Merge pull request #41 from justadudewhohacks/disposable

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

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\ 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

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\ No newline at end of file
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\ 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

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\ 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;

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\ No newline at end of file
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\ 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 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);
+    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 };
     }
-        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;
+    function extractBoxPredictorParams(idx) {
+        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: {
+                    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

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\ No newline at end of file
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\ 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

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\ No newline at end of file
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\ 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

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\ 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

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\ No newline at end of file
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\ 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

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\ 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

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\ No newline at end of file
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\ 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

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\ No newline at end of file
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\ 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

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\ 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');
@@ -70,10 +56,11 @@ 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*/, {
+                    params = {
                         conv32_down: conv32_down,
                         conv32_1: conv32_1,
                         conv32_2: conv32_2,
@@ -90,7 +77,9 @@ function loadQuantizedParams(uri) {
                         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

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\ No newline at end of file
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\ 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 box_predictor_1_params = {
-              box_encoding_predictor_params: box_encoding_1_predictor_params,
-              class_predictor_params: class_predictor_1_params
-          };
-          var box_predictor_2_params = {
-              box_encoding_predictor_params: box_encoding_2_predictor_params,
-              class_predictor_params: class_predictor_2_params
-          };
-          var box_predictor_3_params = {
-              box_encoding_predictor_params: box_encoding_3_predictor_params,
-              class_predictor_params: class_predictor_3_params
-          };
-          var box_predictor_4_params = {
-              box_encoding_predictor_params: box_encoding_4_predictor_params,
-              class_predictor_params: class_predictor_4_params
-          };
-          var box_predictor_5_params = {
-              box_encoding_predictor_params: box_encoding_5_predictor_params,
-              class_predictor_params: class_predictor_5_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 = {
+              box_encoding_predictor: box_encoding_1_predictor,
+              class_predictor: class_predictor_1
+          };
+          var box_predictor_2 = {
+              box_encoding_predictor: box_encoding_2_predictor,
+              class_predictor: class_predictor_2
+          };
+          var box_predictor_3 = {
+              box_encoding_predictor: box_encoding_3_predictor,
+              class_predictor: class_predictor_3
+          };
+          var box_predictor_4 = {
+              box_encoding_predictor: box_encoding_4_predictor,
+              class_predictor: class_predictor_4
+          };
+          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); })
-          };
-      }
-      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"]
-              }
+              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)
           };
-          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);
+      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 };
       }
-          return params;
+      function extractBoxPredictorParams(idx) {
+          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: {
+                      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) {
+  function extractParams$1(weights) {
+      var paramMappings = [];
+      var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
+      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
-          };
-      };
+          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 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;
-      }
-      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 _super.call(this, 'FaceLandmarkNet') || this;
       }
-                          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');
@@ -2205,10 +2199,11 @@
                       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*/, {
+                      params = {
                           conv32_down: conv32_down,
                           conv32_1: conv32_1,
                           conv32_2: conv32_2,
@@ -2225,7 +2220,9 @@
                           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();

src/Rect.ts

@@ -32,6 +32,11 @@ export class Rect implements IRect {
     return new Rect(x, y, width, height)
   }
 
+  public pad(padX: number, padY: number): Rect {
+    let { x, y, width, height } = this
+    return new Rect(x - (padX / 2), y - (padY / 2), width + padX, height + padY)
+  }
+
   public floor(): Rect {
     return new Rect(
       Math.floor(this.x),

src/commons/NeuralNetwork.ts

@@ -7,6 +7,8 @@ export class NeuralNetwork<TNetParams> {
   protected _params: TNetParams | undefined = undefined
   protected _paramMappings: ParamMapping[] = []
 
+  constructor(private _name: string) {}
+
   public get params(): TNetParams | undefined {
     return this._params
   }
@@ -53,11 +55,44 @@ export class NeuralNetwork<TNetParams> {
     })
   }
 
-  public dispose() {
-    this.getParamList().forEach(param => param.tensor.dispose())
+  public dispose(throwOnRedispose: boolean = true) {
+    this.getParamList().forEach(param => {
+      if (throwOnRedispose && param.tensor.isDisposed) {
+        throw new Error(`param tensor has already been disposed for path ${param.path}`)
+      }
+      param.tensor.dispose()
+    })
     this._params = undefined
   }
 
+  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {
+    if (weightsOrUrl instanceof Float32Array) {
+      this.extractWeights(weightsOrUrl)
+      return
+    }
+
+    if (weightsOrUrl && typeof weightsOrUrl !== 'string') {
+      throw new Error(`${this._name}.load - expected model uri, or weights as Float32Array`)
+    }
+    const {
+      paramMappings,
+      params
+    } = await this.loadQuantizedParams(weightsOrUrl)
+
+    this._paramMappings = paramMappings
+    this._params = params
+  }
+
+  public extractWeights(weights: Float32Array) {
+    const {
+      paramMappings,
+      params
+    } = this.extractParams(weights)
+
+    this._paramMappings = paramMappings
+    this._params = params
+  }
+
   private traversePropertyPath(paramPath: string) {
     if (!this.params) {
       throw new Error(`traversePropertyPath - model has no loaded params`)
@@ -78,4 +113,12 @@ export class NeuralNetwork<TNetParams> {
 
     return { obj, objProp }
   }
+
+  protected loadQuantizedParams(_: any): Promise<{ params: TNetParams, paramMappings: ParamMapping[] }> {
+    throw new Error(`${this._name}.loadQuantizedParams - not implemented`)
+  }
+
+  protected extractParams(_: any): { params: TNetParams, paramMappings: ParamMapping[] } {
+    throw new Error(`${this._name}.extractParams - not implemented`)
+  }
 }
\ No newline at end of file

src/commons/disposeUnusedWeightTensors.ts

+import { ParamMapping } from './types';
+
+export function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {
+  Object.keys(weightMap).forEach(path => {
+    if (!paramMappings.some(pm => pm.originalPath === path)) {
+      weightMap[path].dispose()
+    }
+  })
+}

src/commons/extractConvParamsFactory.ts

-import * as tf from '@tensorflow/tfjs-core';
-
-import { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';
-
-export function extractConvParamsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {
-  return function (
-    channelsIn: number,
-    channelsOut: number,
-    filterSize: number,
-    mappedPrefix: string
-  ): ConvParams {
-    const filters = tf.tensor4d(
-      extractWeights(channelsIn * channelsOut * filterSize * filterSize),
-      [filterSize, filterSize, channelsIn, channelsOut]
-    )
-    const bias = tf.tensor1d(extractWeights(channelsOut))
-
-    paramMappings.push(
-      { paramPath: `${mappedPrefix}/filters` },
-      { paramPath: `${mappedPrefix}/bias` }
-    )
-
-    return {
-      filters,
-      bias
-    }
-  }
-}
\ No newline at end of file

src/commons/extractWeightEntry.ts

-import { isTensor } from './isTensor';
-
-export function extractWeightEntry(weightMap: any, path: string, paramRank: number) {
-  const tensor = weightMap[path]
-
-  if (!isTensor(tensor, paramRank)) {
-    throw new Error(`expected weightMap[${path}] to be a Tensor${paramRank}D, instead have ${tensor}`)
-  }
-
-  return { path, tensor }
-}
\ No newline at end of file

src/commons/extractWeightEntryFactory.ts

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

src/faceDetectionNet/FaceDetectionNet.ts

 import * as tf from '@tensorflow/tfjs-core';
 
+import { NeuralNetwork } from '../commons/NeuralNetwork';
 import { NetInput } from '../NetInput';
 import { Rect } from '../Rect';
 import { toNetInput } from '../toNetInput';
@@ -13,28 +14,17 @@ import { outputLayer } from './outputLayer';
 import { predictionLayer } from './predictionLayer';
 import { NetParams } from './types';
 
-export class FaceDetectionNet {
+export class FaceDetectionNet extends NeuralNetwork<NetParams> {
 
-  private _params: NetParams
-
-  public async load(weightsOrUrl?: Float32Array | string): Promise<void> {
-    if (weightsOrUrl instanceof Float32Array) {
-      this.extractWeights(weightsOrUrl)
-      return
+  constructor() {
+    super('FaceDetectionNet')
   }
 
