check in latest build

build/Rect.d.ts

@@ -11,5 +11,6 @@ export declare class Rect implements IRect {
     height: number;
     constructor(x: number, y: number, width: number, height: number);
     toSquare(): Rect;
+    pad(padX: number, padY: number): Rect;
     floor(): Rect;
 }

build/Rect.js

@@ -20,6 +20,10 @@ var Rect = /** @class */ (function () {
         }
         return new Rect(x, y, width, height);
     };
+    Rect.prototype.pad = function (padX, padY) {
+        var _a = this, x = _a.x, y = _a.y, width = _a.width, height = _a.height;
+        return new Rect(x - (padX / 2), y - (padY / 2), width + padX, height + padY);
+    };
     Rect.prototype.floor = function () {
         return new Rect(Math.floor(this.x), Math.floor(this.y), Math.floor(this.width), Math.floor(this.height));
     };

build/Rect.js.map

-{"version":3,"file":"Rect.js","sourceRoot":"","sources":["../src/Rect.ts"],"names":[],"mappings":";;AAOA;IAME,cAAY,CAAS,EAAE,CAAS,EAAE,KAAa,EAAE,MAAc;QAC7D,IAAI,CAAC,CAAC,GAAG,CAAC,CAAA;QACV,IAAI,CAAC,CAAC,GAAG,CAAC,CAAA;QACV,IAAI,CAAC,KAAK,GAAG,KAAK,CAAA;QAClB,IAAI,CAAC,MAAM,GAAG,MAAM,CAAA;IACtB,CAAC;IAEM,uBAAQ,GAAf;QACM,IAAA,SAA8B,EAA5B,QAAC,EAAE,QAAC,EAAE,gBAAK,EAAE,kBAAM,CAAS;QAClC,IAAM,IAAI,GAAG,IAAI,CAAC,GAAG,CAAC,KAAK,GAAG,MAAM,CAAC,CAAA;QACrC,IAAI,KAAK,GAAG,MAAM,EAAE;YAClB,CAAC,IAAI,CAAC,IAAI,GAAG,CAAC,CAAC,CAAA;YACf,KAAK,IAAI,IAAI,CAAA;SACd;QACD,IAAI,MAAM,GAAG,KAAK,EAAE;YAClB,CAAC,IAAI,CAAC,IAAI,GAAG,CAAC,CAAC,CAAA;YACf,MAAM,IAAI,IAAI,CAAA;SACf;QACD,OAAO,IAAI,IAAI,CAAC,CAAC,EAAE,CAAC,EAAE,KAAK,EAAE,MAAM,CAAC,CAAA;IACtC,CAAC;IAEM,oBAAK,GAAZ;QACE,OAAO,IAAI,IAAI,CACb,IAAI,CAAC,KAAK,CAAC,IAAI,CAAC,CAAC,CAAC,EAClB,IAAI,CAAC,KAAK,CAAC,IAAI,CAAC,CAAC,CAAC,EAClB,IAAI,CAAC,KAAK,CAAC,IAAI,CAAC,KAAK,CAAC,EACtB,IAAI,CAAC,KAAK,CAAC,IAAI,CAAC,MAAM,CAAC,CACxB,CAAA;IACH,CAAC;IACH,WAAC;AAAD,CAAC,AAnCD,IAmCC;AAnCY,oBAAI"}
\ No newline at end of file
+{"version":3,"file":"Rect.js","sourceRoot":"","sources":["../src/Rect.ts"],"names":[],"mappings":";;AAOA;IAME,cAAY,CAAS,EAAE,CAAS,EAAE,KAAa,EAAE,MAAc;QAC7D,IAAI,CAAC,CAAC,GAAG,CAAC,CAAA;QACV,IAAI,CAAC,CAAC,GAAG,CAAC,CAAA;QACV,IAAI,CAAC,KAAK,GAAG,KAAK,CAAA;QAClB,IAAI,CAAC,MAAM,GAAG,MAAM,CAAA;IACtB,CAAC;IAEM,uBAAQ,GAAf;QACM,IAAA,SAA8B,EAA5B,QAAC,EAAE,QAAC,EAAE,gBAAK,EAAE,kBAAM,CAAS;QAClC,IAAM,IAAI,GAAG,IAAI,CAAC,GAAG,CAAC,KAAK,GAAG,MAAM,CAAC,CAAA;QACrC,IAAI,KAAK,GAAG,MAAM,EAAE;YAClB,CAAC,IAAI,CAAC,IAAI,GAAG,CAAC,CAAC,CAAA;YACf,KAAK,IAAI,IAAI,CAAA;SACd;QACD,IAAI,MAAM,GAAG,KAAK,EAAE;YAClB,CAAC,IAAI,CAAC,IAAI,GAAG,CAAC,CAAC,CAAA;YACf,MAAM,IAAI,IAAI,CAAA;SACf;QACD,OAAO,IAAI,IAAI,CAAC,CAAC,EAAE,CAAC,EAAE,KAAK,EAAE,MAAM,CAAC,CAAA;IACtC,CAAC;IAEM,kBAAG,GAAV,UAAW,IAAY,EAAE,IAAY;QAC/B,IAAA,SAA8B,EAA5B,QAAC,EAAE,QAAC,EAAE,gBAAK,EAAE,kBAAM,CAAS;QAClC,OAAO,IAAI,IAAI,CAAC,CAAC,GAAG,CAAC,IAAI,GAAG,CAAC,CAAC,EAAE,CAAC,GAAG,CAAC,IAAI,GAAG,CAAC,CAAC,EAAE,KAAK,GAAG,IAAI,EAAE,MAAM,GAAG,IAAI,CAAC,CAAA;IAC9E,CAAC;IAEM,oBAAK,GAAZ;QACE,OAAO,IAAI,IAAI,CACb,IAAI,CAAC,KAAK,CAAC,IAAI,CAAC,CAAC,CAAC,EAClB,IAAI,CAAC,KAAK,CAAC,IAAI,CAAC,CAAC,CAAC,EAClB,IAAI,CAAC,KAAK,CAAC,IAAI,CAAC,KAAK,CAAC,EACtB,IAAI,CAAC,KAAK,CAAC,IAAI,CAAC,MAAM,CAAC,CACxB,CAAA;IACH,CAAC;IACH,WAAC;AAAD,CAAC,AAxCD,IAwCC;AAxCY,oBAAI"}
\ No newline at end of file

build/commons/NeuralNetwork.d.ts

 import * as tf from '@tensorflow/tfjs-core';
 import { ParamMapping } from './types';
 export declare class NeuralNetwork<TNetParams> {
+    private _name;
     protected _params: TNetParams | undefined;
     protected _paramMappings: ParamMapping[];
+    constructor(_name: string);
     readonly params: TNetParams | undefined;
     readonly paramMappings: ParamMapping[];
     getParamFromPath(paramPath: string): tf.Tensor;
@@ -21,6 +23,16 @@ export declare class NeuralNetwork<TNetParams> {
     }[];
     variable(): void;
     freeze(): void;
-    dispose(): void;
+    dispose(throwOnRedispose?: boolean): void;
+    load(weightsOrUrl: Float32Array | string | undefined): Promise<void>;
+    extractWeights(weights: Float32Array): void;
     private traversePropertyPath(paramPath);
+    protected loadQuantizedParams(_: any): Promise<{
+        params: TNetParams;
+        paramMappings: ParamMapping[];
+    }>;
+    protected extractParams(_: any): {
+        params: TNetParams;
+        paramMappings: ParamMapping[];
+    };
 }

build/commons/NeuralNetwork.js

 "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
+var tslib_1 = require("tslib");
 var tf = require("@tensorflow/tfjs-core");
 var NeuralNetwork = /** @class */ (function () {
-    function NeuralNetwork() {
+    function NeuralNetwork(_name) {
+        this._name = _name;
         this._params = undefined;
         this._paramMappings = [];
     }
@@ -59,10 +61,44 @@ var NeuralNetwork = /** @class */ (function () {
             _this.reassignParamFromPath(path, tf.tensor(tensor));
         });
     };
-    NeuralNetwork.prototype.dispose = function () {
-        this.getParamList().forEach(function (param) { return param.tensor.dispose(); });
+    NeuralNetwork.prototype.dispose = function (throwOnRedispose) {
+        if (throwOnRedispose === void 0) { throwOnRedispose = true; }
+        this.getParamList().forEach(function (param) {
+            if (throwOnRedispose && param.tensor.isDisposed) {
+                throw new Error("param tensor has already been disposed for path " + param.path);
+            }
+            param.tensor.dispose();
+        });
         this._params = undefined;
     };
+    NeuralNetwork.prototype.load = function (weightsOrUrl) {
+        return tslib_1.__awaiter(this, void 0, void 0, function () {
+            var _a, paramMappings, params;
+            return tslib_1.__generator(this, function (_b) {
+                switch (_b.label) {
+                    case 0:
+                        if (weightsOrUrl instanceof Float32Array) {
+                            this.extractWeights(weightsOrUrl);
+                            return [2 /*return*/];
+                        }
+                        if (weightsOrUrl && typeof weightsOrUrl !== 'string') {
+                            throw new Error(this._name + ".load - expected model uri, or weights as Float32Array");
+                        }
+                        return [4 /*yield*/, this.loadQuantizedParams(weightsOrUrl)];
+                    case 1:
+                        _a = _b.sent(), paramMappings = _a.paramMappings, params = _a.params;
+                        this._paramMappings = paramMappings;
+                        this._params = params;
+                        return [2 /*return*/];
+                }
+            });
+        });
+    };
+    NeuralNetwork.prototype.extractWeights = function (weights) {
+        var _a = this.extractParams(weights), paramMappings = _a.paramMappings, params = _a.params;
+        this._paramMappings = paramMappings;
+        this._params = params;
+    };
     NeuralNetwork.prototype.traversePropertyPath = function (paramPath) {
         if (!this.params) {
             throw new Error("traversePropertyPath - model has no loaded params");
@@ -79,6 +115,12 @@ var NeuralNetwork = /** @class */ (function () {
         }
         return { obj: obj, objProp: objProp };
     };
+    NeuralNetwork.prototype.loadQuantizedParams = function (_) {
+        throw new Error(this._name + ".loadQuantizedParams - not implemented");
+    };
+    NeuralNetwork.prototype.extractParams = function (_) {
+        throw new Error(this._name + ".extractParams - not implemented");
+    };
     return NeuralNetwork;
 }());
 exports.NeuralNetwork = NeuralNetwork;

build/commons/NeuralNetwork.js.map

-{"version":3,"file":"NeuralNetwork.js","sourceRoot":"","sources":["../../src/commons/NeuralNetwork.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAI5C;IAAA;QAEY,YAAO,GAA2B,SAAS,CAAA;QAC3C,mBAAc,GAAmB,EAAE,CAAA;IAyE/C,CAAC;IAvEC,sBAAW,iCAAM;aAAjB;YACE,OAAO,IAAI,CAAC,OAAO,CAAA;QACrB,CAAC;;;OAAA;IAED,sBAAW,wCAAa;aAAxB;YACE,OAAO,IAAI,CAAC,cAAc,CAAA;QAC5B,CAAC;;;OAAA;IAEM,wCAAgB,GAAvB,UAAwB,SAAiB;QACjC,IAAA,yCAAuD,EAArD,YAAG,EAAE,oBAAO,CAAyC;QAC7D,OAAO,GAAG,CAAC,OAAO,CAAC,CAAA;IACrB,CAAC;IAEM,6CAAqB,GAA5B,UAA6B,SAAiB,EAAE,MAAiB;QACzD,IAAA,yCAAuD,EAArD,YAAG,EAAE,oBAAO,CAAyC;QAC7D,GAAG,CAAC,OAAO,CAAC,CAAC,OAAO,EAAE,CAAA;QACtB,GAAG,CAAC,OAAO,CAAC,GAAG,MAAM,CAAA;IACvB,CAAC;IAEM,oCAAY,GAAnB;QAAA,iBAKC;QAJC,OAAO,IAAI,CAAC,cAAc,CAAC,GAAG,CAAC,UAAC,EAAa;gBAAX,wBAAS;YAAO,OAAA,CAAC;gBACjD,IAAI,EAAE,SAAS;gBACf,MAAM,EAAE,KAAI,CAAC,gBAAgB,CAAC,SAAS,CAAC;aACzC,CAAC;QAHgD,CAGhD,CAAC,CAAA;IACL,CAAC;IAEM,0CAAkB,GAAzB;QACE,OAAO,IAAI,CAAC,YAAY,EAAE,CAAC,MAAM,CAAC,UAAA,KAAK,IAAI,OAAA,KAAK,CAAC,MAAM,YAAY,EAAE,CAAC,QAAQ,EAAnC,CAAmC,CAAC,CAAA;IACjF,CAAC;IAEM,uCAAe,GAAtB;QACE,OAAO,IAAI,CAAC,YAAY,EAAE,CAAC,MAAM,CAAC,UAAA,KAAK,IAAI,OAAA,CAAC,CAAC,KAAK,CAAC,MAAM,YAAY,EAAE,CAAC,QAAQ,CAAC,EAAtC,CAAsC,CAAC,CAAA;IACpF,CAAC;IAEM,gCAAQ,GAAf;QAAA,iBAIC;QAHC,IAAI,CAAC,eAAe,EAAE,CAAC,OAAO,CAAC,UAAC,EAAgB;gBAAd,cAAI,EAAE,kBAAM;YAC5C,KAAI,CAAC,qBAAqB,CAAC,IAAI,EAAE,EAAE,CAAC,QAAQ,CAAC,MAAM,CAAC,CAAC,CAAA;QACvD,CAAC,CAAC,CAAA;IACJ,CAAC;IAEM,8BAAM,GAAb;QAAA,iBAIC;QAHC,IAAI,CAAC,kBAAkB,EAAE,CAAC,OAAO,CAAC,UAAC,EAAgB;gBAAd,cAAI,EAAE,kBAAM;YAC/C,KAAI,CAAC,qBAAqB,CAAC,IAAI,EAAE,EAAE,CAAC,MAAM,CAAC,MAAa,CAAC,CAAC,CAAA;QAC5D,CAAC,CAAC,CAAA;IACJ,CAAC;IAEM,+BAAO,GAAd;QACE,IAAI,CAAC,YAAY,EAAE,CAAC,OAAO,CAAC,UAAA,KAAK,IAAI,OAAA,KAAK,CAAC,MAAM,CAAC,OAAO,EAAE,EAAtB,CAAsB,CAAC,CAAA;QAC5D,IAAI,CAAC,OAAO,GAAG,SAAS,CAAA;IAC1B,CAAC;IAEO,4CAAoB,GAA5B,UAA6B,SAAiB;QAC5C,IAAI,CAAC,IAAI,CAAC,MAAM,EAAE;YAChB,MAAM,IAAI,KAAK,CAAC,mDAAmD,CAAC,CAAA;SACrE;QAED,IAAM,MAAM,GAAG,SAAS,CAAC,KAAK,CAAC,GAAG,CAAC,CAAC,MAAM,CAAC,UAAC,GAAkD,EAAE,OAAO;YACrG,IAAI,CAAC,GAAG,CAAC,OAAO,CAAC,cAAc,CAAC,OAAO,CAAC,EAAE;gBACxC,MAAM,IAAI,KAAK,CAAC,0DAAwD,OAAO,mBAAc,SAAW,CAAC,CAAA;aAC1G;YAED,OAAO,EAAE,GAAG,EAAE,GAAG,CAAC,OAAO,EAAE,OAAO,SAAA,EAAE,OAAO,EAAE,GAAG,CAAC,OAAO,CAAC,OAAO,CAAC,EAAE,CAAA;QACrE,CAAC,EAAE,EAAE,OAAO,EAAE,IAAI,CAAC,MAAM,EAAE,CAAC,CAAA;QAEpB,IAAA,gBAAG,EAAE,wBAAO,CAAW;QAC/B,IAAI,CAAC,GAAG,IAAI,CAAC,OAAO,IAAI,CAAC,CAAC,GAAG,CAAC,OAAO,CAAC,YAAY,EAAE,CAAC,MAAM,CAAC,EAAE;YAC5D,MAAM,IAAI,KAAK,CAAC,gEAA8D,SAAW,CAAC,CAAA;SAC3F;QAED,OAAO,EAAE,GAAG,KAAA,EAAE,OAAO,SAAA,EAAE,CAAA;IACzB,CAAC;IACH,oBAAC;AAAD,CAAC,AA5ED,IA4EC;AA5EY,sCAAa"}
\ No newline at end of file
+{"version":3,"file":"NeuralNetwork.js","sourceRoot":"","sources":["../../src/commons/NeuralNetwork.ts"],"names":[],"mappings":";;;AAAA,0CAA4C;AAI5C;IAKE,uBAAoB,KAAa;QAAb,UAAK,GAAL,KAAK,CAAQ;QAHvB,YAAO,GAA2B,SAAS,CAAA;QAC3C,mBAAc,GAAmB,EAAE,CAAA;IAET,CAAC;IAErC,sBAAW,iCAAM;aAAjB;YACE,OAAO,IAAI,CAAC,OAAO,CAAA;QACrB,CAAC;;;OAAA;IAED,sBAAW,wCAAa;aAAxB;YACE,OAAO,IAAI,CAAC,cAAc,CAAA;QAC5B,CAAC;;;OAAA;IAEM,wCAAgB,GAAvB,UAAwB,SAAiB;QACjC,IAAA,yCAAuD,EAArD,YAAG,EAAE,oBAAO,CAAyC;QAC7D,OAAO,GAAG,CAAC,OAAO,CAAC,CAAA;IACrB,CAAC;IAEM,6CAAqB,GAA5B,UAA6B,SAAiB,EAAE,MAAiB;QACzD,IAAA,yCAAuD,EAArD,YAAG,EAAE,oBAAO,CAAyC;QAC7D,GAAG,CAAC,OAAO,CAAC,CAAC,OAAO,EAAE,CAAA;QACtB,GAAG,CAAC,OAAO,CAAC,GAAG,MAAM,CAAA;IACvB,CAAC;IAEM,oCAAY,GAAnB;QAAA,iBAKC;QAJC,OAAO,IAAI,CAAC,cAAc,CAAC,GAAG,CAAC,UAAC,EAAa;gBAAX,wBAAS;YAAO,OAAA,CAAC;gBACjD,IAAI,EAAE,SAAS;gBACf,MAAM,EAAE,KAAI,CAAC,gBAAgB,CAAC,SAAS,CAAC;aACzC,CAAC;QAHgD,CAGhD,CAAC,CAAA;IACL,CAAC;IAEM,0CAAkB,GAAzB;QACE,OAAO,IAAI,CAAC,YAAY,EAAE,CAAC,MAAM,CAAC,UAAA,KAAK,IAAI,OAAA,KAAK,CAAC,MAAM,YAAY,EAAE,CAAC,QAAQ,EAAnC,CAAmC,CAAC,CAAA;IACjF,CAAC;IAEM,uCAAe,GAAtB;QACE,OAAO,IAAI,CAAC,YAAY,EAAE,CAAC,MAAM,CAAC,UAAA,KAAK,IAAI,OAAA,CAAC,CAAC,KAAK,CAAC,MAAM,YAAY,EAAE,CAAC,QAAQ,CAAC,EAAtC,CAAsC,CAAC,CAAA;IACpF,CAAC;IAEM,gCAAQ,GAAf;QAAA,iBAIC;QAHC,IAAI,CAAC,eAAe,EAAE,CAAC,OAAO,CAAC,UAAC,EAAgB;gBAAd,cAAI,EAAE,kBAAM;YAC5C,KAAI,CAAC,qBAAqB,CAAC,IAAI,EAAE,EAAE,CAAC,QAAQ,CAAC,MAAM,CAAC,CAAC,CAAA;QACvD,CAAC,CAAC,CAAA;IACJ,CAAC;IAEM,8BAAM,GAAb;QAAA,iBAIC;QAHC,IAAI,CAAC,kBAAkB,EAAE,CAAC,OAAO,CAAC,UAAC,EAAgB;gBAAd,cAAI,EAAE,kBAAM;YAC/C,KAAI,CAAC,qBAAqB,CAAC,IAAI,EAAE,EAAE,CAAC,MAAM,CAAC,MAAa,CAAC,CAAC,CAAA;QAC5D,CAAC,CAAC,CAAA;IACJ,CAAC;IAEM,+BAAO,GAAd,UAAe,gBAAgC;QAAhC,iCAAA,EAAA,uBAAgC;QAC7C,IAAI,CAAC,YAAY,EAAE,CAAC,OAAO,CAAC,UAAA,KAAK;YAC/B,IAAI,gBAAgB,IAAI,KAAK,CAAC,MAAM,CAAC,UAAU,EAAE;gBAC/C,MAAM,IAAI,KAAK,CAAC,qDAAmD,KAAK,CAAC,IAAM,CAAC,CAAA;aACjF;YACD,KAAK,CAAC,MAAM,CAAC,OAAO,EAAE,CAAA;QACxB,CAAC,CAAC,CAAA;QACF,IAAI,CAAC,OAAO,GAAG,SAAS,CAAA;IAC1B,CAAC;IAEY,4BAAI,GAAjB,UAAkB,YAA+C;;;;;;wBAC/D,IAAI,YAAY,YAAY,YAAY,EAAE;4BACxC,IAAI,CAAC,cAAc,CAAC,YAAY,CAAC,CAAA;4BACjC,sBAAM;yBACP;wBAED,IAAI,YAAY,IAAI,OAAO,YAAY,KAAK,QAAQ,EAAE;4BACpD,MAAM,IAAI,KAAK,CAAI,IAAI,CAAC,KAAK,2DAAwD,CAAC,CAAA;yBACvF;wBAIG,qBAAM,IAAI,CAAC,mBAAmB,CAAC,YAAY,CAAC,EAAA;;wBAH1C,KAGF,SAA4C,EAF9C,aAAa,mBAAA,EACb,MAAM,YAAA;wBAGR,IAAI,CAAC,cAAc,GAAG,aAAa,CAAA;wBACnC,IAAI,CAAC,OAAO,GAAG,MAAM,CAAA;;;;;KACtB;IAEM,sCAAc,GAArB,UAAsB,OAAqB;QACnC,IAAA,gCAGyB,EAF7B,gCAAa,EACb,kBAAM,CACuB;QAE/B,IAAI,CAAC,cAAc,GAAG,aAAa,CAAA;QACnC,IAAI,CAAC,OAAO,GAAG,MAAM,CAAA;IACvB,CAAC;IAEO,4CAAoB,GAA5B,UAA6B,SAAiB;QAC5C,IAAI,CAAC,IAAI,CAAC,MAAM,EAAE;YAChB,MAAM,IAAI,KAAK,CAAC,mDAAmD,CAAC,CAAA;SACrE;QAED,IAAM,MAAM,GAAG,SAAS,CAAC,KAAK,CAAC,GAAG,CAAC,CAAC,MAAM,CAAC,UAAC,GAAkD,EAAE,OAAO;YACrG,IAAI,CAAC,GAAG,CAAC,OAAO,CAAC,cAAc,CAAC,OAAO,CAAC,EAAE;gBACxC,MAAM,IAAI,KAAK,CAAC,0DAAwD,OAAO,mBAAc,SAAW,CAAC,CAAA;aAC1G;YAED,OAAO,EAAE,GAAG,EAAE,GAAG,CAAC,OAAO,EAAE,OAAO,SAAA,EAAE,OAAO,EAAE,GAAG,CAAC,OAAO,CAAC,OAAO,CAAC,EAAE,CAAA;QACrE,CAAC,EAAE,EAAE,OAAO,EAAE,IAAI,CAAC,MAAM,EAAE,CAAC,CAAA;QAEpB,IAAA,gBAAG,EAAE,wBAAO,CAAW;QAC/B,IAAI,CAAC,GAAG,IAAI,CAAC,OAAO,IAAI,CAAC,CAAC,GAAG,CAAC,OAAO,CAAC,YAAY,EAAE,CAAC,MAAM,CAAC,EAAE;YAC5D,MAAM,IAAI,KAAK,CAAC,gEAA8D,SAAW,CAAC,CAAA;SAC3F;QAED,OAAO,EAAE,GAAG,KAAA,EAAE,OAAO,SAAA,EAAE,CAAA;IACzB,CAAC;IAES,2CAAmB,GAA7B,UAA8B,CAAM;QAClC,MAAM,IAAI,KAAK,CAAI,IAAI,CAAC,KAAK,2CAAwC,CAAC,CAAA;IACxE,CAAC;IAES,qCAAa,GAAvB,UAAwB,CAAM;QAC5B,MAAM,IAAI,KAAK,CAAI,IAAI,CAAC,KAAK,qCAAkC,CAAC,CAAA;IAClE,CAAC;IACH,oBAAC;AAAD,CAAC,AAvHD,IAuHC;AAvHY,sCAAa"}
\ No newline at end of file

build/commons/disposeUnusedWeightTensors.d.ts

+import { ParamMapping } from './types';
+export declare function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]): void;

build/commons/disposeUnusedWeightTensors.js

+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+function disposeUnusedWeightTensors(weightMap, paramMappings) {
+    Object.keys(weightMap).forEach(function (path) {
+        if (!paramMappings.some(function (pm) { return pm.originalPath === path; })) {
+            weightMap[path].dispose();
+        }
+    });
+}
+exports.disposeUnusedWeightTensors = disposeUnusedWeightTensors;
+//# sourceMappingURL=disposeUnusedWeightTensors.js.map
\ No newline at end of file

build/commons/disposeUnusedWeightTensors.js.map

+{"version":3,"file":"disposeUnusedWeightTensors.js","sourceRoot":"","sources":["../../src/commons/disposeUnusedWeightTensors.ts"],"names":[],"mappings":";;AAEA,oCAA2C,SAAc,EAAE,aAA6B;IACtF,MAAM,CAAC,IAAI,CAAC,SAAS,CAAC,CAAC,OAAO,CAAC,UAAA,IAAI;QACjC,IAAI,CAAC,aAAa,CAAC,IAAI,CAAC,UAAA,EAAE,IAAI,OAAA,EAAE,CAAC,YAAY,KAAK,IAAI,EAAxB,CAAwB,CAAC,EAAE;YACvD,SAAS,CAAC,IAAI,CAAC,CAAC,OAAO,EAAE,CAAA;SAC1B;IACH,CAAC,CAAC,CAAA;AACJ,CAAC;AAND,gEAMC"}
\ No newline at end of file

build/commons/extractWeightEntryFactory.d.ts

+import { ParamMapping } from './types';
+export declare function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]): <T>(originalPath: string, paramRank: number, mappedPath?: string | undefined) => T;

build/commons/extractWeightEntryFactory.js

+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var isTensor_1 = require("./isTensor");
+function extractWeightEntryFactory(weightMap, paramMappings) {
+    return function (originalPath, paramRank, mappedPath) {
+        var tensor = weightMap[originalPath];
+        if (!isTensor_1.isTensor(tensor, paramRank)) {
+            throw new Error("expected weightMap[" + originalPath + "] to be a Tensor" + paramRank + "D, instead have " + tensor);
+        }
+        paramMappings.push({ originalPath: originalPath, paramPath: mappedPath || originalPath });
+        return tensor;
+    };
+}
+exports.extractWeightEntryFactory = extractWeightEntryFactory;
+//# sourceMappingURL=extractWeightEntryFactory.js.map
\ No newline at end of file

build/commons/extractWeightEntryFactory.js.map

+{"version":3,"file":"extractWeightEntryFactory.js","sourceRoot":"","sources":["../../src/commons/extractWeightEntryFactory.ts"],"names":[],"mappings":";;AAAA,uCAAsC;AAGtC,mCAA0C,SAAc,EAAE,aAA6B;IAErF,OAAO,UAAa,YAAoB,EAAE,SAAiB,EAAE,UAAmB;QAC9E,IAAM,MAAM,GAAG,SAAS,CAAC,YAAY,CAAC,CAAA;QAEtC,IAAI,CAAC,mBAAQ,CAAC,MAAM,EAAE,SAAS,CAAC,EAAE;YAChC,MAAM,IAAI,KAAK,CAAC,wBAAsB,YAAY,wBAAmB,SAAS,wBAAmB,MAAQ,CAAC,CAAA;SAC3G;QAED,aAAa,CAAC,IAAI,CAChB,EAAE,YAAY,cAAA,EAAE,SAAS,EAAE,UAAU,IAAI,YAAY,EAAE,CACxD,CAAA;QAED,OAAO,MAAM,CAAA;IACf,CAAC,CAAA;AAEH,CAAC;AAhBD,8DAgBC"}
\ No newline at end of file

build/faceDetectionNet/FaceDetectionNet.d.ts

 import * as tf from '@tensorflow/tfjs-core';
+import { NeuralNetwork } from '../commons/NeuralNetwork';
 import { NetInput } from '../NetInput';
 import { TNetInput } from '../types';
 import { FaceDetection } from './FaceDetection';
-export declare class FaceDetectionNet {
-    private _params;
-    load(weightsOrUrl?: Float32Array | string): Promise<void>;
-    extractWeights(weights: Float32Array): void;
+import { NetParams } from './types';
+export declare class FaceDetectionNet extends NeuralNetwork<NetParams> {
+    constructor();
     forwardInput(input: NetInput): {
         boxes: tf.Tensor<tf.Rank.R2>[];
         scores: tf.Tensor<tf.Rank.R1>[];
@@ -15,4 +15,18 @@ export declare class FaceDetectionNet {
         scores: tf.Tensor<tf.Rank.R1>[];
     }>;
     locateFaces(input: TNetInput, minConfidence?: number, maxResults?: number): Promise<FaceDetection[]>;
+    protected loadQuantizedParams(uri: string | undefined): Promise<{
+        params: NetParams;
+        paramMappings: {
+            originalPath?: string | undefined;
+            paramPath: string;
+        }[];
+    }>;
+    protected extractParams(weights: Float32Array): {
+        params: NetParams;
+        paramMappings: {
+            originalPath?: string | undefined;
+            paramPath: string;
+        }[];
+    };
 }

build/faceDetectionNet/FaceDetectionNet.js

@@ -2,6 +2,7 @@
 Object.defineProperty(exports, "__esModule", { value: true });
 var tslib_1 = require("tslib");
 var tf = require("@tensorflow/tfjs-core");
+var NeuralNetwork_1 = require("../commons/NeuralNetwork");
 var Rect_1 = require("../Rect");
 var toNetInput_1 = require("../toNetInput");
 var extractParams_1 = require("./extractParams");
@@ -11,45 +12,22 @@ var mobileNetV1_1 = require("./mobileNetV1");
 var nonMaxSuppression_1 = require("./nonMaxSuppression");
 var outputLayer_1 = require("./outputLayer");
 var predictionLayer_1 = require("./predictionLayer");
-var FaceDetectionNet = /** @class */ (function () {
+var FaceDetectionNet = /** @class */ (function (_super) {
+    tslib_1.__extends(FaceDetectionNet, _super);
     function FaceDetectionNet() {
+        return _super.call(this, 'FaceDetectionNet') || this;
     }
-    FaceDetectionNet.prototype.load = function (weightsOrUrl) {
-        return tslib_1.__awaiter(this, void 0, void 0, function () {
-            var _a;
-            return tslib_1.__generator(this, function (_b) {
-                switch (_b.label) {
-                    case 0:
-                        if (weightsOrUrl instanceof Float32Array) {
-                            this.extractWeights(weightsOrUrl);
-                            return [2 /*return*/];
-                        }
-                        if (weightsOrUrl && typeof weightsOrUrl !== 'string') {
-                            throw new Error('FaceDetectionNet.load - expected model uri, or weights as Float32Array');
-                        }
-                        _a = this;
-                        return [4 /*yield*/, loadQuantizedParams_1.loadQuantizedParams(weightsOrUrl)];
-                    case 1:
-                        _a._params = _b.sent();
-                        return [2 /*return*/];
-                }
-            });
-        });
-    };
-    FaceDetectionNet.prototype.extractWeights = function (weights) {
-        this._params = extractParams_1.extractParams(weights);
-    };
     FaceDetectionNet.prototype.forwardInput = function (input) {
-        var _this = this;
-        if (!this._params) {
+        var params = this.params;
+        if (!params) {
             throw new Error('FaceDetectionNet - load model before inference');
         }
         return tf.tidy(function () {
             var batchTensor = input.toBatchTensor(512, false);
             var x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1));
-            var features = mobileNetV1_1.mobileNetV1(x, _this._params.mobilenetv1_params);
-            var _a = predictionLayer_1.predictionLayer(features.out, features.conv11, _this._params.prediction_layer_params), boxPredictions = _a.boxPredictions, classPredictions = _a.classPredictions;
-            return outputLayer_1.outputLayer(boxPredictions, classPredictions, _this._params.output_layer_params);
+            var features = mobileNetV1_1.mobileNetV1(x, params.mobilenetv1);
+            var _a = predictionLayer_1.predictionLayer(features.out, features.conv11, params.prediction_layer), boxPredictions = _a.boxPredictions, classPredictions = _a.classPredictions;
+            return outputLayer_1.outputLayer(boxPredictions, classPredictions, params.output_layer);
         });
     };
     FaceDetectionNet.prototype.forward = function (input) {
@@ -112,7 +90,13 @@ var FaceDetectionNet = /** @class */ (function () {
             });
         });
     };
+    FaceDetectionNet.prototype.loadQuantizedParams = function (uri) {
+        return loadQuantizedParams_1.loadQuantizedParams(uri);
+    };
+    FaceDetectionNet.prototype.extractParams = function (weights) {
+        return extractParams_1.extractParams(weights);
+    };
     return FaceDetectionNet;
-}());
+}(NeuralNetwork_1.NeuralNetwork));
 exports.FaceDetectionNet = FaceDetectionNet;
 //# sourceMappingURL=FaceDetectionNet.js.map
\ No newline at end of file

