diff --git a/CHANGELOG.md b/CHANGELOG.md index 6d89c58..26a4701 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,6 +1,6 @@ # @vladmandic/face-api - Version: **1.7.14** + Version: **1.7.15** 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 ** @@ -9,7 +9,10 @@ ## Changelog -### **HEAD -> master** 2024/09/10 mandic00@live.com +### **1.7.15** 2025/02/05 mandic00@live.com + + +### **origin/master** 2024/09/10 mandic00@live.com ### **1.7.14** 2024/09/10 mandic00@live.com diff --git a/README.md b/README.md index f9565a9..cb688a9 100644 --- a/README.md +++ b/README.md @@ -104,8 +104,11 @@ NodeJS examples are: 2021-03-14 08:42:09 STATE: Main: worker exit: 1888019 0 ``` -Note that `@tensorflow/tfjs-node` or `@tensorflow/tfjs-node-gpu` -must be installed before using any **NodeJS** examples +### NodeJS Notes +- Supported NodeJS versions are **14** up to **22** + NodeJS version **23** and higher are not supported due to incompatibility with TensorFlow/JS +- `@tensorflow/tfjs-node` or `@tensorflow/tfjs-node-gpu` + must be installed before using any **NodeJS** examples

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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 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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 $||o instanceof D?o.forSize(e,r):o}var Lb=Ar;export{Me as AgeGenderNet,Ct as BoundingBox,C as Box,Y as ComposableTask,ft as ComputeAllFaceDescriptorsTask,Be as ComputeFaceDescriptorsTaskBase,lt as ComputeSingleFaceDescriptorTask,$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 9981175..19c28a6 100644 --- a/dist/face-api.esm.js +++ b/dist/face-api.esm.js @@ -5006,5 +5006,5 @@ return a / b;`,jse=` } `}};function 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t.dtype==="string"?d.stringBytes=l.slice(m,m+w.sizeFromShape(i)):r.typedArrayFromHeap(u).set(l.subarray(m,m+w.sizeFromShape(i))),u}if(t.dtype==="string"){let m=um(l,s,i,t.shape,t.dtype);return d.stringBytes=m,u}let c=r.typedArrayFromHeap(u),h=t.shape.length;if(h===2)Uue(l,p[0],c,s,i);else if(h===3)Gue(l,p[0],p[1],c,s,i);else if(h===4)Hue(l,p[0],p[1],p[2],c,s,i);else{let m=um(l,s,i,t.shape,t.dtype);c.set(m)}return u}function Uue(e,t,n,a,r){let s=0,i=a[0],o=a[1],l=i+r[0];for(let u=i;ub*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=zn({inputs:{x:r},backend:n,attrs:{shape:l}}),m=ys({inputs:{x:h},backend:n,attrs:{perm:u}}),f=zn({inputs:{x:m},backend:n,attrs:{shape:p}}),g=Ii({inputs:{x:f},backend:n,attrs:{begin:d,size:c}});return n.disposeData(h.dataId),n.disposeData(m.dataId),n.disposeData(f.dataId),g}var 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 zn({inputs:{x},backend:n,attrs:{shape:v}})}),m=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape}));s=T.computeOutShape(h.map(x=>x.shape),1);let f=h[0].shape[0]===1,g=L1(m,s,t[0].dtype,f),b=T.computeOutShape(i.map(x=>x.shape),a);o.shape=b;let y=n.dataIdMap.get(o.dataId);return y.stringBytes=T.fromStringArrayToUint8(g),h.forEach(x=>n.disposeData(x.dataId)),o}let l=w.sizeFromShape(i[0].shape.slice(0,a)),u=0,p=i.map(h=>{let m=w.sizeFromShape(h.shape.slice(a));return u+=m,m}),d=i.map(h=>n.typedArrayFromHeap(h)),c=n.typedArrayFromHeap(o);for(let h=0;h`cumprod does not support ${r.dtype} tensors in the WASM backend`);let u=T.getAxesPermutation([s],l),p=r;u!==null&&(p=ys({inputs:{x:r},attrs:{perm:u},backend:n}));let d=T.getInnerMostAxes(1,l)[0];T.assertAxesAreInnerMostDims("cumprod",[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;EF(m,i?1:0,o?1:0,h,f,Qe[r.dtype]);let g=c;if(u!==null){let 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 Ape={kernelName:lu,backendName:"wasm",setupFunc:Epe,kernelFunc:_pe},_F;function Fpe(e){_F=e.wasm.cwrap(Vi,null,["number","number","number","number","number","number"])}function $pe(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 ${r.dtype} tensors in the WASM backend`);let u=T.getAxesPermutation([s],l),p=r;u!==null&&(p=ys({inputs:{x:r},attrs:{perm:u},backend:n}));let d=T.getInnerMostAxes(1,l)[0];T.assertAxesAreInnerMostDims("cumsum",[d],l);let c=n.makeOutput(p.shape,p.dtype),h=p.shape[d],m=n.dataIdMap.get(p.dataId).id,f=n.dataIdMap.get(c.dataId).id;_F(m,i?1:0,o?1:0,h,f,Qe[r.dtype]);let g=c;if(u!==null){let b=T.getUndoAxesPermutation(u);g=ys({inputs:{x:c},attrs:{perm:b},backend:n}),n.disposeData(p.dataId),n.disposeData(c.dataId)}return g}var Dpe={kernelName:Vi,backendName:"wasm",setupFunc:Fpe,kernelFunc:$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}, ${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 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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}'. 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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 e$(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(d.dataId).id,n.dataIdMap.get(c.dataId).id,Qe[r.dtype],l,p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.effectiveFilterHeight,p.effectiveFilterWidth,p.padInfo.top,p.padInfo.left),[d,c]}var Ade={kernelName:Vc,backendName:"wasm",setupFunc:Ede,kernelFunc:_de},t$;function Fde(e){t$=e.wasm.cwrap(co,null,["number, number, number"])}function $de(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:p,axes:d,originalAxes:c,inputWasTransposed:h}=Ds(i,r,t),m=d;if(h){let v=t.dataIdMap.get(p.dataId).id;v!==o&&(u=p,l=v,m=T.getInnerMostAxes(m.length,u.shape.length))}T.assertAxesAreInnerMostDims("mean",m,u.shape.length);let[f,g]=T.computeOutAndReduceShapes(u.shape,m),b=w.sizeFromShape(g),y=u;u.dtype!=="float32"&&(y=Rs({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(y.dataId).id);let x=t.makeOutput(f,"float32");if(w.sizeFromShape(u.shape)!==0){let v=t.dataIdMap.get(x.dataId).id;t$(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$=e.wasm.cwrap(ho,null,["number","number","number","number"])}function Mde(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:p,axes:d,originalAxes:c,inputWasTransposed:h}=Ds(i,r,t);if(h){let x=t.dataIdMap.get(p.dataId).id;x!==o&&(u=p,l=x)}let m=u.shape.length;T.assertAxesAreInnerMostDims("min",d,m);let[f,g]=T.computeOutAndReduceShapes(u.shape,d),b=w.sizeFromShape(g),y=t.makeOutput(f,u.dtype);if(w.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(y.dataId).id;n$(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 Ode={kernelName:ho,backendName:"wasm",setupFunc:Rde,kernelFunc:Mde},Pde=!1,Lde=Ut(mo,Pde),xv;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(xv||(xv={}));var a$;function zde(e){a$=e.wasm.cwrap(fo,null,["number","array","number","number","array","array","number","number"])}function Wde(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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aa(s.width,s.height),this._score=t,this._classScore=n,this._className=a,this._box=new mn(r).rescale(this._imageDims)}get score(){return this._score}get classScore(){return this._classScore}get className(){return this._className}get box(){return this._box}get imageDims(){return this._imageDims}get imageWidth(){return this.imageDims.width}get imageHeight(){return this.imageDims.height}get relativeBox(){return new mn(this._box).rescale(this.imageDims.reverse())}forSize(t,n){return new e(this.score,this.classScore,this.className,this.relativeBox,{width:t,height:n})}};var Ft=class e extends yp{constructor(t,n,a){super(t,t,"",n,a)}forSize(t,n){let{score:a,relativeBox:r,imageDims:s}=super.forSize(t,n);return new e(a,r,s)}};function B$(e,t,n=!0){let a=Math.max(0,Math.min(e.right,t.right)-Math.max(e.left,t.left)),r=Math.max(0,Math.min(e.bottom,t.bottom)-Math.max(e.top,t.top)),s=a*r;return n?s/(e.area+t.area-s):s/Math.min(e.area,t.area)}function V$(e){let t=e.map(o=>o.x),n=e.map(o=>o.y),a=t.reduce((o,l)=>lloo({score:i,boxIndex:o})).sort((i,o)=>i.score-o.score).map(i=>i.boxIndex),s=[];for(;r.length>0;){let i=r.pop();s.push(i);let o=r,l=[];for(let u=0;ul[p]<=n)}return s}function br(e,t){return O(()=>{let[n,a,r]=t,s=yn([...e.shape.slice(0,3),1],n,"float32"),i=yn([...e.shape.slice(0,3),1],a,"float32"),o=yn([...e.shape.slice(0,3),1],r,"float32"),l=et([s,i,o],3);return pe(e,l)})}function G$(e,t=!1){return O(()=>{let[n,a]=e.shape.slice(1);if(n===a)return e;let r=Math.abs(n-a),s=Math.round(r*(t?.5:1)),i=n>a?2:1,o=c=>{let h=e.shape.slice();return h[i]=c,yn(h,0,"float32")},l=o(s),u=r-l.shape[i],d=[t&&u?o(u):null,e,l].filter(c=>!!c).map(c=>re(c,"float32"));return et(d,i)})}function y0e(e){let t=e.slice();for(let n=t.length-1;n>0;n--){let a=Math.floor(Math.random()*(n+1)),r=t[n];t[n]=t[a],t[a]=r}return t}function qf(e){return 1/(1+Math.exp(-e))}function v0e(e){return Math.log(e/(1-e))}var xp=class extends mn{constructor(t,n,a,r,s=!1){super({x:t,y:n,width:a,height:r},s)}};var Lfe=.5,zfe=.43,Wfe=.45,ka=class{constructor(t,n,a=new He(0,0)){let{width:r,height:s}=n;this._imgDims=new aa(r,s),this._shift=a,this._positions=t.map(i=>i.mul(new He(r,s)).add(a))}get shift(){return new He(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(t=>t.sub(this._shift).div(new He(this.imageWidth,this.imageHeight)))}forSize(t,n){return new this.constructor(this.relativePositions,{width:t,height:n})}shiftBy(t,n){return new this.constructor(this.relativePositions,this._imgDims,new He(t,n))}shiftByPoint(t){return this.shiftBy(t.x,t.y)}align(t,n={}){if(t){let s=t instanceof Ft?t.box.floor():new mn(t);return this.shiftBy(s.x,s.y).align(null,n)}let{useDlibAlignment:a,minBoxPadding:r}={useDlibAlignment:!1,minBoxPadding:.2,...n};return 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a=re(t.toBatchTensor(112,!0),"float32"),s=br(a,[122.782,117.001,104.298]).div(255),i=zd(s,n.dense0,!0);return i=zd(i,n.dense1),i=zd(i,n.dense2),i=zd(i,n.dense3),i=ya(i,[7,7],[2,2],"valid"),i})}async forward(t){return this.forwardInput(await vt(t))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeightMap(t){return tD(t)}extractParams(t){return eD(t)}};function Bd(e,t){return O(()=>X($e(e,t.weights),t.bias))}function nD(e,t,n){let a=[],{extractWeights:r,getRemainingWeights:s}=An(e),o=eg(r,a)(t,n,"fc");if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{paramMappings:a,params:{fc:o}}}function aD(e){let t=[],n=sa(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:a("fc")};return _n(e,t),{params:r,paramMappings:t}}function rg(e){let t={},n={};return Object.keys(e).forEach(a=>{let r=a.startsWith("fc")?n:t;r[a]=e[a]}),{featureExtractorMap:t,classifierMap:n}}var Ap=class extends fn{constructor(t,n){super(t),this._faceFeatureExtractor=n}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return O(()=>{let a=t instanceof Wr?this.faceFeatureExtractor.forwardInput(t):t;return Bd(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 nD(t,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:a}=rg(t);return this.faceFeatureExtractor.loadFromWeightMap(n),aD(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 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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 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this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var ll=class extends fn{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("SsdMobilenetv1 - load model before inference");return O(()=>{let a=re(t.toBatchTensor(512,!1),"float32"),r=pe(he(a,127.5),1),s=kD(r,n.mobilenetv1),{boxPredictions:i,classPredictions:o}=ND(s.out,s.conv11,n.prediction_layer);return SD(i,o,n.output_layer)})}async forward(t){return this.forwardInput(await vt(t))}async locateFaces(t,n={}){let{maxResults:a,minConfidence:r}=new Ma(n),s=await vt(t),{boxes:i,scores:o}=this.forwardInput(s),l=i[0],u=o[0];for(let x=1;x{let[v,I]=[Math.max(0,b[x][0]),Math.min(1,b[x][2])].map(_=>_*g),[N,C]=[Math.max(0,b[x][1]),Math.min(1,b[x][3])].map(_=>_*f);return new Ft(p[x],new xp(N,v,C-N,I-v),{height:s.getInputHeight(0),width:s.getInputWidth(0)})});return l.dispose(),u.dispose(),y}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return wD(t)}extractParams(t){return vD(t)}};function lge(e){let t=new ll;return t.extractWeights(e),t}function YCe(e){return lge(e)}var TD=class extends ll{};var CD=.4,ED=[new He(.738768,.874946),new He(2.42204,2.65704),new He(4.30971,7.04493),new He(10.246,4.59428),new He(12.6868,11.8741)],_D=[new He(1.603231,2.094468),new He(6.041143,7.080126),new He(2.882459,3.518061),new He(4.266906,5.178857),new He(9.041765,10.66308)],AD=[117.001,114.697,97.404],FD="tiny_yolov2_model",$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 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a=re(t.toBatchTensor(112,!0),"float32"),s=br(a,[122.782,117.001,104.298]).div(255),i=zd(s,n.dense0,!0);return i=zd(i,n.dense1),i=zd(i,n.dense2),i=zd(i,n.dense3),i=ya(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 tD(t)}extractParams(t){return eD(t)}};function Bd(e,t){return O(()=>X($e(e,t.weights),t.bias))}function nD(e,t,n){let a=[],{extractWeights:r,getRemainingWeights:s}=An(e),o=eg(r,a)(t,n,"fc");if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{paramMappings:a,params:{fc:o}}}function aD(e){let t=[],n=sa(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:a("fc")};return _n(e,t),{params:r,paramMappings:t}}function rg(e){let t={},n={};return Object.keys(e).forEach(a=>{let r=a.startsWith("fc")?n:t;r[a]=e[a]}),{featureExtractorMap:t,classifierMap:n}}var Ap=class extends fn{constructor(t,n){super(t),this._faceFeatureExtractor=n}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return O(()=>{let a=t instanceof Wr?this.faceFeatureExtractor.forwardInput(t):t;return Bd(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 nD(t,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:a}=rg(t);return this.faceFeatureExtractor.loadFromWeightMap(n),aD(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 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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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hD(t)}extractParams(t){return mD(t)}};var cg=class extends $p{constructor(t=new pg){super("FaceLandmark68TinyNet",t)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}};var fD=class extends Dp{};function gD(e,t){return X(z(e,t.weights),t.biases)}function Ek(e,t,n,a,r="same"){let{filters:s,bias:i}=t.conv,o=$t(e,s,n,r);return o=X(o,i),o=gD(o,t.scale),a?Ke(o):o}function bD(e,t){return Ek(e,t,[1,1],!0)}function _k(e,t){return Ek(e,t,[1,1],!1)}function dg(e,t){return Ek(e,t,[2,2],!0,"valid")}function Jfe(e,t){function n(o,l,u){let p=e(o),d=p.length/(l*u*u);if(gk(d))throw new Error(`depth has to be an integer: ${d}, weights.length: ${p.length}, numFilters: ${l}, filterSize: ${u}`);return O(()=>De(Fa(p,[l,d,u,u]),[2,3,1,0]))}function a(o,l,u,p){let d=n(o,l,u),c=je(e(l));return t.push({paramPath:`${p}/filters`},{paramPath:`${p}/bias`}),{filters:d,bias:c}}function r(o,l){let u=je(e(o)),p=je(e(o));return 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0,S=o(x||y||b,5*n,1,"conv8");p={conv0:v,conv1:I,conv2:N,conv3:C,conv4:_,conv5:F,conv6:D,conv7:$,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 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R0;(function(r){r.float32=\"float32\",r.int32=\"int32\",r.bool=\"bool\",r.complex64=\"complex64\"})(R0||(R0={}));var F0;(function(r){r.float32=\"float32\",r.int32=\"float32\",r.bool=\"float32\",r.complex64=\"complex64\"})(F0||(F0={}));var O0;(function(r){r.float32=\"complex64\",r.int32=\"complex64\",r.bool=\"complex64\",r.complex64=\"complex64\"})(O0||(O0={}));var NK={float32:F0,int32:$0,bool:R0,complex64:O0};function ur(r,t){if(r===\"string\"||t===\"string\"){if(r===\"string\"&&t===\"string\")return\"string\";throw new Error(`Can not upcast ${r} with ${t}`)}return NK[r][t]}function lc(r){return ur(r,\"int32\")}function rx(r){return r!=null&&typeof r==\"object\"&&\"texture\"in r&&r.texture instanceof WebGLTexture}function nx(r){return typeof GPUBuffer!=\"undefined\"&&r!=null&&typeof r==\"object\"&&\"buffer\"in r&&r.buffer instanceof GPUBuffer}function Xt(r,t){if(r.dtype===t.dtype)return[r,t];let e=ur(r.dtype,t.dtype);return[r.cast(e),t.cast(e)]}function M0(r,t){_(r.dtype===t.dtype,()=>`The dtypes of the first(${r.dtype}) and second(${t.dtype}) input must match`)}function kK(r,t){return t.some(e=>e.id===r.id)}function ih(r){let t=[];return eE(r,t,new Set),t}function eE(r,t,e){if(r==null)return;if(r instanceof Lt){t.push(r);return}if(!TK(r))return;let n=r;for(let o in n){let s=n[o];e.has(s)||(e.add(s),eE(s,t,e))}}function TK(r){return Array.isArray(r)||typeof r==\"object\"}function P0(r){return r.kernelName!=null}var ox=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()}},ah=class r{constructor(t){this.ENV=t,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new ox}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){Yg(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=P0(t)?t.kernelName:this.state.activeScope!=null?this.state.activeScope.name:\"\";if(P0(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=P0(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=v0(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=b0(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=v0(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=ih(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=K_(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?_K(s.shape):n,j_(a,i,l=>this.tidy(l),EK);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 _(_i(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\"),_(_i(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 ox;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}};ah.nextTensorId=0;ah.nextVariableId=0;function _K(r){let t=Yd(jt(r),\"float32\");return T.makeTensor(t,r,\"float32\")}function L0(){let r=C0();if(r._tfengine==null){let t=new Zd(r);r._tfengine=new ah(t)}return T_(r._tfengine.ENV),J_(()=>r._tfengine),r._tfengine}var T=L0();function EK(r,t){let e={a:r,b:t};return T.runKernel(no,e)}var du={};Kt(du,{isBrowser:()=>B0,isMobile:()=>$K,mockIsMobile:()=>DK});function AK(){return typeof navigator!=\"undefined\"&&navigator!=null}var z0;function DK(r){z0=r}function $K(r){if(z0!==void 0)return z0;if(r||AK()){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(!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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vm=k({unsortedSegmentSum_:G5});function W5(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}, ${e.shape.length})`);let n={value:e},o={axis:t};return T.runKernel(Ki,n,o)}var gr=k({unstack_:W5});function OA(r,t){return dh(r,t,\"right\")}function my(r,t=!0,e,n){return T.makeVariable(r,t,e,n)}function fy(r,t){let e=[];for(let s=0;s0,()=>\"mask cannot be scalar\"),$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 ${t}`)}),n.rank<=1)return n.clone();let o={x:n},s={perm:t};return n.dtype===\"complex64\"?B(()=>{let i=bl(n),a=Iu(n);return i=T.runKernel(so,{x:i},s),a=T.runKernel(so,{x:a},s),e&&(a=Ut(a)),Sn(i,a)}):T.runKernel(so,o,s)}var Vt=k({transpose_:K5});function j5(r,t,e,n,o=!0){let s=C(r,\"v\",\"movingAverage\"),i=C(t,\"x\",\"movingAverage\"),a=C(e,\"decay\",\"movingAverage\");M0(s,i),_(sn(s.shape,i.shape),()=>\"Shape mismatch in v and x\");let u=pt(1),l=at(u,a),c=$(at(i,s),l);if(o){_(n!=null,()=>\"When using zeroDebias: true, step is required.\");let p=C(n,\"step\",\"movingAverage\");c=ut(c,at(u,qr(a,p)))}return K(s,c)}var X5=k({movingAverage_:j5});function Y5(r,t,e){Le(e);let n=C(r,\"indices\",\"scatterND\",\"int32\"),o=C(t,\"updates\",\"scatterND\");Im(o,n,e);let s={indices:n,updates:o},i={shape:e};return T.runKernel(nl,s,i)}var Z5=k({scatterND_:Y5});function MA(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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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 Error(`Cannot compute gradient for fused activation ${e}.`)}function wc(r,t){let e=t,n=we(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 cm(r);if(t===\"prelu\")return _u(r,e);if(t===\"leakyrelu\")return Cu(r,n);if(t===\"sigmoid\")return en(r);throw new Error(`Unknown fused activation ${t}.`)}var Cc=(r,t)=>!(r>0)||t===\"linear\";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(u=u||\"linear\",Cc(T.state.gradientDepth,u)===!1){_(o===\"NHWC\",()=>`Error in fused conv2d: got dataFormat of ${o} but only NHWC is currently supported for the case of gradient depth is 0 and 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Got dilations '${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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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 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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 ${r.rank}`),r.rank===2)return i2(r,t);{let e=r.shape.slice(0,r.shape.length-2).reduce((u,l)=>u*l),n=gr(R(r,[e,r.shape[r.shape.length-2],r.shape[r.shape.length-1]]),0),o=[],s=[];n.forEach(u=>{let[l,c]=i2(u,t);o.push(l),s.push(c)});let 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Je;(function(r){r[r.NONE=0]=\"NONE\",r[r.MEAN=1]=\"MEAN\",r[r.SUM=2]=\"SUM\",r[r.SUM_BY_NONZERO_WEIGHTS=3]=\"SUM_BY_NONZERO_WEIGHTS\"})(Je||(Je={}));function B8(r,t,e=Je.SUM_BY_NONZERO_WEIGHTS){let n=C(r,\"losses\",\"computeWeightedLoss\"),o=null;t!=null&&(o=C(t,\"weights\",\"computeWeightedLoss\"));let s=o==null?n:$(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=ut(mt(s),mt(o));return i>1?ut(a,pt(i)):a}}if(e===Je.SUM_BY_NONZERO_WEIGHTS){if(o==null)return ut(mt(s),pt(n.size));{let i=$(o,ar(n.shape)),a=J(mt(ci(i,pt(0))),\"float32\");return ut(mt(s),a)}}throw Error(`Unknown reduction: ${e}`)}var Kr=k({computeWeightedLoss_:B8});function V8(r,t,e,n=Je.SUM_BY_NONZERO_WEIGHTS){let o=C(r,\"labels\",\"absoluteDifference\"),s=C(t,\"predictions\",\"absoluteDifference\"),i=null;e!=null&&(i=C(e,\"weights\",\"absoluteDifference\")),$e(o.shape,s.shape,\"Error in absoluteDifference: \");let a=_e(at(o,s));return Kr(a,i,n)}var l2=k({absoluteDifference_:V8});function G8(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 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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i={inputIndices:n,inputShape:o,newShape:s},a=T.runKernel(il,i);return{outputIndices:a[0],outputShape:a[1]}}var x2=k({sparseReshape_:J8});function Q8(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 ${s.shape}`);let i={data:n,indices:o,segmentIds:s};return T.runKernel(ou,i)}var y2=k({sparseSegmentMean_:Q8});function tY(r,t,e){let n=C(r,\"data\",\"sparseSegmentSum\"),o=C(t,\"indices\",\"sparseSegmentSum\",\"int32\"),s=C(e,\"segmentIds\",\"sparseSegmentSum\",\"int32\");if(n.rank<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(o.rank!==1)throw new Error(`Indices should be Tensor1D but 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className(){return\"Adadelta\"}constructor(t,e,n=null){super(),this.learningRate=t,this.rho=e,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=T.registeredVariables[n],i=!1;this.accumulatedGrads[o]==null&&(this.accumulatedGrads[o]={originalName:`${n}/accum_grad`,variable:B(()=>vt(s).variable(i))}),this.accumulatedUpdates[o]==null&&(this.accumulatedUpdates[o]={originalName:`${n}/accum_var`,variable:B(()=>vt(s).variable(i))});let a=Array.isArray(t)?t[o].tensor:t[n];if(a==null)return;let u=this.accumulatedGrads[o].variable,l=this.accumulatedUpdates[o].variable;B(()=>{let c=K($(u,this.rho),$(Wt(a),1-this.rho)),p=$(ut(ge(K(l,this.epsilon)),ge(K(u,this.epsilon))),a),m=K($(l,this.rho),$(Wt(p),1-this.rho));u.assign(c),l.assign(m);let f=K($(p,-this.learningRate),s);s.assign(f)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Tt(this.accumulatedGrads.map(t=>t.variable)),Tt(this.accumulatedUpdates.map(t=>t.variable)))}async getWeights(){let t=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=t.length/2,n=!1;this.accumulatedGrads=t.slice(0,e).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.accumulatedUpdates=t.slice(e,e*2).map(o=>({originalName:o.name,variable:o.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(t,e){return new t(e.learningRate,e.rho,e.epsilon)}};var Sc=class extends jr{static get className(){return\"Adagrad\"}constructor(t,e=.1){super(),this.learningRate=t,this.initialAccumulatorValue=e,this.accumulatedGrads=[]}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=T.registeredVariables[n];this.accumulatedGrads[o]==null&&(this.accumulatedGrads[o]={originalName:`${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=K(a,Wt(i));a.assign(u);let l=K($(ut(i,ge(K(u,T.backend.epsilon()))),-this.learningRate),s);s.assign(l)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Tt(this.accumulatedGrads.map(t=>t.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=!1;this.accumulatedGrads=t.map(n=>({originalName:n.name,variable:n.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(t,e){return new t(e.learningRate,e.initialAccumulatorValue)}};var Nc=class extends jr{static get className(){return\"Adam\"}constructor(t,e,n,o=null){super(),this.learningRate=t,this.beta1=e,this.beta2=n,this.epsilon=o,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],B(()=>{this.accBeta1=pt(e).variable(),this.accBeta2=pt(n).variable()}),o==null&&(this.epsilon=T.backend.epsilon())}applyGradients(t){let e=Array.isArray(t)?t.map(n=>n.name):Object.keys(t);B(()=>{let n=at(1,this.accBeta1),o=at(1,this.accBeta2);e.forEach((s,i)=>{let a=T.registeredVariables[s],u=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${s}/m`,variable:B(()=>vt(a).variable(u))}),this.accumulatedSecondMoment[i]==null&&(this.accumulatedSecondMoment[i]={originalName:`${s}/v`,variable:B(()=>vt(a).variable(u))});let l=Array.isArray(t)?t[i].tensor:t[s];if(l==null)return;let c=this.accumulatedFirstMoment[i].variable,p=this.accumulatedSecondMoment[i].variable,m=K($(c,this.beta1),$(l,1-this.beta1)),f=K($(p,this.beta2),$(Wt(l),1-this.beta2)),d=ut(m,n),h=ut(f,o);c.assign(m),p.assign(f);let g=K($(ut(d,K(ge(h),this.epsilon)),-this.learningRate),a);a.assign(g)}),this.accBeta1.assign($(this.accBeta1,this.beta1)),this.accBeta2.assign($(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Tt(this.accumulatedFirstMoment.map(t=>t.variable)),this.accumulatedSecondMoment!=null&&Tt(this.accumulatedSecondMoment.map(t=>t.variable))}async getWeights(){let t=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t),B(()=>{this.accBeta1.assign(qr(this.beta1,this.iterations_+1)),this.accBeta2.assign(qr(this.beta2,this.iterations_+1))});let e=t.length/2,n=!1;this.accumulatedFirstMoment=t.slice(0,e).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.accumulatedSecondMoment=t.slice(e,e*2).map(o=>({originalName:o.name,variable:o.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon)}};var kc=class extends jr{static get className(){return\"Adamax\"}constructor(t,e,n,o=null,s=0){super(),this.learningRate=t,this.beta1=e,this.beta2=n,this.epsilon=o,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],B(()=>{this.iteration=pt(0).variable(),this.accBeta1=pt(e).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=ut(-this.learningRate,K($(this.iteration,this.decay),1));e.forEach((s,i)=>{let a=T.registeredVariables[s],u=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${s}/m`,variable:vt(a).variable(u)}),this.accumulatedWeightedInfNorm[i]==null&&(this.accumulatedWeightedInfNorm[i]={originalName:`${s}/v`,variable:vt(a).variable(u)});let l=Array.isArray(t)?t[i].tensor:t[s];if(l==null)return;let c=this.accumulatedFirstMoment[i].variable,p=this.accumulatedWeightedInfNorm[i].variable,m=K($(c,this.beta1),$(l,1-this.beta1)),f=$(p,this.beta2),d=_e(l),h=kn(f,d);c.assign(m),p.assign(h);let g=K($(ut(o,n),ut(m,K(h,this.epsilon))),a);a.assign(g)}),this.iteration.assign(K(this.iteration,1)),this.accBeta1.assign($(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Tt(this.accumulatedFirstMoment.map(t=>t.variable)),this.accumulatedWeightedInfNorm!=null&&Tt(this.accumulatedWeightedInfNorm.map(t=>t.variable))}async getWeights(){throw new 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 jr{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=K($(this.c,s),i);i.assign(a)})}),this.incrementIterations()}setLearningRate(t){this.learningRate=t,this.c!=null&&this.c.dispose(),this.c=De(pt(-t))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(t){if(t=await this.extractIterations(t),t.length!==0)throw new Error(\"SGD 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this.saveIterations()].concat(this.accumulations.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=!1;this.accumulations=t.map(n=>({originalName:n.name,variable:n.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(t,e){return new t(e.learningRate,e.momentum,e.useNesterov)}};var _c=class extends jr{static get className(){return\"RMSProp\"}constructor(t,e=.9,n=0,o=null,s=!1){if(super(),this.learningRate=t,this.decay=e,this.momentum=n,this.epsilon=o,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,o==null&&(this.epsilon=T.backend.epsilon()),t==null)throw new Error(\"learningRate for RMSPropOptimizer must be defined.\")}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=T.registeredVariables[n],i=!1;this.accumulatedMeanSquares[o]==null&&(this.accumulatedMeanSquares[o]={originalName:`${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=K($(u,this.decay),$(Wt(a),1-this.decay));if(this.centered){let p=this.accumulatedMeanGrads[o].variable,m=K($(p,this.decay),$(a,1-this.decay)),f=ut($(a,this.learningRate),ge(at(c,K(Wt(m),this.epsilon)))),d=K($(l,this.momentum),f);u.assign(c),p.assign(m),l.assign(d);let h=at(s,d);s.assign(h)}else{let p=K($(u,this.decay),$(Wt(a),1-this.decay)),m=K($(l,this.momentum),ut($(a,this.learningRate),ge(K(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 mY=[vc,Sc,Nc,kc,Tc,_c,Il];function S2(){for(let r of mY)bN(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 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. Received: ${r.length} elements.`);for(let n=0;n(Me(t),t===\"channelsFirst\"?Vt(r,[0,2,3,1]):r))}function ek(r,t){return B(()=>(Me(t),t===\"channelsFirst\"?Vt(r,[0,2,3,4,1]):r))}function wJ(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 ${e.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 XR(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=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 ${this.padding}`)}build(t){if(t=Gt(t),t.length!==4)throw new z(\"Input should have rank 4; Received input shape: \"+JSON.stringify(t));let e=this.dataFormat===\"channelsFirst\"?1:t.length-1;if(t[e]==null)throw new z(\"The channel dimension of the inputs should be defined. 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Found `None`.\");let n=t[e],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight(\"kernel\",o,\"float32\",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],\"float32\",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ce({ndim:5,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{let n=St(t);if(n.shape.length!==5)throw new z(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${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 ${t.length}.`);let o=t[1],s=t[2];t=t[0],0wr(t),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0wr(o),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,a=this.recurrentDropoutMask,u,l,c,p;0{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};df.className=\"LSTM\";Q.registerClass(df);var jc=class extends Tl{constructor(t){super(t),this.cells=t.cells}get stateSize(){let t=[];for(let e of this.cells.slice().reverse())Array.isArray(e.stateSize)?t.push(...e.stateSize):t.push(e.stateSize);return t}call(t,e){return B(()=>{t=t;let n=t.slice(1),o=[];for(let a of this.cells.slice().reverse())Array.isArray(a.stateSize)?o.push(n.splice(0,a.stateSize.length)):o.push(n.splice(0,1));o.reverse();let s=[],i;for(let a=0;a{fi(`RNNCell_${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)}). 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. 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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 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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 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: ${e}. 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length-${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 '${r}'`),n.arrayBuffer().then(o=>{WebAssembly.instantiate(o,t).then(s=>{e(s.instance,s.module)})})}),{})}function eq(r,t,e){if(OC!=null)return OC;let n=\"tfjs-backend-wasm.wasm\";return r&&t?n=\"tfjs-backend-wasm-threaded-simd.wasm\":r&&(n=\"tfjs-backend-wasm-simd.wasm\"),yg!=null&&yg[n]!=null?yg[n]:e+n}async function nq(){let[r,t]=await Promise.all([L().getAsync(\"WASM_HAS_SIMD_SUPPORT\"),L().getAsync(\"WASM_HAS_MULTITHREAD_SUPPORT\")]);return new Promise((e,n)=>{let o={};o.locateFile=(a,u)=>{if(a.endsWith(\".worker.js\")){let l=rq.wasmWorkerContents.replace(/\\n/g,\"\\\\n\"),c=new Blob([l],{type:\"application/javascript\"});return URL.createObjectURL(c)}return a.endsWith(\".wasm\")?eq(r,t,xg!=null?xg:u):u+a},u_&&(o.instantiateWasm=Lmt(eq(r,t,xg!=null?xg:\"\")));let s=!1;o.onAbort=()=>{if(s||bg)return;bg=!0,n({message:\"Make sure the server can serve the `.wasm` file relative to the bundled js file. 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Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}u_=t}var oq=-1,i_=-1;function Wmt(r){oq=r}function Umt(){if(i_===-1)throw new Error(\"WASM backend not initialized.\");return i_}var Hmt=\"4.22.0\";var qmt=2;Xp(\"wasm\",async()=>{let{wasm:r}=await nq();return new wg(r)},qmt);var sq=\"4.22.0\",Kmt=\"4.22.0\",jmt=\"4.22.0\",Xmt=\"4.22.0\",Ymt=\"4.22.0\",Zmt={tfjs:sq,\"tfjs-core\":sq,\"tfjs-converter\":Kmt,\"tfjs-backend-cpu\":jmt,\"tfjs-backend-webgl\":Xmt,\"tfjs-backend-wasm\":Ymt};export{Ai as Abs,Go as Acos,Wo as Acosh,vc as AdadeltaOptimizer,Sc as AdagradOptimizer,Nc as AdamOptimizer,kc as AdamaxOptimizer,no as Add,Uo as AddN,Ea as All,Aa as Any,Di as ArgMax,$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 // @ts-ignore async-vs-sync mismatch\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\": \"^7.49.2\",\n \"@tensorflow/tfjs\": \"^4.22.0\",\n \"@tensorflow/tfjs-backend-cpu\": \"^4.22.0\",\n \"@tensorflow/tfjs-backend-wasm\": \"^4.22.0\",\n \"@tensorflow/tfjs-backend-webgl\": \"^4.22.0\",\n \"@tensorflow/tfjs-backend-webgpu\": \"4.22.0\",\n \"@tensorflow/tfjs-converter\": \"^4.22.0\",\n \"@tensorflow/tfjs-core\": \"^4.22.0\",\n \"@tensorflow/tfjs-data\": \"^4.22.0\",\n \"@tensorflow/tfjs-layers\": \"^4.22.0\",\n \"@tensorflow/tfjs-node\": \"^4.22.0\",\n \"@tensorflow/tfjs-node-gpu\": \"^4.22.0\",\n \"@types/node\": \"^22.13.1\",\n \"@types/offscreencanvas\": \"^2019.7.3\",\n \"@typescript-eslint/eslint-plugin\": \"^8.5.0\",\n \"@typescript-eslint/parser\": \"^8.5.0\",\n \"@vladmandic/build\": \"^0.10.2\",\n \"@vladmandic/pilogger\": \"^0.5.1\",\n \"ajv\": \"^8.17.1\",\n \"esbuild\": \"^0.24.2\",\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\": \"^2.8.1\",\n \"typedoc\": \"^0.27.6\",\n \"typescript\": \"5.7.3\"\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"], + "sourcesContent": ["/*\n Face-API\n homepage: \n author: '\n*/\n\nvar zq=Object.create;var f0=Object.defineProperty;var Bq=Object.getOwnPropertyDescriptor;var Vq=Object.getOwnPropertyNames;var Gq=Object.getPrototypeOf,Wq=Object.prototype.hasOwnProperty;var br=(r,t)=>()=>(t||r((t={exports:{}}).exports,t),t.exports),Kt=(r,t)=>{for(var e in t)f0(r,e,{get:t[e],enumerable:!0})},Uq=(r,t,e,n)=>{if(t&&typeof 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R0;(function(r){r.float32=\"float32\",r.int32=\"int32\",r.bool=\"bool\",r.complex64=\"complex64\"})(R0||(R0={}));var F0;(function(r){r.float32=\"float32\",r.int32=\"float32\",r.bool=\"float32\",r.complex64=\"complex64\"})(F0||(F0={}));var O0;(function(r){r.float32=\"complex64\",r.int32=\"complex64\",r.bool=\"complex64\",r.complex64=\"complex64\"})(O0||(O0={}));var NK={float32:F0,int32:$0,bool:R0,complex64:O0};function ur(r,t){if(r===\"string\"||t===\"string\"){if(r===\"string\"&&t===\"string\")return\"string\";throw new Error(`Can not upcast ${r} with ${t}`)}return NK[r][t]}function lc(r){return ur(r,\"int32\")}function rx(r){return r!=null&&typeof r==\"object\"&&\"texture\"in r&&r.texture instanceof WebGLTexture}function nx(r){return typeof GPUBuffer!=\"undefined\"&&r!=null&&typeof r==\"object\"&&\"buffer\"in r&&r.buffer instanceof GPUBuffer}function Xt(r,t){if(r.dtype===t.dtype)return[r,t];let e=ur(r.dtype,t.dtype);return[r.cast(e),t.cast(e)]}function M0(r,t){_(r.dtype===t.dtype,()=>`The dtypes of the first(${r.dtype}) and second(${t.dtype}) input must match`)}function kK(r,t){return t.some(e=>e.id===r.id)}function ih(r){let t=[];return eE(r,t,new Set),t}function eE(r,t,e){if(r==null)return;if(r instanceof Lt){t.push(r);return}if(!TK(r))return;let n=r;for(let o in n){let s=n[o];e.has(s)||(e.add(s),eE(s,t,e))}}function TK(r){return Array.isArray(r)||typeof r==\"object\"}function P0(r){return r.kernelName!=null}var ox=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()}},ah=class r{constructor(t){this.ENV=t,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new ox}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){Yg(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=P0(t)?t.kernelName:this.state.activeScope!=null?this.state.activeScope.name:\"\";if(P0(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=P0(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=v0(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=b0(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=v0(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=ih(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=K_(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?_K(s.shape):n,j_(a,i,l=>this.tidy(l),EK);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 _(_i(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\"),_(_i(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 ox;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}};ah.nextTensorId=0;ah.nextVariableId=0;function _K(r){let t=Yd(jt(r),\"float32\");return T.makeTensor(t,r,\"float32\")}function L0(){let r=C0();if(r._tfengine==null){let t=new Zd(r);r._tfengine=new ah(t)}return T_(r._tfengine.ENV),J_(()=>r._tfengine),r._tfengine}var T=L0();function EK(r,t){let e={a:r,b:t};return T.runKernel(no,e)}var du={};Kt(du,{isBrowser:()=>B0,isMobile:()=>$K,mockIsMobile:()=>DK});function AK(){return typeof navigator!=\"undefined\"&&navigator!=null}var z0;function DK(r){z0=r}function $K(r){if(z0!==void 0)return z0;if(r||AK()){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(!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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vm=k({unsortedSegmentSum_:G5});function W5(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}, ${e.shape.length})`);let n={value:e},o={axis:t};return T.runKernel(Ki,n,o)}var gr=k({unstack_:W5});function OA(r,t){return dh(r,t,\"right\")}function my(r,t=!0,e,n){return T.makeVariable(r,t,e,n)}function fy(r,t){let e=[];for(let s=0;s0,()=>\"mask cannot be scalar\"),$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 ${t}`)}),n.rank<=1)return n.clone();let o={x:n},s={perm:t};return n.dtype===\"complex64\"?B(()=>{let i=bl(n),a=Iu(n);return i=T.runKernel(so,{x:i},s),a=T.runKernel(so,{x:a},s),e&&(a=Ut(a)),Sn(i,a)}):T.runKernel(so,o,s)}var Vt=k({transpose_:K5});function j5(r,t,e,n,o=!0){let s=C(r,\"v\",\"movingAverage\"),i=C(t,\"x\",\"movingAverage\"),a=C(e,\"decay\",\"movingAverage\");M0(s,i),_(sn(s.shape,i.shape),()=>\"Shape mismatch in v and x\");let u=pt(1),l=at(u,a),c=$(at(i,s),l);if(o){_(n!=null,()=>\"When using zeroDebias: true, step is required.\");let p=C(n,\"step\",\"movingAverage\");c=ut(c,at(u,qr(a,p)))}return K(s,c)}var X5=k({movingAverage_:j5});function Y5(r,t,e){Le(e);let n=C(r,\"indices\",\"scatterND\",\"int32\"),o=C(t,\"updates\",\"scatterND\");Im(o,n,e);let s={indices:n,updates:o},i={shape:e};return T.runKernel(nl,s,i)}var Z5=k({scatterND_:Y5});function MA(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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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 Error(`Cannot compute gradient for fused activation ${e}.`)}function wc(r,t){let e=t,n=we(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 cm(r);if(t===\"prelu\")return _u(r,e);if(t===\"leakyrelu\")return Cu(r,n);if(t===\"sigmoid\")return en(r);throw new Error(`Unknown fused activation ${t}.`)}var Cc=(r,t)=>!(r>0)||t===\"linear\";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(u=u||\"linear\",Cc(T.state.gradientDepth,u)===!1){_(o===\"NHWC\",()=>`Error in fused conv2d: got dataFormat of ${o} but only NHWC is currently supported for the case of gradient depth is 0 and 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Got dilations '${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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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 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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 ${r.rank}`),r.rank===2)return i2(r,t);{let e=r.shape.slice(0,r.shape.length-2).reduce((u,l)=>u*l),n=gr(R(r,[e,r.shape[r.shape.length-2],r.shape[r.shape.length-1]]),0),o=[],s=[];n.forEach(u=>{let[l,c]=i2(u,t);o.push(l),s.push(c)});let 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Je;(function(r){r[r.NONE=0]=\"NONE\",r[r.MEAN=1]=\"MEAN\",r[r.SUM=2]=\"SUM\",r[r.SUM_BY_NONZERO_WEIGHTS=3]=\"SUM_BY_NONZERO_WEIGHTS\"})(Je||(Je={}));function B8(r,t,e=Je.SUM_BY_NONZERO_WEIGHTS){let n=C(r,\"losses\",\"computeWeightedLoss\"),o=null;t!=null&&(o=C(t,\"weights\",\"computeWeightedLoss\"));let s=o==null?n:$(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=ut(mt(s),mt(o));return i>1?ut(a,pt(i)):a}}if(e===Je.SUM_BY_NONZERO_WEIGHTS){if(o==null)return ut(mt(s),pt(n.size));{let i=$(o,ar(n.shape)),a=J(mt(ci(i,pt(0))),\"float32\");return ut(mt(s),a)}}throw Error(`Unknown reduction: ${e}`)}var Kr=k({computeWeightedLoss_:B8});function V8(r,t,e,n=Je.SUM_BY_NONZERO_WEIGHTS){let o=C(r,\"labels\",\"absoluteDifference\"),s=C(t,\"predictions\",\"absoluteDifference\"),i=null;e!=null&&(i=C(e,\"weights\",\"absoluteDifference\")),$e(o.shape,s.shape,\"Error in absoluteDifference: \");let a=_e(at(o,s));return Kr(a,i,n)}var l2=k({absoluteDifference_:V8});function G8(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 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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i={inputIndices:n,inputShape:o,newShape:s},a=T.runKernel(il,i);return{outputIndices:a[0],outputShape:a[1]}}var x2=k({sparseReshape_:J8});function Q8(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 ${s.shape}`);let i={data:n,indices:o,segmentIds:s};return T.runKernel(ou,i)}var y2=k({sparseSegmentMean_:Q8});function tY(r,t,e){let n=C(r,\"data\",\"sparseSegmentSum\"),o=C(t,\"indices\",\"sparseSegmentSum\",\"int32\"),s=C(e,\"segmentIds\",\"sparseSegmentSum\",\"int32\");if(n.rank<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(o.rank!==1)throw new Error(`Indices should be Tensor1D but 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className(){return\"Adadelta\"}constructor(t,e,n=null){super(),this.learningRate=t,this.rho=e,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=T.registeredVariables[n],i=!1;this.accumulatedGrads[o]==null&&(this.accumulatedGrads[o]={originalName:`${n}/accum_grad`,variable:B(()=>vt(s).variable(i))}),this.accumulatedUpdates[o]==null&&(this.accumulatedUpdates[o]={originalName:`${n}/accum_var`,variable:B(()=>vt(s).variable(i))});let a=Array.isArray(t)?t[o].tensor:t[n];if(a==null)return;let u=this.accumulatedGrads[o].variable,l=this.accumulatedUpdates[o].variable;B(()=>{let c=K($(u,this.rho),$(Wt(a),1-this.rho)),p=$(ut(ge(K(l,this.epsilon)),ge(K(u,this.epsilon))),a),m=K($(l,this.rho),$(Wt(p),1-this.rho));u.assign(c),l.assign(m);let f=K($(p,-this.learningRate),s);s.assign(f)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Tt(this.accumulatedGrads.map(t=>t.variable)),Tt(this.accumulatedUpdates.map(t=>t.variable)))}async getWeights(){let t=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=t.length/2,n=!1;this.accumulatedGrads=t.slice(0,e).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.accumulatedUpdates=t.slice(e,e*2).map(o=>({originalName:o.name,variable:o.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(t,e){return new t(e.learningRate,e.rho,e.epsilon)}};var Sc=class extends jr{static get className(){return\"Adagrad\"}constructor(t,e=.1){super(),this.learningRate=t,this.initialAccumulatorValue=e,this.accumulatedGrads=[]}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=T.registeredVariables[n];this.accumulatedGrads[o]==null&&(this.accumulatedGrads[o]={originalName:`${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=K(a,Wt(i));a.assign(u);let l=K($(ut(i,ge(K(u,T.backend.epsilon()))),-this.learningRate),s);s.assign(l)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Tt(this.accumulatedGrads.map(t=>t.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=!1;this.accumulatedGrads=t.map(n=>({originalName:n.name,variable:n.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(t,e){return new t(e.learningRate,e.initialAccumulatorValue)}};var Nc=class extends jr{static get className(){return\"Adam\"}constructor(t,e,n,o=null){super(),this.learningRate=t,this.beta1=e,this.beta2=n,this.epsilon=o,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],B(()=>{this.accBeta1=pt(e).variable(),this.accBeta2=pt(n).variable()}),o==null&&(this.epsilon=T.backend.epsilon())}applyGradients(t){let e=Array.isArray(t)?t.map(n=>n.name):Object.keys(t);B(()=>{let n=at(1,this.accBeta1),o=at(1,this.accBeta2);e.forEach((s,i)=>{let a=T.registeredVariables[s],u=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${s}/m`,variable:B(()=>vt(a).variable(u))}),this.accumulatedSecondMoment[i]==null&&(this.accumulatedSecondMoment[i]={originalName:`${s}/v`,variable:B(()=>vt(a).variable(u))});let l=Array.isArray(t)?t[i].tensor:t[s];if(l==null)return;let c=this.accumulatedFirstMoment[i].variable,p=this.accumulatedSecondMoment[i].variable,m=K($(c,this.beta1),$(l,1-this.beta1)),f=K($(p,this.beta2),$(Wt(l),1-this.beta2)),d=ut(m,n),h=ut(f,o);c.assign(m),p.assign(f);let g=K($(ut(d,K(ge(h),this.epsilon)),-this.learningRate),a);a.assign(g)}),this.accBeta1.assign($(this.accBeta1,this.beta1)),this.accBeta2.assign($(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Tt(this.accumulatedFirstMoment.map(t=>t.variable)),this.accumulatedSecondMoment!=null&&Tt(this.accumulatedSecondMoment.map(t=>t.variable))}async getWeights(){let t=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t),B(()=>{this.accBeta1.assign(qr(this.beta1,this.iterations_+1)),this.accBeta2.assign(qr(this.beta2,this.iterations_+1))});let e=t.length/2,n=!1;this.accumulatedFirstMoment=t.slice(0,e).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.accumulatedSecondMoment=t.slice(e,e*2).map(o=>({originalName:o.name,variable:o.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon)}};var kc=class extends jr{static get className(){return\"Adamax\"}constructor(t,e,n,o=null,s=0){super(),this.learningRate=t,this.beta1=e,this.beta2=n,this.epsilon=o,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],B(()=>{this.iteration=pt(0).variable(),this.accBeta1=pt(e).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=ut(-this.learningRate,K($(this.iteration,this.decay),1));e.forEach((s,i)=>{let a=T.registeredVariables[s],u=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${s}/m`,variable:vt(a).variable(u)}),this.accumulatedWeightedInfNorm[i]==null&&(this.accumulatedWeightedInfNorm[i]={originalName:`${s}/v`,variable:vt(a).variable(u)});let l=Array.isArray(t)?t[i].tensor:t[s];if(l==null)return;let c=this.accumulatedFirstMoment[i].variable,p=this.accumulatedWeightedInfNorm[i].variable,m=K($(c,this.beta1),$(l,1-this.beta1)),f=$(p,this.beta2),d=_e(l),h=kn(f,d);c.assign(m),p.assign(h);let g=K($(ut(o,n),ut(m,K(h,this.epsilon))),a);a.assign(g)}),this.iteration.assign(K(this.iteration,1)),this.accBeta1.assign($(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Tt(this.accumulatedFirstMoment.map(t=>t.variable)),this.accumulatedWeightedInfNorm!=null&&Tt(this.accumulatedWeightedInfNorm.map(t=>t.variable))}async getWeights(){throw new 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 jr{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=K($(this.c,s),i);i.assign(a)})}),this.incrementIterations()}setLearningRate(t){this.learningRate=t,this.c!=null&&this.c.dispose(),this.c=De(pt(-t))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(t){if(t=await this.extractIterations(t),t.length!==0)throw new Error(\"SGD 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this.saveIterations()].concat(this.accumulations.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=!1;this.accumulations=t.map(n=>({originalName:n.name,variable:n.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(t,e){return new t(e.learningRate,e.momentum,e.useNesterov)}};var _c=class extends jr{static get className(){return\"RMSProp\"}constructor(t,e=.9,n=0,o=null,s=!1){if(super(),this.learningRate=t,this.decay=e,this.momentum=n,this.epsilon=o,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,o==null&&(this.epsilon=T.backend.epsilon()),t==null)throw new Error(\"learningRate for RMSPropOptimizer must be defined.\")}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=T.registeredVariables[n],i=!1;this.accumulatedMeanSquares[o]==null&&(this.accumulatedMeanSquares[o]={originalName:`${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=K($(u,this.decay),$(Wt(a),1-this.decay));if(this.centered){let p=this.accumulatedMeanGrads[o].variable,m=K($(p,this.decay),$(a,1-this.decay)),f=ut($(a,this.learningRate),ge(at(c,K(Wt(m),this.epsilon)))),d=K($(l,this.momentum),f);u.assign(c),p.assign(m),l.assign(d);let h=at(s,d);s.assign(h)}else{let p=K($(u,this.decay),$(Wt(a),1-this.decay)),m=K($(l,this.momentum),ut($(a,this.learningRate),ge(K(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 mY=[vc,Sc,Nc,kc,Tc,_c,Il];function S2(){for(let r of mY)bN(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 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. Received: ${r.length} elements.`);for(let n=0;n(Me(t),t===\"channelsFirst\"?Vt(r,[0,2,3,1]):r))}function ek(r,t){return B(()=>(Me(t),t===\"channelsFirst\"?Vt(r,[0,2,3,4,1]):r))}function wJ(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 ${e.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 XR(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=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 ${this.padding}`)}build(t){if(t=Gt(t),t.length!==4)throw new z(\"Input should have rank 4; Received input shape: \"+JSON.stringify(t));let e=this.dataFormat===\"channelsFirst\"?1:t.length-1;if(t[e]==null)throw new z(\"The channel dimension of the inputs should be defined. 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Found `None`.\");let n=t[e],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight(\"kernel\",o,\"float32\",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],\"float32\",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ce({ndim:5,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{let n=St(t);if(n.shape.length!==5)throw new z(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${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 ${t.length}.`);let o=t[1],s=t[2];t=t[0],0wr(t),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0wr(o),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,a=this.recurrentDropoutMask,u,l,c,p;0{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};df.className=\"LSTM\";Q.registerClass(df);var jc=class extends Tl{constructor(t){super(t),this.cells=t.cells}get stateSize(){let t=[];for(let e of this.cells.slice().reverse())Array.isArray(e.stateSize)?t.push(...e.stateSize):t.push(e.stateSize);return t}call(t,e){return B(()=>{t=t;let n=t.slice(1),o=[];for(let a of this.cells.slice().reverse())Array.isArray(a.stateSize)?o.push(n.splice(0,a.stateSize.length)):o.push(n.splice(0,1));o.reverse();let s=[],i;for(let a=0;a{fi(`RNNCell_${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)}). 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. 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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 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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 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: ${e}. 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length-${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 '${r}'`),n.arrayBuffer().then(o=>{WebAssembly.instantiate(o,t).then(s=>{e(s.instance,s.module)})})}),{})}function eq(r,t,e){if(OC!=null)return OC;let n=\"tfjs-backend-wasm.wasm\";return r&&t?n=\"tfjs-backend-wasm-threaded-simd.wasm\":r&&(n=\"tfjs-backend-wasm-simd.wasm\"),yg!=null&&yg[n]!=null?yg[n]:e+n}async function nq(){let[r,t]=await Promise.all([L().getAsync(\"WASM_HAS_SIMD_SUPPORT\"),L().getAsync(\"WASM_HAS_MULTITHREAD_SUPPORT\")]);return new Promise((e,n)=>{let o={};o.locateFile=(a,u)=>{if(a.endsWith(\".worker.js\")){let l=rq.wasmWorkerContents.replace(/\\n/g,\"\\\\n\"),c=new Blob([l],{type:\"application/javascript\"});return URL.createObjectURL(c)}return a.endsWith(\".wasm\")?eq(r,t,xg!=null?xg:u):u+a},u_&&(o.instantiateWasm=Lmt(eq(r,t,xg!=null?xg:\"\")));let s=!1;o.onAbort=()=>{if(s||bg)return;bg=!0,n({message:\"Make sure the server can serve the `.wasm` file relative to the bundled js file. 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Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}u_=t}var oq=-1,i_=-1;function Wmt(r){oq=r}function Umt(){if(i_===-1)throw new Error(\"WASM backend not initialized.\");return i_}var Hmt=\"4.22.0\";var qmt=2;Xp(\"wasm\",async()=>{let{wasm:r}=await nq();return new wg(r)},qmt);var sq=\"4.22.0\",Kmt=\"4.22.0\",jmt=\"4.22.0\",Xmt=\"4.22.0\",Ymt=\"4.22.0\",Zmt={tfjs:sq,\"tfjs-core\":sq,\"tfjs-converter\":Kmt,\"tfjs-backend-cpu\":jmt,\"tfjs-backend-webgl\":Xmt,\"tfjs-backend-wasm\":Ymt};export{Ai as Abs,Go as Acos,Wo as Acosh,vc as AdadeltaOptimizer,Sc as AdagradOptimizer,Nc as AdamOptimizer,kc as AdamaxOptimizer,no as Add,Uo as AddN,Ea as All,Aa as Any,Di as ArgMax,$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 // @ts-ignore async-vs-sync mismatch\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\": \"1.7.15\",\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 \"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\": \"^7.49.2\",\n \"@tensorflow/tfjs\": \"^4.22.0\",\n \"@tensorflow/tfjs-backend-cpu\": \"^4.22.0\",\n \"@tensorflow/tfjs-backend-wasm\": \"^4.22.0\",\n \"@tensorflow/tfjs-backend-webgl\": \"^4.22.0\",\n \"@tensorflow/tfjs-backend-webgpu\": \"4.22.0\",\n \"@tensorflow/tfjs-converter\": \"^4.22.0\",\n \"@tensorflow/tfjs-core\": \"^4.22.0\",\n \"@tensorflow/tfjs-data\": \"^4.22.0\",\n \"@tensorflow/tfjs-layers\": \"^4.22.0\",\n \"@tensorflow/tfjs-node\": \"^4.22.0\",\n \"@tensorflow/tfjs-node-gpu\": \"^4.22.0\",\n \"@types/node\": \"^22.13.1\",\n \"@types/offscreencanvas\": \"^2019.7.3\",\n \"@typescript-eslint/eslint-plugin\": \"^8.5.0\",\n \"@typescript-eslint/parser\": \"^8.5.0\",\n \"@vladmandic/build\": \"^0.10.2\",\n \"@vladmandic/pilogger\": \"^0.5.1\",\n \"ajv\": \"^8.17.1\",\n \"esbuild\": \"^0.24.2\",\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\": \"^2.8.1\",\n \"typedoc\": \"^0.27.6\",\n \"typescript\": \"5.7.3\"\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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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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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 TD(e){let t=[],{extractMobilenetV1Params:n,extractPredictionLayerParams:a}=wge(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 ${r}`);let s={mobilenetv1:n(),prediction_layer:a(),output_layer:{extra_dim:r}};return _n(e,t),{params:s,paramMappings:t}}function Oa(e,t,n){return O(()=>{let a=$t(e,t.filters,n,"same");return a=X(a,t.batch_norm_offset),an(a,0,6)})}var kge=.0010000000474974513;function Ige(e,t,n){return O(()=>{let a=As(e,t.filters,n,"same");return a=_s(a,t.batch_norm_mean,t.batch_norm_variance,t.batch_norm_offset,t.batch_norm_scale,kge),an(a,0,6)})}function Sge(e){return[2,4,6,12].some(t=>t===e)?[2,2]:[1,1]}function CD(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=Sge(o);a=Ige(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 Nge(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 ED(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=Nge(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 Tge(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 Cge(e,t){let{sizes:n,centers:a}=Tge(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 _D(e,t,n){return O(()=>{let a=e.shape[0],r=Cge(W(On(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 AD(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 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 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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}); 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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 95f1c39..411f317 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: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(n=>fn(n,{width:e,height:r}));if(qt(o)){let n=o.detection.forSize(e,r),s=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return Pe(Vt(o,n),s)}return pt(o)?Vt(o,o.detection.forSize(e,r)):o instanceof H||o instanceof M?o.forSize(e,r):o}var 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${JSON.stringify(o.meanRgb)}`)}var Q=v(x());var K=v(x());function Ee(o){return K.tidy(()=>{let t=K.mul(o,K.scalar(.10000000149011612));return K.add(K.relu(K.sub(o,t)),t)})}function vt(o,t){return Q.tidy(()=>{let e=Q.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=Q.conv2d(e,t.conv.filters,[1,1],"valid"),e=Q.sub(e,t.bn.sub),e=Q.mul(e,t.bn.truediv),e=Q.add(e,t.conv.bias),Ee(e)})}var Lt=v(x());function yt(o,t){return Lt.tidy(()=>{let e=Lt.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=Lt.separableConv2d(e,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),e=Lt.add(e,t.bias),Ee(e)})}var bo=v(x());function fa(o,t){let e=he(o,t);function r(a,i){let c=bo.tensor1d(o(a)),m=bo.tensor1d(o(a));return t.push({paramPath:`${i}/sub`},{paramPath:`${i}/truediv`}),{sub:c,truediv:m}}function n(a,i,c){let m=e(a,i,3,`${c}/conv`),p=r(i,`${c}/bn`);return{conv:m,bn:p}}let s=be(o,t);return{extractConvParams:e,extractConvWithBatchNormParams:n,extractSeparableConvParams:s}}function 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 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/package.json b/package.json index 08e2d61..2c81b55 100644 --- a/package.json +++ b/package.json @@ -24,7 +24,6 @@ "start": "node --no-warnings demo/node.js", "build": "node build.js", "dev": "build --profile development", - "typings": "build --profile typings", "lint": "eslint src/ demo/", "test": "node --trace-warnings test/test-node.js", "scan": "npx auditjs@latest ossi --dev --quiet" diff --git a/typedoc/classes/AgeGenderNet.html b/typedoc/classes/AgeGenderNet.html index 99b98b4..b34ac12 100644 --- a/typedoc/classes/AgeGenderNet.html +++ b/typedoc/classes/AgeGenderNet.html @@ -1,4 +1,4 @@ -AgeGenderNet | @vladmandic/face-api - v1.7.14

