 64, 'dense1');\n const dense2 = extractDenseBlock4Params(64, 128, 'dense2');\n const dense3 = extractDenseBlock4Params(128, 256, 'dense3');\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: {\n dense0, dense1, dense2, dense3,\n },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\n// eslint-disable-next-line no-unused-vars\nexport function loadConvParamsFactory(extractWeightEntry: (originalPath: string, paramRank: number) => T) {\n return (prefix: string): ConvParams => {\n const filters = extractWeightEntry(`${prefix}/filters`, 4);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n\n return { filters, bias };\n };\n}\n", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n function extractDenseBlock3Params(prefix: string, isFirstLayer = false): DenseBlock3Params {\n const conv0 = isFirstLayer\n ? extractConvParams(`${prefix}/conv0`)\n : extractSeparableConvParams(`${prefix}/conv0`);\n const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n\n return { conv0, conv1, conv2 };\n }\n\n function extractDenseBlock4Params(prefix: string, isFirstLayer = false): DenseBlock4Params {\n const conv0 = isFirstLayer\n ? extractConvParams(`${prefix}/conv0`)\n : extractSeparableConvParams(`${prefix}/conv0`);\n const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n const conv3 = extractSeparableConvParams(`${prefix}/conv3`);\n\n return {\n conv0, conv1, conv2, conv3,\n };\n }\n\n return {\n extractDenseBlock3Params,\n extractDenseBlock4Params,\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractDenseBlock4Params,\n } = loadParamsFactory(weightMap, paramMappings);\n\n const params = {\n dense0: extractDenseBlock4Params('dense0', true),\n dense1: extractDenseBlock4Params('dense1'),\n dense2: extractDenseBlock4Params('dense2'),\n dense3: extractDenseBlock4Params('dense3'),\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork implements IFaceFeatureExtractor {\n constructor() {\n super('FaceFeatureExtractor');\n }\n\n public forwardInput(input: NetInput): tf.Tensor4D {\n const { params } = this;\n\n if (!params) {\n throw new Error('FaceFeatureExtractor - load model before inference');\n }\n\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n const meanRgb = [122.782, 117.001, 104.298];\n const normalized = normalize(batchTensor, meanRgb).div(255) as tf.Tensor4D;\n\n let out = denseBlock4(normalized, params.dense0, true);\n out = denseBlock4(out, params.dense1);\n out = denseBlock4(out, params.dense2);\n out = denseBlock4(out, params.dense3);\n out = tf.avgPool(out, [7, 7], [2, 2], 'valid');\n\n return out;\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n protected getDefaultModelName(): string {\n return 'face_feature_extractor_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParams(weights);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from './types';\n\nexport function fullyConnectedLayer(\n x: tf.Tensor2D,\n params: FCParams,\n): tf.Tensor2D {\n return tf.tidy(() => tf.add(\n tf.matMul(x, params.weights),\n params.bias,\n ));\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n const fc = extractFCParams(channelsIn, channelsOut, 'fc');\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { fc },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractFcParams(prefix: string): FCParams {\n const weights = extractWeightEntry(`${prefix}/weights`, 2);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n return { weights, bias };\n }\n\n const params = {\n fc: extractFcParams('fc'),\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n const featureExtractorMap: tf.NamedTensorMap = {};\n const classifierMap: tf.NamedTensorMap = {};\n\n Object.keys(weightMap).forEach((key) => {\n const map = key.startsWith('fc') ? classifierMap : featureExtractorMap;\n map[key] = weightMap[key];\n });\n\n return { featureExtractorMap, classifierMap };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom/index';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n extends NeuralNetwork {\n protected _faceFeatureExtractor: IFaceFeatureExtractor;\n\n constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor) {\n super(_name);\n this._faceFeatureExtractor = faceFeatureExtractor;\n }\n\n public get faceFeatureExtractor(): IFaceFeatureExtractor {\n return this._faceFeatureExtractor;\n }\n\n protected abstract override getDefaultModelName(): string\n\n protected abstract getClassifierChannelsIn(): number\n\n protected abstract getClassifierChannelsOut(): number\n\n public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n const { params } = this;\n\n if (!params) {\n throw new Error(`${this._name} - load model before inference`);\n }\n\n return tf.tidy(() => {\n const bottleneckFeatures = input instanceof NetInput\n ? this.faceFeatureExtractor.forwardInput(input)\n : input;\n return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc);\n });\n }\n\n public override dispose(throwOnRedispose = true) {\n this.faceFeatureExtractor.dispose(throwOnRedispose);\n super.dispose(throwOnRedispose);\n }\n\n public loadClassifierParams(weights: Float32Array) {\n const { params, paramMappings } = this.extractClassifierParams(weights);\n this._params = params;\n this._paramMappings = paramMappings;\n }\n\n public extractClassifierParams(weights: Float32Array) {\n return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut());\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n return extractParamsFromWeightMap(classifierMap);\n }\n\n protected extractParams(weights: Float32Array) {\n const cIn = this.getClassifierChannelsIn();\n const cOut = this.getClassifierChannelsOut();\n const classifierWeightSize = (cOut * cIn) + cOut;\n\n const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n return this.extractClassifierParams(classifierWeights);\n }\n}\n", "export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised'] as const;\n\nexport class FaceExpressions {\n public neutral = 0;\n public happy = 0;\n public sad = 0;\n public angry = 0;\n public fearful = 0;\n public disgusted = 0;\n public surprised = 0;\n\n constructor(probabilities: number[] | Float32Array) {\n if (probabilities.length !== 7) {\n throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`);\n }\n\n FACE_EXPRESSION_LABELS.forEach((expression, idx) => {\n this[expression] = probabilities[idx];\n });\n }\n\n asSortedArray() {\n return FACE_EXPRESSION_LABELS\n .map((expression) => ({ expression, probability: this[expression] as number }))\n .sort((e0, e1) => e1.probability - e0.probability);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor {\n constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n super('FaceExpressionNet', faceFeatureExtractor);\n }\n\n public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n return tf.tidy(() => tf.softmax(this.runNet(input)));\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async predictExpressions(input: TNetInput) {\n const netInput = await toNetInput(input);\n const out = await this.forwardInput(netInput);\n const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async (t) => {\n const data = t.dataSync();\n t.dispose();\n return data;\n }));\n out.dispose();\n\n const predictionsByBatch = probabilitesByBatch\n .map((probabilites) => new FaceExpressions(probabilites as Float32Array));\n\n return netInput.isBatchInput\n ? predictionsByBatch\n : predictionsByBatch[0];\n }\n\n protected getDefaultModelName(): string {\n return 'face_expression_model';\n }\n\n protected getClassifierChannelsIn(): number {\n return 256;\n }\n\n protected getClassifierChannelsOut(): number {\n return 7;\n }\n}\n", "import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\n\nexport type WithFaceExpressions = TSource & { expressions: FaceExpressions }\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n return obj.expressions instanceof FaceExpressions;\n}\n\nexport function extendWithFaceExpressions(sourceObj: TSource, expressions: FaceExpressions): WithFaceExpressions {\n const extension = { expressions };\n return { ...sourceObj, ...extension };\n}\n", "import { IPoint, Point } from '../classes/index';\nimport { FaceExpressions } from '../faceExpressionNet/index';\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { round } from '../utils/index';\nimport { DrawTextField } from './DrawTextField';\n\nexport type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>\n\nexport function drawFaceExpressions(canvasArg: string | HTMLCanvasElement, faceExpressions: DrawFaceExpressionsInput | Array, minConfidence = 0.1, textFieldAnchor?: IPoint) {\n const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions];\n\n faceExpressionsArray.forEach((e) => {\n // eslint-disable-next-line no-nested-ternary\n const expr = e instanceof FaceExpressions\n ? e\n : (isWithFaceExpressions(e) ? e.expressions : undefined);\n if (!expr) {\n throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof');\n }\n\n const sorted = expr.asSortedArray();\n const resultsToDisplay = sorted.filter((exprLocal) => exprLocal.probability > minConfidence);\n\n const anchor = isWithFaceDetection(e)\n ? e.detection.box.bottomLeft\n : (textFieldAnchor || new Point(0, 0));\n\n const drawTextField = new DrawTextField(\n resultsToDisplay.map((exprLocal) => `${exprLocal.expression} (${round(exprLocal.probability)})`),\n anchor,\n );\n drawTextField.draw(canvasArg);\n });\n}\n", "import { Point } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { isWithFaceDetection, WithFaceDetection } from './WithFaceDetection';\n\nexport type WithFaceLandmarks<\n TSource extends WithFaceDetection<{}>,\n TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> = TSource & {\n landmarks: TFaceLandmarks;\n unshiftedLandmarks: TFaceLandmarks;\n alignedRect: FaceDetection;\n angle: {\n roll: number | undefined;\n pitch: number | undefined;\n yaw: number | undefined;\n };\n};\n\nexport function isWithFaceLandmarks(\n obj: any,\n): obj is WithFaceLandmarks, FaceLandmarks> {\n return (\n isWithFaceDetection(obj)\n && (obj as any)['landmarks'] instanceof FaceLandmarks\n && (obj as any)['unshiftedLandmarks'] instanceof FaceLandmarks\n && (obj as any)['alignedRect'] instanceof FaceDetection\n );\n}\n\nfunction calculateFaceAngle(mesh: FaceLandmarks) {\n // Helper to convert radians to degrees\n // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n const degrees = (radians: number) => (radians * 180) / Math.PI;\n const calcLengthBetweenTwoPoints = (a: Point, b: Point) => Math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2);\n\n const angle = {\n roll: undefined,\n pitch: undefined,\n yaw: undefined,\n };\n\n const calcYaw = (leftPoint: Point, midPoint: Point, rightPoint: Point) => {\n // Calc x-distance from left side of the face (\"ear\") to facial midpoint (\"nose\")\n const leftToMidpoint = Math.floor(leftPoint.x - midPoint.x);\n // Calc x-distance from facial midpoint (\"nose\") to the right side of the face (\"ear\")\n const rightToMidpoint = Math.floor(midPoint.x - rightPoint.x);\n // Difference in distances coincidentally approximates to angles\n return leftToMidpoint - rightToMidpoint;\n };\n\n const calcRoll = (lever: Point, pivot: Point) => {\n // When rolling, the head seems to pivot from the nose/lips/chin area.\n // So, we'll choose any two points from the facial midline, where the first point should be the pivot, and the other \"lever\"\n // Plan/Execution: get the hypotenuse & opposite sides of a 90deg triangle ==> Calculate angle in radians\n const hypotenuse = Math.hypot(pivot.x - lever.x, pivot.y - lever.y);\n const opposite = pivot.y - lever.y;\n const angleInRadians = Math.asin(opposite / hypotenuse);\n const angleInDegrees = degrees(angleInRadians);\n const normalizeAngle = Math.floor(90 - angleInDegrees);\n // If lever more to the left of the pivot, then we're tilting left\n // \"-\" is negative direction. \"+\", or absence of a sign is positive direction\n const tiltDirection = pivot.x - lever.x < 0 ? -1 : 1;\n const result = normalizeAngle * tiltDirection;\n return result;\n };\n\n const calcPitch = (leftPoint: Point, midPoint: Point, rightPoint: Point) => {\n // Theory: While pitching, the nose is the most salient point --> That's what we'll use to make a trianle.\n // The \"base\" is between point that don't move when we pitch our head (i.e. an imaginary line running ear to ear through the nose).\n // Executuin: Get the opposite & adjacent lengths of the triangle from the ear's perspective. Use it to get angle.\n\n const base = calcLengthBetweenTwoPoints(leftPoint, rightPoint);\n // adjecent is base/2 technically.\n const baseCoords = new Point((leftPoint.x + rightPoint.x) / 2, (leftPoint.y + rightPoint.y) / 2);\n const midToBaseLength = calcLengthBetweenTwoPoints(midPoint, baseCoords);\n const angleInRadians = Math.atan(midToBaseLength / base);\n const angleInDegrees = Math.floor(degrees(angleInRadians));\n // Account for directionality.\n // pitch forwards (_i.e. tilting your head forwards) is positive (or no sign); backward is negative.\n const direction = baseCoords.y - midPoint.y < 0 ? -1 : 1;\n const result = angleInDegrees * direction;\n return result;\n };\n\n if (!mesh || !mesh.positions || mesh.positions.length !== 68) return angle;\n const pt = mesh.positions;\n angle.roll = calcRoll(pt[27], pt[66]);\n angle.pitch = calcPitch(pt[14], pt[30], pt[2]);\n angle.yaw = calcYaw(pt[14], pt[33], pt[2]);\n return angle;\n}\n\nexport function extendWithFaceLandmarks, TFaceLandmarks extends FaceLandmarks = FaceLandmarks68>(\n sourceObj: TSource,\n unshiftedLandmarks: TFaceLandmarks,\n): WithFaceLandmarks {\n const { box: shift } = sourceObj.detection;\n const landmarks = unshiftedLandmarks.shiftBy(shift.x, shift.y);\n const rect = landmarks.align();\n const { imageDims } = sourceObj.detection;\n const alignedRect = new FaceDetection(\n sourceObj.detection.score,\n rect.rescale(imageDims.reverse()),\n imageDims,\n );\n const angle = calculateFaceAngle(unshiftedLandmarks);\n const extension = { landmarks, unshiftedLandmarks, alignedRect, angle };\n return { ...sourceObj, ...extension };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IPoint } from '../classes/index';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { drawContour } from './drawContour';\n\nexport interface IDrawFaceLandmarksOptions {\n drawLines?: boolean\n drawPoints?: boolean\n lineWidth?: number\n pointSize?: number\n lineColor?: string\n pointColor?: string\n}\n\nexport class DrawFaceLandmarksOptions {\n public drawLines: boolean;\n\n public drawPoints: boolean;\n\n public lineWidth: number;\n\n public pointSize: number;\n\n public lineColor: string;\n\n public pointColor: string;\n\n constructor(options: IDrawFaceLandmarksOptions = {}) {\n const {\n drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor,\n } = options;\n this.drawLines = drawLines;\n this.drawPoints = drawPoints;\n this.lineWidth = lineWidth || 1;\n this.pointSize = pointSize || 2;\n this.lineColor = lineColor || 'rgba(0, 255, 255, 1)';\n this.pointColor = pointColor || 'rgba(255, 0, 255, 1)';\n }\n}\n\nexport class DrawFaceLandmarks {\n public faceLandmarks: FaceLandmarks;\n\n public options: DrawFaceLandmarksOptions;\n\n constructor(\n faceLandmarks: FaceLandmarks,\n options: IDrawFaceLandmarksOptions = {},\n ) {\n this.faceLandmarks = faceLandmarks;\n this.options = new DrawFaceLandmarksOptions(options);\n }\n\n draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n const ctx = getContext2dOrThrow(canvasArg);\n\n const {\n drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor,\n } = this.options;\n\n if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\n ctx.strokeStyle = lineColor;\n ctx.lineWidth = lineWidth;\n drawContour(ctx, this.faceLandmarks.getJawOutline());\n drawContour(ctx, this.faceLandmarks.getLeftEyeBrow());\n drawContour(ctx, this.faceLandmarks.getRightEyeBrow());\n drawContour(ctx, this.faceLandmarks.getNose());\n drawContour(ctx, this.faceLandmarks.getLeftEye(), true);\n drawContour(ctx, this.faceLandmarks.getRightEye(), true);\n drawContour(ctx, this.faceLandmarks.getMouth(), true);\n }\n\n if (drawPoints) {\n ctx.strokeStyle = pointColor;\n ctx.fillStyle = pointColor;\n\n const drawPoint = (pt: IPoint) => {\n ctx.beginPath();\n ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI);\n ctx.fill();\n };\n this.faceLandmarks.positions.forEach(drawPoint);\n }\n }\n}\n\nexport type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks>\n\nexport function drawFaceLandmarks(\n canvasArg: string | HTMLCanvasElement,\n faceLandmarks: DrawFaceLandmarksInput | Array,\n) {\n const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks];\n faceLandmarksArray.forEach((f) => {\n // eslint-disable-next-line no-nested-ternary\n const landmarks = f instanceof FaceLandmarks\n ? f\n : (isWithFaceLandmarks(f) ? f.landmarks : undefined);\n if (!landmarks) {\n throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks> or array thereof');\n }\n\n new DrawFaceLandmarks(landmarks).draw(canvasArg);\n });\n}\n", "{\n \"name\": \"@vladmandic/face-api\",\n \"version\": \"1.7.13\",\n \"description\": \"FaceAPI: AI-powered Face Detection & Rotation Tracking, Face Description & Recognition, Age & Gender & Emotion Prediction for Browser and NodeJS using TensorFlow/JS\",\n \"sideEffects\": false,\n \"main\": \"dist/face-api.node.js\",\n \"module\": \"dist/face-api.esm.js\",\n \"browser\": \"dist/face-api.esm.js\",\n \"types\": \"types/face-api.d.ts\",\n \"author\": \"Vladimir Mandic \",\n \"bugs\": {\n \"url\": \"https://github.com/vladmandic/face-api/issues\"\n },\n \"homepage\": \"https://vladmandic.github.io/face-api/demo/webcam.html\",\n \"license\": \"MIT\",\n \"engines\": {\n \"node\": \">=14.0.0\"\n },\n \"repository\": {\n \"type\": \"git\",\n \"url\": \"git+https://github.com/vladmandic/face-api.git\"\n },\n \"scripts\": {\n \"start\": \"node --no-warnings demo/node.js\",\n \"build\": \"node build.js\",\n \"dev\": \"build --profile development\",\n \"typings\": \"build --profile typings\",\n \"lint\": \"eslint src/ demo/\",\n \"test\": \"node --trace-warnings test/test-node.js\",\n \"scan\": \"npx auditjs@latest ossi --dev --quiet\"\n },\n \"keywords\": [\n \"face-api\",\n \"faceapi\",\n \"face-detection\",\n \"age-gender\",\n \"emotion-detection\",\n \"face-recognition\",\n \"face\",\n \"face-description\",\n \"tensorflow\",\n \"tensorflowjs\",\n \"tfjs\"\n ],\n \"devDependencies\": {\n \"@canvas/image\": \"^2.0.0\",\n \"@microsoft/api-extractor\": \"^7.39.1\",\n \"@tensorflow/tfjs\": \"^4.16.0\",\n \"@tensorflow/tfjs-backend-cpu\": \"^4.16.0\",\n \"@tensorflow/tfjs-backend-wasm\": \"^4.16.0\",\n \"@tensorflow/tfjs-backend-webgl\": \"^4.16.0\",\n \"@tensorflow/tfjs-backend-webgpu\": \"4.16.0\",\n \"@tensorflow/tfjs-converter\": \"^4.16.0\",\n \"@tensorflow/tfjs-core\": \"^4.16.0\",\n \"@tensorflow/tfjs-data\": \"^4.16.0\",\n \"@tensorflow/tfjs-layers\": \"^4.16.0\",\n \"@tensorflow/tfjs-node\": \"^4.16.0\",\n \"@tensorflow/tfjs-node-gpu\": \"^4.16.0\",\n \"@types/node\": \"^20.11.5\",\n \"@types/offscreencanvas\": \"^2019.7.3\",\n \"@typescript-eslint/eslint-plugin\": \"^6.19.0\",\n \"@typescript-eslint/parser\": \"^6.19.0\",\n \"@vladmandic/build\": \"^0.9.3\",\n \"@vladmandic/pilogger\": \"^0.4.9\",\n \"esbuild\": \"^0.19.11\",\n \"eslint\": \"^8.56.0\",\n \"eslint-config-airbnb-base\": \"^15.0.0\",\n \"eslint-plugin-import\": \"^2.29.1\",\n \"eslint-plugin-json\": \"^3.1.0\",\n \"eslint-plugin-node\": \"^11.1.0\",\n \"eslint-plugin-promise\": \"^6.1.1\",\n \"node-fetch\": \"^3.3.2\",\n \"rimraf\": \"^5.0.5\",\n \"seedrandom\": \"^3.0.5\",\n \"tslib\": \"^2.6.2\",\n \"typedoc\": \"^0.25.7\",\n \"typescript\": \"5.3.3\"\n }\n}\n", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common/index';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n function extractReductionBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ReductionBlockParams {\n const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`);\n const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`);\n const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`);\n\n return { separable_conv0, separable_conv1, expansion_conv };\n }\n\n function extractMainBlockParams(channels: number, mappedPrefix: string): MainBlockParams {\n const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`);\n const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`);\n const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`);\n\n return { separable_conv0, separable_conv1, separable_conv2 };\n }\n\n return {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n };\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n } = extractorsFactory(extractWeights, paramMappings);\n\n const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in');\n const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0');\n const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1');\n\n const entry_flow = {\n conv_in: entry_flow_conv_in,\n reduction_block_0: entry_flow_reduction_block_0,\n reduction_block_1: entry_flow_reduction_block_1,\n };\n\n const middle_flow: Record<`main_block_${number}`, MainBlockParams> = {};\n range(numMainBlocks, 0, 1).forEach((idx) => {\n middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`);\n });\n\n const exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block');\n const exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv');\n\n const exit_flow = {\n reduction_block: exit_flow_reduction_block,\n separable_conv: exit_flow_separable_conv,\n };\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { entry_flow, middle_flow, exit_flow },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`);\n\n return { separable_conv0, separable_conv1, expansion_conv };\n }\n\n function extractMainBlockParams(mappedPrefix: string): MainBlockParams {\n const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`);\n\n return { separable_conv0, separable_conv1, separable_conv2 };\n }\n\n return {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n };\n}\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n numMainBlocks: number,\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n } = loadParamsFactory(weightMap, paramMappings);\n\n const entry_flow_conv_in = extractConvParams('entry_flow/conv_in');\n const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0');\n const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1');\n\n const entry_flow = {\n conv_in: entry_flow_conv_in,\n reduction_block_0: entry_flow_reduction_block_0,\n reduction_block_1: entry_flow_reduction_block_1,\n };\n\n const middle_flow: Record<`main_block_${number}`, MainBlockParams> = {};\n range(numMainBlocks, 0, 1).forEach((idx) => {\n middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`);\n });\n\n const exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block');\n const exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv');\n\n const exit_flow = {\n reduction_block: exit_flow_reduction_block,\n separable_conv: exit_flow_separable_conv,\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params: { entry_flow, middle_flow, exit_flow }, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common/index';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { range } from '../utils/index';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias);\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput = true): tf.Tensor4D {\n let out = isActivateInput ? tf.relu(x) : x;\n out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1]);\n out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n out = tf.maxPool(out, [3, 3], [2, 2], 'same');\n out = tf.add(out, conv(x, params.expansion_conv, [2, 2]));\n return out;\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1]);\n out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1]);\n out = tf.add(out, x);\n return out;\n}\n\nexport class TinyXception extends NeuralNetwork {\n private _numMainBlocks: number;\n\n constructor(numMainBlocks: number) {\n super('TinyXception');\n this._numMainBlocks = numMainBlocks;\n }\n\n public forwardInput(input: NetInput): tf.Tensor4D {\n const { params } = this;\n if (!params) {\n throw new Error('TinyXception - load model before inference');\n }\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n const meanRgb = [122.782, 117.001, 104.298];\n const normalized = normalize(batchTensor, meanRgb).div(255) as tf.Tensor4D;\n let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]));\n out = reductionBlock(out, params.entry_flow.reduction_block_0, false);\n out = reductionBlock(out, params.entry_flow.reduction_block_1);\n range(this._numMainBlocks, 0, 1).forEach((idx) => {\n out = mainBlock(out, params.middle_flow[`main_block_${idx}`]);\n });\n out = reductionBlock(out, params.exit_flow.reduction_block);\n out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]));\n return out;\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n protected getDefaultModelName(): string {\n return 'tiny_xception_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap, this._numMainBlocks);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParams(weights, this._numMainBlocks);\n }\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n const age = extractFCParams(512, 1, 'fc/age');\n const gender = extractFCParams(512, 2, 'fc/gender');\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { fc: { age, gender } },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractFcParams(prefix: string): FCParams {\n const weights = extractWeightEntry(`${prefix}/weights`, 2);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n return { weights, bias };\n }\n\n const params = {\n fc: {\n age: extractFcParams('fc/age'),\n gender: extractFcParams('fc/gender'),\n },\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from '../common/index';\n\n// eslint-disable-next-line no-shadow\nexport enum Gender {\n // eslint-disable-next-line no-unused-vars\n FEMALE = 'female',\n // eslint-disable-next-line no-unused-vars\n MALE = 'male'\n}\n\nexport type AgeAndGenderPrediction = {\n age: number\n gender: Gender\n genderProbability: number\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n fc: {\n age: FCParams\n gender: FCParams\n }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { AgeAndGenderPrediction, Gender, NetOutput, NetParams } from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\n\nexport class AgeGenderNet extends NeuralNetwork {\n private _faceFeatureExtractor: TinyXception;\n\n constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n super('AgeGenderNet');\n this._faceFeatureExtractor = faceFeatureExtractor;\n }\n\n public get faceFeatureExtractor(): TinyXception {\n return this._faceFeatureExtractor;\n }\n\n public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n const { params } = this;\n\n if (!params) {\n throw new Error(`${this._name} - load model before inference`);\n }\n\n return tf.tidy(() => {\n const bottleneckFeatures = input instanceof NetInput\n ? this.faceFeatureExtractor.forwardInput(input)\n : input;\n\n const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1);\n const age = fullyConnectedLayer(pooled, params.fc.age).as1D();\n const gender = fullyConnectedLayer(pooled, params.fc.gender);\n return { age, gender };\n });\n }\n\n public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n return tf.tidy(() => {\n const { age, gender } = this.runNet(input);\n return { age, gender: tf.softmax(gender) };\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async predictAgeAndGender(input: TNetInput): Promise {\n const netInput = await toNetInput(input);\n const out = await this.forwardInput(netInput);\n\n const ages = tf.unstack(out.age);\n const genders = tf.unstack(out.gender);\n const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n ageTensor,\n genderTensor: genders[i],\n }));\n\n const predictionsByBatch = await Promise.all(\n ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n const age = (ageTensor.dataSync())[0];\n const probMale = (genderTensor.dataSync())[0];\n const isMale = probMale > 0.5;\n const gender = isMale ? Gender.MALE : Gender.FEMALE;\n const genderProbability = isMale ? probMale : (1 - probMale);\n\n ageTensor.dispose();\n genderTensor.dispose();\n return { age, gender, genderProbability };\n }),\n );\n out.age.dispose();\n out.gender.dispose();\n\n return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction;\n }\n\n protected getDefaultModelName(): string {\n return 'age_gender_model';\n }\n\n public override dispose(throwOnRedispose = true) {\n this.faceFeatureExtractor.dispose(throwOnRedispose);\n super.dispose(throwOnRedispose);\n }\n\n public loadClassifierParams(weights: Float32Array) {\n const { params, paramMappings } = this.extractClassifierParams(weights);\n this._params = params;\n this._paramMappings = paramMappings;\n }\n\n public extractClassifierParams(weights: Float32Array) {\n return extractParams(weights);\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n return extractParamsFromWeightMap(classifierMap);\n }\n\n protected extractParams(weights: Float32Array) {\n const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2);\n\n const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n return this.extractClassifierParams(classifierWeights);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { IDimensions, Point } from '../classes/index';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils/index';\n\nexport abstract class FaceLandmark68NetBase<\n TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n extends FaceProcessor {\n public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n const inputDimensions = originalDimensions.map(({ width, height }) => {\n const scale = inputSize / Math.max(height, width);\n return {\n width: width * scale,\n height: height * scale,\n };\n });\n\n const batchSize = inputDimensions.length;\n\n return tf.tidy(() => {\n const createInterleavedTensor = (fillX: number, fillY: number) => tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D();\n\n // eslint-disable-next-line no-unused-vars\n const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n const { width, height } = inputDimensions[batchIdx];\n return cond(width, height) ? Math.abs(width - height) / 2 : 0;\n };\n\n const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h);\n const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w);\n\n const landmarkTensors = output\n .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n getPaddingX(batchIdx),\n getPaddingY(batchIdx),\n ))))\n .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n inputDimensions[batchIdx].width,\n inputDimensions[batchIdx].height,\n ))));\n\n return landmarkTensors as tf.Tensor2D;\n });\n }\n\n public forwardInput(input: NetInput): tf.Tensor2D {\n return tf.tidy(() => {\n const out = this.runNet(input);\n return this.postProcess(\n out,\n input.inputSize as number,\n input.inputDimensions.map(([height, width]) => ({ height, width })),\n );\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async detectLandmarks(input: TNetInput): Promise {\n const netInput = await toNetInput(input);\n const landmarkTensors = tf.tidy(\n () => tf.unstack(this.forwardInput(netInput)),\n );\n\n const landmarksForBatch = await Promise.all(landmarkTensors.map(\n async (landmarkTensor, batchIdx) => {\n const landmarksArray = Array.from(landmarkTensor.dataSync());\n const xCoords = landmarksArray.filter((_, i) => isEven(i));\n const yCoords = landmarksArray.filter((_, i) => !isEven(i));\n\n return new FaceLandmarks68(\n Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n {\n height: netInput.getInputHeight(batchIdx),\n width: netInput.getInputWidth(batchIdx),\n },\n );\n },\n ));\n\n landmarkTensors.forEach((t) => t.dispose());\n\n return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n }\n\n protected getClassifierChannelsOut(): number {\n return 136;\n }\n}\n", "import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68Net extends FaceLandmark68NetBase {\n constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n super('FaceLandmark68Net', faceFeatureExtractor);\n }\n\n protected getDefaultModelName(): string {\n return 'face_landmark_68_model';\n }\n\n protected getClassifierChannelsIn(): number {\n return 256;\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeightMapTiny(\n weightMap: tf.NamedTensorMap,\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractDenseBlock3Params,\n } = loadParamsFactory(weightMap, paramMappings);\n\n const params = {\n dense0: extractDenseBlock3Params('dense0', true),\n dense1: extractDenseBlock3Params('dense1'),\n dense2: extractDenseBlock3Params('dense2'),\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const {\n extractDenseBlock3Params,\n } = extractorsFactory(extractWeights, paramMappings);\n\n const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true);\n const dense1 = extractDenseBlock3Params(32, 64, 'dense1');\n const dense2 = extractDenseBlock3Params(64, 128, 'dense2');\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { dense0, dense1, dense2 },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeightMapTiny } from './extractParamsFromWeightMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork implements IFaceFeatureExtractor {\n constructor() {\n super('TinyFaceFeatureExtractor');\n }\n\n public forwardInput(input: NetInput): tf.Tensor4D {\n const { params } = this;\n\n if (!params) {\n throw new Error('TinyFaceFeatureExtractor - load model before inference');\n }\n\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n const meanRgb = [122.782, 117.001, 104.298];\n const normalized = normalize(batchTensor, meanRgb).div(255) as tf.Tensor4D;\n\n let out = denseBlock3(normalized, params.dense0, true);\n out = denseBlock3(out, params.dense1);\n out = denseBlock3(out, params.dense2);\n out = tf.avgPool(out, [14, 14], [2, 2], 'valid');\n\n return out;\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n protected getDefaultModelName(): string {\n return 'face_feature_extractor_tiny_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMapTiny(weightMap);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParamsTiny(weights);\n }\n}\n", "import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase {\n constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\n super('FaceLandmark68TinyNet', faceFeatureExtractor);\n }\n\n protected getDefaultModelName(): string {\n return 'face_landmark_68_tiny_model';\n }\n\n protected getClassifierChannelsIn(): number {\n return 128;\n }\n}\n", "import { FaceLandmark68Net } from './FaceLandmark68Net';\n\nexport * from './FaceLandmark68Net';\nexport * from './FaceLandmark68TinyNet';\nexport class FaceLandmarkNet extends FaceLandmark68Net {}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ScaleLayerParams } from './types';\n\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\n return tf.add(tf.mul(x, params.weights), params.biases);\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\nfunction convLayer(\n x: tf.Tensor4D,\n params: ConvLayerParams,\n strides: [number, number],\n withRelu: boolean,\n padding: 'valid' | 'same' = 'same',\n): tf.Tensor4D {\n const { filters, bias } = params.conv;\n\n let out = tf.conv2d(x, filters, strides, padding);\n out = tf.add(out, bias);\n out = scale(out, params.scale);\n return withRelu ? tf.relu(out) : out;\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n return convLayer(x, params, [1, 1], true);\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n return convLayer(x, params, [1, 1], false);\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n return convLayer(x, params, [2, 2], true, 'valid');\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from '../common/index';\nimport { isFloat } from '../utils/index';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n const weights = extractWeights(numFilterValues);\n const depth = weights.length / (numFilters * filterSize * filterSize);\n\n if (isFloat(depth)) {\n throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`);\n }\n\n return tf.tidy(\n () => tf.transpose(\n tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n [2, 3, 1, 0],\n ),\n );\n }\n\n function extractConvParams(\n numFilterValues: number,\n numFilters: number,\n filterSize: number,\n mappedPrefix: string,\n ): ConvParams {\n const filters = extractFilterValues(numFilterValues, numFilters, filterSize);\n const bias = tf.tensor1d(extractWeights(numFilters));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/filters` },\n { paramPath: `${mappedPrefix}/bias` },\n );\n\n return { filters, bias };\n }\n\n function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n const weights = tf.tensor1d(extractWeights(numWeights));\n const biases = tf.tensor1d(extractWeights(numWeights));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/weights` },\n { paramPath: `${mappedPrefix}/biases` },\n );\n\n return {\n weights,\n biases,\n };\n }\n\n function extractConvLayerParams(\n numFilterValues: number,\n numFilters: number,\n filterSize: number,\n mappedPrefix: string,\n ): ConvLayerParams {\n const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`);\n const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`);\n\n return { conv, scale };\n }\n\n function extractResidualLayerParams(\n numFilterValues: number,\n numFilters: number,\n filterSize: number,\n mappedPrefix: string,\n isDown = false,\n ): ResidualLayerParams {\n const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`);\n const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`);\n\n return { conv1, conv2 };\n }\n\n return {\n extractConvLayerParams,\n extractResidualLayerParams,\n };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractConvLayerParams,\n extractResidualLayerParams,\n } = extractorsFactory(extractWeights, paramMappings);\n\n const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down');\n const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1');\n const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2');\n const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3');\n\n const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true);\n const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1');\n const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2');\n const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3');\n\n const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true);\n const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1');\n const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2');\n\n const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true);\n const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1');\n const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2');\n const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out');\n\n const fc = tf.tidy(\n () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]),\n );\n paramMappings.push({ paramPath: 'fc' });\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n const params = {\n conv32_down,\n conv32_1,\n conv32_2,\n conv32_3,\n conv64_down,\n conv64_1,\n conv64_2,\n conv64_3,\n conv128_down,\n conv128_1,\n conv128_2,\n conv256_down,\n conv256_1,\n conv256_2,\n conv256_down_out,\n fc,\n };\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common/index';\nimport { isTensor2D } from '../utils/index';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractScaleLayerParams(prefix: string): ScaleLayerParams {\n const weights = extractWeightEntry(`${prefix}/scale/weights`, 1);\n const biases = extractWeightEntry(`${prefix}/scale/biases`, 1);\n\n return { weights, biases };\n }\n\n function extractConvLayerParams(prefix: string): ConvLayerParams {\n const filters = extractWeightEntry(`${prefix}/conv/filters`, 4);\n const bias = extractWeightEntry(`${prefix}/conv/bias`, 1);\n const scale = extractScaleLayerParams(prefix);\n\n return { conv: { filters, bias }, scale };\n }\n\n function extractResidualLayerParams(prefix: string): ResidualLayerParams {\n return {\n conv1: extractConvLayerParams(`${prefix}/conv1`),\n conv2: extractConvLayerParams(`${prefix}/conv2`),\n };\n }\n\n return {\n extractConvLayerParams,\n extractResidualLayerParams,\n };\n}\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractConvLayerParams,\n extractResidualLayerParams,\n } = extractorsFactory(weightMap, paramMappings);\n\n const conv32_down = extractConvLayerParams('conv32_down');\n const conv32_1 = extractResidualLayerParams('conv32_1');\n const conv32_2 = extractResidualLayerParams('conv32_2');\n const conv32_3 = extractResidualLayerParams('conv32_3');\n\n const conv64_down = extractResidualLayerParams('conv64_down');\n const conv64_1 = extractResidualLayerParams('conv64_1');\n const conv64_2 = extractResidualLayerParams('conv64_2');\n const conv64_3 = extractResidualLayerParams('conv64_3');\n\n const conv128_down = extractResidualLayerParams('conv128_down');\n const conv128_1 = extractResidualLayerParams('conv128_1');\n const conv128_2 = extractResidualLayerParams('conv128_2');\n\n const conv256_down = extractResidualLayerParams('conv256_down');\n const conv256_1 = extractResidualLayerParams('conv256_1');\n const conv256_2 = extractResidualLayerParams('conv256_2');\n const conv256_down_out = extractResidualLayerParams('conv256_down_out');\n\n const { fc } = weightMap;\n paramMappings.push({ originalPath: 'fc', paramPath: 'fc' });\n\n if (!isTensor2D(fc)) {\n throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`);\n }\n\n const params = {\n conv32_down,\n conv32_1,\n conv32_2,\n conv32_3,\n conv64_down,\n conv64_1,\n conv64_2,\n conv64_3,\n conv128_down,\n conv128_1,\n conv128_2,\n conv256_down,\n conv256_1,\n conv256_2,\n conv256_down_out,\n fc,\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n let out = conv(x, params.conv1);\n out = convNoRelu(out, params.conv2);\n out = tf.add(out, x);\n out = tf.relu(out);\n return out;\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n let out = convDown(x, params.conv1);\n out = convNoRelu(out, params.conv2);\n\n let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D;\n const zeros = tf.zeros(pooled.shape);\n const isPad = pooled.shape[3] !== out.shape[3];\n const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2];\n\n if (isAdjustShape) {\n const padShapeX = [...out.shape] as [number, number, number, number];\n padShapeX[1] = 1;\n const zerosW = tf.zeros(padShapeX);\n out = tf.concat([out, zerosW], 1);\n\n const padShapeY = [...out.shape] as [number, number, number, number];\n padShapeY[2] = 1;\n const zerosH = tf.zeros(padShapeY);\n out = tf.concat([out, zerosH], 2);\n }\n\n pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled;\n out = tf.add(pooled, out) as tf.Tensor4D;\n\n out = tf.relu(out);\n return out;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\nexport class FaceRecognitionNet extends NeuralNetwork {\n constructor() {\n super('FaceRecognitionNet');\n }\n\n public forwardInput(input: NetInput): tf.Tensor2D {\n const { params } = this;\n\n if (!params) {\n throw new Error('FaceRecognitionNet - load model before inference');\n }\n\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n const meanRgb = [122.782, 117.001, 104.298];\n const normalized = normalize(batchTensor, meanRgb).div(255) as tf.Tensor4D;\n\n let out = convDown(normalized, params.conv32_down);\n out = tf.maxPool(out, 3, 2, 'valid');\n\n out = residual(out, params.conv32_1);\n out = residual(out, params.conv32_2);\n out = residual(out, params.conv32_3);\n\n out = residualDown(out, params.conv64_down);\n out = residual(out, params.conv64_1);\n out = residual(out, params.conv64_2);\n out = residual(out, params.conv64_3);\n\n out = residualDown(out, params.conv128_down);\n out = residual(out, params.conv128_1);\n out = residual(out, params.conv128_2);\n\n out = residualDown(out, params.conv256_down);\n out = residual(out, params.conv256_1);\n out = residual(out, params.conv256_2);\n out = residualDown(out, params.conv256_down_out);\n\n const globalAvg = out.mean([1, 2]) as tf.Tensor2D;\n const fullyConnected = tf.matMul(globalAvg, params.fc);\n\n return fullyConnected as tf.Tensor2D;\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async computeFaceDescriptor(input: TNetInput): Promise {\n // @ts-ignore\n if (input?.shape?.some((dim) => dim <= 0)) return new Float32Array(128);\n const netInput = await toNetInput(input);\n const faceDescriptorTensors = tf.tidy(() => tf.unstack(this.forwardInput(netInput)));\n const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map((t) => t.data())) as Float32Array[];\n faceDescriptorTensors.forEach((t) => t.dispose());\n return netInput.isBatchInput ? faceDescriptorsForBatch : faceDescriptorsForBatch[0];\n }\n\n protected getDefaultModelName(): string {\n return 'face_recognition_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParams(weights);\n }\n}\n", "import { FaceRecognitionNet } from './FaceRecognitionNet';\n\nexport * from './FaceRecognitionNet';\n\nexport function createFaceRecognitionNet(weights: Float32Array) {\n const net = new FaceRecognitionNet();\n net.extractWeights(weights);\n return net;\n}\n", "export type WithFaceDescriptor = TSource & {\n descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n TSource\n>(\n sourceObj: TSource,\n descriptor: Float32Array,\n): WithFaceDescriptor {\n const extension = { descriptor };\n return { ...sourceObj, ...extension };\n}\n", "export type WithAge = TSource & {\n age: number\n}\n\nexport function isWithAge(obj: any): obj is WithAge<{}> {\n return typeof obj.age === 'number';\n}\n\nexport function extendWithAge<\n TSource\n>(\n sourceObj: TSource,\n age: number,\n): WithAge {\n const extension = { age };\n return { ...sourceObj, ...extension };\n}\n", "import { Gender } from '../ageGenderNet/types';\nimport { isValidProbablitiy } from '../utils/index';\n\nexport type WithGender = TSource & {\n gender: Gender\n genderProbability: number\n}\n\nexport function isWithGender(obj: any): obj is WithGender<{}> {\n return (obj.gender === Gender.MALE || obj.gender === Gender.FEMALE)\n && isValidProbablitiy(obj.genderProbability);\n}\n\nexport function extendWithGender<\n TSource\n>(\n sourceObj: TSource,\n gender: Gender,\n genderProbability: number,\n): WithGender {\n const extension = { gender, genderProbability };\n return { ...sourceObj, ...extension };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory } from '../common/index';\nimport { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);\n const batch_norm_scale = tf.tensor1d(extractWeights(numChannels));\n const batch_norm_offset = tf.tensor1d(extractWeights(numChannels));\n const batch_norm_mean = tf.tensor1d(extractWeights(numChannels));\n const batch_norm_variance = tf.tensor1d(extractWeights(numChannels));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/filters` },\n { paramPath: `${mappedPrefix}/batch_norm_scale` },\n { paramPath: `${mappedPrefix}/batch_norm_offset` },\n { paramPath: `${mappedPrefix}/batch_norm_mean` },\n { paramPath: `${mappedPrefix}/batch_norm_variance` },\n );\n\n return {\n filters,\n batch_norm_scale,\n batch_norm_offset,\n batch_norm_mean,\n batch_norm_variance,\n };\n }\n\n function extractConvParams(\n channelsIn: number,\n channelsOut: number,\n filterSize: number,\n mappedPrefix: string,\n isPointwiseConv?: boolean,\n ): ConvParams {\n const filters = tf.tensor4d(\n extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n [filterSize, filterSize, channelsIn, channelsOut],\n );\n const bias = tf.tensor1d(extractWeights(channelsOut));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/filters` },\n { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` },\n );\n\n return { filters, bias };\n }\n\n function extractPointwiseConvParams(\n channelsIn: number,\n channelsOut: number,\n filterSize: number,\n mappedPrefix: string,\n ): PointwiseConvParams {\n const {\n filters,\n bias,\n } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true);\n\n return {\n filters,\n batch_norm_offset: bias,\n };\n }\n\n function extractConvPairParams(\n channelsIn: number,\n channelsOut: number,\n mappedPrefix: string,\n ): MobileNetV1.ConvPairParams {\n const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`);\n const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`);\n\n return { depthwise_conv, pointwise_conv };\n }\n\n function extractMobilenetV1Params(): MobileNetV1.Params {\n const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0');\n const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1');\n const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2');\n const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3');\n const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4');\n const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5');\n const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6');\n const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7');\n const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8');\n const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9');\n const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10');\n const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11');\n const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12');\n const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13');\n return {\n conv_0,\n conv_1,\n conv_2,\n conv_3,\n conv_4,\n conv_5,\n conv_6,\n conv_7,\n conv_8,\n conv_9,\n conv_10,\n conv_11,\n conv_12,\n conv_13,\n };\n }\n\n function extractPredictionLayerParams(): PredictionLayerParams {\n const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0');\n const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1');\n const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2');\n const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3');\n const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4');\n const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5');\n const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6');\n const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7');\n const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor');\n const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor');\n const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor');\n const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor');\n const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor');\n const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor');\n const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor');\n const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor');\n const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor');\n const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor');\n const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor');\n const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor');\n\n const box_predictor_0 = {\n box_encoding_predictor: box_encoding_0_predictor,\n class_predictor: class_predictor_0,\n };\n const box_predictor_1 = {\n box_encoding_predictor: box_encoding_1_predictor,\n class_predictor: class_predictor_1,\n };\n const box_predictor_2 = {\n box_encoding_predictor: box_encoding_2_predictor,\n class_predictor: class_predictor_2,\n };\n const box_predictor_3 = {\n box_encoding_predictor: box_encoding_3_predictor,\n class_predictor: class_predictor_3,\n };\n const box_predictor_4 = {\n box_encoding_predictor: box_encoding_4_predictor,\n class_predictor: class_predictor_4,\n };\n const box_predictor_5 = {\n box_encoding_predictor: box_encoding_5_predictor,\n class_predictor: class_predictor_5,\n };\n return {\n conv_0,\n conv_1,\n conv_2,\n conv_3,\n conv_4,\n conv_5,\n conv_6,\n conv_7,\n box_predictor_0,\n box_predictor_1,\n box_predictor_2,\n box_predictor_3,\n box_predictor_4,\n box_predictor_5,\n };\n }\n\n return {\n extractMobilenetV1Params,\n extractPredictionLayerParams,\n };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n const {\n extractMobilenetV1Params,\n extractPredictionLayerParams,\n } = extractorsFactory(extractWeights, paramMappings);\n const mobilenetv1 = extractMobilenetV1Params();\n const prediction_layer = extractPredictionLayerParams();\n const extra_dim = tf.tensor3d(\n extractWeights(5118 * 4),\n [1, 5118, 4],\n );\n const output_layer = {\n extra_dim,\n };\n paramMappings.push({ paramPath: 'output_layer/extra_dim' });\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n params: {\n mobilenetv1,\n prediction_layer,\n output_layer,\n },\n paramMappings,\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common/index';\nimport { isTensor3D } from '../utils/index';\nimport { BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n const filters = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`);\n const batch_norm_offset = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`);\n return { filters, batch_norm_offset };\n }\n\n function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\n const mappedPrefix = `mobilenetv1/conv_${idx}`;\n const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`;\n const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`;\n const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`;\n\n const filters = extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`);\n const batch_norm_scale = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`);\n const batch_norm_offset = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`);\n const batch_norm_mean = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`);\n const batch_norm_variance = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`);\n\n return {\n depthwise_conv: {\n filters,\n batch_norm_scale,\n batch_norm_offset,\n batch_norm_mean,\n batch_norm_variance,\n },\n pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv),\n };\n }\n\n function extractMobilenetV1Params(): MobileNetV1.Params {\n return {\n conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\n conv_1: extractConvPairParams(1),\n conv_2: extractConvPairParams(2),\n conv_3: extractConvPairParams(3),\n conv_4: extractConvPairParams(4),\n conv_5: extractConvPairParams(5),\n conv_6: extractConvPairParams(6),\n conv_7: extractConvPairParams(7),\n conv_8: extractConvPairParams(8),\n conv_9: extractConvPairParams(9),\n conv_10: extractConvPairParams(10),\n conv_11: extractConvPairParams(11),\n conv_12: extractConvPairParams(12),\n conv_13: extractConvPairParams(13),\n };\n }\n\n function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\n const filters = extractWeightEntry(`${prefix}/weights`, 4, `${mappedPrefix}/filters`);\n const bias = extractWeightEntry(`${prefix}/biases`, 1, `${mappedPrefix}/bias`);\n return { filters, bias };\n }\n\n function extractBoxPredictorParams(idx: number): BoxPredictionParams {\n const box_encoding_predictor = extractConvParams(\n `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\n `prediction_layer/box_predictor_${idx}/box_encoding_predictor`,\n );\n const class_predictor = extractConvParams(\n `Prediction/BoxPredictor_${idx}/ClassPredictor`,\n `prediction_layer/box_predictor_${idx}/class_predictor`,\n );\n return { box_encoding_predictor, class_predictor };\n }\n\n function extractPredictionLayerParams(): PredictionLayerParams {\n return {\n conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\n conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\n conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\n conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\n conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\n conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\n conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\n conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\n box_predictor_0: extractBoxPredictorParams(0),\n box_predictor_1: extractBoxPredictorParams(1),\n box_predictor_2: extractBoxPredictorParams(2),\n box_predictor_3: extractBoxPredictorParams(3),\n box_predictor_4: extractBoxPredictorParams(4),\n box_predictor_5: extractBoxPredictorParams(5),\n };\n }\n\n return {\n extractMobilenetV1Params,\n extractPredictionLayerParams,\n };\n}\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n const {\n extractMobilenetV1Params,\n extractPredictionLayerParams,\n } = extractorsFactory(weightMap, paramMappings);\n const extra_dim = weightMap['Output/extra_dim'];\n paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' });\n if (!isTensor3D(extra_dim)) {\n throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`);\n }\n\n const params = {\n mobilenetv1: extractMobilenetV1Params(),\n prediction_layer: extractPredictionLayerParams(),\n output_layer: {\n extra_dim,\n },\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { PointwiseConvParams } from './types';\n\nexport function pointwiseConvLayer(x: tf.Tensor4D, params: PointwiseConvParams, strides: [number, number]) {\n return tf.tidy(() => {\n let out = tf.conv2d(x, params.filters, strides, 'same');\n out = tf.add(out, params.batch_norm_offset);\n return tf.clipByValue(out, 0, 6);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513;\n\nfunction depthwiseConvLayer(x: tf.Tensor4D, params: MobileNetV1.DepthwiseConvParams, strides: [number, number]) {\n return tf.tidy(() => {\n let out = tf.depthwiseConv2d(x, params.filters, strides, 'same');\n out = tf.batchNorm(\n out,\n params.batch_norm_mean,\n params.batch_norm_variance,\n params.batch_norm_offset,\n params.batch_norm_scale,\n epsilon,\n );\n return tf.clipByValue(out, 0, 6);\n });\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n return [2, 4, 6, 12].some((idx) => idx === layerIdx) ? [2, 2] : [1, 1];\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n return tf.tidy(() => {\n let conv11;\n let out = pointwiseConvLayer(x, params.conv_0, [2, 2]);\n\n const convPairParams = [\n params.conv_1,\n params.conv_2,\n params.conv_3,\n params.conv_4,\n params.conv_5,\n params.conv_6,\n params.conv_7,\n params.conv_8,\n params.conv_9,\n params.conv_10,\n params.conv_11,\n params.conv_12,\n params.conv_13,\n ];\n\n convPairParams.forEach((param, i) => {\n const layerIdx = i + 1;\n const depthwiseConvStrides = getStridesForLayerIdx(layerIdx);\n out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides);\n out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1]);\n if (layerIdx === 11) conv11 = out;\n });\n\n if (conv11 === null) {\n throw new Error('mobileNetV1 - output of conv layer 11 is null');\n }\n\n return {\n out,\n conv11: conv11 as any,\n };\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\n const boxesData = boxes.arraySync();\n const yminI = Math.min(boxesData[i][0], boxesData[i][2]);\n const xminI = Math.min(boxesData[i][1], boxesData[i][3]);\n const ymaxI = Math.max(boxesData[i][0], boxesData[i][2]);\n const xmaxI = Math.max(boxesData[i][1], boxesData[i][3]);\n const yminJ = Math.min(boxesData[j][0], boxesData[j][2]);\n const xminJ = Math.min(boxesData[j][1], boxesData[j][3]);\n const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2]);\n const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3]);\n const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n if (areaI <= 0 || areaJ <= 0) return 0.0;\n const intersectionYmin = Math.max(yminI, yminJ);\n const intersectionXmin = Math.max(xminI, xminJ);\n const intersectionYmax = Math.min(ymaxI, ymaxJ);\n const intersectionXmax = Math.min(xmaxI, xmaxJ);\n const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) * Math.max(intersectionXmax - intersectionXmin, 0.0);\n return intersectionArea / (areaI + areaJ - intersectionArea);\n}\n\nexport function nonMaxSuppression(\n boxes: tf.Tensor2D,\n scores: number[],\n maxOutputSize: number,\n iouThreshold: number,\n scoreThreshold: number,\n): number[] {\n const numBoxes = boxes.shape[0];\n const outputSize = Math.min(maxOutputSize, numBoxes);\n\n const candidates = scores\n .map((score, boxIndex) => ({ score, boxIndex }))\n .filter((c) => c.score > scoreThreshold)\n .sort((c1, c2) => c2.score - c1.score);\n\n const suppressFunc = (x: number) => (x <= iouThreshold ? 1 : 0);\n const selected: number[] = [];\n\n candidates.forEach((c) => {\n if (selected.length >= outputSize) return;\n const originalScore = c.score;\n for (let j = selected.length - 1; j >= 0; --j) {\n const iou = IOU(boxes, c.boxIndex, selected[j]);\n if (iou === 0.0) continue;\n c.score *= suppressFunc(iou);\n if (c.score <= scoreThreshold) break;\n }\n if (originalScore === c.score) {\n selected.push(c.boxIndex);\n }\n });\n return selected;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { OutputLayerParams } from './types';\n\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\n const vec = tf.unstack(tf.transpose(x, [1, 0]));\n\n const sizes = [\n tf.sub(vec[2], vec[0]),\n tf.sub(vec[3], vec[1]),\n ];\n const centers = [\n tf.add(vec[0], tf.div(sizes[0], 2)),\n tf.add(vec[1], tf.div(sizes[1], 2)),\n ];\n return { sizes, centers };\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n const { sizes, centers } = getCenterCoordinatesAndSizesLayer(x0);\n\n const vec = tf.unstack(tf.transpose(x1, [1, 0]));\n const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], 5)), sizes[0]), 2);\n const add0_out = tf.add(tf.mul(tf.div(vec[0], 10), sizes[0]), centers[0]);\n const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], 5)), sizes[1]), 2);\n const add1_out = tf.add(tf.mul(tf.div(vec[1], 10), sizes[1]), centers[1]);\n\n return tf.transpose(\n tf.stack([\n tf.sub(add0_out, div0_out),\n tf.sub(add1_out, div1_out),\n tf.add(add0_out, div0_out),\n tf.add(add1_out, div1_out),\n ]),\n [1, 0],\n );\n}\n\nexport function outputLayer(boxPredictions: tf.Tensor4D, classPredictions: tf.Tensor4D, params: OutputLayerParams) {\n return tf.tidy(() => {\n const batchSize = boxPredictions.shape[0];\n\n let boxes = decodeBoxesLayer(\n tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\n tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D,\n );\n boxes = tf.reshape(boxes, [batchSize, (boxes.shape[0] / batchSize), 4]);\n\n const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]));\n let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor;\n\n scores = tf.reshape(scores, [batchSize, scores.shape[1] as number]);\n\n const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[];\n const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[];\n\n return { boxes: boxesByBatch, scores: scoresByBatch };\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { convLayer } from '../common/index';\nimport { BoxPredictionParams } from './types';\n\nexport function boxPredictionLayer(\n x: tf.Tensor4D,\n params: BoxPredictionParams,\n) {\n return tf.tidy(() => {\n const batchSize = x.shape[0];\n const boxPredictionEncoding = tf.reshape(\n convLayer(x, params.box_encoding_predictor),\n [batchSize, -1, 1, 4],\n );\n const classPrediction = tf.reshape(\n convLayer(x, params.class_predictor),\n [batchSize, -1, 3],\n );\n return { boxPredictionEncoding, classPrediction };\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n x: tf.Tensor4D,\n conv11: tf.Tensor4D,\n params: PredictionLayerParams,\n) {\n return tf.tidy(() => {\n const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]);\n const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]);\n const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]);\n const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2]);\n const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1]);\n const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2]);\n const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1]);\n const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2]);\n\n const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0);\n const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1);\n const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2);\n const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3);\n const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4);\n const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5);\n\n const boxPredictions = tf.concat([\n boxPrediction0.boxPredictionEncoding,\n boxPrediction1.boxPredictionEncoding,\n boxPrediction2.boxPredictionEncoding,\n boxPrediction3.boxPredictionEncoding,\n boxPrediction4.boxPredictionEncoding,\n boxPrediction5.boxPredictionEncoding,\n ], 1) as tf.Tensor4D;\n\n const classPredictions = tf.concat([\n boxPrediction0.classPrediction,\n boxPrediction1.classPrediction,\n boxPrediction2.classPrediction,\n boxPrediction3.classPrediction,\n boxPrediction4.classPrediction,\n boxPrediction5.classPrediction,\n ], 1) as tf.Tensor4D;\n\n return {\n boxPredictions,\n classPredictions,\n };\n });\n}\n", "export interface ISsdMobilenetv1Options {\n minConfidence?: number\n maxResults?: number\n}\n\nexport class SsdMobilenetv1Options {\n protected _name = 'SsdMobilenetv1Options';\n\n private _minConfidence: number;\n\n private _maxResults: number;\n\n constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\n this._minConfidence = minConfidence || 0.5;\n this._maxResults = maxResults || 100;\n\n if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\n throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);\n }\n\n if (typeof this._maxResults !== 'number') {\n throw new Error(`${this._name} - expected maxResults to be a number`);\n }\n }\n\n get minConfidence(): number { return this._minConfidence; }\n\n get maxResults(): number { return this._maxResults; }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\nexport class SsdMobilenetv1 extends NeuralNetwork {\n constructor() {\n super('SsdMobilenetv1');\n }\n\n public forwardInput(input: NetInput) {\n const { params } = this;\n if (!params) throw new Error('SsdMobilenetv1 - load model before inference');\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n const x = tf.sub(tf.div(batchTensor, 127.5), 1) as tf.Tensor4D; // input is normalized -1..1\n const features = mobileNetV1(x, params.mobilenetv1);\n const { boxPredictions, classPredictions } = predictionLayer(features.out, features.conv11, params.prediction_layer);\n return outputLayer(boxPredictions, classPredictions, params.output_layer);\n });\n }\n\n public async forward(input: TNetInput) {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async locateFaces(input: TNetInput, options: ISsdMobilenetv1Options = {}): Promise {\n const { maxResults, minConfidence } = new SsdMobilenetv1Options(options);\n const netInput = await toNetInput(input);\n const { boxes: _boxes, scores: _scores } = this.forwardInput(netInput);\n const boxes = _boxes[0];\n const scores = _scores[0];\n for (let i = 1; i < _boxes.length; i++) {\n _boxes[i].dispose();\n _scores[i].dispose();\n }\n const scoresData = Array.from(scores.dataSync());\n const iouThreshold = 0.5;\n const indices = nonMaxSuppression(boxes, scoresData as number[], maxResults, iouThreshold, minConfidence);\n const reshapedDims = netInput.getReshapedInputDimensions(0);\n const inputSize = netInput.inputSize as number;\n const padX = inputSize / reshapedDims.width;\n const padY = inputSize / reshapedDims.height;\n const boxesData = boxes.arraySync();\n const results = indices\n .map((idx) => {\n const [top, bottom] = [\n Math.max(0, boxesData[idx][0]),\n Math.min(1.0, boxesData[idx][2]),\n ].map((val) => val * padY);\n const [left, right] = [\n Math.max(0, boxesData[idx][1]),\n Math.min(1.0, boxesData[idx][3]),\n ].map((val) => val * padX);\n return new FaceDetection(\n scoresData[idx] as number,\n new Rect(left, top, right - left, bottom - top),\n { height: netInput.getInputHeight(0), width: netInput.getInputWidth(0) },\n );\n });\n boxes.dispose();\n scores.dispose();\n return results;\n }\n\n protected getDefaultModelName(): string {\n return 'ssd_mobilenetv1_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParams(weights);\n }\n}\n", "import { SsdMobilenetv1 } from './SsdMobilenetv1';\n\nexport * from './SsdMobilenetv1';\nexport * from './SsdMobilenetv1Options';\n\nexport function createSsdMobilenetv1(weights: Float32Array) {\n const net = new SsdMobilenetv1();\n net.extractWeights(weights);\n return net;\n}\n\nexport function createFaceDetectionNet(weights: Float32Array) {\n return createSsdMobilenetv1(weights);\n}\n\n// alias for backward compatibily\nexport class FaceDetectionNet extends SsdMobilenetv1 {}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n new Point(0.738768, 0.874946),\n new Point(2.42204, 2.65704),\n new Point(4.30971, 7.04493),\n new Point(10.246, 4.59428),\n new Point(12.6868, 11.8741),\n];\n\nexport const BOX_ANCHORS_SEPARABLE = [\n new Point(1.603231, 2.094468),\n new Point(6.041143, 7.080126),\n new Point(2.882459, 3.518061),\n new Point(4.266906, 5.178857),\n new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404];\n\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model';\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model';\n", "import { Point } from '../classes/Point';\n\nexport type TinyYolov2Config = {\n withSeparableConvs: boolean\n iouThreshold: number\n anchors: Point[]\n classes: string[]\n meanRgb?: [number, number, number]\n withClassScores?: boolean,\n filterSizes?: number[]\n isFirstLayerConv2d?: boolean\n}\n\nconst isNumber = (arg: any) => typeof arg === 'number';\n\nexport function validateConfig(config: any) {\n if (!config) {\n throw new Error(`invalid config: ${config}`);\n }\n\n if (typeof config.withSeparableConvs !== 'boolean') {\n throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`);\n }\n\n if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\n throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`);\n }\n\n if (\n !Array.isArray(config.classes)\n || !config.classes.length\n || !config.classes.every((c: any) => typeof c === 'string')\n ) {\n throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`);\n }\n\n if (\n !Array.isArray(config.anchors)\n || !config.anchors.length\n || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\n ) {\n throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`);\n }\n\n if (config.meanRgb && (\n !Array.isArray(config.meanRgb)\n || config.meanRgb.length !== 3\n || !config.meanRgb.every(isNumber)\n )) {\n throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\n return tf.tidy(() => {\n const min = tf.mul(x, tf.scalar(0.10000000149011612));\n return tf.add(tf.relu(tf.sub(x, min)), min);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D {\n return tf.tidy(() => {\n let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid');\n out = tf.sub(out, params.bn.sub);\n out = tf.mul(out, params.bn.truediv);\n out = tf.add(out, params.conv.bias);\n return leaky(out);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D {\n return tf.tidy(() => {\n let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid');\n out = tf.add(out, params.bias);\n return leaky(out);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { extractConvParamsFactory } from '../common/index';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n\n function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n const sub = tf.tensor1d(extractWeights(size));\n const truediv = tf.tensor1d(extractWeights(size));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/sub` },\n { paramPath: `${mappedPrefix}/truediv` },\n );\n return { sub, truediv };\n }\n\n function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`);\n const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`);\n return { conv, bn };\n }\n const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n return {\n extractConvParams,\n extractConvWithBatchNormParams,\n extractSeparableConvParams,\n };\n}\n\nexport function extractParams(\n weights: Float32Array,\n config: TinyYolov2Config,\n boxEncodingSize: number,\n filterSizes: number[],\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const paramMappings: ParamMapping[] = [];\n const {\n extractConvParams,\n extractConvWithBatchNormParams,\n extractSeparableConvParams,\n } = extractorsFactory(extractWeights, paramMappings);\n let params: TinyYolov2NetParams;\n\n if (config.withSeparableConvs) {\n const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n const conv0 = config.isFirstLayerConv2d\n ? extractConvParams(s0, s1, 3, 'conv0')\n : extractSeparableConvParams(s0, s1, 'conv0');\n const conv1 = extractSeparableConvParams(s1, s2, 'conv1');\n const conv2 = extractSeparableConvParams(s2, s3, 'conv2');\n const conv3 = extractSeparableConvParams(s3, s4, 'conv3');\n const conv4 = extractSeparableConvParams(s4, s5, 'conv4');\n const conv5 = extractSeparableConvParams(s5, s6, 'conv5');\n const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined;\n const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined;\n const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8');\n params = {\n conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n };\n } else {\n const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0');\n const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1');\n const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2');\n const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3');\n const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4');\n const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5');\n const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6');\n const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7');\n const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8');\n params = {\n conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n };\n }\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from '../common/index';\nimport { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\nimport { ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractBatchNormParams(prefix: string): BatchNorm {\n const sub = extractWeightEntry(`${prefix}/sub`, 1);\n const truediv = extractWeightEntry(`${prefix}/truediv`, 1);\n return { sub, truediv };\n }\n\n function extractConvParams(prefix: string): ConvParams {\n const filters = extractWeightEntry(`${prefix}/filters`, 4);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n return { filters, bias };\n }\n\n function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\n const conv = extractConvParams(`${prefix}/conv`);\n const bn = extractBatchNormParams(`${prefix}/bn`);\n return { conv, bn };\n }\n\n const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n return {\n extractConvParams,\n extractConvWithBatchNormParams,\n extractSeparableConvParams,\n };\n}\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n config: TinyYolov2Config,\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractConvParams,\n extractConvWithBatchNormParams,\n extractSeparableConvParams,\n } = extractorsFactory(weightMap, paramMappings);\n\n let params: TinyYolov2NetParams;\n\n if (config.withSeparableConvs) {\n // eslint-disable-next-line no-mixed-operators\n const numFilters = (config.filterSizes && config.filterSizes.length || 9);\n params = {\n conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\n conv1: extractSeparableConvParams('conv1'),\n conv2: extractSeparableConvParams('conv2'),\n conv3: extractSeparableConvParams('conv3'),\n conv4: extractSeparableConvParams('conv4'),\n conv5: extractSeparableConvParams('conv5'),\n conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\n conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\n conv8: extractConvParams('conv8'),\n };\n } else {\n params = {\n conv0: extractConvWithBatchNormParams('conv0'),\n conv1: extractConvWithBatchNormParams('conv1'),\n conv2: extractConvWithBatchNormParams('conv2'),\n conv3: extractConvWithBatchNormParams('conv3'),\n conv4: extractConvWithBatchNormParams('conv4'),\n conv5: extractConvWithBatchNormParams('conv5'),\n conv6: extractConvWithBatchNormParams('conv6'),\n conv7: extractConvWithBatchNormParams('conv7'),\n conv8: extractConvParams('conv8'),\n };\n }\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n return { params, paramMappings };\n}\n", "export interface ITinyYolov2Options {\n inputSize?: number\n scoreThreshold?: number\n}\n\nexport class TinyYolov2Options {\n protected _name = 'TinyYolov2Options';\n\n private _inputSize: number;\n\n private _scoreThreshold: number;\n\n constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\n this._inputSize = inputSize || 416;\n this._scoreThreshold = scoreThreshold || 0.5;\n\n if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\n throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);\n }\n\n if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\n throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`);\n }\n }\n\n get inputSize(): number { return this._inputSize; }\n\n get scoreThreshold(): number { return this._scoreThreshold; }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common/index';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom/index';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops/index';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2ExtractBoxesResult, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork {\n public static DEFAULT_FILTER_SIZES = [3, 16, 32, 64, 128, 256, 512, 1024, 1024];\n\n private _config: TinyYolov2Config;\n\n constructor(config: TinyYolov2Config) {\n super('TinyYolov2');\n validateConfig(config);\n this._config = config;\n }\n\n public get config(): TinyYolov2Config {\n return this._config;\n }\n\n public get withClassScores(): boolean {\n return this.config.withClassScores || this.config.classes.length > 1;\n }\n\n public get boxEncodingSize(): number {\n return 5 + (this.withClassScores ? this.config.classes.length : 0);\n }\n\n public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n let out = convWithBatchNorm(x, params.conv0);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv1);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv2);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv3);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv4);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv5);\n out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n out = convWithBatchNorm(out, params.conv6);\n out = convWithBatchNorm(out, params.conv7);\n return convLayer(out, params.conv8, 'valid', false);\n }\n\n public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n let out = this.config.isFirstLayerConv2d\n ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv1);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv2);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv3);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv4);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv5);\n out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out;\n out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out;\n return convLayer(out, params.conv8, 'valid', false);\n }\n\n public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n const { params } = this;\n\n if (!params) {\n throw new Error('TinyYolov2 - load model before inference');\n }\n\n return tf.tidy(() => {\n let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n batchTensor = this.config.meanRgb\n ? normalize(batchTensor, this.config.meanRgb)\n : batchTensor;\n batchTensor = batchTensor.div(255) as tf.Tensor4D;\n return this.config.withSeparableConvs\n ? this.runMobilenet(batchTensor, params as MobilenetParams)\n : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams);\n });\n }\n\n public async forward(input: TNetInput, inputSize: number): Promise {\n return this.forwardInput(await toNetInput(input), inputSize);\n }\n\n public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise {\n const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams);\n const netInput = await toNetInput(input);\n const out = await this.forwardInput(netInput, inputSize);\n const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D;\n const inputDimensions = {\n width: netInput.getInputWidth(0),\n height: netInput.getInputHeight(0),\n };\n\n const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold);\n out.dispose();\n out0.dispose();\n\n const boxes = results.map((res) => res.box);\n const scores = results.map((res) => res.score);\n const classScores = results.map((res) => res.classScore);\n const classNames = results.map((res) => this.config.classes[res.label]);\n\n const indices = nonMaxSuppression(\n boxes.map((box) => box.rescale(inputSize)),\n scores,\n this.config.iouThreshold,\n true,\n );\n\n const detections = indices.map((idx) => new ObjectDetection(\n scores[idx],\n classScores[idx],\n classNames[idx],\n boxes[idx],\n inputDimensions,\n ));\n return detections;\n }\n\n protected getDefaultModelName(): string {\n return '';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap, this.config);\n }\n\n protected extractParams(weights: Float32Array) {\n const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES;\n\n const numFilters = filterSizes ? filterSizes.length : undefined;\n if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`);\n }\n return extractParams(weights, this.config, this.boxEncodingSize, filterSizes);\n }\n\n protected async extractBoxes(\n outputTensor: tf.Tensor4D,\n inputBlobDimensions: Dimensions,\n scoreThreshold?: number,\n ) {\n const { width, height } = inputBlobDimensions;\n const inputSize = Math.max(width, height);\n const correctionFactorX = inputSize / width;\n const correctionFactorY = inputSize / height;\n\n const numCells = outputTensor.shape[1];\n const numBoxes = this.config.anchors.length;\n\n const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize]);\n\n const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4]);\n const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1]);\n const classScores = this.withClassScores\n ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n : tf.scalar(0);\n return [boxes, scores, classScores];\n });\n\n const results: TinyYolov2ExtractBoxesResult[] = [];\n const scoresData = await scoresTensor.array() as number[][][][];\n const boxesData = await boxesTensor.array() as number[][][][];\n for (let row = 0; row < numCells; row++) {\n for (let col = 0; col < numCells; col++) {\n for (let anchor = 0; anchor < numBoxes; anchor++) {\n const score = sigmoid(scoresData[row][col][anchor][0]);\n if (!scoreThreshold || score > scoreThreshold) {\n const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX;\n const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY;\n const widthLocal = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX;\n const heightLocal = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY;\n const x = (ctX - (widthLocal / 2));\n const y = (ctY - (heightLocal / 2));\n const pos = { row, col, anchor };\n const { classScore, label } = this.withClassScores\n ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n : { classScore: 1, label: 0 };\n results.push({\n box: new BoundingBox(x, y, x + widthLocal, y + heightLocal),\n score,\n classScore: score * classScore,\n label,\n ...pos,\n });\n }\n }\n }\n }\n\n boxesTensor.dispose();\n scoresTensor.dispose();\n classScoresTensor.dispose();\n return results;\n }\n\n private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number }) {\n const { row, col, anchor } = pos;\n const classesData = await classesTensor.array();\n return Array(this.config.classes.length).fill(0)\n .map((_, i) => classesData[row][col][anchor][i])\n .map((classScore, label) => ({\n classScore,\n label,\n }))\n .reduce((max, curr) => (max.classScore > curr.classScore ? max : curr));\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/types';\nimport { TNetInput } from '../dom/types';\nimport {\n BOX_ANCHORS,\n BOX_ANCHORS_SEPARABLE,\n DEFAULT_MODEL_NAME,\n DEFAULT_MODEL_NAME_SEPARABLE_CONV,\n IOU_THRESHOLD,\n MEAN_RGB_SEPARABLE,\n} from './const';\nimport { TinyYolov2Base } from './TinyYolov2Base';\nimport { ITinyYolov2Options } from './TinyYolov2Options';\nimport { TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2 extends TinyYolov2Base {\n constructor(withSeparableConvs = true) {\n const config = {\n withSeparableConvs,\n iouThreshold: IOU_THRESHOLD,\n classes: ['face'],\n ...(withSeparableConvs\n ? {\n anchors: BOX_ANCHORS_SEPARABLE,\n meanRgb: MEAN_RGB_SEPARABLE,\n }\n : {\n anchors: BOX_ANCHORS,\n withClassScores: true,\n }),\n };\n\n super(config);\n }\n\n public get withSeparableConvs(): boolean {\n return this.config.withSeparableConvs;\n }\n\n public get anchors(): Point[] {\n return this.config.anchors;\n }\n\n public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise {\n const objectDetections = await this.detect(input, forwardParams);\n return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n }\n\n protected override getDefaultModelName(): string {\n return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME;\n }\n\n protected override extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n return super.extractParamsFromWeightMap(weightMap);\n }\n}\n", "import { TinyYolov2 } from './TinyYolov2';\n\nexport * from './TinyYolov2Options';\nexport * from './config';\nexport * from './types';\nexport { TinyYolov2 };\n\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs = true) {\n const net = new TinyYolov2(withSeparableConvs);\n net.extractWeights(weights);\n return net;\n}\n", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\n\nexport type ITinyFaceDetectorOptions = ITinyYolov2Options\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n protected override _name = 'TinyFaceDetectorOptions';\n}\n", "export class ComposableTask {\n // eslint-disable-next-line no-unused-vars\n public async then(onfulfilled: (value: T) => T | PromiseLike): Promise {\n return onfulfilled(await this.run());\n }\n\n public async run(): Promise {\n throw new Error('ComposableTask - run is not implemented');\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults, TResult>(\n parentResults: TSource[],\n input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n computeResults: (faces: Array) => Promise,\n extractedFaces?: Array | null,\n // eslint-disable-next-line no-unused-vars\n getRectForAlignment: (parentResult: WithFaceLandmarks) => FaceDetection = ({ alignedRect }) => alignedRect,\n) {\n const faceBoxes = parentResults.map((parentResult) => (isWithFaceLandmarks(parentResult)\n ? getRectForAlignment(parentResult)\n : parentResult.detection));\n const faces: Array = extractedFaces || (\n input instanceof tf.Tensor\n ? await extractFaceTensors(input, faceBoxes)\n : await extractFaces(input, faceBoxes)\n );\n const results = await computeResults(faces);\n faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n return results;\n}\n\nexport async function extractSingleFaceAndComputeResult, TResult>(\n parentResult: TSource,\n input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise,\n extractedFaces?: Array | null,\n // eslint-disable-next-line no-unused-vars\n getRectForAlignment?: (parentResultLocal: WithFaceLandmarks) => FaceDetection,\n) {\n return extractAllFacesAndComputeResults(\n [parentResult],\n input,\n async (faces) => computeResult(faces[0]),\n extractedFaces,\n getRectForAlignment,\n );\n}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n new Point(1.603231, 2.094468),\n new Point(6.041143, 7.080126),\n new Point(2.882459, 3.518061),\n new Point(4.266906, 5.178857),\n new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404];\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/index';\nimport { TNetInput } from '../dom/index';\nimport { ITinyYolov2Options } from '../tinyYolov2/index';\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\nimport { TinyYolov2NetParams } from '../tinyYolov2/types';\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\n\nexport class TinyFaceDetector extends TinyYolov2Base {\n constructor() {\n const config = {\n withSeparableConvs: true,\n iouThreshold: IOU_THRESHOLD,\n classes: ['face'],\n anchors: BOX_ANCHORS,\n meanRgb: MEAN_RGB,\n isFirstLayerConv2d: true,\n filterSizes: [3, 16, 32, 64, 128, 256, 512],\n };\n\n super(config);\n }\n\n public get anchors(): Point[] {\n return this.config.anchors;\n }\n\n public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise {\n const objectDetections = await this.detect(input, forwardParams);\n return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n }\n\n protected override getDefaultModelName(): string {\n return 'tiny_face_detector_model';\n }\n\n protected override extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n return super.extractParamsFromWeightMap(weightMap);\n }\n}\n", "import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2/index';\n\nexport const nets = {\n ssdMobilenetv1: new SsdMobilenetv1(),\n tinyFaceDetector: new TinyFaceDetector(),\n tinyYolov2: new TinyYolov2(),\n faceLandmark68Net: new FaceLandmark68Net(),\n faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\n faceRecognitionNet: new FaceRecognitionNet(),\n faceExpressionNet: new FaceExpressionNet(),\n ageGenderNet: new AgeGenderNet(),\n};\n\n/**\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise => nets.ssdMobilenetv1.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Face Detector.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise => nets.tinyFaceDetector.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise => nets.tinyYolov2.locateFaces(input, options);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarks = (input: TNetInput): Promise => nets.faceLandmark68Net.detectLandmarks(input);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image\n * using a tinier version of the 68 point face landmark model, which is slightly\n * faster at inference, but also slightly less accurate.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise => nets.faceLandmark68TinyNet.detectLandmarks(input);\n\n/**\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\n * which uniquely represents the features of that persons face. The computed face descriptor can\n * be used to measure the similarity between faces, by computing the euclidean distance of two\n * face descriptors.\n *\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\n */\nexport const computeFaceDescriptor = (input: TNetInput): Promise => nets.faceRecognitionNet.computeFaceDescriptor(input);\n\n/**\n * Recognizes the facial expressions from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\n */\nexport const recognizeFaceExpressions = (input: TNetInput): Promise => nets.faceExpressionNet.predictExpressions(input);\n\n/**\n * Predicts age and gender from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\n */\nexport const predictAgeAndGender = (input: TNetInput): Promise => nets.ageGenderNet.predictAgeAndGender(input);\n\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url);\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url);\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url);\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url);\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url);\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url);\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url);\nexport const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url);\n\n// backward compatibility\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model;\nexport const locateFaces = ssdMobilenetv1;\nexport const detectLandmarks = detectFaceLandmarks;\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderTask, PredictSingleAgeAndGenderWithFaceAlignmentTask } from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected parentTask: ComposableTask | Promise,\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n protected extractedFaces?: Array,\n ) {\n super();\n }\n}\n\nexport class PredictAllFaceExpressionsTask> extends PredictFaceExpressionsTaskBase[], TSource[]> {\n public override async run(): Promise[]> {\n const parentResults = await this.parentTask;\n\n const faceExpressionsByFace = await extractAllFacesAndComputeResults(\n parentResults,\n this.input,\n async (faces) => Promise.all(\n faces.map((face) => nets.faceExpressionNet.predictExpressions(face) as Promise),\n ),\n this.extractedFaces,\n );\n\n return parentResults.map(\n (parentResult, i) => extendWithFaceExpressions(parentResult, faceExpressionsByFace[i]),\n );\n }\n\n withAgeAndGender() {\n return new PredictAllAgeAndGenderTask(this, this.input);\n }\n}\n\nexport class PredictSingleFaceExpressionsTask> extends PredictFaceExpressionsTaskBase | undefined, TSource | undefined> {\n public override async run(): Promise | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) {\n return undefined;\n }\n\n const faceExpressions = await extractSingleFaceAndComputeResult(\n parentResult,\n this.input,\n (face) => nets.faceExpressionNet.predictExpressions(face) as Promise,\n this.extractedFaces,\n );\n\n return extendWithFaceExpressions(parentResult, faceExpressions);\n }\n\n withAgeAndGender() {\n return new PredictSingleAgeAndGenderTask(this, this.input);\n }\n}\n\nexport class PredictAllFaceExpressionsWithFaceAlignmentTask>> extends PredictAllFaceExpressionsTask {\n override withAgeAndGender() {\n return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptors() {\n return new ComputeAllFaceDescriptorsTask(this, this.input);\n }\n}\n\nexport class PredictSingleFaceExpressionsWithFaceAlignmentTask>> extends PredictSingleFaceExpressionsTask {\n override withAgeAndGender() {\n return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptor() {\n return new ComputeSingleFaceDescriptorTask(this, this.input);\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { TNetInput } from '../dom/index';\nimport { extendWithAge, WithAge } from '../factories/WithAge';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { extendWithGender, WithGender } from '../factories/WithGender';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport { PredictAllFaceExpressionsTask, PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsTask, PredictSingleFaceExpressionsWithFaceAlignmentTask } from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected parentTask: ComposableTask | Promise,\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n protected extractedFaces?: Array,\n ) {\n super();\n }\n}\n\nexport class PredictAllAgeAndGenderTask> extends PredictAgeAndGenderTaskBase>[], TSource[]> {\n public override async run(): Promise>[]> {\n const parentResults = await this.parentTask;\n const ageAndGenderByFace = await extractAllFacesAndComputeResults(\n parentResults,\n this.input,\n async (faces) => Promise.all(faces.map((face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise)),\n this.extractedFaces,\n );\n return parentResults.map((parentResult, i) => {\n const { age, gender, genderProbability } = ageAndGenderByFace[i];\n return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n });\n }\n\n withFaceExpressions() {\n return new PredictAllFaceExpressionsTask(this, this.input);\n }\n}\n\nexport class PredictSingleAgeAndGenderTask> extends PredictAgeAndGenderTaskBase> | undefined, TSource | undefined> {\n public override async run(): Promise> | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) return undefined;\n const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult(\n parentResult,\n this.input,\n (face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise,\n this.extractedFaces,\n );\n return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n }\n\n withFaceExpressions() {\n return new PredictSingleFaceExpressionsTask(this, this.input);\n }\n}\n\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask>> extends PredictAllAgeAndGenderTask {\n override withFaceExpressions() {\n return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptors() {\n return new ComputeAllFaceDescriptorsTask(this, this.input);\n }\n}\n\nexport class PredictSingleAgeAndGenderWithFaceAlignmentTask>> extends PredictSingleAgeAndGenderTask {\n override withFaceExpressions() {\n return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptor() {\n return new ComputeSingleFaceDescriptorTask(this, this.input);\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderWithFaceAlignmentTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsWithFaceAlignmentTask } from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected parentTask: ComposableTask | Promise,\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n ) {\n super();\n }\n}\n\nexport class ComputeAllFaceDescriptorsTask>> extends ComputeFaceDescriptorsTaskBase[], TSource[]> {\n public override async run(): Promise[]> {\n const parentResults = await this.parentTask;\n const descriptors = await extractAllFacesAndComputeResults(\n parentResults,\n this.input,\n (faces) => Promise.all(faces.map((face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise)),\n null,\n (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n );\n return descriptors.map((descriptor, i) => extendWithFaceDescriptor(parentResults[i], descriptor));\n }\n\n withFaceExpressions() {\n return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withAgeAndGender() {\n return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n}\n\nexport class ComputeSingleFaceDescriptorTask>> extends ComputeFaceDescriptorsTaskBase | undefined, TSource | undefined> {\n public override async run(): Promise | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) return undefined;\n const descriptor = await extractSingleFaceAndComputeResult(\n parentResult,\n this.input,\n (face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise,\n null,\n // eslint-disable-next-line no-shadow, @typescript-eslint/no-shadow\n (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n );\n return extendWithFaceDescriptor(parentResult, descriptor);\n }\n\n withFaceExpressions() {\n return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withAgeAndGender() {\n return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderWithFaceAlignmentTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsWithFaceAlignmentTask } from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected parentTask: ComposableTask | Promise,\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n protected useTinyLandmarkNet: boolean,\n ) {\n super();\n }\n\n protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n return this.useTinyLandmarkNet\n ? nets.faceLandmark68TinyNet\n : nets.faceLandmark68Net;\n }\n}\n\nexport class DetectAllFaceLandmarksTask> extends DetectFaceLandmarksTaskBase[], TSource[]> {\n public override async run(): Promise[]> {\n const parentResults = await this.parentTask;\n const detections = parentResults.map((res) => res.detection);\n const faces: Array = this.input instanceof tf.Tensor\n ? await extractFaceTensors(this.input, detections)\n : await extractFaces(this.input, detections);\n const faceLandmarksByFace = await Promise.all(faces.map((face) => this.landmarkNet.detectLandmarks(face))) as FaceLandmarks68[];\n faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n const result = parentResults\n .filter((_parentResult, i) => faceLandmarksByFace[i])\n .map((parentResult, i) => extendWithFaceLandmarks(parentResult, faceLandmarksByFace[i]));\n return result;\n }\n\n withFaceExpressions() {\n return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withAgeAndGender() {\n return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptors() {\n return new ComputeAllFaceDescriptorsTask(this, this.input);\n }\n}\n\nexport class DetectSingleFaceLandmarksTask> extends DetectFaceLandmarksTaskBase | undefined, TSource | undefined> {\n public override async run(): Promise | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) {\n return undefined;\n }\n const { detection } = parentResult;\n const faces: Array = this.input instanceof tf.Tensor\n ? await extractFaceTensors(this.input, [detection])\n : await extractFaces(this.input, [detection]);\n const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68;\n faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n return extendWithFaceLandmarks(parentResult, landmarks);\n }\n\n withFaceExpressions() {\n return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withAgeAndGender() {\n return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptor() {\n return new ComputeSingleFaceDescriptorTask(this, this.input);\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { TinyYolov2Options } from '../tinyYolov2/index';\nimport { ComposableTask } from './ComposableTask';\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\nimport { FaceDetectionOptions } from './types';\n\nexport class DetectFacesTaskBase extends ComposableTask {\n // eslint-disable-next-line no-unused-vars\n constructor(protected input: TNetInput, protected options: FaceDetectionOptions = new SsdMobilenetv1Options()) {\n super();\n }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase {\n public override async run(): Promise {\n const { input, options } = this;\n let result;\n if (options instanceof TinyFaceDetectorOptions) result = nets.tinyFaceDetector.locateFaces(input, options);\n else if (options instanceof SsdMobilenetv1Options) result = nets.ssdMobilenetv1.locateFaces(input, options);\n else if (options instanceof TinyYolov2Options) result = nets.tinyYolov2.locateFaces(input, options);\n else throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options');\n return result;\n }\n\n private runAndExtendWithFaceDetections(): Promise[]> {\n return new Promise[]>((resolve, reject) => {\n this.run()\n .then((detections) => resolve(detections.map((detection) => extendWithFaceDetection({}, detection))))\n .catch((err) => reject(err));\n });\n }\n\n withFaceLandmarks(useTinyLandmarkNet = false) {\n return new DetectAllFaceLandmarksTask(\n this.runAndExtendWithFaceDetections(),\n this.input,\n useTinyLandmarkNet,\n );\n }\n\n withFaceExpressions() {\n return new PredictAllFaceExpressionsTask(\n this.runAndExtendWithFaceDetections(),\n this.input,\n );\n }\n\n withAgeAndGender() {\n return new PredictAllAgeAndGenderTask(\n this.runAndExtendWithFaceDetections(),\n this.input,\n );\n }\n}\n\nexport class DetectSingleFaceTask extends DetectFacesTaskBase {\n public override async run(): Promise {\n const faceDetections = await new DetectAllFacesTask(this.input, this.options);\n let faceDetectionWithHighestScore = faceDetections[0];\n faceDetections.forEach((faceDetection) => {\n if (faceDetection.score > faceDetectionWithHighestScore.score) faceDetectionWithHighestScore = faceDetection;\n });\n return faceDetectionWithHighestScore;\n }\n\n private runAndExtendWithFaceDetection(): Promise | undefined> {\n // eslint-disable-next-line no-async-promise-executor\n return new Promise | undefined>(async (resolve) => {\n const detection = await this.run();\n resolve(detection ? extendWithFaceDetection<{}>({}, detection) : undefined);\n });\n }\n\n withFaceLandmarks(useTinyLandmarkNet = false) {\n return new DetectSingleFaceLandmarksTask(\n this.runAndExtendWithFaceDetection(),\n this.input,\n useTinyLandmarkNet,\n );\n }\n\n withFaceExpressions() {\n return new PredictSingleFaceExpressionsTask(\n this.runAndExtendWithFaceDetection(),\n this.input,\n );\n }\n\n withAgeAndGender() {\n return new PredictSingleAgeAndGenderTask(\n this.runAndExtendWithFaceDetection(),\n this.input,\n );\n }\n}\n", "import { TNetInput } from '../dom/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\nimport { FaceDetectionOptions } from './types';\n\nexport function detectSingleFace(input: TNetInput, options: FaceDetectionOptions = new SsdMobilenetv1Options()): DetectSingleFaceTask {\n return new DetectSingleFaceTask(input, options);\n}\n\nexport function detectAllFaces(input: TNetInput, options: FaceDetectionOptions = new SsdMobilenetv1Options()): DetectAllFacesTask {\n return new DetectAllFacesTask(input, options);\n}\n", "import { TNetInput } from '../dom/index';\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/index';\nimport { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\nimport { detectAllFaces } from './detectFaces';\n\nexport async function allFacesSsdMobilenetv1(input: TNetInput, minConfidence?: number): Promise>>[]> {\n return detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n .withFaceLandmarks()\n .withFaceDescriptors();\n}\n\nexport async function allFacesTinyYolov2(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise>>[]> {\n return detectAllFaces(input, new TinyYolov2Options(forwardParams))\n .withFaceLandmarks()\n .withFaceDescriptors();\n}\n\nexport const allFaces = allFacesSsdMobilenetv1;\n", "export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\n if (arr1.length !== arr2.length) throw new Error('euclideanDistance: arr1.length !== arr2.length');\n const desc1 = Array.from(arr1);\n const desc2 = Array.from(arr2);\n return Math.sqrt(\n desc1\n .map((val, i) => val - desc2[i])\n .reduce((res, diff) => res + (diff * diff), 0),\n );\n}\n", "import { FaceMatch } from '../classes/FaceMatch';\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\nimport { euclideanDistance } from '../euclideanDistance';\nimport { WithFaceDescriptor } from '../factories/index';\n\nexport class FaceMatcher {\n private _labeledDescriptors: LabeledFaceDescriptors[];\n private _distanceThreshold: number;\n\n constructor(inputs: LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>, distanceThreshold = 0.6) {\n this._distanceThreshold = distanceThreshold;\n const inputArray = Array.isArray(inputs) ? inputs : [inputs];\n if (!inputArray.length) throw new Error('FaceRecognizer.constructor - expected atleast one input');\n let count = 1;\n const createUniqueLabel = () => `person ${count++}`;\n this._labeledDescriptors = inputArray.map((desc) => {\n if (desc instanceof LabeledFaceDescriptors) return desc;\n if (desc instanceof Float32Array) return new LabeledFaceDescriptors(createUniqueLabel(), [desc]);\n if (desc.descriptor && desc.descriptor instanceof Float32Array) return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor]);\n throw new Error('FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>');\n });\n }\n\n public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors; }\n\n public get distanceThreshold(): number { return this._distanceThreshold; }\n\n public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\n return descriptors\n .map((d) => euclideanDistance(d, queryDescriptor))\n .reduce((d1, d2) => d1 + d2, 0) / (descriptors.length || 1);\n }\n\n public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n return this.labeledDescriptors\n .map(({ descriptors, label }) => new FaceMatch(label, this.computeMeanDistance(queryDescriptor, descriptors)))\n .reduce((best, curr) => (best.distance < curr.distance ? best : curr));\n }\n\n public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\n const bestMatch = this.matchDescriptor(queryDescriptor);\n return (bestMatch.distance < this._distanceThreshold) ? bestMatch : new FaceMatch('unknown', bestMatch.distance);\n }\n\n public toJSON(): any {\n return {\n distanceThreshold: this._distanceThreshold,\n labeledDescriptors: this._labeledDescriptors.map((ld) => ld.toJSON()),\n };\n }\n\n public static fromJSON(json: any): FaceMatcher {\n const labeledDescriptors = json.labeledDescriptors.map((ld: any) => LabeledFaceDescriptors.fromJSON(ld));\n return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\n }\n}\n", "import { TinyFaceDetector } from './TinyFaceDetector';\n\nexport * from './TinyFaceDetector';\nexport * from './TinyFaceDetectorOptions';\n\nexport function createTinyFaceDetector(weights: Float32Array) {\n const net = new TinyFaceDetector();\n net.extractWeights(weights);\n return net;\n}\n", "import { Dimensions, IDimensions } from './classes/index';\nimport { FaceDetection } from './classes/FaceDetection';\nimport { FaceLandmarks } from './classes/FaceLandmarks';\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\n\nexport function resizeResults(results: T, dimensions: IDimensions): T {\n const { width, height } = new Dimensions(dimensions.width, dimensions.height);\n\n if (width <= 0 || height <= 0) {\n throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`);\n }\n\n if (Array.isArray(results)) {\n // return results.map(obj => resizeResults(obj, { width, height })) as any as T\n return (results as Array).map((obj) => resizeResults(obj, { width, height } as IDimensions)) as any as T;\n }\n\n if (isWithFaceLandmarks(results)) {\n const resizedDetection = results.detection.forSize(width, height);\n const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height);\n return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks);\n }\n\n if (isWithFaceDetection(results)) {\n return extendWithFaceDetection(results, results.detection.forSize(width, height));\n }\n\n if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\n return (results as any).forSize(width, height);\n }\n\n return results;\n}\n", "import * as tf from '../dist/tfjs.esm';\nimport * as draw from './draw/index';\nimport * as utils from './utils/index';\nimport * as pkg from '../package.json';\n\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index';\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nexport const version = pkg.version as string;\n\n// set webgl defaults\n// if (browser) tf.ENV.set('WEBGL_USE_SHAPES_UNIFORMS', true);\n"], - "mappings": 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this.trackTensor(i,e),i}makeVariable(t,e=!0,n,o){n=n||this.nextVariableId().toString(),o!=null&&o!==t.dtype&&(t=t.cast(o));let s=new ml(t,e,n,this.nextTensorId());if(this.state.registeredVariables[s.name]!=null)throw new Error(`Variable with name ${s.name} was already registered`);return this.state.registeredVariables[s.name]=s,this.incRef(s,this.backend),s}trackTensor(t,e){this.state.numTensors++,t.dtype===\"string\"&&this.state.numStringTensors++;let n=0;t.dtype!==\"complex64\"&&t.dtype!==\"string\"&&(n=t.size*Tp(t.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(t.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(t.dataId,{backend:e||this.backend,dtype:t.dtype,shape:t.shape,bytes:n})),t instanceof 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Ot=k({slice_:Dj});function $j(r){let e={x:C(r,\"x\",\"tanh\",\"float32\")};return T.runKernel(ni,e)}var li=k({tanh_:$j});function Rj(r,t,e,n,o,s){let i=C(r,\"forgetBias\",\"basicLSTMCell\"),a=C(t,\"lstmKernel\",\"basicLSTMCell\"),u=C(e,\"lstmBias\",\"basicLSTMCell\"),l=C(n,\"data\",\"basicLSTMCell\"),c=C(o,\"c\",\"basicLSTMCell\"),p=C(s,\"h\",\"basicLSTMCell\"),m=ie([l,p],1),f=Bt(m,a),d=K(f,u),h=d.shape[0],g=d.shape[1]/4,x=[h,g],b=Ot(d,[0,0],x),w=Ot(d,[0,g],x),I=Ot(d,[0,g*2],x),N=Ot(d,[0,g*3],x),E=K($(en(b),li(w)),$(c,en(K(i,I)))),A=$(li(E),en(N));return[E,A]}var $E=k({basicLSTMCell_:Rj});function Fj(r,t,e){let n=C(r,\"x\",\"batchToSpaceND\"),o=t.reduce((a,u)=>a*u);_(n.rank>=1+t.length,()=>`input rank is ${n.rank} but should be > than blockShape.length ${t.length}`),_(e.length===t.length,()=>`crops.length is ${e.length} but should be equal to blockShape.length ${t.length}`),_(n.shape[0]%o===0,()=>`input tensor batch is ${n.shape[0]} but is not divisible by the product of the elements 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oa=k({batchNorm_:Oj});function Mj(r,t,e,n,o,s){let i=C(r,\"x\",\"batchNorm\"),a=C(t,\"mean\",\"batchNorm\"),u=C(e,\"variance\",\"batchNorm\"),l;o!=null&&(l=C(o,\"scale\",\"batchNorm\"));let c;return n!=null&&(c=C(n,\"offset\",\"batchNorm\")),_(i.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${i.rank}.`),_(a.rank===2||a.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${a.rank}.`),_(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${u.rank}.`),l!=null&&_(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${l.rank}.`),c!=null&&_(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`),oa(i,a,u,c,l,s)}var Cx=k({batchNorm2d_:Mj});function Pj(r,t,e,n,o,s){let i=C(r,\"x\",\"batchNorm\"),a=C(t,\"mean\",\"batchNorm\"),u=C(e,\"variance\",\"batchNorm\"),l;o!=null&&(l=C(o,\"scale\",\"batchNorm\"));let c;return n!=null&&(c=C(n,\"offset\",\"batchNorm\")),_(i.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`),_(a.rank===3||a.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${a.rank}.`),_(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${u.rank}.`),l!=null&&_(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${l.rank}.`),c!=null&&_(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`),oa(i,a,u,c,l,s)}var vx=k({batchNorm3d_:Pj});function Lj(r,t,e,n,o,s){let i=C(r,\"x\",\"batchNorm\"),a=C(t,\"mean\",\"batchNorm\"),u=C(e,\"variance\",\"batchNorm\"),l;o!=null&&(l=C(o,\"scale\",\"batchNorm\"));let c;return n!=null&&(c=C(n,\"offset\",\"batchNorm\")),_(i.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`),_(a.rank===4||a.rank===1,()=>`Error in batchNorm4D: mean must 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e=C(r,\"x\",\"bitwiseAnd\"),n=C(t,\"y\",\"bitwiseAnd\");if(!sn(e.shape,n.shape))throw new Error(`BitwiseAnd: Tensors must have the same shape. x: ${e.shape}, y: ${n.shape}`);if(e.dtype!==\"int32\"||n.dtype!==\"int32\")throw new Error(`BitwiseAnd: Only supports 'int32' values in tensor, found type of x: ${e.dtype} and type of y: ${n.dtype}`);let o={a:e,b:n};return T.runKernel($a,o)}var FE=k({bitwiseAnd_:Bj});function Vj(r,t){let e=C(r,\"s0\",\"broadcastArgs\",\"int32\"),n=C(t,\"s1\",\"broadcastArgs\",\"int32\");if(e.rank!==1)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). 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s={x:C(r,\"x\",\"cumsum\")},i={axis:t,exclusive:e,reverse:n};return T.runKernel(os,s,i)}var im=k({cumsum_:s6});function i6(r,t,e,n=!1){let o=C(r,\"x\",\"denseBincount\"),s=C(t,\"weights\",\"denseBincount\");_(o.dtype===\"int32\",()=>`Error in denseBincount: input dtype must be int32, but got ${o.dtype}`),_(o.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${o.rank}.`),_(e>=0,()=>`size must be non-negative, but got ${e}.`),_(s.size===o.size||s.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${o.shape}, weights shape: ${s.shape}.`);let i={x:o,weights:s},a={size:e,binaryOutput:n};return T.runKernel(jl,i,a)}var mh=k({denseBincount_:i6});function a6(r,t,e=\"NHWC\"){let n=C(r,\"x\",\"depthToSpace\",\"float32\"),o=e===\"NHWC\"?n.shape[1]:n.shape[2],s=e===\"NHWC\"?n.shape[2]:n.shape[3],i=e===\"NHWC\"?n.shape[3]:n.shape[1];_(t>1,()=>`blockSize should be > 1 for depthToSpace, but was: 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Got dilations '${s}'`);let[A,D,F,M]=E,V=bc(N,F,u),G=gy(D.shape,V,A,e,n,s,i),W=hy(D,V,A.shape,e,n,s,i);if(M!=null){let q=wc(g,V);return[G,W,q]}return[G,W]},w={x:f,filter:m,bias:g,preluActivationWeights:x},I={strides:e,pad:n,dataFormat:o,dilations:s,dimRoundingMode:i,activation:u,leakyreluAlpha:c};return a==null?pn((E,A,D)=>{let F=T.runKernel(Zi,w,I);return D([A,E,F]),d&&(F=R(F,[F.shape[1],F.shape[2],F.shape[3]])),{value:F,gradFunc:b}})(f,m):pn((E,A,D,F)=>{let M=T.runKernel(Zi,w,I);return F([A,E,M,D]),d&&(M=R(M,[M.shape[1],M.shape[2],M.shape[3]])),{value:M,gradFunc:b}})(f,m,g)}var zA=k({fusedDepthwiseConv2d_:c8});function p8({a:r,b:t,transposeA:e=!1,transposeB:n=!1,bias:o,activation:s=\"linear\",preluActivationWeights:i,leakyreluAlpha:a=.2}){if(Cc(T.state.gradientDepth,s)===!1){let V=Bt(r,t,e,n);return o!=null&&(V=K(V,o)),Ic(V,s,i,a)}let u=C(r,\"a\",\"fused matMul\"),l=C(t,\"b\",\"fused matMul\");[u,l]=Xt(u,l);let c=e?u.shape[u.rank-2]:u.shape[u.rank-1],p=n?l.shape[l.rank-1]:l.shape[l.rank-2],m=e?u.shape[u.rank-1]:u.shape[u.rank-2],f=n?l.shape[l.rank-2]:l.shape[l.rank-1],d=u.shape.slice(0,-2),h=l.shape.slice(0,-2),g=jt(d),x=jt(h);_(c===p,()=>`Error in fused matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${u.shape} and ${l.shape} and transposeA=${e} and transposeB=${n} must match.`);let w=Mt(u.shape.slice(0,-2),l.shape.slice(0,-2)).concat([m,f]),I=e?R(u,[g,c,m]):R(u,[g,m,c]),N=n?R(l,[x,f,p]):R(l,[x,p,f]),E;o!=null&&(E=C(o,\"bias\",\"fused matMul\"),[E]=Xt(E,u),Mt(w,E.shape));let A;i!=null&&(A=C(i,\"prelu weights\",\"fused matMul\"));let D=(V,G)=>{let[W,q,H,j]=G,Y=bc(R(V,H.shape),H,s),Z,et;if(!e&&!n?(Z=Bt(Y,q,!1,!0),et=Bt(W,Y,!0,!1)):!e&&n?(Z=Bt(Y,q,!1,!1),et=Bt(Y,W,!0,!1)):e&&!n?(Z=Bt(q,Y,!1,!0),et=Bt(W,Y,!1,!1)):(Z=Bt(q,Y,!0,!0),et=Bt(Y,W,!0,!0)),o!=null){let nt=wc(j,Y);return[Z,et,nt]}else return[Z,et]},F={a:I,b:N,bias:E,preluActivationWeights:A},M={transposeA:e,transposeB:n,activation:s,leakyreluAlpha:a};return o==null?pn((G,W,q)=>{let H=T.runKernel(Xi,F,M);return q([G,W,H]),{value:R(H,w),gradFunc:D}})(I,N):pn((G,W,q,H)=>{let j=T.runKernel(Xi,F,M);return H([G,W,j,q]),{value:R(j,w),gradFunc:D}})(I,N,E)}var BA=k({fusedMatMul_:p8});function m8(r){return xh(r,.54,.46)}var VA=k({hammingWindow_:m8});function f8(r){return xh(r,.5,.5)}var xy=k({hannWindow_:f8});function d8(r,t,e,n=!1,o=0){let s=0,i=[];for(;s+t<=r.size;)i.push(Ot(r,s,t)),s+=e;if(n)for(;s`Error in cropAndResize: image must be rank 4,but got rank ${i.rank}.`),_(a.rank===2&&a.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${l},4] but had shape ${a.shape}.`),_(u.rank===1&&u.shape[0]===l,()=>`Error in cropAndResize: boxInd must be have size [${l}] but had shape ${a.shape}.`),_(n.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${n.length}.`),_(n[0]>=1&&n[1]>=1,()=>`cropSize must be atleast [1,1], but was ${n}`),_(o===\"bilinear\"||o===\"nearest\",()=>`method must be bilinear or nearest, but was ${o}`);let c={image:i,boxes:a,boxInd:u},p={method:o,extrapolationValue:s,cropSize:n};return T.runKernel(Ma,c,p)}var WA=k({cropAndResize_:g8});function x8(r){let t=C(r,\"image\",\"flipLeftRight\",\"float32\");_(t.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${t.rank}.`);let e={image:t};return T.runKernel(Ba,e,{})}var UA=k({flipLeftRight_:x8});function y8(r){let t=C(r,\"image\",\"grayscaleToRGB\"),e=t.rank-1,n=t.shape[e];_(t.rank>=2,()=>`Error in grayscaleToRGB: images must be at least rank 2, but got rank ${t.rank}.`),_(n===1,()=>`Error in grayscaleToRGB: last dimension of a grayscale image should be size 1, but got size ${n}.`);let o=new Array(t.rank);return o.fill(1,0,e),o[e]=3,Rr(t,o)}var HA=k({grayscaleToRGB_:y8});function b8(r){let t=C(r,\"image\",\"RGBToGrayscale\"),e=t.rank-1,n=t.shape[e];_(t.rank>=2,()=>`Error in RGBToGrayscale: images must be at least rank 2, but got rank ${t.rank}.`),_(n===3,()=>`Error in RGBToGrayscale: last dimension of an RGB image should be size 3, but got size ${n}.`);let o=t.dtype,s=J(t,\"float32\"),i=Oe([.2989,.587,.114]),a;switch(t.rank){case 2:a=wu(\"ij,j->i\",s,i);break;case 3:a=wu(\"ijk,k->ij\",s,i);break;case 4:a=wu(\"ijkl,l->ijk\",s,i);break;case 5:a=wu(\"ijklm,m->ijkl\",s,i);break;case 6:a=wu(\"ijklmn,n->ijklm\",s,i);break;default:throw new Error(\"Not a valid tensor rank.\")}return a=je(a,-1),J(a,o)}var qA=k({rgbToGrayscale_:b8});function w8(r,t,e=0,n=.5){let o=C(r,\"image\",\"rotateWithOffset\",\"float32\");_(o.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${o.rank}.`);let s={image:o},i={radians:t,fillValue:e,center:n};return T.runKernel(pl,s,i)}var KA=k({rotateWithOffset_:w8});function No(r,t,e,n,o,s){n==null&&(n=.5),o==null&&(o=Number.NEGATIVE_INFINITY),s==null&&(s=0);let i=r.shape[0];return e=Math.min(e,i),_(0<=n&&n<=1,()=>`iouThreshold must be in [0, 1], but was '${n}'`),_(r.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${r.rank}'`),_(r.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${r.shape[1]}`),_(t.rank===1,()=>\"scores must be a 1D tensor\"),_(t.shape[0]===i,()=>`scores has incompatible shape with boxes. 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g=0;go&&l.push({score:t[g],boxIndex:g,suppressBeginIndex:0});l.sort(YA);let c=s>0?-.5/s:0,p=[],m=[];for(;p.length0;){let g=l.pop(),{score:x,boxIndex:b,suppressBeginIndex:w}=g;if(x=w;--N){let E=N8(r,b,p[N]);if(E>=n){I=!0;break}if(g.score=g.score*k8(n,c,E),g.score<=o)break}g.suppressBeginIndex=p.length,I||(g.score===x?(p.push(b),m.push(g.score)):g.score>o&&XA(l,g,YA))}let f=p.length,d=e-f;a&&d>0&&(p.push(...new Array(d).fill(0)),m.push(...new Array(d).fill(0)));let h={selectedIndices:p};return i&&(h.selectedScores=m),u&&(h.validOutputs=f),h}function N8(r,t,e){let n=r.subarray(t*4,t*4+4),o=r.subarray(e*4,e*4+4),s=Math.min(n[0],n[2]),i=Math.min(n[1],n[3]),a=Math.max(n[0],n[2]),u=Math.max(n[1],n[3]),l=Math.min(o[0],o[2]),c=Math.min(o[1],o[3]),p=Math.max(o[0],o[2]),m=Math.max(o[1],o[3]),f=(a-s)*(u-i),d=(p-l)*(m-c);if(f<=0||d<=0)return 0;let h=Math.max(s,l),g=Math.max(i,c),x=Math.min(a,p),b=Math.min(u,m),w=Math.max(x-h,0)*Math.max(b-g,0);return w/(f+d-w)}function k8(r,t,e){let n=Math.exp(t*e*e);return e<=r?n:0}function YA(r,t){return r.score-t.score||r.score===t.score&&t.boxIndex-r.boxIndex}async function T8(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY){let s=C(r,\"boxes\",\"nonMaxSuppressionAsync\"),i=C(t,\"scores\",\"nonMaxSuppressionAsync\"),a=No(s,i,e,n,o);e=a.maxOutputSize,n=a.iouThreshold,o=a.scoreThreshold;let u=await Promise.all([s.data(),i.data()]),l=u[0],c=u[1],{selectedIndices:p}=by(l,c,e,n,o);return s!==r&&s.dispose(),i!==t&&i.dispose(),Oe(p,\"int32\")}var ZA=T8;function _8(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY,s=0){let i=C(r,\"boxes\",\"nonMaxSuppression\"),a=C(t,\"scores\",\"nonMaxSuppression\"),u=No(i,a,e,n,o,s);e=u.maxOutputSize,n=u.iouThreshold,o=u.scoreThreshold,s=u.softNmsSigma;let l={boxes:i,scores:a},c={maxOutputSize:e,iouThreshold:n,scoreThreshold:o,softNmsSigma:s},p=T.runKernel(tl,l,c);return{selectedIndices:p[0],selectedScores:p[1]}}var JA=k({nonMaxSuppressionWithScore_:_8});async function E8(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY,s=0){let i=C(r,\"boxes\",\"nonMaxSuppressionAsync\"),a=C(t,\"scores\",\"nonMaxSuppressionAsync\"),u=No(i,a,e,n,o,s);e=u.maxOutputSize,n=u.iouThreshold,o=u.scoreThreshold,s=u.softNmsSigma;let l=await Promise.all([i.data(),a.data()]),c=l[0],p=l[1],{selectedIndices:m,selectedScores:f}=Iy(c,p,e,n,o,s);return i!==r&&i.dispose(),a!==t&&a.dispose(),{selectedIndices:Oe(m,\"int32\"),selectedScores:Oe(f)}}var QA=E8;function A8(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY,s=!1){let i=C(r,\"boxes\",\"nonMaxSuppression\"),a=C(t,\"scores\",\"nonMaxSuppression\"),u=No(i,a,e,n,o,null),l=u.maxOutputSize,c=u.iouThreshold,p=u.scoreThreshold,m={boxes:i,scores:a},f={maxOutputSize:l,iouThreshold:c,scoreThreshold:p,padToMaxOutputSize:s},d=T.runKernel(Qa,m,f);return{selectedIndices:d[0],validOutputs:d[1]}}var t2=k({nonMaxSuppressionPadded_:A8});async function D8(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY,s=!1){let i=C(r,\"boxes\",\"nonMaxSuppressionAsync\"),a=C(t,\"scores\",\"nonMaxSuppressionAsync\"),u=No(i,a,e,n,o,null),l=u.maxOutputSize,c=u.iouThreshold,p=u.scoreThreshold,[m,f]=await Promise.all([i.data(),a.data()]),{selectedIndices:d,validOutputs:h}=wy(m,f,l,c,p,s);return i!==r&&i.dispose(),a!==t&&a.dispose(),{selectedIndices:Oe(d,\"int32\"),validOutputs:pt(h,\"int32\")}}var e2=D8;function $8(r,t,e=!1,n=!1){let o=C(r,\"images\",\"resizeBilinear\");_(o.rank===3||o.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${o.rank}.`),_(t.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${t}.`),_(n===!1||e===!1,()=>\"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.\");let s=o,i=!1;o.rank===3&&(i=!0,s=R(o,[1,o.shape[0],o.shape[1],o.shape[2]]));let[]=t,a={images:s},u={alignCorners:e,halfPixelCenters:n,size:t},l=T.runKernel(Bs,a,u);return i?R(l,[l.shape[1],l.shape[2],l.shape[3]]):l}var Cy=k({resizeBilinear_:$8});function R8(r,t,e=!1,n=!1){let o=C(r,\"images\",\"resizeNearestNeighbor\");_(o.rank===3||o.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${o.rank}.`),_(t.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t}.`),_(o.dtype===\"float32\"||o.dtype===\"int32\",()=>\"`images` must have `int32` or `float32` as dtype\"),_(n===!1||e===!1,()=>\"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.\");let s=o,i=!1;o.rank===3&&(i=!0,s=R(o,[1,o.shape[0],o.shape[1],o.shape[2]]));let[]=t,a={images:s},u={alignCorners:e,halfPixelCenters:n,size:t},l=T.runKernel(zs,a,u);return i?R(l,[l.shape[1],l.shape[2],l.shape[3]]):l}var vy=k({resizeNearestNeighbor_:R8});function F8(r,t=\"binary\",e=!1,n=.5){let o=C(r,\"image\",\"threshold\"),s=.2989,i=.587,a=.114,u=o.shape[0]*o.shape[1],l=$(Oe([n]),255),c,p,m,f;if(_(o.rank===3,()=>`Error in threshold: image must be rank 3,but got rank 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u={image:i,transforms:a},l={interpolation:e,fillMode:n,fillValue:o,outputShape:s};return T.runKernel(cl,u,l)}var n2=k({transform_:M8});function P8(r,t,e){let n=C(r,\"a\",\"bandPart\");_(n.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${n.rank}.`);let o=n.shape,[s,i]=n.shape.slice(-2),a,u;typeof t==\"number\"?(_(t%1===0,()=>`bandPart(): numLower must be an integer, got ${t}.`),_(t<=s,()=>`bandPart(): numLower (${t}) must not be greater than the number of rows (${s}).`),a=C(t<0?s:t,\"numLower\",\"bandPart\")):(_(t.dtype===\"int32\",()=>\"bandPart(): numLower's dtype must be an int32.\"),a=Ie(yl(t,0),s,lo(t,s))),typeof e==\"number\"?(_(e%1===0,()=>`bandPart(): numUpper must be an integer, got ${e}.`),_(e<=i,()=>`bandPart(): numUpper (${e}) must not be greater than the number of columns (${i}).`),u=C(e<0?i:e,\"numUpper\",\"bandPart\")):(_(e.dtype===\"int32\",()=>\"bandPart(): numUpper's dtype must be an int32.\"),u=Ie(yl(e,0),i,lo(e,i)));let l=R(pa(0,s,1,\"int32\"),[-1,1]),c=pa(0,i,1,\"int32\"),p=at(l,c),m=Fr(Vn(p,a),cn(p,Ut(u))),f=ke([s,i],n.dtype);return R(Fe(gr(R(n,[-1,s,i])).map(d=>Ie(m,d,f))),o)}var o2=k({bandPart_:P8});function L8(r){let t;if(Array.isArray(r)){t=!1,_(r!=null&&r.length>0,()=>\"Gram-Schmidt process: input must not be null, undefined, or empty\");let o=r[0].shape[0];for(let s=1;s`Gram-Schmidt: Non-unique lengths found in the input vectors: (${r[s].shape[0]} vs. ${o})`)}else t=!0,r=hr(r,r.shape[0],0).map(o=>Wn(o,[0]));_(r.length<=r[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${r.length}) exceeds number of dimensions (${r[0].shape[0]}).`);let e=[],n=r;for(let o=0;o{let s=n[o];if(o>0)for(let i=0;i=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${r.rank}`),r.rank===2)return i2(r,t);{let e=r.shape.slice(0,r.shape.length-2).reduce((u,l)=>u*l),n=gr(R(r,[e,r.shape[r.shape.length-2],r.shape[r.shape.length-1]]),0),o=[],s=[];n.forEach(u=>{let[l,c]=i2(u,t);o.push(l),s.push(c)});let 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bD={kernelName:Kl,inputsToSave:[\"x\"],gradFunc:Dy.gradFunc};var wD={kernelName:Oi,saveAllInputs:!0,gradFunc:(r,t,e)=>{let n=t.map(u=>u.shape),{axis:o}=e,s=fr(o,t[0].shape)[0],i=n.map(u=>u[s]);return hr(r,i,s).map(u=>()=>u)}};var ID={kernelName:Qo,inputsToSave:[\"x\",\"filter\"],gradFunc:(r,t,e)=>{let[n,o]=t,{dilations:s,strides:i,pad:a,dataFormat:u}=e;return _(io(s),()=>`Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. 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Add some layers first.\");this.model=new Un({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(t,e,n=console.log){this.built||this.build(),super.summary(t,e,n)}setWeights(t){this.model==null&&this.build(),this.model.setWeights(t)}evaluate(t,e,n={}){if(!this.built)throw new dn(\"The model needs to be compiled before being used.\");return this.model.evaluate(t,e,n)}async evaluateDataset(t,e){if(!this.built)throw new dn(\"The model needs to be compiled before being used.\");return this.model.evaluateDataset(t,e)}predict(t,e={}){return this.model==null&&this.build(),this.model.predict(t,e)}predictOnBatch(t){return this.model==null&&this.build(),this.model.predictOnBatch(t)}compile(t){this.build(),this.model.compile(t),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(t){this.model.optimizer=t}async fit(t,e,n={}){if(!this.built)throw new dn(\"The model needs to be compiled before being used.\");return this.model.fit(t,e,n)}async fitDataset(t,e){if(!this.built)throw new dn(\"The model needs to be compiled before being used.\");return this.model.fitDataset(t,e)}async trainOnBatch(t,e){return this.model.trainOnBatch(t,e)}static fromConfig(t,e,n={},o=!1){let s,i={};if(e instanceof Array){if(e[0].className==null||e[0].className===\"Merge\")throw new z(\"Legacy serialization format not supported yet.\");s=e}else y.assert(e.layers!=null,()=>\"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field.\"),s=e.layers,delete e.layers,i=e;let a=new t(i);if(!(a instanceof r))throw new _t(`Sequential.fromConfig called on non-Sequential input: ${a}`);for(let u of s){let c=wn(u,void 0,o);o&&c.setFastWeightInitDuringBuild(!0),a.add(c)}return a}set stopTraining(t){if(this.model==null)throw new z(\"Cannot set the stopTraining property of a sequential model before it is compiled.\");this.model.stopTraining=t}get stopTraining(){if(this.model==null)throw new z(\"Cannot get the stopTraining property of a sequential model before it is compiled.\");return this.model.stopTraining}getConfig(){let t=[];for(let e of this.layers){let n={};n.className=e.getClassName(),n.config=e.getConfig(),t.push(n)}return{name:this.name,layers:t}}};Wc.className=\"Sequential\";Q.registerClass(Wc);function gJ(r){return new Un(r)}function xJ(r){return new Wc(r)}function JN(r){return Hy(r)}function yJ(r,t){Um.registerCallbackConstructor(r,t)}var Pr=class extends Q.Serializable{getConfig(){return{}}},fb=class extends Pr{apply(t,e=1){return uR(t,e)}};fb.className=\"elu\";Q.registerClass(fb);var db=class extends Pr{apply(t){return fm(t)}};db.className=\"selu\";Q.registerClass(db);var hb=class extends Pr{apply(t){return Or(t)}};hb.className=\"relu\";Q.registerClass(hb);var gb=class extends Pr{apply(t){return B(()=>lo(6,Or(t)))}};gb.className=\"relu6\";Q.registerClass(gb);var xb=class extends Pr{apply(t){return t}};xb.className=\"linear\";Q.registerClass(xb);var yb=class extends Pr{apply(t){return en(t)}};yb.className=\"sigmoid\";Q.registerClass(yb);var bb=class extends Pr{apply(t){return pR(t)}};bb.className=\"hardSigmoid\";Q.registerClass(bb);var wb=class extends Pr{apply(t){return ui(t)}};wb.className=\"softplus\";Q.registerClass(wb);var Ib=class extends Pr{apply(t){return cR(t)}};Ib.className=\"softsign\";Q.registerClass(Ib);var Cb=class extends Pr{apply(t){return li(t)}};Cb.className=\"tanh\";Q.registerClass(Cb);var Ym=class extends Pr{apply(t,e=-1){return Eu(t,e)}};Ym.className=\"softmax\";Q.registerClass(Ym);var vb=class extends Pr{apply(t,e=-1){return lm(t,e)}};vb.className=\"logSoftmax\";Q.registerClass(vb);var Sb=class extends Pr{apply(t){return B(()=>B(()=>{let e=Math.sqrt(2),n=$(.5,K(1,am(ut(t,e))));return $(t,n)}))}};Sb.className=\"gelu\";Q.registerClass(Sb);var Nb=class extends Pr{apply(t){return B(()=>$(.5,$(t,K(1,li($(ge(ut(2,Math.PI)),K(t,$(.044715,qr(t,3)))))))))}};Nb.className=\"gelu_new\";Q.registerClass(Nb);var kb=class extends Pr{apply(t){return B(()=>$(t,li(ui(t))))}};kb.className=\"mish\";Q.registerClass(kb);var Tb=class extends Pr{apply(t,e=1){return B(()=>$(en($(t,e)),t))}};Tb.className=\"swish\";Q.registerClass(Tb);function gi(r){return r.getClassName()}function QN(r,t={}){return fa(r,Q.SerializationMap.getMap().classNameMap,t,\"activation\")}function xi(r){if(r==null){let t={};return t.className=\"linear\",t.config={},QN(t)}if(typeof r==\"string\"){let t={};return t.className=r,t.config={},QN(t)}else return r instanceof Pr?r:QN(r)}function tk(r){if(r!=null&&typeof r!=\"object\")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${r}`)}var _b=class extends Q.Serializable{},Lu=class extends _b{constructor(t){super(),tk(t),this.l1=t==null||t.l1==null?.01:t.l1,this.l2=t==null||t.l2==null?.01:t.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(t){return B(()=>{let e=ke([1]);return this.hasL1&&(e=K(e,mt($(this.l1,_e(t))))),this.hasL2&&(e=K(e,mt($(this.l2,$c(t))))),R(e,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(t,e){return new t({l1:e.l1,l2:e.l2})}};Lu.className=\"L1L2\";Q.registerClass(Lu);function KR(r){return tk(r),new Lu({l1:r!=null?r.l1:null,l2:0})}function jR(r){return tk(r),new Lu({l2:r!=null?r.l2:null,l1:0})}var HR={l1l2:\"L1L2\"};function fe(r){return km(r)}function qR(r,t={}){return fa(r,Q.SerializationMap.getMap().classNameMap,t,\"regularizer\")}function ve(r){if(r==null)return null;if(typeof r==\"string\"){let e={className:r in HR?HR[r]:r,config:{}};return qR(e)}else return r instanceof _b?r:qR(r)}var Zm=class extends Et{constructor(t){super(t==null?{}:t),this.supportsMasking=!0,t!=null&&(this.maxValue=t.maxValue)}call(t,e){t=St(t);let n=Or(t);return this.maxValue!=null&&(n=vr(n,0,this.maxValue)),n}computeOutputShape(t){return t}getConfig(){let t={maxValue:this.maxValue},e=super.getConfig();return Object.assign(t,e),t}};Zm.className=\"ReLU\";Q.registerClass(Zm);var Jm=class extends Et{constructor(t){super(t==null?{}:t),this.DEFAULT_ALPHA=.3,t==null&&(t={}),this.alpha=t.alpha==null?this.DEFAULT_ALPHA:t.alpha}call(t,e){let n=St(t);return Cu(n,this.alpha)}computeOutputShape(t){return t}getConfig(){let t={alpha:this.alpha},e=super.getConfig();return Object.assign(t,e),t}};Jm.className=\"LeakyReLU\";Q.registerClass(Jm);var Qm=class extends Et{constructor(t){if(super(t==null?{}:t),this.DEFAULT_ALPHA_INITIALIZER=\"zeros\",t==null&&(t={}),this.supportsMasking=!0,this.alphaInitializer=xe(t.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=ve(t.alphaRegularizer),this.alphaConstraint=Ge(t.alphaConstraint),t.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(t.sharedAxes))this.sharedAxes=t.sharedAxes;else if(typeof t.sharedAxes==\"number\")this.sharedAxes=[t.sharedAxes];else throw new z(`Expected sharedAxes to be a number or an array of numbers, but got ${t.sharedAxes}`)}build(t){t=Gt(t);let e=t.slice(1);if(this.sharedAxes!=null)for(let o of this.sharedAxes)e[o-1]=1;this.alpha=this.addWeight(\"alpha\",e,\"float32\",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let o=1;o{let n=St(t),o=e.mask;if(o!=null){let s=$(at(ar(n.shape),J(o,n.dtype)),pt(-1e9));n=K(n,s)}return this.axis instanceof Array?this.axis.length>1?Ke(at(n,Su(n,this.axis,!0))):this.softmax(n,this.axis[0]):this.softmax(n,this.axis)})}computeOutputShape(t){return t}getConfig(){let t={axis:this.axis},e=super.getConfig();return Object.assign(t,e),t}};rf.className=\"Softmax\";Q.registerClass(rf);function zu(r,t,e){if(typeof r==\"number\")return To(r,t);if(r.length!==t)throw new z(`The ${e} argument must be an integer or tuple of ${t} integers. 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B(()=>{if(s==null&&(s=xn()),Me(s),r.rank!==3&&r.rank!==4)throw new z(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${r.rank}.`);if(t.rank!==3&&t.rank!==4)throw new z(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${r.rank}.`);let u=Ph(r,s);if(o===\"causal\")throw new _t(\"The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.\");return u=Ru.conv2d({x:u,filter:t,strides:n,pad:o===\"same\"?\"same\":\"valid\",dilations:i,dataFormat:\"NHWC\",bias:e,activation:a}),s===\"channelsFirst\"&&(u=Vt(u,[0,3,1,2])),u})}function IJ(r,t,e,n=[1,1,1],o=\"valid\",s,i){return B(()=>{if(s==null&&(s=xn()),Me(s),r.rank!==4&&r.rank!==5)throw new z(`conv3dWithBias expects input to be of rank 4 or 5, but received ${r.rank}.`);if(t.rank!==4&&t.rank!==5)throw new z(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${r.rank}.`);let a=ek(r,s);if(o===\"causal\")throw new _t(\"The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.\");return a=Dx(a,t,n,o===\"same\"?\"same\":\"valid\",\"NDHWC\",i),e!=null&&(a=yn(a,e)),s===\"channelsFirst\"&&(a=Vt(a,[0,4,1,2,3])),a})}var Mh=class r extends Et{constructor(t,e){if(super(e),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",r.verifyArgs(e),this.rank=t,tr(this.rank,\"rank\"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new _t(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=zu(e.kernelSize,t,\"kernelSize\"),this.strides=zu(e.strides==null?1:e.strides,t,\"strides\"),this.padding=e.padding==null?\"valid\":e.padding,hn(this.padding),this.dataFormat=e.dataFormat==null?\"channelsLast\":e.dataFormat,Me(this.dataFormat),this.activation=xi(e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.biasInitializer=xe(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Ge(e.biasConstraint),this.biasRegularizer=ve(e.biasRegularizer),this.activityRegularizer=ve(e.activityRegularizer),this.dilationRate=zu(e.dilationRate==null?1:e.dilationRate,t,\"dilationRate\"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new z(`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 z(`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 z(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(t){if(co(\"kernelSize\"in t,\"required key 'kernelSize' not in config\"),typeof t.kernelSize!=\"number\"&&!Fy(t.kernelSize,\"number\",1,3))throw new z(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(t.kernelSize)}.`)}getConfig(){let t={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:gi(this.activation),useBias:this.useBias,biasInitializer:Te(this.biasInitializer),biasRegularizer:fe(this.biasRegularizer),activityRegularizer:fe(this.activityRegularizer),biasConstraint:Ve(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}},nf=class r extends Mh{constructor(t,e){super(t,e),this.kernel=null,r.verifyArgs(e),this.filters=e.filters,tr(this.filters,\"filters\"),this.kernelInitializer=xe(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Ge(e.kernelConstraint),this.kernelRegularizer=ve(e.kernelRegularizer)}build(t){t=Gt(t);let e=this.dataFormat===\"channelsFirst\"?1:t.length-1;if(t[e]==null)throw new z(`The channel dimension of the input should be defined. Found ${t[e]}`);let n=t[e],o=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight(\"kernel\",o,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:{[e]:n}}],this.built=!0}call(t,e){return B(()=>{t=St(t);let n,o=this.bias==null?null:this.bias.read(),s=Oy(this.activation.getClassName());if(s!=null&&this.rank===2)n=XR(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)n=wJ(t,this.kernel.read(),o,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=XR(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=IJ(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new _t(\"convolutions greater than 3D are not implemented yet.\");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(t){t=Gt(t);let e=[],n=this.dataFormat===\"channelsLast\"?t.slice(1,t.length-1):t.slice(2);for(let s=0;s 0 but got ${JSON.stringify(t.filters)}`)}},Uc=class r extends nf{constructor(t){super(2,t),r.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!=\"number\"&&!Fy(t.kernelSize,\"number\",1,2))throw new z(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(t.kernelSize)}.`)}};Uc.className=\"Conv2D\";Q.registerClass(Uc);var Hc=class r extends nf{constructor(t){super(3,t),r.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!=\"number\"&&!(Array.isArray(t.kernelSize)&&(t.kernelSize.length===1||t.kernelSize.length===3)))throw new z(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(t.kernelSize)}.`)}};Hc.className=\"Conv3D\";Q.registerClass(Hc);var of=class extends Uc{constructor(t){if(super(t),this.inputSpec=[new Ce({ndim:4})],this.padding!==\"same\"&&this.padding!==\"valid\")throw new z(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(t=Gt(t),t.length!==4)throw new z(\"Input should have rank 4; Received input shape: \"+JSON.stringify(t));let e=this.dataFormat===\"channelsFirst\"?1:t.length-1;if(t[e]==null)throw new z(\"The channel dimension of the inputs should be defined. Found `None`.\");let n=t[e],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight(\"kernel\",o,\"float32\",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],\"float32\",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ce({ndim:4,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{let n=St(t);if(n.shape.length!==4)throw new z(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],i,a;this.dataFormat===\"channelsFirst\"?(i=2,a=3):(i=1,a=2);let u=o[i],l=o[a],c=this.kernelSize[0],p=this.kernelSize[1],m=this.strides[0],f=this.strides[1],d=yi(u,m,c,this.padding),h=yi(l,f,p,this.padding),g=[s,d,h,this.filters];this.dataFormat!==\"channelsLast\"&&(n=Vt(n,[0,2,3,1]));let x=om(n,this.kernel.read(),g,this.strides,this.padding);return this.dataFormat!==\"channelsLast\"&&(x=Vt(x,[0,3,1,2])),this.bias!=null&&(x=yn(x,this.bias.read(),this.dataFormat)),this.activation!=null&&(x=this.activation.apply(x)),x})}computeOutputShape(t){t=Gt(t);let e=t.slice(),n,o,s;this.dataFormat===\"channelsFirst\"?(n=1,o=2,s=3):(n=3,o=1,s=2);let i=this.kernelSize[0],a=this.kernelSize[1],u=this.strides[0],l=this.strides[1];return e[n]=this.filters,e[o]=yi(e[o],u,i,this.padding),e[s]=yi(e[s],l,a,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};of.className=\"Conv2DTranspose\";Q.registerClass(of);var sf=class extends Hc{constructor(t){if(super(t),this.inputSpec=[new Ce({ndim:5})],this.padding!==\"same\"&&this.padding!==\"valid\")throw new z(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(t=Gt(t),t.length!==5)throw new z(\"Input should have rank 5; Received input shape: \"+JSON.stringify(t));let e=this.dataFormat===\"channelsFirst\"?1:t.length-1;if(t[e]==null)throw new z(\"The channel dimension of the inputs should be defined. Found `None`.\");let n=t[e],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight(\"kernel\",o,\"float32\",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],\"float32\",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ce({ndim:5,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{let n=St(t);if(n.shape.length!==5)throw new z(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],i,a,u;this.dataFormat===\"channelsFirst\"?(u=2,i=3,a=4):(u=1,i=2,a=3);let l=o[u],c=o[i],p=o[a],m=this.kernelSize[0],f=this.kernelSize[1],d=this.kernelSize[2],h=this.strides[0],g=this.strides[1],x=this.strides[2],b=yi(l,h,m,this.padding),w=yi(c,g,f,this.padding),I=yi(p,x,d,this.padding),N=[s,b,w,I,this.filters];this.dataFormat!==\"channelsLast\"&&(n=Vt(n,[0,2,3,4,1]));let E=Rx(n,this.kernel.read(),N,this.strides,this.padding);return this.dataFormat!==\"channelsLast\"&&(E=Vt(E,[0,4,1,2,3])),this.bias!==null&&(E=yn(E,this.bias.read(),this.dataFormat)),this.activation!==null&&(E=this.activation.apply(E)),E})}computeOutputShape(t){t=Gt(t);let e=t.slice(),n,o,s,i;this.dataFormat===\"channelsFirst\"?(n=1,o=2,s=3,i=4):(n=4,o=1,s=2,i=3);let a=this.kernelSize[0],u=this.kernelSize[1],l=this.kernelSize[2],c=this.strides[0],p=this.strides[1],m=this.strides[2];return e[n]=this.filters,e[o]=yi(e[o],c,a,this.padding),e[s]=yi(e[s],p,u,this.padding),e[i]=yi(e[i],m,l,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};sf.className=\"Conv3DTranspose\";Q.registerClass(sf);var Eb=class extends nf{constructor(t,e){if(super(t,e),this.DEFAULT_DEPTHWISE_INITIALIZER=\"glorotUniform\",this.DEFAULT_POINTWISE_INITIALIZER=\"glorotUniform\",this.depthwiseKernel=null,this.pointwiseKernel=null,e.filters==null)throw new z(\"The `filters` configuration field is required by SeparableConv, but is unspecified.\");if(e.kernelInitializer!=null||e.kernelRegularizer!=null||e.kernelConstraint!=null)throw new z(\"Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.\");if(e.padding!=null&&e.padding!==\"same\"&&e.padding!==\"valid\")throw new z(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(e.padding)}`);this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=xe(e.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=ve(e.depthwiseRegularizer),this.depthwiseConstraint=Ge(e.depthwiseConstraint),this.pointwiseInitializer=xe(e.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=ve(e.pointwiseRegularizer),this.pointwiseConstraint=Ge(e.pointwiseConstraint)}build(t){if(t=Gt(t),t.length{t=St(t);let n;if(this.rank===1)throw new _t(\"1D separable convolution is not implemented yet.\");return this.rank===2&&(this.dataFormat===\"channelsFirst\"&&(t=Vt(t,[0,2,3,1])),n=dm(t,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,\"NHWC\")),this.useBias&&(n=yn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat===\"channelsFirst\"&&(n=Vt(n,[0,3,1,2])),n})}getConfig(){let t=super.getConfig();return delete t.rank,delete t.kernelInitializer,delete t.kernelRegularizer,delete t.kernelConstraint,t.depthwiseInitializer=Te(this.depthwiseInitializer),t.pointwiseInitializer=Te(this.pointwiseInitializer),t.depthwiseRegularizer=fe(this.depthwiseRegularizer),t.pointwiseRegularizer=fe(this.pointwiseRegularizer),t.depthwiseConstraint=Ve(this.depthwiseConstraint),t.pointwiseConstraint=Ve(this.pointwiseConstraint),t}};Eb.className=\"SeparableConv\";var af=class extends Eb{constructor(t){super(2,t)}};af.className=\"SeparableConv2D\";Q.registerClass(af);var lf=class r extends nf{constructor(t){super(1,t),r.verifyArgs(t),this.inputSpec=[{ndim:3}]}getConfig(){let t=super.getConfig();return delete t.rank,delete t.dataFormat,t}static verifyArgs(t){if(typeof t.kernelSize!=\"number\"&&!Fy(t.kernelSize,\"number\",1,1))throw new z(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(t.kernelSize)}.`)}};lf.className=\"Conv1D\";Q.registerClass(lf);var uf=class extends Et{constructor(t){super(t),typeof t.cropping==\"number\"?this.cropping=[[t.cropping,t.cropping],[t.cropping,t.cropping]]:typeof t.cropping[0]==\"number\"?this.cropping=[[t.cropping[0],t.cropping[0]],[t.cropping[1],t.cropping[1]]]:this.cropping=t.cropping,this.dataFormat=t.dataFormat===void 0?\"channelsLast\":t.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(t){return this.dataFormat===\"channelsFirst\"?[t[0],t[1],t[2]-this.cropping[0][0]-this.cropping[0][1],t[3]-this.cropping[1][0]-this.cropping[1][1]]:[t[0],t[1]-this.cropping[0][0]-this.cropping[0][1],t[2]-this.cropping[1][0]-this.cropping[1][1],t[3]]}call(t,e){return B(()=>{if(t=St(t),this.dataFormat===\"channelsLast\"){let n=kh(t,this.cropping[0][0],t.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return kh(n,this.cropping[1][0],t.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=kh(t,this.cropping[0][0],t.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return kh(n,this.cropping[1][0],t.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let t={cropping:this.cropping,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}};uf.className=\"Cropping2D\";Q.registerClass(uf);var cf=class extends Et{constructor(t){super(t),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=t.size==null?this.DEFAULT_SIZE:t.size,this.dataFormat=t.dataFormat==null?\"channelsLast\":t.dataFormat,Me(this.dataFormat),this.interpolation=t.interpolation==null?\"nearest\":t.interpolation,nR(this.interpolation)}computeOutputShape(t){if(this.dataFormat===\"channelsFirst\"){let e=t[2]==null?null:this.size[0]*t[2],n=t[3]==null?null:this.size[1]*t[3];return[t[0],t[1],e,n]}else{let e=t[1]==null?null:this.size[0]*t[1],n=t[2]==null?null:this.size[1]*t[2];return[t[0],e,n,t[3]]}}call(t,e){return B(()=>{let n=St(t),o=n.shape;if(this.dataFormat===\"channelsFirst\"){n=Vt(n,[0,2,3,1]);let s=this.size[0]*o[2],i=this.size[1]*o[3],a=this.interpolation===\"nearest\"?fn.resizeNearestNeighbor(n,[s,i]):fn.resizeBilinear(n,[s,i]);return Vt(a,[0,3,1,2])}else{let s=this.size[0]*o[1],i=this.size[1]*o[2];return this.interpolation===\"nearest\"?fn.resizeNearestNeighbor(n,[s,i]):fn.resizeBilinear(n,[s,i])}})}getConfig(){let t={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},e=super.getConfig();return Object.assign(t,e),t}};cf.className=\"UpSampling2D\";Q.registerClass(cf);function CJ(r,t,e=[1,1],n=\"valid\",o,s){return B(()=>{o==null&&(o=xn()),Me(o);let i=Ph(r,o);if(r.rank!==4)throw new z(`Input for depthwiseConv2d is required to be 4-D, but is instead ${r.rank}-D`);if(t.rank!==4)throw new z(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=ia(i,t,e,n===\"same\"?\"same\":\"valid\",\"NHWC\",s),o===\"channelsFirst\"&&(i=Vt(i,[0,3,1,2])),i})}var pf=class extends Mh{constructor(t){super(2,t),this.depthwiseKernel=null,this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=xe(t.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Ge(t.depthwiseConstraint),this.depthwiseRegularizer=ve(t.depthwiseRegularizer)}build(t){if(t=Gt(t),t.length<4)throw new z(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(t)}.`);let e=this.dataFormat===\"channelsFirst\"?1:3;if(t[e]==null||t[e]<0)throw new z(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${t[e]}).`);let n=t[e],o=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight(\"depthwise_kernel\",o,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(t,e){return B(()=>{t=St(t);let n=CJ(t,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=yn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(t){t=Gt(t);let e=this.dataFormat===\"channelsFirst\"?t[2]:t[1],n=this.dataFormat===\"channelsFirst\"?t[3]:t[2],o=this.dataFormat===\"channelsFirst\"?t[1]*this.depthMultiplier:t[3]*this.depthMultiplier,s=Tn(e,this.kernelSize[0],this.padding,this.strides[0]),i=Tn(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat===\"channelsFirst\"?[t[0],o,s,i]:[t[0],s,i,o]}getConfig(){let t=super.getConfig();return t.depthMultiplier=this.depthMultiplier,t.depthwiseInitializer=Te(this.depthwiseInitializer),t.depthwiseRegularizer=fe(this.depthwiseRegularizer),t.depthwiseConstraint=Ve(this.depthwiseRegularizer),t}};pf.className=\"DepthwiseConv2D\";Q.registerClass(pf);function rk(r,t,e,n){if(Array.isArray(r)){if(t!=null||e!=null)throw new z(\"When inputs is an array, neither initialState or constants should be provided\");n!=null&&(e=r.slice(r.length-n,r.length),r=r.slice(0,r.length-n)),r.length>1&&(t=r.slice(1,r.length)),r=r[0]}function o(s){return s==null||Array.isArray(s)?s:[s]}return t=o(t),e=o(e),{inputs:r,initialState:t,constants:e}}function nk(r,t,e,n=!1,o,s,i=!1,a=!1){return B(()=>{let u=t.shape.length;if(u<3)throw new z(`Input should be at least 3D, but is ${u}D.`);let l=[1,0].concat(gn(2,u));if(t=Vt(t,l),s!=null)throw new _t(\"The rnn() functoin of the deeplearn.js backend does not support constants yet.\");i&&console.warn(\"Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend.\"),o!=null&&(o=J(J(o,\"bool\"),\"float32\"),o.rank===u-1&&(o=je(o,-1)),o=Vt(o,l)),n&&(t=dr(t,0),o!=null&&(o=dr(o,0)));let c=[],p,m=e,f=t.shape[0],d=gr(t),h;o!=null&&(h=gr(o));for(let x=0;xr(b,m));if(o==null)p=w[0],m=w[1];else{let I=B(()=>{let N=h[x],E=at(wr(N),N),A=K($(w[0],N),$(m[0],E)),D=m.map((F,M)=>K($(w[1][M],N),$(F,E)));return{output:A,newStates:D}});p=I.output,m=I.newStates}a&&c.push(p)}let g;return a&&(g=Fe(c,1)),[p,g,m]})}var po=class r extends Et{constructor(t){super(t);let e;if(t.cell==null)throw new z(\"cell property is missing for the constructor of RNN.\");if(Array.isArray(t.cell)?e=new jc({cells:t.cell}):e=t.cell,e.stateSize==null)throw new z(\"The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).\");this.cell=e,this.returnSequences=t.returnSequences==null?!1:t.returnSequences,this.returnState=t.returnState==null?!1:t.returnState,this.goBackwards=t.goBackwards==null?!1:t.goBackwards,this._stateful=t.stateful==null?!1:t.stateful,this.unroll=t.unroll==null?!1:t.unroll,this.supportsMasking=!0,this.inputSpec=[new Ce({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return gn(0,t).map(e=>null)}else return this.states_}setStates(t){this.states_=t}computeOutputShape(t){Uy(t)&&(t=t[0]),t=t;let e=this.cell.stateSize;Array.isArray(e)||(e=[e]);let n=e[0],o;if(this.returnSequences?o=[t[0],t[1],n]:o=[t[0],n],this.returnState){let s=[];for(let i of e)s.push([t[0],i]);return[o].concat(s)}else return o}computeMask(t,e){return B(()=>{Array.isArray(e)&&(e=e[0]);let n=this.returnSequences?e:null;if(this.returnState){let o=this.states.map(s=>null);return[n].concat(o)}else return n})}get states(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,e=[];for(let n=0;na.shape[a.shape.length-1]),i))throw new z(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=i.map(a=>new Ce({shape:[null,a]}));this.stateful&&this.resetStates()}resetStates(t,e=!1){B(()=>{if(!this.stateful)throw new uo(\"Cannot call resetStates() on an RNN Layer that is not stateful.\");let n=this.inputSpec[0].shape[0];if(n==null)throw new z(\"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(o=>ke([n,o])):this.states_=[ke([n,this.cell.stateSize])];else if(t==null)Tt(this.states_),this.keptStates!=null&&(Tt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>ke([n,o])):this.states_[0]=ke([n,this.cell.stateSize]);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e===!0?this.keptStates.push(this.states_.slice()):Tt(this.states_);for(let o=0;oDe(o.clone()))})}apply(t,e){let n=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=rk(t,n,o,this.numConstants);t=s.inputs,n=s.initialState,o=s.constants;let i=[],a=[];if(n!=null){e.initialState=n,i=i.concat(n),this.stateSpec=[];for(let l of n)this.stateSpec.push(new Ce({shape:l.shape}));a=a.concat(this.stateSpec)}if(o!=null&&(e.constants=o,i=i.concat(o),this.numConstants=o.length),i[0]instanceof nn){let l=[t].concat(i),c=this.inputSpec.concat(a),p=this.inputSpec;this.inputSpec=c;let m=super.apply(l,e);return this.inputSpec=p,m}else return super.apply(t,e)}call(t,e){return B(()=>{let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;t=St(t),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(t));let i=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==i)throw new z(`RNN Layer has ${i} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn(\"Ignoring unroll = true for RNN layer, due to imperative backend.\");let a={training:o},l=nk((d,h)=>{let g=this.cell.call([d].concat(h),a);return[g[0],g.slice(1)]},t,s,this.goBackwards,n,null,this.unroll,this.returnSequences),c=l[0],p=l[1],m=l[2];this.stateful&&this.resetStates(m,o);let f=this.returnSequences?p:c;return this.returnState?[f].concat(m):f})}getInitialState(t){return B(()=>{let e=ke(t.shape);return e=mt(e,[1,2]),e=Sl(e),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Vy(e,[1,n]):e):this.cell.stateSize>1?[Vy(e,[1,this.cell.stateSize])]:[e]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(t)}getConfig(){let t=super.getConfig(),e={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(e.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===r.className&&(e.cell={className:this.cell.getClassName(),config:n}),Object.assign(Object.assign(Object.assign({},n),t),e)}static fromConfig(t,e,n={}){let o=e.cell,s=wn(o,n);return new t(Object.assign(e,{cell:s}))}};po.className=\"RNN\";Q.registerClass(po);var Tl=class extends Et{},qc=class extends Tl{constructor(t){super(t),this.DEFAULT_ACTIVATION=\"tanh\",this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",this.units=t.units,tr(this.units,\"units\"),this.activation=xi(t.activation==null?this.DEFAULT_ACTIVATION:t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=xe(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=xe(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=xe(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=ve(t.kernelRegularizer),this.recurrentRegularizer=ve(t.recurrentRegularizer),this.biasRegularizer=ve(t.biasRegularizer),this.kernelConstraint=Ge(t.kernelConstraint),this.recurrentConstraint=Ge(t.recurrentConstraint),this.biasConstraint=Ge(t.biasConstraint),this.dropout=Dc([1,di([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Dc([1,di([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Gt(t),this.kernel=this.addWeight(\"kernel\",[t[t.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(t,e){return B(()=>{if(t=t,t.length!==2)throw new z(`SimpleRNNCell expects 2 input Tensors, got ${t.length}.`);let n=t[1];t=t[0];let o=e.training==null?!1:e.training;0wr(t),rate:this.dropout,training:o,dropoutFunc:this.dropoutFunc})),0wr(n),rate:this.recurrentDropout,training:o,dropoutFunc:this.dropoutFunc}));let s,i=this.dropoutMask,a=this.recurrentDropoutMask;i!=null?s=Do($(t,i),this.kernel.read()):s=Do(t,this.kernel.read()),this.bias!=null&&(s=yn(s,this.bias.read())),a!=null&&(n=$(n,a));let u=K(s,Do(n,this.recurrentKernel.read()));return this.activation!=null&&(u=this.activation.apply(u)),[u,u]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:gi(this.activation),useBias:this.useBias,kernelInitializer:Te(this.kernelInitializer),recurrentInitializer:Te(this.recurrentInitializer),biasInitializer:Te(this.biasInitializer),kernelRegularizer:fe(this.kernelRegularizer),recurrentRegularizer:fe(this.recurrentRegularizer),biasRegularizer:fe(this.biasRegularizer),activityRegularizer:fe(this.activityRegularizer),kernelConstraint:Ve(this.kernelConstraint),recurrentConstraint:Ve(this.recurrentConstraint),biasConstraint:Ve(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},t),e)}};qc.className=\"SimpleRNNCell\";Q.registerClass(qc);var mf=class extends po{constructor(t){t.cell=new qc(t),super(t)}call(t,e){return B(()=>{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return new t(e)}};mf.className=\"SimpleRNN\";Q.registerClass(mf);var Kc=class extends Tl{constructor(t){if(super(t),this.DEFAULT_ACTIVATION=\"tanh\",this.DEFAULT_RECURRENT_ACTIVATION=\"hardSigmoid\",this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",t.resetAfter)throw new z(\"GRUCell does not support reset_after parameter set to true.\");this.units=t.units,tr(this.units,\"units\"),this.activation=xi(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=xi(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=xe(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=xe(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=xe(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=ve(t.kernelRegularizer),this.recurrentRegularizer=ve(t.recurrentRegularizer),this.biasRegularizer=ve(t.biasRegularizer),this.kernelConstraint=Ge(t.kernelConstraint),this.recurrentConstraint=Ge(t.recurrentConstraint),this.biasConstraint=Ge(t.biasConstraint),this.dropout=Dc([1,di([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Dc([1,di([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Gt(t);let e=t[t.length-1];this.kernel=this.addWeight(\"kernel\",[e,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(t,e){return B(()=>{if(t=t,t.length!==2)throw new z(`GRUCell expects 2 input Tensors (inputs, h, c), got ${t.length}.`);let n=e.training==null?!1:e.training,o=t[1];t=t[0],0wr(t),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0wr(o),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,a,u,l;0{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};ff.className=\"GRU\";Q.registerClass(ff);var _l=class extends Tl{constructor(t){super(t),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=t.units,tr(this.units,\"units\"),this.activation=xi(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=xi(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=xe(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=xe(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=xe(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=t.unitForgetBias,this.kernelRegularizer=ve(t.kernelRegularizer),this.recurrentRegularizer=ve(t.recurrentRegularizer),this.biasRegularizer=ve(t.biasRegularizer),this.kernelConstraint=Ge(t.kernelConstraint),this.recurrentConstraint=Ge(t.recurrentConstraint),this.biasConstraint=Ge(t.biasConstraint),this.dropout=Dc([1,di([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Dc([1,di([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){var e;t=Gt(t);let n=t[t.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 o;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,i=this.units;o=new(e=class extends bn{apply(u,l){let c=s.apply([i]),p=new Mu().apply([i]),m=s.apply([i*2]);return GN(GN(c,p),m)}},e.className=\"CustomInit\",e)}else o=this.biasInitializer;this.bias=this.addWeight(\"bias\",[this.units*4],null,o,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training;if(t=t,t.length!==3)throw new z(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let o=t[1],s=t[2];t=t[0],0wr(t),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0wr(o),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,a=this.recurrentDropoutMask,u,l,c,p;0{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};df.className=\"LSTM\";Q.registerClass(df);var jc=class extends Tl{constructor(t){super(t),this.cells=t.cells}get stateSize(){let t=[];for(let e of this.cells.slice().reverse())Array.isArray(e.stateSize)?t.push(...e.stateSize):t.push(e.stateSize);return t}call(t,e){return B(()=>{t=t;let n=t.slice(1),o=[];for(let a of this.cells.slice().reverse())Array.isArray(a.stateSize)?o.push(n.splice(0,a.stateSize.length)):o.push(n.splice(0,1));o.reverse();let s=[],i;for(let a=0;a{fi(`RNNCell_${o}`,()=>{n.build(t),Array.isArray(n.stateSize)?e=n.stateSize[0]:e=n.stateSize,t=[t[0],e]})}),this.built=!0}getConfig(){let t=super.getConfig(),e=s=>({className:s.getClassName(),config:s.getConfig()}),o={cells:this.cells.map(e)};return Object.assign(Object.assign({},t),o)}static fromConfig(t,e,n={}){let o=[];for(let s of e.cells)o.push(wn(s,n));return new t({cells:o})}get trainableWeights(){if(!this.trainable)return[];let t=[];for(let e of this.cells)t.push(...e.trainableWeights);return t}get nonTrainableWeights(){let t=[];for(let e of this.cells)t.push(...e.nonTrainableWeights);if(!this.trainable){let e=[];for(let n of this.cells)e.push(...n.trainableWeights);return e.concat(t)}return t}getWeights(){let t=[];for(let e of this.cells)t.push(...e.weights);return _h(t)}setWeights(t){let e=[];for(let n of this.cells){let o=n.weights.length,s=t.splice(o);for(let i=0;is!=null?s(t(),e):Wy(t(),e),a=()=>Ou(i,t,n);return!o||o<=1?De(a().clone()):Array(o).fill(void 0).map(a).map(l=>De(l.clone()))}var vJ=function(r,t){var e={};for(var n in r)Object.prototype.hasOwnProperty.call(r,n)&&t.indexOf(n)<0&&(e[n]=r[n]);if(r!=null&&typeof Object.getOwnPropertySymbols==\"function\")for(var o=0,n=Object.getOwnPropertySymbols(r);o{if(this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),e&&e.constants)throw new z(\"ConvRNN2D cell does not support constants\");let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}computeOutputShape(t){let e=this.computeSingleOutputShape(t);return this.returnSequences||(e=[e[0],...e.slice(2)]),this.returnState&&(e=[e,...Array(2).fill([t[0],...e.slice(-3)])]),e}getInitialState(t){return B(()=>{let{stateSize:e}=this.cell,n=t.shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)],i=ke(s);return Array.isArray(e)?Array(e.length).fill(i):[i]})}resetStates(t,e=!1){B(()=>{if(!this.stateful)throw new uo(\"Cannot call resetStates() on an RNN Layer that is not stateful.\");let n=this.inputSpec[0].shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)];if(n[0]==null)throw new z(\"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(()=>ke(s)):this.states_=[ke(s)];else if(t==null)Tt(this.states_),this.keptStates!=null&&(Tt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>ke(s)):this.states_[0]=ke(s);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e?this.keptStates.push(this.states_.slice()):Tt(this.states_);for(let a=0;aDe(a.clone()))})}computeSingleOutputShape(t){let{dataFormat:e,filters:n,kernelSize:o,padding:s,strides:i,dilationRate:a}=this.cell,u=e===\"channelsFirst\",l=t[u?3:2],c=t[u?4:3],p=Tn(l,o[0],s,i[0],a[0]),m=Tn(c,o[1],s,i[1],a[1]);return[...t.slice(0,2),...u?[n,p,m]:[p,m,n]]}};Ab.className=\"ConvRNN2D\";var Xc=class extends _l{constructor(t){let{filters:e,kernelSize:n,strides:o,padding:s,dataFormat:i,dilationRate:a}=t;super(Object.assign(Object.assign({},t),{units:e})),this.filters=e,tr(this.filters,\"filters\"),this.kernelSize=zu(n,2,\"kernelSize\"),this.kernelSize.forEach(u=>tr(u,\"kernelSize\")),this.strides=zu(o||1,2,\"strides\"),this.strides.forEach(u=>tr(u,\"strides\")),this.padding=s||\"valid\",hn(this.padding),this.dataFormat=i||\"channelsLast\",Me(this.dataFormat),this.dilationRate=zu(a||1,2,\"dilationRate\"),this.dilationRate.forEach(u=>tr(u,\"dilationRate\"))}build(t){var e;t=Gt(t);let n=this.dataFormat===\"channelsFirst\"?1:t.length-1;if(t[n]==null)throw new z(`The channel dimension of the input should be defined. Found ${t[n]}`);let o=t[n],s=4,i=this.kernelSize.concat([o,this.filters*s]);this.kernel=this.addWeight(\"kernel\",i,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let a=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight(\"recurrent_kernel\",a,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let u;if(this.unitForgetBias){let l=this.biasInitializer,c=this.filters;u=new(e=class extends bn{apply(m,f){let d=l.apply([c]),h=ar([c]),g=l.apply([c*2]);return _m([d,h,g])}},e.className=\"CustomInit\",e)}else u=this.biasInitializer;this.bias=this.addWeight(\"bias\",[this.filters*s],null,u,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(t,e){return B(()=>{if(t.length!==3)throw new z(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let n=e.training||!1,o=t[0],s=t[1],i=t[2],a=4;0wr(o),rate:this.dropout,training:n,count:a,dropoutFunc:this.dropoutFunc}));let u=this.dropoutMask,l=(nt,st,lt)=>!st||!st[lt]?nt:$(st[lt],nt),c=l(o,u,0),p=l(o,u,1),m=l(o,u,2),f=l(o,u,3);0wr(s),rate:this.recurrentDropout,training:n,count:a,dropoutFunc:this.dropoutFunc}));let d=this.recurrentDropoutMask,h=l(s,d,0),g=l(s,d,1),x=l(s,d,2),b=l(s,d,3),w=3,[I,N,E,A]=hr(this.kernel.read(),a,w),[D,F,M,V]=this.useBias?hr(this.bias.read(),a):[null,null,null,null];c=this.inputConv(c,I,D,this.padding),p=this.inputConv(p,N,F,this.padding),m=this.inputConv(m,E,M,this.padding),f=this.inputConv(f,A,V,this.padding);let[G,W,q,H]=hr(this.recurrentKernel.read(),a,w);h=this.recurrentConv(h,G),g=this.recurrentConv(g,W),x=this.recurrentConv(x,q),b=this.recurrentConv(b,H);let j=this.recurrentActivation.apply(K(c,h)),Y=this.recurrentActivation.apply(K(p,g)),Z=K($(Y,i),$(j,this.activation.apply(K(m,x)))),et=$(this.recurrentActivation.apply(K(f,b)),this.activation.apply(Z));return[et,et,Z]})}getConfig(){let t=super.getConfig(),{units:e}=t,n=vJ(t,[\"units\"]),o={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),o)}inputConv(t,e,n,o){let s=Nn(t,e,this.strides,o||\"valid\",this.dataFormat===\"channelsFirst\"?\"NCHW\":\"NHWC\",this.dilationRate);return n?yn(s,n,this.dataFormat):s}recurrentConv(t,e){return Nn(t,e,1,\"same\",this.dataFormat===\"channelsFirst\"?\"NCHW\":\"NHWC\")}};Xc.className=\"ConvLSTM2DCell\";Q.registerClass(Xc);var hf=class extends Ab{constructor(t){let e=new Xc(t);super(Object.assign(Object.assign({},t),{cell:e}))}static fromConfig(t,e){return new t(e)}};hf.className=\"ConvLSTM2D\";Q.registerClass(hf);var Yc=class extends Et{constructor(t){super(t),this.rate=Math.max(Math.min(t.rate,1),0),this.noiseShape=t.noiseShape,this.seed=t.seed,this.supportsMasking=!0}getNoiseShape(t){if(this.noiseShape==null)return this.noiseShape;let e=t.shape,n=[];for(let o=0;o{this.invokeCallHook(t,e);let n=St(t);if(0Wy(n,this.rate,s,this.seed),()=>n,o)}return t})}getConfig(){let t={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},e=super.getConfig();return Object.assign(t,e),t}dispose(){return super.dispose()}};Yc.className=\"Dropout\";Q.registerClass(Yc);var gf=class extends Yc{constructor(t){super(t),this.inputSpec=[{ndim:3}]}getNoiseShape(t){let e=t.shape;return[e[0],1,e[2]]}};gf.className=\"SpatialDropout1D\";Q.registerClass(gf);var xf=class extends Et{constructor(t){if(super(t),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",t.batchInputShape==null&&t.inputShape==null&&t.inputDim!=null){let e=null;t.batchSize!=null&&(e=t.batchSize),this.batchInputShape=[e,t.inputDim]}this.units=t.units,tr(this.units,\"units\"),this.activation=xi(t.activation),t.useBias!=null&&(this.useBias=t.useBias),this.kernelInitializer=xe(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=xe(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Ge(t.kernelConstraint),this.biasConstraint=Ge(t.biasConstraint),this.kernelRegularizer=ve(t.kernelRegularizer),this.biasRegularizer=ve(t.biasRegularizer),this.activityRegularizer=ve(t.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(t){t=Gt(t);let e=t[t.length-1];this.kernel==null&&(this.kernel=this.addWeight(\"kernel\",[e,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]:e}}],this.built=!0}computeOutputShape(t){t=Gt(t);let e=t.slice();return e[e.length-1]=this.units,e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t),o=Oy(this.activation.getClassName()),s;return o!=null?s=Do(n,this.kernel.read(),o,this.bias?this.bias.read():null):(s=Do(n,this.kernel.read()),this.bias!=null&&(s=yn(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let t={units:this.units,activation:gi(this.activation),useBias:this.useBias,kernelInitializer:Te(this.kernelInitializer),biasInitializer:Te(this.biasInitializer),kernelRegularizer:fe(this.kernelRegularizer),biasRegularizer:fe(this.biasRegularizer),activityRegularizer:fe(this.activityRegularizer),kernelConstraint:Ve(this.kernelConstraint),biasConstraint:Ve(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}};xf.className=\"Dense\";Q.registerClass(xf);var yf=class extends Et{constructor(t){t=t||{},super(t),this.inputSpec=[{minNDim:3}],this.dataFormat=t.dataFormat}computeOutputShape(t){t=Gt(t);for(let e of t.slice(1))if(e==null)throw new z(`The shape of the input to \"Flatten\" is not fully defined (got ${t.slice(1)}). 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${r.shape.length}`),y.assert(r.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof e==\"number\"&&(e=[e,e]),r.dtype===\"complex64\"||t.dtype===\"complex64\")throw new _t(\"batchDot is not implemented for complex64-type Tensors yet.\");let n=r.shape.length,o=t.shape.length;e==null&&(e=[n-1,o-2]);let s=e;return B(()=>{let i;if(n>o){i=n-o;let u=[];for(let l=0;ln){i=o-n;let u=[];for(let l=0;l0){let u;n>o?u=n+o-3:u=n-1;let l=[];for(let c=u;c\"A `Dot` layer should be called on a list of exactly 2 inputs.\");let e=t[0],n=t[1];if(e.length>3||n.length>3)throw new _t(\"Dot layer does not support tensors of 4D or higher rank yet.\");let o=this.interpretAxes(e,n);if(e[o[0]]!==n[o[1]])throw new z(`Dimension incompatibility: ${e[o[0]]} !== ${n[o[1]]}`)}mergeFunction(t){if(t.length!==2)throw new z(`A \\`Dot\\` layer must be called on exactly 2 inputs, but received ${t.length} input(s).`);let e=t[0],n=t[1],o;return Array.isArray(this.axes)?o=this.axes.map((s,i)=>Lh(s,t[i].shape.length)):o=[Lh(this.axes,e.shape.length),Lh(this.axes,n.shape.length)],this.normalize&&(e=Eh(e,o[0]),n=Eh(n,o[1])),SJ(e,n,o)}interpretAxes(t,e){let n;return Array.isArray(this.axes)?n=this.axes:n=[Lh(this.axes,t.length),Lh(this.axes,e.length)],n}computeOutputShape(t){y.assert(Array.isArray(t)&&t.length===2&&Array.isArray(t[0])&&Array.isArray(t[1]),()=>\"A `Dot` layer should be called on a list of exactly 2 inputs.\");let e=t[0].slice(),n=t[1].slice();if(e.length>3||n.length>3)throw new _t(\"Dot layer does not support tensors of 4D or higher rank yet.\");let o=this.interpretAxes(e,n);e.splice(o[0],1),n.splice(o[1],1),n.splice(0,1);let s=e.concat(n);return s.length===1&&s.push(1),s}computeMask(t,e){return null}getConfig(){let t={axes:this.axes,normalize:this.normalize},e=super.getConfig();return Object.assign(t,e),t}};Df.className=\"Dot\";Q.registerClass(Df);var $f=class extends Et{constructor(t){super(t),this.supportsMasking=!0,this.stddev=t.stddev}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={stddev:this.stddev};return Object.assign(e,t),e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);return Ou(()=>K(Em(n.shape,0,this.stddev),n),()=>n,e.training||!1)})}};$f.className=\"GaussianNoise\";Q.registerClass($f);var Rf=class extends Et{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);return this.rate>0&&this.rate<1?Ou(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return $(n,Em(n.shape,1,s))},()=>n,e.training||!1):n})}};Rf.className=\"GaussianDropout\";Q.registerClass(Rf);var Ff=class extends Et{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate,this.noiseShape=t.noiseShape}_getNoiseShape(t){return this.noiseShape||St(t).shape}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return B(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(t);return Ou(()=>{let s=St(t),u=-1.6732632423543772*1.0507009873554805,l=cn(Gn(n),this.rate);l=rn(l,\"float32\");let c=((1-this.rate)*(1+this.rate*u**2))**-.5,p=-c*u*this.rate,m=K($(s,l),$(K(l,-1),u));return K($(m,c),p)},()=>St(t),e.training||!1)}return t})}};Ff.className=\"AlphaDropout\";Q.registerClass(Ff);function zh(r,t,e,n,o,s=.001){let i;if(r.rank===2)i=Cx(r,t,e,n,o,s);else if(r.rank===3)i=vx(r,t,e,n,o,s);else if(r.rank===4)i=Sx(r,t,e,n,o,s);else throw new _t(`batchNormalization is not implemented for array of rank ${r.rank} yet`);return i}function NJ(r,t,e,n,o=.001){return B(()=>{let s=dc(r,n),i=s.mean,a=s.variance;return[zh(r,i,a,e,t,o),i,a]})}function kJ(r,t,e,n,o=.001){return B(()=>{let s=dc(r,n),i=s.mean,a=s.variance,u=[];for(let d of 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e=this.axis>=0?this.axis:this.axis+t.length,n=t[e];if(n==null)throw new z(`Axis ${e} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(t)}.`);this.inputSpec=[new Ce({ndim:t.length,axes:{[e]:n}})];let o=[n];this.scale&&(this.gamma=this.addWeight(\"gamma\",o,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight(\"beta\",o,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight(\"moving_mean\",o,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight(\"moving_variance\",o,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training,o=St(t),s=o.shape,i=s.length,a=gn(0,i),u=this.axis>=0?this.axis:this.axis+i;a.splice(u,1);let l=To(1,i);l[u]=s[u];let c=a.slice();c.sort();let p=!y.arraysEqual(c,gn(0,i).slice(0,i-1)),m=()=>{if(p){let b=R(this.movingMean.read(),l),w=R(this.movingVariance.read(),l),I=this.center?R(this.beta.read(),l):null,N=this.scale?R(this.gamma.read(),l):null;return zh(o,b,w,I,N,this.epsilon)}else return zh(o,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return m();let[f,d,h]=TJ(o,this.gamma.read(),this.beta.read(),a,this.epsilon),g=(b,w,I)=>{B(()=>{let N=1-I,E=b.read(),A=$(at(E,w),N);b.write(at(E,A))})};return(()=>{g(this.movingMean,d,this.momentum),g(this.movingVariance,h,this.momentum)})(),f})}getConfig(){let t={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Te(this.betaInitializer),gammaInitializer:Te(this.gammaInitializer),movingMeanInitializer:Te(this.movingMeanInitializer),movingVarianceInitializer:Te(this.movingVarianceInitializer),betaRegularizer:fe(this.betaRegularizer),gammaRegularizer:fe(this.gammaRegularizer),betaConstraint:Ve(this.betaConstraint),gammaConstraint:Ve(this.gammaConstraint)},e=super.getConfig();return Object.assign(t,e),t}};Of.className=\"BatchNormalization\";Q.registerClass(Of);var Mf=class extends Et{constructor(t){if(t==null&&(t={}),super(t),this.axis=t.axis==null?-1:t.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 e of this.axis)if(!Number.isInteger(e))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=t.epsilon==null?.001:t.epsilon,this.center=t.center==null?!0:t.center,this.scale=t.scale==null?!0:t.scale,this.betaInitializer=xe(t.betaInitializer||\"zeros\"),this.gammaInitializer=xe(t.gammaInitializer||\"ones\"),this.betaRegularizer=ve(t.betaRegularizer),this.gammaRegularizer=ve(t.gammaRegularizer),this.supportsMasking=!0}build(t){t=Gt(t);let e=t.length;typeof this.axis==\"number\"&&(this.axis=[this.axis]);for(let s=0;s=e)throw new Error(`Invalid axis: ${s}`);if(this.axis.length!==Eo(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(s=>t[s]),o=!0;this.scale?this.gamma=this.addWeight(\"gamma\",n,\"float32\",this.gammaInitializer,this.gammaRegularizer,o):this.gamma=null,this.center?this.beta=this.addWeight(\"beta\",n,\"float32\",this.betaInitializer,this.betaRegularizer,o):this.beta=null,this.built=!0}call(t,e){let n=St(t),o=n.shape,s=o.length;return B(()=>{let{mean:a,variance:u}=dc(n,this.axis,!0),l=To(1,s);for(let h of this.axis)l[h]=o[h];let c=h=>h!=null&&h.shape.length!==s?R(h,l):h,p=this.scale?c(this.gamma.read()):null,m=this.center?c(this.beta.read()):null,f=[],d=[];for(let h=0;h{if(r.rank!==4)throw new z(`temporalPadding expects input tensor to be 4-D, but received a ${r.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new z(\"spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.\");if(e==null&&(e=xn()),e!==\"channelsLast\"&&e!==\"channelsFirst\")throw new z(`Unknown data format: ${e}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let n;return e===\"channelsFirst\"?n=[[0,0],[0,0],t[0],t[1]]:n=[[0,0],t[0],t[1],[0,0]],mn(r,n)})}var Pf=class extends Et{constructor(t){if(t==null&&(t={}),super(t),this.dataFormat=t.dataFormat==null?xn():t.dataFormat,t.padding==null)this.padding=[[1,1],[1,1]];else if(typeof t.padding==\"number\")this.padding=[[t.padding,t.padding],[t.padding,t.padding]];else{if(t.padding=t.padding,t.padding.length!==2)throw new z(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${t.padding.length} array.`);let e,n;if(typeof t.padding[0]==\"number\")e=[t.padding[0],t.padding[0]],n=[t.padding[1],t.padding[1]];else{if(t.padding=t.padding,t.padding[0].length!==2)throw new z(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${t.padding[0].length} array.`);if(e=t.padding[0],t.padding[1].length!==2)throw new z(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${t.padding[1].length} array.`);n=t.padding[1]}this.padding=[e,n]}this.inputSpec=[new Ce({ndim:4})]}computeOutputShape(t){t=Gt(t);let e,n;return this.dataFormat===\"channelsFirst\"?(t[2]!=null&&t[2]>=0?e=t[2]+this.padding[0][0]+this.padding[0][1]:e=null,t[3]!=null&&t[3]>=0?n=t[3]+this.padding[1][0]+this.padding[1][1]:n=null,[t[0],t[1],e,n]):(t[1]!=null&&t[1]>=0?e=t[1]+this.padding[0][0]+this.padding[0][1]:e=null,t[2]!=null&&t[2]>=0?n=t[2]+this.padding[1][0]+this.padding[1][1]:n=null,[t[0],e,n,t[3]])}call(t,e){return B(()=>_J(St(t),this.padding,this.dataFormat))}getConfig(){let t={padding:this.padding,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}};Pf.className=\"ZeroPadding2D\";Q.registerClass(Pf);function Mb(r,t,e,n,o,s){return B(()=>{Me(o),LN(s),hn(n),e==null&&(e=[1,1]),n==null&&(n=\"valid\"),o==null&&(o=xn()),s==null&&(s=\"max\"),r=Ph(r,o);let i,a=n===\"same\"?\"same\":\"valid\";return s===\"max\"?i=ku(r,t,e,a):i=xu(r,t,e,a),o===\"channelsFirst\"&&(i=Vt(i,[0,3,1,2])),i})}function YR(r,t,e,n,o,s){return B(()=>{Me(o),LN(s),hn(n),e==null&&(e=[1,1,1]),n==null&&(n=\"valid\"),o==null&&(o=xn()),s==null&&(s=\"max\"),r=ek(r,o);let i,a=n===\"same\"?\"same\":\"valid\";return s===\"max\"?i=Xx(r,t,e,a):i=Ix(r,t,e,a),o===\"channelsFirst\"&&(i=Vt(i,[0,4,1,2,3])),i})}var Db=class extends Et{constructor(t){if(t.poolSize==null&&(t.poolSize=2),super(t),typeof t.poolSize==\"number\")this.poolSize=[t.poolSize];else if(Array.isArray(t.poolSize)&&t.poolSize.length===1&&typeof t.poolSize[0]==\"number\")this.poolSize=t.poolSize;else throw new z(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.poolSize)}`);if(tr(this.poolSize,\"poolSize\"),t.strides==null)this.strides=this.poolSize;else if(typeof t.strides==\"number\")this.strides=[t.strides];else if(Array.isArray(t.strides)&&t.strides.length===1&&typeof t.strides[0]==\"number\")this.strides=t.strides;else throw new z(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.strides)}`);tr(this.strides,\"strides\"),this.padding=t.padding==null?\"valid\":t.padding,hn(this.padding),this.inputSpec=[new Ce({ndim:3})]}computeOutputShape(t){t=Gt(t);let e=Tn(t[1],this.poolSize[0],this.padding,this.strides[0]);return[t[0],e,t[2]]}call(t,e){return B(()=>{this.invokeCallHook(t,e),t=Sl(St(t),2);let n=this.poolingFunction(St(t),[this.poolSize[0],1],[this.strides[0],1],this.padding,\"channelsLast\");return Wn(n,[2])})}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides},e=super.getConfig();return Object.assign(t,e),t}},Lf=class extends Db{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Me(s),hn(o),Mb(t,e,n,o,s,\"max\")}};Lf.className=\"MaxPooling1D\";Q.registerClass(Lf);var zf=class extends Db{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Me(s),hn(o),Mb(t,e,n,o,s,\"avg\")}};zf.className=\"AveragePooling1D\";Q.registerClass(zf);var $b=class extends Et{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==2)throw new z(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides];tr(this.poolSize,\"poolSize\"),tr(this.strides,\"strides\"),this.padding=t.padding==null?\"valid\":t.padding,this.dataFormat=t.dataFormat==null?\"channelsLast\":t.dataFormat,Me(this.dataFormat),hn(this.padding),this.inputSpec=[new Ce({ndim:4})]}computeOutputShape(t){t=Gt(t);let e=this.dataFormat===\"channelsFirst\"?t[2]:t[1],n=this.dataFormat===\"channelsFirst\"?t[3]:t[2];return e=Tn(e,this.poolSize[0],this.padding,this.strides[0]),n=Tn(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat===\"channelsFirst\"?[t[0],t[1],e,n]:[t[0],e,n,t[3]]}call(t,e){return B(()=>(this.invokeCallHook(t,e),this.poolingFunction(St(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Bf=class extends $b{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Me(s),hn(o),Mb(t,e,n,o,s,\"max\")}};Bf.className=\"MaxPooling2D\";Q.registerClass(Bf);var Vf=class extends $b{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Me(s),hn(o),Mb(t,e,n,o,s,\"avg\")}};Vf.className=\"AveragePooling2D\";Q.registerClass(Vf);var Rb=class extends Et{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==3)throw new z(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides,t.strides];tr(this.poolSize,\"poolSize\"),tr(this.strides,\"strides\"),this.padding=t.padding==null?\"valid\":t.padding,this.dataFormat=t.dataFormat==null?\"channelsLast\":t.dataFormat,Me(this.dataFormat),hn(this.padding),this.inputSpec=[new Ce({ndim:5})]}computeOutputShape(t){t=Gt(t);let e=this.dataFormat===\"channelsFirst\"?t[2]:t[1],n=this.dataFormat===\"channelsFirst\"?t[3]:t[2],o=this.dataFormat===\"channelsFirst\"?t[4]:t[3];return e=Tn(e,this.poolSize[0],this.padding,this.strides[0]),n=Tn(n,this.poolSize[1],this.padding,this.strides[1]),o=Tn(o,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat===\"channelsFirst\"?[t[0],t[1],e,n,o]:[t[0],e,n,o,t[4]]}call(t,e){return B(()=>(this.invokeCallHook(t,e),this.poolingFunction(St(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Gf=class extends Rb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Me(s),hn(o),YR(t,e,n,o,s,\"max\")}};Gf.className=\"MaxPooling3D\";Q.registerClass(Gf);var Wf=class extends Rb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Me(s),hn(o),YR(t,e,n,o,s,\"avg\")}};Wf.className=\"AveragePooling3D\";Q.registerClass(Wf);var Fb=class extends Et{constructor(t){super(t),this.inputSpec=[new Ce({ndim:3})]}computeOutputShape(t){return[t[0],t[2]]}call(t,e){throw new _t}},Uf=class extends Fb{constructor(t){super(t||{})}call(t,e){return B(()=>{let n=St(t);return Ne(n,1)})}};Uf.className=\"GlobalAveragePooling1D\";Q.registerClass(Uf);var Hf=class extends Fb{constructor(t){super(t||{})}call(t,e){return B(()=>{let n=St(t);return Sr(n,1)})}};Hf.className=\"GlobalMaxPooling1D\";Q.registerClass(Hf);var Ob=class extends Et{constructor(t){super(t),this.dataFormat=t.dataFormat==null?\"channelsLast\":t.dataFormat,Me(this.dataFormat),this.inputSpec=[new Ce({ndim:4})]}computeOutputShape(t){return t=t,this.dataFormat===\"channelsLast\"?[t[0],t[3]]:[t[0],t[1]]}call(t,e){throw new _t}getConfig(){let t={dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},qf=class extends Ob{call(t,e){return B(()=>{let n=St(t);return this.dataFormat===\"channelsLast\"?Ne(n,[1,2]):Ne(n,[2,3])})}};qf.className=\"GlobalAveragePooling2D\";Q.registerClass(qf);var Kf=class extends Ob{call(t,e){return B(()=>{let n=St(t);return this.dataFormat===\"channelsLast\"?Sr(n,[1,2]):Sr(n,[2,3])})}};Kf.className=\"GlobalMaxPooling2D\";Q.registerClass(Kf);var Pb=class extends Et{constructor(t){super(t),this.layer=t.layer}build(t){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(t){this.layer!=null&&(this.layer.trainable=t)}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(t){this.layer.setWeights(t)}getConfig(){let t={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},e=super.getConfig();return Object.assign(t,e),t}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(t)}static fromConfig(t,e,n={}){let o=e.layer,s=wn(o,n);delete e.layer;let i={layer:s};return Object.assign(i,e),new t(i)}},jf=class extends Pb{constructor(t){super(t),this.supportsMasking=!0}build(t){if(t=Gt(t),t.length<3)throw new z(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(t)}`);this.inputSpec=[{shape:t}];let e=[t[0]].concat(t.slice(2));this.layer.built||(this.layer.build(e),this.layer.built=!0),super.build(t)}computeOutputShape(t){t=Gt(t);let e=[t[0]].concat(t.slice(2)),n=this.layer.computeOutputShape(e),o=t[1];return[n[0],o].concat(n.slice(1))}call(t,e){return B(()=>(t=St(t),nk((i,a)=>[St(this.layer.call(i,e)),[]],t,[],!1,null,null,!1,!0)[1]))}};jf.className=\"TimeDistributed\";Q.registerClass(jf);function EJ(r){da(eR,\"BidirectionalMergeMode\",r)}var AJ=\"concat\",Xf=class extends Pb{constructor(t){super(t);let e=t.layer.getConfig(),n={};n.className=t.layer.getClassName(),n.config=e,this.forwardLayer=wn(n),e.goBackwards=e.goBackwards!==!0;let o={};if(o.className=t.layer.getClassName(),o.config=e,this.backwardLayer=wn(o),this.forwardLayer.name=\"forward_\"+this.forwardLayer.name,this.backwardLayer.name=\"backward_\"+this.backwardLayer.name,this.mergeMode=t.mergeMode===void 0?AJ:t.mergeMode,EJ(this.mergeMode),t.weights)throw new _t(\"weights support is not implemented for Bidirectional layer yet.\");this._stateful=t.layer.stateful,this.returnSequences=t.layer.returnSequences,this.returnState=t.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=t.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(t){this._trainable=t,this.forwardLayer!=null&&(this.forwardLayer.trainable=t),this.backwardLayer!=null&&(this.backwardLayer.trainable=t)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(t){let e=t.length,n=Math.floor(e/2);this.forwardLayer.setWeights(t.slice(0,n)),this.backwardLayer.setWeights(t.slice(n))}computeOutputShape(t){let e=this.forwardLayer.computeOutputShape(t);Array.isArray(e)&&Array.isArray(e[0])||(e=[e]),e=e;let n,o,s;return this.returnState&&(s=e.slice(1)),n=e[0],n=n,this.mergeMode===\"concat\"?(n[n.length-1]*=2,o=[n]):this.mergeMode==null?o=[n,n.slice()]:o=[n],this.returnState?this.mergeMode==null?o.concat(s).concat(s.slice()):[n].concat(s).concat(s.slice()):kr(o)}apply(t,e){let n=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=rk(t,n,o,this.numConstants);if(t=s.inputs,n=s.initialState,o=s.constants,Array.isArray(t)&&(n=t.slice(1),t=t[0]),(n==null||n.length===0)&&o==null)return super.apply(t,e);let i=[],a=[];if(n!=null){let l=n.length;if(l%2>0)throw new z(\"When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.\");e.initialState=n,i.push(...n);let c=n.map(p=>new Ce({shape:p.shape}));this.forwardLayer.stateSpec=c.slice(0,l/2),this.backwardLayer.stateSpec=c.slice(l/2),a.push(...c)}if(o!=null)throw new _t(\"Support for constants in Bidirectional layers is not implemented yet.\");let u=i[0]instanceof nn;for(let l of i)if(l instanceof nn!==u)throw new z(\"The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors\");if(u){let l=[t].concat(i),c=this.inputSpec.concat(a),p=this.inputSpec;this.inputSpec=c;let m=super.apply(l,e);return this.inputSpec=p,m}else return super.apply(t,e)}call(t,e){return B(()=>{let n=e.initialState,o,s;if(n==null)o=this.forwardLayer.call(t,e),s=this.backwardLayer.call(t,e);else{let u=n.slice(0,n.length/2),l=n.slice(n.length/2);o=this.forwardLayer.call(t,Object.assign(e,{initialState:u})),s=this.backwardLayer.call(t,Object.assign(e,{initialState:l}))}let i;this.returnState&&(Array.isArray(o)&&(i=o.slice(1).concat(s.slice(1))),o=o[0],s=s[0]),this.returnSequences&&(s=dr(s,1));let a;return this.mergeMode===\"concat\"?a=_m([o,s]):this.mergeMode===\"sum\"?a=K(o,s):this.mergeMode===\"ave\"?a=$(.5,K(o,s)):this.mergeMode===\"mul\"?a=$(o,s):this.mergeMode==null&&(a=[o,s]),this.returnState?this.mergeMode==null?a.concat(i):[a].concat(i):a})}resetStates(t){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(t){fi(this.forwardLayer.name,()=>{this.forwardLayer.build(t)}),fi(this.backwardLayer.name,()=>{this.backwardLayer.build(t)}),this.built=!0}computeMask(t,e){Array.isArray(e)&&(e=e[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[e,e]:n=e:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let s=this.forwardLayer.states.map(i=>null);return Array.isArray(n)?n.concat(s).concat(s):[n].concat(s).concat(s)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(t),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(t)}getConfig(){let t={mergeMode:this.mergeMode},e=super.getConfig();return Object.assign(t,e),t}static fromConfig(t,e){let n=wn(e.layer);if(delete e.layer,e.numConstants!=null)throw new _t(\"Deserialization of a Bidirectional layer with numConstants present is not supported yet.\");let o=e;return o.layer=n,new t(o)}};Xf.className=\"Bidirectional\";Q.registerClass(Xf);var Yf=class extends Et{constructor(t){super(t),this.scale=t.scale,t.offset?this.offset=t.offset:this.offset=0}getConfig(){let t={scale:this.scale,offset:this.offset},e=super.getConfig();return Object.assign(t,e),t}call(t,e){return B(()=>(t=St(t),t.dtype!==\"float32\"&&(t=rn(t,\"float32\")),K($(t,this.scale),this.offset)))}};Yf.className=\"Rescaling\";Q.registerClass(Yf);var{resizeBilinear:DJ,cropAndResize:$J}=fn,Zf=class extends Et{constructor(t){super(t),this.height=t.height,this.width=t.width}centerCrop(t,e,n,o,s,i,a,u){return B(()=>{let l,c=!1,p=e/i,m=n/a,f=(o+e)/i,d=(s+n)/a,h=[p,m,f,d],g=[];t.rank===3?(c=!0,l=Fe([t])):l=t;for(let N=0;N{let s=DJ(t,[e,n]);return rn(s,o)})}call(t,e){return B(()=>{let n=St(t),o=n.dtype,s=n.shape,i=s[s.length-3],a=s[s.length-2],u=0;i!==this.height&&(u=Math.floor((i-this.height)/2));let l=0;return a!==this.width&&(l=Math.floor((a-this.width)/2),l===0&&(l=1)),u>=0&&l>=0?this.centerCrop(n,u,l,this.height,this.width,i,a,o):this.upsize(t,this.height,this.width,o)})}getConfig(){let t={height:this.height,width:this.width},e=super.getConfig();return Object.assign(t,e),t}computeOutputShape(t){t=Gt(t);let e=t.length-3,n=t.length-2;return t[e]=this.height,t[n]=this.width,t}};Zf.className=\"CenterCrop\";Q.registerClass(Zf);function ZR(r,t,e,n){let o=St(r);if(o.dtype!==\"int32\"&&(o=rn(o,\"int32\")),t===\"int\")return o;let s=o.shape;if(o.rank===0&&(o=je(o,-1)),t===\"oneHot\"&&o.shape[o.shape.length-1]!==1&&(o=je(o,-1)),o.rank>2)throw new z(`When outputMode is not int, maximum output rank is 2 Received outputMode ${t} and input shape ${s} which would result in output rank ${o.rank}.`);let i=[\"multiHot\",\"oneHot\"].includes(t),a=o,u;if(typeof n!=\"undefined\"&&t===\"count\"?u=mh(a,n,e,i):u=mh(a,[],e,i),t!==\"tfIdf\")return u;if(n)return $(u,n);throw new z(\"When outputMode is 'tfIdf', weights must be provided.\")}var Jf=class extends Et{constructor(t){super(t),this.numTokens=t.numTokens,t.outputMode?this.outputMode=t.outputMode:this.outputMode=\"multiHot\"}getConfig(){let t={numTokens:this.numTokens,outputMode:this.outputMode},e=super.getConfig();return Object.assign(t,e),t}computeOutputShape(t){return t=Gt(t),t==null?[this.numTokens]:this.outputMode===\"oneHot\"&&t[t.length-1]!==1?(t.push(this.numTokens),t):(t[t.length-1]=this.numTokens,t)}call(t,e){return B(()=>{t=St(t),t.dtype!==\"int32\"&&(t=rn(t,\"int32\"));let n;if(typeof e.countWeights!=\"undefined\"){if(this.outputMode!==\"count\")throw new z(`countWeights is not used when outputMode !== count.\n Received countWeights=${e.countWeights}`);n=St(e.countWeights)}let o=Sr(t),s=gl(t),i=Re(this.numTokens,o).bufferSync().get(0),a=cn(s,0).bufferSync().get(0);if(!(i&&a))throw new z(`Input values must be between 0 < values <= numTokens with numTokens=${this.numTokens}`);return ZR(t,this.outputMode,this.numTokens,n)})}};Jf.className=\"CategoryEncoding\";Q.registerClass(Jf);var FJ=[\"bilinear\",\"nearest\"],JR=new Set(FJ),Qf=class extends Et{constructor(t){if(super(t),this.height=t.height,this.width=t.width,t.interpolation)if(JR.has(t.interpolation))this.interpolation=t.interpolation;else throw new z(`Invalid interpolation parameter: ${t.interpolation} is not implemented`);else this.interpolation=\"bilinear\";this.cropToAspectRatio=!!t.cropToAspectRatio}computeOutputShape(t){t=Gt(t);let e=t[2];return[this.height,this.width,e]}getConfig(){let t={height:this.height,width:this.width,interpolation:this.interpolation,cropToAspectRatio:this.cropToAspectRatio},e=super.getConfig();return Object.assign(t,e),t}call(t,e){return B(()=>{let n=[this.height,this.width];if(this.interpolation===\"bilinear\")return fn.resizeBilinear(t,n,!this.cropToAspectRatio);if(this.interpolation===\"nearest\")return fn.resizeNearestNeighbor(t,n,!this.cropToAspectRatio);throw new Error(`Interpolation is ${this.interpolation} but only ${[...JR]} are supported`)})}};Qf.className=\"Resizing\";Q.registerClass(Qf);var Bh=class{constructor(t){this.seed=t}next(){if(this.seed!==void 0)return this.seed++}};Bh.className=\"RandomSeed\";var Vh=class extends Et{constructor(t){super(t),this.randomGenerator=new Bh(t.seed)}getConfig(){let 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o=v(\"start\",r,t,e),s=v(\"stop\",r,t,e),i=v(\"step\",r,t,e);return[n.range(o,s,i,v(\"dtype\",r,t,e))]}case\"TruncatedNormal\":{let o=v(\"shape\",r,t,e),s=v(\"mean\",r,t,e),i=v(\"stdDev\",r,t,e),a=v(\"seed\",r,t,e);return[n.truncatedNormal(o,s,i,v(\"dtype\",r,t,e),a)]}case\"Zeros\":return[n.zeros(v(\"shape\",r,t,e),v(\"dtype\",r,t,e))];case\"ZerosLike\":return[n.zerosLike(v(\"x\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function Ek(r,t,e){let n=v(\"boxes\",r,t,e),o=v(\"scores\",r,t,e),s=v(\"maxOutputSize\",r,t,e),i=v(\"iouThreshold\",r,t,e),a=v(\"scoreThreshold\",r,t,e),u=v(\"softNmsSigma\",r,t,e);return{boxes:n,scores:o,maxOutputSize:s,iouThreshold:i,scoreThreshold:a,softNmsSigma:u}}var SF=async(r,t,e,n,o=ae)=>{switch(r.op){case\"NonMaxSuppressionV5\":{let{boxes:s,scores:i,maxOutputSize:a,iouThreshold:u,scoreThreshold:l,softNmsSigma:c}=Ek(r,t,e),p=await 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v(\"x\",r,t,e).map(c=>n.tensor1d(c.shape));case\"Size\":return[n.scalar(v(\"x\",r,t,e).size,\"int32\")];case\"Rank\":return[n.scalar(v(\"x\",r,t,e).rank,\"int32\")];case\"NoOp\":return[n.scalar(1)];case\"Print\":let i=v(\"x\",r,t,e),a=v(\"data\",r,t,e),u=v(\"message\",r,t,e),l=v(\"summarize\",r,t,e);console.warn(\"The graph has a tf.print() operation,usually used for debugging, which slows down performance.\"),console.log(u);for(let c=0;ct.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return pt(this.size(),\"int32\")}async import(t,e){this.checkKeyAndValueTensor(t,e);let n=await t.data();return this.tensorMap.forEach(o=>o.dispose()),this.tensorMap.clear(),B(()=>{let o=gr(e),s=n.length,i=o.length;y.assert(s===i,()=>`The number of elements doesn't match, keys has ${s} elements, the values has ${i} elements.`);for(let a=0;a{let o=[];for(let s=0;s{switch(r.op){case\"HashTable\":case\"HashTableV2\":{let o=n.getHashTableHandleByName(r.name);if(o!=null)return[o];{let s=v(\"keyDType\",r,t,e),i=v(\"valueDType\",r,t,e),a=new rw(s,i);return n.addHashTable(r.name,a),[a.handle]}}case\"InitializeTable\":case\"InitializeTableV2\":case\"LookupTableImport\":case\"LookupTableImportV2\":{let o=v(\"tableHandle\",r,t,e,n),s=v(\"keys\",r,t,e),i=v(\"values\",r,t,e);return[await n.getHashTableById(o.id).import(s,i)]}case\"LookupTableFind\":case\"LookupTableFindV2\":{let o=v(\"tableHandle\",r,t,e,n),s=v(\"keys\",r,t,e),i=v(\"defaultValue\",r,t,e);return[await n.getHashTableById(o.id).find(s,i)]}case\"LookupTableSize\":case\"LookupTableSizeV2\":{let o=v(\"tableHandle\",r,t,e,n);return[n.getHashTableById(o.id).tensorSize()]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var _F=(r,t,e,n=ae)=>{switch(r.op){case\"ResizeBilinear\":{let o=v(\"images\",r,t,e),s=v(\"size\",r,t,e),i=v(\"alignCorners\",r,t,e),a=v(\"halfPixelCenters\",r,t,e);return[n.image.resizeBilinear(o,[s[0],s[1]],i,a)]}case\"ResizeNearestNeighbor\":{let o=v(\"images\",r,t,e),s=v(\"size\",r,t,e),i=v(\"alignCorners\",r,t,e),a=v(\"halfPixelCenters\",r,t,e);return[n.image.resizeNearestNeighbor(o,[s[0],s[1]],i,a)]}case\"CropAndResize\":{let o=v(\"image\",r,t,e),s=v(\"boxes\",r,t,e),i=v(\"boxInd\",r,t,e),a=v(\"cropSize\",r,t,e),u=v(\"method\",r,t,e),l=v(\"extrapolationValue\",r,t,e);return[n.image.cropAndResize(o,s,i,a,u,l)]}case\"ImageProjectiveTransformV3\":{let o=v(\"images\",r,t,e),s=v(\"transforms\",r,t,e),i=v(\"outputShape\",r,t,e),a=v(\"fillValue\",r,t,e),u=v(\"interpolation\",r,t,e),l=v(\"fillMode\",r,t,e);return[n.image.transform(o,s,u.toLowerCase(),l.toLowerCase(),a,i)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var EF=(r,t,e,n=ae)=>{switch(r.op){case\"Equal\":return[n.equal(v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"NotEqual\":return[n.notEqual(v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"Greater\":return[n.greater(v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"GreaterEqual\":return[n.greaterEqual(v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"Less\":return[n.less(v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"LessEqual\":return[n.lessEqual(v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"LogicalAnd\":return[n.logicalAnd(v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"LogicalNot\":return[n.logicalNot(v(\"a\",r,t,e))];case\"LogicalOr\":return[n.logicalOr(v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"Select\":case\"SelectV2\":return[n.where(v(\"condition\",r,t,e),v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"BitwiseAnd\":return[n.bitwiseAnd(v(\"a\",r,t,e),v(\"b\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var AF=(r,t,e,n=ae)=>{switch(r.op){case\"BatchMatMul\":case\"BatchMatMulV2\":case\"MatMul\":return[n.matMul(v(\"a\",r,t,e),v(\"b\",r,t,e),v(\"transposeA\",r,t,e),v(\"transposeB\",r,t,e))];case\"Einsum\":return[n.einsum(v(\"equation\",r,t,e),...v(\"tensors\",r,t,e))];case\"Transpose\":return[n.transpose(v(\"x\",r,t,e),v(\"perm\",r,t,e))];case\"_FusedMatMul\":let[o,s]=v(\"fusedOps\",r,t,e),i=o===\"biasadd\",a=s===\"prelu\",u=v(\"numArgs\",r,t,e),l=v(\"leakyreluAlpha\",r,t,e);if(i){if(a&&u!==2)throw new Error(\"Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!a&&u!==1)throw new Error(\"Fused MatMul with BiasAdd must have one extra argument: bias.\")}let[c,p]=v(\"args\",r,t,e);return[n.fused.matMul({a:v(\"a\",r,t,e),b:v(\"b\",r,t,e),transposeA:v(\"transposeA\",r,t,e),transposeB:v(\"transposeB\",r,t,e),bias:c,activation:s,preluActivationWeights:p,leakyreluAlpha:l})];case\"MatrixBandPart\":return[n.linalg.bandPart(v(\"a\",r,t,e),v(\"numLower\",r,t,e),v(\"numUpper\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var DF=(r,t,e,n=ae)=>{switch(r.op){case\"EuclideanNorm\":return[n.euclideanNorm(v(\"x\",r,t,e),v(\"axis\",r,t,e),v(\"keepDims\",r,t,e))];case\"FusedBatchNorm\":case\"FusedBatchNormV2\":return[n.batchNorm(v(\"x\",r,t,e),v(\"mean\",r,t,e),v(\"variance\",r,t,e),v(\"offset\",r,t,e),v(\"scale\",r,t,e),v(\"epsilon\",r,t,e))];case\"FusedBatchNormV3\":return[n.batchNorm(v(\"x\",r,t,e),v(\"mean\",r,t,e),v(\"variance\",r,t,e),v(\"offset\",r,t,e),v(\"scale\",r,t,e),v(\"epsilon\",r,t,e))];case\"LRN\":return[n.localResponseNormalization(v(\"x\",r,t,e),v(\"radius\",r,t,e),v(\"bias\",r,t,e),v(\"alpha\",r,t,e),v(\"beta\",r,t,e))];case\"Softmax\":return[n.softmax(v(\"x\",r,t,e))];case\"LogSoftmax\":return[n.logSoftmax(v(\"x\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var $F=(r,t,e,n=ae)=>{switch(r.op){case\"RaggedGather\":{let{outputNestedSplits:o,outputDenseValues:s}=n.raggedGather(v(\"paramsNestedSplits\",r,t,e),v(\"paramsDenseValues\",r,t,e),v(\"indices\",r,t,e),v(\"outputRaggedRank\",r,t,e));return o.concat(s)}case\"RaggedRange\":{let{rtNestedSplits:o,rtDenseValues:s}=n.raggedRange(v(\"starts\",r,t,e),v(\"limits\",r,t,e),v(\"splits\",r,t,e));return[o,s]}case\"RaggedTensorToTensor\":return[n.raggedTensorToTensor(v(\"shape\",r,t,e),v(\"values\",r,t,e),v(\"defaultValue\",r,t,e),v(\"rowPartitionTensors\",r,t,e),v(\"rowPartitionTypes\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var RF=(r,t,e,n=ae)=>{switch(r.op){case\"Max\":{let a=v(\"axis\",r,t,e),u=v(\"keepDims\",r,t,e);return[n.max(v(\"x\",r,t,e),a,u)]}case\"Mean\":{let a=v(\"axis\",r,t,e),u=v(\"keepDims\",r,t,e);return[n.mean(v(\"x\",r,t,e),a,u)]}case\"Min\":{let a=v(\"axis\",r,t,e),u=v(\"keepDims\",r,t,e);return[n.min(v(\"x\",r,t,e),a,u)]}case\"Sum\":{let a=v(\"axis\",r,t,e),u=v(\"keepDims\",r,t,e);return[n.sum(v(\"x\",r,t,e),a,u)]}case\"All\":{let a=v(\"axis\",r,t,e),u=v(\"keepDims\",r,t,e);return[n.all(v(\"x\",r,t,e),a,u)]}case\"Any\":{let a=v(\"axis\",r,t,e),u=v(\"keepDims\",r,t,e);return[n.any(v(\"x\",r,t,e),a,u)]}case\"ArgMax\":{let a=v(\"axis\",r,t,e);return[n.argMax(v(\"x\",r,t,e),a)]}case\"ArgMin\":{let a=v(\"axis\",r,t,e);return[n.argMin(v(\"x\",r,t,e),a)]}case\"Prod\":{let a=v(\"axis\",r,t,e),u=v(\"keepDims\",r,t,e);return[n.prod(v(\"x\",r,t,e),a,u)]}case\"Cumprod\":{let a=v(\"axis\",r,t,e),u=v(\"exclusive\",r,t,e),l=v(\"reverse\",r,t,e);return[n.cumprod(v(\"x\",r,t,e),a,u,l)]}case\"Cumsum\":{let a=v(\"axis\",r,t,e),u=v(\"exclusive\",r,t,e),l=v(\"reverse\",r,t,e);return[n.cumsum(v(\"x\",r,t,e),a,u,l)]}case\"Bincount\":let o=v(\"x\",r,t,e),s=v(\"weights\",r,t,e),i=v(\"size\",r,t,e);return[n.bincount(o,s,i)];case\"DenseBincount\":{let a=v(\"x\",r,t,e),u=v(\"weights\",r,t,e),l=v(\"size\",r,t,e),c=v(\"binaryOutput\",r,t,e);return[n.denseBincount(a,u,l,c)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var FF=(r,t,e,n=ae)=>{switch(r.op){case\"ConcatV2\":case\"Concat\":{let o=v(\"n\",r,t,e),s=v(\"axis\",r,t,e),i=v(\"tensors\",r,t,e);return i=i.slice(0,o),[n.concat(i,s)]}case\"Gather\":{let o=v(\"x\",r,t,e),s=v(\"indices\",r,t,e);return[n.gather(o,n.cast(s,\"int32\"),0)]}case\"GatherV2\":{let o=v(\"axis\",r,t,e),s=v(\"batchDims\",r,t,e),i=v(\"x\",r,t,e),a=v(\"indices\",r,t,e);return[n.gather(i,n.cast(a,\"int32\"),o,s)]}case\"Reverse\":{let o=v(\"dims\",r,t,e),s=[];for(let a=0;a{let o=v(\"axis\",r,t,e),s=v(\"tensors\",r,t,e),i=s[0].shape,a=n.squeeze(s[0]).shape,u=s.map(l=>{let c=y.arraysEqual(l.shape,i);if(!c&&!y.arraysEqual(n.squeeze(l).shape,a))throw new Error(\"the input tensors shape does not match\");return c?l:n.reshape(l,i)});return[n.stack(u,o)]});case\"Unpack\":{let o=v(\"axis\",r,t,e),s=v(\"tensor\",r,t,e);return n.unstack(s,o)}case\"Tile\":{let o=v(\"reps\",r,t,e);return[n.tile(v(\"x\",r,t,e),o)]}case\"Split\":case\"SplitV\":{let o=v(\"axis\",r,t,e),s=v(\"numOrSizeSplits\",r,t,e),i=v(\"x\",r,t,e);return n.split(i,s,o)}case\"ScatterNd\":{let o=v(\"indices\",r,t,e),s=v(\"values\",r,t,e),i=v(\"shape\",r,t,e);return[n.scatterND(o,s,i)]}case\"GatherNd\":{let o=v(\"x\",r,t,e),s=v(\"indices\",r,t,e);return[n.gatherND(o,s)]}case\"SparseToDense\":{let o=v(\"sparseIndices\",r,t,e),s=v(\"outputShape\",r,t,e),i=v(\"sparseValues\",r,t,e),a=v(\"defaultValue\",r,t,e);return[n.sparseToDense(o,i,s,i.dtype===a.dtype?a:n.cast(a,i.dtype))]}case\"TensorScatterUpdate\":{let o=v(\"indices\",r,t,e),s=v(\"values\",r,t,e),i=v(\"tensor\",r,t,e);return[n.tensorScatterUpdate(i,o,s)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var OF=(r,t,e,n=ae)=>{switch(r.op){case\"SparseFillEmptyRows\":{let{outputIndices:o,outputValues:s,emptyRowIndicator:i,reverseIndexMap:a}=n.sparse.sparseFillEmptyRows(v(\"indices\",r,t,e),v(\"values\",r,t,e),v(\"denseShape\",r,t,e),v(\"defaultValue\",r,t,e));return[o,s,i,a]}case\"SparseReshape\":{let{outputIndices:o,outputShape:s}=n.sparse.sparseReshape(v(\"inputIndices\",r,t,e),v(\"inputShape\",r,t,e),v(\"newShape\",r,t,e));return[o,s]}case\"SparseSegmentMean\":return[n.sparse.sparseSegmentMean(v(\"data\",r,t,e),v(\"indices\",r,t,e),v(\"segmentIds\",r,t,e))];case\"SparseSegmentSum\":return[n.sparse.sparseSegmentSum(v(\"data\",r,t,e),v(\"indices\",r,t,e),v(\"segmentIds\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var MF=(r,t,e,n=ae)=>{switch(r.op){case\"FFT\":return[n.fft(v(\"x\",r,t,e))];case\"IFFT\":return[n.ifft(v(\"x\",r,t,e))];case\"RFFT\":return[n.rfft(v(\"x\",r,t,e))];case\"IRFFT\":return[n.irfft(v(\"x\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var PF=(r,t,e,n=ae)=>{switch(r.op){case\"StaticRegexReplace\":return[n.string.staticRegexReplace(v(\"input\",r,t,e),v(\"pattern\",r,t,e),v(\"rewrite\",r,t,e),v(\"replaceGlobal\",r,t,e))];case\"StringNGrams\":{let{nGrams:o,nGramsSplits:s}=n.string.stringNGrams(v(\"data\",r,t,e),v(\"dataSplits\",r,t,e),v(\"separator\",r,t,e),v(\"nGramWidths\",r,t,e),v(\"leftPad\",r,t,e),v(\"rightPad\",r,t,e),v(\"padWidth\",r,t,e),v(\"preserveShortSequences\",r,t,e));return[o,s]}case\"StringSplit\":{let{indices:o,values:s,shape:i}=n.string.stringSplit(v(\"input\",r,t,e),v(\"delimiter\",r,t,e),v(\"skipEmpty\",r,t,e));return[o,s,i]}case\"StringToHashBucketFast\":return[n.string.stringToHashBucketFast(v(\"input\",r,t,e),v(\"numBuckets\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var LF=(r,t,e,n=ae)=>{switch(r.op){case\"Cast\":return[n.cast(v(\"x\",r,t,e),v(\"dtype\",r,t,e))];case\"ExpandDims\":{let o=v(\"axis\",r,t,e);return[n.expandDims(v(\"x\",r,t,e),o)]}case\"Squeeze\":{let o=v(\"axis\",r,t,e);return[n.squeeze(v(\"x\",r,t,e),o)]}case\"Reshape\":return[n.reshape(v(\"x\",r,t,e),v(\"shape\",r,t,e))];case\"EnsureShape\":return[n.ensureShape(v(\"x\",r,t,e),v(\"shape\",r,t,e))];case\"MirrorPad\":return[n.mirrorPad(v(\"x\",r,t,e),v(\"padding\",r,t,e),v(\"mode\",r,t,e))];case\"PadV2\":case\"Pad\":return[n.pad(v(\"x\",r,t,e),v(\"padding\",r,t,e),v(\"constantValue\",r,t,e))];case\"SpaceToBatchND\":{let o=v(\"blockShape\",r,t,e),s=v(\"paddings\",r,t,e);return[n.spaceToBatchND(v(\"x\",r,t,e),o,s)]}case\"BatchToSpaceND\":{let o=v(\"blockShape\",r,t,e),s=v(\"crops\",r,t,e);return[n.batchToSpaceND(v(\"x\",r,t,e),o,s)]}case\"DepthToSpace\":{let o=v(\"blockSize\",r,t,e),s=v(\"dataFormat\",r,t,e).toUpperCase();return[n.depthToSpace(v(\"x\",r,t,e),o,s)]}case\"BroadcastTo\":return[n.broadcastTo(v(\"x\",r,t,e),v(\"shape\",r,t,e))];case\"BroadcastArgs\":return[n.broadcastArgs(v(\"s0\",r,t,e),v(\"s1\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function Ak(r,t,e,n,o=B){let s=((i,a,u)=>{switch(i.category){case\"arithmetic\":return o(()=>fF(i,a,u));case\"basic_math\":return o(()=>dF(i,a,u));case\"control\":return wF(i,a,u);case\"convolution\":return o(()=>CF(i,a,u));case\"creation\":return o(()=>vF(i,a,u));case\"dynamic\":return SF(i,a,u);case\"evaluation\":return o(()=>NF(i,a,u));case\"image\":return o(()=>_F(i,a,u));case\"graph\":return o(()=>kF(i,a,u));case\"logical\":return o(()=>EF(i,a,u));case\"matrices\":return o(()=>AF(i,a,u));case\"normalization\":return o(()=>DF(i,a,u));case\"ragged\":return o(()=>$F(i,a,u));case\"reduction\":return o(()=>RF(i,a,u));case\"slice_join\":return o(()=>FF(i,a,u));case\"sparse\":return o(()=>OF(i,a,u));case\"spectral\":return o(()=>MF(i,a,u));case\"string\":return o(()=>PF(i,a,u));case\"transformation\":return o(()=>LF(i,a,u));case\"hash_table\":return TF(i,a,u,n);case\"custom\":let l=Vb(i.op);if(l&&l.customExecutor)return l.customExecutor(new Qb(i,a,u));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(r,t,e);return y.isPromise(s)?s.then(i=>[].concat(i)):[].concat(s)}var Uh=class{constructor(t={},e={},n={},o={},s){this.weightMap=t,this.tensorArrayMap=e,this.tensorListMap=n,this.functionMap=o,this.parseNodeNameCache=s,this.rootContext={id:0,frameName:\"\",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(t,e){return{id:t,frameName:e,iterationId:0}}set currentContext(t){this.contexts!==t&&(this.contexts=t,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let t=[];for(let e=0;ee.id===0&&e.iterationId===0?\"\":`${e.frameName}-${e.iterationId}`).join(\"/\"):\"\"}enterFrame(t){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,t)),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 t=Object.assign({},this.contexts[this.contexts.length-1]);t.iterationId+=1,t.id=this.lastId,this.contexts.splice(-1,1,t),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error(\"Cannot increase frame iteration, the context is empty\")}getWeight(t){return this.weightMap[t]}addTensorArray(t){this.tensorArrayMap[t.id]=t}getTensorArray(t){return this.tensorArrayMap[t]}addTensorList(t){this.tensorListMap[t.id]=t}getTensorList(t){return this.tensorListMap[t]}dispose(t){for(let e in this.tensorArrayMap)this.tensorArrayMap[e].clearAndClose(t);for(let e in this.tensorListMap)this.tensorListMap[e].clearAndClose(t)}};function Dk(r,t,e,n){let o=new Set,s=[],i=null,a=null,u=new Set,l=new Set(Object.keys(r).map(m=>In(m)[0]));n=n||[];let c=new Set(n.map(m=>In(m.name)[0])),p=[...t];for(;p.length>0;){let m=p.pop();if((Bu(m)||ftt(m)||dtt(m))&&i==null&&(i=m,a=i.children.map(f=>f.name).filter(f=>o.has(f))),o.add(m.name),e[m.name]==null&&!l.has(m.name)&&!c.has(m.name)){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(f=>{u.has(f.name)||(u.add(f.name),p.push(f))})}}return{inputs:r,outputs:t,usedNodes:o,missingInputs:s,dynamicNode:i,syncInputs:a}}function zF(r,t){let{usedNodes:e,inputs:n}=t,o=Object.keys(n).map(g=>In(g)[0]).map(g=>r.nodes[g]),s=r.initNodes||[],i=g=>e.has(typeof g==\"string\"?g:g.name);function a(g){return[...new Map(g.map(x=>[x.name,x])).values()]}let u=a([...o,...r.weights,...s]).filter(i),l=a([...u,...Object.values(r.nodes)]).filter(i),c=new Map(l.map(g=>[g.name,g])),p={};for(let g of l){p[g.name]=p[g.name]||0;for(let x of g.children)i(x)||(p[x.name]=Number.POSITIVE_INFINITY),p[x.name]=(p[x.name]||0)+1}let m=Object.entries(p).filter(([,g])=>g===0).map(([g])=>g),f=[...m];for(;m.length>0;){let g=m.pop(),x=c.get(g);for(let b of x.children.filter(i))--p[b.name]===0&&(f.push(b.name),m.push(b.name))}let d=f.map(g=>c.get(g)),h=ltt(d,u);return utt(h,u),h}function ltt(r,t){let e=new Map(r.map(i=>[i.name,i])),n=t.map(i=>i.name),o=new Set(n);for(;n.length>0;){let i=n.pop(),a=e.get(i);for(let u of a.children)!e.has(u.name)||o.has(u.name)||(o.add(u.name),n.push(u.name))}return r.filter(i=>o.has(i.name))}var nd=class extends Error{constructor(t){super(`NodesExecutionOrderError: ${t}`)}};function utt(r,t){let e=new Map(r.map((a,u)=>[a.name,u])),n=new Set(t.map(a=>a.name)),o=a=>n.has(typeof a==\"string\"?a:a.name),s=new Set(r.map(a=>a.name)),i=a=>s.has(typeof a==\"string\"?a:a.name);for(let a of r){for(let u of a.children.filter(i)){if(!e.has(u.name))throw new nd(`Child ${u.name} of node ${a.name} is unreachable.`);if(e.get(a.name)>e.get(u.name))throw new nd(`Node ${a.name} is scheduled to run after its child ${u.name}.`)}if(!o(a))for(let u of a.inputs){if(!e.has(u.name))throw new nd(`Input ${u.name} of node ${a.name} is unreachable.`);if(e.get(u.name)>e.get(a.name))throw new nd(`Node ${a.name} is scheduled to run before its input ${u.name}.`)}}}function BF(r){let t=new Map(r.map((a,u)=>[a.name,u])),e=Number.MAX_SAFE_INTEGER,n=r.map((a,u)=>Bu(a)?e:u),o=a=>{let u=n[t.get(a.name)];return u==null?-1:u},s=r.map((a,u)=>a.children.map(o).reduce((l,c)=>Math.max(l,c),n[u])),i=new Map;for(let a=0;at[n].map(o=>o.id));this._weightIds=[].concat(...e),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let e=t.signatureKey||t.name;return t.defaultOutput?`${e}:${t.defaultOutput}`:e})}get functions(){return Object.keys(this._functions).reduce((t,e)=>(t[e]=this._functions[e].signature,t),{})}constructor(t,e){this.graph=t,this.parent=e,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=\",\",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(n=>{this._functionExecutorMap[n]=new r(t.functions[n],this)})}getCompilationKey(t,e){let n=t.map(s=>s.name).sort(),o=e.map(s=>s.name).sort();return n.join(this.SEPARATOR)+\"--\"+o.join(this.SEPARATOR)}compile(t,e){let n=Dk(t,e,this.weightMap,this._initNodes),{missingInputs:o,dynamicNode:s,syncInputs:i}=n;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${i}]`);if(o.length>0){let l=e.map(p=>p.name),c=Object.keys(t);throw new Error(`Cannot compute the outputs [${l}] from the provided inputs [${c}]. Missing the following inputs: [${o}]`)}let a=zF(this.graph,n),u=BF(a);return{orderedNodes:a,nodeLiveUntilMap:u}}cloneAndKeepTensor(t){if(t==null)return null;let e=t.clone();return De(e),e}cloneTensorList(t){return t?t.map(n=>this.cloneAndKeepTensor(n)):null}cloneTensorMap(t){return Object.fromEntries(Object.entries(t).map(([e,n])=>[e,this.cloneTensorList(n)]))}execute(t,e){this.disposeIntermediateTensors(),t=this.mapInputs(t);let n=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e);let o=n.map(m=>this.graph.nodes[In(m)[0]]),s=e.map(m=>In(m)[0]),i=new Set(s),a=s.map(m=>this.graph.nodes[m]);a.length===0&&(a=this._outputs);let u=this.getCompilationKey(o,a),l=this.compiledMap.get(u);l==null&&(l=this.compile(t,a),this.compiledMap.set(u,l));try{this.keepIntermediateTensors=L().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let c={},p={};return B(()=>{let m=new Uh(this.weightMap,c,p,this.functionExecutorMap,this.parseNodeNameCache),f=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(t).forEach(x=>{let[b,w]=In(x,m),I=[];I[w]=t[x],f[b]=I,this.keepIntermediateTensors&&(this.clonedTensorsMap[b]=this.cloneTensorList(I))});let d=this.getFrozenTensorIds(f),{orderedNodes:h,nodeLiveUntilMap:g}=l;for(let x of h){if(f[x.name])continue;let b=Ak(x,f,m,this._resourceManager);if(y.isPromise(b))throw new Error(`The execution of the op '${x.op}' returned a promise. Please use model.executeAsync() instead.`);f[x.name]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[x.name]=this.cloneTensorList(b)),this.checkTensorForDisposalWithNodeLiveUntilInfo(x,f,m,d,i,g.get(x.name))}return this.parent==null&&m.dispose(d),e.map(x=>pr(x,f,m))})}getFrozenTensorIds(t){let e=[].concat.apply([],Object.keys(t).map(n=>t[n]).map(n=>n.map(o=>o.id)));return new Set(e)}checkTensorForDisposal(t,e,n,o,s,i,a){if(!(Bu(e)||i.has(t))){for(let u of n[t])u!=null&&(a[u.id]=(a[u.id]||0)+e.children.length);for(let u of e.inputs){if(Bu(u))continue;let l=lk(u.name,n,o);if(l!=null)for(let c of l){if(!c||c.kept||s.has(c.id))continue;let p=a[c.id];p===1?(c.dispose(),delete a[c.id]):p!=null&&a[c.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(t,e,n,o,s,i){function a(u){return Bu(u)||s.has(u.name)}if(!(Bu(t)||i==null))for(let u of i){if(a(u))continue;let l=lk(u.name,e,n);for(let c of l)!c||c.kept||o.has(c.id)||c.dispose()}}async executeAsync(t,e){return this._executeAsync(t,e)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(t=>{for(let e of t)e&&!e.isDisposed&&e.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(t,e,n=!1,o={},s={}){this.disposeIntermediateTensors(),n||(t=this.mapInputs(t),this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e));try{this.keepIntermediateTensors=L().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let i=new Uh(this.weightMap,o,s,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let a=await this.executeWithControlFlow(t,i,e,n),u=e.map(m=>pr(m,a,i)),l=u.map(m=>m.id),c=Object.keys(t).map(m=>t[m].id),p=new Set([...l,...c,...this.weightIds]);return Object.values(a).forEach(m=>{m.forEach(f=>{f&&!f.isDisposed&&!p.has(f.id)&&f.dispose()})}),this.parent==null&&i.dispose(p),u}async executeFunctionAsync(t,e,n){let o=t.reduce((s,i,a)=>(s[this.inputs[a].name]=i,s),{});return this._executeAsync(o,this.outputNodes,!0,e,n)}async executeWithControlFlow(t,e,n,o){let s=Object.keys(t),i=s.map(I=>this.graph.nodes[In(I)[0]]),a=n.map(I=>In(I)[0]),u=new Set(a),l=a.map(I=>this.graph.nodes[I]);l.length===0&&(l=this._outputs);let{usedNodes:c,missingInputs:p,dynamicNode:m,syncInputs:f}=Dk(t,l,this.weightMap,this._initNodes),d=[...i,...this.graph.weights,...this._initNodes||[]].map(I=>({node:I,contexts:e.currentContext})),h=Object.assign({},this.weightMap);Object.keys(t).forEach(I=>{let[N,E]=In(I),A=[];A[E]=t[I],h[N]=A});let g={},x=this.getFrozenTensorIds(h),b={};for(;d.length>0;){let I=this.processStack(i,d,e,h,b,x,u,g,c);await Promise.all(I)}m==null&&!o&&console.warn(\"This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.\");let w=l.filter(I=>!Bu(I)&&!pr(I.name,h,e)).map(I=>I.name);if(w.length>0){let I=\"\";throw m!=null&&(I=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${f}]`),new Error(`Cannot compute the outputs [${w}] from the provided inputs [${s}]. Consider providing the following inputs: [${p}]. ${I}`)}return h}processStack(t,e,n,o,s,i,a,u,l){let c=[];for(;e.length>0;){let p=e.pop();n.currentContext=p.contexts;let m=\"\";if(p.node.op===\"Enter\"&&v(\"isConstant\",p.node,o,n)&&([m]=bi(p.node.name,n)),o[p.node.name]==null){let f=Ak(p.node,o,n,this._resourceManager);m||([m]=bi(p.node.name,n));let d=n.currentContext;y.isPromise(f)?c.push(f.then(h=>(o[m]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(h)),n.currentContext=d,this.checkTensorForDisposal(m,p.node,o,n,i,a,u),this.processChildNodes(p.node,e,n,o,s,l),h))):(o[m]=f,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(f)),this.checkTensorForDisposal(m,p.node,o,n,i,a,u),this.processChildNodes(p.node,e,n,o,s,l))}else this.processChildNodes(p.node,e,n,o,s,l)}return c}processChildNodes(t,e,n,o,s,i){t.children.forEach(a=>{let[u]=bi(a.name,n);s[u]||!i.has(a.name)||(a.op===\"Merge\"?a.inputNames.some(l=>!!pr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})):a.inputNames.every(l=>!!pr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(e=>e.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(e=>{let n=t[e],[o]=In(e),s=this.graph.nodes[o];if(s.attrParams.shape&&s.attrParams.shape.value){let i=s.attrParams.shape.value,a=i.length===n.shape.length&&n.shape.every((u,l)=>i[l]===-1||i[l]===u);y.assert(a,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${i}], but was [${n.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(n.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(t){var e,n;let o={};for(let s in t){let i=(n=(e=this._signature)===null||e===void 0?void 0:e.inputs)===null||n===void 0?void 0:n[s];i!=null?o[i.name]=t[s]:o[s]=t[s]}return o}checkInputs(t){let e=Object.keys(t).filter(n=>{let[o]=In(n);return this.graph.nodes[o]==null});if(e.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${e}] that are not part of graph`)}mapOutputs(t){return t.map(e=>{var n,o;let s=(o=(n=this._signature)===null||n===void 0?void 0:n.outputs)===null||o===void 0?void 0:o[e];return s!=null?s.name:e},{})}checkOutputs(t){t.forEach(e=>{let[n]=In(e);if(!this.graph.nodes[n])throw new Error(`The output '${e}' is not found in the graph`)})}};var nw=class{constructor(t={},e={}){this.hashTableNameToHandle=t,this.hashTableMap=e}addHashTable(t,e){this.hashTableNameToHandle[t]=e.handle,this.hashTableMap[e.id]=e}getHashTableHandleByName(t){return this.hashTableNameToHandle[t]}getHashTableById(t){return this.hashTableMap[t]}dispose(){for(let t in this.hashTableMap)this.hashTableMap[t].clearAndClose(),delete this.hashTableMap[t];for(let t in this.hashTableNameToHandle)this.hashTableNameToHandle[t].dispose(),delete this.hashTableNameToHandle[t]}};var htt=\"?tfjs-format=file\",gtt=\"model.json\",qh=class{get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}constructor(t,e={},n=Mr){this.modelUrl=t,this.loadOptions=e,this.version=\"n/a\",this.io=n,e==null&&(this.loadOptions={}),this.resourceManager=new nw}findIOHandler(){let t=this.modelUrl;if(t.load!=null)this.handler=t;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(t,this.loadOptions);else{let e=this.io.getLoadHandlers(t,this.loadOptions);if(e.length===0)e.push(this.io.browserHTTPRequest(t,this.loadOptions));else if(e.length>1)throw new Error(`Found more than one (${e.length}) load handlers for URL '${[t]}'`);this.handler=e[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error(\"Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.\");let t=this.handler.load();return y.isPromise(t)?t.then(e=>e.getWeightStream==null?this.loadSync(e):this.loadStreaming(e)):this.loadSync(t)}loadSync(t){let e=this.io.decodeWeights(t.weightData,t.weightSpecs);return this.loadWithWeightMap(t,e)}async loadStreaming(t){if(t.getWeightStream==null)throw new Error(\"Model artifacts missing streamWeights function\");let e=await ax(t.getWeightStream(),t.weightSpecs);return this.loadWithWeightMap(t,e)}loadWithWeightMap(t,e){this.artifacts=t;let n=this.artifacts.modelTopology,o=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let s=this.artifacts.userDefinedMetadata;s.signature!=null&&(o=s.signature),s.structuredOutputKeys!=null&&(this.structuredOutputKeys=s.structuredOutputKeys)}if(this.signature=o,this.version=`${n.versions.producer}.${n.versions.minConsumer}`,this.executor=new Hh(Wh.Instance.transformGraph(n,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(e),this.executor.resourceManager=this.resourceManager,t.modelInitializer!=null&&t.modelInitializer.node!=null){let s=Wh.Instance.transformGraph(t.modelInitializer);this.initializer=new Hh(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=t.initializerSignature}return!0}async save(t,e){if(typeof t==\"string\"){let n=this.io.getSaveHandlers(t);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${t}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${t}'`);t=n[0]}if(t.save==null)throw new Error(\"GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.\");return t.save(this.artifacts)}addStructuredOutputNames(t){if(this.structuredOutputKeys){let e=t instanceof Lt?[t]:t,n={};return e.forEach((o,s)=>n[this.structuredOutputKeys[s]]=o),n}return t}predict(t,e){let n=this.execute(t,this.outputNodes);return this.addStructuredOutputNames(n)}async predictAsync(t,e){let n=await this.executeAsync(t,this.outputNodes);return this.addStructuredOutputNames(n)}normalizeInputs(t){var e;if(!(t instanceof Lt)&&!Array.isArray(t)){let s=(e=this.signature)===null||e===void 0?void 0:e.inputs;if(s!=null)for(let i in s){let a=s[i];a.resourceId!=null&&(t[i]=this.resourceIdToCapturedInput[a.resourceId])}return t}t=Array.isArray(t)?t:[t];let n=Object.keys(this.resourceIdToCapturedInput).length;if(t.length+n!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-n} non-resource placeholders, while there are ${t.length} input tensors provided.`);let o=0;return this.inputNodes.reduce((s,i)=>{var a,u,l;let c=(l=(u=(a=this.signature)===null||a===void 0?void 0:a.inputs)===null||u===void 0?void 0:u[i])===null||l===void 0?void 0:l.resourceId;return c!=null?s[i]=this.resourceIdToCapturedInput[c]:s[i]=t[o++],s},{})}normalizeOutputs(t){return t=t||this.outputNodes,Array.isArray(t)?t:[t]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return 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er{constructor(t,e,n=!0){super(),this.upstream=t,this.batchSize=e,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let t=[];for(;t.length0?{value:t,done:!1}:{value:null,done:!0};t.push(e.value)}return{value:t,done:!1}}},Bk=class extends er{constructor(t,e){super(),this.upstream=t,this.predicate=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let t=await this.upstream.next();if(t.done||this.predicate(t.value))return t;Tt(t.value)}}},Vk=class extends er{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Map`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=Io.getTensorsInContainer(t.value),n=this.transform(t.value),o=Io.getTensorsInContainer(n);for(let s of e)Io.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},Gk=class extends er{constructor(t,e){super(),this.upstream=t,this.handler=e,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(t){if(!this.handler(t))return{value:null,done:!0}}}},iw=class extends er{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=Io.getTensorsInContainer(t.value),n=await this.transform(t.value),o=Io.getTensorsInContainer(n);for(let s of e)Io.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},Zc=class extends er{constructor(){super(),this.outputQueue=new Kh,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}}},Wk=class extends Zc{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let t=await this.upstream.next();if(t.done)return!1;let e=Io.getTensorsInContainer(t.value),n=this.transform(t.value),o=Io.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let s of e)Io.isTensorInList(s,o)||s.dispose();return!0}},aw=class extends er{constructor(t,e){super(),this.baseErrorHandler=e,this.lastRead=null,this.iterator=null,this.moreIterators=t}summary(){return\"TODO: fill in upstream of chained summaries 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At least one type of data should be returned.\")}summary(){return\"microphone\"}static async create(t={}){if(!L().get(\"IS_BROWSER\"))throw new Error(\"microphone API is only supported in browser environment.\");let e=new r(t);return await e.start(),e}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 t=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new t,!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 e=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,e.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 t,e,n=await this.getAudioData();if(this.includeSpectrogram){let o=this.flattenQueue(n.freqDataQueue);t=this.getTensorFromAudioDataArray(o,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let o=this.flattenQueue(n.timeDataQueue);e=this.getTensorFromAudioDataArray(o,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:t,waveform:e},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let t=[],e=[],n=0;return new Promise(o=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&o({freqDataQueue:t,timeDataQueue:e}),t.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),e.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(s),o({freqDataQueue:t,timeDataQueue:e}))},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(t){let e=t[0].length,n=new Float32Array(t.length*e);return t.forEach((o,s)=>n.set(o,s*e)),n}getTensorFromAudioDataArray(t,e){let n=new Float32Array(y.sizeFromShape(e));return n.set(t,n.length-t.length),ir(n,e)}};var mw=class r extends er{constructor(t,e){if(super(),this.webcamVideoElement=t,this.webcamConfig=e,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Oe([0],\"int32\"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,o=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-n)/2,i=(1-o)/2,a=s+n,u=o+i;this.cropBox=pi([i,s,u,a],[1,4])}else this.cropBox=pi([0,0,1,1],[1,4])}summary(){return\"webcam\"}static async create(t,e={}){if(!L().get(\"IS_BROWSER\"))throw new Error(\"tf.data.webcam is only supported in browser environment.\");if(!t){if(t=document.createElement(\"video\"),!e.resizeWidth||!e.resizeHeight)throw new Error(\"Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.\");t.width=e.resizeWidth,t.height=e.resizeHeight}let n=new r(t,e);return await n.start(),n}async start(){this.webcamConfig.facingMode&&y.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(t){throw t.message=`Error thrown while initializing video stream: ${t.message}`,t}if(!this.stream)throw new Error(\"Could not obtain video from webcam.\");try{this.webcamVideoElement.srcObject=this.stream}catch(t){console.log(t),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(t=>{this.webcamVideoElement.onloadedmetadata=()=>{t()}})}async next(){if(this.isClosed)return{value:null,done:!0};let t;try{t=_y.fromPixels(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: 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extends Yh{constructor(t,e){super(),this.upstream=t,this.impl=new Xk(t,e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Xk=class extends Zc{constructor(t,e){super(),this.upstream=t,this.separator=e,this.carryover=\"\"}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let t=await this.upstream.next();if(t.done)return this.carryover===\"\"?!1:(this.outputQueue.push(this.carryover),this.carryover=\"\",!0);let e=t.value.split(this.separator);e[0]=this.carryover+e[0];for(let n of e.slice(0,-1))this.outputQueue.push(n);return this.carryover=e[e.length-1],!0}};var fw=class extends er{decodeUTF8(){return new Yk(this)}},Yk=class extends Yh{constructor(t){super(),this.upstream=t,this.impl=new Zk(t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Zk=class extends Zc{constructor(t){if(super(),this.upstream=t,L().get(\"IS_BROWSER\"))this.decoder=new 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a=s.reduce((x,b)=>x*b),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=te({inputs:{x:o},backend:e,attrs:{shape:u}}),d=We({inputs:{x:f},backend:e,attrs:{perm:l}}),h=te({inputs:{x:d},backend:e,attrs:{shape:c}}),g=Mo({inputs:{x:h},backend:e,attrs:{begin:p,size:m}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),g}var wM={kernelName:Fi,backendName:\"cpu\",kernelFunc:vet};function Net(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i}=n,a=e.data.get(o.dataId).values,u=e.data.get(s.dataId).values,l=dd(a,u,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,l)}var IM={kernelName:Da,backendName:\"cpu\",kernelFunc:Net};function ket(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.data.get(n.dataId).values,i=e.data.get(o.dataId).values,a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeTensorInfo([a.length],\"int32\",Int32Array.from(a))}var CM={kernelName:ql,backendName:\"cpu\",kernelFunc:ket};var Tet=At(ho,(r,t)=>{let e=t;return r>e.clipValueMax?e.clipValueMax:r{let{x:t}=r.inputs,e=r.backend,n=new Float32Array(y.sizeFromShape(t.shape)),o=e.data.get(t.dataId),s=o.complexTensorInfos.real,i=o.complexTensorInfos.imag,a=e.data.get(s.dataId).values,u=e.data.get(i.dataId).values;for(let l=0;lh.shape);S.assertParamsConsistent(i,s);let a=S.computeOutShape(t.map(h=>h.shape),s);if(y.sizeFromShape(a)===0)return e.makeTensorInfo(a,t[0].dtype,[]);let u=t.filter(h=>y.sizeFromShape(h.shape)>0);if(u.length===1)return Zr({inputs:{x:u[0]},backend:e});if(u[0].dtype===\"complex64\"){let h=u.map(I=>Ro({inputs:{input:I},backend:e})),g=u.map(I=>ba({inputs:{input:I},backend:e})),x=Gu({inputs:h,backend:e,attrs:{axis:s}}),b=Gu({inputs:g,backend:e,attrs:{axis:s}}),w=Ir({inputs:{real:x,imag:b},backend:e});return h.forEach(I=>e.disposeIntermediateTensorInfo(I)),g.forEach(I=>e.disposeIntermediateTensorInfo(I)),e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(b),w}let l=u.map(h=>{let x=[-1,y.sizeFromShape(h.shape.slice(s))];return te({inputs:{x:h},backend:e,attrs:{shape:x}})}),c=l.map(h=>({vals:e.data.get(h.dataId).values,shape:h.shape}));a=S.computeOutShape(l.map(h=>h.shape),1);let p=l[0].shape[0]===1,m=Jc(c,a,t[0].dtype,p),f=S.computeOutShape(u.map(h=>h.shape),s),d=e.makeTensorInfo(f,t[0].dtype,m);return l.forEach(h=>e.disposeIntermediateTensorInfo(h)),d}var kM={kernelName:Oi,backendName:\"cpu\",kernelFunc:Gu};function FT(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dataFormat:u,dilations:l,dimRoundingMode:c}=n;tt([o,s],\"conv2d\");let p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,s.shape,i,l,a,c,!1,p),f=m.filterHeight,d=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,x=m.padInfo.left,b=m.padInfo.top,w=m.dataFormat===\"channelsLast\",I=new le(m.outShape,o.dtype),N=y.computeStrides(o.shape),E=y.computeStrides(s.shape),A=N[0],D=w?N[1]:N[2],F=w?N[2]:1,M=w?1:N[1],V=I.strides[0],G=w?I.strides[1]:I.strides[2],W=w?I.strides[2]:1,q=w?1:I.strides[1],H=e.data.get(o.dataId).values,j=e.data.get(s.dataId).values,Y=I.values;for(let Z=0;Z=m.inHeight)continue;let gt=it*E[0],Ct=et+ft*D;for(let Rt=0;Rt=m.inWidth)continue;let ye=gt+qt*E[1],re=Ct+pe*F,be=ye;for(let de=0;de=l.inDepth)continue;let Z=j*F[0],et=V+Y*D[1];for(let nt=0;nt=l.inHeight)continue;let ft=Z+ot*F[1],gt=et+it*D[2];for(let Ct=0;Ct=l.inWidth)continue;let pe=ft+Ht*F[2],ye=gt+qt*l.inChannels,re=pe;for(let be=0;beMath.cos(r)),RM={kernelName:rs,backendName:\"cpu\",kernelFunc:Fet};var Oet=At(ns,r=>Math.cosh(r)),FM={kernelName:ns,backendName:\"cpu\",kernelFunc:Oet};function Met(r){let{inputs:t,backend:e,attrs:n}=r,{image:o,boxes:s,boxInd:i}=t,{cropSize:a,method:u,extrapolationValue:l}=n,[c,p,m,f]=o.shape,d=s.shape[0],[h,g]=a,x=wt([d,h,g,f],\"float32\"),b=e.data.get(s.dataId).values,w=e.data.get(i.dataId).values,I=e.data.get(o.dataId).values,N=y.computeStrides(o.shape),E=y.computeStrides(x.shape);for(let A=0;A=c)continue;let q=h>1?(V-F)*(p-1)/(h-1):0,H=g>1?(G-M)*(m-1)/(g-1):0;for(let j=0;j1?F*(p-1)+j*q:.5*(F+V)*(p-1);if(Y<0||Y>p-1){for(let Z=0;Z1?M*(m-1)+st*H:.5*(M+G)*(m-1);if(lt<0||lt>m-1){for(let gt=0;gt1?M*(m-1)+Z*H:.5*(M+G)*(m-1);if(et<0||et>m-1){for(let lt=0;ltx+d-b-1:(x,b)=>x+b;for(let x=0;xx+d-b-1:(x,b)=>x+b;for(let x=0;x`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let a=o.shape[0],u=o.shape[1],l=o.shape[2],c=o.shape[3],p=u*s,m=l*s,f=c/(s*s),d=e.data.get(o.dataId).values,h=new Float32Array(a*p*m*f),g=0;for(let x=0;x`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${m}'`);let f=S.computeConv2DInfo(o.shape,s.shape,i,m,a,l,!0),{filterHeight:d,filterWidth:h,dilationHeight:g,dilationWidth:x,padInfo:b}=f,w=b.left,I=b.top,N=f.outChannels/f.inChannels,E=new le(f.outShape,o.dtype),A=e.data.get(o.dataId).values,D=e.data.get(s.dataId).values,F=E.values;for(let M=0;M=f.inHeight)continue;let Z=j*p[0],et=V+Y*c[1];for(let nt=0;nt=f.inWidth)continue;let ft=Z+ot*p[1],gt=et+it*f.inChannels,Ct=st,Rt=ft;for(let Dt=0;Dt{let{x:n,filter:o}=r,{strides:s,pad:i,dilations:a}=e,u=t,l=u.data.get(n.dataId).values,c=n.shape.length,p=u.data.get(o.dataId).values,m=o.shape.length,{batchSize:f,inHeight:d,inWidth:h,inChannels:g,outHeight:x,outWidth:b,padInfo:w,strideHeight:I,strideWidth:N,filterHeight:E,filterWidth:A,dilationHeight:D,dilationWidth:F,outShape:M}=S.computeDilation2DInfo(n.shape,o.shape,s,i,\"NHWC\",a),V=y.sizeFromShape(M),G=M.length,W=y.getArrayFromDType(n.dtype,V);for(let H=0;H=0&&it=0&>st&&(st=Dt)}}}let lt=y.locToIndex([H,j,Z,nt],G,y.computeStrides(M));W[lt]=st}}}return{dataId:u.write(y.toTypedArray(W,n.dtype),M,n.dtype),shape:M,dtype:n.dtype}}};var HM={kernelName:Zl,backendName:\"cpu\",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:n,filter:o,dy:s}=r,{strides:i,pad:a,dilations:u}=e,l=t,c=y.toNestedArray(n.shape,l.data.get(n.dataId).values),p=y.toNestedArray(o.shape,l.data.get(o.dataId).values),{batchSize:m,inHeight:f,inWidth:d,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:w,strideWidth:I,filterHeight:N,filterWidth:E,dilationHeight:A,dilationWidth:D,outShape:F}=S.computeDilation2DInfo(n.shape,o.shape,i,a,\"NHWC\",u);y.assert(s.rank===F.length,()=>`Error in ${Zl}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let M=y.toNestedArray(F,l.data.get(s.dataId).values),V=y.makeZerosNestedTypedArray(o.shape,o.dtype);for(let W=0;W=0&&ot=0&&ftet&&(et=gt,nt=lt,st=it)}}}V[nt][st][Z]+=M[W][q][j][Z]}}}return{dataId:l.write(y.toTypedArray(V,n.dtype),o.shape,o.dtype),shape:o.shape,dtype:o.dtype}}};var qM={kernelName:Yl,backendName:\"cpu\",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:n,filter:o,dy:s}=r,{strides:i,pad:a,dilations:u}=e,l=t,c=y.toNestedArray(n.shape,l.data.get(n.dataId).values),p=y.toNestedArray(o.shape,l.data.get(o.dataId).values),{batchSize:m,inHeight:f,inWidth:d,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:w,strideWidth:I,filterHeight:N,filterWidth:E,dilationHeight:A,dilationWidth:D,outShape:F}=S.computeDilation2DInfo(n.shape,o.shape,i,a,\"NHWC\",u);y.assert(s.rank===F.length,()=>`Error in ${Yl}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let M=y.toNestedArray(F,l.data.get(s.dataId).values),V=y.makeZerosNestedTypedArray(n.shape,n.dtype);for(let W=0;W=0&&ot=0&&ftet&&(et=gt,nt=ot,st=ft)}}}V[W][nt][st][Z]+=M[W][q][j][Z]}}}return{dataId:l.write(y.toTypedArray(V,n.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};function Uet(r){let{inputs:t,backend:e,attrs:n}=r,{image:o}=t,{canvas:s,options:i}=n,{contextOptions:a,imageOptions:u}=i||{},l=(u==null?void 0:u.alpha)||1,c=(a==null?void 0:a.contextType)||\"2d\";if(c!==\"2d\")throw new Error(`Context type ${a.contextType} is not supported by the CPU backend.`);let p=s.getContext(c,(a==null?void 0:a.contextAttributes)||{});if(p==null)throw new Error(`Could not get the context with ${c} type.`);let[m,f]=o.shape.slice(0,2),d=o.shape.length===2?1:o.shape[2],h=e.data.get(o.dataId).values,g=o.dtype===\"float32\"?255:1,x=new Uint8ClampedArray(f*m*4);for(let w=0;w1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${A}.`)}else if(o.dtype===\"int32\"&&(A<0||A>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${A}.`);d===1?(I[0]=A*g,I[1]=A*g,I[2]=A*g):I[E]=A*g}let N=w*4;x[N+0]=Math.round(I[0]),x[N+1]=Math.round(I[1]),x[N+2]=Math.round(I[2]),x[N+3]=Math.round(I[3])}s.width=f,s.height=m;let b=new ImageData(x,f,m);return p.putImageData(b,0,0),o}var KM={kernelName:Qd,backendName:\"cpu\",kernelFunc:Uet};function $l(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n;tt(o,\"sum\");let a;o.dtype===\"bool\"?a=Fo({inputs:{x:o},backend:e,attrs:{dtype:\"int32\"}}):a=Zr({inputs:{x:o},backend:e});let u=a.shape.length,l=y.parseAxisParam(s,a.shape),c=S.getAxesPermutation(l,u),p=l,m=a;c!=null&&(m=We({inputs:{x:a},backend:e,attrs:{perm:c}}),p=S.getInnerMostAxes(p.length,u)),S.assertAxesAreInnerMostDims(\"sum\",p,m.shape.length);let[f,d]=S.computeOutAndReduceShapes(m.shape,p),h=S.upcastType(m.dtype,\"int32\"),g=md(e,f,h),x=y.sizeFromShape(d),b=e.data.get(g.dataId).values,w=e.data.get(m.dataId).values;for(let I=0;I=0&&(m=$l({inputs:{x:m},backend:e,attrs:{axis:l[h]-(i.length-f),keepDims:!1}}),d.push(m)),f--)}for(let h of d)h!==m&&e.disposeIntermediateTensorInfo(h);return m}var XM={kernelName:Fp,backendName:\"cpu\",kernelFunc:Het};function qet(r){let{inputs:t,backend:e}=r,{dy:n,y:o}=t;tt([n,o],\"eluGrad\");let s=new Float32Array(y.sizeFromShape(o.shape)),i=e.data.get(o.dataId).values,a=e.data.get(n.dataId).values;for(let u=0;u=0?s[u]=a[u]:s[u]=a[u]*(l+1)}return e.makeTensorInfo(o.shape,\"float32\",s)}var YM={kernelName:La,backendName:\"cpu\",kernelFunc:qet};var Ket=S.ERF_P,jet=S.ERF_A1,Xet=S.ERF_A2,Yet=S.ERF_A3,Zet=S.ERF_A4,Jet=S.ERF_A5,Qet=At(us,r=>{let t=Math.sign(r),e=Math.abs(r),n=1/(1+Ket*e);return t*(1-((((Jet*n+Zet)*n+Yet)*n+Xet)*n+jet)*n*Math.exp(-e*e))}),ZM={kernelName:us,backendName:\"cpu\",kernelFunc:Qet};function yd(r){let{inputs:t,backend:e,attrs:n}=r,{input:o}=t,{dim:s}=n,i=o.shape.length,a=o.shape.slice(),u=s;return s<0&&(y.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+s+1),a.splice(u,0,1),te({inputs:{x:o},backend:e,attrs:{shape:a}})}var JM={kernelName:Mi,backendName:\"cpu\",kernelFunc:yd};var trt=Qt((r,t)=>r/t),tg=oe(as,trt),eg={kernelName:as,backendName:\"cpu\",kernelFunc:tg};function $w(r,t,e){let n=r.shape,o=n[0],s=n[1],i=e.data.get(r.dataId),a=i.complexTensorInfos.real,u=i.complexTensorInfos.imag,l=[o,s],c=y.sizeFromShape(l),p=y.getTypedArrayFromDType(\"float32\",c),m=y.getTypedArrayFromDType(\"float32\",c);for(let g=0;g{let{image:n}=r,o=e,s=y.getTypedArrayFromDType(n.dtype,y.sizeFromShape(n.shape)),[i,a,u,l]=n.shape,c=o.data.get(n.dataId).values;for(let m=0;m=0&&w=0,()=>`GatherV2: the index value ${N} is not in [0, ${c-1}]`)}let p=a;a==null&&(p=0);let m=y.sizeFromShape(s.shape),f=S.segment_util.collectGatherOpShapeInfo(o,s,u,p),d=te({inputs:{x:o},backend:e,attrs:{shape:[f.batchSize,f.outerSize,f.dimSize,f.sliceSize]}}),h=te({inputs:{x:s},backend:e,attrs:{shape:[f.batchSize,m/f.batchSize]}}),g=[f.batchSize,f.outerSize,m/f.batchSize,f.sliceSize],x=e.bufferSync(h),b=e.bufferSync(d),w=yw(b,x,g);return e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),e.makeTensorInfo(f.outputShape,w.dtype,w.values)}var sP={kernelName:Pi,backendName:\"cpu\",kernelFunc:urt};function crt(r){let{inputs:t,backend:e}=r,{input:n}=t,o=y.sizeFromShape(n.shape),s=n.shape[n.shape.length-1],i=o/s,a=te({inputs:{x:n},backend:e,attrs:{shape:[i,s]}}),u=$w(a,!0,e),l=te({inputs:{x:u},backend:e,attrs:{shape:n.shape}});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(u),l}var iP={kernelName:Mp,backendName:\"cpu\",kernelFunc:crt};var prt=At(gs,r=>Number.isFinite(r)?1:0,\"bool\"),aP={kernelName:gs,backendName:\"cpu\",kernelFunc:prt};var mrt=At(xs,r=>Math.abs(r)===1/0?1:0,\"bool\"),lP={kernelName:xs,backendName:\"cpu\",kernelFunc:mrt};var frt=At(ys,r=>Number.isNaN(r)?1:0,\"bool\"),uP={kernelName:ys,backendName:\"cpu\",kernelFunc:frt};function drt(r){let{backend:t,attrs:e}=r,{start:n,stop:o,num:s}=e,i=bw(n,o,s);return t.makeTensorInfo([i.length],\"float32\",i)}var cP={kernelName:Ha,backendName:\"cpu\",kernelFunc:drt};var hrt=At(Is,r=>Math.log1p(r)),pP={kernelName:Is,backendName:\"cpu\",kernelFunc:hrt};var grt=Qt((r,t)=>r&&t),xrt=oe(qa,grt,null,\"bool\"),mP={kernelName:qa,backendName:\"cpu\",kernelFunc:xrt};var yrt=At(Ka,r=>r?0:1,\"bool\"),fP={kernelName:Ka,backendName:\"cpu\",kernelFunc:yrt};var brt=Qt((r,t)=>r||t),wrt=oe(ja,brt,null,\"bool\"),dP={kernelName:ja,backendName:\"cpu\",kernelFunc:wrt};function Irt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n;tt(o,\"LRN\");let l=o.shape[3],c=l-1,p=e.data.get(o.dataId).values,m=y.sizeFromShape(o.shape),f=new Float32Array(m);function d(h){let g=h%l,x=h-g+Math.max(0,g-s),b=h-g+Math.min(g+s,c),w=0;for(;x<=b;x++){let I=p[x];w+=I*I}return w}for(let h=0;h`Error in maxPool: Either strides or dilations must be 1. 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kt=L();kt.registerFlag(\"HAS_WEBGL\",()=>kt.getNumber(\"WEBGL_VERSION\")>0);kt.registerFlag(\"WEBGL_VERSION\",()=>Vw(2)?2:Vw(1)?1:0);kt.registerFlag(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\",()=>!1);kt.registerFlag(\"WEBGL_BUFFER_SUPPORTED\",()=>kt.get(\"WEBGL_VERSION\")===2);kt.registerFlag(\"WEBGL_CPU_FORWARD\",()=>!0);kt.registerFlag(\"WEBGL_FORCE_F16_TEXTURES\",()=>!1);kt.registerFlag(\"WEBGL_PACK\",()=>kt.getBool(\"HAS_WEBGL\"));kt.registerFlag(\"WEBGL_PACK_NORMALIZATION\",()=>kt.getBool(\"WEBGL_PACK\"));kt.registerFlag(\"WEBGL_PACK_CLIP\",()=>kt.getBool(\"WEBGL_PACK\"));kt.registerFlag(\"WEBGL_PACK_DEPTHWISECONV\",()=>kt.getBool(\"WEBGL_PACK\"));kt.registerFlag(\"WEBGL_PACK_BINARY_OPERATIONS\",()=>kt.getBool(\"WEBGL_PACK\"));kt.registerFlag(\"WEBGL_PACK_UNARY_OPERATIONS\",()=>kt.getBool(\"WEBGL_PACK\"));kt.registerFlag(\"WEBGL_PACK_ARRAY_OPERATIONS\",()=>kt.getBool(\"WEBGL_PACK\"));kt.registerFlag(\"WEBGL_PACK_IMAGE_OPERATIONS\",()=>kt.getBool(\"WEBGL_PACK\"));kt.registerFlag(\"WEBGL_PACK_REDUCE\",()=>kt.getBool(\"WEBGL_PACK\"));kt.registerFlag(\"WEBGL_LAZILY_UNPACK\",()=>kt.getBool(\"WEBGL_PACK\"));kt.registerFlag(\"WEBGL_CONV_IM2COL\",()=>kt.getBool(\"WEBGL_PACK\"));kt.registerFlag(\"WEBGL_PACK_CONV2DTRANSPOSE\",()=>kt.getBool(\"WEBGL_PACK\"));kt.registerFlag(\"WEBGL_MAX_TEXTURE_SIZE\",()=>n1(kt.getNumber(\"WEBGL_VERSION\")));kt.registerFlag(\"WEBGL_MAX_TEXTURES_IN_SHADER\",()=>o1(kt.getNumber(\"WEBGL_VERSION\")));kt.registerFlag(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\",()=>{let r=kt.getNumber(\"WEBGL_VERSION\");return r===0?0:s1(r)});kt.registerFlag(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\",()=>kt.getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0&&!du.isMobile());kt.registerFlag(\"WEBGL_RENDER_FLOAT32_CAPABLE\",()=>i1(kt.getNumber(\"WEBGL_VERSION\")));kt.registerFlag(\"WEBGL_RENDER_FLOAT32_ENABLED\",()=>kt.getBool(\"WEBGL_FORCE_F16_TEXTURES\")?!1:kt.getBool(\"WEBGL_RENDER_FLOAT32_CAPABLE\"));kt.registerFlag(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\",()=>a1(kt.getNumber(\"WEBGL_VERSION\")));kt.registerFlag(\"WEBGL_FENCE_API_ENABLED\",()=>l1(kt.getNumber(\"WEBGL_VERSION\")));kt.registerFlag(\"WEBGL_SIZE_UPLOAD_UNIFORM\",()=>kt.getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?4:0);kt.registerFlag(\"WEBGL_DELETE_TEXTURE_THRESHOLD\",()=>-1,r=>{if(typeof r!=\"number\")throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be a number but got ${r}.`);if(r<0&&r!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${r}.`)});kt.registerFlag(\"WEBGL_FLUSH_THRESHOLD\",()=>du.isMobile()?1:-1,r=>{if(typeof r!=\"number\")throw new Error(`WEBGL_FLUSH_THRESHOLD must be a number but got ${r}.`);if(r<0&&r!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${r}.`)});kt.registerFlag(\"CPU_HANDOFF_SIZE_THRESHOLD\",()=>128);kt.registerFlag(\"WEBGL_USE_SHAPES_UNIFORMS\",()=>!1);kt.registerFlag(\"TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD\",()=>1e5);kt.registerFlag(\"TOPK_K_CPU_HANDOFF_THRESHOLD\",()=>128);kt.registerFlag(\"WEBGL_EXP_CONV\",()=>!1);kt.registerFlag(\"SOFTWARE_WEBGL_ENABLED\",()=>kt.getBool(\"IS_TEST\"));kt.registerFlag(\"WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE\",()=>1/0);kt.registerFlag(\"WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE\",()=>!1);kt.registerFlag(\"WEBGL2_ISNAN_CUSTOM\",()=>!1);kt.registerFlag(\"ENGINE_COMPILE_ONLY\",()=>!1);function Ue(){let r,t,e,n,o,s,i,a,u,l;return L().getNumber(\"WEBGL_VERSION\")===2?(r=\"#version 300 es\",t=\"in\",e=\"out\",n=\"in\",o=\"texture\",s=\"outputColor\",i=\"out vec4 outputColor;\",a=L().getBool(\"WEBGL2_ISNAN_CUSTOM\")?`\n bool isnan_custom(float val) {\n uint floatToUint = floatBitsToUint(val);\n return (floatToUint & 0x7fffffffu) > 0x7f800000u;\n }\n\n bvec4 isnan_custom(vec4 val) {\n return bvec4(isnan_custom(val.x),\n isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));\n }\n\n #define isnan(value) isnan_custom(value)\n `:\"\",u=\"\",l=`\n #define round(value) newRound(value)\n int newRound(float value) {\n return int(floor(value + 0.5));\n }\n\n ivec4 newRound(vec4 value) {\n return ivec4(floor(value + vec4(0.5)));\n }\n `):(r=\"\",t=\"attribute\",e=\"varying\",n=\"varying\",o=\"texture2D\",s=\"gl_FragColor\",i=\"\",a=`\n #define isnan(value) isnan_custom(value)\n bool isnan_custom(float val) {\n return (val > 0. || val < 1. || val == 0.) ? false : true;\n }\n bvec4 isnan_custom(vec4 val) {\n return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));\n }\n `,u=`\n uniform float INFINITY;\n\n bool isinf(float val) {\n return abs(val) == INFINITY;\n }\n bvec4 isinf(vec4 val) {\n return equal(abs(val), vec4(INFINITY));\n }\n `,l=`\n int round(float value) {\n return int(floor(value + 0.5));\n }\n\n ivec4 round(vec4 value) {\n return ivec4(floor(value + vec4(0.5)));\n }\n `),{version:r,attribute:t,varyingVs:e,varyingFs:n,texture2D:o,output:s,defineOutput:i,defineSpecialNaN:a,defineSpecialInf:u,defineRound:l}}function Si(r,t,e=\"index\"){let n=y.computeStrides(t);return n.map((o,s)=>{let i=`int ${r[s]} = ${e} / ${o}`,a=s===n.length-1?`int ${r[s+1]} = ${e} - ${r[s]} * ${o}`:`index -= ${r[s]} * ${o}`;return`${i}; ${a};`}).join(\"\")}function up(r,t,e=\"index\"){let n=y.computeStrides(t);return n.map((o,s)=>{let i=`int ${r[s]} = ${e} / outShapeStrides[${s}]`,a=s===n.length-1?`int ${r[s+1]} = ${e} - ${r[s]} * outShapeStrides[${s}]`:`index -= ${r[s]} * outShapeStrides[${s}]`;return`${i}; ${a};`}).join(\"\")}function Qnt(r,t){let e=r.length,n=r.map(s=>`${t}[${s}]`),o=new Array(e-1);o[e-2]=n[e-1];for(let s=e-3;s>=0;--s)o[s]=`(${o[s+1]} * ${n[s+1]})`;return o}function PL(r,t,e=\"index\"){let n=r.map((s,i)=>i),o=Qnt(n,t);return o.map((s,i)=>{let a=`int ${r[i]} = ${e} / ${o[i]}`,u=i===o.length-1?`int ${r[i+1]} = ${e} - ${r[i]} * ${o[i]}`:`index -= ${r[i]} * ${o[i]}`;return`${a}; ${u};`}).join(\"\")}function vd(r){let t=y.computeStrides(r).map(e=>e.toString());return`\n int getFlatIndex(ivec3 coords) {\n return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;\n }\n`}function Sd(){return`\n int getFlatIndex(ivec3 coords) {\n return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;\n }\n`}var Gw=`\n const float FLOAT_MAX = 1.70141184e38;\n const float FLOAT_MIN = 1.17549435e-38;\n\n lowp vec4 encode_float(highp float v) {\n if (isnan(v)) {\n return vec4(255, 255, 255, 255);\n }\n\n highp float av = abs(v);\n\n if(av < FLOAT_MIN) {\n return vec4(0.0, 0.0, 0.0, 0.0);\n } else if(v > FLOAT_MAX) {\n return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n } else if(v < -FLOAT_MAX) {\n return vec4(0.0, 0.0, 128.0, 255.0) / 255.0;\n }\n\n highp vec4 c = vec4(0,0,0,0);\n\n highp float e = floor(log2(av));\n highp float m = exp2(fract(log2(av))) - 1.0;\n\n c[2] = floor(128.0 * m);\n m -= c[2] / 128.0;\n c[1] = floor(32768.0 * m);\n m -= c[1] / 32768.0;\n c[0] = floor(8388608.0 * m);\n\n highp float ebias = e + 127.0;\n c[3] = floor(ebias / 2.0);\n ebias -= c[3] * 2.0;\n c[2] += floor(ebias) * 128.0;\n\n c[3] += 128.0 * step(0.0, -v);\n\n return c / 255.0;\n }\n`;var{getBroadcastDims:LL}=S;function zL(r,t,e){let n=[];if(r.forEach(f=>{let d=y.sizeFromShape(f.shapeInfo.logicalShape);if(f.shapeInfo.isUniform?n.push(`uniform float ${f.name}${d>1?`[${d}]`:\"\"};`):(n.push(`uniform sampler2D ${f.name};`),n.push(`uniform int offset${f.name};`)),e.enableShapeUniforms){let{uniformShape:h}=Ww(e.packedInputs,f.shapeInfo.logicalShape,f.shapeInfo.texShape);switch(h.length){case 1:n.push(`uniform int ${f.name}Shape;`);break;case 2:n.push(`uniform ivec2 ${f.name}Shape;`);break;case 3:n.push(`uniform ivec3 ${f.name}Shape;`);break;case 4:n.push(`uniform ivec4 ${f.name}Shape;`);break;default:break}n.push(`uniform ivec2 ${f.name}TexShape;`)}}),e.enableShapeUniforms){switch(t.logicalShape.length){case 1:n.push(\"uniform int outShape;\");break;case 2:n.push(\"uniform ivec2 outShape;\"),n.push(\"uniform int outShapeStrides;\");break;case 3:n.push(\"uniform ivec3 outShape;\"),n.push(\"uniform ivec2 outShapeStrides;\");break;case 4:n.push(\"uniform ivec4 outShape;\"),n.push(\"uniform ivec3 outShapeStrides;\");break;default:break}n.push(\"uniform ivec2 outTexShape;\")}e.customUniforms&&e.customUniforms.forEach(f=>{n.push(`uniform ${f.type} ${f.name}${f.arrayIndex?`[${f.arrayIndex}]`:\"\"};`)});let o=n.join(`\n`),s=r.map(f=>tot(f,t,e.packedInputs,e.enableShapeUniforms)).join(`\n`),i=t.texShape,a=Ue(),u=not(a),l,c,p=iot(a);return t.isPacked?(l=eot(t.logicalShape,i,e.enableShapeUniforms),c=sot(a)):(l=rot(t.logicalShape,i,e.enableShapeUniforms),c=oot(a)),e.packedInputs&&(p+=cot),[p,u,c,o,l,s,e.userCode].join(`\n`)}function kd(r,t=!1){let e=r.shapeInfo.logicalShape;switch(e.length){case 0:return Cot(r,t);case 1:return Sot(r,t);case 2:return kot(r,t);case 3:return _ot(r,t);case 4:return Aot(r,t);case 5:return Dot(r);case 6:return $ot(r);default:throw new Error(`${e.length}-D input sampling is not yet supported`)}}function BL(r,t){switch(r.shapeInfo.logicalShape.length){case 0:return Iot(r);case 1:return vot(r,t);case 2:return Not(r,t);case 3:return Tot(r,t);default:return Eot(r,t)}}function tot(r,t,e=!1,n){let o=\"\";e?o+=BL(r,n):o+=kd(r,n);let s=r.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(e?o+=Rot(r,t):o+=Fot(r,t)),o}function eot(r,t,e){switch(r.length){case 0:return VL();case 1:return pot(r,t,e);case 2:return bot(r,t,e);case 3:return fot(r,t,e);default:return hot(r,t,e)}}function rot(r,t,e){switch(r.length){case 0:return VL();case 1:return mot(r,t,e);case 2:return wot(r,t,e);case 3:return dot(r,t,e);case 4:return got(r,t,e);case 5:return xot(r,t);case 6:return yot(r,t);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function not(r){return`\n float sampleTexture(sampler2D textureSampler, vec2 uv) {\n return ${r.texture2D}(textureSampler, uv).r;\n }\n `}function oot(r){return`\n void setOutput(float val) {\n ${r.output} = vec4(val, 0, 0, 0);\n }\n `}function sot(r){return`\n void setOutput(vec4 val) {\n ${r.output} = val;\n }\n `}function iot(r){return`${r.version}\n precision highp float;\n precision highp int;\n precision highp sampler2D;\n ${r.varyingFs} vec2 resultUV;\n ${r.defineOutput}\n const vec2 halfCR = vec2(0.5, 0.5);\n\n struct ivec5\n {\n int x;\n int y;\n int z;\n int w;\n int u;\n };\n\n struct ivec6\n {\n int x;\n int y;\n int z;\n int w;\n int u;\n int v;\n };\n\n uniform float NAN;\n ${r.defineSpecialNaN}\n ${r.defineSpecialInf}\n ${r.defineRound}\n\n int imod(int x, int y) {\n return x - y * (x / y);\n }\n\n int idiv(int a, int b, float sign) {\n int res = a / b;\n int mod = imod(a, b);\n if (sign < 0. && mod != 0) {\n res -= 1;\n }\n return res;\n }\n\n //Based on the work of Dave Hoskins\n //https://www.shadertoy.com/view/4djSRW\n #define HASHSCALE1 443.8975\n float random(float seed){\n vec2 p = resultUV * seed;\n vec3 p3 = fract(vec3(p.xyx) * HASHSCALE1);\n p3 += dot(p3, p3.yzx + 19.19);\n return fract((p3.x + p3.y) * p3.z);\n }\n\n ${aot}\n ${lot}\n ${uot}\n `}var aot=`\nvec2 uvFromFlat(int texNumR, int texNumC, int index) {\n int texR = index / texNumC;\n int texC = index - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n int texelIndex = index / 2;\n int texR = texelIndex / texNumC;\n int texC = texelIndex - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,lot=`\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n int texNumC, int row, int col) {\n int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n int texR = texelIndex / texNumC;\n int texC = texelIndex - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,uot=`\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n int texelsInBatch, int texelsInLogicalRow, int b,\n int row, int col) {\n int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n int texR = index / texNumC;\n int texC = index - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,cot=`\n float getChannel(vec4 frag, vec2 innerDims) {\n vec2 modCoord = mod(innerDims, 2.);\n return modCoord.x == 0. ?\n (modCoord.y == 0. ? frag.r : frag.g) :\n (modCoord.y == 0. ? frag.b : frag.a);\n }\n float getChannel(vec4 frag, int dim) {\n float modCoord = mod(float(dim), 2.);\n return modCoord == 0. ? frag.r : frag.g;\n }\n`;function VL(){return`\n int getOutputCoords() {\n return 0;\n }\n `}function pot(r,t,e){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?e?`\n int getOutputCoords() {\n return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));\n }\n `:`\n int getOutputCoords() {\n return 2 * int(resultUV.x * ${n[1]}.0);\n }\n `:n[1]===1?e?`\n int getOutputCoords() {\n return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));\n }\n `:`\n int getOutputCoords() {\n return 2 * int(resultUV.y * ${n[0]}.0);\n }\n `:e?`\n int getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(packedTexShape[0], packedTexShape[1]));\n return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);\n }\n `:`\n int getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${n[0]}, ${n[1]}));\n return 2 * (resTexRC.x * ${n[1]} + resTexRC.y);\n }\n `}function mot(r,t,e){return t[0]===1?e?`\n int getOutputCoords() {\n return int(resultUV.x * float(outTexShape[1]));\n }\n `:`\n int getOutputCoords() {\n return int(resultUV.x * ${t[1]}.0);\n }\n `:t[1]===1?e?`\n int getOutputCoords() {\n return int(resultUV.y * float(outTexShape[0]));\n }\n `:`\n int getOutputCoords() {\n return int(resultUV.y * ${t[0]}.0);\n }\n `:e?`\n int getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(outTexShape[0], outTexShape[1]));\n return resTexRC.x * outTexShape[1] + resTexRC.y;\n }\n `:`\n int getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${t[0]}, ${t[1]}));\n return resTexRC.x * ${t[1]} + resTexRC.y;\n }\n `}function fot(r,t,e){if(e)return`\n ivec3 getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));\n int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(packedTexShape[0], packedTexShape[1]));\n int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n int b = index / texelsInBatch;\n index -= b * texelsInBatch;\n\n int r = 2 * (index / texelsInLogicalRow);\n int c = imod(index, texelsInLogicalRow) * 2;\n\n return ivec3(b, r, c);\n }\n `;let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],o=Math.ceil(r[2]/2),s=o*Math.ceil(r[1]/2);return`\n ivec3 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${n[0]}, ${n[1]}));\n int index = resTexRC.x * ${n[1]} + resTexRC.y;\n\n int b = index / ${s};\n index -= b * ${s};\n\n int r = 2 * (index / ${o});\n int c = imod(index, ${o}) * 2;\n\n return ivec3(b, r, c);\n }\n `}function dot(r,t,e){if(e)return`\n ivec3 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(outTexShape[0], outTexShape[1]));\n int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n ${up([\"r\",\"c\",\"d\"],r)}\n return ivec3(r, c, d);\n }\n`;let n=Si([\"r\",\"c\",\"d\"],r);return`\n ivec3 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${t[0]}, ${t[1]}));\n int index = resTexRC.x * ${t[1]} + resTexRC.y;\n ${n}\n return ivec3(r, c, d);\n }\n `}function hot(r,t,e){if(e)return`\n ivec4 getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(packedTexShape[0], packedTexShape[1]));\n int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));\n int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));\n int texelsInBatchN = texelsInBatch * outShape[1];\n\n int b2 = index / texelsInBatchN;\n index -= b2 * texelsInBatchN;\n\n int b = index / texelsInBatch;\n index -= b * texelsInBatch;\n\n int r = 2 * (index / texelsInLogicalRow);\n int c = imod(index, texelsInLogicalRow) * 2;\n\n return ivec4(b2, b, r, c);\n }\n `;let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],o=Math.ceil(r[r.length-1]/2),s=o*Math.ceil(r[r.length-2]/2),i=s,a=\"\",u=\"b, r, c\";for(let l=2;l=1?c=\"coords = 0;\":c=a.map(b=>`coords.${p[b+l]} = 0;`).join(`\n`);let m=\"\";i<2&&s>0?m=\"coords\":m=r.shapeInfo.logicalShape.map((b,w)=>`coords.${p[w+l]}`).join(\", \");let f=\"return outputValue;\",h=y.sizeFromShape(r.shapeInfo.logicalShape)===1,x=y.sizeFromShape(t.logicalShape)===1;if(s===1&&!h&&!x)f=`\n return vec4(outputValue.xy, outputValue.xy);\n `;else if(h&&!x)i===1?f=`\n return vec4(outputValue.x, outputValue.x, 0., 0.);\n `:f=`\n return vec4(outputValue.x);\n `;else if(a.length){let b=s-2,w=s-1;a.indexOf(b)>-1&&a.indexOf(w)>-1?f=\"return vec4(outputValue.x);\":a.indexOf(b)>-1?f=\"return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);\":a.indexOf(w)>-1&&(f=\"return vec4(outputValue.xx, outputValue.zz);\")}return`\n vec4 ${o}() {\n ${u} coords = getOutputCoords();\n ${c}\n vec4 outputValue = get${n}(${m});\n ${f}\n }\n `}function Fot(r,t){let e=r.name,n=e.charAt(0).toUpperCase()+e.slice(1),o=\"get\"+n+\"AtOutCoords\",s=t.texShape,i=r.shapeInfo.texShape,a=r.shapeInfo.logicalShape.length,u=t.logicalShape.length;if(!r.shapeInfo.isUniform&&a===u&&r.shapeInfo.flatOffset==null&&y.arraysEqual(i,s))return`\n float ${o}() {\n return sampleTexture(${e}, resultUV);\n }\n `;let l=zt(u),c=LL(r.shapeInfo.logicalShape,t.logicalShape),p=u-a,m,f=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];a===0?m=\"\":u<2&&c.length>=1?m=\"coords = 0;\":m=c.map(h=>`coords.${f[h+p]} = 0;`).join(`\n`);let d=\"\";return u<2&&a>0?d=\"coords\":d=r.shapeInfo.logicalShape.map((h,g)=>`coords.${f[g+p]}`).join(\", \"),`\n float ${o}() {\n ${l} coords = getOutputCoords();\n ${m}\n return get${n}(${d});\n }\n `}function zt(r){if(r<=1)return\"int\";if(r===2)return\"ivec2\";if(r===3)return\"ivec3\";if(r===4)return\"ivec4\";if(r===5)return\"ivec5\";if(r===6)return\"ivec6\";throw Error(`GPU for rank ${r} is not yet supported`)}function Ww(r,t,e){let{newShape:n,keptDims:o}=y.squeezeShape(t),s=t.length,i=r&&s===3&&t[0]===1,a=i?t.slice(1):n,u=!r&&s>1&&!y.arraysEqual(t,e)&&n.lengthr[e]).join(\", \")}function WL(r,t,e,n){let o=e.map((c,p)=>{let m={logicalShape:c.shape,texShape:c.isUniform?null:c.texData.texShape,isUniform:c.isUniform,isPacked:c.isUniform?!1:c.texData.isPacked,flatOffset:null};return c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0&&(m.flatOffset=c.texData.slice.flatOffset),{name:t.variableNames[p],shapeInfo:m}}),s=o.map(c=>c.shapeInfo),i={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked,flatOffset:null},a=zL(o,i,t),u=HT(r.gl,a),l=r.createProgram(u);return L().get(\"ENGINE_COMPILE_ONLY\")?{program:t,fragmentShader:u,source:a,webGLProgram:l,inShapeInfos:s,outShapeInfo:i,variablesLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:(r.buildVao(l),Object.assign({program:t,fragmentShader:u,source:a,webGLProgram:l,inShapeInfos:s,outShapeInfo:i},u1(r,t,l)))}function u1(r,t,e){let n=[],o=[],s,i,a,u=null,l=null;l=r.getUniformLocation(e,\"NAN\",!1),L().getNumber(\"WEBGL_VERSION\")===1&&(u=r.getUniformLocation(e,\"INFINITY\",!1));let c=!1;for(let p of t.variableNames){let m={name:p,uniform:r.getUniformLocation(e,p,c),offset:r.getUniformLocation(e,`offset${p}`,c)};t.enableShapeUniforms&&(m.shape=r.getUniformLocation(e,`${p}Shape`,c),m.texShape=r.getUniformLocation(e,`${p}TexShape`,c)),n.push(m)}if(t.enableShapeUniforms&&(s=r.getUniformLocation(e,\"outShape\",c),a=r.getUniformLocation(e,\"outShapeStrides\",c),i=r.getUniformLocation(e,\"outTexShape\",c)),t.customUniforms)for(let p of t.customUniforms)o.push(r.getUniformLocation(e,p.name,c));return{variablesLocations:n,customUniformLocations:o,infLoc:u,nanLoc:l,outShapeLocation:s,outShapeStridesLocation:a,outTexShapeLocation:i}}function GL(r,t){if(r.length!==t.length)throw Error(`Binary was compiled with ${r.length} inputs, but was executed with ${t.length} inputs`);r.forEach((e,n)=>{let o=e.logicalShape,s=t[n],i=s.shape;if(!y.arraysEqual(o,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${o} and ${i} must match`);if(e.isUniform&&s.isUniform)return;let a=e.texShape,u=s.isUniform?null:s.texData.texShape;if(!y.arraysEqual(a,u))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${a} and ${u} must match`)})}function UL(r,t,e,n,o){t.program.enableShapeUniforms||(GL(t.inShapeInfos,e),GL([t.outShapeInfo],[n]));let s=n.texData.texture,i=n.texData.texShape;n.texData.isPacked?r.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):r.setOutputMatrixTexture(s.texture,i[0],i[1]),r.setProgram(t.webGLProgram),r.bindVertexArray(t.webGLProgram.vao),L().getNumber(\"WEBGL_VERSION\")===1&&t.infLoc!==null&&r.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&r.gl.uniform1f(t.nanLoc,NaN);for(let u=0;u{let a=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(r.enableShapeUniforms&&!i.isUniform){let u=i.texData.texShape,{useSqueezeShape:l,uniformShape:c,keptDims:p}=Ww(r.packedInputs,i.shape,u),m=\"\",f=\"\",d=\"\";if(c.length===1&&r.packedInputs){let N=[Math.ceil(u[0]/2),Math.ceil(u[1]/2)];m=`${N[0]>1}_${N[1]>1}`}else if(c.length===2&&!r.packedInputs)f=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!r.packedInputs){let N=y.computeStrides(c);d=`${N[0]===u[1]}_${N[N.length-1]===u[1]}`}let h=i.shape.length,g=c.length===2&&y.arraysEqual(i.shape,u),x=y.sizeFromShape(i.shape)===1,b=S.getBroadcastDims(i.shape,e.shape),w=!r.packedInputs&&h===e.shape.length&&y.arraysEqual(u,e.texData.texShape),I=r.packedInputs||c.length>2?\"\":`${u[0]>1}_${u[1]>1}`;n+=`${h}_${w}_${l?p:\"\"}_${c.length}_${x}_${b}_${g}_${m}_${f}_${d}_${I}_${a}`}else{let u=i.isUniform?\"uniform\":i.texData.texShape;n+=`${i.shape}_${u}_${a}`}});let o=r.userCode,s=r.constructor.name;return s+=\"_\"+n+\"_\"+o+`${L().getNumber(\"WEBGL_VERSION\")}`,s}function he(r){return L().getBool(\"WEBGL_USE_SHAPES_UNIFORMS\")&&r<=4}var Uw=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Wu.DENSE,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let e=Ue();this.outputShape=t,this.enableShapeUniforms=he(this.outputShape.length),this.userCode=`\n ivec3 outCoordsFromFlatIndex(int index) {\n 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texShape[1]));\n int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n vec4 result = vec4(0.);\n\n for (int i=0; i<4; i++) {\n int flatIndex = index + i;\n ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n }\n\n ${e.output} = result;\n }\n `}};var qw=class{constructor(t){this.variableNames=[\"A\"],this.outTexUsage=Jr.DOWNLOAD;let e=Ue();this.outputShape=t,this.userCode=`\n ${Gw}\n\n void main() {\n float x = getAAtOutCoords();\n ${e.output} = encode_float(x);\n }\n `}};var Kw=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Jr.DOWNLOAD;let e=Ue();this.outputShape=t,this.userCode=`\n ${Gw}\n\n void main() {\n ivec3 coords = getOutputCoords();\n float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));\n ${e.output} = encode_float(x);\n }\n `}};var Pot={R:0,G:1,B:2,A:3},ug=class{constructor(t,e=!1,n=\"RGBA\"){this.variableNames=[\"A\"],this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let o=Ue();this.outputShape=t,this.enableShapeUniforms=he(this.outputShape.length);let s=\"result\";e&&(s=\"floor(result * 255. + 0.5)\");let i=\"\";for(let a=0;ay1,createBufferFromOutputTexture:()=>I1,createFloat16MatrixTexture:()=>d1,createFloat16PackedMatrixTexture:()=>x1,createFloat32MatrixTexture:()=>f1,createIndexBuffer:()=>m1,createPackedMatrixTexture:()=>g1,createUnsignedBytesMatrixTexture:()=>h1,createVertexBuffer:()=>p1,createVertexShader:()=>c1,downloadByteEncodedFloatMatrixFromOutputTexture:()=>v1,downloadFloat32MatrixFromBuffer:()=>C1,downloadMatrixFromPackedOutputTexture:()=>N1,downloadPackedMatrixFromBuffer:()=>S1,getInternalFormatForFloat16MatrixTexture:()=>Yw,getInternalFormatForFloat16PackedMatrixTexture:()=>Qw,getInternalFormatForFloat32MatrixTexture:()=>Xw,getInternalFormatForPackedMatrixTexture:()=>Jw,getInternalFormatForUnsignedBytesMatrixTexture:()=>Zw,uploadDenseMatrixToTexture:()=>b1,uploadPixelDataToTexture:()=>w1});function c1(r){let t=Ue(),e=`${t.version}\n precision highp float;\n ${t.attribute} vec3 clipSpacePos;\n ${t.attribute} vec2 uv;\n ${t.varyingVs} vec2 resultUV;\n\n void main() {\n gl_Position = 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ht(r,()=>r.bufferData(r.PIXEL_PACK_BUFFER,a,r.STREAM_READ)),ht(r,()=>r.readPixels(0,0,e,t,r.RGBA,r.FLOAT,0)),ht(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,null)),o}function C1(r,t,e){let n=r,o=new Float32Array(e);return n.bindBuffer(n.PIXEL_PACK_BUFFER,t),n.getBufferSubData(n.PIXEL_PACK_BUFFER,0,o),n.bindBuffer(n.PIXEL_PACK_BUFFER,null),o}function v1(r,t,e,n){let[o,s]=lp(t,e),i=4,a=new Uint8Array(DL(t*e,i));return ht(r,()=>r.readPixels(0,0,o,s,n.downloadTextureFormat,r.UNSIGNED_BYTE,a)),new Float32Array(a.buffer)}function S1(r,t,e,n,o,s,i,a){let u=r,l=new Float32Array($L(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 N1(r,t,e){let n=new Float32Array(t*e*4);return ht(r,()=>r.readPixels(0,0,e,t,r.RGBA,r.FLOAT,n)),n}var pp=class{constructor(t){this.outputTexture=null,this.program=null,this.disposed=!1,this.itemsToPoll=[];let e=L().getNumber(\"WEBGL_VERSION\");if(t!=null?(this.gl=t,BT(e,t)):this.gl=qn(e),t=this.gl,L().getNumber(\"WEBGL_VERSION\")===2){let s=t;this.createVertexArray=()=>ht(s,()=>s.createVertexArray()),this.bindVertexArray=i=>ht(s,()=>s.bindVertexArray(i)),this.deleteVertexArray=i=>ht(s,()=>s.deleteVertexArray(i)),this.getVertexArray=()=>ht(s,()=>s.getParameter(s.VERTEX_ARRAY_BINDING))}else if(t!=null){let s=t.getExtension(\"OES_vertex_array_object\");if(s==null)throw new Error(\"All WebGL1 implementations are expected to offer OES_vertex_array_object.\");this.createVertexArray=()=>ht(t,()=>s.createVertexArrayOES()),this.bindVertexArray=i=>ht(t,()=>s.bindVertexArrayOES(i)),this.deleteVertexArray=i=>ht(t,()=>s.deleteVertexArrayOES(i)),this.getVertexArray=()=>ht(t,()=>t.getParameter(s.VERTEX_ARRAY_BINDING_OES))}let n=\"WEBGL_color_buffer_float\",o=\"EXT_color_buffer_half_float\";if(this.parallelCompilationExtension=this.gl.getExtension(\"KHR_parallel_shader_compile\"),L().getNumber(\"WEBGL_VERSION\")===1){let s=\"OES_texture_float\",i=\"OES_texture_half_float\";if(this.textureFloatExtension=bd(this.gl,s),Kn(this.gl,i))this.textureHalfFloatExtension=bd(this.gl,i);else if(L().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),Kn(this.gl,o))this.colorBufferHalfFloatExtension=bd(this.gl,o);else if(L().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\",Kn(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Kn(this.gl,o))this.colorBufferHalfFloatExtension=this.gl.getExtension(o);else throw new Error(\"GL context does not support color renderable floats\");this.vertexBuffer=p1(this.gl),this.indexBuffer=m1(this.gl),this.framebuffer=JT(this.gl),this.textureConfig=ig(this.gl,this.textureHalfFloatExtension)}get debug(){return L().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 t=this.gl;ht(t,()=>t.finish()),ht(t,()=>t.bindFramebuffer(t.FRAMEBUFFER,null)),ht(t,()=>t.deleteFramebuffer(this.framebuffer)),ht(t,()=>t.bindBuffer(t.ARRAY_BUFFER,null)),ht(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,null)),ht(t,()=>t.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(t,e){return this.throwIfDisposed(),f1(this.gl,t,e,this.textureConfig)}createFloat16MatrixTexture(t,e){return this.throwIfDisposed(),d1(this.gl,t,e,this.textureConfig)}createUnsignedBytesMatrixTexture(t,e){return this.throwIfDisposed(),h1(this.gl,t,e,this.textureConfig)}uploadPixelDataToTexture(t,e){this.throwIfDisposed(),w1(this.gl,t,e)}uploadDenseMatrixToTexture(t,e,n,o){this.throwIfDisposed(),b1(this.gl,t,e,n,o,this.textureConfig)}createFloat16PackedMatrixTexture(t,e){return this.throwIfDisposed(),x1(this.gl,t,e,this.textureConfig)}createPackedMatrixTexture(t,e){return this.throwIfDisposed(),g1(this.gl,t,e,this.textureConfig)}deleteMatrixTexture(t){this.throwIfDisposed(),this.outputTexture===t&&(Bw(this.gl,this.framebuffer),this.outputTexture=null),ht(this.gl,()=>this.gl.deleteTexture(t))}downloadByteEncodedFloatMatrixFromOutputTexture(t,e,n){return this.downloadMatrixDriver(t,()=>v1(this.gl,e,n,this.textureConfig))}downloadPackedMatrixFromBuffer(t,e,n,o,s,i){return S1(this.gl,t,e,n,o,s,i,this.textureConfig)}downloadFloat32MatrixFromBuffer(t,e){return C1(this.gl,t,e)}createBufferFromTexture(t,e,n){this.bindTextureToFrameBuffer(t);let o=I1(this.gl,e,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),o}createAndWaitForFence(){let t=this.createFence(this.gl);return this.pollFence(t)}createFence(t){let e,n;if(L().getBool(\"WEBGL_FENCE_API_ENABLED\")){let o=t,s=o.fenceSync(o.SYNC_GPU_COMMANDS_COMPLETE,0);t.flush(),n=()=>{let i=o.clientWaitSync(s,0,0);return i===o.ALREADY_SIGNALED||i===o.CONDITION_SATISFIED},e=s}else L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0?(e=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(e,L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))):n=()=>!0;return{query:e,isFencePassed:n}}downloadMatrixFromPackedTexture(t,e,n){return this.downloadMatrixDriver(t,()=>N1(this.gl,e,n))}createProgram(t){this.throwIfDisposed();let e=this.gl;this.vertexShader==null&&(this.vertexShader=c1(e));let n=qT(e);ht(e,()=>e.attachShader(n,this.vertexShader)),ht(e,()=>e.attachShader(n,t)),KT(e,n);let o=Object.assign(n,{vao:this.createVertexArray()});return this.debug&&ag(e,o),o}buildVao(t){this.setProgram(t),this.bindVertexArray(t.vao);let e=this.gl;ht(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),y1(e,t,this.vertexBuffer)}deleteProgram(t){this.throwIfDisposed(),t===this.program&&(this.program=null),t!=null&&(ht(this.gl,()=>this.gl.deleteProgram(t)),this.deleteVertexArray(t.vao))}setProgram(t){this.throwIfDisposed(),this.program=t,this.program!=null&&this.debug&&ag(this.gl,this.program),ht(this.gl,()=>this.gl.useProgram(t))}getUniformLocation(t,e,n=!0){return this.throwIfDisposed(),n?QT(this.gl,t,e):t1(this.gl,t,e)}getAttributeLocation(t,e){return this.throwIfDisposed(),ht(this.gl,()=>this.gl.getAttribLocation(t,e))}getUniformLocationNoThrow(t,e){return this.throwIfDisposed(),this.gl.getUniformLocation(t,e)}setInputMatrixTexture(t,e,n){this.throwIfDisposed(),this.throwIfNoProgram(),e1(this.gl,t,e,n)}setOutputMatrixTexture(t,e,n){this.setOutputMatrixTextureDriver(t,n,e)}setOutputPackedMatrixTexture(t,e,n){this.throwIfDisposed();let[o,s]=wa(e,n);this.setOutputMatrixTextureDriver(t,o,s)}setOutputMatrixWriteRegion(t,e,n,o){this.setOutputMatrixWriteRegionDriver(n,t,o,e)}setOutputPackedMatrixWriteRegion(t,e,n,o){throw new Error(\"setOutputPackedMatrixWriteRegion not implemented.\")}debugValidate(){this.program!=null&&ag(this.gl,this.program),wd(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let t=this.gl;if(this.debug){let e=this.getVertexArray();console.assert(e===this.program.vao,\"VAO changed between setProgram and executeProgram!\"),this.debugValidate()}ht(t,()=>t.drawElements(t.TRIANGLES,6,t.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),ht(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=bd(this.gl,L().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(L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2){let n=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=n.createQuery();return n.beginQuery(o.TIME_ELAPSED_EXT,s),s}let t=this.getQueryTimerExtensionWebGL1(),e=t.createQueryEXT();return t.beginQueryEXT(t.TIME_ELAPSED_EXT,e),e}endQuery(){if(L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2){let e=this.gl,n=this.getQueryTimerExtensionWebGL2();e.endQuery(n.TIME_ELAPSED_EXT);return}let t=this.getQueryTimerExtensionWebGL1();t.endQueryEXT(t.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(t){return await y.repeatedTry(()=>this.disposed||this.isQueryAvailable(t,L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))),this.getQueryTime(t,L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))}getQueryTime(t,e){if(e===0)return null;if(e===2){let n=this.gl;return n.getQueryParameter(t,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(t,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(t,e){if(e===0)return!0;if(e===2){let n=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=n.getQueryParameter(t,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(o.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),o=n.getQueryObjectEXT(t,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),o&&!this.disjoint}}pollFence(t){return new Promise(e=>{this.addItemToPoll(()=>t.isFencePassed(),()=>e())})}pollItems(){let t=Lot(this.itemsToPoll.map(e=>e.isDoneFn));for(let e=0;e<=t;++e){let{resolveFn:n}=this.itemsToPoll[e];n()}this.itemsToPoll=this.itemsToPoll.slice(t+1)}addItemToPoll(t,e){if(this.itemsToPoll.push({isDoneFn:t,resolveFn:e}),this.itemsToPoll.length>1)return;let n;\"setTimeoutCustom\"in L().platform&&(n=L().platform.setTimeoutCustom.bind(L().platform)),y.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,n)}bindTextureToFrameBuffer(t){this.throwIfDisposed(),lg(this.gl,t,this.framebuffer),this.debug&&wd(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(lg(this.gl,this.outputTexture,this.framebuffer),this.debug&&wd(this.gl)):Bw(this.gl,this.framebuffer)}downloadMatrixDriver(t,e){this.bindTextureToFrameBuffer(t);let n=e();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(t,e,n){this.throwIfDisposed();let o=this.gl;lg(o,t,this.framebuffer),this.debug&&wd(o),this.outputTexture=t,ht(o,()=>o.viewport(0,0,e,n)),ht(o,()=>o.scissor(0,0,e,n))}setOutputMatrixWriteRegionDriver(t,e,n,o){this.throwIfDisposed(),ht(this.gl,()=>this.gl.scissor(t,e,n,o))}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 Lot(r){let t=0;for(;t`${r}.${e}`)}function rr(r,t){return t===1?[r]:T1(r,t)}function Pz(r,t){if(r===1)return\"rc\";let e=\"\";for(let n=0;n ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]}`;let e=\"\";for(let n=this.rank-2;n= ${this.enableShapeUniforms?`outShape[${n}]`:this.outputShape[n]}`,n= ${n};\n bool rEdge = rp1 >= ${o};\n `}getOutput(t){let e=this.getSourceCoordsArr(t);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${e[0]}),\n cEdge ? 0. : getA(${e[1]}),\n rEdge ? 0. : getA(${e[2]}),\n rEdge || cEdge ? 0. : getA(${e[3]})`}};var Ad=class{constructor(t,e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"inputShape\",type:\"ivec3\"}],this.outputShape=t,this.enableShapeUniforms=he(this.outputShape.length);let n=\"\";for(let o=0;o<4;o++){let s=\"thisRC = rc;\";o%2===1&&(s+=\"thisRC.z += 1;\"),o>1&&(s+=\"thisRC.y += 1;\"),n+=`\n ${s}\n ${o>0?\"if(thisRC.y < rows && thisRC.z < cols){\":\"\"}\n int flatIndex = getFlatIndex(thisRC);\n\n ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n result[${o}] =\n getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n ${o>0?\"}\":\"\"}\n `}this.userCode=`\n ${zot(e,this.enableShapeUniforms)}\n ${this.enableShapeUniforms?Sd():vd(t)}\n\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0.);\n\n ivec3 thisRC;\n int rows = ${this.enableShapeUniforms?\"outShape[1]\":t[1]};\n int cols = ${this.enableShapeUniforms?\"outShape[2]\":t[2]};\n\n ${n}\n\n setOutput(result);\n }\n `}};function zot(r,t){return`\n ivec3 inputCoordsFromReshapedOutCoords(int index) {\n ${t?PL([\"r\",\"c\",\"d\"],\"inputShape\"):Si([\"r\",\"c\",\"d\"],r)}\n return ivec3(r, c, d);\n }\n `}var oI=class{constructor(t){this.gpgpu=t,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(t,e,n){let o=zz(e,n),s=Bz(t,o,n);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let i=Lz(t,o,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=i,this.log();let u=this.freeTextures[s].pop();return this.usedTextures[s].push(u),u}let a;return o===Lr.PACKED_2X2_FLOAT32?a=this.gpgpu.createPackedMatrixTexture(t[0],t[1]):o===Lr.PACKED_2X2_FLOAT16?a=this.gpgpu.createFloat16PackedMatrixTexture(t[0],t[1]):o===Lr.UNPACKED_FLOAT32?a=this.gpgpu.createFloat32MatrixTexture(t[0],t[1]):o===Lr.UNPACKED_FLOAT16?a=this.gpgpu.createFloat16MatrixTexture(t[0],t[1]):o===Lr.PACKED_4X1_UNSIGNED_BYTE&&(a=this.gpgpu.createUnsignedBytesMatrixTexture(t[0],t[1])),this.usedTextures[s].push(a),this.numUsedTextures++,this._numBytesAllocated+=i,this.log(),a}releaseTexture(t,e,n,o){if(this.freeTextures==null)return;let s=zz(n,o),i=Bz(e,s,o);i in this.freeTextures||(this.freeTextures[i]=[]);let a=Lz(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,o),u=L().getNumber(\"WEBGL_DELETE_TEXTURE_THRESHOLD\");u!==-1&&this._numBytesAllocated>u?(this.gpgpu.deleteMatrixTexture(t.texture),this._numBytesAllocated-=a):(this.freeTextures[i].push(t),this.numFreeTextures++,this._numBytesFree+=a),this.numUsedTextures--;let l=this.usedTextures[i],c=l&&l.indexOf(t);if(c==null||c<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");l[c]=l[l.length-1],l.pop(),this.log()}log(){if(!this.logEnabled)return;let t=this.numFreeTextures+this.numUsedTextures;console.log(\"Free/Used\",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${t})`);let e=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*e)}%)`)}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 t in this.freeTextures)this.freeTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});for(let t in this.usedTextures)this.usedTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function Bot(r,t){let e=r;if(t===e.R32F)return 4;if(t===e.R16F)return 2;if(t===e.RGBA32F)return 16;if(t===r.RGBA)return 16;if(t===e.RGBA16F)return 8;if(t===e.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function Lz(r,t,e,n,o){let s=Vot(t,n),i;if(o){let[u,l]=wa(r[0],r[1]);i=u*l}else{let[u,l]=lp(r[0],r[1]);i=u*l}let a=Bot(e,s);return i*a}function Vot(r,t){switch(r){case Lr.PACKED_2X2_FLOAT32:return Jw(t);case Lr.PACKED_2X2_FLOAT16:return Qw(t);case Lr.UNPACKED_FLOAT32:return Xw(t);case Lr.UNPACKED_FLOAT16:return Yw(t);case Lr.PACKED_4X1_UNSIGNED_BYTE:return Zw(t);default:throw new Error(`Unknown physical texture type ${r}`)}}function Got(r){return L().getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?r?Lr.PACKED_2X2_FLOAT32:Lr.UNPACKED_FLOAT32:r?Lr.PACKED_2X2_FLOAT16:Lr.UNPACKED_FLOAT16}function zz(r,t){if(r===Jr.UPLOAD)return Lr.PACKED_2X2_FLOAT32;if(r===Jr.RENDER||r==null)return Got(t);if(r===Jr.DOWNLOAD||r===Jr.PIXELS)return Lr.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function Bz(r,t,e){return`${r[0]}_${r[1]}_${t}_${e}`}var zr=class{constructor(t,e){this.variableNames=[\"A\"],this.outputShape=t,this.enableShapeUniforms=he(this.outputShape.length),this.userCode=`\n float unaryOperation(float x) {\n ${e}\n }\n\n void main() {\n float x = getAAtOutCoords();\n float y = unaryOperation(x);\n\n setOutput(y);\n }\n `}},xr=\"if (isnan(x)) return x;\",Vz=\"return x;\",_1=\"return abs(x);\";var Gz=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",Wz=xr+`\n return (x < 0.0) ? 0.0 : x;\n`,Uz=xr+`\n return (x < 0.0) ? 0.0 : min(6.0, x);\n`,Ia=\"return x;\",Hz=\"return 1.0 / (1.0 + exp(-1.0 * x));\";var Kz=\"return x;\",jz=`\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n`,Xz=`\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,Yz=`\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,Zz=\"return 1.0 / (1.0 + exp(-1.0 * x));\",Dn=class{constructor(t,e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.enableShapeUniforms=he(this.outputShape.length),this.userCode=`\n vec4 unaryOperation(vec4 x) {\n ${e}\n }\n\n void main() {\n vec4 x = getAAtOutCoords();\n vec4 y = unaryOperation(x);\n\n setOutput(y);\n }\n `}};var sI=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=t,this.enableShapeUniforms=he(this.outputShape.length);let e=t.length,n=rr(\"rc\",e),o=zt(e),s=Pz(e,n),i=n.slice(-2),a=e<=1?\"rc\":`vec2(${i.join(\",\")})`;this.userCode=`\n void main() {\n ${o} rc = getOutputCoords();\n vec4 packedInput = getA(${s});\n\n setOutput(getChannel(packedInput, ${a}));\n }\n `}};var Uot=Xr.whereImpl,Hot=1e-7,qot=1e-4,iI={};function Kot(r){return r in iI||(iI[r]={}),iI[r]}var jot=L().getNumber(\"CPU_HANDOFF_SIZE_THRESHOLD\"),Xot=600;function Yot(){return L().global.screen==null?1024:L().global.screen.height*L().global.screen.width*window.devicePixelRatio*Xot/1024/1024}var Dd=class r extends Bo{nextDataId(){return r.nextDataId++}constructor(t){if(super(),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,!L().getBool(\"HAS_WEBGL\"))throw new Error(\"WebGL is not supported on this device\");let e;if(t!=null){if(t instanceof pp)e=t;else{let n=qn(L().getNumber(\"WEBGL_VERSION\"),t);e=new pp(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=qn(L().getNumber(\"WEBGL_VERSION\"));e=new pp(n),this.binaryCache=Kot(L().getNumber(\"WEBGL_VERSION\")),this.gpgpuCreatedLocally=!0}this.gpgpu=e,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new oI(this.gpgpu),this.numMBBeforeWarning=Yot(),this.texData=new Ta(this,Bn())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(t,e,n,o,s,i){let a=this.makeTensorInfo(e,n),u=this.texData.get(a.dataId);u.isPacked=!1,u.texture={texture:t,texShape:[o,s]},u.texShape=[o,s];let l=Id(e),c=new ug(l,!1,i),p=this.runWebGLProgram(c,[a],n,[[o,s]]);return p.shape=e,u.texture=null,this.disposeIntermediateTensorInfo(a),p.dataId}write(t,e,n){if((L().getBool(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\")||L().getBool(\"DEBUG\"))&&this.checkNumericalProblems(t),n===\"complex64\"&&t!