-    if (weightsOrUrl && typeof weightsOrUrl !== 'string') {
-      throw new Error('FaceDetectionNet.load - expected model uri, or weights as Float32Array')
-    }
-    this._params = await loadQuantizedParams(weightsOrUrl)
-  }
+  public forwardInput(input: NetInput) {
 
-  public extractWeights(weights: Float32Array) {
-    this._params = extractParams(weights)
-  }
+    const { params } = this
 
-  public forwardInput(input: NetInput) {
-    if (!this._params) {
+    if (!params) {
       throw new Error('FaceDetectionNet - load model before inference')
     }
 
@@ -42,14 +32,14 @@ export class FaceDetectionNet {
       const batchTensor = input.toBatchTensor(512, false)
 
       const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D
-      const features = mobileNetV1(x, this._params.mobilenetv1_params)
+      const features = mobileNetV1(x, params.mobilenetv1)
 
       const {
         boxPredictions,
         classPredictions
-      } = predictionLayer(features.out, features.conv11, this._params.prediction_layer_params)
+      } = predictionLayer(features.out, features.conv11, params.prediction_layer)
 
-      return outputLayer(boxPredictions, classPredictions, this._params.output_layer_params)
+      return outputLayer(boxPredictions, classPredictions, params.output_layer)
     })
   }
 
@@ -91,7 +81,6 @@ export class FaceDetectionNet {
       minConfidence
     )
 
-
     const paddedHeightRelative = (netInput.getPaddings(0).y + netInput.getInputHeight(0)) / netInput.getInputHeight(0)
     const paddedWidthRelative = (netInput.getPaddings(0).x + netInput.getInputWidth(0)) / netInput.getInputWidth(0)
 
@@ -125,4 +114,12 @@ export class FaceDetectionNet {
 
     return results
   }
+
+  protected loadQuantizedParams(uri: string | undefined) {
+    return loadQuantizedParams(uri)
+  }
+
+  protected extractParams(weights: Float32Array) {
+    return extractParams(weights)
+  }
 }
\ No newline at end of file

src/faceDetectionNet/boxPredictionLayer.ts

@@ -13,11 +13,11 @@ export function boxPredictionLayer(
     const batchSize = x.shape[0]
 
     const boxPredictionEncoding = tf.reshape(
-      convLayer(x, params.box_encoding_predictor_params),
+      convLayer(x, params.box_encoding_predictor),
       [batchSize, -1, 1, 4]
     )
     const classPrediction = tf.reshape(
-      convLayer(x, params.class_predictor_params),
+      convLayer(x, params.class_predictor),
       [batchSize, -1, 3]
     )
 

src/faceDetectionNet/extractParams.ts

 import * as tf from '@tensorflow/tfjs-core';
 
 import { extractWeightsFactory } from '../commons/extractWeightsFactory';
-import { ConvParams } from '../commons/types';
+import { ConvParams, ExtractWeightsFunction, ParamMapping } from '../commons/types';
 import { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';
 
-function extractorsFactory(extractWeights: (numWeights: number) => Float32Array) {
+function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {
+
+  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {
 
-  function extractDepthwiseConvParams(numChannels: number): MobileNetV1.DepthwiseConvParams {
     const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1])
     const batch_norm_scale = tf.tensor1d(extractWeights(numChannels))
     const batch_norm_offset = tf.tensor1d(extractWeights(numChannels))
     const batch_norm_mean = tf.tensor1d(extractWeights(numChannels))
     const 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,
       batch_norm_scale,
@@ -25,29 +34,36 @@ function extractorsFactory(extractWeights: (numWeights: number) => Float32Array)
   function extractConvParams(
     channelsIn: number,
     channelsOut: number,
-    filterSize: number
+    filterSize: number,
+    mappedPrefix: string,
+    isPointwiseConv?: boolean
   ): ConvParams {
+
     const filters = tf.tensor4d(
       extractWeights(channelsIn * channelsOut * filterSize * filterSize),
       [filterSize, filterSize, channelsIn, channelsOut]
     )
     const bias = tf.tensor1d(extractWeights(channelsOut))
 
-    return {
-      filters,
-      bias
-    }
+    paramMappings.push(
+      { paramPath: `${mappedPrefix}/filters` },
+      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` }
+    )
+
+    return { filters, bias }
   }
 
   function extractPointwiseConvParams(
     channelsIn: number,
     channelsOut: number,
-    filterSize: number
+    filterSize: number,
+    mappedPrefix: string
   ): PointwiseConvParams {
+
     const {
       filters,
       bias
-    } = extractConvParams(channelsIn, channelsOut, filterSize)
+    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true)
 
     return {
       filters,
@@ -57,115 +73,118 @@ function extractorsFactory(extractWeights: (numWeights: number) => Float32Array)
 
   function extractConvPairParams(
     channelsIn: number,
-    channelsOut: number
+    channelsOut: number,
+    mappedPrefix: string
   ): MobileNetV1.ConvPairParams {
-    const depthwise_conv_params = extractDepthwiseConvParams(channelsIn)
-    const pointwise_conv_params = extractPointwiseConvParams(channelsIn, channelsOut, 1)
 
-    return {
-      depthwise_conv_params,
-      pointwise_conv_params
-    }
+    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`)
+    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`)
+
+    return { depthwise_conv, pointwise_conv }
   }
 
   function extractMobilenetV1Params(): MobileNetV1.Params {
 
-    const conv_0_params = extractPointwiseConvParams(3, 32, 3)
-
-    const 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]
-    ]
-
-    const conv_pair_params = channelNumPairs.map(
-      ([channelsIn, channelsOut]) => extractConvPairParams(channelsIn, channelsOut)
-    )
+    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0')
+
+    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1')
+    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2')
+    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3')
+    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4')
+    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5')
+    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6')
+    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7')
+    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8')
+    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9')
+    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10')
+    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11')
+    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12')
+    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13')
 
     return {
-      conv_0_params,
-      conv_pair_params
+      conv_0,
+      conv_1,
+      conv_2,
+      conv_3,
+      conv_4,
+      conv_5,
+      conv_6,
+      conv_7,
+      conv_8,
+      conv_9,
+      conv_10,
+      conv_11,
+      conv_12,
+      conv_13
     }
-
   }
 