build/faceDetectionNet/FaceDetectionNet.js.map

-{"version":3,"file":"FaceDetectionNet.js","sourceRoot":"","sources":["../../src/faceDetectionNet/FaceDetectionNet.ts"],"names":[],"mappings":";;;AAAA,0CAA4C;AAG5C,gCAA+B;AAC/B,4CAA2C;AAE3C,iDAAgD;AAChD,iDAAgD;AAChD,6DAA4D;AAC5D,6CAA4C;AAC5C,yDAAwD;AACxD,6CAA4C;AAC5C,qDAAoD;AAGpD;IAAA;IAgHA,CAAC;IA5Gc,+BAAI,GAAjB,UAAkB,YAAoC;;;;;;wBACpD,IAAI,YAAY,YAAY,YAAY,EAAE;4BACxC,IAAI,CAAC,cAAc,CAAC,YAAY,CAAC,CAAA;4BACjC,sBAAM;yBACP;wBAED,IAAI,YAAY,IAAI,OAAO,YAAY,KAAK,QAAQ,EAAE;4BACpD,MAAM,IAAI,KAAK,CAAC,wEAAwE,CAAC,CAAA;yBAC1F;wBACD,KAAA,IAAI,CAAA;wBAAW,qBAAM,yCAAmB,CAAC,YAAY,CAAC,EAAA;;wBAAtD,GAAK,OAAO,GAAG,SAAuC,CAAA;;;;;KACvD;IAEM,yCAAc,GAArB,UAAsB,OAAqB;QACzC,IAAI,CAAC,OAAO,GAAG,6BAAa,CAAC,OAAO,CAAC,CAAA;IACvC,CAAC;IAEM,uCAAY,GAAnB,UAAoB,KAAe;QAAnC,iBAkBC;QAjBC,IAAI,CAAC,IAAI,CAAC,OAAO,EAAE;YACjB,MAAM,IAAI,KAAK,CAAC,gDAAgD,CAAC,CAAA;SAClE;QAED,OAAO,EAAE,CAAC,IAAI,CAAC;YACb,IAAM,WAAW,GAAG,KAAK,CAAC,aAAa,CAAC,GAAG,EAAE,KAAK,CAAC,CAAA;YAEnD,IAAM,CAAC,GAAG,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,WAAW,EAAE,EAAE,CAAC,MAAM,CAAC,oBAAoB,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAgB,CAAA;YACnG,IAAM,QAAQ,GAAG,yBAAW,CAAC,CAAC,EAAE,KAAI,CAAC,OAAO,CAAC,kBAAkB,CAAC,CAAA;YAE1D,IAAA,4GAGkF,EAFtF,kCAAc,EACd,sCAAgB,CACsE;YAExF,OAAO,yBAAW,CAAC,cAAc,EAAE,gBAAgB,EAAE,KAAI,CAAC,OAAO,CAAC,mBAAmB,CAAC,CAAA;QACxF,CAAC,CAAC,CAAA;IACJ,CAAC;IAEY,kCAAO,GAApB,UAAqB,KAAgB;;;;;;wBAC5B,KAAA,IAAI,CAAC,YAAY,CAAA;wBAAC,qBAAM,uBAAU,CAAC,KAAK,EAAE,IAAI,CAAC,EAAA;4BAAtD,sBAAO,SAAA,IAAI,GAAc,SAA6B,EAAC,EAAA;;;;KACxD;IAEY,sCAAW,GAAxB,UACE,KAAgB,EAChB,aAA2B,EAC3B,UAAwB;QADxB,8BAAA,EAAA,mBAA2B;QAC3B,2BAAA,EAAA,gBAAwB;;;;;4BAGP,qBAAM,uBAAU,CAAC,KAAK,EAAE,IAAI,CAAC,EAAA;;wBAAxC,QAAQ,GAAG,SAA6B;wBAExC,KAGF,IAAI,CAAC,YAAY,CAAC,QAAQ,CAAC,EAFtB,MAAM,WAAA,EACL,OAAO,YAAA,CACc;wBAIzB,KAAK,GAAG,MAAM,CAAC,CAAC,CAAC,CAAA;wBACjB,MAAM,GAAG,OAAO,CAAC,CAAC,CAAC,CAAA;wBACzB,KAAS,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,MAAM,CAAC,MAAM,EAAE,CAAC,EAAE,EAAE;4BACtC,MAAM,CAAC,CAAC,CAAC,CAAC,OAAO,EAAE,CAAA;4BACnB,OAAO,CAAC,CAAC,CAAC,CAAC,OAAO,EAAE,CAAA;yBACrB;wBAGkB,KAAA,CAAA,KAAA,KAAK,CAAA,CAAC,IAAI,CAAA;wBAAC,qBAAM,MAAM,CAAC,IAAI,EAAE,EAAA;;wBAA3C,UAAU,GAAG,cAAW,SAAmB,EAAC;wBAE5C,YAAY,GAAG,GAAG,CAAA;wBAClB,OAAO,GAAG,qCAAiB,CAC/B,KAAK,EACL,UAAU,EACV,UAAU,EACV,YAAY,EACZ,aAAa,CACd,CAAA;wBAGK,oBAAoB,GAAG,CAAC,QAAQ,CAAC,WAAW,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,QAAQ,CAAC,cAAc,CAAC,CAAC,CAAC,CAAC,GAAG,QAAQ,CAAC,cAAc,CAAC,CAAC,CAAC,CAAA;wBAC5G,mBAAmB,GAAG,CAAC,QAAQ,CAAC,WAAW,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,QAAQ,CAAC,aAAa,CAAC,CAAC,CAAC,CAAC,GAAG,QAAQ,CAAC,aAAa,CAAC,CAAC,CAAC,CAAA;wBAEzG,OAAO,GAAG,OAAO;6BACpB,GAAG,CAAC,UAAA,GAAG;4BACA,IAAA;;;wFAGkC,EAHjC,WAAG,EAAE,cAAM,CAGsB;4BAClC,IAAA;;;uFAGiC,EAHhC,YAAI,EAAE,aAAK,CAGqB;4BACvC,OAAO,IAAI,6BAAa,CACtB,UAAU,CAAC,GAAG,CAAC,EACf,IAAI,WAAI,CACN,IAAI,EACJ,GAAG,EACH,KAAK,GAAG,IAAI,EACZ,MAAM,GAAG,GAAG,CACb,EACD;gCACE,MAAM,EAAE,QAAQ,CAAC,cAAc,CAAC,CAAC,CAAC;gCAClC,KAAK,EAAE,QAAQ,CAAC,aAAa,CAAC,CAAC,CAAC;6BACjC,CACF,CAAA;wBACH,CAAC,CAAC,CAAA;wBAEJ,KAAK,CAAC,OAAO,EAAE,CAAA;wBACf,MAAM,CAAC,OAAO,EAAE,CAAA;wBAEhB,sBAAO,OAAO,EAAA;;;;KACf;IACH,uBAAC;AAAD,CAAC,AAhHD,IAgHC;AAhHY,4CAAgB"}
\ No newline at end of file
+{"version":3,"file":"FaceDetectionNet.js","sourceRoot":"","sources":["../../src/faceDetectionNet/FaceDetectionNet.ts"],"names":[],"mappings":";;;AAAA,0CAA4C;AAE5C,0DAAyD;AAEzD,gCAA+B;AAC/B,4CAA2C;AAE3C,iDAAgD;AAChD,iDAAgD;AAChD,6DAA4D;AAC5D,6CAA4C;AAC5C,yDAAwD;AACxD,6CAA4C;AAC5C,qDAAoD;AAGpD;IAAsC,4CAAwB;IAE5D;eACE,kBAAM,kBAAkB,CAAC;IAC3B,CAAC;IAEM,uCAAY,GAAnB,UAAoB,KAAe;QAEzB,IAAA,oBAAM,CAAS;QAEvB,IAAI,CAAC,MAAM,EAAE;YACX,MAAM,IAAI,KAAK,CAAC,gDAAgD,CAAC,CAAA;SAClE;QAED,OAAO,EAAE,CAAC,IAAI,CAAC;YACb,IAAM,WAAW,GAAG,KAAK,CAAC,aAAa,CAAC,GAAG,EAAE,KAAK,CAAC,CAAA;YAEnD,IAAM,CAAC,GAAG,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,WAAW,EAAE,EAAE,CAAC,MAAM,CAAC,oBAAoB,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAgB,CAAA;YACnG,IAAM,QAAQ,GAAG,yBAAW,CAAC,CAAC,EAAE,MAAM,CAAC,WAAW,CAAC,CAAA;YAE7C,IAAA,8FAGqE,EAFzE,kCAAc,EACd,sCAAgB,CACyD;YAE3E,OAAO,yBAAW,CAAC,cAAc,EAAE,gBAAgB,EAAE,MAAM,CAAC,YAAY,CAAC,CAAA;QAC3E,CAAC,CAAC,CAAA;IACJ,CAAC;IAEY,kCAAO,GAApB,UAAqB,KAAgB;;;;;;wBAC5B,KAAA,IAAI,CAAC,YAAY,CAAA;wBAAC,qBAAM,uBAAU,CAAC,KAAK,EAAE,IAAI,CAAC,EAAA;4BAAtD,sBAAO,SAAA,IAAI,GAAc,SAA6B,EAAC,EAAA;;;;KACxD;IAEY,sCAAW,GAAxB,UACE,KAAgB,EAChB,aAA2B,EAC3B,UAAwB;QADxB,8BAAA,EAAA,mBAA2B;QAC3B,2BAAA,EAAA,gBAAwB;;;;;4BAGP,qBAAM,uBAAU,CAAC,KAAK,EAAE,IAAI,CAAC,EAAA;;wBAAxC,QAAQ,GAAG,SAA6B;wBAExC,KAGF,IAAI,CAAC,YAAY,CAAC,QAAQ,CAAC,EAFtB,MAAM,WAAA,EACL,OAAO,YAAA,CACc;wBAIzB,KAAK,GAAG,MAAM,CAAC,CAAC,CAAC,CAAA;wBACjB,MAAM,GAAG,OAAO,CAAC,CAAC,CAAC,CAAA;wBACzB,KAAS,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,MAAM,CAAC,MAAM,EAAE,CAAC,EAAE,EAAE;4BACtC,MAAM,CAAC,CAAC,CAAC,CAAC,OAAO,EAAE,CAAA;4BACnB,OAAO,CAAC,CAAC,CAAC,CAAC,OAAO,EAAE,CAAA;yBACrB;wBAGkB,KAAA,CAAA,KAAA,KAAK,CAAA,CAAC,IAAI,CAAA;wBAAC,qBAAM,MAAM,CAAC,IAAI,EAAE,EAAA;;wBAA3C,UAAU,GAAG,cAAW,SAAmB,EAAC;wBAE5C,YAAY,GAAG,GAAG,CAAA;wBAClB,OAAO,GAAG,qCAAiB,CAC/B,KAAK,EACL,UAAU,EACV,UAAU,EACV,YAAY,EACZ,aAAa,CACd,CAAA;wBAEK,oBAAoB,GAAG,CAAC,QAAQ,CAAC,WAAW,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,QAAQ,CAAC,cAAc,CAAC,CAAC,CAAC,CAAC,GAAG,QAAQ,CAAC,cAAc,CAAC,CAAC,CAAC,CAAA;wBAC5G,mBAAmB,GAAG,CAAC,QAAQ,CAAC,WAAW,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,QAAQ,CAAC,aAAa,CAAC,CAAC,CAAC,CAAC,GAAG,QAAQ,CAAC,aAAa,CAAC,CAAC,CAAC,CAAA;wBAEzG,OAAO,GAAG,OAAO;6BACpB,GAAG,CAAC,UAAA,GAAG;4BACA,IAAA;;;wFAGkC,EAHjC,WAAG,EAAE,cAAM,CAGsB;4BAClC,IAAA;;;uFAGiC,EAHhC,YAAI,EAAE,aAAK,CAGqB;4BACvC,OAAO,IAAI,6BAAa,CACtB,UAAU,CAAC,GAAG,CAAC,EACf,IAAI,WAAI,CACN,IAAI,EACJ,GAAG,EACH,KAAK,GAAG,IAAI,EACZ,MAAM,GAAG,GAAG,CACb,EACD;gCACE,MAAM,EAAE,QAAQ,CAAC,cAAc,CAAC,CAAC,CAAC;gCAClC,KAAK,EAAE,QAAQ,CAAC,aAAa,CAAC,CAAC,CAAC;6BACjC,CACF,CAAA;wBACH,CAAC,CAAC,CAAA;wBAEJ,KAAK,CAAC,OAAO,EAAE,CAAA;wBACf,MAAM,CAAC,OAAO,EAAE,CAAA;wBAEhB,sBAAO,OAAO,EAAA;;;;KACf;IAES,8CAAmB,GAA7B,UAA8B,GAAuB;QACnD,OAAO,yCAAmB,CAAC,GAAG,CAAC,CAAA;IACjC,CAAC;IAES,wCAAa,GAAvB,UAAwB,OAAqB;QAC3C,OAAO,6BAAa,CAAC,OAAO,CAAC,CAAA;IAC/B,CAAC;IACH,uBAAC;AAAD,CAAC,AA5GD,CAAsC,6BAAa,GA4GlD;AA5GY,4CAAgB"}
\ No newline at end of file

build/faceDetectionNet/boxPredictionLayer.js

@@ -5,8 +5,8 @@ var convLayer_1 = require("../commons/convLayer");
 function boxPredictionLayer(x, params) {
     return tf.tidy(function () {
         var batchSize = x.shape[0];
-        var boxPredictionEncoding = tf.reshape(convLayer_1.convLayer(x, params.box_encoding_predictor_params), [batchSize, -1, 1, 4]);
-        var classPrediction = tf.reshape(convLayer_1.convLayer(x, params.class_predictor_params), [batchSize, -1, 3]);
+        var boxPredictionEncoding = tf.reshape(convLayer_1.convLayer(x, params.box_encoding_predictor), [batchSize, -1, 1, 4]);
+        var classPrediction = tf.reshape(convLayer_1.convLayer(x, params.class_predictor), [batchSize, -1, 3]);
         return {
             boxPredictionEncoding: boxPredictionEncoding,
             classPrediction: classPrediction

build/faceDetectionNet/boxPredictionLayer.js.map

-{"version":3,"file":"boxPredictionLayer.js","sourceRoot":"","sources":["../../src/faceDetectionNet/boxPredictionLayer.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAE5C,kDAAiD;AAIjD,4BACE,CAAc,EACd,MAA2B;IAE3B,OAAO,EAAE,CAAC,IAAI,CAAC;QAEb,IAAM,SAAS,GAAG,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAA;QAE5B,IAAM,qBAAqB,GAAG,EAAE,CAAC,OAAO,CACtC,qBAAS,CAAC,CAAC,EAAE,MAAM,CAAC,6BAA6B,CAAC,EAClD,CAAC,SAAS,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CACtB,CAAA;QACD,IAAM,eAAe,GAAG,EAAE,CAAC,OAAO,CAChC,qBAAS,CAAC,CAAC,EAAE,MAAM,CAAC,sBAAsB,CAAC,EAC3C,CAAC,SAAS,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CACnB,CAAA;QAED,OAAO;YACL,qBAAqB,uBAAA;YACrB,eAAe,iBAAA;SAChB,CAAA;IACH,CAAC,CAAC,CAAA;AACJ,CAAC;AAtBD,gDAsBC"}
\ No newline at end of file
+{"version":3,"file":"boxPredictionLayer.js","sourceRoot":"","sources":["../../src/faceDetectionNet/boxPredictionLayer.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAE5C,kDAAiD;AAIjD,4BACE,CAAc,EACd,MAA2B;IAE3B,OAAO,EAAE,CAAC,IAAI,CAAC;QAEb,IAAM,SAAS,GAAG,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAA;QAE5B,IAAM,qBAAqB,GAAG,EAAE,CAAC,OAAO,CACtC,qBAAS,CAAC,CAAC,EAAE,MAAM,CAAC,sBAAsB,CAAC,EAC3C,CAAC,SAAS,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CACtB,CAAA;QACD,IAAM,eAAe,GAAG,EAAE,CAAC,OAAO,CAChC,qBAAS,CAAC,CAAC,EAAE,MAAM,CAAC,eAAe,CAAC,EACpC,CAAC,SAAS,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CACnB,CAAA;QAED,OAAO;YACL,qBAAqB,uBAAA;YACrB,eAAe,iBAAA;SAChB,CAAA;IACH,CAAC,CAAC,CAAA;AACJ,CAAC;AAtBD,gDAsBC"}
\ No newline at end of file

build/faceDetectionNet/extractParams.d.ts

+import { ParamMapping } from '../commons/types';
 import { NetParams } from './types';
-export declare function extractParams(weights: Float32Array): NetParams;
+export declare function extractParams(weights: Float32Array): {
+    params: NetParams;
+    paramMappings: ParamMapping[];
+};

build/faceDetectionNet/extractParams.js

@@ -2,13 +2,14 @@
 Object.defineProperty(exports, "__esModule", { value: true });
 var tf = require("@tensorflow/tfjs-core");
 var extractWeightsFactory_1 = require("../commons/extractWeightsFactory");
-function extractorsFactory(extractWeights) {
-    function extractDepthwiseConvParams(numChannels) {
+function extractorsFactory(extractWeights, paramMappings) {
+    function extractDepthwiseConvParams(numChannels, mappedPrefix) {
         var filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);
         var batch_norm_scale = tf.tensor1d(extractWeights(numChannels));
         var batch_norm_offset = tf.tensor1d(extractWeights(numChannels));
         var batch_norm_mean = tf.tensor1d(extractWeights(numChannels));
         var batch_norm_variance = tf.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: filters,
             batch_norm_scale: batch_norm_scale,
@@ -17,115 +18,116 @@ function extractorsFactory(extractWeights) {
             batch_norm_variance: batch_norm_variance
         };
     }
-    function extractConvParams(channelsIn, channelsOut, filterSize) {
+    function extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, isPointwiseConv) {
         var filters = tf.tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
         var bias = tf.tensor1d(extractWeights(channelsOut));
-        return {
-            filters: filters,
-            bias: bias
-        };
+        paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/" + (isPointwiseConv ? 'batch_norm_offset' : 'bias') });
+        return { filters: filters, bias: bias };
     }
-    function extractPointwiseConvParams(channelsIn, channelsOut, filterSize) {
-        var _a = extractConvParams(channelsIn, channelsOut, filterSize), filters = _a.filters, bias = _a.bias;
+    function extractPointwiseConvParams(channelsIn, channelsOut, filterSize, mappedPrefix) {
+        var _a = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true), filters = _a.filters, bias = _a.bias;
         return {
             filters: filters,
             batch_norm_offset: bias
         };
     }
-    function extractConvPairParams(channelsIn, channelsOut) {
-        var depthwise_conv_params = extractDepthwiseConvParams(channelsIn);
-        var pointwise_conv_params = extractPointwiseConvParams(channelsIn, channelsOut, 1);
-        return {
-            depthwise_conv_params: depthwise_conv_params,
-            pointwise_conv_params: pointwise_conv_params
-        };
+    function extractConvPairParams(channelsIn, channelsOut, mappedPrefix) {
+        var depthwise_conv = extractDepthwiseConvParams(channelsIn, mappedPrefix + "/depthwise_conv");
+        var pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, mappedPrefix + "/pointwise_conv");
+        return { depthwise_conv: depthwise_conv, pointwise_conv: pointwise_conv };
     }
     function extractMobilenetV1Params() {
-        var conv_0_params = extractPointwiseConvParams(3, 32, 3);
-        var channelNumPairs = [
-            [32, 64],
-            [64, 128],
-            [128, 128],
-            [128, 256],
-            [256, 256],
-            [256, 512],
-            [512, 512],
-            [512, 512],
-            [512, 512],
-            [512, 512],
-            [512, 512],
-            [512, 1024],
-            [1024, 1024]
-        ];
-        var conv_pair_params = channelNumPairs.map(function (_a) {
-            var channelsIn = _a[0], channelsOut = _a[1];
-            return extractConvPairParams(channelsIn, channelsOut);
-        });
+        var conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0');
+        var conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1');
+        var conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2');
+        var conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3');
+        var conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4');
+        var conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5');
+        var conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6');
+        var conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7');
+        var conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8');
+        var conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9');
+        var conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10');
+        var conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11');
+        var conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12');
+        var conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13');
         return {
-            conv_0_params: conv_0_params,
-            conv_pair_params: conv_pair_params
+            conv_0: conv_0,
+            conv_1: conv_1,
+            conv_2: conv_2,
+            conv_3: conv_3,
+            conv_4: conv_4,
+            conv_5: conv_5,
+            conv_6: conv_6,
+            conv_7: conv_7,
+            conv_8: conv_8,
+            conv_9: conv_9,
+            conv_10: conv_10,
+            conv_11: conv_11,
+            conv_12: conv_12,
+            conv_13: conv_13
         };
     }
     function extractPredictionLayerParams() {
-        var conv_0_params = extractPointwiseConvParams(1024, 256, 1);
-        var conv_1_params = extractPointwiseConvParams(256, 512, 3);
-        var conv_2_params = extractPointwiseConvParams(512, 128, 1);
-        var conv_3_params = extractPointwiseConvParams(128, 256, 3);
-        var conv_4_params = extractPointwiseConvParams(256, 128, 1);
-        var conv_5_params = extractPointwiseConvParams(128, 256, 3);
-        var conv_6_params = extractPointwiseConvParams(256, 64, 1);
-        var conv_7_params = extractPointwiseConvParams(64, 128, 3);
-        var box_encoding_0_predictor_params = extractConvParams(512, 12, 1);
-        var class_predictor_0_params = extractConvParams(512, 9, 1);
-        var box_encoding_1_predictor_params = extractConvParams(1024, 24, 1);
-        var class_predictor_1_params = extractConvParams(1024, 18, 1);
-        var box_encoding_2_predictor_params = extractConvParams(512, 24, 1);
-        var class_predictor_2_params = extractConvParams(512, 18, 1);
-        var box_encoding_3_predictor_params = extractConvParams(256, 24, 1);
-        var class_predictor_3_params = extractConvParams(256, 18, 1);
-        var box_encoding_4_predictor_params = extractConvParams(256, 24, 1);
-        var class_predictor_4_params = extractConvParams(256, 18, 1);
-        var box_encoding_5_predictor_params = extractConvParams(128, 24, 1);
-        var class_predictor_5_params = extractConvParams(128, 18, 1);
-        var box_predictor_0_params = {
-            box_encoding_predictor_params: box_encoding_0_predictor_params,
-            class_predictor_params: class_predictor_0_params
+        var conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0');
+        var conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1');
+        var conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2');
+        var conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3');
+        var conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4');
+        var conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5');
+        var conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6');
+        var conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7');
+        var box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor');
+        var class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor');
+        var box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor');
+        var class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor');
+        var box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor');
+        var class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor');
+        var box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor');
+        var class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor');
+        var box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor');
+        var class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor');
+        var box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor');
+        var class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor');
+        var box_predictor_0 = {
+            box_encoding_predictor: box_encoding_0_predictor,
+            class_predictor: class_predictor_0
         };
-        var box_predictor_1_params = {
-            box_encoding_predictor_params: box_encoding_1_predictor_params,
-            class_predictor_params: class_predictor_1_params
+        var box_predictor_1 = {
+            box_encoding_predictor: box_encoding_1_predictor,
+            class_predictor: class_predictor_1
         };
-        var box_predictor_2_params = {
-            box_encoding_predictor_params: box_encoding_2_predictor_params,
-            class_predictor_params: class_predictor_2_params
+        var box_predictor_2 = {
+            box_encoding_predictor: box_encoding_2_predictor,
+            class_predictor: class_predictor_2
         };
-        var box_predictor_3_params = {
-            box_encoding_predictor_params: box_encoding_3_predictor_params,
-            class_predictor_params: class_predictor_3_params
+        var box_predictor_3 = {
+            box_encoding_predictor: box_encoding_3_predictor,
+            class_predictor: class_predictor_3
         };
-        var box_predictor_4_params = {
-            box_encoding_predictor_params: box_encoding_4_predictor_params,
-            class_predictor_params: class_predictor_4_params
+        var box_predictor_4 = {
+            box_encoding_predictor: box_encoding_4_predictor,
+            class_predictor: class_predictor_4
         };
-        var box_predictor_5_params = {
-            box_encoding_predictor_params: box_encoding_5_predictor_params,
-            class_predictor_params: class_predictor_5_params
+        var box_predictor_5 = {
+            box_encoding_predictor: box_encoding_5_predictor,
+            class_predictor: class_predictor_5
         };
         return {
-            conv_0_params: conv_0_params,
-            conv_1_params: conv_1_params,
-            conv_2_params: conv_2_params,
-            conv_3_params: conv_3_params,
-            conv_4_params: conv_4_params,
-            conv_5_params: conv_5_params,
-            conv_6_params: conv_6_params,
-            conv_7_params: conv_7_params,
-            box_predictor_0_params: box_predictor_0_params,
-            box_predictor_1_params: box_predictor_1_params,
-            box_predictor_2_params: box_predictor_2_params,
-            box_predictor_3_params: box_predictor_3_params,
-            box_predictor_4_params: box_predictor_4_params,
-            box_predictor_5_params: box_predictor_5_params
+            conv_0: conv_0,
+            conv_1: conv_1,
+            conv_2: conv_2,
+            conv_3: conv_3,
+            conv_4: conv_4,
+            conv_5: conv_5,
+            conv_6: conv_6,
+            conv_7: conv_7,
+            box_predictor_0: box_predictor_0,
+            box_predictor_1: box_predictor_1,
+            box_predictor_2: box_predictor_2,
+            box_predictor_3: box_predictor_3,
+            box_predictor_4: box_predictor_4,
+            box_predictor_5: box_predictor_5
         };
     }
     return {
@@ -134,21 +136,26 @@ function extractorsFactory(extractWeights) {
     };
 }
 function extractParams(weights) {
+    var paramMappings = [];
     var _a = extractWeightsFactory_1.extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
-    var _b = extractorsFactory(extractWeights), extractMobilenetV1Params = _b.extractMobilenetV1Params, extractPredictionLayerParams = _b.extractPredictionLayerParams;
-    var mobilenetv1_params = extractMobilenetV1Params();
-    var prediction_layer_params = extractPredictionLayerParams();
+    var _b = extractorsFactory(extractWeights, paramMappings), extractMobilenetV1Params = _b.extractMobilenetV1Params, extractPredictionLayerParams = _b.extractPredictionLayerParams;
+    var mobilenetv1 = extractMobilenetV1Params();
+    var prediction_layer = extractPredictionLayerParams();
     var extra_dim = tf.tensor3d(extractWeights(5118 * 4), [1, 5118, 4]);
-    var output_layer_params = {
+    var output_layer = {
         extra_dim: extra_dim
     };
+    paramMappings.push({ paramPath: 'output_layer/extra_dim' });
     if (getRemainingWeights().length !== 0) {
         throw new Error("weights remaing after extract: " + getRemainingWeights().length);
     }
     return {
-        mobilenetv1_params: mobilenetv1_params,
-        prediction_layer_params: prediction_layer_params,
-        output_layer_params: output_layer_params
+        params: {
+            mobilenetv1: mobilenetv1,
+            prediction_layer: prediction_layer,
+            output_layer: output_layer
+        },
+        paramMappings: paramMappings
     };
 }
 exports.extractParams = extractParams;

build/faceDetectionNet/extractParams.js.map

-{"version":3,"file":"extractParams.js","sourceRoot":"","sources":["../../src/faceDetectionNet/extractParams.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAE5C,0EAAyE;AAIzE,2BAA2B,cAAoD;IAE7E,oCAAoC,WAAmB;QACrD,IAAM,OAAO,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,CAAC,GAAG,CAAC,GAAG,WAAW,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,WAAW,EAAE,CAAC,CAAC,CAAC,CAAA;QACxF,IAAM,gBAAgB,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,WAAW,CAAC,CAAC,CAAA;QACjE,IAAM,iBAAiB,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,WAAW,CAAC,CAAC,CAAA;QAClE,IAAM,eAAe,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,WAAW,CAAC,CAAC,CAAA;QAChE,IAAM,mBAAmB,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,WAAW,CAAC,CAAC,CAAA;QAEpE,OAAO;YACL,OAAO,SAAA;YACP,gBAAgB,kBAAA;YAChB,iBAAiB,mBAAA;YACjB,eAAe,iBAAA;YACf,mBAAmB,qBAAA;SACpB,CAAA;IACH,CAAC;IAED,2BACE,UAAkB,EAClB,WAAmB,EACnB,UAAkB;QAElB,IAAM,OAAO,GAAG,EAAE,CAAC,QAAQ,CACzB,cAAc,CAAC,UAAU,GAAG,WAAW,GAAG,UAAU,GAAG,UAAU,CAAC,EAClE,CAAC,UAAU,EAAE,UAAU,EAAE,UAAU,EAAE,WAAW,CAAC,CAClD,CAAA;QACD,IAAM,IAAI,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,WAAW,CAAC,CAAC,CAAA;QAErD,OAAO;YACL,OAAO,SAAA;YACP,IAAI,MAAA;SACL,CAAA;IACH,CAAC;IAED,oCACE,UAAkB,EAClB,WAAmB,EACnB,UAAkB;QAEZ,IAAA,2DAGoD,EAFxD,oBAAO,EACP,cAAI,CACoD;QAE1D,OAAO;YACL,OAAO,SAAA;YACP,iBAAiB,EAAE,IAAI;SACxB,CAAA;IACH,CAAC;IAED,+BACE,UAAkB,EAClB,WAAmB;QAEnB,IAAM,qBAAqB,GAAG,0BAA0B,CAAC,UAAU,CAAC,CAAA;QACpE,IAAM,qBAAqB,GAAG,0BAA0B,CAAC,UAAU,EAAE,WAAW,EAAE,CAAC,CAAC,CAAA;QAEpF,OAAO;YACL,qBAAqB,uBAAA;YACrB,qBAAqB,uBAAA;SACtB,CAAA;IACH,CAAC;IAED;QAEE,IAAM,aAAa,GAAG,0BAA0B,CAAC,CAAC,EAAE,EAAE,EAAE,CAAC,CAAC,CAAA;QAE1D,IAAM,eAAe,GAAG;YACtB,CAAC,EAAE,EAAE,EAAE,CAAC;YACR,CAAC,EAAE,EAAE,GAAG,CAAC;YACT,CAAC,GAAG,EAAE,GAAG,CAAC;YACV,CAAC,GAAG,EAAE,GAAG,CAAC;YACV,CAAC,GAAG,EAAE,GAAG,CAAC;YACV,CAAC,GAAG,EAAE,GAAG,CAAC;YACV,CAAC,GAAG,EAAE,GAAG,CAAC;YACV,CAAC,GAAG,EAAE,GAAG,CAAC;YACV,CAAC,GAAG,EAAE,GAAG,CAAC;YACV,CAAC,GAAG,EAAE,GAAG,CAAC;YACV,CAAC,GAAG,EAAE,GAAG,CAAC;YACV,CAAC,GAAG,EAAE,IAAI,CAAC;YACX,CAAC,IAAI,EAAE,IAAI,CAAC;SACb,CAAA;QAED,IAAM,gBAAgB,GAAG,eAAe,CAAC,GAAG,CAC1C,UAAC,EAAyB;gBAAxB,kBAAU,EAAE,mBAAW;YAAM,OAAA,qBAAqB,CAAC,UAAU,EAAE,WAAW,CAAC;QAA9C,CAA8C,CAC9E,CAAA;QAED,OAAO;YACL,aAAa,eAAA;YACb,gBAAgB,kBAAA;SACjB,CAAA;IAEH,CAAC;IAED;QACE,IAAM,aAAa,GAAG,0BAA0B,CAAC,IAAI,EAAE,GAAG,EAAE,CAAC,CAAC,CAAA;QAC9D,IAAM,aAAa,GAAG,0BAA0B,CAAC,GAAG,EAAE,GAAG,EAAE,CAAC,CAAC,CAAA;QAC7D,IAAM,aAAa,GAAG,0BAA0B,CAAC,GAAG,EAAE,GAAG,EAAE,CAAC,CAAC,CAAA;QAC7D,IAAM,aAAa,GAAG,0BAA0B,CAAC,GAAG,EAAE,GAAG,EAAE,CAAC,CAAC,CAAA;QAC7D,IAAM,aAAa,GAAG,0BAA0B,CAAC,GAAG,EAAE,GAAG,EAAE,CAAC,CAAC,CAAA;QAC7D,IAAM,aAAa,GAAG,0BAA0B,CAAC,GAAG,EAAE,GAAG,EAAE,CAAC,CAAC,CAAA;QAC7D,IAAM,aAAa,GAAG,0BAA0B,CAAC,GAAG,EAAE,EAAE,EAAE,CAAC,CAAC,CAAA;QAC5D,IAAM,aAAa,GAAG,0BAA0B,CAAC,EAAE,EAAE,GAAG,EAAE,CAAC,CAAC,CAAA;QAE5D,IAAM,+BAA+B,GAAG,iBAAiB,CAAC,GAAG,EAAE,EAAE,EAAE,CAAC,CAAC,CAAA;QACrE,IAAM,wBAAwB,GAAG,iBAAiB,CAAC,GAAG,EAAE,CAAC,EAAE,CAAC,CAAC,CAAA;QAC7D,IAAM,+BAA+B,GAAG,iBAAiB,CAAC,IAAI,EAAE,EAAE,EAAE,CAAC,CAAC,CAAA;QACtE,IAAM,wBAAwB,GAAG,iBAAiB,CAAC,IAAI,EAAE,EAAE,EAAE,CAAC,CAAC,CAAA;QAC/D,IAAM,+BAA+B,GAAG,iBAAiB,CAAC,GAAG,EAAE,EAAE,EAAE,CAAC,CAAC,CAAA;QACrE,IAAM,wBAAwB,GAAG,iBAAiB,CAAC,GAAG,EAAE,EAAE,EAAE,CAAC,CAAC,CAAA;QAC9D,IAAM,+BAA+B,GAAG,iBAAiB,CAAC,GAAG,EAAE,EAAE,EAAE,CAAC,CAAC,CAAA;QACrE,IAAM,wBAAwB,GAAG,iBAAiB,CAAC,GAAG,EAAE,EAAE,EAAE,CAAC,CAAC,CAAA;QAC9D,IAAM,+BAA+B,GAAG,iBAAiB,CAAC,GAAG,EAAE,EAAE,EAAE,CAAC,CAAC,CAAA;QACrE,IAAM,wBAAwB,GAAG,iBAAiB,CAAC,GAAG,EAAE,EAAE,EAAE,CAAC,CAAC,CAAA;QAC9D,IAAM,+BAA+B,GAAG,iBAAiB,CAAC,GAAG,EAAE,EAAE,EAAE,CAAC,CAAC,CAAA;QACrE,IAAM,wBAAwB,GAAG,iBAAiB,CAAC,GAAG,EAAE,EAAE,EAAE,CAAC,CAAC,CAAA;QAE9D,IAAM,sBAAsB,GAAG;YAC7B,6BAA6B,EAAE,+BAA+B;YAC9D,sBAAsB,EAAE,wBAAwB;SACjD,CAAA;QACD,IAAM,sBAAsB,GAAG;YAC7B,6BAA6B,EAAE,+BAA+B;YAC9D,sBAAsB,EAAE,wBAAwB;SACjD,CAAA;QACD,IAAM,sBAAsB,GAAG;YAC7B,6BAA6B,EAAE,+BAA+B;YAC9D,sBAAsB,EAAE,wBAAwB;SACjD,CAAA;QACD,IAAM,sBAAsB,GAAG;YAC7B,6BAA6B,EAAE,+BAA+B;YAC9D,sBAAsB,EAAE,wBAAwB;SACjD,CAAA;QACD,IAAM,sBAAsB,GAAG;YAC7B,6BAA6B,EAAE,+BAA+B;YAC9D,sBAAsB,EAAE,wBAAwB;SACjD,CAAA;QACD,IAAM,sBAAsB,GAAG;YAC7B,6BAA6B,EAAE,+BAA+B;YAC9D,sBAAsB,EAAE,wBAAwB;SACjD,CAAA;QAED,OAAO;YACL,aAAa,eAAA;YACb,aAAa,eAAA;YACb,aAAa,eAAA;YACb,aAAa,eAAA;YACb,aAAa,eAAA;YACb,aAAa,eAAA;YACb,aAAa,eAAA;YACb,aAAa,eAAA;YACb,sBAAsB,wBAAA;YACtB,sBAAsB,wBAAA;YACtB,sBAAsB,wBAAA;YACtB,sBAAsB,wBAAA;YACtB,sBAAsB,wBAAA;YACtB,sBAAsB,wBAAA;SACvB,CAAA;IACH,CAAC;IAGD,OAAO;QACL,wBAAwB,0BAAA;QACxB,4BAA4B,8BAAA;KAC7B,CAAA;AAEH,CAAC;AAED,uBAA8B,OAAqB;IAC3C,IAAA,2DAG4B,EAFhC,kCAAc,EACd,4CAAmB,CACa;IAE5B,IAAA,sCAG+B,EAFnC,sDAAwB,EACxB,8DAA4B,CACO;IAErC,IAAM,kBAAkB,GAAG,wBAAwB,EAAE,CAAA;IACrD,IAAM,uBAAuB,GAAG,4BAA4B,EAAE,CAAA;IAC9D,IAAM,SAAS,GAAG,EAAE,CAAC,QAAQ,CAC3B,cAAc,CAAC,IAAI,GAAG,CAAC,CAAC,EACxB,CAAC,CAAC,EAAE,IAAI,EAAE,CAAC,CAAC,CACb,CAAA;IACD,IAAM,mBAAmB,GAAG;QAC1B,SAAS,WAAA;KACV,CAAA;IAED,IAAI,mBAAmB,EAAE,CAAC,MAAM,KAAK,CAAC,EAAE;QACtC,MAAM,IAAI,KAAK,CAAC,oCAAkC,mBAAmB,EAAE,CAAC,MAAQ,CAAC,CAAA;KAClF;IAED,OAAO;QACL,kBAAkB,oBAAA;QAClB,uBAAuB,yBAAA;QACvB,mBAAmB,qBAAA;KACpB,CAAA;AACH,CAAC;AA9BD,sCA8BC"}
\ No newline at end of file
+{"version":3,"file":"extractParams.js","sourceRoot":"","sources":["../../src/faceDetectionNet/extractParams.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAE5C,0EAAyE;AAIzE,2BAA2B,cAAsC,EAAE,aAA6B;IAE9F,oCAAoC,WAAmB,EAAE,YAAoB;QAE3E,IAAM,OAAO,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,CAAC,GAAG,CAAC,GAAG,WAAW,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,WAAW,EAAE,CAAC,CAAC,CAAC,CAAA;QACxF,IAAM,gBAAgB,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,WAAW,CAAC,CAAC,CAAA;QACjE,IAAM,iBAAiB,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,WAAW,CAAC,CAAC,CAAA;QAClE,IAAM,eAAe,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,WAAW,CAAC,CAAC,CAAA;QAChE,IAAM,mBAAmB,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,WAAW,CAAC,CAAC,CAAA;QAEpE,aAAa,CAAC,IAAI,CAChB,EAAE,SAAS,EAAK,YAAY,aAAU,EAAE,EACxC,EAAE,SAAS,EAAK,YAAY,sBAAmB,EAAE,EACjD,EAAE,SAAS,EAAK,YAAY,uBAAoB,EAAE,EAClD,EAAE,SAAS,EAAK,YAAY,qBAAkB,EAAE,EAChD,EAAE,SAAS,EAAK,YAAY,yBAAsB,EAAE,CACrD,CAAA;QAED,OAAO;YACL,OAAO,SAAA;YACP,gBAAgB,kBAAA;YAChB,iBAAiB,mBAAA;YACjB,eAAe,iBAAA;YACf,mBAAmB,qBAAA;SACpB,CAAA;IACH,CAAC;IAED,2BACE,UAAkB,EAClB,WAAmB,EACnB,UAAkB,EAClB,YAAoB,EACpB,eAAyB;QAGzB,IAAM,OAAO,GAAG,EAAE,CAAC,QAAQ,CACzB,cAAc,CAAC,UAAU,GAAG,WAAW,GAAG,UAAU,GAAG,UAAU,CAAC,EAClE,CAAC,UAAU,EAAE,UAAU,EAAE,UAAU,EAAE,WAAW,CAAC,CAClD,CAAA;QACD,IAAM,IAAI,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,WAAW,CAAC,CAAC,CAAA;QAErD,aAAa,CAAC,IAAI,CAChB,EAAE,SAAS,EAAK,YAAY,aAAU,EAAE,EACxC,EAAE,SAAS,EAAK,YAAY,UAAI,eAAe,CAAC,CAAC,CAAC,mBAAmB,CAAC,CAAC,CAAC,MAAM,CAAE,EAAE,CACnF,CAAA;QAED,OAAO,EAAE,OAAO,SAAA,EAAE,IAAI,MAAA,EAAE,CAAA;IAC1B,CAAC;IAED,oCACE,UAAkB,EAClB,WAAmB,EACnB,UAAkB,EAClB,YAAoB;QAGd,IAAA,+EAGwE,EAF5E,oBAAO,EACP,cAAI,CACwE;QAE9E,OAAO;YACL,OAAO,SAAA;YACP,iBAAiB,EAAE,IAAI;SACxB,CAAA;IACH,CAAC;IAED,+BACE,UAAkB,EAClB,WAAmB,EACnB,YAAoB;QAGpB,IAAM,cAAc,GAAG,0BAA0B,CAAC,UAAU,EAAK,YAAY,oBAAiB,CAAC,CAAA;QAC/F,IAAM,cAAc,GAAG,0BAA0B,CAAC,UAAU,EAAE,WAAW,EAAE,CAAC,EAAK,YAAY,oBAAiB,CAAC,CAAA;QAE/G,OAAO,EAAE,cAAc,gBAAA,EAAE,cAAc,gBAAA,EAAE,CAAA;IAC3C,CAAC;IAED;QAEE,IAAM,MAAM,GAAG,0BAA0B,CAAC,CAAC,EAAE,EAAE,EAAE,CAAC,EAAE,oBAAoB,CAAC,CAAA;QAEzE,IAAM,MAAM,GAAG,qBAAqB,CAAC,EAAE,EAAE,EAAE,EAAE,oBAAoB,CAAC,CAAA;QAClE,IAAM,MAAM,GAAG,qBAAqB,CAAC,EAAE,EAAE,GAAG,EAAE,oBAAoB,CAAC,CAAA;QACnE,IAAM,MAAM,GAAG,qBAAqB,CAAC,GAAG,EAAE,GAAG,EAAE,oBAAoB,CAAC,CAAA;QACpE,IAAM,MAAM,GAAG,qBAAqB,CAAC,GAAG,EAAE,GAAG,EAAE,oBAAoB,CAAC,CAAA;QACpE,IAAM,MAAM,GAAG,qBAAqB,CAAC,GAAG,EAAE,GAAG,EAAE,oBAAoB,CAAC,CAAA;QACpE,IAAM,MAAM,GAAG,qBAAqB,CAAC,GAAG,EAAE,GAAG,EAAE,oBAAoB,CAAC,CAAA;QACpE,IAAM,MAAM,GAAG,qBAAqB,CAAC,GAAG,EAAE,GAAG,EAAE,oBAAoB,CAAC,CAAA;QACpE,IAAM,MAAM,GAAG,qBAAqB,CAAC,GAAG,EAAE,GAAG,EAAE,oBAAoB,CAAC,CAAA;QACpE,IAAM,MAAM,GAAG,qBAAqB,CAAC,GAAG,EAAE,GAAG,EAAE,oBAAoB,CAAC,CAAA;QACpE,IAAM,OAAO,GAAG,qBAAqB,CAAC,GAAG,EAAE,GAAG,EAAE,qBAAqB,CAAC,CAAA;QACtE,IAAM,OAAO,GAAG,qBAAqB,CAAC,GAAG,EAAE,GAAG,EAAE,qBAAqB,CAAC,CAAA;QACtE,IAAM,OAAO,GAAG,qBAAqB,CAAC,GAAG,EAAE,IAAI,EAAE,qBAAqB,CAAC,CAAA;QACvE,IAAM,OAAO,GAAG,qBAAqB,CAAC,IAAI,EAAE,IAAI,EAAE,qBAAqB,CAAC,CAAA;QAExE,OAAO;YACL,MAAM,QAAA;YACN,MAAM,QAAA;YACN,MAAM,QAAA;YACN,MAAM,QAAA;YACN,MAAM,QAAA;YACN,MAAM,QAAA;YACN,MAAM,QAAA;YACN,MAAM,QAAA;YACN,MAAM,QAAA;YACN,MAAM,QAAA;YACN,OAAO,SAAA;YACP,OAAO,SAAA;YACP,OAAO,SAAA;YACP,OAAO,SAAA;SACR,CAAA;IACH,CAAC;IAED;QACE,IAAM,MAAM,GAAG,0BAA0B,CAAC,IAAI,EAAE,GAAG,EAAE,CAAC,EAAE,yBAAyB,CAAC,CAAA;QAClF,IAAM,MAAM,GAAG,0BAA0B,CAAC,GAAG,EAAE,GAAG,EAAE,CAAC,EAAE,yBAAyB,CAAC,CAAA;QACjF,IAAM,MAAM,GAAG,0BAA0B,CAAC,GAAG,EAAE,GAAG,EAAE,CAAC,EAAE,yBAAyB,CAAC,CAAA;QACjF,IAAM,MAAM,GAAG,0BAA0B,CAAC,GAAG,EAAE,GAAG,EAAE,CAAC,EAAE,yBAAyB,CAAC,CAAA;QACjF,IAAM,MAAM,GAAG,0BAA0B,CAAC,GAAG,EAAE,GAAG,EAAE,CAAC,EAAE,yBAAyB,CAAC,CAAA;QACjF,IAAM,MAAM,GAAG,0BAA0B,CAAC,GAAG,EAAE,GAAG,EAAE,CAAC,EAAE,yBAAyB,CAAC,CAAA;QACjF,IAAM,MAAM,GAAG,0BAA0B,CAAC,GAAG,EAAE,EAAE,EAAE,CAAC,EAAE,yBAAyB,CAAC,CAAA;QAChF,IAAM,MAAM,GAAG,0BAA0B,CAAC,EAAE,EAAE,GAAG,EAAE,CAAC,EAAE,yBAAyB,CAAC,CAAA;QAEhF,IAAM,wBAAwB,GAAG,iBAAiB,CAAC,GAAG,EAAE,EAAE,EAAE,CAAC,EAAE,yDAAyD,CAAC,CAAA;QACzH,IAAM,iBAAiB,GAAG,iBAAiB,CAAC,GAAG,EAAE,CAAC,EAAE,CAAC,EAAE,kDAAkD,CAAC,CAAA;QAC1G,IAAM,wBAAwB,GAAG,iBAAiB,CAAC,IAAI,EAAE,EAAE,EAAE,CAAC,EAAE,yDAAyD,CAAC,CAAA;QAC1H,IAAM,iBAAiB,GAAG,iBAAiB,CAAC,IAAI,EAAE,EAAE,EAAE,CAAC,EAAE,kDAAkD,CAAC,CAAA;QAC5G,IAAM,wBAAwB,GAAG,iBAAiB,CAAC,GAAG,EAAE,EAAE,EAAE,CAAC,EAAE,yDAAyD,CAAC,CAAA;QACzH,IAAM,iBAAiB,GAAG,iBAAiB,CAAC,GAAG,EAAE,EAAE,EAAE,CAAC,EAAE,kDAAkD,CAAC,CAAA;QAC3G,IAAM,wBAAwB,GAAG,iBAAiB,CAAC,GAAG,EAAE,EAAE,EAAE,CAAC,EAAE,yDAAyD,CAAC,CAAA;QACzH,IAAM,iBAAiB,GAAG,iBAAiB,CAAC,GAAG,EAAE,EAAE,EAAE,CAAC,EAAE,kDAAkD,CAAC,CAAA;QAC3G,IAAM,wBAAwB,GAAG,iBAAiB,CAAC,GAAG,EAAE,EAAE,EAAE,CAAC,EAAE,yDAAyD,CAAC,CAAA;QACzH,IAAM,iBAAiB,GAAG,iBAAiB,CAAC,GAAG,EAAE,EAAE,EAAE,CAAC,EAAE,kDAAkD,CAAC,CAAA;QAC3G,IAAM,wBAAwB,GAAG,iBAAiB,CAAC,GAAG,EAAE,EAAE,EAAE,CAAC,EAAE,yDAAyD,CAAC,CAAA;QACzH,IAAM,iBAAiB,GAAG,iBAAiB,CAAC,GAAG,EAAE,EAAE,EAAE,CAAC,EAAE,kDAAkD,CAAC,CAAA;QAE3G,IAAM,eAAe,GAAG;YACtB,sBAAsB,EAAE,wBAAwB;YAChD,eAAe,EAAE,iBAAiB;SACnC,CAAA;QACD,IAAM,eAAe,GAAG;YACtB,sBAAsB,EAAE,wBAAwB;YAChD,eAAe,EAAE,iBAAiB;SACnC,CAAA;QACD,IAAM,eAAe,GAAG;YACtB,sBAAsB,EAAE,wBAAwB;YAChD,eAAe,EAAE,iBAAiB;SACnC,CAAA;QACD,IAAM,eAAe,GAAG;YACtB,sBAAsB,EAAE,wBAAwB;YAChD,eAAe,EAAE,iBAAiB;SACnC,CAAA;QACD,IAAM,eAAe,GAAG;YACtB,sBAAsB,EAAE,wBAAwB;YAChD,eAAe,EAAE,iBAAiB;SACnC,CAAA;QACD,IAAM,eAAe,GAAG;YACtB,sBAAsB,EAAE,wBAAwB;YAChD,eAAe,EAAE,iBAAiB;SACnC,CAAA;QAED,OAAO;YACL,MAAM,QAAA;YACN,MAAM,QAAA;YACN,MAAM,QAAA;YACN,MAAM,QAAA;YACN,MAAM,QAAA;YACN,MAAM,QAAA;YACN,MAAM,QAAA;YACN,MAAM,QAAA;YACN,eAAe,iBAAA;YACf,eAAe,iBAAA;YACf,eAAe,iBAAA;YACf,eAAe,iBAAA;YACf,eAAe,iBAAA;YACf,eAAe,iBAAA;SAChB,CAAA;IACH,CAAC;IAED,OAAO;QACL,wBAAwB,0BAAA;QACxB,4BAA4B,8BAAA;KAC7B,CAAA;AAEH,CAAC;AAED,uBAA8B,OAAqB;IAEjD,IAAM,aAAa,GAAmB,EAAE,CAAA;IAElC,IAAA,2DAG4B,EAFhC,kCAAc,EACd,4CAAmB,CACa;IAE5B,IAAA,qDAG8C,EAFlD,sDAAwB,EACxB,8DAA4B,CACsB;IAEpD,IAAM,WAAW,GAAG,wBAAwB,EAAE,CAAA;IAC9C,IAAM,gBAAgB,GAAG,4BAA4B,EAAE,CAAA;IACvD,IAAM,SAAS,GAAG,EAAE,CAAC,QAAQ,CAC3B,cAAc,CAAC,IAAI,GAAG,CAAC,CAAC,EACxB,CAAC,CAAC,EAAE,IAAI,EAAE,CAAC,CAAC,CACb,CAAA;IACD,IAAM,YAAY,GAAG;QACnB,SAAS,WAAA;KACV,CAAA;IAED,aAAa,CAAC,IAAI,CAAC,EAAE,SAAS,EAAE,wBAAwB,EAAE,CAAC,CAAA;IAE3D,IAAI,mBAAmB,EAAE,CAAC,MAAM,KAAK,CAAC,EAAE;QACtC,MAAM,IAAI,KAAK,CAAC,oCAAkC,mBAAmB,EAAE,CAAC,MAAQ,CAAC,CAAA;KAClF;IAED,OAAO;QACL,MAAM,EAAE;YACN,WAAW,aAAA;YACX,gBAAgB,kBAAA;YAChB,YAAY,cAAA;SACb;QACD,aAAa,eAAA;KACd,CAAA;AACH,CAAC;AAtCD,sCAsCC"}
\ No newline at end of file