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let o={id:this.nextDataId()};return this.texData.set(o,{shape:e,dtype:n,values:t,usage:Jr.UPLOAD,refCount:1}),o}refCount(t){return this.texData.has(t)?this.texData.get(t).refCount:0}incRef(t){let e=this.texData.get(t);e.refCount++}decRef(t){if(this.texData.has(t)){let e=this.texData.get(t);e.refCount--}}move(t,e,n,o,s){if(L().getBool(\"DEBUG\")&&this.checkNumericalProblems(e),o===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");this.texData.set(t,{shape:n,dtype:o,values:e,usage:Jr.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(t){this.disposeData(t.dataId)}readSync(t){let e=this.texData.get(t),{values:n,dtype:o,complexTensorInfos:s,slice:i,shape:a,isPacked:u}=e;if(i!=null){let m;u?m=new Dn(a,Ia):m=new zr(a,Ia);let f=this.runWebGLProgram(m,[{dataId:t,shape:a,dtype:o}],o),d=this.readSync(f.dataId);return this.disposeIntermediateTensorInfo(f),d}if(n!=null)return this.convertAndCacheOnCPU(t);if(o===\"string\")return n;let l=this.activeTimers!=null,c;l&&(c=y.now());let p;if(o===\"complex64\"){let m=this.readSync(s.real.dataId),f=this.readSync(s.imag.dataId);p=S.mergeRealAndImagArrays(m,f)}else p=this.getValuesFromTexture(t);return l&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(t,p)}async read(t){if(this.pendingRead.has(t)){let d=this.pendingRead.get(t);return new Promise(h=>d.push(h))}let e=this.texData.get(t),{values:n,shape:o,slice:s,dtype:i,complexTensorInfos:a,isPacked:u}=e;if(s!=null){let d;u?d=new Dn(o,Ia):d=new zr(o,Ia);let h=this.runWebGLProgram(d,[{dataId:t,shape:o,dtype:i}],i),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(n!=null)return this.convertAndCacheOnCPU(t);if(L().getBool(\"DEBUG\")&&!L().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")&&L().getNumber(\"WEBGL_VERSION\")===2)throw new Error(\"tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.\");let l=null,c;if(i!==\"complex64\"&&L().get(\"WEBGL_BUFFER_SUPPORTED\")){c=this.decode(t);let d=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(d.texture.texture,...sg(o))}this.pendingRead.set(t,[]),i!==\"complex64\"&&await this.gpgpu.createAndWaitForFence();let p;if(i===\"complex64\"){let d=await Promise.all([this.read(a.real.dataId),this.read(a.imag.dataId)]),h=d[0],g=d[1];p=S.mergeRealAndImagArrays(h,g)}else if(l==null)p=this.getValuesFromTexture(t);else{let d=y.sizeFromShape(o);p=this.gpgpu.downloadFloat32MatrixFromBuffer(l,d)}if(c!=null&&this.disposeIntermediateTensorInfo(c),l!=null){let d=this.gpgpu.gl;ht(d,()=>d.deleteBuffer(l))}let m=this.convertAndCacheOnCPU(t,p),f=this.pendingRead.get(t);return this.pendingRead.delete(t),f.forEach(d=>d(m)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t)&&Bn().removeDataId(t,this),this.pendingDeletes--),m}readToGPU(t,e={}){let n=this.texData.get(t),{values:o,shape:s,slice:i,dtype:a,isPacked:u,texture:l}=n;if(a===\"complex64\")throw new Error(\"Does not support reading texture for complex64 dtype.\");if(i!=null){let f;u?f=new Dn(s,Ia):f=new zr(s,Ia);let d=this.runWebGLProgram(f,[{dataId:t,shape:s,dtype:a}],a),h=this.readToGPU(d,e);return this.disposeIntermediateTensorInfo(d),h}if(l==null)throw o!=null?new Error(\"Data is not on GPU but on CPU.\"):new Error(\"There is no data on GPU or CPU.\");let c=this.decode(t,e.customTexShape),p=Bn().makeTensorFromTensorInfo(c),m=this.texData.get(c.dataId);return Object.assign({tensorRef:p},m.texture)}bufferSync(t){let e=this.readSync(t.dataId);if(t.dtype===\"string\")try{let n=e.map(o=>y.decodeString(o));return wt(t.shape,t.dtype,n)}catch(n){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return wt(t.shape,t.dtype,e)}checkNumericalProblems(t){if(t!=null)for(let e=0;e0}time(t){let e=this.activeTimers,n=[],o=!1;this.programTimersStack==null?(this.programTimersStack=n,o=!0):this.activeTimers.push(n),this.activeTimers=n,t();let s=y.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),i=y.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=e,o&&(this.programTimersStack=null);let a={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0){let u=await Promise.all(s);a.kernelMs=y.sum(u),a.getExtraProfileInfo=()=>u.map((l,c)=>({name:i[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(\", \")}else a.kernelMs={error:\"WebGL query timers are not supported in this environment.\"};return this.uploadWaitMs=0,this.downloadWaitMs=0,a})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(t){return L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?(this.gpgpu.endQuery(),t):(t.endMs=y.now(),t)}async getQueryTime(t){if(L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0)return this.gpgpu.waitForQueryAndGetTime(t);let e=t;return e.endMs-e.startMs}disposeData(t,e=!1){if(this.pendingDisposal.has(t))return!1;if(!this.texData.has(t))return!0;if(e?this.texData.get(t).refCount=0:this.texData.get(t).refCount--,!e&&this.texData.get(t).refCount>0)return!1;if(this.pendingRead.has(t))return this.pendingDisposal.add(t),this.pendingDeletes++,!1;this.releaseGPUData(t);let{complexTensorInfos:n}=this.texData.get(t);return n!=null&&(this.disposeData(n.real.dataId,e),this.disposeData(n.imag.dataId,e)),this.texData.delete(t),!0}releaseGPUData(t){let{texture:e,dtype:n,texShape:o,usage:s,isPacked:i,slice:a}=this.texData.get(t),u=a&&a.origDataId||t,l=this.dataRefCount.get(u);l>1?this.dataRefCount.set(u,l-1):(this.dataRefCount.delete(u),e!=null&&(this.numBytesInGPU-=this.computeBytes(o,n),this.textureManager.releaseTexture(e,o,s,i)));let c=this.texData.get(t);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture.texture}getDataInfo(t){return this.texData.get(t)}shouldExecuteOnCPU(t,e=jot){return L().getBool(\"WEBGL_CPU_FORWARD\")&&t.every(n=>this.texData.get(n.dataId).texture==null&&y.sizeFromShape(n.shape)0&&y.isString(n[0])){let s=n.map(i=>y.encodeString(i));o=this.write(s,t,e)}else o=this.write(n,t,e);return this.texData.get(o).usage=null,{dataId:o,shape:t,dtype:e}}makeOutput(t,e,n){return Bn().makeTensorFromTensorInfo(this.makeTensorInfo(t,e,n),this)}unpackTensor(t){let e=new sI(t.shape);return this.runWebGLProgram(e,[t],t.dtype)}packTensor(t){let e=new nI(t.shape);return this.runWebGLProgram(e,[t],t.dtype,null,!0)}packedReshape(t,e){let n=[Fl(t.shape),...Ol(t.shape)],o={dtype:t.dtype,shape:n,dataId:t.dataId},s=[Fl(e),...Ol(e)],i=new Ad(s,n),a=!0,u=[n],l=this.runWebGLProgram(i,[o],t.dtype,u,a);return{dataId:l.dataId,shape:e,dtype:l.dtype}}decode(t,e){let n=this.texData.get(t),{isPacked:o,shape:s,dtype:i}=n;if(e!=null){let m=y.sizeFromShape(s),f=e[0]*e[1]*4;y.assert(m<=f,()=>\"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.\")}let a=Id(s),u;o?u=new Hw(a):u=new Uw(a);let l=!0,c=[e!=null?e:sg(a)],p=this.runWebGLProgram(u,[{shape:a,dtype:i,dataId:t}],i,c,l,e);return{dtype:i,shape:s,dataId:p.dataId}}runWebGLProgram(t,e,n,o,s=!1,i){let a=this.makeTensorInfo(t.outputShape,n),u=this.texData.get(a.dataId);if(t.packedOutput&&(u.isPacked=!0),t.outPackingScheme===Wu.DENSE){let x=i!=null?i:sg(t.outputShape);u.texShape=x.map(b=>b*2)}if(t.outTexUsage!=null&&(u.usage=t.outTexUsage),y.sizeFromShape(a.shape)===0)return u.values=y.getTypedArrayFromDType(a.dtype,0),a;let l=[],c=e.map(x=>{if(x.dtype===\"complex64\")throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");let b=this.texData.get(x.dataId);if(b.texture==null){if(!t.packedInputs&&y.sizeFromShape(x.shape)<=L().getNumber(\"WEBGL_SIZE_UPLOAD_UNIFORM\"))return{shape:x.shape,texData:null,isUniform:!0,uniformValues:b.values};t.packedInputs&&(b.isPacked=!0,b.shape=x.shape)}if(this.uploadToGPU(x.dataId),!!b.isPacked!=!!t.packedInputs)x=b.isPacked?this.unpackTensor(x):this.packTensor(x),l.push(x),b=this.texData.get(x.dataId);else if(b.isPacked&&!Uu(b.shape,x.shape)){let w=x,I=x.shape;x.shape=b.shape,x=this.packedReshape(x,I),l.push(x),b=this.texData.get(x.dataId),w.shape=I}return{shape:x.shape,texData:b,isUniform:!1}});this.uploadToGPU(a.dataId);let p={shape:a.shape,texData:u,isUniform:!1},m=HL(t,c,p),f=this.getAndSaveBinary(m,()=>WL(this.gpgpu,t,c,p)),d=this.activeTimers!=null,h;d&&(h=this.startTimer()),L().get(\"ENGINE_COMPILE_ONLY\")||UL(this.gpgpu,f,c,p,o),l.forEach(x=>this.disposeIntermediateTensorInfo(x)),d&&(h=this.endTimer(h),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(h)}));let g=L().getNumber(\"WEBGL_FLUSH_THRESHOLD\");if(g>0){let x=y.now();x-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=x)}if(!L().getBool(\"WEBGL_LAZILY_UNPACK\")&&u.isPacked&&s===!1){let x=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),x}return a}compileAndRun(t,e,n,o,s=!1){return n=n||e[0].dtype,this.runWebGLProgram(t,e,n,o,s)}getAndSaveBinary(t,e){return t in this.binaryCache||(this.binaryCache[t]=e()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(L().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=B(()=>{if(!L().get(\"WEBGL_RENDER_FLOAT32_ENABLED\")){let t=L().getBool(\"DEBUG\");L().set(\"DEBUG\",!1);let e=this.abs(pt(1e-8)).dataSync()[0];if(L().set(\"DEBUG\",t),e>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?Hot:qot}uploadToGPU(t){let e=this.texData.get(t),{shape:n,dtype:o,values:s,texture:i,usage:a,isPacked:u}=e;if(i!=null)return;let l=this.activeTimers!=null,c;l&&(c=y.now());let p=e.texShape;if(p==null&&(p=r1(n,u),e.texShape=p),s!=null){let m=Id(n),f,d=p[1],h=p[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(u||!g)&&([d,h]=wa(p[0],p[1])),u?f=new jw(m,g):f=new ug(m,g);let x=g?[h,d]:p,b=this.makeTensorInfo(x,o),w=this.texData.get(b.dataId);g?w.usage=Jr.PIXELS:w.usage=Jr.UPLOAD,w.texShape=x,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),d,h,s);let I=[[h,d]],E=this.runWebGLProgram(f,[b],o,I,!0),A=this.texData.get(E.dataId);e.texShape=A.texShape,e.isPacked=A.isPacked,e.usage=A.usage,L().get(\"ENGINE_COMPILE_ONLY\")?this.disposeData(E.dataId):(e.texture=A.texture,e.values=null,this.texData.delete(E.dataId)),this.disposeIntermediateTensorInfo(b),l&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(p,a,o,u);e.texture=m}}convertAndCacheOnCPU(t,e){let n=this.texData.get(t),{dtype:o}=n;return e!=null&&(n.values=Zot(e,o)),n.values}acquireTexture(t,e,n,o){if(this.numBytesInGPU+=this.computeBytes(t,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(t,e,o)}computeBytes(t,e){return t[0]*t[1]*y.bytesPerElement(e)}checkCompileCompletion(){for(let[,t]of Object.entries(this.binaryCache))this.checkCompletion_(t)}async checkCompileCompletionAsync(){let t=[];if(this.gpgpu.parallelCompilationExtension){for(let[,e]of Object.entries(this.binaryCache))t.push(this.checkCompletionAsync_(e));return Promise.all(t)}else{for(let[,e]of Object.entries(this.binaryCache)){let n=new Promise(o=>{try{this.checkCompletion_(e),o(!0)}catch(s){throw s}});t.push(n)}return Promise.all(t)}}async checkCompletionAsync_(t){return this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(t):(await Ch(),this.checkCompletionAsync_(t))}checkCompletion_(t){if(this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(t.webGLProgram)),this.gpgpu.gl.getShaderParameter(t.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(Lw(t.source,this.gpgpu.gl.getShaderInfoLog(t.fragmentShader)),new Error(\"Failed to compile fragment shader.\")):new Error(\"Failed to link vertex and fragment shaders.\");return!0}getUniformLocations(){for(let t of Object.values(this.binaryCache)){this.gpgpu.buildVao(t.webGLProgram);let{variablesLocations:e,customUniformLocations:n,infLoc:o,nanLoc:s,outShapeLocation:i,outShapeStridesLocation:a,outTexShapeLocation:u}=u1(this.gpgpu,t.program,t.webGLProgram);t.variablesLocations=e,t.customUniformLocations=n,t.infLoc=o,t.nanLoc=s,t.outShapeLocation=i,t.outShapeStridesLocation=a,t.outTexShapeLocation=u}}createTensorFromGPUData(t,e,n){t.channels=t.channels||\"RGBA\";let{texture:o,height:s,width:i,channels:a}=t,u=Bn().backend;if(!u.gpgpu.gl.isTexture(o))throw new Error(\"The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.\");let l=u.writeTexture(o,e,n,s,i,a);return Bn().makeTensorFromDataId(l,e,n,u)}};Dd.nextDataId=0;function Zot(r,t){if(t===\"float32\"||t===\"complex64\")return r;if(t===\"int32\"||t===\"bool\"){let e=t===\"int32\"?new Int32Array(r.length):new Uint8Array(r.length);for(let n=0;nnew Dd,2);var T$e={forceHalfFloat:Qz};var $d=`\n if (isnan(a)) return a;\n if (isnan(b)) return b;\n`;var $n=class{constructor(t,e,n){this.variableNames=[\"A\",\"B\"],this.outputShape=S.assertAndGetBroadcastShape(e,n),this.enableShapeUniforms=he(this.outputShape.length),this.userCode=`\n float binaryOperation(float a, float b) {\n ${t}\n }\n\n void main() {\n float a = getAAtOutCoords();\n float b = getBAtOutCoords();\n setOutput(binaryOperation(a, b));\n }\n `}};var Xn=`\n result.r = isNaN.r ? NAN : result.r;\n result.g = isNaN.g ? NAN : result.g;\n result.b = isNaN.b ? NAN : result.b;\n result.a = isNaN.a ? NAN : result.a;\n`;var jn=class{constructor(t,e,n,o=!1){this.variableNames=[\"A\",\"B\"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=S.assertAndGetBroadcastShape(e,n);let s=this.outputShape.length;this.enableShapeUniforms=he(s);let i=\"\";if(o)if(s===0||y.sizeFromShape(this.outputShape)===1)i=`\n result.y = 0.;\n result.z = 0.;\n result.w = 0.;\n `;else if(i=`\n ${zt(s)} coords = getOutputCoords();\n `,s===1)this.enableShapeUniforms?i+=`\n result.y = (coords + 1) >= outShape ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `:i+=`\n result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `;else{let u=rr(\"coords\",s);this.enableShapeUniforms?i+=`\n bool nextRowOutOfBounds =\n (${u[s-2]} + 1) >= outShape[${s} - 2];\n bool nextColOutOfBounds =\n (${u[s-1]} + 1) >= outShape[${s} - 1];\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `:i+=`\n bool nextRowOutOfBounds =\n (${u[s-2]} + 1) >= ${this.outputShape[s-2]};\n bool nextColOutOfBounds =\n (${u[s-1]} + 1) >= ${this.outputShape[s-1]};\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `}this.userCode=`\n vec4 binaryOperation(vec4 a, vec4 b) {\n ${t}\n }\n\n void main() {\n vec4 a = getAAtOutCoords();\n vec4 b = getBAtOutCoords();\n\n vec4 result = binaryOperation(a, b);\n ${i}\n\n setOutput(result);\n }\n `}};function nr(r){let{inputs:t,backend:e}=r,{x:n}=t;return e.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var t3={kernelName:go,backendName:\"webgl\",kernelFunc:nr};function Rn(r){let{inputs:t,backend:e}=r,{real:n,imag:o}=t,s=e.makeTensorInfo(n.shape,\"complex64\"),i=e.texData.get(s.dataId),a=nr({inputs:{x:n},backend:e}),u=nr({inputs:{x:o},backend:e});return i.complexTensorInfos={real:a,imag:u},s}var e3={kernelName:Ap,backendName:\"webgl\",kernelFunc:Rn};var E1=\"return (a < 0.) ? b * a : a;\",A1=`\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function Jot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{alpha:s}=n,i=e.makeTensorInfo([],\"float32\",y.createScalarValue(s,\"float32\")),a=L().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new jn(A1,o.shape,i.shape):new $n(E1,o.shape,i.shape),u=e.runWebGLProgram(a,[o,i],\"float32\");return e.disposeIntermediateTensorInfo(i),u}var r3={kernelName:bs,backendName:\"webgl\",kernelFunc:Jot};var D1=\"return (a < 0.) ? b * a : a;\",$1=`\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function Qot(r){let{inputs:t,backend:e}=r,{x:n,alpha:o}=t,s=L().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new jn($1,n.shape,o.shape):new $n(D1,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],\"float32\")}var n3={kernelName:Os,backendName:\"webgl\",kernelFunc:Qot};var Po=\"if (isnan(x)) return x;\";function It({opSnippet:r,packedOpSnippet:t,cpuKernelImpl:e,dtype:n}){return({inputs:o,backend:s})=>{let{x:i}=o,a=s,u=n||i.dtype;if(a.shouldExecuteOnCPU([i])&&e!=null){let p=a.texData.get(i.dataId),m=e(p.values,u);return a.makeTensorInfo(i.shape,u,m)}let l=L().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")&&t!=null,c;return l?c=new Dn(i.shape,t):c=new zr(i.shape,r),a.runWebGLProgram(c,[i],u)}}function ce({opSnippet:r,packedOpSnippet:t,checkOutOfBounds:e=!1,supportsComplex:n=!1,cpuKernelImpl:o,dtype:s}){return({inputs:i,backend:a})=>{let{a:u,b:l}=i,c=a;if(n&&u.dtype===\"complex64\"){let d=c.texData.get(u.dataId),h=c.texData.get(l.dataId),[g,x]=[[d.complexTensorInfos.real,h.complexTensorInfos.real],[d.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(w=>{let[I,N]=w,E={dataId:I.dataId,dtype:I.dtype,shape:u.shape},A={dataId:N.dataId,dtype:N.dtype,shape:l.shape},D=new $n(r,u.shape,l.shape);return c.runWebGLProgram(D,[E,A],ur(I.dtype,N.dtype))}),b=Rn({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let p=s||ur(u.dtype,l.dtype);if((u.dtype===\"string\"||l.dtype===\"string\"||c.shouldExecuteOnCPU([u,l]))&&o!=null){let d=c.texData.get(u.dataId).values,h=c.texData.get(l.dataId).values,g=u.dtype===\"string\"?S.fromUint8ToStringArray(d):d,x=u.dtype===\"string\"?S.fromUint8ToStringArray(h):h,[b,w]=o(u.shape,l.shape,g,x,p),I=c.makeTensorInfo(w,p),N=c.texData.get(I.dataId);return N.values=b,I}let m=L().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")&&t!=null,f;return m?f=new jn(t,u.shape,l.shape,e):f=new $n(r,u.shape,l.shape),c.runWebGLProgram(f,[u,l],p)}}function Ml(r,t=!1){if(r===\"linear\")return t?Kz:Vz;if(r===\"relu\")return t?Xz:Wz;if(r===\"elu\")return t?jz:Gz;if(r===\"relu6\")return t?Yz:Uz;if(r===\"prelu\")return t?$1:D1;if(r===\"leakyrelu\")return t?A1:E1;if(r===\"sigmoid\")return t?Zz:Hz;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var Rd=class{constructor(t,e,n,o=!1,s=!1,i=!1,a=null,u=!1,l=!1){this.variableNames=[\"matrixA\",\"matrixB\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=he(this.outputShape.length);let c=o?t[1]:t[2],p=Math.ceil(c/2),m=o?\"i * 2, rc.y\":\"rc.y, i * 2\",f=s?\"rc.z, i * 2\":\"i * 2, rc.z\",d=o?[\"a.xxyy\",\"a.zzww\"]:[\"a.xxzz\",\"a.yyww\"],h=s?[\"b.xzxz\",\"b.ywyw\"]:[\"b.xyxy\",\"b.zwzw\"],g=\"\",x=\"\";a&&(u?g=`vec4 activation(vec4 a) {\n vec4 b = getPreluActivationWeightsAtOutCoords();\n ${a}\n }`:l?g=`vec4 activation(vec4 a) {\n vec4 b = getLeakyreluAlphaAtOutCoords();\n ${a}\n }`:g=`vec4 activation(vec4 x) {\n ${a}\n }`,x=\"result = activation(result);\");let b=i?\"result += getBiasAtOutCoords();\":\"\";i&&this.variableNames.push(\"bias\"),u&&this.variableNames.push(\"preluActivationWeights\"),l&&this.variableNames.push(\"leakyreluAlpha\");let w=\"rc.x\",I=\"rc.x\";t[0]`The new shape (${u}) has ${l} elements and the old shape (${o.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`);let c=i.texData.get(o.dataId);return c.isPacked&&!Uu(o.shape,u)&&!(c.texture!==null&&Uu(c.shape,u))?i3(o,u,i):(i.incRef(o.dataId),{dataId:o.dataId,shape:u,dtype:o.dtype})}var a3={kernelName:Gi,backendName:\"webgl\",kernelFunc:rt};var fg=class{constructor(t,e){this.variableNames=[\"x\"];let{windowSize:n,batchSize:o,inSize:s,outSize:i}=t;this.outputShape=[o,i];let a=Math.floor(n/4)*4,u=n%4,l=\"sumValue += dot(values, ones);\";if(e!=null){let p=1/e;l=`sumValue += dot(values * ${y.isInt(p)?p.toPrecision(2):p}, ones);`}let c=\"\";s%n>0&&(c=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return 0.0;\n }\n `),this.userCode=`\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n ${c}\n return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${n};\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${a}; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n ${l}\n }\n\n int inIdx = inOffset + ${a};\n if (${u===1}) {\n vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n ${l}\n } else if (${u===2}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1), 0.0, 0.0);\n\n ${l}\n } else if (${u===3}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2), 0.0);\n\n ${l}\n }\n setOutput(sumValue);\n }\n `}};var aI=class{constructor(t,e){this.variableNames=[\"x\"];let{windowSize:n,batchSize:o,inSize:s,outSize:i}=t;this.outputShape=[o,i];let a=\"0.0\",u=\"\";e===\"prod\"?a=\"1.0\":e===\"min\"?(a=\"1.0 / 1e-20\",u=\"min\"):e===\"max\"&&(a=\"-1.0 / 1e-20\",u=\"max\");let l=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), 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);\n\n ${m}\n } else if (${p===2}) {\n ${f} values = ${f}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n ${m}\n } else if (${p===3}) {\n ${f} values = ${f}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n ${m}\n }\n setOutput(${l});\n }\n `}};function est(r){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let e=t.length?t[t.length-1].outSize:r[1],n=S.computeOptimalWindowSize(e);t.push({inSize:e,windowSize:n,outSize:Math.ceil(e/n)})}return t}function Yn(r,t,e,n){let o=est(r.shape),s=r;for(let i=0;i6)throw Error(`Transpose for rank ${t} is not yet supported`);let e=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\",\"resRC.v\"],n=new Array(t);for(let o=0;o6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let o=zt(this.rank),s=T1(\"rc\",this.rank),i=new Array(this.rank);for(let c=0;c`Error in matMul: inner shapes (${p}) and (${m}) of Tensors with shapes ${r.shape} and ${t.shape} and transposeA=${e} and transposeB=${n} must match.`);let N=e?[x,p,f]:[x,f,p],E=n?[b,d,m]:[b,m,d],A=rt({inputs:{x:r},backend:o,attrs:{shape:N}}),D=rt({inputs:{x:t},backend:o,attrs:{shape:E}}),F=[A,D],M=Math.max(x,b),V=e?A.shape[1]:A.shape[2],G=s!=null,W=i!=null,q=u===\"leakyrelu\",H=u!=null?Ml(u,!0):null,j=G||W||q||H!=null,Y;if((f===1||d===1)&&V>F1&&j===!1){let et=A,nt=D;e&&(et=Pe({inputs:{x:A},backend:o,attrs:{perm:[0,2,1]}}),F.push(et)),n&&(nt=Pe({inputs:{x:D},backend:o,attrs:{perm:[0,2,1]}}),F.push(nt));let st=d!==1,lt=d===1,ot=et;st&&(ot=rt({inputs:{x:et},backend:o,attrs:{shape:[M,V,1]}}),F.push(ot));let it=d===1?2:1,ft=nt;lt&&(ft=rt({inputs:{x:nt},backend:o,attrs:{shape:[M,1,V]}}),F.push(ft));let gt=mg({inputs:{a:ot,b:ft},backend:o});Y=fp({inputs:{x:gt},backend:o,attrs:{axis:it,keepDims:!0}}),F.push(gt)}else{let et=ur(r.dtype,t.dtype),nt=new Rd(N,E,[M,f,d],e,n,G,H,W,q),st=[A,D];if(s!=null&&st.push(s),W&&st.push(i),q){let lt=o.makeTensorInfo([],\"float32\",y.createScalarValue(a,\"float32\"));st.push(lt),F.push(lt)}Y=o.runWebGLProgram(nt,st,et)}let Z=rt({inputs:{x:Y},backend:o,attrs:{shape:I}});F.push(Y);for(let et of F)o.disposeIntermediateTensorInfo(et);return Z}function nst(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s,bias:i,preluActivationWeights:a}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=n;return dp({a:o,b:s,transposeA:u,transposeB:l,backend:e,bias:i,preluActivationWeights:a,leakyreluAlpha:p,activation:c})}var p3={kernelName:Xi,backendName:\"webgl\",kernelFunc:nst};var m3=\"return abs(x);\";function ost(r){let{inputs:t,backend:e}=r,{x:n}=t;if(e.shouldExecuteOnCPU([n])&&n.dtype!==\"complex64\"){let s=e.texData.get(n.dataId),i=eI(s.values);return e.makeTensorInfo(n.shape,n.dtype,i)}let o;return L().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")?o=new Dn(n.shape,m3):o=new zr(n.shape,m3),e.runWebGLProgram(o,[n],n.dtype)}var f3={kernelName:Ai,backendName:\"webgl\",kernelFunc:ost};var sst=xr+`\n if (abs(x) > 1.) {\n return NAN;\n }\n return acos(x);\n`,ist=It({opSnippet:sst}),d3={kernelName:Go,backendName:\"webgl\",kernelFunc:ist};var ast=xr+`\n if (x < 1.0) return NAN;\nreturn log(x + sqrt(x * x - 1.0));`,lst=It({opSnippet:ast}),h3={kernelName:Wo,backendName:\"webgl\",kernelFunc:lst};var g3=\"return a + b;\",ust=ce({opSnippet:g3,packedOpSnippet:g3,supportsComplex:!0,cpuKernelImpl:qL}),x3={kernelName:no,backendName:\"webgl\",kernelFunc:ust};var cI=class{constructor(t,e){this.outputShape=[],this.outputShape=t,this.variableNames=e.map((s,i)=>`T${i}`);let n=[];this.variableNames.forEach(s=>{n.push(`float v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(\" + \");this.userCode=`\n void main() {\n ${n.join(`\n `)}\n\n float result = ${o};\n setOutput(result);\n }\n `}};var pI=class{constructor(t,e){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.variableNames=e.map((s,i)=>`T${i}`);let n=[];this.variableNames.forEach(s=>{n.push(`vec4 v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(\" + \");this.userCode=`\n void main() {\n ${n.join(`\n `)}\n\n vec4 result = ${o};\n setOutput(result);\n }\n `}};function mI(r){let{inputs:t,backend:e}=r,n=t;if(n.length===1)return nr({inputs:{x:n[0]},backend:e});if(n.length>L().getNumber(\"WEBGL_MAX_TEXTURES_IN_SHADER\")){let u=Math.floor(n.length/2),l=mI({inputs:n.slice(0,u),backend:e}),c=mI({inputs:n.slice(u),backend:e});return mI({inputs:[l,c],backend:e})}let o=n.map(u=>u.dtype).reduce((u,l)=>ur(u,l)),s=n.map(u=>u.shape),a=L().getBool(\"WEBGL_PACK\")?new pI(n[0].shape,s):new cI(n[0].shape,s);return e.runWebGLProgram(a,n,o)}var y3={kernelName:Uo,backendName:\"webgl\",kernelFunc:mI};function cst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=o;c!=null&&(p=Pe({inputs:{x:o},backend:e,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,a)),S.assertAxesAreInnerMostDims(\"all\",l,a);let[m,f]=S.computeOutAndReduceShapes(p.shape,l),d=y.sizeFromShape(f),h=rt({inputs:{x:p},backend:e,attrs:{shape:[-1,d]}}),g=Yn(h,h.dtype,\"all\",e),x;if(i){let b=S.expandShapeToKeepDim(m,u);x=rt({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=rt({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(p),x}var b3={kernelName:Ea,backendName:\"webgl\",kernelFunc:cst};function pst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=o;c!=null&&(p=Pe({inputs:{x:o},backend:e,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,a)),S.assertAxesAreInnerMostDims(\"any\",l,a);let[m,f]=S.computeOutAndReduceShapes(p.shape,l),d=y.sizeFromShape(f),h=rt({inputs:{x:p},backend:e,attrs:{shape:[-1,d]}}),g=Yn(h,h.dtype,\"any\",e),x;if(i){let b=S.expandShapeToKeepDim(m,u);x=rt({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=rt({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(p),x}var w3={kernelName:Aa,backendName:\"webgl\",kernelFunc:pst};var fI=class{constructor(t,e,n){this.variableNames=[\"A\"];let{windowSize:o,batchSize:s,outSize:i}=t;n||this.variableNames.push(\"bestIndicesA\"),this.outputShape=[s,i];let a=e===\"max\"?\">\":\"<\",u=n?\"inOffset + i;\":\"round(getBestIndicesA(batch, inOffset + i));\";this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${o};\n\n int bestIndex = inOffset;\n float bestValue = getA(batch, bestIndex);\n\n for (int i = 0; i < ${o}; i++) {\n int inIdx = ${u};\n float candidate = getA(batch, inIdx);\n if (candidate ${a} bestValue) {\n bestValue = candidate;\n bestIndex = inIdx;\n }\n }\n setOutput(float(bestIndex));\n }\n `}};var dI=class{constructor(t,e,n,o){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,y.assert(t.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let s=t[t.length-1],i=Math.ceil(s/e);this.outputShape=t.slice(0,-1),i>1&&this.outputShape.push(i),o||this.variableNames.push(\"bestIndicesA\");let a=this.outputShape,u=a.length,l=zt(u),c=rr(\"coords\",u),p,m;if(i===1){m=u+1;let D=zt(m);p=`\n ${D} sourceLocR = ${D}(${c.join()}, 0);\n ++${c[u-1]};\n ${D} sourceLocG = ${D}(${c.join()}, 0);\n ++${c[u-2]};\n ${D} sourceLocA = ${D}(${c.join()}, 0);\n --${c[u-1]};\n ${D} sourceLocB = ${D}(${c.join()}, 0);\n --${c[u-2]};`}else m=u,p=`\n ${l} sourceLocR = coords;\n ++${c[u-1]};\n ${l} sourceLocG = coords;\n ++${c[u-2]};\n ${l} sourceLocA = coords;\n --${c[u-1]};\n ${l} sourceLocB = coords;\n --${c[u-2]};`;let f=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,m),d=\".\"+f[m-1],h=f.map(D=>\"int \"+D),g=rr(\"sourceLocR\",m-1).concat(\"inIdx.r\"),x=rr(\"sourceLocG\",m-1).concat(\"inIdx.g\"),b=rr(\"sourceLocB\",m-1).concat(\"inIdx.b\"),w=rr(\"sourceLocA\",m-1).concat(\"inIdx.a\"),I=n===\"max\"?\"greaterThan\":\"lessThan\",N=o?\"\":`\n inIdx = round(vec4(getBestIndicesAChannel(${g.join()}),\n getBestIndicesAChannel(${x.join()}),\n getBestIndicesAChannel(${b.join()}),\n getBestIndicesAChannel(${w.join()})));`,E=`vec4(\n getAChannel(${g.join()}),\n hasNextCol ? getAChannel(${x.join()}) : 0.,\n hasNextRow ? getAChannel(${b.join()}) : 0.,\n hasNextRow && hasNextCol ? getAChannel(${w.join()}) : 0.)`,A=o?\"\":`\n float getBestIndicesAChannel(${h.join()}) {\n return getChannel(getBestIndicesA(${f.join()}),\n vec2(${f.slice(-2).join()}));\n }`;this.userCode=`\n float getAChannel(${h.join()}) {\n return getChannel(getA(${f.join()}),\n vec2(${f.slice(-2).join()}));\n }\n ${A}\n void main() {\n ${l} coords = getOutputCoords();\n bool hasNextCol = ${c[u-1]} < ${a[u-1]-1};\n bool hasNextRow = ${c[u-2]} < ${a[u-2]-1};\n ${p}\n ivec4 srcIdx = ivec4(sourceLocR${d}, sourceLocG${d},\n sourceLocB${d}, sourceLocA${d}) * ${e};\n ivec4 inIdx = srcIdx;\n vec4 bestIndex = vec4(inIdx);\n vec4 bestValue = ${E};\n\n for (int i = 0; i < ${e}; i++) {\n inIdx = srcIdx;\n ${N}\n vec4 candidate = ${E};\n bvec4 nan = isnan(candidate);\n bvec4 replace = bvec4(\n vec4(${I}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n bestValue = vec4(replace.x ? candidate.x : bestValue.x,\n replace.y ? candidate.y : bestValue.y,\n replace.z ? candidate.z : bestValue.z,\n replace.w ? candidate.w : bestValue.w);\n bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n srcIdx++;\n }\n setOutput(bestIndex);\n }\n `}};function I3(r,t,e,n=null){let o=t.shape[0],s=t.shape[1];n!=null&&(o=n.shape[0],s=n.shape[1]);let i=S.computeOptimalWindowSize(s),a={windowSize:i,inSize:s,batchSize:o,outSize:Math.ceil(s/i)},u=new fI(a,e,n==null),l=[t];n!=null&&l.push(n);let c=r.runWebGLProgram(u,l,\"int32\");if(c.shape[1]===1)return c;let p=I3(r,t,e,c);return r.disposeIntermediateTensorInfo(c),p}function C3(r,t,e,n=null){let o=n!=null?n.shape:t.shape,s=o[o.length-1],i=S.computeOptimalWindowSize(s),a=new dI(o,i,e,n==null),u=n==null?[t]:[t,n],l=r.runWebGLProgram(a,u,\"int32\");if(l.shape.length===t.shape.length){let c=C3(r,t,e,l);return r.disposeIntermediateTensorInfo(l),c}return l}function hI(r,t,e,n){let o=[e];if(S.assertAxesAreInnerMostDims(\"arg\"+n.charAt(0).toUpperCase()+n.slice(1),o,t.shape.length),!L().getBool(\"WEBGL_PACK_REDUCE\")||t.shape.length<=2){let s=[],i=r.texData.get(t.dataId),a=i!==null&&i.isPacked,u=t;a&&(u=r.unpackTensor(t),s.push(u));let[l,c]=S.computeOutAndReduceShapes(u.shape,o),p=y.sizeFromShape(c),m=rt({inputs:{x:u},backend:r,attrs:{shape:[-1,p]}});s.push(m);let f=I3(r,m,n);s.push(f);let d=rt({inputs:{x:f},backend:r,attrs:{shape:l}});return s.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}return C3(r,t,n)}function mst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s}=n,i=y.parseAxisParam(s,o.shape),a=S.getAxesPermutation(i,o.shape.length),u=o,l=[];a!=null&&(u=Pe({inputs:{x:o},backend:e,attrs:{perm:a}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims(\"argMax\",[i[0]],u.shape.length);let c=hI(e,u,i[0],\"max\");return l.forEach(p=>e.disposeIntermediateTensorInfo(p)),c}var v3={kernelName:Di,backendName:\"webgl\",kernelFunc:mst};function fst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s}=n,i=y.parseAxisParam(s,o.shape),a=S.getAxesPermutation(i,o.shape.length),u=o,l=[];a!=null&&(u=Pe({inputs:{x:o},backend:e,attrs:{perm:a}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims(\"argMin\",[i[0]],u.shape.length);let c=hI(e,u,i[0],\"min\");return l.forEach(p=>e.disposeIntermediateTensorInfo(p)),c}var S3={kernelName:$i,backendName:\"webgl\",kernelFunc:fst};var dst=xr+`\n if (abs(x) > 1.) {\n return NAN;\n }\n return asin(x);\n`,hst=It({opSnippet:dst}),N3={kernelName:Ho,backendName:\"webgl\",kernelFunc:hst};var gst=xr+\"return log(x + sqrt(x * x + 1.0));\",xst=It({opSnippet:gst}),k3={kernelName:qo,backendName:\"webgl\",kernelFunc:xst};var yst=xr+`\n return atan(x);\n`,bst=It({opSnippet:yst}),T3={kernelName:Ko,backendName:\"webgl\",kernelFunc:bst};var wst=$d+`\n return atan(a, b);\n`,Ist=`\n vec4 result = atan(a, b);\n bvec4 isNaNA = isnan(a);\n bvec4 isNaNB = isnan(b);\n bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);\n `+Xn+`\n return result;\n`,Cst=ce({opSnippet:wst,packedOpSnippet:Ist}),_3={kernelName:Xo,backendName:\"webgl\",kernelFunc:Cst};var vst=xr+`\n if ((x < -1.0) || (x > 1.0)) return NAN;\nreturn (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sst=It({opSnippet:vst}),E3={kernelName:jo,backendName:\"webgl\",kernelFunc:Sst};var Ni=class{constructor(t,e,n,o=!1,s=!1){if(this.variableNames=[\"x\"],e===\"avg\"&&n)throw new Error(\"Cannot compute positions for average pool.\");let i=t.filterWidth,a=t.strideHeight,u=t.strideWidth,l=t.dilationHeight,c=t.dilationWidth,p=t.effectiveFilterHeight,m=t.effectiveFilterWidth,f=t.padInfo.top,d=t.padInfo.left;this.outputShape=t.outShape;let h=e===\"avg\",g=`((batch * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + d`,x=`(xR * ${t.inWidth} + xC) * ${t.inChannels} + d`,b=\"0.0\";if(h||(b=\"-1.0 / 1e-20\"),n){let D=\">=\";this.userCode=`\n const ivec2 strides = ivec2(${a}, ${u});\n const ivec2 pads = ivec2(${f}, ${d});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n float avgValue = 0.0;\n\n for (int wR = 0; wR < ${p};\n wR += ${l}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${m};\n wC += ${c}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xR, xC, d);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${D} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${o?s?g:x:`wR * ${m} + wC`};\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `;return}let w=\"max\",I=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e===\"avg\"&&(I=\"avgValue / max(count, 1.0)\");let N=Math.floor(i/4)*4,E=i%4,A=`\n if (${h}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = ${w}(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec2 strides = ivec2(${a}, ${u});\n const ivec2 pads = ivec2(${f}, ${d});\n const float initializationValue = ${b};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xR, int xC, int d) {\n if (xC < 0 || xC >= ${t.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xR, xC, d);\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n vec4 minMaxValue = vec4(${b});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wR = 0; wR < ${p};\n wR += ${l}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${N}; wC += 4) {\n int xC = xCCorner + wC * ${c};\n\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n getValue(batch, xR, xC + 2 * ${c}, d),\n getValue(batch, xR, xC + 3 * ${c}, d)\n );\n\n ${A}\n }\n\n int xC = xCCorner + ${N};\n if (${E===1}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${A}\n } else if (${E===2}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n initializationValue,\n initializationValue\n );\n\n ${A}\n } else if (${E===3}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n getValue(batch, xR, xC + 2 * ${c}, d),\n initializationValue\n );\n\n ${A}\n }\n }\n setOutput(${I});\n }\n `}},qu=class{constructor(t,e,n,o=!1,s=!1){if(this.variableNames=[\"x\"],e===\"avg\"&&n)throw new Error(\"Cannot compute positions for average pool.\");let i=t.filterWidth,a=t.strideDepth,u=t.strideHeight,l=t.strideWidth,c=t.dilationDepth,p=t.dilationHeight,m=t.dilationWidth,f=t.effectiveFilterDepth,d=t.effectiveFilterHeight,h=t.effectiveFilterWidth,g=t.padInfo.front,x=t.padInfo.top,b=t.padInfo.left;this.outputShape=t.outShape;let w=e===\"avg\",I=\"0.0\";if(w||(I=\"-1.0 / 1e-20\"),n){let M=\">=\";this.userCode=`\n const ivec3 strides =\n ivec3(${a}, ${u}, ${l});\n const ivec3 pads = ivec3(${g}, ${x}, ${b});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n\n for (int wD = 0; wD < ${f};\n wD += ${c}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${t.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${d};\n wR += ${p}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${h};\n wC += ${m}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xD, xR, xC, ch);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${M} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${o?s?`(((batch * ${t.inDepth} + xD) * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + ch`:`((xD * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + ch`:`wD * ${d} * ${h} +\n wR * ${h} + wC`};\n }\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `;return}let N=\"max\",E=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e===\"avg\"&&(E=\"avgValue / max(count, 1.0)\");let A=Math.floor(i/4)*4,D=i%4,F=`\n if (${w}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = ${N}(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec3 strides =\n ivec3(${a}, ${u}, ${l});\n const ivec3 pads = ivec3(${g}, ${x}, ${b});\n const float initializationValue = ${I};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xD, int xR, int xC, int ch) {\n if (xC < 0 || xC >= ${t.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xD, xR, xC, ch);\n }\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n // ? = to be determined\n vec4 minMaxValue = vec4(${I});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wD = 0; wD < ${f};\n wD += ${c}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${t.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${d};\n wR += ${p}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${A}; wC += 4) {\n int xC = xCCorner + wC * ${m};\n\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n getValue(batch, xD, xR, xC + 2 * ${m}, ch),\n getValue(batch, xD, xR, xC + 3 * ${m}, ch)\n );\n\n ${F}\n }\n\n int xC = xCCorner + ${A};\n if (${D===1}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${F}\n } else if (${D===2}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n initializationValue,\n initializationValue\n );\n\n ${F}\n } else if (${D===3}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n getValue(batch, xD, xR, xC + 2 * ${m}, ch),\n initializationValue\n );\n\n ${F}\n }\n }\n }\n setOutput(${E});\n }\n `}};function Nst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;vi(o,\"avgPool\");let{filterSize:s,strides:i,pad:a,dimRoundingMode:u}=n,l=1;y.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,l,a,u);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return nr({inputs:{x:o},backend:e});let p=new Ni(c,\"avg\",!1);return e.runWebGLProgram(p,[o],\"float32\")}var A3={kernelName:Yo,backendName:\"webgl\",kernelFunc:Nst};function kst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dimRoundingMode:u,dataFormat:l}=n,c=[1,1,1],p=S.computePool3DInfo(o.shape,s,i,c,a,u,l),m=new qu(p,\"avg\",!1);return e.runWebGLProgram(m,[o],\"float32\")}var D3={kernelName:Ri,backendName:\"webgl\",kernelFunc:kst};var gI=class{constructor(t){this.variableNames=[\"dy\"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=t.dilationHeight,a=t.dilationWidth,u=t.effectiveFilterHeight,l=t.effectiveFilterWidth,c=u-1-t.padInfo.top,p=l-1-t.padInfo.left,m=1/(e*n);this.userCode=`\n const ivec2 pads = ivec2(${c}, ${p});\n const float avgMultiplier = float(${m});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${u};\n wR += ${i}) {\n float dyR = float(dyRCorner + wR) / ${o}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${l};\n wC+= ${a}) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n setOutput(dotProd);\n }\n `}},xI=class{constructor(t){this.variableNames=[\"dy\"],this.outputShape=t.inShape;let e=t.filterDepth,n=t.filterHeight,o=t.filterWidth,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,c=t.dilationWidth,p=t.effectiveFilterDepth,m=t.effectiveFilterHeight,f=t.effectiveFilterWidth,d=p-1-t.padInfo.front,h=m-1-t.padInfo.top,g=f-1-t.padInfo.left,x=1/(e*n*o);this.userCode=`\n const ivec3 pads = ivec3(${d}, ${h}, ${g});\n const float avgMultiplier = float(${x});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${p};\n wD += ${u}) {\n float dyD = float(dyDCorner + wD) / ${s}.0;\n\n if (dyD < 0.0 || dyD >= ${t.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${m};\n wR += ${l}) {\n float dyR = float(dyRCorner + wR) / ${i}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${f};\n wC += ${c}) {\n float dyC = float(dyCCorner + wC) / ${a}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function Tst(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s,{filterSize:a,strides:u,pad:l,dimRoundingMode:c}=n,p=[1,1,1],m=S.computePool3DInfo(i.shape,a,u,p,l,c),f=new xI(m);return e.runWebGLProgram(f,[o],i.dtype)}var $3={kernelName:Hl,backendName:\"webgl\",kernelFunc:Tst};function _st(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s;vi([o,s],\"avgPoolGrad\");let{filterSize:a,strides:u,pad:l}=n,c=S.computePool2DInfo(i.shape,a,u,1,l),p=new gI(c);return e.runWebGLProgram(p,[o],i.dtype)}var R3={kernelName:Ul,backendName:\"webgl\",kernelFunc:_st};function Est(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s}=t,{transposeA:i,transposeB:a}=n;return dp({a:o,b:s,transposeA:i,transposeB:a,backend:e})}var F3={kernelName:Zo,backendName:\"webgl\",kernelFunc:Est};var yI=class{constructor(t,e,n,o,s,i){this.outputShape=[],this.variableNames=[\"x\",\"mean\",\"variance\"],S.assertAndGetBroadcastShape(t,e),S.assertAndGetBroadcastShape(t,n);let a=\"0.0\";o!=null&&(S.assertAndGetBroadcastShape(t,o),this.variableNames.push(\"offset\"),a=\"getOffsetAtOutCoords()\");let u=\"1.0\";s!=null&&(S.assertAndGetBroadcastShape(t,s),this.variableNames.push(\"scale\"),u=\"getScaleAtOutCoords()\"),this.outputShape=t,this.userCode=`\n void main() {\n float x = getXAtOutCoords();\n float mean = getMeanAtOutCoords();\n float variance = getVarianceAtOutCoords();\n float offset = ${a};\n float scale = ${u};\n float inv = scale * inversesqrt(variance + float(${i}));\n setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n }\n `}};var bI=class{constructor(t,e,n,o,s,i){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=[\"x\",\"mean\",\"variance\"],S.assertAndGetBroadcastShape(t,e),S.assertAndGetBroadcastShape(t,n);let a=\"vec4(0.0)\";o!=null&&(S.assertAndGetBroadcastShape(t,o),this.variableNames.push(\"offset\"),a=\"getOffsetAtOutCoords()\");let u=\"vec4(1.0)\";s!=null&&(S.assertAndGetBroadcastShape(t,s),this.variableNames.push(\"scale\"),u=\"getScaleAtOutCoords()\"),this.outputShape=t,this.userCode=`\n void main() {\n vec4 offset = ${a};\n vec4 scale = ${u};\n\n vec4 x = getXAtOutCoords();\n vec4 mean = getMeanAtOutCoords();\n vec4 variance = getVarianceAtOutCoords();\n\n vec4 inv = scale * inversesqrt(variance + vec4(${i}));\n\n setOutput((x - mean) * inv + offset);\n }\n `}};var Ast=({inputs:r,backend:t,attrs:e})=>{let{x:n,mean:o,variance:s,offset:i,scale:a}=r;y.assert(o.shape.length===s.shape.length,()=>\"Batch normalization gradient requires mean and variance to have equal ranks.\"),y.assert(i==null||o.shape.length===i.shape.length,()=>\"Batch normalization gradient requires mean and offset to have equal ranks.\"),y.assert(a==null||o.shape.length===a.shape.length,()=>\"Batch normalization gradient requires mean and scale to have equal ranks.\");let{varianceEpsilon:u}=e;u==null&&(u=.001);let l=[n,o,s],c=null;i!=null&&(c=i.shape,l.push(i));let p=null;a!=null&&(p=a.shape,l.push(a));let m=L().getBool(\"WEBGL_PACK_NORMALIZATION\")?new bI(n.shape,o.shape,s.shape,c,p,u):new yI(n.shape,o.shape,s.shape,c,p,u);return t.runWebGLProgram(m,l,l[0].dtype)},O3={kernelName:ds,backendName:\"webgl\",kernelFunc:Ast};var wI=class{constructor(t){this.variableNames=[\"source\"],this.outputShape=t,this.rank=t.length;let e=zt(this.rank);this.customUniforms=[{name:\"start\",arrayIndex:this.rank,type:\"int\"}];let n=Dst(this.rank),o,s=t.map((i,a)=>`sourceLoc.${O1[a]} = start[${a}] + coords.${O1[a]};`);o=`\n ${e} sourceLoc;\n ${e} coords = getOutputCoords();\n ${s.join(`\n`)}\n `,this.userCode=`\n void main() {\n ${o}\n setOutput(getSource(${n}));\n }\n `}},O1=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];function Dst(r){if(r===1)return\"sourceLoc\";if(r<=6)return O1.slice(0,r).map(t=>\"sourceLoc.\"+t).join(\",\");throw Error(`Slicing for rank ${r} is not yet supported`)}var II=class{constructor(t){this.variableNames=[\"source\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.rank=t.length,this.customUniforms=[{name:\"start\",arrayIndex:this.rank,type:\"int\"}];let e=zt(this.rank),n=rr(\"coords\",this.rank),o=rr(\"sourceLoc\",this.rank),s=this.rank===1?\"sourceLoc\":`vec2(${o.slice(-2).join()})`,i=`getChannel(getSource(${o.join()}), ${s})`,a=`\n result.x = ${i};\n if (++${n[this.rank-1]} < ${t[this.rank-1]}) {\n ++${o[this.rank-1]};\n result.y = ${i};\n --${o[this.rank-1]};\n }\n `,u=this.rank===1?\"\":`\n --${n[this.rank-1]};\n if (++${n[this.rank-2]} < ${t[this.rank-2]}) {\n ++${o[this.rank-2]};\n result.z = ${i};\n if (++${n[this.rank-1]} < ${t[this.rank-1]}) {\n ++${o[this.rank-1]};\n result.w = ${i};\n }\n }\n `,l=this.rank<=4?`sourceLoc = coords +\n ${e}(${t.map((c,p)=>`start[${p}]`).join()});`:t.map((c,p)=>`${o[p]} = ${n[p]} + start[${p}];`).join(`\n`);this.userCode=`\n void main() {\n ${e} coords = getOutputCoords();\n ${e} sourceLoc;\n ${l}\n vec4 result = vec4(0.);\n ${a}\n ${u}\n setOutput(result);\n }\n `}};function $st(r,t,e,n){let o=n.texData.get(r.dataId),s=n.makeTensorInfo(e,r.dtype),i=n.texData.get(s.dataId);Object.assign(i,o),i.refCount=1,i.shape=e,i.dtype=r.dtype;let a=Be.computeFlatOffset(t,y.computeStrides(r.shape));o.slice&&(a+=o.slice.flatOffset),i.slice={flatOffset:a,origDataId:o.slice&&o.slice.origDataId||r.dataId};let u=n.dataRefCount.get(i.slice.origDataId)||1;return n.dataRefCount.set(i.slice.origDataId,u+1),s}function ki(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{begin:s,size:i}=n,[a,u]=Be.parseSliceParams(o,s,i);if(Be.assertParamsValid(o,a,u),y.sizeFromShape(u)===0)return e.makeTensorInfo(u,o.dtype,[]);if(e.shouldExecuteOnCPU([o])||o.dtype===\"string\"){let p=e.texData.get(o.dataId),m=Sz(p.values,a,u,o.shape,o.dtype);return e.makeTensorInfo(u,o.dtype,m)}let{isPacked:l}=e.texData.get(o.dataId),c=Be.isSliceContinous(o.shape,a,u);if(l||!c){let p=L().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new II(u):new wI(u),m=[a];return e.runWebGLProgram(p,[o],o.dtype,m)}return e.uploadToGPU(o.dataId),$st(o,a,u,e)}var M3={kernelName:Ui,backendName:\"webgl\",kernelFunc:ki};var Rst=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,crops:i}=n;y.assert(o.shape.length<=4,()=>\"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet\");let a=s.reduce((b,w)=>b*w),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=[],d=rt({inputs:{x:o},backend:e,attrs:{shape:u}}),h=Pe({inputs:{x:d},backend:e,attrs:{perm:l}}),g=rt({inputs:{x:h},backend:e,attrs:{shape:c}}),x=ki({inputs:{x:g},backend:e,attrs:{begin:p,size:m}});return f.push(d),f.push(h),f.push(g),f.forEach(b=>e.disposeIntermediateTensorInfo(b)),x},P3={kernelName:Fi,backendName:\"webgl\",kernelFunc:Rst};function Fst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i}=n,a=e.readSync(o.dataId),u=e.readSync(s.dataId),l=tI(a,u,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,l)}var L3={kernelName:Da,backendName:\"webgl\",kernelFunc:Fst};var Ost=`\n int r = int(a.r) & int(b.r);\n int g = int(a.g) & int(b.g);\n int rb = int(a.b) & int(b.b);\n int ra = int(a.a) & int(b.a);\n return vec4(r, g, rb, ra);\n`,Mst=`\n return float(int(a.r) & int(b.r));\n`;function Pst(r){let{inputs:t,backend:e}=r,{a:n,b:o}=t,s=L().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\"),i=L().getNumber(\"WEBGL_VERSION\");if(e.shouldExecuteOnCPU([n,o])||i===1){let u=e.texData.get(n.dataId).values,l=e.texData.get(o.dataId).values,[c,p]=jL(n.shape,o.shape,u,l,n.dtype),m=e.makeTensorInfo(p,n.dtype),f=e.texData.get(m.dataId);return f.values=c,m}let a;return s?a=new jn(Ost,n.shape,o.shape,!1):a=new $n(Mst,n.shape,o.shape),e.runWebGLProgram(a,[n,o],n.dtype)}var z3={kernelName:$a,backendName:\"webgl\",kernelFunc:Pst};function Lst(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.readSync(n.dataId),i=e.readSync(o.dataId),a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeTensorInfo([a.length],\"int32\",Int32Array.from(a))}var B3={kernelName:ql,backendName:\"webgl\",kernelFunc:Lst};var zst=\"return float(a != b);\",M1=ce({opSnippet:zst,cpuKernelImpl:hz,dtype:\"bool\"}),V3={kernelName:Za,backendName:\"webgl\",kernelFunc:M1};function 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i=e.texData.get(o.dataId).values,[a,u,l]=XL(i,o.shape,o.dtype,s);return e.makeTensorInfo(a,u,l)}if(s===\"int32\")return W3(o,e);if(s===\"bool\"){let i=e.makeTensorInfo([],\"bool\",y.getTypedArrayFromDType(\"bool\",1)),u=M1({inputs:{a:o,b:i},backend:e});return e.disposeIntermediateTensorInfo(i),u}throw new Error(`Error in Cast: failed to cast ${o.dtype} to ${s}`)}var U3={kernelName:fo,backendName:\"webgl\",kernelFunc:P1};var H3=\"return ceil(x);\",Vst=It({opSnippet:H3,packedOpSnippet:H3,cpuKernelImpl:YL}),q3={kernelName:Jo,backendName:\"webgl\",kernelFunc:Vst};var CI=class{constructor(t){this.variableNames=[\"A\"],this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=t,this.userCode=`\n\n void main() {\n float value = getAAtOutCoords();\n if (isnan(value)) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, minVal, maxVal));\n }\n `}};var vI=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=t,this.userCode=`\n void main() {\n vec4 value = getAAtOutCoords();\n\n if (any(isnan(value))) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n }\n `}};function Gst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{clipValueMin:s,clipValueMax:i}=n,a;L().getBool(\"WEBGL_PACK_CLIP\")?a=new vI(o.shape):a=new CI(o.shape);let u=[[s],[i]];return e.runWebGLProgram(a,[o],o.dtype,u)}var K3={kernelName:ho,backendName:\"webgl\",kernelFunc:Gst};var SI=class{constructor(t){this.variableNames=[\"real\",\"imag\"],this.outputShape=t,this.userCode=`\n void main() {\n float re = abs(getRealAtOutCoords());\n float im = abs(getImagAtOutCoords());\n float mx = max(re, im);\n\n // sadly the length function in glsl is not underflow-safe\n // (at least not on Intel GPUs). So the safe solution is\n // to ensure underflow-safety in all cases.\n setOutput(\n mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n );\n }\n `}};function j3(r,t){return{dataId:t.dataId,dtype:t.dtype,shape:r.shape}}function Wst(r){let{inputs:t,backend:e}=r,{x:n}=t,o=e.texData.get(n.dataId),s=new SI(n.shape),i=[j3(n,o.complexTensorInfos.real),j3(n,o.complexTensorInfos.imag)];return e.runWebGLProgram(s,i,i[0].dtype)}var X3={kernelName:Kl,backendName:\"webgl\",kernelFunc:Wst};var NI=class{constructor(t){this.outputShape=[],this.outputShape=S.computeOutShape(t,1),this.variableNames=t.map((i,a)=>`T${a}`);let e=new Array(t.length-1);e[0]=t[0][1];for(let i=1;i`T${g}`);let u=new Array(t.length-1);u[0]=t[0][e];for(let h=1;h= ${u[h-1]}) {\n return getChannel(\n getT${h}(${kI(a,l,g)}),\n vec2(${kI(c,l,g)}));\n }`}let f=u.length,d=u[u.length-1];m+=`\n return getChannel(\n getT${f}(${kI(a,l,d)}),\n vec2(${kI(c,l,d)}));`,this.userCode=`\n float getValue(${a.map(h=>\"int \"+h)}) {\n ${m}\n }\n\n void main() {\n ${s} coords = getOutputCoords();\n vec4 result = vec4(getValue(${i}), 0., 0., 0.);\n\n ${i[o-1]} = ${i[o-1]} + 1;\n if (${i[o-1]} < ${n[o-1]}) {\n result.g = getValue(${i});\n }\n\n ${i[o-2]} = ${i[o-2]} + 1;\n if (${i[o-2]} < ${n[o-2]}) {\n result.a = getValue(${i});\n }\n\n ${i[o-1]} = ${i[o-1]} - 1;\n if (${i[o-2]} < ${n[o-2]} &&\n ${i[o-1]} < ${n[o-1]}) {\n result.b = getValue(${i});\n }\n setOutput(result);\n }\n `}};function kI(r,t,e){let n=r.indexOf(t);return r.map((s,i)=>i===n?`${s} - ${e}`:s).join()}function hp(r){let{inputs:t,backend:e}=r,{input:n}=t,o=e.texData.get(n.dataId);return nr({inputs:{x:o.complexTensorInfos.imag},backend:e})}var Y3={kernelName:Pp,backendName:\"webgl\",kernelFunc:hp};function Fd(r,t,e){let n=r[0].dtype;if(n===\"complex64\"){let f=r.map(b=>Pl({inputs:{input:b},backend:e})),d=r.map(b=>hp({inputs:{input:b},backend:e})),h=Fd(f,t,e),g=Fd(d,t,e),x=Rn({inputs:{real:h,imag:g},backend:e});return f.forEach(b=>e.disposeIntermediateTensorInfo(b)),d.forEach(b=>e.disposeIntermediateTensorInfo(b)),e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),x}let o=e.shouldExecuteOnCPU(r);if(n===\"string\"&&(o=!0),o){let f=r.map(I=>{let E=[-1,y.sizeFromShape(I.shape.slice(t))];return rt({inputs:{x:I},backend:e,attrs:{shape:E}})}),d=f.map(I=>({vals:e.readSync(I.dataId),shape:I.shape})),h=S.computeOutShape(f.map(I=>I.shape),1),g=f[0].shape[0]===1,x=ZL(d,h,n,g),b=S.computeOutShape(r.map(I=>I.shape),t),w=e.makeTensorInfo(b,n,x);return f.forEach(I=>e.disposeIntermediateTensorInfo(I)),w}let s=r.filter(f=>y.sizeFromShape(f.shape)>0),i=L().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")&&s[0].shape.length>1;if(s.length===1){let f=i?new zr(r[0].shape,Ia):new Dn(r[0].shape,Ia);return e.runWebGLProgram(f,r,n)}let a=L().getNumber(\"WEBGL_MAX_TEXTURES_IN_SHADER\");if(s.length>a){let f=[];for(let h=0;hd.shape),t);return e.runWebGLProgram(f,s,n)}let{tensors2D:u,outShape:l}=Ust(s,t,e),c=new NI(u.map(f=>f.shape)),p=e.runWebGLProgram(c,u,n);u.forEach(f=>e.disposeIntermediateTensorInfo(f));let m=rt({inputs:{x:p},attrs:{shape:l},backend:e});return e.disposeIntermediateTensorInfo(p),m}function Ust(r,t,e){let n=S.computeOutShape(r.map(s=>s.shape),t);return{tensors2D:r.map(s=>rt({inputs:{x:s},attrs:{shape:[-1,y.sizeFromShape(s.shape.slice(t))]},backend:e})),outShape:n}}function L1(r){let{inputs:t,backend:e,attrs:n}=r,{axis:o}=n,s=y.parseAxisParam(o,t[0].shape)[0],i=t.map(l=>l.shape);S.assertParamsConsistent(i,s);let a=S.computeOutShape(t.map(l=>l.shape),s);if(y.sizeFromShape(a)===0)return e.makeTensorInfo(a,t[0].dtype,[]);let u=t.filter(l=>y.sizeFromShape(l.shape)>0);return u.length===1?nr({inputs:{x:u[0]},backend:e}):Fd(u,s,e)}var Z3={kernelName:Oi,backendName:\"webgl\",kernelFunc:L1};var Od=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.outputShape=t.outShape;let i=t.padInfo.top,a=t.padInfo.left,u=t.strideHeight,l=t.strideWidth,c=t.dilationHeight,p=t.dilationWidth,m=t.filterHeight,f=t.filterWidth,d=Math.floor(t.inChannels/4)*4,h=t.inChannels%4,g=t.dataFormat===\"channelsLast\",x=g?1:2,b=g?2:3,w=g?3:1,I=\"\",N=\"\";n&&(o?I=`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${n}\n }`:s?I=`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${n}\n }`:I=`\n float activation(float x) {\n ${n}\n }\n `,N=\"result = activation(result);\");let E=e?\"result += getBiasAtOutCoords();\":\"\";e&&this.variableNames.push(\"bias\"),o&&this.variableNames.push(\"preluActivationWeights\"),s&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n ${I}\n\n const ivec2 strides = ivec2(${u}, ${l});\n const ivec2 pads = ivec2(${i}, ${a});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d2 = coords[${w}];\n\n ivec2 xRCCorner =\n ivec2(coords[${x}], coords[${b}]) * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${m}; wR++) {\n int xR = xRCorner + wR * ${c};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${f}; wC++) {\n int xC = xCCorner + wC * ${p};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${d}; d1 += 4) {\n vec4 wValues = vec4(\n getW(wR, wC, d1, d2),\n getW(wR, wC, d1 + 1, d2),\n getW(wR, wC, d1 + 2, d2),\n getW(wR, wC, d1 + 3, d2)\n );\n\n if (${g}) {\n vec4 xValues = vec4(\n getX(batch, xR, xC, d1),\n getX(batch, xR, xC, d1 + 1),\n getX(batch, xR, xC, d1 + 2),\n getX(batch, xR, xC, d1 + 3)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec4 xValues = vec4(\n getX(batch, d1, xR, xC),\n getX(batch, d1 + 1, xR, xC),\n getX(batch, d1 + 2, xR, xC),\n getX(batch, d1 + 3, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n\n if (${h===1}) {\n\n if (${g}) {\n dotProd +=\n getX(batch, xR, xC, ${d}) *\n getW(wR, wC, ${d}, d2);\n } else {\n dotProd +=\n getX(batch, ${d}, xR, xC) *\n getW(wR, wC, ${d}, d2);\n }\n\n } else if (${h===2}) {\n vec2 wValues = vec2(\n getW(wR, wC, ${d}, d2),\n getW(wR, wC, ${d} + 1, d2)\n );\n\n if (${g}) {\n vec2 xValues = vec2(\n getX(batch, xR, xC, ${d}),\n getX(batch, xR, xC, ${d} + 1)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec2 xValues = vec2(\n getX(batch, ${d}, xR, xC),\n getX(batch, ${d} + 1, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n } else if (${h===3}) {\n vec3 wValues = vec3(\n getW(wR, wC, ${d}, d2),\n getW(wR, wC, ${d} + 1, d2),\n getW(wR, wC, ${d} + 2, d2)\n );\n\n if (${g}) {\n vec3 xValues = vec3(\n getX(batch, xR, xC, ${d}),\n getX(batch, xR, xC, ${d} + 1),\n getX(batch, xR, xC, ${d} + 2)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec3 xValues = vec3(\n getX(batch, ${d}, xR, xC),\n getX(batch, ${d} + 1, xR, xC),\n getX(batch, ${d} + 2, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n }\n }\n }\n\n float result = dotProd;\n ${E}\n ${N}\n setOutput(result);\n }\n `}},_I=class{constructor(t){this.variableNames=[\"x\",\"W\"],this.outputShape=t.outShape;let e=t.padInfo.front,n=t.padInfo.top,o=t.padInfo.left,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,c=t.dilationWidth,p=t.filterDepth,m=t.filterHeight,f=t.filterWidth,d=Math.floor(t.inChannels/4)*4,h=t.inChannels%4;this.userCode=`\n const ivec3 strides = ivec3(${s}, ${i}, ${a});\n const ivec3 pads = ivec3(${e}, ${n}, ${o});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d2 = coords.u;\n\n ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xFCorner = xFRCCorner.x;\n int xRCorner = xFRCCorner.y;\n int xCCorner = xFRCCorner.z;\n\n // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n // y(yF, yR, yC, d2). ? = to be determined. : = across all\n // values in that axis.\n float dotProd = 0.0;\n for (int wF = 0; wF < ${p}; wF++) {\n int xF = xFCorner + wF * ${u};\n\n if (xF < 0 || xF >= ${t.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${m}; wR++) {\n int xR = xRCorner + wR * ${l};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${f}; wC++) {\n int xC = xCCorner + wC * ${c};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${d}; d1 += 4) {\n vec4 xValues = vec4(\n getX(batch, xF, xR, xC, d1),\n getX(batch, xF, xR, xC, d1 + 1),\n getX(batch, xF, xR, xC, d1 + 2),\n getX(batch, xF, xR, xC, d1 + 3)\n );\n vec4 wValues = vec4(\n getW(wF, wR, wC, d1, d2),\n getW(wF, wR, wC, d1 + 1, d2),\n getW(wF, wR, wC, d1 + 2, d2),\n getW(wF, wR, wC, d1 + 3, d2)\n );\n\n dotProd += dot(xValues, wValues);\n }\n\n if (${h===1}) {\n dotProd +=\n getX(batch, xF, xR, xC, ${d}) *\n getW(wF, wR, wC, ${d}, d2);\n } else if (${h===2}) {\n vec2 xValues = vec2(\n getX(batch, xF, xR, xC, ${d}),\n getX(batch, xF, xR, xC, ${d} + 1)\n );\n vec2 wValues = vec2(\n getW(wF, wR, wC, ${d}, d2),\n getW(wF, wR, wC, ${d} + 1, d2)\n );\n dotProd += dot(xValues, wValues);\n } else if (${h===3}) {\n vec3 xValues = vec3(\n getX(batch, xF, xR, xC, ${d}),\n getX(batch, xF, xR, xC, ${d} + 1),\n getX(batch, xF, xR, xC, ${d} + 2)\n );\n vec3 wValues = vec3(\n getW(wF, wR, wC, ${d}, d2),\n getW(wF, wR, wC, ${d} + 1, d2),\n getW(wF, wR, wC, ${d} + 2, d2)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}};var Md=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=t.outShape,this.enableShapeUniforms=he(this.outputShape.length);let i=t.padInfo.left,a=t.strideWidth,u=t.dilationWidth,l=t.filterHeight,c=t.filterWidth,p=c,m=`\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g=0 && xR < inDims[0]) {\n `;for(let g=0;g<(p+1)/2;g++){let x=g*2;if(m+=`\n xC = xCCorner + ${x*u};\n `,a===1){if(x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n `,u===1&&x>0?m+=`\n xC${x} = vec4(xTexelC${x-2}.zw, xTexelC${x}.xy);\n `:m+=`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n previous.zw = vec2(0.0);\n }\n\n xC${x} = vec4(previous.zw, xTexelC${x}.xy);\n } else {\n xC${x} = vec4(0.0, 0.0, xTexelC${x}.xy);\n }\n `):m+=`\n if (xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n xC${x} = xTexelC${x};\n `,x+1= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n `,u>1?m+=`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n xC${x+1} = vec4(previous.zw, xTexelC${x+1}.xy);\n } else {\n xC${x+1} = vec4(0.0, 0.0, xTexelC${x+1}.xy);\n }\n `:m+=`\n xC${x+1} = vec4(xTexelC${x}.zw, xTexelC${x+1}.xy);\n `):b===1?m+=`\n xC${x+1} = xTexelC${x};\n `:m+=`\n xCOffset = xC + ${b};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x+1} = xTexelC${x+1};\n `}}else x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x} = vec4(xTexelC${x}.zw, xTexelC${x+1}.zw);\n `,x+1= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${x+1} = vec4(xTexelC${x+1}.xy, final.xy);\n `)):(m+=`\n if(xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x} = vec4(\n xTexelC${x}.xy, xTexelC${x+1}.xy);\n `,x+1= 0) {\n // Use custom imod instead mod. On Intel GPU, mod may generate\n // unexpected value.\n // https://github.com/tensorflow/tfjs/issues/5447\n offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];\n d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /\n inChannels);\n\n if(d1 < inputShape[${a}] && d1 >= 0) {\n\n ch = imod(pos, inChannels);\n\n if (${s}) {\n innerDims = vec2(d1, ch);\n result[${c*2+p}] = getChannel(\n getA(rc.x, d0, int(innerDims.x),\n int(innerDims.y)), innerDims);\n } else {\n innerDims = vec2(d0, d1);\n result[${c*2+p}] = getChannel(\n getA(rc.x, ch, int(innerDims.x),\n int(innerDims.y)), innerDims);\n }\n }\n }\n }\n `;this.userCode=`\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0);\n\n int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n vec2 innerDims;\n\n ${l}\n\n ${o.output} = result;\n }\n `}};function AI(r,t){let e=r.length;return e>=3?t?[...r.slice(0,-3),r[e-3]*r[e-2],r[e-1]]:[...r.slice(0,-3),r[e-3],r[e-2]*r[e-1]]:!t&&e===1&&r[0]>1?[r[0],1]:null}function DI({x:r,filter:t,convInfo:e,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:a=null}){let u=r.shape,l=n.texData.get(r.dataId),c=e.inChannels,p=u[0]*u[1]*u[2],m=e.outChannels,f=e.dataFormat===\"channelsLast\",d=!1,h=!1,g,x=[];if(s!=null){let I=AI(s.shape,f);I!=null&&(s=rt({inputs:{x:s},backend:n,attrs:{shape:I}}),x.push(s))}if(o!=null){let I=AI(o.shape,f);I!=null&&(o=rt({inputs:{x:o},backend:n,attrs:{shape:I}}),x.push(o))}if(!((p===1||m===1)&&c>F1)&&l.isPacked&&f&&l.texture!=null&&u[2]%2!==0&&y.arraysEqual(l.shape.slice(-3),u.slice(-3))){let I=u[0]*u[1]*(u[2]+1),N={dataId:r.dataId,shape:[1,I,e.inChannels],dtype:r.dtype},E=l.shape;l.shape=l.shape.slice(),l.shape[l.shape.length-2]++,y.assert(Uu(l.shape,N.shape),()=>`packed reshape ${l.shape} to ${N.shape} isn't free`);let A=rt({inputs:{x:t},backend:n,attrs:{shape:[1,e.inChannels,e.outChannels]}});x.push(A);let D=dp({a:N,b:A,backend:n,transposeA:d,transposeB:h,bias:o,activation:a,preluActivationWeights:s,leakyreluAlpha:i}),F=n.texData.get(D.dataId);y.assert(F.isPacked,()=>\"batchMatMul result is expected to be packed\"),l.shape=E,F.shape=e.outShape,g=nr({inputs:{x:D},backend:n}),g.shape=e.outShape,x.push(D)}else{let I=e.outHeight*e.outWidth,N=rt({inputs:{x:r},backend:n,attrs:{shape:f?[e.batchSize,I,e.inChannels]:[e.batchSize,e.inChannels,I]}}),E=rt({inputs:{x:t},backend:n,attrs:{shape:[1,e.inChannels,e.outChannels]}}),A=dp({a:f?N:E,b:f?E:N,transposeA:!f,transposeB:h,backend:n,bias:o,activation:a,preluActivationWeights:s,leakyreluAlpha:i});g=rt({inputs:{x:A},backend:n,attrs:{shape:e.outShape}}),x.push(N),x.push(E),x.push(A)}for(let I of x)n.disposeIntermediateTensorInfo(I);return g}function $I({x:r,filter:t,convInfo:e,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:a=null}){let{filterWidth:u,filterHeight:l,inChannels:c,outWidth:p,outHeight:m,dataFormat:f}=e,d=f===\"channelsLast\",h=u*l*c,g=m*p,x=[e.batchSize,h,g],b=!0,w=!1,I=[];if(s!=null){let Z=AI(s.shape,d);Z!=null&&(s=rt({inputs:{x:s},backend:n,attrs:{shape:Z}}),I.push(s))}if(o!=null){let Z=AI(o.shape,d);Z!=null&&(o=rt({inputs:{x:o},backend:n,attrs:{shape:Z}}),I.push(o))}let N=rt({inputs:{x:t},backend:n,attrs:{shape:[1,h,y.sizeFromShape(t.shape)/h]}});I.push(N);let E=new EI(x,e),A=[r.shape,[e.padInfo.top,e.padInfo.left],[e.strideHeight,e.strideWidth],[e.dilationHeight,e.dilationWidth],[e.inChannels],[e.filterWidth*e.inChannels],[e.outWidth]],D=n.runWebGLProgram(E,[r],\"float32\",A),F=rt({inputs:{x:D},backend:n,attrs:{shape:x}});I.push(D),I.push(F);let M=o!=null,V=s!=null,G=a===\"leakyrelu\",W=a?Ml(a,!0):null,q=new Rd(d?F.shape:N.shape,d?N.shape:F.shape,d?[e.batchSize,g,e.outChannels]:[e.batchSize,e.outChannels,g],b,w,M,W,V,G),H=d?[F,N]:[N,F];if(o&&H.push(o),V&&H.push(s),G){let Z=n.makeTensorInfo([],\"float32\",y.createScalarValue(i,\"float32\"));H.push(Z),I.push(Z)}let j=n.runWebGLProgram(q,H,\"float32\"),Y=rt({inputs:{x:j},backend:n,attrs:{shape:e.outShape}});I.push(j);for(let Z of I)n.disposeIntermediateTensorInfo(Z);return Y}function Hst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dataFormat:u,dilations:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,s.shape,i,l,a,c,!1,p),f;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type===\"SAME\"||m.padInfo.type===\"VALID\"))f=DI({x:o,filter:s,convInfo:m,backend:e});else if(m.strideWidth<=2&&p===\"channelsLast\"&&L().getBool(\"WEBGL_EXP_CONV\")){let h=new Md(m),g=[[m.padInfo.top,m.padInfo.left],[m.strideHeight,m.strideWidth],[m.dilationHeight,m.dilationWidth],[m.inHeight,m.inWidth]];f=e.runWebGLProgram(h,[o,s],\"float32\",g)}else if(L().getBool(\"WEBGL_CONV_IM2COL\"))f=$I({x:o,filter:s,convInfo:m,backend:e});else{let h=new Od(m);f=e.runWebGLProgram(h,[o,s],\"float32\")}let d=rt({inputs:{x:f},backend:e,attrs:{shape:m.outShape}});return e.disposeIntermediateTensorInfo(f),d}var J3={kernelName:Qo,backendName:\"webgl\",kernelFunc:Hst};var RI=class{constructor(t){this.variableNames=[\"x\",\"dy\"],this.outputShape=t.filterShape;let e=t.strideHeight,n=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,i=t.dataFormat===\"channelsLast\";this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int d2 = coords.w;\n\n // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int b = 0; b < ${t.batchSize}; b++) {\n for (int yR = 0; yR < ${t.outHeight}; yR++) {\n int xR = wR + yR * ${e} - ${o};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${t.outWidth}; yC++) {\n int xC = wC + yC * ${n} - ${s};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n ${i?`float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);`:`float dyValue = getDy(b, d2, yR, yC);\n float xValue = getX(b, d1, xR, xC);\n dotProd += (xValue * dyValue);`}\n }\n }\n }\n setOutput(dotProd);\n }\n `}},FI=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=t.dataFormat===\"channelsLast\",a=e-1-t.padInfo.top,u=n-1-t.padInfo.left,l=i?1:2,c=i?2:3,p=i?3:1;this.userCode=`\n const ivec2 pads = ivec2(${a}, ${u});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[${p}];\n\n ivec2 dyCorner = ivec2(coords[${l}], coords[${c}]) - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${e}; wR++) {\n float dyR = float(dyRCorner + wR) / ${o}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${e} - 1 - wR;\n\n for (int wC = 0; wC < ${n}; wC++) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${n} - 1 - wC;\n\n for (int d2 = 0; d2 < ${t.outChannels}; d2++) {\n\n if (${i}) {\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n } else {\n float xValue = getDy(batch, d2, idyR, idyC);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n\n }\n }\n }\n setOutput(dotProd);\n }\n `}},OI=class{constructor(t){this.variableNames=[\"x\",\"dy\"],this.outputShape=t.filterShape;let e=t.strideDepth,n=t.strideHeight,o=t.strideWidth,s=t.padInfo.front,i=t.padInfo.top,a=t.padInfo.left;this.userCode=`\n void main() {\n ivec5 coords = getOutputCoords();\n int wF = coords.x;\n int wR = coords.y;\n int wC = coords.z;\n int d1 = coords.w;\n int d2 = coords.u;\n\n float dotProd = 0.0;\n\n for (int b = 0; b < ${t.batchSize}; b++) {\n for (int yF = 0; yF < ${t.outDepth}; yF++) {\n int xF = wF + yF * ${e} - ${s};\n\n if (xF < 0 || xF >= ${t.inDepth}) {\n continue;\n }\n\n for (int yR = 0; yR < ${t.outHeight}; yR++) {\n int xR = wR + yR * ${n} - ${i};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${t.outWidth}; yC++) {\n int xC = wC + yC * ${o} - ${a};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yF, yR, yC, d2);\n float xValue = getX(b, xF, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}},MI=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.outputShape=t.inShape;let e=t.filterDepth,n=t.filterHeight,o=t.filterWidth,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=e-1-t.padInfo.front,l=n-1-t.padInfo.top,c=o-1-t.padInfo.left;this.userCode=`\n const ivec3 pads = ivec3(${u}, ${l}, ${c});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.u;\n\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyFCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n float dotProd = 0.0;\n for (int wF = 0; wF < ${e}; wF++) {\n float dyF = float(dyFCorner + wF) / ${s}.0;\n\n if (dyF < 0.0 || dyF >= ${t.outDepth}.0 || fract(dyF) > 0.0) {\n continue;\n }\n int idyF = int(dyF);\n\n int wFPerm = ${e} - 1 - wF;\n\n for (int wR = 0; wR < ${n}; wR++) {\n float dyR = float(dyRCorner + wR) / ${i}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${n} - 1 - wR;\n\n for (int wC = 0; wC < ${o}; wC++) {\n float dyC = float(dyCCorner + wC) / ${a}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${o} - 1 - wC;\n\n for (int d2 = 0; d2 < ${t.outChannels}; d2++) {\n float xValue = getDy(batch, idyF, idyR, idyC, d2);\n float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function qst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,dataFormat:u,dimRoundingMode:l,filterShape:c}=n,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,c,i,1,a,l,!1,p),f=new RI(m);return e.runWebGLProgram(f,[o,s],\"float32\")}var Q3={kernelName:Dp,backendName:\"webgl\",kernelFunc:qst};var PI=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"strides\",type:\"vec2\"}],this.outputShape=t.inShape,this.enableShapeUniforms=he(this.outputShape.length);let e=t.filterHeight,n=t.filterWidth,o=e-1-t.padInfo.top,s=n-1-t.padInfo.left;this.userCode=`\n const ivec2 pads = ivec2(${o}, ${s});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n\n ivec2 dyCorner = ivec2(coords[1], coords[2]) - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n vec4 result = vec4(0.);\n for (int wR = 0; wR < ${e}; wR++) {\n float dyR = float(dyRCorner + wR) / strides[0];\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n int wRPerm = ${e} - 1 - wR;\n\n for (int wC = 0; wC < ${n}; wC++) {\n int wCPerm = ${n} - 1 - wC;\n\n float dyC = float(dyCCorner + wC) / strides[1];\n bool idyCVal = (dyC >= 0.0) && (dyC < ${t.outWidth}.0)\n && (fract(dyC) == 0.0);\n int idyC = int(dyC);\n\n float dyC2 = float(dyCCorner + wC + 1) / strides[1];\n bool idyCVal2 = (dyC2 >= 0.0) && (dyC2 < ${t.outWidth}.0)\n && (fract(dyC2) == 0.0);\n int idyC2 = int(dyC2);\n\n if (idyCVal && idyCVal2) {\n for (int d2 = 0; d2 < ${t.outChannels}; d2 += 2) {\n vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n vec4 dySample = getDy(batch, idyR, idyC, d2);\n vec4 dySample2 = (idyC / 2 == idyC2 / 2) ?\n dySample : getDy(batch, idyR, idyC2, d2);\n\n vec2 dyValue = mod(float(idyC), 2.) == 0. ?\n dySample.xy : dySample.zw;\n result.xy += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n\n dyValue = mod(float(idyC2), 2.) == 0. ?\n dySample2.xy : dySample2.zw;\n result.zw += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n }\n } else if (idyCVal) {\n for (int d2 = 0; d2 < ${t.outChannels}; d2 += 2) {\n vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n vec4 dySample = getDy(batch, idyR, idyC, d2);\n vec2 dyValue = mod(float(idyC), 2.) == 0. ?\n dySample.xy : dySample.zw;\n result.xy += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n }\n } else if (idyCVal2) {\n for (int d2 = 0; d2 < ${t.outChannels}; d2 += 2) {\n vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n vec4 dySample = getDy(batch, idyR, idyC2, d2);\n vec2 dyValue = mod(float(idyC2), 2.) == 0. ?\n dySample.xy : dySample.zw;\n result.zw += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n }\n }\n }\n }\n setOutput(result);\n }\n `}};function Kst(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{inputShape:i,strides:a,pad:u,dataFormat:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(l),m=S.computeConv2DInfo(i,s.shape,a,1,u,c,!1,p);if(L().getBool(\"WEBGL_PACK_CONV2DTRANSPOSE\")&&p===\"channelsLast\"){let f=[[m.strideHeight,m.strideWidth]],d=new PI(m);return e.runWebGLProgram(d,[o,s],\"float32\",f)}else{let f=new FI(m);return e.runWebGLProgram(f,[o,s],\"float32\")}}var tB={kernelName:ts,backendName:\"webgl\",kernelFunc:Kst};function jst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n,l=S.computeConv3DInfo(o.shape,s.shape,i,u,a),c=new _I(l);return e.runWebGLProgram(c,[o,s],\"float32\")}var eB={kernelName:es,backendName:\"webgl\",kernelFunc:jst};function Xst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,filterShape:u}=n,l=S.computeConv3DInfo(o.shape,u,i,1,a),c=new OI(l);return e.runWebGLProgram(c,[o,s],\"float32\")}var rB={kernelName:Ra,backendName:\"webgl\",kernelFunc:Xst};function Yst(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{pad:i,strides:a,inputShape:u}=n,l=S.computeConv3DInfo(u,s.shape,a,1,i),c=new MI(l);return e.runWebGLProgram(c,[o,s],\"float32\")}var nB={kernelName:Fa,backendName:\"webgl\",kernelFunc:Yst};var Zst=Po+`\n return cos(x);\n`,Jst=`\n vec4 result = cos(x);\n bvec4 isNaN = isnan(x);\n ${Xn}\n return result;\n`,Qst=It({opSnippet:Zst,packedOpSnippet:Jst}),oB={kernelName:rs,backendName:\"webgl\",kernelFunc:Qst};var tit=`\n float e2x = exp(-x);\n return (e2x + 1.0 / e2x) / 2.0;\n`,eit=It({opSnippet:tit}),sB={kernelName:ns,backendName:\"webgl\",kernelFunc:eit};var LI=class{constructor(t,e,n,o,s){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.outputShape=[];let[i,a,u,l]=t,[c]=e,[p,m]=n;this.outputShape=[c,p,m,l];let f=o===\"bilinear\"?1:0,[d,h]=[`${a-1}.0`,`${u-1}.0`],[g,x,b]=p>1?[`${(a-1)/(p-1)}`,\"(y2-y1) * height_ratio\",`y1*${d} + float(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${d}`],[w,I,N]=m>1?[`${(u-1)/(m-1)}`,\"(x2-x1) * width_ratio\",`x1*${h} + float(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${h}`];this.userCode=`\n const float height_ratio = float(${g});\n const float width_ratio = float(${w});\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int y = coords[1];\n int x = coords[2];\n int d = coords[3];\n\n // get box vals\n float y1 = getBoxes(b,0);\n float x1 = getBoxes(b,1);\n float y2 = getBoxes(b,2);\n float x2 = getBoxes(b,3);\n\n // get image in batch index\n int bInd = round(getBoxInd(b));\n if(bInd < 0 || bInd >= ${i}) {\n return;\n }\n\n float height_scale = ${x};\n float width_scale = ${I};\n\n float in_y = ${b};\n if( in_y < 0.0 || in_y > ${d} ) {\n setOutput(float(${s}));\n return;\n }\n float in_x = ${N};\n if( in_x < 0.0 || in_x > ${h} ) {\n setOutput(float(${s}));\n return;\n }\n\n vec2 sourceFracIndexCR = vec2(in_x,in_y);\n if(${f} == 1) {\n // Compute the four integer indices.\n ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n float top = topLeft + (topRight - topLeft) * fracCR.x;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n float newValue = top + (bottom - top) * fracCR.y;\n setOutput(newValue);\n } else {\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestCR = ivec2(floor(\n sourceFracIndexCR + vec2(0.5,0.5)));\n float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n setOutput(newValue);\n }\n }\n `}};var rit=r=>{let{inputs:t,backend:e,attrs:n}=r,{image:o,boxes:s,boxInd:i}=t,{cropSize:a,method:u,extrapolationValue:l}=n,c=new LI(o.shape,s.shape,a,u,l);return e.runWebGLProgram(c,[o,s,i],\"float32\")},iB={kernelName:Ma,backendName:\"webgl\",kernelFunc:rit};var gp;(function(r){r.Prod=\"*\",r.Sum=\"+\"})(gp||(gp={}));var dg=class{constructor(t,e,n,o){this.op=t,this.outputShape=e,this.variableNames=[\"x\"],this.customUniforms=[{name:\"index\",type:\"float\"}];let s=this.outputShape.length,i=this.op===gp.Prod?\"1.0\":\"0.0\",a=n?i:`getX(${aB(s,\"coords\",this.op)})`,u=this.outputShape[this.outputShape.length-1],l=\"\",c=\"\";n?(l=o?`end != ${u-1}`:\"end != 0\",c=o?\"end + 1\":\"end - 1\"):(l=o?`end + pow2 < ${u}`:\"end >= pow2\",c=o?\"end + pow2\":\"end - pow2\"),this.userCode=`\n void main() {\n ${zt(s)} coords = getOutputCoords();\n int end = ${lB(s,\"coords\",this.op)};\n float val = ${a};\n int pow2 = int(pow(2.0, index));\n if (${l}) {\n int idx = ${c};\n ${lB(s,\"coords\",this.op)} = idx;\n val ${this.op}= getX(${aB(s,\"coords\",this.op)});\n }\n setOutput(val);\n }\n `}};function aB(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.x, ${t}.y`;if(r===3)return`${t}.x, ${t}.y, ${t}.z`;if(r===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function lB(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.y`;if(r===3)return`${t}.z`;if(r===4)return`${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function zI(r,t,e,n,o,s){let i=t.shape.length,a=S.getAxesPermutation([n],i),u=t;a!=null&&(u=Pe({inputs:{x:t},backend:e,attrs:{perm:a}}));let l=S.getInnerMostAxes(1,i)[0];if(l!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let c=u.shape[l],p=nr({inputs:{x:u},backend:e});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let f=new dg(r,u.shape,!1,s),d=[[m]],h=p;p=e.runWebGLProgram(f,[p],p.dtype,d),e.disposeIntermediateTensorInfo(h)}if(o){let m=new dg(r,u.shape,o,s),f=p;p=e.runWebGLProgram(m,[p],p.dtype),e.disposeIntermediateTensorInfo(f)}if(a!=null){let m=S.getUndoAxesPermutation(a),f=Pe({inputs:{x:p},backend:e,attrs:{perm:m}});return e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(u),f}return p}function nit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;return zI(gp.Prod,o,e,s,i,a)}var uB={kernelName:Oa,backendName:\"webgl\",kernelFunc:nit};function oit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;return zI(gp.Sum,o,e,s,i,a)}var cB={kernelName:os,backendName:\"webgl\",kernelFunc:oit};function sit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i,binaryOutput:a}=n;if(o.shape.length===1){let u=e.readSync(o.dataId),l=e.readSync(s.dataId),c=tI(u,l,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,c)}else if(o.shape.length===2){let u=e.bufferSync(o),l=e.bufferSync(s),c=KL(u,l,i,a);return e.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${o.shape.length}.`)}var pB={kernelName:jl,backendName:\"webgl\",kernelFunc:sit};var BI=class{constructor(t,e,n){this.variableNames=[\"x\"],this.outputShape=[],this.outputShape=t,this.blockSize=e,this.dataFormat=n,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int h = ${this.getHeightCoordString()};\n int w = ${this.getWidthCoordString()};\n int d = ${this.getDepthCoordString()};\n\n int in_h = h / ${e};\n int offset_h = imod(h, ${e});\n int in_w = w / ${e};\n int offset_w = imod(w, ${e});\n int offset_d = (offset_h * ${e} + offset_w) *\n ${this.getOutputDepthSize()};\n int in_d = d + offset_d;\n\n float result = ${this.getInputSamplingString()};\n setOutput(result);\n }\n `}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 iit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockSize:s,dataFormat:i}=n,a=o.shape[0],u=i===\"NHWC\"?o.shape[1]:o.shape[2],l=i===\"NHWC\"?o.shape[2]:o.shape[3],c=i===\"NHWC\"?o.shape[3]:o.shape[1],p=u*s,m=l*s,f=c/(s*s),d=i===\"NHWC\"?[a,p,m,f]:[a,f,p,m],h=new BI(d,s,i);return e.runWebGLProgram(h,[o],o.dtype)}var mB={kernelName:Pa,backendName:\"webgl\",kernelFunc:iit};var Pd=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=t.outShape,this.enableShapeUniforms=he(this.outputShape.length);let i=t.filterHeight,a=t.filterWidth,u=t.outChannels/t.inChannels,l=\"\",c=\"\";n&&(o?l=`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${n}\n }`:s?l=`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${n}\n }`:l=`\n float activation(float x) {\n ${n}\n }\n `,c=\"result = activation(result);\");let p=e?\"result += getBiasAtOutCoords();\":\"\";e&&this.variableNames.push(\"bias\"),o&&this.variableNames.push(\"preluActivationWeights\"),s&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n ${l}\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n ivec2 xRCCorner = coords.yz * strides - pads;\n int d2 = coords.w;\n int d1 = d2 / ${u};\n int q = d2 - d1 * ${u};\n\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n for (int wR = 0; wR < ${i}; wR++) {\n int xR = xRCorner + wR * dilations[0];\n\n if (xR < 0 || xR >= inDims[0]) {\n continue;\n }\n\n for (int wC = 0; wC < ${a}; wC++) {\n int xC = xCCorner + wC * dilations[1];\n\n if (xC < 0 || xC >= inDims[1]) {\n continue;\n }\n\n float xVal = getX(batch, xR, xC, d1);\n float wVal = getW(wR, wC, d1, q);\n dotProd += xVal * wVal;\n }\n }\n\n float result = dotProd;\n ${p}\n ${c}\n setOutput(result);\n }\n `}};var Ld=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=t.outShape,this.enableShapeUniforms=he(this.outputShape.length);let i=t.outChannels/t.inChannels,a=t.padInfo.left,u=t.strideWidth,l=t.dilationWidth,c=t.filterHeight,p=t.filterWidth,m=p,f=`\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;`;for(let x=0;x=0 && xR < inDims[0]) {\n `;for(let x=0;x<(m+1)/2;x++){let b=x*2;if(f+=`\n xC = xCCorner + ${b*l};\n `,u===1){if(b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n `,l===1&&b>0?f+=`\n xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.xy);\n `:f+=`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n previous.zw = vec2(0.0);\n }\n\n xC${b} = vec4(previous.zw, xTexelC${b}.xy);\n } else {\n xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy);\n }\n `):f+=`\n if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n xC${b} = xTexelC${b};\n `,b+1= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n `,l>1?f+=`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n xC${b+1} = vec4(previous.zw, xTexelC${b+1}.xy);\n } else {\n xC${b+1} = vec4(0.0, 0.0, xTexelC${b+1}.xy);\n }\n `:f+=`\n xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy);\n `):w===1?f+=`\n xC${b+1} = xTexelC${b};\n `:f+=`\n xCOffset = xC + ${w};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b+1} = xTexelC${b+1};\n `}}else b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);\n `,b+1= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy);\n `)):(f+=`\n if(xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b} = vec4(\n xTexelC${b}.xy, xTexelC${b+1}.xy);\n `,b+1`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let p=S.computeConv2DInfo(o.shape,s.shape,i,c,a,l,!0),m;L().getBool(\"WEBGL_PACK_DEPTHWISECONV\")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?m=new Ld(p):m=new Pd(p);let f=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return e.runWebGLProgram(m,[o,s],\"float32\",f)}var fB={kernelName:ss,backendName:\"webgl\",kernelFunc:ait};var VI=class{constructor(t){this.variableNames=[\"x\",\"dy\"],this.outputShape=t.filterShape;let e=t.strideHeight,n=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,i=t.outChannels/t.inChannels;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int dm = coords.w;\n int d2 = d1 * ${i} + dm;\n\n float dotProd = 0.0;\n\n // TO DO: Vec4 over the batch size\n for (int b = 0; b < ${t.batchSize}; b++) {\n for (int yR = 0; yR < ${t.outHeight}; yR++) {\n int xR = wR + yR * ${e} - ${o};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${t.outWidth}; yC++) {\n int xC = wC + yC * ${n} - ${s};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n setOutput(dotProd);\n }\n `}},GI=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=e-1-t.padInfo.top,a=n-1-t.padInfo.left,u=t.outChannels/t.inChannels;this.userCode=`\n const ivec2 pads = ivec2(${i}, ${a});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n ivec2 dyCorner = coords.yz - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n float dotProd = 0.0;\n\n for (int wR = 0; wR < ${e}; wR++) {\n float dyR = float(dyRCorner + wR) / ${o}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${e} - 1 - wR;\n\n for (int wC = 0; wC < ${n}; wC++) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${n} - 1 - wC;\n\n // TO DO: Vec4 over the channelMul\n for (int dm = 0; dm < ${u}; dm++) {\n int d2 = d1 * ${u} + dm;\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, dm);\n dotProd += xValue * wValue;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function lit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,filterShape:c}=n,p=S.computeConv2DInfo(o.shape,c,i,a,u,l,!0),m=new VI(p);return e.runWebGLProgram(m,[o,s],\"float32\")}var dB={kernelName:$p,backendName:\"webgl\",kernelFunc:lit};function uit(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,inputShape:c}=n,p=S.computeConv2DInfo(c,s.shape,i,a,u,l,!0),m=new GI(p);return e.runWebGLProgram(m,[o,s],\"float32\")}var hB={kernelName:Rp,backendName:\"webgl\",kernelFunc:uit};var WI=class{constructor(t){this.variableNames=[\"X\"],this.outputShape=[t,t],this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n setOutput(val);\n }\n `}};function cit(r){let{inputs:t,backend:e}=r,{x:n}=t,o=[...n.shape,...n.shape],s=y.sizeFromShape(n.shape),i=rt({inputs:{x:n},backend:e,attrs:{shape:[s]}}),a=new WI(s),u=e.runWebGLProgram(a,[i],i.dtype),l=rt({inputs:{x:u},backend:e,attrs:{shape:o}});return e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(u),l}var gB={kernelName:Xl,backendName:\"webgl\",kernelFunc:cit};var UI=class{constructor(t){this.variableNames=[\"x\",\"W\"],this.outputShape=t.outShape;let{inHeight:e,inWidth:n,padInfo:o,strideHeight:s,strideWidth:i,filterHeight:a,filterWidth:u,dilationHeight:l,dilationWidth:c}=t,{top:p,left:m}=o;this.userCode=`\n const ivec2 strides = ivec2(${s}, ${i});\n const ivec2 pads = ivec2(${p}, ${m});\n const float neg_infinity = -3.4e38;\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.w;\n ivec2 outTopLeftCorner =\n coords.yz * strides - pads;\n int hBeg = outTopLeftCorner.x;\n int wBeg = outTopLeftCorner.y;\n\n float curVal = neg_infinity;\n for (int h = 0; h < ${a}; h++) {\n int hIn = hBeg + h * ${l};\n\n if (hIn >= 0 && hIn < ${e}) {\n for (int w = 0; w < ${u}; w++) {\n int wIn = wBeg + w * ${c};\n\n if (wIn >= 0 && wIn < ${n}) {\n float xVal = getX(batch, hIn, wIn, d1);\n float wVal = getW(h, w, d1);\n\n float val = xVal + wVal;\n if (val > curVal) {\n curVal = val;\n }\n }\n }\n }\n }\n\n float result = curVal;\n setOutput(result);\n }\n `}};function pit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n,l=S.computeDilation2DInfo(o.shape,s.shape,i,a,\"NHWC\",u),c,p=new UI(l);c=e.runWebGLProgram(p,[o,s],\"float32\");let m=rt({inputs:{x:c},backend:e,attrs:{shape:l.outShape}});return e.disposeIntermediateTensorInfo(c),m}var xB={kernelName:is,backendName:\"webgl\",kernelFunc:pit};function mit(r){let{inputs:t,backend:e,attrs:n}=r,{equation:o}=n,s=t,{allDims:i,summedDims:a,idDims:u}=S.decodeEinsumEquation(o,s.length);S.checkEinsumDimSizes(i.length,u,s);let{path:l,steps:c}=S.getEinsumComputePath(a,u),p=c.length,m=null,f=i.length,d=[];for(let h=0;h=0&&(m=fp({inputs:{x:m},backend:e,attrs:{axis:l[h]-(i.length-f),keepDims:!1}}),d.push(m)),f--)}for(let h of d)h!==m&&e.disposeIntermediateTensorInfo(h);return m}var yB={kernelName:Fp,backendName:\"webgl\",kernelFunc:mit};var fit=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",dit=`\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n`,hit=It({opSnippet:fit,packedOpSnippet:dit}),bB={kernelName:ls,backendName:\"webgl\",kernelFunc:hit};var git=\"return (b >= 0.0) ? a : a * (b + 1.0);\",xit=`\n vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n`,yit=r=>{let{inputs:t,backend:e}=r,{dy:n,y:o}=t,s=L().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new jn(xit,n.shape,o.shape):new $n(git,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],n.dtype)},wB={kernelName:La,backendName:\"webgl\",kernelFunc:yit};var bit=`\n return vec4(equal(a, b));\n`,wit=\"return float(a == b);\",Iit=ce({opSnippet:wit,packedOpSnippet:bit,dtype:\"bool\",cpuKernelImpl:JL}),IB={kernelName:za,backendName:\"webgl\",kernelFunc:Iit};var Cit=`\n // Error function is calculated approximately with elementary function.\n // See \"Handbook of Mathematical Functions with Formulas,\n // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n float p = ${S.ERF_P};\n float a1 = ${S.ERF_A1};\n float a2 = ${S.ERF_A2};\n float a3 = ${S.ERF_A3};\n float a4 = ${S.ERF_A4};\n float a5 = ${S.ERF_A5};\n\n float sign = sign(x);\n x = abs(x);\n float t = 1.0 / (1.0 + p * x);\n return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n`,vit=It({opSnippet:Cit}),CB={kernelName:us,backendName:\"webgl\",kernelFunc:vit};var Sit=Po+`\n return exp(x);\n`,Nit=`\n vec4 result = exp(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,z1=It({opSnippet:Sit,packedOpSnippet:Nit,cpuKernelImpl:QL,dtype:\"float32\"}),vB={kernelName:cs,backendName:\"webgl\",kernelFunc:z1};function HI(r){let{inputs:t,attrs:e,backend:n}=r,{dim:o}=e,{input:s}=t,i=s.shape.length,a=s.shape.slice(),u=o;return o<0&&(y.assert(-(i+1)<=o,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+o+1),a.splice(u,0,1),rt({inputs:{x:s},backend:n,attrs:{shape:a}})}var SB={kernelName:Mi,backendName:\"webgl\",kernelFunc:HI};var NB=\"return exp(x) - 1.0;\",kit=It({opSnippet:NB,packedOpSnippet:NB,cpuKernelImpl:tz}),kB={kernelName:ps,backendName:\"webgl\",kernelFunc:kit};var hg=class{constructor(t,e,n){this.variableNames=[\"real\",\"imag\"];let o=e[1];this.outputShape=e;let s=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,i=n?`${o}.0`:\"1.0\",a;if(t===\"real\")a=\"return real * expR - imag * expI;\";else if(t===\"imag\")a=\"return real * expI + imag * expR;\";else throw new Error(`FFT component must be either \"real\" or \"imag\", got ${t}.`);this.userCode=`\n const float exponentMultiplier = ${s};\n\n float unaryOpComplex(float real, float expR, float imag, float expI) {\n ${a}\n }\n\n float mulMatDFT(int batch, int index) {\n float indexRatio = float(index) / float(${o});\n float exponentMultiplierTimesIndexRatio =\n exponentMultiplier * indexRatio;\n\n float result = 0.0;\n\n for (int i = 0; i < ${o}; i++) {\n // x = (-2|2 * PI / N) * index * i;\n float x = exponentMultiplierTimesIndexRatio * float(i);\n float expR = cos(x);\n float expI = sin(x);\n float real = getReal(batch, i);\n float imag = getImag(batch, i);\n\n result +=\n unaryOpComplex(real, expR, imag, expI) / ${i};\n }\n\n return result;\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n setOutput(mulMatDFT(coords[0], coords[1]));\n }\n `}};function qI(r,t,e){let n=e.texData.get(r.dataId),o=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],i=o/s,a=rt({inputs:{x:r},backend:e,attrs:{shape:[i,s]}}),u=a.shape,l=new hg(\"real\",u,t),c=new hg(\"imag\",u,t),p=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:u},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:u}],m=e.runWebGLProgram(l,p,\"float32\"),f=e.runWebGLProgram(c,p,\"float32\"),d=Rn({inputs:{real:m,imag:f},backend:e});e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f);let h=rt({inputs:{x:d},backend:e,attrs:{shape:r.shape}});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(d),h}function Tit(r){let{inputs:t,backend:e}=r,{input:n}=t;return qI(n,!1,e)}var TB={kernelName:Op,backendName:\"webgl\",kernelFunc:Tit};var KI=class{constructor(t,e){this.outputShape=[],this.customUniforms=[{name:\"value\",type:\"float\"}],this.variableNames=[\"x\"],this.outputShape=t,this.userCode=`\n void main() {\n // Input can be obtained from uniform value.\n setOutput(value);\n }\n `}};function Ll(r){let{backend:t,attrs:e}=r,{shape:n,value:o}=e,{dtype:s}=e;if(s=s||y.inferDtype(o),s===\"string\"){let i=y.getArrayFromDType(s,y.sizeFromShape(n));return i.fill(o),t.makeTensorInfo(n,s,i)}else{let i=new KI(n,o),a=[[o]];return t.runWebGLProgram(i,[],s,a)}}var _B={kernelName:Jl,backendName:\"webgl\",kernelFunc:Ll};var jI=class{constructor(t){this.variableNames=[\"Image\"],this.outputShape=[];let e=t[2];this.outputShape=t,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n\n int coordX = ${e} - x - 1;\n float outputValue;\n if(coordX >= 0 && coordX < ${e}) {\n outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n } else {\n outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n }\n setOutput(outputValue);\n }\n `}};var EB={kernelName:Ba,backendName:\"webgl\",kernelFunc:({inputs:r,backend:t})=>{let{image:e}=r,n=t,o=new jI(e.shape);return n.runWebGLProgram(o,[e],e.dtype)}};var AB=\"return floor(x);\",_it=It({opSnippet:AB,packedOpSnippet:AB,cpuKernelImpl:ez}),DB={kernelName:ms,backendName:\"webgl\",kernelFunc:_it};var Eit=`\n float s = sign(a) * sign(b);\n int ia = round(a);\n int ib = round(b);\n if (ib != 0) {\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n return float(idiv(ia, ib, s));\n } else {\n return NAN;\n }\n`,Ait=`\n ivec4 ia = round(a);\n ivec4 ib = round(b);\n bvec4 cond = notEqual(ib, ivec4(0));\n ivec4 result = ivec4(0);\n vec4 s = sign(a) * sign(b);\n\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n if (cond[0]) {\n result[0] = idiv(ia[0], ib[0], s[0]);\n }\n if (cond[1]) {\n result[1] = idiv(ia[1], ib[1], s[1]);\n }\n if (cond[2]) {\n result[2] = idiv(ia[2], ib[2], s[2]);\n }\n if (cond[3]) {\n result[3] = idiv(ia[3], ib[3], s[3]);\n }\n return vec4(result);\n`,Dit=ce({opSnippet:Eit,packedOpSnippet:Ait,dtype:\"int32\"}),$B={kernelName:fs,backendName:\"webgl\",kernelFunc:Dit};var XI=class{constructor(t){this.variableNames=[\"A\"];let e=Ue(),[n,o]=t;this.outputShape=t,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${o}.0, ${n}.0);\n\n vec4 values = ${e.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n setOutput(floor(value * 255.0 + 0.5));\n }\n `}};var YI=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0;let e=Ue(),[n,o]=t;this.outputShape=t,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n\n vec4 result = vec4(0.);\n\n for(int row=0; row<=1; row++) {\n for(int col=0; col<=1; col++) {\n texC = coords[1] + row;\n depth = coords[2] + col;\n\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2(${o}.0, ${n}.0);\n vec4 values = ${e.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n result[row * 2 + col] = floor(value * 255.0 + 0.5);\n }\n }\n\n ${e.output} = result;\n }\n `}};var RB={kernelName:th,backendName:\"webgl\",kernelFunc:$it},zd,B1=L().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");function $it(r){let{inputs:t,backend:e,attrs:n}=r,{pixels:o}=t,{numChannels:s}=n,i=typeof HTMLVideoElement!=\"undefined\"&&o instanceof HTMLVideoElement,a=typeof HTMLImageElement!=\"undefined\"&&o instanceof HTMLImageElement,[u,l]=i?[o.videoWidth,o.videoHeight]:[o.width,o.height],c=[l,u],p=[l,u,s];if(a||i){let h=L().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");(zd==null||h!==B1)&&(B1=h,zd=document.createElement(\"canvas\").getContext(\"2d\",{willReadFrequently:B1})),zd.canvas.width=u,zd.canvas.height=l,zd.drawImage(o,0,0,u,l),o=zd.canvas}let m=e.makeTensorInfo(c,\"int32\");e.texData.get(m.dataId).usage=Jr.PIXELS,e.gpgpu.uploadPixelDataToTexture(e.getTexture(m.dataId),o);let f=L().getBool(\"WEBGL_PACK\")?new YI(p):new XI(p),d=e.runWebGLProgram(f,[m],\"int32\");return e.disposeData(m.dataId),d}function Rit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=n,h=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(o.shape,s.shape,u,p,l,m,!1,h),x,b=[],w=i!=null,I=a!=null,N=f===\"leakyrelu\",E=()=>{let D=[o,s],F=(M,V)=>{if(V===\"NCHW\"&&M.shape.length===1&&M.shape[0]!==1){let G=rt({inputs:{x:M},backend:e,attrs:{shape:[M.shape[0],1,1]}});return b.push(G),G}return M};if(w&&D.push(F(i,c)),I&&D.push(F(a,c)),N){let M=e.makeTensorInfo([],\"float32\",y.createScalarValue(d,\"float32\"));D.push(M),b.push(M)}return D};if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type===\"SAME\"||g.padInfo.type===\"VALID\"))x=DI({x:o,filter:s,convInfo:g,backend:e,bias:i,activation:f,preluActivationWeights:a,leakyreluAlpha:d});else if(g.strideWidth<=2&&h===\"channelsLast\"&&L().getBool(\"WEBGL_EXP_CONV\")){let D=f?Ml(f,!0):null,F=new Md(g,w,D,I,N),M=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],V=E();x=e.runWebGLProgram(F,V,\"float32\",M)}else if(L().getBool(\"WEBGL_CONV_IM2COL\"))x=$I({x:o,filter:s,convInfo:g,backend:e,bias:i,activation:f,preluActivationWeights:a,leakyreluAlpha:d});else{let D=f?Ml(f,!1):null,F=new Od(g,w,D,I,N),M=E();x=e.runWebGLProgram(F,M,\"float32\")}let A=rt({inputs:{x},backend:e,attrs:{shape:g.outShape}});return b.push(x),b.forEach(D=>e.disposeIntermediateTensorInfo(D)),A}var FB={kernelName:Yi,backendName:\"webgl\",kernelFunc:Rit};function Fit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:p,activation:m,leakyreluAlpha:f}=n,d=[],h=c;h==null&&(h=[1,1]),y.assert(S.eitherStridesOrDilationsAreOne(u,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${h}'`);let g=S.computeConv2DInfo(o.shape,s.shape,u,h,l,p,!0),x=L().getBool(\"WEBGL_PACK_DEPTHWISECONV\")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,b=m?Ml(m,x):null,w=[o,s],I=i!=null,N=a!=null,E=m===\"leakyrelu\";if(I&&w.push(i),N&&w.push(a),E){let M=e.makeTensorInfo([],\"float32\",y.createScalarValue(f,\"float32\"));w.push(M),d.push(M)}let A;x?A=new Ld(g,I,b,N,E):A=new Pd(g,I,b,N,E);let D=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],F=e.runWebGLProgram(A,w,\"float32\",D);return d.forEach(M=>e.disposeIntermediateTensorInfo(M)),F}var OB={kernelName:Zi,backendName:\"webgl\",kernelFunc:Fit};var ZI=class{constructor(t,e,n,o){this.sliceDim=t,this.strides=e,this.paramsShape=o,this.variableNames=[\"x\",\"indices\"],this.outputShape=n;let s=zt(n.length),i=`\n int index;`;for(let a=0;a= ${this.paramsShape[a]};\n flattenIndex += index * ${this.strides[a]};`;this.userCode=`\n void main() {\n ${s} coords = getOutputCoords();\n int flattenIndex = 0;\n bool out_of_bounds = false;\n\n ${i}\n\n setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));\n }\n `}};function Oit(r){let{inputs:t,backend:e}=r,{params:n,indices:o}=t,s=o.shape,i=s[s.length-1],a=y.sizeFromShape(n.shape),[u,l,c,p]=S.prepareAndValidate(n,o),m=rt({inputs:{x:o},backend:e,attrs:{shape:[l,i]}}),f=rt({inputs:{x:n},backend:e,attrs:{shape:[y.sizeFromShape(n.shape)/c,c]}});if(e.shouldExecuteOnCPU([n,o])||n.dtype===\"string\"){let x=e.readSync(o.dataId),b=e.bufferSync(n),w=rz(x,b,n.dtype,l,i,c,p,n.shape,a);return e.makeTensorInfo(u,n.dtype,w.values)}let d=new ZI(i,p,[l,c],n.shape),h=e.runWebGLProgram(d,[f,m],f.dtype),g=rt({inputs:{x:h},backend:e,attrs:{shape:u}});return e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(h),g}var MB={kernelName:Va,backendName:\"webgl\",kernelFunc:Oit};var JI=class{constructor(t,e){this.variableNames=[\"A\",\"indices\"],this.outputShape=e,this.rank=e.length;let n=zt(this.rank),o=Mit(t,2);this.userCode=`\n void main() {\n ${n} resRC = getOutputCoords();\n int index = int(getIndices(resRC.x, resRC.z));\n float inBounds = (index >= 0) && (index < ${t[2]}) ? 1.0 : 0.0;\n setOutput(inBounds * getA(${o}));\n }\n `}};function Mit(r,t){let e=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],n=[];for(let o=0;o=0,()=>`GatherV2: the index value ${N} is not in [0, ${w-1}]`)}}let l=S.segment_util.collectGatherOpShapeInfo(o,s,u,a),c=y.sizeFromShape(s.shape),p=[],m=rt({inputs:{x:o},backend:e,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),f=rt({inputs:{x:s},backend:e,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(m),p.push(f);let d=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(e.shouldExecuteOnCPU([o,s])||o.dtype===\"string\"){let b=e.bufferSync(f),w=e.bufferSync(m),I=nz(w,b,d);return p.forEach(N=>e.disposeIntermediateTensorInfo(N)),e.makeTensorInfo(l.outputShape,I.dtype,I.values)}let h=new JI(m.shape,d),g=e.runWebGLProgram(h,[m,f],m.dtype);p.push(g);let x=rt({inputs:{x:g},backend:e,attrs:{shape:l.outputShape}});return p.forEach(b=>e.disposeIntermediateTensorInfo(b)),x}var PB={kernelName:Pi,backendName:\"webgl\",kernelFunc:V1};var Pit=\"return float(a > b);\",Lit=`\n return vec4(greaterThan(a, b));\n`,zit=ce({opSnippet:Pit,packedOpSnippet:Lit,cpuKernelImpl:oz,dtype:\"bool\"}),LB={kernelName:Ga,backendName:\"webgl\",kernelFunc:zit};var Bit=\"return float(a >= b);\",Vit=`\n return vec4(greaterThanEqual(a, b));\n`,Git=ce({opSnippet:Bit,packedOpSnippet:Vit,dtype:\"bool\",cpuKernelImpl:sz}),zB={kernelName:hs,backendName:\"webgl\",kernelFunc:Git};function Wit(r){let{inputs:t,backend:e}=r,{input:n}=t;return qI(n,!0,e)}var BB={kernelName:Mp,backendName:\"webgl\",kernelFunc:Wit};var Uit=\"return float(!isnan(x) && !isinf(x));\",Hit=It({opSnippet:Uit,dtype:\"bool\"}),VB={kernelName:gs,backendName:\"webgl\",kernelFunc:Hit};var qit=\"return float(isinf(x));\",Kit=It({opSnippet:qit,dtype:\"bool\"}),GB={kernelName:xs,backendName:\"webgl\",kernelFunc:Kit};var jit=\"return float(isnan(x));\",Xit=It({opSnippet:jit,dtype:\"bool\"}),WB={kernelName:ys,backendName:\"webgl\",kernelFunc:Xit};var Yit=\"return float(a < b);\",Zit=`\n return vec4(lessThan(a, b));\n`,Jit=ce({opSnippet:Yit,packedOpSnippet:Zit,cpuKernelImpl:iz,dtype:\"bool\"}),UB={kernelName:Wa,backendName:\"webgl\",kernelFunc:Jit};var Qit=\"return float(a <= b);\",tat=`\n return vec4(lessThanEqual(a, b));\n`,eat=ce({opSnippet:Qit,packedOpSnippet:tat,cpuKernelImpl:az,dtype:\"bool\"}),HB={kernelName:Ua,backendName:\"webgl\",kernelFunc:eat};function rat(r){let{backend:t,attrs:e}=r,{start:n,stop:o,num:s}=e,i=lz(n,o,s);return t.makeTensorInfo([i.length],\"float32\",i)}var qB={kernelName:Ha,backendName:\"webgl\",kernelFunc:rat};var nat=Po+`\n return x < 0.0 ? 0./0. : log(x);\n`,oat=`\n vec4 result = log(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);\n result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);\n result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);\n result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);\n return result;\n`,sat=It({opSnippet:nat,packedOpSnippet:oat,cpuKernelImpl:uz}),KB={kernelName:ws,backendName:\"webgl\",kernelFunc:sat};var iat=Po+`\n return log(1.0 + x);\n`,aat=It({opSnippet:iat}),jB={kernelName:Is,backendName:\"webgl\",kernelFunc:aat};var lat=\"return float(a >= 1.0 && b >= 1.0);\",uat=`\n return vec4(\n vec4(greaterThanEqual(a, vec4(1.0))) *\n vec4(greaterThanEqual(b, vec4(1.0))));\n`,cat=ce({opSnippet:lat,packedOpSnippet:uat,dtype:\"bool\"}),XB={kernelName:qa,backendName:\"webgl\",kernelFunc:cat};var pat=\"return float(!(x >= 1.0));\",mat=It({opSnippet:pat}),YB={kernelName:Ka,backendName:\"webgl\",kernelFunc:mat};var fat=\"return float(a >= 1.0 || b >= 1.0);\",dat=`\n return min(\n vec4(greaterThanEqual(a, vec4(1.0))) +\n vec4(greaterThanEqual(b, vec4(1.0))),\n vec4(1.0));\n`,hat=ce({opSnippet:fat,packedOpSnippet:dat,dtype:\"bool\"}),ZB={kernelName:ja,backendName:\"webgl\",kernelFunc:hat};var QI=class{constructor(t,e,n,o,s){this.variableNames=[\"x\"],this.outputShape=[];let i=e,a=t[3]-1;this.outputShape=t;let u,l=`float(${n}) + float(${o}) * sum`;s===.5?u=`inversesqrt(${l})`:s===1?u=`1.0/(${l})`:u=`exp(log(${l}) * float(-${s}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n int d = coords[3];\n float x = getX(b, r, c, d);\n float sum = 0.0;\n for (int j = -${i}; j <= ${i}; j++) {\n int idx = d + j;\n if (idx >= 0 && idx <= ${a}) {\n float z = getX(b, r, c, idx);\n sum += z * z;\n }\n }\n float val = x * ${u};\n setOutput(val);\n }\n `}};var tC=class{constructor(t,e,n,o,s){this.variableNames=[\"x\"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let i=e,a=t[3]-1;this.outputShape=t;let u,l=`float(${n}) + float(${o}) * sum`;s===.5?u=`inversesqrt(${l})`:s===1?u=`1.0/(${l})`:u=`exp(log(${l}) * float(-${s}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords.x;\n int r = coords.y;\n int c = coords.z;\n int d = coords.w;\n\n bool hasNextCol = d < ${this.outputShape[3]};\n bool hasNextRow = c < ${this.outputShape[2]};\n\n vec4 sum = vec4(0.);\n vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n vec4 xAtOutputCoords = vec4(\n getChannel(xFragAtOutputCoords, vec2(c, d)),\n hasNextCol ?\n getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n hasNextRow ?\n getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n (hasNextRow && hasNextCol) ?\n getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n );\n\n int firstChannel = d - ${i};\n vec2 cache = vec2(0.);\n if(firstChannel >= 0){\n vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n if(hasNextRow){\n cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n }\n }\n\n ivec2 depth = ivec2(d, d + 1);\n for (int j = - ${i}; j <= ${i}; j++) {\n ivec2 idx = depth + j;\n bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${a}));\n\n bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n if(depthInRange || depthPlusOneInRange){\n vec4 z = vec4(0.);\n vec4 xFragAtCurrentDepth;\n z.xz = cache.xy;\n if(depthPlusOneInRange && hasNextCol){\n xFragAtCurrentDepth = idx.y != d ?\n getX(b, r, c, idx.y) : xFragAtOutputCoords;\n z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n if(hasNextRow){\n z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n }\n }\n cache.xy = z.yw;\n sum += z * z;\n }\n }\n vec4 result = xAtOutputCoords * ${u};\n setOutput(result);\n }\n `}};var gat=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n,l=L().getBool(\"WEBGL_PACK_NORMALIZATION\")?new tC(o.shape,s,i,a,u):new QI(o.shape,s,i,a,u);return e.runWebGLProgram(l,[o],o.dtype)},JB={kernelName:Cs,backendName:\"webgl\",kernelFunc:gat};var eC=class{constructor(t,e,n,o,s){this.variableNames=[\"inputImage\",\"outputImage\",\"dy\"],this.outputShape=[],this.outputShape=t,this.depth=t[3],this.depthRadius=e,this.bias=n,this.alpha=o,this.beta=s,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n\n float result = 0.0;\n for (int d = 0; d < ${this.depth}; ++d) {\n int depthBegin = int(max(0.0, float(d - ${e})));\n int depthEnd = int(min(float(${this.depth}),\n float(d + ${e} + 1)));\n\n const int MIN_DEPTH_BEGIN = 0;\n const int MAX_DEPTH_END = ${this.depth};\n\n float norm = 0.0;\n for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd) {\n norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n }\n else {\n break;\n }\n }\n\n norm = float(${o}) * norm + float(${n});\n\n for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd){\n float dyi = -2.0 * float(${o})\n * float(${s})\n * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d)\n / norm;\n if (k == d) {\n dyi += pow(norm, -1.0 * ${s});\n }\n if (k == coords[3]) {\n dyi *= getDy(b, r, c, d);\n result += dyi;\n }\n }\n else {\n break;\n }\n }\n }\n setOutput(result);\n }\n `}};var xat=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o,y:s,dy:i}=t,{depthRadius:a,bias:u,alpha:l,beta:c}=n,p=new eC(o.shape,a,u,l,c);return e.runWebGLProgram(p,[o,s,i],o.dtype)},QB={kernelName:Xa,backendName:\"webgl\",kernelFunc:xat};function tV(r,t,e,n){let o=y.sizeFromShape(t),i=y.sizeFromShape(r.shape)/o,a=rt({inputs:{x:r},attrs:{shape:[i,o]},backend:n}),u=Yn(a,r.dtype,\"max\",n),l=rt({inputs:{x:u},attrs:{shape:e},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(u),l}function G1(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{reductionIndices:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=c!=null,m=e.shouldExecuteOnCPU([o]),f=o;if(p){if(m){let w=e.texData.get(f.dataId).values,I=new Array(a);for(let A=0;A`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,l,a,u);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return nr({inputs:{x:o},backend:e});let p=new Ni(c,\"max\",!1);return e.runWebGLProgram(p,[o],o.dtype)}var nV={kernelName:Ns,backendName:\"webgl\",kernelFunc:Iat};function Cat(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dataFormat:u,dimRoundingMode:l}=n,c=[1,1,1],p=S.computePool3DInfo(o.shape,s,i,c,a,l,u),m=new qu(p,\"max\",!1);return e.runWebGLProgram(m,[o],o.dtype)}var oV={kernelName:Li,backendName:\"webgl\",kernelFunc:Cat};var rC=class{constructor(t){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=t.inShape;let e=t.strideHeight,n=t.strideWidth,o=t.dilationHeight,s=t.effectiveFilterHeight,i=t.effectiveFilterWidth,a=s-1-t.padInfo.top,u=i-1-t.padInfo.left,l=s*i-1;this.userCode=`\n const ivec2 pads = ivec2(${a}, ${u});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${s};\n wR += ${o}) {\n float dyR = float(dyRCorner + wR) / ${e}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${i}; wC++) {\n float dyC = float(dyCCorner + wC) / ${n}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n int maxPosValue = ${l} - int(getMaxPos(b, idyR, idyC, d));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue = wR * ${i} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n setOutput(dotProd);\n }\n `}},nC=class{constructor(t){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=t.inShape;let e=t.strideDepth,n=t.strideHeight,o=t.strideWidth,s=t.dilationDepth,i=t.dilationHeight,a=t.dilationWidth,u=t.effectiveFilterDepth,l=t.effectiveFilterHeight,c=t.effectiveFilterWidth,p=u-1-t.padInfo.front,m=l-1-t.padInfo.top,f=c-1-t.padInfo.left,d=u*l*c-1;this.userCode=`\n const ivec3 pads = ivec3(${p}, ${m}, ${f});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${u};\n wD += ${s}) {\n float dyD = float(dyDCorner + wD) / ${e}.0;\n\n if (dyD < 0.0 || dyD >= ${t.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${l};\n wR += ${i}) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${c};\n wC += ${a}) {\n float dyC = float(dyCCorner + wC) / ${o}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n int maxPosValue = ${d} -\n int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue =\n wD * ${l} * ${c} +\n wR * ${c} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function vat(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s,{filterSize:a,strides:u,pad:l,dimRoundingMode:c}=n,p=[1,1,1],m=S.computePool3DInfo(i.shape,a,u,p,l,c),f=new qu(m,\"max\",!0),d=e.runWebGLProgram(f,[i],i.dtype),h=new nC(m),g=e.runWebGLProgram(h,[o,d],i.dtype);return e.disposeIntermediateTensorInfo(d),g}var sV={kernelName:tu,backendName:\"webgl\",kernelFunc:vat};function Sat(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s,output:i}=t,a=s;vi([s,i],\"maxPoolGrad\");let{filterSize:u,strides:l,pad:c,dimRoundingMode:p}=n,m=S.computePool2DInfo(a.shape,u,l,1,c,p),f=!0,d=new Ni(m,\"max\",f),h=e.runWebGLProgram(d,[a],a.dtype),g=new rC(m),x=e.runWebGLProgram(g,[o,h],a.dtype);return e.disposeIntermediateTensorInfo(h),x}var iV={kernelName:Ql,backendName:\"webgl\",kernelFunc:Sat};function aV(r,t,e,n){let o=new Ni(e,\"max\",!1),s=n.runWebGLProgram(o,[r],\"float32\");o=new Ni(e,\"max\",!0,!0,t);let i=n.runWebGLProgram(o,[r],\"float32\");return[s,i]}var lV={kernelName:eu,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:n}=r,{filterSize:o,strides:s,pad:i,includeBatchInIndex:a}=t,u=e;y.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let l=[1,1];y.assert(S.eitherStridesOrDilationsAreOne(s,l),()=>`Error in maxPool: Either strides or dilations must be 1. 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uC=class{constructor(t,e,n){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"value\",type:\"float\"}],this.outputShape=e.map((h,g)=>h[0]+t[g]+h[1]);let o=t.length,s=zt(o),i=e.map(h=>h[0]).join(\",\"),a=e.map((h,g)=>h[0]+t[g]).join(\",\"),u=rr(\"rc\",o),l=rr(\"source\",o),c=`${u[o-1]} < ${this.outputShape[o-1]}`,p=o===1?\"source\":`vec2(${l.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${u[o-1]} += 1;\n if(${c}) {\n `,o===1?\"\":`}\n rc = outputLoc;\n ${u[o-2]} += 1;\n if(${u[o-2]} < ${this.outputShape[o-2]}) {`,o===1?\"\":` ${u[o-1]} += 1;\n if(${c}) {`],f=o===1?\"rc < start || rc >= end\":\"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))\",d=\"\";for(let h=0,g=o===1?2:4;h{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{paddings:s,constantValue:i}=n;if(y.sizeFromShape(o.shape)===0){let l=s.map((c,p)=>c[0]+o.shape[p]+c[1]);return Ll({backend:e,attrs:{shape:l,value:i,dtype:o.dtype}})}let a=L().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new uC(o.shape,s,i):new lC(o.shape,s,i),u=[[i]];return e.runWebGLProgram(a,[o],o.dtype,u)},EV={kernelName:Rs,backendName:\"webgl\",kernelFunc:q1};var Kat=`\n if(a < 0.0 && floor(b) < b){\n return NAN;\n }\n if (b == 0.0) {\n return 1.0;\n }\n return (round(mod(b, 2.0)) != 1) ?\n pow(abs(a), b) : sign(a) * pow(abs(a), b);\n`,jat=`\n // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n vec4 result = multiplier * pow(abs(a), b);\n\n // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n bvec4 isExpZero = equal(b, vec4(0.0));\n result.r = isExpZero.r ? 1.0 : result.r;\n result.g = isExpZero.g ? 1.0 : result.g;\n result.b = isExpZero.b ? 1.0 : result.b;\n result.a = isExpZero.a ? 1.0 : result.a;\n\n bvec4 isNaN1 = lessThan(a, vec4(0.0));\n bvec4 isNaN2 = 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d=S.expandShapeToKeepDim(f.shape,l);f=rt({inputs:{x:f},backend:e,attrs:{shape:d}})}return u.forEach(d=>e.disposeIntermediateTensorInfo(d)),f}var DV={kernelName:Ms,backendName:\"webgl\",kernelFunc:Yat};function Zat(r){let{inputs:t,backend:e,attrs:n}=r,{paramsNestedSplits:o,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:a}=n,u=o.map(x=>e.readSync(x.dataId)),l=o.map(x=>x.shape),c=e.readSync(s.dataId),p=e.readSync(i.dataId),[m,f,d]=xz(u,l,c,s.shape,s.dtype,p,i.shape,a),h=m.map(x=>e.makeTensorInfo([x.length],\"int32\",x)),g=e.makeTensorInfo(d,s.dtype,f);return h.concat([g])}var $V={kernelName:Lp,backendName:\"webgl\",kernelFunc:Zat};function Jat(r){let{inputs:t,backend:e}=r,{starts:n,limits:o,deltas:s}=t,i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=yz(i,n.shape,n.dtype,a,o.shape,u,s.shape),p=e.makeTensorInfo([l.length],\"int32\",l),m=e.makeTensorInfo([c.length],n.dtype,c);return[p,m]}var RV={kernelName:zp,backendName:\"webgl\",kernelFunc:Jat};function Qat(r){let{inputs:t,backend:e,attrs:n}=r,{shape:o,values:s,defaultValue:i,rowPartitionTensors:a}=t,{rowPartitionTypes:u}=n,l=e.readSync(o.dataId),c=e.readSync(s.dataId),p=e.readSync(i.dataId),m=a.map(g=>e.readSync(g.dataId)),f=a.map(g=>g.shape),[d,h]=bz(l,o.shape,c,s.shape,s.dtype,p,i.shape,m,f,u);return e.makeTensorInfo(d,s.dtype,h)}var FV={kernelName:Bp,backendName:\"webgl\",kernelFunc:Qat};var K1=r=>{let{backend:t,attrs:e}=r,{start:n,stop:o,step:s,dtype:i}=e,a=wz(n,o,s,i);return t.makeTensorInfo([a.length],i,a)},OV={kernelName:ru,backendName:\"webgl\",kernelFunc:K1};var tlt=\"return 1.0 / x;\",elt=It({opSnippet:tlt}),MV={kernelName:Ps,backendName:\"webgl\",kernelFunc:elt};var rlt=xr+`\n return (x < 0.0) ? 0.0 : x;\n`,nlt=`\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,olt=It({opSnippet:rlt,packedOpSnippet:nlt}),PV={kernelName:Ls,backendName:\"webgl\",kernelFunc:olt};var slt=xr+`\n return (x < 0.0) ? 0.0 : min(6.0, x);\n`,ilt=`\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,alt=It({opSnippet:slt,packedOpSnippet:ilt}),LV={kernelName:Vs,backendName:\"webgl\",kernelFunc:alt};var cC=class{constructor(t,e,n,o,s){this.variableNames=[\"A\"],this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m;s?m=\"(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)\":m=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${c[0]/p[0]},\n ${c[1]/p[1]});\n const vec2 inputShapeRC = vec2(${a}.0, ${u}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${m};\n\n // Compute the four integer indices.\n ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));\n ivec2 sourceCeilRC = ivec2(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n float top = topLeft + (topRight - topLeft) * fracRC.y;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n float newValue = top + (bottom - top) * fracRC.x;\n\n setOutput(newValue);\n }\n `}};var pC=class{constructor(t,e,n,o,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m;s?m=\"(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)\":m=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${c[0]/p[0]},\n ${c[1]/p[1]},\n ${c[1]/p[1]});\n const vec3 inputShapeRC = vec3(${a}.0, ${u}.0,\n ${u}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${m};\n\n // Compute the four integer indices.\n ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));\n ivec3 sourceCeilRC = ivec3(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${l-1};\n bool hasNextRow = coords.z < ${n-1};\n\n // In parallel, construct four corners for all four components in\n // packed 2x2 cell.\n vec4 topLeft = vec4(\n getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 bottomLeft = vec4(\n getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 topRight = vec4(\n getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec4 bottomRight = vec4(\n getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n vec4 newValue = mix(top, bottom, fracRC.x);\n\n setOutput(newValue);\n }\n `}};function llt(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n,[u,l]=a,c=L().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new pC(o.shape,u,l,s,i):new cC(o.shape,u,l,s,i);return e.runWebGLProgram(c,[o],\"float32\")}var zV={kernelName:Bs,backendName:\"webgl\",kernelFunc:llt};var mC=class{constructor(t,e,n){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=e;let[,o,s]=e,[,i,a]=t,u=[n&&i>1?o-1:o,n&&a>1?s-1:s],l=[n&&i>1?i-1:i,n&&a>1?a-1:a],c=u[0]/l[0],p=u[1]/l[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${c});\n const float widthScale = float(${p});\n\n const float invHeightScale = float(${m});\n const float invWidthScale = float(${f});\n\n const int winHeight = int(${d});\n const int winWidth = int(${h});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(startRLerp - float(winHeight / 2));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(startCLerp - float(winWidth / 2));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${i}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${a}) {\n continue;\n }\n\n float dxR = float(dyR) * heightScale;\n int topDxRIndex = int(floor(dxR));\n int bottomDxRIndex = int(min(ceil(dxR), ${o-1}.0));\n float dxRLerp = dxR - float(topDxRIndex);\n float inverseDxRLerp = 1.0 - dxRLerp;\n\n float dxC = float(dyC) * widthScale;\n int leftDxCIndex = int(floor(dxC));\n int rightDxCIndex = int(min(ceil(dxC), ${s-1}.0));\n float dxCLerp = dxC - float(leftDxCIndex);\n float inverseDxCLerp = 1.0 - dxCLerp;\n\n if (r == topDxRIndex && c == leftDxCIndex) {\n // topLeft\n accumulator +=\n getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n }\n\n if (r == topDxRIndex && c == rightDxCIndex) {\n // topRight\n accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n }\n\n if (r == bottomDxRIndex && c == leftDxCIndex) {\n // bottomLeft\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n }\n\n if (r == bottomDxRIndex && c == rightDxCIndex) {\n // bottomRight\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}};function ult(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=new mC(s.shape,o.shape,i);return e.runWebGLProgram(a,[s],s.dtype)}var BV={kernelName:rl,backendName:\"webgl\",kernelFunc:ult};var fC=class{constructor(t,e,n,o,s){this.variableNames=[\"A\"],this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m=o?\"0.5\":\"0.0\",f;s?f=\"max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))\":f=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${c[0]/p[0]},\n ${c[1]/p[1]});\n const vec2 inputShapeRC = vec2(${a}.0, ${u}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${f};\n\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestRC = ivec2(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));\n float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n setOutput(newValue);\n }\n `}};var dC=class{constructor(t,e,n,o,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m=o?\"0.5\":\"0.0\",f;s?f=\"max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))\":f=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${c[0]/p[0]},\n ${c[1]/p[1]},\n ${c[1]/p[1]});\n const vec3 inputShapeRC = vec3(${a}.0, ${u}.0,\n ${u}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${f};\n\n // Compute the coordinators of nearest neighbor point.\n ivec3 sourceNearestRC = ivec3(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${l-1};\n bool hasNextRow = coords.z < ${n-1};\n\n vec4 newValue = vec4(\n getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),\n hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);\n\n setOutput(newValue);\n }\n `}};function clt(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n,[u,l]=a,c=L().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new dC(o.shape,u,l,s,i):new fC(o.shape,u,l,s,i);return e.runWebGLProgram(c,[o],o.dtype)}var VV={kernelName:zs,backendName:\"webgl\",kernelFunc:clt};var hC=class{constructor(t,e,n){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=e;let[,o,s]=e,[,i,a]=t,u=[n&&i>1?o-1:o,n&&a>1?s-1:s],l=[n&&i>1?i-1:i,n&&a>1?a-1:a],c=u[0]/l[0],p=u[1]/l[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${c});\n const float widthScale = float(${p});\n\n const float invHeightScale = float(${m});\n const float invWidthScale = float(${f});\n\n const int winHeight = int(${d});\n const int winWidth = int(${h});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${i}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${a}) {\n continue;\n }\n\n float sourceFracRow =\n float(${u[0]}) *\n (float(dyR) / float(${l[0]}));\n\n float sourceFracCol =\n float(${u[1]}) *\n (float(dyC) / float(${l[1]}));\n\n int sourceNearestRow = int(min(\n float(int(${o}) - 1),\n ${n} ? float(round(sourceFracRow)) :\n float(floor(sourceFracRow))));\n\n int sourceNearestCol = int(min(\n float(int(${s}) - 1),\n ${n} ? float(round(sourceFracCol)) :\n float(floor(sourceFracCol))));\n\n if (r == sourceNearestRow && c == sourceNearestCol) {\n accumulator += getDy(b, dyR, dyC, d);\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}};function plt(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=new hC(s.shape,o.shape,i);return e.runWebGLProgram(a,[s],s.dtype)}var GV={kernelName:el,backendName:\"webgl\",kernelFunc:plt};var gC=class{constructor(t,e){this.variableNames=[\"x\"];let n=t.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=t,n===1){this.userCode=`\n void main() {\n int coord = getOutputCoords();\n setOutput(getX(${t[0]} - coord - 1));\n }\n `;return}let o=a=>e.indexOf(a)!==-1&&t[a]!==1?`${t[a]} - coords[${a}] - 1`:`coords[${a}]`,s=t.map((a,u)=>o(u)).join(\",\"),i=zt(n);this.userCode=`\n void main() {\n ${i} coords = getOutputCoords();\n setOutput(getX(${s}));\n }\n `}};var xC=class{constructor(t,e){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0;let n=t.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=t;let o=rr(\"rc\",n),s=`${o[n-1]} + 1 < ${this.outputShape[n-1]}`,i=`${o[n-2]} + 1 < ${this.outputShape[n-2]}`,a=zt(n);n===1?this.userCode=`\n void main(){\n int rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = getChannel(getX(${t[0]} - rc - 1),\n ${t[0]} - rc - 1);\n if(${s}){\n result.g = getChannel(getX(${t[0]} - (rc + 1) - 1),\n ${t[0]} - (rc + 1) - 1);\n }\n setOutput(result);\n }\n `:this.userCode=`\n void main() {\n ${a} rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = ${u(o.slice())};\n if(${s}){\n result.g = ${l(o.slice())};\n }\n if(${i}) {\n result.b = ${c(o.slice())};\n if(${s}) {\n result.a = ${p(o.slice())};\n }\n }\n setOutput(result);\n }\n `;function u(d){return m(d)}function l(d){return d[n-1]=\"(\"+d[n-1]+\" + 1)\",m(d)}function c(d){return d[n-2]=\"(\"+d[n-2]+\" + 1)\",m(d)}function p(d){return d[n-1]=\"(\"+d[n-1]+\" + 1)\",d[n-2]=\"(\"+d[n-2]+\" + 1)\",m(d)}function m(d){let h=t.map((b,w)=>f(w,d)),g=h.join(\",\"),x=h.slice(-2).join(\",\");return`getChannel(getX(${g}), vec2(${x}))`}function f(d,h){return e.indexOf(d)!==-1&&t[d]!==1?`${t[d]} - ${h[d]} - 1`:`${h[d]}`}}};function mlt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{dims:s}=n,i=o.shape.length,a=y.parseAxisParam(s,o.shape);if(i===0)return nr({inputs:{x:o},backend:e});let u=L().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new xC(o.shape,a):new gC(o.shape,a);return e.runWebGLProgram(u,[o],o.dtype)}var WV={kernelName:Gs,backendName:\"webgl\",kernelFunc:mlt};var yC=class{constructor(t,e){this.variableNames=[\"Image\"],this.outputShape=[],this.customUniforms=[{name:\"params\",type:\"vec4\"}];let n=t[1],o=t[2];this.outputShape=t;let s=\"\";typeof e==\"number\"?s=`float outputValue = ${e.toFixed(2)};`:s=`\n vec3 fill = vec3(${e.join(\",\")});\n float outputValue = fill[coords[3]];`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n int y = coords[1];\n float coordXFloat = (float(x) - params[0]) * params[3] -\n (float(y) - params[1]) * params[2];\n float coordYFloat = (float(x) - params[0]) * params[2] +\n (float(y) - params[1]) * params[3];\n int coordX = int(round(coordXFloat + params[0]));\n int coordY = int(round(coordYFloat + params[1]));\n ${s}\n if(coordX >= 0 && coordX < ${o} && coordY >= 0 && coordY < ${n}) {\n outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n }\n setOutput(outputValue);\n }\n `}};var UV={kernelName:pl,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{image:n}=r,{radians:o,fillValue:s,center:i}=t,a=e,u=new yC(n.shape,s),[l,c]=S.getImageCenter(i,n.shape[1],n.shape[2]),p=[[l,c,Math.sin(o),Math.cos(o)]];return a.runWebGLProgram(u,[n],n.dtype,p)}};var flt=`\n // OpenGL ES does not support round function.\n // The algorithm is based on banker's rounding.\n float base = floor(x);\n if ((x - base) < 0.5) {\n return floor(x);\n } else if ((x - base) > 0.5) {\n return ceil(x);\n } else {\n if (mod(base, 2.0) == 0.0) {\n return base;\n } else {\n return base + 1.0;\n }\n }\n`,dlt=It({opSnippet:flt}),HV={kernelName:Ws,backendName:\"webgl\",kernelFunc:dlt};var hlt=\"return inversesqrt(x);\",glt=It({opSnippet:hlt,cpuKernelImpl:Iz}),qV={kernelName:Us,backendName:\"webgl\",kernelFunc:glt};var Ku=class{constructor(t,e,n,o,s,i,a=!0,u=!1){this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.outputShape=i;let l=zt(s.length),c=zt(i.length),p=\"\";n===1?p=\"i\":n===2&&(p=\"i, j\");let m=`getIndices(${p})`,f=\"\";o===1?f=\"i\":o===2&&(f=\"i, coords[1]\");let d=`getUpdates(${f})`,h=\"\";u&&(h=\"coords[0], coords[1]\");let g=`getDefaultValue(${h})`,x=e>1?\"strides[j]\":\"strides\";this.userCode=`\n ${l} strides = ${l}(${s});\n\n void main() {\n ${c} coords = getOutputCoords();\n float sum = 0.0;\n bool found = false;\n for (int i = 0; i < ${t}; i++) {\n int flattenedIndex = 0;\n for (int j = 0; j < ${e}; j++) {\n int index = round(${m});\n flattenedIndex += index * ${x};\n }\n if (flattenedIndex == coords[0]) {\n sum += ${d};\n found = true;\n }\n }\n setOutput(mix(${g}, sum, float(found)));\n }\n `}};var bC=class{constructor(t,e,n,o,s,i,a=!0,u=!1){this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=i;let l=zt(s.length),c=zt(i.length),p=\"\";n===1?p=\"i\":n===2&&(p=\"i, j\");let m=`getIndices(${p})`,f=\"\";o===1?f=\"i\":o===2&&(f=\"i, coords[1]\");let d=`getUpdates(${f})`,h=\"\";u&&(h=\"coords[0], coords[1]\");let g=`getDefaultValue(${h})`,x=e>1?\"strides[j]\":\"strides\",b=e>1?\"strides[j + 1]\":\"strides\";this.userCode=`\n ${l} strides = ${l}(${s});\n\n void main() {\n ${c} coords = getOutputCoords();\n vec4 sum = vec4(0.);\n vec4 found = vec4(0.);\n for (int i = 0; i < ${t}; i+=2) {\n ivec2 flattenedIndex = ivec2(0);\n for (int j = 0; j < ${e}; j+=2) {\n ivec4 index = round(${m});\n flattenedIndex += index.xz * ${x};\n if (j + 1 < ${e}) {\n flattenedIndex += index.yw * ${b};\n }\n }\n if (flattenedIndex[0] == coords[0] || flattenedIndex[1] == coords[0] ||\n flattenedIndex[0] == coords[0] + 1 || flattenedIndex[1] == coords[0] + 1) {\n vec4 updVals = ${d};\n if (flattenedIndex[0] == coords[0]) {\n sum.xy += updVals.xy;\n found.xy = vec2(1.);\n } else if (flattenedIndex[0] == coords[0] + 1) {\n sum.zw += updVals.xy;\n found.zw = vec2(1.);\n }\n if (flattenedIndex[1] == coords[0]) {\n sum.xy += updVals.zw;\n found.xy = vec2(1.);\n } else if (flattenedIndex[1] == coords[0] + 1) {\n sum.zw += updVals.zw;\n found.zw = vec2(1.);\n }\n }\n }\n setOutput(mix(${g}, sum, found));\n }\n `}};function xlt(r){let{inputs:t,backend:e,attrs:n}=r,{indices:o,updates:s}=t,{shape:i}=n,{sliceRank:a,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=S.calculateShapes(s,o,i),m=[p/l,l];if(p===0)return e.makeTensorInfo(i,o.dtype);let f=rt({inputs:{x:o},backend:e,attrs:{shape:[u,a]}}),d=rt({inputs:{x:s},backend:e,attrs:{shape:[u,l]}}),h=e.makeTensorInfo([],\"float32\",new Float32Array([0])),g;L().getBool(\"WEBGL_PACK\")?g=new bC(u,a,f.shape.length,d.shape.length,c,m):g=new Ku(u,a,f.shape.length,d.shape.length,c,m);let x=e.runWebGLProgram(g,[d,f,h],d.dtype),b=rt({inputs:{x},backend:e,attrs:{shape:i}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(h),b}var KV={kernelName:nl,backendName:\"webgl\",kernelFunc:xlt};var wC=class{constructor(t,e,n,o){this.variableNames=[\"sortedSequence\",\"values\"],this.customUniforms=[{name:\"numInputs\",type:\"int\"}],this.outputShape=[t,n];let s=\"while (left < right) {\",i=`for (int i = 0; i < ${Math.ceil(Math.log2(e+1))}; ++i) { if (left >= right) break;`,a=L().getNumber(\"WEBGL_VERSION\")===2?s:i,u=o===\"left\"?\"<\":\"<=\";this.userCode=`\n int findBound(int batch, float value) {\n int left = 0;\n int right = numInputs;\n int mid;\n ${a}\n mid = (left + right) / 2;\n if (getSortedSequence(batch, mid) ${u} value) {\n left = mid + 1;\n } else {\n right = mid;\n }\n }\n return right;\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int valueIndex = coords[1];\n\n float value = getValues(batch, valueIndex);\n\n setOutput(float(findBound(batch, value)));\n }\n `}};function ylt(r){let{inputs:t,backend:e,attrs:n}=r,{sortedSequence:o,values:s}=t,{side:i}=n,a=new wC(o.shape[0],o.shape[1],s.shape[1],i),u=[[o.shape[1]]];return e.runWebGLProgram(a,[o,s],\"int32\",u)}var jV={kernelName:sl,backendName:\"webgl\",kernelFunc:ylt};var IC=class{constructor(t,e,n){this.variableNames=[\"c\",\"a\",\"b\"],this.outputShape=e;let o,s;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)s=\"resRC\",o=\"resRC\";else{let a=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],u=[],l=[];for(let c=0;c= 1.0) {\n setOutput(getA(${s}));\n } else {\n setOutput(getB(${s}));\n }\n }\n `}};function blt(r){let{inputs:t,backend:e}=r,{condition:n,t:o,e:s}=t,i=new IC(n.shape.length,o.shape,o.shape.length);return e.runWebGLProgram(i,[n,o,s],ur(o.dtype,s.dtype))}var XV={kernelName:Wi,backendName:\"webgl\",kernelFunc:blt};var wlt=`\n // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n // see: https://arxiv.org/abs/1706.02515\n float scaleAlpha = ${S.SELU_SCALEALPHA};\n float scale = ${S.SELU_SCALE};\n return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`,Ilt=It({opSnippet:wlt}),YV={kernelName:Hs,backendName:\"webgl\",kernelFunc:Ilt};var Clt=Po+`\n return 1.0 / (1.0 + exp(-1.0 * x));\n`,vlt=`\n vec4 result = 1.0 / (1.0 + exp(-1.0 * x));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,Slt=It({opSnippet:Clt,packedOpSnippet:vlt,cpuKernelImpl:vz}),ZV={kernelName:Xs,backendName:\"webgl\",kernelFunc:Slt};var Nlt=`\n if (isnan(x)) { return 0.0; }\n return sign(x);\n`,klt=It({opSnippet:Nlt}),JV={kernelName:js,backendName:\"webgl\",kernelFunc:klt};var Tlt=Po+`\n return sin(x);\n`,_lt=`\n vec4 result = sin(x);\n bvec4 isNaN = isnan(x);\n ${Xn}\n return result;\n`,Elt=It({opSnippet:Tlt,packedOpSnippet:_lt}),QV={kernelName:qs,backendName:\"webgl\",kernelFunc:Elt};var Alt=`\n float e2x = exp(x);\n return (e2x - 1.0 / e2x) / 2.0;\n`,Dlt=It({opSnippet:Alt}),tG={kernelName:Ks,backendName:\"webgl\",kernelFunc:Dlt};var $lt=`\n float epsilon = 1.1920928955078125e-7;\n float threshold = log(epsilon) + 2.0;\n\n bool too_large = x > -threshold;\n bool too_small = x < threshold;\n\n float result;\n float exp_x = exp(x);\n\n if (too_large){\n result = x;\n }\n else if (too_small){\n result = exp_x;\n }\n else{\n result = log(exp_x + 1.0);\n }\n return result;\n`,Rlt=It({opSnippet:$lt}),eG={kernelName:Ys,backendName:\"webgl\",kernelFunc:Rlt};var Flt=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,paddings:i}=n;y.assert(o.shape.length<=4,()=>\"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet\");let a=s.reduce((x,b)=>x*b),u=[[0,0]];u.push(...i);for(let x=1+s.length;xe.disposeIntermediateTensorInfo(x)),g},rG={kernelName:Hi,backendName:\"webgl\",kernelFunc:Flt};function Olt(r){let{inputs:t,backend:e}=r,{indices:n,values:o,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:\n ${s.shape}`);if(n.shape.length!==2)throw new Error(`Indices must be a matrix, saw:\n ${n.shape}`);if(o.shape.length!==1)throw new Error(`Values must be a vector, saw:\n ${o.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:\n ${i.shape}`);let a=e.readSync(n.dataId),u=e.readSync(o.dataId),l=e.readSync(s.dataId),c=e.readSync(i.dataId)[0],[p,m,f,d,h]=Nz(a,n.shape,n.dtype,u,o.dtype,l,c);return[e.makeTensorInfo(m,n.dtype,p),e.makeTensorInfo([m[0]],o.dtype,f),e.makeTensorInfo([d.length],\"bool\",new Uint8Array(d.map(g=>Number(g)))),e.makeTensorInfo([h.length],n.dtype,new Int32Array(h))]}var nG={kernelName:nu,backendName:\"webgl\",kernelFunc:Olt};function Mlt(r){let{inputs:t,backend:e}=r,{inputIndices:n,inputShape:o,newShape:s}=t;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${n.shape}`);if(o.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${o.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(e.readSync(o.dataId)),a=e.readSync(n.dataId),u=Array.from(e.readSync(s.dataId)),[l,c,p]=kz(a,n.shape,n.dtype,i,u);return[e.makeTensorInfo(c,n.dtype,l),e.makeTensorInfo([p.length],s.dtype,new Int32Array(p))]}var oG={kernelName:il,backendName:\"webgl\",kernelFunc:Mlt};function Plt(r){let{inputs:t,backend:e}=r,{data:n,indices:o,segmentIds:s}=t;if(n.shape.length<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(o.shape.length!==1)throw new Error(`Indices should be a vector but received shape\n ${o.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape\n ${s.shape}`);let i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=rI(i,n.shape,n.dtype,a,u,!0);return e.makeTensorInfo(c,n.dtype,l)}var sG={kernelName:ou,backendName:\"webgl\",kernelFunc:Plt};function Llt(r){let{inputs:t,backend:e}=r,{data:n,indices:o,segmentIds:s}=t;if(n.shape.length<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(o.shape.length!==1)throw new Error(`Indices should be a vector but received shape\n ${o.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape\n ${s.shape}`);let i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=rI(i,n.shape,n.dtype,a,u);return e.makeTensorInfo(c,n.dtype,l)}var iG={kernelName:su,backendName:\"webgl\",kernelFunc:Llt};function zlt(r){let{inputs:t,backend:e,attrs:n}=r,{sparseIndices:o,sparseValues:s,defaultValue:i}=t,{outputShape:a}=n,{sliceRank:u,numUpdates:l,sliceSize:c,strides:p,outputSize:m}=S.calculateShapes(s,o,a),f=!1;if(s.dtype===\"string\"){let x=e.bufferSync(o),b=e.bufferSync(s),w=y.decodeString(e.readSync(i.dataId)[0]),I=Cz(x,b,a,m,c,l,u,p,w,f);return e.makeTensorInfo(a,I.dtype,I.values)}let d=new Ku(l,u,o.shape.length,s.shape.length,p,[m,1],f),h=e.runWebGLProgram(d,[s,o,i],s.dtype),g=rt({inputs:{x:h},backend:e,attrs:{shape:a}});return e.disposeIntermediateTensorInfo(h),g}var aG={kernelName:al,backendName:\"webgl\",kernelFunc:zlt};function Blt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{numOrSizeSplits:s,axis:i}=n,a=y.parseAxisParam(i,o.shape)[0],u=S.prepareSplitSize(o,s,a),l=o.shape.length,c=new Array(l).fill(0),p=o.shape.slice();return u.map(m=>{let f=[...p];f[a]=m;let d=ki({inputs:{x:o},backend:e,attrs:{begin:c,size:f}});return c[a]+=m,d})}var lG={kernelName:qi,backendName:\"webgl\",kernelFunc:Blt};var uG=\"return sqrt(x);\",Vlt=It({opSnippet:uG,packedOpSnippet:uG,cpuKernelImpl:Tz}),cG={kernelName:Zs,backendName:\"webgl\",kernelFunc:Vlt};var Glt=\"return x * x;\",Wlt=It({opSnippet:Glt}),pG={kernelName:iu,backendName:\"webgl\",kernelFunc:Wlt};var mG=\"return (a - b) * (a - b);\",Ult=ce({opSnippet:mG,packedOpSnippet:mG}),fG={kernelName:ti,backendName:\"webgl\",kernelFunc:Ult};function Hlt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;if(o.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");let s=e.readSync(o.dataId),i=S.fromUint8ToStringArray(s),a=_z(i,\"string\",n);return e.makeTensorInfo(o.shape,\"string\",a)}var dG={kernelName:ec,backendName:\"webgl\",kernelFunc:Hlt};function qlt({inputs:r,attrs:t,backend:e}){let{x:n}=r,o=xr+`\n return x > 0.0 ? 1.0 : float(${t.alpha});\n `,s=new zr(n.shape,o);return e.runWebGLProgram(s,[n],n.dtype)}var hG={kernelName:xo,backendName:\"webgl\",kernelFunc:qlt};var CC=class{constructor(t,e,n){this.variableNames=[\"x\"],this.outputShape=n;let o=n.length,s=zt(n.length),i=zt(n.length),a=\"\";if(o===1)a=\"coords * strides + begin\";else{let u=0;a=n.map((l,c)=>(u++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${u-1}] * strides[${c}] + begin[${c}]`)).join(\",\")}this.userCode=`\n ${s} begin = ${s}(${t});\n ${s} strides = ${s}(${e});\n\n void main() {\n ${i} coords = getOutputCoords();\n setOutput(getX(${a}));\n }\n `}};function Klt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{begin:s,end:i,strides:a,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:m}=n,{finalShapeSparse:f,finalShape:d,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:w,strides:I}=Be.sliceInfo(o.shape,s,i,a,u,l,c,p,m),N;if(h)N=rt({inputs:{x:o},backend:e,attrs:{shape:d}});else if(g||x){y.assert(o.shape.length>=1,()=>`Input must have rank at least 1, got: ${o.shape.length}`);let A=Be.computeOutShape(b,w,I),D=ki({inputs:{x:o},backend:e,attrs:{begin:b,size:A}});N=rt({inputs:{x:D},backend:e,attrs:{shape:d}}),e.disposeIntermediateTensorInfo(D)}else if(e.shouldExecuteOnCPU([o])){let D=e.readSync(o.dataId),F=wt(o.shape,o.dtype,D),M=Ez(f,F,I,b);N=e.makeTensorInfo(d,o.dtype,M.values)}else{let D=new CC(b,I,f);N=e.runWebGLProgram(D,[o],o.dtype)}let E=rt({inputs:{x:N},backend:e,attrs:{shape:d}});return e.disposeIntermediateTensorInfo(N),E}var gG={kernelName:ll,backendName:\"webgl\",kernelFunc:Klt};function jlt(r){let{inputs:t,backend:e,attrs:n}=r,{separator:o,nGramWidths:s,leftPad:i,rightPad:a,padWidth:u,preserveShortSequences:l}=n,{data:c,dataSplits:p}=t,m=e.readSync(c.dataId),f=e.readSync(p.dataId),[d,h]=Az(m,f,o,s,i,a,u,l);return[e.makeTensorInfo([d.length],\"string\",d),e.makeTensorInfo(p.shape,\"int32\",h)]}var xG={kernelName:au,backendName:\"webgl\",kernelFunc:jlt};function Xlt(r){let{inputs:t,backend:e,attrs:n}=r,{skipEmpty:o}=n,{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: ${i.shape}`);let a=e.readSync(s.dataId),u=e.readSync(i.dataId)[0],[l,c,p]=Dz(a,u,o),m=c.length;return[e.makeTensorInfo([m,2],\"int32\",l),e.makeTensorInfo([m],\"string\",c),e.makeTensorInfo([2],\"int32\",new Int32Array(p))]}var yG={kernelName:lu,backendName:\"webgl\",kernelFunc:Xlt};function Ylt(r){let{inputs:t,backend:e,attrs:n}=r,{numBuckets:o}=n,{input:s}=t;if(s.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");if(o<=0)throw new Error(\"Number of buckets must be at least 1\");let i=e.readSync(s.dataId),a=$z(i,o);return e.makeTensorInfo(s.shape,\"int32\",a)}var bG={kernelName:uu,backendName:\"webgl\",kernelFunc:Ylt};var Zlt=\"return tan(x);\",Jlt=It({opSnippet:Zlt}),wG={kernelName:ri,backendName:\"webgl\",kernelFunc:Jlt};var Qlt=`\n float e2x = exp(-2.0 * abs(x));\n return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n`,tut=It({opSnippet:Qlt}),IG={kernelName:ni,backendName:\"webgl\",kernelFunc:tut};function eut(r){let{inputs:t,backend:e,attrs:n}=r,{tensor:o,indices:s,updates:i}=t,{}=n,{sliceRank:a,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=S.calculateShapes(i,s,o.shape),m=[p/l,l];if(p===0)return e.makeTensorInfo(o.shape,s.dtype);let f=rt({inputs:{x:s},backend:e,attrs:{shape:[u,a]}}),d=rt({inputs:{x:i},backend:e,attrs:{shape:[u,l]}}),h=rt({inputs:{x:o},backend:e,attrs:{shape:m}}),g=new Ku(u,a,f.shape.length,d.shape.length,c,m,!1,!0),x=e.runWebGLProgram(g,[d,f,h],h.dtype),b=rt({inputs:{x},backend:e,attrs:{shape:o.shape}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(x),b}var CG={kernelName:ol,backendName:\"webgl\",kernelFunc:eut};var vC=class{constructor(t,e){this.variableNames=[\"A\"];let n=new Array(t.length);for(let i=0;i5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${r[0]})`;let e=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\"],n=[];for(let o=0;o5){let u=e.readSync(o.dataId),l=o.dtype===\"string\"?u.map(m=>y.decodeString(m)):u,c=wt(o.shape,o.dtype,l),p=Fz(c,s);return e.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new vC(o.shape,s);return e.runWebGLProgram(i,[o],o.dtype)}var vG={kernelName:oo,backendName:\"webgl\",kernelFunc:j1};var SC=class{constructor(t){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"negativeInf\",type:\"float\"},{name:\"dir\",type:\"int\"},{name:\"inc\",type:\"int\"}],this.outputShape=t,this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int elemIdx = coords[1];\n\n // We compare elements pair-wise within a group of size 2 * inc.\n // The comparing rule for each group alternates between ascending\n // and descending. Within each group, we compare each pair at\n // positions i and i+inc. To decide whether an element at position i\n // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n // inc, it is in the first half of the group, we denote it as x0,\n // otherwise we denote it as x1.\n // For example, as shown in the Bitonic top K paper referenced above,\n // Figure5(a) shows that element[1] is in the\n // second half of the group when group size is 2, but it is in the\n // first half of the group when group size is 4.\n\n bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;\n int i = isFirstInPair ? elemIdx : elemIdx - inc;\n\n int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));\n float x0 = i0 < n ? getX(batch, i0) : negativeInf;\n float x1 = i1 < n ? getX(batch, i1) : negativeInf;\n\n // Denotes which direction indices are in (ascending or descending).\n bool reverse = imod(elemIdx, 2 * dir) >= dir;\n bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n if (reverse == isGreater) { // Elements in opposite order of direction\n int iTemp = i0;\n i0 = i1;\n i1 = iTemp;\n }\n if (isFirstInPair) {\n setOutput(float(i0));\n } else {\n setOutput(float(i1));\n }\n }\n `}},NC=class{constructor(t){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"k\",type:\"int\"}],this.outputShape=t,this.userCode=`\n void main() {\n // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int elemIdx = coords[1];\n\n // The output size is half of the previous size.\n // If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ (k=4),\n // we only need to output the indices at positions |, the indices at\n // positions _ can be thrown away, see Figure5(b) After Phase 2\n // (Merge phase) in the Bitonic Top K paper referenced above.\n // For example, the paper shows we only need to output the orange bars.\n // The output sequence should look like this | | | | | | | |.\n // Because the sequence is halved, to map the output index back\n // to the previous sequence to find the corresponding value,\n // we need to double the index. When we double the index,\n // we basically interpolate a position, so 2i looks like\n // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position\n // of each 2k positions by - elemIdx % k. E.g. for output at\n // index 4,5,6,7, we want to get the corresponding element at\n // original index 8,9,10,11, for output at index 8,9,10,11,\n // we want to get the corresponding element at original index\n // 16,17,18,19, so on and so forth.\n\n int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));\n int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));\n\n float x0 = getX(batch, i0);\n float x1 = i1 < n ? getX(batch, i1) : x0;\n\n setOutput(x0 >= x1 ? float(i0) : float(i1));\n }\n `}};function xp(r,t){t!==null&&r.disposeIntermediateTensorInfo(t)}function SG(r){let t=1;for(;tu){let M=e.readSync(o.dataId),[V,G]=Oz(M,l,o.dtype,s,i);return[e.makeTensorInfo(V.shape,V.dtype,V.values),e.makeTensorInfo(G.shape,G.dtype,G.values)]}if(s===0)return l[l.length-1]=0,[e.makeTensorInfo(l,o.dtype,[]),e.makeTensorInfo(l,\"int32\",[])];if(c===1)return[o,Ll({attrs:{shape:l,dtype:\"int32\",value:0},backend:e})];let p=e.texData.get(o.dataId),m=p!==null&&p.isPacked,f=m?e.unpackTensor(o):o,h=y.sizeFromShape(l)/c,g=rt({inputs:{x:f},attrs:{shape:[h,c]},backend:e});m&&xp(e,f);let x=SG(s),b=SG(c),w=null,I=()=>w===null?[g,g]:[g,w],N=(M,V,G)=>{let W=I(),q=new SC(G),j=[[c],[w===null?1:0],[Number.NEGATIVE_INFINITY],[M],[V]],Y=w;w=e.runWebGLProgram(q,W,\"int32\",j),xp(e,Y)};for(let M=1;M=1;G/=2)N(V,G,[h,b])}for(let M=b;M>x;M/=2){let V=I(),G=new NC([h,M/2]),q=[[c],[w===null?1:0],[x]],H=w;w=e.runWebGLProgram(G,V,\"int32\",q),xp(e,H);let j=x/2,Y=j*2;for(let Z=j;Z>=1;Z/=2)N(Y,Z,w.shape)}let E=w;w=ki({inputs:{x:w},backend:e,attrs:{begin:0,size:[h,s]}}),xp(e,E);let A=V1({inputs:{x:g,indices:w},backend:e,attrs:{axis:1,batchDims:1}});xp(e,g);let D=l.slice(0,-1);D.push(s),E=w,w=rt({inputs:{x:w},attrs:{shape:D},backend:e}),xp(e,E);let F=A;return A=rt({inputs:{x:A},attrs:{shape:D},backend:e}),xp(e,F),[A,w]}var NG={kernelName:ul,backendName:\"webgl\",kernelFunc:nut};var kC=class{constructor(t,e,n,o,s,i){this.variableNames=[\"Image\",\"Transforms\"],this.outputShape=i;let a=n===\"nearest\"?1:2,u;switch(o){case\"constant\":u=1;break;case\"reflect\":u=2;break;case\"wrap\":u=3;break;case\"nearest\":u=4;break;default:u=1;break}this.userCode=`\n float mapCoord(float outCoord, float len) {\n float inCoord = outCoord;\n if(${u} == 2) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz2 = 2.0 * len;\n if (inCoord < sz2) {\n inCoord = sz2 * float(int(float(-inCoord / sz2))) +\n inCoord;\n }\n inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz2 = 2.0 * len;\n inCoord -= sz2 * float(int(float(inCoord / sz2)));\n if (inCoord >= len) {\n inCoord = sz2 - inCoord - 1.0;\n }\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (${u} == 3) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord -= len * float(int(float(inCoord / sz)));\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (${u} == 4) {\n return clamp(outCoord, 0.0, len - 1.0);\n } else {\n return outCoord;\n }\n }\n\n float readWithFillValue(int batch, int coordY, int coordX,\n int channel) {\n float outputValue;\n if (0 <= coordY && coordY < ${t} && 0 <= coordX && coordX < ${e}) {\n outputValue = getImage(batch, coordY, coordX, channel);\n } else {\n outputValue = float(${s});\n }\n return outputValue;\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n float outputValue;\n int batch = coords[0];\n int x = coords[2];\n int y = coords[1];\n int channel = coords[3];\n float xf = float(x);\n float yf = float(y);\n float a1 = getTransforms(batch, 0);\n float a2 = getTransforms(batch, 1);\n float a3 = getTransforms(batch, 2);\n float b1 = getTransforms(batch, 3);\n float b2 = getTransforms(batch, 4);\n float b3 = getTransforms(batch, 5);\n float c1 = getTransforms(batch, 6);\n float c2 = getTransforms(batch, 7);\n float projection = c1 * xf + c2 * yf + 1.0;\n if (projection == 0.0) {\n outputValue = float(${s});\n } else {\n float inX = (a1 * xf + a2 * yf + a3) / projection;\n float inY = (b1 * xf + b2 * yf + b3) / projection;\n float mapX = mapCoord(inX, float(${e}));\n float mapY = mapCoord(inY, float(${t}));\n\n if (${a} == 1) {\n int coordY = int(round(mapY));\n int coordX = int(round(mapX));\n outputValue = readWithFillValue(batch, coordY, coordX,\n channel);\n } else {\n float yFloor = floor(mapY);\n float xFloor = floor(mapX);\n float yCeil = yFloor + 1.0;\n float xCeil = xFloor + 1.0;\n float valueYFloor = (xCeil - mapX) *\n readWithFillValue(batch, int(yFloor), int(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, int(yFloor), int(xCeil), channel);\n float valueYCeil = (xCeil - mapX) *\n readWithFillValue(batch, int(yCeil), int(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, int(yCeil), int(xCeil), channel);\n outputValue = (yCeil - mapY) * valueYFloor +\n (mapY - yFloor) * valueYCeil;\n }\n }\n setOutput(outputValue);\n }\n `}};function out(r){let{inputs:t,backend:e,attrs:n}=r,{image:o,transforms:s}=t,{interpolation:i,fillMode:a,fillValue:u,outputShape:l}=n,[c,p,m,f]=o.shape,[d,h]=l!=null?l:[p,m],g=[c,d,h,f],x=new kC(p,m,i,a,u,g);return e.runWebGLProgram(x,[o,s],\"float32\")}var kG={kernelName:cl,backendName:\"webgl\",kernelFunc:out};function sut(r){let{inputs:t,attrs:e,backend:n}=r,{axis:o}=e,{x:s}=t;vi(s,\"unique\"),console.warn(\"WARNING: \",\"UI might be locked temporarily as data is being downloaded\");let i=n.readSync(s.dataId),{outputValues:a,outputShape:u,indices:l}=Mz(i,o,s.shape,s.dtype);return[n.makeTensorInfo(u,s.dtype,a),n.makeTensorInfo([l.length],\"int32\",l)]}var TG={kernelName:cu,backendName:\"webgl\",kernelFunc:sut};function iut(r){let{inputs:t,backend:e,attrs:n}=r,{value:o}=t,{axis:s}=n;s<0&&(s+=o.shape.length);let i=o,a=i.shape.length,u=o.shape[s],l=new Array(a-1),c=0;for(let h=0;he.disposeIntermediateTensorInfo(h)),d}var _G={kernelName:Ki,backendName:\"webgl\",kernelFunc:iut};var TC=class{constructor(t,e){this.variableNames=[\"x\",\"segmentIds\"];let n=t.windowSize,o=t.batchSize,s=t.inSize,i=t.numSegments,a=i*Math.ceil(s/n);this.outputShape=[o,a];let u=\"0.0\",l=\"sumValue\",c=Math.floor(n/4)*4,p=n%4,m=`\n sumValue += dot(values, segFilter);\n `,f=\"\";s%n>0&&(f=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return initializationValue;\n }\n `);let d=\"\";s%n>0&&(d=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return -1.0;\n }\n `),this.userCode=`\n const float initializationValue = ${u};\n\n float getValue(int batch, int inIdx) {\n ${f}\n return getX(batch, inIdx);\n }\n\n float getSegmentIdAtIndex(int inIdx) {\n ${d}\n return getSegmentIds(inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = int(floor(float(outIdx) / float(\n ${i})) * float(${n}));\n int currentSeg = int(mod(float(outIdx), float(${i})));\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${c}; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n );\n\n ${m}\n }\n\n int inIdx = inOffset + ${c};\n if (${p===1}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n 0,\n 0,\n 0\n );\n\n ${m}\n } else if (${p===2}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n 0,\n 0\n );\n\n ${m}\n } else if (${p===3}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n 0\n );\n\n ${m}\n }\n setOutput(${l});\n }\n `}};function aut(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,segmentIds:s}=t,{numSegments:i}=n,a=o.shape.length,u=[],l=0,c=S.getAxesPermutation([l],a),p=o;c!=null&&(p=Pe({inputs:{x:o},backend:e,attrs:{perm:c}}),u.push(p),l=S.getInnerMostAxes(1,a)[0]);let m=S.segment_util.computeOutShape(p.shape,l,i),f=y.sizeFromShape([p.shape[l]]),d=rt({inputs:{x:p},backend:e,attrs:{shape:[-1,f]}});u.push(d);let h=lc(o.dtype),g=(I,N,E,A,D)=>{let F=I.shape[0],M=I.shape[1],V=S.segment_util.segOpComputeOptimalWindowSize(M,D),G={windowSize:V,inSize:M,batchSize:F,numSegments:D},W=new TC(G,N),q=e.compileAndRun(W,[I,E],A);if(u.push(q),q.shape[1]===D)return q;let H=K1({backend:e,attrs:{start:0,stop:D,step:1,dtype:\"float32\"}}),j=j1({inputs:{x:H},backend:e,attrs:{reps:[M/V]}});return u.push(H),u.push(j),g(q,N,j,A,D)},x=g(d,\"unsortedSegmentSum\",s,h,i),b=rt({inputs:{x},backend:e,attrs:{shape:m}}),w=b;if(c!=null){u.push(b);let I=S.getUndoAxesPermutation(c);w=Pe({inputs:{x:w},backend:e,attrs:{perm:I}})}return u.forEach(I=>e.disposeIntermediateTensorInfo(I)),w}var EG={kernelName:pu,backendName:\"webgl\",kernelFunc:aut};var lut=[p3,f3,d3,h3,x3,y3,b3,w3,v3,S3,N3,k3,T3,_3,E3,A3,D3,$3,R3,F3,O3,P3,L3,z3,B3,U3,q3,K3,e3,X3,Z3,J3,Q3,tB,eB,rB,nB,oB,sB,iB,uB,cB,pB,mB,fB,dB,hB,gB,xB,yB,bB,wB,IB,CB,vB,SB,kB,TB,_B,EB,DB,$B,RB,FB,OB,MB,PB,LB,zB,t3,BB,Y3,VB,GB,WB,r3,UB,HB,qB,KB,jB,XB,YB,ZB,JB,QB,eV,rV,nV,oV,sV,iV,lV,cV,pV,mV,fV,dV,bV,s3,wV,IV,CV,vV,V3,SV,TV,_V,EV,AV,n3,DV,$V,RV,FV,OV,G3,hV,MV,PV,LV,a3,zV,BV,VV,GV,WV,UV,HV,qV,KV,jV,XV,YV,ZV,JV,QV,tG,M3,yV,eG,rG,nG,oG,sG,iG,aG,lG,cG,pG,fG,dG,hG,gG,xG,yG,bG,xV,u3,wG,IG,CG,vG,NG,kG,c3,TG,_G,EG,NV];for(let r of lut)rc(r);var Nt;(function(r){r[r.float32=0]=\"float32\",r[r.int32=1]=\"int32\",r[r.bool=2]=\"bool\",r[r.string=3]=\"string\",r[r.complex64=4]=\"complex64\"})(Nt||(Nt={}));var ju;(function(r){r[r.linear=0]=\"linear\",r[r.relu=1]=\"relu\",r[r.relu6=2]=\"relu6\",r[r.prelu=3]=\"prelu\",r[r.leakyrelu=4]=\"leakyrelu\",r[r.sigmoid=5]=\"sigmoid\",r[r.elu=6]=\"elu\"})(ju||(ju={}));var AG;function uut(r){AG=r.wasm.cwrap(Xi,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function cut(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s,bias:i,preluActivationWeights:a}=t;if(o.dtype!==\"float32\"||s.dtype!==\"float32\")throw new Error(\"_FusedMatMul for non non-float32 tensors not yet supported.\");let{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=n,m=e.dataIdMap.get(o.dataId).id,f=e.dataIdMap.get(s.dataId).id,d=0;if(i!=null){let D=e.dataIdMap.get(i.dataId);if(D.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${D.shape.length}.`);d=D.id}let h=a==null?0:e.dataIdMap.get(a.dataId).id,g=ju[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let x=u?o.shape[2]:o.shape[1],b=l?s.shape[1]:s.shape[2],w=Hr.assertAndGetBroadcastShape(o.shape.slice(0,-2),s.shape.slice(0,-2)),I=e.makeOutput([...w,x,b],o.dtype),N=e.dataIdMap.get(I.dataId).id,E=new Uint8Array(new Int32Array(o.shape).buffer),A=new Uint8Array(new Int32Array(s.shape).buffer);return AG(m,E,o.shape.length,f,A,s.shape.length,u,l,g,d,h,p||0,N),I}var DG={kernelName:Xi,backendName:\"wasm\",setupFunc:uut,kernelFunc:cut};function yt(r,t){let e;function n(s){e=s.wasm.cwrap(r,null,[\"number\",\"number\",\"number\"])}function o(s){let{backend:i,inputs:{x:a}}=s,u=i.dataIdMap.get(a.dataId).id,l=i.makeOutput(a.shape,t||a.dtype),c=i.dataIdMap.get(l.dataId).id;return y.sizeFromShape(l.shape)===0||e(u,Nt[a.dtype],c),l}return{kernelName:r,backendName:\"wasm\",setupFunc:n,kernelFunc:o}}var $G=yt(Ai);var RG=yt(Go);var FG=yt(Wo);function ee(r,t,e){let n;function o(i){n=i.wasm.cwrap(r,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\"])}function s(i){let{backend:a,inputs:u}=i,{a:l,b:c}=u,p=a.dataIdMap.get(l.dataId).id,m=a.dataIdMap.get(c.dataId).id,f=e!=null?e:l.dtype,d=S.assertAndGetBroadcastShape(l.shape,c.shape),h=a.makeOutput(d,f);if(y.sizeFromShape(d)===0)return h;let g=new Uint8Array(new Int32Array(l.shape).buffer),x=new Uint8Array(new Int32Array(c.shape).buffer),b=a.dataIdMap.get(h.dataId).id;return n(p,g,l.shape.length,m,x,c.shape.length,Nt[l.dtype],b),h}return{kernelName:r,backendName:\"wasm\",setupFunc:o,kernelFunc:s}}var put=!0,OG=ee(no,put);var MG;function mut(r){MG=r.wasm.cwrap(Uo,null,[\"array\",\"number\",\"number\",\"number\"])}function fut(r){let{inputs:t,backend:e}=r,n=e.makeOutput(t[0].shape,t[0].dtype);if(y.sizeFromShape(n.shape)===0)return n;let o=t.map(a=>e.dataIdMap.get(a.dataId).id),s=new Uint8Array(new Int32Array(o).buffer),i=e.dataIdMap.get(n.dataId).id;return MG(s,o.length,Nt[n.dtype],i),n}var PG={kernelName:Uo,backendName:\"wasm\",setupFunc:mut,kernelFunc:fut};function yp(r){let{inputs:{x:t},backend:e}=r;if(t.dtype===\"string\")return ir(e.readSync(t.dataId),t.shape,t.dtype);let n=e.makeOutput(t.shape,t.dtype),o=e.typedArrayFromHeap(t);return e.typedArrayFromHeap(n).set(o),n}var LG={kernelName:go,backendName:\"wasm\",kernelFunc:yp};var zG;function dut(r){zG=r.wasm.cwrap(so,null,[\"number\",\"array\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function mo(r){let{inputs:t,backend:e,attrs:n}=r,[o,s]=gut(t.x.shape,n.perm),i=!0;for(let d=0;d=o&&(s===-1||n[s]>n[i])&&(s=i);n[s]=o}return[e,n]}var BG={kernelName:so,backendName:\"wasm\",kernelFunc:mo,setupFunc:dut};function Cn(r,t,e){let n=r.shape,o=r.shape.length,s=y.parseAxisParam(t,n),i=s,a=S.getAxesPermutation(i,o),u=null,l=!1;if(a!=null){let c=new Array(o);for(let f=0;f`new shape: ${i}, old shape: ${n.shape}. New shape and old shape must have the same number of elements.`),r.backend.incRef(n.dataId),{dataId:n.dataId,shape:i,dtype:n.dtype}}var iW={kernelName:Gi,backendName:\"wasm\",kernelFunc:mr};var aW;function Eut(r){aW=r.wasm.cwrap(Zo,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"number\"])}function Aut(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s}=t,{transposeA:i,transposeB:a}=n;if(o.dtype!==\"float32\"||s.dtype!==\"float32\")throw new Error(\"BatchMatMul for non non-float32 tensors not yet supported.\");let u=o.shape.length,l=s.shape.length,c=i?o.shape[u-2]:o.shape[u-1],p=a?s.shape[l-1]:s.shape[l-2],m=i?o.shape[u-1]:o.shape[u-2],f=a?s.shape[l-2]:s.shape[l-1],d=o.shape.slice(0,-2),h=s.shape.slice(0,-2),g=y.sizeFromShape(d),x=y.sizeFromShape(h),w=Hr.assertAndGetBroadcastShape(o.shape.slice(0,-2),s.shape.slice(0,-2)).concat([m,f]);y.assert(c===p,()=>`Error in matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${o.shape} and 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t.dtype===\"string\"?p.stringBytes=u.slice(d,d+y.sizeFromShape(i)):o.typedArrayFromHeap(l).set(u.subarray(d,d+y.sizeFromShape(i))),l}if(t.dtype===\"string\"){let d=ep(u,s,i,t.shape,t.dtype);return p.stringBytes=d,l}let m=o.typedArrayFromHeap(l),f=t.shape.length;if(f===2)Dut(u,c[0],m,s,i);else if(f===3)$ut(u,c[0],c[1],m,s,i);else if(f===4)Rut(u,c[0],c[1],c[2],m,s,i);else{let d=ep(u,s,i,t.shape,t.dtype);m.set(d)}return l}function Dut(r,t,e,n,o){let s=0,i=n[0],a=n[1],u=i+o[0];for(let l=i;lx*b),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=mr({inputs:{x:o},backend:e,attrs:{shape:u}}),d=mo({inputs:{x:f},backend:e,attrs:{perm:l}}),h=mr({inputs:{x:d},backend:e,attrs:{shape:c}}),g=Lo({inputs:{x:h},backend:e,attrs:{begin:p,size:m}});return e.disposeData(f.dataId),e.disposeData(d.dataId),e.disposeData(h.dataId),g}var cW={kernelName:Fi,backendName:\"wasm\",kernelFunc:Fut};var pW;function Out(r){pW=r.wasm.cwrap(Da,null,[\"number\",\"number\",\"boolean\",\"number\",\"number\",\"number\"])}function Mut(r){let{backend:t,inputs:e,attrs:n}=r,{x:o,weights:s}=e,{size:i}=n,a=s.shape.reduce((p,m)=>p*m,1)!==0,u=o.shape.length===1?[i]:[o.shape[0],i],l=t.makeOutput(u,s.dtype);function c(p){return t.dataIdMap.get(p.dataId).id}return pW(c(o),i,a,c(s),Nt[s.dtype],c(l)),l}var mW={kernelName:Da,backendName:\"wasm\",setupFunc:Out,kernelFunc:Mut};var Put=!0,fW=ee($a,Put);function Lut(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.typedArrayFromHeap(n),i=e.typedArrayFromHeap(o),a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeOutput([a.length],\"int32\",void 0,new Int32Array(a))}var dW={kernelName:ql,backendName:\"wasm\",kernelFunc:Lut};function Fn(r){let{inputs:{x:t},attrs:{dtype:e},backend:n}=r,o=n.makeOutput(t.shape,e),s=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(o).set(s),o}var hW={kernelName:fo,backendName:\"wasm\",kernelFunc:Fn};var gW=yt(Jo);var xW;function zut(r){xW=r.wasm.cwrap(ho,null,[\"number\",\"number\",\"number\",\"number\"])}function But(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{clipValueMin:s,clipValueMax:i}=n,a=e.dataIdMap.get(o.dataId).id,u=e.makeOutput(o.shape,o.dtype),l=e.dataIdMap.get(u.dataId).id;return xW(a,s,i,l),u}var yW={kernelName:ho,backendName:\"wasm\",setupFunc:zut,kernelFunc:But};function X1(r){let{inputs:t,backend:e}=r,n=y.parseAxisParam(r.attrs.axis,t[0].shape)[0],o=t.map(f=>f.shape);S.assertParamsConsistent(o,n);let s=S.computeOutShape(t.map(f=>f.shape),n),i=t.filter(f=>y.sizeFromShape(f.shape)>0);if(i.length===1)return yp({inputs:{x:i[0]},backend:e});let a=e.makeOutput(s,t[0].dtype);if(y.sizeFromShape(s)===0)return a;if(i[0].dtype===\"string\"){let f=i.map(w=>{let N=[-1,y.sizeFromShape(w.shape.slice(n))];return 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x=S.getUndoAxesPermutation(l);g=mo({inputs:{x:m},attrs:{perm:x},backend:e}),e.disposeData(c.dataId),e.disposeData(m.dataId)}return g}var OW={kernelName:Oa,backendName:\"wasm\",setupFunc:Qut,kernelFunc:tct};var MW;function ect(r){MW=r.wasm.cwrap(os,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function rct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n,u=o.shape.length;y.assert(o.dtype===\"float32\"||o.dtype===\"int32\",()=>`cumsum does not support ${o.dtype} tensors in the WASM backend`);let l=S.getAxesPermutation([s],u),c=o;l!==null&&(c=mo({inputs:{x:o},attrs:{perm:l},backend:e}));let p=S.getInnerMostAxes(1,u)[0];S.assertAxesAreInnerMostDims(\"cumsum\",[p],u);let m=e.makeOutput(c.shape,c.dtype),f=c.shape[p],d=e.dataIdMap.get(c.dataId).id,h=e.dataIdMap.get(m.dataId).id;MW(d,i?1:0,a?1:0,f,h,Nt[o.dtype]);let g=m;if(l!==null){let x=S.getUndoAxesPermutation(l);g=mo({inputs:{x:m},attrs:{perm:x},backend:e}),e.disposeData(c.dataId),e.disposeData(m.dataId)}return g}var PW={kernelName:os,backendName:\"wasm\",setupFunc:ect,kernelFunc:rct};var LW;function nct(r){LW=r.wasm.cwrap(\"DenseBincount\",null,[\"number\",\"array\",\"number\",\"number\",\"boolean\",\"number\",\"number\",\"boolean\",\"number\"])}function oct(r){let{backend:t,inputs:e,attrs:n}=r,{x:o,weights:s}=e,{size:i,binaryOutput:a}=n,u=s.shape.reduce((m,f)=>m*f,1)!==0,l=o.shape.length===1?[i]:[o.shape[0],i],c=t.makeOutput(l,s.dtype);function p(m){return t.dataIdMap.get(m.dataId).id}return LW(p(o),new Uint8Array(new Int32Array(o.shape).buffer),o.shape.length,i,u,p(s),Nt[s.dtype],a,p(c)),c}var zW={kernelName:jl,backendName:\"wasm\",setupFunc:nct,kernelFunc:oct};var BW;function sct(r){BW=r.wasm.cwrap(Pa,null,[\"number\",\"number\",\"number\",\"array\",\"number\",\"array\",\"array\",\"number\",\"number\"])}function ict(r){let{backend:t,inputs:e,attrs:n}=r,{x:o}=e,{blockSize:s,dataFormat:i}=n,a=o.shape[0],u=i===\"NHWC\"?o.shape[1]:o.shape[2],l=i===\"NHWC\"?o.shape[2]:o.shape[3],c=i===\"NHWC\"?o.shape[3]:o.shape[1],p=u*s,m=l*s,f=c/(s*s),d=i===\"NHWC\"?[a,p,m,f]:[a,f,p,m],h=t.makeOutput(d,\"float32\"),x=t.dataIdMap.get(o.dataId).id,b=new Uint8Array(new Int32Array(y.computeStrides(o.shape)).buffer),w=new Uint8Array(new Int32Array(d).buffer),I=new Uint8Array(new Int32Array(y.computeStrides(d)).buffer),N=t.dataIdMap.get(h.dataId).id;return BW(x,s,i===\"NHWC\"?1:0,b,o.shape.length-1,w,I,d.length,N),h}var VW={kernelName:Pa,backendName:\"wasm\",setupFunc:sct,kernelFunc:ict};var GW;function act(r){GW=r.wasm.cwrap(ss,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function lct(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s}=t,i=n.dataIdMap.get(o.dataId).id,a=n.dataIdMap.get(s.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p}=e,m=l==null?[1,1]:l,f=S.computeConv2DInfo(o.shape,s.shape,u,m,c,p,!0),d=f.filterHeight,h=f.filterWidth,g=f.padInfo.top,x=f.padInfo.right,b=f.padInfo.bottom,w=f.padInfo.left,I=f.dilationHeight,N=f.dilationWidth,E=f.strideHeight,A=f.strideWidth,D=f.inChannels,F=f.outChannels,M=f.padInfo.type===\"SAME\"?1:0;if(f.dataFormat!==\"channelsLast\")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`);let V=n.makeOutput(f.outShape,\"float32\"),G=n.dataIdMap.get(V.dataId).id;return GW(i,o.shape[0],o.shape[1],o.shape[2],a,d,h,g,x,b,w,M,I,N,E,A,D,F,G),V}var WW={kernelName:ss,backendName:\"wasm\",setupFunc:act,kernelFunc:lct};var UW;function uct(r){UW=r.wasm.cwrap(\"Diag\",null,[\"number\",\"number\",\"number\",\"number\"])}function cct(r){let{inputs:t,backend:e}=r,{x:n}=t,o=y.sizeFromShape(n.shape),s=e.makeOutput([...n.shape,...n.shape],n.dtype);return UW(e.dataIdMap.get(n.dataId).id,Nt[n.dtype],o,e.dataIdMap.get(s.dataId).id),s}var HW={kernelName:Xl,backendName:\"wasm\",setupFunc:uct,kernelFunc:cct};var qW;function pct(r){qW=r.wasm.cwrap(is,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function mct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n;if(o.dtype!==s.dtype)throw new Error(`Dilation2D error: x must have the same dtype as filter. 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Got ${o.dtype}, ${s.dtype}, and ${i.dtype}`);let c=S.computeDilation2DInfo(o.shape,s.shape,a,u,\"NHWC\",l),p=e.makeOutput(s.shape,s.dtype);return jW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,e.dataIdMap.get(p.dataId).id,Nt[o.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),p}var XW={kernelName:Zl,backendName:\"wasm\",setupFunc:fct,kernelFunc:dct};var YW;function hct(r){YW=r.wasm.cwrap(Yl,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function gct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,dy:i}=t,{strides:a,pad:u,dilations:l}=n;if(o.dtype!==s.dtype||o.dtype!==i.dtype)throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${o.dtype}, ${s.dtype}, and ${i.dtype}`);let c=S.computeDilation2DInfo(o.shape,s.shape,a,u,\"NHWC\",l),p=e.makeOutput(o.shape,o.dtype);return YW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,e.dataIdMap.get(p.dataId).id,Nt[o.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),p}var ZW={kernelName:Yl,backendName:\"wasm\",setupFunc:hct,kernelFunc:gct};var JW=yt(ls);var QW;function xct(r){QW=r.wasm.cwrap(La,null,[\"number\",\"number\",\"number\"])}function yct(r){let{inputs:t,backend:e}=r,{dy:n,y:o}=t,s=e.makeOutput(o.shape,\"float32\"),i=a=>e.dataIdMap.get(a.dataId).id;return QW(i(o),i(n),i(s)),s}var tU={kernelName:La,backendName:\"wasm\",setupFunc:xct,kernelFunc:yct};var bct=!1,eU=ee(za,bct,\"bool\");var rU=yt(us);var nU=yt(cs,\"float32\");function EC(r){let{inputs:t,attrs:e,backend:n}=r,{input:o}=t,{dim:s}=e,i=o.shape.length,a=o.shape.slice(),u=s;return s<0&&(y.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+s+1),a.splice(u,0,1),mr({inputs:{x:o},backend:n,attrs:{shape:a}})}var oU={kernelName:Mi,backendName:\"wasm\",kernelFunc:EC};var sU=yt(ps,\"float32\");function Z1(r){let{attrs:{shape:t,value:e},backend:n}=r,{attrs:{dtype:o}}=r;o=o||y.inferDtype(e);let s=n.makeOutput(t,o);return n.typedArrayFromHeap(s).fill(e),s}var iU={kernelName:Jl,backendName:\"wasm\",kernelFunc:Z1};var aU;function wct(r){aU=r.wasm.cwrap(Ba,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Ict(r){let{inputs:t,backend:e}=r,{image:n}=t,o=e.makeOutput(n.shape,n.dtype),s=e.dataIdMap.get(n.dataId).id,i=e.dataIdMap.get(o.dataId).id,[a,u,l,c]=n.shape;return aU(s,a,u,l,c,i),o}var lU={kernelName:Ba,backendName:\"wasm\",kernelFunc:Ict,setupFunc:wct};var uU=yt(ms);var Cct=!1,cU=ee(fs,Cct);var pU;function vct(r){pU=r.wasm.cwrap(ds,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Sct(r){let{backend:t,inputs:e,attrs:n}=r,{varianceEpsilon:o}=n,{x:s,mean:i,variance:a,offset:u,scale:l}=e,c=t.dataIdMap.get(s.dataId).id,p=t.dataIdMap.get(i.dataId).id,m=t.dataIdMap.get(a.dataId).id,f=u!=null?t.dataIdMap.get(u.dataId).id:0,d=l!=null?t.dataIdMap.get(l.dataId).id:0,h=t.makeOutput(s.shape,s.dtype);if(y.sizeFromShape(s.shape)===0)return h;let g=t.dataIdMap.get(h.dataId).id;return pU(c,p,m,f,d,o,g),h}var mU={kernelName:ds,backendName:\"wasm\",setupFunc:vct,kernelFunc:Sct};var fU;function Nct(r){fU=r.wasm.cwrap(Yi,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 kct(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=e,h=S.computeConv2DInfo(o.shape,s.shape,u,c,l,m),g=ju[f];if(g==null)throw new Error(`${f} activation not yet supported for FusedConv2D in the wasm backend.`);let x=n.dataIdMap.get(o.dataId).id,b=n.dataIdMap.get(s.dataId).id,w=h.outChannels,I=0;if(i!=null){let ot=n.dataIdMap.get(i.dataId);if(ot.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ot.shape.length}.`);if(ot.shape[0]!==w)throw new Error(`FusedConv2D bias shape (${ot.shape}) does not match the number of output channels (${w})`);I=ot.id}let N=h.filterHeight,E=h.filterWidth,A=h.padInfo.top,D=h.padInfo.right,F=h.padInfo.bottom,M=h.padInfo.left,V=h.dilationHeight,G=h.dilationWidth,W=h.strideHeight,q=h.strideWidth,H=h.inChannels,j=h.padInfo.type===\"SAME\"?1:0,Y=h.batchSize,Z=h.inHeight,et=h.inWidth;if(p!==\"NHWC\")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let nt=n.makeOutput(h.outShape,\"float32\"),st=n.dataIdMap.get(nt.dataId).id,lt=a==null?0:n.dataIdMap.get(a.dataId).id;return fU(x,Y,Z,et,b,N,E,I,A,D,F,M,j,V,G,W,q,H,w,g,lt,d||0,st),nt}var dU={kernelName:Yi,backendName:\"wasm\",setupFunc:Nct,kernelFunc:kct};var hU;function Tct(r){hU=r.wasm.cwrap(Zi,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 _ct(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=e,h=S.computeConv2DInfo(o.shape,s.shape,u,c,l,m,!0),g=ju[f];if(g==null)throw new Error(`${f} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let x=n.dataIdMap.get(o.dataId).id,b=n.dataIdMap.get(s.dataId).id,w=h.outChannels,I=0;if(i!=null){let ot=n.dataIdMap.get(i.dataId);if(ot.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ot.shape.length}.`);if(ot.shape[0]!==w)throw new Error(`FusedDepthwiseConv2D bias shape (${ot.shape}) does not match the number of output channels (${w})`);I=ot.id}let N=h.filterHeight,E=h.filterWidth,A=h.padInfo.top,D=h.padInfo.right,F=h.padInfo.bottom,M=h.padInfo.left,V=h.dilationHeight,G=h.dilationWidth,W=h.strideHeight,q=h.strideWidth,H=h.inChannels,j=h.padInfo.type===\"SAME\"?1:0,Y=h.batchSize,Z=h.inHeight,et=h.inWidth;if(p!==\"NHWC\")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let nt=n.makeOutput(h.outShape,\"float32\"),st=n.dataIdMap.get(nt.dataId).id,lt=a==null?0:n.dataIdMap.get(a.dataId).id;return hU(x,Y,Z,et,b,N,E,I,A,D,F,M,j,V,G,W,q,H,w,g,lt,d||0,st),nt}var gU={kernelName:Zi,backendName:\"wasm\",setupFunc:Tct,kernelFunc:_ct};var xU;function Ect(r){xU=r.wasm.cwrap(Va,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function Act(r){let{backend:t,inputs:e}=r,{params:n,indices:o}=e,[s,i,a,u]=Ey.prepareAndValidate(n,o),l=t.makeOutput(s,n.dtype);if(i===0)return l;let c=o.shape,p=c[c.length-1],f=t.dataIdMap.get(n.dataId).id,h=t.dataIdMap.get(o.dataId).id,g=new Uint8Array(new Int32Array(u).buffer),x=t.dataIdMap.get(l.dataId).id;return xU(f,Nt[n.dtype],h,i,p,a,g,x),l}var yU={kernelName:Va,backendName:\"wasm\",setupFunc:Ect,kernelFunc:Act};var bU;function Dct(r){bU=r.wasm.cwrap(\"Gather\",null,[\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function $ct(r){let{backend:t,inputs:e,attrs:n}=r,{x:o,indices:s}=e,{axis:i,batchDims:a}=n,u=y.parseAxisParam(i,o.shape)[0],l=t.readSync(s.dataId),c=o.shape[u];for(let F=0;F=0,()=>`GatherV2: the index value ${M} is not in [0, ${c-1}]`)}let p=S.segment_util.collectGatherOpShapeInfo(o,s,u,a),m=mr({inputs:{x:o},attrs:{shape:[p.batchSize,p.outerSize,p.dimSize,p.sliceSize]},backend:t}),f=y.sizeFromShape(s.shape),d=mr({inputs:{x:s},attrs:{shape:[p.batchSize,f/p.batchSize]},backend:t}),h=[p.batchSize,p.outerSize,f/p.batchSize,p.sliceSize],g=t.makeOutput(h,o.dtype);if(y.sizeFromShape(o.shape)===0)return g;let x=m.shape.length-1,w=t.dataIdMap.get(m.dataId).id,N=t.dataIdMap.get(d.dataId).id,E=t.dataIdMap.get(g.dataId).id,A=new Uint8Array(new Int32Array(y.computeStrides(m.shape)).buffer),D=new Uint8Array(new Int32Array(y.computeStrides(h)).buffer);return bU(w,Nt[o.dtype],A,x,N,p.batchSize,D,E),t.disposeData(m.dataId),t.disposeData(d.dataId),g.shape=p.outputShape,g}var wU={kernelName:Pi,backendName:\"wasm\",setupFunc:Dct,kernelFunc:$ct};var Rct=!1,IU=ee(Ga,Rct,\"bool\");var Fct=!1,CU=ee(hs,Fct,\"bool\");var vU=yt(gs,\"bool\");var SU=yt(xs,\"bool\");var NU=yt(ys,\"bool\");var kU;function Oct(r){kU=r.wasm.cwrap(bs,null,[\"number\",\"number\",\"number\",\"number\"])}function Mct(r){let{inputs:{x:t},attrs:{alpha:e},backend:n}=r,o=n.dataIdMap.get(t.dataId).id,s=n.makeOutput(t.shape,\"float32\");if(y.sizeFromShape(t.shape)!==0){let i=n.dataIdMap.get(s.dataId).id;kU(o,Nt[t.dtype],e,i)}return s}var TU={kernelName:bs,backendName:\"wasm\",setupFunc:Oct,kernelFunc:Mct};var Pct=!1,_U=ee(Wa,Pct,\"bool\");var Lct=!1,EU=ee(Ua,Lct,\"bool\");var AU;function zct(r){AU=r.wasm.cwrap(Ha,null,[\"number\",\"number\",\"number\",\"number\"])}function Bct(r){let{attrs:t,backend:e}=r,{start:n,stop:o,num:s}=t,i=Math.floor(s),a=e.makeOutput([i],\"float32\");return AU(e.dataIdMap.get(a.dataId).id,n,o,i),a}var DU={kernelName:Ha,backendName:\"wasm\",setupFunc:zct,kernelFunc:Bct};var $U=yt(ws);var RU=yt(Is);var Vct=!1,FU=ee(qa,Vct,\"bool\");var OU=yt(Ka);var Gct=!1,MU=ee(ja,Gct,\"bool\");var Wct=!1,PU=ee(E_,Wct,\"bool\");var LU;function Uct(r){LU=r.wasm.cwrap(Cs,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Hct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n;if(o.dtype!==\"float32\")throw new Error(\"LRN error: x must have dtype float32\");let l=e.makeOutput(o.shape,o.dtype);return LU(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(l.dataId).id,o.shape[3],s,i,a,u),l}var zU={kernelName:Cs,backendName:\"wasm\",setupFunc:Uct,kernelFunc:Hct};var BU;function qct(r){BU=r.wasm.cwrap(Xa,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Kct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,y:s,dy:i}=t,{depthRadius:a,bias:u,alpha:l,beta:c}=n;if(o.dtype!==\"float32\"||s.dtype!==\"float32\"||i.dtype!==\"float32\")throw new Error(\"LRNGrad error: x, y, and dy must have dtype float32\");let p=e.makeOutput(o.shape,o.dtype);return BU(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,e.dataIdMap.get(p.dataId).id,i.shape[3],a,u,l,c),p}var VU={kernelName:Xa,backendName:\"wasm\",setupFunc:qct,kernelFunc:Kct};var GU;function jct(r){GU=r.wasm.cwrap(vs,null,[\"number\",\"number\",\"number\",\"number\"])}function Xct(r){let{backend:t,inputs:e,attrs:n}=r,{reductionIndices:o,keepDims:s}=n,{x:i}=e,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=Cn(i,o,t);if(f){let w=t.dataIdMap.get(c.dataId).id;l=c,u=w}let d=l.shape.length;S.assertAxesAreInnerMostDims(\"max\",p,d);let[h,g]=S.computeOutAndReduceShapes(l.shape,p),x=y.sizeFromShape(g),b=t.makeOutput(h,i.dtype);if(y.sizeFromShape(l.shape)!==0){let w=t.dataIdMap.get(b.dataId).id;GU(u,Nt[i.dtype],x,w)}if(f&&t.disposeData(c.dataId),s){let w=S.expandShapeToKeepDim(b.shape,m);b.shape=w}return b}var WU={kernelName:vs,backendName:\"wasm\",setupFunc:jct,kernelFunc:Xct};var Yct=!1,UU=ee(Ss,Yct);var HU;function Zct(r){HU=r.wasm.cwrap(Ns,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Jct(r){let{inputs:t,attrs:e,backend:n}=r,o=t.x,s=n.dataIdMap.get(o.dataId).id;y.assert(o.dtype===\"float32\",()=>`Error in MaxPool: only float32 input is supported. Got ${o.dtype}.`);let{filterSize:i,strides:a,pad:u,dimRoundingMode:l}=e,c=S.computePool2DInfo(o.shape,i,a,1,u,l),p=c.filterHeight,m=c.filterWidth,f=c.padInfo.top,d=c.padInfo.right,h=c.padInfo.bottom,g=c.padInfo.left,x=c.dilationHeight,b=c.dilationWidth,w=c.strideHeight,I=c.strideWidth,N=c.inChannels,E=c.outChannels;if(c.dataFormat!==\"channelsLast\")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let A=n.makeOutput(c.outShape,\"float32\"),D=n.dataIdMap.get(A.dataId).id;return HU(s,o.shape[0],o.shape[1],o.shape[2],p,m,f,d,h,g,x,b,w,I,N,E,D),A}var qU={kernelName:Ns,backendName:\"wasm\",setupFunc:Zct,kernelFunc:Jct};var KU;function Qct(r){KU=r.wasm.cwrap(\"MaxPool3D\",null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function tpt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dimRoundingMode:u,dataFormat:l}=n,c=S.computePool3DInfo(o.shape,s,i,1,a,u,l),p=e.makeOutput(c.outShape,o.dtype);return KU(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(p.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),p}var jU={kernelName:Li,backendName:\"wasm\",setupFunc:Qct,kernelFunc:tpt};var XU;function ept(r){XU=r.wasm.cwrap(\"MaxPool3DGrad\",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 rpt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,{filterSize:i,strides:a,pad:u,dimRoundingMode:l}=n,c=S.computePool3DInfo(s.shape,i,a,1,u,l),p=e.makeOutput(s.shape,s.dtype);return XU(e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(p.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),p}var YU={kernelName:tu,backendName:\"wasm\",setupFunc:ept,kernelFunc:rpt};var ZU;function npt(r){ZU=r.wasm.cwrap(\"MaxPoolGrad\",null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function opt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,{filterSize:i,strides:a,pad:u,dimRoundingMode:l}=n,c=S.computePool2DInfo(s.shape,i,a,1,u,l),p=e.makeOutput(s.shape,s.dtype);return ZU(e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(p.dataId).id,c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.top,c.padInfo.left),p}var JU={kernelName:Ql,backendName:\"wasm\",setupFunc:npt,kernelFunc:opt};var QU;function spt(r){QU=r.wasm.cwrap(\"MaxPoolWithArgmax\",null,[\"number\",\"number\",\"number\",\"number\",\"boolean\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function ipt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,includeBatchInIndex:u}=n;y.assert(o.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.shape.length}.`);let l=[1,1];y.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,[1,1],a),p=e.makeOutput(c.outShape,o.dtype),m=e.makeOutput(c.outShape,\"int32\");return QU(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(p.dataId).id,e.dataIdMap.get(m.dataId).id,Nt[o.dtype],u,c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.top,c.padInfo.left),[p,m]}var t4={kernelName:eu,backendName:\"wasm\",setupFunc:spt,kernelFunc:ipt};var e4;function apt(r){e4=r.wasm.cwrap(ks,null,[\"number, number, number\"])}function lpt(r){let{backend:t,inputs:e,attrs:n}=r,{axis:o,keepDims:s}=n,{x:i}=e,a=t.dataIdMap.get(i.dataId).id,u=a,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=Cn(i,o,t),d=p;if(f){let I=t.dataIdMap.get(c.dataId).id;I!==a&&(l=c,u=I,d=S.getInnerMostAxes(d.length,l.shape.length))}S.assertAxesAreInnerMostDims(\"mean\",d,l.shape.length);let[h,g]=S.computeOutAndReduceShapes(l.shape,d),x=y.sizeFromShape(g),b=l;l.dtype!==\"float32\"&&(b=Fn({backend:t,inputs:{x:l},attrs:{dtype:\"float32\"}}),u=t.dataIdMap.get(b.dataId).id);let w=t.makeOutput(h,\"float32\");if(y.sizeFromShape(l.shape)!==0){let I=t.dataIdMap.get(w.dataId).id;e4(u,x,I)}if(f&&t.disposeData(c.dataId),s){let I=S.expandShapeToKeepDim(w.shape,m);w.shape=I}return l.dtype!==\"float32\"&&t.disposeData(b.dataId),w}var r4={kernelName:ks,backendName:\"wasm\",setupFunc:apt,kernelFunc:lpt};var n4;function upt(r){n4=r.wasm.cwrap(Ts,null,[\"number\",\"number\",\"number\",\"number\"])}function cpt(r){let{backend:t,inputs:e,attrs:n}=r,{axis:o,keepDims:s}=n,{x:i}=e,a=t.dataIdMap.get(i.dataId).id,u=a,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=Cn(i,o,t);if(f){let 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wasm binary file at '${r}'`),n.arrayBuffer().then(o=>{WebAssembly.instantiate(o,t).then(s=>{e(s.instance,s.module)})})}),{})}function eq(r,t,e){if(OC!=null)return OC;let n=\"tfjs-backend-wasm.wasm\";return r&&t?n=\"tfjs-backend-wasm-threaded-simd.wasm\":r&&(n=\"tfjs-backend-wasm-simd.wasm\"),yg!=null&&yg[n]!=null?yg[n]:e+n}async function nq(){let[r,t]=await Promise.all([L().getAsync(\"WASM_HAS_SIMD_SUPPORT\"),L().getAsync(\"WASM_HAS_MULTITHREAD_SUPPORT\")]);return new Promise((e,n)=>{let o={};o.locateFile=(a,u)=>{if(a.endsWith(\".worker.js\")){let l=rq.wasmWorkerContents.replace(/\\n/g,\"\\\\n\"),c=new Blob([l],{type:\"application/javascript\"});return URL.createObjectURL(c)}return a.endsWith(\".wasm\")?eq(r,t,xg!=null?xg:u):u+a},u_&&(o.instantiateWasm=Lmt(eq(r,t,xg!=null?xg:\"\")));let s=!1;o.onAbort=()=>{if(s||bg)return;bg=!0,n({message:\"Make sure the server can serve the `.wasm` file relative to the bundled js file. 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Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}u_=t}var oq=-1,i_=-1;function Wmt(r){oq=r}function Umt(){if(i_===-1)throw new Error(\"WASM backend not initialized.\");return i_}var Hmt=\"4.21.0\";var qmt=2;Xp(\"wasm\",async()=>{let{wasm:r}=await nq();return new wg(r)},qmt);var sq=\"4.21.0\",Kmt=\"4.21.0\",jmt=\"4.21.0\",Xmt=\"4.21.0\",Ymt=\"4.21.0\",Zmt={tfjs:sq,\"tfjs-core\":sq,\"tfjs-converter\":Kmt,\"tfjs-backend-cpu\":jmt,\"tfjs-backend-webgl\":Xmt,\"tfjs-backend-wasm\":Ymt};export{Ai as Abs,Go as Acos,Wo as Acosh,vc as AdadeltaOptimizer,Sc as AdagradOptimizer,Nc as AdamOptimizer,kc as AdamaxOptimizer,no as Add,Uo as AddN,Ea as All,Aa as Any,Di as ArgMax,$i as ArgMin,Ho as Asin,qo as Asinh,Ko as Atan,Xo as Atan2,jo as Atanh,Yo as AvgPool,Ri as AvgPool3D,Hl as AvgPool3DGrad,Ul as AvgPoolGrad,wg as BackendWasm,Zo as BatchMatMul,Fi as BatchToSpaceND,Da as Bincount,$a as BitwiseAnd,ql as BroadcastArgs,__ as BroadcastTo,zb as Callback,Xy as CallbackList,fo as Cast,Jo as Ceil,ho as ClipByValue,Ap as Complex,Kl as ComplexAbs,Oi as Concat,Qo as Conv2D,Dp as Conv2DBackpropFilter,ts as Conv2DBackpropInput,es as Conv3D,Ra as Conv3DBackpropFilterV2,Fa as Conv3DBackpropInputV2,rs as Cos,ns as Cosh,Ma as CropAndResize,Oa as Cumprod,os as Cumsum,Zy as CustomCallback,Ta as DataStorage,jl as DenseBincount,Pa as DepthToSpace,ss as DepthwiseConv2dNative,$p as DepthwiseConv2dNativeBackpropFilter,Rp as DepthwiseConv2dNativeBackpropInput,Xl as Diag,is as Dilation2D,Zl as Dilation2DBackpropFilter,Yl as Dilation2DBackpropInput,Qd as Draw,w0 as ENV,Bb as EarlyStopping,Fp as Einsum,ls as Elu,La as EluGrad,Zd as Environment,za as Equal,us as Erf,cs as Exp,Mi as ExpandDims,ps as Expm1,Op as FFT,Jl as Fill,Ba as FlipLeftRight,ms as Floor,fs as FloorDiv,th as FromPixels,ds as FusedBatchNorm,Yi as FusedConv2D,Zi as FusedDepthwiseConv2D,pp as GPGPUContext,Va as GatherNd,Pi as GatherV2,qh as GraphModel,Ga as Greater,hs as GreaterEqual,Yy as History,Mp as IFFT,go as Identity,Pp as Imag,Ce as InputSpec,gs as IsFinite,xs as IsInf,ys as IsNan,Bo as KernelBackend,Cs as LRN,Xa as LRNGrad,Th as LayerVariable,Un as LayersModel,bs as LeakyRelu,Wa as Less,Ua as LessEqual,Ha as LinSpace,ws as Log,Is as Log1p,A_ as LogSoftmax,qa as LogicalAnd,Ka as LogicalNot,ja as LogicalOr,E_ as LogicalXor,nft as LowerBound,pd as MathBackendCPU,Dd as MathBackendWebGL,oft as MatrixBandPart,vs as Max,Ns as MaxPool,Li as MaxPool3D,tu as MaxPool3DGrad,Ql as MaxPoolGrad,eu as MaxPoolWithArgmax,Ss as Maximum,ks as Mean,Ts as Min,_s as Minimum,Es as MirrorPad,As as Mod,Tc as MomentumOptimizer,Ya as Multinomial,Ds as Multiply,zi as Neg,Ja as NonMaxSuppressionV3,Qa as NonMaxSuppressionV4,tl as NonMaxSuppressionV5,Za as NotEqual,V0 as OP_SCOPE_SUFFIX,$s as OneHot,Bi as OnesLike,jr as Optimizer,Ih as OptimizerConstructors,Vi as Pack,Rs as PadV2,sft as Pool,Fs as Pow,Os as Prelu,Ms as Prod,_c as RMSPropOptimizer,po as RNN,Lp as RaggedGather,zp as RaggedRange,Bp as RaggedTensorToTensor,ru as Range,D0 as Rank,Vp as Real,as as RealDiv,Ps as Reciprocal,Je as Reduction,Ls as Relu,Vs as Relu6,Gi as Reshape,Bs as ResizeBilinear,rl as ResizeBilinearGrad,zs as ResizeNearestNeighbor,el as ResizeNearestNeighborGrad,Gs as Reverse,pl as RotateWithOffset,Ws as Round,Us as Rsqrt,Il as SGDOptimizer,nl as ScatterNd,sl as SearchSorted,Wi as Select,Hs as Selu,Wc as Sequential,Xs as Sigmoid,js as Sign,qs as Sin,Ks as Sinh,Ui as Slice,Qs as Softmax,Ys as Softplus,Hi as SpaceToBatchND,nu as SparseFillEmptyRows,il as SparseReshape,ou as SparseSegmentMean,su as SparseSegmentSum,al as SparseToDense,qi as SplitV,Zs as Sqrt,iu as Square,ti as SquaredDifference,ec as StaticRegexReplace,xo as Step,ll as StridedSlice,au as StringNGrams,lu as StringSplit,uu as StringToHashBucketFast,ei as Sub,Js as Sum,nn as SymbolicTensor,ri as Tan,ni as Tanh,Lt as Tensor,le as TensorBuffer,ol as TensorScatterUpdate,oo as Tile,ul as TopK,cl as Transform,so as Transpose,cu as Unique,Ki as Unpack,pu as UnsortedSegmentSum,ift as UpperBound,ml as Variable,ji as ZerosLike,Xi as _FusedMatMul,_e as abs,fx as acos,dx as acosh,K as add,EE as addN,tm as all,cc as any,ra as argMax,hx as argMin,gx as asin,xx as asinh,yx as atan,bx as atan2,wx as atanh,xu as avgPool,Ix as avgPool3d,sx as backend,S as backend_util,$E as basicLSTMCell,oa as batchNorm,Cx as batchNorm2d,vx as batchNorm3d,Sx as batchNorm4d,yu as batchToSpaceND,Nx as bincount,FE as bitwiseAnd,q5 as booleanMaskAsync,OE as broadcastArgs,sa as broadcastTo,Hr as broadcast_util,_y as browser,wt as buffer,xQ as callbacks,J as cast,kx as ceil,vr as clipByValue,un as clone,Sn as complex,ie as concat,Tx as concat1d,_x as concat2d,Ex as concat3d,Ax as concat4d,CR as constraints,rm as conv1d,Nn as conv2d,om as conv2dTranspose,Dx as conv3d,Rx as conv3dTranspose,fft as copyRegisteredKernels,bu as cos,sm as cosh,xh as cosineWindow,mc as cumprod,im as cumsum,pn as customGrad,aO as data,mh as denseBincount,G0 as deprecationWarn,Fx as depthToSpace,ia as depthwiseConv2d,IQ as deregisterOp,du as device_util,ME as diag,Ox as dilation2d,Sdt as disableDeprecationWarnings,Tt as dispose,Ndt as disposeVariables,ut as div,Mx as divNoNan,Px as dot,dN as dropout,wu as einsum,aa as elu,vdt as enableDebugMode,Cdt as enableProdMode,hN as enclosingPowerOfTwo,Bn as engine,LE as ensureShape,L as env,$r as equal,am as erf,Lx as euclideanNorm,Ke as exp,je as expandDims,zx as expm1,fc as eye,Au as fft,Co as fill,Adt as findBackend,Ddt as findBackendFactory,la as floor,Qp as floorDiv,Qz as forceHalfFloat,Ru as fused,ua as gather,r8 as gatherND,Ey as gather_util,oE as getBackend,v0 as getGradient,Wp as getKernel,Yg as getKernelsForBackend,Umt as getThreadsCount,k1 as gpgpu_util,X6 as grad,Y6 as grads,Re as greater,cn as greaterEqual,wl as ifft,Iu as imag,fn as image,s8 as inTopKAsync,vR as initializers,JN as input,Mr as io,bm as irfft,Bx as isFinite,Vx as isInf,Gx as isNaN,De as keep,Xr as kernel_impls,oF as layers,Cu as leakyRelu,yl as less,Vn as lessEqual,xN as linalg,VE as linspace,xtt as loadGraphModel,ytt as loadGraphModelSync,UR as loadLayersModel,Wx as localResponseNormalization,Nr as log,vu as log1p,qx as logSigmoid,lm as logSoftmax,Su as logSumExp,Fr as logicalAnd,Nu as logicalNot,um as logicalOr,Kx as logicalXor,aY as losses,GE as lowerBound,Bt as matMul,O2 as math,Sr as max,ku as maxPool,Xx as maxPool3d,WE as maxPoolWithArgmax,kn as maximum,Ne as mean,lh as memory,UE as meshgrid,sF as metrics,gl as min,lo as minimum,Yx as mirrorPad,Zx as mod,gJ as model,iF as models,dc as moments,X5 as movingAverage,$ as mul,HE as multiRNNCell,qE as multinomial,Ut as neg,Ch as nextFrame,xl as norm,ci as notEqual,ca as oneHot,ar as ones,wr as onesLike,k as op,KE as outerProduct,mn as pad,jE as pad1d,XE as pad2d,YE as pad3d,ZE as pad4d,Jx as pool,qr as pow,_u as prelu,mx as print,Qx as prod,kdt as profile,JE as raggedGather,QE as raggedRange,tA as raggedTensorToTensor,eA as rand,CA as randomGamma,xc as randomNormal,vA as randomStandardNormal,Gn as randomUniform,SA as randomUniformInt,pa as range,_dt as ready,bl as real,sy as reciprocal,Xp as registerBackend,yJ as registerCallbackConstructor,$_ as registerGradient,rc as registerKernel,wQ as registerOp,aF as regularizers,Or as relu,cm as relu6,Edt as removeBackend,R as reshape,dr as reverse,NA as reverse1d,kA as reverse2d,TA as reverse3d,_A as reverse4d,Du as rfft,pm as round,mm as rsqrt,pt as scalar,Z5 as scatterND,$u as scatter_util,dh as searchSorted,fm as selu,dm as separableConv2d,xJ as sequential,Q as serialization,OK as setBackend,$dt as setPlatform,Wmt as setThreadsCount,Vmt as setWasmPath,Gmt as setWasmPaths,BT as setWebGLContext,EA as setdiff1dAsync,Ew as shared,en as sigmoid,iy as sign,iY as signal,hm as sin,gm as sinh,Ot as slice,xm as slice1d,gh as slice2d,ym as slice3d,yc as slice4d,Be as slice_util,Eu as softmax,ui as softplus,Tu as spaceToBatchND,lY as sparse,t8 as sparseToDense,sY as spectral,hr as split,ge as sqrt,Wt as square,wm as squaredDifference,Wn as squeeze,Fe as stack,So as step,ay as stridedSlice,uY as string,at as sub,mt as sum,lc as sumOutType,ly as tan,li as tanh,ir as tensor,Oe as tensor1d,pi as tensor2d,uy as tensor3d,AA as tensor4d,DA as tensor5d,$A as tensor6d,FA as tensorScatterUpdate,Io as tensor_util,IA as test_util,B as tidy,Rr as tile,Tdt as time,cy as topk,Ac as train,Vt as transpose,Cm as truncatedNormal,py as unique,mft as unregisterGradient,pft as unregisterKernel,vm as unsortedSegmentSum,gr as unstack,ur as upcastType,OA as upperBound,y as util,Z6 as valueAndGrad,J6 as valueAndGrads,my as variable,Ux as variableGrads,Zmt as version,VF as version_converter,X2 as version_core,KO as version_cpu,jm as version_layers,Hmt as version_wasm,Jz as version_webgl,T$e as webgl,Cd as webgl_util,Ie as where,dy as whereAsync,ke as zeros,vt as zerosLike};\n", "export * from './drawContour';\nexport * from './drawDetections';\nexport * from './drawFaceExpressions';\nexport * from './DrawBox';\nexport * from './DrawFaceLandmarks';\nexport * from './DrawTextField';\n", "import { Point } from '../classes/index';\n\nexport function drawContour(\n ctx: CanvasRenderingContext2D,\n points: Point[],\n isClosed = false,\n) {\n ctx.beginPath();\n\n points.slice(1).forEach(({ x, y }, prevIdx) => {\n const from = points[prevIdx];\n ctx.moveTo(from.x, from.y);\n ctx.lineTo(x, y);\n });\n\n if (isClosed) {\n const from = points[points.length - 1];\n const to = points[0];\n if (!from || !to) {\n return;\n }\n\n ctx.moveTo(from.x, from.y);\n ctx.lineTo(to.x, to.y);\n }\n\n ctx.stroke();\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Point } from '../classes/index';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n return tensor instanceof tf.Tensor && tensor.shape.length === dim;\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n return isTensor(tensor, 1);\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n return isTensor(tensor, 2);\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n return isTensor(tensor, 3);\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n return isTensor(tensor, 4);\n}\n\nexport function isFloat(num: number) {\n return num % 1 !== 0;\n}\n\nexport function isEven(num: number) {\n return num % 2 === 0;\n}\n\nexport function round(num: number, prec = 2) {\n const f = 10 ** prec;\n return Math.floor(num * f) / f;\n}\n\nexport function isDimensions(obj: any): boolean {\n return obj && obj.width && obj.height;\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n const scale = inputSize / Math.max(height, width);\n return new Dimensions(Math.round(width * scale), Math.round(height * scale));\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n .div(new Point(pts.length, pts.length));\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n return Array(num).fill(0).map((_, i) => start + (i * step));\n}\n\nexport function isValidNumber(num: any) {\n return !!num && (num !== Infinity) && (num !== -Infinity) && !Number.isNaN(num) || num === 0;\n}\n\nexport function isValidProbablitiy(num: any) {\n return isValidNumber(num) && num >= 0 && num <= 1.0;\n}\n", "import { isValidNumber } from '../utils/index';\n\nexport interface IDimensions {\n width: number\n height: number\n}\n\nexport class Dimensions implements IDimensions {\n private _width: number;\n\n private _height: number;\n\n constructor(width: number, height: number) {\n if (!isValidNumber(width) || !isValidNumber(height)) {\n throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`);\n }\n\n this._width = width;\n this._height = height;\n }\n\n public get width(): number { return this._width; }\n\n public get height(): number { return this._height; }\n\n public reverse(): Dimensions {\n return new Dimensions(1 / this.width, 1 / this.height);\n }\n}\n", "export interface IPoint {\n x: number\n y: number\n}\n\nexport class Point implements IPoint {\n private _x: number;\n\n private _y: number;\n\n constructor(x: number, y: number) {\n this._x = x;\n this._y = y;\n }\n\n get x(): number { return this._x; }\n\n get y(): number { return this._y; }\n\n public add(pt: IPoint): Point {\n return new Point(this.x + pt.x, this.y + pt.y);\n }\n\n public sub(pt: IPoint): Point {\n return new Point(this.x - pt.x, this.y - pt.y);\n }\n\n public mul(pt: IPoint): Point {\n return new Point(this.x * pt.x, this.y * pt.y);\n }\n\n public div(pt: IPoint): Point {\n return new Point(this.x / pt.x, this.y / pt.y);\n }\n\n public abs(): Point {\n return new Point(Math.abs(this.x), Math.abs(this.y));\n }\n\n public magnitude(): number {\n return Math.sqrt((this.x ** 2) + (this.y ** 2));\n }\n\n public floor(): Point {\n return new Point(Math.floor(this.x), Math.floor(this.y));\n }\n}\n", "import { isDimensions, isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box implements IBoundingBox, IRect {\n public static isRect(rect: any): boolean {\n return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber);\n }\n\n public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions = false) {\n if (!Box.isRect(box)) {\n throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`);\n }\n\n if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\n throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`);\n }\n }\n\n private _x: number;\n\n private _y: number;\n\n private _width: number;\n\n private _height: number;\n\n constructor(_box: IBoundingBox | IRect, allowNegativeDimensions = true) {\n const box = (_box || {}) as any;\n\n const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber);\n const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber);\n\n if (!isRect && !isBbox) {\n throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`);\n }\n\n const [x, y, width, height] = isRect\n ? [box.x, box.y, box.width, box.height]\n : [box.left, box.top, box.right - box.left, box.bottom - box.top];\n\n Box.assertIsValidBox({\n x, y, width, height,\n }, 'Box.constructor', allowNegativeDimensions);\n\n this._x = x;\n this._y = y;\n this._width = width;\n this._height = height;\n }\n\n public get x(): number { return this._x; }\n\n public get y(): number { return this._y; }\n\n public get width(): number { return this._width; }\n\n public get height(): number { return this._height; }\n\n public get left(): number { return this.x; }\n\n public get top(): number { return this.y; }\n\n public get right(): number { return this.x + this.width; }\n\n public get bottom(): number { return this.y + this.height; }\n\n public get area(): number { return this.width * this.height; }\n\n public get topLeft(): Point { return new Point(this.left, this.top); }\n\n public get topRight(): Point { return new Point(this.right, this.top); }\n\n public get bottomLeft(): Point { return new Point(this.left, this.bottom); }\n\n public get bottomRight(): Point { return new Point(this.right, this.bottom); }\n\n public round(): Box {\n const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n .map((val) => Math.round(val));\n return new Box({\n x, y, width, height,\n });\n }\n\n public floor(): Box {\n const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n .map((val) => Math.floor(val));\n return new Box({\n x, y, width, height,\n });\n }\n\n public toSquare(): Box {\n let {\n x, y, width, height,\n } = this;\n const diff = Math.abs(width - height);\n if (width < height) {\n x -= (diff / 2);\n width += diff;\n }\n if (height < width) {\n y -= (diff / 2);\n height += diff;\n }\n\n return new Box({ x, y, width, height });\n }\n\n public rescale(s: IDimensions | number): Box {\n const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number;\n const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number;\n return new Box({\n x: this.x * scaleX,\n y: this.y * scaleY,\n width: this.width * scaleX,\n height: this.height * scaleY,\n });\n }\n\n public pad(padX: number, padY: number): Box {\n const [x, y, width, height] = [\n this.x - (padX / 2),\n this.y - (padY / 2),\n this.width + padX,\n this.height + padY,\n ];\n return new Box({ x, y, width, height });\n }\n\n public clipAtImageBorders(imgWidth: number, imgHeight: number): Box {\n const { x, y, right, bottom } = this;\n const clippedX = Math.max(x, 0);\n const clippedY = Math.max(y, 0);\n\n const newWidth = right - clippedX;\n const newHeight = bottom - clippedY;\n const clippedWidth = Math.min(newWidth, imgWidth - clippedX);\n const clippedHeight = Math.min(newHeight, imgHeight - clippedY);\n\n return (new Box({ x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight })).floor();\n }\n\n public shift(sx: number, sy: number): Box {\n const { width, height } = this;\n const x = this.x + sx;\n const y = this.y + sy;\n\n return new Box({ x, y, width, height });\n }\n\n public padAtBorders(imageHeight: number, imageWidth: number) {\n const w = this.width + 1;\n const h = this.height + 1;\n\n const dx = 1;\n const dy = 1;\n let edx = w;\n let edy = h;\n\n let x = this.left;\n let y = this.top;\n let ex = this.right;\n let ey = this.bottom;\n\n if (ex > imageWidth) {\n edx = -ex + imageWidth + w;\n ex = imageWidth;\n }\n if (ey > imageHeight) {\n edy = -ey + imageHeight + h;\n ey = imageHeight;\n }\n if (x < 1) {\n edy = 2 - x;\n x = 1;\n }\n if (y < 1) {\n edy = 2 - y;\n y = 1;\n }\n\n return { dy, edy, dx, edx, y, ey, x, ex, w, h };\n }\n\n public calibrate(region: Box) {\n return new Box({\n left: this.left + (region.left * this.width),\n top: this.top + (region.top * this.height),\n right: this.right + (region.right * this.width),\n bottom: this.bottom + (region.bottom * this.height),\n }).toSquare().round();\n }\n}\n", "import { Box } from './Box';\n\nexport interface IBoundingBox {\n left: number\n top: number\n right: number\n bottom: number\n}\n\nexport class BoundingBox extends Box implements IBoundingBox {\n constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions = false) {\n super({ left, top, right, bottom }, allowNegativeDimensions);\n }\n}\n", "import { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { IRect, Rect } from './Rect';\n\nexport class ObjectDetection {\n private _score: number;\n\n private _classScore: number;\n\n private _className: string;\n\n private _box: Rect;\n\n private _imageDims: Dimensions;\n\n constructor(\n score: number,\n classScore: number,\n className: string,\n relativeBox: IRect,\n imageDims: IDimensions,\n ) {\n this._imageDims = new Dimensions(imageDims.width, imageDims.height);\n this._score = score;\n this._classScore = classScore;\n this._className = className;\n this._box = new Box(relativeBox).rescale(this._imageDims);\n }\n\n public get score(): number { return this._score; }\n\n public get classScore(): number { return this._classScore; }\n\n public get className(): string { return this._className; }\n\n public get box(): Box { return this._box; }\n\n public get imageDims(): Dimensions { return this._imageDims; }\n\n public get imageWidth(): number { return this.imageDims.width; }\n\n public get imageHeight(): number { return this.imageDims.height; }\n\n public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()); }\n\n public forSize(width: number, height: number): ObjectDetection {\n return new ObjectDetection(\n this.score,\n this.classScore,\n this.className,\n this.relativeBox,\n { width, height },\n );\n }\n}\n", "import { Box } from './Box';\nimport { IDimensions } from './Dimensions';\nimport { ObjectDetection } from './ObjectDetection';\nimport { Rect } from './Rect';\n\nexport interface IFaceDetecion {\n score: number\n box: Box\n}\n\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\n constructor(\n score: number,\n relativeBox: Rect,\n imageDims: IDimensions,\n ) {\n super(score, score, '', relativeBox, imageDims);\n }\n\n public override forSize(width: number, height: number): FaceDetection {\n const { score, relativeBox, imageDims } = super.forSize(width, height);\n return new FaceDetection(score, relativeBox, imageDims);\n }\n}\n", "import { Box } from '../classes/Box';\n\nexport function iou(box1: Box, box2: Box, isIOU = true) {\n const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left));\n const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top));\n const interSection = width * height;\n\n return isIOU\n ? interSection / (box1.area + box2.area - interSection)\n : interSection / Math.min(box1.area, box2.area);\n}\n", "import { BoundingBox, IPoint } from '../classes/index';\n\nexport function minBbox(pts: IPoint[]): BoundingBox {\n const xs = pts.map((pt) => pt.x);\n const ys = pts.map((pt) => pt.y);\n const minX = xs.reduce((min, x) => (x < min ? x : min), Infinity);\n const minY = ys.reduce((min, y) => (y < min ? y : min), Infinity);\n const maxX = xs.reduce((max, x) => (max < x ? x : max), 0);\n const maxY = ys.reduce((max, y) => (max < y ? y : max), 0);\n\n return new BoundingBox(minX, minY, maxX, maxY);\n}\n", "import { Box } from '../classes/Box';\nimport { iou } from './iou';\n\nexport function nonMaxSuppression(\n boxes: Box[],\n scores: number[],\n iouThreshold: number,\n isIOU = true,\n): number[] {\n let indicesSortedByScore = scores\n .map((score, boxIndex) => ({ score, boxIndex }))\n .sort((c1, c2) => c1.score - c2.score)\n .map((c) => c.boxIndex);\n\n const pick: number[] = [];\n\n while (indicesSortedByScore.length > 0) {\n const curr = indicesSortedByScore.pop() as number;\n pick.push(curr);\n\n const indices = indicesSortedByScore;\n\n const outputs: number[] = [];\n for (let i = 0; i < indices.length; i++) {\n const idx = indices[i];\n\n const currBox = boxes[curr];\n const idxBox = boxes[idx];\n\n outputs.push(iou(currBox, idxBox, isIOU));\n }\n\n indicesSortedByScore = indicesSortedByScore.filter(\n (_, j) => outputs[j] <= iouThreshold,\n );\n }\n\n return pick;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {\n return tf.tidy(() => {\n const [r, g, b] = meanRgb;\n const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32');\n const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32');\n const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32');\n const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3);\n\n return tf.sub(x, avg_rgb);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n/**\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\n *\n * @param imgTensor The image tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The padded tensor with width === height.\n */\nexport function padToSquare(imgTensor: tf.Tensor4D, isCenterImage = false): tf.Tensor4D {\n return tf.tidy(() => {\n const [height, width] = imgTensor.shape.slice(1);\n if (height === width) return imgTensor;\n const dimDiff = Math.abs(height - width);\n const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1));\n const paddingAxis = height > width ? 2 : 1;\n const createPaddingTensor = (paddingAmountLocal: number): tf.Tensor => {\n const paddingTensorShape = imgTensor.shape.slice();\n paddingTensorShape[paddingAxis] = paddingAmountLocal;\n return tf.fill(paddingTensorShape, 0, 'float32');\n };\n const paddingTensorAppend = createPaddingTensor(paddingAmount);\n const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number);\n const paddingTensorPrepend = isCenterImage && remainingPaddingAmount ? createPaddingTensor(remainingPaddingAmount) : null;\n const tensorsToStack = [paddingTensorPrepend, imgTensor, paddingTensorAppend]\n .filter((t) => !!t)\n .map((t) => tf.cast(t as tf.Tensor4D, 'float32')) as tf.Tensor4D[];\n return tf.concat(tensorsToStack, paddingAxis);\n });\n}\n", "export function shuffleArray(inputArray: any[]) {\n const array = inputArray.slice();\n for (let i = array.length - 1; i > 0; i--) {\n const j = Math.floor(Math.random() * (i + 1));\n const x = array[i];\n array[i] = array[j];\n array[j] = x;\n }\n return array;\n}\n", "export * from './iou';\nexport * from './minBbox';\nexport * from './nonMaxSuppression';\nexport * from './normalize';\nexport * from './padToSquare';\nexport * from './shuffleArray';\n\nexport function sigmoid(x: number) {\n return 1 / (1 + Math.exp(-x));\n}\n\nexport function inverseSigmoid(x: number) {\n return Math.log(x / (1 - x));\n}\n", "import { Box } from './Box';\n\nexport interface IRect {\n x: number\n y: number\n width: number\n height: number\n}\n\nexport class Rect extends Box implements IRect {\n constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions = false) {\n super({ x, y, width, height }, allowNegativeDimensions);\n }\n}\n", "import { minBbox } from '../ops/index';\nimport { getCenterPoint } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { FaceDetection } from './FaceDetection';\nimport { Point } from './Point';\nimport { IRect, Rect } from './Rect';\n\n// face alignment constants\nconst relX = 0.5;\nconst relY = 0.43;\nconst relScale = 0.45;\n\nexport interface IFaceLandmarks {\n positions: Point[]\n shift: Point\n}\n\nexport class FaceLandmarks implements IFaceLandmarks {\n protected _shift: Point;\n\n protected _positions: Point[];\n\n protected _imgDims: Dimensions;\n\n constructor(\n relativeFaceLandmarkPositions: Point[],\n imgDims: IDimensions,\n shift: Point = new Point(0, 0),\n ) {\n const { width, height } = imgDims;\n this._imgDims = new Dimensions(width, height);\n this._shift = shift;\n this._positions = relativeFaceLandmarkPositions.map(\n (pt) => pt.mul(new Point(width, height)).add(shift),\n );\n }\n\n public get shift(): Point { return new Point(this._shift.x, this._shift.y); }\n\n public get imageWidth(): number { return this._imgDims.width; }\n\n public get imageHeight(): number { return this._imgDims.height; }\n\n public get positions(): Point[] { return this._positions; }\n\n public get relativePositions(): Point[] {\n return this._positions.map(\n (pt) => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight)),\n );\n }\n\n public forSize(width: number, height: number): T {\n return new (this.constructor as any)(\n this.relativePositions,\n { width, height },\n );\n }\n\n public shiftBy(x: number, y: number): T {\n return new (this.constructor as any)(\n this.relativePositions,\n this._imgDims,\n new Point(x, y),\n );\n }\n\n public shiftByPoint(pt: Point): T {\n return this.shiftBy(pt.x, pt.y);\n }\n\n /**\n * Aligns the face landmarks after face detection from the relative positions of the faces\n * bounding box, or it's current shift. This function should be used to align the face images\n * after face detection has been performed, before they are passed to the face recognition net.\n * This will make the computed face descriptor more accurate.\n *\n * @param detection (optional) The bounding box of the face or the face detection result. If\n * no argument was passed the position of the face landmarks are assumed to be relative to\n * it's current shift.\n * @returns The bounding box of the aligned face.\n */\n public align(\n detection?: FaceDetection | IRect | IBoundingBox | null,\n options: { useDlibAlignment?: boolean, minBoxPadding?: number } = { },\n ): Box {\n if (detection) {\n const box = detection instanceof FaceDetection\n ? detection.box.floor()\n : new Box(detection);\n\n return this.shiftBy(box.x, box.y).align(null, options);\n }\n\n const { useDlibAlignment, minBoxPadding } = { useDlibAlignment: false, minBoxPadding: 0.2, ...options };\n\n if (useDlibAlignment) {\n return this.alignDlib();\n }\n\n return this.alignMinBbox(minBoxPadding);\n }\n\n private alignDlib(): Box {\n const centers = this.getRefPointsForAlignment();\n\n const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers;\n const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude();\n const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2;\n\n const size = Math.floor(eyeToMouthDist / relScale);\n\n const refPoint = getCenterPoint(centers);\n // TODO: pad in case rectangle is out of image bounds\n const x = Math.floor(Math.max(0, refPoint.x - (relX * size)));\n const y = Math.floor(Math.max(0, refPoint.y - (relY * size)));\n\n return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y));\n }\n\n private alignMinBbox(padding: number): Box {\n const box = minBbox(this.positions);\n return box.pad(box.width * padding, box.height * padding);\n }\n\n protected getRefPointsForAlignment(): Point[] {\n throw new Error('getRefPointsForAlignment not implemented by base class');\n }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks5 extends FaceLandmarks {\n protected override getRefPointsForAlignment(): Point[] {\n const pts = this.positions;\n return [\n pts[0],\n pts[1],\n getCenterPoint([pts[3], pts[4]]),\n ];\n }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks68 extends FaceLandmarks {\n public getJawOutline(): Point[] {\n return this.positions.slice(0, 17);\n }\n\n public getLeftEyeBrow(): Point[] {\n return this.positions.slice(17, 22);\n }\n\n public getRightEyeBrow(): Point[] {\n return this.positions.slice(22, 27);\n }\n\n public getNose(): Point[] {\n return this.positions.slice(27, 36);\n }\n\n public getLeftEye(): Point[] {\n return this.positions.slice(36, 42);\n }\n\n public getRightEye(): Point[] {\n return this.positions.slice(42, 48);\n }\n\n public getMouth(): Point[] {\n return this.positions.slice(48, 68);\n }\n\n protected override getRefPointsForAlignment(): Point[] {\n return [\n this.getLeftEye(),\n this.getRightEye(),\n this.getMouth(),\n ].map(getCenterPoint);\n }\n}\n", "import { round } from '../utils/index';\n\nexport interface IFaceMatch {\n label: string\n distance: number\n}\n\nexport class FaceMatch implements IFaceMatch {\n private _label: string;\n private _distance: number;\n\n constructor(label: string, distance: number) {\n this._label = label;\n this._distance = distance;\n }\n\n public get label(): string { return this._label; }\n\n public get distance(): number { return this._distance; }\n\n public toString(withDistance = true): string {\n return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`;\n }\n}\n", "import { isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { IRect } from './Rect';\n\nexport class LabeledBox extends Box {\n public static assertIsValidLabeledBox(box: any, callee: string) {\n Box.assertIsValidBox(box, callee);\n if (!isValidNumber(box.label)) {\n throw new Error(`${callee} - expected property label (${box.label}) to be a number`);\n }\n }\n\n private _label: number;\n\n constructor(box: IBoundingBox | IRect | any, label: number) {\n super(box);\n this._label = label;\n }\n\n public get label(): number { return this._label; }\n}\n", "export class LabeledFaceDescriptors {\n private _label: string;\n\n private _descriptors: Float32Array[];\n\n constructor(label: string, descriptors: Float32Array[]) {\n if (!(typeof label === 'string')) {\n throw new Error('LabeledFaceDescriptors - constructor expected label to be a string');\n }\n\n if (!Array.isArray(descriptors) || descriptors.some((desc) => !(desc instanceof Float32Array))) {\n throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array');\n }\n\n this._label = label;\n this._descriptors = descriptors;\n }\n\n public get label(): string { return this._label; }\n\n public get descriptors(): Float32Array[] { return this._descriptors; }\n\n public toJSON(): any {\n return {\n label: this.label,\n descriptors: this.descriptors.map((d) => Array.from(d)),\n };\n }\n\n public static fromJSON(json: any): LabeledFaceDescriptors {\n const descriptors = json.descriptors.map((d: any) => new Float32Array(d));\n return new LabeledFaceDescriptors(json.label, descriptors);\n }\n}\n", "import { isValidProbablitiy } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { LabeledBox } from './LabeledBox';\nimport { IRect } from './Rect';\n\nexport class PredictedBox extends LabeledBox {\n public static assertIsValidPredictedBox(box: any, callee: string) {\n LabeledBox.assertIsValidLabeledBox(box, callee);\n\n if (\n !isValidProbablitiy(box.score)\n || !isValidProbablitiy(box.classScore)\n ) {\n throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`);\n }\n }\n\n private _score: number;\n\n private _classScore: number;\n\n constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {\n super(box, label);\n this._score = score;\n this._classScore = classScore;\n }\n\n public get score(): number { return this._score; }\n\n public get classScore(): number { return this._classScore; }\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\n\nexport type WithFaceDetection = TSource & {\n detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n return obj.detection instanceof FaceDetection;\n}\n\nexport function extendWithFaceDetection(sourceObj: TSource, detection: FaceDetection): WithFaceDetection {\n const extension = { detection };\n return { ...sourceObj, ...extension };\n}\n", "import { Environment } from './types';\n\nexport function createBrowserEnv(): Environment {\n const fetch = window.fetch;\n if (!fetch) throw new Error('fetch - missing fetch implementation for browser environment');\n\n const readFile = () => {\n throw new Error('readFile - filesystem not available for browser environment');\n };\n\n return {\n Canvas: HTMLCanvasElement,\n CanvasRenderingContext2D,\n Image: HTMLImageElement,\n ImageData,\n Video: HTMLVideoElement,\n createCanvasElement: () => document.createElement('canvas'),\n createImageElement: () => document.createElement('img'),\n createVideoElement: () => document.createElement('video'),\n fetch,\n readFile,\n };\n}\n", "export function isNodejs(): boolean {\n return typeof global === 'object'\n && typeof process !== 'undefined'\n && process.versions != null\n && process.versions.node != null;\n}\n", "import { FileSystem } from './types';\nimport { isNodejs } from './isNodejs';\n\nexport function createFileSystem(fs?: any): FileSystem {\n let requireFsError = '';\n if (!fs && isNodejs()) {\n try {\n // eslint-disable-next-line global-require, @typescript-eslint/no-require-imports\n fs = require('fs');\n } catch (err) {\n requireFsError = (err as any).toString();\n }\n }\n\n const readFile = fs\n // eslint-disable-next-line no-undef\n ? (filePath: string) => new Promise((resolve, reject) => { fs.readFile(filePath, (err: NodeJS.ErrnoException | null, buffer: string | Buffer) => (err ? reject(err) : resolve(buffer))); })\n : () => { throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`); };\n return { readFile };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n const Canvas: (new () => HTMLCanvasElement) = (global as any)['Canvas'] || global.HTMLCanvasElement;\n const Image = global.Image || global.HTMLImageElement;\n const Video: (new () => HTMLVideoElement) = (global as any)['Video'] || global.HTMLVideoElement;\n\n const createCanvasElement = () => {\n if (Canvas) return new Canvas();\n throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment');\n };\n\n const createImageElement = () => {\n if (Image) return new Image();\n throw new Error('createImageElement - missing Image implementation for nodejs environment');\n };\n\n const createVideoElement = () => {\n if (Video) return new Video();\n throw new Error('createVideoElement - missing Video implementation for nodejs environment');\n };\n\n const fetch = global.fetch;\n // if (!fetch) throw new Error('fetch - missing fetch implementation for nodejs environment');\n\n const fileSystem = createFileSystem();\n\n return {\n Canvas: Canvas || class {},\n CanvasRenderingContext2D: global.CanvasRenderingContext2D || class {},\n Image: Image || class {},\n ImageData: global.ImageData || class {},\n Video: global.HTMLVideoElement || class {},\n createCanvasElement,\n createImageElement,\n createVideoElement,\n fetch,\n ...fileSystem,\n };\n}\n", "export function isBrowser(): boolean {\n return typeof window === 'object'\n && typeof document !== 'undefined'\n && typeof HTMLImageElement !== 'undefined'\n && typeof HTMLCanvasElement !== 'undefined'\n && typeof HTMLVideoElement !== 'undefined'\n && typeof ImageData !== 'undefined'\n && typeof CanvasRenderingContext2D !== 'undefined';\n}\n", "import { createBrowserEnv } from './createBrowserEnv';\nimport { createFileSystem } from './createFileSystem';\nimport { createNodejsEnv } from './createNodejsEnv';\nimport { isBrowser } from './isBrowser';\nimport { isNodejs } from './isNodejs';\nimport { Environment } from './types';\n\nlet environment: Environment | null;\n\nfunction getEnv(): Environment {\n if (!environment) {\n throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()');\n }\n return environment;\n}\n\nfunction setEnv(env: Environment) {\n environment = env;\n}\n\nfunction initialize() {\n // check for isBrowser() first to prevent electron renderer process\n // to be initialized with wrong environment due to isNodejs() returning true\n if (isBrowser()) return setEnv(createBrowserEnv());\n if (isNodejs()) return setEnv(createNodejsEnv());\n return null;\n}\n\nfunction monkeyPatch(env: Partial) {\n if (!environment) {\n initialize();\n }\n\n if (!environment) {\n throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()');\n }\n\n const { Canvas = environment.Canvas, Image = environment.Image } = env;\n environment.Canvas = Canvas;\n environment.Image = Image;\n environment.createCanvasElement = env.createCanvasElement || (() => new Canvas());\n environment.createImageElement = env.createImageElement || (() => new Image());\n\n environment.ImageData = env.ImageData || environment.ImageData;\n environment.Video = env.Video || environment.Video;\n environment.fetch = env.fetch || environment.fetch;\n environment.readFile = env.readFile || environment.readFile;\n}\n\nexport const env = {\n getEnv,\n setEnv,\n initialize,\n createBrowserEnv,\n createFileSystem,\n createNodejsEnv,\n monkeyPatch,\n isBrowser,\n isNodejs,\n};\n\ninitialize();\n\nexport * from './types';\n", "import { env } from '../env/index';\n\nexport function resolveInput(arg: string | any) {\n if (!env.isNodejs() && typeof arg === 'string') {\n return document.getElementById(arg);\n }\n return arg;\n}\n", "import { env } from '../env/index';\nimport { resolveInput } from './resolveInput';\n\nexport function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D {\n const { Canvas, CanvasRenderingContext2D } = env.getEnv();\n if (canvasArg instanceof CanvasRenderingContext2D) return canvasArg;\n const canvas = resolveInput(canvasArg);\n if (!(canvas instanceof Canvas)) throw new Error('resolveContext2d - expected canvas to be of instance of Canvas');\n const ctx = canvas.getContext('2d', { willReadFrequently: true });\n if (!ctx) throw new Error('resolveContext2d - canvas 2d context is null');\n return ctx;\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IDimensions, IPoint } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { resolveInput } from '../dom/resolveInput';\n\n// eslint-disable-next-line no-shadow\nexport enum AnchorPosition {\n // eslint-disable-next-line no-unused-vars\n TOP_LEFT = 'TOP_LEFT',\n // eslint-disable-next-line no-unused-vars\n TOP_RIGHT = 'TOP_RIGHT',\n // eslint-disable-next-line no-unused-vars\n BOTTOM_LEFT = 'BOTTOM_LEFT',\n // eslint-disable-next-line no-unused-vars\n BOTTOM_RIGHT = 'BOTTOM_RIGHT'\n}\n\nexport interface IDrawTextFieldOptions {\n anchorPosition?: AnchorPosition\n backgroundColor?: string\n fontColor?: string\n fontSize?: number\n fontStyle?: string\n padding?: number\n}\n\nexport class DrawTextFieldOptions implements IDrawTextFieldOptions {\n public anchorPosition: AnchorPosition;\n\n public backgroundColor: string;\n\n public fontColor: string;\n\n public fontSize: number;\n\n public fontStyle: string;\n\n public padding: number;\n\n constructor(options: IDrawTextFieldOptions = {}) {\n const {\n anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding,\n } = options;\n this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT;\n this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)';\n this.fontColor = fontColor || 'rgba(255, 255, 255, 1)';\n this.fontSize = fontSize || 14;\n this.fontStyle = fontStyle || 'Georgia';\n this.padding = padding || 4;\n }\n}\n\nexport class DrawTextField {\n public text: string[];\n\n public anchor : IPoint;\n\n public options: DrawTextFieldOptions;\n\n constructor(\n text: string | string[] | DrawTextField,\n anchor: IPoint,\n options: IDrawTextFieldOptions = {},\n ) {\n // eslint-disable-next-line no-nested-ternary\n this.text = typeof text === 'string'\n ? [text]\n : (text instanceof DrawTextField ? text.text : text);\n this.anchor = anchor;\n this.options = new DrawTextFieldOptions(options);\n }\n\n measureWidth(ctx: CanvasRenderingContext2D): number {\n const { padding } = this.options;\n return this.text.map((l) => ctx.measureText(l).width).reduce((w0, w1) => (w0 < w1 ? w1 : w0), 0) + (2 * padding);\n }\n\n measureHeight(): number {\n const { fontSize, padding } = this.options;\n return this.text.length * fontSize + (2 * padding);\n }\n\n getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint {\n const { anchorPosition } = this.options;\n const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT;\n const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT;\n\n const textFieldWidth = this.measureWidth(ctx);\n const textFieldHeight = this.measureHeight();\n const x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x);\n const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y;\n\n // adjust anchor if text box exceeds canvas borders\n if (canvasDims) {\n const { width, height } = canvasDims;\n const newX = Math.max(Math.min(x, width - textFieldWidth), 0);\n const newY = Math.max(Math.min(y, height - textFieldHeight), 0);\n return { x: newX, y: newY };\n }\n return { x, y };\n }\n\n draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n const canvas = resolveInput(canvasArg);\n const ctx = getContext2dOrThrow(canvas);\n\n const {\n backgroundColor, fontColor, fontSize, fontStyle, padding,\n } = this.options;\n\n ctx.font = `${fontSize}px ${fontStyle}`;\n const maxTextWidth = this.measureWidth(ctx);\n const textHeight = this.measureHeight();\n\n ctx.fillStyle = backgroundColor;\n const upperLeft = this.getUpperLeft(ctx, canvas);\n ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight);\n\n ctx.fillStyle = fontColor;\n this.text.forEach((textLine, i) => {\n const x = padding + upperLeft.x;\n const y = padding + upperLeft.y + ((i + 1) * fontSize);\n ctx.fillText(textLine, x, y);\n });\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { Box, IBoundingBox, IRect } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions } from './DrawTextField';\n\nexport interface IDrawBoxOptions {\n boxColor?: string\n lineWidth?: number\n drawLabelOptions?: IDrawTextFieldOptions\n label?: string\n}\n\nexport class DrawBoxOptions {\n public boxColor: string;\n\n public lineWidth: number;\n\n public drawLabelOptions: DrawTextFieldOptions;\n\n public label?: string;\n\n constructor(options: IDrawBoxOptions = {}) {\n const {\n boxColor, lineWidth, label, drawLabelOptions,\n } = options;\n this.boxColor = boxColor || 'rgba(0, 0, 255, 1)';\n this.lineWidth = lineWidth || 2;\n this.label = label;\n\n const defaultDrawLabelOptions = {\n anchorPosition: AnchorPosition.BOTTOM_LEFT,\n backgroundColor: this.boxColor,\n };\n this.drawLabelOptions = new DrawTextFieldOptions({ ...defaultDrawLabelOptions, ...drawLabelOptions });\n }\n}\n\nexport class DrawBox {\n public box: Box;\n\n public options: DrawBoxOptions;\n\n constructor(\n box: IBoundingBox | IRect,\n options: IDrawBoxOptions = {},\n ) {\n this.box = new Box(box);\n this.options = new DrawBoxOptions(options);\n }\n\n draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n const ctx = getContext2dOrThrow(canvasArg);\n\n const { boxColor, lineWidth } = this.options;\n\n const {\n x, y, width, height,\n } = this.box;\n ctx.strokeStyle = boxColor;\n ctx.lineWidth = lineWidth;\n ctx.strokeRect(x, y, width, height);\n\n const { label } = this.options;\n if (label) {\n new DrawTextField([label], { x: x - (lineWidth / 2), y }, this.options.drawLabelOptions).draw(canvasArg);\n }\n }\n}\n", "import { Box, IBoundingBox, IRect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { round } from '../utils/index';\nimport { DrawBox } from './DrawBox';\n\nexport type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>\n\nexport function drawDetections(\n canvasArg: string | HTMLCanvasElement,\n detections: TDrawDetectionsInput | Array,\n) {\n const detectionsArray = Array.isArray(detections) ? detections : [detections];\n\n detectionsArray.forEach((det) => {\n // eslint-disable-next-line no-nested-ternary\n const score = det instanceof FaceDetection\n ? det.score\n : (isWithFaceDetection(det) ? det.detection.score : undefined);\n\n // eslint-disable-next-line no-nested-ternary\n const box = det instanceof FaceDetection\n ? det.box\n : (isWithFaceDetection(det) ? det.detection.box : new Box(det));\n\n const label = score ? `${round(score)}` : undefined;\n new DrawBox(box, { label }).draw(canvasArg);\n });\n}\n", "import { env } from '../env/index';\n\nexport function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {\n const { Image, Video } = env.getEnv();\n\n return (media instanceof Image && media.complete)\n || (media instanceof Video && media.readyState >= 3);\n}\n", "import { env } from '../env/index';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {\n // eslint-disable-next-line consistent-return\n return new Promise((resolve, reject) => {\n if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {\n resolve(null);\n return;\n }\n\n function onError(e: Event) {\n if (!e.currentTarget) return;\n // eslint-disable-next-line no-use-before-define\n e.currentTarget.removeEventListener('load', onLoad);\n e.currentTarget.removeEventListener('error', onError);\n reject(e);\n }\n\n function onLoad(e: Event) {\n if (!e.currentTarget) return;\n e.currentTarget.removeEventListener('load', onLoad);\n e.currentTarget.removeEventListener('error', onError);\n resolve(e);\n }\n\n media.addEventListener('load', onLoad);\n media.addEventListener('error', onError);\n });\n}\n", "import { env } from '../env/index';\n\nexport function bufferToImage(buf: Blob): Promise {\n return new Promise((resolve, reject) => {\n if (!(buf instanceof Blob)) reject(new Error('bufferToImage - expected buf to be of type: Blob'));\n const reader = new FileReader();\n reader.onload = () => {\n if (typeof reader.result !== 'string') reject(new Error('bufferToImage - expected reader.result to be a string, in onload'));\n const img = env.getEnv().createImageElement();\n img.onload = () => resolve(img);\n img.onerror = reject;\n img.src = reader.result as string;\n };\n reader.onerror = reject;\n reader.readAsDataURL(buf);\n });\n}\n", "import { Dimensions, IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\n\nexport function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {\n const { Image, Video } = env.getEnv();\n\n if (input instanceof Image) {\n return new Dimensions(input.naturalWidth, input.naturalHeight);\n }\n if (input instanceof Video) {\n return new Dimensions(input.videoWidth, input.videoHeight);\n }\n return new Dimensions(input.width, input.height);\n}\n", "import { IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {\n const { createCanvasElement } = env.getEnv();\n const canvas = createCanvasElement();\n canvas.width = width;\n canvas.height = height;\n return canvas;\n}\n\nexport function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {\n const { ImageData } = env.getEnv();\n\n if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\n throw new Error('createCanvasFromMedia - media has not finished loading yet');\n }\n\n const { width, height } = dims || getMediaDimensions(media);\n const canvas = createCanvas({ width, height });\n\n if (media instanceof ImageData) {\n getContext2dOrThrow(canvas).putImageData(media, 0, 0);\n } else {\n getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);\n }\n return canvas;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { env } from '../env/index';\nimport { isTensor4D } from '../utils/index';\n\nexport async function imageTensorToCanvas(\n imgTensor: tf.Tensor,\n canvas?: HTMLCanvasElement,\n): Promise {\n const targetCanvas = canvas || env.getEnv().createCanvasElement();\n\n const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0);\n const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt());\n await tf['browser'].toPixels(imgTensor3D, targetCanvas);\n\n imgTensor3D.dispose();\n\n return targetCanvas;\n}\n", "import { env } from '../env/index';\n\nexport function isMediaElement(input: any) {\n const { Image, Canvas, Video } = env.getEnv();\n\n return input instanceof Image\n || input instanceof Canvas\n || input instanceof Video;\n}\n", "import { env } from '../env/index';\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage = false) {\n const { Image, Canvas } = env.getEnv();\n\n if (!(input instanceof Image || input instanceof Canvas)) {\n throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement');\n }\n\n if (inputSize <= 0) return createCanvas({ width: 1, height: 1 });\n const dims = getMediaDimensions(input);\n const scale = inputSize / Math.max(dims.height, dims.width);\n const width = scale * dims.width;\n const height = scale * dims.height;\n\n const targetCanvas = createCanvas({ width: inputSize, height: inputSize });\n const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input);\n\n const offset = Math.abs(width - height) / 2;\n const dx = centerImage && width < height ? offset : 0;\n const dy = centerImage && height < width ? offset : 0;\n if (inputCanvas.width > 0 && inputCanvas.height > 0) getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height);\n\n return targetCanvas;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { padToSquare } from '../ops/padToSquare';\nimport { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils/index';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n private _imageTensors: Array = [];\n\n private _canvases: HTMLCanvasElement[] = [];\n\n private _batchSize: number;\n\n private _treatAsBatchInput = false;\n\n private _inputDimensions: number[][] = [];\n\n private _inputSize = 0;\n\n constructor(inputs: Array, treatAsBatchInput = false) {\n if (!Array.isArray(inputs)) {\n throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`);\n }\n\n this._treatAsBatchInput = treatAsBatchInput;\n this._batchSize = inputs.length;\n\n inputs.forEach((input, idx) => {\n if (isTensor3D(input)) {\n this._imageTensors[idx] = input;\n this._inputDimensions[idx] = input.shape;\n return;\n }\n\n if (isTensor4D(input)) {\n const batchSize = (input as any).shape[0];\n if (batchSize !== 1) {\n throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n }\n\n this._imageTensors[idx] = input;\n this._inputDimensions[idx] = (input as any).shape.slice(1);\n return;\n }\n\n // @ts-ignore\n const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input);\n this._canvases[idx] = canvas as HTMLCanvasElement;\n this._inputDimensions[idx] = [canvas.height, canvas.width, 3];\n });\n }\n\n public get imageTensors(): Array {\n return this._imageTensors;\n }\n\n public get canvases(): HTMLCanvasElement[] {\n return this._canvases;\n }\n\n public get isBatchInput(): boolean {\n return this.batchSize > 1 || this._treatAsBatchInput;\n }\n\n public get batchSize(): number {\n return this._batchSize;\n }\n\n public get inputDimensions(): number[][] {\n return this._inputDimensions;\n }\n\n public get inputSize(): number | undefined {\n return this._inputSize;\n }\n\n public get reshapedInputDimensions(): Dimensions[] {\n return range(this.batchSize, 0, 1).map(\n (_, batchIdx) => this.getReshapedInputDimensions(batchIdx),\n );\n }\n\n public getInput(batchIdx: number): tf.Tensor3D | tf.Tensor4D | HTMLCanvasElement {\n return this.canvases[batchIdx] || this.imageTensors[batchIdx];\n }\n\n public getInputDimensions(batchIdx: number): number[] {\n return this._inputDimensions[batchIdx];\n }\n\n public getInputHeight(batchIdx: number): number {\n return this._inputDimensions[batchIdx][0];\n }\n\n public getInputWidth(batchIdx: number): number {\n return this._inputDimensions[batchIdx][1];\n }\n\n public getReshapedInputDimensions(batchIdx: number): Dimensions {\n if (typeof this.inputSize !== 'number') {\n throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet');\n }\n\n const width = this.getInputWidth(batchIdx);\n const height = this.getInputHeight(batchIdx);\n return computeReshapedDimensions({ width, height }, this.inputSize);\n }\n\n /**\n * Create a batch tensor from all input canvases and tensors\n * with size [batchSize, inputSize, inputSize, 3].\n *\n * @param inputSize Height and width of the tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The batch tensor.\n */\n public toBatchTensor(inputSize: number, isCenterInputs = true): tf.Tensor4D {\n this._inputSize = inputSize;\n\n return tf.tidy(() => {\n const inputTensors = range(this.batchSize, 0, 1).map((batchIdx) => {\n const input = this.getInput(batchIdx);\n\n if (input instanceof tf.Tensor) {\n let imgTensor = isTensor4D(input) ? input : tf.expandDims(input);\n imgTensor = padToSquare(imgTensor as tf.Tensor4D, isCenterInputs);\n\n if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n imgTensor = tf['image'].resizeBilinear(imgTensor as tf.Tensor4D, [inputSize, inputSize], false, false);\n }\n\n return imgTensor.as3D(inputSize, inputSize, 3);\n }\n\n if (input instanceof env.getEnv().Canvas) {\n return tf['browser'].fromPixels(imageToSquare(input, inputSize, isCenterInputs));\n }\n\n throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`);\n });\n\n const batchTensor = tf.stack(inputTensors.map((t) => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3);\n // const batchTensor = tf.stack(inputTensors.map((t) => tf.cast(t, 'float32'))) as tf.Tensor4D;\n\n return batchTensor;\n });\n }\n}\n", "import { isTensor3D, isTensor4D } from '../utils/index';\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\nimport { isMediaElement } from './isMediaElement';\nimport { NetInput } from './NetInput';\nimport { resolveInput } from './resolveInput';\nimport { TNetInput } from './types';\n\n/**\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\n * to be finished loading.\n *\n * @param input The input, which can be a media element or an array of different media elements.\n * @returns A NetInput instance, which can be passed into one of the neural networks.\n */\nexport async function toNetInput(inputs: TNetInput): Promise {\n if (inputs instanceof NetInput) return inputs;\n const inputArgArray = Array.isArray(inputs) ? inputs : [inputs];\n if (!inputArgArray.length) throw new Error('toNetInput - empty array passed as input');\n const getIdxHint = (idx: number) => (Array.isArray(inputs) ? ` at input index ${idx}:` : '');\n const inputArray = inputArgArray.map(resolveInput);\n inputArray.forEach((input, i) => {\n if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n if (typeof inputArgArray[i] === 'string') throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`);\n throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);\n }\n if (isTensor4D(input)) {\n // if tf.Tensor4D is passed in the input array, the batch size has to be 1\n const batchSize = input.shape[0];\n if (batchSize !== 1) throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n }\n });\n // wait for all media elements being loaded\n await Promise.all(inputArray.map((input) => isMediaElement(input) && awaitMediaLoaded(input)));\n return new NetInput(inputArray, Array.isArray(inputs));\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\nimport { Rect } from '../classes/Rect';\nimport { env } from '../env/index';\nimport { createCanvas } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\nimport { toNetInput } from './toNetInput';\nimport { TNetInput } from './types';\n\n/**\n * Extracts the image regions containing the detected faces.\n *\n * @param input The image that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns The Canvases of the corresponding image region for each detected face.\n */\nexport async function extractFaces(input: TNetInput, detections: Array): Promise {\n const { Canvas } = env.getEnv();\n let canvas = input as HTMLCanvasElement;\n if (!(input instanceof Canvas)) {\n const netInput = await toNetInput(input);\n if (netInput.batchSize > 1) throw new Error('extractFaces - batchSize > 1 not supported');\n const tensorOrCanvas = netInput.getInput(0);\n canvas = tensorOrCanvas instanceof Canvas ? tensorOrCanvas : await imageTensorToCanvas(tensorOrCanvas);\n }\n const ctx = getContext2dOrThrow(canvas);\n const boxes = detections\n .map((det) => (det instanceof FaceDetection ? det.forSize(canvas.width, canvas.height).box.floor() : det))\n .map((box) => box.clipAtImageBorders(canvas.width, canvas.height));\n return boxes.map(({ x, y, width, height }) => {\n const faceImg = createCanvas({ width, height });\n if (width > 0 && height > 0) getContext2dOrThrow(faceImg).putImageData(ctx.getImageData(x, y, width, height), 0, 0);\n return faceImg;\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils/index';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(imageTensor: tf.Tensor3D | tf.Tensor4D, detections: Array): Promise {\n if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D');\n }\n\n if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n throw new Error('extractFaceTensors - batchSize > 1 not supported');\n }\n\n return tf.tidy(() => {\n const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0);\n const boxes = detections.map((det) => (det instanceof FaceDetection ? det.forSize(imgWidth, imgHeight).box : det))\n .map((box) => box.clipAtImageBorders(imgWidth, imgHeight));\n const faceTensors = boxes\n .filter((box) => box.width > 0 && box.height > 0)\n .map(({ x, y, width, height }) => tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]));\n return faceTensors;\n });\n}\n", "import { env } from '../env/index';\n\nexport async function fetchOrThrow(\n url: string,\n // eslint-disable-next-line no-undef\n init?: RequestInit,\n): Promise {\n const { fetch } = env.getEnv();\n const res = await fetch(url, init);\n if (!(res.status < 400)) {\n throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);\n }\n return res;\n}\n", "import { bufferToImage } from './bufferToImage';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchImage(uri: string): Promise {\n const res = await fetchOrThrow(uri);\n const blob = await (res).blob();\n\n if (!blob.type.startsWith('image/')) {\n throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`);\n }\n return bufferToImage(blob);\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson(uri: string): Promise {\n return (await fetchOrThrow(uri)).json();\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise {\n return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer());\n}\n", "import { env } from '../env/index';\n\nexport function bufferToVideo(buf: Blob): Promise {\n return new Promise((resolve, reject) => {\n if (!(buf instanceof Blob)) reject(new Error('bufferToVideo - expected buf to be of type: Blob'));\n\n const video = env.getEnv().createVideoElement();\n video.oncanplay = () => resolve(video);\n video.onerror = reject;\n video.playsInline = true;\n video.muted = true;\n video.src = URL.createObjectURL(buf);\n video.play();\n });\n}\n", "import { bufferToVideo } from './bufferToVideo';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchVideo(uri: string): Promise {\n const res = await fetchOrThrow(uri);\n const blob = await (res).blob();\n\n if (!blob.type.startsWith('video/')) {\n throw new Error(`fetchVideo - expected blob type to be of type video/*, instead have: ${blob.type}, for url: ${res.url}`);\n }\n return bufferToVideo(blob);\n}\n", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`;\n\n if (!uri) {\n return {\n modelBaseUri: '',\n manifestUri: defaultManifestFilename,\n };\n }\n\n if (uri === '/') {\n return {\n modelBaseUri: '/',\n manifestUri: `/${defaultManifestFilename}`,\n };\n }\n // eslint-disable-next-line no-nested-ternary\n const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\n uri = uri.replace(protocol, '');\n\n const parts = uri.split('/').filter((s) => s);\n\n const manifestFile = uri.endsWith('.json')\n ? parts[parts.length - 1]\n : defaultManifestFilename;\n\n let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/');\n modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri;\n\n return {\n modelBaseUri,\n manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`,\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n uri: string | undefined,\n defaultModelName: string,\n): Promise {\n const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName);\n // @ts-ignore\n const manifest = await fetchJson(manifestUri);\n // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n return tf['io'].loadWeights(manifest, modelBaseUri);\n}\n", "import { IDimensions } from '../classes/index';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions = false) {\n const { width, height } = useMediaDimensions\n ? getMediaDimensions(reference)\n : reference;\n input.width = width;\n input.height = height;\n return { width, height };\n}\n", "import * as tf from '../dist/tfjs.esm';\n\nimport { ParamMapping } from './common/index';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom/index';\nimport { env } from './env/index';\n\nexport abstract class NeuralNetwork {\n constructor(name: string) {\n this._name = name;\n }\n\n protected _params: TNetParams | undefined = undefined;\n\n protected _paramMappings: ParamMapping[] = [];\n\n public _name: any;\n\n public get params(): TNetParams | undefined { return this._params; }\n\n public get paramMappings(): ParamMapping[] { return this._paramMappings; }\n\n public get isLoaded(): boolean { return !!this.params; }\n\n public getParamFromPath(paramPath: string): tf.Tensor {\n const { obj, objProp } = this.traversePropertyPath(paramPath);\n return obj[objProp];\n }\n\n public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n const { obj, objProp } = this.traversePropertyPath(paramPath);\n obj[objProp].dispose();\n obj[objProp] = tensor;\n }\n\n public getParamList() {\n return this._paramMappings.map(({ paramPath }) => ({\n path: paramPath,\n tensor: this.getParamFromPath(paramPath),\n }));\n }\n\n public getTrainableParams() {\n return this.getParamList().filter((param) => param.tensor instanceof tf.Variable);\n }\n\n public getFrozenParams() {\n return this.getParamList().filter((param) => !(param.tensor instanceof tf.Variable));\n }\n\n public variable() {\n this.getFrozenParams().forEach(({ path, tensor }) => {\n this.reassignParamFromPath(path, tensor.variable());\n });\n }\n\n public freeze() {\n this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n const tensor = tf.tensor(variable.dataSync());\n variable.dispose();\n this.reassignParamFromPath(path, tensor);\n });\n }\n\n public dispose(throwOnRedispose = true) {\n this.getParamList().forEach((param) => {\n if (throwOnRedispose && param.tensor.isDisposed) {\n throw new Error(`param tensor has already been disposed for path ${param.path}`);\n }\n param.tensor.dispose();\n });\n this._params = undefined;\n }\n\n public serializeParams(): Float32Array {\n return new Float32Array(\n this.getParamList()\n .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n .reduce((flat, arr) => flat.concat(arr)),\n );\n }\n\n public async load(weightsOrUrl: Float32Array | string | undefined): Promise {\n if (weightsOrUrl instanceof Float32Array) {\n this.extractWeights(weightsOrUrl);\n return;\n }\n await this.loadFromUri(weightsOrUrl);\n }\n\n public async loadFromUri(uri: string | undefined) {\n if (uri && typeof uri !== 'string') {\n throw new Error(`${this._name}.loadFromUri - expected model uri`);\n }\n const weightMap = await loadWeightMap(uri, this.getDefaultModelName());\n this.loadFromWeightMap(weightMap);\n }\n\n public async loadFromDisk(filePath: string | undefined) {\n if (filePath && typeof filePath !== 'string') {\n throw new Error(`${this._name}.loadFromDisk - expected model file path`);\n }\n const { readFile } = env.getEnv();\n const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName());\n const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(filePaths.map((fp) => readFile(fp).then((buf) => (typeof buf === 'string' ? Buffer.from(buf) : buf.buffer))));\n const loadWeights = tf['io'].weightsLoaderFactory(fetchWeightsFromDisk);\n const manifest = JSON.parse((await readFile(manifestUri)).toString());\n const weightMap = await loadWeights(manifest, modelBaseUri);\n this.loadFromWeightMap(weightMap);\n }\n\n public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n const { paramMappings, params } = this.extractParamsFromWeightMap(weightMap);\n this._paramMappings = paramMappings;\n this._params = params;\n }\n\n public extractWeights(weights: Float32Array) {\n const { paramMappings, params } = this.extractParams(weights);\n this._paramMappings = paramMappings;\n this._params = params;\n }\n\n private traversePropertyPath(paramPath: string) {\n if (!this.params) {\n throw new Error('traversePropertyPath - model has no loaded params');\n }\n\n const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n // eslint-disable-next-line no-prototype-builtins\n if (!res.nextObj.hasOwnProperty(objProp)) {\n throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`);\n }\n return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] };\n }, { nextObj: this.params });\n\n const { obj, objProp } = result;\n if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);\n }\n\n return { obj, objProp };\n }\n\n protected abstract getDefaultModelName(): string\n\n // eslint-disable-next-line no-unused-vars\n protected abstract extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n\n // eslint-disable-next-line no-unused-vars\n protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n x: tf.Tensor4D,\n params: SeparableConvParams,\n stride: [number, number],\n): tf.Tensor4D {\n return tf.tidy(() => {\n let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same');\n out = tf.add(out, params.bias);\n return out;\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, SeparableConvParams } from '../common/index';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n x: tf.Tensor4D,\n denseBlockParams: DenseBlock3Params,\n isFirstLayer = false,\n): tf.Tensor4D {\n return tf.tidy(() => {\n const out1 = tf.relu(\n isFirstLayer\n ? tf.add(\n tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n denseBlockParams.conv0.bias,\n )\n : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2]),\n ) as tf.Tensor4D;\n const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n });\n}\n\nexport function denseBlock4(\n x: tf.Tensor4D,\n denseBlockParams: DenseBlock4Params,\n isFirstLayer = false,\n isScaleDown = true,\n): tf.Tensor4D {\n return tf.tidy(() => {\n const out1 = tf.relu(\n isFirstLayer\n ? tf.add(\n tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n denseBlockParams.conv0.bias,\n )\n : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1]),\n ) as tf.Tensor4D;\n const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1]);\n\n return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D;\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\nexport function convLayer(\n x: tf.Tensor4D,\n params: ConvParams,\n padding: 'valid' | 'same' = 'same',\n withRelu = false,\n): tf.Tensor4D {\n return tf.tidy(() => {\n const out = tf.add(\n tf.conv2d(x, params.filters, [1, 1], padding),\n params.bias,\n ) as tf.Tensor4D;\n\n return withRelu ? tf.relu(out) : out;\n });\n}\n", "import { ParamMapping } from './types';\n\nexport function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {\n Object.keys(weightMap).forEach((path) => {\n if (!paramMappings.some((pm) => pm.originalPath === path)) {\n weightMap[path].dispose();\n }\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';\n\nexport function extractConvParamsFactory(\n extractWeights: ExtractWeightsFunction,\n paramMappings: ParamMapping[],\n) {\n return (\n channelsIn: number,\n channelsOut: number,\n filterSize: number,\n mappedPrefix: string,\n ): ConvParams => {\n const filters = tf.tensor4d(\n extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n [filterSize, filterSize, channelsIn, channelsOut],\n );\n const bias = tf.tensor1d(extractWeights(channelsOut));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/filters` },\n { paramPath: `${mappedPrefix}/bias` },\n );\n\n return { filters, bias };\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, FCParams, ParamMapping } from './types';\n\nexport function extractFCParamsFactory(\n extractWeights: ExtractWeightsFunction,\n paramMappings: ParamMapping[],\n) {\n return (\n channelsIn: number,\n channelsOut: number,\n mappedPrefix: string,\n ): FCParams => {\n const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);\n const fc_bias = tf.tensor1d(extractWeights(channelsOut));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/weights` },\n { paramPath: `${mappedPrefix}/bias` },\n );\n\n return {\n weights: fc_weights,\n bias: fc_bias,\n };\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n// eslint-disable-next-line no-unused-vars\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\n\nexport type ParamMapping = {\n originalPath?: string\n paramPath: string\n}\n\nexport type ConvParams = {\n filters: tf.Tensor4D\n bias: tf.Tensor1D\n}\n\nexport type FCParams = {\n weights: tf.Tensor2D\n bias: tf.Tensor1D\n}\n\nexport class SeparableConvParams {\n // eslint-disable-next-line no-useless-constructor\n constructor(\n // eslint-disable-next-line no-unused-vars\n public depthwise_filter: tf.Tensor4D,\n // eslint-disable-next-line no-unused-vars\n public pointwise_filter: tf.Tensor4D,\n // eslint-disable-next-line no-unused-vars\n public bias: tf.Tensor1D,\n // eslint-disable-next-line no-empty-function\n ) {}\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n extractWeights: ExtractWeightsFunction,\n paramMappings: ParamMapping[],\n) {\n return (channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams => {\n const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);\n const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);\n const bias = tf.tensor1d(extractWeights(channelsOut));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/depthwise_filter` },\n { paramPath: `${mappedPrefix}/pointwise_filter` },\n { paramPath: `${mappedPrefix}/bias` },\n );\n\n return new SeparableConvParams(\n depthwise_filter,\n pointwise_filter,\n bias,\n );\n };\n}\n\nexport function loadSeparableConvParamsFactory(\n // eslint-disable-next-line no-unused-vars\n extractWeightEntry: (originalPath: string, paramRank: number) => T,\n) {\n return (prefix: string): SeparableConvParams => {\n const depthwise_filter = extractWeightEntry(`${prefix}/depthwise_filter`, 4);\n const pointwise_filter = extractWeightEntry(`${prefix}/pointwise_filter`, 4);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n\n return new SeparableConvParams(\n depthwise_filter,\n pointwise_filter,\n bias,\n );\n };\n}\n", "import { isTensor } from '../utils/index';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n return (originalPath: string, paramRank: number, mappedPath?: string) => {\n const tensor = weightMap[originalPath];\n\n if (!isTensor(tensor, paramRank)) {\n throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`);\n }\n\n paramMappings.push(\n { originalPath, paramPath: mappedPath || originalPath },\n );\n\n return tensor;\n };\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n let remainingWeights = weights;\n\n function extractWeights(numWeights: number): Float32Array {\n const ret = remainingWeights.slice(0, numWeights);\n remainingWeights = remainingWeights.slice(numWeights);\n return ret;\n }\n\n function getRemainingWeights(): Float32Array {\n return remainingWeights;\n }\n\n return {\n extractWeights,\n getRemainingWeights,\n };\n}\n", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, ExtractWeightsFunction, ParamMapping } from '../common/index';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer = false): DenseBlock3Params {\n const conv0 = isFirstLayer\n ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`);\n const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`);\n const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`);\n\n return { conv0, conv1, conv2 };\n }\n\n function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer = false): DenseBlock4Params {\n const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer);\n const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`);\n\n return {\n conv0, conv1, conv2, conv3,\n };\n }\n\n return {\n extractDenseBlock3Params,\n extractDenseBlock4Params,\n };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const {\n extractDenseBlock4Params,\n } = extractorsFactory(extractWeights, paramMappings);\n\n const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true);\n const dense1 = extractDenseBlock4Params(32, 64, 'dense1');\n const dense2 = extractDenseBlock4Params(64, 128, 'dense2');\n const dense3 = extractDenseBlock4Params(128, 256, 'dense3');\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: {\n dense0, dense1, dense2, dense3,\n },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\n// eslint-disable-next-line no-unused-vars\nexport function loadConvParamsFactory(extractWeightEntry: (originalPath: string, paramRank: number) => T) {\n return (prefix: string): ConvParams => {\n const filters = extractWeightEntry(`${prefix}/filters`, 4);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n\n return { filters, bias };\n };\n}\n", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n function extractDenseBlock3Params(prefix: string, isFirstLayer = false): DenseBlock3Params {\n const conv0 = isFirstLayer\n ? extractConvParams(`${prefix}/conv0`)\n : extractSeparableConvParams(`${prefix}/conv0`);\n const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n\n return { conv0, conv1, conv2 };\n }\n\n function extractDenseBlock4Params(prefix: string, isFirstLayer = false): DenseBlock4Params {\n const conv0 = isFirstLayer\n ? extractConvParams(`${prefix}/conv0`)\n : extractSeparableConvParams(`${prefix}/conv0`);\n const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n const conv3 = extractSeparableConvParams(`${prefix}/conv3`);\n\n return {\n conv0, conv1, conv2, conv3,\n };\n }\n\n return {\n extractDenseBlock3Params,\n extractDenseBlock4Params,\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractDenseBlock4Params,\n } = loadParamsFactory(weightMap, paramMappings);\n\n const params = {\n dense0: extractDenseBlock4Params('dense0', true),\n dense1: extractDenseBlock4Params('dense1'),\n dense2: extractDenseBlock4Params('dense2'),\n dense3: extractDenseBlock4Params('dense3'),\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork implements IFaceFeatureExtractor {\n constructor() {\n super('FaceFeatureExtractor');\n }\n\n public forwardInput(input: NetInput): tf.Tensor4D {\n const { params } = this;\n\n if (!params) {\n throw new Error('FaceFeatureExtractor - load model before inference');\n }\n\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n const meanRgb = [122.782, 117.001, 104.298];\n const normalized = normalize(batchTensor, meanRgb).div(255) as tf.Tensor4D;\n\n let out = denseBlock4(normalized, params.dense0, true);\n out = denseBlock4(out, params.dense1);\n out = denseBlock4(out, params.dense2);\n out = denseBlock4(out, params.dense3);\n out = tf.avgPool(out, [7, 7], [2, 2], 'valid');\n\n return out;\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n protected getDefaultModelName(): string {\n return 'face_feature_extractor_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParams(weights);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from './types';\n\nexport function fullyConnectedLayer(\n x: tf.Tensor2D,\n params: FCParams,\n): tf.Tensor2D {\n return tf.tidy(() => tf.add(\n tf.matMul(x, params.weights),\n params.bias,\n ));\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n const fc = extractFCParams(channelsIn, channelsOut, 'fc');\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { fc },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractFcParams(prefix: string): FCParams {\n const weights = extractWeightEntry(`${prefix}/weights`, 2);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n return { weights, bias };\n }\n\n const params = {\n fc: extractFcParams('fc'),\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n const featureExtractorMap: tf.NamedTensorMap = {};\n const classifierMap: tf.NamedTensorMap = {};\n\n Object.keys(weightMap).forEach((key) => {\n const map = key.startsWith('fc') ? classifierMap : featureExtractorMap;\n map[key] = weightMap[key];\n });\n\n return { featureExtractorMap, classifierMap };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom/index';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n extends NeuralNetwork {\n protected _faceFeatureExtractor: IFaceFeatureExtractor;\n\n constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor) {\n super(_name);\n this._faceFeatureExtractor = faceFeatureExtractor;\n }\n\n public get faceFeatureExtractor(): IFaceFeatureExtractor {\n return this._faceFeatureExtractor;\n }\n\n protected abstract override getDefaultModelName(): string\n\n protected abstract getClassifierChannelsIn(): number\n\n protected abstract getClassifierChannelsOut(): number\n\n public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n const { params } = this;\n\n if (!params) {\n throw new Error(`${this._name} - load model before inference`);\n }\n\n return tf.tidy(() => {\n const bottleneckFeatures = input instanceof NetInput\n ? this.faceFeatureExtractor.forwardInput(input)\n : input;\n return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc);\n });\n }\n\n public override dispose(throwOnRedispose = true) {\n this.faceFeatureExtractor.dispose(throwOnRedispose);\n super.dispose(throwOnRedispose);\n }\n\n public loadClassifierParams(weights: Float32Array) {\n const { params, paramMappings } = this.extractClassifierParams(weights);\n this._params = params;\n this._paramMappings = paramMappings;\n }\n\n public extractClassifierParams(weights: Float32Array) {\n return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut());\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n return extractParamsFromWeightMap(classifierMap);\n }\n\n protected extractParams(weights: Float32Array) {\n const cIn = this.getClassifierChannelsIn();\n const cOut = this.getClassifierChannelsOut();\n const classifierWeightSize = (cOut * cIn) + cOut;\n\n const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n return this.extractClassifierParams(classifierWeights);\n }\n}\n", "export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised'] as const;\n\nexport class FaceExpressions {\n public neutral = 0;\n public happy = 0;\n public sad = 0;\n public angry = 0;\n public fearful = 0;\n public disgusted = 0;\n public surprised = 0;\n\n constructor(probabilities: number[] | Float32Array) {\n if (probabilities.length !== 7) {\n throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`);\n }\n\n FACE_EXPRESSION_LABELS.forEach((expression, idx) => {\n this[expression] = probabilities[idx];\n });\n }\n\n asSortedArray() {\n return FACE_EXPRESSION_LABELS\n .map((expression) => ({ expression, probability: this[expression] as number }))\n .sort((e0, e1) => e1.probability - e0.probability);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor {\n constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n super('FaceExpressionNet', faceFeatureExtractor);\n }\n\n public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n return tf.tidy(() => tf.softmax(this.runNet(input)));\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async predictExpressions(input: TNetInput) {\n const netInput = await toNetInput(input);\n const out = await this.forwardInput(netInput);\n const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async (t) => {\n const data = t.dataSync();\n t.dispose();\n return data;\n }));\n out.dispose();\n\n const predictionsByBatch = probabilitesByBatch\n .map((probabilites) => new FaceExpressions(probabilites as Float32Array));\n\n return netInput.isBatchInput\n ? predictionsByBatch\n : predictionsByBatch[0];\n }\n\n protected getDefaultModelName(): string {\n return 'face_expression_model';\n }\n\n protected getClassifierChannelsIn(): number {\n return 256;\n }\n\n protected getClassifierChannelsOut(): number {\n return 7;\n }\n}\n", "import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\n\nexport type WithFaceExpressions = TSource & { expressions: FaceExpressions }\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n return obj.expressions instanceof FaceExpressions;\n}\n\nexport function extendWithFaceExpressions(sourceObj: TSource, expressions: FaceExpressions): WithFaceExpressions {\n const extension = { expressions };\n return { ...sourceObj, ...extension };\n}\n", "import { IPoint, Point } from '../classes/index';\nimport { FaceExpressions } from '../faceExpressionNet/index';\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { round } from '../utils/index';\nimport { DrawTextField } from './DrawTextField';\n\nexport type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>\n\nexport function drawFaceExpressions(canvasArg: string | HTMLCanvasElement, faceExpressions: DrawFaceExpressionsInput | Array, minConfidence = 0.1, textFieldAnchor?: IPoint) {\n const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions];\n\n faceExpressionsArray.forEach((e) => {\n // eslint-disable-next-line no-nested-ternary\n const expr = e instanceof FaceExpressions\n ? e\n : (isWithFaceExpressions(e) ? e.expressions : undefined);\n if (!expr) {\n throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof');\n }\n\n const sorted = expr.asSortedArray();\n const resultsToDisplay = sorted.filter((exprLocal) => exprLocal.probability > minConfidence);\n\n const anchor = isWithFaceDetection(e)\n ? e.detection.box.bottomLeft\n : (textFieldAnchor || new Point(0, 0));\n\n const drawTextField = new DrawTextField(\n resultsToDisplay.map((exprLocal) => `${exprLocal.expression} (${round(exprLocal.probability)})`),\n anchor,\n );\n drawTextField.draw(canvasArg);\n });\n}\n", "import { Point } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { isWithFaceDetection, WithFaceDetection } from './WithFaceDetection';\n\nexport type WithFaceLandmarks<\n TSource extends WithFaceDetection<{}>,\n TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> = TSource & {\n landmarks: TFaceLandmarks;\n unshiftedLandmarks: TFaceLandmarks;\n alignedRect: FaceDetection;\n angle: {\n roll: number | undefined;\n pitch: number | undefined;\n yaw: number | undefined;\n };\n};\n\nexport function isWithFaceLandmarks(\n obj: any,\n): obj is WithFaceLandmarks, FaceLandmarks> {\n return (\n isWithFaceDetection(obj)\n && (obj as any)['landmarks'] instanceof FaceLandmarks\n && (obj as any)['unshiftedLandmarks'] instanceof FaceLandmarks\n && (obj as any)['alignedRect'] instanceof FaceDetection\n );\n}\n\nfunction calculateFaceAngle(mesh: FaceLandmarks) {\n // Helper to convert radians to degrees\n // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n const degrees = (radians: number) => (radians * 180) / Math.PI;\n const calcLengthBetweenTwoPoints = (a: Point, b: Point) => Math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2);\n\n const angle = {\n roll: undefined,\n pitch: undefined,\n yaw: undefined,\n };\n\n const calcYaw = (leftPoint: Point, midPoint: Point, rightPoint: Point) => {\n // Calc x-distance from left side of the face (\"ear\") to facial midpoint (\"nose\")\n const leftToMidpoint = Math.floor(leftPoint.x - midPoint.x);\n // Calc x-distance from facial midpoint (\"nose\") to the right side of the face (\"ear\")\n const rightToMidpoint = Math.floor(midPoint.x - rightPoint.x);\n // Difference in distances coincidentally approximates to angles\n return leftToMidpoint - rightToMidpoint;\n };\n\n const calcRoll = (lever: Point, pivot: Point) => {\n // When rolling, the head seems to pivot from the nose/lips/chin area.\n // So, we'll choose any two points from the facial midline, where the first point should be the pivot, and the other \"lever\"\n // Plan/Execution: get the hypotenuse & opposite sides of a 90deg triangle ==> Calculate angle in radians\n const hypotenuse = Math.hypot(pivot.x - lever.x, pivot.y - lever.y);\n const opposite = pivot.y - lever.y;\n const angleInRadians = Math.asin(opposite / hypotenuse);\n const angleInDegrees = degrees(angleInRadians);\n const normalizeAngle = Math.floor(90 - angleInDegrees);\n // If lever more to the left of the pivot, then we're tilting left\n // \"-\" is negative direction. \"+\", or absence of a sign is positive direction\n const tiltDirection = pivot.x - lever.x < 0 ? -1 : 1;\n const result = normalizeAngle * tiltDirection;\n return result;\n };\n\n const calcPitch = (leftPoint: Point, midPoint: Point, rightPoint: Point) => {\n // Theory: While pitching, the nose is the most salient point --> That's what we'll use to make a trianle.\n // The \"base\" is between point that don't move when we pitch our head (i.e. an imaginary line running ear to ear through the nose).\n // Executuin: Get the opposite & adjacent lengths of the triangle from the ear's perspective. Use it to get angle.\n\n const base = calcLengthBetweenTwoPoints(leftPoint, rightPoint);\n // adjecent is base/2 technically.\n const baseCoords = new Point((leftPoint.x + rightPoint.x) / 2, (leftPoint.y + rightPoint.y) / 2);\n const midToBaseLength = calcLengthBetweenTwoPoints(midPoint, baseCoords);\n const angleInRadians = Math.atan(midToBaseLength / base);\n const angleInDegrees = Math.floor(degrees(angleInRadians));\n // Account for directionality.\n // pitch forwards (_i.e. tilting your head forwards) is positive (or no sign); backward is negative.\n const direction = baseCoords.y - midPoint.y < 0 ? -1 : 1;\n const result = angleInDegrees * direction;\n return result;\n };\n\n if (!mesh || !mesh.positions || mesh.positions.length !== 68) return angle;\n const pt = mesh.positions;\n angle.roll = calcRoll(pt[27], pt[66]);\n angle.pitch = calcPitch(pt[14], pt[30], pt[2]);\n angle.yaw = calcYaw(pt[14], pt[33], pt[2]);\n return angle;\n}\n\nexport function extendWithFaceLandmarks, TFaceLandmarks extends FaceLandmarks = FaceLandmarks68>(\n sourceObj: TSource,\n unshiftedLandmarks: TFaceLandmarks,\n): WithFaceLandmarks {\n const { box: shift } = sourceObj.detection;\n const landmarks = unshiftedLandmarks.shiftBy(shift.x, shift.y);\n const rect = landmarks.align();\n const { imageDims } = sourceObj.detection;\n const alignedRect = new FaceDetection(\n sourceObj.detection.score,\n rect.rescale(imageDims.reverse()),\n imageDims,\n );\n const angle = calculateFaceAngle(unshiftedLandmarks);\n const extension = { landmarks, unshiftedLandmarks, alignedRect, angle };\n return { ...sourceObj, ...extension };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IPoint } from '../classes/index';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { drawContour } from './drawContour';\n\nexport interface IDrawFaceLandmarksOptions {\n drawLines?: boolean\n drawPoints?: boolean\n lineWidth?: number\n pointSize?: number\n lineColor?: string\n pointColor?: string\n}\n\nexport class DrawFaceLandmarksOptions {\n public drawLines: boolean;\n\n public drawPoints: boolean;\n\n public lineWidth: number;\n\n public pointSize: number;\n\n public lineColor: string;\n\n public pointColor: string;\n\n constructor(options: IDrawFaceLandmarksOptions = {}) {\n const {\n drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor,\n } = options;\n this.drawLines = drawLines;\n this.drawPoints = drawPoints;\n this.lineWidth = lineWidth || 1;\n this.pointSize = pointSize || 2;\n this.lineColor = lineColor || 'rgba(0, 255, 255, 1)';\n this.pointColor = pointColor || 'rgba(255, 0, 255, 1)';\n }\n}\n\nexport class DrawFaceLandmarks {\n public faceLandmarks: FaceLandmarks;\n\n public options: DrawFaceLandmarksOptions;\n\n constructor(\n faceLandmarks: FaceLandmarks,\n options: IDrawFaceLandmarksOptions = {},\n ) {\n this.faceLandmarks = faceLandmarks;\n this.options = new DrawFaceLandmarksOptions(options);\n }\n\n draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n const ctx = getContext2dOrThrow(canvasArg);\n\n const {\n drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor,\n } = this.options;\n\n if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\n ctx.strokeStyle = lineColor;\n ctx.lineWidth = lineWidth;\n drawContour(ctx, this.faceLandmarks.getJawOutline());\n drawContour(ctx, this.faceLandmarks.getLeftEyeBrow());\n drawContour(ctx, this.faceLandmarks.getRightEyeBrow());\n drawContour(ctx, this.faceLandmarks.getNose());\n drawContour(ctx, this.faceLandmarks.getLeftEye(), true);\n drawContour(ctx, this.faceLandmarks.getRightEye(), true);\n drawContour(ctx, this.faceLandmarks.getMouth(), true);\n }\n\n if (drawPoints) {\n ctx.strokeStyle = pointColor;\n ctx.fillStyle = pointColor;\n\n const drawPoint = (pt: IPoint) => {\n ctx.beginPath();\n ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI);\n ctx.fill();\n };\n this.faceLandmarks.positions.forEach(drawPoint);\n }\n }\n}\n\nexport type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks>\n\nexport function drawFaceLandmarks(\n canvasArg: string | HTMLCanvasElement,\n faceLandmarks: DrawFaceLandmarksInput | Array,\n) {\n const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks];\n faceLandmarksArray.forEach((f) => {\n // eslint-disable-next-line no-nested-ternary\n const landmarks = f instanceof FaceLandmarks\n ? f\n : (isWithFaceLandmarks(f) ? f.landmarks : undefined);\n if (!landmarks) {\n throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks> or array thereof');\n }\n\n new DrawFaceLandmarks(landmarks).draw(canvasArg);\n });\n}\n", "{\n \"name\": \"@vladmandic/face-api\",\n \"version\": \"1.7.14\",\n \"description\": \"FaceAPI: AI-powered Face Detection & Rotation Tracking, Face Description & Recognition, Age & Gender & Emotion Prediction for Browser and NodeJS using TensorFlow/JS\",\n \"sideEffects\": false,\n \"main\": \"dist/face-api.node.js\",\n \"module\": \"dist/face-api.esm.js\",\n \"browser\": \"dist/face-api.esm.js\",\n \"types\": \"types/face-api.d.ts\",\n \"author\": \"Vladimir Mandic \",\n \"bugs\": {\n \"url\": \"https://github.com/vladmandic/face-api/issues\"\n },\n \"homepage\": \"https://vladmandic.github.io/face-api/demo/webcam.html\",\n \"license\": \"MIT\",\n \"engines\": {\n \"node\": \">=14.0.0\"\n },\n \"repository\": {\n \"type\": \"git\",\n \"url\": \"git+https://github.com/vladmandic/face-api.git\"\n },\n \"scripts\": {\n \"start\": \"node --no-warnings demo/node.js\",\n \"build\": \"node build.js\",\n \"dev\": \"build --profile development\",\n \"typings\": \"build --profile typings\",\n \"lint\": \"eslint src/ demo/\",\n \"test\": \"node --trace-warnings test/test-node.js\",\n \"scan\": \"npx auditjs@latest ossi --dev --quiet\"\n },\n \"keywords\": [\n \"face-api\",\n \"faceapi\",\n \"face-detection\",\n \"age-gender\",\n \"emotion-detection\",\n \"face-recognition\",\n \"face\",\n \"face-description\",\n \"tensorflow\",\n \"tensorflowjs\",\n \"tfjs\"\n ],\n \"devDependencies\": {\n \"@canvas/image\": \"^2.0.0\",\n \"@microsoft/api-extractor\": \"^7.47.7\",\n \"@tensorflow/tfjs\": \"^4.21.0\",\n \"@tensorflow/tfjs-backend-cpu\": \"^4.21.0\",\n \"@tensorflow/tfjs-backend-wasm\": \"^4.21.0\",\n \"@tensorflow/tfjs-backend-webgl\": \"^4.21.0\",\n \"@tensorflow/tfjs-backend-webgpu\": \"4.21.0\",\n \"@tensorflow/tfjs-converter\": \"^4.21.0\",\n \"@tensorflow/tfjs-core\": \"^4.21.0\",\n \"@tensorflow/tfjs-data\": \"^4.21.0\",\n \"@tensorflow/tfjs-layers\": \"^4.21.0\",\n \"@tensorflow/tfjs-node\": \"^4.21.0\",\n \"@tensorflow/tfjs-node-gpu\": \"^4.21.0\",\n \"@types/node\": \"^22.5.4\",\n \"@types/offscreencanvas\": \"^2019.7.3\",\n \"@typescript-eslint/eslint-plugin\": \"^8.5.0\",\n \"@typescript-eslint/parser\": \"^8.5.0\",\n \"@vladmandic/build\": \"^0.10.1\",\n \"@vladmandic/pilogger\": \"^0.5.1\",\n \"ajv\": \"^8.17.1\",\n \"esbuild\": \"^0.23.1\",\n \"eslint\": \"8.57.0\",\n \"eslint-config-airbnb-base\": \"^15.0.0\",\n \"eslint-plugin-import\": \"^2.30.0\",\n \"eslint-plugin-json\": \"^4.0.1\",\n \"eslint-plugin-node\": \"^11.1.0\",\n \"eslint-plugin-promise\": \"^7.1.0\",\n \"node-fetch\": \"^3.3.2\",\n \"rimraf\": \"^6.0.1\",\n \"seedrandom\": \"^3.0.5\",\n \"tslib\": \"^2.7.0\",\n \"typedoc\": \"^0.26.7\",\n \"typescript\": \"5.5.4\"\n }\n}\n", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common/index';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n function extractReductionBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ReductionBlockParams {\n const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`);\n const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`);\n const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`);\n\n return { separable_conv0, separable_conv1, expansion_conv };\n }\n\n function extractMainBlockParams(channels: number, mappedPrefix: string): MainBlockParams {\n const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`);\n const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`);\n const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`);\n\n return { separable_conv0, separable_conv1, separable_conv2 };\n }\n\n return {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n };\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n } = extractorsFactory(extractWeights, paramMappings);\n\n const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in');\n const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0');\n const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1');\n\n const entry_flow = {\n conv_in: entry_flow_conv_in,\n reduction_block_0: entry_flow_reduction_block_0,\n reduction_block_1: entry_flow_reduction_block_1,\n };\n\n const middle_flow: Record<`main_block_${number}`, MainBlockParams> = {};\n range(numMainBlocks, 0, 1).forEach((idx) => {\n middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`);\n });\n\n const exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block');\n const exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv');\n\n const exit_flow = {\n reduction_block: exit_flow_reduction_block,\n separable_conv: exit_flow_separable_conv,\n };\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { entry_flow, middle_flow, exit_flow },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`);\n\n return { separable_conv0, separable_conv1, expansion_conv };\n }\n\n function extractMainBlockParams(mappedPrefix: string): MainBlockParams {\n const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`);\n\n return { separable_conv0, separable_conv1, separable_conv2 };\n }\n\n return {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n };\n}\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n numMainBlocks: number,\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n } = loadParamsFactory(weightMap, paramMappings);\n\n const entry_flow_conv_in = extractConvParams('entry_flow/conv_in');\n const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0');\n const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1');\n\n const entry_flow = {\n conv_in: entry_flow_conv_in,\n reduction_block_0: entry_flow_reduction_block_0,\n reduction_block_1: entry_flow_reduction_block_1,\n };\n\n const middle_flow: Record<`main_block_${number}`, MainBlockParams> = {};\n range(numMainBlocks, 0, 1).forEach((idx) => {\n middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`);\n });\n\n const exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block');\n const exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv');\n\n const exit_flow = {\n reduction_block: exit_flow_reduction_block,\n separable_conv: exit_flow_separable_conv,\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params: { entry_flow, middle_flow, exit_flow }, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common/index';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { range } from '../utils/index';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias);\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput = true): tf.Tensor4D {\n let out = isActivateInput ? tf.relu(x) : x;\n out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1]);\n out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n out = tf.maxPool(out, [3, 3], [2, 2], 'same');\n out = tf.add(out, conv(x, params.expansion_conv, [2, 2]));\n return out;\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1]);\n out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1]);\n out = tf.add(out, x);\n return out;\n}\n\nexport class TinyXception extends NeuralNetwork {\n private _numMainBlocks: number;\n\n constructor(numMainBlocks: number) {\n super('TinyXception');\n this._numMainBlocks = numMainBlocks;\n }\n\n public forwardInput(input: NetInput): tf.Tensor4D {\n const { params } = this;\n if (!params) {\n throw new Error('TinyXception - load model before inference');\n }\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n const meanRgb = [122.782, 117.001, 104.298];\n const normalized = normalize(batchTensor, meanRgb).div(255) as tf.Tensor4D;\n let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]));\n out = reductionBlock(out, params.entry_flow.reduction_block_0, false);\n out = reductionBlock(out, params.entry_flow.reduction_block_1);\n range(this._numMainBlocks, 0, 1).forEach((idx) => {\n out = mainBlock(out, params.middle_flow[`main_block_${idx}`]);\n });\n out = reductionBlock(out, params.exit_flow.reduction_block);\n out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]));\n return out;\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n protected getDefaultModelName(): string {\n return 'tiny_xception_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap, this._numMainBlocks);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParams(weights, this._numMainBlocks);\n }\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n const age = extractFCParams(512, 1, 'fc/age');\n const gender = extractFCParams(512, 2, 'fc/gender');\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { fc: { age, gender } },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractFcParams(prefix: string): FCParams {\n const weights = extractWeightEntry(`${prefix}/weights`, 2);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n return { weights, bias };\n }\n\n const params = {\n fc: {\n age: extractFcParams('fc/age'),\n gender: extractFcParams('fc/gender'),\n },\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from '../common/index';\n\n// eslint-disable-next-line no-shadow\nexport enum Gender {\n // eslint-disable-next-line no-unused-vars\n FEMALE = 'female',\n // eslint-disable-next-line no-unused-vars\n MALE = 'male'\n}\n\nexport type AgeAndGenderPrediction = {\n age: number\n gender: Gender\n genderProbability: number\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n fc: {\n age: FCParams\n gender: FCParams\n }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { AgeAndGenderPrediction, Gender, NetOutput, NetParams } from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\n\nexport class AgeGenderNet extends NeuralNetwork {\n private _faceFeatureExtractor: TinyXception;\n\n constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n super('AgeGenderNet');\n this._faceFeatureExtractor = faceFeatureExtractor;\n }\n\n public get faceFeatureExtractor(): TinyXception {\n return this._faceFeatureExtractor;\n }\n\n public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n const { params } = this;\n\n if (!params) {\n throw new Error(`${this._name} - load model before inference`);\n }\n\n return tf.tidy(() => {\n const bottleneckFeatures = input instanceof NetInput\n ? this.faceFeatureExtractor.forwardInput(input)\n : input;\n\n const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1);\n const age = fullyConnectedLayer(pooled, params.fc.age).as1D();\n const gender = fullyConnectedLayer(pooled, params.fc.gender);\n return { age, gender };\n });\n }\n\n public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n return tf.tidy(() => {\n const { age, gender } = this.runNet(input);\n return { age, gender: tf.softmax(gender) };\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async predictAgeAndGender(input: TNetInput): Promise {\n const netInput = await toNetInput(input);\n const out = await this.forwardInput(netInput);\n\n const ages = tf.unstack(out.age);\n const genders = tf.unstack(out.gender);\n const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n ageTensor,\n genderTensor: genders[i],\n }));\n\n const predictionsByBatch = await Promise.all(\n ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n const age = (ageTensor.dataSync())[0];\n const probMale = (genderTensor.dataSync())[0];\n const isMale = probMale > 0.5;\n const gender = isMale ? Gender.MALE : Gender.FEMALE;\n const genderProbability = isMale ? probMale : (1 - probMale);\n\n ageTensor.dispose();\n genderTensor.dispose();\n return { age, gender, genderProbability };\n }),\n );\n out.age.dispose();\n out.gender.dispose();\n\n return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction;\n }\n\n protected getDefaultModelName(): string {\n return 'age_gender_model';\n }\n\n public override dispose(throwOnRedispose = true) {\n this.faceFeatureExtractor.dispose(throwOnRedispose);\n super.dispose(throwOnRedispose);\n }\n\n public loadClassifierParams(weights: Float32Array) {\n const { params, paramMappings } = this.extractClassifierParams(weights);\n this._params = params;\n this._paramMappings = paramMappings;\n }\n\n public extractClassifierParams(weights: Float32Array) {\n return extractParams(weights);\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n return extractParamsFromWeightMap(classifierMap);\n }\n\n protected extractParams(weights: Float32Array) {\n const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2);\n\n const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n return this.extractClassifierParams(classifierWeights);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { IDimensions, Point } from '../classes/index';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils/index';\n\nexport abstract class FaceLandmark68NetBase<\n TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n extends FaceProcessor {\n public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n const inputDimensions = originalDimensions.map(({ width, height }) => {\n const scale = inputSize / Math.max(height, width);\n return {\n width: width * scale,\n height: height * scale,\n };\n });\n\n const batchSize = inputDimensions.length;\n\n return tf.tidy(() => {\n const createInterleavedTensor = (fillX: number, fillY: number) => tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D();\n\n // eslint-disable-next-line no-unused-vars\n const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n const { width, height } = inputDimensions[batchIdx];\n return cond(width, height) ? Math.abs(width - height) / 2 : 0;\n };\n\n const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h);\n const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w);\n\n const landmarkTensors = output\n .