diff --git a/CHANGELOG.md b/CHANGELOG.md index 9b0303f..d8a6eff 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,6 +1,6 @@ # @vladmandic/face-api -Version: **1.1.6** +Version: **1.1.7** Description: **FaceAPI: AI-powered Face Detection, Description & Recognition using Tensorflow/JS** Author: **Vladimir Mandic ** @@ -9,6 +9,10 @@ Repository: **** ## Changelog +### **1.1.7** 2021/03/31 mandic00@live.com + +- enable minify + ### **1.1.6** 2021/03/26 mandic00@live.com diff --git a/demo/node.js b/demo/node.js index 583bc43..af452da 100644 --- a/demo/node.js +++ b/demo/node.js @@ -26,13 +26,33 @@ async function image(img) { } async function detect(tensor) { - const result = await faceapi + try { + const result = await faceapi + .detectAllFaces(tensor, optionsSSDMobileNet) + .withFaceLandmarks() + .withFaceExpressions() + .withFaceDescriptors() + .withAgeAndGender(); + return result; + } catch (err) { + log.error('Caught error', err.message); + return []; + } +} + +// eslint-disable-next-line no-unused-vars +function detectPromise(tensor) { + return new Promise((resolve) => faceapi .detectAllFaces(tensor, optionsSSDMobileNet) .withFaceLandmarks() .withFaceExpressions() .withFaceDescriptors() - .withAgeAndGender(); - return result; + .withAgeAndGender() + .then((res) => resolve(res)) + .catch((err) => { + log.error('Caught error', err.message); + resolve([]); + })); } function print(face) { @@ -80,6 +100,7 @@ async function main() { if (fs.existsSync(param)) { const tensor = await image(param); const result = await detect(tensor); + // const result = await detectPromise(null); log.data('Image:', param, 'Detected faces:', result.length); for (const face of result) print(face); tensor.dispose(); diff --git a/dist/face-api.esm-nobundle.js b/dist/face-api.esm-nobundle.js index 44c6637..1a9f7d3 100644 --- a/dist/face-api.esm-nobundle.js +++ b/dist/face-api.esm-nobundle.js @@ -5,5 +5,4437 @@ author: ' */ -var pr=Object.defineProperty;var 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s(m,p,d){let u=e(m,`${d}/depthwise_conv`),l=a(m,p,1,`${d}/pointwise_conv`);return{depthwise_conv:u,pointwise_conv:l}}function i(){let m=a(3,32,3,"mobilenetv1/conv_0"),p=s(32,64,"mobilenetv1/conv_1"),d=s(64,128,"mobilenetv1/conv_2"),u=s(128,128,"mobilenetv1/conv_3"),l=s(128,256,"mobilenetv1/conv_4"),f=s(256,256,"mobilenetv1/conv_5"),b=s(256,512,"mobilenetv1/conv_6"),F=s(512,512,"mobilenetv1/conv_7"),h=s(512,512,"mobilenetv1/conv_8"),g=s(512,512,"mobilenetv1/conv_9"),y=s(512,512,"mobilenetv1/conv_10"),v=s(512,512,"mobilenetv1/conv_11"),E=s(512,1024,"mobilenetv1/conv_12"),W=s(1024,1024,"mobilenetv1/conv_13");return{conv_0:m,conv_1:p,conv_2:d,conv_3:u,conv_4:l,conv_5:f,conv_6:b,conv_7:F,conv_8:h,conv_9:g,conv_10:y,conv_11:v,conv_12:E,conv_13:W}}function c(){let 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d=i(`Prediction/BoxPredictor_${p}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${p}/box_encoding_predictor`),u=i(`Prediction/BoxPredictor_${p}/ClassPredictor`,`prediction_layer/box_predictor_${p}/class_predictor`);return{box_encoding_predictor:d,class_predictor:u}}function m(){return{conv_0:r("Prediction",0,"prediction_layer/conv_0"),conv_1:r("Prediction",1,"prediction_layer/conv_1"),conv_2:r("Prediction",2,"prediction_layer/conv_2"),conv_3:r("Prediction",3,"prediction_layer/conv_3"),conv_4:r("Prediction",4,"prediction_layer/conv_4"),conv_5:r("Prediction",5,"prediction_layer/conv_5"),conv_6:r("Prediction",6,"prediction_layer/conv_6"),conv_7:r("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:c(0),box_predictor_1:c(1),box_predictor_2:c(2),box_predictor_3:c(3),box_predictor_4:c(4),box_predictor_5:c(5)}}return{extractMobilenetV1Params:s,extractPredictionLayerParams:m}}function jr(o){let t=[],{extractMobilenetV1Params:e,extractPredictionLayerParams:r}=No(o,t),a=o["Output/extra_dim"];if(t.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!Z(a))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${a}`);let s={mobilenetv1:e(),prediction_layer:r(),output_layer:{extra_dim:a}};return N(o,t),{params:s,paramMappings:t}}function O(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 Io=.0010000000474974513;function Lo(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,Io),n.clipByValue(r,0,6)})}function ko(o){return[2,4,6,12].some(t=>t===o)?[2,2]:[1,1]}function Hr(o,t){return n.tidy(()=>{let e,r=O(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((s,i)=>{let c=i+1,m=ko(c);r=Lo(r,s.depthwise_conv,m),r=O(r,s.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 So(o,t,e){let r=o.arraySync(),a=Math.min(r[t][0],r[t][2]),s=Math.min(r[t][1],r[t][3]),i=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]),d=Math.max(r[e][0],r[e][2]),u=Math.max(r[e][1],r[e][3]),l=(i-a)*(c-s),f=(d-m)*(u-p);if(l<=0||f<=0)return 0;let b=Math.max(a,m),F=Math.max(s,p),h=Math.min(i,d),g=Math.min(c,u),y=Math.max(h-b,0)*Math.max(g-F,0);return y/(l+f-y)}function Yr(o,t,e,r,a){let s=o.shape[0],i=Math.min(e,s),c=t.map((d,u)=>({score:d,boxIndex:u})).filter(d=>d.score>a).sort((d,u)=>u.score-d.score),m=d=>d<=r?1:0,p=[];return c.forEach(d=>{if(p.length>=i)return;let u=d.score;for(let l=p.length-1;l>=0;--l){let f=So(o,d.boxIndex,p[l]);if(f!==0&&(d.score*=m(f),d.score<=a))break}u===d.score&&p.push(d.boxIndex)}),p}function Ao(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 Wo(o,t){let{sizes:e,centers:r}=Ao(o),a=n.unstack(n.transpose(t,[1,0])),s=n.div(n.mul(n.exp(n.div(a[2],5)),e[0]),2),i=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(i,s),n.sub(m,c),n.add(i,s),n.add(m,c)]),[1,0])}function Gr(o,t,e){return n.tidy(()=>{let r=o.shape[0],a=Wo(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 s=n.sigmoid(n.slice(t,[0,0,1],[-1,-1,-1])),i=n.slice(s,[0,0,0],[-1,-1,1]);i=n.reshape(i,[r,i.shape[1]]);let c=n.unstack(a),m=n.unstack(i);return{boxes:c,scores:m}})}function ht(o,t){return n.tidy(()=>{let e=o.shape[0],r=n.reshape(lt(o,t.box_encoding_predictor),[e,-1,1,4]),a=n.reshape(lt(o,t.class_predictor),[e,-1,3]);return{boxPredictionEncoding:r,classPrediction:a}})}function zr(o,t,e){return n.tidy(()=>{let r=O(o,e.conv_0,[1,1]),a=O(r,e.conv_1,[2,2]),s=O(a,e.conv_2,[1,1]),i=O(s,e.conv_3,[2,2]),c=O(i,e.conv_4,[1,1]),m=O(c,e.conv_5,[2,2]),p=O(m,e.conv_6,[1,1]),d=O(p,e.conv_7,[2,2]),u=ht(t,e.box_predictor_0),l=ht(o,e.box_predictor_1),f=ht(a,e.box_predictor_2),b=ht(i,e.box_predictor_3),F=ht(m,e.box_predictor_4),h=ht(d,e.box_predictor_5),g=n.concat([u.boxPredictionEncoding,l.boxPredictionEncoding,f.boxPredictionEncoding,b.boxPredictionEncoding,F.boxPredictionEncoding,h.boxPredictionEncoding],1),y=n.concat([u.classPrediction,l.classPrediction,f.classPrediction,b.classPrediction,F.classPrediction,h.classPrediction],1);return{boxPredictions:g,classPredictions:y}})}var G=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 At=class extends C{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),s=Hr(a,e.mobilenetv1),{boxPredictions:i,classPredictions:c}=zr(s.out,s.conv11,e.prediction_layer);return Gr(i,c,e.output_layer)})}async forward(t){return this.forwardInput(await P(t))}async locateFaces(t,e={}){let{maxResults:r,minConfidence:a}=new G(e),s=await P(t),{boxes:i,scores:c}=this.forwardInput(s),m=i[0],p=c[0];for(let v=1;v{let[E,W]=[Math.max(0,g[v][0]),Math.min(1,g[v][2])].map(R=>R*h),[j,V]=[Math.max(0,g[v][1]),Math.min(1,g[v][3])].map(R=>R*F);return new D(d[v],new Gt(j,E,V-j,W-E),{height:s.getInputHeight(0),width:s.getInputWidth(0)})});return m.dispose(),p.dispose(),y}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return jr(t)}extractParams(t){return Or(t)}};function Bo(o){let t=new At;return t.extractWeights(o),t}function Cu(o){return Bo(o)}var Ro=class extends At{};var Vr=.4,Ur=[new x(.738768,.874946),new x(2.42204,2.65704),new x(4.30971,7.04493),new x(10.246,4.59428),new x(12.6868,11.8741)],Xr=[new x(1.603231,2.094468),new x(6.041143,7.080126),new x(2.882459,3.518061),new x(4.266906,5.178857),new x(9.041765,10.66308)],Jr=[117.001,114.697,97.404],qr="tiny_yolov2_model",Zr="tiny_yolov2_separable_conv_model";var we=o=>typeof o=="number";function Kr(o){if(!o)throw new Error(`invalid config: ${o}`);if(typeof o.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${o.withSeparableConvs}`);if(!we(o.iouThreshold)||o.iouThreshold<0||o.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${o.iouThreshold}`);if(!Array.isArray(o.classes)||!o.classes.length||!o.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(o.classes)}`);if(!Array.isArray(o.anchors)||!o.anchors.length||!o.anchors.map(t=>t||{}).every(t=>we(t.x)&&we(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(o.anchors)}`);if(o.meanRgb&&(!Array.isArray(o.meanRgb)||o.meanRgb.length!==3||!o.meanRgb.every(we)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: 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Qr(o,t,e,r){let{extractWeights:a,getRemainingWeights:s}=I(o),i=[],{extractConvParams:c,extractConvWithBatchNormParams:m,extractSeparableConvParams:p}=$o(a,i),d;if(t.withSeparableConvs){let[u,l,f,b,F,h,g,y,v]=r,E=t.isFirstLayerConv2d?c(u,l,3,"conv0"):p(u,l,"conv0"),W=p(l,f,"conv1"),j=p(f,b,"conv2"),V=p(b,F,"conv3"),R=p(F,h,"conv4"),rt=p(h,g,"conv5"),ot=y?p(g,y,"conv6"):void 0,nt=v?p(y,v,"conv7"):void 0,_t=c(v||y||g,5*e,1,"conv8");d={conv0:E,conv1:W,conv2:j,conv3:V,conv4:R,conv5:rt,conv6:ot,conv7:nt,conv8:_t}}else{let[u,l,f,b,F,h,g,y,v]=r,E=m(u,l,"conv0"),W=m(l,f,"conv1"),j=m(f,b,"conv2"),V=m(b,F,"conv3"),R=m(F,h,"conv4"),rt=m(h,g,"conv5"),ot=m(g,y,"conv6"),nt=m(y,v,"conv7"),_t=c(v,5*e,1,"conv8");d={conv0:E,conv1:W,conv2:j,conv3:V,conv4:R,conv5:rt,conv6:ot,conv7:nt,conv8:_t}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:d,paramMappings:i}}function Oo(o,t){let e=A(o,t);function r(c){let m=e(`${c}/sub`,1),p=e(`${c}/truediv`,1);return{sub:m,truediv:p}}function a(c){let m=e(`${c}/filters`,4),p=e(`${c}/bias`,1);return{filters:m,bias:p}}function s(c){let m=a(`${c}/conv`),p=r(`${c}/bn`);return{conv:m,bn:p}}let i=kt(e);return{extractConvParams:a,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function to(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}=Oo(o,e),i;if(t.withSeparableConvs){let c=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?r("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:c>7?s("conv6"):void 0,conv7:c>8?s("conv7"):void 0,conv8:r("conv8")}}else i={conv0:a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:a("conv6"),conv7:a("conv7"),conv8:r("conv8")};return N(o,e),{params:i,paramMappings:e}}var et=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${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 Ze=class extends C{constructor(t){super("TinyYolov2");Kr(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=Q(t,e.conv0);return r=n.maxPool(r,[2,2],[2,2],"same"),r=Q(r,e.conv1),r=n.maxPool(r,[2,2],[2,2],"same"),r=Q(r,e.conv2),r=n.maxPool(r,[2,2],[2,2],"same"),r=Q(r,e.conv3),r=n.maxPool(r,[2,2],[2,2],"same"),r=Q(r,e.conv4),r=n.maxPool(r,[2,2],[2,2],"same"),r=Q(r,e.conv5),r=n.maxPool(r,[2,2],[1,1],"same"),r=Q(r,e.conv6),r=Q(r,e.conv7),lt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?Wt(lt(t,e.conv0,"valid",!1)):tt(t,e.conv0);return r=n.maxPool(r,[2,2],[2,2],"same"),r=tt(r,e.conv1),r=n.maxPool(r,[2,2],[2,2],"same"),r=tt(r,e.conv2),r=n.maxPool(r,[2,2],[2,2],"same"),r=tt(r,e.conv3),r=n.maxPool(r,[2,2],[2,2],"same"),r=tt(r,e.conv4),r=n.maxPool(r,[2,2],[2,2],"same"),r=tt(r,e.conv5),r=n.maxPool(r,[2,2],[1,1],"same"),r=e.conv6?tt(r,e.conv6):r,r=e.conv7?tt(r,e.conv7):r,lt(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?X(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 P(t),e)}async detect(t,e={}){let{inputSize:r,scoreThreshold:a}=new et(e),s=await P(t),i=await this.forwardInput(s,r),c=n.tidy(()=>n.unstack(i)[0].expandDims()),m={width:s.getInputWidth(0),height:s.getInputHeight(0)},p=await this.extractBoxes(c,s.getReshapedInputDimensions(0),a);i.dispose(),c.dispose();let d=p.map(h=>h.box),u=p.map(h=>h.score),l=p.map(h=>h.classScore),f=p.map(h=>this.config.classes[h.label]);return fr(d.map(h=>h.rescale(r)),u,this.config.iouThreshold,!0).map(h=>new ut(u[h],l[h],f[h],d[h],m))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return to(t,this.config)}extractParams(t){let e=this.config.filterSizes||Ze.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 Qr(t,this.config,this.boxEncodingSize,e)}async extractBoxes(t,e,r){let{width:a,height:s}=e,i=Math.max(a,s),c=i/a,m=i/s,p=t.shape[1],d=this.config.anchors.length,[u,l,f]=n.tidy(()=>{let g=t.reshape([p,p,d,this.boxEncodingSize]),y=g.slice([0,0,0,0],[p,p,d,4]),v=g.slice([0,0,0,4],[p,p,d,1]),E=this.withClassScores?n.softmax(g.slice([0,0,0,5],[p,p,d,this.config.classes.length]),3):n.scalar(0);return[y,v,E]}),b=[],F=await l.array(),h=await u.array();for(let g=0;gr){let W=(y+ce(h[g][y][v][0]))/p*c,j=(g+ce(h[g][y][v][1]))/p*m,V=Math.exp(h[g][y][v][2])*this.config.anchors[v].x/p*c,R=Math.exp(h[g][y][v][3])*this.config.anchors[v].y/p*m,rt=W-V/2,ot=j-R/2,nt={row:g,col:y,anchor:v},{classScore:_t,label:mr}=this.withClassScores?await this.extractPredictedClass(f,nt):{classScore:1,label:0};b.push({box:new Yt(rt,ot,rt+V,ot+R),score:E,classScore:E*_t,label:mr,...nt})}}return u.dispose(),l.dispose(),f.dispose(),b}async extractPredictedClass(t,e){let{row:r,col:a,anchor:s}=e,i=await t.array();return Array(this.config.classes.length).fill(0).map((c,m)=>i[r][a][s][m]).map((c,m)=>({classScore:c,label:m})).reduce((c,m)=>c.classScore>m.classScore?c:m)}},Bt=Ze;Bt.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var ne=class extends Bt{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:Vr,classes:["face"],...t?{anchors:Xr,meanRgb:Jr}:{anchors:Ur,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?Zr:qr}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function Dl(o,t=!0){let e=new ne(t);return e.extractWeights(o),e}var Ke=class extends et{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var z=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function xt(o,t,e,r,a=({alignedRect:s})=>s){let s=o.map(m=>St(m)?a(m):m.detection),i=r||(t instanceof n.Tensor?await Xt(t,s):await Ut(t,s)),c=await e(i);return i.forEach(m=>m instanceof n.Tensor&&m.dispose()),c}async function Rt(o,t,e,r,a){return xt([o],t,async s=>e(s[0]),r,a)}var eo=.4,ro=[new x(1.603231,2.094468),new x(6.041143,7.080126),new x(2.882459,3.518061),new x(4.266906,5.178857),new x(9.041765,10.66308)],oo=[117.001,114.697,97.404];var ae=class extends Bt{constructor(){let t={withSeparableConvs:!0,iouThreshold:eo,classes:["face"],anchors:ro,meanRgb:oo,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 T={ssdMobilenetv1:new At,tinyFaceDetector:new ae,tinyYolov2:new ne,faceLandmark68Net:new ee,faceLandmark68TinyNet:new ze,faceRecognitionNet:new oe,faceExpressionNet:new Be,ageGenderNet:new Ye},jo=(o,t)=>T.ssdMobilenetv1.locateFaces(o,t),tf=(o,t)=>T.tinyFaceDetector.locateFaces(o,t),ef=(o,t)=>T.tinyYolov2.locateFaces(o,t),Ho=o=>T.faceLandmark68Net.detectLandmarks(o),rf=o=>T.faceLandmark68TinyNet.detectLandmarks(o),of=o=>T.faceRecognitionNet.computeFaceDescriptor(o),nf=o=>T.faceExpressionNet.predictExpressions(o),af=o=>T.ageGenderNet.predictAgeAndGender(o),Yo=o=>T.ssdMobilenetv1.load(o),sf=o=>T.tinyFaceDetector.load(o),cf=o=>T.tinyYolov2.load(o),mf=o=>T.faceLandmark68Net.load(o),pf=o=>T.faceLandmark68TinyNet.load(o),df=o=>T.faceRecognitionNet.load(o),uf=o=>T.faceExpressionNet.load(o),lf=o=>T.ageGenderNet.load(o),ff=Yo,hf=jo,xf=Ho;var Qe=class extends z{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},jt=class extends Qe{async run(){let t=await this.parentTask,e=await xt(t,this.input,async r=>Promise.all(r.map(a=>T.faceExpressionNet.predictExpressions(a))),this.extractedFaces);return t.map((r,a)=>Re(r,e[a]))}withAgeAndGender(){return new $t(this,this.input)}},Ht=class extends Qe{async run(){let t=await this.parentTask;if(!t)return;let e=await Rt(t,this.input,r=>T.faceExpressionNet.predictExpressions(r),this.extractedFaces);return Re(t,e)}withAgeAndGender(){return new Ot(this,this.input)}},Ft=class extends jt{withAgeAndGender(){return new vt(this,this.input)}withFaceDescriptors(){return new bt(this,this.input)}},Tt=class extends Ht{withAgeAndGender(){return new yt(this,this.input)}withFaceDescriptor(){return new gt(this,this.input)}};var tr=class extends z{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.extractedFaces=r}},$t=class extends tr{async run(){let t=await this.parentTask,e=await xt(t,this.input,async r=>Promise.all(r.map(a=>T.ageGenderNet.predictAgeAndGender(a))),this.extractedFaces);return t.map((r,a)=>{let{age:s,gender:i,genderProbability:c}=e[a];return Je(qe(r,i,c),s)})}withFaceExpressions(){return new jt(this,this.input)}},Ot=class extends tr{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:a}=await Rt(t,this.input,s=>T.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return Je(qe(t,r,a),e)}withFaceExpressions(){return new Ht(this,this.input)}},vt=class extends $t{withFaceExpressions(){return new Ft(this,this.input)}withFaceDescriptors(){return new bt(this,this.input)}},yt=class extends Ot{withFaceExpressions(){return new Tt(this,this.input)}withFaceDescriptor(){return new gt(this,this.input)}};var er=class extends z{constructor(t,e){super();this.parentTask=t;this.input=e}},bt=class extends er{async run(){let t=await this.parentTask;return(await xt(t,this.input,r=>Promise.all(r.map(a=>T.faceRecognitionNet.computeFaceDescriptor(a))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,a)=>Xe(t[a],r))}withFaceExpressions(){return new Ft(this,this.input)}withAgeAndGender(){return new vt(this,this.input)}},gt=class extends er{async run(){let t=await this.parentTask;if(!t)return;let e=await Rt(t,this.input,r=>T.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return Xe(t,e)}withFaceExpressions(){return new Tt(this,this.input)}withAgeAndGender(){return new yt(this,this.input)}};var rr=class extends z{constructor(t,e,r){super();this.parentTask=t;this.input=e;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?T.faceLandmark68TinyNet:T.faceLandmark68Net}},or=class extends rr{async run(){let t=await this.parentTask,e=t.map(s=>s.detection),r=this.input instanceof n.Tensor?await Xt(this.input,e):await Ut(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.map((s,i)=>Qt(s,a[i]))}withFaceExpressions(){return new Ft(this,this.input)}withAgeAndGender(){return new vt(this,this.input)}withFaceDescriptors(){return new bt(this,this.input)}},nr=class extends rr{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof n.Tensor?await Xt(this.input,[e]):await Ut(this.input,[e]),a=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(s=>s instanceof n.Tensor&&s.dispose()),Qt(t,a)}withFaceExpressions(){return new Tt(this,this.input)}withAgeAndGender(){return new yt(this,this.input)}withFaceDescriptor(){return new gt(this,this.input)}};var ar=class extends z{constructor(t,e=new G){super();this.input=t;this.options=e}},Pe=class extends ar{async run(){let{input:t,options:e}=this,r;if(e instanceof Ke)r=T.tinyFaceDetector.locateFaces(t,e);else if(e instanceof G)r=T.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof et)r=T.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise(async t=>{let e=await this.run();t(e.map(r=>Dt({},r)))})}withFaceLandmarks(t=!1){return new or(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new jt(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new $t(this.runAndExtendWithFaceDetections(),this.input)}},sr=class extends ar{async run(){let t=await new Pe(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?Dt({},e):void 0)})}withFaceLandmarks(t=!1){return new nr(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Ht(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Ot(this.runAndExtendWithFaceDetection(),this.input)}};function fh(o,t=new G){return new sr(o,t)}function ir(o,t=new G){return new Pe(o,t)}async function Go(o,t){return ir(o,new G(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function yh(o,t={}){return ir(o,new et(t)).withFaceLandmarks().withFaceDescriptors()}var Fh=Go;function no(o,t){if(o.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let e=Array.from(o),r=Array.from(t);return Math.sqrt(e.map((a,s)=>a-r[s]).reduce((a,s)=>a+s**2,0))}var cr=class{constructor(t,e=.6){this._distanceThreshold=e;let r=Array.isArray(t)?t:[t];if(!r.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let a=1,s=()=>`person ${a++}`;this._labeledDescriptors=r.map(i=>{if(i instanceof at)return i;if(i instanceof Float32Array)return new at(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new at(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,e){return e.map(r=>no(r,t)).reduce((r,a)=>r+a,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=>at.fromJSON(r));return new cr(e,t.distanceThreshold)}};function Ih(o){let t=new ae;return t.extractWeights(o),t}function zo(o,t){let{width:e,height:r}=new L(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=>zo(a,{width:e,height:r}));if(St(o)){let a=o.detection.forSize(e,r),s=o.unshiftedLandmarks.forSize(a.box.width,a.box.height);return Qt(Dt(o,a),s)}return K(o)?Dt(o,o.detection.forSize(e,r)):o instanceof $||o instanceof D?o.forSize(e,r):o}var Vo=typeof process!="undefined",Uo=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",ox={faceapi:Mr,node:Vo,browser:Uo};export{Ye as AgeGenderNet,Yt as BoundingBox,w as Box,z as ComposableTask,bt as ComputeAllFaceDescriptorsTask,er as ComputeFaceDescriptorsTaskBase,gt as ComputeSingleFaceDescriptorTask,or as DetectAllFaceLandmarksTask,Pe as DetectAllFacesTask,rr as DetectFaceLandmarksTaskBase,ar as DetectFacesTaskBase,nr as DetectSingleFaceLandmarksTask,sr as DetectSingleFaceTask,L as Dimensions,Dr as FACE_EXPRESSION_LABELS,D as FaceDetection,Ro as FaceDetectionNet,Be as FaceExpressionNet,ft as FaceExpressions,ee as FaceLandmark68Net,ze as FaceLandmark68TinyNet,Do as FaceLandmarkNet,$ as FaceLandmarks,fo as FaceLandmarks5,zt as FaceLandmarks68,me as FaceMatch,cr as FaceMatcher,oe as FaceRecognitionNet,ct as Gender,pe as LabeledBox,at as LabeledFaceDescriptors,it as NetInput,C as NeuralNetwork,ut as ObjectDetection,x as Point,ho as PredictedBox,Gt as Rect,At as SsdMobilenetv1,G as SsdMobilenetv1Options,ae as TinyFaceDetector,Ke as TinyFaceDetectorOptions,ne as TinyYolov2,et as TinyYolov2Options,Fh as allFaces,Go as allFacesSsdMobilenetv1,yh as allFacesTinyYolov2,xr as awaitMediaLoaded,br as bufferToImage,of as computeFaceDescriptor,Ct as createCanvas,fe as createCanvasFromMedia,Cu as createFaceDetectionNet,Md as createFaceRecognitionNet,Bo as createSsdMobilenetv1,Ih as createTinyFaceDetector,Dl as createTinyYolov2,ir as detectAllFaces,Ho as detectFaceLandmarks,rf as detectFaceLandmarksTiny,xf as detectLandmarks,fh as detectSingleFace,To as draw,_ as env,no as euclideanDistance,Je as extendWithAge,Xe as extendWithFaceDescriptor,Dt as extendWithFaceDetection,Re as extendWithFaceExpressions,Qt as extendWithFaceLandmarks,qe as extendWithGender,Xt as extractFaceTensors,Ut as extractFaces,si as fetchImage,yr as fetchJson,di as fetchNetWeights,Nt as fetchOrThrow,S as getContext2dOrThrow,Mt as getMediaDimensions,gr as imageTensorToCanvas,vr as imageToSquare,On as inverseSigmoid,ur as iou,We as isMediaElement,le as isMediaLoaded,Id as isWithAge,K as isWithFaceDetection,Er as isWithFaceExpressions,St as isWithFaceLandmarks,Ad as isWithGender,lf as loadAgeGenderModel,ff as loadFaceDetectionModel,uf as loadFaceExpressionModel,mf as loadFaceLandmarkModel,pf as loadFaceLandmarkTinyModel,df as loadFaceRecognitionModel,Yo as loadSsdMobilenetv1Model,sf as loadTinyFaceDetectorModel,cf as loadTinyYolov2Model,Fr as loadWeightMap,hf as locateFaces,vi as matchDimensions,lr as minBbox,T as nets,fr as nonMaxSuppression,X as normalize,hr as padToSquare,af as predictAgeAndGender,nf as recognizeFaceExpressions,zo as resizeResults,Et as resolveInput,Ln as shuffleArray,ce as sigmoid,jo as ssdMobilenetv1,n as tf,tf as tinyFaceDetector,ef as tinyYolov2,P as toNetInput,co as utils,Kr as validateConfig,ox as version}; +var __defProp = Object.defineProperty; +var __hasOwnProp = Object.prototype.hasOwnProperty; +var __getOwnPropNames = Object.getOwnPropertyNames; +var __getOwnPropDesc = Object.getOwnPropertyDescriptor; +var __export = (target, all) => { + for (var name in all) + __defProp(target, name, {get: all[name], enumerable: true}); +}; +var __exportStar = (target, module2, desc) => { + if (module2 && typeof module2 === "object" || typeof module2 === "function") { + for (let key of __getOwnPropNames(module2)) + if (!__hasOwnProp.call(target, key) && key !== "default") + __defProp(target, key, {get: () => module2[key], enumerable: !(desc = __getOwnPropDesc(module2, key)) || desc.enumerable}); + } + return target; +}; + +// dist/tfjs.esm.js +var tfjs_esm_exports = {}; +__exportStar(tfjs_esm_exports, dist_star); +__exportStar(tfjs_esm_exports, tfjs_backend_wasm_star); +import * as dist_star from "@tensorflow/tfjs/dist/index.js"; +import * as tfjs_backend_wasm_star from "@tensorflow/tfjs-backend-wasm"; + +// src/draw/index.ts +var draw_exports = {}; +__export(draw_exports, { + AnchorPosition: () => AnchorPosition, + DrawBox: () => DrawBox, + DrawBoxOptions: () => DrawBoxOptions, + DrawFaceLandmarks: () => DrawFaceLandmarks, + DrawFaceLandmarksOptions: () => DrawFaceLandmarksOptions, + DrawTextField: () => DrawTextField, + DrawTextFieldOptions: () => DrawTextFieldOptions, + drawContour: () => drawContour, + drawDetections: () => drawDetections, + drawFaceExpressions: () => drawFaceExpressions, + drawFaceLandmarks: () => drawFaceLandmarks +}); + +// src/draw/drawContour.ts +function drawContour(ctx, points, isClosed = false) { + ctx.beginPath(); + points.slice(1).forEach(({x, y}, prevIdx) => { + const from = points[prevIdx]; + ctx.moveTo(from.x, from.y); + ctx.lineTo(x, y); + }); + if (isClosed) { + const from = points[points.length - 1]; + const to = points[0]; + if (!from || !to) { + return; + } + ctx.moveTo(from.x, from.y); + ctx.lineTo(to.x, to.y); + } + ctx.stroke(); +} + +// src/utils/index.ts +var utils_exports = {}; +__export(utils_exports, { + computeReshapedDimensions: () => computeReshapedDimensions, + getCenterPoint: () => getCenterPoint, + isDimensions: () => isDimensions, + isEven: () => isEven, + isFloat: () => isFloat, + isTensor: () => isTensor, + isTensor1D: () => isTensor1D, + isTensor2D: () => isTensor2D, + isTensor3D: () => isTensor3D, + isTensor4D: () => isTensor4D, + isValidNumber: () => isValidNumber, + isValidProbablitiy: () => isValidProbablitiy, + range: () => range, + round: () => round +}); + +// src/classes/Dimensions.ts +var Dimensions = class { + constructor(width, height) { + if (!isValidNumber(width) || !isValidNumber(height)) { + throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({width, height})}`); + } + this._width = width; + this._height = height; + } + get width() { + return this._width; + } + get height() { + return this._height; + } + reverse() { + return new Dimensions(1 / this.width, 1 / this.height); + } +}; + +// src/utils/index.ts +function isTensor(tensor2, dim) { + return tensor2 instanceof tfjs_esm_exports.Tensor && tensor2.shape.length === dim; +} +function isTensor1D(tensor2) { + return isTensor(tensor2, 1); +} +function isTensor2D(tensor2) { + return isTensor(tensor2, 2); +} +function isTensor3D(tensor2) { + return isTensor(tensor2, 3); +} +function isTensor4D(tensor2) { + return isTensor(tensor2, 4); +} +function isFloat(num) { + return num % 1 !== 0; +} +function isEven(num) { + return num % 2 === 0; +} +function round(num, prec = 2) { + const f = 10 ** prec; + return Math.floor(num * f) / f; +} +function isDimensions(obj) { + return obj && obj.width && obj.height; +} +function computeReshapedDimensions({width, height}, inputSize) { + const scale2 = inputSize / Math.max(height, width); + return new Dimensions(Math.round(width * scale2), Math.round(height * scale2)); +} +function getCenterPoint(pts) { + return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0)).div(new Point(pts.length, pts.length)); +} +function range(num, start, step) { + return Array(num).fill(0).map((_, i) => start + i * step); +} +function isValidNumber(num) { + return !!num && num !== Infinity && num !== -Infinity && !Number.isNaN(num) || num === 0; +} +function isValidProbablitiy(num) { + return isValidNumber(num) && num >= 0 && num <= 1; +} + +// src/classes/Point.ts +var Point = class { + constructor(x, y) { + this._x = x; + this._y = y; + } + get x() { + return this._x; + } + get y() { + return this._y; + } + add(pt) { + return new Point(this.x + pt.x, this.y + pt.y); + } + sub(pt) { + return new Point(this.x - pt.x, this.y - pt.y); + } + mul(pt) { + return new Point(this.x * pt.x, this.y * pt.y); + } + div(pt) { + return new Point(this.x / pt.x, this.y / pt.y); + } + abs() { + return new Point(Math.abs(this.x), Math.abs(this.y)); + } + magnitude() { + return Math.sqrt(this.x ** 2 + this.y ** 2); + } + floor() { + return new Point(Math.floor(this.x), Math.floor(this.y)); + } +}; + +// src/classes/Box.ts +var Box = class { + static isRect(rect) { + return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber); + } + static assertIsValidBox(box, callee, allowNegativeDimensions = false) { + if (!Box.isRect(box)) { + throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`); + } + if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) { + throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`); + } + } + constructor(_box, allowNegativeDimensions = true) { + const box = _box || {}; + const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber); + const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber); + if (!isRect && !isBbox) { + throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`); + } + const [x, y, width, height] = isRect ? [box.x, box.y, box.width, box.height] : [box.left, box.top, box.right - box.left, box.bottom - box.top]; + Box.assertIsValidBox({ + x, + y, + width, + height + }, "Box.constructor", allowNegativeDimensions); + this._x = x; + this._y = y; + this._width = width; + this._height = height; + } + get x() { + return this._x; + } + get y() { + return this._y; + } + get width() { + return this._width; + } + get height() { + return this._height; + } + get left() { + return this.x; + } + get top() { + return this.y; + } + get right() { + return this.x + this.width; + } + get bottom() { + return this.y + this.height; + } + get area() { + return this.width * this.height; + } + get topLeft() { + return new Point(this.left, this.top); + } + get topRight() { + return new Point(this.right, this.top); + } + get bottomLeft() { + return new Point(this.left, this.bottom); + } + get bottomRight() { + return new Point(this.right, this.bottom); + } + round() { + const [x, y, width, height] = [this.x, this.y, this.width, this.height].map((val) => Math.round(val)); + return new Box({ + x, + y, + width, + height + }); + } + floor() { + const [x, y, width, height] = [this.x, this.y, this.width, this.height].map((val) => Math.floor(val)); + return new Box({ + x, + y, + width, + height + }); + } + toSquare() { + let { + x, + y, + width, + height + } = this; + const diff = Math.abs(width - height); + if (width < height) { + x -= diff / 2; + width += diff; + } + if (height < width) { + y -= diff / 2; + height += diff; + } + return new Box({x, y, width, height}); + } + rescale(s) { + const scaleX = isDimensions(s) ? s.width : s; + const scaleY = isDimensions(s) ? s.height : s; + return new Box({ + x: this.x * scaleX, + y: this.y * scaleY, + width: this.width * scaleX, + height: this.height * scaleY + }); + } + pad(padX, padY) { + const [x, y, width, height] = [ + this.x - padX / 2, + this.y - padY / 2, + this.width + padX, + this.height + padY + ]; + return new Box({ + x, + y, + width, + height + }); + } + clipAtImageBorders(imgWidth, imgHeight) { + const {x, y, right, bottom} = this; + const clippedX = Math.max(x, 0); + const clippedY = Math.max(y, 0); + const newWidth = right - clippedX; + const newHeight = bottom - clippedY; + const clippedWidth = Math.min(newWidth, imgWidth - clippedX); + const clippedHeight = Math.min(newHeight, imgHeight - clippedY); + return new Box({ + x: clippedX, + y: clippedY, + width: clippedWidth, + height: clippedHeight + }).floor(); + } + shift(sx, sy) { + const {width, height} = this; + const x = this.x + sx; + const y = this.y + sy; + return new Box({ + x, + y, + width, + height + }); + } + padAtBorders(imageHeight, imageWidth) { + const w = this.width + 1; + const h = this.height + 1; + const dx = 1; + const dy = 1; + let edx = w; + let edy = h; + let x = this.left; + let y = this.top; + let ex = this.right; + let ey = this.bottom; + if (ex > imageWidth) { + edx = -ex + imageWidth + w; + ex = imageWidth; + } + if (ey > imageHeight) { + edy = -ey + imageHeight + h; + ey = imageHeight; + } + if (x < 1) { + edy = 2 - x; + x = 1; + } + if (y < 1) { + edy = 2 - y; + y = 1; + } + return { + dy, + edy, + dx, + edx, + y, + ey, + x, + ex, + w, + h + }; + } + calibrate(region) { + return new Box({ + left: this.left + region.left * this.width, + top: this.top + region.top * this.height, + right: this.right + region.right * this.width, + bottom: this.bottom + region.bottom * this.height + }).toSquare().round(); + } +}; + +// src/classes/BoundingBox.ts +var BoundingBox = class extends Box { + constructor(left, top, right, bottom, allowNegativeDimensions = false) { + super({ + left, + top, + right, + bottom + }, allowNegativeDimensions); + } +}; + +// src/classes/ObjectDetection.ts +var ObjectDetection = class { + constructor(score, classScore, className, relativeBox, imageDims) { + this._imageDims = new Dimensions(imageDims.width, imageDims.height); + this._score = score; + this._classScore = classScore; + this._className = className; + this._box = new Box(relativeBox).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 Box(this._box).rescale(this.imageDims.reverse()); + } + forSize(width, height) { + return new ObjectDetection(this.score, this.classScore, this.className, this.relativeBox, {width, height}); + } +}; + +// src/classes/FaceDetection.ts +var FaceDetection = class extends ObjectDetection { + constructor(score, relativeBox, imageDims) { + super(score, score, "", relativeBox, imageDims); + } + forSize(width, height) { + const {score, relativeBox, imageDims} = super.forSize(width, height); + return new FaceDetection(score, relativeBox, imageDims); + } +}; + +// src/ops/iou.ts +function iou(box1, box2, isIOU = true) { + const width = Math.max(0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left)); + const height = Math.max(0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top)); + const interSection = width * height; + return isIOU ? interSection / (box1.area + box2.area - interSection) : interSection / Math.min(box1.area, box2.area); +} + +// src/ops/minBbox.ts +function minBbox(pts) { + const xs = pts.map((pt) => pt.x); + const ys = pts.map((pt) => pt.y); + const minX = xs.reduce((min, x) => x < min ? x : min, Infinity); + const minY = ys.reduce((min, y) => y < min ? y : min, Infinity); + const maxX = xs.reduce((max, x) => max < x ? x : max, 0); + const maxY = ys.reduce((max, y) => max < y ? y : max, 0); + return new BoundingBox(minX, minY, maxX, maxY); +} + +// src/ops/nonMaxSuppression.ts +function nonMaxSuppression(boxes, scores, iouThreshold, isIOU = true) { + let indicesSortedByScore = scores.map((score, boxIndex) => ({score, boxIndex})).sort((c1, c2) => c1.score - c2.score).map((c) => c.boxIndex); + const pick = []; + while (indicesSortedByScore.length > 0) { + const curr = indicesSortedByScore.pop(); + pick.push(curr); + const indices = indicesSortedByScore; + const outputs = []; + for (let i = 0; i < indices.length; i++) { + const idx = indices[i]; + const currBox = boxes[curr]; + const idxBox = boxes[idx]; + outputs.push(iou(currBox, idxBox, isIOU)); + } + indicesSortedByScore = indicesSortedByScore.filter((_, j) => outputs[j] <= iouThreshold); + } + return pick; +} + +// src/ops/normalize.ts +function normalize(x, meanRgb) { + return tfjs_esm_exports.tidy(() => { + const [r, g, b] = meanRgb; + const avg_r = tfjs_esm_exports.fill([...x.shape.slice(0, 3), 1], r, "float32"); + const avg_g = tfjs_esm_exports.fill([...x.shape.slice(0, 3), 1], g, "float32"); + const avg_b = tfjs_esm_exports.fill([...x.shape.slice(0, 3), 1], b, "float32"); + const avg_rgb = tfjs_esm_exports.concat([avg_r, avg_g, avg_b], 3); + return tfjs_esm_exports.sub(x, avg_rgb); + }); +} + +// src/ops/padToSquare.ts +function padToSquare(imgTensor, isCenterImage = false) { + return tfjs_esm_exports.tidy(() => { + const [height, width] = imgTensor.shape.slice(1); + if (height === width) { + return imgTensor; + } + const dimDiff = Math.abs(height - width); + const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1)); + const paddingAxis = height > width ? 2 : 1; + const createPaddingTensor = (paddingAmountLocal) => { + const paddingTensorShape = imgTensor.shape.slice(); + paddingTensorShape[paddingAxis] = paddingAmountLocal; + return tfjs_esm_exports.fill(paddingTensorShape, 0, "float32"); + }; + const paddingTensorAppend = createPaddingTensor(paddingAmount); + const remainingPaddingAmount = dimDiff - paddingTensorAppend.shape[paddingAxis]; + const paddingTensorPrepend = isCenterImage && remainingPaddingAmount ? createPaddingTensor(remainingPaddingAmount) : null; + const tensorsToStack = [ + paddingTensorPrepend, + imgTensor, + paddingTensorAppend + ].filter((t) => !!t).map((t) => tfjs_esm_exports.cast(t, "float32")); + return tfjs_esm_exports.concat(tensorsToStack, paddingAxis); + }); +} + +// src/ops/shuffleArray.ts +function shuffleArray(inputArray) { + const array = inputArray.slice(); + for (let i = array.length - 1; i > 0; i--) { + const j = Math.floor(Math.random() * (i + 1)); + const x = array[i]; + array[i] = array[j]; + array[j] = x; + } + return array; +} + +// src/ops/index.ts +function sigmoid(x) { + return 1 / (1 + Math.exp(-x)); +} +function inverseSigmoid(x) { + return Math.log(x / (1 - x)); +} + +// src/classes/Rect.ts +var Rect = class extends Box { + constructor(x, y, width, height, allowNegativeDimensions = false) { + super({ + x, + y, + width, + height + }, allowNegativeDimensions); + } +}; + +// src/classes/FaceLandmarks.ts +var relX = 0.5; +var relY = 0.43; +var relScale = 0.45; +var FaceLandmarks = class { + constructor(relativeFaceLandmarkPositions, imgDims, shift = new Point(0, 0)) { + const {width, height} = imgDims; + this._imgDims = new Dimensions(width, height); + this._shift = shift; + this._positions = relativeFaceLandmarkPositions.map((pt) => pt.mul(new Point(width, height)).add(shift)); + } + get shift() { + return new Point(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((pt) => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight))); + } + forSize(width, height) { + return new this.constructor(this.relativePositions, {width, height}); + } + shiftBy(x, y) { + return new this.constructor(this.relativePositions, this._imgDims, new Point(x, y)); + } + shiftByPoint(pt) { + return this.shiftBy(pt.x, pt.y); + } + align(detection, options = {}) { + if (detection) { + const box = detection instanceof FaceDetection ? detection.box.floor() : new Box(detection); + return this.shiftBy(box.x, box.y).align(null, options); + } + const {useDlibAlignment, minBoxPadding} = {useDlibAlignment: false, minBoxPadding: 0.2, ...options}; + if (useDlibAlignment) { + return this.alignDlib(); + } + return this.alignMinBbox(minBoxPadding); + } + alignDlib() { + const centers = this.getRefPointsForAlignment(); + const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers; + const distToMouth = (pt) => mouthCenter.sub(pt).magnitude(); + const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2; + const size = Math.floor(eyeToMouthDist / relScale); + const refPoint = getCenterPoint(centers); + const x = Math.floor(Math.max(0, refPoint.x - relX * size)); + const y = Math.floor(Math.max(0, refPoint.y - relY * size)); + return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y)); + } + alignMinBbox(padding) { + const box = minBbox(this.positions); + return box.pad(box.width * padding, box.height * padding); + } + getRefPointsForAlignment() { + throw new Error("getRefPointsForAlignment not implemented by base class"); + } +}; + +// src/classes/FaceLandmarks5.ts +var FaceLandmarks5 = class extends FaceLandmarks { + getRefPointsForAlignment() { + const pts = this.positions; + return [ + pts[0], + pts[1], + getCenterPoint([pts[3], pts[4]]) + ]; + } +}; + +// src/classes/FaceLandmarks68.ts +var FaceLandmarks68 = class extends FaceLandmarks { + 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(getCenterPoint); + } +}; + +// src/classes/FaceMatch.ts +var FaceMatch = class { + constructor(label, distance) { + this._label = label; + this._distance = distance; + } + get label() { + return this._label; + } + get distance() { + return this._distance; + } + toString(withDistance = true) { + return `${this.label}${withDistance ? ` (${round(this.distance)})` : ""}`; + } +}; + +// src/classes/LabeledBox.ts +var LabeledBox = class extends Box { + static assertIsValidLabeledBox(box, callee) { + Box.assertIsValidBox(box, callee); + if (!isValidNumber(box.label)) { + throw new Error(`${callee} - expected property label (${box.label}) to be a number`); + } + } + constructor(box, label) { + super(box); + this._label = label; + } + get label() { + return this._label; + } +}; + +// src/classes/LabeledFaceDescriptors.ts +var LabeledFaceDescriptors = class { + constructor(label, descriptors) { + if (!(typeof label === "string")) { + throw new Error("LabeledFaceDescriptors - constructor expected label to be a string"); + } + if (!Array.isArray(descriptors) || descriptors.some((desc) => !(desc instanceof Float32Array))) { + throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array"); + } + this._label = label; + this._descriptors = descriptors; + } + get label() { + return this._label; + } + get descriptors() { + return this._descriptors; + } + toJSON() { + return { + label: this.label, + descriptors: this.descriptors.map((d) => Array.from(d)) + }; + } + static fromJSON(json) { + const descriptors = json.descriptors.map((d) => new Float32Array(d)); + return new LabeledFaceDescriptors(json.label, descriptors); + } +}; + +// src/classes/PredictedBox.ts +var PredictedBox = class extends LabeledBox { + static assertIsValidPredictedBox(box, callee) { + LabeledBox.assertIsValidLabeledBox(box, callee); + if (!isValidProbablitiy(box.score) || !isValidProbablitiy(box.classScore)) { + throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`); + } + } + constructor(box, label, score, classScore) { + super(box, label); + this._score = score; + this._classScore = classScore; + } + get score() { + return this._score; + } + get classScore() { + return this._classScore; + } +}; + +// src/factories/WithFaceDetection.ts +function isWithFaceDetection(obj) { + return obj.detection instanceof FaceDetection; +} +function extendWithFaceDetection(sourceObj, detection) { + const extension = {detection}; + return {...sourceObj, ...extension}; +} + +// src/env/createBrowserEnv.ts +function createBrowserEnv() { + const fetch = window.fetch; + if (!fetch) + throw new Error("fetch - missing fetch implementation for browser environment"); + const readFile = () => { + throw new Error("readFile - filesystem not available for browser environment"); + }; + return { + Canvas: HTMLCanvasElement, + CanvasRenderingContext2D, + Image: HTMLImageElement, + ImageData, + Video: HTMLVideoElement, + createCanvasElement: () => document.createElement("canvas"), + createImageElement: () => document.createElement("img"), + fetch, + readFile + }; +} + +// src/env/createFileSystem.ts +function createFileSystem(fs) { + let requireFsError = ""; + if (!fs) { + try { + fs = require("fs"); + } catch (err) { + requireFsError = err.toString(); + } + } + const readFile = fs ? (filePath) => new Promise((resolve, reject) => { + fs.readFile(filePath, (err, buffer) => err ? reject(err) : resolve(buffer)); + }) : () => { + throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`); + }; + return { + readFile + }; +} + +// src/env/createNodejsEnv.ts +function createNodejsEnv() { + const Canvas = global["Canvas"] || global.HTMLCanvasElement; + const Image = global.Image || global.HTMLImageElement; + const createCanvasElement = () => { + if (Canvas) + return new Canvas(); + throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment"); + }; + const createImageElement = () => { + if (Image) + return new Image(); + throw new Error("createImageElement - missing Image implementation for nodejs environment"); + }; + const fetch = global.fetch; + const fileSystem = createFileSystem(); + return { + Canvas: Canvas || class { + }, + CanvasRenderingContext2D: global.CanvasRenderingContext2D || class { + }, + Image: Image || class { + }, + ImageData: global.ImageData || class { + }, + Video: global.HTMLVideoElement || class { + }, + createCanvasElement, + createImageElement, + fetch, + ...fileSystem + }; +} + +// src/env/isBrowser.ts +function isBrowser() { + return typeof window === "object" && typeof document !== "undefined" && typeof HTMLImageElement !== "undefined" && typeof HTMLCanvasElement !== "undefined" && typeof HTMLVideoElement !== "undefined" && typeof ImageData !== "undefined" && typeof CanvasRenderingContext2D !== "undefined"; +} + +// src/env/isNodejs.ts +function isNodejs() { + return typeof global === "object" && true && typeof module !== "undefined" && typeof process !== "undefined" && !!process.version; +} + +// src/env/index.ts +var environment; +function getEnv() { + if (!environment) { + throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()"); + } + return environment; +} +function setEnv(env2) { + environment = env2; +} +function initialize() { + if (isBrowser()) + return setEnv(createBrowserEnv()); + if (isNodejs()) + return setEnv(createNodejsEnv()); + return null; +} +function monkeyPatch(env2) { + if (!environment) { + initialize(); + } + if (!environment) { + throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()"); + } + const {Canvas = environment.Canvas, Image = environment.Image} = env2; + environment.Canvas = Canvas; + environment.Image = Image; + environment.createCanvasElement = env2.createCanvasElement || (() => new Canvas()); + environment.createImageElement = env2.createImageElement || (() => new Image()); + environment.ImageData = env2.ImageData || environment.ImageData; + environment.Video = env2.Video || environment.Video; + environment.fetch = env2.fetch || environment.fetch; + environment.readFile = env2.readFile || environment.readFile; +} +var env = { + getEnv, + setEnv, + initialize, + createBrowserEnv, + createFileSystem, + createNodejsEnv, + monkeyPatch, + isBrowser, + isNodejs +}; +initialize(); + +// src/dom/resolveInput.ts +function resolveInput(arg) { + if (!env.isNodejs() && typeof arg === "string") { + return document.getElementById(arg); + } + return arg; +} + +// src/dom/getContext2dOrThrow.ts +function getContext2dOrThrow(canvasArg) { + const {Canvas, CanvasRenderingContext2D: CanvasRenderingContext2D2} = env.getEnv(); + if (canvasArg instanceof CanvasRenderingContext2D2) { + return canvasArg; + } + const canvas = resolveInput(canvasArg); + if (!(canvas instanceof Canvas)) { + throw new Error("resolveContext2d - expected canvas to be of instance of Canvas"); + } + const ctx = canvas.getContext("2d"); + if (!ctx) { + throw new Error("resolveContext2d - canvas 2d context is null"); + } + return ctx; +} + +// src/draw/DrawTextField.ts +var AnchorPosition; +(function(AnchorPosition2) { + AnchorPosition2["TOP_LEFT"] = "TOP_LEFT"; + AnchorPosition2["TOP_RIGHT"] = "TOP_RIGHT"; + AnchorPosition2["BOTTOM_LEFT"] = "BOTTOM_LEFT"; + AnchorPosition2["BOTTOM_RIGHT"] = "BOTTOM_RIGHT"; +})(AnchorPosition || (AnchorPosition = {})); +var DrawTextFieldOptions = class { + constructor(options = {}) { + const { + anchorPosition, + backgroundColor, + fontColor, + fontSize, + fontStyle, + padding + } = options; + this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT; + this.backgroundColor = backgroundColor || "rgba(0, 0, 0, 0.5)"; + this.fontColor = fontColor || "rgba(255, 255, 255, 1)"; + this.fontSize = fontSize || 14; + this.fontStyle = fontStyle || "Georgia"; + this.padding = padding || 4; + } +}; +var DrawTextField = class { + constructor(text, anchor, options = {}) { + this.text = typeof text === "string" ? [text] : text instanceof DrawTextField ? text.text : text; + this.anchor = anchor; + this.options = new DrawTextFieldOptions(options); + } + measureWidth(ctx) { + const {padding} = this.options; + return this.text.map((l) => ctx.measureText(l).width).reduce((w0, w1) => w0 < w1 ? w1 : w0, 0) + 2 * padding; + } + measureHeight() { + const {fontSize, padding} = this.options; + return this.text.length * fontSize + 2 * padding; + } + getUpperLeft(ctx, canvasDims) { + const {anchorPosition} = this.options; + const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT; + const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT; + const textFieldWidth = this.measureWidth(ctx); + const textFieldHeight = this.measureHeight(); + const x = isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x; + const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y; + if (canvasDims) { + const {width, height} = canvasDims; + const newX = Math.max(Math.min(x, width - textFieldWidth), 0); + const newY = Math.max(Math.min(y, height - textFieldHeight), 0); + return {x: newX, y: newY}; + } + return {x, y}; + } + draw(canvasArg) { + const canvas = resolveInput(canvasArg); + const ctx = getContext2dOrThrow(canvas); + const { + backgroundColor, + fontColor, + fontSize, + fontStyle, + padding + } = this.options; + ctx.font = `${fontSize}px ${fontStyle}`; + const maxTextWidth = this.measureWidth(ctx); + const textHeight = this.measureHeight(); + ctx.fillStyle = backgroundColor; + const upperLeft = this.getUpperLeft(ctx, canvas); + ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight); + ctx.fillStyle = fontColor; + this.text.forEach((textLine, i) => { + const x = padding + upperLeft.x; + const y = padding + upperLeft.y + (i + 1) * fontSize; + ctx.fillText(textLine, x, y); + }); + } +}; + +// src/draw/DrawBox.ts +var DrawBoxOptions = class { + constructor(options = {}) { + const { + boxColor, + lineWidth, + label, + drawLabelOptions + } = options; + this.boxColor = boxColor || "rgba(0, 0, 255, 1)"; + this.lineWidth = lineWidth || 2; + this.label = label; + const defaultDrawLabelOptions = { + anchorPosition: AnchorPosition.BOTTOM_LEFT, + backgroundColor: this.boxColor + }; + this.drawLabelOptions = new DrawTextFieldOptions({...defaultDrawLabelOptions, ...drawLabelOptions}); + } +}; +var DrawBox = class { + constructor(box, options = {}) { + this.box = new Box(box); + this.options = new DrawBoxOptions(options); + } + draw(canvasArg) { + const ctx = getContext2dOrThrow(canvasArg); + const {boxColor, lineWidth} = this.options; + const { + x, + y, + width, + height + } = this.box; + ctx.strokeStyle = boxColor; + ctx.lineWidth = lineWidth; + ctx.strokeRect(x, y, width, height); + const {label} = this.options; + if (label) { + new DrawTextField([label], {x: x - lineWidth / 2, y}, this.options.drawLabelOptions).draw(canvasArg); + } + } +}; + +// src/draw/drawDetections.ts +function drawDetections(canvasArg, detections) { + const detectionsArray = Array.isArray(detections) ? detections : [detections]; + detectionsArray.forEach((det) => { + const score = det instanceof FaceDetection ? det.score : isWithFaceDetection(det) ? det.detection.score : void 0; + const box = det instanceof FaceDetection ? det.box : isWithFaceDetection(det) ? det.detection.box : new Box(det); + const label = score ? `${round(score)}` : void 0; + new DrawBox(box, {label}).draw(canvasArg); + }); +} + +// src/dom/isMediaLoaded.ts +function isMediaLoaded(media) { + const {Image, Video} = env.getEnv(); + return media instanceof Image && media.complete || media instanceof Video && media.readyState >= 3; +} + +// src/dom/awaitMediaLoaded.ts +function awaitMediaLoaded(media) { + return new Promise((resolve, reject) => { + if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) + return resolve(null); + function onError(e) { + if (!e.currentTarget) + return; + e.currentTarget.removeEventListener("load", onLoad); + e.currentTarget.removeEventListener("error", onError); + reject(e); + } + function onLoad(e) { + if (!e.currentTarget) + return; + e.currentTarget.removeEventListener("load", onLoad); + e.currentTarget.removeEventListener("error", onError); + resolve(e); + } + media.addEventListener("load", onLoad); + media.addEventListener("error", onError); + }); +} + +// src/dom/bufferToImage.ts +function bufferToImage(buf) { + return new Promise((resolve, reject) => { + if (!(buf instanceof Blob)) + reject(new Error("bufferToImage - expected buf to be of type: Blob")); + const reader = new FileReader(); + reader.onload = () => { + if (typeof reader.result !== "string") + reject(new Error("bufferToImage - expected reader.result to be a string, in onload")); + const img = env.getEnv().createImageElement(); + img.onload = () => resolve(img); + img.onerror = reject; + img.src = reader.result; + }; + reader.onerror = reject; + reader.readAsDataURL(buf); + }); +} + +// src/dom/getMediaDimensions.ts +function getMediaDimensions(input) { + const {Image, Video} = env.getEnv(); + if (input instanceof Image) { + return new Dimensions(input.naturalWidth, input.naturalHeight); + } + if (input instanceof Video) { + return new Dimensions(input.videoWidth, input.videoHeight); + } + return new Dimensions(input.width, input.height); +} + +// src/dom/createCanvas.ts +function createCanvas({width, height}) { + const {createCanvasElement} = env.getEnv(); + const canvas = createCanvasElement(); + canvas.width = width; + canvas.height = height; + return canvas; +} +function createCanvasFromMedia(media, dims) { + const {ImageData: ImageData2} = env.getEnv(); + if (!(media instanceof ImageData2) && !isMediaLoaded(media)) { + throw new Error("createCanvasFromMedia - media has not finished loading yet"); + } + const {width, height} = dims || getMediaDimensions(media); + const canvas = createCanvas({width, height}); + if (media instanceof ImageData2) { + getContext2dOrThrow(canvas).putImageData(media, 0, 0); + } else { + getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height); + } + return canvas; +} + +// src/dom/imageTensorToCanvas.ts +async function imageTensorToCanvas(imgTensor, canvas) { + const targetCanvas = canvas || env.getEnv().createCanvasElement(); + const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0); + const imgTensor3D = tfjs_esm_exports.tidy(() => imgTensor.as3D(height, width, numChannels).toInt()); + await tfjs_esm_exports.browser.toPixels(imgTensor3D, targetCanvas); + imgTensor3D.dispose(); + return targetCanvas; +} + +// src/dom/isMediaElement.ts +function isMediaElement(input) { + const {Image, Canvas, Video} = env.getEnv(); + return input instanceof Image || input instanceof Canvas || input instanceof Video; +} + +// src/dom/imageToSquare.ts +function imageToSquare(input, inputSize, centerImage = false) { + const {Image, Canvas} = env.getEnv(); + if (!(input instanceof Image || input instanceof Canvas)) { + throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement"); + } + if (inputSize <= 0) + return createCanvas({width: 1, height: 1}); + const dims = getMediaDimensions(input); + const scale2 = inputSize / Math.max(dims.height, dims.width); + const width = scale2 * dims.width; + const height = scale2 * dims.height; + const targetCanvas = createCanvas({width: inputSize, height: inputSize}); + const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input); + const offset = Math.abs(width - height) / 2; + const dx = centerImage && width < height ? offset : 0; + const dy = centerImage && height < width ? offset : 0; + if (inputCanvas.width > 0 && inputCanvas.height > 0) + getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height); + return targetCanvas; +} + +// src/dom/NetInput.ts +var NetInput = class { + constructor(inputs, treatAsBatchInput = false) { + this._imageTensors = []; + this._canvases = []; + this._treatAsBatchInput = false; + this._inputDimensions = []; + if (!Array.isArray(inputs)) { + throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`); + } + this._treatAsBatchInput = treatAsBatchInput; + this._batchSize = inputs.length; + inputs.forEach((input, idx) => { + if (isTensor3D(input)) { + this._imageTensors[idx] = input; + this._inputDimensions[idx] = input.shape; + return; + } + if (isTensor4D(input)) { + const batchSize = input.shape[0]; + if (batchSize !== 1) { + throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`); + } + this._imageTensors[idx] = input; + this._inputDimensions[idx] = input.shape.slice(1); + return; + } + const canvas = input instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input); + this._canvases[idx] = canvas; + this._inputDimensions[idx] = [canvas.height, canvas.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 range(this.batchSize, 0, 1).map((_, batchIdx) => this.getReshapedInputDimensions(batchIdx)); + } + getInput(batchIdx) { + return this.canvases[batchIdx] || this.imageTensors[batchIdx]; + } + getInputDimensions(batchIdx) { + return this._inputDimensions[batchIdx]; + } + getInputHeight(batchIdx) { + return this._inputDimensions[batchIdx][0]; + } + getInputWidth(batchIdx) { + return this._inputDimensions[batchIdx][1]; + } + getReshapedInputDimensions(batchIdx) { + if (typeof this.inputSize !== "number") { + throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet"); + } + const width = this.getInputWidth(batchIdx); + const height = this.getInputHeight(batchIdx); + return computeReshapedDimensions({width, height}, this.inputSize); + } + toBatchTensor(inputSize, isCenterInputs = true) { + this._inputSize = inputSize; + return tfjs_esm_exports.tidy(() => { + const inputTensors = range(this.batchSize, 0, 1).map((batchIdx) => { + const input = this.getInput(batchIdx); + if (input instanceof tfjs_esm_exports.Tensor) { + let imgTensor = isTensor4D(input) ? input : tfjs_esm_exports.expandDims(input); + imgTensor = padToSquare(imgTensor, isCenterInputs); + if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) { + imgTensor = tfjs_esm_exports.image.resizeBilinear(imgTensor, [inputSize, inputSize], false, false); + } + return imgTensor.as3D(inputSize, inputSize, 3); + } + if (input instanceof env.getEnv().Canvas) { + return tfjs_esm_exports.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs)); + } + throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`); + }); + const batchTensor = tfjs_esm_exports.stack(inputTensors.map((t) => tfjs_esm_exports.cast(t, "float32"))).as4D(this.batchSize, inputSize, inputSize, 3); + return batchTensor; + }); + } +}; + +// src/dom/toNetInput.ts +async function toNetInput(inputs) { + if (inputs instanceof NetInput) + return inputs; + const inputArgArray = Array.isArray(inputs) ? inputs : [inputs]; + if (!inputArgArray.length) + throw new Error("toNetInput - empty array passed as input"); + const getIdxHint = (idx) => Array.isArray(inputs) ? ` at input index ${idx}:` : ""; + const inputArray = inputArgArray.map(resolveInput); + inputArray.forEach((input, i) => { + if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) { + if (typeof inputArgArray[i] === "string") + throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`); + throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`); + } + if (isTensor4D(input)) { + const batchSize = input.shape[0]; + if (batchSize !== 1) + throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`); + } + }); + await Promise.all(inputArray.map((input) => isMediaElement(input) && awaitMediaLoaded(input))); + return new NetInput(inputArray, Array.isArray(inputs)); +} + +// src/dom/extractFaces.ts +async function extractFaces(input, detections) { + const {Canvas} = env.getEnv(); + let canvas = input; + if (!(input instanceof Canvas)) { + const netInput = await toNetInput(input); + if (netInput.batchSize > 1) + throw new Error("extractFaces - batchSize > 1 not supported"); + const tensorOrCanvas = netInput.getInput(0); + canvas = tensorOrCanvas instanceof Canvas ? tensorOrCanvas : await imageTensorToCanvas(tensorOrCanvas); + } + const ctx = getContext2dOrThrow(canvas); + const boxes = detections.map((det) => det instanceof FaceDetection ? det.forSize(canvas.width, canvas.height).box.floor() : det).map((box) => box.clipAtImageBorders(canvas.width, canvas.height)); + return boxes.map(({x, y, width, height}) => { + const faceImg = createCanvas({width, height}); + if (width > 0 && height > 0) + getContext2dOrThrow(faceImg).putImageData(ctx.getImageData(x, y, width, height), 0, 0); + return faceImg; + }); +} + +// src/dom/extractFaceTensors.ts +async function extractFaceTensors(imageTensor, detections) { + if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) { + throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D"); + } + if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) { + throw new Error("extractFaceTensors - batchSize > 1 not supported"); + } + return tfjs_esm_exports.tidy(() => { + const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0); + const boxes = detections.map((det) => det instanceof FaceDetection ? det.forSize(imgWidth, imgHeight).box : det).map((box) => box.clipAtImageBorders(imgWidth, imgHeight)); + const faceTensors = boxes.map(({ + x, + y, + width, + height + }) => tfjs_esm_exports.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels])); + return faceTensors; + }); +} + +// src/dom/fetchOrThrow.ts +async function fetchOrThrow(url, init) { + const {fetch} = env.getEnv(); + const res = await fetch(url, init); + if (!(res.status < 400)) { + throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`); + } + return res; +} + +// src/dom/fetchImage.ts +async function fetchImage(uri) { + const res = await fetchOrThrow(uri); + const blob = await res.blob(); + if (!blob.type.startsWith("image/")) { + throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`); + } + return bufferToImage(blob); +} + +// src/dom/fetchJson.ts +async function fetchJson(uri) { + return (await fetchOrThrow(uri)).json(); +} + +// src/dom/fetchNetWeights.ts +async function fetchNetWeights(uri) { + return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer()); +} + +// src/common/getModelUris.ts +function getModelUris(uri, defaultModelName) { + const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`; + if (!uri) { + return { + modelBaseUri: "", + manifestUri: defaultManifestFilename + }; + } + if (uri === "/") { + return { + modelBaseUri: "/", + manifestUri: `/${defaultManifestFilename}` + }; + } + const protocol = uri.startsWith("http://") ? "http://" : uri.startsWith("https://") ? "https://" : ""; + uri = uri.replace(protocol, ""); + const parts = uri.split("/").filter((s) => s); + const manifestFile = uri.endsWith(".json") ? parts[parts.length - 1] : defaultManifestFilename; + let modelBaseUri = protocol + (uri.endsWith(".json") ? parts.slice(0, parts.length - 1) : parts).join("/"); + modelBaseUri = uri.startsWith("/") ? `/${modelBaseUri}` : modelBaseUri; + return { + modelBaseUri, + manifestUri: modelBaseUri === "/" ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}` + }; +} + +// src/dom/loadWeightMap.ts +async function loadWeightMap(uri, defaultModelName) { + const {manifestUri, modelBaseUri} = getModelUris(uri, defaultModelName); + const manifest = await fetchJson(manifestUri); + return tfjs_esm_exports.io.loadWeights(manifest, modelBaseUri); +} + +// src/dom/matchDimensions.ts +function matchDimensions(input, reference, useMediaDimensions = false) { + const {width, height} = useMediaDimensions ? getMediaDimensions(reference) : reference; + input.width = width; + input.height = height; + return {width, height}; +} + +// src/NeuralNetwork.ts +var NeuralNetwork = class { + constructor(name) { + this._params = void 0; + this._paramMappings = []; + this._name = name; + } + get params() { + return this._params; + } + get paramMappings() { + return this._paramMappings; + } + get isLoaded() { + return !!this.params; + } + getParamFromPath(paramPath) { + const {obj, objProp} = this.traversePropertyPath(paramPath); + return obj[objProp]; + } + reassignParamFromPath(paramPath, tensor2) { + const {obj, objProp} = this.traversePropertyPath(paramPath); + obj[objProp].dispose(); + obj[objProp] = tensor2; + } + getParamList() { + return this._paramMappings.map(({paramPath}) => ({ + path: paramPath, + tensor: this.getParamFromPath(paramPath) + })); + } + getTrainableParams() { + return this.getParamList().filter((param) => param.tensor instanceof tfjs_esm_exports.Variable); + } + getFrozenParams() { + return this.getParamList().filter((param) => !(param.tensor instanceof tfjs_esm_exports.Variable)); + } + variable() { + this.getFrozenParams().forEach(({path, tensor: tensor2}) => { + this.reassignParamFromPath(path, tensor2.variable()); + }); + } + freeze() { + this.getTrainableParams().forEach(({path, tensor: variable}) => { + const tensor2 = tfjs_esm_exports.tensor(variable.dataSync()); + variable.dispose(); + this.reassignParamFromPath(path, tensor2); + }); + } + dispose(throwOnRedispose = true) { + this.getParamList().forEach((param) => { + if (throwOnRedispose && param.tensor.isDisposed) { + throw new Error(`param tensor has already been disposed for path ${param.path}`); + } + param.tensor.dispose(); + }); + this._params = void 0; + } + serializeParams() { + return new Float32Array(this.getParamList().map(({tensor: tensor2}) => Array.from(tensor2.dataSync())).reduce((flat, arr) => flat.concat(arr))); + } + async load(weightsOrUrl) { + if (weightsOrUrl instanceof Float32Array) { + this.extractWeights(weightsOrUrl); + return; + } + await this.loadFromUri(weightsOrUrl); + } + async loadFromUri(uri) { + if (uri && typeof uri !== "string") { + throw new Error(`${this._name}.loadFromUri - expected model uri`); + } + const weightMap = await loadWeightMap(uri, this.getDefaultModelName()); + this.loadFromWeightMap(weightMap); + } + async loadFromDisk(filePath) { + if (filePath && typeof filePath !== "string") { + throw new Error(`${this._name}.loadFromDisk - expected model file path`); + } + const {readFile} = env.getEnv(); + const {manifestUri, modelBaseUri} = getModelUris(filePath, this.getDefaultModelName()); + const fetchWeightsFromDisk = (filePaths) => Promise.all(filePaths.map((fp) => readFile(fp).then((buf) => buf.buffer))); + const loadWeights = tfjs_esm_exports.io.weightsLoaderFactory(fetchWeightsFromDisk); + const manifest = JSON.parse((await readFile(manifestUri)).toString()); + const weightMap = await loadWeights(manifest, modelBaseUri); + this.loadFromWeightMap(weightMap); + } + loadFromWeightMap(weightMap) { + const {paramMappings, params} = this.extractParamsFromWeightMap(weightMap); + this._paramMappings = paramMappings; + this._params = params; + } + extractWeights(weights) { + const {paramMappings, params} = this.extractParams(weights); + this._paramMappings = paramMappings; + this._params = params; + } + traversePropertyPath(paramPath) { + if (!this.params) { + throw new Error("traversePropertyPath - model has no loaded params"); + } + const result = paramPath.split("/").reduce((res, objProp2) => { + if (!res.nextObj.hasOwnProperty(objProp2)) { + throw new Error(`traversePropertyPath - object does not have property ${objProp2}, for path ${paramPath}`); + } + return {obj: res.nextObj, objProp: objProp2, nextObj: res.nextObj[objProp2]}; + }, {nextObj: this.params}); + const {obj, objProp} = result; + if (!obj || !objProp || !(obj[objProp] instanceof tfjs_esm_exports.Tensor)) { + throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`); + } + return {obj, objProp}; + } +}; + +// src/common/depthwiseSeparableConv.ts +function depthwiseSeparableConv(x, params, stride) { + return tfjs_esm_exports.tidy(() => { + let out = tfjs_esm_exports.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, "same"); + out = tfjs_esm_exports.add(out, params.bias); + return out; + }); +} + +// src/faceFeatureExtractor/denseBlock.ts +function denseBlock3(x, denseBlockParams, isFirstLayer = false) { + return tfjs_esm_exports.tidy(() => { + const out1 = tfjs_esm_exports.relu(isFirstLayer ? tfjs_esm_exports.add(tfjs_esm_exports.conv2d(x, denseBlockParams.conv0.filters, [2, 2], "same"), denseBlockParams.conv0.bias) : depthwiseSeparableConv(x, denseBlockParams.conv0, [2, 2])); + const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]); + const in3 = tfjs_esm_exports.relu(tfjs_esm_exports.add(out1, out2)); + const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]); + return tfjs_esm_exports.relu(tfjs_esm_exports.add(out1, tfjs_esm_exports.add(out2, out3))); + }); +} +function denseBlock4(x, denseBlockParams, isFirstLayer = false, isScaleDown = true) { + return tfjs_esm_exports.tidy(() => { + const out1 = tfjs_esm_exports.relu(isFirstLayer ? tfjs_esm_exports.add(tfjs_esm_exports.conv2d(x, denseBlockParams.conv0.filters, isScaleDown ? [2, 2] : [1, 1], "same"), denseBlockParams.conv0.bias) : depthwiseSeparableConv(x, denseBlockParams.conv0, isScaleDown ? [2, 2] : [1, 1])); + const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]); + const in3 = tfjs_esm_exports.relu(tfjs_esm_exports.add(out1, out2)); + const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]); + const in4 = tfjs_esm_exports.relu(tfjs_esm_exports.add(out1, tfjs_esm_exports.add(out2, out3))); + const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1]); + return tfjs_esm_exports.relu(tfjs_esm_exports.add(out1, tfjs_esm_exports.add(out2, tfjs_esm_exports.add(out3, out4)))); + }); +} + +// src/common/convLayer.ts +function convLayer(x, params, padding = "same", withRelu = false) { + return tfjs_esm_exports.tidy(() => { + const out = tfjs_esm_exports.add(tfjs_esm_exports.conv2d(x, params.filters, [1, 1], padding), params.bias); + return withRelu ? tfjs_esm_exports.relu(out) : out; + }); +} + +// src/common/disposeUnusedWeightTensors.ts +function disposeUnusedWeightTensors(weightMap, paramMappings) { + Object.keys(weightMap).forEach((path) => { + if (!paramMappings.some((pm) => pm.originalPath === path)) { + weightMap[path].dispose(); + } + }); +} + +// src/common/extractConvParamsFactory.ts +function extractConvParamsFactory(extractWeights, paramMappings) { + return (channelsIn, channelsOut, filterSize, mappedPrefix) => { + const filters = tfjs_esm_exports.tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]); + const bias = tfjs_esm_exports.tensor1d(extractWeights(channelsOut)); + paramMappings.push({paramPath: `${mappedPrefix}/filters`}, {paramPath: `${mappedPrefix}/bias`}); + return {filters, bias}; + }; +} + +// src/common/extractFCParamsFactory.ts +function extractFCParamsFactory(extractWeights, paramMappings) { + return (channelsIn, channelsOut, mappedPrefix) => { + const fc_weights = tfjs_esm_exports.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]); + const fc_bias = tfjs_esm_exports.tensor1d(extractWeights(channelsOut)); + paramMappings.push({paramPath: `${mappedPrefix}/weights`}, {paramPath: `${mappedPrefix}/bias`}); + return { + weights: fc_weights, + bias: fc_bias + }; + }; +} + +// src/common/types.ts +var SeparableConvParams = class { + constructor(depthwise_filter, pointwise_filter, bias) { + this.depthwise_filter = depthwise_filter; + this.pointwise_filter = pointwise_filter; + this.bias = bias; + } +}; + +// src/common/extractSeparableConvParamsFactory.ts +function extractSeparableConvParamsFactory(extractWeights, paramMappings) { + return (channelsIn, channelsOut, mappedPrefix) => { + const depthwise_filter = tfjs_esm_exports.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]); + const pointwise_filter = tfjs_esm_exports.