build/faceDetectionNet/loadQuantizedParams.d.ts

-export declare function loadQuantizedParams(uri: string | undefined): Promise<any>;
+import { ParamMapping } from '../commons/types';
+import { NetParams } from './types';
+export declare function loadQuantizedParams(uri: string | undefined): Promise<{
+    params: NetParams;
+    paramMappings: ParamMapping[];
+}>;

build/faceDetectionNet/loadQuantizedParams.js

 "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
 var tslib_1 = require("tslib");
+var disposeUnusedWeightTensors_1 = require("../commons/disposeUnusedWeightTensors");
+var extractWeightEntryFactory_1 = require("../commons/extractWeightEntryFactory");
 var isTensor_1 = require("../commons/isTensor");
 var loadWeightMap_1 = require("../commons/loadWeightMap");
 var DEFAULT_MODEL_NAME = 'face_detection_model';
-function extractorsFactory(weightMap) {
-    function extractPointwiseConvParams(prefix, idx) {
-        var pointwise_conv_params = {
-            filters: weightMap[prefix + "/Conv2d_" + idx + "_pointwise/weights"],
-            batch_norm_offset: weightMap[prefix + "/Conv2d_" + idx + "_pointwise/convolution_bn_offset"]
-        };
-        if (!isTensor_1.isTensor4D(pointwise_conv_params.filters)) {
-            throw new Error("expected weightMap[" + prefix + "/Conv2d_" + idx + "_pointwise/weights] to be a Tensor4D, instead have " + pointwise_conv_params.filters);
-        }
-        if (!isTensor_1.isTensor1D(pointwise_conv_params.batch_norm_offset)) {
-            throw new Error("expected weightMap[" + prefix + "/Conv2d_" + idx + "_pointwise/convolution_bn_offset] to be a Tensor1D, instead have " + pointwise_conv_params.batch_norm_offset);
-        }
-        return pointwise_conv_params;
+function extractorsFactory(weightMap, paramMappings) {
+    var extractWeightEntry = extractWeightEntryFactory_1.extractWeightEntryFactory(weightMap, paramMappings);
+    function extractPointwiseConvParams(prefix, idx, mappedPrefix) {
+        var filters = extractWeightEntry(prefix + "/Conv2d_" + idx + "_pointwise/weights", 4, mappedPrefix + "/filters");
+        var batch_norm_offset = extractWeightEntry(prefix + "/Conv2d_" + idx + "_pointwise/convolution_bn_offset", 1, mappedPrefix + "/batch_norm_offset");
+        return { filters: filters, batch_norm_offset: batch_norm_offset };
     }
     function extractConvPairParams(idx) {
-        var depthwise_conv_params = {
-            filters: weightMap["MobilenetV1/Conv2d_" + idx + "_depthwise/depthwise_weights"],
-            batch_norm_scale: weightMap["MobilenetV1/Conv2d_" + idx + "_depthwise/BatchNorm/gamma"],
-            batch_norm_offset: weightMap["MobilenetV1/Conv2d_" + idx + "_depthwise/BatchNorm/beta"],
-            batch_norm_mean: weightMap["MobilenetV1/Conv2d_" + idx + "_depthwise/BatchNorm/moving_mean"],
-            batch_norm_variance: weightMap["MobilenetV1/Conv2d_" + idx + "_depthwise/BatchNorm/moving_variance"],
-        };
-        if (!isTensor_1.isTensor4D(depthwise_conv_params.filters)) {
-            throw new Error("expected weightMap[MobilenetV1/Conv2d_" + idx + "_depthwise/depthwise_weights] to be a Tensor4D, instead have " + depthwise_conv_params.filters);
-        }
-        if (!isTensor_1.isTensor1D(depthwise_conv_params.batch_norm_scale)) {
-            throw new Error("expected weightMap[MobilenetV1/Conv2d_" + idx + "_depthwise/BatchNorm/gamma] to be a Tensor1D, instead have " + depthwise_conv_params.batch_norm_scale);
-        }
-        if (!isTensor_1.isTensor1D(depthwise_conv_params.batch_norm_offset)) {
-            throw new Error("expected weightMap[MobilenetV1/Conv2d_" + idx + "_depthwise/BatchNorm/beta] to be a Tensor1D, instead have " + depthwise_conv_params.batch_norm_offset);
-        }
-        if (!isTensor_1.isTensor1D(depthwise_conv_params.batch_norm_mean)) {
-            throw new Error("expected weightMap[MobilenetV1/Conv2d_" + idx + "_depthwise/BatchNorm/moving_mean] to be a Tensor1D, instead have " + depthwise_conv_params.batch_norm_mean);
-        }
-        if (!isTensor_1.isTensor1D(depthwise_conv_params.batch_norm_variance)) {
-            throw new Error("expected weightMap[MobilenetV1/Conv2d_" + idx + "_depthwise/BatchNorm/moving_variance] to be a Tensor1D, instead have " + depthwise_conv_params.batch_norm_variance);
-        }
+        var mappedPrefix = "mobilenetv1/conv_" + idx;
+        var prefixDepthwiseConv = "MobilenetV1/Conv2d_" + idx + "_depthwise";
+        var mappedPrefixDepthwiseConv = mappedPrefix + "/depthwise_conv";
+        var mappedPrefixPointwiseConv = mappedPrefix + "/pointwise_conv";
+        var filters = extractWeightEntry(prefixDepthwiseConv + "/depthwise_weights", 4, mappedPrefixDepthwiseConv + "/filters");
+        var batch_norm_scale = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/gamma", 1, mappedPrefixDepthwiseConv + "/batch_norm_scale");
+        var batch_norm_offset = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/beta", 1, mappedPrefixDepthwiseConv + "/batch_norm_offset");
+        var batch_norm_mean = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/moving_mean", 1, mappedPrefixDepthwiseConv + "/batch_norm_mean");
+        var batch_norm_variance = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/moving_variance", 1, mappedPrefixDepthwiseConv + "/batch_norm_variance");
         return {
-            depthwise_conv_params: depthwise_conv_params,
-            pointwise_conv_params: extractPointwiseConvParams('MobilenetV1', idx)
+            depthwise_conv: {
+                filters: filters,
+                batch_norm_scale: batch_norm_scale,
+                batch_norm_offset: batch_norm_offset,
+                batch_norm_mean: batch_norm_mean,
+                batch_norm_variance: batch_norm_variance
+            },
+            pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv)
         };
     }
     function extractMobilenetV1Params() {
         return {
-            conv_0_params: extractPointwiseConvParams('MobilenetV1', 0),
-            conv_pair_params: Array(13).fill(0).map(function (_, i) { return extractConvPairParams(i + 1); })
+            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) {
+        var filters = extractWeightEntry(prefix + "/weights", 4, mappedPrefix + "/filters");
+        var bias = extractWeightEntry(prefix + "/biases", 1, mappedPrefix + "/bias");
+        return { filters: filters, bias: bias };
+    }
     function extractBoxPredictorParams(idx) {
-        var params = {
-            box_encoding_predictor_params: {
-                filters: weightMap["Prediction/BoxPredictor_" + idx + "/BoxEncodingPredictor/weights"],
-                bias: weightMap["Prediction/BoxPredictor_" + idx + "/BoxEncodingPredictor/biases"]
-            },
-            class_predictor_params: {
-                filters: weightMap["Prediction/BoxPredictor_" + idx + "/ClassPredictor/weights"],
-                bias: weightMap["Prediction/BoxPredictor_" + idx + "/ClassPredictor/biases"]
-            }
-        };
-        if (!isTensor_1.isTensor4D(params.box_encoding_predictor_params.filters)) {
-            throw new Error("expected weightMap[Prediction/BoxPredictor_" + idx + "/BoxEncodingPredictor/weights] to be a Tensor4D, instead have " + params.box_encoding_predictor_params.filters);
-        }
-        if (!isTensor_1.isTensor1D(params.box_encoding_predictor_params.bias)) {
-            throw new Error("expected weightMap[Prediction/BoxPredictor_" + idx + "/BoxEncodingPredictor/biases] to be a Tensor1D, instead have " + params.box_encoding_predictor_params.bias);
-        }
-        if (!isTensor_1.isTensor4D(params.class_predictor_params.filters)) {
-            throw new Error("expected weightMap[Prediction/BoxPredictor_" + idx + "/ClassPredictor/weights] to be a Tensor4D, instead have " + params.class_predictor_params.filters);
-        }
-        if (!isTensor_1.isTensor1D(params.class_predictor_params.bias)) {
-            throw new Error("expected weightMap[Prediction/BoxPredictor_" + idx + "/ClassPredictor/biases] to be a Tensor1D, instead have " + params.class_predictor_params.bias);
-        }
-        return params;
+        var box_encoding_predictor = extractConvParams("Prediction/BoxPredictor_" + idx + "/BoxEncodingPredictor", "prediction_layer/box_predictor_" + idx + "/box_encoding_predictor");
+        var class_predictor = extractConvParams("Prediction/BoxPredictor_" + idx + "/ClassPredictor", "prediction_layer/box_predictor_" + idx + "/class_predictor");
+        return { box_encoding_predictor: box_encoding_predictor, class_predictor: class_predictor };
     }
     function extractPredictionLayerParams() {
         return {
-            conv_0_params: extractPointwiseConvParams('Prediction', 0),
-            conv_1_params: extractPointwiseConvParams('Prediction', 1),
-            conv_2_params: extractPointwiseConvParams('Prediction', 2),
-            conv_3_params: extractPointwiseConvParams('Prediction', 3),
-            conv_4_params: extractPointwiseConvParams('Prediction', 4),
-            conv_5_params: extractPointwiseConvParams('Prediction', 5),
-            conv_6_params: extractPointwiseConvParams('Prediction', 6),
-            conv_7_params: extractPointwiseConvParams('Prediction', 7),
-            box_predictor_0_params: extractBoxPredictorParams(0),
-            box_predictor_1_params: extractBoxPredictorParams(1),
-            box_predictor_2_params: extractBoxPredictorParams(2),
-            box_predictor_3_params: extractBoxPredictorParams(3),
-            box_predictor_4_params: extractBoxPredictorParams(4),
-            box_predictor_5_params: extractBoxPredictorParams(5)
+            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 {
@@ -102,24 +87,28 @@ function extractorsFactory(weightMap) {
 }
 function loadQuantizedParams(uri) {
     return tslib_1.__awaiter(this, void 0, void 0, function () {
-        var weightMap, _a, extractMobilenetV1Params, extractPredictionLayerParams, extra_dim;
+        var weightMap, paramMappings, _a, extractMobilenetV1Params, extractPredictionLayerParams, extra_dim, params;
         return tslib_1.__generator(this, function (_b) {
             switch (_b.label) {
                 case 0: return [4 /*yield*/, loadWeightMap_1.loadWeightMap(uri, DEFAULT_MODEL_NAME)];
                 case 1:
                     weightMap = _b.sent();
-                    _a = extractorsFactory(weightMap), extractMobilenetV1Params = _a.extractMobilenetV1Params, extractPredictionLayerParams = _a.extractPredictionLayerParams;
+                    paramMappings = [];
+                    _a = extractorsFactory(weightMap, paramMappings), extractMobilenetV1Params = _a.extractMobilenetV1Params, extractPredictionLayerParams = _a.extractPredictionLayerParams;
                     extra_dim = weightMap['Output/extra_dim'];
+                    paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' });
                     if (!isTensor_1.isTensor3D(extra_dim)) {
                         throw new Error("expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have " + extra_dim);
                     }
-                    return [2 /*return*/, {
-                            mobilenetv1_params: extractMobilenetV1Params(),
-                            prediction_layer_params: extractPredictionLayerParams(),
-                            output_layer_params: {
-                                extra_dim: extra_dim
-                            }
-                        }];
+                    params = {
+                        mobilenetv1: extractMobilenetV1Params(),
+                        prediction_layer: extractPredictionLayerParams(),
+                        output_layer: {
+                            extra_dim: extra_dim
+                        }
+                    };
+                    disposeUnusedWeightTensors_1.disposeUnusedWeightTensors(weightMap, paramMappings);
+                    return [2 /*return*/, { params: params, paramMappings: paramMappings }];
             }
         });
     });

build/faceDetectionNet/loadQuantizedParams.js.map

-{"version":3,"file":"loadQuantizedParams.js","sourceRoot":"","sources":["../../src/faceDetectionNet/loadQuantizedParams.ts"],"names":[],"mappings":";;;AAAA,gDAAyE;AACzE,0DAAyD;AAGzD,IAAM,kBAAkB,GAAG,sBAAsB,CAAA;AAEjD,2BAA2B,SAAc;IAEvC,oCAAoC,MAAc,EAAE,GAAW;QAE7D,IAAM,qBAAqB,GAAG;YAC5B,OAAO,EAAE,SAAS,CAAI,MAAM,gBAAW,GAAG,uBAAoB,CAAC;YAC/D,iBAAiB,EAAE,SAAS,CAAI,MAAM,gBAAW,GAAG,qCAAkC,CAAC;SACxF,CAAA;QAED,IAAI,CAAC,qBAAU,CAAC,qBAAqB,CAAC,OAAO,CAAC,EAAE;YAC9C,MAAM,IAAI,KAAK,CAAC,wBAAsB,MAAM,gBAAW,GAAG,2DAAsD,qBAAqB,CAAC,OAAS,CAAC,CAAA;SACjJ;QAED,IAAI,CAAC,qBAAU,CAAC,qBAAqB,CAAC,iBAAiB,CAAC,EAAE;YACxD,MAAM,IAAI,KAAK,CAAC,wBAAsB,MAAM,gBAAW,GAAG,yEAAoE,qBAAqB,CAAC,iBAAmB,CAAC,CAAA;SACzK;QAED,OAAO,qBAAqB,CAAA;IAC9B,CAAC;IAED,+BAA+B,GAAW;QAExC,IAAM,qBAAqB,GAAG;YAC5B,OAAO,EAAE,SAAS,CAAC,wBAAsB,GAAG,iCAA8B,CAAC;YAC3E,gBAAgB,EAAE,SAAS,CAAC,wBAAsB,GAAG,+BAA4B,CAAC;YAClF,iBAAiB,EAAE,SAAS,CAAC,wBAAsB,GAAG,8BAA2B,CAAC;YAClF,eAAe,EAAE,SAAS,CAAC,wBAAsB,GAAG,qCAAkC,CAAC;YACvF,mBAAmB,EAAE,SAAS,CAAC,wBAAsB,GAAG,yCAAsC,CAAC;SAChG,CAAA;QAED,IAAI,CAAC,qBAAU,CAAC,qBAAqB,CAAC,OAAO,CAAC,EAAE;YAC9C,MAAM,IAAI,KAAK,CAAC,2CAAyC,GAAG,qEAAgE,qBAAqB,CAAC,OAAS,CAAC,CAAA;SAC7J;QAED,IAAI,CAAC,qBAAU,CAAC,qBAAqB,CAAC,gBAAgB,CAAC,EAAE;YACvD,MAAM,IAAI,KAAK,CAAC,2CAAyC,GAAG,mEAA8D,qBAAqB,CAAC,gBAAkB,CAAC,CAAA;SACpK;QAED,IAAI,CAAC,qBAAU,CAAC,qBAAqB,CAAC,iBAAiB,CAAC,EAAE;YACxD,MAAM,IAAI,KAAK,CAAC,2CAAyC,GAAG,kEAA6D,qBAAqB,CAAC,iBAAmB,CAAC,CAAA;SACpK;QAED,IAAI,CAAC,qBAAU,CAAC,qBAAqB,CAAC,eAAe,CAAC,EAAE;YACtD,MAAM,IAAI,KAAK,CAAC,2CAAyC,GAAG,yEAAoE,qBAAqB,CAAC,eAAiB,CAAC,CAAA;SACzK;QAED,IAAI,CAAC,qBAAU,CAAC,qBAAqB,CAAC,mBAAmB,CAAC,EAAE;YAC1D,MAAM,IAAI,KAAK,CAAC,2CAAyC,GAAG,6EAAwE,qBAAqB,CAAC,mBAAqB,CAAC,CAAA;SACjL;QAED,OAAO;YACL,qBAAqB,uBAAA;YACrB,qBAAqB,EAAE,0BAA0B,CAAC,aAAa,EAAE,GAAG,CAAC;SACtE,CAAA;IACH,CAAC;IAED;QACE,OAAO;YACL,aAAa,EAAE,0BAA0B,CAAC,aAAa,EAAE,CAAC,CAAC;YAC3D,gBAAgB,EAAE,KAAK,CAAC,EAAE,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,UAAC,CAAC,EAAE,CAAC,IAAK,OAAA,qBAAqB,CAAC,CAAC,GAAG,CAAC,CAAC,EAA5B,CAA4B,CAAC;SAChF,CAAA;IACH,CAAC;IAED,mCAAmC,GAAW;QAE5C,IAAM,MAAM,GAAG;YACb,6BAA6B,EAAE;gBAC7B,OAAO,EAAE,SAAS,CAAC,6BAA2B,GAAG,kCAA+B,CAAC;gBACjF,IAAI,EAAE,SAAS,CAAC,6BAA2B,GAAG,iCAA8B,CAAC;aAC9E;YACD,sBAAsB,EAAE;gBACtB,OAAO,EAAE,SAAS,CAAC,6BAA2B,GAAG,4BAAyB,CAAC;gBAC3E,IAAI,EAAE,SAAS,CAAC,6BAA2B,GAAG,2BAAwB,CAAC;aACxE;SACF,CAAA;QAED,IAAI,CAAC,qBAAU,CAAC,MAAM,CAAC,6BAA6B,CAAC,OAAO,CAAC,EAAE;YAC7D,MAAM,IAAI,KAAK,CAAC,gDAA8C,GAAG,sEAAiE,MAAM,CAAC,6BAA6B,CAAC,OAAS,CAAC,CAAA;SAClL;QAED,IAAI,CAAC,qBAAU,CAAC,MAAM,CAAC,6BAA6B,CAAC,IAAI,CAAC,EAAE;YAC1D,MAAM,IAAI,KAAK,CAAC,gDAA8C,GAAG,qEAAgE,MAAM,CAAC,6BAA6B,CAAC,IAAM,CAAC,CAAA;SAC9K;QAED,IAAI,CAAC,qBAAU,CAAC,MAAM,CAAC,sBAAsB,CAAC,OAAO,CAAC,EAAE;YACtD,MAAM,IAAI,KAAK,CAAC,gDAA8C,GAAG,gEAA2D,MAAM,CAAC,sBAAsB,CAAC,OAAS,CAAC,CAAA;SACrK;QAED,IAAI,CAAC,qBAAU,CAAC,MAAM,CAAC,sBAAsB,CAAC,IAAI,CAAC,EAAE;YACnD,MAAM,IAAI,KAAK,CAAC,gDAA8C,GAAG,+DAA0D,MAAM,CAAC,sBAAsB,CAAC,IAAM,CAAC,CAAA;SACjK;QAED,OAAO,MAAM,CAAA;IACf,CAAC;IAED;QACE,OAAO;YACL,aAAa,EAAE,0BAA0B,CAAC,YAAY,EAAE,CAAC,CAAC;YAC1D,aAAa,EAAE,0BAA0B,CAAC,YAAY,EAAE,CAAC,CAAC;YAC1D,aAAa,EAAE,0BAA0B,CAAC,YAAY,EAAE,CAAC,CAAC;YAC1D,aAAa,EAAE,0BAA0B,CAAC,YAAY,EAAE,CAAC,CAAC;YAC1D,aAAa,EAAE,0BAA0B,CAAC,YAAY,EAAE,CAAC,CAAC;YAC1D,aAAa,EAAE,0BAA0B,CAAC,YAAY,EAAE,CAAC,CAAC;YAC1D,aAAa,EAAE,0BAA0B,CAAC,YAAY,EAAE,CAAC,CAAC;YAC1D,aAAa,EAAE,0BAA0B,CAAC,YAAY,EAAE,CAAC,CAAC;YAC1D,sBAAsB,EAAE,yBAAyB,CAAC,CAAC,CAAC;YACpD,sBAAsB,EAAE,yBAAyB,CAAC,CAAC,CAAC;YACpD,sBAAsB,EAAE,yBAAyB,CAAC,CAAC,CAAC;YACpD,sBAAsB,EAAE,yBAAyB,CAAC,CAAC,CAAC;YACpD,sBAAsB,EAAE,yBAAyB,CAAC,CAAC,CAAC;YACpD,sBAAsB,EAAE,yBAAyB,CAAC,CAAC,CAAC;SACrD,CAAA;IACH,CAAC;IAED,OAAO;QACL,wBAAwB,0BAAA;QACxB,4BAA4B,8BAAA;KAC7B,CAAA;AACH,CAAC;AAED,6BAA0C,GAAuB;;;;;wBAC7C,qBAAM,6BAAa,CAAC,GAAG,EAAE,kBAAkB,CAAC,EAAA;;oBAAxD,SAAS,GAAG,SAA4C;oBAExD,KAGF,iBAAiB,CAAC,SAAS,CAAC,EAF9B,wBAAwB,8BAAA,EACxB,4BAA4B,kCAAA,CACE;oBAE1B,SAAS,GAAG,SAAS,CAAC,kBAAkB,CAAC,CAAA;oBAC/C,IAAI,CAAC,qBAAU,CAAC,SAAS,CAAC,EAAE;wBAC1B,MAAM,IAAI,KAAK,CAAC,2EAAyE,SAAW,CAAC,CAAA;qBACtG;oBAED,sBAAO;4BACL,kBAAkB,EAAE,wBAAwB,EAAE;4BAC9C,uBAAuB,EAAE,4BAA4B,EAAE;4BACvD,mBAAmB,EAAE;gCACnB,SAAS,WAAA;6BACV;yBACF,EAAA;;;;CACF;AApBD,kDAoBC"}
\ No newline at end of file
+{"version":3,"file":"loadQuantizedParams.js","sourceRoot":"","sources":["../../src/faceDetectionNet/loadQuantizedParams.ts"],"names":[],"mappings":";;;AACA,oFAAmF;AACnF,kFAAiF;AACjF,gDAAyE;AACzE,0DAAyD;AAIzD,IAAM,kBAAkB,GAAG,sBAAsB,CAAA;AAEjD,2BAA2B,SAAc,EAAE,aAA6B;IAEtE,IAAM,kBAAkB,GAAG,qDAAyB,CAAC,SAAS,EAAE,aAAa,CAAC,CAAA;IAE9E,oCAAoC,MAAc,EAAE,GAAW,EAAE,YAAoB;QAEnF,IAAM,OAAO,GAAG,kBAAkB,CAAiB,MAAM,gBAAW,GAAG,uBAAoB,EAAE,CAAC,EAAK,YAAY,aAAU,CAAC,CAAA;QAC1H,IAAM,iBAAiB,GAAG,kBAAkB,CAAiB,MAAM,gBAAW,GAAG,qCAAkC,EAAE,CAAC,EAAK,YAAY,uBAAoB,CAAC,CAAA;QAE5J,OAAO,EAAE,OAAO,SAAA,EAAE,iBAAiB,mBAAA,EAAE,CAAA;IACvC,CAAC;IAED,+BAA+B,GAAW;QAExC,IAAM,YAAY,GAAG,sBAAoB,GAAK,CAAA;QAC9C,IAAM,mBAAmB,GAAG,wBAAsB,GAAG,eAAY,CAAA;QACjE,IAAM,yBAAyB,GAAM,YAAY,oBAAiB,CAAA;QAClE,IAAM,yBAAyB,GAAM,YAAY,oBAAiB,CAAA;QAElE,IAAM,OAAO,GAAG,kBAAkB,CAAiB,mBAAmB,uBAAoB,EAAE,CAAC,EAAK,yBAAyB,aAAU,CAAC,CAAA;QACtI,IAAM,gBAAgB,GAAG,kBAAkB,CAAiB,mBAAmB,qBAAkB,EAAE,CAAC,EAAK,yBAAyB,sBAAmB,CAAC,CAAA;QACtJ,IAAM,iBAAiB,GAAG,kBAAkB,CAAiB,mBAAmB,oBAAiB,EAAE,CAAC,EAAK,yBAAyB,uBAAoB,CAAC,CAAA;QACvJ,IAAM,eAAe,GAAG,kBAAkB,CAAiB,mBAAmB,2BAAwB,EAAE,CAAC,EAAK,yBAAyB,qBAAkB,CAAC,CAAA;QAC1J,IAAM,mBAAmB,GAAG,kBAAkB,CAAiB,mBAAmB,+BAA4B,EAAE,CAAC,EAAK,yBAAyB,yBAAsB,CAAC,CAAA;QAEtK,OAAO;YACL,cAAc,EAAE;gBACd,OAAO,SAAA;gBACP,gBAAgB,kBAAA;gBAChB,iBAAiB,mBAAA;gBACjB,eAAe,iBAAA;gBACf,mBAAmB,qBAAA;aACpB;YACD,cAAc,EAAE,0BAA0B,CAAC,aAAa,EAAE,GAAG,EAAE,yBAAyB,CAAC;SAC1F,CAAA;IACH,CAAC;IAED;QACE,OAAO;YACL,MAAM,EAAE,0BAA0B,CAAC,aAAa,EAAE,CAAC,EAAE,oBAAoB,CAAC;YAC1E,MAAM,EAAE,qBAAqB,CAAC,CAAC,CAAC;YAChC,MAAM,EAAE,qBAAqB,CAAC,CAAC,CAAC;YAChC,MAAM,EAAE,qBAAqB,CAAC,CAAC,CAAC;YAChC,MAAM,EAAE,qBAAqB,CAAC,CAAC,CAAC;YAChC,MAAM,EAAE,qBAAqB,CAAC,CAAC,CAAC;YAChC,MAAM,EAAE,qBAAqB,CAAC,CAAC,CAAC;YAChC,MAAM,EAAE,qBAAqB,CAAC,CAAC,CAAC;YAChC,MAAM,EAAE,qBAAqB,CAAC,CAAC,CAAC;YAChC,MAAM,EAAE,qBAAqB,CAAC,CAAC,CAAC;YAChC,OAAO,EAAE,qBAAqB,CAAC,EAAE,CAAC;YAClC,OAAO,EAAE,qBAAqB,CAAC,EAAE,CAAC;YAClC,OAAO,EAAE,qBAAqB,CAAC,EAAE,CAAC;YAClC,OAAO,EAAE,qBAAqB,CAAC,EAAE,CAAC;SACnC,CAAA;IACH,CAAC;IAED,2BAA2B,MAAc,EAAE,YAAoB;QAC7D,IAAM,OAAO,GAAG,kBAAkB,CAAiB,MAAM,aAAU,EAAE,CAAC,EAAK,YAAY,aAAU,CAAC,CAAA;QAClG,IAAM,IAAI,GAAG,kBAAkB,CAAiB,MAAM,YAAS,EAAE,CAAC,EAAK,YAAY,UAAO,CAAC,CAAA;QAE3F,OAAO,EAAE,OAAO,SAAA,EAAE,IAAI,MAAA,EAAE,CAAA;IAC1B,CAAC;IAED,mCAAmC,GAAW;QAE5C,IAAM,sBAAsB,GAAG,iBAAiB,CAC9C,6BAA2B,GAAG,0BAAuB,EACrD,oCAAkC,GAAG,4BAAyB,CAC/D,CAAA;QACD,IAAM,eAAe,GAAG,iBAAiB,CACvC,6BAA2B,GAAG,oBAAiB,EAC/C,oCAAkC,GAAG,qBAAkB,CACxD,CAAA;QAED,OAAO,EAAE,sBAAsB,wBAAA,EAAE,eAAe,iBAAA,EAAE,CAAA;IACpD,CAAC;IAED;QACE,OAAO;YACL,MAAM,EAAE,0BAA0B,CAAC,YAAY,EAAE,CAAC,EAAE,yBAAyB,CAAC;YAC9E,MAAM,EAAE,0BAA0B,CAAC,YAAY,EAAE,CAAC,EAAE,yBAAyB,CAAC;YAC9E,MAAM,EAAE,0BAA0B,CAAC,YAAY,EAAE,CAAC,EAAE,yBAAyB,CAAC;YAC9E,MAAM,EAAE,0BAA0B,CAAC,YAAY,EAAE,CAAC,EAAE,yBAAyB,CAAC;YAC9E,MAAM,EAAE,0BAA0B,CAAC,YAAY,EAAE,CAAC,EAAE,yBAAyB,CAAC;YAC9E,MAAM,EAAE,0BAA0B,CAAC,YAAY,EAAE,CAAC,EAAE,yBAAyB,CAAC;YAC9E,MAAM,EAAE,0BAA0B,CAAC,YAAY,EAAE,CAAC,EAAE,yBAAyB,CAAC;YAC9E,MAAM,EAAE,0BAA0B,CAAC,YAAY,EAAE,CAAC,EAAE,yBAAyB,CAAC;YAC9E,eAAe,EAAE,yBAAyB,CAAC,CAAC,CAAC;YAC7C,eAAe,EAAE,yBAAyB,CAAC,CAAC,CAAC;YAC7C,eAAe,EAAE,yBAAyB,CAAC,CAAC,CAAC;YAC7C,eAAe,EAAE,yBAAyB,CAAC,CAAC,CAAC;YAC7C,eAAe,EAAE,yBAAyB,CAAC,CAAC,CAAC;YAC7C,eAAe,EAAE,yBAAyB,CAAC,CAAC,CAAC;SAC9C,CAAA;IACH,CAAC;IAED,OAAO;QACL,wBAAwB,0BAAA;QACxB,4BAA4B,8BAAA;KAC7B,CAAA;AACH,CAAC;AAED,6BACE,GAAuB;;;;;wBAGL,qBAAM,6BAAa,CAAC,GAAG,EAAE,kBAAkB,CAAC,EAAA;;oBAAxD,SAAS,GAAG,SAA4C;oBACxD,aAAa,GAAmB,EAAE,CAAA;oBAElC,KAGF,iBAAiB,CAAC,SAAS,EAAE,aAAa,CAAC,EAF7C,wBAAwB,8BAAA,EACxB,4BAA4B,kCAAA,CACiB;oBAEzC,SAAS,GAAG,SAAS,CAAC,kBAAkB,CAAC,CAAA;oBAC/C,aAAa,CAAC,IAAI,CAAC,EAAE,YAAY,EAAE,kBAAkB,EAAE,SAAS,EAAE,wBAAwB,EAAE,CAAC,CAAA;oBAE7F,IAAI,CAAC,qBAAU,CAAC,SAAS,CAAC,EAAE;wBAC1B,MAAM,IAAI,KAAK,CAAC,2EAAyE,SAAW,CAAC,CAAA;qBACtG;oBAEK,MAAM,GAAG;wBACb,WAAW,EAAE,wBAAwB,EAAE;wBACvC,gBAAgB,EAAE,4BAA4B,EAAE;wBAChD,YAAY,EAAE;4BACZ,SAAS,WAAA;yBACV;qBACF,CAAA;oBAED,uDAA0B,CAAC,SAAS,EAAE,aAAa,CAAC,CAAA;oBAEpD,sBAAO,EAAE,MAAM,QAAA,EAAE,aAAa,eAAA,EAAE,EAAA;;;;CACjC;AA9BD,kDA8BC"}
\ No newline at end of file