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n getPaddingX(batchIdx),\n getPaddingY(batchIdx),\n ))))\n .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n inputDimensions[batchIdx].width,\n inputDimensions[batchIdx].height,\n ))));\n\n return landmarkTensors as tf.Tensor2D;\n });\n }\n\n public forwardInput(input: NetInput): tf.Tensor2D {\n return tf.tidy(() => {\n const out = this.runNet(input);\n return this.postProcess(\n out,\n input.inputSize as number,\n input.inputDimensions.map(([height, width]) => ({ height, width })),\n );\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async detectLandmarks(input: TNetInput): Promise {\n const netInput = await toNetInput(input);\n const landmarkTensors = tf.tidy(\n () => tf.unstack(this.forwardInput(netInput)),\n );\n\n const landmarksForBatch = await Promise.all(landmarkTensors.map(\n async (landmarkTensor, batchIdx) => {\n const landmarksArray = Array.from(landmarkTensor.dataSync());\n const xCoords = landmarksArray.filter((_, i) => isEven(i));\n const yCoords = landmarksArray.filter((_, i) => !isEven(i));\n\n return new FaceLandmarks68(\n Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n {\n height: netInput.getInputHeight(batchIdx),\n width: netInput.getInputWidth(batchIdx),\n },\n );\n },\n ));\n\n landmarkTensors.forEach((t) => t.dispose());\n\n return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n }\n\n protected getClassifierChannelsOut(): number {\n return 136;\n }\n}\n", "import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68Net extends FaceLandmark68NetBase {\n constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n super('FaceLandmark68Net', faceFeatureExtractor);\n }\n\n protected getDefaultModelName(): string {\n return 'face_landmark_68_model';\n }\n\n protected getClassifierChannelsIn(): number {\n return 256;\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeightMapTiny(\n weightMap: tf.NamedTensorMap,\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractDenseBlock3Params,\n } = loadParamsFactory(weightMap, paramMappings);\n\n const params = {\n dense0: extractDenseBlock3Params('dense0', true),\n dense1: extractDenseBlock3Params('dense1'),\n dense2: extractDenseBlock3Params('dense2'),\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const {\n extractDenseBlock3Params,\n } = extractorsFactory(extractWeights, paramMappings);\n\n const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true);\n const dense1 = extractDenseBlock3Params(32, 64, 'dense1');\n const dense2 = extractDenseBlock3Params(64, 128, 'dense2');\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { dense0, dense1, dense2 },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeightMapTiny } from './extractParamsFromWeightMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork implements IFaceFeatureExtractor {\n constructor() {\n super('TinyFaceFeatureExtractor');\n }\n\n public forwardInput(input: NetInput): tf.Tensor4D {\n const { params } = this;\n\n if (!params) {\n throw new Error('TinyFaceFeatureExtractor - load model before inference');\n }\n\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n const meanRgb = [122.782, 117.001, 104.298];\n const normalized = normalize(batchTensor, meanRgb).div(255) as tf.Tensor4D;\n\n let out = denseBlock3(normalized, params.dense0, true);\n out = denseBlock3(out, params.dense1);\n out = denseBlock3(out, params.dense2);\n out = tf.avgPool(out, [14, 14], [2, 2], 'valid');\n\n return out;\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n protected getDefaultModelName(): string {\n return 'face_feature_extractor_tiny_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMapTiny(weightMap);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParamsTiny(weights);\n }\n}\n", "import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase {\n constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\n super('FaceLandmark68TinyNet', faceFeatureExtractor);\n }\n\n protected getDefaultModelName(): string {\n return 'face_landmark_68_tiny_model';\n }\n\n protected getClassifierChannelsIn(): number {\n return 128;\n }\n}\n", "import { FaceLandmark68Net } from './FaceLandmark68Net';\n\nexport * from './FaceLandmark68Net';\nexport * from './FaceLandmark68TinyNet';\nexport class FaceLandmarkNet extends FaceLandmark68Net {}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ScaleLayerParams } from './types';\n\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\n return tf.add(tf.mul(x, params.weights), params.biases);\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\nfunction convLayer(\n x: tf.Tensor4D,\n params: ConvLayerParams,\n strides: [number, number],\n withRelu: boolean,\n padding: 'valid' | 'same' = 'same',\n): tf.Tensor4D {\n const { filters, bias } = params.conv;\n\n let out = tf.conv2d(x, filters, strides, padding);\n out = tf.add(out, bias);\n out = scale(out, params.scale);\n return withRelu ? tf.relu(out) : out;\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n return convLayer(x, params, [1, 1], true);\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n return convLayer(x, params, [1, 1], false);\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n return convLayer(x, params, [2, 2], true, 'valid');\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from '../common/index';\nimport { isFloat } from '../utils/index';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n const weights = extractWeights(numFilterValues);\n const depth = weights.length / (numFilters * filterSize * filterSize);\n\n if (isFloat(depth)) {\n throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`);\n }\n\n return tf.tidy(\n () => tf.transpose(\n tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n [2, 3, 1, 0],\n ),\n );\n }\n\n function extractConvParams(\n numFilterValues: number,\n numFilters: number,\n filterSize: number,\n mappedPrefix: string,\n ): ConvParams {\n const filters = extractFilterValues(numFilterValues, numFilters, filterSize);\n const bias = tf.tensor1d(extractWeights(numFilters));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/filters` },\n { paramPath: `${mappedPrefix}/bias` },\n );\n\n return { filters, bias };\n }\n\n function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n const weights = tf.tensor1d(extractWeights(numWeights));\n const biases = tf.tensor1d(extractWeights(numWeights));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/weights` },\n { paramPath: `${mappedPrefix}/biases` },\n );\n\n return {\n weights,\n biases,\n };\n }\n\n function extractConvLayerParams(\n numFilterValues: number,\n numFilters: number,\n filterSize: number,\n mappedPrefix: string,\n ): ConvLayerParams {\n const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`);\n const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`);\n\n return { conv, scale };\n }\n\n function extractResidualLayerParams(\n numFilterValues: number,\n numFilters: number,\n filterSize: number,\n mappedPrefix: string,\n isDown = false,\n ): ResidualLayerParams {\n const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`);\n const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`);\n\n return { conv1, conv2 };\n }\n\n return {\n extractConvLayerParams,\n extractResidualLayerParams,\n };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractConvLayerParams,\n extractResidualLayerParams,\n } = extractorsFactory(extractWeights, paramMappings);\n\n const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down');\n const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1');\n const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2');\n const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3');\n\n const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true);\n const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1');\n const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2');\n const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3');\n\n const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true);\n const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1');\n const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2');\n\n const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true);\n const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1');\n const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2');\n const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out');\n\n const fc = tf.tidy(\n () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]),\n );\n paramMappings.push({ paramPath: 'fc' });\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n const params = {\n conv32_down,\n conv32_1,\n conv32_2,\n conv32_3,\n conv64_down,\n conv64_1,\n conv64_2,\n conv64_3,\n conv128_down,\n conv128_1,\n conv128_2,\n conv256_down,\n conv256_1,\n conv256_2,\n conv256_down_out,\n fc,\n };\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common/index';\nimport { isTensor2D } from '../utils/index';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractScaleLayerParams(prefix: string): ScaleLayerParams {\n const weights = extractWeightEntry(`${prefix}/scale/weights`, 1);\n const biases = extractWeightEntry(`${prefix}/scale/biases`, 1);\n\n return { weights, biases };\n }\n\n function extractConvLayerParams(prefix: string): ConvLayerParams {\n const filters = extractWeightEntry(`${prefix}/conv/filters`, 4);\n const bias = extractWeightEntry(`${prefix}/conv/bias`, 1);\n const scale = extractScaleLayerParams(prefix);\n\n return { conv: { filters, bias }, scale };\n }\n\n function extractResidualLayerParams(prefix: string): ResidualLayerParams {\n return {\n conv1: extractConvLayerParams(`${prefix}/conv1`),\n conv2: extractConvLayerParams(`${prefix}/conv2`),\n };\n }\n\n return {\n extractConvLayerParams,\n extractResidualLayerParams,\n };\n}\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractConvLayerParams,\n extractResidualLayerParams,\n } = extractorsFactory(weightMap, paramMappings);\n\n const conv32_down = extractConvLayerParams('conv32_down');\n const conv32_1 = extractResidualLayerParams('conv32_1');\n const conv32_2 = extractResidualLayerParams('conv32_2');\n const conv32_3 = extractResidualLayerParams('conv32_3');\n\n const conv64_down = extractResidualLayerParams('conv64_down');\n const conv64_1 = extractResidualLayerParams('conv64_1');\n const conv64_2 = extractResidualLayerParams('conv64_2');\n const conv64_3 = extractResidualLayerParams('conv64_3');\n\n const conv128_down = extractResidualLayerParams('conv128_down');\n const conv128_1 = extractResidualLayerParams('conv128_1');\n const conv128_2 = extractResidualLayerParams('conv128_2');\n\n const conv256_down = extractResidualLayerParams('conv256_down');\n const conv256_1 = extractResidualLayerParams('conv256_1');\n const conv256_2 = extractResidualLayerParams('conv256_2');\n const conv256_down_out = extractResidualLayerParams('conv256_down_out');\n\n const { fc } = weightMap;\n paramMappings.push({ originalPath: 'fc', paramPath: 'fc' });\n\n if (!isTensor2D(fc)) {\n throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`);\n }\n\n const params = {\n conv32_down,\n conv32_1,\n conv32_2,\n conv32_3,\n conv64_down,\n conv64_1,\n conv64_2,\n conv64_3,\n conv128_down,\n conv128_1,\n conv128_2,\n conv256_down,\n conv256_1,\n conv256_2,\n conv256_down_out,\n fc,\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n let out = conv(x, params.conv1);\n out = convNoRelu(out, params.conv2);\n out = tf.add(out, x);\n out = tf.relu(out);\n return out;\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n let out = convDown(x, params.conv1);\n out = convNoRelu(out, params.conv2);\n\n let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D;\n const zeros = tf.zeros(pooled.shape);\n const isPad = pooled.shape[3] !== out.shape[3];\n const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2];\n\n if (isAdjustShape) {\n const padShapeX = [...out.shape] as [number, number, number, number];\n padShapeX[1] = 1;\n const zerosW = tf.zeros(padShapeX);\n out = tf.concat([out, zerosW], 1);\n\n const padShapeY = [...out.shape] as [number, number, number, number];\n padShapeY[2] = 1;\n const zerosH = tf.zeros(padShapeY);\n out = tf.concat([out, zerosH], 2);\n }\n\n pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled;\n out = tf.add(pooled, out) as tf.Tensor4D;\n\n out = tf.relu(out);\n return out;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\nexport class FaceRecognitionNet extends NeuralNetwork {\n constructor() {\n super('FaceRecognitionNet');\n }\n\n public forwardInput(input: NetInput): tf.Tensor2D {\n const { params } = this;\n\n if (!params) {\n throw new Error('FaceRecognitionNet - load model before inference');\n }\n\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n const meanRgb = [122.782, 117.001, 104.298];\n const normalized = normalize(batchTensor, meanRgb).div(255) as tf.Tensor4D;\n\n let out = convDown(normalized, params.conv32_down);\n out = tf.maxPool(out, 3, 2, 'valid');\n\n out = residual(out, params.conv32_1);\n out = residual(out, params.conv32_2);\n out = residual(out, params.conv32_3);\n\n out = residualDown(out, params.conv64_down);\n out = residual(out, params.conv64_1);\n out = residual(out, params.conv64_2);\n out = residual(out, params.conv64_3);\n\n out = residualDown(out, params.conv128_down);\n out = residual(out, params.conv128_1);\n out = residual(out, params.conv128_2);\n\n out = residualDown(out, params.conv256_down);\n out = residual(out, params.conv256_1);\n out = residual(out, params.conv256_2);\n out = residualDown(out, params.conv256_down_out);\n\n const globalAvg = out.mean([1, 2]) as tf.Tensor2D;\n const fullyConnected = tf.matMul(globalAvg, params.fc);\n\n return fullyConnected as tf.Tensor2D;\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async computeFaceDescriptor(input: TNetInput): Promise {\n // @ts-ignore\n if (input?.shape?.some((dim) => dim <= 0)) return new Float32Array(128);\n const netInput = await toNetInput(input);\n const faceDescriptorTensors = tf.tidy(() => tf.unstack(this.forwardInput(netInput)));\n const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map((t) => t.data())) as Float32Array[];\n faceDescriptorTensors.forEach((t) => t.dispose());\n return netInput.isBatchInput ? faceDescriptorsForBatch : faceDescriptorsForBatch[0];\n }\n\n protected getDefaultModelName(): string {\n return 'face_recognition_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParams(weights);\n }\n}\n", "import { FaceRecognitionNet } from './FaceRecognitionNet';\n\nexport * from './FaceRecognitionNet';\n\nexport function createFaceRecognitionNet(weights: Float32Array) {\n const net = new FaceRecognitionNet();\n net.extractWeights(weights);\n return net;\n}\n", "export type WithFaceDescriptor = TSource & {\n descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n TSource\n>(\n sourceObj: TSource,\n descriptor: Float32Array,\n): WithFaceDescriptor {\n const extension = { descriptor };\n return { ...sourceObj, ...extension };\n}\n", "export type WithAge = TSource & {\n age: number\n}\n\nexport function isWithAge(obj: any): obj is WithAge<{}> {\n return typeof obj.age === 'number';\n}\n\nexport function extendWithAge<\n TSource\n>(\n sourceObj: TSource,\n age: number,\n): WithAge {\n const extension = { age };\n return { ...sourceObj, ...extension };\n}\n", "import { Gender } from '../ageGenderNet/types';\nimport { isValidProbablitiy } from '../utils/index';\n\nexport type WithGender = TSource & {\n gender: Gender\n genderProbability: number\n}\n\nexport function isWithGender(obj: any): obj is WithGender<{}> {\n return (obj.gender === Gender.MALE || obj.gender === Gender.FEMALE)\n && isValidProbablitiy(obj.genderProbability);\n}\n\nexport function extendWithGender<\n TSource\n>(\n sourceObj: TSource,\n gender: Gender,\n genderProbability: number,\n): WithGender {\n const extension = { gender, genderProbability };\n return { ...sourceObj, ...extension };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory } from '../common/index';\nimport { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);\n const batch_norm_scale = tf.tensor1d(extractWeights(numChannels));\n const batch_norm_offset = tf.tensor1d(extractWeights(numChannels));\n const batch_norm_mean = tf.tensor1d(extractWeights(numChannels));\n const batch_norm_variance = tf.tensor1d(extractWeights(numChannels));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/filters` },\n { paramPath: `${mappedPrefix}/batch_norm_scale` },\n { paramPath: `${mappedPrefix}/batch_norm_offset` },\n { paramPath: `${mappedPrefix}/batch_norm_mean` },\n { paramPath: `${mappedPrefix}/batch_norm_variance` },\n );\n\n return {\n filters,\n batch_norm_scale,\n batch_norm_offset,\n batch_norm_mean,\n batch_norm_variance,\n };\n }\n\n function extractConvParams(\n channelsIn: number,\n channelsOut: number,\n filterSize: number,\n mappedPrefix: string,\n isPointwiseConv?: boolean,\n ): ConvParams {\n const filters = tf.tensor4d(\n extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n [filterSize, filterSize, channelsIn, channelsOut],\n );\n const bias = tf.tensor1d(extractWeights(channelsOut));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/filters` },\n { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` },\n );\n\n return { filters, bias };\n }\n\n function extractPointwiseConvParams(\n channelsIn: number,\n channelsOut: number,\n filterSize: number,\n mappedPrefix: string,\n ): PointwiseConvParams {\n const {\n filters,\n bias,\n } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true);\n\n return {\n filters,\n batch_norm_offset: bias,\n };\n }\n\n function extractConvPairParams(\n channelsIn: number,\n channelsOut: number,\n mappedPrefix: string,\n ): MobileNetV1.ConvPairParams {\n const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`);\n const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`);\n\n return { depthwise_conv, pointwise_conv };\n }\n\n function extractMobilenetV1Params(): MobileNetV1.Params {\n const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0');\n const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1');\n const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2');\n const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3');\n const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4');\n const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5');\n const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6');\n const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7');\n const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8');\n const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9');\n const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10');\n const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11');\n const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12');\n const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13');\n return {\n conv_0,\n conv_1,\n conv_2,\n conv_3,\n conv_4,\n conv_5,\n conv_6,\n conv_7,\n conv_8,\n conv_9,\n conv_10,\n conv_11,\n conv_12,\n conv_13,\n };\n }\n\n function extractPredictionLayerParams(): PredictionLayerParams {\n const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0');\n const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1');\n const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2');\n const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3');\n const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4');\n const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5');\n const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6');\n const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7');\n const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor');\n const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor');\n const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor');\n const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor');\n const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor');\n const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor');\n const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor');\n const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor');\n const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor');\n const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor');\n const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor');\n const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor');\n\n const box_predictor_0 = {\n box_encoding_predictor: box_encoding_0_predictor,\n class_predictor: class_predictor_0,\n };\n const box_predictor_1 = {\n box_encoding_predictor: box_encoding_1_predictor,\n class_predictor: class_predictor_1,\n };\n const box_predictor_2 = {\n box_encoding_predictor: box_encoding_2_predictor,\n class_predictor: class_predictor_2,\n };\n const box_predictor_3 = {\n box_encoding_predictor: box_encoding_3_predictor,\n class_predictor: class_predictor_3,\n };\n const box_predictor_4 = {\n box_encoding_predictor: box_encoding_4_predictor,\n class_predictor: class_predictor_4,\n };\n const box_predictor_5 = {\n box_encoding_predictor: box_encoding_5_predictor,\n class_predictor: class_predictor_5,\n };\n return {\n conv_0,\n conv_1,\n conv_2,\n conv_3,\n conv_4,\n conv_5,\n conv_6,\n conv_7,\n box_predictor_0,\n box_predictor_1,\n box_predictor_2,\n box_predictor_3,\n box_predictor_4,\n box_predictor_5,\n };\n }\n\n return {\n extractMobilenetV1Params,\n extractPredictionLayerParams,\n };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n const {\n extractMobilenetV1Params,\n extractPredictionLayerParams,\n } = extractorsFactory(extractWeights, paramMappings);\n const mobilenetv1 = extractMobilenetV1Params();\n const prediction_layer = extractPredictionLayerParams();\n const extra_dim = tf.tensor3d(\n extractWeights(5118 * 4),\n [1, 5118, 4],\n );\n const output_layer = {\n extra_dim,\n };\n paramMappings.push({ paramPath: 'output_layer/extra_dim' });\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n params: {\n mobilenetv1,\n prediction_layer,\n output_layer,\n },\n paramMappings,\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common/index';\nimport { isTensor3D } from '../utils/index';\nimport { BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n const filters = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`);\n const batch_norm_offset = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`);\n return { filters, batch_norm_offset };\n }\n\n function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\n const mappedPrefix = `mobilenetv1/conv_${idx}`;\n const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`;\n const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`;\n const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`;\n\n const filters = extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`);\n const batch_norm_scale = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`);\n const batch_norm_offset = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`);\n const batch_norm_mean = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`);\n const batch_norm_variance = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`);\n\n return {\n depthwise_conv: {\n filters,\n batch_norm_scale,\n batch_norm_offset,\n batch_norm_mean,\n batch_norm_variance,\n },\n pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv),\n };\n }\n\n function extractMobilenetV1Params(): MobileNetV1.Params {\n return {\n conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\n conv_1: extractConvPairParams(1),\n conv_2: extractConvPairParams(2),\n conv_3: extractConvPairParams(3),\n conv_4: extractConvPairParams(4),\n conv_5: extractConvPairParams(5),\n conv_6: extractConvPairParams(6),\n conv_7: extractConvPairParams(7),\n conv_8: extractConvPairParams(8),\n conv_9: extractConvPairParams(9),\n conv_10: extractConvPairParams(10),\n conv_11: extractConvPairParams(11),\n conv_12: extractConvPairParams(12),\n conv_13: extractConvPairParams(13),\n };\n }\n\n function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\n const filters = extractWeightEntry(`${prefix}/weights`, 4, `${mappedPrefix}/filters`);\n const bias = extractWeightEntry(`${prefix}/biases`, 1, `${mappedPrefix}/bias`);\n return { filters, bias };\n }\n\n function extractBoxPredictorParams(idx: number): BoxPredictionParams {\n const box_encoding_predictor = extractConvParams(\n `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\n `prediction_layer/box_predictor_${idx}/box_encoding_predictor`,\n );\n const class_predictor = extractConvParams(\n `Prediction/BoxPredictor_${idx}/ClassPredictor`,\n `prediction_layer/box_predictor_${idx}/class_predictor`,\n );\n return { box_encoding_predictor, class_predictor };\n }\n\n function extractPredictionLayerParams(): PredictionLayerParams {\n return {\n conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\n conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\n conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\n conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\n conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\n conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\n conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\n conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\n box_predictor_0: extractBoxPredictorParams(0),\n box_predictor_1: extractBoxPredictorParams(1),\n box_predictor_2: extractBoxPredictorParams(2),\n box_predictor_3: extractBoxPredictorParams(3),\n box_predictor_4: extractBoxPredictorParams(4),\n box_predictor_5: extractBoxPredictorParams(5),\n };\n }\n\n return {\n extractMobilenetV1Params,\n extractPredictionLayerParams,\n };\n}\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n const {\n extractMobilenetV1Params,\n extractPredictionLayerParams,\n } = extractorsFactory(weightMap, paramMappings);\n const extra_dim = weightMap['Output/extra_dim'];\n paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' });\n if (!isTensor3D(extra_dim)) {\n throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`);\n }\n\n const params = {\n mobilenetv1: extractMobilenetV1Params(),\n prediction_layer: extractPredictionLayerParams(),\n output_layer: {\n extra_dim,\n },\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { PointwiseConvParams } from './types';\n\nexport function pointwiseConvLayer(x: tf.Tensor4D, params: PointwiseConvParams, strides: [number, number]) {\n return tf.tidy(() => {\n let out = tf.conv2d(x, params.filters, strides, 'same');\n out = tf.add(out, params.batch_norm_offset);\n return tf.clipByValue(out, 0, 6);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513;\n\nfunction depthwiseConvLayer(x: tf.Tensor4D, params: MobileNetV1.DepthwiseConvParams, strides: [number, number]) {\n return tf.tidy(() => {\n let out = tf.depthwiseConv2d(x, params.filters, strides, 'same');\n out = tf.batchNorm(\n out,\n params.batch_norm_mean,\n params.batch_norm_variance,\n params.batch_norm_offset,\n params.batch_norm_scale,\n epsilon,\n );\n return tf.clipByValue(out, 0, 6);\n });\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n return [2, 4, 6, 12].some((idx) => idx === layerIdx) ? [2, 2] : [1, 1];\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n return tf.tidy(() => {\n let conv11;\n let out = pointwiseConvLayer(x, params.conv_0, [2, 2]);\n\n const convPairParams = [\n params.conv_1,\n params.conv_2,\n params.conv_3,\n params.conv_4,\n params.conv_5,\n params.conv_6,\n params.conv_7,\n params.conv_8,\n params.conv_9,\n params.conv_10,\n params.conv_11,\n params.conv_12,\n params.conv_13,\n ];\n\n convPairParams.forEach((param, i) => {\n const layerIdx = i + 1;\n const depthwiseConvStrides = getStridesForLayerIdx(layerIdx);\n out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides);\n out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1]);\n if (layerIdx === 11) conv11 = out;\n });\n\n if (conv11 === null) {\n throw new Error('mobileNetV1 - output of conv layer 11 is null');\n }\n\n return {\n out,\n conv11: conv11 as any,\n };\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\n const boxesData = boxes.arraySync();\n const yminI = Math.min(boxesData[i][0], boxesData[i][2]);\n const xminI = Math.min(boxesData[i][1], boxesData[i][3]);\n const ymaxI = Math.max(boxesData[i][0], boxesData[i][2]);\n const xmaxI = Math.max(boxesData[i][1], boxesData[i][3]);\n const yminJ = Math.min(boxesData[j][0], boxesData[j][2]);\n const xminJ = Math.min(boxesData[j][1], boxesData[j][3]);\n const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2]);\n const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3]);\n const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n if (areaI <= 0 || areaJ <= 0) return 0.0;\n const intersectionYmin = Math.max(yminI, yminJ);\n const intersectionXmin = Math.max(xminI, xminJ);\n const intersectionYmax = Math.min(ymaxI, ymaxJ);\n const intersectionXmax = Math.min(xmaxI, xmaxJ);\n const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) * Math.max(intersectionXmax - intersectionXmin, 0.0);\n return intersectionArea / (areaI + areaJ - intersectionArea);\n}\n\nexport function nonMaxSuppression(\n boxes: tf.Tensor2D,\n scores: number[],\n maxOutputSize: number,\n iouThreshold: number,\n scoreThreshold: number,\n): number[] {\n const numBoxes = boxes.shape[0];\n const outputSize = Math.min(maxOutputSize, numBoxes);\n\n const candidates = scores\n .map((score, boxIndex) => ({ score, boxIndex }))\n .filter((c) => c.score > scoreThreshold)\n .sort((c1, c2) => c2.score - c1.score);\n\n const suppressFunc = (x: number) => (x <= iouThreshold ? 1 : 0);\n const selected: number[] = [];\n\n candidates.forEach((c) => {\n if (selected.length >= outputSize) return;\n const originalScore = c.score;\n for (let j = selected.length - 1; j >= 0; --j) {\n const iou = IOU(boxes, c.boxIndex, selected[j]);\n if (iou === 0.0) continue;\n c.score *= suppressFunc(iou);\n if (c.score <= scoreThreshold) break;\n }\n if (originalScore === c.score) {\n selected.push(c.boxIndex);\n }\n });\n return selected;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { OutputLayerParams } from './types';\n\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\n const vec = tf.unstack(tf.transpose(x, [1, 0]));\n\n const sizes = [\n tf.sub(vec[2], vec[0]),\n tf.sub(vec[3], vec[1]),\n ];\n const centers = [\n tf.add(vec[0], tf.div(sizes[0], 2)),\n tf.add(vec[1], tf.div(sizes[1], 2)),\n ];\n return { sizes, centers };\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n const { sizes, centers } = getCenterCoordinatesAndSizesLayer(x0);\n\n const vec = tf.unstack(tf.transpose(x1, [1, 0]));\n const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], 5)), sizes[0]), 2);\n const add0_out = tf.add(tf.mul(tf.div(vec[0], 10), sizes[0]), centers[0]);\n const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], 5)), sizes[1]), 2);\n const add1_out = tf.add(tf.mul(tf.div(vec[1], 10), sizes[1]), centers[1]);\n\n return tf.transpose(\n tf.stack([\n tf.sub(add0_out, div0_out),\n tf.sub(add1_out, div1_out),\n tf.add(add0_out, div0_out),\n tf.add(add1_out, div1_out),\n ]),\n [1, 0],\n );\n}\n\nexport function outputLayer(boxPredictions: tf.Tensor4D, classPredictions: tf.Tensor4D, params: OutputLayerParams) {\n return tf.tidy(() => {\n const batchSize = boxPredictions.shape[0];\n\n let boxes = decodeBoxesLayer(\n tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\n tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D,\n );\n boxes = tf.reshape(boxes, [batchSize, (boxes.shape[0] / batchSize), 4]);\n\n const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]));\n let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor;\n\n scores = tf.reshape(scores, [batchSize, scores.shape[1] as number]);\n\n const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[];\n const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[];\n\n return { boxes: boxesByBatch, scores: scoresByBatch };\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { convLayer } from '../common/index';\nimport { BoxPredictionParams } from './types';\n\nexport function boxPredictionLayer(\n x: tf.Tensor4D,\n params: BoxPredictionParams,\n) {\n return tf.tidy(() => {\n const batchSize = x.shape[0];\n const boxPredictionEncoding = tf.reshape(\n convLayer(x, params.box_encoding_predictor),\n [batchSize, -1, 1, 4],\n );\n const classPrediction = tf.reshape(\n convLayer(x, params.class_predictor),\n [batchSize, -1, 3],\n );\n return { boxPredictionEncoding, classPrediction };\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n x: tf.Tensor4D,\n conv11: tf.Tensor4D,\n params: PredictionLayerParams,\n) {\n return tf.tidy(() => {\n const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]);\n const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]);\n const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]);\n const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2]);\n const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1]);\n const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2]);\n const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1]);\n const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2]);\n\n const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0);\n const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1);\n const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2);\n const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3);\n const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4);\n const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5);\n\n const boxPredictions = tf.concat([\n boxPrediction0.boxPredictionEncoding,\n boxPrediction1.boxPredictionEncoding,\n boxPrediction2.boxPredictionEncoding,\n boxPrediction3.boxPredictionEncoding,\n boxPrediction4.boxPredictionEncoding,\n boxPrediction5.boxPredictionEncoding,\n ], 1) as tf.Tensor4D;\n\n const classPredictions = tf.concat([\n boxPrediction0.classPrediction,\n boxPrediction1.classPrediction,\n boxPrediction2.classPrediction,\n boxPrediction3.classPrediction,\n boxPrediction4.classPrediction,\n boxPrediction5.classPrediction,\n ], 1) as tf.Tensor4D;\n\n return {\n boxPredictions,\n classPredictions,\n };\n });\n}\n", "export interface ISsdMobilenetv1Options {\n minConfidence?: number\n maxResults?: number\n}\n\nexport class SsdMobilenetv1Options {\n protected _name = 'SsdMobilenetv1Options';\n\n private _minConfidence: number;\n\n private _maxResults: number;\n\n constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\n this._minConfidence = minConfidence || 0.5;\n this._maxResults = maxResults || 100;\n\n if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\n throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);\n }\n\n if (typeof this._maxResults !== 'number') {\n throw new Error(`${this._name} - expected maxResults to be a number`);\n }\n }\n\n get minConfidence(): number { return this._minConfidence; }\n\n get maxResults(): number { return this._maxResults; }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\nexport class SsdMobilenetv1 extends NeuralNetwork {\n constructor() {\n super('SsdMobilenetv1');\n }\n\n public forwardInput(input: NetInput) {\n const { params } = this;\n if (!params) throw new Error('SsdMobilenetv1 - load model before inference');\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n const x = tf.sub(tf.div(batchTensor, 127.5), 1) as tf.Tensor4D; // input is normalized -1..1\n const features = mobileNetV1(x, params.mobilenetv1);\n const { boxPredictions, classPredictions } = predictionLayer(features.out, features.conv11, params.prediction_layer);\n return outputLayer(boxPredictions, classPredictions, params.output_layer);\n });\n }\n\n public async forward(input: TNetInput) {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async locateFaces(input: TNetInput, options: ISsdMobilenetv1Options = {}): Promise {\n const { maxResults, minConfidence } = new SsdMobilenetv1Options(options);\n const netInput = await toNetInput(input);\n const { boxes: _boxes, scores: _scores } = this.forwardInput(netInput);\n const boxes = _boxes[0];\n const scores = _scores[0];\n for (let i = 1; i < _boxes.length; i++) {\n _boxes[i].dispose();\n _scores[i].dispose();\n }\n const scoresData = Array.from(scores.dataSync());\n const iouThreshold = 0.5;\n const indices = nonMaxSuppression(boxes, scoresData as number[], maxResults, iouThreshold, minConfidence);\n const reshapedDims = netInput.getReshapedInputDimensions(0);\n const inputSize = netInput.inputSize as number;\n const padX = inputSize / reshapedDims.width;\n const padY = inputSize / reshapedDims.height;\n const boxesData = boxes.arraySync();\n const results = indices\n .map((idx) => {\n const [top, bottom] = [\n Math.max(0, boxesData[idx][0]),\n Math.min(1.0, boxesData[idx][2]),\n ].map((val) => val * padY);\n const [left, right] = [\n Math.max(0, boxesData[idx][1]),\n Math.min(1.0, boxesData[idx][3]),\n ].map((val) => val * padX);\n return new FaceDetection(\n scoresData[idx] as number,\n new Rect(left, top, right - left, bottom - top),\n { height: netInput.getInputHeight(0), width: netInput.getInputWidth(0) },\n );\n });\n boxes.dispose();\n scores.dispose();\n return results;\n }\n\n protected getDefaultModelName(): string {\n return 'ssd_mobilenetv1_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParams(weights);\n }\n}\n", "import { SsdMobilenetv1 } from './SsdMobilenetv1';\n\nexport * from './SsdMobilenetv1';\nexport * from './SsdMobilenetv1Options';\n\nexport function createSsdMobilenetv1(weights: Float32Array) {\n const net = new SsdMobilenetv1();\n net.extractWeights(weights);\n return net;\n}\n\nexport function createFaceDetectionNet(weights: Float32Array) {\n return createSsdMobilenetv1(weights);\n}\n\n// alias for backward compatibily\nexport class FaceDetectionNet extends SsdMobilenetv1 {}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n new Point(0.738768, 0.874946),\n new Point(2.42204, 2.65704),\n new Point(4.30971, 7.04493),\n new Point(10.246, 4.59428),\n new Point(12.6868, 11.8741),\n];\n\nexport const BOX_ANCHORS_SEPARABLE = [\n new Point(1.603231, 2.094468),\n new Point(6.041143, 7.080126),\n new Point(2.882459, 3.518061),\n new Point(4.266906, 5.178857),\n new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404];\n\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model';\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model';\n", "import { Point } from '../classes/Point';\n\nexport type TinyYolov2Config = {\n withSeparableConvs: boolean\n iouThreshold: number\n anchors: Point[]\n classes: string[]\n meanRgb?: [number, number, number]\n withClassScores?: boolean,\n filterSizes?: number[]\n isFirstLayerConv2d?: boolean\n}\n\nconst isNumber = (arg: any) => typeof arg === 'number';\n\nexport function validateConfig(config: any) {\n if (!config) {\n throw new Error(`invalid config: ${config}`);\n }\n\n if (typeof config.withSeparableConvs !== 'boolean') {\n throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`);\n }\n\n if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\n throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`);\n }\n\n if (\n !Array.isArray(config.classes)\n || !config.classes.length\n || !config.classes.every((c: any) => typeof c === 'string')\n ) {\n throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`);\n }\n\n if (\n !Array.isArray(config.anchors)\n || !config.anchors.length\n || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\n ) {\n throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`);\n }\n\n if (config.meanRgb && (\n !Array.isArray(config.meanRgb)\n || config.meanRgb.length !== 3\n || !config.meanRgb.every(isNumber)\n )) {\n throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\n return tf.tidy(() => {\n const min = tf.mul(x, tf.scalar(0.10000000149011612));\n return tf.add(tf.relu(tf.sub(x, min)), min);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D {\n return tf.tidy(() => {\n let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid');\n out = tf.sub(out, params.bn.sub);\n out = tf.mul(out, params.bn.truediv);\n out = tf.add(out, params.conv.bias);\n return leaky(out);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D {\n return tf.tidy(() => {\n let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid');\n out = tf.add(out, params.bias);\n return leaky(out);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { extractConvParamsFactory } from '../common/index';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n\n function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n const sub = tf.tensor1d(extractWeights(size));\n const truediv = tf.tensor1d(extractWeights(size));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/sub` },\n { paramPath: `${mappedPrefix}/truediv` },\n );\n return { sub, truediv };\n }\n\n function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`);\n const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`);\n return { conv, bn };\n }\n const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n return {\n extractConvParams,\n extractConvWithBatchNormParams,\n extractSeparableConvParams,\n };\n}\n\nexport function extractParams(\n weights: Float32Array,\n config: TinyYolov2Config,\n boxEncodingSize: number,\n filterSizes: number[],\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const paramMappings: ParamMapping[] = [];\n const {\n extractConvParams,\n extractConvWithBatchNormParams,\n extractSeparableConvParams,\n } = extractorsFactory(extractWeights, paramMappings);\n let params: TinyYolov2NetParams;\n\n if (config.withSeparableConvs) {\n const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n const conv0 = config.isFirstLayerConv2d\n ? extractConvParams(s0, s1, 3, 'conv0')\n : extractSeparableConvParams(s0, s1, 'conv0');\n const conv1 = extractSeparableConvParams(s1, s2, 'conv1');\n const conv2 = extractSeparableConvParams(s2, s3, 'conv2');\n const conv3 = extractSeparableConvParams(s3, s4, 'conv3');\n const conv4 = extractSeparableConvParams(s4, s5, 'conv4');\n const conv5 = extractSeparableConvParams(s5, s6, 'conv5');\n const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined;\n const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined;\n const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8');\n params = {\n conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n };\n } else {\n const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0');\n const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1');\n const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2');\n const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3');\n const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4');\n const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5');\n const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6');\n const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7');\n const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8');\n params = {\n conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n };\n }\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from '../common/index';\nimport { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\nimport { ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractBatchNormParams(prefix: string): BatchNorm {\n const sub = extractWeightEntry(`${prefix}/sub`, 1);\n const truediv = extractWeightEntry(`${prefix}/truediv`, 1);\n return { sub, truediv };\n }\n\n function extractConvParams(prefix: string): ConvParams {\n const filters = extractWeightEntry(`${prefix}/filters`, 4);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n return { filters, bias };\n }\n\n function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\n const conv = extractConvParams(`${prefix}/conv`);\n const bn = extractBatchNormParams(`${prefix}/bn`);\n return { conv, bn };\n }\n\n const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n return {\n extractConvParams,\n extractConvWithBatchNormParams,\n extractSeparableConvParams,\n };\n}\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n config: TinyYolov2Config,\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractConvParams,\n extractConvWithBatchNormParams,\n extractSeparableConvParams,\n } = extractorsFactory(weightMap, paramMappings);\n\n let params: TinyYolov2NetParams;\n\n if (config.withSeparableConvs) {\n // eslint-disable-next-line no-mixed-operators\n const numFilters = (config.filterSizes && config.filterSizes.length || 9);\n params = {\n conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\n conv1: extractSeparableConvParams('conv1'),\n conv2: extractSeparableConvParams('conv2'),\n conv3: extractSeparableConvParams('conv3'),\n conv4: extractSeparableConvParams('conv4'),\n conv5: extractSeparableConvParams('conv5'),\n conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\n conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\n conv8: extractConvParams('conv8'),\n };\n } else {\n params = {\n conv0: extractConvWithBatchNormParams('conv0'),\n conv1: extractConvWithBatchNormParams('conv1'),\n conv2: extractConvWithBatchNormParams('conv2'),\n conv3: extractConvWithBatchNormParams('conv3'),\n conv4: extractConvWithBatchNormParams('conv4'),\n conv5: extractConvWithBatchNormParams('conv5'),\n conv6: extractConvWithBatchNormParams('conv6'),\n conv7: extractConvWithBatchNormParams('conv7'),\n conv8: extractConvParams('conv8'),\n };\n }\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n return { params, paramMappings };\n}\n", "export interface ITinyYolov2Options {\n inputSize?: number\n scoreThreshold?: number\n}\n\nexport class TinyYolov2Options {\n protected _name = 'TinyYolov2Options';\n\n private _inputSize: number;\n\n private _scoreThreshold: number;\n\n constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\n this._inputSize = inputSize || 416;\n this._scoreThreshold = scoreThreshold || 0.5;\n\n if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\n throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);\n }\n\n if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\n throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`);\n }\n }\n\n get inputSize(): number { return this._inputSize; }\n\n get scoreThreshold(): number { return this._scoreThreshold; }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common/index';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom/index';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops/index';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2ExtractBoxesResult, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork {\n public static DEFAULT_FILTER_SIZES = [3, 16, 32, 64, 128, 256, 512, 1024, 1024];\n\n private _config: TinyYolov2Config;\n\n constructor(config: TinyYolov2Config) {\n super('TinyYolov2');\n validateConfig(config);\n this._config = config;\n }\n\n public get config(): TinyYolov2Config {\n return this._config;\n }\n\n public get withClassScores(): boolean {\n return this.config.withClassScores || this.config.classes.length > 1;\n }\n\n public get boxEncodingSize(): number {\n return 5 + (this.withClassScores ? this.config.classes.length : 0);\n }\n\n public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n let out = convWithBatchNorm(x, params.conv0);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv1);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv2);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv3);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv4);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv5);\n out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n out = convWithBatchNorm(out, params.conv6);\n out = convWithBatchNorm(out, params.conv7);\n return convLayer(out, params.conv8, 'valid', false);\n }\n\n public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n let out = this.config.isFirstLayerConv2d\n ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv1);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv2);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv3);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv4);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv5);\n out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out;\n out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out;\n return convLayer(out, params.conv8, 'valid', false);\n }\n\n public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n const { params } = this;\n\n if (!params) {\n throw new Error('TinyYolov2 - load model before inference');\n }\n\n return tf.tidy(() => {\n let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n batchTensor = this.config.meanRgb\n ? normalize(batchTensor, this.config.meanRgb)\n : batchTensor;\n batchTensor = batchTensor.div(255) as tf.Tensor4D;\n return this.config.withSeparableConvs\n ? this.runMobilenet(batchTensor, params as MobilenetParams)\n : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams);\n });\n }\n\n public async forward(input: TNetInput, inputSize: number): Promise {\n return this.forwardInput(await toNetInput(input), inputSize);\n }\n\n public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise {\n const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams);\n const netInput = await toNetInput(input);\n const out = await this.forwardInput(netInput, inputSize);\n const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D;\n const inputDimensions = {\n width: netInput.getInputWidth(0),\n height: netInput.getInputHeight(0),\n };\n\n const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold);\n out.dispose();\n out0.dispose();\n\n const boxes = results.map((res) => res.box);\n const scores = results.map((res) => res.score);\n const classScores = results.map((res) => res.classScore);\n const classNames = results.map((res) => this.config.classes[res.label]);\n\n const indices = nonMaxSuppression(\n boxes.map((box) => box.rescale(inputSize)),\n scores,\n this.config.iouThreshold,\n true,\n );\n\n const detections = indices.map((idx) => new ObjectDetection(\n scores[idx],\n classScores[idx],\n classNames[idx],\n boxes[idx],\n inputDimensions,\n ));\n return detections;\n }\n\n protected getDefaultModelName(): string {\n return '';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap, this.config);\n }\n\n protected extractParams(weights: Float32Array) {\n const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES;\n\n const numFilters = filterSizes ? filterSizes.length : undefined;\n if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`);\n }\n return extractParams(weights, this.config, this.boxEncodingSize, filterSizes);\n }\n\n protected async extractBoxes(\n outputTensor: tf.Tensor4D,\n inputBlobDimensions: Dimensions,\n scoreThreshold?: number,\n ) {\n const { width, height } = inputBlobDimensions;\n const inputSize = Math.max(width, height);\n const correctionFactorX = inputSize / width;\n const correctionFactorY = inputSize / height;\n\n const numCells = outputTensor.shape[1];\n const numBoxes = this.config.anchors.length;\n\n const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize]);\n\n const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4]);\n const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1]);\n const classScores = this.withClassScores\n ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n : tf.scalar(0);\n return [boxes, scores, classScores];\n });\n\n const results: TinyYolov2ExtractBoxesResult[] = [];\n const scoresData = await scoresTensor.array() as number[][][][];\n const boxesData = await boxesTensor.array() as number[][][][];\n for (let row = 0; row < numCells; row++) {\n for (let col = 0; col < numCells; col++) {\n for (let anchor = 0; anchor < numBoxes; anchor++) {\n const score = sigmoid(scoresData[row][col][anchor][0]);\n if (!scoreThreshold || score > scoreThreshold) {\n const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX;\n const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY;\n const widthLocal = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX;\n const heightLocal = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY;\n const x = (ctX - (widthLocal / 2));\n const y = (ctY - (heightLocal / 2));\n const pos = { row, col, anchor };\n const { classScore, label } = this.withClassScores\n ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n : { classScore: 1, label: 0 };\n results.push({\n box: new BoundingBox(x, y, x + widthLocal, y + heightLocal),\n score,\n classScore: score * classScore,\n label,\n ...pos,\n });\n }\n }\n }\n }\n\n boxesTensor.dispose();\n scoresTensor.dispose();\n classScoresTensor.dispose();\n return results;\n }\n\n private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number }) {\n const { row, col, anchor } = pos;\n const classesData = await classesTensor.array();\n return Array(this.config.classes.length).fill(0)\n .map((_, i) => classesData[row][col][anchor][i])\n .map((classScore, label) => ({\n classScore,\n label,\n }))\n .reduce((max, curr) => (max.classScore > curr.classScore ? max : curr));\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/types';\nimport { TNetInput } from '../dom/types';\nimport {\n BOX_ANCHORS,\n BOX_ANCHORS_SEPARABLE,\n DEFAULT_MODEL_NAME,\n DEFAULT_MODEL_NAME_SEPARABLE_CONV,\n IOU_THRESHOLD,\n MEAN_RGB_SEPARABLE,\n} from './const';\nimport { TinyYolov2Base } from './TinyYolov2Base';\nimport { ITinyYolov2Options } from './TinyYolov2Options';\nimport { TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2 extends TinyYolov2Base {\n constructor(withSeparableConvs = true) {\n const config = {\n withSeparableConvs,\n iouThreshold: IOU_THRESHOLD,\n classes: ['face'],\n ...(withSeparableConvs\n ? {\n anchors: BOX_ANCHORS_SEPARABLE,\n meanRgb: MEAN_RGB_SEPARABLE,\n }\n : {\n anchors: BOX_ANCHORS,\n withClassScores: true,\n }),\n };\n\n super(config);\n }\n\n public get withSeparableConvs(): boolean {\n return this.config.withSeparableConvs;\n }\n\n public get anchors(): Point[] {\n return this.config.anchors;\n }\n\n public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise {\n const objectDetections = await this.detect(input, forwardParams);\n return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n }\n\n protected override getDefaultModelName(): string {\n return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME;\n }\n\n protected override extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n return super.extractParamsFromWeightMap(weightMap);\n }\n}\n", "import { TinyYolov2 } from './TinyYolov2';\n\nexport * from './TinyYolov2Options';\nexport * from './config';\nexport * from './types';\nexport { TinyYolov2 };\n\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs = true) {\n const net = new TinyYolov2(withSeparableConvs);\n net.extractWeights(weights);\n return net;\n}\n", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\n\nexport type ITinyFaceDetectorOptions = ITinyYolov2Options\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n protected override _name = 'TinyFaceDetectorOptions';\n}\n", "export class ComposableTask {\n // eslint-disable-next-line no-unused-vars\n public async then(onfulfilled: (value: T) => T | PromiseLike): Promise {\n return onfulfilled(await this.run());\n }\n\n public async run(): Promise {\n throw new Error('ComposableTask - run is not implemented');\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults, TResult>(\n parentResults: TSource[],\n input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n computeResults: (faces: Array) => Promise,\n extractedFaces?: Array | null,\n // eslint-disable-next-line no-unused-vars\n getRectForAlignment: (parentResult: WithFaceLandmarks) => FaceDetection = ({ alignedRect }) => alignedRect,\n) {\n const faceBoxes = parentResults.map((parentResult) => (isWithFaceLandmarks(parentResult)\n ? getRectForAlignment(parentResult)\n : parentResult.detection));\n const faces: Array = extractedFaces || (\n input instanceof tf.Tensor\n ? await extractFaceTensors(input, faceBoxes)\n : await extractFaces(input, faceBoxes)\n );\n const results = await computeResults(faces);\n faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n return results;\n}\n\nexport async function extractSingleFaceAndComputeResult, TResult>(\n parentResult: TSource,\n input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise,\n extractedFaces?: Array | null,\n // eslint-disable-next-line no-unused-vars\n getRectForAlignment?: (parentResultLocal: WithFaceLandmarks) => FaceDetection,\n) {\n return extractAllFacesAndComputeResults(\n [parentResult],\n input,\n async (faces) => computeResult(faces[0]),\n extractedFaces,\n getRectForAlignment,\n );\n}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n new Point(1.603231, 2.094468),\n new Point(6.041143, 7.080126),\n new Point(2.882459, 3.518061),\n new Point(4.266906, 5.178857),\n new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404];\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/index';\nimport { TNetInput } from '../dom/index';\nimport { ITinyYolov2Options } from '../tinyYolov2/index';\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\nimport { TinyYolov2NetParams } from '../tinyYolov2/types';\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\n\nexport class TinyFaceDetector extends TinyYolov2Base {\n constructor() {\n const config = {\n withSeparableConvs: true,\n iouThreshold: IOU_THRESHOLD,\n classes: ['face'],\n anchors: BOX_ANCHORS,\n meanRgb: MEAN_RGB,\n isFirstLayerConv2d: true,\n filterSizes: [3, 16, 32, 64, 128, 256, 512],\n };\n\n super(config);\n }\n\n public get anchors(): Point[] {\n return this.config.anchors;\n }\n\n public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise {\n const objectDetections = await this.detect(input, forwardParams);\n return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n }\n\n protected override getDefaultModelName(): string {\n return 'tiny_face_detector_model';\n }\n\n protected override extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n return super.extractParamsFromWeightMap(weightMap);\n }\n}\n", "import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2/index';\n\nexport const nets = {\n ssdMobilenetv1: new SsdMobilenetv1(),\n tinyFaceDetector: new TinyFaceDetector(),\n tinyYolov2: new TinyYolov2(),\n faceLandmark68Net: new FaceLandmark68Net(),\n faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\n faceRecognitionNet: new FaceRecognitionNet(),\n faceExpressionNet: new FaceExpressionNet(),\n ageGenderNet: new AgeGenderNet(),\n};\n\n/**\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise => nets.ssdMobilenetv1.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Face Detector.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise => nets.tinyFaceDetector.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise => nets.tinyYolov2.locateFaces(input, options);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarks = (input: TNetInput): Promise => nets.faceLandmark68Net.detectLandmarks(input);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image\n * using a tinier version of the 68 point face landmark model, which is slightly\n * faster at inference, but also slightly less accurate.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise => nets.faceLandmark68TinyNet.detectLandmarks(input);\n\n/**\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\n * which uniquely represents the features of that persons face. The computed face descriptor can\n * be used to measure the similarity between faces, by computing the euclidean distance of two\n * face descriptors.\n *\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\n */\nexport const computeFaceDescriptor = (input: TNetInput): Promise => nets.faceRecognitionNet.computeFaceDescriptor(input);\n\n/**\n * Recognizes the facial expressions from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\n */\nexport const recognizeFaceExpressions = (input: TNetInput): Promise => nets.faceExpressionNet.predictExpressions(input);\n\n/**\n * Predicts age and gender from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\n */\nexport const predictAgeAndGender = (input: TNetInput): Promise => nets.ageGenderNet.predictAgeAndGender(input);\n\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url);\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url);\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url);\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url);\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url);\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url);\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url);\nexport const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url);\n\n// backward compatibility\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model;\nexport const locateFaces = ssdMobilenetv1;\nexport const detectLandmarks = detectFaceLandmarks;\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderTask, PredictSingleAgeAndGenderWithFaceAlignmentTask } from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected parentTask: ComposableTask | Promise,\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n protected extractedFaces?: Array,\n ) {\n super();\n }\n}\n\nexport class PredictAllFaceExpressionsTask> extends PredictFaceExpressionsTaskBase[], TSource[]> {\n public override async run(): Promise[]> {\n const parentResults = await this.parentTask;\n\n const faceExpressionsByFace = await extractAllFacesAndComputeResults(\n parentResults,\n this.input,\n async (faces) => Promise.all(\n faces.map((face) => nets.faceExpressionNet.predictExpressions(face) as Promise),\n ),\n this.extractedFaces,\n );\n\n return parentResults.map(\n (parentResult, i) => extendWithFaceExpressions(parentResult, faceExpressionsByFace[i]),\n );\n }\n\n withAgeAndGender() {\n return new PredictAllAgeAndGenderTask(this, this.input);\n }\n}\n\nexport class PredictSingleFaceExpressionsTask> extends PredictFaceExpressionsTaskBase | undefined, TSource | undefined> {\n public override async run(): Promise | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) {\n return undefined;\n }\n\n const faceExpressions = await extractSingleFaceAndComputeResult(\n parentResult,\n this.input,\n (face) => nets.faceExpressionNet.predictExpressions(face) as Promise,\n this.extractedFaces,\n );\n\n return extendWithFaceExpressions(parentResult, faceExpressions);\n }\n\n withAgeAndGender() {\n return new PredictSingleAgeAndGenderTask(this, this.input);\n }\n}\n\nexport class PredictAllFaceExpressionsWithFaceAlignmentTask>> extends PredictAllFaceExpressionsTask {\n override withAgeAndGender() {\n return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptors() {\n return new ComputeAllFaceDescriptorsTask(this, this.input);\n }\n}\n\nexport class PredictSingleFaceExpressionsWithFaceAlignmentTask>> extends PredictSingleFaceExpressionsTask {\n override withAgeAndGender() {\n return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptor() {\n return new ComputeSingleFaceDescriptorTask(this, this.input);\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { TNetInput } from '../dom/index';\nimport { extendWithAge, WithAge } from '../factories/WithAge';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { extendWithGender, WithGender } from '../factories/WithGender';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport { PredictAllFaceExpressionsTask, PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsTask, PredictSingleFaceExpressionsWithFaceAlignmentTask } from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected parentTask: ComposableTask | Promise,\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n protected extractedFaces?: Array,\n ) {\n super();\n }\n}\n\nexport class PredictAllAgeAndGenderTask> extends PredictAgeAndGenderTaskBase>[], TSource[]> {\n public override async run(): Promise>[]> {\n const parentResults = await this.parentTask;\n const ageAndGenderByFace = await extractAllFacesAndComputeResults(\n parentResults,\n this.input,\n async (faces) => Promise.all(faces.map((face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise)),\n this.extractedFaces,\n );\n return parentResults.map((parentResult, i) => {\n const { age, gender, genderProbability } = ageAndGenderByFace[i];\n return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n });\n }\n\n withFaceExpressions() {\n return new PredictAllFaceExpressionsTask(this, this.input);\n }\n}\n\nexport class PredictSingleAgeAndGenderTask> extends PredictAgeAndGenderTaskBase> | undefined, TSource | undefined> {\n public override async run(): Promise> | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) return undefined;\n const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult(\n parentResult,\n this.input,\n (face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise,\n this.extractedFaces,\n );\n return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n }\n\n withFaceExpressions() {\n return new PredictSingleFaceExpressionsTask(this, this.input);\n }\n}\n\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask>> extends PredictAllAgeAndGenderTask {\n override withFaceExpressions() {\n return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptors() {\n return new ComputeAllFaceDescriptorsTask(this, this.input);\n }\n}\n\nexport class PredictSingleAgeAndGenderWithFaceAlignmentTask>> extends PredictSingleAgeAndGenderTask {\n override withFaceExpressions() {\n return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptor() {\n return new ComputeSingleFaceDescriptorTask(this, this.input);\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderWithFaceAlignmentTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsWithFaceAlignmentTask } from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected parentTask: ComposableTask | Promise,\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n ) {\n super();\n }\n}\n\nexport class ComputeAllFaceDescriptorsTask>> extends ComputeFaceDescriptorsTaskBase[], TSource[]> {\n public override async run(): Promise[]> {\n const parentResults = await this.parentTask;\n const descriptors = await extractAllFacesAndComputeResults(\n parentResults,\n this.input,\n (faces) => Promise.all(faces.map((face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise)),\n null,\n (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n );\n return descriptors.map((descriptor, i) => extendWithFaceDescriptor(parentResults[i], descriptor));\n }\n\n withFaceExpressions() {\n return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withAgeAndGender() {\n return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n}\n\nexport class ComputeSingleFaceDescriptorTask>> extends ComputeFaceDescriptorsTaskBase | undefined, TSource | undefined> {\n public override async run(): Promise | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) return undefined;\n const descriptor = await extractSingleFaceAndComputeResult(\n parentResult,\n this.input,\n (face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise,\n null,\n // eslint-disable-next-line no-shadow, @typescript-eslint/no-shadow\n (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n );\n return extendWithFaceDescriptor(parentResult, descriptor);\n }\n\n withFaceExpressions() {\n return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withAgeAndGender() {\n return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderWithFaceAlignmentTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsWithFaceAlignmentTask } from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected parentTask: ComposableTask | Promise,\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n protected useTinyLandmarkNet: boolean,\n ) {\n super();\n }\n\n protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n return this.useTinyLandmarkNet\n ? nets.faceLandmark68TinyNet\n : nets.faceLandmark68Net;\n }\n}\n\nexport class DetectAllFaceLandmarksTask> extends DetectFaceLandmarksTaskBase[], TSource[]> {\n public override async run(): Promise[]> {\n const parentResults = await this.parentTask;\n const detections = parentResults.map((res) => res.detection);\n const faces: Array = this.input instanceof tf.Tensor\n ? await extractFaceTensors(this.input, detections)\n : await extractFaces(this.input, detections);\n const faceLandmarksByFace = await Promise.all(faces.map((face) => this.landmarkNet.detectLandmarks(face))) as FaceLandmarks68[];\n faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n const result = parentResults\n .filter((_parentResult, i) => faceLandmarksByFace[i])\n .map((parentResult, i) => extendWithFaceLandmarks(parentResult, faceLandmarksByFace[i]));\n return result;\n }\n\n withFaceExpressions() {\n return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withAgeAndGender() {\n return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptors() {\n return new ComputeAllFaceDescriptorsTask(this, this.input);\n }\n}\n\nexport class DetectSingleFaceLandmarksTask> extends DetectFaceLandmarksTaskBase | undefined, TSource | undefined> {\n public override async run(): Promise | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) {\n return undefined;\n }\n const { detection } = parentResult;\n const faces: Array = this.input instanceof tf.Tensor\n ? await extractFaceTensors(this.input, [detection])\n : await extractFaces(this.input, [detection]);\n const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68;\n faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n return extendWithFaceLandmarks(parentResult, landmarks);\n }\n\n withFaceExpressions() {\n return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withAgeAndGender() {\n return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptor() {\n return new ComputeSingleFaceDescriptorTask(this, this.input);\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { TinyYolov2Options } from '../tinyYolov2/index';\nimport { ComposableTask } from './ComposableTask';\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\nimport { FaceDetectionOptions } from './types';\n\nexport class DetectFacesTaskBase extends ComposableTask {\n // eslint-disable-next-line no-unused-vars\n constructor(protected input: TNetInput, protected options: FaceDetectionOptions = new SsdMobilenetv1Options()) {\n super();\n }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase {\n public override async run(): Promise {\n const { input, options } = this;\n let result;\n if (options instanceof TinyFaceDetectorOptions) result = nets.tinyFaceDetector.locateFaces(input, options);\n else if (options instanceof SsdMobilenetv1Options) result = nets.ssdMobilenetv1.locateFaces(input, options);\n else if (options instanceof TinyYolov2Options) result = nets.tinyYolov2.locateFaces(input, options);\n else throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options');\n return result;\n }\n\n private runAndExtendWithFaceDetections(): Promise[]> {\n return new Promise[]>((resolve, reject) => {\n this.run()\n .then((detections) => resolve(detections.map((detection) => extendWithFaceDetection({}, detection))))\n .catch((err) => reject(err));\n });\n }\n\n withFaceLandmarks(useTinyLandmarkNet = false) {\n return new DetectAllFaceLandmarksTask(\n this.runAndExtendWithFaceDetections(),\n this.input,\n useTinyLandmarkNet,\n );\n }\n\n withFaceExpressions() {\n return new PredictAllFaceExpressionsTask(\n this.runAndExtendWithFaceDetections(),\n this.input,\n );\n }\n\n withAgeAndGender() {\n return new PredictAllAgeAndGenderTask(\n this.runAndExtendWithFaceDetections(),\n this.input,\n );\n }\n}\n\nexport class DetectSingleFaceTask extends DetectFacesTaskBase {\n public override async run(): Promise {\n const faceDetections = await new DetectAllFacesTask(this.input, this.options);\n let faceDetectionWithHighestScore = faceDetections[0];\n faceDetections.forEach((faceDetection) => {\n if (faceDetection.score > faceDetectionWithHighestScore.score) faceDetectionWithHighestScore = faceDetection;\n });\n return faceDetectionWithHighestScore;\n }\n\n private runAndExtendWithFaceDetection(): Promise | undefined> {\n // eslint-disable-next-line no-async-promise-executor\n return new Promise | undefined>(async (resolve) => {\n const detection = await this.run();\n resolve(detection ? extendWithFaceDetection<{}>({}, detection) : undefined);\n });\n }\n\n withFaceLandmarks(useTinyLandmarkNet = false) {\n return new DetectSingleFaceLandmarksTask(\n this.runAndExtendWithFaceDetection(),\n this.input,\n useTinyLandmarkNet,\n );\n }\n\n withFaceExpressions() {\n return new PredictSingleFaceExpressionsTask(\n this.runAndExtendWithFaceDetection(),\n this.input,\n );\n }\n\n withAgeAndGender() {\n return new PredictSingleAgeAndGenderTask(\n this.runAndExtendWithFaceDetection(),\n this.input,\n );\n }\n}\n", "import { TNetInput } from '../dom/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\nimport { FaceDetectionOptions } from './types';\n\nexport function detectSingleFace(input: TNetInput, options: FaceDetectionOptions = new SsdMobilenetv1Options()): DetectSingleFaceTask {\n return new DetectSingleFaceTask(input, options);\n}\n\nexport function detectAllFaces(input: TNetInput, options: FaceDetectionOptions = new SsdMobilenetv1Options()): DetectAllFacesTask {\n return new DetectAllFacesTask(input, options);\n}\n", "import { TNetInput } from '../dom/index';\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/index';\nimport { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\nimport { detectAllFaces } from './detectFaces';\n\nexport async function allFacesSsdMobilenetv1(input: TNetInput, minConfidence?: number): Promise>>[]> {\n return detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n .withFaceLandmarks()\n .withFaceDescriptors();\n}\n\nexport async function allFacesTinyYolov2(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise>>[]> {\n return detectAllFaces(input, new TinyYolov2Options(forwardParams))\n .withFaceLandmarks()\n .withFaceDescriptors();\n}\n\nexport const allFaces = allFacesSsdMobilenetv1;\n", "export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\n if (arr1.length !== arr2.length) throw new Error('euclideanDistance: arr1.length !== arr2.length');\n const desc1 = Array.from(arr1);\n const desc2 = Array.from(arr2);\n return Math.sqrt(\n desc1\n .map((val, i) => val - desc2[i])\n .reduce((res, diff) => res + (diff * diff), 0),\n );\n}\n", "import { FaceMatch } from '../classes/FaceMatch';\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\nimport { euclideanDistance } from '../euclideanDistance';\nimport { WithFaceDescriptor } from '../factories/index';\n\nexport class FaceMatcher {\n private _labeledDescriptors: LabeledFaceDescriptors[];\n private _distanceThreshold: number;\n\n constructor(inputs: LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>, distanceThreshold = 0.6) {\n this._distanceThreshold = distanceThreshold;\n const inputArray = Array.isArray(inputs) ? inputs : [inputs];\n if (!inputArray.length) throw new Error('FaceRecognizer.constructor - expected atleast one input');\n let count = 1;\n const createUniqueLabel = () => `person ${count++}`;\n this._labeledDescriptors = inputArray.map((desc) => {\n if (desc instanceof LabeledFaceDescriptors) return desc;\n if (desc instanceof Float32Array) return new LabeledFaceDescriptors(createUniqueLabel(), [desc]);\n if (desc.descriptor && desc.descriptor instanceof Float32Array) return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor]);\n throw new Error('FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>');\n });\n }\n\n public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors; }\n\n public get distanceThreshold(): number { return this._distanceThreshold; }\n\n public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\n return descriptors\n .map((d) => euclideanDistance(d, queryDescriptor))\n .reduce((d1, d2) => d1 + d2, 0) / (descriptors.length || 1);\n }\n\n public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n return this.labeledDescriptors\n .map(({ descriptors, label }) => new FaceMatch(label, this.computeMeanDistance(queryDescriptor, descriptors)))\n .reduce((best, curr) => (best.distance < curr.distance ? best : curr));\n }\n\n public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\n const bestMatch = this.matchDescriptor(queryDescriptor);\n return (bestMatch.distance < this._distanceThreshold) ? bestMatch : new FaceMatch('unknown', bestMatch.distance);\n }\n\n public toJSON(): any {\n return {\n distanceThreshold: this._distanceThreshold,\n labeledDescriptors: this._labeledDescriptors.map((ld) => ld.toJSON()),\n };\n }\n\n public static fromJSON(json: any): FaceMatcher {\n const labeledDescriptors = json.labeledDescriptors.map((ld: any) => LabeledFaceDescriptors.fromJSON(ld));\n return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\n }\n}\n", "import { TinyFaceDetector } from './TinyFaceDetector';\n\nexport * from './TinyFaceDetector';\nexport * from './TinyFaceDetectorOptions';\n\nexport function createTinyFaceDetector(weights: Float32Array) {\n const net = new TinyFaceDetector();\n net.extractWeights(weights);\n return net;\n}\n", "import { Dimensions, IDimensions } from './classes/index';\nimport { FaceDetection } from './classes/FaceDetection';\nimport { FaceLandmarks } from './classes/FaceLandmarks';\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\n\nexport function resizeResults(results: T, dimensions: IDimensions): T {\n const { width, height } = new Dimensions(dimensions.width, dimensions.height);\n\n if (width <= 0 || height <= 0) {\n throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`);\n }\n\n if (Array.isArray(results)) {\n // return results.map(obj => resizeResults(obj, { width, height })) as any as T\n return (results as Array).map((obj) => resizeResults(obj, { width, height } as IDimensions)) as any as T;\n }\n\n if (isWithFaceLandmarks(results)) {\n const resizedDetection = results.detection.forSize(width, height);\n const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height);\n return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks);\n }\n\n if (isWithFaceDetection(results)) {\n return extendWithFaceDetection(results, results.detection.forSize(width, height));\n }\n\n if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\n return (results as any).forSize(width, height);\n }\n\n return results;\n}\n", "import * as tf from '../dist/tfjs.esm';\nimport * as draw from './draw/index';\nimport * as utils from './utils/index';\nimport * as pkg from '../package.json';\n\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index';\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nexport const version = pkg.version as string;\n\n// set webgl defaults\n// if (browser) tf.ENV.set('WEBGL_USE_SHAPES_UNIFORMS', true);\n"], + "mappings": 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Rx{constructor(t){this.ENV=t,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new yI}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let t=this.getSortedBackends();for(let n=0;n{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(t){im(t).forEach(n=>{n.disposeFunc!=null&&n.disposeFunc(this.registry[t])})}initializeBackend(t){let n=this.registryFactory[t];if(n==null)throw new Error(`Cannot initialize backend ${t}, no registration found.`);try{let a=n.factory();if(a&&!(a instanceof Pc)&&typeof a.then=="function"){let r=++this.pendingBackendInitId,s=a.then(i=>r(rthis.registryFactory[n].priority-this.registryFactory[t].priority)}initializeBackendsAndReturnBest(){let t=this.getSortedBackends();for(let n=0;nthis.startScope(a),()=>this.endScope(r),()=>(r=n(),r instanceof Promise&&console.error("Cannot return a Promise inside of 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t.kept=!0,t}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(t){let n={track:[],name:"unnamed scope",id:this.state.nextScopeId++};t&&(n.name=t),this.state.scopeStack.push(n),this.state.activeScope=n}endScope(t){let n=Ov(t),a=new Set(n.map(s=>s.id));for(let s=0;s{!s.kept&&s.scopeId===r.id&&this.track(s)})}gradients(t,n,a,r=!1){if(A(n.length>0,()=>"gradients() received an empty list of xs."),a!=null&&a.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${a.dtype}'`);let s=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",t));A(s instanceof Ce,()=>"The result y returned by f() must be a tensor.");let i=WM(this.state.activeTape,n,s);if(!r&&i.length===0&&n.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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Zz(e,t,n,a,r,s="NHWC",i){let o=e;e.rank===3&&(o=W(e,[1,e.shape[0],e.shape[1],e.shape[2]]));let l=t;l.rank===3&&(l=W(t,[1,t.shape[0],t.shape[1],t.shape[2]])),A(o.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${o.shape}.`),A(l.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${l.shape}.`),A(n.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${n}.`);let u=s==="NHWC"?o.shape[3]:o.shape[1],p=s==="NHWC"?l.shape[3]:l.shape[1];A(u===n[2],()=>`Error in conv2dDerFilter: depth of input ${u}) must match input depth in filter (${n[2]}.`),A(p===n[3],()=>`Error in conv2dDerFilter: depth of dy (${p}) must match output depth for filter (${n[3]}).`),Tn("conv2dDerFilter",r,i);let d={x:o,dy:l},c={strides:a,pad:r,dataFormat:s,dimRoundingMode:i,filterShape:n};return P.runKernel(Om,d,c)}var qw=L({conv2DBackpropFilter_:Zz});function Nf(e,t,n){if(n==null||n==="linear")return e;if(n==="relu")return z(e,Ko(t));throw new 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d=E(e,"x","conv2d","float32"),c=E(t,"filter","conv2d","float32"),h=d,m=!1;d.rank===3&&(m=!0,h=W(d,[1,d.shape[0],d.shape[1],d.shape[2]])),A(h.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${h.rank}.`),A(c.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${c.rank}.`),Tn("fused conv2d",a,i);let f=r==="NHWC"?h.shape[3]:h.shape[1];A(c.shape[2]===f,()=>`Error in conv2d: depth of input (${f}) must match input depth for filter ${c.shape[2]}.`),A(mr(n,s),()=>`Error in conv2D: Either strides or dilations must be 1. 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p=n?l.shape[l.rank-2]:l.shape[l.rank-1],d=a?u.shape[u.rank-1]:u.shape[u.rank-2],c=n?l.shape[l.rank-1]:l.shape[l.rank-2],h=a?u.shape[u.rank-2]:u.shape[u.rank-1],m=l.shape.slice(0,-2),f=u.shape.slice(0,-2),g=ot(m),b=ot(f);A(p===d,()=>`Error in fused matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${l.shape} and ${u.shape} and transposeA=${n} and transposeB=${a} must match.`);let y=ct(l.shape.slice(0,-2),u.shape.slice(0,-2)).concat([c,h]),x=n?W(l,[g,p,c]):W(l,[g,c,p]),v=a?W(u,[b,h,d]):W(u,[b,d,h]),I;r!=null&&(I=E(r,"bias","fused matMul"),[I]=At(I,l),ct(y,I.shape));let N;i!=null&&(N=E(i,"prelu weights","fused matMul"));let C=(D,$)=>{let[S,M,B,U]=$,H=Nf(W(D,B.shape),B,s),q,K;if(!n&&!a?(q=$e(H,M,!1,!0),K=$e(S,H,!0,!1)):!n&&a?(q=$e(H,M,!1,!1),K=$e(H,S,!0,!1)):n&&!a?(q=$e(M,H,!1,!0),K=$e(S,H,!1,!1)):(q=$e(M,H,!0,!0),K=$e(H,S,!0,!0)),r!=null){let Z=Tf(U,H);return[q,K,Z]}else 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t=E(e,"image","RGBToGrayscale"),n=t.rank-1,a=t.shape[n];A(t.rank>=2,()=>`Error in RGBToGrayscale: images must be at least rank 2, but got rank ${t.rank}.`),A(a===3,()=>`Error in RGBToGrayscale: last dimension of an RGB image should be size 3, but got size ${a}.`);let r=t.dtype,s=re(t,"float32"),i=je([.2989,.587,.114]),o;switch(t.rank){case 2:o=Qs("ij,j->i",s,i);break;case 3:o=Qs("ijk,k->ij",s,i);break;case 4:o=Qs("ijkl,l->ijk",s,i);break;case 5:o=Qs("ijklm,m->ijkl",s,i);break;case 6:o=Qs("ijklmn,n->ijklm",s,i);break;default:throw new Error("Not a valid tensor rank.")}return o=Gt(o,-1),re(o,r)}var xW=L({rgbToGrayscale_:yW});function vW(e,t,n=0,a=.5){let r=E(e,"image","rotateWithOffset","float32");A(r.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${r.rank}.`);let s={image:r},i={radians:t,fillValue:n,center:a};return P.runKernel(pp,s,i)}var wW=L({rotateWithOffset_:vW});function gp(e,t,n,a,r,s){a==null&&(a=.5),r==null&&(r=Number.NEGATIVE_INFINITY),s==null&&(s=0);let i=e.shape[0];return n=Math.min(n,i),A(0<=a&&a<=1,()=>`iouThreshold must be in [0, 1], but was '${a}'`),A(e.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${e.rank}'`),A(e.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${e.shape[1]}`),A(t.rank===1,()=>"scores must be a 1D tensor"),A(t.shape[0]===i,()=>`scores has incompatible shape with boxes. 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g=0;gr&&u.push({score:t[g],boxIndex:g,suppressBeginIndex:0});u.sort(II);let p=s>0?-.5/s:0,d=[],c=[];for(;d.length0;){let g=u.pop(),{score:b,boxIndex:y,suppressBeginIndex:x}=g;if(b=x;--I){let N=EW(e,y,d[I]);if(N>=a){v=!0;break}if(g.score=g.score*_W(a,p,N),g.score<=r)break}g.suppressBeginIndex=d.length,v||(g.score===b?(d.push(y),c.push(g.score)):g.score>r&&SW(u,g,II))}let h=d.length,m=n-h;o&&m>0&&(d.push(...new Array(m).fill(0)),c.push(...new Array(m).fill(0)));let f={selectedIndices:d};return i&&(f.selectedScores=c),l&&(f.validOutputs=h),f}function EW(e,t,n){let a=e.subarray(t*4,t*4+4),r=e.subarray(n*4,n*4+4),s=Math.min(a[0],a[2]),i=Math.min(a[1],a[3]),o=Math.max(a[0],a[2]),l=Math.max(a[1],a[3]),u=Math.min(r[0],r[2]),p=Math.min(r[1],r[3]),d=Math.max(r[0],r[2]),c=Math.max(r[1],r[3]),h=(o-s)*(l-i),m=(d-u)*(c-p);if(h<=0||m<=0)return 0;let f=Math.max(s,u),g=Math.max(i,p),b=Math.min(o,d),y=Math.min(l,c),x=Math.max(b-f,0)*Math.max(y-g,0);return x/(h+m-x)}function _W(e,t,n){let 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className(){return"Adadelta"}constructor(e,t,n=null){super(),this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=P.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=P.registeredVariables[t],r=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${t}/accum_grad`,variable:O(()=>je(a).variable(r))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${t}/accum_var`,variable:O(()=>je(a).variable(r))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[n].variable,o=this.accumulatedUpdates[n].variable;O(()=>{let l=X(z(i,this.rho),z(pt(s),1-this.rho)),u=z(he(hn(X(o,this.epsilon)),hn(X(i,this.epsilon))),s),p=X(z(o,this.rho),z(pt(u),1-this.rho));i.assign(l),o.assign(p);let d=X(z(u,-this.learningRate),a);a.assign(d)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Ee(this.accumulatedGrads.map(e=>e.variable)),Ee(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}},Zw=class extends Or{static get className(){return"Adagrad"}constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=P.registeredVariables[t];this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:O(()=>yn(a.shape,this.initialAccumulatorValue).variable(!1))});let r=Array.isArray(e)?e[n].tensor:e[t];if(r==null)return;let s=this.accumulatedGrads[n].variable;O(()=>{let i=X(s,pt(r));s.assign(i);let o=X(z(he(r,hn(X(i,P.backend.epsilon()))),-this.learningRate),a);a.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Ee(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}},Jw=class extends Or{static get className(){return"Adam"}constructor(e,t,n,a=null){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=a,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],O(()=>{this.accBeta1=ve(t).variable(),this.accBeta2=ve(n).variable()}),a==null&&(this.epsilon=P.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);O(()=>{let n=pe(1,this.accBeta1),a=pe(1,this.accBeta2);t.forEach((r,s)=>{let 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f=X(z(he(h,X(hn(m),this.epsilon)),-this.learningRate),i);i.assign(f)}),this.accBeta1.assign(z(this.accBeta1,this.beta1)),this.accBeta2.assign(z(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Ee(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Ee(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),O(()=>{this.accBeta1.assign(Fr(this.beta1,this.iterations_+1)),this.accBeta2.assign(Fr(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)}},Qw=class extends Or{static get className(){return"Adamax"}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=[],O(()=>{this.iteration=ve(0).variable(),this.accBeta1=ve(t).variable()}),a==null&&(this.epsilon=P.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);O(()=>{let n=pe(1,this.accBeta1),a=he(-this.learningRate,X(z(this.iteration,this.decay),1));t.forEach((r,s)=>{let 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this.model.stopTraining}getConfig(){let t=[];for(let n of this.layers){let a={};a.className=n.getClassName(),a.config=n.getConfig(),t.push(a)}return{name:this.name,layers:t}}};Bf.className="Sequential";ne.registerClass(Bf);function y6(e){return new _r(e)}function x6(e){return new Bf(e)}function mC(e){return q2(e)}function v6(e,t){I0.registerCallbackConstructor(e,t)}var Vn=class extends ne.Serializable{getConfig(){return{}}},fC=class extends Vn{apply(e,t=1){return WG(e,t)}};fC.className="elu";ne.registerClass(fC);var gC=class extends Vn{apply(e){return hf(e)}};gC.className="selu";ne.registerClass(gC);var bC=class extends Vn{apply(e){return Ke(e)}};bC.className="relu";ne.registerClass(bC);var yC=class extends Vn{apply(e){return O(()=>fs(6,Ke(e)))}};yC.className="relu6";ne.registerClass(yC);var xC=class extends Vn{apply(e){return e}};xC.className="linear";ne.registerClass(xC);var vC=class extends Vn{apply(e){return ma(e)}};vC.className="sigmoid";ne.registerClass(vC);var wC=class extends Vn{apply(e){return VG(e)}};wC.className="hardSigmoid";ne.registerClass(wC);var kC=class extends Vn{apply(e){return qo(e)}};kC.className="softplus";ne.registerClass(kC);var IC=class extends Vn{apply(e){return BG(e)}};IC.className="softsign";ne.registerClass(IC);var SC=class extends Vn{apply(e){return fi(e)}};SC.className="tanh";ne.registerClass(SC);var _0=class extends Vn{apply(e,t=-1){return ja(e,t)}};_0.className="softmax";ne.registerClass(_0);var NC=class extends Vn{apply(e,t=-1){return sf(e,t)}};NC.className="logSoftmax";ne.registerClass(NC);var TC=class extends Vn{apply(e,t=1){return O(()=>z(ma(z(e,t)),e))}};TC.className="swish";ne.registerClass(TC);var CC=class extends Vn{apply(e){return O(()=>z(e,fi(qo(e))))}};CC.className="mish";ne.registerClass(CC);function bs(e){return e.getClassName()}function kx(e,t={}){return Td(e,ne.SerializationMap.getMap().classNameMap,t,"activation")}function ys(e){if(e==null){let t={};return t.className="linear",t.config={},kx(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},kx(t)}else return e instanceof Vn?e:kx(e)}function A0(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 EC=class extends ne.Serializable{},Fd=class extends EC{constructor(e){super(),A0(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 O(()=>{let t=It([1]);return this.hasL1&&(t=X(t,fe(z(this.l1,Lt(e))))),this.hasL2&&(t=X(t,fe(z(this.l2,Ed(e))))),W(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Fd.className="L1L2";ne.registerClass(Fd);function w6(e){return A0(e),new Fd({l1:e!=null?e.l1:null,l2:0})}function k6(e){return A0(e),new Fd({l2:e!=null?e.l2:null,l1:0})}var QI={l1l2:"L1L2"};function ft(e){return o0(e)}function eS(e,t={}){return Td(e,ne.SerializationMap.getMap().classNameMap,t,"regularizer")}function Nt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in QI?QI[e]:e,config:{}};return eS(t)}else return e instanceof EC?e:eS(e)}var F0=class extends We{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Te(e);let n=Ke(e);return this.maxValue!=null&&(n=an(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};F0.className="ReLU";ne.registerClass(F0);var $0=class extends We{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=Te(e);return fd(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};$0.className="LeakyReLU";ne.registerClass($0);var D0=class extends We{constructor(e){if(super(e==null?{}:e),this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=St(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Nt(e.alphaRegularizer),this.alphaConstraint=Yt(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 V(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=Je(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{let n=Te(e),a=t.mask;if(a!=null){let r=z(pe(On(n.shape),re(a,n.dtype)),ve(-1e9));n=X(n,r)}return this.axis instanceof Array?this.axis.length>1?mn(pe(n,bd(n,this.axis,!0))):this.softmax(n,this.axis[0]):this.softmax(n,this.axis)})}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};O0.className="Softmax";ne.registerClass(O0);function Gl(e,t,n){if(typeof e=="number")return xi(e,t);if(e.length!==t)throw new V(`The ${n} argument must be an integer or tuple of ${t} integers. 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V(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new V(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=P0(e,s);if(r==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=tu.conv2d({x:l,filter:t,strides:a,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=De(l,[0,3,1,2])),l})}function S6(e,t,n,a=[1,1,1],r="valid",s,i){return O(()=>{if(s==null&&(s=Ga()),Rt(s),e.rank!==4&&e.rank!==5)throw new V(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new V(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=_C(e,s);if(r==="causal")throw new ze("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=pw(o,t,a,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Ka(o,n)),s==="channelsFirst"&&(o=De(o,[0,4,1,2,3])),o})}var AC=class FC extends We{constructor(t,n){if(super(n),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",FC.verifyArgs(n),this.rank=t,tn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new ze(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Gl(n.kernelSize,t,"kernelSize"),this.strides=Gl(n.strides==null?1:n.strides,t,"strides"),this.padding=n.padding==null?"valid":n.padding,wa(this.padding),this.dataFormat=n.dataFormat==null?"channelsLast":n.dataFormat,Rt(this.dataFormat),this.activation=ys(n.activation),this.useBias=n.useBias==null?!0:n.useBias,this.biasInitializer=St(n.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Yt(n.biasConstraint),this.biasRegularizer=Nt(n.biasRegularizer),this.activityRegularizer=Nt(n.activityRegularizer),this.dilationRate=Gl(n.dilationRate==null?1:n.dilationRate,t,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new V(`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 V(`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 V(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(t){if(ar("kernelSize"in t,"required key 'kernelSize' not in config"),typeof t.kernelSize!="number"&&!l0(t.kernelSize,"number",1,3))throw new V(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(t.kernelSize)}.`)}getConfig(){let t={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:bs(this.activation),useBias:this.useBias,biasInitializer:_t(this.biasInitializer),biasRegularizer:ft(this.biasRegularizer),activityRegularizer:ft(this.activityRegularizer),biasConstraint:Xt(this.biasConstraint)},n=super.getConfig();return Object.assign(t,n),t}},Vf=class $C extends AC{constructor(t,n){super(t,n),this.kernel=null,$C.verifyArgs(n),this.filters=n.filters,tn(this.filters,"filters"),this.kernelInitializer=St(n.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Yt(n.kernelConstraint),this.kernelRegularizer=Nt(n.kernelRegularizer)}build(t){t=Je(t);let n=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[n]==null)throw new V(`The channel dimension of the input should be defined. 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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 zt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return O(()=>{let n=Te(e);if(n.shape.length!==5)throw new V(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i,o;this.dataFormat==="channelsFirst"?(o=2,s=3,i=4):(o=1,s=2,i=3);let l=a[o],u=a[s],p=a[i],d=this.kernelSize[0],c=this.kernelSize[1],h=this.kernelSize[2],m=this.strides[0],f=this.strides[1],g=this.strides[2],b=rr(l,m,d,this.padding),y=rr(u,f,c,this.padding),x=rr(p,g,h,this.padding),v=[r,b,y,x,this.filters];this.dataFormat!=="channelsLast"&&(n=De(n,[0,2,3,4,1]));let I=cw(n,this.kernel.read(),v,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(I=De(I,[0,4,1,2,3])),this.bias!==null&&(I=Ka(I,this.bias.read(),this.dataFormat)),this.activation!==null&&(I=this.activation.apply(I)),I})}computeOutputShape(e){e=Je(e);let t=e.slice(),n,a,r,s;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3,s=4):(n=4,a=1,r=2,s=3);let i=this.kernelSize[0],o=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],p=this.strides[1],d=this.strides[2];return t[n]=this.filters,t[a]=rr(t[a],u,i,this.padding),t[r]=rr(t[r],p,o,this.padding),t[s]=rr(t[s],d,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};z0.className="Conv3DTranspose";ne.registerClass(z0);var MC=class extends Vf{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new V("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new V("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new V(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=St(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Nt(t.depthwiseRegularizer),this.depthwiseConstraint=Yt(t.depthwiseConstraint),this.pointwiseInitializer=St(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Nt(t.pointwiseRegularizer),this.pointwiseConstraint=Yt(t.pointwiseConstraint)}build(e){if(e=Je(e),e.length{e=Te(e);let n;if(this.rank===1)throw new ze("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=De(e,[0,2,3,1])),n=$s(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Ka(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=De(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=_t(this.depthwiseInitializer),e.pointwiseInitializer=_t(this.pointwiseInitializer),e.depthwiseRegularizer=ft(this.depthwiseRegularizer),e.pointwiseRegularizer=ft(this.pointwiseRegularizer),e.depthwiseConstraint=Xt(this.depthwiseConstraint),e.pointwiseConstraint=Xt(this.pointwiseConstraint),e}};MC.className="SeparableConv";var W0=class extends MC{constructor(e){super(2,e)}};W0.className="SeparableConv2D";ne.registerClass(W0);var B0=class OC extends Vf{constructor(t){super(1,t),OC.verifyArgs(t),this.inputSpec=[{ndim:3}]}getConfig(){let t=super.getConfig();return delete t.rank,delete t.dataFormat,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!l0(t.kernelSize,"number",1,1))throw new V(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(t.kernelSize)}.`)}};B0.className="Conv1D";ne.registerClass(B0);var V0=class extends We{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 O(()=>{if(e=Te(e),this.dataFormat==="channelsLast"){let n=Ph(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Ph(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Ph(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Ph(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}};V0.className="Cropping2D";ne.registerClass(V0);var U0=class extends We{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,Rt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,DG(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 O(()=>{let n=Te(e),a=n.shape;if(this.dataFormat==="channelsFirst"){n=De(n,[0,2,3,1]);let r=this.size[0]*a[2],s=this.size[1]*a[3],i=this.interpolation==="nearest"?Qn.resizeNearestNeighbor(n,[r,s]):Qn.resizeBilinear(n,[r,s]);return De(i,[0,3,1,2])}else{let r=this.size[0]*a[1],s=this.size[1]*a[2];return this.interpolation==="nearest"?Qn.resizeNearestNeighbor(n,[r,s]):Qn.resizeBilinear(n,[r,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},t=super.getConfig();return Object.assign(e,t),e}};U0.className="UpSampling2D";ne.registerClass(U0);function N6(e,t,n=[1,1],a="valid",r,s){return O(()=>{r==null&&(r=Ga()),Rt(r);let i=P0(e,r);if(e.rank!==4)throw new V(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new V(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=_s(i,t,n,a==="same"?"same":"valid","NHWC",s),r==="channelsFirst"&&(i=De(i,[0,3,1,2])),i})}var G0=class extends AC{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=St(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Yt(e.depthwiseConstraint),this.depthwiseRegularizer=Nt(e.depthwiseRegularizer)}build(e){if(e=Je(e),e.length<4)throw new V(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new V(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${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 O(()=>{e=Te(e);let n=N6(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Ka(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=Je(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=Va(t,this.kernelSize[0],this.padding,this.strides[0]),s=Va(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=_t(this.depthwiseInitializer),e.depthwiseRegularizer=ft(this.depthwiseRegularizer),e.depthwiseConstraint=Xt(this.depthwiseRegularizer),e}};G0.className="DepthwiseConv2D";ne.registerClass(G0);function PC(e,t,n,a){if(Array.isArray(e)){if(t!=null||n!=null)throw new V("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 LC(e,t,n,a=!1,r,s,i=!1,o=!1){return O(()=>{let l=t.shape.length;if(l<3)throw new V(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(Ua(2,l));if(t=De(t,u),s!=null)throw new ze("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=re(re(r,"bool"),"float32"),r.rank===l-1&&(r=Gt(r,-1)),r=De(r,u)),a&&(t=ya(t,0),r!=null&&(r=ya(r,0)));let p=[],d,c=n,h=t.shape[0],m=dt(t),f;r!=null&&(f=dt(r));for(let b=0;be(y,c));if(r==null)d=x[0],c=x[1];else{let v=O(()=>{let I=f[b],N=pe(na(I),I),C=X(z(x[0],I),z(c[0],N)),_=c.map((F,D)=>X(z(x[1][D],I),z(F,N)));return{output:C,newStates:_}});d=v.output,c=v.newStates}o&&p.push(d)}let g;return o&&(g=Ft(p,1)),[d,g,c]})}var Pr=class zC extends We{constructor(t){super(t);let n;if(t.cell==null)throw new V("cell property is missing for the constructor of RNN.");if(Array.isArray(t.cell)?n=new jf({cells:t.cell}):n=t.cell,n.stateSize==null)throw new V("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=n,this.returnSequences=t.returnSequences==null?!1:t.returnSequences,this.returnState=t.returnState==null?!1:t.returnState,this.goBackwards=t.goBackwards==null?!1:t.goBackwards,this._stateful=t.stateful==null?!1:t.stateful,this.unroll=t.unroll==null?!1:t.unroll,this.supportsMasking=!0,this.inputSpec=[new zt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Ua(0,t).map(n=>null)}else return this.states_}setStates(t){this.states_=t}computeOutputShape(t){Xx(t)&&(t=t[0]),t=t;let n=this.cell.stateSize;Array.isArray(n)||(n=[n]);let a=n[0],r;if(this.returnSequences?r=[t[0],t[1],a]:r=[t[0],a],this.returnState){let s=[];for(let i of n)s.push([t[0],i]);return[r].concat(s)}else return r}computeMask(t,n){return O(()=>{Array.isArray(n)&&(n=n[0]);let a=this.returnSequences?n:null;if(this.returnState){let r=this.states.map(s=>null);return[a].concat(r)}else return a})}get states(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,n=[];for(let a=0;ai.shape[i.shape.length-1]),s))throw new V(`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 zt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(t,n=!1){O(()=>{if(!this.stateful)throw new Qr("Cannot call resetStates() on an RNN Layer that is not stateful.");let a=this.inputSpec[0].shape[0];if(a==null)throw new V("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(r=>It([a,r])):this.states_=[It([a,this.cell.stateSize])];else if(t==null)Ee(this.states_),this.keptStates!=null&&(Ee(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>It([a,r])):this.states_[0]=It([a,this.cell.stateSize]);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new V(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);n===!0?this.keptStates.push(this.states_.slice()):Ee(this.states_);for(let r=0;rHt(r.clone()))})}apply(t,n){let a=n==null?null:n.initialState,r=n==null?null:n.constants;n==null&&(n={});let s=PC(t,a,r,this.numConstants);t=s.inputs,a=s.initialState,r=s.constants;let i=[],o=[];if(a!=null){n.initialState=a,i=i.concat(a),this.stateSpec=[];for(let l of a)this.stateSpec.push(new zt({shape:l.shape}));o=o.concat(this.stateSpec)}if(r!=null&&(n.constants=r,i=i.concat(r),this.numConstants=r.length),i[0]instanceof Ha){let l=[t].concat(i),u=this.inputSpec.concat(o),p=this.inputSpec;this.inputSpec=u;let d=super.apply(l,n);return this.inputSpec=p,d}else return super.apply(t,n)}call(t,n){return O(()=>{let a=n==null?null:n.mask,r=n==null?null:n.training,s=n==null?null:n.initialState;t=Te(t),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(t));let i=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==i)throw new V(`RNN Layer has ${i} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let o={training:r},l=LC((h,m)=>{let f=this.cell.call([h].concat(m),o);return[f[0],f.slice(1)]},t,s,this.goBackwards,a,null,this.unroll,this.returnSequences),u=l[0],p=l[1],d=l[2];this.stateful&&this.resetStates(d,r);let c=this.returnSequences?p:u;return this.returnState?[c].concat(d):c})}getInitialState(t){return O(()=>{let n=It(t.shape);return n=fe(n,[1,2]),n=Cd(n),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(a=>a>1?jx(n,[1,a]):n):this.cell.stateSize>1?[jx(n,[1,this.cell.stateSize])]:[n]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(t)}getConfig(){let t=super.getConfig(),n={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(n.numConstants=this.numConstants);let a=this.cell.getConfig();return this.getClassName()===zC.className&&(n.cell={className:this.cell.getClassName(),config:a}),Object.assign(Object.assign(Object.assign({},a),t),n)}static fromConfig(t,n,a={}){let r=n.cell,s=Ba(r,a);return new t(Object.assign(n,{cell:s}))}};Pr.className="RNN";ne.registerClass(Pr);var $d=class extends We{},Hf=class extends $d{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,tn(this.units,"units"),this.activation=ys(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=nu([1,gs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=nu([1,gs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Je(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 O(()=>{if(e=e,e.length!==2)throw new V(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let a=t.training==null?!1:t.training;0na(e),rate:this.dropout,training:a,dropoutFunc:this.dropoutFunc})),0na(n),rate:this.recurrentDropout,training:a,dropoutFunc:this.dropoutFunc}));let r,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?r=ur(z(e,s),this.kernel.read()):r=ur(e,this.kernel.read()),this.bias!=null&&(r=Ka(r,this.bias.read())),i!=null&&(n=z(n,i));let o=X(r,ur(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:bs(this.activation),useBias:this.useBias,kernelInitializer:_t(this.kernelInitializer),recurrentInitializer:_t(this.recurrentInitializer),biasInitializer:_t(this.biasInitializer),kernelRegularizer:ft(this.kernelRegularizer),recurrentRegularizer:ft(this.recurrentRegularizer),biasRegularizer:ft(this.biasRegularizer),activityRegularizer:ft(this.activityRegularizer),kernelConstraint:Xt(this.kernelConstraint),recurrentConstraint:Xt(this.recurrentConstraint),biasConstraint:Xt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},e),t)}};Hf.className="SimpleRNNCell";ne.registerClass(Hf);var H0=class extends Pr{constructor(e){e.cell=new Hf(e),super(e)}call(e,t){return O(()=>{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(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)}};H0.className="SimpleRNN";ne.registerClass(H0);var qf=class extends $d{constructor(e){if(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",e.resetAfter)throw new V("GRUCell does not support reset_after parameter set to true.");this.units=e.units,tn(this.units,"units"),this.activation=ys(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=ys(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=nu([1,gs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=nu([1,gs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Je(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 O(()=>{if(e=e,e.length!==2)throw new V(`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],0na(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0na(a),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(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)}};q0.className="GRU";ne.registerClass(q0);var Dd=class extends $d{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,tn(this.units,"units"),this.activation=ys(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=ys(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=nu([1,gs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=nu([1,gs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=Je(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 Ra{apply(i,o){let l=r.apply([s]),u=new Ff().apply([s]),p=r.apply([s*2]);return zI(zI(l,u),p)}},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 O(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new V(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let a=e[1],r=e[2];e=e[0],0na(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0na(a),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,p;0{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(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)}};j0.className="LSTM";ne.registerClass(j0);var jf=class extends $d{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 O(()=>{e=e;let n=e.slice(1),a=[];for(let i of 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a=n.weights.length,r=e.splice(a);for(let s=0;ss!=null?s(t(),n):U2(t(),n),o=()=>_d(i,t,a);return!r||r<=1?Ht(o().clone()):Array(r).fill(void 0).map(o).map(l=>Ht(l.clone()))}var T6=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{if(this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new V("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 O(()=>{let{stateSize:t}=this.cell,n=e.shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)],s=It(r);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){O(()=>{if(!this.stateful)throw new Qr("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 V("If an RNN is stateful, it needs to know its batch size. 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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 l=this.biasInitializer,u=this.filters;o=new(t=class extends Ra{apply(p,d){let c=l.apply([u]),h=On([u]),m=l.apply([u*2]);return u0([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 O(()=>{if(e.length!==3)throw new V(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0na(a),rate:this.dropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,l=(Z,J,ee)=>!J||!J[ee]?Z:z(J[ee],Z),u=l(a,o,0),p=l(a,o,1),d=l(a,o,2),c=l(a,o,3);0na(r),rate:this.recurrentDropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,m=l(r,h,0),f=l(r,h,1),g=l(r,h,2),b=l(r,h,3),y=3,[x,v,I,N]=Pn(this.kernel.read(),i,y),[C,_,F,D]=this.useBias?Pn(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,x,C,this.padding),p=this.inputConv(p,v,_,this.padding),d=this.inputConv(d,I,F,this.padding),c=this.inputConv(c,N,D,this.padding);let[$,S,M,B]=Pn(this.recurrentKernel.read(),i,y);m=this.recurrentConv(m,$),f=this.recurrentConv(f,S),g=this.recurrentConv(g,M),b=this.recurrentConv(b,B);let U=this.recurrentActivation.apply(X(u,m)),H=this.recurrentActivation.apply(X(p,f)),j=X(z(H,s),z(U,this.activation.apply(X(d,g)))),K=z(this.recurrentActivation.apply(X(c,b)),this.activation.apply(j));return[K,K,j]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=T6(e,["units"]),a={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),a)}inputConv(e,t,n,a){let r=$t(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Ka(r,n,this.dataFormat):r}recurrentConv(e,t){return $t(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Kf.className="ConvLSTM2DCell";ne.registerClass(Kf);var K0=class extends WC{constructor(e){let t=new Kf(e);super(Object.assign(Object.assign({},e),{cell:t}))}static fromConfig(e,t){return new e(t)}};K0.className="ConvLSTM2D";ne.registerClass(K0);var Xf=class extends We{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.invokeCallHook(e,t);let n=Te(e);if(0U2(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()}};Xf.className="Dropout";ne.registerClass(Xf);var X0=class extends Xf{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};X0.className="SpatialDropout1D";ne.registerClass(X0);var Y0=class extends We{constructor(e){if(super(e),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,tn(this.units,"units"),this.activation=ys(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Yt(e.kernelConstraint),this.biasConstraint=Yt(e.biasConstraint),this.kernelRegularizer=Nt(e.kernelRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.activityRegularizer=Nt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=Je(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=Je(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return O(()=>{this.invokeCallHook(e,t);let n=Te(e),a=O2(this.activation.getClassName()),r;return a!=null?r=ur(n,this.kernel.read(),a,this.bias?this.bias.read():null):(r=ur(n,this.kernel.read()),this.bias!=null&&(r=Ka(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:bs(this.activation),useBias:this.useBias,kernelInitializer:_t(this.kernelInitializer),biasInitializer:_t(this.biasInitializer),kernelRegularizer:ft(this.kernelRegularizer),biasRegularizer:ft(this.biasRegularizer),activityRegularizer:ft(this.activityRegularizer),kernelConstraint:Xt(this.kernelConstraint),biasConstraint:Xt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Y0.className="Dense";ne.registerClass(Y0);var Z0=class extends We{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=Je(e);for(let t of e.slice(1))if(t==null)throw new V(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). 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s==="max"?i=Dt(e,t,n,o):i=xa(e,t,n,o),r==="channelsFirst"&&(i=De(i,[0,3,1,2])),i})}function BC(e,t,n,a,r,s){return O(()=>{Rt(r),L2(s),wa(a),n==null&&(n=[1,1,1]),a==null&&(a="valid"),r==null&&(r=Ga()),s==null&&(s="max"),e=_C(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=Cw(e,t,n,o):i=Qv(e,t,n,o),r==="channelsFirst"&&(i=De(i,[0,4,1,2,3])),i})}var VC=class extends We{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 V(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(tn(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 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t=Va(t,this.poolSize[0],this.padding,this.strides[0]),n=Va(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 O(()=>(this.invokeCallHook(e,t),this.poolingFunction(Te(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}},x1=class extends UC{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),wa(a),Yf(e,t,n,a,r,"max")}};x1.className="MaxPooling2D";ne.registerClass(x1);var v1=class extends UC{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),wa(a),Yf(e,t,n,a,r,"avg")}};v1.className="AveragePooling2D";ne.registerClass(v1);var GC=class extends We{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 V(`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];tn(this.poolSize,"poolSize"),tn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),wa(this.padding),this.inputSpec=[new zt({ndim:5})]}computeOutputShape(e){e=Je(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=Va(t,this.poolSize[0],this.padding,this.strides[0]),n=Va(n,this.poolSize[1],this.padding,this.strides[1]),a=Va(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 O(()=>(this.invokeCallHook(e,t),this.poolingFunction(Te(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}},w1=class extends GC{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),wa(a),BC(e,t,n,a,r,"max")}};w1.className="MaxPooling3D";ne.registerClass(w1);var k1=class extends GC{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),wa(a),BC(e,t,n,a,r,"avg")}};k1.className="AveragePooling3D";ne.registerClass(k1);var HC=class extends We{constructor(e){super(e),this.inputSpec=[new zt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new 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e({l1:t.l1,l2:t.l2})}};Fd.className="L1L2";ne.registerClass(Fd);function Sj(e){return A0(e),new Fd({l1:e!=null?e.l1:null,l2:0})}function Nj(e){return A0(e),new Fd({l2:e!=null?e.l2:null,l1:0})}var QI={l1l2:"L1L2"};function ft(e){return o0(e)}function eS(e,t={}){return Td(e,ne.SerializationMap.getMap().classNameMap,t,"regularizer")}function Nt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in QI?QI[e]:e,config:{}};return eS(t)}else return e instanceof AC?e:eS(e)}var F0=class extends We{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Te(e);let n=Ke(e);return this.maxValue!=null&&(n=an(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};F0.className="ReLU";ne.registerClass(F0);var $0=class extends We{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=Te(e);return fd(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};$0.className="LeakyReLU";ne.registerClass($0);var D0=class extends We{constructor(e){if(super(e==null?{}:e),this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=St(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Nt(e.alphaRegularizer),this.alphaConstraint=Yt(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 V(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=Je(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{let n=Te(e),a=t.mask;if(a!=null){let r=z(pe(Pn(n.shape),re(a,n.dtype)),xe(-1e9));n=X(n,r)}return this.axis instanceof Array?this.axis.length>1?mn(pe(n,bd(n,this.axis,!0))):this.softmax(n,this.axis[0]):this.softmax(n,this.axis)})}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};O0.className="Softmax";ne.registerClass(O0);function Gl(e,t,n){if(typeof e=="number")return xi(e,t);if(e.length!==t)throw new V(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let a=0;a(Rt(t),t==="channelsFirst"?De(e,[0,2,3,1]):e))}function FC(e,t){return O(()=>(Rt(t),t==="channelsFirst"?De(e,[0,2,3,4,1]):e))}function Tj(e,t,n,a=1,r="valid",s,i=1){return O(()=>{if(s==null&&(s=Ga()),Rt(s),e.shape.length!==3)throw new V(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new V(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new V(`The bias for a conv1dWithBias operation should be 1, but is ${n.shape.length} instead`);if(s==="channelsFirst"&&(e=De(e,[0,2,1])),r==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=Qm(e,t,a,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=Ka(o,n)),o})}function tS(e,t,n,a=[1,1],r="valid",s,i,o=null){return O(()=>{if(s==null&&(s=Ga()),Rt(s),e.rank!==3&&e.rank!==4)throw new V(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new V(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=P0(e,s);if(r==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=tu.conv2d({x:l,filter:t,strides:a,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=De(l,[0,3,1,2])),l})}function Cj(e,t,n,a=[1,1,1],r="valid",s,i){return O(()=>{if(s==null&&(s=Ga()),Rt(s),e.rank!==4&&e.rank!==5)throw new V(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new V(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=FC(e,s);if(r==="causal")throw new ze("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=cw(o,t,a,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Ka(o,n)),s==="channelsFirst"&&(o=De(o,[0,4,1,2,3])),o})}var $C=class DC extends We{constructor(t,n){if(super(n),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",DC.verifyArgs(n),this.rank=t,tn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new ze(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Gl(n.kernelSize,t,"kernelSize"),this.strides=Gl(n.strides==null?1:n.strides,t,"strides"),this.padding=n.padding==null?"valid":n.padding,wa(this.padding),this.dataFormat=n.dataFormat==null?"channelsLast":n.dataFormat,Rt(this.dataFormat),this.activation=xs(n.activation),this.useBias=n.useBias==null?!0:n.useBias,this.biasInitializer=St(n.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Yt(n.biasConstraint),this.biasRegularizer=Nt(n.biasRegularizer),this.activityRegularizer=Nt(n.activityRegularizer),this.dilationRate=Gl(n.dilationRate==null?1:n.dilationRate,t,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new V(`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 V(`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 V(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(t){if(ar("kernelSize"in t,"required key 'kernelSize' not in config"),typeof t.kernelSize!="number"&&!l0(t.kernelSize,"number",1,3))throw new V(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(t.kernelSize)}.`)}getConfig(){let t={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:ys(this.activation),useBias:this.useBias,biasInitializer:_t(this.biasInitializer),biasRegularizer:ft(this.biasRegularizer),activityRegularizer:ft(this.activityRegularizer),biasConstraint:Xt(this.biasConstraint)},n=super.getConfig();return Object.assign(t,n),t}},Uf=class RC extends $C{constructor(t,n){super(t,n),this.kernel=null,RC.verifyArgs(n),this.filters=n.filters,tn(this.filters,"filters"),this.kernelInitializer=St(n.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Yt(n.kernelConstraint),this.kernelRegularizer=Nt(n.kernelRegularizer)}build(t){t=Je(t);let n=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[n]==null)throw new V(`The channel dimension of the input should be defined. Found ${t[n]}`);let a=t[n],r=this.kernelSize.concat([a,this.filters]);this.kernel=this.addWeight("kernel",r,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:{[n]:a}}],this.built=!0}call(t,n){return O(()=>{t=Te(t);let a,r=this.bias==null?null:this.bias.read(),s=O2(this.activation.getClassName());if(s!=null&&this.rank===2)a=tS(t,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)a=Tj(t,this.kernel.read(),r,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)a=tS(t,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)a=Cj(t,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new ze("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(a=this.activation.apply(a))}return a})}computeOutputShape(t){t=Je(t);let n=[],a=this.dataFormat==="channelsLast"?t.slice(1,t.length-1):t.slice(2);for(let s=0;s 0 but got ${JSON.stringify(t.filters)}`)}},Gf=class MC extends Uf{constructor(t){super(2,t),MC.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!l0(t.kernelSize,"number",1,2))throw new V(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(t.kernelSize)}.`)}};Gf.className="Conv2D";ne.registerClass(Gf);var Hf=class OC extends Uf{constructor(t){super(3,t),OC.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!(Array.isArray(t.kernelSize)&&(t.kernelSize.length===1||t.kernelSize.length===3)))throw new V(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(t.kernelSize)}.`)}};Hf.className="Conv3D";ne.registerClass(Hf);var L0=class extends Gf{constructor(e){if(super(e),this.inputSpec=[new zt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new V(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Je(e),e.length!==4)throw new V("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new zt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return O(()=>{let n=Te(e);if(n.shape.length!==4)throw new V(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${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],l=a[i],u=this.kernelSize[0],p=this.kernelSize[1],d=this.strides[0],c=this.strides[1],h=rr(o,d,u,this.padding),m=rr(l,c,p,this.padding),f=[r,h,m,this.filters];this.dataFormat!=="channelsLast"&&(n=De(n,[0,2,3,1]));let g=ef(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=De(g,[0,3,1,2])),this.bias!=null&&(g=Ka(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=Je(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],l=this.strides[1];return t[n]=this.filters,t[a]=rr(t[a],o,s,this.padding),t[r]=rr(t[r],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};L0.className="Conv2DTranspose";ne.registerClass(L0);var z0=class extends Hf{constructor(e){if(super(e),this.inputSpec=[new zt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new V(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Je(e),e.length!==5)throw new V("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new zt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return O(()=>{let n=Te(e);if(n.shape.length!==5)throw new V(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${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 l=a[o],u=a[s],p=a[i],d=this.kernelSize[0],c=this.kernelSize[1],h=this.kernelSize[2],m=this.strides[0],f=this.strides[1],g=this.strides[2],b=rr(l,m,d,this.padding),y=rr(u,f,c,this.padding),x=rr(p,g,h,this.padding),v=[r,b,y,x,this.filters];this.dataFormat!=="channelsLast"&&(n=De(n,[0,2,3,4,1]));let I=dw(n,this.kernel.read(),v,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(I=De(I,[0,4,1,2,3])),this.bias!==null&&(I=Ka(I,this.bias.read(),this.dataFormat)),this.activation!==null&&(I=this.activation.apply(I)),I})}computeOutputShape(e){e=Je(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],l=this.kernelSize[2],u=this.strides[0],p=this.strides[1],d=this.strides[2];return t[n]=this.filters,t[a]=rr(t[a],u,i,this.padding),t[r]=rr(t[r],p,o,this.padding),t[s]=rr(t[s],d,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};z0.className="Conv3DTranspose";ne.registerClass(z0);var PC=class extends Uf{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new V("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new V("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 V(`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=St(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Nt(t.depthwiseRegularizer),this.depthwiseConstraint=Yt(t.depthwiseConstraint),this.pointwiseInitializer=St(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Nt(t.pointwiseRegularizer),this.pointwiseConstraint=Yt(t.pointwiseConstraint)}build(e){if(e=Je(e),e.length{e=Te(e);let n;if(this.rank===1)throw new ze("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=De(e,[0,2,3,1])),n=Ds(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Ka(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=De(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=_t(this.depthwiseInitializer),e.pointwiseInitializer=_t(this.pointwiseInitializer),e.depthwiseRegularizer=ft(this.depthwiseRegularizer),e.pointwiseRegularizer=ft(this.pointwiseRegularizer),e.depthwiseConstraint=Xt(this.depthwiseConstraint),e.pointwiseConstraint=Xt(this.pointwiseConstraint),e}};PC.className="SeparableConv";var W0=class extends PC{constructor(e){super(2,e)}};W0.className="SeparableConv2D";ne.registerClass(W0);var B0=class LC extends Uf{constructor(t){super(1,t),LC.verifyArgs(t),this.inputSpec=[{ndim:3}]}getConfig(){let t=super.getConfig();return delete t.rank,delete t.dataFormat,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!l0(t.kernelSize,"number",1,1))throw new V(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(t.kernelSize)}.`)}};B0.className="Conv1D";ne.registerClass(B0);var V0=class extends We{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 O(()=>{if(e=Te(e),this.dataFormat==="channelsLast"){let n=Ph(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Ph(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Ph(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Ph(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}};V0.className="Cropping2D";ne.registerClass(V0);var U0=class extends We{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,Rt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,MG(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 O(()=>{let n=Te(e),a=n.shape;if(this.dataFormat==="channelsFirst"){n=De(n,[0,2,3,1]);let r=this.size[0]*a[2],s=this.size[1]*a[3],i=this.interpolation==="nearest"?Qn.resizeNearestNeighbor(n,[r,s]):Qn.resizeBilinear(n,[r,s]);return De(i,[0,3,1,2])}else{let r=this.size[0]*a[1],s=this.size[1]*a[2];return this.interpolation==="nearest"?Qn.resizeNearestNeighbor(n,[r,s]):Qn.resizeBilinear(n,[r,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},t=super.getConfig();return Object.assign(e,t),e}};U0.className="UpSampling2D";ne.registerClass(U0);function Ej(e,t,n=[1,1],a="valid",r,s){return O(()=>{r==null&&(r=Ga()),Rt(r);let i=P0(e,r);if(e.rank!==4)throw new V(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new V(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=As(i,t,n,a==="same"?"same":"valid","NHWC",s),r==="channelsFirst"&&(i=De(i,[0,3,1,2])),i})}var G0=class extends $C{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=St(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Yt(e.depthwiseConstraint),this.depthwiseRegularizer=Nt(e.depthwiseRegularizer)}build(e){if(e=Je(e),e.length<4)throw new V(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new V(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${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 O(()=>{e=Te(e);let n=Ej(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Ka(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=Je(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=Va(t,this.kernelSize[0],this.padding,this.strides[0]),s=Va(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=_t(this.depthwiseInitializer),e.depthwiseRegularizer=ft(this.depthwiseRegularizer),e.depthwiseConstraint=Xt(this.depthwiseRegularizer),e}};G0.className="DepthwiseConv2D";ne.registerClass(G0);function zC(e,t,n,a){if(Array.isArray(e)){if(t!=null||n!=null)throw new V("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 WC(e,t,n,a=!1,r,s,i=!1,o=!1){return O(()=>{let l=t.shape.length;if(l<3)throw new V(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(Ua(2,l));if(t=De(t,u),s!=null)throw new ze("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=re(re(r,"bool"),"float32"),r.rank===l-1&&(r=Gt(r,-1)),r=De(r,u)),a&&(t=ya(t,0),r!=null&&(r=ya(r,0)));let p=[],d,c=n,h=t.shape[0],m=dt(t),f;r!=null&&(f=dt(r));for(let b=0;be(y,c));if(r==null)d=x[0],c=x[1];else{let v=O(()=>{let I=f[b],N=pe(na(I),I),C=X(z(x[0],I),z(c[0],N)),_=c.map((F,D)=>X(z(x[1][D],I),z(F,N)));return{output:C,newStates:_}});d=v.output,c=v.newStates}o&&p.push(d)}let g;return o&&(g=Ft(p,1)),[d,g,c]})}var Pr=class BC extends We{constructor(t){super(t);let n;if(t.cell==null)throw new V("cell property is missing for the constructor of RNN.");if(Array.isArray(t.cell)?n=new Kf({cells:t.cell}):n=t.cell,n.stateSize==null)throw new V("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=n,this.returnSequences=t.returnSequences==null?!1:t.returnSequences,this.returnState=t.returnState==null?!1:t.returnState,this.goBackwards=t.goBackwards==null?!1:t.goBackwards,this._stateful=t.stateful==null?!1:t.stateful,this.unroll=t.unroll==null?!1:t.unroll,this.supportsMasking=!0,this.inputSpec=[new zt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Ua(0,t).map(n=>null)}else return this.states_}setStates(t){this.states_=t}computeOutputShape(t){Yx(t)&&(t=t[0]),t=t;let n=this.cell.stateSize;Array.isArray(n)||(n=[n]);let a=n[0],r;if(this.returnSequences?r=[t[0],t[1],a]:r=[t[0],a],this.returnState){let s=[];for(let i of n)s.push([t[0],i]);return[r].concat(s)}else return r}computeMask(t,n){return O(()=>{Array.isArray(n)&&(n=n[0]);let a=this.returnSequences?n:null;if(this.returnState){let r=this.states.map(s=>null);return[a].concat(r)}else return a})}get states(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,n=[];for(let a=0;ai.shape[i.shape.length-1]),s))throw new V(`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 zt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(t,n=!1){O(()=>{if(!this.stateful)throw new Qr("Cannot call resetStates() on an RNN Layer that is not stateful.");let a=this.inputSpec[0].shape[0];if(a==null)throw new V("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(r=>It([a,r])):this.states_=[It([a,this.cell.stateSize])];else if(t==null)Ee(this.states_),this.keptStates!=null&&(Ee(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>It([a,r])):this.states_[0]=It([a,this.cell.stateSize]);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new V(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);n===!0?this.keptStates.push(this.states_.slice()):Ee(this.states_);for(let r=0;rHt(r.clone()))})}apply(t,n){let a=n==null?null:n.initialState,r=n==null?null:n.constants;n==null&&(n={});let s=zC(t,a,r,this.numConstants);t=s.inputs,a=s.initialState,r=s.constants;let i=[],o=[];if(a!=null){n.initialState=a,i=i.concat(a),this.stateSpec=[];for(let l of a)this.stateSpec.push(new zt({shape:l.shape}));o=o.concat(this.stateSpec)}if(r!=null&&(n.constants=r,i=i.concat(r),this.numConstants=r.length),i[0]instanceof Ha){let l=[t].concat(i),u=this.inputSpec.concat(o),p=this.inputSpec;this.inputSpec=u;let d=super.apply(l,n);return this.inputSpec=p,d}else return super.apply(t,n)}call(t,n){return O(()=>{let a=n==null?null:n.mask,r=n==null?null:n.training,s=n==null?null:n.initialState;t=Te(t),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(t));let i=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==i)throw new V(`RNN Layer has ${i} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let o={training:r},l=WC((h,m)=>{let f=this.cell.call([h].concat(m),o);return[f[0],f.slice(1)]},t,s,this.goBackwards,a,null,this.unroll,this.returnSequences),u=l[0],p=l[1],d=l[2];this.stateful&&this.resetStates(d,r);let c=this.returnSequences?p:u;return this.returnState?[c].concat(d):c})}getInitialState(t){return O(()=>{let n=It(t.shape);return n=fe(n,[1,2]),n=Cd(n),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(a=>a>1?Kx(n,[1,a]):n):this.cell.stateSize>1?[Kx(n,[1,this.cell.stateSize])]:[n]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(t)}getConfig(){let t=super.getConfig(),n={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(n.numConstants=this.numConstants);let a=this.cell.getConfig();return this.getClassName()===BC.className&&(n.cell={className:this.cell.getClassName(),config:a}),Object.assign(Object.assign(Object.assign({},a),t),n)}static fromConfig(t,n,a={}){let r=n.cell,s=Ba(r,a);return new t(Object.assign(n,{cell:s}))}};Pr.className="RNN";ne.registerClass(Pr);var $d=class extends We{},jf=class extends $d{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,tn(this.units,"units"),this.activation=xs(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=nu([1,bs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=nu([1,bs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Je(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 O(()=>{if(e=e,e.length!==2)throw new V(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let a=t.training==null?!1:t.training;0na(e),rate:this.dropout,training:a,dropoutFunc:this.dropoutFunc})),0na(n),rate:this.recurrentDropout,training:a,dropoutFunc:this.dropoutFunc}));let r,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?r=ur(z(e,s),this.kernel.read()):r=ur(e,this.kernel.read()),this.bias!=null&&(r=Ka(r,this.bias.read())),i!=null&&(n=z(n,i));let o=X(r,ur(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:ys(this.activation),useBias:this.useBias,kernelInitializer:_t(this.kernelInitializer),recurrentInitializer:_t(this.recurrentInitializer),biasInitializer:_t(this.biasInitializer),kernelRegularizer:ft(this.kernelRegularizer),recurrentRegularizer:ft(this.recurrentRegularizer),biasRegularizer:ft(this.biasRegularizer),activityRegularizer:ft(this.activityRegularizer),kernelConstraint:Xt(this.kernelConstraint),recurrentConstraint:Xt(this.recurrentConstraint),biasConstraint:Xt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},e),t)}};jf.className="SimpleRNNCell";ne.registerClass(jf);var H0=class extends Pr{constructor(e){e.cell=new jf(e),super(e)}call(e,t){return O(()=>{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(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)}};H0.className="SimpleRNN";ne.registerClass(H0);var qf=class extends $d{constructor(e){if(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",e.resetAfter)throw new V("GRUCell does not support reset_after parameter set to true.");this.units=e.units,tn(this.units,"units"),this.activation=xs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=xs(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=nu([1,bs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=nu([1,bs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Je(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 O(()=>{if(e=e,e.length!==2)throw new V(`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],0na(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0na(a),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(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)}};j0.className="GRU";ne.registerClass(j0);var Dd=class extends $d{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,tn(this.units,"units"),this.activation=xs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=xs(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=nu([1,bs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=nu([1,bs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=Je(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 Ra{apply(i,o){let l=r.apply([s]),u=new $f().apply([s]),p=r.apply([s*2]);return zI(zI(l,u),p)}},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 O(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new V(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let a=e[1],r=e[2];e=e[0],0na(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0na(a),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,p;0{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(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)}};q0.className="LSTM";ne.registerClass(q0);var Kf=class extends $d{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 O(()=>{e=e;let n=e.slice(1),a=[];for(let i of 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this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return O(()=>{let{stateSize:t}=this.cell,n=e.shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)],s=It(r);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){O(()=>{if(!this.stateful)throw new Qr("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 V("If an RNN is stateful, it needs to know its batch size. 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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 l=this.biasInitializer,u=this.filters;o=new(t=class extends Ra{apply(p,d){let c=l.apply([u]),h=Pn([u]),m=l.apply([u*2]);return u0([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 O(()=>{if(e.length!==3)throw new V(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0na(a),rate:this.dropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,l=(Z,J,ee)=>!J||!J[ee]?Z:z(J[ee],Z),u=l(a,o,0),p=l(a,o,1),d=l(a,o,2),c=l(a,o,3);0na(r),rate:this.recurrentDropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,m=l(r,h,0),f=l(r,h,1),g=l(r,h,2),b=l(r,h,3),y=3,[x,v,I,N]=Ln(this.kernel.read(),i,y),[C,_,F,D]=this.useBias?Ln(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,x,C,this.padding),p=this.inputConv(p,v,_,this.padding),d=this.inputConv(d,I,F,this.padding),c=this.inputConv(c,N,D,this.padding);let[$,S,M,B]=Ln(this.recurrentKernel.read(),i,y);m=this.recurrentConv(m,$),f=this.recurrentConv(f,S),g=this.recurrentConv(g,M),b=this.recurrentConv(b,B);let U=this.recurrentActivation.apply(X(u,m)),H=this.recurrentActivation.apply(X(p,f)),q=X(z(H,s),z(U,this.activation.apply(X(d,g)))),K=z(this.recurrentActivation.apply(X(c,b)),this.activation.apply(q));return[K,K,q]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=_j(e,["units"]),a={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),a)}inputConv(e,t,n,a){let r=$t(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Ka(r,n,this.dataFormat):r}recurrentConv(e,t){return $t(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Xf.className="ConvLSTM2DCell";ne.registerClass(Xf);var K0=class extends VC{constructor(e){let t=new Xf(e);super(Object.assign(Object.assign({},e),{cell:t}))}static fromConfig(e,t){return new e(t)}};K0.className="ConvLSTM2D";ne.registerClass(K0);var Yf=class extends We{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.invokeCallHook(e,t);let n=Te(e);if(0U2(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()}};Yf.className="Dropout";ne.registerClass(Yf);var X0=class extends Yf{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};X0.className="SpatialDropout1D";ne.registerClass(X0);var Y0=class extends We{constructor(e){if(super(e),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,tn(this.units,"units"),this.activation=xs(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Yt(e.kernelConstraint),this.biasConstraint=Yt(e.biasConstraint),this.kernelRegularizer=Nt(e.kernelRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.activityRegularizer=Nt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=Je(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=Je(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return O(()=>{this.invokeCallHook(e,t);let n=Te(e),a=O2(this.activation.getClassName()),r;return a!=null?r=ur(n,this.kernel.read(),a,this.bias?this.bias.read():null):(r=ur(n,this.kernel.read()),this.bias!=null&&(r=Ka(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:ys(this.activation),useBias:this.useBias,kernelInitializer:_t(this.kernelInitializer),biasInitializer:_t(this.biasInitializer),kernelRegularizer:ft(this.kernelRegularizer),biasRegularizer:ft(this.biasRegularizer),activityRegularizer:ft(this.activityRegularizer),kernelConstraint:Xt(this.kernelConstraint),biasConstraint:Xt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Y0.className="Dense";ne.registerClass(Y0);var Z0=class extends We{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=Je(e);for(let t of e.slice(1))if(t==null)throw new V(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). 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t=Va(t,this.poolSize[0],this.padding,this.strides[0]),n=Va(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 O(()=>(this.invokeCallHook(e,t),this.poolingFunction(Te(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}},x1=class extends HC{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),wa(a),Zf(e,t,n,a,r,"max")}};x1.className="MaxPooling2D";ne.registerClass(x1);var v1=class extends HC{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),wa(a),Zf(e,t,n,a,r,"avg")}};v1.className="AveragePooling2D";ne.registerClass(v1);var jC=class extends We{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 V(`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];tn(this.poolSize,"poolSize"),tn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),wa(this.padding),this.inputSpec=[new zt({ndim:5})]}computeOutputShape(e){e=Je(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=Va(t,this.poolSize[0],this.padding,this.strides[0]),n=Va(n,this.poolSize[1],this.padding,this.strides[1]),a=Va(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 O(()=>(this.invokeCallHook(e,t),this.poolingFunction(Te(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}},w1=class extends jC{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),wa(a),UC(e,t,n,a,r,"max")}};w1.className="MaxPooling3D";ne.registerClass(w1);var k1=class extends jC{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),wa(a),UC(e,t,n,a,r,"avg")}};k1.className="AveragePooling3D";ne.registerClass(k1);var qC=class extends We{constructor(e){super(e),this.inputSpec=[new zt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new 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TypeError(`Node type ${e.op} is not implemented`)}};function pS(e,t,n){let[a,r]=k("fusedOps",e,t,n),s=a==="biasadd",i=!s,o=r==="prelu",l=a==="fusedbatchnorm",u=k("numArgs",e,t,n);if(s){if(o&&u!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!o&&s&&u!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(l)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");let p=k("strides",e,t,n),d=Xh(e,t,n),c=k("dataFormat",e,t,n).toUpperCase(),h=k("dilations",e,t,n),[m,f]=k("args",e,t,n);i&&(f=m,m=void 0);let g=k("leakyreluAlpha",e,t,n);return{stride:p,pad:d,dataFormat:c,dilations:h,biasArg:m,preluArg:f,activationFunc:r,leakyreluAlpha:g}}var Jj=(e,t,n,a=sn)=>{switch(e.op){case"Conv1D":{let r=k("stride",e,t,n),s=k("pad",e,t,n),i=k("dataFormat",e,t,n).toUpperCase(),o=k("dilation",e,t,n);return[a.conv1d(k("x",e,t,n),k("filter",e,t,n),r,s,i,o)]}case"Conv2D":{let 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performance."),console.log(l);for(let p=0;pe.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return ve(this.size(),"int32")}async import(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return this.tensorMap.forEach(a=>a.dispose()),this.tensorMap.clear(),O(()=>{let a=dt(t),r=n.length,s=a.length;w.assert(r===s,()=>`The number of elements doesn't match, keys has ${r} elements, the values has ${s} elements.`);for(let i=0;i{let a=[];for(let r=0;r{switch(e.op){case"HashTable":case"HashTableV2":{let r=a.getHashTableHandleByName(e.name);if(r!=null)return[r];{let s=k("keyDType",e,t,n),i=k("valueDType",e,t,n),o=new a5(s,i);return a.addHashTable(e.name,o),[o.handle]}}case"InitializeTable":case"InitializeTableV2":case"LookupTableImport":case"LookupTableImportV2":{let r=k("tableHandle",e,t,n,a),s=k("keys",e,t,n),i=k("values",e,t,n);return[await a.getHashTableById(r.id).import(s,i)]}case"LookupTableFind":case"LookupTableFindV2":{let r=k("tableHandle",e,t,n,a),s=k("keys",e,t,n),i=k("defaultValue",e,t,n);return[await a.getHashTableById(r.id).find(s,i)]}case"LookupTableSize":case"LookupTableSizeV2":{let r=k("tableHandle",e,t,n,a);return[a.getHashTableById(r.id).tensorSize()]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},s5=(e,t,n,a=sn)=>{switch(e.op){case"ResizeBilinear":{let r=k("images",e,t,n),s=k("size",e,t,n),i=k("alignCorners",e,t,n),o=k("halfPixelCenters",e,t,n);return[a.image.resizeBilinear(r,[s[0],s[1]],i,o)]}case"ResizeNearestNeighbor":{let r=k("images",e,t,n),s=k("size",e,t,n),i=k("alignCorners",e,t,n),o=k("halfPixelCenters",e,t,n);return[a.image.resizeNearestNeighbor(r,[s[0],s[1]],i,o)]}case"CropAndResize":{let r=k("image",e,t,n),s=k("boxes",e,t,n),i=k("boxInd",e,t,n),o=k("cropSize",e,t,n),l=k("method",e,t,n),u=k("extrapolationValue",e,t,n);return[a.image.cropAndResize(r,s,i,o,l,u)]}case"ImageProjectiveTransformV3":{let r=k("images",e,t,n),s=k("transforms",e,t,n),i=k("outputShape",e,t,n),o=k("fillValue",e,t,n),l=k("interpolation",e,t,n),u=k("fillMode",e,t,n);return[a.image.transform(r,s,l.toLowerCase(),u.toLowerCase(),o,i)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},i5=(e,t,n,a=sn)=>{switch(e.op){case"Equal":return[a.equal(k("a",e,t,n),k("b",e,t,n))];case"NotEqual":return[a.notEqual(k("a",e,t,n),k("b",e,t,n))];case"Greater":return[a.greater(k("a",e,t,n),k("b",e,t,n))];case"GreaterEqual":return[a.greaterEqual(k("a",e,t,n),k("b",e,t,n))];case"Less":return[a.less(k("a",e,t,n),k("b",e,t,n))];case"LessEqual":return[a.lessEqual(k("a",e,t,n),k("b",e,t,n))];case"LogicalAnd":return[a.logicalAnd(k("a",e,t,n),k("b",e,t,n))];case"LogicalNot":return[a.logicalNot(k("a",e,t,n))];case"LogicalOr":return[a.logicalOr(k("a",e,t,n),k("b",e,t,n))];case"Select":case"SelectV2":return[a.where(k("condition",e,t,n),k("a",e,t,n),k("b",e,t,n))];case"BitwiseAnd":return[a.bitwiseAnd(k("a",e,t,n),k("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},o5=(e,t,n,a=sn)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[a.matMul(k("a",e,t,n),k("b",e,t,n),k("transposeA",e,t,n),k("transposeB",e,t,n))];case"Einsum":return[a.einsum(k("equation",e,t,n),...k("tensors",e,t,n))];case"Transpose":return[a.transpose(k("x",e,t,n),k("perm",e,t,n))];case"_FusedMatMul":let[r,s]=k("fusedOps",e,t,n),i=r==="biasadd",o=s==="prelu",l=k("numArgs",e,t,n),u=k("leakyreluAlpha",e,t,n);if(i){if(o&&l!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!o&&l!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[p,d]=k("args",e,t,n);return[a.fused.matMul({a:k("a",e,t,n),b:k("b",e,t,n),transposeA:k("transposeA",e,t,n),transposeB:k("transposeB",e,t,n),bias:p,activation:s,preluActivationWeights:d,leakyreluAlpha:u})];case"MatrixBandPart":return[a.linalg.bandPart(k("a",e,t,n),k("numLower",e,t,n),k("numUpper",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},l5=(e,t,n,a=sn)=>{switch(e.op){case"EuclideanNorm":return[a.euclideanNorm(k("x",e,t,n),k("axis",e,t,n),k("keepDims",e,t,n))];case"FusedBatchNorm":case"FusedBatchNormV2":return[a.batchNorm(k("x",e,t,n),k("mean",e,t,n),k("variance",e,t,n),k("offset",e,t,n),k("scale",e,t,n),k("epsilon",e,t,n))];case"FusedBatchNormV3":return[a.batchNorm(k("x",e,t,n),k("mean",e,t,n),k("variance",e,t,n),k("offset",e,t,n),k("scale",e,t,n),k("epsilon",e,t,n))];case"LRN":return[a.localResponseNormalization(k("x",e,t,n),k("radius",e,t,n),k("bias",e,t,n),k("alpha",e,t,n),k("beta",e,t,n))];case"Softmax":return[a.softmax(k("x",e,t,n))];case"LogSoftmax":return[a.logSoftmax(k("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},u5=(e,t,n,a=sn)=>{switch(e.op){case"RaggedGather":{let{outputNestedSplits:r,outputDenseValues:s}=a.raggedGather(k("paramsNestedSplits",e,t,n),k("paramsDenseValues",e,t,n),k("indices",e,t,n),k("outputRaggedRank",e,t,n));return r.concat(s)}case"RaggedRange":{let{rtNestedSplits:r,rtDenseValues:s}=a.raggedRange(k("starts",e,t,n),k("limits",e,t,n),k("splits",e,t,n));return[r,s]}case"RaggedTensorToTensor":return[a.raggedTensorToTensor(k("shape",e,t,n),k("values",e,t,n),k("defaultValue",e,t,n),k("rowPartitionTensors",e,t,n),k("rowPartitionTypes",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},p5=(e,t,n,a=sn)=>{switch(e.op){case"Max":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.max(k("x",e,t,n),o,l)]}case"Mean":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.mean(k("x",e,t,n),o,l)]}case"Min":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.min(k("x",e,t,n),o,l)]}case"Sum":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.sum(k("x",e,t,n),o,l)]}case"All":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.all(k("x",e,t,n),o,l)]}case"Any":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.any(k("x",e,t,n),o,l)]}case"ArgMax":{let o=k("axis",e,t,n);return[a.argMax(k("x",e,t,n),o)]}case"ArgMin":{let o=k("axis",e,t,n);return[a.argMin(k("x",e,t,n),o)]}case"Prod":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.prod(k("x",e,t,n),o,l)]}case"Cumprod":{let o=k("axis",e,t,n),l=k("exclusive",e,t,n),u=k("reverse",e,t,n);return[a.cumprod(k("x",e,t,n),o,l,u)]}case"Cumsum":{let o=k("axis",e,t,n),l=k("exclusive",e,t,n),u=k("reverse",e,t,n);return[a.cumsum(k("x",e,t,n),o,l,u)]}case"Bincount":let r=k("x",e,t,n),s=k("weights",e,t,n),i=k("size",e,t,n);return[a.bincount(r,s,i)];case"DenseBincount":{let o=k("x",e,t,n),l=k("weights",e,t,n),u=k("size",e,t,n),p=k("binaryOutput",e,t,n);return[a.denseBincount(o,l,u,p)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},c5=(e,t,n,a=sn)=>{switch(e.op){case"ConcatV2":case"Concat":{let r=k("n",e,t,n),s=k("axis",e,t,n),i=k("tensors",e,t,n);return i=i.slice(0,r),[a.concat(i,s)]}case"Gather":{let r=k("x",e,t,n),s=k("indices",e,t,n);return[a.gather(r,a.cast(s,"int32"),0)]}case"GatherV2":{let r=k("axis",e,t,n),s=k("batchDims",e,t,n),i=k("x",e,t,n),o=k("indices",e,t,n);return[a.gather(i,a.cast(o,"int32"),r,s)]}case"Reverse":{let r=k("dims",e,t,n),s=[];for(let o=0;o{let r=k("axis",e,t,n),s=k("tensors",e,t,n),i=s[0].shape,o=a.squeeze(s[0]).shape,l=s.map(u=>{let p=w.arraysEqual(u.shape,i);if(!p&&!w.arraysEqual(a.squeeze(u).shape,o))throw new Error("the input tensors shape does not match");return p?u:a.reshape(u,i)});return[a.stack(l,r)]});case"Unpack":{let r=k("axis",e,t,n),s=k("tensor",e,t,n);return a.unstack(s,r)}case"Tile":{let r=k("reps",e,t,n);return[a.tile(k("x",e,t,n),r)]}case"Split":case"SplitV":{let r=k("axis",e,t,n),s=k("numOrSizeSplits",e,t,n),i=k("x",e,t,n);return a.split(i,s,r)}case"ScatterNd":{let r=k("indices",e,t,n),s=k("values",e,t,n),i=k("shape",e,t,n);return[a.scatterND(r,s,i)]}case"GatherNd":{let r=k("x",e,t,n),s=k("indices",e,t,n);return[a.gatherND(r,s)]}case"SparseToDense":{let r=k("sparseIndices",e,t,n),s=k("outputShape",e,t,n),i=k("sparseValues",e,t,n),o=k("defaultValue",e,t,n);return[a.sparseToDense(r,i,s,i.dtype===o.dtype?o:a.cast(o,i.dtype))]}case"TensorScatterUpdate":{let r=k("indices",e,t,n),s=k("values",e,t,n),i=k("tensor",e,t,n);return[a.tensorScatterUpdate(i,r,s)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},d5=(e,t,n,a=sn)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:r,outputValues:s,emptyRowIndicator:i,reverseIndexMap:o}=a.sparse.sparseFillEmptyRows(k("indices",e,t,n),k("values",e,t,n),k("denseShape",e,t,n),k("defaultValue",e,t,n));return[r,s,i,o]}case"SparseReshape":{let{outputIndices:r,outputShape:s}=a.sparse.sparseReshape(k("inputIndices",e,t,n),k("inputShape",e,t,n),k("newShape",e,t,n));return[r,s]}case"SparseSegmentMean":return[a.sparse.sparseSegmentMean(k("data",e,t,n),k("indices",e,t,n),k("segmentIds",e,t,n))];case"SparseSegmentSum":return[a.sparse.sparseSegmentSum(k("data",e,t,n),k("indices",e,t,n),k("segmentIds",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},h5=(e,t,n,a=sn)=>{switch(e.op){case"FFT":return[a.fft(k("x",e,t,n))];case"IFFT":return[a.ifft(k("x",e,t,n))];case"RFFT":return[a.rfft(k("x",e,t,n))];case"IRFFT":return[a.irfft(k("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},m5=(e,t,n,a=sn)=>{switch(e.op){case"StaticRegexReplace":return[a.string.staticRegexReplace(k("input",e,t,n),k("pattern",e,t,n),k("rewrite",e,t,n),k("replaceGlobal",e,t,n))];case"StringNGrams":{let{nGrams:r,nGramsSplits:s}=a.string.stringNGrams(k("data",e,t,n),k("dataSplits",e,t,n),k("separator",e,t,n),k("nGramWidths",e,t,n),k("leftPad",e,t,n),k("rightPad",e,t,n),k("padWidth",e,t,n),k("preserveShortSequences",e,t,n));return[r,s]}case"StringSplit":{let{indices:r,values:s,shape:i}=a.string.stringSplit(k("input",e,t,n),k("delimiter",e,t,n),k("skipEmpty",e,t,n));return[r,s,i]}case"StringToHashBucketFast":return[a.string.stringToHashBucketFast(k("input",e,t,n),k("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},f5=(e,t,n,a=sn)=>{switch(e.op){case"Cast":return[a.cast(k("x",e,t,n),k("dtype",e,t,n))];case"ExpandDims":{let r=k("axis",e,t,n);return[a.expandDims(k("x",e,t,n),r)]}case"Squeeze":{let r=k("axis",e,t,n);return[a.squeeze(k("x",e,t,n),r)]}case"Reshape":return[a.reshape(k("x",e,t,n),k("shape",e,t,n))];case"EnsureShape":return[a.ensureShape(k("x",e,t,n),k("shape",e,t,n))];case"MirrorPad":return[a.mirrorPad(k("x",e,t,n),k("padding",e,t,n),k("mode",e,t,n))];case"PadV2":case"Pad":return[a.pad(k("x",e,t,n),k("padding",e,t,n),k("constantValue",e,t,n))];case"SpaceToBatchND":{let r=k("blockShape",e,t,n),s=k("paddings",e,t,n);return[a.spaceToBatchND(k("x",e,t,n),r,s)]}case"BatchToSpaceND":{let r=k("blockShape",e,t,n),s=k("crops",e,t,n);return[a.batchToSpaceND(k("x",e,t,n),r,s)]}case"DepthToSpace":{let r=k("blockSize",e,t,n),s=k("dataFormat",e,t,n).toUpperCase();return[a.depthToSpace(k("x",e,t,n),r,s)]}case"BroadcastTo":return[a.broadcastTo(k("x",e,t,n),k("shape",e,t,n))];case"BroadcastArgs":return[a.broadcastArgs(k("s0",e,t,n),k("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function cS(e,t,n,a,r=O){let s=((i,o,l)=>{switch(i.category){case"arithmetic":return r(()=>Gj(i,o,l));case"basic_math":return r(()=>Hj(i,o,l));case"control":return Zj(i,o,l);case"convolution":return r(()=>Jj(i,o,l));case"creation":return r(()=>Qj(i,o,l));case"dynamic":return e5(i,o,l);case"evaluation":return r(()=>t5(i,o,l));case"image":return r(()=>s5(i,o,l));case"graph":return r(()=>n5(i,o,l));case"logical":return r(()=>i5(i,o,l));case"matrices":return r(()=>o5(i,o,l));case"normalization":return r(()=>l5(i,o,l));case"ragged":return r(()=>u5(i,o,l));case"reduction":return r(()=>p5(i,o,l));case"slice_join":return r(()=>c5(i,o,l));case"sparse":return r(()=>d5(i,o,l));case"spectral":return r(()=>h5(i,o,l));case"string":return r(()=>m5(i,o,l));case"transformation":return r(()=>f5(i,o,l));case"hash_table":return r5(i,o,l,a);case"custom":let u=sE(i.op);if(u&&u.customExecutor)return u.customExecutor(new Uj(i,o,l));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.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 w.isPromise(s)?s.then(i=>[].concat(i)):[].concat(s)}var dS=class{constructor(e={},t={},n={},a={},r){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=a,this.parseNodeNameCache=r,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;tt.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 hS(e,t,n,a){let r=new Set,s=[],i=null,o=null,l=new Set,u=new Set(Object.keys(e).map(c=>Zn(c)[0]));a=a||[];let p=new Set(a.map(c=>Zn(c.name)[0])),d=[...t];for(;d.length>0;){let c=d.pop();if((ei(c)||I5(c)||S5(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&&!u.has(c.name)&&!p.has(c.name)){if(c.inputs.length===0){s.push(c.name);continue}c.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),d.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function g5(e,t){let{usedNodes:n,inputs:a}=t,r=Object.keys(a).map(g=>Zn(g)[0]).map(g=>e.nodes[g]),s=e.initNodes||[],i=g=>n.has(typeof g=="string"?g:g.name);function o(g){return[...new Map(g.map(b=>[b.name,b])).values()]}let l=o([...r,...e.weights,...s]).filter(i),u=o([...l,...Object.values(e.nodes)]).filter(i),p=new Map(u.map(g=>[g.name,g])),d={};for(let g of u){d[g.name]=d[g.name]||0;for(let b of g.children)i(b)||(d[b.name]=Number.POSITIVE_INFINITY),d[b.name]=(d[b.name]||0)+1}let c=Object.entries(d).filter(([,g])=>g===0).map(([g])=>g),h=[...c];for(;c.length>0;){let g=c.pop(),b=p.get(g);for(let y of b.children.filter(i))--d[y.name]===0&&(h.push(y.name),c.push(y.name))}let m=h.map(g=>p.get(g)),f=b5(m,l);return y5(f,l),f}function b5(e,t){let n=new Map(e.map(s=>[s.name,s])),a=t.map(s=>s.name),r=new Set(a);for(;a.length>0;){let s=a.pop(),i=n.get(s);for(let o of i.children)!n.has(o.name)||r.has(o.name)||(r.add(o.name),a.push(o.name))}return e.filter(s=>r.has(s.name))}var Wh=class extends Error{constructor(e){super(`NodesExecutionOrderError: ${e}`)}};function y5(e,t){let n=new Map(e.map((o,l)=>[o.name,l])),a=new Set(t.map(o=>o.name)),r=o=>a.has(typeof o=="string"?o:o.name),s=new Set(e.map(o=>o.name)),i=o=>s.has(typeof o=="string"?o:o.name);for(let o of e){for(let l of o.children.filter(i)){if(!n.has(l.name))throw new Wh(`Child ${l.name} of node ${o.name} is unreachable.`);if(n.get(o.name)>n.get(l.name))throw new Wh(`Node ${o.name} is scheduled to run after its child ${l.name}.`)}if(!r(o))for(let l of o.inputs){if(!n.has(l.name))throw new Wh(`Input ${l.name} of node ${o.name} is unreachable.`);if(n.get(l.name)>n.get(o.name))throw new Wh(`Node ${o.name} is scheduled to run before its input ${l.name}.`)}}}function x5(e){let t=new Map(e.map((o,l)=>[o.name,l])),n=Number.MAX_SAFE_INTEGER,a=e.map((o,l)=>ei(o)?n:l),r=o=>{let l=a[t.get(o.name)];return l==null?-1:l},s=e.map((o,l)=>o.children.map(r).reduce((u,p)=>Math.max(u,p),a[l])),i=new Map;for(let o=0;ot[a].map(r=>r.id));this._weightIds=[].concat(...n),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let n=t.signatureKey||t.name;return t.defaultOutput?`${n}:${t.defaultOutput}`:n})}get functions(){return Object.keys(this._functions).reduce((t,n)=>(t[n]=this._functions[n].signature,t),{})}constructor(t,n){this.graph=t,this.parent=n,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(a=>{this._functionExecutorMap[a]=new CE(t.functions[a],this)})}getCompilationKey(t,n){let a=t.map(s=>s.name).sort(),r=n.map(s=>s.name).sort();return a.join(this.SEPARATOR)+"--"+r.join(this.SEPARATOR)}compile(t,n){let a=hS(t,n,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:s,syncInputs:i}=a;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${i}]`);if(r.length>0){let u=n.map(d=>d.name),p=Object.keys(t);throw new Error(`Cannot compute the outputs [${u}] from the provided inputs [${p}]. Missing the following inputs: [${r}]`)}let o=g5(this.graph,a),l=x5(o);return{orderedNodes:o,nodeLiveUntilMap:l}}cloneAndKeepTensor(t){if(t==null)return null;let n=t.clone();return Ht(n),n}cloneTensorList(t){return t?t.map(n=>this.cloneAndKeepTensor(n)):null}cloneTensorMap(t){return Object.fromEntries(Object.entries(t).map(([n,a])=>[n,this.cloneTensorList(a)]))}execute(t,n){this.disposeIntermediateTensors(),t=this.mapInputs(t);let a=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),n=this.mapOutputs(n),this.checkOutputs(n);let r=a.map(c=>this.graph.nodes[Zn(c)[0]]),s=n.map(c=>Zn(c)[0]),i=new Set(s),o=s.map(c=>this.graph.nodes[c]);o.length===0&&(o=this._outputs);let l=this.getCompilationKey(r,o),u=this.compiledMap.get(l);u==null&&(u=this.compile(t,o),this.compiledMap.set(l,u));try{this.keepIntermediateTensors=G().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(c){this.keepIntermediateTensors=!1,console.warn(c.message)}let p={},d={};return O(()=>{let c=new dS(this.weightMap,p,d,this.functionExecutorMap,this.parseNodeNameCache),h=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(t).forEach(b=>{let[y,x]=Zn(b,c),v=[];v[x]=t[b],h[y]=v,this.keepIntermediateTensors&&(this.clonedTensorsMap[y]=this.cloneTensorList(v))});let m=this.getFrozenTensorIds(h),{orderedNodes:f,nodeLiveUntilMap:g}=u;for(let b of f){if(h[b.name])continue;let y=cS(b,h,c,this._resourceManager);if(w.isPromise(y))throw new Error(`The execution of the op '${b.op}' returned a promise. 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You can use model.execute() instead.");let x=u.filter(v=>!ei(v)&&!cn(v.name,f,n)).map(v=>v.name);if(x.length>0){let v="";throw c!=null&&(v=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${h}]`),new Error(`Cannot compute the outputs [${x}] from the provided inputs [${s}]. 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p}processChildNodes(t,n,a,r,s,i){t.children.forEach(o=>{let[l]=Nr(o.name,a);s[l]||!i.has(o.name)||(o.op==="Merge"?o.inputNames.some(u=>!!cn(u,r,a))&&(s[l]=!0,n.push({contexts:a.currentContext,node:o})):o.inputNames.every(u=>!!cn(u,r,a))&&(s[l]=!0,n.push({contexts:a.currentContext,node:o})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(n=>n.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(n=>{let a=t[n],[r]=Zn(n),s=this.graph.nodes[r];if(s.attrParams.shape&&s.attrParams.shape.value){let i=s.attrParams.shape.value,o=i.length===a.shape.length&&a.shape.every((l,u)=>i[u]===-1||i[u]===l);w.assert(o,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${i}], but was [${a.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&w.assert(a.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${a.dtype}`)})}mapInputs(t){var n,a;let r={};for(let s in t){let i=(a=(n=this._signature)===null||n===void 0?void 0:n.inputs)===null||a===void 0?void 0:a[s];i!=null?r[i.name]=t[s]:r[s]=t[s]}return r}checkInputs(t){let n=Object.keys(t).filter(a=>{let[r]=Zn(a);return this.graph.nodes[r]==null});if(n.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${n}] that are not part of graph`)}mapOutputs(t){return t.map(n=>{var a,r;let s=(r=(a=this._signature)===null||a===void 0?void 0:a.outputs)===null||r===void 0?void 0:r[n];return s!=null?s.name:n},{})}checkOutputs(t){t.forEach(n=>{let[a]=Zn(n);if(!this.graph.nodes[a])throw new Error(`The output '${n}' is not found in the graph`)})}},N5=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in 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Error(`Cannot find any save handlers for URL '${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)}addStructuredOutputNames(e){if(this.structuredOutputKeys){let t=e instanceof Ce?[e]:e,n={};return t.forEach((a,r)=>n[this.structuredOutputKeys[r]]=a),n}return e}predict(e,t){let n=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(n)}async predictAsync(e,t){let n=await this.executeAsync(e,this.outputNodes);return this.addStructuredOutputNames(n)}normalizeInputs(e){var t;if(!(e instanceof Ce)&&!Array.isArray(e)){let r=(t=this.signature)===null||t===void 0?void 0:t.inputs;if(r!=null)for(let s in r){let i=r[s];i.resourceId!=null&&(e[s]=this.resourceIdToCapturedInput[i.resourceId])}return e}e=Array.isArray(e)?e:[e];let n=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+n!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-n} non-resource placeholders, while there are ${e.length} input tensors provided.`);let a=0;return this.inputNodes.reduce((r,s)=>{var i,o,l;let u=(l=(o=(i=this.signature)===null||i===void 0?void 0:i.inputs)===null||o===void 0?void 0:o[s])===null||l===void 0?void 0:l.resourceId;return u!=null?r[s]=this.resourceIdToCapturedInput[u]:r[s]=e[a++],r},{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return 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rn{constructor(e,t,n=!0){super(),this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},K5=class extends rn{constructor(e,t){super(),this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Ee(e.value)}}},X5=class extends rn{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Wa.getTensorsInContainer(e.value),n=this.transform(e.value),a=Wa.getTensorsInContainer(n);for(let r of t)Wa.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},Y5=class extends rn{constructor(e,t){super(),this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},fS=class extends rn{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Wa.getTensorsInContainer(e.value),n=await this.transform(e.value),a=Wa.getTensorsInContainer(n);for(let r of t)Wa.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},B1=class extends rn{constructor(){super(),this.outputQueue=new DE,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}}},Z5=class extends B1{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=Wa.getTensorsInContainer(e.value),n=this.transform(e.value),a=Wa.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)Wa.isTensorInList(r,a)||r.dispose();return!0}},OE=class extends rn{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}},rs;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(rs||(rs={}));var J5=class extends rn{constructor(e,t=rs.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 rn?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let r=await FE(this.iterators,a);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case rs.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case rs.SHORTEST:return{value:null,done:!0};case rs.LONGEST:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},PE=class extends rn{constructor(e,t){super(),this.upstream=e,this.bufferSize=t,this.buffer=new $E(t)}summary(){return`${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()}},Q5=class extends PE{constructor(e,t,n){super(e,t),this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=D5.alea(n||w.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}}},xp=class{constructor(){this.size=null}batch(e,t=!0){let n=this;w.assert(e>0,()=>`batchSize needs to be positive, but it is - ${e}`);let a;return this.size===1/0||this.size==null?a=this.size:t?a=Math.ceil(this.size/e):a=Math.floor(this.size/e),Yn(async()=>(await n.iterator()).columnMajorBatch(e,t,nK),a)}concatenate(e){let t=this,n;return this.size===1/0||e.size===1/0?n=1/0:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,Yn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===1/0?n=1/0:n=null,Yn(async()=>(await t.iterator()).filter(a=>O(()=>e(a))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return Yn(async()=>(await t.iterator()).map(n=>O(()=>e(n))),this.size)}mapAsync(e){let t=this;return Yn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return Yn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=1/0:n=null,Yn(async()=>{let a=W1(async()=>({value:await t.iterator(),done:!1}));return W5(a.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let a=this,r=$5.alea(t||w.now().toString());return Yn(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,Yn(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};xp.MAX_BUFFER_SIZE=1e4;function Yn(e,t=null){return new class extends xp{constructor(){super(...arguments),this.size=t}async iterator(){return e()}}}function eK(e){return Yn(async()=>ME(e),e.length)}function tK(e){if(!ru(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{let n=await FE(e,a=>{if(a instanceof xp)return{value:a.iterator(),recurse:!1};if(ru(a))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return B5(n,rs.SHORTEST)},t)}function nK(e){if(e===null)return null;let t=e[0];return O5(t)?{value:aK(e),recurse:!1}:{value:null,recurse:!0}}function aK(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Ce?Ft(e):bn(e)}var LE=class extends xp{constructor(e){super(),this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(` -`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},Bh='"',rc=Symbol("out"),gS=Symbol("field"),Vh=Symbol("quote"),Sx=Symbol("quoteafterquote"),bS=Symbol("quoteinquote"),zE=class extends xp{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&&w.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(w.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}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 LE(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(w.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 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;r14||!Number.isInteger(n))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=t.numFramesPerSpectrogram||43,this.sampleRateHz=t.sampleRateHz,this.columnTruncateLength=t.columnTruncateLength||this.fftSize,this.audioTrackConstraints=t.audioTrackConstraints,this.smoothingTimeConstant=t.smoothingTimeConstant||0,this.includeSpectrogram=t.includeSpectrogram!==!1,this.includeWaveform=t.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(t={}){if(!G().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let n=new WE(t);return await n.start(),n}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(a){throw new Error(`Error thrown while initializing video stream: ${a.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let t=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new t,!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 n=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,n.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 t,n,a=await this.getAudioData();if(this.includeSpectrogram){let r=this.flattenQueue(a.freqDataQueue);t=this.getTensorFromAudioDataArray(r,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let r=this.flattenQueue(a.timeDataQueue);n=this.getTensorFromAudioDataArray(r,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:t,waveform:n},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let t=[],n=[],a=0;return new Promise(r=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&r({freqDataQueue:t,timeDataQueue:n}),t.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),n.push(this.timeData.slice())),++a===this.numFrames&&(clearInterval(s),r({freqDataQueue:t,timeDataQueue:n}))},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(t){let n=t[0].length,a=new Float32Array(t.length*n);return t.forEach((r,s)=>a.set(r,s*n)),a}getTensorFromAudioDataArray(t,n){let a=new Float32Array(w.sizeFromShape(n));return a.set(t,a.length-t.length),bn(a,n)}},sK=class BE extends rn{constructor(t,n){if(super(),this.webcamVideoElement=t,this.webcamConfig=n,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=qe([0],"int32"),this.webcamConfig.centerCrop){let a=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,r=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-a)/2,i=(1-r)/2,o=s+a,l=r+i;this.cropBox=Aa([i,s,l,o],[1,4])}else this.cropBox=Aa([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(t,n={}){if(!G().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!t){if(t=document.createElement("video"),!n.resizeWidth||!n.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");t.width=n.resizeWidth,t.height=n.resizeHeight}let a=new BE(t,n);return await a.start(),a}async start(){this.webcamConfig.facingMode&&w.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(t){throw t.message=`Error thrown while initializing video stream: ${t.message}`,t}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(t){console.log(t),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(t=>{this.webcamVideoElement.onloadedmetadata=()=>{t()}})}async next(){if(this.isClosed)return{value:null,done:!0};let t;try{t=Xo.fromPixels(this.webcamVideoElement)}catch(n){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(n)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(t),done:!1}}catch(n){throw new Error(`Error thrown cropping the video: ${n.message}`)}finally{t.dispose()}else return{value:t,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(t){return O(()=>{let n=Gt(re(t,"float32"),0),a;a=Qn.cropAndResize(n,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let r=a.shape;return W(a,r.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},VE=class{},UE=class extends rn{split(e){return new iK(this,e)}},iK=class extends UE{constructor(e,t){super(),this.upstream=e,this.impl=new oK(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},oK=class extends B1{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},lK=class extends rn{decodeUTF8(){return new uK(this)}},uK=class extends UE{constructor(e){super(),this.upstream=e,this.impl=new pK(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},pK=class extends B1{constructor(e){if(super(),this.upstream=e,G().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=QS();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return G().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},GE=class extends lK{constructor(e,t={}){super(),this.file=e,this.options=t,w.assert(e instanceof Uint8Array||(G().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 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implemented`)}},c5=(e,t,n,a=on)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[a.matMul(k("a",e,t,n),k("b",e,t,n),k("transposeA",e,t,n),k("transposeB",e,t,n))];case"Einsum":return[a.einsum(k("equation",e,t,n),...k("tensors",e,t,n))];case"Transpose":return[a.transpose(k("x",e,t,n),k("perm",e,t,n))];case"_FusedMatMul":let[r,s]=k("fusedOps",e,t,n),i=r==="biasadd",o=s==="prelu",l=k("numArgs",e,t,n),u=k("leakyreluAlpha",e,t,n);if(i){if(o&&l!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!o&&l!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[p,d]=k("args",e,t,n);return[a.fused.matMul({a:k("a",e,t,n),b:k("b",e,t,n),transposeA:k("transposeA",e,t,n),transposeB:k("transposeB",e,t,n),bias:p,activation:s,preluActivationWeights:d,leakyreluAlpha:u})];case"MatrixBandPart":return[a.linalg.bandPart(k("a",e,t,n),k("numLower",e,t,n),k("numUpper",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},d5=(e,t,n,a=on)=>{switch(e.op){case"EuclideanNorm":return[a.euclideanNorm(k("x",e,t,n),k("axis",e,t,n),k("keepDims",e,t,n))];case"FusedBatchNorm":case"FusedBatchNormV2":return[a.batchNorm(k("x",e,t,n),k("mean",e,t,n),k("variance",e,t,n),k("offset",e,t,n),k("scale",e,t,n),k("epsilon",e,t,n))];case"FusedBatchNormV3":return[a.batchNorm(k("x",e,t,n),k("mean",e,t,n),k("variance",e,t,n),k("offset",e,t,n),k("scale",e,t,n),k("epsilon",e,t,n))];case"LRN":return[a.localResponseNormalization(k("x",e,t,n),k("radius",e,t,n),k("bias",e,t,n),k("alpha",e,t,n),k("beta",e,t,n))];case"Softmax":return[a.softmax(k("x",e,t,n))];case"LogSoftmax":return[a.logSoftmax(k("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not 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s=((i,o,l)=>{switch(i.category){case"arithmetic":return r(()=>Kq(i,o,l));case"basic_math":return r(()=>Xq(i,o,l));case"control":return t5(i,o,l);case"convolution":return r(()=>n5(i,o,l));case"creation":return r(()=>a5(i,o,l));case"dynamic":return r5(i,o,l);case"evaluation":return r(()=>s5(i,o,l));case"image":return r(()=>u5(i,o,l));case"graph":return r(()=>i5(i,o,l));case"logical":return r(()=>p5(i,o,l));case"matrices":return r(()=>c5(i,o,l));case"normalization":return r(()=>d5(i,o,l));case"ragged":return r(()=>h5(i,o,l));case"reduction":return r(()=>m5(i,o,l));case"slice_join":return r(()=>f5(i,o,l));case"sparse":return r(()=>g5(i,o,l));case"spectral":return r(()=>b5(i,o,l));case"string":return r(()=>y5(i,o,l));case"transformation":return r(()=>x5(i,o,l));case"hash_table":return l5(i,o,l,a);case"custom":let u=oE(i.op);if(u&&u.customExecutor)return u.customExecutor(new qq(i,o,l));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. 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t=0;tt.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 hS(e,t,n,a){let r=new Set,s=[],i=null,o=null,l=new Set,u=new Set(Object.keys(e).map(c=>Zn(c)[0]));a=a||[];let p=new Set(a.map(c=>Zn(c.name)[0])),d=[...t];for(;d.length>0;){let c=d.pop();if((ti(c)||C5(c)||E5(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&&!u.has(c.name)&&!p.has(c.name)){if(c.inputs.length===0){s.push(c.name);continue}c.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),d.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function v5(e,t){let{usedNodes:n,inputs:a}=t,r=Object.keys(a).map(g=>Zn(g)[0]).map(g=>e.nodes[g]),s=e.initNodes||[],i=g=>n.has(typeof g=="string"?g:g.name);function o(g){return[...new Map(g.map(b=>[b.name,b])).values()]}let l=o([...r,...e.weights,...s]).filter(i),u=o([...l,...Object.values(e.nodes)]).filter(i),p=new Map(u.map(g=>[g.name,g])),d={};for(let g of u){d[g.name]=d[g.name]||0;for(let b of g.children)i(b)||(d[b.name]=Number.POSITIVE_INFINITY),d[b.name]=(d[b.name]||0)+1}let c=Object.entries(d).filter(([,g])=>g===0).map(([g])=>g),h=[...c];for(;c.length>0;){let g=c.pop(),b=p.get(g);for(let y of b.children.filter(i))--d[y.name]===0&&(h.push(y.name),c.push(y.name))}let m=h.map(g=>p.get(g)),f=w5(m,l);return k5(f,l),f}function w5(e,t){let n=new Map(e.map(s=>[s.name,s])),a=t.map(s=>s.name),r=new Set(a);for(;a.length>0;){let s=a.pop(),i=n.get(s);for(let o of i.children)!n.has(o.name)||r.has(o.name)||(r.add(o.name),a.push(o.name))}return e.filter(s=>r.has(s.name))}var Wh=class extends Error{constructor(e){super(`NodesExecutionOrderError: ${e}`)}};function k5(e,t){let n=new Map(e.map((o,l)=>[o.name,l])),a=new Set(t.map(o=>o.name)),r=o=>a.has(typeof o=="string"?o:o.name),s=new Set(e.map(o=>o.name)),i=o=>s.has(typeof o=="string"?o:o.name);for(let o of e){for(let l of o.children.filter(i)){if(!n.has(l.name))throw new Wh(`Child ${l.name} of node ${o.name} is unreachable.`);if(n.get(o.name)>n.get(l.name))throw new Wh(`Node ${o.name} is scheduled to run after its child ${l.name}.`)}if(!r(o))for(let l of o.inputs){if(!n.has(l.name))throw new Wh(`Input ${l.name} of node ${o.name} is unreachable.`);if(n.get(l.name)>n.get(o.name))throw new Wh(`Node ${o.name} is scheduled to run before its input ${l.name}.`)}}}function I5(e){let t=new Map(e.map((o,l)=>[o.name,l])),n=Number.MAX_SAFE_INTEGER,a=e.map((o,l)=>ti(o)?n:l),r=o=>{let l=a[t.get(o.name)];return l==null?-1:l},s=e.map((o,l)=>o.children.map(r).reduce((u,p)=>Math.max(u,p),a[l])),i=new Map;for(let o=0;ot[a].map(r=>r.id));this._weightIds=[].concat(...n),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let n=t.signatureKey||t.name;return t.defaultOutput?`${n}:${t.defaultOutput}`:n})}get functions(){return Object.keys(this._functions).reduce((t,n)=>(t[n]=this._functions[n].signature,t),{})}constructor(t,n){this.graph=t,this.parent=n,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(a=>{this._functionExecutorMap[a]=new _E(t.functions[a],this)})}getCompilationKey(t,n){let a=t.map(s=>s.name).sort(),r=n.map(s=>s.name).sort();return a.join(this.SEPARATOR)+"--"+r.join(this.SEPARATOR)}compile(t,n){let a=hS(t,n,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:s,syncInputs:i}=a;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. 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Missing the following inputs: [${r}]`)}let o=v5(this.graph,a),l=I5(o);return{orderedNodes:o,nodeLiveUntilMap:l}}cloneAndKeepTensor(t){if(t==null)return null;let n=t.clone();return Ht(n),n}cloneTensorList(t){return t?t.map(n=>this.cloneAndKeepTensor(n)):null}cloneTensorMap(t){return Object.fromEntries(Object.entries(t).map(([n,a])=>[n,this.cloneTensorList(a)]))}execute(t,n){this.disposeIntermediateTensors(),t=this.mapInputs(t);let a=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),n=this.mapOutputs(n),this.checkOutputs(n);let r=a.map(c=>this.graph.nodes[Zn(c)[0]]),s=n.map(c=>Zn(c)[0]),i=new Set(s),o=s.map(c=>this.graph.nodes[c]);o.length===0&&(o=this._outputs);let l=this.getCompilationKey(r,o),u=this.compiledMap.get(l);u==null&&(u=this.compile(t,o),this.compiledMap.set(l,u));try{this.keepIntermediateTensors=G().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(c){this.keepIntermediateTensors=!1,console.warn(c.message)}let p={},d={};return O(()=>{let c=new dS(this.weightMap,p,d,this.functionExecutorMap,this.parseNodeNameCache),h=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(t).forEach(b=>{let[y,x]=Zn(b,c),v=[];v[x]=t[b],h[y]=v,this.keepIntermediateTensors&&(this.clonedTensorsMap[y]=this.cloneTensorList(v))});let m=this.getFrozenTensorIds(h),{orderedNodes:f,nodeLiveUntilMap:g}=u;for(let b of f){if(h[b.name])continue;let y=cS(b,h,c,this._resourceManager);if(w.isPromise(y))throw new Error(`The execution of the op '${b.op}' returned a promise. 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p}processChildNodes(t,n,a,r,s,i){t.children.forEach(o=>{let[l]=Tr(o.name,a);s[l]||!i.has(o.name)||(o.op==="Merge"?o.inputNames.some(u=>!!dn(u,r,a))&&(s[l]=!0,n.push({contexts:a.currentContext,node:o})):o.inputNames.every(u=>!!dn(u,r,a))&&(s[l]=!0,n.push({contexts:a.currentContext,node:o})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(n=>n.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(n=>{let a=t[n],[r]=Zn(n),s=this.graph.nodes[r];if(s.attrParams.shape&&s.attrParams.shape.value){let i=s.attrParams.shape.value,o=i.length===a.shape.length&&a.shape.every((l,u)=>i[u]===-1||i[u]===l);w.assert(o,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${i}], but was [${a.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&w.assert(a.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${a.dtype}`)})}mapInputs(t){var n,a;let r={};for(let s in t){let i=(a=(n=this._signature)===null||n===void 0?void 0:n.inputs)===null||a===void 0?void 0:a[s];i!=null?r[i.name]=t[s]:r[s]=t[s]}return r}checkInputs(t){let n=Object.keys(t).filter(a=>{let[r]=Zn(a);return this.graph.nodes[r]==null});if(n.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${n}] that are not part of graph`)}mapOutputs(t){return t.map(n=>{var a,r;let s=(r=(a=this._signature)===null||a===void 0?void 0:a.outputs)===null||r===void 0?void 0:r[n];return s!=null?s.name:n},{})}checkOutputs(t){t.forEach(n=>{let[a]=Zn(n);if(!this.graph.nodes[a])throw new Error(`The output '${n}' is not found in the graph`)})}},_5=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in 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n=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+n!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-n} non-resource placeholders, while there are ${e.length} input tensors provided.`);let a=0;return this.inputNodes.reduce((r,s)=>{var i,o,l;let u=(l=(o=(i=this.signature)===null||i===void 0?void 0:i.inputs)===null||o===void 0?void 0:o[s])===null||l===void 0?void 0:l.resourceId;return u!=null?r[s]=this.resourceIdToCapturedInput[u]:r[s]=e[a++],r},{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return 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sn{constructor(e,t,n=!0){super(),this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},J5=class extends sn{constructor(e,t){super(),this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Ee(e.value)}}},Q5=class extends sn{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Wa.getTensorsInContainer(e.value),n=this.transform(e.value),a=Wa.getTensorsInContainer(n);for(let r of t)Wa.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},e8=class extends sn{constructor(e,t){super(),this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},fS=class extends sn{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Wa.getTensorsInContainer(e.value),n=await this.transform(e.value),a=Wa.getTensorsInContainer(n);for(let r of t)Wa.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},B1=class extends sn{constructor(){super(),this.outputQueue=new ME,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}}},t8=class extends B1{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=Wa.getTensorsInContainer(e.value),n=this.transform(e.value),a=Wa.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)Wa.isTensorInList(r,a)||r.dispose();return!0}},LE=class extends sn{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}},rs;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(rs||(rs={}));var n8=class extends sn{constructor(e,t=rs.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 sn?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let r=await DE(this.iterators,a);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case rs.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case rs.SHORTEST:return{value:null,done:!0};case rs.LONGEST:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},zE=class extends sn{constructor(e,t){super(),this.upstream=e,this.bufferSize=t,this.buffer=new RE(t)}summary(){return`${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()}},a8=class extends zE{constructor(e,t,n){super(e,t),this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=P5.alea(n||w.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}}},xp=class{constructor(){this.size=null}batch(e,t=!0){let n=this;w.assert(e>0,()=>`batchSize needs to be positive, but it is + ${e}`);let a;return this.size===1/0||this.size==null?a=this.size:t?a=Math.ceil(this.size/e):a=Math.floor(this.size/e),Yn(async()=>(await n.iterator()).columnMajorBatch(e,t,i8),a)}concatenate(e){let t=this,n;return this.size===1/0||e.size===1/0?n=1/0:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,Yn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===1/0?n=1/0:n=null,Yn(async()=>(await t.iterator()).filter(a=>O(()=>e(a))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return Yn(async()=>(await t.iterator()).map(n=>O(()=>e(n))),this.size)}mapAsync(e){let t=this;return Yn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return Yn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=1/0:n=null,Yn(async()=>{let a=W1(async()=>({value:await t.iterator(),done:!1}));return G5(a.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let a=this,r=O5.alea(t||w.now().toString());return Yn(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,Yn(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};xp.MAX_BUFFER_SIZE=1e4;function Yn(e,t=null){return new class extends xp{constructor(){super(...arguments),this.size=t}async iterator(){return e()}}}function r8(e){return Yn(async()=>PE(e),e.length)}function s8(e){if(!ru(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{let n=await DE(e,a=>{if(a instanceof xp)return{value:a.iterator(),recurse:!1};if(ru(a))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return H5(n,rs.SHORTEST)},t)}function i8(e){if(e===null)return null;let t=e[0];return W5(t)?{value:o8(e),recurse:!1}:{value:null,recurse:!0}}function o8(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Ce?Ft(e):bn(e)}var WE=class extends xp{constructor(e){super(),this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(` +`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},Bh='"',rc=Symbol("out"),gS=Symbol("field"),Vh=Symbol("quote"),Nx=Symbol("quoteafterquote"),bS=Symbol("quoteinquote"),BE=class extends xp{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&&w.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(w.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}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 WE(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(w.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 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;r14||!Number.isInteger(n))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=t.numFramesPerSpectrogram||43,this.sampleRateHz=t.sampleRateHz,this.columnTruncateLength=t.columnTruncateLength||this.fftSize,this.audioTrackConstraints=t.audioTrackConstraints,this.smoothingTimeConstant=t.smoothingTimeConstant||0,this.includeSpectrogram=t.includeSpectrogram!==!1,this.includeWaveform=t.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(t={}){if(!G().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let n=new VE(t);return await n.start(),n}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(a){throw new Error(`Error thrown while initializing video stream: ${a.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let t=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new t,!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 n=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,n.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 t,n,a=await this.getAudioData();if(this.includeSpectrogram){let r=this.flattenQueue(a.freqDataQueue);t=this.getTensorFromAudioDataArray(r,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let r=this.flattenQueue(a.timeDataQueue);n=this.getTensorFromAudioDataArray(r,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:t,waveform:n},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let t=[],n=[],a=0;return new Promise(r=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&r({freqDataQueue:t,timeDataQueue:n}),t.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),n.push(this.timeData.slice())),++a===this.numFrames&&(clearInterval(s),r({freqDataQueue:t,timeDataQueue:n}))},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(t){let n=t[0].length,a=new Float32Array(t.length*n);return t.forEach((r,s)=>a.set(r,s*n)),a}getTensorFromAudioDataArray(t,n){let a=new Float32Array(w.sizeFromShape(n));return a.set(t,a.length-t.length),bn(a,n)}},u8=class UE extends sn{constructor(t,n){if(super(),this.webcamVideoElement=t,this.webcamConfig=n,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=je([0],"int32"),this.webcamConfig.centerCrop){let a=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,r=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-a)/2,i=(1-r)/2,o=s+a,l=r+i;this.cropBox=Aa([i,s,l,o],[1,4])}else this.cropBox=Aa([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(t,n={}){if(!G().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!t){if(t=document.createElement("video"),!n.resizeWidth||!n.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");t.width=n.resizeWidth,t.height=n.resizeHeight}let a=new UE(t,n);return await a.start(),a}async start(){this.webcamConfig.facingMode&&w.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(t){throw t.message=`Error thrown while initializing video stream: ${t.message}`,t}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(t){console.log(t),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(t=>{this.webcamVideoElement.onloadedmetadata=()=>{t()}})}async next(){if(this.isClosed)return{value:null,done:!0};let t;try{t=Xo.fromPixels(this.webcamVideoElement)}catch(n){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(n)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(t),done:!1}}catch(n){throw new Error(`Error thrown cropping the video: ${n.message}`)}finally{t.dispose()}else return{value:t,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(t){return O(()=>{let n=Gt(re(t,"float32"),0),a;a=Qn.cropAndResize(n,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let r=a.shape;return W(a,r.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},GE=class{},HE=class extends sn{split(e){return new p8(this,e)}},p8=class extends HE{constructor(e,t){super(),this.upstream=e,this.impl=new c8(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},c8=class extends B1{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},d8=class extends sn{decodeUTF8(){return new h8(this)}},h8=class extends HE{constructor(e){super(),this.upstream=e,this.impl=new m8(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},m8=class extends B1{constructor(e){if(super(),this.upstream=e,G().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=QS();this.decoder=new 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p=T.computePool3DInfo(s.shape,i,o,1,l,u),d=p.strideDepth,c=p.strideHeight,h=p.strideWidth,m=p.filterDepth,f=p.filterHeight,g=p.filterWidth,b=p.dilationDepth,y=p.dilationHeight,x=p.dilationWidth,v=p.effectiveFilterDepth,I=p.effectiveFilterHeight,N=p.effectiveFilterWidth,C=v-1-p.padInfo.front,_=N-1-p.padInfo.left,F=I-1-p.padInfo.top,D=Pe(s.shape,"float32"),$=1/(m*f*g),S=n.bufferSync(r);for(let M=0;M=p.outDepth||Math.floor(te)!==te))for(let se=0;se=p.outHeight||Math.floor(ie)!==ie))for(let xe=0;xe=p.outWidth||Math.floor(ue)!==ue)continue;let ye=S.get(M,te,ie,ue,B);ee+=ye}}}D.set(ee*$,M,U,H,j,B)}return n.makeTensorInfo(D.shape,D.dtype,D.values)}var vX={kernelName:Wc,backendName:"cpu",kernelFunc:xX};function wX(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;ge([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,p=T.computePool2DInfo(i.shape,o,l,1,u),d=p.strideHeight,c=p.strideWidth,h=p.filterHeight,m=p.filterWidth,f=p.dilationHeight,g=p.dilationWidth,b=p.effectiveFilterHeight,y=p.effectiveFilterWidth,x=y-1-p.padInfo.left,v=b-1-p.padInfo.top,I=Pe(i.shape,"float32"),N=1/(h*m),C=n.data.get(r.dataId).values,_=Pe(r.shape,"float32",C);for(let F=0;F=p.outHeight||Math.floor(j)!==j))for(let K=0;K=p.outWidth||Math.floor(Z)!==Z)continue;let J=_.get(F,j,Z,D);U+=J}}I.set(U*N,F,$,S,D)}return n.makeTensorInfo(I.shape,I.dtype,I.values)}var kX={kernelName:zc,backendName:"cpu",kernelFunc:wX};function IX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,scale:s,offset:i,mean:o,variance:l}=t;w.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient 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o=s.reduce((b,y)=>b*y),l=T.getReshaped(r.shape,s,o),u=T.getPermuted(l.length,s.length),p=T.getReshapedPermuted(r.shape,s,o),d=T.getSliceBeginCoords(i,s.length),c=T.getSliceSize(p,i,s.length),h=xt({inputs:{x:r},backend:n,attrs:{shape:l}}),m=Bn({inputs:{x:h},backend:n,attrs:{perm:u}}),f=xt({inputs:{x:m},backend:n,attrs:{shape:p}}),g=wi({inputs:{x:f},backend:n,attrs:{begin:d,size:c}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),g}var TX={kernelName:fu,backendName:"cpu",kernelFunc:NX};function CX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.data.get(r.dataId).values,l=n.data.get(s.dataId).values,u=H1(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var EX={kernelName:gu,backendName:"cpu",kernelFunc:CX};function _X(e){let{inputs:t,backend:n}=e,{s0:a,s1:r}=t,s=n.data.get(a.dataId).values,i=n.data.get(r.dataId).values,o=T.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var AX={kernelName:Bc,backendName:"cpu",kernelFunc:_X},FX=lt(Ss,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e{let{x:t}=e.inputs,n=e.backend,a=new Float32Array(w.sizeFromShape(t.shape)),r=n.data.get(t.dataId),s=r.complexTensorInfos.real,i=r.complexTensorInfos.imag,o=n.data.get(s.dataId).values,l=n.data.get(i.dataId).values;for(let u=0;uf.shape);T.assertParamsConsistent(i,s);let o=T.computeOutShape(t.map(f=>f.shape),s);if(w.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(f=>w.sizeFromShape(f.shape)>0);if(l.length===1)return dr({inputs:{x:l[0]},backend:n});if(l[0].dtype==="complex64"){let f=l.map(v=>vi({inputs:{input:v},backend:n})),g=l.map(v=>iu({inputs:{input:v},backend:n})),b=ou({inputs:f,backend:n,attrs:{axis:s}}),y=ou({inputs:g,backend:n,attrs:{axis:s}}),x=Jn({inputs:{real:b,imag:y},backend:n});return f.forEach(v=>n.disposeIntermediateTensorInfo(v)),g.forEach(v=>n.disposeIntermediateTensorInfo(v)),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(y),x}let u=l.map(f=>{let g=[-1,w.sizeFromShape(f.shape.slice(s))];return xt({inputs:{x:f},backend:n,attrs:{shape:g}})}),p=u.map(f=>({vals:n.data.get(f.dataId).values,shape:f.shape}));o=T.computeOutShape(u.map(f=>f.shape),1);let d=u[0].shape[0]===1,c=q1(p,o,t[0].dtype,d),h=T.computeOutShape(l.map(f=>f.shape),s),m=n.makeTensorInfo(h,t[0].dtype,c);return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var OX={kernelName:yu,backendName:"cpu",kernelFunc:ou};function G_(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=a;ge([r,s],"conv2d");let d=T.convertConv2DDataFormat(l),c=T.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,d),h=c.filterHeight,m=c.filterWidth,f=c.dilationHeight,g=c.dilationWidth,b=c.padInfo.left,y=c.padInfo.top,x=c.dataFormat==="channelsLast",v=new Wt(c.outShape,r.dtype),I=w.computeStrides(r.shape),N=w.computeStrides(s.shape),C=I[0],_=x?I[1]:I[2],F=x?I[2]:1,D=x?1:I[1],$=v.strides[0],S=x?v.strides[1]:v.strides[2],M=x?v.strides[2]:1,B=x?1:v.strides[1],U=n.data.get(r.dataId).values,H=n.data.get(s.dataId).values,j=v.values;for(let K=0;K=c.inHeight)continue;let xe=se*N[0],ue=Z+ie*_;for(let ye=0;ye=c.inWidth)continue;let mt=xe+Le*N[1],st=ue+Ge*F,at=mt;for(let rt=0;rt=u.inDepth)continue;let K=H*F[0],Z=$+j*_[1];for(let J=0;J=u.inHeight)continue;let ie=K+te*F[1],xe=Z+se*_[2];for(let ue=0;ue=u.inWidth)continue;let Ge=ie+Se*F[2],mt=xe+Le*u.inChannels,st=Ge;for(let at=0;atMath.cos(e)),XX={kernelName:Vi,backendName:"cpu",kernelFunc:KX},YX=lt(Ui,e=>Math.cosh(e)),ZX={kernelName:Ui,backendName:"cpu",kernelFunc:YX};function JX(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,[p,d,c,h]=r.shape,m=s.shape[0],[f,g]=o,b=Pe([m,f,g,h],"float32"),y=n.data.get(s.dataId).values,x=n.data.get(i.dataId).values,v=n.data.get(r.dataId).values,I=w.computeStrides(r.shape),N=w.computeStrides(b.shape);for(let C=0;C=p)continue;let B=f>1?($-F)*(d-1)/(f-1):0,U=g>1?(S-D)*(c-1)/(g-1):0;for(let H=0;H1?F*(d-1)+H*B:.5*(F+$)*(d-1);if(j<0||j>d-1){for(let K=0;K1?D*(c-1)+ee*U:.5*(D+S)*(c-1);if(ae<0||ae>c-1){for(let xe=0;xe1?D*(c-1)+K*U:.5*(D+S)*(c-1);if(Z<0||Z>c-1){for(let ae=0;aeb+m-y-1:(b,y)=>b+y;for(let b=0;bb+m-y-1:(b,y)=>b+y;for(let b=0;b`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let o=r.shape[0],l=r.shape[1],u=r.shape[2],p=r.shape[3],d=l*s,c=u*s,h=p/(s*s),m=n.data.get(r.dataId).values,f=new Float32Array(o*d*c*h),g=0;for(let b=0;b`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let h=T.computeConv2DInfo(r.shape,s.shape,i,c,o,u,!0),{filterHeight:m,filterWidth:f,dilationHeight:g,dilationWidth:b,padInfo:y}=h,x=y.left,v=y.top,I=h.outChannels/h.inChannels,N=new Wt(h.outShape,r.dtype),C=n.data.get(r.dataId).values,_=n.data.get(s.dataId).values,F=N.values;for(let D=0;D=h.inHeight)continue;let K=H*d[0],Z=$+j*p[1];for(let J=0;J=h.inWidth)continue;let ie=K+te*d[1],xe=Z+se*h.inChannels,ue=ee,ye=ie;for(let ke=0;ke{let{x:a,filter:r}=e,{strides:s,pad:i,dilations:o}=n,l=t,u=l.data.get(a.dataId).values,p=a.shape.length,d=l.data.get(r.dataId).values,c=r.shape.length,{batchSize:h,inHeight:m,inWidth:f,inChannels:g,outHeight:b,outWidth:y,padInfo:x,strideHeight:v,strideWidth:I,filterHeight:N,filterWidth:C,dilationHeight:_,dilationWidth:F,outShape:D}=T.computeDilation2DInfo(a.shape,r.shape,s,i,"NHWC",o),$=w.sizeFromShape(D),S=D.length,M=w.getArrayFromDType(a.dtype,$);for(let B=0;B=0&&te=0&&ieJ&&(J=ye)}}}let ee=w.locToIndex([B,U,j,Z],S,w.computeStrides(D));M[ee]=J}}}return{dataId:l.write(w.toTypedArray(M,a.dtype),D,a.dtype),shape:D,dtype:a.dtype}}},gY={kernelName:jl,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,p=w.toNestedArray(a.shape,u.data.get(a.dataId).values),d=w.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:b,padInfo:y,strideHeight:x,strideWidth:v,filterHeight:I,filterWidth:N,dilationHeight:C,dilationWidth:_,outShape:F}=T.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);w.assert(s.rank===F.length,()=>`Error in ${jl}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let D=w.toNestedArray(F,u.data.get(s.dataId).values),$=w.makeZerosNestedTypedArray(r.shape,r.dtype);for(let S=0;S=0&&ae=0&&seK&&(K=ie,Z=ee,J=te)}}}$[Z][J][j]+=D[S][M][U][j]}}}return{dataId:u.write(w.toTypedArray($,a.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},bY={kernelName:ql,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,p=w.toNestedArray(a.shape,u.data.get(a.dataId).values),d=w.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:b,padInfo:y,strideHeight:x,strideWidth:v,filterHeight:I,filterWidth:N,dilationHeight:C,dilationWidth:_,outShape:F}=T.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);w.assert(s.rank===F.length,()=>`Error in ${ql}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let D=w.toNestedArray(F,u.data.get(s.dataId).values),$=w.makeZerosNestedTypedArray(a.shape,a.dtype);for(let S=0;S=0&&ae=0&&seK&&(K=ie,Z=ae,J=se)}}}$[S][Z][J][j]+=D[S][M][U][j]}}}return{dataId:u.write(w.toTypedArray($,a.dtype),a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}};function yY(e){let{inputs:t,backend:n,attrs:a}=e,{image:r}=t,{canvas:s,options:i}=a,{contextOptions:o,imageOptions:l}=i||{},u=(l==null?void 0:l.alpha)||1,p=(o==null?void 0:o.contextType)||"2d";if(p!=="2d")throw new Error(`Context type ${o.contextType} is not supported by the CPU backend.`);let d=s.getContext(p,(o==null?void 0:o.contextAttributes)||{});if(d==null)throw new Error(`Could not get the context with ${p} type.`);let[c,h]=r.shape.slice(0,2),m=r.shape.length===2?1:r.shape[2],f=n.data.get(r.dataId).values,g=r.dtype==="float32"?255:1,b=new Uint8ClampedArray(h*c*4);for(let x=0;x1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 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Got strides ${i} and dilations '${u}'`);let p=T.computePool2DInfo(r.shape,s,i,u,o,l),d;if(p.filterWidth===1&&p.filterHeight===1&&w.arraysEqual(p.inShape,p.outShape))d=dr({inputs:{x:r},backend:n});else{let c=n.data.get(r.dataId).values,h=w.computeStrides(r.shape),m=nk(c,r.shape,r.dtype,h,p,"max");d=n.makeTensorInfo(p.outShape,r.dtype,m.values)}return d}var I7={kernelName:ho,backendName:"cpu",kernelFunc:k7};function S7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=a;ge(r,"maxPool3d");let p=T.computePool3DInfo(r.shape,s,i,1,o,l,u),d=n.data.get(r.dataId).values,c=U_(d,r.shape,r.dtype,w.computeStrides(r.shape),p,"max");return n.makeTensorInfo(c.shape,"float32",c.values)}var N7={kernelName:Lu,backendName:"cpu",kernelFunc:S7};function T7(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=a;ge([r,s],"maxPool3DGrad");let p=T.computePool3DInfo(s.shape,i,o,1,l,u),d=n.bufferSync(s),c=mX(d,p),h=p.strideDepth,m=p.strideHeight,f=p.strideWidth,g=p.dilationDepth,b=p.dilationHeight,y=p.dilationWidth,x=p.effectiveFilterDepth,v=p.effectiveFilterHeight,I=p.effectiveFilterWidth,N=x-1-p.padInfo.front,C=I-1-p.padInfo.left,_=v-1-p.padInfo.top,F=Pe(s.shape,"float32"),D=n.bufferSync(r);for(let $=0;$=p.outDepth||Math.floor(ee)!==ee))for(let ae=0;ae=p.outHeight||Math.floor(te)!==te))for(let se=0;se=p.outWidth||Math.floor(ie)!==ie)continue;let xe=x*v*I-1-c.get($,ee,te,ie,S),ue=J*v*I+ae*I+se,ye=xe===ue?1:0;if(ye===0)continue;let ke=D.get($,ee,te,ie,S);Z+=ke*ye}}}F.set(Z,$,M,B,U,S)}return n.makeTensorInfo(F.shape,F.dtype,F.values)}var C7={kernelName:jc,backendName:"cpu",kernelFunc:T7};function E7(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;ge([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:p,dimRoundingMode:d}=a,c=T.computePool2DInfo(o.shape,l,u,1,p,d),h=n.data.get(o.dataId).values,m=Pe(c.outShape,o.dtype,V_(h,o.shape,o.dtype,c).values),f=c.strideHeight,g=c.strideWidth,b=c.dilationHeight,y=c.dilationWidth,x=c.effectiveFilterHeight,v=c.effectiveFilterWidth,I=v-1-c.padInfo.left,N=x-1-c.padInfo.top,C=Pe(o.shape,"float32"),_=n.data.get(r.dataId).values,F=Pe(r.shape,"float32",_);for(let D=0;D=c.outHeight||Math.floor(K)!==K))for(let Z=0;Z=c.outWidth||Math.floor(J)!==J)continue;let ee=x*v-1-m.get(D,K,J,$),ae=j*v+Z,te=ee===ae?1:0;if(te===0)continue;let se=F.get(D,K,J,$);H+=se*te}}C.set(H,D,S,M,$)}return n.makeTensorInfo(C.shape,C.dtype,C.values)}var _7={kernelName:qc,backendName:"cpu",kernelFunc:E7};function A7(e,t,n,a,r){let s=w.computeStrides(t),i=nk(e,t,n,s,r,"max"),o=V_(e,t,n,r,!0,a);return[i.values,o.values]}var F7={kernelName:Kc,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;ge(a,"MaxPoolWithArgmax");let u=l.data.get(a.dataId).values,p=T.computePool2DInfo(a.shape,r,s,[1,1],i),[d,c]=A7(u,a.shape,a.dtype,o,p),h=l.write(d,p.outShape,a.dtype),m=l.write(c,p.outShape,a.dtype);return[{dataId:h,shape:p.outShape,dtype:a.dtype},{dataId:m,shape:p.outShape,dtype:"int32"}]}};function $7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=w.parseAxisParam(s,r.shape),l=T.computeOutAndReduceShapes(r.shape,o)[1],u=w.sizeFromShape(l),p=[],d=n.makeTensorInfo([],"float32",new Float32Array([u]));p.push(d);let c=vs({inputs:{x:r},backend:n,attrs:{dtype:"float32"}});p.push(c);let h=ak({inputs:{a:c,b:d},backend:n});p.push(h);let m=Rd({inputs:{x:h},backend:n,attrs:{axis:s,keepDims:i}});return p.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var D7={kernelName:mo,backendName:"cpu",kernelFunc:$7};function R7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;ge(r,"min");let o=w.parseAxisParam(s,r.shape),l=o,u=T.getAxesPermutation(l,r.shape.length),p=r;u!=null&&(p=Bn({inputs:{x:r},backend:n,attrs:{perm:u}}),l=T.getInnerMostAxes(l.length,r.shape.length)),T.assertAxesAreInnerMostDims("min",l,p.shape.length);let[d,c]=T.computeOutAndReduceShapes(p.shape,l),h=w.sizeFromShape(c),m=w.makeZerosTypedArray(w.sizeFromShape(d),p.dtype),f=n.data.get(p.dataId).values;for(let b=0;by[0]+r.shape[x]+y[1]),l=s.map(y=>y[0]),u=s.map((y,x)=>y[0]+r.shape[x]),p=i==="reflect"?0:1,d=n.data.get(r.dataId).values,c=r.shape.length,h=w.computeStrides(r.shape),m=w.sizeFromShape(o),f=o.length,g=w.computeStrides(o),b=w.getTypedArrayFromDType(r.dtype,m);for(let y=0;y=u[I]&&(x[I]=(u[I]-1)*2-x[I]+p);x=x.map((I,N)=>I-l[N]);let v=w.locToIndex(x,c,h);b[y]=d[v]}return{dataId:n.write(b,o,r.dtype),shape:o,dtype:r.dtype}}var P7={kernelName:bo,backendName:"cpu",kernelFunc:O7},L7=Mt((e,t)=>{let n=e%t;return e<0&&t<0||e>=0&&t>=0?n:(n+t)%t}),z7=Zt(yo,L7),W7={kernelName:yo,backendName:"cpu",kernelFunc:z7},B7=ks(Fm());function K_(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=r.shape.length,o=s;if(o===-1&&(o=i-1),o!==i-1)throw Error(`Softmax along a non-last dimension is not yet supported. 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x}function yX(e,t){let n=Pe(t.outShape,"int32"),a=t.strideDepth,r=t.strideHeight,s=t.strideWidth,i=t.dilationDepth,o=t.dilationHeight,l=t.dilationWidth,u=t.effectiveFilterDepth,p=t.effectiveFilterHeight,d=t.effectiveFilterWidth,c=t.padInfo.front,h=t.padInfo.top,m=t.padInfo.left;for(let f=0;f=M&&(M=ee,B=H*p*d+K*p+J)}}}n.set(B,f,b,I,F,g)}}}return n}function xX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;ge(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;w.assert(T.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. 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p=T.computePool3DInfo(s.shape,i,o,1,l,u),d=p.strideDepth,c=p.strideHeight,h=p.strideWidth,m=p.filterDepth,f=p.filterHeight,g=p.filterWidth,b=p.dilationDepth,y=p.dilationHeight,x=p.dilationWidth,v=p.effectiveFilterDepth,I=p.effectiveFilterHeight,N=p.effectiveFilterWidth,C=v-1-p.padInfo.front,_=N-1-p.padInfo.left,F=I-1-p.padInfo.top,D=Pe(s.shape,"float32"),$=1/(m*f*g),S=n.bufferSync(r);for(let M=0;M=p.outDepth||Math.floor(te)!==te))for(let se=0;se=p.outHeight||Math.floor(ie)!==ie))for(let ve=0;ve=p.outWidth||Math.floor(ue)!==ue)continue;let ye=S.get(M,te,ie,ue,B);ee+=ye}}}D.set(ee*$,M,U,H,q,B)}return n.makeTensorInfo(D.shape,D.dtype,D.values)}var SX={kernelName:Wc,backendName:"cpu",kernelFunc:IX};function NX(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;ge([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,p=T.computePool2DInfo(i.shape,o,l,1,u),d=p.strideHeight,c=p.strideWidth,h=p.filterHeight,m=p.filterWidth,f=p.dilationHeight,g=p.dilationWidth,b=p.effectiveFilterHeight,y=p.effectiveFilterWidth,x=y-1-p.padInfo.left,v=b-1-p.padInfo.top,I=Pe(i.shape,"float32"),N=1/(h*m),C=n.data.get(r.dataId).values,_=Pe(r.shape,"float32",C);for(let F=0;F=p.outHeight||Math.floor(q)!==q))for(let K=0;K=p.outWidth||Math.floor(Z)!==Z)continue;let J=_.get(F,q,Z,D);U+=J}}I.set(U*N,F,$,S,D)}return n.makeTensorInfo(I.shape,I.dtype,I.values)}var TX={kernelName:zc,backendName:"cpu",kernelFunc:NX};function CX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,scale:s,offset:i,mean:o,variance:l}=t;w.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient 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o=s.reduce((b,y)=>b*y),l=T.getReshaped(r.shape,s,o),u=T.getPermuted(l.length,s.length),p=T.getReshapedPermuted(r.shape,s,o),d=T.getSliceBeginCoords(i,s.length),c=T.getSliceSize(p,i,s.length),h=xt({inputs:{x:r},backend:n,attrs:{shape:l}}),m=Vn({inputs:{x:h},backend:n,attrs:{perm:u}}),f=xt({inputs:{x:m},backend:n,attrs:{shape:p}}),g=wi({inputs:{x:f},backend:n,attrs:{begin:d,size:c}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),g}var AX={kernelName:fu,backendName:"cpu",kernelFunc:_X};function FX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.data.get(r.dataId).values,l=n.data.get(s.dataId).values,u=H1(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var $X={kernelName:gu,backendName:"cpu",kernelFunc:FX};function DX(e){let{inputs:t,backend:n}=e,{s0:a,s1:r}=t,s=n.data.get(a.dataId).values,i=n.data.get(r.dataId).values,o=T.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var RX={kernelName:Bc,backendName:"cpu",kernelFunc:DX},MX=lt(Ns,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e{let{x:t}=e.inputs,n=e.backend,a=new Float32Array(w.sizeFromShape(t.shape)),r=n.data.get(t.dataId),s=r.complexTensorInfos.real,i=r.complexTensorInfos.imag,o=n.data.get(s.dataId).values,l=n.data.get(i.dataId).values;for(let u=0;uf.shape);T.assertParamsConsistent(i,s);let o=T.computeOutShape(t.map(f=>f.shape),s);if(w.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(f=>w.sizeFromShape(f.shape)>0);if(l.length===1)return hr({inputs:{x:l[0]},backend:n});if(l[0].dtype==="complex64"){let f=l.map(v=>vi({inputs:{input:v},backend:n})),g=l.map(v=>iu({inputs:{input:v},backend:n})),b=ou({inputs:f,backend:n,attrs:{axis:s}}),y=ou({inputs:g,backend:n,attrs:{axis:s}}),x=Jn({inputs:{real:b,imag:y},backend:n});return f.forEach(v=>n.disposeIntermediateTensorInfo(v)),g.forEach(v=>n.disposeIntermediateTensorInfo(v)),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(y),x}let u=l.map(f=>{let g=[-1,w.sizeFromShape(f.shape.slice(s))];return xt({inputs:{x:f},backend:n,attrs:{shape:g}})}),p=u.map(f=>({vals:n.data.get(f.dataId).values,shape:f.shape}));o=T.computeOutShape(u.map(f=>f.shape),1);let d=u[0].shape[0]===1,c=j1(p,o,t[0].dtype,d),h=T.computeOutShape(l.map(f=>f.shape),s),m=n.makeTensorInfo(h,t[0].dtype,c);return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var WX={kernelName:yu,backendName:"cpu",kernelFunc:ou};function j_(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=a;ge([r,s],"conv2d");let d=T.convertConv2DDataFormat(l),c=T.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,d),h=c.filterHeight,m=c.filterWidth,f=c.dilationHeight,g=c.dilationWidth,b=c.padInfo.left,y=c.padInfo.top,x=c.dataFormat==="channelsLast",v=new Wt(c.outShape,r.dtype),I=w.computeStrides(r.shape),N=w.computeStrides(s.shape),C=I[0],_=x?I[1]:I[2],F=x?I[2]:1,D=x?1:I[1],$=v.strides[0],S=x?v.strides[1]:v.strides[2],M=x?v.strides[2]:1,B=x?1:v.strides[1],U=n.data.get(r.dataId).values,H=n.data.get(s.dataId).values,q=v.values;for(let K=0;K=c.inHeight)continue;let ve=se*N[0],ue=Z+ie*_;for(let ye=0;ye=c.inWidth)continue;let mt=ve+Le*N[1],st=ue+Ge*F,at=mt;for(let rt=0;rt=u.inDepth)continue;let K=H*F[0],Z=$+q*_[1];for(let J=0;J=u.inHeight)continue;let ie=K+te*F[1],ve=Z+se*_[2];for(let ue=0;ue=u.inWidth)continue;let Ge=ie+Se*F[2],mt=ve+Le*u.inChannels,st=Ge;for(let at=0;atMath.cos(e)),QX={kernelName:Vi,backendName:"cpu",kernelFunc:JX},eY=lt(Ui,e=>Math.cosh(e)),tY={kernelName:Ui,backendName:"cpu",kernelFunc:eY};function nY(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,[p,d,c,h]=r.shape,m=s.shape[0],[f,g]=o,b=Pe([m,f,g,h],"float32"),y=n.data.get(s.dataId).values,x=n.data.get(i.dataId).values,v=n.data.get(r.dataId).values,I=w.computeStrides(r.shape),N=w.computeStrides(b.shape);for(let C=0;C=p)continue;let B=f>1?($-F)*(d-1)/(f-1):0,U=g>1?(S-D)*(c-1)/(g-1):0;for(let H=0;H1?F*(d-1)+H*B:.5*(F+$)*(d-1);if(q<0||q>d-1){for(let K=0;K1?D*(c-1)+ee*U:.5*(D+S)*(c-1);if(ae<0||ae>c-1){for(let ve=0;ve1?D*(c-1)+K*U:.5*(D+S)*(c-1);if(Z<0||Z>c-1){for(let ae=0;aeb+m-y-1:(b,y)=>b+y;for(let b=0;bb+m-y-1:(b,y)=>b+y;for(let b=0;b`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let o=r.shape[0],l=r.shape[1],u=r.shape[2],p=r.shape[3],d=l*s,c=u*s,h=p/(s*s),m=n.data.get(r.dataId).values,f=new Float32Array(o*d*c*h),g=0;for(let b=0;b`Error in depthwiseConv2d: Either strides or dilations must be 1. 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r=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,r),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,a,0);let s=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(a),e.deleteFramebuffer(r),s}function NA(e){return e!==2?!1:ja(e).fenceSync!=null}function wp(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&w.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var be=G();be.registerFlag("HAS_WEBGL",()=>be.getNumber("WEBGL_VERSION")>0);be.registerFlag("WEBGL_VERSION",()=>vv(2)?2:vv(1)?1:0);be.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);be.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>be.get("WEBGL_VERSION")===2);be.registerFlag("WEBGL_CPU_FORWARD",()=>!0);be.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);be.registerFlag("WEBGL_PACK",()=>be.getBool("HAS_WEBGL"));be.registerFlag("WEBGL_PACK_NORMALIZATION",()=>be.getBool("WEBGL_PACK"));be.registerFlag("WEBGL_PACK_CLIP",()=>be.getBool("WEBGL_PACK"));be.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>be.getBool("WEBGL_PACK"));be.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>be.getBool("WEBGL_PACK"));be.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>be.getBool("WEBGL_PACK"));be.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>be.getBool("WEBGL_PACK"));be.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>be.getBool("WEBGL_PACK"));be.registerFlag("WEBGL_PACK_REDUCE",()=>be.getBool("WEBGL_PACK"));be.registerFlag("WEBGL_LAZILY_UNPACK",()=>be.getBool("WEBGL_PACK"));be.registerFlag("WEBGL_CONV_IM2COL",()=>be.getBool("WEBGL_PACK"));be.registerFlag("WEBGL_PACK_CONV2DTRANSPOSE",()=>be.getBool("WEBGL_PACK"));be.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>vA(be.getNumber("WEBGL_VERSION")));be.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>wA(be.getNumber("WEBGL_VERSION")));be.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let 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es",t="in",n="out",a="in",r="texture",s="outputColor",i="out vec4 outputColor;",o=G().getBool("WEBGL2_ISNAN_CUSTOM")?` bool isnan_custom(float val) { uint floatToUint = floatBitsToUint(val); return (floatToUint & 0x7fffffffu) > 0x7f800000u; @@ -112,7 +112,7 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram 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:l,defineRound:u}}function Qo(e,t,n="index"){let a=w.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 Qf(e,t,n="index"){let a=w.computeStrides(t);return a.map((r,s)=>{let i=`int ${e[s]} = ${n} / outShapeStrides[${s}]`,o=s===a.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * outShapeStrides[${s}]`:`index -= ${e[s]} * outShapeStrides[${s}]`;return`${i}; ${o};`}).join("")}function p9(e,t){let n=e.length,a=e.map(s=>`${t}[${s}]`),r=new Array(n-1);r[n-2]=a[n-1];for(let s=n-3;s>=0;--s)r[s]=`(${r[s+1]} * ${a[s+1]})`;return r}function c9(e,t,n="index"){let a=e.map((s,i)=>i),r=p9(a,t);return r.map((s,i)=>{let o=`int ${e[i]} = ${n} / ${r[i]}`,l=i===r.length-1?`int ${e[i+1]} = ${n} - ${e[i]} * ${r[i]}`:`index -= ${e[i]} * ${r[i]}`;return`${o}; ${l};`}).join("")}function ok(e){let t=w.computeStrides(e).map(n=>n.toString());return` + `),{version:e,attribute:t,varyingVs:n,varyingFs:a,texture2D:r,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:u}}function Qo(e,t,n="index"){let a=w.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 eg(e,t,n="index"){let a=w.computeStrides(t);return a.map((r,s)=>{let i=`int ${e[s]} = ${n} / outShapeStrides[${s}]`,o=s===a.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * outShapeStrides[${s}]`:`index -= ${e[s]} * outShapeStrides[${s}]`;return`${i}; ${o};`}).join("")}function m9(e,t){let n=e.length,a=e.map(s=>`${t}[${s}]`),r=new Array(n-1);r[n-2]=a[n-1];for(let s=n-3;s>=0;--s)r[s]=`(${r[s+1]} * ${a[s+1]})`;return r}function f9(e,t,n="index"){let a=e.map((s,i)=>i),r=m9(a,t);return r.map((s,i)=>{let o=`int ${e[i]} = ${n} / ${r[i]}`,l=i===r.length-1?`int ${e[i+1]} = ${n} - ${e[i]} * ${r[i]}`:`index -= ${e[i]} * ${r[i]}`;return`${o}; ${l};`}).join("")}function ok(e){let t=w.computeStrides(e).map(n=>n.toString());return` int getFlatIndex(ivec3 coords) { return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z; } @@ -120,7 +120,7 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram int getFlatIndex(ivec3 coords) { return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z; } -`}var SA=` +`}var TA=` const float FLOAT_MAX = 1.70141184e38; const float FLOAT_MIN = 1.17549435e-38; @@ -159,22 +159,22 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram return c / 255.0; } -`,{getBroadcastDims:NA}=T;function d9(e,t,n){let a=[];if(e.forEach(c=>{let h=w.sizeFromShape(c.shapeInfo.logicalShape);if(c.shapeInfo.isUniform?a.push(`uniform float ${c.name}${h>1?`[${h}]`:""};`):(a.push(`uniform sampler2D ${c.name};`),a.push(`uniform int offset${c.name};`)),n.enableShapeUniforms){let{uniformShape:m}=uk(n.packedInputs,c.shapeInfo.logicalShape,c.shapeInfo.texShape);switch(m.length){case 1:a.push(`uniform int ${c.name}Shape;`);break;case 2:a.push(`uniform ivec2 ${c.name}Shape;`);break;case 3:a.push(`uniform ivec3 ${c.name}Shape;`);break;case 4:a.push(`uniform ivec4 ${c.name}Shape;`);break;default:break}a.push(`uniform ivec2 ${c.name}TexShape;`)}}),n.enableShapeUniforms){switch(t.logicalShape.length){case 1:a.push("uniform int outShape;");break;case 2:a.push("uniform ivec2 outShape;"),a.push("uniform int outShapeStrides;");break;case 3:a.push("uniform ivec3 outShape;"),a.push("uniform ivec2 outShapeStrides;");break;case 4:a.push("uniform ivec4 outShape;"),a.push("uniform ivec3 outShapeStrides;");break;default:break}a.push("uniform ivec2 outTexShape;")}n.customUniforms&&n.customUniforms.forEach(c=>{a.push(`uniform ${c.type} ${c.name}${c.arrayIndex?`[${c.arrayIndex}]`:""};`)});let r=a.join(` -`),s=e.map(c=>h9(c,t,n.packedInputs,n.enableShapeUniforms)).join(` -`),i=t.texShape,o=Cn(),l=g9(o),u,p,d=x9(o);return t.isPacked?(u=m9(t.logicalShape,i,n.enableShapeUniforms),p=y9(o)):(u=f9(t.logicalShape,i,n.enableShapeUniforms),p=b9(o)),n.packedInputs&&(d+=I9),[d,l,p,r,u,s,n.userCode].join(` -`)}function kp(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return M9(e,t);case 1:return P9(e,t);case 2:return z9(e,t);case 3:return B9(e,t);case 4:return U9(e,t);case 5:return G9(e);case 6:return H9(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function TA(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return R9(e);case 1:return O9(e,t);case 2:return L9(e,t);case 3:return W9(e,t);default:return V9(e,t)}}function h9(e,t,n=!1,a){let r="";n?r+=TA(e,a):r+=kp(e,a);let s=e.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(n?r+=q9(e,t):r+=j9(e,t)),r}function m9(e,t,n){switch(e.length){case 0:return CA();case 1:return S9(e,t,n);case 2:return $9(e,t,n);case 3:return T9(e,t,n);default:return E9(e,t,n)}}function f9(e,t,n){switch(e.length){case 0:return CA();case 1:return N9(e,t,n);case 2:return D9(e,t,n);case 3:return C9(e,t,n);case 4:return _9(e,t,n);case 5:return A9(e,t);case 6:return F9(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function g9(e){return` +`,{getBroadcastDims:CA}=T;function g9(e,t,n){let a=[];if(e.forEach(c=>{let h=w.sizeFromShape(c.shapeInfo.logicalShape);if(c.shapeInfo.isUniform?a.push(`uniform float ${c.name}${h>1?`[${h}]`:""};`):(a.push(`uniform sampler2D ${c.name};`),a.push(`uniform int offset${c.name};`)),n.enableShapeUniforms){let{uniformShape:m}=uk(n.packedInputs,c.shapeInfo.logicalShape,c.shapeInfo.texShape);switch(m.length){case 1:a.push(`uniform int ${c.name}Shape;`);break;case 2:a.push(`uniform ivec2 ${c.name}Shape;`);break;case 3:a.push(`uniform ivec3 ${c.name}Shape;`);break;case 4:a.push(`uniform ivec4 ${c.name}Shape;`);break;default:break}a.push(`uniform ivec2 ${c.name}TexShape;`)}}),n.enableShapeUniforms){switch(t.logicalShape.length){case 1:a.push("uniform int outShape;");break;case 2:a.push("uniform ivec2 outShape;"),a.push("uniform int outShapeStrides;");break;case 3:a.push("uniform ivec3 outShape;"),a.push("uniform ivec2 outShapeStrides;");break;case 4:a.push("uniform ivec4 outShape;"),a.push("uniform ivec3 outShapeStrides;");break;default:break}a.push("uniform ivec2 outTexShape;")}n.customUniforms&&n.customUniforms.forEach(c=>{a.push(`uniform ${c.type} ${c.name}${c.arrayIndex?`[${c.arrayIndex}]`:""};`)});let r=a.join(` +`),s=e.map(c=>b9(c,t,n.packedInputs,n.enableShapeUniforms)).join(` +`),i=t.texShape,o=En(),l=v9(o),u,p,d=I9(o);return t.isPacked?(u=y9(t.logicalShape,i,n.enableShapeUniforms),p=k9(o)):(u=x9(t.logicalShape,i,n.enableShapeUniforms),p=w9(o)),n.packedInputs&&(d+=C9),[d,l,p,r,u,s,n.userCode].join(` +`)}function kp(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return z9(e,t);case 1:return B9(e,t);case 2:return U9(e,t);case 3:return H9(e,t);case 4:return q9(e,t);case 5:return K9(e);case 6:return X9(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function EA(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return L9(e);case 1:return W9(e,t);case 2:return V9(e,t);case 3:return G9(e,t);default:return j9(e,t)}}function b9(e,t,n=!1,a){let r="";n?r+=EA(e,a):r+=kp(e,a);let s=e.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(n?r+=Y9(e,t):r+=Z9(e,t)),r}function y9(e,t,n){switch(e.length){case 0:return _A();case 1:return E9(e,t,n);case 2:return O9(e,t,n);case 3:return A9(e,t,n);default:return $9(e,t,n)}}function x9(e,t,n){switch(e.length){case 0:return _A();case 1:return _9(e,t,n);case 2:return P9(e,t,n);case 3:return F9(e,t,n);case 4:return D9(e,t,n);case 5:return R9(e,t);case 6:return M9(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function v9(e){return` float sampleTexture(sampler2D textureSampler, vec2 uv) { return ${e.texture2D}(textureSampler, uv).r; } - `}function b9(e){return` + `}function w9(e){return` void setOutput(float val) { ${e.output} = vec4(val, 0, 0, 0); } - `}function y9(e){return` + `}function k9(e){return` void setOutput(vec4 val) { ${e.output} = val; } - `}function x9(e){return`${e.version} + `}function I9(e){return`${e.version} precision highp float; precision highp int; precision highp sampler2D; @@ -229,10 +229,10 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram return fract((p3.x + p3.y) * p3.z); } - ${v9} - ${w9} - ${k9} - `}var v9=` + ${S9} + ${N9} + ${T9} + `}var S9=` vec2 uvFromFlat(int texNumR, int texNumC, int index) { int texR = index / texNumC; int texC = index - texR * texNumC; @@ -244,7 +244,7 @@ vec2 packedUVfrom1D(int texNumR, int texNumC, int index) { int texC = texelIndex - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,w9=` +`,N9=` vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR, int texNumC, int row, int col) { int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2); @@ -252,7 +252,7 @@ vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR, int texC = texelIndex - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,k9=` +`,T9=` vec2 packedUVfrom3D(int texNumR, int texNumC, int texelsInBatch, int texelsInLogicalRow, int b, int row, int col) { @@ -261,7 +261,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, int texC = index - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,I9=` +`,C9=` float getChannel(vec4 frag, vec2 innerDims) { vec2 modCoord = mod(innerDims, 2.); return modCoord.x == 0. ? @@ -272,11 +272,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float modCoord = mod(float(dim), 2.); return modCoord == 0. ? frag.r : frag.g; } -`;function CA(){return` +`;function _A(){return` int getOutputCoords() { return 0; } - `}function S9(e,t,n){let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return a[0]===1?n?` + `}function E9(e,t,n){let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return a[0]===1?n?` int getOutputCoords() { return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0)); } @@ -305,7 +305,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2(${a[0]}, ${a[1]})); return 2 * (resTexRC.x * ${a[1]} + resTexRC.y); } - `}function N9(e,t,n){return t[0]===1?n?` + `}function _9(e,t,n){return t[0]===1?n?` int getOutputCoords() { return int(resultUV.x * float(outTexShape[1])); } @@ -333,7 +333,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2(${t[0]}, ${t[1]})); return resTexRC.x * ${t[1]} + resTexRC.y; } - `}function T9(e,t,n){if(n)return` + `}function A9(e,t,n){if(n)return` ivec3 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0)); @@ -364,12 +364,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return ivec3(b, r, c); } - `}function C9(e,t,n){if(n)return` + `}function F9(e,t,n){if(n)return` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); int index = resTexRC.x * outTexShape[1] + resTexRC.y; - ${Qf(["r","c","d"],e)} + ${eg(["r","c","d"],e)} return ivec3(r, c, d); } `;let a=Qo(["r","c","d"],e);return` @@ -380,7 +380,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${a} return ivec3(r, c, d); } - `}function E9(e,t,n){if(n)return` + `}function $9(e,t,n){if(n)return` ivec4 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); ivec2 resTexRC = ivec2(resultUV.yx * @@ -421,12 +421,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return ivec${e.length}(${l}); } - `}function _9(e,t,n){if(n)return` + `}function D9(e,t,n){if(n)return` ivec4 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); int index = resTexRC.x * outTexShape[1] + resTexRC.y; - ${Qf(["r","c","d","d2"],e)} + ${eg(["r","c","d","d2"],e)} return ivec4(r, c, d, d2); } `;let a=Qo(["r","c","d","d2"],e);return` @@ -437,7 +437,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${a} return ivec4(r, c, d, d2); } - `}function A9(e,t){let n=Qo(["r","c","d","d2","d3"],e);return` + `}function R9(e,t){let n=Qo(["r","c","d","d2","d3"],e);return` ivec5 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -449,7 +449,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ivec5 outShape = ivec5(r, c, d, d2, d3); return outShape; } - `}function F9(e,t){let n=Qo(["r","c","d","d2","d3","d4"],e);return` + `}function M9(e,t){let n=Qo(["r","c","d","d2","d3","d4"],e);return` ivec6 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -460,7 +460,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ivec6 result = ivec6(r, c, d, d2, d3, d4); return result; } - `}function $9(e,t,n){let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(w.arraysEqual(e,t))return n?` + `}function O9(e,t,n){let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(w.arraysEqual(e,t))return n?` ivec2 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1])); @@ -493,7 +493,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return ivec2(r, c); } - `}function D9(e,t,n){return w.arraysEqual(e,t)?n?` + `}function P9(e,t,n){return w.arraysEqual(e,t)?n?` ivec2 getOutputCoords() { return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); } @@ -547,11 +547,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, int c = index - r * ${e[1]}; return ivec2(r, c); } - `}function el(e){return`offset${e}`}function R9(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=Cn();return` + `}function el(e){return`offset${e}`}function L9(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=En();return` vec4 ${n}() { return ${a.texture2D}(${t}, halfCR); } - `}function M9(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${a}() {return ${n};}`;let[r,s]=e.shapeInfo.texShape;if(r===1&&s===1)return` + `}function z9(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${a}() {return ${n};}`;let[r,s]=e.shapeInfo.texShape;if(r===1&&s===1)return` float ${a}() { return sampleTexture(${n}, halfCR); } @@ -565,7 +565,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = uvFromFlat(${o}, ${l}, ${i}); return sampleTexture(${n}, uv); } - `}function O9(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,s=Cn();if(t)return` + `}function W9(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,s=En();if(t)return` vec4 ${a}(int index) { ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0)); vec2 uv = packedUVfrom1D( @@ -578,7 +578,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${i[0]}, ${i[1]}, index); return ${s.texture2D}(${n}, uv); } - `}function P9(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return` + `}function B9(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return` float ${a}(int index) { ${Ip(e)} } @@ -616,7 +616,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = uvFromFlat(${s}, ${i}, index + ${o}); return sampleTexture(${n}, uv); } - `}function L9(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape,i=s[0],o=s[1],l=Cn();if(s!=null&&w.arraysEqual(n,s))return t?` + `}function V9(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape,i=s[0],o=s[1],l=En();if(s!=null&&w.arraysEqual(n,s))return t?` vec4 ${r}(int row, int col) { vec2 uv = (vec2(col, row) + halfCR) / vec2(${a}TexShape[1], ${a}TexShape[0]); @@ -640,7 +640,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = packedUVfrom2D(${p}, ${u[0]}, ${u[1]}, row, col); return ${l.texture2D}(${a}, uv); } - `}function z9(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape;if(s!=null&&w.arraysEqual(n,s)){if(t)return` + `}function U9(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape;if(s!=null&&w.arraysEqual(n,s)){if(t)return` float ${r}(int row, int col) { vec2 uv = (vec2(col, row) + halfCR) / vec2(${a}TexShape[1], ${a}TexShape[0]); return sampleTexture(${a}, uv); @@ -698,12 +698,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = uvFromFlat(${u}, ${p}, index); return sampleTexture(${a}, uv); } -`}function W9(e,t){let n=e.shapeInfo.logicalShape,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)];if(n[0]===1){let c=n.slice(1),h=[1,2],m=Sp(e,c),f=["b","row","col"];return` - ${TA(m,t)} +`}function G9(e,t){let n=e.shapeInfo.logicalShape,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)];if(n[0]===1){let c=n.slice(1),h=[1,2],m=Sp(e,c),f=["b","row","col"];return` + ${EA(m,t)} vec4 ${r}(int b, int row, int col) { return ${r}(${Np(f,h)}); } - `}let o=Cn();if(t)return` + `}let o=En();if(t)return` vec4 ${r}(int b, int row, int col) { ivec2 packedTexShape = ivec2(ceil(float(${a}TexShape[0]) / 2.0), ceil(float(${a}TexShape[1]) / 2.0)); int valuesPerRow = int(ceil(float(${a}Shape[2]) / 2.0)); @@ -718,7 +718,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${l}, ${u}, ${d}, ${p}, b, row, col); return ${o.texture2D}(${a}, uv); } - `}function B9(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=n[1]*n[2],i=n[2],{newShape:o,keptDims:l}=w.squeezeShape(n),u=o;if(u.length=1?p="coords = 0;":p=o.map(g=>`coords.${d[g+u]} = 0;`).join(` + `}function Y9(e,t){let n=e.name,a=n.charAt(0).toUpperCase()+n.slice(1),r="get"+a+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=CA(e.shapeInfo.logicalShape,t.logicalShape),l=ht(i),u=i-s,p,d=["x","y","z","w","u","v"];s===0?p="":i<2&&o.length>=1?p="coords = 0;":p=o.map(g=>`coords.${d[g+u]} = 0;`).join(` `);let c="";i<2&&s>0?c="coords":c=e.shapeInfo.logicalShape.map((g,b)=>`coords.${d[b+u]}`).join(", ");let h="return outputValue;",m=w.sizeFromShape(e.shapeInfo.logicalShape)===1,f=w.sizeFromShape(t.logicalShape)===1;if(s===1&&!m&&!f)h=` return vec4(outputValue.xy, outputValue.xy); 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Shape ${o} and ${l} must match`)})}function Q9(e,t,n,a,r){t.program.enableShapeUniforms||(SS(t.inShapeInfos,n),SS([t.outShapeInfo],[a]));let s=a.texData.texture,i=a.texData.texShape;a.texData.isPacked?e.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):e.setOutputMatrixTexture(s.texture,i[0],i[1]),e.setProgram(t.webGLProgram),e.bindVertexArray(t.webGLProgram.vao),G().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN);for(let l=0;l{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let l=i.texData.texShape,{useSqueezeShape:u,uniformShape:p,keptDims:d}=uk(e.packedInputs,i.shape,l),c="",h="",m="";if(p.length===1&&e.packedInputs){let I=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];c=`${I[0]>1}_${I[1]>1}`}else if(p.length===2&&!e.packedInputs)h=`${p[0]>1}_${p[1]>1}`;else if(p.length>2&&!e.packedInputs){let I=w.computeStrides(p);m=`${I[0]===l[1]}_${I[I.length-1]===l[1]}`}let f=i.shape.length,g=p.length===2&&w.arraysEqual(i.shape,l),b=w.sizeFromShape(i.shape)===1,y=T.getBroadcastDims(i.shape,n.shape),x=!e.packedInputs&&f===n.shape.length&&w.arraysEqual(l,n.texData.texShape),v=e.packedInputs||p.length>2?"":`${l[0]>1}_${l[1]>1}`;a+=`${f}_${x}_${u?d:""}_${p.length}_${b}_${y}_${g}_${c}_${h}_${m}_${v}_${o}`}else{let l=i.isUniform?"uniform":i.texData.texShape;a+=`${i.shape}_${l}_${o}`}});let r=e.userCode,s=e.constructor.name;return s+="_"+a+"_"+r+`${G().getNumber("WEBGL_VERSION")}`,s}function vn(e){return G().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var tQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Fc.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=En();this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` ivec3 outCoordsFromFlatIndex(int index) { - ${this.enableShapeUniforms?Qf(["r","c","d"],e):Qo(["r","c","d"],e)} + ${this.enableShapeUniforms?eg(["r","c","d"],e):Qo(["r","c","d"],e)} return ivec3(r, c, d); } @@ -1008,9 +1008,9 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${t.output} = result; } - `}},J9=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Fc.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Cn();this.outputShape=e,this.enableShapeUniforms=xn(this.outputShape.length),this.userCode=` + `}},nQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Fc.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=En();this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` ivec3 outCoordsFromFlatIndex(int index) { - ${this.enableShapeUniforms?Qf(["r","c","d"],e):Qo(["r","c","d"],e)} + ${this.enableShapeUniforms?eg(["r","c","d"],e):Qo(["r","c","d"],e)} return ivec3(r, c, d); } @@ -1028,24 +1028,24 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${t.output} = result; } - `}},Q9=class{constructor(e){this.variableNames=["A"],this.outTexUsage=da.DOWNLOAD;let t=Cn();this.outputShape=e,this.userCode=` - ${SA} + `}},aQ=class{constructor(e){this.variableNames=["A"],this.outTexUsage=da.DOWNLOAD;let t=En();this.outputShape=e,this.userCode=` + ${TA} void main() { float x = getAAtOutCoords(); ${t.output} = encode_float(x); } - `}},eQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=da.DOWNLOAD;let t=Cn();this.outputShape=e,this.userCode=` - ${SA} + `}},rQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=da.DOWNLOAD;let t=En();this.outputShape=e,this.userCode=` + ${TA} void main() { ivec3 coords = getOutputCoords(); float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z)); ${t.output} = encode_float(x); } - `}},tQ={R:0,G:1,B:2,A:3},NS=class{constructor(e,t=!1,n="RGBA"){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let a=Cn();this.outputShape=e,this.enableShapeUniforms=xn(this.outputShape.length);let r="result";t&&(r="floor(result * 255. + 0.5)");let s="";for(let i=0;iLA,createBufferFromOutputTexture:()=>BA,createFloat16MatrixTexture:()=>RA,createFloat16PackedMatrixTexture:()=>PA,createFloat32MatrixTexture:()=>DA,createIndexBuffer:()=>$A,createPackedMatrixTexture:()=>OA,createUnsignedBytesMatrixTexture:()=>MA,createVertexBuffer:()=>FA,createVertexShader:()=>AA,downloadByteEncodedFloatMatrixFromOutputTexture:()=>UA,downloadFloat32MatrixFromBuffer:()=>VA,downloadMatrixFromPackedOutputTexture:()=>HA,downloadPackedMatrixFromBuffer:()=>GA,getInternalFormatForFloat16MatrixTexture:()=>ck,getInternalFormatForFloat16PackedMatrixTexture:()=>mk,getInternalFormatForFloat32MatrixTexture:()=>pk,getInternalFormatForPackedMatrixTexture:()=>hk,getInternalFormatForUnsignedBytesMatrixTexture:()=>dk,uploadDenseMatrixToTexture:()=>zA,uploadPixelDataToTexture:()=>WA});function AA(e){let t=Cn(),n=`${t.version} + `}},FA={};_e(FA,{bindVertexProgramAttributeStreams:()=>WA,createBufferFromOutputTexture:()=>UA,createFloat16MatrixTexture:()=>OA,createFloat16PackedMatrixTexture:()=>zA,createFloat32MatrixTexture:()=>MA,createIndexBuffer:()=>RA,createPackedMatrixTexture:()=>LA,createUnsignedBytesMatrixTexture:()=>PA,createVertexBuffer:()=>DA,createVertexShader:()=>$A,downloadByteEncodedFloatMatrixFromOutputTexture:()=>HA,downloadFloat32MatrixFromBuffer:()=>GA,downloadMatrixFromPackedOutputTexture:()=>qA,downloadPackedMatrixFromBuffer:()=>jA,getInternalFormatForFloat16MatrixTexture:()=>ck,getInternalFormatForFloat16PackedMatrixTexture:()=>mk,getInternalFormatForFloat32MatrixTexture:()=>pk,getInternalFormatForPackedMatrixTexture:()=>hk,getInternalFormatForUnsignedBytesMatrixTexture:()=>dk,uploadDenseMatrixToTexture:()=>BA,uploadPixelDataToTexture:()=>VA});function $A(e){let t=En(),n=`${t.version} precision highp float; ${t.attribute} vec3 clipSpacePos; ${t.attribute} vec2 uv; @@ -1119,11 +1119,11 @@ vec2 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this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let a=this.gl;Zh(a,e,this.framebuffer),this.debug&&cc(a),this.outputTexture=e,de(a,()=>a.viewport(0,0,t,n)),de(a,()=>a.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,a){this.throwIfDisposed(),de(this.gl,()=>this.gl.scissor(e,t,n,a))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function oQ(e){let t=0;for(;t`${e}.${n}`)}function In(e,t){return t===1?[e]:ZA(e,t)}function QQ(e,t){if(e===1)return"rc";let n="";for(let a=0;a= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}), cEdge ? 0. : getA(${t[1]}), rEdge ? 0. : getA(${t[2]}), - rEdge || cEdge ? 0. : getA(${t[3]})`}},YA=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=xn(this.outputShape.length);let n="";for(let a=0;a<4;a++){let r="thisRC = rc;";a%2===1&&(r+="thisRC.z += 1;"),a>1&&(r+="thisRC.y += 1;"),n+=` + rEdge || cEdge ? 0. : getA(${t[3]})`}},JA=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length);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); @@ -1158,7 +1158,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims); ${a>0?"}":""} `}this.userCode=` - ${ZQ(t,this.enableShapeUniforms)} + ${tee(t,this.enableShapeUniforms)} ${this.enableShapeUniforms?lk():ok(e)} void main() { @@ -1174,12 +1174,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(result); } - `}};function ZQ(e,t){return` + `}};function tee(e,t){return` ivec3 inputCoordsFromReshapedOutCoords(int index) { - ${t?c9(["r","c","d"],"inputShape"):Qo(["r","c","d"],e)} + ${t?f9(["r","c","d"],"inputShape"):Qo(["r","c","d"],e)} return ivec3(r, c, d); } - `}var JQ=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(e,t,n){let a=CS(t,n),r=ES(e,a,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let s=TS(e,a,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[r].pop();return this.usedTextures[r].push(o),o}let i;return a===dn.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):a===dn.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):a===dn.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):a===dn.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):a===dn.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,a){if(this.freeTextures==null)return;let r=CS(n,a),s=ES(t,r,a);s in this.freeTextures||(this.freeTextures[s]=[]);let i=TS(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,a),o=G().getNumber("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l&&l.indexOf(e);if(u==null||u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l[u]=l[l.length-1],l.pop(),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function QQ(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F||t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function TS(e,t,n,a,r){let s=eee(t,a),i;if(r){let[l,u]=vp(e[0],e[1]);i=l*u}else{let[l,u]=Md(e[0],e[1]);i=l*u}let o=QQ(n,s);return i*o}function eee(e,t){switch(e){case dn.PACKED_2X2_FLOAT32:return hk(t);case dn.PACKED_2X2_FLOAT16:return mk(t);case dn.UNPACKED_FLOAT32:return pk(t);case dn.UNPACKED_FLOAT16:return ck(t);case dn.PACKED_4X1_UNSIGNED_BYTE:return dk(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function tee(e){return G().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?dn.PACKED_2X2_FLOAT32:dn.UNPACKED_FLOAT32:e?dn.PACKED_2X2_FLOAT16:dn.UNPACKED_FLOAT16}function CS(e,t){if(e===da.UPLOAD)return dn.PACKED_2X2_FLOAT32;if(e===da.RENDER||e==null)return tee(t);if(e===da.DOWNLOAD||e===da.PIXELS)return dn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function ES(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var ir=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=xn(this.outputShape.length),this.userCode=` + `}var nee=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(e,t,n){let a=CS(t,n),r=ES(e,a,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let s=TS(e,a,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[r].pop();return this.usedTextures[r].push(o),o}let i;return a===hn.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):a===hn.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):a===hn.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):a===hn.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):a===hn.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,a){if(this.freeTextures==null)return;let r=CS(n,a),s=ES(t,r,a);s in this.freeTextures||(this.freeTextures[s]=[]);let i=TS(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,a),o=G().getNumber("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l&&l.indexOf(e);if(u==null||u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l[u]=l[l.length-1],l.pop(),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function aee(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;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function TS(e,t,n,a,r){let s=ree(t,a),i;if(r){let[l,u]=vp(e[0],e[1]);i=l*u}else{let[l,u]=Md(e[0],e[1]);i=l*u}let o=aee(n,s);return i*o}function ree(e,t){switch(e){case hn.PACKED_2X2_FLOAT32:return hk(t);case hn.PACKED_2X2_FLOAT16:return mk(t);case hn.UNPACKED_FLOAT32:return pk(t);case hn.UNPACKED_FLOAT16:return ck(t);case hn.PACKED_4X1_UNSIGNED_BYTE:return dk(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function see(e){return G().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?hn.PACKED_2X2_FLOAT32:hn.UNPACKED_FLOAT32:e?hn.PACKED_2X2_FLOAT16:hn.UNPACKED_FLOAT16}function CS(e,t){if(e===da.UPLOAD)return hn.PACKED_2X2_FLOAT32;if(e===da.RENDER||e==null)return see(t);if(e===da.DOWNLOAD||e===da.PIXELS)return hn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function ES(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var ir=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` float unaryOperation(float x) { ${t} } @@ -1190,11 +1190,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(y); } - `}},Ma="if (isnan(x)) return x;",nee="return x;",_S="return abs(x);",aee="return (x >= 0.0) ? x : (exp(x) - 1.0);",ree=Ma+` + `}},Ma="if (isnan(x)) return x;",iee="return x;",_S="return abs(x);",oee="return (x >= 0.0) ? x : (exp(x) - 1.0);",lee=Ma+` return (x < 0.0) ? 0.0 : x; -`,see=Ma+` +`,uee=Ma+` return (x < 0.0) ? 0.0 : min(6.0, x); -`,es="return x;",iee="return 1.0 / (1.0 + exp(-1.0 * x));",oee="return x;",lee=` +`,es="return x;",pee="return 1.0 / (1.0 + exp(-1.0 * x));",cee="return x;",dee=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -1203,7 +1203,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; -`,uee=` +`,hee=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -1213,7 +1213,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = isNaN.a ? x.a : result.a; return result; -`,pee=` +`,mee=` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -1223,7 +1223,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = isNaN.a ? x.a : result.a; return result; -`,cee="return 1.0 / (1.0 + exp(-1.0 * x));",ss=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=xn(this.outputShape.length),this.userCode=` +`,fee="return 1.0 / (1.0 + exp(-1.0 * x));",ss=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` vec4 unaryOperation(vec4 x) { ${t} } @@ -1234,17 +1234,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(y); } - `}},dee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=xn(this.outputShape.length);let t=e.length,n=kn("rc",t),a=ht(t),r=XQ(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=` + `}},gee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length);let t=e.length,n=In("rc",t),a=ht(t),r=QQ(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})); } - `}},hee=fr.whereImpl,mee=1e-7,fee=1e-4,Nx={};function gee(e){return e in Nx||(Nx[e]={}),Nx[e]}var bee=G().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),yee=600;function xee(){return G().global.screen==null?1024:G().global.screen.height*G().global.screen.width*window.devicePixelRatio*yee/1024/1024}var gk=class ZA extends Pc{nextDataId(){return ZA.nextDataId++}constructor(t){if(super(),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,!G().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let n;if(t!=null){if(t instanceof em)n=t;else{let a=qa(G().getNumber("WEBGL_VERSION"),t);n=new em(a)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let a=qa(G().getNumber("WEBGL_VERSION"));n=new em(a),this.binaryCache=gee(G().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=n,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new JQ(this.gpgpu),this.numMBBeforeWarning=xee(),this.texData=new $m(this,Ea())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(t,n,a,r,s,i){let o=this.makeTensorInfo(n,a),l=this.texData.get(o.dataId);l.isPacked=!1,l.texture={texture:t,texShape:[r,s]},l.texShape=[r,s];let u=dc(n),p=new NS(u,!1,i),d=this.runWebGLProgram(p,[o],a,[[r,s]]);return d.shape=n,l.texture=null,this.disposeIntermediateTensorInfo(o),d.dataId}write(t,n,a){if((G().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||G().getBool("DEBUG"))&&this.checkNumericalProblems(t),a==="complex64"&&t!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let r={id:this.nextDataId()};return this.texData.set(r,{shape:n,dtype:a,values:t,usage:da.UPLOAD,refCount:1}),r}refCount(t){return this.texData.has(t)?this.texData.get(t).refCount:0}incRef(t){let n=this.texData.get(t);n.refCount++}decRef(t){if(this.texData.has(t)){let n=this.texData.get(t);n.refCount--}}move(t,n,a,r,s){if(G().getBool("DEBUG")&&this.checkNumericalProblems(n),r==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(t,{shape:a,dtype:r,values:n,usage:da.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(t){this.disposeData(t.dataId)}readSync(t){let n=this.texData.get(t),{values:a,dtype:r,complexTensorInfos:s,slice:i,shape:o,isPacked:l}=n;if(i!=null){let c;l?c=new ss(o,es):c=new ir(o,es);let h=this.runWebGLProgram(c,[{dataId:t,shape:o,dtype:r}],r),m=this.readSync(h.dataId);return this.disposeIntermediateTensorInfo(h),m}if(a!=null)return this.convertAndCacheOnCPU(t);if(r==="string")return a;let u=this.activeTimers!=null,p;u&&(p=w.now());let d;if(r==="complex64"){let c=this.readSync(s.real.dataId),h=this.readSync(s.imag.dataId);d=T.mergeRealAndImagArrays(c,h)}else d=this.getValuesFromTexture(t);return u&&(this.downloadWaitMs+=w.now()-p),this.convertAndCacheOnCPU(t,d)}async read(t){if(this.pendingRead.has(t)){let m=this.pendingRead.get(t);return new Promise(f=>m.push(f))}let n=this.texData.get(t),{values:a,shape:r,slice:s,dtype:i,complexTensorInfos:o,isPacked:l}=n;if(s!=null){let m;l?m=new ss(r,es):m=new ir(r,es);let f=this.runWebGLProgram(m,[{dataId:t,shape:r,dtype:i}],i),g=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),g}if(a!=null)return this.convertAndCacheOnCPU(t);if(G().getBool("DEBUG")&&!G().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&G().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,p;if(i!=="complex64"&&G().get("WEBGL_BUFFER_SUPPORTED")){p=this.decode(t);let m=this.texData.get(p.dataId);u=this.gpgpu.createBufferFromTexture(m.texture.texture,...Gh(r))}this.pendingRead.set(t,[]),i!=="complex64"&&await this.gpgpu.createAndWaitForFence();let d;if(i==="complex64"){let m=await Promise.all([this.read(o.real.dataId),this.read(o.imag.dataId)]),f=m[0],g=m[1];d=T.mergeRealAndImagArrays(f,g)}else if(u==null)d=this.getValuesFromTexture(t);else{let m=w.sizeFromShape(r);d=this.gpgpu.downloadFloat32MatrixFromBuffer(u,m)}if(p!=null&&this.disposeIntermediateTensorInfo(p),u!=null){let m=this.gpgpu.gl;de(m,()=>m.deleteBuffer(u))}let c=this.convertAndCacheOnCPU(t,d),h=this.pendingRead.get(t);return this.pendingRead.delete(t),h.forEach(m=>m(c)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t)&&Ea().removeDataId(t,this),this.pendingDeletes--),c}readToGPU(t,n={}){let a=this.texData.get(t),{values:r,shape:s,slice:i,dtype:o,isPacked:l,texture:u}=a;if(o==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(i!=null){let h;l?h=new ss(s,es):h=new ir(s,es);let m=this.runWebGLProgram(h,[{dataId:t,shape:s,dtype:o}],o),f=this.readToGPU(m,n);return this.disposeIntermediateTensorInfo(m),f}if(u==null)throw r!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let p=this.decode(t,n.customTexShape),d=Ea().makeTensorFromTensorInfo(p),c=this.texData.get(p.dataId);return Object.assign({tensorRef:d},c.texture)}bufferSync(t){let n=this.readSync(t.dataId);if(t.dtype==="string")try{let a=n.map(r=>w.decodeString(r));return Pe(t.shape,t.dtype,a)}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return Pe(t.shape,t.dtype,n)}checkNumericalProblems(t){if(t!=null)for(let n=0;n0}time(t){let n=this.activeTimers,a=[],r=!1;this.programTimersStack==null?(this.programTimersStack=a,r=!0):this.activeTimers.push(a),this.activeTimers=a,t();let s=w.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),i=w.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=n,r&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let l=await Promise.all(s);o.kernelMs=w.sum(l),o.getExtraProfileInfo=()=>l.map((u,p)=>({name:i[p],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else o.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,o})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(t){return G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),t):(t.endMs=w.now(),t)}async getQueryTime(t){if(G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(t);let n=t;return n.endMs-n.startMs}disposeData(t,n=!1){if(this.pendingDisposal.has(t))return!1;if(!this.texData.has(t))return!0;if(n?this.texData.get(t).refCount=0:this.texData.get(t).refCount--,!n&&this.texData.get(t).refCount>0)return!1;if(this.pendingRead.has(t))return this.pendingDisposal.add(t),this.pendingDeletes++,!1;this.releaseGPUData(t);let{complexTensorInfos:a}=this.texData.get(t);return a!=null&&(this.disposeData(a.real.dataId,n),this.disposeData(a.imag.dataId,n)),this.texData.delete(t),!0}releaseGPUData(t){let{texture:n,dtype:a,texShape:r,usage:s,isPacked:i,slice:o}=this.texData.get(t),l=o&&o.origDataId||t,u=this.dataRefCount.get(l);u>1?this.dataRefCount.set(l,u-1):(this.dataRefCount.delete(l),n!=null&&(this.numBytesInGPU-=this.computeBytes(r,a),this.textureManager.releaseTexture(n,r,s,i)));let p=this.texData.get(t);p.texture=null,p.texShape=null,p.isPacked=!1,p.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture.texture}getDataInfo(t){return this.texData.get(t)}shouldExecuteOnCPU(t,n=bee){return G().getBool("WEBGL_CPU_FORWARD")&&t.every(a=>this.texData.get(a.dataId).texture==null&&w.sizeFromShape(a.shape)0&&w.isString(a[0])){let s=a.map(i=>w.encodeString(i));r=this.write(s,t,n)}else r=this.write(a,t,n);return this.texData.get(r).usage=null,{dataId:r,shape:t,dtype:n}}makeOutput(t,n,a){return Ea().makeTensorFromTensorInfo(this.makeTensorInfo(t,n,a),this)}unpackTensor(t){let n=new dee(t.shape);return this.runWebGLProgram(n,[t],t.dtype)}packTensor(t){let n=new YQ(t.shape);return this.runWebGLProgram(n,[t],t.dtype,null,!0)}packedReshape(t,n){let a=[ki(t.shape),...Ii(t.shape)],r={dtype:t.dtype,shape:a,dataId:t.dataId},s=[ki(n),...Ii(n)],i=new YA(s,a),o=!0,l=[a],u=this.runWebGLProgram(i,[r],t.dtype,l,o);return{dataId:u.dataId,shape:n,dtype:u.dtype}}decode(t,n){let a=this.texData.get(t),{isPacked:r,shape:s,dtype:i}=a;if(n!=null){let c=w.sizeFromShape(s),h=n[0]*n[1]*4;w.assert(c<=h,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let o=dc(s),l;r?l=new J9(o):l=new Z9(o);let u=!0,p=[n!=null?n:Gh(o)],d=this.runWebGLProgram(l,[{shape:o,dtype:i,dataId:t}],i,p,u,n);return{dtype:i,shape:s,dataId:d.dataId}}runWebGLProgram(t,n,a,r,s=!1,i){let o=this.makeTensorInfo(t.outputShape,a),l=this.texData.get(o.dataId);if(t.packedOutput&&(l.isPacked=!0),t.outPackingScheme===Fc.DENSE){let b=i!=null?i:Gh(t.outputShape);l.texShape=b.map(y=>y*2)}if(t.outTexUsage!=null&&(l.usage=t.outTexUsage),w.sizeFromShape(o.shape)===0)return l.values=w.getTypedArrayFromDType(o.dtype,0),o;let u=[],p=n.map(b=>{if(b.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let y=this.texData.get(b.dataId);if(y.texture==null){if(!t.packedInputs&&w.sizeFromShape(b.shape)<=G().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:b.shape,texData:null,isUniform:!0,uniformValues:y.values};t.packedInputs&&(y.isPacked=!0,y.shape=b.shape)}if(this.uploadToGPU(b.dataId),!!y.isPacked!=!!t.packedInputs)b=y.isPacked?this.unpackTensor(b):this.packTensor(b),u.push(b),y=this.texData.get(b.dataId);else if(y.isPacked&&!$c(y.shape,b.shape)){let x=b,v=b.shape;b.shape=y.shape,b=this.packedReshape(b,v),u.push(b),y=this.texData.get(b.dataId),x.shape=v}return{shape:b.shape,texData:y,isUniform:!1}});this.uploadToGPU(o.dataId);let d={shape:o.shape,texData:l,isUniform:!1},c=Y9(t,p,d),h=this.getAndSaveBinary(c,()=>K9(this.gpgpu,t,p,d)),m=this.activeTimers!=null,f;m&&(f=this.startTimer()),G().get("ENGINE_COMPILE_ONLY")||X9(this.gpgpu,h,p,d,r),u.forEach(b=>this.disposeIntermediateTensorInfo(b)),m&&(f=this.endTimer(f),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(f)}));let g=G().getNumber("WEBGL_FLUSH_THRESHOLD");if(g>0){let b=w.now();b-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=b)}if(!G().getBool("WEBGL_LAZILY_UNPACK")&&l.isPacked&&s===!1){let b=this.unpackTensor(o);return this.disposeIntermediateTensorInfo(o),b}return o}compileAndRun(t,n,a,r,s=!1){return a=a||n[0].dtype,this.runWebGLProgram(t,n,a,r,s)}getAndSaveBinary(t,n){return t in this.binaryCache||(this.binaryCache[t]=n()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(G().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=O(()=>{if(!G().get("WEBGL_RENDER_FLOAT32_ENABLED")){let t=G().getBool("DEBUG");G().set("DEBUG",!1);let n=this.abs(ve(1e-8)).dataSync()[0];if(G().set("DEBUG",t),n>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?mee:fee}uploadToGPU(t){let n=this.texData.get(t),{shape:a,dtype:r,values:s,texture:i,usage:o,isPacked:l}=n;if(i!=null)return;let u=this.activeTimers!=null,p;u&&(p=w.now());let d=n.texShape;if(d==null&&(d=bA(a,l),n.texShape=d),s!=null){let c=dc(a),h,m=d[1],f=d[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(l||!g)&&([m,f]=vp(d[0],d[1])),l?h=new nQ(c,g):h=new NS(c,g);let b=g?[f,m]:d,y=this.makeTensorInfo(b,r),x=this.texData.get(y.dataId);g?x.usage=da.PIXELS:x.usage=da.UPLOAD,x.texShape=b,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),m,f,s);let v=[[f,m]],I=this.runWebGLProgram(h,[y],r,v,!0),N=this.texData.get(I.dataId);n.texShape=N.texShape,n.isPacked=N.isPacked,n.usage=N.usage,G().get("ENGINE_COMPILE_ONLY")?this.disposeData(I.dataId):(n.texture=N.texture,n.values=null,this.texData.delete(I.dataId)),this.disposeIntermediateTensorInfo(y),u&&(this.uploadWaitMs+=w.now()-p)}else{let c=this.acquireTexture(d,o,r,l);n.texture=c}}convertAndCacheOnCPU(t,n){let a=this.texData.get(t),{dtype:r}=a;return n!=null&&(a.values=vee(n,r)),a.values}acquireTexture(t,n,a,r){if(this.numBytesInGPU+=this.computeBytes(t,a),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(t,n,r)}computeBytes(t,n){return t[0]*t[1]*w.bytesPerElement(n)}checkCompileCompletion(){for(let[,t]of Object.entries(this.binaryCache))this.checkCompletion_(t)}async checkCompileCompletionAsync(){let t=[];if(this.gpgpu.parallelCompilationExtension){for(let[,n]of Object.entries(this.binaryCache))t.push(this.checkCompletionAsync_(n));return Promise.all(t)}else{for(let[,n]of Object.entries(this.binaryCache)){let a=new Promise(r=>{try{this.checkCompletion_(n),r(!0)}catch(s){throw s}});t.push(a)}return Promise.all(t)}}async checkCompletionAsync_(t){return this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(t):(await s0(),this.checkCompletionAsync_(t))}checkCompletion_(t){if(this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(t.webGLProgram)),this.gpgpu.gl.getShaderParameter(t.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(ik(t.source,this.gpgpu.gl.getShaderInfoLog(t.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let t of Object.values(this.binaryCache)){this.gpgpu.buildVao(t.webGLProgram);let{variablesLocations:n,customUniformLocations:a,infLoc:r,nanLoc:s,outShapeLocation:i,outShapeStridesLocation:o,outTexShapeLocation:l}=EA(this.gpgpu,t.program,t.webGLProgram);t.variablesLocations=n,t.customUniformLocations=a,t.infLoc=r,t.nanLoc=s,t.outShapeLocation=i,t.outShapeStridesLocation=o,t.outTexShapeLocation=l}}createTensorFromGPUData(t,n,a){t.channels=t.channels||"RGBA";let{texture:r,height:s,width:i,channels:o}=t,l=Ea().backend;if(!l.gpgpu.gl.isTexture(r))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let u=l.writeTexture(r,n,a,s,i,o);return Ea().makeTensorFromDataId(u,n,a,l)}};gk.nextDataId=0;function vee(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;anew gk,2);var kee={forceHalfFloat:JA},bk=` + `}},bee=gr.whereImpl,yee=1e-7,xee=1e-4,Tx={};function vee(e){return e in Tx||(Tx[e]={}),Tx[e]}var wee=G().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),kee=600;function Iee(){return G().global.screen==null?1024:G().global.screen.height*G().global.screen.width*window.devicePixelRatio*kee/1024/1024}var gk=class QA extends Pc{nextDataId(){return QA.nextDataId++}constructor(t){if(super(),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,!G().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let n;if(t!=null){if(t instanceof em)n=t;else{let a=ja(G().getNumber("WEBGL_VERSION"),t);n=new em(a)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let a=ja(G().getNumber("WEBGL_VERSION"));n=new em(a),this.binaryCache=vee(G().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=n,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new nee(this.gpgpu),this.numMBBeforeWarning=Iee(),this.texData=new $m(this,Ea())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(t,n,a,r,s,i){let o=this.makeTensorInfo(n,a),l=this.texData.get(o.dataId);l.isPacked=!1,l.texture={texture:t,texShape:[r,s]},l.texShape=[r,s];let u=dc(n),p=new NS(u,!1,i),d=this.runWebGLProgram(p,[o],a,[[r,s]]);return d.shape=n,l.texture=null,this.disposeIntermediateTensorInfo(o),d.dataId}write(t,n,a){if((G().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||G().getBool("DEBUG"))&&this.checkNumericalProblems(t),a==="complex64"&&t!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let r={id:this.nextDataId()};return this.texData.set(r,{shape:n,dtype:a,values:t,usage:da.UPLOAD,refCount:1}),r}refCount(t){return this.texData.has(t)?this.texData.get(t).refCount:0}incRef(t){let n=this.texData.get(t);n.refCount++}decRef(t){if(this.texData.has(t)){let n=this.texData.get(t);n.refCount--}}move(t,n,a,r,s){if(G().getBool("DEBUG")&&this.checkNumericalProblems(n),r==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(t,{shape:a,dtype:r,values:n,usage:da.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(t){this.disposeData(t.dataId)}readSync(t){let n=this.texData.get(t),{values:a,dtype:r,complexTensorInfos:s,slice:i,shape:o,isPacked:l}=n;if(i!=null){let c;l?c=new ss(o,es):c=new ir(o,es);let h=this.runWebGLProgram(c,[{dataId:t,shape:o,dtype:r}],r),m=this.readSync(h.dataId);return this.disposeIntermediateTensorInfo(h),m}if(a!=null)return this.convertAndCacheOnCPU(t);if(r==="string")return a;let u=this.activeTimers!=null,p;u&&(p=w.now());let d;if(r==="complex64"){let c=this.readSync(s.real.dataId),h=this.readSync(s.imag.dataId);d=T.mergeRealAndImagArrays(c,h)}else d=this.getValuesFromTexture(t);return u&&(this.downloadWaitMs+=w.now()-p),this.convertAndCacheOnCPU(t,d)}async read(t){if(this.pendingRead.has(t)){let m=this.pendingRead.get(t);return new Promise(f=>m.push(f))}let n=this.texData.get(t),{values:a,shape:r,slice:s,dtype:i,complexTensorInfos:o,isPacked:l}=n;if(s!=null){let m;l?m=new ss(r,es):m=new ir(r,es);let f=this.runWebGLProgram(m,[{dataId:t,shape:r,dtype:i}],i),g=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),g}if(a!=null)return this.convertAndCacheOnCPU(t);if(G().getBool("DEBUG")&&!G().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&G().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,p;if(i!=="complex64"&&G().get("WEBGL_BUFFER_SUPPORTED")){p=this.decode(t);let m=this.texData.get(p.dataId);u=this.gpgpu.createBufferFromTexture(m.texture.texture,...Gh(r))}this.pendingRead.set(t,[]),i!=="complex64"&&await this.gpgpu.createAndWaitForFence();let d;if(i==="complex64"){let m=await Promise.all([this.read(o.real.dataId),this.read(o.imag.dataId)]),f=m[0],g=m[1];d=T.mergeRealAndImagArrays(f,g)}else if(u==null)d=this.getValuesFromTexture(t);else{let m=w.sizeFromShape(r);d=this.gpgpu.downloadFloat32MatrixFromBuffer(u,m)}if(p!=null&&this.disposeIntermediateTensorInfo(p),u!=null){let m=this.gpgpu.gl;de(m,()=>m.deleteBuffer(u))}let c=this.convertAndCacheOnCPU(t,d),h=this.pendingRead.get(t);return this.pendingRead.delete(t),h.forEach(m=>m(c)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t)&&Ea().removeDataId(t,this),this.pendingDeletes--),c}readToGPU(t,n={}){let a=this.texData.get(t),{values:r,shape:s,slice:i,dtype:o,isPacked:l,texture:u}=a;if(o==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(i!=null){let h;l?h=new ss(s,es):h=new ir(s,es);let m=this.runWebGLProgram(h,[{dataId:t,shape:s,dtype:o}],o),f=this.readToGPU(m,n);return this.disposeIntermediateTensorInfo(m),f}if(u==null)throw r!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let p=this.decode(t,n.customTexShape),d=Ea().makeTensorFromTensorInfo(p),c=this.texData.get(p.dataId);return Object.assign({tensorRef:d},c.texture)}bufferSync(t){let n=this.readSync(t.dataId);if(t.dtype==="string")try{let a=n.map(r=>w.decodeString(r));return Pe(t.shape,t.dtype,a)}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return Pe(t.shape,t.dtype,n)}checkNumericalProblems(t){if(t!=null)for(let n=0;n0}time(t){let n=this.activeTimers,a=[],r=!1;this.programTimersStack==null?(this.programTimersStack=a,r=!0):this.activeTimers.push(a),this.activeTimers=a,t();let s=w.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),i=w.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=n,r&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let l=await Promise.all(s);o.kernelMs=w.sum(l),o.getExtraProfileInfo=()=>l.map((u,p)=>({name:i[p],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else o.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,o})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(t){return G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),t):(t.endMs=w.now(),t)}async getQueryTime(t){if(G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(t);let n=t;return n.endMs-n.startMs}disposeData(t,n=!1){if(this.pendingDisposal.has(t))return!1;if(!this.texData.has(t))return!0;if(n?this.texData.get(t).refCount=0:this.texData.get(t).refCount--,!n&&this.texData.get(t).refCount>0)return!1;if(this.pendingRead.has(t))return this.pendingDisposal.add(t),this.pendingDeletes++,!1;this.releaseGPUData(t);let{complexTensorInfos:a}=this.texData.get(t);return a!=null&&(this.disposeData(a.real.dataId,n),this.disposeData(a.imag.dataId,n)),this.texData.delete(t),!0}releaseGPUData(t){let{texture:n,dtype:a,texShape:r,usage:s,isPacked:i,slice:o}=this.texData.get(t),l=o&&o.origDataId||t,u=this.dataRefCount.get(l);u>1?this.dataRefCount.set(l,u-1):(this.dataRefCount.delete(l),n!=null&&(this.numBytesInGPU-=this.computeBytes(r,a),this.textureManager.releaseTexture(n,r,s,i)));let p=this.texData.get(t);p.texture=null,p.texShape=null,p.isPacked=!1,p.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture.texture}getDataInfo(t){return this.texData.get(t)}shouldExecuteOnCPU(t,n=wee){return G().getBool("WEBGL_CPU_FORWARD")&&t.every(a=>this.texData.get(a.dataId).texture==null&&w.sizeFromShape(a.shape)0&&w.isString(a[0])){let s=a.map(i=>w.encodeString(i));r=this.write(s,t,n)}else r=this.write(a,t,n);return this.texData.get(r).usage=null,{dataId:r,shape:t,dtype:n}}makeOutput(t,n,a){return Ea().makeTensorFromTensorInfo(this.makeTensorInfo(t,n,a),this)}unpackTensor(t){let n=new gee(t.shape);return this.runWebGLProgram(n,[t],t.dtype)}packTensor(t){let n=new eee(t.shape);return this.runWebGLProgram(n,[t],t.dtype,null,!0)}packedReshape(t,n){let a=[ki(t.shape),...Ii(t.shape)],r={dtype:t.dtype,shape:a,dataId:t.dataId},s=[ki(n),...Ii(n)],i=new JA(s,a),o=!0,l=[a],u=this.runWebGLProgram(i,[r],t.dtype,l,o);return{dataId:u.dataId,shape:n,dtype:u.dtype}}decode(t,n){let a=this.texData.get(t),{isPacked:r,shape:s,dtype:i}=a;if(n!=null){let c=w.sizeFromShape(s),h=n[0]*n[1]*4;w.assert(c<=h,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let o=dc(s),l;r?l=new nQ(o):l=new tQ(o);let u=!0,p=[n!=null?n:Gh(o)],d=this.runWebGLProgram(l,[{shape:o,dtype:i,dataId:t}],i,p,u,n);return{dtype:i,shape:s,dataId:d.dataId}}runWebGLProgram(t,n,a,r,s=!1,i){let o=this.makeTensorInfo(t.outputShape,a),l=this.texData.get(o.dataId);if(t.packedOutput&&(l.isPacked=!0),t.outPackingScheme===Fc.DENSE){let b=i!=null?i:Gh(t.outputShape);l.texShape=b.map(y=>y*2)}if(t.outTexUsage!=null&&(l.usage=t.outTexUsage),w.sizeFromShape(o.shape)===0)return l.values=w.getTypedArrayFromDType(o.dtype,0),o;let u=[],p=n.map(b=>{if(b.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let y=this.texData.get(b.dataId);if(y.texture==null){if(!t.packedInputs&&w.sizeFromShape(b.shape)<=G().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:b.shape,texData:null,isUniform:!0,uniformValues:y.values};t.packedInputs&&(y.isPacked=!0,y.shape=b.shape)}if(this.uploadToGPU(b.dataId),!!y.isPacked!=!!t.packedInputs)b=y.isPacked?this.unpackTensor(b):this.packTensor(b),u.push(b),y=this.texData.get(b.dataId);else if(y.isPacked&&!$c(y.shape,b.shape)){let x=b,v=b.shape;b.shape=y.shape,b=this.packedReshape(b,v),u.push(b),y=this.texData.get(b.dataId),x.shape=v}return{shape:b.shape,texData:y,isUniform:!1}});this.uploadToGPU(o.dataId);let d={shape:o.shape,texData:l,isUniform:!1},c=eQ(t,p,d),h=this.getAndSaveBinary(c,()=>J9(this.gpgpu,t,p,d)),m=this.activeTimers!=null,f;m&&(f=this.startTimer()),G().get("ENGINE_COMPILE_ONLY")||Q9(this.gpgpu,h,p,d,r),u.forEach(b=>this.disposeIntermediateTensorInfo(b)),m&&(f=this.endTimer(f),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(f)}));let g=G().getNumber("WEBGL_FLUSH_THRESHOLD");if(g>0){let b=w.now();b-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=b)}if(!G().getBool("WEBGL_LAZILY_UNPACK")&&l.isPacked&&s===!1){let b=this.unpackTensor(o);return this.disposeIntermediateTensorInfo(o),b}return o}compileAndRun(t,n,a,r,s=!1){return a=a||n[0].dtype,this.runWebGLProgram(t,n,a,r,s)}getAndSaveBinary(t,n){return t in this.binaryCache||(this.binaryCache[t]=n()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(G().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=O(()=>{if(!G().get("WEBGL_RENDER_FLOAT32_ENABLED")){let t=G().getBool("DEBUG");G().set("DEBUG",!1);let n=this.abs(xe(1e-8)).dataSync()[0];if(G().set("DEBUG",t),n>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?yee:xee}uploadToGPU(t){let n=this.texData.get(t),{shape:a,dtype:r,values:s,texture:i,usage:o,isPacked:l}=n;if(i!=null)return;let u=this.activeTimers!=null,p;u&&(p=w.now());let d=n.texShape;if(d==null&&(d=xA(a,l),n.texShape=d),s!=null){let c=dc(a),h,m=d[1],f=d[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(l||!g)&&([m,f]=vp(d[0],d[1])),l?h=new iQ(c,g):h=new NS(c,g);let b=g?[f,m]:d,y=this.makeTensorInfo(b,r),x=this.texData.get(y.dataId);g?x.usage=da.PIXELS:x.usage=da.UPLOAD,x.texShape=b,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),m,f,s);let v=[[f,m]],I=this.runWebGLProgram(h,[y],r,v,!0),N=this.texData.get(I.dataId);n.texShape=N.texShape,n.isPacked=N.isPacked,n.usage=N.usage,G().get("ENGINE_COMPILE_ONLY")?this.disposeData(I.dataId):(n.texture=N.texture,n.values=null,this.texData.delete(I.dataId)),this.disposeIntermediateTensorInfo(y),u&&(this.uploadWaitMs+=w.now()-p)}else{let c=this.acquireTexture(d,o,r,l);n.texture=c}}convertAndCacheOnCPU(t,n){let a=this.texData.get(t),{dtype:r}=a;return n!=null&&(a.values=See(n,r)),a.values}acquireTexture(t,n,a,r){if(this.numBytesInGPU+=this.computeBytes(t,a),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(t,n,r)}computeBytes(t,n){return t[0]*t[1]*w.bytesPerElement(n)}checkCompileCompletion(){for(let[,t]of Object.entries(this.binaryCache))this.checkCompletion_(t)}async checkCompileCompletionAsync(){let t=[];if(this.gpgpu.parallelCompilationExtension){for(let[,n]of Object.entries(this.binaryCache))t.push(this.checkCompletionAsync_(n));return Promise.all(t)}else{for(let[,n]of Object.entries(this.binaryCache)){let a=new Promise(r=>{try{this.checkCompletion_(n),r(!0)}catch(s){throw s}});t.push(a)}return Promise.all(t)}}async checkCompletionAsync_(t){return this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(t):(await s0(),this.checkCompletionAsync_(t))}checkCompletion_(t){if(this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(t.webGLProgram)),this.gpgpu.gl.getShaderParameter(t.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(ik(t.source,this.gpgpu.gl.getShaderInfoLog(t.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let t of Object.values(this.binaryCache)){this.gpgpu.buildVao(t.webGLProgram);let{variablesLocations:n,customUniformLocations:a,infLoc:r,nanLoc:s,outShapeLocation:i,outShapeStridesLocation:o,outTexShapeLocation:l}=AA(this.gpgpu,t.program,t.webGLProgram);t.variablesLocations=n,t.customUniformLocations=a,t.infLoc=r,t.nanLoc=s,t.outShapeLocation=i,t.outShapeStridesLocation=o,t.outTexShapeLocation=l}}createTensorFromGPUData(t,n,a){t.channels=t.channels||"RGBA";let{texture:r,height:s,width:i,channels:o}=t,l=Ea().backend;if(!l.gpgpu.gl.isTexture(r))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let u=l.writeTexture(r,n,a,s,i,o);return Ea().makeTensorFromDataId(u,n,a,l)}};gk.nextDataId=0;function See(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;anew gk,2);var Tee={forceHalfFloat:eF},bk=` if (isnan(a)) return a; if (isnan(b)) return b; -`,Si=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=T.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=xn(this.outputShape.length),this.userCode=` +`,Si=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=T.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` float binaryOperation(float a, float b) { ${e} } @@ -1259,7 +1259,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.g = isNaN.g ? NAN : result.g; result.b = isNaN.b ? NAN : result.b; result.a = isNaN.a ? NAN : result.a; -`,Tp=class{constructor(e,t,n,a=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=T.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=xn(r);let s="";if(a)if(r===0||w.sizeFromShape(this.outputShape)===1)s=` +`,Tp=class{constructor(e,t,n,a=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=T.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=vn(r);let s="";if(a)if(r===0||w.sizeFromShape(this.outputShape)===1)s=` result.y = 0.; result.z = 0.; result.w = 0.; @@ -1273,7 +1273,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y; result.z = 0.; result.w = 0.; - `;else{let i=kn("coords",r);this.enableShapeUniforms?s+=` + `;else{let i=In("coords",r);this.enableShapeUniforms?s+=` bool nextRowOutOfBounds = (${i[r-2]} + 1) >= outShape[${r} - 2]; bool nextColOutOfBounds = @@ -1303,13 +1303,13 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(result); } - `}};function aa(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 Iee={kernelName:no,backendName:"webgl",kernelFunc:aa};function Ms(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=aa({inputs:{x:a},backend:n}),l=aa({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var See={kernelName:Mm,backendName:"webgl",kernelFunc:Ms},QA="return (a < 0.) ? b * a : a;",eF=` + `}};function aa(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 Cee={kernelName:no,backendName:"webgl",kernelFunc:aa};function Os(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=aa({inputs:{x:a},backend:n}),l=aa({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var Eee={kernelName:Mm,backendName:"webgl",kernelFunc:Os},tF="return (a < 0.) ? b * a : a;",nF=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function Nee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a,i=n.makeTensorInfo([],"float32",w.createScalarValue(s,"float32")),o=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Tp(eF,r.shape,i.shape):new Si(QA,r.shape,i.shape),l=n.runWebGLProgram(o,[r,i],"float32");return n.disposeIntermediateTensorInfo(i),l}var Tee={kernelName:io,backendName:"webgl",kernelFunc:Nee},tF="return (a < 0.) ? b * a : a;",nF=` +`;function _ee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a,i=n.makeTensorInfo([],"float32",w.createScalarValue(s,"float32")),o=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Tp(nF,r.shape,i.shape):new Si(tF,r.shape,i.shape),l=n.runWebGLProgram(o,[r,i],"float32");return n.disposeIntermediateTensorInfo(i),l}var Aee={kernelName:io,backendName:"webgl",kernelFunc:_ee},aF="return (a < 0.) ? b * a : a;",rF=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function Cee(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Tp(nF,a.shape,r.shape):new Si(tF,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],"float32")}var Eee={kernelName:Io,backendName:"webgl",kernelFunc:Cee},Cp="if (isnan(x)) return x;";function Ze({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:a}){return({inputs:r,backend:s})=>{let{x:i}=r,o=s,l=a||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let d=o.texData.get(i.dataId),c=n(d.values,l);return o.makeTensorInfo(i.shape,l,c)}let u=G().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,p;return u?p=new ss(i.shape,t):p=new ir(i.shape,e),o.runWebGLProgram(p,[i],l)}}function fn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:a=!1,cpuKernelImpl:r,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,p=o;if(a&&l.dtype==="complex64"){let m=p.texData.get(l.dataId),f=p.texData.get(u.dataId),[g,b]=[[m.complexTensorInfos.real,f.complexTensorInfos.real],[m.complexTensorInfos.imag,f.complexTensorInfos.imag]].map(x=>{let[v,I]=x,N={dataId:v.dataId,dtype:v.dtype,shape:l.shape},C={dataId:I.dataId,dtype:I.dtype,shape:u.shape},_=new Si(e,l.shape,u.shape);return p.runWebGLProgram(_,[N,C],ga(v.dtype,I.dtype))}),y=Ms({inputs:{real:g,imag:b},backend:p});return p.disposeIntermediateTensorInfo(g),p.disposeIntermediateTensorInfo(b),y}let d=s||ga(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||p.shouldExecuteOnCPU([l,u]))&&r!=null){let m=p.texData.get(l.dataId).values,f=p.texData.get(u.dataId).values,g=l.dtype==="string"?T.fromUint8ToStringArray(m):m,b=l.dtype==="string"?T.fromUint8ToStringArray(f):f,[y,x]=r(l.shape,u.shape,g,b,d),v=p.makeTensorInfo(x,d),I=p.texData.get(v.dataId);return I.values=y,v}let c=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return c?h=new Tp(t,l.shape,u.shape,n):h=new Si(e,l.shape,u.shape),p.runWebGLProgram(h,[l,u],d)}}function Dc(e,t=!1){if(e==="linear")return t?oee:nee;if(e==="relu")return t?uee:ree;if(e==="elu")return t?lee:aee;if(e==="relu6")return t?pee:see;if(e==="prelu")return t?nF:tF;if(e==="leakyrelu")return t?eF:QA;if(e==="sigmoid")return t?cee:iee;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var aF=class{constructor(e,t,n,a=!1,r=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=xn(this.outputShape.length);let u=a?e[1]:e[2],p=Math.ceil(u/2),d=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="",g="";i&&(o?f=`vec4 activation(vec4 a) { +`;function Fee(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Tp(rF,a.shape,r.shape):new Si(aF,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],"float32")}var $ee={kernelName:Io,backendName:"webgl",kernelFunc:Fee},Cp="if (isnan(x)) return x;";function Ze({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:a}){return({inputs:r,backend:s})=>{let{x:i}=r,o=s,l=a||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let d=o.texData.get(i.dataId),c=n(d.values,l);return o.makeTensorInfo(i.shape,l,c)}let u=G().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,p;return u?p=new ss(i.shape,t):p=new ir(i.shape,e),o.runWebGLProgram(p,[i],l)}}function fn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:a=!1,cpuKernelImpl:r,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,p=o;if(a&&l.dtype==="complex64"){let m=p.texData.get(l.dataId),f=p.texData.get(u.dataId),[g,b]=[[m.complexTensorInfos.real,f.complexTensorInfos.real],[m.complexTensorInfos.imag,f.complexTensorInfos.imag]].map(x=>{let[v,I]=x,N={dataId:v.dataId,dtype:v.dtype,shape:l.shape},C={dataId:I.dataId,dtype:I.dtype,shape:u.shape},_=new Si(e,l.shape,u.shape);return p.runWebGLProgram(_,[N,C],ga(v.dtype,I.dtype))}),y=Os({inputs:{real:g,imag:b},backend:p});return p.disposeIntermediateTensorInfo(g),p.disposeIntermediateTensorInfo(b),y}let d=s||ga(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||p.shouldExecuteOnCPU([l,u]))&&r!=null){let m=p.texData.get(l.dataId).values,f=p.texData.get(u.dataId).values,g=l.dtype==="string"?T.fromUint8ToStringArray(m):m,b=l.dtype==="string"?T.fromUint8ToStringArray(f):f,[y,x]=r(l.shape,u.shape,g,b,d),v=p.makeTensorInfo(x,d),I=p.texData.get(v.dataId);return I.values=y,v}let c=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return c?h=new Tp(t,l.shape,u.shape,n):h=new Si(e,l.shape,u.shape),p.runWebGLProgram(h,[l,u],d)}}function Dc(e,t=!1){if(e==="linear")return t?cee:iee;if(e==="relu")return t?hee:lee;if(e==="elu")return t?dee:oee;if(e==="relu6")return t?mee:uee;if(e==="prelu")return t?rF:aF;if(e==="leakyrelu")return t?nF:tF;if(e==="sigmoid")return t?fee:pee;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var sF=class{constructor(e,t,n,a=!1,r=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=vn(this.outputShape.length);let u=a?e[1]:e[2],p=Math.ceil(u/2),d=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="",g="";i&&(o?f=`vec4 activation(vec4 a) { vec4 b = getPreluActivationWeightsAtOutCoords(); ${i} }`:l?f=`vec4 activation(vec4 a) { @@ -1361,7 +1361,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float bimag = getBImagAtOutCoords(); setOutput(binaryOpComplex(areal, aimag, breal, bimag)); } - `}},$S="return a * b;";function yk(e){let{inputs:t,backend:n}=e,{a,b:r}=t,s=T.upcastType(a.dtype,r.dtype);if(a.dtype==="complex64"){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),u=new FS(AS.REAL,a.shape,r.shape),p=new FS(AS.IMAG,a.shape,r.shape),d=[{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:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],c=n.runWebGLProgram(u,d,"float32"),h=n.runWebGLProgram(p,d,"float32"),m=Ms({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),l=n.texData.get(r.dataId),[u,p]=NQ(a.shape,r.shape,o.values,l.values,s),d=n.makeTensorInfo(p,s),c=n.texData.get(d.dataId);return c.values=u,d}let i;return G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Tp($S,a.shape,r.shape):i=new Si($S,a.shape,r.shape),n.runWebGLProgram(i,[a,r],s)}var _ee={kernelName:xo,backendName:"webgl",kernelFunc:yk};function Aee(e,t,n){let a=[ki(e.shape),...Ii(e.shape)],r={dtype:e.dtype,shape:a,dataId:e.dataId},s=[ki(t),...Ii(t)],i=new YA(s,a),o=!0,l=[a],u=n.runWebGLProgram(i,[r],e.dtype,l,o);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function ce(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{shape:s}=a,i=n,o=w.sizeFromShape(r.shape),l=w.inferFromImplicitShape(s,o),u=w.sizeFromShape(l);w.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let p=i.texData.get(r.dataId);return p.isPacked&&!$c(r.shape,l)&&!(p.texture!==null&&$c(p.shape,l))?Aee(r,l,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var Fee={kernelName:ju,backendName:"webgl",kernelFunc:ce},DS=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,l="sumValue += dot(values, ones);";if(t!