Class AgeGenderNet

Hierarchy (View Summary)

Constructors

constructor +AgeGenderNet | @vladmandic/face-api - v1.7.15

Class AgeGenderNet

Hierarchy (View Summary)

Constructors

constructor

Properties

_name

Accessors

faceFeatureExtractor isLoaded @@ -24,4 +24,4 @@ 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

  • load(weightsOrUrl: undefined | string | Float32Array): Promise<void>

  • Parameters

    • weightsOrUrl: undefined | string | Float32Array

    Returns Promise<void>

loadClassifierParams

loadFromDisk

loadFromUri

loadFromWeightMap

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_name

_name: any

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faceFeatureExtractor

isLoaded

paramMappings

  • get paramMappings(): ParamMapping[]

  • Returns ParamMapping[]

params

Methods

dispose

extractClassifierParams

extractWeights

forward

forwardInput

freeze

getFrozenParams

getParamFromPath

getParamList

getTrainableParams

load

  • load(weightsOrUrl: undefined | string | Float32Array): Promise<void>

  • Parameters

    • weightsOrUrl: undefined | string | Float32Array

    Returns Promise<void>

loadClassifierParams

loadFromDisk

loadFromUri

loadFromWeightMap

predictAgeAndGender

reassignParamFromPath

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serializeParams

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diff --git a/typedoc/classes/BoundingBox.html b/typedoc/classes/BoundingBox.html index 7354c5d..109c9e0 100644 --- a/typedoc/classes/BoundingBox.html +++ b/typedoc/classes/BoundingBox.html @@ -1,4 +1,4 @@ -BoundingBox | @vladmandic/face-api - v1.7.14

Class BoundingBox

Hierarchy (View Summary)

Implements

Constructors

constructor +BoundingBox | @vladmandic/face-api - v1.7.15

Class BoundingBox

Hierarchy (View Summary)

Implements

Constructors

constructor

Accessors

area bottom bottomLeft @@ -23,4 +23,4 @@ toSquare assertIsValidBox isRect -

Constructors

constructor

  • new BoundingBox(
        left: number,
        top: number,
        right: number,
        bottom: number,
        allowNegativeDimensions?: boolean,
    ): BoundingBox

  • Parameters

    • left: number
    • top: number
    • right: number
    • bottom: number
    • allowNegativeDimensions: boolean = false

    Returns BoundingBox

Accessors

area

bottom

bottomLeft

bottomRight

height

left

right

top

topLeft

topRight

width

x

y

Methods

calibrate

clipAtImageBorders

floor

pad

padAtBorders

  • padAtBorders(
        imageHeight: number,
        imageWidth: number,
    ): {
        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;
    }

rescale

round

shift

toSquare

StaticassertIsValidBox

  • assertIsValidBox(
        box: any,
        callee: string,
        allowNegativeDimensions?: boolean,
    ): void

  • Parameters

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

    Returns void

StaticisRect

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constructor

  • new BoundingBox(
        left: number,
        top: number,
        right: number,
        bottom: number,
        allowNegativeDimensions?: boolean,
    ): BoundingBox

  • Parameters

    • left: number
    • top: number
    • right: number
    • bottom: number
    • allowNegativeDimensions: boolean = false

    Returns BoundingBox

Accessors

area

bottom

bottomLeft

bottomRight

height

left

right

top

topLeft

topRight

width

x

y

Methods

calibrate

clipAtImageBorders

floor

pad

padAtBorders

  • padAtBorders(
        imageHeight: number,
        imageWidth: number,
    ): {
        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;
    }

rescale

round

shift

toSquare

StaticassertIsValidBox

  • assertIsValidBox(
        box: any,
        callee: string,
        allowNegativeDimensions?: boolean,
    ): 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 c8e0eb3..7416d70 100644 --- a/typedoc/classes/Box.html +++ b/typedoc/classes/Box.html @@ -1,4 +1,4 @@ -Box | @vladmandic/face-api - v1.7.14

Class Box<BoxType>

Type Parameters

  • BoxType = any

Hierarchy (View Summary)

Implements

Constructors

constructor +Box | @vladmandic/face-api - v1.7.15

Class Box<BoxType>

Type Parameters

  • BoxType = any

Hierarchy (View Summary)

Implements

Constructors

constructor

Accessors

area bottom bottomLeft @@ -23,4 +23,4 @@ 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: number,
        imageWidth: number,
    ): {
        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;
    }

rescale

round

shift

toSquare

StaticassertIsValidBox

  • assertIsValidBox(
        box: any,
        callee: string,
        allowNegativeDimensions?: boolean,
    ): void

  • Parameters

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

    Returns void

StaticisRect

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constructor

Accessors

area

bottom

bottomLeft

bottomRight

height

left

right

top

topLeft

topRight

width

x

y

Methods

calibrate

clipAtImageBorders

floor

pad

padAtBorders

  • padAtBorders(
        imageHeight: number,
        imageWidth: number,
    ): {
        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;
    }

rescale

round

shift

toSquare

StaticassertIsValidBox

  • assertIsValidBox(
        box: any,
        callee: string,
        allowNegativeDimensions?: boolean,
    ): 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 829e1bc..99d373a 100644 --- a/typedoc/classes/ComposableTask.html +++ b/typedoc/classes/ComposableTask.html @@ -1,4 +1,4 @@ -ComposableTask | @vladmandic/face-api - v1.7.14

Class ComposableTask<T>

Type Parameters

  • T

Hierarchy (View Summary)

Constructors

constructor +ComposableTask | @vladmandic/face-api - v1.7.15

Class ComposableTask<T>

Type Parameters

  • T

Hierarchy (View Summary)

Constructors

constructor

Methods

run then -

Constructors

constructor

Methods

run

then

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diff --git a/typedoc/classes/ComputeAllFaceDescriptorsTask.html b/typedoc/classes/ComputeAllFaceDescriptorsTask.html index a17ab82..7d61ee8 100644 --- a/typedoc/classes/ComputeAllFaceDescriptorsTask.html +++ b/typedoc/classes/ComputeAllFaceDescriptorsTask.html @@ -1,6 +1,6 @@ -ComputeAllFaceDescriptorsTask | @vladmandic/face-api - v1.7.14

Class ComputeAllFaceDescriptorsTask<TSource>

Type Parameters

Hierarchy (View Summary)

Constructors

constructor +ComputeAllFaceDescriptorsTask | @vladmandic/face-api - v1.7.15

Class ComputeAllFaceDescriptorsTask<TSource>

Type Parameters

Hierarchy (View Summary)

Constructors

constructor

Methods

run then withAgeAndGender withFaceExpressions -

Constructors

constructor

Methods

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then

withAgeAndGender

withFaceExpressions

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diff --git a/typedoc/classes/ComputeFaceDescriptorsTaskBase.html b/typedoc/classes/ComputeFaceDescriptorsTaskBase.html index ffc9fcc..2fff17c 100644 --- a/typedoc/classes/ComputeFaceDescriptorsTaskBase.html +++ b/typedoc/classes/ComputeFaceDescriptorsTaskBase.html @@ -1,4 +1,4 @@ -ComputeFaceDescriptorsTaskBase | @vladmandic/face-api - v1.7.14

Class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn>

Type Parameters

  • TReturn
  • TParentReturn

Hierarchy (View Summary)

Constructors

constructor +ComputeFaceDescriptorsTaskBase | @vladmandic/face-api - v1.7.15

Class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn>

Type Parameters

  • TReturn
  • TParentReturn

Hierarchy (View Summary)

Constructors

constructor

Methods

run then -

Constructors

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run

then

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diff --git a/typedoc/classes/ComputeSingleFaceDescriptorTask.html b/typedoc/classes/ComputeSingleFaceDescriptorTask.html index 9feaf37..3bc841e 100644 --- a/typedoc/classes/ComputeSingleFaceDescriptorTask.html +++ b/typedoc/classes/ComputeSingleFaceDescriptorTask.html @@ -1,6 +1,6 @@ -ComputeSingleFaceDescriptorTask | @vladmandic/face-api - v1.7.14

Class ComputeSingleFaceDescriptorTask<TSource>

Type Parameters

Hierarchy (View Summary)

Constructors

constructor +ComputeSingleFaceDescriptorTask | @vladmandic/face-api - v1.7.15

Class ComputeSingleFaceDescriptorTask<TSource>

Type Parameters

Hierarchy (View Summary)

Constructors

constructor

Methods

run then withAgeAndGender withFaceExpressions -

Constructors

constructor

Methods

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then

withAgeAndGender

withFaceExpressions

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diff --git a/typedoc/classes/DetectAllFaceLandmarksTask.html b/typedoc/classes/DetectAllFaceLandmarksTask.html index d3f704d..97bef83 100644 --- a/typedoc/classes/DetectAllFaceLandmarksTask.html +++ b/typedoc/classes/DetectAllFaceLandmarksTask.html @@ -1,7 +1,7 @@ -DetectAllFaceLandmarksTask | @vladmandic/face-api - v1.7.14

Class DetectAllFaceLandmarksTask<TSource>

Type Parameters

Hierarchy (View Summary)

Constructors

constructor +DetectAllFaceLandmarksTask | @vladmandic/face-api - v1.7.15

Class DetectAllFaceLandmarksTask<TSource>

Type Parameters

Hierarchy (View Summary)

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 4c12daf..1640b1a 100644 --- a/typedoc/classes/DetectAllFacesTask.html +++ b/typedoc/classes/DetectAllFacesTask.html @@ -1,7 +1,7 @@ -DetectAllFacesTask | @vladmandic/face-api - v1.7.14

Class DetectAllFacesTask

Hierarchy (View Summary)

Constructors

constructor +DetectAllFacesTask | @vladmandic/face-api - v1.7.15

Class DetectAllFacesTask

Hierarchy (View Summary)

Constructors

constructor

Methods

run then withAgeAndGender withFaceExpressions withFaceLandmarks -

Constructors

constructor

Methods

run

then

withAgeAndGender

withFaceExpressions

withFaceLandmarks

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withFaceLandmarks

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diff --git a/typedoc/classes/DetectFaceLandmarksTaskBase.html b/typedoc/classes/DetectFaceLandmarksTaskBase.html index 21056f1..577676b 100644 --- a/typedoc/classes/DetectFaceLandmarksTaskBase.html +++ b/typedoc/classes/DetectFaceLandmarksTaskBase.html @@ -1,4 +1,4 @@ -DetectFaceLandmarksTaskBase | @vladmandic/face-api - v1.7.14

Class DetectFaceLandmarksTaskBase<TReturn, TParentReturn>

Type Parameters

  • TReturn
  • TParentReturn

Hierarchy (View Summary)

Constructors

constructor +DetectFaceLandmarksTaskBase | @vladmandic/face-api - v1.7.15

Class DetectFaceLandmarksTaskBase<TReturn, TParentReturn>

Type Parameters

  • TReturn
  • TParentReturn

Hierarchy (View Summary)

Constructors

constructor

Methods

run then -

Constructors

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run

then

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diff --git a/typedoc/classes/DetectFacesTaskBase.html b/typedoc/classes/DetectFacesTaskBase.html index 1d8a404..3c45da4 100644 --- a/typedoc/classes/DetectFacesTaskBase.html +++ b/typedoc/classes/DetectFacesTaskBase.html @@ -1,4 +1,4 @@ -DetectFacesTaskBase | @vladmandic/face-api - v1.7.14

Class DetectFacesTaskBase<TReturn>

Type Parameters

  • TReturn

Hierarchy (View Summary)

Constructors

constructor +DetectFacesTaskBase | @vladmandic/face-api - v1.7.15

Class DetectFacesTaskBase<TReturn>

Type Parameters

  • TReturn

Hierarchy (View Summary)

Constructors

constructor

Methods

run then -

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then

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diff --git a/typedoc/classes/DetectSingleFaceLandmarksTask.html b/typedoc/classes/DetectSingleFaceLandmarksTask.html index a7b0ed4..5bd20d0 100644 --- a/typedoc/classes/DetectSingleFaceLandmarksTask.html +++ b/typedoc/classes/DetectSingleFaceLandmarksTask.html @@ -1,7 +1,7 @@ -DetectSingleFaceLandmarksTask | @vladmandic/face-api - v1.7.14

Class DetectSingleFaceLandmarksTask<TSource>

Type Parameters

Hierarchy (View Summary)

Constructors

constructor +DetectSingleFaceLandmarksTask | @vladmandic/face-api - v1.7.15

Class DetectSingleFaceLandmarksTask<TSource>

Type Parameters

Hierarchy (View Summary)

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 c32c67a..de871ea 100644 --- a/typedoc/classes/DetectSingleFaceTask.html +++ b/typedoc/classes/DetectSingleFaceTask.html @@ -1,7 +1,7 @@ -DetectSingleFaceTask | @vladmandic/face-api - v1.7.14

Class DetectSingleFaceTask

Hierarchy (View Summary)

Constructors

constructor +DetectSingleFaceTask | @vladmandic/face-api - v1.7.15

Class DetectSingleFaceTask

Hierarchy (View Summary)

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/Dimensions.html b/typedoc/classes/Dimensions.html index d32e411..4944137 100644 --- a/typedoc/classes/Dimensions.html +++ b/typedoc/classes/Dimensions.html @@ -1,5 +1,5 @@ -Dimensions | @vladmandic/face-api - v1.7.14

Class Dimensions

Implements

Constructors

constructor +Dimensions | @vladmandic/face-api - v1.7.15

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 8db6a8a..2714c66 100644 --- a/typedoc/classes/FaceDetection.html +++ b/typedoc/classes/FaceDetection.html @@ -1,4 +1,4 @@ -FaceDetection | @vladmandic/face-api - v1.7.14

Class FaceDetection

Hierarchy (View Summary)

Implements

Constructors

constructor +FaceDetection | @vladmandic/face-api - v1.7.15

Class FaceDetection

Hierarchy (View Summary)

Implements

Constructors

constructor

Accessors

box className classScore @@ -8,4 +8,4 @@ relativeBox score

Methods

forSize -

Constructors

constructor

Accessors

box

className

classScore

imageDims

imageHeight

imageWidth

relativeBox

score

Methods

forSize

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+

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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 183bfb7..fe50f34 100644 --- a/typedoc/classes/FaceDetectionNet.html +++ b/typedoc/classes/FaceDetectionNet.html @@ -1,4 +1,4 @@ -FaceDetectionNet | @vladmandic/face-api - v1.7.14

Class FaceDetectionNet

Hierarchy (View Summary)

Constructors

constructor +FaceDetectionNet | @vladmandic/face-api - v1.7.15

Class FaceDetectionNet

Hierarchy (View Summary)

Constructors

constructor

Properties

_name

Accessors

isLoaded paramMappings @@ -20,4 +20,4 @@ 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

  • load(weightsOrUrl: undefined | string | Float32Array): Promise<void>

  • Parameters

    • weightsOrUrl: undefined | string | Float32Array

    Returns Promise<void>

loadFromDisk

loadFromUri

loadFromWeightMap

locateFaces

reassignParamFromPath

serializeParams

variable

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Methods
+

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

  • load(weightsOrUrl: undefined | string | Float32Array): Promise<void>

  • Parameters

    • weightsOrUrl: undefined | string | Float32Array

    Returns Promise<void>

loadFromDisk

loadFromUri

loadFromWeightMap

locateFaces

reassignParamFromPath

serializeParams

variable

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diff --git a/typedoc/classes/FaceExpressionNet.html b/typedoc/classes/FaceExpressionNet.html index 0ec140c..058f42b 100644 --- a/typedoc/classes/FaceExpressionNet.html +++ b/typedoc/classes/FaceExpressionNet.html @@ -1,4 +1,4 @@ -FaceExpressionNet | @vladmandic/face-api - v1.7.14

Class FaceExpressionNet

Hierarchy

  • FaceProcessor<FaceFeatureExtractorParams>
    • FaceExpressionNet

Constructors

constructor +FaceExpressionNet | @vladmandic/face-api - v1.7.15

Class FaceExpressionNet

Hierarchy

  • FaceProcessor<FaceFeatureExtractorParams>
    • FaceExpressionNet

Constructors

constructor

Properties

_name

Accessors

faceFeatureExtractor isLoaded @@ -24,4 +24,4 @@ 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: Float32Array,
    ): { paramMappings: ParamMapping[]; params: NetParams }

  • Parameters

    • weights: Float32Array

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

extractWeights

  • extractWeights(weights: Float32Array): void

  • Parameters

    • weights: Float32Array

    Returns void

forward

forwardInput

freeze

getFrozenParams

getParamFromPath

getParamList

getTrainableParams

load

  • load(weightsOrUrl: undefined | string | Float32Array): Promise<void>

  • Parameters

    • weightsOrUrl: undefined | string | Float32Array

    Returns Promise<void>

loadClassifierParams

  • loadClassifierParams(weights: Float32Array): void

  • Parameters

    • weights: Float32Array

    Returns void

loadFromDisk

  • loadFromDisk(filePath: undefined | string): Promise<void>

  • Parameters

    • filePath: undefined | string

    Returns Promise<void>

loadFromUri

  • loadFromUri(uri: undefined | string): Promise<void>

  • Parameters

    • uri: undefined | string

    Returns Promise<void>

loadFromWeightMap

predictExpressions

reassignParamFromPath

  • reassignParamFromPath(paramPath: string, tensor: tf.Tensor): void

  • Parameters

    Returns void

runNet

serializeParams

  • serializeParams(): Float32Array

  • Returns Float32Array

variable

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Properties

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faceFeatureExtractor

  • get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams>

  • Returns IFaceFeatureExtractor<TExtractorParams>

isLoaded

paramMappings

  • get paramMappings(): ParamMapping[]

  • Returns ParamMapping[]

params

Methods

dispose

extractClassifierParams

  • extractClassifierParams(
        weights: Float32Array,
    ): { paramMappings: ParamMapping[]; params: NetParams }

  • Parameters

    • weights: Float32Array

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

extractWeights

  • extractWeights(weights: Float32Array): void

  • Parameters

    • weights: Float32Array

    Returns void

forward

forwardInput

freeze

getFrozenParams

getParamFromPath

getParamList

getTrainableParams

load

  • load(weightsOrUrl: undefined | string | Float32Array): Promise<void>

  • Parameters

    • weightsOrUrl: undefined | string | Float32Array

    Returns Promise<void>

loadClassifierParams

  • loadClassifierParams(weights: Float32Array): void

  • Parameters

    • weights: Float32Array

    Returns void

loadFromDisk

  • loadFromDisk(filePath: undefined | string): Promise<void>

  • Parameters

    • filePath: undefined | string

    Returns Promise<void>

loadFromUri

  • loadFromUri(uri: undefined | string): Promise<void>

  • Parameters

    • uri: undefined | string

    Returns Promise<void>

loadFromWeightMap

predictExpressions

reassignParamFromPath

  • reassignParamFromPath(paramPath: string, tensor: tf.Tensor): void

  • Parameters

    Returns void

runNet

serializeParams

  • serializeParams(): Float32Array

  • Returns Float32Array

variable

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diff --git a/typedoc/classes/FaceExpressions.html b/typedoc/classes/FaceExpressions.html index 7f730ea..23cab45 100644 --- a/typedoc/classes/FaceExpressions.html +++ b/typedoc/classes/FaceExpressions.html @@ -1,4 +1,4 @@ -FaceExpressions | @vladmandic/face-api - v1.7.14

Class FaceExpressions

Constructors

constructor +FaceExpressions | @vladmandic/face-api - v1.7.15

Class FaceExpressions

Constructors

constructor

Properties

angry disgusted fearful @@ -7,4 +7,4 @@ 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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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 1a5dddd..a32de90 100644 --- a/typedoc/classes/FaceLandmark68Net.html +++ b/typedoc/classes/FaceLandmark68Net.html @@ -1,4 +1,4 @@ -FaceLandmark68Net | @vladmandic/face-api - v1.7.14

Class FaceLandmark68Net

Hierarchy (View Summary)

  • FaceLandmark68NetBase<FaceFeatureExtractorParams>

Constructors

constructor +FaceLandmark68Net | @vladmandic/face-api - v1.7.15

Class FaceLandmark68Net

Hierarchy (View Summary)

  • FaceLandmark68NetBase<FaceFeatureExtractorParams>

Constructors

constructor

Properties

_name

Accessors

faceFeatureExtractor isLoaded @@ -25,4 +25,4 @@ 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

  • dispose(throwOnRedispose?: boolean): void

  • Parameters

    • throwOnRedispose: boolean = true

    Returns void

extractClassifierParams

  • extractClassifierParams(
        weights: Float32Array,
    ): { paramMappings: ParamMapping[]; params: NetParams }