build/faceDetectionNet/mobileNetV1.js

@@ -16,12 +16,27 @@ function getStridesForLayerIdx(layerIdx) {
 function mobileNetV1(x, params) {
     return tf.tidy(function () {
         var conv11 = null;
-        var out = pointwiseConvLayer_1.pointwiseConvLayer(x, params.conv_0_params, [2, 2]);
-        params.conv_pair_params.forEach(function (param, i) {
+        var out = pointwiseConvLayer_1.pointwiseConvLayer(x, params.conv_0, [2, 2]);
+        var 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(function (param, i) {
             var layerIdx = i + 1;
             var depthwiseConvStrides = getStridesForLayerIdx(layerIdx);
-            out = depthwiseConvLayer(out, param.depthwise_conv_params, depthwiseConvStrides);
-            out = pointwiseConvLayer_1.pointwiseConvLayer(out, param.pointwise_conv_params, [1, 1]);
+            out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides);
+            out = pointwiseConvLayer_1.pointwiseConvLayer(out, param.pointwise_conv, [1, 1]);
             if (layerIdx === 11) {
                 conv11 = out;
             }

build/faceDetectionNet/mobileNetV1.js.map

-{"version":3,"file":"mobileNetV1.js","sourceRoot":"","sources":["../../src/faceDetectionNet/mobileNetV1.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAE5C,2DAA0D;AAG1D,IAAM,OAAO,GAAG,qBAAqB,CAAA;AAErC,4BACE,CAAc,EACd,MAAuC,EACvC,OAAyB;IAEzB,OAAO,EAAE,CAAC,IAAI,CAAC;QAEb,IAAI,GAAG,GAAG,EAAE,CAAC,eAAe,CAAC,CAAC,EAAE,MAAM,CAAC,OAAO,EAAE,OAAO,EAAE,MAAM,CAAC,CAAA;QAChE,GAAG,GAAG,EAAE,CAAC,kBAAkB,CACzB,GAAG,EACH,MAAM,CAAC,eAAe,EACtB,MAAM,CAAC,mBAAmB,EAC1B,OAAO,EACP,MAAM,CAAC,gBAAgB,EACvB,MAAM,CAAC,iBAAiB,CACzB,CAAA;QACD,OAAO,EAAE,CAAC,WAAW,CAAC,GAAG,EAAE,CAAC,EAAE,CAAC,CAAC,CAAA;IAElC,CAAC,CAAC,CAAA;AACJ,CAAC;AAED,+BAA+B,QAAgB;IAC7C,OAAO,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,CAAC,IAAI,CAAC,UAAA,GAAG,IAAI,OAAA,GAAG,KAAK,QAAQ,EAAhB,CAAgB,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAA;AACtE,CAAC;AAED,qBAA4B,CAAc,EAAE,MAA0B;IACpE,OAAO,EAAE,CAAC,IAAI,CAAC;QAEb,IAAI,MAAM,GAAG,IAAI,CAAA;QACjB,IAAI,GAAG,GAAG,uCAAkB,CAAC,CAAC,EAAE,MAAM,CAAC,aAAa,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAE7D,MAAM,CAAC,gBAAgB,CAAC,OAAO,CAAC,UAAC,KAAK,EAAE,CAAC;YACvC,IAAM,QAAQ,GAAG,CAAC,GAAG,CAAC,CAAA;YACtB,IAAM,oBAAoB,GAAG,qBAAqB,CAAC,QAAQ,CAAC,CAAA;YAC5D,GAAG,GAAG,kBAAkB,CAAC,GAAG,EAAE,KAAK,CAAC,qBAAqB,EAAE,oBAAoB,CAAC,CAAA;YAChF,GAAG,GAAG,uCAAkB,CAAC,GAAG,EAAE,KAAK,CAAC,qBAAqB,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;YAClE,IAAI,QAAQ,KAAK,EAAE,EAAE;gBACnB,MAAM,GAAG,GAAG,CAAA;aACb;QACH,CAAC,CAAC,CAAA;QAEF,IAAI,MAAM,KAAK,IAAI,EAAE;YACnB,MAAM,IAAI,KAAK,CAAC,+CAA+C,CAAC,CAAA;SACjE;QAED,OAAO;YACL,GAAG,KAAA;YACH,MAAM,EAAE,MAAa;SACtB,CAAA;IAEH,CAAC,CAAC,CAAA;AACJ,CAAC;AA1BD,kCA0BC"}
\ No newline at end of file
+{"version":3,"file":"mobileNetV1.js","sourceRoot":"","sources":["../../src/faceDetectionNet/mobileNetV1.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAE5C,2DAA0D;AAG1D,IAAM,OAAO,GAAG,qBAAqB,CAAA;AAErC,4BACE,CAAc,EACd,MAAuC,EACvC,OAAyB;IAEzB,OAAO,EAAE,CAAC,IAAI,CAAC;QAEb,IAAI,GAAG,GAAG,EAAE,CAAC,eAAe,CAAC,CAAC,EAAE,MAAM,CAAC,OAAO,EAAE,OAAO,EAAE,MAAM,CAAC,CAAA;QAChE,GAAG,GAAG,EAAE,CAAC,kBAAkB,CACzB,GAAG,EACH,MAAM,CAAC,eAAe,EACtB,MAAM,CAAC,mBAAmB,EAC1B,OAAO,EACP,MAAM,CAAC,gBAAgB,EACvB,MAAM,CAAC,iBAAiB,CACzB,CAAA;QACD,OAAO,EAAE,CAAC,WAAW,CAAC,GAAG,EAAE,CAAC,EAAE,CAAC,CAAC,CAAA;IAElC,CAAC,CAAC,CAAA;AACJ,CAAC;AAED,+BAA+B,QAAgB;IAC7C,OAAO,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,CAAC,IAAI,CAAC,UAAA,GAAG,IAAI,OAAA,GAAG,KAAK,QAAQ,EAAhB,CAAgB,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAA;AACtE,CAAC;AAED,qBAA4B,CAAc,EAAE,MAA0B;IACpE,OAAO,EAAE,CAAC,IAAI,CAAC;QAEb,IAAI,MAAM,GAAG,IAAI,CAAA;QACjB,IAAI,GAAG,GAAG,uCAAkB,CAAC,CAAC,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAEtD,IAAM,cAAc,GAAG;YACrB,MAAM,CAAC,MAAM;YACb,MAAM,CAAC,MAAM;YACb,MAAM,CAAC,MAAM;YACb,MAAM,CAAC,MAAM;YACb,MAAM,CAAC,MAAM;YACb,MAAM,CAAC,MAAM;YACb,MAAM,CAAC,MAAM;YACb,MAAM,CAAC,MAAM;YACb,MAAM,CAAC,MAAM;YACb,MAAM,CAAC,OAAO;YACd,MAAM,CAAC,OAAO;YACd,MAAM,CAAC,OAAO;YACd,MAAM,CAAC,OAAO;SACf,CAAA;QAED,cAAc,CAAC,OAAO,CAAC,UAAC,KAAK,EAAE,CAAC;YAC9B,IAAM,QAAQ,GAAG,CAAC,GAAG,CAAC,CAAA;YACtB,IAAM,oBAAoB,GAAG,qBAAqB,CAAC,QAAQ,CAAC,CAAA;YAC5D,GAAG,GAAG,kBAAkB,CAAC,GAAG,EAAE,KAAK,CAAC,cAAc,EAAE,oBAAoB,CAAC,CAAA;YACzE,GAAG,GAAG,uCAAkB,CAAC,GAAG,EAAE,KAAK,CAAC,cAAc,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;YAC3D,IAAI,QAAQ,KAAK,EAAE,EAAE;gBACnB,MAAM,GAAG,GAAG,CAAA;aACb;QACH,CAAC,CAAC,CAAA;QAEF,IAAI,MAAM,KAAK,IAAI,EAAE;YACnB,MAAM,IAAI,KAAK,CAAC,+CAA+C,CAAC,CAAA;SACjE;QAED,OAAO;YACL,GAAG,KAAA;YACH,MAAM,EAAE,MAAa;SACtB,CAAA;IAEH,CAAC,CAAC,CAAA;AACJ,CAAC;AA1CD,kCA0CC"}
\ No newline at end of file

build/faceDetectionNet/predictionLayer.js

@@ -5,20 +5,20 @@ var boxPredictionLayer_1 = require("./boxPredictionLayer");
 var pointwiseConvLayer_1 = require("./pointwiseConvLayer");
 function predictionLayer(x, conv11, params) {
     return tf.tidy(function () {
-        var conv0 = pointwiseConvLayer_1.pointwiseConvLayer(x, params.conv_0_params, [1, 1]);
-        var conv1 = pointwiseConvLayer_1.pointwiseConvLayer(conv0, params.conv_1_params, [2, 2]);
-        var conv2 = pointwiseConvLayer_1.pointwiseConvLayer(conv1, params.conv_2_params, [1, 1]);
-        var conv3 = pointwiseConvLayer_1.pointwiseConvLayer(conv2, params.conv_3_params, [2, 2]);
-        var conv4 = pointwiseConvLayer_1.pointwiseConvLayer(conv3, params.conv_4_params, [1, 1]);
-        var conv5 = pointwiseConvLayer_1.pointwiseConvLayer(conv4, params.conv_5_params, [2, 2]);
-        var conv6 = pointwiseConvLayer_1.pointwiseConvLayer(conv5, params.conv_6_params, [1, 1]);
-        var conv7 = pointwiseConvLayer_1.pointwiseConvLayer(conv6, params.conv_7_params, [2, 2]);
-        var boxPrediction0 = boxPredictionLayer_1.boxPredictionLayer(conv11, params.box_predictor_0_params);
-        var boxPrediction1 = boxPredictionLayer_1.boxPredictionLayer(x, params.box_predictor_1_params);
-        var boxPrediction2 = boxPredictionLayer_1.boxPredictionLayer(conv1, params.box_predictor_2_params);
-        var boxPrediction3 = boxPredictionLayer_1.boxPredictionLayer(conv3, params.box_predictor_3_params);
-        var boxPrediction4 = boxPredictionLayer_1.boxPredictionLayer(conv5, params.box_predictor_4_params);
-        var boxPrediction5 = boxPredictionLayer_1.boxPredictionLayer(conv7, params.box_predictor_5_params);
+        var conv0 = pointwiseConvLayer_1.pointwiseConvLayer(x, params.conv_0, [1, 1]);
+        var conv1 = pointwiseConvLayer_1.pointwiseConvLayer(conv0, params.conv_1, [2, 2]);
+        var conv2 = pointwiseConvLayer_1.pointwiseConvLayer(conv1, params.conv_2, [1, 1]);
+        var conv3 = pointwiseConvLayer_1.pointwiseConvLayer(conv2, params.conv_3, [2, 2]);
+        var conv4 = pointwiseConvLayer_1.pointwiseConvLayer(conv3, params.conv_4, [1, 1]);
+        var conv5 = pointwiseConvLayer_1.pointwiseConvLayer(conv4, params.conv_5, [2, 2]);
+        var conv6 = pointwiseConvLayer_1.pointwiseConvLayer(conv5, params.conv_6, [1, 1]);
+        var conv7 = pointwiseConvLayer_1.pointwiseConvLayer(conv6, params.conv_7, [2, 2]);
+        var boxPrediction0 = boxPredictionLayer_1.boxPredictionLayer(conv11, params.box_predictor_0);
+        var boxPrediction1 = boxPredictionLayer_1.boxPredictionLayer(x, params.box_predictor_1);
+        var boxPrediction2 = boxPredictionLayer_1.boxPredictionLayer(conv1, params.box_predictor_2);
+        var boxPrediction3 = boxPredictionLayer_1.boxPredictionLayer(conv3, params.box_predictor_3);
+        var boxPrediction4 = boxPredictionLayer_1.boxPredictionLayer(conv5, params.box_predictor_4);
+        var boxPrediction5 = boxPredictionLayer_1.boxPredictionLayer(conv7, params.box_predictor_5);
         var boxPredictions = tf.concat([
             boxPrediction0.boxPredictionEncoding,
             boxPrediction1.boxPredictionEncoding,

build/faceDetectionNet/predictionLayer.js.map

-{"version":3,"file":"predictionLayer.js","sourceRoot":"","sources":["../../src/faceDetectionNet/predictionLayer.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAE5C,2DAA0D;AAC1D,2DAA0D;AAG1D,yBACE,CAAc,EACd,MAAmB,EACnB,MAA6B;IAE7B,OAAO,EAAE,CAAC,IAAI,CAAC;QAEb,IAAM,KAAK,GAAG,uCAAkB,CAAC,CAAC,EAAE,MAAM,CAAC,aAAa,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QACjE,IAAM,KAAK,GAAG,uCAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,aAAa,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QACrE,IAAM,KAAK,GAAG,uCAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,aAAa,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QACrE,IAAM,KAAK,GAAG,uCAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,aAAa,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QACrE,IAAM,KAAK,GAAG,uCAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,aAAa,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QACrE,IAAM,KAAK,GAAG,uCAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,aAAa,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QACrE,IAAM,KAAK,GAAG,uCAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,aAAa,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QACrE,IAAM,KAAK,GAAG,uCAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,aAAa,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAErE,IAAM,cAAc,GAAG,uCAAkB,CAAC,MAAM,EAAE,MAAM,CAAC,sBAAsB,CAAC,CAAA;QAChF,IAAM,cAAc,GAAG,uCAAkB,CAAC,CAAC,EAAE,MAAM,CAAC,sBAAsB,CAAC,CAAA;QAC3E,IAAM,cAAc,GAAG,uCAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,sBAAsB,CAAC,CAAA;QAC/E,IAAM,cAAc,GAAG,uCAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,sBAAsB,CAAC,CAAA;QAC/E,IAAM,cAAc,GAAG,uCAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,sBAAsB,CAAC,CAAA;QAC/E,IAAM,cAAc,GAAG,uCAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,sBAAsB,CAAC,CAAA;QAE/E,IAAM,cAAc,GAAG,EAAE,CAAC,MAAM,CAAC;YAC/B,cAAc,CAAC,qBAAqB;YACpC,cAAc,CAAC,qBAAqB;YACpC,cAAc,CAAC,qBAAqB;YACpC,cAAc,CAAC,qBAAqB;YACpC,cAAc,CAAC,qBAAqB;YACpC,cAAc,CAAC,qBAAqB;SACrC,EAAE,CAAC,CAAgB,CAAA;QAEpB,IAAM,gBAAgB,GAAG,EAAE,CAAC,MAAM,CAAC;YACjC,cAAc,CAAC,eAAe;YAC9B,cAAc,CAAC,eAAe;YAC9B,cAAc,CAAC,eAAe;YAC9B,cAAc,CAAC,eAAe;YAC9B,cAAc,CAAC,eAAe;YAC9B,cAAc,CAAC,eAAe;SAC/B,EAAE,CAAC,CAAgB,CAAA;QAEpB,OAAO;YACL,cAAc,gBAAA;YACd,gBAAgB,kBAAA;SACjB,CAAA;IACH,CAAC,CAAC,CAAA;AACJ,CAAC;AA9CD,0CA8CC"}
\ No newline at end of file
+{"version":3,"file":"predictionLayer.js","sourceRoot":"","sources":["../../src/faceDetectionNet/predictionLayer.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAE5C,2DAA0D;AAC1D,2DAA0D;AAG1D,yBACE,CAAc,EACd,MAAmB,EACnB,MAA6B;IAE7B,OAAO,EAAE,CAAC,IAAI,CAAC;QAEb,IAAM,KAAK,GAAG,uCAAkB,CAAC,CAAC,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAC1D,IAAM,KAAK,GAAG,uCAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAC9D,IAAM,KAAK,GAAG,uCAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAC9D,IAAM,KAAK,GAAG,uCAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAC9D,IAAM,KAAK,GAAG,uCAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAC9D,IAAM,KAAK,GAAG,uCAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAC9D,IAAM,KAAK,GAAG,uCAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAC9D,IAAM,KAAK,GAAG,uCAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAE9D,IAAM,cAAc,GAAG,uCAAkB,CAAC,MAAM,EAAE,MAAM,CAAC,eAAe,CAAC,CAAA;QACzE,IAAM,cAAc,GAAG,uCAAkB,CAAC,CAAC,EAAE,MAAM,CAAC,eAAe,CAAC,CAAA;QACpE,IAAM,cAAc,GAAG,uCAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,eAAe,CAAC,CAAA;QACxE,IAAM,cAAc,GAAG,uCAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,eAAe,CAAC,CAAA;QACxE,IAAM,cAAc,GAAG,uCAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,eAAe,CAAC,CAAA;QACxE,IAAM,cAAc,GAAG,uCAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,eAAe,CAAC,CAAA;QAExE,IAAM,cAAc,GAAG,EAAE,CAAC,MAAM,CAAC;YAC/B,cAAc,CAAC,qBAAqB;YACpC,cAAc,CAAC,qBAAqB;YACpC,cAAc,CAAC,qBAAqB;YACpC,cAAc,CAAC,qBAAqB;YACpC,cAAc,CAAC,qBAAqB;YACpC,cAAc,CAAC,qBAAqB;SACrC,EAAE,CAAC,CAAgB,CAAA;QAEpB,IAAM,gBAAgB,GAAG,EAAE,CAAC,MAAM,CAAC;YACjC,cAAc,CAAC,eAAe;YAC9B,cAAc,CAAC,eAAe;YAC9B,cAAc,CAAC,eAAe;YAC9B,cAAc,CAAC,eAAe;YAC9B,cAAc,CAAC,eAAe;YAC9B,cAAc,CAAC,eAAe;SAC/B,EAAE,CAAC,CAAgB,CAAA;QAEpB,OAAO;YACL,cAAc,gBAAA;YACd,gBAAgB,kBAAA;SACjB,CAAA;IACH,CAAC,CAAC,CAAA;AACJ,CAAC;AA9CD,0CA8CC"}
\ No newline at end of file

build/faceDetectionNet/types.d.ts

@@ -13,39 +13,51 @@ export declare namespace MobileNetV1 {
         batch_norm_variance: tf.Tensor1D;
     };
     type ConvPairParams = {
-        depthwise_conv_params: DepthwiseConvParams;
-        pointwise_conv_params: PointwiseConvParams;
+        depthwise_conv: DepthwiseConvParams;
+        pointwise_conv: PointwiseConvParams;
     };
     type Params = {
-        conv_0_params: PointwiseConvParams;
-        conv_pair_params: ConvPairParams[];
+        conv_0: PointwiseConvParams;
+        conv_1: ConvPairParams;
+        conv_2: ConvPairParams;
+        conv_3: ConvPairParams;
+        conv_4: ConvPairParams;
+        conv_5: ConvPairParams;
+        conv_6: ConvPairParams;
+        conv_7: ConvPairParams;
+        conv_8: ConvPairParams;
+        conv_9: ConvPairParams;
+        conv_10: ConvPairParams;
+        conv_11: ConvPairParams;
+        conv_12: ConvPairParams;
+        conv_13: ConvPairParams;
     };
 }
 export declare type BoxPredictionParams = {
-    box_encoding_predictor_params: ConvParams;
-    class_predictor_params: ConvParams;
+    box_encoding_predictor: ConvParams;
+    class_predictor: ConvParams;
 };
 export declare type PredictionLayerParams = {
-    conv_0_params: PointwiseConvParams;
-    conv_1_params: PointwiseConvParams;
-    conv_2_params: PointwiseConvParams;
-    conv_3_params: PointwiseConvParams;
-    conv_4_params: PointwiseConvParams;
-    conv_5_params: PointwiseConvParams;
-    conv_6_params: PointwiseConvParams;
-    conv_7_params: PointwiseConvParams;
-    box_predictor_0_params: BoxPredictionParams;
-    box_predictor_1_params: BoxPredictionParams;
-    box_predictor_2_params: BoxPredictionParams;
-    box_predictor_3_params: BoxPredictionParams;
-    box_predictor_4_params: BoxPredictionParams;
-    box_predictor_5_params: BoxPredictionParams;
+    conv_0: PointwiseConvParams;
+    conv_1: PointwiseConvParams;
+    conv_2: PointwiseConvParams;
+    conv_3: PointwiseConvParams;
+    conv_4: PointwiseConvParams;
+    conv_5: PointwiseConvParams;
+    conv_6: PointwiseConvParams;
+    conv_7: PointwiseConvParams;
+    box_predictor_0: BoxPredictionParams;
+    box_predictor_1: BoxPredictionParams;
+    box_predictor_2: BoxPredictionParams;
+    box_predictor_3: BoxPredictionParams;
+    box_predictor_4: BoxPredictionParams;
+    box_predictor_5: BoxPredictionParams;
 };
 export declare type OutputLayerParams = {
     extra_dim: tf.Tensor3D;
 };
 export declare type NetParams = {
-    mobilenetv1_params: MobileNetV1.Params;
-    prediction_layer_params: PredictionLayerParams;
-    output_layer_params: OutputLayerParams;
+    mobilenetv1: MobileNetV1.Params;
+    prediction_layer: PredictionLayerParams;
+    output_layer: OutputLayerParams;
 };

build/faceLandmarkNet/FaceLandmarkNet.d.ts

@@ -5,9 +5,22 @@ import { TNetInput } from '../types';
 import { FaceLandmarks } from './FaceLandmarks';
 import { NetParams } from './types';
 export declare class FaceLandmarkNet extends NeuralNetwork<NetParams> {
-    load(weightsOrUrl: Float32Array | string | undefined): Promise<void>;
-    extractWeights(weights: Float32Array): void;
+    constructor();
     forwardInput(input: NetInput): tf.Tensor2D;
     forward(input: TNetInput): Promise<tf.Tensor2D>;
     detectLandmarks(input: TNetInput): Promise<FaceLandmarks | FaceLandmarks[]>;
+    protected loadQuantizedParams(uri: string | undefined): Promise<{
+        params: NetParams;
+        paramMappings: {
+            originalPath?: string | undefined;
+            paramPath: string;
+        }[];
+    }>;
+    protected extractParams(weights: Float32Array): {
+        params: NetParams;
+        paramMappings: {
+            originalPath?: string | undefined;
+            paramPath: string;
+        }[];
+    };
 }

build/faceLandmarkNet/FaceLandmarkNet.js

@@ -21,57 +21,29 @@ function maxPool(x, strides) {
 var FaceLandmarkNet = /** @class */ (function (_super) {
     tslib_1.__extends(FaceLandmarkNet, _super);
     function FaceLandmarkNet() {
-        return _super !== null && _super.apply(this, arguments) || this;
+        return _super.call(this, 'FaceLandmarkNet') || this;
     }
-    FaceLandmarkNet.prototype.load = function (weightsOrUrl) {
-        return tslib_1.__awaiter(this, void 0, void 0, function () {
-            var _a, paramMappings, params;
-            return tslib_1.__generator(this, function (_b) {
-                switch (_b.label) {
-                    case 0:
-                        if (weightsOrUrl instanceof Float32Array) {
-                            this.extractWeights(weightsOrUrl);
-                            return [2 /*return*/];
-                        }
-                        if (weightsOrUrl && typeof weightsOrUrl !== 'string') {
-                            throw new Error('FaceLandmarkNet.load - expected model uri, or weights as Float32Array');
-                        }
-                        return [4 /*yield*/, loadQuantizedParams_1.loadQuantizedParams(weightsOrUrl)];
-                    case 1:
-                        _a = _b.sent(), paramMappings = _a.paramMappings, params = _a.params;
-                        this._paramMappings = paramMappings;
-                        this._params = params;
-                        return [2 /*return*/];
-                }
-            });
-        });
-    };
-    FaceLandmarkNet.prototype.extractWeights = function (weights) {
-        var _a = extractParams_1.extractParams(weights), paramMappings = _a.paramMappings, params = _a.params;
-        this._paramMappings = paramMappings;
-        this._params = params;
-    };
     FaceLandmarkNet.prototype.forwardInput = function (input) {
-        var params = this._params;
+        var params = this.params;
         if (!params) {
             throw new Error('FaceLandmarkNet - load model before inference');
         }
         return tf.tidy(function () {
             var batchTensor = input.toBatchTensor(128, true);
-            var out = conv(batchTensor, params.conv0_params);
+            var out = conv(batchTensor, params.conv0);
             out = maxPool(out);
-            out = conv(out, params.conv1_params);
-            out = conv(out, params.conv2_params);
+            out = conv(out, params.conv1);
+            out = conv(out, params.conv2);
             out = maxPool(out);
-            out = conv(out, params.conv3_params);
-            out = conv(out, params.conv4_params);
+            out = conv(out, params.conv3);
+            out = conv(out, params.conv4);
             out = maxPool(out);
-            out = conv(out, params.conv5_params);
-            out = conv(out, params.conv6_params);
+            out = conv(out, params.conv5);
+            out = conv(out, params.conv6);
             out = maxPool(out, [1, 1]);
-            out = conv(out, params.conv7_params);
-            var fc0 = tf.relu(fullyConnectedLayer_1.fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc0_params));
-            var fc1 = fullyConnectedLayer_1.fullyConnectedLayer(fc0, params.fc1_params);
+            out = conv(out, params.conv7);
+            var fc0 = tf.relu(fullyConnectedLayer_1.fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc0));
+            var fc1 = fullyConnectedLayer_1.fullyConnectedLayer(fc0, params.fc1);
             var createInterleavedTensor = function (fillX, fillY) {
                 return tf.stack([
                     tf.fill([68], fillX),
@@ -146,6 +118,12 @@ var FaceLandmarkNet = /** @class */ (function (_super) {
             });
         });
     };
+    FaceLandmarkNet.prototype.loadQuantizedParams = function (uri) {
+        return loadQuantizedParams_1.loadQuantizedParams(uri);
+    };
+    FaceLandmarkNet.prototype.extractParams = function (weights) {
+        return extractParams_1.extractParams(weights);
+    };
     return FaceLandmarkNet;
 }(NeuralNetwork_1.NeuralNetwork));
 exports.FaceLandmarkNet = FaceLandmarkNet;

build/faceLandmarkNet/FaceLandmarkNet.js.map

-{"version":3,"file":"FaceLandmarkNet.js","sourceRoot":"","sources":["../../src/faceLandmarkNet/FaceLandmarkNet.ts"],"names":[],"mappings":";;;AAAA,0CAA4C;AAE5C,kDAAiD;AACjD,0DAAyD;AAGzD,kCAAiC;AACjC,4CAA2C;AAE3C,kCAAkC;AAClC,iDAAgD;AAChD,iDAAgD;AAChD,6DAA4D;AAC5D,6DAA4D;AAG5D,cAAc,CAAc,EAAE,MAAkB;IAC9C,OAAO,qBAAS,CAAC,CAAC,EAAE,MAAM,EAAE,OAAO,EAAE,IAAI,CAAC,CAAA;AAC5C,CAAC;AAED,iBAAiB,CAAc,EAAE,OAAkC;IAAlC,wBAAA,EAAA,WAA6B,CAAC,EAAE,CAAC,CAAC;IACjE,OAAO,EAAE,CAAC,OAAO,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,EAAE,OAAO,CAAC,CAAA;AAChD,CAAC;AAED;IAAqC,2CAAwB;IAA7D;;IA2HA,CAAC;IAzHc,8BAAI,GAAjB,UAAkB,YAA+C;;;;;;wBAC/D,IAAI,YAAY,YAAY,YAAY,EAAE;4BACxC,IAAI,CAAC,cAAc,CAAC,YAAY,CAAC,CAAA;4BACjC,sBAAM;yBACP;wBAED,IAAI,YAAY,IAAI,OAAO,YAAY,KAAK,QAAQ,EAAE;4BACpD,MAAM,IAAI,KAAK,CAAC,uEAAuE,CAAC,CAAA;yBACzF;wBAIG,qBAAM,yCAAmB,CAAC,YAAY,CAAC,EAAA;;wBAHrC,KAGF,SAAuC,EAFzC,aAAa,mBAAA,EACb,MAAM,YAAA;wBAGR,IAAI,CAAC,cAAc,GAAG,aAAa,CAAA;wBACnC,IAAI,CAAC,OAAO,GAAG,MAAM,CAAA;;;;;KACtB;IAEM,wCAAc,GAArB,UAAsB,OAAqB;QACnC,IAAA,2CAGoB,EAFxB,gCAAa,EACb,kBAAM,CACkB;QAE1B,IAAI,CAAC,cAAc,GAAG,aAAa,CAAA;QACnC,IAAI,CAAC,OAAO,GAAG,MAAM,CAAA;IACvB,CAAC;IAEM,sCAAY,GAAnB,UAAoB,KAAe;QACjC,IAAM,MAAM,GAAG,IAAI,CAAC,OAAO,CAAA;QAE3B,IAAI,CAAC,MAAM,EAAE;YACX,MAAM,IAAI,KAAK,CAAC,+CAA+C,CAAC,CAAA;SACjE;QAED,OAAO,EAAE,CAAC,IAAI,CAAC;YACb,IAAM,WAAW,GAAG,KAAK,CAAC,aAAa,CAAC,GAAG,EAAE,IAAI,CAAC,CAAA;YAElD,IAAI,GAAG,GAAG,IAAI,CAAC,WAAW,EAAE,MAAM,CAAC,YAAY,CAAC,CAAA;YAChD,GAAG,GAAG,OAAO,CAAC,GAAG,CAAC,CAAA;YAClB,GAAG,GAAG,IAAI,CAAC,GAAG,EAAE,MAAM,CAAC,YAAY,CAAC,CAAA;YACpC,GAAG,GAAG,IAAI,CAAC,GAAG,EAAE,MAAM,CAAC,YAAY,CAAC,CAAA;YACpC,GAAG,GAAG,OAAO,CAAC,GAAG,CAAC,CAAA;YAClB,GAAG,GAAG,IAAI,CAAC,GAAG,EAAE,MAAM,CAAC,YAAY,CAAC,CAAA;YACpC,GAAG,GAAG,IAAI,CAAC,GAAG,EAAE,MAAM,CAAC,YAAY,CAAC,CAAA;YACpC,GAAG,GAAG,OAAO,CAAC,GAAG,CAAC,CAAA;YAClB,GAAG,GAAG,IAAI,CAAC,GAAG,EAAE,MAAM,CAAC,YAAY,CAAC,CAAA;YACpC,GAAG,GAAG,IAAI,CAAC,GAAG,EAAE,MAAM,CAAC,YAAY,CAAC,CAAA;YACpC,GAAG,GAAG,OAAO,CAAC,GAAG,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;YAC1B,GAAG,GAAG,IAAI,CAAC,GAAG,EAAE,MAAM,CAAC,YAAY,CAAC,CAAA;YACpC,IAAM,GAAG,GAAG,EAAE,CAAC,IAAI,CAAC,yCAAmB,CAAC,GAAG,CAAC,IAAI,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,MAAM,CAAC,UAAU,CAAC,CAAC,CAAA;YACvF,IAAM,GAAG,GAAG,yCAAmB,CAAC,GAAG,EAAE,MAAM,CAAC,UAAU,CAAC,CAAA;YAEvD,IAAM,uBAAuB,GAAG,UAAC,KAAa,EAAE,KAAa;gBAC3D,OAAA,EAAE,CAAC,KAAK,CAAC;oBACP,EAAE,CAAC,IAAI,CAAC,CAAC,EAAE,CAAC,EAAE,KAAK,CAAC;oBACpB,EAAE,CAAC,IAAI,CAAC,CAAC,EAAE,CAAC,EAAE,KAAK,CAAC;iBACrB,EAAE,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,EAAE,GAAG,CAAC,CAAC,IAAI,EAAE;YAHzB,CAGyB,CAAA;YAE3B;;;cAGE;YAEF,IAAM,eAAe,GAAG,GAAG;iBACxB,GAAG,CAAC,EAAE,CAAC,KAAK,CAAC,KAAK,CAAC,IAAI,CAAC,KAAK,CAAC,KAAK,CAAC,SAAS,CAAC,EAAE,UAAC,CAAC,EAAE,QAAQ;gBAC3D,OAAA,uBAAuB,CACrB,KAAK,CAAC,WAAW,CAAC,QAAQ,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,aAAa,CAAC,QAAQ,CAAC,EAC7D,KAAK,CAAC,WAAW,CAAC,QAAQ,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,cAAc,CAAC,QAAQ,CAAC,CAC/D;YAHD,CAGC,CACF,CAAC,CAAC;iBACF,GAAG,CAAC,EAAE,CAAC,KAAK,CAAC,KAAK,CAAC,IAAI,CAAC,KAAK,CAAC,KAAK,CAAC,SAAS,CAAC,EAAE,UAAC,CAAC,EAAE,QAAQ;gBAC3D,OAAA,uBAAuB,CACrB,IAAI,CAAC,KAAK,CAAC,KAAK,CAAC,WAAW,CAAC,QAAQ,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAC7C,IAAI,CAAC,KAAK,CAAC,KAAK,CAAC,WAAW,CAAC,QAAQ,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAC9C;YAHD,CAGC,CACF,CAAC,CAAC;iBACF,GAAG,CAAC,EAAE,CAAC,KAAK,CAAC,KAAK,CAAC,IAAI,CAAC,KAAK,CAAC,KAAK,CAAC,SAAS,CAAC,EAAE,UAAC,CAAC,EAAE,QAAQ;gBAC3D,OAAA,uBAAuB,CACrB,KAAK,CAAC,aAAa,CAAC,QAAQ,CAAC,EAC7B,KAAK,CAAC,cAAc,CAAC,QAAQ,CAAC,CAC/B;YAHD,CAGC,CACF,CAAC,CAAC,CAAA;YAEL,OAAO,eAA8B,CAAA;QACvC,CAAC,CAAC,CAAA;IACJ,CAAC;IAEY,iCAAO,GAApB,UAAqB,KAAgB;;;;;;wBAC5B,KAAA,IAAI,CAAC,YAAY,CAAA;wBAAC,qBAAM,uBAAU,CAAC,KAAK,EAAE,IAAI,CAAC,EAAA;4BAAtD,sBAAO,SAAA,IAAI,GAAc,SAA6B,EAAC,EAAA;;;;KACxD;IAEY,yCAAe,GAA5B,UAA6B,KAAgB;;;;;;4BAC1B,qBAAM,uBAAU,CAAC,KAAK,EAAE,IAAI,CAAC,EAAA;;wBAAxC,QAAQ,GAAG,SAA6B;wBAExC,eAAe,GAAG,EAAE,CAAC,IAAI,CAC7B,cAAM,OAAA,EAAE,CAAC,OAAO,CAAC,KAAI,CAAC,YAAY,CAAC,QAAQ,CAAC,CAAC,EAAvC,CAAuC,CAC9C,CAAA;wBAEyB,qBAAM,OAAO,CAAC,GAAG,CAAC,eAAe,CAAC,GAAG,CAC7D,UAAO,cAAc,EAAE,QAAQ;;;;;4CACN,KAAA,CAAA,KAAA,KAAK,CAAA,CAAC,IAAI,CAAA;4CAAC,qBAAM,cAAc,CAAC,IAAI,EAAE,EAAA;;4CAAvD,cAAc,GAAG,cAAW,SAA2B,EAAC;4CACxD,OAAO,GAAG,cAAc,CAAC,MAAM,CAAC,UAAC,CAAC,EAAE,CAAC,IAAK,OAAA,cAAM,CAAC,CAAC,CAAC,EAAT,CAAS,CAAC,CAAA;4CACpD,OAAO,GAAG,cAAc,CAAC,MAAM,CAAC,UAAC,CAAC,EAAE,CAAC,IAAK,OAAA,CAAC,cAAM,CAAC,CAAC,CAAC,EAAV,CAAU,CAAC,CAAA;4CAE3D,sBAAO,IAAI,6BAAa,CACtB,KAAK,CAAC,EAAE,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,UAAC,CAAC,EAAE,CAAC,IAAK,OAAA,IAAI,aAAK,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC,CAAC,CAAC,EAAjC,CAAiC,CAAC,EAClE;oDACE,MAAM,EAAE,QAAQ,CAAC,cAAc,CAAC,QAAQ,CAAC;oDACzC,KAAK,EAAG,QAAQ,CAAC,aAAa,CAAC,QAAQ,CAAC;iDACzC,CACF,EAAA;;;iCACF,CACF,CAAC,EAAA;;wBAdI,iBAAiB,GAAG,SAcxB;wBAEF,eAAe,CAAC,OAAO,CAAC,UAAA,CAAC,IAAI,OAAA,CAAC,CAAC,OAAO,EAAE,EAAX,CAAW,CAAC,CAAA;wBAEzC,sBAAO,QAAQ,CAAC,YAAY;gCAC1B,CAAC,CAAC,iBAAiB;gCACnB,CAAC,CAAC,iBAAiB,CAAC,CAAC,CAAC,EAAA;;;;KACzB;IACH,sBAAC;AAAD,CAAC,AA3HD,CAAqC,6BAAa,GA2HjD;AA3HY,0CAAe"}
\ No newline at end of file
+{"version":3,"file":"FaceLandmarkNet.js","sourceRoot":"","sources":["../../src/faceLandmarkNet/FaceLandmarkNet.ts"],"names":[],"mappings":";;;AAAA,0CAA4C;AAE5C,kDAAiD;AACjD,0DAAyD;AAGzD,kCAAiC;AACjC,4CAA2C;AAE3C,kCAAkC;AAClC,iDAAgD;AAChD,iDAAgD;AAChD,6DAA4D;AAC5D,6DAA4D;AAG5D,cAAc,CAAc,EAAE,MAAkB;IAC9C,OAAO,qBAAS,CAAC,CAAC,EAAE,MAAM,EAAE,OAAO,EAAE,IAAI,CAAC,CAAA;AAC5C,CAAC;AAED,iBAAiB,CAAc,EAAE,OAAkC;IAAlC,wBAAA,EAAA,WAA6B,CAAC,EAAE,CAAC,CAAC;IACjE,OAAO,EAAE,CAAC,OAAO,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,EAAE,OAAO,CAAC,CAAA;AAChD,CAAC;AAED;IAAqC,2CAAwB;IAE3D;eACE,kBAAM,iBAAiB,CAAC;IAC1B,CAAC;IAEM,sCAAY,GAAnB,UAAoB,KAAe;QAEzB,IAAA,oBAAM,CAAS;QAEvB,IAAI,CAAC,MAAM,EAAE;YACX,MAAM,IAAI,KAAK,CAAC,+CAA+C,CAAC,CAAA;SACjE;QAED,OAAO,EAAE,CAAC,IAAI,CAAC;YACb,IAAM,WAAW,GAAG,KAAK,CAAC,aAAa,CAAC,GAAG,EAAE,IAAI,CAAC,CAAA;YAElD,IAAI,GAAG,GAAG,IAAI,CAAC,WAAW,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;YACzC,GAAG,GAAG,OAAO,CAAC,GAAG,CAAC,CAAA;YAClB,GAAG,GAAG,IAAI,CAAC,GAAG,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;YAC7B,GAAG,GAAG,IAAI,CAAC,GAAG,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;YAC7B,GAAG,GAAG,OAAO,CAAC,GAAG,CAAC,CAAA;YAClB,GAAG,GAAG,IAAI,CAAC,GAAG,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;YAC7B,GAAG,GAAG,IAAI,CAAC,GAAG,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;YAC7B,GAAG,GAAG,OAAO,CAAC,GAAG,CAAC,CAAA;YAClB,GAAG,GAAG,IAAI,CAAC,GAAG,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;YAC7B,GAAG,GAAG,IAAI,CAAC,GAAG,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;YAC7B,GAAG,GAAG,OAAO,CAAC,GAAG,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;YAC1B,GAAG,GAAG,IAAI,CAAC,GAAG,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;YAC7B,IAAM,GAAG,GAAG,EAAE,CAAC,IAAI,CAAC,yCAAmB,CAAC,GAAG,CAAC,IAAI,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,MAAM,CAAC,GAAG,CAAC,CAAC,CAAA;YAChF,IAAM,GAAG,GAAG,yCAAmB,CAAC,GAAG,EAAE,MAAM,CAAC,GAAG,CAAC,CAAA;YAEhD,IAAM,uBAAuB,GAAG,UAAC,KAAa,EAAE,KAAa;gBAC3D,OAAA,EAAE,CAAC,KAAK,CAAC;oBACP,EAAE,CAAC,IAAI,CAAC,CAAC,EAAE,CAAC,EAAE,KAAK,CAAC;oBACpB,EAAE,CAAC,IAAI,CAAC,CAAC,EAAE,CAAC,EAAE,KAAK,CAAC;iBACrB,EAAE,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,EAAE,GAAG,CAAC,CAAC,IAAI,EAAE;YAHzB,CAGyB,CAAA;YAE3B;;;cAGE;YAEF,IAAM,eAAe,GAAG,GAAG;iBACxB,GAAG,CAAC,EAAE,CAAC,KAAK,CAAC,KAAK,CAAC,IAAI,CAAC,KAAK,CAAC,KAAK,CAAC,SAAS,CAAC,EAAE,UAAC,CAAC,EAAE,QAAQ;gBAC3D,OAAA,uBAAuB,CACrB,KAAK,CAAC,WAAW,CAAC,QAAQ,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,aAAa,CAAC,QAAQ,CAAC,EAC7D,KAAK,CAAC,WAAW,CAAC,QAAQ,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,cAAc,CAAC,QAAQ,CAAC,CAC/D;YAHD,CAGC,CACF,CAAC,CAAC;iBACF,GAAG,CAAC,EAAE,CAAC,KAAK,CAAC,KAAK,CAAC,IAAI,CAAC,KAAK,CAAC,KAAK,CAAC,SAAS,CAAC,EAAE,UAAC,CAAC,EAAE,QAAQ;gBAC3D,OAAA,uBAAuB,CACrB,IAAI,CAAC,KAAK,CAAC,KAAK,CAAC,WAAW,CAAC,QAAQ,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAC7C,IAAI,CAAC,KAAK,CAAC,KAAK,CAAC,WAAW,CAAC,QAAQ,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAC9C;YAHD,CAGC,CACF,CAAC,CAAC;iBACF,GAAG,CAAC,EAAE,CAAC,KAAK,CAAC,KAAK,CAAC,IAAI,CAAC,KAAK,CAAC,KAAK,CAAC,SAAS,CAAC,EAAE,UAAC,CAAC,EAAE,QAAQ;gBAC3D,OAAA,uBAAuB,CACrB,KAAK,CAAC,aAAa,CAAC,QAAQ,CAAC,EAC7B,KAAK,CAAC,cAAc,CAAC,QAAQ,CAAC,CAC/B;YAHD,CAGC,CACF,CAAC,CAAC,CAAA;YAEL,OAAO,eAA8B,CAAA;QACvC,CAAC,CAAC,CAAA;IACJ,CAAC;IAEY,iCAAO,GAApB,UAAqB,KAAgB;;;;;;wBAC5B,KAAA,IAAI,CAAC,YAAY,CAAA;wBAAC,qBAAM,uBAAU,CAAC,KAAK,EAAE,IAAI,CAAC,EAAA;4BAAtD,sBAAO,SAAA,IAAI,GAAc,SAA6B,EAAC,EAAA;;;;KACxD;IAEY,yCAAe,GAA5B,UAA6B,KAAgB;;;;;;4BAC1B,qBAAM,uBAAU,CAAC,KAAK,EAAE,IAAI,CAAC,EAAA;;wBAAxC,QAAQ,GAAG,SAA6B;wBAExC,eAAe,GAAG,EAAE,CAAC,IAAI,CAC7B,cAAM,OAAA,EAAE,CAAC,OAAO,CAAC,KAAI,CAAC,YAAY,CAAC,QAAQ,CAAC,CAAC,EAAvC,CAAuC,CAC9C,CAAA;wBAEyB,qBAAM,OAAO,CAAC,GAAG,CAAC,eAAe,CAAC,GAAG,CAC7D,UAAO,cAAc,EAAE,QAAQ;;;;;4CACN,KAAA,CAAA,KAAA,KAAK,CAAA,CAAC,IAAI,CAAA;4CAAC,qBAAM,cAAc,CAAC,IAAI,EAAE,EAAA;;4CAAvD,cAAc,GAAG,cAAW,SAA2B,EAAC;4CACxD,OAAO,GAAG,cAAc,CAAC,MAAM,CAAC,UAAC,CAAC,EAAE,CAAC,IAAK,OAAA,cAAM,CAAC,CAAC,CAAC,EAAT,CAAS,CAAC,CAAA;4CACpD,OAAO,GAAG,cAAc,CAAC,MAAM,CAAC,UAAC,CAAC,EAAE,CAAC,IAAK,OAAA,CAAC,cAAM,CAAC,CAAC,CAAC,EAAV,CAAU,CAAC,CAAA;4CAE3D,sBAAO,IAAI,6BAAa,CACtB,KAAK,CAAC,EAAE,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,UAAC,CAAC,EAAE,CAAC,IAAK,OAAA,IAAI,aAAK,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC,CAAC,CAAC,EAAjC,CAAiC,CAAC,EAClE;oDACE,MAAM,EAAE,QAAQ,CAAC,cAAc,CAAC,QAAQ,CAAC;oDACzC,KAAK,EAAG,QAAQ,CAAC,aAAa,CAAC,QAAQ,CAAC;iDACzC,CACF,EAAA;;;iCACF,CACF,CAAC,EAAA;;wBAdI,iBAAiB,GAAG,SAcxB;wBAEF,eAAe,CAAC,OAAO,CAAC,UAAA,CAAC,IAAI,OAAA,CAAC,CAAC,OAAO,EAAE,EAAX,CAAW,CAAC,CAAA;wBAEzC,sBAAO,QAAQ,CAAC,YAAY;gCAC1B,CAAC,CAAC,iBAAiB;gCACnB,CAAC,CAAC,iBAAiB,CAAC,CAAC,CAAC,EAAA;;;;KACzB;IAES,6CAAmB,GAA7B,UAA8B,GAAuB;QACnD,OAAO,yCAAmB,CAAC,GAAG,CAAC,CAAA;IACjC,CAAC;IAES,uCAAa,GAAvB,UAAwB,OAAqB;QAC3C,OAAO,6BAAa,CAAC,OAAO,CAAC,CAAA;IAC/B,CAAC;IACH,sBAAC;AAAD,CAAC,AA5GD,CAAqC,6BAAa,GA4GjD;AA5GY,0CAAe"}
\ No newline at end of file

