check in latest build

build/commons/NeuralNetwork.d.ts

+import * as tf from '@tensorflow/tfjs-core';
+import { ParamMapping } from './types';
+export declare class NeuralNetwork<TNetParams> {
+    protected _params: TNetParams | undefined;
+    protected _paramMappings: ParamMapping[];
+    readonly params: TNetParams | undefined;
+    readonly paramMappings: ParamMapping[];
+    getParamFromPath(paramPath: string): tf.Tensor;
+    reassignParamFromPath(paramPath: string, tensor: tf.Tensor): void;
+    getParamList(): {
+        path: string;
+        tensor: tf.Tensor<tf.Rank>;
+    }[];
+    getTrainableParams(): {
+        path: string;
+        tensor: tf.Tensor<tf.Rank>;
+    }[];
+    getFrozenParams(): {
+        path: string;
+        tensor: tf.Tensor<tf.Rank>;
+    }[];
+    variable(): void;
+    freeze(): void;
+    dispose(): void;
+    private traversePropertyPath(paramPath);
+}

build/commons/NeuralNetwork.js

+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("@tensorflow/tfjs-core");
+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) {
+        var _a = this.traversePropertyPath(paramPath), obj = _a.obj, objProp = _a.objProp;
+        obj[objProp].dispose();
+        obj[objProp] = tensor;
+    };
+    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 tf.Variable; });
+    };
+    NeuralNetwork.prototype.getFrozenParams = function () {
+        return this.getParamList().filter(function (param) { return !(param.tensor instanceof tf.Variable); });
+    };
+    NeuralNetwork.prototype.variable = function () {
+        var _this = this;
+        this.getFrozenParams().forEach(function (_a) {
+            var path = _a.path, tensor = _a.tensor;
+            _this.reassignParamFromPath(path, tf.variable(tensor));
+        });
+    };
+    NeuralNetwork.prototype.freeze = function () {
+        var _this = this;
+        this.getTrainableParams().forEach(function (_a) {
+            var path = _a.path, tensor = _a.tensor;
+            _this.reassignParamFromPath(path, tf.tensor(tensor));
+        });
+    };
+    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 tf.Tensor)) {
+            throw new Error("traversePropertyPath - parameter is not a tensor, for path " + paramPath);
+        }
+        return { obj: obj, objProp: objProp };
+    };
+    return NeuralNetwork;
+}());
+exports.NeuralNetwork = NeuralNetwork;
+//# sourceMappingURL=NeuralNetwork.js.map
\ No newline at end of file

build/commons/NeuralNetwork.js.map

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\ No newline at end of file

build/commons/extractConvParamsFactory.d.ts

-import { ConvParams, ExtractWeightsFunction } from './types';
-export declare function extractConvParamsFactory(extractWeights: ExtractWeightsFunction): (channelsIn: number, channelsOut: number, filterSize: number) => ConvParams;
+import { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';
+export declare function extractConvParamsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]): (channelsIn: number, channelsOut: number, filterSize: number, mappedPrefix: string) => ConvParams;

build/commons/extractConvParamsFactory.js

 "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
 var tf = require("@tensorflow/tfjs-core");
-function extractConvParamsFactory(extractWeights) {
-    return function (channelsIn, channelsOut, filterSize) {
+function extractConvParamsFactory(extractWeights, paramMappings) {
+    return function (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

build/commons/extractConvParamsFactory.js.map

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+{"version":3,"file":"extractConvParamsFactory.js","sourceRoot":"","sources":["../../src/commons/extractConvParamsFactory.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAI5C,kCAAyC,cAAsC,EAAE,aAA6B;IAC5G,OAAO,UACL,UAAkB,EAClB,WAAmB,EACnB,UAAkB,EAClB,YAAoB;QAEpB,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;YACL,OAAO,SAAA;YACP,IAAI,MAAA;SACL,CAAA;IACH,CAAC,CAAA;AACH,CAAC;AAvBD,4DAuBC"}
\ No newline at end of file

build/commons/extractWeightEntry.d.ts

+export declare function extractWeightEntry(weightMap: any, path: string, paramRank: number): {
+    path: string;
+    tensor: any;
+};

