Merge pull request #16 from justadudewhohacks/quantize-weights

README.md

@@ -8,6 +8,7 @@
   * **[Face Recognition](#about-face-recognition)**
   * **[Face Landmark Detection](#about-face-landmark-detection)**
 * **[Usage](#usage)**
+  * **[Loading the Models](#usage-load-models)**
   * **[Face Detection](#usage-face-detection)**
   * **[Face Recognition](#usage-face-recognition)**
   * **[Face Landmark Detection](#usage-face-landmark-detection)**
@@ -95,19 +96,46 @@ Or install the package:
 npm i face-api.js
 ```
 
-<a name="usage-face-detection"></a>
+<a name="usage-load-models"></a>
 
-### Face Detection
+### Loading the Models
+
+To load a model, you have provide the corresponding manifest.json file as well as the model weight files (shards) as assets. Simply copy them to your public or assets folder. The manifest.json and shard files of a model have to be located in the same directory / accessible under the same route.
+
+Assuming the models reside in **public/model**:
+
+``` javascript
+const net = new faceapi.FaceDetectionNet()
+// accordingly for the other models:
+// const net = new faceapi.FaceLandmarkNet()
+// const net = new faceapi.FaceRecognitionNet()
 
-Download the weights file from your server and initialize the net (note, that your server has to host the *face_detection_model.weights* file).
+await net.load('/models/face_detection_model-weights_manifest.json')
+// await net.load('/models/face_landmark_68_model-weights_manifest.json')
+// await net.load('/models/face_recognition_model-weights_manifest.json')
+
+// or simply
+await net.load('/models')
+```
+
+Alternatively you can load the weights as a Float32Array (in case you want to use the uncompressed models):
 
 ``` javascript
-// initialize the face detector
-const res = await axios.get('face_detection_model.weights', { responseType: 'arraybuffer' })
+// using fetch
+const res = await fetch('/models/face_detection_model.weights')
+const weights = new Float32Array(await res.arrayBuffer())
+net.load(weights)
+
+// using axios
+const res = await axios.get('/models/face_detection_model.weights', { responseType: 'arraybuffer' })
 const weights = new Float32Array(res.data)
-const detectionNet = faceapi.faceDetectionNet(weights)
+net.load(weights)
 ```
 
+<a name="usage-face-detection"></a>
+
+### Face Detection
+
 Detect faces and get the bounding boxes and scores:
 
 ``` javascript
@@ -141,15 +169,6 @@ const { boxes, scores } = detectionNet.forward('myImg')
 
 ### Face Recognition
 
-Download the weights file from your server and initialize the net (note, that your server has to host the *face_recognition_model.weights* file).
-
-``` javascript
-// initialize the face recognizer
-const res = await axios.get('face_recognition_model.weights', { responseType: 'arraybuffer' })
-const weights = new Float32Array(res.data)
-const recognitionNet = faceapi.faceRecognitionNet(weights)
-```
-
 Compute and compare the descriptors of two face images:
 
 ``` javascript
@@ -180,15 +199,6 @@ const t = recognitionNet.forward('myImg')
 
 ### Face Landmark Detection
 
-Download the weights file from your server and initialize the net (note, that your server has to host the *face_landmark_68_model.weights* file).
-
-``` javascript
-// initialize the face recognizer
-const res = await axios.get('face_landmark_68_model.weights', { responseType: 'arraybuffer' })
-const weights = new Float32Array(res.data)
-const faceLandmarkNet = faceapi.faceLandmarkNet(weights)
-```
-
 Detect face landmarks:
 
 ``` javascript

dist/commons/isTensor.d.ts

+import * as tf from '@tensorflow/tfjs-core';
+export declare function isTensor(tensor: tf.Tensor, dim: number): boolean;
+export declare function isTensor1D(tensor: tf.Tensor): boolean;
+export declare function isTensor2D(tensor: tf.Tensor): boolean;
+export declare function isTensor3D(tensor: tf.Tensor): boolean;
+export declare function isTensor4D(tensor: tf.Tensor): boolean;