  • Parameters

    • weights: Float32Array

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

extractWeights

  • extractWeights(weights: Float32Array): void

  • Parameters

    • weights: Float32Array

    Returns void

forward

forwardInput

freeze

getFrozenParams

getParamFromPath

getParamList

getTrainableParams

load

  • load(weightsOrUrl: undefined | string | Float32Array): Promise<void>

  • Parameters

    • weightsOrUrl: undefined | string | Float32Array

    Returns Promise<void>

loadClassifierParams

  • loadClassifierParams(weights: Float32Array): void

  • Parameters

    • weights: Float32Array

    Returns void

loadFromDisk

  • loadFromDisk(filePath: undefined | string): Promise<void>

  • Parameters

    • filePath: undefined | string

    Returns Promise<void>

loadFromUri

  • loadFromUri(uri: undefined | string): Promise<void>

  • Parameters

    • uri: undefined | string

    Returns Promise<void>

loadFromWeightMap

postProcess

reassignParamFromPath

  • reassignParamFromPath(paramPath: string, tensor: tf.Tensor): void

  • Parameters

    Returns void

runNet

serializeParams

  • serializeParams(): Float32Array

  • Returns Float32Array

variable

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constructor

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_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

  • dispose(throwOnRedispose?: boolean): void

  • Parameters

    • throwOnRedispose: boolean = true

    Returns void

extractClassifierParams

  • extractClassifierParams(
        weights: Float32Array,
    ): { paramMappings: ParamMapping[]; params: NetParams }

  • Parameters

    • weights: Float32Array

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

extractWeights

  • extractWeights(weights: Float32Array): void

  • Parameters

    • weights: Float32Array

    Returns void

forward

forwardInput

freeze

getFrozenParams

getParamFromPath

getParamList

getTrainableParams

load

  • load(weightsOrUrl: undefined | string | Float32Array): Promise<void>

  • Parameters

    • weightsOrUrl: undefined | string | Float32Array

    Returns Promise<void>

loadClassifierParams

  • loadClassifierParams(weights: Float32Array): void

  • Parameters

    • weights: Float32Array

    Returns void

loadFromDisk

  • loadFromDisk(filePath: undefined | string): Promise<void>

  • Parameters

    • filePath: undefined | string

    Returns Promise<void>

loadFromUri

  • loadFromUri(uri: undefined | string): Promise<void>

  • Parameters

    • uri: undefined | string

    Returns Promise<void>

loadFromWeightMap

postProcess

reassignParamFromPath

  • reassignParamFromPath(paramPath: string, tensor: tf.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 8ad8446..df89c1c 100644 --- a/typedoc/classes/FaceLandmark68TinyNet.html +++ b/typedoc/classes/FaceLandmark68TinyNet.html @@ -1,4 +1,4 @@ -FaceLandmark68TinyNet | @vladmandic/face-api - v1.7.14

Class FaceLandmark68TinyNet

Hierarchy

  • FaceLandmark68NetBase<TinyFaceFeatureExtractorParams>
    • FaceLandmark68TinyNet

Constructors

constructor +FaceLandmark68TinyNet | @vladmandic/face-api - v1.7.15

Class FaceLandmark68TinyNet

Hierarchy

  • FaceLandmark68NetBase<TinyFaceFeatureExtractorParams>
    • FaceLandmark68TinyNet

Constructors

constructor

Properties

_name

Accessors

faceFeatureExtractor isLoaded @@ -25,4 +25,4 @@ 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

  • dispose(throwOnRedispose?: boolean): void

  • Parameters

    • throwOnRedispose: boolean = true

    Returns void

extractClassifierParams

  • extractClassifierParams(
        weights: Float32Array,
    ): { paramMappings: ParamMapping[]; params: NetParams }

  • Parameters

    • weights: Float32Array

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

extractWeights

  • extractWeights(weights: Float32Array): void

  • Parameters

    • weights: Float32Array

    Returns void

forward

forwardInput

freeze

getFrozenParams

getParamFromPath

getParamList

getTrainableParams

load

  • load(weightsOrUrl: undefined | string | Float32Array): Promise<void>

  • Parameters

    • weightsOrUrl: undefined | string | Float32Array

    Returns Promise<void>

loadClassifierParams

  • loadClassifierParams(weights: Float32Array): void

  • Parameters

    • weights: Float32Array

    Returns void

loadFromDisk

  • loadFromDisk(filePath: undefined | string): Promise<void>

  • Parameters

    • filePath: undefined | string

    Returns Promise<void>

loadFromUri

  • loadFromUri(uri: undefined | string): Promise<void>

  • Parameters

    • uri: undefined | string

    Returns Promise<void>

loadFromWeightMap

postProcess

reassignParamFromPath

  • reassignParamFromPath(paramPath: string, tensor: tf.Tensor): void

  • Parameters

    Returns void

runNet

serializeParams

  • serializeParams(): Float32Array

  • Returns Float32Array

variable

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+

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

  • dispose(throwOnRedispose?: boolean): void

  • Parameters

    • throwOnRedispose: boolean = true

    Returns void

extractClassifierParams

  • extractClassifierParams(
        weights: Float32Array,
    ): { paramMappings: ParamMapping[]; params: NetParams }

  • Parameters

    • weights: Float32Array

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

extractWeights

  • extractWeights(weights: Float32Array): void

  • Parameters

    • weights: Float32Array

    Returns void

forward

forwardInput

freeze

getFrozenParams

getParamFromPath

getParamList

getTrainableParams

load

  • load(weightsOrUrl: undefined | string | Float32Array): Promise<void>

  • Parameters

    • weightsOrUrl: undefined | string | Float32Array

    Returns Promise<void>

loadClassifierParams

  • loadClassifierParams(weights: Float32Array): void

  • Parameters

    • weights: Float32Array

    Returns void

loadFromDisk

  • loadFromDisk(filePath: undefined | string): Promise<void>

  • Parameters

    • filePath: undefined | string

    Returns Promise<void>

loadFromUri

  • loadFromUri(uri: undefined | string): Promise<void>

  • Parameters

    • uri: undefined | string

    Returns Promise<void>

loadFromWeightMap

postProcess

reassignParamFromPath

  • reassignParamFromPath(paramPath: string, tensor: tf.Tensor): void

  • Parameters

    Returns void

runNet

serializeParams

  • serializeParams(): Float32Array

  • Returns Float32Array

variable

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diff --git a/typedoc/classes/FaceLandmarkNet.html b/typedoc/classes/FaceLandmarkNet.html index 6ed132c..e6f21dd 100644 --- a/typedoc/classes/FaceLandmarkNet.html +++ b/typedoc/classes/FaceLandmarkNet.html @@ -1,4 +1,4 @@ -FaceLandmarkNet | @vladmandic/face-api - v1.7.14

Class FaceLandmarkNet

Hierarchy (View Summary)

Constructors

constructor +FaceLandmarkNet | @vladmandic/face-api - v1.7.15

Class FaceLandmarkNet

Hierarchy (View Summary)

Constructors

constructor

Properties

_name

Accessors

faceFeatureExtractor isLoaded @@ -25,4 +25,4 @@ 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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Properties
Accessors
Methods
+

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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diff --git a/typedoc/classes/FaceLandmarks.html b/typedoc/classes/FaceLandmarks.html index ac2f1e6..c27d13a 100644 --- a/typedoc/classes/FaceLandmarks.html +++ b/typedoc/classes/FaceLandmarks.html @@ -1,4 +1,4 @@ -FaceLandmarks | @vladmandic/face-api - v1.7.14

Class FaceLandmarks

Hierarchy (View Summary)

Implements

Constructors

constructor +FaceLandmarks | @vladmandic/face-api - v1.7.15

Class FaceLandmarks

Hierarchy (View Summary)

Implements

Constructors

constructor

Accessors

imageHeight imageWidth positions @@ -16,4 +16,4 @@ This will make the computed face descriptor more accurate.

no argument was passed the position of the face landmarks are assumed to be relative to it's current shift.

  • options: { minBoxPadding?: number; useDlibAlignment?: boolean } = {}

    • Returns Box

    The bounding box of the aligned face.

    -

    forSize

    shiftBy

    shiftByPoint

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    +

    forSize

    shiftBy

    shiftByPoint

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    diff --git a/typedoc/classes/FaceLandmarks5.html b/typedoc/classes/FaceLandmarks5.html index 6e5cd53..ab04b4f 100644 --- a/typedoc/classes/FaceLandmarks5.html +++ b/typedoc/classes/FaceLandmarks5.html @@ -1,4 +1,4 @@ -FaceLandmarks5 | @vladmandic/face-api - v1.7.14

    Class FaceLandmarks5

    Hierarchy (View Summary)

    Constructors

    constructor +FaceLandmarks5 | @vladmandic/face-api - v1.7.15

    Class FaceLandmarks5

    Hierarchy (View Summary)

    Constructors

    constructor

    Accessors

    imageHeight imageWidth positions @@ -16,4 +16,4 @@ This will make the computed face descriptor more accurate.

    no argument was passed the position of the face landmarks are assumed to be relative to it's current shift.

  • options: { minBoxPadding?: number; useDlibAlignment?: boolean } = {}

    • Returns Box

    The bounding box of the aligned face.

    -

    forSize

    shiftBy

    shiftByPoint

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    +

    forSize

    shiftBy

    shiftByPoint

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    diff --git a/typedoc/classes/FaceLandmarks68.html b/typedoc/classes/FaceLandmarks68.html index 95aa4e2..19e2412 100644 --- a/typedoc/classes/FaceLandmarks68.html +++ b/typedoc/classes/FaceLandmarks68.html @@ -1,4 +1,4 @@ -FaceLandmarks68 | @vladmandic/face-api - v1.7.14

    Class FaceLandmarks68

    Hierarchy (View Summary)

    Constructors

    constructor +FaceLandmarks68 | @vladmandic/face-api - v1.7.15

    Class FaceLandmarks68

    Hierarchy (View Summary)

    Constructors

    constructor

    Accessors

    imageHeight imageWidth positions @@ -23,4 +23,4 @@ This will make the computed face descriptor more accurate.

    no argument was passed the position of the face landmarks are assumed to be relative to it's current shift.

  • options: { minBoxPadding?: number; useDlibAlignment?: boolean } = {}

    • Returns Box

    The bounding box of the aligned face.

    -

    forSize

    getJawOutline

    getLeftEye

    getLeftEyeBrow

    getMouth

    getNose

    getRightEye

    getRightEyeBrow

    shiftBy

    shiftByPoint

    Settings

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    Methods
    +

    forSize

    getJawOutline

    getLeftEye

    getLeftEyeBrow

    getMouth

    getNose

    getRightEye

    getRightEyeBrow

    shiftBy

    shiftByPoint

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    diff --git a/typedoc/classes/FaceMatch.html b/typedoc/classes/FaceMatch.html index 2b9036e..c3ba308 100644 --- a/typedoc/classes/FaceMatch.html +++ b/typedoc/classes/FaceMatch.html @@ -1,6 +1,6 @@ -FaceMatch | @vladmandic/face-api - v1.7.14

    Class FaceMatch

    Implements

    Constructors

    constructor +FaceMatch | @vladmandic/face-api - v1.7.15

    Class FaceMatch

    Implements

    Constructors

    constructor

    Accessors

    distance label

    Methods

    toString

    Constructors

    constructor

    Accessors

    distance

    label

    Methods

    toString

    • toString(withDistance?: boolean): string

    • Returns a string representation of an object.

      -

      Parameters

      • withDistance: boolean = true

      Returns string

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    Parameters

    • withDistance: boolean = true

    Returns string

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    diff --git a/typedoc/classes/FaceMatcher.html b/typedoc/classes/FaceMatcher.html index 9aca31a..491da44 100644 --- a/typedoc/classes/FaceMatcher.html +++ b/typedoc/classes/FaceMatcher.html @@ -1,4 +1,4 @@ -FaceMatcher | @vladmandic/face-api - v1.7.14

    Class FaceMatcher

    Constructors

    constructor +FaceMatcher | @vladmandic/face-api - v1.7.15

    Class FaceMatcher

    Constructors

    constructor

    Accessors

    distanceThreshold labeledDescriptors

    Methods

    computeMeanDistance @@ -6,4 +6,4 @@ matchDescriptor toJSON fromJSON -

    Constructors

    constructor

    Accessors

    distanceThreshold

    labeledDescriptors

    Methods

    computeMeanDistance

    • computeMeanDistance(
          queryDescriptor: Float32Array,
          descriptors: Float32Array[],
      ): number

    • Parameters

      • queryDescriptor: Float32Array
      • descriptors: Float32Array[]

      Returns number

    findBestMatch

    matchDescriptor

    toJSON

    StaticfromJSON

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    +

    Constructors

    constructor

    Accessors

    distanceThreshold

    labeledDescriptors

    Methods

    computeMeanDistance

    • computeMeanDistance(
          queryDescriptor: Float32Array,
          descriptors: Float32Array[],
      ): number

    • Parameters

      • queryDescriptor: Float32Array
      • descriptors: Float32Array[]

      Returns number

    findBestMatch

    matchDescriptor

    toJSON

    StaticfromJSON

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    diff --git a/typedoc/classes/FaceRecognitionNet.html b/typedoc/classes/FaceRecognitionNet.html index c97b40d..2e7864b 100644 --- a/typedoc/classes/FaceRecognitionNet.html +++ b/typedoc/classes/FaceRecognitionNet.html @@ -1,4 +1,4 @@ -FaceRecognitionNet | @vladmandic/face-api - v1.7.14

    Class FaceRecognitionNet

    Hierarchy (View Summary)

    Constructors

    constructor +FaceRecognitionNet | @vladmandic/face-api - v1.7.15

    Class FaceRecognitionNet

    Hierarchy (View Summary)

    Constructors

    constructor

    Properties

    _name

    Accessors

    isLoaded paramMappings @@ -20,4 +20,4 @@ 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

    • load(weightsOrUrl: undefined | string | Float32Array): Promise<void>

    • Parameters

      • weightsOrUrl: undefined | string | Float32Array

      Returns Promise<void>

    loadFromDisk

    loadFromUri

    loadFromWeightMap

    reassignParamFromPath

    serializeParams

    variable

    Settings

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    Accessors
    Methods
    +

    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

    • load(weightsOrUrl: undefined | string | Float32Array): Promise<void>

    • Parameters

      • weightsOrUrl: undefined | string | Float32Array

      Returns Promise<void>

    loadFromDisk

    loadFromUri

    loadFromWeightMap

    reassignParamFromPath

    serializeParams

    variable

    Settings

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    diff --git a/typedoc/classes/LabeledBox.html b/typedoc/classes/LabeledBox.html index 51e0198..7eaa749 100644 --- a/typedoc/classes/LabeledBox.html +++ b/typedoc/classes/LabeledBox.html @@ -1,4 +1,4 @@ -LabeledBox | @vladmandic/face-api - v1.7.14

    Class LabeledBox

    Hierarchy (View Summary)

    Constructors

    constructor +LabeledBox | @vladmandic/face-api - v1.7.15

    Class LabeledBox

    Hierarchy (View Summary)

    Constructors

    constructor

    Accessors

    area bottom bottomLeft @@ -25,4 +25,4 @@ 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: number,
          imageWidth: number,
      ): {
          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;
      }

    rescale

    round

    shift

    toSquare

    StaticassertIsValidBox

    • assertIsValidBox(
          box: any,
          callee: string,
          allowNegativeDimensions?: boolean,
      ): void

    • Parameters

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

      Returns void

    StaticassertIsValidLabeledBox

    • assertIsValidLabeledBox(box: any, callee: string): void

    • Parameters

      • box: any
      • callee: string

      Returns void

    StaticisRect

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    top

    topLeft

    topRight

    width

    x

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    Methods

    calibrate

    clipAtImageBorders

    floor

    pad

    padAtBorders

    • padAtBorders(
          imageHeight: number,
          imageWidth: number,
      ): {
          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;
      }

    rescale

    round

    shift

    toSquare

    StaticassertIsValidBox

    • assertIsValidBox(
          box: any,
          callee: string,
          allowNegativeDimensions?: boolean,
      ): void

    • Parameters

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

      Returns void

    StaticassertIsValidLabeledBox

    • assertIsValidLabeledBox(box: any, callee: string): void

    • Parameters

      • box: any
      • callee: string

      Returns void

    StaticisRect

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    diff --git a/typedoc/classes/LabeledFaceDescriptors.html b/typedoc/classes/LabeledFaceDescriptors.html index 3cebdba..ea4792b 100644 --- a/typedoc/classes/LabeledFaceDescriptors.html +++ b/typedoc/classes/LabeledFaceDescriptors.html @@ -1,6 +1,6 @@ -LabeledFaceDescriptors | @vladmandic/face-api - v1.7.14

    Class LabeledFaceDescriptors

    Constructors

    constructor +LabeledFaceDescriptors | @vladmandic/face-api - v1.7.15

    Class LabeledFaceDescriptors

    Constructors

    constructor

    Accessors

    descriptors label

    Methods

    toJSON fromJSON -

    Constructors

    constructor

    Accessors

    descriptors

    label

    Methods

    toJSON

    StaticfromJSON

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    diff --git a/typedoc/classes/NetInput.html b/typedoc/classes/NetInput.html index 8748754..24a1651 100644 --- a/typedoc/classes/NetInput.html +++ b/typedoc/classes/NetInput.html @@ -1,4 +1,4 @@ -NetInput | @vladmandic/face-api - v1.7.14

    Class NetInput

    Constructors

    constructor +NetInput | @vladmandic/face-api - v1.7.15

    Class NetInput

    Constructors

    constructor

    Accessors

    batchSize canvases imageTensors @@ -16,4 +16,4 @@ with size [batchSize, inputSize, inputSize, 3].

    Parameters

    • inputSize: number

      Height and width of the tensor.

    • isCenterInputs: boolean = true

    Returns Tensor4D

    The batch tensor.

    -

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    diff --git a/typedoc/classes/NeuralNetwork.html b/typedoc/classes/NeuralNetwork.html index a8e2fd7..ba2aeb2 100644 --- a/typedoc/classes/NeuralNetwork.html +++ b/typedoc/classes/NeuralNetwork.html @@ -1,4 +1,4 @@ -NeuralNetwork | @vladmandic/face-api - v1.7.14

    Class NeuralNetwork<TNetParams>Abstract

    Type Parameters

    • TNetParams

    Hierarchy (View Summary)

    Constructors

    constructor +NeuralNetwork | @vladmandic/face-api - v1.7.15

    Class NeuralNetwork<TNetParams>Abstract

    Type Parameters

    • TNetParams

    Hierarchy (View Summary)

    Constructors

    constructor

    Properties

    _name

    Accessors

    isLoaded paramMappings @@ -17,4 +17,4 @@ reassignParamFromPath serializeParams variable -

    Constructors

    constructor

    Properties

    _name

    _name: any

    Accessors

    isLoaded

    paramMappings

    params

    Methods

    dispose

    • dispose(throwOnRedispose?: boolean): void

    • Parameters

      • throwOnRedispose: boolean = true

      Returns void

    extractWeights

    • extractWeights(weights: Float32Array): void

    • Parameters

      • weights: Float32Array

      Returns void

    freeze

    getFrozenParams

    getParamFromPath

    getParamList

    getTrainableParams

    load

    • load(weightsOrUrl: undefined | string | Float32Array): Promise<void>

    • Parameters

      • weightsOrUrl: undefined | string | Float32Array

      Returns Promise<void>

    loadFromDisk

    • loadFromDisk(filePath: undefined | string): Promise<void>

    • Parameters

      • filePath: undefined | string

      Returns Promise<void>

    loadFromUri

    • loadFromUri(uri: undefined | string): Promise<void>

    • Parameters

      • uri: undefined | string

      Returns Promise<void>

    loadFromWeightMap

    reassignParamFromPath

    serializeParams

    variable

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    _name

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    isLoaded

    paramMappings

    params

    Methods

    dispose

    • dispose(throwOnRedispose?: boolean): void

    • Parameters

      • throwOnRedispose: boolean = true

      Returns void

    extractWeights

    • extractWeights(weights: Float32Array): void

    • Parameters

      • weights: Float32Array

      Returns void

    freeze

    getFrozenParams

    getParamFromPath

    getParamList

    getTrainableParams

    load

    • load(weightsOrUrl: undefined | string | Float32Array): Promise<void>

    • Parameters

      • weightsOrUrl: undefined | string | Float32Array

      Returns Promise<void>

    loadFromDisk

    • loadFromDisk(filePath: undefined | string): Promise<void>

    • Parameters

      • filePath: undefined | string

      Returns Promise<void>

    loadFromUri

    • loadFromUri(uri: undefined | string): Promise<void>

    • Parameters

      • uri: undefined | string

      Returns Promise<void>

    loadFromWeightMap

    reassignParamFromPath

    serializeParams

    variable

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    diff --git a/typedoc/classes/ObjectDetection.html b/typedoc/classes/ObjectDetection.html index 9cbc0ec..36e01c9 100644 --- a/typedoc/classes/ObjectDetection.html +++ b/typedoc/classes/ObjectDetection.html @@ -1,4 +1,4 @@ -ObjectDetection | @vladmandic/face-api - v1.7.14

    Class ObjectDetection

    Hierarchy (View Summary)

    Constructors

    constructor +ObjectDetection | @vladmandic/face-api - v1.7.15

    Class ObjectDetection

    Hierarchy (View Summary)

    Constructors

    constructor

    Accessors

    box className classScore @@ -8,4 +8,4 @@ relativeBox score

    Methods

    forSize -

    Constructors

    constructor

    Accessors

    box

    className

    classScore

    imageDims

    imageHeight

    imageWidth

    relativeBox

    score

    Methods

    forSize

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    +

    Constructors

    constructor

    Accessors

    box

    className

    classScore

    imageDims

    imageHeight

    imageWidth

    relativeBox

    score

    Methods

    forSize

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    diff --git a/typedoc/classes/Point.html b/typedoc/classes/Point.html index 12ffce2..ff82afd 100644 --- a/typedoc/classes/Point.html +++ b/typedoc/classes/Point.html @@ -1,4 +1,4 @@ -Point | @vladmandic/face-api - v1.7.14

    Class Point

    Implements

    Constructors

    constructor +Point | @vladmandic/face-api - v1.7.15

    Class Point

    Implements

    Constructors

    constructor

    Accessors

    x y

    Methods

    abs @@ -8,4 +8,4 @@ magnitude mul sub -

    Constructors

    constructor

    Accessors

    x

    y

    Methods

    abs

    add

    div

    floor

    magnitude

    mul

    sub

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    diff --git a/typedoc/classes/PredictedBox.html b/typedoc/classes/PredictedBox.html index 12ee8a5..19a0b65 100644 --- a/typedoc/classes/PredictedBox.html +++ b/typedoc/classes/PredictedBox.html @@ -1,4 +1,4 @@ -PredictedBox | @vladmandic/face-api - v1.7.14

    Class PredictedBox

    Hierarchy (View Summary)

    Constructors

    constructor +PredictedBox | @vladmandic/face-api - v1.7.15

    Class PredictedBox

    Hierarchy (View Summary)

    Constructors

    constructor

    Accessors

    area bottom bottomLeft @@ -28,4 +28,4 @@ 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: number,
          imageWidth: number,
      ): {
          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;
      }

    rescale

    round

    shift

    toSquare

    StaticassertIsValidBox

    • assertIsValidBox(
          box: any,
          callee: string,
          allowNegativeDimensions?: boolean,
      ): void

    • Parameters

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

      Returns void

    StaticassertIsValidLabeledBox

    StaticassertIsValidPredictedBox

    StaticisRect

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    area

    bottom

    bottomLeft

    bottomRight

    classScore

    height

    label

    left

    right

    score

    top

    topLeft

    topRight

    width

    x

    y

    Methods

    calibrate

    clipAtImageBorders

    floor

    pad

    padAtBorders

    • padAtBorders(
          imageHeight: number,
          imageWidth: number,
      ): {
          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;
      }

    rescale

    round

    shift

    toSquare

    StaticassertIsValidBox

    • assertIsValidBox(
          box: any,
          callee: string,
          allowNegativeDimensions?: boolean,
      ): void

    • Parameters

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

      Returns void

    StaticassertIsValidLabeledBox

    StaticassertIsValidPredictedBox

    StaticisRect

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    diff --git a/typedoc/classes/Rect.html b/typedoc/classes/Rect.html index 3956c7a..0339d26 100644 --- a/typedoc/classes/Rect.html +++ b/typedoc/classes/Rect.html @@ -1,4 +1,4 @@ -Rect | @vladmandic/face-api - v1.7.14

    Class Rect

    Hierarchy (View Summary)

    Implements

    Constructors

    constructor +Rect | @vladmandic/face-api - v1.7.15

    Class Rect

    Hierarchy (View Summary)

    Implements

    Constructors

    constructor

    Accessors

    area bottom bottomLeft @@ -23,4 +23,4 @@ toSquare assertIsValidBox isRect -

    Constructors

    constructor

    • new Rect(
          x: number,
          y: number,
          width: number,
          height: number,
          allowNegativeDimensions?: boolean,
      ): 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: number,
          imageWidth: number,
      ): {
          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;
      }

    rescale

    round

    shift

    toSquare

    StaticassertIsValidBox

    • assertIsValidBox(
          box: any,
          callee: string,
          allowNegativeDimensions?: boolean,
      ): void

    • Parameters

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

      Returns void

    StaticisRect

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    constructor

    • new Rect(
          x: number,
          y: number,
          width: number,
          height: number,
          allowNegativeDimensions?: boolean,
      ): 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: number,
          imageWidth: number,
      ): {
          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;
      }

    rescale

    round

    shift

    toSquare

    StaticassertIsValidBox

    • assertIsValidBox(
          box: any,
          callee: string,
          allowNegativeDimensions?: boolean,
      ): 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 db2f22b..688b69a 100644 --- a/typedoc/classes/SsdMobilenetv1.html +++ b/typedoc/classes/SsdMobilenetv1.html @@ -1,4 +1,4 @@ -SsdMobilenetv1 | @vladmandic/face-api - v1.7.14

    Class SsdMobilenetv1

    Hierarchy (View Summary)

    Constructors

    constructor +SsdMobilenetv1 | @vladmandic/face-api - v1.7.15

    Class SsdMobilenetv1

    Hierarchy (View Summary)

    Constructors

    constructor

    Properties

    _name

    Accessors

    isLoaded paramMappings @@ -20,4 +20,4 @@ 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

    • load(weightsOrUrl: undefined | string | Float32Array): Promise<void>

    • Parameters

      • weightsOrUrl: undefined | string | Float32Array

      Returns Promise<void>

    loadFromDisk

    loadFromUri

    loadFromWeightMap

    locateFaces

    reassignParamFromPath

    serializeParams

    variable

    Settings

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    Accessors
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    +

    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

    • load(weightsOrUrl: undefined | string | Float32Array): Promise<void>

    • Parameters

      • weightsOrUrl: undefined | string | Float32Array

      Returns Promise<void>

    loadFromDisk

    loadFromUri

    loadFromWeightMap

    locateFaces

    reassignParamFromPath

    serializeParams

    variable

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    diff --git a/typedoc/classes/SsdMobilenetv1Options.html b/typedoc/classes/SsdMobilenetv1Options.html index 739dbe6..7ab9d98 100644 --- a/typedoc/classes/SsdMobilenetv1Options.html +++ b/typedoc/classes/SsdMobilenetv1Options.html @@ -1,4 +1,4 @@ -SsdMobilenetv1Options | @vladmandic/face-api - v1.7.14

    Class SsdMobilenetv1Options

    Constructors

    constructor +SsdMobilenetv1Options | @vladmandic/face-api - v1.7.15

    Class SsdMobilenetv1Options

    Constructors

    constructor

    Accessors

    maxResults minConfidence -

    Constructors

    constructor

    Accessors

    maxResults

    minConfidence

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    minConfidence

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    diff --git a/typedoc/classes/TinyFaceDetector.html b/typedoc/classes/TinyFaceDetector.html index bd5da1a..bea6cd2 100644 --- a/typedoc/classes/TinyFaceDetector.html +++ b/typedoc/classes/TinyFaceDetector.html @@ -1,4 +1,4 @@ -TinyFaceDetector | @vladmandic/face-api - v1.7.14

    Class TinyFaceDetector

    Hierarchy

    • TinyYolov2Base
      • TinyFaceDetector

    Constructors

    constructor +TinyFaceDetector | @vladmandic/face-api - v1.7.15

    Class TinyFaceDetector

    Hierarchy

    • TinyYolov2Base
      • TinyFaceDetector

    Constructors

    constructor

    Properties

    _name DEFAULT_FILTER_SIZES

    Accessors

    anchors @@ -28,4 +28,4 @@ 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?: boolean): void

    • Parameters

      • throwOnRedispose: boolean = true

      Returns void

    extractWeights

    • extractWeights(weights: Float32Array): void

    • Parameters

      • weights: Float32Array

      Returns void

    forward

    forwardInput

    freeze

    getFrozenParams

    getParamFromPath

    getParamList

    getTrainableParams

    load

    • load(weightsOrUrl: undefined | string | Float32Array): Promise<void>

    • Parameters

      • weightsOrUrl: undefined | string | Float32Array

      Returns Promise<void>

    loadFromDisk

    • loadFromDisk(filePath: undefined | string): Promise<void>

    • Parameters

      • filePath: undefined | string

      Returns Promise<void>

    loadFromUri

    • loadFromUri(uri: undefined | string): Promise<void>

    • Parameters

      • uri: undefined | string

      Returns Promise<void>

    loadFromWeightMap

    locateFaces

    reassignParamFromPath

    • reassignParamFromPath(paramPath: string, tensor: tf.Tensor): void

    • Parameters

      Returns void

    runMobilenet

    runTinyYolov2

    serializeParams

    • serializeParams(): Float32Array

    • Returns Float32Array

    variable

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    Accessors
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    +

    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?: boolean): void

    • Parameters

      • throwOnRedispose: boolean = true

      Returns void

    extractWeights

    • extractWeights(weights: Float32Array): void

    • Parameters

      • weights: Float32Array

      Returns void

    forward

    forwardInput

    freeze

    getFrozenParams

    getParamFromPath

    getParamList

    getTrainableParams

    load

    • load(weightsOrUrl: undefined | string | Float32Array): Promise<void>

    • Parameters

      • weightsOrUrl: undefined | string | Float32Array

      Returns Promise<void>

    loadFromDisk

    • loadFromDisk(filePath: undefined | string): Promise<void>

    • Parameters

      • filePath: undefined | string

      Returns Promise<void>

    loadFromUri

    • loadFromUri(uri: undefined | string): Promise<void>

    • Parameters

      • uri: undefined | string

      Returns Promise<void>

    loadFromWeightMap

    locateFaces

    reassignParamFromPath

    • reassignParamFromPath(paramPath: string, tensor: tf.Tensor): void

    • Parameters

      Returns void

    runMobilenet

    runTinyYolov2

    serializeParams

    • serializeParams(): Float32Array

    • Returns Float32Array

    variable

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    diff --git a/typedoc/classes/TinyFaceDetectorOptions.html b/typedoc/classes/TinyFaceDetectorOptions.html index 2245337..621660b 100644 --- a/typedoc/classes/TinyFaceDetectorOptions.html +++ b/typedoc/classes/TinyFaceDetectorOptions.html @@ -1,4 +1,4 @@ -TinyFaceDetectorOptions | @vladmandic/face-api - v1.7.14

    Class TinyFaceDetectorOptions

    Hierarchy (View Summary)

    Constructors

    constructor +TinyFaceDetectorOptions | @vladmandic/face-api - v1.7.15

    Class TinyFaceDetectorOptions

    Hierarchy (View Summary)

    Constructors

    constructor

    Accessors

    inputSize scoreThreshold -

    Constructors

    constructor

    Accessors

    inputSize

    scoreThreshold

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    inputSize

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    diff --git a/typedoc/classes/TinyYolov2.html b/typedoc/classes/TinyYolov2.html index b9017d6..1454e47 100644 --- a/typedoc/classes/TinyYolov2.html +++ b/typedoc/classes/TinyYolov2.html @@ -1,4 +1,4 @@ -TinyYolov2 | @vladmandic/face-api - v1.7.14

    Class TinyYolov2

    Hierarchy

    • TinyYolov2Base
      • TinyYolov2

    Constructors

    constructor +TinyYolov2 | @vladmandic/face-api - v1.7.15

    Class TinyYolov2

    Hierarchy

    • TinyYolov2Base
      • TinyYolov2

    Constructors

    constructor

    Properties

    _name DEFAULT_FILTER_SIZES

    Accessors

    anchors @@ -29,4 +29,4 @@ 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?: boolean): void

    • Parameters

      • throwOnRedispose: boolean = true

      Returns void

    extractWeights

    • extractWeights(weights: Float32Array): void

    • Parameters

      • weights: Float32Array

      Returns void

    forward

    forwardInput

    freeze

    getFrozenParams

    getParamFromPath

    getParamList

    getTrainableParams

    load

    • load(weightsOrUrl: undefined | string | Float32Array): Promise<void>

    • Parameters

      • weightsOrUrl: undefined | string | Float32Array

      Returns Promise<void>

    loadFromDisk

    • loadFromDisk(filePath: undefined | string): Promise<void>

    • Parameters

      • filePath: undefined | string

      Returns Promise<void>

    loadFromUri

    • loadFromUri(uri: undefined | string): Promise<void>

    • Parameters

      • uri: undefined | string

      Returns Promise<void>

    loadFromWeightMap

    locateFaces

    reassignParamFromPath

    • reassignParamFromPath(paramPath: string, tensor: tf.Tensor): void

    • Parameters

      Returns void

    runMobilenet

    runTinyYolov2

    serializeParams

    • serializeParams(): Float32Array

    • Returns Float32Array

    variable

    Settings

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    +

    Constructors

    constructor

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    _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?: boolean): void

    • Parameters

      • throwOnRedispose: boolean = true

      Returns void

    extractWeights

    • extractWeights(weights: Float32Array): void

    • Parameters

      • weights: Float32Array

      Returns void

    forward

    forwardInput

    freeze

    getFrozenParams

    getParamFromPath

    getParamList

    getTrainableParams

    load

    • load(weightsOrUrl: undefined | string | Float32Array): Promise<void>

    • Parameters

      • weightsOrUrl: undefined | string | Float32Array

      Returns Promise<void>

    loadFromDisk

    • loadFromDisk(filePath: undefined | string): Promise<void>

    • Parameters

      • filePath: undefined | string

      Returns Promise<void>

    loadFromUri

    • loadFromUri(uri: undefined | string): Promise<void>

    • Parameters

      • uri: undefined | string

      Returns Promise<void>

    loadFromWeightMap

    locateFaces

    reassignParamFromPath

    • reassignParamFromPath(paramPath: string, tensor: tf.Tensor): void

    • Parameters

      Returns void

    runMobilenet

    runTinyYolov2

    serializeParams

    • serializeParams(): Float32Array

    • Returns Float32Array

    variable

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    diff --git a/typedoc/classes/TinyYolov2Options.html b/typedoc/classes/TinyYolov2Options.html index ca17271..8e2c72f 100644 --- a/typedoc/classes/TinyYolov2Options.html +++ b/typedoc/classes/TinyYolov2Options.html @@ -1,4 +1,4 @@ -TinyYolov2Options | @vladmandic/face-api - v1.7.14

    Class TinyYolov2Options

    Hierarchy (View Summary)

    Constructors

    constructor +TinyYolov2Options | @vladmandic/face-api - v1.7.15

    Class TinyYolov2Options

    Hierarchy (View Summary)

    Constructors

    constructor

    Accessors

    inputSize scoreThreshold -

    Constructors

    constructor

    Accessors

    inputSize

    scoreThreshold

    Settings

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    +

    Constructors

    constructor

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    inputSize

    scoreThreshold

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    diff --git a/typedoc/classes/draw.DrawBox.html b/typedoc/classes/draw.DrawBox.html index 949b356..bf1b578 100644 --- a/typedoc/classes/draw.DrawBox.html +++ b/typedoc/classes/draw.DrawBox.html @@ -1,5 +1,5 @@ -DrawBox | @vladmandic/face-api - v1.7.14

    Class DrawBox

    Constructors

    constructor +DrawBox | @vladmandic/face-api - v1.7.15

    Class DrawBox

    Constructors

    constructor

    Properties

    box options

    Methods

    draw -

    Constructors

    constructor

    Properties

    box

    box: Box

    options

    options: DrawBoxOptions

    Methods

    draw

    • draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): void

    • Parameters

      • canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D

      Returns void

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    Constructors

    constructor

    Properties

    box

    box: Box

    options

    options: DrawBoxOptions

    Methods

    draw

    • draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): void

    • Parameters

      • canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D

      Returns void

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    diff --git a/typedoc/classes/draw.DrawBoxOptions.html b/typedoc/classes/draw.DrawBoxOptions.html index 6dbd435..bd67fcb 100644 --- a/typedoc/classes/draw.DrawBoxOptions.html +++ b/typedoc/classes/draw.DrawBoxOptions.html @@ -1,6 +1,6 @@ -DrawBoxOptions | @vladmandic/face-api - v1.7.14

    Class DrawBoxOptions

    Constructors

    constructor +DrawBoxOptions | @vladmandic/face-api - v1.7.15

    Class DrawBoxOptions

    Constructors

    constructor

    Properties

    boxColor drawLabelOptions label? lineWidth -

    Constructors

    constructor

    Properties

    boxColor

    boxColor: string

    drawLabelOptions

    drawLabelOptions: DrawTextFieldOptions

    Optionallabel

    label?: string

    lineWidth

    lineWidth: number

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    +

    Constructors

    constructor

    Properties

    boxColor

    boxColor: string

    drawLabelOptions

    drawLabelOptions: DrawTextFieldOptions

    Optionallabel

    label?: string

    lineWidth

    lineWidth: number

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    diff --git a/typedoc/classes/draw.DrawFaceLandmarks.html b/typedoc/classes/draw.DrawFaceLandmarks.html index 6d1b0e6..8dda7e8 100644 --- a/typedoc/classes/draw.DrawFaceLandmarks.html +++ b/typedoc/classes/draw.DrawFaceLandmarks.html @@ -1,5 +1,5 @@ -DrawFaceLandmarks | @vladmandic/face-api - v1.7.14

    Class DrawFaceLandmarks

    Constructors

    constructor +DrawFaceLandmarks | @vladmandic/face-api - v1.7.15

    Class DrawFaceLandmarks

    Constructors

    constructor

    Properties

    faceLandmarks options

    Methods

    draw -

    Constructors

    constructor

    Properties

    faceLandmarks

    faceLandmarks: FaceLandmarks

    options

    options: DrawFaceLandmarksOptions

    Methods

    draw

    • draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): void

    • Parameters

      • canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D

      Returns void

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    constructor

    Properties

    faceLandmarks

    faceLandmarks: FaceLandmarks

    options

    options: DrawFaceLandmarksOptions

    Methods

    draw

    • draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): void

    • Parameters

      • canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D

      Returns void

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    diff --git a/typedoc/classes/draw.DrawFaceLandmarksOptions.html b/typedoc/classes/draw.DrawFaceLandmarksOptions.html index d0f6b43..85729db 100644 --- a/typedoc/classes/draw.DrawFaceLandmarksOptions.html +++ b/typedoc/classes/draw.DrawFaceLandmarksOptions.html @@ -1,8 +1,8 @@ -DrawFaceLandmarksOptions | @vladmandic/face-api - v1.7.14

    Class DrawFaceLandmarksOptions

    Constructors

    constructor +DrawFaceLandmarksOptions | @vladmandic/face-api - v1.7.15

    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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    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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    diff --git a/typedoc/classes/draw.DrawTextField.html b/typedoc/classes/draw.DrawTextField.html index 509c78a..a885358 100644 --- a/typedoc/classes/draw.DrawTextField.html +++ b/typedoc/classes/draw.DrawTextField.html @@ -1,4 +1,4 @@ -DrawTextField | @vladmandic/face-api - v1.7.14

    Class DrawTextField

    Constructors

    constructor +DrawTextField | @vladmandic/face-api - v1.7.15

    Class DrawTextField

    Constructors

    constructor

    Properties

    anchor options text @@ -6,4 +6,4 @@ getUpperLeft measureHeight measureWidth -

    Constructors

    constructor

    Properties

    anchor

    anchor: IPoint

    options

    options: DrawTextFieldOptions

    text

    text: string[]

    Methods

    draw

    • draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): void

    • Parameters

      • canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D

      Returns void

    getUpperLeft

    measureHeight

    measureWidth

    • measureWidth(ctx: CanvasRenderingContext2D): number

    • Parameters

      • ctx: CanvasRenderingContext2D

      Returns number

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    constructor

    Properties

    anchor

    anchor: IPoint

    options

    options: DrawTextFieldOptions

    text

    text: string[]

    Methods

    draw

    • draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): void

    • Parameters

      • canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D

      Returns void

    getUpperLeft

    measureHeight

    measureWidth

    • measureWidth(ctx: CanvasRenderingContext2D): number

    • Parameters

      • ctx: CanvasRenderingContext2D

      Returns number

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    diff --git a/typedoc/classes/draw.DrawTextFieldOptions.html b/typedoc/classes/draw.DrawTextFieldOptions.html index ed73204..7cc6926 100644 --- a/typedoc/classes/draw.DrawTextFieldOptions.html +++ b/typedoc/classes/draw.DrawTextFieldOptions.html @@ -1,8 +1,8 @@ -DrawTextFieldOptions | @vladmandic/face-api - v1.7.14