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]); + const bias = tfjs_esm_exports.tensor1d(extractWeights(channelsOut)); + paramMappings.push({paramPath: `${mappedPrefix}/depthwise_filter`}, {paramPath: `${mappedPrefix}/pointwise_filter`}, {paramPath: `${mappedPrefix}/bias`}); + return new SeparableConvParams(depthwise_filter, pointwise_filter, bias); + }; +} +function loadSeparableConvParamsFactory(extractWeightEntry) { + return (prefix) => { + const depthwise_filter = extractWeightEntry(`${prefix}/depthwise_filter`, 4); + const pointwise_filter = extractWeightEntry(`${prefix}/pointwise_filter`, 4); + const bias = extractWeightEntry(`${prefix}/bias`, 1); + return new SeparableConvParams(depthwise_filter, pointwise_filter, bias); + }; +} + +// src/common/extractWeightEntryFactory.ts +function extractWeightEntryFactory(weightMap, paramMappings) { + return (originalPath, paramRank, mappedPath) => { + const tensor2 = weightMap[originalPath]; + if (!isTensor(tensor2, paramRank)) { + throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor2}`); + } + paramMappings.push({originalPath, paramPath: mappedPath || originalPath}); + return tensor2; + }; +} + +// src/common/extractWeightsFactory.ts +function extractWeightsFactory(weights) { + let remainingWeights = weights; + function extractWeights(numWeights) { + const ret = remainingWeights.slice(0, numWeights); + remainingWeights = remainingWeights.slice(numWeights); + return ret; + } + function getRemainingWeights() { + return remainingWeights; + } + return { + extractWeights, + getRemainingWeights + }; +} + +// src/faceFeatureExtractor/extractorsFactory.ts +function extractorsFactory(extractWeights, paramMappings) { + const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings); + const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings); + function extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer = false) { + const conv0 = isFirstLayer ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`) : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`); + const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`); + const conv22 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`); + return {conv0, conv1, conv2: conv22}; + } + function extractDenseBlock4Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer = false) { + const {conv0, conv1, conv2: conv22} = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer); + const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`); + return { + conv0, + conv1, + conv2: conv22, + conv3 + }; + } + return { + extractDenseBlock3Params, + extractDenseBlock4Params + }; +} + +// src/faceFeatureExtractor/extractParams.ts +function extractParams(weights) { + const paramMappings = []; + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const { + extractDenseBlock4Params + } = extractorsFactory(extractWeights, paramMappings); + const dense0 = extractDenseBlock4Params(3, 32, "dense0", true); + const dense1 = extractDenseBlock4Params(32, 64, "dense1"); + const dense2 = extractDenseBlock4Params(64, 128, "dense2"); + const dense3 = extractDenseBlock4Params(128, 256, "dense3"); + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + return { + paramMappings, + params: { + dense0, + dense1, + dense2, + dense3 + } + }; +} + +// src/common/loadConvParamsFactory.ts +function loadConvParamsFactory(extractWeightEntry) { + return (prefix) => { + const filters = extractWeightEntry(`${prefix}/filters`, 4); + const bias = extractWeightEntry(`${prefix}/bias`, 1); + return {filters, bias}; + }; +} + +// src/faceFeatureExtractor/loadParamsFactory.ts +function loadParamsFactory(weightMap, paramMappings) { + const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); + const extractConvParams = loadConvParamsFactory(extractWeightEntry); + const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry); + function extractDenseBlock3Params(prefix, isFirstLayer = false) { + const conv0 = isFirstLayer ? extractConvParams(`${prefix}/conv0`) : extractSeparableConvParams(`${prefix}/conv0`); + const conv1 = extractSeparableConvParams(`${prefix}/conv1`); + const conv22 = extractSeparableConvParams(`${prefix}/conv2`); + return {conv0, conv1, conv2: conv22}; + } + function extractDenseBlock4Params(prefix, isFirstLayer = false) { + const conv0 = isFirstLayer ? extractConvParams(`${prefix}/conv0`) : extractSeparableConvParams(`${prefix}/conv0`); + const conv1 = extractSeparableConvParams(`${prefix}/conv1`); + const conv22 = extractSeparableConvParams(`${prefix}/conv2`); + const conv3 = extractSeparableConvParams(`${prefix}/conv3`); + return { + conv0, + conv1, + conv2: conv22, + conv3 + }; + } + return { + extractDenseBlock3Params, + extractDenseBlock4Params + }; +} + +// src/faceFeatureExtractor/extractParamsFromWeightMap.ts +function extractParamsFromWeightMap(weightMap) { + const paramMappings = []; + const { + extractDenseBlock4Params + } = loadParamsFactory(weightMap, paramMappings); + const params = { + dense0: extractDenseBlock4Params("dense0", true), + dense1: extractDenseBlock4Params("dense1"), + dense2: extractDenseBlock4Params("dense2"), + dense3: extractDenseBlock4Params("dense3") + }; + disposeUnusedWeightTensors(weightMap, paramMappings); + return {params, paramMappings}; +} + +// src/faceFeatureExtractor/FaceFeatureExtractor.ts +var FaceFeatureExtractor = class extends NeuralNetwork { + constructor() { + super("FaceFeatureExtractor"); + } + forwardInput(input) { + const {params} = this; + if (!params) { + throw new Error("FaceFeatureExtractor - load model before inference"); + } + return tfjs_esm_exports.tidy(() => { + const batchTensor = tfjs_esm_exports.cast(input.toBatchTensor(112, true), "float32"); + const meanRgb = [122.782, 117.001, 104.298]; + const normalized = normalize(batchTensor, meanRgb).div(255); + let out = denseBlock4(normalized, params.dense0, true); + out = denseBlock4(out, params.dense1); + out = denseBlock4(out, params.dense2); + out = denseBlock4(out, params.dense3); + out = tfjs_esm_exports.avgPool(out, [7, 7], [2, 2], "valid"); + return out; + }); + } + async forward(input) { + return this.forwardInput(await toNetInput(input)); + } + getDefaultModelName() { + return "face_feature_extractor_model"; + } + extractParamsFromWeightMap(weightMap) { + return extractParamsFromWeightMap(weightMap); + } + extractParams(weights) { + return extractParams(weights); + } +}; + +// src/common/fullyConnectedLayer.ts +function fullyConnectedLayer(x, params) { + return tfjs_esm_exports.tidy(() => tfjs_esm_exports.add(tfjs_esm_exports.matMul(x, params.weights), params.bias)); +} + +// src/faceProcessor/extractParams.ts +function extractParams2(weights, channelsIn, channelsOut) { + const paramMappings = []; + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings); + const fc = extractFCParams(channelsIn, channelsOut, "fc"); + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + return { + paramMappings, + params: {fc} + }; +} + +// src/faceProcessor/extractParamsFromWeightMap.ts +function extractParamsFromWeightMap2(weightMap) { + const paramMappings = []; + const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); + function extractFcParams(prefix) { + const weights = extractWeightEntry(`${prefix}/weights`, 2); + const bias = extractWeightEntry(`${prefix}/bias`, 1); + return {weights, bias}; + } + const params = { + fc: extractFcParams("fc") + }; + disposeUnusedWeightTensors(weightMap, paramMappings); + return {params, paramMappings}; +} + +// src/faceProcessor/util.ts +function seperateWeightMaps(weightMap) { + const featureExtractorMap = {}; + const classifierMap = {}; + Object.keys(weightMap).forEach((key) => { + const map = key.startsWith("fc") ? classifierMap : featureExtractorMap; + map[key] = weightMap[key]; + }); + return {featureExtractorMap, classifierMap}; +} + +// src/faceProcessor/FaceProcessor.ts +var FaceProcessor = class extends NeuralNetwork { + constructor(_name, faceFeatureExtractor) { + super(_name); + this._faceFeatureExtractor = faceFeatureExtractor; + } + get faceFeatureExtractor() { + return this._faceFeatureExtractor; + } + runNet(input) { + const {params} = this; + if (!params) { + throw new Error(`${this._name} - load model before inference`); + } + return tfjs_esm_exports.tidy(() => { + const bottleneckFeatures = input instanceof NetInput ? this.faceFeatureExtractor.forwardInput(input) : input; + return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc); + }); + } + dispose(throwOnRedispose = true) { + this.faceFeatureExtractor.dispose(throwOnRedispose); + super.dispose(throwOnRedispose); + } + loadClassifierParams(weights) { + const {params, paramMappings} = this.extractClassifierParams(weights); + this._params = params; + this._paramMappings = paramMappings; + } + extractClassifierParams(weights) { + return extractParams2(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut()); + } + extractParamsFromWeightMap(weightMap) { + const {featureExtractorMap, classifierMap} = seperateWeightMaps(weightMap); + this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap); + return extractParamsFromWeightMap2(classifierMap); + } + extractParams(weights) { + const cIn = this.getClassifierChannelsIn(); + const cOut = this.getClassifierChannelsOut(); + const classifierWeightSize = cOut * cIn + cOut; + const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize); + const classifierWeights = weights.slice(weights.length - classifierWeightSize); + this.faceFeatureExtractor.extractWeights(featureExtractorWeights); + return this.extractClassifierParams(classifierWeights); + } +}; + +// src/faceExpressionNet/FaceExpressions.ts +var FACE_EXPRESSION_LABELS = ["neutral", "happy", "sad", "angry", "fearful", "disgusted", "surprised"]; +var FaceExpressions = class { + constructor(probabilities) { + if (probabilities.length !== 7) { + throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`); + } + FACE_EXPRESSION_LABELS.forEach((expression, idx) => { + this[expression] = probabilities[idx]; + }); + } + asSortedArray() { + return FACE_EXPRESSION_LABELS.map((expression) => ({expression, probability: this[expression]})).sort((e0, e1) => e1.probability - e0.probability); + } +}; + +// src/faceExpressionNet/FaceExpressionNet.ts +var FaceExpressionNet = class extends FaceProcessor { + constructor(faceFeatureExtractor = new FaceFeatureExtractor()) { + super("FaceExpressionNet", faceFeatureExtractor); + } + forwardInput(input) { + return tfjs_esm_exports.tidy(() => tfjs_esm_exports.softmax(this.runNet(input))); + } + async forward(input) { + return this.forwardInput(await toNetInput(input)); + } + async predictExpressions(input) { + const netInput = await toNetInput(input); + const out = await this.forwardInput(netInput); + const probabilitesByBatch = await Promise.all(tfjs_esm_exports.unstack(out).map(async (t) => { + const data = t.dataSync(); + t.dispose(); + return data; + })); + out.dispose(); + const predictionsByBatch = probabilitesByBatch.map((probabilites) => new FaceExpressions(probabilites)); + return netInput.isBatchInput ? predictionsByBatch : predictionsByBatch[0]; + } + getDefaultModelName() { + return "face_expression_model"; + } + getClassifierChannelsIn() { + return 256; + } + getClassifierChannelsOut() { + return 7; + } +}; + +// src/factories/WithFaceExpressions.ts +function isWithFaceExpressions(obj) { + return obj.expressions instanceof FaceExpressions; +} +function extendWithFaceExpressions(sourceObj, expressions) { + const extension = {expressions}; + return {...sourceObj, ...extension}; +} + +// src/draw/drawFaceExpressions.ts +function drawFaceExpressions(canvasArg, faceExpressions, minConfidence = 0.1, textFieldAnchor) { + const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions]; + faceExpressionsArray.forEach((e) => { + const expr = e instanceof FaceExpressions ? e : isWithFaceExpressions(e) ? e.expressions : void 0; + if (!expr) { + throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof"); + } + const sorted = expr.asSortedArray(); + const resultsToDisplay = sorted.filter((exprLocal) => exprLocal.probability > minConfidence); + const anchor = isWithFaceDetection(e) ? e.detection.box.bottomLeft : textFieldAnchor || new Point(0, 0); + const drawTextField = new DrawTextField(resultsToDisplay.map((exprLocal) => `${exprLocal.expression} (${round(exprLocal.probability)})`), anchor); + drawTextField.draw(canvasArg); + }); +} + +// src/factories/WithFaceLandmarks.ts +function isWithFaceLandmarks(obj) { + return isWithFaceDetection(obj) && obj["landmarks"] instanceof FaceLandmarks && obj["unshiftedLandmarks"] instanceof FaceLandmarks && obj["alignedRect"] instanceof FaceDetection; +} +function calculateFaceAngle(mesh) { + const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1) % Math.PI; + const degrees = (theta) => theta * 180 / Math.PI; + const angle = {roll: void 0, pitch: void 0, yaw: void 0}; + if (!mesh || !mesh._positions || mesh._positions.length !== 68) + return angle; + const pt = mesh._positions; + angle.roll = -radians(pt[36]._x, pt[36]._y, pt[45]._x, pt[45]._y); + angle.pitch = radians(0, Math.abs(pt[0]._x - pt[30]._x) / pt[30]._x, Math.PI, Math.abs(pt[16]._x - pt[30]._x) / pt[30]._x); + const bottom = pt.reduce((prev, cur) => prev < cur._y ? prev : cur._y, Infinity); + const top = pt.reduce((prev, cur) => prev > cur._y ? prev : cur._y, -Infinity); + angle.yaw = Math.PI * (mesh._imgDims._height / (top - bottom) / 1.4 - 1); + return angle; +} +function extendWithFaceLandmarks(sourceObj, unshiftedLandmarks) { + const {box: shift} = sourceObj.detection; + const landmarks = unshiftedLandmarks.shiftBy(shift.x, shift.y); + const rect = landmarks.align(); + const {imageDims} = sourceObj.detection; + const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims); + const angle = calculateFaceAngle(unshiftedLandmarks); + const extension = { + landmarks, + unshiftedLandmarks, + alignedRect, + angle + }; + return {...sourceObj, ...extension}; +} + +// src/draw/DrawFaceLandmarks.ts +var DrawFaceLandmarksOptions = class { + constructor(options = {}) { + const { + drawLines = true, + drawPoints = true, + lineWidth, + lineColor, + pointSize, + pointColor + } = options; + this.drawLines = drawLines; + this.drawPoints = drawPoints; + this.lineWidth = lineWidth || 1; + this.pointSize = pointSize || 2; + this.lineColor = lineColor || "rgba(0, 255, 255, 1)"; + this.pointColor = pointColor || "rgba(255, 0, 255, 1)"; + } +}; +var DrawFaceLandmarks = class { + constructor(faceLandmarks, options = {}) { + this.faceLandmarks = faceLandmarks; + this.options = new DrawFaceLandmarksOptions(options); + } + draw(canvasArg) { + const ctx = getContext2dOrThrow(canvasArg); + const { + drawLines, + drawPoints, + lineWidth, + lineColor, + pointSize, + pointColor + } = this.options; + if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) { + ctx.strokeStyle = lineColor; + ctx.lineWidth = lineWidth; + drawContour(ctx, this.faceLandmarks.getJawOutline()); + drawContour(ctx, this.faceLandmarks.getLeftEyeBrow()); + drawContour(ctx, this.faceLandmarks.getRightEyeBrow()); + drawContour(ctx, this.faceLandmarks.getNose()); + drawContour(ctx, this.faceLandmarks.getLeftEye(), true); + drawContour(ctx, this.faceLandmarks.getRightEye(), true); + drawContour(ctx, this.faceLandmarks.getMouth(), true); + } + if (drawPoints) { + ctx.strokeStyle = pointColor; + ctx.fillStyle = pointColor; + const drawPoint = (pt) => { + ctx.beginPath(); + ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI); + ctx.fill(); + }; + this.faceLandmarks.positions.forEach(drawPoint); + } + } +}; +function drawFaceLandmarks(canvasArg, faceLandmarks) { + const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks]; + faceLandmarksArray.forEach((f) => { + const landmarks = f instanceof FaceLandmarks ? f : isWithFaceLandmarks(f) ? f.landmarks : void 0; + if (!landmarks) { + throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks> or array thereof"); + } + new DrawFaceLandmarks(landmarks).draw(canvasArg); + }); +} + +// package.json +var version = "1.1.7"; + +// src/xception/extractParams.ts +function extractorsFactory2(extractWeights, paramMappings) { + const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings); + const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings); + function extractReductionBlockParams(channelsIn, channelsOut, mappedPrefix) { + const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`); + const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`); + const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`); + return {separable_conv0, separable_conv1, expansion_conv}; + } + function extractMainBlockParams(channels, mappedPrefix) { + const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`); + const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`); + const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`); + return {separable_conv0, separable_conv1, separable_conv2}; + } + return { + extractConvParams, + extractSeparableConvParams, + extractReductionBlockParams, + extractMainBlockParams + }; +} +function extractParams3(weights, numMainBlocks) { + const paramMappings = []; + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const { + extractConvParams, + extractSeparableConvParams, + extractReductionBlockParams, + extractMainBlockParams + } = extractorsFactory2(extractWeights, paramMappings); + const entry_flow_conv_in = extractConvParams(3, 32, 3, "entry_flow/conv_in"); + const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, "entry_flow/reduction_block_0"); + const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, "entry_flow/reduction_block_1"); + const entry_flow = { + conv_in: entry_flow_conv_in, + reduction_block_0: entry_flow_reduction_block_0, + reduction_block_1: entry_flow_reduction_block_1 + }; + const middle_flow = {}; + range(numMainBlocks, 0, 1).forEach((idx) => { + middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`); + }); + const exit_flow_reduction_block = extractReductionBlockParams(128, 256, "exit_flow/reduction_block"); + const exit_flow_separable_conv = extractSeparableConvParams(256, 512, "exit_flow/separable_conv"); + const exit_flow = { + reduction_block: exit_flow_reduction_block, + separable_conv: exit_flow_separable_conv + }; + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + return { + paramMappings, + params: {entry_flow, middle_flow, exit_flow} + }; +} + +// src/xception/extractParamsFromWeightMap.ts +function loadParamsFactory2(weightMap, paramMappings) { + const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); + const extractConvParams = loadConvParamsFactory(extractWeightEntry); + const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry); + function extractReductionBlockParams(mappedPrefix) { + const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`); + const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`); + const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`); + return {separable_conv0, separable_conv1, expansion_conv}; + } + function extractMainBlockParams(mappedPrefix) { + const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`); + const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`); + const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`); + return {separable_conv0, separable_conv1, separable_conv2}; + } + return { + extractConvParams, + extractSeparableConvParams, + extractReductionBlockParams, + extractMainBlockParams + }; +} +function extractParamsFromWeightMap3(weightMap, numMainBlocks) { + const paramMappings = []; + const { + extractConvParams, + extractSeparableConvParams, + extractReductionBlockParams, + extractMainBlockParams + } = loadParamsFactory2(weightMap, paramMappings); + const entry_flow_conv_in = extractConvParams("entry_flow/conv_in"); + const entry_flow_reduction_block_0 = extractReductionBlockParams("entry_flow/reduction_block_0"); + const entry_flow_reduction_block_1 = extractReductionBlockParams("entry_flow/reduction_block_1"); + const entry_flow = { + conv_in: entry_flow_conv_in, + reduction_block_0: entry_flow_reduction_block_0, + reduction_block_1: entry_flow_reduction_block_1 + }; + const middle_flow = {}; + range(numMainBlocks, 0, 1).forEach((idx) => { + middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`); + }); + const exit_flow_reduction_block = extractReductionBlockParams("exit_flow/reduction_block"); + const exit_flow_separable_conv = extractSeparableConvParams("exit_flow/separable_conv"); + const exit_flow = { + reduction_block: exit_flow_reduction_block, + separable_conv: exit_flow_separable_conv + }; + disposeUnusedWeightTensors(weightMap, paramMappings); + return {params: {entry_flow, middle_flow, exit_flow}, paramMappings}; +} + +// src/xception/TinyXception.ts +function conv(x, params, stride) { + return tfjs_esm_exports.add(tfjs_esm_exports.conv2d(x, params.filters, stride, "same"), params.bias); +} +function reductionBlock(x, params, isActivateInput = true) { + let out = isActivateInput ? tfjs_esm_exports.relu(x) : x; + out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1]); + out = depthwiseSeparableConv(tfjs_esm_exports.relu(out), params.separable_conv1, [1, 1]); + out = tfjs_esm_exports.maxPool(out, [3, 3], [2, 2], "same"); + out = tfjs_esm_exports.add(out, conv(x, params.expansion_conv, [2, 2])); + return out; +} +function mainBlock(x, params) { + let out = depthwiseSeparableConv(tfjs_esm_exports.relu(x), params.separable_conv0, [1, 1]); + out = depthwiseSeparableConv(tfjs_esm_exports.relu(out), params.separable_conv1, [1, 1]); + out = depthwiseSeparableConv(tfjs_esm_exports.relu(out), params.separable_conv2, [1, 1]); + out = tfjs_esm_exports.add(out, x); + return out; +} +var TinyXception = class extends NeuralNetwork { + constructor(numMainBlocks) { + super("TinyXception"); + this._numMainBlocks = numMainBlocks; + } + forwardInput(input) { + const {params} = this; + if (!params) { + throw new Error("TinyXception - load model before inference"); + } + return tfjs_esm_exports.tidy(() => { + const batchTensor = tfjs_esm_exports.cast(input.toBatchTensor(112, true), "float32"); + const meanRgb = [122.782, 117.001, 104.298]; + const normalized = normalize(batchTensor, meanRgb).div(255); + let out = tfjs_esm_exports.relu(conv(normalized, params.entry_flow.conv_in, [2, 2])); + out = reductionBlock(out, params.entry_flow.reduction_block_0, false); + out = reductionBlock(out, params.entry_flow.reduction_block_1); + range(this._numMainBlocks, 0, 1).forEach((idx) => { + out = mainBlock(out, params.middle_flow[`main_block_${idx}`]); + }); + out = reductionBlock(out, params.exit_flow.reduction_block); + out = tfjs_esm_exports.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1])); + return out; + }); + } + async forward(input) { + return this.forwardInput(await toNetInput(input)); + } + getDefaultModelName() { + return "tiny_xception_model"; + } + extractParamsFromWeightMap(weightMap) { + return extractParamsFromWeightMap3(weightMap, this._numMainBlocks); + } + extractParams(weights) { + return extractParams3(weights, this._numMainBlocks); + } +}; + +// src/ageGenderNet/extractParams.ts +function extractParams4(weights) { + const paramMappings = []; + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings); + const age = extractFCParams(512, 1, "fc/age"); + const gender = extractFCParams(512, 2, "fc/gender"); + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + return { + paramMappings, + params: {fc: {age, gender}} + }; +} + +// src/ageGenderNet/extractParamsFromWeightMap.ts +function extractParamsFromWeightMap4(weightMap) { + const paramMappings = []; + const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); + function extractFcParams(prefix) { + const weights = extractWeightEntry(`${prefix}/weights`, 2); + const bias = extractWeightEntry(`${prefix}/bias`, 1); + return {weights, bias}; + } + const params = { + fc: { + age: extractFcParams("fc/age"), + gender: extractFcParams("fc/gender") + } + }; + disposeUnusedWeightTensors(weightMap, paramMappings); + return {params, paramMappings}; +} + +// src/ageGenderNet/types.ts +var Gender; +(function(Gender2) { + Gender2["FEMALE"] = "female"; + Gender2["MALE"] = "male"; +})(Gender || (Gender = {})); + +// src/ageGenderNet/AgeGenderNet.ts +var AgeGenderNet = class extends NeuralNetwork { + constructor(faceFeatureExtractor = new TinyXception(2)) { + super("AgeGenderNet"); + this._faceFeatureExtractor = faceFeatureExtractor; + } + get faceFeatureExtractor() { + return this._faceFeatureExtractor; + } + runNet(input) { + const {params} = this; + if (!params) { + throw new Error(`${this._name} - load model before inference`); + } + return tfjs_esm_exports.tidy(() => { + const bottleneckFeatures = input instanceof NetInput ? this.faceFeatureExtractor.forwardInput(input) : input; + const pooled = tfjs_esm_exports.avgPool(bottleneckFeatures, [7, 7], [2, 2], "valid").as2D(bottleneckFeatures.shape[0], -1); + const age = fullyConnectedLayer(pooled, params.fc.age).as1D(); + const gender = fullyConnectedLayer(pooled, params.fc.gender); + return {age, gender}; + }); + } + forwardInput(input) { + return tfjs_esm_exports.tidy(() => { + const {age, gender} = this.runNet(input); + return {age, gender: tfjs_esm_exports.softmax(gender)}; + }); + } + async forward(input) { + return this.forwardInput(await toNetInput(input)); + } + async predictAgeAndGender(input) { + const netInput = await toNetInput(input); + const out = await this.forwardInput(netInput); + const ages = tfjs_esm_exports.unstack(out.age); + const genders = tfjs_esm_exports.unstack(out.gender); + const ageAndGenderTensors = ages.map((ageTensor, i) => ({ + ageTensor, + genderTensor: genders[i] + })); + const predictionsByBatch = await Promise.all(ageAndGenderTensors.map(async ({ageTensor, genderTensor}) => { + const age = ageTensor.dataSync()[0]; + const probMale = genderTensor.dataSync()[0]; + const isMale = probMale > 0.5; + const gender = isMale ? Gender.MALE : Gender.FEMALE; + const genderProbability = isMale ? probMale : 1 - probMale; + ageTensor.dispose(); + genderTensor.dispose(); + return {age, gender, genderProbability}; + })); + out.age.dispose(); + out.gender.dispose(); + return netInput.isBatchInput ? predictionsByBatch : predictionsByBatch[0]; + } + getDefaultModelName() { + return "age_gender_model"; + } + dispose(throwOnRedispose = true) { + this.faceFeatureExtractor.dispose(throwOnRedispose); + super.dispose(throwOnRedispose); + } + loadClassifierParams(weights) { + const {params, paramMappings} = this.extractClassifierParams(weights); + this._params = params; + this._paramMappings = paramMappings; + } + extractClassifierParams(weights) { + return extractParams4(weights); + } + extractParamsFromWeightMap(weightMap) { + const {featureExtractorMap, classifierMap} = seperateWeightMaps(weightMap); + this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap); + return extractParamsFromWeightMap4(classifierMap); + } + extractParams(weights) { + const classifierWeightSize = 512 * 1 + 1 + (512 * 2 + 2); + const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize); + const classifierWeights = weights.slice(weights.length - classifierWeightSize); + this.faceFeatureExtractor.extractWeights(featureExtractorWeights); + return this.extractClassifierParams(classifierWeights); + } +}; + +// src/faceLandmarkNet/FaceLandmark68NetBase.ts +var FaceLandmark68NetBase = class extends FaceProcessor { + postProcess(output, inputSize, originalDimensions) { + const inputDimensions = originalDimensions.map(({width, height}) => { + const scale2 = inputSize / Math.max(height, width); + return { + width: width * scale2, + height: height * scale2 + }; + }); + const batchSize = inputDimensions.length; + return tfjs_esm_exports.tidy(() => { + const createInterleavedTensor = (fillX, fillY) => tfjs_esm_exports.stack([tfjs_esm_exports.fill([68], fillX, "float32"), tfjs_esm_exports.fill([68], fillY, "float32")], 1).as2D(1, 136).as1D(); + const getPadding = (batchIdx, cond) => { + const {width, height} = inputDimensions[batchIdx]; + return cond(width, height) ? Math.abs(width - height) / 2 : 0; + }; + const getPaddingX = (batchIdx) => getPadding(batchIdx, (w, h) => w < h); + const getPaddingY = (batchIdx) => getPadding(batchIdx, (w, h) => h < w); + const landmarkTensors = output.mul(tfjs_esm_exports.fill([batchSize, 136], inputSize, "float32")).sub(tfjs_esm_exports.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(getPaddingX(batchIdx), getPaddingY(batchIdx))))).div(tfjs_esm_exports.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(inputDimensions[batchIdx].width, inputDimensions[batchIdx].height)))); + return landmarkTensors; + }); + } + forwardInput(input) { + return tfjs_esm_exports.tidy(() => { + const out = this.runNet(input); + return this.postProcess(out, input.inputSize, input.inputDimensions.map(([height, width]) => ({height, width}))); + }); + } + async forward(input) { + return this.forwardInput(await toNetInput(input)); + } + async detectLandmarks(input) { + const netInput = await toNetInput(input); + const landmarkTensors = tfjs_esm_exports.tidy(() => tfjs_esm_exports.unstack(this.forwardInput(netInput))); + const landmarksForBatch = await Promise.all(landmarkTensors.map(async (landmarkTensor, batchIdx) => { + const landmarksArray = Array.from(landmarkTensor.dataSync()); + const xCoords = landmarksArray.filter((_, i) => isEven(i)); + const yCoords = landmarksArray.filter((_, i) => !isEven(i)); + return new FaceLandmarks68(Array(68).fill(0).map((_, i) => new Point(xCoords[i], yCoords[i])), { + height: netInput.getInputHeight(batchIdx), + width: netInput.getInputWidth(batchIdx) + }); + })); + landmarkTensors.forEach((t) => t.dispose()); + return netInput.isBatchInput ? landmarksForBatch : landmarksForBatch[0]; + } + getClassifierChannelsOut() { + return 136; + } +}; + +// src/faceLandmarkNet/FaceLandmark68Net.ts +var FaceLandmark68Net = class extends FaceLandmark68NetBase { + constructor(faceFeatureExtractor = new FaceFeatureExtractor()) { + super("FaceLandmark68Net", faceFeatureExtractor); + } + getDefaultModelName() { + return "face_landmark_68_model"; + } + getClassifierChannelsIn() { + return 256; + } +}; + +// src/faceFeatureExtractor/extractParamsFromWeightMapTiny.ts +function extractParamsFromWeightMapTiny(weightMap) { + const paramMappings = []; + const { + extractDenseBlock3Params + } = loadParamsFactory(weightMap, paramMappings); + const params = { + dense0: extractDenseBlock3Params("dense0", true), + dense1: extractDenseBlock3Params("dense1"), + dense2: extractDenseBlock3Params("dense2") + }; + disposeUnusedWeightTensors(weightMap, paramMappings); + return {params, paramMappings}; +} + +// src/faceFeatureExtractor/extractParamsTiny.ts +function extractParamsTiny(weights) { + const paramMappings = []; + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const { + extractDenseBlock3Params + } = extractorsFactory(extractWeights, paramMappings); + const dense0 = extractDenseBlock3Params(3, 32, "dense0", true); + const dense1 = extractDenseBlock3Params(32, 64, "dense1"); + const dense2 = extractDenseBlock3Params(64, 128, "dense2"); + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + return { + paramMappings, + params: {dense0, dense1, dense2} + }; +} + +// src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts +var TinyFaceFeatureExtractor = class extends NeuralNetwork { + constructor() { + super("TinyFaceFeatureExtractor"); + } + forwardInput(input) { + const {params} = this; + if (!params) { + throw new Error("TinyFaceFeatureExtractor - load model before inference"); + } + return tfjs_esm_exports.tidy(() => { + const batchTensor = tfjs_esm_exports.cast(input.toBatchTensor(112, true), "float32"); + const meanRgb = [122.782, 117.001, 104.298]; + const normalized = normalize(batchTensor, meanRgb).div(255); + let out = denseBlock3(normalized, params.dense0, true); + out = denseBlock3(out, params.dense1); + out = denseBlock3(out, params.dense2); + out = tfjs_esm_exports.avgPool(out, [14, 14], [2, 2], "valid"); + return out; + }); + } + async forward(input) { + return this.forwardInput(await toNetInput(input)); + } + getDefaultModelName() { + return "face_feature_extractor_tiny_model"; + } + extractParamsFromWeightMap(weightMap) { + return extractParamsFromWeightMapTiny(weightMap); + } + extractParams(weights) { + return extractParamsTiny(weights); + } +}; + +// src/faceLandmarkNet/FaceLandmark68TinyNet.ts +var FaceLandmark68TinyNet = class extends FaceLandmark68NetBase { + constructor(faceFeatureExtractor = new TinyFaceFeatureExtractor()) { + super("FaceLandmark68TinyNet", faceFeatureExtractor); + } + getDefaultModelName() { + return "face_landmark_68_tiny_model"; + } + getClassifierChannelsIn() { + return 128; + } +}; + +// src/faceLandmarkNet/index.ts +var FaceLandmarkNet = class extends FaceLandmark68Net { +}; + +// src/faceRecognitionNet/scaleLayer.ts +function scale(x, params) { + return tfjs_esm_exports.add(tfjs_esm_exports.mul(x, params.weights), params.biases); +} + +// src/faceRecognitionNet/convLayer.ts +function convLayer2(x, params, strides, withRelu, padding = "same") { + const {filters, bias} = params.conv; + let out = tfjs_esm_exports.conv2d(x, filters, strides, padding); + out = tfjs_esm_exports.add(out, bias); + out = scale(out, params.scale); + return withRelu ? tfjs_esm_exports.relu(out) : out; +} +function conv2(x, params) { + return convLayer2(x, params, [1, 1], true); +} +function convNoRelu(x, params) { + return convLayer2(x, params, [1, 1], false); +} +function convDown(x, params) { + return convLayer2(x, params, [2, 2], true, "valid"); +} + +// src/faceRecognitionNet/extractParams.ts +function extractorsFactory3(extractWeights, paramMappings) { + function extractFilterValues(numFilterValues, numFilters, filterSize) { + const weights = extractWeights(numFilterValues); + const depth = weights.length / (numFilters * filterSize * filterSize); + if (isFloat(depth)) { + throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`); + } + return tfjs_esm_exports.tidy(() => tfjs_esm_exports.transpose(tfjs_esm_exports.tensor4d(weights, [numFilters, depth, filterSize, filterSize]), [2, 3, 1, 0])); + } + function extractConvParams(numFilterValues, numFilters, filterSize, mappedPrefix) { + const filters = extractFilterValues(numFilterValues, numFilters, filterSize); + const bias = tfjs_esm_exports.tensor1d(extractWeights(numFilters)); + paramMappings.push({paramPath: `${mappedPrefix}/filters`}, {paramPath: `${mappedPrefix}/bias`}); + return {filters, bias}; + } + function extractScaleLayerParams(numWeights, mappedPrefix) { + const weights = tfjs_esm_exports.tensor1d(extractWeights(numWeights)); + const biases = tfjs_esm_exports.tensor1d(extractWeights(numWeights)); + paramMappings.push({paramPath: `${mappedPrefix}/weights`}, {paramPath: `${mappedPrefix}/biases`}); + return { + weights, + biases + }; + } + function extractConvLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix) { + const conv3 = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`); + const scale2 = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`); + return {conv: conv3, scale: scale2}; + } + function extractResidualLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix, isDown = false) { + const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`); + const conv22 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`); + return {conv1, conv2: conv22}; + } + return { + extractConvLayerParams, + extractResidualLayerParams + }; +} +function extractParams5(weights) { + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const paramMappings = []; + const { + extractConvLayerParams, + extractResidualLayerParams + } = extractorsFactory3(extractWeights, paramMappings); + const conv32_down = extractConvLayerParams(4704, 32, 7, "conv32_down"); + const conv32_1 = extractResidualLayerParams(9216, 32, 3, "conv32_1"); + const conv32_2 = extractResidualLayerParams(9216, 32, 3, "conv32_2"); + const conv32_3 = extractResidualLayerParams(9216, 32, 3, "conv32_3"); + const conv64_down = extractResidualLayerParams(36864, 64, 3, "conv64_down", true); + const conv64_1 = extractResidualLayerParams(36864, 64, 3, "conv64_1"); + const conv64_2 = extractResidualLayerParams(36864, 64, 3, "conv64_2"); + const conv64_3 = extractResidualLayerParams(36864, 64, 3, "conv64_3"); + const conv128_down = extractResidualLayerParams(147456, 128, 3, "conv128_down", true); + const conv128_1 = extractResidualLayerParams(147456, 128, 3, "conv128_1"); + const conv128_2 = extractResidualLayerParams(147456, 128, 3, "conv128_2"); + const conv256_down = extractResidualLayerParams(589824, 256, 3, "conv256_down", true); + const conv256_1 = extractResidualLayerParams(589824, 256, 3, "conv256_1"); + const conv256_2 = extractResidualLayerParams(589824, 256, 3, "conv256_2"); + const conv256_down_out = extractResidualLayerParams(589824, 256, 3, "conv256_down_out"); + const fc = tfjs_esm_exports.tidy(() => tfjs_esm_exports.transpose(tfjs_esm_exports.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0])); + paramMappings.push({paramPath: "fc"}); + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + const params = { + conv32_down, + conv32_1, + conv32_2, + conv32_3, + conv64_down, + conv64_1, + conv64_2, + conv64_3, + conv128_down, + conv128_1, + conv128_2, + conv256_down, + conv256_1, + conv256_2, + conv256_down_out, + fc + }; + return {params, paramMappings}; +} + +// src/faceRecognitionNet/extractParamsFromWeightMap.ts +function extractorsFactory4(weightMap, paramMappings) { + const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); + function extractScaleLayerParams(prefix) { + const weights = extractWeightEntry(`${prefix}/scale/weights`, 1); + const biases = extractWeightEntry(`${prefix}/scale/biases`, 1); + return {weights, biases}; + } + function extractConvLayerParams(prefix) { + const filters = extractWeightEntry(`${prefix}/conv/filters`, 4); + const bias = extractWeightEntry(`${prefix}/conv/bias`, 1); + const scale2 = extractScaleLayerParams(prefix); + return {conv: {filters, bias}, scale: scale2}; + } + function extractResidualLayerParams(prefix) { + return { + conv1: extractConvLayerParams(`${prefix}/conv1`), + conv2: extractConvLayerParams(`${prefix}/conv2`) + }; + } + return { + extractConvLayerParams, + extractResidualLayerParams + }; +} +function extractParamsFromWeightMap5(weightMap) { + const paramMappings = []; + const { + extractConvLayerParams, + extractResidualLayerParams + } = extractorsFactory4(weightMap, paramMappings); + const conv32_down = extractConvLayerParams("conv32_down"); + const conv32_1 = extractResidualLayerParams("conv32_1"); + const conv32_2 = extractResidualLayerParams("conv32_2"); + const conv32_3 = extractResidualLayerParams("conv32_3"); + const conv64_down = extractResidualLayerParams("conv64_down"); + const conv64_1 = extractResidualLayerParams("conv64_1"); + const conv64_2 = extractResidualLayerParams("conv64_2"); + const conv64_3 = extractResidualLayerParams("conv64_3"); + const conv128_down = extractResidualLayerParams("conv128_down"); + const conv128_1 = extractResidualLayerParams("conv128_1"); + const conv128_2 = extractResidualLayerParams("conv128_2"); + const conv256_down = extractResidualLayerParams("conv256_down"); + const conv256_1 = extractResidualLayerParams("conv256_1"); + const conv256_2 = extractResidualLayerParams("conv256_2"); + const conv256_down_out = extractResidualLayerParams("conv256_down_out"); + const {fc} = weightMap; + paramMappings.push({originalPath: "fc", paramPath: "fc"}); + if (!isTensor2D(fc)) { + throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`); + } + const params = { + conv32_down, + conv32_1, + conv32_2, + conv32_3, + conv64_down, + conv64_1, + conv64_2, + conv64_3, + conv128_down, + conv128_1, + conv128_2, + conv256_down, + conv256_1, + conv256_2, + conv256_down_out, + fc + }; + disposeUnusedWeightTensors(weightMap, paramMappings); + return {params, paramMappings}; +} + +// src/faceRecognitionNet/residualLayer.ts +function residual(x, params) { + let out = conv2(x, params.conv1); + out = convNoRelu(out, params.conv2); + out = tfjs_esm_exports.add(out, x); + out = tfjs_esm_exports.relu(out); + return out; +} +function residualDown(x, params) { + let out = convDown(x, params.conv1); + out = convNoRelu(out, params.conv2); + let pooled = tfjs_esm_exports.avgPool(x, 2, 2, "valid"); + const zeros2 = tfjs_esm_exports.zeros(pooled.shape); + const isPad = pooled.shape[3] !== out.shape[3]; + const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2]; + if (isAdjustShape) { + const padShapeX = [...out.shape]; + padShapeX[1] = 1; + const zerosW = tfjs_esm_exports.zeros(padShapeX); + out = tfjs_esm_exports.concat([out, zerosW], 1); + const padShapeY = [...out.shape]; + padShapeY[2] = 1; + const zerosH = tfjs_esm_exports.zeros(padShapeY); + out = tfjs_esm_exports.concat([out, zerosH], 2); + } + pooled = isPad ? tfjs_esm_exports.concat([pooled, zeros2], 3) : pooled; + out = tfjs_esm_exports.add(pooled, out); + out = tfjs_esm_exports.relu(out); + return out; +} + +// src/faceRecognitionNet/FaceRecognitionNet.ts +var FaceRecognitionNet = class extends NeuralNetwork { + constructor() { + super("FaceRecognitionNet"); + } + forwardInput(input) { + const {params} = this; + if (!params) { + throw new Error("FaceRecognitionNet - load model before inference"); + } + return tfjs_esm_exports.tidy(() => { + const batchTensor = tfjs_esm_exports.cast(input.toBatchTensor(150, true), "float32"); + const meanRgb = [122.782, 117.001, 104.298]; + const normalized = normalize(batchTensor, meanRgb).div(255); + let out = convDown(normalized, params.conv32_down); + out = tfjs_esm_exports.maxPool(out, 3, 2, "valid"); + out = residual(out, params.conv32_1); + out = residual(out, params.conv32_2); + out = residual(out, params.conv32_3); + out = residualDown(out, params.conv64_down); + out = residual(out, params.conv64_1); + out = residual(out, params.conv64_2); + out = residual(out, params.conv64_3); + out = residualDown(out, params.conv128_down); + out = residual(out, params.conv128_1); + out = residual(out, params.conv128_2); + out = residualDown(out, params.conv256_down); + out = residual(out, params.conv256_1); + out = residual(out, params.conv256_2); + out = residualDown(out, params.conv256_down_out); + const globalAvg = out.mean([1, 2]); + const fullyConnected = tfjs_esm_exports.matMul(globalAvg, params.fc); + return fullyConnected; + }); + } + async forward(input) { + return this.forwardInput(await toNetInput(input)); + } + async computeFaceDescriptor(input) { + var _a; + if ((_a = input == null ? void 0 : input.shape) == null ? void 0 : _a.some((dim) => dim <= 0)) + return new Float32Array(128); + const netInput = await toNetInput(input); + const faceDescriptorTensors = tfjs_esm_exports.tidy(() => tfjs_esm_exports.unstack(this.forwardInput(netInput))); + const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map((t) => t.data())); + faceDescriptorTensors.forEach((t) => t.dispose()); + return netInput.isBatchInput ? faceDescriptorsForBatch : faceDescriptorsForBatch[0]; + } + getDefaultModelName() { + return "face_recognition_model"; + } + extractParamsFromWeightMap(weightMap) { + return extractParamsFromWeightMap5(weightMap); + } + extractParams(weights) { + return extractParams5(weights); + } +}; + +// src/faceRecognitionNet/index.ts +function createFaceRecognitionNet(weights) { + const net = new FaceRecognitionNet(); + net.extractWeights(weights); + return net; +} + +// src/factories/WithFaceDescriptor.ts +function extendWithFaceDescriptor(sourceObj, descriptor) { + const extension = {descriptor}; + return {...sourceObj, ...extension}; +} + +// src/factories/WithAge.ts +function isWithAge(obj) { + return typeof obj.age === "number"; +} +function extendWithAge(sourceObj, age) { + const extension = {age}; + return {...sourceObj, ...extension}; +} + +// src/factories/WithGender.ts +function isWithGender(obj) { + return (obj.gender === Gender.MALE || obj.gender === Gender.FEMALE) && isValidProbablitiy(obj.genderProbability); +} +function extendWithGender(sourceObj, gender, genderProbability) { + const extension = {gender, genderProbability}; + return {...sourceObj, ...extension}; +} + +// src/ssdMobilenetv1/extractParams.ts +function extractorsFactory5(extractWeights, paramMappings) { + function extractDepthwiseConvParams(numChannels, mappedPrefix) { + const filters = tfjs_esm_exports.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]); + const batch_norm_scale = tfjs_esm_exports.tensor1d(extractWeights(numChannels)); + const batch_norm_offset = tfjs_esm_exports.tensor1d(extractWeights(numChannels)); + const batch_norm_mean = tfjs_esm_exports.tensor1d(extractWeights(numChannels)); + const batch_norm_variance = tfjs_esm_exports.tensor1d(extractWeights(numChannels)); + paramMappings.push({paramPath: `${mappedPrefix}/filters`}, {paramPath: `${mappedPrefix}/batch_norm_scale`}, {paramPath: `${mappedPrefix}/batch_norm_offset`}, {paramPath: `${mappedPrefix}/batch_norm_mean`}, {paramPath: `${mappedPrefix}/batch_norm_variance`}); + return { + filters, + batch_norm_scale, + batch_norm_offset, + batch_norm_mean, + batch_norm_variance + }; + } + function extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, isPointwiseConv) { + const filters = tfjs_esm_exports.tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]); + const bias = tfjs_esm_exports.tensor1d(extractWeights(channelsOut)); + paramMappings.push({paramPath: `${mappedPrefix}/filters`}, {paramPath: `${mappedPrefix}/${isPointwiseConv ? "batch_norm_offset" : "bias"}`}); + return {filters, bias}; + } + function extractPointwiseConvParams(channelsIn, channelsOut, filterSize, mappedPrefix) { + const { + filters, + bias + } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true); + return { + filters, + batch_norm_offset: bias + }; + } + function extractConvPairParams(channelsIn, channelsOut, mappedPrefix) { + const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`); + const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`); + return {depthwise_conv, pointwise_conv}; + } + function extractMobilenetV1Params() { + const conv_0 = extractPointwiseConvParams(3, 32, 3, "mobilenetv1/conv_0"); + const conv_1 = extractConvPairParams(32, 64, "mobilenetv1/conv_1"); + const conv_2 = extractConvPairParams(64, 128, "mobilenetv1/conv_2"); + const conv_3 = extractConvPairParams(128, 128, "mobilenetv1/conv_3"); + const conv_4 = extractConvPairParams(128, 256, "mobilenetv1/conv_4"); + const conv_5 = extractConvPairParams(256, 256, "mobilenetv1/conv_5"); + const conv_6 = extractConvPairParams(256, 512, "mobilenetv1/conv_6"); + const conv_7 = extractConvPairParams(512, 512, "mobilenetv1/conv_7"); + const conv_8 = extractConvPairParams(512, 512, "mobilenetv1/conv_8"); + const conv_9 = extractConvPairParams(512, 512, "mobilenetv1/conv_9"); + const conv_10 = extractConvPairParams(512, 512, "mobilenetv1/conv_10"); + const conv_11 = extractConvPairParams(512, 512, "mobilenetv1/conv_11"); + const conv_12 = extractConvPairParams(512, 1024, "mobilenetv1/conv_12"); + const conv_13 = extractConvPairParams(1024, 1024, "mobilenetv1/conv_13"); + return { + conv_0, + conv_1, + conv_2, + conv_3, + conv_4, + conv_5, + conv_6, + conv_7, + conv_8, + conv_9, + conv_10, + conv_11, + conv_12, + conv_13 + }; + } + function extractPredictionLayerParams() { + const conv_0 = extractPointwiseConvParams(1024, 256, 1, "prediction_layer/conv_0"); + const conv_1 = extractPointwiseConvParams(256, 512, 3, "prediction_layer/conv_1"); + const conv_2 = extractPointwiseConvParams(512, 128, 1, "prediction_layer/conv_2"); + const conv_3 = extractPointwiseConvParams(128, 256, 3, "prediction_layer/conv_3"); + const conv_4 = extractPointwiseConvParams(256, 128, 1, "prediction_layer/conv_4"); + const conv_5 = extractPointwiseConvParams(128, 256, 3, "prediction_layer/conv_5"); + const conv_6 = extractPointwiseConvParams(256, 64, 1, "prediction_layer/conv_6"); + const conv_7 = extractPointwiseConvParams(64, 128, 3, "prediction_layer/conv_7"); + const box_encoding_0_predictor = extractConvParams(512, 12, 1, "prediction_layer/box_predictor_0/box_encoding_predictor"); + const class_predictor_0 = extractConvParams(512, 9, 1, "prediction_layer/box_predictor_0/class_predictor"); + const box_encoding_1_predictor = extractConvParams(1024, 24, 1, "prediction_layer/box_predictor_1/box_encoding_predictor"); + const class_predictor_1 = extractConvParams(1024, 18, 1, "prediction_layer/box_predictor_1/class_predictor"); + const box_encoding_2_predictor = extractConvParams(512, 24, 1, "prediction_layer/box_predictor_2/box_encoding_predictor"); + const class_predictor_2 = extractConvParams(512, 18, 1, "prediction_layer/box_predictor_2/class_predictor"); + const box_encoding_3_predictor = extractConvParams(256, 24, 1, "prediction_layer/box_predictor_3/box_encoding_predictor"); + const class_predictor_3 = extractConvParams(256, 18, 1, "prediction_layer/box_predictor_3/class_predictor"); + const box_encoding_4_predictor = extractConvParams(256, 24, 1, "prediction_layer/box_predictor_4/box_encoding_predictor"); + const class_predictor_4 = extractConvParams(256, 18, 1, "prediction_layer/box_predictor_4/class_predictor"); + const box_encoding_5_predictor = extractConvParams(128, 24, 1, "prediction_layer/box_predictor_5/box_encoding_predictor"); + const class_predictor_5 = extractConvParams(128, 18, 1, "prediction_layer/box_predictor_5/class_predictor"); + const box_predictor_0 = { + box_encoding_predictor: box_encoding_0_predictor, + class_predictor: class_predictor_0 + }; + const box_predictor_1 = { + box_encoding_predictor: box_encoding_1_predictor, + class_predictor: class_predictor_1 + }; + const box_predictor_2 = { + box_encoding_predictor: box_encoding_2_predictor, + class_predictor: class_predictor_2 + }; + const box_predictor_3 = { + box_encoding_predictor: box_encoding_3_predictor, + class_predictor: class_predictor_3 + }; + const box_predictor_4 = { + box_encoding_predictor: box_encoding_4_predictor, + class_predictor: class_predictor_4 + }; + const box_predictor_5 = { + box_encoding_predictor: box_encoding_5_predictor, + class_predictor: class_predictor_5 + }; + return { + conv_0, + conv_1, + conv_2, + conv_3, + conv_4, + conv_5, + conv_6, + conv_7, + box_predictor_0, + box_predictor_1, + box_predictor_2, + box_predictor_3, + box_predictor_4, + box_predictor_5 + }; + } + return { + extractMobilenetV1Params, + extractPredictionLayerParams + }; +} +function extractParams6(weights) { + const paramMappings = []; + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const { + extractMobilenetV1Params, + extractPredictionLayerParams + } = extractorsFactory5(extractWeights, paramMappings); + const mobilenetv1 = extractMobilenetV1Params(); + const prediction_layer = extractPredictionLayerParams(); + const extra_dim = tfjs_esm_exports.tensor3d(extractWeights(5118 * 4), [1, 5118, 4]); + const output_layer = { + extra_dim + }; + paramMappings.push({paramPath: "output_layer/extra_dim"}); + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + return { + params: { + mobilenetv1, + prediction_layer, + output_layer + }, + paramMappings + }; +} + +// src/ssdMobilenetv1/extractParamsFromWeightMap.ts +function extractorsFactory6(weightMap, paramMappings) { + const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); + function extractPointwiseConvParams(prefix, idx, mappedPrefix) { + const filters = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`); + const batch_norm_offset = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`); + return {filters, batch_norm_offset}; + } + function extractConvPairParams(idx) { + const mappedPrefix = `mobilenetv1/conv_${idx}`; + const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`; + const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`; + const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`; + const filters = extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`); + const batch_norm_scale = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`); + const batch_norm_offset = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`); + const batch_norm_mean = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`); + const batch_norm_variance = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`); + return { + depthwise_conv: { + filters, + batch_norm_scale, + batch_norm_offset, + batch_norm_mean, + batch_norm_variance + }, + pointwise_conv: extractPointwiseConvParams("MobilenetV1", idx, mappedPrefixPointwiseConv) + }; + } + function extractMobilenetV1Params() { + return { + conv_0: extractPointwiseConvParams("MobilenetV1", 0, "mobilenetv1/conv_0"), + conv_1: extractConvPairParams(1), + conv_2: extractConvPairParams(2), + conv_3: extractConvPairParams(3), + conv_4: extractConvPairParams(4), + conv_5: extractConvPairParams(5), + conv_6: extractConvPairParams(6), + conv_7: extractConvPairParams(7), + conv_8: extractConvPairParams(8), + conv_9: extractConvPairParams(9), + conv_10: extractConvPairParams(10), + conv_11: extractConvPairParams(11), + conv_12: extractConvPairParams(12), + conv_13: extractConvPairParams(13) + }; + } + function extractConvParams(prefix, mappedPrefix) { + const filters = extractWeightEntry(`${prefix}/weights`, 4, `${mappedPrefix}/filters`); + const bias = extractWeightEntry(`${prefix}/biases`, 1, `${mappedPrefix}/bias`); + return {filters, bias}; + } + function extractBoxPredictorParams(idx) { + const box_encoding_predictor = extractConvParams(`Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`, `prediction_layer/box_predictor_${idx}/box_encoding_predictor`); + const class_predictor = extractConvParams(`Prediction/BoxPredictor_${idx}/ClassPredictor`, `prediction_layer/box_predictor_${idx}/class_predictor`); + return {box_encoding_predictor, class_predictor}; + } + function extractPredictionLayerParams() { + return { + conv_0: extractPointwiseConvParams("Prediction", 0, "prediction_layer/conv_0"), + conv_1: extractPointwiseConvParams("Prediction", 1, "prediction_layer/conv_1"), + conv_2: extractPointwiseConvParams("Prediction", 2, "prediction_layer/conv_2"), + conv_3: extractPointwiseConvParams("Prediction", 3, "prediction_layer/conv_3"), + conv_4: extractPointwiseConvParams("Prediction", 4, "prediction_layer/conv_4"), + conv_5: extractPointwiseConvParams("Prediction", 5, "prediction_layer/conv_5"), + conv_6: extractPointwiseConvParams("Prediction", 6, "prediction_layer/conv_6"), + conv_7: extractPointwiseConvParams("Prediction", 7, "prediction_layer/conv_7"), + box_predictor_0: extractBoxPredictorParams(0), + box_predictor_1: extractBoxPredictorParams(1), + box_predictor_2: extractBoxPredictorParams(2), + box_predictor_3: extractBoxPredictorParams(3), + box_predictor_4: extractBoxPredictorParams(4), + box_predictor_5: extractBoxPredictorParams(5) + }; + } + return { + extractMobilenetV1Params, + extractPredictionLayerParams + }; +} +function extractParamsFromWeightMap6(weightMap) { + const paramMappings = []; + const { + extractMobilenetV1Params, + extractPredictionLayerParams + } = extractorsFactory6(weightMap, paramMappings); + const extra_dim = weightMap["Output/extra_dim"]; + paramMappings.push({originalPath: "Output/extra_dim", paramPath: "output_layer/extra_dim"}); + if (!isTensor3D(extra_dim)) { + throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`); + } + const params = { + mobilenetv1: extractMobilenetV1Params(), + prediction_layer: extractPredictionLayerParams(), + output_layer: { + extra_dim + } + }; + disposeUnusedWeightTensors(weightMap, paramMappings); + return {params, paramMappings}; +} + +// src/ssdMobilenetv1/pointwiseConvLayer.ts +function pointwiseConvLayer(x, params, strides) { + return tfjs_esm_exports.tidy(() => { + let out = tfjs_esm_exports.conv2d(x, params.filters, strides, "same"); + out = tfjs_esm_exports.add(out, params.batch_norm_offset); + return tfjs_esm_exports.clipByValue(out, 0, 6); + }); +} + +// src/ssdMobilenetv1/mobileNetV1.ts +var epsilon = 0.0010000000474974513; +function depthwiseConvLayer(x, params, strides) { + return tfjs_esm_exports.tidy(() => { + let out = tfjs_esm_exports.depthwiseConv2d(x, params.filters, strides, "same"); + out = tfjs_esm_exports.batchNorm(out, params.batch_norm_mean, params.batch_norm_variance, params.batch_norm_offset, params.batch_norm_scale, epsilon); + return tfjs_esm_exports.clipByValue(out, 0, 6); + }); +} +function getStridesForLayerIdx(layerIdx) { + return [2, 4, 6, 12].some((idx) => idx === layerIdx) ? [2, 2] : [1, 1]; +} +function mobileNetV1(x, params) { + return tfjs_esm_exports.tidy(() => { + let conv11; + let out = pointwiseConvLayer(x, params.conv_0, [2, 2]); + const convPairParams = [ + params.conv_1, + params.conv_2, + params.conv_3, + params.conv_4, + params.conv_5, + params.conv_6, + params.conv_7, + params.conv_8, + params.conv_9, + params.conv_10, + params.conv_11, + params.conv_12, + params.conv_13 + ]; + convPairParams.forEach((param, i) => { + const layerIdx = i + 1; + const depthwiseConvStrides = getStridesForLayerIdx(layerIdx); + out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides); + out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1]); + if (layerIdx === 11) + conv11 = out; + }); + if (conv11 === null) { + throw new Error("mobileNetV1 - output of conv layer 11 is null"); + } + return { + out, + conv11 + }; + }); +} + +// src/ssdMobilenetv1/nonMaxSuppression.ts +function IOU(boxes, i, j) { + const boxesData = boxes.arraySync(); + const yminI = Math.min(boxesData[i][0], boxesData[i][2]); + const xminI = Math.min(boxesData[i][1], boxesData[i][3]); + const ymaxI = Math.max(boxesData[i][0], boxesData[i][2]); + const xmaxI = Math.max(boxesData[i][1], boxesData[i][3]); + const yminJ = Math.min(boxesData[j][0], boxesData[j][2]); + const xminJ = Math.min(boxesData[j][1], boxesData[j][3]); + const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2]); + const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3]); + const areaI = (ymaxI - yminI) * (xmaxI - xminI); + const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ); + if (areaI <= 0 || areaJ <= 0) + return 0; + const intersectionYmin = Math.max(yminI, yminJ); + const intersectionXmin = Math.max(xminI, xminJ); + const intersectionYmax = Math.min(ymaxI, ymaxJ); + const intersectionXmax = Math.min(xmaxI, xmaxJ); + const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0) * Math.max(intersectionXmax - intersectionXmin, 0); + return intersectionArea / (areaI + areaJ - intersectionArea); +} +function nonMaxSuppression2(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) { + const numBoxes = boxes.shape[0]; + const outputSize = Math.min(maxOutputSize, numBoxes); + const candidates = scores.map((score, boxIndex) => ({score, boxIndex})).filter((c) => c.score > scoreThreshold).sort((c1, c2) => c2.score - c1.score); + const suppressFunc = (x) => x <= iouThreshold ? 1 : 0; + const selected = []; + candidates.forEach((c) => { + if (selected.length >= outputSize) + return; + const originalScore = c.score; + for (let j = selected.length - 1; j >= 0; --j) { + const iou2 = IOU(boxes, c.boxIndex, selected[j]); + if (iou2 === 0) + continue; + c.score *= suppressFunc(iou2); + if (c.score <= scoreThreshold) + break; + } + if (originalScore === c.score) { + selected.push(c.boxIndex); + } + }); + return selected; +} + +// src/ssdMobilenetv1/outputLayer.ts +function getCenterCoordinatesAndSizesLayer(x) { + const vec = tfjs_esm_exports.unstack(tfjs_esm_exports.transpose(x, [1, 0])); + const sizes = [ + tfjs_esm_exports.sub(vec[2], vec[0]), + tfjs_esm_exports.sub(vec[3], vec[1]) + ]; + const centers = [ + tfjs_esm_exports.add(vec[0], tfjs_esm_exports.div(sizes[0], 2)), + tfjs_esm_exports.add(vec[1], tfjs_esm_exports.div(sizes[1], 2)) + ]; + return {sizes, centers}; +} +function decodeBoxesLayer(x0, x1) { + const {sizes, centers} = getCenterCoordinatesAndSizesLayer(x0); + const vec = tfjs_esm_exports.unstack(tfjs_esm_exports.transpose(x1, [1, 0])); + const div0_out = tfjs_esm_exports.div(tfjs_esm_exports.mul(tfjs_esm_exports.exp(tfjs_esm_exports.div(vec[2], 5)), sizes[0]), 2); + const add0_out = tfjs_esm_exports.add(tfjs_esm_exports.mul(tfjs_esm_exports.div(vec[0], 10), sizes[0]), centers[0]); + const div1_out = tfjs_esm_exports.div(tfjs_esm_exports.mul(tfjs_esm_exports.exp(tfjs_esm_exports.div(vec[3], 5)), sizes[1]), 2); + const add1_out = tfjs_esm_exports.add(tfjs_esm_exports.mul(tfjs_esm_exports.div(vec[1], 10), sizes[1]), centers[1]); + return tfjs_esm_exports.transpose(tfjs_esm_exports.stack([ + tfjs_esm_exports.sub(add0_out, div0_out), + tfjs_esm_exports.sub(add1_out, div1_out), + tfjs_esm_exports.add(add0_out, div0_out), + tfjs_esm_exports.add(add1_out, div1_out) + ]), [1, 0]); +} +function outputLayer(boxPredictions, classPredictions, params) { + return tfjs_esm_exports.tidy(() => { + const batchSize = boxPredictions.shape[0]; + let boxes = decodeBoxesLayer(tfjs_esm_exports.reshape(tfjs_esm_exports.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]), tfjs_esm_exports.reshape(boxPredictions, [-1, 4])); + boxes = tfjs_esm_exports.reshape(boxes, [batchSize, boxes.shape[0] / batchSize, 4]); + const scoresAndClasses = tfjs_esm_exports.sigmoid(tfjs_esm_exports.slice(classPredictions, [0, 0, 1], [-1, -1, -1])); + let scores = tfjs_esm_exports.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]); + scores = tfjs_esm_exports.reshape(scores, [batchSize, scores.shape[1]]); + const boxesByBatch = tfjs_esm_exports.unstack(boxes); + const scoresByBatch = tfjs_esm_exports.unstack(scores); + return {boxes: boxesByBatch, scores: scoresByBatch}; + }); +} + +// src/ssdMobilenetv1/boxPredictionLayer.ts +function boxPredictionLayer(x, params) { + return tfjs_esm_exports.tidy(() => { + const batchSize = x.shape[0]; + const boxPredictionEncoding = tfjs_esm_exports.reshape(convLayer(x, params.box_encoding_predictor), [batchSize, -1, 1, 4]); + const classPrediction = tfjs_esm_exports.reshape(convLayer(x, params.class_predictor), [batchSize, -1, 3]); + return {boxPredictionEncoding, classPrediction}; + }); +} + +// src/ssdMobilenetv1/predictionLayer.ts +function predictionLayer(x, conv11, params) { + return tfjs_esm_exports.tidy(() => { + const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]); + const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]); + const conv22 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]); + const conv3 = pointwiseConvLayer(conv22, params.conv_3, [2, 2]); + const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1]); + const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2]); + const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1]); + const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2]); + const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0); + const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1); + const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2); + const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3); + const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4); + const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5); + const boxPredictions = tfjs_esm_exports.concat([ + boxPrediction0.boxPredictionEncoding, + boxPrediction1.boxPredictionEncoding, + boxPrediction2.boxPredictionEncoding, + boxPrediction3.boxPredictionEncoding, + boxPrediction4.boxPredictionEncoding, + boxPrediction5.boxPredictionEncoding + ], 1); + const classPredictions = tfjs_esm_exports.concat([ + boxPrediction0.classPrediction, + boxPrediction1.classPrediction, + boxPrediction2.classPrediction, + boxPrediction3.classPrediction, + boxPrediction4.classPrediction, + boxPrediction5.classPrediction + ], 1); + return { + boxPredictions, + classPredictions + }; + }); +} + +// src/ssdMobilenetv1/SsdMobilenetv1Options.ts +var SsdMobilenetv1Options = class { + constructor({minConfidence, maxResults} = {}) { + this._name = "SsdMobilenetv1Options"; + this._minConfidence = minConfidence || 0.5; + this._maxResults = maxResults || 100; + if (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; + } +}; + +// src/ssdMobilenetv1/SsdMobilenetv1.ts +var SsdMobilenetv1 = class extends NeuralNetwork { + constructor() { + super("SsdMobilenetv1"); + } + forwardInput(input) { + const {params} = this; + if (!params) + throw new Error("SsdMobilenetv1 - load model before inference"); + return tfjs_esm_exports.tidy(() => { + const batchTensor = tfjs_esm_exports.cast(input.toBatchTensor(512, false), "float32"); + const x = tfjs_esm_exports.sub(tfjs_esm_exports.div(batchTensor, 127.5), 1); + const features = mobileNetV1(x, params.mobilenetv1); + const {boxPredictions, classPredictions} = predictionLayer(features.out, features.conv11, params.prediction_layer); + return outputLayer(boxPredictions, classPredictions, params.output_layer); + }); + } + async forward(input) { + return this.forwardInput(await toNetInput(input)); + } + async locateFaces(input, options = {}) { + const {maxResults, minConfidence} = new SsdMobilenetv1Options(options); + const netInput = await toNetInput(input); + const {boxes: _boxes, scores: _scores} = this.forwardInput(netInput); + const boxes = _boxes[0]; + const scores = _scores[0]; + for (let i = 1; i < _boxes.length; i++) { + _boxes[i].dispose(); + _scores[i].dispose(); + } + const scoresData = Array.from(scores.dataSync()); + const iouThreshold = 0.5; + const indices = nonMaxSuppression2(boxes, scoresData, maxResults, iouThreshold, minConfidence); + const reshapedDims = netInput.getReshapedInputDimensions(0); + const inputSize = netInput.inputSize; + const padX = inputSize / reshapedDims.width; + const padY = inputSize / reshapedDims.height; + const boxesData = boxes.arraySync(); + const results = indices.map((idx) => { + const [top, bottom] = [ + Math.max(0, boxesData[idx][0]), + Math.min(1, boxesData[idx][2]) + ].map((val) => val * padY); + const [left, right] = [ + Math.max(0, boxesData[idx][1]), + Math.min(1, boxesData[idx][3]) + ].map((val) => val * padX); + return new FaceDetection(scoresData[idx], new Rect(left, top, right - left, bottom - top), {height: netInput.getInputHeight(0), width: netInput.getInputWidth(0)}); + }); + boxes.dispose(); + scores.dispose(); + return results; + } + getDefaultModelName() { + return "ssd_mobilenetv1_model"; + } + extractParamsFromWeightMap(weightMap) { + return extractParamsFromWeightMap6(weightMap); + } + extractParams(weights) { + return extractParams6(weights); + } +}; + +// src/ssdMobilenetv1/index.ts +function createSsdMobilenetv1(weights) { + const net = new SsdMobilenetv1(); + net.extractWeights(weights); + return net; +} +function createFaceDetectionNet(weights) { + return createSsdMobilenetv1(weights); +} +var FaceDetectionNet = class extends SsdMobilenetv1 { +}; + +// src/tinyYolov2/const.ts +var IOU_THRESHOLD = 0.4; +var BOX_ANCHORS = [ + new Point(0.738768, 0.874946), + new Point(2.42204, 2.65704), + new Point(4.30971, 7.04493), + new Point(10.246, 4.59428), + new Point(12.6868, 11.8741) +]; +var BOX_ANCHORS_SEPARABLE = [ + new Point(1.603231, 2.094468), + new Point(6.041143, 7.080126), + new Point(2.882459, 3.518061), + new Point(4.266906, 5.178857), + new Point(9.041765, 10.66308) +]; +var MEAN_RGB_SEPARABLE = [117.001, 114.697, 97.404]; +var DEFAULT_MODEL_NAME = "tiny_yolov2_model"; +var DEFAULT_MODEL_NAME_SEPARABLE_CONV = "tiny_yolov2_separable_conv_model"; + +// src/tinyYolov2/config.ts +var isNumber = (arg) => typeof arg === "number"; +function validateConfig(config) { + if (!config) { + throw new Error(`invalid config: ${config}`); + } + if (typeof config.withSeparableConvs !== "boolean") { + throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`); + } + if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1) { + throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`); + } + if (!Array.isArray(config.classes) || !config.classes.length || !config.classes.every((c) => typeof c === "string")) { + throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`); + } + if (!Array.isArray(config.anchors) || !config.anchors.length || !config.anchors.map((a) => a || {}).every((a) => isNumber(a.x) && isNumber(a.y))) { + throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`); + } + if (config.meanRgb && (!Array.isArray(config.meanRgb) || config.meanRgb.length !== 3 || !config.meanRgb.every(isNumber))) { + throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`); + } +} + +// src/tinyYolov2/leaky.ts +function leaky(x) { + return tfjs_esm_exports.tidy(() => { + const min = tfjs_esm_exports.mul(x, tfjs_esm_exports.scalar(0.10000000149011612)); + return tfjs_esm_exports.add(tfjs_esm_exports.relu(tfjs_esm_exports.sub(x, min)), min); + }); +} + +// src/tinyYolov2/convWithBatchNorm.ts +function convWithBatchNorm(x, params) { + return tfjs_esm_exports.tidy(() => { + let out = tfjs_esm_exports.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]); + out = tfjs_esm_exports.conv2d(out, params.conv.filters, [1, 1], "valid"); + out = tfjs_esm_exports.sub(out, params.bn.sub); + out = tfjs_esm_exports.mul(out, params.bn.truediv); + out = tfjs_esm_exports.add(out, params.conv.bias); + return leaky(out); + }); +} + +// src/tinyYolov2/depthwiseSeparableConv.ts +function depthwiseSeparableConv2(x, params) { + return tfjs_esm_exports.tidy(() => { + let out = tfjs_esm_exports.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]); + out = tfjs_esm_exports.