build/commons/extractWeightEntry.js

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

build/commons/extractWeightEntry.js.map

+{"version":3,"file":"extractWeightEntry.js","sourceRoot":"","sources":["../../src/commons/extractWeightEntry.ts"],"names":[],"mappings":";;AAAA,uCAAsC;AAEtC,4BAAmC,SAAc,EAAE,IAAY,EAAE,SAAiB;IAChF,IAAM,MAAM,GAAG,SAAS,CAAC,IAAI,CAAC,CAAA;IAE9B,IAAI,CAAC,mBAAQ,CAAC,MAAM,EAAE,SAAS,CAAC,EAAE;QAChC,MAAM,IAAI,KAAK,CAAC,wBAAsB,IAAI,wBAAmB,SAAS,wBAAmB,MAAQ,CAAC,CAAA;KACnG;IAED,OAAO,EAAE,IAAI,MAAA,EAAE,MAAM,QAAA,EAAE,CAAA;AACzB,CAAC;AARD,gDAQC"}
\ No newline at end of file

build/commons/types.d.ts

@@ -10,3 +10,7 @@ export declare type BatchReshapeInfo = {
     paddingX: number;
     paddingY: number;
 };
+export declare type ParamMapping = {
+    originalPath?: string;
+    paramPath: string;
+};

build/faceLandmarkNet/FaceLandmarkNet.d.ts

 import * as tf from '@tensorflow/tfjs-core';
+import { NeuralNetwork } from '../commons/NeuralNetwork';
 import { NetInput } from '../NetInput';
 import { TNetInput } from '../types';
 import { FaceLandmarks } from './FaceLandmarks';
-export declare class FaceLandmarkNet {
-    private _params;
+import { NetParams } from './types';
+export declare class FaceLandmarkNet extends NeuralNetwork<NetParams> {
     load(weightsOrUrl: Float32Array | string | undefined): Promise<void>;
     extractWeights(weights: Float32Array): void;
     forwardInput(input: NetInput): tf.Tensor2D;

build/faceLandmarkNet/FaceLandmarkNet.js

@@ -3,6 +3,7 @@ Object.defineProperty(exports, "__esModule", { value: true });
 var tslib_1 = require("tslib");
 var tf = require("@tensorflow/tfjs-core");
 var convLayer_1 = require("../commons/convLayer");
+var NeuralNetwork_1 = require("../commons/NeuralNetwork");
 var Point_1 = require("../Point");
 var toNetInput_1 = require("../toNetInput");
 var utils_1 = require("../utils");
@@ -17,12 +18,14 @@ function maxPool(x, strides) {
     if (strides === void 0) { strides = [2, 2]; }
     return tf.maxPool(x, [2, 2], strides, 'valid');
 }
-var FaceLandmarkNet = /** @class */ (function () {
+var FaceLandmarkNet = /** @class */ (function (_super) {
+    tslib_1.__extends(FaceLandmarkNet, _super);
     function FaceLandmarkNet() {
+        return _super !== null && _super.apply(this, arguments) || this;
     }
     FaceLandmarkNet.prototype.load = function (weightsOrUrl) {
         return tslib_1.__awaiter(this, void 0, void 0, function () {
-            var _a;
+            var _a, paramMappings, params;
             return tslib_1.__generator(this, function (_b) {
                 switch (_b.label) {
                     case 0:
@@ -33,17 +36,20 @@ var FaceLandmarkNet = /** @class */ (function () {
                         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();
+                        _a = _b.sent(), paramMappings = _a.paramMappings, params = _a.params;
+                        this._paramMappings = paramMappings;
+                        this._params = params;
                         return [2 /*return*/];
                 }
             });
         });
     };
     FaceLandmarkNet.prototype.extractWeights = function (weights) {
-        this._params = extractParams_1.extractParams(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;
@@ -141,6 +147,6 @@ var FaceLandmarkNet = /** @class */ (function () {
         });
     };
     return FaceLandmarkNet;
-}());
+}(NeuralNetwork_1.NeuralNetwork));
 exports.FaceLandmarkNet = FaceLandmarkNet;
 //# sourceMappingURL=FaceLandmarkNet.js.map
\ No newline at end of file

build/faceLandmarkNet/FaceLandmarkNet.js.map

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\ No newline at end of file

build/faceLandmarkNet/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/faceLandmarkNet/extractParams.js