dist/commons/isTensor.js

+import * as tf from '@tensorflow/tfjs-core';
+export function isTensor(tensor, dim) {
+    return tensor instanceof tf.Tensor && tensor.shape.length === dim;
+}
+export function isTensor1D(tensor) {
+    return isTensor(tensor, 1);
+}
+export function isTensor2D(tensor) {
+    return isTensor(tensor, 2);
+}
+export function isTensor3D(tensor) {
+    return isTensor(tensor, 3);
+}
+export function isTensor4D(tensor) {
+    return isTensor(tensor, 4);
+}
+//# sourceMappingURL=isTensor.js.map
\ No newline at end of file

dist/commons/isTensor.js.map

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\ No newline at end of file

dist/commons/loadWeightMap.d.ts

+export declare function getModelUris(uri: string | undefined, defaultModelName: string): {
+    manifestUri: string;
+    modelBaseUri: string;
+};
+export declare function loadWeightMap(uri: string | undefined, defaultModelName: string): Promise<any>;

dist/commons/loadWeightMap.js

+import * as tslib_1 from "tslib";
+import * as tf from '@tensorflow/tfjs-core';
+export function getModelUris(uri, defaultModelName) {
+    var parts = (uri || '').split('/');
+    var modelBaseUri = ((uri || '').endsWith('.json')
+        ? parts.slice(0, parts.length - 1)
+        : parts).filter(function (s) { return s; }).join('/');
+    var defaultManifestFilename = defaultModelName + "-weights_manifest.json";
+    var manifestUri = !uri || !modelBaseUri
+        ? defaultManifestFilename
+        : (uri.endsWith('.json')
+            ? uri
+            : modelBaseUri + "/" + defaultManifestFilename);
+    return { manifestUri: manifestUri, modelBaseUri: modelBaseUri };
+}
+export function loadWeightMap(uri, defaultModelName) {
+    return tslib_1.__awaiter(this, void 0, void 0, function () {
+        var _a, manifestUri, modelBaseUri, manifest;
+        return tslib_1.__generator(this, function (_b) {
+            switch (_b.label) {
+                case 0:
+                    _a = getModelUris(uri, defaultModelName), manifestUri = _a.manifestUri, modelBaseUri = _a.modelBaseUri;
+                    return [4 /*yield*/, fetch(manifestUri)];
+                case 1: return [4 /*yield*/, (_b.sent()).json()];
+                case 2:
+                    manifest = _b.sent();
+                    return [2 /*return*/, tf.io.loadWeights(manifest, modelBaseUri)];
+            }
+        });
+    });
+}
+//# sourceMappingURL=loadWeightMap.js.map
\ No newline at end of file

dist/commons/loadWeightMap.js.map

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\ No newline at end of file

dist/faceDetectionNet/FaceDetectionNet.d.ts

+import * as tf from '@tensorflow/tfjs-core';
+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;
+    private forwardTensor(imgTensor);
+    forward(input: tf.Tensor | NetInput | TNetInput): {
+        boxes: tf.Tensor<tf.Rank.R2>[];
+        scores: tf.Tensor<tf.Rank.R1>[];
+    };
+    locateFaces(input: tf.Tensor | NetInput | TNetInput, minConfidence?: number, maxResults?: number): Promise<FaceDetection[]>;
+}