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], "valid"); + out = tfjs_esm_exports.add(out, params.bias); + return leaky(out); + }); +} + +// src/tinyYolov2/extractParams.ts +function extractorsFactory7(extractWeights, paramMappings) { + const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings); + function extractBatchNormParams(size, mappedPrefix) { + const sub6 = tfjs_esm_exports.tensor1d(extractWeights(size)); + const truediv = tfjs_esm_exports.tensor1d(extractWeights(size)); + paramMappings.push({paramPath: `${mappedPrefix}/sub`}, {paramPath: `${mappedPrefix}/truediv`}); + return {sub: sub6, truediv}; + } + function extractConvWithBatchNormParams(channelsIn, channelsOut, mappedPrefix) { + const conv3 = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`); + const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`); + return {conv: conv3, bn}; + } + const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings); + return { + extractConvParams, + extractConvWithBatchNormParams, + extractSeparableConvParams + }; +} +function extractParams7(weights, config, boxEncodingSize, filterSizes) { + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const paramMappings = []; + const { + extractConvParams, + extractConvWithBatchNormParams, + extractSeparableConvParams + } = extractorsFactory7(extractWeights, paramMappings); + let params; + if (config.withSeparableConvs) { + const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes; + const conv0 = config.isFirstLayerConv2d ? extractConvParams(s0, s1, 3, "conv0") : extractSeparableConvParams(s0, s1, "conv0"); + const conv1 = extractSeparableConvParams(s1, s2, "conv1"); + const conv22 = extractSeparableConvParams(s2, s3, "conv2"); + const conv3 = extractSeparableConvParams(s3, s4, "conv3"); + const conv4 = extractSeparableConvParams(s4, s5, "conv4"); + const conv5 = extractSeparableConvParams(s5, s6, "conv5"); + const conv6 = s7 ? extractSeparableConvParams(s6, s7, "conv6") : void 0; + const conv7 = s8 ? extractSeparableConvParams(s7, s8, "conv7") : void 0; + const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, "conv8"); + params = { + conv0, + conv1, + conv2: conv22, + conv3, + conv4, + conv5, + conv6, + conv7, + conv8 + }; + } else { + const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes; + const conv0 = extractConvWithBatchNormParams(s0, s1, "conv0"); + const conv1 = extractConvWithBatchNormParams(s1, s2, "conv1"); + const conv22 = extractConvWithBatchNormParams(s2, s3, "conv2"); + const conv3 = extractConvWithBatchNormParams(s3, s4, "conv3"); + const conv4 = extractConvWithBatchNormParams(s4, s5, "conv4"); + const conv5 = extractConvWithBatchNormParams(s5, s6, "conv5"); + const conv6 = extractConvWithBatchNormParams(s6, s7, "conv6"); + const conv7 = extractConvWithBatchNormParams(s7, s8, "conv7"); + const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, "conv8"); + params = { + conv0, + conv1, + conv2: conv22, + conv3, + conv4, + conv5, + conv6, + conv7, + conv8 + }; + } + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + return {params, paramMappings}; +} + +// src/tinyYolov2/extractParamsFromWeightMap.ts +function extractorsFactory8(weightMap, paramMappings) { + const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); + function extractBatchNormParams(prefix) { + const sub6 = extractWeightEntry(`${prefix}/sub`, 1); + const truediv = extractWeightEntry(`${prefix}/truediv`, 1); + return {sub: sub6, truediv}; + } + function extractConvParams(prefix) { + const filters = extractWeightEntry(`${prefix}/filters`, 4); + const bias = extractWeightEntry(`${prefix}/bias`, 1); + return {filters, bias}; + } + function extractConvWithBatchNormParams(prefix) { + const conv3 = extractConvParams(`${prefix}/conv`); + const bn = extractBatchNormParams(`${prefix}/bn`); + return {conv: conv3, bn}; + } + const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry); + return { + extractConvParams, + extractConvWithBatchNormParams, + extractSeparableConvParams + }; +} +function extractParamsFromWeightMap7(weightMap, config) { + const paramMappings = []; + const { + extractConvParams, + extractConvWithBatchNormParams, + extractSeparableConvParams + } = extractorsFactory8(weightMap, paramMappings); + let params; + if (config.withSeparableConvs) { + const numFilters = config.filterSizes && config.filterSizes.length || 9; + params = { + conv0: config.isFirstLayerConv2d ? extractConvParams("conv0") : extractSeparableConvParams("conv0"), + conv1: extractSeparableConvParams("conv1"), + conv2: extractSeparableConvParams("conv2"), + conv3: extractSeparableConvParams("conv3"), + conv4: extractSeparableConvParams("conv4"), + conv5: extractSeparableConvParams("conv5"), + conv6: numFilters > 7 ? extractSeparableConvParams("conv6") : void 0, + conv7: numFilters > 8 ? extractSeparableConvParams("conv7") : void 0, + conv8: extractConvParams("conv8") + }; + } else { + params = { + conv0: extractConvWithBatchNormParams("conv0"), + conv1: extractConvWithBatchNormParams("conv1"), + conv2: extractConvWithBatchNormParams("conv2"), + conv3: extractConvWithBatchNormParams("conv3"), + conv4: extractConvWithBatchNormParams("conv4"), + conv5: extractConvWithBatchNormParams("conv5"), + conv6: extractConvWithBatchNormParams("conv6"), + conv7: extractConvWithBatchNormParams("conv7"), + conv8: extractConvParams("conv8") + }; + } + disposeUnusedWeightTensors(weightMap, paramMappings); + return {params, paramMappings}; +} + +// src/tinyYolov2/TinyYolov2Options.ts +var TinyYolov2Options = class { + constructor({inputSize, scoreThreshold} = {}) { + this._name = "TinyYolov2Options"; + this._inputSize = inputSize || 416; + this._scoreThreshold = scoreThreshold || 0.5; + if (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; + } +}; + +// src/tinyYolov2/TinyYolov2Base.ts +var _TinyYolov2Base = class extends NeuralNetwork { + constructor(config) { + super("TinyYolov2"); + validateConfig(config); + this._config = config; + } + 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(x, params) { + let out = convWithBatchNorm(x, params.conv0); + out = tfjs_esm_exports.maxPool(out, [2, 2], [2, 2], "same"); + out = convWithBatchNorm(out, params.conv1); + out = tfjs_esm_exports.maxPool(out, [2, 2], [2, 2], "same"); + out = convWithBatchNorm(out, params.conv2); + out = tfjs_esm_exports.maxPool(out, [2, 2], [2, 2], "same"); + out = convWithBatchNorm(out, params.conv3); + out = tfjs_esm_exports.maxPool(out, [2, 2], [2, 2], "same"); + out = convWithBatchNorm(out, params.conv4); + out = tfjs_esm_exports.maxPool(out, [2, 2], [2, 2], "same"); + out = convWithBatchNorm(out, params.conv5); + out = tfjs_esm_exports.maxPool(out, [2, 2], [1, 1], "same"); + out = convWithBatchNorm(out, params.conv6); + out = convWithBatchNorm(out, params.conv7); + return convLayer(out, params.conv8, "valid", false); + } + runMobilenet(x, params) { + let out = this.config.isFirstLayerConv2d ? leaky(convLayer(x, params.conv0, "valid", false)) : depthwiseSeparableConv2(x, params.conv0); + out = tfjs_esm_exports.maxPool(out, [2, 2], [2, 2], "same"); + out = depthwiseSeparableConv2(out, params.conv1); + out = tfjs_esm_exports.maxPool(out, [2, 2], [2, 2], "same"); + out = depthwiseSeparableConv2(out, params.conv2); + out = tfjs_esm_exports.maxPool(out, [2, 2], [2, 2], "same"); + out = depthwiseSeparableConv2(out, params.conv3); + out = tfjs_esm_exports.maxPool(out, [2, 2], [2, 2], "same"); + out = depthwiseSeparableConv2(out, params.conv4); + out = tfjs_esm_exports.maxPool(out, [2, 2], [2, 2], "same"); + out = depthwiseSeparableConv2(out, params.conv5); + out = tfjs_esm_exports.maxPool(out, [2, 2], [1, 1], "same"); + out = params.conv6 ? depthwiseSeparableConv2(out, params.conv6) : out; + out = params.conv7 ? depthwiseSeparableConv2(out, params.conv7) : out; + return convLayer(out, params.conv8, "valid", false); + } + forwardInput(input, inputSize) { + const {params} = this; + if (!params) { + throw new Error("TinyYolov2 - load model before inference"); + } + return tfjs_esm_exports.tidy(() => { + let batchTensor = tfjs_esm_exports.cast(input.toBatchTensor(inputSize, false), "float32"); + batchTensor = this.config.meanRgb ? normalize(batchTensor, this.config.meanRgb) : batchTensor; + batchTensor = batchTensor.div(255); + return this.config.withSeparableConvs ? this.runMobilenet(batchTensor, params) : this.runTinyYolov2(batchTensor, params); + }); + } + async forward(input, inputSize) { + return this.forwardInput(await toNetInput(input), inputSize); + } + async detect(input, forwardParams = {}) { + const {inputSize, scoreThreshold} = new TinyYolov2Options(forwardParams); + const netInput = await toNetInput(input); + const out = await this.forwardInput(netInput, inputSize); + const out0 = tfjs_esm_exports.tidy(() => tfjs_esm_exports.unstack(out)[0].expandDims()); + const inputDimensions = { + width: netInput.getInputWidth(0), + height: netInput.getInputHeight(0) + }; + const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold); + out.dispose(); + out0.dispose(); + const boxes = results.map((res) => res.box); + const scores = results.map((res) => res.score); + const classScores = results.map((res) => res.classScore); + const classNames = results.map((res) => this.config.classes[res.label]); + const indices = nonMaxSuppression(boxes.map((box) => box.rescale(inputSize)), scores, this.config.iouThreshold, true); + const detections = indices.map((idx) => new ObjectDetection(scores[idx], classScores[idx], classNames[idx], boxes[idx], inputDimensions)); + return detections; + } + getDefaultModelName() { + return ""; + } + extractParamsFromWeightMap(weightMap) { + return extractParamsFromWeightMap7(weightMap, this.config); + } + extractParams(weights) { + const filterSizes = this.config.filterSizes || _TinyYolov2Base.DEFAULT_FILTER_SIZES; + const numFilters = filterSizes ? filterSizes.length : void 0; + if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) { + throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`); + } + return extractParams7(weights, this.config, this.boxEncodingSize, filterSizes); + } + async extractBoxes(outputTensor, inputBlobDimensions, scoreThreshold) { + const {width, height} = inputBlobDimensions; + const inputSize = Math.max(width, height); + const correctionFactorX = inputSize / width; + const correctionFactorY = inputSize / height; + const numCells = outputTensor.shape[1]; + const numBoxes = this.config.anchors.length; + const [boxesTensor, scoresTensor, classScoresTensor] = tfjs_esm_exports.tidy(() => { + const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize]); + const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4]); + const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1]); + const classScores = this.withClassScores ? tfjs_esm_exports.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3) : tfjs_esm_exports.scalar(0); + return [boxes, scores, classScores]; + }); + const results = []; + const scoresData = await scoresTensor.array(); + const boxesData = await boxesTensor.array(); + for (let row = 0; row < numCells; row++) { + for (let col = 0; col < numCells; col++) { + for (let anchor = 0; anchor < numBoxes; anchor++) { + const score = sigmoid(scoresData[row][col][anchor][0]); + if (!scoreThreshold || score > scoreThreshold) { + const ctX = (col + sigmoid(boxesData[row][col][anchor][0])) / numCells * correctionFactorX; + const ctY = (row + sigmoid(boxesData[row][col][anchor][1])) / numCells * correctionFactorY; + const widthLocal = Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x / numCells * correctionFactorX; + const heightLocal = Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y / numCells * correctionFactorY; + const x = ctX - widthLocal / 2; + const y = ctY - heightLocal / 2; + const pos = {row, col, anchor}; + const {classScore, label} = this.withClassScores ? await this.extractPredictedClass(classScoresTensor, pos) : {classScore: 1, label: 0}; + results.push({ + box: new BoundingBox(x, y, x + widthLocal, y + heightLocal), + score, + classScore: score * classScore, + label, + ...pos + }); + } + } + } + } + boxesTensor.dispose(); + scoresTensor.dispose(); + classScoresTensor.dispose(); + return results; + } + async extractPredictedClass(classesTensor, pos) { + const {row, col, anchor} = pos; + const classesData = await classesTensor.array(); + return Array(this.config.classes.length).fill(0).map((_, i) => classesData[row][col][anchor][i]).map((classScore, label) => ({ + classScore, + label + })).reduce((max, curr) => max.classScore > curr.classScore ? max : curr); + } +}; +var TinyYolov2Base = _TinyYolov2Base; +TinyYolov2Base.DEFAULT_FILTER_SIZES = [3, 16, 32, 64, 128, 256, 512, 1024, 1024]; + +// src/tinyYolov2/TinyYolov2.ts +var TinyYolov2 = class extends TinyYolov2Base { + constructor(withSeparableConvs = true) { + const config = { + withSeparableConvs, + iouThreshold: IOU_THRESHOLD, + classes: ["face"], + ...withSeparableConvs ? { + anchors: BOX_ANCHORS_SEPARABLE, + meanRgb: MEAN_RGB_SEPARABLE + } : { + anchors: BOX_ANCHORS, + withClassScores: true + } + }; + super(config); + } + get withSeparableConvs() { + return this.config.withSeparableConvs; + } + get anchors() { + return this.config.anchors; + } + async locateFaces(input, forwardParams) { + const objectDetections = await this.detect(input, forwardParams); + return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, {width: det.imageWidth, height: det.imageHeight})); + } + getDefaultModelName() { + return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME; + } + extractParamsFromWeightMap(weightMap) { + return super.extractParamsFromWeightMap(weightMap); + } +}; + +// src/tinyYolov2/index.ts +function createTinyYolov2(weights, withSeparableConvs = true) { + const net = new TinyYolov2(withSeparableConvs); + net.extractWeights(weights); + return net; +} + +// src/tinyFaceDetector/TinyFaceDetectorOptions.ts +var TinyFaceDetectorOptions = class extends TinyYolov2Options { + constructor() { + super(...arguments); + this._name = "TinyFaceDetectorOptions"; + } +}; + +// src/globalApi/ComposableTask.ts +var ComposableTask = class { + async then(onfulfilled) { + return onfulfilled(await this.run()); + } + async run() { + throw new Error("ComposableTask - run is not implemented"); + } +}; + +// src/globalApi/extractFacesAndComputeResults.ts +async function extractAllFacesAndComputeResults(parentResults, input, computeResults, extractedFaces, getRectForAlignment = ({alignedRect}) => alignedRect) { + const faceBoxes = parentResults.map((parentResult) => isWithFaceLandmarks(parentResult) ? getRectForAlignment(parentResult) : parentResult.detection); + const faces = extractedFaces || (input instanceof tfjs_esm_exports.Tensor ? await extractFaceTensors(input, faceBoxes) : await extractFaces(input, faceBoxes)); + const results = await computeResults(faces); + faces.forEach((f) => f instanceof tfjs_esm_exports.Tensor && f.dispose()); + return results; +} +async function extractSingleFaceAndComputeResult(parentResult, input, computeResult, extractedFaces, getRectForAlignment) { + return extractAllFacesAndComputeResults([parentResult], input, async (faces) => computeResult(faces[0]), extractedFaces, getRectForAlignment); +} + +// src/tinyFaceDetector/const.ts +var IOU_THRESHOLD2 = 0.4; +var BOX_ANCHORS2 = [ + new Point(1.603231, 2.094468), + new Point(6.041143, 7.080126), + new Point(2.882459, 3.518061), + new Point(4.266906, 5.178857), + new Point(9.041765, 10.66308) +]; +var MEAN_RGB = [117.001, 114.697, 97.404]; + +// src/tinyFaceDetector/TinyFaceDetector.ts +var TinyFaceDetector = class extends TinyYolov2Base { + constructor() { + const config = { + withSeparableConvs: true, + iouThreshold: IOU_THRESHOLD2, + classes: ["face"], + anchors: BOX_ANCHORS2, + meanRgb: MEAN_RGB, + isFirstLayerConv2d: true, + filterSizes: [3, 16, 32, 64, 128, 256, 512] + }; + super(config); + } + get anchors() { + return this.config.anchors; + } + async locateFaces(input, forwardParams) { + const objectDetections = await this.detect(input, forwardParams); + return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, {width: det.imageWidth, height: det.imageHeight})); + } + getDefaultModelName() { + return "tiny_face_detector_model"; + } + extractParamsFromWeightMap(weightMap) { + return super.extractParamsFromWeightMap(weightMap); + } +}; + +// src/globalApi/nets.ts +var nets = { + ssdMobilenetv1: new SsdMobilenetv1(), + tinyFaceDetector: new TinyFaceDetector(), + tinyYolov2: new TinyYolov2(), + faceLandmark68Net: new FaceLandmark68Net(), + faceLandmark68TinyNet: new FaceLandmark68TinyNet(), + faceRecognitionNet: new FaceRecognitionNet(), + faceExpressionNet: new FaceExpressionNet(), + ageGenderNet: new AgeGenderNet() +}; +var ssdMobilenetv1 = (input, options) => nets.ssdMobilenetv1.locateFaces(input, options); +var tinyFaceDetector = (input, options) => nets.tinyFaceDetector.locateFaces(input, options); +var tinyYolov2 = (input, options) => nets.tinyYolov2.locateFaces(input, options); +var detectFaceLandmarks = (input) => nets.faceLandmark68Net.detectLandmarks(input); +var detectFaceLandmarksTiny = (input) => nets.faceLandmark68TinyNet.detectLandmarks(input); +var computeFaceDescriptor = (input) => nets.faceRecognitionNet.computeFaceDescriptor(input); +var recognizeFaceExpressions = (input) => nets.faceExpressionNet.predictExpressions(input); +var predictAgeAndGender = (input) => nets.ageGenderNet.predictAgeAndGender(input); +var loadSsdMobilenetv1Model = (url) => nets.ssdMobilenetv1.load(url); +var loadTinyFaceDetectorModel = (url) => nets.tinyFaceDetector.load(url); +var loadTinyYolov2Model = (url) => nets.tinyYolov2.load(url); +var loadFaceLandmarkModel = (url) => nets.faceLandmark68Net.load(url); +var loadFaceLandmarkTinyModel = (url) => nets.faceLandmark68TinyNet.load(url); +var loadFaceRecognitionModel = (url) => nets.faceRecognitionNet.load(url); +var loadFaceExpressionModel = (url) => nets.faceExpressionNet.load(url); +var loadAgeGenderModel = (url) => nets.ageGenderNet.load(url); +var loadFaceDetectionModel = loadSsdMobilenetv1Model; +var locateFaces = ssdMobilenetv1; +var detectLandmarks = detectFaceLandmarks; + +// src/globalApi/PredictFaceExpressionsTask.ts +var PredictFaceExpressionsTaskBase = class extends ComposableTask { + constructor(parentTask, input, extractedFaces) { + super(); + this.parentTask = parentTask; + this.input = input; + this.extractedFaces = extractedFaces; + } +}; +var PredictAllFaceExpressionsTask = class extends PredictFaceExpressionsTaskBase { + async run() { + const parentResults = await this.parentTask; + const faceExpressionsByFace = await extractAllFacesAndComputeResults(parentResults, this.input, async (faces) => Promise.all(faces.map((face) => nets.faceExpressionNet.predictExpressions(face))), this.extractedFaces); + return parentResults.map((parentResult, i) => extendWithFaceExpressions(parentResult, faceExpressionsByFace[i])); + } + withAgeAndGender() { + return new PredictAllAgeAndGenderTask(this, this.input); + } +}; +var PredictSingleFaceExpressionsTask = class extends PredictFaceExpressionsTaskBase { + async run() { + const parentResult = await this.parentTask; + if (!parentResult) { + return void 0; + } + const faceExpressions = await extractSingleFaceAndComputeResult(parentResult, this.input, (face) => nets.faceExpressionNet.predictExpressions(face), this.extractedFaces); + return extendWithFaceExpressions(parentResult, faceExpressions); + } + withAgeAndGender() { + return new PredictSingleAgeAndGenderTask(this, this.input); + } +}; +var PredictAllFaceExpressionsWithFaceAlignmentTask = class extends PredictAllFaceExpressionsTask { + withAgeAndGender() { + return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input); + } + withFaceDescriptors() { + return new ComputeAllFaceDescriptorsTask(this, this.input); + } +}; +var PredictSingleFaceExpressionsWithFaceAlignmentTask = class extends PredictSingleFaceExpressionsTask { + withAgeAndGender() { + return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input); + } + withFaceDescriptor() { + return new ComputeSingleFaceDescriptorTask(this, this.input); + } +}; + +// src/globalApi/PredictAgeAndGenderTask.ts +var PredictAgeAndGenderTaskBase = class extends ComposableTask { + constructor(parentTask, input, extractedFaces) { + super(); + this.parentTask = parentTask; + this.input = input; + this.extractedFaces = extractedFaces; + } +}; +var PredictAllAgeAndGenderTask = class extends PredictAgeAndGenderTaskBase { + async run() { + const parentResults = await this.parentTask; + const ageAndGenderByFace = await extractAllFacesAndComputeResults(parentResults, this.input, async (faces) => Promise.all(faces.map((face) => nets.ageGenderNet.predictAgeAndGender(face))), this.extractedFaces); + return parentResults.map((parentResult, i) => { + const {age, gender, genderProbability} = ageAndGenderByFace[i]; + return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age); + }); + } + withFaceExpressions() { + return new PredictAllFaceExpressionsTask(this, this.input); + } +}; +var PredictSingleAgeAndGenderTask = class extends PredictAgeAndGenderTaskBase { + async run() { + const parentResult = await this.parentTask; + if (!parentResult) + return void 0; + const {age, gender, genderProbability} = await extractSingleFaceAndComputeResult(parentResult, this.input, (face) => nets.ageGenderNet.predictAgeAndGender(face), this.extractedFaces); + return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age); + } + withFaceExpressions() { + return new PredictSingleFaceExpressionsTask(this, this.input); + } +}; +var PredictAllAgeAndGenderWithFaceAlignmentTask = class extends PredictAllAgeAndGenderTask { + withFaceExpressions() { + return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input); + } + withFaceDescriptors() { + return new ComputeAllFaceDescriptorsTask(this, this.input); + } +}; +var PredictSingleAgeAndGenderWithFaceAlignmentTask = class extends PredictSingleAgeAndGenderTask { + withFaceExpressions() { + return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input); + } + withFaceDescriptor() { + return new ComputeSingleFaceDescriptorTask(this, this.input); + } +}; + +// src/globalApi/ComputeFaceDescriptorsTasks.ts +var ComputeFaceDescriptorsTaskBase = class extends ComposableTask { + constructor(parentTask, input) { + super(); + this.parentTask = parentTask; + this.input = input; + } +}; +var ComputeAllFaceDescriptorsTask = class extends ComputeFaceDescriptorsTaskBase { + async run() { + const parentResults = await this.parentTask; + const descriptors = await extractAllFacesAndComputeResults(parentResults, this.input, (faces) => Promise.all(faces.map((face) => nets.faceRecognitionNet.computeFaceDescriptor(face))), null, (parentResult) => parentResult.landmarks.align(null, {useDlibAlignment: true})); + return descriptors.map((descriptor, i) => extendWithFaceDescriptor(parentResults[i], descriptor)); + } + withFaceExpressions() { + return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input); + } + withAgeAndGender() { + return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input); + } +}; +var ComputeSingleFaceDescriptorTask = class extends ComputeFaceDescriptorsTaskBase { + async run() { + const parentResult = await this.parentTask; + if (!parentResult) { + return void 0; + } + const descriptor = await extractSingleFaceAndComputeResult(parentResult, this.input, (face) => nets.faceRecognitionNet.computeFaceDescriptor(face), null, (parentResult2) => parentResult2.landmarks.align(null, {useDlibAlignment: true})); + return extendWithFaceDescriptor(parentResult, descriptor); + } + withFaceExpressions() { + return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input); + } + withAgeAndGender() { + return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input); + } +}; + +// src/globalApi/DetectFaceLandmarksTasks.ts +var DetectFaceLandmarksTaskBase = class extends ComposableTask { + constructor(parentTask, input, useTinyLandmarkNet) { + super(); + this.parentTask = parentTask; + this.input = input; + this.useTinyLandmarkNet = useTinyLandmarkNet; + } + get landmarkNet() { + return this.useTinyLandmarkNet ? nets.faceLandmark68TinyNet : nets.faceLandmark68Net; + } +}; +var DetectAllFaceLandmarksTask = class extends DetectFaceLandmarksTaskBase { + async run() { + const parentResults = await this.parentTask; + const detections = parentResults.map((res) => res.detection); + const faces = this.input instanceof tfjs_esm_exports.Tensor ? await extractFaceTensors(this.input, detections) : await extractFaces(this.input, detections); + const faceLandmarksByFace = await Promise.all(faces.map((face) => this.landmarkNet.detectLandmarks(face))); + faces.forEach((f) => f instanceof tfjs_esm_exports.Tensor && f.dispose()); + return parentResults.map((parentResult, i) => extendWithFaceLandmarks(parentResult, faceLandmarksByFace[i])); + } + withFaceExpressions() { + return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input); + } + withAgeAndGender() { + return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input); + } + withFaceDescriptors() { + return new ComputeAllFaceDescriptorsTask(this, this.input); + } +}; +var DetectSingleFaceLandmarksTask = class extends DetectFaceLandmarksTaskBase { + async run() { + const parentResult = await this.parentTask; + if (!parentResult) { + return void 0; + } + const {detection} = parentResult; + const faces = this.input instanceof tfjs_esm_exports.Tensor ? await extractFaceTensors(this.input, [detection]) : await extractFaces(this.input, [detection]); + const landmarks = await this.landmarkNet.detectLandmarks(faces[0]); + faces.forEach((f) => f instanceof tfjs_esm_exports.Tensor && f.dispose()); + return extendWithFaceLandmarks(parentResult, landmarks); + } + withFaceExpressions() { + return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input); + } + withAgeAndGender() { + return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input); + } + withFaceDescriptor() { + return new ComputeSingleFaceDescriptorTask(this, this.input); + } +}; + +// src/globalApi/DetectFacesTasks.ts +var DetectFacesTaskBase = class extends ComposableTask { + constructor(input, options = new SsdMobilenetv1Options()) { + super(); + this.input = input; + this.options = options; + } +}; +var DetectAllFacesTask = class extends DetectFacesTaskBase { + async run() { + const {input, options} = this; + let result; + if (options instanceof TinyFaceDetectorOptions) + result = nets.tinyFaceDetector.locateFaces(input, options); + else if (options instanceof SsdMobilenetv1Options) + result = nets.ssdMobilenetv1.locateFaces(input, options); + else if (options instanceof TinyYolov2Options) + result = nets.tinyYolov2.locateFaces(input, options); + else + throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options"); + return result; + } + runAndExtendWithFaceDetections() { + return new Promise((resolve, reject) => { + this.run().then((detections) => resolve(detections.map((detection) => extendWithFaceDetection({}, detection)))).catch((err) => reject(err)); + }); + } + withFaceLandmarks(useTinyLandmarkNet = false) { + return new DetectAllFaceLandmarksTask(this.runAndExtendWithFaceDetections(), this.input, useTinyLandmarkNet); + } + withFaceExpressions() { + return new PredictAllFaceExpressionsTask(this.runAndExtendWithFaceDetections(), this.input); + } + withAgeAndGender() { + return new PredictAllAgeAndGenderTask(this.runAndExtendWithFaceDetections(), this.input); + } +}; +var DetectSingleFaceTask = class extends DetectFacesTaskBase { + async run() { + const faceDetections = await new DetectAllFacesTask(this.input, this.options); + let faceDetectionWithHighestScore = faceDetections[0]; + faceDetections.forEach((faceDetection) => { + if (faceDetection.score > faceDetectionWithHighestScore.score) + faceDetectionWithHighestScore = faceDetection; + }); + return faceDetectionWithHighestScore; + } + runAndExtendWithFaceDetection() { + return new Promise(async (resolve) => { + const detection = await this.run(); + resolve(detection ? extendWithFaceDetection({}, detection) : void 0); + }); + } + withFaceLandmarks(useTinyLandmarkNet = false) { + return new DetectSingleFaceLandmarksTask(this.runAndExtendWithFaceDetection(), this.input, useTinyLandmarkNet); + } + withFaceExpressions() { + return new PredictSingleFaceExpressionsTask(this.runAndExtendWithFaceDetection(), this.input); + } + withAgeAndGender() { + return new PredictSingleAgeAndGenderTask(this.runAndExtendWithFaceDetection(), this.input); + } +}; + +// src/globalApi/detectFaces.ts +function detectSingleFace(input, options = new SsdMobilenetv1Options()) { + return new DetectSingleFaceTask(input, options); +} +function detectAllFaces(input, options = new SsdMobilenetv1Options()) { + return new DetectAllFacesTask(input, options); +} + +// src/globalApi/allFaces.ts +async function allFacesSsdMobilenetv1(input, minConfidence) { + return detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? {minConfidence} : {})).withFaceLandmarks().withFaceDescriptors(); +} +async function allFacesTinyYolov2(input, forwardParams = {}) { + return detectAllFaces(input, new TinyYolov2Options(forwardParams)).withFaceLandmarks().withFaceDescriptors(); +} +var allFaces = allFacesSsdMobilenetv1; + +// src/euclideanDistance.ts +function euclideanDistance(arr1, arr2) { + if (arr1.length !== arr2.length) + throw new Error("euclideanDistance: arr1.length !== arr2.length"); + const desc1 = Array.from(arr1); + const desc2 = Array.from(arr2); + return Math.sqrt(desc1.map((val, i) => val - desc2[i]).reduce((res, diff) => res + diff ** 2, 0)); +} + +// src/globalApi/FaceMatcher.ts +var FaceMatcher = class { + constructor(inputs, distanceThreshold = 0.6) { + this._distanceThreshold = distanceThreshold; + const inputArray = Array.isArray(inputs) ? inputs : [inputs]; + if (!inputArray.length) { + throw new Error("FaceRecognizer.constructor - expected atleast one input"); + } + let count = 1; + const createUniqueLabel = () => `person ${count++}`; + this._labeledDescriptors = inputArray.map((desc) => { + if (desc instanceof LabeledFaceDescriptors) { + return desc; + } + if (desc instanceof Float32Array) { + return new LabeledFaceDescriptors(createUniqueLabel(), [desc]); + } + if (desc.descriptor && desc.descriptor instanceof Float32Array) { + return new LabeledFaceDescriptors(createUniqueLabel(), [desc.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(queryDescriptor, descriptors) { + return descriptors.map((d) => euclideanDistance(d, queryDescriptor)).reduce((d1, d2) => d1 + d2, 0) / (descriptors.length || 1); + } + matchDescriptor(queryDescriptor) { + return this.labeledDescriptors.map(({descriptors, label}) => new FaceMatch(label, this.computeMeanDistance(queryDescriptor, descriptors))).reduce((best, curr) => best.distance < curr.distance ? best : curr); + } + findBestMatch(queryDescriptor) { + const bestMatch = this.matchDescriptor(queryDescriptor); + return bestMatch.distance < this.distanceThreshold ? bestMatch : new FaceMatch("unknown", bestMatch.distance); + } + toJSON() { + return { + distanceThreshold: this.distanceThreshold, + labeledDescriptors: this.labeledDescriptors.map((ld) => ld.toJSON()) + }; + } + static fromJSON(json) { + const labeledDescriptors = json.labeledDescriptors.map((ld) => LabeledFaceDescriptors.fromJSON(ld)); + return new FaceMatcher(labeledDescriptors, json.distanceThreshold); + } +}; + +// src/tinyFaceDetector/index.ts +function createTinyFaceDetector(weights) { + const net = new TinyFaceDetector(); + net.extractWeights(weights); + return net; +} + +// src/resizeResults.ts +function resizeResults(results, dimensions) { + const {width, height} = new Dimensions(dimensions.width, dimensions.height); + if (width <= 0 || height <= 0) { + throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width, height})}`); + } + if (Array.isArray(results)) { + return results.map((obj) => resizeResults(obj, {width, height})); + } + if (isWithFaceLandmarks(results)) { + const resizedDetection = results.detection.forSize(width, height); + const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height); + return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks); + } + if (isWithFaceDetection(results)) { + return extendWithFaceDetection(results, results.detection.forSize(width, height)); + } + if (results instanceof FaceLandmarks || results instanceof FaceDetection) { + return results.forSize(width, height); + } + return results; +} + +// src/index.ts +var node = typeof process !== "undefined"; +var browser3 = typeof navigator !== "undefined" && typeof navigator.userAgent !== "undefined"; +var version2 = {faceapi: version, node, browser: browser3}; +export { + 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_exports as draw, + env, + euclideanDistance, + extendWithAge, + extendWithFaceDescriptor, + extendWithFaceDetection, + extendWithFaceExpressions, + extendWithFaceLandmarks, + extendWithGender, + extractFaceTensors, + extractFaces, + fetchImage, + fetchJson, + fetchNetWeights, + fetchOrThrow, + 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, + tfjs_esm_exports as tf, + tinyFaceDetector, + tinyYolov2, + toNetInput, + utils_exports as utils, + validateConfig, + version2 as version +}; //# sourceMappingURL=face-api.esm-nobundle.js.map diff --git a/dist/face-api.esm-nobundle.js.map b/dist/face-api.esm-nobundle.js.map index 0eda986..8bc44d6 100644 --- a/dist/face-api.esm-nobundle.js.map +++ b/dist/face-api.esm-nobundle.js.map @@ -1,7 +1,7 @@ { "version": 3, "sources": ["../src/tfjs/tf-browser.ts", "../src/draw/index.ts", "../src/draw/drawContour.ts", "../src/utils/index.ts", "../src/classes/Dimensions.ts", "../src/classes/Point.ts", "../src/classes/Box.ts", "../src/classes/BoundingBox.ts", "../src/classes/ObjectDetection.ts", "../src/classes/FaceDetection.ts", "../src/ops/iou.ts", "../src/ops/minBbox.ts", "../src/ops/nonMaxSuppression.ts", "../src/ops/normalize.ts", "../src/ops/padToSquare.ts", "../src/ops/shuffleArray.ts", "../src/ops/index.ts", "../src/classes/Rect.ts", "../src/classes/FaceLandmarks.ts", "../src/classes/FaceLandmarks5.ts", "../src/classes/FaceLandmarks68.ts", "../src/classes/FaceMatch.ts", "../src/classes/LabeledBox.ts", "../src/classes/LabeledFaceDescriptors.ts", "../src/classes/PredictedBox.ts", "../src/factories/WithFaceDetection.ts", "../src/env/createBrowserEnv.ts", "../src/env/createFileSystem.ts", "../src/env/createNodejsEnv.ts", "../src/env/isBrowser.ts", "../src/env/isNodejs.ts", "../src/env/index.ts", "../src/dom/resolveInput.ts", "../src/dom/getContext2dOrThrow.ts", "../src/draw/DrawTextField.ts", "../src/draw/DrawBox.ts", "../src/draw/drawDetections.ts", "../src/dom/isMediaLoaded.ts", "../src/dom/awaitMediaLoaded.ts", "../src/dom/bufferToImage.ts", "../src/dom/getMediaDimensions.ts", "../src/dom/createCanvas.ts", "../src/dom/imageTensorToCanvas.ts", "../src/dom/isMediaElement.ts", "../src/dom/imageToSquare.ts", "../src/dom/NetInput.ts", "../src/dom/toNetInput.ts", "../src/dom/extractFaces.ts", "../src/dom/extractFaceTensors.ts", "../src/dom/fetchOrThrow.ts", "../src/dom/fetchImage.ts", "../src/dom/fetchJson.ts", "../src/dom/fetchNetWeights.ts", "../src/common/getModelUris.ts", "../src/dom/loadWeightMap.ts", "../src/dom/matchDimensions.ts", "../src/NeuralNetwork.ts", "../src/common/depthwiseSeparableConv.ts", "../src/faceFeatureExtractor/denseBlock.ts", "../src/common/convLayer.ts", "../src/common/disposeUnusedWeightTensors.ts", "../src/common/extractConvParamsFactory.ts", "../src/common/extractFCParamsFactory.ts", "../src/common/types.ts", "../src/common/extractSeparableConvParamsFactory.ts", "../src/common/extractWeightEntryFactory.ts", "../src/common/extractWeightsFactory.ts", "../src/faceFeatureExtractor/extractorsFactory.ts", "../src/faceFeatureExtractor/extractParams.ts", "../src/common/loadConvParamsFactory.ts", "../src/faceFeatureExtractor/loadParamsFactory.ts", "../src/faceFeatureExtractor/extractParamsFromWeightMap.ts", "../src/faceFeatureExtractor/FaceFeatureExtractor.ts", "../src/common/fullyConnectedLayer.ts", "../src/faceProcessor/extractParams.ts", "../src/faceProcessor/extractParamsFromWeightMap.ts", "../src/faceProcessor/util.ts", "../src/faceProcessor/FaceProcessor.ts", "../src/faceExpressionNet/FaceExpressions.ts", "../src/faceExpressionNet/FaceExpressionNet.ts", "../src/factories/WithFaceExpressions.ts", "../src/draw/drawFaceExpressions.ts", "../src/factories/WithFaceLandmarks.ts", "../src/draw/DrawFaceLandmarks.ts", "../src/xception/extractParams.ts", "../src/xception/extractParamsFromWeightMap.ts", "../src/xception/TinyXception.ts", "../src/ageGenderNet/extractParams.ts", "../src/ageGenderNet/extractParamsFromWeightMap.ts", "../src/ageGenderNet/types.ts", "../src/ageGenderNet/AgeGenderNet.ts", "../src/faceLandmarkNet/FaceLandmark68NetBase.ts", "../src/faceLandmarkNet/FaceLandmark68Net.ts", "../src/faceFeatureExtractor/extractParamsFromWeightMapTiny.ts", "../src/faceFeatureExtractor/extractParamsTiny.ts", "../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts", "../src/faceLandmarkNet/FaceLandmark68TinyNet.ts", "../src/faceLandmarkNet/index.ts", "../src/faceRecognitionNet/scaleLayer.ts", "../src/faceRecognitionNet/convLayer.ts", "../src/faceRecognitionNet/extractParams.ts", "../src/faceRecognitionNet/extractParamsFromWeightMap.ts", "../src/faceRecognitionNet/residualLayer.ts", "../src/faceRecognitionNet/FaceRecognitionNet.ts", "../src/faceRecognitionNet/index.ts", "../src/factories/WithFaceDescriptor.ts", "../src/factories/WithAge.ts", "../src/factories/WithGender.ts", "../src/ssdMobilenetv1/extractParams.ts", "../src/ssdMobilenetv1/extractParamsFromWeightMap.ts", "../src/ssdMobilenetv1/pointwiseConvLayer.ts", "../src/ssdMobilenetv1/mobileNetV1.ts", "../src/ssdMobilenetv1/nonMaxSuppression.ts", "../src/ssdMobilenetv1/outputLayer.ts", "../src/ssdMobilenetv1/boxPredictionLayer.ts", "../src/ssdMobilenetv1/predictionLayer.ts", "../src/ssdMobilenetv1/SsdMobilenetv1Options.ts", "../src/ssdMobilenetv1/SsdMobilenetv1.ts", "../src/ssdMobilenetv1/index.ts", "../src/tinyYolov2/const.ts", "../src/tinyYolov2/config.ts", "../src/tinyYolov2/leaky.ts", "../src/tinyYolov2/convWithBatchNorm.ts", "../src/tinyYolov2/depthwiseSeparableConv.ts", "../src/tinyYolov2/extractParams.ts", "../src/tinyYolov2/extractParamsFromWeightMap.ts", "../src/tinyYolov2/TinyYolov2Options.ts", "../src/tinyYolov2/TinyYolov2Base.ts", "../src/tinyYolov2/TinyYolov2.ts", "../src/tinyYolov2/index.ts", "../src/tinyFaceDetector/TinyFaceDetectorOptions.ts", "../src/globalApi/ComposableTask.ts", "../src/globalApi/extractFacesAndComputeResults.ts", "../src/tinyFaceDetector/const.ts", "../src/tinyFaceDetector/TinyFaceDetector.ts", "../src/globalApi/nets.ts", "../src/globalApi/PredictFaceExpressionsTask.ts", "../src/globalApi/PredictAgeAndGenderTask.ts", "../src/globalApi/ComputeFaceDescriptorsTasks.ts", "../src/globalApi/DetectFaceLandmarksTasks.ts", "../src/globalApi/DetectFacesTasks.ts", "../src/globalApi/detectFaces.ts", "../src/globalApi/allFaces.ts", "../src/euclideanDistance.ts", "../src/globalApi/FaceMatcher.ts", "../src/tinyFaceDetector/index.ts", "../src/resizeResults.ts", "../src/index.ts"], - "sourcesContent": ["/* eslint-disable import/no-extraneous-dependencies */\n/* eslint-disable node/no-unpublished-import */\n\n// wrapper to load tfjs in a single place so version can be changed quickly\n\nexport * from '@tensorflow/tfjs/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-wasm';\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: boolean = 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: number = 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 ${JSON.stringify({ width, height })}`);\n }\n\n this._width = width;\n this._height = height;\n }\n\n public get width(): number { return this._width; }\n\n public get height(): number { return this._height; }\n\n public reverse(): Dimensions {\n return new Dimensions(1 / this.width, 1 / this.height);\n }\n}\n", "export interface IPoint {\n x: number\n y: number\n}\n\nexport class Point implements IPoint {\n private _x: number\n\n private _y: number\n\n constructor(x: number, y: number) {\n this._x = x;\n this._y = y;\n }\n\n get x(): number { return this._x; }\n\n get y(): number { return this._y; }\n\n public add(pt: IPoint): Point {\n return new Point(this.x + pt.x, this.y + pt.y);\n }\n\n public sub(pt: IPoint): Point {\n return new Point(this.x - pt.x, this.y - pt.y);\n }\n\n public mul(pt: IPoint): Point {\n return new Point(this.x * pt.x, this.y * pt.y);\n }\n\n public div(pt: IPoint): Point {\n return new Point(this.x / pt.x, this.y / pt.y);\n }\n\n public abs(): Point {\n return new Point(Math.abs(this.x), Math.abs(this.y));\n }\n\n public magnitude(): number {\n return Math.sqrt((this.x ** 2) + (this.y ** 2));\n }\n\n public floor(): Point {\n return new Point(Math.floor(this.x), Math.floor(this.y));\n }\n}\n", "import { isDimensions, isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box implements IBoundingBox, IRect {\n public static isRect(rect: any): boolean {\n return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber);\n }\n\n public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions: boolean = false) {\n if (!Box.isRect(box)) {\n throw new Error(`${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: boolean = 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({\n x, y, width, height,\n });\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({\n x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight,\n })).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({\n x, y, width, height,\n });\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 {\n dy, edy, dx, edx, y, ey, x, ex, w, h,\n };\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: boolean = false) {\n super({\n left, top, right, bottom,\n }, 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 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: boolean = 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: boolean = 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(\n imgTensor: tf.Tensor4D,\n isCenterImage: boolean = false,\n): tf.Tensor4D {\n return tf.tidy(() => {\n const [height, width] = imgTensor.shape.slice(1);\n if (height === width) {\n return imgTensor;\n }\n\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\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\n const paddingTensorAppend = createPaddingTensor(paddingAmount);\n const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number);\n\n const paddingTensorPrepend = isCenterImage && remainingPaddingAmount\n ? createPaddingTensor(remainingPaddingAmount)\n : null;\n\n const tensorsToStack = [\n paddingTensorPrepend,\n imgTensor,\n paddingTensorAppend,\n ]\n .filter((t) => !!t)\n .map((t: tf.Tensor) => tf.cast(t, '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: boolean = false) {\n super({\n x, y, width, height,\n }, 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 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 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\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: boolean = 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\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 fetch,\n readFile,\n };\n}\n", "import { FileSystem } from './types';\n\nexport function createFileSystem(fs?: any): FileSystem {\n let requireFsError = '';\n\n if (!fs) {\n try {\n // eslint-disable-next-line global-require\n fs = require('fs');\n } catch (err) {\n requireFsError = err.toString();\n }\n }\n\n const readFile = fs\n ? (filePath: string) => new Promise((resolve, reject) => {\n fs.readFile(filePath, (err: any, buffer: Buffer) => (err ? reject(err) : resolve(buffer)));\n })\n : () => {\n throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`);\n };\n\n return {\n readFile,\n };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n // eslint-disable-next-line dot-notation\n const Canvas = global['Canvas'] || global.HTMLCanvasElement;\n const Image = global.Image || global.HTMLImageElement;\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 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 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", "export function isNodejs(): boolean {\n return typeof global === 'object'\n && typeof require === 'function'\n && typeof module !== 'undefined'\n && typeof process !== 'undefined' && !!process.version;\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\n if (canvasArg instanceof CanvasRenderingContext2D) {\n return canvasArg;\n }\n\n const canvas = resolveInput(canvasArg);\n\n if (!(canvas instanceof Canvas)) {\n throw new Error('resolveContext2d - expected canvas to be of instance of Canvas');\n }\n\n const ctx = canvas.getContext('2d');\n if (!ctx) {\n throw new Error('resolveContext2d - canvas 2d context is null');\n }\n\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)) return resolve(null);\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: boolean = 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: boolean = false\n\n private _inputDimensions: number[][] = []\n\n private _inputSize: number\n\n constructor(inputs: Array, treatAsBatchInput: boolean = 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 const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input);\n this._canvases[idx] = canvas;\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: boolean = 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, isCenterInputs);\n\n if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n imgTensor = tf.image.resizeBilinear(imgTensor, [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\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\n const boxes = detections\n .map((det) => (det instanceof FaceDetection\n ? det.forSize(imgWidth, imgHeight).box\n : det))\n .map((box) => box.clipAtImageBorders(imgWidth, imgHeight));\n\n const faceTensors = boxes.map(({\n x, y, width, height,\n }) => tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]));\n\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: ${res.url}`);\n }\n return bufferToImage(blob);\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson(uri: string): Promise {\n return (await fetchOrThrow(uri)).json();\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise {\n return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer());\n}\n", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n const defaultManifestFilename = `${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 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: boolean = 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: boolean = 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) => buf.buffer)));\n const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk);\n const manifest = JSON.parse((await readFile(manifestUri)).toString());\n const weightMap = await loadWeights(manifest, modelBaseUri);\n this.loadFromWeightMap(weightMap);\n }\n\n public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n const { paramMappings, params } = this.extractParamsFromWeightMap(weightMap);\n this._paramMappings = paramMappings;\n this._params = params;\n }\n\n public extractWeights(weights: Float32Array) {\n const { paramMappings, params } = this.extractParams(weights);\n this._paramMappings = paramMappings;\n this._params = params;\n }\n\n private traversePropertyPath(paramPath: string) {\n if (!this.params) {\n throw new Error('traversePropertyPath - model has no loaded params');\n }\n\n const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n // eslint-disable-next-line no-prototype-builtins\n if (!res.nextObj.hasOwnProperty(objProp)) {\n throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`);\n }\n return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] };\n }, { nextObj: this.params });\n\n const { obj, objProp } = result;\n if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);\n }\n\n return { obj, objProp };\n }\n\n protected abstract getDefaultModelName(): string\n\n // eslint-disable-next-line no-unused-vars\n protected abstract extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n\n // eslint-disable-next-line no-unused-vars\n protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n x: tf.Tensor4D,\n params: SeparableConvParams,\n stride: [number, number],\n): tf.Tensor4D {\n return tf.tidy(() => {\n let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same');\n out = tf.add(out, params.bias);\n return out;\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, SeparableConvParams } from '../common/index';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n x: tf.Tensor4D,\n denseBlockParams: DenseBlock3Params,\n isFirstLayer: boolean = 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: boolean = false,\n isScaleDown: boolean = 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: boolean = 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 ${tensor}`);\n }\n\n paramMappings.push(\n { originalPath, paramPath: mappedPath || originalPath },\n );\n\n return tensor;\n };\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n let remainingWeights = weights;\n\n function extractWeights(numWeights: number): Float32Array {\n const ret = remainingWeights.slice(0, numWeights);\n remainingWeights = remainingWeights.slice(numWeights);\n return ret;\n }\n\n function getRemainingWeights(): Float32Array {\n return remainingWeights;\n }\n\n return {\n extractWeights,\n getRemainingWeights,\n };\n}\n", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, ExtractWeightsFunction, ParamMapping } from '../common/index';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n const conv0 = isFirstLayer\n ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`);\n const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`);\n const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`);\n\n return { conv0, conv1, conv2 };\n }\n\n function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer);\n const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${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(`${prefix}/bias`, 1);\n\n return { filters, bias };\n };\n}\n", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n function extractDenseBlock3Params(prefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n const conv0 = isFirstLayer\n ? extractConvParams(`${prefix}/conv0`)\n : extractSeparableConvParams(`${prefix}/conv0`);\n const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n\n return { conv0, conv1, conv2 };\n }\n\n function extractDenseBlock4Params(prefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n const conv0 = isFirstLayer\n ? extractConvParams(`${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(`${prefix}/bias`, 1);\n return { weights, bias };\n }\n\n const params = {\n fc: extractFcParams('fc'),\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n const featureExtractorMap: tf.NamedTensorMap = {};\n const classifierMap: tf.NamedTensorMap = {};\n\n Object.keys(weightMap).forEach((key) => {\n const map = key.startsWith('fc') ? classifierMap : featureExtractorMap;\n map[key] = weightMap[key];\n });\n\n return { featureExtractorMap, classifierMap };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom/index';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n extends NeuralNetwork {\n protected _faceFeatureExtractor: IFaceFeatureExtractor\n\n constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor) {\n super(_name);\n this._faceFeatureExtractor = faceFeatureExtractor;\n }\n\n public get faceFeatureExtractor(): IFaceFeatureExtractor {\n return this._faceFeatureExtractor;\n }\n\n protected abstract getDefaultModelName(): string\n\n protected abstract getClassifierChannelsIn(): number\n\n protected abstract getClassifierChannelsOut(): number\n\n public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n const { params } = this;\n\n if (!params) {\n throw new Error(`${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 dispose(throwOnRedispose: boolean = 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'];\n\nexport class FaceExpressions {\n public neutral: number\n\n public happy: number\n\n public sad: number\n\n public angry: number\n\n public fearful: number\n\n public disgusted: number\n\n public surprised: number\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 & {\n expressions: FaceExpressions\n}\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n return obj.expressions instanceof FaceExpressions;\n}\n\nexport function extendWithFaceExpressions<\n TSource\n>(\n sourceObj: TSource,\n expressions: FaceExpressions,\n): 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(\n canvasArg: string | HTMLCanvasElement,\n faceExpressions: DrawFaceExpressionsInput | Array,\n minConfidence = 0.1,\n textFieldAnchor?: IPoint,\n) {\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 { 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 > = TSource & {\n landmarks: TFaceLandmarks,\n unshiftedLandmarks: TFaceLandmarks,\n alignedRect: FaceDetection,\n angle: { roll: number | undefined, pitch: number | undefined, yaw: number | undefined },\n }\n\nexport function isWithFaceLandmarks(obj: any): obj is WithFaceLandmarks, FaceLandmarks> {\n return isWithFaceDetection(obj)\n // eslint-disable-next-line dot-notation\n && obj['landmarks'] instanceof FaceLandmarks\n // eslint-disable-next-line dot-notation\n && obj['unshiftedLandmarks'] instanceof FaceLandmarks\n // eslint-disable-next-line dot-notation\n && obj['alignedRect'] instanceof FaceDetection;\n}\n\nfunction calculateFaceAngle(mesh) {\n // returns the angle in the plane (in radians) between the positive x-axis and the ray from (0,0) to the point (x,y)\n const radians = (a1, a2, b1, b2) => (Math.atan2(b2 - a2, b1 - a1) % Math.PI);\n // convert radians to degrees\n // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n const degrees = (theta) => (theta * 180) / Math.PI;\n\n const angle = { roll: undefined, pitch: undefined, yaw: undefined };\n\n if (!mesh || !mesh._positions || mesh._positions.length !== 68) return angle;\n const pt = mesh._positions;\n\n // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees\n // value of 0 means center\n\n // roll is face lean from left to right\n // comparing x,y of outside corners of leftEye and rightEye\n angle.roll = -radians(pt[36]._x, pt[36]._y, pt[45]._x, pt[45]._y);\n\n // pitch is face turn from left right\n // comparing x distance of top of nose to left and right edge of face\n // precision is lacking since coordinates are not precise enough\n angle.pitch = radians(0, Math.abs(pt[0]._x - pt[30]._x) / pt[30]._x, Math.PI, Math.abs(pt[16]._x - pt[30]._x) / pt[30]._x);\n\n // yaw is face move from up to down\n // comparing size of the box around the face with top and bottom of detected landmarks\n // silly hack, but this gives us face compression on y-axis\n // e.g., tilting head up hides the forehead that doesn't have any landmarks so ratio drops\n const bottom = pt.reduce((prev, cur) => (prev < cur._y ? prev : cur._y), +Infinity);\n const top = pt.reduce((prev, cur) => (prev > cur._y ? prev : cur._y), -Infinity);\n angle.yaw = Math.PI * (mesh._imgDims._height / (top - bottom) / 1.40 - 1);\n\n return angle;\n}\n\nexport function extendWithFaceLandmarks<\n TSource extends WithFaceDetection<{}>,\n TFaceLandmarks extends FaceLandmarks = FaceLandmarks68 >(sourceObj: TSource, unshiftedLandmarks: TFaceLandmarks): WithFaceLandmarks {\n const { box: shift } = sourceObj.detection;\n const landmarks = unshiftedLandmarks.shiftBy(shift.x, shift.y);\n\n const rect = landmarks.align();\n const { imageDims } = sourceObj.detection;\n const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims);\n const angle = calculateFaceAngle(unshiftedLandmarks);\n\n const extension = {\n landmarks,\n unshiftedLandmarks,\n alignedRect,\n angle,\n };\n\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", "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, `${mappedPrefix}/separable_conv2`);\n\n return { separable_conv0, separable_conv1, separable_conv2 };\n }\n\n return {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n };\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n } = extractorsFactory(extractWeights, paramMappings);\n\n const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in');\n const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0');\n const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1');\n\n const entry_flow = {\n conv_in: entry_flow_conv_in,\n reduction_block_0: entry_flow_reduction_block_0,\n reduction_block_1: entry_flow_reduction_block_1,\n };\n\n const middle_flow = {};\n range(numMainBlocks, 0, 1).forEach((idx) => {\n middle_flow[`main_block_${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(`${mappedPrefix}/separable_conv2`);\n\n return { separable_conv0, separable_conv1, separable_conv2 };\n }\n\n return {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n };\n}\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n numMainBlocks: number,\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n } = loadParamsFactory(weightMap, paramMappings);\n\n const entry_flow_conv_in = extractConvParams('entry_flow/conv_in');\n const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0');\n const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1');\n\n const entry_flow = {\n conv_in: entry_flow_conv_in,\n reduction_block_0: entry_flow_reduction_block_0,\n reduction_block_1: entry_flow_reduction_block_1,\n };\n\n const middle_flow = {};\n range(numMainBlocks, 0, 1).forEach((idx) => {\n middle_flow[`main_block_${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: boolean = 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(`${prefix}/bias`, 1);\n return { weights, bias };\n }\n\n const params = {\n fc: {\n age: extractFcParams('fc/age'),\n gender: extractFcParams('fc/gender'),\n },\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from '../common/index';\n\n// eslint-disable-next-line no-shadow\nexport enum Gender {\n // eslint-disable-next-line no-unused-vars\n FEMALE = 'female',\n // eslint-disable-next-line no-unused-vars\n MALE = 'male'\n}\n\nexport type AgeAndGenderPrediction = {\n age: number\n gender: Gender\n genderProbability: number\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n fc: {\n age: FCParams\n gender: FCParams\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { AgeAndGenderPrediction, Gender, NetOutput, NetParams } from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\n\nexport class AgeGenderNet extends NeuralNetwork {\n private _faceFeatureExtractor: TinyXception\n\n constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n super('AgeGenderNet');\n this._faceFeatureExtractor = faceFeatureExtractor;\n }\n\n public get faceFeatureExtractor(): TinyXception {\n return this._faceFeatureExtractor;\n }\n\n public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n const { params } = this;\n\n if (!params) {\n throw new Error(`${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 dispose(throwOnRedispose: boolean = 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, `${mappedPrefix}/scale`);\n\n return { conv, scale };\n }\n\n function extractResidualLayerParams(\n numFilterValues: number,\n numFilters: number,\n filterSize: number,\n mappedPrefix: string,\n isDown: boolean = false,\n ): ResidualLayerParams {\n const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${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;\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 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 /*\n if (x.shape[1] === 512 && x.shape[3] === 3) {\n console.log('Input:', x.shape, x.size, 'sum:', x.reshape([786432]).sum().dataSync()[0]); // input does not change (checked values)\n console.log('Filter:', params.filters.shape, params.filters.size, 'sum:', params.filters.reshape([864]).sum().dataSync()[0]); // params do not change (checked values)\n console.log('Strides', strides);\n console.log('Conv2d 1st 5 values:', out.shape, out.size, out.dataSync().slice(0, 5)); // output has different values!\n console.log('Conv2D sum of all values:', tf.reshape(out, [2097152]).sum().dataSync()[0]); // silly sum just to see how much results diverged\n }\n */\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: string = '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\n if (!params) {\n throw new Error('SsdMobilenetv1 - load model before inference');\n }\n\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\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\n const { boxes: _boxes, scores: _scores } = this.forwardInput(netInput);\n\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\n const scoresData = Array.from(scores.dataSync());\n const iouThreshold = 0.5;\n const indices = nonMaxSuppression(boxes, scoresData as number[], maxResults, iouThreshold, minConfidence);\n\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\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\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: string = '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, 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 = [] as any;\n const scoresData = await scoresTensor.array();\n const boxesData = await boxesTensor.array();\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: boolean = 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 getDefaultModelName(): string {\n return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME;\n }\n\n protected 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: boolean = true) {\n const net = new TinyYolov2(withSeparableConvs);\n net.extractWeights(weights);\n return net;\n}\n", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\n\nexport interface ITinyFaceDetectorOptions extends ITinyYolov2Options {}\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n protected _name: string = 'TinyFaceDetectorOptions'\n}\n", "export class ComposableTask {\n public async then(\n // eslint-disable-next-line no-unused-vars\n onfulfilled: (value: T) => T | PromiseLike,\n ): 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\n const faces: Array = extractedFaces || (\n input instanceof tf.Tensor\n ? await extractFaceTensors(input, faceBoxes)\n : await extractFaces(input, faceBoxes)\n );\n\n const results = await computeResults(faces);\n\n faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\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 getDefaultModelName(): string {\n return 'tiny_face_detector_model';\n }\n\n protected 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<\n TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase[], TSource[]> {\n public 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(faces.map(\n (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<\n TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase | undefined, TSource | undefined> {\n public 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<\n TSource extends WithFaceLandmarks>\n> extends PredictAllFaceExpressionsTask {\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 PredictSingleFaceExpressionsWithFaceAlignmentTask<\n TSource extends WithFaceLandmarks>\n> extends PredictSingleFaceExpressionsTask {\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 * 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<\n TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase>[], TSource[]> {\n public async run(): Promise>[]> {\n const parentResults = await this.parentTask;\n\n const ageAndGenderByFace = await extractAllFacesAndComputeResults(\n parentResults,\n this.input,\n async (faces) => Promise.all(faces.map(\n (face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise,\n )),\n this.extractedFaces,\n );\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<\n TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase> | undefined, TSource | undefined> {\n public async run(): Promise> | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) {\n return undefined;\n }\n\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\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<\n TSource extends WithFaceLandmarks>\n> extends PredictAllAgeAndGenderTask {\n 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<\n TSource extends WithFaceLandmarks>\n> extends PredictSingleAgeAndGenderTask {\n 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<\n TSource extends WithFaceLandmarks>\n> extends ComputeFaceDescriptorsTaskBase[], TSource[]> {\n public async run(): Promise[]> {\n const parentResults = await this.parentTask;\n\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\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<\n TSource extends WithFaceLandmarks>\n> extends ComputeFaceDescriptorsTaskBase | undefined, TSource | undefined> {\n public async run(): Promise | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) {\n return undefined;\n }\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\n (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n );\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<\n TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase[], TSource[]> {\n public async run(): Promise[]> {\n const parentResults = await this.parentTask;\n const detections = parentResults.map((res) => res.detection);\n\n const faces: Array = this.input instanceof tf.Tensor\n ? await extractFaceTensors(this.input, detections)\n : await extractFaces(this.input, detections);\n\n const faceLandmarksByFace = await Promise.all(faces.map(\n (face) => this.landmarkNet.detectLandmarks(face),\n )) as FaceLandmarks68[];\n\n faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\n return parentResults.map((parentResult, i) => extendWithFaceLandmarks(parentResult, faceLandmarksByFace[i]));\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 async run(): Promise | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) {\n return undefined;\n }\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\n const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68;\n\n faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n\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 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\n return result;\n }\n\n private runAndExtendWithFaceDetections(): Promise[]> {\n // eslint-disable-next-line no-async-promise-executor\n return new Promise[]>(async (resolve) => {\n const detections = await this.run();\n resolve(detections.map((detection) => extendWithFaceDetection({}, detection)));\n });\n }\n\n withFaceLandmarks(useTinyLandmarkNet: boolean = 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 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: boolean = 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\n const desc1 = Array.from(arr1);\n const desc2 = Array.from(arr2);\n\n return Math.sqrt(\n desc1\n .map((val, i) => val - desc2[i])\n .reduce((res, diff) => res + (diff ** 2), 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\n private _distanceThreshold: number\n\n constructor(\n inputs: LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>,\n distanceThreshold: number = 0.6,\n ) {\n this._distanceThreshold = distanceThreshold;\n\n const inputArray = Array.isArray(inputs) ? inputs : [inputs];\n\n if (!inputArray.length) {\n throw new Error('FaceRecognizer.constructor - expected atleast one input');\n }\n\n let count = 1;\n const createUniqueLabel = () => `person ${count++}`;\n\n this._labeledDescriptors = inputArray.map((desc) => {\n if (desc instanceof LabeledFaceDescriptors) {\n return desc;\n }\n\n if (desc instanceof Float32Array) {\n return new LabeledFaceDescriptors(createUniqueLabel(), [desc]);\n }\n\n if (desc.descriptor && desc.descriptor instanceof Float32Array) {\n return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor]);\n }\n\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)\n / (descriptors.length || 1);\n }\n\n public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n return this.labeledDescriptors\n .map(({ descriptors, label }) => new FaceMatch(\n label,\n this.computeMeanDistance(queryDescriptor, descriptors),\n ))\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\n ? bestMatch\n : 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\n .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\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version as string, node, browser };\n"], - "mappings": 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+ "sourcesContent": ["/* eslint-disable import/no-extraneous-dependencies */\n/* eslint-disable node/no-unpublished-import */\n\n// wrapper to load tfjs in a single place so version can be changed quickly\n\nexport * from '@tensorflow/tfjs/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-wasm';\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: boolean = 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: number = 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 ${JSON.stringify({ width, height })}`);\n }\n\n this._width = width;\n this._height = height;\n }\n\n public get width(): number { return this._width; }\n\n public get height(): number { return this._height; }\n\n public reverse(): Dimensions {\n return new Dimensions(1 / this.width, 1 / this.height);\n }\n}\n", "export interface IPoint {\n x: number\n y: number\n}\n\nexport class Point implements IPoint {\n private _x: number\n\n private _y: number\n\n constructor(x: number, y: number) {\n this._x = x;\n this._y = y;\n }\n\n get x(): number { return this._x; }\n\n get y(): number { return this._y; }\n\n public add(pt: IPoint): Point {\n return new Point(this.x + pt.x, this.y + pt.y);\n }\n\n public sub(pt: IPoint): Point {\n return new Point(this.x - pt.x, this.y - pt.y);\n }\n\n public mul(pt: IPoint): Point {\n return new Point(this.x * pt.x, this.y * pt.y);\n }\n\n public div(pt: IPoint): Point {\n return new Point(this.x / pt.x, this.y / pt.y);\n }\n\n public abs(): Point {\n return new Point(Math.abs(this.x), Math.abs(this.y));\n }\n\n public magnitude(): number {\n return Math.sqrt((this.x ** 2) + (this.y ** 2));\n }\n\n public floor(): Point {\n return new Point(Math.floor(this.x), Math.floor(this.y));\n }\n}\n", "import { isDimensions, isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box implements IBoundingBox, IRect {\n public static isRect(rect: any): boolean {\n return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber);\n }\n\n public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions: boolean = false) {\n if (!Box.isRect(box)) {\n throw new Error(`${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: boolean = 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({\n x, y, width, height,\n });\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({\n x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight,\n })).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({\n x, y, width, height,\n });\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 {\n dy, edy, dx, edx, y, ey, x, ex, w, h,\n };\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: boolean = false) {\n super({\n left, top, right, bottom,\n }, 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 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: boolean = 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: boolean = 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(\n imgTensor: tf.Tensor4D,\n isCenterImage: boolean = false,\n): tf.Tensor4D {\n return tf.tidy(() => {\n const [height, width] = imgTensor.shape.slice(1);\n if (height === width) {\n return imgTensor;\n }\n\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\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\n const paddingTensorAppend = createPaddingTensor(paddingAmount);\n const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number);\n\n const paddingTensorPrepend = isCenterImage && remainingPaddingAmount\n ? createPaddingTensor(remainingPaddingAmount)\n : null;\n\n const tensorsToStack = [\n paddingTensorPrepend,\n imgTensor,\n paddingTensorAppend,\n ]\n .filter((t) => !!t)\n .map((t: tf.Tensor) => tf.cast(t, '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: boolean = false) {\n super({\n x, y, width, height,\n }, 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 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 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\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: boolean = 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\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 fetch,\n readFile,\n };\n}\n", "import { FileSystem } from './types';\n\nexport function createFileSystem(fs?: any): FileSystem {\n let requireFsError = '';\n\n if (!fs) {\n try {\n // eslint-disable-next-line global-require\n fs = require('fs');\n } catch (err) {\n requireFsError = err.toString();\n }\n }\n\n const readFile = fs\n ? (filePath: string) => new Promise((resolve, reject) => {\n fs.readFile(filePath, (err: any, buffer: Buffer) => (err ? reject(err) : resolve(buffer)));\n })\n : () => {\n throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`);\n };\n\n return {\n readFile,\n };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n // eslint-disable-next-line dot-notation\n const Canvas = global['Canvas'] || global.HTMLCanvasElement;\n const Image = global.Image || global.HTMLImageElement;\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 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 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", "export function isNodejs(): boolean {\n return typeof global === 'object'\n && typeof require === 'function'\n && typeof module !== 'undefined'\n && typeof process !== 'undefined' && !!process.version;\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\n if (canvasArg instanceof CanvasRenderingContext2D) {\n return canvasArg;\n }\n\n const canvas = resolveInput(canvasArg);\n\n if (!