@@ -4,40 +4,45 @@ 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);
-    function extractFcParams(channelsIn, channelsOut) {
+    var extractConvParams = extractConvParamsFactory_1.extractConvParamsFactory(extractWeights, paramMappings);
+    function extractFcParams(channelsIn, channelsOut, mappedPrefix) {
         var fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);
         var fc_bias = tf.tensor1d(extractWeights(channelsOut));
+        paramMappings.push({ paramPath: mappedPrefix + "/weights" }, { paramPath: mappedPrefix + "/bias" });
         return {
             weights: fc_weights,
             bias: fc_bias
         };
     }
-    var conv0_params = extractConvParams(3, 32, 3);
-    var conv1_params = extractConvParams(32, 64, 3);
-    var conv2_params = extractConvParams(64, 64, 3);
-    var conv3_params = extractConvParams(64, 64, 3);
-    var conv4_params = extractConvParams(64, 64, 3);
-    var conv5_params = extractConvParams(64, 128, 3);
-    var conv6_params = extractConvParams(128, 128, 3);
-    var conv7_params = extractConvParams(128, 256, 3);
-    var fc0_params = extractFcParams(6400, 1024);
-    var fc1_params = extractFcParams(1024, 136);
+    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');
     if (getRemainingWeights().length !== 0) {
         throw new Error("weights remaing after extract: " + getRemainingWeights().length);
     }
     return {
-        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
+        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
+        }
     };
 }
 exports.extractParams = extractParams;

build/faceLandmarkNet/extractParams.js.map

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\ No newline at end of file

build/faceLandmarkNet/loadQuantizedParams.d.ts

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

build/faceLandmarkNet/loadQuantizedParams.js

 "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
 var tslib_1 = require("tslib");
+var extractWeightEntry_1 = require("../commons/extractWeightEntry");
 var loadWeightMap_1 = require("../commons/loadWeightMap");
-var isTensor_1 = require("../commons/isTensor");
 var DEFAULT_MODEL_NAME = 'face_landmark_68_model';
-function extractorsFactory(weightMap) {
-    function extractConvParams(prefix) {
-        var params = {
-            filters: weightMap[prefix + "/kernel"],
-            bias: weightMap[prefix + "/bias"]
+function extractorsFactory(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
         };
-        if (!isTensor_1.isTensor4D(params.filters)) {
-            throw new Error("expected weightMap[" + prefix + "/kernel] to be a Tensor4D, instead have " + params.filters);
-        }
-        if (!isTensor_1.isTensor1D(params.bias)) {
-            throw new Error("expected weightMap[" + prefix + "/bias] to be a Tensor1D, instead have " + params.bias);
-        }
-        return params;
     }
-    function extractFcParams(prefix) {
-        var params = {
-            weights: weightMap[prefix + "/kernel"],
-            bias: weightMap[prefix + "/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
         };
-        if (!isTensor_1.isTensor2D(params.weights)) {
-            throw new Error("expected weightMap[" + prefix + "/kernel] to be a Tensor2D, instead have " + params.weights);
-        }
-        if (!isTensor_1.isTensor1D(params.bias)) {
-            throw new Error("expected weightMap[" + prefix + "/bias] to be a Tensor1D, instead have " + params.bias);
-        }
-        return params;
     }
     return {
         extractConvParams: extractConvParams,
@@ -38,25 +30,27 @@ function extractorsFactory(weightMap) {
 }
 function loadQuantizedParams(uri) {
     return tslib_1.__awaiter(this, void 0, void 0, function () {
-        var weightMap, _a, extractConvParams, extractFcParams;
+        var weightMap, paramMappings, _a, extractConvParams, extractFcParams, 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), extractConvParams = _a.extractConvParams, extractFcParams = _a.extractFcParams;
-                    return [2 /*return*/, {
-                            conv0_params: extractConvParams('conv2d_0'),
-                            conv1_params: extractConvParams('conv2d_1'),
-                            conv2_params: extractConvParams('conv2d_2'),
-                            conv3_params: extractConvParams('conv2d_3'),
-                            conv4_params: extractConvParams('conv2d_4'),
-                            conv5_params: extractConvParams('conv2d_5'),
-                            conv6_params: extractConvParams('conv2d_6'),
-                            conv7_params: extractConvParams('conv2d_7'),
-                            fc0_params: extractFcParams('dense'),
-                            fc1_params: extractFcParams('logits')
-                        }];
+                    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')
+                    };
+                    return [2 /*return*/, { params: params, paramMappings: paramMappings }];
             }
         });
     });

build/faceLandmarkNet/loadQuantizedParams.js.map

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\ No newline at end of file