    Class DrawTextFieldOptions

    Implements

    Constructors

    constructor +DrawTextFieldOptions | @vladmandic/face-api - v1.7.15

    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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    Properties

    anchorPosition

    anchorPosition: AnchorPosition

    backgroundColor

    backgroundColor: string

    fontColor

    fontColor: string

    fontSize

    fontSize: number

    fontStyle

    fontStyle: string

    padding

    padding: number

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    diff --git a/typedoc/classes/tf.Tensor-1.html b/typedoc/classes/tf.Tensor-1.html index d92e1cf..50d6a7f 100644 --- a/typedoc/classes/tf.Tensor-1.html +++ b/typedoc/classes/tf.Tensor-1.html @@ -1,4 +1,4 @@ -Tensor | @vladmandic/face-api - v1.7.14

    Class Tensor<R>

    A tf.Tensor object represents an immutable, multidimensional array of +Tensor | @vladmandic/face-api - v1.7.15

    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 @@ -222,4 +222,4 @@ Remember to dispose the GPUData after it is used by

    Parameters

    • Optionalverbose: boolean

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

    Returns void

    Doc

    prod

    • prod<T extends tf.Tensor>(
          this: T,
          axis?: number | number[],
          keepDims?: boolean,
      ): T

    • Type Parameters

      Parameters

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

      Returns T

    reciprocal

    • reciprocal<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    relu

    • relu<T extends tf.Tensor>(): T

    • Type Parameters

      Returns T

    relu6

    • relu6<T extends tf.Tensor>(): T

    • Type Parameters

      Returns T

    reshape

    • reshape<T extends tf.Tensor>(shape: number[]): T

    • Type Parameters

      Parameters

      • shape: number[]

      Returns T

    reshapeAs

    • reshapeAs<T extends tf.Tensor>(x: T): T

    • Type Parameters

      Parameters

      Returns T

    resizeBilinear

    • resizeBilinear<T extends Tensor4D | Tensor3D>(
          newShape2D: [number, number],
          alignCorners?: boolean,
          halfPixelCenters?: boolean,
      ): T

    • Type Parameters

      Parameters

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

      Returns T

    resizeNearestNeighbor

    • resizeNearestNeighbor<T extends Tensor4D | Tensor3D>(
          newShape2D: [number, number],
          alignCorners?: boolean,
          halfFloatCenters?: boolean,
      ): T

    • Type Parameters

      Parameters

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

      Returns T

    reverse

    • reverse<T extends tf.Tensor>(this: T, axis?: number | number[]): T

    • Type Parameters

      Parameters

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

      Returns T

    rfft

    • rfft<T extends tf.Tensor>(this: tf.Tensor): tf.Tensor

    • Type Parameters

      Parameters

      Returns tf.Tensor

    round

    • round<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    rsqrt

    • rsqrt<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    selu

    • selu<T extends tf.Tensor>(): T

    • Type Parameters

      Returns T

    separableConv2d

    • separableConv2d<T extends Tensor4D | Tensor3D>(
          depthwiseFilter: Tensor4D | TensorLike4D,
          pointwiseFilter: Tensor4D | TensorLike,
          strides: number | [number, number],
          pad: "valid" | "same",
          dilation?: number | [number, number],
          dataFormat?: "NHWC" | "NCHW",
      ): 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 extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    sign

    • sign<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    sin

    • sin<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    sinh

    • sinh<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    slice

    • slice<T extends tf.Tensor>(
          this: T,
          begin: number | number[],
          size?: number | number[],
      ): T

    • Type Parameters

      Parameters

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

      Returns T

    softmax

    • softmax<T extends tf.Tensor>(this: T, dim?: number): T

    • Type Parameters

      Parameters

      • this: T
      • Optionaldim: number

      Returns T

    softplus

    • softplus<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    spaceToBatchND

    • spaceToBatchND<R extends Rank>(
          blockShape: number[],
          paddings: number[][],
      ): tf.Tensor<R>

    • Type Parameters

      Parameters

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

      Returns tf.Tensor<R>

    split

    • split<T extends tf.Tensor>(
          numOrSizeSplits: number | number[],
          axis?: number,
      ): T[]

    • Type Parameters

      Parameters

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

      Returns T[]

    sqrt

    • sqrt<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    square

    • square<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    squaredDifference

    • squaredDifference<T extends tf.Tensor>(b: TensorLike | tf.Tensor): T

    • Type Parameters

      Parameters

      Returns T

    squeeze

    • squeeze<T extends tf.Tensor>(axis?: number[]): T

    • Type Parameters

      Parameters

      • Optionalaxis: number[]

      Returns T

    stack

    • stack<T extends tf.Tensor>(x: tf.Tensor | tf.Tensor[], axis?: number): T

    • Type Parameters

      Parameters

      Returns T

    step

    • step<T extends tf.Tensor>(this: T, alpha?: number): T

    • Type Parameters

      Parameters

      • this: T
      • Optionalalpha: number

      Returns T

    stridedSlice

    • stridedSlice<T extends tf.Tensor>(
          this: tf.Tensor,
          begin: number[],
          end: number[],
          strides: number[],
          beginMask?: number,
          endMask?: number,
          ellipsisMask?: number,
          newAxisMask?: number,
          shrinkAxisMask?: number,
      ): tf.Tensor

    • Type Parameters

      Parameters

      • this: tf.Tensor
      • begin: number[]
      • end: number[]
      • strides: number[]
      • OptionalbeginMask: number
      • OptionalendMask: number
      • OptionalellipsisMask: number
      • OptionalnewAxisMask: number
      • OptionalshrinkAxisMask: number

      Returns tf.Tensor

    sub

    sum

    • sum<T extends tf.Tensor>(axis?: number | number[], keepDims?: boolean): T

    • Type Parameters

      Parameters

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

      Returns T

    tan

    • tan<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    tanh

    • tanh<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    throwIfDisposed

    • throwIfDisposed(): void

    • Returns void

    tile

    • tile<T extends tf.Tensor>(b: number[]): T

    • Type Parameters

      Parameters

      • b: number[]

      Returns T

    toBool

    • toBool<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    toFloat

    • toFloat<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    toInt

    • toInt<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    topk

    • topk<T extends tf.Tensor>(
          this: T,
          k?: number,
          sorted?: boolean,
      ): { indices: T; values: T }

    • Type Parameters

      Parameters

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

      Returns { indices: T; values: T }

    toString

    • toString(verbose?: boolean): string

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

      -

      Parameters

      • Optionalverbose: boolean

      Returns string

      Doc

    transpose

    • transpose<T extends tf.Tensor>(perm?: number[]): T

    • Type Parameters

      Parameters

      • Optionalperm: number[]

      Returns T

    unique

    • unique<T extends tf.Tensor>(this: T, axis?: number): { indices: T; values: T }

    • Type Parameters

      Parameters

      • this: T
      • Optionalaxis: number

      Returns { indices: T; values: T }

    unsortedSegmentSum

    • unsortedSegmentSum<T extends tf.Tensor>(
          this: T,
          segmentIds: Tensor1D | TensorLike1D,
          numSegments: number,
      ): T

    • Type Parameters

      Parameters

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

      Returns T

    unstack

    • unstack<T extends tf.Tensor>(axis?: number): T[]

    • Type Parameters

      Parameters

      • Optionalaxis: number

      Returns T[]

    variable

    • variable(
          trainable?: boolean,
          name?: string,
          dtype?: keyof DataTypeMap,
      ): Variable<R>

    • Parameters

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

      Returns Variable<R>

    where

    zerosLike

    • zerosLike<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

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    absacosacoshaddallanyargMaxargMinarrayarraySyncas1Das2Das3Das4Das5DasinasinhasScalarasTypeatanatan2atanhavgPoolbatchNormbatchToSpaceNDbroadcastTobufferbufferSyncbytescastceilclipByValuecloneconcatconv1dconv2dconv2dTransposecoscoshcumprodcumsumdatadataSyncdataToGPUdepthToSpacedepthwiseConv2ddilation2ddisposedivdivNoNandoteluequalerfeuclideanNormexpexpandDimsexpm1fftflattenfloorfloorDivgathergreatergreaterEqualifftirfftisFiniteisInfisNaNleakyRelulesslessEquallocalResponseNormalizationloglog1plogicalAndlogicalNotlogicalOrlogicalXorlogSigmoidlogSoftmaxlogSumExpmatMulmaxmaximummaxPoolmeanminminimummirrorPadmodmulnegnormnotEqualoneHotonesLikepadpoolpowpreluprintprodreciprocalrelurelu6reshapereshapeAsresizeBilinearresizeNearestNeighborreverserfftroundrsqrtseluseparableConv2dsigmoidsignsinsinhslicesoftmaxsoftplusspaceToBatchNDsplitsqrtsquaresquaredDifferencesqueezestackstepstridedSlicesubsumtantanhthrowIfDisposedtiletoBooltoFloattoInttopktoStringtransposeuniqueunsortedSegmentSumunstackvariablewherezerosLike
    +

    Parameters

    • Optionalverbose: boolean

    Returns string

    Doc

    transpose

    • transpose<T extends tf.Tensor>(perm?: number[]): T

    • Type Parameters

      Parameters

      • Optionalperm: number[]

      Returns T

    unique

    • unique<T extends tf.Tensor>(this: T, axis?: number): { indices: T; values: T }

    • Type Parameters

      Parameters

      • this: T
      • Optionalaxis: number

      Returns { indices: T; values: T }

    unsortedSegmentSum

    • unsortedSegmentSum<T extends tf.Tensor>(
          this: T,
          segmentIds: Tensor1D | TensorLike1D,
          numSegments: number,
      ): T

    • Type Parameters

      Parameters

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

      Returns T

    unstack

    • unstack<T extends tf.Tensor>(axis?: number): T[]

    • Type Parameters

      Parameters

      • Optionalaxis: number

      Returns T[]

    variable

    • variable(
          trainable?: boolean,
          name?: string,
          dtype?: keyof DataTypeMap,
      ): Variable<R>

    • Parameters

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

      Returns Variable<R>

    where

    zerosLike

    • zerosLike<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

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    absacosacoshaddallanyargMaxargMinarrayarraySyncas1Das2Das3Das4Das5DasinasinhasScalarasTypeatanatan2atanhavgPoolbatchNormbatchToSpaceNDbroadcastTobufferbufferSyncbytescastceilclipByValuecloneconcatconv1dconv2dconv2dTransposecoscoshcumprodcumsumdatadataSyncdataToGPUdepthToSpacedepthwiseConv2ddilation2ddisposedivdivNoNandoteluequalerfeuclideanNormexpexpandDimsexpm1fftflattenfloorfloorDivgathergreatergreaterEqualifftirfftisFiniteisInfisNaNleakyRelulesslessEquallocalResponseNormalizationloglog1plogicalAndlogicalNotlogicalOrlogicalXorlogSigmoidlogSoftmaxlogSumExpmatMulmaxmaximummaxPoolmeanminminimummirrorPadmodmulnegnormnotEqualoneHotonesLikepadpoolpowpreluprintprodreciprocalrelurelu6reshapereshapeAsresizeBilinearresizeNearestNeighborreverserfftroundrsqrtseluseparableConv2dsigmoidsignsinsinhslicesoftmaxsoftplusspaceToBatchNDsplitsqrtsquaresquaredDifferencesqueezestackstepstridedSlicesubsumtantanhthrowIfDisposedtiletoBooltoFloattoInttopktoStringtransposeuniqueunsortedSegmentSumunstackvariablewherezerosLike
    diff --git a/typedoc/classes/tf.Variable.html b/typedoc/classes/tf.Variable.html index f1e668d..3cc5619 100644 --- a/typedoc/classes/tf.Variable.html +++ b/typedoc/classes/tf.Variable.html @@ -1,4 +1,4 @@ -Variable | @vladmandic/face-api - v1.7.14

    Class Variable<R>

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

    +Variable | @vladmandic/face-api - v1.7.15

    Class Variable<R>

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

    Doc

    Type Parameters

    Hierarchy (View Summary)

    Constructors

    constructor

    Properties

    dataId dtype @@ -221,4 +221,4 @@ Remember to dispose the GPUData after it is used by

    Parameters

    • Optionalverbose: boolean

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

    Returns void

    Doc

    prod

    • prod<T extends tf.Tensor>(
          this: T,
          axis?: number | number[],
          keepDims?: boolean,
      ): T

    • Type Parameters

      Parameters

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

      Returns T

    reciprocal

    • reciprocal<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    relu

    • relu<T extends tf.Tensor>(): T

    • Type Parameters

      Returns T

    relu6

    • relu6<T extends tf.Tensor>(): T

    • Type Parameters

      Returns T

    reshape

    • reshape<T extends tf.Tensor>(shape: number[]): T

    • Type Parameters

      Parameters

      • shape: number[]

      Returns T

    reshapeAs

    • reshapeAs<T extends tf.Tensor>(x: T): T

    • Type Parameters

      Parameters

      Returns T

    resizeBilinear

    • resizeBilinear<T extends Tensor4D | Tensor3D>(
          newShape2D: [number, number],
          alignCorners?: boolean,
          halfPixelCenters?: boolean,
      ): T

    • Type Parameters

      Parameters

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

      Returns T

    resizeNearestNeighbor

    • resizeNearestNeighbor<T extends Tensor4D | Tensor3D>(
          newShape2D: [number, number],
          alignCorners?: boolean,
          halfFloatCenters?: boolean,
      ): T

    • Type Parameters

      Parameters

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

      Returns T

    reverse

    • reverse<T extends tf.Tensor>(this: T, axis?: number | number[]): T

    • Type Parameters

      Parameters

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

      Returns T

    rfft

    round

    • round<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    rsqrt

    • rsqrt<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    selu

    • selu<T extends tf.Tensor>(): T

    • Type Parameters

      Returns T

    separableConv2d

    • separableConv2d<T extends Tensor4D | Tensor3D>(
          depthwiseFilter: Tensor4D | TensorLike4D,
          pointwiseFilter: Tensor4D | TensorLike,
          strides: number | [number, number],
          pad: "valid" | "same",
          dilation?: number | [number, number],
          dataFormat?: "NHWC" | "NCHW",
      ): 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 extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    sign

    • sign<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    sin

    • sin<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    sinh

    • sinh<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    slice

    • slice<T extends tf.Tensor>(
          this: T,
          begin: number | number[],
          size?: number | number[],
      ): T

    • Type Parameters

      Parameters

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

      Returns T

    softmax

    • softmax<T extends tf.Tensor>(this: T, dim?: number): T

    • Type Parameters

      Parameters

      • this: T
      • Optionaldim: number

      Returns T

    softplus

    • softplus<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    spaceToBatchND

    • spaceToBatchND<R extends Rank>(
          blockShape: number[],
          paddings: number[][],
      ): tf.Tensor<R>

    • Type Parameters

      Parameters

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

      Returns tf.Tensor<R>

    split

    • split<T extends tf.Tensor>(
          numOrSizeSplits: number | number[],
          axis?: number,
      ): T[]

    • Type Parameters

      Parameters

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

      Returns T[]

    sqrt

    • sqrt<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    square

    • square<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    squaredDifference

    squeeze

    • squeeze<T extends tf.Tensor>(axis?: number[]): T

    • Type Parameters

      Parameters

      • Optionalaxis: number[]

      Returns T

    stack

    • stack<T extends tf.Tensor>(x: tf.Tensor | tf.Tensor[], axis?: number): T

    • Type Parameters

      Parameters

      Returns T

    step

    • step<T extends tf.Tensor>(this: T, alpha?: number): T

    • Type Parameters

      Parameters

      • this: T
      • Optionalalpha: number

      Returns T

    stridedSlice

    • stridedSlice<T extends tf.Tensor>(
          this: tf.Tensor,
          begin: number[],
          end: number[],
          strides: number[],
          beginMask?: number,
          endMask?: number,
          ellipsisMask?: number,
          newAxisMask?: number,
          shrinkAxisMask?: number,
      ): tf.Tensor

    • Type Parameters

      Parameters

      • this: tf.Tensor
      • begin: number[]
      • end: number[]
      • strides: number[]
      • OptionalbeginMask: number
      • OptionalendMask: number
      • OptionalellipsisMask: number
      • OptionalnewAxisMask: number
      • OptionalshrinkAxisMask: number

      Returns tf.Tensor

    sub

    sum

    • sum<T extends tf.Tensor>(axis?: number | number[], keepDims?: boolean): T

    • Type Parameters

      Parameters

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

      Returns T

    tan

    • tan<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    tanh

    • tanh<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    throwIfDisposed

    • throwIfDisposed(): void

    • Returns void

    tile

    • tile<T extends tf.Tensor>(b: number[]): T

    • Type Parameters

      Parameters

      • b: number[]

      Returns T

    toBool

    • toBool<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    toFloat

    • toFloat<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    toInt

    • toInt<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    topk

    • topk<T extends tf.Tensor>(
          this: T,
          k?: number,
          sorted?: boolean,
      ): { indices: T; values: T }

    • Type Parameters

      Parameters

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

      Returns { indices: T; values: T }

    toString

    • toString(verbose?: boolean): string

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

      -

      Parameters

      • Optionalverbose: boolean

      Returns string

      Doc

    transpose

    • transpose<T extends tf.Tensor>(perm?: number[]): T

    • Type Parameters

      Parameters

      • Optionalperm: number[]

      Returns T

    unique

    • unique<T extends tf.Tensor>(this: T, axis?: number): { indices: T; values: T }

    • Type Parameters

      Parameters

      • this: T
      • Optionalaxis: number

      Returns { indices: T; values: T }

    unsortedSegmentSum

    • unsortedSegmentSum<T extends tf.Tensor>(
          this: T,
          segmentIds: Tensor1D | TensorLike1D,
          numSegments: number,
      ): T

    • Type Parameters

      Parameters

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

      Returns T

    unstack

    • unstack<T extends tf.Tensor>(axis?: number): T[]

    • Type Parameters

      Parameters

      • Optionalaxis: number

      Returns T[]

    variable

    • variable(
          trainable?: boolean,
          name?: string,
          dtype?: keyof DataTypeMap,
      ): Variable<R>

    • Parameters

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

      Returns Variable<R>

    where

    zerosLike

    • zerosLike<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

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    absacosacoshaddallanyargMaxargMinarrayarraySyncas1Das2Das3Das4Das5DasinasinhasScalarassignasTypeatanatan2atanhavgPoolbatchNormbatchToSpaceNDbroadcastTobufferbufferSyncbytescastceilclipByValuecloneconcatconv1dconv2dconv2dTransposecoscoshcumprodcumsumdatadataSyncdataToGPUdepthToSpacedepthwiseConv2ddilation2ddisposedivdivNoNandoteluequalerfeuclideanNormexpexpandDimsexpm1fftflattenfloorfloorDivgathergreatergreaterEqualifftirfftisFiniteisInfisNaNleakyRelulesslessEquallocalResponseNormalizationloglog1plogicalAndlogicalNotlogicalOrlogicalXorlogSigmoidlogSoftmaxlogSumExpmatMulmaxmaximummaxPoolmeanminminimummirrorPadmodmulnegnormnotEqualoneHotonesLikepadpoolpowpreluprintprodreciprocalrelurelu6reshapereshapeAsresizeBilinearresizeNearestNeighborreverserfftroundrsqrtseluseparableConv2dsigmoidsignsinsinhslicesoftmaxsoftplusspaceToBatchNDsplitsqrtsquaresquaredDifferencesqueezestackstepstridedSlicesubsumtantanhthrowIfDisposedtiletoBooltoFloattoInttopktoStringtransposeuniqueunsortedSegmentSumunstackvariablewherezerosLike
    +

    Parameters

    • Optionalverbose: boolean

    Returns string

    Doc

    transpose

    • transpose<T extends tf.Tensor>(perm?: number[]): T

    • Type Parameters

      Parameters

      • Optionalperm: number[]

      Returns T

    unique

    • unique<T extends tf.Tensor>(this: T, axis?: number): { indices: T; values: T }

    • Type Parameters

      Parameters

      • this: T
      • Optionalaxis: number

      Returns { indices: T; values: T }

    unsortedSegmentSum

    • unsortedSegmentSum<T extends tf.Tensor>(
          this: T,
          segmentIds: Tensor1D | TensorLike1D,
          numSegments: number,
      ): T

    • Type Parameters

      Parameters

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

      Returns T

    unstack

    • unstack<T extends tf.Tensor>(axis?: number): T[]

    • Type Parameters

      Parameters

      • Optionalaxis: number

      Returns T[]

    variable

    • variable(
          trainable?: boolean,
          name?: string,
          dtype?: keyof DataTypeMap,
      ): Variable<R>

    • Parameters

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

      Returns Variable<R>

    where

    zerosLike

    • zerosLike<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

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    absacosacoshaddallanyargMaxargMinarrayarraySyncas1Das2Das3Das4Das5DasinasinhasScalarassignasTypeatanatan2atanhavgPoolbatchNormbatchToSpaceNDbroadcastTobufferbufferSyncbytescastceilclipByValuecloneconcatconv1dconv2dconv2dTransposecoscoshcumprodcumsumdatadataSyncdataToGPUdepthToSpacedepthwiseConv2ddilation2ddisposedivdivNoNandoteluequalerfeuclideanNormexpexpandDimsexpm1fftflattenfloorfloorDivgathergreatergreaterEqualifftirfftisFiniteisInfisNaNleakyRelulesslessEquallocalResponseNormalizationloglog1plogicalAndlogicalNotlogicalOrlogicalXorlogSigmoidlogSoftmaxlogSumExpmatMulmaxmaximummaxPoolmeanminminimummirrorPadmodmulnegnormnotEqualoneHotonesLikepadpoolpowpreluprintprodreciprocalrelurelu6reshapereshapeAsresizeBilinearresizeNearestNeighborreverserfftroundrsqrtseluseparableConv2dsigmoidsignsinsinhslicesoftmaxsoftplusspaceToBatchNDsplitsqrtsquaresquaredDifferencesqueezestackstepstridedSlicesubsumtantanhthrowIfDisposedtiletoBooltoFloattoInttopktoStringtransposeuniqueunsortedSegmentSumunstackvariablewherezerosLike
    diff --git a/typedoc/classes/tf.io.CompositeArrayBuffer.html b/typedoc/classes/tf.io.CompositeArrayBuffer.html index c302c7c..193a41f 100644 --- a/typedoc/classes/tf.io.CompositeArrayBuffer.html +++ b/typedoc/classes/tf.io.CompositeArrayBuffer.html @@ -1,4 +1,4 @@ -CompositeArrayBuffer | @vladmandic/face-api - v1.7.14

    Class CompositeArrayBuffer

    Wraps a list of ArrayBuffers into a slice()-able object without allocating +CompositeArrayBuffer | @vladmandic/face-api - v1.7.15

    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 @@ -13,4 +13,4 @@ tensors out of it, but for large models, a different approach is needed.

    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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    +

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    diff --git a/typedoc/enums/Gender.html b/typedoc/enums/Gender.html index e666bb4..fccfbf2 100644 --- a/typedoc/enums/Gender.html +++ b/typedoc/enums/Gender.html @@ -1,3 +1,3 @@ -Gender | @vladmandic/face-api - v1.7.14

    Enumeration Gender

    Enumeration Members

    FEMALE +Gender | @vladmandic/face-api - v1.7.15

    Enumeration Gender

    Enumeration Members

    FEMALE MALE -

    Enumeration Members

    FEMALE

    FEMALE: "female"

    MALE

    MALE: "male"

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    +

    Enumeration Members

    FEMALE

    FEMALE: "female"

    MALE

    MALE: "male"

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    diff --git a/typedoc/enums/draw.AnchorPosition.html b/typedoc/enums/draw.AnchorPosition.html index 1d87eba..ea1b362 100644 --- a/typedoc/enums/draw.AnchorPosition.html +++ b/typedoc/enums/draw.AnchorPosition.html @@ -1,5 +1,5 @@ -AnchorPosition | @vladmandic/face-api - v1.7.14

    Enumeration AnchorPosition

    Enumeration Members

    BOTTOM_LEFT +AnchorPosition | @vladmandic/face-api - v1.7.15

    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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    +

    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"

    Settings

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    Enumeration Members
    diff --git a/typedoc/enums/tf.Rank.html b/typedoc/enums/tf.Rank.html index 0033ea6..f0a6516 100644 --- a/typedoc/enums/tf.Rank.html +++ b/typedoc/enums/tf.Rank.html @@ -1,8 +1,8 @@ -Rank | @vladmandic/face-api - v1.7.14

    Enumeration Rank

    Enumeration Members

    R0 +Rank | @vladmandic/face-api - v1.7.15

    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 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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    diff --git a/typedoc/functions/allFaces.html b/typedoc/functions/allFaces.html index 519c362..916a656 100644 --- a/typedoc/functions/allFaces.html +++ b/typedoc/functions/allFaces.html @@ -1 +1 @@ -allFaces | @vladmandic/face-api - v1.7.14

    Function allFaces

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    +allFaces | @vladmandic/face-api - v1.7.15

    Function allFaces

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    diff --git a/typedoc/functions/allFacesSsdMobilenetv1.html b/typedoc/functions/allFacesSsdMobilenetv1.html index 0938a20..3505d93 100644 --- a/typedoc/functions/allFacesSsdMobilenetv1.html +++ b/typedoc/functions/allFacesSsdMobilenetv1.html @@ -1 +1 @@ -allFacesSsdMobilenetv1 | @vladmandic/face-api - v1.7.14

    Function allFacesSsdMobilenetv1

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    +allFacesSsdMobilenetv1 | @vladmandic/face-api - v1.7.15

    Function allFacesSsdMobilenetv1

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    diff --git a/typedoc/functions/allFacesTinyYolov2.html b/typedoc/functions/allFacesTinyYolov2.html index 0f24d17..39fad7e 100644 --- a/typedoc/functions/allFacesTinyYolov2.html +++ b/typedoc/functions/allFacesTinyYolov2.html @@ -1 +1 @@ -allFacesTinyYolov2 | @vladmandic/face-api - v1.7.14

    Function allFacesTinyYolov2

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    +allFacesTinyYolov2 | @vladmandic/face-api - v1.7.15

    Function allFacesTinyYolov2

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    diff --git a/typedoc/functions/awaitMediaLoaded.html b/typedoc/functions/awaitMediaLoaded.html index a28e43b..ce7680e 100644 --- a/typedoc/functions/awaitMediaLoaded.html +++ b/typedoc/functions/awaitMediaLoaded.html @@ -1 +1 @@ -awaitMediaLoaded | @vladmandic/face-api - v1.7.14

    Function awaitMediaLoaded

    • awaitMediaLoaded(
          media: HTMLCanvasElement | HTMLImageElement | HTMLVideoElement,
      ): Promise<unknown>

    • Parameters

      • media: HTMLCanvasElement | HTMLImageElement | HTMLVideoElement

      Returns Promise<unknown>

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    +awaitMediaLoaded | @vladmandic/face-api - v1.7.15

    Function awaitMediaLoaded

    • awaitMediaLoaded(
          media: HTMLCanvasElement | HTMLImageElement | HTMLVideoElement,
      ): 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 12847f3..72fda92 100644 --- a/typedoc/functions/bufferToImage.html +++ b/typedoc/functions/bufferToImage.html @@ -1 +1 @@ -bufferToImage | @vladmandic/face-api - v1.7.14

    Function bufferToImage

    • bufferToImage(buf: Blob): Promise<HTMLImageElement>

    • Parameters

      • buf: Blob

      Returns Promise<HTMLImageElement>

    Settings

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    +bufferToImage | @vladmandic/face-api - v1.7.15

    Function bufferToImage

    • bufferToImage(buf: Blob): 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 2c1898d..3f581f9 100644 --- a/typedoc/functions/computeFaceDescriptor.html +++ b/typedoc/functions/computeFaceDescriptor.html @@ -1,6 +1,6 @@ -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, +computeFaceDescriptor | @vladmandic/face-api - v1.7.15

    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.

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

    • Parameters

      Returns Promise<Float32Array | Float32Array[]>

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

      -

    Settings

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    +

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    diff --git a/typedoc/functions/createCanvas.html b/typedoc/functions/createCanvas.html index 0ae6a42..23633c9 100644 --- a/typedoc/functions/createCanvas.html +++ b/typedoc/functions/createCanvas.html @@ -1 +1 @@ -createCanvas | @vladmandic/face-api - v1.7.14

    Function createCanvas

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    +createCanvas | @vladmandic/face-api - v1.7.15

    Function createCanvas

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    diff --git a/typedoc/functions/createCanvasFromMedia.html b/typedoc/functions/createCanvasFromMedia.html index 254199c..84047bf 100644 --- a/typedoc/functions/createCanvasFromMedia.html +++ b/typedoc/functions/createCanvasFromMedia.html @@ -1 +1 @@ -createCanvasFromMedia | @vladmandic/face-api - v1.7.14

    Function createCanvasFromMedia

    • createCanvasFromMedia(
          media: HTMLImageElement | HTMLVideoElement | ImageData,
          dims?: IDimensions,
      ): HTMLCanvasElement

    • Parameters

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

      Returns HTMLCanvasElement

    Settings

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    +createCanvasFromMedia | @vladmandic/face-api - v1.7.15

    Function createCanvasFromMedia

    • createCanvasFromMedia(
          media: HTMLImageElement | HTMLVideoElement | ImageData,
          dims?: IDimensions,
      ): HTMLCanvasElement

    • Parameters

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

      Returns HTMLCanvasElement

    Settings

    Member Visibility
    diff --git a/typedoc/functions/createFaceDetectionNet.html b/typedoc/functions/createFaceDetectionNet.html index f8e9b17..c449127 100644 --- a/typedoc/functions/createFaceDetectionNet.html +++ b/typedoc/functions/createFaceDetectionNet.html @@ -1 +1 @@ -createFaceDetectionNet | @vladmandic/face-api - v1.7.14

    Function createFaceDetectionNet

    Settings

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    +createFaceDetectionNet | @vladmandic/face-api - v1.7.15

    Function createFaceDetectionNet

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    diff --git a/typedoc/functions/createFaceRecognitionNet.html b/typedoc/functions/createFaceRecognitionNet.html index 79838ff..05435f0 100644 --- a/typedoc/functions/createFaceRecognitionNet.html +++ b/typedoc/functions/createFaceRecognitionNet.html @@ -1 +1 @@ -createFaceRecognitionNet | @vladmandic/face-api - v1.7.14

    Function createFaceRecognitionNet

    Settings

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    +createFaceRecognitionNet | @vladmandic/face-api - v1.7.15

    Function createFaceRecognitionNet

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    diff --git a/typedoc/functions/createSsdMobilenetv1.html b/typedoc/functions/createSsdMobilenetv1.html index 8f80f55..320e68c 100644 --- a/typedoc/functions/createSsdMobilenetv1.html +++ b/typedoc/functions/createSsdMobilenetv1.html @@ -1 +1 @@ -createSsdMobilenetv1 | @vladmandic/face-api - v1.7.14

    Function createSsdMobilenetv1

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    +createSsdMobilenetv1 | @vladmandic/face-api - v1.7.15

    Function createSsdMobilenetv1

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    diff --git a/typedoc/functions/createTinyFaceDetector.html b/typedoc/functions/createTinyFaceDetector.html index c0fb3b4..655c194 100644 --- a/typedoc/functions/createTinyFaceDetector.html +++ b/typedoc/functions/createTinyFaceDetector.html @@ -1 +1 @@ -createTinyFaceDetector | @vladmandic/face-api - v1.7.14

    Function createTinyFaceDetector

    Settings

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    +createTinyFaceDetector | @vladmandic/face-api - v1.7.15

    Function createTinyFaceDetector

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    diff --git a/typedoc/functions/createTinyYolov2.html b/typedoc/functions/createTinyYolov2.html index 9692afe..da11e95 100644 --- a/typedoc/functions/createTinyYolov2.html +++ b/typedoc/functions/createTinyYolov2.html @@ -1 +1 @@ -createTinyYolov2 | @vladmandic/face-api - v1.7.14

    Function createTinyYolov2

    Settings

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    +createTinyYolov2 | @vladmandic/face-api - v1.7.15

    Function createTinyYolov2

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    diff --git a/typedoc/functions/detectAllFaces.html b/typedoc/functions/detectAllFaces.html index 3543d41..1d54d3f 100644 --- a/typedoc/functions/detectAllFaces.html +++ b/typedoc/functions/detectAllFaces.html @@ -1 +1 @@ -detectAllFaces | @vladmandic/face-api - v1.7.14

    Function detectAllFaces

    Settings

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    +detectAllFaces | @vladmandic/face-api - v1.7.15

    Function detectAllFaces

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    diff --git a/typedoc/functions/detectFaceLandmarks.html b/typedoc/functions/detectFaceLandmarks.html index da931b4..30227e4 100644 --- a/typedoc/functions/detectFaceLandmarks.html +++ b/typedoc/functions/detectFaceLandmarks.html @@ -1,3 +1,3 @@ -detectFaceLandmarks | @vladmandic/face-api - v1.7.14

    Function detectFaceLandmarks

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

    +detectFaceLandmarks | @vladmandic/face-api - v1.7.15

    Function detectFaceLandmarks

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

    Settings

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    +

    Settings

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    diff --git a/typedoc/functions/detectFaceLandmarksTiny.html b/typedoc/functions/detectFaceLandmarksTiny.html index 59a7fe3..e5f01dd 100644 --- a/typedoc/functions/detectFaceLandmarksTiny.html +++ b/typedoc/functions/detectFaceLandmarksTiny.html @@ -1,5 +1,5 @@ -detectFaceLandmarksTiny | @vladmandic/face-api - v1.7.14

    Function detectFaceLandmarksTiny

    Detects the 68 point face landmark positions of the face shown in an image +detectFaceLandmarksTiny | @vladmandic/face-api - v1.7.15

    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.

    Settings

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    +

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    diff --git a/typedoc/functions/detectLandmarks.html b/typedoc/functions/detectLandmarks.html index d26d73f..70dab85 100644 --- a/typedoc/functions/detectLandmarks.html +++ b/typedoc/functions/detectLandmarks.html @@ -1,3 +1,3 @@ -detectLandmarks | @vladmandic/face-api - v1.7.14

    Function detectLandmarks

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    diff --git a/typedoc/functions/detectSingleFace.html b/typedoc/functions/detectSingleFace.html index e1564bb..387d037 100644 --- a/typedoc/functions/detectSingleFace.html +++ b/typedoc/functions/detectSingleFace.html @@ -1 +1 @@ -detectSingleFace | @vladmandic/face-api - v1.7.14

    Function detectSingleFace

    Settings

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    +detectSingleFace | @vladmandic/face-api - v1.7.15

    Function detectSingleFace

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    diff --git a/typedoc/functions/draw.drawContour.html b/typedoc/functions/draw.drawContour.html index f1645b9..ecbe92c 100644 --- a/typedoc/functions/draw.drawContour.html +++ b/typedoc/functions/draw.drawContour.html @@ -1 +1 @@ -drawContour | @vladmandic/face-api - v1.7.14

    Function drawContour

    • drawContour(
          ctx: CanvasRenderingContext2D,
          points: Point[],
          isClosed?: boolean,
      ): void

    • Parameters

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

      Returns void

    Settings

    Member Visibility
    +drawContour | @vladmandic/face-api - v1.7.15

    Function drawContour

    • drawContour(
          ctx: CanvasRenderingContext2D,
          points: Point[],
          isClosed?: boolean,
      ): void

    • Parameters

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

      Returns void

    Settings

    Member Visibility
    diff --git a/typedoc/functions/draw.drawDetections.html b/typedoc/functions/draw.drawDetections.html index 2225247..f66af5f 100644 --- a/typedoc/functions/draw.drawDetections.html +++ b/typedoc/functions/draw.drawDetections.html @@ -1 +1 @@ -drawDetections | @vladmandic/face-api - v1.7.14

    Function drawDetections

    Settings

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    +drawDetections | @vladmandic/face-api - v1.7.15

    Function drawDetections

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    diff --git a/typedoc/functions/draw.drawFaceExpressions.html b/typedoc/functions/draw.drawFaceExpressions.html index 1be370d..52f7d72 100644 --- a/typedoc/functions/draw.drawFaceExpressions.html +++ b/typedoc/functions/draw.drawFaceExpressions.html @@ -1 +1 @@ -drawFaceExpressions | @vladmandic/face-api - v1.7.14

    Function drawFaceExpressions

    Settings

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    +drawFaceExpressions | @vladmandic/face-api - v1.7.15

    Function drawFaceExpressions

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    diff --git a/typedoc/functions/draw.drawFaceLandmarks-1.html b/typedoc/functions/draw.drawFaceLandmarks-1.html index b5de6b0..6d01ece 100644 --- a/typedoc/functions/draw.drawFaceLandmarks-1.html +++ b/typedoc/functions/draw.drawFaceLandmarks-1.html @@ -1 +1 @@ -drawFaceLandmarks | @vladmandic/face-api - v1.7.14

    Function drawFaceLandmarks

    Settings

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    +drawFaceLandmarks | @vladmandic/face-api - v1.7.15

    Function drawFaceLandmarks

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    diff --git a/typedoc/functions/euclideanDistance.html b/typedoc/functions/euclideanDistance.html index 13e6197..3d0e9d8 100644 --- a/typedoc/functions/euclideanDistance.html +++ b/typedoc/functions/euclideanDistance.html @@ -1 +1 @@ -euclideanDistance | @vladmandic/face-api - v1.7.14

    Function euclideanDistance

    • euclideanDistance(
          arr1: Float32Array | number[],
          arr2: Float32Array | number[],
      ): number

    • Parameters

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

      Returns number

    Settings

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    +euclideanDistance | @vladmandic/face-api - v1.7.15

    Function euclideanDistance

    • euclideanDistance(
          arr1: Float32Array | number[],
          arr2: Float32Array | number[],
      ): number

    • Parameters

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

      Returns number

    Settings

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    diff --git a/typedoc/functions/extendWithAge.html b/typedoc/functions/extendWithAge.html index 5243d21..a66b2a6 100644 --- a/typedoc/functions/extendWithAge.html +++ b/typedoc/functions/extendWithAge.html @@ -1 +1 @@ -extendWithAge | @vladmandic/face-api - v1.7.14

    Function extendWithAge

    Settings

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    +extendWithAge | @vladmandic/face-api - v1.7.15

    Function extendWithAge

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    diff --git a/typedoc/functions/extendWithFaceDescriptor.html b/typedoc/functions/extendWithFaceDescriptor.html index d7bdda0..c10f10d 100644 --- a/typedoc/functions/extendWithFaceDescriptor.html +++ b/typedoc/functions/extendWithFaceDescriptor.html @@ -1 +1 @@ -extendWithFaceDescriptor | @vladmandic/face-api - v1.7.14

    Function extendWithFaceDescriptor

    Settings

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    +extendWithFaceDescriptor | @vladmandic/face-api - v1.7.15

    Function extendWithFaceDescriptor

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    diff --git a/typedoc/functions/extendWithFaceDetection.html b/typedoc/functions/extendWithFaceDetection.html index 7519d79..6f696b2 100644 --- a/typedoc/functions/extendWithFaceDetection.html +++ b/typedoc/functions/extendWithFaceDetection.html @@ -1 +1 @@ -extendWithFaceDetection | @vladmandic/face-api - v1.7.14

    Function extendWithFaceDetection

    Settings

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    +extendWithFaceDetection | @vladmandic/face-api - v1.7.15

    Function extendWithFaceDetection

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    diff --git a/typedoc/functions/extendWithFaceExpressions.html b/typedoc/functions/extendWithFaceExpressions.html index eb33e9f..410e625 100644 --- a/typedoc/functions/extendWithFaceExpressions.html +++ b/typedoc/functions/extendWithFaceExpressions.html @@ -1 +1 @@ -extendWithFaceExpressions | @vladmandic/face-api - v1.7.14

    Function extendWithFaceExpressions

    Settings

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    +extendWithFaceExpressions | @vladmandic/face-api - v1.7.15

    Function extendWithFaceExpressions

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    diff --git a/typedoc/functions/extendWithFaceLandmarks.html b/typedoc/functions/extendWithFaceLandmarks.html index c78ac5d..a84910c 100644 --- a/typedoc/functions/extendWithFaceLandmarks.html +++ b/typedoc/functions/extendWithFaceLandmarks.html @@ -1 +1 @@ -extendWithFaceLandmarks | @vladmandic/face-api - v1.7.14

    Function extendWithFaceLandmarks

    Settings

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    +extendWithFaceLandmarks | @vladmandic/face-api - v1.7.15

    Function extendWithFaceLandmarks

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    diff --git a/typedoc/functions/extendWithGender.html b/typedoc/functions/extendWithGender.html index 9d83e48..d2a963d 100644 --- a/typedoc/functions/extendWithGender.html +++ b/typedoc/functions/extendWithGender.html @@ -1 +1 @@ -extendWithGender | @vladmandic/face-api - v1.7.14

    Function extendWithGender

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    +extendWithGender | @vladmandic/face-api - v1.7.15

    Function extendWithGender

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    diff --git a/typedoc/functions/extractFaceTensors.html b/typedoc/functions/extractFaceTensors.html index 19aa9f5..ec79704 100644 --- a/typedoc/functions/extractFaceTensors.html +++ b/typedoc/functions/extractFaceTensors.html @@ -1,8 +1,8 @@ -extractFaceTensors | @vladmandic/face-api - v1.7.14

    Function extractFaceTensors

    • extractFaceTensors(
          imageTensor: Tensor4D | Tensor3D,
          detections: (Rect | FaceDetection)[],
      ): Promise<Tensor3D[]>

    • Extracts the tensors of the image regions containing the detected faces. +extractFaceTensors | @vladmandic/face-api - v1.7.15

      Function extractFaceTensors

      • extractFaceTensors(
            imageTensor: Tensor4D | Tensor3D,
            detections: (Rect | FaceDetection)[],
        ): Promise<Tensor3D[]>

      • Extracts the tensors of the image regions containing the detected faces. Useful if you want to compute the face descriptors for the face images. Using this method is faster then extracting a canvas for each face and converting them to tensors individually.