(canvas instanceof Canvas)) {\n throw new Error('resolveContext2d - expected canvas to be of instance of Canvas');\n }\n\n const ctx = canvas.getContext('2d');\n if (!ctx) {\n throw new Error('resolveContext2d - canvas 2d context is null');\n }\n\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)) return resolve(null);\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: boolean = 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: boolean = false\n\n private _inputDimensions: number[][] = []\n\n private _inputSize: number\n\n constructor(inputs: Array, treatAsBatchInput: boolean = 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 const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input);\n this._canvases[idx] = canvas;\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: boolean = 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, isCenterInputs);\n\n if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n imgTensor = tf.image.resizeBilinear(imgTensor, [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\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\n const boxes = detections\n .map((det) => (det instanceof FaceDetection\n ? det.forSize(imgWidth, imgHeight).box\n : det))\n .map((box) => box.clipAtImageBorders(imgWidth, imgHeight));\n\n const faceTensors = boxes.map(({\n x, y, width, height,\n }) => tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]));\n\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: ${res.url}`);\n }\n return bufferToImage(blob);\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson(uri: string): Promise {\n return (await fetchOrThrow(uri)).json();\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise {\n return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer());\n}\n", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n const defaultManifestFilename = `${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 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: boolean = 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: boolean = 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) => buf.buffer)));\n const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk);\n const manifest = JSON.parse((await readFile(manifestUri)).toString());\n const weightMap = await loadWeights(manifest, modelBaseUri);\n this.loadFromWeightMap(weightMap);\n }\n\n public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n const { paramMappings, params } = this.extractParamsFromWeightMap(weightMap);\n this._paramMappings = paramMappings;\n this._params = params;\n }\n\n public extractWeights(weights: Float32Array) {\n const { paramMappings, params } = this.extractParams(weights);\n this._paramMappings = paramMappings;\n this._params = params;\n }\n\n private traversePropertyPath(paramPath: string) {\n if (!this.params) {\n throw new Error('traversePropertyPath - model has no loaded params');\n }\n\n const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n // eslint-disable-next-line no-prototype-builtins\n if (!res.nextObj.hasOwnProperty(objProp)) {\n throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`);\n }\n return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] };\n }, { nextObj: this.params });\n\n const { obj, objProp } = result;\n if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);\n }\n\n return { obj, objProp };\n }\n\n protected abstract getDefaultModelName(): string\n\n // eslint-disable-next-line no-unused-vars\n protected abstract extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n\n // eslint-disable-next-line no-unused-vars\n protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n x: tf.Tensor4D,\n params: SeparableConvParams,\n stride: [number, number],\n): tf.Tensor4D {\n return tf.tidy(() => {\n let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same');\n out = tf.add(out, params.bias);\n return out;\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, SeparableConvParams } from '../common/index';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n x: tf.Tensor4D,\n denseBlockParams: DenseBlock3Params,\n isFirstLayer: boolean = 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: boolean = false,\n isScaleDown: boolean = 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: boolean = 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 ${tensor}`);\n }\n\n paramMappings.push(\n { originalPath, paramPath: mappedPath || originalPath },\n );\n\n return tensor;\n };\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n let remainingWeights = weights;\n\n function extractWeights(numWeights: number): Float32Array {\n const ret = remainingWeights.slice(0, numWeights);\n remainingWeights = remainingWeights.slice(numWeights);\n return ret;\n }\n\n function getRemainingWeights(): Float32Array {\n return remainingWeights;\n }\n\n return {\n extractWeights,\n getRemainingWeights,\n };\n}\n", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, ExtractWeightsFunction, ParamMapping } from '../common/index';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n const conv0 = isFirstLayer\n ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`);\n const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`);\n const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`);\n\n return { conv0, conv1, conv2 };\n }\n\n function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer);\n const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${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(`${prefix}/bias`, 1);\n\n return { filters, bias };\n };\n}\n", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n function extractDenseBlock3Params(prefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n const conv0 = isFirstLayer\n ? extractConvParams(`${prefix}/conv0`)\n : extractSeparableConvParams(`${prefix}/conv0`);\n const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n\n return { conv0, conv1, conv2 };\n }\n\n function extractDenseBlock4Params(prefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n const conv0 = isFirstLayer\n ? extractConvParams(`${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(`${prefix}/bias`, 1);\n return { weights, bias };\n }\n\n const params = {\n fc: extractFcParams('fc'),\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n const featureExtractorMap: tf.NamedTensorMap = {};\n const classifierMap: tf.NamedTensorMap = {};\n\n Object.keys(weightMap).forEach((key) => {\n const map = key.startsWith('fc') ? classifierMap : featureExtractorMap;\n map[key] = weightMap[key];\n });\n\n return { featureExtractorMap, classifierMap };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom/index';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n extends NeuralNetwork {\n protected _faceFeatureExtractor: IFaceFeatureExtractor\n\n constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor) {\n super(_name);\n this._faceFeatureExtractor = faceFeatureExtractor;\n }\n\n public get faceFeatureExtractor(): IFaceFeatureExtractor {\n return this._faceFeatureExtractor;\n }\n\n protected abstract getDefaultModelName(): string\n\n protected abstract getClassifierChannelsIn(): number\n\n protected abstract getClassifierChannelsOut(): number\n\n public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n const { params } = this;\n\n if (!params) {\n throw new Error(`${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 dispose(throwOnRedispose: boolean = 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'];\n\nexport class FaceExpressions {\n public neutral: number\n\n public happy: number\n\n public sad: number\n\n public angry: number\n\n public fearful: number\n\n public disgusted: number\n\n public surprised: number\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(\n canvasArg: string | HTMLCanvasElement,\n faceExpressions: DrawFaceExpressionsInput | Array,\n minConfidence = 0.1,\n textFieldAnchor?: IPoint,\n) {\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 { 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 > = TSource & {\n landmarks: TFaceLandmarks,\n unshiftedLandmarks: TFaceLandmarks,\n alignedRect: FaceDetection,\n angle: { roll: number | undefined, pitch: number | undefined, yaw: number | undefined },\n }\n\nexport function isWithFaceLandmarks(obj: any): obj is WithFaceLandmarks, FaceLandmarks> {\n return isWithFaceDetection(obj)\n // eslint-disable-next-line dot-notation\n && obj['landmarks'] instanceof FaceLandmarks\n // eslint-disable-next-line dot-notation\n && obj['unshiftedLandmarks'] instanceof FaceLandmarks\n // eslint-disable-next-line dot-notation\n && obj['alignedRect'] instanceof FaceDetection;\n}\n\nfunction calculateFaceAngle(mesh) {\n // returns the angle in the plane (in radians) between the positive x-axis and the ray from (0,0) to the point (x,y)\n const radians = (a1, a2, b1, b2) => (Math.atan2(b2 - a2, b1 - a1) % Math.PI);\n // convert radians to degrees\n // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n const degrees = (theta) => (theta * 180) / Math.PI;\n\n const angle = { roll: undefined, pitch: undefined, yaw: undefined };\n\n if (!mesh || !mesh._positions || mesh._positions.length !== 68) return angle;\n const pt = mesh._positions;\n\n // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees\n // value of 0 means center\n\n // roll is face lean from left to right\n // comparing x,y of outside corners of leftEye and rightEye\n angle.roll = -radians(pt[36]._x, pt[36]._y, pt[45]._x, pt[45]._y);\n\n // pitch is face turn from left right\n // comparing x distance of top of nose to left and right edge of face\n // precision is lacking since coordinates are not precise enough\n angle.pitch = radians(0, Math.abs(pt[0]._x - pt[30]._x) / pt[30]._x, Math.PI, Math.abs(pt[16]._x - pt[30]._x) / pt[30]._x);\n\n // yaw is face move from up to down\n // comparing size of the box around the face with top and bottom of detected landmarks\n // silly hack, but this gives us face compression on y-axis\n // e.g., tilting head up hides the forehead that doesn't have any landmarks so ratio drops\n const bottom = pt.reduce((prev, cur) => (prev < cur._y ? prev : cur._y), +Infinity);\n const top = pt.reduce((prev, cur) => (prev > cur._y ? prev : cur._y), -Infinity);\n angle.yaw = Math.PI * (mesh._imgDims._height / (top - bottom) / 1.40 - 1);\n\n return angle;\n}\n\nexport function extendWithFaceLandmarks<\n TSource extends WithFaceDetection<{}>,\n TFaceLandmarks extends FaceLandmarks = FaceLandmarks68 >(sourceObj: TSource, unshiftedLandmarks: TFaceLandmarks): WithFaceLandmarks {\n const { box: shift } = sourceObj.detection;\n const landmarks = unshiftedLandmarks.shiftBy(shift.x, shift.y);\n\n const rect = landmarks.align();\n const { imageDims } = sourceObj.detection;\n const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims);\n const angle = calculateFaceAngle(unshiftedLandmarks);\n\n const extension = {\n landmarks,\n unshiftedLandmarks,\n alignedRect,\n angle,\n };\n\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", "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, `${mappedPrefix}/separable_conv2`);\n\n return { separable_conv0, separable_conv1, separable_conv2 };\n }\n\n return {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n };\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n } = extractorsFactory(extractWeights, paramMappings);\n\n const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in');\n const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0');\n const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1');\n\n const entry_flow = {\n conv_in: entry_flow_conv_in,\n reduction_block_0: entry_flow_reduction_block_0,\n reduction_block_1: entry_flow_reduction_block_1,\n };\n\n const middle_flow = {};\n range(numMainBlocks, 0, 1).forEach((idx) => {\n middle_flow[`main_block_${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(`${mappedPrefix}/separable_conv2`);\n\n return { separable_conv0, separable_conv1, separable_conv2 };\n }\n\n return {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n };\n}\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n numMainBlocks: number,\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n } = loadParamsFactory(weightMap, paramMappings);\n\n const entry_flow_conv_in = extractConvParams('entry_flow/conv_in');\n const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0');\n const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1');\n\n const entry_flow = {\n conv_in: entry_flow_conv_in,\n reduction_block_0: entry_flow_reduction_block_0,\n reduction_block_1: entry_flow_reduction_block_1,\n };\n\n const middle_flow = {};\n range(numMainBlocks, 0, 1).forEach((idx) => {\n middle_flow[`main_block_${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: boolean = 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(`${prefix}/bias`, 1);\n return { weights, bias };\n }\n\n const params = {\n fc: {\n age: extractFcParams('fc/age'),\n gender: extractFcParams('fc/gender'),\n },\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from '../common/index';\n\n// eslint-disable-next-line no-shadow\nexport enum Gender {\n // eslint-disable-next-line no-unused-vars\n FEMALE = 'female',\n // eslint-disable-next-line no-unused-vars\n MALE = 'male'\n}\n\nexport type AgeAndGenderPrediction = {\n age: number\n gender: Gender\n genderProbability: number\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n fc: {\n age: FCParams\n gender: FCParams\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { AgeAndGenderPrediction, Gender, NetOutput, NetParams } from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\n\nexport class AgeGenderNet extends NeuralNetwork {\n private _faceFeatureExtractor: TinyXception\n\n constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n super('AgeGenderNet');\n this._faceFeatureExtractor = faceFeatureExtractor;\n }\n\n public get faceFeatureExtractor(): TinyXception {\n return this._faceFeatureExtractor;\n }\n\n public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n const { params } = this;\n\n if (!params) {\n throw new Error(`${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 dispose(throwOnRedispose: boolean = 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, `${mappedPrefix}/scale`);\n\n return { conv, scale };\n }\n\n function extractResidualLayerParams(\n numFilterValues: number,\n numFilters: number,\n filterSize: number,\n mappedPrefix: string,\n isDown: boolean = false,\n ): ResidualLayerParams {\n const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${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;\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 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 /*\n if (x.shape[1] === 512 && x.shape[3] === 3) {\n console.log('Input:', x.shape, x.size, 'sum:', x.reshape([786432]).sum().dataSync()[0]); // input does not change (checked values)\n console.log('Filter:', params.filters.shape, params.filters.size, 'sum:', params.filters.reshape([864]).sum().dataSync()[0]); // params do not change (checked values)\n console.log('Strides', strides);\n console.log('Conv2d 1st 5 values:', out.shape, out.size, out.dataSync().slice(0, 5)); // output has different values!\n console.log('Conv2D sum of all values:', tf.reshape(out, [2097152]).sum().dataSync()[0]); // silly sum just to see how much results diverged\n }\n */\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: string = '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: string = '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, 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 = [] as any;\n const scoresData = await scoresTensor.array();\n const boxesData = await boxesTensor.array();\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: boolean = 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 getDefaultModelName(): string {\n return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME;\n }\n\n protected 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: boolean = true) {\n const net = new TinyYolov2(withSeparableConvs);\n net.extractWeights(weights);\n return net;\n}\n", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\n\nexport interface ITinyFaceDetectorOptions extends ITinyYolov2Options {}\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n protected _name: string = '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 getDefaultModelName(): string {\n return 'tiny_face_detector_model';\n }\n\n protected 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 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 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 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 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 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 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 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 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 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 async run(): Promise | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) {\n return undefined;\n }\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\n (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n );\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 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(\n faces.map((face) => this.landmarkNet.detectLandmarks(face)),\n ) as FaceLandmarks68[];\n faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n return parentResults.map((parentResult, i) => extendWithFaceLandmarks(parentResult, faceLandmarksByFace[i]));\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 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 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: boolean = 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 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: boolean = 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\n const desc1 = Array.from(arr1);\n const desc2 = Array.from(arr2);\n\n return Math.sqrt(\n desc1\n .map((val, i) => val - desc2[i])\n .reduce((res, diff) => res + (diff ** 2), 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\n private _distanceThreshold: number\n\n constructor(\n inputs: LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>,\n distanceThreshold: number = 0.6,\n ) {\n this._distanceThreshold = distanceThreshold;\n\n const inputArray = Array.isArray(inputs) ? inputs : [inputs];\n\n if (!inputArray.length) {\n throw new Error('FaceRecognizer.constructor - expected atleast one input');\n }\n\n let count = 1;\n const createUniqueLabel = () => `person ${count++}`;\n\n this._labeledDescriptors = inputArray.map((desc) => {\n if (desc instanceof LabeledFaceDescriptors) {\n return desc;\n }\n\n if (desc instanceof Float32Array) {\n return new LabeledFaceDescriptors(createUniqueLabel(), [desc]);\n }\n\n if (desc.descriptor && desc.descriptor instanceof Float32Array) {\n return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor]);\n }\n\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)\n / (descriptors.length || 1);\n }\n\n public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n return this.labeledDescriptors\n .map(({ descriptors, label }) => new FaceMatch(\n label,\n this.computeMeanDistance(queryDescriptor, descriptors),\n ))\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\n ? bestMatch\n : 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\n .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\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version as string, node, browser };\n"], + "mappings": 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i=M.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:D(()=>Ge(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${r}/v`,variable:D(()=>Ge(i).variable(o))});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let c=this.accumulatedFirstMoment[s].variable,u=this.accumulatedSecondMoment[s].variable,p=J(W(c,this.beta1),W(l,1-this.beta1)),d=J(W(u,this.beta2),W(ot(l),1-this.beta2)),h=ye(p,n),m=ye(d,a);c.assign(p),u.assign(d);let f=J(W(ye(h,J(rn(m),this.epsilon)),-this.learningRate),i);i.assign(f)}),this.accBeta1.assign(W(this.accBeta1,this.beta1)),this.accBeta2.assign(W(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Ae(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Ae(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),D(()=>{this.accBeta1.assign(mr(this.beta1,this.iterations_+1)),this.accBeta2.assign(mr(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};Th.className="Adam";qr(Th);var Nh=class extends gr{constructor(e,t,n,a=null,r=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=a,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],D(()=>{this.iteration=ve(0).variable(),this.accBeta1=ve(t).variable()}),a==null&&(this.epsilon=M.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);D(()=>{let n=he(1,this.accBeta1),a=ye(-this.learningRate,J(W(this.iteration,this.decay),1));t.forEach((r,s)=>{let i=M.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:Ge(i).variable(o)}),this.accumulatedWeightedInfNorm[s]==null&&(this.accumulatedWeightedInfNorm[s]={originalName:`${r}/v`,variable:Ge(i).variable(o)});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let c=this.accumulatedFirstMoment[s].variable,u=this.accumulatedWeightedInfNorm[s].variable,p=J(W(c,this.beta1),W(l,1-this.beta1)),d=W(u,this.beta2),h=zt(l),m=ja(d,h);c.assign(p),u.assign(m);let f=J(W(ye(a,n),ye(p,J(m,this.epsilon))),i);i.assign(f)}),this.iteration.assign(J(this.iteration,1)),this.accBeta1.assign(W(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Ae(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Ae(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};Nh.className="Adamax";qr(Nh);var Cc=class extends gr{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=Array.isArray(e)?e[n].tensor:e[t];if(a==null)return;let r=M.registeredVariables[t];D(()=>{let s=J(W(this.c,a),r);r.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=qt(ve(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};Sh.className="Momentum";qr(Sh);var Ch=class extends gr{constructor(e,t=.9,n=0,a=null,r=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=a,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,a==null&&(this.epsilon=M.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=M.registeredVariables[t],r=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${t}/rms`,variable:D(()=>Ge(a).variable(r))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${t}/momentum`,variable:D(()=>Ge(a).variable(r))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${t}/mg`,variable:D(()=>Ge(a).variable(r))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedMeanSquares[n].variable,o=this.accumulatedMoments[n].variable;D(()=>{let l=J(W(i,this.decay),W(ot(s),1-this.decay));if(this.centered){let c=this.accumulatedMeanGrads[n].variable,u=J(W(c,this.decay),W(s,1-this.decay)),p=ye(W(s,this.learningRate),rn(he(l,J(ot(u),this.epsilon)))),d=J(W(o,this.momentum),p);i.assign(l),c.assign(u),o.assign(d);let h=he(a,d);a.assign(h)}else{let 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t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};Ch.className="RMSProp";qr(Ch);var Si=class{static sgd(e){return new Cc(e)}static momentum(e,t,n=!1){return new Sh(e,t,n)}static rmsprop(e,t=.9,n=0,a=null,r=!1){return new Ch(e,t,n,a,r)}static adam(e=.001,t=.9,n=.999,a=null){return new Th(e,t,n,a)}static adadelta(e=.001,t=.95,n=null){return new kh(e,t,n)}static adamax(e=.002,t=.9,n=.999,a=null,r=0){return new Nh(e,t,n,a,r)}static 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Add some layers first.");this.model=new xr({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new Ca("The model needs to be compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new Ca("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new Ca("The model needs to be compiled before being used.");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new Ca("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},a=!1){let r,s={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new B("Legacy serialization format not supported yet.");r=t}else w.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),r=t.layers,delete t.layers,s=t;let i=new e(s);if(!(i instanceof Vl))throw new $e(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of r){let l=Fa(o,void 0,a);a&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new B("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new B("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}};Vl.className="Sequential";re.registerClass(Vl);function tB(e){return new xr(e)}function nB(e){return new Vl(e)}function aB(e,t){return t==null&&(t={}),ZW(e,t)}function o1(e){return g1(e)}function rB(e,t){ma.registerCallbackConstructor(e,t)}var zn=class extends re.Serializable{getConfig(){return{}}},G1=class extends zn{apply(e,t=1){return P3(e,t)}};G1.className="elu";re.registerClass(G1);var H1=class extends zn{apply(e){return uh(e)}};H1.className="selu";re.registerClass(H1);var j1=class extends zn{apply(e){return qe(e)}};j1.className="relu";re.registerClass(j1);var q1=class extends zn{apply(e){return D(()=>Al(6,qe(e)))}};q1.className="relu6";re.registerClass(q1);var K1=class extends zn{apply(e){return e}};K1.className="linear";re.registerClass(K1);var X1=class extends zn{apply(e){return ua(e)}};X1.className="sigmoid";re.registerClass(X1);var Y1=class extends zn{apply(e){return L3(e)}};Y1.className="hardSigmoid";re.registerClass(Y1);var J1=class extends zn{apply(e){return El(e)}};J1.className="softplus";re.registerClass(J1);var Q1=class extends zn{apply(e){return O3(e)}};Q1.className="softsign";re.registerClass(Q1);var Z1=class extends zn{apply(e){return Sl(e)}};Z1.className="tanh";re.registerClass(Z1);var _b=class extends zn{apply(e,t=-1){return Ia(e,t)}};_b.className="softmax";re.registerClass(_b);var eI=class extends zn{apply(e,t=-1){return nh(e,t)}};eI.className="logSoftmax";re.registerClass(eI);var tI=class extends zn{apply(e,t=1){return D(()=>ua(e.mul(t)).mul(e))}};tI.className="swish";re.registerClass(tI);function rs(e){return e.getClassName()}function Eb(e,t={}){return _c(e,re.SerializationMap.getMap().classNameMap,t,"activation")}function ss(e){if(e==null){let t={};return t.className="linear",t.config={},Eb(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},Eb(t)}else return e instanceof zn?e:Eb(e)}function Fb(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var nI=class extends re.Serializable{},Wc=class extends nI{constructor(e){super();Fb(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return D(()=>{let t=bt([1]);return this.hasL1&&(t=J(t,Se(W(this.l1,zt(e))))),this.hasL2&&(t=J(t,Se(W(this.l2,Dc(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Wc.className="L1L2";re.registerClass(Wc);function sB(e){return Fb(e),new Wc({l1:e!=null?e.l1:null,l2:0})}function iB(e){return Fb(e),new Wc({l2:e!=null?e.l2:null,l1:0})}var aI={l1l2:"L1L2"};function ct(e){return Hy(e)}function rI(e,t={}){return _c(e,re.SerializationMap.getMap().classNameMap,t,"regularizer")}function vt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in aI?aI[e]:e,config:{}};return rI(t)}else return e instanceof nI?e:rI(e)}var Ab=class extends je{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Pe(e);let n=qe(e);return this.maxValue!=null&&(n=Kt(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};Ab.className="ReLU";re.registerClass(Ab);var $b=class extends je{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Pe(e);return xc(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};$b.className="LeakyReLU";re.registerClass($b);var Db=class extends je{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=xt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=vt(e.alphaRegularizer),this.alphaConstraint=Ut(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new B(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=ut(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let a of this.sharedAxes)t[a-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let a=1;a(Rt(t),t==="channelsFirst"?Ve(e,[0,2,3,1]):e))}function sI(e,t){return D(()=>(Rt(t),t==="channelsFirst"?Ve(e,[0,2,3,4,1]):e))}function oB(e,t,n,a=1,r="valid",s,i=1){return D(()=>{if(s==null&&(s=Sa()),Rt(s),e.shape.length!==3)throw new B(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new B(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new B(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=Ve(e,[0,2,1])),r==="causal")throw new $e("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=Kd(e,t,a,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=Qa(o,n)),o})}function iI(e,t,n,a=[1,1],r="valid",s,i,o=null){return D(()=>{if(s==null&&(s=Sa()),Rt(s),e.rank!==3&&e.rank!==4)throw new B(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new B(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=Ob(e,s);if(r==="causal")throw new $e("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Zr.conv2d({x:l,filter:t,strides:a,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=Ve(l,[0,3,1,2])),l})}function lB(e,t,n,a=[1,1,1],r="valid",s,i){return D(()=>{if(s==null&&(s=Sa()),Rt(s),e.rank!==4&&e.rank!==5)throw new B(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new B(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=sI(e,s);if(r==="causal")throw new $e("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=my(o,t,a,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Qa(o,n)),s==="channelsFirst"&&(o=Ve(o,[0,4,1,2,3])),o})}var Lb=class extends je{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Lb.verifyArgs(t),this.rank=e,Xt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new $e(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Ul(t.kernelSize,e,"kernelSize"),this.strides=Ul(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,ta(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Rt(this.dataFormat),this.activation=ss(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=xt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Ut(t.biasConstraint),this.biasRegularizer=vt(t.biasRegularizer),this.activityRegularizer=vt(t.activityRegularizer),this.dilationRate=Ul(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new B(`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 B(`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 B(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Ya("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!qy(e.kernelSize,"number",1,3))throw new B(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:rs(this.activation),useBias:this.useBias,biasInitializer:_t(this.biasInitializer),biasRegularizer:ct(this.biasRegularizer),activityRegularizer:ct(this.activityRegularizer),biasConstraint:Vt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Bc=class extends Lb{constructor(e,t){super(e,t);this.kernel=null,Bc.verifyArgs(t),this.filters=t.filters,Xt(this.filters,"filters"),this.kernelInitializer=xt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Ut(t.kernelConstraint),this.kernelRegularizer=vt(t.kernelRegularizer)}build(e){e=ut(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],a=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return D(()=>{e=Pe(e);let n,a=this.bias==null?null:this.bias.read(),r=Hk(this.activation.getClassName());if(r!=null&&this.rank===2)n=iI(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=oB(e,this.kernel.read(),a,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=iI(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=lB(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new $e("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=ut(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r 0 but got ${JSON.stringify(e.filters)}`)}},Vc=class extends Bc{constructor(e){super(2,e);Vc.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!qy(e.kernelSize,"number",1,2))throw new B(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};Vc.className="Conv2D";re.registerClass(Vc);var em=class extends Bc{constructor(e){super(3,e);em.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new B(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};em.className="Conv3D";re.registerClass(em);var zb=class extends Vc{constructor(e){super(e);if(this.inputSpec=[new Yt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new B(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=ut(e),e.length!==4)throw new B("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Yt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return D(()=>{let n=Pe(e);if(n.shape.length!==4)throw new B(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=a[s],l=a[i],c=this.kernelSize[0],u=this.kernelSize[1],p=this.strides[0],d=this.strides[1],h=Zh(o,p,c,this.padding),m=Zh(l,d,u,this.padding),f=[r,h,m,this.filters];this.dataFormat!=="channelsLast"&&(n=Ve(n,[0,2,3,1]));let g=Xd(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=Ve(g,[0,3,1,2])),this.bias!=null&&(g=Qa(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=ut(e);let t=e.slice(),n,a,r;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3):(n=3,a=1,r=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[a]=Zh(t[a],o,s,this.padding),t[r]=Zh(t[r],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};zb.className="Conv2DTranspose";re.registerClass(zb);var oI=class extends Bc{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new B("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new B("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new B(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=xt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=vt(t.depthwiseRegularizer),this.depthwiseConstraint=Ut(t.depthwiseConstraint),this.pointwiseInitializer=xt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=vt(t.pointwiseRegularizer),this.pointwiseConstraint=Ut(t.pointwiseConstraint)}build(e){if(e=ut(e),e.length{e=Pe(e);let n;if(this.rank===1)throw new $e("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Ve(e,[0,2,3,1])),n=Ni(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Qa(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Ve(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=_t(this.depthwiseInitializer),e.pointwiseInitializer=_t(this.pointwiseInitializer),e.depthwiseRegularizer=ct(this.depthwiseRegularizer),e.pointwiseRegularizer=ct(this.pointwiseRegularizer),e.depthwiseConstraint=Vt(this.depthwiseConstraint),e.pointwiseConstraint=Vt(this.pointwiseConstraint),e}};oI.className="SeparableConv";var Wb=class extends oI{constructor(e){super(2,e)}};Wb.className="SeparableConv2D";re.registerClass(Wb);var tm=class extends Bc{constructor(e){super(1,e);tm.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!qy(e.kernelSize,"number",1,1))throw new B(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};tm.className="Conv1D";re.registerClass(tm);var Bb=class extends je{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return D(()=>{if(e=Pe(e),this.dataFormat==="channelsLast"){let n=Fh(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Fh(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Fh(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Fh(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Bb.className="Cropping2D";re.registerClass(Bb);var Vb=class extends je{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,E3(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return D(()=>{let n=Pe(e),a=n.shape;if(this.dataFormat==="channelsFirst"){n=Ve(n,[0,2,3,1]);let r=this.size[0]*a[2],s=this.size[1]*a[3],i=this.interpolation==="nearest"?n.resizeNearestNeighbor([r,s]):n.resizeBilinear([r,s]);return Ve(i,[0,3,1,2])}else{let r=this.size[0]*a[1],s=this.size[1]*a[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([r,s]):n.resizeBilinear([r,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Vb.className="UpSampling2D";re.registerClass(Vb);function uB(e,t,n=[1,1],a="valid",r,s){return D(()=>{r==null&&(r=Sa()),Rt(r);let i=Ob(e,r);if(e.rank!==4)throw new B(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new B(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=Xr(i,t,n,a==="same"?"same":"valid","NHWC",s),r==="channelsFirst"&&(i=Ve(i,[0,3,1,2])),i})}var Ub=class extends Lb{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=xt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Ut(e.depthwiseConstraint),this.depthwiseRegularizer=vt(e.depthwiseRegularizer)}build(e){if(e=ut(e),e.length<4)throw new B(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],a=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",a,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return D(()=>{e=Pe(e);let n=uB(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Qa(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=ut(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=Aa(t,this.kernelSize[0],this.padding,this.strides[0]),s=Aa(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],a,r,s]:[e[0],r,s,a]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=_t(this.depthwiseInitializer),e.depthwiseRegularizer=ct(this.depthwiseRegularizer),e.depthwiseConstraint=Vt(this.depthwiseRegularizer),e}};Ub.className="DepthwiseConv2D";re.registerClass(Ub);function lI(e,t,n,a){if(Array.isArray(e)){if(t!=null||n!=null)throw new B("When inputs is an array, neither initialState or constants should be provided");a!=null&&(n=e.slice(e.length-a,e.length),e=e.slice(0,e.length-a)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(s){return s==null||Array.isArray(s)?s:[s]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function uI(e,t,n,a=!1,r,s,i=!1,o=!1){return D(()=>{let l=t.shape.length;if(l<3)throw new B(`Input should be at least 3D, but is ${l}D.`);let c=[1,0].concat(_a(2,l));if(t=Ve(t,c),s!=null)throw new $e("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=r.asType("bool").asType("float32"),r.rank===l-1&&(r=hn(r,-1)),r=Ve(r,c)),a&&(t=On(t,0),r!=null&&(r=On(r,0)));let u=[],p,d=n,h=t.shape[0],m=lt(t),f;r!=null&&(f=lt(r));for(let y=0;ye(b,d));if(r==null)p=x[0],d=x[1];else{let v=D(()=>{let T=f[y],k=Pn(T).sub(T),S=x[0].mul(T).add(d[0].mul(k)),F=d.map((A,R)=>x[1][R].mul(T).add(A.mul(k)));return{output:S,newStates:F}});p=v.output,d=v.newStates}o&&u.push(p)}let g;return o&&(g=Dt(u,1)),[p,g,d]})}var Za=class extends je{constructor(e){super(e);let t;if(e.cell==null)throw new B("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new nm({cells:e.cell}):t=e.cell,t.stateSize==null)throw new B("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Yt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return _a(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){hb(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],a;if(this.returnSequences?a=[e[0],e[1],n]:a=[e[0],n],this.returnState){let r=[];for(let s of t)r.push([e[0],s]);return[a].concat(r)}else return a}computeMask(e,t){return D(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let a=this.states.map(r=>null);return[n].concat(a)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;ni.shape[i.shape.length-1]),s))throw new B(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=s.map(i=>new Yt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){D(()=>{if(!this.stateful)throw new yr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new B("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>bt([n,a])):this.states_=[bt([n,this.cell.stateSize])];else if(e==null)Ae(this.states_),this.keptStates!=null&&(Ae(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>bt([n,a])):this.states_[0]=bt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Ae(this.states_);for(let a=0;aqt(a.clone()))})}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=lI(e,n,a,this.numConstants);e=r.inputs,n=r.initialState,a=r.constants;let s=[],i=[];if(n!=null){t.initialState=n,s=s.concat(n),this.stateSpec=[];for(let o of n)this.stateSpec.push(new Yt({shape:o.shape}));i=i.concat(this.stateSpec)}if(a!=null&&(t.constants=a,s=s.concat(a),this.numConstants=a.length),s[0]instanceof Ea){let o=[e].concat(s),l=this.inputSpec.concat(i),c=this.inputSpec;this.inputSpec=l;let u=super.apply(o,t);return this.inputSpec=c,u}else return super.apply(e,t)}call(e,t){return D(()=>{let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;e=Pe(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==s)throw new B(`RNN Layer has ${s} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:a},o=uI((d,h)=>{let m=this.cell.call([d].concat(h),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],c=o[1],u=o[2];this.stateful&&this.resetStates(u,a);let p=this.returnSequences?c:l;return this.returnState?[p].concat(u):p})}getInitialState(e){return D(()=>{let t=bt(e.shape);return t=Se(t,[1,2]),t=$c(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Zy(t,[1,n]):t):this.cell.stateSize>1?[Zy(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===Za.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let a=t.cell,r=Fa(a,n);return new e(Object.assign(t,{cell:r}))}};Za.className="RNN";re.registerClass(Za);var Mc=class extends je{},am=class extends Mc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Xt(this.units,"units"),this.activation=ss(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=xt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=xt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=xt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=vt(e.kernelRegularizer),this.recurrentRegularizer=vt(e.recurrentRegularizer),this.biasRegularizer=vt(e.biasRegularizer),this.kernelConstraint=Ut(e.kernelConstraint),this.recurrentConstraint=Ut(e.recurrentConstraint),this.biasConstraint=Ut(e.biasConstraint),this.dropout=Ll([1,ns([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ll([1,ns([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ut(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return D(()=>{if(e=e,e.length!==2)throw new B(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let a=t.training==null?!1:t.training;0Pn(e),rate:this.dropout,training:a})),0Pn(n),rate:this.recurrentDropout,training:a}));let r,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?r=Ja(W(e,s),this.kernel.read()):r=Ja(e,this.kernel.read()),this.bias!=null&&(r=Qa(r,this.bias.read())),i!=null&&(n=W(n,i));let o=J(r,Ja(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:rs(this.activation),useBias:this.useBias,kernelInitializer:_t(this.kernelInitializer),recurrentInitializer:_t(this.recurrentInitializer),biasInitializer:_t(this.biasInitializer),kernelRegularizer:ct(this.kernelRegularizer),recurrentRegularizer:ct(this.recurrentRegularizer),biasRegularizer:ct(this.biasRegularizer),activityRegularizer:ct(this.activityRegularizer),kernelConstraint:Vt(this.kernelConstraint),recurrentConstraint:Vt(this.recurrentConstraint),biasConstraint:Vt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};am.className="SimpleRNNCell";re.registerClass(am);var Gb=class extends Za{constructor(e){e.cell=new am(e),super(e)}call(e,t){return D(()=>{this.cell.dropoutMask!=null&&(Ae(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ae(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return new e(t)}};Gb.className="SimpleRNN";re.registerClass(Gb);var rm=class extends Mc{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new B("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Xt(this.units,"units"),this.activation=ss(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=ss(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=xt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=xt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=xt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=vt(e.kernelRegularizer),this.recurrentRegularizer=vt(e.recurrentRegularizer),this.biasRegularizer=vt(e.biasRegularizer),this.kernelConstraint=Ut(e.kernelConstraint),this.recurrentConstraint=Ut(e.recurrentConstraint),this.biasConstraint=Ut(e.biasConstraint),this.dropout=Ll([1,ns([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ll([1,ns([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ut(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return D(()=>{if(e=e,e.length!==2)throw new B(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,a=e[1];e=e[0],0Pn(e),rate:this.dropout,training:n,count:3})),0Pn(a),rate:this.recurrentDropout,training:n,count:3}));let r=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0{this.cell.dropoutMask!=null&&(Ae(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ae(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Hb.className="GRU";re.registerClass(Hb);var Uc=class extends Mc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Xt(this.units,"units"),this.activation=ss(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=ss(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=xt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=xt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=xt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=vt(e.kernelRegularizer),this.recurrentRegularizer=vt(e.recurrentRegularizer),this.biasRegularizer=vt(e.biasRegularizer),this.kernelConstraint=Ut(e.kernelConstraint),this.recurrentConstraint=Ut(e.recurrentConstraint),this.biasConstraint=Ut(e.biasConstraint),this.dropout=Ll([1,ns([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ll([1,ns([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=ut(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let a;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,s=this.units;a=new(t=class extends ha{apply(i,o){let l=r.apply([s]),c=new $h().apply([s]),u=r.apply([s*2]);return t1(t1(l,c),u)}},t.className="CustomInit",t)}else a=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,a,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return D(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new B(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let a=e[1],r=e[2];e=e[0],0Pn(e),rate:this.dropout,training:n,count:4})),0Pn(a),rate:this.recurrentDropout,training:n,count:4}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,c,u;0{this.cell.dropoutMask!=null&&(Ae(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ae(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};jb.className="LSTM";re.registerClass(jb);var nm=class extends Mc{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return D(()=>{e=e;let n=e.slice(1),a=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?a.push(n.splice(0,i.stateSize.length)):a.push(n.splice(0,1));a.reverse();let r=[],s;for(let i=0;i{Ai(`RNNCell_${a}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=a=>({className:a.getClassName(),config:a.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,n={}){let a=[];for(let r of t.cells)a.push(Fa(r,n));return new e({cells:a})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return mb(e)}setWeights(e){let t=[];for(let n of this.cells){let a=n.weights.length,r=e.splice(a);for(let s=0;sa1(t(),n),i=()=>Rc(s,t,a);return!r||r<=1?qt(i().clone()):Array(r).fill(void 0).map(i).map(o=>qt(o.clone()))}var cB=function(e,t){var n={};for(var a in e)Object.prototype.hasOwnProperty.call(e,a)&&t.indexOf(a)<0&&(n[a]=e[a]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,a=Object.getOwnPropertySymbols(e);r{if(this.cell.dropoutMask!=null&&(Ae(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ae(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new B("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return D(()=>{let{stateSize:t}=this.cell,n=e.shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)],s=bt(r);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){D(()=>{if(!this.stateful)throw new yr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)];if(n[0]==null)throw new B("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(()=>bt(r)):this.states_=[bt(r)];else if(e==null)Ae(this.states_),this.keptStates!=null&&(Ae(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>bt(r)):this.states_[0]=bt(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Ae(this.states_);for(let s=0;sqt(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:a,padding:r,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],c=e[o?4:3],u=Aa(l,a[0],r,s[0],i[0]),p=Aa(c,a[1],r,s[1],i[1]);return[...e.slice(0,2),...o?[n,u,p]:[u,p,n]]}};cI.className="ConvRNN2D";var sm=class extends Uc{constructor(e){let{filters:t,kernelSize:n,strides:a,padding:r,dataFormat:s,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Xt(this.filters,"filters"),this.kernelSize=Ul(n,2,"kernelSize"),this.kernelSize.forEach(o=>Xt(o,"kernelSize")),this.strides=Ul(a||1,2,"strides"),this.strides.forEach(o=>Xt(o,"strides")),this.padding=r||"valid",ta(this.padding),this.dataFormat=s||"channelsLast",Rt(this.dataFormat),this.dilationRate=Ul(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Xt(o,"dilationRate"))}build(e){var t;e=ut(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[n]}`);let a=e[n],r=4,s=this.kernelSize.concat([a,this.filters*r]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,c=this.filters;o=new(t=class extends ha{apply(u,p){let d=l.apply([c]),h=qa([c]),m=l.apply([c*2]);return tb([d,h,m])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return D(()=>{if(e.length!==3)throw new B(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0Pn(a),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(Q,se,ne)=>!se||!se[ne]?Q:W(se[ne],Q),c=l(a,o,0),u=l(a,o,1),p=l(a,o,2),d=l(a,o,3);0Pn(r),rate:this.recurrentDropout,training:n,count:i}));let h=this.recurrentDropoutMask,m=l(r,h,0),f=l(r,h,1),g=l(r,h,2),y=l(r,h,3),b=3,[x,v,T,k]=Ln(this.kernel.read(),i,b),[S,F,A,R]=this.useBias?Ln(this.bias.read(),i):[null,null,null,null];c=this.inputConv(c,x,S,this.padding),u=this.inputConv(u,v,F,this.padding),p=this.inputConv(p,T,A,this.padding),d=this.inputConv(d,k,R,this.padding);let[P,z,V,G]=Ln(this.recurrentKernel.read(),i,b);m=this.recurrentConv(m,P),f=this.recurrentConv(f,z),g=this.recurrentConv(g,V),y=this.recurrentConv(y,G);let H=this.recurrentActivation.apply(J(c,m)),K=this.recurrentActivation.apply(J(u,f)),j=J(W(K,s),W(H,this.activation.apply(J(p,g)))),te=W(this.recurrentActivation.apply(J(d,y)),this.activation.apply(j));return[te,te,j]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=cB(e,["units"]),a={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,a)}inputConv(e,t,n,a){let r=At(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Qa(r,n,this.dataFormat):r}recurrentConv(e,t){return At(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};sm.className="ConvLSTM2DCell";re.registerClass(sm);var qb=class extends cI{constructor(e){let t=new sm(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};qb.className="ConvLSTM2D";re.registerClass(qb);var im=class extends je{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let a=0;a{this.invokeCallHook(e,t);let n=Pe(e);if(0a1(n,this.rate,r,this.seed),()=>n,a)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};im.className="Dropout";re.registerClass(im);var Kb=class extends im{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Kb.className="SpatialDropout1D";re.registerClass(Kb);var Xb=class extends je{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Xt(this.units,"units"),this.activation=ss(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=xt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=xt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Ut(e.kernelConstraint),this.biasConstraint=Ut(e.biasConstraint),this.kernelRegularizer=vt(e.kernelRegularizer),this.biasRegularizer=vt(e.biasRegularizer),this.activityRegularizer=vt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=ut(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=ut(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Pe(e),a=Hk(this.activation.getClassName()),r;return a!=null?r=Ja(n,this.kernel.read(),a,this.bias?this.bias.read():null):(r=Ja(n,this.kernel.read()),this.bias!=null&&(r=Qa(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:rs(this.activation),useBias:this.useBias,kernelInitializer:_t(this.kernelInitializer),biasInitializer:_t(this.biasInitializer),kernelRegularizer:ct(this.kernelRegularizer),biasRegularizer:ct(this.biasRegularizer),activityRegularizer:ct(this.activityRegularizer),kernelConstraint:Vt(this.kernelConstraint),biasConstraint:Vt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Xb.className="Dense";re.registerClass(Xb);var Yb=class extends je{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=ut(e);for(let t of e.slice(1))if(t==null)throw new B(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],ts(e,1)]}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Pe(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let a=[0];for(let r=2;r{this.invokeCallHook(e,t);let n=Pe(e);return this.activation.apply(n)})}getConfig(){let e={activation:rs(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};Jb.className="Activation";re.registerClass(Jb);var Qb=class extends je{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return D(()=>(e=Pe(e),D3(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Qb.className="RepeatVector";re.registerClass(Qb);var Zb=class extends je{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t{this.invokeCallHook(e,t);let n=Pe(e),a=n.shape,r=a.slice(0,1).concat(this.fixUnknownDimension(a.slice(1),this.targetShape));return n.reshape(r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};Zb.className="Reshape";re.registerClass(Zb);var ex=class extends je{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=_a(1,e.dims.length+1);if(!w.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Yt({ndim:this.dims.length+1})]}computeOutputShape(e){e=ut(e);let t=e.slice();return this.dims.forEach((n,a)=>{t[a+1]=e[n]}),t}call(e,t){return Ve(Pe(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};ex.className="Permute";re.registerClass(ex);var tx=class extends je{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Pe(e),a=-1;return hc(Ti(n,this.maskValue),a)}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Pe(e),a=-1,r=!0,s=hc(Ti(n,this.maskValue),a,r);return n.mul(s.asType(n.dtype))})}};tx.className="Masking";re.registerClass(tx);var nx=class extends je{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(mt(e.inputLength))}this.inputDim=e.inputDim,Xt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Xt(this.outputDim,"outputDim"),this.embeddingsInitializer=xt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=vt(e.embeddingsRegularizer),this.activityRegularizer=vt(e.activityRegularizer),this.embeddingsConstraint=Ut(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return D(()=>this.maskZero?(e=Pe(e),Ti(e,Ge(e))):null)}computeOutputShape(e){if(e=ut(e),this.inputLength==null)return[...e,this.outputDim];let t=mt(this.inputLength);if(t.length!==e.length-1)throw new B(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let a=0;a{this.invokeCallHook(e,t);let n=Pe(e);return n.dtype!=="int32"&&(n=Ac(n,"int32")),n1(this.embeddings.read(),n.as1D()).reshape(ut(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:_t(this.embeddingsInitializer),embeddingsRegularizer:ct(this.embeddingsRegularizer),activityRegularizer:ct(this.activityRegularizer),embeddingsConstraint:Vt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};nx.className="Embedding";re.registerClass(nx);var Pi=class extends je{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new $e}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length1)throw new B(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;rr.length);e.indexOf(null)===-1&&es(a).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return D(()=>{if(e=e,this.reshapeRequired){let n=[],a=e.map(r=>r.rank);if(a.indexOf(null)===-1){let r=ns(a);for(let s of e){let i=s.rank;for(let o=0;o1){let c=_a(1,l).concat([0]);n.push(Ve(o,c)),r=!0}else n.push(o)}let s=this.mergeFunction(n),i=s.rank;if(r){if(i==null){let o=s.shape,l=o.length,c=o[l-1],u=[c].concat(o.slice(0,o.length-1));s=Ve(s.reshape([-1,c]),[1,0]).reshape(u)}else if(i>1){let o=[i-1].concat(_a(0,i-1));s=Ve(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let a=1;a{if(t==null)return null;if(!Array.isArray(t))throw new B("`mask` should be an Array");if(!Array.isArray(e))throw new B("`inputs` should be an Array");if(t.length!==e.length)throw new B(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(a=>a==null))return null;t=t.map(a=>a==null?a:hn(a,0));let n=t[0];for(let a=1;a{let t=e[0].clone();for(let n=1;n{let t=e[0].clone();for(let n=1;n{let t=e[0].clone();for(let n=1;n{let t=e[0];for(let n=1;n{let t=e[0];for(let n=1;n1)throw new B("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return D(()=>tb(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new B("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),a=this.axis<0?n.length+this.axis:this.axis;for(let r of t.slice(1)){if(n[a]==null||r[a]==null){n[a]=null;break}n[a]+=r[a]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new B("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new B("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new B(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return D(()=>{let n=!0;if(t.forEach(s=>{if(s!=null){n=!1;return}}),n)return null;let a=[];for(let s=0;s3||t.shape.length>3)throw new $e("batchDot is not implemented for tensors of 4D or higher rank yet");if(w.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),w.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new $e("batchDot is not implemented for complex64-type Tensors yet.");let a=e.shape.length,r=t.shape.length;n==null&&(n=[a-1,r-2]);let s=n;return D(()=>{let i;if(a>r){i=a-r;let l=[];for(let c=0;ca){i=r-a;let l=[];for(let c=0;c0){let l;a>r?l=a+r-3:l=a-1;let c=[];for(let u=l;u"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new $e("Dot layer does not support tensors of 4D or higher rank yet.");let a=this.interpretAxes(t,n);if(t[a[0]]!==n[a[1]])throw new B(`Dimension incompatibility: ${t[a[0]]} !== ${n[a[1]]}`)}mergeFunction(e){if(e.length!==2)throw new B(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],a;return Array.isArray(this.axes)?a=this.axes.map((r,s)=>Gc(r,e[s].shape.length)):a=[Gc(this.axes,t.shape.length),Gc(this.axes,n.shape.length)],this.normalize&&(t=Gh(t,a[0]),n=Gh(n,a[1])),pB(t,n,a)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Gc(this.axes,e.length),Gc(this.axes,t.length)],n}computeOutputShape(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new $e("Dot layer does not support tensors of 4D or higher rank yet.");let a=this.interpretAxes(t,n);t.splice(a[0],1),n.splice(a[1],1),n.splice(0,1);let r=t.concat(n);return r.length===1&&r.push(1),r}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};ux.className="Dot";re.registerClass(ux);var cx=class extends je{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Pe(e);return Rc(()=>Ah(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};cx.className="GaussianNoise";re.registerClass(cx);var px=class extends je{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Pe(e);return this.rate>0&&this.rate<1?Rc(()=>{let a=Math.sqrt(this.rate/(1-this.rate));return n.mul(Ah(n.shape,1,a))},()=>n,t.training||!1):n})}};px.className="GaussianDropout";re.registerClass(px);var dx=class extends je{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Pe(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return D(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Rc(()=>{let a=Pe(e),r=1.6732632423543772,s=1.0507009873554805,i=-r*s,o=Jr($l(n),this.rate);o=Ac(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,c=-l*i*this.rate;return a.mul(o).add(o.add(-1).mul(i)).mul(l).add(c)},()=>Pe(e),t.training||!1)}return e})}};dx.className="AlphaDropout";re.registerClass(dx);function Hc(e,t,n,a,r,s=.001){let i;if(e.rank===2)i=z0(e,t,n,a,r,s);else if(e.rank===3)i=W0(e,t,n,a,r,s);else if(e.rank===4)i=B0(e,t,n,a,r,s);else throw new $e(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function dB(e,t,n,a,r=.001){return D(()=>{let s=rh(e,a),i=s.mean,o=s.variance;return[Hc(e,i,o,n,t,r),i,o]})}function hB(e,t,n,a,r=.001){return D(()=>{let s=rh(e,a),i=s.mean,o=s.variance,l=[];for(let h of _a(0,e.rank))a.indexOf(h)!==-1?l.push(1):l.push(e.shape[h]);let c=i.reshape(l),u=o.reshape(l),p=t==null?null:t.reshape(l),d=n==null?null:n.reshape(l);return[Hc(e,c,u,d,p,r),i,o]})}function mB(e,t,n,a,r=.001){return w.arraysEqual(a.slice().sort(),_a(0,e.rank-1))?dB(e,t,n,a,r):hB(e,t,n,a,r)}var hx=class extends je{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=xt(e.betaInitializer||"zeros"),this.gammaInitializer=xt(e.gammaInitializer||"ones"),this.movingMeanInitializer=xt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=xt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Ut(e.betaConstraint),this.gammaConstraint=Ut(e.gammaConstraint),this.betaRegularizer=vt(e.betaRegularizer),this.gammaRegularizer=vt(e.gammaRegularizer)}build(e){e=ut(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new B(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Yt({ndim:e.length,axes:{[t]:n}})];let a=[n];this.scale&&(this.gamma=this.addWeight("gamma",a,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",a,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",a,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",a,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return D(()=>{let n=t.training==null?!1:t.training,a=Pe(e),r=a.shape,s=r.length,i=_a(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=_i(1,s);l[o]=r[o];let c=i.slice();c.sort();let u=!w.arraysEqual(c,_a(0,s).slice(0,s-1)),p=()=>{if(u){let g=this.movingMean.read().reshape(l),y=this.movingVariance.read().reshape(l),b=this.center?this.beta.read().reshape(l):null,x=this.scale?this.gamma.read().reshape(l):null;return Hc(a,g,y,b,x,this.epsilon)}else return Hc(a,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return p();let[d,h,m]=mB(a,this.gamma.read(),this.beta.read(),i,this.epsilon),f=(g,y,b)=>{D(()=>{let x=1-b,v=g.read(),T=v.sub(y).mul(x);g.write(v.sub(T))})};return(()=>{f(this.movingMean,h,this.momentum),f(this.movingVariance,m,this.momentum)})(),d})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:_t(this.betaInitializer),gammaInitializer:_t(this.gammaInitializer),movingMeanInitializer:_t(this.movingMeanInitializer),movingVarianceInitializer:_t(this.movingVarianceInitializer),betaRegularizer:ct(this.betaRegularizer),gammaRegularizer:ct(this.gammaRegularizer),betaConstraint:Vt(this.betaConstraint),gammaConstraint:Vt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};hx.className="BatchNormalization";re.registerClass(hx);var mx=class extends je{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=xt(e.betaInitializer||"zeros"),this.gammaInitializer=xt(e.gammaInitializer||"ones"),this.betaRegularizer=vt(e.betaRegularizer),this.gammaRegularizer=vt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=ut(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let r=0;r=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==es(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),a=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,a):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,a):this.beta=null,this.built=!0}call(e,t){let n=Pe(e),a=n.shape,r=a.length;return D(()=>{let s=!0,{mean:i,variance:o}=rh(n,this.axis,s),l=_i(1,r);for(let m of this.axis)l[m]=a[m];let c=m=>m!=null&&m.shape.length!==r&&this.axis!==[r-1]?m.reshape(l):m,u=c(this.gamma.read()),p=c(this.beta.read()),d=[],h=[];for(let m=0;m{if(e.rank!==4)throw new B(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new B("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=Sa()),n!=="channelsLast"&&n!=="channelsFirst")throw new B(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let a;return n==="channelsFirst"?a=[[0,0],[0,0],t[0],t[1]]:a=[[0,0],t[0],t[1],[0,0]],ea(e,a)})}var fx=class extends je{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?Sa():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new B(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new B(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new B(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Yt({ndim:4})]}computeOutputShape(e){e=ut(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return D(()=>fB(Pe(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};fx.className="ZeroPadding2D";re.registerClass(fx);function om(e,t,n,a,r,s){return D(()=>{Rt(r),Xk(s),ta(a),n==null&&(n=[1,1]),a==null&&(a="valid"),r==null&&(r=Sa()),s==null&&(s="max"),e=Ob(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=$t(e,t,n,o):i=Qn(e,t,n,o),r==="channelsFirst"&&(i=Ve(i,[0,3,1,2])),i})}function pI(e,t,n,a,r,s){return D(()=>{Rt(r),Xk(s),ta(a),n==null&&(n=[1,1,1]),a==null&&(a="valid"),r==null&&(r=Sa()),s==null&&(s="max"),e=sI(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=Ny(e,t,n,o):i=py(e,t,n,o),r==="channelsFirst"&&(i=Ve(i,[0,4,1,2,3])),i})}var dI=class extends je{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new B(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Xt(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new B(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Xt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,ta(this.padding),this.inputSpec=[new Yt({ndim:3})]}computeOutputShape(e){e=ut(e);let t=Aa(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return D(()=>{this.invokeCallHook(e,t),e=$c(Pe(e),2);let n=this.poolingFunction(Pe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Qr(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},gx=class extends dI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),ta(a),om(e,t,n,a,r,"max")}};gx.className="MaxPooling1D";re.registerClass(gx);var yx=class extends dI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),ta(a),om(e,t,n,a,r,"avg")}};yx.className="AveragePooling1D";re.registerClass(yx);var hI=class extends je{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new B(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Xt(this.poolSize,"poolSize"),Xt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),ta(this.padding),this.inputSpec=[new Yt({ndim:4})]}computeOutputShape(e){e=ut(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Aa(t,this.poolSize[0],this.padding,this.strides[0]),n=Aa(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return D(()=>(this.invokeCallHook(e,t),this.poolingFunction(Pe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},bx=class extends hI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),ta(a),om(e,t,n,a,r,"max")}};bx.className="MaxPooling2D";re.registerClass(bx);var xx=class extends hI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),ta(a),om(e,t,n,a,r,"avg")}};xx.className="AveragePooling2D";re.registerClass(xx);var mI=class extends je{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new B(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Xt(this.poolSize,"poolSize"),Xt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),ta(this.padding),this.inputSpec=[new Yt({ndim:5})]}computeOutputShape(e){e=ut(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Aa(t,this.poolSize[0],this.padding,this.strides[0]),n=Aa(n,this.poolSize[1],this.padding,this.strides[1]),a=Aa(a,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,a]:[e[0],t,n,a,e[4]]}call(e,t){return D(()=>(this.invokeCallHook(e,t),this.poolingFunction(Pe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},vx=class extends mI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),ta(a),pI(e,t,n,a,r,"max")}};vx.className="MaxPooling3D";re.registerClass(vx);var wx=class extends mI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),ta(a),pI(e,t,n,a,r,"avg")}};wx.className="AveragePooling3D";re.registerClass(wx);var fI=class extends je{constructor(e){super(e);this.inputSpec=[new Yt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new 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l4=class{constructor(e,t,n,a,r,s,i){this.name=e,this.dtype=t,this.maxSize=n,this.elementShape=a,this.identicalElementShapes=r,this.dynamicSize=s,this.clearAfterRead=i,this.tensors=[],this.closed_=!1,this.idTensor=ve(0),qt(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.tensor.id))&&t.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(e){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||e>=this.size())throw new Error(`Tried to read from index ${e}, but array size is: ${this.size()}`);let t=this.tensors[e];if(t.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${e} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(t.cleared=!0),t.read=!0,t.tensor}readMany(e){return e.map(t=>this.read(t))}write(e,t){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||!this.dynamicSize&&e>=this.maxSize)throw new Error(`Tried to write to index ${e}, but array is not resizeable and size is: ${this.maxSize}`);let n=this.tensors[e]||{};if(t.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, - because the value dtype is ${t.dtype}, but TensorArray dtype is ${this.dtype}.`);if(this.size()===0&&(this.elementShape==null||this.elementShape.length===0)&&(this.elementShape=t.shape),fa(this.elementShape,t.shape,`TensorArray ${this.name}: Could not write to TensorArray index ${e}.`),n.read)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been read.`);if(n.written)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been written.`);n.tensor=t,qt(t),n.written=!0,this.tensors[e]=n}writeMany(e,t){if(e.length!==t.length)throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${e.length} is not the same as tensors size: ${t.length}.`);e.forEach((n,a)=>this.write(n,t[a]))}gather(e,t){if(!!t&&t!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${t}`);if(e)e=e.slice(0,this.size());else{e=[];for(let a=0;a=this.maxSize)throw new Error(`Max index must be < array size (${n} vs. ${this.maxSize})`);this.writeMany(e,lt(t,0))}split(e,t){if(t.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);let n=0,a=e.map(o=>(n+=o,n));if(n!