build/faceLandmarkNet/extractParams.js

 "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
 var tf = require("@tensorflow/tfjs-core");
-var extractConvParamsFactory_1 = require("../commons/extractConvParamsFactory");
 var extractWeightsFactory_1 = require("../commons/extractWeightsFactory");
 function extractParams(weights) {
     var paramMappings = [];
     var _a = extractWeightsFactory_1.extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
-    var extractConvParams = extractConvParamsFactory_1.extractConvParamsFactory(extractWeights, paramMappings);
+    function extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix) {
+        var filters = tf.tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
+        var bias = tf.tensor1d(extractWeights(channelsOut));
+        paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/bias" });
+        return { filters: filters, bias: bias };
+    }
     function extractFcParams(channelsIn, channelsOut, mappedPrefix) {
         var fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);
         var fc_bias = tf.tensor1d(extractWeights(channelsOut));
@@ -16,32 +20,32 @@ function extractParams(weights) {
             bias: fc_bias
         };
     }
-    var conv0_params = extractConvParams(3, 32, 3, 'conv0_params');
-    var conv1_params = extractConvParams(32, 64, 3, 'conv1_params');
-    var conv2_params = extractConvParams(64, 64, 3, 'conv2_params');
-    var conv3_params = extractConvParams(64, 64, 3, 'conv3_params');
-    var conv4_params = extractConvParams(64, 64, 3, 'conv4_params');
-    var conv5_params = extractConvParams(64, 128, 3, 'conv5_params');
-    var conv6_params = extractConvParams(128, 128, 3, 'conv6_params');
-    var conv7_params = extractConvParams(128, 256, 3, 'conv7_params');
-    var fc0_params = extractFcParams(6400, 1024, 'fc0_params');
-    var fc1_params = extractFcParams(1024, 136, 'fc1_params');
+    var conv0 = extractConvParams(3, 32, 3, 'conv0');
+    var conv1 = extractConvParams(32, 64, 3, 'conv1');
+    var conv2 = extractConvParams(64, 64, 3, 'conv2');
+    var conv3 = extractConvParams(64, 64, 3, 'conv3');
+    var conv4 = extractConvParams(64, 64, 3, 'conv4');
+    var conv5 = extractConvParams(64, 128, 3, 'conv5');
+    var conv6 = extractConvParams(128, 128, 3, 'conv6');
+    var conv7 = extractConvParams(128, 256, 3, 'conv7');
+    var fc0 = extractFcParams(6400, 1024, 'fc0');
+    var fc1 = extractFcParams(1024, 136, 'fc1');
     if (getRemainingWeights().length !== 0) {
         throw new Error("weights remaing after extract: " + getRemainingWeights().length);
     }
     return {
         paramMappings: paramMappings,
         params: {
-            conv0_params: conv0_params,
-            conv1_params: conv1_params,
-            conv2_params: conv2_params,
-            conv3_params: conv3_params,
-            conv4_params: conv4_params,
-            conv5_params: conv5_params,
-            conv6_params: conv6_params,
-            conv7_params: conv7_params,
-            fc0_params: fc0_params,
-            fc1_params: fc1_params
+            conv0: conv0,
+            conv1: conv1,
+            conv2: conv2,
+            conv3: conv3,
+            conv4: conv4,
+            conv5: conv5,
+            conv6: conv6,
+            conv7: conv7,
+            fc0: fc0,
+            fc1: fc1
         }
     };
 }

build/faceLandmarkNet/extractParams.js.map

-{"version":3,"file":"extractParams.js","sourceRoot":"","sources":["../../src/faceLandmarkNet/extractParams.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAE5C,gFAA+E;AAC/E,0EAAyE;AAIzE,uBAA8B,OAAqB;IACjD,IAAM,aAAa,GAAmB,EAAE,CAAA;IAElC,IAAA,2DAG4B,EAFhC,kCAAc,EACd,4CAAmB,CACa;IAElC,IAAM,iBAAiB,GAAG,mDAAwB,CAAC,cAAc,EAAE,aAAa,CAAC,CAAA;IAEjF,yBAAyB,UAAkB,EAAE,WAAmB,EAAE,YAAoB;QACpF,IAAM,UAAU,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,UAAU,GAAG,WAAW,CAAC,EAAE,CAAC,UAAU,EAAE,WAAW,CAAC,CAAC,CAAA;QACnG,IAAM,OAAO,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,WAAW,CAAC,CAAC,CAAA;QAExD,aAAa,CAAC,IAAI,CAChB,EAAE,SAAS,EAAK,YAAY,aAAU,EAAE,EACxC,EAAE,SAAS,EAAK,YAAY,UAAO,EAAE,CACtC,CAAA;QAED,OAAO;YACL,OAAO,EAAE,UAAU;YACnB,IAAI,EAAE,OAAO;SACd,CAAA;IACH,CAAC;IAED,IAAM,YAAY,GAAG,iBAAiB,CAAC,CAAC,EAAE,EAAE,EAAE,CAAC,EAAE,cAAc,CAAC,CAAA;IAChE,IAAM,YAAY,GAAG,iBAAiB,CAAC,EAAE,EAAE,EAAE,EAAE,CAAC,EAAE,cAAc,CAAC,CAAA;IACjE,IAAM,YAAY,GAAG,iBAAiB,CAAC,EAAE,EAAE,EAAE,EAAE,CAAC,EAAE,cAAc,CAAC,CAAA;IACjE,IAAM,YAAY,GAAG,iBAAiB,CAAC,EAAE,EAAE,EAAE,EAAE,CAAC,EAAE,cAAc,CAAC,CAAA;IACjE,IAAM,YAAY,GAAG,iBAAiB,CAAC,EAAE,EAAE,EAAE,EAAE,CAAC,EAAE,cAAc,CAAC,CAAA;IACjE,IAAM,YAAY,GAAG,iBAAiB,CAAC,EAAE,EAAE,GAAG,EAAE,CAAC,EAAE,cAAc,CAAC,CAAA;IAClE,IAAM,YAAY,GAAG,iBAAiB,CAAC,GAAG,EAAE,GAAG,EAAE,CAAC,EAAE,cAAc,CAAC,CAAA;IACnE,IAAM,YAAY,GAAG,iBAAiB,CAAC,GAAG,EAAE,GAAG,EAAE,CAAC,EAAE,cAAc,CAAC,CAAA;IACnE,IAAM,UAAU,GAAG,eAAe,CAAC,IAAI,EAAE,IAAI,EAAE,YAAY,CAAC,CAAA;IAC5D,IAAM,UAAU,GAAG,eAAe,CAAC,IAAI,EAAE,GAAG,EAAE,YAAY,CAAC,CAAA;IAE3D,IAAI,mBAAmB,EAAE,CAAC,MAAM,KAAK,CAAC,EAAE;QACtC,MAAM,IAAI,KAAK,CAAC,oCAAkC,mBAAmB,EAAE,CAAC,MAAQ,CAAC,CAAA;KAClF;IAED,OAAO;QACL,aAAa,eAAA;QACb,MAAM,EAAE;YACN,YAAY,cAAA;YACZ,YAAY,cAAA;YACZ,YAAY,cAAA;YACZ,YAAY,cAAA;YACZ,YAAY,cAAA;YACZ,YAAY,cAAA;YACZ,YAAY,cAAA;YACZ,YAAY,cAAA;YACZ,UAAU,YAAA;YACV,UAAU,YAAA;SACX;KACF,CAAA;AACH,CAAC;AAvDD,sCAuDC"}
\ No newline at end of file
+{"version":3,"file":"extractParams.js","sourceRoot":"","sources":["../../src/faceLandmarkNet/extractParams.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAE5C,0EAAyE;AAIzE,uBAA8B,OAAqB;IAEjD,IAAM,aAAa,GAAmB,EAAE,CAAA;IAElC,IAAA,2DAG4B,EAFhC,kCAAc,EACd,4CAAmB,CACa;IAElC,2BACE,UAAkB,EAClB,WAAmB,EACnB,UAAkB,EAClB,YAAoB;QAGpB,IAAM,OAAO,GAAG,EAAE,CAAC,QAAQ,CACzB,cAAc,CAAC,UAAU,GAAG,WAAW,GAAG,UAAU,GAAG,UAAU,CAAC,EAClE,CAAC,UAAU,EAAE,UAAU,EAAE,UAAU,EAAE,WAAW,CAAC,CAClD,CAAA;QACD,IAAM,IAAI,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,WAAW,CAAC,CAAC,CAAA;QAErD,aAAa,CAAC,IAAI,CAChB,EAAE,SAAS,EAAK,YAAY,aAAU,EAAE,EACxC,EAAE,SAAS,EAAK,YAAY,UAAO,EAAE,CACtC,CAAA;QAED,OAAO,EAAE,OAAO,SAAA,EAAE,IAAI,MAAA,EAAE,CAAA;IAC1B,CAAC;IAED,yBAAyB,UAAkB,EAAE,WAAmB,EAAE,YAAoB;QAEpF,IAAM,UAAU,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,UAAU,GAAG,WAAW,CAAC,EAAE,CAAC,UAAU,EAAE,WAAW,CAAC,CAAC,CAAA;QACnG,IAAM,OAAO,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,WAAW,CAAC,CAAC,CAAA;QAExD,aAAa,CAAC,IAAI,CAChB,EAAE,SAAS,EAAK,YAAY,aAAU,EAAE,EACxC,EAAE,SAAS,EAAK,YAAY,UAAO,EAAE,CACtC,CAAA;QAED,OAAO;YACL,OAAO,EAAE,UAAU;YACnB,IAAI,EAAE,OAAO;SACd,CAAA;IACH,CAAC;IAED,IAAM,KAAK,GAAG,iBAAiB,CAAC,CAAC,EAAE,EAAE,EAAE,CAAC,EAAE,OAAO,CAAC,CAAA;IAClD,IAAM,KAAK,GAAG,iBAAiB,CAAC,EAAE,EAAE,EAAE,EAAE,CAAC,EAAE,OAAO,CAAC,CAAA;IACnD,IAAM,KAAK,GAAG,iBAAiB,CAAC,EAAE,EAAE,EAAE,EAAE,CAAC,EAAE,OAAO,CAAC,CAAA;IACnD,IAAM,KAAK,GAAG,iBAAiB,CAAC,EAAE,EAAE,EAAE,EAAE,CAAC,EAAE,OAAO,CAAC,CAAA;IACnD,IAAM,KAAK,GAAG,iBAAiB,CAAC,EAAE,EAAE,EAAE,EAAE,CAAC,EAAE,OAAO,CAAC,CAAA;IACnD,IAAM,KAAK,GAAG,iBAAiB,CAAC,EAAE,EAAE,GAAG,EAAE,CAAC,EAAE,OAAO,CAAC,CAAA;IACpD,IAAM,KAAK,GAAG,iBAAiB,CAAC,GAAG,EAAE,GAAG,EAAE,CAAC,EAAE,OAAO,CAAC,CAAA;IACrD,IAAM,KAAK,GAAG,iBAAiB,CAAC,GAAG,EAAE,GAAG,EAAE,CAAC,EAAE,OAAO,CAAC,CAAA;IACrD,IAAM,GAAG,GAAG,eAAe,CAAC,IAAI,EAAE,IAAI,EAAE,KAAK,CAAC,CAAA;IAC9C,IAAM,GAAG,GAAG,eAAe,CAAC,IAAI,EAAE,GAAG,EAAE,KAAK,CAAC,CAAA;IAE7C,IAAI,mBAAmB,EAAE,CAAC,MAAM,KAAK,CAAC,EAAE;QACtC,MAAM,IAAI,KAAK,CAAC,oCAAkC,mBAAmB,EAAE,CAAC,MAAQ,CAAC,CAAA;KAClF;IAED,OAAO;QACL,aAAa,eAAA;QACb,MAAM,EAAE;YACN,KAAK,OAAA;YACL,KAAK,OAAA;YACL,KAAK,OAAA;YACL,KAAK,OAAA;YACL,KAAK,OAAA;YACL,KAAK,OAAA;YACL,KAAK,OAAA;YACL,KAAK,OAAA;YACL,GAAG,KAAA;YACH,GAAG,KAAA;SACJ;KACF,CAAA;AACH,CAAC;AA5ED,sCA4EC"}
\ No newline at end of file

build/faceLandmarkNet/loadQuantizedParams.js

 "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
 var tslib_1 = require("tslib");
-var extractWeightEntry_1 = require("../commons/extractWeightEntry");
+var disposeUnusedWeightTensors_1 = require("../commons/disposeUnusedWeightTensors");
+var extractWeightEntryFactory_1 = require("../commons/extractWeightEntryFactory");
 var loadWeightMap_1 = require("../commons/loadWeightMap");
 var DEFAULT_MODEL_NAME = 'face_landmark_68_model';
 function extractorsFactory(weightMap, paramMappings) {
+    var extractWeightEntry = extractWeightEntryFactory_1.extractWeightEntryFactory(weightMap, paramMappings);
     function extractConvParams(prefix, mappedPrefix) {
-        var filtersEntry = extractWeightEntry_1.extractWeightEntry(weightMap, prefix + "/kernel", 4);
-        var biasEntry = extractWeightEntry_1.extractWeightEntry(weightMap, prefix + "/bias", 1);
-        paramMappings.push({ originalPath: filtersEntry.path, paramPath: mappedPrefix + "/filters" }, { originalPath: biasEntry.path, paramPath: mappedPrefix + "/bias" });
-        return {
-            filters: filtersEntry.tensor,
-            bias: biasEntry.tensor
-        };
+        var filters = extractWeightEntry(prefix + "/kernel", 4, mappedPrefix + "/filters");
+        var bias = extractWeightEntry(prefix + "/bias", 1, mappedPrefix + "/bias");
+        return { filters: filters, bias: bias };
     }
     function extractFcParams(prefix, mappedPrefix) {
-        var weightsEntry = extractWeightEntry_1.extractWeightEntry(weightMap, prefix + "/kernel", 2);
-        var biasEntry = extractWeightEntry_1.extractWeightEntry(weightMap, prefix + "/bias", 1);
-        paramMappings.push({ originalPath: weightsEntry.path, paramPath: mappedPrefix + "/weights" }, { originalPath: biasEntry.path, paramPath: mappedPrefix + "/bias" });
-        return {
-            weights: weightsEntry.tensor,
-            bias: biasEntry.tensor
-        };
+        var weights = extractWeightEntry(prefix + "/kernel", 2, mappedPrefix + "/weights");
+        var bias = extractWeightEntry(prefix + "/bias", 1, mappedPrefix + "/bias");
+        return { weights: weights, bias: bias };
     }
     return {
         extractConvParams: extractConvParams,
@@ -39,17 +33,18 @@ function loadQuantizedParams(uri) {
                     paramMappings = [];
                     _a = extractorsFactory(weightMap, paramMappings), extractConvParams = _a.extractConvParams, extractFcParams = _a.extractFcParams;
                     params = {
-                        conv0_params: extractConvParams('conv2d_0', 'conv0_params'),
-                        conv1_params: extractConvParams('conv2d_1', 'conv1_params'),
-                        conv2_params: extractConvParams('conv2d_2', 'conv2_params'),
-                        conv3_params: extractConvParams('conv2d_3', 'conv3_params'),
-                        conv4_params: extractConvParams('conv2d_4', 'conv4_params'),
-                        conv5_params: extractConvParams('conv2d_5', 'conv5_params'),
-                        conv6_params: extractConvParams('conv2d_6', 'conv6_params'),
-                        conv7_params: extractConvParams('conv2d_7', 'conv7_params'),
-                        fc0_params: extractFcParams('dense', 'fc0_params'),
-                        fc1_params: extractFcParams('logits', 'fc1_params')
+                        conv0: extractConvParams('conv2d_0', 'conv0'),
+                        conv1: extractConvParams('conv2d_1', 'conv1'),
+                        conv2: extractConvParams('conv2d_2', 'conv2'),
+                        conv3: extractConvParams('conv2d_3', 'conv3'),
+                        conv4: extractConvParams('conv2d_4', 'conv4'),
+                        conv5: extractConvParams('conv2d_5', 'conv5'),
+                        conv6: extractConvParams('conv2d_6', 'conv6'),
+                        conv7: extractConvParams('conv2d_7', 'conv7'),
+                        fc0: extractFcParams('dense', 'fc0'),
+                        fc1: extractFcParams('logits', 'fc1')
                     };
+                    disposeUnusedWeightTensors_1.disposeUnusedWeightTensors(weightMap, paramMappings);
                     return [2 /*return*/, { params: params, paramMappings: paramMappings }];
             }
         });

build/faceLandmarkNet/loadQuantizedParams.js.map

-{"version":3,"file":"loadQuantizedParams.js","sourceRoot":"","sources":["../../src/faceLandmarkNet/loadQuantizedParams.ts"],"names":[],"mappings":";;;AAEA,oEAAmE;AACnE,0DAAyD;AAIzD,IAAM,kBAAkB,GAAG,wBAAwB,CAAA;AAEnD,2BAA2B,SAAc,EAAE,aAA6B;IAEtE,2BAA2B,MAAc,EAAE,YAAoB;QAC7D,IAAM,YAAY,GAAG,uCAAkB,CAAC,SAAS,EAAK,MAAM,YAAS,EAAE,CAAC,CAAC,CAAA;QACzE,IAAM,SAAS,GAAG,uCAAkB,CAAC,SAAS,EAAK,MAAM,UAAO,EAAE,CAAC,CAAC,CAAA;QACpE,aAAa,CAAC,IAAI,CAChB,EAAE,YAAY,EAAE,YAAY,CAAC,IAAI,EAAE,SAAS,EAAK,YAAY,aAAU,EAAE,EACzE,EAAE,YAAY,EAAE,SAAS,CAAC,IAAI,EAAE,SAAS,EAAK,YAAY,UAAO,EAAE,CACpE,CAAA;QACD,OAAO;YACL,OAAO,EAAE,YAAY,CAAC,MAAqB;YAC3C,IAAI,EAAE,SAAS,CAAC,MAAqB;SACtC,CAAA;IACH,CAAC;IAED,yBAAyB,MAAc,EAAE,YAAoB;QAC3D,IAAM,YAAY,GAAG,uCAAkB,CAAC,SAAS,EAAK,MAAM,YAAS,EAAE,CAAC,CAAC,CAAA;QACzE,IAAM,SAAS,GAAG,uCAAkB,CAAC,SAAS,EAAK,MAAM,UAAO,EAAE,CAAC,CAAC,CAAA;QACpE,aAAa,CAAC,IAAI,CAChB,EAAE,YAAY,EAAE,YAAY,CAAC,IAAI,EAAE,SAAS,EAAK,YAAY,aAAU,EAAE,EACzE,EAAE,YAAY,EAAE,SAAS,CAAC,IAAI,EAAE,SAAS,EAAK,YAAY,UAAO,EAAE,CACpE,CAAA;QACD,OAAO;YACL,OAAO,EAAE,YAAY,CAAC,MAAqB;YAC3C,IAAI,EAAE,SAAS,CAAC,MAAqB;SACtC,CAAA;IACH,CAAC;IAED,OAAO;QACL,iBAAiB,mBAAA;QACjB,eAAe,iBAAA;KAChB,CAAA;AACH,CAAC;AAED,6BACE,GAAuB;;;;;wBAGL,qBAAM,6BAAa,CAAC,GAAG,EAAE,kBAAkB,CAAC,EAAA;;oBAAxD,SAAS,GAAG,SAA4C;oBACxD,aAAa,GAAmB,EAAE,CAAA;oBAElC,KAGF,iBAAiB,CAAC,SAAS,EAAE,aAAa,CAAC,EAF7C,iBAAiB,uBAAA,EACjB,eAAe,qBAAA,CAC8B;oBAEzC,MAAM,GAAG;wBACb,YAAY,EAAE,iBAAiB,CAAC,UAAU,EAAE,cAAc,CAAC;wBAC3D,YAAY,EAAE,iBAAiB,CAAC,UAAU,EAAE,cAAc,CAAC;wBAC3D,YAAY,EAAE,iBAAiB,CAAC,UAAU,EAAE,cAAc,CAAC;wBAC3D,YAAY,EAAE,iBAAiB,CAAC,UAAU,EAAE,cAAc,CAAC;wBAC3D,YAAY,EAAE,iBAAiB,CAAC,UAAU,EAAE,cAAc,CAAC;wBAC3D,YAAY,EAAE,iBAAiB,CAAC,UAAU,EAAE,cAAc,CAAC;wBAC3D,YAAY,EAAE,iBAAiB,CAAC,UAAU,EAAE,cAAc,CAAC;wBAC3D,YAAY,EAAE,iBAAiB,CAAC,UAAU,EAAE,cAAc,CAAC;wBAC3D,UAAU,EAAE,eAAe,CAAC,OAAO,EAAE,YAAY,CAAC;wBAClD,UAAU,EAAE,eAAe,CAAC,QAAQ,EAAE,YAAY,CAAC;qBACpD,CAAA;oBAED,sBAAO,EAAE,MAAM,QAAA,EAAE,aAAa,eAAA,EAAE,EAAA;;;;CACjC;AA1BD,kDA0BC"}
\ No newline at end of file
+{"version":3,"file":"loadQuantizedParams.js","sourceRoot":"","sources":["../../src/faceLandmarkNet/loadQuantizedParams.ts"],"names":[],"mappings":";;;AAEA,oFAAmF;AACnF,kFAAiF;AACjF,0DAAyD;AAIzD,IAAM,kBAAkB,GAAG,wBAAwB,CAAA;AAEnD,2BAA2B,SAAc,EAAE,aAA6B;IAEtE,IAAM,kBAAkB,GAAG,qDAAyB,CAAC,SAAS,EAAE,aAAa,CAAC,CAAA;IAE9E,2BAA2B,MAAc,EAAE,YAAoB;QAC7D,IAAM,OAAO,GAAG,kBAAkB,CAAiB,MAAM,YAAS,EAAE,CAAC,EAAK,YAAY,aAAU,CAAC,CAAA;QACjG,IAAM,IAAI,GAAG,kBAAkB,CAAiB,MAAM,UAAO,EAAE,CAAC,EAAK,YAAY,UAAO,CAAC,CAAA;QAEzF,OAAO,EAAE,OAAO,SAAA,EAAE,IAAI,MAAA,EAAE,CAAA;IAC1B,CAAC;IAED,yBAAyB,MAAc,EAAE,YAAoB;QAC3D,IAAM,OAAO,GAAG,kBAAkB,CAAiB,MAAM,YAAS,EAAE,CAAC,EAAK,YAAY,aAAU,CAAC,CAAA;QACjG,IAAM,IAAI,GAAG,kBAAkB,CAAiB,MAAM,UAAO,EAAE,CAAC,EAAK,YAAY,UAAO,CAAC,CAAA;QAEzF,OAAO,EAAE,OAAO,SAAA,EAAE,IAAI,MAAA,EAAE,CAAA;IAC1B,CAAC;IAED,OAAO;QACL,iBAAiB,mBAAA;QACjB,eAAe,iBAAA;KAChB,CAAA;AACH,CAAC;AAED,6BACE,GAAuB;;;;;wBAGL,qBAAM,6BAAa,CAAC,GAAG,EAAE,kBAAkB,CAAC,EAAA;;oBAAxD,SAAS,GAAG,SAA4C;oBACxD,aAAa,GAAmB,EAAE,CAAA;oBAElC,KAGF,iBAAiB,CAAC,SAAS,EAAE,aAAa,CAAC,EAF7C,iBAAiB,uBAAA,EACjB,eAAe,qBAAA,CAC8B;oBAEzC,MAAM,GAAG;wBACb,KAAK,EAAE,iBAAiB,CAAC,UAAU,EAAE,OAAO,CAAC;wBAC7C,KAAK,EAAE,iBAAiB,CAAC,UAAU,EAAE,OAAO,CAAC;wBAC7C,KAAK,EAAE,iBAAiB,CAAC,UAAU,EAAE,OAAO,CAAC;wBAC7C,KAAK,EAAE,iBAAiB,CAAC,UAAU,EAAE,OAAO,CAAC;wBAC7C,KAAK,EAAE,iBAAiB,CAAC,UAAU,EAAE,OAAO,CAAC;wBAC7C,KAAK,EAAE,iBAAiB,CAAC,UAAU,EAAE,OAAO,CAAC;wBAC7C,KAAK,EAAE,iBAAiB,CAAC,UAAU,EAAE,OAAO,CAAC;wBAC7C,KAAK,EAAE,iBAAiB,CAAC,UAAU,EAAE,OAAO,CAAC;wBAC7C,GAAG,EAAE,eAAe,CAAC,OAAO,EAAE,KAAK,CAAC;wBACpC,GAAG,EAAE,eAAe,CAAC,QAAQ,EAAE,KAAK,CAAC;qBACtC,CAAA;oBAED,uDAA0B,CAAC,SAAS,EAAE,aAAa,CAAC,CAAA;oBAEpD,sBAAO,EAAE,MAAM,QAAA,EAAE,aAAa,eAAA,EAAE,EAAA;;;;CACjC;AA5BD,kDA4BC"}
\ No newline at end of file

build/faceLandmarkNet/types.d.ts

@@ -5,14 +5,14 @@ export declare type FCParams = {
     bias: tf.Tensor1D;
 };
 export declare type NetParams = {
-    conv0_params: ConvParams;
-    conv1_params: ConvParams;
-    conv2_params: ConvParams;
-    conv3_params: ConvParams;
-    conv4_params: ConvParams;
-    conv5_params: ConvParams;
-    conv6_params: ConvParams;
-    conv7_params: ConvParams;
-    fc0_params: FCParams;
-    fc1_params: FCParams;
+    conv0: ConvParams;
+    conv1: ConvParams;
+    conv2: ConvParams;
+    conv3: ConvParams;
+    conv4: ConvParams;
+    conv5: ConvParams;
+    conv6: ConvParams;
+    conv7: ConvParams;
+    fc0: FCParams;
+    fc1: FCParams;
 };

build/faceRecognitionNet/FaceRecognitionNet.d.ts

 import * as tf from '@tensorflow/tfjs-core';
+import { NeuralNetwork } from '../commons/NeuralNetwork';
 import { NetInput } from '../NetInput';
 import { TNetInput } from '../types';
-export declare class FaceRecognitionNet {
-    private _params;
-    load(weightsOrUrl: Float32Array | string | undefined): Promise<void>;
-    extractWeights(weights: Float32Array): void;
+import { NetParams } from './types';
+export declare class FaceRecognitionNet extends NeuralNetwork<NetParams> {
+    constructor();
     forwardInput(input: NetInput): tf.Tensor2D;
     forward(input: TNetInput): Promise<tf.Tensor2D>;
     computeFaceDescriptor(input: TNetInput): Promise<Float32Array | Float32Array[]>;
+    protected loadQuantizedParams(uri: string | undefined): Promise<{
+        params: NetParams;
+        paramMappings: {
+            originalPath?: string | undefined;
+            paramPath: string;
+        }[];
+    }>;
+    protected extractParams(weights: Float32Array): {
+        params: NetParams;
+        paramMappings: {
+            originalPath?: string | undefined;
+            paramPath: string;
+        }[];
+    };
 }