   function extractPredictionLayerParams(): PredictionLayerParams {
-    const conv_0_params = extractPointwiseConvParams(1024, 256, 1)
-    const conv_1_params = extractPointwiseConvParams(256, 512, 3)
-    const conv_2_params = extractPointwiseConvParams(512, 128, 1)
-    const conv_3_params = extractPointwiseConvParams(128, 256, 3)
-    const conv_4_params = extractPointwiseConvParams(256, 128, 1)
-    const conv_5_params = extractPointwiseConvParams(128, 256, 3)
-    const conv_6_params = extractPointwiseConvParams(256, 64, 1)
-    const conv_7_params = extractPointwiseConvParams(64, 128, 3)
-
-    const box_encoding_0_predictor_params = extractConvParams(512, 12, 1)
-    const class_predictor_0_params = extractConvParams(512, 9, 1)
-    const box_encoding_1_predictor_params = extractConvParams(1024, 24, 1)
-    const class_predictor_1_params = extractConvParams(1024, 18, 1)
-    const box_encoding_2_predictor_params = extractConvParams(512, 24, 1)
-    const class_predictor_2_params = extractConvParams(512, 18, 1)
-    const box_encoding_3_predictor_params = extractConvParams(256, 24, 1)
-    const class_predictor_3_params = extractConvParams(256, 18, 1)
-    const box_encoding_4_predictor_params = extractConvParams(256, 24, 1)
-    const class_predictor_4_params = extractConvParams(256, 18, 1)
-    const box_encoding_5_predictor_params = extractConvParams(128, 24, 1)
-    const class_predictor_5_params = extractConvParams(128, 18, 1)
-
-    const box_predictor_0_params = {
-      box_encoding_predictor_params: box_encoding_0_predictor_params,
-      class_predictor_params: class_predictor_0_params
-    }
-    const box_predictor_1_params = {
-      box_encoding_predictor_params: box_encoding_1_predictor_params,
-      class_predictor_params: class_predictor_1_params
-    }
-    const box_predictor_2_params = {
-      box_encoding_predictor_params: box_encoding_2_predictor_params,
-      class_predictor_params: class_predictor_2_params
-    }
-    const box_predictor_3_params = {
-      box_encoding_predictor_params: box_encoding_3_predictor_params,
-      class_predictor_params: class_predictor_3_params
-    }
-    const box_predictor_4_params = {
-      box_encoding_predictor_params: box_encoding_4_predictor_params,
-      class_predictor_params: class_predictor_4_params
-    }
-    const box_predictor_5_params = {
-      box_encoding_predictor_params: box_encoding_5_predictor_params,
-      class_predictor_params: class_predictor_5_params
+    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0')
+    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1')
+    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2')
+    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3')
+    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4')
+    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5')
+    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6')
+    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7')
+
+    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor')
+    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor')
+    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor')
+    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor')
+    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor')
+    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor')
+    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor')
+    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor')
+    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor')
+    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor')
+    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor')
+    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor')
+
+    const box_predictor_0 = {
+      box_encoding_predictor: box_encoding_0_predictor,
+      class_predictor: class_predictor_0
+    }
+    const box_predictor_1 = {
+      box_encoding_predictor: box_encoding_1_predictor,
+      class_predictor: class_predictor_1
+    }
+    const box_predictor_2 = {
+      box_encoding_predictor: box_encoding_2_predictor,
+      class_predictor: class_predictor_2
+    }
+    const box_predictor_3 = {
+      box_encoding_predictor: box_encoding_3_predictor,
+      class_predictor: class_predictor_3
+    }
+    const box_predictor_4 = {
+      box_encoding_predictor: box_encoding_4_predictor,
+      class_predictor: class_predictor_4
+    }
+    const box_predictor_5 = {
+      box_encoding_predictor: box_encoding_5_predictor,
+      class_predictor: class_predictor_5
     }
 
     return {
-      conv_0_params,
-      conv_1_params,
-      conv_2_params,
-      conv_3_params,
-      conv_4_params,
-      conv_5_params,
-      conv_6_params,
-      conv_7_params,
-      box_predictor_0_params,
-      box_predictor_1_params,
-      box_predictor_2_params,
-      box_predictor_3_params,
-      box_predictor_4_params,
-      box_predictor_5_params
+      conv_0,
+      conv_1,
+      conv_2,
+      conv_3,
+      conv_4,
+      conv_5,
+      conv_6,
+      conv_7,
+      box_predictor_0,
+      box_predictor_1,
+      box_predictor_2,
+      box_predictor_3,
+      box_predictor_4,
+      box_predictor_5
     }
   }
 
-
   return {
     extractMobilenetV1Params,
     extractPredictionLayerParams
@@ -173,7 +192,10 @@ function extractorsFactory(extractWeights: (numWeights: number) => Float32Array)
 
 }
 
-export function extractParams(weights: Float32Array): NetParams {
+export function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {
+
+  const paramMappings: ParamMapping[] = []
+
   const {
     extractWeights,
     getRemainingWeights
@@ -182,25 +204,30 @@ export function extractParams(weights: Float32Array): NetParams {
   const {
     extractMobilenetV1Params,
     extractPredictionLayerParams
-  } = extractorsFactory(extractWeights)
+  } = extractorsFactory(extractWeights, paramMappings)
 
-  const mobilenetv1_params = extractMobilenetV1Params()
-  const prediction_layer_params = extractPredictionLayerParams()
+  const mobilenetv1 = extractMobilenetV1Params()
+  const prediction_layer = extractPredictionLayerParams()
   const extra_dim = tf.tensor3d(
     extractWeights(5118 * 4),
     [1, 5118, 4]
   )
-  const output_layer_params = {
+  const output_layer = {
     extra_dim
   }
 
+  paramMappings.push({ paramPath: 'output_layer/extra_dim' })
+
   if (getRemainingWeights().length !== 0) {
     throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)
   }
 
   return {
-    mobilenetv1_params,
-    prediction_layer_params,
-    output_layer_params
+    params: {
+      mobilenetv1,
+      prediction_layer,
+      output_layer
+    },
+    paramMappings
   }
 }
\ No newline at end of file

src/faceDetectionNet/loadQuantizedParams.ts

-import { isTensor1D, isTensor4D, isTensor3D } from '../commons/isTensor';
+import { tf } from '..';
+import { disposeUnusedWeightTensors } from '../commons/disposeUnusedWeightTensors';
+import { extractWeightEntryFactory } from '../commons/extractWeightEntryFactory';
+import { isTensor1D, isTensor3D, isTensor4D } from '../commons/isTensor';
 import { loadWeightMap } from '../commons/loadWeightMap';
-import { BoxPredictionParams, MobileNetV1, PointwiseConvParams, PredictionLayerParams } from './types';
+import { ConvParams, ParamMapping } from '../commons/types';
+import { BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';
 
 const DEFAULT_MODEL_NAME = 'face_detection_model'
 
-function extractorsFactory(weightMap: any) {
+function extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {
 
-  function extractPointwiseConvParams(prefix: string, idx: number): PointwiseConvParams {
+  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)
 
-    const 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}`)
-    }
+  function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {
 
-    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 ${pointwise_conv_params.batch_norm_offset}`)
-    }
+    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`)
+    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`)
 
-    return pointwise_conv_params
+    return { filters, batch_norm_offset }
   }
 
   function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {
 
-    const 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 ${depthwise_conv_params.batch_norm_mean}`)
-    }
+    const mappedPrefix = `mobilenetv1/conv_${idx}`
+    const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`
+    const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`
+    const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`
 
-    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 ${depthwise_conv_params.batch_norm_variance}`)
-    }
+    const filters = extractWeightEntry<tf.Tensor4D>(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`)
+    const batch_norm_scale = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`)
+    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`)
+    const batch_norm_mean = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`)
+    const batch_norm_variance = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`)
 
     return {
-      depthwise_conv_params,
-      pointwise_conv_params: extractPointwiseConvParams('MobilenetV1', idx)
+      depthwise_conv: {
+        filters,
+        batch_norm_scale,
+        batch_norm_offset,
+        batch_norm_mean,
+        batch_norm_variance
+      },
+      pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv)
     }
   }
 
   function extractMobilenetV1Params(): MobileNetV1.Params {
     return {
-      conv_0_params: extractPointwiseConvParams('MobilenetV1', 0),
-      conv_pair_params: Array(13).fill(0).map((_, i) => extractConvPairParams(i + 1))
-    }
-  }
-
-  function extractBoxPredictorParams(idx: number): BoxPredictionParams {
-
-    const 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`]
+      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)
     }
   }
 
-    if (!isTensor4D(params.box_encoding_predictor_params.filters)) {
-      throw new Error(`expected weightMap[Prediction/BoxPredictor_${idx}/BoxEncodingPredictor/weights] to be a Tensor4D, instead have ${params.box_encoding_predictor_params.filters}`)
-    }
+  function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {
+    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/weights`, 4, `${mappedPrefix}/filters`)
+    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/biases`, 1, `${mappedPrefix}/bias`)
 
-    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}`)
+    return { filters, 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}`)
-    }
+  function extractBoxPredictorParams(idx: number): BoxPredictionParams {
 
-    if (!isTensor1D(params.class_predictor_params.bias)) {
-      throw new Error(`expected weightMap[Prediction/BoxPredictor_${idx}/ClassPredictor/biases] to be a Tensor1D, instead have ${params.class_predictor_params.bias}`)
-    }
+    const box_encoding_predictor = extractConvParams(
+      `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,
+      `prediction_layer/box_predictor_${idx}/box_encoding_predictor`
+    )
+    const class_predictor = extractConvParams(
+      `Prediction/BoxPredictor_${idx}/ClassPredictor`,
+      `prediction_layer/box_predictor_${idx}/class_predictor`
+    )
 
-    return params
+    return { box_encoding_predictor, class_predictor }
   }
 
   function extractPredictionLayerParams(): PredictionLayerParams {
     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)
     }
   }
 