==t.shape[0])throw new Error(`Expected sum of lengths to be equal to +var __defProp = Object.defineProperty; +var __export = (target, all4) => { + for (var name in all4) + __defProp(target, name, {get: all4[name], enumerable: true}); +}; + +// dist/tfjs.esm.js +var tfjs_esm_exports = {}; +__export(tfjs_esm_exports, { + Abs: () => Abs, + Acos: () => Acos, + Acosh: () => Acosh, + AdadeltaOptimizer: () => AdadeltaOptimizer, + AdagradOptimizer: () => AdagradOptimizer, + AdamOptimizer: () => AdamOptimizer, + AdamaxOptimizer: () => AdamaxOptimizer, + Add: () => Add, + AddN: () => AddN, + All: () => All, + Any: () => Any, + ArgMax: () => ArgMax, + ArgMin: () => ArgMin, + Asin: () => Asin, + Asinh: () => Asinh, + Atan: () => Atan, + Atan2: () => Atan2, + Atanh: () => Atanh, + AvgPool: () => AvgPool, + AvgPool3D: () => AvgPool3D, + AvgPool3DGrad: () => AvgPool3DGrad, + AvgPoolGrad: () => AvgPoolGrad, + BackendWasm: () => BackendWasm, + BatchMatMul: () => BatchMatMul, + BatchToSpaceND: () => BatchToSpaceND, + Bincount: () => Bincount, + BroadcastTo: () => BroadcastTo, + Callback: () => Callback, + CallbackList: () => CallbackList, + Cast: () => Cast, + Ceil: () => Ceil, + ClipByValue: () => ClipByValue, + Complex: () => Complex, + ComplexAbs: () => ComplexAbs, + Concat: () => Concat, + Conv2D: () => Conv2D, + Conv2DBackpropFilter: () => Conv2DBackpropFilter, + Conv2DBackpropInput: () => Conv2DBackpropInput, + Conv3D: () => Conv3D, + Conv3DBackpropFilterV2: () => Conv3DBackpropFilterV2, + Conv3DBackpropInputV2: () => Conv3DBackpropInputV2, + Cos: () => Cos, + Cosh: () => Cosh, + CropAndResize: () => CropAndResize, + Cumsum: () => Cumsum, + CustomCallback: () => CustomCallback, + DataStorage: () => DataStorage, + DenseBincount: () => DenseBincount, + DepthToSpace: () => DepthToSpace, + DepthwiseConv2dNative: () => DepthwiseConv2dNative, + DepthwiseConv2dNativeBackpropFilter: () => DepthwiseConv2dNativeBackpropFilter, + DepthwiseConv2dNativeBackpropInput: () => DepthwiseConv2dNativeBackpropInput, + Diag: () => Diag, + Dilation2D: () => Dilation2D, + Dilation2DBackpropFilter: () => Dilation2DBackpropFilter, + Dilation2DBackpropInput: () => Dilation2DBackpropInput, + ENV: () => ENV, + EarlyStopping: () => EarlyStopping, + Elu: () => Elu, + EluGrad: () => EluGrad, + Environment: () => Environment, + Equal: () => Equal, + Erf: () => Erf, + Exp: () => Exp, + ExpandDims: () => ExpandDims, + Expm1: () => Expm1, + FFT: () => FFT, + Fill: () => Fill, + FlipLeftRight: () => FlipLeftRight, + Floor: () => Floor, + FloorDiv: () => FloorDiv, + FromPixels: () => FromPixels, + FusedBatchNorm: () => FusedBatchNorm, + FusedConv2D: () => FusedConv2D, + FusedDepthwiseConv2D: () => FusedDepthwiseConv2D, + GatherNd: () => GatherNd, + GatherV2: () => GatherV2, + GraphModel: () => GraphModel, + Greater: () => Greater, + GreaterEqual: () => GreaterEqual, + History: () => History, + IFFT: () => IFFT, + Identity: () => Identity, + Imag: () => Imag, + InputSpec: () => InputSpec, + IsFinite: () => IsFinite, + IsInf: () => IsInf, + IsNan: () => IsNan, + KernelBackend: () => KernelBackend, + LRN: () => LRN, + LRNGrad: () => LRNGrad, + LayerVariable: () => LayerVariable, + LayersModel: () => LayersModel, + LeakyRelu: () => LeakyRelu, + Less: () => Less, + LessEqual: () => LessEqual, + LinSpace: () => LinSpace, + Log: () => Log, + Log1p: () => Log1p, + LogSoftmax: () => LogSoftmax, + LogicalAnd: () => LogicalAnd, + LogicalNot: () => LogicalNot, + LogicalOr: () => LogicalOr, + Max: () => Max, + MaxPool: () => MaxPool, + MaxPool3D: () => MaxPool3D, + MaxPool3DGrad: () => MaxPool3DGrad, + MaxPoolGrad: () => MaxPoolGrad, + MaxPoolWithArgmax: () => MaxPoolWithArgmax, + Maximum: () => Maximum, + Mean: () => Mean, + Min: () => Min, + Minimum: () => Minimum, + MirrorPad: () => MirrorPad, + Mod: () => Mod, + MomentumOptimizer: () => MomentumOptimizer, + Multinomial: () => Multinomial, + Multiply: () => Multiply, + Neg: () => Neg, + NonMaxSuppressionV3: () => NonMaxSuppressionV3, + NonMaxSuppressionV4: () => NonMaxSuppressionV4, + NonMaxSuppressionV5: () => NonMaxSuppressionV5, + NotEqual: () => NotEqual, + OP_SCOPE_SUFFIX: () => OP_SCOPE_SUFFIX, + OneHot: () => OneHot, + OnesLike: () => OnesLike, + Optimizer: () => Optimizer, + Pack: () => Pack, + PadV2: () => PadV2, + Pool: () => Pool, + Pow: () => Pow, + Prelu: () => Prelu, + Prod: () => Prod, + RMSPropOptimizer: () => RMSPropOptimizer, + RNN: () => RNN, + Range: () => Range, + Rank: () => Rank, + Real: () => Real, + RealDiv: () => RealDiv, + Reciprocal: () => Reciprocal, + Reduction: () => Reduction, + Relu: () => Relu, + Relu6: () => Relu6, + Reshape: () => Reshape, + ResizeBilinear: () => ResizeBilinear, + ResizeBilinearGrad: () => ResizeBilinearGrad, + ResizeNearestNeighbor: () => ResizeNearestNeighbor, + ResizeNearestNeighborGrad: () => ResizeNearestNeighborGrad, + Reverse: () => Reverse, + RotateWithOffset: () => RotateWithOffset, + Round: () => Round, + Rsqrt: () => Rsqrt, + SGDOptimizer: () => SGDOptimizer, + ScatterNd: () => ScatterNd, + Select: () => Select, + Selu: () => Selu, + Sequential: () => Sequential, + Sigmoid: () => Sigmoid, + Sign: () => Sign, + Sin: () => Sin, + Sinh: () => Sinh, + Slice: () => Slice, + Softmax: () => Softmax, + Softplus: () => Softplus, + SpaceToBatchND: () => SpaceToBatchND, + SparseToDense: () => SparseToDense, + SplitV: () => SplitV, + Sqrt: () => Sqrt, + Square: () => Square, + SquaredDifference: () => SquaredDifference, + Step: () => Step, + StridedSlice: () => StridedSlice, + Sub: () => Sub, + Sum: () => Sum, + SymbolicTensor: () => SymbolicTensor, + Tan: () => Tan, + Tanh: () => Tanh, + Tensor: () => Tensor, + TensorBuffer: () => TensorBuffer, + Tile: () => Tile, + TopK: () => TopK, + Transform: () => Transform, + Transpose: () => Transpose, + Unique: () => Unique, + Unpack: () => Unpack, + UnsortedSegmentSum: () => UnsortedSegmentSum, + Variable: () => Variable, + ZerosLike: () => ZerosLike, + _FusedMatMul: () => _FusedMatMul, + abs: () => abs, + acos: () => acos, + acosh: () => acosh, + add: () => add2, + addN: () => addN, + all: () => all, + any: () => any, + argMax: () => argMax, + argMin: () => argMin, + asin: () => asin, + asinh: () => asinh, + atan: () => atan, + atan2: () => atan2, + atanh: () => atanh, + avgPool: () => avgPool, + avgPool3d: () => avgPool3d, + backend: () => backend, + backend_util: () => backend_util_exports, + basicLSTMCell: () => basicLSTMCell, + batchNorm: () => batchNorm, + batchNorm2d: () => batchNorm2d, + batchNorm3d: () => batchNorm3d, + batchNorm4d: () => batchNorm4d, + batchToSpaceND: () => batchToSpaceND, + bincount: () => bincount, + booleanMaskAsync: () => booleanMaskAsync, + broadcastTo: () => broadcastTo, + browser: () => browser_exports, + buffer: () => buffer, + callbacks: () => callbacks, + cast: () => cast, + ceil: () => ceil, + clipByValue: () => clipByValue, + clone: () => clone, + complex: () => complex, + concat: () => concat, + concat1d: () => concat1d, + concat2d: () => concat2d, + concat3d: () => concat3d, + concat4d: () => concat4d, + constraints: () => exports_constraints_exports, + conv1d: () => conv1d, + conv2d: () => conv2d, + conv2dTranspose: () => conv2dTranspose, + conv3d: () => conv3d, + conv3dTranspose: () => conv3dTranspose, + copyRegisteredKernels: () => copyRegisteredKernels, + cos: () => cos, + cosh: () => cosh, + cosineWindow: () => cosineWindow, + cumsum: () => cumsum, + customGrad: () => customGrad, + data: () => dist_exports, + denseBincount: () => denseBincount, + deprecationWarn: () => deprecationWarn, + depthToSpace: () => depthToSpace, + depthwiseConv2d: () => depthwiseConv2d, + deregisterOp: () => deregisterOp, + device_util: () => device_util_exports, + diag: () => diag, + dilation2d: () => dilation2d, + disableDeprecationWarnings: () => disableDeprecationWarnings, + dispose: () => dispose, + disposeVariables: () => disposeVariables, + div: () => div, + divNoNan: () => divNoNan, + dot: () => dot, + dropout: () => dropout, + elu: () => elu, + enableDebugMode: () => enableDebugMode, + enableProdMode: () => enableProdMode, + enclosingPowerOfTwo: () => enclosingPowerOfTwo, + engine: () => engine, + env: () => env, + equal: () => equal, + erf: () => erf, + exp: () => exp, + expandDims: () => expandDims, + expm1: () => expm1, + eye: () => eye, + fft: () => fft, + fill: () => fill, + findBackend: () => findBackend, + findBackendFactory: () => findBackendFactory, + floor: () => floor, + floorDiv: () => floorDiv, + fused: () => fused_ops_exports, + gather: () => gather, + gatherND: () => gatherND, + gather_util: () => gather_nd_util_exports, + getBackend: () => getBackend, + getGradient: () => getGradient, + getKernel: () => getKernel, + getKernelsForBackend: () => getKernelsForBackend, + grad: () => grad, + grads: () => grads, + greater: () => greater, + greaterEqual: () => greaterEqual, + ifft: () => ifft, + imag: () => imag, + image: () => image, + inTopKAsync: () => inTopKAsync, + initializers: () => exports_initializers_exports, + input: () => input, + io: () => io_exports, + irfft: () => irfft, + isFinite: () => isFinite2, + isInf: () => isInf, + isNaN: () => isNaN2, + keep: () => keep, + kernel_impls: () => kernel_impls_exports, + layers: () => exports_layers_exports, + leakyRelu: () => leakyRelu, + less: () => less, + lessEqual: () => lessEqual, + linalg: () => linalg, + linspace: () => linspace, + loadGraphModel: () => loadGraphModel, + loadLayersModel: () => loadLayersModel, + localResponseNormalization: () => localResponseNormalization, + log: () => log, + log1p: () => log1p, + logSigmoid: () => logSigmoid, + logSoftmax: () => logSoftmax, + logSumExp: () => logSumExp, + logicalAnd: () => logicalAnd, + logicalNot: () => logicalNot, + logicalOr: () => logicalOr, + logicalXor: () => logicalXor, + losses: () => losses, + matMul: () => matMul, + math: () => math_exports, + max: () => max, + maxPool: () => maxPool, + maxPool3d: () => maxPool3d, + maxPoolWithArgmax: () => maxPoolWithArgmax, + maximum: () => maximum, + mean: () => mean, + memory: () => memory, + metrics: () => exports_metrics_exports, + min: () => min, + minimum: () => minimum, + mirrorPad: () => mirrorPad, + mod: () => mod, + model: () => model, + models: () => exports_models_exports, + moments: () => moments, + movingAverage: () => movingAverage, + mul: () => mul, + multiRNNCell: () => multiRNNCell, + multinomial: () => multinomial, + neg: () => neg, + nextFrame: () => nextFrame, + norm: () => norm, + notEqual: () => notEqual, + oneHot: () => oneHot, + ones: () => ones2, + onesLike: () => onesLike, + op: () => op, + outerProduct: () => outerProduct, + pad: () => pad, + pad1d: () => pad1d, + pad2d: () => pad2d, + pad3d: () => pad3d, + pad4d: () => pad4d, + pool: () => pool, + pow: () => pow, + prelu: () => prelu, + print: () => print2, + prod: () => prod, + profile: () => profile, + rand: () => rand, + randomGamma: () => randomGamma, + randomNormal: () => randomNormal, + randomUniform: () => randomUniform, + range: () => range, + ready: () => ready, + real: () => real, + reciprocal: () => reciprocal, + registerBackend: () => registerBackend, + registerCallbackConstructor: () => registerCallbackConstructor, + registerGradient: () => registerGradient, + registerKernel: () => registerKernel, + registerOp: () => registerOp, + regularizers: () => exports_regularizers_exports, + relu: () => relu, + relu6: () => relu6, + removeBackend: () => removeBackend, + reshape: () => reshape, + reverse: () => reverse, + reverse1d: () => reverse1d, + reverse2d: () => reverse2d, + reverse3d: () => reverse3d, + reverse4d: () => reverse4d, + rfft: () => rfft, + round: () => round2, + rsqrt: () => rsqrt, + scalar: () => scalar, + scatterND: () => scatterND, + scatter_util: () => scatter_nd_util_exports, + selu: () => selu, + separableConv2d: () => separableConv2d, + sequential: () => sequential, + serialization: () => serialization_exports, + setBackend: () => setBackend, + setPlatform: () => setPlatform, + setWasmPath: () => setWasmPath, + setWasmPaths: () => setWasmPaths, + setdiff1dAsync: () => setdiff1dAsync, + sigmoid: () => sigmoid, + sign: () => sign, + signal: () => signal, + sin: () => sin, + sinh: () => sinh, + slice: () => slice, + slice1d: () => slice1d, + slice2d: () => slice2d, + slice3d: () => slice3d, + slice4d: () => slice4d, + slice_util: () => slice_util_exports, + softmax: () => softmax, + softplus: () => softplus, + spaceToBatchND: () => spaceToBatchND, + sparseToDense: () => sparseToDense, + spectral: () => spectral, + split: () => split, + sqrt: () => sqrt, + square: () => square, + squaredDifference: () => squaredDifference, + squeeze: () => squeeze, + stack: () => stack, + step: () => step, + stridedSlice: () => stridedSlice, + sub: () => sub, + sum: () => sum2, + sumOutType: () => sumOutType, + tan: () => tan, + tanh: () => tanh2, + tensor: () => tensor, + tensor1d: () => tensor1d, + tensor2d: () => tensor2d, + tensor3d: () => tensor3d, + tensor4d: () => tensor4d, + tensor5d: () => tensor5d, + tensor6d: () => tensor6d, + tensor_util: () => tensor_util_exports, + test_util: () => test_util_exports, + tidy: () => tidy, + tile: () => tile, + time: () => time, + topk: () => topk, + train: () => train, + transpose: () => transpose, + truncatedNormal: () => truncatedNormal, + unique: () => unique, + unregisterGradient: () => unregisterGradient, + unregisterKernel: () => unregisterKernel, + unsortedSegmentSum: () => unsortedSegmentSum, + unstack: () => unstack, + upcastType: () => upcastType, + util: () => util_exports, + valueAndGrad: () => valueAndGrad, + valueAndGrads: () => valueAndGrads, + variable: () => variable, + variableGrads: () => variableGrads, + version: () => version8, + version_converter: () => version3, + version_core: () => version, + version_layers: () => version2, + version_wasm: () => version9, + where: () => where, + whereAsync: () => whereAsync, + zeros: () => zeros, + zerosLike: () => zerosLike +}); +var __create = Object.create; +var __defProp2 = Object.defineProperty; +var __getProtoOf = Object.getPrototypeOf; +var __hasOwnProp = Object.prototype.hasOwnProperty; +var __getOwnPropNames = Object.getOwnPropertyNames; +var __getOwnPropDesc = Object.getOwnPropertyDescriptor; +var __markAsModule = (target) => __defProp2(target, "__esModule", {value: true}); +var __commonJS = (cb, mod4) => () => (mod4 || cb((mod4 = {exports: {}}).exports, mod4), mod4.exports); +var __export2 = (target, all4) => { + for (var name in all4) + __defProp2(target, name, {get: all4[name], enumerable: true}); +}; +var __exportStar = (target, module2, desc) => { + if (module2 && typeof module2 === "object" || typeof module2 === "function") { + for (let key of __getOwnPropNames(module2)) + if (!__hasOwnProp.call(target, key) && key !== "default") + __defProp2(target, key, {get: () => module2[key], enumerable: !(desc = __getOwnPropDesc(module2, key)) || desc.enumerable}); + } + return target; +}; +var __toModule = (module2) => { + return __exportStar(__markAsModule(__defProp2(module2 != null ? __create(__getProtoOf(module2)) : {}, "default", module2 && module2.__esModule && "default" in module2 ? {get: () => module2.default, enumerable: true} : {value: module2, enumerable: true})), module2); +}; +var require_browser = __commonJS(() => { +}); +var require_alea = __commonJS((exports, module2) => { + (function(global2, module22, define2) { + function Alea(seed) { + var me = this, mash = Mash(); + me.next = function() { + var t = 2091639 * me.s0 + me.c * 23283064365386963e-26; + me.s0 = me.s1; + me.s1 = me.s2; + return me.s2 = t - (me.c = t | 0); + }; + me.c = 1; + me.s0 = mash(" "); + me.s1 = mash(" "); + me.s2 = mash(" "); + me.s0 -= mash(seed); + if (me.s0 < 0) { + me.s0 += 1; + } + me.s1 -= mash(seed); + if (me.s1 < 0) { + me.s1 += 1; + } + me.s2 -= mash(seed); + if (me.s2 < 0) { + me.s2 += 1; + } + mash = null; + } + function copy(f, t) { + t.c = f.c; + t.s0 = f.s0; + t.s1 = f.s1; + t.s2 = f.s2; + return t; + } + function impl(seed, opts) { + var xg = new Alea(seed), state = opts && opts.state, prng = xg.next; + prng.int32 = function() { + return xg.next() * 4294967296 | 0; + }; + prng.double = function() { + return prng() + (prng() * 2097152 | 0) * 11102230246251565e-32; + }; + prng.quick = prng; + if (state) { + if (typeof state == "object") + copy(state, xg); + prng.state = function() { + return copy(xg, {}); + }; + } + return prng; + } + function Mash() { + var n = 4022871197; + var mash = function(data) { + data = data.toString(); + for (var i = 0; i < data.length; i++) { + n += data.charCodeAt(i); + var h = 0.02519603282416938 * n; + n = h >>> 0; + h -= n; + h *= n; + n = h >>> 0; + h -= n; + n += h * 4294967296; + } + return (n >>> 0) * 23283064365386963e-26; + }; + return mash; + } + if (module22 && module22.exports) { + module22.exports = impl; + } else if (define2 && define2.amd) { + define2(function() { + return impl; + }); + } else { + this.alea = impl; + } + })(exports, typeof module2 == "object" && module2, typeof define == "function" && define); +}); +var require_xor128 = __commonJS((exports, module2) => { + (function(global2, module22, define2) { + function XorGen(seed) { + var me = this, strseed = ""; + me.x = 0; + me.y = 0; + me.z = 0; + me.w = 0; + me.next = function() { + var t = me.x ^ me.x << 11; + me.x = me.y; + me.y = me.z; + me.z = me.w; + return me.w ^= me.w >>> 19 ^ t ^ t >>> 8; + }; + if (seed === (seed | 0)) { + me.x = seed; + } else { + strseed += seed; + } + for (var k = 0; k < strseed.length + 64; k++) { + me.x ^= strseed.charCodeAt(k) | 0; + me.next(); + } + } + function copy(f, t) { + t.x = f.x; + t.y = f.y; + t.z = f.z; + t.w = f.w; + return t; + } + function impl(seed, opts) { + var xg = new XorGen(seed), state = opts && opts.state, prng = function() { + return (xg.next() >>> 0) / 4294967296; + }; + prng.double = function() { + do { + var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21); + } while (result === 0); + return result; + }; + prng.int32 = xg.next; + prng.quick = prng; + if (state) { + if (typeof state == "object") + copy(state, xg); + prng.state = function() { + return copy(xg, {}); + }; + } + return prng; + } + if (module22 && module22.exports) { + module22.exports = impl; + } else if (define2 && define2.amd) { + define2(function() { + return impl; + }); + } else { + this.xor128 = impl; + } + })(exports, typeof module2 == "object" && module2, typeof define == "function" && define); +}); +var require_xorwow = __commonJS((exports, module2) => { + (function(global2, module22, define2) { + function XorGen(seed) { + var me = this, strseed = ""; + me.next = function() { + var t = me.x ^ me.x >>> 2; + me.x = me.y; + me.y = me.z; + me.z = me.w; + me.w = me.v; + return (me.d = me.d + 362437 | 0) + (me.v = me.v ^ me.v << 4 ^ (t ^ t << 1)) | 0; + }; + me.x = 0; + me.y = 0; + me.z = 0; + me.w = 0; + me.v = 0; + if (seed === (seed | 0)) { + me.x = seed; + } else { + strseed += seed; + } + for (var k = 0; k < strseed.length + 64; k++) { + me.x ^= strseed.charCodeAt(k) | 0; + if (k == strseed.length) { + me.d = me.x << 10 ^ me.x >>> 4; + } + me.next(); + } + } + function copy(f, t) { + t.x = f.x; + t.y = f.y; + t.z = f.z; + t.w = f.w; + t.v = f.v; + t.d = f.d; + return t; + } + function impl(seed, opts) { + var xg = new XorGen(seed), state = opts && opts.state, prng = function() { + return (xg.next() >>> 0) / 4294967296; + }; + prng.double = function() { + do { + var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21); + } while (result === 0); + return result; + }; + prng.int32 = xg.next; + prng.quick = prng; + if (state) { + if (typeof state == "object") + copy(state, xg); + prng.state = function() { + return copy(xg, {}); + }; + } + return prng; + } + if (module22 && module22.exports) { + module22.exports = impl; + } else if (define2 && define2.amd) { + define2(function() { + return impl; + }); + } else { + this.xorwow = impl; + } + })(exports, typeof module2 == "object" && module2, typeof define == "function" && define); +}); +var require_xorshift7 = __commonJS((exports, module2) => { + (function(global2, module22, define2) { + function XorGen(seed) { + var me = this; + me.next = function() { + var X = me.x, i = me.i, t, v, w; + t = X[i]; + t ^= t >>> 7; + v = t ^ t << 24; + t = X[i + 1 & 7]; + v ^= t ^ t >>> 10; + t = X[i + 3 & 7]; + v ^= t ^ t >>> 3; + t = X[i + 4 & 7]; + v ^= t ^ t << 7; + t = X[i + 7 & 7]; + t = t ^ t << 13; + v ^= t ^ t << 9; + X[i] = v; + me.i = i + 1 & 7; + return v; + }; + function init2(me2, seed2) { + var j, w, X = []; + if (seed2 === (seed2 | 0)) { + w = X[0] = seed2; + } else { + seed2 = "" + seed2; + for (j = 0; j < seed2.length; ++j) { + X[j & 7] = X[j & 7] << 15 ^ seed2.charCodeAt(j) + X[j + 1 & 7] << 13; + } + } + while (X.length < 8) + X.push(0); + for (j = 0; j < 8 && X[j] === 0; ++j) + ; + if (j == 8) + w = X[7] = -1; + else + w = X[j]; + me2.x = X; + me2.i = 0; + for (j = 256; j > 0; --j) { + me2.next(); + } + } + init2(me, seed); + } + function copy(f, t) { + t.x = f.x.slice(); + t.i = f.i; + return t; + } + function impl(seed, opts) { + if (seed == null) + seed = +new Date(); + var xg = new XorGen(seed), state = opts && opts.state, prng = function() { + return (xg.next() >>> 0) / 4294967296; + }; + prng.double = function() { + do { + var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21); + } while (result === 0); + return result; + }; + prng.int32 = xg.next; + prng.quick = prng; + if (state) { + if (state.x) + copy(state, xg); + prng.state = function() { + return copy(xg, {}); + }; + } + return prng; + } + if (module22 && module22.exports) { + module22.exports = impl; + } else if (define2 && define2.amd) { + define2(function() { + return impl; + }); + } else { + this.xorshift7 = impl; + } + })(exports, typeof module2 == "object" && module2, typeof define == "function" && define); +}); +var require_xor4096 = __commonJS((exports, module2) => { + (function(global2, module22, define2) { + function XorGen(seed) { + var me = this; + me.next = function() { + var w = me.w, X = me.X, i = me.i, t, v; + me.w = w = w + 1640531527 | 0; + v = X[i + 34 & 127]; + t = X[i = i + 1 & 127]; + v ^= v << 13; + t ^= t << 17; + v ^= v >>> 15; + t ^= t >>> 12; + v = X[i] = v ^ t; + me.i = i; + return v + (w ^ w >>> 16) | 0; + }; + function init2(me2, seed2) { + var t, v, i, j, w, X = [], limit = 128; + if (seed2 === (seed2 | 0)) { + v = seed2; + seed2 = null; + } else { + seed2 = seed2 + "\0"; + v = 0; + limit = Math.max(limit, seed2.length); + } + for (i = 0, j = -32; j < limit; ++j) { + if (seed2) + v ^= seed2.charCodeAt((j + 32) % seed2.length); + if (j === 0) + w = v; + v ^= v << 10; + v ^= v >>> 15; + v ^= v << 4; + v ^= v >>> 13; + if (j >= 0) { + w = w + 1640531527 | 0; + t = X[j & 127] ^= v + w; + i = t == 0 ? i + 1 : 0; + } + } + if (i >= 128) { + X[(seed2 && seed2.length || 0) & 127] = -1; + } + i = 127; + for (j = 4 * 128; j > 0; --j) { + v = X[i + 34 & 127]; + t = X[i = i + 1 & 127]; + v ^= v << 13; + t ^= t << 17; + v ^= v >>> 15; + t ^= t >>> 12; + X[i] = v ^ t; + } + me2.w = w; + me2.X = X; + me2.i = i; + } + init2(me, seed); + } + function copy(f, t) { + t.i = f.i; + t.w = f.w; + t.X = f.X.slice(); + return t; + } + ; + function impl(seed, opts) { + if (seed == null) + seed = +new Date(); + var xg = new XorGen(seed), state = opts && opts.state, prng = function() { + return (xg.next() >>> 0) / 4294967296; + }; + prng.double = function() { + do { + var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21); + } while (result === 0); + return result; + }; + prng.int32 = xg.next; + prng.quick = prng; + if (state) { + if (state.X) + copy(state, xg); + prng.state = function() { + return copy(xg, {}); + }; + } + return prng; + } + if (module22 && module22.exports) { + module22.exports = impl; + } else if (define2 && define2.amd) { + define2(function() { + return impl; + }); + } else { + this.xor4096 = impl; + } + })(exports, typeof module2 == "object" && module2, typeof define == "function" && define); +}); +var require_tychei = __commonJS((exports, module2) => { + (function(global2, module22, define2) { + function XorGen(seed) { + var me = this, strseed = ""; + me.next = function() { + var b = me.b, c = me.c, d = me.d, a = me.a; + b = b << 25 ^ b >>> 7 ^ c; + c = c - d | 0; + d = d << 24 ^ d >>> 8 ^ a; + a = a - b | 0; + me.b = b = b << 20 ^ b >>> 12 ^ c; + me.c = c = c - d | 0; + me.d = d << 16 ^ c >>> 16 ^ a; + return me.a = a - b | 0; + }; + me.a = 0; + me.b = 0; + me.c = 2654435769 | 0; + me.d = 1367130551; + if (seed === Math.floor(seed)) { + me.a = seed / 4294967296 | 0; + me.b = seed | 0; + } else { + strseed += seed; + } + for (var k = 0; k < strseed.length + 20; k++) { + me.b ^= strseed.charCodeAt(k) | 0; + me.next(); + } + } + function copy(f, t) { + t.a = f.a; + t.b = f.b; + t.c = f.c; + t.d = f.d; + return t; + } + ; + function impl(seed, opts) { + var xg = new XorGen(seed), state = opts && opts.state, prng = function() { + return (xg.next() >>> 0) / 4294967296; + }; + prng.double = function() { + do { + var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21); + } while (result === 0); + return result; + }; + prng.int32 = xg.next; + prng.quick = prng; + if (state) { + if (typeof state == "object") + copy(state, xg); + prng.state = function() { + return copy(xg, {}); + }; + } + return prng; + } + if (module22 && module22.exports) { + module22.exports = impl; + } else if (define2 && define2.amd) { + define2(function() { + return impl; + }); + } else { + this.tychei = impl; + } + })(exports, typeof module2 == "object" && module2, typeof define == "function" && define); +}); +var require_crypto = __commonJS(() => { +}); +var require_seedrandom = __commonJS((exports, module2) => { + (function(pool3, math) { + var global2 = this, width = 256, chunks = 6, digits = 52, rngname = "random", startdenom = math.pow(width, chunks), significance = math.pow(2, digits), overflow = significance * 2, mask = width - 1, nodecrypto; + function seedrandom5(seed, options, callback) { + var key = []; + options = options == true ? {entropy: true} : options || {}; + var shortseed = mixkey(flatten4(options.entropy ? [seed, tostring(pool3)] : seed == null ? autoseed() : seed, 3), key); + var arc4 = new ARC4(key); + var prng = function() { + var n = arc4.g(chunks), d = startdenom, x = 0; + while (n < significance) { + n = (n + x) * width; + d *= width; + x = arc4.g(1); + } + while (n >= overflow) { + n /= 2; + d /= 2; + x >>>= 1; + } + return (n + x) / d; + }; + prng.int32 = function() { + return arc4.g(4) | 0; + }; + prng.quick = function() { + return arc4.g(4) / 4294967296; + }; + prng.double = prng; + mixkey(tostring(arc4.S), pool3); + return (options.pass || callback || function(prng2, seed2, is_math_call, state) { + if (state) { + if (state.S) { + copy(state, arc4); + } + prng2.state = function() { + return copy(arc4, {}); + }; + } + if (is_math_call) { + math[rngname] = prng2; + return seed2; + } else + return prng2; + })(prng, shortseed, "global" in options ? options.global : this == math, options.state); + } + math["seed" + rngname] = seedrandom5; + function ARC4(key) { + var t, keylen = key.length, me = this, i = 0, j = me.i = me.j = 0, s = me.S = []; + if (!keylen) { + key = [keylen++]; + } + while (i < width) { + s[i] = i++; + } + for (i = 0; i < width; i++) { + s[i] = s[j = mask & j + key[i % keylen] + (t = s[i])]; + s[j] = t; + } + (me.g = function(count2) { + var t2, r = 0, i2 = me.i, j2 = me.j, s2 = me.S; + while (count2--) { + t2 = s2[i2 = mask & i2 + 1]; + r = r * width + s2[mask & (s2[i2] = s2[j2 = mask & j2 + t2]) + (s2[j2] = t2)]; + } + me.i = i2; + me.j = j2; + return r; + })(width); + } + function copy(f, t) { + t.i = f.i; + t.j = f.j; + t.S = f.S.slice(); + return t; + } + ; + function flatten4(obj, depth) { + var result = [], typ = typeof obj, prop; + if (depth && typ == "object") { + for (prop in obj) { + try { + result.push(flatten4(obj[prop], depth - 1)); + } catch (e) { + } + } + } + return result.length ? result : typ == "string" ? obj : obj + "\0"; + } + function mixkey(seed, key) { + var stringseed = seed + "", smear, j = 0; + while (j < stringseed.length) { + key[mask & j] = mask & (smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++); + } + return tostring(key); + } + function autoseed() { + try { + var out; + if (nodecrypto && (out = nodecrypto.randomBytes)) { + out = out(width); + } else { + out = new Uint8Array(width); + (global2.crypto || global2.msCrypto).getRandomValues(out); + } + return tostring(out); + } catch (e) { + var browser2 = global2.navigator, plugins = browser2 && browser2.plugins; + return [+new Date(), global2, plugins, global2.screen, tostring(pool3)]; + } + } + function tostring(a) { + return String.fromCharCode.apply(0, a); + } + mixkey(math.random(), pool3); + if (typeof module2 == "object" && module2.exports) { + module2.exports = seedrandom5; + try { + nodecrypto = require_crypto(); + } catch (ex) { + } + } else if (typeof define == "function" && define.amd) { + define(function() { + return seedrandom5; + }); + } + })([], Math); +}); +var require_seedrandom2 = __commonJS((exports, module2) => { + var alea5 = require_alea(); + var xor128 = require_xor128(); + var xorwow = require_xorwow(); + var xorshift7 = require_xorshift7(); + var xor4096 = require_xor4096(); + var tychei = require_tychei(); + var sr = require_seedrandom(); + sr.alea = alea5; + sr.xor128 = xor128; + sr.xorwow = xorwow; + sr.xorshift7 = xorshift7; + sr.xor4096 = xor4096; + sr.tychei = tychei; + module2.exports = sr; +}); +var require_alea2 = __commonJS((exports, module2) => { + (function(global2, module22, define2) { + function Alea(seed) { + var me = this, mash = Mash(); + me.next = function() { + var t = 2091639 * me.s0 + me.c * 23283064365386963e-26; + me.s0 = me.s1; + me.s1 = me.s2; + return me.s2 = t - (me.c = t | 0); + }; + me.c = 1; + me.s0 = mash(" "); + me.s1 = mash(" "); + me.s2 = mash(" "); + me.s0 -= mash(seed); + if (me.s0 < 0) { + me.s0 += 1; + } + me.s1 -= mash(seed); + if (me.s1 < 0) { + me.s1 += 1; + } + me.s2 -= mash(seed); + if (me.s2 < 0) { + me.s2 += 1; + } + mash = null; + } + function copy(f, t) { + t.c = f.c; + t.s0 = f.s0; + t.s1 = f.s1; + t.s2 = f.s2; + return t; + } + function impl(seed, opts) { + var xg = new Alea(seed), state = opts && opts.state, prng = xg.next; + prng.int32 = function() { + return xg.next() * 4294967296 | 0; + }; + prng.double = function() { + return prng() + (prng() * 2097152 | 0) * 11102230246251565e-32; + }; + prng.quick = prng; + if (state) { + if (typeof state == "object") + copy(state, xg); + prng.state = function() { + return copy(xg, {}); + }; + } + return prng; + } + function Mash() { + var n = 4022871197; + var mash = function(data) { + data = String(data); + for (var i = 0; i < data.length; i++) { + n += data.charCodeAt(i); + var h = 0.02519603282416938 * n; + n = h >>> 0; + h -= n; + h *= n; + n = h >>> 0; + h -= n; + n += h * 4294967296; + } + return (n >>> 0) * 23283064365386963e-26; + }; + return mash; + } + if (module22 && module22.exports) { + module22.exports = impl; + } else if (define2 && define2.amd) { + define2(function() { + return impl; + }); + } else { + this.alea = impl; + } + })(exports, typeof module2 == "object" && module2, typeof define == "function" && define); +}); +var require_xor1282 = __commonJS((exports, module2) => { + (function(global2, module22, define2) { + function XorGen(seed) { + var me = this, strseed = ""; + me.x = 0; + me.y = 0; + me.z = 0; + me.w = 0; + me.next = function() { + var t = me.x ^ me.x << 11; + me.x = me.y; + me.y = me.z; + me.z = me.w; + return me.w ^= me.w >>> 19 ^ t ^ t >>> 8; + }; + if (seed === (seed | 0)) { + me.x = seed; + } else { + strseed += seed; + } + for (var k = 0; k < strseed.length + 64; k++) { + me.x ^= strseed.charCodeAt(k) | 0; + me.next(); + } + } + function copy(f, t) { + t.x = f.x; + t.y = f.y; + t.z = f.z; + t.w = f.w; + return t; + } + function impl(seed, opts) { + var xg = new XorGen(seed), state = opts && opts.state, prng = function() { + return (xg.next() >>> 0) / 4294967296; + }; + prng.double = function() { + do { + var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21); + } while (result === 0); + return result; + }; + prng.int32 = xg.next; + prng.quick = prng; + if (state) { + if (typeof state == "object") + copy(state, xg); + prng.state = function() { + return copy(xg, {}); + }; + } + return prng; + } + if (module22 && module22.exports) { + module22.exports = impl; + } else if (define2 && define2.amd) { + define2(function() { + return impl; + }); + } else { + this.xor128 = impl; + } + })(exports, typeof module2 == "object" && module2, typeof define == "function" && define); +}); +var require_xorwow2 = __commonJS((exports, module2) => { + (function(global2, module22, define2) { + function XorGen(seed) { + var me = this, strseed = ""; + me.next = function() { + var t = me.x ^ me.x >>> 2; + me.x = me.y; + me.y = me.z; + me.z = me.w; + me.w = me.v; + return (me.d = me.d + 362437 | 0) + (me.v = me.v ^ me.v << 4 ^ (t ^ t << 1)) | 0; + }; + me.x = 0; + me.y = 0; + me.z = 0; + me.w = 0; + me.v = 0; + if (seed === (seed | 0)) { + me.x = seed; + } else { + strseed += seed; + } + for (var k = 0; k < strseed.length + 64; k++) { + me.x ^= strseed.charCodeAt(k) | 0; + if (k == strseed.length) { + me.d = me.x << 10 ^ me.x >>> 4; + } + me.next(); + } + } + function copy(f, t) { + t.x = f.x; + t.y = f.y; + t.z = f.z; + t.w = f.w; + t.v = f.v; + t.d = f.d; + return t; + } + function impl(seed, opts) { + var xg = new XorGen(seed), state = opts && opts.state, prng = function() { + return (xg.next() >>> 0) / 4294967296; + }; + prng.double = function() { + do { + var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21); + } while (result === 0); + return result; + }; + prng.int32 = xg.next; + prng.quick = prng; + if (state) { + if (typeof state == "object") + copy(state, xg); + prng.state = function() { + return copy(xg, {}); + }; + } + return prng; + } + if (module22 && module22.exports) { + module22.exports = impl; + } else if (define2 && define2.amd) { + define2(function() { + return impl; + }); + } else { + this.xorwow = impl; + } + })(exports, typeof module2 == "object" && module2, typeof define == "function" && define); +}); +var require_xorshift72 = __commonJS((exports, module2) => { + (function(global2, module22, define2) { + function XorGen(seed) { + var me = this; + me.next = function() { + var X = me.x, i = me.i, t, v, w; + t = X[i]; + t ^= t >>> 7; + v = t ^ t << 24; + t = X[i + 1 & 7]; + v ^= t ^ t >>> 10; + t = X[i + 3 & 7]; + v ^= t ^ t >>> 3; + t = X[i + 4 & 7]; + v ^= t ^ t << 7; + t = X[i + 7 & 7]; + t = t ^ t << 13; + v ^= t ^ t << 9; + X[i] = v; + me.i = i + 1 & 7; + return v; + }; + function init2(me2, seed2) { + var j, w, X = []; + if (seed2 === (seed2 | 0)) { + w = X[0] = seed2; + } else { + seed2 = "" + seed2; + for (j = 0; j < seed2.length; ++j) { + X[j & 7] = X[j & 7] << 15 ^ seed2.charCodeAt(j) + X[j + 1 & 7] << 13; + } + } + while (X.length < 8) + X.push(0); + for (j = 0; j < 8 && X[j] === 0; ++j) + ; + if (j == 8) + w = X[7] = -1; + else + w = X[j]; + me2.x = X; + me2.i = 0; + for (j = 256; j > 0; --j) { + me2.next(); + } + } + init2(me, seed); + } + function copy(f, t) { + t.x = f.x.slice(); + t.i = f.i; + return t; + } + function impl(seed, opts) { + if (seed == null) + seed = +new Date(); + var xg = new XorGen(seed), state = opts && opts.state, prng = function() { + return (xg.next() >>> 0) / 4294967296; + }; + prng.double = function() { + do { + var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21); + } while (result === 0); + return result; + }; + prng.int32 = xg.next; + prng.quick = prng; + if (state) { + if (state.x) + copy(state, xg); + prng.state = function() { + return copy(xg, {}); + }; + } + return prng; + } + if (module22 && module22.exports) { + module22.exports = impl; + } else if (define2 && define2.amd) { + define2(function() { + return impl; + }); + } else { + this.xorshift7 = impl; + } + })(exports, typeof module2 == "object" && module2, typeof define == "function" && define); +}); +var require_xor40962 = __commonJS((exports, module2) => { + (function(global2, module22, define2) { + function XorGen(seed) { + var me = this; + me.next = function() { + var w = me.w, X = me.X, i = me.i, t, v; + me.w = w = w + 1640531527 | 0; + v = X[i + 34 & 127]; + t = X[i = i + 1 & 127]; + v ^= v << 13; + t ^= t << 17; + v ^= v >>> 15; + t ^= t >>> 12; + v = X[i] = v ^ t; + me.i = i; + return v + (w ^ w >>> 16) | 0; + }; + function init2(me2, seed2) { + var t, v, i, j, w, X = [], limit = 128; + if (seed2 === (seed2 | 0)) { + v = seed2; + seed2 = null; + } else { + seed2 = seed2 + "\0"; + v = 0; + limit = Math.max(limit, seed2.length); + } + for (i = 0, j = -32; j < limit; ++j) { + if (seed2) + v ^= seed2.charCodeAt((j + 32) % seed2.length); + if (j === 0) + w = v; + v ^= v << 10; + v ^= v >>> 15; + v ^= v << 4; + v ^= v >>> 13; + if (j >= 0) { + w = w + 1640531527 | 0; + t = X[j & 127] ^= v + w; + i = t == 0 ? i + 1 : 0; + } + } + if (i >= 128) { + X[(seed2 && seed2.length || 0) & 127] = -1; + } + i = 127; + for (j = 4 * 128; j > 0; --j) { + v = X[i + 34 & 127]; + t = X[i = i + 1 & 127]; + v ^= v << 13; + t ^= t << 17; + v ^= v >>> 15; + t ^= t >>> 12; + X[i] = v ^ t; + } + me2.w = w; + me2.X = X; + me2.i = i; + } + init2(me, seed); + } + function copy(f, t) { + t.i = f.i; + t.w = f.w; + t.X = f.X.slice(); + return t; + } + ; + function impl(seed, opts) { + if (seed == null) + seed = +new Date(); + var xg = new XorGen(seed), state = opts && opts.state, prng = function() { + return (xg.next() >>> 0) / 4294967296; + }; + prng.double = function() { + do { + var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21); + } while (result === 0); + return result; + }; + prng.int32 = xg.next; + prng.quick = prng; + if (state) { + if (state.X) + copy(state, xg); + prng.state = function() { + return copy(xg, {}); + }; + } + return prng; + } + if (module22 && module22.exports) { + module22.exports = impl; + } else if (define2 && define2.amd) { + define2(function() { + return impl; + }); + } else { + this.xor4096 = impl; + } + })(exports, typeof module2 == "object" && module2, typeof define == "function" && define); +}); +var require_tychei2 = __commonJS((exports, module2) => { + (function(global2, module22, define2) { + function XorGen(seed) { + var me = this, strseed = ""; + me.next = function() { + var b = me.b, c = me.c, d = me.d, a = me.a; + b = b << 25 ^ b >>> 7 ^ c; + c = c - d | 0; + d = d << 24 ^ d >>> 8 ^ a; + a = a - b | 0; + me.b = b = b << 20 ^ b >>> 12 ^ c; + me.c = c = c - d | 0; + me.d = d << 16 ^ c >>> 16 ^ a; + return me.a = a - b | 0; + }; + me.a = 0; + me.b = 0; + me.c = 2654435769 | 0; + me.d = 1367130551; + if (seed === Math.floor(seed)) { + me.a = seed / 4294967296 | 0; + me.b = seed | 0; + } else { + strseed += seed; + } + for (var k = 0; k < strseed.length + 20; k++) { + me.b ^= strseed.charCodeAt(k) | 0; + me.next(); + } + } + function copy(f, t) { + t.a = f.a; + t.b = f.b; + t.c = f.c; + t.d = f.d; + return t; + } + ; + function impl(seed, opts) { + var xg = new XorGen(seed), state = opts && opts.state, prng = function() { + return (xg.next() >>> 0) / 4294967296; + }; + prng.double = function() { + do { + var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21); + } while (result === 0); + return result; + }; + prng.int32 = xg.next; + prng.quick = prng; + if (state) { + if (typeof state == "object") + copy(state, xg); + prng.state = function() { + return copy(xg, {}); + }; + } + return prng; + } + if (module22 && module22.exports) { + module22.exports = impl; + } else if (define2 && define2.amd) { + define2(function() { + return impl; + }); + } else { + this.tychei = impl; + } + })(exports, typeof module2 == "object" && module2, typeof define == "function" && define); +}); +var require_seedrandom3 = __commonJS((exports, module2) => { + (function(global2, pool3, math) { + var width = 256, chunks = 6, digits = 52, rngname = "random", startdenom = math.pow(width, chunks), significance = math.pow(2, digits), overflow = significance * 2, mask = width - 1, nodecrypto; + function seedrandom5(seed, options, callback) { + var key = []; + options = options == true ? {entropy: true} : options || {}; + var shortseed = mixkey(flatten4(options.entropy ? [seed, tostring(pool3)] : seed == null ? autoseed() : seed, 3), key); + var arc4 = new ARC4(key); + var prng = function() { + var n = arc4.g(chunks), d = startdenom, x = 0; + while (n < significance) { + n = (n + x) * width; + d *= width; + x = arc4.g(1); + } + while (n >= overflow) { + n /= 2; + d /= 2; + x >>>= 1; + } + return (n + x) / d; + }; + prng.int32 = function() { + return arc4.g(4) | 0; + }; + prng.quick = function() { + return arc4.g(4) / 4294967296; + }; + prng.double = prng; + mixkey(tostring(arc4.S), pool3); + return (options.pass || callback || function(prng2, seed2, is_math_call, state) { + if (state) { + if (state.S) { + copy(state, arc4); + } + prng2.state = function() { + return copy(arc4, {}); + }; + } + if (is_math_call) { + math[rngname] = prng2; + return seed2; + } else + return prng2; + })(prng, shortseed, "global" in options ? options.global : this == math, options.state); + } + function ARC4(key) { + var t, keylen = key.length, me = this, i = 0, j = me.i = me.j = 0, s = me.S = []; + if (!keylen) { + key = [keylen++]; + } + while (i < width) { + s[i] = i++; + } + for (i = 0; i < width; i++) { + s[i] = s[j = mask & j + key[i % keylen] + (t = s[i])]; + s[j] = t; + } + (me.g = function(count2) { + var t2, r = 0, i2 = me.i, j2 = me.j, s2 = me.S; + while (count2--) { + t2 = s2[i2 = mask & i2 + 1]; + r = r * width + s2[mask & (s2[i2] = s2[j2 = mask & j2 + t2]) + (s2[j2] = t2)]; + } + me.i = i2; + me.j = j2; + return r; + })(width); + } + function copy(f, t) { + t.i = f.i; + t.j = f.j; + t.S = f.S.slice(); + return t; + } + ; + function flatten4(obj, depth) { + var result = [], typ = typeof obj, prop; + if (depth && typ == "object") { + for (prop in obj) { + try { + result.push(flatten4(obj[prop], depth - 1)); + } catch (e) { + } + } + } + return result.length ? result : typ == "string" ? obj : obj + "\0"; + } + function mixkey(seed, key) { + var stringseed = seed + "", smear, j = 0; + while (j < stringseed.length) { + key[mask & j] = mask & (smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++); + } + return tostring(key); + } + function autoseed() { + try { + var out; + if (nodecrypto && (out = nodecrypto.randomBytes)) { + out = out(width); + } else { + out = new Uint8Array(width); + (global2.crypto || global2.msCrypto).getRandomValues(out); + } + return tostring(out); + } catch (e) { + var browser2 = global2.navigator, plugins = browser2 && browser2.plugins; + return [+new Date(), global2, plugins, global2.screen, tostring(pool3)]; + } + } + function tostring(a) { + return String.fromCharCode.apply(0, a); + } + mixkey(math.random(), pool3); + if (typeof module2 == "object" && module2.exports) { + module2.exports = seedrandom5; + try { + nodecrypto = require_crypto(); + } catch (ex) { + } + } else if (typeof define == "function" && define.amd) { + define(function() { + return seedrandom5; + }); + } else { + math["seed" + rngname] = seedrandom5; + } + })(typeof self !== "undefined" ? self : exports, [], Math); +}); +var require_seedrandom4 = __commonJS((exports, module2) => { + var alea5 = require_alea2(); + var xor128 = require_xor1282(); + var xorwow = require_xorwow2(); + var xorshift7 = require_xorshift72(); + var xor4096 = require_xor40962(); + var tychei = require_tychei2(); + var sr = require_seedrandom3(); + sr.alea = alea5; + sr.xor128 = xor128; + sr.xorwow = xorwow; + sr.xorshift7 = xorshift7; + sr.xor4096 = xor4096; + sr.tychei = tychei; + module2.exports = sr; +}); +var require_string_decoder = __commonJS(() => { +}); +var require_path = __commonJS(() => { +}); +var require_worker_threads = __commonJS(() => { +}); +var require_perf_hooks = __commonJS(() => { +}); +var require_tfjs_backend_wasm_threaded_simd = __commonJS((exports, module2) => { + var WasmBackendModuleThreadedSimd = function() { + var _scriptDir = typeof document !== "undefined" && document.currentScript ? document.currentScript.src : void 0; + if (typeof __filename !== "undefined") + _scriptDir = _scriptDir || __filename; + return function(WasmBackendModuleThreadedSimd2) { + WasmBackendModuleThreadedSimd2 = WasmBackendModuleThreadedSimd2 || {}; + function GROWABLE_HEAP_I8() { + if (wasmMemory.buffer != buffer2) { + updateGlobalBufferAndViews(wasmMemory.buffer); + } + return HEAP8; + } + function GROWABLE_HEAP_U8() { + if (wasmMemory.buffer != buffer2) { + updateGlobalBufferAndViews(wasmMemory.buffer); + } + return HEAPU8; + } + function GROWABLE_HEAP_I32() { + if (wasmMemory.buffer != buffer2) { + updateGlobalBufferAndViews(wasmMemory.buffer); + } + return HEAP32; + } + function GROWABLE_HEAP_U32() { + if (wasmMemory.buffer != buffer2) { + updateGlobalBufferAndViews(wasmMemory.buffer); + } + return HEAPU32; + } + function GROWABLE_HEAP_F64() { + if (wasmMemory.buffer != buffer2) { + updateGlobalBufferAndViews(wasmMemory.buffer); + } + return HEAPF64; + } + var Module = typeof WasmBackendModuleThreadedSimd2 !== "undefined" ? WasmBackendModuleThreadedSimd2 : {}; + var readyPromiseResolve, readyPromiseReject; + Module["ready"] = new Promise(function(resolve, reject) { + readyPromiseResolve = resolve; + readyPromiseReject = reject; + }); + var moduleOverrides = {}; + var key; + for (key in Module) { + if (Module.hasOwnProperty(key)) { + moduleOverrides[key] = Module[key]; + } + } + var arguments_ = []; + var thisProgram = "./this.program"; + var quit_ = function(status, toThrow) { + throw toThrow; + }; + var ENVIRONMENT_IS_WEB = false; + var ENVIRONMENT_IS_WORKER = false; + var ENVIRONMENT_IS_NODE = false; + var ENVIRONMENT_IS_SHELL = false; + ENVIRONMENT_IS_WEB = typeof window === "object"; + ENVIRONMENT_IS_WORKER = typeof importScripts === "function"; + ENVIRONMENT_IS_NODE = typeof process === "object" && typeof process.versions === "object" && typeof process.versions.node === "string"; + ENVIRONMENT_IS_SHELL = !ENVIRONMENT_IS_WEB && !ENVIRONMENT_IS_NODE && !ENVIRONMENT_IS_WORKER; + var ENVIRONMENT_IS_PTHREAD = Module["ENVIRONMENT_IS_PTHREAD"] || false; + if (ENVIRONMENT_IS_PTHREAD) { + buffer2 = Module["buffer"]; + } + var scriptDirectory = ""; + function locateFile(path) { + if (Module["locateFile"]) { + return Module["locateFile"](path, scriptDirectory); + } + return scriptDirectory + path; + } + var read_, readAsync, readBinary, setWindowTitle; + var nodeFS; + var nodePath; + if (ENVIRONMENT_IS_NODE) { + if (ENVIRONMENT_IS_WORKER) { + scriptDirectory = require_path().dirname(scriptDirectory) + "/"; + } else { + scriptDirectory = __dirname + "/"; + } + read_ = function shell_read(filename, binary) { + if (!nodeFS) + nodeFS = require("fs"); + if (!nodePath) + nodePath = require_path(); + filename = nodePath["normalize"](filename); + return nodeFS["readFileSync"](filename, binary ? null : "utf8"); + }; + readBinary = function readBinary2(filename) { + var ret = read_(filename, true); + if (!ret.buffer) { + ret = new Uint8Array(ret); + } + assert3(ret.buffer); + return ret; + }; + if (process["argv"].length > 1) { + thisProgram = process["argv"][1].replace(/\\/g, "/"); + } + arguments_ = process["argv"].slice(2); + process["on"]("uncaughtException", function(ex) { + if (!(ex instanceof ExitStatus)) { + throw ex; + } + }); + process["on"]("unhandledRejection", abort); + quit_ = function(status) { + process["exit"](status); + }; + Module["inspect"] = function() { + return "[Emscripten Module object]"; + }; + var nodeWorkerThreads; + try { + nodeWorkerThreads = require_worker_threads(); + } catch (e) { + console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'); + throw e; + } + global.Worker = nodeWorkerThreads.Worker; + } else if (ENVIRONMENT_IS_SHELL) { + if (typeof read != "undefined") { + read_ = function shell_read(f) { + return read(f); + }; + } + readBinary = function readBinary2(f) { + var data; + if (typeof readbuffer === "function") { + return new Uint8Array(readbuffer(f)); + } + data = read(f, "binary"); + assert3(typeof data === "object"); + return data; + }; + if (typeof scriptArgs != "undefined") { + arguments_ = scriptArgs; + } else if (typeof arguments != "undefined") { + arguments_ = arguments; + } + if (typeof quit === "function") { + quit_ = function(status) { + quit(status); + }; + } + if (typeof print !== "undefined") { + if (typeof console === "undefined") + console = {}; + console.log = print; + console.warn = console.error = typeof printErr !== "undefined" ? printErr : print; + } + } else if (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER) { + if (ENVIRONMENT_IS_WORKER) { + scriptDirectory = self.location.href; + } else if (typeof document !== "undefined" && document.currentScript) { + scriptDirectory = document.currentScript.src; + } + if (typeof _scriptDir !== "undefined" && _scriptDir) { + scriptDirectory = _scriptDir; + } + if (scriptDirectory.indexOf("blob:") !== 0) { + scriptDirectory = scriptDirectory.substr(0, scriptDirectory.lastIndexOf("/") + 1); + } else { + scriptDirectory = ""; + } + if (ENVIRONMENT_IS_NODE) { + read_ = function shell_read(filename, binary) { + if (!nodeFS) + nodeFS = require("fs"); + if (!nodePath) + nodePath = require_path(); + filename = nodePath["normalize"](filename); + return nodeFS["readFileSync"](filename, binary ? null : "utf8"); + }; + readBinary = function readBinary2(filename) { + var ret = read_(filename, true); + if (!ret.buffer) { + ret = new Uint8Array(ret); + } + assert3(ret.buffer); + return ret; + }; + } else { + read_ = function(url) { + var xhr = new XMLHttpRequest(); + xhr.open("GET", url, false); + xhr.send(null); + return xhr.responseText; + }; + if (ENVIRONMENT_IS_WORKER) { + readBinary = function(url) { + var xhr = new XMLHttpRequest(); + xhr.open("GET", url, false); + xhr.responseType = "arraybuffer"; + xhr.send(null); + return new Uint8Array(xhr.response); + }; + } + readAsync = function(url, onload, onerror) { + var xhr = new XMLHttpRequest(); + xhr.open("GET", url, true); + xhr.responseType = "arraybuffer"; + xhr.onload = function() { + if (xhr.status == 200 || xhr.status == 0 && xhr.response) { + onload(xhr.response); + return; + } + onerror(); + }; + xhr.onerror = onerror; + xhr.send(null); + }; + } + setWindowTitle = function(title) { + document.title = title; + }; + } else { + } + if (ENVIRONMENT_IS_NODE) { + if (typeof performance === "undefined") { + global.performance = require_perf_hooks().performance; + } + } + var out = Module["print"] || console.log.bind(console); + var err = Module["printErr"] || console.warn.bind(console); + for (key in moduleOverrides) { + if (moduleOverrides.hasOwnProperty(key)) { + Module[key] = moduleOverrides[key]; + } + } + moduleOverrides = null; + if (Module["arguments"]) + arguments_ = Module["arguments"]; + if (Module["thisProgram"]) + thisProgram = Module["thisProgram"]; + if (Module["quit"]) + quit_ = Module["quit"]; + var Atomics_load = Atomics.load; + var Atomics_store = Atomics.store; + var Atomics_compareExchange = Atomics.compareExchange; + var wasmBinary; + if (Module["wasmBinary"]) + wasmBinary = Module["wasmBinary"]; + var noExitRuntime = Module["noExitRuntime"] || true; + if (typeof WebAssembly !== "object") { + abort("no native wasm support detected"); + } + var wasmMemory; + var wasmModule; + var ABORT = false; + var EXITSTATUS; + function assert3(condition, text) { + if (!condition) { + abort("Assertion failed: " + text); + } + } + function getCFunc(ident) { + var func2 = Module["_" + ident]; + assert3(func2, "Cannot call unknown function " + ident + ", make sure it is exported"); + return func2; + } + function ccall(ident, returnType, argTypes, args, opts) { + var toC = {string: function(str) { + var ret2 = 0; + if (str !== null && str !== void 0 && str !== 0) { + var len = (str.length << 2) + 1; + ret2 = stackAlloc(len); + stringToUTF8(str, ret2, len); + } + return ret2; + }, array: function(arr) { + var ret2 = stackAlloc(arr.length); + writeArrayToMemory(arr, ret2); + return ret2; + }}; + function convertReturnValue(ret2) { + if (returnType === "string") + return UTF8ToString(ret2); + if (returnType === "boolean") + return Boolean(ret2); + return ret2; + } + var func2 = getCFunc(ident); + var cArgs = []; + var stack2 = 0; + if (args) { + for (var i = 0; i < args.length; i++) { + var converter = toC[argTypes[i]]; + if (converter) { + if (stack2 === 0) + stack2 = stackSave(); + cArgs[i] = converter(args[i]); + } else { + cArgs[i] = args[i]; + } + } + } + var ret = func2.apply(null, cArgs); + ret = convertReturnValue(ret); + if (stack2 !== 0) + stackRestore(stack2); + return ret; + } + function cwrap(ident, returnType, argTypes, opts) { + argTypes = argTypes || []; + var numericArgs = argTypes.every(function(type) { + return type === "number"; + }); + var numericRet = returnType !== "string"; + if (numericRet && numericArgs && !opts) { + return getCFunc(ident); + } + return function() { + return ccall(ident, returnType, argTypes, arguments, opts); + }; + } + function UTF8ArrayToString(heap, idx, maxBytesToRead) { + var endIdx = idx + maxBytesToRead; + var str = ""; + while (!(idx >= endIdx)) { + var u0 = heap[idx++]; + if (!u0) + return str; + if (!(u0 & 128)) { + str += String.fromCharCode(u0); + continue; + } + var u1 = heap[idx++] & 63; + if ((u0 & 224) == 192) { + str += String.fromCharCode((u0 & 31) << 6 | u1); + continue; + } + var u2 = heap[idx++] & 63; + if ((u0 & 240) == 224) { + u0 = (u0 & 15) << 12 | u1 << 6 | u2; + } else { + u0 = (u0 & 7) << 18 | u1 << 12 | u2 << 6 | heap[idx++] & 63; + } + if (u0 < 65536) { + str += String.fromCharCode(u0); + } else { + var ch = u0 - 65536; + str += String.fromCharCode(55296 | ch >> 10, 56320 | ch & 1023); + } + } + return str; + } + function UTF8ToString(ptr, maxBytesToRead) { + return ptr ? UTF8ArrayToString(GROWABLE_HEAP_U8(), ptr, maxBytesToRead) : ""; + } + function stringToUTF8Array(str, heap, outIdx, maxBytesToWrite) { + if (!(maxBytesToWrite > 0)) + return 0; + var startIdx = outIdx; + var endIdx = outIdx + maxBytesToWrite - 1; + for (var i = 0; i < str.length; ++i) { + var u = str.charCodeAt(i); + if (u >= 55296 && u <= 57343) { + var u1 = str.charCodeAt(++i); + u = 65536 + ((u & 1023) << 10) | u1 & 1023; + } + if (u <= 127) { + if (outIdx >= endIdx) + break; + heap[outIdx++] = u; + } else if (u <= 2047) { + if (outIdx + 1 >= endIdx) + break; + heap[outIdx++] = 192 | u >> 6; + heap[outIdx++] = 128 | u & 63; + } else if (u <= 65535) { + if (outIdx + 2 >= endIdx) + break; + heap[outIdx++] = 224 | u >> 12; + heap[outIdx++] = 128 | u >> 6 & 63; + heap[outIdx++] = 128 | u & 63; + } else { + if (outIdx + 3 >= endIdx) + break; + heap[outIdx++] = 240 | u >> 18; + heap[outIdx++] = 128 | u >> 12 & 63; + heap[outIdx++] = 128 | u >> 6 & 63; + heap[outIdx++] = 128 | u & 63; + } + } + heap[outIdx] = 0; + return outIdx - startIdx; + } + function stringToUTF8(str, outPtr, maxBytesToWrite) { + return stringToUTF8Array(str, GROWABLE_HEAP_U8(), outPtr, maxBytesToWrite); + } + function lengthBytesUTF8(str) { + var len = 0; + for (var i = 0; i < str.length; ++i) { + var u = str.charCodeAt(i); + if (u >= 55296 && u <= 57343) + u = 65536 + ((u & 1023) << 10) | str.charCodeAt(++i) & 1023; + if (u <= 127) + ++len; + else if (u <= 2047) + len += 2; + else if (u <= 65535) + len += 3; + else + len += 4; + } + return len; + } + function writeArrayToMemory(array2, buffer3) { + GROWABLE_HEAP_I8().set(array2, buffer3); + } + function alignUp(x, multiple) { + if (x % multiple > 0) { + x += multiple - x % multiple; + } + return x; + } + var buffer2, HEAP8, HEAPU8, HEAP16, HEAPU16, HEAP32, HEAPU32, HEAPF32, HEAPF64; + function updateGlobalBufferAndViews(buf) { + buffer2 = buf; + Module["HEAP8"] = HEAP8 = new Int8Array(buf); + Module["HEAP16"] = HEAP16 = new Int16Array(buf); + Module["HEAP32"] = HEAP32 = new Int32Array(buf); + Module["HEAPU8"] = HEAPU8 = new Uint8Array(buf); + Module["HEAPU16"] = HEAPU16 = new Uint16Array(buf); + Module["HEAPU32"] = HEAPU32 = new Uint32Array(buf); + Module["HEAPF32"] = HEAPF32 = new Float32Array(buf); + Module["HEAPF64"] = HEAPF64 = new Float64Array(buf); + } + var INITIAL_MEMORY = Module["INITIAL_MEMORY"] || 16777216; + if (ENVIRONMENT_IS_PTHREAD) { + wasmMemory = Module["wasmMemory"]; + buffer2 = Module["buffer"]; + } else { + if (Module["wasmMemory"]) { + wasmMemory = Module["wasmMemory"]; + } else { + wasmMemory = new WebAssembly.Memory({initial: INITIAL_MEMORY / 65536, maximum: 2147483648 / 65536, shared: true}); + if (!(wasmMemory.buffer instanceof SharedArrayBuffer)) { + err("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag"); + if (ENVIRONMENT_IS_NODE) { + console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)"); + } + throw Error("bad memory"); + } + } + } + if (wasmMemory) { + buffer2 = wasmMemory.buffer; + } + INITIAL_MEMORY = buffer2.byteLength; + updateGlobalBufferAndViews(buffer2); + var wasmTable; + var __ATPRERUN__ = []; + var __ATINIT__ = []; + var __ATMAIN__ = []; + var __ATEXIT__ = []; + var __ATPOSTRUN__ = []; + var runtimeInitialized = false; + var runtimeExited = false; + if (!ENVIRONMENT_IS_PTHREAD) + __ATINIT__.push({func: function() { + ___wasm_call_ctors(); + }}); + if (ENVIRONMENT_IS_PTHREAD) + runtimeInitialized = true; + function preRun() { + if (ENVIRONMENT_IS_PTHREAD) + return; + if (Module["preRun"]) { + if (typeof Module["preRun"] == "function") + Module["preRun"] = [Module["preRun"]]; + while (Module["preRun"].length) { + addOnPreRun(Module["preRun"].shift()); + } + } + callRuntimeCallbacks(__ATPRERUN__); + } + function initRuntime() { + runtimeInitialized = true; + callRuntimeCallbacks(__ATINIT__); + } + function preMain() { + if (ENVIRONMENT_IS_PTHREAD) + return; + callRuntimeCallbacks(__ATMAIN__); + } + function exitRuntime() { + if (ENVIRONMENT_IS_PTHREAD) + return; + runtimeExited = true; + } + function postRun() { + if (ENVIRONMENT_IS_PTHREAD) + return; + if (Module["postRun"]) { + if (typeof Module["postRun"] == "function") + Module["postRun"] = [Module["postRun"]]; + while (Module["postRun"].length) { + addOnPostRun(Module["postRun"].shift()); + } + } + callRuntimeCallbacks(__ATPOSTRUN__); + } + function addOnPreRun(cb) { + __ATPRERUN__.unshift(cb); + } + function addOnPostRun(cb) { + __ATPOSTRUN__.unshift(cb); + } + var runDependencies = 0; + var runDependencyWatcher = null; + var dependenciesFulfilled = null; + function addRunDependency(id) { + assert3(!ENVIRONMENT_IS_PTHREAD, "addRunDependency cannot be used in a pthread worker"); + runDependencies++; + if (Module["monitorRunDependencies"]) { + Module["monitorRunDependencies"](runDependencies); + } + } + function removeRunDependency(id) { + runDependencies--; + if (Module["monitorRunDependencies"]) { + Module["monitorRunDependencies"](runDependencies); + } + if (runDependencies == 0) { + if (runDependencyWatcher !== null) { + clearInterval(runDependencyWatcher); + runDependencyWatcher = null; + } + if (dependenciesFulfilled) { + var callback = dependenciesFulfilled; + dependenciesFulfilled = null; + callback(); + } + } + } + Module["preloadedImages"] = {}; + Module["preloadedAudios"] = {}; + function abort(what) { + if (Module["onAbort"]) { + Module["onAbort"](what); + } + if (ENVIRONMENT_IS_PTHREAD) + console.error("Pthread aborting at " + new Error().stack); + what += ""; + err(what); + ABORT = true; + EXITSTATUS = 1; + what = "abort(" + what + "). Build with -s ASSERTIONS=1 for more info."; + var e = new WebAssembly.RuntimeError(what); + readyPromiseReject(e); + throw e; + } + function hasPrefix(str, prefix) { + return String.prototype.startsWith ? str.startsWith(prefix) : str.indexOf(prefix) === 0; + } + var dataURIPrefix = "data:application/octet-stream;base64,"; + function isDataURI(filename) { + return hasPrefix(filename, dataURIPrefix); + } + var fileURIPrefix = "file://"; + function isFileURI(filename) { + return hasPrefix(filename, fileURIPrefix); + } + var wasmBinaryFile = "tfjs-backend-wasm-threaded-simd.wasm"; + if (!isDataURI(wasmBinaryFile)) { + wasmBinaryFile = locateFile(wasmBinaryFile); + } + function getBinary(file) { + try { + if (file == wasmBinaryFile && wasmBinary) { + return new Uint8Array(wasmBinary); + } + if (readBinary) { + return readBinary(file); + } else { + throw "both async and sync fetching of the wasm failed"; + } + } catch (err2) { + abort(err2); + } + } + function getBinaryPromise() { + if (!wasmBinary && (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER)) { + if (typeof fetch === "function" && !isFileURI(wasmBinaryFile)) { + return fetch(wasmBinaryFile, {credentials: "same-origin"}).then(function(response) { + if (!response["ok"]) { + throw "failed to load wasm binary file at '" + wasmBinaryFile + "'"; + } + return response["arrayBuffer"](); + }).catch(function() { + return getBinary(wasmBinaryFile); + }); + } else { + if (readAsync) { + return new Promise(function(resolve, reject) { + readAsync(wasmBinaryFile, function(response) { + resolve(new Uint8Array(response)); + }, reject); + }); + } + } + } + return Promise.resolve().then(function() { + return getBinary(wasmBinaryFile); + }); + } + function createWasm() { + var info = {a: asmLibraryArg}; + function receiveInstance(instance, module22) { + var exports3 = instance.exports; + Module["asm"] = exports3; + wasmTable = Module["asm"]["F"]; + wasmModule = module22; + if (!ENVIRONMENT_IS_PTHREAD) { + var numWorkersToLoad = PThread.unusedWorkers.length; + PThread.unusedWorkers.forEach(function(w) { + PThread.loadWasmModuleToWorker(w, function() { + if (!--numWorkersToLoad) + removeRunDependency("wasm-instantiate"); + }); + }); + } + } + if (!ENVIRONMENT_IS_PTHREAD) { + addRunDependency("wasm-instantiate"); + } + function receiveInstantiatedSource(output) { + receiveInstance(output["instance"], output["module"]); + } + function instantiateArrayBuffer(receiver) { + return getBinaryPromise().then(function(binary) { + return WebAssembly.instantiate(binary, info); + }).then(receiver, function(reason) { + err("failed to asynchronously prepare wasm: " + reason); + abort(reason); + }); + } + function instantiateAsync() { + if (!wasmBinary && typeof WebAssembly.instantiateStreaming === "function" && !isDataURI(wasmBinaryFile) && !isFileURI(wasmBinaryFile) && typeof fetch === "function") { + return fetch(wasmBinaryFile, {credentials: "same-origin"}).then(function(response) { + var result = WebAssembly.instantiateStreaming(response, info); + return result.then(receiveInstantiatedSource, function(reason) { + err("wasm streaming compile failed: " + reason); + err("falling back to ArrayBuffer instantiation"); + return instantiateArrayBuffer(receiveInstantiatedSource); + }); + }); + } else { + return instantiateArrayBuffer(receiveInstantiatedSource); + } + } + if (Module["instantiateWasm"]) { + try { + var exports2 = Module["instantiateWasm"](info, receiveInstance); + return exports2; + } catch (e) { + err("Module.instantiateWasm callback failed with error: " + e); + return false; + } + } + instantiateAsync().catch(readyPromiseReject); + return {}; + } + var ASM_CONSTS = {8991: function($0, $1) { + setTimeout(function() { + __emscripten_do_dispatch_to_thread($0, $1); + }, 0); + }}; + function initPthreadsJS() { + PThread.initRuntime(); + } + function callRuntimeCallbacks(callbacks2) { + while (callbacks2.length > 0) { + var callback = callbacks2.shift(); + if (typeof callback == "function") { + callback(Module); + continue; + } + var func2 = callback.func; + if (typeof func2 === "number") { + if (callback.arg === void 0) { + wasmTable.get(func2)(); + } else { + wasmTable.get(func2)(callback.arg); + } + } else { + func2(callback.arg === void 0 ? null : callback.arg); + } + } + } + function _emscripten_futex_wake(addr, count2) { + if (addr <= 0 || addr > GROWABLE_HEAP_I8().length || addr & true || count2 < 0) + return -28; + if (count2 == 0) + return 0; + if (count2 >= 2147483647) + count2 = Infinity; + var mainThreadWaitAddress = Atomics.load(GROWABLE_HEAP_I32(), __emscripten_main_thread_futex >> 2); + var mainThreadWoken = 0; + if (mainThreadWaitAddress == addr) { + var loadedAddr = Atomics.compareExchange(GROWABLE_HEAP_I32(), __emscripten_main_thread_futex >> 2, mainThreadWaitAddress, 0); + if (loadedAddr == mainThreadWaitAddress) { + --count2; + mainThreadWoken = 1; + if (count2 <= 0) + return 1; + } + } + var ret = Atomics.notify(GROWABLE_HEAP_I32(), addr >> 2, count2); + if (ret >= 0) + return ret + mainThreadWoken; + throw "Atomics.notify returned an unexpected value " + ret; + } + Module["_emscripten_futex_wake"] = _emscripten_futex_wake; + function killThread(pthread_ptr) { + if (ENVIRONMENT_IS_PTHREAD) + throw "Internal Error! killThread() can only ever be called from main application thread!"; + if (!pthread_ptr) + throw "Internal Error! Null pthread_ptr in killThread!"; + GROWABLE_HEAP_I32()[pthread_ptr + 12 >> 2] = 0; + var pthread = PThread.pthreads[pthread_ptr]; + pthread.worker.terminate(); + PThread.freeThreadData(pthread); + PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker), 1); + pthread.worker.pthread = void 0; + } + function cancelThread(pthread_ptr) { + if (ENVIRONMENT_IS_PTHREAD) + throw "Internal Error! cancelThread() can only ever be called from main application thread!"; + if (!pthread_ptr) + throw "Internal Error! Null pthread_ptr in cancelThread!"; + var pthread = PThread.pthreads[pthread_ptr]; + pthread.worker.postMessage({cmd: "cancel"}); + } + function cleanupThread(pthread_ptr) { + if (ENVIRONMENT_IS_PTHREAD) + throw "Internal Error! cleanupThread() can only ever be called from main application thread!"; + if (!pthread_ptr) + throw "Internal Error! Null pthread_ptr in cleanupThread!"; + GROWABLE_HEAP_I32()[pthread_ptr + 12 >> 2] = 0; + var pthread = PThread.pthreads[pthread_ptr]; + if (pthread) { + var worker = pthread.worker; + PThread.returnWorkerToPool(worker); + } + } + var PThread = {unusedWorkers: [], runningWorkers: [], initMainThreadBlock: function() { + var pthreadPoolSize = 8; + for (var i = 0; i < pthreadPoolSize; ++i) { + PThread.allocateUnusedWorker(); + } + }, initRuntime: function() { + var tb = _malloc(228); + for (var i = 0; i < 228 / 4; ++i) + GROWABLE_HEAP_U32()[tb / 4 + i] = 0; + GROWABLE_HEAP_I32()[tb + 12 >> 2] = tb; + var headPtr = tb + 152; + GROWABLE_HEAP_I32()[headPtr >> 2] = headPtr; + var tlsMemory = _malloc(512); + for (var i = 0; i < 128; ++i) + GROWABLE_HEAP_U32()[tlsMemory / 4 + i] = 0; + Atomics.store(GROWABLE_HEAP_U32(), tb + 100 >> 2, tlsMemory); + Atomics.store(GROWABLE_HEAP_U32(), tb + 40 >> 2, tb); + __emscripten_thread_init(tb, !ENVIRONMENT_IS_WORKER, 1); + _emscripten_register_main_browser_thread_id(tb); + }, initWorker: function() { + }, pthreads: {}, threadExitHandlers: [], setThreadStatus: function() { + }, runExitHandlers: function() { + while (PThread.threadExitHandlers.length > 0) { + PThread.threadExitHandlers.pop()(); + } + if (ENVIRONMENT_IS_PTHREAD && _pthread_self()) + ___pthread_tsd_run_dtors(); + }, threadExit: function(exitCode) { + var tb = _pthread_self(); + if (tb) { + Atomics.store(GROWABLE_HEAP_U32(), tb + 4 >> 2, exitCode); + Atomics.store(GROWABLE_HEAP_U32(), tb + 0 >> 2, 1); + Atomics.store(GROWABLE_HEAP_U32(), tb + 56 >> 2, 1); + Atomics.store(GROWABLE_HEAP_U32(), tb + 60 >> 2, 0); + PThread.runExitHandlers(); + _emscripten_futex_wake(tb + 0, 2147483647); + __emscripten_thread_init(0, 0, 0); + if (ENVIRONMENT_IS_PTHREAD) { + postMessage({cmd: "exit"}); + } + } + }, threadCancel: function() { + PThread.runExitHandlers(); + var tb = _pthread_self(); + Atomics.store(GROWABLE_HEAP_U32(), tb + 4 >> 2, -1); + Atomics.store(GROWABLE_HEAP_U32(), tb + 0 >> 2, 1); + _emscripten_futex_wake(tb + 0, 2147483647); + __emscripten_thread_init(0, 0, 0); + postMessage({cmd: "cancelDone"}); + }, terminateAllThreads: function() { + for (var t in PThread.pthreads) { + var pthread = PThread.pthreads[t]; + if (pthread && pthread.worker) { + PThread.returnWorkerToPool(pthread.worker); + } + } + PThread.pthreads = {}; + for (var i = 0; i < PThread.unusedWorkers.length; ++i) { + var worker = PThread.unusedWorkers[i]; + worker.terminate(); + } + PThread.unusedWorkers = []; + for (var i = 0; i < PThread.runningWorkers.length; ++i) { + var worker = PThread.runningWorkers[i]; + var pthread = worker.pthread; + PThread.freeThreadData(pthread); + worker.terminate(); + } + PThread.runningWorkers = []; + }, freeThreadData: function(pthread) { + if (!pthread) + return; + if (pthread.threadInfoStruct) { + var tlsMemory = GROWABLE_HEAP_I32()[pthread.threadInfoStruct + 100 >> 2]; + GROWABLE_HEAP_I32()[pthread.threadInfoStruct + 100 >> 2] = 0; + _free(tlsMemory); + _free(pthread.threadInfoStruct); + } + pthread.threadInfoStruct = 0; + if (pthread.allocatedOwnStack && pthread.stackBase) + _free(pthread.stackBase); + pthread.stackBase = 0; + if (pthread.worker) + pthread.worker.pthread = null; + }, returnWorkerToPool: function(worker) { + PThread.runWithoutMainThreadQueuedCalls(function() { + delete PThread.pthreads[worker.pthread.threadInfoStruct]; + PThread.unusedWorkers.push(worker); + PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker), 1); + PThread.freeThreadData(worker.pthread); + worker.pthread = void 0; + }); + }, runWithoutMainThreadQueuedCalls: function(func2) { + GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls >> 2] = 0; + try { + func2(); + } finally { + GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls >> 2] = 1; + } + }, receiveObjectTransfer: function(data) { + }, loadWasmModuleToWorker: function(worker, onFinishedLoading) { + worker.onmessage = function(e) { + var d = e["data"]; + var cmd = d["cmd"]; + if (worker.pthread) + PThread.currentProxiedOperationCallerThread = worker.pthread.threadInfoStruct; + if (d["targetThread"] && d["targetThread"] != _pthread_self()) { + var thread = PThread.pthreads[d.targetThread]; + if (thread) { + thread.worker.postMessage(e.data, d["transferList"]); + } else { + console.error('Internal error! Worker sent a message "' + cmd + '" to target pthread ' + d["targetThread"] + ", but that thread no longer exists!"); + } + PThread.currentProxiedOperationCallerThread = void 0; + return; + } + if (cmd === "processQueuedMainThreadWork") { + _emscripten_main_thread_process_queued_calls(); + } else if (cmd === "spawnThread") { + spawnThread(e.data); + } else if (cmd === "cleanupThread") { + cleanupThread(d["thread"]); + } else if (cmd === "killThread") { + killThread(d["thread"]); + } else if (cmd === "cancelThread") { + cancelThread(d["thread"]); + } else if (cmd === "loaded") { + worker.loaded = true; + if (onFinishedLoading) + onFinishedLoading(worker); + if (worker.runPthread) { + worker.runPthread(); + delete worker.runPthread; + } + } else if (cmd === "print") { + out("Thread " + d["threadId"] + ": " + d["text"]); + } else if (cmd === "printErr") { + err("Thread " + d["threadId"] + ": " + d["text"]); + } else if (cmd === "alert") { + alert("Thread " + d["threadId"] + ": " + d["text"]); + } else if (cmd === "exit") { + var detached = worker.pthread && Atomics.load(GROWABLE_HEAP_U32(), worker.pthread.threadInfoStruct + 64 >> 2); + if (detached) { + PThread.returnWorkerToPool(worker); + } + } else if (cmd === "exitProcess") { + try { + exit(d["returnCode"]); + } catch (e2) { + if (e2 instanceof ExitStatus) + return; + throw e2; + } + } else if (cmd === "cancelDone") { + PThread.returnWorkerToPool(worker); + } else if (cmd === "objectTransfer") { + PThread.receiveObjectTransfer(e.data); + } else if (e.data.target === "setimmediate") { + worker.postMessage(e.data); + } else { + err("worker sent an unknown command " + cmd); + } + PThread.currentProxiedOperationCallerThread = void 0; + }; + worker.onerror = function(e) { + err("pthread sent an error! " + e.filename + ":" + e.lineno + ": " + e.message); + }; + if (ENVIRONMENT_IS_NODE) { + worker.on("message", function(data) { + worker.onmessage({data}); + }); + worker.on("error", function(data) { + worker.onerror(data); + }); + worker.on("exit", function(data) { + }); + } + worker.postMessage({cmd: "load", urlOrBlob: Module["mainScriptUrlOrBlob"] || _scriptDir, wasmMemory, wasmModule}); + }, allocateUnusedWorker: function() { + var pthreadMainJs = locateFile("tfjs-backend-wasm-threaded-simd.worker.js"); + PThread.unusedWorkers.push(new Worker(pthreadMainJs)); + }, getNewWorker: function() { + if (PThread.unusedWorkers.length == 0) { + PThread.allocateUnusedWorker(); + PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0]); + } + if (PThread.unusedWorkers.length > 0) + return PThread.unusedWorkers.pop(); + else + return null; + }, busySpinWait: function(msecs) { + var t = performance.now() + msecs; + while (performance.now() < t) { + } + }}; + function establishStackSpace(stackTop, stackMax) { + _emscripten_stack_set_limits(stackTop, stackMax); + stackRestore(stackTop); + } + Module["establishStackSpace"] = establishStackSpace; + function getNoExitRuntime() { + return noExitRuntime; + } + Module["getNoExitRuntime"] = getNoExitRuntime; + function invokeEntryPoint(ptr, arg) { + return wasmTable.get(ptr)(arg); + } + Module["invokeEntryPoint"] = invokeEntryPoint; + function ___assert_fail(condition, filename, line, func2) { + abort("Assertion failed: " + UTF8ToString(condition) + ", at: " + [filename ? UTF8ToString(filename) : "unknown filename", line, func2 ? UTF8ToString(func2) : "unknown function"]); + } + function ___call_main(argc, argv) { + var returnCode = _main(argc, argv); + } + var _emscripten_get_now; + if (ENVIRONMENT_IS_NODE) { + _emscripten_get_now = function() { + var t = process["hrtime"](); + return t[0] * 1e3 + t[1] / 1e6; + }; + } else if (ENVIRONMENT_IS_PTHREAD) { + _emscripten_get_now = function() { + return performance.now() - Module["__performance_now_clock_drift"]; + }; + } else if (typeof dateNow !