build/faceRecognitionNet/FaceRecognitionNet.js

@@ -2,66 +2,44 @@
 Object.defineProperty(exports, "__esModule", { value: true });
 var tslib_1 = require("tslib");
 var tf = require("@tensorflow/tfjs-core");
+var NeuralNetwork_1 = require("../commons/NeuralNetwork");
 var toNetInput_1 = require("../toNetInput");
 var convLayer_1 = require("./convLayer");
 var extractParams_1 = require("./extractParams");
 var loadQuantizedParams_1 = require("./loadQuantizedParams");
 var normalize_1 = require("./normalize");
 var residualLayer_1 = require("./residualLayer");
-var FaceRecognitionNet = /** @class */ (function () {
+var FaceRecognitionNet = /** @class */ (function (_super) {
+    tslib_1.__extends(FaceRecognitionNet, _super);
     function FaceRecognitionNet() {
+        return _super.call(this, 'FaceRecognitionNet') || this;
     }
-    FaceRecognitionNet.prototype.load = function (weightsOrUrl) {
-        return tslib_1.__awaiter(this, void 0, void 0, function () {
-            var _a;
-            return tslib_1.__generator(this, function (_b) {
-                switch (_b.label) {
-                    case 0:
-                        if (weightsOrUrl instanceof Float32Array) {
-                            this.extractWeights(weightsOrUrl);
-                            return [2 /*return*/];
-                        }
-                        if (weightsOrUrl && typeof weightsOrUrl !== 'string') {
-                            throw new Error('FaceLandmarkNet.load - expected model uri, or weights as Float32Array');
-                        }
-                        _a = this;
-                        return [4 /*yield*/, loadQuantizedParams_1.loadQuantizedParams(weightsOrUrl)];
-                    case 1:
-                        _a._params = _b.sent();
-                        return [2 /*return*/];
-                }
-            });
-        });
-    };
-    FaceRecognitionNet.prototype.extractWeights = function (weights) {
-        this._params = extractParams_1.extractParams(weights);
-    };
     FaceRecognitionNet.prototype.forwardInput = function (input) {
-        var _this = this;
-        if (!this._params) {
+        var params = this.params;
+        if (!params) {
             throw new Error('FaceRecognitionNet - load model before inference');
         }
         return tf.tidy(function () {
             var batchTensor = input.toBatchTensor(150, true);
             var normalized = normalize_1.normalize(batchTensor);
-            var out = convLayer_1.convDown(normalized, _this._params.conv32_down);
+            var out = convLayer_1.convDown(normalized, params.conv32_down);
             out = tf.maxPool(out, 3, 2, 'valid');
-            out = residualLayer_1.residual(out, _this._params.conv32_1);
-            out = residualLayer_1.residual(out, _this._params.conv32_2);
-            out = residualLayer_1.residual(out, _this._params.conv32_3);
-            out = residualLayer_1.residualDown(out, _this._params.conv64_down);
-            out = residualLayer_1.residual(out, _this._params.conv64_1);
-            out = residualLayer_1.residual(out, _this._params.conv64_2);
-            out = residualLayer_1.residual(out, _this._params.conv64_3);
-            out = residualLayer_1.residualDown(out, _this._params.conv128_down);
-            out = residualLayer_1.residual(out, _this._params.conv128_1);
-            out = residualLayer_1.residual(out, _this._params.conv128_2);
-            out = residualLayer_1.residualDown(out, _this._params.conv256_down);
-            out = residualLayer_1.residual(out, _this._params.conv256_1);
-            out = residualLayer_1.residual(out, _this._params.conv256_2);
-            out = residualLayer_1.residualDown(out, _this._params.conv256_down_out);
+            out = residualLayer_1.residual(out, params.conv32_1);
+            out = residualLayer_1.residual(out, params.conv32_2);
+            out = residualLayer_1.residual(out, params.conv32_3);
+            out = residualLayer_1.residualDown(out, params.conv64_down);
+            out = residualLayer_1.residual(out, params.conv64_1);
+            out = residualLayer_1.residual(out, params.conv64_2);
+            out = residualLayer_1.residual(out, params.conv64_3);
+            out = residualLayer_1.residualDown(out, params.conv128_down);
+            out = residualLayer_1.residual(out, params.conv128_1);
+            out = residualLayer_1.residual(out, params.conv128_2);
+            out = residualLayer_1.residualDown(out, params.conv256_down);
+            out = residualLayer_1.residual(out, params.conv256_1);
+            out = residualLayer_1.residual(out, params.conv256_2);
+            out = residualLayer_1.residualDown(out, params.conv256_down_out);
             var globalAvg = out.mean([1, 2]);
-            var fullyConnected = tf.matMul(globalAvg, _this._params.fc);
+            var fullyConnected = tf.matMul(globalAvg, params.fc);
             return fullyConnected;
         });
     };
@@ -99,7 +77,13 @@ var FaceRecognitionNet = /** @class */ (function () {
             });
         });
     };
+    FaceRecognitionNet.prototype.loadQuantizedParams = function (uri) {
+        return loadQuantizedParams_1.loadQuantizedParams(uri);
+    };
+    FaceRecognitionNet.prototype.extractParams = function (weights) {
+        return extractParams_1.extractParams(weights);
+    };
     return FaceRecognitionNet;
-}());
+}(NeuralNetwork_1.NeuralNetwork));
 exports.FaceRecognitionNet = FaceRecognitionNet;
 //# sourceMappingURL=FaceRecognitionNet.js.map
\ No newline at end of file

build/faceRecognitionNet/FaceRecognitionNet.js.map

-{"version":3,"file":"FaceRecognitionNet.js","sourceRoot":"","sources":["../../src/faceRecognitionNet/FaceRecognitionNet.ts"],"names":[],"mappings":";;;AAAA,0CAA4C;AAG5C,4CAA2C;AAE3C,yCAAuC;AACvC,iDAAgD;AAChD,6DAA4D;AAC5D,yCAAwC;AACxC,iDAAyD;AAGzD;IAAA;IA+EA,CAAC;IA3Ec,iCAAI,GAAjB,UAAkB,YAA+C;;;;;;wBAC/D,IAAI,YAAY,YAAY,YAAY,EAAE;4BACxC,IAAI,CAAC,cAAc,CAAC,YAAY,CAAC,CAAA;4BACjC,sBAAM;yBACP;wBAED,IAAI,YAAY,IAAI,OAAO,YAAY,KAAK,QAAQ,EAAE;4BACpD,MAAM,IAAI,KAAK,CAAC,uEAAuE,CAAC,CAAA;yBACzF;wBACD,KAAA,IAAI,CAAA;wBAAW,qBAAM,yCAAmB,CAAC,YAAY,CAAC,EAAA;;wBAAtD,GAAK,OAAO,GAAG,SAAuC,CAAA;;;;;KACvD;IAEM,2CAAc,GAArB,UAAsB,OAAqB;QACzC,IAAI,CAAC,OAAO,GAAG,6BAAa,CAAC,OAAO,CAAC,CAAA;IACvC,CAAC;IAEM,yCAAY,GAAnB,UAAoB,KAAe;QAAnC,iBAoCC;QAnCC,IAAI,CAAC,IAAI,CAAC,OAAO,EAAE;YACjB,MAAM,IAAI,KAAK,CAAC,kDAAkD,CAAC,CAAA;SACpE;QAED,OAAO,EAAE,CAAC,IAAI,CAAC;YACb,IAAM,WAAW,GAAG,KAAK,CAAC,aAAa,CAAC,GAAG,EAAE,IAAI,CAAC,CAAA;YAElD,IAAM,UAAU,GAAG,qBAAS,CAAC,WAAW,CAAC,CAAA;YAEzC,IAAI,GAAG,GAAG,oBAAQ,CAAC,UAAU,EAAE,KAAI,CAAC,OAAO,CAAC,WAAW,CAAC,CAAA;YACxD,GAAG,GAAG,EAAE,CAAC,OAAO,CAAC,GAAG,EAAE,CAAC,EAAE,CAAC,EAAE,OAAO,CAAC,CAAA;YAEpC,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,KAAI,CAAC,OAAO,CAAC,QAAQ,CAAC,CAAA;YAC1C,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,KAAI,CAAC,OAAO,CAAC,QAAQ,CAAC,CAAA;YAC1C,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,KAAI,CAAC,OAAO,CAAC,QAAQ,CAAC,CAAA;YAE1C,GAAG,GAAG,4BAAY,CAAC,GAAG,EAAE,KAAI,CAAC,OAAO,CAAC,WAAW,CAAC,CAAA;YACjD,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,KAAI,CAAC,OAAO,CAAC,QAAQ,CAAC,CAAA;YAC1C,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,KAAI,CAAC,OAAO,CAAC,QAAQ,CAAC,CAAA;YAC1C,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,KAAI,CAAC,OAAO,CAAC,QAAQ,CAAC,CAAA;YAE1C,GAAG,GAAG,4BAAY,CAAC,GAAG,EAAE,KAAI,CAAC,OAAO,CAAC,YAAY,CAAC,CAAA;YAClD,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,KAAI,CAAC,OAAO,CAAC,SAAS,CAAC,CAAA;YAC3C,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,KAAI,CAAC,OAAO,CAAC,SAAS,CAAC,CAAA;YAE3C,GAAG,GAAG,4BAAY,CAAC,GAAG,EAAE,KAAI,CAAC,OAAO,CAAC,YAAY,CAAC,CAAA;YAClD,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,KAAI,CAAC,OAAO,CAAC,SAAS,CAAC,CAAA;YAC3C,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,KAAI,CAAC,OAAO,CAAC,SAAS,CAAC,CAAA;YAC3C,GAAG,GAAG,4BAAY,CAAC,GAAG,EAAE,KAAI,CAAC,OAAO,CAAC,gBAAgB,CAAC,CAAA;YAEtD,IAAM,SAAS,GAAG,GAAG,CAAC,IAAI,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAgB,CAAA;YACjD,IAAM,cAAc,GAAG,EAAE,CAAC,MAAM,CAAC,SAAS,EAAE,KAAI,CAAC,OAAO,CAAC,EAAE,CAAC,CAAA;YAE5D,OAAO,cAAc,CAAA;QACvB,CAAC,CAAC,CAAA;IACJ,CAAC;IAEY,oCAAO,GAApB,UAAqB,KAAgB;;;;;;wBAC5B,KAAA,IAAI,CAAC,YAAY,CAAA;wBAAC,qBAAM,uBAAU,CAAC,KAAK,EAAE,IAAI,CAAC,EAAA;4BAAtD,sBAAO,SAAA,IAAI,GAAc,SAA6B,EAAC,EAAA;;;;KACxD;IAEY,kDAAqB,GAAlC,UAAmC,KAAgB;;;;;;4BAChC,qBAAM,uBAAU,CAAC,KAAK,EAAE,IAAI,CAAC,EAAA;;wBAAxC,QAAQ,GAAG,SAA6B;wBAExC,qBAAqB,GAAG,EAAE,CAAC,IAAI,CACnC,cAAM,OAAA,EAAE,CAAC,OAAO,CAAC,KAAI,CAAC,YAAY,CAAC,QAAQ,CAAC,CAAC,EAAvC,CAAuC,CAC9C,CAAA;wBAE+B,qBAAM,OAAO,CAAC,GAAG,CAAC,qBAAqB,CAAC,GAAG,CACzE,UAAA,CAAC,IAAI,OAAA,CAAC,CAAC,IAAI,EAAE,EAAR,CAAQ,CACd,CAAC,EAAA;;wBAFI,uBAAuB,GAAG,SAEZ;wBAEpB,qBAAqB,CAAC,OAAO,CAAC,UAAA,CAAC,IAAI,OAAA,CAAC,CAAC,OAAO,EAAE,EAAX,CAAW,CAAC,CAAA;wBAE/C,sBAAO,QAAQ,CAAC,YAAY;gCAC1B,CAAC,CAAC,uBAAuB;gCACzB,CAAC,CAAC,uBAAuB,CAAC,CAAC,CAAC,EAAA;;;;KAC/B;IACH,yBAAC;AAAD,CAAC,AA/ED,IA+EC;AA/EY,gDAAkB"}
\ No newline at end of file
+{"version":3,"file":"FaceRecognitionNet.js","sourceRoot":"","sources":["../../src/faceRecognitionNet/FaceRecognitionNet.ts"],"names":[],"mappings":";;;AAAA,0CAA4C;AAE5C,0DAAyD;AAEzD,4CAA2C;AAE3C,yCAAuC;AACvC,iDAAgD;AAChD,6DAA4D;AAC5D,yCAAwC;AACxC,iDAAyD;AAGzD;IAAwC,8CAAwB;IAE9D;eACE,kBAAM,oBAAoB,CAAC;IAC7B,CAAC;IAEM,yCAAY,GAAnB,UAAoB,KAAe;QAEzB,IAAA,oBAAM,CAAS;QAEvB,IAAI,CAAC,MAAM,EAAE;YACX,MAAM,IAAI,KAAK,CAAC,kDAAkD,CAAC,CAAA;SACpE;QAED,OAAO,EAAE,CAAC,IAAI,CAAC;YACb,IAAM,WAAW,GAAG,KAAK,CAAC,aAAa,CAAC,GAAG,EAAE,IAAI,CAAC,CAAA;YAElD,IAAM,UAAU,GAAG,qBAAS,CAAC,WAAW,CAAC,CAAA;YAEzC,IAAI,GAAG,GAAG,oBAAQ,CAAC,UAAU,EAAE,MAAM,CAAC,WAAW,CAAC,CAAA;YAClD,GAAG,GAAG,EAAE,CAAC,OAAO,CAAC,GAAG,EAAE,CAAC,EAAE,CAAC,EAAE,OAAO,CAAC,CAAA;YAEpC,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,QAAQ,CAAC,CAAA;YACpC,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,QAAQ,CAAC,CAAA;YACpC,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,QAAQ,CAAC,CAAA;YAEpC,GAAG,GAAG,4BAAY,CAAC,GAAG,EAAE,MAAM,CAAC,WAAW,CAAC,CAAA;YAC3C,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,QAAQ,CAAC,CAAA;YACpC,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,QAAQ,CAAC,CAAA;YACpC,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,QAAQ,CAAC,CAAA;YAEpC,GAAG,GAAG,4BAAY,CAAC,GAAG,EAAE,MAAM,CAAC,YAAY,CAAC,CAAA;YAC5C,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,SAAS,CAAC,CAAA;YACrC,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,SAAS,CAAC,CAAA;YAErC,GAAG,GAAG,4BAAY,CAAC,GAAG,EAAE,MAAM,CAAC,YAAY,CAAC,CAAA;YAC5C,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,SAAS,CAAC,CAAA;YACrC,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,SAAS,CAAC,CAAA;YACrC,GAAG,GAAG,4BAAY,CAAC,GAAG,EAAE,MAAM,CAAC,gBAAgB,CAAC,CAAA;YAEhD,IAAM,SAAS,GAAG,GAAG,CAAC,IAAI,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAgB,CAAA;YACjD,IAAM,cAAc,GAAG,EAAE,CAAC,MAAM,CAAC,SAAS,EAAE,MAAM,CAAC,EAAE,CAAC,CAAA;YAEtD,OAAO,cAAc,CAAA;QACvB,CAAC,CAAC,CAAA;IACJ,CAAC;IAEY,oCAAO,GAApB,UAAqB,KAAgB;;;;;;wBAC5B,KAAA,IAAI,CAAC,YAAY,CAAA;wBAAC,qBAAM,uBAAU,CAAC,KAAK,EAAE,IAAI,CAAC,EAAA;4BAAtD,sBAAO,SAAA,IAAI,GAAc,SAA6B,EAAC,EAAA;;;;KACxD;IAEY,kDAAqB,GAAlC,UAAmC,KAAgB;;;;;;4BAChC,qBAAM,uBAAU,CAAC,KAAK,EAAE,IAAI,CAAC,EAAA;;wBAAxC,QAAQ,GAAG,SAA6B;wBAExC,qBAAqB,GAAG,EAAE,CAAC,IAAI,CACnC,cAAM,OAAA,EAAE,CAAC,OAAO,CAAC,KAAI,CAAC,YAAY,CAAC,QAAQ,CAAC,CAAC,EAAvC,CAAuC,CAC9C,CAAA;wBAE+B,qBAAM,OAAO,CAAC,GAAG,CAAC,qBAAqB,CAAC,GAAG,CACzE,UAAA,CAAC,IAAI,OAAA,CAAC,CAAC,IAAI,EAAE,EAAR,CAAQ,CACd,CAAC,EAAA;;wBAFI,uBAAuB,GAAG,SAEZ;wBAEpB,qBAAqB,CAAC,OAAO,CAAC,UAAA,CAAC,IAAI,OAAA,CAAC,CAAC,OAAO,EAAE,EAAX,CAAW,CAAC,CAAA;wBAE/C,sBAAO,QAAQ,CAAC,YAAY;gCAC1B,CAAC,CAAC,uBAAuB;gCACzB,CAAC,CAAC,uBAAuB,CAAC,CAAC,CAAC,EAAA;;;;KAC/B;IAES,gDAAmB,GAA7B,UAA8B,GAAuB;QACnD,OAAO,yCAAmB,CAAC,GAAG,CAAC,CAAA;IACjC,CAAC;IAES,0CAAa,GAAvB,UAAwB,OAAqB;QAC3C,OAAO,6BAAa,CAAC,OAAO,CAAC,CAAA;IAC/B,CAAC;IACH,yBAAC;AAAD,CAAC,AA5ED,CAAwC,6BAAa,GA4EpD;AA5EY,gDAAkB"}
\ No newline at end of file

build/faceRecognitionNet/extractParams.d.ts

+import { ParamMapping } from '../commons/types';
 import { NetParams } from './types';
-export declare function extractParams(weights: Float32Array): NetParams;
+export declare function extractParams(weights: Float32Array): {
+    params: NetParams;
+    paramMappings: ParamMapping[];
+};

build/faceRecognitionNet/extractParams.js

@@ -3,43 +3,40 @@ Object.defineProperty(exports, "__esModule", { value: true });
 var tf = require("@tensorflow/tfjs-core");
 var extractWeightsFactory_1 = require("../commons/extractWeightsFactory");
 var utils_1 = require("../utils");
-function extractorsFactory(extractWeights) {
+function extractorsFactory(extractWeights, paramMappings) {
     function extractFilterValues(numFilterValues, numFilters, filterSize) {
         var weights = extractWeights(numFilterValues);
         var depth = weights.length / (numFilters * filterSize * filterSize);
         if (utils_1.isFloat(depth)) {
             throw new Error("depth has to be an integer: " + depth + ", weights.length: " + weights.length + ", numFilters: " + numFilters + ", filterSize: " + filterSize);
         }
-        return tf.transpose(tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]), [2, 3, 1, 0]);
+        return tf.tidy(function () { return tf.transpose(tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]), [2, 3, 1, 0]); });
     }
-    function extractScaleLayerParams(numWeights) {
+    function extractConvParams(numFilterValues, numFilters, filterSize, mappedPrefix) {
+        var filters = extractFilterValues(numFilterValues, numFilters, filterSize);
+        var bias = tf.tensor1d(extractWeights(numFilters));
+        paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/bias" });
+        return { filters: filters, bias: bias };
+    }
+    function extractScaleLayerParams(numWeights, mappedPrefix) {
         var weights = tf.tensor1d(extractWeights(numWeights));
         var biases = tf.tensor1d(extractWeights(numWeights));
+        paramMappings.push({ paramPath: mappedPrefix + "/weights" }, { paramPath: mappedPrefix + "/biases" });
         return {
             weights: weights,
             biases: biases
         };
     }
-    function extractConvLayerParams(numFilterValues, numFilters, filterSize) {
-        var conv_filters = extractFilterValues(numFilterValues, numFilters, filterSize);
-        var conv_bias = tf.tensor1d(extractWeights(numFilters));
-        var scale = extractScaleLayerParams(numFilters);
-        return {
-            conv: {
-                filters: conv_filters,
-                bias: conv_bias
-            },
-            scale: scale
-        };
+    function extractConvLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix) {
+        var conv = extractConvParams(numFilterValues, numFilters, filterSize, mappedPrefix + "/conv");
+        var scale = extractScaleLayerParams(numFilters, mappedPrefix + "/scale");
+        return { conv: conv, scale: scale };
     }
-    function extractResidualLayerParams(numFilterValues, numFilters, filterSize, isDown) {
+    function extractResidualLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix, isDown) {
         if (isDown === void 0) { isDown = false; }
-        var conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize);
-        var conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize);
-        return {
-            conv1: conv1,
-            conv2: conv2
-        };
+        var conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, mappedPrefix + "/conv1");
+        var conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix + "/conv2");
+        return { conv1: conv1, conv2: conv2 };
     }
     return {
         extractConvLayerParams: extractConvLayerParams,
@@ -48,27 +45,29 @@ function extractorsFactory(extractWeights) {
 }
 function extractParams(weights) {
     var _a = extractWeightsFactory_1.extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
-    var _b = extractorsFactory(extractWeights), extractConvLayerParams = _b.extractConvLayerParams, extractResidualLayerParams = _b.extractResidualLayerParams;
-    var conv32_down = extractConvLayerParams(4704, 32, 7);
-    var conv32_1 = extractResidualLayerParams(9216, 32, 3);
-    var conv32_2 = extractResidualLayerParams(9216, 32, 3);
-    var conv32_3 = extractResidualLayerParams(9216, 32, 3);
-    var conv64_down = extractResidualLayerParams(36864, 64, 3, true);
-    var conv64_1 = extractResidualLayerParams(36864, 64, 3);
-    var conv64_2 = extractResidualLayerParams(36864, 64, 3);
-    var conv64_3 = extractResidualLayerParams(36864, 64, 3);
-    var conv128_down = extractResidualLayerParams(147456, 128, 3, true);
-    var conv128_1 = extractResidualLayerParams(147456, 128, 3);
-    var conv128_2 = extractResidualLayerParams(147456, 128, 3);
-    var conv256_down = extractResidualLayerParams(589824, 256, 3, true);
-    var conv256_1 = extractResidualLayerParams(589824, 256, 3);
-    var conv256_2 = extractResidualLayerParams(589824, 256, 3);
-    var conv256_down_out = extractResidualLayerParams(589824, 256, 3);
-    var fc = tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]);
+    var paramMappings = [];
+    var _b = extractorsFactory(extractWeights, paramMappings), extractConvLayerParams = _b.extractConvLayerParams, extractResidualLayerParams = _b.extractResidualLayerParams;
+    var conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down');
+    var conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1');
+    var conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2');
+    var conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3');
+    var conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true);
+    var conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1');
+    var conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2');
+    var conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3');
+    var conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true);
+    var conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1');
+    var conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2');
+    var conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true);
+    var conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1');
+    var conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2');
+    var conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out');
+    var fc = tf.tidy(function () { return tf.transpose(tf.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);
     }
-    return {
+    var params = {
         conv32_down: conv32_down,
         conv32_1: conv32_1,
         conv32_2: conv32_2,
@@ -86,6 +85,7 @@ function extractParams(weights) {
         conv256_down_out: conv256_down_out,
         fc: fc
     };
+    return { params: params, paramMappings: paramMappings };
 }
 exports.extractParams = extractParams;
 //# sourceMappingURL=extractParams.js.map
\ No newline at end of file

build/faceRecognitionNet/extractParams.js.map

-{"version":3,"file":"extractParams.js","sourceRoot":"","sources":["../../src/faceRecognitionNet/extractParams.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAE5C,0EAAyE;AAEzE,kCAAmC;AAGnC,2BAA2B,cAAsC;IAE/D,6BAA6B,eAAuB,EAAE,UAAkB,EAAE,UAAkB;QAC1F,IAAM,OAAO,GAAG,cAAc,CAAC,eAAe,CAAC,CAAA;QAC/C,IAAM,KAAK,GAAG,OAAO,CAAC,MAAM,GAAG,CAAC,UAAU,GAAG,UAAU,GAAG,UAAU,CAAC,CAAA;QAErE,IAAI,eAAO,CAAC,KAAK,CAAC,EAAE;YAClB,MAAM,IAAI,KAAK,CAAC,iCAA+B,KAAK,0BAAqB,OAAO,CAAC,MAAM,sBAAiB,UAAU,sBAAiB,UAAY,CAAC,CAAA;SACjJ;QAED,OAAO,EAAE,CAAC,SAAS,CACjB,EAAE,CAAC,QAAQ,CAAC,OAAO,EAAE,CAAC,UAAU,EAAE,KAAK,EAAE,UAAU,EAAE,UAAU,CAAC,CAAC,EACjE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CACb,CAAA;IACH,CAAC;IAED,iCAAiC,UAAkB;QACjD,IAAM,OAAO,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,UAAU,CAAC,CAAC,CAAA;QACvD,IAAM,MAAM,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,UAAU,CAAC,CAAC,CAAA;QACtD,OAAO;YACL,OAAO,SAAA;YACP,MAAM,QAAA;SACP,CAAA;IACH,CAAC;IAED,gCACE,eAAuB,EACvB,UAAkB,EAClB,UAAkB;QAElB,IAAM,YAAY,GAAG,mBAAmB,CAAC,eAAe,EAAE,UAAU,EAAE,UAAU,CAAC,CAAA;QACjF,IAAM,SAAS,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,UAAU,CAAC,CAAC,CAAA;QACzD,IAAM,KAAK,GAAG,uBAAuB,CAAC,UAAU,CAAC,CAAA;QAEjD,OAAO;YACL,IAAI,EAAE;gBACJ,OAAO,EAAE,YAAY;gBACrB,IAAI,EAAE,SAAS;aAChB;YACD,KAAK,OAAA;SACN,CAAA;IACH,CAAC;IAED,oCACE,eAAuB,EACvB,UAAkB,EAClB,UAAkB,EAClB,MAAuB;QAAvB,uBAAA,EAAA,cAAuB;QAEvB,IAAM,KAAK,GAAoB,sBAAsB,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,eAAe,EAAE,UAAU,EAAE,UAAU,CAAC,CAAA;QACnH,IAAM,KAAK,GAAoB,sBAAsB,CAAC,eAAe,EAAE,UAAU,EAAE,UAAU,CAAC,CAAA;QAE9F,OAAO;YACL,KAAK,OAAA;YACL,KAAK,OAAA;SACN,CAAA;IACH,CAAC;IAED,OAAO;QACL,sBAAsB,wBAAA;QACtB,0BAA0B,4BAAA;KAC3B,CAAA;AAEH,CAAC;AAED,uBAA8B,OAAqB;IAC3C,IAAA,2DAG4B,EAFhC,kCAAc,EACd,4CAAmB,CACa;IAE5B,IAAA,sCAG+B,EAFnC,kDAAsB,EACtB,0DAA0B,CACS;IAErC,IAAM,WAAW,GAAG,sBAAsB,CAAC,IAAI,EAAE,EAAE,EAAE,CAAC,CAAC,CAAA;IACvD,IAAM,QAAQ,GAAG,0BAA0B,CAAC,IAAI,EAAE,EAAE,EAAE,CAAC,CAAC,CAAA;IACxD,IAAM,QAAQ,GAAG,0BAA0B,CAAC,IAAI,EAAE,EAAE,EAAE,CAAC,CAAC,CAAA;IACxD,IAAM,QAAQ,GAAG,0BAA0B,CAAC,IAAI,EAAE,EAAE,EAAE,CAAC,CAAC,CAAA;IAExD,IAAM,WAAW,GAAG,0BAA0B,CAAC,KAAK,EAAE,EAAE,EAAE,CAAC,EAAE,IAAI,CAAC,CAAA;IAClE,IAAM,QAAQ,GAAG,0BAA0B,CAAC,KAAK,EAAE,EAAE,EAAE,CAAC,CAAC,CAAA;IACzD,IAAM,QAAQ,GAAG,0BAA0B,CAAC,KAAK,EAAE,EAAE,EAAE,CAAC,CAAC,CAAA;IACzD,IAAM,QAAQ,GAAG,0BAA0B,CAAC,KAAK,EAAE,EAAE,EAAE,CAAC,CAAC,CAAA;IAEzD,IAAM,YAAY,GAAG,0BAA0B,CAAC,MAAM,EAAE,GAAG,EAAE,CAAC,EAAE,IAAI,CAAC,CAAA;IACrE,IAAM,SAAS,GAAG,0BAA0B,CAAC,MAAM,EAAE,GAAG,EAAE,CAAC,CAAC,CAAA;IAC5D,IAAM,SAAS,GAAG,0BAA0B,CAAC,MAAM,EAAE,GAAG,EAAE,CAAC,CAAC,CAAA;IAE5D,IAAM,YAAY,GAAG,0BAA0B,CAAC,MAAM,EAAE,GAAG,EAAE,CAAC,EAAE,IAAI,CAAC,CAAA;IACrE,IAAM,SAAS,GAAG,0BAA0B,CAAC,MAAM,EAAE,GAAG,EAAE,CAAC,CAAC,CAAA;IAC5D,IAAM,SAAS,GAAG,0BAA0B,CAAC,MAAM,EAAE,GAAG,EAAE,CAAC,CAAC,CAAA;IAC5D,IAAM,gBAAgB,GAAG,0BAA0B,CAAC,MAAM,EAAE,GAAG,EAAE,CAAC,CAAC,CAAA;IAEnE,IAAM,EAAE,GAAG,EAAE,CAAC,SAAS,CAAC,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,GAAG,GAAG,GAAG,CAAC,EAAE,CAAC,GAAG,EAAE,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;IAEnF,IAAI,mBAAmB,EAAE,CAAC,MAAM,KAAK,CAAC,EAAE;QACtC,MAAM,IAAI,KAAK,CAAC,oCAAkC,mBAAmB,EAAE,CAAC,MAAQ,CAAC,CAAA;KAClF;IAED,OAAO;QACL,WAAW,aAAA;QACX,QAAQ,UAAA;QACR,QAAQ,UAAA;QACR,QAAQ,UAAA;QACR,WAAW,aAAA;QACX,QAAQ,UAAA;QACR,QAAQ,UAAA;QACR,QAAQ,UAAA;QACR,YAAY,cAAA;QACZ,SAAS,WAAA;QACT,SAAS,WAAA;QACT,YAAY,cAAA;QACZ,SAAS,WAAA;QACT,SAAS,WAAA;QACT,gBAAgB,kBAAA;QAChB,EAAE,IAAA;KACH,CAAA;AACH,CAAC;AAtDD,sCAsDC"}
\ No newline at end of file
+{"version":3,"file":"extractParams.js","sourceRoot":"","sources":["../../src/faceRecognitionNet/extractParams.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAE5C,0EAAyE;AAEzE,kCAAmC;AAGnC,2BAA2B,cAAsC,EAAE,aAA6B;IAE9F,6BAA6B,eAAuB,EAAE,UAAkB,EAAE,UAAkB;QAC1F,IAAM,OAAO,GAAG,cAAc,CAAC,eAAe,CAAC,CAAA;QAC/C,IAAM,KAAK,GAAG,OAAO,CAAC,MAAM,GAAG,CAAC,UAAU,GAAG,UAAU,GAAG,UAAU,CAAC,CAAA;QAErE,IAAI,eAAO,CAAC,KAAK,CAAC,EAAE;YAClB,MAAM,IAAI,KAAK,CAAC,iCAA+B,KAAK,0BAAqB,OAAO,CAAC,MAAM,sBAAiB,UAAU,sBAAiB,UAAY,CAAC,CAAA;SACjJ;QAED,OAAO,EAAE,CAAC,IAAI,CACZ,cAAM,OAAA,EAAE,CAAC,SAAS,CAChB,EAAE,CAAC,QAAQ,CAAC,OAAO,EAAE,CAAC,UAAU,EAAE,KAAK,EAAE,UAAU,EAAE,UAAU,CAAC,CAAC,EACjE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CACb,EAHK,CAGL,CACF,CAAA;IACH,CAAC;IAED,2BACE,eAAuB,EACvB,UAAkB,EAClB,UAAkB,EAClB,YAAoB;QAGpB,IAAM,OAAO,GAAG,mBAAmB,CAAC,eAAe,EAAE,UAAU,EAAE,UAAU,CAAC,CAAA;QAC5E,IAAM,IAAI,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,UAAU,CAAC,CAAC,CAAA;QAEpD,aAAa,CAAC,IAAI,CAChB,EAAE,SAAS,EAAK,YAAY,aAAU,EAAE,EACxC,EAAE,SAAS,EAAK,YAAY,UAAO,EAAE,CACtC,CAAA;QAED,OAAO,EAAE,OAAO,SAAA,EAAE,IAAI,MAAA,EAAE,CAAA;IAC1B,CAAC;IAED,iCAAiC,UAAkB,EAAE,YAAoB;QAEvE,IAAM,OAAO,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,UAAU,CAAC,CAAC,CAAA;QACvD,IAAM,MAAM,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,UAAU,CAAC,CAAC,CAAA;QAEtD,aAAa,CAAC,IAAI,CAChB,EAAE,SAAS,EAAK,YAAY,aAAU,EAAE,EACxC,EAAE,SAAS,EAAK,YAAY,YAAS,EAAE,CACxC,CAAA;QAED,OAAO;YACL,OAAO,SAAA;YACP,MAAM,QAAA;SACP,CAAA;IACH,CAAC;IAED,gCACE,eAAuB,EACvB,UAAkB,EAClB,UAAkB,EAClB,YAAoB;QAGpB,IAAM,IAAI,GAAG,iBAAiB,CAAC,eAAe,EAAE,UAAU,EAAE,UAAU,EAAK,YAAY,UAAO,CAAC,CAAA;QAC/F,IAAM,KAAK,GAAG,uBAAuB,CAAC,UAAU,EAAK,YAAY,WAAQ,CAAC,CAAA;QAE1E,OAAO,EAAE,IAAI,MAAA,EAAE,KAAK,OAAA,EAAE,CAAA;IACxB,CAAC;IAED,oCACE,eAAuB,EACvB,UAAkB,EAClB,UAAkB,EAClB,YAAoB,EACpB,MAAuB;QAAvB,uBAAA,EAAA,cAAuB;QAGvB,IAAM,KAAK,GAAG,sBAAsB,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,eAAe,EAAE,UAAU,EAAE,UAAU,EAAK,YAAY,WAAQ,CAAC,CAAA;QAC3H,IAAM,KAAK,GAAG,sBAAsB,CAAC,eAAe,EAAE,UAAU,EAAE,UAAU,EAAK,YAAY,WAAQ,CAAC,CAAA;QAEtG,OAAO,EAAE,KAAK,OAAA,EAAE,KAAK,OAAA,EAAE,CAAA;IACzB,CAAC;IAED,OAAO;QACL,sBAAsB,wBAAA;QACtB,0BAA0B,4BAAA;KAC3B,CAAA;AAEH,CAAC;AAED,uBAA8B,OAAqB;IAE3C,IAAA,2DAG4B,EAFhC,kCAAc,EACd,4CAAmB,CACa;IAElC,IAAM,aAAa,GAAmB,EAAE,CAAA;IAElC,IAAA,qDAG8C,EAFlD,kDAAsB,EACtB,0DAA0B,CACwB;IAEpD,IAAM,WAAW,GAAG,sBAAsB,CAAC,IAAI,EAAE,EAAE,EAAE,CAAC,EAAE,aAAa,CAAC,CAAA;IACtE,IAAM,QAAQ,GAAG,0BAA0B,CAAC,IAAI,EAAE,EAAE,EAAE,CAAC,EAAE,UAAU,CAAC,CAAA;IACpE,IAAM,QAAQ,GAAG,0BAA0B,CAAC,IAAI,EAAE,EAAE,EAAE,CAAC,EAAE,UAAU,CAAC,CAAA;IACpE,IAAM,QAAQ,GAAG,0BAA0B,CAAC,IAAI,EAAE,EAAE,EAAE,CAAC,EAAE,UAAU,CAAC,CAAA;IAEpE,IAAM,WAAW,GAAG,0BAA0B,CAAC,KAAK,EAAE,EAAE,EAAE,CAAC,EAAE,aAAa,EAAE,IAAI,CAAC,CAAA;IACjF,IAAM,QAAQ,GAAG,0BAA0B,CAAC,KAAK,EAAE,EAAE,EAAE,CAAC,EAAE,UAAU,CAAC,CAAA;IACrE,IAAM,QAAQ,GAAG,0BAA0B,CAAC,KAAK,EAAE,EAAE,EAAE,CAAC,EAAE,UAAU,CAAC,CAAA;IACrE,IAAM,QAAQ,GAAG,0BAA0B,CAAC,KAAK,EAAE,EAAE,EAAE,CAAC,EAAE,UAAU,CAAC,CAAA;IAErE,IAAM,YAAY,GAAG,0BAA0B,CAAC,MAAM,EAAE,GAAG,EAAE,CAAC,EAAE,cAAc,EAAE,IAAI,CAAC,CAAA;IACrF,IAAM,SAAS,GAAG,0BAA0B,CAAC,MAAM,EAAE,GAAG,EAAE,CAAC,EAAE,WAAW,CAAC,CAAA;IACzE,IAAM,SAAS,GAAG,0BAA0B,CAAC,MAAM,EAAE,GAAG,EAAE,CAAC,EAAE,WAAW,CAAC,CAAA;IAEzE,IAAM,YAAY,GAAG,0BAA0B,CAAC,MAAM,EAAE,GAAG,EAAE,CAAC,EAAE,cAAc,EAAE,IAAI,CAAC,CAAA;IACrF,IAAM,SAAS,GAAG,0BAA0B,CAAC,MAAM,EAAE,GAAG,EAAE,CAAC,EAAE,WAAW,CAAC,CAAA;IACzE,IAAM,SAAS,GAAG,0BAA0B,CAAC,MAAM,EAAE,GAAG,EAAE,CAAC,EAAE,WAAW,CAAC,CAAA;IACzE,IAAM,gBAAgB,GAAG,0BAA0B,CAAC,MAAM,EAAE,GAAG,EAAE,CAAC,EAAE,kBAAkB,CAAC,CAAA;IAEvF,IAAM,EAAE,GAAG,EAAE,CAAC,IAAI,CAChB,cAAM,OAAA,EAAE,CAAC,SAAS,CAAC,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,GAAG,GAAG,GAAG,CAAC,EAAE,CAAC,GAAG,EAAE,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAxE,CAAwE,CAC/E,CAAA;IACD,aAAa,CAAC,IAAI,CAAC,EAAE,SAAS,EAAE,IAAI,EAAE,CAAC,CAAA;IAEvC,IAAI,mBAAmB,EAAE,CAAC,MAAM,KAAK,CAAC,EAAE;QACtC,MAAM,IAAI,KAAK,CAAC,oCAAkC,mBAAmB,EAAE,CAAC,MAAQ,CAAC,CAAA;KAClF;IAED,IAAM,MAAM,GAAG;QACb,WAAW,aAAA;QACX,QAAQ,UAAA;QACR,QAAQ,UAAA;QACR,QAAQ,UAAA;QACR,WAAW,aAAA;QACX,QAAQ,UAAA;QACR,QAAQ,UAAA;QACR,QAAQ,UAAA;QACR,YAAY,cAAA;QACZ,SAAS,WAAA;QACT,SAAS,WAAA;QACT,YAAY,cAAA;QACZ,SAAS,WAAA;QACT,SAAS,WAAA;QACT,gBAAgB,kBAAA;QAChB,EAAE,IAAA;KACH,CAAA;IAED,OAAO,EAAE,MAAM,QAAA,EAAE,aAAa,eAAA,EAAE,CAAA;AAClC,CAAC;AA9DD,sCA8DC"}
\ No newline at end of file

build/faceRecognitionNet/loadQuantizedParams.d.ts

-export declare function loadQuantizedParams(uri: string | undefined): Promise<any>;
+import { ParamMapping } from '../commons/types';
+import { NetParams } from './types';
+export declare function loadQuantizedParams(uri: string | undefined): Promise<{
+    params: NetParams;
+    paramMappings: ParamMapping[];
+}>;