@@ -124,24 +110,34 @@ function extractorsFactory(weightMap: any) {
   }
 }
 
-export async function loadQuantizedParams(uri: string | undefined): Promise<any> {//Promise<NetParams> {
+export async function loadQuantizedParams(
+  uri: string | undefined
+): Promise<{ params: NetParams, paramMappings: ParamMapping[] }> {
+
   const weightMap = await loadWeightMap(uri, DEFAULT_MODEL_NAME)
+  const paramMappings: ParamMapping[] = []
 
   const {
     extractMobilenetV1Params,
     extractPredictionLayerParams
-  } = extractorsFactory(weightMap)
+  } = extractorsFactory(weightMap, paramMappings)
 
   const extra_dim = weightMap['Output/extra_dim']
+  paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' })
+
   if (!isTensor3D(extra_dim)) {
     throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`)
   }
 
-  return {
-    mobilenetv1_params: extractMobilenetV1Params(),
-    prediction_layer_params: extractPredictionLayerParams(),
-    output_layer_params: {
+  const params = {
+    mobilenetv1: extractMobilenetV1Params(),
+    prediction_layer: extractPredictionLayerParams(),
+    output_layer: {
       extra_dim
     }
   }
+
+  disposeUnusedWeightTensors(weightMap, paramMappings)
+
+  return { params, paramMappings }
 }
\ No newline at end of file

src/faceDetectionNet/mobileNetV1.ts

@@ -34,13 +34,29 @@ export function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {
   return tf.tidy(() => {
 
     let conv11 = null
-    let out = pointwiseConvLayer(x, params.conv_0_params, [2, 2])
+    let out = pointwiseConvLayer(x, params.conv_0, [2, 2])
 
-    params.conv_pair_params.forEach((param, i) => {
+    const convPairParams = [
+      params.conv_1,
+      params.conv_2,
+      params.conv_3,
+      params.conv_4,
+      params.conv_5,
+      params.conv_6,
+      params.conv_7,
+      params.conv_8,
+      params.conv_9,
+      params.conv_10,
+      params.conv_11,
+      params.conv_12,
+      params.conv_13
+    ]
+
+    convPairParams.forEach((param, i) => {
       const layerIdx = i + 1
       const depthwiseConvStrides = getStridesForLayerIdx(layerIdx)
-      out = depthwiseConvLayer(out, param.depthwise_conv_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
       }

src/faceDetectionNet/predictionLayer.ts

@@ -11,21 +11,21 @@ export function predictionLayer(
 ) {
   return tf.tidy(() => {
 
-    const conv0 = pointwiseConvLayer(x, params.conv_0_params, [1, 1])
-    const conv1 = pointwiseConvLayer(conv0, params.conv_1_params, [2, 2])
-    const conv2 = pointwiseConvLayer(conv1, params.conv_2_params, [1, 1])
-    const conv3 = pointwiseConvLayer(conv2, params.conv_3_params, [2, 2])
-    const conv4 = pointwiseConvLayer(conv3, params.conv_4_params, [1, 1])
-    const conv5 = pointwiseConvLayer(conv4, params.conv_5_params, [2, 2])
-    const conv6 = pointwiseConvLayer(conv5, params.conv_6_params, [1, 1])
-    const conv7 = pointwiseConvLayer(conv6, params.conv_7_params, [2, 2])
+    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1])
+    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2])
+    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1])
+    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2])
+    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1])
+    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2])
+    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1])
+    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2])
 
-    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0_params)
-    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1_params)
-    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2_params)
-    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3_params)
-    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4_params)
-    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5_params)
+    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0)
+    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1)
+    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2)
+    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3)
+    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4)
+    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5)
 
     const boxPredictions = tf.concat([
       boxPrediction0.boxPredictionEncoding,

src/faceDetectionNet/types.ts

@@ -18,37 +18,49 @@ export namespace MobileNetV1 {
   }
 
   export type ConvPairParams = {
-    depthwise_conv_params: DepthwiseConvParams
-    pointwise_conv_params: PointwiseConvParams
+    depthwise_conv: DepthwiseConvParams
+    pointwise_conv: PointwiseConvParams
   }
 
   export 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 type BoxPredictionParams = {
-  box_encoding_predictor_params: ConvParams
-  class_predictor_params: ConvParams
+  box_encoding_predictor: ConvParams
+  class_predictor: ConvParams
 }
 
 export 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 type OutputLayerParams = {
@@ -56,7 +68,7 @@ export type OutputLayerParams = {
 }
 
 export type NetParams = {
-  mobilenetv1_params: MobileNetV1.Params,
-  prediction_layer_params: PredictionLayerParams,
-  output_layer_params: OutputLayerParams
+  mobilenetv1: MobileNetV1.Params,
+  prediction_layer: PredictionLayerParams,
+  output_layer: OutputLayerParams
 }

src/faceLandmarkNet/FaceLandmarkNet.ts

@@ -24,36 +24,13 @@ function maxPool(x: tf.Tensor4D, strides: [number, number] = [2, 2]): tf.Tensor4
 
 export class FaceLandmarkNet extends NeuralNetwork<NetParams> {
 
-  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {
-    if (weightsOrUrl instanceof Float32Array) {
-      this.extractWeights(weightsOrUrl)
-      return
-    }
-
-    if (weightsOrUrl && typeof weightsOrUrl !== 'string') {
-      throw new Error('FaceLandmarkNet.load - expected model uri, or weights as Float32Array')
-    }
-    const {
-      paramMappings,
-      params
-    } = await loadQuantizedParams(weightsOrUrl)
-
-    this._paramMappings = paramMappings
-    this._params = params
-  }
-
-  public extractWeights(weights: Float32Array) {
-    const {
-      paramMappings,
-      params
-    } = extractParams(weights)
-
-    this._paramMappings = paramMappings
-    this._params = params
+  constructor() {
+    super('FaceLandmarkNet')
   }
 
   public forwardInput(input: NetInput): tf.Tensor2D {
-    const params = this._params
+
+    const { params } = this
 
     if (!params) {
       throw new Error('FaceLandmarkNet - load model before inference')
@@ -62,20 +39,20 @@ export class FaceLandmarkNet extends NeuralNetwork<NetParams> {
     return tf.tidy(() => {
       const batchTensor = input.toBatchTensor(128, true)
 
-      let out = conv(batchTensor, params.conv0_params)
+      let 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)
-      const fc0 = tf.relu(fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc0_params))
-      const fc1 = fullyConnectedLayer(fc0, params.fc1_params)
+      out = conv(out, params.conv7)
+      const fc0 = tf.relu(fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc0))
+      const fc1 = fullyConnectedLayer(fc0, params.fc1)
 
       const createInterleavedTensor = (fillX: number, fillY: number) =>
         tf.stack([
@@ -145,4 +122,12 @@ export class FaceLandmarkNet extends NeuralNetwork<NetParams> {
       ? landmarksForBatch
       : landmarksForBatch[0]
   }
+
+  protected loadQuantizedParams(uri: string | undefined) {
+    return loadQuantizedParams(uri)
+  }
+
+  protected extractParams(weights: Float32Array) {
+    return extractParams(weights)
+  }
 }
\ No newline at end of file

src/faceLandmarkNet/extractParams.ts

 import * as tf from '@tensorflow/tfjs-core';
 
-import { extractConvParamsFactory } from '../commons/extractConvParamsFactory';
 import { extractWeightsFactory } from '../commons/extractWeightsFactory';
-import { ParamMapping } from '../commons/types';
+import { ConvParams, ParamMapping } from '../commons/types';
 import { FCParams, NetParams } from './types';
 
 export function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {
+
   const paramMappings: ParamMapping[] = []
 
   const {
@@ -13,9 +13,29 @@ export function extractParams(weights: Float32Array): { params: NetParams, param
     getRemainingWeights
   } = extractWeightsFactory(weights)
 
