"use strict";
|
Object.defineProperty(exports, "__esModule", { value: true });
|
var tslib_1 = require("tslib");
|
var tf = require("@tensorflow/tfjs-core");
|
var fullyConnectedLayer_1 = require("../common/fullyConnectedLayer");
|
var util_1 = require("../faceProcessor/util");
|
var TinyXception_1 = require("../xception/TinyXception");
|
var extractParams_1 = require("./extractParams");
|
var extractParamsFromWeigthMap_1 = require("./extractParamsFromWeigthMap");
|
var types_1 = require("./types");
|
var NeuralNetwork_1 = require("../NeuralNetwork");
|
var dom_1 = require("../dom");
|
var AgeGenderNet = /** @class */ (function (_super) {
|
tslib_1.__extends(AgeGenderNet, _super);
|
function AgeGenderNet(faceFeatureExtractor) {
|
if (faceFeatureExtractor === void 0) { faceFeatureExtractor = new TinyXception_1.TinyXception(2); }
|
var _this = _super.call(this, 'AgeGenderNet') || this;
|
_this._faceFeatureExtractor = faceFeatureExtractor;
|
return _this;
|
}
|
Object.defineProperty(AgeGenderNet.prototype, "faceFeatureExtractor", {
|
get: function () {
|
return this._faceFeatureExtractor;
|
},
|
enumerable: true,
|
configurable: true
|
});
|
AgeGenderNet.prototype.runNet = function (input) {
|
var _this = this;
|
var params = this.params;
|
if (!params) {
|
throw new Error(this._name + " - load model before inference");
|
}
|
return tf.tidy(function () {
|
var bottleneckFeatures = input instanceof dom_1.NetInput
|
? _this.faceFeatureExtractor.forwardInput(input)
|
: input;
|
var pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1);
|
var age = fullyConnectedLayer_1.fullyConnectedLayer(pooled, params.fc.age).as1D();
|
var gender = fullyConnectedLayer_1.fullyConnectedLayer(pooled, params.fc.gender);
|
return { age: age, gender: gender };
|
});
|
};
|
AgeGenderNet.prototype.forwardInput = function (input) {
|
var _this = this;
|
return tf.tidy(function () {
|
var _a = _this.runNet(input), age = _a.age, gender = _a.gender;
|
return { age: age, gender: tf.softmax(gender) };
|
});
|
};
|
AgeGenderNet.prototype.forward = function (input) {
|
return tslib_1.__awaiter(this, void 0, void 0, function () {
|
var _a;
|
return tslib_1.__generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
_a = this.forwardInput;
|
return [4 /*yield*/, dom_1.toNetInput(input)];
|
case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
|
}
|
});
|
});
|
};
|
AgeGenderNet.prototype.predictAgeAndGender = function (input) {
|
return tslib_1.__awaiter(this, void 0, void 0, function () {
|
var netInput, out, ages, genders, ageAndGenderTensors, predictionsByBatch;
|
var _this = this;
|
return tslib_1.__generator(this, function (_a) {
|
switch (_a.label) {
|
case 0: return [4 /*yield*/, dom_1.toNetInput(input)];
|
case 1:
|
netInput = _a.sent();
|
return [4 /*yield*/, this.forwardInput(netInput)];
|
case 2:
|
out = _a.sent();
|
ages = tf.unstack(out.age);
|
genders = tf.unstack(out.gender);
|
ageAndGenderTensors = ages.map(function (ageTensor, i) { return ({
|
ageTensor: ageTensor,
|
genderTensor: genders[i]
|
}); });
|
return [4 /*yield*/, Promise.all(ageAndGenderTensors.map(function (_a) {
|
var ageTensor = _a.ageTensor, genderTensor = _a.genderTensor;
|
return tslib_1.__awaiter(_this, void 0, void 0, function () {
|
var age, probMale, isMale, gender, genderProbability;
|
return tslib_1.__generator(this, function (_b) {
|
switch (_b.label) {
|
case 0: return [4 /*yield*/, ageTensor.data()];
|
case 1:
|
age = (_b.sent())[0];
|
return [4 /*yield*/, genderTensor.data()];
|
case 2:
|
probMale = (_b.sent())[0];
|
isMale = probMale > 0.5;
|
gender = isMale ? types_1.Gender.MALE : types_1.Gender.FEMALE;
|
genderProbability = isMale ? probMale : (1 - probMale);
|
ageTensor.dispose();
|
genderTensor.dispose();
|
return [2 /*return*/, { age: age, gender: gender, genderProbability: genderProbability }];
|
}
|
});
|
});
|
}))];
|
case 3:
|
predictionsByBatch = _a.sent();
|
out.age.dispose();
|
out.gender.dispose();
|
return [2 /*return*/, netInput.isBatchInput
|
? predictionsByBatch
|
: predictionsByBatch[0]];
|
}
|
});
|
});
|
};
|
AgeGenderNet.prototype.getDefaultModelName = function () {
|
return 'age_gender_model';
|
};
|
AgeGenderNet.prototype.dispose = function (throwOnRedispose) {
|
if (throwOnRedispose === void 0) { throwOnRedispose = true; }
|
this.faceFeatureExtractor.dispose(throwOnRedispose);
|
_super.prototype.dispose.call(this, throwOnRedispose);
|
};
|
AgeGenderNet.prototype.loadClassifierParams = function (weights) {
|
var _a = this.extractClassifierParams(weights), params = _a.params, paramMappings = _a.paramMappings;
|
this._params = params;
|
this._paramMappings = paramMappings;
|
};
|
AgeGenderNet.prototype.extractClassifierParams = function (weights) {
|
return extractParams_1.extractParams(weights);
|
};
|
AgeGenderNet.prototype.extractParamsFromWeigthMap = function (weightMap) {
|
var _a = util_1.seperateWeightMaps(weightMap), featureExtractorMap = _a.featureExtractorMap, classifierMap = _a.classifierMap;
|
this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);
|
return extractParamsFromWeigthMap_1.extractParamsFromWeigthMap(classifierMap);
|
};
|
AgeGenderNet.prototype.extractParams = function (weights) {
|
var classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2);
|
var featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);
|
var classifierWeights = weights.slice(weights.length - classifierWeightSize);
|
this.faceFeatureExtractor.extractWeights(featureExtractorWeights);
|
return this.extractClassifierParams(classifierWeights);
|
};
|
return AgeGenderNet;
|
}(NeuralNetwork_1.NeuralNetwork));
|
exports.AgeGenderNet = AgeGenderNet;
|
//# sourceMappingURL=AgeGenderNet.js.map
|