build/faceRecognitionNet/loadQuantizedParams.js

 "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
 var tslib_1 = require("tslib");
+var disposeUnusedWeightTensors_1 = require("../commons/disposeUnusedWeightTensors");
+var extractWeightEntryFactory_1 = require("../commons/extractWeightEntryFactory");
 var isTensor_1 = require("../commons/isTensor");
 var loadWeightMap_1 = require("../commons/loadWeightMap");
 var DEFAULT_MODEL_NAME = 'face_recognition_model';
-function extractorsFactory(weightMap) {
+function extractorsFactory(weightMap, paramMappings) {
+    var extractWeightEntry = extractWeightEntryFactory_1.extractWeightEntryFactory(weightMap, paramMappings);
     function extractScaleLayerParams(prefix) {
-        var params = {
-            weights: weightMap[prefix + "/scale/weights"],
-            biases: weightMap[prefix + "/scale/biases"]
-        };
-        if (!isTensor_1.isTensor1D(params.weights)) {
-            throw new Error("expected weightMap[" + prefix + "/scale/weights] to be a Tensor1D, instead have " + params.weights);
-        }
-        if (!isTensor_1.isTensor1D(params.biases)) {
-            throw new Error("expected weightMap[" + prefix + "/scale/biases] to be a Tensor1D, instead have " + params.biases);
-        }
-        return params;
+        var weights = extractWeightEntry(prefix + "/scale/weights", 1);
+        var biases = extractWeightEntry(prefix + "/scale/biases", 1);
+        return { weights: weights, biases: biases };
     }
     function extractConvLayerParams(prefix) {
-        var params = {
-            filters: weightMap[prefix + "/conv/filters"],
-            bias: weightMap[prefix + "/conv/bias"]
-        };
-        if (!isTensor_1.isTensor4D(params.filters)) {
-            throw new Error("expected weightMap[" + prefix + "/conv/filters] to be a Tensor1D, instead have " + params.filters);
-        }
-        if (!isTensor_1.isTensor1D(params.bias)) {
-            throw new Error("expected weightMap[" + prefix + "/conv/bias] to be a Tensor1D, instead have " + params.bias);
-        }
-        return {
-            conv: params,
-            scale: extractScaleLayerParams(prefix)
-        };
+        var filters = extractWeightEntry(prefix + "/conv/filters", 4);
+        var bias = extractWeightEntry(prefix + "/conv/bias", 1);
+        var scale = extractScaleLayerParams(prefix);
+        return { conv: { filters: filters, bias: bias }, scale: scale };
     }
     function extractResidualLayerParams(prefix) {
         return {
@@ -47,13 +32,14 @@ function extractorsFactory(weightMap) {
 }
 function loadQuantizedParams(uri) {
     return tslib_1.__awaiter(this, void 0, void 0, function () {
-        var weightMap, _a, extractConvLayerParams, extractResidualLayerParams, 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;
+        var weightMap, paramMappings, _a, extractConvLayerParams, extractResidualLayerParams, 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, params;
         return tslib_1.__generator(this, function (_b) {
             switch (_b.label) {
                 case 0: return [4 /*yield*/, loadWeightMap_1.loadWeightMap(uri, DEFAULT_MODEL_NAME)];
                 case 1:
                     weightMap = _b.sent();
-                    _a = extractorsFactory(weightMap), extractConvLayerParams = _a.extractConvLayerParams, extractResidualLayerParams = _a.extractResidualLayerParams;
+                    paramMappings = [];
+                    _a = extractorsFactory(weightMap, paramMappings), extractConvLayerParams = _a.extractConvLayerParams, extractResidualLayerParams = _a.extractResidualLayerParams;
                     conv32_down = extractConvLayerParams('conv32_down');
                     conv32_1 = extractResidualLayerParams('conv32_1');
                     conv32_2 = extractResidualLayerParams('conv32_2');
@@ -70,27 +56,30 @@ function loadQuantizedParams(uri) {
                     conv256_2 = extractResidualLayerParams('conv256_2');
                     conv256_down_out = extractResidualLayerParams('conv256_down_out');
                     fc = weightMap['fc'];
+                    paramMappings.push({ originalPath: 'fc', paramPath: 'fc' });
                     if (!isTensor_1.isTensor2D(fc)) {
                         throw new Error("expected weightMap[fc] to be a Tensor2D, instead have " + fc);
                     }
-                    return [2 /*return*/, {
-                            conv32_down: conv32_down,
-                            conv32_1: conv32_1,
-                            conv32_2: conv32_2,
-                            conv32_3: conv32_3,
-                            conv64_down: conv64_down,
-                            conv64_1: conv64_1,
-                            conv64_2: conv64_2,
-                            conv64_3: conv64_3,
-                            conv128_down: conv128_down,
-                            conv128_1: conv128_1,
-                            conv128_2: conv128_2,
-                            conv256_down: conv256_down,
-                            conv256_1: conv256_1,
-                            conv256_2: conv256_2,
-                            conv256_down_out: conv256_down_out,
-                            fc: fc
-                        }];
+                    params = {
+                        conv32_down: conv32_down,
+                        conv32_1: conv32_1,
+                        conv32_2: conv32_2,
+                        conv32_3: conv32_3,
+                        conv64_down: conv64_down,
+                        conv64_1: conv64_1,
+                        conv64_2: conv64_2,
+                        conv64_3: conv64_3,
+                        conv128_down: conv128_down,
+                        conv128_1: conv128_1,
+                        conv128_2: conv128_2,
+                        conv256_down: conv256_down,
+                        conv256_1: conv256_1,
+                        conv256_2: conv256_2,
+                        conv256_down_out: conv256_down_out,
+                        fc: fc
+                    };
+                    disposeUnusedWeightTensors_1.disposeUnusedWeightTensors(weightMap, paramMappings);
+                    return [2 /*return*/, { params: params, paramMappings: paramMappings }];
             }
         });
     });

build/faceRecognitionNet/loadQuantizedParams.js.map

-{"version":3,"file":"loadQuantizedParams.js","sourceRoot":"","sources":["../../src/faceRecognitionNet/loadQuantizedParams.ts"],"names":[],"mappings":";;;AAAA,gDAAyE;AACzE,0DAAyD;AAGzD,IAAM,kBAAkB,GAAG,wBAAwB,CAAA;AAEnD,2BAA2B,SAAc;IAEvC,iCAAiC,MAAc;QAC7C,IAAM,MAAM,GAAG;YACb,OAAO,EAAE,SAAS,CAAI,MAAM,mBAAgB,CAAC;YAC7C,MAAM,EAAE,SAAS,CAAI,MAAM,kBAAe,CAAC;SAC5C,CAAA;QAED,IAAI,CAAC,qBAAU,CAAC,MAAM,CAAC,OAAO,CAAC,EAAE;YAC/B,MAAM,IAAI,KAAK,CAAC,wBAAsB,MAAM,uDAAkD,MAAM,CAAC,OAAS,CAAC,CAAA;SAChH;QAED,IAAI,CAAC,qBAAU,CAAC,MAAM,CAAC,MAAM,CAAC,EAAE;YAC9B,MAAM,IAAI,KAAK,CAAC,wBAAsB,MAAM,sDAAiD,MAAM,CAAC,MAAQ,CAAC,CAAA;SAC9G;QAED,OAAO,MAAM,CAAA;IACf,CAAC;IAED,gCAAgC,MAAc;QAC5C,IAAM,MAAM,GAAG;YACb,OAAO,EAAE,SAAS,CAAI,MAAM,kBAAe,CAAC;YAC5C,IAAI,EAAE,SAAS,CAAI,MAAM,eAAY,CAAC;SACvC,CAAA;QAED,IAAI,CAAC,qBAAU,CAAC,MAAM,CAAC,OAAO,CAAC,EAAE;YAC/B,MAAM,IAAI,KAAK,CAAC,wBAAsB,MAAM,sDAAiD,MAAM,CAAC,OAAS,CAAC,CAAA;SAC/G;QAED,IAAI,CAAC,qBAAU,CAAC,MAAM,CAAC,IAAI,CAAC,EAAE;YAC5B,MAAM,IAAI,KAAK,CAAC,wBAAsB,MAAM,mDAA8C,MAAM,CAAC,IAAM,CAAC,CAAA;SACzG;QAED,OAAO;YACL,IAAI,EAAE,MAAM;YACZ,KAAK,EAAE,uBAAuB,CAAC,MAAM,CAAC;SACvC,CAAA;IACH,CAAC;IAED,oCAAoC,MAAc;QAChD,OAAO;YACL,KAAK,EAAE,sBAAsB,CAAI,MAAM,WAAQ,CAAC;YAChD,KAAK,EAAE,sBAAsB,CAAI,MAAM,WAAQ,CAAC;SACjD,CAAA;IACH,CAAC;IAED,OAAO;QACL,sBAAsB,wBAAA;QACtB,0BAA0B,4BAAA;KAC3B,CAAA;AAEH,CAAC;AAED,6BAA0C,GAAuB;;;;;wBAC7C,qBAAM,6BAAa,CAAC,GAAG,EAAE,kBAAkB,CAAC,EAAA;;oBAAxD,SAAS,GAAG,SAA4C;oBAExD,KAGF,iBAAiB,CAAC,SAAS,CAAC,EAF9B,sBAAsB,4BAAA,EACtB,0BAA0B,gCAAA,CACI;oBAE1B,WAAW,GAAG,sBAAsB,CAAC,aAAa,CAAC,CAAA;oBACnD,QAAQ,GAAG,0BAA0B,CAAC,UAAU,CAAC,CAAA;oBACjD,QAAQ,GAAG,0BAA0B,CAAC,UAAU,CAAC,CAAA;oBACjD,QAAQ,GAAG,0BAA0B,CAAC,UAAU,CAAC,CAAA;oBAEjD,WAAW,GAAG,0BAA0B,CAAC,aAAa,CAAC,CAAA;oBACvD,QAAQ,GAAG,0BAA0B,CAAC,UAAU,CAAC,CAAA;oBACjD,QAAQ,GAAG,0BAA0B,CAAC,UAAU,CAAC,CAAA;oBACjD,QAAQ,GAAG,0BAA0B,CAAC,UAAU,CAAC,CAAA;oBAEjD,YAAY,GAAG,0BAA0B,CAAC,cAAc,CAAC,CAAA;oBACzD,SAAS,GAAG,0BAA0B,CAAC,WAAW,CAAC,CAAA;oBACnD,SAAS,GAAG,0BAA0B,CAAC,WAAW,CAAC,CAAA;oBAEnD,YAAY,GAAG,0BAA0B,CAAC,cAAc,CAAC,CAAA;oBACzD,SAAS,GAAG,0BAA0B,CAAC,WAAW,CAAC,CAAA;oBACnD,SAAS,GAAG,0BAA0B,CAAC,WAAW,CAAC,CAAA;oBACnD,gBAAgB,GAAG,0BAA0B,CAAC,kBAAkB,CAAC,CAAA;oBAEjE,EAAE,GAAG,SAAS,CAAC,IAAI,CAAC,CAAA;oBAE1B,IAAI,CAAC,qBAAU,CAAC,EAAE,CAAC,EAAE;wBACnB,MAAM,IAAI,KAAK,CAAC,2DAAyD,EAAI,CAAC,CAAA;qBAC/E;oBAED,sBAAO;4BACL,WAAW,aAAA;4BACX,QAAQ,UAAA;4BACR,QAAQ,UAAA;4BACR,QAAQ,UAAA;4BACR,WAAW,aAAA;4BACX,QAAQ,UAAA;4BACR,QAAQ,UAAA;4BACR,QAAQ,UAAA;4BACR,YAAY,cAAA;4BACZ,SAAS,WAAA;4BACT,SAAS,WAAA;4BACT,YAAY,cAAA;4BACZ,SAAS,WAAA;4BACT,SAAS,WAAA;4BACT,gBAAgB,kBAAA;4BAChB,EAAE,IAAA;yBACH,EAAA;;;;CACF;AAnDD,kDAmDC"}
\ No newline at end of file
+{"version":3,"file":"loadQuantizedParams.js","sourceRoot":"","sources":["../../src/faceRecognitionNet/loadQuantizedParams.ts"],"names":[],"mappings":";;;AAEA,oFAAmF;AACnF,kFAAiF;AACjF,gDAAiD;AACjD,0DAAyD;AAIzD,IAAM,kBAAkB,GAAG,wBAAwB,CAAA;AAEnD,2BAA2B,SAAc,EAAE,aAA6B;IAEtE,IAAM,kBAAkB,GAAG,qDAAyB,CAAC,SAAS,EAAE,aAAa,CAAC,CAAA;IAE9E,iCAAiC,MAAc;QAE7C,IAAM,OAAO,GAAG,kBAAkB,CAAiB,MAAM,mBAAgB,EAAE,CAAC,CAAC,CAAA;QAC7E,IAAM,MAAM,GAAG,kBAAkB,CAAiB,MAAM,kBAAe,EAAE,CAAC,CAAC,CAAA;QAE3E,OAAO,EAAE,OAAO,SAAA,EAAE,MAAM,QAAA,EAAE,CAAA;IAC5B,CAAC;IAED,gCAAgC,MAAc;QAE5C,IAAM,OAAO,GAAG,kBAAkB,CAAiB,MAAM,kBAAe,EAAE,CAAC,CAAC,CAAA;QAC5E,IAAM,IAAI,GAAG,kBAAkB,CAAiB,MAAM,eAAY,EAAE,CAAC,CAAC,CAAA;QACtE,IAAM,KAAK,GAAG,uBAAuB,CAAC,MAAM,CAAC,CAAA;QAE7C,OAAO,EAAE,IAAI,EAAE,EAAE,OAAO,SAAA,EAAE,IAAI,MAAA,EAAE,EAAE,KAAK,OAAA,EAAE,CAAA;IAC3C,CAAC;IAED,oCAAoC,MAAc;QAChD,OAAO;YACL,KAAK,EAAE,sBAAsB,CAAI,MAAM,WAAQ,CAAC;YAChD,KAAK,EAAE,sBAAsB,CAAI,MAAM,WAAQ,CAAC;SACjD,CAAA;IACH,CAAC;IAED,OAAO;QACL,sBAAsB,wBAAA;QACtB,0BAA0B,4BAAA;KAC3B,CAAA;AAEH,CAAC;AAED,6BACE,GAAuB;;;;;wBAGL,qBAAM,6BAAa,CAAC,GAAG,EAAE,kBAAkB,CAAC,EAAA;;oBAAxD,SAAS,GAAG,SAA4C;oBACxD,aAAa,GAAmB,EAAE,CAAA;oBAElC,KAGF,iBAAiB,CAAC,SAAS,EAAE,aAAa,CAAC,EAF7C,sBAAsB,4BAAA,EACtB,0BAA0B,gCAAA,CACmB;oBAEzC,WAAW,GAAG,sBAAsB,CAAC,aAAa,CAAC,CAAA;oBACnD,QAAQ,GAAG,0BAA0B,CAAC,UAAU,CAAC,CAAA;oBACjD,QAAQ,GAAG,0BAA0B,CAAC,UAAU,CAAC,CAAA;oBACjD,QAAQ,GAAG,0BAA0B,CAAC,UAAU,CAAC,CAAA;oBAEjD,WAAW,GAAG,0BAA0B,CAAC,aAAa,CAAC,CAAA;oBACvD,QAAQ,GAAG,0BAA0B,CAAC,UAAU,CAAC,CAAA;oBACjD,QAAQ,GAAG,0BAA0B,CAAC,UAAU,CAAC,CAAA;oBACjD,QAAQ,GAAG,0BAA0B,CAAC,UAAU,CAAC,CAAA;oBAEjD,YAAY,GAAG,0BAA0B,CAAC,cAAc,CAAC,CAAA;oBACzD,SAAS,GAAG,0BAA0B,CAAC,WAAW,CAAC,CAAA;oBACnD,SAAS,GAAG,0BAA0B,CAAC,WAAW,CAAC,CAAA;oBAEnD,YAAY,GAAG,0BAA0B,CAAC,cAAc,CAAC,CAAA;oBACzD,SAAS,GAAG,0BAA0B,CAAC,WAAW,CAAC,CAAA;oBACnD,SAAS,GAAG,0BAA0B,CAAC,WAAW,CAAC,CAAA;oBACnD,gBAAgB,GAAG,0BAA0B,CAAC,kBAAkB,CAAC,CAAA;oBAEjE,EAAE,GAAG,SAAS,CAAC,IAAI,CAAC,CAAA;oBAC1B,aAAa,CAAC,IAAI,CAAC,EAAE,YAAY,EAAE,IAAI,EAAE,SAAS,EAAE,IAAI,EAAE,CAAC,CAAA;oBAE3D,IAAI,CAAC,qBAAU,CAAC,EAAE,CAAC,EAAE;wBACnB,MAAM,IAAI,KAAK,CAAC,2DAAyD,EAAI,CAAC,CAAA;qBAC/E;oBAEK,MAAM,GAAG;wBACb,WAAW,aAAA;wBACX,QAAQ,UAAA;wBACR,QAAQ,UAAA;wBACR,QAAQ,UAAA;wBACR,WAAW,aAAA;wBACX,QAAQ,UAAA;wBACR,QAAQ,UAAA;wBACR,QAAQ,UAAA;wBACR,YAAY,cAAA;wBACZ,SAAS,WAAA;wBACT,SAAS,WAAA;wBACT,YAAY,cAAA;wBACZ,SAAS,WAAA;wBACT,SAAS,WAAA;wBACT,gBAAgB,kBAAA;wBAChB,EAAE,IAAA;qBACH,CAAA;oBAED,uDAA0B,CAAC,SAAS,EAAE,aAAa,CAAC,CAAA;oBAEpD,sBAAO,EAAE,MAAM,QAAA,EAAE,aAAa,eAAA,EAAE,EAAA;;;;CACjC;AA5DD,kDA4DC"}
\ No newline at end of file

dist/face-api.js

@@ -271,9 +271,6 @@
   function isTensor(tensor$$1, dim) {
       return tensor$$1 instanceof Tensor && tensor$$1.shape.length === dim;
   }
-  function isTensor1D(tensor$$1) {
-      return isTensor(tensor$$1, 1);
-  }
   function isTensor2D(tensor$$1) {
       return isTensor(tensor$$1, 2);
   }
@@ -663,6 +660,10 @@
           }
           return new Rect(x, y, width, height);
       };
+      Rect.prototype.pad = function (padX, padY) {
+          var _a = this, x = _a.x, y = _a.y, width = _a.width, height = _a.height;
+          return new Rect(x - (padX / 2), y - (padY / 2), width + padX, height + padY);
+      };
       Rect.prototype.floor = function () {
           return new Rect(Math.floor(this.x), Math.floor(this.y), Math.floor(this.width), Math.floor(this.height));
       };
@@ -955,6 +956,128 @@
       });
   }
 
+  var NeuralNetwork = /** @class */ (function () {
+      function NeuralNetwork(_name) {
+          this._name = _name;
+          this._params = undefined;
+          this._paramMappings = [];
+      }
+      Object.defineProperty(NeuralNetwork.prototype, "params", {
+          get: function () {
+              return this._params;
+          },
+          enumerable: true,
+          configurable: true
+      });
+      Object.defineProperty(NeuralNetwork.prototype, "paramMappings", {
+          get: function () {
+              return this._paramMappings;
+          },
+          enumerable: true,
+          configurable: true
+      });
+      NeuralNetwork.prototype.getParamFromPath = function (paramPath) {
+          var _a = this.traversePropertyPath(paramPath), obj = _a.obj, objProp = _a.objProp;
+          return obj[objProp];
+      };
+      NeuralNetwork.prototype.reassignParamFromPath = function (paramPath, tensor$$1) {
+          var _a = this.traversePropertyPath(paramPath), obj = _a.obj, objProp = _a.objProp;
+          obj[objProp].dispose();
+          obj[objProp] = tensor$$1;
+      };
+      NeuralNetwork.prototype.getParamList = function () {
+          var _this = this;
+          return this._paramMappings.map(function (_a) {
+              var paramPath = _a.paramPath;
+              return ({
+                  path: paramPath,
+                  tensor: _this.getParamFromPath(paramPath)
+              });
+          });
+      };
+      NeuralNetwork.prototype.getTrainableParams = function () {
+          return this.getParamList().filter(function (param) { return param.tensor instanceof Variable; });
+      };
+      NeuralNetwork.prototype.getFrozenParams = function () {
+          return this.getParamList().filter(function (param) { return !(param.tensor instanceof Variable); });
+      };
+      NeuralNetwork.prototype.variable = function () {
+          var _this = this;
+          this.getFrozenParams().forEach(function (_a) {
+              var path = _a.path, tensor$$1 = _a.tensor;
+              _this.reassignParamFromPath(path, variable(tensor$$1));
+          });
+      };
+      NeuralNetwork.prototype.freeze = function () {
+          var _this = this;
+          this.getTrainableParams().forEach(function (_a) {
+              var path = _a.path, tensor$$1 = _a.tensor;
+              _this.reassignParamFromPath(path, tensor(tensor$$1));
+          });
+      };
+      NeuralNetwork.prototype.dispose = function (throwOnRedispose) {
+          if (throwOnRedispose === void 0) { throwOnRedispose = true; }
+          this.getParamList().forEach(function (param) {
+              if (throwOnRedispose && param.tensor.isDisposed) {
+                  throw new Error("param tensor has already been disposed for path " + param.path);
+              }
+              param.tensor.dispose();
+          });
+          this._params = undefined;
+      };
+      NeuralNetwork.prototype.load = function (weightsOrUrl) {
+          return __awaiter$1(this, void 0, void 0, function () {
+              var _a, paramMappings, params;
+              return __generator$1(this, function (_b) {
+                  switch (_b.label) {
+                      case 0:
+                          if (weightsOrUrl instanceof Float32Array) {
+                              this.extractWeights(weightsOrUrl);
+                              return [2 /*return*/];
+                          }
+                          if (weightsOrUrl && typeof weightsOrUrl !== 'string') {
+                              throw new Error(this._name + ".load - expected model uri, or weights as Float32Array");
+                          }
+                          return [4 /*yield*/, this.loadQuantizedParams(weightsOrUrl)];
+                      case 1:
+                          _a = _b.sent(), paramMappings = _a.paramMappings, params = _a.params;
+                          this._paramMappings = paramMappings;
+                          this._params = params;
+                          return [2 /*return*/];
+                  }
+              });
+          });
+      };
+      NeuralNetwork.prototype.extractWeights = function (weights) {
+          var _a = this.extractParams(weights), paramMappings = _a.paramMappings, params = _a.params;
+          this._paramMappings = paramMappings;
+          this._params = params;
+      };
+      NeuralNetwork.prototype.traversePropertyPath = function (paramPath) {
+          if (!this.params) {
+              throw new Error("traversePropertyPath - model has no loaded params");
+          }
+          var result = paramPath.split('/').reduce(function (res, objProp) {
+              if (!res.nextObj.hasOwnProperty(objProp)) {
+                  throw new Error("traversePropertyPath - object does not have property " + objProp + ", for path " + paramPath);
+              }
+              return { obj: res.nextObj, objProp: objProp, nextObj: res.nextObj[objProp] };
+          }, { nextObj: this.params });
+          var obj = result.obj, objProp = result.objProp;
+          if (!obj || !objProp || !(obj[objProp] instanceof Tensor)) {
+              throw new Error("traversePropertyPath - parameter is not a tensor, for path " + paramPath);
+          }
+          return { obj: obj, objProp: objProp };
+      };
+      NeuralNetwork.prototype.loadQuantizedParams = function (_) {
+          throw new Error(this._name + ".loadQuantizedParams - not implemented");
+      };
+      NeuralNetwork.prototype.extractParams = function (_) {
+          throw new Error(this._name + ".extractParams - not implemented");
+      };
+      return NeuralNetwork;
+  }());
+
   function extractWeightsFactory(weights) {
       var remainingWeights = weights;
       function extractWeights(numWeights) {
@@ -971,13 +1094,14 @@
       };
   }
 
-  function extractorsFactory(extractWeights) {
-      function extractDepthwiseConvParams(numChannels) {
+  function extractorsFactory(extractWeights, paramMappings) {
+      function extractDepthwiseConvParams(numChannels, mappedPrefix) {
           var filters = tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);
           var batch_norm_scale = tensor1d(extractWeights(numChannels));
           var batch_norm_offset = tensor1d(extractWeights(numChannels));
           var batch_norm_mean = tensor1d(extractWeights(numChannels));
           var batch_norm_variance = 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: filters,
               batch_norm_scale: batch_norm_scale,
@@ -986,115 +1110,116 @@
               batch_norm_variance: batch_norm_variance
           };
       }
-      function extractConvParams(channelsIn, channelsOut, filterSize) {
+      function extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, isPointwiseConv) {
           var filters = tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
           var bias = tensor1d(extractWeights(channelsOut));
-          return {
-              filters: filters,
-              bias: bias
-          };
+          paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/" + (isPointwiseConv ? 'batch_norm_offset' : 'bias') });
+          return { filters: filters, bias: bias };
       }
-      function extractPointwiseConvParams(channelsIn, channelsOut, filterSize) {
-          var _a = extractConvParams(channelsIn, channelsOut, filterSize), filters = _a.filters, bias = _a.bias;
+      function extractPointwiseConvParams(channelsIn, channelsOut, filterSize, mappedPrefix) {
+          var _a = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true), filters = _a.filters, bias = _a.bias;
           return {
               filters: filters,
               batch_norm_offset: bias
           };
       }
-      function extractConvPairParams(channelsIn, channelsOut) {
-          var depthwise_conv_params = extractDepthwiseConvParams(channelsIn);
-          var pointwise_conv_params = extractPointwiseConvParams(channelsIn, channelsOut, 1);
-          return {
-              depthwise_conv_params: depthwise_conv_params,
-              pointwise_conv_params: pointwise_conv_params
-          };
+      function extractConvPairParams(channelsIn, channelsOut, mappedPrefix) {
+          var depthwise_conv = extractDepthwiseConvParams(channelsIn, mappedPrefix + "/depthwise_conv");
+          var pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, mappedPrefix + "/pointwise_conv");
+          return { depthwise_conv: depthwise_conv, pointwise_conv: pointwise_conv };
       }
       function extractMobilenetV1Params() {
-          var conv_0_params = extractPointwiseConvParams(3, 32, 3);
-          var channelNumPairs = [
-              [32, 64],
-              [64, 128],
-              [128, 128],
-              [128, 256],
-              [256, 256],
-              [256, 512],
-              [512, 512],
-              [512, 512],
-              [512, 512],
-              [512, 512],
-              [512, 512],
-              [512, 1024],
-              [1024, 1024]
-          ];
-          var conv_pair_params = channelNumPairs.map(function (_a) {
-              var channelsIn = _a[0], channelsOut = _a[1];
-              return extractConvPairParams(channelsIn, channelsOut);
-          });
+          var conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0');
+          var conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1');
+          var conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2');
+          var conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3');
+          var conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4');
+          var conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5');
+          var conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6');
+          var conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7');
+          var conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8');
+          var conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9');
+          var conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10');
+          var conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11');
+          var conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12');
+          var conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13');
           return {
-              conv_0_params: conv_0_params,
-              conv_pair_params: conv_pair_params
+              conv_0: conv_0,
+              conv_1: conv_1,
+              conv_2: conv_2,
+              conv_3: conv_3,
+              conv_4: conv_4,
+              conv_5: conv_5,
+              conv_6: conv_6,
+              conv_7: conv_7,
+              conv_8: conv_8,
+              conv_9: conv_9,
+              conv_10: conv_10,
+              conv_11: conv_11,
+              conv_12: conv_12,
+              conv_13: conv_13
           };
       }
       function extractPredictionLayerParams() {
-          var conv_0_params = extractPointwiseConvParams(1024, 256, 1);
-          var conv_1_params = extractPointwiseConvParams(256, 512, 3);
-          var conv_2_params = extractPointwiseConvParams(512, 128, 1);
-          var conv_3_params = extractPointwiseConvParams(128, 256, 3);
-          var conv_4_params = extractPointwiseConvParams(256, 128, 1);
-          var conv_5_params = extractPointwiseConvParams(128, 256, 3);
-          var conv_6_params = extractPointwiseConvParams(256, 64, 1);
-          var conv_7_params = extractPointwiseConvParams(64, 128, 3);
-          var box_encoding_0_predictor_params = extractConvParams(512, 12, 1);
-          var class_predictor_0_params = extractConvParams(512, 9, 1);
-          var box_encoding_1_predictor_params = extractConvParams(1024, 24, 1);
-          var class_predictor_1_params = extractConvParams(1024, 18, 1);
-          var box_encoding_2_predictor_params = extractConvParams(512, 24, 1);
-          var class_predictor_2_params = extractConvParams(512, 18, 1);
-          var box_encoding_3_predictor_params = extractConvParams(256, 24, 1);
-          var class_predictor_3_params = extractConvParams(256, 18, 1);
-          var box_encoding_4_predictor_params = extractConvParams(256, 24, 1);
-          var class_predictor_4_params = extractConvParams(256, 18, 1);
-          var box_encoding_5_predictor_params = extractConvParams(128, 24, 1);
-          var class_predictor_5_params = extractConvParams(128, 18, 1);
-          var box_predictor_0_params = {
-              box_encoding_predictor_params: box_encoding_0_predictor_params,
-              class_predictor_params: class_predictor_0_params
+          var conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0');
+          var conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1');
+          var conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2');
+          var conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3');
+          var conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4');
+          var conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5');
+          var conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6');
+          var conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7');
+          var box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor');
+          var class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor');
+          var box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor');
+          var class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor');
+          var box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor');
+          var class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor');
+          var box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor');
+          var class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor');
+          var box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor');
+          var class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor');
+          var box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor');
+          var class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor');
+          var box_predictor_0 = {
+              box_encoding_predictor: box_encoding_0_predictor,
+              class_predictor: class_predictor_0
           };
-          var box_predictor_1_params = {
-              box_encoding_predictor_params: box_encoding_1_predictor_params,
-              class_predictor_params: class_predictor_1_params
+          var box_predictor_1 = {
+              box_encoding_predictor: box_encoding_1_predictor,
+              class_predictor: class_predictor_1
           };
-          var box_predictor_2_params = {
-              box_encoding_predictor_params: box_encoding_2_predictor_params,
-              class_predictor_params: class_predictor_2_params
+          var box_predictor_2 = {
+              box_encoding_predictor: box_encoding_2_predictor,
+              class_predictor: class_predictor_2
           };
-          var box_predictor_3_params = {
-              box_encoding_predictor_params: box_encoding_3_predictor_params,
-              class_predictor_params: class_predictor_3_params
+          var box_predictor_3 = {
+              box_encoding_predictor: box_encoding_3_predictor,
+              class_predictor: class_predictor_3
           };
-          var box_predictor_4_params = {
-              box_encoding_predictor_params: box_encoding_4_predictor_params,
-              class_predictor_params: class_predictor_4_params
+          var box_predictor_4 = {
+              box_encoding_predictor: box_encoding_4_predictor,
+              class_predictor: class_predictor_4
           };
-          var box_predictor_5_params = {
-              box_encoding_predictor_params: box_encoding_5_predictor_params,
-              class_predictor_params: class_predictor_5_params
+          var box_predictor_5 = {
+              box_encoding_predictor: box_encoding_5_predictor,
+              class_predictor: class_predictor_5
           };
           return {
-              conv_0_params: conv_0_params,
-              conv_1_params: conv_1_params,
-              conv_2_params: conv_2_params,
-              conv_3_params: conv_3_params,
-              conv_4_params: conv_4_params,
-              conv_5_params: conv_5_params,
-              conv_6_params: conv_6_params,
-              conv_7_params: conv_7_params,
-              box_predictor_0_params: box_predictor_0_params,
-              box_predictor_1_params: box_predictor_1_params,
-              box_predictor_2_params: box_predictor_2_params,
-              box_predictor_3_params: box_predictor_3_params,
-              box_predictor_4_params: box_predictor_4_params,
-              box_predictor_5_params: box_predictor_5_params
+              conv_0: conv_0,
+              conv_1: conv_1,
+              conv_2: conv_2,
+              conv_3: conv_3,
+              conv_4: conv_4,
+              conv_5: conv_5,
+              conv_6: conv_6,
+              conv_7: conv_7,
+              box_predictor_0: box_predictor_0,
+              box_predictor_1: box_predictor_1,
+              box_predictor_2: box_predictor_2,
+              box_predictor_3: box_predictor_3,
+              box_predictor_4: box_predictor_4,
+              box_predictor_5: box_predictor_5
           };
       }
       return {
@@ -1103,21 +1228,45 @@
       };
   }
   function extractParams(weights) {
+      var paramMappings = [];
       var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
-      var _b = extractorsFactory(extractWeights), extractMobilenetV1Params = _b.extractMobilenetV1Params, extractPredictionLayerParams = _b.extractPredictionLayerParams;
-      var mobilenetv1_params = extractMobilenetV1Params();
-      var prediction_layer_params = extractPredictionLayerParams();
+      var _b = extractorsFactory(extractWeights, paramMappings), extractMobilenetV1Params = _b.extractMobilenetV1Params, extractPredictionLayerParams = _b.extractPredictionLayerParams;
+      var mobilenetv1 = extractMobilenetV1Params();
+      var prediction_layer = extractPredictionLayerParams();
       var extra_dim = tensor3d(extractWeights(5118 * 4), [1, 5118, 4]);
-      var output_layer_params = {
+      var output_layer = {
           extra_dim: extra_dim
       };
+      paramMappings.push({ paramPath: 'output_layer/extra_dim' });
       if (getRemainingWeights().length !== 0) {
           throw new Error("weights remaing after extract: " + getRemainingWeights().length);
       }
       return {
-          mobilenetv1_params: mobilenetv1_params,
-          prediction_layer_params: prediction_layer_params,
-          output_layer_params: output_layer_params
+          params: {
+              mobilenetv1: mobilenetv1,
+              prediction_layer: prediction_layer,
+              output_layer: output_layer
+          },
+          paramMappings: paramMappings
+      };
+  }
+
+  function disposeUnusedWeightTensors(weightMap, paramMappings) {
+      Object.keys(weightMap).forEach(function (path) {
+          if (!paramMappings.some(function (pm) { return pm.originalPath === path; })) {
+              weightMap[path].dispose();
+          }
+      });
+  }
+
+  function extractWeightEntryFactory(weightMap, paramMappings) {
+      return function (originalPath, paramRank, mappedPath) {
+          var tensor = weightMap[originalPath];
+          if (!isTensor(tensor, paramRank)) {
+              throw new Error("expected weightMap[" + originalPath + "] to be a Tensor" + paramRank + "D, instead have " + tensor);
+          }
+          paramMappings.push({ originalPath: originalPath, paramPath: mappedPath || originalPath });
+          return tensor;
       };
   }
 