-  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)
+  function extractConvParams(
+    channelsIn: number,
+    channelsOut: number,
+    filterSize: number,
+    mappedPrefix: string
+  ): ConvParams {
+
+    const filters = tf.tensor4d(
+      extractWeights(channelsIn * channelsOut * filterSize * filterSize),
+      [filterSize, filterSize, channelsIn, channelsOut]
+    )
+    const bias = tf.tensor1d(extractWeights(channelsOut))
+
+    paramMappings.push(
+      { paramPath: `${mappedPrefix}/filters` },
+      { paramPath: `${mappedPrefix}/bias` }
+    )
+
+    return { filters, bias }
+  }
 
   function extractFcParams(channelsIn: number, channelsOut: number, mappedPrefix: string): FCParams {
+
     const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut])
     const fc_bias = tf.tensor1d(extractWeights(channelsOut))
 
@@ -30,16 +50,16 @@ export function extractParams(weights: Float32Array): { params: NetParams, param
     }
   }
 
-  const conv0_params = extractConvParams(3, 32, 3, 'conv0_params')
-  const conv1_params = extractConvParams(32, 64, 3, 'conv1_params')
-  const conv2_params = extractConvParams(64, 64, 3, 'conv2_params')
-  const conv3_params = extractConvParams(64, 64, 3, 'conv3_params')
-  const conv4_params = extractConvParams(64, 64, 3, 'conv4_params')
-  const conv5_params = extractConvParams(64, 128, 3, 'conv5_params')
-  const conv6_params = extractConvParams(128, 128, 3, 'conv6_params')
-  const conv7_params = extractConvParams(128, 256, 3, 'conv7_params')
-  const fc0_params = extractFcParams(6400, 1024, 'fc0_params')
-  const fc1_params = extractFcParams(1024, 136, 'fc1_params')
+  const conv0 = extractConvParams(3, 32, 3, 'conv0')
+  const conv1 = extractConvParams(32, 64, 3, 'conv1')
+  const conv2 = extractConvParams(64, 64, 3, 'conv2')
+  const conv3 = extractConvParams(64, 64, 3, 'conv3')
+  const conv4 = extractConvParams(64, 64, 3, 'conv4')
+  const conv5 = extractConvParams(64, 128, 3, 'conv5')
+  const conv6 = extractConvParams(128, 128, 3, 'conv6')
+  const conv7 = extractConvParams(128, 256, 3, 'conv7')
+  const fc0 = extractFcParams(6400, 1024, 'fc0')
+  const fc1 = extractFcParams(1024, 136, 'fc1')
 
   if (getRemainingWeights().length !== 0) {
     throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)
@@ -48,16 +68,16 @@ export function extractParams(weights: Float32Array): { params: NetParams, param
   return {
     paramMappings,
     params: {
-      conv0_params,
-      conv1_params,
-      conv2_params,
-      conv3_params,
-      conv4_params,
-      conv5_params,
-      conv6_params,
-      conv7_params,
-      fc0_params,
-      fc1_params
+      conv0,
+      conv1,
+      conv2,
+      conv3,
+      conv4,
+      conv5,
+      conv6,
+      conv7,
+      fc0,
+      fc1
     }
   }
 }
\ No newline at end of file

src/faceLandmarkNet/loadQuantizedParams.ts

 import * as tf from '@tensorflow/tfjs-core';
 
-import { extractWeightEntry } from '../commons/extractWeightEntry';
+import { disposeUnusedWeightTensors } from '../commons/disposeUnusedWeightTensors';
+import { extractWeightEntryFactory } from '../commons/extractWeightEntryFactory';
 import { loadWeightMap } from '../commons/loadWeightMap';
 import { ConvParams, ParamMapping } from '../commons/types';
 import { FCParams, NetParams } from './types';
@@ -9,30 +10,20 @@ const DEFAULT_MODEL_NAME = 'face_landmark_68_model'
 
 function extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {
 
+  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)
+
   function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {
-    const filtersEntry = extractWeightEntry(weightMap, `${prefix}/kernel`, 4)
-    const biasEntry = extractWeightEntry(weightMap, `${prefix}/bias`, 1)
-    paramMappings.push(
-      { originalPath: filtersEntry.path, paramPath: `${mappedPrefix}/filters` },
-      { originalPath: biasEntry.path, paramPath: `${mappedPrefix}/bias` }
-    )
-    return {
-      filters: filtersEntry.tensor as tf.Tensor4D,
-      bias: biasEntry.tensor as tf.Tensor1D
-    }
+    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/kernel`, 4, `${mappedPrefix}/filters`)
+    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1, `${mappedPrefix}/bias`)
+
+    return { filters, bias }
   }
 
   function extractFcParams(prefix: string, mappedPrefix: string): FCParams {
-    const weightsEntry = extractWeightEntry(weightMap, `${prefix}/kernel`, 2)
-    const biasEntry = extractWeightEntry(weightMap, `${prefix}/bias`, 1)
-    paramMappings.push(
-      { originalPath: weightsEntry.path, paramPath: `${mappedPrefix}/weights` },
-      { originalPath: biasEntry.path, paramPath: `${mappedPrefix}/bias` }
-    )
-    return {
-      weights: weightsEntry.tensor as tf.Tensor2D,
-      bias: biasEntry.tensor as tf.Tensor1D
-    }
+    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/kernel`, 2, `${mappedPrefix}/weights`)
+    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1, `${mappedPrefix}/bias`)
+
+    return { weights, bias }
   }
 
   return {
@@ -54,17 +45,19 @@ export async function loadQuantizedParams(
   } = extractorsFactory(weightMap, paramMappings)
 
   const 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 { params, paramMappings }
 }
\ No newline at end of file

src/faceLandmarkNet/types.ts

@@ -8,14 +8,14 @@ export type FCParams = {
 }
 
 export 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
 }
\ No newline at end of file

src/faceRecognitionNet/FaceRecognitionNet.ts

 import * as tf from '@tensorflow/tfjs-core';
 
+import { NeuralNetwork } from '../commons/NeuralNetwork';
 import { NetInput } from '../NetInput';
 import { toNetInput } from '../toNetInput';
 import { TNetInput } from '../types';
@@ -10,28 +11,17 @@ import { normalize } from './normalize';
 import { residual, residualDown } from './residualLayer';
 import { NetParams } from './types';
 
-export class FaceRecognitionNet {
+export class FaceRecognitionNet extends NeuralNetwork<NetParams> {
 
-  private _params: NetParams
-
-  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {
-    if (weightsOrUrl instanceof Float32Array) {
-      this.extractWeights(weightsOrUrl)
-      return
+  constructor() {
+    super('FaceRecognitionNet')
   }
 
-    if (weightsOrUrl && typeof weightsOrUrl !== 'string') {
-      throw new Error('FaceLandmarkNet.load - expected model uri, or weights as Float32Array')
-    }
-    this._params = await loadQuantizedParams(weightsOrUrl)
-  }
+  public forwardInput(input: NetInput): tf.Tensor2D {
 
-  public extractWeights(weights: Float32Array) {
-    this._params = extractParams(weights)
-  }
+    const { params } = this
 
-  public forwardInput(input: NetInput): tf.Tensor2D {
-    if (!this._params) {
+    if (!params) {
       throw new Error('FaceRecognitionNet - load model before inference')
     }
 
@@ -40,29 +30,29 @@ export class FaceRecognitionNet {
 
       const normalized = normalize(batchTensor)
 
-      let out = convDown(normalized, this._params.conv32_down)
+      let out = convDown(normalized, params.conv32_down)
       out = tf.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 = residual(out, params.conv32_1)
+      out = residual(out, params.conv32_2)
+      out = residual(out, 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, params.conv64_down)
+      out = residual(out, params.conv64_1)
+      out = residual(out, params.conv64_2)
+      out = residual(out, 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, params.conv128_down)
+      out = residual(out, params.conv128_1)
+      out = residual(out, 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 = residualDown(out, params.conv256_down)
+      out = residual(out, params.conv256_1)
+      out = residual(out, params.conv256_2)
+      out = residualDown(out, params.conv256_down_out)
 
       const globalAvg = out.mean([1, 2]) as tf.Tensor2D
-      const fullyConnected = tf.matMul(globalAvg, this._params.fc)
+      const fullyConnected = tf.matMul(globalAvg, params.fc)
 
       return fullyConnected
     })
@@ -89,4 +79,12 @@ export class FaceRecognitionNet {
       ? faceDescriptorsForBatch
       : faceDescriptorsForBatch[0]
   }
+
+  protected loadQuantizedParams(uri: string | undefined) {
+    return loadQuantizedParams(uri)
+  }
+
+  protected extractParams(weights: Float32Array) {
+    return extractParams(weights)
+  }
 }
\ No newline at end of file

src/faceRecognitionNet/extractParams.ts

 import * as tf from '@tensorflow/tfjs-core';
 
 import { extractWeightsFactory } from '../commons/extractWeightsFactory';
-import { ExtractWeightsFunction } from '../commons/types';
+import { ConvParams, ExtractWeightsFunction, ParamMapping } from '../commons/types';
 import { isFloat } from '../utils';
 import { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';
 