== "undefined") { + _emscripten_get_now = dateNow; + } else + _emscripten_get_now = function() { + return performance.now(); + }; + function setErrNo(value) { + GROWABLE_HEAP_I32()[___errno_location() >> 2] = value; + return value; + } + function _atexit(func2, arg) { + if (ENVIRONMENT_IS_PTHREAD) + return _emscripten_proxy_to_main_thread_js(1, 1, func2, arg); + } + function __emscripten_notify_thread_queue(targetThreadId, mainThreadId) { + if (targetThreadId == mainThreadId) { + postMessage({cmd: "processQueuedMainThreadWork"}); + } else if (ENVIRONMENT_IS_PTHREAD) { + postMessage({targetThread: targetThreadId, cmd: "processThreadQueue"}); + } else { + var pthread = PThread.pthreads[targetThreadId]; + var worker = pthread && pthread.worker; + if (!worker) { + return; + } + worker.postMessage({cmd: "processThreadQueue"}); + } + return 1; + } + function _abort() { + abort(); + } + function _emscripten_asm_const_int(code, sigPtr, argbuf) { + var args = readAsmConstArgs(sigPtr, argbuf); + return ASM_CONSTS[code].apply(null, args); + } + function _emscripten_conditional_set_current_thread_status(expectedStatus, newStatus) { + } + function _emscripten_futex_wait(addr, val, timeout) { + if (addr <= 0 || addr > GROWABLE_HEAP_I8().length || addr & true) + return -28; + if (!ENVIRONMENT_IS_WEB) { + var ret = Atomics.wait(GROWABLE_HEAP_I32(), addr >> 2, val, timeout); + if (ret === "timed-out") + return -73; + if (ret === "not-equal") + return -6; + if (ret === "ok") + return 0; + throw "Atomics.wait returned an unexpected value " + ret; + } else { + if (Atomics.load(GROWABLE_HEAP_I32(), addr >> 2) != val) { + return -6; + } + var tNow = performance.now(); + var tEnd = tNow + timeout; + var lastAddr = Atomics.exchange(GROWABLE_HEAP_I32(), __emscripten_main_thread_futex >> 2, addr); + while (1) { + tNow = performance.now(); + if (tNow > tEnd) { + lastAddr = Atomics.exchange(GROWABLE_HEAP_I32(), __emscripten_main_thread_futex >> 2, 0); + return -73; + } + lastAddr = Atomics.exchange(GROWABLE_HEAP_I32(), __emscripten_main_thread_futex >> 2, 0); + if (lastAddr == 0) { + break; + } + _emscripten_main_thread_process_queued_calls(); + if (Atomics.load(GROWABLE_HEAP_I32(), addr >> 2) != val) { + return -6; + } + lastAddr = Atomics.exchange(GROWABLE_HEAP_I32(), __emscripten_main_thread_futex >> 2, addr); + } + return 0; + } + } + function _emscripten_memcpy_big(dest, src, num) { + GROWABLE_HEAP_U8().copyWithin(dest, src, src + num); + } + function _emscripten_num_logical_cores() { + if (ENVIRONMENT_IS_NODE) + return require("os").cpus().length; + return navigator["hardwareConcurrency"]; + } + function _emscripten_proxy_to_main_thread_js(index, sync) { + var numCallArgs = arguments.length - 2; + var stack2 = stackSave(); + var serializedNumCallArgs = numCallArgs; + var args = stackAlloc(serializedNumCallArgs * 8); + var b = args >> 3; + for (var i = 0; i < numCallArgs; i++) { + var arg = arguments[2 + i]; + GROWABLE_HEAP_F64()[b + i] = arg; + } + var ret = _emscripten_run_in_main_runtime_thread_js(index, serializedNumCallArgs, args, sync); + stackRestore(stack2); + return ret; + } + var _emscripten_receive_on_main_thread_js_callArgs = []; + var readAsmConstArgsArray = []; + function readAsmConstArgs(sigPtr, buf) { + readAsmConstArgsArray.length = 0; + var ch; + buf >>= 2; + while (ch = GROWABLE_HEAP_U8()[sigPtr++]) { + var double = ch < 105; + if (double && buf & 1) + buf++; + readAsmConstArgsArray.push(double ? GROWABLE_HEAP_F64()[buf++ >> 1] : GROWABLE_HEAP_I32()[buf]); + ++buf; + } + return readAsmConstArgsArray; + } + function _emscripten_receive_on_main_thread_js(index, numCallArgs, args) { + _emscripten_receive_on_main_thread_js_callArgs.length = numCallArgs; + var b = args >> 3; + for (var i = 0; i < numCallArgs; i++) { + _emscripten_receive_on_main_thread_js_callArgs[i] = GROWABLE_HEAP_F64()[b + i]; + } + var isEmAsmConst = index < 0; + var func2 = !isEmAsmConst ? proxiedFunctionTable[index] : ASM_CONSTS[-index - 1]; + return func2.apply(null, _emscripten_receive_on_main_thread_js_callArgs); + } + function _emscripten_get_heap_size() { + return GROWABLE_HEAP_U8().length; + } + function emscripten_realloc_buffer(size) { + try { + wasmMemory.grow(size - buffer2.byteLength + 65535 >>> 16); + updateGlobalBufferAndViews(wasmMemory.buffer); + return 1; + } catch (e) { + } + } + function _emscripten_resize_heap(requestedSize) { + var oldSize = _emscripten_get_heap_size(); + if (requestedSize <= oldSize) { + return false; + } + var maxHeapSize = 2147483648; + if (requestedSize > maxHeapSize) { + return false; + } + for (var cutDown = 1; cutDown <= 4; cutDown *= 2) { + var overGrownHeapSize = oldSize * (1 + 0.2 / cutDown); + overGrownHeapSize = Math.min(overGrownHeapSize, requestedSize + 100663296); + var newSize = Math.min(maxHeapSize, alignUp(Math.max(requestedSize, overGrownHeapSize), 65536)); + var replacement = emscripten_realloc_buffer(newSize); + if (replacement) { + return true; + } + } + return false; + } + var JSEvents = {inEventHandler: 0, removeAllEventListeners: function() { + for (var i = JSEvents.eventHandlers.length - 1; i >= 0; --i) { + JSEvents._removeHandler(i); + } + JSEvents.eventHandlers = []; + JSEvents.deferredCalls = []; + }, registerRemoveEventListeners: function() { + if (!JSEvents.removeEventListenersRegistered) { + __ATEXIT__.push(JSEvents.removeAllEventListeners); + JSEvents.removeEventListenersRegistered = true; + } + }, deferredCalls: [], deferCall: function(targetFunction, precedence, argsList) { + function arraysHaveEqualContent(arrA, arrB) { + if (arrA.length != arrB.length) + return false; + for (var i2 in arrA) { + if (arrA[i2] != arrB[i2]) + return false; + } + return true; + } + for (var i in JSEvents.deferredCalls) { + var call = JSEvents.deferredCalls[i]; + if (call.targetFunction == targetFunction && arraysHaveEqualContent(call.argsList, argsList)) { + return; + } + } + JSEvents.deferredCalls.push({targetFunction, precedence, argsList}); + JSEvents.deferredCalls.sort(function(x, y) { + return x.precedence < y.precedence; + }); + }, removeDeferredCalls: function(targetFunction) { + for (var i = 0; i < JSEvents.deferredCalls.length; ++i) { + if (JSEvents.deferredCalls[i].targetFunction == targetFunction) { + JSEvents.deferredCalls.splice(i, 1); + --i; + } + } + }, canPerformEventHandlerRequests: function() { + return JSEvents.inEventHandler && JSEvents.currentEventHandler.allowsDeferredCalls; + }, runDeferredCalls: function() { + if (!JSEvents.canPerformEventHandlerRequests()) { + return; + } + for (var i = 0; i < JSEvents.deferredCalls.length; ++i) { + var call = JSEvents.deferredCalls[i]; + JSEvents.deferredCalls.splice(i, 1); + --i; + call.targetFunction.apply(null, call.argsList); + } + }, eventHandlers: [], removeAllHandlersOnTarget: function(target, eventTypeString) { + for (var i = 0; i < JSEvents.eventHandlers.length; ++i) { + if (JSEvents.eventHandlers[i].target == target && (!eventTypeString || eventTypeString == JSEvents.eventHandlers[i].eventTypeString)) { + JSEvents._removeHandler(i--); + } + } + }, _removeHandler: function(i) { + var h = JSEvents.eventHandlers[i]; + h.target.removeEventListener(h.eventTypeString, h.eventListenerFunc, h.useCapture); + JSEvents.eventHandlers.splice(i, 1); + }, registerOrRemoveHandler: function(eventHandler) { + var jsEventHandler = function jsEventHandler2(event) { + ++JSEvents.inEventHandler; + JSEvents.currentEventHandler = eventHandler; + JSEvents.runDeferredCalls(); + eventHandler.handlerFunc(event); + JSEvents.runDeferredCalls(); + --JSEvents.inEventHandler; + }; + if (eventHandler.callbackfunc) { + eventHandler.eventListenerFunc = jsEventHandler; + eventHandler.target.addEventListener(eventHandler.eventTypeString, jsEventHandler, eventHandler.useCapture); + JSEvents.eventHandlers.push(eventHandler); + JSEvents.registerRemoveEventListeners(); + } else { + for (var i = 0; i < JSEvents.eventHandlers.length; ++i) { + if (JSEvents.eventHandlers[i].target == eventHandler.target && JSEvents.eventHandlers[i].eventTypeString == eventHandler.eventTypeString) { + JSEvents._removeHandler(i--); + } + } + } + }, queueEventHandlerOnThread_iiii: function(targetThread, eventHandlerFunc, eventTypeId, eventData, userData) { + var stackTop = stackSave(); + var varargs = stackAlloc(12); + GROWABLE_HEAP_I32()[varargs >> 2] = eventTypeId; + GROWABLE_HEAP_I32()[varargs + 4 >> 2] = eventData; + GROWABLE_HEAP_I32()[varargs + 8 >> 2] = userData; + __emscripten_call_on_thread(0, targetThread, 637534208, eventHandlerFunc, eventData, varargs); + stackRestore(stackTop); + }, getTargetThreadForEventCallback: function(targetThread) { + switch (targetThread) { + case 1: + return 0; + case 2: + return PThread.currentProxiedOperationCallerThread; + default: + return targetThread; + } + }, getNodeNameForTarget: function(target) { + if (!target) + return ""; + if (target == window) + return "#window"; + if (target == screen) + return "#screen"; + return target && target.nodeName ? target.nodeName : ""; + }, fullscreenEnabled: function() { + return document.fullscreenEnabled || document.webkitFullscreenEnabled; + }}; + function stringToNewUTF8(jsString) { + var length = lengthBytesUTF8(jsString) + 1; + var cString = _malloc(length); + stringToUTF8(jsString, cString, length); + return cString; + } + function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread, targetCanvas, width, height) { + var stackTop = stackSave(); + var varargs = stackAlloc(12); + var targetCanvasPtr = 0; + if (targetCanvas) { + targetCanvasPtr = stringToNewUTF8(targetCanvas); + } + GROWABLE_HEAP_I32()[varargs >> 2] = targetCanvasPtr; + GROWABLE_HEAP_I32()[varargs + 4 >> 2] = width; + GROWABLE_HEAP_I32()[varargs + 8 >> 2] = height; + __emscripten_call_on_thread(0, targetThread, 657457152, 0, targetCanvasPtr, varargs); + stackRestore(stackTop); + } + function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread, targetCanvas, width, height) { + targetCanvas = targetCanvas ? UTF8ToString(targetCanvas) : ""; + _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread, targetCanvas, width, height); + } + function maybeCStringToJsString(cString) { + return cString > 2 ? UTF8ToString(cString) : cString; + } + var specialHTMLTargets = [0, typeof document !== "undefined" ? document : 0, typeof window !== "undefined" ? window : 0]; + function findEventTarget(target) { + target = maybeCStringToJsString(target); + var domElement = specialHTMLTargets[target] || (typeof document !== "undefined" ? document.querySelector(target) : void 0); + return domElement; + } + function findCanvasEventTarget(target) { + return findEventTarget(target); + } + function _emscripten_set_canvas_element_size_calling_thread(target, width, height) { + var canvas = findCanvasEventTarget(target); + if (!canvas) + return -4; + if (canvas.canvasSharedPtr) { + GROWABLE_HEAP_I32()[canvas.canvasSharedPtr >> 2] = width; + GROWABLE_HEAP_I32()[canvas.canvasSharedPtr + 4 >> 2] = height; + } + if (canvas.offscreenCanvas || !canvas.controlTransferredOffscreen) { + if (canvas.offscreenCanvas) + canvas = canvas.offscreenCanvas; + var autoResizeViewport = false; + if (canvas.GLctxObject && canvas.GLctxObject.GLctx) { + var prevViewport = canvas.GLctxObject.GLctx.getParameter(2978); + autoResizeViewport = prevViewport[0] === 0 && prevViewport[1] === 0 && prevViewport[2] === canvas.width && prevViewport[3] === canvas.height; + } + canvas.width = width; + canvas.height = height; + if (autoResizeViewport) { + canvas.GLctxObject.GLctx.viewport(0, 0, width, height); + } + } else if (canvas.canvasSharedPtr) { + var targetThread = GROWABLE_HEAP_I32()[canvas.canvasSharedPtr + 8 >> 2]; + _emscripten_set_offscreencanvas_size_on_target_thread(targetThread, target, width, height); + return 1; + } else { + return -4; + } + return 0; + } + function _emscripten_set_canvas_element_size_main_thread(target, width, height) { + if (ENVIRONMENT_IS_PTHREAD) + return _emscripten_proxy_to_main_thread_js(2, 1, target, width, height); + return _emscripten_set_canvas_element_size_calling_thread(target, width, height); + } + function _emscripten_set_canvas_element_size(target, width, height) { + var canvas = findCanvasEventTarget(target); + if (canvas) { + return _emscripten_set_canvas_element_size_calling_thread(target, width, height); + } else { + return _emscripten_set_canvas_element_size_main_thread(target, width, height); + } + } + function _emscripten_set_current_thread_status(newStatus) { + } + function _emscripten_set_thread_name(threadId, name) { + } + function __webgl_enable_ANGLE_instanced_arrays(ctx) { + var ext = ctx.getExtension("ANGLE_instanced_arrays"); + if (ext) { + ctx["vertexAttribDivisor"] = function(index, divisor) { + ext["vertexAttribDivisorANGLE"](index, divisor); + }; + ctx["drawArraysInstanced"] = function(mode, first, count2, primcount) { + ext["drawArraysInstancedANGLE"](mode, first, count2, primcount); + }; + ctx["drawElementsInstanced"] = function(mode, count2, type, indices, primcount) { + ext["drawElementsInstancedANGLE"](mode, count2, type, indices, primcount); + }; + return 1; + } + } + function __webgl_enable_OES_vertex_array_object(ctx) { + var ext = ctx.getExtension("OES_vertex_array_object"); + if (ext) { + ctx["createVertexArray"] = function() { + return ext["createVertexArrayOES"](); + }; + ctx["deleteVertexArray"] = function(vao) { + ext["deleteVertexArrayOES"](vao); + }; + ctx["bindVertexArray"] = function(vao) { + ext["bindVertexArrayOES"](vao); + }; + ctx["isVertexArray"] = function(vao) { + return ext["isVertexArrayOES"](vao); + }; + return 1; + } + } + function __webgl_enable_WEBGL_draw_buffers(ctx) { + var ext = ctx.getExtension("WEBGL_draw_buffers"); + if (ext) { + ctx["drawBuffers"] = function(n, bufs) { + ext["drawBuffersWEBGL"](n, bufs); + }; + return 1; + } + } + function __webgl_enable_WEBGL_multi_draw(ctx) { + return !!(ctx.multiDrawWebgl = ctx.getExtension("WEBGL_multi_draw")); + } + var GL = {counter: 1, buffers: [], programs: [], framebuffers: [], renderbuffers: [], textures: [], uniforms: [], shaders: [], vaos: [], contexts: {}, offscreenCanvases: {}, timerQueriesEXT: [], programInfos: {}, stringCache: {}, unpackAlignment: 4, recordError: function recordError(errorCode) { + if (!GL.lastError) { + GL.lastError = errorCode; + } + }, getNewId: function(table) { + var ret = GL.counter++; + for (var i = table.length; i < ret; i++) { + table[i] = null; + } + return ret; + }, getSource: function(shader, count2, string, length) { + var source = ""; + for (var i = 0; i < count2; ++i) { + var len = length ? GROWABLE_HEAP_I32()[length + i * 4 >> 2] : -1; + source += UTF8ToString(GROWABLE_HEAP_I32()[string + i * 4 >> 2], len < 0 ? void 0 : len); + } + return source; + }, createContext: function(canvas, webGLContextAttributes) { + var ctx = canvas.getContext("webgl", webGLContextAttributes); + if (!ctx) + return 0; + var handle = GL.registerContext(ctx, webGLContextAttributes); + return handle; + }, registerContext: function(ctx, webGLContextAttributes) { + var handle = _malloc(8); + GROWABLE_HEAP_I32()[handle + 4 >> 2] = _pthread_self(); + var context = {handle, attributes: webGLContextAttributes, version: webGLContextAttributes.majorVersion, GLctx: ctx}; + if (ctx.canvas) + ctx.canvas.GLctxObject = context; + GL.contexts[handle] = context; + if (typeof webGLContextAttributes.enableExtensionsByDefault === "undefined" || webGLContextAttributes.enableExtensionsByDefault) { + GL.initExtensions(context); + } + return handle; + }, makeContextCurrent: function(contextHandle) { + GL.currentContext = GL.contexts[contextHandle]; + Module.ctx = GLctx = GL.currentContext && GL.currentContext.GLctx; + return !(contextHandle && !GLctx); + }, getContext: function(contextHandle) { + return GL.contexts[contextHandle]; + }, deleteContext: function(contextHandle) { + if (GL.currentContext === GL.contexts[contextHandle]) + GL.currentContext = null; + if (typeof JSEvents === "object") + JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas); + if (GL.contexts[contextHandle] && GL.contexts[contextHandle].GLctx.canvas) + GL.contexts[contextHandle].GLctx.canvas.GLctxObject = void 0; + _free(GL.contexts[contextHandle].handle); + GL.contexts[contextHandle] = null; + }, initExtensions: function(context) { + if (!context) + context = GL.currentContext; + if (context.initExtensionsDone) + return; + context.initExtensionsDone = true; + var GLctx2 = context.GLctx; + __webgl_enable_ANGLE_instanced_arrays(GLctx2); + __webgl_enable_OES_vertex_array_object(GLctx2); + __webgl_enable_WEBGL_draw_buffers(GLctx2); + GLctx2.disjointTimerQueryExt = GLctx2.getExtension("EXT_disjoint_timer_query"); + __webgl_enable_WEBGL_multi_draw(GLctx2); + var exts = GLctx2.getSupportedExtensions() || []; + exts.forEach(function(ext) { + if (ext.indexOf("lose_context") < 0 && ext.indexOf("debug") < 0) { + GLctx2.getExtension(ext); + } + }); + }, populateUniformTable: function(program) { + var p2 = GL.programs[program]; + var ptable = GL.programInfos[program] = {uniforms: {}, maxUniformLength: 0, maxAttributeLength: -1, maxUniformBlockNameLength: -1}; + var utable = ptable.uniforms; + var numUniforms = GLctx.getProgramParameter(p2, 35718); + for (var i = 0; i < numUniforms; ++i) { + var u = GLctx.getActiveUniform(p2, i); + var name = u.name; + ptable.maxUniformLength = Math.max(ptable.maxUniformLength, name.length + 1); + if (name.slice(-1) == "]") { + name = name.slice(0, name.lastIndexOf("[")); + } + var loc = GLctx.getUniformLocation(p2, name); + if (loc) { + var id = GL.getNewId(GL.uniforms); + utable[name] = [u.size, id]; + GL.uniforms[id] = loc; + for (var j = 1; j < u.size; ++j) { + var n = name + "[" + j + "]"; + loc = GLctx.getUniformLocation(p2, n); + id = GL.getNewId(GL.uniforms); + GL.uniforms[id] = loc; + } + } + } + }}; + var __emscripten_webgl_power_preferences = ["default", "low-power", "high-performance"]; + function _emscripten_webgl_do_create_context(target, attributes) { + var a = attributes >> 2; + var powerPreference = GROWABLE_HEAP_I32()[a + (24 >> 2)]; + var contextAttributes = {alpha: !!GROWABLE_HEAP_I32()[a + (0 >> 2)], depth: !!GROWABLE_HEAP_I32()[a + (4 >> 2)], stencil: !!GROWABLE_HEAP_I32()[a + (8 >> 2)], antialias: !!GROWABLE_HEAP_I32()[a + (12 >> 2)], premultipliedAlpha: !!GROWABLE_HEAP_I32()[a + (16 >> 2)], preserveDrawingBuffer: !!GROWABLE_HEAP_I32()[a + (20 >> 2)], powerPreference: __emscripten_webgl_power_preferences[powerPreference], failIfMajorPerformanceCaveat: !!GROWABLE_HEAP_I32()[a + (28 >> 2)], majorVersion: GROWABLE_HEAP_I32()[a + (32 >> 2)], minorVersion: GROWABLE_HEAP_I32()[a + (36 >> 2)], enableExtensionsByDefault: GROWABLE_HEAP_I32()[a + (40 >> 2)], explicitSwapControl: GROWABLE_HEAP_I32()[a + (44 >> 2)], proxyContextToMainThread: GROWABLE_HEAP_I32()[a + (48 >> 2)], renderViaOffscreenBackBuffer: GROWABLE_HEAP_I32()[a + (52 >> 2)]}; + var canvas = findCanvasEventTarget(target); + if (!canvas) { + return 0; + } + if (contextAttributes.explicitSwapControl) { + return 0; + } + var contextHandle = GL.createContext(canvas, contextAttributes); + return contextHandle; + } + function _emscripten_webgl_create_context(a0, a12) { + return _emscripten_webgl_do_create_context(a0, a12); + } + var SYSCALLS = {mappings: {}, buffers: [null, [], []], printChar: function(stream, curr) { + var buffer3 = SYSCALLS.buffers[stream]; + if (curr === 0 || curr === 10) { + (stream === 1 ? out : err)(UTF8ArrayToString(buffer3, 0)); + buffer3.length = 0; + } else { + buffer3.push(curr); + } + }, varargs: void 0, get: function() { + SYSCALLS.varargs += 4; + var ret = GROWABLE_HEAP_I32()[SYSCALLS.varargs - 4 >> 2]; + return ret; + }, getStr: function(ptr) { + var ret = UTF8ToString(ptr); + return ret; + }, get64: function(low, high) { + return low; + }}; + function _fd_close(fd) { + if (ENVIRONMENT_IS_PTHREAD) + return _emscripten_proxy_to_main_thread_js(3, 1, fd); + return 0; + } + function _fd_seek(fd, offset_low, offset_high, whence, newOffset) { + if (ENVIRONMENT_IS_PTHREAD) + return _emscripten_proxy_to_main_thread_js(4, 1, fd, offset_low, offset_high, whence, newOffset); + } + function _fd_write(fd, iov, iovcnt, pnum) { + if (ENVIRONMENT_IS_PTHREAD) + return _emscripten_proxy_to_main_thread_js(5, 1, fd, iov, iovcnt, pnum); + var num = 0; + for (var i = 0; i < iovcnt; i++) { + var ptr = GROWABLE_HEAP_I32()[iov + i * 8 >> 2]; + var len = GROWABLE_HEAP_I32()[iov + (i * 8 + 4) >> 2]; + for (var j = 0; j < len; j++) { + SYSCALLS.printChar(fd, GROWABLE_HEAP_U8()[ptr + j]); + } + num += len; + } + GROWABLE_HEAP_I32()[pnum >> 2] = num; + return 0; + } + function _pthread_cleanup_pop(execute2) { + var routine = PThread.threadExitHandlers.pop(); + if (execute2) + routine(); + } + function _pthread_cleanup_push(routine, arg) { + PThread.threadExitHandlers.push(function() { + wasmTable.get(routine)(arg); + }); + } + function spawnThread(threadParams) { + if (ENVIRONMENT_IS_PTHREAD) + throw "Internal Error! spawnThread() can only ever be called from main application thread!"; + var worker = PThread.getNewWorker(); + if (worker.pthread !== void 0) + throw "Internal error!"; + if (!threadParams.pthread_ptr) + throw "Internal error, no pthread ptr!"; + PThread.runningWorkers.push(worker); + var tlsMemory = _malloc(128 * 4); + for (var i = 0; i < 128; ++i) { + GROWABLE_HEAP_I32()[tlsMemory + i * 4 >> 2] = 0; + } + var stackHigh = threadParams.stackBase + threadParams.stackSize; + var pthread = PThread.pthreads[threadParams.pthread_ptr] = {worker, stackBase: threadParams.stackBase, stackSize: threadParams.stackSize, allocatedOwnStack: threadParams.allocatedOwnStack, threadInfoStruct: threadParams.pthread_ptr}; + var tis = pthread.threadInfoStruct >> 2; + Atomics.store(GROWABLE_HEAP_U32(), tis + (64 >> 2), threadParams.detached); + Atomics.store(GROWABLE_HEAP_U32(), tis + (100 >> 2), tlsMemory); + Atomics.store(GROWABLE_HEAP_U32(), tis + (40 >> 2), pthread.threadInfoStruct); + Atomics.store(GROWABLE_HEAP_U32(), tis + (80 >> 2), threadParams.stackSize); + Atomics.store(GROWABLE_HEAP_U32(), tis + (76 >> 2), stackHigh); + Atomics.store(GROWABLE_HEAP_U32(), tis + (104 >> 2), threadParams.stackSize); + Atomics.store(GROWABLE_HEAP_U32(), tis + (104 + 8 >> 2), stackHigh); + Atomics.store(GROWABLE_HEAP_U32(), tis + (104 + 12 >> 2), threadParams.detached); + var global_libc = _emscripten_get_global_libc(); + var global_locale = global_libc + 40; + Atomics.store(GROWABLE_HEAP_U32(), tis + (172 >> 2), global_locale); + worker.pthread = pthread; + var msg = {cmd: "run", start_routine: threadParams.startRoutine, arg: threadParams.arg, threadInfoStruct: threadParams.pthread_ptr, stackBase: threadParams.stackBase, stackSize: threadParams.stackSize}; + worker.runPthread = function() { + msg.time = performance.now(); + worker.postMessage(msg, threadParams.transferList); + }; + if (worker.loaded) { + worker.runPthread(); + delete worker.runPthread; + } + } + function _pthread_create(pthread_ptr, attr, start_routine, arg) { + if (typeof SharedArrayBuffer === "undefined") { + err("Current environment does not support SharedArrayBuffer, pthreads are not available!"); + return 6; + } + if (!pthread_ptr) { + err("pthread_create called with a null thread pointer!"); + return 28; + } + var transferList = []; + var error = 0; + if (ENVIRONMENT_IS_PTHREAD && (transferList.length === 0 || error)) { + return _emscripten_sync_run_in_main_thread_4(687865856, pthread_ptr, attr, start_routine, arg); + } + if (error) + return error; + var stackSize = 0; + var stackBase = 0; + var detached = 0; + if (attr && attr != -1) { + stackSize = GROWABLE_HEAP_I32()[attr >> 2]; + stackSize += 81920; + stackBase = GROWABLE_HEAP_I32()[attr + 8 >> 2]; + detached = GROWABLE_HEAP_I32()[attr + 12 >> 2] !== 0; + } else { + stackSize = 2097152; + } + var allocatedOwnStack = stackBase == 0; + if (allocatedOwnStack) { + stackBase = _memalign(16, stackSize); + } else { + stackBase -= stackSize; + assert3(stackBase > 0); + } + var threadInfoStruct = _malloc(228); + for (var i = 0; i < 228 >> 2; ++i) + GROWABLE_HEAP_U32()[(threadInfoStruct >> 2) + i] = 0; + GROWABLE_HEAP_I32()[pthread_ptr >> 2] = threadInfoStruct; + GROWABLE_HEAP_I32()[threadInfoStruct + 12 >> 2] = threadInfoStruct; + var headPtr = threadInfoStruct + 152; + GROWABLE_HEAP_I32()[headPtr >> 2] = headPtr; + var threadParams = {stackBase, stackSize, allocatedOwnStack, detached, startRoutine: start_routine, pthread_ptr: threadInfoStruct, arg, transferList}; + if (ENVIRONMENT_IS_PTHREAD) { + threadParams.cmd = "spawnThread"; + postMessage(threadParams, transferList); + } else { + spawnThread(threadParams); + } + return 0; + } + function _sysconf(name) { + if (ENVIRONMENT_IS_PTHREAD) + return _emscripten_proxy_to_main_thread_js(6, 1, name); + switch (name) { + case 30: + return 16384; + case 85: + var maxHeapSize = 2147483648; + return maxHeapSize / 16384; + case 132: + case 133: + case 12: + case 137: + case 138: + case 15: + case 235: + case 16: + case 17: + case 18: + case 19: + case 20: + case 149: + case 13: + case 10: + case 236: + case 153: + case 9: + case 21: + case 22: + case 159: + case 154: + case 14: + case 77: + case 78: + case 139: + case 82: + case 68: + case 67: + case 164: + case 11: + case 29: + case 47: + case 48: + case 95: + case 52: + case 51: + case 46: + return 200809; + case 27: + case 246: + case 127: + case 128: + case 23: + case 24: + case 160: + case 161: + case 181: + case 182: + case 242: + case 183: + case 184: + case 243: + case 244: + case 245: + case 165: + case 178: + case 179: + case 49: + case 50: + case 168: + case 169: + case 175: + case 170: + case 171: + case 172: + case 97: + case 76: + case 32: + case 173: + case 35: + case 80: + case 81: + case 79: + return -1; + case 176: + case 177: + case 7: + case 155: + case 8: + case 157: + case 125: + case 126: + case 92: + case 93: + case 129: + case 130: + case 131: + case 94: + case 91: + return 1; + case 74: + case 60: + case 69: + case 70: + case 4: + return 1024; + case 31: + case 42: + case 72: + return 32; + case 87: + case 26: + case 33: + return 2147483647; + case 34: + case 1: + return 47839; + case 38: + case 36: + return 99; + case 43: + case 37: + return 2048; + case 0: + return 2097152; + case 3: + return 65536; + case 28: + return 32768; + case 44: + return 32767; + case 75: + return 16384; + case 39: + return 1e3; + case 89: + return 700; + case 71: + return 256; + case 40: + return 255; + case 2: + return 100; + case 180: + return 64; + case 25: + return 20; + case 5: + return 16; + case 6: + return 6; + case 73: + return 4; + case 84: { + if (typeof navigator === "object") + return navigator["hardwareConcurrency"] || 1; + return 1; + } + } + setErrNo(28); + return -1; + } + if (!ENVIRONMENT_IS_PTHREAD) + PThread.initMainThreadBlock(); + var GLctx; + var proxiedFunctionTable = [null, _atexit, _emscripten_set_canvas_element_size_main_thread, _fd_close, _fd_seek, _fd_write, _sysconf]; + var asmLibraryArg = {e: ___assert_fail, r: ___call_main, x: __emscripten_notify_thread_queue, b: _abort, y: _emscripten_asm_const_int, j: _emscripten_conditional_set_current_thread_status, c: _emscripten_futex_wait, d: _emscripten_futex_wake, f: _emscripten_get_now, p: _emscripten_memcpy_big, z: _emscripten_num_logical_cores, u: _emscripten_receive_on_main_thread_js, q: _emscripten_resize_heap, v: _emscripten_set_canvas_element_size, i: _emscripten_set_current_thread_status, t: _emscripten_set_thread_name, w: _emscripten_webgl_create_context, m: _fd_close, n: _fd_seek, g: _fd_write, o: initPthreadsJS, a: wasmMemory || Module["wasmMemory"], k: _pthread_cleanup_pop, l: _pthread_cleanup_push, h: _pthread_create, s: _sysconf}; + var asm = createWasm(); + var ___wasm_call_ctors = Module["___wasm_call_ctors"] = function() { + return (___wasm_call_ctors = Module["___wasm_call_ctors"] = Module["asm"]["A"]).apply(null, arguments); + }; + var _init = Module["_init"] = function() { + return (_init = Module["_init"] = Module["asm"]["B"]).apply(null, arguments); + }; + var _register_tensor = Module["_register_tensor"] = function() { + return (_register_tensor = Module["_register_tensor"] = Module["asm"]["C"]).apply(null, arguments); + }; + var _dispose_data = Module["_dispose_data"] = function() { + return (_dispose_data = Module["_dispose_data"] = Module["asm"]["D"]).apply(null, arguments); + }; + var _dispose = Module["_dispose"] = function() { + return (_dispose = Module["_dispose"] = Module["asm"]["E"]).apply(null, arguments); + }; + var _Abs = Module["_Abs"] = function() { + return (_Abs = Module["_Abs"] = Module["asm"]["G"]).apply(null, arguments); + }; + var _Add = Module["_Add"] = function() { + return (_Add = Module["_Add"] = Module["asm"]["H"]).apply(null, arguments); + }; + var _AddN = Module["_AddN"] = function() { + return (_AddN = Module["_AddN"] = Module["asm"]["I"]).apply(null, arguments); + }; + var _ArgMax = Module["_ArgMax"] = function() { + return (_ArgMax = Module["_ArgMax"] = Module["asm"]["J"]).apply(null, arguments); + }; + var _AvgPool = Module["_AvgPool"] = function() { + return (_AvgPool = Module["_AvgPool"] = Module["asm"]["K"]).apply(null, arguments); + }; + var _BatchMatMul = Module["_BatchMatMul"] = function() { + return (_BatchMatMul = Module["_BatchMatMul"] = Module["asm"]["L"]).apply(null, arguments); + }; + var _Ceil = Module["_Ceil"] = function() { + return (_Ceil = Module["_Ceil"] = Module["asm"]["M"]).apply(null, arguments); + }; + var _ClipByValue = Module["_ClipByValue"] = function() { + return (_ClipByValue = Module["_ClipByValue"] = Module["asm"]["N"]).apply(null, arguments); + }; + var _Conv2D = Module["_Conv2D"] = function() { + return (_Conv2D = Module["_Conv2D"] = Module["asm"]["O"]).apply(null, arguments); + }; + var _Conv2DBackpropInput = Module["_Conv2DBackpropInput"] = function() { + return (_Conv2DBackpropInput = Module["_Conv2DBackpropInput"] = Module["asm"]["P"]).apply(null, arguments); + }; + var _Cos = Module["_Cos"] = function() { + return (_Cos = Module["_Cos"] = Module["asm"]["Q"]).apply(null, arguments); + }; + var _CropAndResize = Module["_CropAndResize"] = function() { + return (_CropAndResize = Module["_CropAndResize"] = Module["asm"]["R"]).apply(null, arguments); + }; + var _Cumsum = Module["_Cumsum"] = function() { + return (_Cumsum = Module["_Cumsum"] = Module["asm"]["S"]).apply(null, arguments); + }; + var _DepthToSpace = Module["_DepthToSpace"] = function() { + return (_DepthToSpace = Module["_DepthToSpace"] = Module["asm"]["T"]).apply(null, arguments); + }; + var _DepthwiseConv2dNative = Module["_DepthwiseConv2dNative"] = function() { + return (_DepthwiseConv2dNative = Module["_DepthwiseConv2dNative"] = Module["asm"]["U"]).apply(null, arguments); + }; + var _Equal = Module["_Equal"] = function() { + return (_Equal = Module["_Equal"] = Module["asm"]["V"]).apply(null, arguments); + }; + var _Exp = Module["_Exp"] = function() { + return (_Exp = Module["_Exp"] = Module["asm"]["W"]).apply(null, arguments); + }; + var _FlipLeftRight = Module["_FlipLeftRight"] = function() { + return (_FlipLeftRight = Module["_FlipLeftRight"] = Module["asm"]["X"]).apply(null, arguments); + }; + var _Floor = Module["_Floor"] = function() { + return (_Floor = Module["_Floor"] = Module["asm"]["Y"]).apply(null, arguments); + }; + var _FloorDiv = Module["_FloorDiv"] = function() { + return (_FloorDiv = Module["_FloorDiv"] = Module["asm"]["Z"]).apply(null, arguments); + }; + var _FusedBatchNorm = Module["_FusedBatchNorm"] = function() { + return (_FusedBatchNorm = Module["_FusedBatchNorm"] = Module["asm"]["_"]).apply(null, arguments); + }; + var _FusedConv2D = Module["_FusedConv2D"] = function() { + return (_FusedConv2D = Module["_FusedConv2D"] = Module["asm"]["$"]).apply(null, arguments); + }; + var _FusedDepthwiseConv2D = Module["_FusedDepthwiseConv2D"] = function() { + return (_FusedDepthwiseConv2D = Module["_FusedDepthwiseConv2D"] = Module["asm"]["aa"]).apply(null, arguments); + }; + var _Gather = Module["_Gather"] = function() { + return (_Gather = Module["_Gather"] = Module["asm"]["ba"]).apply(null, arguments); + }; + var _GatherNd = Module["_GatherNd"] = function() { + return (_GatherNd = Module["_GatherNd"] = Module["asm"]["ca"]).apply(null, arguments); + }; + var _Greater = Module["_Greater"] = function() { + return (_Greater = Module["_Greater"] = Module["asm"]["da"]).apply(null, arguments); + }; + var _GreaterEqual = Module["_GreaterEqual"] = function() { + return (_GreaterEqual = Module["_GreaterEqual"] = Module["asm"]["ea"]).apply(null, arguments); + }; + var _LeakyRelu = Module["_LeakyRelu"] = function() { + return (_LeakyRelu = Module["_LeakyRelu"] = Module["asm"]["fa"]).apply(null, arguments); + }; + var _Less = Module["_Less"] = function() { + return (_Less = Module["_Less"] = Module["asm"]["ga"]).apply(null, arguments); + }; + var _LessEqual = Module["_LessEqual"] = function() { + return (_LessEqual = Module["_LessEqual"] = Module["asm"]["ha"]).apply(null, arguments); + }; + var _Log = Module["_Log"] = function() { + return (_Log = Module["_Log"] = Module["asm"]["ia"]).apply(null, arguments); + }; + var _LogicalAnd = Module["_LogicalAnd"] = function() { + return (_LogicalAnd = Module["_LogicalAnd"] = Module["asm"]["ja"]).apply(null, arguments); + }; + var _Max = Module["_Max"] = function() { + return (_Max = Module["_Max"] = Module["asm"]["ka"]).apply(null, arguments); + }; + var _MaxPool = Module["_MaxPool"] = function() { + return (_MaxPool = Module["_MaxPool"] = Module["asm"]["la"]).apply(null, arguments); + }; + var _Maximum = Module["_Maximum"] = function() { + return (_Maximum = Module["_Maximum"] = Module["asm"]["ma"]).apply(null, arguments); + }; + var _Mean = Module["_Mean"] = function() { + return (_Mean = Module["_Mean"] = Module["asm"]["na"]).apply(null, arguments); + }; + var _Min = Module["_Min"] = function() { + return (_Min = Module["_Min"] = Module["asm"]["oa"]).apply(null, arguments); + }; + var _Minimum = Module["_Minimum"] = function() { + return (_Minimum = Module["_Minimum"] = Module["asm"]["pa"]).apply(null, arguments); + }; + var _Multiply = Module["_Multiply"] = function() { + return (_Multiply = Module["_Multiply"] = Module["asm"]["qa"]).apply(null, arguments); + }; + var _Neg = Module["_Neg"] = function() { + return (_Neg = Module["_Neg"] = Module["asm"]["ra"]).apply(null, arguments); + }; + var _NonMaxSuppressionV3 = Module["_NonMaxSuppressionV3"] = function() { + return (_NonMaxSuppressionV3 = Module["_NonMaxSuppressionV3"] = Module["asm"]["sa"]).apply(null, arguments); + }; + var _NonMaxSuppressionV4 = Module["_NonMaxSuppressionV4"] = function() { + return (_NonMaxSuppressionV4 = Module["_NonMaxSuppressionV4"] = Module["asm"]["ta"]).apply(null, arguments); + }; + var _NonMaxSuppressionV5 = Module["_NonMaxSuppressionV5"] = function() { + return (_NonMaxSuppressionV5 = Module["_NonMaxSuppressionV5"] = Module["asm"]["ua"]).apply(null, arguments); + }; + var _NotEqual = Module["_NotEqual"] = function() { + return (_NotEqual = Module["_NotEqual"] = Module["asm"]["va"]).apply(null, arguments); + }; + var _OneHot = Module["_OneHot"] = function() { + return (_OneHot = Module["_OneHot"] = Module["asm"]["wa"]).apply(null, arguments); + }; + var _PadV2 = Module["_PadV2"] = function() { + return (_PadV2 = Module["_PadV2"] = Module["asm"]["xa"]).apply(null, arguments); + }; + var _Pow = Module["_Pow"] = function() { + return (_Pow = Module["_Pow"] = Module["asm"]["ya"]).apply(null, arguments); + }; + var _Prelu = Module["_Prelu"] = function() { + return (_Prelu = Module["_Prelu"] = Module["asm"]["za"]).apply(null, arguments); + }; + var _Prod = Module["_Prod"] = function() { + return (_Prod = Module["_Prod"] = Module["asm"]["Aa"]).apply(null, arguments); + }; + var _RealDiv = Module["_RealDiv"] = function() { + return (_RealDiv = Module["_RealDiv"] = Module["asm"]["Ba"]).apply(null, arguments); + }; + var _Relu = Module["_Relu"] = function() { + return (_Relu = Module["_Relu"] = Module["asm"]["Ca"]).apply(null, arguments); + }; + var _Relu6 = Module["_Relu6"] = function() { + return (_Relu6 = Module["_Relu6"] = Module["asm"]["Da"]).apply(null, arguments); + }; + var _ResizeBilinear = Module["_ResizeBilinear"] = function() { + return (_ResizeBilinear = Module["_ResizeBilinear"] = Module["asm"]["Ea"]).apply(null, arguments); + }; + var _Reverse = Module["_Reverse"] = function() { + return (_Reverse = Module["_Reverse"] = Module["asm"]["Fa"]).apply(null, arguments); + }; + var _RotateWithOffset = Module["_RotateWithOffset"] = function() { + return (_RotateWithOffset = Module["_RotateWithOffset"] = Module["asm"]["Ga"]).apply(null, arguments); + }; + var _Round = Module["_Round"] = function() { + return (_Round = Module["_Round"] = Module["asm"]["Ha"]).apply(null, arguments); + }; + var _Rsqrt = Module["_Rsqrt"] = function() { + return (_Rsqrt = Module["_Rsqrt"] = Module["asm"]["Ia"]).apply(null, arguments); + }; + var _ScatterNd = Module["_ScatterNd"] = function() { + return (_ScatterNd = Module["_ScatterNd"] = Module["asm"]["Ja"]).apply(null, arguments); + }; + var _SelectV2 = Module["_SelectV2"] = function() { + return (_SelectV2 = Module["_SelectV2"] = Module["asm"]["Ka"]).apply(null, arguments); + }; + var _Sigmoid = Module["_Sigmoid"] = function() { + return (_Sigmoid = Module["_Sigmoid"] = Module["asm"]["La"]).apply(null, arguments); + }; + var _Sin = Module["_Sin"] = function() { + return (_Sin = Module["_Sin"] = Module["asm"]["Ma"]).apply(null, arguments); + }; + var _Softmax = Module["_Softmax"] = function() { + return (_Softmax = Module["_Softmax"] = Module["asm"]["Na"]).apply(null, arguments); + }; + var _Sqrt = Module["_Sqrt"] = function() { + return (_Sqrt = Module["_Sqrt"] = Module["asm"]["Oa"]).apply(null, arguments); + }; + var _Square = Module["_Square"] = function() { + return (_Square = Module["_Square"] = Module["asm"]["Pa"]).apply(null, arguments); + }; + var _SquaredDifference = Module["_SquaredDifference"] = function() { + return (_SquaredDifference = Module["_SquaredDifference"] = Module["asm"]["Qa"]).apply(null, arguments); + }; + var _Step = Module["_Step"] = function() { + return (_Step = Module["_Step"] = Module["asm"]["Ra"]).apply(null, arguments); + }; + var _StridedSlice = Module["_StridedSlice"] = function() { + return (_StridedSlice = Module["_StridedSlice"] = Module["asm"]["Sa"]).apply(null, arguments); + }; + var _Sub = Module["_Sub"] = function() { + return (_Sub = Module["_Sub"] = Module["asm"]["Ta"]).apply(null, arguments); + }; + var _Sum = Module["_Sum"] = function() { + return (_Sum = Module["_Sum"] = Module["asm"]["Ua"]).apply(null, arguments); + }; + var _Tanh = Module["_Tanh"] = function() { + return (_Tanh = Module["_Tanh"] = Module["asm"]["Va"]).apply(null, arguments); + }; + var _Tile = Module["_Tile"] = function() { + return (_Tile = Module["_Tile"] = Module["asm"]["Wa"]).apply(null, arguments); + }; + var _TopK = Module["_TopK"] = function() { + return (_TopK = Module["_TopK"] = Module["asm"]["Xa"]).apply(null, arguments); + }; + var _Transpose = Module["_Transpose"] = function() { + return (_Transpose = Module["_Transpose"] = Module["asm"]["Ya"]).apply(null, arguments); + }; + var __FusedMatMul = Module["__FusedMatMul"] = function() { + return (__FusedMatMul = Module["__FusedMatMul"] = Module["asm"]["Za"]).apply(null, arguments); + }; + var _malloc = Module["_malloc"] = function() { + return (_malloc = Module["_malloc"] = Module["asm"]["_a"]).apply(null, arguments); + }; + var _free = Module["_free"] = function() { + return (_free = Module["_free"] = Module["asm"]["$a"]).apply(null, arguments); + }; + var ___errno_location = Module["___errno_location"] = function() { + return (___errno_location = Module["___errno_location"] = Module["asm"]["ab"]).apply(null, arguments); + }; + var _emscripten_get_global_libc = Module["_emscripten_get_global_libc"] = function() { + return (_emscripten_get_global_libc = Module["_emscripten_get_global_libc"] = Module["asm"]["bb"]).apply(null, arguments); + }; + var _pthread_self = Module["_pthread_self"] = function() { + return (_pthread_self = Module["_pthread_self"] = Module["asm"]["cb"]).apply(null, arguments); + }; + var ___pthread_tsd_run_dtors = Module["___pthread_tsd_run_dtors"] = function() { + return (___pthread_tsd_run_dtors = Module["___pthread_tsd_run_dtors"] = Module["asm"]["db"]).apply(null, arguments); + }; + var _emscripten_main_thread_process_queued_calls = Module["_emscripten_main_thread_process_queued_calls"] = function() { + return (_emscripten_main_thread_process_queued_calls = Module["_emscripten_main_thread_process_queued_calls"] = Module["asm"]["eb"]).apply(null, arguments); + }; + var _emscripten_current_thread_process_queued_calls = Module["_emscripten_current_thread_process_queued_calls"] = function() { + return (_emscripten_current_thread_process_queued_calls = Module["_emscripten_current_thread_process_queued_calls"] = Module["asm"]["fb"]).apply(null, arguments); + }; + var _emscripten_register_main_browser_thread_id = Module["_emscripten_register_main_browser_thread_id"] = function() { + return (_emscripten_register_main_browser_thread_id = Module["_emscripten_register_main_browser_thread_id"] = Module["asm"]["gb"]).apply(null, arguments); + }; + var __emscripten_do_dispatch_to_thread = Module["__emscripten_do_dispatch_to_thread"] = function() { + return (__emscripten_do_dispatch_to_thread = Module["__emscripten_do_dispatch_to_thread"] = Module["asm"]["hb"]).apply(null, arguments); + }; + var _emscripten_sync_run_in_main_thread_4 = Module["_emscripten_sync_run_in_main_thread_4"] = function() { + return (_emscripten_sync_run_in_main_thread_4 = Module["_emscripten_sync_run_in_main_thread_4"] = Module["asm"]["ib"]).apply(null, arguments); + }; + var _emscripten_run_in_main_runtime_thread_js = Module["_emscripten_run_in_main_runtime_thread_js"] = function() { + return (_emscripten_run_in_main_runtime_thread_js = Module["_emscripten_run_in_main_runtime_thread_js"] = Module["asm"]["jb"]).apply(null, arguments); + }; + var __emscripten_call_on_thread = Module["__emscripten_call_on_thread"] = function() { + return (__emscripten_call_on_thread = Module["__emscripten_call_on_thread"] = Module["asm"]["kb"]).apply(null, arguments); + }; + var _emscripten_tls_init = Module["_emscripten_tls_init"] = function() { + return (_emscripten_tls_init = Module["_emscripten_tls_init"] = Module["asm"]["lb"]).apply(null, arguments); + }; + var __emscripten_thread_init = Module["__emscripten_thread_init"] = function() { + return (__emscripten_thread_init = Module["__emscripten_thread_init"] = Module["asm"]["mb"]).apply(null, arguments); + }; + var stackSave = Module["stackSave"] = function() { + return (stackSave = Module["stackSave"] = Module["asm"]["nb"]).apply(null, arguments); + }; + var stackRestore = Module["stackRestore"] = function() { + return (stackRestore = Module["stackRestore"] = Module["asm"]["ob"]).apply(null, arguments); + }; + var stackAlloc = Module["stackAlloc"] = function() { + return (stackAlloc = Module["stackAlloc"] = Module["asm"]["pb"]).apply(null, arguments); + }; + var _emscripten_stack_set_limits = Module["_emscripten_stack_set_limits"] = function() { + return (_emscripten_stack_set_limits = Module["_emscripten_stack_set_limits"] = Module["asm"]["qb"]).apply(null, arguments); + }; + var _memalign = Module["_memalign"] = function() { + return (_memalign = Module["_memalign"] = Module["asm"]["rb"]).apply(null, arguments); + }; + var __emscripten_allow_main_runtime_queued_calls = Module["__emscripten_allow_main_runtime_queued_calls"] = 9880; + var __emscripten_main_thread_futex = Module["__emscripten_main_thread_futex"] = 11368; + Module["cwrap"] = cwrap; + Module["PThread"] = PThread; + Module["PThread"] = PThread; + Module["wasmMemory"] = wasmMemory; + Module["ExitStatus"] = ExitStatus; + var calledRun; + function ExitStatus(status) { + this.name = "ExitStatus"; + this.message = "Program terminated with exit(" + status + ")"; + this.status = status; + } + dependenciesFulfilled = function runCaller() { + if (!calledRun) + run(); + if (!calledRun) + dependenciesFulfilled = runCaller; + }; + function run(args) { + args = args || arguments_; + if (runDependencies > 0) { + return; + } + if (ENVIRONMENT_IS_PTHREAD) { + readyPromiseResolve(Module); + postMessage({cmd: "loaded"}); + return; + } + preRun(); + if (runDependencies > 0) { + return; + } + function doRun() { + if (calledRun) + return; + calledRun = true; + Module["calledRun"] = true; + if (ABORT) + return; + initRuntime(); + preMain(); + readyPromiseResolve(Module); + if (Module["onRuntimeInitialized"]) + Module["onRuntimeInitialized"](); + postRun(); + } + if (Module["setStatus"]) { + Module["setStatus"]("Running..."); + setTimeout(function() { + setTimeout(function() { + Module["setStatus"](""); + }, 1); + doRun(); + }, 1); + } else { + doRun(); + } + } + Module["run"] = run; + function exit(status, implicit) { + if (implicit && noExitRuntime && status === 0) { + return; + } + if (!implicit) { + if (ENVIRONMENT_IS_PTHREAD) { + postMessage({cmd: "exitProcess", returnCode: status}); + throw new ExitStatus(status); + } else { + } + } + if (noExitRuntime) { + } else { + PThread.terminateAllThreads(); + EXITSTATUS = status; + exitRuntime(); + if (Module["onExit"]) + Module["onExit"](status); + ABORT = true; + } + quit_(status, new ExitStatus(status)); + } + if (Module["preInit"]) { + if (typeof Module["preInit"] == "function") + Module["preInit"] = [Module["preInit"]]; + while (Module["preInit"].length > 0) { + Module["preInit"].pop()(); + } + } + if (ENVIRONMENT_IS_PTHREAD) { + noExitRuntime = false; + PThread.initWorker(); + } + run(); + return WasmBackendModuleThreadedSimd2.ready; + }; + }(); + if (typeof exports === "object" && typeof module2 === "object") + module2.exports = WasmBackendModuleThreadedSimd; + else if (typeof define === "function" && define["amd"]) + define([], function() { + return WasmBackendModuleThreadedSimd; + }); + else if (typeof exports === "object") + exports["WasmBackendModuleThreadedSimd"] = WasmBackendModuleThreadedSimd; +}); +var require_tfjs_backend_wasm = __commonJS((exports, module2) => { + var WasmBackendModule = function() { + var _scriptDir = typeof document !== "undefined" && document.currentScript ? document.currentScript.src : void 0; + if (typeof __filename !== "undefined") + _scriptDir = _scriptDir || __filename; + return function(WasmBackendModule2) { + WasmBackendModule2 = WasmBackendModule2 || {}; + var Module = typeof WasmBackendModule2 !== "undefined" ? WasmBackendModule2 : {}; + var readyPromiseResolve, readyPromiseReject; + Module["ready"] = new Promise(function(resolve, reject) { + readyPromiseResolve = resolve; + readyPromiseReject = reject; + }); + var moduleOverrides = {}; + var key; + for (key in Module) { + if (Module.hasOwnProperty(key)) { + moduleOverrides[key] = Module[key]; + } + } + var arguments_ = []; + var thisProgram = "./this.program"; + var quit_ = function(status, toThrow) { + throw toThrow; + }; + var ENVIRONMENT_IS_WEB = false; + var ENVIRONMENT_IS_WORKER = false; + var ENVIRONMENT_IS_NODE = false; + var ENVIRONMENT_IS_SHELL = false; + ENVIRONMENT_IS_WEB = typeof window === "object"; + ENVIRONMENT_IS_WORKER = typeof importScripts === "function"; + ENVIRONMENT_IS_NODE = typeof process === "object" && typeof process.versions === "object" && typeof process.versions.node === "string"; + ENVIRONMENT_IS_SHELL = !ENVIRONMENT_IS_WEB && !ENVIRONMENT_IS_NODE && !ENVIRONMENT_IS_WORKER; + var scriptDirectory = ""; + function locateFile(path) { + if (Module["locateFile"]) { + return Module["locateFile"](path, scriptDirectory); + } + return scriptDirectory + path; + } + var read_, readAsync, readBinary, setWindowTitle; + var nodeFS; + var nodePath; + if (ENVIRONMENT_IS_NODE) { + if (ENVIRONMENT_IS_WORKER) { + scriptDirectory = require_path().dirname(scriptDirectory) + "/"; + } else { + scriptDirectory = __dirname + "/"; + } + read_ = function shell_read(filename, binary) { + if (!nodeFS) + nodeFS = require("fs"); + if (!nodePath) + nodePath = require_path(); + filename = nodePath["normalize"](filename); + return nodeFS["readFileSync"](filename, binary ? null : "utf8"); + }; + readBinary = function readBinary2(filename) { + var ret = read_(filename, true); + if (!ret.buffer) { + ret = new Uint8Array(ret); + } + assert3(ret.buffer); + return ret; + }; + if (process["argv"].length > 1) { + thisProgram = process["argv"][1].replace(/\\/g, "/"); + } + arguments_ = process["argv"].slice(2); + process["on"]("uncaughtException", function(ex) { + if (!(ex instanceof ExitStatus)) { + throw ex; + } + }); + process["on"]("unhandledRejection", abort); + quit_ = function(status) { + process["exit"](status); + }; + Module["inspect"] = function() { + return "[Emscripten Module object]"; + }; + } else if (ENVIRONMENT_IS_SHELL) { + if (typeof read != "undefined") { + read_ = function shell_read(f) { + return read(f); + }; + } + readBinary = function readBinary2(f) { + var data; + if (typeof readbuffer === "function") { + return new Uint8Array(readbuffer(f)); + } + data = read(f, "binary"); + assert3(typeof data === "object"); + return data; + }; + if (typeof scriptArgs != "undefined") { + arguments_ = scriptArgs; + } else if (typeof arguments != "undefined") { + arguments_ = arguments; + } + if (typeof quit === "function") { + quit_ = function(status) { + quit(status); + }; + } + if (typeof print !== "undefined") { + if (typeof console === "undefined") + console = {}; + console.log = print; + console.warn = console.error = typeof printErr !== "undefined" ? printErr : print; + } + } else if (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER) { + if (ENVIRONMENT_IS_WORKER) { + scriptDirectory = self.location.href; + } else if (typeof document !== "undefined" && document.currentScript) { + scriptDirectory = document.currentScript.src; + } + if (_scriptDir) { + scriptDirectory = _scriptDir; + } + if (scriptDirectory.indexOf("blob:") !== 0) { + scriptDirectory = scriptDirectory.substr(0, scriptDirectory.lastIndexOf("/") + 1); + } else { + scriptDirectory = ""; + } + { + read_ = function(url) { + var xhr = new XMLHttpRequest(); + xhr.open("GET", url, false); + xhr.send(null); + return xhr.responseText; + }; + if (ENVIRONMENT_IS_WORKER) { + readBinary = function(url) { + var xhr = new XMLHttpRequest(); + xhr.open("GET", url, false); + xhr.responseType = "arraybuffer"; + xhr.send(null); + return new Uint8Array(xhr.response); + }; + } + readAsync = function(url, onload, onerror) { + var xhr = new XMLHttpRequest(); + xhr.open("GET", url, true); + xhr.responseType = "arraybuffer"; + xhr.onload = function() { + if (xhr.status == 200 || xhr.status == 0 && xhr.response) { + onload(xhr.response); + return; + } + onerror(); + }; + xhr.onerror = onerror; + xhr.send(null); + }; + } + setWindowTitle = function(title) { + document.title = title; + }; + } else { + } + var out = Module["print"] || console.log.bind(console); + var err = Module["printErr"] || console.warn.bind(console); + for (key in moduleOverrides) { + if (moduleOverrides.hasOwnProperty(key)) { + Module[key] = moduleOverrides[key]; + } + } + moduleOverrides = null; + if (Module["arguments"]) + arguments_ = Module["arguments"]; + if (Module["thisProgram"]) + thisProgram = Module["thisProgram"]; + if (Module["quit"]) + quit_ = Module["quit"]; + var wasmBinary; + if (Module["wasmBinary"]) + wasmBinary = Module["wasmBinary"]; + var noExitRuntime = Module["noExitRuntime"] || true; + if (typeof WebAssembly !== "object") { + abort("no native wasm support detected"); + } + var wasmMemory; + var ABORT = false; + var EXITSTATUS; + function assert3(condition, text) { + if (!condition) { + abort("Assertion failed: " + text); + } + } + function getCFunc(ident) { + var func2 = Module["_" + ident]; + assert3(func2, "Cannot call unknown function " + ident + ", make sure it is exported"); + return func2; + } + function ccall(ident, returnType, argTypes, args, opts) { + var toC = {string: function(str) { + var ret2 = 0; + if (str !== null && str !== void 0 && str !== 0) { + var len = (str.length << 2) + 1; + ret2 = stackAlloc(len); + stringToUTF8(str, ret2, len); + } + return ret2; + }, array: function(arr) { + var ret2 = stackAlloc(arr.length); + writeArrayToMemory(arr, ret2); + return ret2; + }}; + function convertReturnValue(ret2) { + if (returnType === "string") + return UTF8ToString(ret2); + if (returnType === "boolean") + return Boolean(ret2); + return ret2; + } + var func2 = getCFunc(ident); + var cArgs = []; + var stack2 = 0; + if (args) { + for (var i = 0; i < args.length; i++) { + var converter = toC[argTypes[i]]; + if (converter) { + if (stack2 === 0) + stack2 = stackSave(); + cArgs[i] = converter(args[i]); + } else { + cArgs[i] = args[i]; + } + } + } + var ret = func2.apply(null, cArgs); + ret = convertReturnValue(ret); + if (stack2 !== 0) + stackRestore(stack2); + return ret; + } + function cwrap(ident, returnType, argTypes, opts) { + argTypes = argTypes || []; + var numericArgs = argTypes.every(function(type) { + return type === "number"; + }); + var numericRet = returnType !== "string"; + if (numericRet && numericArgs && !opts) { + return getCFunc(ident); + } + return function() { + return ccall(ident, returnType, argTypes, arguments, opts); + }; + } + var UTF8Decoder = typeof TextDecoder !== "undefined" ? new TextDecoder("utf8") : void 0; + function UTF8ArrayToString(heap, idx, maxBytesToRead) { + var endIdx = idx + maxBytesToRead; + var endPtr = idx; + while (heap[endPtr] && !(endPtr >= endIdx)) + ++endPtr; + if (endPtr - idx > 16 && heap.subarray && UTF8Decoder) { + return UTF8Decoder.decode(heap.subarray(idx, endPtr)); + } else { + var str = ""; + while (idx < endPtr) { + var u0 = heap[idx++]; + if (!(u0 & 128)) { + str += String.fromCharCode(u0); + continue; + } + var u1 = heap[idx++] & 63; + if ((u0 & 224) == 192) { + str += String.fromCharCode((u0 & 31) << 6 | u1); + continue; + } + var u2 = heap[idx++] & 63; + if ((u0 & 240) == 224) { + u0 = (u0 & 15) << 12 | u1 << 6 | u2; + } else { + u0 = (u0 & 7) << 18 | u1 << 12 | u2 << 6 | heap[idx++] & 63; + } + if (u0 < 65536) { + str += String.fromCharCode(u0); + } else { + var ch = u0 - 65536; + str += String.fromCharCode(55296 | ch >> 10, 56320 | ch & 1023); + } + } + } + return str; + } + function UTF8ToString(ptr, maxBytesToRead) { + return ptr ? UTF8ArrayToString(HEAPU8, ptr, maxBytesToRead) : ""; + } + function stringToUTF8Array(str, heap, outIdx, maxBytesToWrite) { + if (!(maxBytesToWrite > 0)) + return 0; + var startIdx = outIdx; + var endIdx = outIdx + maxBytesToWrite - 1; + for (var i = 0; i < str.length; ++i) { + var u = str.charCodeAt(i); + if (u >= 55296 && u <= 57343) { + var u1 = str.charCodeAt(++i); + u = 65536 + ((u & 1023) << 10) | u1 & 1023; + } + if (u <= 127) { + if (outIdx >= endIdx) + break; + heap[outIdx++] = u; + } else if (u <= 2047) { + if (outIdx + 1 >= endIdx) + break; + heap[outIdx++] = 192 | u >> 6; + heap[outIdx++] = 128 | u & 63; + } else if (u <= 65535) { + if (outIdx + 2 >= endIdx) + break; + heap[outIdx++] = 224 | u >> 12; + heap[outIdx++] = 128 | u >> 6 & 63; + heap[outIdx++] = 128 | u & 63; + } else { + if (outIdx + 3 >= endIdx) + break; + heap[outIdx++] = 240 | u >> 18; + heap[outIdx++] = 128 | u >> 12 & 63; + heap[outIdx++] = 128 | u >> 6 & 63; + heap[outIdx++] = 128 | u & 63; + } + } + heap[outIdx] = 0; + return outIdx - startIdx; + } + function stringToUTF8(str, outPtr, maxBytesToWrite) { + return stringToUTF8Array(str, HEAPU8, outPtr, maxBytesToWrite); + } + function writeArrayToMemory(array2, buffer3) { + HEAP8.set(array2, buffer3); + } + function alignUp(x, multiple) { + if (x % multiple > 0) { + x += multiple - x % multiple; + } + return x; + } + var buffer2, HEAP8, HEAPU8, HEAP16, HEAPU16, HEAP32, HEAPU32, HEAPF32, HEAPF64; + function updateGlobalBufferAndViews(buf) { + buffer2 = buf; + Module["HEAP8"] = HEAP8 = new Int8Array(buf); + Module["HEAP16"] = HEAP16 = new Int16Array(buf); + Module["HEAP32"] = HEAP32 = new Int32Array(buf); + Module["HEAPU8"] = HEAPU8 = new Uint8Array(buf); + Module["HEAPU16"] = HEAPU16 = new Uint16Array(buf); + Module["HEAPU32"] = HEAPU32 = new Uint32Array(buf); + Module["HEAPF32"] = HEAPF32 = new Float32Array(buf); + Module["HEAPF64"] = HEAPF64 = new Float64Array(buf); + } + var INITIAL_MEMORY = Module["INITIAL_MEMORY"] || 16777216; + var wasmTable; + var __ATPRERUN__ = []; + var __ATINIT__ = []; + var __ATMAIN__ = []; + var __ATPOSTRUN__ = []; + var runtimeInitialized = false; + __ATINIT__.push({func: function() { + ___wasm_call_ctors(); + }}); + function preRun() { + if (Module["preRun"]) { + if (typeof Module["preRun"] == "function") + Module["preRun"] = [Module["preRun"]]; + while (Module["preRun"].length) { + addOnPreRun(Module["preRun"].shift()); + } + } + callRuntimeCallbacks(__ATPRERUN__); + } + function initRuntime() { + runtimeInitialized = true; + callRuntimeCallbacks(__ATINIT__); + } + function preMain() { + callRuntimeCallbacks(__ATMAIN__); + } + function postRun() { + if (Module["postRun"]) { + if (typeof Module["postRun"] == "function") + Module["postRun"] = [Module["postRun"]]; + while (Module["postRun"].length) { + addOnPostRun(Module["postRun"].shift()); + } + } + callRuntimeCallbacks(__ATPOSTRUN__); + } + function addOnPreRun(cb) { + __ATPRERUN__.unshift(cb); + } + function addOnPostRun(cb) { + __ATPOSTRUN__.unshift(cb); + } + var runDependencies = 0; + var runDependencyWatcher = null; + var dependenciesFulfilled = null; + function addRunDependency(id) { + runDependencies++; + if (Module["monitorRunDependencies"]) { + Module["monitorRunDependencies"](runDependencies); + } + } + function removeRunDependency(id) { + runDependencies--; + if (Module["monitorRunDependencies"]) { + Module["monitorRunDependencies"](runDependencies); + } + if (runDependencies == 0) { + if (runDependencyWatcher !== null) { + clearInterval(runDependencyWatcher); + runDependencyWatcher = null; + } + if (dependenciesFulfilled) { + var callback = dependenciesFulfilled; + dependenciesFulfilled = null; + callback(); + } + } + } + Module["preloadedImages"] = {}; + Module["preloadedAudios"] = {}; + function abort(what) { + if (Module["onAbort"]) { + Module["onAbort"](what); + } + what += ""; + err(what); + ABORT = true; + EXITSTATUS = 1; + what = "abort(" + what + "). Build with -s ASSERTIONS=1 for more info."; + var e = new WebAssembly.RuntimeError(what); + readyPromiseReject(e); + throw e; + } + function hasPrefix(str, prefix) { + return String.prototype.startsWith ? str.startsWith(prefix) : str.indexOf(prefix) === 0; + } + var dataURIPrefix = "data:application/octet-stream;base64,"; + function isDataURI(filename) { + return hasPrefix(filename, dataURIPrefix); + } + var fileURIPrefix = "file://"; + function isFileURI(filename) { + return hasPrefix(filename, fileURIPrefix); + } + var wasmBinaryFile = "tfjs-backend-wasm.wasm"; + if (!isDataURI(wasmBinaryFile)) { + wasmBinaryFile = locateFile(wasmBinaryFile); + } + function getBinary(file) { + try { + if (file == wasmBinaryFile && wasmBinary) { + return new Uint8Array(wasmBinary); + } + if (readBinary) { + return readBinary(file); + } else { + throw "both async and sync fetching of the wasm failed"; + } + } catch (err2) { + abort(err2); + } + } + function getBinaryPromise() { + if (!wasmBinary && (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER)) { + if (typeof fetch === "function" && !isFileURI(wasmBinaryFile)) { + return fetch(wasmBinaryFile, {credentials: "same-origin"}).then(function(response) { + if (!response["ok"]) { + throw "failed to load wasm binary file at '" + wasmBinaryFile + "'"; + } + return response["arrayBuffer"](); + }).catch(function() { + return getBinary(wasmBinaryFile); + }); + } else { + if (readAsync) { + return new Promise(function(resolve, reject) { + readAsync(wasmBinaryFile, function(response) { + resolve(new Uint8Array(response)); + }, reject); + }); + } + } + } + return Promise.resolve().then(function() { + return getBinary(wasmBinaryFile); + }); + } + function createWasm() { + var info = {a: asmLibraryArg}; + function receiveInstance(instance, module22) { + var exports3 = instance.exports; + Module["asm"] = exports3; + wasmMemory = Module["asm"]["g"]; + updateGlobalBufferAndViews(wasmMemory.buffer); + wasmTable = Module["asm"]["m"]; + removeRunDependency("wasm-instantiate"); + } + addRunDependency("wasm-instantiate"); + function receiveInstantiatedSource(output) { + receiveInstance(output["instance"]); + } + function instantiateArrayBuffer(receiver) { + return getBinaryPromise().then(function(binary) { + return WebAssembly.instantiate(binary, info); + }).then(receiver, function(reason) { + err("failed to asynchronously prepare wasm: " + reason); + abort(reason); + }); + } + function instantiateAsync() { + if (!wasmBinary && typeof WebAssembly.instantiateStreaming === "function" && !isDataURI(wasmBinaryFile) && !isFileURI(wasmBinaryFile) && typeof fetch === "function") { + return fetch(wasmBinaryFile, {credentials: "same-origin"}).then(function(response) { + var result = WebAssembly.instantiateStreaming(response, info); + return result.then(receiveInstantiatedSource, function(reason) { + err("wasm streaming compile failed: " + reason); + err("falling back to ArrayBuffer instantiation"); + return instantiateArrayBuffer(receiveInstantiatedSource); + }); + }); + } else { + return instantiateArrayBuffer(receiveInstantiatedSource); + } + } + if (Module["instantiateWasm"]) { + try { + var exports2 = Module["instantiateWasm"](info, receiveInstance); + return exports2; + } catch (e) { + err("Module.instantiateWasm callback failed with error: " + e); + return false; + } + } + instantiateAsync().catch(readyPromiseReject); + return {}; + } + function callRuntimeCallbacks(callbacks2) { + while (callbacks2.length > 0) { + var callback = callbacks2.shift(); + if (typeof callback == "function") { + callback(Module); + continue; + } + var func2 = callback.func; + if (typeof func2 === "number") { + if (callback.arg === void 0) { + wasmTable.get(func2)(); + } else { + wasmTable.get(func2)(callback.arg); + } + } else { + func2(callback.arg === void 0 ? null : callback.arg); + } + } + } + function _abort() { + abort(); + } + function _emscripten_memcpy_big(dest, src, num) { + HEAPU8.copyWithin(dest, src, src + num); + } + function _emscripten_get_heap_size() { + return HEAPU8.length; + } + function emscripten_realloc_buffer(size) { + try { + wasmMemory.grow(size - buffer2.byteLength + 65535 >>> 16); + updateGlobalBufferAndViews(wasmMemory.buffer); + return 1; + } catch (e) { + } + } + function _emscripten_resize_heap(requestedSize) { + var oldSize = _emscripten_get_heap_size(); + var maxHeapSize = 2147483648; + if (requestedSize > maxHeapSize) { + return false; + } + for (var cutDown = 1; cutDown <= 4; cutDown *= 2) { + var overGrownHeapSize = oldSize * (1 + 0.2 / cutDown); + overGrownHeapSize = Math.min(overGrownHeapSize, requestedSize + 100663296); + var newSize = Math.min(maxHeapSize, alignUp(Math.max(requestedSize, overGrownHeapSize), 65536)); + var replacement = emscripten_realloc_buffer(newSize); + if (replacement) { + return true; + } + } + return false; + } + var SYSCALLS = {mappings: {}, buffers: [null, [], []], printChar: function(stream, curr) { + var buffer3 = SYSCALLS.buffers[stream]; + if (curr === 0 || curr === 10) { + (stream === 1 ? out : err)(UTF8ArrayToString(buffer3, 0)); + buffer3.length = 0; + } else { + buffer3.push(curr); + } + }, varargs: void 0, get: function() { + SYSCALLS.varargs += 4; + var ret = HEAP32[SYSCALLS.varargs - 4 >> 2]; + return ret; + }, getStr: function(ptr) { + var ret = UTF8ToString(ptr); + return ret; + }, get64: function(low, high) { + return low; + }}; + function _fd_close(fd) { + return 0; + } + function _fd_seek(fd, offset_low, offset_high, whence, newOffset) { + } + function _fd_write(fd, iov, iovcnt, pnum) { + var num = 0; + for (var i = 0; i < iovcnt; i++) { + var ptr = HEAP32[iov + i * 8 >> 2]; + var len = HEAP32[iov + (i * 8 + 4) >> 2]; + for (var j = 0; j < len; j++) { + SYSCALLS.printChar(fd, HEAPU8[ptr + j]); + } + num += len; + } + HEAP32[pnum >> 2] = num; + return 0; + } + var asmLibraryArg = {a: _abort, d: _emscripten_memcpy_big, e: _emscripten_resize_heap, f: _fd_close, c: _fd_seek, b: _fd_write}; + var asm = createWasm(); + var ___wasm_call_ctors = Module["___wasm_call_ctors"] = function() { + return (___wasm_call_ctors = Module["___wasm_call_ctors"] = Module["asm"]["h"]).apply(null, arguments); + }; + var _init = Module["_init"] = function() { + return (_init = Module["_init"] = Module["asm"]["i"]).apply(null, arguments); + }; + var _register_tensor = Module["_register_tensor"] = function() { + return (_register_tensor = Module["_register_tensor"] = Module["asm"]["j"]).apply(null, arguments); + }; + var _dispose_data = Module["_dispose_data"] = function() { + return (_dispose_data = Module["_dispose_data"] = Module["asm"]["k"]).apply(null, arguments); + }; + var _dispose = Module["_dispose"] = function() { + return (_dispose = Module["_dispose"] = Module["asm"]["l"]).apply(null, arguments); + }; + var _Abs = Module["_Abs"] = function() { + return (_Abs = Module["_Abs"] = Module["asm"]["n"]).apply(null, arguments); + }; + var _Add = Module["_Add"] = function() { + return (_Add = Module["_Add"] = Module["asm"]["o"]).apply(null, arguments); + }; + var _AddN = Module["_AddN"] = function() { + return (_AddN = Module["_AddN"] = Module["asm"]["p"]).apply(null, arguments); + }; + var _ArgMax = Module["_ArgMax"] = function() { + return (_ArgMax = Module["_ArgMax"] = Module["asm"]["q"]).apply(null, arguments); + }; + var _AvgPool = Module["_AvgPool"] = function() { + return (_AvgPool = Module["_AvgPool"] = Module["asm"]["r"]).apply(null, arguments); + }; + var _BatchMatMul = Module["_BatchMatMul"] = function() { + return (_BatchMatMul = Module["_BatchMatMul"] = Module["asm"]["s"]).apply(null, arguments); + }; + var _Ceil = Module["_Ceil"] = function() { + return (_Ceil = Module["_Ceil"] = Module["asm"]["t"]).apply(null, arguments); + }; + var _ClipByValue = Module["_ClipByValue"] = function() { + return (_ClipByValue = Module["_ClipByValue"] = Module["asm"]["u"]).apply(null, arguments); + }; + var _Conv2D = Module["_Conv2D"] = function() { + return (_Conv2D = Module["_Conv2D"] = Module["asm"]["v"]).apply(null, arguments); + }; + var _Conv2DBackpropInput = Module["_Conv2DBackpropInput"] = function() { + return (_Conv2DBackpropInput = Module["_Conv2DBackpropInput"] = Module["asm"]["w"]).apply(null, arguments); + }; + var _Cos = Module["_Cos"] = function() { + return (_Cos = Module["_Cos"] = Module["asm"]["x"]).apply(null, arguments); + }; + var _CropAndResize = Module["_CropAndResize"] = function() { + return (_CropAndResize = Module["_CropAndResize"] = Module["asm"]["y"]).apply(null, arguments); + }; + var _Cumsum = Module["_Cumsum"] = function() { + return (_Cumsum = Module["_Cumsum"] = Module["asm"]["z"]).apply(null, arguments); + }; + var _DepthToSpace = Module["_DepthToSpace"] = function() { + return (_DepthToSpace = Module["_DepthToSpace"] = Module["asm"]["A"]).apply(null, arguments); + }; + var _DepthwiseConv2dNative = Module["_DepthwiseConv2dNative"] = function() { + return (_DepthwiseConv2dNative = Module["_DepthwiseConv2dNative"] = Module["asm"]["B"]).apply(null, arguments); + }; + var _Equal = Module["_Equal"] = function() { + return (_Equal = Module["_Equal"] = Module["asm"]["C"]).apply(null, arguments); + }; + var _Exp = Module["_Exp"] = function() { + return (_Exp = Module["_Exp"] = Module["asm"]["D"]).apply(null, arguments); + }; + var _FlipLeftRight = Module["_FlipLeftRight"] = function() { + return (_FlipLeftRight = Module["_FlipLeftRight"] = Module["asm"]["E"]).apply(null, arguments); + }; + var _Floor = Module["_Floor"] = function() { + return (_Floor = Module["_Floor"] = Module["asm"]["F"]).apply(null, arguments); + }; + var _FloorDiv = Module["_FloorDiv"] = function() { + return (_FloorDiv = Module["_FloorDiv"] = Module["asm"]["G"]).apply(null, arguments); + }; + var _FusedBatchNorm = Module["_FusedBatchNorm"] = function() { + return (_FusedBatchNorm = Module["_FusedBatchNorm"] = Module["asm"]["H"]).apply(null, arguments); + }; + var _FusedConv2D = Module["_FusedConv2D"] = function() { + return (_FusedConv2D = Module["_FusedConv2D"] = Module["asm"]["I"]).apply(null, arguments); + }; + var _FusedDepthwiseConv2D = Module["_FusedDepthwiseConv2D"] = function() { + return (_FusedDepthwiseConv2D = Module["_FusedDepthwiseConv2D"] = Module["asm"]["J"]).apply(null, arguments); + }; + var _Gather = Module["_Gather"] = function() { + return (_Gather = Module["_Gather"] = Module["asm"]["K"]).apply(null, arguments); + }; + var _GatherNd = Module["_GatherNd"] = function() { + return (_GatherNd = Module["_GatherNd"] = Module["asm"]["L"]).apply(null, arguments); + }; + var _Greater = Module["_Greater"] = function() { + return (_Greater = Module["_Greater"] = Module["asm"]["M"]).apply(null, arguments); + }; + var _GreaterEqual = Module["_GreaterEqual"] = function() { + return (_GreaterEqual = Module["_GreaterEqual"] = Module["asm"]["N"]).apply(null, arguments); + }; + var _LeakyRelu = Module["_LeakyRelu"] = function() { + return (_LeakyRelu = Module["_LeakyRelu"] = Module["asm"]["O"]).apply(null, arguments); + }; + var _Less = Module["_Less"] = function() { + return (_Less = Module["_Less"] = Module["asm"]["P"]).apply(null, arguments); + }; + var _LessEqual = Module["_LessEqual"] = function() { + return (_LessEqual = Module["_LessEqual"] = Module["asm"]["Q"]).apply(null, arguments); + }; + var _Log = Module["_Log"] = function() { + return (_Log = Module["_Log"] = Module["asm"]["R"]).apply(null, arguments); + }; + var _LogicalAnd = Module["_LogicalAnd"] = function() { + return (_LogicalAnd = Module["_LogicalAnd"] = Module["asm"]["S"]).apply(null, arguments); + }; + var _Max = Module["_Max"] = function() { + return (_Max = Module["_Max"] = Module["asm"]["T"]).apply(null, arguments); + }; + var _MaxPool = Module["_MaxPool"] = function() { + return (_MaxPool = Module["_MaxPool"] = Module["asm"]["U"]).apply(null, arguments); + }; + var _Maximum = Module["_Maximum"] = function() { + return (_Maximum = Module["_Maximum"] = Module["asm"]["V"]).apply(null, arguments); + }; + var _Mean = Module["_Mean"] = function() { + return (_Mean = Module["_Mean"] = Module["asm"]["W"]).apply(null, arguments); + }; + var _Min = Module["_Min"] = function() { + return (_Min = Module["_Min"] = Module["asm"]["X"]).apply(null, arguments); + }; + var _Minimum = Module["_Minimum"] = function() { + return (_Minimum = Module["_Minimum"] = Module["asm"]["Y"]).apply(null, arguments); + }; + var _Multiply = Module["_Multiply"] = function() { + return (_Multiply = Module["_Multiply"] = Module["asm"]["Z"]).apply(null, arguments); + }; + var _Neg = Module["_Neg"] = function() { + return (_Neg = Module["_Neg"] = Module["asm"]["_"]).apply(null, arguments); + }; + var _NonMaxSuppressionV3 = Module["_NonMaxSuppressionV3"] = function() { + return (_NonMaxSuppressionV3 = Module["_NonMaxSuppressionV3"] = Module["asm"]["$"]).apply(null, arguments); + }; + var _NonMaxSuppressionV4 = Module["_NonMaxSuppressionV4"] = function() { + return (_NonMaxSuppressionV4 = Module["_NonMaxSuppressionV4"] = Module["asm"]["aa"]).apply(null, arguments); + }; + var _NonMaxSuppressionV5 = Module["_NonMaxSuppressionV5"] = function() { + return (_NonMaxSuppressionV5 = Module["_NonMaxSuppressionV5"] = Module["asm"]["ba"]).apply(null, arguments); + }; + var _NotEqual = Module["_NotEqual"] = function() { + return (_NotEqual = Module["_NotEqual"] = Module["asm"]["ca"]).apply(null, arguments); + }; + var _OneHot = Module["_OneHot"] = function() { + return (_OneHot = Module["_OneHot"] = Module["asm"]["da"]).apply(null, arguments); + }; + var _PadV2 = Module["_PadV2"] = function() { + return (_PadV2 = Module["_PadV2"] = Module["asm"]["ea"]).apply(null, arguments); + }; + var _Pow = Module["_Pow"] = function() { + return (_Pow = Module["_Pow"] = Module["asm"]["fa"]).apply(null, arguments); + }; + var _Prelu = Module["_Prelu"] = function() { + return (_Prelu = Module["_Prelu"] = Module["asm"]["ga"]).apply(null, arguments); + }; + var _Prod = Module["_Prod"] = function() { + return (_Prod = Module["_Prod"] = Module["asm"]["ha"]).apply(null, arguments); + }; + var _RealDiv = Module["_RealDiv"] = function() { + return (_RealDiv = Module["_RealDiv"] = Module["asm"]["ia"]).apply(null, arguments); + }; + var _Relu = Module["_Relu"] = function() { + return (_Relu = Module["_Relu"] = Module["asm"]["ja"]).apply(null, arguments); + }; + var _Relu6 = Module["_Relu6"] = function() { + return (_Relu6 = Module["_Relu6"] = Module["asm"]["ka"]).apply(null, arguments); + }; + var _ResizeBilinear = Module["_ResizeBilinear"] = function() { + return (_ResizeBilinear = Module["_ResizeBilinear"] = Module["asm"]["la"]).apply(null, arguments); + }; + var _Reverse = Module["_Reverse"] = function() { + return (_Reverse = Module["_Reverse"] = Module["asm"]["ma"]).apply(null, arguments); + }; + var _RotateWithOffset = Module["_RotateWithOffset"] = function() { + return (_RotateWithOffset = Module["_RotateWithOffset"] = Module["asm"]["na"]).apply(null, arguments); + }; + var _Round = Module["_Round"] = function() { + return (_Round = Module["_Round"] = Module["asm"]["oa"]).apply(null, arguments); + }; + var _Rsqrt = Module["_Rsqrt"] = function() { + return (_Rsqrt = Module["_Rsqrt"] = Module["asm"]["pa"]).apply(null, arguments); + }; + var _ScatterNd = Module["_ScatterNd"] = function() { + return (_ScatterNd = Module["_ScatterNd"] = Module["asm"]["qa"]).apply(null, arguments); + }; + var _SelectV2 = Module["_SelectV2"] = function() { + return (_SelectV2 = Module["_SelectV2"] = Module["asm"]["ra"]).apply(null, arguments); + }; + var _Sigmoid = Module["_Sigmoid"] = function() { + return (_Sigmoid = Module["_Sigmoid"] = Module["asm"]["sa"]).apply(null, arguments); + }; + var _Sin = Module["_Sin"] = function() { + return (_Sin = Module["_Sin"] = Module["asm"]["ta"]).apply(null, arguments); + }; + var _Softmax = Module["_Softmax"] = function() { + return (_Softmax = Module["_Softmax"] = Module["asm"]["ua"]).apply(null, arguments); + }; + var _Sqrt = Module["_Sqrt"] = function() { + return (_Sqrt = Module["_Sqrt"] = Module["asm"]["va"]).apply(null, arguments); + }; + var _Square = Module["_Square"] = function() { + return (_Square = Module["_Square"] = Module["asm"]["wa"]).apply(null, arguments); + }; + var _SquaredDifference = Module["_SquaredDifference"] = function() { + return (_SquaredDifference = Module["_SquaredDifference"] = Module["asm"]["xa"]).apply(null, arguments); + }; + var _Step = Module["_Step"] = function() { + return (_Step = Module["_Step"] = Module["asm"]["ya"]).apply(null, arguments); + }; + var _StridedSlice = Module["_StridedSlice"] = function() { + return (_StridedSlice = Module["_StridedSlice"] = Module["asm"]["za"]).apply(null, arguments); + }; + var _Sub = Module["_Sub"] = function() { + return (_Sub = Module["_Sub"] = Module["asm"]["Aa"]).apply(null, arguments); + }; + var _Sum = Module["_Sum"] = function() { + return (_Sum = Module["_Sum"] = Module["asm"]["Ba"]).apply(null, arguments); + }; + var _Tanh = Module["_Tanh"] = function() { + return (_Tanh = Module["_Tanh"] = Module["asm"]["Ca"]).apply(null, arguments); + }; + var _Tile = Module["_Tile"] = function() { + return (_Tile = Module["_Tile"] = Module["asm"]["Da"]).apply(null, arguments); + }; + var _TopK = Module["_TopK"] = function() { + return (_TopK = Module["_TopK"] = Module["asm"]["Ea"]).apply(null, arguments); + }; + var _Transpose = Module["_Transpose"] = function() { + return (_Transpose = Module["_Transpose"] = Module["asm"]["Fa"]).apply(null, arguments); + }; + var __FusedMatMul = Module["__FusedMatMul"] = function() { + return (__FusedMatMul = Module["__FusedMatMul"] = Module["asm"]["Ga"]).apply(null, arguments); + }; + var _malloc = Module["_malloc"] = function() { + return (_malloc = Module["_malloc"] = Module["asm"]["Ha"]).apply(null, arguments); + }; + var _free = Module["_free"] = function() { + return (_free = Module["_free"] = Module["asm"]["Ia"]).apply(null, arguments); + }; + var stackSave = Module["stackSave"] = function() { + return (stackSave = Module["stackSave"] = Module["asm"]["Ja"]).apply(null, arguments); + }; + var stackRestore = Module["stackRestore"] = function() { + return (stackRestore = Module["stackRestore"] = Module["asm"]["Ka"]).apply(null, arguments); + }; + var stackAlloc = Module["stackAlloc"] = function() { + return (stackAlloc = Module["stackAlloc"] = Module["asm"]["La"]).apply(null, arguments); + }; + Module["cwrap"] = cwrap; + var calledRun; + function ExitStatus(status) { + this.name = "ExitStatus"; + this.message = "Program terminated with exit(" + status + ")"; + this.status = status; + } + dependenciesFulfilled = function runCaller() { + if (!calledRun) + run(); + if (!calledRun) + dependenciesFulfilled = runCaller; + }; + function run(args) { + args = args || arguments_; + if (runDependencies > 0) { + return; + } + preRun(); + if (runDependencies > 0) { + return; + } + function doRun() { + if (calledRun) + return; + calledRun = true; + Module["calledRun"] = true; + if (ABORT) + return; + initRuntime(); + preMain(); + readyPromiseResolve(Module); + if (Module["onRuntimeInitialized"]) + Module["onRuntimeInitialized"](); + postRun(); + } + if (Module["setStatus"]) { + Module["setStatus"]("Running..."); + setTimeout(function() { + setTimeout(function() { + Module["setStatus"](""); + }, 1); + doRun(); + }, 1); + } else { + doRun(); + } + } + Module["run"] = run; + if (Module["preInit"]) { + if (typeof Module["preInit"] == "function") + Module["preInit"] = [Module["preInit"]]; + while (Module["preInit"].length > 0) { + Module["preInit"].pop()(); + } + } + run(); + return WasmBackendModule2.ready; + }; + }(); + if (typeof exports === "object" && typeof module2 === "object") + module2.exports = WasmBackendModule; + else if (typeof define === "function" && define["amd"]) + define([], function() { + return WasmBackendModule; + }); + else if (typeof exports === "object") + exports["WasmBackendModule"] = WasmBackendModule; +}); +/** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +var EPSILON_FLOAT32 = 1e-7; +var EPSILON_FLOAT16 = 1e-4; +var DataStorage = class { + constructor(backend2, dataMover) { + this.backend = backend2; + this.dataMover = dataMover; + this.data = new WeakMap(); + this.dataIdsCount = 0; + } + get(dataId) { + if (!this.data.has(dataId)) { + this.dataMover.moveData(this.backend, dataId); + } + return this.data.get(dataId); + } + set(dataId, value) { + this.dataIdsCount++; + this.data.set(dataId, value); + } + has(dataId) { + return this.data.has(dataId); + } + delete(dataId) { + this.dataIdsCount--; + return this.data.delete(dataId); + } + numDataIds() { + return this.dataIdsCount; + } +}; +var KernelBackend = class { + refCount(dataId) { + return notYetImplemented("refCount"); + } + incRef(dataId) { + return notYetImplemented("incRef"); + } + timerAvailable() { + return true; + } + time(f) { + return notYetImplemented("time"); + } + read(dataId) { + return notYetImplemented("read"); + } + readSync(dataId) { + return notYetImplemented("readSync"); + } + numDataIds() { + return notYetImplemented("numDataIds"); + } + disposeData(dataId, force) { + return notYetImplemented("disposeData"); + } + write(values, shape, dtype) { + return notYetImplemented("write"); + } + move(dataId, values, shape, dtype, refCount) { + return notYetImplemented("move"); + } + memory() { + return notYetImplemented("memory"); + } + floatPrecision() { + return notYetImplemented("floatPrecision"); + } + epsilon() { + return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16; + } + dispose() { + return notYetImplemented("dispose"); + } +}; +function notYetImplemented(kernelName) { + throw new Error(`'${kernelName}' not yet implemented or not found in the registry. This kernel may not be supported by the tfjs backend you have chosen`); +} +/** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +function shuffle(array2) { + let counter = array2.length; + let temp = 0; + let index = 0; + while (counter > 0) { + index = Math.random() * counter | 0; + counter--; + temp = array2[counter]; + array2[counter] = array2[index]; + array2[index] = temp; + } +} +function shuffleCombo(array2, array22) { + if (array2.length !