@@ -1152,95 +1301,78 @@
   }
 
   var DEFAULT_MODEL_NAME = 'face_detection_model';
-  function extractorsFactory$1(weightMap) {
-      function extractPointwiseConvParams(prefix, idx) {
-          var pointwise_conv_params = {
-              filters: weightMap[prefix + "/Conv2d_" + idx + "_pointwise/weights"],
-              batch_norm_offset: weightMap[prefix + "/Conv2d_" + idx + "_pointwise/convolution_bn_offset"]
-          };
-          if (!isTensor4D(pointwise_conv_params.filters)) {
-              throw new Error("expected weightMap[" + prefix + "/Conv2d_" + idx + "_pointwise/weights] to be a Tensor4D, instead have " + pointwise_conv_params.filters);
-          }
-          if (!isTensor1D(pointwise_conv_params.batch_norm_offset)) {
-              throw new Error("expected weightMap[" + prefix + "/Conv2d_" + idx + "_pointwise/convolution_bn_offset] to be a Tensor1D, instead have " + pointwise_conv_params.batch_norm_offset);
-          }
-          return pointwise_conv_params;
+  function extractorsFactory$1(weightMap, paramMappings) {
+      var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
+      function extractPointwiseConvParams(prefix, idx, mappedPrefix) {
+          var filters = extractWeightEntry(prefix + "/Conv2d_" + idx + "_pointwise/weights", 4, mappedPrefix + "/filters");
+          var batch_norm_offset = extractWeightEntry(prefix + "/Conv2d_" + idx + "_pointwise/convolution_bn_offset", 1, mappedPrefix + "/batch_norm_offset");
+          return { filters: filters, batch_norm_offset: batch_norm_offset };
       }
       function extractConvPairParams(idx) {
-          var depthwise_conv_params = {
-              filters: weightMap["MobilenetV1/Conv2d_" + idx + "_depthwise/depthwise_weights"],
-              batch_norm_scale: weightMap["MobilenetV1/Conv2d_" + idx + "_depthwise/BatchNorm/gamma"],
-              batch_norm_offset: weightMap["MobilenetV1/Conv2d_" + idx + "_depthwise/BatchNorm/beta"],
-              batch_norm_mean: weightMap["MobilenetV1/Conv2d_" + idx + "_depthwise/BatchNorm/moving_mean"],
-              batch_norm_variance: weightMap["MobilenetV1/Conv2d_" + idx + "_depthwise/BatchNorm/moving_variance"],
-          };
-          if (!isTensor4D(depthwise_conv_params.filters)) {
-              throw new Error("expected weightMap[MobilenetV1/Conv2d_" + idx + "_depthwise/depthwise_weights] to be a Tensor4D, instead have " + depthwise_conv_params.filters);
-          }
-          if (!isTensor1D(depthwise_conv_params.batch_norm_scale)) {
-              throw new Error("expected weightMap[MobilenetV1/Conv2d_" + idx + "_depthwise/BatchNorm/gamma] to be a Tensor1D, instead have " + depthwise_conv_params.batch_norm_scale);
-          }
-          if (!isTensor1D(depthwise_conv_params.batch_norm_offset)) {
-              throw new Error("expected weightMap[MobilenetV1/Conv2d_" + idx + "_depthwise/BatchNorm/beta] to be a Tensor1D, instead have " + depthwise_conv_params.batch_norm_offset);
-          }
-          if (!isTensor1D(depthwise_conv_params.batch_norm_mean)) {
-              throw new Error("expected weightMap[MobilenetV1/Conv2d_" + idx + "_depthwise/BatchNorm/moving_mean] to be a Tensor1D, instead have " + depthwise_conv_params.batch_norm_mean);
-          }
-          if (!isTensor1D(depthwise_conv_params.batch_norm_variance)) {
-              throw new Error("expected weightMap[MobilenetV1/Conv2d_" + idx + "_depthwise/BatchNorm/moving_variance] to be a Tensor1D, instead have " + depthwise_conv_params.batch_norm_variance);
-          }
+          var mappedPrefix = "mobilenetv1/conv_" + idx;
+          var prefixDepthwiseConv = "MobilenetV1/Conv2d_" + idx + "_depthwise";
+          var mappedPrefixDepthwiseConv = mappedPrefix + "/depthwise_conv";
+          var mappedPrefixPointwiseConv = mappedPrefix + "/pointwise_conv";
+          var filters = extractWeightEntry(prefixDepthwiseConv + "/depthwise_weights", 4, mappedPrefixDepthwiseConv + "/filters");
+          var batch_norm_scale = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/gamma", 1, mappedPrefixDepthwiseConv + "/batch_norm_scale");
+          var batch_norm_offset = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/beta", 1, mappedPrefixDepthwiseConv + "/batch_norm_offset");
+          var batch_norm_mean = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/moving_mean", 1, mappedPrefixDepthwiseConv + "/batch_norm_mean");
+          var batch_norm_variance = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/moving_variance", 1, mappedPrefixDepthwiseConv + "/batch_norm_variance");
           return {
-              depthwise_conv_params: depthwise_conv_params,
-              pointwise_conv_params: extractPointwiseConvParams('MobilenetV1', idx)
+              depthwise_conv: {
+                  filters: filters,
+                  batch_norm_scale: batch_norm_scale,
+                  batch_norm_offset: batch_norm_offset,
+                  batch_norm_mean: batch_norm_mean,
+                  batch_norm_variance: batch_norm_variance
+              },
+              pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv)
           };
       }
       function extractMobilenetV1Params() {
           return {
-              conv_0_params: extractPointwiseConvParams('MobilenetV1', 0),
-              conv_pair_params: Array(13).fill(0).map(function (_, i) { return extractConvPairParams(i + 1); })
+              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) {
+          var filters = extractWeightEntry(prefix + "/weights", 4, mappedPrefix + "/filters");
+          var bias = extractWeightEntry(prefix + "/biases", 1, mappedPrefix + "/bias");
+          return { filters: filters, bias: bias };
+      }
       function extractBoxPredictorParams(idx) {
-          var params = {
-              box_encoding_predictor_params: {
-                  filters: weightMap["Prediction/BoxPredictor_" + idx + "/BoxEncodingPredictor/weights"],
-                  bias: weightMap["Prediction/BoxPredictor_" + idx + "/BoxEncodingPredictor/biases"]
-              },
-              class_predictor_params: {
-                  filters: weightMap["Prediction/BoxPredictor_" + idx + "/ClassPredictor/weights"],
-                  bias: weightMap["Prediction/BoxPredictor_" + idx + "/ClassPredictor/biases"]
-              }
-          };
-          if (!isTensor4D(params.box_encoding_predictor_params.filters)) {
-              throw new Error("expected weightMap[Prediction/BoxPredictor_" + idx + "/BoxEncodingPredictor/weights] to be a Tensor4D, instead have " + params.box_encoding_predictor_params.filters);
-          }
-          if (!isTensor1D(params.box_encoding_predictor_params.bias)) {
-              throw new Error("expected weightMap[Prediction/BoxPredictor_" + idx + "/BoxEncodingPredictor/biases] to be a Tensor1D, instead have " + params.box_encoding_predictor_params.bias);
-          }
-          if (!isTensor4D(params.class_predictor_params.filters)) {
-              throw new Error("expected weightMap[Prediction/BoxPredictor_" + idx + "/ClassPredictor/weights] to be a Tensor4D, instead have " + params.class_predictor_params.filters);
-          }
-          if (!isTensor1D(params.class_predictor_params.bias)) {
-              throw new Error("expected weightMap[Prediction/BoxPredictor_" + idx + "/ClassPredictor/biases] to be a Tensor1D, instead have " + params.class_predictor_params.bias);
-          }
-          return params;
+          var box_encoding_predictor = extractConvParams("Prediction/BoxPredictor_" + idx + "/BoxEncodingPredictor", "prediction_layer/box_predictor_" + idx + "/box_encoding_predictor");
+          var class_predictor = extractConvParams("Prediction/BoxPredictor_" + idx + "/ClassPredictor", "prediction_layer/box_predictor_" + idx + "/class_predictor");
+          return { box_encoding_predictor: box_encoding_predictor, class_predictor: class_predictor };
       }
       function extractPredictionLayerParams() {
           return {
-              conv_0_params: extractPointwiseConvParams('Prediction', 0),
-              conv_1_params: extractPointwiseConvParams('Prediction', 1),
-              conv_2_params: extractPointwiseConvParams('Prediction', 2),
-              conv_3_params: extractPointwiseConvParams('Prediction', 3),
-              conv_4_params: extractPointwiseConvParams('Prediction', 4),
-              conv_5_params: extractPointwiseConvParams('Prediction', 5),
-              conv_6_params: extractPointwiseConvParams('Prediction', 6),
-              conv_7_params: extractPointwiseConvParams('Prediction', 7),
-              box_predictor_0_params: extractBoxPredictorParams(0),
-              box_predictor_1_params: extractBoxPredictorParams(1),
-              box_predictor_2_params: extractBoxPredictorParams(2),
-              box_predictor_3_params: extractBoxPredictorParams(3),
-              box_predictor_4_params: extractBoxPredictorParams(4),
-              box_predictor_5_params: extractBoxPredictorParams(5)
+              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 {
@@ -1250,24 +1382,28 @@
   }
   function loadQuantizedParams(uri) {
       return __awaiter$1(this, void 0, void 0, function () {
-          var weightMap, _a, extractMobilenetV1Params, extractPredictionLayerParams, extra_dim;
+          var weightMap, paramMappings, _a, extractMobilenetV1Params, extractPredictionLayerParams, extra_dim, params;
           return __generator$1(this, function (_b) {
               switch (_b.label) {
                   case 0: return [4 /*yield*/, loadWeightMap(uri, DEFAULT_MODEL_NAME)];
                   case 1:
                       weightMap = _b.sent();
-                      _a = extractorsFactory$1(weightMap), extractMobilenetV1Params = _a.extractMobilenetV1Params, extractPredictionLayerParams = _a.extractPredictionLayerParams;
+                      paramMappings = [];
+                      _a = extractorsFactory$1(weightMap, paramMappings), extractMobilenetV1Params = _a.extractMobilenetV1Params, extractPredictionLayerParams = _a.extractPredictionLayerParams;
                       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);
                       }
-                      return [2 /*return*/, {
-                              mobilenetv1_params: extractMobilenetV1Params(),
-                              prediction_layer_params: extractPredictionLayerParams(),
-                              output_layer_params: {
-                                  extra_dim: extra_dim
-                              }
-                          }];
+                      params = {
+                          mobilenetv1: extractMobilenetV1Params(),
+                          prediction_layer: extractPredictionLayerParams(),
+                          output_layer: {
+                              extra_dim: extra_dim
+                          }
+                      };
+                      disposeUnusedWeightTensors(weightMap, paramMappings);
+                      return [2 /*return*/, { params: params, paramMappings: paramMappings }];
               }
           });
       });
@@ -1295,12 +1431,27 @@
   function mobileNetV1(x, params) {
       return tidy(function () {
           var conv11 = null;
-          var out = pointwiseConvLayer(x, params.conv_0_params, [2, 2]);
-          params.conv_pair_params.forEach(function (param, i) {
+          var out = pointwiseConvLayer(x, params.conv_0, [2, 2]);
+          var 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(function (param, i) {
               var layerIdx = i + 1;
               var depthwiseConvStrides = getStridesForLayerIdx(layerIdx);
-              out = depthwiseConvLayer(out, param.depthwise_conv_params, depthwiseConvStrides);
-              out = pointwiseConvLayer(out, param.pointwise_conv_params, [1, 1]);
+              out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides);
+              out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1]);
               if (layerIdx === 11) {
                   conv11 = out;
               }
@@ -1426,8 +1577,8 @@
   function boxPredictionLayer(x, params) {
       return tidy(function () {
           var batchSize = x.shape[0];
-          var boxPredictionEncoding = reshape(convLayer(x, params.box_encoding_predictor_params), [batchSize, -1, 1, 4]);
-          var classPrediction = reshape(convLayer(x, params.class_predictor_params), [batchSize, -1, 3]);
+          var boxPredictionEncoding = reshape(convLayer(x, params.box_encoding_predictor), [batchSize, -1, 1, 4]);
+          var classPrediction = reshape(convLayer(x, params.class_predictor), [batchSize, -1, 3]);
           return {
               boxPredictionEncoding: boxPredictionEncoding,
               classPrediction: classPrediction
@@ -1437,20 +1588,20 @@
 
   function predictionLayer(x, conv11, params) {
       return tidy(function () {
-          var conv0 = pointwiseConvLayer(x, params.conv_0_params, [1, 1]);
-          var conv1 = pointwiseConvLayer(conv0, params.conv_1_params, [2, 2]);
-          var conv2 = pointwiseConvLayer(conv1, params.conv_2_params, [1, 1]);
-          var conv3 = pointwiseConvLayer(conv2, params.conv_3_params, [2, 2]);
-          var conv4 = pointwiseConvLayer(conv3, params.conv_4_params, [1, 1]);
-          var conv5 = pointwiseConvLayer(conv4, params.conv_5_params, [2, 2]);
-          var conv6 = pointwiseConvLayer(conv5, params.conv_6_params, [1, 1]);
-          var conv7 = pointwiseConvLayer(conv6, params.conv_7_params, [2, 2]);
-          var boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0_params);
-          var boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1_params);
-          var boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2_params);
-          var boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3_params);
-          var boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4_params);
-          var boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5_params);
+          var conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]);
+          var conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]);
+          var conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]);
+          var conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2]);
+          var conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1]);
+          var conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2]);
+          var conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1]);
+          var conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2]);
+          var boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0);
+          var boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1);
+          var boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2);
+          var boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3);
+          var boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4);
+          var boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5);
           var boxPredictions = concat([
               boxPrediction0.boxPredictionEncoding,
               boxPrediction1.boxPredictionEncoding,
@@ -1474,45 +1625,22 @@
       });
   }
 
-  var FaceDetectionNet = /** @class */ (function () {
+  var FaceDetectionNet = /** @class */ (function (_super) {
+      __extends$1(FaceDetectionNet, _super);
       function FaceDetectionNet() {
+          return _super.call(this, 'FaceDetectionNet') || this;
       }
-      FaceDetectionNet.prototype.load = function (weightsOrUrl) {
-          return __awaiter$1(this, void 0, void 0, function () {
-              var _a;
-              return __generator$1(this, function (_b) {
-                  switch (_b.label) {
-                      case 0:
-                          if (weightsOrUrl instanceof Float32Array) {
-                              this.extractWeights(weightsOrUrl);
-                              return [2 /*return*/];
-                          }
-                          if (weightsOrUrl && typeof weightsOrUrl !== 'string') {
-                              throw new Error('FaceDetectionNet.load - expected model uri, or weights as Float32Array');
-                          }
-                          _a = this;
-                          return [4 /*yield*/, loadQuantizedParams(weightsOrUrl)];
-                      case 1:
-                          _a._params = _b.sent();
-                          return [2 /*return*/];
-                  }
-              });
-          });
-      };
-      FaceDetectionNet.prototype.extractWeights = function (weights) {
-          this._params = extractParams(weights);
-      };
       FaceDetectionNet.prototype.forwardInput = function (input) {
-          var _this = this;
-          if (!this._params) {
+          var params = this.params;
+          if (!params) {
               throw new Error('FaceDetectionNet - load model before inference');
           }
           return tidy(function () {
               var batchTensor = input.toBatchTensor(512, false);
               var x = sub(mul(batchTensor, scalar(0.007843137718737125)), scalar(1));
-              var features = mobileNetV1(x, _this._params.mobilenetv1_params);
-              var _a = predictionLayer(features.out, features.conv11, _this._params.prediction_layer_params), boxPredictions = _a.boxPredictions, classPredictions = _a.classPredictions;
-              return outputLayer(boxPredictions, classPredictions, _this._params.output_layer_params);
+              var features = mobileNetV1(x, params.mobilenetv1);
+              var _a = predictionLayer(features.out, features.conv11, params.prediction_layer), boxPredictions = _a.boxPredictions, classPredictions = _a.classPredictions;
+              return outputLayer(boxPredictions, classPredictions, params.output_layer);
           });
       };
       FaceDetectionNet.prototype.forward = function (input) {
@@ -1575,8 +1703,14 @@
               });
           });
       };
+      FaceDetectionNet.prototype.loadQuantizedParams = function (uri) {
+          return loadQuantizedParams(uri);
+      };
+      FaceDetectionNet.prototype.extractParams = function (weights) {
+          return extractParams(weights);
+      };
       return FaceDetectionNet;
-  }());
+  }(NeuralNetwork));
 
   function faceDetectionNet(weights) {
       var net = new FaceDetectionNet();
@@ -1584,103 +1718,15 @@
       return net;
   }
 
-  var NeuralNetwork = /** @class */ (function () {
-      function NeuralNetwork() {
-          this._params = undefined;
-          this._paramMappings = [];
-      }
-      Object.defineProperty(NeuralNetwork.prototype, "params", {
-          get: function () {
-              return this._params;
-          },
-          enumerable: true,
-          configurable: true
-      });
-      Object.defineProperty(NeuralNetwork.prototype, "paramMappings", {
-          get: function () {
-              return this._paramMappings;
-          },
-          enumerable: true,
-          configurable: true
-      });
-      NeuralNetwork.prototype.getParamFromPath = function (paramPath) {
-          var _a = this.traversePropertyPath(paramPath), obj = _a.obj, objProp = _a.objProp;
-          return obj[objProp];
-      };
-      NeuralNetwork.prototype.reassignParamFromPath = function (paramPath, tensor$$1) {
-          var _a = this.traversePropertyPath(paramPath), obj = _a.obj, objProp = _a.objProp;
-          obj[objProp].dispose();
-          obj[objProp] = tensor$$1;
-      };
-      NeuralNetwork.prototype.getParamList = function () {
-          var _this = this;
-          return this._paramMappings.map(function (_a) {
-              var paramPath = _a.paramPath;
-              return ({
-                  path: paramPath,
-                  tensor: _this.getParamFromPath(paramPath)
-              });
-          });
-      };
-      NeuralNetwork.prototype.getTrainableParams = function () {
-          return this.getParamList().filter(function (param) { return param.tensor instanceof Variable; });
-      };
-      NeuralNetwork.prototype.getFrozenParams = function () {
-          return this.getParamList().filter(function (param) { return !(param.tensor instanceof Variable); });
-      };
-      NeuralNetwork.prototype.variable = function () {
-          var _this = this;
-          this.getFrozenParams().forEach(function (_a) {
-              var path = _a.path, tensor$$1 = _a.tensor;
-              _this.reassignParamFromPath(path, variable(tensor$$1));
-          });
-      };
-      NeuralNetwork.prototype.freeze = function () {
-          var _this = this;
-          this.getTrainableParams().forEach(function (_a) {
-              var path = _a.path, tensor$$1 = _a.tensor;
-              _this.reassignParamFromPath(path, tensor(tensor$$1));
-          });
-      };
-      NeuralNetwork.prototype.dispose = function () {
-          this.getParamList().forEach(function (param) { return param.tensor.dispose(); });
-          this._params = undefined;
-      };
-      NeuralNetwork.prototype.traversePropertyPath = function (paramPath) {
-          if (!this.params) {
-              throw new Error("traversePropertyPath - model has no loaded params");
-          }
-          var result = paramPath.split('/').reduce(function (res, objProp) {
-              if (!res.nextObj.hasOwnProperty(objProp)) {
-                  throw new Error("traversePropertyPath - object does not have property " + objProp + ", for path " + paramPath);
-              }
-              return { obj: res.nextObj, objProp: objProp, nextObj: res.nextObj[objProp] };
-          }, { nextObj: this.params });
-          var obj = result.obj, objProp = result.objProp;
-          if (!obj || !objProp || !(obj[objProp] instanceof Tensor)) {
-              throw new Error("traversePropertyPath - parameter is not a tensor, for path " + paramPath);
-          }
-          return { obj: obj, objProp: objProp };
-      };
-      return NeuralNetwork;
-  }());
-
-  function extractConvParamsFactory(extractWeights, paramMappings) {
-      return function (channelsIn, channelsOut, filterSize, mappedPrefix) {
-          var filters = tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
-          var bias = tensor1d(extractWeights(channelsOut));
-          paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/bias" });
-          return {
-              filters: filters,
-              bias: bias
-          };
-      };
-  }
-
   function extractParams$1(weights) {
       var paramMappings = [];
       var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
-      var extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);
+      function extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix) {
+          var filters = tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
+          var bias = tensor1d(extractWeights(channelsOut));
+          paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/bias" });
+          return { filters: filters, bias: bias };
+      }
       function extractFcParams(channelsIn, channelsOut, mappedPrefix) {
           var fc_weights = tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);
           var fc_bias = tensor1d(extractWeights(channelsOut));
@@ -1690,32 +1736,32 @@
               bias: fc_bias
           };
       }
-      var conv0_params = extractConvParams(3, 32, 3, 'conv0_params');
-      var conv1_params = extractConvParams(32, 64, 3, 'conv1_params');
-      var conv2_params = extractConvParams(64, 64, 3, 'conv2_params');
-      var conv3_params = extractConvParams(64, 64, 3, 'conv3_params');
-      var conv4_params = extractConvParams(64, 64, 3, 'conv4_params');
-      var conv5_params = extractConvParams(64, 128, 3, 'conv5_params');
-      var conv6_params = extractConvParams(128, 128, 3, 'conv6_params');
-      var conv7_params = extractConvParams(128, 256, 3, 'conv7_params');
-      var fc0_params = extractFcParams(6400, 1024, 'fc0_params');
-      var fc1_params = extractFcParams(1024, 136, 'fc1_params');
+      var conv0 = extractConvParams(3, 32, 3, 'conv0');
+      var conv1 = extractConvParams(32, 64, 3, 'conv1');
+      var conv2 = extractConvParams(64, 64, 3, 'conv2');
+      var conv3 = extractConvParams(64, 64, 3, 'conv3');
+      var conv4 = extractConvParams(64, 64, 3, 'conv4');
+      var conv5 = extractConvParams(64, 128, 3, 'conv5');
+      var conv6 = extractConvParams(128, 128, 3, 'conv6');
+      var conv7 = extractConvParams(128, 256, 3, 'conv7');
+      var fc0 = extractFcParams(6400, 1024, 'fc0');
+      var fc1 = extractFcParams(1024, 136, 'fc1');
       if (getRemainingWeights().length !== 0) {
           throw new Error("weights remaing after extract: " + getRemainingWeights().length);
       }
       return {
           paramMappings: paramMappings,
           params: {
-              conv0_params: conv0_params,
-              conv1_params: conv1_params,
-              conv2_params: conv2_params,
-              conv3_params: conv3_params,
-              conv4_params: conv4_params,
-              conv5_params: conv5_params,
-              conv6_params: conv6_params,
-              conv7_params: conv7_params,
-              fc0_params: fc0_params,
-              fc1_params: fc1_params
+              conv0: conv0,
+              conv1: conv1,
+              conv2: conv2,
+              conv3: conv3,
+              conv4: conv4,
+              conv5: conv5,
+              conv6: conv6,
+              conv7: conv7,
+              fc0: fc0,
+              fc1: fc1
           }
       };
   }
@@ -1826,33 +1872,18 @@
       });
   }
 
-  function extractWeightEntry(weightMap, path, paramRank) {
-      var tensor = weightMap[path];
-      if (!isTensor(tensor, paramRank)) {
-          throw new Error("expected weightMap[" + path + "] to be a Tensor" + paramRank + "D, instead have " + tensor);
-      }
-      return { path: path, tensor: tensor };
-  }
-
   var DEFAULT_MODEL_NAME$1 = 'face_landmark_68_model';
   function extractorsFactory$2(weightMap, paramMappings) {
+      var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
       function extractConvParams(prefix, mappedPrefix) {
-          var filtersEntry = extractWeightEntry(weightMap, prefix + "/kernel", 4);
-          var biasEntry = extractWeightEntry(weightMap, prefix + "/bias", 1);
-          paramMappings.push({ originalPath: filtersEntry.path, paramPath: mappedPrefix + "/filters" }, { originalPath: biasEntry.path, paramPath: mappedPrefix + "/bias" });
-          return {
-              filters: filtersEntry.tensor,
-              bias: biasEntry.tensor
-          };
+          var filters = extractWeightEntry(prefix + "/kernel", 4, mappedPrefix + "/filters");
+          var bias = extractWeightEntry(prefix + "/bias", 1, mappedPrefix + "/bias");
+          return { filters: filters, bias: bias };
       }
       function extractFcParams(prefix, mappedPrefix) {
-          var weightsEntry = extractWeightEntry(weightMap, prefix + "/kernel", 2);
-          var biasEntry = extractWeightEntry(weightMap, prefix + "/bias", 1);
-          paramMappings.push({ originalPath: weightsEntry.path, paramPath: mappedPrefix + "/weights" }, { originalPath: biasEntry.path, paramPath: mappedPrefix + "/bias" });
-          return {
-              weights: weightsEntry.tensor,
-              bias: biasEntry.tensor
-          };
+          var weights = extractWeightEntry(prefix + "/kernel", 2, mappedPrefix + "/weights");
+          var bias = extractWeightEntry(prefix + "/bias", 1, mappedPrefix + "/bias");
+          return { weights: weights, bias: bias };
       }
       return {
           extractConvParams: extractConvParams,
@@ -1870,17 +1901,18 @@
                       paramMappings = [];
                       _a = extractorsFactory$2(weightMap, paramMappings), extractConvParams = _a.extractConvParams, extractFcParams = _a.extractFcParams;
                       params = {
-                          conv0_params: extractConvParams('conv2d_0', 'conv0_params'),
-                          conv1_params: extractConvParams('conv2d_1', 'conv1_params'),
-                          conv2_params: extractConvParams('conv2d_2', 'conv2_params'),
-                          conv3_params: extractConvParams('conv2d_3', 'conv3_params'),
-                          conv4_params: extractConvParams('conv2d_4', 'conv4_params'),
-                          conv5_params: extractConvParams('conv2d_5', 'conv5_params'),
-                          conv6_params: extractConvParams('conv2d_6', 'conv6_params'),
-                          conv7_params: extractConvParams('conv2d_7', 'conv7_params'),
-                          fc0_params: extractFcParams('dense', 'fc0_params'),
-                          fc1_params: extractFcParams('logits', 'fc1_params')
+                          conv0: extractConvParams('conv2d_0', 'conv0'),
+                          conv1: extractConvParams('conv2d_1', 'conv1'),
+                          conv2: extractConvParams('conv2d_2', 'conv2'),
+                          conv3: extractConvParams('conv2d_3', 'conv3'),
+                          conv4: extractConvParams('conv2d_4', 'conv4'),
+                          conv5: extractConvParams('conv2d_5', 'conv5'),
+                          conv6: extractConvParams('conv2d_6', 'conv6'),
+                          conv7: extractConvParams('conv2d_7', 'conv7'),
+                          fc0: extractFcParams('dense', 'fc0'),
+                          fc1: extractFcParams('logits', 'fc1')
                       };
+                      disposeUnusedWeightTensors(weightMap, paramMappings);
                       return [2 /*return*/, { params: params, paramMappings: paramMappings }];
               }
           });
@@ -1897,57 +1929,29 @@
   var FaceLandmarkNet = /** @class */ (function (_super) {
       __extends$1(FaceLandmarkNet, _super);
       function FaceLandmarkNet() {
-          return _super !== null && _super.apply(this, arguments) || this;
+          return _super.call(this, 'FaceLandmarkNet') || this;
       }
-      FaceLandmarkNet.prototype.load = function (weightsOrUrl) {
-          return __awaiter$1(this, void 0, void 0, function () {
-              var _a, paramMappings, params;
-              return __generator$1(this, function (_b) {
-                  switch (_b.label) {
-                      case 0:
-                          if (weightsOrUrl instanceof Float32Array) {
-                              this.extractWeights(weightsOrUrl);
-                              return [2 /*return*/];
-                          }
-                          if (weightsOrUrl && typeof weightsOrUrl !== 'string') {
-                              throw new Error('FaceLandmarkNet.load - expected model uri, or weights as Float32Array');
-                          }
-                          return [4 /*yield*/, loadQuantizedParams$1(weightsOrUrl)];
-                      case 1:
-                          _a = _b.sent(), paramMappings = _a.paramMappings, params = _a.params;
-                          this._paramMappings = paramMappings;
-                          this._params = params;
-                          return [2 /*return*/];
-                  }
-              });
-          });
-      };
-      FaceLandmarkNet.prototype.extractWeights = function (weights) {
-          var _a = extractParams$1(weights), paramMappings = _a.paramMappings, params = _a.params;
-          this._paramMappings = paramMappings;
-          this._params = params;
-      };
       FaceLandmarkNet.prototype.forwardInput = function (input) {
-          var params = this._params;
+          var params = this.params;
           if (!params) {
               throw new Error('FaceLandmarkNet - load model before inference');
           }
           return tidy(function () {
               var batchTensor = input.toBatchTensor(128, true);
-              var out = conv(batchTensor, params.conv0_params);
+              var out = conv(batchTensor, params.conv0);
               out = maxPool$1(out);
-              out = conv(out, params.conv1_params);
-              out = conv(out, params.conv2_params);
+              out = conv(out, params.conv1);
+              out = conv(out, params.conv2);
               out = maxPool$1(out);
-              out = conv(out, params.conv3_params);
-              out = conv(out, params.conv4_params);
+              out = conv(out, params.conv3);
+              out = conv(out, params.conv4);
               out = maxPool$1(out);
-              out = conv(out, params.conv5_params);
-              out = conv(out, params.conv6_params);
+              out = conv(out, params.conv5);
+              out = conv(out, params.conv6);
               out = maxPool$1(out, [1, 1]);
-              out = conv(out, params.conv7_params);
-              var fc0 = relu(fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc0_params));
-              var fc1 = fullyConnectedLayer(fc0, params.fc1_params);
+              out = conv(out, params.conv7);
+              var fc0 = relu(fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc0));
+              var fc1 = fullyConnectedLayer(fc0, params.fc1);
               var createInterleavedTensor = function (fillX, fillY) {
                   return stack([
                       fill([68], fillX),
@@ -2022,6 +2026,12 @@
               });
           });
       };
+      FaceLandmarkNet.prototype.loadQuantizedParams = function (uri) {
+          return loadQuantizedParams$1(uri);
+      };
+      FaceLandmarkNet.prototype.extractParams = function (weights) {
+          return extractParams$1(weights);
+      };
       return FaceLandmarkNet;
   }(NeuralNetwork));
 
@@ -2053,43 +2063,40 @@
       return convLayer$1(x, params, [2, 2], true, 'valid');
   }
 
-  function extractorsFactory$3(extractWeights) {
+  function extractorsFactory$3(extractWeights, paramMappings) {
       function extractFilterValues(numFilterValues, numFilters, filterSize) {
           var weights = extractWeights(numFilterValues);
           var 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 transpose(tensor4d(weights, [numFilters, depth, filterSize, filterSize]), [2, 3, 1, 0]);
+          return tidy(function () { return transpose(tensor4d(weights, [numFilters, depth, filterSize, filterSize]), [2, 3, 1, 0]); });
       }
-      function extractScaleLayerParams(numWeights) {
+      function extractConvParams(numFilterValues, numFilters, filterSize, mappedPrefix) {
+          var filters = extractFilterValues(numFilterValues, numFilters, filterSize);
+          var bias = tensor1d(extractWeights(numFilters));
+          paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/bias" });
+          return { filters: filters, bias: bias };
+      }
+      function extractScaleLayerParams(numWeights, mappedPrefix) {
           var weights = tensor1d(extractWeights(numWeights));
           var biases = tensor1d(extractWeights(numWeights));
+          paramMappings.push({ paramPath: mappedPrefix + "/weights" }, { paramPath: mappedPrefix + "/biases" });
           return {
               weights: weights,
               biases: biases
           };
       }
-      function extractConvLayerParams(numFilterValues, numFilters, filterSize) {
-          var conv_filters = extractFilterValues(numFilterValues, numFilters, filterSize);
-          var conv_bias = tensor1d(extractWeights(numFilters));
-          var scale = extractScaleLayerParams(numFilters);
-          return {
-              conv: {
-                  filters: conv_filters,
-                  bias: conv_bias
-              },
-              scale: scale
-          };
+      function extractConvLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix) {
+          var conv = extractConvParams(numFilterValues, numFilters, filterSize, mappedPrefix + "/conv");
+          var scale = extractScaleLayerParams(numFilters, mappedPrefix + "/scale");
+          return { conv: conv, scale: scale };
       }
-      function extractResidualLayerParams(numFilterValues, numFilters, filterSize, isDown) {
+      function extractResidualLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix, isDown) {
           if (isDown === void 0) { isDown = false; }
-          var conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize);
-          var conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize);
-          return {
-              conv1: conv1,
-              conv2: conv2
-          };
+          var conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, mappedPrefix + "/conv1");
+          var conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix + "/conv2");
+          return { conv1: conv1, conv2: conv2 };
       }
       return {
           extractConvLayerParams: extractConvLayerParams,
@@ -2098,27 +2105,29 @@
   }
   function extractParams$2(weights) {
       var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
-      var _b = extractorsFactory$3(extractWeights), extractConvLayerParams = _b.extractConvLayerParams, extractResidualLayerParams = _b.extractResidualLayerParams;
-      var conv32_down = extractConvLayerParams(4704, 32, 7);
-      var conv32_1 = extractResidualLayerParams(9216, 32, 3);
-      var conv32_2 = extractResidualLayerParams(9216, 32, 3);
-      var conv32_3 = extractResidualLayerParams(9216, 32, 3);
-      var conv64_down = extractResidualLayerParams(36864, 64, 3, true);
-      var conv64_1 = extractResidualLayerParams(36864, 64, 3);
-      var conv64_2 = extractResidualLayerParams(36864, 64, 3);
-      var conv64_3 = extractResidualLayerParams(36864, 64, 3);
-      var conv128_down = extractResidualLayerParams(147456, 128, 3, true);
-      var conv128_1 = extractResidualLayerParams(147456, 128, 3);
-      var conv128_2 = extractResidualLayerParams(147456, 128, 3);
-      var conv256_down = extractResidualLayerParams(589824, 256, 3, true);
-      var conv256_1 = extractResidualLayerParams(589824, 256, 3);
-      var conv256_2 = extractResidualLayerParams(589824, 256, 3);
-      var conv256_down_out = extractResidualLayerParams(589824, 256, 3);
-      var fc = transpose(tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]);
+      var paramMappings = [];
+      var _b = extractorsFactory$3(extractWeights, paramMappings), extractConvLayerParams = _b.extractConvLayerParams, extractResidualLayerParams = _b.extractResidualLayerParams;
+      var conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down');
+      var conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1');
+      var conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2');
+      var conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3');
+      var conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true);
+      var conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1');
+      var conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2');
+      var conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3');
+      var conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true);
+      var conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1');
+      var conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2');
+      var conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true);
+      var conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1');
+      var conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2');
+      var conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out');
+      var fc = tidy(function () { return transpose(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);
       }
-      return {
+      var params = {
           conv32_down: conv32_down,
           conv32_1: conv32_1,
           conv32_2: conv32_2,
@@ -2136,38 +2145,22 @@
           conv256_down_out: conv256_down_out,
           fc: fc
       };
+      return { params: params, paramMappings: paramMappings };
   }
 
   var DEFAULT_MODEL_NAME$2 = 'face_recognition_model';
-  function extractorsFactory$4(weightMap) {
+  function extractorsFactory$4(weightMap, paramMappings) {
+      var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
       function extractScaleLayerParams(prefix) {
-          var params = {
-              weights: weightMap[prefix + "/scale/weights"],
-              biases: weightMap[prefix + "/scale/biases"]
-          };
-          if (!isTensor1D(params.weights)) {
-              throw new Error("expected weightMap[" + prefix + "/scale/weights] to be a Tensor1D, instead have " + params.weights);
-          }
-          if (!isTensor1D(params.biases)) {
-              throw new Error("expected weightMap[" + prefix + "/scale/biases] to be a Tensor1D, instead have " + params.biases);
-          }
-          return params;
+          var weights = extractWeightEntry(prefix + "/scale/weights", 1);
+          var biases = extractWeightEntry(prefix + "/scale/biases", 1);
+          return { weights: weights, biases: biases };
       }
       function extractConvLayerParams(prefix) {
-          var params = {
-              filters: weightMap[prefix + "/conv/filters"],
-              bias: weightMap[prefix + "/conv/bias"]
-          };
-          if (!isTensor4D(params.filters)) {
-              throw new Error("expected weightMap[" + prefix + "/conv/filters] to be a Tensor1D, instead have " + params.filters);
-          }
-          if (!isTensor1D(params.bias)) {
-              throw new Error("expected weightMap[" + prefix + "/conv/bias] to be a Tensor1D, instead have " + params.bias);
-          }
-          return {
-              conv: params,
-              scale: extractScaleLayerParams(prefix)
-          };
+          var filters = extractWeightEntry(prefix + "/conv/filters", 4);
+          var bias = extractWeightEntry(prefix + "/conv/bias", 1);
+          var scale = extractScaleLayerParams(prefix);
+          return { conv: { filters: filters, bias: bias }, scale: scale };
       }
       function extractResidualLayerParams(prefix) {
           return {
@@ -2182,13 +2175,14 @@
   }
   function loadQuantizedParams$2(uri) {
       return __awaiter$1(this, void 0, void 0, function () {
-          var weightMap, _a, extractConvLayerParams, extractResidualLayerParams, 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;
+          var weightMap, paramMappings, _a, extractConvLayerParams, extractResidualLayerParams, 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, params;
           return __generator$1(this, function (_b) {
               switch (_b.label) {
                   case 0: return [4 /*yield*/, loadWeightMap(uri, DEFAULT_MODEL_NAME$2)];
                   case 1:
                       weightMap = _b.sent();
-                      _a = extractorsFactory$4(weightMap), extractConvLayerParams = _a.extractConvLayerParams, extractResidualLayerParams = _a.extractResidualLayerParams;
+                      paramMappings = [];
+                      _a = extractorsFactory$4(weightMap, paramMappings), extractConvLayerParams = _a.extractConvLayerParams, extractResidualLayerParams = _a.extractResidualLayerParams;
                       conv32_down = extractConvLayerParams('conv32_down');
                       conv32_1 = extractResidualLayerParams('conv32_1');
                       conv32_2 = extractResidualLayerParams('conv32_2');
@@ -2205,27 +2199,30 @@
                       conv256_2 = extractResidualLayerParams('conv256_2');
                       conv256_down_out = extractResidualLayerParams('conv256_down_out');
                       fc = weightMap['fc'];
+                      paramMappings.push({ originalPath: 'fc', paramPath: 'fc' });
                       if (!isTensor2D(fc)) {
                           throw new Error("expected weightMap[fc] to be a Tensor2D, instead have " + fc);
                       }
-                      return [2 /*return*/, {
-                              conv32_down: conv32_down,
-                              conv32_1: conv32_1,
-                              conv32_2: conv32_2,
-                              conv32_3: conv32_3,
-                              conv64_down: conv64_down,
-                              conv64_1: conv64_1,
-                              conv64_2: conv64_2,
-                              conv64_3: conv64_3,
-                              conv128_down: conv128_down,
-                              conv128_1: conv128_1,
-                              conv128_2: conv128_2,
-                              conv256_down: conv256_down,
-                              conv256_1: conv256_1,
-                              conv256_2: conv256_2,
-                              conv256_down_out: conv256_down_out,
-                              fc: fc
-                          }];
+                      params = {
+                          conv32_down: conv32_down,
+                          conv32_1: conv32_1,
+                          conv32_2: conv32_2,
+                          conv32_3: conv32_3,
+                          conv64_down: conv64_down,
+                          conv64_1: conv64_1,
+                          conv64_2: conv64_2,
+                          conv64_3: conv64_3,
+                          conv128_down: conv128_down,
+                          conv128_1: conv128_1,
+                          conv128_2: conv128_2,
+                          conv256_down: conv256_down,
+                          conv256_1: conv256_1,
+                          conv256_2: conv256_2,
+                          conv256_down_out: conv256_down_out,
+                          fc: fc
+                      };
+                      disposeUnusedWeightTensors(weightMap, paramMappings);
+                      return [2 /*return*/, { params: params, paramMappings: paramMappings }];
               }
           });
       });
@@ -2271,60 +2268,37 @@
       return out;
   }
 
-  var FaceRecognitionNet = /** @class */ (function () {
+  var FaceRecognitionNet = /** @class */ (function (_super) {
+      __extends$1(FaceRecognitionNet, _super);
       function FaceRecognitionNet() {
+          return _super.call(this, 'FaceRecognitionNet') || this;
       }
-      FaceRecognitionNet.prototype.load = function (weightsOrUrl) {
-          return __awaiter$1(this, void 0, void 0, function () {
-              var _a;
-              return __generator$1(this, function (_b) {
-                  switch (_b.label) {
-                      case 0:
-                          if (weightsOrUrl instanceof Float32Array) {
-                              this.extractWeights(weightsOrUrl);
-                              return [2 /*return*/];
-                          }
-                          if (weightsOrUrl && typeof weightsOrUrl !== 'string') {
-                              throw new Error('FaceLandmarkNet.load - expected model uri, or weights as Float32Array');
-                          }
-                          _a = this;
-                          return [4 /*yield*/, loadQuantizedParams$2(weightsOrUrl)];
-                      case 1:
-                          _a._params = _b.sent();
-                          return [2 /*return*/];
-                  }
-              });
-          });
-      };
-      FaceRecognitionNet.prototype.extractWeights = function (weights) {
-          this._params = extractParams$2(weights);
-      };
       FaceRecognitionNet.prototype.forwardInput = function (input) {
-          var _this = this;
-          if (!this._params) {
+          var params = this.params;
+          if (!params) {
               throw new Error('FaceRecognitionNet - load model before inference');
           }
           return tidy(function () {
               var batchTensor = input.toBatchTensor(150, true);
               var normalized = normalize(batchTensor);
-              var out = convDown(normalized, _this._params.conv32_down);
+              var out = convDown(normalized, params.conv32_down);
               out = maxPool(out, 3, 2, 'valid');
-              out = residual(out, _this._params.conv32_1);
-              out = residual(out, _this._params.conv32_2);
-              out = residual(out, _this._params.conv32_3);
-              out = residualDown(out, _this._params.conv64_down);
-              out = residual(out, _this._params.conv64_1);
-              out = residual(out, _this._params.conv64_2);
-              out = residual(out, _this._params.conv64_3);
-              out = residualDown(out, _this._params.conv128_down);
-              out = residual(out, _this._params.conv128_1);
-              out = residual(out, _this._params.conv128_2);
-              out = residualDown(out, _this._params.conv256_down);
-              out = residual(out, _this._params.conv256_1);
-              out = residual(out, _this._params.conv256_2);
-              out = residualDown(out, _this._params.conv256_down_out);
+              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);
               var globalAvg = out.mean([1, 2]);
-              var fullyConnected = matMul(globalAvg, _this._params.fc);
+              var fullyConnected = matMul(globalAvg, params.fc);
               return fullyConnected;
           });
       };
@@ -2362,8 +2336,14 @@
               });
           });
       };
+      FaceRecognitionNet.prototype.loadQuantizedParams = function (uri) {
+          return loadQuantizedParams$2(uri);
+      };
+      FaceRecognitionNet.prototype.extractParams = function (weights) {
+          return extractParams$2(weights);
+      };
       return FaceRecognitionNet;
-  }());
+  }(NeuralNetwork));
 
   function faceRecognitionNet(weights) {
       var net = new FaceRecognitionNet();