-function extractorsFactory(extractWeights: ExtractWeightsFunction) {
+function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {
 
   function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {
     const weights = extractWeights(numFilterValues)
@@ -15,15 +15,42 @@ function extractorsFactory(extractWeights: ExtractWeightsFunction) {
       throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`)
     }
 
-    return tf.transpose(
+    return tf.tidy(
+      () => tf.transpose(
         tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),
         [2, 3, 1, 0]
       )
+    )
   }
 
-  function extractScaleLayerParams(numWeights: number): ScaleLayerParams {
+  function extractConvParams(
+    numFilterValues: number,
+    numFilters: number,
+    filterSize: number,
+    mappedPrefix: string
+  ): ConvParams {
+
+    const filters = extractFilterValues(numFilterValues, numFilters, filterSize)
+    const bias = tf.tensor1d(extractWeights(numFilters))
+
+    paramMappings.push(
+      { paramPath: `${mappedPrefix}/filters` },
+      { paramPath: `${mappedPrefix}/bias` }
+    )
+
+    return { filters, bias }
+  }
+
+  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {
+
     const weights = tf.tensor1d(extractWeights(numWeights))
     const biases = tf.tensor1d(extractWeights(numWeights))
+
+    paramMappings.push(
+      { paramPath: `${mappedPrefix}/weights` },
+      { paramPath: `${mappedPrefix}/biases` }
+    )
+
     return {
       weights,
       biases
@@ -33,34 +60,28 @@ function extractorsFactory(extractWeights: ExtractWeightsFunction) {
   function extractConvLayerParams(
     numFilterValues: number,
     numFilters: number,
-    filterSize: number
+    filterSize: number,
+    mappedPrefix: string
   ): ConvLayerParams {
-    const conv_filters = extractFilterValues(numFilterValues, numFilters, filterSize)
-    const conv_bias = tf.tensor1d(extractWeights(numFilters))
-    const scale = extractScaleLayerParams(numFilters)
 
-    return {
-      conv: {
-        filters: conv_filters,
-        bias: conv_bias
-      },
-      scale
-    }
+    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`)
+    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`)
+
+    return { conv, scale }
   }
 
   function extractResidualLayerParams(
     numFilterValues: number,
     numFilters: number,
     filterSize: number,
+    mappedPrefix: string,
     isDown: boolean = false
   ): ResidualLayerParams {
-    const conv1: ConvLayerParams = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize)
-    const conv2: ConvLayerParams = extractConvLayerParams(numFilterValues, numFilters, filterSize)
 
-    return {
-      conv1,
-      conv2
-    }
+    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`)
+    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`)
+
+    return { conv1, conv2 }
   }
 
   return {
@@ -70,43 +91,49 @@ function extractorsFactory(extractWeights: ExtractWeightsFunction) {
 
 }
 
-export function extractParams(weights: Float32Array): NetParams {
+export function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {
+
   const {
     extractWeights,
     getRemainingWeights
   } = extractWeightsFactory(weights)
 
+  const paramMappings: ParamMapping[] = []
+
   const {
     extractConvLayerParams,
     extractResidualLayerParams
-  } = extractorsFactory(extractWeights)
+  } = extractorsFactory(extractWeights, paramMappings)
 
-  const conv32_down = extractConvLayerParams(4704, 32, 7)
-  const conv32_1 = extractResidualLayerParams(9216, 32, 3)
-  const conv32_2 = extractResidualLayerParams(9216, 32, 3)
-  const conv32_3 = extractResidualLayerParams(9216, 32, 3)
+  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down')
+  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1')
+  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2')
+  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3')
 
-  const conv64_down = extractResidualLayerParams(36864, 64, 3, true)
-  const conv64_1 = extractResidualLayerParams(36864, 64, 3)
-  const conv64_2 = extractResidualLayerParams(36864, 64, 3)
-  const conv64_3 = extractResidualLayerParams(36864, 64, 3)
+  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true)
+  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1')
+  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2')
+  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3')
 
-  const conv128_down = extractResidualLayerParams(147456, 128, 3, true)
-  const conv128_1 = extractResidualLayerParams(147456, 128, 3)
-  const conv128_2 = extractResidualLayerParams(147456, 128, 3)
+  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true)
+  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1')
+  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2')
 
-  const conv256_down = extractResidualLayerParams(589824, 256, 3, true)
-  const conv256_1 = extractResidualLayerParams(589824, 256, 3)
-  const conv256_2 = extractResidualLayerParams(589824, 256, 3)
-  const conv256_down_out = extractResidualLayerParams(589824, 256, 3)
+  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true)
+  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1')
+  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2')
+  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out')
 
-  const fc = tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0])
+  const fc = tf.tidy(
+    () => 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 {
+  const params = {
     conv32_down,
     conv32_1,
     conv32_2,
@@ -124,4 +151,6 @@ export function extractParams(weights: Float32Array): NetParams {
     conv256_down_out,
     fc
   }
+
+  return { params, paramMappings }
 }
\ No newline at end of file

src/faceRecognitionNet/loadQuantizedParams.ts

-import { isTensor1D, isTensor2D, isTensor4D } from '../commons/isTensor';
+import * as tf from '@tensorflow/tfjs-core';
+
+import { disposeUnusedWeightTensors } from '../commons/disposeUnusedWeightTensors';
+import { extractWeightEntryFactory } from '../commons/extractWeightEntryFactory';
+import { isTensor2D } from '../commons/isTensor';
 import { loadWeightMap } from '../commons/loadWeightMap';
-import { ConvLayerParams, ResidualLayerParams, ScaleLayerParams } from './types';
+import { ParamMapping } from '../commons/types';
+import { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';
 
 const DEFAULT_MODEL_NAME = 'face_recognition_model'
 
-function extractorsFactory(weightMap: any) {
+function extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {
 
-  function extractScaleLayerParams(prefix: string): ScaleLayerParams {
-    const params = {
-      weights: weightMap[`${prefix}/scale/weights`],
-      biases: weightMap[`${prefix}/scale/biases`]
-    }
+  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)
 
-    if (!isTensor1D(params.weights)) {
-      throw new Error(`expected weightMap[${prefix}/scale/weights] to be a Tensor1D, instead have ${params.weights}`)
-    }
+  function extractScaleLayerParams(prefix: string): ScaleLayerParams {
 
-    if (!isTensor1D(params.biases)) {
-      throw new Error(`expected weightMap[${prefix}/scale/biases] to be a Tensor1D, instead have ${params.biases}`)
-    }
+    const weights = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/weights`, 1)
+    const biases = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/biases`, 1)
 
-    return params
+    return { weights, biases }
   }
 
   function extractConvLayerParams(prefix: string): ConvLayerParams {
-    const 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 ${params.filters}`)
-    }
+    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/conv/filters`, 4)
+    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/conv/bias`, 1)
+    const scale = extractScaleLayerParams(prefix)
 
-    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)
-    }
+    return { conv: { filters, bias }, scale }
   }
 
   function extractResidualLayerParams(prefix: string): ResidualLayerParams {
@@ -57,13 +44,17 @@ function extractorsFactory(weightMap: any) {
 