        Parameters

        • imageTensor: Tensor4D | Tensor3D

          The image tensor that face detection has been performed on.

        • detections: (Rect | FaceDetection)[]

          The face detection results or face bounding boxes for that image.

        Returns Promise<Tensor3D[]>

        Tensors of the corresponding image region for each detected face.

        -

      Settings

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      +

    Settings

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    diff --git a/typedoc/functions/extractFaces.html b/typedoc/functions/extractFaces.html index 32f9b3a..49c9f7f 100644 --- a/typedoc/functions/extractFaces.html +++ b/typedoc/functions/extractFaces.html @@ -1,5 +1,5 @@ -extractFaces | @vladmandic/face-api - v1.7.14

    Function extractFaces

    • extractFaces(
          input: TNetInput,
          detections: (Rect | FaceDetection)[],
      ): Promise<HTMLCanvasElement[]>

    • Extracts the image regions containing the detected faces.

      +extractFaces | @vladmandic/face-api - v1.7.15

      Function extractFaces

      • extractFaces(
            input: TNetInput,
            detections: (Rect | FaceDetection)[],
        ): Promise<HTMLCanvasElement[]>

      • Extracts the image regions containing the detected faces.

        Parameters

        • input: TNetInput

          The image that face detection has been performed on.

        • detections: (Rect | FaceDetection)[]

          The face detection results or face bounding boxes for that image.

        Returns Promise<HTMLCanvasElement[]>

        The Canvases of the corresponding image region for each detected face.

        -

      Settings

      Member Visibility
      +

    Settings

    Member Visibility
    diff --git a/typedoc/functions/fetchImage.html b/typedoc/functions/fetchImage.html index 3c85585..ecb95e9 100644 --- a/typedoc/functions/fetchImage.html +++ b/typedoc/functions/fetchImage.html @@ -1 +1 @@ -fetchImage | @vladmandic/face-api - v1.7.14

    Function fetchImage

    • fetchImage(uri: string): Promise<HTMLImageElement>

    • Parameters

      • uri: string

      Returns Promise<HTMLImageElement>

    Settings

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    +fetchImage | @vladmandic/face-api - v1.7.15

    Function fetchImage

    • fetchImage(uri: string): Promise<HTMLImageElement>

    • Parameters

      • uri: string

      Returns Promise<HTMLImageElement>

    Settings

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    diff --git a/typedoc/functions/fetchJson.html b/typedoc/functions/fetchJson.html index fbb05de..c3dff34 100644 --- a/typedoc/functions/fetchJson.html +++ b/typedoc/functions/fetchJson.html @@ -1 +1 @@ -fetchJson | @vladmandic/face-api - v1.7.14

    Function fetchJson

    • fetchJson<T>(uri: string): Promise<T>

    • Type Parameters

      • T

      Parameters

      • uri: string

      Returns Promise<T>

    Settings

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    +fetchJson | @vladmandic/face-api - v1.7.15

    Function fetchJson

    • fetchJson<T>(uri: string): Promise<T>

    • Type Parameters

      • T

      Parameters

      • uri: string

      Returns Promise<T>

    Settings

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    diff --git a/typedoc/functions/fetchNetWeights.html b/typedoc/functions/fetchNetWeights.html index c4ef9a7..20f8339 100644 --- a/typedoc/functions/fetchNetWeights.html +++ b/typedoc/functions/fetchNetWeights.html @@ -1 +1 @@ -fetchNetWeights | @vladmandic/face-api - v1.7.14

    Function fetchNetWeights

    • fetchNetWeights(uri: string): Promise<Float32Array>

    • Parameters

      • uri: string

      Returns Promise<Float32Array>

    Settings

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    +fetchNetWeights | @vladmandic/face-api - v1.7.15

    Function fetchNetWeights

    • fetchNetWeights(uri: string): Promise<Float32Array>

    • Parameters

      • uri: string

      Returns Promise<Float32Array>

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    diff --git a/typedoc/functions/fetchOrThrow.html b/typedoc/functions/fetchOrThrow.html index be4babf..ce50b43 100644 --- a/typedoc/functions/fetchOrThrow.html +++ b/typedoc/functions/fetchOrThrow.html @@ -1 +1 @@ -fetchOrThrow | @vladmandic/face-api - v1.7.14

    Function fetchOrThrow

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

    • Parameters

      • url: string
      • Optionalinit: RequestInit

      Returns Promise<Response>

    Settings

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    +fetchOrThrow | @vladmandic/face-api - v1.7.15

    Function fetchOrThrow

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

    • Parameters

      • url: string
      • Optionalinit: RequestInit

      Returns Promise<Response>

    Settings

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    diff --git a/typedoc/functions/fetchVideo.html b/typedoc/functions/fetchVideo.html index cad6a3f..6fc7b57 100644 --- a/typedoc/functions/fetchVideo.html +++ b/typedoc/functions/fetchVideo.html @@ -1 +1 @@ -fetchVideo | @vladmandic/face-api - v1.7.14

    Function fetchVideo

    • fetchVideo(uri: string): Promise<HTMLVideoElement>

    • Parameters

      • uri: string

      Returns Promise<HTMLVideoElement>

    Settings

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    +fetchVideo | @vladmandic/face-api - v1.7.15

    Function fetchVideo

    • fetchVideo(uri: string): 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 ff4c0a5..61b99db 100644 --- a/typedoc/functions/getContext2dOrThrow.html +++ b/typedoc/functions/getContext2dOrThrow.html @@ -1 +1 @@ -getContext2dOrThrow | @vladmandic/face-api - v1.7.14

    Function getContext2dOrThrow

    • getContext2dOrThrow(
          canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D,
      ): CanvasRenderingContext2D

    • Parameters

      • canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D

      Returns CanvasRenderingContext2D

    Settings

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    +getContext2dOrThrow | @vladmandic/face-api - v1.7.15

    Function getContext2dOrThrow

    • getContext2dOrThrow(
          canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D,
      ): CanvasRenderingContext2D

    • Parameters

      • canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D

      Returns CanvasRenderingContext2D

    Settings

    Member Visibility
    diff --git a/typedoc/functions/getMediaDimensions.html b/typedoc/functions/getMediaDimensions.html index a1bf1e0..3c0aa4b 100644 --- a/typedoc/functions/getMediaDimensions.html +++ b/typedoc/functions/getMediaDimensions.html @@ -1 +1 @@ -getMediaDimensions | @vladmandic/face-api - v1.7.14

    Function getMediaDimensions

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    +getMediaDimensions | @vladmandic/face-api - v1.7.15

    Function getMediaDimensions

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    diff --git a/typedoc/functions/imageTensorToCanvas.html b/typedoc/functions/imageTensorToCanvas.html index 137f746..e38155c 100644 --- a/typedoc/functions/imageTensorToCanvas.html +++ b/typedoc/functions/imageTensorToCanvas.html @@ -1 +1 @@ -imageTensorToCanvas | @vladmandic/face-api - v1.7.14

    Function imageTensorToCanvas

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

    • Parameters

      • imgTensor: tf.Tensor
      • Optionalcanvas: HTMLCanvasElement

      Returns Promise<HTMLCanvasElement>

    Settings

    Member Visibility
    +imageTensorToCanvas | @vladmandic/face-api - v1.7.15

    Function imageTensorToCanvas

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

    • Parameters

      • imgTensor: tf.Tensor
      • Optionalcanvas: HTMLCanvasElement

      Returns Promise<HTMLCanvasElement>

    Settings

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    diff --git a/typedoc/functions/imageToSquare.html b/typedoc/functions/imageToSquare.html index 102aa3f..db89fa5 100644 --- a/typedoc/functions/imageToSquare.html +++ b/typedoc/functions/imageToSquare.html @@ -1 +1 @@ -imageToSquare | @vladmandic/face-api - v1.7.14

    Function imageToSquare

    • imageToSquare(
          input: HTMLCanvasElement | HTMLImageElement,
          inputSize: number,
          centerImage?: boolean,
      ): HTMLCanvasElement

    • Parameters

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

      Returns HTMLCanvasElement

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    +imageToSquare | @vladmandic/face-api - v1.7.15

    Function imageToSquare

    • imageToSquare(
          input: HTMLCanvasElement | HTMLImageElement,
          inputSize: number,
          centerImage?: boolean,
      ): 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 5090651..6ffbbec 100644 --- a/typedoc/functions/inverseSigmoid.html +++ b/typedoc/functions/inverseSigmoid.html @@ -1 +1 @@ -inverseSigmoid | @vladmandic/face-api - v1.7.14

    Function inverseSigmoid

    • inverseSigmoid(x: number): number

    • Parameters

      • x: number

      Returns number

    Settings

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    +inverseSigmoid | @vladmandic/face-api - v1.7.15

    Function inverseSigmoid

    • inverseSigmoid(x: number): number

    • Parameters

      • x: number

      Returns number

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    diff --git a/typedoc/functions/iou.html b/typedoc/functions/iou.html index 6a180a6..f557c26 100644 --- a/typedoc/functions/iou.html +++ b/typedoc/functions/iou.html @@ -1 +1 @@ -iou | @vladmandic/face-api - v1.7.14

    Function iou

    • iou(box1: Box, box2: Box, isIOU?: boolean): number

    • Parameters

      • box1: Box
      • box2: Box
      • isIOU: boolean = true

      Returns number

    Settings

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    +iou | @vladmandic/face-api - v1.7.15

    Function iou

    • iou(box1: Box, box2: Box, isIOU?: boolean): number

    • Parameters

      • box1: Box
      • box2: Box
      • isIOU: boolean = true

      Returns number

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    diff --git a/typedoc/functions/isMediaElement.html b/typedoc/functions/isMediaElement.html index c11e0b7..ddae07e 100644 --- a/typedoc/functions/isMediaElement.html +++ b/typedoc/functions/isMediaElement.html @@ -1 +1 @@ -isMediaElement | @vladmandic/face-api - v1.7.14

    Function isMediaElement

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

    • Parameters

      • input: any

      Returns input is HTMLCanvasElement | HTMLImageElement | HTMLVideoElement

    Settings

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    +isMediaElement | @vladmandic/face-api - v1.7.15

    Function isMediaElement

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

    • Parameters

      • input: any

      Returns input is HTMLCanvasElement | HTMLImageElement | HTMLVideoElement

    Settings

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    diff --git a/typedoc/functions/isMediaLoaded.html b/typedoc/functions/isMediaLoaded.html index 5ba2ff2..f5531d2 100644 --- a/typedoc/functions/isMediaLoaded.html +++ b/typedoc/functions/isMediaLoaded.html @@ -1 +1 @@ -isMediaLoaded | @vladmandic/face-api - v1.7.14

    Function isMediaLoaded

    • isMediaLoaded(media: HTMLImageElement | HTMLVideoElement): boolean

    • Parameters

      • media: HTMLImageElement | HTMLVideoElement

      Returns boolean

    Settings

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    +isMediaLoaded | @vladmandic/face-api - v1.7.15

    Function isMediaLoaded

    • isMediaLoaded(media: HTMLImageElement | HTMLVideoElement): boolean

    • Parameters

      • media: HTMLImageElement | HTMLVideoElement

      Returns boolean

    Settings

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    diff --git a/typedoc/functions/isWithAge.html b/typedoc/functions/isWithAge.html index acc0308..b586f2a 100644 --- a/typedoc/functions/isWithAge.html +++ b/typedoc/functions/isWithAge.html @@ -1 +1 @@ -isWithAge | @vladmandic/face-api - v1.7.14

    Function isWithAge

    Settings

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    +isWithAge | @vladmandic/face-api - v1.7.15

    Function isWithAge

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    diff --git a/typedoc/functions/isWithFaceDetection.html b/typedoc/functions/isWithFaceDetection.html index 73ff339..07d9926 100644 --- a/typedoc/functions/isWithFaceDetection.html +++ b/typedoc/functions/isWithFaceDetection.html @@ -1 +1 @@ -isWithFaceDetection | @vladmandic/face-api - v1.7.14

    Function isWithFaceDetection

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    +isWithFaceDetection | @vladmandic/face-api - v1.7.15

    Function isWithFaceDetection

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    diff --git a/typedoc/functions/isWithFaceExpressions.html b/typedoc/functions/isWithFaceExpressions.html index c129b27..8e2bd64 100644 --- a/typedoc/functions/isWithFaceExpressions.html +++ b/typedoc/functions/isWithFaceExpressions.html @@ -1 +1 @@ -isWithFaceExpressions | @vladmandic/face-api - v1.7.14

    Function isWithFaceExpressions

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    +isWithFaceExpressions | @vladmandic/face-api - v1.7.15

    Function isWithFaceExpressions

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    diff --git a/typedoc/functions/isWithFaceLandmarks.html b/typedoc/functions/isWithFaceLandmarks.html index 4862e3f..5ee9602 100644 --- a/typedoc/functions/isWithFaceLandmarks.html +++ b/typedoc/functions/isWithFaceLandmarks.html @@ -1 +1 @@ -isWithFaceLandmarks | @vladmandic/face-api - v1.7.14

    Function isWithFaceLandmarks

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    +isWithFaceLandmarks | @vladmandic/face-api - v1.7.15

    Function isWithFaceLandmarks

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    diff --git a/typedoc/functions/isWithGender.html b/typedoc/functions/isWithGender.html index 06c7374..1b9926a 100644 --- a/typedoc/functions/isWithGender.html +++ b/typedoc/functions/isWithGender.html @@ -1 +1 @@ -isWithGender | @vladmandic/face-api - v1.7.14

    Function isWithGender

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    +isWithGender | @vladmandic/face-api - v1.7.15

    Function isWithGender

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    diff --git a/typedoc/functions/loadAgeGenderModel.html b/typedoc/functions/loadAgeGenderModel.html index 0593f37..1830b22 100644 --- a/typedoc/functions/loadAgeGenderModel.html +++ b/typedoc/functions/loadAgeGenderModel.html @@ -1 +1 @@ -loadAgeGenderModel | @vladmandic/face-api - v1.7.14

    Function loadAgeGenderModel

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    +loadAgeGenderModel | @vladmandic/face-api - v1.7.15

    Function loadAgeGenderModel

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    diff --git a/typedoc/functions/loadFaceDetectionModel.html b/typedoc/functions/loadFaceDetectionModel.html index 599e3de..ee641f6 100644 --- a/typedoc/functions/loadFaceDetectionModel.html +++ b/typedoc/functions/loadFaceDetectionModel.html @@ -1 +1 @@ -loadFaceDetectionModel | @vladmandic/face-api - v1.7.14

    Function loadFaceDetectionModel

    • loadFaceDetectionModel(url: string): Promise<void>

    • Parameters

      • url: string

      Returns Promise<void>

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    +loadFaceDetectionModel | @vladmandic/face-api - v1.7.15

    Function loadFaceDetectionModel

    • loadFaceDetectionModel(url: string): Promise<void>

    • Parameters

      • url: string

      Returns Promise<void>

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    diff --git a/typedoc/functions/loadFaceExpressionModel.html b/typedoc/functions/loadFaceExpressionModel.html index dfd3580..6b312c7 100644 --- a/typedoc/functions/loadFaceExpressionModel.html +++ b/typedoc/functions/loadFaceExpressionModel.html @@ -1 +1 @@ -loadFaceExpressionModel | @vladmandic/face-api - v1.7.14

    Function loadFaceExpressionModel

    • loadFaceExpressionModel(url: string): Promise<void>

    • Parameters

      • url: string

      Returns Promise<void>

    Settings

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    +loadFaceExpressionModel | @vladmandic/face-api - v1.7.15

    Function loadFaceExpressionModel

    • loadFaceExpressionModel(url: string): Promise<void>

    • Parameters

      • url: string

      Returns Promise<void>

    Settings

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    diff --git a/typedoc/functions/loadFaceLandmarkModel.html b/typedoc/functions/loadFaceLandmarkModel.html index 46612d2..179fd0b 100644 --- a/typedoc/functions/loadFaceLandmarkModel.html +++ b/typedoc/functions/loadFaceLandmarkModel.html @@ -1 +1 @@ -loadFaceLandmarkModel | @vladmandic/face-api - v1.7.14

    Function loadFaceLandmarkModel

    • loadFaceLandmarkModel(url: string): Promise<void>

    • Parameters

      • url: string

      Returns Promise<void>

    Settings

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    +loadFaceLandmarkModel | @vladmandic/face-api - v1.7.15

    Function loadFaceLandmarkModel

    • loadFaceLandmarkModel(url: string): Promise<void>

    • Parameters

      • url: string

      Returns Promise<void>

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    diff --git a/typedoc/functions/loadFaceLandmarkTinyModel.html b/typedoc/functions/loadFaceLandmarkTinyModel.html index 9a6ac67..9893796 100644 --- a/typedoc/functions/loadFaceLandmarkTinyModel.html +++ b/typedoc/functions/loadFaceLandmarkTinyModel.html @@ -1 +1 @@ -loadFaceLandmarkTinyModel | @vladmandic/face-api - v1.7.14

    Function loadFaceLandmarkTinyModel

    • loadFaceLandmarkTinyModel(url: string): Promise<void>

    • Parameters

      • url: string

      Returns Promise<void>

    Settings

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    +loadFaceLandmarkTinyModel | @vladmandic/face-api - v1.7.15

    Function loadFaceLandmarkTinyModel

    • loadFaceLandmarkTinyModel(url: string): Promise<void>

    • Parameters

      • url: string

      Returns Promise<void>

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    diff --git a/typedoc/functions/loadFaceRecognitionModel.html b/typedoc/functions/loadFaceRecognitionModel.html index 0f38c1f..7d1931a 100644 --- a/typedoc/functions/loadFaceRecognitionModel.html +++ b/typedoc/functions/loadFaceRecognitionModel.html @@ -1 +1 @@ -loadFaceRecognitionModel | @vladmandic/face-api - v1.7.14

    Function loadFaceRecognitionModel

    • loadFaceRecognitionModel(url: string): Promise<void>

    • Parameters

      • url: string

      Returns Promise<void>

    Settings

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    +loadFaceRecognitionModel | @vladmandic/face-api - v1.7.15

    Function loadFaceRecognitionModel

    • loadFaceRecognitionModel(url: string): Promise<void>

    • Parameters

      • url: string

      Returns Promise<void>

    Settings

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    diff --git a/typedoc/functions/loadSsdMobilenetv1Model.html b/typedoc/functions/loadSsdMobilenetv1Model.html index 1e01b8d..a27f7b4 100644 --- a/typedoc/functions/loadSsdMobilenetv1Model.html +++ b/typedoc/functions/loadSsdMobilenetv1Model.html @@ -1 +1 @@ -loadSsdMobilenetv1Model | @vladmandic/face-api - v1.7.14

    Function loadSsdMobilenetv1Model

    • loadSsdMobilenetv1Model(url: string): Promise<void>

    • Parameters

      • url: string

      Returns Promise<void>

    Settings

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    +loadSsdMobilenetv1Model | @vladmandic/face-api - v1.7.15

    Function loadSsdMobilenetv1Model

    • loadSsdMobilenetv1Model(url: string): Promise<void>

    • Parameters

      • url: string

      Returns Promise<void>

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    diff --git a/typedoc/functions/loadTinyFaceDetectorModel.html b/typedoc/functions/loadTinyFaceDetectorModel.html index bfe2606..675a16b 100644 --- a/typedoc/functions/loadTinyFaceDetectorModel.html +++ b/typedoc/functions/loadTinyFaceDetectorModel.html @@ -1 +1 @@ -loadTinyFaceDetectorModel | @vladmandic/face-api - v1.7.14

    Function loadTinyFaceDetectorModel

    • loadTinyFaceDetectorModel(url: string): Promise<void>

    • Parameters

      • url: string

      Returns Promise<void>

    Settings

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    +loadTinyFaceDetectorModel | @vladmandic/face-api - v1.7.15

    Function loadTinyFaceDetectorModel

    • loadTinyFaceDetectorModel(url: string): Promise<void>

    • Parameters

      • url: string

      Returns Promise<void>

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    diff --git a/typedoc/functions/loadTinyYolov2Model.html b/typedoc/functions/loadTinyYolov2Model.html index f312669..05adf0c 100644 --- a/typedoc/functions/loadTinyYolov2Model.html +++ b/typedoc/functions/loadTinyYolov2Model.html @@ -1 +1 @@ -loadTinyYolov2Model | @vladmandic/face-api - v1.7.14

    Function loadTinyYolov2Model

    • loadTinyYolov2Model(url: string): Promise<void>

    • Parameters

      • url: string

      Returns Promise<void>

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    +loadTinyYolov2Model | @vladmandic/face-api - v1.7.15

    Function loadTinyYolov2Model

    • loadTinyYolov2Model(url: string): Promise<void>

    • Parameters

      • url: string

      Returns Promise<void>

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    diff --git a/typedoc/functions/loadWeightMap.html b/typedoc/functions/loadWeightMap.html index 2c8ff00..a5dc565 100644 --- a/typedoc/functions/loadWeightMap.html +++ b/typedoc/functions/loadWeightMap.html @@ -1 +1 @@ -loadWeightMap | @vladmandic/face-api - v1.7.14

    Function loadWeightMap

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    +loadWeightMap | @vladmandic/face-api - v1.7.15

    Function loadWeightMap

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    diff --git a/typedoc/functions/locateFaces.html b/typedoc/functions/locateFaces.html index ed8c3bc..af44e63 100644 --- a/typedoc/functions/locateFaces.html +++ b/typedoc/functions/locateFaces.html @@ -1,5 +1,5 @@ -locateFaces | @vladmandic/face-api - v1.7.14

    Function locateFaces

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    diff --git a/typedoc/functions/matchDimensions.html b/typedoc/functions/matchDimensions.html index 18a1839..c21e27f 100644 --- a/typedoc/functions/matchDimensions.html +++ b/typedoc/functions/matchDimensions.html @@ -1 +1 @@ -matchDimensions | @vladmandic/face-api - v1.7.14

    Function matchDimensions

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    +matchDimensions | @vladmandic/face-api - v1.7.15

    Function matchDimensions

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    diff --git a/typedoc/functions/minBbox.html b/typedoc/functions/minBbox.html index 9854174..3ca712e 100644 --- a/typedoc/functions/minBbox.html +++ b/typedoc/functions/minBbox.html @@ -1 +1 @@ -minBbox | @vladmandic/face-api - v1.7.14

    Function minBbox

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    +minBbox | @vladmandic/face-api - v1.7.15

    Function minBbox

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    diff --git a/typedoc/functions/nonMaxSuppression.html b/typedoc/functions/nonMaxSuppression.html index e169f71..128ad3f 100644 --- a/typedoc/functions/nonMaxSuppression.html +++ b/typedoc/functions/nonMaxSuppression.html @@ -1 +1 @@ -nonMaxSuppression | @vladmandic/face-api - v1.7.14

    Function nonMaxSuppression

    • nonMaxSuppression(
          boxes: Box[],
          scores: number[],
          iouThreshold: number,
          isIOU?: boolean,
      ): number[]

    • Parameters

      • boxes: Box[]
      • scores: number[]
      • iouThreshold: number
      • isIOU: boolean = true

      Returns number[]

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    +nonMaxSuppression | @vladmandic/face-api - v1.7.15

    Function nonMaxSuppression

    • nonMaxSuppression(
          boxes: Box[],
          scores: number[],
          iouThreshold: number,
          isIOU?: boolean,
      ): number[]

    • Parameters

      • boxes: Box[]
      • scores: number[]
      • iouThreshold: number
      • isIOU: boolean = true

      Returns number[]

    Settings

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    diff --git a/typedoc/functions/normalize.html b/typedoc/functions/normalize.html index 7d3e52f..3b9025a 100644 --- a/typedoc/functions/normalize.html +++ b/typedoc/functions/normalize.html @@ -1 +1 @@ -normalize | @vladmandic/face-api - v1.7.14

    Function normalize

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    +normalize | @vladmandic/face-api - v1.7.15

    Function normalize

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    diff --git a/typedoc/functions/padToSquare.html b/typedoc/functions/padToSquare.html index 01bd94d..0822642 100644 --- a/typedoc/functions/padToSquare.html +++ b/typedoc/functions/padToSquare.html @@ -1,6 +1,6 @@ -padToSquare | @vladmandic/face-api - v1.7.14

    Function padToSquare

    • padToSquare(imgTensor: Tensor4D, isCenterImage?: boolean): Tensor4D

    • Pads the smaller dimension of an image tensor with zeros, such that width === height.

      +padToSquare | @vladmandic/face-api - v1.7.15

      Function padToSquare

      • padToSquare(imgTensor: Tensor4D, isCenterImage?: boolean): Tensor4D

      • Pads the smaller dimension of an image tensor with zeros, such that width === height.

        Parameters

        • imgTensor: Tensor4D

          The image tensor.

        • isCenterImage: boolean = false

          (optional, default: false) If true, add an equal amount of padding on both sides of the minor dimension oof the image.

        Returns Tensor4D

        The padded tensor with width === height.

        -

      Settings

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      +

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    diff --git a/typedoc/functions/predictAgeAndGender.html b/typedoc/functions/predictAgeAndGender.html index 09290ad..9a10c60 100644 --- a/typedoc/functions/predictAgeAndGender.html +++ b/typedoc/functions/predictAgeAndGender.html @@ -1,3 +1,3 @@ -predictAgeAndGender | @vladmandic/face-api - v1.7.14

    Function predictAgeAndGender

    Predicts age and gender from a face image.

    +predictAgeAndGender | @vladmandic/face-api - v1.7.15

    Function predictAgeAndGender

    Predicts age and gender from a face image.

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    +

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    diff --git a/typedoc/functions/recognizeFaceExpressions.html b/typedoc/functions/recognizeFaceExpressions.html index 1afcd61..95d1d90 100644 --- a/typedoc/functions/recognizeFaceExpressions.html +++ b/typedoc/functions/recognizeFaceExpressions.html @@ -1,3 +1,3 @@ -recognizeFaceExpressions | @vladmandic/face-api - v1.7.14

    Function recognizeFaceExpressions

    Recognizes the facial expressions from a face image.

    +recognizeFaceExpressions | @vladmandic/face-api - v1.7.15

    Function recognizeFaceExpressions

    Recognizes the facial expressions from a face image.

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    +

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    diff --git a/typedoc/functions/resizeResults.html b/typedoc/functions/resizeResults.html index 7b2afb3..3f4bdf5 100644 --- a/typedoc/functions/resizeResults.html +++ b/typedoc/functions/resizeResults.html @@ -1 +1 @@ -resizeResults | @vladmandic/face-api - v1.7.14

    Function resizeResults

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    +resizeResults | @vladmandic/face-api - v1.7.15

    Function resizeResults

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    diff --git a/typedoc/functions/resolveInput.html b/typedoc/functions/resolveInput.html index e56c98f..08a8694 100644 --- a/typedoc/functions/resolveInput.html +++ b/typedoc/functions/resolveInput.html @@ -1 +1 @@ -resolveInput | @vladmandic/face-api - v1.7.14

    Function resolveInput

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    +resolveInput | @vladmandic/face-api - v1.7.15

    Function resolveInput

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    diff --git a/typedoc/functions/shuffleArray.html b/typedoc/functions/shuffleArray.html index 70c58e6..916c573 100644 --- a/typedoc/functions/shuffleArray.html +++ b/typedoc/functions/shuffleArray.html @@ -1 +1 @@ -shuffleArray | @vladmandic/face-api - v1.7.14

    Function shuffleArray

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    +shuffleArray | @vladmandic/face-api - v1.7.15

    Function shuffleArray

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    diff --git a/typedoc/functions/sigmoid.html b/typedoc/functions/sigmoid.html index b045c7b..4da41ab 100644 --- a/typedoc/functions/sigmoid.html +++ b/typedoc/functions/sigmoid.html @@ -1 +1 @@ -sigmoid | @vladmandic/face-api - v1.7.14

    Function sigmoid

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    +sigmoid | @vladmandic/face-api - v1.7.15

    Function sigmoid

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    diff --git a/typedoc/functions/ssdMobilenetv1-1.html b/typedoc/functions/ssdMobilenetv1-1.html index 639efe8..2667cc6 100644 --- a/typedoc/functions/ssdMobilenetv1-1.html +++ b/typedoc/functions/ssdMobilenetv1-1.html @@ -1,5 +1,5 @@ -ssdMobilenetv1 | @vladmandic/face-api - v1.7.14

    Function ssdMobilenetv1

    Attempts to detect all faces in an image using SSD Mobilenetv1 Network.

    +ssdMobilenetv1 | @vladmandic/face-api - v1.7.15

    Function ssdMobilenetv1

    Attempts to detect all faces in an image using SSD Mobilenetv1 Network.

    Settings

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    +

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    diff --git a/typedoc/functions/tf.add.html b/typedoc/functions/tf.add.html index 0ae97ac..811a5ae 100644 --- a/typedoc/functions/tf.add.html +++ b/typedoc/functions/tf.add.html @@ -1,4 +1,4 @@ -add | @vladmandic/face-api - v1.7.14

    Function add

    • add<T extends tf.Tensor>(
          a: TensorLike | tf.Tensor,
          b: TensorLike | tf.Tensor,
      ): T

    • Adds two tf.Tensors element-wise, A + B. Supports broadcasting.

      +add | @vladmandic/face-api - v1.7.15

      Function add

      • add<T extends tf.Tensor>(
            a: TensorLike | tf.Tensor,
            b: TensorLike | tf.Tensor,
        ): 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)
        @@ -7,4 +7,4 @@

        Type Parameters

        Parameters

        Returns T

        Doc

      Settings

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      +

    Returns T

    Doc

    Settings

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    diff --git a/typedoc/functions/tf.avgPool.html b/typedoc/functions/tf.avgPool.html index fc9cb8f..17d65d5 100644 --- a/typedoc/functions/tf.avgPool.html +++ b/typedoc/functions/tf.avgPool.html @@ -1,4 +1,4 @@ -avgPool | @vladmandic/face-api - v1.7.14

    Function avgPool

    • avgPool<T extends Tensor4D | Tensor3D>(
          x: TensorLike | T,
          filterSize: number | [number, number],
          strides: number | [number, number],
          pad: number | "valid" | "same" | ExplicitPadding,
          dimRoundingMode?: "floor" | "round" | "ceil",
      ): T

    • Computes the 2D average pooling of an image.

      +avgPool | @vladmandic/face-api - v1.7.15

      Function avgPool

      • avgPool<T extends Tensor4D | Tensor3D>(
            x: TensorLike | T,
            filterSize: number | [number, number],
            strides: number | [number, number],
            pad: number | "valid" | "same" | ExplicitPadding,
            dimRoundingMode?: "floor" | "round" | "ceil",
        ): T

      • Computes the 2D average pooling of an image.

        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.

        • filterSize: number | [number, number]

          The filter size: [filterHeight, filterWidth]. If @@ -16,4 +16,4 @@ than 1x1.

      • 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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    +

    Returns T

    Doc

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    diff --git a/typedoc/functions/tf.batchNorm.html b/typedoc/functions/tf.batchNorm.html index 40cc5ba..9419e43 100644 --- a/typedoc/functions/tf.batchNorm.html +++ b/typedoc/functions/tf.batchNorm.html @@ -1,4 +1,4 @@ -batchNorm | @vladmandic/face-api - v1.7.14

    Function batchNorm

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    +

    Returns tf.Tensor<R>

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    diff --git a/typedoc/functions/tf.browser.draw.html b/typedoc/functions/tf.browser.draw.html index 6a68d7f..ed8a89b 100644 --- a/typedoc/functions/tf.browser.draw.html +++ b/typedoc/functions/tf.browser.draw.html @@ -1,4 +1,4 @@ -draw | @vladmandic/face-api - v1.7.14

    Function draw

    • draw(
          image: Tensor2D | TensorLike | Tensor3D,
          canvas: HTMLCanvasElement,
          options?: DrawOptions,
      ): void

    • Draws a tf.Tensor to a canvas.

      +draw | @vladmandic/face-api - v1.7.15

      Function draw

      • draw(
            image: Tensor2D | TensorLike | Tensor3D,
            canvas: HTMLCanvasElement,
            options?: DrawOptions,
        ): void

      • Draws a tf.Tensor to a canvas.

        When the dtype of the input is 'float32', we assume values in the range [0-1]. Otherwise, when input is 'int32', we assume values in the range [0-255].

        @@ -14,4 +14,4 @@ these shapes:

      • canvas: HTMLCanvasElement

        The canvas to draw to.

      • Optionaloptions: DrawOptions

        The configuration arguments for image to be drawn and the canvas to draw to.

        -

      Returns void

      Doc

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    Returns void

    Doc

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    diff --git a/typedoc/functions/tf.browser.fromPixels.html b/typedoc/functions/tf.browser.fromPixels.html index 341e635..4a0cb08 100644 --- a/typedoc/functions/tf.browser.fromPixels.html +++ b/typedoc/functions/tf.browser.fromPixels.html @@ -1,4 +1,4 @@ -fromPixels | @vladmandic/face-api - v1.7.14

    Function fromPixels

    • fromPixels(
          pixels:
              | HTMLCanvasElement
              | HTMLImageElement
              | HTMLVideoElement
              | ImageData
              | ImageBitmap
              | PixelData,
          numChannels?: number,
      ): Tensor3D

    • Creates a tf.Tensor from an image.

      +fromPixels | @vladmandic/face-api - v1.7.15

      Function fromPixels

      • fromPixels(
            pixels:
                | HTMLCanvasElement
                | HTMLImageElement
                | HTMLVideoElement
                | ImageData
                | ImageBitmap
                | PixelData,
            numChannels?: number,
        ): Tensor3D

      • Creates a tf.Tensor from an image.

        const image = new ImageData(1, 1);
        image.data[0] = 100;
        image.data[1] = 150;
        image.data[2] = 200;
        image.data[3] = 255;

        tf.browser.fromPixels(image).print();
        @@ -16,4 +16,4 @@ engines. This means that results from different browsers, or even same browser with CPU and GPU rendering engines can be different. See discussion in details: https://github.com/tensorflow/tfjs/issues/5482

        -

        Doc

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    diff --git a/typedoc/functions/tf.browser.fromPixelsAsync.html b/typedoc/functions/tf.browser.fromPixelsAsync.html index aaaf6ef..0b49b77 100644 --- a/typedoc/functions/tf.browser.fromPixelsAsync.html +++ b/typedoc/functions/tf.browser.fromPixelsAsync.html @@ -1,4 +1,4 @@ -fromPixelsAsync | @vladmandic/face-api - v1.7.14

    Function fromPixelsAsync

    • fromPixelsAsync(
          pixels:
              | HTMLCanvasElement
              | HTMLImageElement
              | HTMLVideoElement
              | ImageData
              | ImageBitmap
              | PixelData,
          numChannels?: number,
      ): Promise<Tensor3D>

    • Creates a tf.Tensor from an image in async way.

      +fromPixelsAsync | @vladmandic/face-api - v1.7.15

      Function fromPixelsAsync

      • fromPixelsAsync(
            pixels:
                | HTMLCanvasElement
                | HTMLImageElement
                | HTMLVideoElement
                | ImageData
                | ImageBitmap
                | PixelData,
            numChannels?: number,
        ): 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();
        @@ -12,4 +12,4 @@ object with following attributes:
      • 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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    Returns Promise<Tensor3D>

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    diff --git a/typedoc/functions/tf.browser.toPixels.html b/typedoc/functions/tf.browser.toPixels.html index 2b4c82d..23e5262 100644 --- a/typedoc/functions/tf.browser.toPixels.html +++ b/typedoc/functions/tf.browser.toPixels.html @@ -1,4 +1,4 @@ -toPixels | @vladmandic/face-api - v1.7.14

    Function toPixels

    • toPixels(
          img: Tensor2D | TensorLike | Tensor3D,
          canvas?: HTMLCanvasElement,
      ): Promise<Uint8ClampedArray>

    • Draws a tf.Tensor of pixel values to a byte array or optionally a +toPixels | @vladmandic/face-api - v1.7.15

      Function toPixels

      • toPixels(
            img: Tensor2D | TensorLike | Tensor3D,
            canvas?: HTMLCanvasElement,
        ): Promise<Uint8ClampedArray>

      • Draws a tf.Tensor of pixel values to a byte array or optionally a canvas.

        When the dtype of the input is 'float32', we assume values in the range [0-1]. Otherwise, when input is 'int32', we assume values in the range @@ -11,4 +11,4 @@ grayscale. When depth of 3, we draw with the first three components of the depth dimension corresponding to r, g, b and alpha = 1. When depth of 4, all four components of the depth dimension correspond to r, g, b, a.

      • Optionalcanvas: HTMLCanvasElement

        The canvas to draw to.

        -

      Returns Promise<Uint8ClampedArray>

      Doc

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    +

    Returns Promise<Uint8ClampedArray>

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    diff --git a/typedoc/functions/tf.cast.html b/typedoc/functions/tf.cast.html index 3cb3898..b160ca7 100644 --- a/typedoc/functions/tf.cast.html +++ b/typedoc/functions/tf.cast.html @@ -1,7 +1,7 @@ -cast | @vladmandic/face-api - v1.7.14

    Function cast

    • cast<T extends tf.Tensor>(x: TensorLike | T, dtype: keyof DataTypeMap): T

    • Casts a tf.Tensor to a new dtype.

      +cast | @vladmandic/face-api - v1.7.15

      Function cast

      • cast<T extends tf.Tensor>(x: TensorLike | T, dtype: keyof DataTypeMap): 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

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    Returns T

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    diff --git a/typedoc/functions/tf.clipByValue.html b/typedoc/functions/tf.clipByValue.html index d288b1d..3cf9ed7 100644 --- a/typedoc/functions/tf.clipByValue.html +++ b/typedoc/functions/tf.clipByValue.html @@ -1,8 +1,8 @@ -clipByValue | @vladmandic/face-api - v1.7.14

    Function clipByValue

    • clipByValue<T extends tf.Tensor>(
          x: TensorLike | T,
          clipValueMin: number,
          clipValueMax: number,
      ): T

    • Clips values element-wise. max(min(x, clipValueMax), clipValueMin)

      +clipByValue | @vladmandic/face-api - v1.7.15

      Function clipByValue

      • clipByValue<T extends tf.Tensor>(
            x: TensorLike | T,
            clipValueMin: number,
            clipValueMax: number,
        ): 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

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    Returns T

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    diff --git a/typedoc/functions/tf.concat.html b/typedoc/functions/tf.concat.html index 6adea10..9a0b587 100644 --- a/typedoc/functions/tf.concat.html +++ b/typedoc/functions/tf.concat.html @@ -1,4 +1,4 @@ -concat | @vladmandic/face-api - v1.7.14

    Function concat

    • concat<T extends tf.Tensor>(tensors: (TensorLike | T)[], axis?: number): T

    • Concatenates a list of tf.Tensors along a given axis.

      +concat | @vladmandic/face-api - v1.7.15

      Function concat

      • concat<T extends tf.Tensor>(tensors: (TensorLike | T)[], axis?: number): 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 @@ -22,4 +22,4 @@ same signature as this method.

        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

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    diff --git a/typedoc/functions/tf.conv2d.html b/typedoc/functions/tf.conv2d.html index cf8dba4..7ed8d7c 100644 --- a/typedoc/functions/tf.conv2d.html +++ b/typedoc/functions/tf.conv2d.html @@ -1,4 +1,4 @@ -conv2d | @vladmandic/face-api - v1.7.14

    Function conv2d

    • conv2d<T extends Tensor4D | Tensor3D>(
          x: TensorLike | T,
          filter: Tensor4D | TensorLike,
          strides: number | [number, number],
          pad: number | "valid" | "same" | ExplicitPadding,
          dataFormat?: "NHWC" | "NCHW",
          dilations?: number | [number, number],
          dimRoundingMode?: "floor" | "round" | "ceil",
      ): T

    • Computes a 2D convolution over the input x.

      +conv2d | @vladmandic/face-api - v1.7.15

      Function conv2d

      • conv2d<T extends Tensor4D | Tensor3D>(
            x: TensorLike | T,
            filter: Tensor4D | TensorLike,
            strides: number | [number, number],
            pad: number | "valid" | "same" | ExplicitPadding,
            dataFormat?: "NHWC" | "NCHW",
            dilations?: number | [number, number],
            dimRoundingMode?: "floor" | "round" | "ceil",
        ): 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.

          @@ -21,4 +21,4 @@ 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

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    diff --git a/typedoc/functions/tf.depthwiseConv2d.html b/typedoc/functions/tf.depthwiseConv2d.html index 5cd1182..f77fa52 100644 --- a/typedoc/functions/tf.depthwiseConv2d.html +++ b/typedoc/functions/tf.depthwiseConv2d.html @@ -1,4 +1,4 @@ -depthwiseConv2d | @vladmandic/face-api - v1.7.14

    Function depthwiseConv2d

    • depthwiseConv2d<T extends Tensor4D | Tensor3D>(
          x: TensorLike | T,
          filter: Tensor4D | TensorLike,
          strides: number | [number, number],
          pad: number | "valid" | "same" | ExplicitPadding,
          dataFormat?: "NHWC" | "NCHW",
          dilations?: number | [number, number],
          dimRoundingMode?: "floor" | "round" | "ceil",
      ): T

    • Depthwise 2D convolution.