== array22.length) { + throw new Error(`Array sizes must match to be shuffled together First array length was ${array2.length}Second array length was ${array22.length}`); + } + let counter = array2.length; + let temp, temp2; + let index = 0; + while (counter > 0) { + index = Math.random() * counter | 0; + counter--; + temp = array2[counter]; + temp2 = array22[counter]; + array2[counter] = array2[index]; + array22[counter] = array22[index]; + array2[index] = temp; + array22[index] = temp2; + } +} +function clamp(min6, x, max6) { + return Math.max(min6, Math.min(x, max6)); +} +function nearestLargerEven(val) { + return val % 2 === 0 ? val : val + 1; +} +function sum(arr) { + let sum6 = 0; + for (let i = 0; i < arr.length; i++) { + sum6 += arr[i]; + } + return sum6; +} +function randUniform(a, b) { + const r = Math.random(); + return b * r + (1 - r) * a; +} +function distSquared(a, b) { + let result = 0; + for (let i = 0; i < a.length; i++) { + const diff = Number(a[i]) - Number(b[i]); + result += diff * diff; + } + return result; +} +function assert(expr, msg) { + if (!expr) { + throw new Error(typeof msg === "string" ? msg : msg()); + } +} +function assertShapesMatch(shapeA, shapeB, errorMessagePrefix = "") { + assert(arraysEqual(shapeA, shapeB), () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`); +} +function assertNonNull(a) { + assert(a != null, () => `The input to the tensor constructor must be a non-null value.`); +} +function flatten(arr, result = [], skipTypedArray = false) { + if (result == null) { + result = []; + } + if (Array.isArray(arr) || isTypedArray(arr) && !skipTypedArray) { + for (let i = 0; i < arr.length; ++i) { + flatten(arr[i], result, skipTypedArray); + } + } else { + result.push(arr); + } + return result; +} +function sizeFromShape(shape) { + if (shape.length === 0) { + return 1; + } + let size = shape[0]; + for (let i = 1; i < shape.length; i++) { + size *= shape[i]; + } + return size; +} +function isScalarShape(shape) { + return shape.length === 0; +} +function arraysEqual(n1, n2) { + if (n1 === n2) { + return true; + } + if (n1 == null || n2 == null) { + return false; + } + if (n1.length !== n2.length) { + return false; + } + for (let i = 0; i < n1.length; i++) { + if (n1[i] !== n2[i]) { + return false; + } + } + return true; +} +function isInt(a) { + return a % 1 === 0; +} +function tanh(x) { + if (Math.tanh != null) { + return Math.tanh(x); + } + if (x === Infinity) { + return 1; + } else if (x === -Infinity) { + return -1; + } else { + const e2x = Math.exp(2 * x); + return (e2x - 1) / (e2x + 1); + } +} +function sizeToSquarishShape(size) { + const width = Math.ceil(Math.sqrt(size)); + return [width, Math.ceil(size / width)]; +} +function createShuffledIndices(n) { + const shuffledIndices = new Uint32Array(n); + for (let i = 0; i < n; ++i) { + shuffledIndices[i] = i; + } + shuffle(shuffledIndices); + return shuffledIndices; +} +function rightPad(a, size) { + if (size <= a.length) { + return a; + } + return a + " ".repeat(size - a.length); +} +function repeatedTry(checkFn, delayFn = (counter) => 0, maxCounter) { + return new Promise((resolve, reject) => { + let tryCount = 0; + const tryFn = () => { + if (checkFn()) { + resolve(); + return; + } + tryCount++; + const nextBackoff = delayFn(tryCount); + if (maxCounter != null && tryCount >= maxCounter) { + reject(); + return; + } + setTimeout(tryFn, nextBackoff); + }; + tryFn(); + }); +} +function inferFromImplicitShape(shape, size) { + let shapeProd = 1; + let implicitIdx = -1; + for (let i = 0; i < shape.length; ++i) { + if (shape[i] >= 0) { + shapeProd *= shape[i]; + } else if (shape[i] === -1) { + if (implicitIdx !== -1) { + throw Error(`Shapes can only have 1 implicit size. Found -1 at dim ${implicitIdx} and dim ${i}`); + } + implicitIdx = i; + } else if (shape[i] < 0) { + throw Error(`Shapes can not be < 0. Found ${shape[i]} at dim ${i}`); + } + } + if (implicitIdx === -1) { + if (size > 0 && size !== shapeProd) { + throw Error(`Size(${size}) must match the product of shape ${shape}`); + } + return shape; + } + if (shapeProd === 0) { + throw Error(`Cannot infer the missing size in [${shape}] when there are 0 elements`); + } + if (size % shapeProd !== 0) { + throw Error(`The implicit shape can't be a fractional number. Got ${size} / ${shapeProd}`); + } + const newShape = shape.slice(); + newShape[implicitIdx] = size / shapeProd; + return newShape; +} +function parseAxisParam(axis, shape) { + const rank = shape.length; + axis = axis == null ? shape.map((s, i) => i) : [].concat(axis); + assert(axis.every((ax) => ax >= -rank && ax < rank), () => `All values in axis param must be in range [-${rank}, ${rank}) but got axis ${axis}`); + assert(axis.every((ax) => isInt(ax)), () => `All values in axis param must be integers but got axis ${axis}`); + return axis.map((a) => a < 0 ? rank + a : a); +} +function squeezeShape(shape, axis) { + const newShape = []; + const keptDims = []; + const isEmptyArray = axis != null && Array.isArray(axis) && axis.length === 0; + const axes = axis == null || isEmptyArray ? null : parseAxisParam(axis, shape).sort(); + let j = 0; + for (let i = 0; i < shape.length; ++i) { + if (axes != null) { + if (axes[j] === i && shape[i] !== 1) { + throw new Error(`Can't squeeze axis ${i} since its dim '${shape[i]}' is not 1`); + } + if ((axes[j] == null || axes[j] > i) && shape[i] === 1) { + newShape.push(shape[i]); + keptDims.push(i); + } + if (axes[j] <= i) { + j++; + } + } + if (shape[i] !== 1) { + newShape.push(shape[i]); + keptDims.push(i); + } + } + return {newShape, keptDims}; +} +function getTypedArrayFromDType(dtype, size) { + let values = null; + if (dtype == null || dtype === "float32") { + values = new Float32Array(size); + } else if (dtype === "int32") { + values = new Int32Array(size); + } else if (dtype === "bool") { + values = new Uint8Array(size); + } else { + throw new Error(`Unknown data type ${dtype}`); + } + return values; +} +function getArrayFromDType(dtype, size) { + let values = null; + if (dtype == null || dtype === "float32") { + values = new Float32Array(size); + } else if (dtype === "int32") { + values = new Int32Array(size); + } else if (dtype === "bool") { + values = new Uint8Array(size); + } else if (dtype === "string") { + values = new Array(size); + } else { + throw new Error(`Unknown data type ${dtype}`); + } + return values; +} +function checkConversionForErrors(vals, dtype) { + for (let i = 0; i < vals.length; i++) { + const num = vals[i]; + if (isNaN(num) || !isFinite(num)) { + throw Error(`A tensor of type ${dtype} being uploaded contains ${num}.`); + } + } +} +function isValidDtype(dtype) { + return dtype === "bool" || dtype === "complex64" || dtype === "float32" || dtype === "int32" || dtype === "string"; +} +function hasEncodingLoss(oldType, newType) { + if (newType === "complex64") { + return false; + } + if (newType === "float32" && oldType !== "complex64") { + return false; + } + if (newType === "int32" && oldType !== "float32" && oldType !== "complex64") { + return false; + } + if (newType === "bool" && oldType === "bool") { + return false; + } + return true; +} +function isTypedArray(a) { + return a instanceof Float32Array || a instanceof Int32Array || a instanceof Uint8Array; +} +function bytesPerElement(dtype) { + if (dtype === "float32" || dtype === "int32") { + return 4; + } else if (dtype === "complex64") { + return 8; + } else if (dtype === "bool") { + return 1; + } else { + throw new Error(`Unknown dtype ${dtype}`); + } +} +function bytesFromStringArray(arr) { + if (arr == null) { + return 0; + } + let bytes = 0; + arr.forEach((x) => bytes += x.length); + return bytes; +} +function isString(value) { + return typeof value === "string" || value instanceof String; +} +function isBoolean(value) { + return typeof value === "boolean"; +} +function isNumber(value) { + return typeof value === "number"; +} +function inferDtype(values) { + if (Array.isArray(values)) { + return inferDtype(values[0]); + } + if (values instanceof Float32Array) { + return "float32"; + } else if (values instanceof Int32Array || values instanceof Uint8Array) { + return "int32"; + } else if (isNumber(values)) { + return "float32"; + } else if (isString(values)) { + return "string"; + } else if (isBoolean(values)) { + return "bool"; + } + return "float32"; +} +function isFunction(f) { + return !!(f && f.constructor && f.call && f.apply); +} +function nearestDivisor(size, start) { + for (let i = start; i < size; ++i) { + if (size % i === 0) { + return i; + } + } + return size; +} +function computeStrides(shape) { + const rank = shape.length; + if (rank < 2) { + return []; + } + const strides = new Array(rank - 1); + strides[rank - 2] = shape[rank - 1]; + for (let i = rank - 3; i >= 0; --i) { + strides[i] = strides[i + 1] * shape[i + 1]; + } + return strides; +} +function createNestedArray(offset, shape, a) { + const ret = new Array(); + if (shape.length === 1) { + const d = shape[0]; + for (let i = 0; i < d; i++) { + ret[i] = a[offset + i]; + } + } else { + const d = shape[0]; + const rest = shape.slice(1); + const len = rest.reduce((acc, c) => acc * c); + for (let i = 0; i < d; i++) { + ret[i] = createNestedArray(offset + i * len, rest, a); + } + } + return ret; +} +function toNestedArray(shape, a) { + if (shape.length === 0) { + return a[0]; + } + const size = shape.reduce((acc, c) => acc * c); + if (size === 0) { + return []; + } + if (size !== a.length) { + throw new Error(`[${shape}] does not match the input size ${a.length}.`); + } + return createNestedArray(0, shape, a); +} +function makeOnesTypedArray(size, dtype) { + const array2 = makeZerosTypedArray(size, dtype); + for (let i = 0; i < array2.length; i++) { + array2[i] = 1; + } + return array2; +} +function makeZerosTypedArray(size, dtype) { + if (dtype == null || dtype === "float32" || dtype === "complex64") { + return new Float32Array(size); + } else if (dtype === "int32") { + return new Int32Array(size); + } else if (dtype === "bool") { + return new Uint8Array(size); + } else { + throw new Error(`Unknown data type ${dtype}`); + } +} +function makeZerosNestedTypedArray(shape, dtype) { + const size = shape.reduce((prev, curr) => prev * curr, 1); + if (dtype == null || dtype === "float32") { + return toNestedArray(shape, new Float32Array(size)); + } else if (dtype === "int32") { + return toNestedArray(shape, new Int32Array(size)); + } else if (dtype === "bool") { + return toNestedArray(shape, new Uint8Array(size)); + } else { + throw new Error(`Unknown data type ${dtype}`); + } +} +function assertNonNegativeIntegerDimensions(shape) { + shape.forEach((dimSize) => { + assert(Number.isInteger(dimSize) && dimSize >= 0, () => `Tensor must have a shape comprised of positive integers but got shape [${shape}].`); + }); +} +function locToIndex(locs, rank, strides) { + if (rank === 0) { + return 0; + } else if (rank === 1) { + return locs[0]; + } + let index = locs[locs.length - 1]; + for (let i = 0; i < locs.length - 1; ++i) { + index += strides[i] * locs[i]; + } + return index; +} +function indexToLoc(index, rank, strides) { + if (rank === 0) { + return []; + } else if (rank === 1) { + return [index]; + } + const locs = new Array(rank); + for (let i = 0; i < locs.length - 1; ++i) { + locs[i] = Math.floor(index / strides[i]); + index -= locs[i] * strides[i]; + } + locs[locs.length - 1] = index; + return locs; +} +function isPromise(object) { + return object && object.then && typeof object.then === "function"; +} +/** + * @license + * Copyright 2017 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +var TENSORFLOWJS_FLAGS_PREFIX = "tfjsflags"; +var Environment = class { + constructor(global2) { + this.global = global2; + this.flags = {}; + this.flagRegistry = {}; + this.urlFlags = {}; + this.populateURLFlags(); + } + setPlatform(platformName, platform) { + if (this.platform != null) { + console.warn(`Platform ${this.platformName} has already been set. Overwriting the platform with ${platform}.`); + } + this.platformName = platformName; + this.platform = platform; + } + registerFlag(flagName, evaluationFn, setHook) { + this.flagRegistry[flagName] = {evaluationFn, setHook}; + if (this.urlFlags[flagName] != null) { + const flagValue = this.urlFlags[flagName]; + console.warn(`Setting feature override from URL ${flagName}: ${flagValue}.`); + this.set(flagName, flagValue); + } + } + async getAsync(flagName) { + if (flagName in this.flags) { + return this.flags[flagName]; + } + this.flags[flagName] = await this.evaluateFlag(flagName); + return this.flags[flagName]; + } + get(flagName) { + if (flagName in this.flags) { + return this.flags[flagName]; + } + const flagValue = this.evaluateFlag(flagName); + if (isPromise(flagValue)) { + throw new Error(`Flag ${flagName} cannot be synchronously evaluated. Please use getAsync() instead.`); + } + this.flags[flagName] = flagValue; + return this.flags[flagName]; + } + getNumber(flagName) { + return this.get(flagName); + } + getBool(flagName) { + return this.get(flagName); + } + getFlags() { + return this.flags; + } + get features() { + return this.flags; + } + set(flagName, value) { + if (this.flagRegistry[flagName] == null) { + throw new Error(`Cannot set flag ${flagName} as it has not been registered.`); + } + this.flags[flagName] = value; + if (this.flagRegistry[flagName].setHook != null) { + this.flagRegistry[flagName].setHook(value); + } + } + evaluateFlag(flagName) { + if (this.flagRegistry[flagName] == null) { + throw new Error(`Cannot evaluate flag '${flagName}': no evaluation function found.`); + } + return this.flagRegistry[flagName].evaluationFn(); + } + setFlags(flags) { + this.flags = Object.assign({}, flags); + } + reset() { + this.flags = {}; + this.urlFlags = {}; + this.populateURLFlags(); + } + populateURLFlags() { + if (typeof this.global === "undefined" || typeof this.global.location === "undefined" || typeof this.global.location.search === "undefined") { + return; + } + const urlParams = getQueryParams(this.global.location.search); + if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) { + const keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(","); + keyValues.forEach((keyValue) => { + const [key, value] = keyValue.split(":"); + this.urlFlags[key] = parseValue(key, value); + }); + } + } +}; +function getQueryParams(queryString) { + const params = {}; + queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (s, ...t) => { + decodeParam(params, t[0], t[1]); + return t.join("="); + }); + return params; +} +function decodeParam(params, name, value) { + params[decodeURIComponent(name)] = decodeURIComponent(value || ""); +} +function parseValue(flagName, value) { + value = value.toLowerCase(); + if (value === "true" || value === "false") { + return value === "true"; + } else if (`${+value}` === value) { + return +value; + } + throw new Error(`Could not parse value flag value ${value} for flag ${flagName}.`); +} +function env() { + return ENV; +} +var ENV = null; +function setEnvironmentGlobal(environment2) { + ENV = environment2; +} +/** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +var globalNameSpace; +function getGlobalNamespace() { + if (globalNameSpace == null) { + let ns; + if (typeof window !== "undefined") { + ns = window; + } else if (typeof global !== "undefined") { + ns = global; + } else if (typeof process !== "undefined") { + ns = process; + } else if (typeof self !== "undefined") { + ns = self; + } else { + throw new Error("Could not find a global object"); + } + globalNameSpace = ns; + } + return globalNameSpace; +} +function getGlobalMap() { + const ns = getGlobalNamespace(); + if (ns._tfGlobals == null) { + ns._tfGlobals = new Map(); + } + return ns._tfGlobals; +} +function getGlobal(key, init2) { + const globalMap = getGlobalMap(); + if (globalMap.has(key)) { + return globalMap.get(key); + } else { + const singleton = init2(); + globalMap.set(key, singleton); + return globalMap.get(key); + } +} +var Abs = "Abs"; +var Acos = "Acos"; +var Acosh = "Acosh"; +var Add = "Add"; +var AddN = "AddN"; +var All = "All"; +var Any = "Any"; +var ArgMax = "ArgMax"; +var ArgMin = "ArgMin"; +var Asin = "Asin"; +var Asinh = "Asinh"; +var Atan = "Atan"; +var Atanh = "Atanh"; +var Atan2 = "Atan2"; +var AvgPool = "AvgPool"; +var AvgPoolGrad = "AvgPoolGrad"; +var AvgPool3D = "AvgPool3D"; +var AvgPool3DGrad = "AvgPool3DGrad"; +var BatchMatMul = "BatchMatMul"; +var BatchToSpaceND = "BatchToSpaceND"; +var Bincount = "Bincount"; +var BroadcastTo = "BroadcastTo"; +var Cast = "Cast"; +var Ceil = "Ceil"; +var ClipByValue = "ClipByValue"; +var Complex = "Complex"; +var ComplexAbs = "ComplexAbs"; +var Concat = "Concat"; +var Conv2D = "Conv2D"; +var Conv2DBackpropFilter = "Conv2DBackpropFilter"; +var Conv2DBackpropInput = "Conv2DBackpropInput"; +var Conv3D = "Conv3D"; +var Conv3DBackpropFilterV2 = "Conv3DBackpropFilterV2"; +var Conv3DBackpropInputV2 = "Conv3DBackpropInputV2"; +var Cos = "Cos"; +var Cosh = "Cosh"; +var Cumsum = "Cumsum"; +var CropAndResize = "CropAndResize"; +var DenseBincount = "DenseBincount"; +var DepthToSpace = "DepthToSpace"; +var DepthwiseConv2dNative = "DepthwiseConv2dNative"; +var DepthwiseConv2dNativeBackpropFilter = "DepthwiseConv2dNativeBackpropFilter"; +var DepthwiseConv2dNativeBackpropInput = "DepthwiseConv2dNativeBackpropInput"; +var Diag = "Diag"; +var Dilation2D = "Dilation2D"; +var Dilation2DBackpropInput = "Dilation2DBackpropInput"; +var Dilation2DBackpropFilter = "Dilation2DBackpropFilter"; +var RealDiv = "RealDiv"; +var Elu = "Elu"; +var EluGrad = "EluGrad"; +var Erf = "Erf"; +var Equal = "Equal"; +var Exp = "Exp"; +var ExpandDims = "ExpandDims"; +var Expm1 = "Expm1"; +var FFT = "FFT"; +var Fill = "Fill"; +var FlipLeftRight = "FlipLeftRight"; +var Floor = "Floor"; +var FloorDiv = "FloorDiv"; +var FusedBatchNorm = "FusedBatchNorm"; +var GatherV2 = "GatherV2"; +var GatherNd = "GatherNd"; +var Greater = "Greater"; +var GreaterEqual = "GreaterEqual"; +var Identity = "Identity"; +var IFFT = "IFFT"; +var Imag = "Imag"; +var IsFinite = "IsFinite"; +var IsInf = "IsInf"; +var IsNan = "IsNan"; +var LeakyRelu = "LeakyRelu"; +var Less = "Less"; +var LessEqual = "LessEqual"; +var LinSpace = "LinSpace"; +var Log = "Log"; +var Log1p = "Log1p"; +var LogicalAnd = "LogicalAnd"; +var LogicalNot = "LogicalNot"; +var LogicalOr = "LogicalOr"; +var LogSoftmax = "LogSoftmax"; +var LRN = "LRN"; +var LRNGrad = "LRNGrad"; +var Max = "Max"; +var Maximum = "Maximum"; +var MaxPool = "MaxPool"; +var MaxPoolGrad = "MaxPoolGrad"; +var MaxPool3D = "MaxPool3D"; +var MaxPool3DGrad = "MaxPool3DGrad"; +var MaxPoolWithArgmax = "MaxPoolWithArgmax"; +var Mean = "Mean"; +var Min = "Min"; +var Minimum = "Minimum"; +var MirrorPad = "MirrorPad"; +var Mod = "Mod"; +var Multinomial = "Multinomial"; +var Multiply = "Multiply"; +var Neg = "Neg"; +var NotEqual = "NotEqual"; +var NonMaxSuppressionV3 = "NonMaxSuppressionV3"; +var NonMaxSuppressionV4 = "NonMaxSuppressionV4"; +var NonMaxSuppressionV5 = "NonMaxSuppressionV5"; +var OnesLike = "OnesLike"; +var OneHot = "OneHot"; +var Pack = "Pack"; +var PadV2 = "PadV2"; +var Pool = "Pool"; +var Pow = "Pow"; +var Prelu = "Prelu"; +var Prod = "Prod"; +var Range = "Range"; +var Real = "Real"; +var Reciprocal = "Reciprocal"; +var Relu = "Relu"; +var Reshape = "Reshape"; +var ResizeNearestNeighbor = "ResizeNearestNeighbor"; +var ResizeNearestNeighborGrad = "ResizeNearestNeighborGrad"; +var ResizeBilinear = "ResizeBilinear"; +var ResizeBilinearGrad = "ResizeBilinearGrad"; +var Relu6 = "Relu6"; +var Reverse = "Reverse"; +var Round = "Round"; +var Rsqrt = "Rsqrt"; +var ScatterNd = "ScatterNd"; +var Select = "Select"; +var Selu = "Selu"; +var Slice = "Slice"; +var Sin = "Sin"; +var Sinh = "Sinh"; +var Sign = "Sign"; +var Sigmoid = "Sigmoid"; +var Softplus = "Softplus"; +var Sqrt = "Sqrt"; +var Sum = "Sum"; +var SpaceToBatchND = "SpaceToBatchND"; +var SplitV = "SplitV"; +var Softmax = "Softmax"; +var SquaredDifference = "SquaredDifference"; +var Square = "Square"; +var Sub = "Sub"; +var SparseToDense = "SparseToDense"; +var StridedSlice = "StridedSlice"; +var Tan = "Tan"; +var Tanh = "Tanh"; +var Tile = "Tile"; +var TopK = "TopK"; +var Transform = "Transform"; +var Transpose = "Transpose"; +var Unique = "Unique"; +var Unpack = "Unpack"; +var UnsortedSegmentSum = "UnsortedSegmentSum"; +var ZerosLike = "ZerosLike"; +var Step = "Step"; +var FromPixels = "FromPixels"; +var RotateWithOffset = "RotateWithOffset"; +var _FusedMatMul = "_FusedMatMul"; +var FusedConv2D = "FusedConv2D"; +var FusedDepthwiseConv2D = "FusedDepthwiseConv2D"; +/** + * @license + * Copyright 2019 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +var kernelRegistry = getGlobal("kernelRegistry", () => new Map()); +var gradRegistry = getGlobal("gradRegistry", () => new Map()); +function getKernel(kernelName, backendName) { + const key = makeKey(kernelName, backendName); + return kernelRegistry.get(key); +} +function getGradient(kernelName) { + return gradRegistry.get(kernelName); +} +function getKernelsForBackend(backendName) { + const it = kernelRegistry.entries(); + const result = []; + while (true) { + const {done, value} = it.next(); + if (done) { + break; + } + const [key, config] = value; + const [backend2] = key.split("_"); + if (backend2 === backendName) { + result.push(config); + } + } + return result; +} +function registerKernel(config) { + const {kernelName, backendName} = config; + const key = makeKey(kernelName, backendName); + if (kernelRegistry.has(key)) { + console.warn(`The kernel '${kernelName}' for backend '${backendName}' is already registered`); + } + kernelRegistry.set(key, config); +} +function registerGradient(config) { + const {kernelName} = config; + if (gradRegistry.has(kernelName)) { + if (env().getBool("DEBUG")) { + console.warn(`Overriding the gradient for '${kernelName}'`); + } + } + gradRegistry.set(kernelName, config); +} +function unregisterKernel(kernelName, backendName) { + const key = makeKey(kernelName, backendName); + if (!kernelRegistry.has(key)) { + throw new Error(`The kernel '${kernelName}' for backend '${backendName}' is not registered`); + } + kernelRegistry.delete(key); +} +function unregisterGradient(kernelName) { + if (!gradRegistry.has(kernelName)) { + throw new Error(`The gradient '${kernelName}' for backend is not registered`); + } + gradRegistry.delete(kernelName); +} +function copyRegisteredKernels(registeredBackendName, newBackendName) { + const kernels = getKernelsForBackend(registeredBackendName); + kernels.forEach((kernelConfig) => { + const newKernelConfig = Object.assign({}, kernelConfig, {backendName: newBackendName}); + registerKernel(newKernelConfig); + }); +} +function makeKey(kernelName, backendName) { + return `${backendName}_${kernelName}`; +} +var util_exports = {}; +__export2(util_exports, { + arraysEqual: () => arraysEqual, + assert: () => assert, + assertNonNegativeIntegerDimensions: () => assertNonNegativeIntegerDimensions, + assertNonNull: () => assertNonNull, + assertShapesMatch: () => assertShapesMatch, + bytesFromStringArray: () => bytesFromStringArray, + bytesPerElement: () => bytesPerElement, + checkConversionForErrors: () => checkConversionForErrors, + clamp: () => clamp, + computeStrides: () => computeStrides, + createScalarValue: () => createScalarValue, + createShuffledIndices: () => createShuffledIndices, + decodeString: () => decodeString, + distSquared: () => distSquared, + encodeString: () => encodeString, + fetch: () => fetch2, + flatten: () => flatten, + getArrayFromDType: () => getArrayFromDType, + getTypedArrayFromDType: () => getTypedArrayFromDType, + hasEncodingLoss: () => hasEncodingLoss, + indexToLoc: () => indexToLoc, + inferDtype: () => inferDtype, + inferFromImplicitShape: () => inferFromImplicitShape, + isBoolean: () => isBoolean, + isFunction: () => isFunction, + isInt: () => isInt, + isNumber: () => isNumber, + isPromise: () => isPromise, + isScalarShape: () => isScalarShape, + isString: () => isString, + isTypedArray: () => isTypedArray, + isValidDtype: () => isValidDtype, + locToIndex: () => locToIndex, + makeOnesTypedArray: () => makeOnesTypedArray, + makeZerosNestedTypedArray: () => makeZerosNestedTypedArray, + makeZerosTypedArray: () => makeZerosTypedArray, + nearestDivisor: () => nearestDivisor, + nearestLargerEven: () => nearestLargerEven, + now: () => now, + parseAxisParam: () => parseAxisParam, + randUniform: () => randUniform, + repeatedTry: () => repeatedTry, + rightPad: () => rightPad, + shuffle: () => shuffle, + shuffleCombo: () => shuffleCombo, + sizeFromShape: () => sizeFromShape, + sizeToSquarishShape: () => sizeToSquarishShape, + squeezeShape: () => squeezeShape, + sum: () => sum, + tanh: () => tanh, + toNestedArray: () => toNestedArray, + toTypedArray: () => toTypedArray +}); +/** + * @license + * Copyright 2017 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +function createScalarValue(value, dtype) { + if (dtype === "string") { + return encodeString(value); + } + return toTypedArray([value], dtype); +} +function noConversionNeeded(a, dtype) { + return a instanceof Float32Array && dtype === "float32" || a instanceof Int32Array && dtype === "int32" || a instanceof Uint8Array && dtype === "bool"; +} +function toTypedArray(a, dtype) { + if (dtype === "string") { + throw new Error("Cannot convert a string[] to a TypedArray"); + } + if (Array.isArray(a)) { + a = flatten(a); + } + if (env().getBool("DEBUG")) { + checkConversionForErrors(a, dtype); + } + if (noConversionNeeded(a, dtype)) { + return a; + } + if (dtype == null || dtype === "float32" || dtype === "complex64") { + return new Float32Array(a); + } else if (dtype === "int32") { + return new Int32Array(a); + } else if (dtype === "bool") { + const bool = new Uint8Array(a.length); + for (let i = 0; i < bool.length; ++i) { + if (Math.round(a[i]) !== 0) { + bool[i] = 1; + } + } + return bool; + } else { + throw new Error(`Unknown data type ${dtype}`); + } +} +function now() { + return env().platform.now(); +} +function fetch2(path, requestInits) { + return env().platform.fetch(path, requestInits); +} +function encodeString(s, encoding = "utf-8") { + encoding = encoding || "utf-8"; + return env().platform.encode(s, encoding); +} +function decodeString(bytes, encoding = "utf-8") { + encoding = encoding || "utf-8"; + return env().platform.decode(bytes, encoding); +} +/** + * @license + * Copyright 2018 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +var Profiler = class { + constructor(backendTimer, logger) { + this.backendTimer = backendTimer; + this.logger = logger; + if (logger == null) { + this.logger = new Logger(); + } + } + profileKernel(kernelName, inputs, f) { + let outputs; + const holdResultWrapperFn = () => { + outputs = f(); + }; + let timer; + const start = now(); + if (this.backendTimer.timerAvailable()) { + timer = this.backendTimer.time(holdResultWrapperFn); + } else { + holdResultWrapperFn(); + for (const output of outputs) { + output.dataSync(); + } + timer = Promise.resolve({kernelMs: now() - start}); + } + if (env().getBool("CHECK_COMPUTATION_FOR_ERRORS")) { + for (let i = 0; i < outputs.length; i++) { + const output = outputs[i]; + output.data().then((tensorVals) => { + checkComputationForErrors(tensorVals, output.dtype, kernelName); + }); + } + } + const kernelProfile = { + kernelName, + outputs, + inputs, + timeMs: timer.then((timing) => timing.kernelMs), + extraInfo: timer.then((timing) => timing.getExtraProfileInfo != null ? timing.getExtraProfileInfo() : "") + }; + return kernelProfile; + } + logKernelProfile(kernelProfile) { + const {kernelName, outputs, timeMs, inputs, extraInfo} = kernelProfile; + outputs.forEach((result) => { + Promise.all([result.data(), timeMs, extraInfo]).then((valueContainer) => { + this.logger.logKernelProfile(kernelName, result, valueContainer[0], valueContainer[1], inputs, valueContainer[2]); + }); + }); + } +}; +function checkComputationForErrors(vals, dtype, kernelName) { + if (dtype !== "float32") { + return false; + } + for (let i = 0; i < vals.length; i++) { + const num = vals[i]; + if (isNaN(num) || !isFinite(num)) { + console.warn(`Found ${num} in the result of '${kernelName}'`); + return true; + } + } + return false; +} +var Logger = class { + logKernelProfile(name, result, vals, timeMs, inputs, extraInfo) { + const time2 = typeof timeMs === "number" ? rightPad(`${timeMs}ms`, 9) : timeMs["error"]; + const paddedName = rightPad(name, 25); + const rank = result.rank; + const size = result.size; + const shape = rightPad(result.shape.toString(), 14); + let inputShapesDescription = ""; + for (const name2 in inputs) { + const input2 = inputs[name2]; + if (input2 != null) { + const inputShape = input2.shape || result.shape; + const inputRank = inputShape.length; + inputShapesDescription += `${name2}: ${inputRank}D ${inputRank > 0 ? inputShape : ""} `; + } + } + console.log(`%c${paddedName} %c${time2} %c${rank}D ${shape} %c${size} %c${inputShapesDescription} %c${extraInfo}`, "font-weight:bold", "color:red", "color:blue", "color: orange", "color: green", "color: steelblue"); + } +}; +/** + * @license + * Copyright 2017 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +function getFilteredNodesXToY(tape, xs, y) { + const tensorsFromX = {}; + const nodesFromX = {}; + for (let i = 0; i < xs.length; i++) { + tensorsFromX[xs[i].id] = true; + } + for (let i = 0; i < tape.length; i++) { + const node2 = tape[i]; + const nodeInputs = node2.inputs; + for (const inputName in nodeInputs) { + const input2 = nodeInputs[inputName]; + let anyInputFromX = false; + for (let j = 0; j < xs.length; j++) { + if (tensorsFromX[input2.id]) { + node2.outputs.forEach((output) => tensorsFromX[output.id] = true); + anyInputFromX = true; + nodesFromX[node2.id] = true; + break; + } + } + if (anyInputFromX) { + break; + } + } + } + const tensorsLeadToY = {}; + tensorsLeadToY[y.id] = true; + const nodesToY = {}; + for (let i = tape.length - 1; i >= 0; i--) { + const node2 = tape[i]; + const nodeInputs = node2.inputs; + for (let j = 0; j < node2.outputs.length; j++) { + if (tensorsLeadToY[node2.outputs[j].id]) { + for (const inputName in nodeInputs) { + tensorsLeadToY[nodeInputs[inputName].id] = true; + nodesToY[node2.id] = true; + } + break; + } + } + } + const filteredTape = []; + for (let i = 0; i < tape.length; i++) { + const node2 = tape[i]; + if (nodesFromX[node2.id] && nodesToY[node2.id]) { + const prunedInputs = {}; + for (const inputName in node2.inputs) { + const nodeInput = node2.inputs[inputName]; + if (tensorsFromX[nodeInput.id]) { + prunedInputs[inputName] = nodeInput; + } + } + const prunedNode = Object.assign({}, node2); + prunedNode.inputs = prunedInputs; + prunedNode.outputs = node2.outputs; + filteredTape.push(prunedNode); + } + } + return filteredTape; +} +function backpropagateGradients(tensorAccumulatedGradientMap, filteredTape, tidy2, add5) { + for (let i = filteredTape.length - 1; i >= 0; i--) { + const node2 = filteredTape[i]; + const dys = []; + node2.outputs.forEach((o) => { + const gradTensor = tensorAccumulatedGradientMap[o.id]; + if (gradTensor != null) { + dys.push(gradTensor); + } else { + dys.push(null); + } + }); + if (node2.gradient == null) { + throw new Error(`Cannot compute gradient: gradient function not found for ${node2.kernelName}.`); + } + const inputGradients = node2.gradient(dys); + for (const inputName in node2.inputs) { + if (!(inputName in inputGradients)) { + throw new Error(`Cannot backprop through input ${inputName}. Available gradients found: ${Object.keys(inputGradients)}.`); + } + const dx = tidy2(() => inputGradients[inputName]()); + if (dx.dtype !== "float32") { + throw new Error(`Error in gradient for op ${node2.kernelName}. The gradient of input ${inputName} must have 'float32' dtype, but has '${dx.dtype}'`); + } + const x = node2.inputs[inputName]; + if (!arraysEqual(dx.shape, x.shape)) { + throw new Error(`Error in gradient for op ${node2.kernelName}. The gradient of input '${inputName}' has shape '${dx.shape}', which does not match the shape of the input '${x.shape}'`); + } + if (tensorAccumulatedGradientMap[x.id] == null) { + tensorAccumulatedGradientMap[x.id] = dx; + } else { + const curGradient = tensorAccumulatedGradientMap[x.id]; + tensorAccumulatedGradientMap[x.id] = add5(curGradient, dx); + curGradient.dispose(); + } + } + } +} +/** + * @license + * Copyright 2018 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +var FORMAT_LIMIT_NUM_VALS = 20; +var FORMAT_NUM_FIRST_LAST_VALS = 3; +var FORMAT_NUM_SIG_DIGITS = 7; +function tensorToString(vals, shape, dtype, verbose) { + const strides = computeStrides(shape); + const padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides); + const rank = shape.length; + const valsLines = subTensorToString(vals, shape, dtype, strides, padPerCol); + const lines = ["Tensor"]; + if (verbose) { + lines.push(` dtype: ${dtype}`); + lines.push(` rank: ${rank}`); + lines.push(` shape: [${shape}]`); + lines.push(` values:`); + } + lines.push(valsLines.map((l) => " " + l).join("\n")); + return lines.join("\n"); +} +function computeMaxSizePerColumn(vals, shape, dtype, strides) { + const n = sizeFromShape(shape); + const numCols = strides[strides.length - 1]; + const padPerCol = new Array(numCols).fill(0); + const rank = shape.length; + const valuesOrTuples = dtype === "complex64" ? createComplexTuples(vals) : vals; + if (rank > 1) { + for (let row = 0; row < n / numCols; row++) { + const offset = row * numCols; + for (let j = 0; j < numCols; j++) { + padPerCol[j] = Math.max(padPerCol[j], valToString(valuesOrTuples[offset + j], 0, dtype).length); + } + } + } + return padPerCol; +} +function valToString(val, pad3, dtype) { + let valStr; + if (Array.isArray(val)) { + valStr = `${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`; + } else if (isString(val)) { + valStr = `'${val}'`; + } else if (dtype === "bool") { + valStr = boolNumToString(val); + } else { + valStr = parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString(); + } + return rightPad(valStr, pad3); +} +function boolNumToString(v) { + return v === 0 ? "false" : "true"; +} +function subTensorToString(vals, shape, dtype, strides, padPerCol, isLast = true) { + const storagePerElement = dtype === "complex64" ? 2 : 1; + const size = shape[0]; + const rank = shape.length; + if (rank === 0) { + if (dtype === "complex64") { + const complexTuple = createComplexTuples(vals); + return [valToString(complexTuple[0], 0, dtype)]; + } + if (dtype === "bool") { + return [boolNumToString(vals[0])]; + } + return [vals[0].toString()]; + } + if (rank === 1) { + if (size > FORMAT_LIMIT_NUM_VALS) { + const firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement; + let firstVals = Array.from(vals.slice(0, firstValsSize)); + let lastVals = Array.from(vals.slice((size - FORMAT_NUM_FIRST_LAST_VALS) * storagePerElement, size * storagePerElement)); + if (dtype === "complex64") { + firstVals = createComplexTuples(firstVals); + lastVals = createComplexTuples(lastVals); + } + return [ + "[" + firstVals.map((x, i) => valToString(x, padPerCol[i], dtype)).join(", ") + ", ..., " + lastVals.map((x, i) => valToString(x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i], dtype)).join(", ") + "]" + ]; + } + const displayVals = dtype === "complex64" ? createComplexTuples(vals) : Array.from(vals); + return [ + "[" + displayVals.map((x, i) => valToString(x, padPerCol[i], dtype)).join(", ") + "]" + ]; + } + const subshape = shape.slice(1); + const substrides = strides.slice(1); + const stride = strides[0] * storagePerElement; + const lines = []; + if (size > FORMAT_LIMIT_NUM_VALS) { + for (let i = 0; i < FORMAT_NUM_FIRST_LAST_VALS; i++) { + const start = i * stride; + const end = start + stride; + lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, false)); + } + lines.push("..."); + for (let i = size - FORMAT_NUM_FIRST_LAST_VALS; i < size; i++) { + const start = i * stride; + const end = start + stride; + lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1)); + } + } else { + for (let i = 0; i < size; i++) { + const start = i * stride; + const end = start + stride; + lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1)); + } + } + const sep = rank === 2 ? "," : ""; + lines[0] = "[" + lines[0] + sep; + for (let i = 1; i < lines.length - 1; i++) { + lines[i] = " " + lines[i] + sep; + } + let newLineSep = ",\n"; + for (let i = 2; i < rank; i++) { + newLineSep += "\n"; + } + lines[lines.length - 1] = " " + lines[lines.length - 1] + "]" + (isLast ? "" : newLineSep); + return lines; +} +function createComplexTuples(vals) { + const complexTuples = []; + for (let i = 0; i < vals.length; i += 2) { + complexTuples.push([vals[i], vals[i + 1]]); + } + return complexTuples; +} +/** + * @license + * Copyright 2017 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +var TensorBuffer = class { + constructor(shape, dtype, values) { + this.dtype = dtype; + this.shape = shape.slice(); + this.size = sizeFromShape(shape); + if (values != null) { + const n = values.length; + assert(n === this.size, () => `Length of values '${n}' does not match the size inferred by the shape '${this.size}'.`); + } + if (dtype === "complex64") { + throw new Error(`complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).`); + } + this.values = values || getArrayFromDType(dtype, this.size); + this.strides = computeStrides(shape); + } + set(value, ...locs) { + if (locs.length === 0) { + locs = [0]; + } + assert(locs.length === this.rank, () => `The number of provided coordinates (${locs.length}) must match the rank (${this.rank})`); + const index = this.locToIndex(locs); + this.values[index] = value; + } + get(...locs) { + if (locs.length === 0) { + locs = [0]; + } + let i = 0; + for (const loc of locs) { + if (loc < 0 || loc >= this.shape[i]) { + const msg = `Requested out of range element at ${locs}. Buffer shape=${this.shape}`; + throw new Error(msg); + } + i++; + } + let index = locs[locs.length - 1]; + for (let i2 = 0; i2 < locs.length - 1; ++i2) { + index += this.strides[i2] * locs[i2]; + } + return this.values[index]; + } + locToIndex(locs) { + if (this.rank === 0) { + return 0; + } else if (this.rank === 1) { + return locs[0]; + } + let index = locs[locs.length - 1]; + for (let i = 0; i < locs.length - 1; ++i) { + index += this.strides[i] * locs[i]; + } + return index; + } + indexToLoc(index) { + if (this.rank === 0) { + return []; + } else if (this.rank === 1) { + return [index]; + } + const locs = new Array(this.shape.length); + for (let i = 0; i < locs.length - 1; ++i) { + locs[i] = Math.floor(index / this.strides[i]); + index -= locs[i] * this.strides[i]; + } + locs[locs.length - 1] = index; + return locs; + } + get rank() { + return this.shape.length; + } + toTensor() { + return trackerFn().makeTensor(this.values, this.shape, this.dtype); + } +}; +var trackerFn = null; +var opHandler = null; +var deprecationWarningFn = null; +function setTensorTracker(fn) { + trackerFn = fn; +} +function setOpHandler(handler) { + opHandler = handler; +} +function setDeprecationWarningFn(fn) { + deprecationWarningFn = fn; +} +var Tensor = class { + constructor(shape, dtype, dataId, id) { + this.kept = false; + this.isDisposedInternal = false; + this.shape = shape.slice(); + this.dtype = dtype || "float32"; + this.size = sizeFromShape(shape); + this.strides = computeStrides(shape); + this.dataId = dataId; + this.id = id; + this.rankType = this.rank < 5 ? this.rank.toString() : "higher"; + } + get rank() { + return this.shape.length; + } + async buffer() { + const vals = await this.data(); + return opHandler.buffer(this.shape, this.dtype, vals); + } + bufferSync() { + return opHandler.buffer(this.shape, this.dtype, this.dataSync()); + } + async array() { + const vals = await this.data(); + return toNestedArray(this.shape, vals); + } + arraySync() { + return toNestedArray(this.shape, this.dataSync()); + } + async data() { + this.throwIfDisposed(); + const data = trackerFn().read(this.dataId); + if (this.dtype === "string") { + const bytes = await data; + try { + return bytes.map((b) => decodeString(b)); + } catch (_a) { + throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes()."); + } + } + return data; + } + dataSync() { + this.throwIfDisposed(); + const data = trackerFn().readSync(this.dataId); + if (this.dtype === "string") { + try { + return data.map((b) => decodeString(b)); + } catch (_a) { + throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes()."); + } + } + return data; + } + async bytes() { + this.throwIfDisposed(); + const data = await trackerFn().read(this.dataId); + if (this.dtype === "string") { + return data; + } else { + return new Uint8Array(data.buffer); + } + } + dispose() { + if (this.isDisposed) { + return; + } + trackerFn().disposeTensor(this); + this.isDisposedInternal = true; + } + get isDisposed() { + return this.isDisposedInternal; + } + throwIfDisposed() { + if (this.isDisposed) { + throw new Error(`Tensor is disposed.`); + } + } + print(verbose = false) { + return opHandler.print(this, verbose); + } + clone() { + this.throwIfDisposed(); + return opHandler.clone(this); + } + toString(verbose = false) { + const vals = this.dataSync(); + return tensorToString(vals, this.shape, this.dtype, verbose); + } + cast(dtype) { + this.throwIfDisposed(); + return opHandler.cast(this, dtype); + } + variable(trainable = true, name, dtype) { + this.throwIfDisposed(); + return trackerFn().makeVariable(this, trainable, name, dtype); + } +}; +Object.defineProperty(Tensor, Symbol.hasInstance, { + value: (instance) => { + return !!instance && instance.data != null && instance.dataSync != null && instance.throwIfDisposed != null; + } +}); +function getGlobalTensorClass() { + return getGlobal("Tensor", () => { + return Tensor; + }); +} +getGlobalTensorClass(); +var Variable = class extends Tensor { + constructor(initialValue, trainable, name, tensorId) { + super(initialValue.shape, initialValue.dtype, initialValue.dataId, tensorId); + this.trainable = trainable; + this.name = name; + } + assign(newValue) { + if (newValue.dtype !== this.dtype) { + throw new Error(`dtype of the new value (${newValue.dtype}) and previous value (${this.dtype}) must match`); + } + if (!arraysEqual(newValue.shape, this.shape)) { + throw new Error(`shape of the new value (${newValue.shape}) and previous value (${this.shape}) must match`); + } + trackerFn().disposeTensor(this); + this.dataId = newValue.dataId; + trackerFn().incRef(this, null); + } + dispose() { + trackerFn().disposeVariable(this); + this.isDisposedInternal = true; + } +}; +Object.defineProperty(Variable, Symbol.hasInstance, { + value: (instance) => { + return instance instanceof Tensor && instance.assign != null && instance.assign instanceof Function; + } +}); +var tensor_util_exports = {}; +__export2(tensor_util_exports, { + assertTypesMatch: () => assertTypesMatch, + getTensorsInContainer: () => getTensorsInContainer, + isTensorInList: () => isTensorInList, + makeTypesMatch: () => makeTypesMatch +}); +/** + * @license + * Copyright 2017 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +var Rank; +(function(Rank2) { + Rank2["R0"] = "R0"; + Rank2["R1"] = "R1"; + Rank2["R2"] = "R2"; + Rank2["R3"] = "R3"; + Rank2["R4"] = "R4"; + Rank2["R5"] = "R5"; + Rank2["R6"] = "R6"; +})(Rank || (Rank = {})); +var UpcastInt32AndMap; +(function(UpcastInt32AndMap2) { + UpcastInt32AndMap2["float32"] = "float32"; + UpcastInt32AndMap2["int32"] = "int32"; + UpcastInt32AndMap2["bool"] = "int32"; + UpcastInt32AndMap2["complex64"] = "complex64"; +})(UpcastInt32AndMap || (UpcastInt32AndMap = {})); +var UpcastBoolAndMap; +(function(UpcastBoolAndMap2) { + UpcastBoolAndMap2["float32"] = "float32"; + UpcastBoolAndMap2["int32"] = "int32"; + UpcastBoolAndMap2["bool"] = "bool"; + UpcastBoolAndMap2["complex64"] = "complex64"; +})(UpcastBoolAndMap || (UpcastBoolAndMap = {})); +var UpcastFloat32AndMap; +(function(UpcastFloat32AndMap2) { + UpcastFloat32AndMap2["float32"] = "float32"; + UpcastFloat32AndMap2["int32"] = "float32"; + UpcastFloat32AndMap2["bool"] = "float32"; + UpcastFloat32AndMap2["complex64"] = "complex64"; +})(UpcastFloat32AndMap || (UpcastFloat32AndMap = {})); +var UpcastComplex64AndMap; +(function(UpcastComplex64AndMap2) { + UpcastComplex64AndMap2["float32"] = "complex64"; + UpcastComplex64AndMap2["int32"] = "complex64"; + UpcastComplex64AndMap2["bool"] = "complex64"; + UpcastComplex64AndMap2["complex64"] = "complex64"; +})(UpcastComplex64AndMap || (UpcastComplex64AndMap = {})); +var upcastTypeMap = { + float32: UpcastFloat32AndMap, + int32: UpcastInt32AndMap, + bool: UpcastBoolAndMap, + complex64: UpcastComplex64AndMap +}; +function upcastType(typeA, typeB) { + if (typeA === "string" || typeB === "string") { + if (typeA === "string" && typeB === "string") { + return "string"; + } + throw new Error(`Can not upcast ${typeA} with ${typeB}`); + } + return upcastTypeMap[typeA][typeB]; +} +function sumOutType(type) { + return upcastType(type, "int32"); +} +/** + * @license + * Copyright 2018 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +function makeTypesMatch(a, b) { + if (a.dtype === b.dtype) { + return [a, b]; + } + const dtype = upcastType(a.dtype, b.dtype); + return [a.cast(dtype), b.cast(dtype)]; +} +function assertTypesMatch(a, b) { + assert(a.dtype === b.dtype, () => `The dtypes of the first(${a.dtype}) and second(${b.dtype}) input must match`); +} +function isTensorInList(tensor2, tensorList) { + return tensorList.some((x) => x.id === tensor2.id); +} +function getTensorsInContainer(result) { + const list = []; + const seen = new Set(); + walkTensorContainer(result, list, seen); + return list; +} +function walkTensorContainer(container, list, seen) { + if (container == null) { + return; + } + if (container instanceof Tensor) { + list.push(container); + return; + } + if (!isIterable(container)) { + return; + } + const iterable = container; + for (const k in iterable) { + const val = iterable[k]; + if (!seen.has(val)) { + seen.add(val); + walkTensorContainer(val, list, seen); + } + } +} +function isIterable(obj) { + return Array.isArray(obj) || typeof obj === "object"; +} +/** + * @license + * Copyright 2018 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +function isRegisteredKernelInvocation(kernelInvocation) { + return kernelInvocation.kernelName != null; +} +var EngineState = 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 = false; + this.activeProfile = { + newBytes: 0, + newTensors: 0, + peakBytes: 0, + kernels: [], + result: null, + get kernelNames() { + return Array.from(new Set(this.kernels.map((k) => k.name))); + } + }; + } + dispose() { + for (const variableName in this.registeredVariables) { + this.registeredVariables[variableName].dispose(); + } + } +}; +var Engine = class { + constructor(ENV5) { + this.ENV = ENV5; + this.registry = {}; + this.registryFactory = {}; + this.pendingBackendInitId = 0; + this.state = new EngineState(); + } + async ready() { + if (this.pendingBackendInit != null) { + return this.pendingBackendInit.then(() => { + }); + } + if (this.backendInstance != null) { + return; + } + const sortedBackends = this.getSortedBackends(); + for (let i = 0; i < sortedBackends.length; i++) { + const backendName = sortedBackends[i]; + const success = await this.initializeBackend(backendName).success; + if (success) { + await this.setBackend(backendName); + return; + } + } + throw new Error(`Could not initialize any backends, all backend initializations failed.`); + } + get backend() { + if (this.pendingBackendInit != null) { + throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`); + } + if (this.backendInstance == null) { + const {name, asyncInit} = this.initializeBackendsAndReturnBest(); + if (asyncInit) { + throw new Error(`The highest priority backend '${name}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`); + } + this.setBackend(name); + } + return this.backendInstance; + } + backendNames() { + return Object.keys(this.registryFactory); + } + findBackend(backendName) { + if (!(backendName in this.registry)) { + if (backendName in this.registryFactory) { + const {asyncInit} = this.initializeBackend(backendName); + if (asyncInit) { + return null; + } + } else { + return null; + } + } + return this.registry[backendName]; + } + findBackendFactory(backendName) { + if (!(backendName in this.registryFactory)) { + return null; + } + return this.registryFactory[backendName].factory; + } + registerBackend(backendName, factory, priority = 1) { + if (backendName in this.registryFactory) { + console.warn(`${backendName} backend was already registered. Reusing existing backend factory.`); + return false; + } + this.registryFactory[backendName] = {factory, priority}; + return true; + } + async setBackend(backendName) { + if (this.registryFactory[backendName] == null) { + throw new Error(`Backend name '${backendName}' not found in registry`); + } + this.backendName = backendName; + if (this.registry[backendName] == null) { + this.backendInstance = null; + const {success, asyncInit} = this.initializeBackend(backendName); + const result = asyncInit ? await success : success; + if (!result) { + return false; + } + } + this.backendInstance = this.registry[backendName]; + this.setupRegisteredKernels(); + this.profiler = new Profiler(this.backendInstance); + return true; + } + setupRegisteredKernels() { + const kernels = getKernelsForBackend(this.backendName); + kernels.forEach((kernel) => { + if (kernel.setupFunc != null) { + kernel.setupFunc(this.backendInstance); + } + }); + } + disposeRegisteredKernels(backendName) { + const kernels = getKernelsForBackend(backendName); + kernels.forEach((kernel) => { + if (kernel.disposeFunc != null) { + kernel.disposeFunc(this.registry[backendName]); + } + }); + } + initializeBackend(backendName) { + const registryFactoryEntry = this.registryFactory[backendName]; + if (registryFactoryEntry == null) { + throw new Error(`Cannot initialize backend ${backendName}, no registration found.`); + } + try { + const backend2 = registryFactoryEntry.factory(); + if (backend2 && !(backend2 instanceof KernelBackend) && typeof backend2.then === "function") { + const promiseId = ++this.pendingBackendInitId; + const success = backend2.then((backendInstance) => { + if (promiseId < this.pendingBackendInitId) { + return false; + } + this.registry[backendName] = backendInstance; + this.pendingBackendInit = null; + return true; + }).catch((err) => { + if (promiseId < this.pendingBackendInitId) { + return false; + } + this.pendingBackendInit = null; + console.warn(`Initialization of backend ${backendName} failed`); + console.warn(err.stack || err.message); + return false; + }); + this.pendingBackendInit = success; + return {success, asyncInit: true}; + } else { + this.registry[backendName] = backend2; + return {success: true, asyncInit: false}; + } + } catch (err) { + console.warn(`Initialization of backend ${backendName} failed`); + console.warn(err.stack || err.message); + return {success: false, asyncInit: false}; + } + } + removeBackend(backendName) { + if (!(backendName in this.registryFactory)) { + throw new Error(`${backendName} backend not found in registry`); + } + if (this.backendName === backendName && this.pendingBackendInit != null) { + this.pendingBackendInitId++; + } + if (backendName in this.registry) { + this.disposeRegisteredKernels(backendName); + this.registry[backendName].dispose(); + delete this.registry[backendName]; + } + delete this.registryFactory[backendName]; + if (this.backendName === backendName) { + this.pendingBackendInit = null; + this.backendName = null; + this.backendInstance = null; + } + } + getSortedBackends() { + if (Object.keys(this.registryFactory).length === 0) { + throw new Error("No backend found in registry."); + } + return Object.keys(this.registryFactory).sort((a, b) => { + return this.registryFactory[b].priority - this.registryFactory[a].priority; + }); + } + initializeBackendsAndReturnBest() { + const sortedBackends = this.getSortedBackends(); + for (let i = 0; i < sortedBackends.length; i++) { + const backendName = sortedBackends[i]; + const {success, asyncInit} = this.initializeBackend(backendName); + if (asyncInit || success) { + return {name: backendName, asyncInit}; + } + } + throw new Error(`Could not initialize any backends, all backend initializations failed.`); + } + moveData(backend2, dataId) { + const info = this.state.tensorInfo.get(dataId); + const srcBackend = info.backend; + const values = this.readSync(dataId); + const refCount = srcBackend.refCount(dataId); + srcBackend.disposeData(dataId, true); + info.backend = backend2; + backend2.move(dataId, values, info.shape, info.dtype, refCount); + if (this.shouldCheckForMemLeaks()) { + this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++; + } + } + tidy(nameOrFn, fn) { + let name = null; + if (fn == null) { + if (typeof nameOrFn !== "function") { + throw new Error("Please provide a function to tidy()"); + } + fn = nameOrFn; + } else { + if (typeof nameOrFn !== "string" && !(nameOrFn instanceof String)) { + throw new Error("When calling with two arguments, the first argument to tidy() must be a string"); + } + if (typeof fn !== "function") { + throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function"); + } + name = nameOrFn; + } + let result; + return this.scopedRun(() => this.startScope(name), () => this.endScope(result), () => { + result = fn(); + if (result instanceof Promise) { + console.error("Cannot return a Promise inside of tidy."); + } + return result; + }); + } + scopedRun(start, end, f) { + start(); + try { + const res = f(); + end(); + return res; + } catch (ex) { + end(); + throw ex; + } + } + nextTensorId() { + return Engine.nextTensorId++; + } + nextVariableId() { + return Engine.nextVariableId++; + } + clone(x) { + const y = ENGINE.runKernel(Identity, {x}); + const inputs = {x}; + const grad2 = (dy) => ({ + x: () => { + const dtype = "float32"; + const gradInputs = {x: dy}; + const attrs = {dtype}; + return ENGINE.runKernel(Cast, gradInputs, attrs); + } + }); + const saved = []; + this.addTapeNode(this.state.activeScope.name, inputs, [y], grad2, saved, {}); + return y; + } + runKernel(kernelName, inputs, attrs) { + const hasKernel = getKernel(kernelName, this.backendName) != null; + if (!hasKernel) { + throw new Error(`Kernel '${kernelName}' not registered for backend '${this.backendName}'`); + } + return this.runKernelFunc({kernelName, inputs, attrs}); + } + shouldCheckForMemLeaks() { + return this.ENV.getBool("IS_TEST"); + } + checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos) { + const numDataIdsAfter = this.backend.numDataIds(); + let numOutputDataIds = 0; + outInfos.forEach((info) => { + numOutputDataIds += info.dtype === "complex64" ? 3 : 1; + }); + const numMoves = this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]; + const dataIdsLeaked = numDataIdsAfter - numDataIdsBefore - numOutputDataIds - numMoves; + if (dataIdsLeaked > 0) { + throw new Error(`Backend '${this.backendName}' has an internal memory leak (${dataIdsLeaked} data ids) after running '${kernelName}'`); + } + } + runKernelFunc(kernelParams) { + let outputs; + let saved = []; + const isTapeOn = this.isTapeOn(); + const startingBytecount = this.state.numBytes; + const startingNumTensors = this.state.numTensors; + if (this.shouldCheckForMemLeaks()) { + this.state.numDataMovesStack.push(0); + } + let kernelFunc3; + if (this.backendName == null) { + this.backend; + } + let out; + const kernelOrScopeName = isRegisteredKernelInvocation(kernelParams) ? kernelParams.kernelName : this.state.activeScope != null ? this.state.activeScope.name : ""; + if (isRegisteredKernelInvocation(kernelParams)) { + const {kernelName, inputs: inputs2, attrs: attrs2} = kernelParams; + if (this.backendName == null) { + this.backend; + } + const kernel = getKernel(kernelName, this.backendName); + assert(kernel != null, () => `Cannot find registered kernel '${kernelName}' for backend '${this.backendName}'`); + kernelFunc3 = () => { + const numDataIdsBefore = this.backend.numDataIds(); + out = kernel.kernelFunc({inputs: inputs2, attrs: attrs2, backend: this.backend}); + const outInfos = Array.isArray(out) ? out : [out]; + if (this.shouldCheckForMemLeaks()) { + this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos); + } + const outTensors = outInfos.map((outInfo) => { + if (outInfo.rank != null) { + return outInfo; + } + const {dataId, shape, dtype} = outInfo; + return this.makeTensorFromDataId(dataId, shape, dtype); + }); + if (isTapeOn) { + const tensorsToSave = this.getTensorsForGradient(kernelName, inputs2, outTensors); + saved = this.saveTensorsForBackwardMode(tensorsToSave); + } + return outTensors; + }; + } else { + const {forwardFunc} = kernelParams; + const saveFunc = (tensors) => { + if (!isTapeOn) { + return; + } + saved = tensors.map((tensor2) => this.keep(this.clone(tensor2))); + }; + kernelFunc3 = () => { + const numDataIdsBefore = this.backend.numDataIds(); + out = this.tidy(() => forwardFunc(this.backend, saveFunc)); + const outs = Array.isArray(out) ? out : [out]; + if (this.shouldCheckForMemLeaks()) { + this.checkKernelForMemLeak(kernelOrScopeName, numDataIdsBefore, outs); + } + return outs; + }; + } + const {inputs, attrs} = kernelParams; + const backwardsFunc = isRegisteredKernelInvocation(kernelParams) ? null : kernelParams.backwardsFunc; + let kernelProfile; + this.scopedRun(() => this.state.kernelDepth++, () => this.state.kernelDepth--, () => { + if (!this.ENV.getBool("DEBUG") && !this.state.profiling) { + outputs = kernelFunc3(); + } else { + kernelProfile = this.profiler.profileKernel(kernelOrScopeName, inputs, () => kernelFunc3()); + if (this.ENV.getBool("DEBUG")) { + this.profiler.logKernelProfile(kernelProfile); + } + outputs = kernelProfile.outputs; + } + }); + if (isTapeOn) { + this.addTapeNode(kernelOrScopeName, inputs, outputs, backwardsFunc, saved, attrs); + } + if (this.state.profiling) { + this.state.activeProfile.kernels.push({ + name: kernelOrScopeName, + bytesAdded: this.state.numBytes - startingBytecount, + totalBytesSnapshot: this.state.numBytes, + tensorsAdded: this.state.numTensors - startingNumTensors, + totalTensorsSnapshot: this.state.numTensors, + inputShapes: Object.keys(inputs).map((key) => inputs[key] != null ? inputs[key].shape : null), + outputShapes: outputs.map((item) => item.shape), + kernelTimeMs: kernelProfile.timeMs, + extraInfo: kernelProfile.extraInfo + }); + } + return Array.isArray(out) ? outputs : outputs[0]; + } + saveTensorsForBackwardMode(tensors) { + const saved = tensors.map((tensor2) => this.keep(this.clone(tensor2))); + return saved; + } + getTensorsForGradient(kernelName, inputs, outputs) { + const gradConfig = getGradient(kernelName); + if (gradConfig != null) { + const inputsToSave = gradConfig.inputsToSave || []; + const outputsToSave = gradConfig.outputsToSave || []; + let inputTensorsToSave; + if (gradConfig.saveAllInputs) { + assert(Array.isArray(inputs), () => "saveAllInputs is true, expected inputs to be an array."); + inputTensorsToSave = Object.keys(inputs).map((key) => inputs[key]); + } else { + inputTensorsToSave = inputsToSave.map((inputName) => inputs[inputName]); + } + const outputTensorsToSave = outputs.filter((_, i) => outputsToSave[i]); + return inputTensorsToSave.concat(outputTensorsToSave); + } + return []; + } + makeTensor(values, shape, dtype, backend2) { + if (values == null) { + throw new Error("Values passed to engine.makeTensor() are null"); + } + dtype = dtype || "float32"; + backend2 = backend2 || this.backend; + let backendVals = values; + if (dtype === "string" && isString(values[0])) { + backendVals = values.map((d) => encodeString(d)); + } + const dataId = backend2.write(backendVals, shape, dtype); + const t = new Tensor(shape, dtype, dataId, this.nextTensorId()); + this.trackTensor(t, backend2); + if (dtype === "string") { + const info = this.state.tensorInfo.get(dataId); + const newBytes = bytesFromStringArray(backendVals); + this.state.numBytes += newBytes - info.bytes; + info.bytes = newBytes; + } + return t; + } + makeTensorFromDataId(dataId, shape, dtype, backend2) { + dtype = dtype || "float32"; + const t = new Tensor(shape, dtype, dataId, this.nextTensorId()); + this.trackTensor(t, backend2); + return t; + } + makeVariable(initialValue, trainable = true, name, dtype) { + name = name || this.nextVariableId().toString(); + if (dtype != null && dtype !== initialValue.dtype) { + initialValue = initialValue.cast(dtype); + } + const v = new Variable(initialValue, trainable, name, this.nextTensorId()); + if (this.state.registeredVariables[v.name] != null) { + throw new Error(`Variable with name ${v.name} was already registered`); + } + this.state.registeredVariables[v.name] = v; + this.incRef(v, this.backend); + return v; + } + trackTensor(a, backend2) { + this.state.numTensors++; + if (a.dtype === "string") { + this.state.numStringTensors++; + } + let bytes = 0; + if (a.dtype !== "complex64" && a.dtype !== "string") { + bytes = a.size * bytesPerElement(a.dtype); + } + this.state.numBytes += bytes; + if (!this.state.tensorInfo.has(a.dataId)) { + this.state.numDataBuffers++; + this.state.tensorInfo.set(a.dataId, { + backend: backend2 || this.backend, + dtype: a.dtype, + shape: a.shape, + bytes + }); + } + if (!(a instanceof Variable)) { + this.track(a); + } + } + incRef(a, backend2) { + this.trackTensor(a, backend2); + this.backend.incRef(a.dataId); + } + removeDataId(dataId, backend2) { + if (this.state.tensorInfo.has(dataId) && this.state.tensorInfo.get(dataId).backend === backend2) { + this.state.tensorInfo.delete(dataId); + this.state.numDataBuffers--; + } + } + disposeTensor(a) { + if (!this.state.tensorInfo.has(a.dataId)) { + return; + } + const info = this.state.tensorInfo.get(a.dataId); + this.state.numTensors--; + if (a.dtype === "string") { + this.state.numStringTensors--; + this.state.numBytes -= info.bytes; + } + if (a.dtype !== "complex64" && a.dtype !== "string") { + const bytes = a.size * bytesPerElement(a.dtype); + this.state.numBytes -= bytes; + } + if (info.backend.disposeData(a.dataId)) { + this.removeDataId(a.dataId, info.backend); + } + } + disposeVariables() { + for (const varName in this.state.registeredVariables) { + const v = this.state.registeredVariables[varName]; + this.disposeVariable(v); + } + } + disposeVariable(v) { + this.disposeTensor(v); + if (this.state.registeredVariables[v.name] != null) { + delete this.state.registeredVariables[v.name]; + } + } + memory() { + const info = this.backend.memory(); + info.numTensors = this.state.numTensors; + info.numDataBuffers = this.state.numDataBuffers; + info.numBytes = this.state.numBytes; + if (this.state.numStringTensors > 0) { + info.unreliable = true; + if (info.reasons == null) { + info.reasons = []; + } + info.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)"); + } + return info; + } + async profile(query) { + this.state.profiling = true; + const startBytes = this.state.numBytes; + const startNumTensors = this.state.numTensors; + this.state.activeProfile.kernels = []; + this.state.activeProfile.result = await query(); + this.state.profiling = false; + this.state.activeProfile.peakBytes = Math.max(...this.state.activeProfile.kernels.map((d) => d.totalBytesSnapshot)); + this.state.activeProfile.newBytes = this.state.numBytes - startBytes; + this.state.activeProfile.newTensors = this.state.numTensors - startNumTensors; + for (const kernel of this.state.activeProfile.kernels) { + kernel.kernelTimeMs = await kernel.kernelTimeMs; + kernel.extraInfo = await kernel.extraInfo; + } + return this.state.activeProfile; + } + isTapeOn() { + return this.state.gradientDepth > 0 && this.state.kernelDepth === 0; + } + addTapeNode(kernelName, inputs, outputs, gradientsFunc, saved, attrs) { + const tapeNode = {id: this.state.nextTapeNodeId++, kernelName, inputs, outputs, saved}; + const gradConfig = getGradient(kernelName); + if (gradConfig != null) { + gradientsFunc = gradConfig.gradFunc; + } + if (gradientsFunc != null) { + tapeNode.gradient = (dys) => { + dys = dys.map((dy, i) => { + if (dy == null) { + const output = outputs[i]; + const vals = makeZerosTypedArray(output.size, output.dtype); + return this.makeTensor(vals, output.shape, output.dtype); + } + return dy; + }); + return gradientsFunc(dys.length > 1 ? dys : dys[0], saved, attrs); + }; + } + this.state.activeTape.push(tapeNode); + } + keep(result) { + result.kept = true; + return result; + } + startTape() { + if (this.state.gradientDepth === 0) { + this.state.activeTape = []; + } + this.state.gradientDepth++; + } + endTape() { + this.state.gradientDepth--; + } + startScope(name) { + const scopeInfo = { + track: [], + name: "unnamed scope", + id: this.state.nextScopeId++ + }; + if (name) { + scopeInfo.name = name; + } + this.state.scopeStack.push(scopeInfo); + this.state.activeScope = scopeInfo; + } + endScope(result) { + const tensorsToTrackInParent = getTensorsInContainer(result); + const tensorsToTrackInParentSet = new Set(tensorsToTrackInParent.map((t) => t.id)); + for (let i = 0; i < this.state.activeScope.track.length; i++) { + const tensor2 = this.state.activeScope.track[i]; + if (!tensor2.kept && !tensorsToTrackInParentSet.has(tensor2.id)) { + tensor2.dispose(); + } + } + const oldScope = this.state.scopeStack.pop(); + this.state.activeScope = this.state.scopeStack.length === 0 ? null : this.state.scopeStack[this.state.scopeStack.length - 1]; + tensorsToTrackInParent.forEach((tensor2) => { + if (!tensor2.kept && tensor2.scopeId === oldScope.id) { + this.track(tensor2); + } + }); + } + gradients(f, xs, dy, allowNoGradients = false) { + assert(xs.length > 0, () => "gradients() received an empty list of xs."); + if (dy != null && dy.dtype !