 }
 
-export async function loadQuantizedParams(uri: string | undefined): Promise<any> {
+export async function loadQuantizedParams(
+  uri: string | undefined
+): Promise<{ params: NetParams, paramMappings: ParamMapping[] }> {
+
   const weightMap = await loadWeightMap(uri, DEFAULT_MODEL_NAME)
+  const paramMappings: ParamMapping[] = []
 
   const {
     extractConvLayerParams,
     extractResidualLayerParams
-  } = extractorsFactory(weightMap)
+  } = extractorsFactory(weightMap, paramMappings)
 
   const conv32_down = extractConvLayerParams('conv32_down')
   const conv32_1 = extractResidualLayerParams('conv32_1')
@@ -85,12 +76,13 @@ export async function loadQuantizedParams(uri: string | undefined): Promise<any>
   const conv256_down_out = extractResidualLayerParams('conv256_down_out')
 
   const 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 {
+  const params = {
     conv32_down,
     conv32_1,
     conv32_2,
@@ -108,4 +100,8 @@ export async function loadQuantizedParams(uri: string | undefined): Promise<any>
     conv256_down_out,
     fc
   }
+
+  disposeUnusedWeightTensors(weightMap, paramMappings)
+
+  return { params, paramMappings }
 }
\ No newline at end of file

test/tests/commons/NeuralNetwork.test.ts

-import { NeuralNetwork } from '../../../src/commons/NeuralNetwork';
 import * as tf from '@tensorflow/tfjs-core';
 
+import { NeuralNetwork } from '../../../src/commons/NeuralNetwork';
+
 class FakeNeuralNetwork extends NeuralNetwork<any> {
   constructor(
     convFilter: tf.Tensor = tf.tensor(0),
     convBias: tf.Tensor = tf.tensor(0),
     fcWeights: tf.Tensor = tf.tensor(0)
   ) {
-    super()
+    super('FakeNeuralNetwork')
     this._params = {
       conv: {
         filter: convFilter,

test/tests/e2e/faceDetectionNet.test.ts

 import * as faceapi from '../../../src';
 import { FaceDetection } from '../../../src/faceDetectionNet/FaceDetection';
 import { IRect } from '../../../src/Rect';
-import { expectMaxDelta } from '../../utils';
+import { describeWithNets, expectAllTensorsReleased, expectMaxDelta } from '../../utils';
 
 function expectRectClose(
   result: IRect,
@@ -33,19 +33,11 @@ describe('faceDetectionNet', () => {
     imgEl = await faceapi.bufferToImage(img)
   })
 
-  describe('uncompressed weights', () => {
-
-    let faceDetectionNet: faceapi.FaceDetectionNet
+  describeWithNets('uncompressed weights', { withFaceDetectionNet: { quantized: false } }, ({ faceDetectionNet }) => {
 
     const expectedScores = [0.98, 0.89, 0.82, 0.75, 0.58, 0.55]
     const maxBoxDelta = 1
 
-    beforeAll(async () => {
-      const res = await fetch('base/weights/uncompressed/face_detection_model.weights')
-      const weights = new Float32Array(await res.arrayBuffer())
-      faceDetectionNet = faceapi.faceDetectionNet(weights)
-    })
-
     it('scores > 0.8', async () => {
       const detections = await faceDetectionNet.locateFaces(imgEl) as FaceDetection[]
 
@@ -72,18 +64,11 @@ describe('faceDetectionNet', () => {
 
   })
 
-  describe('quantized weights', () => {
-
-    let faceDetectionNet: faceapi.FaceDetectionNet
+  describeWithNets('quantized weights', { withFaceDetectionNet: { quantized: true } }, ({ faceDetectionNet }) => {
 
     const expectedScores = [0.97, 0.88, 0.83, 0.82, 0.59, 0.52]
     const maxBoxDelta = 5
 
-    beforeAll(async () => {
-      faceDetectionNet = new faceapi.FaceDetectionNet()
-      await faceDetectionNet.load('base/weights')
-    })
-
     it('scores > 0.8', async () => {
       const detections = await faceDetectionNet.locateFaces(imgEl) as FaceDetection[]
 
@@ -110,4 +95,33 @@ describe('faceDetectionNet', () => {
 
   })
 
+  describe('no memory leaks', () => {
+
+    describe('NeuralNetwork, uncompressed model', () => {
+
+      it('disposes all param tensors', async () => {
+        await expectAllTensorsReleased(async () => {
+          const res = await fetch('base/weights/uncompressed/face_detection_model.weights')
+          const weights = new Float32Array(await res.arrayBuffer())
+          const net = faceapi.faceDetectionNet(weights)
+          net.dispose()
+        })
+      })
+
+    })
+
+    describe('NeuralNetwork, quantized model', () => {
+
+      it('disposes all param tensors', async () => {
+        await expectAllTensorsReleased(async () => {
+          const net = new faceapi.FaceDetectionNet()
+          await net.load('base/weights')
+          net.dispose()
+        })
+      })
+
+    })
+
+  })
+
 })
\ No newline at end of file

test/tests/e2e/faceLandmarkNet.test.ts

@@ -5,7 +5,7 @@ import { isTensor3D } from '../../../src/commons/isTensor';
 import { FaceLandmarks } from '../../../src/faceLandmarkNet/FaceLandmarks';
 import { Point } from '../../../src/Point';
 import { Dimensions, TMediaElement } from '../../../src/types';
-import { expectMaxDelta, expectAllTensorsReleased, tensor3D } from '../../utils';
+import { expectMaxDelta, expectAllTensorsReleased, tensor3D, describeWithNets } from '../../utils';
 import { NetInput } from '../../../src/NetInput';
 import { toNetInput } from '../../../src';
 
@@ -38,15 +38,7 @@ describe('faceLandmarkNet', () => {
     faceLandmarkPositionsRect = await (await fetch('base/test/data/faceLandmarkPositionsRect.json')).json()
   })
 
-  describe('uncompressed weights', () => {
-
-    let faceLandmarkNet: faceapi.FaceLandmarkNet
-
-    beforeAll(async () => {
-      const res = await fetch('base/weights/uncompressed/face_landmark_68_model.weights')
-      const weights = new Float32Array(await res.arrayBuffer())
-      faceLandmarkNet = faceapi.faceLandmarkNet(weights)
-    })
+  describeWithNets('uncompressed weights', { withFaceLandmarkNet: { quantized: false } }, ({ faceLandmarkNet }) => {
 
     it('computes face landmarks for squared input', async () => {
       const { width, height } = imgEl1
@@ -78,14 +70,7 @@ describe('faceLandmarkNet', () => {
 
   })
 
-  describe('quantized weights', () => {
-
-    let faceLandmarkNet: faceapi.FaceLandmarkNet
-
-    beforeAll(async () => {
-      faceLandmarkNet = new faceapi.FaceLandmarkNet()
-      await faceLandmarkNet.load('base/weights')
-    })
+  describeWithNets('quantized weights', { withFaceLandmarkNet: { quantized: true } }, ({ faceLandmarkNet }) => {
 
     it('computes face landmarks for squared input', async () => {
       const { width, height } = imgEl1
@@ -117,15 +102,7 @@ describe('faceLandmarkNet', () => {
 
   })
 
-  describe('batch inputs', () => {
-
-    let faceLandmarkNet: faceapi.FaceLandmarkNet
-
-    beforeAll(async () => {
-      const res = await fetch('base/weights/uncompressed/face_landmark_68_model.weights')
-      const weights = new Float32Array(await res.arrayBuffer())
-      faceLandmarkNet = faceapi.faceLandmarkNet(weights)
-    })
+  describeWithNets('batch inputs', { withFaceLandmarkNet: { quantized: false } }, ({ faceLandmarkNet }) => {
 
     it('computes face landmarks for batch of image elements', async () => {
       const inputs = [imgEl1, imgEl2, imgElRect]
@@ -229,13 +206,31 @@ describe('faceLandmarkNet', () => {
 
   })
 
-  describe('no memory leaks', () => {
+  describeWithNets('no memory leaks', { withFaceLandmarkNet: { quantized: true } }, ({ faceLandmarkNet }) => {
 