=null){let p=1/t;l=`sumValue += dot(values * ${w.isInt(p)?p.toPrecision(2):p}, ones);`}let u="";r%n>0&&(u=` + `}},$S="return a * b;";function yk(e){let{inputs:t,backend:n}=e,{a,b:r}=t,s=T.upcastType(a.dtype,r.dtype);if(a.dtype==="complex64"){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),u=new FS(AS.REAL,a.shape,r.shape),p=new FS(AS.IMAG,a.shape,r.shape),d=[{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:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],c=n.runWebGLProgram(u,d,"float32"),h=n.runWebGLProgram(p,d,"float32"),m=Os({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),l=n.texData.get(r.dataId),[u,p]=_Q(a.shape,r.shape,o.values,l.values,s),d=n.makeTensorInfo(p,s),c=n.texData.get(d.dataId);return c.values=u,d}let i;return G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Tp($S,a.shape,r.shape):i=new Si($S,a.shape,r.shape),n.runWebGLProgram(i,[a,r],s)}var Dee={kernelName:xo,backendName:"webgl",kernelFunc:yk};function Ree(e,t,n){let a=[ki(e.shape),...Ii(e.shape)],r={dtype:e.dtype,shape:a,dataId:e.dataId},s=[ki(t),...Ii(t)],i=new JA(s,a),o=!0,l=[a],u=n.runWebGLProgram(i,[r],e.dtype,l,o);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function ce(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{shape:s}=a,i=n,o=w.sizeFromShape(r.shape),l=w.inferFromImplicitShape(s,o),u=w.sizeFromShape(l);w.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let p=i.texData.get(r.dataId);return p.isPacked&&!$c(r.shape,l)&&!(p.texture!==null&&$c(p.shape,l))?Ree(r,l,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var Mee={kernelName:qu,backendName:"webgl",kernelFunc:ce},DS=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,l="sumValue += dot(values, ones);";if(t!=null){let p=1/t;l=`sumValue += dot(values * ${w.isInt(p)?p.toPrecision(2):p}, ones);`}let u="";r%n>0&&(u=` if (inIdx < 0 || inIdx >= ${r}) { return 0.0; } @@ -1414,7 +1414,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, } setOutput(sumValue); } - `}},$ee=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 l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let u=Math.floor(n/4)*4,p=n%4,d=` + `}},Oee=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let u=Math.floor(n/4)*4,p=n%4,d=` if (${t==="sum"}) { sumValue += dot(values, ones); } else if (${t==="prod"}) { @@ -1506,12 +1506,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, } setOutput(${l}); } - `}};function Dee(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=T.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:a,outSize:Math.ceil(n/a)})}return t}function nl(e,t,n,a){let r=Dee(e.shape),s=e;for(let i=0;i6)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;r6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let a=ht(this.rank),r=XA("rc",this.rank),s=new Array(this.rank);for(let u=0;u6)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;r6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let a=ht(this.rank),r=ZA("rc",this.rank),s=new Array(this.rank);for(let u=0;u`Error in matMul: inner shapes (${d}) and (${c}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${a} must match.`);let v=n?[b,d,h]:[b,h,d],I=a?[y,m,c]:[y,c,m],N=ce({inputs:{x:e},backend:r,attrs:{shape:v}}),C=ce({inputs:{x:t},backend:r,attrs:{shape:I}}),_=[N,C],F=Math.max(b,y),D=n?N.shape[1]:N.shape[2],$=s!=null,S=i!=null,M=l==="leakyrelu",B=l!=null?Dc(l,!0):null,U=$||S||M||B!=null,H;if((h===1||m===1)&&D>rF&&U===!1){let K=N,Z=C;n&&(K=In({inputs:{x:N},backend:r,attrs:{perm:[0,2,1]}}),_.push(K)),a&&(Z=In({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),_.push(Z));let J=m!==1,ee=m===1,ae=K;J&&(ae=ce({inputs:{x:K},backend:r,attrs:{shape:[F,D,1]}}),_.push(ae));let te=m===1?2:1,se=Z;ee&&(se=ce({inputs:{x:Z},backend:r,attrs:{shape:[F,1,D]}}),_.push(se));let ie=yk({inputs:{a:ae,b:se},backend:r});H=tg({inputs:{x:ie},backend:r,attrs:{axis:te,keepDims:!0}}),_.push(ie)}else{let K=ga(e.dtype,t.dtype),Z=new aF(v,I,[F,h,m],n,a,$,B,S,M),J=[N,C];if(s!=null&&J.push(s),S&&J.push(i),M){let ee=r.makeTensorInfo([],"float32",w.createScalarValue(o,"float32"));J.push(ee),_.push(ee)}H=r.runWebGLProgram(Z,J,K)}let j=ce({inputs:{x:H},backend:r,attrs:{shape:x}});_.push(H);for(let K of _)r.disposeIntermediateTensorInfo(K);return j}function Wee(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:d}=a;return Cm({a:r,b:s,transposeA:l,transposeB:u,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:d,activation:p})}var Bee={kernelName:oi,backendName:"webgl",kernelFunc:Wee},RS="return abs(x);";function Vee(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=jA(s.values);return n.makeTensorInfo(a.shape,a.dtype,i)}let r;return G().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new ss(a.shape,RS):r=new ir(a.shape,RS),n.runWebGLProgram(r,[a],a.dtype)}var Uee={kernelName:uu,backendName:"webgl",kernelFunc:Vee},Gee=Ma+` + `}};function tg(e,t,n){let a=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Wee(e.shape,t):new Lee(e.shape,t);return n.runWebGLProgram(a,[e],e.dtype)}function Bee(e,t,n,a){let r=t,s=e.shape.length,i=w.parseAxisParam(r,e.shape),o=i,l=T.getAxesPermutation(o,s),u=l!=null,p=e;u&&(p=tg(e,l,a),o=T.getInnerMostAxes(o.length,s)),T.assertAxesAreInnerMostDims("sum",o,s);let[d,c]=T.computeOutAndReduceShapes(p.shape,o),h=d;n&&(h=T.expandShapeToKeepDim(d,i));let m=w.sizeFromShape(c),f=w.sizeFromShape(e.shape)/m,g=ce({inputs:{x:p},attrs:{shape:[f,m]},backend:a}),b=Xm(e.dtype),y=nl(g,b,"sum",a),x=ce({inputs:{x:y},attrs:{shape:h},backend:a});return a.disposeIntermediateTensorInfo(g),a.disposeIntermediateTensorInfo(y),u&&a.disposeIntermediateTensorInfo(p),x}function ng(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;return Bee(r,s,i,n)}var Vee={kernelName:Wo,backendName:"webgl",kernelFunc:ng};function Sn(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{perm:s}=a,i=n,o=r.shape.length,l=new Array(o);for(let p=0;p`Error in matMul: inner shapes (${d}) and (${c}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${a} must match.`);let v=n?[b,d,h]:[b,h,d],I=a?[y,m,c]:[y,c,m],N=ce({inputs:{x:e},backend:r,attrs:{shape:v}}),C=ce({inputs:{x:t},backend:r,attrs:{shape:I}}),_=[N,C],F=Math.max(b,y),D=n?N.shape[1]:N.shape[2],$=s!=null,S=i!=null,M=l==="leakyrelu",B=l!=null?Dc(l,!0):null,U=$||S||M||B!=null,H;if((h===1||m===1)&&D>iF&&U===!1){let K=N,Z=C;n&&(K=Sn({inputs:{x:N},backend:r,attrs:{perm:[0,2,1]}}),_.push(K)),a&&(Z=Sn({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),_.push(Z));let J=m!==1,ee=m===1,ae=K;J&&(ae=ce({inputs:{x:K},backend:r,attrs:{shape:[F,D,1]}}),_.push(ae));let te=m===1?2:1,se=Z;ee&&(se=ce({inputs:{x:Z},backend:r,attrs:{shape:[F,1,D]}}),_.push(se));let ie=yk({inputs:{a:ae,b:se},backend:r});H=ng({inputs:{x:ie},backend:r,attrs:{axis:te,keepDims:!0}}),_.push(ie)}else{let K=ga(e.dtype,t.dtype),Z=new sF(v,I,[F,h,m],n,a,$,B,S,M),J=[N,C];if(s!=null&&J.push(s),S&&J.push(i),M){let ee=r.makeTensorInfo([],"float32",w.createScalarValue(o,"float32"));J.push(ee),_.push(ee)}H=r.runWebGLProgram(Z,J,K)}let q=ce({inputs:{x:H},backend:r,attrs:{shape:x}});_.push(H);for(let K of _)r.disposeIntermediateTensorInfo(K);return q}function Gee(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:d}=a;return Cm({a:r,b:s,transposeA:l,transposeB:u,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:d,activation:p})}var Hee={kernelName:li,backendName:"webgl",kernelFunc:Gee},RS="return abs(x);";function jee(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=XA(s.values);return n.makeTensorInfo(a.shape,a.dtype,i)}let r;return G().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new ss(a.shape,RS):r=new ir(a.shape,RS),n.runWebGLProgram(r,[a],a.dtype)}var qee={kernelName:uu,backendName:"webgl",kernelFunc:jee},Kee=Ma+` if (abs(x) > 1.) { return NAN; } return acos(x); -`,Hee=Ze({opSnippet:Gee}),qee={kernelName:Ci,backendName:"webgl",kernelFunc:Hee},jee=Ma+` +`,Xee=Ze({opSnippet:Kee}),Yee={kernelName:Ci,backendName:"webgl",kernelFunc:Xee},Zee=Ma+` if (x < 1.0) return NAN; -return log(x + sqrt(x * x - 1.0));`,Kee=Ze({opSnippet:jee}),Xee={kernelName:Ei,backendName:"webgl",kernelFunc:Kee},MS="return a + b;",Yee=fn({opSnippet:MS,packedOpSnippet:MS,supportsComplex:!0,cpuKernelImpl:rQ}),Zee={kernelName:Is,backendName:"webgl",kernelFunc:Yee},Jee=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=` +return log(x + sqrt(x * x - 1.0));`,Jee=Ze({opSnippet:Zee}),Qee={kernelName:Ei,backendName:"webgl",kernelFunc:Jee},MS="return a + b;",ete=fn({opSnippet:MS,packedOpSnippet:MS,supportsComplex:!0,cpuKernelImpl:lQ}),tte={kernelName:Ss,backendName:"webgl",kernelFunc:ete},nte=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(` `)} @@ -1543,7 +1543,7 @@ return log(x + sqrt(x * x - 1.0));`,Kee=Ze({opSnippet:jee}),Xee={kernelName:Ei,b float result = ${a}; setOutput(result); } - `}},Qee=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let n=[];this.variableNames.forEach(r=>{n.push(`vec4 v${r} = get${r}AtOutCoords();`)});let a=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=` + `}},ate=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,s)=>`T${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(` `)} @@ -1551,7 +1551,7 @@ return log(x + sqrt(x * x - 1.0));`,Kee=Ze({opSnippet:jee}),Xee={kernelName:Ei,b vec4 result = ${a}; setOutput(result); } - `}};function tm(e){let{inputs:t,backend:n}=e,a=t;if(a.length===1)return aa({inputs:{x:a[0]},backend:n});if(a.length>G().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(a.length/2),l=tm({inputs:a.slice(0,o),backend:n}),u=tm({inputs:a.slice(o),backend:n});return tm({inputs:[l,u],backend:n})}let r=a.map(o=>o.dtype).reduce((o,l)=>ga(o,l)),s=a.map(o=>o.shape),i=G().getBool("WEBGL_PACK")?new Qee(a[0].shape,s):new Jee(a[0].shape,s);return n.runWebGLProgram(i,a,r)}var ete={kernelName:_i,backendName:"webgl",kernelFunc:tm};function tte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=w.parseAxisParam(s,r.shape),u=l,p=T.getAxesPermutation(u,o),d=r;p!=null&&(d=In({inputs:{x:r},backend:n,attrs:{perm:p}}),u=T.getInnerMostAxes(u.length,o)),T.assertAxesAreInnerMostDims("all",u,o);let[c,h]=T.computeOutAndReduceShapes(d.shape,u),m=w.sizeFromShape(h),f=ce({inputs:{x:d},backend:n,attrs:{shape:[-1,m]}}),g=nl(f,f.dtype,"all",n),b;if(i){let y=T.expandShapeToKeepDim(c,l);b=ce({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=ce({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),p!=null&&n.disposeIntermediateTensorInfo(d),b}var nte={kernelName:pu,backendName:"webgl",kernelFunc:tte};function ate(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=w.parseAxisParam(s,r.shape),u=l,p=T.getAxesPermutation(u,o),d=r;p!=null&&(d=In({inputs:{x:r},backend:n,attrs:{perm:p}}),u=T.getInnerMostAxes(u.length,o)),T.assertAxesAreInnerMostDims("any",u,o);let[c,h]=T.computeOutAndReduceShapes(d.shape,u),m=w.sizeFromShape(h),f=ce({inputs:{x:d},backend:n,attrs:{shape:[-1,m]}}),g=nl(f,f.dtype,"any",n),b;if(i){let y=T.expandShapeToKeepDim(c,l);b=ce({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=ce({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),p!=null&&n.disposeIntermediateTensorInfo(d),b}var rte={kernelName:cu,backendName:"webgl",kernelFunc:ate},ste=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=` + `}};function tm(e){let{inputs:t,backend:n}=e,a=t;if(a.length===1)return aa({inputs:{x:a[0]},backend:n});if(a.length>G().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(a.length/2),l=tm({inputs:a.slice(0,o),backend:n}),u=tm({inputs:a.slice(o),backend:n});return tm({inputs:[l,u],backend:n})}let r=a.map(o=>o.dtype).reduce((o,l)=>ga(o,l)),s=a.map(o=>o.shape),i=G().getBool("WEBGL_PACK")?new ate(a[0].shape,s):new nte(a[0].shape,s);return n.runWebGLProgram(i,a,r)}var rte={kernelName:_i,backendName:"webgl",kernelFunc:tm};function ste(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=w.parseAxisParam(s,r.shape),u=l,p=T.getAxesPermutation(u,o),d=r;p!=null&&(d=Sn({inputs:{x:r},backend:n,attrs:{perm:p}}),u=T.getInnerMostAxes(u.length,o)),T.assertAxesAreInnerMostDims("all",u,o);let[c,h]=T.computeOutAndReduceShapes(d.shape,u),m=w.sizeFromShape(h),f=ce({inputs:{x:d},backend:n,attrs:{shape:[-1,m]}}),g=nl(f,f.dtype,"all",n),b;if(i){let y=T.expandShapeToKeepDim(c,l);b=ce({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=ce({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),p!=null&&n.disposeIntermediateTensorInfo(d),b}var ite={kernelName:pu,backendName:"webgl",kernelFunc:ste};function ote(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=w.parseAxisParam(s,r.shape),u=l,p=T.getAxesPermutation(u,o),d=r;p!=null&&(d=Sn({inputs:{x:r},backend:n,attrs:{perm:p}}),u=T.getInnerMostAxes(u.length,o)),T.assertAxesAreInnerMostDims("any",u,o);let[c,h]=T.computeOutAndReduceShapes(d.shape,u),m=w.sizeFromShape(h),f=ce({inputs:{x:d},backend:n,attrs:{shape:[-1,m]}}),g=nl(f,f.dtype,"any",n),b;if(i){let y=T.expandShapeToKeepDim(c,l);b=ce({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=ce({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),p!=null&&n.disposeIntermediateTensorInfo(d),b}var lte={kernelName:cu,backendName:"webgl",kernelFunc:ote},ute=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]; @@ -1571,7 +1571,7 @@ return log(x + sqrt(x * x - 1.0));`,Kee=Ze({opSnippet:jee}),Xee={kernelName:Ei,b } setOutput(float(bestIndex)); } - `}},ite=class{constructor(e,t,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,w.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,l=ht(o),u=kn("coords",o),p,d;if(s===1){d=o+1;let C=ht(d);p=` + `}},pte=class{constructor(e,t,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,w.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,l=ht(o),u=In("coords",o),p,d;if(s===1){d=o+1;let C=ht(d);p=` ${C} sourceLocR = ${C}(${u.join()}, 0); ++${u[o-1]}; ${C} sourceLocG = ${C}(${u.join()}, 0); @@ -1587,7 +1587,7 @@ return log(x + sqrt(x * x - 1.0));`,Kee=Ze({opSnippet:jee}),Xee={kernelName:Ei,b ${l} sourceLocA = coords; --${u[o-1]}; ${l} sourceLocB = coords; - --${u[o-2]};`;let c=["x","y","z","w","u","v"].slice(0,d),h="."+c[d-1],m=c.map(C=>"int "+C),f=kn("sourceLocR",d-1).concat("inIdx.r"),g=kn("sourceLocG",d-1).concat("inIdx.g"),b=kn("sourceLocB",d-1).concat("inIdx.b"),y=kn("sourceLocA",d-1).concat("inIdx.a"),x=n==="max"?"greaterThan":"lessThan",v=a?"":` + --${u[o-2]};`;let c=["x","y","z","w","u","v"].slice(0,d),h="."+c[d-1],m=c.map(C=>"int "+C),f=In("sourceLocR",d-1).concat("inIdx.r"),g=In("sourceLocG",d-1).concat("inIdx.g"),b=In("sourceLocB",d-1).concat("inIdx.b"),y=In("sourceLocA",d-1).concat("inIdx.a"),x=n==="max"?"greaterThan":"lessThan",v=a?"":` inIdx = round(vec4(getBestIndicesAChannel(${f.join()}), getBestIndicesAChannel(${g.join()}), getBestIndicesAChannel(${b.join()}), @@ -1633,25 +1633,25 @@ return log(x + sqrt(x * x - 1.0));`,Kee=Ze({opSnippet:jee}),Xee={kernelName:Ei,b } setOutput(bestIndex); } - `}};function sF(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=T.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},l=new ste(o,n,a==null),u=[t];a!=null&&u.push(a);let p=e.runWebGLProgram(l,u,"int32");if(p.shape[1]===1)return p;let d=sF(e,t,n,p);return e.disposeIntermediateTensorInfo(p),d}function iF(e,t,n,a=null){let r=a!=null?a.shape:t.shape,s=r[r.length-1],i=T.computeOptimalWindowSize(s),o=new ite(r,i,n,a==null),l=a==null?[t]:[t,a],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let p=iF(e,t,n,u);return e.disposeIntermediateTensorInfo(u),p}return u}function oF(e,t,n,a){let r=[n];if(T.assertAxesAreInnerMostDims("arg"+a.charAt(0).toUpperCase()+a.slice(1),r,t.shape.length),!G().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,l=t;o&&(l=e.unpackTensor(t),s.push(l));let[u,p]=T.computeOutAndReduceShapes(l.shape,r),d=w.sizeFromShape(p),c=ce({inputs:{x:l},backend:e,attrs:{shape:[-1,d]}});s.push(c);let h=sF(e,c,a);s.push(h);let m=ce({inputs:{x:h},backend:e,attrs:{shape:u}});return s.forEach(f=>e.disposeIntermediateTensorInfo(f)),m}return iF(e,t,a)}function ote(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=w.parseAxisParam(s,r.shape),o=T.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=In({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=T.getInnerMostAxes(i.length,l.shape.length)),T.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let p=oF(n,l,i[0],"max");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),p}var lte={kernelName:du,backendName:"webgl",kernelFunc:ote};function ute(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=w.parseAxisParam(s,r.shape),o=T.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=In({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=T.getInnerMostAxes(i.length,l.shape.length)),T.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let p=oF(n,l,i[0],"min");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),p}var pte={kernelName:hu,backendName:"webgl",kernelFunc:ute},cte=Ma+` + `}};function oF(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=T.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},l=new ute(o,n,a==null),u=[t];a!=null&&u.push(a);let p=e.runWebGLProgram(l,u,"int32");if(p.shape[1]===1)return p;let d=oF(e,t,n,p);return e.disposeIntermediateTensorInfo(p),d}function lF(e,t,n,a=null){let r=a!=null?a.shape:t.shape,s=r[r.length-1],i=T.computeOptimalWindowSize(s),o=new pte(r,i,n,a==null),l=a==null?[t]:[t,a],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let p=lF(e,t,n,u);return e.disposeIntermediateTensorInfo(u),p}return u}function uF(e,t,n,a){let r=[n];if(T.assertAxesAreInnerMostDims("arg"+a.charAt(0).toUpperCase()+a.slice(1),r,t.shape.length),!G().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,l=t;o&&(l=e.unpackTensor(t),s.push(l));let[u,p]=T.computeOutAndReduceShapes(l.shape,r),d=w.sizeFromShape(p),c=ce({inputs:{x:l},backend:e,attrs:{shape:[-1,d]}});s.push(c);let h=oF(e,c,a);s.push(h);let m=ce({inputs:{x:h},backend:e,attrs:{shape:u}});return s.forEach(f=>e.disposeIntermediateTensorInfo(f)),m}return lF(e,t,a)}function cte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=w.parseAxisParam(s,r.shape),o=T.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Sn({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=T.getInnerMostAxes(i.length,l.shape.length)),T.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let p=uF(n,l,i[0],"max");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),p}var dte={kernelName:du,backendName:"webgl",kernelFunc:cte};function hte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=w.parseAxisParam(s,r.shape),o=T.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Sn({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=T.getInnerMostAxes(i.length,l.shape.length)),T.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let p=uF(n,l,i[0],"min");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),p}var mte={kernelName:hu,backendName:"webgl",kernelFunc:hte},fte=Ma+` if (abs(x) > 1.) { return NAN; } return asin(x); -`,dte=Ze({opSnippet:cte}),hte={kernelName:Ai,backendName:"webgl",kernelFunc:dte},mte=Ma+"return log(x + sqrt(x * x + 1.0));",fte=Ze({opSnippet:mte}),gte={kernelName:Fi,backendName:"webgl",kernelFunc:fte},bte=Ma+` +`,gte=Ze({opSnippet:fte}),bte={kernelName:Ai,backendName:"webgl",kernelFunc:gte},yte=Ma+"return log(x + sqrt(x * x + 1.0));",xte=Ze({opSnippet:yte}),vte={kernelName:Fi,backendName:"webgl",kernelFunc:xte},wte=Ma+` return atan(x); -`,yte=Ze({opSnippet:bte}),xte={kernelName:$i,backendName:"webgl",kernelFunc:yte},vte=bk+` +`,kte=Ze({opSnippet:wte}),Ite={kernelName:$i,backendName:"webgl",kernelFunc:kte},Ste=bk+` return atan(a, b); -`,wte=` +`,Nte=` vec4 result = atan(a, b); bvec4 isNaNA = isnan(a); bvec4 isNaNB = isnan(b); bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w); `+tl+` return result; -`,kte=fn({opSnippet:vte,packedOpSnippet:wte}),Ite={kernelName:Ri,backendName:"webgl",kernelFunc:kte},Ste=Ma+` +`,Tte=fn({opSnippet:Ste,packedOpSnippet:Nte}),Cte={kernelName:Ri,backendName:"webgl",kernelFunc:Tte},Ete=Ma+` if ((x < -1.0) || (x > 1.0)) return NAN; -return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nte=Ze({opSnippet:Ste}),Tte={kernelName:Di,backendName:"webgl",kernelFunc:Nte},Rc=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,l=e.dilationHeight,u=e.dilationWidth,p=e.effectiveFilterHeight,d=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`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b="0.0";if(m||(b="-1.0 / 1e-20"),n){let C=">=";this.userCode=` +return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,_te=Ze({opSnippet:Ete}),Ate={kernelName:Di,backendName:"webgl",kernelFunc:_te},Rc=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,l=e.dilationHeight,u=e.dilationWidth,p=e.effectiveFilterHeight,d=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`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b="0.0";if(m||(b="-1.0 / 1e-20"),n){let C=">=";this.userCode=` const ivec2 strides = ivec2(${i}, ${o}); const ivec2 pads = ivec2(${c}, ${h}); @@ -1955,7 +1955,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nte=Ze({opSnippet:Ste}),Tte={kernel } setOutput(${I}); } - `}};function Cte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;wp(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;w.assert(T.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=T.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&w.arraysEqual(p.inShape,p.outShape))return aa({inputs:{x:r},backend:n});let d=new Rc(p,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var Ete={kernelName:Mi,backendName:"webgl",kernelFunc:Cte};function _te(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=a,p=[1,1,1],d=T.computePool3DInfo(r.shape,s,i,p,o,l,u),c=new xk(d,"avg",!1);return n.runWebGLProgram(c,[r],"float32")}var Ate={kernelName:mu,backendName:"webgl",kernelFunc:_te},Fte=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,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,p=l-1-e.padInfo.left,d=1/(t*n);this.userCode=` + `}};function Fte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;wp(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;w.assert(T.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=T.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&w.arraysEqual(p.inShape,p.outShape))return aa({inputs:{x:r},backend:n});let d=new Rc(p,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var $te={kernelName:Mi,backendName:"webgl",kernelFunc:Fte};function Dte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=a,p=[1,1,1],d=T.computePool3DInfo(r.shape,s,i,p,o,l,u),c=new xk(d,"avg",!1);return n.runWebGLProgram(c,[r],"float32")}var Rte={kernelName:mu,backendName:"webgl",kernelFunc:Dte},Mte=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,p=l-1-e.padInfo.left,d=1/(t*n);this.userCode=` const ivec2 pads = ivec2(${u}, ${p}); const float avgMultiplier = float(${d}); @@ -1997,7 +1997,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nte=Ze({opSnippet:Ste}),Tte={kernel } setOutput(dotProd); } - `}},$te=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,p=e.effectiveFilterDepth,d=e.effectiveFilterHeight,c=e.effectiveFilterWidth,h=p-1-e.padInfo.front,m=d-1-e.padInfo.top,f=c-1-e.padInfo.left,g=1/(t*n*a);this.userCode=` + `}},Ote=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,p=e.effectiveFilterDepth,d=e.effectiveFilterHeight,c=e.effectiveFilterWidth,h=p-1-e.padInfo.front,m=d-1-e.padInfo.top,f=c-1-e.padInfo.left,g=1/(t*n*a);this.userCode=` const ivec3 pads = ivec3(${h}, ${m}, ${f}); const float avgMultiplier = float(${g}); @@ -2053,7 +2053,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nte=Ze({opSnippet:Ste}),Tte={kernel } setOutput(dotProd); } - `}};function Dte(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=a,d=[1,1,1],c=T.computePool3DInfo(i.shape,o,l,d,u,p),h=new $te(c);return n.runWebGLProgram(h,[r],i.dtype)}var Rte={kernelName:Wc,backendName:"webgl",kernelFunc:Dte};function Mte(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;wp([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,p=T.computePool2DInfo(i.shape,o,l,1,u),d=new Fte(p);return n.runWebGLProgram(d,[r],i.dtype)}var Ote={kernelName:zc,backendName:"webgl",kernelFunc:Mte};function Pte(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;return Cm({a:r,b:s,transposeA:i,transposeB:o,backend:n})}var Lte={kernelName:Oi,backendName:"webgl",kernelFunc:Pte},zte=class{constructor(e,t,n,a,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],T.assertAndGetBroadcastShape(e,t),T.assertAndGetBroadcastShape(e,n);let i="0.0";a!=null&&(T.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(T.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` + `}};function Pte(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=a,d=[1,1,1],c=T.computePool3DInfo(i.shape,o,l,d,u,p),h=new Ote(c);return n.runWebGLProgram(h,[r],i.dtype)}var Lte={kernelName:Wc,backendName:"webgl",kernelFunc:Pte};function zte(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;wp([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,p=T.computePool2DInfo(i.shape,o,l,1,u),d=new Mte(p);return n.runWebGLProgram(d,[r],i.dtype)}var Wte={kernelName:zc,backendName:"webgl",kernelFunc:zte};function Bte(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;return Cm({a:r,b:s,transposeA:i,transposeB:o,backend:n})}var Vte={kernelName:Oi,backendName:"webgl",kernelFunc:Bte},Ute=class{constructor(e,t,n,a,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],T.assertAndGetBroadcastShape(e,t),T.assertAndGetBroadcastShape(e,n);let i="0.0";a!=null&&(T.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(T.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { float x = getXAtOutCoords(); float mean = getMeanAtOutCoords(); @@ -2063,7 +2063,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nte=Ze({opSnippet:Ste}),Tte={kernel float inv = scale * inversesqrt(variance + float(${s})); setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1))); } - `}},Wte=class{constructor(e,t,n,a,r,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],T.assertAndGetBroadcastShape(e,t),T.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";a!=null&&(T.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(T.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` + `}},Gte=class{constructor(e,t,n,a,r,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],T.assertAndGetBroadcastShape(e,t),T.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";a!=null&&(T.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(T.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { vec4 offset = ${i}; vec4 scale = ${o}; @@ -2076,7 +2076,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nte=Ze({opSnippet:Ste}),Tte={kernel setOutput((x - mean) * inv + offset); } - `}},Bte=({inputs:e,backend:t,attrs:n})=>{let{x:a,mean:r,variance:s,offset:i,scale:o}=e;w.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let u=[a,r,s],p=null;i!=null&&(p=i.shape,u.push(i));let d=null;o!=null&&(d=o.shape,u.push(o));let c=G().getBool("WEBGL_PACK_NORMALIZATION")?new Wte(a.shape,r.shape,s.shape,p,d,l):new zte(a.shape,r.shape,s.shape,p,d,l);return t.runWebGLProgram(c,u,u[0].dtype)},Vte={kernelName:eo,backendName:"webgl",kernelFunc:Bte},Ute=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ht(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=Gte(this.rank),a,r=e.map((s,i)=>`sourceLoc.${wv[i]} = start[${i}] + coords.${wv[i]};`);a=` + `}},Hte=({inputs:e,backend:t,attrs:n})=>{let{x:a,mean:r,variance:s,offset:i,scale:o}=e;w.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let u=[a,r,s],p=null;i!=null&&(p=i.shape,u.push(i));let d=null;o!=null&&(d=o.shape,u.push(o));let c=G().getBool("WEBGL_PACK_NORMALIZATION")?new Gte(a.shape,r.shape,s.shape,p,d,l):new Ute(a.shape,r.shape,s.shape,p,d,l);return t.runWebGLProgram(c,u,u[0].dtype)},jte={kernelName:eo,backendName:"webgl",kernelFunc:Hte},qte=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ht(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=Kte(this.rank),a,r=e.map((s,i)=>`sourceLoc.${kv[i]} = start[${i}] + coords.${kv[i]};`);a=` ${t} sourceLoc; ${t} coords = getOutputCoords(); ${r.join(` @@ -2086,7 +2086,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nte=Ze({opSnippet:Ste}),Tte={kernel ${a} setOutput(getSource(${n})); } - `}},wv=["x","y","z","w","u","v"];function Gte(e){if(e===1)return"sourceLoc";if(e<=6)return wv.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var Hte=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=ht(this.rank),n=kn("coords",this.rank),a=kn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${a.slice(-2).join()})`,s=`getChannel(getSource(${a.join()}), ${r})`,i=` + `}},kv=["x","y","z","w","u","v"];function Kte(e){if(e===1)return"sourceLoc";if(e<=6)return kv.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var Xte=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=ht(this.rank),n=In("coords",this.rank),a=In("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${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]}; @@ -2115,15 +2115,15 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nte=Ze({opSnippet:Ste}),Tte={kernel ${o} setOutput(result); } - `}};function qte(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=Kt.computeFlatOffset(t,w.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=a.dataRefCount.get(i.slice.origDataId)||1;return a.dataRefCount.set(i.slice.origDataId,l+1),s}function Ep(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a,[o,l]=Kt.parseSliceParams(r,s,i);if(Kt.assertParamsValid(r,o,l),w.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.texData.get(r.dataId),c=OQ(d.values,o,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,c)}let{isPacked:u}=n.texData.get(r.dataId),p=Kt.isSliceContinous(r.shape,o,l);if(u||!p){let d=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Hte(l):new Ute(l),c=[o];return n.runWebGLProgram(d,[r],r.dtype,c)}return n.uploadToGPU(r.dataId),qte(r,o,l,n)}var jte={kernelName:ep,backendName:"webgl",kernelFunc:Ep},Kte=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;w.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,x)=>y*x),l=T.getReshaped(r.shape,s,o),u=T.getPermuted(l.length,s.length),p=T.getReshapedPermuted(r.shape,s,o),d=T.getSliceBeginCoords(i,s.length),c=T.getSliceSize(p,i,s.length),h=[],m=ce({inputs:{x:r},backend:n,attrs:{shape:l}}),f=In({inputs:{x:m},backend:n,attrs:{perm:u}}),g=ce({inputs:{x:f},backend:n,attrs:{shape:p}}),b=Ep({inputs:{x:g},backend:n,attrs:{begin:d,size:c}});return h.push(m),h.push(f),h.push(g),h.forEach(y=>n.disposeIntermediateTensorInfo(y)),b},Xte={kernelName:fu,backendName:"webgl",kernelFunc:Kte};function Yte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.readSync(r.dataId),l=n.readSync(s.dataId),u=qA(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var Zte={kernelName:gu,backendName:"webgl",kernelFunc:Yte},Jte=` + `}};function Yte(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=Kt.computeFlatOffset(t,w.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=a.dataRefCount.get(i.slice.origDataId)||1;return a.dataRefCount.set(i.slice.origDataId,l+1),s}function Ep(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a,[o,l]=Kt.parseSliceParams(r,s,i);if(Kt.assertParamsValid(r,o,l),w.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.texData.get(r.dataId),c=WQ(d.values,o,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,c)}let{isPacked:u}=n.texData.get(r.dataId),p=Kt.isSliceContinous(r.shape,o,l);if(u||!p){let d=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Xte(l):new qte(l),c=[o];return n.runWebGLProgram(d,[r],r.dtype,c)}return n.uploadToGPU(r.dataId),Yte(r,o,l,n)}var Zte={kernelName:ep,backendName:"webgl",kernelFunc:Ep},Jte=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;w.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let 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l=n.texData.get(a.dataId).values,u=n.texData.get(r.dataId).values,[p,d]=pQ(a.shape,r.shape,l,u,a.dtype),c=n.makeTensorInfo(d,a.dtype),h=n.texData.get(c.dataId);return h.values=p,c}let o;return s?o=new Tp(nne,a.shape,r.shape,!1):o=new Si(ane,a.shape,r.shape),n.runWebGLProgram(o,[a,r],a.dtype)}var sne={kernelName:bu,backendName:"webgl",kernelFunc:rne};function ine(e){let{inputs:t,backend:n}=e,{s0:a,s1:r}=t,s=n.readSync(a.dataId),i=n.readSync(r.dataId),o=T.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var one={kernelName:Bc,backendName:"webgl",kernelFunc:ine},lne="return float(a != b);",pF=fn({opSnippet:lne,cpuKernelImpl:FQ,dtype:"bool"}),une={kernelName:Bu,backendName:"webgl",kernelFunc:pF};function Pd(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return aa({inputs:{x:r.complexTensorInfos.real},backend:n})}var pne={kernelName:qm,backendName:"webgl",kernelFunc:Pd},cne="return float(int(x));";function dne(e,t){let n=new ir(e.shape,cne),a=t.runWebGLProgram(n,[e],"int32");return{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}function Iv(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dtype:s}=a;if(s==="complex64"){if(r.dtype==="complex64")return aa({inputs:{x:r},backend:n});let i=It(r.shape),o=Iv({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=Os({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=Pd({inputs:{input:r},backend:n}),o=Iv({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!w.hasEncodingLoss(r.dtype,s)){let i=aa({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(n.shouldExecuteOnCPU([r])){let i=n.texData.get(r.dataId).values,[o,l,u]=cQ(i,r.shape,r.dtype,s);return n.makeTensorInfo(o,l,u)}if(s==="int32")return dne(r,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),o=pF({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 hne={kernelName:Pi,backendName:"webgl",kernelFunc:Iv},OS="return ceil(x);",mne=Ze({opSnippet:OS,packedOpSnippet:OS,cpuKernelImpl:dQ}),fne={kernelName:Li,backendName:"webgl",kernelFunc:mne},gne=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` void main() { float value = getAAtOutCoords(); @@ -2134,7 +2134,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nte=Ze({opSnippet:Ste}),Tte={kernel setOutput(clamp(value, minVal, maxVal)); } - `}},hne=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` + `}},bne=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` void main() { vec4 value = getAAtOutCoords(); @@ -2145,7 +2145,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nte=Ze({opSnippet:Ste}),Tte={kernel setOutput(clamp(value, vec4(minVal), vec4(maxVal))); } - `}};function mne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o;G().getBool("WEBGL_PACK_CLIP")?o=new hne(r.shape):o=new dne(r.shape);let l=[[s],[i]];return n.runWebGLProgram(o,[r],r.dtype,l)}var fne={kernelName:Ss,backendName:"webgl",kernelFunc:mne},gne=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=` + `}};function yne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o;G().getBool("WEBGL_PACK_CLIP")?o=new bne(r.shape):o=new gne(r.shape);let l=[[s],[i]];return n.runWebGLProgram(o,[r],r.dtype,l)}var xne={kernelName:Ns,backendName:"webgl",kernelFunc:yne},vne=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=` void main() { float re = abs(getRealAtOutCoords()); float im = abs(getImagAtOutCoords()); @@ -2158,7 +2158,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nte=Ze({opSnippet:Ste}),Tte={kernel mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx)) ); } - `}};function PS(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function bne(e){let{inputs:t,backend:n}=e,{x:a}=t,r=n.texData.get(a.dataId),s=new gne(a.shape),i=[PS(a,r.complexTensorInfos.real),PS(a,r.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var yne={kernelName:Vc,backendName:"webgl",kernelFunc:bne},xne=class{constructor(e){this.outputShape=[],this.outputShape=T.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${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s`T${f}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let m=1;m`T${f}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let m=1;m= ${o[m-1]}) { return getChannel( - getT${m}(${qh(i,l,f)}), - vec2(${qh(u,l,f)})); + getT${m}(${jh(i,l,f)}), + vec2(${jh(u,l,f)})); }`}let c=o.length,h=o[o.length-1];d+=` return getChannel( - getT${c}(${qh(i,l,h)}), - vec2(${qh(u,l,h)}));`,this.userCode=` + getT${c}(${jh(i,l,h)}), + vec2(${jh(u,l,h)}));`,this.userCode=` float getValue(${i.map(m=>"int "+m)}) { ${d} } @@ -2204,7 +2204,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nte=Ze({opSnippet:Ste}),Tte={kernel } setOutput(result); } - `}};function qh(e,t,n){let a=e.indexOf(t);return e.map((r,s)=>s===a?`${r} - ${n}`:r).join()}function ng(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return aa({inputs:{x:r.complexTensorInfos.imag},backend:n})}var wne={kernelName:Um,backendName:"webgl",kernelFunc:ng};function hc(e,t,n){let a=e[0].dtype;if(a==="complex64"){let h=e.map(y=>Pd({inputs:{input:y},backend:n})),m=e.map(y=>ng({inputs:{input:y},backend:n})),f=hc(h,t,n),g=hc(m,t,n),b=Ms({inputs:{real:f,imag:g},backend:n});return h.forEach(y=>n.disposeIntermediateTensorInfo(y)),m.forEach(y=>n.disposeIntermediateTensorInfo(y)),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),b}let r=n.shouldExecuteOnCPU(e);if(a==="string"&&(r=!0),r){let h=e.map(v=>{let I=[-1,w.sizeFromShape(v.shape.slice(t))];return ce({inputs:{x:v},backend:n,attrs:{shape:I}})}),m=h.map(v=>({vals:n.readSync(v.dataId),shape:v.shape})),f=T.computeOutShape(h.map(v=>v.shape),1),g=h[0].shape[0]===1,b=uQ(m,f,a,g),y=T.computeOutShape(e.map(v=>v.shape),t),x=n.makeTensorInfo(y,a,b);return h.forEach(v=>n.disposeIntermediateTensorInfo(v)),x}let s=e.filter(h=>w.sizeFromShape(h.shape)>0),i=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&s[0].shape.length>1;if(s.length===1){let h=i?new ir(e[0].shape,es):new ss(e[0].shape,es);return n.runWebGLProgram(h,e,a)}let o=G().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(s.length>o){let h=[];for(let f=0;fm.shape),t);return n.runWebGLProgram(h,s,a)}let{tensors2D:l,outShape:u}=kne(s,t,n),p=new xne(l.map(h=>h.shape)),d=n.runWebGLProgram(p,l,a);l.forEach(h=>n.disposeIntermediateTensorInfo(h));let c=ce({inputs:{x:d},attrs:{shape:u},backend:n});return n.disposeIntermediateTensorInfo(d),c}function kne(e,t,n){let a=T.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>ce({inputs:{x:r},attrs:{shape:[-1,w.sizeFromShape(r.shape.slice(t))]},backend:n})),outShape:a}}function uF(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=w.parseAxisParam(r,t[0].shape)[0],i=t.map(u=>u.shape);T.assertParamsConsistent(i,s);let o=T.computeOutShape(t.map(u=>u.shape),s);if(w.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(u=>w.sizeFromShape(u.shape)>0);return l.length===1?aa({inputs:{x:l[0]},backend:n}):hc(l,s,n)}var Ine={kernelName:yu,backendName:"webgl",kernelFunc:uF},pF=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,l=e.strideWidth,u=e.dilationHeight,p=e.dilationWidth,d=e.filterHeight,c=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4,f=e.dataFormat==="channelsLast",g=f?1:2,b=f?2:3,y=f?3:1,x="",v="";n&&(a?x=`float activation(float a) { + `}};function jh(e,t,n){let a=e.indexOf(t);return e.map((r,s)=>s===a?`${r} - ${n}`:r).join()}function ag(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return aa({inputs:{x:r.complexTensorInfos.imag},backend:n})}var Nne={kernelName:Um,backendName:"webgl",kernelFunc:ag};function hc(e,t,n){let a=e[0].dtype;if(a==="complex64"){let h=e.map(y=>Pd({inputs:{input:y},backend:n})),m=e.map(y=>ag({inputs:{input:y},backend:n})),f=hc(h,t,n),g=hc(m,t,n),b=Os({inputs:{real:f,imag:g},backend:n});return h.forEach(y=>n.disposeIntermediateTensorInfo(y)),m.forEach(y=>n.disposeIntermediateTensorInfo(y)),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),b}let r=n.shouldExecuteOnCPU(e);if(a==="string"&&(r=!0),r){let h=e.map(v=>{let I=[-1,w.sizeFromShape(v.shape.slice(t))];return ce({inputs:{x:v},backend:n,attrs:{shape:I}})}),m=h.map(v=>({vals:n.readSync(v.dataId),shape:v.shape})),f=T.computeOutShape(h.map(v=>v.shape),1),g=h[0].shape[0]===1,b=hQ(m,f,a,g),y=T.computeOutShape(e.map(v=>v.shape),t),x=n.makeTensorInfo(y,a,b);return h.forEach(v=>n.disposeIntermediateTensorInfo(v)),x}let s=e.filter(h=>w.sizeFromShape(h.shape)>0),i=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&s[0].shape.length>1;if(s.length===1){let h=i?new ir(e[0].shape,es):new ss(e[0].shape,es);return n.runWebGLProgram(h,e,a)}let o=G().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(s.length>o){let h=[];for(let f=0;fm.shape),t);return n.runWebGLProgram(h,s,a)}let{tensors2D:l,outShape:u}=Tne(s,t,n),p=new Ine(l.map(h=>h.shape)),d=n.runWebGLProgram(p,l,a);l.forEach(h=>n.disposeIntermediateTensorInfo(h));let c=ce({inputs:{x:d},attrs:{shape:u},backend:n});return n.disposeIntermediateTensorInfo(d),c}function Tne(e,t,n){let a=T.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>ce({inputs:{x:r},attrs:{shape:[-1,w.sizeFromShape(r.shape.slice(t))]},backend:n})),outShape:a}}function cF(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=w.parseAxisParam(r,t[0].shape)[0],i=t.map(u=>u.shape);T.assertParamsConsistent(i,s);let o=T.computeOutShape(t.map(u=>u.shape),s);if(w.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(u=>w.sizeFromShape(u.shape)>0);return l.length===1?aa({inputs:{x:l[0]},backend:n}):hc(l,s,n)}var Cne={kernelName:yu,backendName:"webgl",kernelFunc:cF},dF=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,l=e.strideWidth,u=e.dilationHeight,p=e.dilationWidth,d=e.filterHeight,c=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4,f=e.dataFormat==="channelsLast",g=f?1:2,b=f?2:3,y=f?3:1,x="",v="";n&&(a?x=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${n} }`:r?x=`float activation(float a) { @@ -2338,7 +2338,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nte=Ze({opSnippet:Ste}),Tte={kernel ${v} setOutput(result); } - `}},Sne=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,a=e.padInfo.left,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,p=e.filterDepth,d=e.filterHeight,c=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=` + `}},Ene=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,a=e.padInfo.left,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,p=e.filterDepth,d=e.filterHeight,c=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=` const ivec3 strides = ivec3(${r}, ${s}, ${i}); const ivec3 pads = ivec3(${t}, ${n}, ${a}); @@ -2426,7 +2426,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nte=Ze({opSnippet:Ste}),Tte={kernel } setOutput(dotProd); } - `}},cF=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=xn(this.outputShape.length);let s=e.padInfo.left,i=e.strideWidth,o=e.dilationWidth,l=e.filterHeight,u=e.filterWidth,p=u,d=` + `}},hF=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=vn(this.outputShape.length);let s=e.padInfo.left,i=e.strideWidth,o=e.dilationWidth,l=e.filterHeight,u=e.filterWidth,p=u,d=` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`;for(let f=0;f=3?t?[...e.slice(0,-3),e[n-3]*e[n-2],e[n-1]]:[...e.slice(0,-3),e[n-3],e[n-2]*e[n-1]]:!t&&n===1&&e[0]>1?[e[0],1]:null}function dF({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=a.texData.get(e.dataId),p=n.inChannels,d=l[0]*l[1]*l[2],c=n.outChannels,h=n.dataFormat==="channelsLast",m=!1,f=!1,g,b=[];if(s!=null){let y=Em(s.shape,h);y!=null&&(s=ce({inputs:{x:s},backend:a,attrs:{shape:y}}),b.push(s))}if(r!=null){let y=Em(r.shape,h);y!=null&&(r=ce({inputs:{x:r},backend:a,attrs:{shape:y}}),b.push(r))}if(!((d===1||c===1)&&p>rF)&&u.isPacked&&h&&u.texture!=null&&l[2]%2!==0&&w.arraysEqual(u.shape.slice(-3),l.slice(-3))){let y=l[0]*l[1]*(l[2]+1),x={dataId:e.dataId,shape:[1,y,n.inChannels],dtype:e.dtype},v=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,w.assert($c(u.shape,x.shape),()=>`packed reshape ${u.shape} to ${x.shape} isn't free`);let I=ce({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}});b.push(I);let N=Cm({a:x,b:I,backend:a,transposeA:m,transposeB:f,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),C=a.texData.get(N.dataId);w.assert(C.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=v,C.shape=n.outShape,g=aa({inputs:{x:N},backend:a}),g.shape=n.outShape,b.push(N)}else{let y=n.outHeight*n.outWidth,x=ce({inputs:{x:e},backend:a,attrs:{shape:h?[n.batchSize,y,n.inChannels]:[n.batchSize,n.inChannels,y]}}),v=ce({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}}),I=Cm({a:h?x:v,b:h?v:x,transposeA:!h,transposeB:f,backend:a,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=ce({inputs:{x:I},backend:a,attrs:{shape:n.outShape}}),b.push(x),b.push(v),b.push(I)}for(let y of b)a.disposeIntermediateTensorInfo(y);return g}function hF({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:p,outWidth:d,outHeight:c,dataFormat:h}=n,m=h==="channelsLast",f=l*u*p,g=c*d,b=[n.batchSize,f,g],y=!0,x=!1,v=[];if(s!=null){let K=Em(s.shape,m);K!=null&&(s=ce({inputs:{x:s},backend:a,attrs:{shape:K}}),v.push(s))}if(r!=null){let K=Em(r.shape,m);K!=null&&(r=ce({inputs:{x:r},backend:a,attrs:{shape:K}}),v.push(r))}let I=ce({inputs:{x:t},backend:a,attrs:{shape:[1,f,w.sizeFromShape(t.shape)/f]}});v.push(I);let N=new Nne(b,n),C=[e.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],_=a.runWebGLProgram(N,[e],"float32",C),F=ce({inputs:{x:_},backend:a,attrs:{shape:b}});v.push(_),v.push(F);let D=r!=null,$=s!=null,S=o==="leakyrelu",M=o?Dc(o,!0):null,B=new aF(m?F.shape:I.shape,m?I.shape:F.shape,m?[n.batchSize,g,n.outChannels]:[n.batchSize,n.outChannels,g],y,x,D,M,$,S),U=m?[F,I]:[I,F];if(r&&U.push(r),$&&U.push(s),S){let K=a.makeTensorInfo([],"float32",w.createScalarValue(i,"float32"));U.push(K),v.push(K)}let H=a.runWebGLProgram(B,U,"float32"),j=ce({inputs:{x:H},backend:a,attrs:{shape:n.outShape}});v.push(H);for(let K of v)a.disposeIntermediateTensorInfo(K);return j}function Tne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=a,d=T.convertConv2DDataFormat(l),c=T.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,d),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=dF({x:r,filter:s,convInfo:c,backend:n});else if(c.strideWidth<=2&&d==="channelsLast"&&G().getBool("WEBGL_EXP_CONV")){let f=new cF(c),g=[[c.padInfo.top,c.padInfo.left],[c.strideHeight,c.strideWidth],[c.dilationHeight,c.dilationWidth],[c.inHeight,c.inWidth]];h=n.runWebGLProgram(f,[r,s],"float32",g)}else if(G().getBool("WEBGL_CONV_IM2COL"))h=hF({x:r,filter:s,convInfo:c,backend:n});else{let f=new pF(c);h=n.runWebGLProgram(f,[r,s],"float32")}let m=ce({inputs:{x:h},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(h),m}var Cne={kernelName:zi,backendName:"webgl",kernelFunc:Tne},Ene=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=` + `}};function Em(e,t){let n=e.length;return n>=3?t?[...e.slice(0,-3),e[n-3]*e[n-2],e[n-1]]:[...e.slice(0,-3),e[n-3],e[n-2]*e[n-1]]:!t&&n===1&&e[0]>1?[e[0],1]:null}function mF({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=a.texData.get(e.dataId),p=n.inChannels,d=l[0]*l[1]*l[2],c=n.outChannels,h=n.dataFormat==="channelsLast",m=!1,f=!1,g,b=[];if(s!=null){let y=Em(s.shape,h);y!=null&&(s=ce({inputs:{x:s},backend:a,attrs:{shape:y}}),b.push(s))}if(r!=null){let y=Em(r.shape,h);y!=null&&(r=ce({inputs:{x:r},backend:a,attrs:{shape:y}}),b.push(r))}if(!((d===1||c===1)&&p>iF)&&u.isPacked&&h&&u.texture!=null&&l[2]%2!==0&&w.arraysEqual(u.shape.slice(-3),l.slice(-3))){let y=l[0]*l[1]*(l[2]+1),x={dataId:e.dataId,shape:[1,y,n.inChannels],dtype:e.dtype},v=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,w.assert($c(u.shape,x.shape),()=>`packed reshape ${u.shape} to ${x.shape} isn't free`);let I=ce({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}});b.push(I);let N=Cm({a:x,b:I,backend:a,transposeA:m,transposeB:f,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),C=a.texData.get(N.dataId);w.assert(C.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=v,C.shape=n.outShape,g=aa({inputs:{x:N},backend:a}),g.shape=n.outShape,b.push(N)}else{let y=n.outHeight*n.outWidth,x=ce({inputs:{x:e},backend:a,attrs:{shape:h?[n.batchSize,y,n.inChannels]:[n.batchSize,n.inChannels,y]}}),v=ce({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}}),I=Cm({a:h?x:v,b:h?v:x,transposeA:!h,transposeB:f,backend:a,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=ce({inputs:{x:I},backend:a,attrs:{shape:n.outShape}}),b.push(x),b.push(v),b.push(I)}for(let y of b)a.disposeIntermediateTensorInfo(y);return g}function fF({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:p,outWidth:d,outHeight:c,dataFormat:h}=n,m=h==="channelsLast",f=l*u*p,g=c*d,b=[n.batchSize,f,g],y=!0,x=!1,v=[];if(s!=null){let K=Em(s.shape,m);K!=null&&(s=ce({inputs:{x:s},backend:a,attrs:{shape:K}}),v.push(s))}if(r!=null){let K=Em(r.shape,m);K!=null&&(r=ce({inputs:{x:r},backend:a,attrs:{shape:K}}),v.push(r))}let I=ce({inputs:{x:t},backend:a,attrs:{shape:[1,f,w.sizeFromShape(t.shape)/f]}});v.push(I);let N=new _ne(b,n),C=[e.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],_=a.runWebGLProgram(N,[e],"float32",C),F=ce({inputs:{x:_},backend:a,attrs:{shape:b}});v.push(_),v.push(F);let D=r!=null,$=s!=null,S=o==="leakyrelu",M=o?Dc(o,!0):null,B=new sF(m?F.shape:I.shape,m?I.shape:F.shape,m?[n.batchSize,g,n.outChannels]:[n.batchSize,n.outChannels,g],y,x,D,M,$,S),U=m?[F,I]:[I,F];if(r&&U.push(r),$&&U.push(s),S){let K=a.makeTensorInfo([],"float32",w.createScalarValue(i,"float32"));U.push(K),v.push(K)}let H=a.runWebGLProgram(B,U,"float32"),q=ce({inputs:{x:H},backend:a,attrs:{shape:n.outShape}});v.push(H);for(let K of v)a.disposeIntermediateTensorInfo(K);return q}function Ane(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=a,d=T.convertConv2DDataFormat(l),c=T.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,d),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=mF({x:r,filter:s,convInfo:c,backend:n});else if(c.strideWidth<=2&&d==="channelsLast"&&G().getBool("WEBGL_EXP_CONV")){let f=new hF(c),g=[[c.padInfo.top,c.padInfo.left],[c.strideHeight,c.strideWidth],[c.dilationHeight,c.dilationWidth],[c.inHeight,c.inWidth]];h=n.runWebGLProgram(f,[r,s],"float32",g)}else if(G().getBool("WEBGL_CONV_IM2COL"))h=fF({x:r,filter:s,convInfo:c,backend:n});else{let f=new dF(c);h=n.runWebGLProgram(f,[r,s],"float32")}let m=ce({inputs:{x:h},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(h),m}var Fne={kernelName:zi,backendName:"webgl",kernelFunc:Ane},$ne=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; @@ -2706,7 +2706,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nte=Ze({opSnippet:Ste}),Tte={kernel } setOutput(dotProd); } - `}},_ne=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=s?1:2,u=s?2:3,p=s?3:1;this.userCode=` + `}},Dne=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=s?1:2,u=s?2:3,p=s?3:1;this.userCode=` const ivec2 pads = ivec2(${i}, ${o}); void main() { @@ -2759,7 +2759,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nte=Ze({opSnippet:Ste}),Tte={kernel } setOutput(dotProd); } - `}},Ane=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=` + `}},Rne=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=` void main() { ivec5 coords = getOutputCoords(); int wF = coords.x; @@ -2801,7 +2801,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nte=Ze({opSnippet:Ste}),Tte={kernel } setOutput(dotProd); } - `}},Fne=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=a-1-e.padInfo.left;this.userCode=` + `}},Mne=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=a-1-e.padInfo.left;this.userCode=` const ivec3 pads = ivec3(${o}, ${l}, ${u}); void main() { @@ -2858,7 +2858,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nte=Ze({opSnippet:Ste}),Tte={kernel } setOutput(dotProd); } - `}};function $ne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=a,d=T.convertConv2DDataFormat(l),c=T.computeConv2DInfo(r.shape,p,i,1,o,u,!1,d),h=new Ene(c);return n.runWebGLProgram(h,[r,s],"float32")}var Dne={kernelName:Om,backendName:"webgl",kernelFunc:$ne},Rne=class{constructor(e){this.variableNames=["dy","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"strides",type:"vec2"}],this.outputShape=e.inShape,this.enableShapeUniforms=xn(this.outputShape.length);let t=e.filterHeight,n=e.filterWidth,a=t-1-e.padInfo.top,r=n-1-e.padInfo.left;this.userCode=` + `}};function One(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=a,d=T.convertConv2DDataFormat(l),c=T.computeConv2DInfo(r.shape,p,i,1,o,u,!1,d),h=new $ne(c);return n.runWebGLProgram(h,[r,s],"float32")}var Pne={kernelName:Om,backendName:"webgl",kernelFunc:One},Lne=class{constructor(e){this.variableNames=["dy","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"strides",type:"vec2"}],this.outputShape=e.inShape,this.enableShapeUniforms=vn(this.outputShape.length);let t=e.filterHeight,n=e.filterWidth,a=t-1-e.padInfo.top,r=n-1-e.padInfo.left;this.userCode=` const ivec2 pads = ivec2(${a}, ${r}); void main() { @@ -2932,17 +2932,17 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nte=Ze({opSnippet:Ste}),Tte={kernel } setOutput(result); } - `}};function Mne(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:p}=a,d=T.convertConv2DDataFormat(u),c=T.computeConv2DInfo(i,s.shape,o,1,l,p,!1,d);if(G().getBool("WEBGL_PACK_CONV2DTRANSPOSE")&&d==="channelsLast"){let h=[[c.strideHeight,c.strideWidth]],m=new Rne(c);return n.runWebGLProgram(m,[r,s],"float32",h)}else{let h=new _ne(c);return n.runWebGLProgram(h,[r,s],"float32")}}var One={kernelName:Wi,backendName:"webgl",kernelFunc:Mne};function Pne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=T.computeConv3DInfo(r.shape,s.shape,i,l,o),p=new Sne(u);return n.runWebGLProgram(p,[r,s],"float32")}var Lne={kernelName:Bi,backendName:"webgl",kernelFunc:Pne};function zne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=a,u=T.computeConv3DInfo(r.shape,l,i,1,o),p=new Ane(u);return n.runWebGLProgram(p,[r,s],"float32")}var Wne={kernelName:xu,backendName:"webgl",kernelFunc:zne};function Bne(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a,u=T.computeConv3DInfo(l,s.shape,o,1,i),p=new Fne(u);return n.runWebGLProgram(p,[r,s],"float32")}var Vne={kernelName:vu,backendName:"webgl",kernelFunc:Bne},Une=Cp+` + `}};function zne(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:p}=a,d=T.convertConv2DDataFormat(u),c=T.computeConv2DInfo(i,s.shape,o,1,l,p,!1,d);if(G().getBool("WEBGL_PACK_CONV2DTRANSPOSE")&&d==="channelsLast"){let h=[[c.strideHeight,c.strideWidth]],m=new Lne(c);return n.runWebGLProgram(m,[r,s],"float32",h)}else{let h=new Dne(c);return n.runWebGLProgram(h,[r,s],"float32")}}var Wne={kernelName:Wi,backendName:"webgl",kernelFunc:zne};function Bne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=T.computeConv3DInfo(r.shape,s.shape,i,l,o),p=new Ene(u);return n.runWebGLProgram(p,[r,s],"float32")}var Vne={kernelName:Bi,backendName:"webgl",kernelFunc:Bne};function Une(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=a,u=T.computeConv3DInfo(r.shape,l,i,1,o),p=new Rne(u);return n.runWebGLProgram(p,[r,s],"float32")}var Gne={kernelName:xu,backendName:"webgl",kernelFunc:Une};function Hne(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a,u=T.computeConv3DInfo(l,s.shape,o,1,i),p=new Mne(u);return n.runWebGLProgram(p,[r,s],"float32")}var jne={kernelName:vu,backendName:"webgl",kernelFunc:Hne},qne=Cp+` return cos(x); -`,Gne=` +`,Kne=` vec4 result = cos(x); bvec4 isNaN = isnan(x); ${tl} return result; -`,Hne=Ze({opSnippet:Une,packedOpSnippet:Gne}),qne={kernelName:Vi,backendName:"webgl",kernelFunc:Hne},jne=` +`,Xne=Ze({opSnippet:qne,packedOpSnippet:Kne}),Yne={kernelName:Vi,backendName:"webgl",kernelFunc:Xne},Zne=` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; -`,Kne=Ze({opSnippet:jne}),Xne={kernelName:Ui,backendName:"webgl",kernelFunc:Kne},Yne=class{constructor(e,t,n,a,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[p,d]=n;this.outputShape=[u,p,d,l];let c=a==="bilinear"?1:0,[h,m]=[`${i-1}.0`,`${o-1}.0`],[f,g,b]=p>1?[`${(i-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[y,x,v]=d>1?[`${(o-1)/(d-1)}`,"(x2-x1) * width_ratio",`x1*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=` +`,Jne=Ze({opSnippet:Zne}),Qne={kernelName:Ui,backendName:"webgl",kernelFunc:Jne},eae=class{constructor(e,t,n,a,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[p,d]=n;this.outputShape=[u,p,d,l];let c=a==="bilinear"?1:0,[h,m]=[`${i-1}.0`,`${o-1}.0`],[f,g,b]=p>1?[`${(i-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[y,x,v]=d>1?[`${(o-1)/(d-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(${y}); void main() { @@ -3003,7 +3003,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nte=Ze({opSnippet:Ste}),Tte={kernel setOutput(newValue); } } - `}},Zne=e=>{let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,p=new Yne(r.shape,s.shape,o,l,u);return n.runWebGLProgram(p,[r,s,i],"float32")},Jne={kernelName:ku,backendName:"webgl",kernelFunc:Zne},Mc;(function(e){e.Prod="*",e.Sum="+"})(Mc||(Mc={}));var LS=class{constructor(e,t,n,a){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let r=this.outputShape.length,s=this.op===Mc.Prod?"1.0":"0.0",i=n?s:`getX(${zS(r,"coords",this.op)})`,o=this.outputShape[this.outputShape.length-1],l="",u="";n?(l=a?`end != ${o-1}`:"end != 0",u=a?"end + 1":"end - 1"):(l=a?`end + pow2 < ${o}`:"end >= pow2",u=a?"end + pow2":"end - pow2"),this.userCode=` + `}},tae=e=>{let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,p=new eae(r.shape,s.shape,o,l,u);return n.runWebGLProgram(p,[r,s,i],"float32")},nae={kernelName:ku,backendName:"webgl",kernelFunc:tae},Mc;(function(e){e.Prod="*",e.Sum="+"})(Mc||(Mc={}));var LS=class{constructor(e,t,n,a){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let r=this.outputShape.length,s=this.op===Mc.Prod?"1.0":"0.0",i=n?s:`getX(${zS(r,"coords",this.op)})`,o=this.outputShape[this.outputShape.length-1],l="",u="";n?(l=a?`end != ${o-1}`:"end != 0",u=a?"end + 1":"end - 1"):(l=a?`end + pow2 < ${o}`:"end >= pow2",u=a?"end + pow2":"end - pow2"),this.userCode=` void main() { ${ht(r)} coords = getOutputCoords(); int end = ${WS(r,"coords",this.op)}; @@ -3016,7 +3016,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nte=Ze({opSnippet:Ste}),Tte={kernel } setOutput(val); } - `}};function zS(e,t,n){if(e===1)return`${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 new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function WS(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function mF(e,t,n,a,r,s){let i=t.shape.length,o=T.getAxesPermutation([a],i),l=t;o!=null&&(l=In({inputs:{x:t},backend:n,attrs:{perm:o}}));let u=T.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${a}`);let p=l.shape[u],d=aa({inputs:{x:l},backend:n});for(let c=0;c<=Math.ceil(Math.log2(p))-1;c++){let h=new LS(e,l.shape,!1,s),m=[[c]],f=d;d=n.runWebGLProgram(h,[d],d.dtype,m),n.disposeIntermediateTensorInfo(f)}if(r){let c=new LS(e,l.shape,r,s),h=d;d=n.runWebGLProgram(c,[d],d.dtype),n.disposeIntermediateTensorInfo(h)}if(o!=null){let c=T.getUndoAxesPermutation(o),h=In({inputs:{x:d},backend:n,attrs:{perm:c}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(l),h}return d}function Qne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;return mF(Mc.Prod,r,n,s,i,o)}var eae={kernelName:wu,backendName:"webgl",kernelFunc:Qne};function tae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;return mF(Mc.Sum,r,n,s,i,o)}var nae={kernelName:Gi,backendName:"webgl",kernelFunc:tae};function aae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.readSync(r.dataId),u=n.readSync(s.dataId),p=qA(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,p)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(s),p=sQ(l,u,i,o);return n.makeTensorInfo(p.shape,s.dtype,p.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var rae={kernelName:Uc,backendName:"webgl",kernelFunc:aae},sae=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=` + `}};function zS(e,t,n){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 new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function WS(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function gF(e,t,n,a,r,s){let i=t.shape.length,o=T.getAxesPermutation([a],i),l=t;o!=null&&(l=Sn({inputs:{x:t},backend:n,attrs:{perm:o}}));let u=T.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${a}`);let p=l.shape[u],d=aa({inputs:{x:l},backend:n});for(let c=0;c<=Math.ceil(Math.log2(p))-1;c++){let h=new LS(e,l.shape,!1,s),m=[[c]],f=d;d=n.runWebGLProgram(h,[d],d.dtype,m),n.disposeIntermediateTensorInfo(f)}if(r){let c=new LS(e,l.shape,r,s),h=d;d=n.runWebGLProgram(c,[d],d.dtype),n.disposeIntermediateTensorInfo(h)}if(o!=null){let c=T.getUndoAxesPermutation(o),h=Sn({inputs:{x:d},backend:n,attrs:{perm:c}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(l),h}return d}function aae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;return gF(Mc.Prod,r,n,s,i,o)}var rae={kernelName:wu,backendName:"webgl",kernelFunc:aae};function sae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;return gF(Mc.Sum,r,n,s,i,o)}var iae={kernelName:Gi,backendName:"webgl",kernelFunc:sae};function oae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.readSync(r.dataId),u=n.readSync(s.dataId),p=KA(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,p)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(s),p=uQ(l,u,i,o);return n.makeTensorInfo(p.shape,s.dtype,p.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var lae={kernelName:Uc,backendName:"webgl",kernelFunc:oae},uae=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]; @@ -3035,7 +3035,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nte=Ze({opSnippet:Ste}),Tte={kernel 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 iae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],d=l*s,c=u*s,h=p/(s*s),m=i==="NHWC"?[o,d,c,h]:[o,h,d,c],f=new sae(m,s,i);return n.runWebGLProgram(f,[r],r.dtype)}var oae={kernelName:Iu,backendName:"webgl",kernelFunc:iae},fF=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=xn(this.outputShape.length);let s=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,l="",u="";n&&(a?l=`float activation(float a) { + `}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 pae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],d=l*s,c=u*s,h=p/(s*s),m=i==="NHWC"?[o,d,c,h]:[o,h,d,c],f=new uae(m,s,i);return n.runWebGLProgram(f,[r],r.dtype)}var cae={kernelName:Iu,backendName:"webgl",kernelFunc:pae},bF=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=vn(this.outputShape.length);let s=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,l="",u="";n&&(a?l=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${n} }`:r?l=`float activation(float a) { @@ -3088,7 +3088,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nte=Ze({opSnippet:Ste}),Tte={kernel ${u} setOutput(result); } - `}},gF=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=xn(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,p=e.filterWidth,d=p,c=` + `}},yF=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=vn(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,p=e.filterWidth,d=p,c=` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${p}'`);let d=T.computeConv2DInfo(r.shape,s.shape,i,p,o,u,!0),c;G().getBool("WEBGL_PACK_DEPTHWISECONV")&&d.strideWidth<=2&&d.outChannels/d.inChannels===1?c=new gF(d):c=new fF(d);let h=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];return n.runWebGLProgram(c,[r,s],"float32",h)}var uae={kernelName:Hi,backendName:"webgl",kernelFunc:lae},pae=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=` + `}};function dae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=a,p=l;p==null&&(p=[1,1]),w.assert(T.eitherStridesOrDilationsAreOne(i,p),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${p}'`);let d=T.computeConv2DInfo(r.shape,s.shape,i,p,o,u,!0),c;G().getBool("WEBGL_PACK_DEPTHWISECONV")&&d.strideWidth<=2&&d.outChannels/d.inChannels===1?c=new yF(d):c=new bF(d);let h=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];return n.runWebGLProgram(c,[r,s],"float32",h)}var hae={kernelName:Hi,backendName:"webgl",kernelFunc:dae},mae=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; @@ -3312,7 +3312,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nte=Ze({opSnippet:Ste}),Tte={kernel } setOutput(dotProd); } - `}},cae=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=` + `}},fae=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() { @@ -3357,13 +3357,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nte=Ze({opSnippet:Ste}),Tte={kernel } setOutput(dotProd); } - `}};function dae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:p}=a,d=T.computeConv2DInfo(r.shape,p,i,o,l,u,!0),c=new pae(d);return n.runWebGLProgram(c,[r,s],"float32")}var hae={kernelName:Pm,backendName:"webgl",kernelFunc:dae};function mae(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:p}=a,d=T.computeConv2DInfo(p,s.shape,i,o,l,u,!0),c=new cae(d);return n.runWebGLProgram(c,[r,s],"float32")}var fae={kernelName:Lm,backendName:"webgl",kernelFunc:mae},gae=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=` + `}};function gae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:p}=a,d=T.computeConv2DInfo(r.shape,p,i,o,l,u,!0),c=new mae(d);return n.runWebGLProgram(c,[r,s],"float32")}var bae={kernelName:Pm,backendName:"webgl",kernelFunc:gae};function yae(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:p}=a,d=T.computeConv2DInfo(p,s.shape,i,o,l,u,!0),c=new fae(d);return n.runWebGLProgram(c,[r,s],"float32")}var xae={kernelName:Lm,backendName:"webgl",kernelFunc:yae},vae=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 bae(e){let{inputs:t,backend:n}=e,{x:a}=t,r=[...a.shape,...a.shape],s=w.sizeFromShape(a.shape),i=ce({inputs:{x:a},backend:n,attrs:{shape:[s]}}),o=new gae(s),l=n.runWebGLProgram(o,[i],i.dtype),u=ce({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var yae={kernelName:Gc,backendName:"webgl",kernelFunc:bae},xae=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:l,dilationWidth:u}=e,{top:p,left:d}=a;this.userCode=` + `}};function wae(e){let{inputs:t,backend:n}=e,{x:a}=t,r=[...a.shape,...a.shape],s=w.sizeFromShape(a.shape),i=ce({inputs:{x:a},backend:n,attrs:{shape:[s]}}),o=new vae(s),l=n.runWebGLProgram(o,[i],i.dtype),u=ce({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var kae={kernelName:Gc,backendName:"webgl",kernelFunc:wae},Iae=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:l,dilationWidth:u}=e,{top:p,left:d}=a;this.userCode=` const ivec2 strides = ivec2(${r}, ${s}); const ivec2 pads = ivec2(${p}, ${d}); const float neg_infinity = -3.4e38; @@ -3401,7 +3401,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nte=Ze({opSnippet:Ste}),Tte={kernel float result = curVal; setOutput(result); } - `}};function vae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=T.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p,d=new xae(u);p=n.runWebGLProgram(d,[r,s],"float32");let c=ce({inputs:{x:p},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(p),c}var wae={kernelName:qi,backendName:"webgl",kernelFunc:vae};function kae(e){let{inputs:t,backend:n,attrs:a}=e,{equation:r}=a,s=t,{allDims:i,summedDims:o,idDims:l}=T.decodeEinsumEquation(r,s.length);T.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:p}=T.getEinsumComputePath(o,l),d=p.length,c=null,h=i.length,m=[];for(let f=0;f=0&&(c=tg({inputs:{x:c},backend:n,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(c)),h--)}for(let f of m)f!==c&&n.disposeIntermediateTensorInfo(f);return c}var Iae={kernelName:Wm,backendName:"webgl",kernelFunc:kae},Sae="return (x >= 0.0) ? x : (exp(x) - 1.0);",Nae=` + `}};function Sae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=T.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p,d=new Iae(u);p=n.runWebGLProgram(d,[r,s],"float32");let c=ce({inputs:{x:p},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(p),c}var Nae={kernelName:ji,backendName:"webgl",kernelFunc:Sae};function Tae(e){let{inputs:t,backend:n,attrs:a}=e,{equation:r}=a,s=t,{allDims:i,summedDims:o,idDims:l}=T.decodeEinsumEquation(r,s.length);T.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:p}=T.getEinsumComputePath(o,l),d=p.length,c=null,h=i.length,m=[];for(let f=0;f=0&&(c=ng({inputs:{x:c},backend:n,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(c)),h--)}for(let f of m)f!==c&&n.disposeIntermediateTensorInfo(f);return c}var Cae={kernelName:Wm,backendName:"webgl",kernelFunc:Tae},Eae="return (x >= 0.0) ? x : (exp(x) - 1.0);",_ae=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -3410,12 +3410,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nte=Ze({opSnippet:Ste}),Tte={kernel result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; -`,Tae=Ze({opSnippet:Sae,packedOpSnippet:Nae}),Cae={kernelName:Ki,backendName:"webgl",kernelFunc:Tae},Eae="return (b >= 0.0) ? a : a * (b + 1.0);",_ae=` +`,Aae=Ze({opSnippet:Eae,packedOpSnippet:_ae}),Fae={kernelName:Ki,backendName:"webgl",kernelFunc:Aae},$ae="return (b >= 0.0) ? a : a * (b + 1.0);",Dae=` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); -`,Aae=e=>{let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Tp(_ae,a.shape,r.shape):new Si(Eae,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)},Fae={kernelName:Su,backendName:"webgl",kernelFunc:Aae},$ae=` +`,Rae=e=>{let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Tp(Dae,a.shape,r.shape):new Si($ae,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)},Mae={kernelName:Su,backendName:"webgl",kernelFunc:Rae},Oae=` return vec4(equal(a, b)); -`,Dae="return float(a == b);",Rae=fn({opSnippet:Dae,packedOpSnippet:$ae,dtype:"bool",cpuKernelImpl:pQ}),Mae={kernelName:Nu,backendName:"webgl",kernelFunc:Rae},Oae=` +`,Pae="return float(a == b);",Lae=fn({opSnippet:Pae,packedOpSnippet:Oae,dtype:"bool",cpuKernelImpl:mQ}),zae={kernelName:Nu,backendName:"webgl",kernelFunc:Lae},Wae=` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. @@ -3430,9 +3430,9 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nte=Ze({opSnippet:Ste}),Tte={kernel x = abs(x); float t = 1.0 / (1.0 + p * x); return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x)); -`,Pae=Ze({opSnippet:Oae}),Lae={kernelName:Xi,backendName:"webgl",kernelFunc:Pae},zae=Cp+` +`,Bae=Ze({opSnippet:Wae}),Vae={kernelName:Xi,backendName:"webgl",kernelFunc:Bae},Uae=Cp+` return exp(x); -`,Wae=` +`,Gae=` vec4 result = exp(x); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : result.r; @@ -3441,7 +3441,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nte=Ze({opSnippet:Ste}),Tte={kernel result.a = isNaN.a ? x.a : result.a; return result; -`,bF=Ze({opSnippet:zae,packedOpSnippet:Wae,cpuKernelImpl:cQ,dtype:"float32"}),Bae={kernelName:Yi,backendName:"webgl",kernelFunc:bF};function Iv(e){let{inputs:t,attrs:n,backend:a}=e,{dim:r}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(w.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),ce({inputs:{x:s},backend:a,attrs:{shape:o}})}var Vae={kernelName:Tu,backendName:"webgl",kernelFunc:Iv},BS="return exp(x) - 1.0;",Uae=Ze({opSnippet:BS,packedOpSnippet:BS,cpuKernelImpl:dQ}),Gae={kernelName:Zi,backendName:"webgl",kernelFunc:Uae},VS=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 ${e}.`);this.userCode=` +`,xF=Ze({opSnippet:Uae,packedOpSnippet:Gae,cpuKernelImpl:fQ,dtype:"float32"}),Hae={kernelName:Yi,backendName:"webgl",kernelFunc:xF};function Sv(e){let{inputs:t,attrs:n,backend:a}=e,{dim:r}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(w.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),ce({inputs:{x:s},backend:a,attrs:{shape:o}})}var jae={kernelName:Tu,backendName:"webgl",kernelFunc:Sv},BS="return exp(x) - 1.0;",qae=Ze({opSnippet:BS,packedOpSnippet:BS,cpuKernelImpl:gQ}),Kae={kernelName:Zi,backendName:"webgl",kernelFunc:qae},VS=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) { @@ -3474,12 +3474,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nte=Ze({opSnippet:Ste}),Tte={kernel ivec2 coords = getOutputCoords(); setOutput(mulMatDFT(coords[0], coords[1])); } - `}};function yF(e,t,n){let a=n.texData.get(e.dataId),r=w.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=ce({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,u=new VS("real",l,t),p=new VS("imag",l,t),d=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:l},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:l}],c=n.runWebGLProgram(u,d,"float32"),h=n.runWebGLProgram(p,d,"float32"),m=Ms({inputs:{real:c,imag:h},backend:n});n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h);let f=ce({inputs:{x:m},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(m),f}function Hae(e){let{inputs:t,backend:n}=e,{input:a}=t;return yF(a,!1,n)}var qae={kernelName:Bm,backendName:"webgl",kernelFunc:Hae},jae=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=` + `}};function vF(e,t,n){let a=n.texData.get(e.dataId),r=w.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=ce({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,u=new VS("real",l,t),p=new VS("imag",l,t),d=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:l},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:l}],c=n.runWebGLProgram(u,d,"float32"),h=n.runWebGLProgram(p,d,"float32"),m=Os({inputs:{real:c,imag:h},backend:n});n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h);let f=ce({inputs:{x:m},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(m),f}function Xae(e){let{inputs:t,backend:n}=e,{input:a}=t;return vF(a,!1,n)}var Yae={kernelName:Bm,backendName:"webgl",kernelFunc:Xae},Zae=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=` void main() { // Input can be obtained from uniform value. setOutput(value); } - `}};function Ld(e){let{backend:t,attrs:n}=e,{shape:a,value:r}=n,{dtype:s}=n;if(s=s||w.inferDtype(r),s==="string"){let i=w.getArrayFromDType(s,w.sizeFromShape(a));return i.fill(r),t.makeTensorInfo(a,s,i)}else{let i=new jae(a,r),o=[[r]];return t.runWebGLProgram(i,[],s,o)}}var Kae={kernelName:Hc,backendName:"webgl",kernelFunc:Ld},Xae=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=` + `}};function Ld(e){let{backend:t,attrs:n}=e,{shape:a,value:r}=n,{dtype:s}=n;if(s=s||w.inferDtype(r),s==="string"){let i=w.getArrayFromDType(s,w.sizeFromShape(a));return i.fill(r),t.makeTensorInfo(a,s,i)}else{let i=new Zae(a,r),o=[[r]];return t.runWebGLProgram(i,[],s,o)}}var Jae={kernelName:Hc,backendName:"webgl",kernelFunc:Ld},Qae=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]; @@ -3493,7 +3493,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nte=Ze({opSnippet:Ste}),Tte={kernel } setOutput(outputValue); } - `}},Yae={kernelName:Cu,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,a=t,r=new Xae(n.shape);return a.runWebGLProgram(r,[n],n.dtype)}},US="return floor(x);",Zae=Ze({opSnippet:US,packedOpSnippet:US,cpuKernelImpl:hQ}),Jae={kernelName:Ji,backendName:"webgl",kernelFunc:Zae},Qae=` + `}},ere={kernelName:Cu,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,a=t,r=new Qae(n.shape);return a.runWebGLProgram(r,[n],n.dtype)}},US="return floor(x);",tre=Ze({opSnippet:US,packedOpSnippet:US,cpuKernelImpl:bQ}),nre={kernelName:Ji,backendName:"webgl",kernelFunc:tre},are=` float s = sign(a) * sign(b); int ia = round(a); int ib = round(b); @@ -3503,7 +3503,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nte=Ze({opSnippet:Ste}),Tte={kernel } else { return NAN; } -`,ere=` +`,rre=` ivec4 ia = round(a); ivec4 ib = round(b); bvec4 cond = notEqual(ib, ivec4(0)); @@ -3524,7 +3524,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nte=Ze({opSnippet:Ste}),Tte={kernel result[3] = idiv(ia[3], ib[3], s[3]); } return vec4(result); -`,tre=fn({opSnippet:Qae,packedOpSnippet:ere,dtype:"int32"}),nre={kernelName:Qi,backendName:"webgl",kernelFunc:tre},are=class{constructor(e){this.variableNames=["A"];let t=Cn(),[n,a]=e;this.outputShape=e,this.userCode=` +`,sre=fn({opSnippet:are,packedOpSnippet:rre,dtype:"int32"}),ire={kernelName:Qi,backendName:"webgl",kernelFunc:sre},ore=class{constructor(e){this.variableNames=["A"];let t=En(),[n,a]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; @@ -3546,7 +3546,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nte=Ze({opSnippet:Ste}),Tte={kernel setOutput(floor(value * 255.0 + 0.5)); } - `}},rre=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Cn(),[n,a]=e;this.outputShape=e,this.userCode=` + `}},lre=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=En(),[n,a]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); 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-`,xre=fn({opSnippet:bre,packedOpSnippet:yre,cpuKernelImpl:gQ,dtype:"bool"}),vre={kernelName:Au,backendName:"webgl",kernelFunc:xre},wre="return float(a >= b);",kre=` +`,Ire=fn({opSnippet:wre,packedOpSnippet:kre,cpuKernelImpl:vQ,dtype:"bool"}),Sre={kernelName:Au,backendName:"webgl",kernelFunc:Ire},Nre="return float(a >= b);",Tre=` return vec4(greaterThanEqual(a, b)); -`,Ire=fn({opSnippet:wre,packedOpSnippet:kre,dtype:"bool",cpuKernelImpl:bQ}),Sre={kernelName:to,backendName:"webgl",kernelFunc:Ire};function Nre(e){let{inputs:t,backend:n}=e,{input:a}=t;return yF(a,!0,n)}var Tre={kernelName:Vm,backendName:"webgl",kernelFunc:Nre},Cre="return float(!isnan(x) && !isinf(x));",Ere=Ze({opSnippet:Cre,dtype:"bool"}),_re={kernelName:ao,backendName:"webgl",kernelFunc:Ere},Are="return float(isinf(x));",Fre=Ze({opSnippet:Are,dtype:"bool"}),$re={kernelName:ro,backendName:"webgl",kernelFunc:Fre},Dre="return float(isnan(x));",Rre=Ze({opSnippet:Dre,dtype:"bool"}),Mre={kernelName:so,backendName:"webgl",kernelFunc:Rre},Ore="return float(a < b);",Pre=` +`,Cre=fn({opSnippet:Nre,packedOpSnippet:Tre,dtype:"bool",cpuKernelImpl:wQ}),Ere={kernelName:to,backendName:"webgl",kernelFunc:Cre};function _re(e){let{inputs:t,backend:n}=e,{input:a}=t;return vF(a,!0,n)}var Are={kernelName:Vm,backendName:"webgl",kernelFunc:_re},Fre="return float(!isnan(x) && !isinf(x));",$re=Ze({opSnippet:Fre,dtype:"bool"}),Dre={kernelName:ao,backendName:"webgl",kernelFunc:$re},Rre="return float(isinf(x));",Mre=Ze({opSnippet:Rre,dtype:"bool"}),Ore={kernelName:ro,backendName:"webgl",kernelFunc:Mre},Pre="return float(isnan(x));",Lre=Ze({opSnippet:Pre,dtype:"bool"}),zre={kernelName:so,backendName:"webgl",kernelFunc:Lre},Wre="return float(a < b);",Bre=` return vec4(lessThan(a, b)); -`,Lre=fn({opSnippet:Ore,packedOpSnippet:Pre,cpuKernelImpl:yQ,dtype:"bool"}),zre={kernelName:Fu,backendName:"webgl",kernelFunc:Lre},Wre="return float(a <= b);",Bre=` +`,Vre=fn({opSnippet:Wre,packedOpSnippet:Bre,cpuKernelImpl:kQ,dtype:"bool"}),Ure={kernelName:Fu,backendName:"webgl",kernelFunc:Vre},Gre="return float(a <= b);",Hre=` return vec4(lessThanEqual(a, b)); -`,Vre=fn({opSnippet:Wre,packedOpSnippet:Bre,cpuKernelImpl:xQ,dtype:"bool"}),Ure={kernelName:$u,backendName:"webgl",kernelFunc:Vre};function Gre(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=vQ(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var Hre={kernelName:Du,backendName:"webgl",kernelFunc:Gre},qre=Cp+` +`,jre=fn({opSnippet:Gre,packedOpSnippet:Hre,cpuKernelImpl:IQ,dtype:"bool"}),qre={kernelName:$u,backendName:"webgl",kernelFunc:jre};function Kre(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=SQ(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var Xre={kernelName:Du,backendName:"webgl",kernelFunc:Kre},Yre=Cp+` return x < 0.0 ? 0./0. : log(x); -`,jre=` +`,Zre=` vec4 result = log(x); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r); @@ -3620,18 +3620,18 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nte=Ze({opSnippet:Ste}),Tte={kernel result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b); result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a); return result; -`,Kre=Ze({opSnippet:qre,packedOpSnippet:jre,cpuKernelImpl:wQ}),Xre={kernelName:oo,backendName:"webgl",kernelFunc:Kre},Yre=Cp+` +`,Jre=Ze({opSnippet:Yre,packedOpSnippet:Zre,cpuKernelImpl:NQ}),Qre={kernelName:oo,backendName:"webgl",kernelFunc:Jre},ese=Cp+` return log(1.0 + x); -`,Zre=Ze({opSnippet:Yre}),Jre={kernelName:lo,backendName:"webgl",kernelFunc:Zre},Qre="return float(a >= 1.0 && b >= 1.0);",ese=` +`,tse=Ze({opSnippet:ese}),nse={kernelName:lo,backendName:"webgl",kernelFunc:tse},ase="return float(a >= 1.0 && b >= 1.0);",rse=` return vec4( vec4(greaterThanEqual(a, vec4(1.0))) * vec4(greaterThanEqual(b, vec4(1.0)))); -`,tse=fn({opSnippet:Qre,packedOpSnippet:ese,dtype:"bool"}),nse={kernelName:Ru,backendName:"webgl",kernelFunc:tse},ase="return float(!(x >= 1.0));",rse=Ze({opSnippet:ase}),sse={kernelName:Mu,backendName:"webgl",kernelFunc:rse},ise="return float(a >= 1.0 || b >= 1.0);",ose=` +`,sse=fn({opSnippet:ase,packedOpSnippet:rse,dtype:"bool"}),ise={kernelName:Ru,backendName:"webgl",kernelFunc:sse},ose="return float(!(x >= 1.0));",lse=Ze({opSnippet:ose}),use={kernelName:Mu,backendName:"webgl",kernelFunc:lse},pse="return float(a >= 1.0 || b >= 1.0);",cse=` return min( vec4(greaterThanEqual(a, vec4(1.0))) + vec4(greaterThanEqual(b, vec4(1.0))), vec4(1.0)); -`,lse=fn({opSnippet:ise,packedOpSnippet:ose,dtype:"bool"}),use={kernelName:Ou,backendName:"webgl",kernelFunc:lse},pse=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=` +`,dse=fn({opSnippet:pse,packedOpSnippet:cse,dtype:"bool"}),hse={kernelName:Ou,backendName:"webgl",kernelFunc:dse},mse=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3650,7 +3650,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nte=Ze({opSnippet:Ste}),Tte={kernel float val = x * ${o}; setOutput(val); } - `}},cse=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=` + `}},fse=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords.x; @@ -3712,7 +3712,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nte=Ze({opSnippet:Ste}),Tte={kernel vec4 result = xAtOutputCoords * ${o}; setOutput(result); } - `}},dse=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a,u=G().getBool("WEBGL_PACK_NORMALIZATION")?new cse(r.shape,s,i,o,l):new pse(r.shape,s,i,o,l);return n.runWebGLProgram(u,[r],r.dtype)},hse={kernelName:uo,backendName:"webgl",kernelFunc:dse},mse=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=` + `}},gse=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a,u=G().getBool("WEBGL_PACK_NORMALIZATION")?new fse(r.shape,s,i,o,l):new mse(r.shape,s,i,o,l);return n.runWebGLProgram(u,[r],r.dtype)},bse={kernelName:uo,backendName:"webgl",kernelFunc:gse},yse=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]; @@ -3767,16 +3767,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nte=Ze({opSnippet:Ste}),Tte={kernel } setOutput(result); } - `}},fse=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=a,d=new mse(r.shape,o,l,u,p);return n.runWebGLProgram(d,[r,s,i],r.dtype)},gse={kernelName:Pu,backendName:"webgl",kernelFunc:fse};function bse(e,t,n,a){let r=w.sizeFromShape(t),s=w.sizeFromShape(e.shape)/r,i=ce({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=nl(i,e.dtype,"max",a),l=ce({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}function vF(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=r.shape.length,l=w.parseAxisParam(s,r.shape),u=l,p=T.getAxesPermutation(u,o),d=p!=null,c=n.shouldExecuteOnCPU([r]),h=r;if(d){if(c){let y=n.texData.get(h.dataId).values,x=new Array(o);for(let N=0;N{let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=a,d=new yse(r.shape,o,l,u,p);return n.runWebGLProgram(d,[r,s,i],r.dtype)},vse={kernelName:Pu,backendName:"webgl",kernelFunc:xse};function wse(e,t,n,a){let r=w.sizeFromShape(t),s=w.sizeFromShape(e.shape)/r,i=ce({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=nl(i,e.dtype,"max",a),l=ce({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}function kF(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=r.shape.length,l=w.parseAxisParam(s,r.shape),u=l,p=T.getAxesPermutation(u,o),d=p!=null,c=n.shouldExecuteOnCPU([r]),h=r;if(d){if(c){let y=n.texData.get(h.dataId).values,x=new Array(o);for(let N=0;N`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=T.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&w.arraysEqual(p.inShape,p.outShape))return aa({inputs:{x:r},backend:n});let d=new Rc(p,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var Sse={kernelName:ho,backendName:"webgl",kernelFunc:Ise};function Nse(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=a,p=[1,1,1],d=T.computePool3DInfo(r.shape,s,i,p,o,u,l),c=new xk(d,"max",!1);return n.runWebGLProgram(c,[r],r.dtype)}var Tse={kernelName:Lu,backendName:"webgl",kernelFunc:Nse},Cse=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,l=r*s-1;this.userCode=` +`,Nse=fn({opSnippet:Ise,packedOpSnippet:Sse,cpuKernelImpl:CQ}),Tse={kernelName:co,backendName:"webgl",kernelFunc:Nse};function Cse(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;wp(r,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;w.assert(T.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=T.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&w.arraysEqual(p.inShape,p.outShape))return aa({inputs:{x:r},backend:n});let d=new Rc(p,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var Ese={kernelName:ho,backendName:"webgl",kernelFunc:Cse};function _se(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=a,p=[1,1,1],d=T.computePool3DInfo(r.shape,s,i,p,o,u,l),c=new xk(d,"max",!1);return n.runWebGLProgram(c,[r],r.dtype)}var Ase={kernelName:Lu,backendName:"webgl",kernelFunc:_se},Fse=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,l=r*s-1;this.userCode=` const ivec2 pads = ivec2(${i}, ${o}); void main() { @@ -3822,7 +3822,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nte=Ze({opSnippet:Ste}),Tte={kernel } setOutput(dotProd); } - `}},Ese=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,p=o-1-e.padInfo.front,d=l-1-e.padInfo.top,c=u-1-e.padInfo.left,h=o*l*u-1;this.userCode=` + `}},$se=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,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,p=o-1-e.padInfo.front,d=l-1-e.padInfo.top,c=u-1-e.padInfo.left,h=o*l*u-1;this.userCode=` const ivec3 pads = ivec3(${p}, ${d}, ${c}); void main() { @@ -3886,16 +3886,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nte=Ze({opSnippet:Ste}),Tte={kernel } setOutput(dotProd); } - `}};function _se(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=a,d=[1,1,1],c=T.computePool3DInfo(i.shape,o,l,d,u,p),h=new xk(c,"max",!0),m=n.runWebGLProgram(h,[i],i.dtype),f=new Ese(c),g=n.runWebGLProgram(f,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),g}var Ase={kernelName:jc,backendName:"webgl",kernelFunc:_se};function Fse(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;wp([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:p,dimRoundingMode:d}=a,c=T.computePool2DInfo(o.shape,l,u,1,p,d),h=!0,m=new Rc(c,"max",h),f=n.runWebGLProgram(m,[o],o.dtype),g=new Cse(c),b=n.runWebGLProgram(g,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),b}var $se={kernelName:qc,backendName:"webgl",kernelFunc:Fse};function Dse(e,t,n,a){let r=new Rc(n,"max",!1),s=a.runWebGLProgram(r,[e],"float32");r=new Rc(n,"max",!0,!0,t);let i=a.runWebGLProgram(r,[e],"float32");return[s,i]}var Rse={kernelName:Kc,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;w.assert(a.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${a.shape.length}.`);let u=[1,1];w.assert(T.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let p=T.computePool2DInfo(a.shape,r,s,u,i),[d,c]=Dse(a,o,p,l);return[d,c]}};function Mse(e,t,n,a){let r=w.sizeFromShape(t),s=w.sizeFromShape(e.shape)/r,i=ce({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=nl(i,"float32","mean",a),l=ce({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}var Ose={kernelName:mo,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{keepDims:r,axis:s}=t,i=n,o=a.shape.length,l=w.parseAxisParam(s,a.shape),u=l,p=T.getAxesPermutation(u,o),d=p!=null,c=i.shouldExecuteOnCPU([a]),h=[],m=a;if(d){if(c){let x=i.texData.get(m.dataId).values,v=new Array(o);for(let C=0;C{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;w.assert(a.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${a.shape.length}.`);let u=[1,1];w.assert(T.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let p=T.computePool2DInfo(a.shape,r,s,u,i),[d,c]=Pse(a,o,p,l);return[d,c]}};function zse(e,t,n,a){let r=w.sizeFromShape(t),s=w.sizeFromShape(e.shape)/r,i=ce({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=nl(i,"float32","mean",a),l=ce({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}var Wse={kernelName:mo,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{keepDims:r,axis:s}=t,i=n,o=a.shape.length,l=w.parseAxisParam(s,a.shape),u=l,p=T.getAxesPermutation(u,o),d=p!=null,c=i.shouldExecuteOnCPU([a]),h=[],m=a;if(d){if(c){let x=i.texData.get(m.dataId).values,v=new Array(o);for(let C=0;Cu[0]+e[p]+u[1]);let a=e.length,r=ht(a),s=t.map(u=>u[0]).join(","),i=t.map((u,p)=>u[0]+e[p]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a),l=n==="reflect"?0:1;if(a===1){this.userCode=` +`,Hse=fn({opSnippet:Use,packedOpSnippet:Gse,cpuKernelImpl:EQ}),jse={kernelName:go,backendName:"webgl",kernelFunc:Hse},qse=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((u,p)=>u[0]+e[p]+u[1]);let a=e.length,r=ht(a),s=t.map(u=>u[0]).join(","),i=t.map((u,p)=>u[0]+e[p]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a),l=n==="reflect"?0:1;if(a===1){this.userCode=` int start = ${s}; int end = ${i}; @@ -3924,7 +3924,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nte=Ze({opSnippet:Ste}),Tte={kernel ${r} coords = outC - start; setOutput(getX(${o})); } - `}},Gse=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=ht(a),s=t.map(h=>h[0]).join(","),i=t.map((h,m)=>h[0]+e[m]).join(","),o=kn("rc",a),l=kn("source",a),u=`${o[a-1]} < ${this.outputShape[a-1]}`,p=a===1?"source":`vec2(${l.slice(-2).join()})`,d=n==="reflect"?0:1,c="";if(a===1){let h=` + `}},Kse=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=ht(a),s=t.map(h=>h[0]).join(","),i=t.map((h,m)=>h[0]+e[m]).join(","),o=In("rc",a),l=In("source",a),u=`${o[a-1]} < ${this.outputShape[a-1]}`,p=a===1?"source":`vec2(${l.slice(-2).join()})`,d=n==="reflect"?0:1,c="";if(a===1){let h=` ${r} source = rc; if (source < start) { source = start * 2 - source - ${d}; @@ -3980,13 +3980,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nte=Ze({opSnippet:Ste}),Tte={kernel ${c} setOutput(result); } - `}},Hse=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{paddings:r,mode:s}=n,i=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Gse(a.shape,r,s):new Use(a.shape,r,s);return t.runWebGLProgram(i,[a],a.dtype)},qse={kernelName:bo,backendName:"webgl",kernelFunc:Hse},jse=`if (b == 0.0) return NAN; - return mod(a, b);`,Kse=` + `}},Xse=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{paddings:r,mode:s}=n,i=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Kse(a.shape,r,s):new qse(a.shape,r,s);return t.runWebGLProgram(i,[a],a.dtype)},Yse={kernelName:bo,backendName:"webgl",kernelFunc:Xse},Zse=`if (b == 0.0) return NAN; + return mod(a, b);`,Jse=` vec4 result = mod(a, b); bvec4 isNaN = equal(b, vec4(0.0)); `+tl+` return result; -`,Xse=fn({opSnippet:jse,packedOpSnippet:Kse}),Yse={kernelName:yo,backendName:"webgl",kernelFunc:Xse},Zse=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=` +`,Qse=fn({opSnippet:Zse,packedOpSnippet:Jse}),eie={kernelName:yo,backendName:"webgl",kernelFunc:Qse},tie=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -4006,11 +4006,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nte=Ze({opSnippet:Ste}),Tte={kernel // If no other event happened, last event happened. setOutput(float(${t-1})); } - `}},Jse=` + `}},nie=` if (a == b) { return 1.0; }; -return a / b;`,Qse=` +return a / b;`,aie=` // vec4 one = vec4(equal(a, b)); // return one + (vec4(1.0) - one) * a / b; vec4 result = a / b; @@ -4028,9 +4028,9 @@ return a / b;`,Qse=` } return result; -`,wF=fn({opSnippet:Jse,packedOpSnippet:Qse,checkOutOfBounds:!0}),eie={kernelName:ji,backendName:"webgl",kernelFunc:wF},GS="return a - b;",kF=fn({opSnippet:GS,packedOpSnippet:GS,supportsComplex:!0,cpuKernelImpl:HQ}),tie={kernelName:Uo,backendName:"webgl",kernelFunc:kF};function IF(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=w.parseAxisParam([s],r.shape),o=vF({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=T.expandShapeToKeepDim(o.shape,i),u=ce({inputs:{x:o},backend:n,attrs:{shape:l}}),p=kF({inputs:{a:r,b:u},backend:n}),d=bF({inputs:{x:p},backend:n}),c=tg({inputs:{x:d},backend:n,attrs:{axis:i,keepDims:!1}}),h=ce({inputs:{x:c},backend:n,attrs:{shape:l}}),m=wF({inputs:{a:d,b:h},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}var nie={kernelName:Bo,backendName:"webgl",kernelFunc:IF};function aie(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a,l=o?r:IF({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),u=l.shape[0],p=l.shape[1],d=new Zse(u,p,s),c=[[i]],h=n.runWebGLProgram(d,[l],"int32",c);return o||n.disposeIntermediateTensorInfo(l),h}var rie={kernelName:zu,backendName:"webgl",kernelFunc:aie},sie=Ma+` +`,IF=fn({opSnippet:nie,packedOpSnippet:aie,checkOutOfBounds:!0}),rie={kernelName:qi,backendName:"webgl",kernelFunc:IF},GS="return a - b;",SF=fn({opSnippet:GS,packedOpSnippet:GS,supportsComplex:!0,cpuKernelImpl:XQ}),sie={kernelName:Uo,backendName:"webgl",kernelFunc:SF};function NF(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=w.parseAxisParam([s],r.shape),o=kF({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=T.expandShapeToKeepDim(o.shape,i),u=ce({inputs:{x:o},backend:n,attrs:{shape:l}}),p=SF({inputs:{a:r,b:u},backend:n}),d=xF({inputs:{x:p},backend:n}),c=ng({inputs:{x:d},backend:n,attrs:{axis:i,keepDims:!1}}),h=ce({inputs:{x:c},backend:n,attrs:{shape:l}}),m=IF({inputs:{a:d,b:h},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}var iie={kernelName:Bo,backendName:"webgl",kernelFunc:NF};function oie(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a,l=o?r:NF({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),u=l.shape[0],p=l.shape[1],d=new tie(u,p,s),c=[[i]],h=n.runWebGLProgram(d,[l],"int32",c);return o||n.disposeIntermediateTensorInfo(l),h}var lie={kernelName:zu,backendName:"webgl",kernelFunc:oie},uie=Ma+` return -x; -`,iie=` +`,pie=` vec4 result = -x; bvec4 isNaN = isnan(x); @@ -4040,14 +4040,14 @@ return a / b;`,Qse=` result.a = isNaN.a ? x.a : result.a; return result; -`;function oie(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])){let s=n.texData.get(a.dataId),[i,o]=TQ(s.values,a.shape,a.dtype);return n.makeTensorInfo(o,a.dtype,i)}let r;return G().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new ss(a.shape,iie):r=new ir(a.shape,sie),n.runWebGLProgram(r,[a],a.dtype)}var lie={kernelName:Wu,backendName:"webgl",kernelFunc:oie},uie=fr.nonMaxSuppressionV3Impl;function pie(e){T.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:l}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),{selectedIndices:d}=uie(u,p,i,o,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var cie={kernelName:Vu,backendName:"webgl",kernelFunc:pie},die=fr.nonMaxSuppressionV4Impl;function hie(e){T.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:l,padToMaxOutputSize:u}=a,p=n.readSync(r.dataId),d=n.readSync(s.dataId),{selectedIndices:c,validOutputs:h}=die(p,d,i,o,l,u);return[n.makeTensorInfo([c.length],"int32",new Int32Array(c)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var mie={kernelName:Uu,backendName:"webgl",kernelFunc:hie},fie=fr.nonMaxSuppressionV5Impl;function gie(e){T.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:l,softNmsSigma:u}=a,p=n.readSync(r.dataId),d=n.readSync(s.dataId),c=i,h=o,m=l,f=u,{selectedIndices:g,selectedScores:b}=fie(p,d,c,h,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([b.length],"float32",new Float32Array(b))]}var bie={kernelName:Gu,backendName:"webgl",kernelFunc:gie},yie=class{constructor(e,t,n,a){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=` +`;function cie(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])){let s=n.texData.get(a.dataId),[i,o]=AQ(s.values,a.shape,a.dtype);return n.makeTensorInfo(o,a.dtype,i)}let r;return G().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new ss(a.shape,pie):r=new ir(a.shape,uie),n.runWebGLProgram(r,[a],a.dtype)}var die={kernelName:Wu,backendName:"webgl",kernelFunc:cie},hie=gr.nonMaxSuppressionV3Impl;function mie(e){T.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:l}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),{selectedIndices:d}=hie(u,p,i,o,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var fie={kernelName:Vu,backendName:"webgl",kernelFunc:mie},gie=gr.nonMaxSuppressionV4Impl;function bie(e){T.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:l,padToMaxOutputSize:u}=a,p=n.readSync(r.dataId),d=n.readSync(s.dataId),{selectedIndices:c,validOutputs:h}=gie(p,d,i,o,l,u);return[n.makeTensorInfo([c.length],"int32",new Int32Array(c)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var yie={kernelName:Uu,backendName:"webgl",kernelFunc:bie},xie=gr.nonMaxSuppressionV5Impl;function vie(e){T.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:l,softNmsSigma:u}=a,p=n.readSync(r.dataId),d=n.readSync(s.dataId),c=i,h=o,m=l,f=u,{selectedIndices:g,selectedScores:b}=xie(p,d,c,h,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([b.length],"float32",new Float32Array(b))]}var wie={kernelName:Gu,backendName:"webgl",kernelFunc:vie},kie=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))); } - `}},xie=e=>{let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=a,u=w.sizeFromShape(r.shape),p=new yie(u,i,o,l),d=ce({inputs:{x:r},backend:n,attrs:{shape:[u]}}),c=n.runWebGLProgram(p,[d],s);n.disposeIntermediateTensorInfo(d);let h=[...r.shape,i],m=ce({inputs:{x:c},backend:n,attrs:{shape:h}});return n.disposeIntermediateTensorInfo(c),m},vie={kernelName:vo,backendName:"webgl",kernelFunc:xie};function _m(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="complex64"){let r=Pd({inputs:{input:a},backend:n}),s=_m({inputs:{x:r},backend:n}),i=ng({inputs:{input:a},backend:n}),o=_m({inputs:{x:i},backend:n}),l=Ms({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Ld({attrs:{shape:a.shape,dtype:a.dtype,value:a.dtype==="string"?"":0},backend:n})}var wie={kernelName:up,backendName:"webgl",kernelFunc:_m};function SF(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=Pd({inputs:{input:a},backend:n}),s=SF({inputs:{x:r},backend:n}),i=ng({inputs:{input:a},backend:n}),o=_m({inputs:{x:i},backend:n}),l=Ms({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Ld({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:n})}var kie={kernelName:Hu,backendName:"webgl",kernelFunc:SF};function Iie(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return Iv({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{w.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let d=Iv({inputs:{input:p},backend:n,attrs:{dim:r}});return o.push(d),d}),u=uF({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var Sie={kernelName:qu,backendName:"webgl",kernelFunc:Iie},Nie=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let a=e.length,r=ht(a),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a);if(a===1){this.userCode=` + `}},Iie=e=>{let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=a,u=w.sizeFromShape(r.shape),p=new kie(u,i,o,l),d=ce({inputs:{x:r},backend:n,attrs:{shape:[u]}}),c=n.runWebGLProgram(p,[d],s);n.disposeIntermediateTensorInfo(d);let h=[...r.shape,i],m=ce({inputs:{x:c},backend:n,attrs:{shape:h}});return n.disposeIntermediateTensorInfo(c),m},Sie={kernelName:vo,backendName:"webgl",kernelFunc:Iie};function _m(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="complex64"){let r=Pd({inputs:{input:a},backend:n}),s=_m({inputs:{x:r},backend:n}),i=ag({inputs:{input:a},backend:n}),o=_m({inputs:{x:i},backend:n}),l=Os({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Ld({attrs:{shape:a.shape,dtype:a.dtype,value:a.dtype==="string"?"":0},backend:n})}var Nie={kernelName:up,backendName:"webgl",kernelFunc:_m};function TF(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=Pd({inputs:{input:a},backend:n}),s=TF({inputs:{x:r},backend:n}),i=ag({inputs:{input:a},backend:n}),o=_m({inputs:{x:i},backend:n}),l=Os({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Ld({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:n})}var Tie={kernelName:Hu,backendName:"webgl",kernelFunc:TF};function Cie(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return Sv({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{w.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let d=Sv({inputs:{input:p},backend:n,attrs:{dim:r}});return o.push(d),d}),u=cF({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var Eie={kernelName:ju,backendName:"webgl",kernelFunc:Cie},_ie=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let a=e.length,r=ht(a),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a);if(a===1){this.userCode=` int start = ${s}; int end = ${i}; @@ -4072,7 +4072,7 @@ return a / b;`,Qse=` setOutput(getX(${o})); } } - `}},Tie=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((m,f)=>m[0]+e[f]+m[1]);let a=e.length,r=ht(a),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[0]+e[f]).join(","),o=kn("rc",a),l=kn("source",a),u=`${o[a-1]} < ${this.outputShape[a-1]}`,p=a===1?"source":`vec2(${l.slice(-2).join()})`,d=[`${r} rc = outputLoc;`,`${o[a-1]} += 1; + `}},Aie=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((m,f)=>m[0]+e[f]+m[1]);let a=e.length,r=ht(a),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[0]+e[f]).join(","),o=In("rc",a),l=In("source",a),u=`${o[a-1]} < ${this.outputShape[a-1]}`,p=a===1?"source":`vec2(${l.slice(-2).join()})`,d=[`${r} rc = outputLoc;`,`${o[a-1]} += 1; if(${u}) { `,a===1?"":`} rc = outputLoc; @@ -4096,7 +4096,7 @@ return a / b;`,Qse=` ${h} setOutput(result); } - `}},NF=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a;if(w.sizeFromShape(r.shape)===0){let u=s.map((p,d)=>p[0]+r.shape[d]+p[1]);return Ld({backend:n,attrs:{shape:u,value:i,dtype:r.dtype}})}let o=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Tie(r.shape,s,i):new Nie(r.shape,s,i),l=[[i]];return n.runWebGLProgram(o,[r],r.dtype,l)},Cie={kernelName:wo,backendName:"webgl",kernelFunc:NF},Eie=` + `}},CF=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a;if(w.sizeFromShape(r.shape)===0){let u=s.map((p,d)=>p[0]+r.shape[d]+p[1]);return Ld({backend:n,attrs:{shape:u,value:i,dtype:r.dtype}})}let o=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Aie(r.shape,s,i):new _ie(r.shape,s,i),l=[[i]];return n.runWebGLProgram(o,[r],r.dtype,l)},Fie={kernelName:wo,backendName:"webgl",kernelFunc:CF},$ie=` if(a < 0.0 && floor(b) < b){ return NAN; } @@ -4105,7 +4105,7 @@ return a / b;`,Qse=` } return (round(mod(b, 2.0)) != 1) ? pow(abs(a), b) : sign(a) * pow(abs(a), b); -`,_ie=` +`,Die=` // 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); @@ -4123,9 +4123,9 @@ return a / b;`,Qse=` bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w); `+tl+` return result; -`,Aie=fn({opSnippet:Eie,packedOpSnippet:_ie}),Fie={kernelName:ko,backendName:"webgl",kernelFunc:Aie};function $ie(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=[],u=w.parseAxisParam(s,r.shape),p=u,d=T.getAxesPermutation(p,o),c=r;d!=null&&(c=In({inputs:{x:r},backend:n,attrs:{perm:d}}),p=T.getInnerMostAxes(p.length,o),l.push(c)),T.assertAxesAreInnerMostDims("prod",p,o);let h;if(n.shouldExecuteOnCPU([c])){let m=n.texData.get(c.dataId).values,{outVals:f,outShape:g,outDtype:b}=EQ(c.shape,c.dtype,m,p);h=n.makeTensorInfo(g,b,f)}else{let[m,f]=T.computeOutAndReduceShapes(c.shape,p),g=w.sizeFromShape(f),b=ce({inputs:{x:c},backend:n,attrs:{shape:[-1,g]}}),y=Xm(r.dtype),x=nl(b,y,"prod",n);h=ce({inputs:{x},backend:n,attrs:{shape:m}}),l.push(b),l.push(x)}if(i){l.push(h);let m=T.expandShapeToKeepDim(h.shape,u);h=ce({inputs:{x:h},backend:n,attrs:{shape:m}})}return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var Die={kernelName:So,backendName:"webgl",kernelFunc:$ie};function 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Lie(e){let{inputs:t,backend:n,attrs:a}=e,{shape:r,values:s,defaultValue:i,rowPartitionTensors:o}=t,{rowPartitionTypes:l}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),d=n.readSync(i.dataId),c=o.map(g=>n.readSync(g.dataId)),h=o.map(g=>g.shape),[m,f]=FQ(u,r.shape,p,s.shape,s.dtype,d,i.shape,c,h,l);return n.makeTensorInfo(m,s.dtype,f)}var zie={kernelName:qm,backendName:"webgl",kernelFunc:Lie},TF=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=$Q(a,r,s,i);return t.makeTensorInfo([o.length],i,o)},Wie={kernelName:Xc,backendName:"webgl",kernelFunc:TF},Bie="return 1.0 / x;",Vie=Ze({opSnippet:Bie}),Uie={kernelName:No,backendName:"webgl",kernelFunc:Vie},Gie=Ma+` +`,Rie=fn({opSnippet:$ie,packedOpSnippet:Die}),Mie={kernelName:ko,backendName:"webgl",kernelFunc:Rie};function Oie(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=[],u=w.parseAxisParam(s,r.shape),p=u,d=T.getAxesPermutation(p,o),c=r;d!=null&&(c=Sn({inputs:{x:r},backend:n,attrs:{perm:d}}),p=T.getInnerMostAxes(p.length,o),l.push(c)),T.assertAxesAreInnerMostDims("prod",p,o);let h;if(n.shouldExecuteOnCPU([c])){let m=n.texData.get(c.dataId).values,{outVals:f,outShape:g,outDtype:b}=$Q(c.shape,c.dtype,m,p);h=n.makeTensorInfo(g,b,f)}else{let[m,f]=T.computeOutAndReduceShapes(c.shape,p),g=w.sizeFromShape(f),b=ce({inputs:{x:c},backend:n,attrs:{shape:[-1,g]}}),y=Xm(r.dtype),x=nl(b,y,"prod",n);h=ce({inputs:{x},backend:n,attrs:{shape:m}}),l.push(b),l.push(x)}if(i){l.push(h);let m=T.expandShapeToKeepDim(h.shape,u);h=ce({inputs:{x:h},backend:n,attrs:{shape:m}})}return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var Pie={kernelName:So,backendName:"webgl",kernelFunc:Oie};function Lie(e){let{inputs:t,backend:n,attrs:a}=e,{paramsNestedSplits:r,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:o}=a,l=r.map(b=>n.readSync(b.dataId)),u=r.map(b=>b.shape),p=n.readSync(s.dataId),d=n.readSync(i.dataId),[c,h,m]=DQ(l,u,p,s.shape,s.dtype,d,i.shape,o),f=c.map(b=>n.makeTensorInfo([b.length],"int32",b)),g=n.makeTensorInfo(m,s.dtype,h);return f.concat([g])}var zie={kernelName:Gm,backendName:"webgl",kernelFunc:Lie};function Wie(e){let{inputs:t,backend:n}=e,{starts:a,limits:r,deltas:s}=t,i=n.readSync(a.dataId),o=n.readSync(r.dataId),l=n.readSync(s.dataId),[u,p]=RQ(i,a.shape,a.dtype,o,r.shape,l,s.shape),d=n.makeTensorInfo([u.length],"int32",u),c=n.makeTensorInfo([p.length],a.dtype,p);return[d,c]}var Bie={kernelName:Hm,backendName:"webgl",kernelFunc:Wie};function Vie(e){let{inputs:t,backend:n,attrs:a}=e,{shape:r,values:s,defaultValue:i,rowPartitionTensors:o}=t,{rowPartitionTypes:l}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),d=n.readSync(i.dataId),c=o.map(g=>n.readSync(g.dataId)),h=o.map(g=>g.shape),[m,f]=MQ(u,r.shape,p,s.shape,s.dtype,d,i.shape,c,h,l);return n.makeTensorInfo(m,s.dtype,f)}var Uie={kernelName:jm,backendName:"webgl",kernelFunc:Vie},EF=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=OQ(a,r,s,i);return t.makeTensorInfo([o.length],i,o)},Gie={kernelName:Xc,backendName:"webgl",kernelFunc:EF},Hie="return 1.0 / x;",jie=Ze({opSnippet:Hie}),qie={kernelName:No,backendName:"webgl",kernelFunc:jie},Kie=Ma+` return (x < 0.0) ? 0.0 : x; -`,Hie=` +`,Xie=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -4135,9 +4135,9 @@ return a / b;`,Qse=` result.a = isNaN.a ? x.a : result.a; return result; -`,qie=Ze({opSnippet:Gie,packedOpSnippet:Hie}),jie={kernelName:To,backendName:"webgl",kernelFunc:qie},Kie=Ma+` +`,Yie=Ze({opSnippet:Kie,packedOpSnippet:Xie}),Zie={kernelName:To,backendName:"webgl",kernelFunc:Yie},Jie=Ma+` return (x < 0.0) ? 0.0 : min(6.0, x); -`,Xie=` +`,Qie=` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -4147,7 +4147,7 @@ return a / b;`,Qse=` result.a = isNaN.a ? x.a : result.a; return result; -`,Yie=Ze({opSnippet:Kie,packedOpSnippet:Xie}),Zie={kernelName:_o,backendName:"webgl",kernelFunc:Yie},Jie=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d;r?d="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` +`,eoe=Ze({opSnippet:Jie,packedOpSnippet:Qie}),toe={kernelName:_o,backendName:"webgl",kernelFunc:eoe},noe=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d;r?d="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${u[0]/p[0]}, ${u[1]/p[1]}); @@ -4180,7 +4180,7 @@ return a / b;`,Qse=` setOutput(newValue); } - `}},Qie=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d;r?d="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}},aoe=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d;r?d="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${u[0]/p[0]}, ${u[1]/p[1]}, @@ -4257,7 +4257,7 @@ return a / b;`,Qse=` setOutput(newValue); } - `}};function eoe(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,p=G().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Qie(r.shape,l,u,s,i):new Jie(r.shape,l,u,s,i);return n.runWebGLProgram(p,[r],"float32")}var toe={kernelName:Eo,backendName:"webgl",kernelFunc:eoe},noe=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],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],d=1/u,c=1/p,h=Math.ceil(d)*2+2,m=Math.ceil(c)*2+2;this.userCode=` + `}};function roe(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,p=G().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new aoe(r.shape,l,u,s,i):new noe(r.shape,l,u,s,i);return n.runWebGLProgram(p,[r],"float32")}var soe={kernelName:Eo,backendName:"webgl",kernelFunc:roe},ioe=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],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],d=1/u,c=1/p,h=Math.ceil(d)*2+2,m=Math.ceil(c)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -4338,7 +4338,7 @@ return a / b;`,Qse=` setOutput(accumulator); } - `}};function aoe(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new noe(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var roe={kernelName:Xu,backendName:"webgl",kernelFunc:aoe},soe=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d=a?"0.5":"0.0",c;r?c="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":c="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}};function ooe(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new ioe(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var loe={kernelName:Xu,backendName:"webgl",kernelFunc:ooe},uoe=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d=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( ${u[0]/p[0]}, ${u[1]/p[1]}); @@ -4360,7 +4360,7 @@ return a / b;`,Qse=` setOutput(newValue); } - `}},ioe=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d=a?"0.5":"0.0",c;r?c="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":c="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}},poe=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d=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( ${u[0]/p[0]}, ${u[1]/p[1]}, @@ -4401,7 +4401,7 @@ return a / b;`,Qse=` setOutput(newValue); } - `}};function ooe(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,p=G().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new ioe(r.shape,l,u,s,i):new soe(r.shape,l,u,s,i);return n.runWebGLProgram(p,[r],r.dtype)}var loe={kernelName:Co,backendName:"webgl",kernelFunc:ooe},uoe=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],d=1/u,c=1/p,h=Math.ceil(d)*2+2,m=Math.ceil(c)*2+2;this.userCode=` + `}};function coe(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,p=G().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new poe(r.shape,l,u,s,i):new uoe(r.shape,l,u,s,i);return n.runWebGLProgram(p,[r],r.dtype)}var doe={kernelName:Co,backendName:"webgl",kernelFunc:coe},hoe=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],d=1/u,c=1/p,h=Math.ceil(d)*2+2,m=Math.ceil(c)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -4471,7 +4471,7 @@ return a / b;`,Qse=` setOutput(accumulator); } - `}};function poe(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new uoe(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var coe={kernelName:Ku,backendName:"webgl",kernelFunc:poe},doe=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=` + `}};function moe(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new hoe(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var foe={kernelName:Ku,backendName:"webgl",kernelFunc:moe},goe=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=` void main() { int coord = getOutputCoords(); setOutput(getX(${e[0]} - coord - 1)); @@ -4481,7 +4481,7 @@ return a / b;`,Qse=` ${s} coords = getOutputCoords(); setOutput(getX(${r})); } - `}},hoe=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let a=kn("rc",n),r=`${a[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${a[n-2]} + 1 < ${this.outputShape[n-2]}`,i=ht(n);n===1?this.userCode=` + `}},boe=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let a=In("rc",n),r=`${a[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${a[n-2]} + 1 < ${this.outputShape[n-2]}`,i=ht(n);n===1?this.userCode=` void main(){ int rc = getOutputCoords(); vec4 result = vec4(0.); @@ -4509,7 +4509,7 @@ return a / b;`,Qse=` } setOutput(result); } - `;function o(h){return d(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",d(h)}function u(h){return h[n-2]="("+h[n-2]+" + 1)",d(h)}function p(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",d(h)}function d(h){let m=e.map((b,y)=>c(y,h)),f=m.join(","),g=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${g}))`}function c(h,m){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${m[h]} - 1`:`${m[h]}`}}};function moe(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=r.shape.length,o=w.parseAxisParam(s,r.shape);if(i===0)return aa({inputs:{x:r},backend:n});let l=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new hoe(r.shape,o):new doe(r.shape,o);return n.runWebGLProgram(l,[r],r.dtype)}var foe={kernelName:Ao,backendName:"webgl",kernelFunc:moe},goe=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],a=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=` + `;function o(h){return d(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",d(h)}function u(h){return h[n-2]="("+h[n-2]+" + 1)",d(h)}function p(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",d(h)}function d(h){let m=e.map((b,y)=>c(y,h)),f=m.join(","),g=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${g}))`}function c(h,m){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${m[h]} - 1`:`${m[h]}`}}};function yoe(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=r.shape.length,o=w.parseAxisParam(s,r.shape);if(i===0)return aa({inputs:{x:r},backend:n});let l=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new boe(r.shape,o):new goe(r.shape,o);return n.runWebGLProgram(l,[r],r.dtype)}var xoe={kernelName:Ao,backendName:"webgl",kernelFunc:yoe},voe=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];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=` void main() { @@ -4528,7 +4528,7 @@ return a / b;`,Qse=` } setOutput(outputValue); } - `}},boe={kernelName:pp,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,{radians:r,fillValue:s,center:i}=t,o=n,l=new goe(a.shape,s),[u,p]=T.getImageCenter(i,a.shape[1],a.shape[2]),d=[[u,p,Math.sin(r),Math.cos(r)]];return o.runWebGLProgram(l,[a],a.dtype,d)}},yoe=` + `}},woe={kernelName:pp,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,{radians:r,fillValue:s,center:i}=t,o=n,l=new voe(a.shape,s),[u,p]=T.getImageCenter(i,a.shape[1],a.shape[2]),d=[[u,p,Math.sin(r),Math.cos(r)]];return o.runWebGLProgram(l,[a],a.dtype,d)}},koe=` // OpenGL ES does not support round function. // The algorithm is based on banker's rounding. float base = floor(x); @@ -4543,7 +4543,7 @@ return a / b;`,Qse=` return base + 1.0; } } -`,xoe=Ze({opSnippet:yoe}),voe={kernelName:Fo,backendName:"webgl",kernelFunc:xoe},woe="return inversesqrt(x);",koe=Ze({opSnippet:woe,cpuKernelImpl:DQ}),Ioe={kernelName:$o,backendName:"webgl",kernelFunc:koe},vk=class{constructor(e,t,n,a,r,s,i=!0,o=!1){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let l=ht(r.length),u=ht(s.length),p="";n===1?p="i":n===2&&(p="i, j");let d=`getIndices(${p})`,c="";a===1?c="i":a===2&&(c="i, coords[1]");let h=`getUpdates(${c})`,m="";o&&(m="coords[0], coords[1]");let f=`getDefaultValue(${m})`,g=t>1?"strides[j]":"strides";this.userCode=` +`,Ioe=Ze({opSnippet:koe}),Soe={kernelName:Fo,backendName:"webgl",kernelFunc:Ioe},Noe="return inversesqrt(x);",Toe=Ze({opSnippet:Noe,cpuKernelImpl:PQ}),Coe={kernelName:$o,backendName:"webgl",kernelFunc:Toe},vk=class{constructor(e,t,n,a,r,s,i=!0,o=!1){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let l=ht(r.length),u=ht(s.length),p="";n===1?p="i":n===2&&(p="i, j");let d=`getIndices(${p})`,c="";a===1?c="i":a===2&&(c="i, coords[1]");let h=`getUpdates(${c})`,m="";o&&(m="coords[0], coords[1]");let f=`getDefaultValue(${m})`,g=t>1?"strides[j]":"strides";this.userCode=` ${l} strides = ${l}(${r}); void main() { @@ -4563,7 +4563,7 @@ return a / b;`,Qse=` } setOutput(mix(${f}, sum, float(found))); } - `}},Soe=class{constructor(e,t,n,a,r,s,i=!0,o=!1){this.variableNames=["updates","indices","defaultValue"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=s;let l=ht(r.length),u=ht(s.length),p="";n===1?p="i":n===2&&(p="i, j");let d=`getIndices(${p})`,c="";a===1?c="i":a===2&&(c="i, coords[1]");let h=`getUpdates(${c})`,m="";o&&(m="coords[0], coords[1]");let f=`getDefaultValue(${m})`,g=t>1?"strides[j]":"strides",b=t>1?"strides[j + 1]":"strides";this.userCode=` + `}},Eoe=class{constructor(e,t,n,a,r,s,i=!0,o=!1){this.variableNames=["updates","indices","defaultValue"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=s;let l=ht(r.length),u=ht(s.length),p="";n===1?p="i":n===2&&(p="i, j");let d=`getIndices(${p})`,c="";a===1?c="i":a===2&&(c="i, coords[1]");let h=`getUpdates(${c})`,m="";o&&(m="coords[0], coords[1]");let f=`getDefaultValue(${m})`,g=t>1?"strides[j]":"strides",b=t>1?"strides[j + 1]":"strides";this.userCode=` ${l} strides = ${l}(${r}); void main() { @@ -4600,7 +4600,7 @@ return a / b;`,Qse=` } setOutput(mix(${f}, sum, found)); } - `}};function Noe(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r,updates:s}=t,{shape:i}=a,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:d}=T.calculateShapes(s,r,i),c=[d/u,u];if(d===0)return n.makeTensorInfo(i,r.dtype);let h=ce({inputs:{x:r},backend:n,attrs:{shape:[l,o]}}),m=ce({inputs:{x:s},backend:n,attrs:{shape:[l,u]}}),f=n.makeTensorInfo([],"float32",new Float32Array([0])),g;G().getBool("WEBGL_PACK")?g=new Soe(l,o,h.shape.length,m.shape.length,p,c):g=new vk(l,o,h.shape.length,m.shape.length,p,c);let b=n.runWebGLProgram(g,[m,h,f],m.dtype),y=ce({inputs:{x:b},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(f),y}var Toe={kernelName:Yu,backendName:"webgl",kernelFunc:Noe},Coe=class{constructor(e,t,n,a){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,n];let r="while (left < right) {",s=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,i=G().getNumber("WEBGL_VERSION")===2?r:s,o=a==="left"?"<":"<=";this.userCode=` + `}};function _oe(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r,updates:s}=t,{shape:i}=a,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:d}=T.calculateShapes(s,r,i),c=[d/u,u];if(d===0)return n.makeTensorInfo(i,r.dtype);let h=ce({inputs:{x:r},backend:n,attrs:{shape:[l,o]}}),m=ce({inputs:{x:s},backend:n,attrs:{shape:[l,u]}}),f=n.makeTensorInfo([],"float32",new Float32Array([0])),g;G().getBool("WEBGL_PACK")?g=new Eoe(l,o,h.shape.length,m.shape.length,p,c):g=new vk(l,o,h.shape.length,m.shape.length,p,c);let b=n.runWebGLProgram(g,[m,h,f],m.dtype),y=ce({inputs:{x:b},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(f),y}var Aoe={kernelName:Yu,backendName:"webgl",kernelFunc:_oe},Foe=class{constructor(e,t,n,a){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,n];let r="while (left < right) {",s=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,i=G().getNumber("WEBGL_VERSION")===2?r:s,o=a==="left"?"<":"<=";this.userCode=` int findBound(int batch, float value) { int left = 0; int right = numInputs; @@ -4625,7 +4625,7 @@ return a / b;`,Qse=` setOutput(float(findBound(batch, value))); } - `}};function Eoe(e){let{inputs:t,backend:n,attrs:a}=e,{sortedSequence:r,values:s}=t,{side:i}=a,o=new Coe(r.shape[0],r.shape[1],s.shape[1],i),l=[[r.shape[1]]];return n.runWebGLProgram(o,[r,s],"int32",l)}var _oe={kernelName:Ju,backendName:"webgl",kernelFunc:Eoe},Aoe=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=[],l=[];for(let u=0;u4)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=[],l=[];for(let u=0;u= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0); -`,Roe=Ze({opSnippet:Doe}),Moe={kernelName:Do,backendName:"webgl",kernelFunc:Roe},Ooe=Cp+` +`,Loe=Ze({opSnippet:Poe}),zoe={kernelName:Do,backendName:"webgl",kernelFunc:Loe},Woe=Cp+` return 1.0 / (1.0 + exp(-1.0 * x)); -`,Poe=` +`,Boe=` vec4 result = 1.0 / (1.0 + exp(-1.0 * x)); bvec4 isNaN = isnan(x); @@ -4653,20 +4653,20 @@ return a / b;`,Qse=` result.a = isNaN.a ? x.a : result.a; return result; -`,Loe=Ze({opSnippet:Ooe,packedOpSnippet:Poe,cpuKernelImpl:MQ}),zoe={kernelName:Po,backendName:"webgl",kernelFunc:Loe},Woe=` +`,Voe=Ze({opSnippet:Woe,packedOpSnippet:Boe,cpuKernelImpl:zQ}),Uoe={kernelName:Po,backendName:"webgl",kernelFunc:Voe},Goe=` if (isnan(x)) { return 0.0; } return sign(x); -`,Boe=Ze({opSnippet:Woe}),Voe={kernelName:Oo,backendName:"webgl",kernelFunc:Boe},Uoe=Cp+` +`,Hoe=Ze({opSnippet:Goe}),joe={kernelName:Oo,backendName:"webgl",kernelFunc:Hoe},qoe=Cp+` return sin(x); -`,Goe=` +`,Koe=` vec4 result = sin(x); bvec4 isNaN = isnan(x); ${tl} return result; -`,Hoe=Ze({opSnippet:Uoe,packedOpSnippet:Goe}),qoe={kernelName:Ro,backendName:"webgl",kernelFunc:Hoe},joe=` +`,Xoe=Ze({opSnippet:qoe,packedOpSnippet:Koe}),Yoe={kernelName:Ro,backendName:"webgl",kernelFunc:Xoe},Zoe=` float e2x = exp(x); return (e2x - 1.0 / e2x) / 2.0; -`,Koe=Ze({opSnippet:joe}),Xoe={kernelName:Mo,backendName:"webgl",kernelFunc:Koe},Yoe=` +`,Joe=Ze({opSnippet:Zoe}),Qoe={kernelName:Mo,backendName:"webgl",kernelFunc:Joe},ele=` float epsilon = 1.1920928955078125e-7; float threshold = log(epsilon) + 2.0; @@ -4686,17 +4686,17 @@ return a / b;`,Qse=` result = log(exp_x + 1.0); } return result; -`,Zoe=Ze({opSnippet:Yoe}),Joe={kernelName:Lo,backendName:"webgl",kernelFunc:Zoe},Qoe=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,paddings:i}=a;w.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((b,y)=>b*y),l=[[0,0]];l.push(...i);for(let b=1+s.length;bn.disposeIntermediateTensorInfo(b)),g},ele={kernelName:tp,backendName:"webgl",kernelFunc:Qoe};function tle(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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i=Array.from(n.readSync(r.dataId)),o=n.readSync(a.dataId),l=Array.from(n.readSync(s.dataId)),[u,p,d]=LQ(o,a.shape,a.dtype,i,l);return[n.makeTensorInfo(p,a.dtype,u),n.makeTensorInfo([d.length],s.dtype,new Int32Array(d))]}var rle={kernelName:ap,backendName:"webgl",kernelFunc:ale};function sle(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 + ${i.shape}`);let o=n.readSync(a.dataId),l=n.readSync(r.dataId),u=n.readSync(s.dataId),p=n.readSync(i.dataId)[0],[d,c,h,m,f]=BQ(o,a.shape,a.dtype,l,r.dtype,u,p);return[n.makeTensorInfo(c,a.dtype,d),n.makeTensorInfo([c[0]],r.dtype,h),n.makeTensorInfo([m.length],"bool",new Uint8Array(m.map(g=>Number(g)))),n.makeTensorInfo([f.length],a.dtype,new Int32Array(f))]}var ile={kernelName:Yc,backendName:"webgl",kernelFunc:sle};function ole(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),l=Array.from(n.readSync(s.dataId)),[u,p,d]=VQ(o,a.shape,a.dtype,i,l);return[n.makeTensorInfo(p,a.dtype,u),n.makeTensorInfo([d.length],s.dtype,new Int32Array(d))]}var lle={kernelName:ap,backendName:"webgl",kernelFunc:ole};function ule(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),l=n.readSync(s.dataId),[u,p]=KA(i,a.shape,a.dtype,o,l,!0);return n.makeTensorInfo(p,a.dtype,u)}var ile={kernelName:Zc,backendName:"webgl",kernelFunc:sle};function ole(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 + ${s.shape}`);let i=n.readSync(a.dataId),o=n.readSync(r.dataId),l=n.readSync(s.dataId),[u,p]=YA(i,a.shape,a.dtype,o,l,!0);return n.makeTensorInfo(p,a.dtype,u)}var ple={kernelName:Zc,backendName:"webgl",kernelFunc:ule};function cle(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),l=n.readSync(s.dataId),[u,p]=KA(i,a.shape,a.dtype,o,l);return n.makeTensorInfo(p,a.dtype,u)}var lle={kernelName:Jc,backendName:"webgl",kernelFunc:ole};function ule(e){let{inputs:t,backend:n,attrs:a}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=a,{sliceRank:l,numUpdates:u,sliceSize:p,strides:d,outputSize:c}=T.calculateShapes(s,r,o),h=!1;if(s.dtype==="string"){let b=n.bufferSync(r),y=n.bufferSync(s),x=w.decodeString(n.readSync(i.dataId)[0]),v=RQ(b,y,o,c,p,u,l,d,x,h);return n.makeTensorInfo(o,v.dtype,v.values)}let m=new vk(u,l,r.shape.length,s.shape.length,d,[c,1],h),f=n.runWebGLProgram(m,[s,r,i],s.dtype),g=ce({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(f),g}var ple={kernelName:rp,backendName:"webgl",kernelFunc:ule};function cle(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=a,o=w.parseAxisParam(i,r.shape)[0],l=T.prepareSplitSize(r,s,o),u=r.shape.length,p=new Array(u).fill(0),d=r.shape.slice();return l.map(c=>{let h=[...d];h[o]=c;let m=Ep({inputs:{x:r},backend:n,attrs:{begin:p,size:h}});return p[o]+=c,m})}var dle={kernelName:np,backendName:"webgl",kernelFunc:cle},HS="return sqrt(x);",hle=Ze({opSnippet:HS,packedOpSnippet:HS,cpuKernelImpl:zQ}),mle={kernelName:zo,backendName:"webgl",kernelFunc:hle},fle="return x * x;",gle=Ze({opSnippet:fle}),ble={kernelName:Qc,backendName:"webgl",kernelFunc:gle},qS="return (a - b) * (a - b);",yle=fn({opSnippet:qS,packedOpSnippet:qS}),xle={kernelName:Vo,backendName:"webgl",kernelFunc:yle};function vle(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;if(r.dtype!=="string")throw new Error("Input must be of datatype string");let s=n.readSync(r.dataId),i=T.fromUint8ToStringArray(s),o=WQ(i,"string",a);return n.makeTensorInfo(r.shape,"string",o)}var wle={kernelName:ed,backendName:"webgl",kernelFunc:vle};function kle({inputs:e,attrs:t,backend:n}){let{x:a}=e,r=Ma+` + ${s.shape}`);let i=n.readSync(a.dataId),o=n.readSync(r.dataId),l=n.readSync(s.dataId),[u,p]=YA(i,a.shape,a.dtype,o,l);return n.makeTensorInfo(p,a.dtype,u)}var dle={kernelName:Jc,backendName:"webgl",kernelFunc:cle};function hle(e){let{inputs:t,backend:n,attrs:a}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=a,{sliceRank:l,numUpdates:u,sliceSize:p,strides:d,outputSize:c}=T.calculateShapes(s,r,o),h=!1;if(s.dtype==="string"){let b=n.bufferSync(r),y=n.bufferSync(s),x=w.decodeString(n.readSync(i.dataId)[0]),v=LQ(b,y,o,c,p,u,l,d,x,h);return n.makeTensorInfo(o,v.dtype,v.values)}let m=new vk(u,l,r.shape.length,s.shape.length,d,[c,1],h),f=n.runWebGLProgram(m,[s,r,i],s.dtype),g=ce({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(f),g}var mle={kernelName:rp,backendName:"webgl",kernelFunc:hle};function fle(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=a,o=w.parseAxisParam(i,r.shape)[0],l=T.prepareSplitSize(r,s,o),u=r.shape.length,p=new Array(u).fill(0),d=r.shape.slice();return l.map(c=>{let h=[...d];h[o]=c;let m=Ep({inputs:{x:r},backend:n,attrs:{begin:p,size:h}});return p[o]+=c,m})}var gle={kernelName:np,backendName:"webgl",kernelFunc:fle},HS="return sqrt(x);",ble=Ze({opSnippet:HS,packedOpSnippet:HS,cpuKernelImpl:UQ}),yle={kernelName:zo,backendName:"webgl",kernelFunc:ble},xle="return x * x;",vle=Ze({opSnippet:xle}),wle={kernelName:Qc,backendName:"webgl",kernelFunc:vle},jS="return (a - b) * (a - b);",kle=fn({opSnippet:jS,packedOpSnippet:jS}),Ile={kernelName:Vo,backendName:"webgl",kernelFunc:kle};function Sle(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;if(r.dtype!=="string")throw new Error("Input must be of datatype string");let s=n.readSync(r.dataId),i=T.fromUint8ToStringArray(s),o=GQ(i,"string",a);return n.makeTensorInfo(r.shape,"string",o)}var Nle={kernelName:ed,backendName:"webgl",kernelFunc:Sle};function Tle({inputs:e,attrs:t,backend:n}){let{x:a}=e,r=Ma+` return x > 0.0 ? 1.0 : float(${t.alpha}); - `,s=new ir(a.shape,r);return n.runWebGLProgram(s,[a],a.dtype)}var Ile={kernelName:Ts,backendName:"webgl",kernelFunc:kle},Sle=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let a=n.length,r=ht(n.length),s=ht(n.length),i="";if(a===1)i="coords * strides + begin";else{let o=0;i=n.map((l,u)=>(o++,n.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=` + `,s=new ir(a.shape,r);return n.runWebGLProgram(s,[a],a.dtype)}var Cle={kernelName:Cs,backendName:"webgl",kernelFunc:Tle},Ele=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let a=n.length,r=ht(n.length),s=ht(n.length),i="";if(a===1)i="coords * strides + begin";else{let o=0;i=n.map((l,u)=>(o++,n.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=` ${r} begin = ${r}(${e}); ${r} strides = ${r}(${t}); @@ -4704,15 +4704,15 @@ return a / b;`,Qse=` ${s} coords = getOutputCoords(); setOutput(getX(${i})); } - `}};function Nle(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:p,newAxisMask:d,shrinkAxisMask:c}=a,{finalShapeSparse:h,finalShape:m,isIdentity:f,sliceDim0:g,isSimpleSlice:b,begin:y,end:x,strides:v}=Kt.sliceInfo(r.shape,s,i,o,l,u,p,d,c),I;if(f)I=ce({inputs:{x:r},backend:n,attrs:{shape:m}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let C=Kt.computeOutShape(y,x,v),_=Ep({inputs:{x:r},backend:n,attrs:{begin:y,size:C}});I=ce({inputs:{x:_},backend:n,attrs:{shape:m}}),n.disposeIntermediateTensorInfo(_)}else if(n.shouldExecuteOnCPU([r])){let C=n.readSync(r.dataId),_=Pe(r.shape,r.dtype,C),F=BQ(h,_,v,y);I=n.makeTensorInfo(m,r.dtype,F.values)}else{let C=new 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o=n.readSync(s.dataId),l=n.readSync(i.dataId)[0],[u,p,d]=UQ(o,l,r),c=p.length;return[n.makeTensorInfo([c,2],"int32",u),n.makeTensorInfo([c],"string",p),n.makeTensorInfo([2],"int32",new Int32Array(d))]}var Ale={kernelName:nd,backendName:"webgl",kernelFunc:_le};function Fle(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=GQ(i,r);return n.makeTensorInfo(s.shape,"int32",o)}var $le={kernelName:ad,backendName:"webgl",kernelFunc:Fle},Dle="return tan(x);",Rle=Ze({opSnippet:Dle}),Mle={kernelName:Go,backendName:"webgl",kernelFunc:Rle},Ole=` + `}};function 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Fle(e){let{inputs:t,backend:n,attrs:a}=e,{separator:r,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=a,{data:p,dataSplits:d}=t,c=n.readSync(p.dataId),h=n.readSync(d.dataId),[m,f]=jQ(c,h,r,s,i,o,l,u);return[n.makeTensorInfo([m.length],"string",m),n.makeTensorInfo(d.shape,"int32",f)]}var $le={kernelName:td,backendName:"webgl",kernelFunc:Fle};function Dle(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),l=n.readSync(i.dataId)[0],[u,p,d]=qQ(o,l,r),c=p.length;return[n.makeTensorInfo([c,2],"int32",u),n.makeTensorInfo([c],"string",p),n.makeTensorInfo([2],"int32",new Int32Array(d))]}var Rle={kernelName:nd,backendName:"webgl",kernelFunc:Dle};function 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h=ce({inputs:{x:s},backend:n,attrs:{shape:[l,o]}}),m=ce({inputs:{x:i},backend:n,attrs:{shape:[l,u]}}),f=ce({inputs:{x:r},backend:n,attrs:{shape:c}}),g=new vk(l,o,h.shape.length,m.shape.length,p,c,!1,!0),b=n.runWebGLProgram(g,[m,h,f],f.dtype),y=ce({inputs:{x:b},backend:n,attrs:{shape:r.shape}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(b),y}var Wle={kernelName:Zu,backendName:"webgl",kernelFunc:zle},Ble=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s5)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;r5){let o=n.readSync(r.dataId),l=r.dtype==="string"?o.map(d=>w.decodeString(d)):o,u=Pe(r.shape,r.dtype,l),p=qQ(u,s);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new Ble(r.shape,s);return n.runWebGLProgram(i,[r],r.dtype)}var Ule={kernelName:Ns,backendName:"webgl",kernelFunc:CF},Gle=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=` + `}};function jle(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],a=[];for(let r=0;r5){let o=n.readSync(r.dataId),l=r.dtype==="string"?o.map(d=>w.decodeString(d)):o,u=Pe(r.shape,r.dtype,l),p=YQ(u,s);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new Hle(r.shape,s);return n.runWebGLProgram(i,[r],r.dtype)}var qle={kernelName:Ts,backendName:"webgl",kernelFunc:_F},Kle=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -4752,7 +4752,7 @@ return a / b;`,Qse=` setOutput(float(i1)); } } - `}},Hle=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode=` + `}},Xle=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode=` void main() { // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ... ivec2 coords = getOutputCoords(); @@ -4786,7 +4786,7 @@ return a / b;`,Qse=` setOutput(x0 >= x1 ? float(i0) : float(i1)); } - `}};function Ks(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function jS(e){let t=1;for(;tl){let F=n.readSync(r.dataId),[D,$]=jQ(F,u,r.dtype,s,i);return[n.makeTensorInfo(D.shape,D.dtype,D.values),n.makeTensorInfo($.shape,$.dtype,$.values)]}if(s===0)return u[u.length-1]=0,[n.makeTensorInfo(u,r.dtype,[]),n.makeTensorInfo(u,"int32",[])];if(p===1)return[r,Ld({attrs:{shape:u,dtype:"int32",value:0},backend:n})];let d=n.texData.get(r.dataId),c=d!==null&&d.isPacked,h=c?n.unpackTensor(r):r,m=w.sizeFromShape(u)/p,f=ce({inputs:{x:h},attrs:{shape:[m,p]},backend:n});c&&Ks(n,h);let g=jS(s),b=jS(p),y=null,x=()=>y===null?[f,f]:[f,y],v=(F,D,$)=>{let S=x(),M=new Gle($),B=[[p],[y===null?1:0],[Number.NEGATIVE_INFINITY],[F],[D]],U=y;y=n.runWebGLProgram(M,S,"int32",B),Ks(n,U)};for(let F=1;F=1;$/=2)v(D,$,[m,b])}for(let F=b;F>g;F/=2){let D=x(),$=new Hle([m,F/2]),S=[[p],[y===null?1:0],[g]],M=y;y=n.runWebGLProgram($,D,"int32",S),Ks(n,M);let 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Oc;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu"})(Oc||(Oc={}));var EF;function sue(e){EF=e.wasm.cwrap(oi,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function iue(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:l,transposeB:u,activation:p,leakyreluAlpha:d}=a,c=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(s.dataId).id,m=0;if(i!=null){let _=n.dataIdMap.get(i.dataId);if(_.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${_.shape.length}.`);m=_.id}let f=o==null?0:n.dataIdMap.get(o.dataId).id,g=Oc[p];if(g==null)throw new Error(`${p} activation not yet supported for FusedConv2D in the 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Got ${r.dtype} and ${s.dtype}`);let u=T.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p=n.makeOutput(u.outShape,r.dtype);return e$(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(p.dataId).id,Qe[r.dtype],u.batchSize,u.inChannels,u.inHeight,u.inWidth,u.outHeight,u.outWidth,u.strideHeight,u.strideWidth,u.dilationHeight,u.dilationWidth,u.filterHeight,u.filterWidth,u.padInfo.top,u.padInfo.left),p}var tce={kernelName:qi,backendName:"wasm",setupFunc:Qpe,kernelFunc:ece},t$;function nce(e){t$=e.wasm.cwrap(jl,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function ace(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,dy:i}=t,{strides:o,pad:l,dilations:u}=a;if(r.dtype!==s.dtype||r.dtype!==i.dtype)throw new Error(`Dilation2DBackpropFilter error: x must have the same dtype as filter and dy. 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Got ${r.dtype}, ${s.dtype}, and ${i.dtype}`);let p=T.computeDilation2DInfo(r.shape,s.shape,o,l,"NHWC",u),d=n.makeOutput(r.shape,r.dtype);return n$(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(i.dataId).id,n.dataIdMap.get(d.dataId).id,Qe[r.dtype],p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.filterHeight,p.filterWidth,p.padInfo.top,p.padInfo.left),d}var oce={kernelName:ql,backendName:"wasm",setupFunc:sce,kernelFunc:ice},lce=Xe(Ki),a$;function uce(e){a$=e.wasm.cwrap(Su,null,["number","number","number"])}function pce(e){let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=n.makeOutput(r.shape,"float32"),i=o=>n.dataIdMap.get(o.dataId).id;return a$(i(r),i(a),i(s)),s}var cce={kernelName:Su,backendName:"wasm",setupFunc:uce,kernelFunc:pce},dce=!1,hce=Ut(Nu,dce,"bool"),mce=Xe(Xi),fce=Xe(Yi,"float32");function Nv(e){let{inputs:t,attrs:n,backend:a}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(w.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Ln({inputs:{x:r},backend:a,attrs:{shape:o}})}var gce={kernelName:Tu,backendName:"wasm",kernelFunc:Nv},bce=Xe(Zi,"float32");function r$(e){let{attrs:{shape:t,value:n},backend:a}=e,{attrs:{dtype:r}}=e;r=r||w.inferDtype(n);let s=a.makeOutput(t,r);return a.typedArrayFromHeap(s).fill(n),s}var yce={kernelName:Hc,backendName:"wasm",kernelFunc:r$},s$;function xce(e){s$=e.wasm.cwrap(Cu,null,["number","number","number","number","number","number"])}function vce(e){let{inputs:t,backend:n}=e,{image:a}=t,r=n.makeOutput(a.shape,a.dtype),s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,l,u,p]=a.shape;return s$(s,o,l,u,p,i),r}var wce={kernelName:Cu,backendName:"wasm",kernelFunc:vce,setupFunc:xce},kce=Xe(Ji),Ice=!1,Sce=Ut(Qi,Ice),i$;function Nce(e){i$=e.wasm.cwrap(eo,null,["number","number","number","number","number","number","number"])}function Tce(e){let{backend:t,inputs:n,attrs:a}=e,{varianceEpsilon:r}=a,{x:s,mean:i,variance:o,offset:l,scale:u}=n,p=t.dataIdMap.get(s.dataId).id,d=t.dataIdMap.get(i.dataId).id,c=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=u!=null?t.dataIdMap.get(u.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(w.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return i$(p,d,c,h,m,r,g),f}var Cce={kernelName:eo,backendName:"wasm",setupFunc:Nce,kernelFunc:Tce},o$;function Ece(e){o$=e.wasm.cwrap(li,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function _ce(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=T.computeConv2DInfo(r.shape,s.shape,l,p,u,c),g=Oc[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let b=a.dataIdMap.get(r.dataId).id,y=a.dataIdMap.get(s.dataId).id,x=f.outChannels,v=0;if(i!=null){let te=a.dataIdMap.get(i.dataId);if(te.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);if(te.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${te.shape}) does not match the number of output channels (${x})`);v=te.id}let I=f.filterHeight,N=f.filterWidth,C=f.padInfo.top,_=f.padInfo.right,F=f.padInfo.bottom,D=f.padInfo.left,$=f.dilationHeight,S=f.dilationWidth,M=f.strideHeight,B=f.strideWidth,U=f.inChannels,H=f.padInfo.type==="SAME"?1:0,j=f.batchSize,K=f.inHeight,Z=f.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let J=a.makeOutput(f.outShape,"float32"),ee=a.dataIdMap.get(J.dataId).id,ae=o==null?0:a.dataIdMap.get(o.dataId).id;return o$(b,j,K,Z,y,I,N,v,C,_,F,D,H,$,S,M,B,U,x,g,ae,m||0,ee),J}var Ace={kernelName:li,backendName:"wasm",setupFunc:Ece,kernelFunc:_ce},l$;function Fce(e){l$=e.wasm.cwrap(ui,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 $ce(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=T.computeConv2DInfo(r.shape,s.shape,l,p,u,c,!0),g=Oc[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let b=a.dataIdMap.get(r.dataId).id,y=a.dataIdMap.get(s.dataId).id,x=f.outChannels,v=0;if(i!=null){let te=a.dataIdMap.get(i.dataId);if(te.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);if(te.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${te.shape}) does not match the number of output channels (${x})`);v=te.id}let I=f.filterHeight,N=f.filterWidth,C=f.padInfo.top,_=f.padInfo.right,F=f.padInfo.bottom,D=f.padInfo.left,$=f.dilationHeight,S=f.dilationWidth,M=f.strideHeight,B=f.strideWidth,U=f.inChannels,H=f.padInfo.type==="SAME"?1:0,j=f.batchSize,K=f.inHeight,Z=f.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let J=a.makeOutput(f.outShape,"float32"),ee=a.dataIdMap.get(J.dataId).id,ae=o==null?0:a.dataIdMap.get(o.dataId).id;return l$(b,j,K,Z,y,I,N,v,C,_,F,D,H,$,S,M,B,U,x,g,ae,m||0,ee),J}var Dce={kernelName:ui,backendName:"wasm",setupFunc:Fce,kernelFunc:$ce},u$;function Rce(e){u$=e.wasm.cwrap(_u,null,["number","number","number","number","number","number","array","number"])}function Mce(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=r0.prepareAndValidate(a,r),u=t.makeOutput(s,a.dtype);if(i===0)return u;let p=r.shape,d=p[p.length-1],c=t.dataIdMap.get(a.dataId).id,h=t.dataIdMap.get(r.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(u.dataId).id;return u$(c,Qe[a.dtype],h,i,d,o,m,f),u}var Oce={kernelName:_u,backendName:"wasm",setupFunc:Rce,kernelFunc:Mce},p$;function Pce(e){p$=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function Lce(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=w.parseAxisParam(i,r.shape)[0],u=t.readSync(s.dataId),p=r.shape[l];for(let C=0;C=0,()=>`GatherV2: the index value ${_} is not in [0, ${p-1}]`)}let d=T.segment_util.collectGatherOpShapeInfo(r,s,l,o),c=Ln({inputs:{x:r},attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]},backend:t}),h=w.sizeFromShape(s.shape),m=Ln({inputs:{x:s},attrs:{shape:[d.batchSize,h/d.batchSize]},backend:t}),f=[d.batchSize,d.outerSize,h/d.batchSize,d.sliceSize],g=t.makeOutput(f,r.dtype);if(w.sizeFromShape(r.shape)===0)return g;let b=c.shape.length-1,y=t.dataIdMap.get(c.dataId).id,x=t.dataIdMap.get(m.dataId).id,v=t.dataIdMap.get(g.dataId).id,I=new Uint8Array(new Int32Array(w.computeStrides(c.shape)).buffer),N=new Uint8Array(new Int32Array(w.computeStrides(f)).buffer);return p$(y,Qe[r.dtype],I,b,x,d.batchSize,N,v),t.disposeData(c.dataId),t.disposeData(m.dataId),g.shape=d.outputShape,g}var zce={kernelName:Eu,backendName:"wasm",setupFunc:Pce,kernelFunc:Lce},Wce=!1,Bce=Ut(Au,Wce,"bool"),Vce=!1,Uce=Ut(to,Vce,"bool"),Gce=Xe(ao,"bool"),Hce=Xe(ro,"bool"),qce=Xe(so,"bool"),c$;function jce(e){c$=e.wasm.cwrap(io,null,["number","number","number","number"])}function Kce(e){let{inputs:{x:t},attrs:{alpha:n},backend:a}=e,r=a.dataIdMap.get(t.dataId).id,s=a.makeOutput(t.shape,"float32");if(w.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;c$(r,Qe[t.dtype],n,i)}return s}var Xce={kernelName:io,backendName:"wasm",setupFunc:jce,kernelFunc:Kce},Yce=!1,Zce=Ut(Fu,Yce,"bool"),Jce=!1,Qce=Ut($u,Jce,"bool"),d$;function ede(e){d$=e.wasm.cwrap(Du,null,["number","number","number","number"])}function tde(e){let{attrs:t,backend:n}=e,{start:a,stop:r,num:s}=t,i=Math.floor(s),o=n.makeOutput([i],"float32");return d$(n.dataIdMap.get(o.dataId).id,a,r,i),o}var nde={kernelName:Du,backendName:"wasm",setupFunc:ede,kernelFunc:tde},ade=Xe(oo),rde=Xe(lo),sde=!1,ide=Ut(Ru,sde,"bool"),ode=Xe(Mu),lde=!1,ude=Ut(Ou,lde,"bool"),pde=!1,cde=Ut(mN,pde,"bool"),h$;function dde(e){h$=e.wasm.cwrap(uo,null,["number","number","number","number","number","number","number"])}function hde(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a;if(r.dtype!=="float32")throw new Error("LRN error: x must have dtype float32");let u=n.makeOutput(r.shape,r.dtype);return h$(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(u.dataId).id,r.shape[3],s,i,o,l),u}var mde={kernelName:uo,backendName:"wasm",setupFunc:dde,kernelFunc:hde},m$;function fde(e){m$=e.wasm.cwrap(Pu,null,["number","number","number","number","number","number","number","number","number"])}function gde(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=a;if(r.dtype!=="float32"||s.dtype!=="float32"||i.dtype!=="float32")throw new Error("LRNGrad 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vde={kernelName:po,backendName:"wasm",setupFunc:yde,kernelFunc:xde},wde=!1,kde=Ut(co,wde),g$;function Ide(e){g$=e.wasm.cwrap(ho,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Sde(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id;w.assert(r.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${r.dtype}.`);let{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,p=T.computePool2DInfo(r.shape,i,o,1,l,u),d=p.filterHeight,c=p.filterWidth,h=p.padInfo.top,m=p.padInfo.right,f=p.padInfo.bottom,g=p.padInfo.left,b=p.dilationHeight,y=p.dilationWidth,x=p.strideHeight,v=p.strideWidth,I=p.inChannels,N=p.outChannels;if(p.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${p.dataFormat}'. 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Qe;(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"})(Qe||(Qe={}));var Oc;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu"})(Oc||(Oc={}));var AF;function uue(e){AF=e.wasm.cwrap(li,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function pue(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:l,transposeB:u,activation:p,leakyreluAlpha:d}=a,c=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(s.dataId).id,m=0;if(i!=null){let _=n.dataIdMap.get(i.dataId);if(_.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank 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due=Xe(uu),hue=Xe(Ci),mue=Xe(Ei);function Ut(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:l}=i,{a:u,b:p}=l,d=o.dataIdMap.get(u.dataId).id,c=o.dataIdMap.get(p.dataId).id,h=n!=null?n:u.dtype,m=T.assertAndGetBroadcastShape(u.shape,p.shape),f=o.makeOutput(m,h);if(w.sizeFromShape(m)===0)return f;let g=new Uint8Array(new Int32Array(u.shape).buffer),b=new Uint8Array(new Int32Array(p.shape).buffer),y=o.dataIdMap.get(f.dataId).id;return a(d,g,u.shape.length,c,b,p.shape.length,Qe[u.dtype],y),f}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:s}}var fue=!0,gue=Ut(Ss,fue),FF;function bue(e){FF=e.wasm.cwrap(_i,null,["array","number","number","number"])}function yue(e){let{inputs:t,backend:n}=e,a=n.makeOutput(t[0].shape,t[0].dtype);if(w.sizeFromShape(a.shape)===0)return a;let r=t.map(o=>n.dataIdMap.get(o.dataId).id),s=new Uint8Array(new 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Got ${r.dtype}, ${s.dtype}, and ${i.dtype}`);let p=T.computeDilation2DInfo(r.shape,s.shape,o,l,"NHWC",u),d=n.makeOutput(s.shape,s.dtype);return a$(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(i.dataId).id,n.dataIdMap.get(d.dataId).id,Qe[r.dtype],p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.filterHeight,p.filterWidth,p.padInfo.top,p.padInfo.left),d}var lce={kernelName:ql,backendName:"wasm",setupFunc:ice,kernelFunc:oce},r$;function uce(e){r$=e.wasm.cwrap(jl,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function pce(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,dy:i}=t,{strides:o,pad:l,dilations:u}=a;if(r.dtype!==s.dtype||r.dtype!==i.dtype)throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. 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Uce={kernelName:Eu,backendName:"wasm",setupFunc:Bce,kernelFunc:Vce},Gce=!1,Hce=Ut(Au,Gce,"bool"),jce=!1,qce=Ut(to,jce,"bool"),Kce=Xe(ao,"bool"),Xce=Xe(ro,"bool"),Yce=Xe(so,"bool"),h$;function Zce(e){h$=e.wasm.cwrap(io,null,["number","number","number","number"])}function Jce(e){let{inputs:{x:t},attrs:{alpha:n},backend:a}=e,r=a.dataIdMap.get(t.dataId).id,s=a.makeOutput(t.shape,"float32");if(w.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;h$(r,Qe[t.dtype],n,i)}return s}var Qce={kernelName:io,backendName:"wasm",setupFunc:Zce,kernelFunc:Jce},ede=!1,tde=Ut(Fu,ede,"bool"),nde=!1,ade=Ut($u,nde,"bool"),m$;function rde(e){m$=e.wasm.cwrap(Du,null,["number","number","number","number"])}function sde(e){let{attrs:t,backend:n}=e,{start:a,stop:r,num:s}=t,i=Math.floor(s),o=n.makeOutput([i],"float32");return m$(n.dataIdMap.get(o.dataId).id,a,r,i),o}var ide={kernelName:Du,backendName:"wasm",setupFunc:rde,kernelFunc:sde},ode=Xe(oo),lde=Xe(lo),ude=!1,pde=Ut(Ru,ude,"bool"),cde=Xe(Mu),dde=!1,hde=Ut(Ou,dde,"bool"),mde=!1,fde=Ut(mN,mde,"bool"),f$;function gde(e){f$=e.wasm.cwrap(uo,null,["number","number","number","number","number","number","number"])}function bde(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a;if(r.dtype!=="float32")throw new Error("LRN error: x must have dtype float32");let u=n.makeOutput(r.shape,r.dtype);return f$(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(u.dataId).id,r.shape[3],s,i,o,l),u}var yde={kernelName:uo,backendName:"wasm",setupFunc:gde,kernelFunc:bde},g$;function xde(e){g$=e.wasm.cwrap(Pu,null,["number","number","number","number","number","number","number","number","number"])}function vde(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=a;if(r.dtype!=="float32"||s.dtype!=="float32"||i.dtype!=="float32")throw new Error("LRNGrad 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Sde={kernelName:po,backendName:"wasm",setupFunc:kde,kernelFunc:Ide},Nde=!1,Tde=Ut(co,Nde),y$;function Cde(e){y$=e.wasm.cwrap(ho,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Ede(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id;w.assert(r.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. 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Kd=class extends un{constructor(t=new xg(2)){super("AgeGenderNet"),this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return O(()=>{let a=t instanceof xr?this.faceFeatureExtractor.forwardInput(t):t,r=xa(a,[7,7],[2,2],"valid").as2D(a.shape[0],-1),s=qd(r,n.fc.age).as1D(),i=qd(r,n.fc.gender);return{age:s,gender:i}})}forwardInput(t){return O(()=>{let{age:n,gender:a}=this.runNet(t);return{age:n,gender:ja(a)}})}async forward(t){return this.forwardInput(await vt(t))}async predictAgeAndGender(t){let n=await vt(t),a=await this.forwardInput(n),r=dt(a.age),s=dt(a.gender),i=r.map((l,u)=>({ageTensor:l,genderTensor:s[u]})),o=await Promise.all(i.map(async({ageTensor:l,genderTensor:u})=>{let p=l.dataSync()[0],d=u.dataSync()[0],c=d>.5,h=c?"male":"female",m=c?d:1-d;return l.dispose(),u.dispose(),{age:p,gender:h,genderProbability:m}}));return 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p=`mobilenetv1/conv_${u}`,d=`MobilenetV1/Conv2d_${u}_depthwise`,c=`${p}/depthwise_conv`,h=`${p}/pointwise_conv`,m=n(`${d}/depthwise_weights`,4,`${c}/filters`),f=n(`${d}/BatchNorm/gamma`,1,`${c}/batch_norm_scale`),g=n(`${d}/BatchNorm/beta`,1,`${c}/batch_norm_offset`),b=n(`${d}/BatchNorm/moving_mean`,1,`${c}/batch_norm_mean`),y=n(`${d}/BatchNorm/moving_variance`,1,`${c}/batch_norm_variance`);return{depthwise_conv:{filters:m,batch_norm_scale:f,batch_norm_offset:g,batch_norm_mean:b,batch_norm_variance:y},pointwise_conv:a("MobilenetV1",u,h)}}function s(){return{conv_0:a("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:r(1),conv_2:r(2),conv_3:r(3),conv_4:r(4),conv_5:r(5),conv_6:r(6),conv_7:r(7),conv_8:r(8),conv_9:r(9),conv_10:r(10),conv_11:r(11),conv_12:r(12),conv_13:r(13)}}function i(u,p){let d=n(`${u}/weights`,4,`${p}/filters`),c=n(`${u}/biases`,1,`${p}/bias`);return{filters:d,bias:c}}function o(u){let p=i(`Prediction/BoxPredictor_${u}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${u}/box_encoding_predictor`),d=i(`Prediction/BoxPredictor_${u}/ClassPredictor`,`prediction_layer/box_predictor_${u}/class_predictor`);return{box_encoding_predictor:p,class_predictor:d}}function l(){return{conv_0:a("Prediction",0,"prediction_layer/conv_0"),conv_1:a("Prediction",1,"prediction_layer/conv_1"),conv_2:a("Prediction",2,"prediction_layer/conv_2"),conv_3:a("Prediction",3,"prediction_layer/conv_3"),conv_4:a("Prediction",4,"prediction_layer/conv_4"),conv_5:a("Prediction",5,"prediction_layer/conv_5"),conv_6:a("Prediction",6,"prediction_layer/conv_6"),conv_7:a("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:o(0),box_predictor_1:o(1),box_predictor_2:o(2),box_predictor_3:o(3),box_predictor_4:o(4),box_predictor_5:o(5)}}return{extractMobilenetV1Params:s,extractPredictionLayerParams:l}}function SD(e){let t=[],{extractMobilenetV1Params:n,extractPredictionLayerParams:a}=bge(e,t),r=e["Output/extra_dim"];if(t.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!Wr(r))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${r}`);let s={mobilenetv1:n(),prediction_layer:a(),output_layer:{extra_dim:r}};return En(e,t),{params:s,paramMappings:t}}function Oa(e,t,n){return O(()=>{let a=$t(e,t.filters,n,"same");return a=X(a,t.batch_norm_offset),an(a,0,6)})}var yge=.0010000000474974513;function xge(e,t,n){return O(()=>{let a=_s(e,t.filters,n,"same");return a=Es(a,t.batch_norm_mean,t.batch_norm_variance,t.batch_norm_offset,t.batch_norm_scale,yge),an(a,0,6)})}function vge(e){return[2,4,6,12].some(t=>t===e)?[2,2]:[1,1]}function ND(e,t){return O(()=>{let n,a=Oa(e,t.conv_0,[2,2]);if([t.conv_1,t.conv_2,t.conv_3,t.conv_4,t.conv_5,t.conv_6,t.conv_7,t.conv_8,t.conv_9,t.conv_10,t.conv_11,t.conv_12,t.conv_13].forEach((s,i)=>{let o=i+1,l=vge(o);a=xge(a,s.depthwise_conv,l),a=Oa(a,s.pointwise_conv,[1,1]),o===11&&(n=a)}),n===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out:a,conv11:n}})}function wge(e,t,n){let a=e.arraySync(),r=Math.min(a[t][0],a[t][2]),s=Math.min(a[t][1],a[t][3]),i=Math.max(a[t][0],a[t][2]),o=Math.max(a[t][1],a[t][3]),l=Math.min(a[n][0],a[n][2]),u=Math.min(a[n][1],a[n][3]),p=Math.max(a[n][0],a[n][2]),d=Math.max(a[n][1],a[n][3]),c=(i-r)*(o-s),h=(p-l)*(d-u);if(c<=0||h<=0)return 0;let m=Math.max(r,l),f=Math.max(s,u),g=Math.min(i,p),b=Math.min(o,d),y=Math.max(g-m,0)*Math.max(b-f,0);return y/(c+h-y)}function TD(e,t,n,a,r){let s=e.shape[0],i=Math.min(n,s),o=t.map((p,d)=>({score:p,boxIndex:d})).filter(p=>p.score>r).sort((p,d)=>d.score-p.score),l=p=>p<=a?1:0,u=[];return o.forEach(p=>{if(u.length>=i)return;let d=p.score;for(let c=u.length-1;c>=0;--c){let h=wge(e,p.boxIndex,u[c]);if(h!==0&&(p.score*=l(h),p.score<=r))break}d===p.score&&u.push(p.boxIndex)}),u}function kge(e){let t=dt(De(e,[1,0])),n=[pe(t[2],t[0]),pe(t[3],t[1])],a=[X(t[0],he(n[0],2)),X(t[1],he(n[1],2))];return{sizes:n,centers:a}}function Ige(e,t){let{sizes:n,centers:a}=kge(e),r=dt(De(t,[1,0])),s=he(z(mn(he(r[2],5)),n[0]),2),i=X(z(he(r[0],10),n[0]),a[0]),o=he(z(mn(he(r[3],5)),n[1]),2),l=X(z(he(r[1],10),n[1]),a[1]);return De(Ft([pe(i,s),pe(l,o),X(i,s),X(l,o)]),[1,0])}function CD(e,t,n){return O(()=>{let a=e.shape[0],r=Ige(W(Mn(n.extra_dim,[a,1,1]),[-1,4]),W(e,[-1,4]));r=W(r,[a,r.shape[0]/a,4]);let s=ma(Ve(t,[0,0,1],[-1,-1,-1])),i=Ve(s,[0,0,0],[-1,-1,1]);i=W(i,[a,i.shape[1]]);let o=dt(r),l=dt(i);return{boxes:o,scores:l}})}function xl(e,t){return O(()=>{let n=e.shape[0],a=W(fl(e,t.box_encoding_predictor),[n,-1,1,4]),r=W(fl(e,t.class_predictor),[n,-1,3]);return{boxPredictionEncoding:a,classPrediction:r}})}function ED(e,t,n){return O(()=>{let a=Oa(e,n.conv_0,[1,1]),r=Oa(a,n.conv_1,[2,2]),s=Oa(r,n.conv_2,[1,1]),i=Oa(s,n.conv_3,[2,2]),o=Oa(i,n.conv_4,[1,1]),l=Oa(o,n.conv_5,[2,2]),u=Oa(l,n.conv_6,[1,1]),p=Oa(u,n.conv_7,[2,2]),d=xl(t,n.box_predictor_0),c=xl(e,n.box_predictor_1),h=xl(r,n.box_predictor_2),m=xl(i,n.box_predictor_3),f=xl(l,n.box_predictor_4),g=xl(p,n.box_predictor_5),b=et([d.boxPredictionEncoding,c.boxPredictionEncoding,h.boxPredictionEncoding,m.boxPredictionEncoding,f.boxPredictionEncoding,g.boxPredictionEncoding],1),y=et([d.classPrediction,c.classPrediction,h.classPrediction,m.classPrediction,f.classPrediction,g.classPrediction],1);return{boxPredictions:b,classPredictions:y}})}var Ia=class{constructor({minConfidence:t,maxResults:n}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=t||.5,this._maxResults=n||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var zs=class extends un{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("SsdMobilenetv1 - load model before inference");return O(()=>{let a=re(t.toBatchTensor(512,!1),"float32"),r=pe(he(a,127.5),1),s=ND(r,n.mobilenetv1),{boxPredictions:i,classPredictions:o}=ED(s.out,s.conv11,n.prediction_layer);return CD(i,o,n.output_layer)})}async forward(t){return this.forwardInput(await vt(t))}async locateFaces(t,n={}){let{maxResults:a,minConfidence:r}=new Ia(n),s=await vt(t),{boxes:i,scores:o}=this.forwardInput(s),l=i[0],u=o[0];for(let x=1;x{let[v,I]=[Math.max(0,b[x][0]),Math.min(1,b[x][2])].map(_=>_*g),[N,C]=[Math.max(0,b[x][1]),Math.min(1,b[x][3])].map(_=>_*f);return new Tt(p[x],new ll(N,v,C-N,I-v),{height:s.getInputHeight(0),width:s.getInputWidth(0)})});return l.dispose(),u.dispose(),y}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return SD(t)}extractParams(t){return ID(t)}};function _D(e){let t=new zs;return t.extractWeights(e),t}function Sge(e){return _D(e)}var Jk=class extends zs{};var AD=.4,FD=[new Ue(.738768,.874946),new Ue(2.42204,2.65704),new Ue(4.30971,7.04493),new Ue(10.246,4.59428),new Ue(12.6868,11.8741)],$D=[new Ue(1.603231,2.094468),new Ue(6.041143,7.080126),new Ue(2.882459,3.518061),new Ue(4.266906,5.178857),new Ue(9.041765,10.66308)],DD=[117.001,114.697,97.404],RD="tiny_yolov2_model",MD="tiny_yolov2_separable_conv_model";var Tg=e=>typeof e=="number";function Qk(e){if(!e)throw new Error(`invalid config: ${e}`);if(typeof e.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${e.withSeparableConvs}`);if(!Tg(e.iouThreshold)||e.iouThreshold<0||e.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${e.iouThreshold}`);if(!Array.isArray(e.classes)||!e.classes.length||!e.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(e.classes)}`);if(!Array.isArray(e.anchors)||!e.anchors.length||!e.anchors.map(t=>t||{}).every(t=>Tg(t.x)&&Tg(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(e.anchors)}`);if(e.meanRgb&&(!Array.isArray(e.meanRgb)||e.meanRgb.length!==3||!e.meanRgb.every(Tg)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(e.meanRgb)}`)}function Vp(e){return O(()=>{let t=z(e,ve(.10000000149011612));return X(Ke(pe(e,t)),t)})}function Ur(e,t){return O(()=>{let n=va(e,[[0,0],[1,1],[1,1],[0,0]]);return n=$t(n,t.conv.filters,[1,1],"valid"),n=pe(n,t.bn.sub),n=z(n,t.bn.truediv),n=X(n,t.conv.bias),Vp(n)})}function Gr(e,t){return O(()=>{let n=va(e,[[0,0],[1,1],[1,1],[0,0]]);return n=$s(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=X(n,t.bias),Vp(n)})}function Nge(e,t){let n=Mp(e,t);function a(i,o){let l=qe(e(i)),u=qe(e(i));return t.push({paramPath:`${o}/sub`},{paramPath:`${o}/truediv`}),{sub:l,truediv:u}}function r(i,o,l){let u=n(i,o,3,`${l}/conv`),p=a(o,`${l}/bn`);return{conv:u,bn:p}}let s=Op(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}}function OD(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=_n(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:u}=Nge(r,i),p;if(t.withSeparableConvs){let[d,c,h,m,f,g,b,y,x]=a,v=t.isFirstLayerConv2d?o(d,c,3,"conv0"):u(d,c,"conv0"),I=u(c,h,"conv1"),N=u(h,m,"conv2"),C=u(m,f,"conv3"),_=u(f,g,"conv4"),F=u(g,b,"conv5"),D=y?u(b,y,"conv6"):void 0,$=x?u(y,x,"conv7"):void 0,S=o(x||y||b,5*n,1,"conv8");p={conv0:v,conv1:I,conv2:N,conv3:C,conv4:_,conv5:F,conv6:D,conv7:$,conv8:S}}else{let[d,c,h,m,f,g,b,y,x]=a,v=l(d,c,"conv0"),I=l(c,h,"conv1"),N=l(h,m,"conv2"),C=l(m,f,"conv3"),_=l(f,g,"conv4"),F=l(g,b,"conv5"),D=l(b,y,"conv6"),$=l(y,x,"conv7"),S=o(x,5*n,1,"conv8");p={conv0:v,conv1:I,conv2:N,conv3:C,conv4:_,conv5:F,conv6:D,conv7:$,conv8:S}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:p,paramMappings:i}}function Tge(e,t){let n=ia(e,t);function a(o){let l=n(`${o}/sub`,1),u=n(`${o}/truediv`,1);return{sub:l,truediv:u}}function r(o){let l=n(`${o}/filters`,4),u=n(`${o}/bias`,1);return{filters:l,bias:u}}function s(o){let l=r(`${o}/conv`),u=a(`${o}/bn`);return{conv:l,bn:u}}let i=Pp(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function PD(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=Tge(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return En(e,n),{params:i,paramMappings:n}}var Ja=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var Cg=class Cg extends un{constructor(t){super("TinyYolov2"),Qk(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,n){let a=Ur(t,n.conv0);return a=Dt(a,[2,2],[2,2],"same"),a=Ur(a,n.conv1),a=Dt(a,[2,2],[2,2],"same"),a=Ur(a,n.conv2),a=Dt(a,[2,2],[2,2],"same"),a=Ur(a,n.conv3),a=Dt(a,[2,2],[2,2],"same"),a=Ur(a,n.conv4),a=Dt(a,[2,2],[2,2],"same"),a=Ur(a,n.conv5),a=Dt(a,[2,2],[1,1],"same"),a=Ur(a,n.conv6),a=Ur(a,n.conv7),fl(a,n.conv8,"valid",!1)}runMobilenet(t,n){let a=this.config.isFirstLayerConv2d?Vp(fl(t,n.conv0,"valid",!1)):Gr(t,n.conv0);return a=Dt(a,[2,2],[2,2],"same"),a=Gr(a,n.conv1),a=Dt(a,[2,2],[2,2],"same"),a=Gr(a,n.conv2),a=Dt(a,[2,2],[2,2],"same"),a=Gr(a,n.conv3),a=Dt(a,[2,2],[2,2],"same"),a=Gr(a,n.conv4),a=Dt(a,[2,2],[2,2],"same"),a=Gr(a,n.conv5),a=Dt(a,[2,2],[1,1],"same"),a=n.conv6?Gr(a,n.conv6):a,a=n.conv7?Gr(a,n.conv7):a,fl(a,n.conv8,"valid",!1)}forwardInput(t,n){let{params:a}=this;if(!a)throw new Error("TinyYolov2 - load model before inference");return O(()=>{let r=re(t.toBatchTensor(n,!1),"float32");return r=this.config.meanRgb?Ya(r,this.config.meanRgb):r,r=r.div(255),this.config.withSeparableConvs?this.runMobilenet(r,a):this.runTinyYolov2(r,a)})}async forward(t,n){return this.forwardInput(await vt(t),n)}async detect(t,n={}){let{inputSize:a,scoreThreshold:r}=new Ja(n),s=await vt(t),i=await this.forwardInput(s,a),o=O(()=>dt(i)[0].expandDims()),l={width:s.getInputWidth(0),height:s.getInputHeight(0)},u=await this.extractBoxes(o,s.getReshapedInputDimensions(0),r);i.dispose(),o.dispose();let p=u.map(g=>g.box),d=u.map(g=>g.score),c=u.map(g=>g.classScore),h=u.map(g=>this.config.classes[g.label]);return _k(p.map(g=>g.rescale(a)),d,this.config.iouThreshold,!0).map(g=>new ol(d[g],c[g],h[g],p[g],l))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return PD(t,this.config)}extractParams(t){let n=this.config.filterSizes||Cg.DEFAULT_FILTER_SIZES,a=n?n.length:void 0;if(a!==7&&a!==8&&a!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${a} filterSizes in config`);return OD(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,a){let{width:r,height:s}=n,i=Math.max(r,s),o=i/r,l=i/s,u=t.shape[1],p=this.config.anchors.length,[d,c,h]=O(()=>{let b=t.reshape([u,u,p,this.boxEncodingSize]),y=b.slice([0,0,0,0],[u,u,p,4]),x=b.slice([0,0,0,4],[u,u,p,1]),v=this.withClassScores?ja(b.slice([0,0,0,5],[u,u,p,this.config.classes.length]),3):ve(0);return[y,x,v]}),m=[],f=await c.array(),g=await d.array();for(let b=0;ba){let I=(y+zd(g[b][y][x][0]))/u*o,N=(b+zd(g[b][y][x][1]))/u*l,C=Math.exp(g[b][y][x][2])*this.config.anchors[x].x/u*o,_=Math.exp(g[b][y][x][3])*this.config.anchors[x].y/u*l,F=I-C/2,D=N-_/2,$={row:b,col:y,anchor:x},{classScore:S,label:M}=this.withClassScores?await this.extractPredictedClass(h,$):{classScore:1,label:0};m.push({box:new il(F,D,F+C,D+_),score:v,classScore:v*S,label:M,...$})}}return d.dispose(),c.dispose(),h.dispose(),m}async extractPredictedClass(t,n){let{row:a,col:r,anchor:s}=n,i=await t.array();return Array(this.config.classes.length).fill(0).map((o,l)=>i[a][r][s][l]).map((o,l)=>({classScore:o,label:l})).reduce((o,l)=>o.classScore>l.classScore?o:l)}};Cg.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Up=Cg;var vl=class extends Up{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:AD,classes:["face"],...t?{anchors:$D,meanRgb:DD}:{anchors:FD,withClassScores:!0}};super(n)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return 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ln{constructor(t,n,a,r,s=!1){super({x:t,y:n,width:a,height:r},s)}};var Zfe=.5,Jfe=.43,Qfe=.45,sa=class{constructor(t,n,a=new Ue(0,0)){let{width:r,height:s}=n;this._imgDims=new Un(r,s),this._shift=a,this._positions=t.map(i=>i.mul(new Ue(r,s)).add(a))}get shift(){return new Ue(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(t=>t.sub(this._shift).div(new Ue(this.imageWidth,this.imageHeight)))}forSize(t,n){return new this.constructor(this.relativePositions,{width:t,height:n})}shiftBy(t,n){return new this.constructor(this.relativePositions,this._imgDims,new Ue(t,n))}shiftByPoint(t){return this.shiftBy(t.x,t.y)}align(t,n={}){if(t){let s=t instanceof Tt?t.box.floor():new ln(t);return this.shiftBy(s.x,s.y).align(null,n)}let{useDlibAlignment:a,minBoxPadding:r}={useDlibAlignment:!1,minBoxPadding:.2,...n};return a?this.alignDlib():this.alignMinBbox(r)}alignDlib(){let t=this.getRefPointsForAlignment(),[n,a,r]=t,s=d=>r.sub(d).magnitude(),i=(s(n)+s(a))/2,o=Math.floor(i/Qfe),l=sl(t),u=Math.floor(Math.max(0,l.x-Zfe*o)),p=Math.floor(Math.max(0,l.y-Jfe*o));return new ll(u,p,Math.min(o,this.imageWidth+u),Math.min(o,this.imageHeight+p))}alignMinBbox(t){let n=Ek(this.positions);return n.pad(n.width*t,n.height*t)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var Fk=class extends sa{getRefPointsForAlignment(){let t=this.positions;return[t[0],t[1],sl([t[3],t[4]])]}};var ul=class extends sa{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(sl)}};var Ap=class{constructor(t,n){this._label=t,this._distance=n}get label(){return this._label}get distance(){return this._distance}toString(t=!0){return`${this.label}${t?` (${rl(this.distance)})`:""}`}};var Fp=class extends ln{static assertIsValidLabeledBox(t,n){if(ln.assertIsValidBox(t,n),!Xa(t.label))throw new Error(`${n} - expected property label (${t.label}) to be a number`)}constructor(t,n){super(t),this._label=n}get label(){return this._label}};var zs=class e{constructor(t,n){if(typeof t!="string")throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(n)||n.some(a=>!(a instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=t,this._descriptors=n}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(t=>Array.from(t))}}static fromJSON(t){let n=t.descriptors.map(a=>new Float32Array(a));return new e(t.label,n)}};var $k=class extends Fp{static assertIsValidPredictedBox(t,n){if(Fp.assertIsValidLabeledBox(t,n),!_p(t.score)||!_p(t.classScore))throw new Error(`${n} - expected properties score (${t.score}) and (${t.classScore}) to be a number between [0, 1]`)}constructor(t,n,a,r){super(t,n),this._score=a,this._classScore=r}get score(){return this._score}get classScore(){return this._classScore}};function xr(e){return e.detection instanceof Tt}function pl(e,t){return{...e,...{detection:t}}}function Dk(){let e=window.fetch;if(!e)throw new Error("fetch - missing fetch implementation for browser 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Mk()?Ok(Dk()):Wd()?Ok(Rk()):null}function tge(e){if(un||Pk(),!un)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{Canvas:t=un.Canvas,Image:n=un.Image}=e;un.Canvas=t,un.Image=n,un.createCanvasElement=e.createCanvasElement||(()=>new t),un.createImageElement=e.createImageElement||(()=>new n),un.ImageData=e.ImageData||un.ImageData,un.Video=e.Video||un.Video,un.fetch=e.fetch||un.fetch,un.readFile=e.readFile||un.readFile}var tt={getEnv:ege,setEnv:Ok,initialize:Pk,createBrowserEnv:Dk,createFileSystem:og,createNodejsEnv:Rk,monkeyPatch:tge,isBrowser:Mk,isNodejs:Wd};Pk();function cl(e){return!tt.isNodejs()&&typeof e=="string"?document.getElementById(e):e}function Gn(e){let{Canvas:t,CanvasRenderingContext2D:n}=tt.getEnv();if(e instanceof n)return e;let a=cl(e);if(!(a instanceof t))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");let r=a.getContext("2d",{willReadFrequently:!0});if(!r)throw new Error("resolveContext2d 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i={anchorPosition:"BOTTOM_LEFT",backgroundColor:this.boxColor};this.drawLabelOptions=new $p({...i,...s})}},Bd=class{constructor(t,n={}){this.box=new ln(t),this.options=new lg(n)}draw(t){let n=Gn(t),{boxColor:a,lineWidth:r}=this.options,{x:s,y:i,width:o,height:l}=this.box;n.strokeStyle=a,n.lineWidth=r,n.strokeRect(s,i,o,l);let{label:u}=this.options;u&&new dl([u],{x:s-r/2,y:i},this.options.drawLabelOptions).draw(t)}};function nge(e,t){(Array.isArray(t)?t:[t]).forEach(a=>{let r=a instanceof Tt?a.score:xr(a)?a.detection.score:void 0,s=a instanceof Tt?a.box:xr(a)?a.detection.box:new ln(a),i=r?`${rl(r)}`:void 0;new Bd(s,{label:i}).draw(e)})}function Vd(e){let{Image:t,Video:n}=tt.getEnv();return e instanceof t&&e.complete||e instanceof n&&e.readyState>=3}function zk(e){return new Promise((t,n)=>{if(e instanceof tt.getEnv().Canvas||Vd(e)){t(null);return}function a(s){s.currentTarget&&(s.currentTarget.removeEventListener("load",r),s.currentTarget.removeEventListener("error",a),n(s))}function r(s){s.currentTarget&&(s.currentTarget.removeEventListener("load",r),s.currentTarget.removeEventListener("error",a),t(s))}e.addEventListener("load",r),e.addEventListener("error",a)})}function Wk(e){return new Promise((t,n)=>{e instanceof Blob||n(new Error("bufferToImage - expected buf to be of type: Blob"));let a=new FileReader;a.onload=()=>{typeof a.result!="string"&&n(new Error("bufferToImage - expected reader.result to be a string, in onload"));let r=tt.getEnv().createImageElement();r.onload=()=>t(r),r.onerror=n,r.src=a.result},a.onerror=n,a.readAsDataURL(e)})}function hl(e){let{Image:t,Video:n}=tt.getEnv();return e instanceof t?new Un(e.naturalWidth,e.naturalHeight):e instanceof n?new Un(e.videoWidth,e.videoHeight):new Un(e.width,e.height)}function ml({width:e,height:t}){let{createCanvasElement:n}=tt.getEnv(),a=n();return a.width=e,a.height=t,a}function Ud(e,t){let{ImageData:n}=tt.getEnv();if(!(e instanceof n)&&!Vd(e))throw new Error("createCanvasFromMedia - media has not finished 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vr=class{constructor(t,n=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];this._inputSize=0;if(!Array.isArray(t))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${t}`);this._treatAsBatchInput=n,this._batchSize=t.length,t.forEach((a,r)=>{if(Wr(a)){this._imageTensors[r]=a,this._inputDimensions[r]=a.shape;return}if(ka(a)){let i=a.shape[0];if(i!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${i} passed, but not supported in input array`);this._imageTensors[r]=a,this._inputDimensions[r]=a.shape.slice(1);return}let s=a instanceof tt.getEnv().Canvas?a:Ud(a);this._canvases[r]=s,this._inputDimensions[r]=[s.height,s.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return yr(this.batchSize,0,1).map((t,n)=>this.getReshapedInputDimensions(n))}getInput(t){return this.canvases[t]||this.imageTensors[t]}getInputDimensions(t){return this._inputDimensions[t]}getInputHeight(t){return this._inputDimensions[t][0]}getInputWidth(t){return this._inputDimensions[t][1]}getReshapedInputDimensions(t){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");let n=this.getInputWidth(t),a=this.getInputHeight(t);return Nk({width:n,height:a},this.inputSize)}toBatchTensor(t,n=!0){return this._inputSize=t,O(()=>{let a=yr(this.batchSize,0,1).map(s=>{let i=this.getInput(s);if(i instanceof Ce){let o=ka(i)?i:Gt(i);return o=Ak(o,n),(o.shape[1]!==t||o.shape[2]!==t)&&(o=Qn.resizeBilinear(o,[t,t],!1,!1)),o.as3D(t,t,3)}if(i instanceof tt.getEnv().Canvas)return Xo.fromPixels(Vk(i,t,n));throw new Error(`toBatchTensor - at batchIdx ${s}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${i}`)});return Ft(a.map(s=>re(s,"float32"))).as4D(this.batchSize,t,t,3)})}};async function vt(e){if(e instanceof vr)return e;let t=Array.isArray(e)?e:[e];if(!t.length)throw new Error("toNetInput - empty array passed as input");let n=r=>Array.isArray(e)?` at input index ${r}:`:"",a=t.map(cl);return a.forEach((r,s)=>{if(!ug(r)&&!Wr(r)&&!ka(r))throw typeof t[s]=="string"?new Error(`toNetInput -${n(s)} string passed, but could not resolve HTMLElement for element id ${t[s]}`):new Error(`toNetInput -${n(s)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);if(ka(r)){let i=r.shape[0];if(i!==1)throw new Error(`toNetInput -${n(s)} tf.Tensor4D with batchSize ${i} passed, but not supported in input array`)}}),await Promise.all(a.map(r=>ug(r)&&zk(r))),new vr(a,Array.isArray(e))}async function Dp(e,t){let{Canvas:n}=tt.getEnv(),a=e;if(!(e instanceof n)){let i=await vt(e);if(i.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");let o=i.getInput(0);a=o instanceof n?o:await Bk(o)}let r=Gn(a);return t.map(i=>i instanceof Tt?i.forSize(a.width,a.height).box.floor():i).map(i=>i.clipAtImageBorders(a.width,a.height)).map(({x:i,y:o,width:l,height:u})=>{let p=ml({width:l,height:u});return l>0&&u>0&&Gn(p).putImageData(r.getImageData(i,o,l,u),0,0),p})}async function Rp(e,t){if(!Wr(e)&&!ka(e))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(ka(e)&&e.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return O(()=>{let[n,a,r]=e.shape.slice(ka(e)?1:0);return t.map(o=>o instanceof Tt?o.forSize(a,n).box:o).map(o=>o.clipAtImageBorders(a,n)).filter(o=>o.width>0&&o.height>0).map(({x:o,y:l,width:u,height:p})=>qo(e.as3D(n,a,r),[l,o,0],[p,u,r]))})}async function Br(e,t){let{fetch:n}=tt.getEnv(),a=await n(e,t);if(!(a.status<400))throw new Error(`failed to fetch: (${a.status}) ${a.statusText}, from url: ${a.url}`);return a}async function age(e){let t=await Br(e),n=await t.blob();if(!n.type.startsWith("image/"))throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${n.type}, for url: ${t.url}`);return Wk(n)}async function Uk(e){return(await Br(e)).json()}async function rge(e){return new Float32Array(await(await Br(e)).arrayBuffer())}function lD(e){return new Promise((t,n)=>{e instanceof Blob||n(new Error("bufferToVideo - expected buf to be of type: Blob"));let a=tt.getEnv().createVideoElement();a.oncanplay=()=>t(a),a.onerror=n,a.playsInline=!0,a.muted=!0,a.src=URL.createObjectURL(e),a.play()})}async function sge(e){let t=await Br(e),n=await t.blob();if(!n.type.startsWith("video/"))throw new Error(`fetchVideo - expected blob type to be of type video/*, instead have: ${n.type}, for url: ${t.url}`);return lD(n)}function pg(e,t){let n=`${t}-weights_manifest.json`;if(!e)return{modelBaseUri:"",manifestUri:n};if(e==="/")return{modelBaseUri:"/",manifestUri:`/${n}`};let a=e.startsWith("http://")?"http://":e.startsWith("https://")?"https://":"";e=e.replace(a,"");let r=e.split("/").filter(o=>o),s=e.endsWith(".json")?r[r.length-1]:n,i=a+(e.endsWith(".json")?r.slice(0,r.length-1):r).join("/");return i=e.startsWith("/")?`/${i}`:i,{modelBaseUri:i,manifestUri:i==="/"?`/${s}`:`${i}/${s}`}}async function Gk(e,t){let{manifestUri:n,modelBaseUri:a}=pg(e,t),r=await Uk(n);return jt.loadWeights(r,a)}function ige(e,t,n=!1){let{width:a,height:r}=n?hl(t):t;return e.width=a,e.height=r,{width:a,height:r}}var pn=class{constructor(t){this._params=void 0;this._paramMappings=[];this._name=t}get params(){return this._params}get paramMappings(){return this._paramMappings}get isLoaded(){return!!this.params}getParamFromPath(t){let{obj:n,objProp:a}=this.traversePropertyPath(t);return 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Float32Array){this.extractWeights(t);return}await this.loadFromUri(t)}async loadFromUri(t){if(t&&typeof t!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);let n=await Gk(t,this.getDefaultModelName());this.loadFromWeightMap(n)}async loadFromDisk(t){if(t&&typeof t!="string")throw new Error(`${this._name}.loadFromDisk - expected model file path`);let{readFile:n}=tt.getEnv(),{manifestUri:a,modelBaseUri:r}=pg(t,this.getDefaultModelName()),s=u=>Promise.all(u.map(p=>n(p).then(d=>typeof d=="string"?Buffer.from(d):d.buffer))),i=jt.weightsLoaderFactory(s),o=JSON.parse((await n(a)).toString()),l=await i(o,r);this.loadFromWeightMap(l)}loadFromWeightMap(t){let{paramMappings:n,params:a}=this.extractParamsFromWeightMap(t);this._paramMappings=n,this._params=a}extractWeights(t){let{paramMappings:n,params:a}=this.extractParams(t);this._paramMappings=n,this._params=a}traversePropertyPath(t){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");let 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a=re(t.toBatchTensor(112,!0),"float32"),s=Ya(a,[122.782,117.001,104.298]).div(255),i=Gd(s,n.dense0,!0);return i=Gd(i,n.dense1),i=Gd(i,n.dense2),i=Gd(i,n.dense3),i=xa(i,[7,7],[2,2],"valid"),i})}async forward(t){return this.forwardInput(await vt(t))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeightMap(t){return pD(t)}extractParams(t){return uD(t)}};function jd(e,t){return O(()=>X($e(e,t.weights),t.bias))}function cD(e,t,n){let a=[],{extractWeights:r,getRemainingWeights:s}=An(e),o=dg(r,a)(t,n,"fc");if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{paramMappings:a,params:{fc:o}}}function dD(e){let t=[],n=ia(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:a("fc")};return _n(e,t),{params:r,paramMappings:t}}function gg(e){let t={},n={};return Object.keys(e).forEach(a=>{let r=a.startsWith("fc")?n:t;r[a]=e[a]}),{featureExtractorMap:t,classifierMap:n}}var zp=class extends pn{constructor(t,n){super(t),this._faceFeatureExtractor=n}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return O(()=>{let a=t instanceof vr?this.faceFeatureExtractor.forwardInput(t):t;return jd(a.as2D(a.shape[0],-1),n.fc)})}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:n,paramMappings:a}=this.extractClassifierParams(t);this._params=n,this._paramMappings=a}extractClassifierParams(t){return cD(t,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:a}=gg(t);return this.faceFeatureExtractor.loadFromWeightMap(n),dD(a)}extractParams(t){let n=this.getClassifierChannelsIn(),a=this.getClassifierChannelsOut(),r=a*n+a,s=t.slice(0,t.length-r),i=t.slice(t.length-r);return this.faceFeatureExtractor.extractWeights(s),this.extractClassifierParams(i)}};var Hk=["neutral","happy","sad","angry","fearful","disgusted","surprised"],Vr=class{constructor(t){this.neutral=0;this.happy=0;this.sad=0;this.angry=0;this.fearful=0;this.disgusted=0;this.surprised=0;if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${t.length}`);Hk.forEach((n,a)=>{this[n]=t[a]})}asSortedArray(){return Hk.map(t=>({expression:t,probability:this[t]})).sort((t,n)=>n.probability-t.probability)}};var qd=class extends zp{constructor(t=new Lp){super("FaceExpressionNet",t)}forwardInput(t){return O(()=>qa(this.runNet(t)))}async forward(t){return this.forwardInput(await vt(t))}async predictExpressions(t){let n=await vt(t),a=await this.forwardInput(n),r=await Promise.all(dt(a).map(async i=>{let o=i.dataSync();return i.dispose(),o}));a.dispose();let s=r.map(i=>new Vr(i));return n.isBatchInput?s:s[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function jk(e){return e.expressions instanceof Vr}function bg(e,t){return{...e,...{expressions:t}}}function oge(e,t,n=.1,a){(Array.isArray(t)?t:[t]).forEach(s=>{let i=s instanceof Vr?s:jk(s)?s.expressions:void 0;if(!i)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let l=i.asSortedArray().filter(d=>d.probability>n),u=xr(s)?s.detection.box.bottomLeft:a||new Ue(0,0);new dl(l.map(d=>`${d.expression} (${rl(d.probability)})`),u).draw(e)})}function gl(e){return xr(e)&&e.landmarks instanceof sa&&e.unshiftedLandmarks instanceof sa&&e.alignedRect instanceof Tt}function lge(e){let t=l=>l*180/Math.PI,n=(l,u)=>Math.sqrt((l.x-u.x)**2+(l.y-u.y)**2),a={roll:void 0,pitch:void 0,yaw:void 0},r=(l,u,p)=>{let d=Math.floor(l.x-u.x),c=Math.floor(u.x-p.x);return d-c},s=(l,u)=>{let p=Math.hypot(u.x-l.x,u.y-l.y),d=u.y-l.y,c=Math.asin(d/p),h=t(c),m=Math.floor(90-h),f=u.x-l.x<0?-1:1;return m*f},i=(l,u,p)=>{let d=n(l,p),c=new 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l=a(i,i,`${o}/separable_conv0`),u=a(i,i,`${o}/separable_conv1`),p=a(i,i,`${o}/separable_conv2`);return{separable_conv0:l,separable_conv1:u,separable_conv2:p}}return{extractConvParams:n,extractSeparableConvParams:a,extractReductionBlockParams:r,extractMainBlockParams:s}}function mD(e,t){let n=[],{extractWeights:a,getRemainingWeights:r}=An(e),{extractConvParams:s,extractSeparableConvParams:i,extractReductionBlockParams:o,extractMainBlockParams:l}=dge(a,n),u=s(3,32,3,"entry_flow/conv_in"),p=o(32,64,"entry_flow/reduction_block_0"),d=o(64,128,"entry_flow/reduction_block_1"),c={conv_in:u,reduction_block_0:p,reduction_block_1:d},h={};yr(t,0,1).forEach(b=>{h[`main_block_${b}`]=l(128,`middle_flow/main_block_${b}`)});let m=o(128,256,"exit_flow/reduction_block"),f=i(256,512,"exit_flow/separable_conv"),g={reduction_block:m,separable_conv:f};if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:n,params:{entry_flow:c,middle_flow:h,exit_flow:g}}}function hge(e,t){let n=ia(e,t),a=mg(n),r=Pp(n);function s(o){let l=r(`${o}/separable_conv0`),u=r(`${o}/separable_conv1`),p=a(`${o}/expansion_conv`);return{separable_conv0:l,separable_conv1:u,expansion_conv:p}}function i(o){let l=r(`${o}/separable_conv0`),u=r(`${o}/separable_conv1`),p=r(`${o}/separable_conv2`);return{separable_conv0:l,separable_conv1:u,separable_conv2:p}}return{extractConvParams:a,extractSeparableConvParams:r,extractReductionBlockParams:s,extractMainBlockParams:i}}function fD(e,t){let n=[],{extractConvParams:a,extractSeparableConvParams:r,extractReductionBlockParams:s,extractMainBlockParams:i}=hge(e,n),o=a("entry_flow/conv_in"),l=s("entry_flow/reduction_block_0"),u=s("entry_flow/reduction_block_1"),p={conv_in:o,reduction_block_0:l,reduction_block_1:u},d={};yr(t,0,1).forEach(f=>{d[`main_block_${f}`]=i(`middle_flow/main_block_${f}`)});let c=s("exit_flow/reduction_block"),h=r("exit_flow/separable_conv"),m={reduction_block:c,separable_conv:h};return _n(e,n),{params:{entry_flow:p,middle_flow:d,exit_flow:m},paramMappings:n}}function gD(e,t,n){return X($t(e,t.filters,n,"same"),t.bias)}function Kk(e,t,n=!0){let a=n?Ke(e):e;return a=Hn(a,t.separable_conv0,[1,1]),a=Hn(Ke(a),t.separable_conv1,[1,1]),a=Dt(a,[3,3],[2,2],"same"),a=X(a,gD(e,t.expansion_conv,[2,2])),a}function mge(e,t){let n=Hn(Ke(e),t.separable_conv0,[1,1]);return n=Hn(Ke(n),t.separable_conv1,[1,1]),n=Hn(Ke(n),t.separable_conv2,[1,1]),n=X(n,e),n}var vg=class extends pn{constructor(t){super("TinyXception"),this._numMainBlocks=t}forwardInput(t){let{params:n}=this;if(!n)throw new Error("TinyXception - load model before inference");return O(()=>{let a=re(t.toBatchTensor(112,!0),"float32"),s=Ya(a,[122.782,117.001,104.298]).div(255),i=Ke(gD(s,n.entry_flow.conv_in,[2,2]));return i=Kk(i,n.entry_flow.reduction_block_0,!1),i=Kk(i,n.entry_flow.reduction_block_1),yr(this._numMainBlocks,0,1).forEach(o=>{i=mge(i,n.middle_flow[`main_block_${o}`])}),i=Kk(i,n.exit_flow.reduction_block),i=Ke(Hn(i,n.exit_flow.separable_conv,[1,1])),i})}async forward(t){return this.forwardInput(await vt(t))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(t){return fD(t,this._numMainBlocks)}extractParams(t){return mD(t,this._numMainBlocks)}};function bD(e){let t=[],{extractWeights:n,getRemainingWeights:a}=An(e),r=dg(n,t),s=r(512,1,"fc/age"),i=r(512,2,"fc/gender");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:t,params:{fc:{age:s,gender:i}}}}function yD(e){let t=[],n=ia(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:{age:a("fc/age"),gender:a("fc/gender")}};return _n(e,t),{params:r,paramMappings:t}}var wg=(n=>(n.FEMALE="female",n.MALE="male",n))(wg||{});var Kd=class extends pn{constructor(t=new vg(2)){super("AgeGenderNet"),this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return O(()=>{let a=t instanceof vr?this.faceFeatureExtractor.forwardInput(t):t,r=xa(a,[7,7],[2,2],"valid").as2D(a.shape[0],-1),s=jd(r,n.fc.age).as1D(),i=jd(r,n.fc.gender);return{age:s,gender:i}})}forwardInput(t){return O(()=>{let{age:n,gender:a}=this.runNet(t);return{age:n,gender:qa(a)}})}async forward(t){return this.forwardInput(await vt(t))}async predictAgeAndGender(t){let n=await vt(t),a=await this.forwardInput(n),r=dt(a.age),s=dt(a.gender),i=r.map((l,u)=>({ageTensor:l,genderTensor:s[u]})),o=await Promise.all(i.map(async({ageTensor:l,genderTensor:u})=>{let p=l.dataSync()[0],d=u.dataSync()[0],c=d>.5,h=c?"male":"female",m=c?d:1-d;return l.dispose(),u.dispose(),{age:p,gender:h,genderProbability:m}}));return 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this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var Ws=class extends pn{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("SsdMobilenetv1 - load model before inference");return O(()=>{let a=re(t.toBatchTensor(512,!1),"float32"),r=pe(he(a,127.5),1),s=CD(r,n.mobilenetv1),{boxPredictions:i,classPredictions:o}=AD(s.out,s.conv11,n.prediction_layer);return _D(i,o,n.output_layer)})}async forward(t){return this.forwardInput(await vt(t))}async locateFaces(t,n={}){let{maxResults:a,minConfidence:r}=new Ia(n),s=await vt(t),{boxes:i,scores:o}=this.forwardInput(s),l=i[0],u=o[0];for(let x=1;x{let[v,I]=[Math.max(0,b[x][0]),Math.min(1,b[x][2])].map(_=>_*g),[N,C]=[Math.max(0,b[x][1]),Math.min(1,b[x][3])].map(_=>_*f);return new Tt(p[x],new ll(N,v,C-N,I-v),{height:s.getInputHeight(0),width:s.getInputWidth(0)})});return l.dispose(),u.dispose(),y}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return TD(t)}extractParams(t){return ND(t)}};function FD(e){let t=new Ws;return t.extractWeights(e),t}function Ege(e){return FD(e)}var Jk=class extends Ws{};var $D=.4,DD=[new Ue(.738768,.874946),new Ue(2.42204,2.65704),new Ue(4.30971,7.04493),new Ue(10.246,4.59428),new Ue(12.6868,11.8741)],RD=[new Ue(1.603231,2.094468),new Ue(6.041143,7.080126),new Ue(2.882459,3.518061),new Ue(4.266906,5.178857),new Ue(9.041765,10.66308)],MD=[117.001,114.697,97.404],OD="tiny_yolov2_model",PD="tiny_yolov2_separable_conv_model";var Cg=e=>typeof e=="number";function Qk(e){if(!e)throw new Error(`invalid config: ${e}`);if(typeof e.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${e.withSeparableConvs}`);if(!Cg(e.iouThreshold)||e.iouThreshold<0||e.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${e.iouThreshold}`);if(!Array.isArray(e.classes)||!e.classes.length||!e.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(e.classes)}`);if(!Array.isArray(e.anchors)||!e.anchors.length||!e.anchors.map(t=>t||{}).every(t=>Cg(t.x)&&Cg(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(e.anchors)}`);if(e.meanRgb&&(!Array.isArray(e.meanRgb)||e.meanRgb.length!==3||!e.meanRgb.every(Cg)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(e.meanRgb)}`)}function Vp(e){return O(()=>{let t=z(e,xe(.10000000149011612));return X(Ke(pe(e,t)),t)})}function Ur(e,t){return O(()=>{let n=va(e,[[0,0],[1,1],[1,1],[0,0]]);return n=$t(n,t.conv.filters,[1,1],"valid"),n=pe(n,t.bn.sub),n=z(n,t.bn.truediv),n=X(n,t.conv.bias),Vp(n)})}function Gr(e,t){return O(()=>{let n=va(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Ds(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=X(n,t.bias),Vp(n)})}function _ge(e,t){let n=Mp(e,t);function a(i,o){let l=je(e(i)),u=je(e(i));return t.push({paramPath:`${o}/sub`},{paramPath:`${o}/truediv`}),{sub:l,truediv:u}}function r(i,o,l){let u=n(i,o,3,`${l}/conv`),p=a(o,`${l}/bn`);return{conv:u,bn:p}}let s=Op(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}}function LD(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=An(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:u}=_ge(r,i),p;if(t.withSeparableConvs){let[d,c,h,m,f,g,b,y,x]=a,v=t.isFirstLayerConv2d?o(d,c,3,"conv0"):u(d,c,"conv0"),I=u(c,h,"conv1"),N=u(h,m,"conv2"),C=u(m,f,"conv3"),_=u(f,g,"conv4"),F=u(g,b,"conv5"),D=y?u(b,y,"conv6"):void 0,$=x?u(y,x,"conv7"):void 0,S=o(x||y||b,5*n,1,"conv8");p={conv0:v,conv1:I,conv2:N,conv3:C,conv4:_,conv5:F,conv6:D,conv7:$,conv8:S}}else{let[d,c,h,m,f,g,b,y,x]=a,v=l(d,c,"conv0"),I=l(c,h,"conv1"),N=l(h,m,"conv2"),C=l(m,f,"conv3"),_=l(f,g,"conv4"),F=l(g,b,"conv5"),D=l(b,y,"conv6"),$=l(y,x,"conv7"),S=o(x,5*n,1,"conv8");p={conv0:v,conv1:I,conv2:N,conv3:C,conv4:_,conv5:F,conv6:D,conv7:$,conv8:S}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:p,paramMappings:i}}function Age(e,t){let n=ia(e,t);function a(o){let l=n(`${o}/sub`,1),u=n(`${o}/truediv`,1);return{sub:l,truediv:u}}function r(o){let l=n(`${o}/filters`,4),u=n(`${o}/bias`,1);return{filters:l,bias:u}}function s(o){let l=r(`${o}/conv`),u=a(`${o}/bn`);return{conv:l,bn:u}}let i=Pp(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function zD(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=Age(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return _n(e,n),{params:i,paramMappings:n}}var Ja=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var Eg=class Eg extends pn{constructor(t){super("TinyYolov2"),Qk(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,n){let a=Ur(t,n.conv0);return a=Dt(a,[2,2],[2,2],"same"),a=Ur(a,n.conv1),a=Dt(a,[2,2],[2,2],"same"),a=Ur(a,n.conv2),a=Dt(a,[2,2],[2,2],"same"),a=Ur(a,n.conv3),a=Dt(a,[2,2],[2,2],"same"),a=Ur(a,n.conv4),a=Dt(a,[2,2],[2,2],"same"),a=Ur(a,n.conv5),a=Dt(a,[2,2],[1,1],"same"),a=Ur(a,n.conv6),a=Ur(a,n.conv7),fl(a,n.conv8,"valid",!1)}runMobilenet(t,n){let a=this.config.isFirstLayerConv2d?Vp(fl(t,n.conv0,"valid",!1)):Gr(t,n.conv0);return a=Dt(a,[2,2],[2,2],"same"),a=Gr(a,n.conv1),a=Dt(a,[2,2],[2,2],"same"),a=Gr(a,n.conv2),a=Dt(a,[2,2],[2,2],"same"),a=Gr(a,n.conv3),a=Dt(a,[2,2],[2,2],"same"),a=Gr(a,n.conv4),a=Dt(a,[2,2],[2,2],"same"),a=Gr(a,n.conv5),a=Dt(a,[2,2],[1,1],"same"),a=n.conv6?Gr(a,n.conv6):a,a=n.conv7?Gr(a,n.conv7):a,fl(a,n.conv8,"valid",!1)}forwardInput(t,n){let{params:a}=this;if(!a)throw new Error("TinyYolov2 - load model before inference");return O(()=>{let r=re(t.toBatchTensor(n,!1),"float32");return r=this.config.meanRgb?Ya(r,this.config.meanRgb):r,r=r.div(255),this.config.withSeparableConvs?this.runMobilenet(r,a):this.runTinyYolov2(r,a)})}async forward(t,n){return this.forwardInput(await vt(t),n)}async detect(t,n={}){let{inputSize:a,scoreThreshold:r}=new Ja(n),s=await vt(t),i=await this.forwardInput(s,a),o=O(()=>dt(i)[0].expandDims()),l={width:s.getInputWidth(0),height:s.getInputHeight(0)},u=await this.extractBoxes(o,s.getReshapedInputDimensions(0),r);i.dispose(),o.dispose();let p=u.map(g=>g.box),d=u.map(g=>g.score),c=u.map(g=>g.classScore),h=u.map(g=>this.config.classes[g.label]);return _k(p.map(g=>g.rescale(a)),d,this.config.iouThreshold,!0).map(g=>new ol(d[g],c[g],h[g],p[g],l))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return zD(t,this.config)}extractParams(t){let n=this.config.filterSizes||Eg.DEFAULT_FILTER_SIZES,a=n?n.length:void 0;if(a!==7&&a!==8&&a!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${a} filterSizes in config`);return LD(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,a){let{width:r,height:s}=n,i=Math.max(r,s),o=i/r,l=i/s,u=t.shape[1],p=this.config.anchors.length,[d,c,h]=O(()=>{let b=t.reshape([u,u,p,this.boxEncodingSize]),y=b.slice([0,0,0,0],[u,u,p,4]),x=b.slice([0,0,0,4],[u,u,p,1]),v=this.withClassScores?qa(b.slice([0,0,0,5],[u,u,p,this.config.classes.length]),3):xe(0);return[y,x,v]}),m=[],f=await c.array(),g=await d.array();for(let b=0;ba){let I=(y+zd(g[b][y][x][0]))/u*o,N=(b+zd(g[b][y][x][1]))/u*l,C=Math.exp(g[b][y][x][2])*this.config.anchors[x].x/u*o,_=Math.exp(g[b][y][x][3])*this.config.anchors[x].y/u*l,F=I-C/2,D=N-_/2,$={row:b,col:y,anchor:x},{classScore:S,label:M}=this.withClassScores?await this.extractPredictedClass(h,$):{classScore:1,label:0};m.push({box:new il(F,D,F+C,D+_),score:v,classScore:v*S,label:M,...$})}}return d.dispose(),c.dispose(),h.dispose(),m}async extractPredictedClass(t,n){let{row:a,col:r,anchor:s}=n,i=await t.array();return Array(this.config.classes.length).fill(0).map((o,l)=>i[a][r][s][l]).map((o,l)=>({classScore:o,label:l})).reduce((o,l)=>o.classScore>l.classScore?o:l)}};Eg.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Up=Eg;var vl=class extends Up{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:$D,classes:["face"],...t?{anchors:RD,meanRgb:MD}:{anchors:DD,withClassScores:!0}};super(n)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new Tt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?PD:OD}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function Fge(e,t=!0){let n=new vl(t);return n.extractWeights(e),n}var Zd=class extends Ja{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var Sa=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function wl(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>gl(l)?r(l):l.detection),i=a||(t instanceof Ce?await Rp(t,s):await Dp(t,s)),o=await n(i);return i.forEach(l=>l instanceof Ce&&l.dispose()),o}async function Gp(e,t,n,a,r){return wl([e],t,async s=>n(s[0]),a,r)}var WD=.4,BD=[new Ue(1.603231,2.094468),new Ue(6.041143,7.080126),new Ue(2.882459,3.518061),new Ue(4.266906,5.178857),new Ue(9.041765,10.66308)],VD=[117.001,114.697,97.404];var kl=class extends Up{constructor(){let t={withSeparableConvs:!0,iouThreshold:WD,classes:["face"],anchors:BD,meanRgb:VD,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new Tt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var nt={ssdMobilenetv1:new Ws,tinyFaceDetector:new kl,tinyYolov2:new vl,faceLandmark68Net:new bl,faceLandmark68TinyNet:new Xd,faceRecognitionNet:new yl,faceExpressionNet:new qd,ageGenderNet:new Kd},UD=(e,t)=>nt.ssdMobilenetv1.locateFaces(e,t),$ge=(e,t)=>nt.tinyFaceDetector.locateFaces(e,t),Dge=(e,t)=>nt.tinyYolov2.locateFaces(e,t),GD=e=>nt.faceLandmark68Net.detectLandmarks(e),Rge=e=>nt.faceLandmark68TinyNet.detectLandmarks(e),Mge=e=>nt.faceRecognitionNet.computeFaceDescriptor(e),Oge=e=>nt.faceExpressionNet.predictExpressions(e),Pge=e=>nt.ageGenderNet.predictAgeAndGender(e),HD=e=>nt.ssdMobilenetv1.load(e),Lge=e=>nt.tinyFaceDetector.load(e),zge=e=>nt.tinyYolov2.load(e),Wge=e=>nt.faceLandmark68Net.load(e),Bge=e=>nt.faceLandmark68TinyNet.load(e),Vge=e=>nt.faceRecognitionNet.load(e),Uge=e=>nt.faceExpressionNet.load(e),Gge=e=>nt.ageGenderNet.load(e),Hge=HD,jge=UD,qge=GD;var _g=class extends Sa{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.extractedFaces=r}},Il=class extends _g{async run(){let t=await this.parentTask,n=await wl(t,this.input,async a=>Promise.all(a.map(r=>nt.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>bg(a,n[r]))}withAgeAndGender(){return new Nl(this,this.input)}},Sl=class extends _g{async run(){let t=await this.parentTask;if(!t)return;let n=await Gp(t,this.input,a=>nt.faceExpressionNet.predictExpressions(a),this.extractedFaces);return bg(t,n)}withAgeAndGender(){return new Tl(this,this.input)}},Bs=class extends Il{withAgeAndGender(){return new Us(this,this.input)}withFaceDescriptors(){return new Hr(this,this.input)}},Vs=class extends Sl{withAgeAndGender(){return new Gs(this,this.input)}withFaceDescriptor(){return new jr(this,this.input)}};var Ag=class extends Sa{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.extractedFaces=r}},Nl=class extends Ag{async run(){let t=await this.parentTask,n=await wl(t,this.input,async a=>Promise.all(a.map(r=>nt.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((a,r)=>{let{age:s,gender:i,genderProbability:o}=n[r];return Ng(Tg(a,i,o),s)})}withFaceExpressions(){return new Il(this,this.input)}},Tl=class extends Ag{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await Gp(t,this.input,s=>nt.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return Ng(Tg(t,a,r),n)}withFaceExpressions(){return new Sl(this,this.input)}},Us=class extends Nl{withFaceExpressions(){return new Bs(this,this.input)}withFaceDescriptors(){return new Hr(this,this.input)}},Gs=class extends Tl{withFaceExpressions(){return new Vs(this,this.input)}withFaceDescriptor(){return new jr(this,this.input)}};var Jd=class extends Sa{constructor(n,a){super();this.parentTask=n;this.input=a}},Hr=class extends Jd{async run(){let t=await this.parentTask;return(await wl(t,this.input,a=>Promise.all(a.map(r=>nt.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>Sg(t[r],a))}withFaceExpressions(){return new Bs(this,this.input)}withAgeAndGender(){return new Us(this,this.input)}},jr=class extends Jd{async run(){let t=await this.parentTask;if(!t)return;let n=await Gp(t,this.input,a=>nt.faceRecognitionNet.computeFaceDescriptor(a),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}));return Sg(t,n)}withFaceExpressions(){return new Vs(this,this.input)}withAgeAndGender(){return new Gs(this,this.input)}};var Qd=class extends Sa{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?nt.faceLandmark68TinyNet:nt.faceLandmark68Net}},eh=class extends Qd{async run(){let t=await this.parentTask,n=t.map(i=>i.detection),a=this.input instanceof Ce?await Rp(this.input,n):await Dp(this.input,n),r=await Promise.all(a.map(i=>this.landmarkNet.detectLandmarks(i)));return a.forEach(i=>i instanceof Ce&&i.dispose()),t.filter((i,o)=>r[o]).map((i,o)=>Wp(i,r[o]))}withFaceExpressions(){return new Bs(this,this.input)}withAgeAndGender(){return new Us(this,this.input)}withFaceDescriptors(){return new Hr(this,this.input)}},th=class extends Qd{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof Ce?await Rp(this.input,[n]):await Dp(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof Ce&&s.dispose()),Wp(t,r)}withFaceExpressions(){return new Vs(this,this.input)}withAgeAndGender(){return new Gs(this,this.input)}withFaceDescriptor(){return new jr(this,this.input)}};var nh=class extends Sa{constructor(n,a=new Ia){super();this.input=n;this.options=a}},Hp=class extends nh{async run(){let{input:t,options:n}=this,a;if(n instanceof Zd)a=nt.tinyFaceDetector.locateFaces(t,n);else if(n instanceof Ia)a=nt.ssdMobilenetv1.locateFaces(t,n);else if(n instanceof Ja)a=nt.tinyYolov2.locateFaces(t,n);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return a}runAndExtendWithFaceDetections(){return new Promise((t,n)=>{this.run().then(a=>t(a.map(r=>pl({},r)))).catch(a=>n(a))})}withFaceLandmarks(t=!1){return new eh(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Il(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Nl(this.runAndExtendWithFaceDetections(),this.input)}},ah=class extends nh{async run(){let t=await new Hp(this.input,this.options),n=t[0];return t.forEach(a=>{a.score>n.score&&(n=a)}),n}runAndExtendWithFaceDetection(){return new Promise(async t=>{let n=await this.run();t(n?pl({},n):void 0)})}withFaceLandmarks(t=!1){return new th(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Sl(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Tl(this.runAndExtendWithFaceDetection(),this.input)}};function Kge(e,t=new Ia){return new ah(e,t)}function Fg(e,t=new Ia){return new Hp(e,t)}async function jD(e,t){return Fg(e,new 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rn(o,t,e,r){let{extractWeights:n,getRemainingWeights:s}=k(o),a=[],{extractConvParams:i,extractConvWithBatchNormParams:c,extractSeparableConvParams:m}=fa(n,a),p;if(t.withSeparableConvs){let[u,f,l,b,T,h,g,P,y]=r,I=t.isFirstLayerConv2d?i(u,f,3,"conv0"):m(u,f,"conv0"),j=m(f,l,"conv1"),tt=m(l,b,"conv2"),it=m(b,T,"conv3"),q=m(T,h,"conv4"),Pt=m(h,g,"conv5"),wt=P?m(g,P,"conv6"):void 0,Ft=y?m(P,y,"conv7"):void 0,ie=i(y||P||g,5*e,1,"conv8");p={conv0:I,conv1:j,conv2:tt,conv3:it,conv4:q,conv5:Pt,conv6:wt,conv7:Ft,conv8:ie}}else{let[u,f,l,b,T,h,g,P,y]=r,I=c(u,f,"conv0"),j=c(f,l,"conv1"),tt=c(l,b,"conv2"),it=c(b,T,"conv3"),q=c(T,h,"conv4"),Pt=c(h,g,"conv5"),wt=c(g,P,"conv6"),Ft=c(P,y,"conv7"),ie=i(y,5*e,1,"conv8");p={conv0:I,conv1:j,conv2:tt,conv3:it,conv4:q,conv5:Pt,conv6:wt,conv7:Ft,conv8:ie}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:p,paramMappings:a}}function la(o,t){let e=Y(o,t);function r(i){let c=e(`${i}/sub`,1),m=e(`${i}/truediv`,1);return{sub:c,truediv:m}}function n(i){let c=e(`${i}/filters`,4),m=e(`${i}/bias`,1);return{filters:c,bias:m}}function s(i){let c=n(`${i}/conv`),m=r(`${i}/bn`);return{conv:c,bn:m}}let a=ge(e);return{extractConvParams:n,extractConvWithBatchNormParams:s,extractSeparableConvParams:a}}function on(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:s}=la(o,e),a;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;a={conv0:t.isFirstLayerConv2d?r("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:i>7?s("conv6"):void 0,conv7:i>8?s("conv7"):void 0,conv8:r("conv8")}}else a={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return W(o,e),{params:a,paramMappings:e}}var st=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var Cr=class Cr extends A{constructor(t){super("TinyYolov2"),ho(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,e){let r=vt(t,e.conv0);return r=C.maxPool(r,[2,2],[2,2],"same"),r=vt(r,e.conv1),r=C.maxPool(r,[2,2],[2,2],"same"),r=vt(r,e.conv2),r=C.maxPool(r,[2,2],[2,2],"same"),r=vt(r,e.conv3),r=C.maxPool(r,[2,2],[2,2],"same"),r=vt(r,e.conv4),r=C.maxPool(r,[2,2],[2,2],"same"),r=vt(r,e.conv5),r=C.maxPool(r,[2,2],[1,1],"same"),r=vt(r,e.conv6),r=vt(r,e.conv7),Jt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?Ee(Jt(t,e.conv0,"valid",!1)):yt(t,e.conv0);return r=C.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv1),r=C.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv2),r=C.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv3),r=C.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv4),r=C.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv5),r=C.maxPool(r,[2,2],[1,1],"same"),r=e.conv6?yt(r,e.conv6):r,r=e.conv7?yt(r,e.conv7):r,Jt(r,e.conv8,"valid",!1)}forwardInput(t,e){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return C.tidy(()=>{let n=C.cast(t.toBatchTensor(e,!1),"float32");return n=this.config.meanRgb?rt(n,this.config.meanRgb):n,n=n.div(255),this.config.withSeparableConvs?this.runMobilenet(n,r):this.runTinyYolov2(n,r)})}async forward(t,e){return this.forwardInput(await D(t),e)}async detect(t,e={}){let{inputSize:r,scoreThreshold:n}=new st(e),s=await D(t),a=await this.forwardInput(s,r),i=C.tidy(()=>C.unstack(a)[0].expandDims()),c={width:s.getInputWidth(0),height:s.getInputHeight(0)},m=await this.extractBoxes(i,s.getReshapedInputDimensions(0),n);a.dispose(),i.dispose();let p=m.map(h=>h.box),u=m.map(h=>h.score),f=m.map(h=>h.classScore),l=m.map(h=>this.config.classes[h.label]);return Vr(p.map(h=>h.rescale(r)),u,this.config.iouThreshold,!0).map(h=>new Ht(u[h],f[h],l[h],p[h],c))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return on(t,this.config)}extractParams(t){let e=this.config.filterSizes||Cr.DEFAULT_FILTER_SIZES,r=e?e.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return rn(t,this.config,this.boxEncodingSize,e)}async extractBoxes(t,e,r){let{width:n,height:s}=e,a=Math.max(n,s),i=a/n,c=a/s,m=t.shape[1],p=this.config.anchors.length,[u,f,l]=C.tidy(()=>{let g=t.reshape([m,m,p,this.boxEncodingSize]),P=g.slice([0,0,0,0],[m,m,p,4]),y=g.slice([0,0,0,4],[m,m,p,1]),I=this.withClassScores?C.softmax(g.slice([0,0,0,5],[m,m,p,this.config.classes.length]),3):C.scalar(0);return[P,y,I]}),b=[],T=await f.array(),h=await u.array();for(let g=0;gr){let j=(P+Ne(h[g][P][y][0]))/m*i,tt=(g+Ne(h[g][P][y][1]))/m*c,it=Math.exp(h[g][P][y][2])*this.config.anchors[y].x/m*i,q=Math.exp(h[g][P][y][3])*this.config.anchors[y].y/m*c,Pt=j-it/2,wt=tt-q/2,Ft={row:g,col:P,anchor:y},{classScore:ie,label:yo}=this.withClassScores?await this.extractPredictedClass(l,Ft):{classScore:1,label:0};b.push({box:new Ot(Pt,wt,Pt+it,wt+q),score:I,classScore:I*ie,label:yo,...Ft})}}return u.dispose(),f.dispose(),l.dispose(),b}async extractPredictedClass(t,e){let{row:r,col:n,anchor:s}=e,a=await t.array();return Array(this.config.classes.length).fill(0).map((i,c)=>a[r][n][s][c]).map((i,c)=>({classScore:i,label:c})).reduce((i,c)=>i.classScore>c.classScore?i:c)}};Cr.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Me=Cr;var te=class extends Me{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:qo,classes:["face"],...t?{anchors:Ko,meanRgb:Qo}:{anchors:Zo,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?en:tn}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function da(o,t=!0){let e=new te(t);return e.extractWeights(o),e}var je=class extends st{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var J=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var Xe=v(x());var go=v(x());async function ee(o,t,e,r,n=({alignedRect:s})=>s){let s=o.map(c=>qt(c)?n(c):c.detection),a=r||(t instanceof go.Tensor?await le(t,s):await fe(t,s)),i=await e(a);return a.forEach(c=>c instanceof go.Tensor&&c.dispose()),i}async function Ce(o,t,e,r,n){return ee([o],t,async s=>e(s[0]),r,n)}var nn=.4,an=[new _(1.603231,2.094468),new _(6.041143,7.080126),new _(2.882459,3.518061),new _(4.266906,5.178857),new _(9.041765,10.66308)],sn=[117.001,114.697,97.404];var re=class extends Me{constructor(){let t={withSeparableConvs:!0,iouThreshold:nn,classes:["face"],anchors:an,meanRgb:sn,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var F={ssdMobilenetv1:new It,tinyFaceDetector:new re,tinyYolov2:new te,faceLandmark68Net:new Zt,faceLandmark68TinyNet:new ze,faceRecognitionNet:new Kt,faceExpressionNet:new Oe,ageGenderNet:new He},cn=(o,t)=>F.ssdMobilenetv1.locateFaces(o,t),ha=(o,t)=>F.tinyFaceDetector.locateFaces(o,t),ba=(o,t)=>F.tinyYolov2.locateFaces(o,t),mn=o=>F.faceLandmark68Net.detectLandmarks(o),ga=o=>F.faceLandmark68TinyNet.detectLandmarks(o),xa=o=>F.faceRecognitionNet.computeFaceDescriptor(o),va=o=>F.faceExpressionNet.predictExpressions(o),ya=o=>F.ageGenderNet.predictAgeAndGender(o),pn=o=>F.ssdMobilenetv1.load(o),_a=o=>F.tinyFaceDetector.load(o),Ta=o=>F.tinyYolov2.load(o),Pa=o=>F.faceLandmark68Net.load(o),wa=o=>F.faceLandmark68TinyNet.load(o),Fa=o=>F.faceRecognitionNet.load(o),Da=o=>F.faceExpressionNet.load(o),Ea=o=>F.ageGenderNet.load(o),Ma=pn,Ca=cn,Ia=mn;var Ir=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},oe=class extends Ir{async run(){let t=await this.parentTask,e=await ee(t,this.input,async r=>Promise.all(r.map(n=>F.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>gr(r,e[n]))}withAgeAndGender(){return new ae(this,this.input)}},ne=class extends Ir{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>F.faceExpressionNet.predictExpressions(r),this.extractedFaces);return gr(t,e)}withAgeAndGender(){return new se(this,this.input)}},St=class extends oe{withAgeAndGender(){return new Wt(this,this.input)}withFaceDescriptors(){return new _t(this,this.input)}},At=class extends ne{withAgeAndGender(){return new kt(this,this.input)}withFaceDescriptor(){return new Tt(this,this.input)}};var Nr=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ae=class extends Nr{async run(){let t=await this.parentTask,e=await ee(t,this.input,async r=>Promise.all(r.map(n=>F.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:s,gender:a,genderProbability:i}=e[n];return Dr(Er(r,a,i),s)})}withFaceExpressions(){return new oe(this,this.input)}},se=class extends Nr{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await Ce(t,this.input,s=>F.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return Dr(Er(t,r,n),e)}withFaceExpressions(){return new ne(this,this.input)}},Wt=class extends ae{withFaceExpressions(){return new St(this,this.input)}withFaceDescriptors(){return new _t(this,this.input)}},kt=class extends se{withFaceExpressions(){return new At(this,this.input)}withFaceDescriptor(){return new Tt(this,this.input)}};var Ue=class extends J{constructor(e,r){super();this.parentTask=e;this.input=r}},_t=class extends Ue{async run(){let t=await this.parentTask;return(await ee(t,this.input,r=>Promise.all(r.map(n=>F.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>Fr(t[n],r))}withFaceExpressions(){return new St(this,this.input)}withAgeAndGender(){return new Wt(this,this.input)}},Tt=class extends Ue{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>F.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return Fr(t,e)}withFaceExpressions(){return new At(this,this.input)}withAgeAndGender(){return new kt(this,this.input)}};var Je=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.useTinyLandmarkNet=n}get landmarkNet(){return this.useTinyLandmarkNet?F.faceLandmark68TinyNet:F.faceLandmark68Net}},qe=class extends Je{async run(){let t=await this.parentTask,e=t.map(a=>a.detection),r=this.input instanceof Xe.Tensor?await le(this.input,e):await fe(this.input,e),n=await Promise.all(r.map(a=>this.landmarkNet.detectLandmarks(a)));return r.forEach(a=>a instanceof Xe.Tensor&&a.dispose()),t.filter((a,i)=>n[i]).map((a,i)=>Pe(a,n[i]))}withFaceExpressions(){return new St(this,this.input)}withAgeAndGender(){return new Wt(this,this.input)}withFaceDescriptors(){return new _t(this,this.input)}},Ze=class extends Je{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof Xe.Tensor?await le(this.input,[e]):await fe(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(s=>s instanceof Xe.Tensor&&s.dispose()),Pe(t,n)}withFaceExpressions(){return new At(this,this.input)}withAgeAndGender(){return new kt(this,this.input)}withFaceDescriptor(){return new Tt(this,this.input)}};var Ke=class extends J{constructor(e,r=new X){super();this.input=e;this.options=r}},Ie=class extends Ke{async run(){let{input:t,options:e}=this,r;if(e instanceof je)r=F.tinyFaceDetector.locateFaces(t,e);else if(e instanceof X)r=F.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof st)r=F.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise((t,e)=>{this.run().then(r=>t(r.map(n=>Vt({},n)))).catch(r=>e(r))})}withFaceLandmarks(t=!1){return new qe(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new oe(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new ae(this.runAndExtendWithFaceDetections(),this.input)}},Qe=class extends Ke{async run(){let t=await new Ie(this.input,this.options),e=t[0];return t.forEach(r=>{r.score>e.score&&(e=r)}),e}runAndExtendWithFaceDetection(){return new Promise(async t=>{let e=await this.run();t(e?Vt({},e):void 0)})}withFaceLandmarks(t=!1){return new Ze(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new ne(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new se(this.runAndExtendWithFaceDetection(),this.input)}};function Na(o,t=new X){return new Qe(o,t)}function Lr(o,t=new X){return new Ie(o,t)}async function un(o,t){return Lr(o,new X(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function La(o,t={}){return Lr(o,new st(t)).withFaceLandmarks().withFaceDescriptors()}var Sa=un;function xo(o,t){if(o.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let e=Array.from(o),r=Array.from(t);return Math.sqrt(e.map((n,s)=>n-r[s]).reduce((n,s)=>n+s*s,0))}var vo=class o{constructor(t,e=.6){this._distanceThreshold=e;let r=Array.isArray(t)?t:[t];if(!r.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let n=1,s=()=>`person ${n++}`;this._labeledDescriptors=r.map(a=>{if(a instanceof Et)return a;if(a instanceof Float32Array)return new Et(s(),[a]);if(a.descriptor&&a.descriptor instanceof Float32Array)return new Et(s(),[a.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>xo(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new me(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distancet.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>Et.fromJSON(r));return new o(e,t.distanceThreshold)}};function Aa(o){let t=new re;return t.extractWeights(o),t}function fn(o,t){let{width:e,height:r}=new R(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(n=>fn(n,{width:e,height:r}));if(qt(o)){let n=o.detection.forSize(e,r),s=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return Pe(Vt(o,n),s)}return pt(o)?Vt(o,o.detection.forSize(e,r)):o instanceof H||o instanceof M?o.forSize(e,r):o}var 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c=e(`${i}/sub`,1),m=e(`${i}/truediv`,1);return{sub:c,truediv:m}}function n(i){let c=e(`${i}/filters`,4),m=e(`${i}/bias`,1);return{filters:c,bias:m}}function s(i){let c=n(`${i}/conv`),m=r(`${i}/bn`);return{conv:c,bn:m}}let a=ge(e);return{extractConvParams:n,extractConvWithBatchNormParams:s,extractSeparableConvParams:a}}function on(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:s}=la(o,e),a;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;a={conv0:t.isFirstLayerConv2d?r("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:i>7?s("conv6"):void 0,conv7:i>8?s("conv7"):void 0,conv8:r("conv8")}}else a={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return W(o,e),{params:a,paramMappings:e}}var st=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var Cr=class Cr extends A{constructor(t){super("TinyYolov2"),ho(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,e){let r=vt(t,e.conv0);return r=C.maxPool(r,[2,2],[2,2],"same"),r=vt(r,e.conv1),r=C.maxPool(r,[2,2],[2,2],"same"),r=vt(r,e.conv2),r=C.maxPool(r,[2,2],[2,2],"same"),r=vt(r,e.conv3),r=C.maxPool(r,[2,2],[2,2],"same"),r=vt(r,e.conv4),r=C.maxPool(r,[2,2],[2,2],"same"),r=vt(r,e.conv5),r=C.maxPool(r,[2,2],[1,1],"same"),r=vt(r,e.conv6),r=vt(r,e.conv7),Jt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?Ee(Jt(t,e.conv0,"valid",!1)):yt(t,e.conv0);return r=C.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv1),r=C.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv2),r=C.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv3),r=C.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv4),r=C.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv5),r=C.maxPool(r,[2,2],[1,1],"same"),r=e.conv6?yt(r,e.conv6):r,r=e.conv7?yt(r,e.conv7):r,Jt(r,e.conv8,"valid",!1)}forwardInput(t,e){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return C.tidy(()=>{let n=C.cast(t.toBatchTensor(e,!1),"float32");return n=this.config.meanRgb?rt(n,this.config.meanRgb):n,n=n.div(255),this.config.withSeparableConvs?this.runMobilenet(n,r):this.runTinyYolov2(n,r)})}async forward(t,e){return this.forwardInput(await D(t),e)}async detect(t,e={}){let{inputSize:r,scoreThreshold:n}=new st(e),s=await D(t),a=await this.forwardInput(s,r),i=C.tidy(()=>C.unstack(a)[0].expandDims()),c={width:s.getInputWidth(0),height:s.getInputHeight(0)},m=await this.extractBoxes(i,s.getReshapedInputDimensions(0),n);a.dispose(),i.dispose();let p=m.map(h=>h.box),u=m.map(h=>h.score),f=m.map(h=>h.classScore),l=m.map(h=>this.config.classes[h.label]);return Vr(p.map(h=>h.rescale(r)),u,this.config.iouThreshold,!0).map(h=>new Ht(u[h],f[h],l[h],p[h],c))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return on(t,this.config)}extractParams(t){let e=this.config.filterSizes||Cr.DEFAULT_FILTER_SIZES,r=e?e.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return rn(t,this.config,this.boxEncodingSize,e)}async extractBoxes(t,e,r){let{width:n,height:s}=e,a=Math.max(n,s),i=a/n,c=a/s,m=t.shape[1],p=this.config.anchors.length,[u,f,l]=C.tidy(()=>{let g=t.reshape([m,m,p,this.boxEncodingSize]),P=g.slice([0,0,0,0],[m,m,p,4]),y=g.slice([0,0,0,4],[m,m,p,1]),I=this.withClassScores?C.softmax(g.slice([0,0,0,5],[m,m,p,this.config.classes.length]),3):C.scalar(0);return[P,y,I]}),b=[],T=await f.array(),h=await u.array();for(let g=0;gr){let j=(P+Ne(h[g][P][y][0]))/m*i,tt=(g+Ne(h[g][P][y][1]))/m*c,it=Math.exp(h[g][P][y][2])*this.config.anchors[y].x/m*i,q=Math.exp(h[g][P][y][3])*this.config.anchors[y].y/m*c,Pt=j-it/2,wt=tt-q/2,Ft={row:g,col:P,anchor:y},{classScore:ie,label:yo}=this.withClassScores?await this.extractPredictedClass(l,Ft):{classScore:1,label:0};b.push({box:new Ot(Pt,wt,Pt+it,wt+q),score:I,classScore:I*ie,label:yo,...Ft})}}return u.dispose(),f.dispose(),l.dispose(),b}async extractPredictedClass(t,e){let{row:r,col:n,anchor:s}=e,a=await t.array();return Array(this.config.classes.length).fill(0).map((i,c)=>a[r][n][s][c]).map((i,c)=>({classScore:i,label:c})).reduce((i,c)=>i.classScore>c.classScore?i:c)}};Cr.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Me=Cr;var te=class extends Me{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:qo,classes:["face"],...t?{anchors:Ko,meanRgb:Qo}:{anchors:Zo,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?en:tn}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function da(o,t=!0){let e=new te(t);return e.extractWeights(o),e}var je=class extends st{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var J=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var Xe=v(x());var go=v(x());async function ee(o,t,e,r,n=({alignedRect:s})=>s){let s=o.map(c=>qt(c)?n(c):c.detection),a=r||(t instanceof go.Tensor?await le(t,s):await fe(t,s)),i=await e(a);return a.forEach(c=>c instanceof go.Tensor&&c.dispose()),i}async function Ce(o,t,e,r,n){return ee([o],t,async s=>e(s[0]),r,n)}var nn=.4,an=[new _(1.603231,2.094468),new _(6.041143,7.080126),new _(2.882459,3.518061),new _(4.266906,5.178857),new _(9.041765,10.66308)],sn=[117.001,114.697,97.404];var re=class extends Me{constructor(){let t={withSeparableConvs:!0,iouThreshold:nn,classes:["face"],anchors:an,meanRgb:sn,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var F={ssdMobilenetv1:new It,tinyFaceDetector:new re,tinyYolov2:new te,faceLandmark68Net:new Zt,faceLandmark68TinyNet:new ze,faceRecognitionNet:new Kt,faceExpressionNet:new Oe,ageGenderNet:new He},cn=(o,t)=>F.ssdMobilenetv1.locateFaces(o,t),ha=(o,t)=>F.tinyFaceDetector.locateFaces(o,t),ba=(o,t)=>F.tinyYolov2.locateFaces(o,t),mn=o=>F.faceLandmark68Net.detectLandmarks(o),ga=o=>F.faceLandmark68TinyNet.detectLandmarks(o),xa=o=>F.faceRecognitionNet.computeFaceDescriptor(o),va=o=>F.faceExpressionNet.predictExpressions(o),ya=o=>F.ageGenderNet.predictAgeAndGender(o),pn=o=>F.ssdMobilenetv1.load(o),_a=o=>F.tinyFaceDetector.load(o),Ta=o=>F.tinyYolov2.load(o),Pa=o=>F.faceLandmark68Net.load(o),wa=o=>F.faceLandmark68TinyNet.load(o),Fa=o=>F.faceRecognitionNet.load(o),Da=o=>F.faceExpressionNet.load(o),Ea=o=>F.ageGenderNet.load(o),Ma=pn,Ca=cn,Ia=mn;var Ir=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},oe=class extends Ir{async run(){let t=await this.parentTask,e=await ee(t,this.input,async r=>Promise.all(r.map(n=>F.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>gr(r,e[n]))}withAgeAndGender(){return new ae(this,this.input)}},ne=class extends Ir{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>F.faceExpressionNet.predictExpressions(r),this.extractedFaces);return gr(t,e)}withAgeAndGender(){return new se(this,this.input)}},St=class extends oe{withAgeAndGender(){return new Wt(this,this.input)}withFaceDescriptors(){return new _t(this,this.input)}},At=class extends ne{withAgeAndGender(){return new kt(this,this.input)}withFaceDescriptor(){return new Tt(this,this.input)}};var Nr=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ae=class extends Nr{async run(){let t=await this.parentTask,e=await ee(t,this.input,async r=>Promise.all(r.map(n=>F.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:s,gender:a,genderProbability:i}=e[n];return Dr(Er(r,a,i),s)})}withFaceExpressions(){return new oe(this,this.input)}},se=class extends Nr{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await Ce(t,this.input,s=>F.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return Dr(Er(t,r,n),e)}withFaceExpressions(){return new ne(this,this.input)}},Wt=class extends ae{withFaceExpressions(){return new St(this,this.input)}withFaceDescriptors(){return new _t(this,this.input)}},kt=class extends se{withFaceExpressions(){return new At(this,this.input)}withFaceDescriptor(){return new Tt(this,this.input)}};var Ue=class extends J{constructor(e,r){super();this.parentTask=e;this.input=r}},_t=class extends Ue{async run(){let t=await this.parentTask;return(await ee(t,this.input,r=>Promise.all(r.map(n=>F.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>Fr(t[n],r))}withFaceExpressions(){return new St(this,this.input)}withAgeAndGender(){return new Wt(this,this.input)}},Tt=class extends Ue{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>F.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return Fr(t,e)}withFaceExpressions(){return new At(this,this.input)}withAgeAndGender(){return new kt(this,this.input)}};var Je=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.useTinyLandmarkNet=n}get landmarkNet(){return this.useTinyLandmarkNet?F.faceLandmark68TinyNet:F.faceLandmark68Net}},qe=class extends Je{async run(){let t=await this.parentTask,e=t.map(a=>a.detection),r=this.input instanceof Xe.Tensor?await le(this.input,e):await fe(this.input,e),n=await Promise.all(r.map(a=>this.landmarkNet.detectLandmarks(a)));return r.forEach(a=>a instanceof Xe.Tensor&&a.dispose()),t.filter((a,i)=>n[i]).map((a,i)=>Pe(a,n[i]))}withFaceExpressions(){return new St(this,this.input)}withAgeAndGender(){return new Wt(this,this.input)}withFaceDescriptors(){return new _t(this,this.input)}},Ze=class extends Je{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof Xe.Tensor?await le(this.input,[e]):await fe(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(s=>s instanceof Xe.Tensor&&s.dispose()),Pe(t,n)}withFaceExpressions(){return new At(this,this.input)}withAgeAndGender(){return new kt(this,this.input)}withFaceDescriptor(){return new Tt(this,this.input)}};var Ke=class extends J{constructor(e,r=new X){super();this.input=e;this.options=r}},Ie=class extends Ke{async run(){let{input:t,options:e}=this,r;if(e instanceof je)r=F.tinyFaceDetector.locateFaces(t,e);else if(e instanceof X)r=F.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof st)r=F.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise((t,e)=>{this.run().then(r=>t(r.map(n=>Vt({},n)))).catch(r=>e(r))})}withFaceLandmarks(t=!1){return new qe(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new oe(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new ae(this.runAndExtendWithFaceDetections(),this.input)}},Qe=class extends Ke{async run(){let t=await new Ie(this.input,this.options),e=t[0];return t.forEach(r=>{r.score>e.score&&(e=r)}),e}runAndExtendWithFaceDetection(){return new Promise(async t=>{let e=await this.run();t(e?Vt({},e):void 0)})}withFaceLandmarks(t=!1){return new Ze(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new ne(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new se(this.runAndExtendWithFaceDetection(),this.input)}};function Na(o,t=new X){return new Qe(o,t)}function Lr(o,t=new X){return new Ie(o,t)}async function un(o,t){return Lr(o,new X(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function La(o,t={}){return Lr(o,new st(t)).withFaceLandmarks().withFaceDescriptors()}var Sa=un;function xo(o,t){if(o.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let e=Array.from(o),r=Array.from(t);return Math.sqrt(e.map((n,s)=>n-r[s]).reduce((n,s)=>n+s*s,0))}var vo=class o{constructor(t,e=.6){this._distanceThreshold=e;let r=Array.isArray(t)?t:[t];if(!r.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let n=1,s=()=>`person ${n++}`;this._labeledDescriptors=r.map(a=>{if(a instanceof Et)return a;if(a instanceof Float32Array)return new Et(s(),[a]);if(a.descriptor&&a.descriptor instanceof Float32Array)return new Et(s(),[a.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>xo(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new me(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distancet.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>Et.fromJSON(r));return new o(e,t.distanceThreshold)}};function Aa(o){let t=new re;return t.extractWeights(o),t}function fn(o,t){let{width:e,height:r}=new R(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(n=>fn(n,{width:e,height:r}));if(qt(o)){let n=o.detection.forSize(e,r),s=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return Pe(Vt(o,n),s)}return pt(o)?Vt(o,o.detection.forSize(e,r)):o instanceof H||o instanceof M?o.forSize(e,r):o}var ka=No;0&&(module.exports={AgeGenderNet,BoundingBox,Box,ComposableTask,ComputeAllFaceDescriptorsTask,ComputeFaceDescriptorsTaskBase,ComputeSingleFaceDescriptorTask,DetectAllFaceLandmarksTask,DetectAllFacesTask,DetectFaceLandmarksTaskBase,DetectFacesTaskBase,DetectSingleFaceLandmarksTask,DetectSingleFaceTask,Dimensions,FACE_EXPRESSION_LABELS,FaceDetection,FaceDetectionNet,FaceExpressionNet,FaceExpressions,FaceLandmark68Net,FaceLandmark68TinyNet,FaceLandmarkNet,FaceLandmarks,FaceLandmarks5,FaceLandmarks68,FaceMatch,FaceMatcher,FaceRecognitionNet,Gender,LabeledBox,LabeledFaceDescriptors,NetInput,NeuralNetwork,ObjectDetection,Point,PredictedBox,Rect,SsdMobilenetv1,SsdMobilenetv1Options,TinyFaceDetector,TinyFaceDetectorOptions,TinyYolov2,TinyYolov2Options,allFaces,allFacesSsdMobilenetv1,allFacesTinyYolov2,awaitMediaLoaded,bufferToImage,computeFaceDescriptor,createCanvas,createCanvasFromMedia,createFaceDetectionNet,createFaceRecognitionNet,createSsdMobilenetv1,createTinyFaceDetector,createTinyYolov2,detectAllFaces,detectFaceLandmarks,detectFaceLandmarksTiny,detectLandmarks,detectSingleFace,draw,env,euclideanDistance,extendWithAge,extendWithFaceDescriptor,extendWithFaceDetection,extendWithFaceExpressions,extendWithFaceLandmarks,extendWithGender,extractFaceTensors,extractFaces,fetchImage,fetchJson,fetchNetWeights,fetchOrThrow,fetchVideo,getContext2dOrThrow,getMediaDimensions,imageTensorToCanvas,imageToSquare,inverseSigmoid,iou,isMediaElement,isMediaLoaded,isWithAge,isWithFaceDetection,isWithFaceExpressions,isWithFaceLandmarks,isWithGender,loadAgeGenderModel,loadFaceDetectionModel,loadFaceExpressionModel,loadFaceLandmarkModel,loadFaceLandmarkTinyModel,loadFaceRecognitionModel,loadSsdMobilenetv1Model,loadTinyFaceDetectorModel,loadTinyYolov2Model,loadWeightMap,locateFaces,matchDimensions,minBbox,nets,nonMaxSuppression,normalize,padToSquare,predictAgeAndGender,recognizeFaceExpressions,resizeResults,resolveInput,shuffleArray,sigmoid,ssdMobilenetv1,tf,tinyFaceDetector,tinyYolov2,toNetInput,utils,validateConfig,version}); diff --git a/dist/face-api.node-wasm.js b/dist/face-api.node-wasm.js index 7477f90..d08f9b9 100644 --- a/dist/face-api.node-wasm.js +++ b/dist/face-api.node-wasm.js @@ -4,4 +4,4 @@ author: ' */ -"use strict";var dn=Object.create;var tr=Object.defineProperty;var hn=Object.getOwnPropertyDescriptor;var bn=Object.getOwnPropertyNames;var gn=Object.getPrototypeOf,xn=Object.prototype.hasOwnProperty;var vn=(o,t)=>()=>(t||o((t={exports:{}}).exports,t),t.exports),Wr=(o,t)=>{for(var e in t)tr(o,e,{get:t[e],enumerable:!0})},_o=(o,t,e,r)=>{if(t&&typeof t=="object"||typeof t=="function")for(let n of bn(t))!xn.call(o,n)&&n!==e&&tr(o,n,{get:()=>t[n],enumerable:!(r=hn(t,n))||r.enumerable});return o};var v=(o,t,e)=>(e=o!=null?dn(gn(o)):{},_o(t||!o||!o.__esModule?tr(e,"default",{value:o,enumerable:!0}):e,o)),yn=o=>_o(tr({},"__esModule",{value:!0}),o);var x=vn((Ya,rr)=>{"use strict";var 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st=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var Nr=class Nr extends A{constructor(t){super("TinyYolov2"),ho(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,e){let r=vt(t,e.conv0);return 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n=this.config.meanRgb?rt(n,this.config.meanRgb):n,n=n.div(255),this.config.withSeparableConvs?this.runMobilenet(n,r):this.runTinyYolov2(n,r)})}async forward(t,e){return this.forwardInput(await D(t),e)}async detect(t,e={}){let{inputSize:r,scoreThreshold:n}=new st(e),s=await D(t),a=await this.forwardInput(s,r),i=C.tidy(()=>C.unstack(a)[0].expandDims()),c={width:s.getInputWidth(0),height:s.getInputHeight(0)},m=await this.extractBoxes(i,s.getReshapedInputDimensions(0),n);a.dispose(),i.dispose();let p=m.map(h=>h.box),u=m.map(h=>h.score),f=m.map(h=>h.classScore),l=m.map(h=>this.config.classes[h.label]);return Vr(p.map(h=>h.rescale(r)),u,this.config.iouThreshold,!0).map(h=>new Ht(u[h],f[h],l[h],p[h],c))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return nn(t,this.config)}extractParams(t){let e=this.config.filterSizes||Nr.DEFAULT_FILTER_SIZES,r=e?e.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return on(t,this.config,this.boxEncodingSize,e)}async extractBoxes(t,e,r){let{width:n,height:s}=e,a=Math.max(n,s),i=a/n,c=a/s,m=t.shape[1],p=this.config.anchors.length,[u,f,l]=C.tidy(()=>{let g=t.reshape([m,m,p,this.boxEncodingSize]),P=g.slice([0,0,0,0],[m,m,p,4]),y=g.slice([0,0,0,4],[m,m,p,1]),I=this.withClassScores?C.softmax(g.slice([0,0,0,5],[m,m,p,this.config.classes.length]),3):C.scalar(0);return[P,y,I]}),b=[],T=await f.array(),h=await u.array();for(let g=0;gr){let j=(P+Ne(h[g][P][y][0]))/m*i,tt=(g+Ne(h[g][P][y][1]))/m*c,it=Math.exp(h[g][P][y][2])*this.config.anchors[y].x/m*i,q=Math.exp(h[g][P][y][3])*this.config.anchors[y].y/m*c,Pt=j-it/2,wt=tt-q/2,Ft={row:g,col:P,anchor:y},{classScore:ie,label:yo}=this.withClassScores?await this.extractPredictedClass(l,Ft):{classScore:1,label:0};b.push({box:new Ot(Pt,wt,Pt+it,wt+q),score:I,classScore:I*ie,label:yo,...Ft})}}return u.dispose(),f.dispose(),l.dispose(),b}async extractPredictedClass(t,e){let{row:r,col:n,anchor:s}=e,a=await t.array();return Array(this.config.classes.length).fill(0).map((i,c)=>a[r][n][s][c]).map((i,c)=>({classScore:i,label:c})).reduce((i,c)=>i.classScore>c.classScore?i:c)}};Nr.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Me=Nr;var te=class extends Me{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:Zo,classes:["face"],...t?{anchors:Qo,meanRgb:tn}:{anchors:Ko,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?rn:en}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function da(o,t=!0){let e=new te(t);return e.extractWeights(o),e}var je=class extends st{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var J=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var Xe=v(x());var go=v(x());async function ee(o,t,e,r,n=({alignedRect:s})=>s){let s=o.map(c=>qt(c)?n(c):c.detection),a=r||(t instanceof go.Tensor?await le(t,s):await fe(t,s)),i=await e(a);return a.forEach(c=>c instanceof go.Tensor&&c.dispose()),i}async function Ce(o,t,e,r,n){return ee([o],t,async s=>e(s[0]),r,n)}var an=.4,sn=[new _(1.603231,2.094468),new _(6.041143,7.080126),new _(2.882459,3.518061),new _(4.266906,5.178857),new _(9.041765,10.66308)],cn=[117.001,114.697,97.404];var re=class extends Me{constructor(){let t={withSeparableConvs:!0,iouThreshold:an,classes:["face"],anchors:sn,meanRgb:cn,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var F={ssdMobilenetv1:new It,tinyFaceDetector:new re,tinyYolov2:new te,faceLandmark68Net:new Zt,faceLandmark68TinyNet:new ze,faceRecognitionNet:new Kt,faceExpressionNet:new Oe,ageGenderNet:new He},mn=(o,t)=>F.ssdMobilenetv1.locateFaces(o,t),ha=(o,t)=>F.tinyFaceDetector.locateFaces(o,t),ba=(o,t)=>F.tinyYolov2.locateFaces(o,t),pn=o=>F.faceLandmark68Net.detectLandmarks(o),ga=o=>F.faceLandmark68TinyNet.detectLandmarks(o),xa=o=>F.faceRecognitionNet.computeFaceDescriptor(o),va=o=>F.faceExpressionNet.predictExpressions(o),ya=o=>F.ageGenderNet.predictAgeAndGender(o),un=o=>F.ssdMobilenetv1.load(o),_a=o=>F.tinyFaceDetector.load(o),Ta=o=>F.tinyYolov2.load(o),Pa=o=>F.faceLandmark68Net.load(o),wa=o=>F.faceLandmark68TinyNet.load(o),Fa=o=>F.faceRecognitionNet.load(o),Da=o=>F.faceExpressionNet.load(o),Ea=o=>F.ageGenderNet.load(o),Ma=un,Ca=mn,Ia=pn;var Lr=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},oe=class extends Lr{async run(){let t=await this.parentTask,e=await ee(t,this.input,async r=>Promise.all(r.map(n=>F.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>vr(r,e[n]))}withAgeAndGender(){return new ae(this,this.input)}},ne=class extends Lr{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>F.faceExpressionNet.predictExpressions(r),this.extractedFaces);return vr(t,e)}withAgeAndGender(){return new se(this,this.input)}},St=class extends oe{withAgeAndGender(){return new Wt(this,this.input)}withFaceDescriptors(){return new _t(this,this.input)}},At=class extends ne{withAgeAndGender(){return new kt(this,this.input)}withFaceDescriptor(){return new Tt(this,this.input)}};var Sr=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ae=class extends Sr{async run(){let t=await this.parentTask,e=await ee(t,this.input,async r=>Promise.all(r.map(n=>F.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:s,gender:a,genderProbability:i}=e[n];return Mr(Cr(r,a,i),s)})}withFaceExpressions(){return new oe(this,this.input)}},se=class extends Sr{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await Ce(t,this.input,s=>F.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return Mr(Cr(t,r,n),e)}withFaceExpressions(){return new ne(this,this.input)}},Wt=class extends ae{withFaceExpressions(){return new St(this,this.input)}withFaceDescriptors(){return new _t(this,this.input)}},kt=class extends se{withFaceExpressions(){return new At(this,this.input)}withFaceDescriptor(){return new Tt(this,this.input)}};var Ue=class extends J{constructor(e,r){super();this.parentTask=e;this.input=r}},_t=class extends Ue{async run(){let t=await this.parentTask;return(await ee(t,this.input,r=>Promise.all(r.map(n=>F.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>Er(t[n],r))}withFaceExpressions(){return new St(this,this.input)}withAgeAndGender(){return new Wt(this,this.input)}},Tt=class extends Ue{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>F.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return Er(t,e)}withFaceExpressions(){return new At(this,this.input)}withAgeAndGender(){return new kt(this,this.input)}};var Je=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.useTinyLandmarkNet=n}get landmarkNet(){return this.useTinyLandmarkNet?F.faceLandmark68TinyNet:F.faceLandmark68Net}},qe=class extends Je{async run(){let t=await this.parentTask,e=t.map(a=>a.detection),r=this.input instanceof Xe.Tensor?await le(this.input,e):await fe(this.input,e),n=await Promise.all(r.map(a=>this.landmarkNet.detectLandmarks(a)));return r.forEach(a=>a instanceof Xe.Tensor&&a.dispose()),t.filter((a,i)=>n[i]).map((a,i)=>Pe(a,n[i]))}withFaceExpressions(){return new St(this,this.input)}withAgeAndGender(){return new Wt(this,this.input)}withFaceDescriptors(){return new _t(this,this.input)}},Ze=class extends Je{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof Xe.Tensor?await le(this.input,[e]):await fe(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(s=>s instanceof Xe.Tensor&&s.dispose()),Pe(t,n)}withFaceExpressions(){return new At(this,this.input)}withAgeAndGender(){return new kt(this,this.input)}withFaceDescriptor(){return new Tt(this,this.input)}};var Ke=class extends J{constructor(e,r=new X){super();this.input=e;this.options=r}},Ie=class extends Ke{async run(){let{input:t,options:e}=this,r;if(e instanceof je)r=F.tinyFaceDetector.locateFaces(t,e);else if(e instanceof X)r=F.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof st)r=F.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise((t,e)=>{this.run().then(r=>t(r.map(n=>Vt({},n)))).catch(r=>e(r))})}withFaceLandmarks(t=!1){return new qe(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new oe(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new ae(this.runAndExtendWithFaceDetections(),this.input)}},Qe=class extends Ke{async run(){let t=await new Ie(this.input,this.options),e=t[0];return t.forEach(r=>{r.score>e.score&&(e=r)}),e}runAndExtendWithFaceDetection(){return new Promise(async t=>{let e=await this.run();t(e?Vt({},e):void 0)})}withFaceLandmarks(t=!1){return new Ze(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new ne(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new se(this.runAndExtendWithFaceDetection(),this.input)}};function Na(o,t=new X){return new Qe(o,t)}function Ar(o,t=new X){return new Ie(o,t)}async function fn(o,t){return Ar(o,new X(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function La(o,t={}){return Ar(o,new st(t)).withFaceLandmarks().withFaceDescriptors()}var Sa=fn;function xo(o,t){if(o.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let e=Array.from(o),r=Array.from(t);return Math.sqrt(e.map((n,s)=>n-r[s]).reduce((n,s)=>n+s*s,0))}var vo=class o{constructor(t,e=.6){this._distanceThreshold=e;let r=Array.isArray(t)?t:[t];if(!r.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let n=1,s=()=>`person ${n++}`;this._labeledDescriptors=r.map(a=>{if(a instanceof Et)return a;if(a instanceof Float32Array)return new Et(s(),[a]);if(a.descriptor&&a.descriptor instanceof Float32Array)return new Et(s(),[a.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>xo(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new me(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distancet.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>Et.fromJSON(r));return new o(e,t.distanceThreshold)}};function Aa(o){let t=new re;return t.extractWeights(o),t}function ln(o,t){let{width:e,height:r}=new R(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(n=>ln(n,{width:e,height:r}));if(qt(o)){let n=o.detection.forSize(e,r),s=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return Pe(Vt(o,n),s)}return pt(o)?Vt(o,o.detection.forSize(e,r)):o instanceof H||o instanceof M?o.forSize(e,r):o}var 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c=e(`${i}/sub`,1),m=e(`${i}/truediv`,1);return{sub:c,truediv:m}}function n(i){let c=e(`${i}/filters`,4),m=e(`${i}/bias`,1);return{filters:c,bias:m}}function s(i){let c=n(`${i}/conv`),m=r(`${i}/bn`);return{conv:c,bn:m}}let a=ge(e);return{extractConvParams:n,extractConvWithBatchNormParams:s,extractSeparableConvParams:a}}function nn(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:s}=la(o,e),a;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;a={conv0:t.isFirstLayerConv2d?r("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:i>7?s("conv6"):void 0,conv7:i>8?s("conv7"):void 0,conv8:r("conv8")}}else a={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return W(o,e),{params:a,paramMappings:e}}var st=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var Nr=class Nr extends A{constructor(t){super("TinyYolov2"),ho(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,e){let r=vt(t,e.conv0);return r=C.maxPool(r,[2,2],[2,2],"same"),r=vt(r,e.conv1),r=C.maxPool(r,[2,2],[2,2],"same"),r=vt(r,e.conv2),r=C.maxPool(r,[2,2],[2,2],"same"),r=vt(r,e.conv3),r=C.maxPool(r,[2,2],[2,2],"same"),r=vt(r,e.conv4),r=C.maxPool(r,[2,2],[2,2],"same"),r=vt(r,e.conv5),r=C.maxPool(r,[2,2],[1,1],"same"),r=vt(r,e.conv6),r=vt(r,e.conv7),Jt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?Ee(Jt(t,e.conv0,"valid",!1)):yt(t,e.conv0);return r=C.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv1),r=C.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv2),r=C.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv3),r=C.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv4),r=C.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv5),r=C.maxPool(r,[2,2],[1,1],"same"),r=e.conv6?yt(r,e.conv6):r,r=e.conv7?yt(r,e.conv7):r,Jt(r,e.conv8,"valid",!1)}forwardInput(t,e){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return C.tidy(()=>{let n=C.cast(t.toBatchTensor(e,!1),"float32");return n=this.config.meanRgb?rt(n,this.config.meanRgb):n,n=n.div(255),this.config.withSeparableConvs?this.runMobilenet(n,r):this.runTinyYolov2(n,r)})}async forward(t,e){return this.forwardInput(await D(t),e)}async detect(t,e={}){let{inputSize:r,scoreThreshold:n}=new st(e),s=await D(t),a=await this.forwardInput(s,r),i=C.tidy(()=>C.unstack(a)[0].expandDims()),c={width:s.getInputWidth(0),height:s.getInputHeight(0)},m=await this.extractBoxes(i,s.getReshapedInputDimensions(0),n);a.dispose(),i.dispose();let p=m.map(h=>h.box),u=m.map(h=>h.score),f=m.map(h=>h.classScore),l=m.map(h=>this.config.classes[h.label]);return Vr(p.map(h=>h.rescale(r)),u,this.config.iouThreshold,!0).map(h=>new Ht(u[h],f[h],l[h],p[h],c))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return nn(t,this.config)}extractParams(t){let e=this.config.filterSizes||Nr.DEFAULT_FILTER_SIZES,r=e?e.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return on(t,this.config,this.boxEncodingSize,e)}async extractBoxes(t,e,r){let{width:n,height:s}=e,a=Math.max(n,s),i=a/n,c=a/s,m=t.shape[1],p=this.config.anchors.length,[u,f,l]=C.tidy(()=>{let g=t.reshape([m,m,p,this.boxEncodingSize]),P=g.slice([0,0,0,0],[m,m,p,4]),y=g.slice([0,0,0,4],[m,m,p,1]),I=this.withClassScores?C.softmax(g.slice([0,0,0,5],[m,m,p,this.config.classes.length]),3):C.scalar(0);return[P,y,I]}),b=[],T=await f.array(),h=await u.array();for(let g=0;gr){let j=(P+Ne(h[g][P][y][0]))/m*i,tt=(g+Ne(h[g][P][y][1]))/m*c,it=Math.exp(h[g][P][y][2])*this.config.anchors[y].x/m*i,q=Math.exp(h[g][P][y][3])*this.config.anchors[y].y/m*c,Pt=j-it/2,wt=tt-q/2,Ft={row:g,col:P,anchor:y},{classScore:ie,label:yo}=this.withClassScores?await this.extractPredictedClass(l,Ft):{classScore:1,label:0};b.push({box:new Ot(Pt,wt,Pt+it,wt+q),score:I,classScore:I*ie,label:yo,...Ft})}}return u.dispose(),f.dispose(),l.dispose(),b}async extractPredictedClass(t,e){let{row:r,col:n,anchor:s}=e,a=await t.array();return Array(this.config.classes.length).fill(0).map((i,c)=>a[r][n][s][c]).map((i,c)=>({classScore:i,label:c})).reduce((i,c)=>i.classScore>c.classScore?i:c)}};Nr.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Me=Nr;var te=class extends Me{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:Zo,classes:["face"],...t?{anchors:Qo,meanRgb:tn}:{anchors:Ko,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?rn:en}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function da(o,t=!0){let e=new te(t);return e.extractWeights(o),e}var je=class extends st{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var J=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var Xe=v(x());var go=v(x());async function ee(o,t,e,r,n=({alignedRect:s})=>s){let s=o.map(c=>qt(c)?n(c):c.detection),a=r||(t instanceof go.Tensor?await le(t,s):await fe(t,s)),i=await e(a);return a.forEach(c=>c instanceof go.Tensor&&c.dispose()),i}async function Ce(o,t,e,r,n){return ee([o],t,async s=>e(s[0]),r,n)}var an=.4,sn=[new _(1.603231,2.094468),new _(6.041143,7.080126),new _(2.882459,3.518061),new _(4.266906,5.178857),new _(9.041765,10.66308)],cn=[117.001,114.697,97.404];var re=class extends Me{constructor(){let t={withSeparableConvs:!0,iouThreshold:an,classes:["face"],anchors:sn,meanRgb:cn,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var F={ssdMobilenetv1:new It,tinyFaceDetector:new re,tinyYolov2:new te,faceLandmark68Net:new Zt,faceLandmark68TinyNet:new ze,faceRecognitionNet:new Kt,faceExpressionNet:new Oe,ageGenderNet:new He},mn=(o,t)=>F.ssdMobilenetv1.locateFaces(o,t),ha=(o,t)=>F.tinyFaceDetector.locateFaces(o,t),ba=(o,t)=>F.tinyYolov2.locateFaces(o,t),pn=o=>F.faceLandmark68Net.detectLandmarks(o),ga=o=>F.faceLandmark68TinyNet.detectLandmarks(o),xa=o=>F.faceRecognitionNet.computeFaceDescriptor(o),va=o=>F.faceExpressionNet.predictExpressions(o),ya=o=>F.ageGenderNet.predictAgeAndGender(o),un=o=>F.ssdMobilenetv1.load(o),_a=o=>F.tinyFaceDetector.load(o),Ta=o=>F.tinyYolov2.load(o),Pa=o=>F.faceLandmark68Net.load(o),wa=o=>F.faceLandmark68TinyNet.load(o),Fa=o=>F.faceRecognitionNet.load(o),Da=o=>F.faceExpressionNet.load(o),Ea=o=>F.ageGenderNet.load(o),Ma=un,Ca=mn,Ia=pn;var Lr=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},oe=class extends Lr{async run(){let t=await this.parentTask,e=await ee(t,this.input,async r=>Promise.all(r.map(n=>F.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>vr(r,e[n]))}withAgeAndGender(){return new ae(this,this.input)}},ne=class extends Lr{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>F.faceExpressionNet.predictExpressions(r),this.extractedFaces);return vr(t,e)}withAgeAndGender(){return new se(this,this.input)}},St=class extends oe{withAgeAndGender(){return new Wt(this,this.input)}withFaceDescriptors(){return new _t(this,this.input)}},At=class extends ne{withAgeAndGender(){return new kt(this,this.input)}withFaceDescriptor(){return new Tt(this,this.input)}};var Sr=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ae=class extends Sr{async run(){let t=await this.parentTask,e=await ee(t,this.input,async r=>Promise.all(r.map(n=>F.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:s,gender:a,genderProbability:i}=e[n];return Mr(Cr(r,a,i),s)})}withFaceExpressions(){return new oe(this,this.input)}},se=class extends Sr{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await Ce(t,this.input,s=>F.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return Mr(Cr(t,r,n),e)}withFaceExpressions(){return new ne(this,this.input)}},Wt=class extends ae{withFaceExpressions(){return new St(this,this.input)}withFaceDescriptors(){return new _t(this,this.input)}},kt=class extends se{withFaceExpressions(){return new At(this,this.input)}withFaceDescriptor(){return new Tt(this,this.input)}};var Ue=class extends J{constructor(e,r){super();this.parentTask=e;this.input=r}},_t=class extends Ue{async run(){let t=await this.parentTask;return(await ee(t,this.input,r=>Promise.all(r.map(n=>F.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>Er(t[n],r))}withFaceExpressions(){return new St(this,this.input)}withAgeAndGender(){return new Wt(this,this.input)}},Tt=class extends Ue{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>F.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return Er(t,e)}withFaceExpressions(){return new At(this,this.input)}withAgeAndGender(){return new kt(this,this.input)}};var Je=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.useTinyLandmarkNet=n}get landmarkNet(){return this.useTinyLandmarkNet?F.faceLandmark68TinyNet:F.faceLandmark68Net}},qe=class extends Je{async run(){let t=await this.parentTask,e=t.map(a=>a.detection),r=this.input instanceof Xe.Tensor?await le(this.input,e):await fe(this.input,e),n=await Promise.all(r.map(a=>this.landmarkNet.detectLandmarks(a)));return r.forEach(a=>a instanceof Xe.Tensor&&a.dispose()),t.filter((a,i)=>n[i]).map((a,i)=>Pe(a,n[i]))}withFaceExpressions(){return new St(this,this.input)}withAgeAndGender(){return new Wt(this,this.input)}withFaceDescriptors(){return new _t(this,this.input)}},Ze=class extends Je{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof Xe.Tensor?await le(this.input,[e]):await fe(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(s=>s instanceof Xe.Tensor&&s.dispose()),Pe(t,n)}withFaceExpressions(){return new At(this,this.input)}withAgeAndGender(){return new kt(this,this.input)}withFaceDescriptor(){return new Tt(this,this.input)}};var Ke=class extends J{constructor(e,r=new X){super();this.input=e;this.options=r}},Ie=class extends Ke{async run(){let{input:t,options:e}=this,r;if(e instanceof je)r=F.tinyFaceDetector.locateFaces(t,e);else if(e instanceof X)r=F.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof st)r=F.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise((t,e)=>{this.run().then(r=>t(r.map(n=>Vt({},n)))).catch(r=>e(r))})}withFaceLandmarks(t=!1){return new qe(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new oe(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new ae(this.runAndExtendWithFaceDetections(),this.input)}},Qe=class extends Ke{async run(){let t=await new Ie(this.input,this.options),e=t[0];return t.forEach(r=>{r.score>e.score&&(e=r)}),e}runAndExtendWithFaceDetection(){return new Promise(async t=>{let e=await this.run();t(e?Vt({},e):void 0)})}withFaceLandmarks(t=!1){return new Ze(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new ne(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new se(this.runAndExtendWithFaceDetection(),this.input)}};function Na(o,t=new X){return new Qe(o,t)}function Ar(o,t=new X){return new Ie(o,t)}async function fn(o,t){return Ar(o,new X(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function La(o,t={}){return Ar(o,new st(t)).withFaceLandmarks().withFaceDescriptors()}var Sa=fn;function xo(o,t){if(o.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let e=Array.from(o),r=Array.from(t);return Math.sqrt(e.map((n,s)=>n-r[s]).reduce((n,s)=>n+s*s,0))}var vo=class o{constructor(t,e=.6){this._distanceThreshold=e;let r=Array.isArray(t)?t:[t];if(!r.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let n=1,s=()=>`person ${n++}`;this._labeledDescriptors=r.map(a=>{if(a instanceof Et)return a;if(a instanceof Float32Array)return new Et(s(),[a]);if(a.descriptor&&a.descriptor instanceof Float32Array)return new Et(s(),[a.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>xo(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new me(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distancet.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>Et.fromJSON(r));return new o(e,t.distanceThreshold)}};function Aa(o){let t=new re;return t.extractWeights(o),t}function ln(o,t){let{width:e,height:r}=new R(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(n=>ln(n,{width:e,height:r}));if(qt(o)){let n=o.detection.forSize(e,r),s=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return Pe(Vt(o,n),s)}return pt(o)?Vt(o,o.detection.forSize(e,r)):o instanceof H||o instanceof M?o.forSize(e,r):o}var ka=Lo;0&&(module.exports={AgeGenderNet,BoundingBox,Box,ComposableTask,ComputeAllFaceDescriptorsTask,ComputeFaceDescriptorsTaskBase,ComputeSingleFaceDescriptorTask,DetectAllFaceLandmarksTask,DetectAllFacesTask,DetectFaceLandmarksTaskBase,DetectFacesTaskBase,DetectSingleFaceLandmarksTask,DetectSingleFaceTask,Dimensions,FACE_EXPRESSION_LABELS,FaceDetection,FaceDetectionNet,FaceExpressionNet,FaceExpressions,FaceLandmark68Net,FaceLandmark68TinyNet,FaceLandmarkNet,FaceLandmarks,FaceLandmarks5,FaceLandmarks68,FaceMatch,FaceMatcher,FaceRecognitionNet,Gender,LabeledBox,LabeledFaceDescriptors,NetInput,NeuralNetwork,ObjectDetection,Point,PredictedBox,Rect,SsdMobilenetv1,SsdMobilenetv1Options,TinyFaceDetector,TinyFaceDetectorOptions,TinyYolov2,TinyYolov2Options,allFaces,allFacesSsdMobilenetv1,allFacesTinyYolov2,awaitMediaLoaded,bufferToImage,computeFaceDescriptor,createCanvas,createCanvasFromMedia,createFaceDetectionNet,createFaceRecognitionNet,createSsdMobilenetv1,createTinyFaceDetector,createTinyYolov2,detectAllFaces,detectFaceLandmarks,detectFaceLandmarksTiny,detectLandmarks,detectSingleFace,draw,env,euclideanDistance,extendWithAge,extendWithFaceDescriptor,extendWithFaceDetection,extendWithFaceExpressions,extendWithFaceLandmarks,extendWithGender,extractFaceTensors,extractFaces,fetchImage,fetchJson,fetchNetWeights,fetchOrThrow,fetchVideo,getContext2dOrThrow,getMediaDimensions,imageTensorToCanvas,imageToSquare,inverseSigmoid,iou,isMediaElement,isMediaLoaded,isWithAge,isWithFaceDetection,isWithFaceExpressions,isWithFaceLandmarks,isWithGender,loadAgeGenderModel,loadFaceDetectionModel,loadFaceExpressionModel,loadFaceLandmarkModel,loadFaceLandmarkTinyModel,loadFaceRecognitionModel,loadSsdMobilenetv1Model,loadTinyFaceDetectorModel,loadTinyYolov2Model,loadWeightMap,locateFaces,matchDimensions,minBbox,nets,nonMaxSuppression,normalize,padToSquare,predictAgeAndGender,recognizeFaceExpressions,resizeResults,resolveInput,shuffleArray,sigmoid,ssdMobilenetv1,tf,tinyFaceDetector,tinyYolov2,toNetInput,utils,validateConfig,version}); diff --git a/dist/face-api.node.js b/dist/face-api.node.js index 52b0525..8589ca5 100644 --- a/dist/face-api.node.js +++ b/dist/face-api.node.js @@ -4,4 +4,4 @@ author: ' */ -"use strict";var ln=Object.create;var tr=Object.defineProperty;var dn=Object.getOwnPropertyDescriptor;var hn=Object.getOwnPropertyNames;var bn=Object.getPrototypeOf,gn=Object.prototype.hasOwnProperty;var xn=(o,t)=>()=>(t||o((t={exports:{}}).exports,t),t.exports),Sr=(o,t)=>{for(var e in t)tr(o,e,{get:t[e],enumerable:!0})},_o=(o,t,e,r)=>{if(t&&typeof t=="object"||typeof t=="function")for(let n of hn(t))!gn.call(o,n)&&n!==e&&tr(o,n,{get:()=>t[n],enumerable:!(r=dn(t,n))||r.enumerable});return o};var v=(o,t,e)=>(e=o!=null?ln(bn(o)):{},_o(t||!o||!o.__esModule?tr(e,"default",{value:o,enumerable:!0}):e,o)),vn=o=>_o(tr({},"__esModule",{value:!0}),o);var x=xn((Ya,Br)=>{"use strict";var 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st=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var Cr=class Cr extends A{constructor(t){super("TinyYolov2"),ho(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,e){let r=vt(t,e.conv0);return 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n=this.config.meanRgb?rt(n,this.config.meanRgb):n,n=n.div(255),this.config.withSeparableConvs?this.runMobilenet(n,r):this.runTinyYolov2(n,r)})}async forward(t,e){return this.forwardInput(await D(t),e)}async detect(t,e={}){let{inputSize:r,scoreThreshold:n}=new st(e),s=await D(t),a=await this.forwardInput(s,r),i=C.tidy(()=>C.unstack(a)[0].expandDims()),c={width:s.getInputWidth(0),height:s.getInputHeight(0)},m=await this.extractBoxes(i,s.getReshapedInputDimensions(0),n);a.dispose(),i.dispose();let p=m.map(h=>h.box),u=m.map(h=>h.score),f=m.map(h=>h.classScore),l=m.map(h=>this.config.classes[h.label]);return Vr(p.map(h=>h.rescale(r)),u,this.config.iouThreshold,!0).map(h=>new Ht(u[h],f[h],l[h],p[h],c))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return on(t,this.config)}extractParams(t){let e=this.config.filterSizes||Cr.DEFAULT_FILTER_SIZES,r=e?e.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return rn(t,this.config,this.boxEncodingSize,e)}async extractBoxes(t,e,r){let{width:n,height:s}=e,a=Math.max(n,s),i=a/n,c=a/s,m=t.shape[1],p=this.config.anchors.length,[u,f,l]=C.tidy(()=>{let g=t.reshape([m,m,p,this.boxEncodingSize]),P=g.slice([0,0,0,0],[m,m,p,4]),y=g.slice([0,0,0,4],[m,m,p,1]),I=this.withClassScores?C.softmax(g.slice([0,0,0,5],[m,m,p,this.config.classes.length]),3):C.scalar(0);return[P,y,I]}),b=[],T=await f.array(),h=await u.array();for(let g=0;gr){let j=(P+Ne(h[g][P][y][0]))/m*i,tt=(g+Ne(h[g][P][y][1]))/m*c,it=Math.exp(h[g][P][y][2])*this.config.anchors[y].x/m*i,q=Math.exp(h[g][P][y][3])*this.config.anchors[y].y/m*c,Pt=j-it/2,wt=tt-q/2,Ft={row:g,col:P,anchor:y},{classScore:ie,label:yo}=this.withClassScores?await this.extractPredictedClass(l,Ft):{classScore:1,label:0};b.push({box:new Ot(Pt,wt,Pt+it,wt+q),score:I,classScore:I*ie,label:yo,...Ft})}}return u.dispose(),f.dispose(),l.dispose(),b}async extractPredictedClass(t,e){let{row:r,col:n,anchor:s}=e,a=await t.array();return Array(this.config.classes.length).fill(0).map((i,c)=>a[r][n][s][c]).map((i,c)=>({classScore:i,label:c})).reduce((i,c)=>i.classScore>c.classScore?i:c)}};Cr.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Me=Cr;var te=class extends Me{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:qo,classes:["face"],...t?{anchors:Ko,meanRgb:Qo}:{anchors:Zo,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?en:tn}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function da(o,t=!0){let e=new te(t);return e.extractWeights(o),e}var je=class extends st{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var J=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var Xe=v(x());var go=v(x());async function ee(o,t,e,r,n=({alignedRect:s})=>s){let s=o.map(c=>qt(c)?n(c):c.detection),a=r||(t instanceof go.Tensor?await le(t,s):await fe(t,s)),i=await e(a);return a.forEach(c=>c instanceof go.Tensor&&c.dispose()),i}async function Ce(o,t,e,r,n){return ee([o],t,async s=>e(s[0]),r,n)}var nn=.4,an=[new _(1.603231,2.094468),new _(6.041143,7.080126),new _(2.882459,3.518061),new _(4.266906,5.178857),new _(9.041765,10.66308)],sn=[117.001,114.697,97.404];var re=class extends Me{constructor(){let t={withSeparableConvs:!0,iouThreshold:nn,classes:["face"],anchors:an,meanRgb:sn,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var F={ssdMobilenetv1:new It,tinyFaceDetector:new re,tinyYolov2:new te,faceLandmark68Net:new Zt,faceLandmark68TinyNet:new ze,faceRecognitionNet:new Kt,faceExpressionNet:new Oe,ageGenderNet:new He},cn=(o,t)=>F.ssdMobilenetv1.locateFaces(o,t),ha=(o,t)=>F.tinyFaceDetector.locateFaces(o,t),ba=(o,t)=>F.tinyYolov2.locateFaces(o,t),mn=o=>F.faceLandmark68Net.detectLandmarks(o),ga=o=>F.faceLandmark68TinyNet.detectLandmarks(o),xa=o=>F.faceRecognitionNet.computeFaceDescriptor(o),va=o=>F.faceExpressionNet.predictExpressions(o),ya=o=>F.ageGenderNet.predictAgeAndGender(o),pn=o=>F.ssdMobilenetv1.load(o),_a=o=>F.tinyFaceDetector.load(o),Ta=o=>F.tinyYolov2.load(o),Pa=o=>F.faceLandmark68Net.load(o),wa=o=>F.faceLandmark68TinyNet.load(o),Fa=o=>F.faceRecognitionNet.load(o),Da=o=>F.faceExpressionNet.load(o),Ea=o=>F.ageGenderNet.load(o),Ma=pn,Ca=cn,Ia=mn;var Ir=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},oe=class extends Ir{async run(){let t=await this.parentTask,e=await ee(t,this.input,async r=>Promise.all(r.map(n=>F.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>gr(r,e[n]))}withAgeAndGender(){return new ae(this,this.input)}},ne=class extends Ir{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>F.faceExpressionNet.predictExpressions(r),this.extractedFaces);return gr(t,e)}withAgeAndGender(){return new se(this,this.input)}},St=class extends oe{withAgeAndGender(){return new Wt(this,this.input)}withFaceDescriptors(){return new _t(this,this.input)}},At=class extends ne{withAgeAndGender(){return new kt(this,this.input)}withFaceDescriptor(){return new Tt(this,this.input)}};var Nr=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ae=class extends Nr{async run(){let t=await this.parentTask,e=await ee(t,this.input,async r=>Promise.all(r.map(n=>F.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:s,gender:a,genderProbability:i}=e[n];return Dr(Er(r,a,i),s)})}withFaceExpressions(){return new oe(this,this.input)}},se=class extends Nr{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await Ce(t,this.input,s=>F.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return Dr(Er(t,r,n),e)}withFaceExpressions(){return new ne(this,this.input)}},Wt=class extends ae{withFaceExpressions(){return new St(this,this.input)}withFaceDescriptors(){return new _t(this,this.input)}},kt=class extends se{withFaceExpressions(){return new At(this,this.input)}withFaceDescriptor(){return new Tt(this,this.input)}};var Ue=class extends J{constructor(e,r){super();this.parentTask=e;this.input=r}},_t=class extends Ue{async run(){let t=await this.parentTask;return(await ee(t,this.input,r=>Promise.all(r.map(n=>F.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>Fr(t[n],r))}withFaceExpressions(){return new St(this,this.input)}withAgeAndGender(){return new Wt(this,this.input)}},Tt=class extends Ue{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>F.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return Fr(t,e)}withFaceExpressions(){return new At(this,this.input)}withAgeAndGender(){return new kt(this,this.input)}};var Je=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.useTinyLandmarkNet=n}get landmarkNet(){return this.useTinyLandmarkNet?F.faceLandmark68TinyNet:F.faceLandmark68Net}},qe=class extends Je{async run(){let t=await this.parentTask,e=t.map(a=>a.detection),r=this.input instanceof Xe.Tensor?await le(this.input,e):await fe(this.input,e),n=await Promise.all(r.map(a=>this.landmarkNet.detectLandmarks(a)));return r.forEach(a=>a instanceof Xe.Tensor&&a.dispose()),t.filter((a,i)=>n[i]).map((a,i)=>Pe(a,n[i]))}withFaceExpressions(){return new St(this,this.input)}withAgeAndGender(){return new Wt(this,this.input)}withFaceDescriptors(){return new _t(this,this.input)}},Ze=class extends Je{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof Xe.Tensor?await le(this.input,[e]):await fe(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(s=>s instanceof Xe.Tensor&&s.dispose()),Pe(t,n)}withFaceExpressions(){return new At(this,this.input)}withAgeAndGender(){return new kt(this,this.input)}withFaceDescriptor(){return new Tt(this,this.input)}};var Ke=class extends J{constructor(e,r=new X){super();this.input=e;this.options=r}},Ie=class extends Ke{async run(){let{input:t,options:e}=this,r;if(e instanceof je)r=F.tinyFaceDetector.locateFaces(t,e);else if(e instanceof X)r=F.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof st)r=F.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise((t,e)=>{this.run().then(r=>t(r.map(n=>Vt({},n)))).catch(r=>e(r))})}withFaceLandmarks(t=!1){return new qe(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new oe(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new ae(this.runAndExtendWithFaceDetections(),this.input)}},Qe=class extends Ke{async run(){let t=await new Ie(this.input,this.options),e=t[0];return t.forEach(r=>{r.score>e.score&&(e=r)}),e}runAndExtendWithFaceDetection(){return new Promise(async t=>{let e=await this.run();t(e?Vt({},e):void 0)})}withFaceLandmarks(t=!1){return new Ze(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new ne(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new se(this.runAndExtendWithFaceDetection(),this.input)}};function Na(o,t=new X){return new Qe(o,t)}function Lr(o,t=new X){return new Ie(o,t)}async function un(o,t){return Lr(o,new X(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function La(o,t={}){return Lr(o,new st(t)).withFaceLandmarks().withFaceDescriptors()}var Sa=un;function xo(o,t){if(o.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let e=Array.from(o),r=Array.from(t);return Math.sqrt(e.map((n,s)=>n-r[s]).reduce((n,s)=>n+s*s,0))}var vo=class o{constructor(t,e=.6){this._distanceThreshold=e;let r=Array.isArray(t)?t:[t];if(!r.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let n=1,s=()=>`person ${n++}`;this._labeledDescriptors=r.map(a=>{if(a instanceof Et)return a;if(a instanceof Float32Array)return new Et(s(),[a]);if(a.descriptor&&a.descriptor instanceof Float32Array)return new Et(s(),[a.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>xo(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new me(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distancet.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>Et.fromJSON(r));return new o(e,t.distanceThreshold)}};function Aa(o){let t=new re;return t.extractWeights(o),t}function fn(o,t){let{width:e,height:r}=new R(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(n=>fn(n,{width:e,height:r}));if(qt(o)){let n=o.detection.forSize(e,r),s=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return Pe(Vt(o,n),s)}return pt(o)?Vt(o,o.detection.forSize(e,r)):o instanceof H||o instanceof M?o.forSize(e,r):o}var 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st=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var Cr=class Cr extends A{constructor(t){super("TinyYolov2"),ho(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,e){let r=vt(t,e.conv0);return 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n=this.config.meanRgb?rt(n,this.config.meanRgb):n,n=n.div(255),this.config.withSeparableConvs?this.runMobilenet(n,r):this.runTinyYolov2(n,r)})}async forward(t,e){return this.forwardInput(await D(t),e)}async detect(t,e={}){let{inputSize:r,scoreThreshold:n}=new st(e),s=await D(t),a=await this.forwardInput(s,r),i=C.tidy(()=>C.unstack(a)[0].expandDims()),c={width:s.getInputWidth(0),height:s.getInputHeight(0)},m=await this.extractBoxes(i,s.getReshapedInputDimensions(0),n);a.dispose(),i.dispose();let p=m.map(h=>h.box),u=m.map(h=>h.score),f=m.map(h=>h.classScore),l=m.map(h=>this.config.classes[h.label]);return Vr(p.map(h=>h.rescale(r)),u,this.config.iouThreshold,!0).map(h=>new Ht(u[h],f[h],l[h],p[h],c))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return on(t,this.config)}extractParams(t){let e=this.config.filterSizes||Cr.DEFAULT_FILTER_SIZES,r=e?e.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return rn(t,this.config,this.boxEncodingSize,e)}async extractBoxes(t,e,r){let{width:n,height:s}=e,a=Math.max(n,s),i=a/n,c=a/s,m=t.shape[1],p=this.config.anchors.length,[u,f,l]=C.tidy(()=>{let g=t.reshape([m,m,p,this.boxEncodingSize]),P=g.slice([0,0,0,0],[m,m,p,4]),y=g.slice([0,0,0,4],[m,m,p,1]),I=this.withClassScores?C.softmax(g.slice([0,0,0,5],[m,m,p,this.config.classes.length]),3):C.scalar(0);return[P,y,I]}),b=[],T=await f.array(),h=await u.array();for(let g=0;gr){let j=(P+Ne(h[g][P][y][0]))/m*i,tt=(g+Ne(h[g][P][y][1]))/m*c,it=Math.exp(h[g][P][y][2])*this.config.anchors[y].x/m*i,q=Math.exp(h[g][P][y][3])*this.config.anchors[y].y/m*c,Pt=j-it/2,wt=tt-q/2,Ft={row:g,col:P,anchor:y},{classScore:ie,label:yo}=this.withClassScores?await this.extractPredictedClass(l,Ft):{classScore:1,label:0};b.push({box:new Ot(Pt,wt,Pt+it,wt+q),score:I,classScore:I*ie,label:yo,...Ft})}}return u.dispose(),f.dispose(),l.dispose(),b}async extractPredictedClass(t,e){let{row:r,col:n,anchor:s}=e,a=await t.array();return Array(this.config.classes.length).fill(0).map((i,c)=>a[r][n][s][c]).map((i,c)=>({classScore:i,label:c})).reduce((i,c)=>i.classScore>c.classScore?i:c)}};Cr.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Me=Cr;var te=class extends Me{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:qo,classes:["face"],...t?{anchors:Ko,meanRgb:Qo}:{anchors:Zo,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?en:tn}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function da(o,t=!0){let e=new te(t);return e.extractWeights(o),e}var je=class extends st{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var J=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var Xe=v(x());var go=v(x());async function ee(o,t,e,r,n=({alignedRect:s})=>s){let s=o.map(c=>qt(c)?n(c):c.detection),a=r||(t instanceof go.Tensor?await le(t,s):await fe(t,s)),i=await e(a);return a.forEach(c=>c instanceof go.Tensor&&c.dispose()),i}async function Ce(o,t,e,r,n){return ee([o],t,async s=>e(s[0]),r,n)}var nn=.4,an=[new _(1.603231,2.094468),new _(6.041143,7.080126),new _(2.882459,3.518061),new _(4.266906,5.178857),new _(9.041765,10.66308)],sn=[117.001,114.697,97.404];var re=class extends Me{constructor(){let t={withSeparableConvs:!0,iouThreshold:nn,classes:["face"],anchors:an,meanRgb:sn,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var F={ssdMobilenetv1:new It,tinyFaceDetector:new re,tinyYolov2:new te,faceLandmark68Net:new Zt,faceLandmark68TinyNet:new ze,faceRecognitionNet:new Kt,faceExpressionNet:new Oe,ageGenderNet:new He},cn=(o,t)=>F.ssdMobilenetv1.locateFaces(o,t),ha=(o,t)=>F.tinyFaceDetector.locateFaces(o,t),ba=(o,t)=>F.tinyYolov2.locateFaces(o,t),mn=o=>F.faceLandmark68Net.detectLandmarks(o),ga=o=>F.faceLandmark68TinyNet.detectLandmarks(o),xa=o=>F.faceRecognitionNet.computeFaceDescriptor(o),va=o=>F.faceExpressionNet.predictExpressions(o),ya=o=>F.ageGenderNet.predictAgeAndGender(o),pn=o=>F.ssdMobilenetv1.load(o),_a=o=>F.tinyFaceDetector.load(o),Ta=o=>F.tinyYolov2.load(o),Pa=o=>F.faceLandmark68Net.load(o),wa=o=>F.faceLandmark68TinyNet.load(o),Fa=o=>F.faceRecognitionNet.load(o),Da=o=>F.faceExpressionNet.load(o),Ea=o=>F.ageGenderNet.load(o),Ma=pn,Ca=cn,Ia=mn;var Ir=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},oe=class extends Ir{async run(){let t=await this.parentTask,e=await ee(t,this.input,async r=>Promise.all(r.map(n=>F.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>gr(r,e[n]))}withAgeAndGender(){return new ae(this,this.input)}},ne=class extends Ir{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>F.faceExpressionNet.predictExpressions(r),this.extractedFaces);return gr(t,e)}withAgeAndGender(){return new se(this,this.input)}},St=class extends oe{withAgeAndGender(){return new Wt(this,this.input)}withFaceDescriptors(){return new _t(this,this.input)}},At=class extends ne{withAgeAndGender(){return new kt(this,this.input)}withFaceDescriptor(){return new Tt(this,this.input)}};var Nr=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ae=class extends Nr{async run(){let t=await this.parentTask,e=await ee(t,this.input,async r=>Promise.all(r.map(n=>F.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:s,gender:a,genderProbability:i}=e[n];return Dr(Er(r,a,i),s)})}withFaceExpressions(){return new oe(this,this.input)}},se=class extends Nr{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await Ce(t,this.input,s=>F.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return Dr(Er(t,r,n),e)}withFaceExpressions(){return new ne(this,this.input)}},Wt=class extends ae{withFaceExpressions(){return new St(this,this.input)}withFaceDescriptors(){return new _t(this,this.input)}},kt=class extends se{withFaceExpressions(){return new At(this,this.input)}withFaceDescriptor(){return new Tt(this,this.input)}};var Ue=class extends J{constructor(e,r){super();this.parentTask=e;this.input=r}},_t=class extends Ue{async run(){let t=await this.parentTask;return(await ee(t,this.input,r=>Promise.all(r.map(n=>F.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>Fr(t[n],r))}withFaceExpressions(){return new St(this,this.input)}withAgeAndGender(){return new Wt(this,this.input)}},Tt=class extends Ue{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>F.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return Fr(t,e)}withFaceExpressions(){return new At(this,this.input)}withAgeAndGender(){return new kt(this,this.input)}};var Je=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.useTinyLandmarkNet=n}get landmarkNet(){return this.useTinyLandmarkNet?F.faceLandmark68TinyNet:F.faceLandmark68Net}},qe=class extends Je{async run(){let t=await this.parentTask,e=t.map(a=>a.detection),r=this.input instanceof Xe.Tensor?await le(this.input,e):await fe(this.input,e),n=await Promise.all(r.map(a=>this.landmarkNet.detectLandmarks(a)));return r.forEach(a=>a instanceof Xe.Tensor&&a.dispose()),t.filter((a,i)=>n[i]).map((a,i)=>Pe(a,n[i]))}withFaceExpressions(){return new St(this,this.input)}withAgeAndGender(){return new Wt(this,this.input)}withFaceDescriptors(){return new _t(this,this.input)}},Ze=class extends Je{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof Xe.Tensor?await le(this.input,[e]):await fe(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(s=>s instanceof Xe.Tensor&&s.dispose()),Pe(t,n)}withFaceExpressions(){return new At(this,this.input)}withAgeAndGender(){return new kt(this,this.input)}withFaceDescriptor(){return new Tt(this,this.input)}};var Ke=class extends J{constructor(e,r=new X){super();this.input=e;this.options=r}},Ie=class extends Ke{async run(){let{input:t,options:e}=this,r;if(e instanceof je)r=F.tinyFaceDetector.locateFaces(t,e);else if(e instanceof X)r=F.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof st)r=F.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise((t,e)=>{this.run().then(r=>t(r.map(n=>Vt({},n)))).catch(r=>e(r))})}withFaceLandmarks(t=!1){return new qe(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new oe(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new ae(this.runAndExtendWithFaceDetections(),this.input)}},Qe=class extends Ke{async run(){let t=await new Ie(this.input,this.options),e=t[0];return t.forEach(r=>{r.score>e.score&&(e=r)}),e}runAndExtendWithFaceDetection(){return new Promise(async t=>{let e=await this.run();t(e?Vt({},e):void 0)})}withFaceLandmarks(t=!1){return new Ze(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new ne(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new se(this.runAndExtendWithFaceDetection(),this.input)}};function Na(o,t=new X){return new Qe(o,t)}function Lr(o,t=new X){return new Ie(o,t)}async function un(o,t){return Lr(o,new X(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function La(o,t={}){return Lr(o,new st(t)).withFaceLandmarks().withFaceDescriptors()}var Sa=un;function xo(o,t){if(o.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let e=Array.from(o),r=Array.from(t);return Math.sqrt(e.map((n,s)=>n-r[s]).reduce((n,s)=>n+s*s,0))}var vo=class o{constructor(t,e=.6){this._distanceThreshold=e;let r=Array.isArray(t)?t:[t];if(!r.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let n=1,s=()=>`person ${n++}`;this._labeledDescriptors=r.map(a=>{if(a instanceof Et)return a;if(a instanceof Float32Array)return new Et(s(),[a]);if(a.descriptor&&a.descriptor instanceof Float32Array)return new Et(s(),[a.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>xo(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new me(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distancet.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>Et.fromJSON(r));return new o(e,t.distanceThreshold)}};function Aa(o){let t=new re;return t.extractWeights(o),t}function fn(o,t){let{width:e,height:r}=new R(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(n=>fn(n,{width:e,height:r}));if(qt(o)){let n=o.detection.forSize(e,r),s=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return Pe(Vt(o,n),s)}return pt(o)?Vt(o,o.detection.forSize(e,r)):o instanceof H||o instanceof M?o.forSize(e,r):o}var ka=No;0&&(module.exports={AgeGenderNet,BoundingBox,Box,ComposableTask,ComputeAllFaceDescriptorsTask,ComputeFaceDescriptorsTaskBase,ComputeSingleFaceDescriptorTask,DetectAllFaceLandmarksTask,DetectAllFacesTask,DetectFaceLandmarksTaskBase,DetectFacesTaskBase,DetectSingleFaceLandmarksTask,DetectSingleFaceTask,Dimensions,FACE_EXPRESSION_LABELS,FaceDetection,FaceDetectionNet,FaceExpressionNet,FaceExpressions,FaceLandmark68Net,FaceLandmark68TinyNet,FaceLandmarkNet,FaceLandmarks,FaceLandmarks5,FaceLandmarks68,FaceMatch,FaceMatcher,FaceRecognitionNet,Gender,LabeledBox,LabeledFaceDescriptors,NetInput,NeuralNetwork,ObjectDetection,Point,PredictedBox,Rect,SsdMobilenetv1,SsdMobilenetv1Options,TinyFaceDetector,TinyFaceDetectorOptions,TinyYolov2,TinyYolov2Options,allFaces,allFacesSsdMobilenetv1,allFacesTinyYolov2,awaitMediaLoaded,bufferToImage,computeFaceDescriptor,createCanvas,createCanvasFromMedia,createFaceDetectionNet,createFaceRecognitionNet,createSsdMobilenetv1,createTinyFaceDetector,createTinyYolov2,detectAllFaces,detectFaceLandmarks,detectFaceLandmarksTiny,detectLandmarks,detectSingleFace,draw,env,euclideanDistance,extendWithAge,extendWithFaceDescriptor,extendWithFaceDetection,extendWithFaceExpressions,extendWithFaceLandmarks,extendWithGender,extractFaceTensors,extractFaces,fetchImage,fetchJson,fetchNetWeights,fetchOrThrow,fetchVideo,getContext2dOrThrow,getMediaDimensions,imageTensorToCanvas,imageToSquare,inverseSigmoid,iou,isMediaElement,isMediaLoaded,isWithAge,isWithFaceDetection,isWithFaceExpressions,isWithFaceLandmarks,isWithGender,loadAgeGenderModel,loadFaceDetectionModel,loadFaceExpressionModel,loadFaceLandmarkModel,loadFaceLandmarkTinyModel,loadFaceRecognitionModel,loadSsdMobilenetv1Model,loadTinyFaceDetectorModel,loadTinyYolov2Model,loadWeightMap,locateFaces,matchDimensions,minBbox,nets,nonMaxSuppression,normalize,padToSquare,predictAgeAndGender,recognizeFaceExpressions,resizeResults,resolveInput,shuffleArray,sigmoid,ssdMobilenetv1,tf,tinyFaceDetector,tinyYolov2,toNetInput,utils,validateConfig,version}); 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D0;(function(r){r.float32="float32",r.int32="int32",r.bool="bool",r.complex64="complex64"})(D0||(D0={}));var $0;(function(r){r.float32="float32",r.int32="float32",r.bool="float32",r.complex64="complex64"})($0||($0={}));var R0;(function(r){r.float32="complex64",r.int32="complex64",r.bool="complex64",r.complex64="complex64"})(R0||(R0={}));var CK={float32:$0,int32:A0,bool:D0,complex64:R0};function ur(r,t){if(r==="string"||t==="string"){if(r==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${r} with ${t}`)}return CK[r][t]}function lc(r){return ur(r,"int32")}function ex(r){return r!=null&&typeof r=="object"&&"texture"in r&&r.texture instanceof WebGLTexture}function rx(r){return typeof GPUBuffer!="undefined"&&r!=null&&typeof r=="object"&&"buffer"in r&&r.buffer instanceof GPUBuffer}function Xt(r,t){if(r.dtype===t.dtype)return[r,t];let e=ur(r.dtype,t.dtype);return[r.cast(e),t.cast(e)]}function F0(r,t){_(r.dtype===t.dtype,()=>`The dtypes of the first(${r.dtype}) and 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r{constructor(t){this.ENV=t,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new nx}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let t=this.getSortedBackends();for(let e=0;e{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(t){Xg(t).forEach(n=>{n.disposeFunc!=null&&n.disposeFunc(this.registry[t])})}initializeBackend(t){let e=this.registryFactory[t];if(e==null)throw new Error(`Cannot initialize backend ${t}, no registration found.`);try{let n=e.factory();if(n&&!(n instanceof Bo)&&typeof n.then=="function"){let o=++this.pendingBackendInitId,s=n.then(i=>o(othis.registryFactory[e].priority-this.registryFactory[t].priority)}initializeBackendsAndReturnBest(){let t=this.getSortedBackends();for(let e=0;ethis.startScope(n),()=>this.endScope(o),()=>(o=e(),o instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),o))}scopedRun(t,e,n){t();try{let o=n();return e(),o}catch(o){throw e(),o}}nextTensorId(){return r.nextTensorId++}nextVariableId(){return r.nextVariableId++}clone(t){let e=T.runKernel(go,{x:t}),n={x:t},o=i=>({x:()=>{let a="float32",u={x:i},l={dtype:a};return T.runKernel(fo,u,l)}}),s=[];return this.addTapeNode(this.state.activeScope.name,n,[e],o,s,{}),e}runKernel(t,e,n){if(this.backendName==null&&this.backend,!(Wp(t,this.backendName)!=null))throw new Error(`Kernel '${t}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:t,inputs:e,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(t,e,n){let o=this.backend.numDataIds(),s=0;n.forEach(u=>{s+=u.dtype==="complex64"?3:1});let i=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],a=o-e-s-i;if(a>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${a} data ids) after running '${t}'`)}runKernelFunc(t){let e,n=[],o=this.isTapeOn(),s=this.state.numBytes,i=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let a;this.backendName==null&&this.backend;let u,l=O0(t)?t.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(O0(t)){let{kernelName:d,inputs:h,attrs:g}=t;this.backendName==null&&this.backend;let x=Wp(d,this.backendName);_(x!=null,()=>`Cannot find registered kernel '${d}' for backend '${this.backendName}'`),a=()=>{let b=this.backend.numDataIds();u=x.kernelFunc({inputs:h,attrs:g,backend:this.backend});let w=Array.isArray(u)?u:[u];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(d,b,w);let I=w.map(N=>N.rank!=null?N:this.makeTensorFromTensorInfo(N));if(o){let N=this.getTensorsForGradient(d,h,I);n=this.saveTensorsForBackwardMode(N)}return I}}else{let{forwardFunc:d}=t,h=g=>{o&&(n=g.map(x=>this.keep(this.clone(x))))};a=()=>{let g=this.backend.numDataIds();u=this.tidy(()=>d(this.backend,h));let x=Array.isArray(u)?u:[u];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,g,x),x}}let{inputs:c,attrs:p}=t,m=O0(t)?null:t.backwardsFunc,f;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?e=a():(f=this.profiler.profileKernel(l,c,()=>a()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(f),e=f.outputs)}),o&&this.addTapeNode(l,c,e,m,n,p),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-i,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(d=>c[d]!=null?c[d].shape:null),outputShapes:e.map(d=>d.shape),kernelTimeMs:f.timeMs,extraInfo:f.extraInfo}),Array.isArray(u)?e:e[0]}saveTensorsForBackwardMode(t){return t.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(t,e,n){let o=I0(t);if(o!=null){let s=o.inputsToSave||[],i=o.outputsToSave||[],a;o.saveAllInputs?(_(Array.isArray(e),()=>"saveAllInputs is true, expected inputs to be an array."),a=Object.keys(e).map(l=>e[l])):a=s.map(l=>e[l]);let u=n.filter((l,c)=>i[c]);return a.concat(u)}return[]}makeTensor(t,e,n,o){if(t==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",o=o||this.backend;let s=t;n==="string"&&Vo(t[0])&&(s=t.map(u=>fu(u)));let i=o.write(s,e,n),a=new Lt(e,n,i,this.nextTensorId());if(this.trackTensor(a,o),n==="string"){let u=this.state.tensorInfo.get(i),l=x0(s);this.state.numBytes+=l-u.bytes,u.bytes=l}return a}makeTensorFromDataId(t,e,n,o){n=n||"float32";let s={dataId:t,shape:e,dtype:n};return this.makeTensorFromTensorInfo(s,o)}makeTensorFromTensorInfo(t,e){let{dataId:n,shape:o,dtype:s}=t,i=new Lt(o,s,n,this.nextTensorId());return this.trackTensor(i,e),i}makeVariable(t,e=!0,n,o){n=n||this.nextVariableId().toString(),o!=null&&o!==t.dtype&&(t=t.cast(o));let s=new ml(t,e,n,this.nextTensorId());if(this.state.registeredVariables[s.name]!=null)throw new Error(`Variable with name ${s.name} was already registered`);return this.state.registeredVariables[s.name]=s,this.incRef(s,this.backend),s}trackTensor(t,e){this.state.numTensors++,t.dtype==="string"&&this.state.numStringTensors++;let n=0;t.dtype!=="complex64"&&t.dtype!=="string"&&(n=t.size*Tp(t.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(t.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(t.dataId,{backend:e||this.backend,dtype:t.dtype,shape:t.shape,bytes:n})),t instanceof ml||this.track(t)}incRef(t,e){this.trackTensor(t,e),this.backend.incRef(t.dataId)}removeDataId(t,e){this.state.tensorInfo.has(t)&&this.state.tensorInfo.get(t).backend===e&&(this.state.tensorInfo.delete(t),this.state.numDataBuffers--)}disposeTensor(t){if(!this.state.tensorInfo.has(t.dataId))return;let e=this.state.tensorInfo.get(t.dataId);if(this.state.numTensors--,t.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=e.bytes),t.dtype!=="complex64"&&t.dtype!=="string"){let n=t.size*Tp(t.dtype);this.state.numBytes-=n}e.backend.disposeData(t.dataId)&&this.removeDataId(t.dataId,e.backend)}disposeVariables(){for(let t in this.state.registeredVariables){let e=this.state.registeredVariables[t];this.disposeVariable(e)}}disposeVariable(t){this.disposeTensor(t),this.state.registeredVariables[t.name]!=null&&delete this.state.registeredVariables[t.name]}memory(){let t=this.backend.memory();return t.numTensors=this.state.numTensors,t.numDataBuffers=this.state.numDataBuffers,t.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(t.unreliable=!0,t.reasons==null&&(t.reasons=[]),t.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),t}async profile(t){this.state.profiling=!0;let e=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await t(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(o=>o.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-e,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let o of this.state.activeProfile.kernels)o.kernelTimeMs=await o.kernelTimeMs,o.extraInfo=await o.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(t,e,n,o,s,i){let a={id:this.state.nextTapeNodeId++,kernelName:t,inputs:e,outputs:n,saved:s},u=I0(t);u!=null&&(o=u.gradFunc),o!=null&&(a.gradient=l=>(l=l.map((c,p)=>{if(c==null){let m=n[p],f=Ep(m.size,m.dtype);return this.makeTensor(f,m.shape,m.dtype)}return c}),o(l.length>1?l:l[0],s,i))),this.state.activeTape.push(a)}keep(t){return t.kept=!0,t}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(t){let e={track:[],name:"unnamed scope",id:this.state.nextScopeId++};t&&(e.name=t),this.state.scopeStack.push(e),this.state.activeScope=e}endScope(t){let e=sh(t),n=new Set(e.map(s=>s.id));for(let s=0;s{!s.kept&&s.scopeId===o.id&&this.track(s)})}gradients(t,e,n,o=!1){if(_(e.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 s=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",t));_(s instanceof Lt,()=>"The result y returned by f() must be a tensor.");let i=H_(this.state.activeTape,e,s);if(!o&&i.length===0&&e.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{let a={};a[s.id]=n==null?NK(s.shape):n,q_(a,i,l=>this.tidy(l),kK);let u=e.map(l=>a[l.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(l=>{for(let c of l.saved)c.dispose()}),this.state.activeTape=null),{value:s,grads:u}})}customGrad(t){return _(Ti(t),()=>"The f passed in customGrad(f) must be a function."),(...e)=>{_(e.every(a=>a instanceof Lt),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let n,o={};e.forEach((a,u)=>{o[u]=a});let s=(a,u)=>(n=t(...e,u),_(n.value instanceof Lt,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),_(Ti(n.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),n.value),i=(a,u)=>{let l=n.gradFunc(a,u),c=Array.isArray(l)?l:[l];_(c.length===e.length,()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),_(c.every(m=>m instanceof Lt),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");let p={};return c.forEach((m,f)=>{p[f]=()=>m}),p};return this.runKernelFunc({forwardFunc:s,backwardsFunc:i,inputs:o})}}readSync(t){return this.state.tensorInfo.get(t).backend.readSync(t)}read(t){return this.state.tensorInfo.get(t).backend.read(t)}readToGPU(t,e){return this.state.tensorInfo.get(t).backend.readToGPU(t,e)}async time(t){let e=ac(),n=await this.backend.time(t);return n.wallMs=ac()-e,n}track(t){return this.state.activeScope!=null&&(t.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(t)),t}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new nx;for(let t in this.registry)this.disposeRegisteredKernels(t),this.registry[t].dispose(),delete this.registry[t];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}};ih.nextTensorId=0;ih.nextVariableId=0;function NK(r){let t=Xd(jt(r),"float32");return T.makeTensor(t,r,"float32")}function M0(){let r=w0();if(r._tfengine==null){let t=new Yd(r);r._tfengine=new ih(t)}return N_(r._tfengine.ENV),Y_(()=>r._tfengine),r._tfengine}var T=M0();function kK(r,t){let e={a:r,b:t};return T.runKernel(no,e)}var du={};Kt(du,{isBrowser:()=>L0,isMobile:()=>EK,mockIsMobile:()=>_K});function TK(){return typeof navigator!