      +depthwiseConv2d | @vladmandic/face-api - v1.7.15

      Function depthwiseConv2d

      • depthwiseConv2d<T extends Tensor4D | Tensor3D>(
            x: TensorLike | T,
            filter: Tensor4D | TensorLike,
            strides: number | [number, number],
            pad: number | "valid" | "same" | ExplicitPadding,
            dataFormat?: "NHWC" | "NCHW",
            dilations?: number | [number, number],
            dimRoundingMode?: "floor" | "round" | "ceil",
        ): 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 @@ -30,4 +30,4 @@ 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

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    diff --git a/typedoc/functions/tf.div.html b/typedoc/functions/tf.div.html index 8d77343..cabd16b 100644 --- a/typedoc/functions/tf.div.html +++ b/typedoc/functions/tf.div.html @@ -1,4 +1,4 @@ -div | @vladmandic/face-api - v1.7.14

    Function div

    • div<T extends tf.Tensor>(
          a: TensorLike | tf.Tensor,
          b: TensorLike | tf.Tensor,
      ): T

    • Divides two tf.Tensors element-wise, A / B. Supports broadcasting.

      +div | @vladmandic/face-api - v1.7.15

      Function div

      • div<T extends tf.Tensor>(
            a: TensorLike | tf.Tensor,
            b: TensorLike | tf.Tensor,
        ): T

      • Divides two tf.Tensors element-wise, A / B. Supports broadcasting.

        const a = tf.tensor1d([1, 4, 9, 16]);
        const b = tf.tensor1d([1, 2, 3, 4]);

        a.div(b).print();  // or tf.div(a, b)
        @@ -8,4 +8,4 @@

        Type Parameters

        Parameters

        Returns T

        Doc

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    Returns T

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    diff --git a/typedoc/functions/tf.exp.html b/typedoc/functions/tf.exp.html index a0c97d8..95bf64e 100644 --- a/typedoc/functions/tf.exp.html +++ b/typedoc/functions/tf.exp.html @@ -1,6 +1,6 @@ -exp | @vladmandic/face-api - v1.7.14

    Function exp

    • exp<T extends tf.Tensor>(x: TensorLike | T): T

    • Computes exponential of the input tf.Tensor element-wise. e ^ x

      +exp | @vladmandic/face-api - v1.7.15

      Function exp

      • exp<T extends tf.Tensor>(x: TensorLike | T): 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 1ccd12d..b448372 100644 --- a/typedoc/functions/tf.expandDims.html +++ b/typedoc/functions/tf.expandDims.html @@ -1,4 +1,4 @@ -expandDims | @vladmandic/face-api - v1.7.14

    Function expandDims

    • expandDims<T extends tf.Tensor>(x: TensorLike | tf.Tensor, axis?: number): T

    • Returns a tf.Tensor that has expanded rank, by inserting a dimension +expandDims | @vladmandic/face-api - v1.7.15

      Function expandDims

      • expandDims<T extends tf.Tensor>(x: TensorLike | tf.Tensor, axis?: number): 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();
        @@ -6,4 +6,4 @@ into the tensor's shape.

        Type Parameters

        Parameters

        • x: TensorLike | tf.Tensor

          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

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    Returns T

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    diff --git a/typedoc/functions/tf.fill.html b/typedoc/functions/tf.fill.html index d5b34ca..55c26a0 100644 --- a/typedoc/functions/tf.fill.html +++ b/typedoc/functions/tf.fill.html @@ -1,4 +1,4 @@ -fill | @vladmandic/face-api - v1.7.14

    Function fill

    • fill<R extends Rank>(
          shape: ShapeMap[R],
          value: string | number,
          dtype?: keyof DataTypeMap,
      ): tf.Tensor<R>

    • Creates a tf.Tensor filled with a scalar value.

      +fill | @vladmandic/face-api - v1.7.15

      Function fill

      • fill<R extends Rank>(
            shape: ShapeMap[R],
            value: string | number,
            dtype?: keyof DataTypeMap,
        ): tf.Tensor<R>

      • Creates a tf.Tensor filled with a scalar value.

        tf.fill([2, 2], 4).print();
        @@ -6,4 +6,4 @@
      • 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 tf.Tensor<R>

      Doc

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    +

    Returns tf.Tensor<R>

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    diff --git a/typedoc/functions/tf.io.browserFiles.html b/typedoc/functions/tf.io.browserFiles.html index 5214314..962dfe1 100644 --- a/typedoc/functions/tf.io.browserFiles.html +++ b/typedoc/functions/tf.io.browserFiles.html @@ -1,4 +1,4 @@ -browserFiles | @vladmandic/face-api - v1.7.14

    Function browserFiles

    • browserFiles(files: File[]): IOHandler

    • Creates an IOHandler that loads model artifacts from user-selected files.

      +browserFiles | @vladmandic/face-api - v1.7.15

      Function browserFiles

      • browserFiles(files: File[]): 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 @@ -20,4 +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.

    -

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    diff --git a/typedoc/functions/tf.io.browserHTTPRequest.html b/typedoc/functions/tf.io.browserHTTPRequest.html index 03ec45e..acca72f 100644 --- a/typedoc/functions/tf.io.browserHTTPRequest.html +++ b/typedoc/functions/tf.io.browserHTTPRequest.html @@ -1,2 +1,2 @@ -browserHTTPRequest | @vladmandic/face-api - v1.7.14

    Function browserHTTPRequest

    • browserHTTPRequest(path: string, loadOptions?: LoadOptions): IOHandler

    • Deprecated. Use tf.io.http.

      -

      Parameters

      Returns IOHandler

    Settings

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    +browserHTTPRequest | @vladmandic/face-api - v1.7.15

    Function browserHTTPRequest

    • browserHTTPRequest(path: string, loadOptions?: LoadOptions): IOHandler

    • Deprecated. Use tf.io.http.

      +

      Parameters

      Returns IOHandler

    Settings

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    diff --git a/typedoc/functions/tf.io.concatenateArrayBuffers.html b/typedoc/functions/tf.io.concatenateArrayBuffers.html index 09a0ce0..3d8783a 100644 --- a/typedoc/functions/tf.io.concatenateArrayBuffers.html +++ b/typedoc/functions/tf.io.concatenateArrayBuffers.html @@ -1,6 +1,6 @@ -concatenateArrayBuffers | @vladmandic/face-api - v1.7.14

    Function concatenateArrayBuffers

    • concatenateArrayBuffers(buffers: ArrayBuffer | ArrayBuffer[]): ArrayBuffer

    • Concatenate a number of ArrayBuffers into one.

      +concatenateArrayBuffers | @vladmandic/face-api - v1.7.15

      Function concatenateArrayBuffers

      • concatenateArrayBuffers(buffers: ArrayBuffer | ArrayBuffer[]): ArrayBuffer

      • Concatenate a number of ArrayBuffers into one.

        Parameters

        • buffers: ArrayBuffer | ArrayBuffer[]

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

        Returns ArrayBuffer

        Result of concatenating buffers in order.

        Deprecated

        Use tf.io.CompositeArrayBuffer.join() instead.

        -

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    diff --git a/typedoc/functions/tf.io.copyModel.html b/typedoc/functions/tf.io.copyModel.html index af5c060..5c7c886 100644 --- a/typedoc/functions/tf.io.copyModel.html +++ b/typedoc/functions/tf.io.copyModel.html @@ -1,4 +1,4 @@ -copyModel | @vladmandic/face-api - v1.7.14

    Function copyModel

    • copyModel(sourceURL: string, destURL: string): Promise<ModelArtifactsInfo>

    • Copy a model from one URL to another.

      +copyModel | @vladmandic/face-api - v1.7.15

      Function copyModel

      • copyModel(sourceURL: string, destURL: string): Promise<ModelArtifactsInfo>

      • Copy a model from one URL to another.

        This function supports:

        1. Copying within a storage medium, e.g., @@ -15,4 +15,4 @@ is successful).

          Throws

          Error if copying fails, e.g., if no model exists at sourceURL, or if oldPath and newPath are identical.

          -

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    diff --git a/typedoc/functions/tf.io.decodeWeights.html b/typedoc/functions/tf.io.decodeWeights.html index 9e3e353..4cbc33b 100644 --- a/typedoc/functions/tf.io.decodeWeights.html +++ b/typedoc/functions/tf.io.decodeWeights.html @@ -1,4 +1,4 @@ -decodeWeights | @vladmandic/face-api - v1.7.14

    Function decodeWeights

    • decodeWeights(
          weightData: WeightData,
          specs: WeightsManifestEntry[],
      ): NamedTensorMap

    • Decode flat ArrayBuffer as weights.

      +decodeWeights | @vladmandic/face-api - v1.7.15

      Function decodeWeights

      • decodeWeights(
            weightData: WeightData,
            specs: WeightsManifestEntry[],
        ): NamedTensorMap

      • Decode flat ArrayBuffer as weights.

        This function does not handle sharding.

        This function is the reverse of encodeWeights.

        Parameters

        • weightData: WeightData

          A flat ArrayBuffer or an array of ArrayBuffers carrying the @@ -9,4 +9,4 @@ whose value are encoded by buffer.

        Returns NamedTensorMap

        A map from tensor name to tensor value, with the names corresponding to names in specs.

        Throws

        Error, if any of the tensors has unsupported dtype.

        -

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    diff --git a/typedoc/functions/tf.io.decodeWeightsStream.html b/typedoc/functions/tf.io.decodeWeightsStream.html index 0a95168..307abb6 100644 --- a/typedoc/functions/tf.io.decodeWeightsStream.html +++ b/typedoc/functions/tf.io.decodeWeightsStream.html @@ -1 +1 @@ -decodeWeightsStream | @vladmandic/face-api - v1.7.14

    Function decodeWeightsStream

    • decodeWeightsStream(
          weightStream: ReadableStream<ArrayBuffer>,
          specs: WeightsManifestEntry[],
      ): Promise<NamedTensorMap>

    • Parameters

      Returns Promise<NamedTensorMap>

    Settings

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    +decodeWeightsStream | @vladmandic/face-api - v1.7.15

    Function decodeWeightsStream

    • decodeWeightsStream(
          weightStream: ReadableStream<ArrayBuffer>,
          specs: WeightsManifestEntry[],
      ): 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 87b402b..08be17d 100644 --- a/typedoc/functions/tf.io.encodeWeights.html +++ b/typedoc/functions/tf.io.encodeWeights.html @@ -1,4 +1,4 @@ -encodeWeights | @vladmandic/face-api - v1.7.14

    Function encodeWeights

    • encodeWeights(
          tensors: NamedTensorMap | NamedTensor[],
          group?: WeightGroup,
      ): Promise<{ data: ArrayBuffer; specs: WeightsManifestEntry[] }>

    • Encode a map from names to weight values as an ArrayBuffer, along with an +encodeWeights | @vladmandic/face-api - v1.7.15

      Function encodeWeights

      • encodeWeights(
            tensors: NamedTensorMap | NamedTensor[],
            group?: WeightGroup,
        ): Promise<{ data: ArrayBuffer; specs: WeightsManifestEntry[] }>

      • Encode a map from names to weight values as an ArrayBuffer, along with an Array of WeightsManifestEntry as specification of the encoded weights.

        This function does not perform sharding.

        This function is the reverse of decodeWeights.

        @@ -12,4 +12,4 @@ concatenated.
        • tensor names, dtypes and shapes.

      Throws

      Error: on unsupported tensor dtype.

      -

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    diff --git a/typedoc/functions/tf.io.fromMemory.html b/typedoc/functions/tf.io.fromMemory.html index 20ac761..26d61bc 100644 --- a/typedoc/functions/tf.io.fromMemory.html +++ b/typedoc/functions/tf.io.fromMemory.html @@ -1,4 +1,4 @@ -fromMemory | @vladmandic/face-api - v1.7.14

    Function fromMemory

    • fromMemory(
          modelArtifacts: {} | ModelArtifacts,
          weightSpecs?: WeightsManifestEntry[],
          weightData?: WeightData,
          trainingConfig?: TrainingConfig,
      ): IOHandler

    • Creates an IOHandler that loads model artifacts from memory.

      +fromMemory | @vladmandic/face-api - v1.7.15

      Function fromMemory

      • fromMemory(
            modelArtifacts: {} | ModelArtifacts,
            weightSpecs?: WeightsManifestEntry[],
            weightData?: WeightData,
            trainingConfig?: TrainingConfig,
        ): IOHandler

      • Creates an IOHandler that loads model artifacts from memory.

        When used in conjunction with tf.loadLayersModel, an instance of tf.LayersModel (Keras-style) can be constructed from the loaded artifacts.

        const model = await tf.loadLayersModel(tf.io.fromMemory(
            modelTopology, weightSpecs, weightData));
@@ -12,4 +12,4 @@ names, shapes, types, and quantization of the weight data. Optional.
        
 concatenated in the order described by the weightSpecs. Optional.
      • OptionaltrainingConfig: TrainingConfig

        Model training configuration. Optional.

      Returns IOHandler

      A passthrough IOHandler that simply loads the provided data.

      -

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    diff --git a/typedoc/functions/tf.io.fromMemorySync.html b/typedoc/functions/tf.io.fromMemorySync.html index 53916ce..fed1cf7 100644 --- a/typedoc/functions/tf.io.fromMemorySync.html +++ b/typedoc/functions/tf.io.fromMemorySync.html @@ -1,4 +1,4 @@ -fromMemorySync | @vladmandic/face-api - v1.7.14

    Function fromMemorySync

    • fromMemorySync(
          modelArtifacts: {} | ModelArtifacts,
          weightSpecs?: WeightsManifestEntry[],
          weightData?: WeightData,
          trainingConfig?: TrainingConfig,
      ): IOHandlerSync

    • Creates an IOHandler that loads model artifacts from memory.

      +fromMemorySync | @vladmandic/face-api - v1.7.15

      Function fromMemorySync

      • fromMemorySync(
            modelArtifacts: {} | ModelArtifacts,
            weightSpecs?: WeightsManifestEntry[],
            weightData?: WeightData,
            trainingConfig?: TrainingConfig,
        ): IOHandlerSync

      • Creates an IOHandler that loads model artifacts from memory.

        When used in conjunction with tf.loadLayersModel, an instance of tf.LayersModel (Keras-style) can be constructed from the loaded artifacts.

        const model = await tf.loadLayersModel(tf.io.fromMemory(
            modelTopology, weightSpecs, weightData));
@@ -12,4 +12,4 @@ names, shapes, types, and quantization of the weight data. Optional.
        
 concatenated in the order described by the weightSpecs. Optional.
      • OptionaltrainingConfig: TrainingConfig

        Model training configuration. Optional.

      Returns IOHandlerSync

      A passthrough IOHandlerSync that simply loads the provided data.

      -

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    diff --git a/typedoc/functions/tf.io.getLoadHandlers.html b/typedoc/functions/tf.io.getLoadHandlers.html index 3733cf6..c89004c 100644 --- a/typedoc/functions/tf.io.getLoadHandlers.html +++ b/typedoc/functions/tf.io.getLoadHandlers.html @@ -1 +1 @@ -getLoadHandlers | @vladmandic/face-api - v1.7.14

    Function getLoadHandlers

    • getLoadHandlers(url: string | string[], loadOptions?: LoadOptions): IOHandler[]

    • Parameters

      Returns IOHandler[]

    Settings

    Member Visibility
    +getLoadHandlers | @vladmandic/face-api - v1.7.15

    Function getLoadHandlers

    • getLoadHandlers(url: string | string[], loadOptions?: LoadOptions): IOHandler[]

    • Parameters

      Returns IOHandler[]

    Settings

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    diff --git a/typedoc/functions/tf.io.getModelArtifactsForJSON.html b/typedoc/functions/tf.io.getModelArtifactsForJSON.html index 1f6aa42..0b246e5 100644 --- a/typedoc/functions/tf.io.getModelArtifactsForJSON.html +++ b/typedoc/functions/tf.io.getModelArtifactsForJSON.html @@ -1,7 +1,7 @@ -getModelArtifactsForJSON | @vladmandic/face-api - v1.7.14

    Function getModelArtifactsForJSON

    • getModelArtifactsForJSON(
          modelJSON: ModelJSON,
          loadWeights: (
              weightsManifest: WeightsManifestConfig,
          ) => Promise<[WeightsManifestEntry[], WeightData]>,
      ): Promise<ModelArtifacts>

    • Create ModelArtifacts from a JSON file.

      +getModelArtifactsForJSON | @vladmandic/face-api - v1.7.15

      Function getModelArtifactsForJSON

      • getModelArtifactsForJSON(
            modelJSON: ModelJSON,
            loadWeights: (
                weightsManifest: WeightsManifestConfig,
            ) => Promise<[WeightsManifestEntry[], WeightData]>,
        ): Promise<ModelArtifacts>

      • Create ModelArtifacts from a JSON file.

        Parameters

        • modelJSON: ModelJSON

          Object containing the parsed JSON of model.json

        • loadWeights: (
              weightsManifest: WeightsManifestConfig,
          ) => Promise<[WeightsManifestEntry[], WeightData]>

          Function that takes the JSON file's weights manifest, reads weights from the listed path(s), and returns a Promise of the weight manifest entries along with the weights data.

        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 05e681e..0c822cb 100644 --- a/typedoc/functions/tf.io.getModelArtifactsForJSONSync.html +++ b/typedoc/functions/tf.io.getModelArtifactsForJSONSync.html @@ -1,4 +1,4 @@ -getModelArtifactsForJSONSync | @vladmandic/face-api - v1.7.14

    Function getModelArtifactsForJSONSync

    • getModelArtifactsForJSONSync(
          modelJSON: ModelJSON,
          weightSpecs?: WeightsManifestEntry[],
          weightData?: WeightData,
      ): ModelArtifacts

    • Create ModelArtifacts from a JSON file and weights.

      +getModelArtifactsForJSONSync | @vladmandic/face-api - v1.7.15

      Function getModelArtifactsForJSONSync

      • getModelArtifactsForJSONSync(
            modelJSON: ModelJSON,
            weightSpecs?: WeightsManifestEntry[],
            weightData?: WeightData,
        ): ModelArtifacts

      • Create ModelArtifacts from a JSON file and weights.

        Parameters

        • modelJSON: ModelJSON

          Object containing the parsed JSON of model.json

        • OptionalweightSpecs: WeightsManifestEntry[]

          The list of WeightsManifestEntry for the model. Must be passed if the modelJSON has a weightsManifest.

          @@ -6,4 +6,4 @@ passed if the modelJSON has a weightsManifest.

          the model corresponding to the weights in weightSpecs. Must be passed if the modelJSON has a weightsManifest.

        Returns ModelArtifacts

        A Promise of the ModelArtifacts, as described by the JSON file.

        -

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    diff --git a/typedoc/functions/tf.io.getModelArtifactsInfoForJSON.html b/typedoc/functions/tf.io.getModelArtifactsInfoForJSON.html index 9223641..51baa41 100644 --- a/typedoc/functions/tf.io.getModelArtifactsInfoForJSON.html +++ b/typedoc/functions/tf.io.getModelArtifactsInfoForJSON.html @@ -1,3 +1,3 @@ -getModelArtifactsInfoForJSON | @vladmandic/face-api - v1.7.14

    Function getModelArtifactsInfoForJSON

    • getModelArtifactsInfoForJSON(modelArtifacts: ModelArtifacts): ModelArtifactsInfo

    • Populate ModelArtifactsInfo fields for a model with JSON topology.

      +getModelArtifactsInfoForJSON | @vladmandic/face-api - v1.7.15

      Function getModelArtifactsInfoForJSON

      • getModelArtifactsInfoForJSON(modelArtifacts: 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 3aefc42..dc5adf4 100644 --- a/typedoc/functions/tf.io.getSaveHandlers.html +++ b/typedoc/functions/tf.io.getSaveHandlers.html @@ -1 +1 @@ -getSaveHandlers | @vladmandic/face-api - v1.7.14

    Function getSaveHandlers

    • getSaveHandlers(url: string | string[]): IOHandler[]

    • Parameters

      • url: string | string[]

      Returns IOHandler[]

    Settings

    Member Visibility
    +getSaveHandlers | @vladmandic/face-api - v1.7.15

    Function getSaveHandlers

    • getSaveHandlers(url: string | string[]): IOHandler[]

    • Parameters

      • url: string | string[]

      Returns IOHandler[]

    Settings

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    diff --git a/typedoc/functions/tf.io.getWeightSpecs.html b/typedoc/functions/tf.io.getWeightSpecs.html index 54a1830..8d98e04 100644 --- a/typedoc/functions/tf.io.getWeightSpecs.html +++ b/typedoc/functions/tf.io.getWeightSpecs.html @@ -1,5 +1,5 @@ -getWeightSpecs | @vladmandic/face-api - v1.7.14

    Function getWeightSpecs

    • getWeightSpecs(weightsManifest: WeightsManifestConfig): WeightsManifestEntry[]

    • Concatenate the weights stored in a WeightsManifestConfig into a list of +getWeightSpecs | @vladmandic/face-api - v1.7.15

      Function getWeightSpecs

      • getWeightSpecs(weightsManifest: WeightsManifestConfig): WeightsManifestEntry[]

      • Concatenate the weights stored in a WeightsManifestConfig into a list of WeightsManifestEntry

        Parameters

        Returns WeightsManifestEntry[]

        A list of WeightsManifestEntry of the weights in the weightsManifest

        -

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    diff --git a/typedoc/functions/tf.io.http.html b/typedoc/functions/tf.io.http.html index 75fd027..8c371f0 100644 --- a/typedoc/functions/tf.io.http.html +++ b/typedoc/functions/tf.io.http.html @@ -1,4 +1,4 @@ -http | @vladmandic/face-api - v1.7.14

    Function http

    • http(path: string, loadOptions?: LoadOptions): IOHandler

    • Creates an IOHandler subtype that sends model artifacts to HTTP server.

      +http | @vladmandic/face-api - v1.7.15

      Function http

      • http(path: string, loadOptions?: 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 @@ -40,4 +40,4 @@ the fetch from node-fetch can be used here.

        • before the load is completed.

    Returns IOHandler

    An instance of IOHandler.

    -

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    diff --git a/typedoc/functions/tf.io.isHTTPScheme.html b/typedoc/functions/tf.io.isHTTPScheme.html index 13cc8c8..b92a417 100644 --- a/typedoc/functions/tf.io.isHTTPScheme.html +++ b/typedoc/functions/tf.io.isHTTPScheme.html @@ -1 +1 @@ -isHTTPScheme | @vladmandic/face-api - v1.7.14

    Function isHTTPScheme

    • isHTTPScheme(url: string): boolean

    • Parameters

      • url: string

      Returns boolean

    Settings

    Member Visibility
    +isHTTPScheme | @vladmandic/face-api - v1.7.15

    Function isHTTPScheme

    • isHTTPScheme(url: string): boolean

    • Parameters

      • url: string

      Returns boolean

    Settings

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    diff --git a/typedoc/functions/tf.io.listModels.html b/typedoc/functions/tf.io.listModels.html index 8a78dd0..f60500e 100644 --- a/typedoc/functions/tf.io.listModels.html +++ b/typedoc/functions/tf.io.listModels.html @@ -1,4 +1,4 @@ -listModels | @vladmandic/face-api - v1.7.14

    Function listModels

    • listModels(): Promise<{ [url: string]: ModelArtifactsInfo }>

    • List all models stored in registered storage mediums.

      +listModels | @vladmandic/face-api - v1.7.15

      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()));
@@ -8,4 +8,4 @@ IndexedDB.
        
 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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    diff --git a/typedoc/functions/tf.io.loadWeights.html b/typedoc/functions/tf.io.loadWeights.html index 1fb95ce..203cbb9 100644 --- a/typedoc/functions/tf.io.loadWeights.html +++ b/typedoc/functions/tf.io.loadWeights.html @@ -1,7 +1,7 @@ -loadWeights | @vladmandic/face-api - v1.7.14

    Function loadWeights

    • loadWeights(
          manifest: WeightsManifestConfig,
          filePathPrefix?: string,
          weightNames?: string[],
          requestInit?: RequestInit,
      ): Promise<NamedTensorMap>

    • Reads a weights manifest JSON configuration, fetches the weights and +loadWeights | @vladmandic/face-api - v1.7.15

      Function loadWeights

      • loadWeights(
            manifest: WeightsManifestConfig,
            filePathPrefix?: string,
            weightNames?: string[],
            requestInit?: RequestInit,
        ): Promise<NamedTensorMap>

      • Reads a weights manifest JSON configuration, fetches the weights and returns them as Tensors.

        Parameters

        • manifest: WeightsManifestConfig

          The weights manifest JSON.

        • OptionalfilePathPrefix: string

          The path prefix for filenames given in the manifest. Defaults to the empty string.

        • OptionalweightNames: string[]

          The names of the weights to be fetched.

          -
        • OptionalrequestInit: RequestInit

        Returns Promise<NamedTensorMap>

      Settings

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      +
    • OptionalrequestInit: RequestInit

    Returns Promise<NamedTensorMap>

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    diff --git a/typedoc/functions/tf.io.moveModel.html b/typedoc/functions/tf.io.moveModel.html index f59e26d..f837445 100644 --- a/typedoc/functions/tf.io.moveModel.html +++ b/typedoc/functions/tf.io.moveModel.html @@ -1,4 +1,4 @@ -moveModel | @vladmandic/face-api - v1.7.14

    Function moveModel

    • moveModel(sourceURL: string, destURL: string): Promise<ModelArtifactsInfo>

    • Move a model from one URL to another.

      +moveModel | @vladmandic/face-api - v1.7.15

      Function moveModel

      • moveModel(sourceURL: string, destURL: string): Promise<ModelArtifactsInfo>

      • Move a model from one URL to another.

        This function supports:

        1. Moving within a storage medium, e.g., @@ -15,4 +15,4 @@ 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 9bc0ad0..dff24d5 100644 --- a/typedoc/functions/tf.io.registerLoadRouter.html +++ b/typedoc/functions/tf.io.registerLoadRouter.html @@ -1 +1 @@ -registerLoadRouter | @vladmandic/face-api - v1.7.14

    Function registerLoadRouter

    • registerLoadRouter(loudRouter: IORouter): void

    • Parameters

      • loudRouter: IORouter

      Returns void

    Settings

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    +registerLoadRouter | @vladmandic/face-api - v1.7.15

    Function registerLoadRouter

    • registerLoadRouter(loudRouter: IORouter): void

    • Parameters

      • loudRouter: IORouter

      Returns void

    Settings

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    diff --git a/typedoc/functions/tf.io.registerSaveRouter.html b/typedoc/functions/tf.io.registerSaveRouter.html index 566a950..97cd70d 100644 --- a/typedoc/functions/tf.io.registerSaveRouter.html +++ b/typedoc/functions/tf.io.registerSaveRouter.html @@ -1 +1 @@ -registerSaveRouter | @vladmandic/face-api - v1.7.14

    Function registerSaveRouter

    • registerSaveRouter(loudRouter: IORouter): void

    • Parameters

      • loudRouter: IORouter

      Returns void

    Settings

    Member Visibility
    +registerSaveRouter | @vladmandic/face-api - v1.7.15

    Function registerSaveRouter

    • registerSaveRouter(loudRouter: IORouter): void

    • Parameters

      • loudRouter: IORouter

      Returns void

    Settings

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    diff --git a/typedoc/functions/tf.io.removeModel.html b/typedoc/functions/tf.io.removeModel.html index aaa6d48..78589fd 100644 --- a/typedoc/functions/tf.io.removeModel.html +++ b/typedoc/functions/tf.io.removeModel.html @@ -1,4 +1,4 @@ -removeModel | @vladmandic/face-api - v1.7.14

    Function removeModel

    • removeModel(url: string): Promise<ModelArtifactsInfo>

    • Remove a model specified by URL from a registered storage medium.

      +removeModel | @vladmandic/face-api - v1.7.15

      Function removeModel

      • removeModel(url: string): 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()));
        @@ -7,4 +7,4 @@

      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/functions/tf.io.weightsLoaderFactory.html b/typedoc/functions/tf.io.weightsLoaderFactory.html index e0b628d..1be060e 100644 --- a/typedoc/functions/tf.io.weightsLoaderFactory.html +++ b/typedoc/functions/tf.io.weightsLoaderFactory.html @@ -1,4 +1,4 @@ -weightsLoaderFactory | @vladmandic/face-api - v1.7.14

    Function weightsLoaderFactory

    • weightsLoaderFactory(
          fetchWeightsFunction: (fetchUrls: string[]) => Promise<ArrayBuffer[]>,
      ): (
          manifest: WeightsManifestConfig,
          filePathPrefix?: string,
          weightNames?: string[],
      ) => Promise<NamedTensorMap>

    • Creates a function, which reads a weights manifest JSON configuration, +weightsLoaderFactory | @vladmandic/face-api - v1.7.15

      Function weightsLoaderFactory

      • weightsLoaderFactory(
            fetchWeightsFunction: (fetchUrls: string[]) => Promise<ArrayBuffer[]>,
        ): (
            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, './')
@@ -6,4 +6,4 @@ fetches the weight files using the specified function and returns them as

        Parameters

        • fetchWeightsFunction: (fetchUrls: string[]) => Promise<ArrayBuffer[]>

          The function used for fetching the weight files.

        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 d7045d2..c68a815 100644 --- a/typedoc/functions/tf.io.withSaveHandler.html +++ b/typedoc/functions/tf.io.withSaveHandler.html @@ -1,7 +1,7 @@ -withSaveHandler | @vladmandic/face-api - v1.7.14

    Function withSaveHandler

    • withSaveHandler(
          saveHandler: (artifacts: ModelArtifacts) => Promise<SaveResult>,
      ): IOHandler

    • Creates an IOHandler that passes saved model artifacts to a callback.

      +withSaveHandler | @vladmandic/face-api - v1.7.15

      Function withSaveHandler

      • withSaveHandler(
            saveHandler: (artifacts: ModelArtifacts) => Promise<SaveResult>,
        ): 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: ModelArtifacts) => Promise<SaveResult>

          A function that accepts a ModelArtifacts and returns a promise that resolves to a SaveResult.

          -

        Returns IOHandler

      Settings

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    Returns IOHandler

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    diff --git a/typedoc/functions/tf.io.withSaveHandlerSync.html b/typedoc/functions/tf.io.withSaveHandlerSync.html index 63a5c52..5c9a338 100644 --- a/typedoc/functions/tf.io.withSaveHandlerSync.html +++ b/typedoc/functions/tf.io.withSaveHandlerSync.html @@ -1,7 +1,7 @@ -withSaveHandlerSync | @vladmandic/face-api - v1.7.14

    Function withSaveHandlerSync

    • withSaveHandlerSync(
          saveHandler: (artifacts: ModelArtifacts) => SaveResult,
      ): IOHandlerSync

    • Creates an IOHandlerSync that passes saved model artifacts to a callback.

      +withSaveHandlerSync | @vladmandic/face-api - v1.7.15

      Function withSaveHandlerSync

      • withSaveHandlerSync(
            saveHandler: (artifacts: ModelArtifacts) => SaveResult,
        ): 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

        • saveHandler: (artifacts: ModelArtifacts) => SaveResult

          A function that accepts a ModelArtifacts and returns a SaveResult.

          -

        Returns IOHandlerSync

      Settings

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    Returns IOHandlerSync

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    diff --git a/typedoc/functions/tf.matMul.html b/typedoc/functions/tf.matMul.html index f211232..6563c92 100644 --- a/typedoc/functions/tf.matMul.html +++ b/typedoc/functions/tf.matMul.html @@ -1,4 +1,4 @@ -matMul | @vladmandic/face-api - v1.7.14

    Function matMul

    • matMul<T extends tf.Tensor>(
          a: TensorLike | tf.Tensor,
          b: TensorLike | tf.Tensor,
          transposeA?: boolean,
          transposeB?: boolean,
      ): T

    • Computes the dot product of two matrices, A * B. These must be matrices.

      +matMul | @vladmandic/face-api - v1.7.15

      Function matMul

      • matMul<T extends tf.Tensor>(
            a: TensorLike | tf.Tensor,
            b: TensorLike | tf.Tensor,
            transposeA?: boolean,
            transposeB?: boolean,
        ): 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)
        @@ -6,4 +6,4 @@
      • b: TensorLike | tf.Tensor

        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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    Returns T

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    diff --git a/typedoc/functions/tf.maxPool.html b/typedoc/functions/tf.maxPool.html index aaabb30..5425a70 100644 --- a/typedoc/functions/tf.maxPool.html +++ b/typedoc/functions/tf.maxPool.html @@ -1,4 +1,4 @@ -maxPool | @vladmandic/face-api - v1.7.14

    Function maxPool

    • maxPool<T extends Tensor4D | Tensor3D>(
          x: TensorLike | T,
          filterSize: number | [number, number],
          strides: number | [number, number],
          pad: number | "valid" | "same" | ExplicitPadding,
          dimRoundingMode?: "floor" | "round" | "ceil",
      ): T

    • Computes the 2D max pooling of an image.

      +maxPool | @vladmandic/face-api - v1.7.15

      Function maxPool

      • maxPool<T extends Tensor4D | Tensor3D>(
            x: TensorLike | T,
            filterSize: number | [number, number],
            strides: number | [number, number],
            pad: number | "valid" | "same" | ExplicitPadding,
            dimRoundingMode?: "floor" | "round" | "ceil",
        ): T

      • Computes the 2D max pooling of an image.

        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.

        • filterSize: number | [number, number]

          The filter size: [filterHeight, filterWidth]. If @@ -16,4 +16,4 @@ than 1x1.

      • OptionaldimRoundingMode: "floor" | "round" | "ceil"

        A string from: 'ceil', 'round', 'floor'. If none is provided, it will default to truncate.

        -

      Returns T

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    Returns T

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    diff --git a/typedoc/functions/tf.mul.html b/typedoc/functions/tf.mul.html index 4f1685c..5120a56 100644 --- a/typedoc/functions/tf.mul.html +++ b/typedoc/functions/tf.mul.html @@ -1,4 +1,4 @@ -mul | @vladmandic/face-api - v1.7.14

    Function mul

    • mul<T extends tf.Tensor>(
          a: TensorLike | tf.Tensor,
          b: TensorLike | tf.Tensor,
      ): T

    • Multiplies two tf.Tensors element-wise, A * B. Supports broadcasting.

      +mul | @vladmandic/face-api - v1.7.15

      Function mul

      • mul<T extends tf.Tensor>(
            a: TensorLike | tf.Tensor,
            b: TensorLike | tf.Tensor,
        ): T

      • Multiplies two tf.Tensors element-wise, A * B. Supports broadcasting.

        We also expose tf.mulStrict which has the same signature as this op and asserts that a and b are the same shape (does not broadcast).

        const a = tf.tensor1d([1, 2, 3, 4]);
        const b = tf.tensor1d([2, 3, 4, 5]);

        a.mul(b).print();  // or tf.mul(a, b)
@@ -9,4 +9,4 @@ asserts that a and b are the same shape (does not broa

        Type Parameters

        Parameters

        Returns T

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    Returns T

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    diff --git a/typedoc/functions/tf.pad.html b/typedoc/functions/tf.pad.html index eabe551..fb5d97d 100644 --- a/typedoc/functions/tf.pad.html +++ b/typedoc/functions/tf.pad.html @@ -1,4 +1,4 @@ -pad | @vladmandic/face-api - v1.7.14

    Function pad

    • pad<T extends tf.Tensor>(
          x: TensorLike | T,
          paddings: [number, number][],
          constantValue?: number,
      ): T

    • Pads a tf.Tensor with a given value and paddings.

      +pad | @vladmandic/face-api - v1.7.15

      Function pad

      • pad<T extends tf.Tensor>(
            x: TensorLike | T,
            paddings: [number, number][],
            constantValue?: number,
        ): 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 @@ -17,4 +17,4 @@ as this method that assert that paddings is of given length.

        each element is a length-2 tuple of ints [padBefore, padAfter], specifying how much to pad along each dimension of the tensor.

      • OptionalconstantValue: number

        The pad value to use. Defaults to 0.

        -

      Returns T

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    Returns T

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    diff --git a/typedoc/functions/tf.relu.html b/typedoc/functions/tf.relu.html index 546ffb9..ee49fdc 100644 --- a/typedoc/functions/tf.relu.html +++ b/typedoc/functions/tf.relu.html @@ -1,7 +1,7 @@ -relu | @vladmandic/face-api - v1.7.14

    Function relu

    • relu<T extends tf.Tensor>(x: T | TensorLike): T

    • Computes rectified linear element-wise: max(x, 0).

      +relu | @vladmandic/face-api - v1.7.15

      Function relu

      • relu<T extends tf.Tensor>(x: T | TensorLike): 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

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    Returns T

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    diff --git a/typedoc/functions/tf.reshape.html b/typedoc/functions/tf.reshape.html index c140c86..5c423a3 100644 --- a/typedoc/functions/tf.reshape.html +++ b/typedoc/functions/tf.reshape.html @@ -1,4 +1,4 @@ -reshape | @vladmandic/face-api - v1.7.14

    Function reshape

    • reshape<R extends Rank>(
          x: TensorLike | tf.Tensor,
          shape: ShapeMap[R],
      ): tf.Tensor<R>

    • Reshapes a tf.Tensor to a given shape.

      +reshape | @vladmandic/face-api - v1.7.15

      Function reshape

      • reshape<R extends Rank>(
            x: TensorLike | tf.Tensor,
            shape: ShapeMap[R],
        ): tf.Tensor<R>

      • Reshapes a tf.Tensor to a given shape.

        Given an input tensor, returns a new tensor with the same values as the input tensor with shape shape.

        If one component of shape is the special value -1, the size of that @@ -14,4 +14,4 @@ tensor.

        Type Parameters

        Parameters

        • x: TensorLike | tf.Tensor

          The input tensor to be reshaped.

        • shape: ShapeMap[R]

          An array of integers defining the output tensor shape.

          -

        Returns tf.Tensor<R>

        Doc

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    Returns tf.Tensor<R>

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    diff --git a/typedoc/functions/tf.scalar.html b/typedoc/functions/tf.scalar.html index 361fabd..f1c65d0 100644 --- a/typedoc/functions/tf.scalar.html +++ b/typedoc/functions/tf.scalar.html @@ -1,4 +1,4 @@ -scalar | @vladmandic/face-api - v1.7.14

    Function scalar

    • scalar(
          value: string | number | boolean | Uint8Array,
          dtype?: keyof DataTypeMap,
      ): Scalar

    • Creates rank-0 tf.Tensor (scalar) with the provided value and dtype.

      +scalar | @vladmandic/face-api - v1.7.15

      Function scalar

      • scalar(
            value: string | number | boolean | Uint8Array,
            dtype?: keyof DataTypeMap,
        ): Scalar

      • Creates rank-0 tf.Tensor (scalar) with the provided value and dtype.

        The same functionality can be achieved with tf.tensor, but in general we recommend using tf.scalar as it makes the code more readable.

        tf.scalar(3.14).print();
@@ -6,4 +6,4 @@ we recommend using tf.scalar as it makes the code more readable.
        Parameters
        • value: string | number | boolean | Uint8Array
          The value of the scalar.
          
 
        • Optionaldtype: keyof DataTypeMap
          The data type.
          
-
        Returns Scalar
        Doc

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    Returns Scalar

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    diff --git a/typedoc/functions/tf.separableConv2d.html b/typedoc/functions/tf.separableConv2d.html index 1faf6da..581c700 100644 --- a/typedoc/functions/tf.separableConv2d.html +++ b/typedoc/functions/tf.separableConv2d.html @@ -1,4 +1,4 @@ -separableConv2d | @vladmandic/face-api - v1.7.14

    Function separableConv2d

    • separableConv2d<T extends Tensor4D | Tensor3D>(
          x: TensorLike | T,
          depthwiseFilter: Tensor4D | TensorLike,
          pointwiseFilter: Tensor4D | TensorLike,
          strides: number | [number, number],
          pad: "valid" | "same",
          dilation?: number | [number, number],
          dataFormat?: "NHWC" | "NCHW",
      ): T

    • 2-D convolution with separable filters.

      +separableConv2d | @vladmandic/face-api - v1.7.15

      Function separableConv2d

      • separableConv2d<T extends Tensor4D | Tensor3D>(
            x: TensorLike | T,
            depthwiseFilter: Tensor4D | TensorLike,
            pointwiseFilter: Tensor4D | TensorLike,
            strides: number | [number, number],
            pad: "valid" | "same",
            dilation?: number | [number, number],
            dataFormat?: "NHWC" | "NCHW",
        ): T

      • 2-D convolution with separable filters.

        Performs a depthwise convolution that acts separately on channels followed by a pointwise convolution that mixes channels. Note that this is separability between dimensions [1, 2] and 3, not spatial separability @@ -25,4 +25,4 @@ than 1x1.