== "float32") { + throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`); + } + const y = this.scopedRun(() => this.startTape(), () => this.endTape(), () => this.tidy("forward", f)); + assert(y instanceof Tensor, () => "The result y returned by f() must be a tensor."); + const filteredTape = getFilteredNodesXToY(this.state.activeTape, xs, y); + if (!allowNoGradients && filteredTape.length === 0 && xs.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", () => { + const accumulatedGradientMap = {}; + accumulatedGradientMap[y.id] = dy == null ? ones(y.shape) : dy; + backpropagateGradients(accumulatedGradientMap, filteredTape, (f2) => this.tidy(f2), add); + const grads2 = xs.map((x) => accumulatedGradientMap[x.id]); + if (this.state.gradientDepth === 0) { + this.state.activeTape.forEach((node2) => { + for (const tensor2 of node2.saved) { + tensor2.dispose(); + } + }); + this.state.activeTape = null; + } + return {value: y, grads: grads2}; + }); + } + customGrad(f) { + assert(isFunction(f), () => "The f passed in customGrad(f) must be a function."); + return (...inputs) => { + assert(inputs.every((t) => t instanceof Tensor), () => "The args passed in customGrad(f)(x1, x2,...) must all be tensors"); + let res; + const inputMap = {}; + inputs.forEach((input2, i) => { + inputMap[i] = input2; + }); + const forwardFunc = (_, save) => { + res = f(...[...inputs, save]); + assert(res.value instanceof Tensor, () => "The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"); + assert(isFunction(res.gradFunc), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."); + return res.value; + }; + const backwardsFunc = (dy, saved) => { + const gradRes = res.gradFunc(dy, saved); + const grads2 = Array.isArray(gradRes) ? gradRes : [gradRes]; + assert(grads2.length === inputs.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(...)."); + assert(grads2.every((t) => t instanceof Tensor), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors."); + const gradMap = {}; + grads2.forEach((grad2, i) => { + gradMap[i] = () => grad2; + }); + return gradMap; + }; + return this.runKernelFunc({ + forwardFunc, + backwardsFunc, + inputs: inputMap + }); + }; + } + readSync(dataId) { + const info = this.state.tensorInfo.get(dataId); + return info.backend.readSync(dataId); + } + read(dataId) { + const info = this.state.tensorInfo.get(dataId); + return info.backend.read(dataId); + } + async time(query) { + const start = now(); + const timingInfo = await this.backend.time(query); + timingInfo.wallMs = now() - start; + return timingInfo; + } + track(result) { + if (this.state.activeScope != null) { + result.scopeId = this.state.activeScope.id; + this.state.activeScope.track.push(result); + } + return result; + } + get registeredVariables() { + return this.state.registeredVariables; + } + reset() { + this.pendingBackendInitId++; + this.state.dispose(); + this.ENV.reset(); + this.state = new EngineState(); + for (const backendName in this.registry) { + this.disposeRegisteredKernels(backendName); + this.registry[backendName].dispose(); + delete this.registry[backendName]; + } + this.backendName = null; + this.backendInstance = null; + this.pendingBackendInit = null; + } +}; +Engine.nextTensorId = 0; +Engine.nextVariableId = 0; +function ones(shape) { + const values = makeOnesTypedArray(sizeFromShape(shape), "float32"); + return ENGINE.makeTensor(values, shape, "float32"); +} +function getOrMakeEngine() { + const ns = getGlobalNamespace(); + if (ns._tfengine == null) { + const environment2 = new Environment(ns); + ns._tfengine = new Engine(environment2); + } + setEnvironmentGlobal(ns._tfengine.ENV); + setTensorTracker(() => ns._tfengine); + return ns._tfengine; +} +var ENGINE = getOrMakeEngine(); +function add(a, b) { + const inputs = {a, b}; + return ENGINE.runKernel(Add, inputs); +} +var device_util_exports = {}; +__export2(device_util_exports, { + isBrowser: () => isBrowser, + isMobile: () => isMobile +}); +/** + * @license + * Copyright 2017 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +function _isNavigatorDefined() { + return typeof navigator !== "undefined" && navigator != null; +} +function isMobile() { + if (_isNavigatorDefined()) { + const a = navigator.userAgent || navigator.vendor || window.opera; + return /(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(a) || /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(a.substr(0, 4)); + } + return false; +} +function isBrowser() { + return typeof window !== "undefined" && window.document != null || typeof WorkerGlobalScope !== "undefined"; +} +/** + * @license + * Copyright 2019 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +var ENV2 = env(); +ENV2.registerFlag("DEBUG", () => false, (debugValue) => { + if (debugValue) { + console.warn("Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance."); + } +}); +ENV2.registerFlag("IS_BROWSER", () => isBrowser()); +ENV2.registerFlag("IS_NODE", () => typeof process !== "undefined" && typeof process.versions !== "undefined" && typeof process.versions.node !== "undefined"); +ENV2.registerFlag("IS_CHROME", () => typeof navigator !== "undefined" && navigator != null && navigator.userAgent != null && /Chrome/.test(navigator.userAgent) && /Google Inc/.test(navigator.vendor)); +ENV2.registerFlag("PROD", () => false); +ENV2.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY", () => ENV2.getBool("DEBUG")); +ENV2.registerFlag("DEPRECATION_WARNINGS_ENABLED", () => true); +ENV2.registerFlag("IS_TEST", () => false); +ENV2.registerFlag("CHECK_COMPUTATION_FOR_ERRORS", () => true); +ENV2.registerFlag("WRAP_TO_IMAGEBITMAP", () => false); +/** + * @license + * Copyright 2018 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +function inferShape(val, dtype) { + let firstElem = val; + if (isTypedArray(val)) { + return dtype === "string" ? [] : [val.length]; + } + if (!Array.isArray(val)) { + return []; + } + const shape = []; + while (Array.isArray(firstElem) || isTypedArray(firstElem) && dtype !== "string") { + shape.push(firstElem.length); + firstElem = firstElem[0]; + } + if (Array.isArray(val) && env().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")) { + deepAssertShapeConsistency(val, shape, []); + } + return shape; +} +function deepAssertShapeConsistency(val, shape, indices) { + indices = indices || []; + if (!Array.isArray(val) && !isTypedArray(val)) { + assert(shape.length === 0, () => `Element arr[${indices.join("][")}] is a primitive, but should be an array/TypedArray of ${shape[0]} elements`); + return; + } + assert(shape.length > 0, () => `Element arr[${indices.join("][")}] should be a primitive, but is an array of ${val.length} elements`); + assert(val.length === shape[0], () => `Element arr[${indices.join("][")}] should have ${shape[0]} elements, but has ${val.length} elements`); + const subShape = shape.slice(1); + for (let i = 0; i < val.length; ++i) { + deepAssertShapeConsistency(val[i], subShape, indices.concat(i)); + } +} +function assertDtype(expectedDtype, actualDType, argName, functionName) { + if (expectedDtype === "string_or_numeric") { + return; + } + if (expectedDtype == null) { + throw new Error(`Expected dtype cannot be null.`); + } + if (expectedDtype !== "numeric" && expectedDtype !== actualDType || expectedDtype === "numeric" && actualDType === "string") { + throw new Error(`Argument '${argName}' passed to '${functionName}' must be ${expectedDtype} tensor, but got ${actualDType} tensor`); + } +} +function convertToTensor(x, argName, functionName, parseAsDtype = "numeric") { + if (x instanceof Tensor) { + assertDtype(parseAsDtype, x.dtype, argName, functionName); + return x; + } + let inferredDtype = inferDtype(x); + if (inferredDtype !== "string" && ["bool", "int32", "float32"].indexOf(parseAsDtype) >= 0) { + inferredDtype = parseAsDtype; + } + assertDtype(parseAsDtype, inferredDtype, argName, functionName); + if (x == null || !isTypedArray(x) && !Array.isArray(x) && typeof x !== "number" && typeof x !== "boolean" && typeof x !== "string") { + const type = x == null ? "null" : x.constructor.name; + throw new Error(`Argument '${argName}' passed to '${functionName}' must be a Tensor or TensorLike, but got '${type}'`); + } + const inferredShape = inferShape(x, inferredDtype); + if (!isTypedArray(x) && !Array.isArray(x)) { + x = [x]; + } + const skipTypedArray = true; + const values = inferredDtype !== "string" ? toTypedArray(x, inferredDtype) : flatten(x, [], skipTypedArray); + return ENGINE.makeTensor(values, inferredShape, inferredDtype); +} +function convertToTensorArray(arg, argName, functionName, parseAsDtype = "numeric") { + if (!Array.isArray(arg)) { + throw new Error(`Argument ${argName} passed to ${functionName} must be a \`Tensor[]\` or \`TensorLike[]\``); + } + const tensors = arg; + return tensors.map((t, i) => convertToTensor(t, `${argName}[${i}]`, functionName, parseAsDtype)); +} +/** + * @license + * Copyright 2018 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +var OP_SCOPE_SUFFIX = "__op"; +function op(f) { + const keys = Object.keys(f); + if (keys.length !== 1) { + throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${keys.length} keys.`); + } + let opName = keys[0]; + const fn = f[opName]; + if (opName.endsWith("_")) { + opName = opName.substring(0, opName.length - 1); + } + opName = opName + OP_SCOPE_SUFFIX; + const f2 = (...args) => { + ENGINE.startScope(opName); + try { + const result = fn(...args); + if (isPromise(result)) { + console.error("Cannot return a Promise inside of tidy."); + } + ENGINE.endScope(result); + return result; + } catch (ex) { + ENGINE.endScope(null); + throw ex; + } + }; + Object.defineProperty(f2, "name", {value: opName, configurable: true}); + return f2; +} +/** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +function complex_(real4, imag4) { + const $real = convertToTensor(real4, "real", "complex"); + const $imag = convertToTensor(imag4, "imag", "complex"); + assertShapesMatch($real.shape, $imag.shape, `real and imag shapes, ${$real.shape} and ${$imag.shape}, must match in call to tf.complex().`); + const inputs = {real: $real, imag: $imag}; + return ENGINE.runKernel(Complex, inputs); +} +var complex = op({complex_}); +/** + * @license + * Copyright 2018 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +function makeTensor(values, shape, inferredShape, dtype) { + if (dtype == null) { + dtype = inferDtype(values); + } + if (dtype === "complex64") { + throw new Error(`Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).`); + } + if (!isTypedArray(values) && !Array.isArray(values) && typeof values !== "number" && typeof values !== "boolean" && typeof values !== "string") { + throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray"); + } + if (shape != null) { + assertNonNegativeIntegerDimensions(shape); + const providedSize = sizeFromShape(shape); + const inferredSize = sizeFromShape(inferredShape); + assert(providedSize === inferredSize, () => `Based on the provided shape, [${shape}], the tensor should have ${providedSize} values but has ${inferredSize}`); + for (let i = 0; i < inferredShape.length; ++i) { + const inferred = inferredShape[i]; + const flatDimsDontMatch = i === inferredShape.length - 1 ? inferred !== sizeFromShape(shape.slice(i)) : true; + assert(inferredShape[i] === shape[i] || !flatDimsDontMatch, () => `Error creating a new Tensor. Inferred shape (${inferredShape}) does not match the provided shape (${shape}). `); + } + } + if (!isTypedArray(values) && !Array.isArray(values)) { + values = [values]; + } + shape = shape || inferredShape; + values = dtype !== "string" ? toTypedArray(values, dtype) : flatten(values, [], true); + return ENGINE.makeTensor(values, shape, dtype); +} +/** + * @license + * Copyright 2018 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +function tensor(values, shape, dtype) { + const inferredShape = inferShape(values, dtype); + return makeTensor(values, shape, inferredShape, dtype); +} +/** + * @license + * Copyright 2018 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +var DTYPE_VALUE_SIZE_MAP = { + float32: 4, + float16: 2, + int32: 4, + uint16: 2, + uint8: 1, + bool: 1, + complex64: 8 +}; +/** + * @license + * Copyright 2018 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +var NUM_BYTES_STRING_LENGTH = 4; +async function encodeWeights(tensors, group) { + const specs = []; + const dataPromises = []; + const names = Array.isArray(tensors) ? tensors.map((tensor2) => tensor2.name) : Object.keys(tensors); + for (let i = 0; i < names.length; ++i) { + const name = names[i]; + const t = Array.isArray(tensors) ? tensors[i].tensor : tensors[name]; + if (t.dtype !== "float32" && t.dtype !== "int32" && t.dtype !== "bool" && t.dtype !== "string" && t.dtype !== "complex64") { + throw new Error(`Unsupported dtype in weight '${name}': ${t.dtype}`); + } + const spec = {name, shape: t.shape, dtype: t.dtype}; + if (t.dtype === "string") { + const utf8bytes = new Promise(async (resolve) => { + const vals = await t.bytes(); + const totalNumBytes = vals.reduce((p2, c) => p2 + c.length, 0) + NUM_BYTES_STRING_LENGTH * vals.length; + const bytes = new Uint8Array(totalNumBytes); + let offset = 0; + for (let i2 = 0; i2 < vals.length; i2++) { + const val = vals[i2]; + const bytesOfLength = new Uint8Array(new Uint32Array([val.length]).buffer); + bytes.set(bytesOfLength, offset); + offset += NUM_BYTES_STRING_LENGTH; + bytes.set(val, offset); + offset += val.length; + } + resolve(bytes); + }); + dataPromises.push(utf8bytes); + } else { + dataPromises.push(t.data()); + } + if (group != null) { + spec.group = group; + } + specs.push(spec); + } + const tensorValues = await Promise.all(dataPromises); + return {data: concatenateTypedArrays(tensorValues), specs}; +} +function decodeWeights(buffer2, specs) { + const out = {}; + let float16Decode; + let offset = 0; + for (const spec of specs) { + const name = spec.name; + const dtype = spec.dtype; + const shape = spec.shape; + const size = sizeFromShape(shape); + let values; + if ("quantization" in spec) { + const quantization = spec.quantization; + if (quantization.dtype === "uint8" || quantization.dtype === "uint16") { + if (!("min" in quantization && "scale" in quantization)) { + throw new Error(`Weight ${spec.name} with quantization ${quantization.dtype} doesn't have corresponding metadata min and scale.`); + } + } else if (quantization.dtype === "float16") { + if (dtype !== "float32") { + throw new Error(`Weight ${spec.name} is quantized with ${quantization.dtype} which only supports weights of type float32 not ${dtype}.`); + } + } else { + throw new Error(`Weight ${spec.name} has unknown quantization dtype ${quantization.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`); + } + const quantizationSizeFactor = DTYPE_VALUE_SIZE_MAP[quantization.dtype]; + const byteBuffer = buffer2.slice(offset, offset + size * quantizationSizeFactor); + const quantizedArray = quantization.dtype === "uint8" ? new Uint8Array(byteBuffer) : new Uint16Array(byteBuffer); + if (dtype === "float32") { + if (quantization.dtype === "uint8" || quantization.dtype === "uint16") { + values = new Float32Array(quantizedArray.length); + for (let i = 0; i < quantizedArray.length; i++) { + const v = quantizedArray[i]; + values[i] = v * quantization.scale + quantization.min; + } + } else if (quantization.dtype === "float16") { + if (float16Decode === void 0) { + float16Decode = getFloat16Decoder(); + } + values = float16Decode(quantizedArray); + } else { + throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type float32.`); + } + } else if (dtype === "int32") { + if (quantization.dtype !== "uint8" && quantization.dtype !== "uint16") { + throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type int32.`); + } + values = new Int32Array(quantizedArray.length); + for (let i = 0; i < quantizedArray.length; i++) { + const v = quantizedArray[i]; + values[i] = Math.round(v * quantization.scale + quantization.min); + } + } else { + throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`); + } + offset += size * quantizationSizeFactor; + } else if (dtype === "string") { + const size2 = sizeFromShape(spec.shape); + values = []; + for (let i = 0; i < size2; i++) { + const byteLength = new Uint32Array(buffer2.slice(offset, offset + NUM_BYTES_STRING_LENGTH))[0]; + offset += NUM_BYTES_STRING_LENGTH; + const bytes = new Uint8Array(buffer2.slice(offset, offset + byteLength)); + values.push(bytes); + offset += byteLength; + } + } else { + const dtypeFactor = DTYPE_VALUE_SIZE_MAP[dtype]; + const byteBuffer = buffer2.slice(offset, offset + size * dtypeFactor); + if (dtype === "float32") { + values = new Float32Array(byteBuffer); + } else if (dtype === "int32") { + values = new Int32Array(byteBuffer); + } else if (dtype === "bool") { + values = new Uint8Array(byteBuffer); + } else if (dtype === "complex64") { + values = new Float32Array(byteBuffer); + const real4 = new Float32Array(values.length / 2); + const image3 = new Float32Array(values.length / 2); + for (let i = 0; i < real4.length; i++) { + real4[i] = values[i * 2]; + image3[i] = values[i * 2 + 1]; + } + const realTensor = tensor(real4, shape, "float32"); + const imageTensor = tensor(image3, shape, "float32"); + out[name] = complex(realTensor, imageTensor); + realTensor.dispose(); + imageTensor.dispose(); + } else { + throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`); + } + offset += size * dtypeFactor; + } + if (dtype !== "complex64") { + out[name] = tensor(values, shape, dtype); + } + } + return out; +} +function concatenateTypedArrays(xs) { + if (xs === null) { + throw new Error(`Invalid input value: ${JSON.stringify(xs)}`); + } + let totalByteLength = 0; + const normalizedXs = []; + xs.forEach((x) => { + totalByteLength += x.byteLength; + normalizedXs.push(x.byteLength === x.buffer.byteLength ? x : new x.constructor(x)); + if (!(x instanceof Float32Array || x instanceof Int32Array || x instanceof Uint8Array)) { + throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`); + } + }); + const y = new Uint8Array(totalByteLength); + let offset = 0; + normalizedXs.forEach((x) => { + y.set(new Uint8Array(x.buffer), offset); + offset += x.byteLength; + }); + return y.buffer; +} +var useNodeBuffer = typeof Buffer !== "undefined" && (typeof Blob === "undefined" || typeof atob === "undefined" || typeof btoa === "undefined"); +function stringByteLength(str) { + if (useNodeBuffer) { + return Buffer.byteLength(str); + } + return new Blob([str]).size; +} +function arrayBufferToBase64String(buffer2) { + if (useNodeBuffer) { + return Buffer.from(buffer2).toString("base64"); + } + const buf = new Uint8Array(buffer2); + let s = ""; + for (let i = 0, l = buf.length; i < l; i++) { + s += String.fromCharCode(buf[i]); + } + return btoa(s); +} +function base64StringToArrayBuffer(str) { + if (useNodeBuffer) { + const buf = Buffer.from(str, "base64"); + return buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength); + } + const s = atob(str); + const buffer2 = new Uint8Array(s.length); + for (let i = 0; i < s.length; ++i) { + buffer2.set([s.charCodeAt(i)], i); + } + return buffer2.buffer; +} +function concatenateArrayBuffers(buffers) { + if (buffers.length === 1) { + return buffers[0]; + } + let totalByteLength = 0; + buffers.forEach((buffer2) => { + totalByteLength += buffer2.byteLength; + }); + const temp = new Uint8Array(totalByteLength); + let offset = 0; + buffers.forEach((buffer2) => { + temp.set(new Uint8Array(buffer2), offset); + offset += buffer2.byteLength; + }); + return temp.buffer; +} +function basename(path) { + const SEPARATOR = "/"; + path = path.trim(); + while (path.endsWith(SEPARATOR)) { + path = path.slice(0, path.length - 1); + } + const items = path.split(SEPARATOR); + return items[items.length - 1]; +} +function getModelArtifactsInfoForJSON(modelArtifacts) { + if (modelArtifacts.modelTopology instanceof ArrayBuffer) { + throw new Error("Expected JSON model topology, received ArrayBuffer."); + } + return { + dateSaved: new Date(), + modelTopologyType: "JSON", + modelTopologyBytes: modelArtifacts.modelTopology == null ? 0 : stringByteLength(JSON.stringify(modelArtifacts.modelTopology)), + weightSpecsBytes: modelArtifacts.weightSpecs == null ? 0 : stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)), + weightDataBytes: modelArtifacts.weightData == null ? 0 : modelArtifacts.weightData.byteLength + }; +} +function computeFloat16MantisaTable() { + const convertMantissa = (i) => { + let m = i << 13; + let e = 0; + while ((m & 8388608) === 0) { + e -= 8388608; + m <<= 1; + } + m &= ~8388608; + e += 947912704; + return m | e; + }; + const mantisaTable = new Uint32Array(2048); + mantisaTable[0] = 0; + for (let i = 1; i < 1024; i++) { + mantisaTable[i] = convertMantissa(i); + } + for (let i = 1024; i < 2048; i++) { + mantisaTable[i] = 939524096 + (i - 1024 << 13); + } + return mantisaTable; +} +function computeFloat16ExponentTable() { + const exponentTable = new Uint32Array(64); + exponentTable[0] = 0; + exponentTable[31] = 1199570944; + exponentTable[32] = 2147483648; + exponentTable[63] = 3347054592; + for (let i = 1; i < 31; i++) { + exponentTable[i] = i << 23; + } + for (let i = 33; i < 63; i++) { + exponentTable[i] = 2147483648 + (i - 32 << 23); + } + return exponentTable; +} +function computeFloat16OffsetTable() { + const offsetTable = new Uint32Array(64); + for (let i = 0; i < 64; i++) { + offsetTable[i] = 1024; + } + offsetTable[0] = offsetTable[32] = 0; + return offsetTable; +} +function getFloat16Decoder() { + const mantisaTable = computeFloat16MantisaTable(); + const exponentTable = computeFloat16ExponentTable(); + const offsetTable = computeFloat16OffsetTable(); + return (quantizedArray) => { + const buffer2 = new ArrayBuffer(4 * quantizedArray.length); + const bufferUint32View = new Uint32Array(buffer2); + for (let index = 0; index < quantizedArray.length; index++) { + const float16Bits = quantizedArray[index]; + const float32Bits = mantisaTable[offsetTable[float16Bits >> 10] + (float16Bits & 1023)] + exponentTable[float16Bits >> 10]; + bufferUint32View[index] = float32Bits; + } + return new Float32Array(buffer2); + }; +} +/** + * @license + * Copyright 2018 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +var IORouterRegistry = class { + constructor() { + this.saveRouters = []; + this.loadRouters = []; + } + static getInstance() { + if (IORouterRegistry.instance == null) { + IORouterRegistry.instance = new IORouterRegistry(); + } + return IORouterRegistry.instance; + } + static registerSaveRouter(saveRouter) { + IORouterRegistry.getInstance().saveRouters.push(saveRouter); + } + static registerLoadRouter(loadRouter) { + IORouterRegistry.getInstance().loadRouters.push(loadRouter); + } + static getSaveHandlers(url) { + return IORouterRegistry.getHandlers(url, "save"); + } + static getLoadHandlers(url, loadOptions) { + return IORouterRegistry.getHandlers(url, "load", loadOptions); + } + static getHandlers(url, handlerType, loadOptions) { + const validHandlers = []; + const routers = handlerType === "load" ? IORouterRegistry.getInstance().loadRouters : IORouterRegistry.getInstance().saveRouters; + routers.forEach((router) => { + const handler = router(url, loadOptions); + if (handler !== null) { + validHandlers.push(handler); + } + }); + return validHandlers; + } +}; +var registerSaveRouter = (loudRouter) => IORouterRegistry.registerSaveRouter(loudRouter); +var registerLoadRouter = (loudRouter) => IORouterRegistry.registerLoadRouter(loudRouter); +var getSaveHandlers = (url) => IORouterRegistry.getSaveHandlers(url); +var getLoadHandlers = (url, loadOptions) => IORouterRegistry.getLoadHandlers(url, loadOptions); +/** + * @license + * Copyright 2018 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +var DATABASE_NAME = "tensorflowjs"; +var DATABASE_VERSION = 1; +var MODEL_STORE_NAME = "models_store"; +var INFO_STORE_NAME = "model_info_store"; +function getIndexedDBFactory() { + if (!env().getBool("IS_BROWSER")) { + throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser."); + } + const theWindow = typeof window === "undefined" ? self : window; + const factory = theWindow.indexedDB || theWindow.mozIndexedDB || theWindow.webkitIndexedDB || theWindow.msIndexedDB || theWindow.shimIndexedDB; + if (factory == null) { + throw new Error("The current browser does not appear to support IndexedDB."); + } + return factory; +} +function setUpDatabase(openRequest) { + const db = openRequest.result; + db.createObjectStore(MODEL_STORE_NAME, {keyPath: "modelPath"}); + db.createObjectStore(INFO_STORE_NAME, {keyPath: "modelPath"}); +} +var BrowserIndexedDB = class { + constructor(modelPath) { + this.indexedDB = getIndexedDBFactory(); + if (modelPath == null || !modelPath) { + throw new Error("For IndexedDB, modelPath must not be null, undefined or empty."); + } + this.modelPath = modelPath; + } + async save(modelArtifacts) { + if (modelArtifacts.modelTopology instanceof ArrayBuffer) { + throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet."); + } + return this.databaseAction(this.modelPath, modelArtifacts); + } + async load() { + return this.databaseAction(this.modelPath); + } + databaseAction(modelPath, modelArtifacts) { + return new Promise((resolve, reject) => { + const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION); + openRequest.onupgradeneeded = () => setUpDatabase(openRequest); + openRequest.onsuccess = () => { + const db = openRequest.result; + if (modelArtifacts == null) { + const modelTx = db.transaction(MODEL_STORE_NAME, "readonly"); + const modelStore = modelTx.objectStore(MODEL_STORE_NAME); + const getRequest = modelStore.get(this.modelPath); + getRequest.onsuccess = () => { + if (getRequest.result == null) { + db.close(); + return reject(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`)); + } else { + resolve(getRequest.result.modelArtifacts); + } + }; + getRequest.onerror = (error) => { + db.close(); + return reject(getRequest.error); + }; + modelTx.oncomplete = () => db.close(); + } else { + const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts); + const infoTx = db.transaction(INFO_STORE_NAME, "readwrite"); + let infoStore = infoTx.objectStore(INFO_STORE_NAME); + const putInfoRequest = infoStore.put({modelPath: this.modelPath, modelArtifactsInfo}); + let modelTx; + putInfoRequest.onsuccess = () => { + modelTx = db.transaction(MODEL_STORE_NAME, "readwrite"); + const modelStore = modelTx.objectStore(MODEL_STORE_NAME); + const putModelRequest = modelStore.put({ + modelPath: this.modelPath, + modelArtifacts, + modelArtifactsInfo + }); + putModelRequest.onsuccess = () => resolve({modelArtifactsInfo}); + putModelRequest.onerror = (error) => { + infoStore = infoTx.objectStore(INFO_STORE_NAME); + const deleteInfoRequest = infoStore.delete(this.modelPath); + deleteInfoRequest.onsuccess = () => { + db.close(); + return reject(putModelRequest.error); + }; + deleteInfoRequest.onerror = (error2) => { + db.close(); + return reject(putModelRequest.error); + }; + }; + }; + putInfoRequest.onerror = (error) => { + db.close(); + return reject(putInfoRequest.error); + }; + infoTx.oncomplete = () => { + if (modelTx == null) { + db.close(); + } else { + modelTx.oncomplete = () => db.close(); + } + }; + } + }; + openRequest.onerror = (error) => reject(openRequest.error); + }); + } +}; +BrowserIndexedDB.URL_SCHEME = "indexeddb://"; +var indexedDBRouter = (url) => { + if (!env().getBool("IS_BROWSER")) { + return null; + } else { + if (!Array.isArray(url) && url.startsWith(BrowserIndexedDB.URL_SCHEME)) { + return browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length)); + } else { + return null; + } + } +}; +IORouterRegistry.registerSaveRouter(indexedDBRouter); +IORouterRegistry.registerLoadRouter(indexedDBRouter); +function browserIndexedDB(modelPath) { + return new BrowserIndexedDB(modelPath); +} +function maybeStripScheme(key) { + return key.startsWith(BrowserIndexedDB.URL_SCHEME) ? key.slice(BrowserIndexedDB.URL_SCHEME.length) : key; +} +var BrowserIndexedDBManager = class { + constructor() { + this.indexedDB = getIndexedDBFactory(); + } + async listModels() { + return new Promise((resolve, reject) => { + const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION); + openRequest.onupgradeneeded = () => setUpDatabase(openRequest); + openRequest.onsuccess = () => { + const db = openRequest.result; + const tx = db.transaction(INFO_STORE_NAME, "readonly"); + const store = tx.objectStore(INFO_STORE_NAME); + const getAllInfoRequest = store.getAll(); + getAllInfoRequest.onsuccess = () => { + const out = {}; + for (const item of getAllInfoRequest.result) { + out[item.modelPath] = item.modelArtifactsInfo; + } + resolve(out); + }; + getAllInfoRequest.onerror = (error) => { + db.close(); + return reject(getAllInfoRequest.error); + }; + tx.oncomplete = () => db.close(); + }; + openRequest.onerror = (error) => reject(openRequest.error); + }); + } + async removeModel(path) { + path = maybeStripScheme(path); + return new Promise((resolve, reject) => { + const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION); + openRequest.onupgradeneeded = () => setUpDatabase(openRequest); + openRequest.onsuccess = () => { + const db = openRequest.result; + const infoTx = db.transaction(INFO_STORE_NAME, "readwrite"); + const infoStore = infoTx.objectStore(INFO_STORE_NAME); + const getInfoRequest = infoStore.get(path); + let modelTx; + getInfoRequest.onsuccess = () => { + if (getInfoRequest.result == null) { + db.close(); + return reject(new Error(`Cannot find model with path '${path}' in IndexedDB.`)); + } else { + const deleteInfoRequest = infoStore.delete(path); + const deleteModelData = () => { + modelTx = db.transaction(MODEL_STORE_NAME, "readwrite"); + const modelStore = modelTx.objectStore(MODEL_STORE_NAME); + const deleteModelRequest = modelStore.delete(path); + deleteModelRequest.onsuccess = () => resolve(getInfoRequest.result.modelArtifactsInfo); + deleteModelRequest.onerror = (error) => reject(getInfoRequest.error); + }; + deleteInfoRequest.onsuccess = deleteModelData; + deleteInfoRequest.onerror = (error) => { + deleteModelData(); + db.close(); + return reject(getInfoRequest.error); + }; + } + }; + getInfoRequest.onerror = (error) => { + db.close(); + return reject(getInfoRequest.error); + }; + infoTx.oncomplete = () => { + if (modelTx == null) { + db.close(); + } else { + modelTx.oncomplete = () => db.close(); + } + }; + }; + openRequest.onerror = (error) => reject(openRequest.error); + }); + } +}; +/** + * @license + * Copyright 2018 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +var PATH_SEPARATOR = "/"; +var PATH_PREFIX = "tensorflowjs_models"; +var INFO_SUFFIX = "info"; +var MODEL_TOPOLOGY_SUFFIX = "model_topology"; +var WEIGHT_SPECS_SUFFIX = "weight_specs"; +var WEIGHT_DATA_SUFFIX = "weight_data"; +var MODEL_METADATA_SUFFIX = "model_metadata"; +function getModelKeys(path) { + return { + info: [PATH_PREFIX, path, INFO_SUFFIX].join(PATH_SEPARATOR), + topology: [PATH_PREFIX, path, MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR), + weightSpecs: [PATH_PREFIX, path, WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR), + weightData: [PATH_PREFIX, path, WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR), + modelMetadata: [PATH_PREFIX, path, MODEL_METADATA_SUFFIX].join(PATH_SEPARATOR) + }; +} +function getModelPathFromKey(key) { + const items = key.split(PATH_SEPARATOR); + if (items.length < 3) { + throw new Error(`Invalid key format: ${key}`); + } + return items.slice(1, items.length - 1).join(PATH_SEPARATOR); +} +function maybeStripScheme2(key) { + return key.startsWith(BrowserLocalStorage.URL_SCHEME) ? key.slice(BrowserLocalStorage.URL_SCHEME.length) : key; +} +var BrowserLocalStorage = class { + constructor(modelPath) { + if (!env().getBool("IS_BROWSER") || typeof window === "undefined" || typeof window.localStorage === "undefined") { + throw new Error("The current environment does not support local storage."); + } + this.LS = window.localStorage; + if (modelPath == null || !modelPath) { + throw new Error("For local storage, modelPath must not be null, undefined or empty."); + } + this.modelPath = modelPath; + this.keys = getModelKeys(this.modelPath); + } + async save(modelArtifacts) { + if (modelArtifacts.modelTopology instanceof ArrayBuffer) { + throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet."); + } else { + const topology = JSON.stringify(modelArtifacts.modelTopology); + const weightSpecs = JSON.stringify(modelArtifacts.weightSpecs); + const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts); + try { + this.LS.setItem(this.keys.info, JSON.stringify(modelArtifactsInfo)); + this.LS.setItem(this.keys.topology, topology); + this.LS.setItem(this.keys.weightSpecs, weightSpecs); + this.LS.setItem(this.keys.weightData, arrayBufferToBase64String(modelArtifacts.weightData)); + const result = { + format: modelArtifacts.format, + generatedBy: modelArtifacts.generatedBy, + convertedBy: modelArtifacts.convertedBy + }; + if (modelArtifacts.signature != null) { + result.signature = modelArtifacts.signature; + } + if (modelArtifacts.userDefinedMetadata != null) { + result.userDefinedMetadata = modelArtifacts.userDefinedMetadata; + } + if (modelArtifacts.modelInitializer != null) { + result.modelInitializer = modelArtifacts.modelInitializer; + } + this.LS.setItem(this.keys.modelMetadata, JSON.stringify(result)); + return {modelArtifactsInfo}; + } catch (err) { + this.LS.removeItem(this.keys.info); + this.LS.removeItem(this.keys.topology); + this.LS.removeItem(this.keys.weightSpecs); + this.LS.removeItem(this.keys.weightData); + this.LS.removeItem(this.keys.modelMetadata); + throw new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`); + } + } + } + async load() { + const info = JSON.parse(this.LS.getItem(this.keys.info)); + if (info == null) { + throw new Error(`In local storage, there is no model with name '${this.modelPath}'`); + } + if (info.modelTopologyType !== "JSON") { + throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet."); + } + const out = {}; + const topology = JSON.parse(this.LS.getItem(this.keys.topology)); + if (topology == null) { + throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`); + } + out.modelTopology = topology; + const weightSpecs = JSON.parse(this.LS.getItem(this.keys.weightSpecs)); + if (weightSpecs == null) { + throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`); + } + out.weightSpecs = weightSpecs; + const metadataString = this.LS.getItem(this.keys.modelMetadata); + if (metadataString != null) { + const metadata = JSON.parse(metadataString); + out.format = metadata["format"]; + out.generatedBy = metadata["generatedBy"]; + out.convertedBy = metadata["convertedBy"]; + if (metadata["signature"] != null) { + out.signature = metadata["signature"]; + } + if (metadata["userDefinedMetadata"] != null) { + out.userDefinedMetadata = metadata["userDefinedMetadata"]; + } + if (metadata["modelInitializer"] != null) { + out.modelInitializer = metadata["modelInitializer"]; + } + } + const weightDataBase64 = this.LS.getItem(this.keys.weightData); + if (weightDataBase64 == null) { + throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`); + } + out.weightData = base64StringToArrayBuffer(weightDataBase64); + return out; + } +}; +BrowserLocalStorage.URL_SCHEME = "localstorage://"; +var localStorageRouter = (url) => { + if (!env().getBool("IS_BROWSER")) { + return null; + } else { + if (!Array.isArray(url) && url.startsWith(BrowserLocalStorage.URL_SCHEME)) { + return browserLocalStorage(url.slice(BrowserLocalStorage.URL_SCHEME.length)); + } else { + return null; + } + } +}; +IORouterRegistry.registerSaveRouter(localStorageRouter); +IORouterRegistry.registerLoadRouter(localStorageRouter); +function browserLocalStorage(modelPath) { + return new BrowserLocalStorage(modelPath); +} +var BrowserLocalStorageManager = class { + constructor() { + assert(env().getBool("IS_BROWSER"), () => "Current environment is not a web browser"); + assert(typeof window === "undefined" || typeof window.localStorage !== "undefined", () => "Current browser does not appear to support localStorage"); + this.LS = window.localStorage; + } + async listModels() { + const out = {}; + const prefix = PATH_PREFIX + PATH_SEPARATOR; + const suffix = PATH_SEPARATOR + INFO_SUFFIX; + for (let i = 0; i < this.LS.length; ++i) { + const key = this.LS.key(i); + if (key.startsWith(prefix) && key.endsWith(suffix)) { + const modelPath = getModelPathFromKey(key); + out[modelPath] = JSON.parse(this.LS.getItem(key)); + } + } + return out; + } + async removeModel(path) { + path = maybeStripScheme2(path); + const keys = getModelKeys(path); + if (this.LS.getItem(keys.info) == null) { + throw new Error(`Cannot find model at path '${path}'`); + } + const info = JSON.parse(this.LS.getItem(keys.info)); + this.LS.removeItem(keys.info); + this.LS.removeItem(keys.topology); + this.LS.removeItem(keys.weightSpecs); + this.LS.removeItem(keys.weightData); + return info; + } +}; +/** + * @license + * Copyright 2018 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +var URL_SCHEME_SUFFIX = "://"; +var ModelStoreManagerRegistry = class { + constructor() { + this.managers = {}; + } + static getInstance() { + if (ModelStoreManagerRegistry.instance == null) { + ModelStoreManagerRegistry.instance = new ModelStoreManagerRegistry(); + } + return ModelStoreManagerRegistry.instance; + } + static registerManager(scheme, manager) { + assert(scheme != null, () => "scheme must not be undefined or null."); + if (scheme.endsWith(URL_SCHEME_SUFFIX)) { + scheme = scheme.slice(0, scheme.indexOf(URL_SCHEME_SUFFIX)); + } + assert(scheme.length > 0, () => "scheme must not be an empty string."); + const registry = ModelStoreManagerRegistry.getInstance(); + assert(registry.managers[scheme] == null, () => `A model store manager is already registered for scheme '${scheme}'.`); + registry.managers[scheme] = manager; + } + static getManager(scheme) { + const manager = this.getInstance().managers[scheme]; + if (manager == null) { + throw new Error(`Cannot find model manager for scheme '${scheme}'`); + } + return manager; + } + static getSchemes() { + return Object.keys(this.getInstance().managers); + } +}; +function parseURL(url) { + if (url.indexOf(URL_SCHEME_SUFFIX) === -1) { + throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${ModelStoreManagerRegistry.getSchemes().join(",")}`); + } + return { + scheme: url.split(URL_SCHEME_SUFFIX)[0], + path: url.split(URL_SCHEME_SUFFIX)[1] + }; +} +async function cloneModelInternal(sourceURL, destURL, deleteSource = false) { + assert(sourceURL !== destURL, () => `Old path and new path are the same: '${sourceURL}'`); + const loadHandlers = IORouterRegistry.getLoadHandlers(sourceURL); + assert(loadHandlers.length > 0, () => `Copying failed because no load handler is found for source URL ${sourceURL}.`); + assert(loadHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) load handlers for source URL ${sourceURL}.`); + const loadHandler = loadHandlers[0]; + const saveHandlers = IORouterRegistry.getSaveHandlers(destURL); + assert(saveHandlers.length > 0, () => `Copying failed because no save handler is found for destination URL ${destURL}.`); + assert(saveHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) save handlers for destination URL ${destURL}.`); + const saveHandler = saveHandlers[0]; + const sourceScheme = parseURL(sourceURL).scheme; + const sourcePath = parseURL(sourceURL).path; + const sameMedium = sourceScheme === parseURL(sourceURL).scheme; + const modelArtifacts = await loadHandler.load(); + if (deleteSource && sameMedium) { + await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath); + } + const saveResult = await saveHandler.save(modelArtifacts); + if (deleteSource && !sameMedium) { + await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath); + } + return saveResult.modelArtifactsInfo; +} +async function listModels() { + const schemes = ModelStoreManagerRegistry.getSchemes(); + const out = {}; + for (const scheme of schemes) { + const schemeOut = await ModelStoreManagerRegistry.getManager(scheme).listModels(); + for (const path in schemeOut) { + const url = scheme + URL_SCHEME_SUFFIX + path; + out[url] = schemeOut[path]; + } + } + return out; +} +async function removeModel(url) { + const schemeAndPath = parseURL(url); + const manager = ModelStoreManagerRegistry.getManager(schemeAndPath.scheme); + return manager.removeModel(schemeAndPath.path); +} +async function copyModel(sourceURL, destURL) { + const deleteSource = false; + return cloneModelInternal(sourceURL, destURL, deleteSource); +} +async function moveModel(sourceURL, destURL) { + const deleteSource = true; + return cloneModelInternal(sourceURL, destURL, deleteSource); +} +/** + * @license + * Copyright 2019 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +var PlatformBrowser = class { + fetch(path, init2) { + return fetch(path, init2); + } + now() { + return performance.now(); + } + encode(text, encoding) { + if (encoding !== "utf-8" && encoding !== "utf8") { + throw new Error(`Browser's encoder only supports utf-8, but got ${encoding}`); + } + if (this.textEncoder == null) { + this.textEncoder = new TextEncoder(); + } + return this.textEncoder.encode(text); + } + decode(bytes, encoding) { + return new TextDecoder(encoding).decode(bytes); + } +}; +if (env().get("IS_BROWSER")) { + env().setPlatform("browser", new PlatformBrowser()); + try { + ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME, new BrowserLocalStorageManager()); + } catch (err) { + } + try { + ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME, new BrowserIndexedDBManager()); + } catch (err) { + } +} +/** + * @license + * Copyright 2019 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +var getNodeFetch = { + importFetch: () => require_browser() +}; +var systemFetch; +var PlatformNode = class { + constructor() { + this.util = require("util"); + this.textEncoder = new this.util.TextEncoder(); + } + fetch(path, requestInits) { + if (env().global.fetch != null) { + return env().global.fetch(path, requestInits); + } + if (systemFetch == null) { + systemFetch = getNodeFetch.importFetch(); + } + return systemFetch(path, requestInits); + } + now() { + const time2 = process.hrtime(); + return time2[0] * 1e3 + time2[1] / 1e6; + } + encode(text, encoding) { + if (encoding !== "utf-8" && encoding !== "utf8") { + throw new Error(`Node built-in encoder only supports utf-8, but got ${encoding}`); + } + return this.textEncoder.encode(text); + } + decode(bytes, encoding) { + if (bytes.length === 0) { + return ""; + } + return new this.util.TextDecoder(encoding).decode(bytes); + } +}; +if (env().get("IS_NODE")) { + env().setPlatform("node", new PlatformNode()); +} +/** + * @license + * Copyright 2020 Google Inc. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +function buffer(shape, dtype = "float32", values) { + dtype = dtype || "float32"; + assertNonNegativeIntegerDimensions(shape); + return new TensorBuffer(shape, dtype, values); +} +/** + * @license + * Copyright 2020 Google Inc. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +function cast_(x, dtype) { + const $x = convertToTensor(x, "x", "cast"); + if (!isValidDtype(dtype)) { + throw new Error(`Failed to cast to unknown dtype ${dtype}`); + } + if (dtype === "string" && $x.dtype !== "string" || dtype !== "string" && $x.dtype === "string") { + throw new Error("Only strings can be casted to strings"); + } + const inputs = {x: $x}; + const attrs = {dtype}; + return ENGINE.runKernel(Cast, inputs, attrs); +} +var cast = op({cast_}); +/** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +function clone_(x) { + const $x = convertToTensor(x, "x", "clone", "string_or_numeric"); + const inputs = {x: $x}; + return ENGINE.runKernel(Identity, inputs); +} +var clone = op({clone_}); +/** + * @license + * Copyright 2020 Google Inc. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +function print2(x, verbose = false) { + console.log(x.toString(verbose)); +} +/** + * @license + * Copyright 2020 Google Inc. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +getOrMakeEngine(); +var opHandler2 = { + buffer, + cast, + clone, + print: print2 +}; +setOpHandler(opHandler2); +var io_exports = {}; +__export2(io_exports, { + browserFiles: () => browserFiles, + browserHTTPRequest: () => browserHTTPRequest, + concatenateArrayBuffers: () => concatenateArrayBuffers, + copyModel: () => copyModel, + decodeWeights: () => decodeWeights, + encodeWeights: () => encodeWeights, + fromMemory: () => fromMemory, + getLoadHandlers: () => getLoadHandlers, + getModelArtifactsInfoForJSON: () => getModelArtifactsInfoForJSON, + getSaveHandlers: () => getSaveHandlers, + http: () => http, + isHTTPScheme: () => isHTTPScheme, + listModels: () => listModels, + loadWeights: () => loadWeights, + moveModel: () => moveModel, + registerLoadRouter: () => registerLoadRouter, + registerSaveRouter: () => registerSaveRouter, + removeModel: () => removeModel, + weightsLoaderFactory: () => weightsLoaderFactory, + withSaveHandler: () => withSaveHandler +}); +/** + * @license + * Copyright 2018 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +var DEFAULT_FILE_NAME_PREFIX = "model"; +var DEFAULT_JSON_EXTENSION_NAME = ".json"; +var DEFAULT_WEIGHT_DATA_EXTENSION_NAME = ".weights.bin"; +function defer(f) { + return new Promise((resolve) => setTimeout(resolve)).then(f); +} +var BrowserDownloads = class { + constructor(fileNamePrefix) { + if (!env().getBool("IS_BROWSER")) { + throw new Error("browserDownloads() cannot proceed because the current environment is not a browser."); + } + if (fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)) { + fileNamePrefix = fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length); + } + if (fileNamePrefix == null || fileNamePrefix.length === 0) { + fileNamePrefix = DEFAULT_FILE_NAME_PREFIX; + } + this.modelTopologyFileName = fileNamePrefix + DEFAULT_JSON_EXTENSION_NAME; + this.weightDataFileName = fileNamePrefix + DEFAULT_WEIGHT_DATA_EXTENSION_NAME; + } + async save(modelArtifacts) { + if (typeof document === "undefined") { + throw new Error("Browser downloads are not supported in this environment since `document` is not present"); + } + const weightsURL = window.URL.createObjectURL(new Blob([modelArtifacts.weightData], {type: "application/octet-stream"})); + if (modelArtifacts.modelTopology instanceof ArrayBuffer) { + throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet."); + } else { + const weightsManifest = [{ + paths: ["./" + this.weightDataFileName], + weights: modelArtifacts.weightSpecs + }]; + const modelTopologyAndWeightManifest = { + modelTopology: modelArtifacts.modelTopology, + format: modelArtifacts.format, + generatedBy: modelArtifacts.generatedBy, + convertedBy: modelArtifacts.convertedBy, + weightsManifest + }; + if (modelArtifacts.signature != null) { + modelTopologyAndWeightManifest.signature = modelArtifacts.signature; + } + if (modelArtifacts.userDefinedMetadata != null) { + modelTopologyAndWeightManifest.userDefinedMetadata = modelArtifacts.userDefinedMetadata; + } + if (modelArtifacts.modelInitializer != null) { + modelTopologyAndWeightManifest.modelInitializer = modelArtifacts.modelInitializer; + } + const modelTopologyAndWeightManifestURL = window.URL.createObjectURL(new Blob([JSON.stringify(modelTopologyAndWeightManifest)], {type: "application/json"})); + const jsonAnchor = this.jsonAnchor == null ? document.createElement("a") : this.jsonAnchor; + jsonAnchor.download = this.modelTopologyFileName; + jsonAnchor.href = modelTopologyAndWeightManifestURL; + await defer(() => jsonAnchor.dispatchEvent(new MouseEvent("click"))); + if (modelArtifacts.weightData != null) { + const weightDataAnchor = this.weightDataAnchor == null ? document.createElement("a") : this.weightDataAnchor; + weightDataAnchor.download = this.weightDataFileName; + weightDataAnchor.href = weightsURL; + await defer(() => weightDataAnchor.dispatchEvent(new MouseEvent("click"))); + } + return {modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts)}; + } + } +}; +BrowserDownloads.URL_SCHEME = "downloads://"; +var BrowserFiles = class { + constructor(files) { + if (files == null || files.length < 1) { + throw new Error(`When calling browserFiles, at least 1 file is required, but received ${files}`); + } + this.files = files; + } + async load() { + const jsonFile = this.files[0]; + const weightFiles = this.files.slice(1); + return new Promise((resolve, reject) => { + const jsonReader = new FileReader(); + jsonReader.onload = (event) => { + const modelJSON = JSON.parse(event.target.result); + const modelTopology = modelJSON.modelTopology; + if (modelTopology == null) { + reject(new Error(`modelTopology field is missing from file ${jsonFile.name}`)); + return; + } + if (weightFiles.length === 0) { + resolve({modelTopology}); + } + const weightsManifest = modelJSON.weightsManifest; + if (weightsManifest == null) { + reject(new Error(`weightManifest field is missing from file ${jsonFile.name}`)); + return; + } + let pathToFile; + try { + pathToFile = this.checkManifestAndWeightFiles(weightsManifest, weightFiles); + } catch (err) { + reject(err); + return; + } + const weightSpecs = []; + const paths = []; + const perFileBuffers = []; + weightsManifest.forEach((weightsGroup) => { + weightsGroup.paths.forEach((path) => { + paths.push(path); + perFileBuffers.push(null); + }); + weightSpecs.push(...weightsGroup.weights); + }); + weightsManifest.forEach((weightsGroup) => { + weightsGroup.paths.forEach((path) => { + const weightFileReader = new FileReader(); + weightFileReader.onload = (event2) => { + const weightData = event2.target.result; + const index = paths.indexOf(path); + perFileBuffers[index] = weightData; + if (perFileBuffers.indexOf(null) === -1) { + const result = { + modelTopology, + weightSpecs, + weightData: concatenateArrayBuffers(perFileBuffers), + format: modelJSON.format, + generatedBy: modelJSON.generatedBy, + convertedBy: modelJSON.convertedBy + }; + if (modelJSON.signature != null) { + result.signature = modelJSON.signature; + } + if (modelJSON.userDefinedMetadata != null) { + result.userDefinedMetadata = modelJSON.userDefinedMetadata; + } + if (modelJSON.modelInitializer != null) { + result.modelInitializer = modelJSON.modelInitializer; + } + resolve(result); + } + }; + weightFileReader.onerror = (error) => reject(`Failed to weights data from file of path '${path}'.`); + weightFileReader.readAsArrayBuffer(pathToFile[path]); + }); + }); + }; + jsonReader.onerror = (error) => reject(`Failed to read model topology and weights manifest JSON from file '${jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`); + jsonReader.readAsText(jsonFile); + }); + } + checkManifestAndWeightFiles(manifest, files) { + const basenames = []; + const fileNames = files.map((file) => basename(file.name)); + const pathToFile = {}; + for (const group of manifest) { + group.paths.forEach((path) => { + const pathBasename = basename(path); + if (basenames.indexOf(pathBasename) !== -1) { + throw new Error(`Duplicate file basename found in weights manifest: '${pathBasename}'`); + } + basenames.push(pathBasename); + if (fileNames.indexOf(pathBasename) === -1) { + throw new Error(`Weight file with basename '${pathBasename}' is not provided.`); + } else { + pathToFile[path] = files[fileNames.indexOf(pathBasename)]; + } + }); + } + if (basenames.length !== files.length) { + throw new Error(`Mismatch in the number of files in weights manifest (${basenames.length}) and the number of weight files provided (${files.length}).`); + } + return pathToFile; + } +}; +var browserDownloadsRouter = (url) => { + if (!env().getBool("IS_BROWSER")) { + return null; + } else { + if (!Array.isArray(url) && url.startsWith(BrowserDownloads.URL_SCHEME)) { + return browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length)); + } else { + return null; + } + } +}; +IORouterRegistry.registerSaveRouter(browserDownloadsRouter); +function browserDownloads(fileNamePrefix = "model") { + return new BrowserDownloads(fileNamePrefix); +} +function browserFiles(files) { + return new BrowserFiles(files); +} +/** + * @license + * Copyright 2019 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +function monitorPromisesProgress(promises, onProgress, startFraction, endFraction) { + checkPromises(promises); + startFraction = startFraction == null ? 0 : startFraction; + endFraction = endFraction == null ? 1 : endFraction; + checkFraction(startFraction, endFraction); + let resolvedPromise = 0; + const registerMonitor = (promise) => { + promise.then((value) => { + const fraction = startFraction + ++resolvedPromise / promises.length * (endFraction - startFraction); + onProgress(fraction); + return value; + }); + return promise; + }; + function checkPromises(promises2) { + assert(promises2 != null && Array.isArray(promises2) && promises2.length > 0, () => "promises must be a none empty array"); + } + function checkFraction(startFraction2, endFraction2) { + assert(startFraction2 >= 0 && startFraction2 <= 1, () => `Progress fraction must be in range [0, 1], but got startFraction ${startFraction2}`); + assert(endFraction2 >= 0 && endFraction2 <= 1, () => `Progress fraction must be in range [0, 1], but got endFraction ${endFraction2}`); + assert(endFraction2 >= startFraction2, () => `startFraction must be no more than endFraction, but got startFraction ${startFraction2} and endFraction ${endFraction2}`); + } + return Promise.all(promises.map(registerMonitor)); +} +/** + * @license + * Copyright 2018 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +async function loadWeightsAsArrayBuffer(fetchURLs, loadOptions) { + if (loadOptions == null) { + loadOptions = {}; + } + const fetchFunc = loadOptions.fetchFunc == null ? env().platform.fetch : loadOptions.fetchFunc; + const requests = fetchURLs.map((fetchURL) => fetchFunc(fetchURL, loadOptions.requestInit, {isBinary: true})); + const fetchStartFraction = 0; + const fetchEndFraction = 0.5; + const responses = loadOptions.onProgress == null ? await Promise.all(requests) : await monitorPromisesProgress(requests, loadOptions.onProgress, fetchStartFraction, fetchEndFraction); + const bufferPromises = responses.map((response) => response.arrayBuffer()); + const bufferStartFraction = 0.5; + const bufferEndFraction = 1; + const buffers = loadOptions.onProgress == null ? await Promise.all(bufferPromises) : await monitorPromisesProgress(bufferPromises, loadOptions.onProgress, bufferStartFraction, bufferEndFraction); + return buffers; +} +async function loadWeights(manifest, filePathPrefix = "", weightNames, requestInit) { + const fetchWeights = (fetchUrls) => loadWeightsAsArrayBuffer(fetchUrls, {requestInit}); + const loadWeights2 = weightsLoaderFactory(fetchWeights); + return loadWeights2(manifest, filePathPrefix, weightNames); +} +function weightsLoaderFactory(fetchWeightsFunction) { + return async (manifest, filePathPrefix = "", weightNames) => { + const groupIndicesToFetchMap = manifest.map(() => false); + const groupWeightsToFetch = {}; + const weightsFound = weightNames != null ? weightNames.map(() => false) : []; + const allManifestWeightNames = []; + manifest.forEach((manifestGroupConfig, groupIndex) => { + let groupOffset = 0; + manifestGroupConfig.weights.forEach((weightsEntry) => { + const rawDtype = "quantization" in weightsEntry ? weightsEntry.quantization.dtype : weightsEntry.dtype; + const weightsBytes = DTYPE_VALUE_SIZE_MAP[rawDtype] * sizeFromShape(weightsEntry.shape); + const enqueueWeightsForFetchingFn = () => { + groupIndicesToFetchMap[groupIndex] = true; + if (groupWeightsToFetch[groupIndex] == null) { + groupWeightsToFetch[groupIndex] = []; + } + groupWeightsToFetch[groupIndex].push({ + manifestEntry: weightsEntry, + groupOffset, + sizeBytes: weightsBytes + }); + }; + if (weightNames != null) { + weightNames.forEach((weightName, weightIndex) => { + if (weightName === weightsEntry.name) { + enqueueWeightsForFetchingFn(); + weightsFound[weightIndex] = true; + } + }); + } else { + enqueueWeightsForFetchingFn(); + } + allManifestWeightNames.push(weightsEntry.name); + groupOffset += weightsBytes; + }); + }); + if (!weightsFound.every((found) => found)) { + const weightsNotFound = weightNames.filter((_, i) => !weightsFound[i]); + throw new Error(`Could not find weights in manifest with names: ${weightsNotFound.join(", ")}. +Manifest JSON has weights with names: ${allManifestWeightNames.join(", ")}.`); + } + const groupIndicesToFetch = groupIndicesToFetchMap.reduce((accumulator, shouldFetch, i) => { + if (shouldFetch) { + accumulator.push(i); + } + return accumulator; + }, []); + const fetchUrls = []; + groupIndicesToFetch.forEach((i) => { + manifest[i].paths.forEach((filepath) => { + const fetchUrl = filePathPrefix + (!filePathPrefix.endsWith("/") ? "/" : "") + filepath; + fetchUrls.push(fetchUrl); + }); + }); + const buffers = await fetchWeightsFunction(fetchUrls); + const weightsTensorMap = {}; + let bufferIndexOffset = 0; + groupIndicesToFetch.forEach((i) => { + const numBuffers = manifest[i].paths.length; + let groupBytes = 0; + for (let i2 = 0; i2 < numBuffers; i2++) { + groupBytes += buffers[bufferIndexOffset + i2].byteLength; + } + const groupBuffer = new ArrayBuffer(groupBytes); + const groupByteBuffer = new Uint8Array(groupBuffer); + let groupBufferOffset = 0; + for (let i2 = 0; i2 < numBuffers; i2++) { + const buffer2 = new Uint8Array(buffers[bufferIndexOffset + i2]); + groupByteBuffer.set(buffer2, groupBufferOffset); + groupBufferOffset += buffer2.byteLength; + } + const weightsEntries = groupWeightsToFetch[i]; + weightsEntries.forEach((weightsEntry) => { + const byteBuffer = groupBuffer.slice(weightsEntry.groupOffset, weightsEntry.groupOffset + weightsEntry.sizeBytes); + const nameToTensorMap = decodeWeights(byteBuffer, [weightsEntry.manifestEntry]); + for (const name in nameToTensorMap) { + weightsTensorMap[name] = nameToTensorMap[name]; + } + }); + bufferIndexOffset += numBuffers; + }); + return weightsTensorMap; + }; +} +/** + * @license + * Copyright 2018 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +var OCTET_STREAM_MIME_TYPE = "application/octet-stream"; +var JSON_TYPE = "application/json"; +var HTTPRequest = class { + constructor(path, loadOptions) { + this.DEFAULT_METHOD = "POST"; + if (loadOptions == null) { + loadOptions = {}; + } + this.weightPathPrefix = loadOptions.weightPathPrefix; + this.onProgress = loadOptions.onProgress; + this.weightUrlConverter = loadOptions.weightUrlConverter; + if (loadOptions.fetchFunc != null) { + assert(typeof loadOptions.fetchFunc === "function", () => "Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"); + this.fetch = loadOptions.fetchFunc; + } else { + this.fetch = env().platform.fetch; + } + assert(path != null && path.length > 0, () => "URL path for http must not be null, undefined or empty."); + if (Array.isArray(path)) { + assert(path.length === 2, () => `URL paths for http must have a length of 2, (actual length is ${path.length}).`); + } + this.path = path; + if (loadOptions.requestInit != null && loadOptions.requestInit.body != null) { + throw new Error("requestInit is expected to have no pre-existing body, but has one."); + } + this.requestInit = loadOptions.requestInit || {}; + } + async save(modelArtifacts) { + if (modelArtifacts.modelTopology instanceof ArrayBuffer) { + throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet."); + } + const init2 = Object.assign({method: this.DEFAULT_METHOD}, this.requestInit); + init2.body = new FormData(); + const weightsManifest = [{ + paths: ["./model.weights.bin"], + weights: modelArtifacts.weightSpecs + }]; + const modelTopologyAndWeightManifest = { + modelTopology: modelArtifacts.modelTopology, + format: modelArtifacts.format, + generatedBy: modelArtifacts.generatedBy, + convertedBy: modelArtifacts.convertedBy, + weightsManifest + }; + if (modelArtifacts.signature != null) { + modelTopologyAndWeightManifest.signature = modelArtifacts.signature; + } + if (modelArtifacts.userDefinedMetadata != null) { + modelTopologyAndWeightManifest.userDefinedMetadata = modelArtifacts.userDefinedMetadata; + } + if (modelArtifacts.modelInitializer != null) { + modelTopologyAndWeightManifest.modelInitializer = modelArtifacts.modelInitializer; + } + init2.body.append("model.json", new Blob([JSON.stringify(modelTopologyAndWeightManifest)], {type: JSON_TYPE}), "model.json"); + if (modelArtifacts.weightData != null) { + init2.body.append("model.weights.bin", new Blob([modelArtifacts.weightData], {type: OCTET_STREAM_MIME_TYPE}), "model.weights.bin"); + } + const response = await this.fetch(this.path, init2); + if (response.ok) { + return { + modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts), + responses: [response] + }; + } else { + throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${response.status}.`); + } + } + async load() { + const modelConfigRequest = await this.fetch(this.path, this.requestInit); + if (!modelConfigRequest.ok) { + throw new Error(`Request to ${this.path} failed with status code ${modelConfigRequest.status}. Please verify this URL points to the model JSON of the model to load.`); + } + let modelConfig; + try { + modelConfig = await modelConfigRequest.json(); + } catch (e) { + let message = `Failed to parse model JSON of response from ${this.path}.`; + if (this.path.endsWith(".pb")) { + message += " Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository."; + } else { + message += " Please make sure the server is serving valid JSON for this request."; + } + throw new Error(message); + } + const modelTopology = modelConfig.modelTopology; + const weightsManifest = modelConfig.weightsManifest; + const generatedBy = modelConfig.generatedBy; + const convertedBy = modelConfig.convertedBy; + const format = modelConfig.format; + const signature = modelConfig.signature; + const userDefinedMetadata = modelConfig.userDefinedMetadata; + if (modelTopology == null && weightsManifest == null) { + throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`); + } + let weightSpecs; + let weightData; + if (weightsManifest != null) { + const results = await this.loadWeights(weightsManifest); + [weightSpecs, weightData] = results; + } + const artifacts = { + modelTopology, + weightSpecs, + weightData, + generatedBy, + convertedBy, + format + }; + if (signature != null) { + artifacts.signature = signature; + } + if (userDefinedMetadata != null) { + artifacts.userDefinedMetadata = userDefinedMetadata; + } + const initializer = modelConfig.modelInitializer; + if (initializer) { + artifacts.modelInitializer = initializer; + } + return artifacts; + } + async loadWeights(weightsManifest) { + const weightPath = Array.isArray(this.path) ? this.path[1] : this.path; + const [prefix, suffix] = parseUrl(weightPath); + const pathPrefix = this.weightPathPrefix || prefix; + const weightSpecs = []; + for (const entry of weightsManifest) { + weightSpecs.push(...entry.weights); + } + const fetchURLs = []; + const urlPromises = []; + for (const weightsGroup of weightsManifest) { + for (const path of weightsGroup.paths) { + if (this.weightUrlConverter != null) { + urlPromises.push(this.weightUrlConverter(path)); + } else { + fetchURLs.push(pathPrefix + path + suffix); + } + } + } + if (this.weightUrlConverter) { + fetchURLs.push(...await Promise.all(urlPromises)); + } + const buffers = await loadWeightsAsArrayBuffer(fetchURLs, { + requestInit: this.requestInit, + fetchFunc: this.fetch, + onProgress: this.onProgress + }); + return [weightSpecs, concatenateArrayBuffers(buffers)]; + } +}; +HTTPRequest.URL_SCHEME_REGEX = /^https?:\/\//; +function parseUrl(url) { + const lastSlash = url.lastIndexOf("/"); + const lastSearchParam = url.lastIndexOf("?"); + const prefix = url.substring(0, lastSlash); + const suffix = lastSearchParam > lastSlash ? url.substring(lastSearchParam) : ""; + return [prefix + "/", suffix]; +} +function isHTTPScheme(url) { + return url.match(HTTPRequest.URL_SCHEME_REGEX) != null; +} +var httpRouter = (url, loadOptions) => { + if (typeof fetch === "undefined" && (loadOptions == null || loadOptions.fetchFunc == null)) { + return null; + } else { + let isHTTP = true; + if (Array.isArray(url)) { + isHTTP = url.every((urlItem) => isHTTPScheme(urlItem)); + } else { + isHTTP = isHTTPScheme(url); + } + if (isHTTP) { + return http(url, loadOptions); + } + } + return null; +}; +IORouterRegistry.registerSaveRouter(httpRouter); +IORouterRegistry.registerLoadRouter(httpRouter); +function http(path, loadOptions) { + return new HTTPRequest(path, loadOptions); +} +function browserHTTPRequest(path, loadOptions) { + return http(path, loadOptions); +} +/** + * @license + * Copyright 2018 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +var PassthroughLoader = class { + constructor(modelArtifacts) { + this.modelArtifacts = modelArtifacts; + } + async load() { + return this.modelArtifacts; + } +}; +var PassthroughSaver = class { + constructor(saveHandler) { + this.saveHandler = saveHandler; + } + async save(modelArtifacts) { + return this.saveHandler(modelArtifacts); + } +}; +function fromMemory(modelArtifacts, weightSpecs, weightData, trainingConfig) { + if (arguments.length === 1) { + const isModelArtifacts = modelArtifacts.modelTopology != null || modelArtifacts.weightSpecs != null; + if (isModelArtifacts) { + return new PassthroughLoader(modelArtifacts); + } else { + console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."); + return new PassthroughLoader({modelTopology: modelArtifacts}); + } + } else { + console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."); + return new PassthroughLoader({ + modelTopology: modelArtifacts, + weightSpecs, + weightData, + trainingConfig + }); + } +} +function withSaveHandler(saveHandler) { + return new PassthroughSaver(saveHandler); +} +/** + * @license + * Copyright 2018 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +var math_exports = {}; +__export2(math_exports, { + confusionMatrix: () => confusionMatrix +}); +/** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +function matMul_(a, b, transposeA = false, transposeB = false) { + let $a = convertToTensor(a, "a", "matMul"); + let $b = convertToTensor(b, "b", "matMul"); + [$a, $b] = makeTypesMatch($a, $b); + const inputs = {a: $a, b: $b}; + const attrs = {transposeA, transposeB}; + return ENGINE.runKernel(BatchMatMul, inputs, attrs); +} +var matMul = op({matMul_}); +/** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +function oneHot_(indices, depth, onValue = 1, offValue = 0) { + if (depth < 2) { + throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`); + } + const $indices = convertToTensor(indices, "indices", "oneHot", "int32"); + const inputs = {indices: $indices}; + const attrs = {depth, onValue, offValue}; + return ENGINE.runKernel(OneHot, inputs, attrs); +} +var oneHot = op({oneHot_}); +/** + * @license + * Copyright 2018 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +function transpose_(x, perm) { + const $x = convertToTensor(x, "x", "transpose"); + if (perm == null) { + perm = $x.shape.map((s, i) => i).reverse(); + } + assert($x.rank === perm.length, () => `Error in transpose: rank of input ${$x.rank} must match length of perm ${perm}.`); + perm.forEach((axis) => { + assert(axis >= 0 && axis < $x.rank, () => `All entries in 'perm' must be between 0 and ${$x.rank - 1} but got ${perm}`); + }); + if ($x.rank <= 1) { + return $x.clone(); + } + const inputs = {x: $x}; + const attrs = {perm}; + return ENGINE.runKernel(Transpose, inputs, attrs); +} +var transpose = op({transpose_}); +/** + * @license + * Copyright 2018 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +function confusionMatrix_(labels, predictions, numClasses) { + const $labels = convertToTensor(labels, "labels", "confusionMatrix"); + const $predictions = convertToTensor(predictions, "predictions", "confusionMatrix"); + assert(numClasses == null || numClasses > 0 && Number.isInteger(numClasses), () => `If provided, numClasses must be a positive integer, but got ${numClasses}`); + assert($labels.rank === 1, () => `Expected the rank of labels to be 1, but got ${$labels.rank}`); + assert($predictions.rank === 1, () => `Expected the rank of predictions to be 1, but got ${$predictions.rank}`); + assert($labels.shape[0] === $predictions.shape[0], () => `Mismatch in the number of examples: ${$labels.shape[0]} vs. ${$predictions.shape[0]}. Labels and predictions should have the same number of elements.`); + assert(numClasses > 0 && Number.isInteger(numClasses), () => `numClasses is required to be a positive integer, but got ${numClasses}`); + const oneHotLabels = oneHot(cast($labels, "int32"), numClasses); + const oneHotPredictions = oneHot(cast($predictions, "int32"), numClasses); + const oneHotLabelsT = transpose(oneHotLabels); + const product = matMul(oneHotLabelsT, oneHotPredictions); + return cast(product, "int32"); +} +var confusionMatrix = op({confusionMatrix_}); +/** + * @license + * Copyright 2018 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +var browser_exports = {}; +__export2(browser_exports, { + fromPixels: () => fromPixels, + fromPixelsAsync: () => fromPixelsAsync, + toPixels: () => toPixels +}); +/** + * @license + * Copyright 2018 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +function tensor3d(values, shape, dtype) { + assertNonNull(values); + if (shape != null && shape.length !== 3) { + throw new Error("tensor3d() requires shape to have three numbers"); + } + const inferredShape = inferShape(values, dtype); + if (inferredShape.length !== 3 && inferredShape.length !== 1) { + throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray"); + } + if (inferredShape.length === 1 && shape == null) { + throw new Error("tensor3d() requires shape to be provided when `values` are a flat array"); + } + return makeTensor(values, shape, inferredShape, dtype); +} +/** + * @license + * Copyright 2019 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +var fromPixels2DContext; +function fromPixels_(pixels, numChannels = 3) { + if (numChannels > 4) { + throw new Error("Cannot construct Tensor with more than 4 channels from pixels."); + } + if (pixels == null) { + throw new Error("pixels passed to tf.browser.fromPixels() can not be null"); + } + let isPixelData2 = false; + let isImageData = false; + let isVideo = false; + let isImage = false; + let isCanvasLike = false; + let isImageBitmap = false; + if (pixels.data instanceof Uint8Array) { + isPixelData2 = true; + } else if (typeof ImageData !== "undefined" && pixels instanceof ImageData) { + isImageData = true; + } else if (typeof HTMLVideoElement !== "undefined" && pixels instanceof HTMLVideoElement) { + isVideo = true; + } else if (typeof HTMLImageElement !== "undefined" && pixels instanceof HTMLImageElement) { + isImage = true; + } else if (pixels.getContext != null) { + isCanvasLike = true; + } else if (typeof ImageBitmap !== "undefined" && pixels instanceof ImageBitmap) { + isImageBitmap = true; + } else { + throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${pixels.constructor.name}`); + } + if (isVideo) { + const HAVE_CURRENT_DATA_READY_STATE = 2; + if (isVideo && pixels.readyState < HAVE_CURRENT_DATA_READY_STATE) { + throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the