="undefined"&&navigator!=null}var P0;function _K(r){P0=r}function EK(r){if(P0!==void 0)return P0;if(r||TK()){if(r||(r=navigator),r.product==="ReactNative")return!0;let t=r.userAgent||r.vendor||(typeof window!="undefined"?window.opera:"");if(!t){let e=r;return 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p=J(p,a.dtype),u?R(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var Ix=k({avgPool3d_:Tj});function _j(r,t=0){_(r.length>=1,()=>"Pass at least one tensor to concat");let e=fl(r,"tensors","concat","string_or_numeric");if(e[0].dtype==="complex64"&&e.forEach(s=>{if(s.dtype!=="complex64")throw new Error(`Cannot concatenate complex64 tensors with a tensor + with dtype ${s.dtype}. `)}),e.length===1)return un(e[0]);let n=e,o={axis:t};return T.runKernel(Oi,n,o)}var ie=k({concat_:_j});function Ej(r,t,e=!1,n=!1){let o=C(r,"a","matMul"),s=C(t,"b","matMul");[o,s]=Xt(o,s);let i={a:o,b:s},a={transposeA:e,transposeB:n};return T.runKernel(Zo,i,a)}var Bt=k({matMul_:Ej});function Aj(r){let e={x:C(r,"x","sigmoid","float32")};return T.runKernel(Xs,e)}var en=k({sigmoid_:Aj});function Dj(r,t,e){let n=C(r,"x","slice","string_or_numeric");if(n.rank===0)throw new Error("Slicing scalar is not possible");let o={x:n},s={begin:t,size:e};return T.runKernel(Ui,o,s)}var Ot=k({slice_:Dj});function $j(r){let e={x:C(r,"x","tanh","float32")};return T.runKernel(ni,e)}var li=k({tanh_:$j});function Rj(r,t,e,n,o,s){let i=C(r,"forgetBias","basicLSTMCell"),a=C(t,"lstmKernel","basicLSTMCell"),u=C(e,"lstmBias","basicLSTMCell"),l=C(n,"data","basicLSTMCell"),c=C(o,"c","basicLSTMCell"),p=C(s,"h","basicLSTMCell"),m=ie([l,p],1),f=Bt(m,a),d=K(f,u),h=d.shape[0],g=d.shape[1]/4,x=[h,g],b=Ot(d,[0,0],x),w=Ot(d,[0,g],x),I=Ot(d,[0,g*2],x),N=Ot(d,[0,g*3],x),E=K($(en(b),li(w)),$(c,en(K(i,I)))),A=$(li(E),en(N));return[E,A]}var $E=k({basicLSTMCell_:Rj});function Fj(r,t,e){let n=C(r,"x","batchToSpaceND"),o=t.reduce((a,u)=>a*u);_(n.rank>=1+t.length,()=>`input rank is ${n.rank} but should be > than blockShape.length ${t.length}`),_(e.length===t.length,()=>`crops.length is ${e.length} but should be equal to blockShape.length ${t.length}`),_(n.shape[0]%o===0,()=>`input tensor batch is ${n.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${o}`);let s={x:n},i={blockShape:t,crops:e};return T.runKernel(Fi,s,i)}var yu=k({batchToSpaceND_:Fj});function RE(r){let t;return r.rank===0||r.rank===1?t=R(r,[1,1,1,r.size]):r.rank===2?t=R(r,[1,1,r.shape[0],r.shape[1]]):r.rank===3?t=R(r,[1,r.shape[0],r.shape[1],r.shape[2]]):t=r,t}function Oj(r,t,e,n,o,s){s==null&&(s=.001);let i=C(r,"x","batchNorm"),a=C(t,"mean","batchNorm"),u=C(e,"variance","batchNorm"),l;o!=null&&(l=C(o,"scale","batchNorm"));let c;n!=null&&(c=C(n,"offset","batchNorm")),_(a.rank===u.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),_(c==null||a.rank===c.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),_(l==null||a.rank===l.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let m={x:RE(i),scale:l,offset:c,mean:a,variance:u},f={varianceEpsilon:s},d=T.runKernel(ds,m,f);return R(d,i.shape)}var oa=k({batchNorm_:Oj});function Mj(r,t,e,n,o,s){let i=C(r,"x","batchNorm"),a=C(t,"mean","batchNorm"),u=C(e,"variance","batchNorm"),l;o!=null&&(l=C(o,"scale","batchNorm"));let c;return n!=null&&(c=C(n,"offset","batchNorm")),_(i.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${i.rank}.`),_(a.rank===2||a.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${a.rank}.`),_(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${u.rank}.`),l!=null&&_(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${l.rank}.`),c!=null&&_(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`),oa(i,a,u,c,l,s)}var Cx=k({batchNorm2d_:Mj});function Pj(r,t,e,n,o,s){let i=C(r,"x","batchNorm"),a=C(t,"mean","batchNorm"),u=C(e,"variance","batchNorm"),l;o!=null&&(l=C(o,"scale","batchNorm"));let c;return n!=null&&(c=C(n,"offset","batchNorm")),_(i.rank===3,()=>`Error in batchNorm3D: x 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Has rank ${n.rank}`);let o={s0:e,s1:n};return T.runKernel(ql,o)}var OE=k({broadcastArgs_:Vj});function Gj(r,t){let e=C(r,"broadcastTo","x"),n=e.shape;if(Le(t),t.lengthe.rank){let l=e.shape.slice();for(;l.length=0;l--)if(o[l]===t[l])s[l]=1;else if(e.shape[l]!==1)throw new Error(`broadcastTo(): [${n}] cannot be broadcast to [${t}].`);if(s.map((l,c)=>l>1?c:-1).filter(l=>l>=0).length===0)return un(e);let a={x:e},u={reps:s};return T.runKernel(oo,a,u)}var sa=k({broadcastTo_:Gj});function Wj(r){let e={x:C(r,"x","ceil","float32")};return T.runKernel(Jo,e)}var kx=k({ceil_:Wj});function Co(r,t,e){Le(r),e=e||Wl(t);let n={shape:r,value:t,dtype:e};return T.runKernel(Jl,{},n)}function Uj(r,t,e){let n=C(r,"x","clipByValue");if(_(t<=e,()=>`Error in clip: min (${t}) must be less than or equal to max (${e}).`),t===e)return Co(n.shape,t,n.dtype);let o={x:n},s={clipValueMin:t,clipValueMax:e};return T.runKernel(ho,o,s)}var vr=k({clipByValue_:Uj});function Hj(r){return ie(r,0)}var Tx=k({concat1d_:Hj});function qj(r,t){return ie(r,t)}var _x=k({concat2d_:qj});function Kj(r,t){return ie(r,t)}var Ex=k({concat3d_:Kj});function jj(r,t){return ie(r,t)}var Ax=k({concat4d_:jj});function Xj(r,t,e,n,o="NHWC",s=[1,1],i){let a=C(r,"x","conv2d","float32"),u=C(t,"filter","conv2d","float32"),l=a,c=!1;a.rank===3&&(c=!0,l=R(a,[1,a.shape[0],a.shape[1],a.shape[2]])),_(l.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${l.rank}.`),_(u.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${u.rank}.`),Se("conv2d",n,i);let p=o==="NHWC"?l.shape[3]:l.shape[1];_(p===u.shape[2],()=>`Error in conv2d: depth of input (${p}) must match input depth for filter ${u.shape[2]}.`),_(Dr(e,s),()=>`Error in conv2D: Either strides or dilations must be 1. 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e=C(r,"x","unstack","string_or_numeric");_(t>=-e.shape.length&&t`Axis = ${t} is not in [-${e.shape.length}, ${e.shape.length})`);let n={value:e},o={axis:t};return T.runKernel(qi,n,o)}var gr=k({unstack_:B5});function RA(r,t){return fh(r,t,"right")}function my(r,t=!0,e,n){return T.makeVariable(r,t,e,n)}function fy(r,t){let e=[];for(let s=0;s0,()=>"mask cannot be scalar"),$e(a.slice(s,s+i),o.shape,"mask's shape must match the first K dimensions of tensor's shape,");let u=1;for(let h=s;ha).reverse()),_(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of perm ${t}.`),t.forEach(i=>{_(i>=0&&i`All entries in 'perm' must be between 0 and ${n.rank-1} but got ${t}`)}),n.rank<=1)return n.clone();let o={x:n},s={perm:t};return n.dtype==="complex64"?B(()=>{let i=bl(n),a=Iu(n);return i=T.runKernel(so,{x:i},s),a=T.runKernel(so,{x:a},s),e&&(a=Ut(a)),Sn(i,a)}):T.runKernel(so,o,s)}var Vt=k({transpose_:U5});function H5(r,t,e,n,o=!0){let s=C(r,"v","movingAverage"),i=C(t,"x","movingAverage"),a=C(e,"decay","movingAverage");F0(s,i),_(on(s.shape,i.shape),()=>"Shape mismatch in v and x");let u=pt(1),l=at(u,a),c=$(at(i,s),l);if(o){_(n!=null,()=>"When using zeroDebias: true, step is required.");let p=C(n,"step","movingAverage");c=ct(c,at(u,un(a,p)))}return X(s,c)}var q5=k({movingAverage_:H5});function K5(r,t,e){Le(e);let n=C(r,"indices","scatterND","int32"),o=C(t,"updates","scatterND");wm(o,n,e);let s={indices:n,updates:o},i={shape:e};return T.runKernel(nl,s,i)}var j5=k({scatterND_:K5});function FA(r,t,e,n){if(r.dtype!=="int32")throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${r.dtype}.`);if(r.rank>2)throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${r.shape}.`);let o=r.rank>0?r.shape[0]:1,s=r.rank>1?r.shape[1]:1;if(e.length!==s)throw new Error(`outputShape has incorrect number of elements:, ${e.length}, should be: ${s}.`);let i=t.size;if(!(t.rank===0||t.rank===1&&i===o))throw new Error(`sparseValues has incorrect shape ${t.shape}, should be [] or [${o}]`);if(t.dtype!==n.dtype)throw new Error("sparseValues.dtype must match defaultValues.dtype")}function Y5(r,t,e,n=0){Le(e);let o=C(r,"sparseIndices","sparseToDense","int32"),s=C(t,"sparseValues","sparseToDense","string_or_numeric"),i=C(n,"defaultValue","sparseToDense",s.dtype);FA(o,s,e,i);let a={sparseIndices:o,sparseValues:s,defaultValue:i},u={outputShape:e};return T.runKernel(al,a,u)}var Z5=k({sparseToDense_:Y5});function J5(r,t){let e=C(t,"indices","gatherND","int32"),o={params:C(r,"x","gatherND","string_or_numeric"),indices:e};return T.runKernel(Va,o)}var Q5=k({gatherND_:J5});function OA(r,t){if(t==null)return r.shape.slice();if(on(r.shape,t))return t;if(r.shape.length===t.length){let e=[];for(let n=0;n`x has to be a floating point tensor since it's going to be scaled, but got a ${o.dtype} tensor instead.`),_(t>=0&&t<1,()=>`rate must be a float in the 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n8(r,t,e,n,o,s="NHWC",i){let a=r;r.rank===3&&(a=R(r,[1,r.shape[0],r.shape[1],r.shape[2]]));let u=t;u.rank===3&&(u=R(t,[1,t.shape[0],t.shape[1],t.shape[2]])),_(a.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${a.shape}.`),_(u.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${u.shape}.`),_(e.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${e}.`);let l=s==="NHWC"?a.shape[3]:a.shape[1],c=s==="NHWC"?u.shape[3]:u.shape[1];_(l===e[2],()=>`Error in conv2dDerFilter: depth of input ${l}) must match input depth in filter (${e[2]}.`),_(c===e[3],()=>`Error in conv2dDerFilter: depth of dy (${c}) must match output depth for filter (${e[3]}).`),ve("conv2dDerFilter",o,i);let p={x:a,dy:u},m={strides:n,pad:o,dataFormat:s,dimRoundingMode:i,filterShape:e};return T.runKernel(Dp,p,m)}var vm=k({conv2DBackpropFilter_:n8});function bc(r,t,e){if(e==null||e==="linear")return r;if(e==="relu")return $(r,So(t));throw new 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p=C(r,"x","conv2d","float32"),m=C(t,"filter","conv2d","float32"),f=p,d=!1;p.rank===3&&(d=!0,f=R(p,[1,p.shape[0],p.shape[1],p.shape[2]])),_(f.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${f.rank}.`),_(m.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${m.rank}.`),ve("fused conv2d",n,i);let h=o==="NHWC"?f.shape[3]:f.shape[1];_(m.shape[2]===h,()=>`Error in conv2d: depth of input (${h}) must match input depth for filter ${m.shape[2]}.`),_(Dr(e,s),()=>`Error in conv2D: Either strides or dilations must be 1. 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c=e?u.shape[u.rank-2]:u.shape[u.rank-1],p=n?l.shape[l.rank-1]:l.shape[l.rank-2],m=e?u.shape[u.rank-1]:u.shape[u.rank-2],f=n?l.shape[l.rank-2]:l.shape[l.rank-1],d=u.shape.slice(0,-2),h=l.shape.slice(0,-2),g=jt(d),x=jt(h);_(c===p,()=>`Error in fused matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${u.shape} and ${l.shape} and transposeA=${e} and transposeB=${n} must match.`);let w=Mt(u.shape.slice(0,-2),l.shape.slice(0,-2)).concat([m,f]),I=e?R(u,[g,c,m]):R(u,[g,m,c]),N=n?R(l,[x,f,p]):R(l,[x,p,f]),E;o!=null&&(E=C(o,"bias","fused matMul"),[E]=Xt(E,u),Mt(w,E.shape));let A;i!=null&&(A=C(i,"prelu weights","fused matMul"));let D=(V,G)=>{let[W,q,H,K]=G,Y=bc(R(V,H.shape),H,s),Z,et;if(!e&&!n?(Z=Bt(Y,q,!1,!0),et=Bt(W,Y,!0,!1)):!e&&n?(Z=Bt(Y,q,!1,!1),et=Bt(Y,W,!0,!1)):e&&!n?(Z=Bt(q,Y,!1,!0),et=Bt(W,Y,!1,!1)):(Z=Bt(q,Y,!0,!0),et=Bt(Y,W,!0,!0)),o!=null){let nt=wc(K,Y);return[Z,et,nt]}else return[Z,et]},F={a:I,b:N,bias:E,preluActivationWeights:A},M={transposeA:e,transposeB:n,activation:s,leakyreluAlpha:a};return o==null?pn((G,W,q)=>{let H=T.runKernel(ji,F,M);return q([G,W,H]),{value:R(H,w),gradFunc:D}})(I,N):pn((G,W,q,H)=>{let K=T.runKernel(ji,F,M);return H([G,W,K,q]),{value:R(K,w),gradFunc:D}})(I,N,E)}var LA=k({fusedMatMul_:l8});function u8(r){return gh(r,.54,.46)}var zA=k({hammingWindow_:u8});function c8(r){return gh(r,.5,.5)}var xy=k({hannWindow_:c8});function p8(r,t,e,n=!1,o=0){let s=0,i=[];for(;s+t<=r.size;)i.push(Ot(r,s,t)),s+=e;if(n)for(;s`Error in cropAndResize: image must be rank 4,but got rank ${i.rank}.`),_(a.rank===2&&a.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${l},4] but had shape ${a.shape}.`),_(u.rank===1&&u.shape[0]===l,()=>`Error in cropAndResize: boxInd must be have size [${l}] but had shape ${a.shape}.`),_(n.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${n.length}.`),_(n[0]>=1&&n[1]>=1,()=>`cropSize must be atleast [1,1], but was ${n}`),_(o==="bilinear"||o==="nearest",()=>`method must be bilinear or nearest, but was ${o}`);let c={image:i,boxes:a,boxInd:u},p={method:o,extrapolationValue:s,cropSize:n};return T.runKernel(Ma,c,p)}var VA=k({cropAndResize_:f8});function d8(r){let t=C(r,"image","flipLeftRight","float32");_(t.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${t.rank}.`);let e={image:t};return T.runKernel(Ba,e,{})}var GA=k({flipLeftRight_:d8});function h8(r){let t=C(r,"image","grayscaleToRGB"),e=t.rank-1,n=t.shape[e];_(t.rank>=2,()=>`Error in grayscaleToRGB: images must be at least rank 2, but got rank ${t.rank}.`),_(n===1,()=>`Error in grayscaleToRGB: last dimension of a grayscale image should be size 1, but got size ${n}.`);let o=new Array(t.rank);return o.fill(1,0,e),o[e]=3,Rr(t,o)}var WA=k({grayscaleToRGB_:h8});function g8(r){let t=C(r,"image","RGBToGrayscale"),e=t.rank-1,n=t.shape[e];_(t.rank>=2,()=>`Error in RGBToGrayscale: images must be at least rank 2, but got rank ${t.rank}.`),_(n===3,()=>`Error in RGBToGrayscale: last dimension of an RGB image should be size 3, but got size ${n}.`);let o=t.dtype,s=J(t,"float32"),i=Oe([.2989,.587,.114]),a;switch(t.rank){case 2:a=wu("ij,j->i",s,i);break;case 3:a=wu("ijk,k->ij",s,i);break;case 4:a=wu("ijkl,l->ijk",s,i);break;case 5:a=wu("ijklm,m->ijkl",s,i);break;case 6:a=wu("ijklmn,n->ijklm",s,i);break;default:throw new Error("Not a valid tensor rank.")}return a=je(a,-1),J(a,o)}var UA=k({rgbToGrayscale_:g8});function x8(r,t,e=0,n=.5){let o=C(r,"image","rotateWithOffset","float32");_(o.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${o.rank}.`);let s={image:o},i={radians:t,fillValue:e,center:n};return T.runKernel(pl,s,i)}var HA=k({rotateWithOffset_:x8});function 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T.runKernel(cl,u,l)}var e2=k({transform_:R8});function F8(r,t,e){let n=C(r,"a","bandPart");_(n.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${n.rank}.`);let o=n.shape,[s,i]=n.shape.slice(-2),a,u;typeof t=="number"?(_(t%1===0,()=>`bandPart(): numLower must be an integer, got ${t}.`),_(t<=s,()=>`bandPart(): numLower (${t}) must not be greater than the number of rows (${s}).`),a=C(t<0?s:t,"numLower","bandPart")):(_(t.dtype==="int32",()=>"bandPart(): numLower's dtype must be an int32."),a=we(yl(t,0),s,lo(t,s))),typeof e=="number"?(_(e%1===0,()=>`bandPart(): numUpper must be an integer, got ${e}.`),_(e<=i,()=>`bandPart(): numUpper (${e}) must not be greater than the number of columns (${i}).`),u=C(e<0?i:e,"numUpper","bandPart")):(_(e.dtype==="int32",()=>"bandPart(): numUpper's dtype must be an int32."),u=we(yl(e,0),i,lo(e,i)));let l=R(pa(0,s,1,"int32"),[-1,1]),c=pa(0,i,1,"int32"),p=at(l,c),m=Fr(Vn(p,a),cn(p,Ut(u))),f=ke([s,i],n.dtype);return R(Fe(gr(R(n,[-1,s,i])).map(d=>we(m,d,f))),o)}var r2=k({bandPart_:F8});function O8(r){let t;if(Array.isArray(r)){t=!1,_(r!=null&&r.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let o=r[0].shape[0];for(let s=1;s`Gram-Schmidt: Non-unique lengths found in the input vectors: (${r[s].shape[0]} vs. ${o})`)}else t=!0,r=hr(r,r.shape[0],0).map(o=>Wn(o,[0]));_(r.length<=r[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${r.length}) exceeds number of dimensions (${r[0].shape[0]}).`);let e=[],n=r;for(let o=0;o{let s=n[o];if(o>0)for(let i=0;i=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${r.rank}`),r.rank===2)return o2(r,t);{let e=r.shape.slice(0,r.shape.length-2).reduce((u,l)=>u*l),n=gr(R(r,[e,r.shape[r.shape.length-2],r.shape[r.shape.length-1]]),0),o=[],s=[];n.forEach(u=>{let[l,c]=o2(u,t);o.push(l),s.push(c)});let i=R(Fe(o,0),r.shape),a=R(Fe(s,0),r.shape);return[i,a]}}function o2(r,t=!1){return T.tidy(()=>{_(r.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${r.shape.length}D Tensor.`);let e=r.shape[0],n=r.shape[1],o=fc(e),s=ln(r),i=ci([[1]],[1,1]),a=ln(i),u=e>=n?n:e;for(let l=0;l{let f=Ot(s,[l,l],[e-l,1]),d=xl(f),h=Ot(s,[l,l],[1,1]),g=we(Re(h,0),ci([[-1]]),ci([[1]])),x=at(h,$(g,d)),b=ct(f,x);b.shape[0]===1?a=ln(i):a=ie([i,Ot(b,[1,0],[b.shape[0]-1,b.shape[1]])],0);let w=Ut(ct(Bt(g,x),d)),I=Ot(s,[l,0],[e-l,n]),N=$(w,a),E=Vt(a);if(l===0)s=at(I,Bt(N,Bt(E,I)));else{let F=at(I,Bt(N,Bt(E,I)));s=ie([Ot(s,[0,0],[l,n]),F],0)}let A=Vt(N),D=Ot(o,[0,l],[e,o.shape[1]-l]);if(l===0)o=at(D,Bt(Bt(D,a),A));else{let F=at(D,Bt(Bt(D,a),A));o=ie([Ot(o,[0,0],[e,l]),F],1)}return[a,s,o]}),Tt([c,p,m])}return!t&&e>n&&(o=Ot(o,[0,0],[e,n]),s=Ot(s,[0,0],[n,n])),[o,s]})}var s2=k({qr_:M8});var Je;(function(r){r[r.NONE=0]="NONE",r[r.MEAN=1]="MEAN",r[r.SUM=2]="SUM",r[r.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(Je||(Je={}));function P8(r,t,e=Je.SUM_BY_NONZERO_WEIGHTS){let n=C(r,"losses","computeWeightedLoss"),o=null;t!=null&&(o=C(t,"weights","computeWeightedLoss"));let s=o==null?n:$(n,o);if(e===Je.NONE)return s;if(e===Je.SUM)return mt(s);if(e===Je.MEAN){if(o==null)return Ne(s);{let i=n.size/o.size,a=ct(mt(s),mt(o));return i>1?ct(a,pt(i)):a}}if(e===Je.SUM_BY_NONZERO_WEIGHTS){if(o==null)return ct(mt(s),pt(n.size));{let i=$(o,ar(n.shape)),a=J(mt(ui(i,pt(0))),"float32");return ct(mt(s),a)}}throw Error(`Unknown reduction: ${e}`)}var Hr=k({computeWeightedLoss_:P8});function L8(r,t,e,n=Je.SUM_BY_NONZERO_WEIGHTS){let o=C(r,"labels","absoluteDifference"),s=C(t,"predictions","absoluteDifference"),i=null;e!=null&&(i=C(e,"weights","absoluteDifference")),$e(o.shape,s.shape,"Error in absoluteDifference: ");let a=_e(at(o,s));return Hr(a,i,n)}var i2=k({absoluteDifference_:L8});function z8(r,t,e,n,o=Je.SUM_BY_NONZERO_WEIGHTS){let s=C(r,"labels","cosineDistance"),i=C(t,"predictions","cosineDistance"),a=null;n!=null&&(a=C(n,"weights","cosineDistance")),$e(s.shape,i.shape,"Error in cosineDistance: ");let u=pt(1),l=at(u,mt($(s,i),e,!0));return Hr(l,a,o)}var a2=k({cosineDistance_:z8});function B8(r,t,e,n=Je.SUM_BY_NONZERO_WEIGHTS){let o=C(r,"labels","hingeLoss"),s=C(t,"predictions","hingeLoss"),i=null;e!=null&&(i=C(e,"weights","hingeLoss")),$e(o.shape,s.shape,"Error in hingeLoss: ");let a=pt(1);o=at($(pt(2),o),a);let u=Or(at(a,$(o,s)));return Hr(u,i,n)}var l2=k({hingeLoss_:B8});function V8(r,t,e,n=1,o=Je.SUM_BY_NONZERO_WEIGHTS){let s=C(r,"labels","huberLoss"),i=C(t,"predictions","huberLoss"),a=null;e!=null&&(a=C(e,"weights","huberLoss")),$e(s.shape,i.shape,"Error in huberLoss: ");let u=pt(n),l=_e(at(i,s)),c=lo(l,u),p=at(l,c),m=X($(pt(.5),Wt(c)),$(u,p));return Hr(m,a,o)}var u2=k({huberLoss_:V8});function G8(r,t,e,n=1e-7,o=Je.SUM_BY_NONZERO_WEIGHTS){let s=C(r,"labels","logLoss"),i=C(t,"predictions","logLoss"),a=null;e!=null&&(a=C(e,"weights","logLoss")),$e(s.shape,i.shape,"Error in logLoss: ");let u=pt(1),l=pt(n),c=Ut($(s,Nr(X(i,l)))),p=$(at(u,s),Nr(X(at(u,i),l))),m=at(c,p);return Hr(m,a,o)}var c2=k({logLoss_:G8});function W8(r,t,e,n=Je.SUM_BY_NONZERO_WEIGHTS){let o=C(r,"labels","meanSquaredError"),s=C(t,"predictions","meanSquaredError"),i=null;e!=null&&(i=C(e,"weights","meanSquaredError")),$e(o.shape,s.shape,"Error in meanSquaredError: ");let a=bm(o,s);return Hr(a,i,n)}var p2=k({meanSquaredError_:W8});function U8(r,t){let e=C(r,"labels","sigmoidCrossEntropyWithLogits"),n=C(t,"logits","sigmoidCrossEntropyWithLogits");$e(e.shape,n.shape,"Error in sigmoidCrossEntropyWithLogits: ");let o=Or(n),s=$(n,e),i=vu(Ke(Ut(_e(n))));return X(at(o,s),i)}function H8(r,t,e,n=0,o=Je.SUM_BY_NONZERO_WEIGHTS){let 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i8(r,t,e,n,o,s="NHWC",i){let a=r;r.rank===3&&(a=R(r,[1,r.shape[0],r.shape[1],r.shape[2]]));let u=t;u.rank===3&&(u=R(t,[1,t.shape[0],t.shape[1],t.shape[2]])),_(a.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${a.shape}.`),_(u.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${u.shape}.`),_(e.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${e}.`);let l=s==="NHWC"?a.shape[3]:a.shape[1],c=s==="NHWC"?u.shape[3]:u.shape[1];_(l===e[2],()=>`Error in conv2dDerFilter: depth of input ${l}) must match input depth in filter (${e[2]}.`),_(c===e[3],()=>`Error in conv2dDerFilter: depth of dy (${c}) must match output depth for filter (${e[3]}).`),Se("conv2dDerFilter",o,i);let p={x:a,dy:u},m={strides:n,pad:o,dataFormat:s,dimRoundingMode:i,filterShape:e};return T.runKernel(Dp,p,m)}var Sm=k({conv2DBackpropFilter_:i8});function bc(r,t,e){if(e==null||e==="linear")return r;if(e==="relu")return $(r,So(t));throw new 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p=C(r,"x","conv2d","float32"),m=C(t,"filter","conv2d","float32"),f=p,d=!1;p.rank===3&&(d=!0,f=R(p,[1,p.shape[0],p.shape[1],p.shape[2]])),_(f.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${f.rank}.`),_(m.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${m.rank}.`),Se("fused conv2d",n,i);let h=o==="NHWC"?f.shape[3]:f.shape[1];_(m.shape[2]===h,()=>`Error in conv2d: depth of input (${h}) must match input depth for filter ${m.shape[2]}.`),_(Dr(e,s),()=>`Error in conv2D: Either strides or dilations must be 1. 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c=e?u.shape[u.rank-2]:u.shape[u.rank-1],p=n?l.shape[l.rank-1]:l.shape[l.rank-2],m=e?u.shape[u.rank-1]:u.shape[u.rank-2],f=n?l.shape[l.rank-2]:l.shape[l.rank-1],d=u.shape.slice(0,-2),h=l.shape.slice(0,-2),g=jt(d),x=jt(h);_(c===p,()=>`Error in fused matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${u.shape} and ${l.shape} and transposeA=${e} and transposeB=${n} must match.`);let w=Mt(u.shape.slice(0,-2),l.shape.slice(0,-2)).concat([m,f]),I=e?R(u,[g,c,m]):R(u,[g,m,c]),N=n?R(l,[x,f,p]):R(l,[x,p,f]),E;o!=null&&(E=C(o,"bias","fused matMul"),[E]=Xt(E,u),Mt(w,E.shape));let A;i!=null&&(A=C(i,"prelu weights","fused matMul"));let D=(V,G)=>{let[W,q,H,j]=G,Y=bc(R(V,H.shape),H,s),Z,et;if(!e&&!n?(Z=Bt(Y,q,!1,!0),et=Bt(W,Y,!0,!1)):!e&&n?(Z=Bt(Y,q,!1,!1),et=Bt(Y,W,!0,!1)):e&&!n?(Z=Bt(q,Y,!1,!0),et=Bt(W,Y,!1,!1)):(Z=Bt(q,Y,!0,!0),et=Bt(Y,W,!0,!0)),o!=null){let nt=wc(j,Y);return[Z,et,nt]}else 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t=C(r,"image","RGBToGrayscale"),e=t.rank-1,n=t.shape[e];_(t.rank>=2,()=>`Error in RGBToGrayscale: images must be at least rank 2, but got rank ${t.rank}.`),_(n===3,()=>`Error in RGBToGrayscale: last dimension of an RGB image should be size 3, but got size ${n}.`);let o=t.dtype,s=J(t,"float32"),i=Oe([.2989,.587,.114]),a;switch(t.rank){case 2:a=wu("ij,j->i",s,i);break;case 3:a=wu("ijk,k->ij",s,i);break;case 4:a=wu("ijkl,l->ijk",s,i);break;case 5:a=wu("ijklm,m->ijkl",s,i);break;case 6:a=wu("ijklmn,n->ijklm",s,i);break;default:throw new Error("Not a valid tensor rank.")}return a=je(a,-1),J(a,o)}var qA=k({rgbToGrayscale_:b8});function w8(r,t,e=0,n=.5){let o=C(r,"image","rotateWithOffset","float32");_(o.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${o.rank}.`);let s={image:o},i={radians:t,fillValue:e,center:n};return T.runKernel(pl,s,i)}var KA=k({rotateWithOffset_:w8});function No(r,t,e,n,o,s){n==null&&(n=.5),o==null&&(o=Number.NEGATIVE_INFINITY),s==null&&(s=0);let i=r.shape[0];return e=Math.min(e,i),_(0<=n&&n<=1,()=>`iouThreshold must be in [0, 1], but was '${n}'`),_(r.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${r.rank}'`),_(r.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${r.shape[1]}`),_(t.rank===1,()=>"scores must be a 1D tensor"),_(t.shape[0]===i,()=>`scores has incompatible shape with boxes. 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g=0;go&&l.push({score:t[g],boxIndex:g,suppressBeginIndex:0});l.sort(YA);let c=s>0?-.5/s:0,p=[],m=[];for(;p.length0;){let g=l.pop(),{score:x,boxIndex:b,suppressBeginIndex:w}=g;if(x=w;--N){let E=N8(r,b,p[N]);if(E>=n){I=!0;break}if(g.score=g.score*k8(n,c,E),g.score<=o)break}g.suppressBeginIndex=p.length,I||(g.score===x?(p.push(b),m.push(g.score)):g.score>o&&XA(l,g,YA))}let f=p.length,d=e-f;a&&d>0&&(p.push(...new Array(d).fill(0)),m.push(...new Array(d).fill(0)));let h={selectedIndices:p};return i&&(h.selectedScores=m),u&&(h.validOutputs=f),h}function N8(r,t,e){let n=r.subarray(t*4,t*4+4),o=r.subarray(e*4,e*4+4),s=Math.min(n[0],n[2]),i=Math.min(n[1],n[3]),a=Math.max(n[0],n[2]),u=Math.max(n[1],n[3]),l=Math.min(o[0],o[2]),c=Math.min(o[1],o[3]),p=Math.max(o[0],o[2]),m=Math.max(o[1],o[3]),f=(a-s)*(u-i),d=(p-l)*(m-c);if(f<=0||d<=0)return 0;let h=Math.max(s,l),g=Math.max(i,c),x=Math.min(a,p),b=Math.min(u,m),w=Math.max(x-h,0)*Math.max(b-g,0);return w/(f+d-w)}function k8(r,t,e){let 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T.tidy(()=>{_(r.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${r.shape.length}D Tensor.`);let e=r.shape[0],n=r.shape[1],o=fc(e),s=un(r),i=pi([[1]],[1,1]),a=un(i),u=e>=n?n:e;for(let l=0;l{let f=Ot(s,[l,l],[e-l,1]),d=xl(f),h=Ot(s,[l,l],[1,1]),g=Ie(Re(h,0),pi([[-1]]),pi([[1]])),x=at(h,$(g,d)),b=ut(f,x);b.shape[0]===1?a=un(i):a=ie([i,Ot(b,[1,0],[b.shape[0]-1,b.shape[1]])],0);let w=Ut(ut(Bt(g,x),d)),I=Ot(s,[l,0],[e-l,n]),N=$(w,a),E=Vt(a);if(l===0)s=at(I,Bt(N,Bt(E,I)));else{let F=at(I,Bt(N,Bt(E,I)));s=ie([Ot(s,[0,0],[l,n]),F],0)}let A=Vt(N),D=Ot(o,[0,l],[e,o.shape[1]-l]);if(l===0)o=at(D,Bt(Bt(D,a),A));else{let F=at(D,Bt(Bt(D,a),A));o=ie([Ot(o,[0,0],[e,l]),F],1)}return[a,s,o]}),Tt([c,p,m])}return!t&&e>n&&(o=Ot(o,[0,0],[e,n]),s=Ot(s,[0,0],[n,n])),[o,s]})}var a2=k({qr_:z8});var Je;(function(r){r[r.NONE=0]="NONE",r[r.MEAN=1]="MEAN",r[r.SUM=2]="SUM",r[r.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(Je||(Je={}));function B8(r,t,e=Je.SUM_BY_NONZERO_WEIGHTS){let 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s=C(r,"labels","cosineDistance"),i=C(t,"predictions","cosineDistance"),a=null;n!=null&&(a=C(n,"weights","cosineDistance")),$e(s.shape,i.shape,"Error in cosineDistance: ");let u=pt(1),l=at(u,mt($(s,i),e,!0));return Kr(l,a,o)}var u2=k({cosineDistance_:G8});function W8(r,t,e,n=Je.SUM_BY_NONZERO_WEIGHTS){let o=C(r,"labels","hingeLoss"),s=C(t,"predictions","hingeLoss"),i=null;e!=null&&(i=C(e,"weights","hingeLoss")),$e(o.shape,s.shape,"Error in hingeLoss: ");let a=pt(1);o=at($(pt(2),o),a);let u=Or(at(a,$(o,s)));return Kr(u,i,n)}var c2=k({hingeLoss_:W8});function U8(r,t,e,n=1,o=Je.SUM_BY_NONZERO_WEIGHTS){let s=C(r,"labels","huberLoss"),i=C(t,"predictions","huberLoss"),a=null;e!=null&&(a=C(e,"weights","huberLoss")),$e(s.shape,i.shape,"Error in huberLoss: ");let u=pt(n),l=_e(at(i,s)),c=lo(l,u),p=at(l,c),m=K($(pt(.5),Wt(c)),$(u,p));return Kr(m,a,o)}var p2=k({huberLoss_:U8});function H8(r,t,e,n=1e-7,o=Je.SUM_BY_NONZERO_WEIGHTS){let s=C(r,"labels","logLoss"),i=C(t,"predictions","logLoss"),a=null;e!=null&&(a=C(e,"weights","logLoss")),$e(s.shape,i.shape,"Error in logLoss: ");let u=pt(1),l=pt(n),c=Ut($(s,Nr(K(i,l)))),p=$(at(u,s),Nr(K(at(u,i),l))),m=at(c,p);return Kr(m,a,o)}var m2=k({logLoss_:H8});function q8(r,t,e,n=Je.SUM_BY_NONZERO_WEIGHTS){let o=C(r,"labels","meanSquaredError"),s=C(t,"predictions","meanSquaredError"),i=null;e!=null&&(i=C(e,"weights","meanSquaredError")),$e(o.shape,s.shape,"Error in meanSquaredError: ");let a=wm(o,s);return Kr(a,i,n)}var f2=k({meanSquaredError_:q8});function K8(r,t){let e=C(r,"labels","sigmoidCrossEntropyWithLogits"),n=C(t,"logits","sigmoidCrossEntropyWithLogits");$e(e.shape,n.shape,"Error in sigmoidCrossEntropyWithLogits: ");let o=Or(n),s=$(n,e),i=vu(Ke(Ut(_e(n))));return K(at(o,s),i)}function j8(r,t,e,n=0,o=Je.SUM_BY_NONZERO_WEIGHTS){let s=C(r,"multiClassLabels","sigmoidCrossEntropy"),i=C(t,"logits","sigmoidCrossEntropy"),a=null;if(e!=null&&(a=C(e,"weights","sigmoidCrossEntropy")),$e(s.shape,i.shape,"Error in sigmoidCrossEntropy: "),n>0){let l=pt(n),c=pt(1),p=pt(.5);s=K($(s,at(c,l)),$(p,l))}let u=K8(s,i);return Kr(u,a,o)}var d2=k({sigmoidCrossEntropy_:j8});function X8(r,t,e=-1){if(e===-1&&(e=t.rank-1),e!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. 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xD={kernelName:Kl,inputsToSave:["x"],gradFunc:Dy.gradFunc};var yD={kernelName:Fi,saveAllInputs:!0,gradFunc:(r,t,e)=>{let n=t.map(u=>u.shape),{axis:o}=e,s=fr(o,t[0].shape)[0],i=n.map(u=>u[s]);return hr(r,i,s).map(u=>()=>u)}};var bD={kernelName:Qo,inputsToSave:["x","filter"],gradFunc:(r,t,e)=>{let[n,o]=t,{dilations:s,strides:i,pad:a,dataFormat:u}=e;return _(io(s),()=>`Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. 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this.model.stopTraining}getConfig(){let t=[];for(let e of this.layers){let n={};n.className=e.getClassName(),n.config=e.getConfig(),t.push(n)}return{name:this.name,layers:t}}};Wc.className="Sequential";Q.registerClass(Wc);function fJ(r){return new Un(r)}function dJ(r){return new Wc(r)}function YN(r){return Hy(r)}function hJ(r,t){Wm.registerCallbackConstructor(r,t)}var rn=class extends Q.Serializable{getConfig(){return{}}},fb=class extends rn{apply(t,e=1){return aR(t,e)}};fb.className="elu";Q.registerClass(fb);var db=class extends rn{apply(t){return mm(t)}};db.className="selu";Q.registerClass(db);var hb=class extends rn{apply(t){return Or(t)}};hb.className="relu";Q.registerClass(hb);var gb=class extends rn{apply(t){return B(()=>lo(6,Or(t)))}};gb.className="relu6";Q.registerClass(gb);var xb=class extends rn{apply(t){return t}};xb.className="linear";Q.registerClass(xb);var yb=class extends rn{apply(t){return Qr(t)}};yb.className="sigmoid";Q.registerClass(yb);var bb=class extends rn{apply(t){return uR(t)}};bb.className="hardSigmoid";Q.registerClass(bb);var wb=class extends rn{apply(t){return li(t)}};wb.className="softplus";Q.registerClass(wb);var Ib=class extends rn{apply(t){return lR(t)}};Ib.className="softsign";Q.registerClass(Ib);var Cb=class extends rn{apply(t){return na(t)}};Cb.className="tanh";Q.registerClass(Cb);var Xm=class extends rn{apply(t,e=-1){return Eu(t,e)}};Xm.className="softmax";Q.registerClass(Xm);var vb=class extends rn{apply(t,e=-1){return am(t,e)}};vb.className="logSoftmax";Q.registerClass(vb);var Sb=class extends rn{apply(t,e=1){return B(()=>$(Qr($(t,e)),t))}};Sb.className="swish";Q.registerClass(Sb);var Nb=class extends rn{apply(t){return B(()=>$(t,na(li(t))))}};Nb.className="mish";Q.registerClass(Nb);function hi(r){return r.getClassName()}function ZN(r,t={}){return fa(r,Q.SerializationMap.getMap().classNameMap,t,"activation")}function gi(r){if(r==null){let t={};return t.className="linear",t.config={},ZN(t)}if(typeof r=="string"){let t={};return t.className=r,t.config={},ZN(t)}else return r instanceof rn?r:ZN(r)}function JN(r){if(r!=null&&typeof r!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${r}`)}var kb=class extends Q.Serializable{},Lu=class extends kb{constructor(t){super(),JN(t),this.l1=t==null||t.l1==null?.01:t.l1,this.l2=t==null||t.l2==null?.01:t.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(t){return B(()=>{let e=ke([1]);return this.hasL1&&(e=X(e,mt($(this.l1,_e(t))))),this.hasL2&&(e=X(e,mt($(this.l2,$c(t))))),R(e,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(t,e){return new t({l1:e.l1,l2:e.l2})}};Lu.className="L1L2";Q.registerClass(Lu);function UR(r){return JN(r),new Lu({l1:r!=null?r.l1:null,l2:0})}function HR(r){return JN(r),new Lu({l2:r!=null?r.l2:null,l1:0})}var GR={l1l2:"L1L2"};function fe(r){return Nm(r)}function WR(r,t={}){return fa(r,Q.SerializationMap.getMap().classNameMap,t,"regularizer")}function Ce(r){if(r==null)return null;if(typeof r=="string"){let e={className:r in GR?GR[r]:r,config:{}};return WR(e)}else return r instanceof kb?r:WR(r)}var Ym=class extends Et{constructor(t){super(t==null?{}:t),this.supportsMasking=!0,t!=null&&(this.maxValue=t.maxValue)}call(t,e){t=St(t);let n=Or(t);return this.maxValue!=null&&(n=vr(n,0,this.maxValue)),n}computeOutputShape(t){return t}getConfig(){let t={maxValue:this.maxValue},e=super.getConfig();return Object.assign(t,e),t}};Ym.className="ReLU";Q.registerClass(Ym);var Zm=class extends Et{constructor(t){super(t==null?{}:t),this.DEFAULT_ALPHA=.3,t==null&&(t={}),this.alpha=t.alpha==null?this.DEFAULT_ALPHA:t.alpha}call(t,e){let n=St(t);return Cu(n,this.alpha)}computeOutputShape(t){return t}getConfig(){let t={alpha:this.alpha},e=super.getConfig();return Object.assign(t,e),t}};Zm.className="LeakyReLU";Q.registerClass(Zm);var Jm=class extends Et{constructor(t){if(super(t==null?{}:t),this.DEFAULT_ALPHA_INITIALIZER="zeros",t==null&&(t={}),this.supportsMasking=!0,this.alphaInitializer=ge(t.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Ce(t.alphaRegularizer),this.alphaConstraint=Ge(t.alphaConstraint),t.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(t.sharedAxes))this.sharedAxes=t.sharedAxes;else if(typeof t.sharedAxes=="number")this.sharedAxes=[t.sharedAxes];else throw new z(`Expected sharedAxes to be a number or an array of numbers, but got ${t.sharedAxes}`)}build(t){t=Gt(t);let e=t.slice(1);if(this.sharedAxes!=null)for(let o of this.sharedAxes)e[o-1]=1;this.alpha=this.addWeight("alpha",e,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let o=1;o{let n=St(t),o=e.mask;if(o!=null){let s=$(at(ar(n.shape),J(o,n.dtype)),pt(-1e9));n=X(n,s)}return this.axis instanceof Array?this.axis.length>1?Ke(at(n,Su(n,this.axis,!0))):this.softmax(n,this.axis[0]):this.softmax(n,this.axis)})}computeOutputShape(t){return t}getConfig(){let t={axis:this.axis},e=super.getConfig();return Object.assign(t,e),t}};ef.className="Softmax";Q.registerClass(ef);function zu(r,t,e){if(typeof r=="number")return To(r,t);if(r.length!==t)throw new z(`The ${e} argument must be an integer or tuple of ${t} integers. 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z(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${r.rank}.`);if(t.rank!==3&&t.rank!==4)throw new z(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${r.rank}.`);let u=Mh(r,s);if(o==="causal")throw new _t("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return u=Ru.conv2d({x:u,filter:t,strides:n,pad:o==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:e,activation:a}),s==="channelsFirst"&&(u=Vt(u,[0,3,1,2])),u})}function yJ(r,t,e,n=[1,1,1],o="valid",s,i){return B(()=>{if(s==null&&(s=xn()),Me(s),r.rank!==4&&r.rank!==5)throw new z(`conv3dWithBias expects input to be of rank 4 or 5, but received ${r.rank}.`);if(t.rank!==4&&t.rank!==5)throw new z(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${r.rank}.`);let a=QN(r,s);if(o==="causal")throw new _t("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return a=Ax(a,t,n,o==="same"?"same":"valid","NDHWC",i),e!=null&&(a=yn(a,e)),s==="channelsFirst"&&(a=Vt(a,[0,4,1,2,3])),a})}var Oh=class r extends Et{constructor(t,e){if(super(e),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",r.verifyArgs(e),this.rank=t,tr(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new _t(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=zu(e.kernelSize,t,"kernelSize"),this.strides=zu(e.strides==null?1:e.strides,t,"strides"),this.padding=e.padding==null?"valid":e.padding,hn(this.padding),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Me(this.dataFormat),this.activation=gi(e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.biasInitializer=ge(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Ge(e.biasConstraint),this.biasRegularizer=Ce(e.biasRegularizer),this.activityRegularizer=Ce(e.activityRegularizer),this.dilationRate=zu(e.dilationRate==null?1:e.dilationRate,t,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new z(`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 z(`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 z(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(t){if(co("kernelSize"in t,"required key 'kernelSize' not in config"),typeof t.kernelSize!="number"&&!Fy(t.kernelSize,"number",1,3))throw new z(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(t.kernelSize)}.`)}getConfig(){let t={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:hi(this.activation),useBias:this.useBias,biasInitializer:Te(this.biasInitializer),biasRegularizer:fe(this.biasRegularizer),activityRegularizer:fe(this.activityRegularizer),biasConstraint:Ve(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}},rf=class r extends Oh{constructor(t,e){super(t,e),this.kernel=null,r.verifyArgs(e),this.filters=e.filters,tr(this.filters,"filters"),this.kernelInitializer=ge(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Ge(e.kernelConstraint),this.kernelRegularizer=Ce(e.kernelRegularizer)}build(t){t=Gt(t);let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new z(`The channel dimension of the input should be defined. Found ${t[e]}`);let n=t[e],o=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",o,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:{[e]:n}}],this.built=!0}call(t,e){return B(()=>{t=St(t);let n,o=this.bias==null?null:this.bias.read(),s=Oy(this.activation.getClassName());if(s!=null&&this.rank===2)n=qR(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)n=xJ(t,this.kernel.read(),o,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=qR(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=yJ(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new _t("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(t){t=Gt(t);let e=[],n=this.dataFormat==="channelsLast"?t.slice(1,t.length-1):t.slice(2);for(let s=0;s 0 but got ${JSON.stringify(t.filters)}`)}},Uc=class r extends rf{constructor(t){super(2,t),r.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!Fy(t.kernelSize,"number",1,2))throw new z(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(t.kernelSize)}.`)}};Uc.className="Conv2D";Q.registerClass(Uc);var Hc=class r extends rf{constructor(t){super(3,t),r.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!(Array.isArray(t.kernelSize)&&(t.kernelSize.length===1||t.kernelSize.length===3)))throw new z(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(t.kernelSize)}.`)}};Hc.className="Conv3D";Q.registerClass(Hc);var nf=class extends Uc{constructor(t){if(super(t),this.inputSpec=[new Ie({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new z(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(t=Gt(t),t.length!==4)throw new z("Input should have rank 4; Received input shape: "+JSON.stringify(t));let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new z("The channel dimension of the inputs should be defined. Found `None`.");let n=t[e],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",o,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ie({ndim:4,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{let n=St(t);if(n.shape.length!==4)throw new z(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],i,a;this.dataFormat==="channelsFirst"?(i=2,a=3):(i=1,a=2);let u=o[i],l=o[a],c=this.kernelSize[0],p=this.kernelSize[1],m=this.strides[0],f=this.strides[1],d=xi(u,m,c,this.padding),h=xi(l,f,p,this.padding),g=[s,d,h,this.filters];this.dataFormat!=="channelsLast"&&(n=Vt(n,[0,2,3,1]));let x=om(n,this.kernel.read(),g,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(x=Vt(x,[0,3,1,2])),this.bias!=null&&(x=yn(x,this.bias.read(),this.dataFormat)),this.activation!=null&&(x=this.activation.apply(x)),x})}computeOutputShape(t){t=Gt(t);let e=t.slice(),n,o,s;this.dataFormat==="channelsFirst"?(n=1,o=2,s=3):(n=3,o=1,s=2);let i=this.kernelSize[0],a=this.kernelSize[1],u=this.strides[0],l=this.strides[1];return e[n]=this.filters,e[o]=xi(e[o],u,i,this.padding),e[s]=xi(e[s],l,a,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};nf.className="Conv2DTranspose";Q.registerClass(nf);var of=class extends Hc{constructor(t){if(super(t),this.inputSpec=[new Ie({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new z(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(t=Gt(t),t.length!==5)throw new z("Input should have rank 5; Received input shape: "+JSON.stringify(t));let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new z("The channel dimension of the inputs should be defined. Found `None`.");let n=t[e],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",o,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ie({ndim:5,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{let n=St(t);if(n.shape.length!==5)throw new z(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],i,a,u;this.dataFormat==="channelsFirst"?(u=2,i=3,a=4):(u=1,i=2,a=3);let l=o[u],c=o[i],p=o[a],m=this.kernelSize[0],f=this.kernelSize[1],d=this.kernelSize[2],h=this.strides[0],g=this.strides[1],x=this.strides[2],b=xi(l,h,m,this.padding),w=xi(c,g,f,this.padding),I=xi(p,x,d,this.padding),N=[s,b,w,I,this.filters];this.dataFormat!=="channelsLast"&&(n=Vt(n,[0,2,3,4,1]));let E=$x(n,this.kernel.read(),N,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(E=Vt(E,[0,4,1,2,3])),this.bias!==null&&(E=yn(E,this.bias.read(),this.dataFormat)),this.activation!==null&&(E=this.activation.apply(E)),E})}computeOutputShape(t){t=Gt(t);let e=t.slice(),n,o,s,i;this.dataFormat==="channelsFirst"?(n=1,o=2,s=3,i=4):(n=4,o=1,s=2,i=3);let a=this.kernelSize[0],u=this.kernelSize[1],l=this.kernelSize[2],c=this.strides[0],p=this.strides[1],m=this.strides[2];return e[n]=this.filters,e[o]=xi(e[o],c,a,this.padding),e[s]=xi(e[s],p,u,this.padding),e[i]=xi(e[i],m,l,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};of.className="Conv3DTranspose";Q.registerClass(of);var Tb=class extends rf{constructor(t,e){if(super(t,e),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,e.filters==null)throw new z("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(e.kernelInitializer!=null||e.kernelRegularizer!=null||e.kernelConstraint!=null)throw new z("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(e.padding!=null&&e.padding!=="same"&&e.padding!=="valid")throw new z(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(e.padding)}`);this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=ge(e.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Ce(e.depthwiseRegularizer),this.depthwiseConstraint=Ge(e.depthwiseConstraint),this.pointwiseInitializer=ge(e.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Ce(e.pointwiseRegularizer),this.pointwiseConstraint=Ge(e.pointwiseConstraint)}build(t){if(t=Gt(t),t.length{t=St(t);let n;if(this.rank===1)throw new _t("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(t=Vt(t,[0,2,3,1])),n=fm(t,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=yn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Vt(n,[0,3,1,2])),n})}getConfig(){let t=super.getConfig();return delete t.rank,delete t.kernelInitializer,delete t.kernelRegularizer,delete t.kernelConstraint,t.depthwiseInitializer=Te(this.depthwiseInitializer),t.pointwiseInitializer=Te(this.pointwiseInitializer),t.depthwiseRegularizer=fe(this.depthwiseRegularizer),t.pointwiseRegularizer=fe(this.pointwiseRegularizer),t.depthwiseConstraint=Ve(this.depthwiseConstraint),t.pointwiseConstraint=Ve(this.pointwiseConstraint),t}};Tb.className="SeparableConv";var sf=class extends Tb{constructor(t){super(2,t)}};sf.className="SeparableConv2D";Q.registerClass(sf);var af=class r extends rf{constructor(t){super(1,t),r.verifyArgs(t),this.inputSpec=[{ndim:3}]}getConfig(){let t=super.getConfig();return delete t.rank,delete t.dataFormat,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!Fy(t.kernelSize,"number",1,1))throw new z(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(t.kernelSize)}.`)}};af.className="Conv1D";Q.registerClass(af);var lf=class extends Et{constructor(t){super(t),typeof t.cropping=="number"?this.cropping=[[t.cropping,t.cropping],[t.cropping,t.cropping]]:typeof t.cropping[0]=="number"?this.cropping=[[t.cropping[0],t.cropping[0]],[t.cropping[1],t.cropping[1]]]:this.cropping=t.cropping,this.dataFormat=t.dataFormat===void 0?"channelsLast":t.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(t){return this.dataFormat==="channelsFirst"?[t[0],t[1],t[2]-this.cropping[0][0]-this.cropping[0][1],t[3]-this.cropping[1][0]-this.cropping[1][1]]:[t[0],t[1]-this.cropping[0][0]-this.cropping[0][1],t[2]-this.cropping[1][0]-this.cropping[1][1],t[3]]}call(t,e){return B(()=>{if(t=St(t),this.dataFormat==="channelsLast"){let n=Nh(t,this.cropping[0][0],t.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Nh(n,this.cropping[1][0],t.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Nh(t,this.cropping[0][0],t.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Nh(n,this.cropping[1][0],t.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let t={cropping:this.cropping,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}};lf.className="Cropping2D";Q.registerClass(lf);var uf=class extends Et{constructor(t){super(t),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=t.size==null?this.DEFAULT_SIZE:t.size,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Me(this.dataFormat),this.interpolation=t.interpolation==null?"nearest":t.interpolation,eR(this.interpolation)}computeOutputShape(t){if(this.dataFormat==="channelsFirst"){let e=t[2]==null?null:this.size[0]*t[2],n=t[3]==null?null:this.size[1]*t[3];return[t[0],t[1],e,n]}else{let e=t[1]==null?null:this.size[0]*t[1],n=t[2]==null?null:this.size[1]*t[2];return[t[0],e,n,t[3]]}}call(t,e){return B(()=>{let n=St(t),o=n.shape;if(this.dataFormat==="channelsFirst"){n=Vt(n,[0,2,3,1]);let s=this.size[0]*o[2],i=this.size[1]*o[3],a=this.interpolation==="nearest"?fn.resizeNearestNeighbor(n,[s,i]):fn.resizeBilinear(n,[s,i]);return Vt(a,[0,3,1,2])}else{let s=this.size[0]*o[1],i=this.size[1]*o[2];return this.interpolation==="nearest"?fn.resizeNearestNeighbor(n,[s,i]):fn.resizeBilinear(n,[s,i])}})}getConfig(){let t={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},e=super.getConfig();return Object.assign(t,e),t}};uf.className="UpSampling2D";Q.registerClass(uf);function bJ(r,t,e=[1,1],n="valid",o,s){return B(()=>{o==null&&(o=xn()),Me(o);let i=Mh(r,o);if(r.rank!==4)throw new z(`Input for depthwiseConv2d is required to be 4-D, but is instead ${r.rank}-D`);if(t.rank!==4)throw new z(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=ia(i,t,e,n==="same"?"same":"valid","NHWC",s),o==="channelsFirst"&&(i=Vt(i,[0,3,1,2])),i})}var cf=class extends Oh{constructor(t){super(2,t),this.depthwiseKernel=null,this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=ge(t.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Ge(t.depthwiseConstraint),this.depthwiseRegularizer=Ce(t.depthwiseRegularizer)}build(t){if(t=Gt(t),t.length<4)throw new z(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(t)}.`);let e=this.dataFormat==="channelsFirst"?1:3;if(t[e]==null||t[e]<0)throw new z(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${t[e]}).`);let n=t[e],o=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",o,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(t,e){return B(()=>{t=St(t);let n=bJ(t,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=yn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(t){t=Gt(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],n=this.dataFormat==="channelsFirst"?t[3]:t[2],o=this.dataFormat==="channelsFirst"?t[1]*this.depthMultiplier:t[3]*this.depthMultiplier,s=Tn(e,this.kernelSize[0],this.padding,this.strides[0]),i=Tn(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[t[0],o,s,i]:[t[0],s,i,o]}getConfig(){let t=super.getConfig();return t.depthMultiplier=this.depthMultiplier,t.depthwiseInitializer=Te(this.depthwiseInitializer),t.depthwiseRegularizer=fe(this.depthwiseRegularizer),t.depthwiseConstraint=Ve(this.depthwiseRegularizer),t}};cf.className="DepthwiseConv2D";Q.registerClass(cf);function tk(r,t,e,n){if(Array.isArray(r)){if(t!=null||e!=null)throw new z("When inputs is an array, neither initialState or constants should be provided");n!=null&&(e=r.slice(r.length-n,r.length),r=r.slice(0,r.length-n)),r.length>1&&(t=r.slice(1,r.length)),r=r[0]}function o(s){return s==null||Array.isArray(s)?s:[s]}return t=o(t),e=o(e),{inputs:r,initialState:t,constants:e}}function ek(r,t,e,n=!1,o,s,i=!1,a=!1){return B(()=>{let u=t.shape.length;if(u<3)throw new z(`Input should be at least 3D, but is ${u}D.`);let l=[1,0].concat(gn(2,u));if(t=Vt(t,l),s!=null)throw new _t("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),o!=null&&(o=J(J(o,"bool"),"float32"),o.rank===u-1&&(o=je(o,-1)),o=Vt(o,l)),n&&(t=dr(t,0),o!=null&&(o=dr(o,0)));let c=[],p,m=e,f=t.shape[0],d=gr(t),h;o!=null&&(h=gr(o));for(let x=0;xr(b,m));if(o==null)p=w[0],m=w[1];else{let I=B(()=>{let N=h[x],E=at(wr(N),N),A=X($(w[0],N),$(m[0],E)),D=m.map((F,M)=>X($(w[1][M],N),$(F,E)));return{output:A,newStates:D}});p=I.output,m=I.newStates}a&&c.push(p)}let g;return a&&(g=Fe(c,1)),[p,g,m]})}var po=class r extends Et{constructor(t){super(t);let e;if(t.cell==null)throw new z("cell property is missing for the constructor of RNN.");if(Array.isArray(t.cell)?e=new jc({cells:t.cell}):e=t.cell,e.stateSize==null)throw new z("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=e,this.returnSequences=t.returnSequences==null?!1:t.returnSequences,this.returnState=t.returnState==null?!1:t.returnState,this.goBackwards=t.goBackwards==null?!1:t.goBackwards,this._stateful=t.stateful==null?!1:t.stateful,this.unroll=t.unroll==null?!1:t.unroll,this.supportsMasking=!0,this.inputSpec=[new Ie({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return gn(0,t).map(e=>null)}else return this.states_}setStates(t){this.states_=t}computeOutputShape(t){Uy(t)&&(t=t[0]),t=t;let e=this.cell.stateSize;Array.isArray(e)||(e=[e]);let n=e[0],o;if(this.returnSequences?o=[t[0],t[1],n]:o=[t[0],n],this.returnState){let s=[];for(let i of e)s.push([t[0],i]);return[o].concat(s)}else return o}computeMask(t,e){return B(()=>{Array.isArray(e)&&(e=e[0]);let n=this.returnSequences?e:null;if(this.returnState){let o=this.states.map(s=>null);return[n].concat(o)}else return n})}get states(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,e=[];for(let n=0;na.shape[a.shape.length-1]),i))throw new z(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=i.map(a=>new Ie({shape:[null,a]}));this.stateful&&this.resetStates()}resetStates(t,e=!1){B(()=>{if(!this.stateful)throw new uo("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new z("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(o=>ke([n,o])):this.states_=[ke([n,this.cell.stateSize])];else if(t==null)Tt(this.states_),this.keptStates!=null&&(Tt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>ke([n,o])):this.states_[0]=ke([n,this.cell.stateSize]);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e===!0?this.keptStates.push(this.states_.slice()):Tt(this.states_);for(let o=0;oDe(o.clone()))})}apply(t,e){let n=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=tk(t,n,o,this.numConstants);t=s.inputs,n=s.initialState,o=s.constants;let i=[],a=[];if(n!=null){e.initialState=n,i=i.concat(n),this.stateSpec=[];for(let l of n)this.stateSpec.push(new Ie({shape:l.shape}));a=a.concat(this.stateSpec)}if(o!=null&&(e.constants=o,i=i.concat(o),this.numConstants=o.length),i[0]instanceof en){let l=[t].concat(i),c=this.inputSpec.concat(a),p=this.inputSpec;this.inputSpec=c;let m=super.apply(l,e);return this.inputSpec=p,m}else return super.apply(t,e)}call(t,e){return B(()=>{let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;t=St(t),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(t));let i=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==i)throw new z(`RNN Layer has ${i} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let a={training:o},l=ek((d,h)=>{let g=this.cell.call([d].concat(h),a);return[g[0],g.slice(1)]},t,s,this.goBackwards,n,null,this.unroll,this.returnSequences),c=l[0],p=l[1],m=l[2];this.stateful&&this.resetStates(m,o);let f=this.returnSequences?p:c;return this.returnState?[f].concat(m):f})}getInitialState(t){return B(()=>{let e=ke(t.shape);return e=mt(e,[1,2]),e=Sl(e),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Vy(e,[1,n]):e):this.cell.stateSize>1?[Vy(e,[1,this.cell.stateSize])]:[e]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(t)}getConfig(){let t=super.getConfig(),e={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(e.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===r.className&&(e.cell={className:this.cell.getClassName(),config:n}),Object.assign(Object.assign(Object.assign({},n),t),e)}static fromConfig(t,e,n={}){let o=e.cell,s=wn(o,n);return new t(Object.assign(e,{cell:s}))}};po.className="RNN";Q.registerClass(po);var Tl=class extends Et{},qc=class extends Tl{constructor(t){super(t),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=t.units,tr(this.units,"units"),this.activation=gi(t.activation==null?this.DEFAULT_ACTIVATION:t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=ge(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ge(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ge(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ce(t.kernelRegularizer),this.recurrentRegularizer=Ce(t.recurrentRegularizer),this.biasRegularizer=Ce(t.biasRegularizer),this.kernelConstraint=Ge(t.kernelConstraint),this.recurrentConstraint=Ge(t.recurrentConstraint),this.biasConstraint=Ge(t.biasConstraint),this.dropout=Dc([1,fi([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Dc([1,fi([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Gt(t),this.kernel=this.addWeight("kernel",[t[t.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(t,e){return B(()=>{if(t=t,t.length!==2)throw new z(`SimpleRNNCell expects 2 input Tensors, got ${t.length}.`);let n=t[1];t=t[0];let o=e.training==null?!1:e.training;0wr(t),rate:this.dropout,training:o,dropoutFunc:this.dropoutFunc})),0wr(n),rate:this.recurrentDropout,training:o,dropoutFunc:this.dropoutFunc}));let s,i=this.dropoutMask,a=this.recurrentDropoutMask;i!=null?s=Do($(t,i),this.kernel.read()):s=Do(t,this.kernel.read()),this.bias!=null&&(s=yn(s,this.bias.read())),a!=null&&(n=$(n,a));let u=X(s,Do(n,this.recurrentKernel.read()));return this.activation!=null&&(u=this.activation.apply(u)),[u,u]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:hi(this.activation),useBias:this.useBias,kernelInitializer:Te(this.kernelInitializer),recurrentInitializer:Te(this.recurrentInitializer),biasInitializer:Te(this.biasInitializer),kernelRegularizer:fe(this.kernelRegularizer),recurrentRegularizer:fe(this.recurrentRegularizer),biasRegularizer:fe(this.biasRegularizer),activityRegularizer:fe(this.activityRegularizer),kernelConstraint:Ve(this.kernelConstraint),recurrentConstraint:Ve(this.recurrentConstraint),biasConstraint:Ve(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},t),e)}};qc.className="SimpleRNNCell";Q.registerClass(qc);var pf=class extends po{constructor(t){t.cell=new qc(t),super(t)}call(t,e){return B(()=>{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return new t(e)}};pf.className="SimpleRNN";Q.registerClass(pf);var Kc=class extends Tl{constructor(t){if(super(t),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",t.resetAfter)throw new z("GRUCell does not support reset_after parameter set to true.");this.units=t.units,tr(this.units,"units"),this.activation=gi(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=gi(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=ge(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ge(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ge(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ce(t.kernelRegularizer),this.recurrentRegularizer=Ce(t.recurrentRegularizer),this.biasRegularizer=Ce(t.biasRegularizer),this.kernelConstraint=Ge(t.kernelConstraint),this.recurrentConstraint=Ge(t.recurrentConstraint),this.biasConstraint=Ge(t.biasConstraint),this.dropout=Dc([1,fi([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Dc([1,fi([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Gt(t);let e=t[t.length-1];this.kernel=this.addWeight("kernel",[e,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(t,e){return B(()=>{if(t=t,t.length!==2)throw new z(`GRUCell expects 2 input Tensors (inputs, h, c), got ${t.length}.`);let n=e.training==null?!1:e.training,o=t[1];t=t[0],0wr(t),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0wr(o),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,a,u,l;0{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};mf.className="GRU";Q.registerClass(mf);var _l=class extends Tl{constructor(t){super(t),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=t.units,tr(this.units,"units"),this.activation=gi(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=gi(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=ge(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ge(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ge(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=t.unitForgetBias,this.kernelRegularizer=Ce(t.kernelRegularizer),this.recurrentRegularizer=Ce(t.recurrentRegularizer),this.biasRegularizer=Ce(t.biasRegularizer),this.kernelConstraint=Ge(t.kernelConstraint),this.recurrentConstraint=Ge(t.recurrentConstraint),this.biasConstraint=Ge(t.biasConstraint),this.dropout=Dc([1,fi([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Dc([1,fi([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){var e;t=Gt(t);let n=t[t.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 o;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,i=this.units;o=new(e=class extends bn{apply(u,l){let c=s.apply([i]),p=new Mu().apply([i]),m=s.apply([i*2]);return BN(BN(c,p),m)}},e.className="CustomInit",e)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,o,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training;if(t=t,t.length!==3)throw new z(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let o=t[1],s=t[2];t=t[0],0wr(t),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0wr(o),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,a=this.recurrentDropoutMask,u,l,c,p;0{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};ff.className="LSTM";Q.registerClass(ff);var jc=class extends Tl{constructor(t){super(t),this.cells=t.cells}get stateSize(){let t=[];for(let e of this.cells.slice().reverse())Array.isArray(e.stateSize)?t.push(...e.stateSize):t.push(e.stateSize);return t}call(t,e){return B(()=>{t=t;let n=t.slice(1),o=[];for(let a of 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o=n.weights.length,s=t.splice(o);for(let i=0;is!=null?s(t(),e):Wy(t(),e),a=()=>Ou(i,t,n);return!o||o<=1?De(a().clone()):Array(o).fill(void 0).map(a).map(l=>De(l.clone()))}var wJ=function(r,t){var e={};for(var n in r)Object.prototype.hasOwnProperty.call(r,n)&&t.indexOf(n)<0&&(e[n]=r[n]);if(r!=null&&typeof Object.getOwnPropertySymbols=="function")for(var o=0,n=Object.getOwnPropertySymbols(r);o{if(this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),e&&e.constants)throw new z("ConvRNN2D cell does not support constants");let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}computeOutputShape(t){let e=this.computeSingleOutputShape(t);return this.returnSequences||(e=[e[0],...e.slice(2)]),this.returnState&&(e=[e,...Array(2).fill([t[0],...e.slice(-3)])]),e}getInitialState(t){return B(()=>{let{stateSize:e}=this.cell,n=t.shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)],i=ke(s);return Array.isArray(e)?Array(e.length).fill(i):[i]})}resetStates(t,e=!1){B(()=>{if(!this.stateful)throw new uo("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)];if(n[0]==null)throw new z("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(()=>ke(s)):this.states_=[ke(s)];else if(t==null)Tt(this.states_),this.keptStates!=null&&(Tt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>ke(s)):this.states_[0]=ke(s);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e?this.keptStates.push(this.states_.slice()):Tt(this.states_);for(let a=0;aDe(a.clone()))})}computeSingleOutputShape(t){let{dataFormat:e,filters:n,kernelSize:o,padding:s,strides:i,dilationRate:a}=this.cell,u=e==="channelsFirst",l=t[u?3:2],c=t[u?4:3],p=Tn(l,o[0],s,i[0],a[0]),m=Tn(c,o[1],s,i[1],a[1]);return[...t.slice(0,2),...u?[n,p,m]:[p,m,n]]}};_b.className="ConvRNN2D";var Xc=class extends _l{constructor(t){let{filters:e,kernelSize:n,strides:o,padding:s,dataFormat:i,dilationRate:a}=t;super(Object.assign(Object.assign({},t),{units:e})),this.filters=e,tr(this.filters,"filters"),this.kernelSize=zu(n,2,"kernelSize"),this.kernelSize.forEach(u=>tr(u,"kernelSize")),this.strides=zu(o||1,2,"strides"),this.strides.forEach(u=>tr(u,"strides")),this.padding=s||"valid",hn(this.padding),this.dataFormat=i||"channelsLast",Me(this.dataFormat),this.dilationRate=zu(a||1,2,"dilationRate"),this.dilationRate.forEach(u=>tr(u,"dilationRate"))}build(t){var e;t=Gt(t);let n=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[n]==null)throw new z(`The channel dimension of the input should be defined. Found ${t[n]}`);let o=t[n],s=4,i=this.kernelSize.concat([o,this.filters*s]);this.kernel=this.addWeight("kernel",i,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let a=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",a,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let u;if(this.unitForgetBias){let l=this.biasInitializer,c=this.filters;u=new(e=class extends bn{apply(m,f){let d=l.apply([c]),h=ar([c]),g=l.apply([c*2]);return Tm([d,h,g])}},e.className="CustomInit",e)}else u=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*s],null,u,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(t,e){return B(()=>{if(t.length!==3)throw new z(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let n=e.training||!1,o=t[0],s=t[1],i=t[2],a=4;0wr(o),rate:this.dropout,training:n,count:a,dropoutFunc:this.dropoutFunc}));let u=this.dropoutMask,l=(nt,st,lt)=>!st||!st[lt]?nt:$(st[lt],nt),c=l(o,u,0),p=l(o,u,1),m=l(o,u,2),f=l(o,u,3);0wr(s),rate:this.recurrentDropout,training:n,count:a,dropoutFunc:this.dropoutFunc}));let d=this.recurrentDropoutMask,h=l(s,d,0),g=l(s,d,1),x=l(s,d,2),b=l(s,d,3),w=3,[I,N,E,A]=hr(this.kernel.read(),a,w),[D,F,M,V]=this.useBias?hr(this.bias.read(),a):[null,null,null,null];c=this.inputConv(c,I,D,this.padding),p=this.inputConv(p,N,F,this.padding),m=this.inputConv(m,E,M,this.padding),f=this.inputConv(f,A,V,this.padding);let[G,W,q,H]=hr(this.recurrentKernel.read(),a,w);h=this.recurrentConv(h,G),g=this.recurrentConv(g,W),x=this.recurrentConv(x,q),b=this.recurrentConv(b,H);let K=this.recurrentActivation.apply(X(c,h)),Y=this.recurrentActivation.apply(X(p,g)),Z=X($(Y,i),$(K,this.activation.apply(X(m,x)))),et=$(this.recurrentActivation.apply(X(f,b)),this.activation.apply(Z));return[et,et,Z]})}getConfig(){let t=super.getConfig(),{units:e}=t,n=wJ(t,["units"]),o={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),o)}inputConv(t,e,n,o){let s=Nn(t,e,this.strides,o||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?yn(s,n,this.dataFormat):s}recurrentConv(t,e){return Nn(t,e,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Xc.className="ConvLSTM2DCell";Q.registerClass(Xc);var df=class extends _b{constructor(t){let e=new Xc(t);super(Object.assign(Object.assign({},t),{cell:e}))}static fromConfig(t,e){return new t(e)}};df.className="ConvLSTM2D";Q.registerClass(df);var Yc=class extends Et{constructor(t){super(t),this.rate=Math.max(Math.min(t.rate,1),0),this.noiseShape=t.noiseShape,this.seed=t.seed,this.supportsMasking=!0}getNoiseShape(t){if(this.noiseShape==null)return this.noiseShape;let e=t.shape,n=[];for(let o=0;o{this.invokeCallHook(t,e);let n=St(t);if(0Wy(n,this.rate,s,this.seed),()=>n,o)}return t})}getConfig(){let t={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},e=super.getConfig();return Object.assign(t,e),t}dispose(){return super.dispose()}};Yc.className="Dropout";Q.registerClass(Yc);var hf=class extends Yc{constructor(t){super(t),this.inputSpec=[{ndim:3}]}getNoiseShape(t){let e=t.shape;return[e[0],1,e[2]]}};hf.className="SpatialDropout1D";Q.registerClass(hf);var gf=class extends Et{constructor(t){if(super(t),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",t.batchInputShape==null&&t.inputShape==null&&t.inputDim!=null){let e=null;t.batchSize!=null&&(e=t.batchSize),this.batchInputShape=[e,t.inputDim]}this.units=t.units,tr(this.units,"units"),this.activation=gi(t.activation),t.useBias!=null&&(this.useBias=t.useBias),this.kernelInitializer=ge(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=ge(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Ge(t.kernelConstraint),this.biasConstraint=Ge(t.biasConstraint),this.kernelRegularizer=Ce(t.kernelRegularizer),this.biasRegularizer=Ce(t.biasRegularizer),this.activityRegularizer=Ce(t.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(t){t=Gt(t);let e=t[t.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[e,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]:e}}],this.built=!0}computeOutputShape(t){t=Gt(t);let e=t.slice();return e[e.length-1]=this.units,e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t),o=Oy(this.activation.getClassName()),s;return o!=null?s=Do(n,this.kernel.read(),o,this.bias?this.bias.read():null):(s=Do(n,this.kernel.read()),this.bias!=null&&(s=yn(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let t={units:this.units,activation:hi(this.activation),useBias:this.useBias,kernelInitializer:Te(this.kernelInitializer),biasInitializer:Te(this.biasInitializer),kernelRegularizer:fe(this.kernelRegularizer),biasRegularizer:fe(this.biasRegularizer),activityRegularizer:fe(this.activityRegularizer),kernelConstraint:Ve(this.kernelConstraint),biasConstraint:Ve(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}};gf.className="Dense";Q.registerClass(gf);var xf=class extends Et{constructor(t){t=t||{},super(t),this.inputSpec=[{minNDim:3}],this.dataFormat=t.dataFormat}computeOutputShape(t){t=Gt(t);for(let e of t.slice(1))if(e==null)throw new z(`The shape of the input to "Flatten" is not fully defined (got ${t.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[t[0],Ao(t,1)]}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);if(this.dataFormat==="channelsFirst"&&n.rank>1){let o=[0];for(let s=2;s{this.invokeCallHook(t,e);let n=St(t);return this.activation.apply(n)})}getConfig(){let t={activation:hi(this.activation)},e=super.getConfig();return Object.assign(t,e),t}};yf.className="Activation";Q.registerClass(yf);var bf=class extends Et{constructor(t){super(t),this.n=t.n,this.inputSpec=[{ndim:2}]}computeOutputShape(t){return[t[0],this.n,t[1]]}call(t,e){return B(()=>(t=St(t),oR(t,this.n)))}getConfig(){let t={n:this.n},e=super.getConfig();return Object.assign(t,e),t}};bf.className="RepeatVector";Q.registerClass(bf);var wf=class extends Et{constructor(t){super(t),this.targetShape=t.targetShape;for(let e=0;e{this.invokeCallHook(t,e);let n=St(t),o=n.shape,s=o.slice(0,1).concat(this.fixUnknownDimension(o.slice(1),this.targetShape));return R(n,s)})}getConfig(){let t={targetShape:this.targetShape},e=super.getConfig();return Object.assign(t,e),t}};wf.className="Reshape";Q.registerClass(wf);var If=class extends Et{constructor(t){if(super(t),t.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(t.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${t.dims} instead.`);let e=gn(1,t.dims.length+1);if(!y.arraysEqual(t.dims.slice().sort(),e))throw new Error("Invalid permutation `dims`: "+JSON.stringify(t.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=t.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Ie({ndim:this.dims.length+1})]}computeOutputShape(t){t=Gt(t);let e=t.slice();return this.dims.forEach((n,o)=>{e[o+1]=t[n]}),e}call(t,e){return Vt(St(t),this.dimsIncludingBatch)}getConfig(){let t={dims:this.dims},e=super.getConfig();return Object.assign(t,e),t}};If.className="Permute";Q.registerClass(If);var Cf=class extends Et{constructor(t){super(t==null?{}:t),this.supportsMasking=!0,t!=null?this.maskValue=t.maskValue==null?0:t.maskValue:this.maskValue=0}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={maskValue:this.maskValue};return Object.assign(e,t),e}computeMask(t,e){let n=St(t);return cc(ui(n,this.maskValue),-1)}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t),i=cc(ui(n,this.maskValue),-1,!0);return $(n,J(i,n.dtype))})}};Cf.className="Masking";Q.registerClass(Cf);var vf=class extends Et{constructor(t){if(super(t),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",t.batchInputShape==null&&t.inputShape==null){let e=null;t.batchSize!=null&&(e=t.batchSize),t.inputLength==null?this.batchInputShape=[e,null]:this.batchInputShape=[e].concat(ue(t.inputLength))}this.inputDim=t.inputDim,tr(this.inputDim,"inputDim"),this.outputDim=t.outputDim,tr(this.outputDim,"outputDim"),this.embeddingsInitializer=ge(t.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Ce(t.embeddingsRegularizer),this.activityRegularizer=Ce(t.activityRegularizer),this.embeddingsConstraint=Ge(t.embeddingsConstraint),this.maskZero=t.maskZero,this.supportsMasking=t.maskZero,this.inputLength=t.inputLength}build(t){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(t){}computeMask(t,e){return B(()=>this.maskZero?(t=St(t),ui(t,vt(t))):null)}computeOutputShape(t){if(t=Gt(t),this.inputLength==null)return[...t,this.outputDim];let e=ue(this.inputLength);if(e.length!==t.length-1)throw new z(`"inputLength" is ${this.inputLength}, but received input shape has shape ${t}`);{let n=0;for(let o=0;o{this.invokeCallHook(t,e);let n=St(t);n.dtype!=="int32"&&(n=tn(n,"int32"));let o=Gy(this.embeddings.read(),R(n,[n.size]));return R(o,Gt(this.computeOutputShape(n.shape)))})}getConfig(){let t={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Te(this.embeddingsInitializer),embeddingsRegularizer:fe(this.embeddingsRegularizer),activityRegularizer:fe(this.activityRegularizer),embeddingsConstraint:Ve(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},e=super.getConfig();return Object.assign(t,e),t}};vf.className="Embedding";Q.registerClass(vf);var Al=class extends Et{constructor(t){super(t||{}),this.supportsMasking=!0}mergeFunction(t){throw new _t}computeElementwiseOpOutputShape(t,e){if(t==null||e==null)return null;if(t.length1)throw new z(`Can not merge tensors with different batch sizes. 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Array.isArray(this.axes)?o=this.axes.map((s,i)=>Ph(s,t[i].shape.length)):o=[Ph(this.axes,e.shape.length),Ph(this.axes,n.shape.length)],this.normalize&&(e=_h(e,o[0]),n=_h(n,o[1])),IJ(e,n,o)}interpretAxes(t,e){let n;return Array.isArray(this.axes)?n=this.axes:n=[Ph(this.axes,t.length),Ph(this.axes,e.length)],n}computeOutputShape(t){y.assert(Array.isArray(t)&&t.length===2&&Array.isArray(t[0])&&Array.isArray(t[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let e=t[0].slice(),n=t[1].slice();if(e.length>3||n.length>3)throw new _t("Dot layer does not support tensors of 4D or higher rank yet.");let o=this.interpretAxes(e,n);e.splice(o[0],1),n.splice(o[1],1),n.splice(0,1);let s=e.concat(n);return s.length===1&&s.push(1),s}computeMask(t,e){return null}getConfig(){let t={axes:this.axes,normalize:this.normalize},e=super.getConfig();return Object.assign(t,e),t}};Af.className="Dot";Q.registerClass(Af);var Df=class extends Et{constructor(t){super(t),this.supportsMasking=!0,this.stddev=t.stddev}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={stddev:this.stddev};return Object.assign(e,t),e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);return Ou(()=>X(_m(n.shape,0,this.stddev),n),()=>n,e.training||!1)})}};Df.className="GaussianNoise";Q.registerClass(Df);var $f=class extends Et{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);return this.rate>0&&this.rate<1?Ou(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return $(n,_m(n.shape,1,s))},()=>n,e.training||!1):n})}};$f.className="GaussianDropout";Q.registerClass($f);var Rf=class extends Et{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate,this.noiseShape=t.noiseShape}_getNoiseShape(t){return 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e=this.axis>=0?this.axis:this.axis+t.length,n=t[e];if(n==null)throw new z(`Axis ${e} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(t)}.`);this.inputSpec=[new Ie({ndim:t.length,axes:{[e]:n}})];let o=[n];this.scale&&(this.gamma=this.addWeight("gamma",o,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",o,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",o,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",o,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training,o=St(t),s=o.shape,i=s.length,a=gn(0,i),u=this.axis>=0?this.axis:this.axis+i;a.splice(u,1);let l=To(1,i);l[u]=s[u];let c=a.slice();c.sort();let p=!y.arraysEqual(c,gn(0,i).slice(0,i-1)),m=()=>{if(p){let 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t={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Te(this.betaInitializer),gammaInitializer:Te(this.gammaInitializer),movingMeanInitializer:Te(this.movingMeanInitializer),movingVarianceInitializer:Te(this.movingVarianceInitializer),betaRegularizer:fe(this.betaRegularizer),gammaRegularizer:fe(this.gammaRegularizer),betaConstraint:Ve(this.betaConstraint),gammaConstraint:Ve(this.gammaConstraint)},e=super.getConfig();return Object.assign(t,e),t}};Ff.className="BatchNormalization";Q.registerClass(Ff);var Of=class extends Et{constructor(t){if(t==null&&(t={}),super(t),this.axis=t.axis==null?-1:t.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 e of this.axis)if(!Number.isInteger(e))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new 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n=St(t),o=n.shape,s=o.length;return B(()=>{let{mean:a,variance:u}=dc(n,this.axis,!0),l=To(1,s);for(let h of this.axis)l[h]=o[h];let c=h=>h!=null&&h.shape.length!==s?R(h,l):h,p=this.scale?c(this.gamma.read()):null,m=this.center?c(this.beta.read()):null,f=[],d=[];for(let h=0;h{if(r.rank!==4)throw new z(`temporalPadding expects input tensor to be 4-D, but received a ${r.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new z("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(e==null&&(e=xn()),e!=="channelsLast"&&e!=="channelsFirst")throw new z(`Unknown data format: ${e}. 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s==="max"?i=ku(r,t,e,a):i=xu(r,t,e,a),o==="channelsFirst"&&(i=Vt(i,[0,3,1,2])),i})}function KR(r,t,e,n,o,s){return B(()=>{Me(o),MN(s),hn(n),e==null&&(e=[1,1,1]),n==null&&(n="valid"),o==null&&(o=xn()),s==null&&(s="max"),r=QN(r,o);let i,a=n==="same"?"same":"valid";return s==="max"?i=Xx(r,t,e,a):i=wx(r,t,e,a),o==="channelsFirst"&&(i=Vt(i,[0,4,1,2,3])),i})}var Eb=class extends Et{constructor(t){if(t.poolSize==null&&(t.poolSize=2),super(t),typeof t.poolSize=="number")this.poolSize=[t.poolSize];else if(Array.isArray(t.poolSize)&&t.poolSize.length===1&&typeof t.poolSize[0]=="number")this.poolSize=t.poolSize;else throw new z(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.poolSize)}`);if(tr(this.poolSize,"poolSize"),t.strides==null)this.strides=this.poolSize;else if(typeof t.strides=="number")this.strides=[t.strides];else if(Array.isArray(t.strides)&&t.strides.length===1&&typeof 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Me(s),hn(o),Fb(t,e,n,o,s,"avg")}};Lf.className="AveragePooling1D";Q.registerClass(Lf);var Ab=class extends Et{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==2)throw new z(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides];tr(this.poolSize,"poolSize"),tr(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Me(this.dataFormat),hn(this.padding),this.inputSpec=[new Ie({ndim:4})]}computeOutputShape(t){t=Gt(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],n=this.dataFormat==="channelsFirst"?t[3]:t[2];return e=Tn(e,this.poolSize[0],this.padding,this.strides[0]),n=Tn(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[t[0],t[1],e,n]:[t[0],e,n,t[3]]}call(t,e){return B(()=>(this.invokeCallHook(t,e),this.poolingFunction(St(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},zf=class extends Ab{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Me(s),hn(o),Fb(t,e,n,o,s,"max")}};zf.className="MaxPooling2D";Q.registerClass(zf);var Bf=class extends Ab{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Me(s),hn(o),Fb(t,e,n,o,s,"avg")}};Bf.className="AveragePooling2D";Q.registerClass(Bf);var Db=class extends Et{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==3)throw new z(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides,t.strides];tr(this.poolSize,"poolSize"),tr(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Me(this.dataFormat),hn(this.padding),this.inputSpec=[new Ie({ndim:5})]}computeOutputShape(t){t=Gt(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],n=this.dataFormat==="channelsFirst"?t[3]:t[2],o=this.dataFormat==="channelsFirst"?t[4]:t[3];return e=Tn(e,this.poolSize[0],this.padding,this.strides[0]),n=Tn(n,this.poolSize[1],this.padding,this.strides[1]),o=Tn(o,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[t[0],t[1],e,n,o]:[t[0],e,n,o,t[4]]}call(t,e){return B(()=>(this.invokeCallHook(t,e),this.poolingFunction(St(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Vf=class extends Db{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Me(s),hn(o),KR(t,e,n,o,s,"max")}};Vf.className="MaxPooling3D";Q.registerClass(Vf);var Gf=class extends Db{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Me(s),hn(o),KR(t,e,n,o,s,"avg")}};Gf.className="AveragePooling3D";Q.registerClass(Gf);var $b=class extends Et{constructor(t){super(t),this.inputSpec=[new Ie({ndim:3})]}computeOutputShape(t){return[t[0],t[2]]}call(t,e){throw new _t}},Wf=class extends $b{constructor(t){super(t||{})}call(t,e){return B(()=>{let n=St(t);return Ne(n,1)})}};Wf.className="GlobalAveragePooling1D";Q.registerClass(Wf);var Uf=class extends $b{constructor(t){super(t||{})}call(t,e){return B(()=>{let n=St(t);return Sr(n,1)})}};Uf.className="GlobalMaxPooling1D";Q.registerClass(Uf);var Rb=class extends Et{constructor(t){super(t),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Me(this.dataFormat),this.inputSpec=[new Ie({ndim:4})]}computeOutputShape(t){return t=t,this.dataFormat==="channelsLast"?[t[0],t[3]]:[t[0],t[1]]}call(t,e){throw new _t}getConfig(){let t={dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Hf=class extends Rb{call(t,e){return B(()=>{let n=St(t);return this.dataFormat==="channelsLast"?Ne(n,[1,2]):Ne(n,[2,3])})}};Hf.className="GlobalAveragePooling2D";Q.registerClass(Hf);var qf=class extends Rb{call(t,e){return B(()=>{let n=St(t);return this.dataFormat==="channelsLast"?Sr(n,[1,2]):Sr(n,[2,3])})}};qf.className="GlobalMaxPooling2D";Q.registerClass(qf);var Ob=class extends Et{constructor(t){super(t),this.layer=t.layer}build(t){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(t){this.layer!=null&&(this.layer.trainable=t)}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(t){this.layer.setWeights(t)}getConfig(){let t={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},e=super.getConfig();return Object.assign(t,e),t}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(t)}static fromConfig(t,e,n={}){let o=e.layer,s=wn(o,n);delete e.layer;let i={layer:s};return Object.assign(i,e),new t(i)}},Kf=class extends Ob{constructor(t){super(t),this.supportsMasking=!0}build(t){if(t=Gt(t),t.length<3)throw new z(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(t)}`);this.inputSpec=[{shape:t}];let e=[t[0]].concat(t.slice(2));this.layer.built||(this.layer.build(e),this.layer.built=!0),super.build(t)}computeOutputShape(t){t=Gt(t);let e=[t[0]].concat(t.slice(2)),n=this.layer.computeOutputShape(e),o=t[1];return[n[0],o].concat(n.slice(1))}call(t,e){return B(()=>(t=St(t),ek((i,a)=>[St(this.layer.call(i,e)),[]],t,[],!1,null,null,!1,!0)[1]))}};Kf.className="TimeDistributed";Q.registerClass(Kf);function kJ(r){da(Q$,"BidirectionalMergeMode",r)}var TJ="concat",jf=class extends Ob{constructor(t){super(t);let e=t.layer.getConfig(),n={};n.className=t.layer.getClassName(),n.config=e,this.forwardLayer=wn(n),e.goBackwards=e.goBackwards!==!0;let 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e=t.length,n=Math.floor(e/2);this.forwardLayer.setWeights(t.slice(0,n)),this.backwardLayer.setWeights(t.slice(n))}computeOutputShape(t){let e=this.forwardLayer.computeOutputShape(t);Array.isArray(e)&&Array.isArray(e[0])||(e=[e]),e=e;let n,o,s;return this.returnState&&(s=e.slice(1)),n=e[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,o=[n]):this.mergeMode==null?o=[n,n.slice()]:o=[n],this.returnState?this.mergeMode==null?o.concat(s).concat(s.slice()):[n].concat(s).concat(s.slice()):kr(o)}apply(t,e){let n=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=tk(t,n,o,this.numConstants);if(t=s.inputs,n=s.initialState,o=s.constants,Array.isArray(t)&&(n=t.slice(1),t=t[0]),(n==null||n.length===0)&&o==null)return super.apply(t,e);let i=[],a=[];if(n!=null){let l=n.length;if(l%2>0)throw new z("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");e.initialState=n,i.push(...n);let c=n.map(p=>new 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a;return this.mergeMode==="concat"?a=Tm([o,s]):this.mergeMode==="sum"?a=X(o,s):this.mergeMode==="ave"?a=$(.5,X(o,s)):this.mergeMode==="mul"?a=$(o,s):this.mergeMode==null&&(a=[o,s]),this.returnState?this.mergeMode==null?a.concat(i):[a].concat(i):a})}resetStates(t){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(t){mi(this.forwardLayer.name,()=>{this.forwardLayer.build(t)}),mi(this.backwardLayer.name,()=>{this.backwardLayer.build(t)}),this.built=!0}computeMask(t,e){Array.isArray(e)&&(e=e[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[e,e]:n=e:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let s=this.forwardLayer.states.map(i=>null);return Array.isArray(n)?n.concat(s).concat(s):[n].concat(s).concat(s)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(t),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(t)}getConfig(){let t={mergeMode:this.mergeMode},e=super.getConfig();return Object.assign(t,e),t}static fromConfig(t,e){let n=wn(e.layer);if(delete e.layer,e.numConstants!=null)throw new _t("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let o=e;return o.layer=n,new t(o)}};jf.className="Bidirectional";Q.registerClass(jf);var Xf=class extends Et{constructor(t){super(t),this.scale=t.scale,t.offset?this.offset=t.offset:this.offset=0}getConfig(){let t={scale:this.scale,offset:this.offset},e=super.getConfig();return Object.assign(t,e),t}call(t,e){return 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t[e]=this.height,t[n]=this.width,t}};Yf.className="CenterCrop";Q.registerClass(Yf);function jR(r,t,e,n){let o=St(r);if(o.dtype!=="int32"&&(o=tn(o,"int32")),t==="int")return o;let s=o.shape;if(o.rank===0&&(o=je(o,-1)),t==="oneHot"&&o.shape[o.shape.length-1]!==1&&(o=je(o,-1)),o.rank>2)throw new z(`When outputMode is not int, maximum output rank is 2 Received outputMode ${t} and input shape ${s} which would result in output rank ${o.rank}.`);let i=["multiHot","oneHot"].includes(t),a=o,u;if(typeof n!="undefined"&&t==="count"?u=ph(a,n,e,i):u=ph(a,[],e,i),t!=="tfIdf")return u;if(n)return $(u,n);throw new z("When outputMode is 'tfIdf', weights must be provided.")}var Zf=class extends Et{constructor(t){super(t),this.numTokens=t.numTokens,t.outputMode?this.outputMode=t.outputMode:this.outputMode="multiHot"}getConfig(){let t={numTokens:this.numTokens,outputMode:this.outputMode},e=super.getConfig();return Object.assign(t,e),t}computeOutputShape(t){return t=Gt(t),t==null?[this.numTokens]:this.outputMode==="oneHot"&&t[t.length-1]!==1?(t.push(this.numTokens),t):(t[t.length-1]=this.numTokens,t)}call(t,e){return B(()=>{t=St(t),t.dtype!=="int32"&&(t=tn(t,"int32"));let n;if(typeof e.countWeights!="undefined"){if(this.outputMode!=="count")throw new z(`countWeights is not used when outputMode !== count. - Received countWeights=${e.countWeights}`);n=St(e.countWeights)}let o=Sr(t),s=gl(t),i=Re(this.numTokens,o).bufferSync().get(0),a=cn(s,0).bufferSync().get(0);if(!(i&&a))throw new z(`Input values must be between 0 < values <= numTokens with numTokens=${this.numTokens}`);return jR(t,this.outputMode,this.numTokens,n)})}};Zf.className="CategoryEncoding";Q.registerClass(Zf);var DJ=["bilinear","nearest"],XR=new Set(DJ),Jf=class extends Et{constructor(t){if(super(t),this.height=t.height,this.width=t.width,t.interpolation)if(XR.has(t.interpolation))this.interpolation=t.interpolation;else throw new z(`Invalid interpolation parameter: ${t.interpolation} is not implemented`);else this.interpolation="bilinear";this.cropToAspectRatio=!!t.cropToAspectRatio}computeOutputShape(t){t=Gt(t);let e=t[2];return[this.height,this.width,e]}getConfig(){let t={height:this.height,width:this.width,interpolation:this.interpolation,cropToAspectRatio:this.cropToAspectRatio},e=super.getConfig();return Object.assign(t,e),t}call(t,e){return B(()=>{let n=[this.height,this.width];if(this.interpolation==="bilinear")return fn.resizeBilinear(t,n,!this.cropToAspectRatio);if(this.interpolation==="nearest")return fn.resizeNearestNeighbor(t,n,!this.cropToAspectRatio);throw new Error(`Interpolation is ${this.interpolation} but only ${[...XR]} are supported`)})}};Jf.className="Resizing";Q.registerClass(Jf);var zh=class{constructor(t){this.seed=t}next(){if(this.seed!==void 0)return this.seed++}};zh.className="RandomSeed";var Bh=class extends Et{constructor(t){super(t),this.randomGenerator=new zh(t.seed)}getConfig(){let 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t({l1:e.l1,l2:e.l2})}};Lu.className="L1L2";Q.registerClass(Lu);function KR(r){return tk(r),new Lu({l1:r!=null?r.l1:null,l2:0})}function jR(r){return tk(r),new Lu({l2:r!=null?r.l2:null,l1:0})}var HR={l1l2:"L1L2"};function fe(r){return km(r)}function qR(r,t={}){return fa(r,Q.SerializationMap.getMap().classNameMap,t,"regularizer")}function ve(r){if(r==null)return null;if(typeof r=="string"){let e={className:r in HR?HR[r]:r,config:{}};return qR(e)}else return r instanceof _b?r:qR(r)}var Zm=class extends Et{constructor(t){super(t==null?{}:t),this.supportsMasking=!0,t!=null&&(this.maxValue=t.maxValue)}call(t,e){t=St(t);let n=Or(t);return this.maxValue!=null&&(n=vr(n,0,this.maxValue)),n}computeOutputShape(t){return t}getConfig(){let t={maxValue:this.maxValue},e=super.getConfig();return Object.assign(t,e),t}};Zm.className="ReLU";Q.registerClass(Zm);var Jm=class extends Et{constructor(t){super(t==null?{}:t),this.DEFAULT_ALPHA=.3,t==null&&(t={}),this.alpha=t.alpha==null?this.DEFAULT_ALPHA:t.alpha}call(t,e){let n=St(t);return Cu(n,this.alpha)}computeOutputShape(t){return t}getConfig(){let t={alpha:this.alpha},e=super.getConfig();return Object.assign(t,e),t}};Jm.className="LeakyReLU";Q.registerClass(Jm);var Qm=class extends Et{constructor(t){if(super(t==null?{}:t),this.DEFAULT_ALPHA_INITIALIZER="zeros",t==null&&(t={}),this.supportsMasking=!0,this.alphaInitializer=xe(t.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=ve(t.alphaRegularizer),this.alphaConstraint=Ge(t.alphaConstraint),t.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(t.sharedAxes))this.sharedAxes=t.sharedAxes;else if(typeof t.sharedAxes=="number")this.sharedAxes=[t.sharedAxes];else throw new z(`Expected sharedAxes to be a number or an array of numbers, but got ${t.sharedAxes}`)}build(t){t=Gt(t);let e=t.slice(1);if(this.sharedAxes!=null)for(let o of this.sharedAxes)e[o-1]=1;this.alpha=this.addWeight("alpha",e,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let o=1;o{let n=St(t),o=e.mask;if(o!=null){let s=$(at(ar(n.shape),J(o,n.dtype)),pt(-1e9));n=K(n,s)}return this.axis instanceof Array?this.axis.length>1?Ke(at(n,Su(n,this.axis,!0))):this.softmax(n,this.axis[0]):this.softmax(n,this.axis)})}computeOutputShape(t){return t}getConfig(){let t={axis:this.axis},e=super.getConfig();return Object.assign(t,e),t}};rf.className="Softmax";Q.registerClass(rf);function zu(r,t,e){if(typeof r=="number")return To(r,t);if(r.length!==t)throw new z(`The ${e} argument must be an integer or tuple of ${t} integers. 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z(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${r.rank}.`);if(t.rank!==3&&t.rank!==4)throw new z(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${r.rank}.`);let u=Ph(r,s);if(o==="causal")throw new _t("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return u=Ru.conv2d({x:u,filter:t,strides:n,pad:o==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:e,activation:a}),s==="channelsFirst"&&(u=Vt(u,[0,3,1,2])),u})}function IJ(r,t,e,n=[1,1,1],o="valid",s,i){return B(()=>{if(s==null&&(s=xn()),Me(s),r.rank!==4&&r.rank!==5)throw new z(`conv3dWithBias expects input to be of rank 4 or 5, but received ${r.rank}.`);if(t.rank!==4&&t.rank!==5)throw new z(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${r.rank}.`);let a=ek(r,s);if(o==="causal")throw new _t("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return a=Dx(a,t,n,o==="same"?"same":"valid","NDHWC",i),e!=null&&(a=yn(a,e)),s==="channelsFirst"&&(a=Vt(a,[0,4,1,2,3])),a})}var Mh=class r extends Et{constructor(t,e){if(super(e),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",r.verifyArgs(e),this.rank=t,tr(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new _t(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=zu(e.kernelSize,t,"kernelSize"),this.strides=zu(e.strides==null?1:e.strides,t,"strides"),this.padding=e.padding==null?"valid":e.padding,hn(this.padding),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Me(this.dataFormat),this.activation=xi(e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.biasInitializer=xe(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Ge(e.biasConstraint),this.biasRegularizer=ve(e.biasRegularizer),this.activityRegularizer=ve(e.activityRegularizer),this.dilationRate=zu(e.dilationRate==null?1:e.dilationRate,t,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new z(`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 z(`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 z(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(t){if(co("kernelSize"in t,"required key 'kernelSize' not in config"),typeof t.kernelSize!="number"&&!Fy(t.kernelSize,"number",1,3))throw new z(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(t.kernelSize)}.`)}getConfig(){let t={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:gi(this.activation),useBias:this.useBias,biasInitializer:Te(this.biasInitializer),biasRegularizer:fe(this.biasRegularizer),activityRegularizer:fe(this.activityRegularizer),biasConstraint:Ve(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}},nf=class r extends Mh{constructor(t,e){super(t,e),this.kernel=null,r.verifyArgs(e),this.filters=e.filters,tr(this.filters,"filters"),this.kernelInitializer=xe(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Ge(e.kernelConstraint),this.kernelRegularizer=ve(e.kernelRegularizer)}build(t){t=Gt(t);let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new z(`The channel dimension of the input should be defined. 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Found `None`.");let n=t[e],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",o,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ce({ndim:5,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{let n=St(t);if(n.shape.length!==5)throw new z(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],i,a,u;this.dataFormat==="channelsFirst"?(u=2,i=3,a=4):(u=1,i=2,a=3);let l=o[u],c=o[i],p=o[a],m=this.kernelSize[0],f=this.kernelSize[1],d=this.kernelSize[2],h=this.strides[0],g=this.strides[1],x=this.strides[2],b=yi(l,h,m,this.padding),w=yi(c,g,f,this.padding),I=yi(p,x,d,this.padding),N=[s,b,w,I,this.filters];this.dataFormat!=="channelsLast"&&(n=Vt(n,[0,2,3,4,1]));let E=Rx(n,this.kernel.read(),N,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(E=Vt(E,[0,4,1,2,3])),this.bias!==null&&(E=yn(E,this.bias.read(),this.dataFormat)),this.activation!==null&&(E=this.activation.apply(E)),E})}computeOutputShape(t){t=Gt(t);let e=t.slice(),n,o,s,i;this.dataFormat==="channelsFirst"?(n=1,o=2,s=3,i=4):(n=4,o=1,s=2,i=3);let a=this.kernelSize[0],u=this.kernelSize[1],l=this.kernelSize[2],c=this.strides[0],p=this.strides[1],m=this.strides[2];return e[n]=this.filters,e[o]=yi(e[o],c,a,this.padding),e[s]=yi(e[s],p,u,this.padding),e[i]=yi(e[i],m,l,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};sf.className="Conv3DTranspose";Q.registerClass(sf);var Eb=class extends nf{constructor(t,e){if(super(t,e),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,e.filters==null)throw new z("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(e.kernelInitializer!=null||e.kernelRegularizer!=null||e.kernelConstraint!=null)throw new z("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(e.padding!=null&&e.padding!=="same"&&e.padding!=="valid")throw new z(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(e.padding)}`);this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=xe(e.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=ve(e.depthwiseRegularizer),this.depthwiseConstraint=Ge(e.depthwiseConstraint),this.pointwiseInitializer=xe(e.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=ve(e.pointwiseRegularizer),this.pointwiseConstraint=Ge(e.pointwiseConstraint)}build(t){if(t=Gt(t),t.length{t=St(t);let n;if(this.rank===1)throw new _t("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(t=Vt(t,[0,2,3,1])),n=dm(t,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=yn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Vt(n,[0,3,1,2])),n})}getConfig(){let t=super.getConfig();return delete t.rank,delete t.kernelInitializer,delete t.kernelRegularizer,delete t.kernelConstraint,t.depthwiseInitializer=Te(this.depthwiseInitializer),t.pointwiseInitializer=Te(this.pointwiseInitializer),t.depthwiseRegularizer=fe(this.depthwiseRegularizer),t.pointwiseRegularizer=fe(this.pointwiseRegularizer),t.depthwiseConstraint=Ve(this.depthwiseConstraint),t.pointwiseConstraint=Ve(this.pointwiseConstraint),t}};Eb.className="SeparableConv";var af=class extends Eb{constructor(t){super(2,t)}};af.className="SeparableConv2D";Q.registerClass(af);var lf=class r extends nf{constructor(t){super(1,t),r.verifyArgs(t),this.inputSpec=[{ndim:3}]}getConfig(){let t=super.getConfig();return delete t.rank,delete t.dataFormat,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!Fy(t.kernelSize,"number",1,1))throw new z(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(t.kernelSize)}.`)}};lf.className="Conv1D";Q.registerClass(lf);var uf=class extends Et{constructor(t){super(t),typeof t.cropping=="number"?this.cropping=[[t.cropping,t.cropping],[t.cropping,t.cropping]]:typeof t.cropping[0]=="number"?this.cropping=[[t.cropping[0],t.cropping[0]],[t.cropping[1],t.cropping[1]]]:this.cropping=t.cropping,this.dataFormat=t.dataFormat===void 0?"channelsLast":t.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(t){return this.dataFormat==="channelsFirst"?[t[0],t[1],t[2]-this.cropping[0][0]-this.cropping[0][1],t[3]-this.cropping[1][0]-this.cropping[1][1]]:[t[0],t[1]-this.cropping[0][0]-this.cropping[0][1],t[2]-this.cropping[1][0]-this.cropping[1][1],t[3]]}call(t,e){return B(()=>{if(t=St(t),this.dataFormat==="channelsLast"){let n=kh(t,this.cropping[0][0],t.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return kh(n,this.cropping[1][0],t.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=kh(t,this.cropping[0][0],t.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return kh(n,this.cropping[1][0],t.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let t={cropping:this.cropping,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}};uf.className="Cropping2D";Q.registerClass(uf);var cf=class extends Et{constructor(t){super(t),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=t.size==null?this.DEFAULT_SIZE:t.size,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Me(this.dataFormat),this.interpolation=t.interpolation==null?"nearest":t.interpolation,nR(this.interpolation)}computeOutputShape(t){if(this.dataFormat==="channelsFirst"){let e=t[2]==null?null:this.size[0]*t[2],n=t[3]==null?null:this.size[1]*t[3];return[t[0],t[1],e,n]}else{let e=t[1]==null?null:this.size[0]*t[1],n=t[2]==null?null:this.size[1]*t[2];return[t[0],e,n,t[3]]}}call(t,e){return B(()=>{let n=St(t),o=n.shape;if(this.dataFormat==="channelsFirst"){n=Vt(n,[0,2,3,1]);let s=this.size[0]*o[2],i=this.size[1]*o[3],a=this.interpolation==="nearest"?fn.resizeNearestNeighbor(n,[s,i]):fn.resizeBilinear(n,[s,i]);return Vt(a,[0,3,1,2])}else{let s=this.size[0]*o[1],i=this.size[1]*o[2];return this.interpolation==="nearest"?fn.resizeNearestNeighbor(n,[s,i]):fn.resizeBilinear(n,[s,i])}})}getConfig(){let t={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},e=super.getConfig();return Object.assign(t,e),t}};cf.className="UpSampling2D";Q.registerClass(cf);function CJ(r,t,e=[1,1],n="valid",o,s){return B(()=>{o==null&&(o=xn()),Me(o);let i=Ph(r,o);if(r.rank!==4)throw new z(`Input for depthwiseConv2d is required to be 4-D, but is instead ${r.rank}-D`);if(t.rank!==4)throw new z(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=ia(i,t,e,n==="same"?"same":"valid","NHWC",s),o==="channelsFirst"&&(i=Vt(i,[0,3,1,2])),i})}var pf=class extends Mh{constructor(t){super(2,t),this.depthwiseKernel=null,this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=xe(t.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Ge(t.depthwiseConstraint),this.depthwiseRegularizer=ve(t.depthwiseRegularizer)}build(t){if(t=Gt(t),t.length<4)throw new z(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(t)}.`);let e=this.dataFormat==="channelsFirst"?1:3;if(t[e]==null||t[e]<0)throw new z(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${t[e]}).`);let n=t[e],o=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",o,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(t,e){return B(()=>{t=St(t);let n=CJ(t,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=yn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(t){t=Gt(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],n=this.dataFormat==="channelsFirst"?t[3]:t[2],o=this.dataFormat==="channelsFirst"?t[1]*this.depthMultiplier:t[3]*this.depthMultiplier,s=Tn(e,this.kernelSize[0],this.padding,this.strides[0]),i=Tn(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[t[0],o,s,i]:[t[0],s,i,o]}getConfig(){let t=super.getConfig();return t.depthMultiplier=this.depthMultiplier,t.depthwiseInitializer=Te(this.depthwiseInitializer),t.depthwiseRegularizer=fe(this.depthwiseRegularizer),t.depthwiseConstraint=Ve(this.depthwiseRegularizer),t}};pf.className="DepthwiseConv2D";Q.registerClass(pf);function rk(r,t,e,n){if(Array.isArray(r)){if(t!=null||e!=null)throw new z("When inputs is an array, neither initialState or constants should be provided");n!=null&&(e=r.slice(r.length-n,r.length),r=r.slice(0,r.length-n)),r.length>1&&(t=r.slice(1,r.length)),r=r[0]}function o(s){return s==null||Array.isArray(s)?s:[s]}return t=o(t),e=o(e),{inputs:r,initialState:t,constants:e}}function nk(r,t,e,n=!1,o,s,i=!1,a=!1){return B(()=>{let u=t.shape.length;if(u<3)throw new z(`Input should be at least 3D, but is ${u}D.`);let l=[1,0].concat(gn(2,u));if(t=Vt(t,l),s!=null)throw new _t("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),o!=null&&(o=J(J(o,"bool"),"float32"),o.rank===u-1&&(o=je(o,-1)),o=Vt(o,l)),n&&(t=dr(t,0),o!=null&&(o=dr(o,0)));let c=[],p,m=e,f=t.shape[0],d=gr(t),h;o!=null&&(h=gr(o));for(let x=0;xr(b,m));if(o==null)p=w[0],m=w[1];else{let I=B(()=>{let N=h[x],E=at(wr(N),N),A=K($(w[0],N),$(m[0],E)),D=m.map((F,M)=>K($(w[1][M],N),$(F,E)));return{output:A,newStates:D}});p=I.output,m=I.newStates}a&&c.push(p)}let g;return a&&(g=Fe(c,1)),[p,g,m]})}var po=class r extends Et{constructor(t){super(t);let e;if(t.cell==null)throw new z("cell property is missing for the constructor of RNN.");if(Array.isArray(t.cell)?e=new jc({cells:t.cell}):e=t.cell,e.stateSize==null)throw new z("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=e,this.returnSequences=t.returnSequences==null?!1:t.returnSequences,this.returnState=t.returnState==null?!1:t.returnState,this.goBackwards=t.goBackwards==null?!1:t.goBackwards,this._stateful=t.stateful==null?!1:t.stateful,this.unroll=t.unroll==null?!1:t.unroll,this.supportsMasking=!0,this.inputSpec=[new Ce({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return gn(0,t).map(e=>null)}else return this.states_}setStates(t){this.states_=t}computeOutputShape(t){Uy(t)&&(t=t[0]),t=t;let e=this.cell.stateSize;Array.isArray(e)||(e=[e]);let n=e[0],o;if(this.returnSequences?o=[t[0],t[1],n]:o=[t[0],n],this.returnState){let s=[];for(let i of e)s.push([t[0],i]);return[o].concat(s)}else return o}computeMask(t,e){return B(()=>{Array.isArray(e)&&(e=e[0]);let n=this.returnSequences?e:null;if(this.returnState){let o=this.states.map(s=>null);return[n].concat(o)}else return n})}get states(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,e=[];for(let n=0;na.shape[a.shape.length-1]),i))throw new z(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=i.map(a=>new Ce({shape:[null,a]}));this.stateful&&this.resetStates()}resetStates(t,e=!1){B(()=>{if(!this.stateful)throw new uo("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new z("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(o=>ke([n,o])):this.states_=[ke([n,this.cell.stateSize])];else if(t==null)Tt(this.states_),this.keptStates!=null&&(Tt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>ke([n,o])):this.states_[0]=ke([n,this.cell.stateSize]);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e===!0?this.keptStates.push(this.states_.slice()):Tt(this.states_);for(let o=0;oDe(o.clone()))})}apply(t,e){let n=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=rk(t,n,o,this.numConstants);t=s.inputs,n=s.initialState,o=s.constants;let i=[],a=[];if(n!=null){e.initialState=n,i=i.concat(n),this.stateSpec=[];for(let l of n)this.stateSpec.push(new Ce({shape:l.shape}));a=a.concat(this.stateSpec)}if(o!=null&&(e.constants=o,i=i.concat(o),this.numConstants=o.length),i[0]instanceof nn){let l=[t].concat(i),c=this.inputSpec.concat(a),p=this.inputSpec;this.inputSpec=c;let m=super.apply(l,e);return this.inputSpec=p,m}else return super.apply(t,e)}call(t,e){return B(()=>{let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;t=St(t),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(t));let i=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==i)throw new z(`RNN Layer has ${i} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let a={training:o},l=nk((d,h)=>{let g=this.cell.call([d].concat(h),a);return[g[0],g.slice(1)]},t,s,this.goBackwards,n,null,this.unroll,this.returnSequences),c=l[0],p=l[1],m=l[2];this.stateful&&this.resetStates(m,o);let f=this.returnSequences?p:c;return this.returnState?[f].concat(m):f})}getInitialState(t){return B(()=>{let e=ke(t.shape);return e=mt(e,[1,2]),e=Sl(e),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Vy(e,[1,n]):e):this.cell.stateSize>1?[Vy(e,[1,this.cell.stateSize])]:[e]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(t)}getConfig(){let t=super.getConfig(),e={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(e.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===r.className&&(e.cell={className:this.cell.getClassName(),config:n}),Object.assign(Object.assign(Object.assign({},n),t),e)}static fromConfig(t,e,n={}){let o=e.cell,s=wn(o,n);return new t(Object.assign(e,{cell:s}))}};po.className="RNN";Q.registerClass(po);var Tl=class extends Et{},qc=class extends Tl{constructor(t){super(t),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=t.units,tr(this.units,"units"),this.activation=xi(t.activation==null?this.DEFAULT_ACTIVATION:t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=xe(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=xe(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=xe(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=ve(t.kernelRegularizer),this.recurrentRegularizer=ve(t.recurrentRegularizer),this.biasRegularizer=ve(t.biasRegularizer),this.kernelConstraint=Ge(t.kernelConstraint),this.recurrentConstraint=Ge(t.recurrentConstraint),this.biasConstraint=Ge(t.biasConstraint),this.dropout=Dc([1,di([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Dc([1,di([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Gt(t),this.kernel=this.addWeight("kernel",[t[t.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(t,e){return B(()=>{if(t=t,t.length!==2)throw new z(`SimpleRNNCell expects 2 input Tensors, got ${t.length}.`);let n=t[1];t=t[0];let o=e.training==null?!1:e.training;0wr(t),rate:this.dropout,training:o,dropoutFunc:this.dropoutFunc})),0wr(n),rate:this.recurrentDropout,training:o,dropoutFunc:this.dropoutFunc}));let s,i=this.dropoutMask,a=this.recurrentDropoutMask;i!=null?s=Do($(t,i),this.kernel.read()):s=Do(t,this.kernel.read()),this.bias!=null&&(s=yn(s,this.bias.read())),a!=null&&(n=$(n,a));let u=K(s,Do(n,this.recurrentKernel.read()));return this.activation!=null&&(u=this.activation.apply(u)),[u,u]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:gi(this.activation),useBias:this.useBias,kernelInitializer:Te(this.kernelInitializer),recurrentInitializer:Te(this.recurrentInitializer),biasInitializer:Te(this.biasInitializer),kernelRegularizer:fe(this.kernelRegularizer),recurrentRegularizer:fe(this.recurrentRegularizer),biasRegularizer:fe(this.biasRegularizer),activityRegularizer:fe(this.activityRegularizer),kernelConstraint:Ve(this.kernelConstraint),recurrentConstraint:Ve(this.recurrentConstraint),biasConstraint:Ve(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},t),e)}};qc.className="SimpleRNNCell";Q.registerClass(qc);var mf=class extends po{constructor(t){t.cell=new qc(t),super(t)}call(t,e){return B(()=>{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return new t(e)}};mf.className="SimpleRNN";Q.registerClass(mf);var Kc=class extends Tl{constructor(t){if(super(t),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",t.resetAfter)throw new z("GRUCell does not support reset_after parameter set to true.");this.units=t.units,tr(this.units,"units"),this.activation=xi(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=xi(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=xe(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=xe(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=xe(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=ve(t.kernelRegularizer),this.recurrentRegularizer=ve(t.recurrentRegularizer),this.biasRegularizer=ve(t.biasRegularizer),this.kernelConstraint=Ge(t.kernelConstraint),this.recurrentConstraint=Ge(t.recurrentConstraint),this.biasConstraint=Ge(t.biasConstraint),this.dropout=Dc([1,di([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Dc([1,di([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Gt(t);let e=t[t.length-1];this.kernel=this.addWeight("kernel",[e,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(t,e){return B(()=>{if(t=t,t.length!==2)throw new z(`GRUCell expects 2 input Tensors (inputs, h, c), got ${t.length}.`);let n=e.training==null?!1:e.training,o=t[1];t=t[0],0wr(t),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0wr(o),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,a,u,l;0{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};ff.className="GRU";Q.registerClass(ff);var _l=class extends Tl{constructor(t){super(t),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=t.units,tr(this.units,"units"),this.activation=xi(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=xi(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=xe(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=xe(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=xe(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=t.unitForgetBias,this.kernelRegularizer=ve(t.kernelRegularizer),this.recurrentRegularizer=ve(t.recurrentRegularizer),this.biasRegularizer=ve(t.biasRegularizer),this.kernelConstraint=Ge(t.kernelConstraint),this.recurrentConstraint=Ge(t.recurrentConstraint),this.biasConstraint=Ge(t.biasConstraint),this.dropout=Dc([1,di([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Dc([1,di([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){var e;t=Gt(t);let n=t[t.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 o;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,i=this.units;o=new(e=class extends bn{apply(u,l){let c=s.apply([i]),p=new Mu().apply([i]),m=s.apply([i*2]);return GN(GN(c,p),m)}},e.className="CustomInit",e)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,o,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training;if(t=t,t.length!==3)throw new z(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let o=t[1],s=t[2];t=t[0],0wr(t),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0wr(o),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,a=this.recurrentDropoutMask,u,l,c,p;0{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};df.className="LSTM";Q.registerClass(df);var jc=class extends Tl{constructor(t){super(t),this.cells=t.cells}get stateSize(){let t=[];for(let e of this.cells.slice().reverse())Array.isArray(e.stateSize)?t.push(...e.stateSize):t.push(e.stateSize);return t}call(t,e){return B(()=>{t=t;let n=t.slice(1),o=[];for(let a of 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o=n.weights.length,s=t.splice(o);for(let i=0;is!=null?s(t(),e):Wy(t(),e),a=()=>Ou(i,t,n);return!o||o<=1?De(a().clone()):Array(o).fill(void 0).map(a).map(l=>De(l.clone()))}var vJ=function(r,t){var e={};for(var n in r)Object.prototype.hasOwnProperty.call(r,n)&&t.indexOf(n)<0&&(e[n]=r[n]);if(r!=null&&typeof Object.getOwnPropertySymbols=="function")for(var o=0,n=Object.getOwnPropertySymbols(r);o{if(this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),e&&e.constants)throw new z("ConvRNN2D cell does not support constants");let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}computeOutputShape(t){let e=this.computeSingleOutputShape(t);return this.returnSequences||(e=[e[0],...e.slice(2)]),this.returnState&&(e=[e,...Array(2).fill([t[0],...e.slice(-3)])]),e}getInitialState(t){return B(()=>{let{stateSize:e}=this.cell,n=t.shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)],i=ke(s);return Array.isArray(e)?Array(e.length).fill(i):[i]})}resetStates(t,e=!1){B(()=>{if(!this.stateful)throw new uo("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)];if(n[0]==null)throw new z("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(()=>ke(s)):this.states_=[ke(s)];else if(t==null)Tt(this.states_),this.keptStates!=null&&(Tt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>ke(s)):this.states_[0]=ke(s);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e?this.keptStates.push(this.states_.slice()):Tt(this.states_);for(let a=0;aDe(a.clone()))})}computeSingleOutputShape(t){let{dataFormat:e,filters:n,kernelSize:o,padding:s,strides:i,dilationRate:a}=this.cell,u=e==="channelsFirst",l=t[u?3:2],c=t[u?4:3],p=Tn(l,o[0],s,i[0],a[0]),m=Tn(c,o[1],s,i[1],a[1]);return[...t.slice(0,2),...u?[n,p,m]:[p,m,n]]}};Ab.className="ConvRNN2D";var Xc=class extends _l{constructor(t){let{filters:e,kernelSize:n,strides:o,padding:s,dataFormat:i,dilationRate:a}=t;super(Object.assign(Object.assign({},t),{units:e})),this.filters=e,tr(this.filters,"filters"),this.kernelSize=zu(n,2,"kernelSize"),this.kernelSize.forEach(u=>tr(u,"kernelSize")),this.strides=zu(o||1,2,"strides"),this.strides.forEach(u=>tr(u,"strides")),this.padding=s||"valid",hn(this.padding),this.dataFormat=i||"channelsLast",Me(this.dataFormat),this.dilationRate=zu(a||1,2,"dilationRate"),this.dilationRate.forEach(u=>tr(u,"dilationRate"))}build(t){var e;t=Gt(t);let n=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[n]==null)throw new z(`The channel dimension of the input should be defined. Found ${t[n]}`);let o=t[n],s=4,i=this.kernelSize.concat([o,this.filters*s]);this.kernel=this.addWeight("kernel",i,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let a=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",a,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let u;if(this.unitForgetBias){let l=this.biasInitializer,c=this.filters;u=new(e=class extends bn{apply(m,f){let d=l.apply([c]),h=ar([c]),g=l.apply([c*2]);return _m([d,h,g])}},e.className="CustomInit",e)}else u=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*s],null,u,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(t,e){return B(()=>{if(t.length!==3)throw new z(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let n=e.training||!1,o=t[0],s=t[1],i=t[2],a=4;0wr(o),rate:this.dropout,training:n,count:a,dropoutFunc:this.dropoutFunc}));let u=this.dropoutMask,l=(nt,st,lt)=>!st||!st[lt]?nt:$(st[lt],nt),c=l(o,u,0),p=l(o,u,1),m=l(o,u,2),f=l(o,u,3);0wr(s),rate:this.recurrentDropout,training:n,count:a,dropoutFunc:this.dropoutFunc}));let d=this.recurrentDropoutMask,h=l(s,d,0),g=l(s,d,1),x=l(s,d,2),b=l(s,d,3),w=3,[I,N,E,A]=hr(this.kernel.read(),a,w),[D,F,M,V]=this.useBias?hr(this.bias.read(),a):[null,null,null,null];c=this.inputConv(c,I,D,this.padding),p=this.inputConv(p,N,F,this.padding),m=this.inputConv(m,E,M,this.padding),f=this.inputConv(f,A,V,this.padding);let[G,W,q,H]=hr(this.recurrentKernel.read(),a,w);h=this.recurrentConv(h,G),g=this.recurrentConv(g,W),x=this.recurrentConv(x,q),b=this.recurrentConv(b,H);let j=this.recurrentActivation.apply(K(c,h)),Y=this.recurrentActivation.apply(K(p,g)),Z=K($(Y,i),$(j,this.activation.apply(K(m,x)))),et=$(this.recurrentActivation.apply(K(f,b)),this.activation.apply(Z));return[et,et,Z]})}getConfig(){let t=super.getConfig(),{units:e}=t,n=vJ(t,["units"]),o={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),o)}inputConv(t,e,n,o){let s=Nn(t,e,this.strides,o||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?yn(s,n,this.dataFormat):s}recurrentConv(t,e){return Nn(t,e,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Xc.className="ConvLSTM2DCell";Q.registerClass(Xc);var hf=class extends Ab{constructor(t){let e=new Xc(t);super(Object.assign(Object.assign({},t),{cell:e}))}static fromConfig(t,e){return new t(e)}};hf.className="ConvLSTM2D";Q.registerClass(hf);var Yc=class extends Et{constructor(t){super(t),this.rate=Math.max(Math.min(t.rate,1),0),this.noiseShape=t.noiseShape,this.seed=t.seed,this.supportsMasking=!0}getNoiseShape(t){if(this.noiseShape==null)return this.noiseShape;let e=t.shape,n=[];for(let o=0;o{this.invokeCallHook(t,e);let n=St(t);if(0Wy(n,this.rate,s,this.seed),()=>n,o)}return t})}getConfig(){let t={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},e=super.getConfig();return Object.assign(t,e),t}dispose(){return super.dispose()}};Yc.className="Dropout";Q.registerClass(Yc);var gf=class extends Yc{constructor(t){super(t),this.inputSpec=[{ndim:3}]}getNoiseShape(t){let e=t.shape;return[e[0],1,e[2]]}};gf.className="SpatialDropout1D";Q.registerClass(gf);var xf=class extends Et{constructor(t){if(super(t),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",t.batchInputShape==null&&t.inputShape==null&&t.inputDim!=null){let e=null;t.batchSize!=null&&(e=t.batchSize),this.batchInputShape=[e,t.inputDim]}this.units=t.units,tr(this.units,"units"),this.activation=xi(t.activation),t.useBias!=null&&(this.useBias=t.useBias),this.kernelInitializer=xe(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=xe(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Ge(t.kernelConstraint),this.biasConstraint=Ge(t.biasConstraint),this.kernelRegularizer=ve(t.kernelRegularizer),this.biasRegularizer=ve(t.biasRegularizer),this.activityRegularizer=ve(t.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(t){t=Gt(t);let e=t[t.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[e,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]:e}}],this.built=!0}computeOutputShape(t){t=Gt(t);let e=t.slice();return e[e.length-1]=this.units,e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t),o=Oy(this.activation.getClassName()),s;return o!=null?s=Do(n,this.kernel.read(),o,this.bias?this.bias.read():null):(s=Do(n,this.kernel.read()),this.bias!=null&&(s=yn(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let t={units:this.units,activation:gi(this.activation),useBias:this.useBias,kernelInitializer:Te(this.kernelInitializer),biasInitializer:Te(this.biasInitializer),kernelRegularizer:fe(this.kernelRegularizer),biasRegularizer:fe(this.biasRegularizer),activityRegularizer:fe(this.activityRegularizer),kernelConstraint:Ve(this.kernelConstraint),biasConstraint:Ve(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}};xf.className="Dense";Q.registerClass(xf);var yf=class extends Et{constructor(t){t=t||{},super(t),this.inputSpec=[{minNDim:3}],this.dataFormat=t.dataFormat}computeOutputShape(t){t=Gt(t);for(let e of t.slice(1))if(e==null)throw new z(`The shape of the input to "Flatten" is not fully defined (got ${t.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[t[0],Ao(t,1)]}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);if(this.dataFormat==="channelsFirst"&&n.rank>1){let o=[0];for(let s=2;s{this.invokeCallHook(t,e);let n=St(t);return this.activation.apply(n)})}getConfig(){let t={activation:gi(this.activation)},e=super.getConfig();return Object.assign(t,e),t}};bf.className="Activation";Q.registerClass(bf);var wf=class extends Et{constructor(t){super(t),this.n=t.n,this.inputSpec=[{ndim:2}]}computeOutputShape(t){return[t[0],this.n,t[1]]}call(t,e){return B(()=>(t=St(t),iR(t,this.n)))}getConfig(){let t={n:this.n},e=super.getConfig();return Object.assign(t,e),t}};wf.className="RepeatVector";Q.registerClass(wf);var If=class extends Et{constructor(t){super(t),this.targetShape=t.targetShape;for(let e=0;e{this.invokeCallHook(t,e);let n=St(t),o=n.shape,s=o.slice(0,1).concat(this.fixUnknownDimension(o.slice(1),this.targetShape));return R(n,s)})}getConfig(){let t={targetShape:this.targetShape},e=super.getConfig();return Object.assign(t,e),t}};If.className="Reshape";Q.registerClass(If);var Cf=class extends Et{constructor(t){if(super(t),t.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(t.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${t.dims} instead.`);let e=gn(1,t.dims.length+1);if(!y.arraysEqual(t.dims.slice().sort(),e))throw new Error("Invalid permutation `dims`: "+JSON.stringify(t.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=t.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Ce({ndim:this.dims.length+1})]}computeOutputShape(t){t=Gt(t);let e=t.slice();return this.dims.forEach((n,o)=>{e[o+1]=t[n]}),e}call(t,e){return Vt(St(t),this.dimsIncludingBatch)}getConfig(){let t={dims:this.dims},e=super.getConfig();return Object.assign(t,e),t}};Cf.className="Permute";Q.registerClass(Cf);var vf=class extends Et{constructor(t){super(t==null?{}:t),this.supportsMasking=!0,t!=null?this.maskValue=t.maskValue==null?0:t.maskValue:this.maskValue=0}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={maskValue:this.maskValue};return Object.assign(e,t),e}computeMask(t,e){let n=St(t);return cc(ci(n,this.maskValue),-1)}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t),i=cc(ci(n,this.maskValue),-1,!0);return $(n,J(i,n.dtype))})}};vf.className="Masking";Q.registerClass(vf);var Sf=class extends Et{constructor(t){if(super(t),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",t.batchInputShape==null&&t.inputShape==null){let e=null;t.batchSize!=null&&(e=t.batchSize),t.inputLength==null?this.batchInputShape=[e,null]:this.batchInputShape=[e].concat(ue(t.inputLength))}this.inputDim=t.inputDim,tr(this.inputDim,"inputDim"),this.outputDim=t.outputDim,tr(this.outputDim,"outputDim"),this.embeddingsInitializer=xe(t.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=ve(t.embeddingsRegularizer),this.activityRegularizer=ve(t.activityRegularizer),this.embeddingsConstraint=Ge(t.embeddingsConstraint),this.maskZero=t.maskZero,this.supportsMasking=t.maskZero,this.inputLength=t.inputLength}build(t){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(t){}computeMask(t,e){return B(()=>this.maskZero?(t=St(t),ci(t,vt(t))):null)}computeOutputShape(t){if(t=Gt(t),this.inputLength==null)return[...t,this.outputDim];let e=ue(this.inputLength);if(e.length!==t.length-1)throw new z(`"inputLength" is ${this.inputLength}, but received input shape has shape ${t}`);{let n=0;for(let o=0;o{this.invokeCallHook(t,e);let n=St(t);n.dtype!=="int32"&&(n=rn(n,"int32"));let o=Gy(this.embeddings.read(),R(n,[n.size]));return R(o,Gt(this.computeOutputShape(n.shape)))})}getConfig(){let t={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Te(this.embeddingsInitializer),embeddingsRegularizer:fe(this.embeddingsRegularizer),activityRegularizer:fe(this.activityRegularizer),embeddingsConstraint:Ve(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},e=super.getConfig();return Object.assign(t,e),t}};Sf.className="Embedding";Q.registerClass(Sf);var Al=class extends Et{constructor(t){super(t||{}),this.supportsMasking=!0}mergeFunction(t){throw new _t}computeElementwiseOpOutputShape(t,e){if(t==null||e==null)return null;if(t.length1)throw new z(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(t)}.`);let n=t[0]==null?null:t[0].slice(1);for(let s=1;ss.length);t.indexOf(null)===-1&&Eo(o).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(t,e){return B(()=>{if(t=t,this.reshapeRequired){let n=[],o=t.map(s=>s.rank);if(o.indexOf(null)===-1){let s=di(o);for(let i of t){let a=i.rank;for(let u=0;u1){let c=gn(1,l).concat([0]);n.push(Vt(u,c)),s=!0}else n.push(u)}let i=this.mergeFunction(n),a=i.rank;if(s){if(a==null){let u=i.shape,l=u.length,c=u[l-1],p=[c].concat(u.slice(0,u.length-1));i=R(Vt(R(i,[-1,c]),[1,0]),p)}else if(a>1){let u=[a-1].concat(gn(0,a-1));i=Vt(i,u)}}return i}}else return this.mergeFunction(t)})}computeOutputShape(t){t=t;let e;t[0]==null?e=null:e=t[0].slice(1);for(let o=1;o{if(e==null)return null;if(!Array.isArray(e))throw new z("`mask` should be an Array");if(!Array.isArray(t))throw new z("`inputs` should be an Array");if(e.length!==t.length)throw new z(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${t.length} vs ${e.length})`);if(e.every(o=>o==null))return null;e=e.map(o=>o==null?o:je(o,0));let n=e[0];for(let o=1;o{let e=t[0].clone();for(let n=1;n{let e=t[0].clone();for(let n=1;n{let e=t[0].clone();for(let n=1;n{let e=t[0];for(let n=1;n{let e=t[0];for(let n=1;n1)throw new z("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. 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${r.shape.length}`),y.assert(r.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof e=="number"&&(e=[e,e]),r.dtype==="complex64"||t.dtype==="complex64")throw new _t("batchDot is not implemented for complex64-type Tensors yet.");let n=r.shape.length,o=t.shape.length;e==null&&(e=[n-1,o-2]);let s=e;return B(()=>{let i;if(n>o){i=n-o;let u=[];for(let l=0;ln){i=o-n;let u=[];for(let l=0;l0){let u;n>o?u=n+o-3:u=n-1;let l=[];for(let c=u;c"A `Dot` layer should be called on a list of exactly 2 inputs.");let e=t[0],n=t[1];if(e.length>3||n.length>3)throw new _t("Dot layer does not support tensors of 4D or higher rank yet.");let o=this.interpretAxes(e,n);if(e[o[0]]!==n[o[1]])throw new z(`Dimension incompatibility: ${e[o[0]]} !== ${n[o[1]]}`)}mergeFunction(t){if(t.length!==2)throw new z(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${t.length} input(s).`);let e=t[0],n=t[1],o;return Array.isArray(this.axes)?o=this.axes.map((s,i)=>Lh(s,t[i].shape.length)):o=[Lh(this.axes,e.shape.length),Lh(this.axes,n.shape.length)],this.normalize&&(e=Eh(e,o[0]),n=Eh(n,o[1])),SJ(e,n,o)}interpretAxes(t,e){let n;return Array.isArray(this.axes)?n=this.axes:n=[Lh(this.axes,t.length),Lh(this.axes,e.length)],n}computeOutputShape(t){y.assert(Array.isArray(t)&&t.length===2&&Array.isArray(t[0])&&Array.isArray(t[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let e=t[0].slice(),n=t[1].slice();if(e.length>3||n.length>3)throw new _t("Dot layer does not support tensors of 4D or higher rank yet.");let o=this.interpretAxes(e,n);e.splice(o[0],1),n.splice(o[1],1),n.splice(0,1);let s=e.concat(n);return s.length===1&&s.push(1),s}computeMask(t,e){return null}getConfig(){let t={axes:this.axes,normalize:this.normalize},e=super.getConfig();return Object.assign(t,e),t}};Df.className="Dot";Q.registerClass(Df);var $f=class extends 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e=this.axis>=0?this.axis:this.axis+t.length,n=t[e];if(n==null)throw new z(`Axis ${e} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(t)}.`);this.inputSpec=[new Ce({ndim:t.length,axes:{[e]:n}})];let o=[n];this.scale&&(this.gamma=this.addWeight("gamma",o,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",o,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",o,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",o,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training,o=St(t),s=o.shape,i=s.length,a=gn(0,i),u=this.axis>=0?this.axis:this.axis+i;a.splice(u,1);let l=To(1,i);l[u]=s[u];let c=a.slice();c.sort();let p=!y.arraysEqual(c,gn(0,i).slice(0,i-1)),m=()=>{if(p){let 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t={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Te(this.betaInitializer),gammaInitializer:Te(this.gammaInitializer),movingMeanInitializer:Te(this.movingMeanInitializer),movingVarianceInitializer:Te(this.movingVarianceInitializer),betaRegularizer:fe(this.betaRegularizer),gammaRegularizer:fe(this.gammaRegularizer),betaConstraint:Ve(this.betaConstraint),gammaConstraint:Ve(this.gammaConstraint)},e=super.getConfig();return Object.assign(t,e),t}};Of.className="BatchNormalization";Q.registerClass(Of);var Mf=class extends Et{constructor(t){if(t==null&&(t={}),super(t),this.axis=t.axis==null?-1:t.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 e of this.axis)if(!Number.isInteger(e))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new 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s==="max"?i=ku(r,t,e,a):i=xu(r,t,e,a),o==="channelsFirst"&&(i=Vt(i,[0,3,1,2])),i})}function YR(r,t,e,n,o,s){return B(()=>{Me(o),LN(s),hn(n),e==null&&(e=[1,1,1]),n==null&&(n="valid"),o==null&&(o=xn()),s==null&&(s="max"),r=ek(r,o);let i,a=n==="same"?"same":"valid";return s==="max"?i=Xx(r,t,e,a):i=Ix(r,t,e,a),o==="channelsFirst"&&(i=Vt(i,[0,4,1,2,3])),i})}var Db=class extends Et{constructor(t){if(t.poolSize==null&&(t.poolSize=2),super(t),typeof t.poolSize=="number")this.poolSize=[t.poolSize];else if(Array.isArray(t.poolSize)&&t.poolSize.length===1&&typeof t.poolSize[0]=="number")this.poolSize=t.poolSize;else throw new z(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.poolSize)}`);if(tr(this.poolSize,"poolSize"),t.strides==null)this.strides=this.poolSize;else if(typeof t.strides=="number")this.strides=[t.strides];else if(Array.isArray(t.strides)&&t.strides.length===1&&typeof 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Me(s),hn(o),Mb(t,e,n,o,s,"avg")}};zf.className="AveragePooling1D";Q.registerClass(zf);var $b=class extends Et{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==2)throw new z(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides];tr(this.poolSize,"poolSize"),tr(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Me(this.dataFormat),hn(this.padding),this.inputSpec=[new Ce({ndim:4})]}computeOutputShape(t){t=Gt(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],n=this.dataFormat==="channelsFirst"?t[3]:t[2];return e=Tn(e,this.poolSize[0],this.padding,this.strides[0]),n=Tn(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[t[0],t[1],e,n]:[t[0],e,n,t[3]]}call(t,e){return B(()=>(this.invokeCallHook(t,e),this.poolingFunction(St(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Bf=class extends $b{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Me(s),hn(o),Mb(t,e,n,o,s,"max")}};Bf.className="MaxPooling2D";Q.registerClass(Bf);var Vf=class extends $b{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Me(s),hn(o),Mb(t,e,n,o,s,"avg")}};Vf.className="AveragePooling2D";Q.registerClass(Vf);var Rb=class extends Et{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==3)throw new z(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides,t.strides];tr(this.poolSize,"poolSize"),tr(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Me(this.dataFormat),hn(this.padding),this.inputSpec=[new Ce({ndim:5})]}computeOutputShape(t){t=Gt(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],n=this.dataFormat==="channelsFirst"?t[3]:t[2],o=this.dataFormat==="channelsFirst"?t[4]:t[3];return e=Tn(e,this.poolSize[0],this.padding,this.strides[0]),n=Tn(n,this.poolSize[1],this.padding,this.strides[1]),o=Tn(o,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[t[0],t[1],e,n,o]:[t[0],e,n,o,t[4]]}call(t,e){return B(()=>(this.invokeCallHook(t,e),this.poolingFunction(St(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Gf=class extends Rb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Me(s),hn(o),YR(t,e,n,o,s,"max")}};Gf.className="MaxPooling3D";Q.registerClass(Gf);var Wf=class extends Rb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Me(s),hn(o),YR(t,e,n,o,s,"avg")}};Wf.className="AveragePooling3D";Q.registerClass(Wf);var Fb=class extends Et{constructor(t){super(t),this.inputSpec=[new Ce({ndim:3})]}computeOutputShape(t){return[t[0],t[2]]}call(t,e){throw new 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o=v("logits",r,t,e),s=v("numSamples",r,t,e),i=v("seed",r,t,e);return[n.multinomial(o,s,i)]}case"OneHot":{let o=v("indices",r,t,e),s=v("depth",r,t,e),i=v("onValue",r,t,e),a=v("offValue",r,t,e),u=v("dtype",r,t,e);return[n.oneHot(o,s,i,a,u)]}case"Ones":return[n.ones(v("shape",r,t,e),v("dtype",r,t,e))];case"OnesLike":return[n.onesLike(v("x",r,t,e))];case"RandomStandardNormal":return[n.randomStandardNormal(v("shape",r,t,e),v("dtype",r,t,e),v("seed",r,t,e))];case"RandomUniform":return[n.randomUniform(v("shape",r,t,e),v("minval",r,t,e),v("maxval",r,t,e),v("dtype",r,t,e))];case"RandomUniformInt":return[n.randomUniformInt(v("shape",r,t,e),v("minval",r,t,e),v("maxval",r,t,e),v("seed",r,t,e))];case"Range":{let o=v("start",r,t,e),s=v("stop",r,t,e),i=v("step",r,t,e);return[n.range(o,s,i,v("dtype",r,t,e))]}case"TruncatedNormal":{let 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o=v("x",r,t,e),s=v("axis",r,t,e),i=n.unique(o,s);return[i.values,i.indices]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var vF=(r,t,e,n=ae)=>{switch(r.op){case"Const":return t[r.name];case"PlaceholderWithDefault":let o=v("default",r,t,e);return[pr(r.name,t,e)||o];case"Placeholder":return[pr(r.name,t,e)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let c=v("x",r,t,e);return[bi(c)]}case"IdentityN":return v("x",r,t,e).map(c=>bi(c));case"Snapshot":let s=v("x",r,t,e);return[bi(s)];case"Shape":return[n.tensor1d(v("x",r,t,e).shape,"int32")];case"ShapeN":return v("x",r,t,e).map(c=>n.tensor1d(c.shape));case"Size":return[n.scalar(v("x",r,t,e).size,"int32")];case"Rank":return[n.scalar(v("x",r,t,e).rank,"int32")];case"NoOp":return[n.scalar(1)];case"Print":let i=v("x",r,t,e),a=v("data",r,t,e),u=v("message",r,t,e),l=v("summarize",r,t,e);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(u);for(let c=0;ct.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return pt(this.size(),"int32")}async import(t,e){this.checkKeyAndValueTensor(t,e);let n=await t.data();return this.tensorMap.forEach(o=>o.dispose()),this.tensorMap.clear(),B(()=>{let o=gr(e),s=n.length,i=o.length;y.assert(s===i,()=>`The number of elements doesn't match, keys has ${s} elements, the values has ${i} elements.`);for(let a=0;a{let o=[];for(let s=0;s{switch(r.op){case"HashTable":case"HashTableV2":{let o=n.getHashTableHandleByName(r.name);if(o!=null)return[o];{let s=v("keyDType",r,t,e),i=v("valueDType",r,t,e),a=new tw(s,i);return n.addHashTable(r.name,a),[a.handle]}}case"InitializeTable":case"InitializeTableV2":case"LookupTableImport":case"LookupTableImportV2":{let o=v("tableHandle",r,t,e,n),s=v("keys",r,t,e),i=v("values",r,t,e);return[await n.getHashTableById(o.id).import(s,i)]}case"LookupTableFind":case"LookupTableFindV2":{let 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o=v("images",r,t,e),s=v("transforms",r,t,e),i=v("outputShape",r,t,e),a=v("fillValue",r,t,e),u=v("interpolation",r,t,e),l=v("fillMode",r,t,e);return[n.image.transform(o,s,u.toLowerCase(),l.toLowerCase(),a,i)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var kF=(r,t,e,n=ae)=>{switch(r.op){case"Equal":return[n.equal(v("a",r,t,e),v("b",r,t,e))];case"NotEqual":return[n.notEqual(v("a",r,t,e),v("b",r,t,e))];case"Greater":return[n.greater(v("a",r,t,e),v("b",r,t,e))];case"GreaterEqual":return[n.greaterEqual(v("a",r,t,e),v("b",r,t,e))];case"Less":return[n.less(v("a",r,t,e),v("b",r,t,e))];case"LessEqual":return[n.lessEqual(v("a",r,t,e),v("b",r,t,e))];case"LogicalAnd":return[n.logicalAnd(v("a",r,t,e),v("b",r,t,e))];case"LogicalNot":return[n.logicalNot(v("a",r,t,e))];case"LogicalOr":return[n.logicalOr(v("a",r,t,e),v("b",r,t,e))];case"Select":case"SelectV2":return[n.where(v("condition",r,t,e),v("a",r,t,e),v("b",r,t,e))];case"BitwiseAnd":return[n.bitwiseAnd(v("a",r,t,e),v("b",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var TF=(r,t,e,n=ae)=>{switch(r.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[n.matMul(v("a",r,t,e),v("b",r,t,e),v("transposeA",r,t,e),v("transposeB",r,t,e))];case"Einsum":return[n.einsum(v("equation",r,t,e),...v("tensors",r,t,e))];case"Transpose":return[n.transpose(v("x",r,t,e),v("perm",r,t,e))];case"_FusedMatMul":let[o,s]=v("fusedOps",r,t,e),i=o==="biasadd",a=s==="prelu",u=v("numArgs",r,t,e),l=v("leakyreluAlpha",r,t,e);if(i){if(a&&u!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!a&&u!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[c,p]=v("args",r,t,e);return[n.fused.matMul({a:v("a",r,t,e),b:v("b",r,t,e),transposeA:v("transposeA",r,t,e),transposeB:v("transposeB",r,t,e),bias:c,activation:s,preluActivationWeights:p,leakyreluAlpha:l})];case"MatrixBandPart":return[n.linalg.bandPart(v("a",r,t,e),v("numLower",r,t,e),v("numUpper",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var _F=(r,t,e,n=ae)=>{switch(r.op){case"EuclideanNorm":return[n.euclideanNorm(v("x",r,t,e),v("axis",r,t,e),v("keepDims",r,t,e))];case"FusedBatchNorm":case"FusedBatchNormV2":return[n.batchNorm(v("x",r,t,e),v("mean",r,t,e),v("variance",r,t,e),v("offset",r,t,e),v("scale",r,t,e),v("epsilon",r,t,e))];case"FusedBatchNormV3":return[n.batchNorm(v("x",r,t,e),v("mean",r,t,e),v("variance",r,t,e),v("offset",r,t,e),v("scale",r,t,e),v("epsilon",r,t,e))];case"LRN":return[n.localResponseNormalization(v("x",r,t,e),v("radius",r,t,e),v("bias",r,t,e),v("alpha",r,t,e),v("beta",r,t,e))];case"Softmax":return[n.softmax(v("x",r,t,e))];case"LogSoftmax":return[n.logSoftmax(v("x",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var EF=(r,t,e,n=ae)=>{switch(r.op){case"RaggedGather":{let{outputNestedSplits:o,outputDenseValues:s}=n.raggedGather(v("paramsNestedSplits",r,t,e),v("paramsDenseValues",r,t,e),v("indices",r,t,e),v("outputRaggedRank",r,t,e));return o.concat(s)}case"RaggedRange":{let{rtNestedSplits:o,rtDenseValues:s}=n.raggedRange(v("starts",r,t,e),v("limits",r,t,e),v("splits",r,t,e));return[o,s]}case"RaggedTensorToTensor":return[n.raggedTensorToTensor(v("shape",r,t,e),v("values",r,t,e),v("defaultValue",r,t,e),v("rowPartitionTensors",r,t,e),v("rowPartitionTypes",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var AF=(r,t,e,n=ae)=>{switch(r.op){case"Max":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.max(v("x",r,t,e),a,u)]}case"Mean":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.mean(v("x",r,t,e),a,u)]}case"Min":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.min(v("x",r,t,e),a,u)]}case"Sum":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.sum(v("x",r,t,e),a,u)]}case"All":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.all(v("x",r,t,e),a,u)]}case"Any":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.any(v("x",r,t,e),a,u)]}case"ArgMax":{let 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o=v("x",r,t,e),s=v("indices",r,t,e);return[n.gatherND(o,s)]}case"SparseToDense":{let o=v("sparseIndices",r,t,e),s=v("outputShape",r,t,e),i=v("sparseValues",r,t,e),a=v("defaultValue",r,t,e);return[n.sparseToDense(o,i,s,i.dtype===a.dtype?a:n.cast(a,i.dtype))]}case"TensorScatterUpdate":{let o=v("indices",r,t,e),s=v("values",r,t,e),i=v("tensor",r,t,e);return[n.tensorScatterUpdate(i,o,s)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var $F=(r,t,e,n=ae)=>{switch(r.op){case"SparseFillEmptyRows":{let{outputIndices:o,outputValues:s,emptyRowIndicator:i,reverseIndexMap:a}=n.sparse.sparseFillEmptyRows(v("indices",r,t,e),v("values",r,t,e),v("denseShape",r,t,e),v("defaultValue",r,t,e));return[o,s,i,a]}case"SparseReshape":{let{outputIndices:o,outputShape:s}=n.sparse.sparseReshape(v("inputIndices",r,t,e),v("inputShape",r,t,e),v("newShape",r,t,e));return[o,s]}case"SparseSegmentMean":return[n.sparse.sparseSegmentMean(v("data",r,t,e),v("indices",r,t,e),v("segmentIds",r,t,e))];case"SparseSegmentSum":return[n.sparse.sparseSegmentSum(v("data",r,t,e),v("indices",r,t,e),v("segmentIds",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var RF=(r,t,e,n=ae)=>{switch(r.op){case"FFT":return[n.fft(v("x",r,t,e))];case"IFFT":return[n.ifft(v("x",r,t,e))];case"RFFT":return[n.rfft(v("x",r,t,e))];case"IRFFT":return[n.irfft(v("x",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var FF=(r,t,e,n=ae)=>{switch(r.op){case"StaticRegexReplace":return[n.string.staticRegexReplace(v("input",r,t,e),v("pattern",r,t,e),v("rewrite",r,t,e),v("replaceGlobal",r,t,e))];case"StringNGrams":{let{nGrams:o,nGramsSplits:s}=n.string.stringNGrams(v("data",r,t,e),v("dataSplits",r,t,e),v("separator",r,t,e),v("nGramWidths",r,t,e),v("leftPad",r,t,e),v("rightPad",r,t,e),v("padWidth",r,t,e),v("preserveShortSequences",r,t,e));return[o,s]}case"StringSplit":{let{indices:o,values:s,shape:i}=n.string.stringSplit(v("input",r,t,e),v("delimiter",r,t,e),v("skipEmpty",r,t,e));return[o,s,i]}case"StringToHashBucketFast":return[n.string.stringToHashBucketFast(v("input",r,t,e),v("numBuckets",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var OF=(r,t,e,n=ae)=>{switch(r.op){case"Cast":return[n.cast(v("x",r,t,e),v("dtype",r,t,e))];case"ExpandDims":{let o=v("axis",r,t,e);return[n.expandDims(v("x",r,t,e),o)]}case"Squeeze":{let o=v("axis",r,t,e);return[n.squeeze(v("x",r,t,e),o)]}case"Reshape":return[n.reshape(v("x",r,t,e),v("shape",r,t,e))];case"EnsureShape":return[n.ensureShape(v("x",r,t,e),v("shape",r,t,e))];case"MirrorPad":return[n.mirrorPad(v("x",r,t,e),v("padding",r,t,e),v("mode",r,t,e))];case"PadV2":case"Pad":return[n.pad(v("x",r,t,e),v("padding",r,t,e),v("constantValue",r,t,e))];case"SpaceToBatchND":{let o=v("blockShape",r,t,e),s=v("paddings",r,t,e);return[n.spaceToBatchND(v("x",r,t,e),o,s)]}case"BatchToSpaceND":{let o=v("blockShape",r,t,e),s=v("crops",r,t,e);return[n.batchToSpaceND(v("x",r,t,e),o,s)]}case"DepthToSpace":{let o=v("blockSize",r,t,e),s=v("dataFormat",r,t,e).toUpperCase();return[n.depthToSpace(v("x",r,t,e),o,s)]}case"BroadcastTo":return[n.broadcastTo(v("x",r,t,e),v("shape",r,t,e))];case"BroadcastArgs":return[n.broadcastArgs(v("s0",r,t,e),v("s1",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function _k(r,t,e,n,o=B){let s=((i,a,u)=>{switch(i.category){case"arithmetic":return o(()=>cF(i,a,u));case"basic_math":return o(()=>pF(i,a,u));case"control":return xF(i,a,u);case"convolution":return o(()=>bF(i,a,u));case"creation":return o(()=>wF(i,a,u));case"dynamic":return IF(i,a,u);case"evaluation":return o(()=>CF(i,a,u));case"image":return o(()=>NF(i,a,u));case"graph":return o(()=>vF(i,a,u));case"logical":return o(()=>kF(i,a,u));case"matrices":return o(()=>TF(i,a,u));case"normalization":return o(()=>_F(i,a,u));case"ragged":return o(()=>EF(i,a,u));case"reduction":return o(()=>AF(i,a,u));case"slice_join":return o(()=>DF(i,a,u));case"sparse":return o(()=>$F(i,a,u));case"spectral":return o(()=>RF(i,a,u));case"string":return o(()=>FF(i,a,u));case"transformation":return o(()=>OF(i,a,u));case"hash_table":return SF(i,a,u,n);case"custom":let l=zb(i.op);if(l&&l.customExecutor)return l.customExecutor(new Zb(i,a,u));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(r,t,e);return y.isPromise(s)?s.then(i=>[].concat(i)):[].concat(s)}var Wh=class{constructor(t={},e={},n={},o={},s){this.weightMap=t,this.tensorArrayMap=e,this.tensorListMap=n,this.functionMap=o,this.parseNodeNameCache=s,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(t,e){return{id:t,frameName:e,iterationId:0}}set currentContext(t){this.contexts!==t&&(this.contexts=t,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let t=[];for(let e=0;ee.id===0&&e.iterationId===0?"":`${e.frameName}-${e.iterationId}`).join("/"):""}enterFrame(t){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,t)),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 t=Object.assign({},this.contexts[this.contexts.length-1]);t.iterationId+=1,t.id=this.lastId,this.contexts.splice(-1,1,t),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(t){return this.weightMap[t]}addTensorArray(t){this.tensorArrayMap[t.id]=t}getTensorArray(t){return this.tensorArrayMap[t]}addTensorList(t){this.tensorListMap[t.id]=t}getTensorList(t){return this.tensorListMap[t]}dispose(t){for(let e in this.tensorArrayMap)this.tensorArrayMap[e].clearAndClose(t);for(let e in this.tensorListMap)this.tensorListMap[e].clearAndClose(t)}};function Ek(r,t,e,n){let o=new Set,s=[],i=null,a=null,u=new Set,l=new Set(Object.keys(r).map(m=>In(m)[0]));n=n||[];let c=new Set(n.map(m=>In(m.name)[0])),p=[...t];for(;p.length>0;){let m=p.pop();if((Bu(m)||utt(m)||ctt(m))&&i==null&&(i=m,a=i.children.map(f=>f.name).filter(f=>o.has(f))),o.add(m.name),e[m.name]==null&&!l.has(m.name)&&!c.has(m.name)){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(f=>{u.has(f.name)||(u.add(f.name),p.push(f))})}}return{inputs:r,outputs:t,usedNodes:o,missingInputs:s,dynamicNode:i,syncInputs:a}}function MF(r,t){let{usedNodes:e,inputs:n}=t,o=Object.keys(n).map(g=>In(g)[0]).map(g=>r.nodes[g]),s=r.initNodes||[],i=g=>e.has(typeof g=="string"?g:g.name);function a(g){return[...new Map(g.map(x=>[x.name,x])).values()]}let u=a([...o,...r.weights,...s]).filter(i),l=a([...u,...Object.values(r.nodes)]).filter(i),c=new Map(l.map(g=>[g.name,g])),p={};for(let g of l){p[g.name]=p[g.name]||0;for(let x of g.children)i(x)||(p[x.name]=Number.POSITIVE_INFINITY),p[x.name]=(p[x.name]||0)+1}let m=Object.entries(p).filter(([,g])=>g===0).map(([g])=>g),f=[...m];for(;m.length>0;){let g=m.pop(),x=c.get(g);for(let b of x.children.filter(i))--p[b.name]===0&&(f.push(b.name),m.push(b.name))}let d=f.map(g=>c.get(g)),h=ott(d,u);return stt(h,u),h}function ott(r,t){let e=new Map(r.map(i=>[i.name,i])),n=t.map(i=>i.name),o=new Set(n);for(;n.length>0;){let i=n.pop(),a=e.get(i);for(let u of a.children)!e.has(u.name)||o.has(u.name)||(o.add(u.name),n.push(u.name))}return r.filter(i=>o.has(i.name))}var rd=class extends Error{constructor(t){super(`NodesExecutionOrderError: ${t}`)}};function stt(r,t){let e=new Map(r.map((a,u)=>[a.name,u])),n=new Set(t.map(a=>a.name)),o=a=>n.has(typeof a=="string"?a:a.name),s=new Set(r.map(a=>a.name)),i=a=>s.has(typeof a=="string"?a:a.name);for(let a of r){for(let u of a.children.filter(i)){if(!e.has(u.name))throw new rd(`Child ${u.name} of node ${a.name} is unreachable.`);if(e.get(a.name)>e.get(u.name))throw new rd(`Node ${a.name} is scheduled to run after its child ${u.name}.`)}if(!o(a))for(let u of a.inputs){if(!e.has(u.name))throw new rd(`Input ${u.name} of node ${a.name} is unreachable.`);if(e.get(u.name)>e.get(a.name))throw new rd(`Node ${a.name} is scheduled to run before its input ${u.name}.`)}}}function PF(r){let t=new Map(r.map((a,u)=>[a.name,u])),e=Number.MAX_SAFE_INTEGER,n=r.map((a,u)=>Bu(a)?e:u),o=a=>{let u=n[t.get(a.name)];return u==null?-1:u},s=r.map((a,u)=>a.children.map(o).reduce((l,c)=>Math.max(l,c),n[u])),i=new Map;for(let a=0;at[n].map(o=>o.id));this._weightIds=[].concat(...e),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let e=t.signatureKey||t.name;return t.defaultOutput?`${e}:${t.defaultOutput}`:e})}get functions(){return Object.keys(this._functions).reduce((t,e)=>(t[e]=this._functions[e].signature,t),{})}constructor(t,e){this.graph=t,this.parent=e,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(n=>{this._functionExecutorMap[n]=new r(t.functions[n],this)})}getCompilationKey(t,e){let n=t.map(s=>s.name).sort(),o=e.map(s=>s.name).sort();return n.join(this.SEPARATOR)+"--"+o.join(this.SEPARATOR)}compile(t,e){let n=Ek(t,e,this.weightMap,this._initNodes),{missingInputs:o,dynamicNode:s,syncInputs:i}=n;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${i}]`);if(o.length>0){let l=e.map(p=>p.name),c=Object.keys(t);throw new Error(`Cannot compute the outputs [${l}] from the provided inputs [${c}]. Missing the following inputs: [${o}]`)}let a=MF(this.graph,n),u=PF(a);return{orderedNodes:a,nodeLiveUntilMap:u}}cloneAndKeepTensor(t){if(t==null)return null;let e=t.clone();return De(e),e}cloneTensorList(t){return t?t.map(n=>this.cloneAndKeepTensor(n)):null}cloneTensorMap(t){return Object.fromEntries(Object.entries(t).map(([e,n])=>[e,this.cloneTensorList(n)]))}execute(t,e){this.disposeIntermediateTensors(),t=this.mapInputs(t);let n=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e);let o=n.map(m=>this.graph.nodes[In(m)[0]]),s=e.map(m=>In(m)[0]),i=new Set(s),a=s.map(m=>this.graph.nodes[m]);a.length===0&&(a=this._outputs);let u=this.getCompilationKey(o,a),l=this.compiledMap.get(u);l==null&&(l=this.compile(t,a),this.compiledMap.set(u,l));try{this.keepIntermediateTensors=L().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let c={},p={};return B(()=>{let m=new Wh(this.weightMap,c,p,this.functionExecutorMap,this.parseNodeNameCache),f=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(t).forEach(x=>{let[b,w]=In(x,m),I=[];I[w]=t[x],f[b]=I,this.keepIntermediateTensors&&(this.clonedTensorsMap[b]=this.cloneTensorList(I))});let d=this.getFrozenTensorIds(f),{orderedNodes:h,nodeLiveUntilMap:g}=l;for(let x of h){if(f[x.name])continue;let b=_k(x,f,m,this._resourceManager);if(y.isPromise(b))throw new Error(`The execution of the op '${x.op}' returned a promise. Please use model.executeAsync() instead.`);f[x.name]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[x.name]=this.cloneTensorList(b)),this.checkTensorForDisposalWithNodeLiveUntilInfo(x,f,m,d,i,g.get(x.name))}return this.parent==null&&m.dispose(d),e.map(x=>pr(x,f,m))})}getFrozenTensorIds(t){let e=[].concat.apply([],Object.keys(t).map(n=>t[n]).map(n=>n.map(o=>o.id)));return new Set(e)}checkTensorForDisposal(t,e,n,o,s,i,a){if(!(Bu(e)||i.has(t))){for(let u of n[t])u!=null&&(a[u.id]=(a[u.id]||0)+e.children.length);for(let u of e.inputs){if(Bu(u))continue;let l=ik(u.name,n,o);if(l!=null)for(let c of l){if(!c||c.kept||s.has(c.id))continue;let p=a[c.id];p===1?(c.dispose(),delete a[c.id]):p!=null&&a[c.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(t,e,n,o,s,i){function a(u){return Bu(u)||s.has(u.name)}if(!(Bu(t)||i==null))for(let u of i){if(a(u))continue;let l=ik(u.name,e,n);for(let c of l)!c||c.kept||o.has(c.id)||c.dispose()}}async executeAsync(t,e){return this._executeAsync(t,e)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(t=>{for(let e of t)e&&!e.isDisposed&&e.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(t,e,n=!1,o={},s={}){this.disposeIntermediateTensors(),n||(t=this.mapInputs(t),this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e));try{this.keepIntermediateTensors=L().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let i=new Wh(this.weightMap,o,s,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let a=await this.executeWithControlFlow(t,i,e,n),u=e.map(m=>pr(m,a,i)),l=u.map(m=>m.id),c=Object.keys(t).map(m=>t[m].id),p=new Set([...l,...c,...this.weightIds]);return Object.values(a).forEach(m=>{m.forEach(f=>{f&&!f.isDisposed&&!p.has(f.id)&&f.dispose()})}),this.parent==null&&i.dispose(p),u}async executeFunctionAsync(t,e,n){let o=t.reduce((s,i,a)=>(s[this.inputs[a].name]=i,s),{});return this._executeAsync(o,this.outputNodes,!0,e,n)}async executeWithControlFlow(t,e,n,o){let s=Object.keys(t),i=s.map(I=>this.graph.nodes[In(I)[0]]),a=n.map(I=>In(I)[0]),u=new Set(a),l=a.map(I=>this.graph.nodes[I]);l.length===0&&(l=this._outputs);let{usedNodes:c,missingInputs:p,dynamicNode:m,syncInputs:f}=Ek(t,l,this.weightMap,this._initNodes),d=[...i,...this.graph.weights,...this._initNodes||[]].map(I=>({node:I,contexts:e.currentContext})),h=Object.assign({},this.weightMap);Object.keys(t).forEach(I=>{let[N,E]=In(I),A=[];A[E]=t[I],h[N]=A});let g={},x=this.getFrozenTensorIds(h),b={};for(;d.length>0;){let I=this.processStack(i,d,e,h,b,x,u,g,c);await Promise.all(I)}m==null&&!o&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let w=l.filter(I=>!Bu(I)&&!pr(I.name,h,e)).map(I=>I.name);if(w.length>0){let I="";throw m!=null&&(I=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${f}]`),new Error(`Cannot compute the outputs [${w}] from the provided inputs [${s}]. Consider providing the following inputs: [${p}]. ${I}`)}return h}processStack(t,e,n,o,s,i,a,u,l){let c=[];for(;e.length>0;){let p=e.pop();n.currentContext=p.contexts;let m="";if(p.node.op==="Enter"&&v("isConstant",p.node,o,n)&&([m]=yi(p.node.name,n)),o[p.node.name]==null){let f=_k(p.node,o,n,this._resourceManager);m||([m]=yi(p.node.name,n));let d=n.currentContext;y.isPromise(f)?c.push(f.then(h=>(o[m]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(h)),n.currentContext=d,this.checkTensorForDisposal(m,p.node,o,n,i,a,u),this.processChildNodes(p.node,e,n,o,s,l),h))):(o[m]=f,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(f)),this.checkTensorForDisposal(m,p.node,o,n,i,a,u),this.processChildNodes(p.node,e,n,o,s,l))}else this.processChildNodes(p.node,e,n,o,s,l)}return c}processChildNodes(t,e,n,o,s,i){t.children.forEach(a=>{let[u]=yi(a.name,n);s[u]||!i.has(a.name)||(a.op==="Merge"?a.inputNames.some(l=>!!pr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})):a.inputNames.every(l=>!!pr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(e=>e.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(e=>{let n=t[e],[o]=In(e),s=this.graph.nodes[o];if(s.attrParams.shape&&s.attrParams.shape.value){let i=s.attrParams.shape.value,a=i.length===n.shape.length&&n.shape.every((u,l)=>i[l]===-1||i[l]===u);y.assert(a,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${i}], but was [${n.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(n.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(t){var e,n;let o={};for(let s in t){let i=(n=(e=this._signature)===null||e===void 0?void 0:e.inputs)===null||n===void 0?void 0:n[s];i!=null?o[i.name]=t[s]:o[s]=t[s]}return o}checkInputs(t){let e=Object.keys(t).filter(n=>{let[o]=In(n);return this.graph.nodes[o]==null});if(e.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${e}] that are not part of graph`)}mapOutputs(t){return t.map(e=>{var n,o;let s=(o=(n=this._signature)===null||n===void 0?void 0:n.outputs)===null||o===void 0?void 0:o[e];return s!=null?s.name:e},{})}checkOutputs(t){t.forEach(e=>{let[n]=In(e);if(!this.graph.nodes[n])throw new Error(`The output '${e}' is not found in the graph`)})}};var ew=class{constructor(t={},e={}){this.hashTableNameToHandle=t,this.hashTableMap=e}addHashTable(t,e){this.hashTableNameToHandle[t]=e.handle,this.hashTableMap[e.id]=e}getHashTableHandleByName(t){return this.hashTableNameToHandle[t]}getHashTableById(t){return this.hashTableMap[t]}dispose(){for(let t in this.hashTableMap)this.hashTableMap[t].clearAndClose(),delete this.hashTableMap[t];for(let t in this.hashTableNameToHandle)this.hashTableNameToHandle[t].dispose(),delete this.hashTableNameToHandle[t]}};var ptt="?tfjs-format=file",mtt="model.json",Hh=class{get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}constructor(t,e={},n=Mr){this.modelUrl=t,this.loadOptions=e,this.version="n/a",this.io=n,e==null&&(this.loadOptions={}),this.resourceManager=new ew}findIOHandler(){let t=this.modelUrl;if(t.load!=null)this.handler=t;else 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this.loadWithWeightMap(t,e)}loadWithWeightMap(t,e){this.artifacts=t;let n=this.artifacts.modelTopology,o=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let s=this.artifacts.userDefinedMetadata;s.signature!=null&&(o=s.signature),s.structuredOutputKeys!=null&&(this.structuredOutputKeys=s.structuredOutputKeys)}if(this.signature=o,this.version=`${n.versions.producer}.${n.versions.minConsumer}`,this.executor=new Uh(Gh.Instance.transformGraph(n,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(e),this.executor.resourceManager=this.resourceManager,t.modelInitializer!=null&&t.modelInitializer.node!=null){let s=Gh.Instance.transformGraph(t.modelInitializer);this.initializer=new Uh(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=t.initializerSignature}return!0}async save(t,e){if(typeof t=="string"){let n=this.io.getSaveHandlers(t);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${t}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${t}'`);t=n[0]}if(t.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return t.save(this.artifacts)}addStructuredOutputNames(t){if(this.structuredOutputKeys){let e=t instanceof Lt?[t]:t,n={};return e.forEach((o,s)=>n[this.structuredOutputKeys[s]]=o),n}return t}predict(t,e){let n=this.execute(t,this.outputNodes);return this.addStructuredOutputNames(n)}async predictAsync(t,e){let n=await this.executeAsync(t,this.outputNodes);return this.addStructuredOutputNames(n)}normalizeInputs(t){var e;if(!(t instanceof Lt)&&!Array.isArray(t)){let s=(e=this.signature)===null||e===void 0?void 0:e.inputs;if(s!=null)for(let i in s){let a=s[i];a.resourceId!=null&&(t[i]=this.resourceIdToCapturedInput[a.resourceId])}return t}t=Array.isArray(t)?t:[t];let n=Object.keys(this.resourceIdToCapturedInput).length;if(t.length+n!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-n} non-resource placeholders, while there are ${t.length} input tensors provided.`);let o=0;return this.inputNodes.reduce((s,i)=>{var a,u,l;let c=(l=(u=(a=this.signature)===null||a===void 0?void 0:a.inputs)===null||u===void 0?void 0:u[i])===null||l===void 0?void 0:l.resourceId;return c!=null?s[i]=this.resourceIdToCapturedInput[c]:s[i]=t[o++],s},{})}normalizeOutputs(t){return t=t||this.outputNodes,Array.isArray(t)?t:[t]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(t){if(this.resourceIdToCapturedInput={},this.initializerSignature){let e=this.initializerSignature.outputs,n=Object.keys(e);for(let o=0;o1?n:n[0]}async executeAsync(t,e){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),t=this.normalizeInputs(t),e=this.normalizeOutputs(e);let n=await this.executor.executeAsync(t,e);return n.length>1?n:n[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(t){return Object.keys(t).reduce((e,n)=>(e[n]=[t[n]],e),{})}dispose(){this.executor.dispose(),this.initializer&&(this.initializer.dispose(),this.resourceIdToCapturedInput&&Tt(this.resourceIdToCapturedInput)),this.resourceManager.dispose()}};async function ftt(r,t={},e=Mr){if(r==null)throw new Error("modelUrl in loadGraphModel() cannot be null. 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oO={};Kt(oO,{CSVDataset:()=>sd,Dataset:()=>wi,FileDataSource:()=>ld,TextLineDataset:()=>od,URLDataSource:()=>ud,array:()=>jF,csv:()=>QF,func:()=>tO,generator:()=>eO,microphone:()=>nO,version_data:()=>Yk,webcam:()=>rO,zip:()=>XF});var KF=Gl(dh());var UF=Gl(dh());function zF(r,t){return rw(r,t)}function rw(r,t,e=new Map,n=new Set){if(r==null)return null;if(typeof Blob=="function"&&r instanceof Blob)return r.slice();if(n.has(r))throw new Error("Circular references are not supported.");if(e.has(r))return e.get(r);let o=t(r);if(o.recurse&&o.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(o.recurse)if(Vu(r)){let s=Array.isArray(r)?[]:{};n.add(r);for(let i in r){let a=r[i],u=rw(a,t,e,n);s[i]=u}return n.delete(r),r.__proto__&&(s.__proto__=r.__proto__),s}else throw new Error(`Can't recurse into non-iterable type: ${r}`);else return e.set(r,o.value),o.value}function BF(r,t=Dk){return VF(r,t)}function VF(r,t,e=new Set){let n=r[0];if(e.has(n))throw new Error("Circular references are not supported.");let o=t(r);if(o.recurse&&o.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(o.recurse)if(Vu(n)){let s=Array.isArray(n)?[]:{};e.add(n);for(let i in n){let a=r.map(l=>l[i]),u=VF(a,t,e);s[i]=u}return e.delete(n),s}else throw new Error(`Can't recurse into non-iterable type: ${n}`);else return o.value}function Dk(r){return r===null?null:Vu(r[0])?{value:null,recurse:!0}:{value:r,recurse:!1}}async function nw(r,t){let e=new Map;rw(r,t,e);for(let o of Array.from(e.keys())){let s=e.get(o);if(y.isPromise(s)){let i=await s;e.set(o,i)}}return rw(r,t,e)}function Vu(r){let t=!1;if(L().get("IS_BROWSER"))t=r instanceof TextDecoder;else{let{StringDecoder:e}=Ak();t=r instanceof e}return r!=null&&!ArrayBuffer.isView(r)&&(Array.isArray(r)||typeof r=="object"&&!(r instanceof Lt)&&!(r instanceof Promise)&&!t)}function GF(r){return r==null||gtt(r)||Array.isArray(r)||typeof r=="object"&&r instanceof Lt||y.isTypedArray(r)}function gtt(r){return r===null||typeof r!="object"&&typeof r!="function"}function WF(r){return zF(r,xtt)}function xtt(r){return r instanceof Lt?{value:r.clone(),recurse:!1}:Vu(r)?{value:null,recurse:!0}:{value:r,recurse:!1}}var nd=class{constructor(t){if(this.capacity=t,this.begin=0,this.end=0,t==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(t<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(t),this.doubledCapacity=2*t}wrap(t){for(;t<0;)t+=this.doubledCapacity;return t%this.doubledCapacity}get(t){if(t<0)throw new RangeError("Can't get item at a negative index.");return this.data[t%this.capacity]}set(t,e){if(t<0)throw new RangeError("Can't set item at a negative index.");this.data[t%this.capacity]=e}length(){let t=this.end-this.begin;return t<0&&(t=this.doubledCapacity+t),t}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(t){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,t),this.end=this.wrap(this.end+1)}pushAll(t){for(let e of t)this.push(e)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let t=this.get(this.end);return this.set(this.end,void 0),t}unshift(t){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,t)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),t}shuffleExcise(t){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.wrap(this.begin+t),n=this.get(e);return this.set(e,this.pop()),n}};var qh=class r extends nd{constructor(){super(r.INITIAL_CAPACITY)}isFull(){return!1}push(t){super.isFull()&&this.expand(),super.push(t)}unshift(t){super.isFull()&&this.expand(),super.unshift(t)}expand(){let t=this.capacity*2,e=new Array(t),n=this.length();for(let o=0;oe===!0)}rowMajorBatch(t,e=!0){return new Pk(this,t,e)}columnMajorBatch(t,e=!0,n=Dk){return this.rowMajorBatch(t,e).map(s=>BF(s,n))}concatenate(t,e){return new sw(Uk([this,t]),e)}take(t){return t<0||t==null?this:new Mk(this,t)}skip(t){return t<0||t==null?this:new Ok(this,t)}prefetch(t){return new iw(this,t)}shuffle(t,e){return new Wk(this,t,e)}serial(){return new Fk(this)}},$k=class extends er{constructor(t){super(),this.items=t,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 t=this.items[this.trav];return this.trav++,{value:WF(t),done:!1}}},Rk=class extends er{constructor(t){super(),this.nextFn=t}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(t){throw t.message=`Error thrown while iterating through a dataset: ${t.message}`,t}}},Fk=class extends er{constructor(t){super(),this.upstream=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},Ok=class extends er{constructor(t,e){super(),this.upstream=t,this.maxCount=e,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++ Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},Pk=class extends er{constructor(t,e,n=!0){super(),this.upstream=t,this.batchSize=e,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let t=[];for(;t.length0?{value:t,done:!1}:{value:null,done:!0};t.push(e.value)}return{value:t,done:!1}}},Lk=class extends er{constructor(t,e){super(),this.upstream=t,this.predicate=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let t=await this.upstream.next();if(t.done||this.predicate(t.value))return t;Tt(t.value)}}},zk=class extends er{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Map`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=Io.getTensorsInContainer(t.value),n=this.transform(t.value),o=Io.getTensorsInContainer(n);for(let s of e)Io.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},Bk=class extends er{constructor(t,e){super(),this.upstream=t,this.handler=e,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(t){if(!this.handler(t))return{value:null,done:!0}}}},ow=class extends er{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=Io.getTensorsInContainer(t.value),n=await this.transform(t.value),o=Io.getTensorsInContainer(n);for(let s of e)Io.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},Zc=class extends er{constructor(){super(),this.outputQueue=new qh,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}}},Vk=class extends Zc{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let t=await this.upstream.next();if(t.done)return!1;let e=Io.getTensorsInContainer(t.value),n=this.transform(t.value),o=Io.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let s of e)Io.isTensorInList(s,o)||s.dispose();return!0}},sw=class extends er{constructor(t,e){super(),this.baseErrorHandler=e,this.lastRead=null,this.iterator=null,this.moreIterators=t}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(t){if(await t,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 e=await this.iterator.next();return e.done?(this.iterator=null,this.readFromChain(t)):e}},Dl;(function(r){r[r.FAIL=0]="FAIL",r[r.SHORTEST=1]="SHORTEST",r[r.LONGEST=2]="LONGEST"})(Dl||(Dl={}));var Gk=class extends er{constructor(t,e=Dl.FAIL){super(),this.iterators=t,this.mismatchMode=e,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(t){await t;let e=0,n=0;function o(i){return i instanceof er?{value:i.next().then(u=>(e++,u.done&&n++,u.value)),recurse:!1}:{value:null,recurse:!0}}let s=await nw(this.iterators,o);if(e===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Dl.FAIL:throw new Error(`Zipped streams should have the same length. 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At least one type of data should be returned.")}summary(){return"microphone"}static async create(t={}){if(!L().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let e=new r(t);return await e.start(),e}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 t=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new t,!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 e=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,e.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 t,e,n=await this.getAudioData();if(this.includeSpectrogram){let o=this.flattenQueue(n.freqDataQueue);t=this.getTensorFromAudioDataArray(o,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let o=this.flattenQueue(n.timeDataQueue);e=this.getTensorFromAudioDataArray(o,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:t,waveform:e},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let t=[],e=[],n=0;return new Promise(o=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&o({freqDataQueue:t,timeDataQueue:e}),t.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),e.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(s),o({freqDataQueue:t,timeDataQueue:e}))},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(t){let e=t[0].length,n=new Float32Array(t.length*e);return t.forEach((o,s)=>n.set(o,s*e)),n}getTensorFromAudioDataArray(t,e){let n=new Float32Array(y.sizeFromShape(e));return n.set(t,n.length-t.length),ir(n,e)}};var cw=class r extends er{constructor(t,e){if(super(),this.webcamVideoElement=t,this.webcamConfig=e,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Oe([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,o=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-n)/2,i=(1-o)/2,a=s+n,u=o+i;this.cropBox=ci([i,s,u,a],[1,4])}else this.cropBox=ci([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(t,e={}){if(!L().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!t){if(t=document.createElement("video"),!e.resizeWidth||!e.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");t.width=e.resizeWidth,t.height=e.resizeHeight}let n=new r(t,e);return await n.start(),n}async start(){this.webcamConfig.facingMode&&y.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(t){throw t.message=`Error thrown while initializing video stream: ${t.message}`,t}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(t){console.log(t),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(t=>{this.webcamVideoElement.onloadedmetadata=()=>{t()}})}async next(){if(this.isClosed)return{value:null,done:!0};let t;try{t=_y.fromPixels(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(t),done:!1}}catch(e){throw new Error(`Error thrown cropping the video: ${e.message}`)}finally{t.dispose()}else return{value:t,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(t){return B(()=>{let e=je(J(t,"float32"),0),n;n=fn.cropAndResize(e,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let o=n.shape;return R(n,o.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}};var id=class{};var Xh=class extends er{split(t){return new qk(this,t)}},qk=class extends Xh{constructor(t,e){super(),this.upstream=t,this.impl=new Kk(t,e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Kk=class extends Zc{constructor(t,e){super(),this.upstream=t,this.separator=e,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let t=await this.upstream.next();if(t.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let e=t.value.split(this.separator);e[0]=this.carryover+e[0];for(let n of e.slice(0,-1))this.outputQueue.push(n);return this.carryover=e[e.length-1],!0}};var pw=class extends er{decodeUTF8(){return new jk(this)}},jk=class extends Xh{constructor(t){super(),this.upstream=t,this.impl=new Xk(t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Xk=class extends Zc{constructor(t){if(super(),this.upstream=t,L().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:e}=Ak();this.decoder=new e("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let t=await this.upstream.next(),e;if(t.done)return!1;e=t.value;let n;return L().get("IS_BROWSER")?n=this.decoder.decode(e,{stream:!0}):n=this.decoder.write(Buffer.from(e.buffer)),this.outputQueue.push(n),!0}};var ad=class extends pw{constructor(t,e={}){super(),this.file=t,this.options=e,y.assert(t instanceof Uint8Array||(L().get("IS_BROWSER")?t instanceof File||t instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=e.offset||0,this.chunkSize=e.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,n)=>{let o=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,o)));else{let s=new FileReader;s.onload=a=>{let u=s.result;if(u instanceof 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id{constructor(t,e={}){super(),this.url=t,this.fileOptions=e}async iterator(){return mw(this.url)?new ld(this.url,this.fileOptions).iterator():JF(this.url,this.fileOptions)}};function QF(r,t={}){return new sd(new ud(r),t)}function tO(r){let t=Kh(r);return En(async()=>t)}function eO(r){return En(async()=>{let t=await r();return Kh(()=>t.next())})}async function rO(r,t){return cw.create(r,t)}async function nO(r){return uw.create(r)}var Yk="4.16.0";function tt(r,t){Array.isArray(r)||(r=[r]),r.forEach(e=>{e!=null&&y.assert(e.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var Itt=Kr.whereImpl,cd=class r extends Bo{nextDataId(){return r.nextDataId++}constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new Ta(this,Bn())}write(t,e,n){this.firstUse&&(this.firstUse=!1,L().get("IS_NODE")&&S.warn(` + ${n}, and tensor's shape is: ${r.shape}`);let s=r.shape.slice(1),i=tw(s,e),a=n===0?0:r.size/n,u=B(()=>{let c=[];r=R(r,[1,n,a]);for(let 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g=v("leakyreluAlpha",r,t,e);return{stride:c,pad:p,dataFormat:m,dilations:f,biasArg:d,preluArg:h,activationFunc:o,leakyreluAlpha:g}}var CF=(r,t,e,n=ae)=>{switch(r.op){case"Conv1D":{let o=v("stride",r,t,e),s=v("pad",r,t,e),i=v("dataFormat",r,t,e).toUpperCase(),a=v("dilation",r,t,e);return[n.conv1d(v("x",r,t,e),v("filter",r,t,e),o,s,i,a)]}case"Conv2D":{let 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performance."),console.log(u);for(let c=0;ct.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return pt(this.size(),"int32")}async import(t,e){this.checkKeyAndValueTensor(t,e);let n=await t.data();return this.tensorMap.forEach(o=>o.dispose()),this.tensorMap.clear(),B(()=>{let o=gr(e),s=n.length,i=o.length;y.assert(s===i,()=>`The number of elements doesn't match, keys has ${s} elements, the values has ${i} elements.`);for(let a=0;a{let o=[];for(let s=0;s{switch(r.op){case"HashTable":case"HashTableV2":{let o=n.getHashTableHandleByName(r.name);if(o!=null)return[o];{let s=v("keyDType",r,t,e),i=v("valueDType",r,t,e),a=new rw(s,i);return n.addHashTable(r.name,a),[a.handle]}}case"InitializeTable":case"InitializeTableV2":case"LookupTableImport":case"LookupTableImportV2":{let o=v("tableHandle",r,t,e,n),s=v("keys",r,t,e),i=v("values",r,t,e);return[await n.getHashTableById(o.id).import(s,i)]}case"LookupTableFind":case"LookupTableFindV2":{let 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o=v("images",r,t,e),s=v("transforms",r,t,e),i=v("outputShape",r,t,e),a=v("fillValue",r,t,e),u=v("interpolation",r,t,e),l=v("fillMode",r,t,e);return[n.image.transform(o,s,u.toLowerCase(),l.toLowerCase(),a,i)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var EF=(r,t,e,n=ae)=>{switch(r.op){case"Equal":return[n.equal(v("a",r,t,e),v("b",r,t,e))];case"NotEqual":return[n.notEqual(v("a",r,t,e),v("b",r,t,e))];case"Greater":return[n.greater(v("a",r,t,e),v("b",r,t,e))];case"GreaterEqual":return[n.greaterEqual(v("a",r,t,e),v("b",r,t,e))];case"Less":return[n.less(v("a",r,t,e),v("b",r,t,e))];case"LessEqual":return[n.lessEqual(v("a",r,t,e),v("b",r,t,e))];case"LogicalAnd":return[n.logicalAnd(v("a",r,t,e),v("b",r,t,e))];case"LogicalNot":return[n.logicalNot(v("a",r,t,e))];case"LogicalOr":return[n.logicalOr(v("a",r,t,e),v("b",r,t,e))];case"Select":case"SelectV2":return[n.where(v("condition",r,t,e),v("a",r,t,e),v("b",r,t,e))];case"BitwiseAnd":return[n.bitwiseAnd(v("a",r,t,e),v("b",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var AF=(r,t,e,n=ae)=>{switch(r.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[n.matMul(v("a",r,t,e),v("b",r,t,e),v("transposeA",r,t,e),v("transposeB",r,t,e))];case"Einsum":return[n.einsum(v("equation",r,t,e),...v("tensors",r,t,e))];case"Transpose":return[n.transpose(v("x",r,t,e),v("perm",r,t,e))];case"_FusedMatMul":let[o,s]=v("fusedOps",r,t,e),i=o==="biasadd",a=s==="prelu",u=v("numArgs",r,t,e),l=v("leakyreluAlpha",r,t,e);if(i){if(a&&u!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!a&&u!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[c,p]=v("args",r,t,e);return[n.fused.matMul({a:v("a",r,t,e),b:v("b",r,t,e),transposeA:v("transposeA",r,t,e),transposeB:v("transposeB",r,t,e),bias:c,activation:s,preluActivationWeights:p,leakyreluAlpha:l})];case"MatrixBandPart":return[n.linalg.bandPart(v("a",r,t,e),v("numLower",r,t,e),v("numUpper",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var DF=(r,t,e,n=ae)=>{switch(r.op){case"EuclideanNorm":return[n.euclideanNorm(v("x",r,t,e),v("axis",r,t,e),v("keepDims",r,t,e))];case"FusedBatchNorm":case"FusedBatchNormV2":return[n.batchNorm(v("x",r,t,e),v("mean",r,t,e),v("variance",r,t,e),v("offset",r,t,e),v("scale",r,t,e),v("epsilon",r,t,e))];case"FusedBatchNormV3":return[n.batchNorm(v("x",r,t,e),v("mean",r,t,e),v("variance",r,t,e),v("offset",r,t,e),v("scale",r,t,e),v("epsilon",r,t,e))];case"LRN":return[n.localResponseNormalization(v("x",r,t,e),v("radius",r,t,e),v("bias",r,t,e),v("alpha",r,t,e),v("beta",r,t,e))];case"Softmax":return[n.softmax(v("x",r,t,e))];case"LogSoftmax":return[n.logSoftmax(v("x",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var $F=(r,t,e,n=ae)=>{switch(r.op){case"RaggedGather":{let{outputNestedSplits:o,outputDenseValues:s}=n.raggedGather(v("paramsNestedSplits",r,t,e),v("paramsDenseValues",r,t,e),v("indices",r,t,e),v("outputRaggedRank",r,t,e));return o.concat(s)}case"RaggedRange":{let{rtNestedSplits:o,rtDenseValues:s}=n.raggedRange(v("starts",r,t,e),v("limits",r,t,e),v("splits",r,t,e));return[o,s]}case"RaggedTensorToTensor":return[n.raggedTensorToTensor(v("shape",r,t,e),v("values",r,t,e),v("defaultValue",r,t,e),v("rowPartitionTensors",r,t,e),v("rowPartitionTypes",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var RF=(r,t,e,n=ae)=>{switch(r.op){case"Max":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.max(v("x",r,t,e),a,u)]}case"Mean":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.mean(v("x",r,t,e),a,u)]}case"Min":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.min(v("x",r,t,e),a,u)]}case"Sum":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.sum(v("x",r,t,e),a,u)]}case"All":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.all(v("x",r,t,e),a,u)]}case"Any":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.any(v("x",r,t,e),a,u)]}case"ArgMax":{let a=v("axis",r,t,e);return[n.argMax(v("x",r,t,e),a)]}case"ArgMin":{let a=v("axis",r,t,e);return[n.argMin(v("x",r,t,e),a)]}case"Prod":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.prod(v("x",r,t,e),a,u)]}case"Cumprod":{let a=v("axis",r,t,e),u=v("exclusive",r,t,e),l=v("reverse",r,t,e);return[n.cumprod(v("x",r,t,e),a,u,l)]}case"Cumsum":{let a=v("axis",r,t,e),u=v("exclusive",r,t,e),l=v("reverse",r,t,e);return[n.cumsum(v("x",r,t,e),a,u,l)]}case"Bincount":let o=v("x",r,t,e),s=v("weights",r,t,e),i=v("size",r,t,e);return[n.bincount(o,s,i)];case"DenseBincount":{let a=v("x",r,t,e),u=v("weights",r,t,e),l=v("size",r,t,e),c=v("binaryOutput",r,t,e);return[n.denseBincount(a,u,l,c)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var FF=(r,t,e,n=ae)=>{switch(r.op){case"ConcatV2":case"Concat":{let o=v("n",r,t,e),s=v("axis",r,t,e),i=v("tensors",r,t,e);return i=i.slice(0,o),[n.concat(i,s)]}case"Gather":{let o=v("x",r,t,e),s=v("indices",r,t,e);return[n.gather(o,n.cast(s,"int32"),0)]}case"GatherV2":{let o=v("axis",r,t,e),s=v("batchDims",r,t,e),i=v("x",r,t,e),a=v("indices",r,t,e);return[n.gather(i,n.cast(a,"int32"),o,s)]}case"Reverse":{let o=v("dims",r,t,e),s=[];for(let a=0;a{let o=v("axis",r,t,e),s=v("tensors",r,t,e),i=s[0].shape,a=n.squeeze(s[0]).shape,u=s.map(l=>{let c=y.arraysEqual(l.shape,i);if(!c&&!y.arraysEqual(n.squeeze(l).shape,a))throw new Error("the input tensors shape does not match");return c?l:n.reshape(l,i)});return[n.stack(u,o)]});case"Unpack":{let o=v("axis",r,t,e),s=v("tensor",r,t,e);return n.unstack(s,o)}case"Tile":{let o=v("reps",r,t,e);return[n.tile(v("x",r,t,e),o)]}case"Split":case"SplitV":{let o=v("axis",r,t,e),s=v("numOrSizeSplits",r,t,e),i=v("x",r,t,e);return n.split(i,s,o)}case"ScatterNd":{let o=v("indices",r,t,e),s=v("values",r,t,e),i=v("shape",r,t,e);return[n.scatterND(o,s,i)]}case"GatherNd":{let o=v("x",r,t,e),s=v("indices",r,t,e);return[n.gatherND(o,s)]}case"SparseToDense":{let o=v("sparseIndices",r,t,e),s=v("outputShape",r,t,e),i=v("sparseValues",r,t,e),a=v("defaultValue",r,t,e);return[n.sparseToDense(o,i,s,i.dtype===a.dtype?a:n.cast(a,i.dtype))]}case"TensorScatterUpdate":{let o=v("indices",r,t,e),s=v("values",r,t,e),i=v("tensor",r,t,e);return[n.tensorScatterUpdate(i,o,s)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var OF=(r,t,e,n=ae)=>{switch(r.op){case"SparseFillEmptyRows":{let{outputIndices:o,outputValues:s,emptyRowIndicator:i,reverseIndexMap:a}=n.sparse.sparseFillEmptyRows(v("indices",r,t,e),v("values",r,t,e),v("denseShape",r,t,e),v("defaultValue",r,t,e));return[o,s,i,a]}case"SparseReshape":{let{outputIndices:o,outputShape:s}=n.sparse.sparseReshape(v("inputIndices",r,t,e),v("inputShape",r,t,e),v("newShape",r,t,e));return[o,s]}case"SparseSegmentMean":return[n.sparse.sparseSegmentMean(v("data",r,t,e),v("indices",r,t,e),v("segmentIds",r,t,e))];case"SparseSegmentSum":return[n.sparse.sparseSegmentSum(v("data",r,t,e),v("indices",r,t,e),v("segmentIds",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var MF=(r,t,e,n=ae)=>{switch(r.op){case"FFT":return[n.fft(v("x",r,t,e))];case"IFFT":return[n.ifft(v("x",r,t,e))];case"RFFT":return[n.rfft(v("x",r,t,e))];case"IRFFT":return[n.irfft(v("x",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var PF=(r,t,e,n=ae)=>{switch(r.op){case"StaticRegexReplace":return[n.string.staticRegexReplace(v("input",r,t,e),v("pattern",r,t,e),v("rewrite",r,t,e),v("replaceGlobal",r,t,e))];case"StringNGrams":{let{nGrams:o,nGramsSplits:s}=n.string.stringNGrams(v("data",r,t,e),v("dataSplits",r,t,e),v("separator",r,t,e),v("nGramWidths",r,t,e),v("leftPad",r,t,e),v("rightPad",r,t,e),v("padWidth",r,t,e),v("preserveShortSequences",r,t,e));return[o,s]}case"StringSplit":{let{indices:o,values:s,shape:i}=n.string.stringSplit(v("input",r,t,e),v("delimiter",r,t,e),v("skipEmpty",r,t,e));return[o,s,i]}case"StringToHashBucketFast":return[n.string.stringToHashBucketFast(v("input",r,t,e),v("numBuckets",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var LF=(r,t,e,n=ae)=>{switch(r.op){case"Cast":return[n.cast(v("x",r,t,e),v("dtype",r,t,e))];case"ExpandDims":{let o=v("axis",r,t,e);return[n.expandDims(v("x",r,t,e),o)]}case"Squeeze":{let o=v("axis",r,t,e);return[n.squeeze(v("x",r,t,e),o)]}case"Reshape":return[n.reshape(v("x",r,t,e),v("shape",r,t,e))];case"EnsureShape":return[n.ensureShape(v("x",r,t,e),v("shape",r,t,e))];case"MirrorPad":return[n.mirrorPad(v("x",r,t,e),v("padding",r,t,e),v("mode",r,t,e))];case"PadV2":case"Pad":return[n.pad(v("x",r,t,e),v("padding",r,t,e),v("constantValue",r,t,e))];case"SpaceToBatchND":{let o=v("blockShape",r,t,e),s=v("paddings",r,t,e);return[n.spaceToBatchND(v("x",r,t,e),o,s)]}case"BatchToSpaceND":{let o=v("blockShape",r,t,e),s=v("crops",r,t,e);return[n.batchToSpaceND(v("x",r,t,e),o,s)]}case"DepthToSpace":{let o=v("blockSize",r,t,e),s=v("dataFormat",r,t,e).toUpperCase();return[n.depthToSpace(v("x",r,t,e),o,s)]}case"BroadcastTo":return[n.broadcastTo(v("x",r,t,e),v("shape",r,t,e))];case"BroadcastArgs":return[n.broadcastArgs(v("s0",r,t,e),v("s1",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function Ak(r,t,e,n,o=B){let s=((i,a,u)=>{switch(i.category){case"arithmetic":return o(()=>fF(i,a,u));case"basic_math":return o(()=>dF(i,a,u));case"control":return wF(i,a,u);case"convolution":return o(()=>CF(i,a,u));case"creation":return o(()=>vF(i,a,u));case"dynamic":return SF(i,a,u);case"evaluation":return o(()=>NF(i,a,u));case"image":return o(()=>_F(i,a,u));case"graph":return o(()=>kF(i,a,u));case"logical":return o(()=>EF(i,a,u));case"matrices":return o(()=>AF(i,a,u));case"normalization":return o(()=>DF(i,a,u));case"ragged":return o(()=>$F(i,a,u));case"reduction":return o(()=>RF(i,a,u));case"slice_join":return o(()=>FF(i,a,u));case"sparse":return o(()=>OF(i,a,u));case"spectral":return o(()=>MF(i,a,u));case"string":return o(()=>PF(i,a,u));case"transformation":return o(()=>LF(i,a,u));case"hash_table":return TF(i,a,u,n);case"custom":let l=Vb(i.op);if(l&&l.customExecutor)return l.customExecutor(new Qb(i,a,u));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(r,t,e);return y.isPromise(s)?s.then(i=>[].concat(i)):[].concat(s)}var Uh=class{constructor(t={},e={},n={},o={},s){this.weightMap=t,this.tensorArrayMap=e,this.tensorListMap=n,this.functionMap=o,this.parseNodeNameCache=s,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(t,e){return{id:t,frameName:e,iterationId:0}}set currentContext(t){this.contexts!==t&&(this.contexts=t,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let t=[];for(let e=0;ee.id===0&&e.iterationId===0?"":`${e.frameName}-${e.iterationId}`).join("/"):""}enterFrame(t){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,t)),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 t=Object.assign({},this.contexts[this.contexts.length-1]);t.iterationId+=1,t.id=this.lastId,this.contexts.splice(-1,1,t),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(t){return this.weightMap[t]}addTensorArray(t){this.tensorArrayMap[t.id]=t}getTensorArray(t){return this.tensorArrayMap[t]}addTensorList(t){this.tensorListMap[t.id]=t}getTensorList(t){return this.tensorListMap[t]}dispose(t){for(let e in this.tensorArrayMap)this.tensorArrayMap[e].clearAndClose(t);for(let e in this.tensorListMap)this.tensorListMap[e].clearAndClose(t)}};function Dk(r,t,e,n){let o=new Set,s=[],i=null,a=null,u=new Set,l=new Set(Object.keys(r).map(m=>In(m)[0]));n=n||[];let c=new Set(n.map(m=>In(m.name)[0])),p=[...t];for(;p.length>0;){let m=p.pop();if((Bu(m)||ftt(m)||dtt(m))&&i==null&&(i=m,a=i.children.map(f=>f.name).filter(f=>o.has(f))),o.add(m.name),e[m.name]==null&&!l.has(m.name)&&!c.has(m.name)){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(f=>{u.has(f.name)||(u.add(f.name),p.push(f))})}}return{inputs:r,outputs:t,usedNodes:o,missingInputs:s,dynamicNode:i,syncInputs:a}}function zF(r,t){let{usedNodes:e,inputs:n}=t,o=Object.keys(n).map(g=>In(g)[0]).map(g=>r.nodes[g]),s=r.initNodes||[],i=g=>e.has(typeof g=="string"?g:g.name);function a(g){return[...new Map(g.map(x=>[x.name,x])).values()]}let u=a([...o,...r.weights,...s]).filter(i),l=a([...u,...Object.values(r.nodes)]).filter(i),c=new Map(l.map(g=>[g.name,g])),p={};for(let g of l){p[g.name]=p[g.name]||0;for(let x of g.children)i(x)||(p[x.name]=Number.POSITIVE_INFINITY),p[x.name]=(p[x.name]||0)+1}let m=Object.entries(p).filter(([,g])=>g===0).map(([g])=>g),f=[...m];for(;m.length>0;){let g=m.pop(),x=c.get(g);for(let b of x.children.filter(i))--p[b.name]===0&&(f.push(b.name),m.push(b.name))}let d=f.map(g=>c.get(g)),h=ltt(d,u);return utt(h,u),h}function ltt(r,t){let e=new Map(r.map(i=>[i.name,i])),n=t.map(i=>i.name),o=new Set(n);for(;n.length>0;){let i=n.pop(),a=e.get(i);for(let u of a.children)!e.has(u.name)||o.has(u.name)||(o.add(u.name),n.push(u.name))}return r.filter(i=>o.has(i.name))}var nd=class extends Error{constructor(t){super(`NodesExecutionOrderError: ${t}`)}};function utt(r,t){let e=new Map(r.map((a,u)=>[a.name,u])),n=new Set(t.map(a=>a.name)),o=a=>n.has(typeof a=="string"?a:a.name),s=new Set(r.map(a=>a.name)),i=a=>s.has(typeof a=="string"?a:a.name);for(let a of r){for(let u of a.children.filter(i)){if(!e.has(u.name))throw new nd(`Child ${u.name} of node ${a.name} is unreachable.`);if(e.get(a.name)>e.get(u.name))throw new nd(`Node ${a.name} is scheduled to run after its child ${u.name}.`)}if(!o(a))for(let u of a.inputs){if(!e.has(u.name))throw new nd(`Input ${u.name} of node ${a.name} is unreachable.`);if(e.get(u.name)>e.get(a.name))throw new nd(`Node ${a.name} is scheduled to run before its input ${u.name}.`)}}}function BF(r){let t=new Map(r.map((a,u)=>[a.name,u])),e=Number.MAX_SAFE_INTEGER,n=r.map((a,u)=>Bu(a)?e:u),o=a=>{let u=n[t.get(a.name)];return u==null?-1:u},s=r.map((a,u)=>a.children.map(o).reduce((l,c)=>Math.max(l,c),n[u])),i=new Map;for(let a=0;at[n].map(o=>o.id));this._weightIds=[].concat(...e),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let e=t.signatureKey||t.name;return t.defaultOutput?`${e}:${t.defaultOutput}`:e})}get functions(){return Object.keys(this._functions).reduce((t,e)=>(t[e]=this._functions[e].signature,t),{})}constructor(t,e){this.graph=t,this.parent=e,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(n=>{this._functionExecutorMap[n]=new r(t.functions[n],this)})}getCompilationKey(t,e){let n=t.map(s=>s.name).sort(),o=e.map(s=>s.name).sort();return n.join(this.SEPARATOR)+"--"+o.join(this.SEPARATOR)}compile(t,e){let n=Dk(t,e,this.weightMap,this._initNodes),{missingInputs:o,dynamicNode:s,syncInputs:i}=n;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${i}]`);if(o.length>0){let l=e.map(p=>p.name),c=Object.keys(t);throw new Error(`Cannot compute the outputs [${l}] from the provided inputs [${c}]. Missing the following inputs: [${o}]`)}let a=zF(this.graph,n),u=BF(a);return{orderedNodes:a,nodeLiveUntilMap:u}}cloneAndKeepTensor(t){if(t==null)return null;let e=t.clone();return De(e),e}cloneTensorList(t){return t?t.map(n=>this.cloneAndKeepTensor(n)):null}cloneTensorMap(t){return Object.fromEntries(Object.entries(t).map(([e,n])=>[e,this.cloneTensorList(n)]))}execute(t,e){this.disposeIntermediateTensors(),t=this.mapInputs(t);let n=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e);let o=n.map(m=>this.graph.nodes[In(m)[0]]),s=e.map(m=>In(m)[0]),i=new Set(s),a=s.map(m=>this.graph.nodes[m]);a.length===0&&(a=this._outputs);let u=this.getCompilationKey(o,a),l=this.compiledMap.get(u);l==null&&(l=this.compile(t,a),this.compiledMap.set(u,l));try{this.keepIntermediateTensors=L().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let c={},p={};return B(()=>{let m=new Uh(this.weightMap,c,p,this.functionExecutorMap,this.parseNodeNameCache),f=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(t).forEach(x=>{let[b,w]=In(x,m),I=[];I[w]=t[x],f[b]=I,this.keepIntermediateTensors&&(this.clonedTensorsMap[b]=this.cloneTensorList(I))});let d=this.getFrozenTensorIds(f),{orderedNodes:h,nodeLiveUntilMap:g}=l;for(let x of h){if(f[x.name])continue;let b=Ak(x,f,m,this._resourceManager);if(y.isPromise(b))throw new Error(`The execution of the op '${x.op}' returned a promise. 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c}processChildNodes(t,e,n,o,s,i){t.children.forEach(a=>{let[u]=bi(a.name,n);s[u]||!i.has(a.name)||(a.op==="Merge"?a.inputNames.some(l=>!!pr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})):a.inputNames.every(l=>!!pr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(e=>e.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(e=>{let n=t[e],[o]=In(e),s=this.graph.nodes[o];if(s.attrParams.shape&&s.attrParams.shape.value){let i=s.attrParams.shape.value,a=i.length===n.shape.length&&n.shape.every((u,l)=>i[l]===-1||i[l]===u);y.assert(a,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${i}], but was [${n.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(n.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(t){var e,n;let o={};for(let s in t){let i=(n=(e=this._signature)===null||e===void 0?void 0:e.inputs)===null||n===void 0?void 0:n[s];i!=null?o[i.name]=t[s]:o[s]=t[s]}return o}checkInputs(t){let e=Object.keys(t).filter(n=>{let[o]=In(n);return this.graph.nodes[o]==null});if(e.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${e}] that are not part of graph`)}mapOutputs(t){return t.map(e=>{var n,o;let s=(o=(n=this._signature)===null||n===void 0?void 0:n.outputs)===null||o===void 0?void 0:o[e];return s!=null?s.name:e},{})}checkOutputs(t){t.forEach(e=>{let[n]=In(e);if(!this.graph.nodes[n])throw new Error(`The output '${e}' is not found in the graph`)})}};var nw=class{constructor(t={},e={}){this.hashTableNameToHandle=t,this.hashTableMap=e}addHashTable(t,e){this.hashTableNameToHandle[t]=e.handle,this.hashTableMap[e.id]=e}getHashTableHandleByName(t){return this.hashTableNameToHandle[t]}getHashTableById(t){return this.hashTableMap[t]}dispose(){for(let t in this.hashTableMap)this.hashTableMap[t].clearAndClose(),delete this.hashTableMap[t];for(let t in this.hashTableNameToHandle)this.hashTableNameToHandle[t].dispose(),delete this.hashTableNameToHandle[t]}};var htt="?tfjs-format=file",gtt="model.json",qh=class{get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}constructor(t,e={},n=Mr){this.modelUrl=t,this.loadOptions=e,this.version="n/a",this.io=n,e==null&&(this.loadOptions={}),this.resourceManager=new nw}findIOHandler(){let t=this.modelUrl;if(t.load!=null)this.handler=t;else 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this.loadWithWeightMap(t,e)}loadWithWeightMap(t,e){this.artifacts=t;let n=this.artifacts.modelTopology,o=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let s=this.artifacts.userDefinedMetadata;s.signature!=null&&(o=s.signature),s.structuredOutputKeys!=null&&(this.structuredOutputKeys=s.structuredOutputKeys)}if(this.signature=o,this.version=`${n.versions.producer}.${n.versions.minConsumer}`,this.executor=new Hh(Wh.Instance.transformGraph(n,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(e),this.executor.resourceManager=this.resourceManager,t.modelInitializer!=null&&t.modelInitializer.node!=null){let s=Wh.Instance.transformGraph(t.modelInitializer);this.initializer=new Hh(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=t.initializerSignature}return!0}async save(t,e){if(typeof t=="string"){let n=this.io.getSaveHandlers(t);if(n.length===0)throw new 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n=Object.keys(this.resourceIdToCapturedInput).length;if(t.length+n!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-n} non-resource placeholders, while there are ${t.length} input tensors provided.`);let o=0;return this.inputNodes.reduce((s,i)=>{var a,u,l;let c=(l=(u=(a=this.signature)===null||a===void 0?void 0:a.inputs)===null||u===void 0?void 0:u[i])===null||l===void 0?void 0:l.resourceId;return c!=null?s[i]=this.resourceIdToCapturedInput[c]:s[i]=t[o++],s},{})}normalizeOutputs(t){return t=t||this.outputNodes,Array.isArray(t)?t:[t]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(t){if(this.resourceIdToCapturedInput={},this.initializerSignature){let e=this.initializerSignature.outputs,n=Object.keys(e);for(let o=0;o1?n:n[0]}async executeAsync(t,e){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),t=this.normalizeInputs(t),e=this.normalizeOutputs(e);let n=await this.executor.executeAsync(t,e);return n.length>1?n:n[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(t){return Object.keys(t).reduce((e,n)=>(e[n]=[t[n]],e),{})}dispose(){this.executor.dispose(),this.initializer&&(this.initializer.dispose(),this.resourceIdToCapturedInput&&Tt(this.resourceIdToCapturedInput)),this.resourceManager.dispose()}};async function xtt(r,t={},e=Mr){if(r==null)throw new Error("modelUrl in loadGraphModel() cannot be null. 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r=="object"&&r instanceof Lt||y.isTypedArray(r)}function wtt(r){return r===null||typeof r!="object"&&typeof r!="function"}function qF(r){return GF(r,Itt)}function Itt(r){return r instanceof Lt?{value:r.clone(),recurse:!1}:Vu(r)?{value:null,recurse:!0}:{value:r,recurse:!1}}var od=class{constructor(t){if(this.capacity=t,this.begin=0,this.end=0,t==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(t<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(t),this.doubledCapacity=2*t}wrap(t){for(;t<0;)t+=this.doubledCapacity;return t%this.doubledCapacity}get(t){if(t<0)throw new RangeError("Can't get item at a negative index.");return this.data[t%this.capacity]}set(t,e){if(t<0)throw new RangeError("Can't set item at a negative index.");this.data[t%this.capacity]=e}length(){let t=this.end-this.begin;return t<0&&(t=this.doubledCapacity+t),t}isFull(){return this.length()===this.capacity}isEmpty(){return 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od{constructor(){super(r.INITIAL_CAPACITY)}isFull(){return!1}push(t){super.isFull()&&this.expand(),super.push(t)}unshift(t){super.isFull()&&this.expand(),super.unshift(t)}expand(){let t=this.capacity*2,e=new Array(t),n=this.length();for(let o=0;oe===!0)}rowMajorBatch(t,e=!0){return new zk(this,t,e)}columnMajorBatch(t,e=!0,n=Rk){return this.rowMajorBatch(t,e).map(s=>WF(s,n))}concatenate(t,e){return new aw(qk([this,t]),e)}take(t){return t<0||t==null?this:new Lk(this,t)}skip(t){return t<0||t==null?this:new Pk(this,t)}prefetch(t){return new lw(this,t)}shuffle(t,e){return new Hk(this,t,e)}serial(){return new Mk(this)}},Fk=class extends er{constructor(t){super(),this.items=t,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 t=this.items[this.trav];return this.trav++,{value:qF(t),done:!1}}},Ok=class extends er{constructor(t){super(),this.nextFn=t}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(t){throw t.message=`Error thrown while iterating through a dataset: ${t.message}`,t}}},Mk=class extends er{constructor(t){super(),this.upstream=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},Pk=class extends er{constructor(t,e){super(),this.upstream=t,this.maxCount=e,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++ Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},zk=class extends er{constructor(t,e,n=!0){super(),this.upstream=t,this.batchSize=e,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let t=[];for(;t.length0?{value:t,done:!1}:{value:null,done:!0};t.push(e.value)}return{value:t,done:!1}}},Bk=class extends er{constructor(t,e){super(),this.upstream=t,this.predicate=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let t=await this.upstream.next();if(t.done||this.predicate(t.value))return t;Tt(t.value)}}},Vk=class extends er{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Map`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=Io.getTensorsInContainer(t.value),n=this.transform(t.value),o=Io.getTensorsInContainer(n);for(let s of e)Io.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},Gk=class extends er{constructor(t,e){super(),this.upstream=t,this.handler=e,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(t){if(!this.handler(t))return{value:null,done:!0}}}},iw=class extends er{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=Io.getTensorsInContainer(t.value),n=await this.transform(t.value),o=Io.getTensorsInContainer(n);for(let s of e)Io.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},Zc=class extends er{constructor(){super(),this.outputQueue=new Kh,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}}},Wk=class extends Zc{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let t=await this.upstream.next();if(t.done)return!1;let e=Io.getTensorsInContainer(t.value),n=this.transform(t.value),o=Io.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let s of e)Io.isTensorInList(s,o)||s.dispose();return!0}},aw=class extends er{constructor(t,e){super(),this.baseErrorHandler=e,this.lastRead=null,this.iterator=null,this.moreIterators=t}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(t){if(await t,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 e=await this.iterator.next();return e.done?(this.iterator=null,this.readFromChain(t)):e}},Dl;(function(r){r[r.FAIL=0]="FAIL",r[r.SHORTEST=1]="SHORTEST",r[r.LONGEST=2]="LONGEST"})(Dl||(Dl={}));var Uk=class extends er{constructor(t,e=Dl.FAIL){super(),this.iterators=t,this.mismatchMode=e,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(t){await t;let e=0,n=0;function o(i){return i instanceof er?{value:i.next().then(u=>(e++,u.done&&n++,u.value)),recurse:!1}:{value:null,recurse:!0}}let s=await sw(this.iterators,o);if(e===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Dl.FAIL:throw new Error(`Zipped streams should have the same length. 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At least one type of data should be returned.")}summary(){return"microphone"}static async create(t={}){if(!L().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let e=new r(t);return await e.start(),e}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 t=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new t,!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 e=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,e.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 t,e,n=await this.getAudioData();if(this.includeSpectrogram){let o=this.flattenQueue(n.freqDataQueue);t=this.getTensorFromAudioDataArray(o,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let o=this.flattenQueue(n.timeDataQueue);e=this.getTensorFromAudioDataArray(o,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:t,waveform:e},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let t=[],e=[],n=0;return new Promise(o=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&o({freqDataQueue:t,timeDataQueue:e}),t.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),e.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(s),o({freqDataQueue:t,timeDataQueue:e}))},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(t){let e=t[0].length,n=new Float32Array(t.length*e);return t.forEach((o,s)=>n.set(o,s*e)),n}getTensorFromAudioDataArray(t,e){let n=new Float32Array(y.sizeFromShape(e));return n.set(t,n.length-t.length),ir(n,e)}};var mw=class r extends er{constructor(t,e){if(super(),this.webcamVideoElement=t,this.webcamConfig=e,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Oe([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,o=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-n)/2,i=(1-o)/2,a=s+n,u=o+i;this.cropBox=pi([i,s,u,a],[1,4])}else this.cropBox=pi([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(t,e={}){if(!L().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!t){if(t=document.createElement("video"),!e.resizeWidth||!e.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");t.width=e.resizeWidth,t.height=e.resizeHeight}let n=new r(t,e);return await n.start(),n}async start(){this.webcamConfig.facingMode&&y.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(t){throw t.message=`Error thrown while initializing video stream: ${t.message}`,t}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(t){console.log(t),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(t=>{this.webcamVideoElement.onloadedmetadata=()=>{t()}})}async next(){if(this.isClosed)return{value:null,done:!0};let t;try{t=_y.fromPixels(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(t),done:!1}}catch(e){throw new Error(`Error thrown cropping the video: ${e.message}`)}finally{t.dispose()}else return{value:t,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(t){return B(()=>{let e=je(J(t,"float32"),0),n;n=fn.cropAndResize(e,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let o=n.shape;return R(n,o.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}};var ad=class{};var Yh=class extends er{split(t){return new jk(this,t)}},jk=class extends Yh{constructor(t,e){super(),this.upstream=t,this.impl=new Xk(t,e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Xk=class extends Zc{constructor(t,e){super(),this.upstream=t,this.separator=e,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let t=await this.upstream.next();if(t.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let e=t.value.split(this.separator);e[0]=this.carryover+e[0];for(let n of e.slice(0,-1))this.outputQueue.push(n);return this.carryover=e[e.length-1],!0}};var fw=class extends er{decodeUTF8(){return new Yk(this)}},Yk=class extends Yh{constructor(t){super(),this.upstream=t,this.impl=new Zk(t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Zk=class extends Zc{constructor(t){if(super(),this.upstream=t,L().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:e}=$k();this.decoder=new e("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let t=await this.upstream.next(),e;if(t.done)return!1;e=t.value;let n;return L().get("IS_BROWSER")?n=this.decoder.decode(e,{stream:!0}):n=this.decoder.write(Buffer.from(e.buffer)),this.outputQueue.push(n),!0}};var ld=class extends fw{constructor(t,e={}){super(),this.file=t,this.options=e,y.assert(t instanceof Uint8Array||(L().get("IS_BROWSER")?t instanceof File||t instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=e.offset||0,this.chunkSize=e.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,n)=>{let o=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,o)));else{let s=new FileReader;s.onload=a=>{let u=s.result;if(u instanceof 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c=S.computePool3DInfo(s.shape,i,a,1,u,l),p=c.strideDepth,m=c.strideHeight,f=c.strideWidth,d=c.filterDepth,h=c.filterHeight,g=c.filterWidth,x=c.dilationDepth,b=c.dilationHeight,w=c.dilationWidth,I=c.effectiveFilterDepth,N=c.effectiveFilterHeight,E=c.effectiveFilterWidth,A=I-1-c.padInfo.front,D=E-1-c.padInfo.left,F=N-1-c.padInfo.top,M=wt(s.shape,"float32"),V=1/(d*h*g),G=e.bufferSync(o);for(let W=0;W=c.outDepth||Math.floor(ot)!==ot))for(let it=0;it=c.outHeight||Math.floor(ft)!==ft))for(let gt=0;gt=c.outWidth||Math.floor(Ct)!==Ct)continue;let Rt=G.get(W,ot,ft,Ct,q);st+=Rt}}}M.set(st*V,W,H,K,Y,q)}return e.makeTensorInfo(M.shape,M.dtype,M.values)}var dM={kernelName:Hl,backendName:"cpu",kernelFunc:get};function xet(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s;tt([o,s],"avgPoolGrad");let{filterSize:a,strides:u,pad:l}=n,c=S.computePool2DInfo(i.shape,a,u,1,l),p=c.strideHeight,m=c.strideWidth,f=c.filterHeight,d=c.filterWidth,h=c.dilationHeight,g=c.dilationWidth,x=c.effectiveFilterHeight,b=c.effectiveFilterWidth,w=b-1-c.padInfo.left,I=x-1-c.padInfo.top,N=wt(i.shape,"float32"),E=1/(f*d),A=e.data.get(o.dataId).values,D=wt(o.shape,"float32",A);for(let F=0;F=c.outHeight||Math.floor(Y)!==Y))for(let Z=0;Z=c.outWidth||Math.floor(et)!==et)continue;let nt=D.get(F,Y,et,M);H+=nt}}N.set(H*E,F,V,G,M)}return e.makeTensorInfo(N.shape,N.dtype,N.values)}var hM={kernelName:Ul,backendName:"cpu",kernelFunc:xet};function yet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,scale:s,offset:i,mean:a,variance:u}=t;y.assert(a.shape.length===u.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),y.assert(s==null||a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),tt([o,a,u,s,i],"batchNorm");let{varianceEpsilon:l}=n;l==null&&(l=.001);let c=e.data.get(o.dataId).values,p=e.data.get(a.dataId).values,m=e.data.get(u.dataId).values,f=s?e.data.get(s.dataId).values:new Float32Array([1]),d=i?e.data.get(i.dataId).values:new Float32Array([0]),h=new Float32Array(c.length),g=d.length,x=f.length,b=m.length,w=p.length,I=0,N=0,E=0,A=0;for(let D=0;D=g&&(I=0),N>=w&&(N=0),E>=x&&(E=0),A>=b&&(A=0);return e.makeTensorInfo(o.shape,o.dtype,h)}var gM={kernelName:ds,backendName:"cpu",kernelFunc:yet};function bet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,crops:i}=n;tt([o],"batchToSpaceND");let a=s.reduce((x,b)=>x*b),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=te({inputs:{x:o},backend:e,attrs:{shape:u}}),d=We({inputs:{x:f},backend:e,attrs:{perm:l}}),h=te({inputs:{x:d},backend:e,attrs:{shape:c}}),g=Mo({inputs:{x:h},backend:e,attrs:{begin:p,size:m}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),g}var xM={kernelName:Ri,backendName:"cpu",kernelFunc:bet};function wet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i}=n,a=e.data.get(o.dataId).values,u=e.data.get(s.dataId).values,l=fd(a,u,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,l)}var yM={kernelName:Da,backendName:"cpu",kernelFunc:wet};function Iet(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.data.get(n.dataId).values,i=e.data.get(o.dataId).values,a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeTensorInfo([a.length],"int32",Int32Array.from(a))}var bM={kernelName:ql,backendName:"cpu",kernelFunc:Iet};var Cet=At(ho,(r,t)=>{let e=t;return r>e.clipValueMax?e.clipValueMax:r{let{x:t}=r.inputs,e=r.backend,n=new Float32Array(y.sizeFromShape(t.shape)),o=e.data.get(t.dataId),s=o.complexTensorInfos.real,i=o.complexTensorInfos.imag,a=e.data.get(s.dataId).values,u=e.data.get(i.dataId).values;for(let l=0;lh.shape);S.assertParamsConsistent(i,s);let a=S.computeOutShape(t.map(h=>h.shape),s);if(y.sizeFromShape(a)===0)return e.makeTensorInfo(a,t[0].dtype,[]);let u=t.filter(h=>y.sizeFromShape(h.shape)>0);if(u.length===1)return Xr({inputs:{x:u[0]},backend:e});if(u[0].dtype==="complex64"){let h=u.map(I=>Ro({inputs:{input:I},backend:e})),g=u.map(I=>ba({inputs:{input:I},backend:e})),x=Gu({inputs:h,backend:e,attrs:{axis:s}}),b=Gu({inputs:g,backend:e,attrs:{axis:s}}),w=Ir({inputs:{real:x,imag:b},backend:e});return h.forEach(I=>e.disposeIntermediateTensorInfo(I)),g.forEach(I=>e.disposeIntermediateTensorInfo(I)),e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(b),w}let l=u.map(h=>{let x=[-1,y.sizeFromShape(h.shape.slice(s))];return te({inputs:{x:h},backend:e,attrs:{shape:x}})}),c=l.map(h=>({vals:e.data.get(h.dataId).values,shape:h.shape}));a=S.computeOutShape(l.map(h=>h.shape),1);let p=l[0].shape[0]===1,m=Jc(c,a,t[0].dtype,p),f=S.computeOutShape(u.map(h=>h.shape),s),d=e.makeTensorInfo(f,t[0].dtype,m);return l.forEach(h=>e.disposeIntermediateTensorInfo(h)),d}var vM={kernelName:Fi,backendName:"cpu",kernelFunc:Gu};function $T(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dataFormat:u,dilations:l,dimRoundingMode:c}=n;tt([o,s],"conv2d");let p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,s.shape,i,l,a,c,!1,p),f=m.filterHeight,d=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,x=m.padInfo.left,b=m.padInfo.top,w=m.dataFormat==="channelsLast",I=new le(m.outShape,o.dtype),N=y.computeStrides(o.shape),E=y.computeStrides(s.shape),A=N[0],D=w?N[1]:N[2],F=w?N[2]:1,M=w?1:N[1],V=I.strides[0],G=w?I.strides[1]:I.strides[2],W=w?I.strides[2]:1,q=w?1:I.strides[1],H=e.data.get(o.dataId).values,K=e.data.get(s.dataId).values,Y=I.values;for(let Z=0;Z=m.inHeight)continue;let gt=it*E[0],Ct=et+ft*D;for(let Rt=0;Rt=m.inWidth)continue;let xe=gt+qt*E[1],re=Ct+pe*F,ye=xe;for(let de=0;de=l.inDepth)continue;let Z=K*F[0],et=V+Y*D[1];for(let nt=0;nt=l.inHeight)continue;let ft=Z+ot*F[1],gt=et+it*D[2];for(let Ct=0;Ct=l.inWidth)continue;let pe=ft+Ht*F[2],xe=gt+qt*l.inChannels,re=pe;for(let ye=0;yeMath.cos(r)),AM={kernelName:rs,backendName:"cpu",kernelFunc:Aet};var Det=At(ns,r=>Math.cosh(r)),DM={kernelName:ns,backendName:"cpu",kernelFunc:Det};function $et(r){let{inputs:t,backend:e,attrs:n}=r,{image:o,boxes:s,boxInd:i}=t,{cropSize:a,method:u,extrapolationValue:l}=n,[c,p,m,f]=o.shape,d=s.shape[0],[h,g]=a,x=wt([d,h,g,f],"float32"),b=e.data.get(s.dataId).values,w=e.data.get(i.dataId).values,I=e.data.get(o.dataId).values,N=y.computeStrides(o.shape),E=y.computeStrides(x.shape);for(let A=0;A=c)continue;let q=h>1?(V-F)*(p-1)/(h-1):0,H=g>1?(G-M)*(m-1)/(g-1):0;for(let K=0;K1?F*(p-1)+K*q:.5*(F+V)*(p-1);if(Y<0||Y>p-1){for(let Z=0;Z1?M*(m-1)+st*H:.5*(M+G)*(m-1);if(lt<0||lt>m-1){for(let gt=0;gt1?M*(m-1)+Z*H:.5*(M+G)*(m-1);if(et<0||et>m-1){for(let lt=0;ltx+d-b-1:(x,b)=>x+b;for(let x=0;xx+d-b-1:(x,b)=>x+b;for(let x=0;x`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let a=o.shape[0],u=o.shape[1],l=o.shape[2],c=o.shape[3],p=u*s,m=l*s,f=c/(s*s),d=e.data.get(o.dataId).values,h=new Float32Array(a*p*m*f),g=0;for(let x=0;x`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${m}'`);let f=S.computeConv2DInfo(o.shape,s.shape,i,m,a,l,!0),{filterHeight:d,filterWidth:h,dilationHeight:g,dilationWidth:x,padInfo:b}=f,w=b.left,I=b.top,N=f.outChannels/f.inChannels,E=new le(f.outShape,o.dtype),A=e.data.get(o.dataId).values,D=e.data.get(s.dataId).values,F=E.values;for(let M=0;M=f.inHeight)continue;let Z=K*p[0],et=V+Y*c[1];for(let nt=0;nt=f.inWidth)continue;let ft=Z+ot*p[1],gt=et+it*f.inChannels,Ct=st,Rt=ft;for(let Dt=0;Dt{let{x:n,filter:o}=r,{strides:s,pad:i,dilations:a}=e,u=t,l=u.data.get(n.dataId).values,c=n.shape.length,p=u.data.get(o.dataId).values,m=o.shape.length,{batchSize:f,inHeight:d,inWidth:h,inChannels:g,outHeight:x,outWidth:b,padInfo:w,strideHeight:I,strideWidth:N,filterHeight:E,filterWidth:A,dilationHeight:D,dilationWidth:F,outShape:M}=S.computeDilation2DInfo(n.shape,o.shape,s,i,"NHWC",a),V=y.sizeFromShape(M),G=M.length,W=y.getArrayFromDType(n.dtype,V);for(let H=0;H=0&&it=0&>st&&(st=Dt)}}}let lt=y.locToIndex([H,K,Z,nt],G,y.computeStrides(M));W[lt]=st}}}return{dataId:u.write(y.toTypedArray(W,n.dtype),M,n.dtype),shape:M,dtype:n.dtype}}};var GM={kernelName:Zl,backendName:"cpu",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:n,filter:o,dy:s}=r,{strides:i,pad:a,dilations:u}=e,l=t,c=y.toNestedArray(n.shape,l.data.get(n.dataId).values),p=y.toNestedArray(o.shape,l.data.get(o.dataId).values),{batchSize:m,inHeight:f,inWidth:d,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:w,strideWidth:I,filterHeight:N,filterWidth:E,dilationHeight:A,dilationWidth:D,outShape:F}=S.computeDilation2DInfo(n.shape,o.shape,i,a,"NHWC",u);y.assert(s.rank===F.length,()=>`Error in ${Zl}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let M=y.toNestedArray(F,l.data.get(s.dataId).values),V=y.makeZerosNestedTypedArray(o.shape,o.dtype);for(let W=0;W=0&&ot=0&&ftet&&(et=gt,nt=lt,st=it)}}}V[nt][st][Z]+=M[W][q][K][Z]}}}return{dataId:l.write(y.toTypedArray(V,n.dtype),o.shape,o.dtype),shape:o.shape,dtype:o.dtype}}};var WM={kernelName:Yl,backendName:"cpu",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:n,filter:o,dy:s}=r,{strides:i,pad:a,dilations:u}=e,l=t,c=y.toNestedArray(n.shape,l.data.get(n.dataId).values),p=y.toNestedArray(o.shape,l.data.get(o.dataId).values),{batchSize:m,inHeight:f,inWidth:d,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:w,strideWidth:I,filterHeight:N,filterWidth:E,dilationHeight:A,dilationWidth:D,outShape:F}=S.computeDilation2DInfo(n.shape,o.shape,i,a,"NHWC",u);y.assert(s.rank===F.length,()=>`Error in ${Yl}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let M=y.toNestedArray(F,l.data.get(s.dataId).values),V=y.makeZerosNestedTypedArray(n.shape,n.dtype);for(let W=0;W=0&&ot=0&&ftet&&(et=gt,nt=ot,st=ft)}}}V[W][nt][st][Z]+=M[W][q][K][Z]}}}return{dataId:l.write(y.toTypedArray(V,n.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};function Bet(r){let{inputs:t,backend:e,attrs:n}=r,{image:o}=t,{canvas:s,options:i}=n,{contextOptions:a,imageOptions:u}=i||{},l=(u==null?void 0:u.alpha)||1,c=(a==null?void 0:a.contextType)||"2d";if(c!=="2d")throw new Error(`Context type ${a.contextType} is not supported by the CPU backend.`);let p=s.getContext(c,(a==null?void 0:a.contextAttributes)||{});if(p==null)throw new Error(`Could not get the context with ${c} type.`);let[m,f]=o.shape.slice(0,2),d=o.shape.length===2?1:o.shape[2],h=e.data.get(o.dataId).values,g=o.dtype==="float32"?255:1,x=new Uint8ClampedArray(f*m*4);for(let w=0;w1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${A}.`)}else if(o.dtype==="int32"&&(A<0||A>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${A}.`);d===1?(I[0]=A*g,I[1]=A*g,I[2]=A*g):I[E]=A*g}let N=w*4;x[N+0]=Math.round(I[0]),x[N+1]=Math.round(I[1]),x[N+2]=Math.round(I[2]),x[N+3]=Math.round(I[3])}s.width=f,s.height=m;let b=new ImageData(x,f,m);return p.putImageData(b,0,0),o}var UM={kernelName:Jd,backendName:"cpu",kernelFunc:Bet};function $l(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n;tt(o,"sum");let a;o.dtype==="bool"?a=Fo({inputs:{x:o},backend:e,attrs:{dtype:"int32"}}):a=Xr({inputs:{x:o},backend:e});let u=a.shape.length,l=y.parseAxisParam(s,a.shape),c=S.getAxesPermutation(l,u),p=l,m=a;c!=null&&(m=We({inputs:{x:a},backend:e,attrs:{perm:c}}),p=S.getInnerMostAxes(p.length,u)),S.assertAxesAreInnerMostDims("sum",p,m.shape.length);let[f,d]=S.computeOutAndReduceShapes(m.shape,p),h=S.upcastType(m.dtype,"int32"),g=pd(e,f,h),x=y.sizeFromShape(d),b=e.data.get(g.dataId).values,w=e.data.get(m.dataId).values;for(let I=0;I=0&&(m=$l({inputs:{x:m},backend:e,attrs:{axis:l[h]-(i.length-f),keepDims:!1}}),d.push(m)),f--)}for(let h of d)h!==m&&e.disposeIntermediateTensorInfo(h);return m}var qM={kernelName:Fp,backendName:"cpu",kernelFunc:Vet};function Get(r){let{inputs:t,backend:e}=r,{dy:n,y:o}=t;tt([n,o],"eluGrad");let s=new Float32Array(y.sizeFromShape(o.shape)),i=e.data.get(o.dataId).values,a=e.data.get(n.dataId).values;for(let u=0;u=0?s[u]=a[u]:s[u]=a[u]*(l+1)}return e.makeTensorInfo(o.shape,"float32",s)}var KM={kernelName:La,backendName:"cpu",kernelFunc:Get};var Wet=S.ERF_P,Uet=S.ERF_A1,Het=S.ERF_A2,qet=S.ERF_A3,Ket=S.ERF_A4,jet=S.ERF_A5,Xet=At(us,r=>{let t=Math.sign(r),e=Math.abs(r),n=1/(1+Wet*e);return t*(1-((((jet*n+Ket)*n+qet)*n+Het)*n+Uet)*n*Math.exp(-e*e))}),jM={kernelName:us,backendName:"cpu",kernelFunc:Xet};function xd(r){let{inputs:t,backend:e,attrs:n}=r,{input:o}=t,{dim:s}=n,i=o.shape.length,a=o.shape.slice(),u=s;return s<0&&(y.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+s+1),a.splice(u,0,1),te({inputs:{x:o},backend:e,attrs:{shape:a}})}var XM={kernelName:Oi,backendName:"cpu",kernelFunc:xd};var Yet=Qt((r,t)=>r/t),Qh=oe(as,Yet),tg={kernelName:as,backendName:"cpu",kernelFunc:Qh};function Aw(r,t,e){let n=r.shape,o=n[0],s=n[1],i=e.data.get(r.dataId),a=i.complexTensorInfos.real,u=i.complexTensorInfos.imag,l=[o,s],c=y.sizeFromShape(l),p=y.getTypedArrayFromDType("float32",c),m=y.getTypedArrayFromDType("float32",c);for(let g=0;g{let{image:n}=r,o=e,s=y.getTypedArrayFromDType(n.dtype,y.sizeFromShape(n.shape)),[i,a,u,l]=n.shape,c=o.data.get(n.dataId).values;for(let m=0;m=0&&w=0,()=>`GatherV2: the index value ${N} is not in [0, ${c-1}]`)}let p=a;a==null&&(p=0);let m=y.sizeFromShape(s.shape),f=S.segment_util.collectGatherOpShapeInfo(o,s,u,p),d=te({inputs:{x:o},backend:e,attrs:{shape:[f.batchSize,f.outerSize,f.dimSize,f.sliceSize]}}),h=te({inputs:{x:s},backend:e,attrs:{shape:[f.batchSize,m/f.batchSize]}}),g=[f.batchSize,f.outerSize,m/f.batchSize,f.sliceSize],x=e.bufferSync(h),b=e.bufferSync(d),w=gw(b,x,g);return e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),e.makeTensorInfo(f.outputShape,w.dtype,w.values)}var rP={kernelName:Mi,backendName:"cpu",kernelFunc:srt};function irt(r){let{inputs:t,backend:e}=r,{input:n}=t,o=y.sizeFromShape(n.shape),s=n.shape[n.shape.length-1],i=o/s,a=te({inputs:{x:n},backend:e,attrs:{shape:[i,s]}}),u=Aw(a,!0,e),l=te({inputs:{x:u},backend:e,attrs:{shape:n.shape}});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(u),l}var nP={kernelName:Mp,backendName:"cpu",kernelFunc:irt};var art=At(gs,r=>Number.isFinite(r)?1:0,"bool"),oP={kernelName:gs,backendName:"cpu",kernelFunc:art};var lrt=At(xs,r=>Math.abs(r)===1/0?1:0,"bool"),sP={kernelName:xs,backendName:"cpu",kernelFunc:lrt};var urt=At(ys,r=>Number.isNaN(r)?1:0,"bool"),iP={kernelName:ys,backendName:"cpu",kernelFunc:urt};function crt(r){let{backend:t,attrs:e}=r,{start:n,stop:o,num:s}=e,i=xw(n,o,s);return t.makeTensorInfo([i.length],"float32",i)}var aP={kernelName:Ha,backendName:"cpu",kernelFunc:crt};var prt=At(Is,r=>Math.log1p(r)),lP={kernelName:Is,backendName:"cpu",kernelFunc:prt};var mrt=Qt((r,t)=>r&&t),frt=oe(qa,mrt,null,"bool"),uP={kernelName:qa,backendName:"cpu",kernelFunc:frt};var drt=At(Ka,r=>r?0:1,"bool"),cP={kernelName:Ka,backendName:"cpu",kernelFunc:drt};var hrt=Qt((r,t)=>r||t),grt=oe(ja,hrt,null,"bool"),pP={kernelName:ja,backendName:"cpu",kernelFunc:grt};function xrt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n;tt(o,"LRN");let l=o.shape[3],c=l-1,p=e.data.get(o.dataId).values,m=y.sizeFromShape(o.shape),f=new Float32Array(m);function d(h){let g=h%l,x=h-g+Math.max(0,g-s),b=h-g+Math.min(g+s,c),w=0;for(;x<=b;x++){let I=p[x];w+=I*I}return w}for(let h=0;h`Error in maxPool: Either strides or dilations must be 1. 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yet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;tt(o,"avgPool");let{filterSize:s,strides:i,pad:a,dimRoundingMode:u}=n,l=1;y.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in avgPool: Either strides or dilations must be 1. 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c=S.computePool3DInfo(s.shape,i,a,1,u,l),p=c.strideDepth,m=c.strideHeight,f=c.strideWidth,d=c.filterDepth,h=c.filterHeight,g=c.filterWidth,x=c.dilationDepth,b=c.dilationHeight,w=c.dilationWidth,I=c.effectiveFilterDepth,N=c.effectiveFilterHeight,E=c.effectiveFilterWidth,A=I-1-c.padInfo.front,D=E-1-c.padInfo.left,F=N-1-c.padInfo.top,M=wt(s.shape,"float32"),V=1/(d*h*g),G=e.bufferSync(o);for(let W=0;W=c.outDepth||Math.floor(ot)!==ot))for(let it=0;it=c.outHeight||Math.floor(ft)!==ft))for(let gt=0;gt=c.outWidth||Math.floor(Ct)!==Ct)continue;let Rt=G.get(W,ot,ft,Ct,q);st+=Rt}}}M.set(st*V,W,H,j,Y,q)}return e.makeTensorInfo(M.shape,M.dtype,M.values)}var xM={kernelName:Hl,backendName:"cpu",kernelFunc:wet};function Iet(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s;tt([o,s],"avgPoolGrad");let{filterSize:a,strides:u,pad:l}=n,c=S.computePool2DInfo(i.shape,a,u,1,l),p=c.strideHeight,m=c.strideWidth,f=c.filterHeight,d=c.filterWidth,h=c.dilationHeight,g=c.dilationWidth,x=c.effectiveFilterHeight,b=c.effectiveFilterWidth,w=b-1-c.padInfo.left,I=x-1-c.padInfo.top,N=wt(i.shape,"float32"),E=1/(f*d),A=e.data.get(o.dataId).values,D=wt(o.shape,"float32",A);for(let F=0;F=c.outHeight||Math.floor(Y)!==Y))for(let Z=0;Z=c.outWidth||Math.floor(et)!==et)continue;let nt=D.get(F,Y,et,M);H+=nt}}N.set(H*E,F,V,G,M)}return e.makeTensorInfo(N.shape,N.dtype,N.values)}var yM={kernelName:Ul,backendName:"cpu",kernelFunc:Iet};function Cet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,scale:s,offset:i,mean:a,variance:u}=t;y.assert(a.shape.length===u.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),y.assert(s==null||a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),tt([o,a,u,s,i],"batchNorm");let{varianceEpsilon:l}=n;l==null&&(l=.001);let c=e.data.get(o.dataId).values,p=e.data.get(a.dataId).values,m=e.data.get(u.dataId).values,f=s?e.data.get(s.dataId).values:new Float32Array([1]),d=i?e.data.get(i.dataId).values:new Float32Array([0]),h=new Float32Array(c.length),g=d.length,x=f.length,b=m.length,w=p.length,I=0,N=0,E=0,A=0;for(let D=0;D=g&&(I=0),N>=w&&(N=0),E>=x&&(E=0),A>=b&&(A=0);return e.makeTensorInfo(o.shape,o.dtype,h)}var bM={kernelName:ds,backendName:"cpu",kernelFunc:Cet};function vet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,crops:i}=n;tt([o],"batchToSpaceND");let a=s.reduce((x,b)=>x*b),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=te({inputs:{x:o},backend:e,attrs:{shape:u}}),d=We({inputs:{x:f},backend:e,attrs:{perm:l}}),h=te({inputs:{x:d},backend:e,attrs:{shape:c}}),g=Mo({inputs:{x:h},backend:e,attrs:{begin:p,size:m}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),g}var wM={kernelName:Fi,backendName:"cpu",kernelFunc:vet};function Net(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i}=n,a=e.data.get(o.dataId).values,u=e.data.get(s.dataId).values,l=dd(a,u,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,l)}var IM={kernelName:Da,backendName:"cpu",kernelFunc:Net};function ket(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.data.get(n.dataId).values,i=e.data.get(o.dataId).values,a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeTensorInfo([a.length],"int32",Int32Array.from(a))}var CM={kernelName:ql,backendName:"cpu",kernelFunc:ket};var Tet=At(ho,(r,t)=>{let e=t;return r>e.clipValueMax?e.clipValueMax:r{let{x:t}=r.inputs,e=r.backend,n=new Float32Array(y.sizeFromShape(t.shape)),o=e.data.get(t.dataId),s=o.complexTensorInfos.real,i=o.complexTensorInfos.imag,a=e.data.get(s.dataId).values,u=e.data.get(i.dataId).values;for(let l=0;lh.shape);S.assertParamsConsistent(i,s);let a=S.computeOutShape(t.map(h=>h.shape),s);if(y.sizeFromShape(a)===0)return e.makeTensorInfo(a,t[0].dtype,[]);let u=t.filter(h=>y.sizeFromShape(h.shape)>0);if(u.length===1)return Zr({inputs:{x:u[0]},backend:e});if(u[0].dtype==="complex64"){let h=u.map(I=>Ro({inputs:{input:I},backend:e})),g=u.map(I=>ba({inputs:{input:I},backend:e})),x=Gu({inputs:h,backend:e,attrs:{axis:s}}),b=Gu({inputs:g,backend:e,attrs:{axis:s}}),w=Ir({inputs:{real:x,imag:b},backend:e});return h.forEach(I=>e.disposeIntermediateTensorInfo(I)),g.forEach(I=>e.disposeIntermediateTensorInfo(I)),e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(b),w}let l=u.map(h=>{let x=[-1,y.sizeFromShape(h.shape.slice(s))];return te({inputs:{x:h},backend:e,attrs:{shape:x}})}),c=l.map(h=>({vals:e.data.get(h.dataId).values,shape:h.shape}));a=S.computeOutShape(l.map(h=>h.shape),1);let p=l[0].shape[0]===1,m=Jc(c,a,t[0].dtype,p),f=S.computeOutShape(u.map(h=>h.shape),s),d=e.makeTensorInfo(f,t[0].dtype,m);return l.forEach(h=>e.disposeIntermediateTensorInfo(h)),d}var kM={kernelName:Oi,backendName:"cpu",kernelFunc:Gu};function FT(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dataFormat:u,dilations:l,dimRoundingMode:c}=n;tt([o,s],"conv2d");let p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,s.shape,i,l,a,c,!1,p),f=m.filterHeight,d=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,x=m.padInfo.left,b=m.padInfo.top,w=m.dataFormat==="channelsLast",I=new le(m.outShape,o.dtype),N=y.computeStrides(o.shape),E=y.computeStrides(s.shape),A=N[0],D=w?N[1]:N[2],F=w?N[2]:1,M=w?1:N[1],V=I.strides[0],G=w?I.strides[1]:I.strides[2],W=w?I.strides[2]:1,q=w?1:I.strides[1],H=e.data.get(o.dataId).values,j=e.data.get(s.dataId).values,Y=I.values;for(let Z=0;Z=m.inHeight)continue;let gt=it*E[0],Ct=et+ft*D;for(let Rt=0;Rt=m.inWidth)continue;let ye=gt+qt*E[1],re=Ct+pe*F,be=ye;for(let de=0;de=l.inDepth)continue;let Z=j*F[0],et=V+Y*D[1];for(let nt=0;nt=l.inHeight)continue;let ft=Z+ot*F[1],gt=et+it*D[2];for(let Ct=0;Ct=l.inWidth)continue;let pe=ft+Ht*F[2],ye=gt+qt*l.inChannels,re=pe;for(let be=0;beMath.cos(r)),RM={kernelName:rs,backendName:"cpu",kernelFunc:Fet};var Oet=At(ns,r=>Math.cosh(r)),FM={kernelName:ns,backendName:"cpu",kernelFunc:Oet};function Met(r){let{inputs:t,backend:e,attrs:n}=r,{image:o,boxes:s,boxInd:i}=t,{cropSize:a,method:u,extrapolationValue:l}=n,[c,p,m,f]=o.shape,d=s.shape[0],[h,g]=a,x=wt([d,h,g,f],"float32"),b=e.data.get(s.dataId).values,w=e.data.get(i.dataId).values,I=e.data.get(o.dataId).values,N=y.computeStrides(o.shape),E=y.computeStrides(x.shape);for(let A=0;A=c)continue;let q=h>1?(V-F)*(p-1)/(h-1):0,H=g>1?(G-M)*(m-1)/(g-1):0;for(let j=0;j1?F*(p-1)+j*q:.5*(F+V)*(p-1);if(Y<0||Y>p-1){for(let Z=0;Z1?M*(m-1)+st*H:.5*(M+G)*(m-1);if(lt<0||lt>m-1){for(let gt=0;gt1?M*(m-1)+Z*H:.5*(M+G)*(m-1);if(et<0||et>m-1){for(let lt=0;ltx+d-b-1:(x,b)=>x+b;for(let x=0;xx+d-b-1:(x,b)=>x+b;for(let x=0;x`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let a=o.shape[0],u=o.shape[1],l=o.shape[2],c=o.shape[3],p=u*s,m=l*s,f=c/(s*s),d=e.data.get(o.dataId).values,h=new Float32Array(a*p*m*f),g=0;for(let x=0;x`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${m}'`);let f=S.computeConv2DInfo(o.shape,s.shape,i,m,a,l,!0),{filterHeight:d,filterWidth:h,dilationHeight:g,dilationWidth:x,padInfo:b}=f,w=b.left,I=b.top,N=f.outChannels/f.inChannels,E=new le(f.outShape,o.dtype),A=e.data.get(o.dataId).values,D=e.data.get(s.dataId).values,F=E.values;for(let M=0;M=f.inHeight)continue;let Z=j*p[0],et=V+Y*c[1];for(let nt=0;nt=f.inWidth)continue;let ft=Z+ot*p[1],gt=et+it*f.inChannels,Ct=st,Rt=ft;for(let Dt=0;Dt{let{x:n,filter:o}=r,{strides:s,pad:i,dilations:a}=e,u=t,l=u.data.get(n.dataId).values,c=n.shape.length,p=u.data.get(o.dataId).values,m=o.shape.length,{batchSize:f,inHeight:d,inWidth:h,inChannels:g,outHeight:x,outWidth:b,padInfo:w,strideHeight:I,strideWidth:N,filterHeight:E,filterWidth:A,dilationHeight:D,dilationWidth:F,outShape:M}=S.computeDilation2DInfo(n.shape,o.shape,s,i,"NHWC",a),V=y.sizeFromShape(M),G=M.length,W=y.getArrayFromDType(n.dtype,V);for(let H=0;H=0&&it=0&>st&&(st=Dt)}}}let lt=y.locToIndex([H,j,Z,nt],G,y.computeStrides(M));W[lt]=st}}}return{dataId:u.write(y.toTypedArray(W,n.dtype),M,n.dtype),shape:M,dtype:n.dtype}}};var HM={kernelName:Zl,backendName:"cpu",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:n,filter:o,dy:s}=r,{strides:i,pad:a,dilations:u}=e,l=t,c=y.toNestedArray(n.shape,l.data.get(n.dataId).values),p=y.toNestedArray(o.shape,l.data.get(o.dataId).values),{batchSize:m,inHeight:f,inWidth:d,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:w,strideWidth:I,filterHeight:N,filterWidth:E,dilationHeight:A,dilationWidth:D,outShape:F}=S.computeDilation2DInfo(n.shape,o.shape,i,a,"NHWC",u);y.assert(s.rank===F.length,()=>`Error in ${Zl}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let M=y.toNestedArray(F,l.data.get(s.dataId).values),V=y.makeZerosNestedTypedArray(o.shape,o.dtype);for(let W=0;W=0&&ot=0&&ftet&&(et=gt,nt=lt,st=it)}}}V[nt][st][Z]+=M[W][q][j][Z]}}}return{dataId:l.write(y.toTypedArray(V,n.dtype),o.shape,o.dtype),shape:o.shape,dtype:o.dtype}}};var qM={kernelName:Yl,backendName:"cpu",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:n,filter:o,dy:s}=r,{strides:i,pad:a,dilations:u}=e,l=t,c=y.toNestedArray(n.shape,l.data.get(n.dataId).values),p=y.toNestedArray(o.shape,l.data.get(o.dataId).values),{batchSize:m,inHeight:f,inWidth:d,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:w,strideWidth:I,filterHeight:N,filterWidth:E,dilationHeight:A,dilationWidth:D,outShape:F}=S.computeDilation2DInfo(n.shape,o.shape,i,a,"NHWC",u);y.assert(s.rank===F.length,()=>`Error in ${Yl}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let M=y.toNestedArray(F,l.data.get(s.dataId).values),V=y.makeZerosNestedTypedArray(n.shape,n.dtype);for(let W=0;W=0&&ot=0&&ftet&&(et=gt,nt=ot,st=ft)}}}V[W][nt][st][Z]+=M[W][q][j][Z]}}}return{dataId:l.write(y.toTypedArray(V,n.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};function Uet(r){let{inputs:t,backend:e,attrs:n}=r,{image:o}=t,{canvas:s,options:i}=n,{contextOptions:a,imageOptions:u}=i||{},l=(u==null?void 0:u.alpha)||1,c=(a==null?void 0:a.contextType)||"2d";if(c!=="2d")throw new Error(`Context type ${a.contextType} is not supported by the CPU backend.`);let p=s.getContext(c,(a==null?void 0:a.contextAttributes)||{});if(p==null)throw new Error(`Could not get the context with ${c} type.`);let[m,f]=o.shape.slice(0,2),d=o.shape.length===2?1:o.shape[2],h=e.data.get(o.dataId).values,g=o.dtype==="float32"?255:1,x=new Uint8ClampedArray(f*m*4);for(let w=0;w1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 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Logits was rank ${i} and dim was ${a}`);let u=y.parseAxisParam([a],o.shape),l=PT({inputs:{x:o},backend:e,attrs:{reductionIndices:u,keepDims:!1}}),c=S.expandShapeToKeepDim(l.shape,u),p=te({inputs:{x:l},backend:e,attrs:{shape:c}}),m=Jh({inputs:{a:o,b:p},backend:e}),f=aT({inputs:{x:m},backend:e}),d=$l({inputs:{x:f},backend:e,attrs:{axis:u,keepDims:!1}}),h=te({inputs:{x:d},backend:e,attrs:{shape:c}}),g=tg({inputs:{a:f,b:h},backend:e});return e.disposeIntermediateTensorInfo(l),e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),g}var _P={kernelName:Qs,backendName:"cpu",kernelFunc:LT};function $rt(r){let{inputs:t,backend:e,attrs:n}=r,{logits:o}=t,{numSamples:s,seed:i,normalized:a}=n;tt(o,"multinomial");let u=a?o:LT({inputs:{logits:o},backend:e,attrs:{dim:-1}}),l=u.shape[0],c=u.shape[1],p=e.data.get(u.dataId).values,m=[l,s],f=y.makeZerosTypedArray(y.sizeFromShape(m),"int32");for(let d=0;d=0&&p[m]{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),y.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let a=[],u=t.map(c=>{let p=yd({inputs:{input:c},backend:e,attrs:{dim:o}});return a.push(p),p}),l=Gu({inputs:u,backend:e,attrs:{axis:o}});return a.forEach(c=>e.disposeIntermediateTensorInfo(c)),l}var LP={kernelName:Vi,backendName:"cpu",kernelFunc:zT};function Brt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{paddings:s,constantValue:i}=n;tt(o,"pad");let a=s.map((b,w)=>b[0]+o.shape[w]+b[1]),u=s.map(b=>b[0]),l=e.data.get(o.dataId).values,c=y.sizeFromShape(o.shape),p=o.shape.length,m=y.computeStrides(o.shape),f=y.sizeFromShape(a),d=a.length,h=y.computeStrides(a),g=y.getTypedArrayFromDType(o.dtype,f);i!==0&&g.fill(i);for(let 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Uu(r,t){if(r=r.slice(-2),t=t.slice(-2),y.arraysEqual(r,t)||!r.length||!t.length||r[0]===0||r[1]===0||t[0]===0||t[1]===0)return!0;if(r.length!==t.length){let e=r[r.length-1],n=t[t.length-1];if(e===n||Ow(e)&&Ow(n)&&(r[0]===1||t[0]===1))return!0}return r[1]===t[1]&&Ow(r[0])&&Ow(t[0])}var Mw,Pw;function n1(r){if(Mw==null){let t=qn(r);Mw=t.getParameter(t.MAX_TEXTURE_SIZE)}return Mw}function Ynt(){Mw=null}function Znt(){Pw=null}function o1(r){if(Pw==null){let t=qn(r);Pw=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,Pw)}function s1(r){if(r===0)return 0;let t,e=qn(r);return Kn(e,"EXT_disjoint_timer_query_webgl2")&&r===2?t=2:Kn(e,"EXT_disjoint_timer_query")?t=1:t=0,t}function Kn(r,t){return r.getExtension(t)!=null}function Vw(r){try{if(qn(r)!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function i1(r){if(r===0)return!1;let t=qn(r);if(r===1){if(!Kn(t,"OES_texture_float"))return!1}else 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e=ig(r,t),n=r.createTexture();r.bindTexture(r.TEXTURE_2D,n),r.texImage2D(r.TEXTURE_2D,0,e.internalFormatHalfFloat,1,1,0,e.textureFormatFloat,e.textureTypeHalfFloat,null);let i=r.createFramebuffer();r.bindFramebuffer(r.FRAMEBUFFER,i),r.framebufferTexture2D(r.FRAMEBUFFER,r.COLOR_ATTACHMENT0,r.TEXTURE_2D,n,0);let a=r.checkFramebufferStatus(r.FRAMEBUFFER)===r.FRAMEBUFFER_COMPLETE;return r.bindTexture(r.TEXTURE_2D,null),r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteTexture(n),r.deleteFramebuffer(i),a}function l1(r){return r!==2?!1:qn(r).fenceSync!=null}function vi(r,t){Array.isArray(r)||(r=[r]),r.forEach(e=>{e!=null&&y.assert(e.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var kt=L();kt.registerFlag("HAS_WEBGL",()=>kt.getNumber("WEBGL_VERSION")>0);kt.registerFlag("WEBGL_VERSION",()=>Vw(2)?2:Vw(1)?1:0);kt.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);kt.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>kt.get("WEBGL_VERSION")===2);kt.registerFlag("WEBGL_CPU_FORWARD",()=>!0);kt.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);kt.registerFlag("WEBGL_PACK",()=>kt.getBool("HAS_WEBGL"));kt.registerFlag("WEBGL_PACK_NORMALIZATION",()=>kt.getBool("WEBGL_PACK"));kt.registerFlag("WEBGL_PACK_CLIP",()=>kt.getBool("WEBGL_PACK"));kt.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>kt.getBool("WEBGL_PACK"));kt.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>kt.getBool("WEBGL_PACK"));kt.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>kt.getBool("WEBGL_PACK"));kt.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>kt.getBool("WEBGL_PACK"));kt.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>kt.getBool("WEBGL_PACK"));kt.registerFlag("WEBGL_PACK_REDUCE",()=>kt.getBool("WEBGL_PACK"));kt.registerFlag("WEBGL_LAZILY_UNPACK",()=>kt.getBool("WEBGL_PACK"));kt.registerFlag("WEBGL_CONV_IM2COL",()=>kt.getBool("WEBGL_PACK"));kt.registerFlag("WEBGL_PACK_CONV2DTRANSPOSE",()=>kt.getBool("WEBGL_PACK"));kt.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>n1(kt.getNumber("WEBGL_VERSION")));kt.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>o1(kt.getNumber("WEBGL_VERSION")));kt.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let r=kt.getNumber("WEBGL_VERSION");return r===0?0:s1(r)});kt.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>kt.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!du.isMobile());kt.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>i1(kt.getNumber("WEBGL_VERSION")));kt.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>kt.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:kt.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));kt.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>a1(kt.getNumber("WEBGL_VERSION")));kt.registerFlag("WEBGL_FENCE_API_ENABLED",()=>l1(kt.getNumber("WEBGL_VERSION")));kt.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>kt.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);kt.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,r=>{if(typeof r!="number")throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be a number but got ${r}.`);if(r<0&&r!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${r}.`)});kt.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>du.isMobile()?1:-1,r=>{if(typeof r!="number")throw new Error(`WEBGL_FLUSH_THRESHOLD must be a number but got ${r}.`);if(r<0&&r!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${r}.`)});kt.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD",()=>128);kt.registerFlag("WEBGL_USE_SHAPES_UNIFORMS",()=>!1);kt.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e5);kt.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD",()=>128);kt.registerFlag("WEBGL_EXP_CONV",()=>!1);kt.registerFlag("SOFTWARE_WEBGL_ENABLED",()=>kt.getBool("IS_TEST"));kt.registerFlag("WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE",()=>1/0);kt.registerFlag("WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE",()=>!1);kt.registerFlag("WEBGL2_ISNAN_CUSTOM",()=>!1);kt.registerFlag("ENGINE_COMPILE_ONLY",()=>!1);function Ue(){let r,t,e,n,o,s,i,a,u,l;return L().getNumber("WEBGL_VERSION")===2?(r="#version 300 es",t="in",e="out",n="in",o="texture",s="outputColor",i="out vec4 outputColor;",a=L().getBool("WEBGL2_ISNAN_CUSTOM")?` bool isnan_custom(float val) { uint floatToUint = floatBitsToUint(val); return (floatToUint & 0x7fffffffu) > 0x7f800000u; @@ -112,15 +112,15 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram ivec4 round(vec4 value) { return ivec4(floor(value + vec4(0.5))); } - `),{version:r,attribute:t,varyingVs:e,varyingFs:n,texture2D:o,output:s,defineOutput:i,defineSpecialNaN:a,defineSpecialInf:u,defineRound:l}}function vi(r,t,e="index"){let n=y.computeStrides(t);return n.map((o,s)=>{let i=`int ${r[s]} = ${e} / ${o}`,a=s===n.length-1?`int ${r[s+1]} = ${e} - ${r[s]} * ${o}`:`index -= ${r[s]} * ${o}`;return`${i}; ${a};`}).join("")}function up(r,t,e="index"){let n=y.computeStrides(t);return n.map((o,s)=>{let i=`int ${r[s]} = ${e} / outShapeStrides[${s}]`,a=s===n.length-1?`int ${r[s+1]} = ${e} - ${r[s]} * outShapeStrides[${s}]`:`index -= ${r[s]} * outShapeStrides[${s}]`;return`${i}; ${a};`}).join("")}function Xnt(r,t){let e=r.length,n=r.map(s=>`${t}[${s}]`),o=new Array(e-1);o[e-2]=n[e-1];for(let s=e-3;s>=0;--s)o[s]=`(${o[s+1]} * ${n[s+1]})`;return o}function FL(r,t,e="index"){let n=r.map((s,i)=>i),o=Xnt(n,t);return o.map((s,i)=>{let a=`int ${r[i]} = ${e} / ${o[i]}`,u=i===o.length-1?`int ${r[i+1]} = ${e} - ${r[i]} * ${o[i]}`:`index -= ${r[i]} * ${o[i]}`;return`${a}; ${u};`}).join("")}function Cd(r){let t=y.computeStrides(r).map(e=>e.toString());return` + `),{version:r,attribute:t,varyingVs:e,varyingFs:n,texture2D:o,output:s,defineOutput:i,defineSpecialNaN:a,defineSpecialInf:u,defineRound:l}}function Si(r,t,e="index"){let n=y.computeStrides(t);return n.map((o,s)=>{let i=`int ${r[s]} = ${e} / ${o}`,a=s===n.length-1?`int ${r[s+1]} = ${e} - ${r[s]} * ${o}`:`index -= ${r[s]} * ${o}`;return`${i}; ${a};`}).join("")}function up(r,t,e="index"){let n=y.computeStrides(t);return n.map((o,s)=>{let i=`int ${r[s]} = ${e} / outShapeStrides[${s}]`,a=s===n.length-1?`int ${r[s+1]} = ${e} - ${r[s]} * outShapeStrides[${s}]`:`index -= ${r[s]} * outShapeStrides[${s}]`;return`${i}; ${a};`}).join("")}function Qnt(r,t){let e=r.length,n=r.map(s=>`${t}[${s}]`),o=new Array(e-1);o[e-2]=n[e-1];for(let s=e-3;s>=0;--s)o[s]=`(${o[s+1]} * ${n[s+1]})`;return o}function PL(r,t,e="index"){let n=r.map((s,i)=>i),o=Qnt(n,t);return o.map((s,i)=>{let a=`int ${r[i]} = ${e} / ${o[i]}`,u=i===o.length-1?`int ${r[i+1]} = ${e} - ${r[i]} * ${o[i]}`:`index -= ${r[i]} * ${o[i]}`;return`${a}; ${u};`}).join("")}function vd(r){let t=y.computeStrides(r).map(e=>e.toString());return` int getFlatIndex(ivec3 coords) { return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z; } -`}function vd(){return` +`}function Sd(){return` int getFlatIndex(ivec3 coords) { return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z; } -`}var Bw=` +`}var Gw=` const float FLOAT_MAX = 1.70141184e38; const float FLOAT_MIN = 1.17549435e-38; @@ -159,22 +159,22 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram return c / 255.0; } -`;var{getBroadcastDims:OL}=S;function ML(r,t,e){let n=[];if(r.forEach(f=>{let d=y.sizeFromShape(f.shapeInfo.logicalShape);if(f.shapeInfo.isUniform?n.push(`uniform float ${f.name}${d>1?`[${d}]`:""};`):(n.push(`uniform sampler2D ${f.name};`),n.push(`uniform int offset${f.name};`)),e.enableShapeUniforms){let{uniformShape:h}=Vw(e.packedInputs,f.shapeInfo.logicalShape,f.shapeInfo.texShape);switch(h.length){case 1:n.push(`uniform int ${f.name}Shape;`);break;case 2:n.push(`uniform ivec2 ${f.name}Shape;`);break;case 3:n.push(`uniform ivec3 ${f.name}Shape;`);break;case 4:n.push(`uniform ivec4 ${f.name}Shape;`);break;default:break}n.push(`uniform ivec2 ${f.name}TexShape;`)}}),e.enableShapeUniforms){switch(t.logicalShape.length){case 1:n.push("uniform int outShape;");break;case 2:n.push("uniform ivec2 outShape;"),n.push("uniform int outShapeStrides;");break;case 3:n.push("uniform ivec3 outShape;"),n.push("uniform ivec2 outShapeStrides;");break;case 4:n.push("uniform ivec4 outShape;"),n.push("uniform ivec3 outShapeStrides;");break;default:break}n.push("uniform ivec2 outTexShape;")}e.customUniforms&&e.customUniforms.forEach(f=>{n.push(`uniform ${f.type} ${f.name}${f.arrayIndex?`[${f.arrayIndex}]`:""};`)});let o=n.join(` -`),s=r.map(f=>Ynt(f,t,e.packedInputs,e.enableShapeUniforms)).join(` -`),i=t.texShape,a=Ue(),u=Qnt(a),l,c,p=rot(a);return t.isPacked?(l=Znt(t.logicalShape,i,e.enableShapeUniforms),c=eot(a)):(l=Jnt(t.logicalShape,i,e.enableShapeUniforms),c=tot(a)),e.packedInputs&&(p+=iot),[p,u,c,o,l,s,e.userCode].join(` -`)}function Nd(r,t=!1){let e=r.shapeInfo.logicalShape;switch(e.length){case 0:return yot(r,t);case 1:return wot(r,t);case 2:return Cot(r,t);case 3:return Sot(r,t);case 4:return kot(r,t);case 5:return Tot(r);case 6:return _ot(r);default:throw new Error(`${e.length}-D input sampling is not yet supported`)}}function PL(r,t){switch(r.shapeInfo.logicalShape.length){case 0:return xot(r);case 1:return bot(r,t);case 2:return Iot(r,t);case 3:return vot(r,t);default:return Not(r,t)}}function Ynt(r,t,e=!1,n){let o="";e?o+=PL(r,n):o+=Nd(r,n);let s=r.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(e?o+=Eot(r,t):o+=Aot(r,t)),o}function Znt(r,t,e){switch(r.length){case 0:return LL();case 1:return aot(r,t,e);case 2:return hot(r,t,e);case 3:return uot(r,t,e);default:return pot(r,t,e)}}function Jnt(r,t,e){switch(r.length){case 0:return LL();case 1:return lot(r,t,e);case 2:return got(r,t,e);case 3:return cot(r,t,e);case 4:return mot(r,t,e);case 5:return fot(r,t);case 6:return dot(r,t);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function Qnt(r){return` +`;var{getBroadcastDims:LL}=S;function zL(r,t,e){let n=[];if(r.forEach(f=>{let d=y.sizeFromShape(f.shapeInfo.logicalShape);if(f.shapeInfo.isUniform?n.push(`uniform float ${f.name}${d>1?`[${d}]`:""};`):(n.push(`uniform sampler2D ${f.name};`),n.push(`uniform int offset${f.name};`)),e.enableShapeUniforms){let{uniformShape:h}=Ww(e.packedInputs,f.shapeInfo.logicalShape,f.shapeInfo.texShape);switch(h.length){case 1:n.push(`uniform int ${f.name}Shape;`);break;case 2:n.push(`uniform ivec2 ${f.name}Shape;`);break;case 3:n.push(`uniform ivec3 ${f.name}Shape;`);break;case 4:n.push(`uniform ivec4 ${f.name}Shape;`);break;default:break}n.push(`uniform ivec2 ${f.name}TexShape;`)}}),e.enableShapeUniforms){switch(t.logicalShape.length){case 1:n.push("uniform int outShape;");break;case 2:n.push("uniform ivec2 outShape;"),n.push("uniform int outShapeStrides;");break;case 3:n.push("uniform ivec3 outShape;"),n.push("uniform ivec2 outShapeStrides;");break;case 4:n.push("uniform ivec4 outShape;"),n.push("uniform ivec3 outShapeStrides;");break;default:break}n.push("uniform ivec2 outTexShape;")}e.customUniforms&&e.customUniforms.forEach(f=>{n.push(`uniform ${f.type} ${f.name}${f.arrayIndex?`[${f.arrayIndex}]`:""};`)});let o=n.join(` +`),s=r.map(f=>tot(f,t,e.packedInputs,e.enableShapeUniforms)).join(` +`),i=t.texShape,a=Ue(),u=not(a),l,c,p=iot(a);return t.isPacked?(l=eot(t.logicalShape,i,e.enableShapeUniforms),c=sot(a)):(l=rot(t.logicalShape,i,e.enableShapeUniforms),c=oot(a)),e.packedInputs&&(p+=cot),[p,u,c,o,l,s,e.userCode].join(` +`)}function kd(r,t=!1){let e=r.shapeInfo.logicalShape;switch(e.length){case 0:return Cot(r,t);case 1:return Sot(r,t);case 2:return kot(r,t);case 3:return _ot(r,t);case 4:return Aot(r,t);case 5:return Dot(r);case 6:return $ot(r);default:throw new Error(`${e.length}-D input sampling is not yet supported`)}}function BL(r,t){switch(r.shapeInfo.logicalShape.length){case 0:return Iot(r);case 1:return vot(r,t);case 2:return Not(r,t);case 3:return Tot(r,t);default:return Eot(r,t)}}function tot(r,t,e=!1,n){let o="";e?o+=BL(r,n):o+=kd(r,n);let s=r.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(e?o+=Rot(r,t):o+=Fot(r,t)),o}function eot(r,t,e){switch(r.length){case 0:return VL();case 1:return pot(r,t,e);case 2:return bot(r,t,e);case 3:return fot(r,t,e);default:return hot(r,t,e)}}function rot(r,t,e){switch(r.length){case 0:return VL();case 1:return mot(r,t,e);case 2:return wot(r,t,e);case 3:return dot(r,t,e);case 4:return got(r,t,e);case 5:return xot(r,t);case 6:return yot(r,t);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function not(r){return` float sampleTexture(sampler2D textureSampler, vec2 uv) { return ${r.texture2D}(textureSampler, uv).r; } - `}function tot(r){return` + `}function oot(r){return` void setOutput(float val) { ${r.output} = vec4(val, 0, 0, 0); } - `}function eot(r){return` + `}function sot(r){return` void setOutput(vec4 val) { ${r.output} = val; } - `}function rot(r){return`${r.version} + `}function iot(r){return`${r.version} precision highp float; precision highp int; precision highp sampler2D; @@ -229,10 +229,10 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram return fract((p3.x + p3.y) * p3.z); } - ${not} - ${oot} - ${sot} - `}var not=` + ${aot} + ${lot} + ${uot} + `}var aot=` vec2 uvFromFlat(int texNumR, int texNumC, int index) { int texR = index / texNumC; int texC = index - texR * texNumC; @@ -244,7 +244,7 @@ vec2 packedUVfrom1D(int texNumR, int texNumC, int index) { int texC = texelIndex - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,oot=` +`,lot=` vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR, int texNumC, int row, int col) { int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2); @@ -252,7 +252,7 @@ vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR, int texC = texelIndex - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,sot=` +`,uot=` vec2 packedUVfrom3D(int texNumR, int texNumC, int texelsInBatch, int texelsInLogicalRow, int b, int row, int col) { @@ -261,7 +261,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, int texC = index - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,iot=` +`,cot=` float getChannel(vec4 frag, vec2 innerDims) { vec2 modCoord = mod(innerDims, 2.); return modCoord.x == 0. ? @@ -272,11 +272,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float modCoord = mod(float(dim), 2.); return modCoord == 0. ? frag.r : frag.g; } -`;function LL(){return` +`;function VL(){return` int getOutputCoords() { return 0; } - `}function aot(r,t,e){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?e?` + `}function pot(r,t,e){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?e?` int getOutputCoords() { return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0)); } @@ -305,7 +305,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2(${n[0]}, ${n[1]})); return 2 * (resTexRC.x * ${n[1]} + resTexRC.y); } - `}function lot(r,t,e){return t[0]===1?e?` + `}function mot(r,t,e){return t[0]===1?e?` int getOutputCoords() { return int(resultUV.x * float(outTexShape[1])); } @@ -333,7 +333,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2(${t[0]}, ${t[1]})); return resTexRC.x * ${t[1]} + resTexRC.y; } - `}function uot(r,t,e){if(e)return` + `}function fot(r,t,e){if(e)return` ivec3 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0)); @@ -364,7 +364,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return ivec3(b, r, c); } - `}function cot(r,t,e){if(e)return` + `}function dot(r,t,e){if(e)return` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); @@ -372,7 +372,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${up(["r","c","d"],r)} return ivec3(r, c, d); } -`;let n=vi(["r","c","d"],r);return` +`;let n=Si(["r","c","d"],r);return` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -380,7 +380,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${n} return ivec3(r, c, d); } - `}function pot(r,t,e){if(e)return` + `}function hot(r,t,e){if(e)return` ivec4 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); ivec2 resTexRC = ivec2(resultUV.yx * @@ -421,7 +421,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return ivec${r.length}(${u}); } - `}function mot(r,t,e){if(e)return` + `}function got(r,t,e){if(e)return` ivec4 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); @@ -429,7 +429,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${up(["r","c","d","d2"],r)} return ivec4(r, c, d, d2); } - `;let n=vi(["r","c","d","d2"],r);return` + `;let n=Si(["r","c","d","d2"],r);return` ivec4 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -437,7 +437,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${n} return ivec4(r, c, d, d2); } - `}function fot(r,t){let e=vi(["r","c","d","d2","d3"],r);return` + `}function xot(r,t){let e=Si(["r","c","d","d2","d3"],r);return` ivec5 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -449,7 +449,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ivec5 outShape = ivec5(r, c, d, d2, d3); return outShape; } - `}function dot(r,t){let e=vi(["r","c","d","d2","d3","d4"],r);return` + `}function yot(r,t){let e=Si(["r","c","d","d2","d3","d4"],r);return` ivec6 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -460,7 +460,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ivec6 result = ivec6(r, c, d, d2, d3, d4); return result; } - `}function hot(r,t,e){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(y.arraysEqual(r,t))return e?` + `}function bot(r,t,e){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(y.arraysEqual(r,t))return e?` ivec2 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1])); @@ -493,7 +493,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return ivec2(r, c); } - `}function got(r,t,e){return y.arraysEqual(r,t)?e?` + `}function wot(r,t,e){return y.arraysEqual(r,t)?e?` ivec2 getOutputCoords() { return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); } @@ -547,11 +547,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, int c = index - r * ${r[1]}; return ivec2(r, c); } - `}function cp(r){return`offset${r}`}function xot(r){let t=r.name,e="get"+t.charAt(0).toUpperCase()+t.slice(1),n=Ue();return` + `}function cp(r){return`offset${r}`}function Iot(r){let t=r.name,e="get"+t.charAt(0).toUpperCase()+t.slice(1),n=Ue();return` vec4 ${e}() { return ${n.texture2D}(${t}, halfCR); } - `}function yot(r,t){let e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1);if(r.shapeInfo.isUniform)return`float ${n}() {return ${e};}`;let[o,s]=r.shapeInfo.texShape;if(o===1&&s===1)return` + `}function Cot(r,t){let e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1);if(r.shapeInfo.isUniform)return`float ${n}() {return ${e};}`;let[o,s]=r.shapeInfo.texShape;if(o===1&&s===1)return` float ${n}() { return sampleTexture(${e}, halfCR); } @@ -565,7 +565,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = uvFromFlat(${a}, ${u}, ${i}); return sampleTexture(${e}, uv); } - `}function bot(r,t){let e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1),o=r.shapeInfo.texShape,s=Ue();if(t)return` + `}function vot(r,t){let e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1),o=r.shapeInfo.texShape,s=Ue();if(t)return` vec4 ${n}(int index) { ivec2 packedTexShape = ivec2(ceil(float(${e}TexShape[0]) / 2.0), ceil(float(${e}TexShape[1]) / 2.0)); vec2 uv = packedUVfrom1D( @@ -578,9 +578,9 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${i[0]}, ${i[1]}, index); return ${s.texture2D}(${e}, uv); } - `}function wot(r,t){let e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1);if(r.shapeInfo.isUniform)return` + `}function Sot(r,t){let e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1);if(r.shapeInfo.isUniform)return` float ${n}(int index) { - ${kd(r)} + ${Td(r)} } `;let o=r.shapeInfo.texShape,s=o[0],i=o[1];if(i===1&&s===1)return` float ${n}(int index) { @@ -616,7 +616,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = uvFromFlat(${s}, ${i}, index + ${a}); return sampleTexture(${e}, uv); } - `}function Iot(r,t){let e=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r.shapeInfo.texShape,i=s[0],a=s[1],u=Ue();if(s!=null&&y.arraysEqual(e,s))return t?` + `}function Not(r,t){let e=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r.shapeInfo.texShape,i=s[0],a=s[1],u=Ue();if(s!=null&&y.arraysEqual(e,s))return t?` vec4 ${o}(int row, int col) { vec2 uv = (vec2(col, row) + halfCR) / vec2(${n}TexShape[1], ${n}TexShape[0]); @@ -640,7 +640,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = packedUVfrom2D(${c}, ${l[0]}, ${l[1]}, row, col); return ${u.texture2D}(${n}, uv); } - `}function Cot(r,t){let e=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r.shapeInfo.texShape;if(s!=null&&y.arraysEqual(e,s)){if(t)return` + `}function kot(r,t){let e=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r.shapeInfo.texShape;if(s!=null&&y.arraysEqual(e,s)){if(t)return` float ${o}(int row, int col) { vec2 uv = (vec2(col, row) + halfCR) / vec2(${n}TexShape[1], ${n}TexShape[0]); return sampleTexture(${n}, uv); @@ -650,15 +650,15 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = (vec2(col, row) + halfCR) / vec2(${f}.0, ${m}.0); return sampleTexture(${n}, uv); } - `}let{newShape:i,keptDims:a}=y.squeezeShape(e),u=i;if(u.length=1?c="coords = 0;":c=a.map(b=>`coords.${p[b+l]} = 0;`).join(` + `}function Rot(r,t){let e=r.name,n=e.charAt(0).toUpperCase()+e.slice(1),o="get"+n+"AtOutCoords",s=r.shapeInfo.logicalShape.length,i=t.logicalShape.length,a=LL(r.shapeInfo.logicalShape,t.logicalShape),u=zt(i),l=i-s,c,p=["x","y","z","w","u","v"];s===0?c="":i<2&&a.length>=1?c="coords = 0;":c=a.map(b=>`coords.${p[b+l]} = 0;`).join(` `);let m="";i<2&&s>0?m="coords":m=r.shapeInfo.logicalShape.map((b,w)=>`coords.${p[w+l]}`).join(", ");let f="return outputValue;",h=y.sizeFromShape(r.shapeInfo.logicalShape)===1,x=y.sizeFromShape(t.logicalShape)===1;if(s===1&&!h&&!x)f=` return vec4(outputValue.xy, outputValue.xy); `;else if(h&&!x)i===1?f=` @@ -977,20 +977,20 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec4 outputValue = get${n}(${m}); 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N=y.computeStrides(c);d=`${N[0]===u[1]}_${N[N.length-1]===u[1]}`}let h=i.shape.length,g=c.length===2&&y.arraysEqual(i.shape,u),x=y.sizeFromShape(i.shape)===1,b=S.getBroadcastDims(i.shape,e.shape),w=!r.packedInputs&&h===e.shape.length&&y.arraysEqual(u,e.texData.texShape),I=r.packedInputs||c.length>2?"":`${u[0]>1}_${u[1]>1}`;n+=`${h}_${w}_${l?p:""}_${c.length}_${x}_${b}_${g}_${m}_${f}_${d}_${I}_${a}`}else{let u=i.isUniform?"uniform":i.texData.texShape;n+=`${i.shape}_${u}_${a}`}});let o=r.userCode,s=r.constructor.name;return s+="_"+n+"_"+o+`${L().getNumber("WEBGL_VERSION")}`,s}function he(r){return L().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&r<=4}var Uw=class{constructor(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Wu.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let e=Ue();this.outputShape=t,this.enableShapeUniforms=he(this.outputShape.length),this.userCode=` ivec3 outCoordsFromFlatIndex(int index) { - ${this.enableShapeUniforms?up(["r","c","d"],t):vi(["r","c","d"],t)} + ${this.enableShapeUniforms?up(["r","c","d"],t):Si(["r","c","d"],t)} return ivec3(r, c, d); } @@ -1008,9 +1008,9 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${e.output} = result; } - `}};var Ww=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Wu.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let e=Ue();this.outputShape=t,this.enableShapeUniforms=he(this.outputShape.length),this.userCode=` + `}};var Hw=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Wu.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let e=Ue();this.outputShape=t,this.enableShapeUniforms=he(this.outputShape.length),this.userCode=` ivec3 outCoordsFromFlatIndex(int index) { - ${this.enableShapeUniforms?up(["r","c","d"],t):vi(["r","c","d"],t)} + ${this.enableShapeUniforms?up(["r","c","d"],t):Si(["r","c","d"],t)} return ivec3(r, c, d); } @@ -1028,26 +1028,26 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${e.output} = result; } - `}};var Uw=class{constructor(t){this.variableNames=["A"],this.outTexUsage=Yr.DOWNLOAD;let e=Ue();this.outputShape=t,this.userCode=` - ${Bw} + `}};var qw=class{constructor(t){this.variableNames=["A"],this.outTexUsage=Jr.DOWNLOAD;let e=Ue();this.outputShape=t,this.userCode=` + ${Gw} void main() { float x = getAAtOutCoords(); ${e.output} = encode_float(x); } - `}};var Hw=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Yr.DOWNLOAD;let e=Ue();this.outputShape=t,this.userCode=` - ${Bw} + `}};var Kw=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Jr.DOWNLOAD;let e=Ue();this.outputShape=t,this.userCode=` + ${Gw} void main() { ivec3 coords = getOutputCoords(); float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z)); ${e.output} = encode_float(x); } - `}};var Rot={R:0,G:1,B:2,A:3},lg=class{constructor(t,e=!1,n="RGBA"){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let o=Ue();this.outputShape=t,this.enableShapeUniforms=he(this.outputShape.length);let s="result";e&&(s="floor(result * 255. + 0.5)");let i="";for(let a=0;ag1,createBufferFromOutputTexture:()=>b1,createFloat16MatrixTexture:()=>m1,createFloat16PackedMatrixTexture:()=>h1,createFloat32MatrixTexture:()=>p1,createIndexBuffer:()=>c1,createPackedMatrixTexture:()=>d1,createUnsignedBytesMatrixTexture:()=>f1,createVertexBuffer:()=>u1,createVertexShader:()=>l1,downloadByteEncodedFloatMatrixFromOutputTexture:()=>I1,downloadFloat32MatrixFromBuffer:()=>w1,downloadMatrixFromPackedOutputTexture:()=>v1,downloadPackedMatrixFromBuffer:()=>C1,getInternalFormatForFloat16MatrixTexture:()=>jw,getInternalFormatForFloat16PackedMatrixTexture:()=>Zw,getInternalFormatForFloat32MatrixTexture:()=>Kw,getInternalFormatForPackedMatrixTexture:()=>Yw,getInternalFormatForUnsignedBytesMatrixTexture:()=>Xw,uploadDenseMatrixToTexture:()=>x1,uploadPixelDataToTexture:()=>y1});function l1(r){let t=Ue(),e=`${t.version} + `}};var 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implementations are expected to offer OES_vertex_array_object.");this.createVertexArray=()=>ht(t,()=>s.createVertexArrayOES()),this.bindVertexArray=i=>ht(t,()=>s.bindVertexArrayOES(i)),this.deleteVertexArray=i=>ht(t,()=>s.deleteVertexArrayOES(i)),this.getVertexArray=()=>ht(t,()=>t.getParameter(s.VERTEX_ARRAY_BINDING_OES))}let n="WEBGL_color_buffer_float",o="EXT_color_buffer_half_float";if(this.parallelCompilationExtension=this.gl.getExtension("KHR_parallel_shader_compile"),L().getNumber("WEBGL_VERSION")===1){let s="OES_texture_float",i="OES_texture_half_float";if(this.textureFloatExtension=bd(this.gl,s),Kn(this.gl,i))this.textureHalfFloatExtension=bd(this.gl,i);else if(L().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),Kn(this.gl,o))this.colorBufferHalfFloatExtension=bd(this.gl,o);else if(L().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",Kn(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Kn(this.gl,o))this.colorBufferHalfFloatExtension=this.gl.getExtension(o);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=p1(this.gl),this.indexBuffer=m1(this.gl),this.framebuffer=JT(this.gl),this.textureConfig=ig(this.gl,this.textureHalfFloatExtension)}get debug(){return L().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 t=this.gl;ht(t,()=>t.finish()),ht(t,()=>t.bindFramebuffer(t.FRAMEBUFFER,null)),ht(t,()=>t.deleteFramebuffer(this.framebuffer)),ht(t,()=>t.bindBuffer(t.ARRAY_BUFFER,null)),ht(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,null)),ht(t,()=>t.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(t,e){return this.throwIfDisposed(),f1(this.gl,t,e,this.textureConfig)}createFloat16MatrixTexture(t,e){return this.throwIfDisposed(),d1(this.gl,t,e,this.textureConfig)}createUnsignedBytesMatrixTexture(t,e){return this.throwIfDisposed(),h1(this.gl,t,e,this.textureConfig)}uploadPixelDataToTexture(t,e){this.throwIfDisposed(),w1(this.gl,t,e)}uploadDenseMatrixToTexture(t,e,n,o){this.throwIfDisposed(),b1(this.gl,t,e,n,o,this.textureConfig)}createFloat16PackedMatrixTexture(t,e){return this.throwIfDisposed(),x1(this.gl,t,e,this.textureConfig)}createPackedMatrixTexture(t,e){return this.throwIfDisposed(),g1(this.gl,t,e,this.textureConfig)}deleteMatrixTexture(t){this.throwIfDisposed(),this.outputTexture===t&&(Bw(this.gl,this.framebuffer),this.outputTexture=null),ht(this.gl,()=>this.gl.deleteTexture(t))}downloadByteEncodedFloatMatrixFromOutputTexture(t,e,n){return this.downloadMatrixDriver(t,()=>v1(this.gl,e,n,this.textureConfig))}downloadPackedMatrixFromBuffer(t,e,n,o,s,i){return S1(this.gl,t,e,n,o,s,i,this.textureConfig)}downloadFloat32MatrixFromBuffer(t,e){return C1(this.gl,t,e)}createBufferFromTexture(t,e,n){this.bindTextureToFrameBuffer(t);let o=I1(this.gl,e,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),o}createAndWaitForFence(){let t=this.createFence(this.gl);return this.pollFence(t)}createFence(t){let e,n;if(L().getBool("WEBGL_FENCE_API_ENABLED")){let o=t,s=o.fenceSync(o.SYNC_GPU_COMMANDS_COMPLETE,0);t.flush(),n=()=>{let 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e=this.gl;ht(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),y1(e,t,this.vertexBuffer)}deleteProgram(t){this.throwIfDisposed(),t===this.program&&(this.program=null),t!=null&&(ht(this.gl,()=>this.gl.deleteProgram(t)),this.deleteVertexArray(t.vao))}setProgram(t){this.throwIfDisposed(),this.program=t,this.program!=null&&this.debug&&ag(this.gl,this.program),ht(this.gl,()=>this.gl.useProgram(t))}getUniformLocation(t,e,n=!0){return this.throwIfDisposed(),n?QT(this.gl,t,e):t1(this.gl,t,e)}getAttributeLocation(t,e){return this.throwIfDisposed(),ht(this.gl,()=>this.gl.getAttribLocation(t,e))}getUniformLocationNoThrow(t,e){return this.throwIfDisposed(),this.gl.getUniformLocation(t,e)}setInputMatrixTexture(t,e,n){this.throwIfDisposed(),this.throwIfNoProgram(),e1(this.gl,t,e,n)}setOutputMatrixTexture(t,e,n){this.setOutputMatrixTextureDriver(t,n,e)}setOutputPackedMatrixTexture(t,e,n){this.throwIfDisposed();let[o,s]=wa(e,n);this.setOutputMatrixTextureDriver(t,o,s)}setOutputMatrixWriteRegion(t,e,n,o){this.setOutputMatrixWriteRegionDriver(n,t,o,e)}setOutputPackedMatrixWriteRegion(t,e,n,o){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&ag(this.gl,this.program),wd(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let t=this.gl;if(this.debug){let e=this.getVertexArray();console.assert(e===this.program.vao,"VAO changed between setProgram and executeProgram!"),this.debugValidate()}ht(t,()=>t.drawElements(t.TRIANGLES,6,t.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),ht(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=bd(this.gl,L().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(L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=n.createQuery();return n.beginQuery(o.TIME_ELAPSED_EXT,s),s}let t=this.getQueryTimerExtensionWebGL1(),e=t.createQueryEXT();return t.beginQueryEXT(t.TIME_ELAPSED_EXT,e),e}endQuery(){if(L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let 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this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),o&&!this.disjoint}}pollFence(t){return new Promise(e=>{this.addItemToPoll(()=>t.isFencePassed(),()=>e())})}pollItems(){let t=Lot(this.itemsToPoll.map(e=>e.isDoneFn));for(let e=0;e<=t;++e){let{resolveFn:n}=this.itemsToPoll[e];n()}this.itemsToPoll=this.itemsToPoll.slice(t+1)}addItemToPoll(t,e){if(this.itemsToPoll.push({isDoneFn:t,resolveFn:e}),this.itemsToPoll.length>1)return;let n;"setTimeoutCustom"in L().platform&&(n=L().platform.setTimeoutCustom.bind(L().platform)),y.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,n)}bindTextureToFrameBuffer(t){this.throwIfDisposed(),lg(this.gl,t,this.framebuffer),this.debug&&wd(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(lg(this.gl,this.outputTexture,this.framebuffer),this.debug&&wd(this.gl)):Bw(this.gl,this.framebuffer)}downloadMatrixDriver(t,e){this.bindTextureToFrameBuffer(t);let n=e();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(t,e,n){this.throwIfDisposed();let o=this.gl;lg(o,t,this.framebuffer),this.debug&&wd(o),this.outputTexture=t,ht(o,()=>o.viewport(0,0,e,n)),ht(o,()=>o.scissor(0,0,e,n))}setOutputMatrixWriteRegionDriver(t,e,n,o){this.throwIfDisposed(),ht(this.gl,()=>this.gl.scissor(t,e,n,o))}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 Lot(r){let t=0;for(;t`${r}.${e}`)}function rr(r,t){return t===1?[r]:T1(r,t)}function Pz(r,t){if(r===1)return"rc";let e="";for(let n=0;n= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${e[0]}), cEdge ? 0. : getA(${e[1]}), rEdge ? 0. : getA(${e[2]}), - rEdge || cEdge ? 0. : getA(${e[3]})`}};var Ed=class{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=t,this.enableShapeUniforms=he(this.outputShape.length);let n="";for(let o=0;o<4;o++){let s="thisRC = rc;";o%2===1&&(s+="thisRC.z += 1;"),o>1&&(s+="thisRC.y += 1;"),n+=` + rEdge || cEdge ? 0. : getA(${e[3]})`}};var Ad=class{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=t,this.enableShapeUniforms=he(this.outputShape.length);let n="";for(let o=0;o<4;o++){let s="thisRC = rc;";o%2===1&&(s+="thisRC.z += 1;"),o>1&&(s+="thisRC.y += 1;"),n+=` ${s} ${o>0?"if(thisRC.y < rows && thisRC.z < cols){":""} int flatIndex = getFlatIndex(thisRC); @@ -1158,8 +1158,8 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims); ${o>0?"}":""} `}this.userCode=` - ${Oot(e,this.enableShapeUniforms)} - ${this.enableShapeUniforms?vd():Cd(t)} + ${zot(e,this.enableShapeUniforms)} + ${this.enableShapeUniforms?Sd():vd(t)} void main() { ivec3 rc = getOutputCoords(); @@ -1174,12 +1174,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(result); } - `}};function Oot(r,t){return` + `}};function zot(r,t){return` ivec3 inputCoordsFromReshapedOutCoords(int index) { - ${t?FL(["r","c","d"],"inputShape"):vi(["r","c","d"],r)} + ${t?PL(["r","c","d"],"inputShape"):Si(["r","c","d"],r)} return ivec3(r, c, d); } - `}var rI=class{constructor(t){this.gpgpu=t,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(t,e,n){let o=Mz(e,n),s=Pz(t,o,n);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let i=Oz(t,o,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=i,this.log();let u=this.freeTextures[s].pop();return this.usedTextures[s].push(u),u}let a;return o===Pr.PACKED_2X2_FLOAT32?a=this.gpgpu.createPackedMatrixTexture(t[0],t[1]):o===Pr.PACKED_2X2_FLOAT16?a=this.gpgpu.createFloat16PackedMatrixTexture(t[0],t[1]):o===Pr.UNPACKED_FLOAT32?a=this.gpgpu.createFloat32MatrixTexture(t[0],t[1]):o===Pr.UNPACKED_FLOAT16?a=this.gpgpu.createFloat16MatrixTexture(t[0],t[1]):o===Pr.PACKED_4X1_UNSIGNED_BYTE&&(a=this.gpgpu.createUnsignedBytesMatrixTexture(t[0],t[1])),this.usedTextures[s].push(a),this.numUsedTextures++,this._numBytesAllocated+=i,this.log(),a}releaseTexture(t,e,n,o){if(this.freeTextures==null)return;let s=Mz(n,o),i=Pz(e,s,o);i in this.freeTextures||(this.freeTextures[i]=[]);let a=Oz(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,o),u=L().getNumber("WEBGL_DELETE_TEXTURE_THRESHOLD");u!==-1&&this._numBytesAllocated>u?(this.gpgpu.deleteMatrixTexture(t.texture),this._numBytesAllocated-=a):(this.freeTextures[i].push(t),this.numFreeTextures++,this._numBytesFree+=a),this.numUsedTextures--;let l=this.usedTextures[i],c=l&&l.indexOf(t);if(c==null||c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l[c]=l[l.length-1],l.pop(),this.log()}log(){if(!this.logEnabled)return;let t=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${t})`);let e=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*e)}%)`)}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 t in this.freeTextures)this.freeTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});for(let t in this.usedTextures)this.usedTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function Mot(r,t){let e=r;if(t===e.R32F)return 4;if(t===e.R16F)return 2;if(t===e.RGBA32F)return 16;if(t===r.RGBA)return 16;if(t===e.RGBA16F)return 8;if(t===e.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function Oz(r,t,e,n,o){let s=Pot(t,n),i;if(o){let[u,l]=wa(r[0],r[1]);i=u*l}else{let[u,l]=lp(r[0],r[1]);i=u*l}let a=Mot(e,s);return i*a}function Pot(r,t){switch(r){case Pr.PACKED_2X2_FLOAT32:return Yw(t);case Pr.PACKED_2X2_FLOAT16:return Zw(t);case Pr.UNPACKED_FLOAT32:return Kw(t);case Pr.UNPACKED_FLOAT16:return jw(t);case Pr.PACKED_4X1_UNSIGNED_BYTE:return Xw(t);default:throw new Error(`Unknown physical texture type ${r}`)}}function Lot(r){return L().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?r?Pr.PACKED_2X2_FLOAT32:Pr.UNPACKED_FLOAT32:r?Pr.PACKED_2X2_FLOAT16:Pr.UNPACKED_FLOAT16}function Mz(r,t){if(r===Yr.UPLOAD)return Pr.PACKED_2X2_FLOAT32;if(r===Yr.RENDER||r==null)return Lot(t);if(r===Yr.DOWNLOAD||r===Yr.PIXELS)return Pr.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function Pz(r,t,e){return`${r[0]}_${r[1]}_${t}_${e}`}var Lr=class{constructor(t,e){this.variableNames=["A"],this.outputShape=t,this.enableShapeUniforms=he(this.outputShape.length),this.userCode=` + `}var oI=class{constructor(t){this.gpgpu=t,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(t,e,n){let o=zz(e,n),s=Bz(t,o,n);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let i=Lz(t,o,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=i,this.log();let u=this.freeTextures[s].pop();return this.usedTextures[s].push(u),u}let a;return o===Lr.PACKED_2X2_FLOAT32?a=this.gpgpu.createPackedMatrixTexture(t[0],t[1]):o===Lr.PACKED_2X2_FLOAT16?a=this.gpgpu.createFloat16PackedMatrixTexture(t[0],t[1]):o===Lr.UNPACKED_FLOAT32?a=this.gpgpu.createFloat32MatrixTexture(t[0],t[1]):o===Lr.UNPACKED_FLOAT16?a=this.gpgpu.createFloat16MatrixTexture(t[0],t[1]):o===Lr.PACKED_4X1_UNSIGNED_BYTE&&(a=this.gpgpu.createUnsignedBytesMatrixTexture(t[0],t[1])),this.usedTextures[s].push(a),this.numUsedTextures++,this._numBytesAllocated+=i,this.log(),a}releaseTexture(t,e,n,o){if(this.freeTextures==null)return;let s=zz(n,o),i=Bz(e,s,o);i in this.freeTextures||(this.freeTextures[i]=[]);let a=Lz(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,o),u=L().getNumber("WEBGL_DELETE_TEXTURE_THRESHOLD");u!==-1&&this._numBytesAllocated>u?(this.gpgpu.deleteMatrixTexture(t.texture),this._numBytesAllocated-=a):(this.freeTextures[i].push(t),this.numFreeTextures++,this._numBytesFree+=a),this.numUsedTextures--;let l=this.usedTextures[i],c=l&&l.indexOf(t);if(c==null||c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l[c]=l[l.length-1],l.pop(),this.log()}log(){if(!this.logEnabled)return;let t=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${t})`);let e=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*e)}%)`)}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 t in this.freeTextures)this.freeTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});for(let t in this.usedTextures)this.usedTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function Bot(r,t){let e=r;if(t===e.R32F)return 4;if(t===e.R16F)return 2;if(t===e.RGBA32F)return 16;if(t===r.RGBA)return 16;if(t===e.RGBA16F)return 8;if(t===e.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function Lz(r,t,e,n,o){let s=Vot(t,n),i;if(o){let[u,l]=wa(r[0],r[1]);i=u*l}else{let[u,l]=lp(r[0],r[1]);i=u*l}let a=Bot(e,s);return i*a}function Vot(r,t){switch(r){case Lr.PACKED_2X2_FLOAT32:return Jw(t);case Lr.PACKED_2X2_FLOAT16:return Qw(t);case Lr.UNPACKED_FLOAT32:return Xw(t);case Lr.UNPACKED_FLOAT16:return Yw(t);case Lr.PACKED_4X1_UNSIGNED_BYTE:return Zw(t);default:throw new Error(`Unknown physical texture type ${r}`)}}function Got(r){return L().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?r?Lr.PACKED_2X2_FLOAT32:Lr.UNPACKED_FLOAT32:r?Lr.PACKED_2X2_FLOAT16:Lr.UNPACKED_FLOAT16}function zz(r,t){if(r===Jr.UPLOAD)return Lr.PACKED_2X2_FLOAT32;if(r===Jr.RENDER||r==null)return Got(t);if(r===Jr.DOWNLOAD||r===Jr.PIXELS)return Lr.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function Bz(r,t,e){return`${r[0]}_${r[1]}_${t}_${e}`}var zr=class{constructor(t,e){this.variableNames=["A"],this.outputShape=t,this.enableShapeUniforms=he(this.outputShape.length),this.userCode=` float unaryOperation(float x) { ${e} } @@ -1190,11 +1190,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(y); } - `}},xr="if (isnan(x)) return x;",Lz="return x;",k1="return abs(x);";var zz="return (x >= 0.0) ? x : (exp(x) - 1.0);",Bz=xr+` + `}},xr="if (isnan(x)) return x;",Vz="return x;",_1="return abs(x);";var Gz="return (x >= 0.0) ? x : (exp(x) - 1.0);",Wz=xr+` return (x < 0.0) ? 0.0 : x; -`,Vz=xr+` +`,Uz=xr+` return (x < 0.0) ? 0.0 : min(6.0, x); -`,Ia="return x;",Gz="return 1.0 / (1.0 + exp(-1.0 * x));";var Uz="return x;",Hz=` +`,Ia="return x;",Hz="return 1.0 / (1.0 + exp(-1.0 * x));";var Kz="return x;",jz=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -1203,7 +1203,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; -`,qz=` +`,Xz=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -1213,7 +1213,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = isNaN.a ? x.a : result.a; return result; -`,Kz=` +`,Yz=` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -1223,7 +1223,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = isNaN.a ? x.a : result.a; return result; -`,jz="return 1.0 / (1.0 + exp(-1.0 * x));",Dn=class{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.enableShapeUniforms=he(this.outputShape.length),this.userCode=` +`,Zz="return 1.0 / (1.0 + exp(-1.0 * x));",Dn=class{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.enableShapeUniforms=he(this.outputShape.length),this.userCode=` vec4 unaryOperation(vec4 x) { ${e} } @@ -1234,14 +1234,14 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(y); } - `}};var nI=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=t,this.enableShapeUniforms=he(this.outputShape.length);let e=t.length,n=rr("rc",e),o=zt(e),s=Fz(e,n),i=n.slice(-2),a=e<=1?"rc":`vec2(${i.join(",")})`;this.userCode=` + `}};var sI=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=t,this.enableShapeUniforms=he(this.outputShape.length);let e=t.length,n=rr("rc",e),o=zt(e),s=Pz(e,n),i=n.slice(-2),a=e<=1?"rc":`vec2(${i.join(",")})`;this.userCode=` void main() { ${o} rc = getOutputCoords(); vec4 packedInput = getA(${s}); setOutput(getChannel(packedInput, ${a})); } - `}};var Bot=Kr.whereImpl,Vot=1e-7,Got=1e-4,oI={};function Wot(r){return r in oI||(oI[r]={}),oI[r]}var Uot=L().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),Hot=600;function qot(){return L().global.screen==null?1024:L().global.screen.height*L().global.screen.width*window.devicePixelRatio*Hot/1024/1024}var Ad=class r extends Bo{nextDataId(){return r.nextDataId++}constructor(t){if(super(),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,!L().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let e;if(t!=null){if(t instanceof pp)e=t;else{let n=qn(L().getNumber("WEBGL_VERSION"),t);e=new pp(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=qn(L().getNumber("WEBGL_VERSION"));e=new pp(n),this.binaryCache=Wot(L().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=e,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new rI(this.gpgpu),this.numMBBeforeWarning=qot(),this.texData=new Ta(this,Bn())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(t,e,n,o,s,i){let a=this.makeTensorInfo(e,n),u=this.texData.get(a.dataId);u.isPacked=!1,u.texture={texture:t,texShape:[o,s]},u.texShape=[o,s];let l=wd(e),c=new lg(l,!1,i),p=this.runWebGLProgram(c,[a],n,[[o,s]]);return p.shape=e,u.texture=null,this.disposeIntermediateTensorInfo(a),p.dataId}write(t,e,n){if((L().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||L().getBool("DEBUG"))&&this.checkNumericalProblems(t),n==="complex64"&&t!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let o={id:this.nextDataId()};return this.texData.set(o,{shape:e,dtype:n,values:t,usage:Yr.UPLOAD,refCount:1}),o}refCount(t){return this.texData.has(t)?this.texData.get(t).refCount:0}incRef(t){let e=this.texData.get(t);e.refCount++}decRef(t){if(this.texData.has(t)){let e=this.texData.get(t);e.refCount--}}move(t,e,n,o,s){if(L().getBool("DEBUG")&&this.checkNumericalProblems(e),o==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(t,{shape:n,dtype:o,values:e,usage:Yr.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(t){this.disposeData(t.dataId)}readSync(t){let e=this.texData.get(t),{values:n,dtype:o,complexTensorInfos:s,slice:i,shape:a,isPacked:u}=e;if(i!=null){let m;u?m=new Dn(a,Ia):m=new Lr(a,Ia);let f=this.runWebGLProgram(m,[{dataId:t,shape:a,dtype:o}],o),d=this.readSync(f.dataId);return this.disposeIntermediateTensorInfo(f),d}if(n!=null)return this.convertAndCacheOnCPU(t);if(o==="string")return n;let l=this.activeTimers!=null,c;l&&(c=y.now());let p;if(o==="complex64"){let m=this.readSync(s.real.dataId),f=this.readSync(s.imag.dataId);p=S.mergeRealAndImagArrays(m,f)}else p=this.getValuesFromTexture(t);return l&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(t,p)}async read(t){if(this.pendingRead.has(t)){let d=this.pendingRead.get(t);return new Promise(h=>d.push(h))}let e=this.texData.get(t),{values:n,shape:o,slice:s,dtype:i,complexTensorInfos:a,isPacked:u}=e;if(s!=null){let d;u?d=new Dn(o,Ia):d=new Lr(o,Ia);let h=this.runWebGLProgram(d,[{dataId:t,shape:o,dtype:i}],i),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(n!=null)return this.convertAndCacheOnCPU(t);if(L().getBool("DEBUG")&&!L().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&L().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,c;if(i!=="complex64"&&L().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(t);let d=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(d.texture.texture,...og(o))}this.pendingRead.set(t,[]),i!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(i==="complex64"){let d=await Promise.all([this.read(a.real.dataId),this.read(a.imag.dataId)]),h=d[0],g=d[1];p=S.mergeRealAndImagArrays(h,g)}else if(l==null)p=this.getValuesFromTexture(t);else{let d=y.sizeFromShape(o);p=this.gpgpu.downloadFloat32MatrixFromBuffer(l,d)}if(c!=null&&this.disposeIntermediateTensorInfo(c),l!=null){let d=this.gpgpu.gl;ht(d,()=>d.deleteBuffer(l))}let m=this.convertAndCacheOnCPU(t,p),f=this.pendingRead.get(t);return this.pendingRead.delete(t),f.forEach(d=>d(m)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t)&&Bn().removeDataId(t,this),this.pendingDeletes--),m}readToGPU(t,e={}){let n=this.texData.get(t),{values:o,shape:s,slice:i,dtype:a,isPacked:u,texture:l}=n;if(a==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(i!=null){let f;u?f=new Dn(s,Ia):f=new Lr(s,Ia);let d=this.runWebGLProgram(f,[{dataId:t,shape:s,dtype:a}],a),h=this.readToGPU(d,e);return this.disposeIntermediateTensorInfo(d),h}if(l==null)throw o!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let c=this.decode(t,e.customTexShape),p=Bn().makeTensorFromTensorInfo(c),m=this.texData.get(c.dataId);return Object.assign({tensorRef:p},m.texture)}bufferSync(t){let e=this.readSync(t.dataId);if(t.dtype==="string")try{let n=e.map(o=>y.decodeString(o));return wt(t.shape,t.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return wt(t.shape,t.dtype,e)}checkNumericalProblems(t){if(t!=null)for(let e=0;e0}time(t){let e=this.activeTimers,n=[],o=!1;this.programTimersStack==null?(this.programTimersStack=n,o=!0):this.activeTimers.push(n),this.activeTimers=n,t();let s=y.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),i=y.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=e,o&&(this.programTimersStack=null);let a={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let u=await Promise.all(s);a.kernelMs=y.sum(u),a.getExtraProfileInfo=()=>u.map((l,c)=>({name:i[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else a.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,a})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(t){return L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),t):(t.endMs=y.now(),t)}async getQueryTime(t){if(L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(t);let e=t;return e.endMs-e.startMs}disposeData(t,e=!1){if(this.pendingDisposal.has(t))return!1;if(!this.texData.has(t))return!0;if(e?this.texData.get(t).refCount=0:this.texData.get(t).refCount--,!e&&this.texData.get(t).refCount>0)return!1;if(this.pendingRead.has(t))return this.pendingDisposal.add(t),this.pendingDeletes++,!1;this.releaseGPUData(t);let{complexTensorInfos:n}=this.texData.get(t);return n!=null&&(this.disposeData(n.real.dataId,e),this.disposeData(n.imag.dataId,e)),this.texData.delete(t),!0}releaseGPUData(t){let{texture:e,dtype:n,texShape:o,usage:s,isPacked:i,slice:a}=this.texData.get(t),u=a&&a.origDataId||t,l=this.dataRefCount.get(u);l>1?this.dataRefCount.set(u,l-1):(this.dataRefCount.delete(u),e!=null&&(this.numBytesInGPU-=this.computeBytes(o,n),this.textureManager.releaseTexture(e,o,s,i)));let c=this.texData.get(t);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture.texture}getDataInfo(t){return this.texData.get(t)}shouldExecuteOnCPU(t,e=Uot){return L().getBool("WEBGL_CPU_FORWARD")&&t.every(n=>this.texData.get(n.dataId).texture==null&&y.sizeFromShape(n.shape)0&&y.isString(n[0])){let s=n.map(i=>y.encodeString(i));o=this.write(s,t,e)}else o=this.write(n,t,e);return this.texData.get(o).usage=null,{dataId:o,shape:t,dtype:e}}makeOutput(t,e,n){return Bn().makeTensorFromTensorInfo(this.makeTensorInfo(t,e,n),this)}unpackTensor(t){let e=new nI(t.shape);return this.runWebGLProgram(e,[t],t.dtype)}packTensor(t){let e=new eI(t.shape);return this.runWebGLProgram(e,[t],t.dtype,null,!0)}packedReshape(t,e){let n=[Fl(t.shape),...Ol(t.shape)],o={dtype:t.dtype,shape:n,dataId:t.dataId},s=[Fl(e),...Ol(e)],i=new Ed(s,n),a=!0,u=[n],l=this.runWebGLProgram(i,[o],t.dtype,u,a);return{dataId:l.dataId,shape:e,dtype:l.dtype}}decode(t,e){let n=this.texData.get(t),{isPacked:o,shape:s,dtype:i}=n;if(e!=null){let m=y.sizeFromShape(s),f=e[0]*e[1]*4;y.assert(m<=f,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let a=wd(s),u;o?u=new Ww(a):u=new Gw(a);let l=!0,c=[e!=null?e:og(a)],p=this.runWebGLProgram(u,[{shape:a,dtype:i,dataId:t}],i,c,l,e);return{dtype:i,shape:s,dataId:p.dataId}}runWebGLProgram(t,e,n,o,s=!1,i){let a=this.makeTensorInfo(t.outputShape,n),u=this.texData.get(a.dataId);if(t.packedOutput&&(u.isPacked=!0),t.outPackingScheme===Wu.DENSE){let x=i!=null?i:og(t.outputShape);u.texShape=x.map(b=>b*2)}if(t.outTexUsage!=null&&(u.usage=t.outTexUsage),y.sizeFromShape(a.shape)===0)return u.values=y.getTypedArrayFromDType(a.dtype,0),a;let l=[],c=e.map(x=>{if(x.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let b=this.texData.get(x.dataId);if(b.texture==null){if(!t.packedInputs&&y.sizeFromShape(x.shape)<=L().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:x.shape,texData:null,isUniform:!0,uniformValues:b.values};t.packedInputs&&(b.isPacked=!0,b.shape=x.shape)}if(this.uploadToGPU(x.dataId),!!b.isPacked!=!!t.packedInputs)x=b.isPacked?this.unpackTensor(x):this.packTensor(x),l.push(x),b=this.texData.get(x.dataId);else if(b.isPacked&&!Uu(b.shape,x.shape)){let w=x,I=x.shape;x.shape=b.shape,x=this.packedReshape(x,I),l.push(x),b=this.texData.get(x.dataId),w.shape=I}return{shape:x.shape,texData:b,isUniform:!1}});this.uploadToGPU(a.dataId);let p={shape:a.shape,texData:u,isUniform:!1},m=GL(t,c,p),f=this.getAndSaveBinary(m,()=>BL(this.gpgpu,t,c,p)),d=this.activeTimers!=null,h;d&&(h=this.startTimer()),L().get("ENGINE_COMPILE_ONLY")||VL(this.gpgpu,f,c,p,o),l.forEach(x=>this.disposeIntermediateTensorInfo(x)),d&&(h=this.endTimer(h),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(h)}));let g=L().getNumber("WEBGL_FLUSH_THRESHOLD");if(g>0){let x=y.now();x-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=x)}if(!L().getBool("WEBGL_LAZILY_UNPACK")&&u.isPacked&&s===!1){let x=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),x}return a}compileAndRun(t,e,n,o,s=!1){return n=n||e[0].dtype,this.runWebGLProgram(t,e,n,o,s)}getAndSaveBinary(t,e){return t in this.binaryCache||(this.binaryCache[t]=e()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(L().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=B(()=>{if(!L().get("WEBGL_RENDER_FLOAT32_ENABLED")){let t=L().getBool("DEBUG");L().set("DEBUG",!1);let e=this.abs(pt(1e-8)).dataSync()[0];if(L().set("DEBUG",t),e>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?Vot:Got}uploadToGPU(t){let e=this.texData.get(t),{shape:n,dtype:o,values:s,texture:i,usage:a,isPacked:u}=e;if(i!=null)return;let l=this.activeTimers!=null,c;l&&(c=y.now());let p=e.texShape;if(p==null&&(p=t1(n,u),e.texShape=p),s!=null){let m=wd(n),f,d=p[1],h=p[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(u||!g)&&([d,h]=wa(p[0],p[1])),u?f=new qw(m,g):f=new lg(m,g);let x=g?[h,d]:p,b=this.makeTensorInfo(x,o),w=this.texData.get(b.dataId);g?w.usage=Yr.PIXELS:w.usage=Yr.UPLOAD,w.texShape=x,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),d,h,s);let I=[[h,d]],E=this.runWebGLProgram(f,[b],o,I,!0),A=this.texData.get(E.dataId);e.texShape=A.texShape,e.isPacked=A.isPacked,e.usage=A.usage,L().get("ENGINE_COMPILE_ONLY")?this.disposeData(E.dataId):(e.texture=A.texture,e.values=null,this.texData.delete(E.dataId)),this.disposeIntermediateTensorInfo(b),l&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(p,a,o,u);e.texture=m}}convertAndCacheOnCPU(t,e){let n=this.texData.get(t),{dtype:o}=n;return e!=null&&(n.values=Kot(e,o)),n.values}acquireTexture(t,e,n,o){if(this.numBytesInGPU+=this.computeBytes(t,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(t,e,o)}computeBytes(t,e){return t[0]*t[1]*y.bytesPerElement(e)}checkCompileCompletion(){for(let[,t]of Object.entries(this.binaryCache))this.checkCompletion_(t)}async checkCompileCompletionAsync(){let t=[];if(this.gpgpu.parallelCompilationExtension){for(let[,e]of Object.entries(this.binaryCache))t.push(this.checkCompletionAsync_(e));return Promise.all(t)}else{for(let[,e]of Object.entries(this.binaryCache)){let n=new Promise(o=>{try{this.checkCompletion_(e),o(!0)}catch(s){throw s}});t.push(n)}return Promise.all(t)}}async checkCompletionAsync_(t){return this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(t):(await Ih(),this.checkCompletionAsync_(t))}checkCompletion_(t){if(this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(t.webGLProgram)),this.gpgpu.gl.getShaderParameter(t.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(Mw(t.source,this.gpgpu.gl.getShaderInfoLog(t.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let t of Object.values(this.binaryCache)){this.gpgpu.buildVao(t.webGLProgram);let{variablesLocations:e,customUniformLocations:n,infLoc:o,nanLoc:s,outShapeLocation:i,outShapeStridesLocation:a,outTexShapeLocation:u}=a1(this.gpgpu,t.program,t.webGLProgram);t.variablesLocations=e,t.customUniformLocations=n,t.infLoc=o,t.nanLoc=s,t.outShapeLocation=i,t.outShapeStridesLocation=a,t.outTexShapeLocation=u}}createTensorFromGPUData(t,e,n){t.channels=t.channels||"RGBA";let{texture:o,height:s,width:i,channels:a}=t,u=Bn().backend;if(!u.gpgpu.gl.isTexture(o))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let l=u.writeTexture(o,e,n,s,i,a);return Bn().makeTensorFromDataId(l,e,n,u)}};Ad.nextDataId=0;function Kot(r,t){if(t==="float32"||t==="complex64")return r;if(t==="int32"||t==="bool"){let e=t==="int32"?new Int32Array(r.length):new Uint8Array(r.length);for(let n=0;nnew Ad,2);var N$e={forceHalfFloat:Yz};var Dd=` + `}};var Uot=Xr.whereImpl,Hot=1e-7,qot=1e-4,iI={};function Kot(r){return r in iI||(iI[r]={}),iI[r]}var jot=L().