dist/faceDetectionNet/FaceDetectionNet.js

+import * as tslib_1 from "tslib";
+import * as tf from '@tensorflow/tfjs-core';
+import { getImageTensor } from '../getImageTensor';
+import { padToSquare } from '../padToSquare';
+import { Rect } from '../Rect';
+import { extractParams } from './extractParams';
+import { FaceDetection } from './FaceDetection';
+import { loadQuantizedParams } from './loadQuantizedParams';
+import { mobileNetV1 } from './mobileNetV1';
+import { nonMaxSuppression } from './nonMaxSuppression';
+import { outputLayer } from './outputLayer';
+import { predictionLayer } from './predictionLayer';
+import { resizeLayer } from './resizeLayer';
+var FaceDetectionNet = /** @class */ (function () {
+    function FaceDetectionNet() {
+    }
+    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(weightsOrUrl)];
+                    case 1:
+                        _a._params = _b.sent();
+                        return [2 /*return*/];
+                }
+            });
+        });
+    };
+    FaceDetectionNet.prototype.extractWeights = function (weights) {
+        this._params = extractParams(weights);
+    };
+    FaceDetectionNet.prototype.forwardTensor = function (imgTensor) {
+        var _this = this;
+        if (!this._params) {
+            throw new Error('FaceDetectionNet - load model before inference');
+        }
+        return tf.tidy(function () {
+            var resized = resizeLayer(imgTensor);
+            var features = mobileNetV1(resized, _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);
+        });
+    };
+    FaceDetectionNet.prototype.forward = function (input) {
+        var _this = this;
+        return tf.tidy(function () { return _this.forwardTensor(padToSquare(getImageTensor(input))); });
+    };
+    FaceDetectionNet.prototype.locateFaces = function (input, minConfidence, maxResults) {
+        if (minConfidence === void 0) { minConfidence = 0.8; }
+        if (maxResults === void 0) { maxResults = 100; }
+        return tslib_1.__awaiter(this, void 0, void 0, function () {
+            var _this = this;
+            var paddedHeightRelative, paddedWidthRelative, imageDimensions, _a, _boxes, _scores, boxes, scores, i, scoresData, _b, _c, iouThreshold, indices, results;
+            return tslib_1.__generator(this, function (_d) {
+                switch (_d.label) {
+                    case 0:
+                        paddedHeightRelative = 1, paddedWidthRelative = 1;
+                        _a = tf.tidy(function () {
+                            var imgTensor = getImageTensor(input);
+                            var _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];
+                            imageDimensions = { width: width, height: height };
+                            imgTensor = padToSquare(imgTensor);
+                            paddedHeightRelative = imgTensor.shape[1] / height;
+                            paddedWidthRelative = imgTensor.shape[2] / width;
+                            return _this.forwardTensor(imgTensor);
+                        }), _boxes = _a.boxes, _scores = _a.scores;
+                        boxes = _boxes[0];
+                        scores = _scores[0];
+                        for (i = 1; i < _boxes.length; i++) {
+                            _boxes[i].dispose();
+                            _scores[i].dispose();
+                        }
+                        _c = (_b = Array).from;
+                        return [4 /*yield*/, scores.data()];
+                    case 1:
+                        scoresData = _c.apply(_b, [_d.sent()]);
+                        iouThreshold = 0.5;
+                        indices = nonMaxSuppression(boxes, scoresData, maxResults, iouThreshold, minConfidence);
+                        results = indices
+                            .map(function (idx) {
+                            var _a = [
+                                Math.max(0, boxes.get(idx, 0)),
+                                Math.min(1.0, boxes.get(idx, 2))
+                            ].map(function (val) { return val * paddedHeightRelative; }), top = _a[0], bottom = _a[1];
+                            var _b = [
+                                Math.max(0, boxes.get(idx, 1)),
+                                Math.min(1.0, boxes.get(idx, 3))
+                            ].map(function (val) { return val * paddedWidthRelative; }), left = _b[0], right = _b[1];
+                            return new FaceDetection(scoresData[idx], new Rect(left, top, right - left, bottom - top), imageDimensions);
+                        });
+                        boxes.dispose();
+                        scores.dispose();
+                        return [2 /*return*/, results];
+                }
+            });
+        });
+    };
+    return FaceDetectionNet;
+}());
+export { FaceDetectionNet };
+//# sourceMappingURL=FaceDetectionNet.js.map
\ No newline at end of file

dist/faceDetectionNet/FaceDetectionNet.js.map

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dist/faceDetectionNet/boxPredictionLayer.d.ts

 import * as tf from '@tensorflow/tfjs-core';
-import { FaceDetectionNet } from './types';
-export declare function boxPredictionLayer(x: tf.Tensor4D, params: FaceDetectionNet.BoxPredictionParams): {
+import { BoxPredictionParams } from './types';
+export declare function boxPredictionLayer(x: tf.Tensor4D, params: BoxPredictionParams): {
     boxPredictionEncoding: tf.Tensor<tf.Rank>;
     classPrediction: tf.Tensor<tf.Rank>;
 };

dist/faceDetectionNet/boxPredictionLayer.js.map

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\ No newline at end of file
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\ No newline at end of file

dist/faceDetectionNet/extractParams.d.ts

-import { FaceDetectionNet } from './types';
-export declare function extractParams(weights: Float32Array): FaceDetectionNet.NetParams;
+import { NetParams } from './types';
+export declare function extractParams(weights: Float32Array): NetParams;

dist/faceDetectionNet/extractParams.js.map

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\ No newline at end of file
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\ No newline at end of file