-    let faceLandmarkNet: faceapi.FaceLandmarkNet
+    describe('NeuralNetwork, uncompressed model', () => {
+
+      it('disposes all param tensors', async () => {
+        await expectAllTensorsReleased(async () => {
+          const res = await fetch('base/weights/uncompressed/face_landmark_68_model.weights')
+          const weights = new Float32Array(await res.arrayBuffer())
+          const net = faceapi.faceLandmarkNet(weights)
+          net.dispose()
+        })
+      })
+
+    })
+
+    describe('NeuralNetwork, quantized model', () => {
+
+      it('disposes all param tensors', async () => {
+        await expectAllTensorsReleased(async () => {
+          const net = new faceapi.FaceLandmarkNet()
+          await net.load('base/weights')
+          net.dispose()
+        })
+      })
 
-    beforeAll(async () => {
-      faceLandmarkNet = new faceapi.FaceLandmarkNet()
-      await faceLandmarkNet.load('base/weights')
     })
 
     describe('forwardInput', () => {

test/tests/e2e/faceRecognitionNet.test.ts

@@ -2,7 +2,7 @@ import * as tf from '@tensorflow/tfjs-core';
 
 import * as faceapi from '../../../src';
 import { NetInput } from '../../../src/NetInput';
-import { expectAllTensorsReleased } from '../../utils';
+import { expectAllTensorsReleased, describeWithNets } from '../../utils';
 import { toNetInput } from '../../../src';
 
 describe('faceRecognitionNet', () => {
@@ -26,15 +26,7 @@ describe('faceRecognitionNet', () => {
     faceDescriptorRect = await (await fetch('base/test/data/faceDescriptorRect.json')).json()
   })
 
-  describe('uncompressed weights', () => {
-
-    let faceRecognitionNet: faceapi.FaceRecognitionNet
-
-    beforeAll(async () => {
-      const res = await fetch('base/weights/uncompressed/face_recognition_model.weights')
-      const weights = new Float32Array(await res.arrayBuffer())
-      faceRecognitionNet = faceapi.faceRecognitionNet(weights)
-    })
+  describeWithNets('uncompressed weights', { withFaceRecognitionNet: { quantized: false } }, ({ faceRecognitionNet }) => {
 
     it('computes face descriptor for squared input', async () => {
       const result = await faceRecognitionNet.computeFaceDescriptor(imgEl1) as Float32Array
@@ -52,14 +44,7 @@ describe('faceRecognitionNet', () => {
 
   // TODO: figure out why descriptors return NaN in the test cases
   /*
-  describe('quantized weights', () => {
-
-    let faceRecognitionNet: faceapi.FaceRecognitionNet
-
-    beforeAll(async () => {
-      faceRecognitionNet = new faceapi.FaceRecognitionNet()
-      await faceRecognitionNet.load('base/weights')
-    })
+  describeWithNets('quantized weights', { withFaceRecognitionNet: { quantized: true } }, ({ faceRecognitionNet }) => {
 
     it('computes face descriptor for squared input', async () => {
       const result = await faceRecognitionNet.computeFaceDescriptor(imgEl1) as Float32Array
@@ -76,15 +61,7 @@ describe('faceRecognitionNet', () => {
   })
   */
 
-  describe('batch inputs', () => {
-
-    let faceRecognitionNet: faceapi.FaceRecognitionNet
-
-    beforeAll(async () => {
-      const res = await fetch('base/weights/uncompressed/face_recognition_model.weights')
-      const weights = new Float32Array(await res.arrayBuffer())
-      faceRecognitionNet = faceapi.faceRecognitionNet(weights)
-    })
+  describeWithNets('batch inputs', { withFaceRecognitionNet: { quantized: false } }, ({ faceRecognitionNet }) => {
 
     it('computes face descriptors for batch of image elements', async () => {
       const inputs = [imgEl1, imgEl2, imgElRect]
@@ -156,14 +133,31 @@ describe('faceRecognitionNet', () => {
 
   })
 
-  describe('no memory leaks', () => {
+  describeWithNets('no memory leaks', { withFaceRecognitionNet: { quantized: false } }, ({ faceRecognitionNet }) => {
 
-    let faceRecognitionNet: faceapi.FaceRecognitionNet
+    describe('NeuralNetwork, uncompressed model', () => {
 
-    beforeAll(async () => {
+      it('disposes all param tensors', async () => {
+        await expectAllTensorsReleased(async () => {
           const res = await fetch('base/weights/uncompressed/face_recognition_model.weights')
           const weights = new Float32Array(await res.arrayBuffer())
-      faceRecognitionNet = faceapi.faceRecognitionNet(weights)
+          const net = faceapi.faceRecognitionNet(weights)
+          net.dispose()
+        })
+      })
+
+    })
+
+    describe('NeuralNetwork, quantized model', () => {
+
+      it('disposes all param tensors', async () => {
+        await expectAllTensorsReleased(async () => {
+          const net = new faceapi.FaceRecognitionNet()
+          await net.load('base/weights')
+          net.dispose()
+        })
+      })
+
     })
 
     describe('forwardInput', () => {
@@ -292,5 +286,4 @@ describe('faceRecognitionNet', () => {
     })
   })
 
-
 })
\ No newline at end of file

test/utils.ts

 import * as tf from '@tensorflow/tfjs-core';
 
+import * as faceapi from '../src/';
+import { NeuralNetwork } from '../src/commons/NeuralNetwork';
+
 export function zeros(length: number): Float32Array {
   return new Float32Array(length)
 }
@@ -21,3 +24,84 @@ export async function expectAllTensorsReleased(fn: () => any) {
 export function tensor3D() {
   return tf.tensor3d([[[0]]])
 }
+
+export type WithNetOptions = {
+  quantized?: boolean
+}
+
+export type InjectNetArgs = {
+  faceDetectionNet: faceapi.FaceDetectionNet
+  faceLandmarkNet: faceapi.FaceLandmarkNet
+  faceRecognitionNet: faceapi.FaceRecognitionNet
+}
+
+export type DescribeWithNetsOptions = {
+  withFaceDetectionNet?: WithNetOptions
+  withFaceLandmarkNet?: WithNetOptions
+  withFaceRecognitionNet?: WithNetOptions
+}
+
+async function loadNetWeights(uri: string): Promise<Float32Array> {
+  return new Float32Array(await (await fetch(uri)).arrayBuffer())
+}
+
+async function initNet<TNet extends NeuralNetwork<any>>(
+  net: TNet,
+  uncompressedFilename: string | boolean
+) {
+  await net.load(
+    uncompressedFilename
+      ? await loadNetWeights(`base/weights/uncompressed/${uncompressedFilename}`)
+      : 'base/weights'
+  )
+}
+
+export function describeWithNets(
+  description: string,
+  options: DescribeWithNetsOptions,
+  specDefinitions: (nets: InjectNetArgs) => void
+) {
+
+  describe(description, () => {
+
+    let faceDetectionNet: faceapi.FaceDetectionNet = new faceapi.FaceDetectionNet()
+    let faceLandmarkNet: faceapi.FaceLandmarkNet = new faceapi.FaceLandmarkNet()
+    let faceRecognitionNet: faceapi.FaceRecognitionNet = new faceapi.FaceRecognitionNet()
+
+    beforeAll(async () => {
+      const {
+        withFaceDetectionNet,
+        withFaceLandmarkNet,
+        withFaceRecognitionNet
+      } = options
+
+      if (withFaceDetectionNet) {
+        await initNet<faceapi.FaceDetectionNet>(
+          faceDetectionNet,
+          !withFaceDetectionNet.quantized && 'face_detection_model.weights'
+        )
+      }
+      if (withFaceLandmarkNet) {
+        await initNet<faceapi.FaceLandmarkNet>(
+          faceLandmarkNet,
+          !withFaceLandmarkNet.quantized && 'face_landmark_68_model.weights'
+        )
+      }
+      if (withFaceRecognitionNet) {
+        await initNet<faceapi.FaceRecognitionNet>(
+          faceRecognitionNet,
+          !withFaceRecognitionNet.quantized && 'face_recognition_model.weights'
+        )
+      }
+    })
+
+    afterAll(() => {
+      faceDetectionNet && faceDetectionNet.dispose()
+      faceLandmarkNet && faceLandmarkNet.dispose()
+      faceRecognitionNet && faceRecognitionNet.dispose()
+    })
+
+    specDefinitions({ faceDetectionNet, faceLandmarkNet, faceRecognitionNet })
+  })
+}
+