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),Xot=600;function Yot(){return L().global.screen==null?1024:L().global.screen.height*L().global.screen.width*window.devicePixelRatio*Xot/1024/1024}var Dd=class r extends Bo{nextDataId(){return r.nextDataId++}constructor(t){if(super(),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,!L().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let e;if(t!=null){if(t instanceof pp)e=t;else{let n=qn(L().getNumber("WEBGL_VERSION"),t);e=new pp(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=qn(L().getNumber("WEBGL_VERSION"));e=new pp(n),this.binaryCache=Kot(L().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=e,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new oI(this.gpgpu),this.numMBBeforeWarning=Yot(),this.texData=new Ta(this,Bn())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(t,e,n,o,s,i){let a=this.makeTensorInfo(e,n),u=this.texData.get(a.dataId);u.isPacked=!1,u.texture={texture:t,texShape:[o,s]},u.texShape=[o,s];let l=Id(e),c=new ug(l,!1,i),p=this.runWebGLProgram(c,[a],n,[[o,s]]);return p.shape=e,u.texture=null,this.disposeIntermediateTensorInfo(a),p.dataId}write(t,e,n){if((L().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||L().getBool("DEBUG"))&&this.checkNumericalProblems(t),n==="complex64"&&t!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let o={id:this.nextDataId()};return this.texData.set(o,{shape:e,dtype:n,values:t,usage:Jr.UPLOAD,refCount:1}),o}refCount(t){return this.texData.has(t)?this.texData.get(t).refCount:0}incRef(t){let e=this.texData.get(t);e.refCount++}decRef(t){if(this.texData.has(t)){let e=this.texData.get(t);e.refCount--}}move(t,e,n,o,s){if(L().getBool("DEBUG")&&this.checkNumericalProblems(e),o==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(t,{shape:n,dtype:o,values:e,usage:Jr.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(t){this.disposeData(t.dataId)}readSync(t){let e=this.texData.get(t),{values:n,dtype:o,complexTensorInfos:s,slice:i,shape:a,isPacked:u}=e;if(i!=null){let m;u?m=new Dn(a,Ia):m=new zr(a,Ia);let f=this.runWebGLProgram(m,[{dataId:t,shape:a,dtype:o}],o),d=this.readSync(f.dataId);return this.disposeIntermediateTensorInfo(f),d}if(n!=null)return this.convertAndCacheOnCPU(t);if(o==="string")return n;let l=this.activeTimers!=null,c;l&&(c=y.now());let p;if(o==="complex64"){let m=this.readSync(s.real.dataId),f=this.readSync(s.imag.dataId);p=S.mergeRealAndImagArrays(m,f)}else p=this.getValuesFromTexture(t);return l&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(t,p)}async read(t){if(this.pendingRead.has(t)){let d=this.pendingRead.get(t);return new Promise(h=>d.push(h))}let e=this.texData.get(t),{values:n,shape:o,slice:s,dtype:i,complexTensorInfos:a,isPacked:u}=e;if(s!=null){let d;u?d=new Dn(o,Ia):d=new zr(o,Ia);let h=this.runWebGLProgram(d,[{dataId:t,shape:o,dtype:i}],i),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(n!=null)return this.convertAndCacheOnCPU(t);if(L().getBool("DEBUG")&&!L().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&L().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,c;if(i!=="complex64"&&L().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(t);let d=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(d.texture.texture,...sg(o))}this.pendingRead.set(t,[]),i!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(i==="complex64"){let d=await Promise.all([this.read(a.real.dataId),this.read(a.imag.dataId)]),h=d[0],g=d[1];p=S.mergeRealAndImagArrays(h,g)}else if(l==null)p=this.getValuesFromTexture(t);else{let d=y.sizeFromShape(o);p=this.gpgpu.downloadFloat32MatrixFromBuffer(l,d)}if(c!=null&&this.disposeIntermediateTensorInfo(c),l!=null){let d=this.gpgpu.gl;ht(d,()=>d.deleteBuffer(l))}let m=this.convertAndCacheOnCPU(t,p),f=this.pendingRead.get(t);return this.pendingRead.delete(t),f.forEach(d=>d(m)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t)&&Bn().removeDataId(t,this),this.pendingDeletes--),m}readToGPU(t,e={}){let n=this.texData.get(t),{values:o,shape:s,slice:i,dtype:a,isPacked:u,texture:l}=n;if(a==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(i!=null){let f;u?f=new Dn(s,Ia):f=new zr(s,Ia);let d=this.runWebGLProgram(f,[{dataId:t,shape:s,dtype:a}],a),h=this.readToGPU(d,e);return this.disposeIntermediateTensorInfo(d),h}if(l==null)throw o!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let c=this.decode(t,e.customTexShape),p=Bn().makeTensorFromTensorInfo(c),m=this.texData.get(c.dataId);return Object.assign({tensorRef:p},m.texture)}bufferSync(t){let e=this.readSync(t.dataId);if(t.dtype==="string")try{let n=e.map(o=>y.decodeString(o));return wt(t.shape,t.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return wt(t.shape,t.dtype,e)}checkNumericalProblems(t){if(t!=null)for(let e=0;e0}time(t){let e=this.activeTimers,n=[],o=!1;this.programTimersStack==null?(this.programTimersStack=n,o=!0):this.activeTimers.push(n),this.activeTimers=n,t();let s=y.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),i=y.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=e,o&&(this.programTimersStack=null);let a={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let u=await Promise.all(s);a.kernelMs=y.sum(u),a.getExtraProfileInfo=()=>u.map((l,c)=>({name:i[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else a.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,a})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(t){return L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),t):(t.endMs=y.now(),t)}async getQueryTime(t){if(L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(t);let e=t;return e.endMs-e.startMs}disposeData(t,e=!1){if(this.pendingDisposal.has(t))return!1;if(!this.texData.has(t))return!0;if(e?this.texData.get(t).refCount=0:this.texData.get(t).refCount--,!e&&this.texData.get(t).refCount>0)return!1;if(this.pendingRead.has(t))return this.pendingDisposal.add(t),this.pendingDeletes++,!1;this.releaseGPUData(t);let{complexTensorInfos:n}=this.texData.get(t);return n!=null&&(this.disposeData(n.real.dataId,e),this.disposeData(n.imag.dataId,e)),this.texData.delete(t),!0}releaseGPUData(t){let{texture:e,dtype:n,texShape:o,usage:s,isPacked:i,slice:a}=this.texData.get(t),u=a&&a.origDataId||t,l=this.dataRefCount.get(u);l>1?this.dataRefCount.set(u,l-1):(this.dataRefCount.delete(u),e!=null&&(this.numBytesInGPU-=this.computeBytes(o,n),this.textureManager.releaseTexture(e,o,s,i)));let c=this.texData.get(t);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture.texture}getDataInfo(t){return this.texData.get(t)}shouldExecuteOnCPU(t,e=jot){return L().getBool("WEBGL_CPU_FORWARD")&&t.every(n=>this.texData.get(n.dataId).texture==null&&y.sizeFromShape(n.shape)0&&y.isString(n[0])){let s=n.map(i=>y.encodeString(i));o=this.write(s,t,e)}else o=this.write(n,t,e);return this.texData.get(o).usage=null,{dataId:o,shape:t,dtype:e}}makeOutput(t,e,n){return Bn().makeTensorFromTensorInfo(this.makeTensorInfo(t,e,n),this)}unpackTensor(t){let e=new sI(t.shape);return this.runWebGLProgram(e,[t],t.dtype)}packTensor(t){let e=new nI(t.shape);return this.runWebGLProgram(e,[t],t.dtype,null,!0)}packedReshape(t,e){let n=[Fl(t.shape),...Ol(t.shape)],o={dtype:t.dtype,shape:n,dataId:t.dataId},s=[Fl(e),...Ol(e)],i=new Ad(s,n),a=!0,u=[n],l=this.runWebGLProgram(i,[o],t.dtype,u,a);return{dataId:l.dataId,shape:e,dtype:l.dtype}}decode(t,e){let n=this.texData.get(t),{isPacked:o,shape:s,dtype:i}=n;if(e!=null){let m=y.sizeFromShape(s),f=e[0]*e[1]*4;y.assert(m<=f,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let a=Id(s),u;o?u=new Hw(a):u=new Uw(a);let l=!0,c=[e!=null?e:sg(a)],p=this.runWebGLProgram(u,[{shape:a,dtype:i,dataId:t}],i,c,l,e);return{dtype:i,shape:s,dataId:p.dataId}}runWebGLProgram(t,e,n,o,s=!1,i){let a=this.makeTensorInfo(t.outputShape,n),u=this.texData.get(a.dataId);if(t.packedOutput&&(u.isPacked=!0),t.outPackingScheme===Wu.DENSE){let x=i!=null?i:sg(t.outputShape);u.texShape=x.map(b=>b*2)}if(t.outTexUsage!=null&&(u.usage=t.outTexUsage),y.sizeFromShape(a.shape)===0)return u.values=y.getTypedArrayFromDType(a.dtype,0),a;let l=[],c=e.map(x=>{if(x.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let b=this.texData.get(x.dataId);if(b.texture==null){if(!t.packedInputs&&y.sizeFromShape(x.shape)<=L().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:x.shape,texData:null,isUniform:!0,uniformValues:b.values};t.packedInputs&&(b.isPacked=!0,b.shape=x.shape)}if(this.uploadToGPU(x.dataId),!!b.isPacked!=!!t.packedInputs)x=b.isPacked?this.unpackTensor(x):this.packTensor(x),l.push(x),b=this.texData.get(x.dataId);else if(b.isPacked&&!Uu(b.shape,x.shape)){let w=x,I=x.shape;x.shape=b.shape,x=this.packedReshape(x,I),l.push(x),b=this.texData.get(x.dataId),w.shape=I}return{shape:x.shape,texData:b,isUniform:!1}});this.uploadToGPU(a.dataId);let p={shape:a.shape,texData:u,isUniform:!1},m=HL(t,c,p),f=this.getAndSaveBinary(m,()=>WL(this.gpgpu,t,c,p)),d=this.activeTimers!=null,h;d&&(h=this.startTimer()),L().get("ENGINE_COMPILE_ONLY")||UL(this.gpgpu,f,c,p,o),l.forEach(x=>this.disposeIntermediateTensorInfo(x)),d&&(h=this.endTimer(h),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(h)}));let g=L().getNumber("WEBGL_FLUSH_THRESHOLD");if(g>0){let x=y.now();x-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=x)}if(!L().getBool("WEBGL_LAZILY_UNPACK")&&u.isPacked&&s===!1){let x=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),x}return a}compileAndRun(t,e,n,o,s=!1){return n=n||e[0].dtype,this.runWebGLProgram(t,e,n,o,s)}getAndSaveBinary(t,e){return t in this.binaryCache||(this.binaryCache[t]=e()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(L().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=B(()=>{if(!L().get("WEBGL_RENDER_FLOAT32_ENABLED")){let t=L().getBool("DEBUG");L().set("DEBUG",!1);let e=this.abs(pt(1e-8)).dataSync()[0];if(L().set("DEBUG",t),e>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?Hot:qot}uploadToGPU(t){let e=this.texData.get(t),{shape:n,dtype:o,values:s,texture:i,usage:a,isPacked:u}=e;if(i!=null)return;let l=this.activeTimers!=null,c;l&&(c=y.now());let p=e.texShape;if(p==null&&(p=r1(n,u),e.texShape=p),s!=null){let m=Id(n),f,d=p[1],h=p[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(u||!g)&&([d,h]=wa(p[0],p[1])),u?f=new jw(m,g):f=new ug(m,g);let x=g?[h,d]:p,b=this.makeTensorInfo(x,o),w=this.texData.get(b.dataId);g?w.usage=Jr.PIXELS:w.usage=Jr.UPLOAD,w.texShape=x,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),d,h,s);let I=[[h,d]],E=this.runWebGLProgram(f,[b],o,I,!0),A=this.texData.get(E.dataId);e.texShape=A.texShape,e.isPacked=A.isPacked,e.usage=A.usage,L().get("ENGINE_COMPILE_ONLY")?this.disposeData(E.dataId):(e.texture=A.texture,e.values=null,this.texData.delete(E.dataId)),this.disposeIntermediateTensorInfo(b),l&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(p,a,o,u);e.texture=m}}convertAndCacheOnCPU(t,e){let n=this.texData.get(t),{dtype:o}=n;return e!=null&&(n.values=Zot(e,o)),n.values}acquireTexture(t,e,n,o){if(this.numBytesInGPU+=this.computeBytes(t,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(t,e,o)}computeBytes(t,e){return t[0]*t[1]*y.bytesPerElement(e)}checkCompileCompletion(){for(let[,t]of Object.entries(this.binaryCache))this.checkCompletion_(t)}async checkCompileCompletionAsync(){let t=[];if(this.gpgpu.parallelCompilationExtension){for(let[,e]of Object.entries(this.binaryCache))t.push(this.checkCompletionAsync_(e));return Promise.all(t)}else{for(let[,e]of Object.entries(this.binaryCache)){let n=new Promise(o=>{try{this.checkCompletion_(e),o(!0)}catch(s){throw s}});t.push(n)}return Promise.all(t)}}async checkCompletionAsync_(t){return this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(t):(await Ch(),this.checkCompletionAsync_(t))}checkCompletion_(t){if(this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(t.webGLProgram)),this.gpgpu.gl.getShaderParameter(t.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(Lw(t.source,this.gpgpu.gl.getShaderInfoLog(t.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let t of Object.values(this.binaryCache)){this.gpgpu.buildVao(t.webGLProgram);let{variablesLocations:e,customUniformLocations:n,infLoc:o,nanLoc:s,outShapeLocation:i,outShapeStridesLocation:a,outTexShapeLocation:u}=u1(this.gpgpu,t.program,t.webGLProgram);t.variablesLocations=e,t.customUniformLocations=n,t.infLoc=o,t.nanLoc=s,t.outShapeLocation=i,t.outShapeStridesLocation=a,t.outTexShapeLocation=u}}createTensorFromGPUData(t,e,n){t.channels=t.channels||"RGBA";let{texture:o,height:s,width:i,channels:a}=t,u=Bn().backend;if(!u.gpgpu.gl.isTexture(o))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let l=u.writeTexture(o,e,n,s,i,a);return Bn().makeTensorFromDataId(l,e,n,u)}};Dd.nextDataId=0;function Zot(r,t){if(t==="float32"||t==="complex64")return r;if(t==="int32"||t==="bool"){let e=t==="int32"?new Int32Array(r.length):new Uint8Array(r.length);for(let n=0;nnew Dd,2);var T$e={forceHalfFloat:Qz};var $d=` if (isnan(a)) return a; if (isnan(b)) return b; `;var $n=class{constructor(t,e,n){this.variableNames=["A","B"],this.outputShape=S.assertAndGetBroadcastShape(e,n),this.enableShapeUniforms=he(this.outputShape.length),this.userCode=` @@ -1303,13 +1303,13 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(result); } - `}};function nr(r){let{inputs:t,backend:e}=r,{x:n}=t;return e.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var Zz={kernelName:go,backendName:"webgl",kernelFunc:nr};function Rn(r){let{inputs:t,backend:e}=r,{real:n,imag:o}=t,s=e.makeTensorInfo(n.shape,"complex64"),i=e.texData.get(s.dataId),a=nr({inputs:{x:n},backend:e}),u=nr({inputs:{x:o},backend:e});return i.complexTensorInfos={real:a,imag:u},s}var Jz={kernelName:Ap,backendName:"webgl",kernelFunc:Rn};var T1="return (a < 0.) ? b * a : a;",_1=` + `}};function nr(r){let{inputs:t,backend:e}=r,{x:n}=t;return e.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var t3={kernelName:go,backendName:"webgl",kernelFunc:nr};function Rn(r){let{inputs:t,backend:e}=r,{real:n,imag:o}=t,s=e.makeTensorInfo(n.shape,"complex64"),i=e.texData.get(s.dataId),a=nr({inputs:{x:n},backend:e}),u=nr({inputs:{x:o},backend:e});return i.complexTensorInfos={real:a,imag:u},s}var e3={kernelName:Ap,backendName:"webgl",kernelFunc:Rn};var E1="return (a < 0.) ? b * a : a;",A1=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function jot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{alpha:s}=n,i=e.makeTensorInfo([],"float32",y.createScalarValue(s,"float32")),a=L().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new jn(_1,o.shape,i.shape):new $n(T1,o.shape,i.shape),u=e.runWebGLProgram(a,[o,i],"float32");return e.disposeIntermediateTensorInfo(i),u}var Qz={kernelName:bs,backendName:"webgl",kernelFunc:jot};var E1="return (a < 0.) ? b * a : a;",A1=` +`;function Jot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{alpha:s}=n,i=e.makeTensorInfo([],"float32",y.createScalarValue(s,"float32")),a=L().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new jn(A1,o.shape,i.shape):new $n(E1,o.shape,i.shape),u=e.runWebGLProgram(a,[o,i],"float32");return e.disposeIntermediateTensorInfo(i),u}var r3={kernelName:bs,backendName:"webgl",kernelFunc:Jot};var D1="return (a < 0.) ? b * a : a;",$1=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function Xot(r){let{inputs:t,backend:e}=r,{x:n,alpha:o}=t,s=L().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new jn(A1,n.shape,o.shape):new $n(E1,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],"float32")}var t3={kernelName:Os,backendName:"webgl",kernelFunc:Xot};var Po="if (isnan(x)) return x;";function It({opSnippet:r,packedOpSnippet:t,cpuKernelImpl:e,dtype:n}){return({inputs:o,backend:s})=>{let{x:i}=o,a=s,u=n||i.dtype;if(a.shouldExecuteOnCPU([i])&&e!=null){let p=a.texData.get(i.dataId),m=e(p.values,u);return a.makeTensorInfo(i.shape,u,m)}let l=L().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return l?c=new Dn(i.shape,t):c=new Lr(i.shape,r),a.runWebGLProgram(c,[i],u)}}function ce({opSnippet:r,packedOpSnippet:t,checkOutOfBounds:e=!1,supportsComplex:n=!1,cpuKernelImpl:o,dtype:s}){return({inputs:i,backend:a})=>{let{a:u,b:l}=i,c=a;if(n&&u.dtype==="complex64"){let d=c.texData.get(u.dataId),h=c.texData.get(l.dataId),[g,x]=[[d.complexTensorInfos.real,h.complexTensorInfos.real],[d.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(w=>{let[I,N]=w,E={dataId:I.dataId,dtype:I.dtype,shape:u.shape},A={dataId:N.dataId,dtype:N.dtype,shape:l.shape},D=new $n(r,u.shape,l.shape);return c.runWebGLProgram(D,[E,A],ur(I.dtype,N.dtype))}),b=Rn({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let p=s||ur(u.dtype,l.dtype);if((u.dtype==="string"||l.dtype==="string"||c.shouldExecuteOnCPU([u,l]))&&o!=null){let d=c.texData.get(u.dataId).values,h=c.texData.get(l.dataId).values,g=u.dtype==="string"?S.fromUint8ToStringArray(d):d,x=u.dtype==="string"?S.fromUint8ToStringArray(h):h,[b,w]=o(u.shape,l.shape,g,x,p),I=c.makeTensorInfo(w,p),N=c.texData.get(I.dataId);return N.values=b,I}let m=L().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,f;return m?f=new jn(t,u.shape,l.shape,e):f=new $n(r,u.shape,l.shape),c.runWebGLProgram(f,[u,l],p)}}function Ml(r,t=!1){if(r==="linear")return t?Uz:Lz;if(r==="relu")return t?qz:Bz;if(r==="elu")return t?Hz:zz;if(r==="relu6")return t?Kz:Vz;if(r==="prelu")return t?A1:E1;if(r==="leakyrelu")return t?_1:T1;if(r==="sigmoid")return t?jz:Gz;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var $d=class{constructor(t,e,n,o=!1,s=!1,i=!1,a=null,u=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=he(this.outputShape.length);let c=o?t[1]:t[2],p=Math.ceil(c/2),m=o?"i * 2, rc.y":"rc.y, i * 2",f=s?"rc.z, i * 2":"i * 2, rc.z",d=o?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],h=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],g="",x="";a&&(u?g=`vec4 activation(vec4 a) { +`;function Qot(r){let{inputs:t,backend:e}=r,{x:n,alpha:o}=t,s=L().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new jn($1,n.shape,o.shape):new $n(D1,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],"float32")}var n3={kernelName:Os,backendName:"webgl",kernelFunc:Qot};var Po="if (isnan(x)) return x;";function It({opSnippet:r,packedOpSnippet:t,cpuKernelImpl:e,dtype:n}){return({inputs:o,backend:s})=>{let{x:i}=o,a=s,u=n||i.dtype;if(a.shouldExecuteOnCPU([i])&&e!=null){let p=a.texData.get(i.dataId),m=e(p.values,u);return a.makeTensorInfo(i.shape,u,m)}let l=L().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return l?c=new Dn(i.shape,t):c=new zr(i.shape,r),a.runWebGLProgram(c,[i],u)}}function ce({opSnippet:r,packedOpSnippet:t,checkOutOfBounds:e=!1,supportsComplex:n=!1,cpuKernelImpl:o,dtype:s}){return({inputs:i,backend:a})=>{let{a:u,b:l}=i,c=a;if(n&&u.dtype==="complex64"){let d=c.texData.get(u.dataId),h=c.texData.get(l.dataId),[g,x]=[[d.complexTensorInfos.real,h.complexTensorInfos.real],[d.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(w=>{let[I,N]=w,E={dataId:I.dataId,dtype:I.dtype,shape:u.shape},A={dataId:N.dataId,dtype:N.dtype,shape:l.shape},D=new $n(r,u.shape,l.shape);return c.runWebGLProgram(D,[E,A],ur(I.dtype,N.dtype))}),b=Rn({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let p=s||ur(u.dtype,l.dtype);if((u.dtype==="string"||l.dtype==="string"||c.shouldExecuteOnCPU([u,l]))&&o!=null){let d=c.texData.get(u.dataId).values,h=c.texData.get(l.dataId).values,g=u.dtype==="string"?S.fromUint8ToStringArray(d):d,x=u.dtype==="string"?S.fromUint8ToStringArray(h):h,[b,w]=o(u.shape,l.shape,g,x,p),I=c.makeTensorInfo(w,p),N=c.texData.get(I.dataId);return N.values=b,I}let m=L().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,f;return m?f=new jn(t,u.shape,l.shape,e):f=new $n(r,u.shape,l.shape),c.runWebGLProgram(f,[u,l],p)}}function Ml(r,t=!1){if(r==="linear")return t?Kz:Vz;if(r==="relu")return t?Xz:Wz;if(r==="elu")return t?jz:Gz;if(r==="relu6")return t?Yz:Uz;if(r==="prelu")return t?$1:D1;if(r==="leakyrelu")return t?A1:E1;if(r==="sigmoid")return t?Zz:Hz;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var Rd=class{constructor(t,e,n,o=!1,s=!1,i=!1,a=null,u=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=he(this.outputShape.length);let c=o?t[1]:t[2],p=Math.ceil(c/2),m=o?"i * 2, rc.y":"rc.y, i * 2",f=s?"rc.z, i * 2":"i * 2, rc.z",d=o?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],h=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],g="",x="";a&&(u?g=`vec4 activation(vec4 a) { vec4 b = getPreluActivationWeightsAtOutCoords(); ${a} }`:l?g=`vec4 activation(vec4 a) { @@ -1348,7 +1348,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(result); } - `}};var D1={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},cg=class{constructor(t,e,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=S.assertAndGetBroadcastShape(e,n),this.userCode=` + `}};var R1={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},pg=class{constructor(t,e,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=S.assertAndGetBroadcastShape(e,n),this.userCode=` float binaryOpComplex( float areal, float aimag, float breal, float bimag) { ${t} @@ -1361,7 +1361,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float bimag = getBImagAtOutCoords(); setOutput(binaryOpComplex(areal, aimag, breal, bimag)); } - `}};var e3="return a * b;";function pg(r){let{inputs:t,backend:e}=r,{a:n,b:o}=t,s=S.upcastType(n.dtype,o.dtype);if(n.dtype==="complex64"){let a=e.texData.get(n.dataId),u=e.texData.get(o.dataId),l=new cg(D1.REAL,n.shape,o.shape),c=new cg(D1.IMAG,n.shape,o.shape),p=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:n.shape},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:n.shape},{dataId:u.complexTensorInfos.real.dataId,dtype:u.complexTensorInfos.real.dtype,shape:o.shape},{dataId:u.complexTensorInfos.imag.dataId,dtype:u.complexTensorInfos.imag.dtype,shape:o.shape}],m=e.runWebGLProgram(l,p,"float32"),f=e.runWebGLProgram(c,p,"float32"),d=Rn({inputs:{real:m,imag:f},backend:e});return e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),d}if(e.shouldExecuteOnCPU([n,o])){let a=e.texData.get(n.dataId),u=e.texData.get(o.dataId),[l,c]=cz(n.shape,o.shape,a.values,u.values,s),p=e.makeTensorInfo(c,s),m=e.texData.get(p.dataId);return m.values=l,p}let i;return L().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new jn(e3,n.shape,o.shape):i=new $n(e3,n.shape,o.shape),e.runWebGLProgram(i,[n,o],s)}var r3={kernelName:Ds,backendName:"webgl",kernelFunc:pg};function n3(r,t,e){let n=[Fl(r.shape),...Ol(r.shape)],o={dtype:r.dtype,shape:n,dataId:r.dataId},s=[Fl(t),...Ol(t)],i=new Ed(s,n),a=!0,u=[n],l=e.runWebGLProgram(i,[o],r.dtype,u,a);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function rt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{shape:s}=n,i=e,a=y.sizeFromShape(o.shape),u=y.inferFromImplicitShape(s,a),l=y.sizeFromShape(u);y.assert(a===l,()=>`The new shape (${u}) has ${l} elements and the old shape (${o.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`);let c=i.texData.get(o.dataId);return c.isPacked&&!Uu(o.shape,u)&&!(c.texture!==null&&Uu(c.shape,u))?n3(o,u,i):(i.incRef(o.dataId),{dataId:o.dataId,shape:u,dtype:o.dtype})}var o3={kernelName:Vi,backendName:"webgl",kernelFunc:rt};var mg=class{constructor(t,e){this.variableNames=["x"];let{windowSize:n,batchSize:o,inSize:s,outSize:i}=t;this.outputShape=[o,i];let a=Math.floor(n/4)*4,u=n%4,l="sumValue += dot(values, ones);";if(e!=null){let p=1/e;l=`sumValue += dot(values * ${y.isInt(p)?p.toPrecision(2):p}, ones);`}let c="";s%n>0&&(c=` + `}};var o3="return a * b;";function mg(r){let{inputs:t,backend:e}=r,{a:n,b:o}=t,s=S.upcastType(n.dtype,o.dtype);if(n.dtype==="complex64"){let a=e.texData.get(n.dataId),u=e.texData.get(o.dataId),l=new pg(R1.REAL,n.shape,o.shape),c=new pg(R1.IMAG,n.shape,o.shape),p=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:n.shape},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:n.shape},{dataId:u.complexTensorInfos.real.dataId,dtype:u.complexTensorInfos.real.dtype,shape:o.shape},{dataId:u.complexTensorInfos.imag.dataId,dtype:u.complexTensorInfos.imag.dtype,shape:o.shape}],m=e.runWebGLProgram(l,p,"float32"),f=e.runWebGLProgram(c,p,"float32"),d=Rn({inputs:{real:m,imag:f},backend:e});return e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),d}if(e.shouldExecuteOnCPU([n,o])){let a=e.texData.get(n.dataId),u=e.texData.get(o.dataId),[l,c]=fz(n.shape,o.shape,a.values,u.values,s),p=e.makeTensorInfo(c,s),m=e.texData.get(p.dataId);return m.values=l,p}let i;return L().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new jn(o3,n.shape,o.shape):i=new $n(o3,n.shape,o.shape),e.runWebGLProgram(i,[n,o],s)}var s3={kernelName:Ds,backendName:"webgl",kernelFunc:mg};function i3(r,t,e){let n=[Fl(r.shape),...Ol(r.shape)],o={dtype:r.dtype,shape:n,dataId:r.dataId},s=[Fl(t),...Ol(t)],i=new Ad(s,n),a=!0,u=[n],l=e.runWebGLProgram(i,[o],r.dtype,u,a);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function rt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{shape:s}=n,i=e,a=y.sizeFromShape(o.shape),u=y.inferFromImplicitShape(s,a),l=y.sizeFromShape(u);y.assert(a===l,()=>`The new shape (${u}) has ${l} elements and the old shape (${o.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`);let c=i.texData.get(o.dataId);return c.isPacked&&!Uu(o.shape,u)&&!(c.texture!==null&&Uu(c.shape,u))?i3(o,u,i):(i.incRef(o.dataId),{dataId:o.dataId,shape:u,dtype:o.dtype})}var a3={kernelName:Gi,backendName:"webgl",kernelFunc:rt};var fg=class{constructor(t,e){this.variableNames=["x"];let{windowSize:n,batchSize:o,inSize:s,outSize:i}=t;this.outputShape=[o,i];let a=Math.floor(n/4)*4,u=n%4,l="sumValue += dot(values, ones);";if(e!=null){let p=1/e;l=`sumValue += dot(values * ${y.isInt(p)?p.toPrecision(2):p}, ones);`}let c="";s%n>0&&(c=` if (inIdx < 0 || inIdx >= ${s}) { return 0.0; } @@ -1414,7 +1414,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, } setOutput(sumValue); } - `}};var sI=class{constructor(t,e){this.variableNames=["x"];let{windowSize:n,batchSize:o,inSize:s,outSize:i}=t;this.outputShape=[o,i];let a="0.0",u="";e==="prod"?a="1.0":e==="min"?(a="1.0 / 1e-20",u="min"):e==="max"&&(a="-1.0 / 1e-20",u="max");let l=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="sum"?l="sumValue":e==="prod"?l="prodValue":e==="all"?l="allValue":e==="any"&&(l="anyValue");let c=Math.floor(n/4)*4,p=n%4,m=` + `}};var aI=class{constructor(t,e){this.variableNames=["x"];let{windowSize:n,batchSize:o,inSize:s,outSize:i}=t;this.outputShape=[o,i];let a="0.0",u="";e==="prod"?a="1.0":e==="min"?(a="1.0 / 1e-20",u="min"):e==="max"&&(a="-1.0 / 1e-20",u="max");let l=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="sum"?l="sumValue":e==="prod"?l="prodValue":e==="all"?l="allValue":e==="any"&&(l="anyValue");let c=Math.floor(n/4)*4,p=n%4,m=` if (${e==="sum"}) { sumValue += dot(values, ones); } else if (${e==="prod"}) { @@ -1506,12 +1506,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, } setOutput(${l}); } - `}};function Zot(r){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let e=t.length?t[t.length-1].outSize:r[1],n=S.computeOptimalWindowSize(e);t.push({inSize:e,windowSize:n,outSize:Math.ceil(e/n)})}return t}function Yn(r,t,e,n){let o=Zot(r.shape),s=r;for(let i=0;i6)throw Error(`Transpose for rank ${t} is not yet supported`);let e=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],n=new Array(t);for(let o=0;o6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let o=zt(this.rank),s=N1("rc",this.rank),i=new Array(this.rank);for(let c=0;c6)throw Error(`Transpose for rank ${t} is not yet supported`);let e=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],n=new Array(t);for(let o=0;o6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let o=zt(this.rank),s=T1("rc",this.rank),i=new Array(this.rank);for(let c=0;c`Error in matMul: inner shapes (${p}) and (${m}) of Tensors with shapes ${r.shape} and ${t.shape} and transposeA=${e} and transposeB=${n} must match.`);let N=e?[x,p,f]:[x,f,p],E=n?[b,d,m]:[b,m,d],A=rt({inputs:{x:r},backend:o,attrs:{shape:N}}),D=rt({inputs:{x:t},backend:o,attrs:{shape:E}}),F=[A,D],M=Math.max(x,b),V=e?A.shape[1]:A.shape[2],G=s!=null,W=i!=null,q=u==="leakyrelu",H=u!=null?Ml(u,!0):null,K=G||W||q||H!=null,Y;if((f===1||d===1)&&V>$1&&K===!1){let et=A,nt=D;e&&(et=Pe({inputs:{x:A},backend:o,attrs:{perm:[0,2,1]}}),F.push(et)),n&&(nt=Pe({inputs:{x:D},backend:o,attrs:{perm:[0,2,1]}}),F.push(nt));let st=d!==1,lt=d===1,ot=et;st&&(ot=rt({inputs:{x:et},backend:o,attrs:{shape:[M,V,1]}}),F.push(ot));let it=d===1?2:1,ft=nt;lt&&(ft=rt({inputs:{x:nt},backend:o,attrs:{shape:[M,1,V]}}),F.push(ft));let gt=pg({inputs:{a:ot,b:ft},backend:o});Y=fp({inputs:{x:gt},backend:o,attrs:{axis:it,keepDims:!0}}),F.push(gt)}else{let et=ur(r.dtype,t.dtype),nt=new $d(N,E,[M,f,d],e,n,G,H,W,q),st=[A,D];if(s!=null&&st.push(s),W&&st.push(i),q){let lt=o.makeTensorInfo([],"float32",y.createScalarValue(a,"float32"));st.push(lt),F.push(lt)}Y=o.runWebGLProgram(nt,st,et)}let Z=rt({inputs:{x:Y},backend:o,attrs:{shape:I}});F.push(Y);for(let et of F)o.disposeIntermediateTensorInfo(et);return Z}function Qot(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s,bias:i,preluActivationWeights:a}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=n;return dp({a:o,b:s,transposeA:u,transposeB:l,backend:e,bias:i,preluActivationWeights:a,leakyreluAlpha:p,activation:c})}var l3={kernelName:ji,backendName:"webgl",kernelFunc:Qot};var u3="return abs(x);";function tst(r){let{inputs:t,backend:e}=r,{x:n}=t;if(e.shouldExecuteOnCPU([n])&&n.dtype!=="complex64"){let s=e.texData.get(n.dataId),i=Qw(s.values);return e.makeTensorInfo(n.shape,n.dtype,i)}let o;return L().getBool("WEBGL_PACK_UNARY_OPERATIONS")?o=new Dn(n.shape,u3):o=new Lr(n.shape,u3),e.runWebGLProgram(o,[n],n.dtype)}var c3={kernelName:Ei,backendName:"webgl",kernelFunc:tst};var est=xr+` + `}};function Hu(r,t,e){let n=L().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new uI(r.shape,t):new lI(r.shape,t);return e.runWebGLProgram(n,[r],r.dtype)}function l3(r,t,e,n){let o=t,s=r.shape.length,i=y.parseAxisParam(o,r.shape),a=i,u=S.getAxesPermutation(a,s),l=u!=null,c=r;l&&(c=Hu(r,u,n),a=S.getInnerMostAxes(a.length,s)),S.assertAxesAreInnerMostDims("sum",a,s);let[p,m]=S.computeOutAndReduceShapes(c.shape,a),f=p;e&&(f=S.expandShapeToKeepDim(p,i));let d=y.sizeFromShape(m),g=y.sizeFromShape(r.shape)/d,x=rt({inputs:{x:c},attrs:{shape:[g,d]},backend:n}),b=lc(r.dtype),w=Yn(x,b,"sum",n),I=rt({inputs:{x:w},attrs:{shape:f},backend:n});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(w),l&&n.disposeIntermediateTensorInfo(c),I}function fp(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n;return l3(o,s,i,e)}var u3={kernelName:Js,backendName:"webgl",kernelFunc:fp};function Pe(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{perm:s}=n,i=e,a=o.shape.length,u=new Array(a);for(let c=0;c`Error in matMul: inner shapes (${p}) and (${m}) of Tensors with shapes ${r.shape} and ${t.shape} and transposeA=${e} and transposeB=${n} must match.`);let N=e?[x,p,f]:[x,f,p],E=n?[b,d,m]:[b,m,d],A=rt({inputs:{x:r},backend:o,attrs:{shape:N}}),D=rt({inputs:{x:t},backend:o,attrs:{shape:E}}),F=[A,D],M=Math.max(x,b),V=e?A.shape[1]:A.shape[2],G=s!=null,W=i!=null,q=u==="leakyrelu",H=u!=null?Ml(u,!0):null,j=G||W||q||H!=null,Y;if((f===1||d===1)&&V>F1&&j===!1){let et=A,nt=D;e&&(et=Pe({inputs:{x:A},backend:o,attrs:{perm:[0,2,1]}}),F.push(et)),n&&(nt=Pe({inputs:{x:D},backend:o,attrs:{perm:[0,2,1]}}),F.push(nt));let st=d!==1,lt=d===1,ot=et;st&&(ot=rt({inputs:{x:et},backend:o,attrs:{shape:[M,V,1]}}),F.push(ot));let it=d===1?2:1,ft=nt;lt&&(ft=rt({inputs:{x:nt},backend:o,attrs:{shape:[M,1,V]}}),F.push(ft));let gt=mg({inputs:{a:ot,b:ft},backend:o});Y=fp({inputs:{x:gt},backend:o,attrs:{axis:it,keepDims:!0}}),F.push(gt)}else{let et=ur(r.dtype,t.dtype),nt=new Rd(N,E,[M,f,d],e,n,G,H,W,q),st=[A,D];if(s!=null&&st.push(s),W&&st.push(i),q){let lt=o.makeTensorInfo([],"float32",y.createScalarValue(a,"float32"));st.push(lt),F.push(lt)}Y=o.runWebGLProgram(nt,st,et)}let Z=rt({inputs:{x:Y},backend:o,attrs:{shape:I}});F.push(Y);for(let et of F)o.disposeIntermediateTensorInfo(et);return Z}function nst(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s,bias:i,preluActivationWeights:a}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=n;return dp({a:o,b:s,transposeA:u,transposeB:l,backend:e,bias:i,preluActivationWeights:a,leakyreluAlpha:p,activation:c})}var p3={kernelName:Xi,backendName:"webgl",kernelFunc:nst};var m3="return abs(x);";function ost(r){let{inputs:t,backend:e}=r,{x:n}=t;if(e.shouldExecuteOnCPU([n])&&n.dtype!=="complex64"){let s=e.texData.get(n.dataId),i=eI(s.values);return e.makeTensorInfo(n.shape,n.dtype,i)}let o;return L().getBool("WEBGL_PACK_UNARY_OPERATIONS")?o=new Dn(n.shape,m3):o=new zr(n.shape,m3),e.runWebGLProgram(o,[n],n.dtype)}var f3={kernelName:Ai,backendName:"webgl",kernelFunc:ost};var sst=xr+` if (abs(x) > 1.) { return NAN; } return acos(x); -`,rst=It({opSnippet:est}),p3={kernelName:Go,backendName:"webgl",kernelFunc:rst};var nst=xr+` +`,ist=It({opSnippet:sst}),d3={kernelName:Go,backendName:"webgl",kernelFunc:ist};var ast=xr+` if (x < 1.0) return NAN; -return log(x + sqrt(x * x - 1.0));`,ost=It({opSnippet:nst}),m3={kernelName:Wo,backendName:"webgl",kernelFunc:ost};var f3="return a + b;",sst=ce({opSnippet:f3,packedOpSnippet:f3,supportsComplex:!0,cpuKernelImpl:WL}),d3={kernelName:no,backendName:"webgl",kernelFunc:sst};var lI=class{constructor(t,e){this.outputShape=[],this.outputShape=t,this.variableNames=e.map((s,i)=>`T${i}`);let n=[];this.variableNames.forEach(s=>{n.push(`float v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=` +return log(x + sqrt(x * x - 1.0));`,lst=It({opSnippet:ast}),h3={kernelName:Wo,backendName:"webgl",kernelFunc:lst};var g3="return a + b;",ust=ce({opSnippet:g3,packedOpSnippet:g3,supportsComplex:!0,cpuKernelImpl:qL}),x3={kernelName:no,backendName:"webgl",kernelFunc:ust};var cI=class{constructor(t,e){this.outputShape=[],this.outputShape=t,this.variableNames=e.map((s,i)=>`T${i}`);let n=[];this.variableNames.forEach(s=>{n.push(`float v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=` void main() { ${n.join(` `)} @@ -1543,7 +1543,7 @@ return log(x + sqrt(x * x - 1.0));`,ost=It({opSnippet:nst}),m3={kernelName:Wo,ba float result = ${o}; setOutput(result); } - `}};var uI=class{constructor(t,e){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.variableNames=e.map((s,i)=>`T${i}`);let n=[];this.variableNames.forEach(s=>{n.push(`vec4 v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=` + `}};var pI=class{constructor(t,e){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.variableNames=e.map((s,i)=>`T${i}`);let n=[];this.variableNames.forEach(s=>{n.push(`vec4 v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=` void main() { ${n.join(` `)} @@ -1551,7 +1551,7 @@ return log(x + sqrt(x * x - 1.0));`,ost=It({opSnippet:nst}),m3={kernelName:Wo,ba vec4 result = ${o}; setOutput(result); } - `}};function cI(r){let{inputs:t,backend:e}=r,n=t;if(n.length===1)return nr({inputs:{x:n[0]},backend:e});if(n.length>L().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(n.length/2),l=cI({inputs:n.slice(0,u),backend:e}),c=cI({inputs:n.slice(u),backend:e});return cI({inputs:[l,c],backend:e})}let o=n.map(u=>u.dtype).reduce((u,l)=>ur(u,l)),s=n.map(u=>u.shape),a=L().getBool("WEBGL_PACK")?new uI(n[0].shape,s):new lI(n[0].shape,s);return e.runWebGLProgram(a,n,o)}var h3={kernelName:Uo,backendName:"webgl",kernelFunc:cI};function ist(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=o;c!=null&&(p=Pe({inputs:{x:o},backend:e,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,a)),S.assertAxesAreInnerMostDims("all",l,a);let[m,f]=S.computeOutAndReduceShapes(p.shape,l),d=y.sizeFromShape(f),h=rt({inputs:{x:p},backend:e,attrs:{shape:[-1,d]}}),g=Yn(h,h.dtype,"all",e),x;if(i){let b=S.expandShapeToKeepDim(m,u);x=rt({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=rt({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(p),x}var g3={kernelName:Ea,backendName:"webgl",kernelFunc:ist};function ast(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=o;c!=null&&(p=Pe({inputs:{x:o},backend:e,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,a)),S.assertAxesAreInnerMostDims("any",l,a);let[m,f]=S.computeOutAndReduceShapes(p.shape,l),d=y.sizeFromShape(f),h=rt({inputs:{x:p},backend:e,attrs:{shape:[-1,d]}}),g=Yn(h,h.dtype,"any",e),x;if(i){let b=S.expandShapeToKeepDim(m,u);x=rt({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=rt({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(p),x}var x3={kernelName:Aa,backendName:"webgl",kernelFunc:ast};var pI=class{constructor(t,e,n){this.variableNames=["A"];let{windowSize:o,batchSize:s,outSize:i}=t;n||this.variableNames.push("bestIndicesA"),this.outputShape=[s,i];let a=e==="max"?">":"<",u=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=` + `}};function mI(r){let{inputs:t,backend:e}=r,n=t;if(n.length===1)return nr({inputs:{x:n[0]},backend:e});if(n.length>L().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(n.length/2),l=mI({inputs:n.slice(0,u),backend:e}),c=mI({inputs:n.slice(u),backend:e});return mI({inputs:[l,c],backend:e})}let o=n.map(u=>u.dtype).reduce((u,l)=>ur(u,l)),s=n.map(u=>u.shape),a=L().getBool("WEBGL_PACK")?new pI(n[0].shape,s):new cI(n[0].shape,s);return e.runWebGLProgram(a,n,o)}var y3={kernelName:Uo,backendName:"webgl",kernelFunc:mI};function cst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=o;c!=null&&(p=Pe({inputs:{x:o},backend:e,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,a)),S.assertAxesAreInnerMostDims("all",l,a);let[m,f]=S.computeOutAndReduceShapes(p.shape,l),d=y.sizeFromShape(f),h=rt({inputs:{x:p},backend:e,attrs:{shape:[-1,d]}}),g=Yn(h,h.dtype,"all",e),x;if(i){let b=S.expandShapeToKeepDim(m,u);x=rt({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=rt({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(p),x}var b3={kernelName:Ea,backendName:"webgl",kernelFunc:cst};function pst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=o;c!=null&&(p=Pe({inputs:{x:o},backend:e,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,a)),S.assertAxesAreInnerMostDims("any",l,a);let[m,f]=S.computeOutAndReduceShapes(p.shape,l),d=y.sizeFromShape(f),h=rt({inputs:{x:p},backend:e,attrs:{shape:[-1,d]}}),g=Yn(h,h.dtype,"any",e),x;if(i){let b=S.expandShapeToKeepDim(m,u);x=rt({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=rt({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(p),x}var w3={kernelName:Aa,backendName:"webgl",kernelFunc:pst};var fI=class{constructor(t,e,n){this.variableNames=["A"];let{windowSize:o,batchSize:s,outSize:i}=t;n||this.variableNames.push("bestIndicesA"),this.outputShape=[s,i];let a=e==="max"?">":"<",u=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -1571,7 +1571,7 @@ return log(x + sqrt(x * x - 1.0));`,ost=It({opSnippet:nst}),m3={kernelName:Wo,ba } setOutput(float(bestIndex)); } - `}};var mI=class{constructor(t,e,n,o){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,y.assert(t.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let s=t[t.length-1],i=Math.ceil(s/e);this.outputShape=t.slice(0,-1),i>1&&this.outputShape.push(i),o||this.variableNames.push("bestIndicesA");let a=this.outputShape,u=a.length,l=zt(u),c=rr("coords",u),p,m;if(i===1){m=u+1;let D=zt(m);p=` + `}};var dI=class{constructor(t,e,n,o){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,y.assert(t.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let s=t[t.length-1],i=Math.ceil(s/e);this.outputShape=t.slice(0,-1),i>1&&this.outputShape.push(i),o||this.variableNames.push("bestIndicesA");let a=this.outputShape,u=a.length,l=zt(u),c=rr("coords",u),p,m;if(i===1){m=u+1;let D=zt(m);p=` ${D} sourceLocR = ${D}(${c.join()}, 0); ++${c[u-1]}; ${D} sourceLocG = ${D}(${c.join()}, 0); @@ -1633,25 +1633,25 @@ return log(x + sqrt(x * x - 1.0));`,ost=It({opSnippet:nst}),m3={kernelName:Wo,ba } setOutput(bestIndex); } - `}};function y3(r,t,e,n=null){let o=t.shape[0],s=t.shape[1];n!=null&&(o=n.shape[0],s=n.shape[1]);let i=S.computeOptimalWindowSize(s),a={windowSize:i,inSize:s,batchSize:o,outSize:Math.ceil(s/i)},u=new pI(a,e,n==null),l=[t];n!=null&&l.push(n);let c=r.runWebGLProgram(u,l,"int32");if(c.shape[1]===1)return c;let p=y3(r,t,e,c);return r.disposeIntermediateTensorInfo(c),p}function b3(r,t,e,n=null){let o=n!=null?n.shape:t.shape,s=o[o.length-1],i=S.computeOptimalWindowSize(s),a=new mI(o,i,e,n==null),u=n==null?[t]:[t,n],l=r.runWebGLProgram(a,u,"int32");if(l.shape.length===t.shape.length){let c=b3(r,t,e,l);return r.disposeIntermediateTensorInfo(l),c}return l}function fI(r,t,e,n){let o=[e];if(S.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),o,t.shape.length),!L().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=r.texData.get(t.dataId),a=i!==null&&i.isPacked,u=t;a&&(u=r.unpackTensor(t),s.push(u));let[l,c]=S.computeOutAndReduceShapes(u.shape,o),p=y.sizeFromShape(c),m=rt({inputs:{x:u},backend:r,attrs:{shape:[-1,p]}});s.push(m);let f=y3(r,m,n);s.push(f);let d=rt({inputs:{x:f},backend:r,attrs:{shape:l}});return s.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}return b3(r,t,n)}function lst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s}=n,i=y.parseAxisParam(s,o.shape),a=S.getAxesPermutation(i,o.shape.length),u=o,l=[];a!=null&&(u=Pe({inputs:{x:o},backend:e,attrs:{perm:a}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims("argMax",[i[0]],u.shape.length);let c=fI(e,u,i[0],"max");return l.forEach(p=>e.disposeIntermediateTensorInfo(p)),c}var w3={kernelName:Ai,backendName:"webgl",kernelFunc:lst};function ust(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s}=n,i=y.parseAxisParam(s,o.shape),a=S.getAxesPermutation(i,o.shape.length),u=o,l=[];a!=null&&(u=Pe({inputs:{x:o},backend:e,attrs:{perm:a}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims("argMin",[i[0]],u.shape.length);let c=fI(e,u,i[0],"min");return l.forEach(p=>e.disposeIntermediateTensorInfo(p)),c}var I3={kernelName:Di,backendName:"webgl",kernelFunc:ust};var cst=xr+` + `}};function I3(r,t,e,n=null){let o=t.shape[0],s=t.shape[1];n!=null&&(o=n.shape[0],s=n.shape[1]);let i=S.computeOptimalWindowSize(s),a={windowSize:i,inSize:s,batchSize:o,outSize:Math.ceil(s/i)},u=new fI(a,e,n==null),l=[t];n!=null&&l.push(n);let c=r.runWebGLProgram(u,l,"int32");if(c.shape[1]===1)return c;let p=I3(r,t,e,c);return r.disposeIntermediateTensorInfo(c),p}function C3(r,t,e,n=null){let o=n!=null?n.shape:t.shape,s=o[o.length-1],i=S.computeOptimalWindowSize(s),a=new dI(o,i,e,n==null),u=n==null?[t]:[t,n],l=r.runWebGLProgram(a,u,"int32");if(l.shape.length===t.shape.length){let c=C3(r,t,e,l);return r.disposeIntermediateTensorInfo(l),c}return l}function hI(r,t,e,n){let o=[e];if(S.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),o,t.shape.length),!L().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=r.texData.get(t.dataId),a=i!==null&&i.isPacked,u=t;a&&(u=r.unpackTensor(t),s.push(u));let[l,c]=S.computeOutAndReduceShapes(u.shape,o),p=y.sizeFromShape(c),m=rt({inputs:{x:u},backend:r,attrs:{shape:[-1,p]}});s.push(m);let f=I3(r,m,n);s.push(f);let d=rt({inputs:{x:f},backend:r,attrs:{shape:l}});return s.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}return C3(r,t,n)}function mst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s}=n,i=y.parseAxisParam(s,o.shape),a=S.getAxesPermutation(i,o.shape.length),u=o,l=[];a!=null&&(u=Pe({inputs:{x:o},backend:e,attrs:{perm:a}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims("argMax",[i[0]],u.shape.length);let c=hI(e,u,i[0],"max");return l.forEach(p=>e.disposeIntermediateTensorInfo(p)),c}var v3={kernelName:Di,backendName:"webgl",kernelFunc:mst};function fst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s}=n,i=y.parseAxisParam(s,o.shape),a=S.getAxesPermutation(i,o.shape.length),u=o,l=[];a!=null&&(u=Pe({inputs:{x:o},backend:e,attrs:{perm:a}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims("argMin",[i[0]],u.shape.length);let c=hI(e,u,i[0],"min");return l.forEach(p=>e.disposeIntermediateTensorInfo(p)),c}var S3={kernelName:$i,backendName:"webgl",kernelFunc:fst};var dst=xr+` if (abs(x) > 1.) { return NAN; } return asin(x); -`,pst=It({opSnippet:cst}),C3={kernelName:Ho,backendName:"webgl",kernelFunc:pst};var mst=xr+"return log(x + sqrt(x * x + 1.0));",fst=It({opSnippet:mst}),v3={kernelName:qo,backendName:"webgl",kernelFunc:fst};var dst=xr+` +`,hst=It({opSnippet:dst}),N3={kernelName:Ho,backendName:"webgl",kernelFunc:hst};var gst=xr+"return log(x + sqrt(x * x + 1.0));",xst=It({opSnippet:gst}),k3={kernelName:qo,backendName:"webgl",kernelFunc:xst};var yst=xr+` return atan(x); -`,hst=It({opSnippet:dst}),S3={kernelName:Ko,backendName:"webgl",kernelFunc:hst};var gst=Dd+` +`,bst=It({opSnippet:yst}),T3={kernelName:Ko,backendName:"webgl",kernelFunc:bst};var wst=$d+` return atan(a, b); -`,xst=` +`,Ist=` vec4 result = atan(a, b); bvec4 isNaNA = isnan(a); bvec4 isNaNB = isnan(b); bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w); `+Xn+` return result; -`,yst=ce({opSnippet:gst,packedOpSnippet:xst}),N3={kernelName:Xo,backendName:"webgl",kernelFunc:yst};var bst=xr+` +`,Cst=ce({opSnippet:wst,packedOpSnippet:Ist}),_3={kernelName:Xo,backendName:"webgl",kernelFunc:Cst};var vst=xr+` if ((x < -1.0) || (x > 1.0)) return NAN; -return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wst=It({opSnippet:bst}),k3={kernelName:jo,backendName:"webgl",kernelFunc:wst};var Si=class{constructor(t,e,n,o=!1,s=!1){if(this.variableNames=["x"],e==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let i=t.filterWidth,a=t.strideHeight,u=t.strideWidth,l=t.dilationHeight,c=t.dilationWidth,p=t.effectiveFilterHeight,m=t.effectiveFilterWidth,f=t.padInfo.top,d=t.padInfo.left;this.outputShape=t.outShape;let h=e==="avg",g=`((batch * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + d`,x=`(xR * ${t.inWidth} + xC) * ${t.inChannels} + d`,b="0.0";if(h||(b="-1.0 / 1e-20"),n){let D=">=";this.userCode=` +return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sst=It({opSnippet:vst}),E3={kernelName:jo,backendName:"webgl",kernelFunc:Sst};var Ni=class{constructor(t,e,n,o=!1,s=!1){if(this.variableNames=["x"],e==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let i=t.filterWidth,a=t.strideHeight,u=t.strideWidth,l=t.dilationHeight,c=t.dilationWidth,p=t.effectiveFilterHeight,m=t.effectiveFilterWidth,f=t.padInfo.top,d=t.padInfo.left;this.outputShape=t.outShape;let h=e==="avg",g=`((batch * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + d`,x=`(xR * ${t.inWidth} + xC) * ${t.inChannels} + d`,b="0.0";if(h||(b="-1.0 / 1e-20"),n){let D=">=";this.userCode=` const ivec2 strides = ivec2(${a}, ${u}); const ivec2 pads = ivec2(${f}, ${d}); @@ -1955,7 +1955,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wst=It({opSnippet:bst}),k3={kernelN } setOutput(${E}); } - `}};function Ist(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;Ci(o,"avgPool");let{filterSize:s,strides:i,pad:a,dimRoundingMode:u}=n,l=1;y.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,l,a,u);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return nr({inputs:{x:o},backend:e});let p=new Si(c,"avg",!1);return e.runWebGLProgram(p,[o],"float32")}var T3={kernelName:Yo,backendName:"webgl",kernelFunc:Ist};function Cst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dimRoundingMode:u,dataFormat:l}=n,c=[1,1,1],p=S.computePool3DInfo(o.shape,s,i,c,a,u,l),m=new qu(p,"avg",!1);return e.runWebGLProgram(m,[o],"float32")}var _3={kernelName:$i,backendName:"webgl",kernelFunc:Cst};var dI=class{constructor(t){this.variableNames=["dy"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=t.dilationHeight,a=t.dilationWidth,u=t.effectiveFilterHeight,l=t.effectiveFilterWidth,c=u-1-t.padInfo.top,p=l-1-t.padInfo.left,m=1/(e*n);this.userCode=` + `}};function Nst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;vi(o,"avgPool");let{filterSize:s,strides:i,pad:a,dimRoundingMode:u}=n,l=1;y.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,l,a,u);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return nr({inputs:{x:o},backend:e});let p=new Ni(c,"avg",!1);return e.runWebGLProgram(p,[o],"float32")}var A3={kernelName:Yo,backendName:"webgl",kernelFunc:Nst};function kst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dimRoundingMode:u,dataFormat:l}=n,c=[1,1,1],p=S.computePool3DInfo(o.shape,s,i,c,a,u,l),m=new qu(p,"avg",!1);return e.runWebGLProgram(m,[o],"float32")}var D3={kernelName:Ri,backendName:"webgl",kernelFunc:kst};var gI=class{constructor(t){this.variableNames=["dy"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=t.dilationHeight,a=t.dilationWidth,u=t.effectiveFilterHeight,l=t.effectiveFilterWidth,c=u-1-t.padInfo.top,p=l-1-t.padInfo.left,m=1/(e*n);this.userCode=` const ivec2 pads = ivec2(${c}, ${p}); const float avgMultiplier = float(${m}); @@ -1997,7 +1997,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wst=It({opSnippet:bst}),k3={kernelN } setOutput(dotProd); } - `}},hI=class{constructor(t){this.variableNames=["dy"],this.outputShape=t.inShape;let e=t.filterDepth,n=t.filterHeight,o=t.filterWidth,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,c=t.dilationWidth,p=t.effectiveFilterDepth,m=t.effectiveFilterHeight,f=t.effectiveFilterWidth,d=p-1-t.padInfo.front,h=m-1-t.padInfo.top,g=f-1-t.padInfo.left,x=1/(e*n*o);this.userCode=` + `}},xI=class{constructor(t){this.variableNames=["dy"],this.outputShape=t.inShape;let e=t.filterDepth,n=t.filterHeight,o=t.filterWidth,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,c=t.dilationWidth,p=t.effectiveFilterDepth,m=t.effectiveFilterHeight,f=t.effectiveFilterWidth,d=p-1-t.padInfo.front,h=m-1-t.padInfo.top,g=f-1-t.padInfo.left,x=1/(e*n*o);this.userCode=` const ivec3 pads = ivec3(${d}, ${h}, ${g}); const float avgMultiplier = float(${x}); @@ -2053,7 +2053,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wst=It({opSnippet:bst}),k3={kernelN } setOutput(dotProd); } - `}};function vst(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s,{filterSize:a,strides:u,pad:l,dimRoundingMode:c}=n,p=[1,1,1],m=S.computePool3DInfo(i.shape,a,u,p,l,c),f=new hI(m);return e.runWebGLProgram(f,[o],i.dtype)}var E3={kernelName:Hl,backendName:"webgl",kernelFunc:vst};function Sst(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s;Ci([o,s],"avgPoolGrad");let{filterSize:a,strides:u,pad:l}=n,c=S.computePool2DInfo(i.shape,a,u,1,l),p=new dI(c);return e.runWebGLProgram(p,[o],i.dtype)}var A3={kernelName:Ul,backendName:"webgl",kernelFunc:Sst};function Nst(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s}=t,{transposeA:i,transposeB:a}=n;return dp({a:o,b:s,transposeA:i,transposeB:a,backend:e})}var D3={kernelName:Zo,backendName:"webgl",kernelFunc:Nst};var gI=class{constructor(t,e,n,o,s,i){this.outputShape=[],this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(t,e),S.assertAndGetBroadcastShape(t,n);let a="0.0";o!=null&&(S.assertAndGetBroadcastShape(t,o),this.variableNames.push("offset"),a="getOffsetAtOutCoords()");let u="1.0";s!=null&&(S.assertAndGetBroadcastShape(t,s),this.variableNames.push("scale"),u="getScaleAtOutCoords()"),this.outputShape=t,this.userCode=` + `}};function Tst(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s,{filterSize:a,strides:u,pad:l,dimRoundingMode:c}=n,p=[1,1,1],m=S.computePool3DInfo(i.shape,a,u,p,l,c),f=new xI(m);return e.runWebGLProgram(f,[o],i.dtype)}var $3={kernelName:Hl,backendName:"webgl",kernelFunc:Tst};function _st(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s;vi([o,s],"avgPoolGrad");let{filterSize:a,strides:u,pad:l}=n,c=S.computePool2DInfo(i.shape,a,u,1,l),p=new gI(c);return e.runWebGLProgram(p,[o],i.dtype)}var R3={kernelName:Ul,backendName:"webgl",kernelFunc:_st};function Est(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s}=t,{transposeA:i,transposeB:a}=n;return dp({a:o,b:s,transposeA:i,transposeB:a,backend:e})}var F3={kernelName:Zo,backendName:"webgl",kernelFunc:Est};var yI=class{constructor(t,e,n,o,s,i){this.outputShape=[],this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(t,e),S.assertAndGetBroadcastShape(t,n);let a="0.0";o!=null&&(S.assertAndGetBroadcastShape(t,o),this.variableNames.push("offset"),a="getOffsetAtOutCoords()");let u="1.0";s!=null&&(S.assertAndGetBroadcastShape(t,s),this.variableNames.push("scale"),u="getScaleAtOutCoords()"),this.outputShape=t,this.userCode=` void main() { float x = getXAtOutCoords(); float mean = getMeanAtOutCoords(); @@ -2063,7 +2063,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wst=It({opSnippet:bst}),k3={kernelN float inv = scale * inversesqrt(variance + float(${i})); setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1))); } - `}};var xI=class{constructor(t,e,n,o,s,i){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(t,e),S.assertAndGetBroadcastShape(t,n);let a="vec4(0.0)";o!=null&&(S.assertAndGetBroadcastShape(t,o),this.variableNames.push("offset"),a="getOffsetAtOutCoords()");let u="vec4(1.0)";s!=null&&(S.assertAndGetBroadcastShape(t,s),this.variableNames.push("scale"),u="getScaleAtOutCoords()"),this.outputShape=t,this.userCode=` + `}};var bI=class{constructor(t,e,n,o,s,i){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(t,e),S.assertAndGetBroadcastShape(t,n);let a="vec4(0.0)";o!=null&&(S.assertAndGetBroadcastShape(t,o),this.variableNames.push("offset"),a="getOffsetAtOutCoords()");let u="vec4(1.0)";s!=null&&(S.assertAndGetBroadcastShape(t,s),this.variableNames.push("scale"),u="getScaleAtOutCoords()"),this.outputShape=t,this.userCode=` void main() { vec4 offset = ${a}; vec4 scale = ${u}; @@ -2076,7 +2076,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wst=It({opSnippet:bst}),k3={kernelN setOutput((x - mean) * inv + offset); } - `}};var kst=({inputs:r,backend:t,attrs:e})=>{let{x:n,mean:o,variance:s,offset:i,scale:a}=r;y.assert(o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),y.assert(a==null||o.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:u}=e;u==null&&(u=.001);let l=[n,o,s],c=null;i!=null&&(c=i.shape,l.push(i));let p=null;a!=null&&(p=a.shape,l.push(a));let m=L().getBool("WEBGL_PACK_NORMALIZATION")?new xI(n.shape,o.shape,s.shape,c,p,u):new gI(n.shape,o.shape,s.shape,c,p,u);return t.runWebGLProgram(m,l,l[0].dtype)},$3={kernelName:ds,backendName:"webgl",kernelFunc:kst};var yI=class{constructor(t){this.variableNames=["source"],this.outputShape=t,this.rank=t.length;let e=zt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=Tst(this.rank),o,s=t.map((i,a)=>`sourceLoc.${R1[a]} = start[${a}] + coords.${R1[a]};`);o=` + `}};var Ast=({inputs:r,backend:t,attrs:e})=>{let{x:n,mean:o,variance:s,offset:i,scale:a}=r;y.assert(o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),y.assert(a==null||o.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:u}=e;u==null&&(u=.001);let l=[n,o,s],c=null;i!=null&&(c=i.shape,l.push(i));let p=null;a!=null&&(p=a.shape,l.push(a));let m=L().getBool("WEBGL_PACK_NORMALIZATION")?new bI(n.shape,o.shape,s.shape,c,p,u):new yI(n.shape,o.shape,s.shape,c,p,u);return t.runWebGLProgram(m,l,l[0].dtype)},O3={kernelName:ds,backendName:"webgl",kernelFunc:Ast};var wI=class{constructor(t){this.variableNames=["source"],this.outputShape=t,this.rank=t.length;let e=zt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=Dst(this.rank),o,s=t.map((i,a)=>`sourceLoc.${O1[a]} = start[${a}] + coords.${O1[a]};`);o=` ${e} sourceLoc; ${e} coords = getOutputCoords(); ${s.join(` @@ -2086,7 +2086,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wst=It({opSnippet:bst}),k3={kernelN ${o} setOutput(getSource(${n})); } - `}},R1=["x","y","z","w","u","v"];function Tst(r){if(r===1)return"sourceLoc";if(r<=6)return R1.slice(0,r).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}var bI=class{constructor(t){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.rank=t.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let e=zt(this.rank),n=rr("coords",this.rank),o=rr("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${o.slice(-2).join()})`,i=`getChannel(getSource(${o.join()}), ${s})`,a=` + `}},O1=["x","y","z","w","u","v"];function Dst(r){if(r===1)return"sourceLoc";if(r<=6)return O1.slice(0,r).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}var II=class{constructor(t){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.rank=t.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let e=zt(this.rank),n=rr("coords",this.rank),o=rr("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${o.slice(-2).join()})`,i=`getChannel(getSource(${o.join()}), ${s})`,a=` result.x = ${i}; if (++${n[this.rank-1]} < ${t[this.rank-1]}) { ++${o[this.rank-1]}; @@ -2115,15 +2115,15 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wst=It({opSnippet:bst}),k3={kernelN ${u} setOutput(result); } - `}};function _st(r,t,e,n){let o=n.texData.get(r.dataId),s=n.makeTensorInfo(e,r.dtype),i=n.texData.get(s.dataId);Object.assign(i,o),i.refCount=1,i.shape=e,i.dtype=r.dtype;let a=Be.computeFlatOffset(t,y.computeStrides(r.shape));o.slice&&(a+=o.slice.flatOffset),i.slice={flatOffset:a,origDataId:o.slice&&o.slice.origDataId||r.dataId};let u=n.dataRefCount.get(i.slice.origDataId)||1;return n.dataRefCount.set(i.slice.origDataId,u+1),s}function Ni(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{begin:s,size:i}=n,[a,u]=Be.parseSliceParams(o,s,i);if(Be.assertParamsValid(o,a,u),y.sizeFromShape(u)===0)return e.makeTensorInfo(u,o.dtype,[]);if(e.shouldExecuteOnCPU([o])||o.dtype==="string"){let p=e.texData.get(o.dataId),m=Iz(p.values,a,u,o.shape,o.dtype);return e.makeTensorInfo(u,o.dtype,m)}let{isPacked:l}=e.texData.get(o.dataId),c=Be.isSliceContinous(o.shape,a,u);if(l||!c){let p=L().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new bI(u):new yI(u),m=[a];return e.runWebGLProgram(p,[o],o.dtype,m)}return e.uploadToGPU(o.dataId),_st(o,a,u,e)}var R3={kernelName:Wi,backendName:"webgl",kernelFunc:Ni};var Est=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,crops:i}=n;y.assert(o.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let a=s.reduce((b,w)=>b*w),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=[],d=rt({inputs:{x:o},backend:e,attrs:{shape:u}}),h=Pe({inputs:{x:d},backend:e,attrs:{perm:l}}),g=rt({inputs:{x:h},backend:e,attrs:{shape:c}}),x=Ni({inputs:{x:g},backend:e,attrs:{begin:p,size:m}});return f.push(d),f.push(h),f.push(g),f.forEach(b=>e.disposeIntermediateTensorInfo(b)),x},F3={kernelName:Ri,backendName:"webgl",kernelFunc:Est};function Ast(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i}=n,a=e.readSync(o.dataId),u=e.readSync(s.dataId),l=Jw(a,u,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,l)}var O3={kernelName:Da,backendName:"webgl",kernelFunc:Ast};var Dst=` + `}};function $st(r,t,e,n){let o=n.texData.get(r.dataId),s=n.makeTensorInfo(e,r.dtype),i=n.texData.get(s.dataId);Object.assign(i,o),i.refCount=1,i.shape=e,i.dtype=r.dtype;let a=Be.computeFlatOffset(t,y.computeStrides(r.shape));o.slice&&(a+=o.slice.flatOffset),i.slice={flatOffset:a,origDataId:o.slice&&o.slice.origDataId||r.dataId};let u=n.dataRefCount.get(i.slice.origDataId)||1;return n.dataRefCount.set(i.slice.origDataId,u+1),s}function ki(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{begin:s,size:i}=n,[a,u]=Be.parseSliceParams(o,s,i);if(Be.assertParamsValid(o,a,u),y.sizeFromShape(u)===0)return e.makeTensorInfo(u,o.dtype,[]);if(e.shouldExecuteOnCPU([o])||o.dtype==="string"){let p=e.texData.get(o.dataId),m=Sz(p.values,a,u,o.shape,o.dtype);return e.makeTensorInfo(u,o.dtype,m)}let{isPacked:l}=e.texData.get(o.dataId),c=Be.isSliceContinous(o.shape,a,u);if(l||!c){let p=L().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new II(u):new wI(u),m=[a];return e.runWebGLProgram(p,[o],o.dtype,m)}return e.uploadToGPU(o.dataId),$st(o,a,u,e)}var M3={kernelName:Ui,backendName:"webgl",kernelFunc:ki};var Rst=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,crops:i}=n;y.assert(o.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let a=s.reduce((b,w)=>b*w),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=[],d=rt({inputs:{x:o},backend:e,attrs:{shape:u}}),h=Pe({inputs:{x:d},backend:e,attrs:{perm:l}}),g=rt({inputs:{x:h},backend:e,attrs:{shape:c}}),x=ki({inputs:{x:g},backend:e,attrs:{begin:p,size:m}});return f.push(d),f.push(h),f.push(g),f.forEach(b=>e.disposeIntermediateTensorInfo(b)),x},P3={kernelName:Fi,backendName:"webgl",kernelFunc:Rst};function Fst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i}=n,a=e.readSync(o.dataId),u=e.readSync(s.dataId),l=tI(a,u,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,l)}var L3={kernelName:Da,backendName:"webgl",kernelFunc:Fst};var Ost=` int r = int(a.r) & int(b.r); int g = int(a.g) & int(b.g); int rb = int(a.b) & int(b.b); int ra = int(a.a) & int(b.a); return vec4(r, g, rb, ra); -`,$st=` +`,Mst=` return float(int(a.r) & int(b.r)); -`;function Rst(r){let{inputs:t,backend:e}=r,{a:n,b:o}=t,s=L().getBool("WEBGL_PACK_BINARY_OPERATIONS"),i=L().getNumber("WEBGL_VERSION");if(e.shouldExecuteOnCPU([n,o])||i===1){let u=e.texData.get(n.dataId).values,l=e.texData.get(o.dataId).values,[c,p]=HL(n.shape,o.shape,u,l,n.dtype),m=e.makeTensorInfo(p,n.dtype),f=e.texData.get(m.dataId);return f.values=c,m}let a;return s?a=new jn(Dst,n.shape,o.shape,!1):a=new $n($st,n.shape,o.shape),e.runWebGLProgram(a,[n,o],n.dtype)}var M3={kernelName:$a,backendName:"webgl",kernelFunc:Rst};function Fst(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.readSync(n.dataId),i=e.readSync(o.dataId),a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeTensorInfo([a.length],"int32",Int32Array.from(a))}var P3={kernelName:ql,backendName:"webgl",kernelFunc:Fst};var Ost="return float(a != b);",F1=ce({opSnippet:Ost,cpuKernelImpl:mz,dtype:"bool"}),L3={kernelName:Za,backendName:"webgl",kernelFunc:F1};function Pl(r){let{inputs:t,backend:e}=r,{input:n}=t,o=e.texData.get(n.dataId);return nr({inputs:{x:o.complexTensorInfos.real},backend:e})}var z3={kernelName:Vp,backendName:"webgl",kernelFunc:Pl};var Mst="return float(int(x));";function B3(r,t){let e=new Lr(r.shape,Mst),n=t.runWebGLProgram(e,[r],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function O1(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{dtype:s}=n;if(s==="complex64"){if(o.dtype==="complex64")return nr({inputs:{x:o},backend:e});let i=ke(o.shape),a=O1({inputs:{x:o},backend:e,attrs:{dtype:"float32"}}),u=Rn({inputs:{real:a,imag:i},backend:e});return i.dispose(),e.disposeIntermediateTensorInfo(a),u}if(o.dtype==="complex64"){let i=Pl({inputs:{input:o},backend:e}),a=O1({inputs:{x:i},backend:e,attrs:{dtype:s}});return e.disposeIntermediateTensorInfo(i),a}if(!y.hasEncodingLoss(o.dtype,s)){let i=nr({inputs:{x:o},backend:e});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(e.shouldExecuteOnCPU([o])){let i=e.texData.get(o.dataId).values,[a,u,l]=qL(i,o.shape,o.dtype,s);return e.makeTensorInfo(a,u,l)}if(s==="int32")return B3(o,e);if(s==="bool"){let i=e.makeTensorInfo([],"bool",y.getTypedArrayFromDType("bool",1)),u=F1({inputs:{a:o,b:i},backend:e});return e.disposeIntermediateTensorInfo(i),u}throw new Error(`Error in Cast: failed to cast ${o.dtype} to ${s}`)}var V3={kernelName:fo,backendName:"webgl",kernelFunc:O1};var G3="return ceil(x);",Pst=It({opSnippet:G3,packedOpSnippet:G3,cpuKernelImpl:KL}),W3={kernelName:Jo,backendName:"webgl",kernelFunc:Pst};var wI=class{constructor(t){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=t,this.userCode=` +`;function Pst(r){let{inputs:t,backend:e}=r,{a:n,b:o}=t,s=L().getBool("WEBGL_PACK_BINARY_OPERATIONS"),i=L().getNumber("WEBGL_VERSION");if(e.shouldExecuteOnCPU([n,o])||i===1){let u=e.texData.get(n.dataId).values,l=e.texData.get(o.dataId).values,[c,p]=jL(n.shape,o.shape,u,l,n.dtype),m=e.makeTensorInfo(p,n.dtype),f=e.texData.get(m.dataId);return f.values=c,m}let a;return s?a=new jn(Ost,n.shape,o.shape,!1):a=new $n(Mst,n.shape,o.shape),e.runWebGLProgram(a,[n,o],n.dtype)}var z3={kernelName:$a,backendName:"webgl",kernelFunc:Pst};function Lst(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.readSync(n.dataId),i=e.readSync(o.dataId),a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeTensorInfo([a.length],"int32",Int32Array.from(a))}var B3={kernelName:ql,backendName:"webgl",kernelFunc:Lst};var zst="return float(a != b);",M1=ce({opSnippet:zst,cpuKernelImpl:hz,dtype:"bool"}),V3={kernelName:Za,backendName:"webgl",kernelFunc:M1};function Pl(r){let{inputs:t,backend:e}=r,{input:n}=t,o=e.texData.get(n.dataId);return nr({inputs:{x:o.complexTensorInfos.real},backend:e})}var G3={kernelName:Vp,backendName:"webgl",kernelFunc:Pl};var Bst="return float(int(x));";function W3(r,t){let e=new zr(r.shape,Bst),n=t.runWebGLProgram(e,[r],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function P1(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{dtype:s}=n;if(s==="complex64"){if(o.dtype==="complex64")return nr({inputs:{x:o},backend:e});let i=ke(o.shape),a=P1({inputs:{x:o},backend:e,attrs:{dtype:"float32"}}),u=Rn({inputs:{real:a,imag:i},backend:e});return i.dispose(),e.disposeIntermediateTensorInfo(a),u}if(o.dtype==="complex64"){let i=Pl({inputs:{input:o},backend:e}),a=P1({inputs:{x:i},backend:e,attrs:{dtype:s}});return e.disposeIntermediateTensorInfo(i),a}if(!y.hasEncodingLoss(o.dtype,s)){let i=nr({inputs:{x:o},backend:e});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(e.shouldExecuteOnCPU([o])){let i=e.texData.get(o.dataId).values,[a,u,l]=XL(i,o.shape,o.dtype,s);return e.makeTensorInfo(a,u,l)}if(s==="int32")return W3(o,e);if(s==="bool"){let i=e.makeTensorInfo([],"bool",y.getTypedArrayFromDType("bool",1)),u=M1({inputs:{a:o,b:i},backend:e});return e.disposeIntermediateTensorInfo(i),u}throw new Error(`Error in Cast: failed to cast ${o.dtype} to ${s}`)}var U3={kernelName:fo,backendName:"webgl",kernelFunc:P1};var H3="return ceil(x);",Vst=It({opSnippet:H3,packedOpSnippet:H3,cpuKernelImpl:YL}),q3={kernelName:Jo,backendName:"webgl",kernelFunc:Vst};var CI=class{constructor(t){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=t,this.userCode=` void main() { float value = getAAtOutCoords(); @@ -2134,7 +2134,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wst=It({opSnippet:bst}),k3={kernelN setOutput(clamp(value, minVal, maxVal)); } - `}};var II=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=t,this.userCode=` + `}};var vI=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=t,this.userCode=` void main() { vec4 value = getAAtOutCoords(); @@ -2145,7 +2145,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wst=It({opSnippet:bst}),k3={kernelN setOutput(clamp(value, vec4(minVal), vec4(maxVal))); } - `}};function Lst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{clipValueMin:s,clipValueMax:i}=n,a;L().getBool("WEBGL_PACK_CLIP")?a=new II(o.shape):a=new wI(o.shape);let u=[[s],[i]];return e.runWebGLProgram(a,[o],o.dtype,u)}var U3={kernelName:ho,backendName:"webgl",kernelFunc:Lst};var CI=class{constructor(t){this.variableNames=["real","imag"],this.outputShape=t,this.userCode=` + `}};function Gst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{clipValueMin:s,clipValueMax:i}=n,a;L().getBool("WEBGL_PACK_CLIP")?a=new vI(o.shape):a=new CI(o.shape);let u=[[s],[i]];return e.runWebGLProgram(a,[o],o.dtype,u)}var K3={kernelName:ho,backendName:"webgl",kernelFunc:Gst};var SI=class{constructor(t){this.variableNames=["real","imag"],this.outputShape=t,this.userCode=` void main() { float re = abs(getRealAtOutCoords()); float im = abs(getImagAtOutCoords()); @@ -2158,7 +2158,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wst=It({opSnippet:bst}),k3={kernelN mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx)) ); } - `}};function H3(r,t){return{dataId:t.dataId,dtype:t.dtype,shape:r.shape}}function zst(r){let{inputs:t,backend:e}=r,{x:n}=t,o=e.texData.get(n.dataId),s=new CI(n.shape),i=[H3(n,o.complexTensorInfos.real),H3(n,o.complexTensorInfos.imag)];return e.runWebGLProgram(s,i,i[0].dtype)}var q3={kernelName:Kl,backendName:"webgl",kernelFunc:zst};var vI=class{constructor(t){this.outputShape=[],this.outputShape=S.computeOutShape(t,1),this.variableNames=t.map((i,a)=>`T${a}`);let e=new Array(t.length-1);e[0]=t[0][1];for(let i=1;i`T${a}`);let e=new Array(t.length-1);e[0]=t[0][1];for(let i=1;i`T${g}`);let u=new Array(t.length-1);u[0]=t[0][e];for(let h=1;h`T${g}`);let u=new Array(t.length-1);u[0]=t[0][e];for(let h=1;h= ${u[h-1]}) { return getChannel( - getT${h}(${SI(a,l,g)}), - vec2(${SI(c,l,g)})); + getT${h}(${kI(a,l,g)}), + vec2(${kI(c,l,g)})); }`}let f=u.length,d=u[u.length-1];m+=` return getChannel( - getT${f}(${SI(a,l,d)}), - vec2(${SI(c,l,d)}));`,this.userCode=` + getT${f}(${kI(a,l,d)}), + vec2(${kI(c,l,d)}));`,this.userCode=` float getValue(${a.map(h=>"int "+h)}) { ${m} } @@ -2204,7 +2204,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wst=It({opSnippet:bst}),k3={kernelN } setOutput(result); } - `}};function SI(r,t,e){let n=r.indexOf(t);return r.map((s,i)=>i===n?`${s} - ${e}`:s).join()}function hp(r){let{inputs:t,backend:e}=r,{input:n}=t,o=e.texData.get(n.dataId);return nr({inputs:{x:o.complexTensorInfos.imag},backend:e})}var K3={kernelName:Pp,backendName:"webgl",kernelFunc:hp};function Rd(r,t,e){let n=r[0].dtype;if(n==="complex64"){let f=r.map(b=>Pl({inputs:{input:b},backend:e})),d=r.map(b=>hp({inputs:{input:b},backend:e})),h=Rd(f,t,e),g=Rd(d,t,e),x=Rn({inputs:{real:h,imag:g},backend:e});return f.forEach(b=>e.disposeIntermediateTensorInfo(b)),d.forEach(b=>e.disposeIntermediateTensorInfo(b)),e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),x}let o=e.shouldExecuteOnCPU(r);if(n==="string"&&(o=!0),o){let f=r.map(I=>{let E=[-1,y.sizeFromShape(I.shape.slice(t))];return rt({inputs:{x:I},backend:e,attrs:{shape:E}})}),d=f.map(I=>({vals:e.readSync(I.dataId),shape:I.shape})),h=S.computeOutShape(f.map(I=>I.shape),1),g=f[0].shape[0]===1,x=jL(d,h,n,g),b=S.computeOutShape(r.map(I=>I.shape),t),w=e.makeTensorInfo(b,n,x);return f.forEach(I=>e.disposeIntermediateTensorInfo(I)),w}let s=r.filter(f=>y.sizeFromShape(f.shape)>0),i=L().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&s[0].shape.length>1;if(s.length===1){let f=i?new Lr(r[0].shape,Ia):new Dn(r[0].shape,Ia);return e.runWebGLProgram(f,r,n)}let a=L().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(s.length>a){let f=[];for(let h=0;hd.shape),t);return e.runWebGLProgram(f,s,n)}let{tensors2D:u,outShape:l}=Bst(s,t,e),c=new vI(u.map(f=>f.shape)),p=e.runWebGLProgram(c,u,n);u.forEach(f=>e.disposeIntermediateTensorInfo(f));let m=rt({inputs:{x:p},attrs:{shape:l},backend:e});return e.disposeIntermediateTensorInfo(p),m}function Bst(r,t,e){let n=S.computeOutShape(r.map(s=>s.shape),t);return{tensors2D:r.map(s=>rt({inputs:{x:s},attrs:{shape:[-1,y.sizeFromShape(s.shape.slice(t))]},backend:e})),outShape:n}}function M1(r){let{inputs:t,backend:e,attrs:n}=r,{axis:o}=n,s=y.parseAxisParam(o,t[0].shape)[0],i=t.map(l=>l.shape);S.assertParamsConsistent(i,s);let a=S.computeOutShape(t.map(l=>l.shape),s);if(y.sizeFromShape(a)===0)return e.makeTensorInfo(a,t[0].dtype,[]);let u=t.filter(l=>y.sizeFromShape(l.shape)>0);return u.length===1?nr({inputs:{x:u[0]},backend:e}):Rd(u,s,e)}var j3={kernelName:Fi,backendName:"webgl",kernelFunc:M1};var Fd=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.outputShape=t.outShape;let i=t.padInfo.top,a=t.padInfo.left,u=t.strideHeight,l=t.strideWidth,c=t.dilationHeight,p=t.dilationWidth,m=t.filterHeight,f=t.filterWidth,d=Math.floor(t.inChannels/4)*4,h=t.inChannels%4,g=t.dataFormat==="channelsLast",x=g?1:2,b=g?2:3,w=g?3:1,I="",N="";n&&(o?I=`float activation(float a) { + `}};function kI(r,t,e){let n=r.indexOf(t);return r.map((s,i)=>i===n?`${s} - ${e}`:s).join()}function hp(r){let{inputs:t,backend:e}=r,{input:n}=t,o=e.texData.get(n.dataId);return nr({inputs:{x:o.complexTensorInfos.imag},backend:e})}var Y3={kernelName:Pp,backendName:"webgl",kernelFunc:hp};function Fd(r,t,e){let n=r[0].dtype;if(n==="complex64"){let f=r.map(b=>Pl({inputs:{input:b},backend:e})),d=r.map(b=>hp({inputs:{input:b},backend:e})),h=Fd(f,t,e),g=Fd(d,t,e),x=Rn({inputs:{real:h,imag:g},backend:e});return f.forEach(b=>e.disposeIntermediateTensorInfo(b)),d.forEach(b=>e.disposeIntermediateTensorInfo(b)),e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),x}let o=e.shouldExecuteOnCPU(r);if(n==="string"&&(o=!0),o){let f=r.map(I=>{let E=[-1,y.sizeFromShape(I.shape.slice(t))];return rt({inputs:{x:I},backend:e,attrs:{shape:E}})}),d=f.map(I=>({vals:e.readSync(I.dataId),shape:I.shape})),h=S.computeOutShape(f.map(I=>I.shape),1),g=f[0].shape[0]===1,x=ZL(d,h,n,g),b=S.computeOutShape(r.map(I=>I.shape),t),w=e.makeTensorInfo(b,n,x);return f.forEach(I=>e.disposeIntermediateTensorInfo(I)),w}let s=r.filter(f=>y.sizeFromShape(f.shape)>0),i=L().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&s[0].shape.length>1;if(s.length===1){let f=i?new zr(r[0].shape,Ia):new Dn(r[0].shape,Ia);return e.runWebGLProgram(f,r,n)}let a=L().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(s.length>a){let f=[];for(let h=0;hd.shape),t);return e.runWebGLProgram(f,s,n)}let{tensors2D:u,outShape:l}=Ust(s,t,e),c=new NI(u.map(f=>f.shape)),p=e.runWebGLProgram(c,u,n);u.forEach(f=>e.disposeIntermediateTensorInfo(f));let m=rt({inputs:{x:p},attrs:{shape:l},backend:e});return e.disposeIntermediateTensorInfo(p),m}function Ust(r,t,e){let n=S.computeOutShape(r.map(s=>s.shape),t);return{tensors2D:r.map(s=>rt({inputs:{x:s},attrs:{shape:[-1,y.sizeFromShape(s.shape.slice(t))]},backend:e})),outShape:n}}function L1(r){let{inputs:t,backend:e,attrs:n}=r,{axis:o}=n,s=y.parseAxisParam(o,t[0].shape)[0],i=t.map(l=>l.shape);S.assertParamsConsistent(i,s);let a=S.computeOutShape(t.map(l=>l.shape),s);if(y.sizeFromShape(a)===0)return e.makeTensorInfo(a,t[0].dtype,[]);let u=t.filter(l=>y.sizeFromShape(l.shape)>0);return u.length===1?nr({inputs:{x:u[0]},backend:e}):Fd(u,s,e)}var Z3={kernelName:Oi,backendName:"webgl",kernelFunc:L1};var Od=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.outputShape=t.outShape;let i=t.padInfo.top,a=t.padInfo.left,u=t.strideHeight,l=t.strideWidth,c=t.dilationHeight,p=t.dilationWidth,m=t.filterHeight,f=t.filterWidth,d=Math.floor(t.inChannels/4)*4,h=t.inChannels%4,g=t.dataFormat==="channelsLast",x=g?1:2,b=g?2:3,w=g?3:1,I="",N="";n&&(o?I=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${n} }`:s?I=`float activation(float a) { @@ -2338,7 +2338,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wst=It({opSnippet:bst}),k3={kernelN ${N} setOutput(result); } - `}},kI=class{constructor(t){this.variableNames=["x","W"],this.outputShape=t.outShape;let e=t.padInfo.front,n=t.padInfo.top,o=t.padInfo.left,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,c=t.dilationWidth,p=t.filterDepth,m=t.filterHeight,f=t.filterWidth,d=Math.floor(t.inChannels/4)*4,h=t.inChannels%4;this.userCode=` + `}},_I=class{constructor(t){this.variableNames=["x","W"],this.outputShape=t.outShape;let e=t.padInfo.front,n=t.padInfo.top,o=t.padInfo.left,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,c=t.dilationWidth,p=t.filterDepth,m=t.filterHeight,f=t.filterWidth,d=Math.floor(t.inChannels/4)*4,h=t.inChannels%4;this.userCode=` const ivec3 strides = ivec3(${s}, ${i}, ${a}); const ivec3 pads = ivec3(${e}, ${n}, ${o}); @@ -2426,7 +2426,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wst=It({opSnippet:bst}),k3={kernelN } setOutput(dotProd); } - `}};var Od=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=t.outShape,this.enableShapeUniforms=he(this.outputShape.length);let i=t.padInfo.left,a=t.strideWidth,u=t.dilationWidth,l=t.filterHeight,c=t.filterWidth,p=c,m=` + `}};var Md=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=t.outShape,this.enableShapeUniforms=he(this.outputShape.length);let i=t.padInfo.left,a=t.strideWidth,u=t.dilationWidth,l=t.filterHeight,c=t.filterWidth,p=c,m=` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g=3?t?[...r.slice(0,-3),r[e-3]*r[e-2],r[e-1]]:[...r.slice(0,-3),r[e-3],r[e-2]*r[e-1]]:!t&&e===1&&r[0]>1?[r[0],1]:null}function EI({x:r,filter:t,convInfo:e,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:a=null}){let u=r.shape,l=n.texData.get(r.dataId),c=e.inChannels,p=u[0]*u[1]*u[2],m=e.outChannels,f=e.dataFormat==="channelsLast",d=!1,h=!1,g,x=[];if(s!=null){let I=_I(s.shape,f);I!=null&&(s=rt({inputs:{x:s},backend:n,attrs:{shape:I}}),x.push(s))}if(o!=null){let I=_I(o.shape,f);I!=null&&(o=rt({inputs:{x:o},backend:n,attrs:{shape:I}}),x.push(o))}if(!((p===1||m===1)&&c>$1)&&l.isPacked&&f&&l.texture!=null&&u[2]%2!==0&&y.arraysEqual(l.shape.slice(-3),u.slice(-3))){let I=u[0]*u[1]*(u[2]+1),N={dataId:r.dataId,shape:[1,I,e.inChannels],dtype:r.dtype},E=l.shape;l.shape=l.shape.slice(),l.shape[l.shape.length-2]++,y.assert(Uu(l.shape,N.shape),()=>`packed reshape ${l.shape} to ${N.shape} isn't free`);let A=rt({inputs:{x:t},backend:n,attrs:{shape:[1,e.inChannels,e.outChannels]}});x.push(A);let D=dp({a:N,b:A,backend:n,transposeA:d,transposeB:h,bias:o,activation:a,preluActivationWeights:s,leakyreluAlpha:i}),F=n.texData.get(D.dataId);y.assert(F.isPacked,()=>"batchMatMul result is expected to be packed"),l.shape=E,F.shape=e.outShape,g=nr({inputs:{x:D},backend:n}),g.shape=e.outShape,x.push(D)}else{let I=e.outHeight*e.outWidth,N=rt({inputs:{x:r},backend:n,attrs:{shape:f?[e.batchSize,I,e.inChannels]:[e.batchSize,e.inChannels,I]}}),E=rt({inputs:{x:t},backend:n,attrs:{shape:[1,e.inChannels,e.outChannels]}}),A=dp({a:f?N:E,b:f?E:N,transposeA:!f,transposeB:h,backend:n,bias:o,activation:a,preluActivationWeights:s,leakyreluAlpha:i});g=rt({inputs:{x:A},backend:n,attrs:{shape:e.outShape}}),x.push(N),x.push(E),x.push(A)}for(let I of x)n.disposeIntermediateTensorInfo(I);return g}function AI({x:r,filter:t,convInfo:e,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:a=null}){let{filterWidth:u,filterHeight:l,inChannels:c,outWidth:p,outHeight:m,dataFormat:f}=e,d=f==="channelsLast",h=u*l*c,g=m*p,x=[e.batchSize,h,g],b=!0,w=!1,I=[];if(s!=null){let Z=_I(s.shape,d);Z!=null&&(s=rt({inputs:{x:s},backend:n,attrs:{shape:Z}}),I.push(s))}if(o!=null){let Z=_I(o.shape,d);Z!=null&&(o=rt({inputs:{x:o},backend:n,attrs:{shape:Z}}),I.push(o))}let N=rt({inputs:{x:t},backend:n,attrs:{shape:[1,h,y.sizeFromShape(t.shape)/h]}});I.push(N);let E=new TI(x,e),A=[r.shape,[e.padInfo.top,e.padInfo.left],[e.strideHeight,e.strideWidth],[e.dilationHeight,e.dilationWidth],[e.inChannels],[e.filterWidth*e.inChannels],[e.outWidth]],D=n.runWebGLProgram(E,[r],"float32",A),F=rt({inputs:{x:D},backend:n,attrs:{shape:x}});I.push(D),I.push(F);let M=o!=null,V=s!=null,G=a==="leakyrelu",W=a?Ml(a,!0):null,q=new $d(d?F.shape:N.shape,d?N.shape:F.shape,d?[e.batchSize,g,e.outChannels]:[e.batchSize,e.outChannels,g],b,w,M,W,V,G),H=d?[F,N]:[N,F];if(o&&H.push(o),V&&H.push(s),G){let Z=n.makeTensorInfo([],"float32",y.createScalarValue(i,"float32"));H.push(Z),I.push(Z)}let K=n.runWebGLProgram(q,H,"float32"),Y=rt({inputs:{x:K},backend:n,attrs:{shape:e.outShape}});I.push(K);for(let Z of I)n.disposeIntermediateTensorInfo(Z);return Y}function Vst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dataFormat:u,dilations:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,s.shape,i,l,a,c,!1,p),f;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type==="SAME"||m.padInfo.type==="VALID"))f=EI({x:o,filter:s,convInfo:m,backend:e});else if(m.strideWidth<=2&&p==="channelsLast"&&L().getBool("WEBGL_EXP_CONV")){let h=new Od(m),g=[[m.padInfo.top,m.padInfo.left],[m.strideHeight,m.strideWidth],[m.dilationHeight,m.dilationWidth],[m.inHeight,m.inWidth]];f=e.runWebGLProgram(h,[o,s],"float32",g)}else if(L().getBool("WEBGL_CONV_IM2COL"))f=AI({x:o,filter:s,convInfo:m,backend:e});else{let h=new Fd(m);f=e.runWebGLProgram(h,[o,s],"float32")}let d=rt({inputs:{x:f},backend:e,attrs:{shape:m.outShape}});return e.disposeIntermediateTensorInfo(f),d}var X3={kernelName:Qo,backendName:"webgl",kernelFunc:Vst};var DI=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideHeight,n=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,i=t.dataFormat==="channelsLast";this.userCode=` + `}};function AI(r,t){let e=r.length;return e>=3?t?[...r.slice(0,-3),r[e-3]*r[e-2],r[e-1]]:[...r.slice(0,-3),r[e-3],r[e-2]*r[e-1]]:!t&&e===1&&r[0]>1?[r[0],1]:null}function DI({x:r,filter:t,convInfo:e,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:a=null}){let u=r.shape,l=n.texData.get(r.dataId),c=e.inChannels,p=u[0]*u[1]*u[2],m=e.outChannels,f=e.dataFormat==="channelsLast",d=!1,h=!1,g,x=[];if(s!=null){let I=AI(s.shape,f);I!=null&&(s=rt({inputs:{x:s},backend:n,attrs:{shape:I}}),x.push(s))}if(o!=null){let I=AI(o.shape,f);I!=null&&(o=rt({inputs:{x:o},backend:n,attrs:{shape:I}}),x.push(o))}if(!((p===1||m===1)&&c>F1)&&l.isPacked&&f&&l.texture!=null&&u[2]%2!==0&&y.arraysEqual(l.shape.slice(-3),u.slice(-3))){let I=u[0]*u[1]*(u[2]+1),N={dataId:r.dataId,shape:[1,I,e.inChannels],dtype:r.dtype},E=l.shape;l.shape=l.shape.slice(),l.shape[l.shape.length-2]++,y.assert(Uu(l.shape,N.shape),()=>`packed reshape ${l.shape} to ${N.shape} isn't free`);let A=rt({inputs:{x:t},backend:n,attrs:{shape:[1,e.inChannels,e.outChannels]}});x.push(A);let D=dp({a:N,b:A,backend:n,transposeA:d,transposeB:h,bias:o,activation:a,preluActivationWeights:s,leakyreluAlpha:i}),F=n.texData.get(D.dataId);y.assert(F.isPacked,()=>"batchMatMul result is expected to be packed"),l.shape=E,F.shape=e.outShape,g=nr({inputs:{x:D},backend:n}),g.shape=e.outShape,x.push(D)}else{let I=e.outHeight*e.outWidth,N=rt({inputs:{x:r},backend:n,attrs:{shape:f?[e.batchSize,I,e.inChannels]:[e.batchSize,e.inChannels,I]}}),E=rt({inputs:{x:t},backend:n,attrs:{shape:[1,e.inChannels,e.outChannels]}}),A=dp({a:f?N:E,b:f?E:N,transposeA:!f,transposeB:h,backend:n,bias:o,activation:a,preluActivationWeights:s,leakyreluAlpha:i});g=rt({inputs:{x:A},backend:n,attrs:{shape:e.outShape}}),x.push(N),x.push(E),x.push(A)}for(let I of x)n.disposeIntermediateTensorInfo(I);return g}function $I({x:r,filter:t,convInfo:e,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:a=null}){let{filterWidth:u,filterHeight:l,inChannels:c,outWidth:p,outHeight:m,dataFormat:f}=e,d=f==="channelsLast",h=u*l*c,g=m*p,x=[e.batchSize,h,g],b=!0,w=!1,I=[];if(s!=null){let Z=AI(s.shape,d);Z!=null&&(s=rt({inputs:{x:s},backend:n,attrs:{shape:Z}}),I.push(s))}if(o!=null){let Z=AI(o.shape,d);Z!=null&&(o=rt({inputs:{x:o},backend:n,attrs:{shape:Z}}),I.push(o))}let N=rt({inputs:{x:t},backend:n,attrs:{shape:[1,h,y.sizeFromShape(t.shape)/h]}});I.push(N);let E=new EI(x,e),A=[r.shape,[e.padInfo.top,e.padInfo.left],[e.strideHeight,e.strideWidth],[e.dilationHeight,e.dilationWidth],[e.inChannels],[e.filterWidth*e.inChannels],[e.outWidth]],D=n.runWebGLProgram(E,[r],"float32",A),F=rt({inputs:{x:D},backend:n,attrs:{shape:x}});I.push(D),I.push(F);let M=o!=null,V=s!=null,G=a==="leakyrelu",W=a?Ml(a,!0):null,q=new Rd(d?F.shape:N.shape,d?N.shape:F.shape,d?[e.batchSize,g,e.outChannels]:[e.batchSize,e.outChannels,g],b,w,M,W,V,G),H=d?[F,N]:[N,F];if(o&&H.push(o),V&&H.push(s),G){let Z=n.makeTensorInfo([],"float32",y.createScalarValue(i,"float32"));H.push(Z),I.push(Z)}let j=n.runWebGLProgram(q,H,"float32"),Y=rt({inputs:{x:j},backend:n,attrs:{shape:e.outShape}});I.push(j);for(let Z of I)n.disposeIntermediateTensorInfo(Z);return Y}function Hst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dataFormat:u,dilations:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,s.shape,i,l,a,c,!1,p),f;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type==="SAME"||m.padInfo.type==="VALID"))f=DI({x:o,filter:s,convInfo:m,backend:e});else if(m.strideWidth<=2&&p==="channelsLast"&&L().getBool("WEBGL_EXP_CONV")){let h=new Md(m),g=[[m.padInfo.top,m.padInfo.left],[m.strideHeight,m.strideWidth],[m.dilationHeight,m.dilationWidth],[m.inHeight,m.inWidth]];f=e.runWebGLProgram(h,[o,s],"float32",g)}else if(L().getBool("WEBGL_CONV_IM2COL"))f=$I({x:o,filter:s,convInfo:m,backend:e});else{let h=new Od(m);f=e.runWebGLProgram(h,[o,s],"float32")}let d=rt({inputs:{x:f},backend:e,attrs:{shape:m.outShape}});return e.disposeIntermediateTensorInfo(f),d}var J3={kernelName:Qo,backendName:"webgl",kernelFunc:Hst};var RI=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideHeight,n=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,i=t.dataFormat==="channelsLast";this.userCode=` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; @@ -2706,7 +2706,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wst=It({opSnippet:bst}),k3={kernelN } setOutput(dotProd); } - `}},$I=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=t.dataFormat==="channelsLast",a=e-1-t.padInfo.top,u=n-1-t.padInfo.left,l=i?1:2,c=i?2:3,p=i?3:1;this.userCode=` + `}},FI=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=t.dataFormat==="channelsLast",a=e-1-t.padInfo.top,u=n-1-t.padInfo.left,l=i?1:2,c=i?2:3,p=i?3:1;this.userCode=` const ivec2 pads = ivec2(${a}, ${u}); void main() { @@ -2759,7 +2759,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wst=It({opSnippet:bst}),k3={kernelN } setOutput(dotProd); } - `}},RI=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideDepth,n=t.strideHeight,o=t.strideWidth,s=t.padInfo.front,i=t.padInfo.top,a=t.padInfo.left;this.userCode=` + `}},OI=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideDepth,n=t.strideHeight,o=t.strideWidth,s=t.padInfo.front,i=t.padInfo.top,a=t.padInfo.left;this.userCode=` void main() { ivec5 coords = getOutputCoords(); int wF = coords.x; @@ -2801,7 +2801,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wst=It({opSnippet:bst}),k3={kernelN } setOutput(dotProd); } - `}},FI=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterDepth,n=t.filterHeight,o=t.filterWidth,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=e-1-t.padInfo.front,l=n-1-t.padInfo.top,c=o-1-t.padInfo.left;this.userCode=` + `}},MI=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterDepth,n=t.filterHeight,o=t.filterWidth,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=e-1-t.padInfo.front,l=n-1-t.padInfo.top,c=o-1-t.padInfo.left;this.userCode=` const ivec3 pads = ivec3(${u}, ${l}, ${c}); void main() { @@ -2858,7 +2858,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wst=It({opSnippet:bst}),k3={kernelN } setOutput(dotProd); } - `}};function Gst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,dataFormat:u,dimRoundingMode:l,filterShape:c}=n,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,c,i,1,a,l,!1,p),f=new DI(m);return e.runWebGLProgram(f,[o,s],"float32")}var Y3={kernelName:Dp,backendName:"webgl",kernelFunc:Gst};var OI=class{constructor(t){this.variableNames=["dy","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"strides",type:"vec2"}],this.outputShape=t.inShape,this.enableShapeUniforms=he(this.outputShape.length);let e=t.filterHeight,n=t.filterWidth,o=e-1-t.padInfo.top,s=n-1-t.padInfo.left;this.userCode=` + `}};function qst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,dataFormat:u,dimRoundingMode:l,filterShape:c}=n,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,c,i,1,a,l,!1,p),f=new RI(m);return e.runWebGLProgram(f,[o,s],"float32")}var Q3={kernelName:Dp,backendName:"webgl",kernelFunc:qst};var PI=class{constructor(t){this.variableNames=["dy","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"strides",type:"vec2"}],this.outputShape=t.inShape,this.enableShapeUniforms=he(this.outputShape.length);let e=t.filterHeight,n=t.filterWidth,o=e-1-t.padInfo.top,s=n-1-t.padInfo.left;this.userCode=` const ivec2 pads = ivec2(${o}, ${s}); void main() { @@ -2932,17 +2932,17 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wst=It({opSnippet:bst}),k3={kernelN } setOutput(result); } - `}};function Wst(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{inputShape:i,strides:a,pad:u,dataFormat:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(l),m=S.computeConv2DInfo(i,s.shape,a,1,u,c,!1,p);if(L().getBool("WEBGL_PACK_CONV2DTRANSPOSE")&&p==="channelsLast"){let f=[[m.strideHeight,m.strideWidth]],d=new OI(m);return e.runWebGLProgram(d,[o,s],"float32",f)}else{let f=new $I(m);return e.runWebGLProgram(f,[o,s],"float32")}}var Z3={kernelName:ts,backendName:"webgl",kernelFunc:Wst};function Ust(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n,l=S.computeConv3DInfo(o.shape,s.shape,i,u,a),c=new kI(l);return e.runWebGLProgram(c,[o,s],"float32")}var J3={kernelName:es,backendName:"webgl",kernelFunc:Ust};function Hst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,filterShape:u}=n,l=S.computeConv3DInfo(o.shape,u,i,1,a),c=new RI(l);return e.runWebGLProgram(c,[o,s],"float32")}var Q3={kernelName:Ra,backendName:"webgl",kernelFunc:Hst};function qst(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{pad:i,strides:a,inputShape:u}=n,l=S.computeConv3DInfo(u,s.shape,a,1,i),c=new FI(l);return e.runWebGLProgram(c,[o,s],"float32")}var tB={kernelName:Fa,backendName:"webgl",kernelFunc:qst};var Kst=Po+` + `}};function Kst(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{inputShape:i,strides:a,pad:u,dataFormat:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(l),m=S.computeConv2DInfo(i,s.shape,a,1,u,c,!1,p);if(L().getBool("WEBGL_PACK_CONV2DTRANSPOSE")&&p==="channelsLast"){let f=[[m.strideHeight,m.strideWidth]],d=new PI(m);return e.runWebGLProgram(d,[o,s],"float32",f)}else{let f=new FI(m);return e.runWebGLProgram(f,[o,s],"float32")}}var tB={kernelName:ts,backendName:"webgl",kernelFunc:Kst};function jst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n,l=S.computeConv3DInfo(o.shape,s.shape,i,u,a),c=new _I(l);return e.runWebGLProgram(c,[o,s],"float32")}var eB={kernelName:es,backendName:"webgl",kernelFunc:jst};function Xst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,filterShape:u}=n,l=S.computeConv3DInfo(o.shape,u,i,1,a),c=new OI(l);return e.runWebGLProgram(c,[o,s],"float32")}var rB={kernelName:Ra,backendName:"webgl",kernelFunc:Xst};function Yst(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{pad:i,strides:a,inputShape:u}=n,l=S.computeConv3DInfo(u,s.shape,a,1,i),c=new MI(l);return e.runWebGLProgram(c,[o,s],"float32")}var nB={kernelName:Fa,backendName:"webgl",kernelFunc:Yst};var Zst=Po+` return cos(x); -`,jst=` +`,Jst=` vec4 result = cos(x); bvec4 isNaN = isnan(x); ${Xn} return result; -`,Xst=It({opSnippet:Kst,packedOpSnippet:jst}),eB={kernelName:rs,backendName:"webgl",kernelFunc:Xst};var Yst=` +`,Qst=It({opSnippet:Zst,packedOpSnippet:Jst}),oB={kernelName:rs,backendName:"webgl",kernelFunc:Qst};var tit=` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; -`,Zst=It({opSnippet:Yst}),rB={kernelName:ns,backendName:"webgl",kernelFunc:Zst};var MI=class{constructor(t,e,n,o,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[i,a,u,l]=t,[c]=e,[p,m]=n;this.outputShape=[c,p,m,l];let f=o==="bilinear"?1:0,[d,h]=[`${a-1}.0`,`${u-1}.0`],[g,x,b]=p>1?[`${(a-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${d} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${d}`],[w,I,N]=m>1?[`${(u-1)/(m-1)}`,"(x2-x1) * width_ratio",`x1*${h} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${h}`];this.userCode=` +`,eit=It({opSnippet:tit}),sB={kernelName:ns,backendName:"webgl",kernelFunc:eit};var LI=class{constructor(t,e,n,o,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[i,a,u,l]=t,[c]=e,[p,m]=n;this.outputShape=[c,p,m,l];let f=o==="bilinear"?1:0,[d,h]=[`${a-1}.0`,`${u-1}.0`],[g,x,b]=p>1?[`${(a-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${d} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${d}`],[w,I,N]=m>1?[`${(u-1)/(m-1)}`,"(x2-x1) * width_ratio",`x1*${h} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${h}`];this.userCode=` const float height_ratio = float(${g}); const float width_ratio = float(${w}); void main() { @@ -3003,20 +3003,20 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wst=It({opSnippet:bst}),k3={kernelN setOutput(newValue); } } - `}};var Jst=r=>{let{inputs:t,backend:e,attrs:n}=r,{image:o,boxes:s,boxInd:i}=t,{cropSize:a,method:u,extrapolationValue:l}=n,c=new MI(o.shape,s.shape,a,u,l);return e.runWebGLProgram(c,[o,s,i],"float32")},nB={kernelName:Ma,backendName:"webgl",kernelFunc:Jst};var gp;(function(r){r.Prod="*",r.Sum="+"})(gp||(gp={}));var fg=class{constructor(t,e,n,o){this.op=t,this.outputShape=e,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let s=this.outputShape.length,i=this.op===gp.Prod?"1.0":"0.0",a=n?i:`getX(${oB(s,"coords",this.op)})`,u=this.outputShape[this.outputShape.length-1],l="",c="";n?(l=o?`end != ${u-1}`:"end != 0",c=o?"end + 1":"end - 1"):(l=o?`end + pow2 < ${u}`:"end >= pow2",c=o?"end + pow2":"end - pow2"),this.userCode=` + `}};var rit=r=>{let{inputs:t,backend:e,attrs:n}=r,{image:o,boxes:s,boxInd:i}=t,{cropSize:a,method:u,extrapolationValue:l}=n,c=new LI(o.shape,s.shape,a,u,l);return e.runWebGLProgram(c,[o,s,i],"float32")},iB={kernelName:Ma,backendName:"webgl",kernelFunc:rit};var gp;(function(r){r.Prod="*",r.Sum="+"})(gp||(gp={}));var dg=class{constructor(t,e,n,o){this.op=t,this.outputShape=e,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let s=this.outputShape.length,i=this.op===gp.Prod?"1.0":"0.0",a=n?i:`getX(${aB(s,"coords",this.op)})`,u=this.outputShape[this.outputShape.length-1],l="",c="";n?(l=o?`end != ${u-1}`:"end != 0",c=o?"end + 1":"end - 1"):(l=o?`end + pow2 < ${u}`:"end >= pow2",c=o?"end + pow2":"end - pow2"),this.userCode=` void main() { ${zt(s)} coords = getOutputCoords(); - int end = ${sB(s,"coords",this.op)}; + int end = ${lB(s,"coords",this.op)}; float val = ${a}; int pow2 = int(pow(2.0, index)); if (${l}) { int idx = ${c}; - ${sB(s,"coords",this.op)} = idx; - val ${this.op}= getX(${oB(s,"coords",this.op)}); + ${lB(s,"coords",this.op)} = idx; + val ${this.op}= getX(${aB(s,"coords",this.op)}); } setOutput(val); } - `}};function oB(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.x, ${t}.y`;if(r===3)return`${t}.x, ${t}.y, ${t}.z`;if(r===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function sB(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.y`;if(r===3)return`${t}.z`;if(r===4)return`${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function PI(r,t,e,n,o,s){let i=t.shape.length,a=S.getAxesPermutation([n],i),u=t;a!=null&&(u=Pe({inputs:{x:t},backend:e,attrs:{perm:a}}));let l=S.getInnerMostAxes(1,i)[0];if(l!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let c=u.shape[l],p=nr({inputs:{x:u},backend:e});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let f=new fg(r,u.shape,!1,s),d=[[m]],h=p;p=e.runWebGLProgram(f,[p],p.dtype,d),e.disposeIntermediateTensorInfo(h)}if(o){let m=new fg(r,u.shape,o,s),f=p;p=e.runWebGLProgram(m,[p],p.dtype),e.disposeIntermediateTensorInfo(f)}if(a!=null){let m=S.getUndoAxesPermutation(a),f=Pe({inputs:{x:p},backend:e,attrs:{perm:m}});return e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(u),f}return p}function Qst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;return PI(gp.Prod,o,e,s,i,a)}var iB={kernelName:Oa,backendName:"webgl",kernelFunc:Qst};function tit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;return PI(gp.Sum,o,e,s,i,a)}var aB={kernelName:os,backendName:"webgl",kernelFunc:tit};function eit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i,binaryOutput:a}=n;if(o.shape.length===1){let u=e.readSync(o.dataId),l=e.readSync(s.dataId),c=Jw(u,l,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,c)}else if(o.shape.length===2){let u=e.bufferSync(o),l=e.bufferSync(s),c=UL(u,l,i,a);return e.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${o.shape.length}.`)}var lB={kernelName:jl,backendName:"webgl",kernelFunc:eit};var LI=class{constructor(t,e,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=t,this.blockSize=e,this.dataFormat=n,this.userCode=` + `}};function aB(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.x, ${t}.y`;if(r===3)return`${t}.x, ${t}.y, ${t}.z`;if(r===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function lB(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.y`;if(r===3)return`${t}.z`;if(r===4)return`${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function zI(r,t,e,n,o,s){let i=t.shape.length,a=S.getAxesPermutation([n],i),u=t;a!=null&&(u=Pe({inputs:{x:t},backend:e,attrs:{perm:a}}));let l=S.getInnerMostAxes(1,i)[0];if(l!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let c=u.shape[l],p=nr({inputs:{x:u},backend:e});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let f=new dg(r,u.shape,!1,s),d=[[m]],h=p;p=e.runWebGLProgram(f,[p],p.dtype,d),e.disposeIntermediateTensorInfo(h)}if(o){let m=new dg(r,u.shape,o,s),f=p;p=e.runWebGLProgram(m,[p],p.dtype),e.disposeIntermediateTensorInfo(f)}if(a!=null){let m=S.getUndoAxesPermutation(a),f=Pe({inputs:{x:p},backend:e,attrs:{perm:m}});return e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(u),f}return p}function nit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;return zI(gp.Prod,o,e,s,i,a)}var uB={kernelName:Oa,backendName:"webgl",kernelFunc:nit};function oit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;return zI(gp.Sum,o,e,s,i,a)}var cB={kernelName:os,backendName:"webgl",kernelFunc:oit};function sit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i,binaryOutput:a}=n;if(o.shape.length===1){let u=e.readSync(o.dataId),l=e.readSync(s.dataId),c=tI(u,l,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,c)}else if(o.shape.length===2){let u=e.bufferSync(o),l=e.bufferSync(s),c=KL(u,l,i,a);return e.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${o.shape.length}.`)}var pB={kernelName:jl,backendName:"webgl",kernelFunc:sit};var BI=class{constructor(t,e,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=t,this.blockSize=e,this.dataFormat=n,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3035,7 +3035,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wst=It({opSnippet:bst}),k3={kernelN 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 rit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockSize:s,dataFormat:i}=n,a=o.shape[0],u=i==="NHWC"?o.shape[1]:o.shape[2],l=i==="NHWC"?o.shape[2]:o.shape[3],c=i==="NHWC"?o.shape[3]:o.shape[1],p=u*s,m=l*s,f=c/(s*s),d=i==="NHWC"?[a,p,m,f]:[a,f,p,m],h=new LI(d,s,i);return e.runWebGLProgram(h,[o],o.dtype)}var uB={kernelName:Pa,backendName:"webgl",kernelFunc:rit};var Md=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=t.outShape,this.enableShapeUniforms=he(this.outputShape.length);let i=t.filterHeight,a=t.filterWidth,u=t.outChannels/t.inChannels,l="",c="";n&&(o?l=`float activation(float a) { + `}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 iit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockSize:s,dataFormat:i}=n,a=o.shape[0],u=i==="NHWC"?o.shape[1]:o.shape[2],l=i==="NHWC"?o.shape[2]:o.shape[3],c=i==="NHWC"?o.shape[3]:o.shape[1],p=u*s,m=l*s,f=c/(s*s),d=i==="NHWC"?[a,p,m,f]:[a,f,p,m],h=new BI(d,s,i);return e.runWebGLProgram(h,[o],o.dtype)}var mB={kernelName:Pa,backendName:"webgl",kernelFunc:iit};var Pd=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=t.outShape,this.enableShapeUniforms=he(this.outputShape.length);let i=t.filterHeight,a=t.filterWidth,u=t.outChannels/t.inChannels,l="",c="";n&&(o?l=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${n} }`:s?l=`float activation(float a) { @@ -3088,7 +3088,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wst=It({opSnippet:bst}),k3={kernelN ${c} setOutput(result); } - `}};var Pd=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=t.outShape,this.enableShapeUniforms=he(this.outputShape.length);let i=t.outChannels/t.inChannels,a=t.padInfo.left,u=t.strideWidth,l=t.dilationWidth,c=t.filterHeight,p=t.filterWidth,m=p,f=` + `}};var Ld=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=t.outShape,this.enableShapeUniforms=he(this.outputShape.length);let i=t.outChannels/t.inChannels,a=t.padInfo.left,u=t.strideWidth,l=t.dilationWidth,c=t.filterHeight,p=t.filterWidth,m=p,f=` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`;for(let x=0;x`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let p=S.computeConv2DInfo(o.shape,s.shape,i,c,a,l,!0),m;L().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?m=new Pd(p):m=new Md(p);let f=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return e.runWebGLProgram(m,[o,s],"float32",f)}var cB={kernelName:ss,backendName:"webgl",kernelFunc:nit};var zI=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideHeight,n=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,i=t.outChannels/t.inChannels;this.userCode=` + `}};function ait(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u,dimRoundingMode:l}=n,c=u;c==null&&(c=[1,1]),y.assert(S.eitherStridesOrDilationsAreOne(i,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let p=S.computeConv2DInfo(o.shape,s.shape,i,c,a,l,!0),m;L().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?m=new Ld(p):m=new Pd(p);let f=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return e.runWebGLProgram(m,[o,s],"float32",f)}var fB={kernelName:ss,backendName:"webgl",kernelFunc:ait};var VI=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideHeight,n=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,i=t.outChannels/t.inChannels;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; @@ -3312,7 +3312,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wst=It({opSnippet:bst}),k3={kernelN } setOutput(dotProd); } - `}},BI=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=e-1-t.padInfo.top,a=n-1-t.padInfo.left,u=t.outChannels/t.inChannels;this.userCode=` + `}},GI=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=e-1-t.padInfo.top,a=n-1-t.padInfo.left,u=t.outChannels/t.inChannels;this.userCode=` const ivec2 pads = ivec2(${i}, ${a}); void main() { @@ -3357,13 +3357,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wst=It({opSnippet:bst}),k3={kernelN } setOutput(dotProd); } - `}};function oit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,filterShape:c}=n,p=S.computeConv2DInfo(o.shape,c,i,a,u,l,!0),m=new zI(p);return e.runWebGLProgram(m,[o,s],"float32")}var pB={kernelName:$p,backendName:"webgl",kernelFunc:oit};function sit(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,inputShape:c}=n,p=S.computeConv2DInfo(c,s.shape,i,a,u,l,!0),m=new BI(p);return e.runWebGLProgram(m,[o,s],"float32")}var mB={kernelName:Rp,backendName:"webgl",kernelFunc:sit};var VI=class{constructor(t){this.variableNames=["X"],this.outputShape=[t,t],this.userCode=` + `}};function lit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,filterShape:c}=n,p=S.computeConv2DInfo(o.shape,c,i,a,u,l,!0),m=new VI(p);return e.runWebGLProgram(m,[o,s],"float32")}var dB={kernelName:$p,backendName:"webgl",kernelFunc:lit};function uit(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,inputShape:c}=n,p=S.computeConv2DInfo(c,s.shape,i,a,u,l,!0),m=new GI(p);return e.runWebGLProgram(m,[o,s],"float32")}var hB={kernelName:Rp,backendName:"webgl",kernelFunc:uit};var WI=class{constructor(t){this.variableNames=["X"],this.outputShape=[t,t],this.userCode=` void main() { ivec2 coords = getOutputCoords(); float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0; setOutput(val); } - `}};function iit(r){let{inputs:t,backend:e}=r,{x:n}=t,o=[...n.shape,...n.shape],s=y.sizeFromShape(n.shape),i=rt({inputs:{x:n},backend:e,attrs:{shape:[s]}}),a=new VI(s),u=e.runWebGLProgram(a,[i],i.dtype),l=rt({inputs:{x:u},backend:e,attrs:{shape:o}});return e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(u),l}var fB={kernelName:Xl,backendName:"webgl",kernelFunc:iit};var GI=class{constructor(t){this.variableNames=["x","W"],this.outputShape=t.outShape;let{inHeight:e,inWidth:n,padInfo:o,strideHeight:s,strideWidth:i,filterHeight:a,filterWidth:u,dilationHeight:l,dilationWidth:c}=t,{top:p,left:m}=o;this.userCode=` + `}};function cit(r){let{inputs:t,backend:e}=r,{x:n}=t,o=[...n.shape,...n.shape],s=y.sizeFromShape(n.shape),i=rt({inputs:{x:n},backend:e,attrs:{shape:[s]}}),a=new WI(s),u=e.runWebGLProgram(a,[i],i.dtype),l=rt({inputs:{x:u},backend:e,attrs:{shape:o}});return e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(u),l}var gB={kernelName:Xl,backendName:"webgl",kernelFunc:cit};var UI=class{constructor(t){this.variableNames=["x","W"],this.outputShape=t.outShape;let{inHeight:e,inWidth:n,padInfo:o,strideHeight:s,strideWidth:i,filterHeight:a,filterWidth:u,dilationHeight:l,dilationWidth:c}=t,{top:p,left:m}=o;this.userCode=` const ivec2 strides = ivec2(${s}, ${i}); const ivec2 pads = ivec2(${p}, ${m}); const float neg_infinity = -3.4e38; @@ -3401,7 +3401,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wst=It({opSnippet:bst}),k3={kernelN float result = curVal; setOutput(result); } - `}};function ait(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n,l=S.computeDilation2DInfo(o.shape,s.shape,i,a,"NHWC",u),c,p=new GI(l);c=e.runWebGLProgram(p,[o,s],"float32");let m=rt({inputs:{x:c},backend:e,attrs:{shape:l.outShape}});return e.disposeIntermediateTensorInfo(c),m}var dB={kernelName:is,backendName:"webgl",kernelFunc:ait};function lit(r){let{inputs:t,backend:e,attrs:n}=r,{equation:o}=n,s=t,{allDims:i,summedDims:a,idDims:u}=S.decodeEinsumEquation(o,s.length);S.checkEinsumDimSizes(i.length,u,s);let{path:l,steps:c}=S.getEinsumComputePath(a,u),p=c.length,m=null,f=i.length,d=[];for(let h=0;h=0&&(m=fp({inputs:{x:m},backend:e,attrs:{axis:l[h]-(i.length-f),keepDims:!1}}),d.push(m)),f--)}for(let h of d)h!==m&&e.disposeIntermediateTensorInfo(h);return m}var hB={kernelName:Fp,backendName:"webgl",kernelFunc:lit};var uit="return (x >= 0.0) ? x : (exp(x) - 1.0);",cit=` + `}};function pit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n,l=S.computeDilation2DInfo(o.shape,s.shape,i,a,"NHWC",u),c,p=new UI(l);c=e.runWebGLProgram(p,[o,s],"float32");let m=rt({inputs:{x:c},backend:e,attrs:{shape:l.outShape}});return e.disposeIntermediateTensorInfo(c),m}var xB={kernelName:is,backendName:"webgl",kernelFunc:pit};function mit(r){let{inputs:t,backend:e,attrs:n}=r,{equation:o}=n,s=t,{allDims:i,summedDims:a,idDims:u}=S.decodeEinsumEquation(o,s.length);S.checkEinsumDimSizes(i.length,u,s);let{path:l,steps:c}=S.getEinsumComputePath(a,u),p=c.length,m=null,f=i.length,d=[];for(let h=0;h=0&&(m=fp({inputs:{x:m},backend:e,attrs:{axis:l[h]-(i.length-f),keepDims:!1}}),d.push(m)),f--)}for(let h of d)h!==m&&e.disposeIntermediateTensorInfo(h);return m}var yB={kernelName:Fp,backendName:"webgl",kernelFunc:mit};var fit="return (x >= 0.0) ? x : (exp(x) - 1.0);",dit=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -3410,12 +3410,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wst=It({opSnippet:bst}),k3={kernelN result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; -`,pit=It({opSnippet:uit,packedOpSnippet:cit}),gB={kernelName:ls,backendName:"webgl",kernelFunc:pit};var mit="return (b >= 0.0) ? a : a * (b + 1.0);",fit=` +`,hit=It({opSnippet:fit,packedOpSnippet:dit}),bB={kernelName:ls,backendName:"webgl",kernelFunc:hit};var git="return (b >= 0.0) ? a : a * (b + 1.0);",xit=` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); -`,dit=r=>{let{inputs:t,backend:e}=r,{dy:n,y:o}=t,s=L().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new jn(fit,n.shape,o.shape):new $n(mit,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],n.dtype)},xB={kernelName:La,backendName:"webgl",kernelFunc:dit};var hit=` +`,yit=r=>{let{inputs:t,backend:e}=r,{dy:n,y:o}=t,s=L().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new jn(xit,n.shape,o.shape):new $n(git,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],n.dtype)},wB={kernelName:La,backendName:"webgl",kernelFunc:yit};var bit=` return vec4(equal(a, b)); -`,git="return float(a == b);",xit=ce({opSnippet:git,packedOpSnippet:hit,dtype:"bool",cpuKernelImpl:XL}),yB={kernelName:za,backendName:"webgl",kernelFunc:xit};var yit=` +`,wit="return float(a == b);",Iit=ce({opSnippet:wit,packedOpSnippet:bit,dtype:"bool",cpuKernelImpl:JL}),IB={kernelName:za,backendName:"webgl",kernelFunc:Iit};var Cit=` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. @@ -3430,9 +3430,9 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wst=It({opSnippet:bst}),k3={kernelN x = abs(x); float t = 1.0 / (1.0 + p * x); return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x)); -`,bit=It({opSnippet:yit}),bB={kernelName:us,backendName:"webgl",kernelFunc:bit};var wit=Po+` +`,vit=It({opSnippet:Cit}),CB={kernelName:us,backendName:"webgl",kernelFunc:vit};var Sit=Po+` return exp(x); -`,Iit=` +`,Nit=` vec4 result = exp(x); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : result.r; @@ -3441,7 +3441,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wst=It({opSnippet:bst}),k3={kernelN result.a = isNaN.a ? x.a : result.a; return result; -`,P1=It({opSnippet:wit,packedOpSnippet:Iit,cpuKernelImpl:YL,dtype:"float32"}),wB={kernelName:cs,backendName:"webgl",kernelFunc:P1};function WI(r){let{inputs:t,attrs:e,backend:n}=r,{dim:o}=e,{input:s}=t,i=s.shape.length,a=s.shape.slice(),u=o;return o<0&&(y.assert(-(i+1)<=o,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+o+1),a.splice(u,0,1),rt({inputs:{x:s},backend:n,attrs:{shape:a}})}var IB={kernelName:Oi,backendName:"webgl",kernelFunc:WI};var CB="return exp(x) - 1.0;",Cit=It({opSnippet:CB,packedOpSnippet:CB,cpuKernelImpl:ZL}),vB={kernelName:ps,backendName:"webgl",kernelFunc:Cit};var dg=class{constructor(t,e,n){this.variableNames=["real","imag"];let o=e[1];this.outputShape=e;let s=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,i=n?`${o}.0`:"1.0",a;if(t==="real")a="return real * expR - imag * expI;";else if(t==="imag")a="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${t}.`);this.userCode=` +`,z1=It({opSnippet:Sit,packedOpSnippet:Nit,cpuKernelImpl:QL,dtype:"float32"}),vB={kernelName:cs,backendName:"webgl",kernelFunc:z1};function HI(r){let{inputs:t,attrs:e,backend:n}=r,{dim:o}=e,{input:s}=t,i=s.shape.length,a=s.shape.slice(),u=o;return o<0&&(y.assert(-(i+1)<=o,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+o+1),a.splice(u,0,1),rt({inputs:{x:s},backend:n,attrs:{shape:a}})}var SB={kernelName:Mi,backendName:"webgl",kernelFunc:HI};var NB="return exp(x) - 1.0;",kit=It({opSnippet:NB,packedOpSnippet:NB,cpuKernelImpl:tz}),kB={kernelName:ps,backendName:"webgl",kernelFunc:kit};var hg=class{constructor(t,e,n){this.variableNames=["real","imag"];let o=e[1];this.outputShape=e;let s=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,i=n?`${o}.0`:"1.0",a;if(t==="real")a="return real * expR - imag * expI;";else if(t==="imag")a="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${t}.`);this.userCode=` const float exponentMultiplier = ${s}; float unaryOpComplex(float real, float expR, float imag, float expI) { @@ -3474,12 +3474,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wst=It({opSnippet:bst}),k3={kernelN ivec2 coords = getOutputCoords(); setOutput(mulMatDFT(coords[0], coords[1])); } - `}};function UI(r,t,e){let n=e.texData.get(r.dataId),o=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],i=o/s,a=rt({inputs:{x:r},backend:e,attrs:{shape:[i,s]}}),u=a.shape,l=new dg("real",u,t),c=new dg("imag",u,t),p=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:u},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:u}],m=e.runWebGLProgram(l,p,"float32"),f=e.runWebGLProgram(c,p,"float32"),d=Rn({inputs:{real:m,imag:f},backend:e});e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f);let h=rt({inputs:{x:d},backend:e,attrs:{shape:r.shape}});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(d),h}function vit(r){let{inputs:t,backend:e}=r,{input:n}=t;return UI(n,!1,e)}var SB={kernelName:Op,backendName:"webgl",kernelFunc:vit};var HI=class{constructor(t,e){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=t,this.userCode=` + `}};function qI(r,t,e){let n=e.texData.get(r.dataId),o=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],i=o/s,a=rt({inputs:{x:r},backend:e,attrs:{shape:[i,s]}}),u=a.shape,l=new hg("real",u,t),c=new hg("imag",u,t),p=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:u},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:u}],m=e.runWebGLProgram(l,p,"float32"),f=e.runWebGLProgram(c,p,"float32"),d=Rn({inputs:{real:m,imag:f},backend:e});e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f);let h=rt({inputs:{x:d},backend:e,attrs:{shape:r.shape}});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(d),h}function Tit(r){let{inputs:t,backend:e}=r,{input:n}=t;return qI(n,!1,e)}var TB={kernelName:Op,backendName:"webgl",kernelFunc:Tit};var KI=class{constructor(t,e){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=t,this.userCode=` void main() { // Input can be obtained from uniform value. setOutput(value); } - `}};function Ll(r){let{backend:t,attrs:e}=r,{shape:n,value:o}=e,{dtype:s}=e;if(s=s||y.inferDtype(o),s==="string"){let i=y.getArrayFromDType(s,y.sizeFromShape(n));return i.fill(o),t.makeTensorInfo(n,s,i)}else{let i=new HI(n,o),a=[[o]];return t.runWebGLProgram(i,[],s,a)}}var NB={kernelName:Jl,backendName:"webgl",kernelFunc:Ll};var qI=class{constructor(t){this.variableNames=["Image"],this.outputShape=[];let e=t[2];this.outputShape=t,this.userCode=` + `}};function Ll(r){let{backend:t,attrs:e}=r,{shape:n,value:o}=e,{dtype:s}=e;if(s=s||y.inferDtype(o),s==="string"){let i=y.getArrayFromDType(s,y.sizeFromShape(n));return i.fill(o),t.makeTensorInfo(n,s,i)}else{let i=new KI(n,o),a=[[o]];return t.runWebGLProgram(i,[],s,a)}}var _B={kernelName:Jl,backendName:"webgl",kernelFunc:Ll};var jI=class{constructor(t){this.variableNames=["Image"],this.outputShape=[];let e=t[2];this.outputShape=t,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int x = coords[2]; @@ -3493,7 +3493,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wst=It({opSnippet:bst}),k3={kernelN } setOutput(outputValue); } - `}};var kB={kernelName:Ba,backendName:"webgl",kernelFunc:({inputs:r,backend:t})=>{let{image:e}=r,n=t,o=new qI(e.shape);return n.runWebGLProgram(o,[e],e.dtype)}};var TB="return floor(x);",Sit=It({opSnippet:TB,packedOpSnippet:TB,cpuKernelImpl:JL}),_B={kernelName:ms,backendName:"webgl",kernelFunc:Sit};var Nit=` + `}};var EB={kernelName:Ba,backendName:"webgl",kernelFunc:({inputs:r,backend:t})=>{let{image:e}=r,n=t,o=new jI(e.shape);return n.runWebGLProgram(o,[e],e.dtype)}};var AB="return floor(x);",_it=It({opSnippet:AB,packedOpSnippet:AB,cpuKernelImpl:ez}),DB={kernelName:ms,backendName:"webgl",kernelFunc:_it};var Eit=` float s = sign(a) * sign(b); int ia = round(a); int ib = round(b); @@ -3503,7 +3503,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wst=It({opSnippet:bst}),k3={kernelN } else { return NAN; } -`,kit=` +`,Ait=` ivec4 ia = round(a); ivec4 ib = round(b); bvec4 cond = notEqual(ib, ivec4(0)); @@ -3524,7 +3524,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wst=It({opSnippet:bst}),k3={kernelN result[3] = idiv(ia[3], ib[3], s[3]); } return vec4(result); -`,Tit=ce({opSnippet:Nit,packedOpSnippet:kit,dtype:"int32"}),EB={kernelName:fs,backendName:"webgl",kernelFunc:Tit};var KI=class{constructor(t){this.variableNames=["A"];let e=Ue(),[n,o]=t;this.outputShape=t,this.userCode=` +`,Dit=ce({opSnippet:Eit,packedOpSnippet:Ait,dtype:"int32"}),$B={kernelName:fs,backendName:"webgl",kernelFunc:Dit};var XI=class{constructor(t){this.variableNames=["A"];let e=Ue(),[n,o]=t;this.outputShape=t,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; @@ -3546,7 +3546,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wst=It({opSnippet:bst}),k3={kernelN setOutput(floor(value * 255.0 + 0.5)); } - `}};var jI=class{constructor(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let e=Ue(),[n,o]=t;this.outputShape=t,this.userCode=` + `}};var YI=class{constructor(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let e=Ue(),[n,o]=t;this.outputShape=t,this.userCode=` void main() { ivec3 coords = getOutputCoords(); 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-`,jit=ce({opSnippet:qit,packedOpSnippet:Kit,cpuKernelImpl:nz,dtype:"bool"}),VB={kernelName:Wa,backendName:"webgl",kernelFunc:jit};var Xit="return float(a <= b);",Yit=` +`,Jit=ce({opSnippet:Yit,packedOpSnippet:Zit,cpuKernelImpl:iz,dtype:"bool"}),UB={kernelName:Wa,backendName:"webgl",kernelFunc:Jit};var Qit="return float(a <= b);",tat=` return vec4(lessThanEqual(a, b)); -`,Zit=ce({opSnippet:Xit,packedOpSnippet:Yit,cpuKernelImpl:oz,dtype:"bool"}),GB={kernelName:Ua,backendName:"webgl",kernelFunc:Zit};function Jit(r){let{backend:t,attrs:e}=r,{start:n,stop:o,num:s}=e,i=sz(n,o,s);return t.makeTensorInfo([i.length],"float32",i)}var WB={kernelName:Ha,backendName:"webgl",kernelFunc:Jit};var Qit=Po+` +`,eat=ce({opSnippet:Qit,packedOpSnippet:tat,cpuKernelImpl:az,dtype:"bool"}),HB={kernelName:Ua,backendName:"webgl",kernelFunc:eat};function rat(r){let{backend:t,attrs:e}=r,{start:n,stop:o,num:s}=e,i=lz(n,o,s);return t.makeTensorInfo([i.length],"float32",i)}var qB={kernelName:Ha,backendName:"webgl",kernelFunc:rat};var nat=Po+` return x < 0.0 ? 0./0. : log(x); -`,tat=` +`,oat=` vec4 result = log(x); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r); @@ -3620,18 +3620,18 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wst=It({opSnippet:bst}),k3={kernelN result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b); result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a); return result; -`,eat=It({opSnippet:Qit,packedOpSnippet:tat,cpuKernelImpl:iz}),UB={kernelName:ws,backendName:"webgl",kernelFunc:eat};var rat=Po+` +`,sat=It({opSnippet:nat,packedOpSnippet:oat,cpuKernelImpl:uz}),KB={kernelName:ws,backendName:"webgl",kernelFunc:sat};var iat=Po+` return log(1.0 + x); -`,nat=It({opSnippet:rat}),HB={kernelName:Is,backendName:"webgl",kernelFunc:nat};var oat="return float(a >= 1.0 && b >= 1.0);",sat=` +`,aat=It({opSnippet:iat}),jB={kernelName:Is,backendName:"webgl",kernelFunc:aat};var lat="return float(a >= 1.0 && b >= 1.0);",uat=` return vec4( vec4(greaterThanEqual(a, vec4(1.0))) * vec4(greaterThanEqual(b, vec4(1.0)))); -`,iat=ce({opSnippet:oat,packedOpSnippet:sat,dtype:"bool"}),qB={kernelName:qa,backendName:"webgl",kernelFunc:iat};var aat="return float(!(x >= 1.0));",lat=It({opSnippet:aat}),KB={kernelName:Ka,backendName:"webgl",kernelFunc:lat};var uat="return float(a >= 1.0 || b >= 1.0);",cat=` +`,cat=ce({opSnippet:lat,packedOpSnippet:uat,dtype:"bool"}),XB={kernelName:qa,backendName:"webgl",kernelFunc:cat};var pat="return float(!(x >= 1.0));",mat=It({opSnippet:pat}),YB={kernelName:Ka,backendName:"webgl",kernelFunc:mat};var fat="return float(a >= 1.0 || b >= 1.0);",dat=` return min( vec4(greaterThanEqual(a, vec4(1.0))) + vec4(greaterThanEqual(b, vec4(1.0))), vec4(1.0)); -`,pat=ce({opSnippet:uat,packedOpSnippet:cat,dtype:"bool"}),jB={kernelName:ja,backendName:"webgl",kernelFunc:pat};var ZI=class{constructor(t,e,n,o,s){this.variableNames=["x"],this.outputShape=[];let i=e,a=t[3]-1;this.outputShape=t;let u,l=`float(${n}) + float(${o}) * sum`;s===.5?u=`inversesqrt(${l})`:s===1?u=`1.0/(${l})`:u=`exp(log(${l}) * float(-${s}));`,this.userCode=` +`,hat=ce({opSnippet:fat,packedOpSnippet:dat,dtype:"bool"}),ZB={kernelName:ja,backendName:"webgl",kernelFunc:hat};var QI=class{constructor(t,e,n,o,s){this.variableNames=["x"],this.outputShape=[];let i=e,a=t[3]-1;this.outputShape=t;let u,l=`float(${n}) + float(${o}) * sum`;s===.5?u=`inversesqrt(${l})`:s===1?u=`1.0/(${l})`:u=`exp(log(${l}) * float(-${s}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3650,7 +3650,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wst=It({opSnippet:bst}),k3={kernelN float val = x * ${u}; setOutput(val); } - `}};var JI=class{constructor(t,e,n,o,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let i=e,a=t[3]-1;this.outputShape=t;let u,l=`float(${n}) + float(${o}) * sum`;s===.5?u=`inversesqrt(${l})`:s===1?u=`1.0/(${l})`:u=`exp(log(${l}) * float(-${s}));`,this.userCode=` + `}};var tC=class{constructor(t,e,n,o,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let i=e,a=t[3]-1;this.outputShape=t;let u,l=`float(${n}) + float(${o}) * sum`;s===.5?u=`inversesqrt(${l})`:s===1?u=`1.0/(${l})`:u=`exp(log(${l}) * float(-${s}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords.x; @@ -3712,7 +3712,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wst=It({opSnippet:bst}),k3={kernelN vec4 result = xAtOutputCoords * ${u}; setOutput(result); } - `}};var mat=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n,l=L().getBool("WEBGL_PACK_NORMALIZATION")?new JI(o.shape,s,i,a,u):new ZI(o.shape,s,i,a,u);return e.runWebGLProgram(l,[o],o.dtype)},XB={kernelName:Cs,backendName:"webgl",kernelFunc:mat};var QI=class{constructor(t,e,n,o,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=t,this.depth=t[3],this.depthRadius=e,this.bias=n,this.alpha=o,this.beta=s,this.userCode=` + `}};var gat=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n,l=L().getBool("WEBGL_PACK_NORMALIZATION")?new tC(o.shape,s,i,a,u):new QI(o.shape,s,i,a,u);return e.runWebGLProgram(l,[o],o.dtype)},JB={kernelName:Cs,backendName:"webgl",kernelFunc:gat};var eC=class{constructor(t,e,n,o,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=t,this.depth=t[3],this.depthRadius=e,this.bias=n,this.alpha=o,this.beta=s,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3767,16 +3767,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wst=It({opSnippet:bst}),k3={kernelN } setOutput(result); } - `}};var fat=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o,y:s,dy:i}=t,{depthRadius:a,bias:u,alpha:l,beta:c}=n,p=new QI(o.shape,a,u,l,c);return e.runWebGLProgram(p,[o,s,i],o.dtype)},YB={kernelName:Xa,backendName:"webgl",kernelFunc:fat};function ZB(r,t,e,n){let o=y.sizeFromShape(t),i=y.sizeFromShape(r.shape)/o,a=rt({inputs:{x:r},attrs:{shape:[i,o]},backend:n}),u=Yn(a,r.dtype,"max",n),l=rt({inputs:{x:u},attrs:{shape:e},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(u),l}function B1(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{reductionIndices:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=c!=null,m=e.shouldExecuteOnCPU([o]),f=o;if(p){if(m){let w=e.texData.get(f.dataId).values,I=new Array(a);for(let A=0;A{let{inputs:t,backend:e,attrs:n}=r,{x:o,y:s,dy:i}=t,{depthRadius:a,bias:u,alpha:l,beta:c}=n,p=new eC(o.shape,a,u,l,c);return e.runWebGLProgram(p,[o,s,i],o.dtype)},QB={kernelName:Xa,backendName:"webgl",kernelFunc:xat};function tV(r,t,e,n){let o=y.sizeFromShape(t),i=y.sizeFromShape(r.shape)/o,a=rt({inputs:{x:r},attrs:{shape:[i,o]},backend:n}),u=Yn(a,r.dtype,"max",n),l=rt({inputs:{x:u},attrs:{shape:e},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(u),l}function G1(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{reductionIndices:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=c!=null,m=e.shouldExecuteOnCPU([o]),f=o;if(p){if(m){let w=e.texData.get(f.dataId).values,I=new Array(a);for(let A=0;A`Error in maxPool: Either strides or dilations must be 1. 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Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,l,a,u);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return nr({inputs:{x:o},backend:e});let p=new Ni(c,"max",!1);return e.runWebGLProgram(p,[o],o.dtype)}var nV={kernelName:Ns,backendName:"webgl",kernelFunc:Iat};function Cat(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dataFormat:u,dimRoundingMode:l}=n,c=[1,1,1],p=S.computePool3DInfo(o.shape,s,i,c,a,l,u),m=new qu(p,"max",!1);return e.runWebGLProgram(m,[o],o.dtype)}var oV={kernelName:Li,backendName:"webgl",kernelFunc:Cat};var rC=class{constructor(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;let e=t.strideHeight,n=t.strideWidth,o=t.dilationHeight,s=t.effectiveFilterHeight,i=t.effectiveFilterWidth,a=s-1-t.padInfo.top,u=i-1-t.padInfo.left,l=s*i-1;this.userCode=` const ivec2 pads = ivec2(${a}, ${u}); void main() { @@ -3822,7 +3822,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wst=It({opSnippet:bst}),k3={kernelN } setOutput(dotProd); } - `}},eC=class{constructor(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;let e=t.strideDepth,n=t.strideHeight,o=t.strideWidth,s=t.dilationDepth,i=t.dilationHeight,a=t.dilationWidth,u=t.effectiveFilterDepth,l=t.effectiveFilterHeight,c=t.effectiveFilterWidth,p=u-1-t.padInfo.front,m=l-1-t.padInfo.top,f=c-1-t.padInfo.left,d=u*l*c-1;this.userCode=` + `}},nC=class{constructor(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;let e=t.strideDepth,n=t.strideHeight,o=t.strideWidth,s=t.dilationDepth,i=t.dilationHeight,a=t.dilationWidth,u=t.effectiveFilterDepth,l=t.effectiveFilterHeight,c=t.effectiveFilterWidth,p=u-1-t.padInfo.front,m=l-1-t.padInfo.top,f=c-1-t.padInfo.left,d=u*l*c-1;this.userCode=` const ivec3 pads = ivec3(${p}, ${m}, ${f}); void main() { @@ -3886,16 +3886,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wst=It({opSnippet:bst}),k3={kernelN } setOutput(dotProd); } - `}};function bat(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s,{filterSize:a,strides:u,pad:l,dimRoundingMode:c}=n,p=[1,1,1],m=S.computePool3DInfo(i.shape,a,u,p,l,c),f=new qu(m,"max",!0),d=e.runWebGLProgram(f,[i],i.dtype),h=new eC(m),g=e.runWebGLProgram(h,[o,d],i.dtype);return e.disposeIntermediateTensorInfo(d),g}var rV={kernelName:tu,backendName:"webgl",kernelFunc:bat};function wat(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s,output:i}=t,a=s;Ci([s,i],"maxPoolGrad");let{filterSize:u,strides:l,pad:c,dimRoundingMode:p}=n,m=S.computePool2DInfo(a.shape,u,l,1,c,p),f=!0,d=new Si(m,"max",f),h=e.runWebGLProgram(d,[a],a.dtype),g=new tC(m),x=e.runWebGLProgram(g,[o,h],a.dtype);return e.disposeIntermediateTensorInfo(h),x}var nV={kernelName:Ql,backendName:"webgl",kernelFunc:wat};function oV(r,t,e,n){let o=new Si(e,"max",!1),s=n.runWebGLProgram(o,[r],"float32");o=new Si(e,"max",!0,!0,t);let i=n.runWebGLProgram(o,[r],"float32");return[s,i]}var sV={kernelName:eu,backendName:"webgl",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:n}=r,{filterSize:o,strides:s,pad:i,includeBatchInIndex:a}=t,u=e;y.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let l=[1,1];y.assert(S.eitherStridesOrDilationsAreOne(s,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${l}'`);let c=S.computePool2DInfo(n.shape,o,s,l,i),[p,m]=oV(n,a,c,u);return[p,m]}};function iV(r,t,e,n){let o=y.sizeFromShape(t),i=y.sizeFromShape(r.shape)/o,a=rt({inputs:{x:r},attrs:{shape:[i,o]},backend:n}),u=Yn(a,"float32","mean",n),l=rt({inputs:{x:u},attrs:{shape:e},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(u),l}var aV={kernelName:ks,backendName:"webgl",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:n}=r,{keepDims:o,axis:s}=t,i=e,a=n.shape.length,u=y.parseAxisParam(s,n.shape),l=u,c=S.getAxesPermutation(l,a),p=c!=null,m=i.shouldExecuteOnCPU([n]),f=[],d=n;if(p){if(m){let I=i.texData.get(d.dataId).values,N=new Array(a);for(let D=0;D{let{x:n}=r,{filterSize:o,strides:s,pad:i,includeBatchInIndex:a}=t,u=e;y.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let l=[1,1];y.assert(S.eitherStridesOrDilationsAreOne(s,l),()=>`Error in maxPool: Either strides or dilations must be 1. 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int end = ${a}; @@ -3924,7 +3924,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wst=It({opSnippet:bst}),k3={kernelN ${s} coords = outC - start; setOutput(getX(${u})); } - `}};var nC=class{constructor(t,e,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.map((d,h)=>d[0]+t[h]+d[1]);let o=t.length,s=zt(o),i=e.map(d=>d[0]).join(","),a=e.map((d,h)=>d[0]+t[h]).join(","),u=rr("rc",o),l=rr("source",o),c=`${u[o-1]} < ${this.outputShape[o-1]}`,p=o===1?"source":`vec2(${l.slice(-2).join()})`,m=n==="reflect"?0:1,f="";if(o===1){let d=` + `}};var sC=class{constructor(t,e,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.map((d,h)=>d[0]+t[h]+d[1]);let o=t.length,s=zt(o),i=e.map(d=>d[0]).join(","),a=e.map((d,h)=>d[0]+t[h]).join(","),u=rr("rc",o),l=rr("source",o),c=`${u[o-1]} < ${this.outputShape[o-1]}`,p=o===1?"source":`vec2(${l.slice(-2).join()})`,m=n==="reflect"?0:1,f="";if(o===1){let d=` ${s} source = rc; if (source < start) { source = start * 2 - source - ${m}; @@ -3980,13 +3980,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wst=It({opSnippet:bst}),k3={kernelN ${f} setOutput(result); } - `}};var Nat=({inputs:r,backend:t,attrs:e})=>{let{x:n}=r,{paddings:o,mode:s}=e,i=L().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new nC(n.shape,o,s):new rC(n.shape,o,s);return t.runWebGLProgram(i,[n],n.dtype)},cV={kernelName:Es,backendName:"webgl",kernelFunc:Nat};var kat=`if (b == 0.0) return NAN; - return mod(a, b);`,Tat=` + `}};var Eat=({inputs:r,backend:t,attrs:e})=>{let{x:n}=r,{paddings:o,mode:s}=e,i=L().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new sC(n.shape,o,s):new oC(n.shape,o,s);return t.runWebGLProgram(i,[n],n.dtype)},fV={kernelName:Es,backendName:"webgl",kernelFunc:Eat};var Aat=`if (b == 0.0) return NAN; + return mod(a, b);`,Dat=` vec4 result = mod(a, b); bvec4 isNaN = equal(b, vec4(0.0)); `+Xn+` return result; -`,_at=ce({opSnippet:kat,packedOpSnippet:Tat}),pV={kernelName:As,backendName:"webgl",kernelFunc:_at};var oC=class{constructor(t,e,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[t,n],this.userCode=` +`,$at=ce({opSnippet:Aat,packedOpSnippet:Dat}),dV={kernelName:As,backendName:"webgl",kernelFunc:$at};var iC=class{constructor(t,e,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[t,n],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -4006,11 +4006,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wst=It({opSnippet:bst}),k3={kernelN // If no other event happened, last event happened. setOutput(float(${e-1})); } - `}};var Eat=` + `}};var Rat=` if (a == b) { return 1.0; }; -return a / b;`,Aat=` +return a / b;`,Fat=` // vec4 one = vec4(equal(a, b)); // return one + (vec4(1.0) - one) * a / b; vec4 result = a / b; @@ -4028,9 +4028,9 @@ return a / b;`,Aat=` } return result; -`,V1=ce({opSnippet:Eat,packedOpSnippet:Aat,checkOutOfBounds:!0}),mV={kernelName:as,backendName:"webgl",kernelFunc:V1};var fV="return a - b;",G1=ce({opSnippet:fV,packedOpSnippet:fV,supportsComplex:!0,cpuKernelImpl:Az}),dV={kernelName:ei,backendName:"webgl",kernelFunc:G1};function W1(r){let{inputs:t,backend:e,attrs:n}=r,{logits:o}=t,{dim:s}=n,i=y.parseAxisParam([s],o.shape),a=B1({inputs:{x:o},backend:e,attrs:{reductionIndices:i,keepDims:!1}}),u=S.expandShapeToKeepDim(a.shape,i),l=rt({inputs:{x:a},backend:e,attrs:{shape:u}}),c=G1({inputs:{a:o,b:l},backend:e}),p=P1({inputs:{x:c},backend:e}),m=fp({inputs:{x:p},backend:e,attrs:{axis:i,keepDims:!1}}),f=rt({inputs:{x:m},backend:e,attrs:{shape:u}}),d=V1({inputs:{a:p,b:f},backend:e});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(l),e.disposeIntermediateTensorInfo(c),e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),d}var hV={kernelName:Qs,backendName:"webgl",kernelFunc:W1};function Dat(r){let{inputs:t,backend:e,attrs:n}=r,{logits:o}=t,{numSamples:s,seed:i,normalized:a}=n,u=a?o:W1({inputs:{logits:o},backend:e,attrs:{dim:o.shape.length-1}}),l=u.shape[0],c=u.shape[1],p=new oC(l,c,s),m=[[i]],f=e.runWebGLProgram(p,[u],"int32",m);return a||e.disposeIntermediateTensorInfo(u),f}var gV={kernelName:Ya,backendName:"webgl",kernelFunc:Dat};var $at=xr+` +`,W1=ce({opSnippet:Rat,packedOpSnippet:Fat,checkOutOfBounds:!0}),hV={kernelName:as,backendName:"webgl",kernelFunc:W1};var gV="return a - b;",U1=ce({opSnippet:gV,packedOpSnippet:gV,supportsComplex:!0,cpuKernelImpl:Rz}),xV={kernelName:ei,backendName:"webgl",kernelFunc:U1};function H1(r){let{inputs:t,backend:e,attrs:n}=r,{logits:o}=t,{dim:s}=n,i=y.parseAxisParam([s],o.shape),a=G1({inputs:{x:o},backend:e,attrs:{reductionIndices:i,keepDims:!1}}),u=S.expandShapeToKeepDim(a.shape,i),l=rt({inputs:{x:a},backend:e,attrs:{shape:u}}),c=U1({inputs:{a:o,b:l},backend:e}),p=z1({inputs:{x:c},backend:e}),m=fp({inputs:{x:p},backend:e,attrs:{axis:i,keepDims:!1}}),f=rt({inputs:{x:m},backend:e,attrs:{shape:u}}),d=W1({inputs:{a:p,b:f},backend:e});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(l),e.disposeIntermediateTensorInfo(c),e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),d}var yV={kernelName:Qs,backendName:"webgl",kernelFunc:H1};function Oat(r){let{inputs:t,backend:e,attrs:n}=r,{logits:o}=t,{numSamples:s,seed:i,normalized:a}=n,u=a?o:H1({inputs:{logits:o},backend:e,attrs:{dim:o.shape.length-1}}),l=u.shape[0],c=u.shape[1],p=new iC(l,c,s),m=[[i]],f=e.runWebGLProgram(p,[u],"int32",m);return a||e.disposeIntermediateTensorInfo(u),f}var bV={kernelName:Ya,backendName:"webgl",kernelFunc:Oat};var Mat=xr+` return -x; -`,Rat=` +`,Pat=` vec4 result = -x; bvec4 isNaN = isnan(x); @@ -4040,14 +4040,14 @@ return a / b;`,Aat=` result.a = isNaN.a ? x.a : result.a; return result; -`;function Fat(r){let{inputs:t,backend:e}=r,{x:n}=t;if(e.shouldExecuteOnCPU([n])){let s=e.texData.get(n.dataId),[i,a]=pz(s.values,n.shape,n.dtype);return e.makeTensorInfo(a,n.dtype,i)}let o;return L().getBool("WEBGL_PACK_UNARY_OPERATIONS")?o=new Dn(n.shape,Rat):o=new Lr(n.shape,$at),e.runWebGLProgram(o,[n],n.dtype)}var xV={kernelName:Li,backendName:"webgl",kernelFunc:Fat};var Oat=Kr.nonMaxSuppressionV3Impl;function Mat(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:n}=r,{boxes:o,scores:s}=t,{maxOutputSize:i,iouThreshold:a,scoreThreshold:u}=n,l=e.readSync(o.dataId),c=e.readSync(s.dataId),{selectedIndices:p}=Oat(l,c,i,a,u);return e.makeTensorInfo([p.length],"int32",new Int32Array(p))}var yV={kernelName:Ja,backendName:"webgl",kernelFunc:Mat};var Pat=Kr.nonMaxSuppressionV4Impl;function Lat(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:n}=r,{boxes:o,scores:s}=t,{maxOutputSize:i,iouThreshold:a,scoreThreshold:u,padToMaxOutputSize:l}=n,c=e.readSync(o.dataId),p=e.readSync(s.dataId),{selectedIndices:m,validOutputs:f}=Pat(c,p,i,a,u,l);return[e.makeTensorInfo([m.length],"int32",new Int32Array(m)),e.makeTensorInfo([],"int32",new Int32Array([f]))]}var bV={kernelName:Qa,backendName:"webgl",kernelFunc:Lat};var zat=Kr.nonMaxSuppressionV5Impl;function Bat(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:n}=r,{boxes:o,scores:s}=t,{maxOutputSize:i,iouThreshold:a,scoreThreshold:u,softNmsSigma:l}=n,c=e.readSync(o.dataId),p=e.readSync(s.dataId),m=i,f=a,d=u,h=l,{selectedIndices:g,selectedScores:x}=zat(c,p,m,f,d,h);return[e.makeTensorInfo([g.length],"int32",new Int32Array(g)),e.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var wV={kernelName:tl,backendName:"webgl",kernelFunc:Bat};var sC=class{constructor(t,e,n,o){this.variableNames=["indices"],this.outputShape=[t,e],this.userCode=` +`;function Lat(r){let{inputs:t,backend:e}=r,{x:n}=t;if(e.shouldExecuteOnCPU([n])){let s=e.texData.get(n.dataId),[i,a]=dz(s.values,n.shape,n.dtype);return e.makeTensorInfo(a,n.dtype,i)}let o;return L().getBool("WEBGL_PACK_UNARY_OPERATIONS")?o=new Dn(n.shape,Pat):o=new zr(n.shape,Mat),e.runWebGLProgram(o,[n],n.dtype)}var wV={kernelName:zi,backendName:"webgl",kernelFunc:Lat};var zat=Xr.nonMaxSuppressionV3Impl;function Bat(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:n}=r,{boxes:o,scores:s}=t,{maxOutputSize:i,iouThreshold:a,scoreThreshold:u}=n,l=e.readSync(o.dataId),c=e.readSync(s.dataId),{selectedIndices:p}=zat(l,c,i,a,u);return e.makeTensorInfo([p.length],"int32",new Int32Array(p))}var IV={kernelName:Ja,backendName:"webgl",kernelFunc:Bat};var Vat=Xr.nonMaxSuppressionV4Impl;function Gat(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:n}=r,{boxes:o,scores:s}=t,{maxOutputSize:i,iouThreshold:a,scoreThreshold:u,padToMaxOutputSize:l}=n,c=e.readSync(o.dataId),p=e.readSync(s.dataId),{selectedIndices:m,validOutputs:f}=Vat(c,p,i,a,u,l);return[e.makeTensorInfo([m.length],"int32",new Int32Array(m)),e.makeTensorInfo([],"int32",new Int32Array([f]))]}var CV={kernelName:Qa,backendName:"webgl",kernelFunc:Gat};var Wat=Xr.nonMaxSuppressionV5Impl;function Uat(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:n}=r,{boxes:o,scores:s}=t,{maxOutputSize:i,iouThreshold:a,scoreThreshold:u,softNmsSigma:l}=n,c=e.readSync(o.dataId),p=e.readSync(s.dataId),m=i,f=a,d=u,h=l,{selectedIndices:g,selectedScores:x}=Wat(c,p,m,f,d,h);return[e.makeTensorInfo([g.length],"int32",new Int32Array(g)),e.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var vV={kernelName:tl,backendName:"webgl",kernelFunc:Uat};var aC=class{constructor(t,e,n,o){this.variableNames=["indices"],this.outputShape=[t,e],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int index = round(getIndices(coords.x)); setOutput(mix(float(${o}), float(${n}), float(index == coords.y))); } - `}};var Vat=r=>{let{inputs:t,backend:e,attrs:n}=r,{indices:o}=t,{dtype:s,depth:i,onValue:a,offValue:u}=n,l=y.sizeFromShape(o.shape),c=new sC(l,i,a,u),p=rt({inputs:{x:o},backend:e,attrs:{shape:[l]}}),m=e.runWebGLProgram(c,[p],s);e.disposeIntermediateTensorInfo(p);let f=[...o.shape,i],d=rt({inputs:{x:m},backend:e,attrs:{shape:f}});return e.disposeIntermediateTensorInfo(m),d},IV={kernelName:$s,backendName:"webgl",kernelFunc:Vat};function hg(r){let{inputs:t,backend:e}=r,{x:n}=t;if(n.dtype==="complex64"){let o=Pl({inputs:{input:n},backend:e}),s=hg({inputs:{x:o},backend:e}),i=hp({inputs:{input:n},backend:e}),a=hg({inputs:{x:i},backend:e}),u=Rn({inputs:{real:s,imag:a},backend:e});return e.disposeIntermediateTensorInfo(o),e.disposeIntermediateTensorInfo(s),e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(a),u}else return Ll({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:e})}var CV={kernelName:Ki,backendName:"webgl",kernelFunc:hg};function vV(r){let{inputs:t,backend:e}=r,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let o=Pl({inputs:{input:n},backend:e}),s=vV({inputs:{x:o},backend:e}),i=hp({inputs:{input:n},backend:e}),a=hg({inputs:{x:i},backend:e}),u=Rn({inputs:{real:s,imag:a},backend:e});return e.disposeIntermediateTensorInfo(o),e.disposeIntermediateTensorInfo(s),e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(a),u}else return Ll({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:e})}var SV={kernelName:zi,backendName:"webgl",kernelFunc:vV};function Gat(r){let{inputs:t,backend:e,attrs:n}=r,{axis:o}=n;if(t.length===1)return WI({inputs:{input:t[0]},backend:e,attrs:{dim:o}});let s=t[0].shape,i=t[0].dtype;t.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),y.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let a=[],u=t.map(c=>{let p=WI({inputs:{input:c},backend:e,attrs:{dim:o}});return a.push(p),p}),l=M1({inputs:u,backend:e,attrs:{axis:o}});return a.forEach(c=>e.disposeIntermediateTensorInfo(c)),l}var NV={kernelName:Bi,backendName:"webgl",kernelFunc:Gat};var iC=class{constructor(t,e,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=e.map((l,c)=>l[0]+t[c]+l[1]);let o=t.length,s=zt(o),i=e.map(l=>l[0]).join(","),a=e.map((l,c)=>l[0]+t[c]).join(","),u=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,o);if(o===1){this.userCode=` + `}};var Hat=r=>{let{inputs:t,backend:e,attrs:n}=r,{indices:o}=t,{dtype:s,depth:i,onValue:a,offValue:u}=n,l=y.sizeFromShape(o.shape),c=new aC(l,i,a,u),p=rt({inputs:{x:o},backend:e,attrs:{shape:[l]}}),m=e.runWebGLProgram(c,[p],s);e.disposeIntermediateTensorInfo(p);let f=[...o.shape,i],d=rt({inputs:{x:m},backend:e,attrs:{shape:f}});return e.disposeIntermediateTensorInfo(m),d},SV={kernelName:$s,backendName:"webgl",kernelFunc:Hat};function gg(r){let{inputs:t,backend:e}=r,{x:n}=t;if(n.dtype==="complex64"){let o=Pl({inputs:{input:n},backend:e}),s=gg({inputs:{x:o},backend:e}),i=hp({inputs:{input:n},backend:e}),a=gg({inputs:{x:i},backend:e}),u=Rn({inputs:{real:s,imag:a},backend:e});return e.disposeIntermediateTensorInfo(o),e.disposeIntermediateTensorInfo(s),e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(a),u}else return Ll({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:e})}var NV={kernelName:ji,backendName:"webgl",kernelFunc:gg};function kV(r){let{inputs:t,backend:e}=r,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let o=Pl({inputs:{input:n},backend:e}),s=kV({inputs:{x:o},backend:e}),i=hp({inputs:{input:n},backend:e}),a=gg({inputs:{x:i},backend:e}),u=Rn({inputs:{real:s,imag:a},backend:e});return e.disposeIntermediateTensorInfo(o),e.disposeIntermediateTensorInfo(s),e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(a),u}else return Ll({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:e})}var TV={kernelName:Bi,backendName:"webgl",kernelFunc:kV};function qat(r){let{inputs:t,backend:e,attrs:n}=r,{axis:o}=n;if(t.length===1)return HI({inputs:{input:t[0]},backend:e,attrs:{dim:o}});let s=t[0].shape,i=t[0].dtype;t.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),y.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let a=[],u=t.map(c=>{let p=HI({inputs:{input:c},backend:e,attrs:{dim:o}});return a.push(p),p}),l=L1({inputs:u,backend:e,attrs:{axis:o}});return a.forEach(c=>e.disposeIntermediateTensorInfo(c)),l}var _V={kernelName:Vi,backendName:"webgl",kernelFunc:qat};var lC=class{constructor(t,e,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=e.map((l,c)=>l[0]+t[c]+l[1]);let o=t.length,s=zt(o),i=e.map(l=>l[0]).join(","),a=e.map((l,c)=>l[0]+t[c]).join(","),u=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,o);if(o===1){this.userCode=` int start = ${i}; int end = ${a}; @@ -4072,7 +4072,7 @@ return a / b;`,Aat=` setOutput(getX(${u})); } } - `}};var aC=class{constructor(t,e,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=e.map((h,g)=>h[0]+t[g]+h[1]);let o=t.length,s=zt(o),i=e.map(h=>h[0]).join(","),a=e.map((h,g)=>h[0]+t[g]).join(","),u=rr("rc",o),l=rr("source",o),c=`${u[o-1]} < ${this.outputShape[o-1]}`,p=o===1?"source":`vec2(${l.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${u[o-1]} += 1; + `}};var uC=class{constructor(t,e,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=e.map((h,g)=>h[0]+t[g]+h[1]);let o=t.length,s=zt(o),i=e.map(h=>h[0]).join(","),a=e.map((h,g)=>h[0]+t[g]).join(","),u=rr("rc",o),l=rr("source",o),c=`${u[o-1]} < ${this.outputShape[o-1]}`,p=o===1?"source":`vec2(${l.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${u[o-1]} += 1; if(${c}) { `,o===1?"":`} rc = outputLoc; @@ -4096,7 +4096,7 @@ return a / b;`,Aat=` ${d} setOutput(result); } - `}};var U1=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{paddings:s,constantValue:i}=n;if(y.sizeFromShape(o.shape)===0){let l=s.map((c,p)=>c[0]+o.shape[p]+c[1]);return Ll({backend:e,attrs:{shape:l,value:i,dtype:o.dtype}})}let a=L().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new aC(o.shape,s,i):new iC(o.shape,s,i),u=[[i]];return e.runWebGLProgram(a,[o],o.dtype,u)},kV={kernelName:Rs,backendName:"webgl",kernelFunc:U1};var Wat=` + `}};var q1=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{paddings:s,constantValue:i}=n;if(y.sizeFromShape(o.shape)===0){let l=s.map((c,p)=>c[0]+o.shape[p]+c[1]);return Ll({backend:e,attrs:{shape:l,value:i,dtype:o.dtype}})}let a=L().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new uC(o.shape,s,i):new lC(o.shape,s,i),u=[[i]];return e.runWebGLProgram(a,[o],o.dtype,u)},EV={kernelName:Rs,backendName:"webgl",kernelFunc:q1};var Kat=` if(a < 0.0 && floor(b) < b){ return NAN; } @@ -4105,7 +4105,7 @@ return a / b;`,Aat=` } return (round(mod(b, 2.0)) != 1) ? pow(abs(a), b) : sign(a) * pow(abs(a), b); -`,Uat=` +`,jat=` // 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); @@ -4123,9 +4123,9 @@ return a / b;`,Aat=` bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w); `+Xn+` return result; -`,Hat=ce({opSnippet:Wat,packedOpSnippet:Uat}),TV={kernelName:Fs,backendName:"webgl",kernelFunc:Hat};function qat(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=[],l=y.parseAxisParam(s,o.shape),c=l,p=S.getAxesPermutation(c,a),m=o;p!=null&&(m=Pe({inputs:{x:o},backend:e,attrs:{perm:p}}),c=S.getInnerMostAxes(c.length,a),u.push(m)),S.assertAxesAreInnerMostDims("prod",c,a);let f;if(e.shouldExecuteOnCPU([m])){let d=e.texData.get(m.dataId).values,{outVals:h,outShape:g,outDtype:x}=fz(m.shape,m.dtype,d,c);f=e.makeTensorInfo(g,x,h)}else{let[d,h]=S.computeOutAndReduceShapes(m.shape,c),g=y.sizeFromShape(h),x=rt({inputs:{x:m},backend:e,attrs:{shape:[-1,g]}}),b=lc(o.dtype),w=Yn(x,b,"prod",e);f=rt({inputs:{x:w},backend:e,attrs:{shape:d}}),u.push(x),u.push(w)}if(i){u.push(f);let d=S.expandShapeToKeepDim(f.shape,l);f=rt({inputs:{x:f},backend:e,attrs:{shape:d}})}return u.forEach(d=>e.disposeIntermediateTensorInfo(d)),f}var _V={kernelName:Ms,backendName:"webgl",kernelFunc:qat};function Kat(r){let{inputs:t,backend:e,attrs:n}=r,{paramsNestedSplits:o,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:a}=n,u=o.map(x=>e.readSync(x.dataId)),l=o.map(x=>x.shape),c=e.readSync(s.dataId),p=e.readSync(i.dataId),[m,f,d]=dz(u,l,c,s.shape,s.dtype,p,i.shape,a),h=m.map(x=>e.makeTensorInfo([x.length],"int32",x)),g=e.makeTensorInfo(d,s.dtype,f);return h.concat([g])}var EV={kernelName:Lp,backendName:"webgl",kernelFunc:Kat};function jat(r){let{inputs:t,backend:e}=r,{starts:n,limits:o,deltas:s}=t,i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=hz(i,n.shape,n.dtype,a,o.shape,u,s.shape),p=e.makeTensorInfo([l.length],"int32",l),m=e.makeTensorInfo([c.length],n.dtype,c);return[p,m]}var AV={kernelName:zp,backendName:"webgl",kernelFunc:jat};function Xat(r){let{inputs:t,backend:e,attrs:n}=r,{shape:o,values:s,defaultValue:i,rowPartitionTensors:a}=t,{rowPartitionTypes:u}=n,l=e.readSync(o.dataId),c=e.readSync(s.dataId),p=e.readSync(i.dataId),m=a.map(g=>e.readSync(g.dataId)),f=a.map(g=>g.shape),[d,h]=gz(l,o.shape,c,s.shape,s.dtype,p,i.shape,m,f,u);return e.makeTensorInfo(d,s.dtype,h)}var DV={kernelName:Bp,backendName:"webgl",kernelFunc:Xat};var H1=r=>{let{backend:t,attrs:e}=r,{start:n,stop:o,step:s,dtype:i}=e,a=xz(n,o,s,i);return t.makeTensorInfo([a.length],i,a)},$V={kernelName:ru,backendName:"webgl",kernelFunc:H1};var Yat="return 1.0 / x;",Zat=It({opSnippet:Yat}),RV={kernelName:Ps,backendName:"webgl",kernelFunc:Zat};var Jat=xr+` +`,Xat=ce({opSnippet:Kat,packedOpSnippet:jat}),AV={kernelName:Fs,backendName:"webgl",kernelFunc:Xat};function Yat(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=[],l=y.parseAxisParam(s,o.shape),c=l,p=S.getAxesPermutation(c,a),m=o;p!=null&&(m=Pe({inputs:{x:o},backend:e,attrs:{perm:p}}),c=S.getInnerMostAxes(c.length,a),u.push(m)),S.assertAxesAreInnerMostDims("prod",c,a);let f;if(e.shouldExecuteOnCPU([m])){let d=e.texData.get(m.dataId).values,{outVals:h,outShape:g,outDtype:x}=gz(m.shape,m.dtype,d,c);f=e.makeTensorInfo(g,x,h)}else{let[d,h]=S.computeOutAndReduceShapes(m.shape,c),g=y.sizeFromShape(h),x=rt({inputs:{x:m},backend:e,attrs:{shape:[-1,g]}}),b=lc(o.dtype),w=Yn(x,b,"prod",e);f=rt({inputs:{x:w},backend:e,attrs:{shape:d}}),u.push(x),u.push(w)}if(i){u.push(f);let d=S.expandShapeToKeepDim(f.shape,l);f=rt({inputs:{x:f},backend:e,attrs:{shape:d}})}return u.forEach(d=>e.disposeIntermediateTensorInfo(d)),f}var DV={kernelName:Ms,backendName:"webgl",kernelFunc:Yat};function Zat(r){let{inputs:t,backend:e,attrs:n}=r,{paramsNestedSplits:o,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:a}=n,u=o.map(x=>e.readSync(x.dataId)),l=o.map(x=>x.shape),c=e.readSync(s.dataId),p=e.readSync(i.dataId),[m,f,d]=xz(u,l,c,s.shape,s.dtype,p,i.shape,a),h=m.map(x=>e.makeTensorInfo([x.length],"int32",x)),g=e.makeTensorInfo(d,s.dtype,f);return h.concat([g])}var $V={kernelName:Lp,backendName:"webgl",kernelFunc:Zat};function Jat(r){let{inputs:t,backend:e}=r,{starts:n,limits:o,deltas:s}=t,i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=yz(i,n.shape,n.dtype,a,o.shape,u,s.shape),p=e.makeTensorInfo([l.length],"int32",l),m=e.makeTensorInfo([c.length],n.dtype,c);return[p,m]}var RV={kernelName:zp,backendName:"webgl",kernelFunc:Jat};function Qat(r){let{inputs:t,backend:e,attrs:n}=r,{shape:o,values:s,defaultValue:i,rowPartitionTensors:a}=t,{rowPartitionTypes:u}=n,l=e.readSync(o.dataId),c=e.readSync(s.dataId),p=e.readSync(i.dataId),m=a.map(g=>e.readSync(g.dataId)),f=a.map(g=>g.shape),[d,h]=bz(l,o.shape,c,s.shape,s.dtype,p,i.shape,m,f,u);return e.makeTensorInfo(d,s.dtype,h)}var FV={kernelName:Bp,backendName:"webgl",kernelFunc:Qat};var K1=r=>{let{backend:t,attrs:e}=r,{start:n,stop:o,step:s,dtype:i}=e,a=wz(n,o,s,i);return t.makeTensorInfo([a.length],i,a)},OV={kernelName:ru,backendName:"webgl",kernelFunc:K1};var tlt="return 1.0 / x;",elt=It({opSnippet:tlt}),MV={kernelName:Ps,backendName:"webgl",kernelFunc:elt};var rlt=xr+` return (x < 0.0) ? 0.0 : x; -`,Qat=` +`,nlt=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -4135,9 +4135,9 @@ return a / b;`,Aat=` result.a = isNaN.a ? x.a : result.a; return result; -`,tlt=It({opSnippet:Jat,packedOpSnippet:Qat}),FV={kernelName:Ls,backendName:"webgl",kernelFunc:tlt};var elt=xr+` +`,olt=It({opSnippet:rlt,packedOpSnippet:nlt}),PV={kernelName:Ls,backendName:"webgl",kernelFunc:olt};var slt=xr+` return (x < 0.0) ? 0.0 : min(6.0, x); -`,rlt=` +`,ilt=` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -4147,7 +4147,7 @@ return a / b;`,Aat=` result.a = isNaN.a ? x.a : result.a; return result; -`,nlt=It({opSnippet:elt,packedOpSnippet:rlt}),OV={kernelName:Vs,backendName:"webgl",kernelFunc:nlt};var lC=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m;s?m="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":m="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` +`,alt=It({opSnippet:slt,packedOpSnippet:ilt}),LV={kernelName:Vs,backendName:"webgl",kernelFunc:alt};var cC=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m;s?m="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":m="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${c[0]/p[0]}, ${c[1]/p[1]}); @@ -4180,7 +4180,7 @@ return a / b;`,Aat=` setOutput(newValue); } - `}};var uC=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m;s?m="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":m="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}};var pC=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m;s?m="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":m="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${c[0]/p[0]}, ${c[1]/p[1]}, @@ -4257,7 +4257,7 @@ return a / b;`,Aat=` setOutput(newValue); } - `}};function olt(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n,[u,l]=a,c=L().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new uC(o.shape,u,l,s,i):new lC(o.shape,u,l,s,i);return e.runWebGLProgram(c,[o],"float32")}var MV={kernelName:Bs,backendName:"webgl",kernelFunc:olt};var cC=class{constructor(t,e,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e;let[,o,s]=e,[,i,a]=t,u=[n&&i>1?o-1:o,n&&a>1?s-1:s],l=[n&&i>1?i-1:i,n&&a>1?a-1:a],c=u[0]/l[0],p=u[1]/l[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=` + `}};function llt(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n,[u,l]=a,c=L().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new pC(o.shape,u,l,s,i):new cC(o.shape,u,l,s,i);return e.runWebGLProgram(c,[o],"float32")}var zV={kernelName:Bs,backendName:"webgl",kernelFunc:llt};var mC=class{constructor(t,e,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e;let[,o,s]=e,[,i,a]=t,u=[n&&i>1?o-1:o,n&&a>1?s-1:s],l=[n&&i>1?i-1:i,n&&a>1?a-1:a],c=u[0]/l[0],p=u[1]/l[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -4338,7 +4338,7 @@ return a / b;`,Aat=` setOutput(accumulator); } - `}};function slt(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=new cC(s.shape,o.shape,i);return e.runWebGLProgram(a,[s],s.dtype)}var PV={kernelName:rl,backendName:"webgl",kernelFunc:slt};var pC=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m=o?"0.5":"0.0",f;s?f="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":f="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}};function ult(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=new mC(s.shape,o.shape,i);return e.runWebGLProgram(a,[s],s.dtype)}var BV={kernelName:rl,backendName:"webgl",kernelFunc:ult};var fC=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m=o?"0.5":"0.0",f;s?f="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":f="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${c[0]/p[0]}, ${c[1]/p[1]}); @@ -4360,7 +4360,7 @@ return a / b;`,Aat=` setOutput(newValue); } - `}};var mC=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m=o?"0.5":"0.0",f;s?f="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":f="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}};var dC=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m=o?"0.5":"0.0",f;s?f="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":f="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${c[0]/p[0]}, ${c[1]/p[1]}, @@ -4401,7 +4401,7 @@ return a / b;`,Aat=` setOutput(newValue); } - `}};function ilt(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n,[u,l]=a,c=L().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new mC(o.shape,u,l,s,i):new pC(o.shape,u,l,s,i);return e.runWebGLProgram(c,[o],o.dtype)}var LV={kernelName:zs,backendName:"webgl",kernelFunc:ilt};var fC=class{constructor(t,e,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e;let[,o,s]=e,[,i,a]=t,u=[n&&i>1?o-1:o,n&&a>1?s-1:s],l=[n&&i>1?i-1:i,n&&a>1?a-1:a],c=u[0]/l[0],p=u[1]/l[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=` + `}};function clt(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n,[u,l]=a,c=L().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new dC(o.shape,u,l,s,i):new fC(o.shape,u,l,s,i);return e.runWebGLProgram(c,[o],o.dtype)}var VV={kernelName:zs,backendName:"webgl",kernelFunc:clt};var hC=class{constructor(t,e,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e;let[,o,s]=e,[,i,a]=t,u=[n&&i>1?o-1:o,n&&a>1?s-1:s],l=[n&&i>1?i-1:i,n&&a>1?a-1:a],c=u[0]/l[0],p=u[1]/l[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -4471,7 +4471,7 @@ return a / b;`,Aat=` setOutput(accumulator); } - `}};function alt(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=new fC(s.shape,o.shape,i);return e.runWebGLProgram(a,[s],s.dtype)}var zV={kernelName:el,backendName:"webgl",kernelFunc:alt};var dC=class{constructor(t,e){this.variableNames=["x"];let n=t.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=t,n===1){this.userCode=` + `}};function plt(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=new hC(s.shape,o.shape,i);return e.runWebGLProgram(a,[s],s.dtype)}var GV={kernelName:el,backendName:"webgl",kernelFunc:plt};var gC=class{constructor(t,e){this.variableNames=["x"];let n=t.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=t,n===1){this.userCode=` void main() { int coord = getOutputCoords(); setOutput(getX(${t[0]} - coord - 1)); @@ -4481,7 +4481,7 @@ return a / b;`,Aat=` ${i} coords = getOutputCoords(); setOutput(getX(${s})); } - `}};var hC=class{constructor(t,e){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=t.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=t;let o=rr("rc",n),s=`${o[n-1]} + 1 < ${this.outputShape[n-1]}`,i=`${o[n-2]} + 1 < ${this.outputShape[n-2]}`,a=zt(n);n===1?this.userCode=` + `}};var xC=class{constructor(t,e){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=t.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=t;let o=rr("rc",n),s=`${o[n-1]} + 1 < ${this.outputShape[n-1]}`,i=`${o[n-2]} + 1 < ${this.outputShape[n-2]}`,a=zt(n);n===1?this.userCode=` void main(){ int rc = getOutputCoords(); vec4 result = vec4(0.); @@ -4509,7 +4509,7 @@ return a / b;`,Aat=` } setOutput(result); } - `;function u(d){return m(d)}function l(d){return d[n-1]="("+d[n-1]+" + 1)",m(d)}function c(d){return d[n-2]="("+d[n-2]+" + 1)",m(d)}function p(d){return d[n-1]="("+d[n-1]+" + 1)",d[n-2]="("+d[n-2]+" + 1)",m(d)}function m(d){let h=t.map((b,w)=>f(w,d)),g=h.join(","),x=h.slice(-2).join(",");return`getChannel(getX(${g}), vec2(${x}))`}function f(d,h){return e.indexOf(d)!==-1&&t[d]!==1?`${t[d]} - ${h[d]} - 1`:`${h[d]}`}}};function llt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{dims:s}=n,i=o.shape.length,a=y.parseAxisParam(s,o.shape);if(i===0)return nr({inputs:{x:o},backend:e});let u=L().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new hC(o.shape,a):new dC(o.shape,a);return e.runWebGLProgram(u,[o],o.dtype)}var BV={kernelName:Gs,backendName:"webgl",kernelFunc:llt};var gC=class{constructor(t,e){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=t[1],o=t[2];this.outputShape=t;let s="";typeof e=="number"?s=`float outputValue = ${e.toFixed(2)};`:s=` + `;function u(d){return m(d)}function l(d){return d[n-1]="("+d[n-1]+" + 1)",m(d)}function c(d){return d[n-2]="("+d[n-2]+" + 1)",m(d)}function p(d){return d[n-1]="("+d[n-1]+" + 1)",d[n-2]="("+d[n-2]+" + 1)",m(d)}function m(d){let h=t.map((b,w)=>f(w,d)),g=h.join(","),x=h.slice(-2).join(",");return`getChannel(getX(${g}), vec2(${x}))`}function f(d,h){return e.indexOf(d)!==-1&&t[d]!==1?`${t[d]} - ${h[d]} - 1`:`${h[d]}`}}};function mlt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{dims:s}=n,i=o.shape.length,a=y.parseAxisParam(s,o.shape);if(i===0)return nr({inputs:{x:o},backend:e});let u=L().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new xC(o.shape,a):new gC(o.shape,a);return e.runWebGLProgram(u,[o],o.dtype)}var WV={kernelName:Gs,backendName:"webgl",kernelFunc:mlt};var yC=class{constructor(t,e){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=t[1],o=t[2];this.outputShape=t;let s="";typeof e=="number"?s=`float outputValue = ${e.toFixed(2)};`:s=` vec3 fill = vec3(${e.join(",")}); float outputValue = fill[coords[3]];`,this.userCode=` void main() { @@ -4528,7 +4528,7 @@ return a / b;`,Aat=` } setOutput(outputValue); } - `}};var VV={kernelName:pl,backendName:"webgl",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{image:n}=r,{radians:o,fillValue:s,center:i}=t,a=e,u=new gC(n.shape,s),[l,c]=S.getImageCenter(i,n.shape[1],n.shape[2]),p=[[l,c,Math.sin(o),Math.cos(o)]];return a.runWebGLProgram(u,[n],n.dtype,p)}};var ult=` + `}};var UV={kernelName:pl,backendName:"webgl",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{image:n}=r,{radians:o,fillValue:s,center:i}=t,a=e,u=new yC(n.shape,s),[l,c]=S.getImageCenter(i,n.shape[1],n.shape[2]),p=[[l,c,Math.sin(o),Math.cos(o)]];return a.runWebGLProgram(u,[n],n.dtype,p)}};var flt=` // OpenGL ES does not support round function. // The algorithm is based on banker's rounding. float base = floor(x); @@ -4543,7 +4543,7 @@ return a / b;`,Aat=` return base + 1.0; } } -`,clt=It({opSnippet:ult}),GV={kernelName:Ws,backendName:"webgl",kernelFunc:clt};var plt="return inversesqrt(x);",mlt=It({opSnippet:plt,cpuKernelImpl:yz}),WV={kernelName:Us,backendName:"webgl",kernelFunc:mlt};var Ku=class{constructor(t,e,n,o,s,i,a=!0,u=!1){this.variableNames=["updates","indices","defaultValue"],this.outputShape=i;let l=zt(s.length),c=zt(i.length),p="";n===1?p="i":n===2&&(p="i, j");let m=`getIndices(${p})`,f="";o===1?f="i":o===2&&(f="i, coords[1]");let d=`getUpdates(${f})`,h="";u&&(h="coords[0], coords[1]");let g=`getDefaultValue(${h})`,x=e>1?"strides[j]":"strides";this.userCode=` +`,dlt=It({opSnippet:flt}),HV={kernelName:Ws,backendName:"webgl",kernelFunc:dlt};var hlt="return inversesqrt(x);",glt=It({opSnippet:hlt,cpuKernelImpl:Iz}),qV={kernelName:Us,backendName:"webgl",kernelFunc:glt};var Ku=class{constructor(t,e,n,o,s,i,a=!0,u=!1){this.variableNames=["updates","indices","defaultValue"],this.outputShape=i;let l=zt(s.length),c=zt(i.length),p="";n===1?p="i":n===2&&(p="i, j");let m=`getIndices(${p})`,f="";o===1?f="i":o===2&&(f="i, coords[1]");let d=`getUpdates(${f})`,h="";u&&(h="coords[0], coords[1]");let g=`getDefaultValue(${h})`,x=e>1?"strides[j]":"strides";this.userCode=` ${l} strides = ${l}(${s}); void main() { @@ -4563,7 +4563,7 @@ return a / b;`,Aat=` } setOutput(mix(${g}, sum, float(found))); } - `}};var xC=class{constructor(t,e,n,o,s,i,a=!0,u=!1){this.variableNames=["updates","indices","defaultValue"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=i;let l=zt(s.length),c=zt(i.length),p="";n===1?p="i":n===2&&(p="i, j");let m=`getIndices(${p})`,f="";o===1?f="i":o===2&&(f="i, coords[1]");let d=`getUpdates(${f})`,h="";u&&(h="coords[0], coords[1]");let g=`getDefaultValue(${h})`,x=e>1?"strides[j]":"strides",b=e>1?"strides[j + 1]":"strides";this.userCode=` + `}};var bC=class{constructor(t,e,n,o,s,i,a=!0,u=!1){this.variableNames=["updates","indices","defaultValue"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=i;let l=zt(s.length),c=zt(i.length),p="";n===1?p="i":n===2&&(p="i, j");let m=`getIndices(${p})`,f="";o===1?f="i":o===2&&(f="i, coords[1]");let d=`getUpdates(${f})`,h="";u&&(h="coords[0], coords[1]");let g=`getDefaultValue(${h})`,x=e>1?"strides[j]":"strides",b=e>1?"strides[j + 1]":"strides";this.userCode=` ${l} strides = ${l}(${s}); void main() { @@ -4600,7 +4600,7 @@ return a / b;`,Aat=` } setOutput(mix(${g}, sum, found)); } - `}};function flt(r){let{inputs:t,backend:e,attrs:n}=r,{indices:o,updates:s}=t,{shape:i}=n,{sliceRank:a,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=S.calculateShapes(s,o,i),m=[p/l,l];if(p===0)return e.makeTensorInfo(i,o.dtype);let f=rt({inputs:{x:o},backend:e,attrs:{shape:[u,a]}}),d=rt({inputs:{x:s},backend:e,attrs:{shape:[u,l]}}),h=e.makeTensorInfo([],"float32",new Float32Array([0])),g;L().getBool("WEBGL_PACK")?g=new xC(u,a,f.shape.length,d.shape.length,c,m):g=new Ku(u,a,f.shape.length,d.shape.length,c,m);let x=e.runWebGLProgram(g,[d,f,h],d.dtype),b=rt({inputs:{x},backend:e,attrs:{shape:i}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(h),b}var UV={kernelName:nl,backendName:"webgl",kernelFunc:flt};var yC=class{constructor(t,e,n,o){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[t,n];let s="while (left < right) {",i=`for (int i = 0; i < ${Math.ceil(Math.log2(e+1))}; ++i) { if (left >= right) break;`,a=L().getNumber("WEBGL_VERSION")===2?s:i,u=o==="left"?"<":"<=";this.userCode=` + `}};function xlt(r){let{inputs:t,backend:e,attrs:n}=r,{indices:o,updates:s}=t,{shape:i}=n,{sliceRank:a,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=S.calculateShapes(s,o,i),m=[p/l,l];if(p===0)return e.makeTensorInfo(i,o.dtype);let f=rt({inputs:{x:o},backend:e,attrs:{shape:[u,a]}}),d=rt({inputs:{x:s},backend:e,attrs:{shape:[u,l]}}),h=e.makeTensorInfo([],"float32",new Float32Array([0])),g;L().getBool("WEBGL_PACK")?g=new bC(u,a,f.shape.length,d.shape.length,c,m):g=new Ku(u,a,f.shape.length,d.shape.length,c,m);let x=e.runWebGLProgram(g,[d,f,h],d.dtype),b=rt({inputs:{x},backend:e,attrs:{shape:i}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(h),b}var KV={kernelName:nl,backendName:"webgl",kernelFunc:xlt};var wC=class{constructor(t,e,n,o){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[t,n];let s="while (left < right) {",i=`for (int i = 0; i < ${Math.ceil(Math.log2(e+1))}; ++i) { if (left >= right) break;`,a=L().getNumber("WEBGL_VERSION")===2?s:i,u=o==="left"?"<":"<=";this.userCode=` int findBound(int batch, float value) { int left = 0; int right = numInputs; @@ -4625,7 +4625,7 @@ return a / b;`,Aat=` setOutput(float(findBound(batch, value))); } - `}};function dlt(r){let{inputs:t,backend:e,attrs:n}=r,{sortedSequence:o,values:s}=t,{side:i}=n,a=new yC(o.shape[0],o.shape[1],s.shape[1],i),u=[[o.shape[1]]];return e.runWebGLProgram(a,[o,s],"int32",u)}var HV={kernelName:sl,backendName:"webgl",kernelFunc:dlt};var bC=class{constructor(t,e,n){this.variableNames=["c","a","b"],this.outputShape=e;let o,s;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)s="resRC",o="resRC";else{let a=["resRC.x","resRC.y","resRC.z","resRC.w"],u=[],l=[];for(let c=0;c4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)s="resRC",o="resRC";else{let a=["resRC.x","resRC.y","resRC.z","resRC.w"],u=[],l=[];for(let c=0;c= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0); -`,xlt=It({opSnippet:glt}),KV={kernelName:Hs,backendName:"webgl",kernelFunc:xlt};var ylt=Po+` +`,Ilt=It({opSnippet:wlt}),YV={kernelName:Hs,backendName:"webgl",kernelFunc:Ilt};var Clt=Po+` return 1.0 / (1.0 + exp(-1.0 * x)); -`,blt=` +`,vlt=` vec4 result = 1.0 / (1.0 + exp(-1.0 * x)); bvec4 isNaN = isnan(x); @@ -4653,20 +4653,20 @@ return a / b;`,Aat=` result.a = isNaN.a ? x.a : result.a; return result; -`,wlt=It({opSnippet:ylt,packedOpSnippet:blt,cpuKernelImpl:wz}),jV={kernelName:Xs,backendName:"webgl",kernelFunc:wlt};var Ilt=` +`,Slt=It({opSnippet:Clt,packedOpSnippet:vlt,cpuKernelImpl:vz}),ZV={kernelName:Xs,backendName:"webgl",kernelFunc:Slt};var Nlt=` if (isnan(x)) { return 0.0; } return sign(x); -`,Clt=It({opSnippet:Ilt}),XV={kernelName:js,backendName:"webgl",kernelFunc:Clt};var vlt=Po+` +`,klt=It({opSnippet:Nlt}),JV={kernelName:js,backendName:"webgl",kernelFunc:klt};var Tlt=Po+` return sin(x); -`,Slt=` +`,_lt=` vec4 result = sin(x); bvec4 isNaN = isnan(x); ${Xn} return result; -`,Nlt=It({opSnippet:vlt,packedOpSnippet:Slt}),YV={kernelName:qs,backendName:"webgl",kernelFunc:Nlt};var klt=` +`,Elt=It({opSnippet:Tlt,packedOpSnippet:_lt}),QV={kernelName:qs,backendName:"webgl",kernelFunc:Elt};var Alt=` float e2x = exp(x); return (e2x - 1.0 / e2x) / 2.0; -`,Tlt=It({opSnippet:klt}),ZV={kernelName:Ks,backendName:"webgl",kernelFunc:Tlt};var _lt=` +`,Dlt=It({opSnippet:Alt}),tG={kernelName:Ks,backendName:"webgl",kernelFunc:Dlt};var $lt=` float epsilon = 1.1920928955078125e-7; float threshold = log(epsilon) + 2.0; 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a=s.reduce((x,b)=>x*b),u=[[0,0]];u.push(...i);for(let x=1+s.length;xe.disposeIntermediateTensorInfo(x)),g},rG={kernelName:Hi,backendName:"webgl",kernelFunc:Flt};function Olt(r){let{inputs:t,backend:e}=r,{indices:n,values:o,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw: ${s.shape}`);if(n.shape.length!==2)throw new Error(`Indices must be a matrix, saw: ${n.shape}`);if(o.shape.length!==1)throw new Error(`Values must be a vector, saw: ${o.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw: - ${i.shape}`);let a=e.readSync(n.dataId),u=e.readSync(o.dataId),l=e.readSync(s.dataId),c=e.readSync(i.dataId)[0],[p,m,f,d,h]=Cz(a,n.shape,n.dtype,u,o.dtype,l,c);return[e.makeTensorInfo(m,n.dtype,p),e.makeTensorInfo([m[0]],o.dtype,f),e.makeTensorInfo([d.length],"bool",new Uint8Array(d.map(g=>Number(g)))),e.makeTensorInfo([h.length],n.dtype,new Int32Array(h))]}var tG={kernelName:nu,backendName:"webgl",kernelFunc:Dlt};function $lt(r){let{inputs:t,backend:e}=r,{inputIndices:n,inputShape:o,newShape:s}=t;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${n.shape}`);if(o.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${o.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(e.readSync(o.dataId)),a=e.readSync(n.dataId),u=Array.from(e.readSync(s.dataId)),[l,c,p]=vz(a,n.shape,n.dtype,i,u);return[e.makeTensorInfo(c,n.dtype,l),e.makeTensorInfo([p.length],s.dtype,new Int32Array(p))]}var eG={kernelName:il,backendName:"webgl",kernelFunc:$lt};function Rlt(r){let{inputs:t,backend:e}=r,{data:n,indices:o,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(o.shape.length!==1)throw new Error(`Indices should be a vector but received shape + ${i.shape}`);let a=e.readSync(n.dataId),u=e.readSync(o.dataId),l=e.readSync(s.dataId),c=e.readSync(i.dataId)[0],[p,m,f,d,h]=Nz(a,n.shape,n.dtype,u,o.dtype,l,c);return[e.makeTensorInfo(m,n.dtype,p),e.makeTensorInfo([m[0]],o.dtype,f),e.makeTensorInfo([d.length],"bool",new Uint8Array(d.map(g=>Number(g)))),e.makeTensorInfo([h.length],n.dtype,new Int32Array(h))]}var nG={kernelName:nu,backendName:"webgl",kernelFunc:Olt};function Mlt(r){let{inputs:t,backend:e}=r,{inputIndices:n,inputShape:o,newShape:s}=t;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${n.shape}`);if(o.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${o.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(e.readSync(o.dataId)),a=e.readSync(n.dataId),u=Array.from(e.readSync(s.dataId)),[l,c,p]=kz(a,n.shape,n.dtype,i,u);return[e.makeTensorInfo(c,n.dtype,l),e.makeTensorInfo([p.length],s.dtype,new Int32Array(p))]}var oG={kernelName:il,backendName:"webgl",kernelFunc:Mlt};function Plt(r){let{inputs:t,backend:e}=r,{data:n,indices:o,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(o.shape.length!==1)throw new Error(`Indices should be a vector but received shape ${o.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape - ${s.shape}`);let i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=tI(i,n.shape,n.dtype,a,u,!0);return e.makeTensorInfo(c,n.dtype,l)}var rG={kernelName:ou,backendName:"webgl",kernelFunc:Rlt};function Flt(r){let{inputs:t,backend:e}=r,{data:n,indices:o,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(o.shape.length!==1)throw new Error(`Indices should be a vector but received shape + ${s.shape}`);let i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=rI(i,n.shape,n.dtype,a,u,!0);return e.makeTensorInfo(c,n.dtype,l)}var sG={kernelName:ou,backendName:"webgl",kernelFunc:Plt};function Llt(r){let{inputs:t,backend:e}=r,{data:n,indices:o,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(o.shape.length!==1)throw new Error(`Indices should be a vector but received shape ${o.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape - ${s.shape}`);let i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=tI(i,n.shape,n.dtype,a,u);return e.makeTensorInfo(c,n.dtype,l)}var nG={kernelName:su,backendName:"webgl",kernelFunc:Flt};function Olt(r){let{inputs:t,backend:e,attrs:n}=r,{sparseIndices:o,sparseValues:s,defaultValue:i}=t,{outputShape:a}=n,{sliceRank:u,numUpdates:l,sliceSize:c,strides:p,outputSize:m}=S.calculateShapes(s,o,a),f=!1;if(s.dtype==="string"){let x=e.bufferSync(o),b=e.bufferSync(s),w=y.decodeString(e.readSync(i.dataId)[0]),I=bz(x,b,a,m,c,l,u,p,w,f);return e.makeTensorInfo(a,I.dtype,I.values)}let d=new Ku(l,u,o.shape.length,s.shape.length,p,[m,1],f),h=e.runWebGLProgram(d,[s,o,i],s.dtype),g=rt({inputs:{x:h},backend:e,attrs:{shape:a}});return e.disposeIntermediateTensorInfo(h),g}var oG={kernelName:al,backendName:"webgl",kernelFunc:Olt};function Mlt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{numOrSizeSplits:s,axis:i}=n,a=y.parseAxisParam(i,o.shape)[0],u=S.prepareSplitSize(o,s,a),l=o.shape.length,c=new Array(l).fill(0),p=o.shape.slice();return u.map(m=>{let f=[...p];f[a]=m;let d=Ni({inputs:{x:o},backend:e,attrs:{begin:c,size:f}});return c[a]+=m,d})}var sG={kernelName:Hi,backendName:"webgl",kernelFunc:Mlt};var iG="return sqrt(x);",Plt=It({opSnippet:iG,packedOpSnippet:iG,cpuKernelImpl:Sz}),aG={kernelName:Zs,backendName:"webgl",kernelFunc:Plt};var Llt="return x * x;",zlt=It({opSnippet:Llt}),lG={kernelName:iu,backendName:"webgl",kernelFunc:zlt};var uG="return (a - b) * (a - b);",Blt=ce({opSnippet:uG,packedOpSnippet:uG}),cG={kernelName:ti,backendName:"webgl",kernelFunc:Blt};function Vlt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;if(o.dtype!=="string")throw new Error("Input must be of datatype string");let s=e.readSync(o.dataId),i=S.fromUint8ToStringArray(s),a=Nz(i,"string",n);return e.makeTensorInfo(o.shape,"string",a)}var pG={kernelName:ec,backendName:"webgl",kernelFunc:Vlt};function Glt({inputs:r,attrs:t,backend:e}){let{x:n}=r,o=xr+` + ${s.shape}`);let i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=rI(i,n.shape,n.dtype,a,u);return e.makeTensorInfo(c,n.dtype,l)}var iG={kernelName:su,backendName:"webgl",kernelFunc:Llt};function zlt(r){let{inputs:t,backend:e,attrs:n}=r,{sparseIndices:o,sparseValues:s,defaultValue:i}=t,{outputShape:a}=n,{sliceRank:u,numUpdates:l,sliceSize:c,strides:p,outputSize:m}=S.calculateShapes(s,o,a),f=!1;if(s.dtype==="string"){let x=e.bufferSync(o),b=e.bufferSync(s),w=y.decodeString(e.readSync(i.dataId)[0]),I=Cz(x,b,a,m,c,l,u,p,w,f);return e.makeTensorInfo(a,I.dtype,I.values)}let d=new Ku(l,u,o.shape.length,s.shape.length,p,[m,1],f),h=e.runWebGLProgram(d,[s,o,i],s.dtype),g=rt({inputs:{x:h},backend:e,attrs:{shape:a}});return e.disposeIntermediateTensorInfo(h),g}var aG={kernelName:al,backendName:"webgl",kernelFunc:zlt};function Blt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{numOrSizeSplits:s,axis:i}=n,a=y.parseAxisParam(i,o.shape)[0],u=S.prepareSplitSize(o,s,a),l=o.shape.length,c=new Array(l).fill(0),p=o.shape.slice();return u.map(m=>{let f=[...p];f[a]=m;let d=ki({inputs:{x:o},backend:e,attrs:{begin:c,size:f}});return c[a]+=m,d})}var lG={kernelName:qi,backendName:"webgl",kernelFunc:Blt};var uG="return sqrt(x);",Vlt=It({opSnippet:uG,packedOpSnippet:uG,cpuKernelImpl:Tz}),cG={kernelName:Zs,backendName:"webgl",kernelFunc:Vlt};var Glt="return x * x;",Wlt=It({opSnippet:Glt}),pG={kernelName:iu,backendName:"webgl",kernelFunc:Wlt};var mG="return (a - b) * (a - b);",Ult=ce({opSnippet:mG,packedOpSnippet:mG}),fG={kernelName:ti,backendName:"webgl",kernelFunc:Ult};function Hlt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;if(o.dtype!=="string")throw new Error("Input must be of datatype string");let s=e.readSync(o.dataId),i=S.fromUint8ToStringArray(s),a=_z(i,"string",n);return e.makeTensorInfo(o.shape,"string",a)}var dG={kernelName:ec,backendName:"webgl",kernelFunc:Hlt};function qlt({inputs:r,attrs:t,backend:e}){let{x:n}=r,o=xr+` return x > 0.0 ? 1.0 : float(${t.alpha}); - `,s=new Lr(n.shape,o);return e.runWebGLProgram(s,[n],n.dtype)}var mG={kernelName:xo,backendName:"webgl",kernelFunc:Glt};var wC=class{constructor(t,e,n){this.variableNames=["x"],this.outputShape=n;let o=n.length,s=zt(n.length),i=zt(n.length),a="";if(o===1)a="coords * strides + begin";else{let u=0;a=n.map((l,c)=>(u++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${u-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=` + `,s=new zr(n.shape,o);return e.runWebGLProgram(s,[n],n.dtype)}var hG={kernelName:xo,backendName:"webgl",kernelFunc:qlt};var CC=class{constructor(t,e,n){this.variableNames=["x"],this.outputShape=n;let o=n.length,s=zt(n.length),i=zt(n.length),a="";if(o===1)a="coords * strides + begin";else{let u=0;a=n.map((l,c)=>(u++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${u-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=` ${s} begin = ${s}(${t}); ${s} strides = ${s}(${e}); @@ -4704,15 +4704,15 @@ return a / b;`,Aat=` ${i} coords = getOutputCoords(); setOutput(getX(${a})); } - `}};function Wlt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{begin:s,end:i,strides:a,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:m}=n,{finalShapeSparse:f,finalShape:d,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:w,strides:I}=Be.sliceInfo(o.shape,s,i,a,u,l,c,p,m),N;if(h)N=rt({inputs:{x:o},backend:e,attrs:{shape:d}});else if(g||x){y.assert(o.shape.length>=1,()=>`Input must have rank at least 1, got: ${o.shape.length}`);let A=Be.computeOutShape(b,w,I),D=Ni({inputs:{x:o},backend:e,attrs:{begin:b,size:A}});N=rt({inputs:{x:D},backend:e,attrs:{shape:d}}),e.disposeIntermediateTensorInfo(D)}else if(e.shouldExecuteOnCPU([o])){let D=e.readSync(o.dataId),F=wt(o.shape,o.dtype,D),M=kz(f,F,I,b);N=e.makeTensorInfo(d,o.dtype,M.values)}else{let D=new wC(b,I,f);N=e.runWebGLProgram(D,[o],o.dtype)}let E=rt({inputs:{x:N},backend:e,attrs:{shape:d}});return e.disposeIntermediateTensorInfo(N),E}var fG={kernelName:ll,backendName:"webgl",kernelFunc:Wlt};function Ult(r){let{inputs:t,backend:e,attrs:n}=r,{separator:o,nGramWidths:s,leftPad:i,rightPad:a,padWidth:u,preserveShortSequences:l}=n,{data:c,dataSplits:p}=t,m=e.readSync(c.dataId),f=e.readSync(p.dataId),[d,h]=Tz(m,f,o,s,i,a,u,l);return[e.makeTensorInfo([d.length],"string",d),e.makeTensorInfo(p.shape,"int32",h)]}var dG={kernelName:au,backendName:"webgl",kernelFunc:Ult};function Hlt(r){let{inputs:t,backend:e,attrs:n}=r,{skipEmpty:o}=n,{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 a=e.readSync(s.dataId),u=e.readSync(i.dataId)[0],[l,c,p]=_z(a,u,o),m=c.length;return[e.makeTensorInfo([m,2],"int32",l),e.makeTensorInfo([m],"string",c),e.makeTensorInfo([2],"int32",new Int32Array(p))]}var hG={kernelName:lu,backendName:"webgl",kernelFunc:Hlt};function qlt(r){let{inputs:t,backend:e,attrs:n}=r,{numBuckets:o}=n,{input:s}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(o<=0)throw new Error("Number of buckets must be at least 1");let i=e.readSync(s.dataId),a=Ez(i,o);return e.makeTensorInfo(s.shape,"int32",a)}var gG={kernelName:uu,backendName:"webgl",kernelFunc:qlt};var Klt="return tan(x);",jlt=It({opSnippet:Klt}),xG={kernelName:ri,backendName:"webgl",kernelFunc:jlt};var Xlt=` + `}};function Klt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{begin:s,end:i,strides:a,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:m}=n,{finalShapeSparse:f,finalShape:d,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:w,strides:I}=Be.sliceInfo(o.shape,s,i,a,u,l,c,p,m),N;if(h)N=rt({inputs:{x:o},backend:e,attrs:{shape:d}});else if(g||x){y.assert(o.shape.length>=1,()=>`Input must have rank at least 1, got: ${o.shape.length}`);let A=Be.computeOutShape(b,w,I),D=ki({inputs:{x:o},backend:e,attrs:{begin:b,size:A}});N=rt({inputs:{x:D},backend:e,attrs:{shape:d}}),e.disposeIntermediateTensorInfo(D)}else if(e.shouldExecuteOnCPU([o])){let D=e.readSync(o.dataId),F=wt(o.shape,o.dtype,D),M=Ez(f,F,I,b);N=e.makeTensorInfo(d,o.dtype,M.values)}else{let D=new CC(b,I,f);N=e.runWebGLProgram(D,[o],o.dtype)}let E=rt({inputs:{x:N},backend:e,attrs:{shape:d}});return e.disposeIntermediateTensorInfo(N),E}var gG={kernelName:ll,backendName:"webgl",kernelFunc:Klt};function jlt(r){let{inputs:t,backend:e,attrs:n}=r,{separator:o,nGramWidths:s,leftPad:i,rightPad:a,padWidth:u,preserveShortSequences:l}=n,{data:c,dataSplits:p}=t,m=e.readSync(c.dataId),f=e.readSync(p.dataId),[d,h]=Az(m,f,o,s,i,a,u,l);return[e.makeTensorInfo([d.length],"string",d),e.makeTensorInfo(p.shape,"int32",h)]}var xG={kernelName:au,backendName:"webgl",kernelFunc:jlt};function 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lW;function Dut(r){lW=r.wasm.cwrap(Da,null,["number","number","boolean","number","number","number"])}function $ut(r){let{backend:t,inputs:e,attrs:n}=r,{x:o,weights:s}=e,{size:i}=n,a=s.shape.reduce((p,m)=>p*m,1)!==0,u=o.shape.length===1?[i]:[o.shape[0],i],l=t.makeOutput(u,s.dtype);function c(p){return t.dataIdMap.get(p.dataId).id}return lW(c(o),i,a,c(s),Nt[s.dtype],c(l)),l}var uW={kernelName:Da,backendName:"wasm",setupFunc:Dut,kernelFunc:$ut};var Rut=!0,cW=ee($a,Rut);function Fut(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.typedArrayFromHeap(n),i=e.typedArrayFromHeap(o),a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeOutput([a.length],"int32",void 0,new Int32Array(a))}var pW={kernelName:ql,backendName:"wasm",kernelFunc:Fut};function Fn(r){let{inputs:{x:t},attrs:{dtype:e},backend:n}=r,o=n.makeOutput(t.shape,e),s=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(o).set(s),o}var mW={kernelName:fo,backendName:"wasm",kernelFunc:Fn};var fW=yt(Jo);var dW;function 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$W={kernelName:Oa,backendName:"wasm",setupFunc:Xut,kernelFunc:Yut};var RW;function Zut(r){RW=r.wasm.cwrap(os,null,["number","number","number","number","number","number"])}function Jut(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n,u=o.shape.length;y.assert(o.dtype==="float32"||o.dtype==="int32",()=>`cumsum does not support ${o.dtype} tensors in the WASM backend`);let l=S.getAxesPermutation([s],u),c=o;l!==null&&(c=mo({inputs:{x:o},attrs:{perm:l},backend:e}));let p=S.getInnerMostAxes(1,u)[0];S.assertAxesAreInnerMostDims("cumsum",[p],u);let m=e.makeOutput(c.shape,c.dtype),f=c.shape[p],d=e.dataIdMap.get(c.dataId).id,h=e.dataIdMap.get(m.dataId).id;RW(d,i?1:0,a?1:0,f,h,Nt[o.dtype]);let g=m;if(l!==null){let x=S.getUndoAxesPermutation(l);g=mo({inputs:{x:m},attrs:{perm:x},backend:e}),e.disposeData(c.dataId),e.disposeData(m.dataId)}return g}var FW={kernelName:os,backendName:"wasm",setupFunc:Zut,kernelFunc:Jut};var OW;function 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rct(r){let{backend:t,inputs:e,attrs:n}=r,{x:o}=e,{blockSize:s,dataFormat:i}=n,a=o.shape[0],u=i==="NHWC"?o.shape[1]:o.shape[2],l=i==="NHWC"?o.shape[2]:o.shape[3],c=i==="NHWC"?o.shape[3]:o.shape[1],p=u*s,m=l*s,f=c/(s*s),d=i==="NHWC"?[a,p,m,f]:[a,f,p,m],h=t.makeOutput(d,"float32"),x=t.dataIdMap.get(o.dataId).id,b=new Uint8Array(new Int32Array(y.computeStrides(o.shape)).buffer),w=new Uint8Array(new Int32Array(d).buffer),I=new Uint8Array(new Int32Array(y.computeStrides(d)).buffer),N=t.dataIdMap.get(h.dataId).id;return PW(x,s,i==="NHWC"?1:0,b,o.shape.length-1,w,I,d.length,N),h}var LW={kernelName:Pa,backendName:"wasm",setupFunc:ect,kernelFunc:rct};var zW;function nct(r){zW=r.wasm.cwrap(ss,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function oct(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s}=t,i=n.dataIdMap.get(o.dataId).id,a=n.dataIdMap.get(s.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p}=e,m=l==null?[1,1]:l,f=S.computeConv2DInfo(o.shape,s.shape,u,m,c,p,!0),d=f.filterHeight,h=f.filterWidth,g=f.padInfo.top,x=f.padInfo.right,b=f.padInfo.bottom,w=f.padInfo.left,I=f.dilationHeight,N=f.dilationWidth,E=f.strideHeight,A=f.strideWidth,D=f.inChannels,F=f.outChannels,M=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${f.dataFormat}'. 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Got ${o.dtype}, ${s.dtype}, and ${i.dtype}`);let c=S.computeDilation2DInfo(o.shape,s.shape,a,u,"NHWC",l),p=e.makeOutput(s.shape,s.dtype);return HW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,e.dataIdMap.get(p.dataId).id,Nt[o.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),p}var qW={kernelName:Zl,backendName:"wasm",setupFunc:uct,kernelFunc:cct};var KW;function pct(r){KW=r.wasm.cwrap(Yl,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function mct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,dy:i}=t,{strides:a,pad:u,dilations:l}=n;if(o.dtype!==s.dtype||o.dtype!==i.dtype)throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. 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s(i){let{backend:a,inputs:u}=i,{a:l,b:c}=u,p=a.dataIdMap.get(l.dataId).id,m=a.dataIdMap.get(c.dataId).id,f=e!=null?e:l.dtype,d=S.assertAndGetBroadcastShape(l.shape,c.shape),h=a.makeOutput(d,f);if(y.sizeFromShape(d)===0)return h;let g=new Uint8Array(new Int32Array(l.shape).buffer),x=new Uint8Array(new Int32Array(c.shape).buffer),b=a.dataIdMap.get(h.dataId).id;return n(p,g,l.shape.length,m,x,c.shape.length,Nt[l.dtype],b),h}return{kernelName:r,backendName:"wasm",setupFunc:o,kernelFunc:s}}var put=!0,OG=ee(no,put);var MG;function mut(r){MG=r.wasm.cwrap(Uo,null,["array","number","number","number"])}function fut(r){let{inputs:t,backend:e}=r,n=e.makeOutput(t[0].shape,t[0].dtype);if(y.sizeFromShape(n.shape)===0)return n;let o=t.map(a=>e.dataIdMap.get(a.dataId).id),s=new Uint8Array(new Int32Array(o).buffer),i=e.dataIdMap.get(n.dataId).id;return MG(s,o.length,Nt[n.dtype],i),n}var PG={kernelName:Uo,backendName:"wasm",setupFunc:mut,kernelFunc:fut};function yp(r){let{inputs:{x:t},backend:e}=r;if(t.dtype==="string")return ir(e.readSync(t.dataId),t.shape,t.dtype);let n=e.makeOutput(t.shape,t.dtype),o=e.typedArrayFromHeap(t);return e.typedArrayFromHeap(n).set(o),n}var LG={kernelName:go,backendName:"wasm",kernelFunc:yp};var zG;function dut(r){zG=r.wasm.cwrap(so,null,["number","array","number","number","number","array","number"])}function mo(r){let{inputs:t,backend:e,attrs:n}=r,[o,s]=gut(t.x.shape,n.perm),i=!0;for(let d=0;d=o&&(s===-1||n[s]>n[i])&&(s=i);n[s]=o}return[e,n]}var BG={kernelName:so,backendName:"wasm",kernelFunc:mo,setupFunc:dut};function Cn(r,t,e){let n=r.shape,o=r.shape.length,s=y.parseAxisParam(t,n),i=s,a=S.getAxesPermutation(i,o),u=null,l=!1;if(a!=null){let c=new Array(o);for(let f=0;f`new shape: ${i}, old shape: ${n.shape}. 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pW;function Out(r){pW=r.wasm.cwrap(Da,null,["number","number","boolean","number","number","number"])}function Mut(r){let{backend:t,inputs:e,attrs:n}=r,{x:o,weights:s}=e,{size:i}=n,a=s.shape.reduce((p,m)=>p*m,1)!==0,u=o.shape.length===1?[i]:[o.shape[0],i],l=t.makeOutput(u,s.dtype);function c(p){return t.dataIdMap.get(p.dataId).id}return pW(c(o),i,a,c(s),Nt[s.dtype],c(l)),l}var mW={kernelName:Da,backendName:"wasm",setupFunc:Out,kernelFunc:Mut};var Put=!0,fW=ee($a,Put);function Lut(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.typedArrayFromHeap(n),i=e.typedArrayFromHeap(o),a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeOutput([a.length],"int32",void 0,new Int32Array(a))}var dW={kernelName:ql,backendName:"wasm",kernelFunc:Lut};function Fn(r){let{inputs:{x:t},attrs:{dtype:e},backend:n}=r,o=n.makeOutput(t.shape,e),s=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(o).set(s),o}var hW={kernelName:fo,backendName:"wasm",kernelFunc:Fn};var gW=yt(Jo);var xW;function zut(r){xW=r.wasm.cwrap(ho,null,["number","number","number","number"])}function But(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{clipValueMin:s,clipValueMax:i}=n,a=e.dataIdMap.get(o.dataId).id,u=e.makeOutput(o.shape,o.dtype),l=e.dataIdMap.get(u.dataId).id;return xW(a,s,i,l),u}var yW={kernelName:ho,backendName:"wasm",setupFunc:zut,kernelFunc:But};function X1(r){let{inputs:t,backend:e}=r,n=y.parseAxisParam(r.attrs.axis,t[0].shape)[0],o=t.map(f=>f.shape);S.assertParamsConsistent(o,n);let s=S.computeOutShape(t.map(f=>f.shape),n),i=t.filter(f=>y.sizeFromShape(f.shape)>0);if(i.length===1)return yp({inputs:{x:i[0]},backend:e});let a=e.makeOutput(s,t[0].dtype);if(y.sizeFromShape(s)===0)return a;if(i[0].dtype==="string"){let f=i.map(w=>{let N=[-1,y.sizeFromShape(w.shape.slice(n))];return mr({inputs:{x:w},backend:e,attrs:{shape:N}})}),d=f.map(w=>({vals:e.readSync(w.dataId),shape:w.shape}));s=S.computeOutShape(f.map(w=>w.shape),1);let h=f[0].shape[0]===1,g=Jc(d,s,t[0].dtype,h),x=S.computeOutShape(i.map(w=>w.shape),n);a.shape=x;let b=e.dataIdMap.get(a.dataId);return b.stringBytes=S.fromStringArrayToUint8(g),f.forEach(w=>e.disposeData(w.dataId)),a}let u=y.sizeFromShape(i[0].shape.slice(0,n)),l=0,c=i.map(f=>{let d=y.sizeFromShape(f.shape.slice(n));return l+=d,d}),p=i.map(f=>e.typedArrayFromHeap(f)),m=e.typedArrayFromHeap(a);for(let f=0;f`cumprod does not support ${o.dtype} tensors in the WASM backend`);let l=S.getAxesPermutation([s],u),c=o;l!==null&&(c=mo({inputs:{x:o},attrs:{perm:l},backend:e}));let p=S.getInnerMostAxes(1,u)[0];S.assertAxesAreInnerMostDims("cumprod",[p],u);let m=e.makeOutput(c.shape,c.dtype),f=c.shape[p],d=e.dataIdMap.get(c.dataId).id,h=e.dataIdMap.get(m.dataId).id;FW(d,i?1:0,a?1:0,f,h,Nt[o.dtype]);let g=m;if(l!==null){let x=S.getUndoAxesPermutation(l);g=mo({inputs:{x:m},attrs:{perm:x},backend:e}),e.disposeData(c.dataId),e.disposeData(m.dataId)}return g}var OW={kernelName:Oa,backendName:"wasm",setupFunc:Qut,kernelFunc:tct};var MW;function ect(r){MW=r.wasm.cwrap(os,null,["number","number","number","number","number","number"])}function rct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n,u=o.shape.length;y.assert(o.dtype==="float32"||o.dtype==="int32",()=>`cumsum does not support ${o.dtype} tensors in the WASM backend`);let l=S.getAxesPermutation([s],u),c=o;l!==null&&(c=mo({inputs:{x:o},attrs:{perm:l},backend:e}));let p=S.getInnerMostAxes(1,u)[0];S.assertAxesAreInnerMostDims("cumsum",[p],u);let m=e.makeOutput(c.shape,c.dtype),f=c.shape[p],d=e.dataIdMap.get(c.dataId).id,h=e.dataIdMap.get(m.dataId).id;MW(d,i?1:0,a?1:0,f,h,Nt[o.dtype]);let g=m;if(l!==null){let x=S.getUndoAxesPermutation(l);g=mo({inputs:{x:m},attrs:{perm:x},backend:e}),e.disposeData(c.dataId),e.disposeData(m.dataId)}return g}var PW={kernelName:os,backendName:"wasm",setupFunc:ect,kernelFunc:rct};var LW;function nct(r){LW=r.wasm.cwrap("DenseBincount",null,["number","array","number","number","boolean","number","number","boolean","number"])}function oct(r){let{backend:t,inputs:e,attrs:n}=r,{x:o,weights:s}=e,{size:i,binaryOutput:a}=n,u=s.shape.reduce((m,f)=>m*f,1)!==0,l=o.shape.length===1?[i]:[o.shape[0],i],c=t.makeOutput(l,s.dtype);function p(m){return t.dataIdMap.get(m.dataId).id}return LW(p(o),new Uint8Array(new Int32Array(o.shape).buffer),o.shape.length,i,u,p(s),Nt[s.dtype],a,p(c)),c}var zW={kernelName:jl,backendName:"wasm",setupFunc:nct,kernelFunc:oct};var BW;function sct(r){BW=r.wasm.cwrap(Pa,null,["number","number","number","array","number","array","array","number","number"])}function ict(r){let{backend:t,inputs:e,attrs:n}=r,{x:o}=e,{blockSize:s,dataFormat:i}=n,a=o.shape[0],u=i==="NHWC"?o.shape[1]:o.shape[2],l=i==="NHWC"?o.shape[2]:o.shape[3],c=i==="NHWC"?o.shape[3]:o.shape[1],p=u*s,m=l*s,f=c/(s*s),d=i==="NHWC"?[a,p,m,f]:[a,f,p,m],h=t.makeOutput(d,"float32"),x=t.dataIdMap.get(o.dataId).id,b=new Uint8Array(new Int32Array(y.computeStrides(o.shape)).buffer),w=new Uint8Array(new Int32Array(d).buffer),I=new Uint8Array(new Int32Array(y.computeStrides(d)).buffer),N=t.dataIdMap.get(h.dataId).id;return BW(x,s,i==="NHWC"?1:0,b,o.shape.length-1,w,I,d.length,N),h}var VW={kernelName:Pa,backendName:"wasm",setupFunc:sct,kernelFunc:ict};var GW;function act(r){GW=r.wasm.cwrap(ss,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function lct(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s}=t,i=n.dataIdMap.get(o.dataId).id,a=n.dataIdMap.get(s.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p}=e,m=l==null?[1,1]:l,f=S.computeConv2DInfo(o.shape,s.shape,u,m,c,p,!0),d=f.filterHeight,h=f.filterWidth,g=f.padInfo.top,x=f.padInfo.right,b=f.padInfo.bottom,w=f.padInfo.left,I=f.dilationHeight,N=f.dilationWidth,E=f.strideHeight,A=f.strideWidth,D=f.inChannels,F=f.outChannels,M=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${f.dataFormat}'. 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Got ${o.dtype} and ${s.dtype}`);let l=S.computeDilation2DInfo(o.shape,s.shape,i,a,"NHWC",u),c=e.makeOutput(l.outShape,o.dtype);return qW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(c.dataId).id,Nt[o.dtype],l.batchSize,l.inChannels,l.inHeight,l.inWidth,l.outHeight,l.outWidth,l.strideHeight,l.strideWidth,l.dilationHeight,l.dilationWidth,l.filterHeight,l.filterWidth,l.padInfo.top,l.padInfo.left),c}var KW={kernelName:is,backendName:"wasm",setupFunc:pct,kernelFunc:mct};var jW;function fct(r){jW=r.wasm.cwrap(Zl,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function dct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,dy:i}=t,{strides:a,pad:u,dilations:l}=n;if(o.dtype!==s.dtype||o.dtype!==i.dtype)throw new Error(`Dilation2DBackpropFilter error: x must have the same dtype as filter and dy. Got ${o.dtype}, ${s.dtype}, and ${i.dtype}`);let c=S.computeDilation2DInfo(o.shape,s.shape,a,u,"NHWC",l),p=e.makeOutput(s.shape,s.dtype);return jW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,e.dataIdMap.get(p.dataId).id,Nt[o.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),p}var XW={kernelName:Zl,backendName:"wasm",setupFunc:fct,kernelFunc:dct};var YW;function hct(r){YW=r.wasm.cwrap(Yl,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function gct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,dy:i}=t,{strides:a,pad:u,dilations:l}=n;if(o.dtype!==s.dtype||o.dtype!==i.dtype)throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${o.dtype}, ${s.dtype}, and ${i.dtype}`);let c=S.computeDilation2DInfo(o.shape,s.shape,a,u,"NHWC",l),p=e.makeOutput(o.shape,o.dtype);return YW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,e.dataIdMap.get(p.dataId).id,Nt[o.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),p}var ZW={kernelName:Yl,backendName:"wasm",setupFunc:hct,kernelFunc:gct};var JW=yt(ls);var QW;function xct(r){QW=r.wasm.cwrap(La,null,["number","number","number"])}function yct(r){let{inputs:t,backend:e}=r,{dy:n,y:o}=t,s=e.makeOutput(o.shape,"float32"),i=a=>e.dataIdMap.get(a.dataId).id;return QW(i(o),i(n),i(s)),s}var tU={kernelName:La,backendName:"wasm",setupFunc:xct,kernelFunc:yct};var bct=!1,eU=ee(za,bct,"bool");var rU=yt(us);var nU=yt(cs,"float32");function EC(r){let{inputs:t,attrs:e,backend:n}=r,{input:o}=t,{dim:s}=e,i=o.shape.length,a=o.shape.slice(),u=s;return s<0&&(y.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+s+1),a.splice(u,0,1),mr({inputs:{x:o},backend:n,attrs:{shape:a}})}var oU={kernelName:Mi,backendName:"wasm",kernelFunc:EC};var sU=yt(ps,"float32");function Z1(r){let{attrs:{shape:t,value:e},backend:n}=r,{attrs:{dtype:o}}=r;o=o||y.inferDtype(e);let s=n.makeOutput(t,o);return n.typedArrayFromHeap(s).fill(e),s}var iU={kernelName:Jl,backendName:"wasm",kernelFunc:Z1};var aU;function wct(r){aU=r.wasm.cwrap(Ba,null,["number","number","number","number","number","number"])}function Ict(r){let{inputs:t,backend:e}=r,{image:n}=t,o=e.makeOutput(n.shape,n.dtype),s=e.dataIdMap.get(n.dataId).id,i=e.dataIdMap.get(o.dataId).id,[a,u,l,c]=n.shape;return aU(s,a,u,l,c,i),o}var lU={kernelName:Ba,backendName:"wasm",kernelFunc:Ict,setupFunc:wct};var uU=yt(ms);var Cct=!1,cU=ee(fs,Cct);var pU;function vct(r){pU=r.wasm.cwrap(ds,null,["number","number","number","number","number","number","number"])}function Sct(r){let{backend:t,inputs:e,attrs:n}=r,{varianceEpsilon:o}=n,{x:s,mean:i,variance:a,offset:u,scale:l}=e,c=t.dataIdMap.get(s.dataId).id,p=t.dataIdMap.get(i.dataId).id,m=t.dataIdMap.get(a.dataId).id,f=u!=null?t.dataIdMap.get(u.dataId).id:0,d=l!=null?t.dataIdMap.get(l.dataId).id:0,h=t.makeOutput(s.shape,s.dtype);if(y.sizeFromShape(s.shape)===0)return h;let g=t.dataIdMap.get(h.dataId).id;return pU(c,p,m,f,d,o,g),h}var mU={kernelName:ds,backendName:"wasm",setupFunc:vct,kernelFunc:Sct};var fU;function Nct(r){fU=r.wasm.cwrap(Yi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function kct(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=e,h=S.computeConv2DInfo(o.shape,s.shape,u,c,l,m),g=ju[f];if(g==null)throw new Error(`${f} activation not yet supported for FusedConv2D in the wasm backend.`);let x=n.dataIdMap.get(o.dataId).id,b=n.dataIdMap.get(s.dataId).id,w=h.outChannels,I=0;if(i!=null){let ot=n.dataIdMap.get(i.dataId);if(ot.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ot.shape.length}.`);if(ot.shape[0]!==w)throw new Error(`FusedConv2D bias shape (${ot.shape}) does not match the number of output channels (${w})`);I=ot.id}let N=h.filterHeight,E=h.filterWidth,A=h.padInfo.top,D=h.padInfo.right,F=h.padInfo.bottom,M=h.padInfo.left,V=h.dilationHeight,G=h.dilationWidth,W=h.strideHeight,q=h.strideWidth,H=h.inChannels,j=h.padInfo.type==="SAME"?1:0,Y=h.batchSize,Z=h.inHeight,et=h.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let nt=n.makeOutput(h.outShape,"float32"),st=n.dataIdMap.get(nt.dataId).id,lt=a==null?0:n.dataIdMap.get(a.dataId).id;return fU(x,Y,Z,et,b,N,E,I,A,D,F,M,j,V,G,W,q,H,w,g,lt,d||0,st),nt}var dU={kernelName:Yi,backendName:"wasm",setupFunc:Nct,kernelFunc:kct};var hU;function Tct(r){hU=r.wasm.cwrap(Zi,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 _ct(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=e,h=S.computeConv2DInfo(o.shape,s.shape,u,c,l,m,!0),g=ju[f];if(g==null)throw new Error(`${f} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let x=n.dataIdMap.get(o.dataId).id,b=n.dataIdMap.get(s.dataId).id,w=h.outChannels,I=0;if(i!=null){let ot=n.dataIdMap.get(i.dataId);if(ot.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ot.shape.length}.`);if(ot.shape[0]!==w)throw new Error(`FusedDepthwiseConv2D bias shape (${ot.shape}) does not match the number of output channels (${w})`);I=ot.id}let N=h.filterHeight,E=h.filterWidth,A=h.padInfo.top,D=h.padInfo.right,F=h.padInfo.bottom,M=h.padInfo.left,V=h.dilationHeight,G=h.dilationWidth,W=h.strideHeight,q=h.strideWidth,H=h.inChannels,j=h.padInfo.type==="SAME"?1:0,Y=h.batchSize,Z=h.inHeight,et=h.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let nt=n.makeOutput(h.outShape,"float32"),st=n.dataIdMap.get(nt.dataId).id,lt=a==null?0:n.dataIdMap.get(a.dataId).id;return hU(x,Y,Z,et,b,N,E,I,A,D,F,M,j,V,G,W,q,H,w,g,lt,d||0,st),nt}var gU={kernelName:Zi,backendName:"wasm",setupFunc:Tct,kernelFunc:_ct};var xU;function Ect(r){xU=r.wasm.cwrap(Va,null,["number","number","number","number","number","number","array","number"])}function Act(r){let{backend:t,inputs:e}=r,{params:n,indices:o}=e,[s,i,a,u]=Ey.prepareAndValidate(n,o),l=t.makeOutput(s,n.dtype);if(i===0)return l;let c=o.shape,p=c[c.length-1],f=t.dataIdMap.get(n.dataId).id,h=t.dataIdMap.get(o.dataId).id,g=new Uint8Array(new Int32Array(u).buffer),x=t.dataIdMap.get(l.dataId).id;return xU(f,Nt[n.dtype],h,i,p,a,g,x),l}var yU={kernelName:Va,backendName:"wasm",setupFunc:Ect,kernelFunc:Act};var bU;function Dct(r){bU=r.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function $ct(r){let{backend:t,inputs:e,attrs:n}=r,{x:o,indices:s}=e,{axis:i,batchDims:a}=n,u=y.parseAxisParam(i,o.shape)[0],l=t.readSync(s.dataId),c=o.shape[u];for(let F=0;F=0,()=>`GatherV2: the index value ${M} is not in [0, ${c-1}]`)}let p=S.segment_util.collectGatherOpShapeInfo(o,s,u,a),m=mr({inputs:{x:o},attrs:{shape:[p.batchSize,p.outerSize,p.dimSize,p.sliceSize]},backend:t}),f=y.sizeFromShape(s.shape),d=mr({inputs:{x:s},attrs:{shape:[p.batchSize,f/p.batchSize]},backend:t}),h=[p.batchSize,p.outerSize,f/p.batchSize,p.sliceSize],g=t.makeOutput(h,o.dtype);if(y.sizeFromShape(o.shape)===0)return g;let x=m.shape.length-1,w=t.dataIdMap.get(m.dataId).id,N=t.dataIdMap.get(d.dataId).id,E=t.dataIdMap.get(g.dataId).id,A=new Uint8Array(new Int32Array(y.computeStrides(m.shape)).buffer),D=new Uint8Array(new Int32Array(y.computeStrides(h)).buffer);return bU(w,Nt[o.dtype],A,x,N,p.batchSize,D,E),t.disposeData(m.dataId),t.disposeData(d.dataId),g.shape=p.outputShape,g}var wU={kernelName:Pi,backendName:"wasm",setupFunc:Dct,kernelFunc:$ct};var Rct=!1,IU=ee(Ga,Rct,"bool");var Fct=!1,CU=ee(hs,Fct,"bool");var vU=yt(gs,"bool");var SU=yt(xs,"bool");var NU=yt(ys,"bool");var kU;function Oct(r){kU=r.wasm.cwrap(bs,null,["number","number","number","number"])}function Mct(r){let{inputs:{x:t},attrs:{alpha:e},backend:n}=r,o=n.dataIdMap.get(t.dataId).id,s=n.makeOutput(t.shape,"float32");if(y.sizeFromShape(t.shape)!==0){let i=n.dataIdMap.get(s.dataId).id;kU(o,Nt[t.dtype],e,i)}return s}var TU={kernelName:bs,backendName:"wasm",setupFunc:Oct,kernelFunc:Mct};var Pct=!1,_U=ee(Wa,Pct,"bool");var Lct=!1,EU=ee(Ua,Lct,"bool");var AU;function zct(r){AU=r.wasm.cwrap(Ha,null,["number","number","number","number"])}function Bct(r){let{attrs:t,backend:e}=r,{start:n,stop:o,num:s}=t,i=Math.floor(s),a=e.makeOutput([i],"float32");return AU(e.dataIdMap.get(a.dataId).id,n,o,i),a}var DU={kernelName:Ha,backendName:"wasm",setupFunc:zct,kernelFunc:Bct};var $U=yt(ws);var RU=yt(Is);var Vct=!1,FU=ee(qa,Vct,"bool");var OU=yt(Ka);var Gct=!1,MU=ee(ja,Gct,"bool");var Wct=!1,PU=ee(E_,Wct,"bool");var LU;function Uct(r){LU=r.wasm.cwrap(Cs,null,["number","number","number","number","number","number","number"])}function Hct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n;if(o.dtype!=="float32")throw new Error("LRN error: x must have dtype float32");let l=e.makeOutput(o.shape,o.dtype);return LU(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(l.dataId).id,o.shape[3],s,i,a,u),l}var zU={kernelName:Cs,backendName:"wasm",setupFunc:Uct,kernelFunc:Hct};var BU;function qct(r){BU=r.wasm.cwrap(Xa,null,["number","number","number","number","number","number","number","number","number"])}function Kct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,y:s,dy:i}=t,{depthRadius:a,bias:u,alpha:l,beta:c}=n;if(o.dtype!=="float32"||s.dtype!=="float32"||i.dtype!=="float32")throw new Error("LRNGrad error: x, y, and dy must have dtype float32");let p=e.makeOutput(o.shape,o.dtype);return BU(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,e.dataIdMap.get(p.dataId).id,i.shape[3],a,u,l,c),p}var VU={kernelName:Xa,backendName:"wasm",setupFunc:qct,kernelFunc:Kct};var GU;function jct(r){GU=r.wasm.cwrap(vs,null,["number","number","number","number"])}function Xct(r){let{backend:t,inputs:e,attrs:n}=r,{reductionIndices:o,keepDims:s}=n,{x:i}=e,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=Cn(i,o,t);if(f){let w=t.dataIdMap.get(c.dataId).id;l=c,u=w}let d=l.shape.length;S.assertAxesAreInnerMostDims("max",p,d);let[h,g]=S.computeOutAndReduceShapes(l.shape,p),x=y.sizeFromShape(g),b=t.makeOutput(h,i.dtype);if(y.sizeFromShape(l.shape)!==0){let w=t.dataIdMap.get(b.dataId).id;GU(u,Nt[i.dtype],x,w)}if(f&&t.disposeData(c.dataId),s){let w=S.expandShapeToKeepDim(b.shape,m);b.shape=w}return b}var WU={kernelName:vs,backendName:"wasm",setupFunc:jct,kernelFunc:Xct};var Yct=!1,UU=ee(Ss,Yct);var HU;function Zct(r){HU=r.wasm.cwrap(Ns,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Jct(r){let{inputs:t,attrs:e,backend:n}=r,o=t.x,s=n.dataIdMap.get(o.dataId).id;y.assert(o.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${o.dtype}.`);let{filterSize:i,strides:a,pad:u,dimRoundingMode:l}=e,c=S.computePool2DInfo(o.shape,i,a,1,u,l),p=c.filterHeight,m=c.filterWidth,f=c.padInfo.top,d=c.padInfo.right,h=c.padInfo.bottom,g=c.padInfo.left,x=c.dilationHeight,b=c.dilationWidth,w=c.strideHeight,I=c.strideWidth,N=c.inChannels,E=c.outChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let A=n.makeOutput(c.outShape,"float32"),D=n.dataIdMap.get(A.dataId).id;return HU(s,o.shape[0],o.shape[1],o.shape[2],p,m,f,d,h,g,x,b,w,I,N,E,D),A}var qU={kernelName:Ns,backendName:"wasm",setupFunc:Zct,kernelFunc:Jct};var KU;function Qct(r){KU=r.wasm.cwrap("MaxPool3D",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function tpt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dimRoundingMode:u,dataFormat:l}=n,c=S.computePool3DInfo(o.shape,s,i,1,a,u,l),p=e.makeOutput(c.outShape,o.dtype);return KU(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(p.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),p}var jU={kernelName:Li,backendName:"wasm",setupFunc:Qct,kernelFunc:tpt};var XU;function ept(r){XU=r.wasm.cwrap("MaxPool3DGrad",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 rpt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,{filterSize:i,strides:a,pad:u,dimRoundingMode:l}=n,c=S.computePool3DInfo(s.shape,i,a,1,u,l),p=e.makeOutput(s.shape,s.dtype);return XU(e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(p.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),p}var YU={kernelName:tu,backendName:"wasm",setupFunc:ept,kernelFunc:rpt};var ZU;function npt(r){ZU=r.wasm.cwrap("MaxPoolGrad",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function opt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,{filterSize:i,strides:a,pad:u,dimRoundingMode:l}=n,c=S.computePool2DInfo(s.shape,i,a,1,u,l),p=e.makeOutput(s.shape,s.dtype);return ZU(e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(p.dataId).id,c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.top,c.padInfo.left),p}var JU={kernelName:Ql,backendName:"wasm",setupFunc:npt,kernelFunc:opt};var QU;function spt(r){QU=r.wasm.cwrap("MaxPoolWithArgmax",null,["number","number","number","number","boolean","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function ipt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,includeBatchInIndex:u}=n;y.assert(o.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.shape.length}.`);let l=[1,1];y.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in maxPool: Either strides or dilations must be 1. 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I=t.dataIdMap.get(c.dataId).id;I!==a&&(l=c,u=I,d=S.getInnerMostAxes(d.length,l.shape.length))}S.assertAxesAreInnerMostDims("mean",d,l.shape.length);let[h,g]=S.computeOutAndReduceShapes(l.shape,d),x=y.sizeFromShape(g),b=l;l.dtype!=="float32"&&(b=Fn({backend:t,inputs:{x:l},attrs:{dtype:"float32"}}),u=t.dataIdMap.get(b.dataId).id);let w=t.makeOutput(h,"float32");if(y.sizeFromShape(l.shape)!==0){let I=t.dataIdMap.get(w.dataId).id;e4(u,x,I)}if(f&&t.disposeData(c.dataId),s){let I=S.expandShapeToKeepDim(w.shape,m);w.shape=I}return l.dtype!=="float32"&&t.disposeData(b.dataId),w}var r4={kernelName:ks,backendName:"wasm",setupFunc:apt,kernelFunc:lpt};var n4;function upt(r){n4=r.wasm.cwrap(Ts,null,["number","number","number","number"])}function cpt(r){let{backend:t,inputs:e,attrs:n}=r,{axis:o,keepDims:s}=n,{x:i}=e,a=t.dataIdMap.get(i.dataId).id,u=a,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=Cn(i,o,t);if(f){let w=t.dataIdMap.get(c.dataId).id;w!==a&&(l=c,u=w)}let 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AH={kernelName:Js,backendName:"wasm",setupFunc:bmt,kernelFunc:wmt};var DH=yt(ri);var $H=yt(ni);var RH;function Imt(r){RH=r.wasm.cwrap(ol,null,["number","number","number","number","number","number","array","number","number","number"])}function Cmt(r){let{backend:t,inputs:e,attrs:n}=r,{tensor:o,indices:s,updates:i}=e,{}=n,a=t.makeOutput(o.shape,o.dtype);if(y.sizeFromShape(o.shape)===0)return a;let{sliceRank:u,numUpdates:l,sliceSize:c,strides:p,outputSize:m}=$u.calculateShapes(i,s,o.shape),d=t.dataIdMap.get(s.dataId).id,g=t.dataIdMap.get(i.dataId).id,b=t.dataIdMap.get(o.dataId).id,w=new Uint8Array(new Int32Array(p).buffer),I=t.dataIdMap.get(a.dataId).id;return RH(d,g,Nt[i.dtype],u,l,c,w,m,I,b),a}var FH={kernelName:ol,backendName:"wasm",setupFunc:Imt,kernelFunc:Cmt};var OH;function vmt(r){OH=r.wasm.cwrap(oo,null,["number","array","number","array","number","number"])}function Smt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,s=e.dataIdMap.get(o.dataId).id,{reps:i}=n,a=new Array(o.shape.length);for(let m=0;m{let{x:n}=r,{k:o,sorted:s}=e,i=t.dataIdMap.get(n.dataId).id,a=new Uint8Array(new Int32Array(n.shape).buffer),u=n.shape.slice();u[u.length-1]=o;let l=t.makeOutput(u,n.dtype),c=t.dataIdMap.get(l.dataId).id,p=t.makeOutput(u,"int32"),m=t.dataIdMap.get(p.dataId).id;return PH(i,a,n.shape.length,Nt[n.dtype],o,s,c,m),[l,p]},LH={kernelName:ul,backendName:"wasm",setupFunc:Nmt,kernelFunc:kmt};var zH;function Tmt(r){zH=r.wasm.cwrap(cl,null,["number","number","bool","number","number","number","number","number","number","array","number","array","number","number","number","number","number"])}function _mt(r){let{backend:t,inputs:e,attrs:n}=r,{image:o,transforms:s}=e,{interpolation:i,fillMode:a,fillValue:u,outputShape:l}=n,[c,p,m,f]=o.shape,[d,h]=l!=null?l:[p,m],g=[c,d,h,f],x=new Uint8Array(new Int32Array(y.computeStrides(o.shape)).buffer),b=new Uint8Array(new 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GH={kernelName:qi,backendName:"wasm",kernelFunc:Amt};function Dmt(r){let{inputs:{x:t},backend:e}=r,n=e.makeOutput(t.shape,t.dtype);return e.typedArrayFromHeap(n).fill(0),n}var WH={kernelName:Ki,backendName:"wasm",kernelFunc:Dmt};var $mt=[_G,EG,AG,DG,$G,FG,zG,VG,GG,WG,UG,HG,qG,KG,jG,YG,rW,JG,tW,sW,aW,uW,cW,pW,mW,fW,hW,gW,yW,wW,CW,SW,kW,TW,_W,AW,$W,FW,MW,LW,BW,GW,UW,qW,jW,XW,ZW,JW,QW,tU,eU,rU,nU,sU,iU,aU,uU,pU,fU,hU,xU,yU,bU,OG,wU,IU,CU,SU,NU,kU,_U,AU,EU,DU,$U,RU,FU,MU,LU,BU,VU,WU,HU,KU,XU,ZU,QU,e4,r4,o4,l4,u4,c4,p4,f4,h4,x4,y4,w4,I4,C4,_C,S4,k4,_4,E4,A4,D4,$4,R4,nW,O4,P4,z4,V4,W4,H4,q4,K4,X4,Z4,Q4,tH,rH,nH,oH,sH,iW,i4,iH,aH,uH,pH,fH,dH,gH,xH,yH,bH,wH,CH,SH,NH,kH,TH,_H,AH,DH,$H,FH,MH,LH,BH,PG,VH,GH,WH];for(let r of $mt)rc(r);var J1=L();J1.registerFlag("WASM_HAS_SIMD_SUPPORT",async()=>{try{return WebAssembly.validate(new 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c=e;this.dataIdMap.set(t,{id:i,stringBytes:c,shape:n,dtype:o,memoryOffset:null,refCount:s});return}let a=y.sizeFromShape(n),u=a*y.bytesPerElement(o),l=this.wasm._malloc(u)>>>0;this.dataIdMap.set(t,{id:i,memoryOffset:l,shape:n,dtype:o,refCount:s}),this.wasm.tfjs.registerTensor(i,a,l),e!=null&&this.wasm.HEAPU8.set(new Uint8Array(e.buffer,e.byteOffset,u),l)}async read(t){return this.readSync(t)}readSync(t,e,n){let{memoryOffset:o,dtype:s,shape:i,stringBytes:a}=this.dataIdMap.get(t);if(s==="string")return(e==null||e===0)&&(n==null||n>=a.length)?a:a.slice(e,n);e=e||0,n=n||y.sizeFromShape(i);let u=y.bytesPerElement(s),l=this.wasm.HEAPU8.slice(o+e*u,o+n*u);return Omt(l.buffer,s)}disposeData(t,e=!1){if(this.dataIdMap.has(t)){let n=this.dataIdMap.get(t);if(n.refCount--,!e&&n.refCount>0)return!1;this.wasm._free(n.memoryOffset),this.wasm.tfjs.disposeData(n.id),this.dataIdMap.delete(t)}return!0}refCount(t){return this.dataIdMap.has(t)?this.dataIdMap.get(t).refCount:0}incRef(t){let e=this.dataIdMap.get(t);e!=null&&e.refCount++}floatPrecision(){return 32}getMemoryOffset(t){return this.dataIdMap.get(t).memoryOffset}dispose(){this.wasm.tfjs.dispose(),"PThread"in this.wasm&&this.wasm.PThread.terminateAllThreads(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(t,e,n,o){let s;if(n==null)s=this.write(o!=null?o:null,t,e);else{let i=this.dataIdNextNumber++;s={id:i},this.dataIdMap.set(s,{id:i,memoryOffset:n,shape:t,dtype:e,refCount:1});let a=y.sizeFromShape(t);this.wasm.tfjs.registerTensor(i,a,n)}return{dataId:s,shape:t,dtype:e}}typedArrayFromHeap({shape:t,dtype:e,dataId:n}){let o=this.wasm.HEAPU8.buffer,{memoryOffset:s}=this.dataIdMap.get(n),i=y.sizeFromShape(t);switch(e){case"float32":return new Float32Array(o,s,i);case"int32":return new Int32Array(o,s,i);case"bool":return new Uint8Array(o,s,i);default:throw new Error(`Unknown dtype ${e}`)}}};function Fmt(r){return(t,e)=>(y.fetch(r,{credentials:"same-origin"}).then(n=>{n.ok||t.env.a(`failed to load wasm binary 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Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");RC=r,a_=t}function Lmt(r,t=!1){if(yg)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");if(typeof r=="string")gg=r;else{xg=r;let e=Mmt.filter(n=>xg[n]==null);if(e.length>0)throw new Error(`There were no entries found for the following binaries: ${e.join(",")}. 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WH={kernelName:cl,backendName:"wasm",setupFunc:Dmt,kernelFunc:$mt};function Rmt(r){let{inputs:t,attrs:e,backend:n}=r,{axis:o}=e,{x:s}=t,{outputValues:i,outputShape:a,indices:u}=sp(n.readSync(s.dataId),o,s.shape,s.dtype);return[n.makeOutput(a,s.dtype,void 0,i),n.makeOutput([u.length],"int32",void 0,u)]}var UH={kernelName:cu,backendName:"wasm",kernelFunc:Rmt};function Fmt(r){let{inputs:t,backend:e,attrs:n}=r,{value:o}=t,{axis:s}=n;s<0&&(s+=o.shape.length);let i=o.shape[s],a=o.shape.length,u=new Array(a-1),l=0;for(let f=0;f({dataId:f,dtype:d,shape:u}))}var HH={kernelName:Ki,backendName:"wasm",kernelFunc:Fmt};function Omt(r){let{inputs:{x:t},backend:e}=r,n=e.makeOutput(t.shape,t.dtype);return e.typedArrayFromHeap(n).fill(0),n}var qH={kernelName:ji,backendName:"wasm",kernelFunc:Omt};var Mmt=[DG,$G,RG,FG,OG,PG,GG,UG,HG,qG,KG,jG,XG,YG,ZG,QG,sW,eW,nW,lW,cW,mW,fW,dW,hW,gW,yW,bW,IW,vW,NW,TW,EW,AW,DW,RW,OW,PW,zW,VW,WW,HW,KW,XW,ZW,JW,tU,eU,rU,nU,oU,sU,iU,lU,uU,cU,mU,dU,gU,yU,wU,IU,CU,LG,vU,SU,NU,TU,_U,EU,DU,RU,$U,FU,OU,MU,PU,zU,VU,WU,UU,qU,jU,YU,JU,t4,r4,o4,s4,a4,p4,m4,f4,d4,g4,y4,w4,I4,v4,S4,N4,AC,T4,E4,D4,$4,R4,F4,O4,M4,iW,L4,B4,G4,U4,q4,j4,X4,Y4,J4,tH,rH,nH,sH,iH,aH,lH,uW,u4,uH,cH,mH,dH,gH,xH,bH,wH,IH,CH,vH,NH,TH,_H,EH,AH,DH,RH,FH,OH,PH,zH,VH,WH,BG,UH,HH,qH];for(let r of Mmt)rc(r);var t_=L();t_.registerFlag("WASM_HAS_SIMD_SUPPORT",async()=>{try{return WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,10,9,1,7,0,65,0,253,15,26,11]))}catch(r){return!1}});t_.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(t_.get("IS_NODE"))return!1;try{return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)),WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,5,4,1,3,1,1,10,11,1,9,0,65,0,254,16,2,0,26,11]))}catch(r){return!1}});var a_=Gl(YH()),rq=Gl(JH()),l_=Gl(QH());var tq=a_.default||a_,Pmt=l_.default||l_,wg=class extends Bo{constructor(t){super(),this.wasm=t,this.dataIdNextNumber=1,this.wasm.tfjs.initWithThreadsCount(oq),i_=this.wasm.tfjs.getThreadsCount(),this.dataIdMap=new Ta(this,Bn())}write(t,e,n){let o={id:this.dataIdNextNumber++};return this.move(o,t,e,n,1),o}numDataIds(){return this.dataIdMap.numDataIds()}async time(t){let e=y.now();return t(),{kernelMs:y.now()-e}}move(t,e,n,o,s){let i=this.dataIdNextNumber++;if(o==="string"){let c=e;this.dataIdMap.set(t,{id:i,stringBytes:c,shape:n,dtype:o,memoryOffset:null,refCount:s});return}let a=y.sizeFromShape(n),u=a*y.bytesPerElement(o),l=this.wasm._malloc(u)>>>0;this.dataIdMap.set(t,{id:i,memoryOffset:l,shape:n,dtype:o,refCount:s}),this.wasm.tfjs.registerTensor(i,a,l),e!=null&&this.wasm.HEAPU8.set(new Uint8Array(e.buffer,e.byteOffset,u),l)}async read(t){return this.readSync(t)}readSync(t,e,n){let{memoryOffset:o,dtype:s,shape:i,stringBytes:a}=this.dataIdMap.get(t);if(s==="string")return(e==null||e===0)&&(n==null||n>=a.length)?a:a.slice(e,n);e=e||0,n=n||y.sizeFromShape(i);let u=y.bytesPerElement(s),l=this.wasm.HEAPU8.slice(o+e*u,o+n*u);return zmt(l.buffer,s)}disposeData(t,e=!1){if(this.dataIdMap.has(t)){let n=this.dataIdMap.get(t);if(n.refCount--,!e&&n.refCount>0)return!1;this.wasm._free(n.memoryOffset),this.wasm.tfjs.disposeData(n.id),this.dataIdMap.delete(t)}return!0}refCount(t){return this.dataIdMap.has(t)?this.dataIdMap.get(t).refCount:0}incRef(t){let e=this.dataIdMap.get(t);e!=null&&e.refCount++}floatPrecision(){return 32}getMemoryOffset(t){return this.dataIdMap.get(t).memoryOffset}dispose(){this.wasm.tfjs.dispose(),"PThread"in this.wasm&&this.wasm.PThread.terminateAllThreads(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(t,e,n,o){let s;if(n==null)s=this.write(o!=null?o:null,t,e);else{let i=this.dataIdNextNumber++;s={id:i},this.dataIdMap.set(s,{id:i,memoryOffset:n,shape:t,dtype:e,refCount:1});let a=y.sizeFromShape(t);this.wasm.tfjs.registerTensor(i,a,n)}return{dataId:s,shape:t,dtype:e}}typedArrayFromHeap({shape:t,dtype:e,dataId:n}){let o=this.wasm.HEAPU8.buffer,{memoryOffset:s}=this.dataIdMap.get(n),i=y.sizeFromShape(t);switch(e){case"float32":return new Float32Array(o,s,i);case"int32":return new Int32Array(o,s,i);case"bool":return new Uint8Array(o,s,i);default:throw new Error(`Unknown dtype ${e}`)}}};function Lmt(r){return(t,e)=>(y.fetch(r,{credentials:"same-origin"}).then(n=>{n.ok||t.env.a(`failed to load wasm binary file at '${r}'`),n.arrayBuffer().then(o=>{WebAssembly.instantiate(o,t).then(s=>{e(s.instance,s.module)})})}),{})}function eq(r,t,e){if(OC!=null)return OC;let n="tfjs-backend-wasm.wasm";return r&&t?n="tfjs-backend-wasm-threaded-simd.wasm":r&&(n="tfjs-backend-wasm-simd.wasm"),yg!=null&&yg[n]!=null?yg[n]:e+n}async function nq(){let[r,t]=await Promise.all([L().getAsync("WASM_HAS_SIMD_SUPPORT"),L().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((e,n)=>{let o={};o.locateFile=(a,u)=>{if(a.endsWith(".worker.js")){let l=rq.wasmWorkerContents.replace(/\n/g,"\\n"),c=new Blob([l],{type:"application/javascript"});return URL.createObjectURL(c)}return a.endsWith(".wasm")?eq(r,t,xg!=null?xg:u):u+a},u_&&(o.instantiateWasm=Lmt(eq(r,t,xg!=null?xg:"")));let s=!1;o.onAbort=()=>{if(s||bg)return;bg=!0,n({message:"Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers"})};let i;t&&r&&OC==null?(o.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+tq.toString()],{type:"text/javascript"}),i=tq(o)):i=Pmt(o),i.then(a=>{s=!0,bg=!1;let u=null;a.tfjs={init:a.cwrap("init",null,[]),initWithThreadsCount:a.cwrap("init_with_threads_count",null,["number"]),getThreadsCount:a.cwrap("get_threads_count","number",[]),registerTensor:a.cwrap("register_tensor",null,["number","number","number"]),disposeData:a.cwrap("dispose_data",u,["number"]),dispose:a.cwrap("dispose",u,[])},e({wasm:a})}).catch(n)})}function zmt(r,t){switch(t){case"float32":return new Float32Array(r);case"int32":return new Int32Array(r);case"bool":return new Uint8Array(r);default:throw new Error(`Unknown dtype ${t}`)}}var Bmt=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],OC=null,xg=null,yg={},bg=!1,u_=!1;function Vmt(r,t=!1){if(G0("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),bg)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");OC=r,u_=t}function Gmt(r,t=!1){if(bg)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");if(typeof r=="string")xg=r;else{yg=r;let e=Bmt.filter(n=>yg[n]==null);if(e.length>0)throw new Error(`There were no entries found for the following binaries: ${e.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}u_=t}var oq=-1,i_=-1;function Wmt(r){oq=r}function Umt(){if(i_===-1)throw new Error("WASM backend not initialized.");return i_}var Hmt="4.21.0";var qmt=2;Xp("wasm",async()=>{let{wasm:r}=await nq();return new wg(r)},qmt);var sq="4.21.0",Kmt="4.21.0",jmt="4.21.0",Xmt="4.21.0",Ymt="4.21.0",Zmt={tfjs:sq,"tfjs-core":sq,"tfjs-converter":Kmt,"tfjs-backend-cpu":jmt,"tfjs-backend-webgl":Xmt,"tfjs-backend-wasm":Ymt};export{Ai as Abs,Go as Acos,Wo as Acosh,vc as AdadeltaOptimizer,Sc as AdagradOptimizer,Nc as AdamOptimizer,kc as AdamaxOptimizer,no as Add,Uo as AddN,Ea as All,Aa as Any,Di as ArgMax,$i as ArgMin,Ho as Asin,qo as Asinh,Ko as Atan,Xo as Atan2,jo as Atanh,Yo as AvgPool,Ri as AvgPool3D,Hl as AvgPool3DGrad,Ul as AvgPoolGrad,wg as BackendWasm,Zo as BatchMatMul,Fi as BatchToSpaceND,Da as Bincount,$a as BitwiseAnd,ql as BroadcastArgs,__ as BroadcastTo,zb as Callback,Xy as CallbackList,fo as Cast,Jo as Ceil,ho as ClipByValue,Ap as Complex,Kl as ComplexAbs,Oi as Concat,Qo as Conv2D,Dp as Conv2DBackpropFilter,ts as Conv2DBackpropInput,es as Conv3D,Ra as Conv3DBackpropFilterV2,Fa as Conv3DBackpropInputV2,rs as Cos,ns as Cosh,Ma as CropAndResize,Oa as Cumprod,os as Cumsum,Zy as CustomCallback,Ta as DataStorage,jl as DenseBincount,Pa as DepthToSpace,ss as DepthwiseConv2dNative,$p as DepthwiseConv2dNativeBackpropFilter,Rp as DepthwiseConv2dNativeBackpropInput,Xl as Diag,is as Dilation2D,Zl as Dilation2DBackpropFilter,Yl as Dilation2DBackpropInput,Qd as Draw,w0 as ENV,Bb as EarlyStopping,Fp as Einsum,ls as Elu,La as EluGrad,Zd as Environment,za as Equal,us as Erf,cs as Exp,Mi as ExpandDims,ps as Expm1,Op as FFT,Jl as Fill,Ba as FlipLeftRight,ms as Floor,fs as FloorDiv,th as FromPixels,ds as FusedBatchNorm,Yi as FusedConv2D,Zi as FusedDepthwiseConv2D,pp as GPGPUContext,Va as GatherNd,Pi as GatherV2,qh as GraphModel,Ga as Greater,hs as GreaterEqual,Yy as History,Mp as IFFT,go as Identity,Pp as Imag,Ce as InputSpec,gs as IsFinite,xs as IsInf,ys as IsNan,Bo as KernelBackend,Cs as LRN,Xa as LRNGrad,Th as LayerVariable,Un as LayersModel,bs as LeakyRelu,Wa as Less,Ua as LessEqual,Ha as LinSpace,ws as Log,Is as Log1p,A_ as LogSoftmax,qa as LogicalAnd,Ka as LogicalNot,ja as LogicalOr,E_ as LogicalXor,nft as LowerBound,pd as MathBackendCPU,Dd as MathBackendWebGL,oft as MatrixBandPart,vs as Max,Ns as MaxPool,Li as MaxPool3D,tu as MaxPool3DGrad,Ql as MaxPoolGrad,eu as MaxPoolWithArgmax,Ss as Maximum,ks as Mean,Ts as Min,_s as Minimum,Es as MirrorPad,As as Mod,Tc as MomentumOptimizer,Ya as Multinomial,Ds as Multiply,zi as Neg,Ja as NonMaxSuppressionV3,Qa as NonMaxSuppressionV4,tl as NonMaxSuppressionV5,Za as NotEqual,V0 as OP_SCOPE_SUFFIX,$s as OneHot,Bi as OnesLike,jr as Optimizer,Ih as OptimizerConstructors,Vi as Pack,Rs as PadV2,sft as Pool,Fs as Pow,Os as Prelu,Ms as Prod,_c as RMSPropOptimizer,po as RNN,Lp as RaggedGather,zp as RaggedRange,Bp as RaggedTensorToTensor,ru as Range,D0 as Rank,Vp as Real,as as RealDiv,Ps as Reciprocal,Je as Reduction,Ls as Relu,Vs as Relu6,Gi as Reshape,Bs as ResizeBilinear,rl as ResizeBilinearGrad,zs as ResizeNearestNeighbor,el as ResizeNearestNeighborGrad,Gs as Reverse,pl as RotateWithOffset,Ws as Round,Us as Rsqrt,Il as SGDOptimizer,nl as ScatterNd,sl as SearchSorted,Wi as Select,Hs as Selu,Wc as Sequential,Xs as Sigmoid,js as Sign,qs as Sin,Ks as Sinh,Ui as Slice,Qs as Softmax,Ys as Softplus,Hi as SpaceToBatchND,nu as SparseFillEmptyRows,il as SparseReshape,ou as SparseSegmentMean,su as SparseSegmentSum,al as SparseToDense,qi as SplitV,Zs as Sqrt,iu as Square,ti as SquaredDifference,ec as StaticRegexReplace,xo as Step,ll as StridedSlice,au as StringNGrams,lu as StringSplit,uu as StringToHashBucketFast,ei as Sub,Js as Sum,nn as SymbolicTensor,ri as Tan,ni as Tanh,Lt as Tensor,le as TensorBuffer,ol as TensorScatterUpdate,oo as Tile,ul as TopK,cl as Transform,so as Transpose,cu as Unique,Ki as Unpack,pu as UnsortedSegmentSum,ift as UpperBound,ml as Variable,ji as ZerosLike,Xi as _FusedMatMul,_e as abs,fx as acos,dx as acosh,K as add,EE as addN,tm as all,cc as any,ra as argMax,hx as argMin,gx as asin,xx as asinh,yx as atan,bx as atan2,wx as atanh,xu as avgPool,Ix as avgPool3d,sx as backend,S as backend_util,$E as basicLSTMCell,oa as batchNorm,Cx as batchNorm2d,vx as batchNorm3d,Sx as batchNorm4d,yu as batchToSpaceND,Nx as bincount,FE as bitwiseAnd,q5 as booleanMaskAsync,OE as broadcastArgs,sa as broadcastTo,Hr as broadcast_util,_y as browser,wt as buffer,xQ as callbacks,J as cast,kx as ceil,vr as clipByValue,un as clone,Sn as complex,ie as concat,Tx as concat1d,_x as concat2d,Ex as concat3d,Ax as concat4d,CR as constraints,rm as conv1d,Nn as conv2d,om as conv2dTranspose,Dx as conv3d,Rx as conv3dTranspose,fft as copyRegisteredKernels,bu as cos,sm as cosh,xh as cosineWindow,mc as cumprod,im as cumsum,pn as customGrad,aO as data,mh as denseBincount,G0 as deprecationWarn,Fx as depthToSpace,ia as depthwiseConv2d,IQ as deregisterOp,du as device_util,ME as diag,Ox as dilation2d,Sdt as disableDeprecationWarnings,Tt as dispose,Ndt as disposeVariables,ut as div,Mx as divNoNan,Px as dot,dN as dropout,wu as einsum,aa as elu,vdt as enableDebugMode,Cdt as enableProdMode,hN as enclosingPowerOfTwo,Bn as engine,LE as ensureShape,L as env,$r as equal,am as erf,Lx as euclideanNorm,Ke as exp,je as expandDims,zx as expm1,fc as eye,Au as fft,Co as fill,Adt as findBackend,Ddt as findBackendFactory,la as floor,Qp as floorDiv,Qz as forceHalfFloat,Ru as fused,ua as gather,r8 as gatherND,Ey as gather_util,oE as getBackend,v0 as getGradient,Wp as getKernel,Yg as getKernelsForBackend,Umt as getThreadsCount,k1 as gpgpu_util,X6 as grad,Y6 as grads,Re as greater,cn as greaterEqual,wl as ifft,Iu as imag,fn as image,s8 as inTopKAsync,vR as initializers,JN as input,Mr as io,bm as irfft,Bx as isFinite,Vx as isInf,Gx as isNaN,De as keep,Xr as kernel_impls,oF as layers,Cu as leakyRelu,yl as less,Vn as lessEqual,xN as linalg,VE as linspace,xtt as loadGraphModel,ytt as loadGraphModelSync,UR as loadLayersModel,Wx as localResponseNormalization,Nr as log,vu as log1p,qx as logSigmoid,lm as logSoftmax,Su as logSumExp,Fr as logicalAnd,Nu as logicalNot,um as logicalOr,Kx as logicalXor,aY as losses,GE as lowerBound,Bt as matMul,O2 as math,Sr as max,ku as maxPool,Xx as maxPool3d,WE as maxPoolWithArgmax,kn as maximum,Ne as mean,lh as memory,UE as meshgrid,sF as metrics,gl as min,lo as minimum,Yx as mirrorPad,Zx as mod,gJ as model,iF as models,dc as moments,X5 as movingAverage,$ as mul,HE as multiRNNCell,qE as multinomial,Ut as neg,Ch as nextFrame,xl as norm,ci as notEqual,ca as oneHot,ar as ones,wr as onesLike,k as op,KE as outerProduct,mn as pad,jE as pad1d,XE as pad2d,YE as pad3d,ZE as pad4d,Jx as pool,qr as pow,_u as prelu,mx as print,Qx as prod,kdt as profile,JE as raggedGather,QE as raggedRange,tA as raggedTensorToTensor,eA as rand,CA as randomGamma,xc as randomNormal,vA as randomStandardNormal,Gn as randomUniform,SA as randomUniformInt,pa as range,_dt as ready,bl as real,sy as reciprocal,Xp as registerBackend,yJ as registerCallbackConstructor,$_ as registerGradient,rc as registerKernel,wQ as registerOp,aF as regularizers,Or as relu,cm as relu6,Edt as removeBackend,R as reshape,dr as reverse,NA as reverse1d,kA as reverse2d,TA as reverse3d,_A as reverse4d,Du as rfft,pm as round,mm as rsqrt,pt as scalar,Z5 as scatterND,$u as scatter_util,dh as searchSorted,fm as selu,dm as separableConv2d,xJ as sequential,Q as serialization,OK as setBackend,$dt as setPlatform,Wmt as setThreadsCount,Vmt as setWasmPath,Gmt as setWasmPaths,BT as setWebGLContext,EA as setdiff1dAsync,Ew as shared,en as sigmoid,iy as sign,iY as signal,hm as sin,gm as sinh,Ot as slice,xm as slice1d,gh as slice2d,ym as slice3d,yc as slice4d,Be as slice_util,Eu as softmax,ui as softplus,Tu as spaceToBatchND,lY as sparse,t8 as sparseToDense,sY as spectral,hr as split,ge as sqrt,Wt as square,wm as squaredDifference,Wn as squeeze,Fe as stack,So as step,ay as stridedSlice,uY as string,at as sub,mt as sum,lc as sumOutType,ly as tan,li as tanh,ir as tensor,Oe as tensor1d,pi as tensor2d,uy as tensor3d,AA as tensor4d,DA as tensor5d,$A as tensor6d,FA as tensorScatterUpdate,Io as tensor_util,IA as test_util,B as tidy,Rr as tile,Tdt as time,cy as topk,Ac as train,Vt as transpose,Cm as truncatedNormal,py as unique,mft as unregisterGradient,pft as unregisterKernel,vm as unsortedSegmentSum,gr as unstack,ur as upcastType,OA as upperBound,y as util,Z6 as valueAndGrad,J6 as valueAndGrads,my as variable,Ux as variableGrads,Zmt as version,VF as version_converter,X2 as version_core,KO as version_cpu,jm as version_layers,Hmt as version_wasm,Jz as version_webgl,T$e as webgl,Cd as webgl_util,Ie as where,dy as whereAsync,ke as zeros,vt as zerosLike}; diff --git a/dist/tfjs.version.js b/dist/tfjs.version.js index f1f52a8..0e48bd6 100644 --- a/dist/tfjs.version.js +++ b/dist/tfjs.version.js @@ -4,4 +4,4 @@ author: ' */ -var e="4.16.0";var s="4.16.0";var t="4.16.0";var n="4.16.0";var i="4.16.0";var w={tfjs:e,"tfjs-core":e,"tfjs-converter":s,"tfjs-backend-cpu":t,"tfjs-backend-webgl":n,"tfjs-backend-wasm":i};export{w as version}; +var e="4.21.0";var s="4.21.0";var t="4.21.0";var n="4.21.0";var i="4.21.0";var w={tfjs:e,"tfjs-core":e,"tfjs-converter":s,"tfjs-backend-cpu":t,"tfjs-backend-webgl":n,"tfjs-backend-wasm":i};export{w as version}; diff --git a/package-lock.json b/package-lock.json deleted file mode 100644 index ba999a3..0000000 --- a/package-lock.json +++ /dev/null @@ -1,6109 +0,0 @@ -{ - "name": "@vladmandic/face-api", - "version": "1.7.13", - "lockfileVersion": 3, - "requires": true, - "packages": { - "": { - "name": "@vladmandic/face-api", - "version": "1.7.13", - "license": "MIT", - "devDependencies": { - "@canvas/image": "^1.0.1", - "@microsoft/api-extractor": "^7.35.2", - "@tensorflow/tfjs": "^4.7.0", 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@typescript-eslint/no-require-imports fs = require('fs'); } catch (err) { requireFsError = (err as any).toString(); diff --git a/typedoc/assets/highlight.css b/typedoc/assets/highlight.css index 9cac2e7..efbf1f4 100644 --- a/typedoc/assets/highlight.css +++ b/typedoc/assets/highlight.css @@ -19,8 +19,6 @@ --dark-hl-8: #CE9178; --light-hl-9: #267F99; --dark-hl-9: #4EC9B0; - --light-hl-10: #000000; - --dark-hl-10: #C8C8C8; --light-code-background: #FFFFFF; --dark-code-background: #1E1E1E; } @@ -36,7 +34,6 @@ --hl-7: var(--light-hl-7); --hl-8: var(--light-hl-8); --hl-9: var(--light-hl-9); - --hl-10: var(--light-hl-10); --code-background: var(--light-code-background); } } @@ -51,7 +48,6 @@ --hl-7: var(--dark-hl-7); --hl-8: var(--dark-hl-8); --hl-9: var(--dark-hl-9); - --hl-10: var(--dark-hl-10); --code-background: var(--dark-code-background); } } @@ -66,7 +62,6 @@ --hl-7: var(--light-hl-7); --hl-8: var(--light-hl-8); --hl-9: var(--light-hl-9); - --hl-10: var(--light-hl-10); --code-background: var(--light-code-background); } @@ -81,7 +76,6 @@ --hl-7: var(--dark-hl-7); --hl-8: var(--dark-hl-8); --hl-9: var(--dark-hl-9); - --hl-10: var(--dark-hl-10); --code-background: var(--dark-code-background); } @@ -95,5 +89,4 @@ .hl-7 { color: var(--hl-7); } .hl-8 { color: var(--hl-8); } .hl-9 { color: var(--hl-9); } -.hl-10 { color: var(--hl-10); } pre, code { background: var(--code-background); } diff --git a/typedoc/assets/icons.js b/typedoc/assets/icons.js new file mode 100644 index 0000000..e88e8ca --- /dev/null +++ b/typedoc/assets/icons.js @@ -0,0 +1,18 @@ +(function() { + addIcons(); + function addIcons() { + if (document.readyState === "loading") return document.addEventListener("DOMContentLoaded", addIcons); + const svg = document.body.appendChild(document.createElementNS("http://www.w3.org/2000/svg", "svg")); + svg.innerHTML = `""`; + svg.style.display = "none"; + if (location.protocol === "file:") updateUseElements(); + } + + function updateUseElements() { + document.querySelectorAll("use").forEach(el => { + if (el.getAttribute("href").includes("#icon-")) { + el.setAttribute("href", el.getAttribute("href").replace(/.*#/, "#")); + } + }); + } +})() \ No newline at end of file diff --git a/typedoc/assets/icons.svg b/typedoc/assets/icons.svg new file mode 100644 index 0000000..e371b8b --- /dev/null +++ b/typedoc/assets/icons.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/typedoc/assets/main.js b/typedoc/assets/main.js index 7270cff..21a5d74 100644 --- a/typedoc/assets/main.js +++ b/typedoc/assets/main.js @@ -1,8 +1,9 @@ "use strict"; -"use strict";(()=>{var Pe=Object.create;var ne=Object.defineProperty;var Ie=Object.getOwnPropertyDescriptor;var Oe=Object.getOwnPropertyNames;var _e=Object.getPrototypeOf,Re=Object.prototype.hasOwnProperty;var Me=(t,e)=>()=>(e||t((e={exports:{}}).exports,e),e.exports);var Fe=(t,e,n,r)=>{if(e&&typeof e=="object"||typeof e=="function")for(let i of 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0qxYvq3CsmFxJaTGc/RFvUSULL6fzX81qxhDiLLaf3cKfQ6oWW0/l8+7DdNOudbq2K4svZ0YvjkrV/elpYJ0byxVRfZZe04DFTKX/MltR7d6f4kkloOZ0P69OpUQyXV8EFde81sWBnsWX1fqMZqnuSi2m/X58+aE7sL3LLaT4061NzeK0bSEF6aSvUBiype6v6xLYLf2Pbg07tYWm9x2/bX1pQrE17kgtq/26nmDnPYgvqfbN+o9FLYovpfWjWv/7+s2pl1BddUP/xqPy0+6KL6tepXlTr/nb98HOXOLxd/rQ/KxImhJRtiT27oIX3byHQPmJRT3ZRC+AiaNSCi+yiFjw/vtasJvuiS+pXaV5YZ6Z732zpd93erh++3On621V2aQveaDamgezSFvyoWGX0RZfW/380y9tAdjEL4EagqP0it6DmT7qTpqvgkrpVehfWuX3UAMir4HK6Gw3p6qSW06o5nsYUu/N1Klv5Irig7vbnQX5aKwbUvuhy+jVk/XFZqv6oGjyWHTl2jWKWJqHldKoA225hrrbbn5RL9Z7kYtr3u+avmrn5Irek5uP3218VO9Ke5GLanzRf8NOy3+6Taip8WngefNr/plH627KR2aq971lsQb2a4XHpU8dDc7t9OrQbSMWdn77sghZomnvp1m7rK3Rqi2X1+suQXyoQQ190af1q7Qvr5tIPx0yQEg8vYomUPnrEoFji6Nl2fWwOmjiTq+ByulVwd2m2e9g/azb4Z7Hl9B7/ddC8bie2mN6jarQ7LjzaSbk4IgbgA8vZooWIx+UJoi7fwCDbwGytKqVL61TETy0dAx3m8ZHVdmLL6dVi4ePyTLit8unhWXNd+yq5nPY2DOvd/isf5auISR7IL2jJw1Yxop7FltOrGsgXH8f/9bw+aLr6WW5hzZtvtu1V9Gan+t6YR5a0p2lU6TgugsvpDjIhypo7sQX1NoojkU5qQa1tuofNW+UgG0ovZ0Uv852s3AstqFMBtxaOilVd51n4No/uUs3Sd2qCnGSy1u2yt6JP+69UTOkit6Dmbx/2mqsMV8EFdX+nufd/FltQ75NioOyk5miN5ruOKr5p//NFpvi5yMuzl8d6BgSpdD62t2AUS7Oh/pvLowlmjPyY4na32d5ONOf67Cx7wk6hv9hy+hxXWp53239pbjRd5P74jkmqb+ifizfiv5ZzqSF4eG4XlSzpOuoUk5bqrqJpXQ3L2AZd57g/nJrN2+b+sdmd3moWCOwzC3Zm5Wr0KriY7t8+NJrNyFlsMb0+7bruIKwvOkd/P8vnu/BHDHq6qSQpwXVCjs+gdm2Oz85Y6VtjgVioCFhYUv1yjshQB5MjMkmPeLoQqhn9TfERLUL+R1Ay8tNqIy0mTRDQYGOLlbiWWG7HUBGb2zFJl5TJMdQzyOSY5hk5byM4h8nbmPg2kSyN+Epclsa095JzMsJ7MTkZxzQpMzAO+0OaluAGv5hvMdTC5FucoEfKd8fpGmS8S9In5lIMVQ1zKaZr0b0UnzkxSZuUJ3E4cs/TIWZFHCoaZkVM1qZrPjYHYloPlDIeQucbZDxMeyM5wzS80HiG6bHxIZ5JEdYMfCbFpHdT5E0MlcbyJiZpFrMkhvqGWRKTtIg5EWGdN8iJmNaKkQyI0HxcBsQkXbF8h6EuNt9h2ljIZzeEUfD9jG9LymUIKjADRbIOFr0OlXyYoYXPUwg6NjO+GCErIWh4iO2JxjTwOQhBw27W7CplHMSpdZBxMFkPfzQ/1LON/v7AiJ6jnE0QNB2ZbIKJunS7CSZ3YKIePlMgaoFMgYk6+LyAqMPM0sFnAUQddpYOPucf6nCzdPAZ/lBHPkuH6mvBSJZkHZpxeBC5kqZFONwDJXi6l6yDzcw3VGLmadG01+DMME2LnHUP9DBZ99JW66M59mDZLufYS9crZSlhVA4ylKRpi+fPA31C/ry0Na6QLQ+WuJgtL02HkBsPdGBuvMS1ejQTHq7Y+Ux4aRrFvHcDDIx571L1CFnuBnowy12ynpGcdkOFUk67CZq1CufoUWwXZu0WdJuFeXuFSC46UMPkokvVJATYDBRhjE0aIxnJMwegRMozl64znlWOUStklUskQrEccgiF2BxyifrkjHGojckYl6pLpWaGBv5iH2jYzxhvhUD8UAPG4adpkK5Ggo7Bvcg0LXxeN9ABed3SNIxlcQNdYha3NK18lg3QBfk1UjVEMrQNFHEZ2lL18fnYBqoeowETcS3CNZ5QB97iSdMg51oDLUyutURNQmY11IOZ1dK1CHnUGEWYRy1Jl5w1LdTEZE1L0zOSIw20STnSpuhUq5uuScp/Bmcfs3q5mO0MlAxuxKVpieQ2w5McJrdZoi4hkxkqwkxmiVqEvGWoBfOWJWmJZikLNfFZyhK1RXKSoTYuJ1myNp2iGTqS8o2BZl2+sUR7YtnFUD+bXSxZn3xpbKiPuTeWrE/MHDZUN8wclqpNpWeWBj4r2EBHNu89IjnABqq4HGBT9AkZv1h9mPFrij4+vxer7scZs2Y8mxerDrN5JemTc3eFupjcXYl6xExdqGiYqStVk0rLLA1SfqiBlkF+qDRNQs4tUIM5t9J0aA6MMMNWqgZlezH5tBI1RbJnoS4ue1aaNg35w1xZaRpUn+ac71LIgxVqwDxYaRpUQGKQ9SpRh5zjCvUwOa6SdMkZrUJNTEarVD1i2P5A0zBqPy2GS/PFYLaqNA2qwX+QmypRB5uJClX8NkODlAgJdAwyISVq0Qws884T4jmlQk1CTqlEfZqGm9duYr6ooZJijpZIdijUxGWHmqJNrWuWppHMTwOFUuanCXoVeZ449bE8T4lWiFmdUO8wq1OaJhWymkesxIxNoGSQsSlNi5TWA7QM8nokaZGyMYVKBtmYEnWM5l5CdXLupTTNWqDCZVpK1aRY6A/yKiXqUKmYp0Fxqj8v8kzMkARKBlk70rRo0RaXDylZk5T9aKhqkP0oTddoriPQKOc6StQrZDZCdZjZKE2LasCbOd6JWYtQyyBr0QQ9IzmKOJVSjqJU7VJGooHOQUaiNE3SjW/QM7jvnaiFzzaESpoZpHUstxDqEnILpenkMwmBKsgklKhBgQdmxTSpAolnxRHrAnznxfdKGYBAB2YAStMh5vsBLcN8P4l6xOw+qGiY3SdRk5DLB/VgLp9ELXzmHlTylDbCaNOhDNXo8/TQs3Oy9IxpV2TpQSOmJz0ZNUaRo2fcmtD5+jBdPkNPUleT8/GEqph8PJ+nu03JvtO18MzcOzo7VLl3OIOW6oTzMu/oLIMuosm7g0ZG8+4kdlHlmovLspOkScypE+oZ5tRJ0hLNoBNq4jPojGm7dqqu6Kzncb95fujr8VoidQevUJdZbqDq4DXaTtSrvC1LUHBtoJ/7yTCa3fOjr7T9a0J12XXo+XkVqe2lLxUb19vC15pFa41+hZFaTbTW6LWxSK02WqudWKuL1uom1ppHa80n1lpEa40CfKiV/xJ6d2PhO8i+mf2ZGbFyM79yK1Zu51fuxMrd/MpzsfJ8TuVv1o/Nhur5Yf00VBGWJyiKrohi1b8cX3iD0dxssDmsfxvMBe0fmVfoGR1W8PV+d9o/X+eUu+fdbRsH2NXUE4k3jDdG1PJNc2qo2piiq1SSrkFn+iao67vd0/MJ3OJVcnJzXvLb9W3z+tPToTkex94URGe97jfD+sRXlmTTDMivnf67tsav9p9+fAq9u92dmsPd+vasGOSS9AXr6vf7T1/vH3qLII2ml72nZJX4qGRCe6T5j+3m9CHNhv5j841oZb9fv28eprT8S+bpBdqlrTCxTbpHpinv5ZEUuuF5JX7p/BO7oDazpKRHlWGSeVj9FYwaoPsCpn4Ao+qVnX9m3x81I63fT+n24y0x3uVTenxU8Wfv49M6d6RXK7SR1HQte0Wn6SLnEjqJRAeCRZqsqhNL0sMsP75f7zaP68Ovo4uPi2CHJibP/UFlqrmIe2L6esB/0dtdM1Xpy34FY9MQ+7IRw37ab3enWZZdaljSNOUCRrYsZSmTYthT+7Zv+5mBEw3rV7B0iymWnfEW065Ak1tslmVBDXNNg/kpOjgMBsPZA0PyDCa3StKUljoshONVglXKsWqBoSrRKM0wNX+USjAqYb05b4BKsEk5OM0fmxLbSbkvmDkspTbUQlbFRiSlbX/gGDR38JH8cTf1tV/ik0tYM7oCZ+xQr8UlCxJW5Yx63fpc1s0ju4vsKLALJZN65PWtv9zdftgfftoft8EtcTpA8epCibRV+XVj8O7Hn375/vW375QaXvbkIw0M1ouaf/7uL39NUn1+YKbur3589+7HH5JePHxkGf1Jrw/PTLFgsCN813w6fbttHjaq3SBKT98Jrvnerdf6clDD2HJ48KoS2Fjf/nrvr1Moluq8bcMqljLubq/aQfBm9R9e0qDxTaBsj3L/l2TO6feH6fZ0Dy9l0NN6s+lnoU8w5/roHGNgTSV+8IOJddbHnryy4psgaXGldYkw9ihNShl3lhh2lGYlDTnzRhylRcrRZtZgk2CKbi83fZxJMWV8jJkzxCgtUQ0vU0cXhTF/0HgyZyCRWvzUfBoGokuqO+G5OmncUWu9iM/VO7r/A8XqvR+nGTOmHJ8PY1gJ1MNDC9nw1/ZSst7l+NRcK+6b09+enprD9w1z31gyAh6aa0N8Fw66dTtwTue14z2ftg/XbncOmfJ/1cZMbY8QhHs9YKN6zgLxsYgsiWroRSBKOsZCERVazLiWsZhEhRY7rmUsOFGhxY1rGYtSjGoJbyUNVfjyGfW//thLyTqsvi2eXHt45x8r96UzLP9m+9jspKi3s/1XocmabvePT8+n5mfKZrFRqBWfmGzDfXP6umm3XP7QQ1Qcik3323p338h+a0tn+O3v64ft5s3z4/smNqD1pObq+umwf79+/7A9bX8fU9gTTdPaX0F+ed/8pdltmsObZjjN9Quj4792qTioULVIDGzsTZXhTwa2G/xvm/Xp+dC8/nQ6rJUGCI+lWRKk9Xjeqdry5UVwuq67/eG39WET0vHIu4bis/XqVc7S9nRoNtvb05f3zZe7DUkrNPNPTbdC+vnLoeax37/UaHvYrzdft09v77bN4af1Yc2kcB+qFh6bbkdD38QEU+Qnk6xR3apUa78R/j5+1zdQkVi7TBHmteXo29yQhi/ka1RzXmvkhqfX/cP66Wm7u//8L3lW9J9511/8v+PveJa7Gng6br7YHr/Y7j40h+3J/5Js4sSn7kVPww9wIe0JXkb5JW3ZHr/frzfMz/cOzeiJzrMAmIHvLd8e9o8/rRmCMrSDeWQ5ew5N24nvd6lGSc8t31Lfb5nfbJNbqRNf1I53h/V21+YiU89s7EOL2vTtYf/vZpdiEDyxnDXi7ywPzWB/aHmu/rsDm+iIWWkeBomO5uo+Nodtm/hf3zWGTyxnTbu2Uy4BF9fbDgJ/O2yV6q/Sy1vxzfY4TI0gm9GJL2/HPzxo7t+XHTem/8xyFnUrFqo8YaF1fWCWLVdDYGtGoTX0xyg96MXofPv6hy+/fy1X8fIiwC/xOhPYukdqTqsXI9b628yfaOfZP+imiDVeKE7Wx69QR2odu0ctWC0dh92nq76hh1LUjyy178Nepjbk8tzithCI2z5sT79PNSusYp6Fg+v9zenH59MwfvLy97kdMKxorM9dzVF3M1ZBtGddn5jQmXh1I/0nopFxCCwoLnq7zeZ8h/QqUjgkjinubjX133ixERXDxpn6Ojd3t+O5slBva2NKr4urHeuBqHpiXxwxYrxfjtsRxKh+1Z51bXf3/cuzvRC6fnl8Wg8uYfSP0WO1vXyInZ0HxkmRIvsnnSYSnK7oEAQoRFUdolEJGmXv96fT/lGn7SKbpq53LMT1gfOCUut/+Mm8n5vb4VZ/YPpFTrcm5d8k0NxWczh9R2dmmvd5yTwxxxrt4djADs3ZmNAAIRdTvPPclwSNv49r/H1Zjb+xIUsDrb8NgpRma/7AByoNVH8YhibN1v3ARSeND6mz9fbHWFEtjq+ztR50DT0cbGdrhtFXVM2MvLN1rw/NWjFokdSyPmaD3zg/DwLeltD+s87dPcnFPa5rgEB2cRuUzRAKLzZ98r+FMvzmBr+FMlMv/7vYA73D38Weqfe0f8v/ggDT794Of0Ngbms3x9s1A9uH7X0RXEw39ytmA734K2Yzdd4+bJ++PH33uL5vvtofNs1hyC+HyyLumcUsOn7Yaj75s9iSrf/lSd0GID3HClWARdSAm/7/jEdS9OsSq5G2QRvFynLctpuNjNlU5o1s5ZuFzGw+s50bxc5A05pywqllWnMZM5vPbOcyPv89EsGySGsu1DU/b89cxuOfPndbLtQxP2+/HN4hmWKkfLlkCRsVW3eFjfL1o4k2houV9cP2/WF9UkyPfdE5k3PI5LjetgSDk9lbaOEcxqZha7I2PUNLYmeJzIxlZeoauXGXZWHqGiXmJbIudc0i05JZlrpugVlp8P8EJqVg/ZOYkwrsT2NKOoo/hRlJrCiljQUkEmFBKbVLsCPGehJbPP4CAstJ1DHyGhKrUQ63EpMRWYyyXom5iKxFWW+EqcRYirY1RGYSYSXKunkmIrAQZZ0q5qFjHUqNEtMQWYa+dWLMYoxVyFqUTGIJFrEUg1iIPSzNHJZiDQszhoXYwtJMYSmWsCxDWIodLMwMFmIFyzKCpdjAwkxgIRawLANYZu+/7J5/mb3+59njR/f2kSkzSAA4vJHfD+WJXr+XXCtlKsfaRjaEfdskjzViyBPqGtkhCsr6uY2GLXV2hbKVtNAATdewA9Z81fY8zSkaPcJmPdEjvKZwof2xOTA3oFHRVS5FU/8raTO6+h+eEhJVBgL6L+V4u+9tQqL1vTzLCt03sFAM2eMDKIfKYj/ZIarqfSsXETa7XlC6yBczrFH10YRmSpvQ/YHNN8fovIom6gu/n7BbRPQN+0SM6Cq1+8reak0IpJe3o/1VPbUZnfCiVnA/ccPoxw9mAc3bdpP9zZa55sfo7wsvbwWffk0yY5h3bSk7hBRskiFM7rUFLDk0D+vT9mPDHSswloTiMy3BOWmYIh7H9VhSfGGaeOrymmorfdl/IDJnjKaZD7HQiNI4JJIVwiwlJ9nXt17KLDXhlwPEJPUwa7BQLbnllNpGxgVQKYwLU/VGxgFO8UgORqXm4WcR0Tv+SSi1noePn1K0cw8lWqFdAeGvTyhWQKP6fAf9aoh4pL78lYxzUvSF6eHGlZ7lZ2heP2zv+fUxqDwLpukSB7s8rjNffrjL4+Pd2GzYs3zOAJhPCZbU6tcOifmstZLaEs0gmc9bLWlsUQ6beWzcXMKOxIE014yk0+xKHlpzeWydb4FqsM3l0XYxCxTDbz4y/s63RTEg59KIPEm7OEQXVdyMolp+kO7qnD9KF5XUwPfN6b/Wv/34fGp/yExnDT6imAOj+ttYite/N18d9sNDCMmA8Jm5FvhAi1QT4KG5NrzhckdKut/E0kcmt3xqqy/Y4smtvYDuH/bPY0uCnuKzdLLW5MXQWemM1VDMAu1y6GzG3PXQqC2aBVFgzJwVUcwa5ZLobMvMNVHMksRF0dmihVZF8lejWhadrZm1LpJtUC2Mgm9o6spo1AbF0ggMmbE2kq1RLI7OZsxYHYX6kXj+sD7d8ofLl1I96XxYv28eFJW9PAtGYCMZJsUcbY+n9e5WPuvr6erJpqiDdWTYTn1fjbdRytqxZ7iWZaLpGI4dOEXQFXeIQsvAI4KiUW+wuiDY9HTgfpsJNPXk9Jr6bv++bZOGve5wLdLfzlj3b0bo634pP8e/Vk8k7Z4IKk5M1TKudz12iyT66ikHPqwt2s8QrdB8h/zLaz5E1Bb/EjV6xtt1ZkOO3ntBffp5XKOPD7tBnWmJYDR6hTAcVJyYBkbVc7gbDYOOk5KeQqOVu2+DSlNSwGh08rdxUGtaAhiNXuGuDipOTP+i0cze5BkMhCnJX5S+Za/JMP5NSnui1M1fn2GUp6U70Xta9fIT0r7oLdA1wZSkL+MTMX+/aPCVJaV8GdfK3z5CrWkJX8a1ineThr0tMd2Lop2Fm0uDlk5M9jKumbvXhFpTUr2Ma1TcehosrKYnehm3hwd2aEIaq1O1u3xjinFAepIX1gbVfaqY+sRrVb2qlrhdlW5Z7JKVxrgJd60mGBm7crWAlcwVpyktqdlKLnoPa1JLflYrF/F27HLWEi25TJf8rD1yEV/HLm4t0Y7LdMjP2h+Hp5gTTJRPNRewcHwDPuuK1zQLlTe9BuuQ5GQu7CTMsEMKPD7eHrZPp728IgAxPVO8O+wf/+vtj29SKn7Zeyja+Gj8ZJLGWpFA1WRDUggba4WKtqn1b6a5+mX43FRbwl1Pcre4PDJRf7/7//j+f5rbk3xjCcoXOT3h6lT1MjQ28R4Rqzd+u0yt0T/O3x3iX/eBvT00XTd7X0hWPbgxNEkzd0eI1Rm7VqfWJt8LYnXyN4Oma+YjOmTV8aj/NN1CBIesfCTyX609dueH1S7d+lFq18RCsHrH4u8jOvsn7WHAQf+wWvpx9NEFNl9JJHdZZ8ToDkioWN43DCvuzQR8qMX4W2tH/Z6FmrEeTR09OlM17Uitw42bql0HtQZnqZsh4OvVSsUT6j0+v4/VS8UT6n18Hi7MevVS8YR6N9uPsXqpeEr7vh/OB/32fS+P//F2WN/vtqfnzXD86bdGT2iCDh609+qPJ/qKfs70u2R85EK/cGJcRFr9L2PPCi/XF5oZqTFijxCrEdtXaqwTojeGxiTGbyS3zIQmWaAt1BPDwBLV/CA0gmYjMNQY3wXodEW2AMwrjq//dVr5nfRQ4XD/PKWLjU7CA73zP6nRKXqgUx/FotPJx7EM9aZFsuh0C7EsQ+WJ0SzK3sWdvjOdK+XkXaeZi2kZKk6JatHp5eNahprTIlt0uoXYlqHyxOgWnXY2voWZJlIiXNS+ZsM8WH8nRXmo9fNBHqwBaTEeKZ5XNsKEaJcUK7RNMSXiRbN04WNemC8wKepFo1lYjg80p0W+aDSLsS9cD0yMflG1uRD/wrR6YgSMRjsXAzPUnBIFo9GqiIPh1m2TI2E0NvGxMMx6NSkaRukDOR6GdUZ6RIxghyomJm5CYlRMUNkScTFTrItFxugMnBAbM8nQWHTMIpYyoSfTWlSHExaNkZnYop/Z0oU8H4uUWaZFl+qin7mHLuT3WMTMMu25VAf9zP1zGDczyUw5cmYRKzWb/lnRM1OtVMbPMOuX5AgaafHQPzULOGf/WKotmHtmdqkjdmTmLUg6MbtWGzkwi1UrZ3u+Vj2S5zlWfSTD87X+sdzOqKB3QMCy6VF/aQD31bxEsA3WpgHtq9bJIBv0awH2VbMGXONLjiFVqfunNOQYQpW+hek6eGQa+zCm6xIQafQrma6NRaJXXUkoNK6JQ6BXRSnoM66HR55XTWmoM65LQJxXZYloM66NRZq9gSIFZY76iqV3gb+SkN2oPp7TBQrT4JzGcyMvOQFNarSOveoUFBmbbHgE2fsiktBjTBOPHK+a0lBjTJOIGPs9JhEtRttQQIq9VkxEiTFtHEK8akpBhzEtCmTYWwxMR4UxG3hEeFWbhgZH2lRGgkHjpqNA0KtCgLzKRPTnK1kC+aVYE0N9cYMmIL4kw2Job5ZlDClLazF5szezxWYaFkN3syyb6ckYqpvXYnO72GfqYTP9GENx89prbgf7TP1riNySzJJR2yyrYpvEWWgt1SolUuvN78koDSe93kblt/X29EOz2a6/3683zXUtc/e88/Hqxz+hSBTT9Kp+/3x31xze7f1ChKk3KNdWento1qfm6/Xu4/rI1NkvnlLlt4f9o3/VkbovclolzafTYX17au9GcXb3iydU+a7ZHfs3ytiKOyFt9XfN6faD5LtrYVJ1/3Xs3fSC2tqypMreNKd/eL7CvTdIJFX84+Hdh36aYKi1K06q8u/bTbOXKvSF2urum9PX+92p+XQyG9lQRipBge/ZzI/vBfWDkLZ6f0mIuuK7vfgdM1JpCnDPiVV35epKj/51Xz80j03vskqv1kAgsVpx6A3KtZU+7Ncb6vY/rJ+YSoNybaWPbRbEaJcAiXjFsV1XvMqX+MeRbReK44uozlgmmXQTP4MZMWxs9RKi52kGjhzipFvYO+V505y+2z09D4HBuWCRu11BZapTkItd8XuPOKPyCkF0qsZbP8A1Y9p6YpPf7fhV60SFZ16C6FSN79tK2NuUobq+3OS3a4sjPxoLLziQnqVX8Y59uam6Ds3xw/qp2XyX9K7yU3o7IMe+pg/1xObpUb8o+8A83cIVaF7vyPVnrU7+yjevMn7dW6Hx50ldKvrgNFtOez/k0IA6oh5l1RqL4FvaP3xswp58nUr7xdp10mk/mPiuFV4LR0Id6jLLrxPgO3bt2c69xz+9Uy87B7X+TK+3GRjc1YzlabVLtapqs9eKmt319u7H9WG7fv/QHP/U7D5OX2AOq3k5sohsjRAWYvfN6XXUxK7um4ugqGK4oJr2Gp2qrrovSoXOs3XCSx61L3n8417yGLxkpnrLY/Qtt7vtabt+6M/jsvpA+PO/7VXd+Y1rzQv3rBRemqhW+wtHx+agczLzyOdvAFR6cbymGQYWRxvj2+1D8/b346l5VNsVPPJHNcZV6bkxrL4xehZHG+PNftP8zzGlY/Sf+KOa4qLz3BK5viWu9goN8bjf/dr8/lPw8x6yQaH052+Anr7LN6F6+76h0oh47L4ZzQjVk/0DxsOztss7qwaCq5HiG1N3UJlwEf0j3peUXV5X9alfTJQXasxgRyu13ugyeaXF1jS22BofmQ7NetNKqfTc9KTHFCa6S6V2fKIa6L+aLPvt9e7j9rDfMVuCXkk8Ah5+v+j1p6dDc2x3cG8a/qevAolF+B1fq/p3jUKTpUi3/eG39WHD8wrBAHhmGQsSlc/X+0Q3KK6y/I/NMSawT06wJmRX7S2Cb5v16fnQvKZzSX2XEJ7VnX+rW+zwnND5X16kF7Zisz0+Sb8VyphxFV/Yjvak6Ou2yu3dtjn8tD6smRSIglHCswtb2B1vTzVSfny+narIwjS7boS/jwcfDvUkqpCm4qcl2nv0vW5ITSRwavYLjpy3eQN+WD89bXf3f9DrnrX9B9/6F/+v8m3Pwgt84vDjrUl97GnBoYaxI7UP4EOLW7U9QvjAiEE9+YUH4/vm5HtVGxz101r4EWLGIua5paf1pu3297tJ5kkPf6bW+357VC9A4JnlLXp3WG937Y40bV5ln1zeum8P+383u2TT4LGF7Trv4bUG9eQXtuTu0DTCLzpzC+yz9MJWHJsDEeg0Pw0f+wyL25TF7OexoB1P/nbYphhyfeQz2fPN9vhrqkHdM5/JomE4m9Ks/oOfZ+uBIai6JeH1qflW9Q5Lv/3y69e/vP4/P/38+u3b735888v3X371+vu3DF7kBSfiorGXj8cCTkNFCT8RgsYKa91d83w6rPmfwR7ovQrP0vlh/fTE/9z9QONZdJa+4+iQ12k7rrX4SdS13t0flO92Fp2l765ZH+6YFOKsxqvwLJ2b7fH++XgaXXwfL6DmIj7Pj8+Hp8P2qNXbF0/VC7kw3u4Pp2bz5eGw1voWHkl+bxh2vl/vNo/rw68SpuuVLzbsYJ3isDM2hPeNl5jt/nj66bC/bY78sDqwJXxgUVtGyfnAGJGbL2dNkiFL27DxP+NxFlR6aPjQbJsmcvVhM82l6oo2izD1gT0TiLrGaxGePvTWBJqusEHN0gcGzSbpCutSOPrAwCUoOm+jmqFrbZpH0PtaPj8/X+yddPR8xsvNZucLv6qSnH++N5a5+eBN06m5akpI6VcTiLnehjS/T6flKouirHxgzCRSrhhsVZx8YM0sSq6ZptWMfDhrzybkCa0m8nGxxZLpuM4aDRvnbJpDxnWWjXJxzqzJVFxhU5SJD4yZRMQ1GwiZhw8Xxuk0XGGBhoUPTJlDwpULU/1C9HNojzFw1ogpBDzBFpF/i8Yk0+8Ea+LsWzRpGvnWbxVi3Ftazk2h3rxFEhUqqrGtppdYnAxda1Uj6dDkmTSop38uD4raoyZCPYNmMyGNRYnGLG9HChnq2bMEGxrYNZMO9ZtrKT4UbTsFIerZNIMRxT2ooER9z83gRFE7kklRz6jFWFHUwim0qGfkkrwI7UwmRuN2LcOMSM8fR40WeK80bjTpBRcjR4u9biI7+hxvPU6Pem87nR+NTBtpfWwGQ9LYkdoH5nOkEatUJKln0CyWFB2Mk2hSz6JFeFJ8Wk8mSv1ZfjGmpGq9UarEtNxkrjRmUQpZCu1agi2NWaemS6Fps/lS1C4VYeoZNIsxxTck45Spv8CezpmiVqSQpp45S7Cm0cVtymL281igIU5gyBzmpLJnlDoxBk3mTiqLdOSJMWsee9JsPTT0abgknMOf0CqZQL3b7n4f3852Up+BRPVrnkCjzubPJlKBHfOp1IhdCWQqMGwBOqWzbIJRn8eeNFIV2LUMrWLsm02swuZbjlqNtKWKXAW2zaJXY55VEazQo7Mo1og9E0hWYNyCNGvE0mlEKzB2Wao1tHcC2dLYtxTdOuv6IwnXIu+XSrkmvuiCpGvB106mXZ/n7TXEK3jrOdRrdPpJ73uz6JfOnil9YwkKNmqdkoQFhs2kYSODeSIRCyxbiIqNLRsmkLFwFbEgHVO2poKQsS05g5KNW5ZGytC+ZWjZuJUJxAxNXICajdinJGeBYTPp2diGSUPQwgX/HIo2Yk0aSQvMWoamKRbZqYvqz2eJjqwNDJpH15R2KQgba9gMyqa0TEvaWPPm0jbd1khH3Lil6TzqxmyEBvla17fNz83t/r5Ndhtmr8NfbxqKJt15FrT02yKh9hS8x1SrZntg9VSAxpmgzpOXZEOq+gU03+4fn56pf3zTHG8P26c0T/BPT7BKuS/irEjfFMlto94RcYZM2A4lWRLdC4kGTdoIae2K7oI4kyZtgXR9WbX/4WyatfnR2abf+XAGzt/2pLWguOeJtV7yhkdtk2a3I1g2Z6ujtm90nyMYN3mTo7MsusPhTJq0vVHOcPLehp3g0jc2OjtiRwWcIVPOCXSWaPZXnEVzNlc6y8SdFWdO8rZKb0NsTyWZMmVDlWaRuJuKmZS8lUqzKb6Pihk2bROls06xg+JMm7F9Eu0Kfz622W3+sT19EBa9wY/IsqLR3Q0mLI9oorzlifUHP6N5fbY9oN6yvzLLSGkVYAPISgTJiS2FerChEl/j2gJc8i6upbRJvMS2iisSZSe1F6crbLEJr3Nti2HEBNdiF6lp7RVTIkhOaquhnrClkl+DWuBL9teiL2XpbcJXGJQnv3+/zutbJ5hH7/OXZrdpuOGyX5z+xmK1KJL83lDz9dXTTF0/PLQ95Pj2uPlh/3770Oya08eMMZgXTFXTAsD/Z/+w/2giKq5CqdVHKlWjwa/3j0/7Y7tjeLdmFkVh8SJIkKlShQPBUmHBcvrQ7FQqO8E5ug7POlUkl6gJnTRgdMf2ga/WzEYoLr6YE0dUqJ0aebNUJ4+ZNHB6bDU6zTapU4yZhp1kEcuYTvQljRDwnGQyL71kF4pomJSrMP6a6U6L2Tf2YSfb8VtkOZhgF1/NwnZ+ed98udvAxJxoJNSxoIWxISJm1ZQRYtQy5jN8u93dP8AXHPsQJfklP8Wojjkfo/iy6Z9j3EbFB5lqS8InGbdtwkc5xVbNZzluaMqHmWpl7NOMWzbl41RY11ta0y2B6zPMEhtFtKt2eu5Lee0eCqhX8IRSaCshrQgZmUUGDqle1dqPMzyly4jKk7pJghXcsCQakbKei9swdPa5j7ATxlBkQVcPqp00LTBvkdzkQ0tig79e428iZRrTzz25lDWx2WfMHs2Ek2ZRdI6JmaOaVvS2RIaFof4Jo4Jgw/CDvM4GkU8yFFrwo2QqnvFZwrskf5icNeOfpk6r9uPkbEj9PNMsGv9AYzbpP1G9VYqPVDIp4TPV2RP5UDkbJnyqoh38UunSCRRLpoHswksnvv7EJdTwhaYtpQRjJi6p1FbFl1aCUdOWWDGbxKVW8FB8yglEl196DaufvwQL327qUoyxTL0kU1mQvBhiLJq8KFJbqF8cCealL5KSWq+HqSa3XljHUhaOL+cYk6Yv6ySbYss7zTggSH+WBd+io4H0mjOWgJPGhEQ7pizBFhkZJtiZtCibPz5MbMnINYKEhlRcJ5hkpWoxucxIMW4ZXrP5YX26/cD4tlc2EvwQRNzuH//r7Y9vRmt72ZPkW7pv27RrPIE+7f2doUq4fPCwft88NJvYpDRQzj4zw4bN9nha726bdx8OzfHD/oGPnA1M4B5JsoC5wPNDs95909Wr8QD30GQbHtviketDgf7hA5N13213m6+a48mLKfo6iE/We9rrPq6LXIqm3k+b7ppe6Oz1h0zbP8cPD2JpU5h6Xo5kPfF2CNe1jnxElKzlZvCErHQkhcZpu/v920tQaK/zRbQzz8zSD2FaI5ov0pN13g3ysylUcw8tZAFmflNbcX1wliXC9dQRMwZPzbIh/JVipQn40GQL1vcNremUykE+RW8x+t1fTyVDgS/UMZDiR32tGkXSKhcDK6+F+go3QzwkntAGUjMUtF+OTkkrqVUUvw3cu1TOyWmVHOi7+3cj7buueiRRraqnQ7PZ3p7YjdNVCyOlVdBeownDfH/Yb5oHRokgmaLoHXT5mCpWNlUZfQhjanpSKQr6nTSmYiA3VUlrq1bRRTZVWW9SGdOFoqmqrt/DmCaQTFH05XmiiOkIhVLf43KdZ+w1QkG9mtsuAQY3wvRK00bj8aF+2kWV4EV/fDoxt3o4kek6vu3Mjik5y0zfcYxVOrb94E3mVyXb+8f9dsMtR6hEfd1l97E5HJu3Yn2hgLra/TNX1/5ZW8HjdvfV+/0nppKuRFvRbr/7Yf3p7fPTeXhgqhzI6Cs/PPp7ymylXZl6Il9v3u3f/ut5feCq65VqKzx+eL67e2i+PBzW3DqqX6xePPlMO6O3gzixNBXBtzCW9acvqA4LFDX0MYe65hRQOKh00pHEwH4JHo3l/hmaMzlvtmCPOr1t3JSb/v/octgGFYp1STvO9/tPDY9bpxh6c65urrkj++Tj7f6wpNmX+pa3W50gSjR88c+FW0rFLQmfmG2NMknU0I70FFFSe6gTRA2NmJAeKsGKaHIowZhJqaF0NkUTQw3NmZQWStNnVUmhhvbMSgmlsUufEGpo3Px0UCktJyaDklstORWU0h5NIijWqjlpoJS2jSaBYg2bnAJKY1U0AdTQnEnpn1TLLDn5EzN5pad+0tgQS/w0NGJK2ieNFZqkT0Nr5qR80s3rQsInbkJPTPek1R9L9sSbMSXVU4o1YqIn2ZzkNE8p9sSTPMlGTUvxpLFMkeBpaNaM9E7SqrC3cxW232eTErbd2l0rU6UqwgUsnbJD5VSrMtOKulW70TG1iXtRqG6hneg0I0f2oVpTp+1CJ5o8sgedbrNqBxoxembXj+02Oa3SXlOlWbGz5HSm7Svl91btKjkDEveUSRaIO0rRkOT9pNYecTfJmZK8l9T1x9GdJGfL5H2kzibdLpIzbN4eMq3F2B1krLWS9o9qW8Z2j4JFU/eOaruiO0fBqEn7Rp1F4q6RMyV5z6hc9vA7RnaySdsv6vRLu0XOgNS9os6CsZ0iZ8nUfaJ2PmZ2ifxEnLBH1OuWdoiSCan7wzRL2N1hzJSkvWGaLfLOMGZQ+r5QZ9XIrpAzaeKeULYnv25ivgulMKRiuzs1hzYW8/gnXjJ+aNpfNj5ud1/vd3fbTdMPwtcpeIkP8+tX4WUkg9affm6Ozw+n1Nd9GTw51RRxW44+4LuGpv2nbdKDV03fqw9fFVarfEdQ2KLrBKnWDHuBxpTxHjBmxyBwAYMSxcAFFFQHLogazm+cVnP/Y/rm9bdf/u37d798+933717//Mvb7/7f12911b8UHtUNcoNXSu/9vFWajh9RDr1svbv9wF3A4nVfpdP1ahkBr1lDCfRvfdt+rff6Bifh2Y4HK9pLk/6XZ9/yYIk3Z/jU0na93396vbvdb7a7+7dbZjnP2zV8ar5dcPWYya0ctwmfWdyiy0CqN6j/yLL2RKEzb8+ksKhEe9JMWdwKiupVGnERnm2DgkzyJqSxSf1XLdBJ3ohEPplohUgoI8YkM0q9TSKl5M1J5pTanjpKKnl7JrNK9TinopXCgDeLV6a2HEss462WxCwT7BmjlqJVU7llgm1RcikaNoldaq0S6SVvTjK/VM9ZPMEUpqw0hqmesQSKKUxZiRxTa8UYyeStmcoy9TsHhmZKW4YEnpmiXyKashmpTDPVGpZqxs1J4pqp9shkM25UOtvUWjZCN3mzJvLNqE3hla7vUJa/OiaJTQYsEsqbomcqzlDhvJR2HkVq23abo9rSBrb1H/tMlvlgEDkXTdS8wbNL2ciCQPEKPook9c3Irl5T2yTg16t4PurrXmDaV9G3RIv3BgoZuPS2abdO7x+ar/e7j/zH3lfNPjLdghha7KvVQMWhrhSc2NemBYljbxdBiODOVHio8ewYNkS3TgWGY7ZoUGHfljmQMN4HxvFg347pYHDUijgSBCOmwcC4DaMYsG/DZACoskGrfkHNEdzXVzwB9A30KhFfX2063Bv7AiNYr694AtBTaY6ivIEBkyDemB1RfNc3YRK4i/c2FbLr2zAL1o2MPWpMFwxCswGdroVENMe1TjKUG7VBg+PAkjkgbtSeUQQHxkyGb3FLotitb8Ik4DYyR8ioLZgi0iHbyAwRwWvBFDEBrMU1a5Ba34I5MG1sxSxgtHCpnAjQxnXG0BmqngLNdBaIuIwzIRmU6WyIIzLOkGlwLG6NAov1TZkBxIZ29IP9rhKxQL+BlJ4KDNHPeMUC+Ami6YaGxy4PDQmPwooI39GbwvKWGAXUt3M66UgO5Iu9WhLjm+TkBO0KjjfdwzE7eqzu4/phu1mfmq9DQnEldaFA1LfIq68mQO3EqbE4Wrci8Rhb31jOsYGNwhcZIWTj2m8mwbKunvg1w+3+ediDFBbBc4vYggxPYYYe56ksOFeSYMH1kUUseGzWu5/v36dYcH1kEQsk4qfsphL8m2HR3fbh1BzaUTPJmvCxZb6W47fbw/H0/fr35tB+hibtm+GenmoXDpZftQni3+wPj2DR5e9zh8eworFx8WqOtER5xk7OKrghuRElY/nXD8/NZvtRpe8qm6wTXdL6+B/b0wfJNYPyuS7iKxxz1dBMaXTc77ANoxpvugeUakec+B6zj8Z1v4/9UPeoZnTlBZ3DZposgdK5buSqG3MiGhhx4Uqv7uYsr1I6Nrnud71sGzrd8s8+pOs2ibrln19I120TddsFdbtE3W5B3Xmi7nxB3UWi7mJB3WWi7nJB3VWi7mq6bhwmv2nu1s8Pp+vq5Y0wYsqCcwfPkZrHxtHIGyQMqTojRkbXSCXpA22CRfKYO8siHH4TLJJH4lkW4aCcYJE8Ps+yCIfqBIvkUXuWRTiAJ1gkj+WzLMJhPcEieYSfZREO9gkWyeP+LItwCkiwSJ4NUiySUZ40I6ROBbKG13Ru8FWbpYuuJ4uqhqLLQUShbj1QZN5D2hr1Ev2nmXFDj6abosleNtWm88OLW3V7YVNTTQtqWNw+/7uTU007P7y4VYf9b1NtokeX9+N+civRo4tbRBx6qlGXp5ewq3c40zzfPmw33K9+Xs9nBjLqUOo3zfNh/fCmOf22PwxPuIPSRY70hjWqjvNCM5MC6hiNw5g6jS5dGB2jjomkm6pPDJ6T1LLxcxO0iyFzjGI2ak6lMylQjtEcjZVLtkAXHseYMR4hN7k12KC4SEsM4uKmaB4LheP1x6LhplgRDYDjTRBj4JL1i2FvjGI28i1ZoxDsxuhj4t2StUkhbow6LsotWd9YYBujNxbblqyfDWdjlA4i2iZpkoLYBIVcHNtkvWzoWkTxIHptsmY5YC2ino9ZS7ZhJEyNMSASqabRHvzu6XH77wZTcfV/7LRXzq2q/vvVi+1u03x68ef/fdH+8Fz7g21/fmFu7E394tWLu23zsDm++PM/X5yXMfvHx9a4/+7K/u5v57USJPKn1YtX/1y9KtxNkdv//u9X/zw/4Qv8H7xY9uLVP7NXRX1TliYQywIxI9VmAjH74tU/zStT31RFWJsNxNyLV/+0r2x1k63sK/fK1Td5XgfyLpDPvbypb3LnXuWvbH5TrMpXRVuDM0XwYB48WLAPltyDRfBg2beweuXsTW6qQL4M5Ku+fM29URXI1335bMU9UId+WvkncntTuDx0FDjUe5R3aejTrPVdZljJ0K1Z677Mcv7PQs9mreMyx9YZ+jRrPZXlbJ2hE7PWNVnB1hl6LWudkpWsZOivrHVHVrGSoaey1g9ZzUqGLjKtI8zqlXU3uQ19ZEIfmdYRhvWRge+udYQxr/Lqpizgywt9ZPy3Z9k6Qx+Z1hGG9ZEJfWRaR5iclQx9ZFpHGNZHJvSRaR1hWB+Z0EemdYRhfWRCH5nWEYb1kQl9ZP1ntOIkbegj2zrCsj6yoY9s6wjLfkcWhsfWEZb1kQ19ZP0QyfrIhj6yfnBkfWRDH1k/GrI+sqGPrB/+Su7btKGPrB/4qleuvCnzsM/b0EfWD3msj2zoI7eSZhEXushlopkudJEzopkudJFr/eDYDuJgEmv94NgO4kIXuVx8odBDrnWDY3uSCz3kWjc4tie50EOudYNje5ILPeRaNzi2J7nQQ7nooTz0UN66wbFdLg89lLducOywkIceyr2H2GEhDz2Uew+xXS6HBYbooTz0UN66IWe7Rx56KG/dkLPdIw89lLduyFmv56GH8tYNOTsP56GHitYPuXtVZDemCussQhcVrR9y1utF6KKi9UNecNqL0EVF64ecdWYRuqhwUsMXoYeK1g95xSqHNZ93Uc1Khi4qWj8U7LK2CF1UtH4oMlYydFFRiy8Ueqhs3VAYrsoy9FCZSVWWoYNKIwqG/ilbJxRsPypD/5Sif8rQP2XrhMKxVYb+KQuxSliDe/ewK8QydE/p3cMOM2XonrJ1QsH2zDL0T+X9ww4zVeifqvVCwQ4zVeigqnVDyY4eVeihqnVDyY4eVeihyoljQhW6qBIHuSr0UFWIg0cVuqhq/VCyQ1cFG6RK/H6r0EVVLX6/VeiieiV+v3XoojoTv986dFEtfkN16KHait9vHXqoFr+hOnRQLTqoDh1UF+L3W4cOqkuxytA/dSV+vzVsXcURrsYta+uEkl2kUFlfNpO3mCvYt66MvHVcwc515T8kfkO6gr3rSnQUFfVFW4eU7KxJZX3Z1iUlv39dwQZ25T8ofge7gi3sqvVLye9hV7CJXbWuKfld7Arc5oFCxY5S2QA2tK6pBNoAbvNQoeJ5AwKHTCRJGRIHzxUqvpchc/BkgZ9SMqQOni1UfM9B7uDpQsV3ByQPni/wo2uG7METhorvOkAfMiMuyjPAD5mHDBXfywxCIu81vpcBgsg8aKj4XgYQIvOooeZ7GWCIzMOGmu9lACIyjxv4VWoGKCLzwKEWCBi4zSOHmu9mgCMyIw6RGfCIzFOHmu9lQCQyzx1qvpcBk8g8eaj5ZrBI91rX1CXbI4FLZJ4+1BUHpDIgE5nnDzXfHYBNZJ5AZCu+PwCeyKw4q2XAJzJPIbIV33cAUWQeRGQrw7cEeM7DCKElAFRkHke0qJflouA6TySylQBRwXceSmQrvlM4hLOezq748QSgRebZRLbiRwkAF5kj91Wv8uKmxP4G7CLzhCJb8R0D8EXmIUWW8R0DCEbm5A8PEEbmSUUmgHLAGFkubsEy4BiZpxWZgNUBZWS5PM0By8g8scjaFRLTN3Nk6+JKMgOgkXlskWX8fARMI8vlDw+gRpaT3/iOCVwjy2W/AdjICvKbcBwAfitkvwHcyAryG9/dgW9khew3ABxZQX5jdz4ZQI6skP1W4KEI+Y3/iAB0ZIXsNyAdmecZmeG/N4AdmUw7MsAdmYcaGX/okQHxyGTkkQHzyEo6xeK/N+AeWSn7DcBH5vlGxh+oZAA/slL2G9CPzEOOjD9+yUo8z5L9BggkK8lv/PcGFCQrZb8BBskq8lvBjg9AQjLPOzL+dCcDGJJV5LmKrxk856lH1p7xcMLgOw8+svaYhxMG33n4kdmMnciBjGSef2SWXyEAHMk8AsksP2QDH8k8Bcn4I58MEEnmQUjWnvpwNYMHPQvJLO9BACVZTftwfjEIrCTzSCSz/OYAeElW0yEy70FAJllN58gs28kAm2SejmSOxTsZoJPMA5LMseAmA3qSeUiSOR5LAEHJPCdp4yfZF8RTZe9Bx3sQQIrxsCRzBXsMDCTFEElxJbcUM4BSDKEU/jTFAEsxnpdkrubNgENmT0yyfPUqNze2Blk4ZvbERDjlBppiPDHJ2oOVYcMZwCnGI5OMP1sxwFOMZyZZzn6uBoCK8dAk47fxBoiK8dQky1lnG0AqhgI4ct7ZwFQMxXDk7OdqAKoYCuPI2QHXAFYxFMmR884GrmIomKNgB1wDYMV08Rzs52qArBgK6ShMe3S7wn4EaMV4fCJEYABaMRTXUfABExjZ4QEKv5s2GNvhAUpW8PEVg/AO778i506mDUZ4eISSFYIZ4D/PULKCPR03GOdBgIXvRRjpYch97ILWYLAHERZ2k2ww3MNTlKxgyb/BiA/PUbKSj0kDyGI8SMlKvscBZTGepGQlO0EYwCzGo5SsZCcIA5zFeJaSlfyYAaDFeJiSlSyrNEBajKcpWcn3DEAthlBLyfcMQC3GUkgV722ALcbzFH54AdRiCLWUvLMBtRiPU7KKdzawFkOspeKdDazFEGupeGcDazHEWire2cBaDLGWij1RMcBaDLGWinc2sBZDrKVi13MGWItxsfETWIsh1lLx4wCwFuOBSlbxPQNoiyHaUvHuBtpi8kxekRvgLYZ4S833DeAtxkOVrOb7BhAXQ8Sl5vsGEBfjsUpW830DmIsh5lLzfQOYi/FgJav5vgHUxRB1EWYqoC4mp9BGfiAA7mKIu9S8u4G7GA9Xspp3N5AX4+GKWfEeBPJiiLyseA8CezEUXbLiPQjsxRB7WfEeBPpiPGAxK96DQF8M0ZcV70HgL4b4C79bM8BfDPGXFe9uIDCGCIywogMCY4jArPi+AQzGEIPhN7oGGIwhBrPiOxJQGEMURlhYAoUxZS7vig1wGENhKCu+iwKHMcRhMr6LAokxRGJ4RmCAxBgiMRnfn4HFGI9bTMb3Z2AxpqIgY74/A4sxHreYjO/PwGKMxy2GP4A3wGIMsRhhowQsxlCISsb3Z2AxxuMWk/FdFFiMIRYj7F2BxRiPWwzPbA2wGFORB/mOBCzGeNxiDN+RgMUYj1uM4fsGsBhDgSuG7xvAYgyxGGHbCCzGEIsR9sXAYgzFsBi+1wGLMR63GMP3OmAxhkJZDN/rgMUYj1uMKTj6ACjGUECL4fsRoBi7IgeyXcMCirErciDbNSygGOtpi7Fs17CAYuyKov3ZrmEBxViKa7Fs17DAYiyxGMs60AKMsZ63GMs60AKMsZ63GMs60AKMsZ63GMuOBBZgjPW8xQgB6wBjrOctxvIeBBhjPW8xlvcgwBjreYvhoaUFGGMpwoWHlhZgjM3oygbvQYAx1vMWw0NLCzDGet5iHO9BgDHW8xbjeA8CjLEEYxzvQaAx1gMX43gPAo2xFOnieA8CjrGEY3jQagHHWMIxPMK1gGMs3bhxfN8AHGPp0k3O9w3AMba7d8P3DeAxlngMH7RggcdYun2T8x0JgIylCzh8bKkFIGMp5iXnOxLewrERoG3xIo5nLibnex3exfHMxfDxP3ZwHcd7MOd7Hd7IseRBvtfhpRxLHuT7Bt7LISDDonKLN3Mo9IUPerV4OcdDF8OTVov3czx0MXxEqwUiY+mODh+CaoHIWA9dDB9caoHIWCIy/NmdBSJjPXQxfECfBSJjPXQxPD61QGQsERn+mMECkbEuEkNhgchYIjJ8IIcFImMdeZBdqFkgMpaiX3gya4HIWLrDU9RcpwMgYyn8hYetFoCM9czF8LDVApCxBGT4kxELQMYSkOFPRiwAGUshMDzGtQBkrGcuhse4FoCMpSCYku/OAGRsXolHWxZ4jCUewx+IW+Ax1iMXw/NhCzzGUhwMz4ct8BhLPIbnwxZ4jCUew/NhCzzGEo/hoa8FHmOJx/DQ1wKPscRjeOhrgcfYopTPuC3wGFvQLVS+HwGPscRjeEJsgcdY4jH8Qa0FHmOJx/A42QKPsXQRiMfJFniMJR5T8X0DeIwlHsMfIVrgMZaiYirhHil4kHgMj5Mt8BhLPIbHyRZ4jPXIxfCE2AKPsXJkjAUaY4nG1CymtkBjLNGYmh8IgMZYiozhQyUs0BhLNIYnzxZojK3EKw4WWIwlFiO9HniPWIz0euA9j1sMj78tsBhLcTF84I8FFmOJxfCs3AKLscRieFZugcVYiovhw6AssBhLLIY9h7aAYmwdOdS1gGJsHTnUtYBibC0f6logMbaOHOpaIDG2lg91LYAYW0cOdS2QGFtHDnUtkBi3ihzqOiAxbhU51HVAYtwqcqjrgMS4VeRQ1wGJcavIoa4DEuNWkUNdByTGrSKHug5IjFtFDnUdkBi3Eg91HXAYt4oc6jrgMC6LHOo64DAuixzqOuAwLosEVTjgMI6CYvj53QGHcRQUw8/vDjiMo6AYfsp2wGEcBcXwU7YDDuMoKIY/AXbAYRylOuFPgB1wGEdRMfwJsAMO48xKPgF2wGEccRh+ynbAYRxxGP4E2AGHcRQWw8/vDjiMo7AY/gTYAYdxJpdPgB1wGEcchp+HHXAYZ0r5BNgBh3EUGMPPag44jKPAGH5Wc8BhHHEY/gTYAYdxFBjDnwA74DCOAmP4E2AHHMYRh+FPgB1wGEcchj8BdsBhHHEY/gTYAYdxtpBPgB2AGEcghj8BdgBiHIEY/gTYAYhxFBrDnwA7zJVCIIY/AXaYL4VCY/gTYIcpU5yRT4AdZk2h0Bj+BNgNEqc4+QTYYe4UFwktdJg/hUAMfwLsMIWKK+UTYIdZVAjE8CfADhOpUGgMfwLsAMQ4AjH8CbADEuOIxPAnwA5IjCMSw58AOyAxjq4i8SfADkiM87BFOAF2QGIchcbwJ8AOSIwjEsOfADsgMY5IDH8C7IDEOAqN4U+AHaAYRxeS+BNgByjGEYrhT4AdoBhHKIY/AXaAYhyhGP4E2AGKcYRi+BNgByjG0bUk/gTYAYpxRS6fADtAMY5QDH8C7ADFOAqN4U+AHaAYRyiGPwF2gGIcoRj2BNgBiXHlSj4BdkBiHJEY/gTYAYlxRGL4E2AHJMYRieFPgB2QGEeZWfgTYAckxhGJ4U+AHZAYRySGPwF2QGIckRj+BNgBiXFEYvgTYAckxhGJ4U+AHbAYRyxGyAUGLMYRi+FPgB2wGEeRMfwJsAMW44jF8CfADliMIxbDnwA7oDGOaAx/AuyAxjiiMfwJsAMa44jG8CfADmiMo8gY/gTYAY1xRGP4E2AHNMYRjeFPgB3QGEeRMfwJsAMa4+iWEn8C7ADHOMIx/AmwAxzjKLMLfwLsAMc4iozhT4Ad8BhHkTH8CbADHuPolhJ/AuwAyDiKjOFPgB0AGUehMfwJsAMgk1NoDH8CnAOQybtbSuxZTg5AJqfQGP4EOAcgk1NoDH8CnAOQySk0hj8BzgHI5BQaw58A5wBkcgqN4U+AcwAyeXdPiTsBzoHH5BQZw58A50BkcoqM4U+AcyAyOUXG8CfAORCZnCJj+BPgHIhMTpEx/AlwDkQm77LNsiv+HIhMTpEx/AlwDkQmp8gYnrXmQGTyLHLRLAcikxOR4U+AcyAyOREZ/gQ4ByKTU2QMfwKcA5HJKTKGx7g5EJmcstCyJ8A5AJmc8tDyZDYHIJNTYAxPZnMAMjkBGf4EOAcgkxOQ4U+AcwAyOeWk5ZlvDkAm79LS8t0ZgExOgTH8CXAOQCanwBj2ZCQHHpMTj+FPgHPgMTnFxfAwOQcek1NcDA+Tc+AxOfEYHibnwGNy4jE8TM6Bx+TEY3hCnAOPyYnH8IQ4Bx6TE4/hCXEOPCanrDD8CXAOPCanwBieEOfAY3LiMTwhzoHH5C5yVTcHHpMTj+Fxcg48JqfAGB4n58BjcuIx/AlwDjwm7wJj+C4KPCZ3hXwCnAOPyYnH8Dg5Bx6TE4/hcXIOPCanwBieEOeY2zaS3Baz2xKN4Y9Ic0xwSzSGPyLNMcdtHrksn2OaW6IxPHnOB5luI6luwXvEYqTXA+8Ri5FeD7xHUTE8/s4x6W0XFiO0BXiPWAzPynNgMTmxGJ6V58BickoQw58A58BicmIxNZ/PGFhMTiyGP0LMgcXkFBZT8/mPgcXkxGL4FFw5sJjc4xbLp+DKgcXkHrdYPq9WDiwm97zFrvgUxwBj8pJStLMXs3OAMbnnLZbPlZUDjMnpmhK/O88BxuQlbQT5+QRgTE5hMfzuPAcYk9M1JX53ngOMyT1vsXx+rxxgTF6SB/l82wBjcoIx7EAAKCYvKS8am90rBxSTV+Q/vusDiskr8h/fmwHF5J62WD5vWA4oJve0xfJ5w3JAMbmnLZZPBpYDisk9bbF8hq8cUEzuaYvlGXwOKCb3tMVmjh1DAcXknrZYnsHngGJyT1tsxtHQHEhM7mGL5TNh5UBiciIxbKhLDiAm96xFagsAMXltI20BICavXaQtAMTkdS63BXCYvCb38f0TOExON5SEtgDv1eS9+lWe36xKtALTvnvvGX5HDBimoLgY/mMtAMMUnrRY/oChAAxTrOiHLtiOXwCGKTxpsfwxQAEYpvCkxfLHAAVgmGKVyz2jAAxTeNJi+TODAjBM4VGLNXyKe+AwhUctlj80KIDDFCvyINuPCuAwRSb/kEwBGKbIskgzA4YpMhNpZsAwRRb5AAvAMEXmIs0MGKbwpEVqZsAwRVZEmhkwTJGVke4MGKaIZIspgMIUGfmP/1UBoDCFBy3C51oAhSkoLkb4XAHDFJ60WP73awrAMIWhX5thAVYBGKbwpMXyv2JTAIYpPGmx/A/ZFIBhCk9aLJ/YrAAMU3jSYvmfsykAwxSetFj+F20KwDCFRy1ia4AH6WeC+OOkAjhMQb8UxB8nFcBhCvqxIP44qQAOU9DvBfEL1gI4TNH9ZBDf94HDFPSrQfxPzRTAYYouLob9UADDFPTTQUIzA4Yp6NeDpGYGB9IPCEnNDA50q0gzA4Yp6HeEhGYGDFPQTwkJzQwYpnDkQP5DAQxTdGExbDMDhSkc+Y//qIDCFB60WP7QrgAKU7gyMngBhSk8aLH8D/wUQGEKD1os/xs/BVCYwqMWyyemK4DDFHlsDAUOU+TkQHYXUQCHKTxqsfwP/hTAYQqPWiz/mz8FcJgip9/t4r9AIDGFhy025/sRkJjCwxab830DSEzhYYsVfv8HSEzhYYvNeXfjLxAV8gWzAn+DyLMWK/xiEP4MkWctNue7Bv4SUUEO5L2NP0bkWYsteG/jDxJ51mIL3tuD3ySi317jvY0/S0Qghj9aK/CXiYrIKgZ/m4g4TOHY7wQ4TEEcpshZYeAwBXGYoniVr25MicLgQI9abFHywuBAj1osf6RVAIcpunQxbOR3ARymKMmBNf+C4EDiMOWKF8bflRLT9hZAYQqPWmyZvXL5TekcCIP/PGqxpeGFwX/EYUrb/raoLbJQGDhMQRxGMAM4TEEcRjADOExBHKZ0vBngP+IwZc4Lg/+Iw5QFLwz+Iw5Tlrww+I84TFnxwvjTYOTBmhcGD1LiXj4YvwAQUxCIqVZszQBiCg9bbJXxwuBBIjHs7yEWAGIKAjGV4SsGBxKIqfhOByCmIBBT8V0DSExBJKbiuwaQmMLTFlux58QFoJiCUAz/cygFoJiCUAz/eygFoJhyRQ5kJ58SUEy5kn/mDUBMSSCmZtdoJYCYkkAM3xYlgJiSQAz/UyslgJiSQAz/+yklgJhyJf/sG2CYkjCM9Hrw02+EYaTXg19/IwzD/4pLCRimzMSDwBIoTEkUhv8ZlxIoTJnJP9MHDKYkBiO0BDCYkhiM0BLAYEpiMPyPyZTAYEpiMHXBwcoSGExJDKYueWHwnucstr1LyAmD9wjC1CwzLQHClJ6zOP40qwQIU3rO4lYZN7uXAGFKz1ncyvDC4EGKhWGXZyUwmNJjFtf+JPpwWC6BwZQes7iV44XBgR6zuFX+Ks9uTFmAMDjQYxa3KnhhcCAxGKFrAIMpicEIXQMYTEkMRugawGBKYjBC1wAGU1pyIBt2UQKDKT1mcfzesgQGU1ryILukLIHBlB6zuIxdUpbAYErPWVzG91CAMGWXJIbbepXAYEorZ0orAcGUnrK4jO/5gGBKT1lcZllhQDBldzOJC4AsgcCUHrK4jN3FlEBgSvo954zv+UBgyu4nnQu+ZnCf/KvOJQCYkn7XOeM7HACYkn7aOeM7HACYkn7dOeM7HACYkn7g2fAdDgBMma8i4wsAmJJ+59nwvRMATEk/9SwMcwBgSvq1Z8N3OQAwJf3gs+G7HACY0jMWZ/huBACm9IzFGXYzXAKAKQnA8Gd7JQCY0jMWaeoBAFN6xiJNPQBgykI+RioBwJRFFvEJAJiyMJGuAQCmLGxk6gEAUxYuMvUAgCkJwAhTDwCYkgCMMPUAgCkJwAhTDwCYkiJhhKkHCExZ1JGpB38hulxFph78kegyi0w9+EPRpYlMPfhj0aWNTD34e9F0LYmfevAno+lWEj/14I9Gl0Vk6hn8cHQZmXrwt6MpXS8/9eCvR5d1ZOoBAFNWq8jUAwCm9IxFmnoAwJSVvIMA/FJWNjL1AH4pKxeZegC/lFUemXoAv5RVEZl6AL+UVRkZXwC/lJ6wSFMP4JeyqiPDHOCX0hMWaeoB/FLWWWTqAfxSesQiTT3AX8raRqYe4C9lTTMg/1PowF/KOo8MzcBfyrqIDM3AX0qPWBz/O2Al8Jeylgl2CfilrOuYyaEDq9VKNrkC/FIRfuHvWVQAYKoVOVD4dXgDwuRA9kOpAMBUBGD4tUMFAKbyjMXx4QAVAJhqJZ/iVkBgKg9ZnGU/qgoITLWqYu9XgbB3oGXDhCsgMJXHLI6/iVoBg6mIwQgtBwym8qDF8ddWK6AwlQctzrJfYAUUpvKgxVl2IK+AwlQZOZA9AK+AwlQetDj+19YqoDBVRh5kb7tXQGGqrJJnqgooTBULhamAwlRGXoNWAGEqgjCCFQBhKoIwgk8AwlTGRnwCFKYiCiO0HFCYiiiMYwFdBRSmIgojfFVAYSqiMI4NpKiAwlQetDjH3gCtgMJUHrQ4x14/q4DCVB60OD7QoAIKU1k5GLQCCFMRhBG8DRCmsrEvECBMZWNfIECYiiCM4G2AMBVBGMHbAGEqGxtDgcJUtop4GzBMRRhG8DZgmIowjOBtwDCVRy3OsVS4Ag5TOTnLXQUYpiIMwweKVIBhKsIwfKBIBRimcvQF8qMigJiKQAwf+1EBiKkIxPCxHxWAmIpADB/7UQGIqQjE8LEfFYCYKo9sIyoAMRWBGOFDARBTEYgRPhQAMRWBGOFDARBTEYgRPhQAMRWBGOFDARBTEYgRPhQAMVVeRj4UADEVgRjhQwEQUxGI4QNyKgAxVbESA84rADEVgRj+En0FIKYiEMMH71QAYqpCPomogMNUFAgjWAzu86RFtBjcV0R+u6wCDFMV9AHygwZgmKqgD5BfSgGGqToMw3c5wDBVh2HYfWAFGKYqI1MgUJiK4mD4VgYIUxGE4XeuFUCYqnTyBroCClOVkV1gBRimKiO7wAowTEXXkXhAVwGGqSgQhgd0FXCYigJheEBXAYepKBCGB3QVcJiKOAwP6CrgMFVlZEBXAYmpiMTwgK4CElN1JIblJRWQmKojMXxvBhJTUW4YFtBVAGIqSg3DAroKOExFHIYHdBVwmIo4DA/oKuAwFf1+NQvoKsAwFWEYHtBVgGGqOrYEBQxTEYbhAV0FGKaqxTTLFUCYiiAMD+gqgDAVQRge0FUAYSqCMDygqwDCVHUlA7oKKEzVURh+fAEKUxOF4QFdDRSmXkXOIWqgMHVHYdguVwOFqTsKw3a5GihMvXIyoKuBwtREYXhAVwOFqT1pcXzwag0YpiYMw+eQqQHD1CsZo9VAYeqVfB2wBghTE4Thg2JrgDC15yyOzzdTA4SpCcLwQbE1QJg6k5cvNTCYmhgMT6RqYDB1x2BYIlUDg6k7BsMSqRoYTJ1FzgFrYDA1MRieSNXAYOqslpf5NTCYmiJh+GV+DRCmJgjDE6kaIEzdQRj+0wYIUxOE4X9wuwYIU8dCYWqAMHUsFKYGCFMThOF/nrsGCFMb+UZnDQymJgYjmQwONJERtAYGU1NWGD4bcg0MpraRnCI1QJiasvTyWbhqgDA1/Xw1n0CzBghT089X84mka4AwNUGYgt3H1ABharqNxC5KamAwNTGYgt1B1MBgamIwBYsdamAwNTEYfrtRA4OpicHw240aGExNDIbfbtTAYOoIg6mBwdQucg5RA4OpicEIoyIwmNrJN6prQDA1IRjhMwEEU7vISWANCKYmBMNvZGpAMDUhGH4jUwOCqekyEr+RqQHB1Hkmb2RqQDA1XUbiNzI1IJiaLiPxG5kaEExNCIbfyNSAYGpCMPxGpgYEUxOC4TcyNSCYmhAMv5GpAcHUhGD4jUwNCKamFL3sRqYGAlNTKAw/ZgCBqYnA8BuZGghMTQSG38jUQGDqwoobmRoITE2RMPxGpgYEUxOC4TcyNSCYuijkjUwNCKYuxJssNQCYmgAMv5GpAcDUBGD4jUwNAKYmAMNvZGoAMDXFwfAbmRoITF1GIplqQDA1IRhhIwMIpi5jCxhAMDUhGGEjAwimJgQjbGQAwdQUCSNsZADB1GUV2cgAgqkpFKbgNzKAYGoKhRFaAxBM3SEY3imAYOoOwfBdHxBM3SEYftAHBFMTginYs4IaEExNCEaY4QHB1BQMI8zwwGDqKrIEBQZTV7ElKDCYuootQYHB1BQLIzQzQJiaIIzQzABharqKJMytAGFquoskzK0AYWq6iyTMrYBharqLJMytgGHqLhaGH+oAw9SEYYS5FTBMTRhGmFsBw9SEYYS5FTBMtiIOw0+uXWkgLmcH7QoDaSNOsF1hIG3lKbYrDcSdPMl2pYF4Lk6zXWEgXcgTbVcaiJfyVNuVBuKVPNl2pYG4+OtzXVlfmMAMP+F2pYF4Jk+5XWkgbuRJtysNxK087XalgXjkhKIrDcRzeertSgPxyB6jKw3ES3n67UoD8UqegLvSQLyWp+CutC9OtIafhLvSQNx7tWTpXFcaiHuvluwxdVcaiHuvliyh60oDce/VkkU8XWkg7r1asiimKw3EvVdLlhJ0pYG492rJ7v270kDce7Vkd/9daSDuvVqyy5+utC9OYTQlu5joSgNx79VK8KpFr1IsTSV41aJXKZqmErxq0asUT1MJXrXoVYI5leBVi16lmJpK8KpFrxLRqQSvWvQqMR3+bm9XGoh7r/K3e7vSvrgTb4h2ZYFwJi+hutJA3MiLqK40ECefCh3MoU8juWa6wkDau7QWuqNDlxLi4S8Rd6WBuHcpf424Kw3EvUv5O7xdaSDuXcpfze1K++IUbVPnHPfqSgNx79Ra6I45OpUibvi0sF1pIO6dyud67UoDcf+h8tleu9JAvPVbzt+Q7UoD8cKLC17N0aue7eR8zteuNBCvvLjg1Ry96gFPzud97Ur74p7x5HwW1a40EM+8uODVAr3qOU/OZzztSgNxj4H4M7KuNBAXz5K7skCYfCr0mAJ9WoiX8ruyQJg8KnSvAj1aiPllu7JA2PuTz9balfbFy8jAW6I3PfTJ+eSuXWkgLl6u6coCYevrFnp5ib4sI74s0Zce/OSZ8EmU6Msy4ssSfenRT54J30+JvpRzBXdlgTD5UvjYSvRlFfFlhb6syJfCl1mhL+WLUl1ZIEy+FD7jCn0p/5R2VxYIky+F77JCX1YRX1boy4p8KXyXFfrSc6Cc/wG6rjQQryMrhQqdSSlr+DxQXWkgnsmZoLrSQNwPtEb4kmt0KNEiPt1VVxqIOznhVVcaiOdyrqmuNBAv5GxTXWkgXsr5prrSQDzygdboUsphwyd76kp74hllseHTPXWlgXgm553qSgNxEzEmQ3SUUTYb0RiL4k7OPtWVBuK5nH+qKw3ECzkDVVcaiJdyDqquNBCv5CxUXWkgXst5qLrSvjj9/DafiaorDcQzORdVVxqIGzkbVVcaiFsxH1VXGEg7OSNVVxqI53JOqq40EC/krFRdaSBeynmputJAnEZffsGQITzKPB7KDb8GyBAeZZHrVl1hIO0nU8MvAjJkR5mh0Zef1zNkR5mnQzl/FbUrDcSdF+dn3wzZUebpUM7fwOxKA3G/eTH8HJkhO8o8Hcr5i5VdaSDunWr5eSlDdpR5OpTzuZa70r64jTkV0VHm4VDO/xJzVxqIm8iEnSE6yigjsTBhZ4iOMg+HcsvmEO9KA/E8MmFniI4yW0Qm7AzRUebhkDRhZ4iOMkpOLEzYGaKjjNITCxN2hugoi6CjDNFR5iLZGbvSQDySn7ErDcRtbMJGdJQ5FzUGfUqxQaIx6FPKVSxN2MiOMspWLE3YyI4yylcsTdjIjjLKWCxN2MiOMgoTkiZsZEcZBQpJEzayo8zTIXHCRnaUUbCQNGEjO8ood7E0YSM7yih7sTBhIzrKKH2xNGEjOsoof440YSM6yiiFsTRhIzrKKImxNGEjOsoIHfG/WdyVBuLy/Z+uMJA2scEayVFGyYylwRrJUdalMxYGa2RHGbEjabBGdpR1KY2FwRrpUdYlNRYGa6RHmSdE4mCN/CjrMhsLgzXyo4xyG0uDNRKkrBRTc3ZlgXB0Q4MEKSujGxokSFnpYoM1MqSMchyLxqBPuyzHkjHoU7rgJQ3WSJGyLtex8OUhR8q6bMfCYI0cKevyHQuDNZKkrMt4LAzWSJKyLuexMFgjS8oqGxuskSVllPdYGqyRJmWU+VgarJEmZZT7WBisESdllPxYGqwRJ2WU/lgarBEnZfRDVNJgjTwp61IgC10GeVJW0+KX/WWZrjQQl6Onu8JA2sYGa8RJWYeThMEacVJGAUjSYI04KfPASBysESdlhJOkwRpxUkYpkaXBGoFSVtexwRqBkiGgJAzWBoGS6YASP1gbBEpmJTNfgzjJdDiJHx8N4iTT4SR+fDSIk0yHk/hvwyBOMh1OkowpULyMGlOieBUZrA3iJNPhJP7LM4iTTLaKDNYGcZIhnCQM1gZxkiGcJAzWBnGSIZwkDNYGeZLJXGSwNsiTTMeT+MHaIE8yHU/iB2uDPMl0PIn9lAziJEP5k4XB2iBOMpS7RxisDeIkQz9kJQzWBnmSMVlksDbIkwzxJMsTH4M8yRBP4n//pysNxCMRDgZxkqFftBIGa4M4ydBvWgmDtUGcZDqcxN7j7EoD8So2bCBOMiaG8w3iJGNjON8gTzKUWVkaNpAnGeJJ0rCBPMkQT5KGDeRJhn7jSho2kCcZ4knSsIE8yRBPkoYN5Emm40nCsIE8yRBPkoYN5EnGxuZUgzzJ0A9eSaMMEiVDREkYZRAoGQJK0iiDQMkQUJJGGQRKhoCSNMogUDIElKRRBoGS8cgodzzKNQiUjEdGOf/zXl1pIO7JL/87al1pIO7JL/97YF1pX9wjo5xPvtWVBuJ+9ctnb+pKA3E//vI5mbrSQNyPv+3FUntTOOwzCJSMR0Y5n5epKw3EPXzgMzN1pYG492rOJhjqSgNx71U+O1NXGoh7r/L5mbrSQNx7lU8G1JX2xYko8b/P1ZUG4t6rfJKfrjQQ917lk+Z0pYG49yr/y1tdaSDuvcqnL+hKA3HvVf7Xt7rSQNx7lU810JUG4vLVmK4wkPZOLTJ+zkaiZCgiqTCCODrVM6Ocv93elQbiEUxoECkZD41E0xEpGQpKkkxHpGQ8NMr5G/FdaSCex0xHl5ZF1BZ0KcUlSYMMEiVTkk9z7tZOVxqI19F2RJ9W5FPhU0KiZKqYTxEomcrEGgaBkqmioy8CJVO5WMMgUDIUniQ1DAIlU5FThUEDiZKhACX+qmBXGoiTU4VBA4mSiQUoGQRKpl7F3hSBkiGgJHkJgZKhACXhNoZBomRqOadJVxhIu6gt6NM6j02RCJQMASWpgyFQMnUZ62AIlEwdHXwRKBmPjHLhmopBoGRXkRNyizzJrmI+tciT7MpE2tEiUbKr2IdqkSjZVexDtUiU7Cr2oVokStYzo1y4v2ORKNlVZEK1CJTsKuZTi0DJrmITqkWgZD0yyoW7RBaBks0ig69FnmSz2OBrkSfZLOpT5Ek2i/oUeZL1xCgXrkFZ5Ek2K2LNjjzJemSUC7emLAIlG/lR9K4wkI76FHmSNatYOyJPshSfJLUj8iRLPEnYulnkSZZ4ktSOyJMsxScJ18ksAiVLd9v41F1daSAuJ2fvCgNp8ik//1rkSZbCk4S7ahZ5kqXwJOGumkWeZD0xyoW7ahZ5kqX4JOGumkWeZOlqm3BZwiJPsp4Y5cLVNos8yVo5htsiTbKeF+UVf/5jkSZZz4vy9h4cK44+9bwob+/BseLoU1tFwhIs0iRr68jBmEWaZN0qwlot0iTrskhIuUWcZAkn8b9v3JUG4jbW7oiTrAdGYrsjTrIuj7U74iRLOEm4f2gRJ1nCScL9Q4s4yRJOEkCxRZxkCScJ1xUt4iRL8Un8Tyl3pYE4fanCOIA4yRJOEi4JWsRJlnCScEnQIk6yhJOES4IWcZIlnCRcErSIkyzhJOGSoEWcZAkn8T+e2ZUG4t6rwiVBizjJEk4SLglaxEmWcFLN3+u3iJMs4STh4NgiTrKEk4Q7hRZxkvXAqBDuFFrESTZyt80iTLIF5XVgk9V1pYF44U0ROhjCJOt5USHcV7RIk6znRYVwX9EiTbKeF2WZ8OkhTbKeFxXC9UaLNMl6YFQI1xst4iTrgVEhXG+0iJNsST4V+iPiJOuBUSFcQbSIk6wnRoVwq9AiT7JlbKGEOMlSgJJwrGcRJ9nINTeLMMl6XFRkwqSHMMl6XFRkwkIGYZKtYjejLNIkSzRJGgSQJtkqFvJrkSbZKhbya5Em2SoW8muRJtkqFvJrkSbZKhbya5Em2SoW8muRJtkqFvJrESfZOhbyaxEn2ToW8msRJ9k6FvJrESfZOhbya5En2dpFThgt8iRbx4LOLPIkW0eCziziJFvHgs4s4iRbx4LOLOIkW8eCziziJLeKBZ055EmOeJKwPnXIk9wqEnTmECe5VSzk1yFOcit5QnUIk5zHRdII5hAmOY+LpBHMIUxyFJ4kjGAOaZIjmiSMYA5pkuvCk/ge4JAmuS48iR/BHNIk14Un8SOYQ5zkuvAkvn85xEmuC08S+hfiJEfX3YQRzCFOchSeJIxgDnGSo/AkYQRziJMchScJI5hDnOSyKjKCOeRJrotP4kcwhzzJmVVkBHPIk1wXn8SPYA55kjNGHsEc4iRnbGQEc4iTnHGREcwhTnIUnySMYA5xkqP4JGkEQ57kPDEqhCwGDnmS88SoyPj9u0Oe5AwtlNjM5l1pX9zSQolfzDrkSS7ym2NdYSBtYqYjTnIeGImmI05yHhkVQnYCh0DJdUCJJxUOgZKzNP6yKae70kA8AvMd8iRnyaeVYAv6lHiSNFojT3KOfMpzDYc8yXliVBj+yMUhT3KeGBVGmJiQJzlPjIr2Ci4rjk71xKgwQpdBnuRcLJLQIU9yLhZJ6JAnOU+MCiN0SORJzsUiCR3yJOdikYQOeZLLY5GEDnmSy2ORhA55kstjkYQOeZLLY5GEDnmSy2ORhA55kstjkYQOeZLLY5GEDnmSy2ORhA55kstjkYQOeZLLY5GEDnmSK2KRhA55kisikYQOcZIrYpGEDnGSK2KRhA5xkitikYQOgZIrYpGEDoGSI6Ak3H93CJQcASXh/rtDoOSKyMGbQ57kKH+2kKbMIU9yxJMM+2sMXWkgTuMvj3Ac8iRH4UkCQnfIkxzxJGkeQ57kykggt0Oc5Agn8b9O25UG4kVs2kOe5Cg8SZr2kCe5sopNe0iUHBEladpDouSIKEnTHhIlV2WxaQ+JkqtMbNpDouSq6D4ViZKrYldTHRIlV+WxaQ+JkuuIkjBsIFFyHVESpj0kSq4jSsK0h0TJdURJGJSQKLmOKAmDEhIl1xElYdpDouQo27Y07SFRckSUpGkPiZKjK2/StIdEyXUZlIRpD4mS64iSMO0hUnJ1LN2DQ6TkOqQkTHuIlFyHlITKwal5R5T4aS9HopTTjTdh2suRKOWUQkmY9nJESjkhJWHayxEp5R4bFUIulByhUk6/hibMTDlCpZygkjAz5QiVco+NCiHTSo5QKY/8JlpXGEjHJtUcmVKexSbVHJlS7qnR/9/YtS05iivbf+nnebCQxGX+4HzDjh0dlC27mLbBA7i6anbMv58QkEmmYNH9VNVdVloIKS8rV6Zy0PXFp5iSn1Cj3O4nmHyKKfn5erRd4M+niJI37sBI+hRR8hNmlNv9bJRPESVvjmyqTxElb+ZXuu9W+RRR8hNmlNt9t8qniJI3RzbVp4iSnzCjHNSk+RRR8tn8TvcdJZ8iSn4CjYzfvUFr+av6+ME7TQElP0FG4N6v5a/q4/M73U+7+RRQ8hNklIMqGp8CSj6D18Ysf1Mfnt6oA2c6hZP8DCeBkhufwkl+hpNAyY1P4SRvcasHn6JJfkaT3L5b4lM0yc9oktvn7PgUTfIzmuT2OTs+RZP8hBflbt+t9ima5Gc0ye1npH2KJvkJMModUBkpnORnOMnvpxZ8Cif5CTDK/b4r61M4yc9wkt93ZX0KJ/kZTgL1OT6Fk7zDBeQ+BZP8DCahLZCCSX4Gk9AWSMEk7/zRFkjBJO/yoy2Qgkl+BpPQFkjBJO/Koy2QgkneVUdbIAWTvD8dbYEUTPLeHG2BFEzyE1yUg5orn4JJfoKLclBz5VMwyXucdfMplORnKGn/VtDlr+rjOb61b/mr+niB7+1b/qo+Pnm9uzf3LX9Un55fKbC9KZLk8/mVAmOaIkl+RpLQ2UihJJ/PrxTY3hRK8jMzCZSi+RRK8vkB6uBTJMnnR40efIok+fyo0YNPkSSfHzV68CmS5POjRg8+hZJ8ftTowadQki+OGj34FEryxVGjB59CSb44avTgUyjJF0eNHnwKJfniqNGDT7EkXxw1evApluQntAgFnD7FkvzMTQIBp0+xJD83T9oPOH0KJfm5dxIKOFMoyc+9k1DAmUJJfiYnoYAzhZL83DsJBZwplOQnsCgHVaB+gZL++8e3pv0I/Rgu/9dewue3P//zn2/fv49fz/Dtj/99+97M/5n9MUn99uf/vtlvf/7v3z++udPy088//fJvv/w7d8vPav5ZmOVntvxc/l7k889yGVctf6+Wv1f0/8Xyc5EXmw7Pv5hlRrEx6/ILfcYug2KPwfkXb+gXGkUTjg3xll/owzl9OKcP5/ThnD5Mz2foAWOvseUX+nBBHy6WD1v+pXS0bLx+tKBFuawgDS9OtIannBb1RL/wsi7Di3L5n5JXkucc78CiRaQ1O9ELm24soN9K+q3gz9ELnDrt0288ouARJY8oeUTJI0oeUdEIw3NZ1zJ2GlpepaWxlmdq+dsc7wbP2yHGdvQbv1rL79+R5Oha/Pm/f//9g07H9K94Wr5Pe14cg2ia+CDk/ISGXszU5pOewfEz0D6IPUeWmZeWZ07vIuYRl994dfy84XbmVr8NcmbximieWUaSItVjfvcWPWN97hJBpRS0jHfz2QLj37WASgigbR1pIUDA5aKGW7MON6QVMjq3kTCyPBGc0U2/s+q0CixoPxUnOlTzftmXU7eXW2gvoX/24dKcx6ZrlWj5qMXpYEKzlDaMangudxOfTjvvzD1B9+amJuCckFCS2ivpIJW+hJLuatHlGsWWFctaw6W536/1OaiNE9sTrC8uwxt3GTsMl0f31txDG8YPoyUVStKvZjE27ddXd+8+Mi2lVFLgAWjP712v3ovz4r249SzCLTyJeHZDs9khcjfnpJByelP5bKCgSLXAEQpcHyhySRZtwdo3goJI3K3/UquTyTWOfcHRwC+9UYzcKOQL4OF9qNWGPYmvLSwdRjKiZbkc88qT0argI/W3R/2pJ5fJyTmaHNyJ/e3RtFqClRI8ScBz6Gu9PoVUw3Z5Me50KGD4as9aiFTB5MO4E9wrg9EqtMrlU5AtmDfd7vgsGS8fgrwhhxXuYJPxcv45WQALj8/gkvHSgpAT5ixUi4NX47OT1GY5aTML9cigd0F2kpucfD+HTejQtO9agNyI5K5EJhoSMJzre93rRXDyJdBOyvAmGEI/NsNHfW8ub92nPnViPgVZ0ILebEnOXeXo+JVww8qvuddv4R423ybVd0kuZGXx9hMiF0O7FZoLoZWFdm0YEhNpcrmO2AsZhi4GQZsT7ZU/YjL8zWnMZCov3yAdYyxhrJNtKJURRR7Owlcz1m2mBchHJ6/eYZsz1uk+lk9ADorDq/9xe3ad9iys0EWx0ezsztGucHhX/Kyb8REuTX3v6ktQBzze07oKPcGX+laff4RWDRUPRIHqweBb373ay7m7Jw6CFZsxtycy5mhd3urx/N52/UOtjPSXYnfMeWXoLDrHkUqGFN8kd2j+UdvOSRs2XeR4MHjshmd9TtYoO0n7QbbZOaT/39S2jSml9fs9tDpv3Th2akWs1PsFhS4FhWwFbZqSAq+KHJCqQqp1/pbv93DVrrcVRyOH9n0Z3Te392S49BChTp6Hp98dbxoXPhDF6xRXlyUjHwx5HK/hZnrxdnLxDQQWEDBRMtbiOVqHwctbPAFNe9uYFPkQMDpMRkW4fj1/FPkX9KAlRfGlIUNkOHL3+BV/bk+oEYfAUzjtK3iSus/Qnrv4oOl5isww4XYzbODKg4cOiecu9RWDP1NtEBDRd/XlXA/j2OmTKT0zQkkcDBjf+u7nELRfIXX6Alfgkdfmrh/FSHUOj808+H0cn334+xUGtTuNjDzg0Xldr8nEC2mMCA1agB8oYetbS91Gh+/gRUxSxq55JLBCvPxb2iD4Cr7GcA/tbdSWVXqY0Hx9jcnil3IYGQhnkIBzfW/e+nrUE8/EOhbkWBYVqdITA66sHODxPtftRz3oSbpKLYxDmyTubu0pSLtVFmQPyX2HW/Ucmrs+JDL8IKUXKwzA+Hv0QPWBlfp9Is8cDE1BQicfpCSEtoKnZRIynLs+kSLMaEkgYWXo7TgGPD2eXfOsx2nnvnX9JWg4wRlhAgtSawUpp7IiFbwCwzCMjd/09vVR31/aA85lIEZTzwgZdQ6duvO9a7WkUh5+wrgdXlPtheYKUfI53M/d49kN9ds9jPXwQ/mN8lHM4kxDCc0YpkBiR4tJDAe5ZlHIawwLvnUJw7lvnmPXD9tZOTmrAm7xWaCWpqMcBd5lKxb5iznuTPCtHoKepFWTPFr81xgeEXVthrFuz1pMIadooV+5iOnD8F4/w+XSPEI7NF2rNr+V4GCeI+W9yBqa9nZPHnX7KqRlzKBzcO7ac50oPqlsyopOCW1yrEEnUaGt1W5Tz5nJEBIq0K69NjftsFTK56FEhyvwdmiHsX+dk32VSQVAwZ9ZDIulXJ8nXeMpy5eT2szpwzm5sTlpvpzigYJw1YKwpoJcy5IyZSV9e0nhREkqrySEpySHqjoRaEHJpIpOREWjqpKzaW7NapFnt+YijeXfOJ1o6MGnDvrLb4z/Z2v2izOIWcn/x9+RkcmeejPSb3xyyYWe2s0tv2WEFFvGjB1n2BznxtyaN81XlJkNDq3OROCA2+FDB4XSKfAQA4njTnqg1HDectIOuwNdq9MJuVPG3HJWEHsEUUQSEstgnRYvFgpiCZmeRK4mwYuJbWAUkaTGZGRNQU1GfppzWCVGUWNft8Oz08o5k6FJRplmd6D22w+rH0yZVsvZ9CO19eG0iFKJ4HTG4fJ6LaJSIjhqPHyQXPsIJyWCN/nhshZahNrllkkH/sAafJRahEJPWPN6D538buFvvCncUvorpFqqo2lEGUmkKBDXg4E/m/F9F+GKPGd56PGGeH49ukvQwKFcB2yfE6d5HYPPtjaQEirJyPl1By9sSAI5CfUTMOXw23q+1BIlcOTeiD7UY1hC6qDVqjcq1irgt04y5mhNB7FylU/YQxDjr33846XReb1CgY4wHzgLmj2pMUwJ9SQpnsvobepz9StJfTh3t7bZkeWtSiTDGH2RFbGOr2EMD73EaomK4yVqu0v4a9i8JbU4xfGbxnnxXKI/U8ewIzkxK74utHbLcgXj4MzWKmmbX491rULGwd7rnnV76UMKrBkFBGEf/PV49p22xnJB2TVy/kDE8HpoCTI1QgQl59GSXupR7Xfx6ARUkMFwJ2RyopBGW3SZ1WJf0FbofUQJG0SrlBacaXkGabAoY+xuWg8ZGVxlZLgWbtK+kDDUHzotIyMgGChcwrm7hJ8hYtZaE8s3AhdAjh7GPtT6taqMG5RxrV/38fu1uY+h/x63ZYLWFupwsKcMD9wicT0nbRifdV8/tFxvlWlHB+YSniMlZvSelU9HBDIH2WuTnJ/NEHY8SS8Xm8CljDIPDmLtItTX2VnpK+CtN6kirUSke7/40xO4jmcQZSzQyL1uL4+6/7EFRpQ9sr+Y0S6dKVf0oRK5olrCdh4KNoL5h1mKeiQlppT62kLXekfMLiKjzL6FsN6euEYTgnypbBL02FdRACNS+FOJtUcUs79GMqVnDEdYK2fXVBz+QirX/AUr4KMnqdwJyJFIZRzsUq926fHirQK3cozaZVBrN/c6uklZYk1lSoRyg87Dx9tF1JwklBUVfJRmuL2GURsOn8m3b2CgcGk28WusExAWlAw50aHzlaNc8Tbg6NAwumGYVs7Gz1giThvHaI5jbrirOMYsDua7gTGdVL0lQUIlpEuSjPE9IprdXS9coZxlCKtcGuWQGhktx9tZF/VP0T+lhStImbo0H23XJnwVaTczgiccTE9culEPl2mbkrwpDzdzX//U4aryRynlSyFdThhZni1rnsMgLYreZK6FdA9JicvI7jluEGe5Oh5G4lHAuWvH7qXz2dL19xBcjqM5sNJfL1fHl0dPHjVM+Hz2YdjC5hIT8CXcHVspTft8aQqFDF19CZ2YRdSuyrcyjZoTCT/HejQVtp2VlbAJDBg3gvZeuIyPcuxJ9/XPiUe3u2ckXYmOhMfhQZTVtCGZh2SZ02HPYdI4Cnl2TTsmUiRhhngaOURmopQxfI7XJmiVZSVlPYcorhq/tzSSZZRDZuUlJeVVSmGygqfUBMH8FhswTYORiZKCyi4KMqIlVy2t9TN4yb60ZGlPSZ0V5PaXXBe11uhALzckk5ZZmYJKPArS2CUlTypORJxgkURIZy0jY3LnCwosSvLAKi6bOsGNGO4vbR+k0aIsiIO53InPsxtoKnYbHK0tZq4IG2uBFFyX9qPpu3b6PwUFSU/nBKOK8Per1qQGlUkjHqWDfmzor3q4nD+l2xwEXULfJyGeTMAbqGHD63xvLiCDm6uKDI+/nISkgHKWSbCVkmgOv4Rk20sWNFEHC1rLihJhFWfdTvg5P586npauDzmfnKBzME8WPiM2dmk0UmAkUpBRui0jf9RBZzF8Ph9Gr5iURDrDQfpC+BxDe4mIfkJ38iovkcHColXAAdlA5QUyGM2msha/RotS3AKYqtGigHfjVY4ug3CiFrYfjKp8VAbxlFXUXCGml1weuQwf9c+xr8/jRCNqrk3ot9CTLVQ2aK295EiIox7Dlio7/eIbN5iJK2QIfoIInBg/hnZIwSQVFZ+wip6l7Gh4K5VVzlG/YTLpWuLLlHSTZZxiJ37R1KSeQkCGARlP8CdOJZdwr2jzKNVwQTWNBXl2FX1dxTXEJ4MWMa7e93Unf5+cSL0JpcIzBtKN4Oly0hsuIUdSjb++2o2c/KT4r1BfH+eFFP0HJlGUjB3H0VdKDjRCWk+kiaVMFSCUTLCAoO6R2pE62hiDdFgUcQ31+OrDsvUTzrMkg+Wrg8U0EcPJecMl3gY6ulK35eUms6bcgZz5J7AGQouLMOJGpNS+pmL6CiwRkSI3wtTWh2Sxg9BSbJOcahHKw5fDghQ1wUkGSwmL6JSAvNQSxGKXh1vsEfPwGm6SYw93+zQ2MUGFwp1hWuZXaVgFpXNZvoUVJVeV4bKyFK6A8cI11P31dddHq1TbACqN8Kjvumpc4tp5BXd1GM/vUeXpNRc0BLhh4sgdVnmubB88nHH0X0OirmXxqTlBNHAa3IY9y+kUoHjC5yaK6Prxve9+6vEqvsG7PY7/aC5BlTe4UqlVWDB61QU1mUodkrl3OVw8kOhXqwfBrWuT1M6r3Q0zPHO2cZtsVBQFD5n216a9vIVh3BxwL2sCpos/gYB7PY5BA7YqXUq+4EL52xXRPGMx06beyCgaCuTOXO9dwg5VoTjBDo5bxlBKvaCIrCSIurIMJDCwcoJLH783gcAzma7ICP13kJx87dpxpw5QQnxEn8yxweraMWVBWMm8zonfmGPVGGWMX4m2khFVTgzHHJuKrn9oErLiQ/Nq4+GbqkNZJlsSC7Ukr7mknEtJRfUVBFevXf+z7jVoKDNv+dqBhp0Ew6xOw+UKhjM+2cp7ZR6tY6as4w4PDsLQy5y2eLHkPOQrXsZRhWHG6tpEyHBiKctoipZ7BDmOKtwah2Bt1IeQbCipRPN1IswHXlsfGe7Hw8UZhnks001ny9cbznpxxaDjsMpjX7zvHhsjJcteq5WujG3zxDd7dLp/hTq+UNHz0JQ0k6lc08HwZ/OZRFVGBSIw4b0O3jJ2Top9Bfdc3z3G6/vrTZtIkRFAm+JWj4kjl8kHZro4Jm1t0QgrKQg5GYuCNmtBKFVh0PNQJ53urX5r7s2o3qeVVWMFLOq7hTbEgraE7Cr3VM4wNpQxnkM7hn7KcqhJaDlwecMYU3Thc8wuez6Qssgn2LbiFsEPTRRUKcwThPluYbz23T+h3UN65CNwYGq4y9TaAc1wX65sbaHGbCfLutUxbOI8a0pWYR6yom6ROJjoS1cpJwtCbDQWcA1UFueEz8Ei5T1sKqQVCnCCHCqS8LO56AJOryCNEwT4bmH8q/7ZvcaYl9NBnThPJcyD3cIYXa7wlZAIJDADddA6+C3do5K1X0IVFAV09eW9bi/3tLJIZmMOjtpEFQavsVTUWkg2iFIiPbzux+Zan8fh2vWpWZFIFYw6kKBUQUtfBjrUG2FNe+32ZiZ7Jx7s90f3GpPQXVaAwkDyFsY2IcU4iT6XEF+/LSzFyVTV+sutBGFz9hMM97IzzNUzXCjMTEKTsdtjuU7KsZZx1GFsuohj0SP5wQNO07w3umDYynA355Z5hn2Kteuj4VZ9GbtB2dpQkEuqHLtBznNumIu1/IG5HKne8EBt+ZMyDJBgFaVFhbU59NLFhoUtYvT21JdSwoHOitzevVMvS5uPzuvY100b62j3LJQMNXh3GK4qM5591rXqzbCtYhDT8t5xnJB13M3Tsffqi4NT93o+Q7/pDuJUNHSgThYI5RnOCTtBLNOBcl2Gb3jMXvWXQeP7+iv2jQpj19+Uk2iMbsECBUR2v/YTtR6lDCu28bOInXR1JeVQvhm/ib57qYyqRunRTtX6UsJABW2EghvKEmml4lDrBOHc9/r5TPrlKcAbBq87rob09gsimBQUphYUR5XklZeUn6tIf1XE+qjYKTvBnh+x64Z2asWbQAvZgDYvVvawKmAuKq1qkLAOwb1LOLk7GjBK5cJB0lNzxLkT58BDSnnze3QuSTiCWFPzS+KSTHPkMPZsOI+V5uRkJFfiecDMhpOgVQk7KDQgnSB7W0BuZpMitMqjopjRweKuDS5uVAcdaPumcSmbwkltXJIhqWA6dBKyc47liyupX1dJ27skd6Ik56aCYO70DXuZ70oFFdBAi/Fjt1PVV6p6M2iAZjHd8PerTsooS5XHx1ojCtiERk5qnJKcvpK42SVBdSVFohVUw017vr8uIR6jR/OPNlUqyoZJoaa9NAlLIZNtQhkQtNwkit4fbqzSxDRXfU8gUK/26An6s+voPjYrnBK6KiWgzBhZACjsIEpWbxEGEJOIbRc5RcJgl4zB+ekOMvoNi/4I/RCG5vbotI3IlS9sISGp6bQakO8cDlk8Vw3UineOv4x83jQglN4MfNhOl/aeVJENzEY1Me7b4fbnqp7FY9u7gbGcbLhSwaLepk+KsZwseaoK+Jz9RrvLA0VuloOwcDNM5eI74JDCAyCs0QxvTVtrPFjnLeFpGXY6onmjtjp+7gH1kpHeOqRpxeFT+Yr2lSQGklFzEQf5ts0QPnQWz3rpUGCTPFyj4tEVhSqco/PsYD1LlNEP473+Cv22qDBXiVAP25o1w/Xe1QnwKtEGSC5shujcDuf3kHTcEooXImlNBGj008sojRrFuAJvu20nUisZbzk39ReXU3Byab0FghmdJuOWK2x+DNcuGcdQrWOEwsPsbDNMUFu4h+m/lTlXGxyrvwH1W1XlhyfYQLYZ0pIgFXwQMIO7CDXDXECvj6f6cli60gwbfSZtTEH0voJSWCWZsoro/xUOEXRV/o5fn6uYG/dpbobZcdPbX6bsIJxNQ3VrGithoRwi2TQ4SwbLqAai2DTYJoNlKQeEs2iwSwbLk4NdrbktdPt6vKUGXT42TJVwW+mYdbo3Y6OzTrl8fOyEDLuMaFV0efAEv2AwK2I1Dm2GX7KXFV0YFretggBzWZG1DvYTYi2rlnSwPKNJ+1PsnSkFumDWerNW3u9JsaoNDcwt/tWllwFIXg8Y9CM8k+amsk6D7nmwsCT3R+jr4ZHU8xpZn802wkE+9kT91cCHbCxDxsMTIaIk0kxJKFRZMErGSpHde2iPUf93GShDv3cZDDoKeMVls1CdLlKSNoR6OtKrhbXuk1OjBsocChoU6h9ffVqopGINsvUO+pUb9Fle31AQtF3Q3UEF03PIoagoOqsg++wehqQvoOQDULM9B41rHL8FeSV3zdLGwX2RYwp0y6GSKF9OLkIOw50oZAM5WNnOxVNOwVMKICfXNod+dUwuTek8zfVQXSS+EWqMZHS1NnFyjfkWjmUmnFVa7y9bryLj5m4m4+Q79/s1lqk3fBuScdxc0HEpu4d+Vpwp30q0ab3lK8X6xcc/9qg+qjqRTR3ytapqLchgrtF6jVoGO5HHr1NmdGfiSmnAWIgkrZZ0R5RqzQCtO4kiW7ojSMEA0J6mgqJJ2xGmNiT010iY4GTvyHJKFlSMUVbfPS6NNlJWoow580EMp1DXC/UM81AyZpJlfFOa5RvgHHfDdEyW83y3Gi57pym++kbPUHp3axmE4bkyn4X7TRvuTmo4GjeW66Qck9ddzmlhLhLyWMcvM5wTfo9aZ2gk3JqvfSo4q2mYdWfW7p/rbYfcf9Ty0XdMLHTMh/Tc6RPX/wu2h7KhwtWFZJE4dsf9kv4tpCLEsTrE2u7XUqcEjx4h9S13Nr+qSIIpUhI2O5c7YhSKCSO3KGb31TvVPucEq2dWAUlsLMweVlHn+t6H4dm1Q4gltRF7rtM4JFMYJ7EkHCxsi1Ln/ntJeZUqsF8Lg5hs5hj9cFx0iEGXe6caE2ey3tOSqnGwFc+9u5mnFiBBHzphDhZi37tbE+9mSm5IkbGWJRPtIGF6kdImHU8kBYy79DpIXFykJBx6yeSypF4dbDCyCPlMpUhfhVSLq/Ahu+2A+pkE4izhWJi8HKV01zG5xi1zin5FyULIzY5SXo+kIjuTaseSxsdtxe6ddpDVuYRQZhz1fedcVhoaAKMfdbTNr4vGMmVDowqm3rb1SpKyAsPNKaELqrJV8Y2FJLpZBCD0KRQeZkoe9fjQhVpGgRdkxixhdg4GNpu9I7UYGWsHyQOP+rN5JK0hJRhnySNx8IahR/25ufRKNa0m98ZydyrIhHjUn30YXvcxgRZVkzy+3RjfaBd76OtnkkeKfAMH2xjF8QmnKFflmR5mzx+hHl79Tv7cSkWVw2zkMn4b4kllmUOY7BHZb5qMVerqQzQwAX909o43JBkMWDb7aNq3BBDJT8prhwj+o2mnVvyXkDbOUHGoy1YDevA0232tiKhks2Bm+dHEHiBPHdJmEvK1NBGH9Q05dDvtMFXrBo/1TdL7VeKLls6Ch1Gj5ugqhafIQb81PFJ81TNIdtTaXOBIlsj+6+MlJBHbCx7PKCjlGcsegNDizhDH2PXhUbf1Tc9B93A+XNCxe3b37pbclCjWk68i/h0pm4uOcsVx+h0RaZ+nXHUjhBLaH+HruSmpVPr2BH3TR/cRtn3Lla5GI1PrJwlzFE5aujmCQ+AKnvdpsHRAdN32PDyDhKT40cucpUljU8le9HAp2qDddJnPtQRge8hWa/dKRVS8d4J53TaM3WtM6/Ik6lLAzhW7islKkKWA3JY2JOpEVTNYaJvb8Orreyy/7vofWpmoOwwg77cNr7Gvkzp31fsU+qtt1z7qz+H1JNxLk2ukoWTYwEIi00battJN5VBg/mYj6FlfkhS0Ub1MII8YSNpOTPXcgDzEjbiY89pc1WVUNxBo3rGwnenJAAIWJW/6Y8nMi6Vjv7SpAeM3NLb8pHDBgy8ftxkBSYWxRPTDKbfu7a9wHvd7z8jUbAWb5nVteE+iapnvscTK9FAHdG0Y7s0PTYyRR9FSxYGHyaOuffbdrU+SLDIKgA7rOvRc3+/x0lllkjURE8kgfuT37VVOpe6qciBhQ3iWvT4pJMsJG8ghPStxGY2MpzIixlgqNPSU8ikov1FQPVHJrdkYDT7B1/isL/W4e82dzKYRVFoQ7bPkew+5Jc4J0seiMmlatbxWghU57bYcsvOf9WWPcZurWzktLDSarNWjfj7jRaWaiCSxbm56y6ibYZDacH25YYDbcLLXcOcpw23C+KWZjGurMrsieww1c22VW/miDP17mAneqxGSq0r7IedyoBMD3ea0PgMXtjIgbrhVgFmv0uLnz078hBxTWy51c4afgevIHKcJPDRmO3RMcYgr2M5sGreTHpVRM0GnOaTtz51b0x4TkkeR01Pn0D1N8YxMtve31MrB43MSb0DcgEMSYC4JMy0JYClJy5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"; \ No newline at end of file diff --git a/typedoc/assets/style.css b/typedoc/assets/style.css index 98a4377..9d619a6 100644 --- a/typedoc/assets/style.css +++ b/typedoc/assets/style.css @@ -10,6 +10,7 @@ --light-color-text: #222; --light-color-text-aside: #6e6e6e; --light-color-link: #1f70c2; + --light-color-focus-outline: #3584e4; --light-color-ts-keyword: #056bd6; --light-color-ts-project: #b111c9; @@ -35,6 +36,7 @@ --light-color-ts-set-signature: var(--light-color-ts-accessor); --light-color-ts-type-alias: #d51270; /* reference not included as links will be colored with the kind that it points to */ + --light-color-document: #000000; --light-external-icon: url("data:image/svg+xml;utf8,"); --light-color-scheme: light; @@ -50,6 +52,7 @@ --dark-color-text: #f5f5f5; --dark-color-text-aside: #dddddd; --dark-color-link: #00aff4; + --dark-color-focus-outline: #4c97f2; --dark-color-ts-keyword: #3399ff; --dark-color-ts-project: #e358ff; @@ -75,6 +78,7 @@ --dark-color-ts-set-signature: var(--dark-color-ts-accessor); --dark-color-ts-type-alias: #ff6492; /* reference not included as links will be colored with the kind that it points to */ + --dark-color-document: #ffffff; --dark-external-icon: url("data:image/svg+xml;utf8,"); --dark-color-scheme: dark; @@ -92,6 +96,7 @@ --color-text: var(--light-color-text); --color-text-aside: var(--light-color-text-aside); --color-link: var(--light-color-link); + --color-focus-outline: var(--light-color-focus-outline); --color-ts-keyword: var(--light-color-ts-keyword); --color-ts-module: var(--light-color-ts-module); @@ -116,6 +121,7 @@ --color-ts-get-signature: var(--light-color-ts-get-signature); --color-ts-set-signature: var(--light-color-ts-set-signature); --color-ts-type-alias: var(--light-color-ts-type-alias); + --color-document: var(--light-color-document); --external-icon: var(--light-external-icon); --color-scheme: var(--light-color-scheme); @@ -134,6 +140,7 @@ --color-text: var(--dark-color-text); --color-text-aside: var(--dark-color-text-aside); --color-link: var(--dark-color-link); + --color-focus-outline: var(--dark-color-focus-outline); --color-ts-keyword: var(--dark-color-ts-keyword); --color-ts-module: var(--dark-color-ts-module); @@ -158,6 +165,7 @@ --color-ts-get-signature: var(--dark-color-ts-get-signature); --color-ts-set-signature: var(--dark-color-ts-set-signature); --color-ts-type-alias: var(--dark-color-ts-type-alias); + --color-document: var(--dark-color-document); --external-icon: var(--dark-external-icon); --color-scheme: var(--dark-color-scheme); @@ -183,6 +191,7 @@ body { --color-text: var(--light-color-text); --color-text-aside: var(--light-color-text-aside); --color-link: var(--light-color-link); + --color-focus-outline: var(--light-color-focus-outline); --color-ts-keyword: var(--light-color-ts-keyword); --color-ts-module: var(--light-color-ts-module); @@ -207,6 +216,7 @@ body { --color-ts-get-signature: var(--light-color-ts-get-signature); --color-ts-set-signature: var(--light-color-ts-set-signature); --color-ts-type-alias: var(--light-color-ts-type-alias); + --color-document: var(--light-color-document); --external-icon: var(--light-external-icon); --color-scheme: var(--light-color-scheme); @@ -223,6 +233,7 @@ body { --color-text: var(--dark-color-text); --color-text-aside: var(--dark-color-text-aside); --color-link: var(--dark-color-link); + --color-focus-outline: var(--dark-color-focus-outline); --color-ts-keyword: var(--dark-color-ts-keyword); --color-ts-module: var(--dark-color-ts-module); @@ -247,11 +258,17 @@ body { --color-ts-get-signature: var(--dark-color-ts-get-signature); --color-ts-set-signature: var(--dark-color-ts-set-signature); --color-ts-type-alias: var(--dark-color-ts-type-alias); + --color-document: var(--dark-color-document); --external-icon: var(--dark-external-icon); --color-scheme: var(--dark-color-scheme); } +*:focus-visible, +.tsd-accordion-summary:focus-visible svg { + outline: 2px solid var(--color-focus-outline); +} + .always-visible, .always-visible .tsd-signatures { display: inherit !important; @@ -266,16 +283,6 @@ h6 { line-height: 1.2; } -h1 > a:not(.link), -h2 > a:not(.link), -h3 > a:not(.link), -h4 > a:not(.link), -h5 > a:not(.link), -h6 > a:not(.link) { - text-decoration: none; - color: var(--color-text); -} - h1 { font-size: 1.875rem; margin: 0.67rem 0; @@ -306,10 +313,6 @@ h6 { margin: 2.33rem 0; } -.uppercase { - text-transform: uppercase; -} - dl, menu, ol, @@ -327,17 +330,14 @@ dd { } /* Footer */ -.tsd-generator { +footer { border-top: 1px solid var(--color-accent); padding-top: 1rem; padding-bottom: 1rem; max-height: 3.5rem; } - -.tsd-generator > p { - margin-top: 0; - margin-bottom: 0; - padding: 0 1rem; +footer > p { + margin: 0 1em; } .container-main { @@ -405,7 +405,8 @@ dd { } body { background: var(--color-background); - font-family: "Segoe UI", sans-serif; + font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", "Noto Sans", + Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji"; font-size: 16px; color: var(--color-text); } @@ -423,6 +424,9 @@ a.external[target="_blank"] { background-repeat: no-repeat; padding-right: 13px; } +a.tsd-anchor-link { + color: var(--color-text); +} code, pre { @@ -582,13 +586,13 @@ dl.tsd-comment-tag-group p { } .tsd-filter-input { display: flex; - width: fit-content; width: -moz-fit-content; + width: fit-content; align-items: center; - user-select: none; -webkit-user-select: none; -moz-user-select: none; -ms-user-select: none; + user-select: none; cursor: pointer; } .tsd-filter-input input[type="checkbox"] { @@ -611,11 +615,8 @@ dl.tsd-comment-tag-group p { Don't remove unless you know what you're doing. */ opacity: 0.99; } -.tsd-filter-input input[type="checkbox"]:focus + svg { - transform: scale(0.95); -} -.tsd-filter-input input[type="checkbox"]:focus:not(:focus-visible) + svg { - transform: scale(1); +.tsd-filter-input input[type="checkbox"]:focus-visible + svg { + outline: 2px solid var(--color-focus-outline); } .tsd-checkbox-background { fill: var(--color-accent); @@ -632,13 +633,18 @@ input[type="checkbox"]:checked ~ svg .tsd-checkbox-checkmark { stroke: var(--color-accent); } -.tsd-theme-toggle { - padding-top: 0.75rem; +.settings-label { + font-weight: bold; + text-transform: uppercase; + display: inline-block; } -.tsd-theme-toggle > h4 { - display: inline; - vertical-align: middle; - margin-right: 0.75rem; + +.tsd-filter-visibility .settings-label { + margin: 0.75rem 0 0.5rem 0; +} + +.tsd-theme-toggle .settings-label { + margin: 0.75rem 0.75rem 0 0; } .tsd-hierarchy { @@ -771,6 +777,9 @@ input[type="checkbox"]:checked ~ svg .tsd-checkbox-checkmark { padding: 0; max-width: 100%; } +.tsd-navigation .tsd-nav-link { + display: none; +} .tsd-nested-navigation { margin-left: 3rem; } @@ -784,6 +793,15 @@ input[type="checkbox"]:checked ~ svg .tsd-checkbox-checkmark { margin-left: -1.5rem; } +.tsd-page-navigation-section { + margin-left: 10px; +} +.tsd-page-navigation-section > summary { + padding: 0.25rem; +} +.tsd-page-navigation-section > div { + margin-left: 20px; +} .tsd-page-navigation ul { padding-left: 1.75rem; } @@ -814,10 +832,10 @@ a.tsd-index-link { } .tsd-accordion-summary, .tsd-accordion-summary a { - user-select: none; -moz-user-select: none; -webkit-user-select: none; -ms-user-select: none; + user-select: none; cursor: pointer; } @@ -830,8 +848,9 @@ a.tsd-index-link { padding-top: 0; padding-bottom: 0; } -.tsd-index-accordion .tsd-accordion-summary > svg { +.tsd-accordion .tsd-accordion-summary > svg { margin-left: 0.25rem; + vertical-align: text-top; } .tsd-index-content > :not(:first-child) { margin-top: 0.75rem; @@ -879,7 +898,7 @@ a.tsd-index-link { } .tsd-panel-group { - margin: 4rem 0; + margin: 2rem 0; } .tsd-panel-group.tsd-index-group { margin: 2rem 0; @@ -887,6 +906,9 @@ a.tsd-index-link { .tsd-panel-group.tsd-index-group details { margin: 2rem 0; } +.tsd-panel-group > .tsd-accordion-summary { + margin-bottom: 1rem; +} #tsd-search { transition: background-color 0.2s; @@ -1036,6 +1058,12 @@ a.tsd-index-link { border-width: 1px 0; transition: background-color 0.1s; } +.tsd-signatures .tsd-index-signature:not(:last-child) { + margin-bottom: 1em; +} +.tsd-signatures .tsd-index-signature .tsd-signature { + border-width: 1px; +} .tsd-description .tsd-signatures .tsd-signature { border-width: 1px; } @@ -1349,6 +1377,12 @@ img { .has-menu .tsd-navigation { max-height: 100%; } + #tsd-toolbar-links { + display: none; + } + .tsd-navigation .tsd-nav-link { + display: flex; + } } /* one sidebar */ diff --git a/typedoc/classes/AgeGenderNet.html b/typedoc/classes/AgeGenderNet.html index 2c7460d..56efe0b 100644 --- a/typedoc/classes/AgeGenderNet.html +++ b/typedoc/classes/AgeGenderNet.html @@ -1,27 +1,27 @@ -AgeGenderNet | @vladmandic/face-api - v1.7.13