dist/faceDetectionNet/index.d.ts

-import * as tf from '@tensorflow/tfjs-core';
-import { NetInput } from '../NetInput';
-import { FaceDetection } from './FaceDetection';
-export declare function faceDetectionNet(weights: Float32Array): {
-    forward: (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | tf.Tensor<tf.Rank> | NetInput) => {
-        boxes: tf.Tensor<tf.Rank.R2>[];
-        scores: tf.Tensor<tf.Rank.R1>[];
-    };
-    locateFaces: (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | tf.Tensor<tf.Rank> | NetInput, minConfidence?: number, maxResults?: number) => Promise<FaceDetection[]>;
-};
+import { FaceDetectionNet } from './FaceDetectionNet';
+export * from './FaceDetectionNet';
+export declare function faceDetectionNet(weights: Float32Array): FaceDetectionNet;

dist/faceDetectionNet/index.js

-import * as tslib_1 from "tslib";
-import * as tf from '@tensorflow/tfjs-core';
-import { getImageTensor } from '../getImageTensor';
-import { padToSquare } from '../padToSquare';
-import { extractParams } from './extractParams';
-import { FaceDetection } from './FaceDetection';
-import { mobileNetV1 } from './mobileNetV1';
-import { nonMaxSuppression } from './nonMaxSuppression';
-import { outputLayer } from './outputLayer';
-import { predictionLayer } from './predictionLayer';
-import { resizeLayer } from './resizeLayer';
-import { Rect } from '../Rect';
+import { FaceDetectionNet } from './FaceDetectionNet';
+export * from './FaceDetectionNet';
 export function faceDetectionNet(weights) {
-    var params = extractParams(weights);
-    function forwardTensor(imgTensor) {
-        return tf.tidy(function () {
-            var resized = resizeLayer(imgTensor);
-            var features = mobileNetV1(resized, params.mobilenetv1_params);
-            var _a = predictionLayer(features.out, features.conv11, params.prediction_layer_params), boxPredictions = _a.boxPredictions, classPredictions = _a.classPredictions;
-            return outputLayer(boxPredictions, classPredictions, params.output_layer_params);
-        });
-    }
-    function forward(input) {
-        return tf.tidy(function () { return forwardTensor(padToSquare(getImageTensor(input))); });
-    }
-    function locateFaces(input, minConfidence, maxResults) {
-        if (minConfidence === void 0) { minConfidence = 0.8; }
-        if (maxResults === void 0) { maxResults = 100; }
-        return tslib_1.__awaiter(this, void 0, void 0, function () {
-            var paddedHeightRelative, paddedWidthRelative, imageDimensions, _a, _boxes, _scores, boxes, scores, i, scoresData, _b, _c, iouThreshold, indices, results;
-            return tslib_1.__generator(this, function (_d) {
-                switch (_d.label) {
-                    case 0:
-                        paddedHeightRelative = 1, paddedWidthRelative = 1;
-                        _a = tf.tidy(function () {
-                            var imgTensor = getImageTensor(input);
-                            var _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];
-                            imageDimensions = { width: width, height: height };
-                            imgTensor = padToSquare(imgTensor);
-                            paddedHeightRelative = imgTensor.shape[1] / height;
-                            paddedWidthRelative = imgTensor.shape[2] / width;
-                            return forwardTensor(imgTensor);
-                        }), _boxes = _a.boxes, _scores = _a.scores;
-                        boxes = _boxes[0];
-                        scores = _scores[0];
-                        for (i = 1; i < _boxes.length; i++) {
-                            _boxes[i].dispose();
-                            _scores[i].dispose();
-                        }
-                        _c = (_b = Array).from;
-                        return [4 /*yield*/, scores.data()];
-                    case 1:
-                        scoresData = _c.apply(_b, [_d.sent()]);
-                        iouThreshold = 0.5;
-                        indices = nonMaxSuppression(boxes, scoresData, maxResults, iouThreshold, minConfidence);
-                        results = indices
-                            .map(function (idx) {
-                            var _a = [
-                                Math.max(0, boxes.get(idx, 0)),
-                                Math.min(1.0, boxes.get(idx, 2))
-                            ].map(function (val) { return val * paddedHeightRelative; }), top = _a[0], bottom = _a[1];
-                            var _b = [
-                                Math.max(0, boxes.get(idx, 1)),
-                                Math.min(1.0, boxes.get(idx, 3))
-                            ].map(function (val) { return val * paddedWidthRelative; }), left = _b[0], right = _b[1];
-                            return new FaceDetection(scoresData[idx], new Rect(left, top, right - left, bottom - top), imageDimensions);
-                        });
-                        boxes.dispose();
-                        scores.dispose();
-                        return [2 /*return*/, results];
-                }
-            });
-        });
-    }
-    return {
-        forward: forward,
-        locateFaces: locateFaces
-    };
+    var net = new FaceDetectionNet();
+    net.extractWeights(weights);
+    return net;
 }
 //# sourceMappingURL=index.js.map
\ No newline at end of file