      • For more info, see this guide: https://www.tensorflow.org/api_docs/python/tf/nn/convolution
      -
    • Optionaldilation: number | [number, number]
    • OptionaldataFormat: "NHWC" | "NCHW"

    Returns T

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  • Optionaldilation: number | [number, number]
  • OptionaldataFormat: "NHWC" | "NCHW"

    • Returns T

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    diff --git a/typedoc/functions/tf.sigmoid.html b/typedoc/functions/tf.sigmoid.html index 103cb39..83ee15c 100644 --- a/typedoc/functions/tf.sigmoid.html +++ b/typedoc/functions/tf.sigmoid.html @@ -1,6 +1,6 @@ -sigmoid | @vladmandic/face-api - v1.7.14

    Function sigmoid

    • sigmoid<T extends tf.Tensor>(x: TensorLike | T): T

    • Computes sigmoid element-wise, 1 / (1 + exp(-x))

      +sigmoid | @vladmandic/face-api - v1.7.15

      Function sigmoid

      • sigmoid<T extends tf.Tensor>(x: TensorLike | T): 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

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    Returns T

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    diff --git a/typedoc/functions/tf.slice.html b/typedoc/functions/tf.slice.html index 28be138..bf06bd1 100644 --- a/typedoc/functions/tf.slice.html +++ b/typedoc/functions/tf.slice.html @@ -1,4 +1,4 @@ -slice | @vladmandic/face-api - v1.7.14

    Function slice

    • slice<R extends Rank, T extends tf.Tensor<R>>(
          x: TensorLike | T,
          begin: number | number[],
          size?: number | number[],
      ): T

    • Extracts a slice from a tf.Tensor starting at coordinates begin +slice | @vladmandic/face-api - v1.7.15

      Function slice

      • slice<R extends Rank, T extends tf.Tensor<R>>(
            x: TensorLike | T,
            begin: number | number[],
            size?: number | number[],
        ): 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:

        @@ -23,4 +23,4 @@ first axis.

        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

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    Returns T

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    diff --git a/typedoc/functions/tf.slice3d.html b/typedoc/functions/tf.slice3d.html index f3c686d..6fee790 100644 --- a/typedoc/functions/tf.slice3d.html +++ b/typedoc/functions/tf.slice3d.html @@ -1,3 +1,3 @@ -slice3d | @vladmandic/face-api - v1.7.14

    Function slice3d

    • slice3d(
          x: TensorLike | Tensor3D,
          begin: [number, number, number],
          size: [number, number, number],
      ): Tensor3D

    • Extracts a 3D slice from a 3D array starting at coordinates begin and +slice3d | @vladmandic/face-api - v1.7.15

      Function slice3d

      • slice3d(
            x: TensorLike | Tensor3D,
            begin: [number, number, number],
            size: [number, number, number],
        ): Tensor3D

      • Extracts a 3D slice from a 3D array starting at coordinates begin and is of size size. See slice for details.

        -

        Parameters

        Returns Tensor3D

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      Parameters

      Returns Tensor3D

    Settings

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    diff --git a/typedoc/functions/tf.softmax.html b/typedoc/functions/tf.softmax.html index 87c00ca..deebc12 100644 --- a/typedoc/functions/tf.softmax.html +++ b/typedoc/functions/tf.softmax.html @@ -1,4 +1,4 @@ -softmax | @vladmandic/face-api - v1.7.14

    Function softmax

    • softmax<T extends tf.Tensor>(logits: TensorLike | T, dim?: number): T

    • Computes the softmax normalized vector given the logits.

      +softmax | @vladmandic/face-api - v1.7.15

      Function softmax

      • softmax<T extends tf.Tensor>(logits: TensorLike | T, dim?: number): T

      • Computes the softmax normalized vector given the logits.

        const a = tf.tensor1d([1, 2, 3]);

        a.softmax().print();  // or tf.softmax(a)
        @@ -8,4 +8,4 @@

        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

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    Returns T

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    diff --git a/typedoc/functions/tf.stack.html b/typedoc/functions/tf.stack.html index 3dca08b..1442c30 100644 --- a/typedoc/functions/tf.stack.html +++ b/typedoc/functions/tf.stack.html @@ -1,7 +1,7 @@ -stack | @vladmandic/face-api - v1.7.14

    Function stack

    • stack<T extends tf.Tensor>(
          tensors: (TensorLike | T)[],
          axis?: number,
      ): tf.Tensor

    • Stacks a list of rank-R tf.Tensors into one rank-(R+1) tf.Tensor.

      +stack | @vladmandic/face-api - v1.7.15

      Function stack

      • stack<T extends tf.Tensor>(
            tensors: (TensorLike | T)[],
            axis?: number,
        ): tf.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 tf.Tensor

        Doc

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    Returns tf.Tensor

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    diff --git a/typedoc/functions/tf.sub.html b/typedoc/functions/tf.sub.html index 4ee0f7e..6c698ca 100644 --- a/typedoc/functions/tf.sub.html +++ b/typedoc/functions/tf.sub.html @@ -1,4 +1,4 @@ -sub | @vladmandic/face-api - v1.7.14

    Function sub

    • sub<T extends tf.Tensor>(
          a: TensorLike | tf.Tensor,
          b: TensorLike | tf.Tensor,
      ): T

    • Subtracts two tf.Tensors element-wise, A - B. Supports broadcasting.

      +sub | @vladmandic/face-api - v1.7.15

      Function sub

      • sub<T extends tf.Tensor>(
            a: TensorLike | tf.Tensor,
            b: TensorLike | tf.Tensor,
        ): T

      • Subtracts two tf.Tensors element-wise, A - B. Supports broadcasting.

        const a = tf.tensor1d([10, 20, 30, 40]);
        const b = tf.tensor1d([1, 2, 3, 4]);

        a.sub(b).print();  // or tf.sub(a, b)
        @@ -8,4 +8,4 @@

        Type Parameters

        Parameters

        Returns T

        Doc

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    Returns T

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    diff --git a/typedoc/functions/tf.tensor-2.html b/typedoc/functions/tf.tensor-2.html index bbaea42..db96de8 100644 --- a/typedoc/functions/tf.tensor-2.html +++ b/typedoc/functions/tf.tensor-2.html @@ -1,4 +1,4 @@ -tensor | @vladmandic/face-api - v1.7.14

    Function tensor

    • tensor<R extends Rank>(
          values: TensorLike | WebGLData | WebGPUData,
          shape?: ShapeMap[R],
          dtype?: keyof DataTypeMap,
      ): tf.Tensor<R>

    • Creates a tf.Tensor with the provided values, shape and dtype.

      +tensor | @vladmandic/face-api - v1.7.15

      Function tensor

      • tensor<R extends Rank>(
            values: TensorLike | WebGLData | WebGPUData,
            shape?: ShapeMap[R],
            dtype?: keyof DataTypeMap,
        ): tf.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();
        @@ -43,4 +43,4 @@ by the tensor, so do not destroy this GPUBuffer until all access is done.
    • Optionalshape: ShapeMap[R]

      The shape of the tensor. Optional. If not provided, it is inferred from values.

    • Optionaldtype: keyof DataTypeMap

      The data type.

      -

    Returns tf.Tensor<R>

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    Returns tf.Tensor<R>

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    diff --git a/typedoc/functions/tf.tensor1d-1.html b/typedoc/functions/tf.tensor1d-1.html index 81f7cfa..a72e08e 100644 --- a/typedoc/functions/tf.tensor1d-1.html +++ b/typedoc/functions/tf.tensor1d-1.html @@ -1,4 +1,4 @@ -tensor1d | @vladmandic/face-api - v1.7.14

    Function tensor1d

    • tensor1d(values: TensorLike1D, dtype?: keyof DataTypeMap): Tensor1D

    • Creates rank-1 tf.Tensor with the provided values, shape and dtype.

      +tensor1d | @vladmandic/face-api - v1.7.15

      Function tensor1d

      • tensor1d(values: TensorLike1D, dtype?: keyof DataTypeMap): Tensor1D

      • Creates rank-1 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.tensor1d as it makes the code more readable.

        tf.tensor1d([1, 2, 3]).print();
@@ -7,4 +7,4 @@ we recommend using tf.tensor1d as it makes the code more readable.<

        Parameters

        • values: TensorLike1D

          The values of the tensor. Can be array of numbers, or a TypedArray.

        • Optionaldtype: keyof DataTypeMap

          The data type.

          -

        Returns Tensor1D

        Doc

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    Returns Tensor1D

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    diff --git a/typedoc/functions/tf.tensor2d-1.html b/typedoc/functions/tf.tensor2d-1.html index b824aa6..70377fd 100644 --- a/typedoc/functions/tf.tensor2d-1.html +++ b/typedoc/functions/tf.tensor2d-1.html @@ -1,4 +1,4 @@ -tensor2d | @vladmandic/face-api - v1.7.14

    Function tensor2d

    • tensor2d(
          values: TensorLike2D,
          shape?: [number, number],
          dtype?: keyof DataTypeMap,
      ): Tensor2D

    • Creates rank-2 tf.Tensor with the provided values, shape and dtype.

      +tensor2d | @vladmandic/face-api - v1.7.15

      Function tensor2d

      • tensor2d(
            values: TensorLike2D,
            shape?: [number, number],
            dtype?: keyof DataTypeMap,
        ): 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();
@@ -12,4 +12,4 @@ 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

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    Returns Tensor2D

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    diff --git a/typedoc/functions/tf.tensor3d-1.html b/typedoc/functions/tf.tensor3d-1.html index 8280ff8..3ce2a3f 100644 --- a/typedoc/functions/tf.tensor3d-1.html +++ b/typedoc/functions/tf.tensor3d-1.html @@ -1,4 +1,4 @@ -tensor3d | @vladmandic/face-api - v1.7.14

    Function tensor3d

    • tensor3d(
          values: TensorLike3D,
          shape?: [number, number, number],
          dtype?: keyof DataTypeMap,
      ): Tensor3D

    • Creates rank-3 tf.Tensor with the provided values, shape and dtype.

      +tensor3d | @vladmandic/face-api - v1.7.15

      Function tensor3d

      • tensor3d(
            values: TensorLike3D,
            shape?: [number, number, number],
            dtype?: keyof DataTypeMap,
        ): 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();
@@ -12,4 +12,4 @@ 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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    Returns Tensor3D

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    diff --git a/typedoc/functions/tf.tensor4d-1.html b/typedoc/functions/tf.tensor4d-1.html index 7b0872c..493ea86 100644 --- a/typedoc/functions/tf.tensor4d-1.html +++ b/typedoc/functions/tf.tensor4d-1.html @@ -1,4 +1,4 @@ -tensor4d | @vladmandic/face-api - v1.7.14

    Function tensor4d

    • tensor4d(
          values: TensorLike4D,
          shape?: [number, number, number, number],
          dtype?: keyof DataTypeMap,
      ): Tensor4D

    • Creates rank-4 tf.Tensor with the provided values, shape and dtype.

      +tensor4d | @vladmandic/face-api - v1.7.15

      Function tensor4d

      • tensor4d(
            values: TensorLike4D,
            shape?: [number, number, number, number],
            dtype?: keyof DataTypeMap,
        ): 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();
@@ -12,4 +12,4 @@ 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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    +

    Returns Tensor4D

    Doc

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    diff --git a/typedoc/functions/tf.tidy.html b/typedoc/functions/tf.tidy.html index e923716..16baedc 100644 --- a/typedoc/functions/tf.tidy.html +++ b/typedoc/functions/tf.tidy.html @@ -1,4 +1,4 @@ -tidy | @vladmandic/face-api - v1.7.14

    Function tidy

    • tidy<T extends TensorContainer>(
          nameOrFn: string | ScopeFn<T>,
          fn?: ScopeFn<T>,
      ): T

    • Executes the provided function fn and after it is executed, cleans up all +tidy | @vladmandic/face-api - v1.7.15

      Function tidy

      • tidy<T extends TensorContainer>(
            nameOrFn: string | ScopeFn<T>,
            fn?: ScopeFn<T>,
        ): 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.

        @@ -15,4 +15,4 @@ 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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    +

    Returns T

    Doc

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    diff --git a/typedoc/functions/tf.tile.html b/typedoc/functions/tf.tile.html index a7db387..b77670b 100644 --- a/typedoc/functions/tf.tile.html +++ b/typedoc/functions/tf.tile.html @@ -1,4 +1,4 @@ -tile | @vladmandic/face-api - v1.7.14

    Function tile

    • tile<T extends tf.Tensor>(x: TensorLike | T, reps: number[]): T

    • Construct a tensor by repeating it the number of times given by reps.

      +tile | @vladmandic/face-api - v1.7.15

      Function tile

      • tile<T extends tf.Tensor>(x: TensorLike | T, reps: number[]): T

      • Construct a tensor by repeating it the number of times given by reps.

        This operation creates a new tensor by replicating input reps times. The output tensor's ith dimension has input.shape[i] * reps[i] elements, and the values of input are replicated reps[i] times along the ith dimension. For example, tiling @@ -11,4 +11,4 @@ times. The output tensor's ith dimension has input.shape[i] *

        Type Parameters

        Parameters

        • x: TensorLike | T

          The tensor to tile.

        • reps: number[]

          Determines the number of replications per dimension.

          -

        Returns T

        Doc

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      +

    Returns T

    Doc

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    diff --git a/typedoc/functions/tf.transpose.html b/typedoc/functions/tf.transpose.html index bf54709..431e41c 100644 --- a/typedoc/functions/tf.transpose.html +++ b/typedoc/functions/tf.transpose.html @@ -1,4 +1,4 @@ -transpose | @vladmandic/face-api - v1.7.14

    Function transpose

    • transpose<T extends tf.Tensor>(
          x: TensorLike | T,
          perm?: number[],
          conjugate?: boolean,
      ): T

    • Transposes the tf.Tensor. Permutes the dimensions according to perm.

      +transpose | @vladmandic/face-api - v1.7.15

      Function transpose

      • transpose<T extends tf.Tensor>(
            x: TensorLike | T,
            perm?: number[],
            conjugate?: boolean,
        ): T

      • Transposes the tf.Tensor. Permutes the dimensions according to perm.

        The returned tf.Tensor's dimension i will correspond to the input dimension perm[i]. If perm is not given, it is set to [n-1...0], where n is the rank of the input tf.Tensor. Hence by default, this @@ -9,4 +9,4 @@ operation performs a regular matrix transpose on 2-D input tf.Tensor

        Type Parameters

        Parameters

        • x: TensorLike | T

          The tensor to transpose.

        • Optionalperm: number[]

          The permutation of the dimensions of a.

        • Optionalconjugate: boolean

          Will conjugate complex input if true.

          -

        Returns T

        Doc

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      +

    Returns T

    Doc

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    diff --git a/typedoc/functions/tf.unstack.html b/typedoc/functions/tf.unstack.html index 6a22d36..953156a 100644 --- a/typedoc/functions/tf.unstack.html +++ b/typedoc/functions/tf.unstack.html @@ -1,7 +1,7 @@ -unstack | @vladmandic/face-api - v1.7.14

    Function unstack

    • unstack(x: TensorLike | tf.Tensor, axis?: number): tf.Tensor[]

    • Unstacks a tf.Tensor of rank-R into a list of rank-(R-1) tf.Tensors.

      +unstack | @vladmandic/face-api - v1.7.15

      Function unstack

      • unstack(x: TensorLike | tf.Tensor, axis?: number): tf.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 | tf.Tensor

          A tensor object.

        • Optionalaxis: number

          The axis to unstack along. Defaults to 0 (the first dim).

          -

        Returns tf.Tensor[]

        Doc

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      +

    Returns tf.Tensor[]

    Doc

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    diff --git a/typedoc/functions/tf.zeros.html b/typedoc/functions/tf.zeros.html index c22c3f2..6b4206f 100644 --- a/typedoc/functions/tf.zeros.html +++ b/typedoc/functions/tf.zeros.html @@ -1,8 +1,8 @@ -zeros | @vladmandic/face-api - v1.7.14

    Function zeros

    • zeros<R extends Rank>(
          shape: ShapeMap[R],
          dtype?: keyof DataTypeMap,
      ): tf.Tensor<R>

    • Creates a tf.Tensor with all elements set to 0.

      +zeros | @vladmandic/face-api - v1.7.15

      Function zeros

      • zeros<R extends Rank>(
            shape: ShapeMap[R],
            dtype?: keyof DataTypeMap,
        ): tf.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 tf.Tensor<R>

        Doc

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      +

    Returns tf.Tensor<R>

    Doc

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    diff --git a/typedoc/functions/tinyFaceDetector-1.html b/typedoc/functions/tinyFaceDetector-1.html index 1516e66..0217cf8 100644 --- a/typedoc/functions/tinyFaceDetector-1.html +++ b/typedoc/functions/tinyFaceDetector-1.html @@ -1,5 +1,5 @@ -tinyFaceDetector | @vladmandic/face-api - v1.7.14

    Function tinyFaceDetector

    Attempts to detect all faces in an image using the Tiny Face Detector.

    +tinyFaceDetector | @vladmandic/face-api - v1.7.15

    Function tinyFaceDetector

    Attempts to detect all faces in an image using the Tiny Face Detector.

    Settings

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    +

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    diff --git a/typedoc/functions/tinyYolov2-1.html b/typedoc/functions/tinyYolov2-1.html index d0e0926..eaae2c3 100644 --- a/typedoc/functions/tinyYolov2-1.html +++ b/typedoc/functions/tinyYolov2-1.html @@ -1,5 +1,5 @@ -tinyYolov2 | @vladmandic/face-api - v1.7.14

    Function tinyYolov2

    Attempts to detect all faces in an image using the Tiny Yolov2 Network.

    +tinyYolov2 | @vladmandic/face-api - v1.7.15

    Function tinyYolov2

    Attempts to detect all faces in an image using the Tiny Yolov2 Network.

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    +

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    diff --git a/typedoc/functions/toNetInput.html b/typedoc/functions/toNetInput.html index 5506011..b5096cb 100644 --- a/typedoc/functions/toNetInput.html +++ b/typedoc/functions/toNetInput.html @@ -1,4 +1,4 @@ -toNetInput | @vladmandic/face-api - v1.7.14

    Function toNetInput

    • toNetInput(inputs: TNetInput): Promise<NetInput>

    • Validates the input to make sure, they are valid net inputs and awaits all media elements +toNetInput | @vladmandic/face-api - v1.7.15

      Function toNetInput

      • toNetInput(inputs: TNetInput): Promise<NetInput>

      • Validates the input to make sure, they are valid net inputs and awaits all media elements to be finished loading.

        Parameters

        Returns Promise<NetInput>

        A NetInput instance, which can be passed into one of the neural networks.

        -

      Settings

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      +

    Settings

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    diff --git a/typedoc/functions/utils.computeReshapedDimensions.html b/typedoc/functions/utils.computeReshapedDimensions.html index 030a5d5..f59d358 100644 --- a/typedoc/functions/utils.computeReshapedDimensions.html +++ b/typedoc/functions/utils.computeReshapedDimensions.html @@ -1 +1 @@ -computeReshapedDimensions | @vladmandic/face-api - v1.7.14

    Function computeReshapedDimensions

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    +computeReshapedDimensions | @vladmandic/face-api - v1.7.15

    Function computeReshapedDimensions

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    diff --git a/typedoc/functions/utils.getCenterPoint.html b/typedoc/functions/utils.getCenterPoint.html index 6eb9557..a573c7b 100644 --- a/typedoc/functions/utils.getCenterPoint.html +++ b/typedoc/functions/utils.getCenterPoint.html @@ -1 +1 @@ -getCenterPoint | @vladmandic/face-api - v1.7.14

    Function getCenterPoint

    Settings

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    +getCenterPoint | @vladmandic/face-api - v1.7.15

    Function getCenterPoint

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    diff --git a/typedoc/functions/utils.isDimensions.html b/typedoc/functions/utils.isDimensions.html index 0548aa6..6f05ef1 100644 --- a/typedoc/functions/utils.isDimensions.html +++ b/typedoc/functions/utils.isDimensions.html @@ -1 +1 @@ -isDimensions | @vladmandic/face-api - v1.7.14

    Function isDimensions

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    +isDimensions | @vladmandic/face-api - v1.7.15

    Function isDimensions

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    diff --git a/typedoc/functions/utils.isEven.html b/typedoc/functions/utils.isEven.html index d129d75..28016af 100644 --- a/typedoc/functions/utils.isEven.html +++ b/typedoc/functions/utils.isEven.html @@ -1 +1 @@ -isEven | @vladmandic/face-api - v1.7.14

    Function isEven

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    +isEven | @vladmandic/face-api - v1.7.15

    Function isEven

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    diff --git a/typedoc/functions/utils.isFloat.html b/typedoc/functions/utils.isFloat.html index 02df35e..b98404a 100644 --- a/typedoc/functions/utils.isFloat.html +++ b/typedoc/functions/utils.isFloat.html @@ -1 +1 @@ -isFloat | @vladmandic/face-api - v1.7.14

    Function isFloat

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    +isFloat | @vladmandic/face-api - v1.7.15

    Function isFloat

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    diff --git a/typedoc/functions/utils.isTensor.html b/typedoc/functions/utils.isTensor.html index dd0ffb3..09c60d7 100644 --- a/typedoc/functions/utils.isTensor.html +++ b/typedoc/functions/utils.isTensor.html @@ -1 +1 @@ -isTensor | @vladmandic/face-api - v1.7.14

    Function isTensor

    • isTensor(tensor: any, dim: number): boolean

    • Parameters

      • tensor: any
      • dim: number

      Returns boolean

    Settings

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    +isTensor | @vladmandic/face-api - v1.7.15

    Function isTensor

    • isTensor(tensor: any, dim: number): boolean

    • Parameters

      • tensor: any
      • dim: number

      Returns boolean

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    diff --git a/typedoc/functions/utils.isTensor1D.html b/typedoc/functions/utils.isTensor1D.html index 84462ba..bb1c55e 100644 --- a/typedoc/functions/utils.isTensor1D.html +++ b/typedoc/functions/utils.isTensor1D.html @@ -1 +1 @@ -isTensor1D | @vladmandic/face-api - v1.7.14

    Function isTensor1D

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    +isTensor1D | @vladmandic/face-api - v1.7.15

    Function isTensor1D

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    diff --git a/typedoc/functions/utils.isTensor2D.html b/typedoc/functions/utils.isTensor2D.html index 596307e..0836362 100644 --- a/typedoc/functions/utils.isTensor2D.html +++ b/typedoc/functions/utils.isTensor2D.html @@ -1 +1 @@ -isTensor2D | @vladmandic/face-api - v1.7.14

    Function isTensor2D

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    +isTensor2D | @vladmandic/face-api - v1.7.15

    Function isTensor2D

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    diff --git a/typedoc/functions/utils.isTensor3D.html b/typedoc/functions/utils.isTensor3D.html index 1899344..eabf8a2 100644 --- a/typedoc/functions/utils.isTensor3D.html +++ b/typedoc/functions/utils.isTensor3D.html @@ -1 +1 @@ -isTensor3D | @vladmandic/face-api - v1.7.14

    Function isTensor3D

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    +isTensor3D | @vladmandic/face-api - v1.7.15

    Function isTensor3D

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    diff --git a/typedoc/functions/utils.isTensor4D.html b/typedoc/functions/utils.isTensor4D.html index c869219..77eed34 100644 --- a/typedoc/functions/utils.isTensor4D.html +++ b/typedoc/functions/utils.isTensor4D.html @@ -1 +1 @@ -isTensor4D | @vladmandic/face-api - v1.7.14

    Function isTensor4D

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    +isTensor4D | @vladmandic/face-api - v1.7.15

    Function isTensor4D

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    diff --git a/typedoc/functions/utils.isValidNumber.html b/typedoc/functions/utils.isValidNumber.html index 67cb864..405d88a 100644 --- a/typedoc/functions/utils.isValidNumber.html +++ b/typedoc/functions/utils.isValidNumber.html @@ -1 +1 @@ -isValidNumber | @vladmandic/face-api - v1.7.14

    Function isValidNumber

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    +isValidNumber | @vladmandic/face-api - v1.7.15

    Function isValidNumber

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    diff --git a/typedoc/functions/utils.isValidProbablitiy.html b/typedoc/functions/utils.isValidProbablitiy.html index b0dae4d..10f4840 100644 --- a/typedoc/functions/utils.isValidProbablitiy.html +++ b/typedoc/functions/utils.isValidProbablitiy.html @@ -1 +1 @@ -isValidProbablitiy | @vladmandic/face-api - v1.7.14

    Function isValidProbablitiy

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    +isValidProbablitiy | @vladmandic/face-api - v1.7.15

    Function isValidProbablitiy

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    diff --git a/typedoc/functions/utils.range.html b/typedoc/functions/utils.range.html index 3e15f62..f85fadc 100644 --- a/typedoc/functions/utils.range.html +++ b/typedoc/functions/utils.range.html @@ -1 +1 @@ -range | @vladmandic/face-api - v1.7.14

    Function range

    • range(num: number, start: number, step: number): number[]

    • Parameters

      • num: number
      • start: number
      • step: number

      Returns number[]

    Settings

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    +range | @vladmandic/face-api - v1.7.15

    Function range

    • range(num: number, start: number, step: number): 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 702ab71..9b259b3 100644 --- a/typedoc/functions/utils.round.html +++ b/typedoc/functions/utils.round.html @@ -1 +1 @@ -round | @vladmandic/face-api - v1.7.14

    Function round

    • round(num: number, prec?: number): number

    • Parameters

      • num: number
      • prec: number = 2

      Returns number

    Settings

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    +round | @vladmandic/face-api - v1.7.15

    Function round

    • round(num: number, prec?: number): 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 af54aa6..6aafd31 100644 --- a/typedoc/functions/validateConfig.html +++ b/typedoc/functions/validateConfig.html @@ -1 +1 @@ -validateConfig | @vladmandic/face-api - v1.7.14

    Function validateConfig

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    +validateConfig | @vladmandic/face-api - v1.7.15

    Function validateConfig

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    diff --git a/typedoc/hierarchy.html b/typedoc/hierarchy.html index b759f1a..a1b5c53 100644 --- a/typedoc/hierarchy.html +++ b/typedoc/hierarchy.html @@ -1 +1 @@ -@vladmandic/face-api - v1.7.14

    @vladmandic/face-api - v1.7.14

    Hierarchy Summary

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    +@vladmandic/face-api - v1.7.15

    @vladmandic/face-api - v1.7.15

    Hierarchy Summary

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    diff --git a/typedoc/index.html b/typedoc/index.html index 2c30792..f9a4743 100644 --- a/typedoc/index.html +++ b/typedoc/index.html @@ -1 +1 @@ -@vladmandic/face-api - v1.7.14

    @vladmandic/face-api - v1.7.14

    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

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    On This Page

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    allFacesallFacesSsdMobilenetv1allFacesTinyYolov2awaitMediaLoadedbufferToImagecomputeFaceDescriptorcreateCanvascreateCanvasFromMediacreateFaceDetectionNetcreateFaceRecognitionNetcreateSsdMobilenetv1createTinyFaceDetectorcreateTinyYolov2detectAllFacesdetectFaceLandmarksdetectFaceLandmarksTinydetectLandmarksdetectSingleFaceeuclideanDistanceextendWithAgeextendWithFaceDescriptorextendWithFaceDetectionextendWithFaceExpressionsextendWithFaceLandmarksextendWithGenderextractFacesextractFaceTensorsfetchImagefetchJsonfetchNetWeightsfetchOrThrowfetchVideogetContext2dOrThrowgetMediaDimensionsimageTensorToCanvasimageToSquareinverseSigmoidiouisMediaElementisMediaLoadedisWithAgeisWithFaceDetectionisWithFaceExpressionsisWithFaceLandmarksisWithGenderloadAgeGenderModelloadFaceDetectionModelloadFaceExpressionModelloadFaceLandmarkModelloadFaceLandmarkTinyModelloadFaceRecognitionModelloadSsdMobilenetv1ModelloadTinyFaceDetectorModelloadTinyYolov2ModelloadWeightMaplocateFacesmatchDimensionsminBboxnonMaxSuppressionnormalizepadToSquarepredictAgeAndGenderrecognizeFaceExpressionsresizeResultsresolveInputshuffleArraysigmoidssdMobilenetv1tinyFaceDetectortinyYolov2toNetInputvalidateConfig
    +@vladmandic/face-api - v1.7.15

    @vladmandic/face-api - v1.7.15

    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

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    Type Aliases
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    allFacesallFacesSsdMobilenetv1allFacesTinyYolov2awaitMediaLoadedbufferToImagecomputeFaceDescriptorcreateCanvascreateCanvasFromMediacreateFaceDetectionNetcreateFaceRecognitionNetcreateSsdMobilenetv1createTinyFaceDetectorcreateTinyYolov2detectAllFacesdetectFaceLandmarksdetectFaceLandmarksTinydetectLandmarksdetectSingleFaceeuclideanDistanceextendWithAgeextendWithFaceDescriptorextendWithFaceDetectionextendWithFaceExpressionsextendWithFaceLandmarksextendWithGenderextractFacesextractFaceTensorsfetchImagefetchJsonfetchNetWeightsfetchOrThrowfetchVideogetContext2dOrThrowgetMediaDimensionsimageTensorToCanvasimageToSquareinverseSigmoidiouisMediaElementisMediaLoadedisWithAgeisWithFaceDetectionisWithFaceExpressionsisWithFaceLandmarksisWithGenderloadAgeGenderModelloadFaceDetectionModelloadFaceExpressionModelloadFaceLandmarkModelloadFaceLandmarkTinyModelloadFaceRecognitionModelloadSsdMobilenetv1ModelloadTinyFaceDetectorModelloadTinyYolov2ModelloadWeightMaplocateFacesmatchDimensionsminBboxnonMaxSuppressionnormalizepadToSquarepredictAgeAndGenderrecognizeFaceExpressionsresizeResultsresolveInputshuffleArraysigmoidssdMobilenetv1tinyFaceDetectortinyYolov2toNetInputvalidateConfig
    diff --git a/typedoc/interfaces/IBoundingBox.html b/typedoc/interfaces/IBoundingBox.html index 5a2e233..a98a7a4 100644 --- a/typedoc/interfaces/IBoundingBox.html +++ b/typedoc/interfaces/IBoundingBox.html @@ -1,5 +1,5 @@ -IBoundingBox | @vladmandic/face-api - v1.7.14

    Interface IBoundingBox

    interface IBoundingBox {
        bottom: number;
        left: number;
        right: number;
        top: number;
    }

    Implemented by

    Properties

    bottom +IBoundingBox | @vladmandic/face-api - v1.7.15

    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

    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 a4c2897..6d83d92 100644 --- a/typedoc/interfaces/IDimensions.html +++ b/typedoc/interfaces/IDimensions.html @@ -1,3 +1,3 @@ -IDimensions | @vladmandic/face-api - v1.7.14

    Interface IDimensions

    interface IDimensions {
        height: number;
        width: number;
    }

    Implemented by

    Properties

    height +IDimensions | @vladmandic/face-api - v1.7.15

    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

    height

    height: number

    width

    width: number

    Settings

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    diff --git a/typedoc/interfaces/IFaceDetecion.html b/typedoc/interfaces/IFaceDetecion.html index 9e9b2cb..d52929e 100644 --- a/typedoc/interfaces/IFaceDetecion.html +++ b/typedoc/interfaces/IFaceDetecion.html @@ -1,3 +1,3 @@ -IFaceDetecion | @vladmandic/face-api - v1.7.14

    Interface IFaceDetecion

    interface IFaceDetecion {
        box: Box;
        score: number;
    }

    Implemented by

    Properties

    box +IFaceDetecion | @vladmandic/face-api - v1.7.15

    Interface IFaceDetecion

    interface IFaceDetecion {
        box: Box;
        score: number;
    }

    Implemented by

    Properties

    box score -

    Properties

    box

    box: Box

    score

    score: number

    Settings

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    +

    Properties

    box

    box: Box

    score

    score: number

    Settings

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    diff --git a/typedoc/interfaces/IFaceLandmarks.html b/typedoc/interfaces/IFaceLandmarks.html index 15d21d1..483cb4e 100644 --- a/typedoc/interfaces/IFaceLandmarks.html +++ b/typedoc/interfaces/IFaceLandmarks.html @@ -1,3 +1,3 @@ -IFaceLandmarks | @vladmandic/face-api - v1.7.14

    Interface IFaceLandmarks

    interface IFaceLandmarks {
        positions: Point[];
        shift: Point;
    }

    Implemented by

    Properties

    positions +IFaceLandmarks | @vladmandic/face-api - v1.7.15

    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

    positions

    positions: Point[]

    shift

    shift: Point

    Settings

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    diff --git a/typedoc/interfaces/IFaceMatch.html b/typedoc/interfaces/IFaceMatch.html index 12b7ac4..5291698 100644 --- a/typedoc/interfaces/IFaceMatch.html +++ b/typedoc/interfaces/IFaceMatch.html @@ -1,3 +1,3 @@ -IFaceMatch | @vladmandic/face-api - v1.7.14

    Interface IFaceMatch

    interface IFaceMatch {
        distance: number;
        label: string;
    }

    Implemented by

    Properties

    distance +IFaceMatch | @vladmandic/face-api - v1.7.15

    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

    distance

    distance: number

    label

    label: string

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    diff --git a/typedoc/interfaces/IPoint.html b/typedoc/interfaces/IPoint.html index 37d0ea1..c3989a3 100644 --- a/typedoc/interfaces/IPoint.html +++ b/typedoc/interfaces/IPoint.html @@ -1,3 +1,3 @@ -IPoint | @vladmandic/face-api - v1.7.14

    Interface IPoint

    interface IPoint {
        x: number;
        y: number;
    }

    Implemented by

    Properties

    x +IPoint | @vladmandic/face-api - v1.7.15

    Interface IPoint

    interface IPoint {
        x: number;
        y: number;
    }

    Implemented by

    Properties

    x y -

    Properties

    x

    x: number

    y

    y: number

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    Properties

    x

    x: number

    y

    y: number

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    diff --git a/typedoc/interfaces/IRect.html b/typedoc/interfaces/IRect.html index 116ac00..6e6324f 100644 --- a/typedoc/interfaces/IRect.html +++ b/typedoc/interfaces/IRect.html @@ -1,5 +1,5 @@ -IRect | @vladmandic/face-api - v1.7.14

    Interface IRect

    interface IRect {
        height: number;
        width: number;
        x: number;
        y: number;
    }

    Implemented by

    Properties

    height +IRect | @vladmandic/face-api - v1.7.15

    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

    height

    height: number

    width

    width: number

    x

    x: number

    y

    y: number

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    diff --git a/typedoc/interfaces/ISsdMobilenetv1Options.html b/typedoc/interfaces/ISsdMobilenetv1Options.html index a6e57ed..3714b50 100644 --- a/typedoc/interfaces/ISsdMobilenetv1Options.html +++ b/typedoc/interfaces/ISsdMobilenetv1Options.html @@ -1,3 +1,3 @@ -ISsdMobilenetv1Options | @vladmandic/face-api - v1.7.14

    Interface ISsdMobilenetv1Options

    interface ISsdMobilenetv1Options {
        maxResults?: number;
        minConfidence?: number;
    }

    Properties

    maxResults? +ISsdMobilenetv1Options | @vladmandic/face-api - v1.7.15

    Interface ISsdMobilenetv1Options

    interface ISsdMobilenetv1Options {
        maxResults?: number;
        minConfidence?: number;
    }

    Properties

    maxResults? minConfidence? -

    Properties

    OptionalmaxResults

    maxResults?: number

    OptionalminConfidence

    minConfidence?: number

    Settings

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    +

    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 782fb3e..feddb94 100644 --- a/typedoc/interfaces/ITinyYolov2Options.html +++ b/typedoc/interfaces/ITinyYolov2Options.html @@ -1,3 +1,3 @@ -ITinyYolov2Options | @vladmandic/face-api - v1.7.14

    Interface ITinyYolov2Options

    interface ITinyYolov2Options {
        inputSize?: number;
        scoreThreshold?: number;
    }

    Properties

    inputSize? +ITinyYolov2Options | @vladmandic/face-api - v1.7.15

    Interface ITinyYolov2Options

    interface ITinyYolov2Options {
        inputSize?: number;
        scoreThreshold?: number;
    }

    Properties

    inputSize? scoreThreshold? -

    Properties

    OptionalinputSize

    inputSize?: number

    OptionalscoreThreshold

    scoreThreshold?: number

    Settings

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    +

    Properties

    OptionalinputSize

    inputSize?: number

    OptionalscoreThreshold

    scoreThreshold?: number

    Settings

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    diff --git a/typedoc/interfaces/draw.IDrawBoxOptions.html b/typedoc/interfaces/draw.IDrawBoxOptions.html index 4498e5c..f9c17fc 100644 --- a/typedoc/interfaces/draw.IDrawBoxOptions.html +++ b/typedoc/interfaces/draw.IDrawBoxOptions.html @@ -1,5 +1,5 @@ -IDrawBoxOptions | @vladmandic/face-api - v1.7.14

    Interface IDrawBoxOptions

    interface IDrawBoxOptions {
        boxColor?: string;
        drawLabelOptions?: IDrawTextFieldOptions;
        label?: string;
        lineWidth?: number;
    }

    Properties

    boxColor? +IDrawBoxOptions | @vladmandic/face-api - v1.7.15

    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

    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 2116d63..2b44133 100644 --- a/typedoc/interfaces/draw.IDrawFaceLandmarksOptions.html +++ b/typedoc/interfaces/draw.IDrawFaceLandmarksOptions.html @@ -1,7 +1,7 @@ -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? +IDrawFaceLandmarksOptions | @vladmandic/face-api - v1.7.15

    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

    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 d35fee6..fecb09b 100644 --- a/typedoc/interfaces/draw.IDrawTextFieldOptions.html +++ b/typedoc/interfaces/draw.IDrawTextFieldOptions.html @@ -1,7 +1,7 @@ -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? +IDrawTextFieldOptions | @vladmandic/face-api - v1.7.15

    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

    OptionalanchorPosition

    anchorPosition?: AnchorPosition

    OptionalbackgroundColor

    backgroundColor?: string

    OptionalfontColor

    fontColor?: string

    OptionalfontSize

    fontSize?: number

    OptionalfontStyle

    fontStyle?: string

    Optionalpadding

    padding?: number

    Settings

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    diff --git a/typedoc/interfaces/tf.io.IOHandler.html b/typedoc/interfaces/tf.io.IOHandler.html index c1a705b..4ef7521 100644 --- a/typedoc/interfaces/tf.io.IOHandler.html +++ b/typedoc/interfaces/tf.io.IOHandler.html @@ -1,6 +1,6 @@ -IOHandler | @vladmandic/face-api - v1.7.14

    Interface IOHandler

    Interface for a model import/export handler.

    +IOHandler | @vladmandic/face-api - v1.7.15

    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

    Optionalload

    load?: LoadHandler

    Optionalsave

    save?: SaveHandler

    Settings

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    +

    Properties

    Optionalload

    load?: LoadHandler

    Optionalsave

    save?: SaveHandler

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    diff --git a/typedoc/interfaces/tf.io.LoadOptions.html b/typedoc/interfaces/tf.io.LoadOptions.html index 87b4dfc..4cb1029 100644 --- a/typedoc/interfaces/tf.io.LoadOptions.html +++ b/typedoc/interfaces/tf.io.LoadOptions.html @@ -1,4 +1,4 @@ -LoadOptions | @vladmandic/face-api - v1.7.14

    Interface LoadOptions

    Innamespace

    io

    +LoadOptions | @vladmandic/face-api - v1.7.15

    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? @@ -44,4 +44,4 @@ model.json URL: https://www 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.

    -

    Settings

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    Settings

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    diff --git a/typedoc/interfaces/tf.io.ModelArtifacts.html b/typedoc/interfaces/tf.io.ModelArtifacts.html index 0cb4d60..877fce8 100644 --- a/typedoc/interfaces/tf.io.ModelArtifacts.html +++ b/typedoc/interfaces/tf.io.ModelArtifacts.html @@ -1,4 +1,4 @@ -ModelArtifacts | @vladmandic/face-api - v1.7.14

    Interface ModelArtifacts

    The serialized artifacts of a model, including topology and weights.

    +ModelArtifacts | @vladmandic/face-api - v1.7.15

    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.

    @@ -44,4 +44,4 @@ concatenated together or an Array of ArrayBuffers containing the weights (weights may be sharded across multiple ArrayBuffers).

    OptionalweightSpecs

    weightSpecs?: WeightsManifestEntry[]

    Weight specifications.

    This corresponds to the weightsData below.

    -

    Settings

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    +

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    diff --git a/typedoc/interfaces/tf.io.ModelArtifactsInfo.html b/typedoc/interfaces/tf.io.ModelArtifactsInfo.html index 8012155..0251408 100644 --- a/typedoc/interfaces/tf.io.ModelArtifactsInfo.html +++ b/typedoc/interfaces/tf.io.ModelArtifactsInfo.html @@ -1,4 +1,4 @@ -ModelArtifactsInfo | @vladmandic/face-api - v1.7.14

    Interface ModelArtifactsInfo

    interface ModelArtifactsInfo {
        dateSaved: Date;
        modelTopologyBytes?: number;
        modelTopologyType: "JSON" | "GraphDef";
        weightDataBytes?: number;
        weightSpecsBytes?: number;
    }

    Properties

    dateSaved +ModelArtifactsInfo | @vladmandic/face-api - v1.7.15

    Interface ModelArtifactsInfo

    interface ModelArtifactsInfo {
        dateSaved: Date;
        modelTopologyBytes?: number;
        modelTopologyType: "JSON" | "GraphDef";
        weightDataBytes?: number;
        weightSpecsBytes?: number;
    }

    Properties

    dateSaved modelTopologyBytes? modelTopologyType weightDataBytes? @@ -21,4 +21,4 @@ protocol buffer (binary).

    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 0fd3ead..b31e8c0 100644 --- a/typedoc/interfaces/tf.io.ModelJSON.html +++ b/typedoc/interfaces/tf.io.ModelJSON.html @@ -1,4 +1,4 @@ -ModelJSON | @vladmandic/face-api - v1.7.14

    Interface ModelJSON

    The on-disk format of the model.json file.

    +ModelJSON | @vladmandic/face-api - v1.7.15

    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? @@ -35,4 +35,4 @@ encoding of the GraphDef protocol buffer.