Class AgeGenderNet

Hierarchy (view full)

Constructors

constructor -

Properties

_name -

Accessors

faceFeatureExtractor -isLoaded -paramMappings -params -

Methods

dispose -extractClassifierParams -extractWeights -forward -forwardInput -freeze -getFrozenParams -getParamFromPath -getParamList -getTrainableParams -load -loadClassifierParams -loadFromDisk -loadFromUri -loadFromWeightMap -predictAgeAndGender -reassignParamFromPath -runNet -serializeParams -variable -

Constructors

constructor

Properties

_name

_name: any

Accessors

faceFeatureExtractor

isLoaded

paramMappings

  • get paramMappings(): ParamMapping[]

  • Returns ParamMapping[]

params

Methods

dispose

extractClassifierParams

extractWeights

forward

forwardInput

freeze

getFrozenParams

getParamFromPath

getParamList

getTrainableParams

load

loadClassifierParams

loadFromDisk

loadFromUri

loadFromWeightMap

predictAgeAndGender

reassignParamFromPath

runNet

serializeParams

variable

Settings

Member Visibility

Theme

On This Page

\ No newline at end of file +AgeGenderNet | @vladmandic/face-api - v1.7.14

Class AgeGenderNet

Hierarchy (view full)

Constructors

constructor +

Properties

_name +

Accessors

faceFeatureExtractor +isLoaded +paramMappings +params +

Methods

dispose +extractClassifierParams +extractWeights +forward +forwardInput +freeze +getFrozenParams +getParamFromPath +getParamList +getTrainableParams +load +loadClassifierParams +loadFromDisk +loadFromUri +loadFromWeightMap +predictAgeAndGender +reassignParamFromPath +runNet +serializeParams +variable +

Constructors

constructor

Properties

_name

_name: any

Accessors

faceFeatureExtractor

isLoaded

paramMappings

  • get paramMappings(): ParamMapping[]

  • Returns ParamMapping[]

params

Methods

dispose

extractClassifierParams

extractWeights

forward

forwardInput

freeze

getFrozenParams

getParamFromPath

getParamList

getTrainableParams

load

loadClassifierParams

loadFromDisk

loadFromUri

loadFromWeightMap

predictAgeAndGender

reassignParamFromPath

runNet

serializeParams

variable

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diff --git a/typedoc/classes/BoundingBox.html b/typedoc/classes/BoundingBox.html index 65058ad..0d406ff 100644 --- a/typedoc/classes/BoundingBox.html +++ b/typedoc/classes/BoundingBox.html @@ -1,26 +1,26 @@ -BoundingBox | @vladmandic/face-api - v1.7.13

Class BoundingBox

Hierarchy (view full)

Implements

Constructors

constructor -

Accessors

area -bottom -bottomLeft -bottomRight -height -left -right -top -topLeft -topRight -width -x -y -

Methods

calibrate -clipAtImageBorders -floor -pad -padAtBorders -rescale -round -shift -toSquare -assertIsValidBox -isRect -

Constructors

constructor

Accessors

area

bottom

bottomLeft

bottomRight

height

left

right

top

topLeft

topRight

width

x

y

Methods

calibrate

clipAtImageBorders

floor

pad

padAtBorders

  • padAtBorders(imageHeight, imageWidth): {
        dx: number;
        dy: number;
        edx: number;
        edy: number;
        ex: number;
        ey: number;
        h: number;
        w: number;
        x: number;
        y: number;
    }

  • Parameters

    • imageHeight: number
    • imageWidth: number

    Returns {
        dx: number;
        dy: number;
        edx: number;
        edy: number;
        ex: number;
        ey: number;
        h: number;
        w: number;
        x: number;
        y: number;
    }

    • dx: number
    • dy: number
    • edx: number
    • edy: number
    • ex: number
    • ey: number
    • h: number
    • w: number
    • x: number
    • y: number

rescale

round

shift

toSquare

Static assertIsValidBox

  • assertIsValidBox(box, callee, allowNegativeDimensions?): void

  • Parameters

    • box: any
    • callee: string
    • allowNegativeDimensions: boolean = false

    Returns void

Static isRect

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\ No newline at end of file +BoundingBox | @vladmandic/face-api - v1.7.14

Class BoundingBox

Hierarchy (view full)

Implements

Constructors

constructor +

Accessors

area +bottom +bottomLeft +bottomRight +height +left +right +top +topLeft +topRight +width +x +y +

Methods

calibrate +clipAtImageBorders +floor +pad +padAtBorders +rescale +round +shift +toSquare +assertIsValidBox +isRect +

Constructors

constructor

Accessors

area

bottom

bottomLeft

bottomRight

height

left

right

top

topLeft

topRight

width

x

y

Methods

calibrate

clipAtImageBorders

floor

pad

padAtBorders

  • padAtBorders(imageHeight, imageWidth): {
        dx: number;
        dy: number;
        edx: number;
        edy: number;
        ex: number;
        ey: number;
        h: number;
        w: number;
        x: number;
        y: number;
    }

  • Parameters

    • imageHeight: number
    • imageWidth: number

    Returns {
        dx: number;
        dy: number;
        edx: number;
        edy: number;
        ex: number;
        ey: number;
        h: number;
        w: number;
        x: number;
        y: number;
    }

    • dx: number
    • dy: number
    • edx: number
    • edy: number
    • ex: number
    • ey: number
    • h: number
    • w: number
    • x: number
    • y: number

rescale

round

shift

toSquare

StaticassertIsValidBox

  • assertIsValidBox(box, callee, allowNegativeDimensions?): void

  • Parameters

    • box: any
    • callee: string
    • allowNegativeDimensions: boolean = false

    Returns void

StaticisRect

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diff --git a/typedoc/classes/Box.html b/typedoc/classes/Box.html index a9a3ddb..5fdd9ee 100644 --- a/typedoc/classes/Box.html +++ b/typedoc/classes/Box.html @@ -1,26 +1,26 @@ -Box | @vladmandic/face-api - v1.7.13

Class Box<BoxType>

Type Parameters

  • BoxType = any

Hierarchy (view full)

Implements

Constructors

constructor -

Accessors

area -bottom -bottomLeft -bottomRight -height -left -right -top -topLeft -topRight -width -x -y -

Methods

calibrate -clipAtImageBorders -floor -pad -padAtBorders -rescale -round -shift -toSquare -assertIsValidBox -isRect -

Constructors

constructor

Accessors

area

bottom

bottomLeft

bottomRight

height

left

right

top

topLeft

topRight

width

x

y

Methods

calibrate

clipAtImageBorders

floor

pad

padAtBorders

  • padAtBorders(imageHeight, imageWidth): {
        dx: number;
        dy: number;
        edx: number;
        edy: number;
        ex: number;
        ey: number;
        h: number;
        w: number;
        x: number;
        y: number;
    }

  • Parameters

    • imageHeight: number
    • imageWidth: number

    Returns {
        dx: number;
        dy: number;
        edx: number;
        edy: number;
        ex: number;
        ey: number;
        h: number;
        w: number;
        x: number;
        y: number;
    }

    • dx: number
    • dy: number
    • edx: number
    • edy: number
    • ex: number
    • ey: number
    • h: number
    • w: number
    • x: number
    • y: number

rescale

round

shift

toSquare

Static assertIsValidBox

  • assertIsValidBox(box, callee, allowNegativeDimensions?): void

  • Parameters

    • box: any
    • callee: string
    • allowNegativeDimensions: boolean = false

    Returns void

Static isRect

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\ No newline at end of file +Box | @vladmandic/face-api - v1.7.14

Class Box<BoxType>

Type Parameters

  • BoxType = any

Hierarchy (view full)

Implements

Constructors

constructor +

Accessors

area +bottom +bottomLeft +bottomRight +height +left +right +top +topLeft +topRight +width +x +y +

Methods

calibrate +clipAtImageBorders +floor +pad +padAtBorders +rescale +round +shift +toSquare +assertIsValidBox +isRect +

Constructors

constructor

Accessors

area

bottom

bottomLeft

bottomRight

height

left

right

top

topLeft

topRight

width

x

y

Methods

calibrate

clipAtImageBorders

floor

pad

padAtBorders

  • padAtBorders(imageHeight, imageWidth): {
        dx: number;
        dy: number;
        edx: number;
        edy: number;
        ex: number;
        ey: number;
        h: number;
        w: number;
        x: number;
        y: number;
    }

  • Parameters

    • imageHeight: number
    • imageWidth: number

    Returns {
        dx: number;
        dy: number;
        edx: number;
        edy: number;
        ex: number;
        ey: number;
        h: number;
        w: number;
        x: number;
        y: number;
    }

    • dx: number
    • dy: number
    • edx: number
    • edy: number
    • ex: number
    • ey: number
    • h: number
    • w: number
    • x: number
    • y: number

rescale

round

shift

toSquare

StaticassertIsValidBox

  • assertIsValidBox(box, callee, allowNegativeDimensions?): void

  • Parameters

    • box: any
    • callee: string
    • allowNegativeDimensions: boolean = false

    Returns void

StaticisRect

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diff --git a/typedoc/classes/ComposableTask.html b/typedoc/classes/ComposableTask.html index 0e9128d..d515fb0 100644 --- a/typedoc/classes/ComposableTask.html +++ b/typedoc/classes/ComposableTask.html @@ -1,4 +1,4 @@ -ComposableTask | @vladmandic/face-api - v1.7.13

Class ComposableTask<T>

Type Parameters

  • T

Hierarchy (view full)

Constructors

constructor -

Methods

run -then -

Constructors

constructor

Methods

run

then

  • then(onfulfilled): Promise<T>

  • Parameters

    • onfulfilled: ((value) => T | PromiseLike<T>)
        • (value): T | PromiseLike<T>

        • Parameters

          • value: T

          Returns T | PromiseLike<T>

    Returns Promise<T>

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\ No newline at end of file +ComposableTask | @vladmandic/face-api - v1.7.14

Class ComposableTask<T>

Type Parameters

  • T

Hierarchy (view full)

Constructors

constructor +

Methods

run +then +

Constructors

constructor

Methods

run

then

  • then(onfulfilled): Promise<T>

  • Parameters

    • onfulfilled: ((value: T) => T | PromiseLike<T>)
        • (value): T | PromiseLike<T>

        • Parameters

          • value: T

          Returns T | PromiseLike<T>

    Returns Promise<T>

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diff --git a/typedoc/classes/ComputeAllFaceDescriptorsTask.html b/typedoc/classes/ComputeAllFaceDescriptorsTask.html index 85d4d1d..2b9d320 100644 --- a/typedoc/classes/ComputeAllFaceDescriptorsTask.html +++ b/typedoc/classes/ComputeAllFaceDescriptorsTask.html @@ -1,6 +1,6 @@ -ComputeAllFaceDescriptorsTask | @vladmandic/face-api - v1.7.13

Class ComputeAllFaceDescriptorsTask<TSource>

Type Parameters

Hierarchy (view full)

Constructors

constructor -

Methods

run -then -withAgeAndGender -withFaceExpressions -

Constructors

constructor

Methods

run

then

withAgeAndGender

withFaceExpressions

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\ No newline at end of file +ComputeAllFaceDescriptorsTask | @vladmandic/face-api - v1.7.14

Class ComputeAllFaceDescriptorsTask<TSource>

Type Parameters

Hierarchy (view full)

Constructors

constructor +

Methods

run +then +withAgeAndGender +withFaceExpressions +

Constructors

constructor

Methods

run

then

withAgeAndGender

withFaceExpressions

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diff --git a/typedoc/classes/ComputeFaceDescriptorsTaskBase.html b/typedoc/classes/ComputeFaceDescriptorsTaskBase.html index 3171c85..4629e33 100644 --- a/typedoc/classes/ComputeFaceDescriptorsTaskBase.html +++ b/typedoc/classes/ComputeFaceDescriptorsTaskBase.html @@ -1,4 +1,4 @@ -ComputeFaceDescriptorsTaskBase | @vladmandic/face-api - v1.7.13

Class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn>

Type Parameters

  • TReturn

  • TParentReturn

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Constructors

constructor -

Methods

run -then -

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constructor

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run

then

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\ No newline at end of file +ComputeFaceDescriptorsTaskBase | @vladmandic/face-api - v1.7.14

Class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn>

Type Parameters

  • TReturn
  • TParentReturn

Hierarchy (view full)

Constructors

constructor +

Methods

run +then +

Constructors

constructor

Methods

run

then

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diff --git a/typedoc/classes/ComputeSingleFaceDescriptorTask.html b/typedoc/classes/ComputeSingleFaceDescriptorTask.html index fd68993..471a7eb 100644 --- a/typedoc/classes/ComputeSingleFaceDescriptorTask.html +++ b/typedoc/classes/ComputeSingleFaceDescriptorTask.html @@ -1,6 +1,6 @@ -ComputeSingleFaceDescriptorTask | @vladmandic/face-api - v1.7.13

Class ComputeSingleFaceDescriptorTask<TSource>

Type Parameters

Hierarchy (view full)

Constructors

constructor -

Methods

run -then -withAgeAndGender -withFaceExpressions -

Constructors

constructor

Methods

run

then

withAgeAndGender

withFaceExpressions

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\ No newline at end of file +ComputeSingleFaceDescriptorTask | @vladmandic/face-api - v1.7.14

Class ComputeSingleFaceDescriptorTask<TSource>

Type Parameters

Hierarchy (view full)

Constructors

constructor +

Methods

run +then +withAgeAndGender +withFaceExpressions +

Constructors

constructor

Methods

run

then

withAgeAndGender

withFaceExpressions

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diff --git a/typedoc/classes/DetectAllFaceLandmarksTask.html b/typedoc/classes/DetectAllFaceLandmarksTask.html index 2351088..79e6c0a 100644 --- a/typedoc/classes/DetectAllFaceLandmarksTask.html +++ b/typedoc/classes/DetectAllFaceLandmarksTask.html @@ -1,7 +1,7 @@ -DetectAllFaceLandmarksTask | @vladmandic/face-api - v1.7.13

Class DetectAllFaceLandmarksTask<TSource>

Type Parameters

Hierarchy (view full)

Constructors

constructor -

Methods

run -then -withAgeAndGender -withFaceDescriptors -withFaceExpressions -

Constructors

constructor

Methods

run

then

withAgeAndGender

withFaceDescriptors

withFaceExpressions

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\ No newline at end of file +DetectAllFaceLandmarksTask | @vladmandic/face-api - v1.7.14

Class DetectAllFaceLandmarksTask<TSource>

Type Parameters

Hierarchy (view full)

Constructors

constructor +

Methods

run +then +withAgeAndGender +withFaceDescriptors +withFaceExpressions +

Constructors

constructor

Methods

run

then

withAgeAndGender

withFaceDescriptors

withFaceExpressions

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diff --git a/typedoc/classes/DetectAllFacesTask.html b/typedoc/classes/DetectAllFacesTask.html index 55e70cb..740f238 100644 --- a/typedoc/classes/DetectAllFacesTask.html +++ b/typedoc/classes/DetectAllFacesTask.html @@ -1,7 +1,7 @@ -DetectAllFacesTask | @vladmandic/face-api - v1.7.13

Class DetectAllFacesTask

Hierarchy (view full)

Constructors

constructor -

Methods

run -then -withAgeAndGender -withFaceExpressions -withFaceLandmarks -

Constructors

constructor

Methods

run

then

withAgeAndGender

withFaceExpressions

withFaceLandmarks

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\ No newline at end of file +DetectAllFacesTask | @vladmandic/face-api - v1.7.14

Class DetectAllFacesTask

Hierarchy (view full)

Constructors

constructor +

Methods

run +then +withAgeAndGender +withFaceExpressions +withFaceLandmarks +

Constructors

constructor

Methods

run

then

withAgeAndGender

withFaceExpressions

withFaceLandmarks

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diff --git a/typedoc/classes/DetectFaceLandmarksTaskBase.html b/typedoc/classes/DetectFaceLandmarksTaskBase.html index 7ff0202..ec51461 100644 --- a/typedoc/classes/DetectFaceLandmarksTaskBase.html +++ b/typedoc/classes/DetectFaceLandmarksTaskBase.html @@ -1,4 +1,4 @@ -DetectFaceLandmarksTaskBase | @vladmandic/face-api - v1.7.13

Class DetectFaceLandmarksTaskBase<TReturn, TParentReturn>

Type Parameters

  • TReturn

  • TParentReturn

Hierarchy (view full)

Constructors

constructor -

Methods

run -then -

Constructors

constructor

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run

then

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\ No newline at end of file +DetectFaceLandmarksTaskBase | @vladmandic/face-api - v1.7.14

Class DetectFaceLandmarksTaskBase<TReturn, TParentReturn>

Type Parameters

  • TReturn
  • TParentReturn

Hierarchy (view full)

Constructors

constructor +

Methods

run +then +

Constructors

constructor

Methods

run

then

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diff --git a/typedoc/classes/DetectFacesTaskBase.html b/typedoc/classes/DetectFacesTaskBase.html index 005c567..cd24d0a 100644 --- a/typedoc/classes/DetectFacesTaskBase.html +++ b/typedoc/classes/DetectFacesTaskBase.html @@ -1,4 +1,4 @@ -DetectFacesTaskBase | @vladmandic/face-api - v1.7.13

Class DetectFacesTaskBase<TReturn>

Type Parameters

  • TReturn

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constructor -

Methods

run -then -

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constructor

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run

then

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\ No newline at end of file +DetectFacesTaskBase | @vladmandic/face-api - v1.7.14

Class DetectFacesTaskBase<TReturn>

Type Parameters

  • TReturn

Hierarchy (view full)

Constructors

constructor +

Methods

run +then +

Constructors

constructor

Methods

run

then

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diff --git a/typedoc/classes/DetectSingleFaceLandmarksTask.html b/typedoc/classes/DetectSingleFaceLandmarksTask.html index 0e460f0..c76c736 100644 --- a/typedoc/classes/DetectSingleFaceLandmarksTask.html +++ b/typedoc/classes/DetectSingleFaceLandmarksTask.html @@ -1,7 +1,7 @@ -DetectSingleFaceLandmarksTask | @vladmandic/face-api - v1.7.13

Class DetectSingleFaceLandmarksTask<TSource>

Type Parameters

Hierarchy (view full)

Constructors

constructor -

Methods

run -then -withAgeAndGender -withFaceDescriptor -withFaceExpressions -

Constructors

constructor

Methods

run

then

withAgeAndGender

withFaceDescriptor

withFaceExpressions

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\ No newline at end of file +DetectSingleFaceLandmarksTask | @vladmandic/face-api - v1.7.14

Class DetectSingleFaceLandmarksTask<TSource>

Type Parameters

Hierarchy (view full)

Constructors

constructor +

Methods

run +then +withAgeAndGender +withFaceDescriptor +withFaceExpressions +

Constructors

constructor

Methods

run

then

withAgeAndGender

withFaceDescriptor

withFaceExpressions

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diff --git a/typedoc/classes/DetectSingleFaceTask.html b/typedoc/classes/DetectSingleFaceTask.html index 7f5560a..7c7ee69 100644 --- a/typedoc/classes/DetectSingleFaceTask.html +++ b/typedoc/classes/DetectSingleFaceTask.html @@ -1,7 +1,7 @@ -DetectSingleFaceTask | @vladmandic/face-api - v1.7.13

Class DetectSingleFaceTask

Hierarchy (view full)

Constructors

constructor -

Methods

run -then -withAgeAndGender -withFaceExpressions -withFaceLandmarks -

Constructors

constructor

Methods

run

then

withAgeAndGender

withFaceExpressions

withFaceLandmarks

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\ No newline at end of file +DetectSingleFaceTask | @vladmandic/face-api - v1.7.14

Class DetectSingleFaceTask

Hierarchy (view full)

Constructors

constructor +

Methods

run +then +withAgeAndGender +withFaceExpressions +withFaceLandmarks +

Constructors

constructor

Methods

run

then

withAgeAndGender

withFaceExpressions

withFaceLandmarks

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Methods
diff --git a/typedoc/classes/Dimensions.html b/typedoc/classes/Dimensions.html index 9b8051e..b665f59 100644 --- a/typedoc/classes/Dimensions.html +++ b/typedoc/classes/Dimensions.html @@ -1,5 +1,5 @@ -Dimensions | @vladmandic/face-api - v1.7.13

Class Dimensions

Implements

Constructors

constructor -

Accessors

height -width -

Methods

reverse -

Constructors

constructor

Accessors

height

width

Methods

reverse

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\ No newline at end of file +Dimensions | @vladmandic/face-api - v1.7.14

Class Dimensions

Implements

Constructors

constructor +

Accessors

height +width +

Methods

reverse +

Constructors

constructor

Accessors

height

width

Methods

reverse

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diff --git a/typedoc/classes/FaceDetection.html b/typedoc/classes/FaceDetection.html index 15f28d4..524b231 100644 --- a/typedoc/classes/FaceDetection.html +++ b/typedoc/classes/FaceDetection.html @@ -1,11 +1,11 @@ -FaceDetection | @vladmandic/face-api - v1.7.13

Class FaceDetection

Hierarchy (view full)

Implements

Constructors

constructor -

Accessors

box -className -classScore -imageDims -imageHeight -imageWidth -relativeBox -score -

Methods

forSize -

Constructors

constructor

Accessors

box

className

classScore

imageDims

imageHeight

imageWidth

relativeBox

score

Methods

forSize

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\ No newline at end of file +FaceDetection | @vladmandic/face-api - v1.7.14

Class FaceDetection

Hierarchy (view full)

Implements

Constructors

constructor +

Accessors

box +className +classScore +imageDims +imageHeight +imageWidth +relativeBox +score +

Methods

forSize +

Constructors

constructor

Accessors

box

className

classScore

imageDims

imageHeight

imageWidth

relativeBox

score

Methods

forSize

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diff --git a/typedoc/classes/FaceDetectionNet.html b/typedoc/classes/FaceDetectionNet.html index fcabf9c..eef46f4 100644 --- a/typedoc/classes/FaceDetectionNet.html +++ b/typedoc/classes/FaceDetectionNet.html @@ -1,23 +1,23 @@ -FaceDetectionNet | @vladmandic/face-api - v1.7.13

Class FaceDetectionNet

Hierarchy (view full)

Constructors

constructor -

Properties

_name -

Accessors

isLoaded -paramMappings -params -

Methods

dispose -extractWeights -forward -forwardInput -freeze -getFrozenParams -getParamFromPath -getParamList -getTrainableParams -load -loadFromDisk -loadFromUri -loadFromWeightMap -locateFaces -reassignParamFromPath -serializeParams -variable -

Constructors

constructor

Properties

_name

_name: any

Accessors

isLoaded

paramMappings

  • get paramMappings(): ParamMapping[]

  • Returns ParamMapping[]

params

Methods

dispose

extractWeights

forward

forwardInput

freeze

getFrozenParams

getParamFromPath

getParamList

getTrainableParams

load

loadFromDisk

loadFromUri

loadFromWeightMap

locateFaces

reassignParamFromPath

serializeParams

variable

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\ No newline at end of file +FaceDetectionNet | @vladmandic/face-api - v1.7.14

Class FaceDetectionNet

Hierarchy (view full)

Constructors

constructor +

Properties

_name +

Accessors

isLoaded +paramMappings +params +

Methods

dispose +extractWeights +forward +forwardInput +freeze +getFrozenParams +getParamFromPath +getParamList +getTrainableParams +load +loadFromDisk +loadFromUri +loadFromWeightMap +locateFaces +reassignParamFromPath +serializeParams +variable +

Constructors

constructor

Properties

_name

_name: any

Accessors

isLoaded

paramMappings

  • get paramMappings(): ParamMapping[]

  • Returns ParamMapping[]

params

Methods

dispose

extractWeights

forward

forwardInput

freeze

getFrozenParams

getParamFromPath

getParamList

getTrainableParams

load

loadFromDisk

loadFromUri

loadFromWeightMap

locateFaces

reassignParamFromPath

serializeParams

variable

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Methods
diff --git a/typedoc/classes/FaceExpressionNet.html b/typedoc/classes/FaceExpressionNet.html index 7d8ea56..07c13bc 100644 --- a/typedoc/classes/FaceExpressionNet.html +++ b/typedoc/classes/FaceExpressionNet.html @@ -1,27 +1,27 @@ -FaceExpressionNet | @vladmandic/face-api - v1.7.13

Class FaceExpressionNet

Hierarchy

  • FaceProcessor<FaceFeatureExtractorParams>
    • FaceExpressionNet

Constructors

constructor -

Properties

_name -

Accessors

faceFeatureExtractor -isLoaded -paramMappings -params -

Methods

dispose -extractClassifierParams -extractWeights -forward -forwardInput -freeze -getFrozenParams -getParamFromPath -getParamList -getTrainableParams -load -loadClassifierParams -loadFromDisk -loadFromUri -loadFromWeightMap -predictExpressions -reassignParamFromPath -runNet -serializeParams -variable -

Constructors

constructor

Properties

_name

_name: any

Accessors

faceFeatureExtractor

  • get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams>

  • Returns IFaceFeatureExtractor<TExtractorParams>

isLoaded

paramMappings

  • get paramMappings(): ParamMapping[]

  • Returns ParamMapping[]

params

Methods

dispose

extractClassifierParams

  • extractClassifierParams(weights): {
        paramMappings: ParamMapping[];
        params: NetParams;
    }

  • Parameters

    • weights: Float32Array

    Returns {
        paramMappings: ParamMapping[];
        params: NetParams;
    }

    • paramMappings: ParamMapping[]
    • params: NetParams

extractWeights

  • extractWeights(weights): void

  • Parameters

    • weights: Float32Array

    Returns void

forward

forwardInput

freeze

getFrozenParams

  • getFrozenParams(): {
        path: string;
        tensor: Tensor<Rank>;
    }[]

  • Returns {
        path: string;
        tensor: Tensor<Rank>;
    }[]

getParamFromPath

getParamList

getTrainableParams

  • getTrainableParams(): {
        path: string;
        tensor: Tensor<Rank>;
    }[]

  • Returns {
        path: string;
        tensor: Tensor<Rank>;
    }[]

load

  • load(weightsOrUrl): Promise<void>

  • Parameters

    • weightsOrUrl: undefined | string | Float32Array

    Returns Promise<void>

loadClassifierParams

loadFromDisk

  • loadFromDisk(filePath): Promise<void>

  • Parameters

    • filePath: undefined | string

    Returns Promise<void>

loadFromUri

  • loadFromUri(uri): Promise<void>

  • Parameters

    • uri: undefined | string

    Returns Promise<void>

loadFromWeightMap

predictExpressions

reassignParamFromPath

  • reassignParamFromPath(paramPath, tensor): void

  • Parameters

    Returns void

runNet

serializeParams

  • serializeParams(): Float32Array

  • Returns Float32Array

variable

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\ No newline at end of file +FaceExpressionNet | @vladmandic/face-api - v1.7.14

Class FaceExpressionNet

Hierarchy

  • FaceProcessor<FaceFeatureExtractorParams>
    • FaceExpressionNet

Constructors

constructor +

Properties

_name +

Accessors

faceFeatureExtractor +isLoaded +paramMappings +params +

Methods

dispose +extractClassifierParams +extractWeights +forward +forwardInput +freeze +getFrozenParams +getParamFromPath +getParamList +getTrainableParams +load +loadClassifierParams +loadFromDisk +loadFromUri +loadFromWeightMap +predictExpressions +reassignParamFromPath +runNet +serializeParams +variable +

Constructors

constructor

Properties

_name

_name: any

Accessors

faceFeatureExtractor

  • get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams>

  • Returns IFaceFeatureExtractor<TExtractorParams>

isLoaded

paramMappings

  • get paramMappings(): ParamMapping[]

  • Returns ParamMapping[]

params

Methods

dispose

extractClassifierParams

  • extractClassifierParams(weights): {
        paramMappings: ParamMapping[];
        params: NetParams;
    }

  • Parameters

    • weights: Float32Array

    Returns {
        paramMappings: ParamMapping[];
        params: NetParams;
    }

    • paramMappings: ParamMapping[]
    • params: NetParams

extractWeights

  • extractWeights(weights): void

  • Parameters

    • weights: Float32Array

    Returns void

forward

forwardInput

freeze

getFrozenParams

  • getFrozenParams(): {
        path: string;
        tensor: Tensor<Rank>;
    }[]

  • Returns {
        path: string;
        tensor: Tensor<Rank>;
    }[]

getParamFromPath

getParamList

getTrainableParams

  • getTrainableParams(): {
        path: string;
        tensor: Tensor<Rank>;
    }[]

  • Returns {
        path: string;
        tensor: Tensor<Rank>;
    }[]

load

  • load(weightsOrUrl): Promise<void>

  • Parameters

    • weightsOrUrl: undefined | string | Float32Array

    Returns Promise<void>

loadClassifierParams

loadFromDisk

  • loadFromDisk(filePath): Promise<void>

  • Parameters

    • filePath: undefined | string

    Returns Promise<void>

loadFromUri

  • loadFromUri(uri): Promise<void>

  • Parameters

    • uri: undefined | string

    Returns Promise<void>

loadFromWeightMap

predictExpressions

reassignParamFromPath

  • reassignParamFromPath(paramPath, tensor): void

  • Parameters

    Returns void

runNet

serializeParams

  • serializeParams(): Float32Array

  • Returns Float32Array

variable

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Accessors
Methods
diff --git a/typedoc/classes/FaceExpressions.html b/typedoc/classes/FaceExpressions.html index d767396..d2663e9 100644 --- a/typedoc/classes/FaceExpressions.html +++ b/typedoc/classes/FaceExpressions.html @@ -1,10 +1,10 @@ -FaceExpressions | @vladmandic/face-api - v1.7.13

Class FaceExpressions

Constructors

constructor -

Properties

angry -disgusted -fearful -happy -neutral -sad -surprised -

Methods

asSortedArray -

Constructors

constructor

Properties

angry

angry: number = 0

disgusted

disgusted: number = 0

fearful

fearful: number = 0

happy

happy: number = 0

neutral

neutral: number = 0

sad

sad: number = 0

surprised

surprised: number = 0

Methods

asSortedArray

  • asSortedArray(): {
        expression: "neutral" | "happy" | "sad" | "angry" | "fearful" | "disgusted" | "surprised";
        probability: number;
    }[]

  • Returns {
        expression: "neutral" | "happy" | "sad" | "angry" | "fearful" | "disgusted" | "surprised";
        probability: number;
    }[]

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\ No newline at end of file +FaceExpressions | @vladmandic/face-api - v1.7.14

Class FaceExpressions

Constructors

constructor +

Properties

angry +disgusted +fearful +happy +neutral +sad +surprised +

Methods

asSortedArray +

Constructors

constructor

Properties

angry

angry: number = 0

disgusted

disgusted: number = 0

fearful

fearful: number = 0

happy

happy: number = 0

neutral

neutral: number = 0

sad

sad: number = 0

surprised

surprised: number = 0

Methods

asSortedArray

  • asSortedArray(): {
        expression:
            | "neutral"
            | "happy"
            | "sad"
            | "angry"
            | "fearful"
            | "disgusted"
            | "surprised";
        probability: number;
    }[]

  • Returns {
        expression:
            | "neutral"
            | "happy"
            | "sad"
            | "angry"
            | "fearful"
            | "disgusted"
            | "surprised";
        probability: number;
    }[]

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diff --git a/typedoc/classes/FaceLandmark68Net.html b/typedoc/classes/FaceLandmark68Net.html index c60cffc..76a8bb8 100644 --- a/typedoc/classes/FaceLandmark68Net.html +++ b/typedoc/classes/FaceLandmark68Net.html @@ -1,28 +1,28 @@ -FaceLandmark68Net | @vladmandic/face-api - v1.7.13

Class FaceLandmark68Net

Hierarchy (view full)

  • FaceLandmark68NetBase<FaceFeatureExtractorParams>

Constructors

constructor -

Properties

_name -

Accessors

faceFeatureExtractor -isLoaded -paramMappings -params -

Methods

detectLandmarks -dispose -extractClassifierParams -extractWeights -forward -forwardInput -freeze -getFrozenParams -getParamFromPath -getParamList -getTrainableParams -load -loadClassifierParams -loadFromDisk -loadFromUri -loadFromWeightMap -postProcess -reassignParamFromPath -runNet -serializeParams -variable -

Constructors

constructor

Properties

_name

_name: any

Accessors

faceFeatureExtractor

  • get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams>

  • Returns IFaceFeatureExtractor<TExtractorParams>

isLoaded

  • get isLoaded(): boolean

  • Returns boolean

paramMappings

  • get paramMappings(): ParamMapping[]

  • Returns ParamMapping[]

params

Methods

detectLandmarks

dispose

extractClassifierParams

  • extractClassifierParams(weights): {
        paramMappings: ParamMapping[];
        params: NetParams;
    }

  • Parameters

    • weights: Float32Array

    Returns {
        paramMappings: ParamMapping[];
        params: NetParams;
    }

    • paramMappings: ParamMapping[]
    • params: NetParams

extractWeights

  • extractWeights(weights): void

  • Parameters

    • weights: Float32Array

    Returns void

forward

forwardInput

freeze

getFrozenParams

  • getFrozenParams(): {
        path: string;
        tensor: Tensor<Rank>;
    }[]

  • Returns {
        path: string;
        tensor: Tensor<Rank>;
    }[]

getParamFromPath

getParamList

  • getParamList(): {
        path: string;
        tensor: Tensor<Rank>;
    }[]

  • Returns {
        path: string;
        tensor: Tensor<Rank>;
    }[]

getTrainableParams

  • getTrainableParams(): {
        path: string;
        tensor: Tensor<Rank>;
    }[]

  • Returns {
        path: string;
        tensor: Tensor<Rank>;
    }[]

load

  • load(weightsOrUrl): Promise<void>

  • Parameters

    • weightsOrUrl: undefined | string | Float32Array

    Returns Promise<void>

loadClassifierParams

loadFromDisk

  • loadFromDisk(filePath): Promise<void>

  • Parameters

    • filePath: undefined | string

    Returns Promise<void>

loadFromUri

  • loadFromUri(uri): Promise<void>

  • Parameters

    • uri: undefined | string

    Returns Promise<void>

loadFromWeightMap

postProcess

reassignParamFromPath

  • reassignParamFromPath(paramPath, tensor): void

  • Parameters

    Returns void

runNet

serializeParams

  • serializeParams(): Float32Array

  • Returns Float32Array

variable

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\ No newline at end of file +FaceLandmark68Net | @vladmandic/face-api - v1.7.14

Class FaceLandmark68Net

Hierarchy (view full)

  • FaceLandmark68NetBase<FaceFeatureExtractorParams>

Constructors

constructor +

Properties

_name +

Accessors

faceFeatureExtractor +isLoaded +paramMappings +params +

Methods

detectLandmarks +dispose +extractClassifierParams +extractWeights +forward +forwardInput +freeze +getFrozenParams +getParamFromPath +getParamList +getTrainableParams +load +loadClassifierParams +loadFromDisk +loadFromUri +loadFromWeightMap +postProcess +reassignParamFromPath +runNet +serializeParams +variable +

Constructors

constructor

Properties

_name

_name: any

Accessors

faceFeatureExtractor

  • get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams>

  • Returns IFaceFeatureExtractor<TExtractorParams>

isLoaded

  • get isLoaded(): boolean

  • Returns boolean

paramMappings

  • get paramMappings(): ParamMapping[]

  • Returns ParamMapping[]

params

Methods

detectLandmarks

dispose

extractClassifierParams

  • extractClassifierParams(weights): {
        paramMappings: ParamMapping[];
        params: NetParams;
    }

  • Parameters

    • weights: Float32Array

    Returns {
        paramMappings: ParamMapping[];
        params: NetParams;
    }

    • paramMappings: ParamMapping[]
    • params: NetParams

extractWeights

  • extractWeights(weights): void

  • Parameters

    • weights: Float32Array

    Returns void

forward

forwardInput

freeze

getFrozenParams

  • getFrozenParams(): {
        path: string;
        tensor: Tensor<Rank>;
    }[]

  • Returns {
        path: string;
        tensor: Tensor<Rank>;
    }[]

getParamFromPath

getParamList

  • getParamList(): {
        path: string;
        tensor: Tensor<Rank>;
    }[]

  • Returns {
        path: string;
        tensor: Tensor<Rank>;
    }[]

getTrainableParams

  • getTrainableParams(): {
        path: string;
        tensor: Tensor<Rank>;
    }[]

  • Returns {
        path: string;
        tensor: Tensor<Rank>;
    }[]

load

  • load(weightsOrUrl): Promise<void>

  • Parameters

    • weightsOrUrl: undefined | string | Float32Array

    Returns Promise<void>

loadClassifierParams

loadFromDisk

  • loadFromDisk(filePath): Promise<void>

  • Parameters

    • filePath: undefined | string

    Returns Promise<void>

loadFromUri

  • loadFromUri(uri): Promise<void>

  • Parameters

    • uri: undefined | string

    Returns Promise<void>

loadFromWeightMap

postProcess

reassignParamFromPath

  • reassignParamFromPath(paramPath, tensor): void

  • Parameters

    Returns void

runNet

serializeParams

  • serializeParams(): Float32Array

  • Returns Float32Array

variable

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diff --git a/typedoc/classes/FaceLandmark68TinyNet.html b/typedoc/classes/FaceLandmark68TinyNet.html index 203d142..1d713b0 100644 --- a/typedoc/classes/FaceLandmark68TinyNet.html +++ b/typedoc/classes/FaceLandmark68TinyNet.html @@ -1,28 +1,28 @@ -FaceLandmark68TinyNet | @vladmandic/face-api - v1.7.13

Class FaceLandmark68TinyNet

Hierarchy

  • FaceLandmark68NetBase<TinyFaceFeatureExtractorParams>
    • FaceLandmark68TinyNet

Constructors

constructor -

Properties

_name -

Accessors

faceFeatureExtractor -isLoaded -paramMappings -params -

Methods

detectLandmarks -dispose -extractClassifierParams -extractWeights -forward -forwardInput -freeze -getFrozenParams -getParamFromPath -getParamList -getTrainableParams -load -loadClassifierParams -loadFromDisk -loadFromUri -loadFromWeightMap -postProcess -reassignParamFromPath -runNet -serializeParams -variable -

Constructors

constructor

Properties

_name

_name: any

Accessors

faceFeatureExtractor

  • get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams>

  • Returns IFaceFeatureExtractor<TExtractorParams>

isLoaded

  • get isLoaded(): boolean

  • Returns boolean

paramMappings

  • get paramMappings(): ParamMapping[]

  • Returns ParamMapping[]

params

Methods

detectLandmarks

dispose

extractClassifierParams

  • extractClassifierParams(weights): {
        paramMappings: ParamMapping[];
        params: NetParams;
    }

  • Parameters

    • weights: Float32Array

    Returns {
        paramMappings: ParamMapping[];
        params: NetParams;
    }

    • paramMappings: ParamMapping[]
    • params: NetParams

extractWeights

  • extractWeights(weights): void

  • Parameters

    • weights: Float32Array

    Returns void

forward

forwardInput

freeze

getFrozenParams

  • getFrozenParams(): {
        path: string;
        tensor: Tensor<Rank>;
    }[]

  • Returns {
        path: string;
        tensor: Tensor<Rank>;
    }[]

getParamFromPath

getParamList

  • getParamList(): {
        path: string;
        tensor: Tensor<Rank>;
    }[]

  • Returns {
        path: string;
        tensor: Tensor<Rank>;
    }[]

getTrainableParams

  • getTrainableParams(): {
        path: string;
        tensor: Tensor<Rank>;
    }[]

  • Returns {
        path: string;
        tensor: Tensor<Rank>;
    }[]

load

  • load(weightsOrUrl): Promise<void>

  • Parameters

    • weightsOrUrl: undefined | string | Float32Array

    Returns Promise<void>

loadClassifierParams

loadFromDisk

  • loadFromDisk(filePath): Promise<void>

  • Parameters

    • filePath: undefined | string

    Returns Promise<void>

loadFromUri

  • loadFromUri(uri): Promise<void>

  • Parameters

    • uri: undefined | string

    Returns Promise<void>

loadFromWeightMap

postProcess

reassignParamFromPath

  • reassignParamFromPath(paramPath, tensor): void

  • Parameters

    Returns void

runNet

serializeParams

  • serializeParams(): Float32Array

  • Returns Float32Array

variable

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\ No newline at end of file +FaceLandmark68TinyNet | @vladmandic/face-api - v1.7.14

Class FaceLandmark68TinyNet

Hierarchy

  • FaceLandmark68NetBase<TinyFaceFeatureExtractorParams>
    • FaceLandmark68TinyNet

Constructors

constructor +

Properties

_name +

Accessors

faceFeatureExtractor +isLoaded +paramMappings +params +

Methods

detectLandmarks +dispose +extractClassifierParams +extractWeights +forward +forwardInput +freeze +getFrozenParams +getParamFromPath +getParamList +getTrainableParams +load +loadClassifierParams +loadFromDisk +loadFromUri +loadFromWeightMap +postProcess +reassignParamFromPath +runNet +serializeParams +variable +

Constructors

constructor

Properties

_name

_name: any

Accessors

faceFeatureExtractor

  • get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams>

  • Returns IFaceFeatureExtractor<TExtractorParams>

isLoaded

  • get isLoaded(): boolean

  • Returns boolean

paramMappings

  • get paramMappings(): ParamMapping[]

  • Returns ParamMapping[]

params

Methods

detectLandmarks

dispose

extractClassifierParams

  • extractClassifierParams(weights): {
        paramMappings: ParamMapping[];
        params: NetParams;
    }

  • Parameters

    • weights: Float32Array

    Returns {
        paramMappings: ParamMapping[];
        params: NetParams;
    }

    • paramMappings: ParamMapping[]
    • params: NetParams

extractWeights

  • extractWeights(weights): void

  • Parameters

    • weights: Float32Array

    Returns void

forward

forwardInput

freeze

getFrozenParams

  • getFrozenParams(): {
        path: string;
        tensor: Tensor<Rank>;
    }[]

  • Returns {
        path: string;
        tensor: Tensor<Rank>;
    }[]

getParamFromPath

getParamList

  • getParamList(): {
        path: string;
        tensor: Tensor<Rank>;
    }[]

  • Returns {
        path: string;
        tensor: Tensor<Rank>;
    }[]

getTrainableParams

  • getTrainableParams(): {
        path: string;
        tensor: Tensor<Rank>;
    }[]

  • Returns {
        path: string;
        tensor: Tensor<Rank>;
    }[]

load

  • load(weightsOrUrl): Promise<void>

  • Parameters

    • weightsOrUrl: undefined | string | Float32Array

    Returns Promise<void>

loadClassifierParams

loadFromDisk

  • loadFromDisk(filePath): Promise<void>

  • Parameters

    • filePath: undefined | string

    Returns Promise<void>

loadFromUri

  • loadFromUri(uri): Promise<void>

  • Parameters

    • uri: undefined | string

    Returns Promise<void>

loadFromWeightMap

postProcess

reassignParamFromPath

  • reassignParamFromPath(paramPath, tensor): void

  • Parameters

    Returns void

runNet

serializeParams

  • serializeParams(): Float32Array

  • Returns Float32Array

variable

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Properties
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Methods
diff --git a/typedoc/classes/FaceLandmarkNet.html b/typedoc/classes/FaceLandmarkNet.html index e21ea8c..2162302 100644 --- a/typedoc/classes/FaceLandmarkNet.html +++ b/typedoc/classes/FaceLandmarkNet.html @@ -1,28 +1,28 @@ -FaceLandmarkNet | @vladmandic/face-api - v1.7.13

Class FaceLandmarkNet

Hierarchy (view full)

Constructors

constructor -

Properties

_name -

Accessors

faceFeatureExtractor -isLoaded -paramMappings -params -

Methods

detectLandmarks -dispose -extractClassifierParams -extractWeights -forward -forwardInput -freeze -getFrozenParams -getParamFromPath -getParamList -getTrainableParams -load -loadClassifierParams -loadFromDisk -loadFromUri -loadFromWeightMap -postProcess -reassignParamFromPath -runNet -serializeParams -variable -

Constructors

constructor

Properties

_name

_name: any

Accessors

faceFeatureExtractor

  • get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams>

  • Returns IFaceFeatureExtractor<TExtractorParams>

isLoaded

paramMappings

  • get paramMappings(): ParamMapping[]

  • Returns ParamMapping[]

params

Methods

detectLandmarks

dispose

extractClassifierParams

extractWeights

forward

forwardInput

freeze

getFrozenParams

getParamFromPath

getParamList

getTrainableParams

load

loadClassifierParams

loadFromDisk

loadFromUri

loadFromWeightMap

postProcess

reassignParamFromPath

runNet

serializeParams

variable

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\ No newline at end of file +FaceLandmarkNet | @vladmandic/face-api - v1.7.14

Class FaceLandmarkNet

Hierarchy (view full)

Constructors

constructor +

Properties

_name +

Accessors

faceFeatureExtractor +isLoaded +paramMappings +params +

Methods

detectLandmarks +dispose +extractClassifierParams +extractWeights +forward +forwardInput +freeze +getFrozenParams +getParamFromPath +getParamList +getTrainableParams +load +loadClassifierParams +loadFromDisk +loadFromUri +loadFromWeightMap +postProcess +reassignParamFromPath +runNet +serializeParams +variable +

Constructors

constructor

Properties

_name

_name: any

Accessors

faceFeatureExtractor

  • get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams>

  • Returns IFaceFeatureExtractor<TExtractorParams>

isLoaded

paramMappings

  • get paramMappings(): ParamMapping[]

  • Returns ParamMapping[]

params

Methods

detectLandmarks

dispose

extractClassifierParams

extractWeights

forward

forwardInput

freeze

getFrozenParams

getParamFromPath

getParamList

getTrainableParams

load

loadClassifierParams

loadFromDisk

loadFromUri

loadFromWeightMap

postProcess

reassignParamFromPath

runNet

serializeParams

variable

Settings

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Constructors
Properties
Accessors
Methods
diff --git a/typedoc/classes/FaceLandmarks.html b/typedoc/classes/FaceLandmarks.html index d1ca666..02b415c 100644 --- a/typedoc/classes/FaceLandmarks.html +++ b/typedoc/classes/FaceLandmarks.html @@ -1,19 +1,19 @@ -FaceLandmarks | @vladmandic/face-api - v1.7.13

Class FaceLandmarks

Hierarchy (view full)

Implements

Constructors

constructor -

Accessors

imageHeight -imageWidth -positions -relativePositions -shift -

Methods

align -forSize -shiftBy -shiftByPoint -

Constructors

constructor

Accessors

imageHeight

imageWidth

positions

relativePositions

shift

Methods

align

Returns Box<any>

The bounding box of the aligned face.

+

forSize

shiftBy

shiftByPoint

Settings

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diff --git a/typedoc/classes/FaceLandmarks5.html b/typedoc/classes/FaceLandmarks5.html index 0e31467..abd0a5e 100644 --- a/typedoc/classes/FaceLandmarks5.html +++ b/typedoc/classes/FaceLandmarks5.html @@ -1,19 +1,19 @@ -FaceLandmarks5 | @vladmandic/face-api - v1.7.13

Class FaceLandmarks5

Hierarchy (view full)

Constructors

constructor -

Accessors

imageHeight -imageWidth -positions -relativePositions -shift -

Methods

align -forSize -shiftBy -shiftByPoint -

Constructors

constructor

Accessors

imageHeight

imageWidth

positions

relativePositions

shift

Methods

align

Returns Box<any>

The bounding box of the aligned face.

+

forSize

shiftBy

shiftByPoint

Settings

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Methods
diff --git a/typedoc/classes/FaceLandmarks68.html b/typedoc/classes/FaceLandmarks68.html index 77f61da..053d003 100644 --- a/typedoc/classes/FaceLandmarks68.html +++ b/typedoc/classes/FaceLandmarks68.html @@ -1,26 +1,26 @@ -FaceLandmarks68 | @vladmandic/face-api - v1.7.13

Class FaceLandmarks68

Hierarchy (view full)

Constructors

constructor -

Accessors

imageHeight -imageWidth -positions -relativePositions -shift -

Methods

align -forSize -getJawOutline -getLeftEye -getLeftEyeBrow -getMouth -getNose -getRightEye -getRightEyeBrow -shiftBy -shiftByPoint -

Constructors

constructor

Accessors

imageHeight

imageWidth

positions

relativePositions

shift

Methods

align

Returns Box<any>

The bounding box of the aligned face.

+

forSize

getJawOutline

getLeftEye

getLeftEyeBrow

getMouth

getNose

getRightEye

getRightEyeBrow

shiftBy

shiftByPoint

Settings

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Methods
diff --git a/typedoc/classes/FaceMatch.html b/typedoc/classes/FaceMatch.html index d1f14d3..9bc7975 100644 --- a/typedoc/classes/FaceMatch.html +++ b/typedoc/classes/FaceMatch.html @@ -1,5 +1,6 @@ -FaceMatch | @vladmandic/face-api - v1.7.13

Class FaceMatch

Implements

Constructors

constructor -

Accessors

distance -label -

Methods

toString -

Constructors

constructor

Accessors

distance

label

Methods

toString

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\ No newline at end of file +FaceMatch | @vladmandic/face-api - v1.7.14

Class FaceMatch

Implements

Constructors

constructor +

Accessors

distance +label +

Methods

toString +

Constructors

constructor

Accessors

distance

label

Methods

toString

  • toString(withDistance?): string

  • Returns a string representation of an object.

    +

    Parameters

    • withDistance: boolean = true

    Returns string

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diff --git a/typedoc/classes/FaceMatcher.html b/typedoc/classes/FaceMatcher.html index 8304069..dd7502a 100644 --- a/typedoc/classes/FaceMatcher.html +++ b/typedoc/classes/FaceMatcher.html @@ -1,9 +1,9 @@ -FaceMatcher | @vladmandic/face-api - v1.7.13

Class FaceMatcher

Constructors

constructor -

Accessors

distanceThreshold -labeledDescriptors -

Methods

computeMeanDistance -findBestMatch -matchDescriptor -toJSON -fromJSON -

Constructors

constructor

Accessors

distanceThreshold

labeledDescriptors

Methods

computeMeanDistance

  • computeMeanDistance(queryDescriptor, descriptors): number

  • Parameters

    • queryDescriptor: Float32Array
    • descriptors: Float32Array[]

    Returns number

findBestMatch

matchDescriptor

toJSON

Static fromJSON

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\ No newline at end of file +FaceMatcher | @vladmandic/face-api - v1.7.14

Class FaceMatcher

Constructors

constructor +

Accessors

distanceThreshold +labeledDescriptors +

Methods

computeMeanDistance +findBestMatch +matchDescriptor +toJSON +fromJSON +

Constructors

constructor

Accessors

distanceThreshold

labeledDescriptors

Methods

computeMeanDistance

  • computeMeanDistance(queryDescriptor, descriptors): number

  • Parameters

    • queryDescriptor: Float32Array
    • descriptors: Float32Array[]

    Returns number

findBestMatch

matchDescriptor

toJSON

StaticfromJSON

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Accessors
Methods
diff --git a/typedoc/classes/FaceRecognitionNet.html b/typedoc/classes/FaceRecognitionNet.html index be407a1..7c59ca6 100644 --- a/typedoc/classes/FaceRecognitionNet.html +++ b/typedoc/classes/FaceRecognitionNet.html @@ -1,23 +1,23 @@ -FaceRecognitionNet | @vladmandic/face-api - v1.7.13

Class FaceRecognitionNet

Hierarchy (view full)

Constructors

constructor -

Properties

_name -

Accessors

isLoaded -paramMappings -params -

Methods

computeFaceDescriptor -dispose -extractWeights -forward -forwardInput -freeze -getFrozenParams -getParamFromPath -getParamList -getTrainableParams -load -loadFromDisk -loadFromUri -loadFromWeightMap -reassignParamFromPath -serializeParams -variable -

Constructors

constructor

Properties

_name

_name: any

Accessors

isLoaded

paramMappings

  • get paramMappings(): ParamMapping[]

  • Returns ParamMapping[]

params

Methods

computeFaceDescriptor

dispose

extractWeights

forward

forwardInput

freeze

getFrozenParams

getParamFromPath

getParamList

getTrainableParams

load

loadFromDisk

loadFromUri

loadFromWeightMap

reassignParamFromPath

serializeParams

variable

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\ No newline at end of file +FaceRecognitionNet | @vladmandic/face-api - v1.7.14

Class FaceRecognitionNet

Hierarchy (view full)

Constructors

constructor +

Properties

_name +

Accessors

isLoaded +paramMappings +params +

Methods

computeFaceDescriptor +dispose +extractWeights +forward +forwardInput +freeze +getFrozenParams +getParamFromPath +getParamList +getTrainableParams +load +loadFromDisk +loadFromUri +loadFromWeightMap +reassignParamFromPath +serializeParams +variable +

Constructors

constructor

Properties

_name

_name: any

Accessors

isLoaded

paramMappings

  • get paramMappings(): ParamMapping[]

  • Returns ParamMapping[]

params

Methods

computeFaceDescriptor

dispose

extractWeights

forward

forwardInput

freeze

getFrozenParams

getParamFromPath

getParamList

getTrainableParams

load

loadFromDisk

loadFromUri

loadFromWeightMap

reassignParamFromPath

serializeParams

variable

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Properties
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Methods
diff --git a/typedoc/classes/LabeledBox.html b/typedoc/classes/LabeledBox.html index 91a1b49..843d1be 100644 --- a/typedoc/classes/LabeledBox.html +++ b/typedoc/classes/LabeledBox.html @@ -1,28 +1,28 @@ -LabeledBox | @vladmandic/face-api - v1.7.13

Class LabeledBox

Hierarchy (view full)

Constructors

constructor -

Accessors

area -bottom -bottomLeft -bottomRight -height -label -left -right -top -topLeft -topRight -width -x -y -

Methods

calibrate -clipAtImageBorders -floor -pad -padAtBorders -rescale -round -shift -toSquare -assertIsValidBox -assertIsValidLabeledBox -isRect -

Constructors

constructor

Accessors

area

bottom

bottomLeft

bottomRight

height

label

left

right

top

topLeft

topRight

width

x

y

Methods

calibrate

clipAtImageBorders

floor

pad

padAtBorders

  • padAtBorders(imageHeight, imageWidth): {
        dx: number;
        dy: number;
        edx: number;
        edy: number;
        ex: number;
        ey: number;
        h: number;
        w: number;
        x: number;
        y: number;
    }

  • Parameters

    • imageHeight: number
    • imageWidth: number

    Returns {
        dx: number;
        dy: number;
        edx: number;
        edy: number;
        ex: number;
        ey: number;
        h: number;
        w: number;
        x: number;
        y: number;
    }

    • dx: number
    • dy: number
    • edx: number
    • edy: number
    • ex: number
    • ey: number
    • h: number
    • w: number
    • x: number
    • y: number

rescale

round

shift

toSquare

Static assertIsValidBox

  • assertIsValidBox(box, callee, allowNegativeDimensions?): void

  • Parameters

    • box: any
    • callee: string
    • allowNegativeDimensions: boolean = false

    Returns void

Static assertIsValidLabeledBox

Static isRect

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\ No newline at end of file +LabeledBox | @vladmandic/face-api - v1.7.14

Class LabeledBox

Hierarchy (view full)

Constructors

constructor +

Accessors

area +bottom +bottomLeft +bottomRight +height +label +left +right +top +topLeft +topRight +width +x +y +

Methods

calibrate +clipAtImageBorders +floor +pad +padAtBorders +rescale +round +shift +toSquare +assertIsValidBox +assertIsValidLabeledBox +isRect +

Constructors

constructor

Accessors

area

bottom

bottomLeft

bottomRight

height

label

left

right

top

topLeft

topRight

width

x

y

Methods

calibrate

clipAtImageBorders

floor

pad

padAtBorders

  • padAtBorders(imageHeight, imageWidth): {
        dx: number;
        dy: number;
        edx: number;
        edy: number;
        ex: number;
        ey: number;
        h: number;
        w: number;
        x: number;
        y: number;
    }

  • Parameters

    • imageHeight: number
    • imageWidth: number

    Returns {
        dx: number;
        dy: number;
        edx: number;
        edy: number;
        ex: number;
        ey: number;
        h: number;
        w: number;
        x: number;
        y: number;
    }

    • dx: number
    • dy: number
    • edx: number
    • edy: number
    • ex: number
    • ey: number
    • h: number
    • w: number
    • x: number
    • y: number

rescale

round

shift

toSquare

StaticassertIsValidBox

  • assertIsValidBox(box, callee, allowNegativeDimensions?): void

  • Parameters

    • box: any
    • callee: string
    • allowNegativeDimensions: boolean = false

    Returns void

StaticassertIsValidLabeledBox

StaticisRect

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diff --git a/typedoc/classes/LabeledFaceDescriptors.html b/typedoc/classes/LabeledFaceDescriptors.html index cee9fc2..83a8c2d 100644 --- a/typedoc/classes/LabeledFaceDescriptors.html +++ b/typedoc/classes/LabeledFaceDescriptors.html @@ -1,6 +1,6 @@ -LabeledFaceDescriptors | @vladmandic/face-api - v1.7.13

Class LabeledFaceDescriptors

Constructors

constructor -

Accessors

descriptors -label -

Methods

toJSON -fromJSON -

Constructors

constructor

Accessors

descriptors

label

Methods

toJSON

Static fromJSON

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\ No newline at end of file +LabeledFaceDescriptors | @vladmandic/face-api - v1.7.14

Class LabeledFaceDescriptors

Constructors

constructor +

Accessors

descriptors +label +

Methods

toJSON +fromJSON +

Constructors

constructor

Accessors

descriptors

label

Methods

toJSON

StaticfromJSON

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Accessors
Methods
diff --git a/typedoc/classes/NetInput.html b/typedoc/classes/NetInput.html index 8e5bd95..5e66e3d 100644 --- a/typedoc/classes/NetInput.html +++ b/typedoc/classes/NetInput.html @@ -1,19 +1,19 @@ -NetInput | @vladmandic/face-api - v1.7.13

Class NetInput

Constructors

constructor -

Accessors

batchSize -canvases -imageTensors -inputDimensions -inputSize -isBatchInput -reshapedInputDimensions -

Methods

getInput -getInputDimensions -getInputHeight -getInputWidth -getReshapedInputDimensions -toBatchTensor -

Constructors

constructor

Accessors

batchSize

canvases

imageTensors

inputDimensions

inputSize

isBatchInput

reshapedInputDimensions

Methods

getInput

getInputDimensions

  • getInputDimensions(batchIdx): number[]

  • Parameters

    • batchIdx: number

    Returns number[]

getInputHeight

getInputWidth

getReshapedInputDimensions

toBatchTensor

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diff --git a/typedoc/classes/NeuralNetwork.html b/typedoc/classes/NeuralNetwork.html index a08cefa..0ed02c7 100644 --- a/typedoc/classes/NeuralNetwork.html +++ b/typedoc/classes/NeuralNetwork.html @@ -1,20 +1,20 @@ -NeuralNetwork | @vladmandic/face-api - v1.7.13

Class NeuralNetwork<TNetParams>Abstract

Type Parameters

  • TNetParams

Hierarchy (view full)

Constructors

constructor -

Properties

_name -

Accessors

isLoaded -paramMappings -params -

Methods

dispose -extractWeights -freeze -getFrozenParams -getParamFromPath -getParamList -getTrainableParams -load -loadFromDisk -loadFromUri -loadFromWeightMap -reassignParamFromPath -serializeParams -variable -

Constructors

constructor

Properties

_name

_name: any

Accessors

isLoaded

paramMappings

params

Methods

dispose

  • dispose(throwOnRedispose?): void

  • Parameters

    • throwOnRedispose: boolean = true

    Returns void

extractWeights

freeze

getFrozenParams

getParamFromPath

getParamList

getTrainableParams

load

  • load(weightsOrUrl): Promise<void>

  • Parameters

    • weightsOrUrl: undefined | string | Float32Array

    Returns Promise<void>

loadFromDisk

  • loadFromDisk(filePath): Promise<void>

  • Parameters

    • filePath: undefined | string

    Returns Promise<void>

loadFromUri

  • loadFromUri(uri): Promise<void>

  • Parameters

    • uri: undefined | string

    Returns Promise<void>

loadFromWeightMap

reassignParamFromPath

serializeParams

variable

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\ No newline at end of file +NeuralNetwork | @vladmandic/face-api - v1.7.14

Class NeuralNetwork<TNetParams>Abstract

Type Parameters

  • TNetParams

Hierarchy (view full)

Constructors

constructor +

Properties

_name +

Accessors

isLoaded +paramMappings +params +

Methods

dispose +extractWeights +freeze +getFrozenParams +getParamFromPath +getParamList +getTrainableParams +load +loadFromDisk +loadFromUri +loadFromWeightMap +reassignParamFromPath +serializeParams +variable +

Constructors

constructor

Properties

_name

_name: any

Accessors

isLoaded

paramMappings

params

Methods

dispose

  • dispose(throwOnRedispose?): void

  • Parameters

    • throwOnRedispose: boolean = true

    Returns void

extractWeights

freeze

getFrozenParams

getParamFromPath

getParamList

getTrainableParams

load

  • load(weightsOrUrl): Promise<void>

  • Parameters

    • weightsOrUrl: undefined | string | Float32Array

    Returns Promise<void>

loadFromDisk

  • loadFromDisk(filePath): Promise<void>

  • Parameters

    • filePath: undefined | string

    Returns Promise<void>

loadFromUri

  • loadFromUri(uri): Promise<void>

  • Parameters

    • uri: undefined | string

    Returns Promise<void>

loadFromWeightMap

reassignParamFromPath

serializeParams

variable

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Properties
Accessors
Methods
diff --git a/typedoc/classes/ObjectDetection.html b/typedoc/classes/ObjectDetection.html index 9c51fc5..04c1e96 100644 --- a/typedoc/classes/ObjectDetection.html +++ b/typedoc/classes/ObjectDetection.html @@ -1,11 +1,11 @@ -ObjectDetection | @vladmandic/face-api - v1.7.13

Class ObjectDetection

Hierarchy (view full)

Constructors

constructor -

Accessors

box -className -classScore -imageDims -imageHeight -imageWidth -relativeBox -score -

Methods

forSize -

Constructors

constructor

Accessors

box

className

classScore

imageDims

imageHeight

imageWidth

relativeBox

score

Methods

forSize

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\ No newline at end of file +ObjectDetection | @vladmandic/face-api - v1.7.14

Class ObjectDetection

Hierarchy (view full)

Constructors

constructor +

Accessors

box +className +classScore +imageDims +imageHeight +imageWidth +relativeBox +score +

Methods

forSize +

Constructors

constructor

Accessors

box

className

classScore

imageDims

imageHeight

imageWidth

relativeBox

score

Methods

forSize

Settings

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Accessors
Methods
diff --git a/typedoc/classes/Point.html b/typedoc/classes/Point.html index d2e38f2..591d571 100644 --- a/typedoc/classes/Point.html +++ b/typedoc/classes/Point.html @@ -1,11 +1,11 @@ -Point | @vladmandic/face-api - v1.7.13

Class Point

Implements

Constructors

constructor -

Accessors

x -y -

Methods

abs -add -div -floor -magnitude -mul -sub -

Constructors

constructor

Accessors

x

y

Methods

abs

add

div

floor

magnitude

mul

sub

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\ No newline at end of file +Point | @vladmandic/face-api - v1.7.14

Class Point

Implements

Constructors

constructor +

Accessors

x +y +

Methods

abs +add +div +floor +magnitude +mul +sub +

Constructors

constructor

Accessors

x

y

Methods

abs

add

div

floor

magnitude

mul

sub

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Constructors
Accessors
Methods
diff --git a/typedoc/classes/PredictedBox.html b/typedoc/classes/PredictedBox.html index 365a5bc..523d3fe 100644 --- a/typedoc/classes/PredictedBox.html +++ b/typedoc/classes/PredictedBox.html @@ -1,31 +1,31 @@ -PredictedBox | @vladmandic/face-api - v1.7.13

Class PredictedBox

Hierarchy (view full)

Constructors

constructor -

Accessors

area -bottom -bottomLeft -bottomRight -classScore -height -label -left -right -score -top -topLeft -topRight -width -x -y -

Methods

calibrate -clipAtImageBorders -floor -pad -padAtBorders -rescale -round -shift -toSquare -assertIsValidBox -assertIsValidLabeledBox -assertIsValidPredictedBox -isRect -

Constructors

constructor

Accessors

area

bottom

bottomLeft

bottomRight

classScore

height

label

left

right

score

top

topLeft

topRight

width

x

y

Methods

calibrate

clipAtImageBorders

floor

pad

padAtBorders

  • padAtBorders(imageHeight, imageWidth): {
        dx: number;
        dy: number;
        edx: number;
        edy: number;
        ex: number;
        ey: number;
        h: number;
        w: number;
        x: number;
        y: number;
    }

  • Parameters

    • imageHeight: number
    • imageWidth: number

    Returns {
        dx: number;
        dy: number;
        edx: number;
        edy: number;
        ex: number;
        ey: number;
        h: number;
        w: number;
        x: number;
        y: number;
    }

    • dx: number
    • dy: number
    • edx: number
    • edy: number
    • ex: number
    • ey: number
    • h: number
    • w: number
    • x: number
    • y: number

rescale

round

shift

toSquare

Static assertIsValidBox

Static assertIsValidLabeledBox

Static assertIsValidPredictedBox

Static isRect

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\ No newline at end of file +PredictedBox | @vladmandic/face-api - v1.7.14

Class PredictedBox

Hierarchy (view full)

Constructors

constructor +

Accessors

area +bottom +bottomLeft +bottomRight +classScore +height +label +left +right +score +top +topLeft +topRight +width +x +y +

Methods

calibrate +clipAtImageBorders +floor +pad +padAtBorders +rescale +round +shift +toSquare +assertIsValidBox +assertIsValidLabeledBox +assertIsValidPredictedBox +isRect +

Constructors

constructor

Accessors

area

bottom

bottomLeft

bottomRight

classScore

height

label

left

right

score

top

topLeft

topRight

width

x

y

Methods

calibrate

clipAtImageBorders

floor

pad

padAtBorders

  • padAtBorders(imageHeight, imageWidth): {
        dx: number;
        dy: number;
        edx: number;
        edy: number;
        ex: number;
        ey: number;
        h: number;
        w: number;
        x: number;
        y: number;
    }

  • Parameters

    • imageHeight: number
    • imageWidth: number

    Returns {
        dx: number;
        dy: number;
        edx: number;
        edy: number;
        ex: number;
        ey: number;
        h: number;
        w: number;
        x: number;
        y: number;
    }

    • dx: number
    • dy: number
    • edx: number
    • edy: number
    • ex: number
    • ey: number
    • h: number
    • w: number
    • x: number
    • y: number

rescale

round

shift

toSquare

StaticassertIsValidBox

StaticassertIsValidLabeledBox

StaticassertIsValidPredictedBox

StaticisRect

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Accessors
Methods
diff --git a/typedoc/classes/Rect.html b/typedoc/classes/Rect.html index abe66c9..aa098d8 100644 --- a/typedoc/classes/Rect.html +++ b/typedoc/classes/Rect.html @@ -1,26 +1,26 @@ -Rect | @vladmandic/face-api - v1.7.13

Class Rect

Hierarchy (view full)

Implements

Constructors

constructor -

Accessors

area -bottom -bottomLeft -bottomRight -height -left -right -top -topLeft -topRight -width -x -y -

Methods

calibrate -clipAtImageBorders -floor -pad -padAtBorders -rescale -round -shift -toSquare -assertIsValidBox -isRect -

Constructors

constructor

  • new Rect(x, y, width, height, allowNegativeDimensions?): Rect

  • Parameters

    • x: number
    • y: number
    • width: number
    • height: number
    • allowNegativeDimensions: boolean = false

    Returns Rect

Accessors

area

bottom

bottomLeft

bottomRight

height

left

right

top

topLeft

topRight

width

x

y

Methods

calibrate

clipAtImageBorders

floor

pad

padAtBorders

  • padAtBorders(imageHeight, imageWidth): {
        dx: number;
        dy: number;
        edx: number;
        edy: number;
        ex: number;
        ey: number;
        h: number;
        w: number;
        x: number;
        y: number;
    }

  • Parameters

    • imageHeight: number
    • imageWidth: number

    Returns {
        dx: number;
        dy: number;
        edx: number;
        edy: number;
        ex: number;
        ey: number;
        h: number;
        w: number;
        x: number;
        y: number;
    }

    • dx: number
    • dy: number
    • edx: number
    • edy: number
    • ex: number
    • ey: number
    • h: number
    • w: number
    • x: number
    • y: number

rescale

round

shift

toSquare

Static assertIsValidBox

  • assertIsValidBox(box, callee, allowNegativeDimensions?): void

  • Parameters

    • box: any
    • callee: string
    • allowNegativeDimensions: boolean = false

    Returns void

Static isRect

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\ No newline at end of file +Rect | @vladmandic/face-api - v1.7.14

Class Rect

Hierarchy (view full)

Implements

Constructors

constructor +

Accessors

area +bottom +bottomLeft +bottomRight +height +left +right +top +topLeft +topRight +width +x +y +

Methods

calibrate +clipAtImageBorders +floor +pad +padAtBorders +rescale +round +shift +toSquare +assertIsValidBox +isRect +

Constructors

constructor

  • new Rect(x, y, width, height, allowNegativeDimensions?): Rect

  • Parameters

    • x: number
    • y: number
    • width: number
    • height: number
    • allowNegativeDimensions: boolean = false

    Returns Rect

Accessors

area

bottom

bottomLeft

bottomRight

height

left

right

top

topLeft

topRight

width

x

y

Methods

calibrate

clipAtImageBorders

floor

pad

padAtBorders

  • padAtBorders(imageHeight, imageWidth): {
        dx: number;
        dy: number;
        edx: number;
        edy: number;
        ex: number;
        ey: number;
        h: number;
        w: number;
        x: number;
        y: number;
    }

  • Parameters

    • imageHeight: number
    • imageWidth: number

    Returns {
        dx: number;
        dy: number;
        edx: number;
        edy: number;
        ex: number;
        ey: number;
        h: number;
        w: number;
        x: number;
        y: number;
    }

    • dx: number
    • dy: number
    • edx: number
    • edy: number
    • ex: number
    • ey: number
    • h: number
    • w: number
    • x: number
    • y: number

rescale

round

shift

toSquare

StaticassertIsValidBox

  • assertIsValidBox(box, callee, allowNegativeDimensions?): void

  • Parameters

    • box: any
    • callee: string
    • allowNegativeDimensions: boolean = false

    Returns void

StaticisRect

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diff --git a/typedoc/classes/SsdMobilenetv1.html b/typedoc/classes/SsdMobilenetv1.html index 9aaae83..8972148 100644 --- a/typedoc/classes/SsdMobilenetv1.html +++ b/typedoc/classes/SsdMobilenetv1.html @@ -1,23 +1,23 @@ -SsdMobilenetv1 | @vladmandic/face-api - v1.7.13

Class SsdMobilenetv1

Hierarchy (view full)

Constructors

constructor -

Properties

_name -

Accessors

isLoaded -paramMappings -params -

Methods

dispose -extractWeights -forward -forwardInput -freeze -getFrozenParams -getParamFromPath -getParamList -getTrainableParams -load -loadFromDisk -loadFromUri -loadFromWeightMap -locateFaces -reassignParamFromPath -serializeParams -variable -

Constructors

constructor

Properties

_name

_name: any

Accessors

isLoaded

paramMappings

  • get paramMappings(): ParamMapping[]

  • Returns ParamMapping[]

params

Methods

dispose

extractWeights

forward

forwardInput

freeze

getFrozenParams

getParamFromPath

getParamList

getTrainableParams

load

loadFromDisk

loadFromUri

loadFromWeightMap

locateFaces

reassignParamFromPath

serializeParams

variable

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\ No newline at end of file +SsdMobilenetv1 | @vladmandic/face-api - v1.7.14

Class SsdMobilenetv1

Hierarchy (view full)

Constructors

constructor +

Properties

_name +

Accessors

isLoaded +paramMappings +params +

Methods

dispose +extractWeights +forward +forwardInput +freeze +getFrozenParams +getParamFromPath +getParamList +getTrainableParams +load +loadFromDisk +loadFromUri +loadFromWeightMap +locateFaces +reassignParamFromPath +serializeParams +variable +

Constructors

constructor

Properties

_name

_name: any

Accessors

isLoaded

paramMappings

  • get paramMappings(): ParamMapping[]

  • Returns ParamMapping[]

params

Methods

dispose

extractWeights

forward

forwardInput

freeze

getFrozenParams

getParamFromPath

getParamList

getTrainableParams

load

loadFromDisk

loadFromUri

loadFromWeightMap

locateFaces

reassignParamFromPath

serializeParams

variable

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Constructors
Properties
Accessors
Methods
diff --git a/typedoc/classes/SsdMobilenetv1Options.html b/typedoc/classes/SsdMobilenetv1Options.html index 6d37964..2579cba 100644 --- a/typedoc/classes/SsdMobilenetv1Options.html +++ b/typedoc/classes/SsdMobilenetv1Options.html @@ -1,4 +1,4 @@ -SsdMobilenetv1Options | @vladmandic/face-api - v1.7.13

Class SsdMobilenetv1Options

Constructors

constructor -

Accessors

maxResults -minConfidence -

Constructors

constructor

Accessors

maxResults

minConfidence

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\ No newline at end of file +SsdMobilenetv1Options | @vladmandic/face-api - v1.7.14

Class SsdMobilenetv1Options

Constructors

constructor +

Accessors

maxResults +minConfidence +

Constructors

constructor

Accessors

maxResults

minConfidence

Settings

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On This Page

Constructors
Accessors
diff --git a/typedoc/classes/TinyFaceDetector.html b/typedoc/classes/TinyFaceDetector.html index 4a65cef..8b2de8c 100644 --- a/typedoc/classes/TinyFaceDetector.html +++ b/typedoc/classes/TinyFaceDetector.html @@ -1,31 +1,31 @@ -TinyFaceDetector | @vladmandic/face-api - v1.7.13

Class TinyFaceDetector

Hierarchy

  • TinyYolov2Base
    • TinyFaceDetector

Constructors

constructor -

Properties

_name -DEFAULT_FILTER_SIZES -

Accessors

anchors -boxEncodingSize -config -isLoaded -paramMappings -params -withClassScores -

Methods

detect -dispose -extractWeights -forward -forwardInput -freeze -getFrozenParams -getParamFromPath -getParamList -getTrainableParams -load -loadFromDisk -loadFromUri -loadFromWeightMap -locateFaces -reassignParamFromPath -runMobilenet -runTinyYolov2 -serializeParams -variable -

Constructors

constructor

Properties

_name

_name: any

Static DEFAULT_FILTER_SIZES

DEFAULT_FILTER_SIZES: number[] = ...

Accessors

anchors

boxEncodingSize

config

isLoaded

paramMappings

  • get paramMappings(): ParamMapping[]

  • Returns ParamMapping[]

params

withClassScores

Methods

detect

dispose

  • dispose(throwOnRedispose?): void

  • Parameters

    • throwOnRedispose: boolean = true

    Returns void

extractWeights

  • extractWeights(weights): void

  • Parameters

    • weights: Float32Array

    Returns void

forward

forwardInput

freeze

getFrozenParams

  • getFrozenParams(): {
        path: string;
        tensor: Tensor<Rank>;
    }[]

  • Returns {
        path: string;
        tensor: Tensor<Rank>;
    }[]

getParamFromPath

getParamList

getTrainableParams

  • getTrainableParams(): {
        path: string;
        tensor: Tensor<Rank>;
    }[]

  • Returns {
        path: string;
        tensor: Tensor<Rank>;
    }[]

load

  • load(weightsOrUrl): Promise<void>

  • Parameters

    • weightsOrUrl: undefined | string | Float32Array

    Returns Promise<void>

loadFromDisk

  • loadFromDisk(filePath): Promise<void>

  • Parameters

    • filePath: undefined | string

    Returns Promise<void>

loadFromUri

  • loadFromUri(uri): Promise<void>

  • Parameters

    • uri: undefined | string

    Returns Promise<void>

loadFromWeightMap

locateFaces

reassignParamFromPath

  • reassignParamFromPath(paramPath, tensor): void

  • Parameters

    Returns void

runMobilenet

runTinyYolov2

serializeParams

  • serializeParams(): Float32Array

  • Returns Float32Array

variable

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\ No newline at end of file +TinyFaceDetector | @vladmandic/face-api - v1.7.14

Class TinyFaceDetector

Hierarchy

  • TinyYolov2Base
    • TinyFaceDetector

Constructors

constructor +

Properties

_name +DEFAULT_FILTER_SIZES +

Accessors

anchors +boxEncodingSize +config +isLoaded +paramMappings +params +withClassScores +

Methods

detect +dispose +extractWeights +forward +forwardInput +freeze +getFrozenParams +getParamFromPath +getParamList +getTrainableParams +load +loadFromDisk +loadFromUri +loadFromWeightMap +locateFaces +reassignParamFromPath +runMobilenet +runTinyYolov2 +serializeParams +variable +

Constructors

constructor

Properties

_name

_name: any

StaticDEFAULT_FILTER_SIZES

DEFAULT_FILTER_SIZES: number[] = ...

Accessors

anchors

boxEncodingSize

config

isLoaded

paramMappings

  • get paramMappings(): ParamMapping[]

  • Returns ParamMapping[]

params

withClassScores

Methods

detect

dispose

  • dispose(throwOnRedispose?): void

  • Parameters

    • throwOnRedispose: boolean = true

    Returns void

extractWeights

  • extractWeights(weights): void

  • Parameters

    • weights: Float32Array

    Returns void

forward

forwardInput

freeze

getFrozenParams

  • getFrozenParams(): {
        path: string;
        tensor: Tensor<Rank>;
    }[]

  • Returns {
        path: string;
        tensor: Tensor<Rank>;
    }[]

getParamFromPath

getParamList

getTrainableParams

  • getTrainableParams(): {
        path: string;
        tensor: Tensor<Rank>;
    }[]

  • Returns {
        path: string;
        tensor: Tensor<Rank>;
    }[]

load

  • load(weightsOrUrl): Promise<void>

  • Parameters

    • weightsOrUrl: undefined | string | Float32Array

    Returns Promise<void>

loadFromDisk

  • loadFromDisk(filePath): Promise<void>

  • Parameters

    • filePath: undefined | string

    Returns Promise<void>

loadFromUri

  • loadFromUri(uri): Promise<void>

  • Parameters

    • uri: undefined | string

    Returns Promise<void>

loadFromWeightMap

locateFaces

reassignParamFromPath

  • reassignParamFromPath(paramPath, tensor): void

  • Parameters

    Returns void

runMobilenet

runTinyYolov2

serializeParams

  • serializeParams(): Float32Array

  • Returns Float32Array

variable

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Properties
Accessors
Methods
diff --git a/typedoc/classes/TinyFaceDetectorOptions.html b/typedoc/classes/TinyFaceDetectorOptions.html index 6b320b5..b0fd4d8 100644 --- a/typedoc/classes/TinyFaceDetectorOptions.html +++ b/typedoc/classes/TinyFaceDetectorOptions.html @@ -1,4 +1,4 @@ -TinyFaceDetectorOptions | @vladmandic/face-api - v1.7.13

Class TinyFaceDetectorOptions

Hierarchy (view full)

Constructors

constructor -

Accessors

inputSize -scoreThreshold -

Constructors

constructor

Accessors

inputSize

scoreThreshold

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\ No newline at end of file +TinyFaceDetectorOptions | @vladmandic/face-api - v1.7.14

Class TinyFaceDetectorOptions

Hierarchy (view full)

Constructors

constructor +

Accessors

inputSize +scoreThreshold +

Constructors

constructor

Accessors

inputSize

scoreThreshold

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Constructors
Accessors
diff --git a/typedoc/classes/TinyYolov2.html b/typedoc/classes/TinyYolov2.html index b727992..0c99d99 100644 --- a/typedoc/classes/TinyYolov2.html +++ b/typedoc/classes/TinyYolov2.html @@ -1,32 +1,32 @@ -TinyYolov2 | @vladmandic/face-api - v1.7.13

Class TinyYolov2

Hierarchy

  • TinyYolov2Base
    • TinyYolov2

Constructors

constructor -

Properties

_name -DEFAULT_FILTER_SIZES -

Accessors

anchors -boxEncodingSize -config -isLoaded -paramMappings -params -withClassScores -withSeparableConvs -

Methods

detect -dispose -extractWeights -forward -forwardInput -freeze -getFrozenParams -getParamFromPath -getParamList -getTrainableParams -load -loadFromDisk -loadFromUri -loadFromWeightMap -locateFaces -reassignParamFromPath -runMobilenet -runTinyYolov2 -serializeParams -variable -

Constructors

constructor

Properties

_name

_name: any

Static DEFAULT_FILTER_SIZES

DEFAULT_FILTER_SIZES: number[] = ...

Accessors

anchors

boxEncodingSize

config

isLoaded

paramMappings

  • get paramMappings(): ParamMapping[]

  • Returns ParamMapping[]

params

withClassScores

withSeparableConvs

Methods

detect

dispose

  • dispose(throwOnRedispose?): void

  • Parameters

    • throwOnRedispose: boolean = true

    Returns void

extractWeights

  • extractWeights(weights): void

  • Parameters

    • weights: Float32Array

    Returns void

forward

forwardInput

freeze

getFrozenParams

  • getFrozenParams(): {
        path: string;
        tensor: Tensor<Rank>;
    }[]

  • Returns {
        path: string;
        tensor: Tensor<Rank>;
    }[]

getParamFromPath

getParamList

getTrainableParams

  • getTrainableParams(): {
        path: string;
        tensor: Tensor<Rank>;
    }[]

  • Returns {
        path: string;
        tensor: Tensor<Rank>;
    }[]

load

  • load(weightsOrUrl): Promise<void>

  • Parameters

    • weightsOrUrl: undefined | string | Float32Array

    Returns Promise<void>

loadFromDisk

  • loadFromDisk(filePath): Promise<void>

  • Parameters

    • filePath: undefined | string

    Returns Promise<void>

loadFromUri

  • loadFromUri(uri): Promise<void>

  • Parameters

    • uri: undefined | string

    Returns Promise<void>

loadFromWeightMap

locateFaces

reassignParamFromPath

  • reassignParamFromPath(paramPath, tensor): void

  • Parameters

    Returns void

runMobilenet

runTinyYolov2

serializeParams

  • serializeParams(): Float32Array

  • Returns Float32Array

variable

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\ No newline at end of file +TinyYolov2 | @vladmandic/face-api - v1.7.14

Class TinyYolov2

Hierarchy

  • TinyYolov2Base
    • TinyYolov2

Constructors

constructor +

Properties

_name +DEFAULT_FILTER_SIZES +

Accessors

anchors +boxEncodingSize +config +isLoaded +paramMappings +params +withClassScores +withSeparableConvs +

Methods

detect +dispose +extractWeights +forward +forwardInput +freeze +getFrozenParams +getParamFromPath +getParamList +getTrainableParams +load +loadFromDisk +loadFromUri +loadFromWeightMap +locateFaces +reassignParamFromPath +runMobilenet +runTinyYolov2 +serializeParams +variable +

Constructors

constructor

Properties

_name

_name: any

StaticDEFAULT_FILTER_SIZES

DEFAULT_FILTER_SIZES: number[] = ...

Accessors

anchors

boxEncodingSize

config

isLoaded

paramMappings

  • get paramMappings(): ParamMapping[]

  • Returns ParamMapping[]

params

withClassScores

withSeparableConvs

Methods

detect

dispose

  • dispose(throwOnRedispose?): void

  • Parameters

    • throwOnRedispose: boolean = true

    Returns void

extractWeights

  • extractWeights(weights): void

  • Parameters

    • weights: Float32Array

    Returns void

forward

forwardInput

freeze

getFrozenParams

  • getFrozenParams(): {
        path: string;
        tensor: Tensor<Rank>;
    }[]

  • Returns {
        path: string;
        tensor: Tensor<Rank>;
    }[]

getParamFromPath

getParamList

getTrainableParams

  • getTrainableParams(): {
        path: string;
        tensor: Tensor<Rank>;
    }[]

  • Returns {
        path: string;
        tensor: Tensor<Rank>;
    }[]

load

  • load(weightsOrUrl): Promise<void>

  • Parameters

    • weightsOrUrl: undefined | string | Float32Array

    Returns Promise<void>

loadFromDisk

  • loadFromDisk(filePath): Promise<void>

  • Parameters

    • filePath: undefined | string

    Returns Promise<void>

loadFromUri

  • loadFromUri(uri): Promise<void>

  • Parameters

    • uri: undefined | string

    Returns Promise<void>

loadFromWeightMap

locateFaces

reassignParamFromPath

  • reassignParamFromPath(paramPath, tensor): void

  • Parameters

    Returns void

runMobilenet

runTinyYolov2

serializeParams

  • serializeParams(): Float32Array

  • Returns Float32Array

variable

Settings

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Constructors
Properties
Accessors
Methods
diff --git a/typedoc/classes/TinyYolov2Options.html b/typedoc/classes/TinyYolov2Options.html index 4f229ea..49bc7a1 100644 --- a/typedoc/classes/TinyYolov2Options.html +++ b/typedoc/classes/TinyYolov2Options.html @@ -1,4 +1,4 @@ -TinyYolov2Options | @vladmandic/face-api - v1.7.13

Class TinyYolov2Options

Hierarchy (view full)

Constructors

constructor -

Accessors

inputSize -scoreThreshold -

Constructors

constructor

Accessors

inputSize

scoreThreshold

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\ No newline at end of file +TinyYolov2Options | @vladmandic/face-api - v1.7.14

Class TinyYolov2Options

Hierarchy (view full)

Constructors

constructor +

Accessors

inputSize +scoreThreshold +

Constructors

constructor

Accessors

inputSize

scoreThreshold

Settings

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Constructors
Accessors
diff --git a/typedoc/classes/draw.DrawBox.html b/typedoc/classes/draw.DrawBox.html index 7551301..6d96dc9 100644 --- a/typedoc/classes/draw.DrawBox.html +++ b/typedoc/classes/draw.DrawBox.html @@ -1,5 +1,5 @@ -DrawBox | @vladmandic/face-api - v1.7.13

Class DrawBox

Constructors

constructor -

Properties

box -options -

Methods

draw -

Constructors

constructor

Properties

box

box: Box<any>

options

options: DrawBoxOptions

Methods

draw

  • draw(canvasArg): void

  • Parameters

    • canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D

    Returns void

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\ No newline at end of file +DrawBox | @vladmandic/face-api - v1.7.14

Class DrawBox

Constructors

constructor +

Properties

box +options +

Methods

draw +

Constructors

constructor

Properties

box

box: Box<any>

options

options: DrawBoxOptions

Methods

draw

  • draw(canvasArg): void

  • Parameters

    • canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D

    Returns void

Settings

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Properties
Methods
diff --git a/typedoc/classes/draw.DrawBoxOptions.html b/typedoc/classes/draw.DrawBoxOptions.html index 38fc0bd..ae63065 100644 --- a/typedoc/classes/draw.DrawBoxOptions.html +++ b/typedoc/classes/draw.DrawBoxOptions.html @@ -1,6 +1,6 @@ -DrawBoxOptions | @vladmandic/face-api - v1.7.13

Class DrawBoxOptions

Constructors

constructor -

Properties

boxColor -drawLabelOptions -label? -lineWidth -

Constructors

constructor

Properties

boxColor

boxColor: string

drawLabelOptions

drawLabelOptions: DrawTextFieldOptions

Optional label

label?: string

lineWidth

lineWidth: number

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\ No newline at end of file +DrawBoxOptions | @vladmandic/face-api - v1.7.14

Class DrawBoxOptions

Constructors

constructor +

Properties

boxColor +drawLabelOptions +label? +lineWidth +

Constructors

constructor

Properties

boxColor

boxColor: string

drawLabelOptions

drawLabelOptions: DrawTextFieldOptions

Optionallabel

label?: string

lineWidth

lineWidth: number

Settings

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Constructors
Properties
diff --git a/typedoc/classes/draw.DrawFaceLandmarks.html b/typedoc/classes/draw.DrawFaceLandmarks.html index 080cb62..a818ad0 100644 --- a/typedoc/classes/draw.DrawFaceLandmarks.html +++ b/typedoc/classes/draw.DrawFaceLandmarks.html @@ -1,5 +1,5 @@ -DrawFaceLandmarks | @vladmandic/face-api - v1.7.13

Class DrawFaceLandmarks

Constructors

constructor -

Properties

faceLandmarks -options -

Methods

draw -

Constructors

constructor

Properties

faceLandmarks

faceLandmarks: FaceLandmarks

options

options: DrawFaceLandmarksOptions

Methods

draw

Settings

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\ No newline at end of file +DrawFaceLandmarks | @vladmandic/face-api - v1.7.14

Class DrawFaceLandmarks

Constructors

constructor +

Properties

faceLandmarks +options +

Methods

draw +

Constructors

constructor

Properties

faceLandmarks

faceLandmarks: FaceLandmarks

options

options: DrawFaceLandmarksOptions

Methods

draw

Settings

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Constructors
Properties
Methods
diff --git a/typedoc/classes/draw.DrawFaceLandmarksOptions.html b/typedoc/classes/draw.DrawFaceLandmarksOptions.html index 1a63df6..288d263 100644 --- a/typedoc/classes/draw.DrawFaceLandmarksOptions.html +++ b/typedoc/classes/draw.DrawFaceLandmarksOptions.html @@ -1,8 +1,8 @@ -DrawFaceLandmarksOptions | @vladmandic/face-api - v1.7.13

Class DrawFaceLandmarksOptions

Constructors

constructor -

Properties

drawLines -drawPoints -lineColor -lineWidth -pointColor -pointSize -

Constructors

constructor

Properties

drawLines

drawLines: boolean

drawPoints

drawPoints: boolean

lineColor

lineColor: string

lineWidth

lineWidth: number

pointColor

pointColor: string

pointSize

pointSize: number

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\ No newline at end of file +DrawFaceLandmarksOptions | @vladmandic/face-api - v1.7.14

Class DrawFaceLandmarksOptions

Constructors

constructor +

Properties

drawLines +drawPoints +lineColor +lineWidth +pointColor +pointSize +

Constructors

constructor

Properties

drawLines

drawLines: boolean

drawPoints

drawPoints: boolean

lineColor

lineColor: string

lineWidth

lineWidth: number

pointColor

pointColor: string

pointSize

pointSize: number

Settings

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Constructors
Properties
diff --git a/typedoc/classes/draw.DrawTextField.html b/typedoc/classes/draw.DrawTextField.html index 9f7a7ee..ea20cdc 100644 --- a/typedoc/classes/draw.DrawTextField.html +++ b/typedoc/classes/draw.DrawTextField.html @@ -1,9 +1,9 @@ -DrawTextField | @vladmandic/face-api - v1.7.13

Class DrawTextField

Constructors

constructor -

Properties

anchor -options -text -

Methods

draw -getUpperLeft -measureHeight -measureWidth -

Constructors

constructor

Properties

anchor

anchor: IPoint

options

options: DrawTextFieldOptions

text

text: string[]

Methods

draw

  • draw(canvasArg): void

  • Parameters

    • canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D

    Returns void

getUpperLeft

measureHeight

measureWidth

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\ No newline at end of file +DrawTextField | @vladmandic/face-api - v1.7.14

Class DrawTextField

Constructors

constructor +

Properties

anchor +options +text +

Methods

draw +getUpperLeft +measureHeight +measureWidth +

Constructors

constructor

Properties

anchor

anchor: IPoint

options

options: DrawTextFieldOptions

text

text: string[]

Methods

draw

  • draw(canvasArg): void

  • Parameters

    • canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D

    Returns void

getUpperLeft

measureHeight

measureWidth

Settings

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Constructors
Properties
Methods
diff --git a/typedoc/classes/draw.DrawTextFieldOptions.html b/typedoc/classes/draw.DrawTextFieldOptions.html index 731edfe..b700429 100644 --- a/typedoc/classes/draw.DrawTextFieldOptions.html +++ b/typedoc/classes/draw.DrawTextFieldOptions.html @@ -1,8 +1,8 @@ -DrawTextFieldOptions | @vladmandic/face-api - v1.7.13

Class DrawTextFieldOptions

Implements

Constructors

constructor -

Properties

anchorPosition -backgroundColor -fontColor -fontSize -fontStyle -padding -

Constructors

constructor

Properties

anchorPosition

anchorPosition: AnchorPosition

backgroundColor

backgroundColor: string

fontColor

fontColor: string

fontSize

fontSize: number

fontStyle

fontStyle: string

padding

padding: number

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\ No newline at end of file +DrawTextFieldOptions | @vladmandic/face-api - v1.7.14

Class DrawTextFieldOptions

Implements

Constructors

constructor +

Properties

anchorPosition +backgroundColor +fontColor +fontSize +fontStyle +padding +

Constructors

constructor

Properties

anchorPosition

anchorPosition: AnchorPosition

backgroundColor

backgroundColor: string

fontColor

fontColor: string

fontSize

fontSize: number

fontStyle

fontStyle: string

padding

padding: number

Settings

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Properties
diff --git a/typedoc/classes/tf.Tensor-1.html b/typedoc/classes/tf.Tensor-1.html index 8218dca..014be3d 100644 --- a/typedoc/classes/tf.Tensor-1.html +++ b/typedoc/classes/tf.Tensor-1.html @@ -1,4 +1,4 @@ -Tensor | @vladmandic/face-api - v1.7.13

Class Tensor<R>

A tf.Tensor object represents an immutable, multidimensional array of +Tensor | @vladmandic/face-api - v1.7.14

Class Tensor<R>

A tf.Tensor object represents an immutable, multidimensional array of numbers that has a shape and a data type.

For performance reasons, functions that create tensors do not necessarily perform a copy of the data passed to them (e.g. if the data is passed as a @@ -6,213 +6,220 @@ perform a copy of the data passed to them (e.g. if the data is passed as a a feature and is not supported. To avoid this behavior, use the tensor before changing the input data or create a copy with copy = tf.add(yourTensor, 0).

See tf.tensor for details on how to create a tf.Tensor.

-

Doc

Type Parameters

Hierarchy (view full)

Implements

  • TensorInfo

Constructors

constructor -

Properties

dataId -dtype -id -kept -kerasMask? -rankType -scopeId -shape -size -strides -

Accessors

isDisposed -rank -

Methods

abs -acos -acosh -add -all -any -argMax -argMin -array -arraySync -as1D -as2D -as3D -as4D -as5D -asScalar -asType -asin -asinh -atan -atan2 -atanh -avgPool -batchNorm -batchToSpaceND -broadcastTo -buffer -bufferSync -bytes -cast -ceil -clipByValue -clone -concat -conv1d -conv2d -conv2dTranspose -cos -cosh -cumprod -cumsum -data -dataSync -dataToGPU -depthToSpace -depthwiseConv2d -dilation2d -dispose -div -divNoNan -dot -elu -equal -erf -euclideanNorm -exp -expandDims -expm1 -fft -flatten -floor -floorDiv -gather -greater -greaterEqual -ifft -irfft -isFinite -isInf -isNaN -leakyRelu -less -lessEqual -localResponseNormalization -log -log1p -logSigmoid -logSoftmax -logSumExp -logicalAnd -logicalNot -logicalOr -logicalXor -matMul -max -maxPool -maximum -mean -min -minimum -mirrorPad -mod -mul -neg -norm -notEqual -oneHot -onesLike -pad -pool -pow -prelu -print -prod -reciprocal -relu -relu6 -reshape -reshapeAs -resizeBilinear -resizeNearestNeighbor -reverse -rfft -round -rsqrt -selu -separableConv2d -sigmoid -sign -sin -sinh -slice -softmax -softplus -spaceToBatchND -split -sqrt -square -squaredDifference -squeeze -stack -step -stridedSlice -sub -sum -tan -tanh -throwIfDisposed -tile -toBool -toFloat -toInt -toString -topk -transpose -unique -unsortedSegmentSum -unstack -variable -where -zerosLike -

Constructors

constructor

  • new Tensor<R>(shape, dtype, dataId, id): Tensor<R>

  • Type Parameters

    Parameters

    • shape: ShapeMap[R]
    • dtype: keyof DataTypeMap
    • dataId: object
    • id: number

    Returns Tensor<R>

Properties

dataId

dataId: object

Id of the bucket holding the data for this tensor. Multiple arrays can +

Doc

Type Parameters

Hierarchy (view full)

Implements

  • TensorInfo

Constructors

constructor +

Properties

dataId +dtype +id +kept +kerasMask? +rankType +scopeId +shape +size +strides +

Accessors

isDisposed +rank +

Methods

abs +acos +acosh +add +all +any +argMax +argMin +array +arraySync +as1D +as2D +as3D +as4D +as5D +asin +asinh +asScalar +asType +atan +atan2 +atanh +avgPool +batchNorm +batchToSpaceND +broadcastTo +buffer +bufferSync +bytes +cast +ceil +clipByValue +clone +concat +conv1d +conv2d +conv2dTranspose +cos +cosh +cumprod +cumsum +data +dataSync +dataToGPU +depthToSpace +depthwiseConv2d +dilation2d +dispose +div +divNoNan +dot +elu +equal +erf +euclideanNorm +exp +expandDims +expm1 +fft +flatten +floor +floorDiv +gather +greater +greaterEqual +ifft +irfft +isFinite +isInf +isNaN +leakyRelu +less +lessEqual +localResponseNormalization +log +log1p +logicalAnd +logicalNot +logicalOr +logicalXor +logSigmoid +logSoftmax +logSumExp +matMul +max +maximum +maxPool +mean +min +minimum +mirrorPad +mod +mul +neg +norm +notEqual +oneHot +onesLike +pad +pool +pow +prelu +print +prod +reciprocal +relu +relu6 +reshape +reshapeAs +resizeBilinear +resizeNearestNeighbor +reverse +rfft +round +rsqrt +selu +separableConv2d +sigmoid +sign +sin +sinh +slice +softmax +softplus +spaceToBatchND +split +sqrt +square +squaredDifference +squeeze +stack +step +stridedSlice +sub +sum +tan +tanh +throwIfDisposed +tile +toBool +toFloat +toInt +topk +toString +transpose +unique +unsortedSegmentSum +unstack +variable +where +zerosLike +

Constructors

constructor

  • new Tensor<R>(shape, dtype, dataId, id): Tensor<R>

  • Type Parameters

    Parameters

    • shape: ShapeMap[R]
    • dtype: keyof DataTypeMap
    • dataId: object
    • id: number

    Returns Tensor<R>

Properties

dataId

dataId: object

Id of the bucket holding the data for this tensor. Multiple arrays can point to the same bucket (e.g. when calling array.reshape()).

-

Readonly dtype

dtype: keyof DataTypeMap

The data type for the array.

-

Readonly id

id: number

Unique id of this tensor.

-

kept

kept: boolean

Whether this tensor has been globally kept.

-

Optional kerasMask

kerasMask?: Tensor<Rank>

The keras mask that some keras layers attach to the tensor

-

Readonly rankType

rankType: R

The rank type for the array (see Rank enum).

-

scopeId

scopeId: number

The id of the scope this tensor is being tracked in.

-

Readonly shape

shape: ShapeMap[R]

The shape of the tensor.

-

Readonly size

size: number

Number of elements in the tensor.

-

Readonly strides

strides: number[]

Number of elements to skip in each dimension when indexing. See -https://docs.scipy.org/doc/numpy/reference/generated/\ +

Readonlydtype

dtype: keyof DataTypeMap

The data type for the array.

+

Readonlyid

id: number

Unique id of this tensor.

+

kept

kept: boolean

Whether this tensor has been globally kept.

+

OptionalkerasMask

kerasMask?: Tensor<Rank>

The keras mask that some keras layers attach to the tensor

+

ReadonlyrankType

rankType: R

The rank type for the array (see Rank enum).

+

scopeId

scopeId: number

The id of the scope this tensor is being tracked in.

+

Readonlyshape

shape: ShapeMap[R]

The shape of the tensor.

+

Readonlysize

size: number

Number of elements in the tensor.

+

Readonlystrides

strides: number[]

Number of elements to skip in each dimension when indexing. See +https://docs.scipy.org/doc/numpy/reference/generated/\ numpy.ndarray.strides.html

-

Accessors

isDisposed

  • get isDisposed(): boolean

  • Returns boolean

rank

  • get rank(): number

  • Returns number

Methods

abs

  • abs<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

acos

  • acos<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

acosh

  • acosh<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

add

  • add<T>(b): T

  • Type Parameters

    Parameters

    Returns T

all

  • all<T>(this, axis?, keepDims?): T

  • Type Parameters

    Parameters

    • this: T
    • Optional axis: number | number[]
    • Optional keepDims: boolean

    Returns T

any

  • any<T>(this, axis?, keepDims?): T

  • Type Parameters

    Parameters

    • this: T
    • Optional axis: number | number[]
    • Optional keepDims: boolean

    Returns T

argMax

  • argMax<T>(axis?): T

  • Type Parameters

    Parameters

    • Optional axis: number

    Returns T

argMin

  • argMin<T>(axis?): T

  • Type Parameters

    Parameters

    • Optional axis: number

    Returns T

array

  • array(): Promise<ArrayMap[R]>

  • Returns the tensor data as a nested array. The transfer of data is done +

Accessors

isDisposed

  • get isDisposed(): boolean

  • Returns boolean

rank

  • get rank(): number

  • Returns number

Methods

abs

  • abs<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

acos

  • acos<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

acosh

  • acosh<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

add

  • add<T>(b): T

  • Type Parameters

    Parameters

    Returns T

all

  • all<T>(this, axis?, keepDims?): T

  • Type Parameters

    Parameters

    • this: T
    • Optionalaxis: number | number[]
    • OptionalkeepDims: boolean

    Returns T

any

  • any<T>(this, axis?, keepDims?): T

  • Type Parameters

    Parameters

    • this: T
    • Optionalaxis: number | number[]
    • OptionalkeepDims: boolean

    Returns T

argMax

  • argMax<T>(axis?): T

  • Type Parameters

    Parameters

    • Optionalaxis: number

    Returns T

argMin

  • argMin<T>(axis?): T

  • Type Parameters

    Parameters

    • Optionalaxis: number

    Returns T

array

  • array(): Promise<ArrayMap[R]>

  • Returns the tensor data as a nested array. The transfer of data is done asynchronously.

    -

    Returns Promise<ArrayMap[R]>

    Doc

arraySync

  • arraySync(): ArrayMap[R]

  • Returns the tensor data as a nested array. The transfer of data is done +

    Returns Promise<ArrayMap[R]>

    Doc

arraySync

  • arraySync(): ArrayMap[R]

  • Returns the tensor data as a nested array. The transfer of data is done synchronously.

    -

    Returns ArrayMap[R]

    Doc

as1D

  • as1D<T>(): Tensor1D

  • Type Parameters

    Returns Tensor1D

as2D

  • as2D<T>(rows, columns): Tensor2D

  • Type Parameters

    Parameters

    • rows: number
    • columns: number

    Returns Tensor2D

as3D

  • as3D<T>(rows, columns, depth): Tensor3D

  • Type Parameters

    Parameters

    • rows: number
    • columns: number
    • depth: number

    Returns Tensor3D

as4D

  • as4D<T>(rows, columns, depth, depth2): Tensor4D

  • Type Parameters

    Parameters

    • rows: number
    • columns: number
    • depth: number
    • depth2: number

    Returns Tensor4D

as5D

  • as5D<T>(rows, columns, depth, depth2, depth3): Tensor5D

  • Type Parameters

    Parameters

    • rows: number
    • columns: number
    • depth: number
    • depth2: number
    • depth3: number

    Returns Tensor5D

asScalar

  • asScalar<T>(): Scalar

  • Type Parameters

    Returns Scalar

asType

  • asType<T>(this, dtype): T

  • Type Parameters

    Parameters

    • this: T
    • dtype: keyof DataTypeMap

    Returns T

asin

  • asin<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

asinh

  • asinh<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

atan

  • atan<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

atan2

  • atan2<T>(b): T

  • Type Parameters

    Parameters

    Returns T

atanh

  • atanh<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

avgPool

  • avgPool<T>(filterSize, strides, pad, dimRoundingMode?): T

  • Type Parameters

    Parameters

    • filterSize: number | [number, number]
    • strides: number | [number, number]
    • pad: number | "valid" | "same" | ExplicitPadding
    • Optional dimRoundingMode: "floor" | "round" | "ceil"

    Returns T

batchNorm

batchToSpaceND

  • batchToSpaceND<R>(blockShape, crops): Tensor<R>

  • Type Parameters

    Parameters

    • blockShape: number[]
    • crops: number[][]

    Returns Tensor<R>

broadcastTo

  • broadcastTo<R>(shape): Tensor<R>

  • Type Parameters

    Parameters

    • shape: ShapeMap[R]

    Returns Tensor<R>

buffer

  • buffer<D>(): Promise<TensorBuffer<R, D>>

  • Returns a promise of tf.TensorBuffer that holds the underlying data.

    -

    Type Parameters

    • D extends keyof DataTypeMap = "float32"

    Returns Promise<TensorBuffer<R, D>>

    Doc

bufferSync

  • bufferSync<D>(): TensorBuffer<R, D>

  • Returns a tf.TensorBuffer that holds the underlying data.

    -

    Type Parameters

    • D extends keyof DataTypeMap = "float32"

    Returns TensorBuffer<R, D>

    Doc

bytes

  • bytes(): Promise<Uint8Array | Uint8Array[]>

  • Returns the underlying bytes of the tensor's data.

    -

    Returns Promise<Uint8Array | Uint8Array[]>

cast

  • cast<T>(dtype): T

  • Type Parameters

    Parameters

    • dtype: keyof DataTypeMap

    Returns T

ceil

  • ceil<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

clipByValue

  • clipByValue<T>(min, max): Tensor<Rank>

  • Type Parameters

    Parameters

    • min: number
    • max: number

    Returns Tensor<Rank>

clone

  • clone<T>(this): T

  • Returns a copy of the tensor. See tf.clone for details.

    -

    Type Parameters

    Parameters

    • this: T

    Returns T

    Doc

concat

  • concat<T>(tensors, axis?): T

  • Type Parameters

    Parameters

    Returns T

conv1d

  • conv1d<T>(filter, stride, pad, dataFormat?, dilation?, dimRoundingMode?): T

  • Type Parameters

    Parameters

    • filter: Tensor3D | TensorLike3D
    • stride: number
    • pad: number | "valid" | "same" | ExplicitPadding
    • Optional dataFormat: "NWC" | "NCW"
    • Optional dilation: number
    • Optional dimRoundingMode: "floor" | "round" | "ceil"

    Returns T

conv2d

  • conv2d<T>(filter, strides, pad, dataFormat?, dilations?, dimRoundingMode?): T

  • Type Parameters

    Parameters

    • filter: Tensor4D | TensorLike4D
    • strides: number | [number, number]
    • pad: number | "valid" | "same"
    • Optional dataFormat: "NHWC" | "NCHW"
    • Optional dilations: number | [number, number]
    • Optional dimRoundingMode: "floor" | "round" | "ceil"

    Returns T

conv2dTranspose

  • conv2dTranspose<T>(filter, outputShape, strides, pad, dimRoundingMode?): T

  • Type Parameters

    Parameters

    • filter: Tensor4D | TensorLike4D
    • outputShape: [number, number, number, number] | [number, number, number]
    • strides: number | [number, number]
    • pad: number | "valid" | "same"
    • Optional dimRoundingMode: "floor" | "round" | "ceil"

    Returns T

cos

  • cos<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

cosh

  • cosh<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

cumprod

  • cumprod<R>(axis?, exclusive?, reverse?): Tensor<R>

  • Type Parameters

    Parameters

    • Optional axis: number
    • Optional exclusive: boolean
    • Optional reverse: boolean

    Returns Tensor<R>

cumsum

  • cumsum<R>(axis?, exclusive?, reverse?): Tensor<R>

  • Type Parameters

    Parameters

    • Optional axis: number
    • Optional exclusive: boolean
    • Optional reverse: boolean

    Returns Tensor<R>

data

  • data<D>(): Promise<DataTypeMap[D]>

  • Asynchronously downloads the values from the tf.Tensor. Returns a +

    Returns ArrayMap[R]

    Doc

as1D

  • as1D<T>(): Tensor1D

  • Type Parameters

    Returns Tensor1D

as2D

  • as2D<T>(rows, columns): Tensor2D

  • Type Parameters

    Parameters

    • rows: number
    • columns: number

    Returns Tensor2D

as3D

  • as3D<T>(rows, columns, depth): Tensor3D

  • Type Parameters

    Parameters

    • rows: number
    • columns: number
    • depth: number

    Returns Tensor3D

as4D

  • as4D<T>(rows, columns, depth, depth2): Tensor4D

  • Type Parameters

    Parameters

    • rows: number
    • columns: number
    • depth: number
    • depth2: number

    Returns Tensor4D

as5D

  • as5D<T>(rows, columns, depth, depth2, depth3): Tensor5D

  • Type Parameters

    Parameters

    • rows: number
    • columns: number
    • depth: number
    • depth2: number
    • depth3: number

    Returns Tensor5D

asin

  • asin<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

asinh

  • asinh<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

asScalar

  • asScalar<T>(): Scalar

  • Type Parameters

    Returns Scalar

asType

  • asType<T>(this, dtype): T

  • Type Parameters

    Parameters

    • this: T
    • dtype: keyof DataTypeMap

    Returns T

atan

  • atan<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

atan2

  • atan2<T>(b): T

  • Type Parameters

    Parameters

    Returns T

atanh

  • atanh<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

avgPool

  • avgPool<T>(filterSize, strides, pad, dimRoundingMode?): T

  • Type Parameters

    Parameters

    • filterSize: number | [number, number]
    • strides: number | [number, number]
    • pad:
          | number
          | "valid"
          | "same"
          | ExplicitPadding
    • OptionaldimRoundingMode: "floor" | "round" | "ceil"

    Returns T

batchNorm

batchToSpaceND

  • batchToSpaceND<R>(blockShape, crops): Tensor<R>

  • Type Parameters

    Parameters

    • blockShape: number[]
    • crops: number[][]

    Returns Tensor<R>

broadcastTo

  • broadcastTo<R>(shape): Tensor<R>

  • Type Parameters

    Parameters

    • shape: ShapeMap[R]

    Returns Tensor<R>

buffer

  • buffer<D>(): Promise<TensorBuffer<R, D>>

  • Returns a promise of tf.TensorBuffer that holds the underlying data.

    +

    Type Parameters

    • D extends keyof DataTypeMap = "float32"

    Returns Promise<TensorBuffer<R, D>>

    Doc

bufferSync

  • bufferSync<D>(): TensorBuffer<R, D>

  • Returns a tf.TensorBuffer that holds the underlying data.

    +

    Type Parameters

    • D extends keyof DataTypeMap = "float32"

    Returns TensorBuffer<R, D>

    Doc

bytes

  • bytes(): Promise<Uint8Array | Uint8Array[]>

  • Returns the underlying bytes of the tensor's data.

    +

    Returns Promise<Uint8Array | Uint8Array[]>

cast

  • cast<T>(dtype): T

  • Type Parameters

    Parameters

    • dtype: keyof DataTypeMap

    Returns T

ceil

  • ceil<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

clipByValue

  • clipByValue<T>(min, max): Tensor<Rank>

  • Type Parameters

    Parameters

    • min: number
    • max: number

    Returns Tensor<Rank>

clone

  • clone<T>(this): T

  • Returns a copy of the tensor. See tf.clone for details.

    +

    Type Parameters

    Parameters

    • this: T

    Returns T

    Doc

concat

  • concat<T>(tensors, axis?): T

  • Type Parameters

    Parameters

    Returns T

conv1d

  • conv1d<T>(filter, stride, pad, dataFormat?, dilation?, dimRoundingMode?): T

  • Type Parameters

    Parameters

    • filter: Tensor3D | TensorLike3D
    • stride: number
    • pad:
          | number
          | "valid"
          | "same"
          | ExplicitPadding
    • OptionaldataFormat: "NWC" | "NCW"
    • Optionaldilation: number
    • OptionaldimRoundingMode: "floor" | "round" | "ceil"

    Returns T

conv2d

  • conv2d<T>(filter, strides, pad, dataFormat?, dilations?, dimRoundingMode?): T

  • Type Parameters

    Parameters

    • filter: Tensor4D | TensorLike4D
    • strides: number | [number, number]
    • pad: number | "valid" | "same"
    • OptionaldataFormat: "NHWC" | "NCHW"
    • Optionaldilations: number | [number, number]
    • OptionaldimRoundingMode: "floor" | "round" | "ceil"

    Returns T

conv2dTranspose

  • conv2dTranspose<T>(filter, outputShape, strides, pad, dimRoundingMode?): T

  • Type Parameters

    Parameters

    • filter: Tensor4D | TensorLike4D
    • outputShape: [number, number, number, number] | [number, number, number]
    • strides: number | [number, number]
    • pad: number | "valid" | "same"
    • OptionaldimRoundingMode: "floor" | "round" | "ceil"

    Returns T

cos

  • cos<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

cosh

  • cosh<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

cumprod

  • cumprod<R>(axis?, exclusive?, reverse?): Tensor<R>

  • Type Parameters

    Parameters

    • Optionalaxis: number
    • Optionalexclusive: boolean
    • Optionalreverse: boolean

    Returns Tensor<R>

cumsum

  • cumsum<R>(axis?, exclusive?, reverse?): Tensor<R>

  • Type Parameters

    Parameters

    • Optionalaxis: number
    • Optionalexclusive: boolean
    • Optionalreverse: boolean

    Returns Tensor<R>

data

  • data<D>(): Promise<DataTypeMap[D]>

  • Asynchronously downloads the values from the tf.Tensor. Returns a promise of TypedArray that resolves when the computation has finished.

    -

    Type Parameters

    • D extends keyof DataTypeMap = NumericDataType

    Returns Promise<DataTypeMap[D]>

    Doc

dataSync

  • dataSync<D>(): DataTypeMap[D]

  • Synchronously downloads the values from the tf.Tensor. This blocks the +

    Type Parameters

    • D extends keyof DataTypeMap = NumericDataType

    Returns Promise<DataTypeMap[D]>

    Doc

dataSync

  • dataSync<D>(): DataTypeMap[D]

  • Synchronously downloads the values from the tf.Tensor. This blocks the UI thread until the values are ready, which can cause performance issues.

    -

    Type Parameters

    • D extends keyof DataTypeMap = NumericDataType

    Returns DataTypeMap[D]

    Doc

dataToGPU

  • dataToGPU(options?): GPUData

  • Copy the tensor's data to a new GPU resource. Comparing to the dataSync() +

    Type Parameters

    • D extends keyof DataTypeMap = NumericDataType

    Returns DataTypeMap[D]

    Doc

dataToGPU

  • dataToGPU(options?): GPUData

  • Copy the tensor's data to a new GPU resource. Comparing to the dataSync() and data(), this method prevents data from being downloaded to CPU.

    For WebGL backend, the data will be stored on a densely packed texture. This means that the texture will use the RGBA channels to store value.

    For WebGPU backend, the data will be stored on a buffer. There is no parameter, so can not use a user-defined size to create the buffer.

    -

    Parameters

    • Optional options: DataToGPUWebGLOption

    Returns GPUData

    For WebGL backend, a GPUData contains the new texture and - its information. - { - tensorRef: The tensor that is associated with this texture, - texture: WebGLTexture, - texShape: [number, number] // [height, width] - }

    -
    For WebGPU backend, a GPUData contains the new buffer.
    {
       tensorRef: The tensor that is associated with this buffer,
       buffer: GPUBuffer,
    }

    Remember to dispose the GPUData after it is used by
    `res.tensorRef.dispose()`.
-
    -

    Doc

depthToSpace

  • depthToSpace<T>(blockSize, dataFormat): T

  • Type Parameters

    Parameters

    • blockSize: number
    • dataFormat: "NHWC" | "NCHW"

    Returns T

depthwiseConv2d

  • depthwiseConv2d<T>(filter, strides, pad, dataFormat?, dilations?, dimRoundingMode?): T

  • Type Parameters

    Parameters

    • filter: Tensor4D | TensorLike4D
    • strides: number | [number, number]
    • pad: number | "valid" | "same"
    • Optional dataFormat: "NHWC" | "NCHW"
    • Optional dilations: number | [number, number]
    • Optional dimRoundingMode: "floor" | "round" | "ceil"

    Returns T

dilation2d

  • dilation2d<T>(filter, strides, pad, dilations?, dataFormat?): T

  • Type Parameters

    Parameters

    • filter: Tensor3D | TensorLike3D
    • strides: number | [number, number]
    • pad: "valid" | "same"
    • Optional dilations: number | [number, number]
    • Optional dataFormat: "NHWC"

    Returns T

dispose

  • dispose(): void

  • Disposes tf.Tensor from memory.

    -

    Returns void

    Doc

div

  • div<T>(b): T

  • Type Parameters

    Parameters

    Returns T

divNoNan

  • divNoNan<T>(b): T

  • Type Parameters

    Parameters

    Returns T

dot

elu

  • elu<T>(): T

  • Type Parameters

    Returns T

equal

  • equal<T>(b): T

  • Type Parameters

    Parameters

    Returns T

erf

  • erf<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

euclideanNorm

  • euclideanNorm<T>(this, axis?, keepDims?): T

  • Type Parameters

    Parameters

    • this: T
    • Optional axis: number | number[]
    • Optional keepDims: boolean

    Returns T

exp

  • exp<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

expandDims

  • expandDims<T>(axis?): T

  • Type Parameters

    Parameters

    • Optional axis: number

    Returns T

expm1

  • expm1<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

fft

  • fft<T>(this): Tensor<Rank>

  • Type Parameters

    Parameters

    Returns Tensor<Rank>

flatten

  • flatten<T>(): Tensor1D

  • Type Parameters

    Returns Tensor1D

floor

  • floor<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

floorDiv

  • floorDiv<T>(b): T

  • Type Parameters

    Parameters

    Returns T

gather

  • gather<T>(this, indices, axis?, batchDims?): T

  • Type Parameters

    Parameters

    Returns T

greater

  • greater<T>(b): T

  • Type Parameters

    Parameters

    Returns T

greaterEqual

  • greaterEqual<T>(b): T

  • Type Parameters

    Parameters

    Returns T

ifft

  • ifft<T>(this): Tensor<Rank>

  • Type Parameters

    Parameters

    Returns Tensor<Rank>

irfft

  • irfft<T>(this): Tensor<Rank>

  • Type Parameters

    Parameters

    Returns Tensor<Rank>

isFinite

  • isFinite<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

isInf

  • isInf<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

isNaN

  • isNaN<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

leakyRelu

  • leakyRelu<T>(alpha): T

  • Type Parameters

    Parameters

    • alpha: number

    Returns T

less

  • less<T>(b): T

  • Type Parameters

    Parameters

    Returns T

lessEqual

  • lessEqual<T>(b): T

  • Type Parameters

    Parameters

    Returns T

localResponseNormalization

  • localResponseNormalization<T>(depthRadius?, bias?, alpha?, beta?): T

  • Type Parameters

    Parameters

    • Optional depthRadius: number
    • Optional bias: number
    • Optional alpha: number
    • Optional beta: number

    Returns T

log

  • log<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

log1p

  • log1p<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

logSigmoid

  • logSigmoid<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

logSoftmax

  • logSoftmax<T>(this, axis?): T

  • Type Parameters

    Parameters

    • this: T
    • Optional axis: number

    Returns T

logSumExp

  • logSumExp<T>(this, axis?, keepDims?): T

  • Type Parameters

    Parameters

    • this: T
    • Optional axis: number | number[]
    • Optional keepDims: boolean

    Returns T

logicalAnd

  • logicalAnd<T>(b): T

  • Type Parameters

    Parameters

    Returns T

logicalNot

  • logicalNot<T>(): T

  • Type Parameters

    Returns T

logicalOr

  • logicalOr<T>(b): T

  • Type Parameters

    Parameters

    Returns T

logicalXor

  • logicalXor<T>(b): T

  • Type Parameters

    Parameters

    Returns T

matMul

  • matMul<T>(b, transposeA?, transposeB?): Tensor<Rank>

  • Type Parameters

    Parameters

    Returns Tensor<Rank>

max

  • max<T>(axis?, keepDims?): T

  • Type Parameters

    Parameters

    • Optional axis: number | number[]
    • Optional keepDims: boolean

    Returns T

maxPool

  • maxPool<T>(filterSize, strides, pad, dimRoundingMode?): T

  • Type Parameters

    Parameters

    • filterSize: number | [number, number]
    • strides: number | [number, number]
    • pad: number | "valid" | "same" | ExplicitPadding
    • Optional dimRoundingMode: "floor" | "round" | "ceil"

    Returns T

maximum

  • maximum<T>(b): T

  • Type Parameters

    Parameters

    Returns T

mean

  • mean<T>(axis?, keepDims?): T

  • Type Parameters

    Parameters

    • Optional axis: number | number[]
    • Optional keepDims: boolean

    Returns T

min

  • min<T>(axis?, keepDims?): T

  • Type Parameters

    Parameters

    • Optional axis: number | number[]
    • Optional keepDims: boolean

    Returns T

minimum

  • minimum<T>(b): T

  • Type Parameters

    Parameters

    Returns T

mirrorPad

  • mirrorPad<T>(paddings, mode): T

  • Type Parameters

    Parameters

    • paddings: [number, number][]
    • mode: "reflect" | "symmetric"

    Returns T

mod

  • mod<T>(b): T

  • Type Parameters

    Parameters

    Returns T

mul

  • mul<T>(b): T

  • Type Parameters

    Parameters

    Returns T

neg

  • neg<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

norm

  • norm<T>(ord?, axis?, keepDims?): Tensor<Rank>

  • Type Parameters

    Parameters

    • Optional ord: number | "euclidean" | "fro"
    • Optional axis: number | number[]
    • Optional keepDims: boolean

    Returns Tensor<Rank>

notEqual

  • notEqual<T>(b): T

  • Type Parameters

    Parameters

    Returns T

oneHot

  • oneHot(depth, onValue, offValue): Tensor<Rank>

  • Parameters

    • depth: number
    • onValue: number
    • offValue: number

    Returns Tensor<Rank>

onesLike

  • onesLike<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

pad

  • pad<T>(paddings, constantValue?): T

  • Type Parameters

    Parameters

    • paddings: [number, number][]
    • Optional constantValue: number

    Returns T

pool

  • pool<T>(windowShape, poolingType, padding, diationRate?, strides?, dimRoundingMode?): T

  • Type Parameters

    Parameters

    • windowShape: number | [number, number]
    • poolingType: "avg" | "max"
    • padding: number | "valid" | "same" | ExplicitPadding
    • Optional diationRate: number | [number, number]
    • Optional strides: number | [number, number]
    • Optional dimRoundingMode: "floor" | "round" | "ceil"

    Returns T

pow

  • pow<T>(exp): T

  • Type Parameters

    Parameters

    Returns T

prelu

  • prelu<T>(alpha): T

  • Type Parameters

    Parameters

    Returns T

print

  • print(verbose?): void

  • Prints the tf.Tensor. See tf.print for details.

    -

    Parameters

    • Optional verbose: boolean

      Whether to print verbose information about the tensor, - including dtype and size.

      -

    Returns void

    Doc

prod

  • prod<T>(this, axis?, keepDims?): T

  • Type Parameters

    Parameters

    • this: T
    • Optional axis: number | number[]
    • Optional keepDims: boolean

    Returns T

reciprocal

  • reciprocal<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

relu

  • relu<T>(): T

  • Type Parameters

    Returns T

relu6

  • relu6<T>(): T

  • Type Parameters

    Returns T

reshape

  • reshape<T>(shape): T

  • Type Parameters

    Parameters

    • shape: number[]

    Returns T

reshapeAs

  • reshapeAs<T>(x): T

  • Type Parameters

    Parameters

    Returns T

resizeBilinear

  • resizeBilinear<T>(newShape2D, alignCorners?, halfPixelCenters?): T

  • Type Parameters

    Parameters

    • newShape2D: [number, number]
    • Optional alignCorners: boolean
    • Optional halfPixelCenters: boolean

    Returns T

resizeNearestNeighbor

  • resizeNearestNeighbor<T>(newShape2D, alignCorners?, halfFloatCenters?): T

  • Type Parameters

    Parameters

    • newShape2D: [number, number]
    • Optional alignCorners: boolean
    • Optional halfFloatCenters: boolean

    Returns T

reverse

  • reverse<T>(this, axis?): T

  • Type Parameters

    Parameters

    • this: T
    • Optional axis: number | number[]

    Returns T

rfft

  • rfft<T>(this): Tensor<Rank>

  • Type Parameters

    Parameters

    Returns Tensor<Rank>

round

  • round<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

rsqrt

  • rsqrt<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

selu

  • selu<T>(): T

  • Type Parameters

    Returns T

separableConv2d

  • separableConv2d<T>(depthwiseFilter, pointwiseFilter, strides, pad, dilation?, dataFormat?): T

  • Type Parameters

    Parameters

    • depthwiseFilter: Tensor4D | TensorLike4D
    • pointwiseFilter: Tensor4D | TensorLike
    • strides: number | [number, number]
    • pad: "valid" | "same"
    • Optional dilation: number | [number, number]
    • Optional dataFormat: "NHWC" | "NCHW"

    Returns T

sigmoid

  • sigmoid<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

sign

  • sign<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

sin

  • sin<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

sinh

  • sinh<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

slice

  • slice<T>(this, begin, size?): T

  • Type Parameters

    Parameters

    • this: T
    • begin: number | number[]
    • Optional size: number | number[]

    Returns T

softmax

  • softmax<T>(this, dim?): T

  • Type Parameters

    Parameters

    • this: T
    • Optional dim: number

    Returns T

softplus

  • softplus<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

spaceToBatchND

  • spaceToBatchND<R>(blockShape, paddings): Tensor<R>

  • Type Parameters

    Parameters

    • blockShape: number[]
    • paddings: number[][]

    Returns Tensor<R>

split

  • split<T>(numOrSizeSplits, axis?): T[]

  • Type Parameters

    Parameters

    • numOrSizeSplits: number | number[]
    • Optional axis: number

    Returns T[]

sqrt

  • sqrt<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

square

  • square<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

squaredDifference

  • squaredDifference<T>(b): T

  • Type Parameters

    Parameters

    Returns T

squeeze

  • squeeze<T>(axis?): T

  • Type Parameters

    Parameters

    • Optional axis: number[]

    Returns T

stack

  • stack<T>(x, axis?): T

  • Type Parameters

    Parameters

    Returns T

step

  • step<T>(this, alpha?): T

  • Type Parameters

    Parameters

    • this: T
    • Optional alpha: number

    Returns T

stridedSlice

  • stridedSlice<T>(this, begin, end, strides, beginMask?, endMask?, ellipsisMask?, newAxisMask?, shrinkAxisMask?): Tensor<Rank>

  • Type Parameters

    Parameters

    • this: Tensor<Rank>
    • begin: number[]
    • end: number[]
    • strides: number[]
    • Optional beginMask: number
    • Optional endMask: number
    • Optional ellipsisMask: number
    • Optional newAxisMask: number
    • Optional shrinkAxisMask: number

    Returns Tensor<Rank>

sub

  • sub<T>(b): T

  • Type Parameters

    Parameters

    Returns T

sum

  • sum<T>(axis?, keepDims?): T

  • Type Parameters

    Parameters

    • Optional axis: number | number[]
    • Optional keepDims: boolean

    Returns T

tan

  • tan<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

tanh

  • tanh<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

throwIfDisposed

  • throwIfDisposed(): void

  • Returns void

tile

  • tile<T>(b): T

  • Type Parameters

    Parameters

    • b: number[]

    Returns T

toBool

  • toBool<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

toFloat

  • toFloat<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

toInt

  • toInt<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

toString

  • toString(verbose?): string

  • Returns a human-readable description of the tensor. Useful for logging.

    -

    Parameters

    • Optional verbose: boolean

    Returns string

    Doc

topk

  • topk<T>(this, k?, sorted?): {
        indices: T;
        values: T;
    }

  • Type Parameters

    Parameters

    • this: T
    • Optional k: number
    • Optional sorted: boolean

    Returns {
        indices: T;
        values: T;
    }

    • indices: T
    • values: T

transpose

  • transpose<T>(perm?): T

  • Type Parameters

    Parameters

    • Optional perm: number[]

    Returns T

unique

  • unique<T>(this, axis?): {
        indices: T;
        values: T;
    }

  • Type Parameters

    Parameters

    • this: T
    • Optional axis: number

    Returns {
        indices: T;
        values: T;
    }

    • indices: T
    • values: T

unsortedSegmentSum

  • unsortedSegmentSum<T>(this, segmentIds, numSegments): T

  • Type Parameters

    Parameters

    • this: T
    • segmentIds: Tensor1D | TensorLike1D
    • numSegments: number

    Returns T

unstack

  • unstack<T>(axis?): T[]

  • Type Parameters

    Parameters

    • Optional axis: number

    Returns T[]

variable

  • variable(trainable?, name?, dtype?): Variable<R>

  • Parameters

    • Optional trainable: boolean
    • Optional name: string
    • Optional dtype: keyof DataTypeMap

    Returns Variable<R>

where

  • where<T>(condition, x): T

  • Type Parameters

    Parameters

    Returns T

zerosLike

  • zerosLike<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

Settings

Member Visibility

Theme

On This Page

\ No newline at end of file +

Parameters

  • Optionaloptions: DataToGPUWebGLOption

Returns GPUData

For WebGL backend, a GPUData contains the new texture and +its information. +{ +tensorRef: The tensor that is associated with this texture, +texture: WebGLTexture, +texShape: [number, number] // [height, width] +}

+
For WebGPU backend, a GPUData contains the new buffer.
+{
+   tensorRef: The tensor that is associated with this buffer,
+   buffer: GPUBuffer,
+}
+
+Remember to dispose the GPUData after it is used by
+`res.tensorRef.dispose()`.
+
+

Doc

depthToSpace

  • depthToSpace<T>(blockSize, dataFormat): T

  • Type Parameters

    Parameters

    • blockSize: number
    • dataFormat: "NHWC" | "NCHW"

    Returns T

depthwiseConv2d

  • depthwiseConv2d<T>(filter, strides, pad, dataFormat?, dilations?, dimRoundingMode?): T

  • Type Parameters

    Parameters

    • filter: Tensor4D | TensorLike4D
    • strides: number | [number, number]
    • pad: number | "valid" | "same"
    • OptionaldataFormat: "NHWC" | "NCHW"
    • Optionaldilations: number | [number, number]
    • OptionaldimRoundingMode: "floor" | "round" | "ceil"

    Returns T

dilation2d

  • dilation2d<T>(filter, strides, pad, dilations?, dataFormat?): T

  • Type Parameters

    Parameters

    • filter: Tensor3D | TensorLike3D
    • strides: number | [number, number]
    • pad: "valid" | "same"
    • Optionaldilations: number | [number, number]
    • OptionaldataFormat: "NHWC"

    Returns T

dispose

  • dispose(): void

  • Disposes tf.Tensor from memory.

    +

    Returns void

    Doc

div

  • div<T>(b): T

  • Type Parameters

    Parameters

    Returns T

divNoNan

  • divNoNan<T>(b): T

  • Type Parameters

    Parameters

    Returns T

dot

elu

  • elu<T>(): T

  • Type Parameters

    Returns T

equal

  • equal<T>(b): T

  • Type Parameters

    Parameters

    Returns T

erf

  • erf<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

euclideanNorm

  • euclideanNorm<T>(this, axis?, keepDims?): T

  • Type Parameters

    Parameters

    • this: T
    • Optionalaxis: number | number[]
    • OptionalkeepDims: boolean

    Returns T

exp

  • exp<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

expandDims

  • expandDims<T>(axis?): T

  • Type Parameters

    Parameters

    • Optionalaxis: number

    Returns T

expm1

  • expm1<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

fft

  • fft<T>(this): Tensor<Rank>

  • Type Parameters

    Parameters

    Returns Tensor<Rank>

flatten

  • flatten<T>(): Tensor1D

  • Type Parameters

    Returns Tensor1D

floor

  • floor<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

floorDiv

  • floorDiv<T>(b): T

  • Type Parameters

    Parameters

    Returns T

gather

  • gather<T>(this, indices, axis?, batchDims?): T

  • Type Parameters

    Parameters

    Returns T

greater

  • greater<T>(b): T

  • Type Parameters

    Parameters

    Returns T

greaterEqual

  • greaterEqual<T>(b): T

  • Type Parameters

    Parameters

    Returns T

ifft

  • ifft<T>(this): Tensor<Rank>

  • Type Parameters

    Parameters

    Returns Tensor<Rank>

irfft

  • irfft<T>(this): Tensor<Rank>

  • Type Parameters

    Parameters

    Returns Tensor<Rank>

isFinite

  • isFinite<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

isInf

  • isInf<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

isNaN

  • isNaN<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

leakyRelu

  • leakyRelu<T>(alpha): T

  • Type Parameters

    Parameters

    • alpha: number

    Returns T

less

  • less<T>(b): T

  • Type Parameters

    Parameters

    Returns T

lessEqual

  • lessEqual<T>(b): T

  • Type Parameters

    Parameters

    Returns T

localResponseNormalization

  • localResponseNormalization<T>(depthRadius?, bias?, alpha?, beta?): T

  • Type Parameters

    Parameters

    • OptionaldepthRadius: number
    • Optionalbias: number
    • Optionalalpha: number
    • Optionalbeta: number

    Returns T

log

  • log<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

log1p

  • log1p<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

logicalAnd

  • logicalAnd<T>(b): T

  • Type Parameters

    Parameters

    Returns T

logicalNot

  • logicalNot<T>(): T

  • Type Parameters

    Returns T

logicalOr

  • logicalOr<T>(b): T

  • Type Parameters

    Parameters

    Returns T

logicalXor

  • logicalXor<T>(b): T

  • Type Parameters

    Parameters

    Returns T

logSigmoid

  • logSigmoid<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

logSoftmax

  • logSoftmax<T>(this, axis?): T

  • Type Parameters

    Parameters

    • this: T
    • Optionalaxis: number

    Returns T

logSumExp

  • logSumExp<T>(this, axis?, keepDims?): T

  • Type Parameters

    Parameters

    • this: T
    • Optionalaxis: number | number[]
    • OptionalkeepDims: boolean

    Returns T

matMul

  • matMul<T>(b, transposeA?, transposeB?): Tensor<Rank>

  • Type Parameters

    Parameters

    Returns Tensor<Rank>

max

  • max<T>(axis?, keepDims?): T

  • Type Parameters

    Parameters

    • Optionalaxis: number | number[]
    • OptionalkeepDims: boolean

    Returns T

maximum

  • maximum<T>(b): T

  • Type Parameters

    Parameters

    Returns T

maxPool

  • maxPool<T>(filterSize, strides, pad, dimRoundingMode?): T

  • Type Parameters

    Parameters

    • filterSize: number | [number, number]
    • strides: number | [number, number]
    • pad:
          | number
          | "valid"
          | "same"
          | ExplicitPadding
    • OptionaldimRoundingMode: "floor" | "round" | "ceil"

    Returns T

mean

  • mean<T>(axis?, keepDims?): T

  • Type Parameters

    Parameters

    • Optionalaxis: number | number[]
    • OptionalkeepDims: boolean

    Returns T

min

  • min<T>(axis?, keepDims?): T

  • Type Parameters

    Parameters

    • Optionalaxis: number | number[]
    • OptionalkeepDims: boolean

    Returns T

minimum

  • minimum<T>(b): T

  • Type Parameters

    Parameters

    Returns T

mirrorPad

  • mirrorPad<T>(paddings, mode): T

  • Type Parameters

    Parameters

    • paddings: [number, number][]
    • mode: "reflect" | "symmetric"

    Returns T

mod

  • mod<T>(b): T

  • Type Parameters

    Parameters

    Returns T

mul

  • mul<T>(b): T

  • Type Parameters

    Parameters

    Returns T

neg

  • neg<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

norm

  • norm<T>(ord?, axis?, keepDims?): Tensor<Rank>

  • Type Parameters

    Parameters

    • Optionalord: number | "euclidean" | "fro"
    • Optionalaxis: number | number[]
    • OptionalkeepDims: boolean

    Returns Tensor<Rank>

notEqual

  • notEqual<T>(b): T

  • Type Parameters

    Parameters

    Returns T

oneHot

  • oneHot(depth, onValue, offValue): Tensor<Rank>

  • Parameters

    • depth: number
    • onValue: number
    • offValue: number

    Returns Tensor<Rank>

onesLike

  • onesLike<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

pad

  • pad<T>(paddings, constantValue?): T

  • Type Parameters

    Parameters

    • paddings: [number, number][]
    • OptionalconstantValue: number

    Returns T

pool

  • pool<T>(windowShape, poolingType, padding, diationRate?, strides?, dimRoundingMode?): T

  • Type Parameters

    Parameters

    • windowShape: number | [number, number]
    • poolingType: "avg" | "max"
    • padding:
          | number
          | "valid"
          | "same"
          | ExplicitPadding
    • OptionaldiationRate: number | [number, number]
    • Optionalstrides: number | [number, number]
    • OptionaldimRoundingMode: "floor" | "round" | "ceil"

    Returns T

pow

  • pow<T>(exp): T

  • Type Parameters

    Parameters

    Returns T

prelu

  • prelu<T>(alpha): T

  • Type Parameters

    Parameters

    Returns T

print

  • print(verbose?): void

  • Prints the tf.Tensor. See tf.print for details.

    +

    Parameters

    • Optionalverbose: boolean

      Whether to print verbose information about the tensor, +including dtype and size.

      +

    Returns void

    Doc

prod

  • prod<T>(this, axis?, keepDims?): T

  • Type Parameters

    Parameters

    • this: T
    • Optionalaxis: number | number[]
    • OptionalkeepDims: boolean

    Returns T

reciprocal

  • reciprocal<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

relu

  • relu<T>(): T

  • Type Parameters

    Returns T

relu6

  • relu6<T>(): T

  • Type Parameters

    Returns T

reshape

  • reshape<T>(shape): T

  • Type Parameters

    Parameters

    • shape: number[]

    Returns T

reshapeAs

  • reshapeAs<T>(x): T

  • Type Parameters

    Parameters

    Returns T

resizeBilinear

  • resizeBilinear<T>(newShape2D, alignCorners?, halfPixelCenters?): T

  • Type Parameters

    Parameters

    • newShape2D: [number, number]
    • OptionalalignCorners: boolean
    • OptionalhalfPixelCenters: boolean

    Returns T

resizeNearestNeighbor

  • resizeNearestNeighbor<T>(newShape2D, alignCorners?, halfFloatCenters?): T

  • Type Parameters

    Parameters

    • newShape2D: [number, number]
    • OptionalalignCorners: boolean
    • OptionalhalfFloatCenters: boolean

    Returns T

reverse

  • reverse<T>(this, axis?): T

  • Type Parameters

    Parameters

    • this: T
    • Optionalaxis: number | number[]

    Returns T

rfft

  • rfft<T>(this): Tensor<Rank>

  • Type Parameters

    Parameters

    Returns Tensor<Rank>

round

  • round<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

rsqrt

  • rsqrt<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

selu

  • selu<T>(): T

  • Type Parameters

    Returns T

separableConv2d

  • separableConv2d<T>(depthwiseFilter, pointwiseFilter, strides, pad, dilation?, dataFormat?): T

  • Type Parameters

    Parameters

    • depthwiseFilter: Tensor4D | TensorLike4D
    • pointwiseFilter: Tensor4D | TensorLike
    • strides: number | [number, number]
    • pad: "valid" | "same"
    • Optionaldilation: number | [number, number]
    • OptionaldataFormat: "NHWC" | "NCHW"

    Returns T

sigmoid

  • sigmoid<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

sign

  • sign<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

sin

  • sin<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

sinh

  • sinh<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

slice

  • slice<T>(this, begin, size?): T

  • Type Parameters

    Parameters

    • this: T
    • begin: number | number[]
    • Optionalsize: number | number[]

    Returns T

softmax

  • softmax<T>(this, dim?): T

  • Type Parameters

    Parameters

    • this: T
    • Optionaldim: number

    Returns T

softplus

  • softplus<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

spaceToBatchND

  • spaceToBatchND<R>(blockShape, paddings): Tensor<R>

  • Type Parameters

    Parameters

    • blockShape: number[]
    • paddings: number[][]

    Returns Tensor<R>

split

  • split<T>(numOrSizeSplits, axis?): T[]

  • Type Parameters

    Parameters

    • numOrSizeSplits: number | number[]
    • Optionalaxis: number

    Returns T[]

sqrt

  • sqrt<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

square

  • square<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

squaredDifference

  • squaredDifference<T>(b): T

  • Type Parameters

    Parameters

    Returns T

squeeze

  • squeeze<T>(axis?): T

  • Type Parameters

    Parameters

    • Optionalaxis: number[]

    Returns T

stack

  • stack<T>(x, axis?): T

  • Type Parameters

    Parameters

    Returns T

step

  • step<T>(this, alpha?): T

  • Type Parameters

    Parameters

    • this: T
    • Optionalalpha: number

    Returns T

stridedSlice

  • stridedSlice<T>(this, begin, end, strides, beginMask?, endMask?, ellipsisMask?, newAxisMask?, shrinkAxisMask?): Tensor<Rank>

  • Type Parameters

    Parameters

    • this: Tensor<Rank>
    • begin: number[]
    • end: number[]
    • strides: number[]
    • OptionalbeginMask: number
    • OptionalendMask: number
    • OptionalellipsisMask: number
    • OptionalnewAxisMask: number
    • OptionalshrinkAxisMask: number

    Returns Tensor<Rank>

sub

  • sub<T>(b): T

  • Type Parameters

    Parameters

    Returns T

sum

  • sum<T>(axis?, keepDims?): T

  • Type Parameters

    Parameters

    • Optionalaxis: number | number[]
    • OptionalkeepDims: boolean

    Returns T

tan

  • tan<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

tanh

  • tanh<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

throwIfDisposed

  • throwIfDisposed(): void

  • Returns void

tile

  • tile<T>(b): T

  • Type Parameters

    Parameters

    • b: number[]

    Returns T

toBool

  • toBool<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

toFloat

  • toFloat<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

toInt

  • toInt<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

topk

  • topk<T>(this, k?, sorted?): {
        indices: T;
        values: T;
    }

  • Type Parameters

    Parameters

    • this: T
    • Optionalk: number
    • Optionalsorted: boolean

    Returns {
        indices: T;
        values: T;
    }

    • indices: T
    • values: T

toString

  • toString(verbose?): string

  • Returns a human-readable description of the tensor. Useful for logging.

    +

    Parameters

    • Optionalverbose: boolean

    Returns string

    Doc

transpose

  • transpose<T>(perm?): T

  • Type Parameters

    Parameters

    • Optionalperm: number[]

    Returns T

unique

  • unique<T>(this, axis?): {
        indices: T;
        values: T;
    }

  • Type Parameters

    Parameters

    • this: T
    • Optionalaxis: number

    Returns {
        indices: T;
        values: T;
    }

    • indices: T
    • values: T

unsortedSegmentSum

  • unsortedSegmentSum<T>(this, segmentIds, numSegments): T

  • Type Parameters

    Parameters

    • this: T
    • segmentIds: Tensor1D | TensorLike1D
    • numSegments: number

    Returns T

unstack

  • unstack<T>(axis?): T[]

  • Type Parameters

    Parameters

    • Optionalaxis: number

    Returns T[]

variable

  • variable(trainable?, name?, dtype?): Variable<R>

  • Parameters

    • Optionaltrainable: boolean
    • Optionalname: string
    • Optionaldtype: keyof DataTypeMap

    Returns Variable<R>

where

  • where<T>(condition, x): T

  • Type Parameters

    Parameters

    Returns T

zerosLike

  • zerosLike<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

Settings

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On This Page

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absacosacoshaddallanyargMaxargMinarrayarraySyncas1Das2Das3Das4Das5DasinasinhasScalarasTypeatanatan2atanhavgPoolbatchNormbatchToSpaceNDbroadcastTobufferbufferSyncbytescastceilclipByValuecloneconcatconv1dconv2dconv2dTransposecoscoshcumprodcumsumdatadataSyncdataToGPUdepthToSpacedepthwiseConv2ddilation2ddisposedivdivNoNandoteluequalerfeuclideanNormexpexpandDimsexpm1fftflattenfloorfloorDivgathergreatergreaterEqualifftirfftisFiniteisInfisNaNleakyRelulesslessEquallocalResponseNormalizationloglog1plogicalAndlogicalNotlogicalOrlogicalXorlogSigmoidlogSoftmaxlogSumExpmatMulmaxmaximummaxPoolmeanminminimummirrorPadmodmulnegnormnotEqualoneHotonesLikepadpoolpowpreluprintprodreciprocalrelurelu6reshapereshapeAsresizeBilinearresizeNearestNeighborreverserfftroundrsqrtseluseparableConv2dsigmoidsignsinsinhslicesoftmaxsoftplusspaceToBatchNDsplitsqrtsquaresquaredDifferencesqueezestackstepstridedSlicesubsumtantanhthrowIfDisposedtiletoBooltoFloattoInttopktoStringtransposeuniqueunsortedSegmentSumunstackvariablewherezerosLike
diff --git a/typedoc/classes/tf.Variable.html b/typedoc/classes/tf.Variable.html index 4c30d82..56761bf 100644 --- a/typedoc/classes/tf.Variable.html +++ b/typedoc/classes/tf.Variable.html @@ -1,217 +1,224 @@ -Variable | @vladmandic/face-api - v1.7.13

Class Variable<R>

A mutable tf.Tensor, useful for persisting state, e.g. for training.

-

Doc

Type Parameters

Hierarchy (view full)

Constructors

constructor -

Properties

dataId -dtype -id -kept -kerasMask? -name -rankType -scopeId -shape -size -strides -trainable -

Accessors

isDisposed -rank -

Methods

abs -acos -acosh -add -all -any -argMax -argMin -array -arraySync -as1D -as2D -as3D -as4D -as5D -asScalar -asType -asin -asinh -assign -atan -atan2 -atanh -avgPool -batchNorm -batchToSpaceND -broadcastTo -buffer -bufferSync -bytes -cast -ceil -clipByValue -clone -concat -conv1d -conv2d -conv2dTranspose -cos -cosh -cumprod -cumsum -data -dataSync -dataToGPU -depthToSpace -depthwiseConv2d -dilation2d -dispose -div -divNoNan -dot -elu -equal -erf -euclideanNorm -exp -expandDims -expm1 -fft -flatten -floor -floorDiv -gather -greater -greaterEqual -ifft -irfft -isFinite -isInf -isNaN -leakyRelu -less -lessEqual -localResponseNormalization -log -log1p -logSigmoid -logSoftmax -logSumExp -logicalAnd -logicalNot -logicalOr -logicalXor -matMul -max -maxPool -maximum -mean -min -minimum -mirrorPad -mod -mul -neg -norm -notEqual -oneHot -onesLike -pad -pool -pow -prelu -print -prod -reciprocal -relu -relu6 -reshape -reshapeAs -resizeBilinear -resizeNearestNeighbor -reverse -rfft -round -rsqrt -selu -separableConv2d -sigmoid -sign -sin -sinh -slice -softmax -softplus -spaceToBatchND -split -sqrt -square -squaredDifference -squeeze -stack -step -stridedSlice -sub -sum -tan -tanh -throwIfDisposed -tile -toBool -toFloat -toInt -toString -topk -transpose -unique -unsortedSegmentSum -unstack -variable -where -zerosLike -

Constructors

constructor

  • new Variable<R>(initialValue, trainable, name, tensorId): Variable<R>

  • Type Parameters

    Parameters

    • initialValue: Tensor<R>
    • trainable: boolean
    • name: string
    • tensorId: number

    Returns Variable<R>

Properties

dataId

dataId: object

Id of the bucket holding the data for this tensor. Multiple arrays can +Variable | @vladmandic/face-api - v1.7.14

Class Variable<R>

A mutable tf.Tensor, useful for persisting state, e.g. for training.

+

Doc

Type Parameters

Hierarchy (view full)

Constructors

constructor +

Properties

dataId +dtype +id +kept +kerasMask? +name +rankType +scopeId +shape +size +strides +trainable +

Accessors

isDisposed +rank +

Methods

abs +acos +acosh +add +all +any +argMax +argMin +array +arraySync +as1D +as2D +as3D +as4D +as5D +asin +asinh +asScalar +assign +asType +atan +atan2 +atanh +avgPool +batchNorm +batchToSpaceND +broadcastTo +buffer +bufferSync +bytes +cast +ceil +clipByValue +clone +concat +conv1d +conv2d +conv2dTranspose +cos +cosh +cumprod +cumsum +data +dataSync +dataToGPU +depthToSpace +depthwiseConv2d +dilation2d +dispose +div +divNoNan +dot +elu +equal +erf +euclideanNorm +exp +expandDims +expm1 +fft +flatten +floor +floorDiv +gather +greater +greaterEqual +ifft +irfft +isFinite +isInf +isNaN +leakyRelu +less +lessEqual +localResponseNormalization +log +log1p +logicalAnd +logicalNot +logicalOr +logicalXor +logSigmoid +logSoftmax +logSumExp +matMul +max +maximum +maxPool +mean +min +minimum +mirrorPad +mod +mul +neg +norm +notEqual +oneHot +onesLike +pad +pool +pow +prelu +print +prod +reciprocal +relu +relu6 +reshape +reshapeAs +resizeBilinear +resizeNearestNeighbor +reverse +rfft +round +rsqrt +selu +separableConv2d +sigmoid +sign +sin +sinh +slice +softmax +softplus +spaceToBatchND +split +sqrt +square +squaredDifference +squeeze +stack +step +stridedSlice +sub +sum +tan +tanh +throwIfDisposed +tile +toBool +toFloat +toInt +topk +toString +transpose +unique +unsortedSegmentSum +unstack +variable +where +zerosLike +

Constructors

constructor

  • new Variable<R>(initialValue, trainable, name, tensorId): Variable<R>

  • Type Parameters

    Parameters

    • initialValue: Tensor<R>
    • trainable: boolean
    • name: string
    • tensorId: number

    Returns Variable<R>

Properties

dataId

dataId: object

Id of the bucket holding the data for this tensor. Multiple arrays can point to the same bucket (e.g. when calling array.reshape()).

-

Readonly dtype

dtype: keyof DataTypeMap

The data type for the array.

-

Readonly id

id: number

Unique id of this tensor.

-

kept

kept: boolean

Whether this tensor has been globally kept.

-

Optional kerasMask

kerasMask?: Tensor<Rank>

The keras mask that some keras layers attach to the tensor

-

name

name: string

Readonly rankType

rankType: R

The rank type for the array (see Rank enum).

-

scopeId

scopeId: number

The id of the scope this tensor is being tracked in.

-

Readonly shape

shape: ShapeMap[R]

The shape of the tensor.

-

Readonly size

size: number

Number of elements in the tensor.

-

Readonly strides

strides: number[]

Number of elements to skip in each dimension when indexing. See -https://docs.scipy.org/doc/numpy/reference/generated/\ +

Readonlydtype

dtype: keyof DataTypeMap

The data type for the array.

+

Readonlyid

id: number

Unique id of this tensor.

+

kept

kept: boolean

Whether this tensor has been globally kept.

+

OptionalkerasMask

kerasMask?: Tensor<Rank>

The keras mask that some keras layers attach to the tensor

+

name

name: string

ReadonlyrankType

rankType: R

The rank type for the array (see Rank enum).

+

scopeId

scopeId: number

The id of the scope this tensor is being tracked in.

+

Readonlyshape

shape: ShapeMap[R]

The shape of the tensor.

+

Readonlysize

size: number

Number of elements in the tensor.

+

Readonlystrides

strides: number[]

Number of elements to skip in each dimension when indexing. See +https://docs.scipy.org/doc/numpy/reference/generated/\ numpy.ndarray.strides.html

-

trainable

trainable: boolean

Accessors

isDisposed

  • get isDisposed(): boolean

  • Returns boolean

rank

  • get rank(): number

  • Returns number

Methods

abs

  • abs<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

acos

  • acos<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

acosh

  • acosh<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

add

  • add<T>(b): T

  • Type Parameters

    Parameters

    Returns T

all

  • all<T>(this, axis?, keepDims?): T

  • Type Parameters

    Parameters

    • this: T
    • Optional axis: number | number[]
    • Optional keepDims: boolean

    Returns T

any

  • any<T>(this, axis?, keepDims?): T

  • Type Parameters

    Parameters

    • this: T
    • Optional axis: number | number[]
    • Optional keepDims: boolean

    Returns T

argMax

  • argMax<T>(axis?): T

  • Type Parameters

    Parameters

    • Optional axis: number

    Returns T

argMin

  • argMin<T>(axis?): T

  • Type Parameters

    Parameters

    • Optional axis: number

    Returns T

array

  • array(): Promise<ArrayMap[R]>

  • Returns the tensor data as a nested array. The transfer of data is done +

trainable

trainable: boolean

Accessors

isDisposed

  • get isDisposed(): boolean

  • Returns boolean

rank

  • get rank(): number

  • Returns number

Methods

abs

  • abs<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

acos

  • acos<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

acosh

  • acosh<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

add

  • add<T>(b): T

  • Type Parameters

    Parameters

    Returns T

all

  • all<T>(this, axis?, keepDims?): T

  • Type Parameters

    Parameters

    • this: T
    • Optionalaxis: number | number[]
    • OptionalkeepDims: boolean

    Returns T

any

  • any<T>(this, axis?, keepDims?): T

  • Type Parameters

    Parameters

    • this: T
    • Optionalaxis: number | number[]
    • OptionalkeepDims: boolean

    Returns T

argMax

  • argMax<T>(axis?): T

  • Type Parameters

    Parameters

    • Optionalaxis: number

    Returns T

argMin

  • argMin<T>(axis?): T

  • Type Parameters

    Parameters

    • Optionalaxis: number

    Returns T

array

  • array(): Promise<ArrayMap[R]>

  • Returns the tensor data as a nested array. The transfer of data is done asynchronously.

    -

    Returns Promise<ArrayMap[R]>

    Doc

arraySync

  • arraySync(): ArrayMap[R]

  • Returns the tensor data as a nested array. The transfer of data is done +

    Returns Promise<ArrayMap[R]>

    Doc

arraySync

  • arraySync(): ArrayMap[R]

  • Returns the tensor data as a nested array. The transfer of data is done synchronously.

    -

    Returns ArrayMap[R]

    Doc

as1D

  • as1D<T>(): Tensor1D

  • Type Parameters

    Returns Tensor1D

as2D

  • as2D<T>(rows, columns): Tensor2D

  • Type Parameters

    Parameters

    • rows: number
    • columns: number

    Returns Tensor2D

as3D

  • as3D<T>(rows, columns, depth): Tensor3D

  • Type Parameters

    Parameters

    • rows: number
    • columns: number
    • depth: number

    Returns Tensor3D

as4D

  • as4D<T>(rows, columns, depth, depth2): Tensor4D

  • Type Parameters

    Parameters

    • rows: number
    • columns: number
    • depth: number
    • depth2: number

    Returns Tensor4D

as5D

  • as5D<T>(rows, columns, depth, depth2, depth3): Tensor5D

  • Type Parameters

    Parameters

    • rows: number
    • columns: number
    • depth: number
    • depth2: number
    • depth3: number

    Returns Tensor5D

asScalar

  • asScalar<T>(): Scalar

  • Type Parameters

    Returns Scalar

asType

  • asType<T>(this, dtype): T

  • Type Parameters

    Parameters

    • this: T
    • dtype: keyof DataTypeMap

    Returns T

asin

  • asin<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

asinh

  • asinh<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

assign

  • assign(newValue): void

  • Assign a new tf.Tensor to this variable. The new tf.Tensor must have +

    Returns ArrayMap[R]

    Doc

as1D

  • as1D<T>(): Tensor1D

  • Type Parameters

    Returns Tensor1D

as2D

  • as2D<T>(rows, columns): Tensor2D

  • Type Parameters

    Parameters

    • rows: number
    • columns: number

    Returns Tensor2D

as3D

  • as3D<T>(rows, columns, depth): Tensor3D

  • Type Parameters

    Parameters

    • rows: number
    • columns: number
    • depth: number

    Returns Tensor3D

as4D

  • as4D<T>(rows, columns, depth, depth2): Tensor4D

  • Type Parameters

    Parameters

    • rows: number
    • columns: number
    • depth: number
    • depth2: number

    Returns Tensor4D

as5D

  • as5D<T>(rows, columns, depth, depth2, depth3): Tensor5D

  • Type Parameters

    Parameters

    • rows: number
    • columns: number
    • depth: number
    • depth2: number
    • depth3: number

    Returns Tensor5D

asin

  • asin<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

asinh

  • asinh<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

asScalar

  • asScalar<T>(): Scalar

  • Type Parameters

    Returns Scalar

assign

  • assign(newValue): void

  • Assign a new tf.Tensor to this variable. The new tf.Tensor must have the same shape and dtype as the old tf.Tensor.

    -

    Parameters

    • newValue: Tensor<R>

      New tensor to be assigned to this variable.

      -

    Returns void

    Doc

atan

  • atan<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

atan2

  • atan2<T>(b): T

  • Type Parameters

    Parameters

    Returns T

atanh

  • atanh<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

avgPool

  • avgPool<T>(filterSize, strides, pad, dimRoundingMode?): T

  • Type Parameters

    Parameters

    • filterSize: number | [number, number]
    • strides: number | [number, number]
    • pad: number | "valid" | "same" | ExplicitPadding
    • Optional dimRoundingMode: "floor" | "round" | "ceil"

    Returns T

batchNorm

batchToSpaceND

  • batchToSpaceND<R>(blockShape, crops): Tensor<R>

  • Type Parameters

    Parameters

    • blockShape: number[]
    • crops: number[][]

    Returns Tensor<R>

broadcastTo

  • broadcastTo<R>(shape): Tensor<R>

  • Type Parameters

    Parameters

    • shape: ShapeMap[R]

    Returns Tensor<R>

buffer

  • buffer<D>(): Promise<TensorBuffer<R, D>>

  • Returns a promise of tf.TensorBuffer that holds the underlying data.

    -

    Type Parameters

    • D extends keyof DataTypeMap = "float32"

    Returns Promise<TensorBuffer<R, D>>

    Doc

bufferSync

  • bufferSync<D>(): TensorBuffer<R, D>

  • Returns a tf.TensorBuffer that holds the underlying data.

    -

    Type Parameters

    • D extends keyof DataTypeMap = "float32"

    Returns TensorBuffer<R, D>

    Doc

bytes

  • bytes(): Promise<Uint8Array | Uint8Array[]>

  • Returns the underlying bytes of the tensor's data.

    -

    Returns Promise<Uint8Array | Uint8Array[]>

cast

  • cast<T>(dtype): T

  • Type Parameters

    Parameters

    • dtype: keyof DataTypeMap

    Returns T

ceil

  • ceil<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

clipByValue

  • clipByValue<T>(min, max): Tensor<Rank>

  • Type Parameters

    Parameters

    • min: number
    • max: number

    Returns Tensor<Rank>

clone

  • clone<T>(this): T

  • Returns a copy of the tensor. See tf.clone for details.

    -

    Type Parameters

    Parameters

    • this: T

    Returns T

    Doc

concat

  • concat<T>(tensors, axis?): T

  • Type Parameters

    Parameters

    Returns T

conv1d

  • conv1d<T>(filter, stride, pad, dataFormat?, dilation?, dimRoundingMode?): T

  • Type Parameters

    Parameters

    • filter: Tensor3D | TensorLike3D
    • stride: number
    • pad: number | "valid" | "same" | ExplicitPadding
    • Optional dataFormat: "NWC" | "NCW"
    • Optional dilation: number
    • Optional dimRoundingMode: "floor" | "round" | "ceil"

    Returns T

conv2d

  • conv2d<T>(filter, strides, pad, dataFormat?, dilations?, dimRoundingMode?): T

  • Type Parameters

    Parameters

    • filter: Tensor4D | TensorLike4D
    • strides: number | [number, number]
    • pad: number | "valid" | "same"
    • Optional dataFormat: "NHWC" | "NCHW"
    • Optional dilations: number | [number, number]
    • Optional dimRoundingMode: "floor" | "round" | "ceil"

    Returns T

conv2dTranspose

  • conv2dTranspose<T>(filter, outputShape, strides, pad, dimRoundingMode?): T

  • Type Parameters

    Parameters

    • filter: Tensor4D | TensorLike4D
    • outputShape: [number, number, number, number] | [number, number, number]
    • strides: number | [number, number]
    • pad: number | "valid" | "same"
    • Optional dimRoundingMode: "floor" | "round" | "ceil"

    Returns T

cos

  • cos<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

cosh

  • cosh<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

cumprod

  • cumprod<R>(axis?, exclusive?, reverse?): Tensor<R>

  • Type Parameters

    Parameters

    • Optional axis: number
    • Optional exclusive: boolean
    • Optional reverse: boolean

    Returns Tensor<R>

cumsum

  • cumsum<R>(axis?, exclusive?, reverse?): Tensor<R>

  • Type Parameters

    Parameters

    • Optional axis: number
    • Optional exclusive: boolean
    • Optional reverse: boolean

    Returns Tensor<R>

data

  • data<D>(): Promise<DataTypeMap[D]>

  • Asynchronously downloads the values from the tf.Tensor. Returns a +

    Parameters

    • newValue: Tensor<R>

      New tensor to be assigned to this variable.

      +

    Returns void

    Doc

asType

  • asType<T>(this, dtype): T

  • Type Parameters

    Parameters

    • this: T
    • dtype: keyof DataTypeMap

    Returns T

atan

  • atan<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

atan2

  • atan2<T>(b): T

  • Type Parameters

    Parameters

    Returns T

atanh

  • atanh<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

avgPool

  • avgPool<T>(filterSize, strides, pad, dimRoundingMode?): T

  • Type Parameters

    Parameters

    • filterSize: number | [number, number]
    • strides: number | [number, number]
    • pad:
          | number
          | "valid"
          | "same"
          | ExplicitPadding
    • OptionaldimRoundingMode: "floor" | "round" | "ceil"

    Returns T

batchNorm

batchToSpaceND

  • batchToSpaceND<R>(blockShape, crops): Tensor<R>

  • Type Parameters

    Parameters

    • blockShape: number[]
    • crops: number[][]

    Returns Tensor<R>

broadcastTo

  • broadcastTo<R>(shape): Tensor<R>

  • Type Parameters

    Parameters

    • shape: ShapeMap[R]

    Returns Tensor<R>

buffer

  • buffer<D>(): Promise<TensorBuffer<R, D>>

  • Returns a promise of tf.TensorBuffer that holds the underlying data.

    +

    Type Parameters

    • D extends keyof DataTypeMap = "float32"

    Returns Promise<TensorBuffer<R, D>>

    Doc

bufferSync

  • bufferSync<D>(): TensorBuffer<R, D>

  • Returns a tf.TensorBuffer that holds the underlying data.

    +

    Type Parameters

    • D extends keyof DataTypeMap = "float32"

    Returns TensorBuffer<R, D>

    Doc

bytes

  • bytes(): Promise<Uint8Array | Uint8Array[]>

  • Returns the underlying bytes of the tensor's data.

    +

    Returns Promise<Uint8Array | Uint8Array[]>

cast

  • cast<T>(dtype): T

  • Type Parameters

    Parameters

    • dtype: keyof DataTypeMap

    Returns T

ceil

  • ceil<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

clipByValue

  • clipByValue<T>(min, max): Tensor<Rank>

  • Type Parameters

    Parameters

    • min: number
    • max: number

    Returns Tensor<Rank>

clone

  • clone<T>(this): T

  • Returns a copy of the tensor. See tf.clone for details.

    +

    Type Parameters

    Parameters

    • this: T

    Returns T

    Doc

concat

  • concat<T>(tensors, axis?): T

  • Type Parameters

    Parameters

    Returns T

conv1d

  • conv1d<T>(filter, stride, pad, dataFormat?, dilation?, dimRoundingMode?): T

  • Type Parameters

    Parameters

    • filter: Tensor3D | TensorLike3D
    • stride: number
    • pad:
          | number
          | "valid"
          | "same"
          | ExplicitPadding
    • OptionaldataFormat: "NWC" | "NCW"
    • Optionaldilation: number
    • OptionaldimRoundingMode: "floor" | "round" | "ceil"

    Returns T

conv2d

  • conv2d<T>(filter, strides, pad, dataFormat?, dilations?, dimRoundingMode?): T

  • Type Parameters

    Parameters

    • filter: Tensor4D | TensorLike4D
    • strides: number | [number, number]
    • pad: number | "valid" | "same"
    • OptionaldataFormat: "NHWC" | "NCHW"
    • Optionaldilations: number | [number, number]
    • OptionaldimRoundingMode: "floor" | "round" | "ceil"

    Returns T

conv2dTranspose

  • conv2dTranspose<T>(filter, outputShape, strides, pad, dimRoundingMode?): T

  • Type Parameters

    Parameters

    • filter: Tensor4D | TensorLike4D
    • outputShape: [number, number, number, number] | [number, number, number]
    • strides: number | [number, number]
    • pad: number | "valid" | "same"
    • OptionaldimRoundingMode: "floor" | "round" | "ceil"

    Returns T

cos

  • cos<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

cosh

  • cosh<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

cumprod

  • cumprod<R>(axis?, exclusive?, reverse?): Tensor<R>

  • Type Parameters

    Parameters

    • Optionalaxis: number
    • Optionalexclusive: boolean
    • Optionalreverse: boolean

    Returns Tensor<R>

cumsum

  • cumsum<R>(axis?, exclusive?, reverse?): Tensor<R>

  • Type Parameters

    Parameters

    • Optionalaxis: number
    • Optionalexclusive: boolean
    • Optionalreverse: boolean

    Returns Tensor<R>

data

  • data<D>(): Promise<DataTypeMap[D]>

  • Asynchronously downloads the values from the tf.Tensor. Returns a promise of TypedArray that resolves when the computation has finished.

    -

    Type Parameters

    • D extends keyof DataTypeMap = NumericDataType

    Returns Promise<DataTypeMap[D]>

    Doc

dataSync

  • dataSync<D>(): DataTypeMap[D]

  • Synchronously downloads the values from the tf.Tensor. This blocks the +

    Type Parameters

    • D extends keyof DataTypeMap = NumericDataType

    Returns Promise<DataTypeMap[D]>

    Doc

dataSync

  • dataSync<D>(): DataTypeMap[D]

  • Synchronously downloads the values from the tf.Tensor. This blocks the UI thread until the values are ready, which can cause performance issues.

    -

    Type Parameters

    • D extends keyof DataTypeMap = NumericDataType

    Returns DataTypeMap[D]

    Doc

dataToGPU

  • dataToGPU(options?): GPUData

  • Copy the tensor's data to a new GPU resource. Comparing to the dataSync() +

    Type Parameters

    • D extends keyof DataTypeMap = NumericDataType

    Returns DataTypeMap[D]

    Doc

dataToGPU

  • dataToGPU(options?): GPUData

  • Copy the tensor's data to a new GPU resource. Comparing to the dataSync() and data(), this method prevents data from being downloaded to CPU.

    For WebGL backend, the data will be stored on a densely packed texture. This means that the texture will use the RGBA channels to store value.

    For WebGPU backend, the data will be stored on a buffer. There is no parameter, so can not use a user-defined size to create the buffer.

    -

    Parameters

    • Optional options: DataToGPUWebGLOption

    Returns GPUData

    For WebGL backend, a GPUData contains the new texture and - its information. - { - tensorRef: The tensor that is associated with this texture, - texture: WebGLTexture, - texShape: [number, number] // [height, width] - }

    -
    For WebGPU backend, a GPUData contains the new buffer.
    {
       tensorRef: The tensor that is associated with this buffer,
       buffer: GPUBuffer,
    }

    Remember to dispose the GPUData after it is used by
    `res.tensorRef.dispose()`.
-
    -

    Doc

depthToSpace

  • depthToSpace<T>(blockSize, dataFormat): T

  • Type Parameters

    Parameters

    • blockSize: number
    • dataFormat: "NHWC" | "NCHW"

    Returns T

depthwiseConv2d

  • depthwiseConv2d<T>(filter, strides, pad, dataFormat?, dilations?, dimRoundingMode?): T

  • Type Parameters

    Parameters

    • filter: Tensor4D | TensorLike4D
    • strides: number | [number, number]
    • pad: number | "valid" | "same"
    • Optional dataFormat: "NHWC" | "NCHW"
    • Optional dilations: number | [number, number]
    • Optional dimRoundingMode: "floor" | "round" | "ceil"

    Returns T

dilation2d

  • dilation2d<T>(filter, strides, pad, dilations?, dataFormat?): T

  • Type Parameters

    Parameters

    • filter: Tensor3D | TensorLike3D
    • strides: number | [number, number]
    • pad: "valid" | "same"
    • Optional dilations: number | [number, number]
    • Optional dataFormat: "NHWC"

    Returns T

dispose

  • dispose(): void

  • Disposes tf.Tensor from memory.

    -

    Returns void

    Doc

div

  • div<T>(b): T

  • Type Parameters

    Parameters

    Returns T

divNoNan

  • divNoNan<T>(b): T

  • Type Parameters

    Parameters

    Returns T

dot

elu

  • elu<T>(): T

  • Type Parameters

    Returns T

equal

  • equal<T>(b): T

  • Type Parameters

    Parameters

    Returns T

erf

  • erf<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

euclideanNorm

  • euclideanNorm<T>(this, axis?, keepDims?): T

  • Type Parameters

    Parameters

    • this: T
    • Optional axis: number | number[]
    • Optional keepDims: boolean

    Returns T

exp

  • exp<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

expandDims

  • expandDims<T>(axis?): T

  • Type Parameters

    Parameters

    • Optional axis: number

    Returns T

expm1

  • expm1<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

fft

  • fft<T>(this): Tensor<Rank>

  • Type Parameters

    Parameters

    Returns Tensor<Rank>

flatten

  • flatten<T>(): Tensor1D

  • Type Parameters

    Returns Tensor1D

floor

  • floor<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

floorDiv

  • floorDiv<T>(b): T

  • Type Parameters

    Parameters

    Returns T

gather

  • gather<T>(this, indices, axis?, batchDims?): T

  • Type Parameters

    Parameters

    Returns T

greater

  • greater<T>(b): T

  • Type Parameters

    Parameters

    Returns T

greaterEqual

  • greaterEqual<T>(b): T

  • Type Parameters

    Parameters

    Returns T

ifft

  • ifft<T>(this): Tensor<Rank>

  • Type Parameters

    Parameters

    Returns Tensor<Rank>

irfft

  • irfft<T>(this): Tensor<Rank>

  • Type Parameters

    Parameters

    Returns Tensor<Rank>

isFinite

  • isFinite<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

isInf

  • isInf<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

isNaN

  • isNaN<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

leakyRelu

  • leakyRelu<T>(alpha): T

  • Type Parameters

    Parameters

    • alpha: number

    Returns T

less

  • less<T>(b): T

  • Type Parameters

    Parameters

    Returns T

lessEqual

  • lessEqual<T>(b): T

  • Type Parameters

    Parameters

    Returns T

localResponseNormalization

  • localResponseNormalization<T>(depthRadius?, bias?, alpha?, beta?): T

  • Type Parameters

    Parameters

    • Optional depthRadius: number
    • Optional bias: number
    • Optional alpha: number
    • Optional beta: number

    Returns T

log

  • log<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

log1p

  • log1p<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

logSigmoid

  • logSigmoid<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

logSoftmax

  • logSoftmax<T>(this, axis?): T

  • Type Parameters

    Parameters

    • this: T
    • Optional axis: number

    Returns T

logSumExp

  • logSumExp<T>(this, axis?, keepDims?): T

  • Type Parameters

    Parameters

    • this: T
    • Optional axis: number | number[]
    • Optional keepDims: boolean

    Returns T

logicalAnd

  • logicalAnd<T>(b): T

  • Type Parameters

    Parameters

    Returns T

logicalNot

  • logicalNot<T>(): T

  • Type Parameters

    Returns T

logicalOr

  • logicalOr<T>(b): T

  • Type Parameters

    Parameters

    Returns T

logicalXor

  • logicalXor<T>(b): T

  • Type Parameters

    Parameters

    Returns T

matMul

  • matMul<T>(b, transposeA?, transposeB?): Tensor<Rank>

  • Type Parameters

    Parameters

    Returns Tensor<Rank>

max

  • max<T>(axis?, keepDims?): T

  • Type Parameters

    Parameters

    • Optional axis: number | number[]
    • Optional keepDims: boolean

    Returns T

maxPool

  • maxPool<T>(filterSize, strides, pad, dimRoundingMode?): T

  • Type Parameters

    Parameters

    • filterSize: number | [number, number]
    • strides: number | [number, number]
    • pad: number | "valid" | "same" | ExplicitPadding
    • Optional dimRoundingMode: "floor" | "round" | "ceil"

    Returns T

maximum

  • maximum<T>(b): T

  • Type Parameters

    Parameters

    Returns T

mean

  • mean<T>(axis?, keepDims?): T

  • Type Parameters

    Parameters

    • Optional axis: number | number[]
    • Optional keepDims: boolean

    Returns T

min

  • min<T>(axis?, keepDims?): T

  • Type Parameters

    Parameters

    • Optional axis: number | number[]
    • Optional keepDims: boolean

    Returns T

minimum

  • minimum<T>(b): T

  • Type Parameters

    Parameters

    Returns T

mirrorPad

  • mirrorPad<T>(paddings, mode): T

  • Type Parameters

    Parameters

    • paddings: [number, number][]
    • mode: "reflect" | "symmetric"

    Returns T

mod

  • mod<T>(b): T

  • Type Parameters

    Parameters

    Returns T

mul

  • mul<T>(b): T

  • Type Parameters

    Parameters

    Returns T

neg

  • neg<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

norm

  • norm<T>(ord?, axis?, keepDims?): Tensor<Rank>

  • Type Parameters

    Parameters

    • Optional ord: number | "euclidean" | "fro"
    • Optional axis: number | number[]
    • Optional keepDims: boolean

    Returns Tensor<Rank>

notEqual

  • notEqual<T>(b): T

  • Type Parameters

    Parameters

    Returns T

oneHot

  • oneHot(depth, onValue, offValue): Tensor<Rank>

  • Parameters

    • depth: number
    • onValue: number
    • offValue: number

    Returns Tensor<Rank>

onesLike

  • onesLike<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

pad

  • pad<T>(paddings, constantValue?): T

  • Type Parameters

    Parameters

    • paddings: [number, number][]
    • Optional constantValue: number

    Returns T

pool

  • pool<T>(windowShape, poolingType, padding, diationRate?, strides?, dimRoundingMode?): T

  • Type Parameters

    Parameters

    • windowShape: number | [number, number]
    • poolingType: "avg" | "max"
    • padding: number | "valid" | "same" | ExplicitPadding
    • Optional diationRate: number | [number, number]
    • Optional strides: number | [number, number]
    • Optional dimRoundingMode: "floor" | "round" | "ceil"

    Returns T

pow

  • pow<T>(exp): T

  • Type Parameters

    Parameters

    Returns T

prelu

  • prelu<T>(alpha): T

  • Type Parameters

    Parameters

    Returns T

print

  • print(verbose?): void

  • Prints the tf.Tensor. See tf.print for details.

    -

    Parameters

    • Optional verbose: boolean

      Whether to print verbose information about the tensor, - including dtype and size.

      -

    Returns void

    Doc

prod

  • prod<T>(this, axis?, keepDims?): T

  • Type Parameters

    Parameters

    • this: T
    • Optional axis: number | number[]
    • Optional keepDims: boolean

    Returns T

reciprocal

  • reciprocal<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

relu

  • relu<T>(): T

  • Type Parameters

    Returns T

relu6

  • relu6<T>(): T

  • Type Parameters

    Returns T

reshape

  • reshape<T>(shape): T

  • Type Parameters

    Parameters

    • shape: number[]

    Returns T

reshapeAs

  • reshapeAs<T>(x): T

  • Type Parameters

    Parameters

    Returns T

resizeBilinear

  • resizeBilinear<T>(newShape2D, alignCorners?, halfPixelCenters?): T

  • Type Parameters

    Parameters

    • newShape2D: [number, number]
    • Optional alignCorners: boolean
    • Optional halfPixelCenters: boolean

    Returns T

resizeNearestNeighbor

  • resizeNearestNeighbor<T>(newShape2D, alignCorners?, halfFloatCenters?): T

  • Type Parameters

    Parameters

    • newShape2D: [number, number]
    • Optional alignCorners: boolean
    • Optional halfFloatCenters: boolean

    Returns T

reverse

  • reverse<T>(this, axis?): T

  • Type Parameters

    Parameters

    • this: T
    • Optional axis: number | number[]

    Returns T

rfft

  • rfft<T>(this): Tensor<Rank>

  • Type Parameters

    Parameters

    Returns Tensor<Rank>

round

  • round<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

rsqrt

  • rsqrt<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

selu

  • selu<T>(): T

  • Type Parameters

    Returns T

separableConv2d

  • separableConv2d<T>(depthwiseFilter, pointwiseFilter, strides, pad, dilation?, dataFormat?): T

  • Type Parameters

    Parameters

    • depthwiseFilter: Tensor4D | TensorLike4D
    • pointwiseFilter: Tensor4D | TensorLike
    • strides: number | [number, number]
    • pad: "valid" | "same"
    • Optional dilation: number | [number, number]
    • Optional dataFormat: "NHWC" | "NCHW"

    Returns T

sigmoid

  • sigmoid<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

sign

  • sign<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

sin

  • sin<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

sinh

  • sinh<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

slice

  • slice<T>(this, begin, size?): T

  • Type Parameters

    Parameters

    • this: T
    • begin: number | number[]
    • Optional size: number | number[]

    Returns T

softmax

  • softmax<T>(this, dim?): T

  • Type Parameters

    Parameters

    • this: T
    • Optional dim: number

    Returns T

softplus

  • softplus<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

spaceToBatchND

  • spaceToBatchND<R>(blockShape, paddings): Tensor<R>

  • Type Parameters

    Parameters

    • blockShape: number[]
    • paddings: number[][]

    Returns Tensor<R>

split

  • split<T>(numOrSizeSplits, axis?): T[]

  • Type Parameters

    Parameters

    • numOrSizeSplits: number | number[]
    • Optional axis: number

    Returns T[]

sqrt

  • sqrt<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

square

  • square<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

squaredDifference

  • squaredDifference<T>(b): T

  • Type Parameters

    Parameters

    Returns T

squeeze

  • squeeze<T>(axis?): T

  • Type Parameters

    Parameters

    • Optional axis: number[]

    Returns T

stack

  • stack<T>(x, axis?): T

  • Type Parameters

    Parameters

    Returns T

step

  • step<T>(this, alpha?): T

  • Type Parameters

    Parameters

    • this: T
    • Optional alpha: number

    Returns T

stridedSlice

  • stridedSlice<T>(this, begin, end, strides, beginMask?, endMask?, ellipsisMask?, newAxisMask?, shrinkAxisMask?): Tensor<Rank>

  • Type Parameters

    Parameters

    • this: Tensor<Rank>
    • begin: number[]
    • end: number[]
    • strides: number[]
    • Optional beginMask: number
    • Optional endMask: number
    • Optional ellipsisMask: number
    • Optional newAxisMask: number
    • Optional shrinkAxisMask: number

    Returns Tensor<Rank>

sub

  • sub<T>(b): T

  • Type Parameters

    Parameters

    Returns T

sum

  • sum<T>(axis?, keepDims?): T

  • Type Parameters

    Parameters

    • Optional axis: number | number[]
    • Optional keepDims: boolean

    Returns T

tan

  • tan<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

tanh

  • tanh<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

throwIfDisposed

  • throwIfDisposed(): void

  • Returns void

tile

  • tile<T>(b): T

  • Type Parameters

    Parameters

    • b: number[]

    Returns T

toBool

  • toBool<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

toFloat

  • toFloat<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

toInt

  • toInt<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

toString

  • toString(verbose?): string

  • Returns a human-readable description of the tensor. Useful for logging.

    -

    Parameters

    • Optional verbose: boolean

    Returns string

    Doc

topk

  • topk<T>(this, k?, sorted?): {
        indices: T;
        values: T;
    }

  • Type Parameters

    Parameters

    • this: T
    • Optional k: number
    • Optional sorted: boolean

    Returns {
        indices: T;
        values: T;
    }

    • indices: T
    • values: T

transpose

  • transpose<T>(perm?): T

  • Type Parameters

    Parameters

    • Optional perm: number[]

    Returns T

unique

  • unique<T>(this, axis?): {
        indices: T;
        values: T;
    }

  • Type Parameters

    Parameters

    • this: T
    • Optional axis: number

    Returns {
        indices: T;
        values: T;
    }

    • indices: T
    • values: T

unsortedSegmentSum

  • unsortedSegmentSum<T>(this, segmentIds, numSegments): T

  • Type Parameters

    Parameters

    • this: T
    • segmentIds: Tensor1D | TensorLike1D
    • numSegments: number

    Returns T

unstack

  • unstack<T>(axis?): T[]

  • Type Parameters

    Parameters

    • Optional axis: number

    Returns T[]

variable

  • variable(trainable?, name?, dtype?): Variable<R>

  • Parameters

    • Optional trainable: boolean
    • Optional name: string
    • Optional dtype: keyof DataTypeMap

    Returns Variable<R>

where

zerosLike

  • zerosLike<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

Settings

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On This Page

\ No newline at end of file +

Parameters

  • Optionaloptions: DataToGPUWebGLOption

Returns GPUData

For WebGL backend, a GPUData contains the new texture and +its information. +{ +tensorRef: The tensor that is associated with this texture, +texture: WebGLTexture, +texShape: [number, number] // [height, width] +}

+
For WebGPU backend, a GPUData contains the new buffer.
+{
+   tensorRef: The tensor that is associated with this buffer,
+   buffer: GPUBuffer,
+}
+
+Remember to dispose the GPUData after it is used by
+`res.tensorRef.dispose()`.
+
+

Doc

depthToSpace

  • depthToSpace<T>(blockSize, dataFormat): T

  • Type Parameters

    Parameters

    • blockSize: number
    • dataFormat: "NHWC" | "NCHW"

    Returns T

depthwiseConv2d

  • depthwiseConv2d<T>(filter, strides, pad, dataFormat?, dilations?, dimRoundingMode?): T

  • Type Parameters

    Parameters

    • filter: Tensor4D | TensorLike4D
    • strides: number | [number, number]
    • pad: number | "valid" | "same"
    • OptionaldataFormat: "NHWC" | "NCHW"
    • Optionaldilations: number | [number, number]
    • OptionaldimRoundingMode: "floor" | "round" | "ceil"

    Returns T

dilation2d

  • dilation2d<T>(filter, strides, pad, dilations?, dataFormat?): T

  • Type Parameters

    Parameters

    • filter: Tensor3D | TensorLike3D
    • strides: number | [number, number]
    • pad: "valid" | "same"
    • Optionaldilations: number | [number, number]
    • OptionaldataFormat: "NHWC"

    Returns T

dispose

  • dispose(): void

  • Disposes tf.Tensor from memory.

    +

    Returns void

    Doc

div

  • div<T>(b): T

  • Type Parameters

    Parameters

    Returns T

divNoNan

  • divNoNan<T>(b): T

  • Type Parameters

    Parameters

    Returns T

dot

elu

  • elu<T>(): T

  • Type Parameters

    Returns T

equal

  • equal<T>(b): T

  • Type Parameters

    Parameters

    Returns T

erf

  • erf<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

euclideanNorm

  • euclideanNorm<T>(this, axis?, keepDims?): T

  • Type Parameters

    Parameters

    • this: T
    • Optionalaxis: number | number[]
    • OptionalkeepDims: boolean

    Returns T

exp

  • exp<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

expandDims

  • expandDims<T>(axis?): T

  • Type Parameters

    Parameters

    • Optionalaxis: number

    Returns T

expm1

  • expm1<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

fft

  • fft<T>(this): Tensor<Rank>

  • Type Parameters

    Parameters

    Returns Tensor<Rank>

flatten

  • flatten<T>(): Tensor1D

  • Type Parameters

    Returns Tensor1D

floor

  • floor<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

floorDiv

  • floorDiv<T>(b): T

  • Type Parameters

    Parameters

    Returns T

gather

  • gather<T>(this, indices, axis?, batchDims?): T

  • Type Parameters

    Parameters

    Returns T

greater

  • greater<T>(b): T

  • Type Parameters

    Parameters

    Returns T

greaterEqual

  • greaterEqual<T>(b): T

  • Type Parameters

    Parameters

    Returns T

ifft

  • ifft<T>(this): Tensor<Rank>

  • Type Parameters

    Parameters

    Returns Tensor<Rank>

irfft

  • irfft<T>(this): Tensor<Rank>

  • Type Parameters

    Parameters

    Returns Tensor<Rank>

isFinite

  • isFinite<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

isInf

  • isInf<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

isNaN

  • isNaN<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

leakyRelu

  • leakyRelu<T>(alpha): T

  • Type Parameters

    Parameters

    • alpha: number

    Returns T

less

  • less<T>(b): T

  • Type Parameters

    Parameters

    Returns T

lessEqual

  • lessEqual<T>(b): T

  • Type Parameters

    Parameters

    Returns T

localResponseNormalization

  • localResponseNormalization<T>(depthRadius?, bias?, alpha?, beta?): T

  • Type Parameters

    Parameters

    • OptionaldepthRadius: number
    • Optionalbias: number
    • Optionalalpha: number
    • Optionalbeta: number

    Returns T

log

  • log<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

log1p

  • log1p<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

logicalAnd

  • logicalAnd<T>(b): T

  • Type Parameters

    Parameters

    Returns T

logicalNot

  • logicalNot<T>(): T

  • Type Parameters

    Returns T

logicalOr

  • logicalOr<T>(b): T

  • Type Parameters

    Parameters

    Returns T

logicalXor

  • logicalXor<T>(b): T

  • Type Parameters

    Parameters

    Returns T

logSigmoid

  • logSigmoid<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

logSoftmax

  • logSoftmax<T>(this, axis?): T

  • Type Parameters

    Parameters

    • this: T
    • Optionalaxis: number

    Returns T

logSumExp

  • logSumExp<T>(this, axis?, keepDims?): T

  • Type Parameters

    Parameters

    • this: T
    • Optionalaxis: number | number[]
    • OptionalkeepDims: boolean

    Returns T

matMul

  • matMul<T>(b, transposeA?, transposeB?): Tensor<Rank>

  • Type Parameters

    Parameters

    Returns Tensor<Rank>

max

  • max<T>(axis?, keepDims?): T

  • Type Parameters

    Parameters

    • Optionalaxis: number | number[]
    • OptionalkeepDims: boolean

    Returns T

maximum

  • maximum<T>(b): T

  • Type Parameters

    Parameters

    Returns T

maxPool

  • maxPool<T>(filterSize, strides, pad, dimRoundingMode?): T

  • Type Parameters

    Parameters

    • filterSize: number | [number, number]
    • strides: number | [number, number]
    • pad:
          | number
          | "valid"
          | "same"
          | ExplicitPadding
    • OptionaldimRoundingMode: "floor" | "round" | "ceil"

    Returns T

mean

  • mean<T>(axis?, keepDims?): T

  • Type Parameters

    Parameters

    • Optionalaxis: number | number[]
    • OptionalkeepDims: boolean

    Returns T

min

  • min<T>(axis?, keepDims?): T

  • Type Parameters

    Parameters

    • Optionalaxis: number | number[]
    • OptionalkeepDims: boolean

    Returns T

minimum

  • minimum<T>(b): T

  • Type Parameters

    Parameters

    Returns T

mirrorPad

  • mirrorPad<T>(paddings, mode): T

  • Type Parameters

    Parameters

    • paddings: [number, number][]
    • mode: "reflect" | "symmetric"

    Returns T

mod

  • mod<T>(b): T

  • Type Parameters

    Parameters

    Returns T

mul

  • mul<T>(b): T

  • Type Parameters

    Parameters

    Returns T

neg

  • neg<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

norm

  • norm<T>(ord?, axis?, keepDims?): Tensor<Rank>

  • Type Parameters

    Parameters

    • Optionalord: number | "euclidean" | "fro"
    • Optionalaxis: number | number[]
    • OptionalkeepDims: boolean

    Returns Tensor<Rank>

notEqual

  • notEqual<T>(b): T

  • Type Parameters

    Parameters

    Returns T

oneHot

  • oneHot(depth, onValue, offValue): Tensor<Rank>

  • Parameters

    • depth: number
    • onValue: number
    • offValue: number

    Returns Tensor<Rank>

onesLike

  • onesLike<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

pad

  • pad<T>(paddings, constantValue?): T

  • Type Parameters

    Parameters

    • paddings: [number, number][]
    • OptionalconstantValue: number

    Returns T

pool

  • pool<T>(windowShape, poolingType, padding, diationRate?, strides?, dimRoundingMode?): T

  • Type Parameters

    Parameters

    • windowShape: number | [number, number]
    • poolingType: "avg" | "max"
    • padding:
          | number
          | "valid"
          | "same"
          | ExplicitPadding
    • OptionaldiationRate: number | [number, number]
    • Optionalstrides: number | [number, number]
    • OptionaldimRoundingMode: "floor" | "round" | "ceil"

    Returns T

pow

  • pow<T>(exp): T

  • Type Parameters

    Parameters

    Returns T

prelu

  • prelu<T>(alpha): T

  • Type Parameters

    Parameters

    Returns T

print

  • print(verbose?): void

  • Prints the tf.Tensor. See tf.print for details.

    +

    Parameters

    • Optionalverbose: boolean

      Whether to print verbose information about the tensor, +including dtype and size.

      +

    Returns void

    Doc

prod

  • prod<T>(this, axis?, keepDims?): T

  • Type Parameters

    Parameters

    • this: T
    • Optionalaxis: number | number[]
    • OptionalkeepDims: boolean

    Returns T

reciprocal

  • reciprocal<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

relu

  • relu<T>(): T

  • Type Parameters

    Returns T

relu6

  • relu6<T>(): T

  • Type Parameters

    Returns T

reshape

  • reshape<T>(shape): T

  • Type Parameters

    Parameters

    • shape: number[]

    Returns T

reshapeAs

  • reshapeAs<T>(x): T

  • Type Parameters

    Parameters

    Returns T

resizeBilinear

  • resizeBilinear<T>(newShape2D, alignCorners?, halfPixelCenters?): T

  • Type Parameters

    Parameters

    • newShape2D: [number, number]
    • OptionalalignCorners: boolean
    • OptionalhalfPixelCenters: boolean

    Returns T

resizeNearestNeighbor

  • resizeNearestNeighbor<T>(newShape2D, alignCorners?, halfFloatCenters?): T

  • Type Parameters

    Parameters

    • newShape2D: [number, number]
    • OptionalalignCorners: boolean
    • OptionalhalfFloatCenters: boolean

    Returns T

reverse

  • reverse<T>(this, axis?): T

  • Type Parameters

    Parameters

    • this: T
    • Optionalaxis: number | number[]

    Returns T

rfft

  • rfft<T>(this): Tensor<Rank>

  • Type Parameters

    Parameters

    Returns Tensor<Rank>

round

  • round<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

rsqrt

  • rsqrt<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

selu

  • selu<T>(): T

  • Type Parameters

    Returns T

separableConv2d

  • separableConv2d<T>(depthwiseFilter, pointwiseFilter, strides, pad, dilation?, dataFormat?): T

  • Type Parameters

    Parameters

    • depthwiseFilter: Tensor4D | TensorLike4D
    • pointwiseFilter: Tensor4D | TensorLike
    • strides: number | [number, number]
    • pad: "valid" | "same"
    • Optionaldilation: number | [number, number]
    • OptionaldataFormat: "NHWC" | "NCHW"

    Returns T

sigmoid

  • sigmoid<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

sign

  • sign<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

sin

  • sin<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

sinh

  • sinh<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

slice

  • slice<T>(this, begin, size?): T

  • Type Parameters

    Parameters

    • this: T
    • begin: number | number[]
    • Optionalsize: number | number[]

    Returns T

softmax

  • softmax<T>(this, dim?): T

  • Type Parameters

    Parameters

    • this: T
    • Optionaldim: number

    Returns T

softplus

  • softplus<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

spaceToBatchND

  • spaceToBatchND<R>(blockShape, paddings): Tensor<R>

  • Type Parameters

    Parameters

    • blockShape: number[]
    • paddings: number[][]

    Returns Tensor<R>

split

  • split<T>(numOrSizeSplits, axis?): T[]

  • Type Parameters

    Parameters

    • numOrSizeSplits: number | number[]
    • Optionalaxis: number

    Returns T[]

sqrt

  • sqrt<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

square

  • square<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

squaredDifference

  • squaredDifference<T>(b): T

  • Type Parameters

    Parameters

    Returns T

squeeze

  • squeeze<T>(axis?): T

  • Type Parameters

    Parameters

    • Optionalaxis: number[]

    Returns T

stack

  • stack<T>(x, axis?): T

  • Type Parameters

    Parameters

    Returns T

step

  • step<T>(this, alpha?): T

  • Type Parameters

    Parameters

    • this: T
    • Optionalalpha: number

    Returns T

stridedSlice

  • stridedSlice<T>(this, begin, end, strides, beginMask?, endMask?, ellipsisMask?, newAxisMask?, shrinkAxisMask?): Tensor<Rank>

  • Type Parameters

    Parameters

    • this: Tensor<Rank>
    • begin: number[]
    • end: number[]
    • strides: number[]
    • OptionalbeginMask: number
    • OptionalendMask: number
    • OptionalellipsisMask: number
    • OptionalnewAxisMask: number
    • OptionalshrinkAxisMask: number

    Returns Tensor<Rank>

sub

  • sub<T>(b): T

  • Type Parameters

    Parameters

    Returns T

sum

  • sum<T>(axis?, keepDims?): T

  • Type Parameters

    Parameters

    • Optionalaxis: number | number[]
    • OptionalkeepDims: boolean

    Returns T

tan

  • tan<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

tanh

  • tanh<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

throwIfDisposed

  • throwIfDisposed(): void

  • Returns void

tile

  • tile<T>(b): T

  • Type Parameters

    Parameters

    • b: number[]

    Returns T

toBool

  • toBool<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

toFloat

  • toFloat<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

toInt

  • toInt<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

topk

  • topk<T>(this, k?, sorted?): {
        indices: T;
        values: T;
    }

  • Type Parameters

    Parameters

    • this: T
    • Optionalk: number
    • Optionalsorted: boolean

    Returns {
        indices: T;
        values: T;
    }

    • indices: T
    • values: T

toString

  • toString(verbose?): string

  • Returns a human-readable description of the tensor. Useful for logging.

    +

    Parameters

    • Optionalverbose: boolean

    Returns string

    Doc

transpose

  • transpose<T>(perm?): T

  • Type Parameters

    Parameters

    • Optionalperm: number[]

    Returns T

unique

  • unique<T>(this, axis?): {
        indices: T;
        values: T;
    }

  • Type Parameters

    Parameters

    • this: T
    • Optionalaxis: number

    Returns {
        indices: T;
        values: T;
    }

    • indices: T
    • values: T

unsortedSegmentSum

  • unsortedSegmentSum<T>(this, segmentIds, numSegments): T

  • Type Parameters

    Parameters

    • this: T
    • segmentIds: Tensor1D | TensorLike1D
    • numSegments: number

    Returns T

unstack

  • unstack<T>(axis?): T[]

  • Type Parameters

    Parameters

    • Optionalaxis: number

    Returns T[]

variable

  • variable(trainable?, name?, dtype?): Variable<R>

  • Parameters

    • Optionaltrainable: boolean
    • Optionalname: string
    • Optionaldtype: keyof DataTypeMap

    Returns Variable<R>

where

zerosLike

  • zerosLike<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

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Methods
absacosacoshaddallanyargMaxargMinarrayarraySyncas1Das2Das3Das4Das5DasinasinhasScalarassignasTypeatanatan2atanhavgPoolbatchNormbatchToSpaceNDbroadcastTobufferbufferSyncbytescastceilclipByValuecloneconcatconv1dconv2dconv2dTransposecoscoshcumprodcumsumdatadataSyncdataToGPUdepthToSpacedepthwiseConv2ddilation2ddisposedivdivNoNandoteluequalerfeuclideanNormexpexpandDimsexpm1fftflattenfloorfloorDivgathergreatergreaterEqualifftirfftisFiniteisInfisNaNleakyRelulesslessEquallocalResponseNormalizationloglog1plogicalAndlogicalNotlogicalOrlogicalXorlogSigmoidlogSoftmaxlogSumExpmatMulmaxmaximummaxPoolmeanminminimummirrorPadmodmulnegnormnotEqualoneHotonesLikepadpoolpowpreluprintprodreciprocalrelurelu6reshapereshapeAsresizeBilinearresizeNearestNeighborreverserfftroundrsqrtseluseparableConv2dsigmoidsignsinsinhslicesoftmaxsoftplusspaceToBatchNDsplitsqrtsquaresquaredDifferencesqueezestackstepstridedSlicesubsumtantanhthrowIfDisposedtiletoBooltoFloattoInttopktoStringtransposeuniqueunsortedSegmentSumunstackvariablewherezerosLike
diff --git a/typedoc/classes/tf.io.CompositeArrayBuffer.html b/typedoc/classes/tf.io.CompositeArrayBuffer.html index 5677e4f..113138e 100644 --- a/typedoc/classes/tf.io.CompositeArrayBuffer.html +++ b/typedoc/classes/tf.io.CompositeArrayBuffer.html @@ -1,16 +1,16 @@ -CompositeArrayBuffer | @vladmandic/face-api - v1.7.13

Class CompositeArrayBuffer

Wraps a list of ArrayBuffers into a slice()-able object without allocating +CompositeArrayBuffer | @vladmandic/face-api - v1.7.14

Class CompositeArrayBuffer

Wraps a list of ArrayBuffers into a slice()-able object without allocating a large ArrayBuffer.

Allocating large ArrayBuffers (~2GB) can be unstable on Chrome. TFJS loads its weights as a list of (usually) 4MB ArrayBuffers and then slices the -weight tensors out of them. For small models, it's safe to concatenate all +weight tensors out of them. For small models, it's safe to concatenate all the weight buffers into a single ArrayBuffer and then slice the weight tensors out of it, but for large models, a different approach is needed.

-

Constructors

constructor -

Properties

byteLength -

Methods

slice -join -

Constructors

constructor

  • new CompositeArrayBuffer(buffers?): CompositeArrayBuffer

  • Parameters

    • Optional buffers: TypedArray | ArrayBuffer | ArrayBuffer[] | TypedArray[]

    Returns CompositeArrayBuffer

Properties

Readonly byteLength

byteLength: number

Methods

slice

  • slice(start?, end?): ArrayBuffer

  • Parameters

    • Optional start: number
    • Optional end: number

    Returns ArrayBuffer

Static join

  • join(buffers?): ArrayBuffer

  • Concatenate a number of ArrayBuffers into one.

    -

    Parameters

    • Optional buffers: ArrayBuffer | ArrayBuffer[]

      An array of ArrayBuffers to concatenate, or a single - ArrayBuffer.

      +

Constructors

constructor +

Properties

byteLength +

Methods

slice +join +

Constructors

constructor

  • new CompositeArrayBuffer(buffers?): CompositeArrayBuffer

  • Parameters

    • Optionalbuffers:
          | TypedArray
          | ArrayBuffer
          | ArrayBuffer[]
          | TypedArray[]

    Returns CompositeArrayBuffer

Properties

ReadonlybyteLength

byteLength: number

Methods

slice

  • slice(start?, end?): ArrayBuffer

  • Parameters

    • Optionalstart: number
    • Optionalend: number

    Returns ArrayBuffer

Staticjoin

  • join(buffers?): ArrayBuffer

  • Concatenate a number of ArrayBuffers into one.

    +

    Parameters

    • Optionalbuffers: ArrayBuffer | ArrayBuffer[]

      An array of ArrayBuffers to concatenate, or a single +ArrayBuffer.

    Returns ArrayBuffer

    Result of concatenating buffers in order.

    -

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Settings

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Properties
Methods
diff --git a/typedoc/enums/Gender.html b/typedoc/enums/Gender.html index f138fbc..b353db2 100644 --- a/typedoc/enums/Gender.html +++ b/typedoc/enums/Gender.html @@ -1,3 +1,3 @@ -Gender | @vladmandic/face-api - v1.7.13

Enumeration Gender

Enumeration Members

FEMALE -MALE -

Enumeration Members

FEMALE

FEMALE: "female"

MALE

MALE: "male"

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\ No newline at end of file +Gender | @vladmandic/face-api - v1.7.14

Enumeration Gender

Enumeration Members

FEMALE +MALE +

Enumeration Members

FEMALE

FEMALE: "female"

MALE

MALE: "male"

Settings

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On This Page

Enumeration Members
diff --git a/typedoc/enums/draw.AnchorPosition.html b/typedoc/enums/draw.AnchorPosition.html index 3d1b068..9339bb9 100644 --- a/typedoc/enums/draw.AnchorPosition.html +++ b/typedoc/enums/draw.AnchorPosition.html @@ -1,5 +1,5 @@ -AnchorPosition | @vladmandic/face-api - v1.7.13

Enumeration AnchorPosition

Enumeration Members

BOTTOM_LEFT -BOTTOM_RIGHT -TOP_LEFT -TOP_RIGHT -

Enumeration Members

BOTTOM_LEFT

BOTTOM_LEFT: "BOTTOM_LEFT"

BOTTOM_RIGHT

BOTTOM_RIGHT: "BOTTOM_RIGHT"

TOP_LEFT

TOP_LEFT: "TOP_LEFT"

TOP_RIGHT

TOP_RIGHT: "TOP_RIGHT"

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\ No newline at end of file +AnchorPosition | @vladmandic/face-api - v1.7.14

Enumeration AnchorPosition

Enumeration Members

BOTTOM_LEFT +BOTTOM_RIGHT +TOP_LEFT +TOP_RIGHT +

Enumeration Members

BOTTOM_LEFT

BOTTOM_LEFT: "BOTTOM_LEFT"

BOTTOM_RIGHT

BOTTOM_RIGHT: "BOTTOM_RIGHT"

TOP_LEFT

TOP_LEFT: "TOP_LEFT"

TOP_RIGHT

TOP_RIGHT: "TOP_RIGHT"

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On This Page

Enumeration Members
diff --git a/typedoc/enums/tf.Rank.html b/typedoc/enums/tf.Rank.html index dad7736..f6bede1 100644 --- a/typedoc/enums/tf.Rank.html +++ b/typedoc/enums/tf.Rank.html @@ -1,8 +1,8 @@ -Rank | @vladmandic/face-api - v1.7.13

Enumeration Rank

Enumeration Members

R0 -R1 -R2 -R3 -R4 -R5 -R6 -

Enumeration Members

R0

R0: "R0"

R1

R1: "R1"

R2

R2: "R2"

R3

R3: "R3"

R4

R4: "R4"

R5

R5: "R5"

R6

R6: "R6"

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Enumeration Rank

Enumeration Members

R0 +R1 +R2 +R3 +R4 +R5 +R6 +

Enumeration Members

R0

R0: "R0"

R1

R1: "R1"

R2

R2: "R2"

R3

R3: "R3"

R4

R4: "R4"

R5

R5: "R5"

R6

R6: "R6"

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On This Page

Enumeration Members
diff --git a/typedoc/functions/allFaces.html b/typedoc/functions/allFaces.html index ff9e218..46db0df 100644 --- a/typedoc/functions/allFaces.html +++ b/typedoc/functions/allFaces.html @@ -1 +1 @@ -allFaces | @vladmandic/face-api - v1.7.13

Function allFaces

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\ No newline at end of file +allFaces | @vladmandic/face-api - v1.7.14

Function allFaces

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diff --git a/typedoc/functions/allFacesSsdMobilenetv1.html b/typedoc/functions/allFacesSsdMobilenetv1.html index 1c9ae40..14cf1f4 100644 --- a/typedoc/functions/allFacesSsdMobilenetv1.html +++ b/typedoc/functions/allFacesSsdMobilenetv1.html @@ -1 +1 @@ -allFacesSsdMobilenetv1 | @vladmandic/face-api - v1.7.13

Function allFacesSsdMobilenetv1

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\ No newline at end of file +allFacesSsdMobilenetv1 | @vladmandic/face-api - v1.7.14

Function allFacesSsdMobilenetv1

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diff --git a/typedoc/functions/allFacesTinyYolov2.html b/typedoc/functions/allFacesTinyYolov2.html index 404fce1..ed134df 100644 --- a/typedoc/functions/allFacesTinyYolov2.html +++ b/typedoc/functions/allFacesTinyYolov2.html @@ -1 +1 @@ -allFacesTinyYolov2 | @vladmandic/face-api - v1.7.13

Function allFacesTinyYolov2

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\ No newline at end of file +allFacesTinyYolov2 | @vladmandic/face-api - v1.7.14

Function allFacesTinyYolov2

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diff --git a/typedoc/functions/awaitMediaLoaded.html b/typedoc/functions/awaitMediaLoaded.html index 7bb1e3e..f1793bf 100644 --- a/typedoc/functions/awaitMediaLoaded.html +++ b/typedoc/functions/awaitMediaLoaded.html @@ -1 +1 @@ -awaitMediaLoaded | @vladmandic/face-api - v1.7.13

Function awaitMediaLoaded

  • awaitMediaLoaded(media): Promise<unknown>

  • Parameters

    • media: HTMLCanvasElement | HTMLImageElement | HTMLVideoElement

    Returns Promise<unknown>

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\ No newline at end of file +awaitMediaLoaded | @vladmandic/face-api - v1.7.14

Function awaitMediaLoaded

  • awaitMediaLoaded(media): Promise<unknown>

  • Parameters

    • media: HTMLCanvasElement | HTMLImageElement | HTMLVideoElement

    Returns Promise<unknown>

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diff --git a/typedoc/functions/bufferToImage.html b/typedoc/functions/bufferToImage.html index 0a0554f..25d881f 100644 --- a/typedoc/functions/bufferToImage.html +++ b/typedoc/functions/bufferToImage.html @@ -1 +1 @@ -bufferToImage | @vladmandic/face-api - v1.7.13

Function bufferToImage

  • bufferToImage(buf): Promise<HTMLImageElement>

  • Parameters

    • buf: Blob

    Returns Promise<HTMLImageElement>

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\ No newline at end of file +bufferToImage | @vladmandic/face-api - v1.7.14

Function bufferToImage

  • bufferToImage(buf): Promise<HTMLImageElement>

  • Parameters

    • buf: Blob

    Returns Promise<HTMLImageElement>

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diff --git a/typedoc/functions/computeFaceDescriptor.html b/typedoc/functions/computeFaceDescriptor.html index 770864a..7e26c0b 100644 --- a/typedoc/functions/computeFaceDescriptor.html +++ b/typedoc/functions/computeFaceDescriptor.html @@ -1,6 +1,6 @@ -computeFaceDescriptor | @vladmandic/face-api - v1.7.13

Function computeFaceDescriptor

  • computeFaceDescriptor(input): Promise<Float32Array | Float32Array[]>

  • Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image, +computeFaceDescriptor | @vladmandic/face-api - v1.7.14

    Function computeFaceDescriptor

    Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image, which uniquely represents the features of that persons face. The computed face descriptor can be used to measure the similarity between faces, by computing the euclidean distance of two face descriptors.

    -

    Parameters

    Returns Promise<Float32Array | Float32Array[]>

    Face descriptor with 128 entries or array thereof in case of batch input.

    -

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  • computeFaceDescriptor(input): Promise<Float32Array | Float32Array[]>

  • Parameters

    Returns Promise<Float32Array | Float32Array[]>

    Face descriptor with 128 entries or array thereof in case of batch input.

    +

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diff --git a/typedoc/functions/createCanvas.html b/typedoc/functions/createCanvas.html index 98a0f0a..131e373 100644 --- a/typedoc/functions/createCanvas.html +++ b/typedoc/functions/createCanvas.html @@ -1 +1 @@ -createCanvas | @vladmandic/face-api - v1.7.13

Function createCanvas

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\ No newline at end of file +createCanvas | @vladmandic/face-api - v1.7.14

Function createCanvas

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diff --git a/typedoc/functions/createCanvasFromMedia.html b/typedoc/functions/createCanvasFromMedia.html index c67fdc0..5017d24 100644 --- a/typedoc/functions/createCanvasFromMedia.html +++ b/typedoc/functions/createCanvasFromMedia.html @@ -1 +1 @@ -createCanvasFromMedia | @vladmandic/face-api - v1.7.13

Function createCanvasFromMedia

  • createCanvasFromMedia(media, dims?): HTMLCanvasElement

  • Parameters

    • media: HTMLImageElement | HTMLVideoElement | ImageData
    • Optional dims: IDimensions

    Returns HTMLCanvasElement

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\ No newline at end of file +createCanvasFromMedia | @vladmandic/face-api - v1.7.14

Function createCanvasFromMedia

  • createCanvasFromMedia(media, dims?): HTMLCanvasElement

  • Parameters

    • media: HTMLImageElement | HTMLVideoElement | ImageData
    • Optionaldims: IDimensions

    Returns HTMLCanvasElement

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diff --git a/typedoc/functions/createFaceDetectionNet.html b/typedoc/functions/createFaceDetectionNet.html index fba078f..ef637c0 100644 --- a/typedoc/functions/createFaceDetectionNet.html +++ b/typedoc/functions/createFaceDetectionNet.html @@ -1 +1 @@ -createFaceDetectionNet | @vladmandic/face-api - v1.7.13

Function createFaceDetectionNet

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\ No newline at end of file +createFaceDetectionNet | @vladmandic/face-api - v1.7.14

Function createFaceDetectionNet

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diff --git a/typedoc/functions/createFaceRecognitionNet.html b/typedoc/functions/createFaceRecognitionNet.html index 97c6a0f..5d60d48 100644 --- a/typedoc/functions/createFaceRecognitionNet.html +++ b/typedoc/functions/createFaceRecognitionNet.html @@ -1 +1 @@ -createFaceRecognitionNet | @vladmandic/face-api - v1.7.13

Function createFaceRecognitionNet

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\ No newline at end of file +createFaceRecognitionNet | @vladmandic/face-api - v1.7.14

Function createFaceRecognitionNet

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diff --git a/typedoc/functions/createSsdMobilenetv1.html b/typedoc/functions/createSsdMobilenetv1.html index bc7af7c..1b8b2e4 100644 --- a/typedoc/functions/createSsdMobilenetv1.html +++ b/typedoc/functions/createSsdMobilenetv1.html @@ -1 +1 @@ -createSsdMobilenetv1 | @vladmandic/face-api - v1.7.13

Function createSsdMobilenetv1

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\ No newline at end of file +createSsdMobilenetv1 | @vladmandic/face-api - v1.7.14

Function createSsdMobilenetv1

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diff --git a/typedoc/functions/createTinyFaceDetector.html b/typedoc/functions/createTinyFaceDetector.html index 686e8c5..aee9222 100644 --- a/typedoc/functions/createTinyFaceDetector.html +++ b/typedoc/functions/createTinyFaceDetector.html @@ -1 +1 @@ -createTinyFaceDetector | @vladmandic/face-api - v1.7.13

Function createTinyFaceDetector

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\ No newline at end of file +createTinyFaceDetector | @vladmandic/face-api - v1.7.14

Function createTinyFaceDetector

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diff --git a/typedoc/functions/createTinyYolov2.html b/typedoc/functions/createTinyYolov2.html index 39136c4..d7b0455 100644 --- a/typedoc/functions/createTinyYolov2.html +++ b/typedoc/functions/createTinyYolov2.html @@ -1 +1 @@ -createTinyYolov2 | @vladmandic/face-api - v1.7.13

Function createTinyYolov2

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\ No newline at end of file +createTinyYolov2 | @vladmandic/face-api - v1.7.14

Function createTinyYolov2

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diff --git a/typedoc/functions/detectAllFaces.html b/typedoc/functions/detectAllFaces.html index c5f55f8..a8aa454 100644 --- a/typedoc/functions/detectAllFaces.html +++ b/typedoc/functions/detectAllFaces.html @@ -1 +1 @@ -detectAllFaces | @vladmandic/face-api - v1.7.13

Function detectAllFaces

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Function detectAllFaces

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diff --git a/typedoc/functions/detectFaceLandmarks.html b/typedoc/functions/detectFaceLandmarks.html index 655a864..5971831 100644 --- a/typedoc/functions/detectFaceLandmarks.html +++ b/typedoc/functions/detectFaceLandmarks.html @@ -1,3 +1,3 @@ -detectFaceLandmarks | @vladmandic/face-api - v1.7.13

Function detectFaceLandmarks

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\ No newline at end of file +detectFaceLandmarks | @vladmandic/face-api - v1.7.14

Function detectFaceLandmarks

Detects the 68 point face landmark positions of the face shown in an image.

+

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diff --git a/typedoc/functions/detectFaceLandmarksTiny.html b/typedoc/functions/detectFaceLandmarksTiny.html index b48f13d..57336ef 100644 --- a/typedoc/functions/detectFaceLandmarksTiny.html +++ b/typedoc/functions/detectFaceLandmarksTiny.html @@ -1,5 +1,5 @@ -detectFaceLandmarksTiny | @vladmandic/face-api - v1.7.13

Function detectFaceLandmarksTiny

  • detectFaceLandmarksTiny(input): Promise<FaceLandmarks68 | FaceLandmarks68[]>

  • Detects the 68 point face landmark positions of the face shown in an image +detectFaceLandmarksTiny | @vladmandic/face-api - v1.7.14

    Function detectFaceLandmarksTiny

    Detects the 68 point face landmark positions of the face shown in an image using a tinier version of the 68 point face landmark model, which is slightly faster at inference, but also slightly less accurate.

    -

    Parameters

    Returns Promise<FaceLandmarks68 | FaceLandmarks68[]>

    68 point face landmarks or array thereof in case of batch input.

    -

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diff --git a/typedoc/functions/detectLandmarks.html b/typedoc/functions/detectLandmarks.html index 7b13db8..82c9bf7 100644 --- a/typedoc/functions/detectLandmarks.html +++ b/typedoc/functions/detectLandmarks.html @@ -1,3 +1,3 @@ -detectLandmarks | @vladmandic/face-api - v1.7.13

Function detectLandmarks

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Function detectLandmarks

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diff --git a/typedoc/functions/detectSingleFace.html b/typedoc/functions/detectSingleFace.html index 5ace810..57345c9 100644 --- a/typedoc/functions/detectSingleFace.html +++ b/typedoc/functions/detectSingleFace.html @@ -1 +1 @@ -detectSingleFace | @vladmandic/face-api - v1.7.13

Function detectSingleFace

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Function detectSingleFace

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diff --git a/typedoc/functions/draw.drawContour.html b/typedoc/functions/draw.drawContour.html index 1515245..b8d5318 100644 --- a/typedoc/functions/draw.drawContour.html +++ b/typedoc/functions/draw.drawContour.html @@ -1 +1 @@ -drawContour | @vladmandic/face-api - v1.7.13

Function drawContour

  • drawContour(ctx, points, isClosed?): void

  • Parameters

    • ctx: CanvasRenderingContext2D
    • points: Point[]
    • isClosed: boolean = false

    Returns void

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\ No newline at end of file +drawContour | @vladmandic/face-api - v1.7.14

Function drawContour

  • drawContour(ctx, points, isClosed?): void

  • Parameters

    • ctx: CanvasRenderingContext2D
    • points: Point[]
    • isClosed: boolean = false

    Returns void

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diff --git a/typedoc/functions/draw.drawDetections.html b/typedoc/functions/draw.drawDetections.html index b0cfa69..5af3324 100644 --- a/typedoc/functions/draw.drawDetections.html +++ b/typedoc/functions/draw.drawDetections.html @@ -1 +1 @@ -drawDetections | @vladmandic/face-api - v1.7.13

Function drawDetections

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Function drawDetections

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diff --git a/typedoc/functions/draw.drawFaceExpressions.html b/typedoc/functions/draw.drawFaceExpressions.html index 63480c2..c113946 100644 --- a/typedoc/functions/draw.drawFaceExpressions.html +++ b/typedoc/functions/draw.drawFaceExpressions.html @@ -1 +1 @@ -drawFaceExpressions | @vladmandic/face-api - v1.7.13

Function drawFaceExpressions

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Function drawFaceExpressions

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diff --git a/typedoc/functions/draw.drawFaceLandmarks-1.html b/typedoc/functions/draw.drawFaceLandmarks-1.html index 312741a..b0fdd84 100644 --- a/typedoc/functions/draw.drawFaceLandmarks-1.html +++ b/typedoc/functions/draw.drawFaceLandmarks-1.html @@ -1 +1 @@ -drawFaceLandmarks | @vladmandic/face-api - v1.7.13

Function drawFaceLandmarks

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Function drawFaceLandmarks

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diff --git a/typedoc/functions/euclideanDistance.html b/typedoc/functions/euclideanDistance.html index 659bade..5eb514b 100644 --- a/typedoc/functions/euclideanDistance.html +++ b/typedoc/functions/euclideanDistance.html @@ -1 +1 @@ -euclideanDistance | @vladmandic/face-api - v1.7.13

Function euclideanDistance

  • euclideanDistance(arr1, arr2): number

  • Parameters

    • arr1: Float32Array | number[]
    • arr2: Float32Array | number[]

    Returns number

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Function euclideanDistance

  • euclideanDistance(arr1, arr2): number

  • Parameters

    • arr1: Float32Array | number[]
    • arr2: Float32Array | number[]

    Returns number

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diff --git a/typedoc/functions/extendWithAge.html b/typedoc/functions/extendWithAge.html index 1e8afb8..e6a36b9 100644 --- a/typedoc/functions/extendWithAge.html +++ b/typedoc/functions/extendWithAge.html @@ -1 +1 @@ -extendWithAge | @vladmandic/face-api - v1.7.13

Function extendWithAge

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Function extendWithAge

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diff --git a/typedoc/functions/extendWithFaceDescriptor.html b/typedoc/functions/extendWithFaceDescriptor.html index 87d13cf..d776982 100644 --- a/typedoc/functions/extendWithFaceDescriptor.html +++ b/typedoc/functions/extendWithFaceDescriptor.html @@ -1 +1 @@ -extendWithFaceDescriptor | @vladmandic/face-api - v1.7.13

Function extendWithFaceDescriptor

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Function extendWithFaceDescriptor

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diff --git a/typedoc/functions/extendWithFaceDetection.html b/typedoc/functions/extendWithFaceDetection.html index 3933386..34f280f 100644 --- a/typedoc/functions/extendWithFaceDetection.html +++ b/typedoc/functions/extendWithFaceDetection.html @@ -1 +1 @@ -extendWithFaceDetection | @vladmandic/face-api - v1.7.13

Function extendWithFaceDetection

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Function extendWithFaceDetection

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diff --git a/typedoc/functions/extendWithFaceExpressions.html b/typedoc/functions/extendWithFaceExpressions.html index b171347..3d211f2 100644 --- a/typedoc/functions/extendWithFaceExpressions.html +++ b/typedoc/functions/extendWithFaceExpressions.html @@ -1 +1 @@ -extendWithFaceExpressions | @vladmandic/face-api - v1.7.13

Function extendWithFaceExpressions

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Function extendWithFaceExpressions

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diff --git a/typedoc/functions/extendWithFaceLandmarks.html b/typedoc/functions/extendWithFaceLandmarks.html index a729c5b..1ec5f89 100644 --- a/typedoc/functions/extendWithFaceLandmarks.html +++ b/typedoc/functions/extendWithFaceLandmarks.html @@ -1 +1 @@ -extendWithFaceLandmarks | @vladmandic/face-api - v1.7.13

Function extendWithFaceLandmarks

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Function extendWithFaceLandmarks

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diff --git a/typedoc/functions/extendWithGender.html b/typedoc/functions/extendWithGender.html index d456118..b96953a 100644 --- a/typedoc/functions/extendWithGender.html +++ b/typedoc/functions/extendWithGender.html @@ -1 +1 @@ -extendWithGender | @vladmandic/face-api - v1.7.13

Function extendWithGender

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Function extendWithGender

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diff --git a/typedoc/functions/extractFaceTensors.html b/typedoc/functions/extractFaceTensors.html index 651aead..3348935 100644 --- a/typedoc/functions/extractFaceTensors.html +++ b/typedoc/functions/extractFaceTensors.html @@ -1,8 +1,8 @@ -extractFaceTensors | @vladmandic/face-api - v1.7.13

Function extractFaceTensors

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diff --git a/typedoc/functions/extractFaces.html b/typedoc/functions/extractFaces.html index be988ad..aa2b867 100644 --- a/typedoc/functions/extractFaces.html +++ b/typedoc/functions/extractFaces.html @@ -1,5 +1,5 @@ -extractFaces | @vladmandic/face-api - v1.7.13

Function extractFaces

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diff --git a/typedoc/functions/fetchImage.html b/typedoc/functions/fetchImage.html index 792305b..cbe874a 100644 --- a/typedoc/functions/fetchImage.html +++ b/typedoc/functions/fetchImage.html @@ -1 +1 @@ -fetchImage | @vladmandic/face-api - v1.7.13

Function fetchImage

  • fetchImage(uri): Promise<HTMLImageElement>

  • Parameters

    • uri: string

    Returns Promise<HTMLImageElement>

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Function fetchImage

  • fetchImage(uri): Promise<HTMLImageElement>

  • Parameters

    • uri: string

    Returns Promise<HTMLImageElement>

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diff --git a/typedoc/functions/fetchJson.html b/typedoc/functions/fetchJson.html index c8e6081..a9df200 100644 --- a/typedoc/functions/fetchJson.html +++ b/typedoc/functions/fetchJson.html @@ -1 +1 @@ -fetchJson | @vladmandic/face-api - v1.7.13

Function fetchJson

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Function fetchJson

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diff --git a/typedoc/functions/fetchNetWeights.html b/typedoc/functions/fetchNetWeights.html index 9bb3752..ae05794 100644 --- a/typedoc/functions/fetchNetWeights.html +++ b/typedoc/functions/fetchNetWeights.html @@ -1 +1 @@ -fetchNetWeights | @vladmandic/face-api - v1.7.13

Function fetchNetWeights

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Function fetchNetWeights

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diff --git a/typedoc/functions/fetchOrThrow.html b/typedoc/functions/fetchOrThrow.html index b0ac197..1c6256c 100644 --- a/typedoc/functions/fetchOrThrow.html +++ b/typedoc/functions/fetchOrThrow.html @@ -1 +1 @@ -fetchOrThrow | @vladmandic/face-api - v1.7.13

Function fetchOrThrow

  • fetchOrThrow(url, init?): Promise<Response>

  • Parameters

    • url: string
    • Optional init: RequestInit

    Returns Promise<Response>

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Function fetchOrThrow

  • fetchOrThrow(url, init?): Promise<Response>

  • Parameters

    • url: string
    • Optionalinit: RequestInit

    Returns Promise<Response>

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diff --git a/typedoc/functions/fetchVideo.html b/typedoc/functions/fetchVideo.html index 1a58fb9..291942d 100644 --- a/typedoc/functions/fetchVideo.html +++ b/typedoc/functions/fetchVideo.html @@ -1 +1 @@ -fetchVideo | @vladmandic/face-api - v1.7.13

Function fetchVideo

  • fetchVideo(uri): Promise<HTMLVideoElement>

  • Parameters

    • uri: string

    Returns Promise<HTMLVideoElement>

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Function fetchVideo

  • fetchVideo(uri): Promise<HTMLVideoElement>

  • Parameters

    • uri: string

    Returns Promise<HTMLVideoElement>

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diff --git a/typedoc/functions/getContext2dOrThrow.html b/typedoc/functions/getContext2dOrThrow.html index fa1f7c8..2b81793 100644 --- a/typedoc/functions/getContext2dOrThrow.html +++ b/typedoc/functions/getContext2dOrThrow.html @@ -1 +1 @@ -getContext2dOrThrow | @vladmandic/face-api - v1.7.13

Function getContext2dOrThrow

  • getContext2dOrThrow(canvasArg): CanvasRenderingContext2D

  • Parameters

    • canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D

    Returns CanvasRenderingContext2D

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Function getContext2dOrThrow

  • getContext2dOrThrow(canvasArg): CanvasRenderingContext2D

  • Parameters

    • canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D

    Returns CanvasRenderingContext2D

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diff --git a/typedoc/functions/getMediaDimensions.html b/typedoc/functions/getMediaDimensions.html index 349b489..ec13614 100644 --- a/typedoc/functions/getMediaDimensions.html +++ b/typedoc/functions/getMediaDimensions.html @@ -1 +1 @@ -getMediaDimensions | @vladmandic/face-api - v1.7.13

Function getMediaDimensions

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Function getMediaDimensions

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diff --git a/typedoc/functions/imageTensorToCanvas.html b/typedoc/functions/imageTensorToCanvas.html index eec6491..238ced8 100644 --- a/typedoc/functions/imageTensorToCanvas.html +++ b/typedoc/functions/imageTensorToCanvas.html @@ -1 +1 @@ -imageTensorToCanvas | @vladmandic/face-api - v1.7.13

Function imageTensorToCanvas

  • imageTensorToCanvas(imgTensor, canvas?): Promise<HTMLCanvasElement>

  • Parameters

    • imgTensor: Tensor<Rank>
    • Optional canvas: HTMLCanvasElement

    Returns Promise<HTMLCanvasElement>

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Function imageTensorToCanvas

  • imageTensorToCanvas(imgTensor, canvas?): Promise<HTMLCanvasElement>

  • Parameters

    • imgTensor: Tensor<Rank>
    • Optionalcanvas: HTMLCanvasElement

    Returns Promise<HTMLCanvasElement>

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diff --git a/typedoc/functions/imageToSquare.html b/typedoc/functions/imageToSquare.html index 871783f..19b5aa5 100644 --- a/typedoc/functions/imageToSquare.html +++ b/typedoc/functions/imageToSquare.html @@ -1 +1 @@ -imageToSquare | @vladmandic/face-api - v1.7.13

Function imageToSquare

  • imageToSquare(input, inputSize, centerImage?): HTMLCanvasElement

  • Parameters

    • input: HTMLCanvasElement | HTMLImageElement
    • inputSize: number
    • centerImage: boolean = false

    Returns HTMLCanvasElement

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Function imageToSquare

  • imageToSquare(input, inputSize, centerImage?): HTMLCanvasElement

  • Parameters

    • input: HTMLCanvasElement | HTMLImageElement
    • inputSize: number
    • centerImage: boolean = false

    Returns HTMLCanvasElement

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diff --git a/typedoc/functions/inverseSigmoid.html b/typedoc/functions/inverseSigmoid.html index 651cd36..e51b93f 100644 --- a/typedoc/functions/inverseSigmoid.html +++ b/typedoc/functions/inverseSigmoid.html @@ -1 +1 @@ -inverseSigmoid | @vladmandic/face-api - v1.7.13

Function inverseSigmoid

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Function inverseSigmoid

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diff --git a/typedoc/functions/iou.html b/typedoc/functions/iou.html index eaedb38..ff48a33 100644 --- a/typedoc/functions/iou.html +++ b/typedoc/functions/iou.html @@ -1 +1 @@ -iou | @vladmandic/face-api - v1.7.13

Function iou

  • iou(box1, box2, isIOU?): number

  • Parameters

    • box1: Box<any>
    • box2: Box<any>
    • isIOU: boolean = true

    Returns number

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Function iou

  • iou(box1, box2, isIOU?): number

  • Parameters

    • box1: Box<any>
    • box2: Box<any>
    • isIOU: boolean = true

    Returns number

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diff --git a/typedoc/functions/isMediaElement.html b/typedoc/functions/isMediaElement.html index 252d08d..ae4c769 100644 --- a/typedoc/functions/isMediaElement.html +++ b/typedoc/functions/isMediaElement.html @@ -1 +1 @@ -isMediaElement | @vladmandic/face-api - v1.7.13

Function isMediaElement

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Function isMediaElement

  • isMediaElement(input): input is HTMLCanvasElement | HTMLImageElement | HTMLVideoElement

  • Parameters

    • input: any

    Returns input is HTMLCanvasElement | HTMLImageElement | HTMLVideoElement

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diff --git a/typedoc/functions/isMediaLoaded.html b/typedoc/functions/isMediaLoaded.html index 42e7bca..be507a1 100644 --- a/typedoc/functions/isMediaLoaded.html +++ b/typedoc/functions/isMediaLoaded.html @@ -1 +1 @@ -isMediaLoaded | @vladmandic/face-api - v1.7.13

Function isMediaLoaded

  • isMediaLoaded(media): boolean

  • Parameters

    • media: HTMLImageElement | HTMLVideoElement

    Returns boolean

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Function isMediaLoaded

  • isMediaLoaded(media): boolean

  • Parameters

    • media: HTMLImageElement | HTMLVideoElement

    Returns boolean

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diff --git a/typedoc/functions/isWithAge.html b/typedoc/functions/isWithAge.html index ff83b8e..64f81fa 100644 --- a/typedoc/functions/isWithAge.html +++ b/typedoc/functions/isWithAge.html @@ -1 +1 @@ -isWithAge | @vladmandic/face-api - v1.7.13

Function isWithAge

  • isWithAge(obj): obj is {
        age: number;
    }

  • Parameters

    • obj: any

    Returns obj is {
        age: number;
    }

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Function isWithAge

  • isWithAge(obj): obj is {
        age: number;
    }

  • Parameters

    • obj: any

    Returns obj is {
        age: number;
    }

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diff --git a/typedoc/functions/isWithFaceDetection.html b/typedoc/functions/isWithFaceDetection.html index 6ad2167..d404fc6 100644 --- a/typedoc/functions/isWithFaceDetection.html +++ b/typedoc/functions/isWithFaceDetection.html @@ -1 +1 @@ -isWithFaceDetection | @vladmandic/face-api - v1.7.13

Function isWithFaceDetection

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Function isWithFaceDetection

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diff --git a/typedoc/functions/isWithFaceExpressions.html b/typedoc/functions/isWithFaceExpressions.html index eeb4fc5..7a3e166 100644 --- a/typedoc/functions/isWithFaceExpressions.html +++ b/typedoc/functions/isWithFaceExpressions.html @@ -1 +1 @@ -isWithFaceExpressions | @vladmandic/face-api - v1.7.13

Function isWithFaceExpressions

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Function isWithFaceExpressions

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diff --git a/typedoc/functions/isWithFaceLandmarks.html b/typedoc/functions/isWithFaceLandmarks.html index 63063e2..a84bdc3 100644 --- a/typedoc/functions/isWithFaceLandmarks.html +++ b/typedoc/functions/isWithFaceLandmarks.html @@ -1 +1 @@ -isWithFaceLandmarks | @vladmandic/face-api - v1.7.13

Function isWithFaceLandmarks

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Function isWithFaceLandmarks

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diff --git a/typedoc/functions/isWithGender.html b/typedoc/functions/isWithGender.html index 61aa125..7331352 100644 --- a/typedoc/functions/isWithGender.html +++ b/typedoc/functions/isWithGender.html @@ -1 +1 @@ -isWithGender | @vladmandic/face-api - v1.7.13

Function isWithGender

  • isWithGender(obj): obj is {
        gender: Gender;
        genderProbability: number;
    }

  • Parameters

    • obj: any

    Returns obj is {
        gender: Gender;
        genderProbability: number;
    }

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Function isWithGender

  • isWithGender(obj): obj is {
        gender: Gender;
        genderProbability: number;
    }

  • Parameters

    • obj: any

    Returns obj is {
        gender: Gender;
        genderProbability: number;
    }

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diff --git a/typedoc/functions/loadAgeGenderModel.html b/typedoc/functions/loadAgeGenderModel.html index ee0bf29..b88137d 100644 --- a/typedoc/functions/loadAgeGenderModel.html +++ b/typedoc/functions/loadAgeGenderModel.html @@ -1 +1 @@ -loadAgeGenderModel | @vladmandic/face-api - v1.7.13

Function loadAgeGenderModel

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Function loadAgeGenderModel

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diff --git a/typedoc/functions/loadFaceDetectionModel.html b/typedoc/functions/loadFaceDetectionModel.html index e10563b..9d6c047 100644 --- a/typedoc/functions/loadFaceDetectionModel.html +++ b/typedoc/functions/loadFaceDetectionModel.html @@ -1 +1 @@ -loadFaceDetectionModel | @vladmandic/face-api - v1.7.13

Function loadFaceDetectionModel

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Function loadFaceDetectionModel

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diff --git a/typedoc/functions/loadFaceExpressionModel.html b/typedoc/functions/loadFaceExpressionModel.html index 2c651fa..d216085 100644 --- a/typedoc/functions/loadFaceExpressionModel.html +++ b/typedoc/functions/loadFaceExpressionModel.html @@ -1 +1 @@ -loadFaceExpressionModel | @vladmandic/face-api - v1.7.13

Function loadFaceExpressionModel

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Function loadFaceExpressionModel

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diff --git a/typedoc/functions/loadFaceLandmarkModel.html b/typedoc/functions/loadFaceLandmarkModel.html index 891aa74..5a7a976 100644 --- a/typedoc/functions/loadFaceLandmarkModel.html +++ b/typedoc/functions/loadFaceLandmarkModel.html @@ -1 +1 @@ -loadFaceLandmarkModel | @vladmandic/face-api - v1.7.13

Function loadFaceLandmarkModel

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Function loadFaceLandmarkModel

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diff --git a/typedoc/functions/loadFaceLandmarkTinyModel.html b/typedoc/functions/loadFaceLandmarkTinyModel.html index ca8ac71..bf7feba 100644 --- a/typedoc/functions/loadFaceLandmarkTinyModel.html +++ b/typedoc/functions/loadFaceLandmarkTinyModel.html @@ -1 +1 @@ -loadFaceLandmarkTinyModel | @vladmandic/face-api - v1.7.13

Function loadFaceLandmarkTinyModel

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\ No newline at end of file +loadFaceLandmarkTinyModel | @vladmandic/face-api - v1.7.14

Function loadFaceLandmarkTinyModel

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diff --git a/typedoc/functions/loadFaceRecognitionModel.html b/typedoc/functions/loadFaceRecognitionModel.html index 79d3b2c..bad924d 100644 --- a/typedoc/functions/loadFaceRecognitionModel.html +++ b/typedoc/functions/loadFaceRecognitionModel.html @@ -1 +1 @@ -loadFaceRecognitionModel | @vladmandic/face-api - v1.7.13

Function loadFaceRecognitionModel

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Function loadFaceRecognitionModel

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diff --git a/typedoc/functions/loadSsdMobilenetv1Model.html b/typedoc/functions/loadSsdMobilenetv1Model.html index 5fb5c53..9e9e5bf 100644 --- a/typedoc/functions/loadSsdMobilenetv1Model.html +++ b/typedoc/functions/loadSsdMobilenetv1Model.html @@ -1 +1 @@ -loadSsdMobilenetv1Model | @vladmandic/face-api - v1.7.13

Function loadSsdMobilenetv1Model

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\ No newline at end of file +loadSsdMobilenetv1Model | @vladmandic/face-api - v1.7.14

Function loadSsdMobilenetv1Model

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diff --git a/typedoc/functions/loadTinyFaceDetectorModel.html b/typedoc/functions/loadTinyFaceDetectorModel.html index 15ffcd2..2272a5c 100644 --- a/typedoc/functions/loadTinyFaceDetectorModel.html +++ b/typedoc/functions/loadTinyFaceDetectorModel.html @@ -1 +1 @@ -loadTinyFaceDetectorModel | @vladmandic/face-api - v1.7.13

Function loadTinyFaceDetectorModel

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Function loadTinyFaceDetectorModel

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diff --git a/typedoc/functions/loadTinyYolov2Model.html b/typedoc/functions/loadTinyYolov2Model.html index 6379ebd..8784199 100644 --- a/typedoc/functions/loadTinyYolov2Model.html +++ b/typedoc/functions/loadTinyYolov2Model.html @@ -1 +1 @@ -loadTinyYolov2Model | @vladmandic/face-api - v1.7.13

Function loadTinyYolov2Model

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Function loadTinyYolov2Model

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diff --git a/typedoc/functions/loadWeightMap.html b/typedoc/functions/loadWeightMap.html index cba085c..332a964 100644 --- a/typedoc/functions/loadWeightMap.html +++ b/typedoc/functions/loadWeightMap.html @@ -1 +1 @@ -loadWeightMap | @vladmandic/face-api - v1.7.13

Function loadWeightMap

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Function loadWeightMap

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diff --git a/typedoc/functions/locateFaces.html b/typedoc/functions/locateFaces.html index becf8fc..2598f8d 100644 --- a/typedoc/functions/locateFaces.html +++ b/typedoc/functions/locateFaces.html @@ -1,5 +1,5 @@ -locateFaces | @vladmandic/face-api - v1.7.13

Function locateFaces

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diff --git a/typedoc/functions/matchDimensions.html b/typedoc/functions/matchDimensions.html index d5fdde2..7fb7d75 100644 --- a/typedoc/functions/matchDimensions.html +++ b/typedoc/functions/matchDimensions.html @@ -1 +1 @@ -matchDimensions | @vladmandic/face-api - v1.7.13

Function matchDimensions

  • matchDimensions(input, reference, useMediaDimensions?): {
        height: number;
        width: number;
    }

  • Parameters

    Returns {
        height: number;
        width: number;
    }

    • height: number
    • width: number

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Function matchDimensions

  • matchDimensions(input, reference, useMediaDimensions?): {
        height: number;
        width: number;
    }

  • Parameters

    Returns {
        height: number;
        width: number;
    }

    • height: number
    • width: number

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diff --git a/typedoc/functions/minBbox.html b/typedoc/functions/minBbox.html index 574b22d..a532b02 100644 --- a/typedoc/functions/minBbox.html +++ b/typedoc/functions/minBbox.html @@ -1 +1 @@ -minBbox | @vladmandic/face-api - v1.7.13

Function minBbox

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Function minBbox

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diff --git a/typedoc/functions/nonMaxSuppression.html b/typedoc/functions/nonMaxSuppression.html index 857e5c0..ff3038f 100644 --- a/typedoc/functions/nonMaxSuppression.html +++ b/typedoc/functions/nonMaxSuppression.html @@ -1 +1 @@ -nonMaxSuppression | @vladmandic/face-api - v1.7.13

Function nonMaxSuppression

  • nonMaxSuppression(boxes, scores, iouThreshold, isIOU?): number[]

  • Parameters

    • boxes: Box<any>[]
    • scores: number[]
    • iouThreshold: number
    • isIOU: boolean = true

    Returns number[]

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\ No newline at end of file +nonMaxSuppression | @vladmandic/face-api - v1.7.14

Function nonMaxSuppression

  • nonMaxSuppression(boxes, scores, iouThreshold, isIOU?): number[]

  • Parameters

    • boxes: Box<any>[]
    • scores: number[]
    • iouThreshold: number
    • isIOU: boolean = true

    Returns number[]

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diff --git a/typedoc/functions/normalize.html b/typedoc/functions/normalize.html index 3662ea6..38776d9 100644 --- a/typedoc/functions/normalize.html +++ b/typedoc/functions/normalize.html @@ -1 +1 @@ -normalize | @vladmandic/face-api - v1.7.13

Function normalize

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Function normalize

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diff --git a/typedoc/functions/padToSquare.html b/typedoc/functions/padToSquare.html index 8d37933..5cd4c5a 100644 --- a/typedoc/functions/padToSquare.html +++ b/typedoc/functions/padToSquare.html @@ -1,6 +1,6 @@ -padToSquare | @vladmandic/face-api - v1.7.13

Function padToSquare

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diff --git a/typedoc/functions/predictAgeAndGender.html b/typedoc/functions/predictAgeAndGender.html index 4023992..dd32ecb 100644 --- a/typedoc/functions/predictAgeAndGender.html +++ b/typedoc/functions/predictAgeAndGender.html @@ -1,3 +1,3 @@ -predictAgeAndGender | @vladmandic/face-api - v1.7.13

Function predictAgeAndGender

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\ No newline at end of file +predictAgeAndGender | @vladmandic/face-api - v1.7.14

Function predictAgeAndGender

Predicts age and gender from a face image.

+

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diff --git a/typedoc/functions/recognizeFaceExpressions.html b/typedoc/functions/recognizeFaceExpressions.html index c9ecb95..4e15b94 100644 --- a/typedoc/functions/recognizeFaceExpressions.html +++ b/typedoc/functions/recognizeFaceExpressions.html @@ -1,3 +1,3 @@ -recognizeFaceExpressions | @vladmandic/face-api - v1.7.13

Function recognizeFaceExpressions

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\ No newline at end of file +recognizeFaceExpressions | @vladmandic/face-api - v1.7.14

Function recognizeFaceExpressions

Recognizes the facial expressions from a face image.

+

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diff --git a/typedoc/functions/resizeResults.html b/typedoc/functions/resizeResults.html index 475a843..30ceaba 100644 --- a/typedoc/functions/resizeResults.html +++ b/typedoc/functions/resizeResults.html @@ -1 +1 @@ -resizeResults | @vladmandic/face-api - v1.7.13

Function resizeResults

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Function resizeResults

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diff --git a/typedoc/functions/resolveInput.html b/typedoc/functions/resolveInput.html index c60ec72..929bd06 100644 --- a/typedoc/functions/resolveInput.html +++ b/typedoc/functions/resolveInput.html @@ -1 +1 @@ -resolveInput | @vladmandic/face-api - v1.7.13

Function resolveInput

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Function resolveInput

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diff --git a/typedoc/functions/shuffleArray.html b/typedoc/functions/shuffleArray.html index 5cb63ca..0464e67 100644 --- a/typedoc/functions/shuffleArray.html +++ b/typedoc/functions/shuffleArray.html @@ -1 +1 @@ -shuffleArray | @vladmandic/face-api - v1.7.13

Function shuffleArray

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Function shuffleArray

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diff --git a/typedoc/functions/sigmoid.html b/typedoc/functions/sigmoid.html index 7909ea0..7e85717 100644 --- a/typedoc/functions/sigmoid.html +++ b/typedoc/functions/sigmoid.html @@ -1 +1 @@ -sigmoid | @vladmandic/face-api - v1.7.13

Function sigmoid

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Function sigmoid

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diff --git a/typedoc/functions/ssdMobilenetv1-1.html b/typedoc/functions/ssdMobilenetv1-1.html index 96f6a16..7ca2153 100644 --- a/typedoc/functions/ssdMobilenetv1-1.html +++ b/typedoc/functions/ssdMobilenetv1-1.html @@ -1,5 +1,5 @@ -ssdMobilenetv1 | @vladmandic/face-api - v1.7.13

Function ssdMobilenetv1

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diff --git a/typedoc/functions/tf.add.html b/typedoc/functions/tf.add.html index 3889348..dac498a 100644 --- a/typedoc/functions/tf.add.html +++ b/typedoc/functions/tf.add.html @@ -1,8 +1,10 @@ -add | @vladmandic/face-api - v1.7.13

Function add

  • add<T>(a, b): T

  • Adds two tf.Tensors element-wise, A + B. Supports broadcasting.

    -
    const a = tf.tensor1d([1, 2, 3, 4]);
    const b = tf.tensor1d([10, 20, 30, 40]);

    a.add(b).print();  // or tf.add(a, b)
-
    -
    // Broadcast add a with b.
    const a = tf.scalar(5);
    const b = tf.tensor1d([10, 20, 30, 40]);

    a.add(b).print();  // or tf.add(a, b)
-
    -

    Type Parameters

    Parameters

    Returns T

    Doc

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\ No newline at end of file +add | @vladmandic/face-api - v1.7.14

Function add

  • add<T>(a, b): T

  • Adds two tf.Tensors element-wise, A + B. Supports broadcasting.

    +
    const a = tf.tensor1d([1, 2, 3, 4]);
    const b = tf.tensor1d([10, 20, 30, 40]);

    a.add(b).print();  // or tf.add(a, b)
+
    + +
    // Broadcast add a with b.
    const a = tf.scalar(5);
    const b = tf.tensor1d([10, 20, 30, 40]);

    a.add(b).print();  // or tf.add(a, b)
+
    + +

    Type Parameters

    Parameters

    Returns T

    Doc

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diff --git a/typedoc/functions/tf.avgPool.html b/typedoc/functions/tf.avgPool.html index 990bf25..413310b 100644 --- a/typedoc/functions/tf.avgPool.html +++ b/typedoc/functions/tf.avgPool.html @@ -1,19 +1,19 @@ -avgPool | @vladmandic/face-api - v1.7.13

Function avgPool

  • avgPool<T>(x, filterSize, strides, pad, dimRoundingMode?): T

  • Computes the 2D average pooling of an image.

    -

    Type Parameters

    Parameters

    Returns T

    Doc

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diff --git a/typedoc/functions/tf.batchNorm.html b/typedoc/functions/tf.batchNorm.html index 8e0a963..f5accde 100644 --- a/typedoc/functions/tf.batchNorm.html +++ b/typedoc/functions/tf.batchNorm.html @@ -1,6 +1,6 @@ -batchNorm | @vladmandic/face-api - v1.7.13

Function batchNorm

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diff --git a/typedoc/functions/tf.browser.draw.html b/typedoc/functions/tf.browser.draw.html index bab0b56..7c110de 100644 --- a/typedoc/functions/tf.browser.draw.html +++ b/typedoc/functions/tf.browser.draw.html @@ -1,8 +1,8 @@ -draw | @vladmandic/face-api - v1.7.13

Function draw

Returns void

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diff --git a/typedoc/functions/tf.browser.fromPixels.html b/typedoc/functions/tf.browser.fromPixels.html index 5ed293e..0ff76cd 100644 --- a/typedoc/functions/tf.browser.fromPixels.html +++ b/typedoc/functions/tf.browser.fromPixels.html @@ -1,11 +1,12 @@ -fromPixels | @vladmandic/face-api - v1.7.13

Function fromPixels

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diff --git a/typedoc/functions/tf.browser.fromPixelsAsync.html b/typedoc/functions/tf.browser.fromPixelsAsync.html index b7009ab..70838bd 100644 --- a/typedoc/functions/tf.browser.fromPixelsAsync.html +++ b/typedoc/functions/tf.browser.fromPixelsAsync.html @@ -1,14 +1,15 @@ -fromPixelsAsync | @vladmandic/face-api - v1.7.13

Function fromPixelsAsync

  • fromPixelsAsync(pixels, numChannels?): Promise<Tensor3D>

  • Creates a tf.Tensor from an image in async way.

    -
    const image = new ImageData(1, 1);
    image.data[0] = 100;
    image.data[1] = 150;
    image.data[2] = 200;
    image.data[3] = 255;

    (await tf.browser.fromPixelsAsync(image)).print();
-
    +fromPixelsAsync | @vladmandic/face-api - v1.7.14

    Function fromPixelsAsync

    • fromPixelsAsync(pixels, numChannels?): Promise<Tensor3D>

    • Creates a tf.Tensor from an image in async way.

      +
      const image = new ImageData(1, 1);
      image.data[0] = 100;
      image.data[1] = 150;
      image.data[2] = 200;
      image.data[3] = 255;

      (await tf.browser.fromPixelsAsync(image)).print();
+
      +

      This API is the async version of fromPixels. The API will first check |WRAP_TO_IMAGEBITMAP| flag, and try to wrap the input to imageBitmap if the flag is set to true.

      -

      Parameters

      • pixels: HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | ImageData | ImageBitmap | PixelData

        The input image to construct the tensor from. The +

        Parameters

        • pixels:
              | HTMLCanvasElement
              | HTMLImageElement
              | HTMLVideoElement
              | ImageData
              | ImageBitmap
              | PixelData

          The input image to construct the tensor from. The supported image types are all 4-channel. You can also pass in an image object with following attributes: {data: Uint8Array; width: number; height: number}

          -
        • Optional numChannels: number

          The number of channels of the output tensor. A +

        • OptionalnumChannels: number

          The number of channels of the output tensor. A numChannels value less than 4 allows you to ignore channels. Defaults to 3 (ignores alpha channel of input image).

          -

        Returns Promise<Tensor3D>

        Doc

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    \ No newline at end of file +

Returns Promise<Tensor3D>

Doc

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diff --git a/typedoc/functions/tf.browser.toPixels.html b/typedoc/functions/tf.browser.toPixels.html index 018a246..8b94f79 100644 --- a/typedoc/functions/tf.browser.toPixels.html +++ b/typedoc/functions/tf.browser.toPixels.html @@ -1,14 +1,14 @@ -toPixels | @vladmandic/face-api - v1.7.13

Function toPixels

Returns Promise<Uint8ClampedArray>

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diff --git a/typedoc/functions/tf.cast.html b/typedoc/functions/tf.cast.html index d5292f3..c3ca94d 100644 --- a/typedoc/functions/tf.cast.html +++ b/typedoc/functions/tf.cast.html @@ -1,6 +1,7 @@ -cast | @vladmandic/face-api - v1.7.13

Function cast

  • cast<T>(x, dtype): T

  • Casts a tf.Tensor to a new dtype.

    -
    const x = tf.tensor1d([1.5, 2.5, 3]);
    tf.cast(x, 'int32').print();
-
    -

    Type Parameters

    Parameters

    • x: TensorLike | T

      The input tensor to be casted.

      -
    • dtype: keyof DataTypeMap

      The dtype to cast the input tensor to.

      -

    Returns T

    Doc

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\ No newline at end of file +cast | @vladmandic/face-api - v1.7.14

Function cast

  • cast<T>(x, dtype): T

  • Casts a tf.Tensor to a new dtype.

    +
    const x = tf.tensor1d([1.5, 2.5, 3]);
    tf.cast(x, 'int32').print();
+
    + +

    Type Parameters

    Parameters

    • x: TensorLike | T

      The input tensor to be casted.

      +
    • dtype: keyof DataTypeMap

      The dtype to cast the input tensor to.

      +

    Returns T

    Doc

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diff --git a/typedoc/functions/tf.clipByValue.html b/typedoc/functions/tf.clipByValue.html index 371fef2..40eba22 100644 --- a/typedoc/functions/tf.clipByValue.html +++ b/typedoc/functions/tf.clipByValue.html @@ -1,7 +1,8 @@ -clipByValue | @vladmandic/face-api - v1.7.13

Function clipByValue

  • clipByValue<T>(x, clipValueMin, clipValueMax): T

  • Clips values element-wise. max(min(x, clipValueMax), clipValueMin)

    -
    const x = tf.tensor1d([-1, 2, -3, 4]);

    x.clipByValue(-2, 3).print();  // or tf.clipByValue(x, -2, 3)
-
    -

    Type Parameters

    Parameters

    • x: TensorLike | T

      The input tensor.

      -
    • clipValueMin: number

      Lower bound of range to be clipped to.

      -
    • clipValueMax: number

      Upper bound of range to be clipped to.

      -

    Returns T

    Doc

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\ No newline at end of file +clipByValue | @vladmandic/face-api - v1.7.14

Function clipByValue

  • clipByValue<T>(x, clipValueMin, clipValueMax): T

  • Clips values element-wise. max(min(x, clipValueMax), clipValueMin)

    +
    const x = tf.tensor1d([-1, 2, -3, 4]);

    x.clipByValue(-2, 3).print();  // or tf.clipByValue(x, -2, 3)
+
    + +

    Type Parameters

    Parameters

    • x: TensorLike | T

      The input tensor.

      +
    • clipValueMin: number

      Lower bound of range to be clipped to.

      +
    • clipValueMax: number

      Upper bound of range to be clipped to.

      +

    Returns T

    Doc

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diff --git a/typedoc/functions/tf.concat.html b/typedoc/functions/tf.concat.html index 937fab3..f47820c 100644 --- a/typedoc/functions/tf.concat.html +++ b/typedoc/functions/tf.concat.html @@ -1,4 +1,4 @@ -concat | @vladmandic/face-api - v1.7.13

Function concat

  • concat<T>(tensors, axis?): T

  • Concatenates a list of tf.Tensors along a given axis.

    +concat | @vladmandic/face-api - v1.7.14

    Function concat

    • concat<T>(tensors, axis?): T

    • Concatenates a list of tf.Tensors along a given axis.

      The tensors ranks and types must match, and their sizes must match in all dimensions except axis.

      Also available are stricter rank-specific methods that assert that @@ -11,12 +11,15 @@ dimensions except axis.

    Except tf.concat1d (which does not have axis param), all methods have same signature as this method.

    -
    const a = tf.tensor1d([1, 2]);
    const b = tf.tensor1d([3, 4]);
    a.concat(b).print();  // or a.concat(b)
-
    -
    const a = tf.tensor1d([1, 2]);
    const b = tf.tensor1d([3, 4]);
    const c = tf.tensor1d([5, 6]);
    tf.concat([a, b, c]).print();
-
    -
    const a = tf.tensor2d([[1, 2], [10, 20]]);
    const b = tf.tensor2d([[3, 4], [30, 40]]);
    const axis = 1;
    tf.concat([a, b], axis).print();
-
    -

    Type Parameters

    Parameters

    • tensors: (TensorLike | T)[]

      A list of tensors to concatenate.

      -
    • Optional axis: number

      The axis to concatenate along. Defaults to 0 (the first dim).

      -

    Returns T

    Doc

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\ No newline at end of file +
const a = tf.tensor1d([1, 2]);
const b = tf.tensor1d([3, 4]);
a.concat(b).print();  // or a.concat(b)
+
+ +
const a = tf.tensor1d([1, 2]);
const b = tf.tensor1d([3, 4]);
const c = tf.tensor1d([5, 6]);
tf.concat([a, b, c]).print();
+
+ +
const a = tf.tensor2d([[1, 2], [10, 20]]);
const b = tf.tensor2d([[3, 4], [30, 40]]);
const axis = 1;
tf.concat([a, b], axis).print();
+
+ +

Type Parameters

Parameters

  • tensors: (TensorLike | T)[]

    A list of tensors to concatenate.

    +
  • Optionalaxis: number

    The axis to concatenate along. Defaults to 0 (the first dim).

    +

Returns T

Doc

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diff --git a/typedoc/functions/tf.conv2d.html b/typedoc/functions/tf.conv2d.html index ea543b4..8174a79 100644 --- a/typedoc/functions/tf.conv2d.html +++ b/typedoc/functions/tf.conv2d.html @@ -1,24 +1,24 @@ -conv2d | @vladmandic/face-api - v1.7.13

Function conv2d

  • conv2d<T>(x, filter, strides, pad, dataFormat?, dilations?, dimRoundingMode?): T

  • Computes a 2D convolution over the input x.

    -

    Type Parameters

    Parameters

    • x: TensorLike | T

      The input tensor, of rank 4 or rank 3, of shape - [batch, height, width, inChannels]. If rank 3, batch of 1 is +conv2d | @vladmandic/face-api - v1.7.14

      Function conv2d

      • conv2d<T>(x, filter, strides, pad, dataFormat?, dilations?, dimRoundingMode?): T

      • Computes a 2D convolution over the input x.

        +

        Type Parameters

        Parameters

        • x: TensorLike | T

          The input tensor, of rank 4 or rank 3, of shape +[batch, height, width, inChannels]. If rank 3, batch of 1 is assumed.

          -
        • filter: Tensor4D | TensorLike

          The filter, rank 4, of shape - [filterHeight, filterWidth, inDepth, outDepth].

          -
        • strides: number | [number, number]

          The strides of the convolution: [strideHeight, strideWidth].

          -
        • pad: number | "valid" | "same" | ExplicitPadding

          The type of padding algorithm.

          +
        • filter: Tensor4D | TensorLike

          The filter, rank 4, of shape +[filterHeight, filterWidth, inDepth, outDepth].

          +
        • strides: number | [number, number]

          The strides of the convolution: [strideHeight, strideWidth].

          +
        • pad:
              | number
              | "valid"
              | "same"
              | ExplicitPadding

          The type of padding algorithm.

          -
        • Optional dataFormat: "NHWC" | "NCHW"
        • Optional dilations: number | [number, number]

          The dilation rates: [dilationHeight, dilationWidth] - in which we sample input values across the height and width dimensions - in atrous convolution. Defaults to [1, 1]. If dilations is a single - number, then dilationHeight == dilationWidth. If it is greater than - 1, then all values of strides must be 1.

          -
        • Optional dimRoundingMode: "floor" | "round" | "ceil"

          A string from: 'ceil', 'round', 'floor'. If none is - provided, it will default to truncate.

          -

        Returns T

        Doc

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      \ No newline at end of file +
    • OptionaldataFormat: "NHWC" | "NCHW"
    • Optionaldilations: number | [number, number]

      The dilation rates: [dilationHeight, dilationWidth] +in which we sample input values across the height and width dimensions +in atrous convolution. Defaults to [1, 1]. If dilations is a single +number, then dilationHeight == dilationWidth. If it is greater than +1, then all values of strides must be 1.

      +
    • OptionaldimRoundingMode: "floor" | "round" | "ceil"

      A string from: 'ceil', 'round', 'floor'. If none is +provided, it will default to truncate.

      +

    Returns T

    Doc

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diff --git a/typedoc/functions/tf.depthwiseConv2d.html b/typedoc/functions/tf.depthwiseConv2d.html index d45d46a..15f67af 100644 --- a/typedoc/functions/tf.depthwiseConv2d.html +++ b/typedoc/functions/tf.depthwiseConv2d.html @@ -1,4 +1,4 @@ -depthwiseConv2d | @vladmandic/face-api - v1.7.13

Function depthwiseConv2d

  • depthwiseConv2d<T>(x, filter, strides, pad, dataFormat?, dilations?, dimRoundingMode?): T

  • Depthwise 2D convolution.

    +depthwiseConv2d | @vladmandic/face-api - v1.7.14

    Function depthwiseConv2d

    • depthwiseConv2d<T>(x, filter, strides, pad, dataFormat?, dilations?, dimRoundingMode?): T

    • Depthwise 2D convolution.

      Given a 4D input array and a filter array of shape [filterHeight, filterWidth, inChannels, channelMultiplier] containing inChannels convolutional filters of depth 1, this op applies a @@ -6,28 +6,28 @@ different filter to each input channel (expanding from 1 channel to channelMultiplier channels for each), then concatenates the results together. The output has inChannels * channelMultiplier channels.

      See -https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d +https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d for more details.

      -

      Type Parameters

      Parameters

      • x: TensorLike | T

        The input tensor, of rank 4 or rank 3, of shape - [batch, height, width, inChannels]. If rank 3, batch of 1 is +

        Type Parameters

        Parameters

        • x: TensorLike | T

          The input tensor, of rank 4 or rank 3, of shape +[batch, height, width, inChannels]. If rank 3, batch of 1 is assumed.

          -
        • filter: Tensor4D | TensorLike

          The filter tensor, rank 4, of shape - [filterHeight, filterWidth, inChannels, channelMultiplier].

          -
        • strides: number | [number, number]

          The strides of the convolution: [strideHeight, strideWidth]. If strides is a single number, then strideHeight == strideWidth.

          -
        • pad: number | "valid" | "same" | ExplicitPadding

          The type of padding algorithm.

          +
        • filter: Tensor4D | TensorLike

          The filter tensor, rank 4, of shape +[filterHeight, filterWidth, inChannels, channelMultiplier].

          +
        • strides: number | [number, number]

          The strides of the convolution: [strideHeight, strideWidth]. If strides is a single number, then strideHeight == strideWidth.

          +
        • pad:
              | number
              | "valid"
              | "same"
              | ExplicitPadding

          The type of padding algorithm.

          -
        • Optional dataFormat: "NHWC" | "NCHW"
        • Optional dilations: number | [number, number]

          The dilation rates: [dilationHeight, dilationWidth] - in which we sample input values across the height and width dimensions - in atrous convolution. Defaults to [1, 1]. If rate is a single - number, then dilationHeight == dilationWidth. If it is greater than - 1, then all values of strides must be 1.

          -
        • Optional dimRoundingMode: "floor" | "round" | "ceil"

          A string from: 'ceil', 'round', 'floor'. If none is - provided, it will default to truncate.

          -

        Returns T

        Doc

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    \ No newline at end of file +
  • OptionaldataFormat: "NHWC" | "NCHW"
  • Optionaldilations: number | [number, number]

    The dilation rates: [dilationHeight, dilationWidth] +in which we sample input values across the height and width dimensions +in atrous convolution. Defaults to [1, 1]. If rate is a single +number, then dilationHeight == dilationWidth. If it is greater than +1, then all values of strides must be 1.

    +
  • OptionaldimRoundingMode: "floor" | "round" | "ceil"

    A string from: 'ceil', 'round', 'floor'. If none is +provided, it will default to truncate.

    +

Returns T

Doc

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diff --git a/typedoc/functions/tf.div.html b/typedoc/functions/tf.div.html index c485006..3744661 100644 --- a/typedoc/functions/tf.div.html +++ b/typedoc/functions/tf.div.html @@ -1,9 +1,11 @@ -div | @vladmandic/face-api - v1.7.13

Function div

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diff --git a/typedoc/functions/tf.exp.html b/typedoc/functions/tf.exp.html index 542b1a2..cc044b8 100644 --- a/typedoc/functions/tf.exp.html +++ b/typedoc/functions/tf.exp.html @@ -1,5 +1,6 @@ -exp | @vladmandic/face-api - v1.7.13

Function exp

  • exp<T>(x): T

  • Computes exponential of the input tf.Tensor element-wise. e ^ x

    -
    const x = tf.tensor1d([1, 2, -3]);

    x.exp().print();  // or tf.exp(x)
-
    -

    Type Parameters

    Parameters

    Returns T

    Doc

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\ No newline at end of file +exp | @vladmandic/face-api - v1.7.14

Function exp

  • exp<T>(x): T

  • Computes exponential of the input tf.Tensor element-wise. e ^ x

    +
    const x = tf.tensor1d([1, 2, -3]);

    x.exp().print();  // or tf.exp(x)
+
    + +

    Type Parameters

    Parameters

    Returns T

    Doc

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diff --git a/typedoc/functions/tf.expandDims.html b/typedoc/functions/tf.expandDims.html index e94be51..1cf1c95 100644 --- a/typedoc/functions/tf.expandDims.html +++ b/typedoc/functions/tf.expandDims.html @@ -1,8 +1,9 @@ -expandDims | @vladmandic/face-api - v1.7.13

Function expandDims

  • expandDims<T>(x, axis?): T

  • Returns a tf.Tensor that has expanded rank, by inserting a dimension -into the tensor's shape.

    -
    const x = tf.tensor1d([1, 2, 3, 4]);
    const axis = 1;
    x.expandDims(axis).print();
-
    -

    Type Parameters

    Parameters

    • x: TensorLike | Tensor<Rank>

      The input tensor whose dimensions are to be expanded.

      -
    • Optional axis: number

      The dimension index at which to insert shape of 1. Defaults - to 0 (the first dimension).

      -

    Returns T

    Doc

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\ No newline at end of file +expandDims | @vladmandic/face-api - v1.7.14

Function expandDims

  • expandDims<T>(x, axis?): T

  • Returns a tf.Tensor that has expanded rank, by inserting a dimension +into the tensor's shape.

    +
    const x = tf.tensor1d([1, 2, 3, 4]);
    const axis = 1;
    x.expandDims(axis).print();
+
    + +

    Type Parameters

    Parameters

    • x: TensorLike | Tensor<Rank>

      The input tensor whose dimensions are to be expanded.

      +
    • Optionalaxis: number

      The dimension index at which to insert shape of 1. Defaults +to 0 (the first dimension).

      +

    Returns T

    Doc

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diff --git a/typedoc/functions/tf.fill.html b/typedoc/functions/tf.fill.html index b36476c..91ddea1 100644 --- a/typedoc/functions/tf.fill.html +++ b/typedoc/functions/tf.fill.html @@ -1,8 +1,9 @@ -fill | @vladmandic/face-api - v1.7.13

Function fill

  • fill<R>(shape, value, dtype?): Tensor<R>

  • Creates a tf.Tensor filled with a scalar value.

    -
    tf.fill([2, 2], 4).print();
-
    -

    Type Parameters

    Parameters

    • shape: ShapeMap[R]

      An array of integers defining the output tensor shape.

      -
    • value: string | number

      The scalar value to fill the tensor with.

      -
    • Optional dtype: keyof DataTypeMap

      The type of an element in the resulting tensor. Defaults to - 'float32' if the given param value is a number, otherwise 'string'.

      -

    Returns Tensor<R>

    Doc

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\ No newline at end of file +fill | @vladmandic/face-api - v1.7.14

Function fill

  • fill<R>(shape, value, dtype?): Tensor<R>

  • Creates a tf.Tensor filled with a scalar value.

    +
    tf.fill([2, 2], 4).print();
+
    + +

    Type Parameters

    Parameters

    • shape: ShapeMap[R]

      An array of integers defining the output tensor shape.

      +
    • value: string | number

      The scalar value to fill the tensor with.

      +
    • Optionaldtype: keyof DataTypeMap

      The type of an element in the resulting tensor. Defaults to +'float32' if the given param value is a number, otherwise 'string'.

      +

    Returns Tensor<R>

    Doc

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diff --git a/typedoc/functions/tf.io.browserFiles.html b/typedoc/functions/tf.io.browserFiles.html index c9e78ff..b407086 100644 --- a/typedoc/functions/tf.io.browserFiles.html +++ b/typedoc/functions/tf.io.browserFiles.html @@ -1,13 +1,14 @@ -browserFiles | @vladmandic/face-api - v1.7.13

Function browserFiles

  • browserFiles(files): IOHandler

  • Creates an IOHandler that loads model artifacts from user-selected files.

    +browserFiles | @vladmandic/face-api - v1.7.14

    Function browserFiles

    • browserFiles(files): IOHandler

    • Creates an IOHandler that loads model artifacts from user-selected files.

      This method can be used for loading from files such as user-selected files in the browser. When used in conjunction with tf.loadLayersModel, an instance of tf.LayersModel (Keras-style) can be constructed from the loaded artifacts.

      -
      // Note: This code snippet won't run properly without the actual file input
      //   elements in the HTML DOM.

      // Suppose there are two HTML file input (`<input type="file" ...>`)
      // elements.
      const uploadJSONInput = document.getElementById('upload-json');
      const uploadWeightsInput = document.getElementById('upload-weights');
      const model = await tf.loadLayersModel(tf.io.browserFiles(
          [uploadJSONInput.files[0], uploadWeightsInput.files[0]]));
-
      -

      Parameters

      • files: File[]

        Files to load from. Currently, this function supports only - loading from files that contain Keras-style models (i.e., tf.Models), for - which an Array of Files is expected (in that order):

        +
        // Note: This code snippet won't run properly without the actual file input
        //   elements in the HTML DOM.

        // Suppose there are two HTML file input (`<input type="file" ...>`)
        // elements.
        const uploadJSONInput = document.getElementById('upload-json');
        const uploadWeightsInput = document.getElementById('upload-weights');
        const model = await tf.loadLayersModel(tf.io.browserFiles(
            [uploadJSONInput.files[0], uploadWeightsInput.files[0]]));
+
        + +

        Parameters

        • files: File[]

          Files to load from. Currently, this function supports only +loading from files that contain Keras-style models (i.e., tf.Models), for +which an Array of Files is expected (in that order):

          • A JSON file containing the model topology and weight manifest.
          • Optionally, one or more binary files containing the binary weights. @@ -19,4 +20,4 @@ Python PIP package. If no weights files are provided, only the model topology will be loaded from the JSON file above.

        Returns IOHandler

        An instance of Files IOHandler.

        -

        Doc

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    \ No newline at end of file +

    Doc

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diff --git a/typedoc/functions/tf.io.browserHTTPRequest.html b/typedoc/functions/tf.io.browserHTTPRequest.html index e1e9bd0..2003b47 100644 --- a/typedoc/functions/tf.io.browserHTTPRequest.html +++ b/typedoc/functions/tf.io.browserHTTPRequest.html @@ -1,2 +1,2 @@ -browserHTTPRequest | @vladmandic/face-api - v1.7.13

Function browserHTTPRequest

  • browserHTTPRequest(path, loadOptions?): IOHandler

  • Deprecated. Use tf.io.http.

    -

    Parameters

    Returns IOHandler

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\ No newline at end of file +browserHTTPRequest | @vladmandic/face-api - v1.7.14

Function browserHTTPRequest

  • browserHTTPRequest(path, loadOptions?): IOHandler

  • Deprecated. Use tf.io.http.

    +

    Parameters

    Returns IOHandler

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diff --git a/typedoc/functions/tf.io.concatenateArrayBuffers.html b/typedoc/functions/tf.io.concatenateArrayBuffers.html index 9d182c4..c345b6f 100644 --- a/typedoc/functions/tf.io.concatenateArrayBuffers.html +++ b/typedoc/functions/tf.io.concatenateArrayBuffers.html @@ -1,6 +1,6 @@ -concatenateArrayBuffers | @vladmandic/face-api - v1.7.13

Function concatenateArrayBuffers

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diff --git a/typedoc/functions/tf.io.copyModel.html b/typedoc/functions/tf.io.copyModel.html index ff46396..66fc871 100644 --- a/typedoc/functions/tf.io.copyModel.html +++ b/typedoc/functions/tf.io.copyModel.html @@ -1,17 +1,18 @@ -copyModel | @vladmandic/face-api - v1.7.13

Function copyModel

  • copyModel(sourceURL, destURL): Promise<ModelArtifactsInfo>

  • Copy a model from one URL to another.

    +copyModel | @vladmandic/face-api - v1.7.14

    Function copyModel

    • copyModel(sourceURL, destURL): Promise<ModelArtifactsInfo>

    • Copy a model from one URL to another.

      This function supports:

      1. Copying within a storage medium, e.g., -tf.io.copyModel('localstorage://model-1', 'localstorage://model-2')
        2. +tf.io.copyModel('localstorage://model-1', 'localstorage://model-2')
      2. Copying between two storage mediums, e.g., -tf.io.copyModel('localstorage://model-1', 'indexeddb://model-1')
        3. +tf.io.copyModel('localstorage://model-1', 'indexeddb://model-1')
      -
      // First create and save a model.
      const model = tf.sequential();
      model.add(tf.layers.dense(
          {units: 1, inputShape: [10], activation: 'sigmoid'}));
      await model.save('localstorage://demo/management/model1');

      // Then list existing models.
      console.log(JSON.stringify(await tf.io.listModels()));

      // Copy the model, from Local Storage to IndexedDB.
      await tf.io.copyModel(
          'localstorage://demo/management/model1',
          'indexeddb://demo/management/model1');

      // List models again.
      console.log(JSON.stringify(await tf.io.listModels()));

      // Remove both models.
      await tf.io.removeModel('localstorage://demo/management/model1');
      await tf.io.removeModel('indexeddb://demo/management/model1');
-
      -

      Parameters

      • sourceURL: string

        Source URL of copying.

        -
      • destURL: string

        Destination URL of copying.

        +
        // First create and save a model.
        const model = tf.sequential();
        model.add(tf.layers.dense(
            {units: 1, inputShape: [10], activation: 'sigmoid'}));
        await model.save('localstorage://demo/management/model1');

        // Then list existing models.
        console.log(JSON.stringify(await tf.io.listModels()));

        // Copy the model, from Local Storage to IndexedDB.
        await tf.io.copyModel(
            'localstorage://demo/management/model1',
            'indexeddb://demo/management/model1');

        // List models again.
        console.log(JSON.stringify(await tf.io.listModels()));

        // Remove both models.
        await tf.io.removeModel('localstorage://demo/management/model1');
        await tf.io.removeModel('indexeddb://demo/management/model1');
+
        + +

        Parameters

        • sourceURL: string

          Source URL of copying.

          +
        • destURL: string

          Destination URL of copying.

        Returns Promise<ModelArtifactsInfo>

        ModelArtifactsInfo of the copied model (if and only if copying - is successful).

        -

        Throws

        Error if copying fails, e.g., if no model exists at sourceURL, or - if oldPath and newPath are identical.

        -

        Doc

    Settings

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    \ No newline at end of file +is successful).

    +

    Throws

    Error if copying fails, e.g., if no model exists at sourceURL, or +if oldPath and newPath are identical.

    +

    Doc

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diff --git a/typedoc/functions/tf.io.decodeWeights.html b/typedoc/functions/tf.io.decodeWeights.html index ba16d73..210b1c7 100644 --- a/typedoc/functions/tf.io.decodeWeights.html +++ b/typedoc/functions/tf.io.decodeWeights.html @@ -1,12 +1,12 @@ -decodeWeights | @vladmandic/face-api - v1.7.13

Function decodeWeights

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diff --git a/typedoc/functions/tf.io.decodeWeightsStream.html b/typedoc/functions/tf.io.decodeWeightsStream.html index 863a359..e9502a7 100644 --- a/typedoc/functions/tf.io.decodeWeightsStream.html +++ b/typedoc/functions/tf.io.decodeWeightsStream.html @@ -1 +1 @@ -decodeWeightsStream | @vladmandic/face-api - v1.7.13

Function decodeWeightsStream

  • decodeWeightsStream(weightStream, specs): Promise<NamedTensorMap>

  • Parameters

    Returns Promise<NamedTensorMap>

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\ No newline at end of file +decodeWeightsStream | @vladmandic/face-api - v1.7.14

Function decodeWeightsStream

  • decodeWeightsStream(weightStream, specs): Promise<NamedTensorMap>

  • Parameters

    Returns Promise<NamedTensorMap>

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diff --git a/typedoc/functions/tf.io.encodeWeights.html b/typedoc/functions/tf.io.encodeWeights.html index e120979..0f499bd 100644 --- a/typedoc/functions/tf.io.encodeWeights.html +++ b/typedoc/functions/tf.io.encodeWeights.html @@ -1,9 +1,9 @@ -encodeWeights | @vladmandic/face-api - v1.7.13

Function encodeWeights

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diff --git a/typedoc/functions/tf.io.fromMemory.html b/typedoc/functions/tf.io.fromMemory.html index 8dc0d23..8b98375 100644 --- a/typedoc/functions/tf.io.fromMemory.html +++ b/typedoc/functions/tf.io.fromMemory.html @@ -1,14 +1,15 @@ -fromMemory | @vladmandic/face-api - v1.7.13

Function fromMemory

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diff --git a/typedoc/functions/tf.io.fromMemorySync.html b/typedoc/functions/tf.io.fromMemorySync.html index c2fd46c..ab3ec5d 100644 --- a/typedoc/functions/tf.io.fromMemorySync.html +++ b/typedoc/functions/tf.io.fromMemorySync.html @@ -1,14 +1,15 @@ -fromMemorySync | @vladmandic/face-api - v1.7.13

Function fromMemorySync

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diff --git a/typedoc/functions/tf.io.getLoadHandlers.html b/typedoc/functions/tf.io.getLoadHandlers.html index 5a4404b..2fcd518 100644 --- a/typedoc/functions/tf.io.getLoadHandlers.html +++ b/typedoc/functions/tf.io.getLoadHandlers.html @@ -1 +1 @@ -getLoadHandlers | @vladmandic/face-api - v1.7.13

Function getLoadHandlers

  • getLoadHandlers(url, loadOptions?): IOHandler[]

  • Parameters

    • url: string | string[]
    • Optional loadOptions: LoadOptions

    Returns IOHandler[]

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\ No newline at end of file +getLoadHandlers | @vladmandic/face-api - v1.7.14

Function getLoadHandlers

  • getLoadHandlers(url, loadOptions?): IOHandler[]

  • Parameters

    Returns IOHandler[]

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diff --git a/typedoc/functions/tf.io.getModelArtifactsForJSON.html b/typedoc/functions/tf.io.getModelArtifactsForJSON.html index 262d6c0..301ef92 100644 --- a/typedoc/functions/tf.io.getModelArtifactsForJSON.html +++ b/typedoc/functions/tf.io.getModelArtifactsForJSON.html @@ -1,7 +1,7 @@ -getModelArtifactsForJSON | @vladmandic/face-api - v1.7.13

Function getModelArtifactsForJSON

  • getModelArtifactsForJSON(modelJSON, loadWeights): Promise<ModelArtifacts>

  • Create ModelArtifacts from a JSON file.

    -

    Parameters

    Returns Promise<ModelArtifacts>

    A Promise of the ModelArtifacts, as described by the JSON file.

    -

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\ No newline at end of file +getModelArtifactsForJSON | @vladmandic/face-api - v1.7.14

Function getModelArtifactsForJSON

  • getModelArtifactsForJSON(modelJSON, loadWeights): Promise<ModelArtifacts>

  • Create ModelArtifacts from a JSON file.

    +

    Parameters

    Returns Promise<ModelArtifacts>

    A Promise of the ModelArtifacts, as described by the JSON file.

    +

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diff --git a/typedoc/functions/tf.io.getModelArtifactsForJSONSync.html b/typedoc/functions/tf.io.getModelArtifactsForJSONSync.html index 613f54a..641738a 100644 --- a/typedoc/functions/tf.io.getModelArtifactsForJSONSync.html +++ b/typedoc/functions/tf.io.getModelArtifactsForJSONSync.html @@ -1,9 +1,9 @@ -getModelArtifactsForJSONSync | @vladmandic/face-api - v1.7.13

Function getModelArtifactsForJSONSync

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diff --git a/typedoc/functions/tf.io.getModelArtifactsInfoForJSON.html b/typedoc/functions/tf.io.getModelArtifactsInfoForJSON.html index 434f5c9..83f7050 100644 --- a/typedoc/functions/tf.io.getModelArtifactsInfoForJSON.html +++ b/typedoc/functions/tf.io.getModelArtifactsInfoForJSON.html @@ -1,3 +1,3 @@ -getModelArtifactsInfoForJSON | @vladmandic/face-api - v1.7.13

Function getModelArtifactsInfoForJSON

  • getModelArtifactsInfoForJSON(modelArtifacts): ModelArtifactsInfo

  • Populate ModelArtifactsInfo fields for a model with JSON topology.

    -

    Parameters

    Returns ModelArtifactsInfo

    A ModelArtifactsInfo object.

    -

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\ No newline at end of file +getModelArtifactsInfoForJSON | @vladmandic/face-api - v1.7.14

Function getModelArtifactsInfoForJSON

  • getModelArtifactsInfoForJSON(modelArtifacts): ModelArtifactsInfo

  • Populate ModelArtifactsInfo fields for a model with JSON topology.

    +

    Parameters

    Returns ModelArtifactsInfo

    A ModelArtifactsInfo object.

    +

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diff --git a/typedoc/functions/tf.io.getSaveHandlers.html b/typedoc/functions/tf.io.getSaveHandlers.html index 582aab6..034b94e 100644 --- a/typedoc/functions/tf.io.getSaveHandlers.html +++ b/typedoc/functions/tf.io.getSaveHandlers.html @@ -1 +1 @@ -getSaveHandlers | @vladmandic/face-api - v1.7.13

Function getSaveHandlers

  • getSaveHandlers(url): IOHandler[]

  • Parameters

    • url: string | string[]

    Returns IOHandler[]

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\ No newline at end of file +getSaveHandlers | @vladmandic/face-api - v1.7.14

Function getSaveHandlers

  • getSaveHandlers(url): IOHandler[]

  • Parameters

    • url: string | string[]

    Returns IOHandler[]

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diff --git a/typedoc/functions/tf.io.getWeightSpecs.html b/typedoc/functions/tf.io.getWeightSpecs.html index f93fff9..79380fc 100644 --- a/typedoc/functions/tf.io.getWeightSpecs.html +++ b/typedoc/functions/tf.io.getWeightSpecs.html @@ -1,5 +1,5 @@ -getWeightSpecs | @vladmandic/face-api - v1.7.13

Function getWeightSpecs

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diff --git a/typedoc/functions/tf.io.http.html b/typedoc/functions/tf.io.http.html index cc52f65..173423e 100644 --- a/typedoc/functions/tf.io.http.html +++ b/typedoc/functions/tf.io.http.html @@ -1,4 +1,4 @@ -http | @vladmandic/face-api - v1.7.13

Function http

  • http(path, loadOptions?): IOHandler

  • Creates an IOHandler subtype that sends model artifacts to HTTP server.

    +http | @vladmandic/face-api - v1.7.14

    Function http

    • http(path, loadOptions?): IOHandler

    • Creates an IOHandler subtype that sends model artifacts to HTTP server.

      An HTTP request of the multipart/form-data mime type will be sent to the path URL. The form data includes artifacts that represent the topology and/or weights of the model. In the case of Keras-style tf.Model, two @@ -7,28 +7,30 @@ blobs (files) exist in form-data:

    • A JSON file consisting of modelTopology and weightsManifest.
    • A binary weights file consisting of the concatenated weight values. These files are in the same format as the one generated by -tfjs_converter.
      • +tfjs_converter.

    The following code snippet exemplifies the client-side code that uses this function:

    -
    const model = tf.sequential();
    model.add(
        tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));

    const saveResult = await model.save(tf.io.http(
        'http://model-server:5000/upload', {requestInit: {method: 'PUT'}}));
    console.log(saveResult);
-
    +
    const model = tf.sequential();
    model.add(
        tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));

    const saveResult = await model.save(tf.io.http(
        'http://model-server:5000/upload', {requestInit: {method: 'PUT'}}));
    console.log(saveResult);
+
    +

    If the default POST method is to be used, without any custom parameters such as headers, you can simply pass an HTTP or HTTPS URL to model.save:

    -
    const saveResult = await model.save('http://model-server:5000/upload');
-
    +
    const saveResult = await model.save('http://model-server:5000/upload');
+
    +

    The following GitHub Gist -https://gist.github.com/dsmilkov/1b6046fd6132d7408d5257b0976f7864 -implements a server based on flask that +https://gist.github.com/dsmilkov/1b6046fd6132d7408d5257b0976f7864 +implements a server based on flask that can receive the request. Upon receiving the model artifacts via the requst, -this particular server reconstitutes instances of Keras +this particular server reconstitutes instances of Keras Models in memory.

    -

    Parameters

    • path: string

      A URL path to the model. - Can be an absolute HTTP path (e.g., - 'http://localhost:8000/model-upload)') or a relative path (e.g., - './model-upload').

      -
    • Optional loadOptions: LoadOptions

      Optional configuration for the loading. It includes the - following fields:

      +

      Parameters

      • path: string

        A URL path to the model. +Can be an absolute HTTP path (e.g., +'http://localhost:8000/model-upload)') or a relative path (e.g., +'./model-upload').

        +
      • OptionalloadOptions: LoadOptions

        Optional configuration for the loading. It includes the +following fields:

        • weightPathPrefix Optional, this specifies the path prefix for weight files, by default this is calculated from the path param.
          • @@ -38,4 +40,4 @@ the fetch from node-fetch can be used here. before the load is completed.

      Returns IOHandler

      An instance of IOHandler.

      -

      Doc

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\ No newline at end of file +

Doc

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diff --git a/typedoc/functions/tf.io.isHTTPScheme.html b/typedoc/functions/tf.io.isHTTPScheme.html index b6d79ec..82861a7 100644 --- a/typedoc/functions/tf.io.isHTTPScheme.html +++ b/typedoc/functions/tf.io.isHTTPScheme.html @@ -1 +1 @@ -isHTTPScheme | @vladmandic/face-api - v1.7.13

Function isHTTPScheme

  • isHTTPScheme(url): boolean

  • Parameters

    • url: string

    Returns boolean

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\ No newline at end of file +isHTTPScheme | @vladmandic/face-api - v1.7.14

Function isHTTPScheme

  • isHTTPScheme(url): boolean

  • Parameters

    • url: string

    Returns boolean

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diff --git a/typedoc/functions/tf.io.listModels.html b/typedoc/functions/tf.io.listModels.html index 7118577..def2bce 100644 --- a/typedoc/functions/tf.io.listModels.html +++ b/typedoc/functions/tf.io.listModels.html @@ -1,10 +1,11 @@ -listModels | @vladmandic/face-api - v1.7.13

Function listModels

  • listModels(): Promise<{
        [url: string]: ModelArtifactsInfo;
    }>

  • List all models stored in registered storage mediums.

    +listModels | @vladmandic/face-api - v1.7.14

    Function listModels

    • listModels(): Promise<{
          [url: string]: ModelArtifactsInfo;
      }>

    • List all models stored in registered storage mediums.

      For a web browser environment, the registered mediums are Local Storage and IndexedDB.

      -
      // First create and save a model.
      const model = tf.sequential();
      model.add(tf.layers.dense(
          {units: 1, inputShape: [10], activation: 'sigmoid'}));
      await model.save('localstorage://demo/management/model1');

      // Then list existing models.
      console.log(JSON.stringify(await tf.io.listModels()));

      // Delete the model.
      await tf.io.removeModel('localstorage://demo/management/model1');

      // List models again.
      console.log(JSON.stringify(await tf.io.listModels()));
-
      +
      // First create and save a model.
      const model = tf.sequential();
      model.add(tf.layers.dense(
          {units: 1, inputShape: [10], activation: 'sigmoid'}));
      await model.save('localstorage://demo/management/model1');

      // Then list existing models.
      console.log(JSON.stringify(await tf.io.listModels()));

      // Delete the model.
      await tf.io.removeModel('localstorage://demo/management/model1');

      // List models again.
      console.log(JSON.stringify(await tf.io.listModels()));
+
      +

      Returns Promise<{
          [url: string]: ModelArtifactsInfo;
      }>

      A Promise of a dictionary mapping URLs of existing models to their model artifacts info. URLs include medium-specific schemes, e.g., - 'indexeddb://my/model/1'. Model artifacts info include type of the -model's topology, byte sizes of the topology, weights, etc.

      -

      Doc

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    \ No newline at end of file +'indexeddb://my/model/1'. Model artifacts info include type of the +model's topology, byte sizes of the topology, weights, etc.

    +

    Doc

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diff --git a/typedoc/functions/tf.io.loadWeights.html b/typedoc/functions/tf.io.loadWeights.html index b6b059b..227f71b 100644 --- a/typedoc/functions/tf.io.loadWeights.html +++ b/typedoc/functions/tf.io.loadWeights.html @@ -1,7 +1,7 @@ -loadWeights | @vladmandic/face-api - v1.7.13

Function loadWeights

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diff --git a/typedoc/functions/tf.io.moveModel.html b/typedoc/functions/tf.io.moveModel.html index 78dff7b..ba18c8d 100644 --- a/typedoc/functions/tf.io.moveModel.html +++ b/typedoc/functions/tf.io.moveModel.html @@ -1,17 +1,18 @@ -moveModel | @vladmandic/face-api - v1.7.13

Function moveModel

  • moveModel(sourceURL, destURL): Promise<ModelArtifactsInfo>

  • Move a model from one URL to another.

    +moveModel | @vladmandic/face-api - v1.7.14

    Function moveModel

    • moveModel(sourceURL, destURL): Promise<ModelArtifactsInfo>

    • Move a model from one URL to another.

      This function supports:

      1. Moving within a storage medium, e.g., -tf.io.moveModel('localstorage://model-1', 'localstorage://model-2')
        2. +tf.io.moveModel('localstorage://model-1', 'localstorage://model-2')
      2. Moving between two storage mediums, e.g., -tf.io.moveModel('localstorage://model-1', 'indexeddb://model-1')
        3. +tf.io.moveModel('localstorage://model-1', 'indexeddb://model-1')
      -
      // First create and save a model.
      const model = tf.sequential();
      model.add(tf.layers.dense(
          {units: 1, inputShape: [10], activation: 'sigmoid'}));
      await model.save('localstorage://demo/management/model1');

      // Then list existing models.
      console.log(JSON.stringify(await tf.io.listModels()));

      // Move the model, from Local Storage to IndexedDB.
      await tf.io.moveModel(
          'localstorage://demo/management/model1',
          'indexeddb://demo/management/model1');

      // List models again.
      console.log(JSON.stringify(await tf.io.listModels()));

      // Remove the moved model.
      await tf.io.removeModel('indexeddb://demo/management/model1');
-
      -

      Parameters

      • sourceURL: string

        Source URL of moving.

        -
      • destURL: string

        Destination URL of moving.

        +
        // First create and save a model.
        const model = tf.sequential();
        model.add(tf.layers.dense(
            {units: 1, inputShape: [10], activation: 'sigmoid'}));
        await model.save('localstorage://demo/management/model1');

        // Then list existing models.
        console.log(JSON.stringify(await tf.io.listModels()));

        // Move the model, from Local Storage to IndexedDB.
        await tf.io.moveModel(
            'localstorage://demo/management/model1',
            'indexeddb://demo/management/model1');

        // List models again.
        console.log(JSON.stringify(await tf.io.listModels()));

        // Remove the moved model.
        await tf.io.removeModel('indexeddb://demo/management/model1');
+
        + +

        Parameters

        • sourceURL: string

          Source URL of moving.

          +
        • destURL: string

          Destination URL of moving.

        Returns Promise<ModelArtifactsInfo>

        ModelArtifactsInfo of the copied model (if and only if copying - is successful).

        -

        Throws

        Error if moving fails, e.g., if no model exists at sourceURL, or - if oldPath and newPath are identical.

        -

        Doc

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    \ No newline at end of file +is successful).

    +

    Throws

    Error if moving fails, e.g., if no model exists at sourceURL, or +if oldPath and newPath are identical.

    +

    Doc

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diff --git a/typedoc/functions/tf.io.registerLoadRouter.html b/typedoc/functions/tf.io.registerLoadRouter.html index 137338f..172a023 100644 --- a/typedoc/functions/tf.io.registerLoadRouter.html +++ b/typedoc/functions/tf.io.registerLoadRouter.html @@ -1 +1 @@ -registerLoadRouter | @vladmandic/face-api - v1.7.13

Function registerLoadRouter

  • registerLoadRouter(loudRouter): void

  • Parameters

    • loudRouter: IORouter

    Returns void

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\ No newline at end of file +registerLoadRouter | @vladmandic/face-api - v1.7.14

Function registerLoadRouter

  • registerLoadRouter(loudRouter): void

  • Parameters

    • loudRouter: IORouter

    Returns void

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diff --git a/typedoc/functions/tf.io.registerSaveRouter.html b/typedoc/functions/tf.io.registerSaveRouter.html index c9152be..afdbad3 100644 --- a/typedoc/functions/tf.io.registerSaveRouter.html +++ b/typedoc/functions/tf.io.registerSaveRouter.html @@ -1 +1 @@ -registerSaveRouter | @vladmandic/face-api - v1.7.13

Function registerSaveRouter

  • registerSaveRouter(loudRouter): void

  • Parameters

    • loudRouter: IORouter

    Returns void

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\ No newline at end of file +registerSaveRouter | @vladmandic/face-api - v1.7.14

Function registerSaveRouter

  • registerSaveRouter(loudRouter): void

  • Parameters

    • loudRouter: IORouter

    Returns void

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diff --git a/typedoc/functions/tf.io.removeModel.html b/typedoc/functions/tf.io.removeModel.html index 3379c9d..bcf4fff 100644 --- a/typedoc/functions/tf.io.removeModel.html +++ b/typedoc/functions/tf.io.removeModel.html @@ -1,9 +1,10 @@ -removeModel | @vladmandic/face-api - v1.7.13

Function removeModel

  • removeModel(url): Promise<ModelArtifactsInfo>

  • Remove a model specified by URL from a registered storage medium.

    -
    // First create and save a model.
    const model = tf.sequential();
    model.add(tf.layers.dense(
        {units: 1, inputShape: [10], activation: 'sigmoid'}));
    await model.save('localstorage://demo/management/model1');

    // Then list existing models.
    console.log(JSON.stringify(await tf.io.listModels()));

    // Delete the model.
    await tf.io.removeModel('localstorage://demo/management/model1');

    // List models again.
    console.log(JSON.stringify(await tf.io.listModels()));
-
    -

    Parameters

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diff --git a/typedoc/functions/tf.io.weightsLoaderFactory.html b/typedoc/functions/tf.io.weightsLoaderFactory.html index 00a333d..58f74ce 100644 --- a/typedoc/functions/tf.io.weightsLoaderFactory.html +++ b/typedoc/functions/tf.io.weightsLoaderFactory.html @@ -1,8 +1,9 @@ -weightsLoaderFactory | @vladmandic/face-api - v1.7.13

Function weightsLoaderFactory

  • weightsLoaderFactory(fetchWeightsFunction): ((manifest, filePathPrefix?, weightNames?) => Promise<NamedTensorMap>)

  • Creates a function, which reads a weights manifest JSON configuration, +weightsLoaderFactory | @vladmandic/face-api - v1.7.14

    Function weightsLoaderFactory

    • weightsLoaderFactory(fetchWeightsFunction): ((manifest: WeightsManifestConfig, filePathPrefix?: string, weightNames?: string[]) => Promise<NamedTensorMap>)

    • Creates a function, which reads a weights manifest JSON configuration, fetches the weight files using the specified function and returns them as Tensors.

      -
      // example for creating a nodejs weight loader, which reads the weight files
      // from disk using fs.readFileSync

      import * as fs from 'fs'

      const fetchWeightsFromDisk = (filePaths: string[]) =>
        filePaths.map(filePath => fs.readFileSync(filePath).buffer)

      const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)

      const manifest = JSON.parse(
        fs.readFileSync('./my_model-weights_manifest').toString()
      )
      const weightMap = await loadWeights(manifest, './')
-
      -

      Parameters

      • fetchWeightsFunction: ((fetchUrls) => Promise<ArrayBuffer[]>)

        The function used for fetching the weight files.

        -
          • (fetchUrls): Promise<ArrayBuffer[]>

          • Parameters

            • fetchUrls: string[]

            Returns Promise<ArrayBuffer[]>

      Returns ((manifest, filePathPrefix?, weightNames?) => Promise<NamedTensorMap>)

      Weight loading function.

      -

    Settings

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    \ No newline at end of file +
    // example for creating a nodejs weight loader, which reads the weight files
    // from disk using fs.readFileSync

    import * as fs from 'fs'

    const fetchWeightsFromDisk = (filePaths: string[]) =>
      filePaths.map(filePath => fs.readFileSync(filePath).buffer)

    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)

    const manifest = JSON.parse(
      fs.readFileSync('./my_model-weights_manifest').toString()
    )
    const weightMap = await loadWeights(manifest, './')
+
    + +

    Parameters

    • fetchWeightsFunction: ((fetchUrls: string[]) => Promise<ArrayBuffer[]>)

      The function used for fetching the weight files.

      +
        • (fetchUrls): Promise<ArrayBuffer[]>

        • Parameters

          • fetchUrls: string[]

          Returns Promise<ArrayBuffer[]>

    Returns ((manifest: WeightsManifestConfig, filePathPrefix?: string, weightNames?: string[]) => Promise<NamedTensorMap>)

    Weight loading function.

    +

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diff --git a/typedoc/functions/tf.io.withSaveHandler.html b/typedoc/functions/tf.io.withSaveHandler.html index f829761..18a8b7d 100644 --- a/typedoc/functions/tf.io.withSaveHandler.html +++ b/typedoc/functions/tf.io.withSaveHandler.html @@ -1,6 +1,7 @@ -withSaveHandler | @vladmandic/face-api - v1.7.13

Function withSaveHandler

  • withSaveHandler(saveHandler): IOHandler

  • Creates an IOHandler that passes saved model artifacts to a callback.

    -
    function handleSave(artifacts) {
      // ... do something with the artifacts ...
      return {modelArtifactsInfo: {...}, ...};
    }

    const saveResult = model.save(tf.io.withSaveHandler(handleSave));
-
    -

    Parameters

    • saveHandler: ((artifacts) => Promise<SaveResult>)

      A function that accepts a ModelArtifacts and returns a - promise that resolves to a SaveResult.

      -

    Returns IOHandler

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\ No newline at end of file +withSaveHandler | @vladmandic/face-api - v1.7.14

Function withSaveHandler

  • withSaveHandler(saveHandler): IOHandler

  • Creates an IOHandler that passes saved model artifacts to a callback.

    +
    function handleSave(artifacts) {
      // ... do something with the artifacts ...
      return {modelArtifactsInfo: {...}, ...};
    }

    const saveResult = model.save(tf.io.withSaveHandler(handleSave));
+
    + +

    Parameters

    Returns IOHandler

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diff --git a/typedoc/functions/tf.io.withSaveHandlerSync.html b/typedoc/functions/tf.io.withSaveHandlerSync.html index 74be6ac..4f2029c 100644 --- a/typedoc/functions/tf.io.withSaveHandlerSync.html +++ b/typedoc/functions/tf.io.withSaveHandlerSync.html @@ -1,6 +1,7 @@ -withSaveHandlerSync | @vladmandic/face-api - v1.7.13

Function withSaveHandlerSync

  • withSaveHandlerSync(saveHandler): IOHandlerSync

  • Creates an IOHandlerSync that passes saved model artifacts to a callback.

    -
    function handleSave(artifacts) {
      // ... do something with the artifacts ...
      return {modelArtifactsInfo: {...}, ...};
    }

    const saveResult = model.save(tf.io.withSaveHandler(handleSave));
-
    -

    Parameters

    Returns IOHandlerSync

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\ No newline at end of file +withSaveHandlerSync | @vladmandic/face-api - v1.7.14

Function withSaveHandlerSync

  • withSaveHandlerSync(saveHandler): IOHandlerSync

  • Creates an IOHandlerSync that passes saved model artifacts to a callback.

    +
    function handleSave(artifacts) {
      // ... do something with the artifacts ...
      return {modelArtifactsInfo: {...}, ...};
    }

    const saveResult = model.save(tf.io.withSaveHandler(handleSave));
+
    + +

    Parameters

    Returns IOHandlerSync

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diff --git a/typedoc/functions/tf.matMul.html b/typedoc/functions/tf.matMul.html index a6af8fa..11615e2 100644 --- a/typedoc/functions/tf.matMul.html +++ b/typedoc/functions/tf.matMul.html @@ -1,8 +1,9 @@ -matMul | @vladmandic/face-api - v1.7.13

Function matMul

  • matMul<T>(a, b, transposeA?, transposeB?): T

  • Computes the dot product of two matrices, A * B. These must be matrices.

    -
    const a = tf.tensor2d([1, 2], [1, 2]);
    const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);

    a.matMul(b).print();  // or tf.matMul(a, b)
-
    -

    Type Parameters

    Parameters

    • a: TensorLike | Tensor<Rank>

      First matrix in dot product operation.

      -
    • b: TensorLike | Tensor<Rank>

      Second matrix in dot product operation.

      -
    • Optional transposeA: boolean

      If true, a is transposed before multiplication.

      -
    • Optional transposeB: boolean

      If true, b is transposed before multiplication.

      -

    Returns T

    Doc

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\ No newline at end of file +matMul | @vladmandic/face-api - v1.7.14

Function matMul

  • matMul<T>(a, b, transposeA?, transposeB?): T

  • Computes the dot product of two matrices, A * B. These must be matrices.

    +
    const a = tf.tensor2d([1, 2], [1, 2]);
    const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);

    a.matMul(b).print();  // or tf.matMul(a, b)
+
    + +

    Type Parameters

    Parameters

    • a: TensorLike | Tensor<Rank>

      First matrix in dot product operation.

      +
    • b: TensorLike | Tensor<Rank>

      Second matrix in dot product operation.

      +
    • OptionaltransposeA: boolean

      If true, a is transposed before multiplication.

      +
    • OptionaltransposeB: boolean

      If true, b is transposed before multiplication.

      +

    Returns T

    Doc

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diff --git a/typedoc/functions/tf.maxPool.html b/typedoc/functions/tf.maxPool.html index 8f619f8..79cb403 100644 --- a/typedoc/functions/tf.maxPool.html +++ b/typedoc/functions/tf.maxPool.html @@ -1,19 +1,19 @@ -maxPool | @vladmandic/face-api - v1.7.13

Function maxPool

  • maxPool<T>(x, filterSize, strides, pad, dimRoundingMode?): T

  • Computes the 2D max pooling of an image.

    -

    Type Parameters

    Parameters

    Returns T

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diff --git a/typedoc/functions/tf.mul.html b/typedoc/functions/tf.mul.html index aba7c3a..36e25e5 100644 --- a/typedoc/functions/tf.mul.html +++ b/typedoc/functions/tf.mul.html @@ -1,10 +1,12 @@ -mul | @vladmandic/face-api - v1.7.13

Function mul

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diff --git a/typedoc/functions/tf.pad.html b/typedoc/functions/tf.pad.html index 81b12ae..349f759 100644 --- a/typedoc/functions/tf.pad.html +++ b/typedoc/functions/tf.pad.html @@ -1,4 +1,4 @@ -pad | @vladmandic/face-api - v1.7.13

Function pad

  • pad<T>(x, paddings, constantValue?): T

  • Pads a tf.Tensor with a given value and paddings.

    +pad | @vladmandic/face-api - v1.7.14

    Function pad

    • pad<T>(x, paddings, constantValue?): T

    • Pads a tf.Tensor with a given value and paddings.

      This operation implements CONSTANT mode. For REFLECT and SYMMETRIC, refer to tf.mirrorPad.

      Also available are stricter rank-specific methods with the same signature @@ -9,11 +9,12 @@ as this method that assert that paddings is of given length.

    • tf.pad3d
    • tf.pad4d
    -
    const x = tf.tensor1d([1, 2, 3, 4]);
    x.pad([[1, 2]]).print();
-
    -

    Type Parameters

    Parameters

    • x: TensorLike | T

      The tensor to pad.

      -
    • paddings: [number, number][]

      An array of length R (the rank of the tensor), where +

      const x = tf.tensor1d([1, 2, 3, 4]);
      x.pad([[1, 2]]).print();
+
      + +

      Type Parameters

      Parameters

      • x: TensorLike | T

        The tensor to pad.

        +
      • paddings: [number, number][]

        An array of length R (the rank of the tensor), where each element is a length-2 tuple of ints [padBefore, padAfter], specifying how much to pad along each dimension of the tensor.

        -
      • Optional constantValue: number

        The pad value to use. Defaults to 0.

        -

      Returns T

      Doc

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\ No newline at end of file +
  • OptionalconstantValue: number

    The pad value to use. Defaults to 0.

    +

    • Returns T

    Doc

    Settings

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    diff --git a/typedoc/functions/tf.relu.html b/typedoc/functions/tf.relu.html index 0adc7ed..580d2ef 100644 --- a/typedoc/functions/tf.relu.html +++ b/typedoc/functions/tf.relu.html @@ -1,6 +1,7 @@ -relu | @vladmandic/face-api - v1.7.13

    Function relu

    • relu<T>(x): T

    • Computes rectified linear element-wise: max(x, 0).

      -
      const x = tf.tensor1d([-1, 2, -3, 4]);

      x.relu().print();  // or tf.relu(x)
-
      -

      Type Parameters

      Parameters

      • x: T | TensorLike

        The input tensor. If the dtype is bool, the output dtype will be - int32.

        -

      Returns T

      Doc

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    \ No newline at end of file +relu | @vladmandic/face-api - v1.7.14

    Function relu

    • relu<T>(x): T

    • Computes rectified linear element-wise: max(x, 0).

      +
      const x = tf.tensor1d([-1, 2, -3, 4]);

      x.relu().print();  // or tf.relu(x)
+
      + +

      Type Parameters

      Parameters

      • x: T | TensorLike

        The input tensor. If the dtype is bool, the output dtype will be +int32.

        +

      Returns T

      Doc

    Settings

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    diff --git a/typedoc/functions/tf.reshape.html b/typedoc/functions/tf.reshape.html index 329c579..7f9a0da 100644 --- a/typedoc/functions/tf.reshape.html +++ b/typedoc/functions/tf.reshape.html @@ -1,4 +1,4 @@ -reshape | @vladmandic/face-api - v1.7.13

    Function reshape

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    diff --git a/typedoc/functions/tf.scalar.html b/typedoc/functions/tf.scalar.html index 92df54a..9b28660 100644 --- a/typedoc/functions/tf.scalar.html +++ b/typedoc/functions/tf.scalar.html @@ -1,8 +1,9 @@ -scalar | @vladmandic/face-api - v1.7.13

    Function scalar

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    diff --git a/typedoc/functions/tf.separableConv2d.html b/typedoc/functions/tf.separableConv2d.html index c021f1b..ad49790 100644 --- a/typedoc/functions/tf.separableConv2d.html +++ b/typedoc/functions/tf.separableConv2d.html @@ -1,28 +1,28 @@ -separableConv2d | @vladmandic/face-api - v1.7.13

    Function separableConv2d

    Returns T

    Doc

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    diff --git a/typedoc/functions/tf.sigmoid.html b/typedoc/functions/tf.sigmoid.html index 6dd392c..d7f7949 100644 --- a/typedoc/functions/tf.sigmoid.html +++ b/typedoc/functions/tf.sigmoid.html @@ -1,5 +1,6 @@ -sigmoid | @vladmandic/face-api - v1.7.13

    Function sigmoid

    • sigmoid<T>(x): T

    • Computes sigmoid element-wise, 1 / (1 + exp(-x))

      -
      const x = tf.tensor1d([0, -1, 2, -3]);

      x.sigmoid().print();  // or tf.sigmoid(x)
-
      -

      Type Parameters

      Parameters

      Returns T

      Doc

    Settings

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    Theme

    \ No newline at end of file +sigmoid | @vladmandic/face-api - v1.7.14

    Function sigmoid

    • sigmoid<T>(x): T

    • Computes sigmoid element-wise, 1 / (1 + exp(-x))

      +
      const x = tf.tensor1d([0, -1, 2, -3]);

      x.sigmoid().print();  // or tf.sigmoid(x)
+
      + +

      Type Parameters

      Parameters

      Returns T

      Doc

    Settings

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    diff --git a/typedoc/functions/tf.slice.html b/typedoc/functions/tf.slice.html index a8edfe2..064adb7 100644 --- a/typedoc/functions/tf.slice.html +++ b/typedoc/functions/tf.slice.html @@ -1,4 +1,4 @@ -slice | @vladmandic/face-api - v1.7.13

    Function slice

    • slice<R, T>(x, begin, size?): T

    • Extracts a slice from a tf.Tensor starting at coordinates begin +slice | @vladmandic/face-api - v1.7.14

      Function slice

      • slice<R, T>(x, begin, size?): T

      • Extracts a slice from a tf.Tensor starting at coordinates begin and is of size size.

        Also available are stricter rank-specific methods with the same signature as this method that assert that x is of the given rank:

        @@ -8,17 +8,19 @@ as this method that assert that x is of the given rank:

      • tf.slice3d
      • tf.slice4d
      -
      const x = tf.tensor1d([1, 2, 3, 4]);

      x.slice([1], [2]).print();
-
      -
      const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);

      x.slice([1, 0], [1, 2]).print();
-
      -

      Type Parameters

      Parameters

      • x: TensorLike | T

        The input tf.Tensor to slice from.

        -
      • begin: number | number[]

        The coordinates to start the slice from. The length can be - less than the rank of x - the rest of the axes will have implicit 0 as - start. Can also be a single number, in which case it specifies the - first axis.

        -
      • Optional size: number | number[]

        The size of the slice. The length can be less than the rank of - x - the rest of the axes will have implicit -1. A value of -1 requests - the rest of the dimensions in the axis. Can also be a single number, - in which case it specifies the size of the first axis.

        -

      Returns T

      Doc

    Settings

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    Theme

    \ No newline at end of file +
    const x = tf.tensor1d([1, 2, 3, 4]);

    x.slice([1], [2]).print();
+
    + +
    const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);

    x.slice([1, 0], [1, 2]).print();
+
    + +

    Type Parameters

    Parameters

    • x: TensorLike | T

      The input tf.Tensor to slice from.

      +
    • begin: number | number[]

      The coordinates to start the slice from. The length can be +less than the rank of x - the rest of the axes will have implicit 0 as +start. Can also be a single number, in which case it specifies the +first axis.

      +
    • Optionalsize: number | number[]

      The size of the slice. The length can be less than the rank of +x - the rest of the axes will have implicit -1. A value of -1 requests +the rest of the dimensions in the axis. Can also be a single number, +in which case it specifies the size of the first axis.

      +

    Returns T

    Doc

    Settings

    Member Visibility
    diff --git a/typedoc/functions/tf.slice3d.html b/typedoc/functions/tf.slice3d.html index a22b9c5..fb08b27 100644 --- a/typedoc/functions/tf.slice3d.html +++ b/typedoc/functions/tf.slice3d.html @@ -1,3 +1,3 @@ -slice3d | @vladmandic/face-api - v1.7.13

    Function slice3d

    Settings

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    diff --git a/typedoc/functions/tf.softmax.html b/typedoc/functions/tf.softmax.html index 3231842..950d2cf 100644 --- a/typedoc/functions/tf.softmax.html +++ b/typedoc/functions/tf.softmax.html @@ -1,9 +1,11 @@ -softmax | @vladmandic/face-api - v1.7.13

    Function softmax

    • softmax<T>(logits, dim?): T

    • Computes the softmax normalized vector given the logits.

      -
      const a = tf.tensor1d([1, 2, 3]);

      a.softmax().print();  // or tf.softmax(a)
-
      -
      const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);

      a.softmax().print();  // or tf.softmax(a)
-
      -

      Type Parameters

      Parameters

      • logits: TensorLike | T

        The logits array.

        -
      • Optional dim: number

        The dimension softmax would be performed on. Defaults to -1 - which indicates the last dimension.

        -

      Returns T

      Doc

    Settings

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    Theme

    \ No newline at end of file +softmax | @vladmandic/face-api - v1.7.14

    Function softmax

    • softmax<T>(logits, dim?): T

    • Computes the softmax normalized vector given the logits.

      +
      const a = tf.tensor1d([1, 2, 3]);

      a.softmax().print();  // or tf.softmax(a)
+
      + +
      const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);

      a.softmax().print();  // or tf.softmax(a)
+
      + +

      Type Parameters

      Parameters

      • logits: TensorLike | T

        The logits array.

        +
      • Optionaldim: number

        The dimension softmax would be performed on. Defaults to -1 +which indicates the last dimension.

        +

      Returns T

      Doc

    Settings

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    diff --git a/typedoc/functions/tf.stack.html b/typedoc/functions/tf.stack.html index f0746e9..f0ff3a5 100644 --- a/typedoc/functions/tf.stack.html +++ b/typedoc/functions/tf.stack.html @@ -1,6 +1,7 @@ -stack | @vladmandic/face-api - v1.7.13

    Function stack

    • stack<T>(tensors, axis?): Tensor

    • Stacks a list of rank-R tf.Tensors into one rank-(R+1) tf.Tensor.

      -
      const a = tf.tensor1d([1, 2]);
      const b = tf.tensor1d([3, 4]);
      const c = tf.tensor1d([5, 6]);
      tf.stack([a, b, c]).print();
-
      -

      Type Parameters

      Parameters

      • tensors: (TensorLike | T)[]

        A list of tensor objects with the same shape and dtype.

        -
      • Optional axis: number

        The axis to stack along. Defaults to 0 (the first dim).

        -

      Returns Tensor

      Doc

    Settings

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    Theme

    \ No newline at end of file +stack | @vladmandic/face-api - v1.7.14

    Function stack

    • stack<T>(tensors, axis?): Tensor

    • Stacks a list of rank-R tf.Tensors into one rank-(R+1) tf.Tensor.

      +
      const a = tf.tensor1d([1, 2]);
      const b = tf.tensor1d([3, 4]);
      const c = tf.tensor1d([5, 6]);
      tf.stack([a, b, c]).print();
+
      + +

      Type Parameters

      Parameters

      • tensors: (TensorLike | T)[]

        A list of tensor objects with the same shape and dtype.

        +
      • Optionalaxis: number

        The axis to stack along. Defaults to 0 (the first dim).

        +

      Returns Tensor

      Doc

    Settings

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    diff --git a/typedoc/functions/tf.sub.html b/typedoc/functions/tf.sub.html index 0d8509d..b233e2d 100644 --- a/typedoc/functions/tf.sub.html +++ b/typedoc/functions/tf.sub.html @@ -1,9 +1,11 @@ -sub | @vladmandic/face-api - v1.7.13

    Function sub

    Settings

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    diff --git a/typedoc/functions/tf.tensor-2.html b/typedoc/functions/tf.tensor-2.html index 1d2f092..3bd2fe0 100644 --- a/typedoc/functions/tf.tensor-2.html +++ b/typedoc/functions/tf.tensor-2.html @@ -1,38 +1,46 @@ -tensor | @vladmandic/face-api - v1.7.13

    Function tensor

    • tensor<R>(values, shape?, dtype?): Tensor<R>

    • Creates a tf.Tensor with the provided values, shape and dtype.

      -
      // Pass an array of values to create a vector.
      tf.tensor([1, 2, 3, 4]).print();
-
      -
      // Pass a nested array of values to make a matrix or a higher
      // dimensional tensor.
      tf.tensor([[1, 2], [3, 4]]).print();
-
      -
      // Pass a flat array and specify a shape yourself.
      tf.tensor([1, 2, 3, 4], [2, 2]).print();
-
      -
      // Pass a `WebGLData` object and specify a shape yourself.

      // This makes it possible for TF.js applications to avoid GPU / CPU sync.
      // For example, if your application includes a preprocessing step on the GPU,
      // you could upload the GPU output directly to TF.js, rather than first
      // downloading the values.

      // Example for WebGL2:
      if (tf.findBackend('custom-webgl') == null) {
        const customCanvas = document.createElement('canvas');
        const customBackend = new tf.MathBackendWebGL(customCanvas);
        tf.registerBackend('custom-webgl', () => customBackend);
      }
      const savedBackend = tf.getBackend();
      await tf.setBackend('custom-webgl');
      const gl = tf.backend().gpgpu.gl;
      const texture = gl.createTexture();
      const tex2d = gl.TEXTURE_2D;
      const width = 2;
      const height = 2;

      gl.bindTexture(tex2d, texture);
      gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);
      gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);
      gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST);
      gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST);
      gl.texImage2D(
        tex2d, 0, gl.RGBA32F, // internalFormat
        width, height, 0,
        gl.RGBA, // textureFormat
        gl.FLOAT, // textureType
        new Float32Array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15])
      );

      // Currently, the `texture` has 4 pixels:
      // Pixel0 is {R:0, G:1, B:2, A:3}
      // Pixel1 is {R:4, G:5, B:6, A:7}
      // Pixel2 is {R:8, G:9, B:10, A:11}
      // Pixel3 is {R:12, G:13, B:14, A:15}

      const logicalShape = [height * width * 2];
      const a = tf.tensor({texture, height, width, channels: 'BR'}, logicalShape);
      a.print();
      // Tensor value will be [2, 0, 6, 4, 10, 8, 14, 12], since [2, 0] is the
      // values of 'B' and 'R' channels of Pixel0, [6, 4] is the values of 'B' and
      'R'
      // channels of Pixel1...

      // For postprocessing on the GPU, it's possible to retrieve the texture
      // backing any tensor by calling the tensor's `dataToGPU` method like
      // so:

      const tex = a.dataToGPU();
      await tf.setBackend(savedBackend);
-
      -
      // Pass a `WebGPUData` object and specify a shape yourself.

      // This makes it possible for TF.js applications to avoid GPU / CPU sync.
      // For example, if your application includes a preprocessing step on the GPU,
      // you could upload the GPU output directly to TF.js, rather than first
      // downloading the values. Unlike WebGL, this optionally supports zero copy
      // by WebGPUData.zeroCopy. When zeroCopy is false or undefined(default), this
      // passing GPUBuffer can be destroyed after tensor is created. When zeroCopy
      // is true, this GPUBuffer is bound directly by the tensor, so do not destroy
      // this GPUBuffer until all access is done.

      // Example for WebGPU:
      function createGPUBufferFromData(device, data, dtype) {
        const bytesPerElement = 4;
        const sizeInBytes = data.length * bytesPerElement;

        const gpuWriteBuffer = device.createBuffer({
          mappedAtCreation: true,
          size: sizeInBytes,
          usage: GPUBufferUsage.MAP_WRITE | GPUBufferUsage.COPY_SRC
        });
        const arrayBuffer = gpuWriteBuffer.getMappedRange();
        if (dtype === 'float32') {
          new Float32Array(arrayBuffer).set(data);
        } else if (dtype === 'int32') {
          new Int32Array(arrayBuffer).set(data);
        } else {
          throw new Error(
              `Creating tensor from GPUBuffer only supports` +
              `'float32'|'int32' dtype, while the dtype is ${dtype}.`);
        }
        gpuWriteBuffer.unmap();

        const gpuReadBuffer = device.createBuffer({
          mappedAtCreation: false,
          size: sizeInBytes,
          usage: GPUBufferUsage.COPY_DST | GPUBufferUsage.STORAGE |
              GPUBufferUsage.COPY_SRC
        });

        const copyEncoder = device.createCommandEncoder();
        copyEncoder.copyBufferToBuffer(
            gpuWriteBuffer, 0, gpuReadBuffer, 0, sizeInBytes);
        const copyCommands = copyEncoder.finish();
        device.queue.submit([copyCommands]);
        gpuWriteBuffer.destroy();
        return gpuReadBuffer;
      }

      const savedBackend = tf.getBackend();
      await tf.setBackend('webgpu').catch(
          () => {throw new Error(
              'Failed to use WebGPU backend. Please use Chrome Canary to run.')});
      const dtype = 'float32';
      const device = tf.backend().device;
      const aData = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16];
      const bData = [1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4];
      const expected = [2, 4, 6, 8, 6, 8, 10, 12, 10, 12, 14, 16, 14, 16, 18, 20];
      const aBuffer = createGPUBufferFromData(device, aData, dtype);
      const shape = [aData.length];
      // To use zeroCopy, use {buffer: aBuffer, zeroCopy: true} instead and destroy
      // aBuffer untill all access is done.
      const a = tf.tensor({buffer: aBuffer}, shape, dtype);
      const b = tf.tensor(bData, shape, dtype);
      const result = tf.add(a, b);
      result.print();
      a.dispose();
      b.dispose();
      result.dispose();
      aBuffer.destroy();
      await tf.setBackend(savedBackend);
-
      -

      Type Parameters

      Parameters

      • values: TensorLike | WebGLData | WebGPUData

        The values of the tensor. Can be nested array of numbers, - or a flat array, or a TypedArray, or a WebGLData object, or a -WebGPUData object. If the values are strings, they will be encoded as utf-8 -and kept as Uint8Array[]. If the values is a WebGLData object, the dtype -could only be 'float32' or 'int32' and the object has to have: 1. texture, a -WebGLTexture, the texture must share the same WebGLRenderingContext with -TFJS's WebGL backend (you could create a custom WebGL backend from your -texture's canvas) and the internal texture format for the input texture must -be floating point or normalized integer; 2. height, the height of the -texture; 3. width, the width of the texture; 4. channels, a non-empty subset -of 'RGBA', indicating the values of which channels will be passed to the -tensor, such as 'R' or 'BR' (The order of the channels affect the order of -tensor values. ). (If the values passed from texture is less than the tensor -size, zeros will be padded at the rear.). If the values is a WebGPUData -object, the dtype could only be 'float32' or 'int32 and the object has to -have: buffer, a GPUBuffer. The buffer must: 1. share the same GPUDevice -with TFJS's WebGPU backend; 2. buffer.usage should at least support -GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC; 3. buffer.size should not -be smaller than the byte size of tensor shape. WebGPUData optionally supports -zero copy by flag zeroCopy. When zeroCopy is false or undefined(default), -this passing GPUBuffer can be destroyed after tensor is created. When -zeroCopy is true, this GPUBuffer is bound directly by the tensor, so do not -destroy this GPUBuffer until all access is done.

        -
      • Optional shape: ShapeMap[R]

        The shape of the tensor. Optional. If not provided, - it is inferred from values.

        -
      • Optional dtype: keyof DataTypeMap

        The data type.

        -

      Returns Tensor<R>

      Doc

    Settings

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    \ No newline at end of file +tensor | @vladmandic/face-api - v1.7.14

    Function tensor

    • tensor<R>(values, shape?, dtype?): Tensor<R>

    • Creates a tf.Tensor with the provided values, shape and dtype.

      +
      // Pass an array of values to create a vector.
      tf.tensor([1, 2, 3, 4]).print();
+
      + +
      // Pass a nested array of values to make a matrix or a higher
      // dimensional tensor.
      tf.tensor([[1, 2], [3, 4]]).print();
+
      + +
      // Pass a flat array and specify a shape yourself.
      tf.tensor([1, 2, 3, 4], [2, 2]).print();
+
      + +
      // Pass a `WebGLData` object and specify a shape yourself.

      // This makes it possible for TF.js applications to avoid GPU / CPU sync.
      // For example, if your application includes a preprocessing step on the GPU,
      // you could upload the GPU output directly to TF.js, rather than first
      // downloading the values.

      // Example for WebGL2:
      if (tf.findBackend('custom-webgl') == null) {
        const customCanvas = document.createElement('canvas');
        const customBackend = new tf.MathBackendWebGL(customCanvas);
        tf.registerBackend('custom-webgl', () => customBackend);
      }
      const savedBackend = tf.getBackend();
      await tf.setBackend('custom-webgl');
      const gl = tf.backend().gpgpu.gl;
      const texture = gl.createTexture();
      const tex2d = gl.TEXTURE_2D;
      const width = 2;
      const height = 2;

      gl.bindTexture(tex2d, texture);
      gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);
      gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);
      gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST);
      gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST);
      gl.texImage2D(
        tex2d, 0, gl.RGBA32F, // internalFormat
        width, height, 0,
        gl.RGBA, // textureFormat
        gl.FLOAT, // textureType
        new Float32Array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15])
      );

      // Currently, the `texture` has 4 pixels:
      // Pixel0 is {R:0, G:1, B:2, A:3}
      // Pixel1 is {R:4, G:5, B:6, A:7}
      // Pixel2 is {R:8, G:9, B:10, A:11}
      // Pixel3 is {R:12, G:13, B:14, A:15}

      const logicalShape = [height * width * 2];
      const a = tf.tensor({texture, height, width, channels: 'BR'}, logicalShape);
      a.print();
      // Tensor value will be [2, 0, 6, 4, 10, 8, 14, 12], since [2, 0] is the
      // values of 'B' and 'R' channels of Pixel0, [6, 4] is the values of 'B' and
      'R'
      // channels of Pixel1...

      // For postprocessing on the GPU, it's possible to retrieve the texture
      // backing any tensor by calling the tensor's `dataToGPU` method like
      // so:

      const tex = a.dataToGPU();
      await tf.setBackend(savedBackend);
+
      + +
      // Pass a `WebGPUData` object and specify a shape yourself.

      // This makes it possible for TF.js applications to avoid GPU / CPU sync.
      // For example, if your application includes a preprocessing step on the GPU,
      // you could upload the GPU output directly to TF.js, rather than first
      // downloading the values. Unlike WebGL, this optionally supports zero copy
      // by WebGPUData.zeroCopy. When zeroCopy is false or undefined(default), this
      // passing GPUBuffer can be destroyed after tensor is created. When zeroCopy
      // is true, this GPUBuffer is bound directly by the tensor, so do not destroy
      // this GPUBuffer until all access is done.

      // Example for WebGPU:
      function createGPUBufferFromData(device, data, dtype) {
        const bytesPerElement = 4;
        const sizeInBytes = data.length * bytesPerElement;

        const gpuWriteBuffer = device.createBuffer({
          mappedAtCreation: true,
          size: sizeInBytes,
          usage: GPUBufferUsage.MAP_WRITE | GPUBufferUsage.COPY_SRC
        });
        const arrayBuffer = gpuWriteBuffer.getMappedRange();
        if (dtype === 'float32') {
          new Float32Array(arrayBuffer).set(data);
        } else if (dtype === 'int32') {
          new Int32Array(arrayBuffer).set(data);
        } else {
          throw new Error(
              `Creating tensor from GPUBuffer only supports` +
              `'float32'|'int32' dtype, while the dtype is ${dtype}.`);
        }
        gpuWriteBuffer.unmap();

        const gpuReadBuffer = device.createBuffer({
          mappedAtCreation: false,
          size: sizeInBytes,
          usage: GPUBufferUsage.COPY_DST | GPUBufferUsage.STORAGE |
              GPUBufferUsage.COPY_SRC
        });

        const copyEncoder = device.createCommandEncoder();
        copyEncoder.copyBufferToBuffer(
            gpuWriteBuffer, 0, gpuReadBuffer, 0, sizeInBytes);
        const copyCommands = copyEncoder.finish();
        device.queue.submit([copyCommands]);
        gpuWriteBuffer.destroy();
        return gpuReadBuffer;
      }

      const savedBackend = tf.getBackend();
      await tf.setBackend('webgpu').catch(
          () => {throw new Error(
              'Failed to use WebGPU backend. Please use Chrome Canary to run.')});
      const dtype = 'float32';
      const device = tf.backend().device;
      const aData = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16];
      const bData = [1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4];
      const expected = [2, 4, 6, 8, 6, 8, 10, 12, 10, 12, 14, 16, 14, 16, 18, 20];
      const aBuffer = createGPUBufferFromData(device, aData, dtype);
      const shape = [aData.length];
      // To use zeroCopy, use {buffer: aBuffer, zeroCopy: true} instead and destroy
      // aBuffer untill all access is done.
      const a = tf.tensor({buffer: aBuffer}, shape, dtype);
      const b = tf.tensor(bData, shape, dtype);
      const result = tf.add(a, b);
      result.print();
      a.dispose();
      b.dispose();
      result.dispose();
      aBuffer.destroy();
      await tf.setBackend(savedBackend);
+
      + +

      Type Parameters

      Parameters

      • values: TensorLike | WebGLData | WebGPUData

        The values of the tensor. Can be nested array of numbers, +or a flat array, or a TypedArray(At the moment it supports Uint8Array, +Uint8ClampedArray, Int32Array, Float32Array) data types, or a WebGLData +object, or a WebGPUData object. If the values are strings, they will be +encoded as utf-8 and kept as Uint8Array[]. If the values is a WebGLData +object, the dtype could only be 'float32' or 'int32' and the object has to +have: 1. texture, a WebGLTexture, the texture must share the same +WebGLRenderingContext with TFJS's WebGL backend (you could create a custom +WebGL backend from your texture's canvas) and the internal texture format +for the input texture must be floating point or normalized integer; 2. +height, the height of the texture; 3. width, the width of the texture; 4. +channels, a non-empty subset of 'RGBA', indicating the values of which +channels will be passed to the tensor, such as 'R' or 'BR' (The order of the +channels affect the order of tensor values. ). (If the values passed from +texture is less than the tensor size, zeros will be padded at the rear.). If +the values is a WebGPUData object, the dtype could only be 'float32' or +'int32 and the object has to have: buffer, a GPUBuffer. The buffer must:

        +
          +
        1. share the same GPUDevice with TFJS's WebGPU backend; 2. buffer.usage +should at least support GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC; 3. +buffer.size should not be smaller than the byte size of tensor shape. +WebGPUData optionally supports zero copy by flag zeroCopy. When zeroCopy is +false or undefined(default),this passing GPUBuffer can be destroyed after +tensor is created. When zeroCopy is true, this GPUBuffer is bound directly +by the tensor, so do not destroy this GPUBuffer until all access is done.
          2. +
        +
      • Optionalshape: ShapeMap[R]

        The shape of the tensor. Optional. If not provided, +it is inferred from values.

        +
      • Optionaldtype: keyof DataTypeMap

        The data type.

        +

      Returns Tensor<R>

      Doc

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    diff --git a/typedoc/functions/tf.tensor1d-1.html b/typedoc/functions/tf.tensor1d-1.html index f3f4ada..80c58a3 100644 --- a/typedoc/functions/tf.tensor1d-1.html +++ b/typedoc/functions/tf.tensor1d-1.html @@ -1,9 +1,10 @@ -tensor1d | @vladmandic/face-api - v1.7.13

    Function tensor1d

    Settings

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    diff --git a/typedoc/functions/tf.tensor2d-1.html b/typedoc/functions/tf.tensor2d-1.html index 49e493c..8ac6d2f 100644 --- a/typedoc/functions/tf.tensor2d-1.html +++ b/typedoc/functions/tf.tensor2d-1.html @@ -1,13 +1,15 @@ -tensor2d | @vladmandic/face-api - v1.7.13

    Function tensor2d

    • tensor2d(values, shape?, dtype?): Tensor2D

    • Creates rank-2 tf.Tensor with the provided values, shape and dtype.

      +tensor2d | @vladmandic/face-api - v1.7.14

      Function tensor2d

      • tensor2d(values, shape?, dtype?): Tensor2D

      • Creates rank-2 tf.Tensor with the provided values, shape and dtype.

        The same functionality can be achieved with tf.tensor, but in general we recommend using tf.tensor2d as it makes the code more readable.

        -
        // Pass a nested array.
        tf.tensor2d([[1, 2], [3, 4]]).print();
-
        -
        // Pass a flat array and specify a shape.
        tf.tensor2d([1, 2, 3, 4], [2, 2]).print();
-
        -

        Parameters

        • values: TensorLike2D

          The values of the tensor. Can be nested array of numbers, - or a flat array, or a TypedArray.

          -
        • Optional shape: [number, number]

          The shape of the tensor. If not provided, it is inferred from - values.

          -
        • Optional dtype: keyof DataTypeMap

          The data type.

          -

        Returns Tensor2D

        Doc

      Settings

      Member Visibility

      Theme

      \ No newline at end of file +
      // Pass a nested array.
      tf.tensor2d([[1, 2], [3, 4]]).print();
+
      + +
      // Pass a flat array and specify a shape.
      tf.tensor2d([1, 2, 3, 4], [2, 2]).print();
+
      + +

      Parameters

      • values: TensorLike2D

        The values of the tensor. Can be nested array of numbers, +or a flat array, or a TypedArray.

        +
      • Optionalshape: [number, number]

        The shape of the tensor. If not provided, it is inferred from +values.

        +
      • Optionaldtype: keyof DataTypeMap

        The data type.

        +

      Returns Tensor2D

      Doc

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    diff --git a/typedoc/functions/tf.tensor3d-1.html b/typedoc/functions/tf.tensor3d-1.html index fa9d68a..3cce6a8 100644 --- a/typedoc/functions/tf.tensor3d-1.html +++ b/typedoc/functions/tf.tensor3d-1.html @@ -1,13 +1,15 @@ -tensor3d | @vladmandic/face-api - v1.7.13

    Function tensor3d

    • tensor3d(values, shape?, dtype?): Tensor3D

    • Creates rank-3 tf.Tensor with the provided values, shape and dtype.

      +tensor3d | @vladmandic/face-api - v1.7.14

      Function tensor3d

      • tensor3d(values, shape?, dtype?): Tensor3D

      • Creates rank-3 tf.Tensor with the provided values, shape and dtype.

        The same functionality can be achieved with tf.tensor, but in general we recommend using tf.tensor3d as it makes the code more readable.

        -
        // Pass a nested array.
        tf.tensor3d([[[1], [2]], [[3], [4]]]).print();
-
        -
        // Pass a flat array and specify a shape.
        tf.tensor3d([1, 2, 3, 4], [2, 2, 1]).print();
-
        -

        Parameters

        • values: TensorLike3D

          The values of the tensor. Can be nested array of numbers, - or a flat array, or a TypedArray.

          -
        • Optional shape: [number, number, number]

          The shape of the tensor. If not provided, it is inferred from - values.

          -
        • Optional dtype: keyof DataTypeMap

          The data type.

          -

        Returns Tensor3D

        Doc

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      Theme

      \ No newline at end of file +
      // Pass a nested array.
      tf.tensor3d([[[1], [2]], [[3], [4]]]).print();
+
      + +
      // Pass a flat array and specify a shape.
      tf.tensor3d([1, 2, 3, 4], [2, 2, 1]).print();
+
      + +

      Parameters

      • values: TensorLike3D

        The values of the tensor. Can be nested array of numbers, +or a flat array, or a TypedArray.

        +
      • Optionalshape: [number, number, number]

        The shape of the tensor. If not provided, it is inferred from +values.

        +
      • Optionaldtype: keyof DataTypeMap

        The data type.

        +

      Returns Tensor3D

      Doc

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    diff --git a/typedoc/functions/tf.tensor4d-1.html b/typedoc/functions/tf.tensor4d-1.html index 8dc9c7d..c018ad3 100644 --- a/typedoc/functions/tf.tensor4d-1.html +++ b/typedoc/functions/tf.tensor4d-1.html @@ -1,13 +1,15 @@ -tensor4d | @vladmandic/face-api - v1.7.13

    Function tensor4d

    • tensor4d(values, shape?, dtype?): Tensor4D

    • Creates rank-4 tf.Tensor with the provided values, shape and dtype.

      +tensor4d | @vladmandic/face-api - v1.7.14

      Function tensor4d

      • tensor4d(values, shape?, dtype?): Tensor4D

      • Creates rank-4 tf.Tensor with the provided values, shape and dtype.

        The same functionality can be achieved with tf.tensor, but in general we recommend using tf.tensor4d as it makes the code more readable.

        -
        // Pass a nested array.
        tf.tensor4d([[[[1], [2]], [[3], [4]]]]).print();
-
        -
        // Pass a flat array and specify a shape.
        tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]).print();
-
        -

        Parameters

        • values: TensorLike4D

          The values of the tensor. Can be nested array of numbers, - or a flat array, or a TypedArray.

          -
        • Optional shape: [number, number, number, number]

          The shape of the tensor. Optional. If not provided, - it is inferred from values.

          -
        • Optional dtype: keyof DataTypeMap

          The data type.

          -

        Returns Tensor4D

        Doc

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      Theme

      \ No newline at end of file +
      // Pass a nested array.
      tf.tensor4d([[[[1], [2]], [[3], [4]]]]).print();
+
      + +
      // Pass a flat array and specify a shape.
      tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]).print();
+
      + +

      Parameters

      • values: TensorLike4D

        The values of the tensor. Can be nested array of numbers, +or a flat array, or a TypedArray.

        +
      • Optionalshape: [number, number, number, number]

        The shape of the tensor. Optional. If not provided, +it is inferred from values.

        +
      • Optionaldtype: keyof DataTypeMap

        The data type.

        +

      Returns Tensor4D

      Doc

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    diff --git a/typedoc/functions/tf.tidy.html b/typedoc/functions/tf.tidy.html index 30979ff..adc7250 100644 --- a/typedoc/functions/tf.tidy.html +++ b/typedoc/functions/tf.tidy.html @@ -1,4 +1,4 @@ -tidy | @vladmandic/face-api - v1.7.13

    Function tidy

    • tidy<T>(nameOrFn, fn?): T

    • Executes the provided function fn and after it is executed, cleans up all +tidy | @vladmandic/face-api - v1.7.14

      Function tidy

      • tidy<T>(nameOrFn, fn?): T

      • Executes the provided function fn and after it is executed, cleans up all intermediate tensors allocated by fn except those returned by fn. fn must not return a Promise (async functions not allowed). The returned result can be a complex object.

        @@ -7,11 +7,12 @@ operations in tf.tidy for automatic memory cleanup.

        NOTE: Variables do not get cleaned up when inside a tidy(). If you want to dispose variables, please use tf.disposeVariables or call dispose() directly on variables.

        -
        // y = 2 ^ 2 + 1
        const y = tf.tidy(() => {
          // a, b, and one will be cleaned up when the tidy ends.
          const one = tf.scalar(1);
          const a = tf.scalar(2);
          const b = a.square();

          console.log('numTensors (in tidy): ' + tf.memory().numTensors);

          // The value returned inside the tidy function will return
          // through the tidy, in this case to the variable y.
          return b.add(one);
        });

        console.log('numTensors (outside tidy): ' + tf.memory().numTensors);
        y.print();
-
        -

        Type Parameters

        • T extends TensorContainer

        Parameters

        • nameOrFn: string | ScopeFn<T>

          The name of the closure, or the function to execute. - If a name is provided, the 2nd argument should be the function. - If debug mode is on, the timing and the memory usage of the function - will be tracked and displayed on the console using the provided name.

          -
        • Optional fn: ScopeFn<T>

          The function to execute.

          -

        Returns T

        Doc

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      Theme

      \ No newline at end of file +
      // y = 2 ^ 2 + 1
      const y = tf.tidy(() => {
        // a, b, and one will be cleaned up when the tidy ends.
        const one = tf.scalar(1);
        const a = tf.scalar(2);
        const b = a.square();

        console.log('numTensors (in tidy): ' + tf.memory().numTensors);

        // The value returned inside the tidy function will return
        // through the tidy, in this case to the variable y.
        return b.add(one);
      });

      console.log('numTensors (outside tidy): ' + tf.memory().numTensors);
      y.print();
+
      + +

      Type Parameters

      • T extends TensorContainer

      Parameters

      • nameOrFn: string | ScopeFn<T>

        The name of the closure, or the function to execute. +If a name is provided, the 2nd argument should be the function. +If debug mode is on, the timing and the memory usage of the function +will be tracked and displayed on the console using the provided name.

        +
      • Optionalfn: ScopeFn<T>

        The function to execute.

        +

      Returns T

      Doc

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    diff --git a/typedoc/functions/tf.tile.html b/typedoc/functions/tf.tile.html index 09c91d9..d83f0aa 100644 --- a/typedoc/functions/tf.tile.html +++ b/typedoc/functions/tf.tile.html @@ -1,12 +1,14 @@ -tile | @vladmandic/face-api - v1.7.13

    Function tile

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    diff --git a/typedoc/functions/tf.transpose.html b/typedoc/functions/tf.transpose.html index cf2784e..00073c0 100644 --- a/typedoc/functions/tf.transpose.html +++ b/typedoc/functions/tf.transpose.html @@ -1,11 +1,12 @@ -transpose | @vladmandic/face-api - v1.7.13

    Function transpose

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    diff --git a/typedoc/functions/tf.unstack.html b/typedoc/functions/tf.unstack.html index 668ff9f..68bf6bf 100644 --- a/typedoc/functions/tf.unstack.html +++ b/typedoc/functions/tf.unstack.html @@ -1,6 +1,7 @@ -unstack | @vladmandic/face-api - v1.7.13

    Function unstack

    • unstack(x, axis?): Tensor[]

    • Unstacks a tf.Tensor of rank-R into a list of rank-(R-1) tf.Tensors.

      -
      const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);

      tf.unstack(a).forEach(tensor => tensor.print());
-
      -

      Parameters

      • x: TensorLike | Tensor<Rank>

        A tensor object.

        -
      • Optional axis: number

        The axis to unstack along. Defaults to 0 (the first dim).

        -

      Returns Tensor[]

      Doc

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    \ No newline at end of file +unstack | @vladmandic/face-api - v1.7.14

    Function unstack

    • unstack(x, axis?): Tensor[]

    • Unstacks a tf.Tensor of rank-R into a list of rank-(R-1) tf.Tensors.

      +
      const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);

      tf.unstack(a).forEach(tensor => tensor.print());
+
      + +

      Parameters

      • x: TensorLike | Tensor<Rank>

        A tensor object.

        +
      • Optionalaxis: number

        The axis to unstack along. Defaults to 0 (the first dim).

        +

      Returns Tensor[]

      Doc

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    diff --git a/typedoc/functions/tf.zeros.html b/typedoc/functions/tf.zeros.html index a43b88a..0b8ef84 100644 --- a/typedoc/functions/tf.zeros.html +++ b/typedoc/functions/tf.zeros.html @@ -1,7 +1,8 @@ -zeros | @vladmandic/face-api - v1.7.13

    Function zeros

    • zeros<R>(shape, dtype?): Tensor<R>

    • Creates a tf.Tensor with all elements set to 0.

      -
      tf.zeros([2, 2]).print();
-
      -

      Type Parameters

      Parameters

      • shape: ShapeMap[R]

        An array of integers defining the output tensor shape.

        -
      • Optional dtype: keyof DataTypeMap

        The type of an element in the resulting tensor. Can - be 'float32', 'int32' or 'bool'. Defaults to 'float'.

        -

      Returns Tensor<R>

      Doc

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    \ No newline at end of file +zeros | @vladmandic/face-api - v1.7.14

    Function zeros

    • zeros<R>(shape, dtype?): Tensor<R>

    • Creates a tf.Tensor with all elements set to 0.

      +
      tf.zeros([2, 2]).print();
+
      + +

      Type Parameters

      Parameters

      • shape: ShapeMap[R]

        An array of integers defining the output tensor shape.

        +
      • Optionaldtype: keyof DataTypeMap

        The type of an element in the resulting tensor. Can +be 'float32', 'int32' or 'bool'. Defaults to 'float'.

        +

      Returns Tensor<R>

      Doc

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    diff --git a/typedoc/functions/tinyFaceDetector-1.html b/typedoc/functions/tinyFaceDetector-1.html index 78b2fcd..31dc1df 100644 --- a/typedoc/functions/tinyFaceDetector-1.html +++ b/typedoc/functions/tinyFaceDetector-1.html @@ -1,5 +1,5 @@ -tinyFaceDetector | @vladmandic/face-api - v1.7.13

    Function tinyFaceDetector

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    diff --git a/typedoc/functions/tinyYolov2-1.html b/typedoc/functions/tinyYolov2-1.html index 73c3e40..0ef703d 100644 --- a/typedoc/functions/tinyYolov2-1.html +++ b/typedoc/functions/tinyYolov2-1.html @@ -1,5 +1,5 @@ -tinyYolov2 | @vladmandic/face-api - v1.7.13

    Function tinyYolov2

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    diff --git a/typedoc/functions/toNetInput.html b/typedoc/functions/toNetInput.html index 5ba1728..12a1064 100644 --- a/typedoc/functions/toNetInput.html +++ b/typedoc/functions/toNetInput.html @@ -1,4 +1,4 @@ -toNetInput | @vladmandic/face-api - v1.7.13

    Function toNetInput

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    diff --git a/typedoc/functions/utils.computeReshapedDimensions.html b/typedoc/functions/utils.computeReshapedDimensions.html index ff0d027..af77d42 100644 --- a/typedoc/functions/utils.computeReshapedDimensions.html +++ b/typedoc/functions/utils.computeReshapedDimensions.html @@ -1 +1 @@ -computeReshapedDimensions | @vladmandic/face-api - v1.7.13

    Function computeReshapedDimensions

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    \ No newline at end of file +computeReshapedDimensions | @vladmandic/face-api - v1.7.14

    Function computeReshapedDimensions

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    diff --git a/typedoc/functions/utils.getCenterPoint.html b/typedoc/functions/utils.getCenterPoint.html index 30ac2e2..4372d5b 100644 --- a/typedoc/functions/utils.getCenterPoint.html +++ b/typedoc/functions/utils.getCenterPoint.html @@ -1 +1 @@ -getCenterPoint | @vladmandic/face-api - v1.7.13

    Function getCenterPoint

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    \ No newline at end of file +getCenterPoint | @vladmandic/face-api - v1.7.14

    Function getCenterPoint

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    diff --git a/typedoc/functions/utils.isDimensions.html b/typedoc/functions/utils.isDimensions.html index cc80c72..2bfd97f 100644 --- a/typedoc/functions/utils.isDimensions.html +++ b/typedoc/functions/utils.isDimensions.html @@ -1 +1 @@ -isDimensions | @vladmandic/face-api - v1.7.13

    Function isDimensions

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    \ No newline at end of file +isDimensions | @vladmandic/face-api - v1.7.14

    Function isDimensions

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    diff --git a/typedoc/functions/utils.isEven.html b/typedoc/functions/utils.isEven.html index 488f12f..41c02fb 100644 --- a/typedoc/functions/utils.isEven.html +++ b/typedoc/functions/utils.isEven.html @@ -1 +1 @@ -isEven | @vladmandic/face-api - v1.7.13

    Function isEven

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    \ No newline at end of file +isEven | @vladmandic/face-api - v1.7.14

    Function isEven

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    diff --git a/typedoc/functions/utils.isFloat.html b/typedoc/functions/utils.isFloat.html index 452fe35..5c44d52 100644 --- a/typedoc/functions/utils.isFloat.html +++ b/typedoc/functions/utils.isFloat.html @@ -1 +1 @@ -isFloat | @vladmandic/face-api - v1.7.13

    Function isFloat

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    \ No newline at end of file +isFloat | @vladmandic/face-api - v1.7.14

    Function isFloat

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    diff --git a/typedoc/functions/utils.isTensor.html b/typedoc/functions/utils.isTensor.html index d0d1557..803be42 100644 --- a/typedoc/functions/utils.isTensor.html +++ b/typedoc/functions/utils.isTensor.html @@ -1 +1 @@ -isTensor | @vladmandic/face-api - v1.7.13

    Function isTensor

    • isTensor(tensor, dim): boolean

    • Parameters

      • tensor: any
      • dim: number

      Returns boolean

    Settings

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    \ No newline at end of file +isTensor | @vladmandic/face-api - v1.7.14

    Function isTensor

    • isTensor(tensor, dim): boolean

    • Parameters

      • tensor: any
      • dim: number

      Returns boolean

    Settings

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    diff --git a/typedoc/functions/utils.isTensor1D.html b/typedoc/functions/utils.isTensor1D.html index 8243bc1..4cf8f55 100644 --- a/typedoc/functions/utils.isTensor1D.html +++ b/typedoc/functions/utils.isTensor1D.html @@ -1 +1 @@ -isTensor1D | @vladmandic/face-api - v1.7.13

    Function isTensor1D

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    \ No newline at end of file +isTensor1D | @vladmandic/face-api - v1.7.14

    Function isTensor1D

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    diff --git a/typedoc/functions/utils.isTensor2D.html b/typedoc/functions/utils.isTensor2D.html index 76c9f83..7c8defb 100644 --- a/typedoc/functions/utils.isTensor2D.html +++ b/typedoc/functions/utils.isTensor2D.html @@ -1 +1 @@ -isTensor2D | @vladmandic/face-api - v1.7.13

    Function isTensor2D

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    \ No newline at end of file +isTensor2D | @vladmandic/face-api - v1.7.14

    Function isTensor2D

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    diff --git a/typedoc/functions/utils.isTensor3D.html b/typedoc/functions/utils.isTensor3D.html index 11a0d14..1d85650 100644 --- a/typedoc/functions/utils.isTensor3D.html +++ b/typedoc/functions/utils.isTensor3D.html @@ -1 +1 @@ -isTensor3D | @vladmandic/face-api - v1.7.13

    Function isTensor3D

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    \ No newline at end of file +isTensor3D | @vladmandic/face-api - v1.7.14

    Function isTensor3D

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    diff --git a/typedoc/functions/utils.isTensor4D.html b/typedoc/functions/utils.isTensor4D.html index 3e303e4..822cbd3 100644 --- a/typedoc/functions/utils.isTensor4D.html +++ b/typedoc/functions/utils.isTensor4D.html @@ -1 +1 @@ -isTensor4D | @vladmandic/face-api - v1.7.13

    Function isTensor4D

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    \ No newline at end of file +isTensor4D | @vladmandic/face-api - v1.7.14

    Function isTensor4D

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    diff --git a/typedoc/functions/utils.isValidNumber.html b/typedoc/functions/utils.isValidNumber.html index 8b6cbfc..93f9b29 100644 --- a/typedoc/functions/utils.isValidNumber.html +++ b/typedoc/functions/utils.isValidNumber.html @@ -1 +1 @@ -isValidNumber | @vladmandic/face-api - v1.7.13

    Function isValidNumber

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    \ No newline at end of file +isValidNumber | @vladmandic/face-api - v1.7.14

    Function isValidNumber

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    diff --git a/typedoc/functions/utils.isValidProbablitiy.html b/typedoc/functions/utils.isValidProbablitiy.html index d795349..ff3bcb5 100644 --- a/typedoc/functions/utils.isValidProbablitiy.html +++ b/typedoc/functions/utils.isValidProbablitiy.html @@ -1 +1 @@ -isValidProbablitiy | @vladmandic/face-api - v1.7.13

    Function isValidProbablitiy

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    Function isValidProbablitiy

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    diff --git a/typedoc/functions/utils.range.html b/typedoc/functions/utils.range.html index 85e96b7..dd7cfa7 100644 --- a/typedoc/functions/utils.range.html +++ b/typedoc/functions/utils.range.html @@ -1 +1 @@ -range | @vladmandic/face-api - v1.7.13

    Function range

    • range(num, start, step): number[]

    • Parameters

      • num: number
      • start: number
      • step: number

      Returns number[]

    Settings

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    Function range

    • range(num, start, step): number[]

    • Parameters

      • num: number
      • start: number
      • step: number

      Returns number[]

    Settings

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    diff --git a/typedoc/functions/utils.round.html b/typedoc/functions/utils.round.html index 16ab455..3add397 100644 --- a/typedoc/functions/utils.round.html +++ b/typedoc/functions/utils.round.html @@ -1 +1 @@ -round | @vladmandic/face-api - v1.7.13

    Function round

    • round(num, prec?): number

    • Parameters

      • num: number
      • prec: number = 2

      Returns number

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    Function round

    • round(num, prec?): number

    • Parameters

      • num: number
      • prec: number = 2

      Returns number

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    diff --git a/typedoc/functions/validateConfig.html b/typedoc/functions/validateConfig.html index 13b0f7f..360a3fe 100644 --- a/typedoc/functions/validateConfig.html +++ b/typedoc/functions/validateConfig.html @@ -1 +1 @@ -validateConfig | @vladmandic/face-api - v1.7.13

    Function validateConfig

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    Function validateConfig

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    diff --git a/typedoc/hierarchy.html b/typedoc/hierarchy.html index c421195..b06934e 100644 --- a/typedoc/hierarchy.html +++ b/typedoc/hierarchy.html @@ -1 +1 @@ -@vladmandic/face-api - v1.7.13

    @vladmandic/face-api - v1.7.13

    Class Hierarchy

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    @vladmandic/face-api - v1.7.14

    Class Hierarchy

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    diff --git a/typedoc/index.html b/typedoc/index.html index fd6bdb2..038acad 100644 --- a/typedoc/index.html +++ b/typedoc/index.html @@ -1,155 +1,155 @@ -@vladmandic/face-api - v1.7.13

    @vladmandic/face-api - v1.7.13

    Index

    Namespaces

    draw -tf -utils -

    Enumerations

    Gender -

    Classes

    AgeGenderNet -BoundingBox -Box -ComposableTask -ComputeAllFaceDescriptorsTask -ComputeFaceDescriptorsTaskBase -ComputeSingleFaceDescriptorTask -DetectAllFaceLandmarksTask -DetectAllFacesTask -DetectFaceLandmarksTaskBase -DetectFacesTaskBase -DetectSingleFaceLandmarksTask -DetectSingleFaceTask -Dimensions -FaceDetection -FaceDetectionNet -FaceExpressionNet -FaceExpressions -FaceLandmark68Net -FaceLandmark68TinyNet -FaceLandmarkNet -FaceLandmarks -FaceLandmarks5 -FaceLandmarks68 -FaceMatch -FaceMatcher -FaceRecognitionNet -LabeledBox -LabeledFaceDescriptors -NetInput -NeuralNetwork -ObjectDetection -Point -PredictedBox -Rect -SsdMobilenetv1 -SsdMobilenetv1Options -TinyFaceDetector -TinyFaceDetectorOptions -TinyYolov2 -TinyYolov2Options -

    Interfaces

    IBoundingBox -IDimensions -IFaceDetecion -IFaceLandmarks -IFaceMatch -IPoint -IRect -ISsdMobilenetv1Options -ITinyYolov2Options -

    Type Aliases

    AgeAndGenderPrediction -BatchNorm -ConvWithBatchNorm -DefaultTinyYolov2NetParams -Environment -FaceDetectionFunction -FaceDetectionOptions -FileSystem -ITinyFaceDetectorOptions -MobilenetParams -NetOutput -NetParams -TMediaElement -TNetInput -TResolvedNetInput -TinyYolov2Config -TinyYolov2ExtractBoxesResult -TinyYolov2NetParams -WithAge -WithFaceDescriptor -WithFaceDetection -WithFaceExpressions -WithFaceLandmarks -WithGender -

    Variables

    FACE_EXPRESSION_LABELS -env -nets -version -

    Functions

    allFaces -allFacesSsdMobilenetv1 -allFacesTinyYolov2 -awaitMediaLoaded -bufferToImage -computeFaceDescriptor -createCanvas -createCanvasFromMedia -createFaceDetectionNet -createFaceRecognitionNet -createSsdMobilenetv1 -createTinyFaceDetector -createTinyYolov2 -detectAllFaces -detectFaceLandmarks -detectFaceLandmarksTiny -detectLandmarks -detectSingleFace -euclideanDistance -extendWithAge -extendWithFaceDescriptor -extendWithFaceDetection -extendWithFaceExpressions -extendWithFaceLandmarks -extendWithGender -extractFaceTensors -extractFaces -fetchImage -fetchJson -fetchNetWeights -fetchOrThrow -fetchVideo -getContext2dOrThrow -getMediaDimensions -imageTensorToCanvas -imageToSquare -inverseSigmoid -iou -isMediaElement -isMediaLoaded -isWithAge -isWithFaceDetection -isWithFaceExpressions -isWithFaceLandmarks -isWithGender -loadAgeGenderModel -loadFaceDetectionModel -loadFaceExpressionModel -loadFaceLandmarkModel -loadFaceLandmarkTinyModel -loadFaceRecognitionModel -loadSsdMobilenetv1Model -loadTinyFaceDetectorModel -loadTinyYolov2Model -loadWeightMap -locateFaces -matchDimensions -minBbox -nonMaxSuppression -normalize -padToSquare -predictAgeAndGender -recognizeFaceExpressions -resizeResults -resolveInput -shuffleArray -sigmoid -ssdMobilenetv1 -tinyFaceDetector -tinyYolov2 -toNetInput -validateConfig -

    Settings

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    \ No newline at end of file +@vladmandic/face-api - v1.7.14

    @vladmandic/face-api - v1.7.14

    Index

    Namespaces

    draw +tf +utils +

    Enumerations

    Gender +

    Classes

    AgeGenderNet +BoundingBox +Box +ComposableTask +ComputeAllFaceDescriptorsTask +ComputeFaceDescriptorsTaskBase +ComputeSingleFaceDescriptorTask +DetectAllFaceLandmarksTask +DetectAllFacesTask +DetectFaceLandmarksTaskBase +DetectFacesTaskBase +DetectSingleFaceLandmarksTask +DetectSingleFaceTask +Dimensions +FaceDetection +FaceDetectionNet +FaceExpressionNet +FaceExpressions +FaceLandmark68Net +FaceLandmark68TinyNet +FaceLandmarkNet +FaceLandmarks +FaceLandmarks5 +FaceLandmarks68 +FaceMatch +FaceMatcher +FaceRecognitionNet +LabeledBox +LabeledFaceDescriptors +NetInput +NeuralNetwork +ObjectDetection +Point +PredictedBox +Rect +SsdMobilenetv1 +SsdMobilenetv1Options +TinyFaceDetector +TinyFaceDetectorOptions +TinyYolov2 +TinyYolov2Options +

    Interfaces

    IBoundingBox +IDimensions +IFaceDetecion +IFaceLandmarks +IFaceMatch +IPoint +IRect +ISsdMobilenetv1Options +ITinyYolov2Options +

    Type Aliases

    AgeAndGenderPrediction +BatchNorm +ConvWithBatchNorm +DefaultTinyYolov2NetParams +Environment +FaceDetectionFunction +FaceDetectionOptions +FileSystem +ITinyFaceDetectorOptions +MobilenetParams +NetOutput +NetParams +TinyYolov2Config +TinyYolov2ExtractBoxesResult +TinyYolov2NetParams +TMediaElement +TNetInput +TResolvedNetInput +WithAge +WithFaceDescriptor +WithFaceDetection +WithFaceExpressions +WithFaceLandmarks +WithGender +

    Variables

    env +FACE_EXPRESSION_LABELS +nets +version +

    Functions

    allFaces +allFacesSsdMobilenetv1 +allFacesTinyYolov2 +awaitMediaLoaded +bufferToImage +computeFaceDescriptor +createCanvas +createCanvasFromMedia +createFaceDetectionNet +createFaceRecognitionNet +createSsdMobilenetv1 +createTinyFaceDetector +createTinyYolov2 +detectAllFaces +detectFaceLandmarks +detectFaceLandmarksTiny +detectLandmarks +detectSingleFace +euclideanDistance +extendWithAge +extendWithFaceDescriptor +extendWithFaceDetection +extendWithFaceExpressions +extendWithFaceLandmarks +extendWithGender +extractFaces +extractFaceTensors +fetchImage +fetchJson +fetchNetWeights +fetchOrThrow +fetchVideo +getContext2dOrThrow +getMediaDimensions +imageTensorToCanvas +imageToSquare +inverseSigmoid +iou +isMediaElement +isMediaLoaded +isWithAge +isWithFaceDetection +isWithFaceExpressions +isWithFaceLandmarks +isWithGender +loadAgeGenderModel +loadFaceDetectionModel +loadFaceExpressionModel +loadFaceLandmarkModel +loadFaceLandmarkTinyModel +loadFaceRecognitionModel +loadSsdMobilenetv1Model +loadTinyFaceDetectorModel +loadTinyYolov2Model +loadWeightMap +locateFaces +matchDimensions +minBbox +nonMaxSuppression +normalize +padToSquare +predictAgeAndGender +recognizeFaceExpressions +resizeResults +resolveInput +shuffleArray +sigmoid +ssdMobilenetv1 +tinyFaceDetector +tinyYolov2 +toNetInput +validateConfig +

    Settings

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    diff --git a/typedoc/interfaces/IBoundingBox.html b/typedoc/interfaces/IBoundingBox.html index 0772cd2..607b8db 100644 --- a/typedoc/interfaces/IBoundingBox.html +++ b/typedoc/interfaces/IBoundingBox.html @@ -1,5 +1,5 @@ -IBoundingBox | @vladmandic/face-api - v1.7.13

    Interface IBoundingBox

    interface IBoundingBox {
        bottom: number;
        left: number;
        right: number;
        top: number;
    }

    Implemented by

    Properties

    bottom -left -right -top -

    Properties

    bottom

    bottom: number

    left

    left: number

    right

    right: number

    top

    top: number

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    \ No newline at end of file +IBoundingBox | @vladmandic/face-api - v1.7.14

    Interface IBoundingBox

    interface IBoundingBox {
        bottom: number;
        left: number;
        right: number;
        top: number;
    }

    Implemented by

    Properties

    bottom +left +right +top +

    Properties

    bottom

    bottom: number

    left

    left: number

    right

    right: number

    top

    top: number

    Settings

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    Properties
    diff --git a/typedoc/interfaces/IDimensions.html b/typedoc/interfaces/IDimensions.html index 7be945e..ef1b49e 100644 --- a/typedoc/interfaces/IDimensions.html +++ b/typedoc/interfaces/IDimensions.html @@ -1,3 +1,3 @@ -IDimensions | @vladmandic/face-api - v1.7.13

    Interface IDimensions

    interface IDimensions {
        height: number;
        width: number;
    }

    Implemented by

    Properties

    height -width -

    Properties

    height

    height: number

    width

    width: number

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    \ No newline at end of file +IDimensions | @vladmandic/face-api - v1.7.14

    Interface IDimensions

    interface IDimensions {
        height: number;
        width: number;
    }

    Implemented by

    Properties

    height +width +

    Properties

    height

    height: number

    width

    width: number

    Settings

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    Properties
    diff --git a/typedoc/interfaces/IFaceDetecion.html b/typedoc/interfaces/IFaceDetecion.html index e9649b4..eb4c087 100644 --- a/typedoc/interfaces/IFaceDetecion.html +++ b/typedoc/interfaces/IFaceDetecion.html @@ -1,3 +1,3 @@ -IFaceDetecion | @vladmandic/face-api - v1.7.13

    Interface IFaceDetecion

    interface IFaceDetecion {
        box: Box<any>;
        score: number;
    }

    Implemented by

    Properties

    box -score -

    Properties

    box

    box: Box<any>

    score

    score: number

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    \ No newline at end of file +IFaceDetecion | @vladmandic/face-api - v1.7.14

    Interface IFaceDetecion

    interface IFaceDetecion {
        box: Box<any>;
        score: number;
    }

    Implemented by

    Properties

    box +score +

    Properties

    box

    box: Box<any>

    score

    score: number

    Settings

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    Properties
    diff --git a/typedoc/interfaces/IFaceLandmarks.html b/typedoc/interfaces/IFaceLandmarks.html index bbf3403..0749a19 100644 --- a/typedoc/interfaces/IFaceLandmarks.html +++ b/typedoc/interfaces/IFaceLandmarks.html @@ -1,3 +1,3 @@ -IFaceLandmarks | @vladmandic/face-api - v1.7.13

    Interface IFaceLandmarks

    interface IFaceLandmarks {
        positions: Point[];
        shift: Point;
    }

    Implemented by

    Properties

    positions -shift -

    Properties

    positions

    positions: Point[]

    shift

    shift: Point

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    \ No newline at end of file +IFaceLandmarks | @vladmandic/face-api - v1.7.14

    Interface IFaceLandmarks

    interface IFaceLandmarks {
        positions: Point[];
        shift: Point;
    }

    Implemented by

    Properties

    positions +shift +

    Properties

    positions

    positions: Point[]

    shift

    shift: Point

    Settings

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    Properties
    diff --git a/typedoc/interfaces/IFaceMatch.html b/typedoc/interfaces/IFaceMatch.html index 5c10bbf..e5001c4 100644 --- a/typedoc/interfaces/IFaceMatch.html +++ b/typedoc/interfaces/IFaceMatch.html @@ -1,3 +1,3 @@ -IFaceMatch | @vladmandic/face-api - v1.7.13

    Interface IFaceMatch

    interface IFaceMatch {
        distance: number;
        label: string;
    }

    Implemented by

    Properties

    distance -label -

    Properties

    distance

    distance: number

    label

    label: string

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    \ No newline at end of file +IFaceMatch | @vladmandic/face-api - v1.7.14

    Interface IFaceMatch

    interface IFaceMatch {
        distance: number;
        label: string;
    }

    Implemented by

    Properties

    distance +label +

    Properties

    distance

    distance: number

    label

    label: string

    Settings

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    Properties
    diff --git a/typedoc/interfaces/IPoint.html b/typedoc/interfaces/IPoint.html index 773f259..1fdb763 100644 --- a/typedoc/interfaces/IPoint.html +++ b/typedoc/interfaces/IPoint.html @@ -1,3 +1,3 @@ -IPoint | @vladmandic/face-api - v1.7.13

    Interface IPoint

    interface IPoint {
        x: number;
        y: number;
    }

    Implemented by

    Properties

    x -y -

    Properties

    x

    x: number

    y

    y: number

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    \ No newline at end of file +IPoint | @vladmandic/face-api - v1.7.14

    Interface IPoint

    interface IPoint {
        x: number;
        y: number;
    }

    Implemented by

    Properties

    x +y +

    Properties

    x

    x: number

    y

    y: number

    Settings

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    Properties
    diff --git a/typedoc/interfaces/IRect.html b/typedoc/interfaces/IRect.html index df90712..f238e42 100644 --- a/typedoc/interfaces/IRect.html +++ b/typedoc/interfaces/IRect.html @@ -1,5 +1,5 @@ -IRect | @vladmandic/face-api - v1.7.13

    Interface IRect

    interface IRect {
        height: number;
        width: number;
        x: number;
        y: number;
    }

    Implemented by

    Properties

    height -width -x -y -

    Properties

    height

    height: number

    width

    width: number

    x

    x: number

    y

    y: number

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    \ No newline at end of file +IRect | @vladmandic/face-api - v1.7.14

    Interface IRect

    interface IRect {
        height: number;
        width: number;
        x: number;
        y: number;
    }

    Implemented by

    Properties

    height +width +x +y +

    Properties

    height

    height: number

    width

    width: number

    x

    x: number

    y

    y: number

    Settings

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    Properties
    diff --git a/typedoc/interfaces/ISsdMobilenetv1Options.html b/typedoc/interfaces/ISsdMobilenetv1Options.html index 27b124b..5fc4337 100644 --- a/typedoc/interfaces/ISsdMobilenetv1Options.html +++ b/typedoc/interfaces/ISsdMobilenetv1Options.html @@ -1,3 +1,3 @@ -ISsdMobilenetv1Options | @vladmandic/face-api - v1.7.13

    Interface ISsdMobilenetv1Options

    interface ISsdMobilenetv1Options {
        maxResults?: number;
        minConfidence?: number;
    }

    Properties

    maxResults? -minConfidence? -

    Properties

    Optional maxResults

    maxResults?: number

    Optional minConfidence

    minConfidence?: number

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    \ No newline at end of file +ISsdMobilenetv1Options | @vladmandic/face-api - v1.7.14

    Interface ISsdMobilenetv1Options

    interface ISsdMobilenetv1Options {
        maxResults?: number;
        minConfidence?: number;
    }

    Properties

    maxResults? +minConfidence? +

    Properties

    OptionalmaxResults

    maxResults?: number

    OptionalminConfidence

    minConfidence?: number

    Settings

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    Properties
    diff --git a/typedoc/interfaces/ITinyYolov2Options.html b/typedoc/interfaces/ITinyYolov2Options.html index 4a81771..776e136 100644 --- a/typedoc/interfaces/ITinyYolov2Options.html +++ b/typedoc/interfaces/ITinyYolov2Options.html @@ -1,3 +1,3 @@ -ITinyYolov2Options | @vladmandic/face-api - v1.7.13

    Interface ITinyYolov2Options

    interface ITinyYolov2Options {
        inputSize?: number;
        scoreThreshold?: number;
    }

    Properties

    inputSize? -scoreThreshold? -

    Properties

    Optional inputSize

    inputSize?: number

    Optional scoreThreshold

    scoreThreshold?: number

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    \ No newline at end of file +ITinyYolov2Options | @vladmandic/face-api - v1.7.14

    Interface ITinyYolov2Options

    interface ITinyYolov2Options {
        inputSize?: number;
        scoreThreshold?: number;
    }

    Properties

    inputSize? +scoreThreshold? +

    Properties

    OptionalinputSize

    inputSize?: number

    OptionalscoreThreshold

    scoreThreshold?: number

    Settings

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    Properties
    diff --git a/typedoc/interfaces/draw.IDrawBoxOptions.html b/typedoc/interfaces/draw.IDrawBoxOptions.html index 1f5ef2c..4e1b7a0 100644 --- a/typedoc/interfaces/draw.IDrawBoxOptions.html +++ b/typedoc/interfaces/draw.IDrawBoxOptions.html @@ -1,5 +1,5 @@ -IDrawBoxOptions | @vladmandic/face-api - v1.7.13

    Interface IDrawBoxOptions

    interface IDrawBoxOptions {
        boxColor?: string;
        drawLabelOptions?: IDrawTextFieldOptions;
        label?: string;
        lineWidth?: number;
    }

    Properties

    boxColor? -drawLabelOptions? -label? -lineWidth? -

    Properties

    Optional boxColor

    boxColor?: string

    Optional drawLabelOptions

    drawLabelOptions?: IDrawTextFieldOptions

    Optional label

    label?: string

    Optional lineWidth

    lineWidth?: number

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    \ No newline at end of file +IDrawBoxOptions | @vladmandic/face-api - v1.7.14

    Interface IDrawBoxOptions

    interface IDrawBoxOptions {
        boxColor?: string;
        drawLabelOptions?: IDrawTextFieldOptions;
        label?: string;
        lineWidth?: number;
    }

    Properties

    boxColor? +drawLabelOptions? +label? +lineWidth? +

    Properties

    OptionalboxColor

    boxColor?: string

    OptionaldrawLabelOptions

    drawLabelOptions?: IDrawTextFieldOptions

    Optionallabel

    label?: string

    OptionallineWidth

    lineWidth?: number

    Settings

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    Properties
    diff --git a/typedoc/interfaces/draw.IDrawFaceLandmarksOptions.html b/typedoc/interfaces/draw.IDrawFaceLandmarksOptions.html index d8c2b4a..33929ac 100644 --- a/typedoc/interfaces/draw.IDrawFaceLandmarksOptions.html +++ b/typedoc/interfaces/draw.IDrawFaceLandmarksOptions.html @@ -1,7 +1,7 @@ -IDrawFaceLandmarksOptions | @vladmandic/face-api - v1.7.13

    Interface IDrawFaceLandmarksOptions

    interface IDrawFaceLandmarksOptions {
        drawLines?: boolean;
        drawPoints?: boolean;
        lineColor?: string;
        lineWidth?: number;
        pointColor?: string;
        pointSize?: number;
    }

    Properties

    drawLines? -drawPoints? -lineColor? -lineWidth? -pointColor? -pointSize? -

    Properties

    Optional drawLines

    drawLines?: boolean

    Optional drawPoints

    drawPoints?: boolean

    Optional lineColor

    lineColor?: string

    Optional lineWidth

    lineWidth?: number

    Optional pointColor

    pointColor?: string

    Optional pointSize

    pointSize?: number

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    \ No newline at end of file +IDrawFaceLandmarksOptions | @vladmandic/face-api - v1.7.14

    Interface IDrawFaceLandmarksOptions

    interface IDrawFaceLandmarksOptions {
        drawLines?: boolean;
        drawPoints?: boolean;
        lineColor?: string;
        lineWidth?: number;
        pointColor?: string;
        pointSize?: number;
    }

    Properties

    drawLines? +drawPoints? +lineColor? +lineWidth? +pointColor? +pointSize? +

    Properties

    OptionaldrawLines

    drawLines?: boolean

    OptionaldrawPoints

    drawPoints?: boolean

    OptionallineColor

    lineColor?: string

    OptionallineWidth

    lineWidth?: number

    OptionalpointColor

    pointColor?: string

    OptionalpointSize

    pointSize?: number

    Settings

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    Properties
    diff --git a/typedoc/interfaces/draw.IDrawTextFieldOptions.html b/typedoc/interfaces/draw.IDrawTextFieldOptions.html index 3256343..5f093eb 100644 --- a/typedoc/interfaces/draw.IDrawTextFieldOptions.html +++ b/typedoc/interfaces/draw.IDrawTextFieldOptions.html @@ -1,7 +1,7 @@ -IDrawTextFieldOptions | @vladmandic/face-api - v1.7.13

    Interface IDrawTextFieldOptions

    interface IDrawTextFieldOptions {
        anchorPosition?: AnchorPosition;
        backgroundColor?: string;
        fontColor?: string;
        fontSize?: number;
        fontStyle?: string;
        padding?: number;
    }

    Implemented by

    Properties

    anchorPosition? -backgroundColor? -fontColor? -fontSize? -fontStyle? -padding? -

    Properties

    Optional anchorPosition

    anchorPosition?: AnchorPosition

    Optional backgroundColor

    backgroundColor?: string

    Optional fontColor

    fontColor?: string

    Optional fontSize

    fontSize?: number

    Optional fontStyle

    fontStyle?: string

    Optional padding

    padding?: number

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    \ No newline at end of file +IDrawTextFieldOptions | @vladmandic/face-api - v1.7.14

    Interface IDrawTextFieldOptions

    interface IDrawTextFieldOptions {
        anchorPosition?: AnchorPosition;
        backgroundColor?: string;
        fontColor?: string;
        fontSize?: number;
        fontStyle?: string;
        padding?: number;
    }

    Implemented by

    Properties

    anchorPosition? +backgroundColor? +fontColor? +fontSize? +fontStyle? +padding? +

    Properties

    OptionalanchorPosition

    anchorPosition?: AnchorPosition

    OptionalbackgroundColor

    backgroundColor?: string

    OptionalfontColor

    fontColor?: string

    OptionalfontSize

    fontSize?: number

    OptionalfontStyle

    fontStyle?: string

    Optionalpadding

    padding?: number

    Settings

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    Properties
    diff --git a/typedoc/interfaces/tf.io.IOHandler.html b/typedoc/interfaces/tf.io.IOHandler.html index 68c2aa5..ee10757 100644 --- a/typedoc/interfaces/tf.io.IOHandler.html +++ b/typedoc/interfaces/tf.io.IOHandler.html @@ -1,6 +1,6 @@ -IOHandler | @vladmandic/face-api - v1.7.13

    Interface IOHandler

    Interface for a model import/export handler.

    +IOHandler | @vladmandic/face-api - v1.7.14

    Interface IOHandler

    Interface for a model import/export handler.

    The save and load handlers are both optional, in order to allow handlers that support only saving or loading.

    -
    interface IOHandler {
        load?: LoadHandler;
        save?: SaveHandler;
    }

    Properties

    load? -save? -

    Properties

    Optional load

    load?: LoadHandler

    Optional save

    save?: SaveHandler

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    \ No newline at end of file +
    interface IOHandler {
        load?: LoadHandler;
        save?: SaveHandler;
    }

    Properties

    load? +save? +

    Properties

    Optionalload

    load?: LoadHandler

    Optionalsave

    save?: SaveHandler

    Settings

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    Properties
    diff --git a/typedoc/interfaces/tf.io.LoadOptions.html b/typedoc/interfaces/tf.io.LoadOptions.html index 08ff8c1..a65aac4 100644 --- a/typedoc/interfaces/tf.io.LoadOptions.html +++ b/typedoc/interfaces/tf.io.LoadOptions.html @@ -1,33 +1,32 @@ -LoadOptions | @vladmandic/face-api - v1.7.13

    Interface LoadOptions

    Innamespace

    io

    -
    interface LoadOptions {
        fetchFunc?: {
            (input, init?): Promise<Response>;
            (input, init?): Promise<Response>;
            (input, init?): Promise<Response>;
        };
        fromTFHub?: boolean;
        onProgress?: OnProgressCallback;
        requestInit?: RequestInit;
        streamWeights?: boolean;
        strict?: boolean;
        weightPathPrefix?: string;
        weightUrlConverter?: ((weightFileName) => Promise<string>);
    }

    Properties

    fetchFunc? -fromTFHub? -onProgress? -requestInit? -streamWeights? -strict? -weightPathPrefix? -weightUrlConverter? -

    Properties

    Optional fetchFunc

    fetchFunc?: {
        (input, init?): Promise<Response>;
        (input, init?): Promise<Response>;
        (input, init?): Promise<Response>;
    }

    A function used to override the window.fetch function.

    -

    Type declaration

      • (input, init?): Promise<Response>

      • A function used to override the window.fetch function.

        -

        Parameters

        • input: RequestInfo | URL
        • Optional init: RequestInit

        Returns Promise<Response>

      • (input, init?): Promise<Response>

      • A function used to override the window.fetch function.

        -

        Parameters

        • input: RequestInfo | URL
        • Optional init: RequestInit

        Returns Promise<Response>

      • (input, init?): Promise<Response>

      • A function used to override the window.fetch function.

        -

        Parameters

        • input: string | Request | URL
        • Optional init: RequestInit

        Returns Promise<Response>

    Optional fromTFHub

    fromTFHub?: boolean

    Whether the module or model is to be loaded from TF Hub.

    +LoadOptions | @vladmandic/face-api - v1.7.14

    Interface LoadOptions

    Innamespace

    io

    +
    interface LoadOptions {
        fetchFunc?: {
            (input: RequestInfo | URL, init?: RequestInit): Promise<Response>;
            (input: RequestInfo | URL, init?: RequestInit): Promise<Response>;
            (input: string | Request | URL, init?: RequestInit): Promise<Response>;
        };
        fromTFHub?: boolean;
        onProgress?: OnProgressCallback;
        requestInit?: RequestInit;
        streamWeights?: boolean;
        strict?: boolean;
        weightPathPrefix?: string;
        weightUrlConverter?: ((weightFileName: string) => Promise<string>);
    }

    Properties

    fetchFunc? +fromTFHub? +onProgress? +requestInit? +streamWeights? +strict? +weightPathPrefix? +weightUrlConverter? +

    Properties

    OptionalfetchFunc

    fetchFunc?: {
        (input: RequestInfo | URL, init?: RequestInit): Promise<Response>;
        (input: RequestInfo | URL, init?: RequestInit): Promise<Response>;
        (input: string | Request | URL, init?: RequestInit): Promise<Response>;
    }

    A function used to override the window.fetch function.

    +

    Type declaration

      • (input, init?): Promise<Response>

      • MDN Reference

        +

        Parameters

        • input: RequestInfo | URL
        • Optionalinit: RequestInit

        Returns Promise<Response>

      • (input, init?): Promise<Response>

      • MDN Reference

        +

        Parameters

        • input: RequestInfo | URL
        • Optionalinit: RequestInit

        Returns Promise<Response>

      • (input, init?): Promise<Response>

      • Parameters

        • input: string | Request | URL
        • Optionalinit: RequestInit

        Returns Promise<Response>

    OptionalfromTFHub

    fromTFHub?: boolean

    Whether the module or model is to be loaded from TF Hub.

    Setting this to true allows passing a TF-Hub module URL, omitting the standard model file name and the query parameters.

    Default: false.

    -

    Optional onProgress

    onProgress?: OnProgressCallback

    Progress callback.

    -

    Optional requestInit

    requestInit?: RequestInit

    RequestInit (options) for HTTP requests.

    +

    OptionalonProgress

    onProgress?: OnProgressCallback

    Progress callback.

    +

    OptionalrequestInit

    requestInit?: RequestInit

    RequestInit (options) for HTTP requests.

    For detailed information on the supported fields, see -https://developer.mozilla.org/en-US/docs/Web/API/Request/Request

    -

    Optional streamWeights

    streamWeights?: boolean

    Whether to stream the model directly to the backend or cache all its +https://developer.mozilla.org/en-US/docs/Web/API/Request/Request

    +

    OptionalstreamWeights

    streamWeights?: boolean

    Whether to stream the model directly to the backend or cache all its weights on CPU first. Useful for large models.

    -

    Optional strict

    strict?: boolean

    Strict loading model: whether extraneous weights or missing +

    Optionalstrict

    strict?: boolean

    Strict loading model: whether extraneous weights or missing weights should trigger an Error.

    If true, require that the provided weights exactly match those required by the layers. false means that both extra weights and missing weights will be silently ignored.

    Default: true.

    -

    Optional weightPathPrefix

    weightPathPrefix?: string

    Path prefix for weight files, by default this is calculated from the +

    OptionalweightPathPrefix

    weightPathPrefix?: string

    Path prefix for weight files, by default this is calculated from the path of the model JSON file.

    For instance, if the path to the model JSON file is http://localhost/foo/model.json, then the default path prefix will be @@ -37,18 +36,11 @@ loaded from http://localhost/foo/group1-shard1of2 by default. Howev if you provide a weightPathPrefix value of http://localhost/foo/alt-weights, then the weight file will be loaded from the path http://localhost/foo/alt-weights/group1-shard1of2 instead.

    -

    Optional weightUrlConverter

    weightUrlConverter?: ((weightFileName) => Promise<string>)

    An async function to convert weight file name to URL. The weight file -names are stored in model.json's weightsManifest.paths field. By default we +

    OptionalweightUrlConverter

    weightUrlConverter?: ((weightFileName: string) => Promise<string>)

    An async function to convert weight file name to URL. The weight file +names are stored in model.json's weightsManifest.paths field. By default we consider weight files are colocated with the model.json file. For example: - model.json URL: https://www.google.com/models/1/model.json - group1-shard1of1.bin url: - https://www.google.com/models/1/group1-shard1of1.bin

    +model.json URL: https://www.google.com/models/1/model.json +group1-shard1of1.bin url: +https://www.google.com/models/1/group1-shard1of1.bin

    With this func you can convert the weight file name to any URL.

    -

    Type declaration

      • (weightFileName): Promise<string>

      • An async function to convert weight file name to URL. The weight file -names are stored in model.json's weightsManifest.paths field. By default we -consider weight files are colocated with the model.json file. For example: - model.json URL: https://www.google.com/models/1/model.json - group1-shard1of1.bin url: - https://www.google.com/models/1/group1-shard1of1.bin

        -

        With this func you can convert the weight file name to any URL.

        -

        Parameters

        • weightFileName: string

        Returns Promise<string>

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    diff --git a/typedoc/interfaces/tf.io.ModelArtifacts.html b/typedoc/interfaces/tf.io.ModelArtifacts.html index e4ce9aa..f8aa529 100644 --- a/typedoc/interfaces/tf.io.ModelArtifacts.html +++ b/typedoc/interfaces/tf.io.ModelArtifacts.html @@ -1,50 +1,47 @@ -ModelArtifacts | @vladmandic/face-api - v1.7.13

    Interface ModelArtifacts

    The serialized artifacts of a model, including topology and weights.

    +ModelArtifacts | @vladmandic/face-api - v1.7.14

    Interface ModelArtifacts

    The serialized artifacts of a model, including topology and weights.

    The modelTopology, trainingConfig, weightSpecs and weightData fields of this interface are optional, in order to support topology- or weights-only saving and loading.

    Note this interface is used internally in IOHandlers. For the file format written to disk as model.json, see ModelJSON.

    -
    interface ModelArtifacts {
        convertedBy?: null | string;
        format?: string;
        generatedBy?: string;
        getWeightStream?: (() => ReadableStream<ArrayBuffer>);
        initializerSignature?: {};
        modelInitializer?: {};
        modelTopology?: {} | ArrayBuffer;
        signature?: {};
        trainingConfig?: TrainingConfig;
        userDefinedMetadata?: {
            [key: string]: {};
        };
        weightData?: WeightData;
        weightSpecs?: WeightsManifestEntry[];
    }

    Properties

    convertedBy? -format? -generatedBy? -getWeightStream? -initializerSignature? -modelInitializer? -modelTopology? -signature? -trainingConfig? -userDefinedMetadata? -weightData? -weightSpecs? -

    Properties

    Optional convertedBy

    convertedBy?: null | string

    What library or tool is responsible for converting the original model +

    interface ModelArtifacts {
        convertedBy?: null | string;
        format?: string;
        generatedBy?: string;
        getWeightStream?: (() => ReadableStream<ArrayBuffer>);
        initializerSignature?: {};
        modelInitializer?: {};
        modelTopology?: {} | ArrayBuffer;
        signature?: {};
        trainingConfig?: TrainingConfig;
        userDefinedMetadata?: {
            [key: string]: {};
        };
        weightData?: WeightData;
        weightSpecs?: WeightsManifestEntry[];
    }

    Properties

    convertedBy? +format? +generatedBy? +getWeightStream? +initializerSignature? +modelInitializer? +modelTopology? +signature? +trainingConfig? +userDefinedMetadata? +weightData? +weightSpecs? +

    Properties

    OptionalconvertedBy

    convertedBy?: null | string

    What library or tool is responsible for converting the original model to this format, applicable only if the model is output by a converter.

    -

    Used for debugging purposes. E.g., 'TensorFlow.js Converter v1.0.0'.

    +

    Used for debugging purposes. E.g., 'TensorFlow.js Converter v1.0.0'.

    A value of null means the model artifacts are generated without any conversion process (e.g., saved directly from a TensorFlow.js tf.LayersModel instance.)

    -

    Optional format

    format?: string

    Hard-coded format name for models saved from TensorFlow.js or converted +

    Optionalformat

    format?: string

    Hard-coded format name for models saved from TensorFlow.js or converted by TensorFlow.js Converter.

    -

    Optional generatedBy

    generatedBy?: string

    What library is responsible for originally generating this artifact.

    -

    Used for debugging purposes. E.g., 'TensorFlow.js v1.0.0'.

    -

    Optional getWeightStream

    getWeightStream?: (() => ReadableStream<ArrayBuffer>)

    Returns a stream of the weights. Some models are too large to fit in -V8's memory heap, and getWeightStream loads their weights without storing +

    OptionalgeneratedBy

    generatedBy?: string

    What library is responsible for originally generating this artifact.

    +

    Used for debugging purposes. E.g., 'TensorFlow.js v1.0.0'.

    +

    OptionalgetWeightStream

    getWeightStream?: (() => ReadableStream<ArrayBuffer>)

    Returns a stream of the weights. Some models are too large to fit in +V8's memory heap, and getWeightStream loads their weights without storing them all in memory at the same time.

    -

    Type declaration

      • (): ReadableStream<ArrayBuffer>

      • Returns a stream of the weights. Some models are too large to fit in -V8's memory heap, and getWeightStream loads their weights without storing -them all in memory at the same time.

        -

        Returns ReadableStream<ArrayBuffer>

    Optional initializerSignature

    initializerSignature?: {}

    Inputs and outputs signature for model initializer.

    -

    Type declaration

      Optional modelInitializer

      modelInitializer?: {}

      Initializer for the model.

      -

      Type declaration

        Optional modelTopology

        modelTopology?: {} | ArrayBuffer

        Model topology.

        +

        OptionalinitializerSignature

        initializerSignature?: {}

        Inputs and outputs signature for model initializer.

        +

        OptionalmodelInitializer

        modelInitializer?: {}

        Initializer for the model.

        +

        OptionalmodelTopology

        modelTopology?: {} | ArrayBuffer

        Model topology.

        For Keras-style tf.Models, this is a JSON object. For TensorFlow-style models (e.g., SavedModel), this is the JSON encoding of the GraphDef protocol buffer.

        -

        Type declaration

          Optional signature

          signature?: {}

          Inputs and outputs signature for saved model.

          -

          Type declaration

            Optional trainingConfig

            trainingConfig?: TrainingConfig

            Serialized configuration for the model's training.

            -

            Optional userDefinedMetadata

            userDefinedMetadata?: {
                [key: string]: {};
            }

            User-defined metadata about the model.

            -

            Type declaration

            • [key: string]: {}

              Optional weightData

              weightData?: WeightData

              Binary buffer(s) for all weight values in the order specified by +

              Optionalsignature

              signature?: {}

              Inputs and outputs signature for saved model.

              +

              OptionaltrainingConfig

              trainingConfig?: TrainingConfig

              Serialized configuration for the model's training.

              +

              OptionaluserDefinedMetadata

              userDefinedMetadata?: {
                  [key: string]: {};
              }

              User-defined metadata about the model.

              +

              OptionalweightData

              weightData?: WeightData

              Binary buffer(s) for all weight values in the order specified by weightSpecs. This may be a single ArrayBuffer of all the weights concatenated together or an Array of ArrayBuffers containing the weights (weights may be sharded across multiple ArrayBuffers).

              -

              Optional weightSpecs

              weightSpecs?: WeightsManifestEntry[]

              Weight specifications.

              +

              OptionalweightSpecs

              weightSpecs?: WeightsManifestEntry[]

              Weight specifications.

              This corresponds to the weightsData below.

              -

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              diff --git a/typedoc/interfaces/tf.io.ModelArtifactsInfo.html b/typedoc/interfaces/tf.io.ModelArtifactsInfo.html index 723ea4d..4015255 100644 --- a/typedoc/interfaces/tf.io.ModelArtifactsInfo.html +++ b/typedoc/interfaces/tf.io.ModelArtifactsInfo.html @@ -1,24 +1,24 @@ -ModelArtifactsInfo | @vladmandic/face-api - v1.7.13

              Interface ModelArtifactsInfo

              interface ModelArtifactsInfo {
                  dateSaved: Date;
                  modelTopologyBytes?: number;
                  modelTopologyType: "JSON" | "GraphDef";
                  weightDataBytes?: number;
                  weightSpecsBytes?: number;
              }

              Properties

              dateSaved -modelTopologyBytes? -modelTopologyType -weightDataBytes? -weightSpecsBytes? -

              Properties

              dateSaved

              dateSaved: Date

              Timestamp for when the model is saved.

              -

              Optional modelTopologyBytes

              modelTopologyBytes?: number

              Size of model topology (Keras JSON or GraphDef), in bytes.

              -

              modelTopologyType

              modelTopologyType: "JSON" | "GraphDef"

              TODO (cais,yassogba) consider removing GraphDef as GraphDefs now +ModelArtifactsInfo | @vladmandic/face-api - v1.7.14

              Interface ModelArtifactsInfo

              interface ModelArtifactsInfo {
                  dateSaved: Date;
                  modelTopologyBytes?: number;
                  modelTopologyType: "JSON" | "GraphDef";
                  weightDataBytes?: number;
                  weightSpecsBytes?: number;
              }

              Properties

              dateSaved +modelTopologyBytes? +modelTopologyType +weightDataBytes? +weightSpecsBytes? +

              Properties

              dateSaved

              dateSaved: Date

              Timestamp for when the model is saved.

              +

              OptionalmodelTopologyBytes

              modelTopologyBytes?: number

              Size of model topology (Keras JSON or GraphDef), in bytes.

              +

              modelTopologyType

              modelTopologyType: "JSON" | "GraphDef"

              TODO (cais,yassogba) consider removing GraphDef as GraphDefs now come in a JSON format and none of our IOHandlers support a non json -format. We could conder replacing this with 'Binary' if we want to +format. We could conder replacing this with 'Binary' if we want to allow future handlers to save to non json formats (though they will -probably want more information than 'Binary'). +probably want more information than 'Binary'). Type of the model topology

              Type of the model topology

              Possible values:

              • JSON: JSON config (human-readable, e.g., Keras JSON).
              • GraphDef: TensorFlow -GraphDef +GraphDef protocol buffer (binary).
              -

              Optional weightDataBytes

              weightDataBytes?: number

              Size of weight value data, in bytes.

              -

              Optional weightSpecsBytes

              weightSpecsBytes?: number

              Size of weight specification or manifest, in bytes.

              -

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              OptionalweightDataBytes

              weightDataBytes?: number

              Size of weight value data, in bytes.

              +

              OptionalweightSpecsBytes

              weightSpecsBytes?: number

              Size of weight specification or manifest, in bytes.

              +

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              diff --git a/typedoc/interfaces/tf.io.ModelJSON.html b/typedoc/interfaces/tf.io.ModelJSON.html index 3941bd5..1a37b6d 100644 --- a/typedoc/interfaces/tf.io.ModelJSON.html +++ b/typedoc/interfaces/tf.io.ModelJSON.html @@ -1,38 +1,38 @@ -ModelJSON | @vladmandic/face-api - v1.7.13

              Interface ModelJSON

              The on-disk format of the model.json file.

              +ModelJSON | @vladmandic/face-api - v1.7.14

              Interface ModelJSON

              The on-disk format of the model.json file.

              TF.js 1.0 always populates the optional fields when writing model.json. Prior versions did not provide those fields.

              -
              interface ModelJSON {
                  convertedBy?: null | string;
                  format?: string;
                  generatedBy?: string;
                  initializerSignature?: {};
                  modelInitializer?: {};
                  modelTopology: {};
                  signature?: {};
                  trainingConfig?: TrainingConfig;
                  userDefinedMetadata?: {
                      [key: string]: {};
                  };
                  weightsManifest: WeightsManifestConfig;
              }

              Properties

              convertedBy? -format? -generatedBy? -initializerSignature? -modelInitializer? -modelTopology -signature? -trainingConfig? -userDefinedMetadata? -weightsManifest -

              Properties

              Optional convertedBy

              convertedBy?: null | string

              What library or tool is responsible for converting the original model +

              interface ModelJSON {
                  convertedBy?: null | string;
                  format?: string;
                  generatedBy?: string;
                  initializerSignature?: {};
                  modelInitializer?: {};
                  modelTopology: {};
                  signature?: {};
                  trainingConfig?: TrainingConfig;
                  userDefinedMetadata?: {
                      [key: string]: {};
                  };
                  weightsManifest: WeightsManifestConfig;
              }

              Properties

              convertedBy? +format? +generatedBy? +initializerSignature? +modelInitializer? +modelTopology +signature? +trainingConfig? +userDefinedMetadata? +weightsManifest +

              Properties

              OptionalconvertedBy

              convertedBy?: null | string

              What library or tool is responsible for converting the original model to this format, applicable only if the model is output by a converter.

              -

              Used for debugging purposes. E.g., 'TensorFlow.js Converter v1.0.0'.

              +

              Used for debugging purposes. E.g., 'TensorFlow.js Converter v1.0.0'.

              A value of null means the model artifacts are generated without any conversion process (e.g., saved directly from a TensorFlow.js tf.LayersModel instance.)

              -

              Optional format

              format?: string

              Hard-coded format name for models saved from TensorFlow.js or converted +

              Optionalformat

              format?: string

              Hard-coded format name for models saved from TensorFlow.js or converted by TensorFlow.js Converter.

              -

              Optional generatedBy

              generatedBy?: string

              What library is responsible for originally generating this artifact.

              -

              Used for debugging purposes. E.g., 'TensorFlow.js v1.0.0'.

              -

              Optional initializerSignature

              initializerSignature?: {}

              Inputs and outputs signature for model initializer.

              -

              Type declaration

                Optional modelInitializer

                modelInitializer?: {}

                Initializer for the model.

                -

                Type declaration

                  modelTopology

                  modelTopology: {}

                  Model topology.

                  +

                  OptionalgeneratedBy

                  generatedBy?: string

                  What library is responsible for originally generating this artifact.

                  +

                  Used for debugging purposes. E.g., 'TensorFlow.js v1.0.0'.

                  +

                  OptionalinitializerSignature

                  initializerSignature?: {}

                  Inputs and outputs signature for model initializer.

                  +

                  OptionalmodelInitializer

                  modelInitializer?: {}

                  Initializer for the model.

                  +

                  modelTopology

                  modelTopology: {}

                  Model topology.

                  For Keras-style tf.Models, this is a JSON object. For TensorFlow-style models (e.g., SavedModel), this is the JSON encoding of the GraphDef protocol buffer.

                  -

                  Type declaration

                    Optional signature

                    signature?: {}

                    Inputs and outputs signature for saved model.

                    -

                    Type declaration

                      Optional trainingConfig

                      trainingConfig?: TrainingConfig

                      Model training configuration.

                      -

                      Optional userDefinedMetadata

                      userDefinedMetadata?: {
                          [key: string]: {};
                      }

                      User-defined metadata about the model.

                      -

                      Type declaration

                      • [key: string]: {}

                        weightsManifest

                        weightsManifest: WeightsManifestConfig

                        Weights manifest.

                        +

                        Optionalsignature

                        signature?: {}

                        Inputs and outputs signature for saved model.

                        +

                        OptionaltrainingConfig

                        trainingConfig?: TrainingConfig

                        Model training configuration.

                        +

                        OptionaluserDefinedMetadata

                        userDefinedMetadata?: {
                            [key: string]: {};
                        }

                        User-defined metadata about the model.

                        +

                        weightsManifest

                        weightsManifest: WeightsManifestConfig

                        Weights manifest.

                        The weights manifest consists of an ordered list of weight-manifest groups. Each weight-manifest group consists of a number of weight values stored in a number of paths. See the documentation of WeightsManifestConfig for more details.

                        -

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                        diff --git a/typedoc/interfaces/tf.io.ModelStoreManager.html b/typedoc/interfaces/tf.io.ModelStoreManager.html index 2b6bc6a..6188cfa 100644 --- a/typedoc/interfaces/tf.io.ModelStoreManager.html +++ b/typedoc/interfaces/tf.io.ModelStoreManager.html @@ -1,4 +1,4 @@ -ModelStoreManager | @vladmandic/face-api - v1.7.13

                        Interface ModelStoreManager

                        An interface for the manager of a model store.

                        +ModelStoreManager | @vladmandic/face-api - v1.7.14

                        Interface ModelStoreManager

                        An interface for the manager of a model store.

                        A model store is defined as a storage medium on which multiple models can be stored. Each stored model has a unique path as its identifier. A ModelStoreManager for the store allows actions including

                        @@ -6,14 +6,14 @@ A ModelStoreManager for the store allows actions including

                      • Listing the models stored in the store.
                      • Deleting a model from the store.
                        • -
                        interface ModelStoreManager {
                            listModels(): Promise<{
                                [path: string]: ModelArtifactsInfo;
                            }>;
                            removeModel(path): Promise<ModelArtifactsInfo>;
                        }

                        Methods

                        listModels -removeModel -

                        Methods

                        listModels

                        • listModels(): Promise<{
                              [path: string]: ModelArtifactsInfo;
                          }>

                        • List all models in the model store.

                          +
                        interface ModelStoreManager {
                            listModels(): Promise<{
                                [path: string]: ModelArtifactsInfo;
                            }>;
                            removeModel(path: string): Promise<ModelArtifactsInfo>;
                        }

                        Methods

                        listModels +removeModel +

                        Methods

                        listModels

                        • listModels(): Promise<{
                              [path: string]: ModelArtifactsInfo;
                          }>

                        • List all models in the model store.

                          Returns Promise<{
                              [path: string]: ModelArtifactsInfo;
                          }>

                          A dictionary mapping paths of existing models to their - model artifacts info. Model artifacts info include type of the model's - topology, byte sizes of the topology, weights, etc.

                          -

                        removeModel

                        • removeModel(path): Promise<ModelArtifactsInfo>

                        • Remove a model specified by path.

                          -

                          Parameters

                          • path: string

                          Returns Promise<ModelArtifactsInfo>

                          ModelArtifactsInfo of the deleted model (if and only if deletion - is successful).

                          -

                          Throws

                          Error if deletion fails, e.g., if no model exists at path.

                          -

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                        \ No newline at end of file +model artifacts info. Model artifacts info include type of the model's +topology, byte sizes of the topology, weights, etc.

                        +

                        removeModel

                        • removeModel(path): Promise<ModelArtifactsInfo>

                        • Remove a model specified by path.

                          +

                          Parameters

                          • path: string

                          Returns Promise<ModelArtifactsInfo>

                          ModelArtifactsInfo of the deleted model (if and only if deletion +is successful).

                          +

                          Throws

                          Error if deletion fails, e.g., if no model exists at path.

                          +

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                        diff --git a/typedoc/interfaces/tf.io.RequestDetails.html b/typedoc/interfaces/tf.io.RequestDetails.html index 8ff23d2..b8fddf4 100644 --- a/typedoc/interfaces/tf.io.RequestDetails.html +++ b/typedoc/interfaces/tf.io.RequestDetails.html @@ -1,4 +1,4 @@ -RequestDetails | @vladmandic/face-api - v1.7.13

                        Interface RequestDetails

                        Additional options for Platform.fetch

                        -
                        interface RequestDetails {
                            isBinary?: boolean;
                        }

                        Properties

                        isBinary? -

                        Properties

                        Optional isBinary

                        isBinary?: boolean

                        Is this request for a binary file (as opposed to a json file)

                        -

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                        \ No newline at end of file +RequestDetails | @vladmandic/face-api - v1.7.14

                        Interface RequestDetails

                        Additional options for Platform.fetch

                        +
                        interface RequestDetails {
                            isBinary?: boolean;
                        }

                        Properties

                        isBinary? +

                        Properties

                        OptionalisBinary

                        isBinary?: boolean

                        Is this request for a binary file (as opposed to a json file)

                        +

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                        diff --git a/typedoc/interfaces/tf.io.SaveConfig.html b/typedoc/interfaces/tf.io.SaveConfig.html index a5cb904..14cba3b 100644 --- a/typedoc/interfaces/tf.io.SaveConfig.html +++ b/typedoc/interfaces/tf.io.SaveConfig.html @@ -1,9 +1,9 @@ -SaveConfig | @vladmandic/face-api - v1.7.13

                        Interface SaveConfig

                        Options for saving a model.

                        -

                        Innamespace

                        io

                        -
                        interface SaveConfig {
                            includeOptimizer?: boolean;
                            trainableOnly?: boolean;
                        }

                        Properties

                        includeOptimizer? -trainableOnly? -

                        Properties

                        Optional includeOptimizer

                        includeOptimizer?: boolean

                        Whether the optimizer will be saved (if exists).

                        +SaveConfig | @vladmandic/face-api - v1.7.14

                        Interface SaveConfig

                        Options for saving a model.

                        +

                        Innamespace

                        io

                        +
                        interface SaveConfig {
                            includeOptimizer?: boolean;
                            trainableOnly?: boolean;
                        }

                        Properties

                        includeOptimizer? +trainableOnly? +

                        Properties

                        OptionalincludeOptimizer

                        includeOptimizer?: boolean

                        Whether the optimizer will be saved (if exists).

                        Default: false.

                        -

                        Optional trainableOnly

                        trainableOnly?: boolean

                        Whether to save only the trainable weights of the model, ignoring the +

                        OptionaltrainableOnly

                        trainableOnly?: boolean

                        Whether to save only the trainable weights of the model, ignoring the non-trainable ones.

                        -

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                        diff --git a/typedoc/interfaces/tf.io.SaveResult.html b/typedoc/interfaces/tf.io.SaveResult.html index 4da6f58..bb406b2 100644 --- a/typedoc/interfaces/tf.io.SaveResult.html +++ b/typedoc/interfaces/tf.io.SaveResult.html @@ -1,9 +1,9 @@ -SaveResult | @vladmandic/face-api - v1.7.13

                        Interface SaveResult

                        Result of a saving operation.

                        -
                        interface SaveResult {
                            errors?: (string | {})[];
                            modelArtifactsInfo: ModelArtifactsInfo;
                            responses?: Response[];
                        }

                        Properties

                        errors? -modelArtifactsInfo -responses? -

                        Properties

                        Optional errors

                        errors?: (string | {})[]

                        Error messages and related data (if any).

                        -

                        modelArtifactsInfo

                        modelArtifactsInfo: ModelArtifactsInfo

                        Information about the model artifacts saved.

                        -

                        Optional responses

                        responses?: Response[]

                        HTTP responses from the server that handled the model-saving request (if +SaveResult | @vladmandic/face-api - v1.7.14

                        Interface SaveResult

                        Result of a saving operation.

                        +
                        interface SaveResult {
                            errors?: (string | {})[];
                            modelArtifactsInfo: ModelArtifactsInfo;
                            responses?: Response[];
                        }

                        Properties

                        errors? +modelArtifactsInfo +responses? +

                        Properties

                        Optionalerrors

                        errors?: (string | {})[]

                        Error messages and related data (if any).

                        +

                        modelArtifactsInfo

                        modelArtifactsInfo: ModelArtifactsInfo

                        Information about the model artifacts saved.

                        +

                        Optionalresponses

                        responses?: Response[]

                        HTTP responses from the server that handled the model-saving request (if any). This is applicable only to server-based saving routes.

                        -

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                        diff --git a/typedoc/interfaces/tf.io.TrainingConfig.html b/typedoc/interfaces/tf.io.TrainingConfig.html index facb840..9713663 100644 --- a/typedoc/interfaces/tf.io.TrainingConfig.html +++ b/typedoc/interfaces/tf.io.TrainingConfig.html @@ -1,11 +1,11 @@ -TrainingConfig | @vladmandic/face-api - v1.7.13

                        Interface TrainingConfig

                        Model training configuration.

                        -
                        interface TrainingConfig {
                            loss: string | string[] | {
                                [key: string]: string;
                            };
                            loss_weights?: number[] | {
                                [key: string]: number;
                            };
                            metrics?: string[] | {
                                [key: string]: string;
                            };
                            optimizer_config: {};
                            sample_weight_mode?: string;
                            weighted_metrics?: string[];
                        }

                        Properties

                        loss -loss_weights? -metrics? -optimizer_config -sample_weight_mode? -weighted_metrics? -

                        Properties

                        loss

                        loss: string | string[] | {
                            [key: string]: string;
                        }

                        Loss function(s) for the model's output(s).

                        -

                        Type declaration

                        • [key: string]: string

                        Optional loss_weights

                        loss_weights?: number[] | {
                            [key: string]: number;
                        }

                        Type declaration

                        • [key: string]: number

                        Optional metrics

                        metrics?: string[] | {
                            [key: string]: string;
                        }

                        Metric function(s) for the model's output(s).

                        -

                        Type declaration

                        • [key: string]: string

                        optimizer_config

                        optimizer_config: {}

                        Optimizer used for the model training.

                        -

                        Type declaration

                          Optional sample_weight_mode

                          sample_weight_mode?: string

                          Optional weighted_metrics

                          weighted_metrics?: string[]

                          Settings

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                          \ No newline at end of file +TrainingConfig | @vladmandic/face-api - v1.7.14

                          Interface TrainingConfig

                          Model training configuration.

                          +
                          interface TrainingConfig {
                              loss: string | string[] | {
                                  [key: string]: string;
                              };
                              loss_weights?: number[] | {
                                  [key: string]: number;
                              };
                              metrics?: string[] | {
                                  [key: string]: string;
                              };
                              optimizer_config: {};
                              sample_weight_mode?: string;
                              weighted_metrics?: string[];
                          }

                          Properties

                          loss +loss_weights? +metrics? +optimizer_config +sample_weight_mode? +weighted_metrics? +

                          Properties

                          loss

                          loss: string | string[] | {
                              [key: string]: string;
                          }

                          Loss function(s) for the model's output(s).

                          +

                          Optionalloss_weights

                          loss_weights?: number[] | {
                              [key: string]: number;
                          }

                          Optionalmetrics

                          metrics?: string[] | {
                              [key: string]: string;
                          }

                          Metric function(s) for the model's output(s).

                          +

                          optimizer_config

                          optimizer_config: {}

                          Optimizer used for the model training.

                          +

                          Optionalsample_weight_mode

                          sample_weight_mode?: string

                          Optionalweighted_metrics

                          weighted_metrics?: string[]

                          Settings

                          Member Visibility

                          On This Page

                          Properties
                          diff --git a/typedoc/interfaces/tf.io.WeightsManifestEntry.html b/typedoc/interfaces/tf.io.WeightsManifestEntry.html index 0cd44dc..1a1e580 100644 --- a/typedoc/interfaces/tf.io.WeightsManifestEntry.html +++ b/typedoc/interfaces/tf.io.WeightsManifestEntry.html @@ -1,16 +1,16 @@ -WeightsManifestEntry | @vladmandic/face-api - v1.7.13

                          Interface WeightsManifestEntry

                          An entry in the weight manifest.

                          +WeightsManifestEntry | @vladmandic/face-api - v1.7.14

                          Interface WeightsManifestEntry

                          An entry in the weight manifest.

                          The entry contains specification of a weight.

                          -
                          interface WeightsManifestEntry {
                              dtype: "string" | "float32" | "int32" | "bool" | "complex64";
                              group?: WeightGroup;
                              name: string;
                              quantization?: {
                                  dtype: "uint16" | "uint8" | "float16";
                                  min?: number;
                                  scale?: number;
                              };
                              shape: number[];
                          }

                          Properties

                          dtype -group? -name -quantization? -shape -

                          Properties

                          dtype

                          dtype: "string" | "float32" | "int32" | "bool" | "complex64"

                          Data type of the weight.

                          -

                          Optional group

                          group?: WeightGroup

                          Type of the weight.

                          +
                          interface WeightsManifestEntry {
                              dtype:
                                  | "string"
                                  | "float32"
                                  | "int32"
                                  | "bool"
                                  | "complex64";
                              group?: WeightGroup;
                              name: string;
                              quantization?: {
                                  dtype: "uint16" | "uint8" | "float16";
                                  min?: number;
                                  scale?: number;
                              };
                              shape: number[];
                          }

                          Properties

                          dtype +group? +name +quantization? +shape +

                          Properties

                          dtype

                          dtype:
                              | "string"
                              | "float32"
                              | "int32"
                              | "bool"
                              | "complex64"

                          Data type of the weight.

                          +

                          Optionalgroup

                          group?: WeightGroup

                          Type of the weight.

                          Optional.

                          -

                          The value 'optimizer' indicates the weight belongs to an optimizer +

                          The value 'optimizer' indicates the weight belongs to an optimizer (i.e., used only during model training and not during inference).

                          -

                          name

                          name: string

                          Name of the weight, e.g., 'Dense_1/bias'

                          -

                          Optional quantization

                          quantization?: {
                              dtype: "uint16" | "uint8" | "float16";
                              min?: number;
                              scale?: number;
                          }

                          Information for dequantization of the weight.

                          -

                          Type declaration

                          • dtype: "uint16" | "uint8" | "float16"
                          • Optional min?: number
                          • Optional scale?: number

                          shape

                          shape: number[]

                          Shape of the weight.

                          -

                          Settings

                          Member Visibility

                          Theme

                          On This Page

                          \ No newline at end of file +

                          name

                          name: string

                          Name of the weight, e.g., 'Dense_1/bias'

                          +

                          Optionalquantization

                          quantization?: {
                              dtype: "uint16" | "uint8" | "float16";
                              min?: number;
                              scale?: number;
                          }

                          Information for dequantization of the weight.

                          +

                          shape

                          shape: number[]

                          Shape of the weight.

                          +

                          Settings

                          Member Visibility

                          On This Page

                          Properties
                          diff --git a/typedoc/modules/draw.html b/typedoc/modules/draw.html index 93597ba..4c1ca81 100644 --- a/typedoc/modules/draw.html +++ b/typedoc/modules/draw.html @@ -1,18 +1,18 @@ -draw | @vladmandic/face-api - v1.7.13

                          Namespace draw

                          Index

                          Enumerations

                          AnchorPosition -

                          Classes

                          DrawBox -DrawBoxOptions -DrawFaceLandmarks -DrawFaceLandmarksOptions -DrawTextField -DrawTextFieldOptions -

                          Interfaces

                          IDrawBoxOptions -IDrawFaceLandmarksOptions -IDrawTextFieldOptions -

                          Type Aliases

                          DrawFaceExpressionsInput -DrawFaceLandmarksInput -TDrawDetectionsInput -

                          Functions

                          drawContour -drawDetections -drawFaceExpressions -drawFaceLandmarks -

                          Settings

                          Member Visibility

                          Theme

                          \ No newline at end of file +draw | @vladmandic/face-api - v1.7.14

                          Namespace draw

                          Index

                          Enumerations

                          AnchorPosition +

                          Classes

                          DrawBox +DrawBoxOptions +DrawFaceLandmarks +DrawFaceLandmarksOptions +DrawTextField +DrawTextFieldOptions +

                          Interfaces

                          IDrawBoxOptions +IDrawFaceLandmarksOptions +IDrawTextFieldOptions +

                          Type Aliases

                          DrawFaceExpressionsInput +DrawFaceLandmarksInput +TDrawDetectionsInput +

                          Functions

                          drawContour +drawDetections +drawFaceExpressions +drawFaceLandmarks +

                          Settings

                          Member Visibility
                          diff --git a/typedoc/modules/tf.Tensor.html b/typedoc/modules/tf.Tensor.html index e43367b..b3f2ceb 100644 --- a/typedoc/modules/tf.Tensor.html +++ b/typedoc/modules/tf.Tensor.html @@ -1 +1 @@ -Tensor | @vladmandic/face-api - v1.7.13

                          Namespace Tensor

                          Settings

                          Member Visibility

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                          \ No newline at end of file +Tensor | @vladmandic/face-api - v1.7.14

                          Namespace Tensor

                          Settings

                          Member Visibility
                          diff --git a/typedoc/modules/tf.browser.html b/typedoc/modules/tf.browser.html index 4c40fcb..5c01cf8 100644 --- a/typedoc/modules/tf.browser.html +++ b/typedoc/modules/tf.browser.html @@ -1,5 +1,5 @@ -browser | @vladmandic/face-api - v1.7.13

                          Namespace browser

                          Index

                          Functions

                          draw -fromPixels -fromPixelsAsync -toPixels -

                          Settings

                          Member Visibility

                          Theme

                          \ No newline at end of file +browser | @vladmandic/face-api - v1.7.14

                          Namespace browser

                          Index

                          Functions

                          draw +fromPixels +fromPixelsAsync +toPixels +

                          Settings

                          Member Visibility
                          diff --git a/typedoc/modules/tf.html b/typedoc/modules/tf.html index 7f8b7e7..e964d7d 100644 --- a/typedoc/modules/tf.html +++ b/typedoc/modules/tf.html @@ -1,53 +1,53 @@ -tf | @vladmandic/face-api - v1.7.13

                          Namespace tf

                          Index

                          Namespaces

                          Tensor -browser -io -

                          Enumerations

                          Rank -

                          Classes

                          Tensor -Variable -

                          Type Aliases

                          NamedTensorMap -Tensor1D -Tensor2D -Tensor3D -Tensor4D -Tensor5D -TensorLike -

                          Variables

                          ENV -image -version -

                          Functions

                          add -avgPool -batchNorm -cast -clipByValue -concat -conv2d -depthwiseConv2d -div -exp -expandDims -fill -matMul -maxPool -mul -pad -relu -reshape -scalar -separableConv2d -sigmoid -slice -slice3d -softmax -stack -sub -tensor -tensor1d -tensor2d -tensor3d -tensor4d -tidy -tile -transpose -unstack -zeros -

                          Settings

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                          Theme

                          \ No newline at end of file +tf | @vladmandic/face-api - v1.7.14

                          Namespace tf

                          Index

                          Namespaces

                          browser +io +Tensor +

                          Enumerations

                          Rank +

                          Classes

                          Tensor +Variable +

                          Type Aliases

                          NamedTensorMap +Tensor1D +Tensor2D +Tensor3D +Tensor4D +Tensor5D +TensorLike +

                          Variables

                          ENV +image +version +

                          Functions

                          add +avgPool +batchNorm +cast +clipByValue +concat +conv2d +depthwiseConv2d +div +exp +expandDims +fill +matMul +maxPool +mul +pad +relu +reshape +scalar +separableConv2d +sigmoid +slice +slice3d +softmax +stack +sub +tensor +tensor1d +tensor2d +tensor3d +tensor4d +tidy +tile +transpose +unstack +zeros +

                          Settings

                          Member Visibility
                          diff --git a/typedoc/modules/tf.io.html b/typedoc/modules/tf.io.html index 62bb3e4..2daeb82 100644 --- a/typedoc/modules/tf.io.html +++ b/typedoc/modules/tf.io.html @@ -1,46 +1,46 @@ -io | @vladmandic/face-api - v1.7.13

                          Namespace io

                          Index

                          Classes

                          CompositeArrayBuffer -

                          Interfaces

                          IOHandler -LoadOptions -ModelArtifacts -ModelArtifactsInfo -ModelJSON -ModelStoreManager -RequestDetails -SaveConfig -SaveResult -TrainingConfig -WeightsManifestEntry -

                          Type Aliases

                          IOHandlerSync -LoadHandler -OnProgressCallback -SaveHandler -WeightData -WeightGroup -WeightsManifestConfig -

                          Functions

                          browserFiles -browserHTTPRequest -concatenateArrayBuffers -copyModel -decodeWeights -decodeWeightsStream -encodeWeights -fromMemory -fromMemorySync -getLoadHandlers -getModelArtifactsForJSON -getModelArtifactsForJSONSync -getModelArtifactsInfoForJSON -getSaveHandlers -getWeightSpecs -http -isHTTPScheme -listModels -loadWeights -moveModel -registerLoadRouter -registerSaveRouter -removeModel -weightsLoaderFactory -withSaveHandler -withSaveHandlerSync -

                          Settings

                          Member Visibility

                          Theme

                          \ No newline at end of file +io | @vladmandic/face-api - v1.7.14

                          Namespace io

                          Index

                          Classes

                          CompositeArrayBuffer +

                          Interfaces

                          IOHandler +LoadOptions +ModelArtifacts +ModelArtifactsInfo +ModelJSON +ModelStoreManager +RequestDetails +SaveConfig +SaveResult +TrainingConfig +WeightsManifestEntry +

                          Type Aliases

                          IOHandlerSync +LoadHandler +OnProgressCallback +SaveHandler +WeightData +WeightGroup +WeightsManifestConfig +

                          Functions

                          browserFiles +browserHTTPRequest +concatenateArrayBuffers +copyModel +decodeWeights +decodeWeightsStream +encodeWeights +fromMemory +fromMemorySync +getLoadHandlers +getModelArtifactsForJSON +getModelArtifactsForJSONSync +getModelArtifactsInfoForJSON +getSaveHandlers +getWeightSpecs +http +isHTTPScheme +listModels +loadWeights +moveModel +registerLoadRouter +registerSaveRouter +removeModel +weightsLoaderFactory +withSaveHandler +withSaveHandlerSync +

                          Settings

                          Member Visibility
                          diff --git a/typedoc/modules/utils.html b/typedoc/modules/utils.html index b908712..47a3eff 100644 --- a/typedoc/modules/utils.html +++ b/typedoc/modules/utils.html @@ -1,15 +1,15 @@ -utils | @vladmandic/face-api - v1.7.13

                          Namespace utils

                          Index

                          Functions

                          computeReshapedDimensions -getCenterPoint -isDimensions -isEven -isFloat -isTensor -isTensor1D -isTensor2D -isTensor3D -isTensor4D -isValidNumber -isValidProbablitiy -range -round -

                          Settings

                          Member Visibility

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                          \ No newline at end of file +utils | @vladmandic/face-api - v1.7.14

                          Namespace utils

                          Index

                          Functions

                          computeReshapedDimensions +getCenterPoint +isDimensions +isEven +isFloat +isTensor +isTensor1D +isTensor2D +isTensor3D +isTensor4D +isValidNumber +isValidProbablitiy +range +round +

                          Settings

                          Member Visibility
                          diff --git a/typedoc/types/AgeAndGenderPrediction.html b/typedoc/types/AgeAndGenderPrediction.html index 10ec8b8..0f323cb 100644 --- a/typedoc/types/AgeAndGenderPrediction.html +++ b/typedoc/types/AgeAndGenderPrediction.html @@ -1 +1 @@ -AgeAndGenderPrediction | @vladmandic/face-api - v1.7.13

                          Type alias AgeAndGenderPrediction

                          AgeAndGenderPrediction: {
                              age: number;
                              gender: Gender;
                              genderProbability: number;
                          }

                          Type declaration

                          • age: number
                          • gender: Gender
                          • genderProbability: number

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                          \ No newline at end of file +AgeAndGenderPrediction | @vladmandic/face-api - v1.7.14

                          Type Alias AgeAndGenderPrediction

                          AgeAndGenderPrediction: {
                              age: number;
                              gender: Gender;
                              genderProbability: number;
                          }

                          Settings

                          Member Visibility
                          diff --git a/typedoc/types/BatchNorm.html b/typedoc/types/BatchNorm.html index 87374db..86097f9 100644 --- a/typedoc/types/BatchNorm.html +++ b/typedoc/types/BatchNorm.html @@ -1 +1 @@ -BatchNorm | @vladmandic/face-api - v1.7.13

                          Type alias BatchNorm

                          BatchNorm: {
                              sub: Tensor1D;
                              truediv: Tensor1D;
                          }

                          Type declaration

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                          \ No newline at end of file +BatchNorm | @vladmandic/face-api - v1.7.14

                          Type Alias BatchNorm

                          BatchNorm: {
                              sub: Tensor1D;
                              truediv: Tensor1D;
                          }

                          Settings

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                          diff --git a/typedoc/types/ConvWithBatchNorm.html b/typedoc/types/ConvWithBatchNorm.html index ee6ea4a..0c13435 100644 --- a/typedoc/types/ConvWithBatchNorm.html +++ b/typedoc/types/ConvWithBatchNorm.html @@ -1 +1 @@ -ConvWithBatchNorm | @vladmandic/face-api - v1.7.13

                          Type alias ConvWithBatchNorm

                          ConvWithBatchNorm: {
                              bn: BatchNorm;
                              conv: ConvParams;
                          }

                          Type declaration

                          Settings

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                          \ No newline at end of file +ConvWithBatchNorm | @vladmandic/face-api - v1.7.14

                          Type Alias ConvWithBatchNorm

                          ConvWithBatchNorm: {
                              bn: BatchNorm;
                              conv: ConvParams;
                          }

                          Settings

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                          diff --git a/typedoc/types/DefaultTinyYolov2NetParams.html b/typedoc/types/DefaultTinyYolov2NetParams.html index d87df83..b18d5ab 100644 --- a/typedoc/types/DefaultTinyYolov2NetParams.html +++ b/typedoc/types/DefaultTinyYolov2NetParams.html @@ -1 +1 @@ -DefaultTinyYolov2NetParams | @vladmandic/face-api - v1.7.13

                          Type alias DefaultTinyYolov2NetParams

                          DefaultTinyYolov2NetParams: {
                              conv0: ConvWithBatchNorm;
                              conv1: ConvWithBatchNorm;
                              conv2: ConvWithBatchNorm;
                              conv3: ConvWithBatchNorm;
                              conv4: ConvWithBatchNorm;
                              conv5: ConvWithBatchNorm;
                              conv6: ConvWithBatchNorm;
                              conv7: ConvWithBatchNorm;
                              conv8: ConvParams;
                          }

                          Type declaration

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                          \ No newline at end of file +DefaultTinyYolov2NetParams | @vladmandic/face-api - v1.7.14

                          Type Alias DefaultTinyYolov2NetParams

                          DefaultTinyYolov2NetParams: {
                              conv0: ConvWithBatchNorm;
                              conv1: ConvWithBatchNorm;
                              conv2: ConvWithBatchNorm;
                              conv3: ConvWithBatchNorm;
                              conv4: ConvWithBatchNorm;
                              conv5: ConvWithBatchNorm;
                              conv6: ConvWithBatchNorm;
                              conv7: ConvWithBatchNorm;
                              conv8: ConvParams;
                          }

                          Settings

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                          diff --git a/typedoc/types/Environment.html b/typedoc/types/Environment.html index c42eea9..a8ac0d0 100644 --- a/typedoc/types/Environment.html +++ b/typedoc/types/Environment.html @@ -1 +1 @@ -Environment | @vladmandic/face-api - v1.7.13

                          Type alias Environment

                          Environment: FileSystem & {
                              Canvas: typeof HTMLCanvasElement;
                              CanvasRenderingContext2D: typeof CanvasRenderingContext2D;
                              Image: typeof HTMLImageElement;
                              ImageData: typeof ImageData;
                              Video: typeof HTMLVideoElement;
                              createCanvasElement: (() => HTMLCanvasElement);
                              createImageElement: (() => HTMLImageElement);
                              createVideoElement: (() => HTMLVideoElement);
                              fetch: ((url, init?) => Promise<Response>);
                          }

                          Type declaration

                          • Canvas: typeof HTMLCanvasElement
                          • CanvasRenderingContext2D: typeof CanvasRenderingContext2D
                          • Image: typeof HTMLImageElement
                          • ImageData: typeof ImageData
                          • Video: typeof HTMLVideoElement
                          • createCanvasElement: (() => HTMLCanvasElement)
                              • (): HTMLCanvasElement

                              • Returns HTMLCanvasElement

                          • createImageElement: (() => HTMLImageElement)
                              • (): HTMLImageElement

                              • Returns HTMLImageElement

                          • createVideoElement: (() => HTMLVideoElement)
                              • (): HTMLVideoElement

                              • Returns HTMLVideoElement

                          • fetch: ((url, init?) => Promise<Response>)
                              • (url, init?): Promise<Response>

                              • Parameters

                                • url: string
                                • Optional init: RequestInit

                                Returns Promise<Response>

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                          \ No newline at end of file +Environment | @vladmandic/face-api - v1.7.14

                          Type Alias Environment

                          Environment: FileSystem & {
                              Canvas: typeof HTMLCanvasElement;
                              CanvasRenderingContext2D: typeof CanvasRenderingContext2D;
                              createCanvasElement: (() => HTMLCanvasElement);
                              createImageElement: (() => HTMLImageElement);
                              createVideoElement: (() => HTMLVideoElement);
                              fetch: ((url: string, init?: RequestInit) => Promise<Response>);
                              Image: typeof HTMLImageElement;
                              ImageData: typeof ImageData;
                              Video: typeof HTMLVideoElement;
                          }

                          Settings

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                          diff --git a/typedoc/types/FaceDetectionFunction.html b/typedoc/types/FaceDetectionFunction.html index 5c712cd..3ebfe99 100644 --- a/typedoc/types/FaceDetectionFunction.html +++ b/typedoc/types/FaceDetectionFunction.html @@ -1 +1 @@ -FaceDetectionFunction | @vladmandic/face-api - v1.7.13

                          Type alias FaceDetectionFunction

                          FaceDetectionFunction: ((input) => Promise<FaceDetection[]>)

                          Type declaration

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                          \ No newline at end of file +FaceDetectionFunction | @vladmandic/face-api - v1.7.14

                          Type Alias FaceDetectionFunction

                          FaceDetectionFunction: ((input: TNetInput) => Promise<FaceDetection[]>)

                          Settings

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                          diff --git a/typedoc/types/FaceDetectionOptions.html b/typedoc/types/FaceDetectionOptions.html index 06fcc0c..d2e6096 100644 --- a/typedoc/types/FaceDetectionOptions.html +++ b/typedoc/types/FaceDetectionOptions.html @@ -1 +1 @@ -FaceDetectionOptions | @vladmandic/face-api - v1.7.13

                          Type alias FaceDetectionOptions

                          FaceDetectionOptions: TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options

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                          \ No newline at end of file +FaceDetectionOptions | @vladmandic/face-api - v1.7.14

                          Type Alias FaceDetectionOptions

                          FaceDetectionOptions: TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options

                          Settings

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                          diff --git a/typedoc/types/FileSystem.html b/typedoc/types/FileSystem.html index 677e3af..178bb7d 100644 --- a/typedoc/types/FileSystem.html +++ b/typedoc/types/FileSystem.html @@ -1 +1 @@ -FileSystem | @vladmandic/face-api - v1.7.13

                          Type alias FileSystem

                          FileSystem: {
                              readFile: ((filePath) => Promise<string | Buffer>);
                          }

                          Type declaration

                          • readFile: ((filePath) => Promise<string | Buffer>)
                              • (filePath): Promise<string | Buffer>

                              • Parameters

                                • filePath: string

                                Returns Promise<string | Buffer>

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                          \ No newline at end of file +FileSystem | @vladmandic/face-api - v1.7.14

                          Type Alias FileSystem

                          FileSystem: {
                              readFile: ((filePath: string) => Promise<string | Buffer>);
                          }

                          Settings

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                          diff --git a/typedoc/types/ITinyFaceDetectorOptions.html b/typedoc/types/ITinyFaceDetectorOptions.html index cc12252..67be6e8 100644 --- a/typedoc/types/ITinyFaceDetectorOptions.html +++ b/typedoc/types/ITinyFaceDetectorOptions.html @@ -1 +1 @@ -ITinyFaceDetectorOptions | @vladmandic/face-api - v1.7.13

                          Type alias ITinyFaceDetectorOptions

                          ITinyFaceDetectorOptions: ITinyYolov2Options

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                          \ No newline at end of file +ITinyFaceDetectorOptions | @vladmandic/face-api - v1.7.14

                          Type Alias ITinyFaceDetectorOptions

                          ITinyFaceDetectorOptions: ITinyYolov2Options

                          Settings

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                          diff --git a/typedoc/types/MobilenetParams.html b/typedoc/types/MobilenetParams.html index eccb1ce..6eead84 100644 --- a/typedoc/types/MobilenetParams.html +++ b/typedoc/types/MobilenetParams.html @@ -1 +1 @@ -MobilenetParams | @vladmandic/face-api - v1.7.13

                          Type alias MobilenetParams

                          MobilenetParams: {
                              conv0: SeparableConvParams | ConvParams;
                              conv1: SeparableConvParams;
                              conv2: SeparableConvParams;
                              conv3: SeparableConvParams;
                              conv4: SeparableConvParams;
                              conv5: SeparableConvParams;
                              conv6?: SeparableConvParams;
                              conv7?: SeparableConvParams;
                              conv8: ConvParams;
                          }

                          Type declaration

                          • conv0: SeparableConvParams | ConvParams
                          • conv1: SeparableConvParams
                          • conv2: SeparableConvParams
                          • conv3: SeparableConvParams
                          • conv4: SeparableConvParams
                          • conv5: SeparableConvParams
                          • Optional conv6?: SeparableConvParams
                          • Optional conv7?: SeparableConvParams
                          • conv8: ConvParams

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                          \ No newline at end of file +MobilenetParams | @vladmandic/face-api - v1.7.14

                          Type Alias MobilenetParams

                          MobilenetParams: {
                              conv0: SeparableConvParams | ConvParams;
                              conv1: SeparableConvParams;
                              conv2: SeparableConvParams;
                              conv3: SeparableConvParams;
                              conv4: SeparableConvParams;
                              conv5: SeparableConvParams;
                              conv6?: SeparableConvParams;
                              conv7?: SeparableConvParams;
                              conv8: ConvParams;
                          }

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                          diff --git a/typedoc/types/NetOutput.html b/typedoc/types/NetOutput.html index e913115..9c10807 100644 --- a/typedoc/types/NetOutput.html +++ b/typedoc/types/NetOutput.html @@ -1 +1 @@ -NetOutput | @vladmandic/face-api - v1.7.13

                          Type alias NetOutput

                          NetOutput: {
                              age: Tensor1D;
                              gender: Tensor2D;
                          }

                          Type declaration

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                          Type Alias NetOutput

                          NetOutput: {
                              age: Tensor1D;
                              gender: Tensor2D;
                          }

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                          diff --git a/typedoc/types/NetParams.html b/typedoc/types/NetParams.html index fc73deb..5ca428f 100644 --- a/typedoc/types/NetParams.html +++ b/typedoc/types/NetParams.html @@ -1 +1 @@ -NetParams | @vladmandic/face-api - v1.7.13

                          Type alias NetParams

                          NetParams: {
                              fc: {
                                  age: FCParams;
                                  gender: FCParams;
                              };
                          }

                          Type declaration

                          • fc: {
                                age: FCParams;
                                gender: FCParams;
                            }
                            • age: FCParams
                            • gender: FCParams

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                          Type Alias NetParams

                          NetParams: {
                              fc: {
                                  age: FCParams;
                                  gender: FCParams;
                              };
                          }

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                          diff --git a/typedoc/types/TMediaElement.html b/typedoc/types/TMediaElement.html index a586e4c..15809dd 100644 --- a/typedoc/types/TMediaElement.html +++ b/typedoc/types/TMediaElement.html @@ -1 +1 @@ -TMediaElement | @vladmandic/face-api - v1.7.13

                          Type alias TMediaElement

                          TMediaElement: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement

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                          \ No newline at end of file +TMediaElement | @vladmandic/face-api - v1.7.14

                          Type Alias TMediaElement

                          TMediaElement: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement

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                          diff --git a/typedoc/types/TNetInput.html b/typedoc/types/TNetInput.html index 855d1e0..5623127 100644 --- a/typedoc/types/TNetInput.html +++ b/typedoc/types/TNetInput.html @@ -1 +1 @@ -TNetInput | @vladmandic/face-api - v1.7.13

                          Type alias TNetInput

                          TNetInput: string | TResolvedNetInput | (string | TResolvedNetInput)[] | NetInput

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                          Type Alias TNetInput

                          TNetInput:
                              | string
                              | TResolvedNetInput
                              | (string | TResolvedNetInput)[]
                              | NetInput

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                          diff --git a/typedoc/types/TResolvedNetInput.html b/typedoc/types/TResolvedNetInput.html index 99a09e2..9f26c6f 100644 --- a/typedoc/types/TResolvedNetInput.html +++ b/typedoc/types/TResolvedNetInput.html @@ -1 +1 @@ -TResolvedNetInput | @vladmandic/face-api - v1.7.13

                          Type alias TResolvedNetInput

                          TResolvedNetInput: TMediaElement | Tensor3D | Tensor4D

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                          Type Alias TResolvedNetInput

                          TResolvedNetInput: TMediaElement | Tensor3D | Tensor4D

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                          diff --git a/typedoc/types/TinyYolov2Config.html b/typedoc/types/TinyYolov2Config.html index b1b6e6c..7760e8d 100644 --- a/typedoc/types/TinyYolov2Config.html +++ b/typedoc/types/TinyYolov2Config.html @@ -1 +1 @@ -TinyYolov2Config | @vladmandic/face-api - v1.7.13

                          Type alias TinyYolov2Config

                          TinyYolov2Config: {
                              anchors: Point[];
                              classes: string[];
                              filterSizes?: number[];
                              iouThreshold: number;
                              isFirstLayerConv2d?: boolean;
                              meanRgb?: [number, number, number];
                              withClassScores?: boolean;
                              withSeparableConvs: boolean;
                          }

                          Type declaration

                          • anchors: Point[]
                          • classes: string[]
                          • Optional filterSizes?: number[]
                          • iouThreshold: number
                          • Optional isFirstLayerConv2d?: boolean
                          • Optional meanRgb?: [number, number, number]
                          • Optional withClassScores?: boolean
                          • withSeparableConvs: boolean

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                          \ No newline at end of file +TinyYolov2Config | @vladmandic/face-api - v1.7.14

                          Type Alias TinyYolov2Config

                          TinyYolov2Config: {
                              anchors: Point[];
                              classes: string[];
                              filterSizes?: number[];
                              iouThreshold: number;
                              isFirstLayerConv2d?: boolean;
                              meanRgb?: [number, number, number];
                              withClassScores?: boolean;
                              withSeparableConvs: boolean;
                          }

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                          diff --git a/typedoc/types/TinyYolov2ExtractBoxesResult.html b/typedoc/types/TinyYolov2ExtractBoxesResult.html index a4c6cc2..230d365 100644 --- a/typedoc/types/TinyYolov2ExtractBoxesResult.html +++ b/typedoc/types/TinyYolov2ExtractBoxesResult.html @@ -1 +1 @@ -TinyYolov2ExtractBoxesResult | @vladmandic/face-api - v1.7.13

                          Type alias TinyYolov2ExtractBoxesResult

                          TinyYolov2ExtractBoxesResult: {
                              anchor: number;
                              box: BoundingBox;
                              classScore: number;
                              col: number;
                              label: number;
                              row: number;
                              score: number;
                          }

                          Type declaration

                          • anchor: number
                          • box: BoundingBox
                          • classScore: number
                          • col: number
                          • label: number
                          • row: number
                          • score: number

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                          \ No newline at end of file +TinyYolov2ExtractBoxesResult | @vladmandic/face-api - v1.7.14

                          Type Alias TinyYolov2ExtractBoxesResult

                          TinyYolov2ExtractBoxesResult: {
                              anchor: number;
                              box: BoundingBox;
                              classScore: number;
                              col: number;
                              label: number;
                              row: number;
                              score: number;
                          }

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                          diff --git a/typedoc/types/TinyYolov2NetParams.html b/typedoc/types/TinyYolov2NetParams.html index 21de7c6..75e81d7 100644 --- a/typedoc/types/TinyYolov2NetParams.html +++ b/typedoc/types/TinyYolov2NetParams.html @@ -1 +1 @@ -TinyYolov2NetParams | @vladmandic/face-api - v1.7.13

                          Type alias TinyYolov2NetParams

                          TinyYolov2NetParams: DefaultTinyYolov2NetParams | MobilenetParams

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                          Type Alias TinyYolov2NetParams

                          TinyYolov2NetParams: DefaultTinyYolov2NetParams | MobilenetParams

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                          diff --git a/typedoc/types/WithAge.html b/typedoc/types/WithAge.html index 3e580d1..080556d 100644 --- a/typedoc/types/WithAge.html +++ b/typedoc/types/WithAge.html @@ -1 +1 @@ -WithAge | @vladmandic/face-api - v1.7.13

                          Type alias WithAge<TSource>

                          WithAge<TSource>: TSource & {
                              age: number;
                          }

                          Type Parameters

                          • TSource

                          Type declaration

                          • age: number

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                          Type Alias WithAge<TSource>

                          WithAge<TSource>: TSource & {
                              age: number;
                          }

                          Type Parameters

                          • TSource

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                          diff --git a/typedoc/types/WithFaceDescriptor.html b/typedoc/types/WithFaceDescriptor.html index 6b54ef5..47ea5ff 100644 --- a/typedoc/types/WithFaceDescriptor.html +++ b/typedoc/types/WithFaceDescriptor.html @@ -1 +1 @@ -WithFaceDescriptor | @vladmandic/face-api - v1.7.13

                          Type alias WithFaceDescriptor<TSource>

                          WithFaceDescriptor<TSource>: TSource & {
                              descriptor: Float32Array;
                          }

                          Type Parameters

                          • TSource

                          Type declaration

                          • descriptor: Float32Array

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                          \ No newline at end of file +WithFaceDescriptor | @vladmandic/face-api - v1.7.14

                          Type Alias WithFaceDescriptor<TSource>

                          WithFaceDescriptor<TSource>: TSource & {
                              descriptor: Float32Array;
                          }

                          Type Parameters

                          • TSource

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                          diff --git a/typedoc/types/WithFaceDetection.html b/typedoc/types/WithFaceDetection.html index 5f976a8..8f1871a 100644 --- a/typedoc/types/WithFaceDetection.html +++ b/typedoc/types/WithFaceDetection.html @@ -1 +1 @@ -WithFaceDetection | @vladmandic/face-api - v1.7.13

                          Type alias WithFaceDetection<TSource>

                          WithFaceDetection<TSource>: TSource & {
                              detection: FaceDetection;
                          }

                          Type Parameters

                          • TSource

                          Type declaration

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                          \ No newline at end of file +WithFaceDetection | @vladmandic/face-api - v1.7.14

                          Type Alias WithFaceDetection<TSource>

                          WithFaceDetection<TSource>: TSource & {
                              detection: FaceDetection;
                          }

                          Type Parameters

                          • TSource

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                          diff --git a/typedoc/types/WithFaceExpressions.html b/typedoc/types/WithFaceExpressions.html index bdfd2f9..1460e62 100644 --- a/typedoc/types/WithFaceExpressions.html +++ b/typedoc/types/WithFaceExpressions.html @@ -1 +1 @@ -WithFaceExpressions | @vladmandic/face-api - v1.7.13

                          Type alias WithFaceExpressions<TSource>

                          WithFaceExpressions<TSource>: TSource & {
                              expressions: FaceExpressions;
                          }

                          Type Parameters

                          • TSource

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                          \ No newline at end of file +WithFaceExpressions | @vladmandic/face-api - v1.7.14

                          Type Alias WithFaceExpressions<TSource>

                          WithFaceExpressions<TSource>: TSource & {
                              expressions: FaceExpressions;
                          }

                          Type Parameters

                          • TSource

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                          diff --git a/typedoc/types/WithFaceLandmarks.html b/typedoc/types/WithFaceLandmarks.html index 5133068..97bb709 100644 --- a/typedoc/types/WithFaceLandmarks.html +++ b/typedoc/types/WithFaceLandmarks.html @@ -1 +1 @@ -WithFaceLandmarks | @vladmandic/face-api - v1.7.13

                          Type alias WithFaceLandmarks<TSource, TFaceLandmarks>

                          WithFaceLandmarks<TSource, TFaceLandmarks>: TSource & {
                              alignedRect: FaceDetection;
                              angle: {
                                  pitch: number | undefined;
                                  roll: number | undefined;
                                  yaw: number | undefined;
                              };
                              landmarks: TFaceLandmarks;
                              unshiftedLandmarks: TFaceLandmarks;
                          }

                          Type Parameters

                          Type declaration

                          • alignedRect: FaceDetection
                          • angle: {
                                pitch: number | undefined;
                                roll: number | undefined;
                                yaw: number | undefined;
                            }
                            • pitch: number | undefined
                            • roll: number | undefined
                            • yaw: number | undefined
                          • landmarks: TFaceLandmarks
                          • unshiftedLandmarks: TFaceLandmarks

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                          Type Alias WithFaceLandmarks<TSource, TFaceLandmarks>

                          WithFaceLandmarks<TSource, TFaceLandmarks>: TSource & {
                              alignedRect: FaceDetection;
                              angle: {
                                  pitch: number | undefined;
                                  roll: number | undefined;
                                  yaw: number | undefined;
                              };
                              landmarks: TFaceLandmarks;
                              unshiftedLandmarks: TFaceLandmarks;
                          }

                          Type Parameters

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                          diff --git a/typedoc/types/WithGender.html b/typedoc/types/WithGender.html index ae7d53f..5d374de 100644 --- a/typedoc/types/WithGender.html +++ b/typedoc/types/WithGender.html @@ -1 +1 @@ -WithGender | @vladmandic/face-api - v1.7.13

                          Type alias WithGender<TSource>

                          WithGender<TSource>: TSource & {
                              gender: Gender;
                              genderProbability: number;
                          }

                          Type Parameters

                          • TSource

                          Type declaration

                          • gender: Gender
                          • genderProbability: number

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                          Type Alias WithGender<TSource>

                          WithGender<TSource>: TSource & {
                              gender: Gender;
                              genderProbability: number;
                          }

                          Type Parameters

                          • TSource

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                          diff --git a/typedoc/types/draw.DrawFaceExpressionsInput.html b/typedoc/types/draw.DrawFaceExpressionsInput.html index 8ccf05a..fdeef04 100644 --- a/typedoc/types/draw.DrawFaceExpressionsInput.html +++ b/typedoc/types/draw.DrawFaceExpressionsInput.html @@ -1 +1 @@ -DrawFaceExpressionsInput | @vladmandic/face-api - v1.7.13

                          Type alias DrawFaceExpressionsInput

                          DrawFaceExpressionsInput: FaceExpressions | WithFaceExpressions<{}>

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                          Type Alias DrawFaceExpressionsInput

                          DrawFaceExpressionsInput: FaceExpressions | WithFaceExpressions<{}>

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                          diff --git a/typedoc/types/draw.DrawFaceLandmarksInput.html b/typedoc/types/draw.DrawFaceLandmarksInput.html index 01c6f67..c2a3f1f 100644 --- a/typedoc/types/draw.DrawFaceLandmarksInput.html +++ b/typedoc/types/draw.DrawFaceLandmarksInput.html @@ -1 +1 @@ -DrawFaceLandmarksInput | @vladmandic/face-api - v1.7.13

                          Type alias DrawFaceLandmarksInput

                          DrawFaceLandmarksInput: FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>>

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                          Type Alias DrawFaceLandmarksInput

                          DrawFaceLandmarksInput: FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>>

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                          diff --git a/typedoc/types/draw.TDrawDetectionsInput.html b/typedoc/types/draw.TDrawDetectionsInput.html index 0fc6ef8..2656fc6 100644 --- a/typedoc/types/draw.TDrawDetectionsInput.html +++ b/typedoc/types/draw.TDrawDetectionsInput.html @@ -1 +1 @@ -TDrawDetectionsInput | @vladmandic/face-api - v1.7.13

                          Type alias TDrawDetectionsInput

                          TDrawDetectionsInput: IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>

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                          Type Alias TDrawDetectionsInput

                          TDrawDetectionsInput:
                              | IRect
                              | IBoundingBox
                              | FaceDetection
                              | WithFaceDetection<{}>

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                          diff --git a/typedoc/types/tf.NamedTensorMap.html b/typedoc/types/tf.NamedTensorMap.html index dbf2678..ed23634 100644 --- a/typedoc/types/tf.NamedTensorMap.html +++ b/typedoc/types/tf.NamedTensorMap.html @@ -1 +1 @@ -NamedTensorMap | @vladmandic/face-api - v1.7.13

                          Type alias NamedTensorMap

                          NamedTensorMap: {
                              [name: string]: Tensor;
                          }

                          Type declaration

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                          Type Alias NamedTensorMap

                          NamedTensorMap: {
                              [name: string]: Tensor;
                          }

                          Docalias

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                          diff --git a/typedoc/types/tf.Tensor1D.html b/typedoc/types/tf.Tensor1D.html index 95c6451..126cb9b 100644 --- a/typedoc/types/tf.Tensor1D.html +++ b/typedoc/types/tf.Tensor1D.html @@ -1,2 +1,2 @@ -Tensor1D | @vladmandic/face-api - v1.7.13

                          Type alias Tensor1D

                          Tensor1D: Tensor<R1>

                          Doclink

                          Tensor

                          -

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                          Type Alias Tensor1D

                          Tensor1D: Tensor<R1>

                          Doclink

                          Tensor

                          +

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                          diff --git a/typedoc/types/tf.Tensor2D.html b/typedoc/types/tf.Tensor2D.html index 569e000..55b0f93 100644 --- a/typedoc/types/tf.Tensor2D.html +++ b/typedoc/types/tf.Tensor2D.html @@ -1,2 +1,2 @@ -Tensor2D | @vladmandic/face-api - v1.7.13

                          Type alias Tensor2D

                          Tensor2D: Tensor<R2>

                          Doclink

                          Tensor

                          -

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                          \ No newline at end of file +Tensor2D | @vladmandic/face-api - v1.7.14

                          Type Alias Tensor2D

                          Tensor2D: Tensor<R2>

                          Doclink

                          Tensor

                          +

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                          diff --git a/typedoc/types/tf.Tensor3D.html b/typedoc/types/tf.Tensor3D.html index 9aabb0f..9274bae 100644 --- a/typedoc/types/tf.Tensor3D.html +++ b/typedoc/types/tf.Tensor3D.html @@ -1,2 +1,2 @@ -Tensor3D | @vladmandic/face-api - v1.7.13

                          Type alias Tensor3D

                          Tensor3D: Tensor<R3>

                          Doclink

                          Tensor

                          -

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                          Type Alias Tensor3D

                          Tensor3D: Tensor<R3>

                          Doclink

                          Tensor

                          +

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                          diff --git a/typedoc/types/tf.Tensor4D.html b/typedoc/types/tf.Tensor4D.html index 67af2bd..a219d78 100644 --- a/typedoc/types/tf.Tensor4D.html +++ b/typedoc/types/tf.Tensor4D.html @@ -1,2 +1,2 @@ -Tensor4D | @vladmandic/face-api - v1.7.13

                          Type alias Tensor4D

                          Tensor4D: Tensor<R4>

                          Doclink

                          Tensor

                          -

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                          \ No newline at end of file +Tensor4D | @vladmandic/face-api - v1.7.14

                          Type Alias Tensor4D

                          Tensor4D: Tensor<R4>

                          Doclink

                          Tensor

                          +

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                          diff --git a/typedoc/types/tf.Tensor5D.html b/typedoc/types/tf.Tensor5D.html index a6adbcf..2ab3025 100644 --- a/typedoc/types/tf.Tensor5D.html +++ b/typedoc/types/tf.Tensor5D.html @@ -1,2 +1,2 @@ -Tensor5D | @vladmandic/face-api - v1.7.13

                          Type alias Tensor5D

                          Tensor5D: Tensor<R5>

                          Doclink

                          Tensor

                          -

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                          Type Alias Tensor5D

                          Tensor5D: Tensor<R5>

                          Doclink

                          Tensor

                          +

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                          diff --git a/typedoc/types/tf.TensorLike.html b/typedoc/types/tf.TensorLike.html index fb69f42..340cfb3 100644 --- a/typedoc/types/tf.TensorLike.html +++ b/typedoc/types/tf.TensorLike.html @@ -1,2 +1,2 @@ -TensorLike | @vladmandic/face-api - v1.7.13

                          Type alias TensorLike

                          TensorLike: TypedArray | number | boolean | string | RecursiveArray<number | number[] | TypedArray> | RecursiveArray<boolean> | RecursiveArray<string> | Uint8Array[]

                          Docalias

                          TypedArray|Array

                          -

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                          \ No newline at end of file +TensorLike | @vladmandic/face-api - v1.7.14

                          Type Alias TensorLike

                          TensorLike:
                              | TypedArray
                              | number
                              | boolean
                              | string
                              | RecursiveArray<number | number[] | TypedArray>
                              | RecursiveArray<boolean>
                              | RecursiveArray<string>
                              | Uint8Array[]

                          Docalias

                          TypedArray|Array

                          +

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                          diff --git a/typedoc/types/tf.io.IOHandlerSync.html b/typedoc/types/tf.io.IOHandlerSync.html index 9da62a8..9fe09f7 100644 --- a/typedoc/types/tf.io.IOHandlerSync.html +++ b/typedoc/types/tf.io.IOHandlerSync.html @@ -1,4 +1,4 @@ -IOHandlerSync | @vladmandic/face-api - v1.7.13

                          Type alias IOHandlerSync

                          IOHandlerSync: {
                              load?: LoadHandlerSync;
                              save?: SaveHandlerSync;
                          }

                          Interface for a synchronous model import/export handler.

                          +IOHandlerSync | @vladmandic/face-api - v1.7.14

                          Type Alias IOHandlerSync

                          IOHandlerSync: {
                              load?: LoadHandlerSync;
                              save?: SaveHandlerSync;
                          }

                          Interface for a synchronous model import/export handler.

                          The save and load handlers are both optional, in order to allow handlers that support only saving or loading.

                          -

                          Type declaration

                          • Optional load?: LoadHandlerSync
                          • Optional save?: SaveHandlerSync

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                          diff --git a/typedoc/types/tf.io.LoadHandler.html b/typedoc/types/tf.io.LoadHandler.html index 4efaacc..a255a7a 100644 --- a/typedoc/types/tf.io.LoadHandler.html +++ b/typedoc/types/tf.io.LoadHandler.html @@ -1,2 +1,2 @@ -LoadHandler | @vladmandic/face-api - v1.7.13

                          Type alias LoadHandler

                          LoadHandler: (() => Promise<ModelArtifacts>)

                          Type definition for handlers of loading operations.

                          -

                          Type declaration

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                          Type Alias LoadHandler

                          LoadHandler: (() => Promise<ModelArtifacts>)

                          Type definition for handlers of loading operations.

                          +

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                          diff --git a/typedoc/types/tf.io.OnProgressCallback.html b/typedoc/types/tf.io.OnProgressCallback.html index 8bb27ad..75b84ea 100644 --- a/typedoc/types/tf.io.OnProgressCallback.html +++ b/typedoc/types/tf.io.OnProgressCallback.html @@ -1,5 +1,5 @@ -OnProgressCallback | @vladmandic/face-api - v1.7.13

                          Type alias OnProgressCallback

                          OnProgressCallback: ((fraction) => void)

                          Callback for the progress of a long-running action such as an HTTP +OnProgressCallback | @vladmandic/face-api - v1.7.14

                          Type Alias OnProgressCallback

                          OnProgressCallback: ((fraction: number) => void)

                          Callback for the progress of a long-running action such as an HTTP request for a large binary object.

                          fraction should be a number in the [0, 1] interval, indicating how much of the action has completed.

                          -

                          Type declaration

                            • (fraction): void

                            • Parameters

                              • fraction: number

                              Returns void

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                          diff --git a/typedoc/types/tf.io.SaveHandler.html b/typedoc/types/tf.io.SaveHandler.html index 57461c5..e5f169b 100644 --- a/typedoc/types/tf.io.SaveHandler.html +++ b/typedoc/types/tf.io.SaveHandler.html @@ -1,2 +1,2 @@ -SaveHandler | @vladmandic/face-api - v1.7.13

                          Type alias SaveHandler

                          SaveHandler: ((modelArtifact) => Promise<SaveResult>)

                          Type definition for handlers of saving operations.

                          -

                          Type declaration

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                          \ No newline at end of file +SaveHandler | @vladmandic/face-api - v1.7.14

                          Type Alias SaveHandler

                          SaveHandler: ((modelArtifact: ModelArtifacts) => Promise<SaveResult>)

                          Type definition for handlers of saving operations.

                          +

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                          diff --git a/typedoc/types/tf.io.WeightData.html b/typedoc/types/tf.io.WeightData.html index 2a6eca4..9da676e 100644 --- a/typedoc/types/tf.io.WeightData.html +++ b/typedoc/types/tf.io.WeightData.html @@ -1 +1 @@ -WeightData | @vladmandic/face-api - v1.7.13

                          Type alias WeightData

                          WeightData: ArrayBuffer | ArrayBuffer[]

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                          Type Alias WeightData

                          WeightData: ArrayBuffer | ArrayBuffer[]

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                          diff --git a/typedoc/types/tf.io.WeightGroup.html b/typedoc/types/tf.io.WeightGroup.html index 5f8d208..8eda845 100644 --- a/typedoc/types/tf.io.WeightGroup.html +++ b/typedoc/types/tf.io.WeightGroup.html @@ -1,5 +1,5 @@ -WeightGroup | @vladmandic/face-api - v1.7.13

                          Type alias WeightGroup

                          WeightGroup: "model" | "optimizer"

                          Group to which the weight belongs.

                          +WeightGroup | @vladmandic/face-api - v1.7.14

                          Type Alias WeightGroup

                          WeightGroup: "model" | "optimizer"

                          Group to which the weight belongs.

                            -
                          • 'optimizer': Weight from a stateful optimizer.
                            • +
                          • 'optimizer': Weight from a stateful optimizer.
                          -

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                          diff --git a/typedoc/types/tf.io.WeightsManifestConfig.html b/typedoc/types/tf.io.WeightsManifestConfig.html index 9903b40..e0c42d4 100644 --- a/typedoc/types/tf.io.WeightsManifestConfig.html +++ b/typedoc/types/tf.io.WeightsManifestConfig.html @@ -1,6 +1,6 @@ -WeightsManifestConfig | @vladmandic/face-api - v1.7.13

                          Type alias WeightsManifestConfig

                          WeightsManifestConfig: WeightsManifestGroupConfig[]

                          A weight manifest.

                          +WeightsManifestConfig | @vladmandic/face-api - v1.7.14

                          Type Alias WeightsManifestConfig

                          WeightsManifestConfig: WeightsManifestGroupConfig[]

                          A weight manifest.

                          The weight manifest consists of an ordered list of weight-manifest groups. Each weight-manifest group ("group" for short hereafter) consists of a number of weight values stored in a number of paths. See the documentation of WeightManifestGroupConfig below for more details.

                          -

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                          diff --git a/typedoc/variables/FACE_EXPRESSION_LABELS.html b/typedoc/variables/FACE_EXPRESSION_LABELS.html index 6b82f36..fec5ac6 100644 --- a/typedoc/variables/FACE_EXPRESSION_LABELS.html +++ b/typedoc/variables/FACE_EXPRESSION_LABELS.html @@ -1 +1 @@ -FACE_EXPRESSION_LABELS | @vladmandic/face-api - v1.7.13

                          Variable FACE_EXPRESSION_LABELSConst

                          FACE_EXPRESSION_LABELS: readonly ["neutral", "happy", "sad", "angry", "fearful", "disgusted", "surprised"] = ...

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                          \ No newline at end of file +FACE_EXPRESSION_LABELS | @vladmandic/face-api - v1.7.14

                          Variable FACE_EXPRESSION_LABELSConst

                          FACE_EXPRESSION_LABELS: readonly ["neutral", "happy", "sad", "angry", "fearful", "disgusted", "surprised"] = ...

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                          diff --git a/typedoc/variables/env.html b/typedoc/variables/env.html index 5be28c5..af3cfea 100644 --- a/typedoc/variables/env.html +++ b/typedoc/variables/env.html @@ -1 +1 @@ -env | @vladmandic/face-api - v1.7.13

                          Variable envConst

                          env: {
                              createBrowserEnv: (() => Environment);
                              createFileSystem: ((fs?) => FileSystem);
                              createNodejsEnv: (() => Environment);
                              getEnv: (() => Environment);
                              initialize: (() => null | void);
                              isBrowser: (() => boolean);
                              isNodejs: (() => boolean);
                              monkeyPatch: ((env) => void);
                              setEnv: ((env) => void);
                          } = ...

                          Type declaration

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                          \ No newline at end of file +env | @vladmandic/face-api - v1.7.14

                          Variable envConst

                          env: {
                              createBrowserEnv: (() => Environment);
                              createFileSystem: ((fs?: any) => FileSystem);
                              createNodejsEnv: (() => Environment);
                              getEnv: (() => Environment);
                              initialize: (() => null | void);
                              isBrowser: (() => boolean);
                              isNodejs: (() => boolean);
                              monkeyPatch: ((env: Partial<Environment>) => void);
                              setEnv: ((env: Environment) => void);
                          } = ...

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                          diff --git a/typedoc/variables/nets.html b/typedoc/variables/nets.html index 5801677..367646c 100644 --- a/typedoc/variables/nets.html +++ b/typedoc/variables/nets.html @@ -1 +1 @@ -nets | @vladmandic/face-api - v1.7.13

                          Variable netsConst

                          nets: {
                              ageGenderNet: AgeGenderNet;
                              faceExpressionNet: FaceExpressionNet;
                              faceLandmark68Net: FaceLandmark68Net;
                              faceLandmark68TinyNet: FaceLandmark68TinyNet;
                              faceRecognitionNet: FaceRecognitionNet;
                              ssdMobilenetv1: SsdMobilenetv1;
                              tinyFaceDetector: TinyFaceDetector;
                              tinyYolov2: TinyYolov2;
                          } = ...

                          Type declaration

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                          \ No newline at end of file +nets | @vladmandic/face-api - v1.7.14

                          Variable netsConst

                          nets: {
                              ageGenderNet: AgeGenderNet;
                              faceExpressionNet: FaceExpressionNet;
                              faceLandmark68Net: FaceLandmark68Net;
                              faceLandmark68TinyNet: FaceLandmark68TinyNet;
                              faceRecognitionNet: FaceRecognitionNet;
                              ssdMobilenetv1: SsdMobilenetv1;
                              tinyFaceDetector: TinyFaceDetector;
                              tinyYolov2: TinyYolov2;
                          } = ...

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                          diff --git a/typedoc/variables/tf.ENV.html b/typedoc/variables/tf.ENV.html index b84aefa..5c35ffe 100644 --- a/typedoc/variables/tf.ENV.html +++ b/typedoc/variables/tf.ENV.html @@ -1 +1 @@ -ENV | @vladmandic/face-api - v1.7.13

                          Variable ENV

                          ENV: Environment

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                          Variable ENV

                          ENV: Environment

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                          diff --git a/typedoc/variables/tf.image.html b/typedoc/variables/tf.image.html index f99d1e2..b577cb9 100644 --- a/typedoc/variables/tf.image.html +++ b/typedoc/variables/tf.image.html @@ -1 +1 @@ -image | @vladmandic/face-api - v1.7.13

                          Variable imageConst

                          image: {
                              cropAndResize: ((image, boxes, boxInd, cropSize, method?, extrapolationValue?) => Tensor4D);
                              flipLeftRight: ((image) => Tensor4D);
                              grayscaleToRGB: (<T>(image) => T);
                              nonMaxSuppression: ((boxes, scores, maxOutputSize, iouThreshold?, scoreThreshold?) => Tensor1D);
                              nonMaxSuppressionAsync: ((boxes, scores, maxOutputSize, iouThreshold?, scoreThreshold?) => Promise<Tensor1D>);
                              nonMaxSuppressionPadded: ((boxes, scores, maxOutputSize, iouThreshold?, scoreThreshold?, padToMaxOutputSize?) => NamedTensorMap);
                              nonMaxSuppressionPaddedAsync: ((boxes, scores, maxOutputSize, iouThreshold?, scoreThreshold?, padToMaxOutputSize?) => Promise<NamedTensorMap>);
                              nonMaxSuppressionWithScore: ((boxes, scores, maxOutputSize, iouThreshold?, scoreThreshold?, softNmsSigma?) => NamedTensorMap);
                              nonMaxSuppressionWithScoreAsync: ((boxes, scores, maxOutputSize, iouThreshold?, scoreThreshold?, softNmsSigma?) => Promise<NamedTensorMap>);
                              resizeBilinear: (<T_2>(images, size, alignCorners?, halfPixelCenters?) => T_2);
                              resizeNearestNeighbor: (<T_1>(images, size, alignCorners?, halfPixelCenters?) => T_1);
                              rgbToGrayscale: (<T_3>(image) => T_3);
                              rotateWithOffset: ((image, radians, fillValue?, center?) => Tensor4D);
                              threshold: ((image, method?, inverted?, threshValue?) => Tensor3D);
                              transform: ((image, transforms, interpolation?, fillMode?, fillValue?, outputShape?) => Tensor4D);
                          }

                          Type declaration

                          • cropAndResize: ((image, boxes, boxInd, cropSize, method?, extrapolationValue?) => Tensor4D)
                              • (image, boxes, boxInd, cropSize, method?, extrapolationValue?): Tensor4D

                              • Parameters

                                • image: TensorLike | Tensor4D
                                • boxes: TensorLike | Tensor2D
                                • boxInd: TensorLike | Tensor1D
                                • cropSize: [number, number]
                                • Optional method: "bilinear" | "nearest"
                                • Optional extrapolationValue: number

                                Returns Tensor4D

                          • flipLeftRight: ((image) => Tensor4D)
                              • (image): Tensor4D

                              • Parameters

                                • image: TensorLike | Tensor4D

                                Returns Tensor4D

                          • grayscaleToRGB: (<T>(image) => T)
                              • <T>(image): T

                              • Type Parameters

                                • T extends Tensor2D | Tensor3D | Tensor4D | Tensor5D | Tensor6D

                                Parameters

                                • image: TensorLike | T

                                Returns T

                          • nonMaxSuppression: ((boxes, scores, maxOutputSize, iouThreshold?, scoreThreshold?) => Tensor1D)
                              • (boxes, scores, maxOutputSize, iouThreshold?, scoreThreshold?): Tensor1D

                              • Parameters

                                • boxes: TensorLike | Tensor2D
                                • scores: TensorLike | Tensor1D
                                • maxOutputSize: number
                                • Optional iouThreshold: number
                                • Optional scoreThreshold: number

                                Returns Tensor1D

                          • nonMaxSuppressionAsync: ((boxes, scores, maxOutputSize, iouThreshold?, scoreThreshold?) => Promise<Tensor1D>)
                              • (boxes, scores, maxOutputSize, iouThreshold?, scoreThreshold?): Promise<Tensor1D>

                              • Parameters

                                • boxes: TensorLike | Tensor2D
                                • scores: TensorLike | Tensor1D
                                • maxOutputSize: number
                                • Optional iouThreshold: number
                                • Optional scoreThreshold: number

                                Returns Promise<Tensor1D>

                          • nonMaxSuppressionPadded: ((boxes, scores, maxOutputSize, iouThreshold?, scoreThreshold?, padToMaxOutputSize?) => NamedTensorMap)
                              • (boxes, scores, maxOutputSize, iouThreshold?, scoreThreshold?, padToMaxOutputSize?): NamedTensorMap

                              • Parameters

                                • boxes: TensorLike | Tensor2D
                                • scores: TensorLike | Tensor1D
                                • maxOutputSize: number
                                • Optional iouThreshold: number
                                • Optional scoreThreshold: number
                                • Optional padToMaxOutputSize: boolean

                                Returns NamedTensorMap

                          • nonMaxSuppressionPaddedAsync: ((boxes, scores, maxOutputSize, iouThreshold?, scoreThreshold?, padToMaxOutputSize?) => Promise<NamedTensorMap>)
                              • (boxes, scores, maxOutputSize, iouThreshold?, scoreThreshold?, padToMaxOutputSize?): Promise<NamedTensorMap>

                              • Parameters

                                • boxes: TensorLike | Tensor2D
                                • scores: TensorLike | Tensor1D
                                • maxOutputSize: number
                                • Optional iouThreshold: number
                                • Optional scoreThreshold: number
                                • Optional padToMaxOutputSize: boolean

                                Returns Promise<NamedTensorMap>

                          • nonMaxSuppressionWithScore: ((boxes, scores, maxOutputSize, iouThreshold?, scoreThreshold?, softNmsSigma?) => NamedTensorMap)
                              • (boxes, scores, maxOutputSize, iouThreshold?, scoreThreshold?, softNmsSigma?): NamedTensorMap

                              • Parameters

                                • boxes: TensorLike | Tensor2D
                                • scores: TensorLike | Tensor1D
                                • maxOutputSize: number
                                • Optional iouThreshold: number
                                • Optional scoreThreshold: number
                                • Optional softNmsSigma: number

                                Returns NamedTensorMap

                          • nonMaxSuppressionWithScoreAsync: ((boxes, scores, maxOutputSize, iouThreshold?, scoreThreshold?, softNmsSigma?) => Promise<NamedTensorMap>)
                              • (boxes, scores, maxOutputSize, iouThreshold?, scoreThreshold?, softNmsSigma?): Promise<NamedTensorMap>

                              • Parameters

                                • boxes: TensorLike | Tensor2D
                                • scores: TensorLike | Tensor1D
                                • maxOutputSize: number
                                • Optional iouThreshold: number
                                • Optional scoreThreshold: number
                                • Optional softNmsSigma: number

                                Returns Promise<NamedTensorMap>

                          • resizeBilinear: (<T_2>(images, size, alignCorners?, halfPixelCenters?) => T_2)
                              • <T_2>(images, size, alignCorners?, halfPixelCenters?): T_2

                              • Type Parameters

                                • T_2 extends Tensor3D | Tensor4D

                                Parameters

                                • images: TensorLike | T_2
                                • size: [number, number]
                                • Optional alignCorners: boolean
                                • Optional halfPixelCenters: boolean

                                Returns T_2

                          • resizeNearestNeighbor: (<T_1>(images, size, alignCorners?, halfPixelCenters?) => T_1)
                              • <T_1>(images, size, alignCorners?, halfPixelCenters?): T_1

                              • Type Parameters

                                • T_1 extends Tensor3D | Tensor4D

                                Parameters

                                • images: TensorLike | T_1
                                • size: [number, number]
                                • Optional alignCorners: boolean
                                • Optional halfPixelCenters: boolean

                                Returns T_1

                          • rgbToGrayscale: (<T_3>(image) => T_3)
                              • <T_3>(image): T_3

                              • Type Parameters

                                • T_3 extends Tensor2D | Tensor3D | Tensor4D | Tensor5D | Tensor6D

                                Parameters

                                • image: TensorLike | T_3

                                Returns T_3

                          • rotateWithOffset: ((image, radians, fillValue?, center?) => Tensor4D)
                              • (image, radians, fillValue?, center?): Tensor4D

                              • Parameters

                                • image: TensorLike | Tensor4D
                                • radians: number
                                • Optional fillValue: number | [number, number, number]
                                • Optional center: number | [number, number]

                                Returns Tensor4D

                          • threshold: ((image, method?, inverted?, threshValue?) => Tensor3D)
                              • (image, method?, inverted?, threshValue?): Tensor3D

                              • Parameters

                                • image: TensorLike | Tensor3D
                                • Optional method: string
                                • Optional inverted: boolean
                                • Optional threshValue: number

                                Returns Tensor3D

                          • transform: ((image, transforms, interpolation?, fillMode?, fillValue?, outputShape?) => Tensor4D)
                              • (image, transforms, interpolation?, fillMode?, fillValue?, outputShape?): Tensor4D

                              • Parameters

                                • image: TensorLike | Tensor4D
                                • transforms: TensorLike | Tensor2D
                                • Optional interpolation: "bilinear" | "nearest"
                                • Optional fillMode: "reflect" | "nearest" | "constant" | "wrap"
                                • Optional fillValue: number
                                • Optional outputShape: [number, number]

                                Returns Tensor4D

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                          \ No newline at end of file +image | @vladmandic/face-api - v1.7.14

                          Variable imageConst

                          image: {
                              cropAndResize: ((image: TensorLike | Tensor4D, boxes: TensorLike | Tensor2D, boxInd: TensorLike | Tensor1D, cropSize: [number, number], method?: "bilinear" | "nearest", extrapolationValue?: number) => Tensor4D);
                              flipLeftRight: ((image: TensorLike | Tensor4D) => Tensor4D);
                              grayscaleToRGB: (<T>(image: TensorLike | T) => T);
                              nonMaxSuppression: ((boxes: TensorLike | Tensor2D, scores: TensorLike | Tensor1D, maxOutputSize: number, iouThreshold?: number, scoreThreshold?: number) => Tensor1D);
                              nonMaxSuppressionAsync: ((boxes: TensorLike | Tensor2D, scores: TensorLike | Tensor1D, maxOutputSize: number, iouThreshold?: number, scoreThreshold?: number) => Promise<Tensor1D>);
                              nonMaxSuppressionPadded: ((boxes: TensorLike | Tensor2D, scores: TensorLike | Tensor1D, maxOutputSize: number, iouThreshold?: number, scoreThreshold?: number, padToMaxOutputSize?: boolean) => NamedTensorMap);
                              nonMaxSuppressionPaddedAsync: ((boxes: TensorLike | Tensor2D, scores: TensorLike | Tensor1D, maxOutputSize: number, iouThreshold?: number, scoreThreshold?: number, padToMaxOutputSize?: boolean) => Promise<NamedTensorMap>);
                              nonMaxSuppressionWithScore: ((boxes: TensorLike | Tensor2D, scores: TensorLike | Tensor1D, maxOutputSize: number, iouThreshold?: number, scoreThreshold?: number, softNmsSigma?: number) => NamedTensorMap);
                              nonMaxSuppressionWithScoreAsync: ((boxes: TensorLike | Tensor2D, scores: TensorLike | Tensor1D, maxOutputSize: number, iouThreshold?: number, scoreThreshold?: number, softNmsSigma?: number) => Promise<NamedTensorMap>);
                              resizeBilinear: (<T_2>(images: TensorLike | T_2, size: [number, number], alignCorners?: boolean, halfPixelCenters?: boolean) => T_2);
                              resizeNearestNeighbor: (<T_1>(images: TensorLike | T_1, size: [number, number], alignCorners?: boolean, halfPixelCenters?: boolean) => T_1);
                              rgbToGrayscale: (<T_3>(image: TensorLike | T_3) => T_3);
                              rotateWithOffset: ((image: TensorLike | Tensor4D, radians: number, fillValue?: number | [number, number, number], center?: number | [number, number]) => Tensor4D);
                              threshold: ((image: TensorLike | Tensor3D, method?: string, inverted?: boolean, threshValue?: number) => Tensor3D);
                              transform: ((image: TensorLike | Tensor4D, transforms: TensorLike | Tensor2D, interpolation?: "bilinear" | "nearest", fillMode?:
                                  | "reflect"
                                  | "nearest"
                                  | "constant"
                                  | "wrap", fillValue?: number, outputShape?: [number, number]) => Tensor4D);
                          }

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                          diff --git a/typedoc/variables/tf.version.html b/typedoc/variables/tf.version.html index 42955d7..2763662 100644 --- a/typedoc/variables/tf.version.html +++ b/typedoc/variables/tf.version.html @@ -1 +1 @@ -version | @vladmandic/face-api - v1.7.13

                          Variable versionConst

                          version: {
                              tfjs: string;
                              tfjs-backend-cpu: string;
                              tfjs-backend-webgl: string;
                              tfjs-converter: string;
                              tfjs-core: string;
                              tfjs-data: string;
                              tfjs-layers: string;
                          }

                          Type declaration

                          • tfjs: string
                          • tfjs-backend-cpu: string
                          • tfjs-backend-webgl: string
                          • tfjs-converter: string
                          • tfjs-core: string
                          • tfjs-data: string
                          • tfjs-layers: string

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                          \ No newline at end of file +version | @vladmandic/face-api - v1.7.14

                          Variable versionConst

                          version: {
                              tfjs: string;
                              tfjs-backend-cpu: string;
                              tfjs-backend-webgl: string;
                              tfjs-converter: string;
                              tfjs-core: string;
                              tfjs-data: string;
                              tfjs-layers: string;
                          }

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                          diff --git a/typedoc/variables/version.html b/typedoc/variables/version.html index 3238d9a..96a65db 100644 --- a/typedoc/variables/version.html +++ b/typedoc/variables/version.html @@ -1 +1 @@ -version | @vladmandic/face-api - v1.7.13

                          Variable versionConst

                          version: string = ...

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                          \ No newline at end of file +version | @vladmandic/face-api - v1.7.14

                          Variable versionConst

                          version: string = ...

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                          diff --git a/types/face-api.d.ts b/types/face-api.d.ts index 7c5cd6a..df827c6 100644 --- a/types/face-api.d.ts +++ b/types/face-api.d.ts @@ -1,5 +1,4 @@ /// -/// declare const add: typeof add_; @@ -1903,7 +1902,7 @@ declare function isFloat(num: number): boolean; declare function isHTTPScheme(url: string): boolean; -export declare function isMediaElement(input: any): boolean; +export declare function isMediaElement(input: any): input is HTMLCanvasElement | HTMLImageElement | HTMLVideoElement; export declare function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement): boolean; @@ -2841,8 +2840,8 @@ export declare const predictAgeAndGender: (input: TNetInput) => Promise extends ComposableTask { protected parentTask: ComposableTask | Promise; protected input: TNetInput; - protected extractedFaces?: (tf.Tensor3D | HTMLCanvasElement)[] | undefined; - constructor(parentTask: ComposableTask | Promise, input: TNetInput, extractedFaces?: (tf.Tensor3D | HTMLCanvasElement)[] | undefined); + protected extractedFaces?: Array | undefined; + constructor(parentTask: ComposableTask | Promise, input: TNetInput, extractedFaces?: Array | undefined); } declare class PredictAllAgeAndGenderTask> extends PredictAgeAndGenderTaskBase>[], TSource[]> { @@ -2877,8 +2876,8 @@ export declare class PredictedBox extends LabeledBox { declare class PredictFaceExpressionsTaskBase extends ComposableTask { protected parentTask: ComposableTask | Promise; protected input: TNetInput; - protected extractedFaces?: (tf.Tensor3D | HTMLCanvasElement)[] | undefined; - constructor(parentTask: ComposableTask | Promise, input: TNetInput, extractedFaces?: (tf.Tensor3D | HTMLCanvasElement)[] | undefined); + protected extractedFaces?: Array | undefined; + constructor(parentTask: ComposableTask | Promise, input: TNetInput, extractedFaces?: Array | undefined); } declare type PredictionLayerParams = { @@ -3715,28 +3714,29 @@ declare class Tensor implements TensorInfo { * await tf.setBackend(savedBackend); * ``` * @param values The values of the tensor. Can be nested array of numbers, - * or a flat array, or a `TypedArray`, or a `WebGLData` object, or a - * `WebGPUData` object. If the values are strings, they will be encoded as utf-8 - * and kept as `Uint8Array[]`. If the values is a `WebGLData` object, the dtype - * could only be 'float32' or 'int32' and the object has to have: 1. texture, a - * `WebGLTexture`, the texture must share the same `WebGLRenderingContext` with - * TFJS's WebGL backend (you could create a custom WebGL backend from your - * texture's canvas) and the internal texture format for the input texture must - * be floating point or normalized integer; 2. height, the height of the - * texture; 3. width, the width of the texture; 4. channels, a non-empty subset - * of 'RGBA', indicating the values of which channels will be passed to the - * tensor, such as 'R' or 'BR' (The order of the channels affect the order of - * tensor values. ). (If the values passed from texture is less than the tensor - * size, zeros will be padded at the rear.). If the values is a `WebGPUData` - * object, the dtype could only be 'float32' or 'int32 and the object has to - * have: buffer, a `GPUBuffer`. The buffer must: 1. share the same `GPUDevice` - * with TFJS's WebGPU backend; 2. buffer.usage should at least support - * GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC; 3. buffer.size should not - * be smaller than the byte size of tensor shape. WebGPUData optionally supports - * zero copy by flag zeroCopy. When zeroCopy is false or undefined(default), - * this passing GPUBuffer can be destroyed after tensor is created. When - * zeroCopy is true, this GPUBuffer is bound directly by the tensor, so do not - * destroy this GPUBuffer until all access is done. + * or a flat array, or a `TypedArray`(At the moment it supports Uint8Array, + * Uint8ClampedArray, Int32Array, Float32Array) data types, or a `WebGLData` + * object, or a `WebGPUData` object. If the values are strings, they will be + * encoded as utf-8 and kept as `Uint8Array[]`. If the values is a `WebGLData` + * object, the dtype could only be 'float32' or 'int32' and the object has to + * have: 1. texture, a `WebGLTexture`, the texture must share the same + * `WebGLRenderingContext` with TFJS's WebGL backend (you could create a custom + * WebGL backend from your texture's canvas) and the internal texture format + * for the input texture must be floating point or normalized integer; 2. + * height, the height of the texture; 3. width, the width of the texture; 4. + * channels, a non-empty subset of 'RGBA', indicating the values of which + * channels will be passed to the tensor, such as 'R' or 'BR' (The order of the + * channels affect the order of tensor values. ). (If the values passed from + * texture is less than the tensor size, zeros will be padded at the rear.). If + * the values is a `WebGPUData` object, the dtype could only be 'float32' or + * 'int32 and the object has to have: buffer, a `GPUBuffer`. The buffer must: + * 1. share the same `GPUDevice` with TFJS's WebGPU backend; 2. buffer.usage + * should at least support GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC; 3. + * buffer.size should not be smaller than the byte size of tensor shape. + * WebGPUData optionally supports zero copy by flag zeroCopy. When zeroCopy is + * false or undefined(default),this passing GPUBuffer can be destroyed after + * tensor is created. When zeroCopy is true, this GPUBuffer is bound directly + * by the tensor, so do not destroy this GPUBuffer until all access is done. * @param shape The shape of the tensor. Optional. If not provided, * it is inferred from `values`. * @param dtype The data type. diff --git a/types/lib/src/dom/isMediaElement.d.ts b/types/lib/src/dom/isMediaElement.d.ts index 1d359c2..4e84a35 100644 --- a/types/lib/src/dom/isMediaElement.d.ts +++ b/types/lib/src/dom/isMediaElement.d.ts @@ -1 +1 @@ -export declare function isMediaElement(input: any): boolean; +export declare function isMediaElement(input: any): input is HTMLCanvasElement | HTMLImageElement | HTMLVideoElement; diff --git a/types/lib/src/env/types.d.ts b/types/lib/src/env/types.d.ts index e26e91a..5a03207 100644 --- a/types/lib/src/env/types.d.ts +++ b/types/lib/src/env/types.d.ts @@ -1,4 +1,3 @@ -/// export type FileSystem = { readFile: (filePath: string) => Promise; }; diff --git a/types/lib/src/globalApi/PredictAgeAndGenderTask.d.ts b/types/lib/src/globalApi/PredictAgeAndGenderTask.d.ts index fc03f3e..a3b7707 100644 --- a/types/lib/src/globalApi/PredictAgeAndGenderTask.d.ts +++ b/types/lib/src/globalApi/PredictAgeAndGenderTask.d.ts @@ -10,8 +10,8 @@ import { PredictAllFaceExpressionsTask, PredictAllFaceExpressionsWithFaceAlignme export declare class PredictAgeAndGenderTaskBase extends ComposableTask { protected parentTask: ComposableTask | Promise; protected input: TNetInput; - protected extractedFaces?: (tf.Tensor3D | HTMLCanvasElement)[] | undefined; - constructor(parentTask: ComposableTask | Promise, input: TNetInput, extractedFaces?: (tf.Tensor3D | HTMLCanvasElement)[] | undefined); + protected extractedFaces?: Array | undefined; + constructor(parentTask: ComposableTask | Promise, input: TNetInput, extractedFaces?: Array | undefined); } export declare class PredictAllAgeAndGenderTask> extends PredictAgeAndGenderTaskBase>[], TSource[]> { run(): Promise>[]>; diff --git a/types/lib/src/globalApi/PredictFaceExpressionsTask.d.ts b/types/lib/src/globalApi/PredictFaceExpressionsTask.d.ts index 0834cb3..e8194dd 100644 --- a/types/lib/src/globalApi/PredictFaceExpressionsTask.d.ts +++ b/types/lib/src/globalApi/PredictFaceExpressionsTask.d.ts @@ -9,8 +9,8 @@ import { PredictAllAgeAndGenderTask, PredictAllAgeAndGenderWithFaceAlignmentTask export declare class PredictFaceExpressionsTaskBase extends ComposableTask { protected parentTask: ComposableTask | Promise; protected input: TNetInput; - protected extractedFaces?: (tf.Tensor3D | HTMLCanvasElement)[] | undefined; - constructor(parentTask: ComposableTask | Promise, input: TNetInput, extractedFaces?: (tf.Tensor3D | HTMLCanvasElement)[] | undefined); + protected extractedFaces?: Array | undefined; + constructor(parentTask: ComposableTask | Promise, input: TNetInput, extractedFaces?: Array | undefined); } export declare class PredictAllFaceExpressionsTask> extends PredictFaceExpressionsTaskBase[], TSource[]> { run(): Promise[]>;