    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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    +

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    diff --git a/typedoc/interfaces/tf.io.ModelStoreManager.html b/typedoc/interfaces/tf.io.ModelStoreManager.html index 698afc7..4fc099f 100644 --- a/typedoc/interfaces/tf.io.ModelStoreManager.html +++ b/typedoc/interfaces/tf.io.ModelStoreManager.html @@ -1,4 +1,4 @@ -ModelStoreManager | @vladmandic/face-api - v1.7.14

    Interface ModelStoreManager

    An interface for the manager of a model store.

    +ModelStoreManager | @vladmandic/face-api - v1.7.15

    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

    @@ -16,4 +16,4 @@ topology, byte sizes of the topology, weights, etc.

    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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    +

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    diff --git a/typedoc/interfaces/tf.io.RequestDetails.html b/typedoc/interfaces/tf.io.RequestDetails.html index 76eece8..90f89ca 100644 --- a/typedoc/interfaces/tf.io.RequestDetails.html +++ b/typedoc/interfaces/tf.io.RequestDetails.html @@ -1,4 +1,4 @@ -RequestDetails | @vladmandic/face-api - v1.7.14

    Interface RequestDetails

    Additional options for Platform.fetch

    +RequestDetails | @vladmandic/face-api - v1.7.15

    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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    +

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    diff --git a/typedoc/interfaces/tf.io.SaveConfig.html b/typedoc/interfaces/tf.io.SaveConfig.html index a6aa632..a8315e2 100644 --- a/typedoc/interfaces/tf.io.SaveConfig.html +++ b/typedoc/interfaces/tf.io.SaveConfig.html @@ -1,4 +1,4 @@ -SaveConfig | @vladmandic/face-api - v1.7.14

    Interface SaveConfig

    Options for saving a model.

    +SaveConfig | @vladmandic/face-api - v1.7.15

    Interface SaveConfig

    Options for saving a model.

    Innamespace

    io

    interface SaveConfig {
        includeOptimizer?: boolean;
        trainableOnly?: boolean;
    }

    Properties

    includeOptimizer? trainableOnly? @@ -6,4 +6,4 @@

    Default: false.

    OptionaltrainableOnly

    trainableOnly?: boolean

    Whether to save only the trainable weights of the model, ignoring the non-trainable ones.

    -

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    +

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    diff --git a/typedoc/interfaces/tf.io.SaveResult.html b/typedoc/interfaces/tf.io.SaveResult.html index 311fb75..79c91dc 100644 --- a/typedoc/interfaces/tf.io.SaveResult.html +++ b/typedoc/interfaces/tf.io.SaveResult.html @@ -1,4 +1,4 @@ -SaveResult | @vladmandic/face-api - v1.7.14

    Interface SaveResult

    Result of a saving operation.

    +SaveResult | @vladmandic/face-api - v1.7.15

    Interface SaveResult

    Result of a saving operation.

    interface SaveResult {
        errors?: (string | {})[];
        modelArtifactsInfo: ModelArtifactsInfo;
        responses?: Response[];
    }

    Properties

    errors? modelArtifactsInfo responses? @@ -6,4 +6,4 @@

    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 4729df2..1d96f35 100644 --- a/typedoc/interfaces/tf.io.TrainingConfig.html +++ b/typedoc/interfaces/tf.io.TrainingConfig.html @@ -1,4 +1,4 @@ -TrainingConfig | @vladmandic/face-api - v1.7.14

    Interface TrainingConfig

    Model training configuration.

    +TrainingConfig | @vladmandic/face-api - v1.7.15

    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? @@ -8,4 +8,4 @@

    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

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    +

    Optionalsample_weight_mode

    sample_weight_mode?: string

    Optionalweighted_metrics

    weighted_metrics?: string[]

    Settings

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    diff --git a/typedoc/interfaces/tf.io.WeightsManifestEntry.html b/typedoc/interfaces/tf.io.WeightsManifestEntry.html index 97f99aa..874b5ee 100644 --- a/typedoc/interfaces/tf.io.WeightsManifestEntry.html +++ b/typedoc/interfaces/tf.io.WeightsManifestEntry.html @@ -1,4 +1,4 @@ -WeightsManifestEntry | @vladmandic/face-api - v1.7.14

    Interface WeightsManifestEntry

    An entry in the weight manifest.

    +WeightsManifestEntry | @vladmandic/face-api - v1.7.15

    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? @@ -13,4 +13,4 @@

    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.

    -

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    +

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    diff --git a/typedoc/modules/draw.html b/typedoc/modules/draw.html index 7f46d9a..bab19a5 100644 --- a/typedoc/modules/draw.html +++ b/typedoc/modules/draw.html @@ -1 +1 @@ -draw | @vladmandic/face-api - v1.7.14

    Namespace draw

    Enumerations

    AnchorPosition

    Classes

    DrawBox
    DrawBoxOptions
    DrawFaceLandmarks
    DrawFaceLandmarksOptions
    DrawTextField
    DrawTextFieldOptions

    Interfaces

    IDrawBoxOptions
    IDrawFaceLandmarksOptions
    IDrawTextFieldOptions

    Type Aliases

    DrawFaceExpressionsInput
    DrawFaceLandmarksInput
    TDrawDetectionsInput

    Functions

    drawContour
    drawDetections
    drawFaceExpressions
    drawFaceLandmarks

    Settings

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    +draw | @vladmandic/face-api - v1.7.15

    Namespace draw

    Enumerations

    AnchorPosition

    Classes

    DrawBox
    DrawBoxOptions
    DrawFaceLandmarks
    DrawFaceLandmarksOptions
    DrawTextField
    DrawTextFieldOptions

    Interfaces

    IDrawBoxOptions
    IDrawFaceLandmarksOptions
    IDrawTextFieldOptions

    Type Aliases

    DrawFaceExpressionsInput
    DrawFaceLandmarksInput
    TDrawDetectionsInput

    Functions

    drawContour
    drawDetections
    drawFaceExpressions
    drawFaceLandmarks

    Settings

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    diff --git a/typedoc/modules/tf.Tensor.html b/typedoc/modules/tf.Tensor.html index 9ce3ad7..08e81bb 100644 --- a/typedoc/modules/tf.Tensor.html +++ b/typedoc/modules/tf.Tensor.html @@ -1 +1 @@ -Tensor | @vladmandic/face-api - v1.7.14

    Namespace Tensor

    Settings

    Member Visibility
    +Tensor | @vladmandic/face-api - v1.7.15

    Namespace Tensor

    Settings

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    diff --git a/typedoc/modules/tf.browser.html b/typedoc/modules/tf.browser.html index 965a26e..341b975 100644 --- a/typedoc/modules/tf.browser.html +++ b/typedoc/modules/tf.browser.html @@ -1 +1 @@ -browser | @vladmandic/face-api - v1.7.14

    Namespace browser

    Functions

    draw
    fromPixels
    fromPixelsAsync
    toPixels

    Settings

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    +browser | @vladmandic/face-api - v1.7.15

    Namespace browser

    Functions

    draw
    fromPixels
    fromPixelsAsync
    toPixels

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    diff --git a/typedoc/modules/tf.html b/typedoc/modules/tf.html index d947f87..a5ba293 100644 --- a/typedoc/modules/tf.html +++ b/typedoc/modules/tf.html @@ -1 +1 @@ -tf | @vladmandic/face-api - v1.7.14

    Namespace tf

    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

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    +tf | @vladmandic/face-api - v1.7.15

    Namespace tf

    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

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    diff --git a/typedoc/modules/tf.io.html b/typedoc/modules/tf.io.html index b6e8ad9..2cdaa50 100644 --- a/typedoc/modules/tf.io.html +++ b/typedoc/modules/tf.io.html @@ -1 +1 @@ -io | @vladmandic/face-api - v1.7.14

    Namespace io

    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

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    Type Aliases
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    +io | @vladmandic/face-api - v1.7.15

    Namespace io

    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

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    diff --git a/typedoc/modules/utils.html b/typedoc/modules/utils.html index 04520db..6fd8833 100644 --- a/typedoc/modules/utils.html +++ b/typedoc/modules/utils.html @@ -1 +1 @@ -utils | @vladmandic/face-api - v1.7.14

    Namespace utils

    Functions

    computeReshapedDimensions
    getCenterPoint
    isDimensions
    isEven
    isFloat
    isTensor
    isTensor1D
    isTensor2D
    isTensor3D
    isTensor4D
    isValidNumber
    isValidProbablitiy
    range
    round

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    +utils | @vladmandic/face-api - v1.7.15

    Namespace utils

    Functions

    computeReshapedDimensions
    getCenterPoint
    isDimensions
    isEven
    isFloat
    isTensor
    isTensor1D
    isTensor2D
    isTensor3D
    isTensor4D
    isValidNumber
    isValidProbablitiy
    range
    round

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    diff --git a/typedoc/types/AgeAndGenderPrediction.html b/typedoc/types/AgeAndGenderPrediction.html index 13a63a8..b4024a1 100644 --- a/typedoc/types/AgeAndGenderPrediction.html +++ b/typedoc/types/AgeAndGenderPrediction.html @@ -1 +1 @@ -AgeAndGenderPrediction | @vladmandic/face-api - v1.7.14

    Type Alias AgeAndGenderPrediction

    AgeAndGenderPrediction: {
        age: number;
        gender: Gender;
        genderProbability: number;
    }

    Type declaration

    • age: number
    • gender: Gender
    • genderProbability: number

    Settings

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    +AgeAndGenderPrediction | @vladmandic/face-api - v1.7.15

    Type Alias AgeAndGenderPrediction

    AgeAndGenderPrediction: {
        age: number;
        gender: Gender;
        genderProbability: number;
    }

    Type declaration

    • age: number
    • gender: Gender
    • genderProbability: number

    Settings

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    diff --git a/typedoc/types/BatchNorm.html b/typedoc/types/BatchNorm.html index f910d86..d7902f0 100644 --- a/typedoc/types/BatchNorm.html +++ b/typedoc/types/BatchNorm.html @@ -1 +1 @@ -BatchNorm | @vladmandic/face-api - v1.7.14

    Type Alias BatchNorm

    BatchNorm: { sub: Tensor1D; truediv: Tensor1D }

    Type declaration

    Settings

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    +BatchNorm | @vladmandic/face-api - v1.7.15

    Type Alias BatchNorm

    BatchNorm: { sub: Tensor1D; truediv: Tensor1D }

    Type declaration

    Settings

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    diff --git a/typedoc/types/ConvWithBatchNorm.html b/typedoc/types/ConvWithBatchNorm.html index 9c3c52b..7872658 100644 --- a/typedoc/types/ConvWithBatchNorm.html +++ b/typedoc/types/ConvWithBatchNorm.html @@ -1 +1 @@ -ConvWithBatchNorm | @vladmandic/face-api - v1.7.14

    Type Alias ConvWithBatchNorm

    ConvWithBatchNorm: { bn: BatchNorm; conv: ConvParams }

    Type declaration

    Settings

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    +ConvWithBatchNorm | @vladmandic/face-api - v1.7.15

    Type Alias ConvWithBatchNorm

    ConvWithBatchNorm: { bn: BatchNorm; conv: ConvParams }

    Type declaration

    Settings

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    diff --git a/typedoc/types/DefaultTinyYolov2NetParams.html b/typedoc/types/DefaultTinyYolov2NetParams.html index 3b35a22..5646bad 100644 --- a/typedoc/types/DefaultTinyYolov2NetParams.html +++ b/typedoc/types/DefaultTinyYolov2NetParams.html @@ -1 +1 @@ -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;
    }

    Type declaration

    Settings

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    +DefaultTinyYolov2NetParams | @vladmandic/face-api - v1.7.15

    Type Alias DefaultTinyYolov2NetParams

    DefaultTinyYolov2NetParams: {
        conv0: ConvWithBatchNorm;
        conv1: ConvWithBatchNorm;
        conv2: ConvWithBatchNorm;
        conv3: ConvWithBatchNorm;
        conv4: ConvWithBatchNorm;
        conv5: ConvWithBatchNorm;
        conv6: ConvWithBatchNorm;
        conv7: ConvWithBatchNorm;
        conv8: ConvParams;
    }

    Type declaration

    Settings

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    diff --git a/typedoc/types/Environment.html b/typedoc/types/Environment.html index 98f94a3..53baf97 100644 --- a/typedoc/types/Environment.html +++ b/typedoc/types/Environment.html @@ -1 +1 @@ -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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    +Environment | @vladmandic/face-api - v1.7.15

    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;
    }

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    diff --git a/typedoc/types/FaceDetectionFunction.html b/typedoc/types/FaceDetectionFunction.html index bbff0ba..9985526 100644 --- a/typedoc/types/FaceDetectionFunction.html +++ b/typedoc/types/FaceDetectionFunction.html @@ -1 +1 @@ -FaceDetectionFunction | @vladmandic/face-api - v1.7.14

    Type Alias FaceDetectionFunction

    FaceDetectionFunction: (input: TNetInput) => Promise<FaceDetection[]>

    Type declaration

    Settings

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    +FaceDetectionFunction | @vladmandic/face-api - v1.7.15

    Type Alias FaceDetectionFunction

    FaceDetectionFunction: (input: TNetInput) => Promise<FaceDetection[]>

    Type declaration

    Settings

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    diff --git a/typedoc/types/FaceDetectionOptions.html b/typedoc/types/FaceDetectionOptions.html index 046167c..5f0fec8 100644 --- a/typedoc/types/FaceDetectionOptions.html +++ b/typedoc/types/FaceDetectionOptions.html @@ -1 +1 @@ -FaceDetectionOptions | @vladmandic/face-api - v1.7.14

    Type Alias FaceDetectionOptions

    FaceDetectionOptions:
        | TinyFaceDetectorOptions
        | SsdMobilenetv1Options
        | TinyYolov2Options

    Settings

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    +FaceDetectionOptions | @vladmandic/face-api - v1.7.15

    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 a5fa931..6dd9a20 100644 --- a/typedoc/types/FileSystem.html +++ b/typedoc/types/FileSystem.html @@ -1 +1 @@ -FileSystem | @vladmandic/face-api - v1.7.14

    Type Alias FileSystem

    FileSystem: { readFile: (filePath: string) => Promise<string | Buffer> }

    Type declaration

    • readFile: (filePath: string) => Promise<string | Buffer>

    Settings

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    +FileSystem | @vladmandic/face-api - v1.7.15

    Type Alias FileSystem

    FileSystem: { readFile: (filePath: string) => Promise<string | Buffer> }

    Type declaration

    • readFile: (filePath: string) => Promise<string | Buffer>

    Settings

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    diff --git a/typedoc/types/ITinyFaceDetectorOptions.html b/typedoc/types/ITinyFaceDetectorOptions.html index 41c63ab..017688e 100644 --- a/typedoc/types/ITinyFaceDetectorOptions.html +++ b/typedoc/types/ITinyFaceDetectorOptions.html @@ -1 +1 @@ -ITinyFaceDetectorOptions | @vladmandic/face-api - v1.7.14

    Type Alias ITinyFaceDetectorOptions

    ITinyFaceDetectorOptions: ITinyYolov2Options

    Settings

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    +ITinyFaceDetectorOptions | @vladmandic/face-api - v1.7.15

    Type Alias ITinyFaceDetectorOptions

    ITinyFaceDetectorOptions: ITinyYolov2Options

    Settings

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    diff --git a/typedoc/types/MobilenetParams.html b/typedoc/types/MobilenetParams.html index 1a4b60c..d267ff3 100644 --- a/typedoc/types/MobilenetParams.html +++ b/typedoc/types/MobilenetParams.html @@ -1 +1 @@ -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;
    }

    Type declaration

    • conv0: SeparableConvParams | ConvParams
    • conv1: SeparableConvParams
    • conv2: SeparableConvParams
    • conv3: SeparableConvParams
    • conv4: SeparableConvParams
    • conv5: SeparableConvParams
    • Optionalconv6?: SeparableConvParams
    • Optionalconv7?: SeparableConvParams
    • conv8: ConvParams

    Settings

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    +MobilenetParams | @vladmandic/face-api - v1.7.15

    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
    • Optionalconv6?: SeparableConvParams
    • Optionalconv7?: SeparableConvParams
    • conv8: ConvParams

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    diff --git a/typedoc/types/NetOutput.html b/typedoc/types/NetOutput.html index f77a960..a016742 100644 --- a/typedoc/types/NetOutput.html +++ b/typedoc/types/NetOutput.html @@ -1 +1 @@ -NetOutput | @vladmandic/face-api - v1.7.14

    Type Alias NetOutput

    NetOutput: { age: Tensor1D; gender: Tensor2D }

    Type declaration

    Settings

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    +NetOutput | @vladmandic/face-api - v1.7.15

    Type Alias NetOutput

    NetOutput: { age: Tensor1D; gender: Tensor2D }

    Type declaration

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    diff --git a/typedoc/types/NetParams.html b/typedoc/types/NetParams.html index c125ae9..5848795 100644 --- a/typedoc/types/NetParams.html +++ b/typedoc/types/NetParams.html @@ -1 +1 @@ -NetParams | @vladmandic/face-api - v1.7.14

    Type Alias NetParams

    NetParams: { fc: { age: FCParams; gender: FCParams } }

    Type declaration

    • fc: { age: FCParams; gender: FCParams }

    Settings

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    +NetParams | @vladmandic/face-api - v1.7.15

    Type Alias NetParams

    NetParams: { fc: { age: FCParams; gender: FCParams } }

    Type declaration

    • fc: { age: FCParams; gender: FCParams }

    Settings

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    diff --git a/typedoc/types/TMediaElement.html b/typedoc/types/TMediaElement.html index b068185..a410764 100644 --- a/typedoc/types/TMediaElement.html +++ b/typedoc/types/TMediaElement.html @@ -1 +1 @@ -TMediaElement | @vladmandic/face-api - v1.7.14

    Type Alias TMediaElement

    TMediaElement: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement

    Settings

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    +TMediaElement | @vladmandic/face-api - v1.7.15

    Type Alias TMediaElement

    TMediaElement: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement

    Settings

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    diff --git a/typedoc/types/TNetInput.html b/typedoc/types/TNetInput.html index de67683..abfd5fc 100644 --- a/typedoc/types/TNetInput.html +++ b/typedoc/types/TNetInput.html @@ -1 +1 @@ -TNetInput | @vladmandic/face-api - v1.7.14

    Type Alias TNetInput

    TNetInput:
        | string
        | TResolvedNetInput
        | (string | TResolvedNetInput)[]
        | NetInput

    Settings

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    +TNetInput | @vladmandic/face-api - v1.7.15

    Type Alias TNetInput

    TNetInput:
        | string
        | TResolvedNetInput
        | (string | TResolvedNetInput)[]
        | NetInput

    Settings

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    diff --git a/typedoc/types/TResolvedNetInput.html b/typedoc/types/TResolvedNetInput.html index c0b7d23..cee29cf 100644 --- a/typedoc/types/TResolvedNetInput.html +++ b/typedoc/types/TResolvedNetInput.html @@ -1 +1 @@ -TResolvedNetInput | @vladmandic/face-api - v1.7.14

    Type Alias TResolvedNetInput

    TResolvedNetInput: TMediaElement | Tensor3D | Tensor4D

    Settings

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    +TResolvedNetInput | @vladmandic/face-api - v1.7.15

    Type Alias TResolvedNetInput

    TResolvedNetInput: TMediaElement | Tensor3D | Tensor4D

    Settings

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    diff --git a/typedoc/types/TinyYolov2Config.html b/typedoc/types/TinyYolov2Config.html index 099f1ba..10794a6 100644 --- a/typedoc/types/TinyYolov2Config.html +++ b/typedoc/types/TinyYolov2Config.html @@ -1 +1 @@ -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;
    }

    Type declaration

    • anchors: Point[]
    • classes: string[]
    • OptionalfilterSizes?: number[]
    • iouThreshold: number
    • OptionalisFirstLayerConv2d?: boolean
    • OptionalmeanRgb?: [number, number, number]
    • OptionalwithClassScores?: boolean
    • withSeparableConvs: boolean

    Settings

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    +TinyYolov2Config | @vladmandic/face-api - v1.7.15

    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[]
    • OptionalfilterSizes?: number[]
    • iouThreshold: number
    • OptionalisFirstLayerConv2d?: boolean
    • OptionalmeanRgb?: [number, number, number]
    • OptionalwithClassScores?: boolean
    • withSeparableConvs: boolean

    Settings

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    diff --git a/typedoc/types/TinyYolov2ExtractBoxesResult.html b/typedoc/types/TinyYolov2ExtractBoxesResult.html index ac695cf..3880814 100644 --- a/typedoc/types/TinyYolov2ExtractBoxesResult.html +++ b/typedoc/types/TinyYolov2ExtractBoxesResult.html @@ -1 +1 @@ -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;
    }

    Type declaration

    • anchor: number
    • box: BoundingBox
    • classScore: number
    • col: number
    • label: number
    • row: number
    • score: number

    Settings

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    +TinyYolov2ExtractBoxesResult | @vladmandic/face-api - v1.7.15

    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

    Settings

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    diff --git a/typedoc/types/TinyYolov2NetParams.html b/typedoc/types/TinyYolov2NetParams.html index d79ff82..cf02b17 100644 --- a/typedoc/types/TinyYolov2NetParams.html +++ b/typedoc/types/TinyYolov2NetParams.html @@ -1 +1 @@ -TinyYolov2NetParams | @vladmandic/face-api - v1.7.14

    Type Alias TinyYolov2NetParams

    TinyYolov2NetParams: DefaultTinyYolov2NetParams | MobilenetParams

    Settings

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    +TinyYolov2NetParams | @vladmandic/face-api - v1.7.15

    Type Alias TinyYolov2NetParams

    TinyYolov2NetParams: DefaultTinyYolov2NetParams | MobilenetParams

    Settings

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    diff --git a/typedoc/types/WithAge.html b/typedoc/types/WithAge.html index cf200ce..782ae31 100644 --- a/typedoc/types/WithAge.html +++ b/typedoc/types/WithAge.html @@ -1 +1 @@ -WithAge | @vladmandic/face-api - v1.7.14

    Type Alias WithAge<TSource>

    WithAge: TSource & { age: number }

    Type Parameters

    • TSource

    Settings

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    +WithAge | @vladmandic/face-api - v1.7.15

    Type Alias WithAge<TSource>

    WithAge: TSource & { age: number }

    Type Parameters

    • TSource

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    diff --git a/typedoc/types/WithFaceDescriptor.html b/typedoc/types/WithFaceDescriptor.html index a1c0523..f3184d0 100644 --- a/typedoc/types/WithFaceDescriptor.html +++ b/typedoc/types/WithFaceDescriptor.html @@ -1 +1 @@ -WithFaceDescriptor | @vladmandic/face-api - v1.7.14

    Type Alias WithFaceDescriptor<TSource>

    WithFaceDescriptor: TSource & { descriptor: Float32Array }

    Type Parameters

    • TSource

    Settings

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    +WithFaceDescriptor | @vladmandic/face-api - v1.7.15

    Type Alias WithFaceDescriptor<TSource>

    WithFaceDescriptor: TSource & { descriptor: Float32Array }

    Type Parameters

    • TSource

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    diff --git a/typedoc/types/WithFaceDetection.html b/typedoc/types/WithFaceDetection.html index 24122c3..dde5fc9 100644 --- a/typedoc/types/WithFaceDetection.html +++ b/typedoc/types/WithFaceDetection.html @@ -1 +1 @@ -WithFaceDetection | @vladmandic/face-api - v1.7.14

    Type Alias WithFaceDetection<TSource>

    WithFaceDetection: TSource & { detection: FaceDetection }

    Type Parameters

    • TSource

    Settings

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    +WithFaceDetection | @vladmandic/face-api - v1.7.15

    Type Alias WithFaceDetection<TSource>

    WithFaceDetection: TSource & { detection: FaceDetection }

    Type Parameters

    • TSource

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    diff --git a/typedoc/types/WithFaceExpressions.html b/typedoc/types/WithFaceExpressions.html index 3dc446f..7f4eeea 100644 --- a/typedoc/types/WithFaceExpressions.html +++ b/typedoc/types/WithFaceExpressions.html @@ -1 +1 @@ -WithFaceExpressions | @vladmandic/face-api - v1.7.14

    Type Alias WithFaceExpressions<TSource>

    WithFaceExpressions: TSource & { expressions: FaceExpressions }

    Type Parameters

    • TSource

    Settings

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    +WithFaceExpressions | @vladmandic/face-api - v1.7.15

    Type Alias WithFaceExpressions<TSource>

    WithFaceExpressions: TSource & { expressions: FaceExpressions }

    Type Parameters

    • TSource

    Settings

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    diff --git a/typedoc/types/WithFaceLandmarks.html b/typedoc/types/WithFaceLandmarks.html index 300cc35..8d9ff94 100644 --- a/typedoc/types/WithFaceLandmarks.html +++ b/typedoc/types/WithFaceLandmarks.html @@ -1 +1 @@ -WithFaceLandmarks | @vladmandic/face-api - v1.7.14

    Type Alias WithFaceLandmarks<TSource, TFaceLandmarks>

    WithFaceLandmarks: TSource & {
        alignedRect: FaceDetection;
        angle: {
            pitch: number | undefined;
            roll: number | undefined;
            yaw: number | undefined;
        };
        landmarks: TFaceLandmarks;
        unshiftedLandmarks: TFaceLandmarks;
    }

    Type Parameters

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    +WithFaceLandmarks | @vladmandic/face-api - v1.7.15

    Type Alias WithFaceLandmarks<TSource, TFaceLandmarks>

    WithFaceLandmarks: 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 6c72b5c..52960eb 100644 --- a/typedoc/types/WithGender.html +++ b/typedoc/types/WithGender.html @@ -1 +1 @@ -WithGender | @vladmandic/face-api - v1.7.14

    Type Alias WithGender<TSource>

    WithGender: TSource & { gender: Gender; genderProbability: number }

    Type Parameters

    • TSource

    Settings

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    +WithGender | @vladmandic/face-api - v1.7.15

    Type Alias WithGender<TSource>

    WithGender: TSource & { gender: Gender; genderProbability: number }

    Type Parameters

    • TSource

    Settings

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    diff --git a/typedoc/types/draw.DrawFaceExpressionsInput.html b/typedoc/types/draw.DrawFaceExpressionsInput.html index 55aae0f..a5a802a 100644 --- a/typedoc/types/draw.DrawFaceExpressionsInput.html +++ b/typedoc/types/draw.DrawFaceExpressionsInput.html @@ -1 +1 @@ -DrawFaceExpressionsInput | @vladmandic/face-api - v1.7.14

    Type Alias DrawFaceExpressionsInput

    DrawFaceExpressionsInput: FaceExpressions | WithFaceExpressions<{}>

    Settings

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    +DrawFaceExpressionsInput | @vladmandic/face-api - v1.7.15

    Type Alias DrawFaceExpressionsInput

    DrawFaceExpressionsInput: FaceExpressions | WithFaceExpressions<{}>

    Settings

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    diff --git a/typedoc/types/draw.DrawFaceLandmarksInput.html b/typedoc/types/draw.DrawFaceLandmarksInput.html index 22f4016..821a42c 100644 --- a/typedoc/types/draw.DrawFaceLandmarksInput.html +++ b/typedoc/types/draw.DrawFaceLandmarksInput.html @@ -1 +1 @@ -DrawFaceLandmarksInput | @vladmandic/face-api - v1.7.14

    Type Alias DrawFaceLandmarksInput

    DrawFaceLandmarksInput: FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>>

    Settings

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    +DrawFaceLandmarksInput | @vladmandic/face-api - v1.7.15

    Type Alias DrawFaceLandmarksInput

    DrawFaceLandmarksInput: FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>>

    Settings

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    diff --git a/typedoc/types/draw.TDrawDetectionsInput.html b/typedoc/types/draw.TDrawDetectionsInput.html index bd953c9..32c7302 100644 --- a/typedoc/types/draw.TDrawDetectionsInput.html +++ b/typedoc/types/draw.TDrawDetectionsInput.html @@ -1 +1 @@ -TDrawDetectionsInput | @vladmandic/face-api - v1.7.14

    Type Alias TDrawDetectionsInput

    TDrawDetectionsInput:
        | IRect
        | IBoundingBox
        | FaceDetection
        | WithFaceDetection<{}>

    Settings

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    +TDrawDetectionsInput | @vladmandic/face-api - v1.7.15

    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 0d01f90..c981437 100644 --- a/typedoc/types/tf.NamedTensorMap.html +++ b/typedoc/types/tf.NamedTensorMap.html @@ -1 +1 @@ -NamedTensorMap | @vladmandic/face-api - v1.7.14

    Type Alias NamedTensorMap

    NamedTensorMap: { [name: string]: tf.Tensor }

    Type declaration

    Docalias

    Settings

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    +NamedTensorMap | @vladmandic/face-api - v1.7.15

    Type Alias NamedTensorMap

    NamedTensorMap: { [name: string]: tf.Tensor }

    Type declaration

    Docalias

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    diff --git a/typedoc/types/tf.Tensor1D.html b/typedoc/types/tf.Tensor1D.html index f0e3ad3..77a52f5 100644 --- a/typedoc/types/tf.Tensor1D.html +++ b/typedoc/types/tf.Tensor1D.html @@ -1,2 +1,2 @@ -Tensor1D | @vladmandic/face-api - v1.7.14

    Type Alias Tensor1D

    Tensor1D: tf.Tensor<R1>

    Doclink

    Tensor

    -

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    +Tensor1D | @vladmandic/face-api - v1.7.15

    Type Alias Tensor1D

    Tensor1D: tf.Tensor<R1>

    Doclink

    Tensor

    +

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    diff --git a/typedoc/types/tf.Tensor2D.html b/typedoc/types/tf.Tensor2D.html index 920d086..be5d713 100644 --- a/typedoc/types/tf.Tensor2D.html +++ b/typedoc/types/tf.Tensor2D.html @@ -1,2 +1,2 @@ -Tensor2D | @vladmandic/face-api - v1.7.14

    Type Alias Tensor2D

    Tensor2D: tf.Tensor<R2>

    Doclink

    Tensor

    -

    Settings

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    +Tensor2D | @vladmandic/face-api - v1.7.15

    Type Alias Tensor2D

    Tensor2D: tf.Tensor<R2>

    Doclink

    Tensor

    +

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    diff --git a/typedoc/types/tf.Tensor3D.html b/typedoc/types/tf.Tensor3D.html index 301e07b..e89340d 100644 --- a/typedoc/types/tf.Tensor3D.html +++ b/typedoc/types/tf.Tensor3D.html @@ -1,2 +1,2 @@ -Tensor3D | @vladmandic/face-api - v1.7.14

    Type Alias Tensor3D

    Tensor3D: tf.Tensor<R3>

    Doclink

    Tensor

    -

    Settings

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    +Tensor3D | @vladmandic/face-api - v1.7.15

    Type Alias Tensor3D

    Tensor3D: tf.Tensor<R3>

    Doclink

    Tensor

    +

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    diff --git a/typedoc/types/tf.Tensor4D.html b/typedoc/types/tf.Tensor4D.html index 4c1382d..7ddcdbe 100644 --- a/typedoc/types/tf.Tensor4D.html +++ b/typedoc/types/tf.Tensor4D.html @@ -1,2 +1,2 @@ -Tensor4D | @vladmandic/face-api - v1.7.14

    Type Alias Tensor4D

    Tensor4D: tf.Tensor<R4>

    Doclink

    Tensor

    -

    Settings

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    +Tensor4D | @vladmandic/face-api - v1.7.15

    Type Alias Tensor4D

    Tensor4D: tf.Tensor<R4>

    Doclink

    Tensor

    +

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    diff --git a/typedoc/types/tf.Tensor5D.html b/typedoc/types/tf.Tensor5D.html index 43a7aba..1bd150c 100644 --- a/typedoc/types/tf.Tensor5D.html +++ b/typedoc/types/tf.Tensor5D.html @@ -1,2 +1,2 @@ -Tensor5D | @vladmandic/face-api - v1.7.14

    Type Alias Tensor5D

    Tensor5D: tf.Tensor<R5>

    Doclink

    Tensor

    -

    Settings

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    +Tensor5D | @vladmandic/face-api - v1.7.15

    Type Alias Tensor5D

    Tensor5D: tf.Tensor<R5>

    Doclink

    Tensor

    +

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    diff --git a/typedoc/types/tf.TensorLike.html b/typedoc/types/tf.TensorLike.html index acb4611..25d5c8b 100644 --- a/typedoc/types/tf.TensorLike.html +++ b/typedoc/types/tf.TensorLike.html @@ -1,2 +1,2 @@ -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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    +TensorLike | @vladmandic/face-api - v1.7.15

    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 63a3a6c..059065e 100644 --- a/typedoc/types/tf.io.IOHandlerSync.html +++ b/typedoc/types/tf.io.IOHandlerSync.html @@ -1,4 +1,4 @@ -IOHandlerSync | @vladmandic/face-api - v1.7.14

    Type Alias IOHandlerSync

    IOHandlerSync: { load?: LoadHandlerSync; save?: SaveHandlerSync }

    Interface for a synchronous model import/export handler.

    +IOHandlerSync | @vladmandic/face-api - v1.7.15

    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

    • Optionalload?: LoadHandlerSync
    • Optionalsave?: SaveHandlerSync

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    +

    Type declaration

    • Optionalload?: LoadHandlerSync
    • Optionalsave?: SaveHandlerSync

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    diff --git a/typedoc/types/tf.io.LoadHandler.html b/typedoc/types/tf.io.LoadHandler.html index 9b8752c..fb5ae76 100644 --- a/typedoc/types/tf.io.LoadHandler.html +++ b/typedoc/types/tf.io.LoadHandler.html @@ -1,2 +1,2 @@ -LoadHandler | @vladmandic/face-api - v1.7.14

    Type Alias LoadHandler

    LoadHandler: () => Promise<ModelArtifacts>

    Type definition for handlers of loading operations.

    -

    Type declaration

    Settings

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    +LoadHandler | @vladmandic/face-api - v1.7.15

    Type Alias LoadHandler

    LoadHandler: () => Promise<ModelArtifacts>

    Type definition for handlers of loading operations.

    +

    Type declaration

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    diff --git a/typedoc/types/tf.io.OnProgressCallback.html b/typedoc/types/tf.io.OnProgressCallback.html index cfdc64b..e02655e 100644 --- a/typedoc/types/tf.io.OnProgressCallback.html +++ b/typedoc/types/tf.io.OnProgressCallback.html @@ -1,5 +1,5 @@ -OnProgressCallback | @vladmandic/face-api - v1.7.14

    Type Alias OnProgressCallback

    OnProgressCallback: (fraction: number) => void

    Callback for the progress of a long-running action such as an HTTP +OnProgressCallback | @vladmandic/face-api - v1.7.15

    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: number): void

      • Parameters

        • fraction: number

        Returns void

    Settings

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    +

    Type declaration

      • (fraction: number): 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 ed5ed3d..991aa0a 100644 --- a/typedoc/types/tf.io.SaveHandler.html +++ b/typedoc/types/tf.io.SaveHandler.html @@ -1,2 +1,2 @@ -SaveHandler | @vladmandic/face-api - v1.7.14

    Type Alias SaveHandler

    SaveHandler: (modelArtifact: ModelArtifacts) => Promise<SaveResult>

    Type definition for handlers of saving operations.

    -

    Type declaration

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    +SaveHandler | @vladmandic/face-api - v1.7.15

    Type Alias SaveHandler

    SaveHandler: (modelArtifact: ModelArtifacts) => Promise<SaveResult>

    Type definition for handlers of saving operations.

    +

    Type declaration

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    diff --git a/typedoc/types/tf.io.WeightData.html b/typedoc/types/tf.io.WeightData.html index 9a414d6..a24cebb 100644 --- a/typedoc/types/tf.io.WeightData.html +++ b/typedoc/types/tf.io.WeightData.html @@ -1 +1 @@ -WeightData | @vladmandic/face-api - v1.7.14

    Type Alias WeightData

    WeightData: ArrayBuffer | ArrayBuffer[]

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    +WeightData | @vladmandic/face-api - v1.7.15

    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 e2d5fc2..ce0bffe 100644 --- a/typedoc/types/tf.io.WeightGroup.html +++ b/typedoc/types/tf.io.WeightGroup.html @@ -1,5 +1,5 @@ -WeightGroup | @vladmandic/face-api - v1.7.14

    Type Alias WeightGroup

    WeightGroup: "model" | "optimizer"

    Group to which the weight belongs.

    +WeightGroup | @vladmandic/face-api - v1.7.15

    Type Alias WeightGroup

    WeightGroup: "model" | "optimizer"

    Group to which the weight belongs.

    • 'optimizer': Weight from a stateful optimizer.
    -

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    +

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    diff --git a/typedoc/types/tf.io.WeightsManifestConfig.html b/typedoc/types/tf.io.WeightsManifestConfig.html index ee22c4f..61a1c68 100644 --- a/typedoc/types/tf.io.WeightsManifestConfig.html +++ b/typedoc/types/tf.io.WeightsManifestConfig.html @@ -1,6 +1,6 @@ -WeightsManifestConfig | @vladmandic/face-api - v1.7.14

    Type Alias WeightsManifestConfig

    WeightsManifestConfig: WeightsManifestGroupConfig[]

    A weight manifest.

    +WeightsManifestConfig | @vladmandic/face-api - v1.7.15

    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 573a630..fc14f35 100644 --- a/typedoc/variables/FACE_EXPRESSION_LABELS.html +++ b/typedoc/variables/FACE_EXPRESSION_LABELS.html @@ -1 +1 @@ -FACE_EXPRESSION_LABELS | @vladmandic/face-api - v1.7.14

    Variable FACE_EXPRESSION_LABELSConst

    FACE_EXPRESSION_LABELS: readonly [
        "neutral",
        "happy",
        "sad",
        "angry",
        "fearful",
        "disgusted",
        "surprised",
    ] = ...

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    +FACE_EXPRESSION_LABELS | @vladmandic/face-api - v1.7.15

    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 c3c51f6..5acb254 100644 --- a/typedoc/variables/env.html +++ b/typedoc/variables/env.html @@ -1 +1 @@ -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;
    } = ...

    Type declaration

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    +env | @vladmandic/face-api - v1.7.15

    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;
    } = ...

    Type declaration

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    diff --git a/typedoc/variables/nets.html b/typedoc/variables/nets.html index a0ec603..06192c1 100644 --- a/typedoc/variables/nets.html +++ b/typedoc/variables/nets.html @@ -1 +1 @@ -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;
    } = ...

    Type declaration

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    +nets | @vladmandic/face-api - v1.7.15

    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 616c842..3906964 100644 --- a/typedoc/variables/tf.ENV.html +++ b/typedoc/variables/tf.ENV.html @@ -1 +1 @@ -ENV | @vladmandic/face-api - v1.7.14

    Variable ENV

    ENV: Environment

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    +ENV | @vladmandic/face-api - v1.7.15

    Variable ENV

    ENV: Environment

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    diff --git a/typedoc/variables/tf.image.html b/typedoc/variables/tf.image.html index c81716d..c82993e 100644 --- a/typedoc/variables/tf.image.html +++ b/typedoc/variables/tf.image.html @@ -1 +1 @@ -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 extends Tensor2D
            | Tensor3D
            | Tensor4D
            | Tensor5D
            | Tensor6D,
        >(
            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 extends Tensor3D | Tensor4D>(
            images: TensorLike | T_2,
            size: [number, number],
            alignCorners?: boolean,
            halfPixelCenters?: boolean,
        ) => T_2;
        resizeNearestNeighbor: <T_1 extends Tensor3D | Tensor4D>(
            images: TensorLike | T_1,
            size: [number, number],
            alignCorners?: boolean,
            halfPixelCenters?: boolean,
        ) => T_1;
        rgbToGrayscale: <
            T_3 extends Tensor2D
            | Tensor3D
            | Tensor4D
            | Tensor5D
            | Tensor6D,
        >(
            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;
    }

    Type declaration

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    +image | @vladmandic/face-api - v1.7.15

    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 extends Tensor2D
            | Tensor3D
            | Tensor4D
            | Tensor5D
            | Tensor6D,
        >(
            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 extends Tensor3D | Tensor4D>(
            images: TensorLike | T_2,
            size: [number, number],
            alignCorners?: boolean,
            halfPixelCenters?: boolean,
        ) => T_2;
        resizeNearestNeighbor: <T_1 extends Tensor3D | Tensor4D>(
            images: TensorLike | T_1,
            size: [number, number],
            alignCorners?: boolean,
            halfPixelCenters?: boolean,
        ) => T_1;
        rgbToGrayscale: <
            T_3 extends Tensor2D
            | Tensor3D
            | Tensor4D
            | Tensor5D
            | Tensor6D,
        >(
            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;
    }

    Type declaration

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    diff --git a/typedoc/variables/tf.version.html b/typedoc/variables/tf.version.html index 804c439..f08cf4e 100644 --- a/typedoc/variables/tf.version.html +++ b/typedoc/variables/tf.version.html @@ -1 +1 @@ -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;
    }

    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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    +version | @vladmandic/face-api - v1.7.15

    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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    diff --git a/typedoc/variables/version.html b/typedoc/variables/version.html index ce01574..66ba321 100644 --- a/typedoc/variables/version.html +++ b/typedoc/variables/version.html @@ -1 +1 @@ -version | @vladmandic/face-api - v1.7.14

    Variable versionConst

    version: string = ...

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    +version | @vladmandic/face-api - v1.7.15

    Variable versionConst

    version: string = ...

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