dist/faceDetectionNet/index.js.map

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\ No newline at end of file

dist/faceDetectionNet/loadQuantizedParams.d.ts

+export declare function loadQuantizedParams(uri: string | undefined): Promise<any>;

dist/faceDetectionNet/loadQuantizedParams.js

+import * as tslib_1 from "tslib";
+import { isTensor1D, isTensor4D, isTensor3D } from '../commons/isTensor';
+import { loadWeightMap } from '../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 (!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 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);
+        }
+        return {
+            depthwise_conv_params: depthwise_conv_params,
+            pointwise_conv_params: extractPointwiseConvParams('MobilenetV1', idx)
+        };
+    }
+    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"]
+            }
+        };
+        if (!isTensor4D(params.box_encoding_predictor_params.filters)) {
+            throw new Error("expected weightMap[Prediction/BoxPredictor_" + idx + "/BoxEncodingPredictor/weights] to be a Tensor4D, instead have " + params.box_encoding_predictor_params.filters);
+        }
+        if (!isTensor1D(params.box_encoding_predictor_params.bias)) {
+            throw new Error("expected weightMap[Prediction/BoxPredictor_" + idx + "/BoxEncodingPredictor/biases] to be a Tensor1D, instead have " + params.box_encoding_predictor_params.bias);
+        }
+        if (!isTensor4D(params.class_predictor_params.filters)) {
+            throw new Error("expected weightMap[Prediction/BoxPredictor_" + idx + "/ClassPredictor/weights] to be a Tensor4D, instead have " + params.class_predictor_params.filters);
+        }
+        if (!isTensor1D(params.class_predictor_params.bias)) {
+            throw new Error("expected weightMap[Prediction/BoxPredictor_" + idx + "/ClassPredictor/biases] to be a Tensor1D, instead have " + params.class_predictor_params.bias);
+        }
+        return params;
+    }
+    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)
+        };
+    }
+    return {
+        extractMobilenetV1Params: extractMobilenetV1Params,
+        extractPredictionLayerParams: extractPredictionLayerParams
+    };
+}
+export function loadQuantizedParams(uri) {
+    return tslib_1.__awaiter(this, void 0, void 0, function () {
+        var weightMap, _a, extractMobilenetV1Params, extractPredictionLayerParams, extra_dim;
+        return tslib_1.__generator(this, function (_b) {
+            switch (_b.label) {
+                case 0: return [4 /*yield*/, loadWeightMap(uri, DEFAULT_MODEL_NAME)];
+                case 1:
+                    weightMap = _b.sent();
+                    _a = extractorsFactory(weightMap), extractMobilenetV1Params = _a.extractMobilenetV1Params, extractPredictionLayerParams = _a.extractPredictionLayerParams;
+                    extra_dim = weightMap['Output/extra_dim'];
+                    if (!isTensor3D(extra_dim)) {
+                        throw new Error("expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have " + extra_dim);
+                    }
+                    return [2 /*return*/, {
+                            mobilenetv1_params: extractMobilenetV1Params(),
+                            prediction_layer_params: extractPredictionLayerParams(),
+                            output_layer_params: {
+                                extra_dim: extra_dim
+                            }
+                        }];
+            }
+        });
+    });
+}
+//# sourceMappingURL=loadQuantizedParams.js.map
\ No newline at end of file

dist/faceDetectionNet/loadQuantizedParams.js.map

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\ No newline at end of file

dist/faceDetectionNet/mobileNetV1.d.ts

 import * as tf from '@tensorflow/tfjs-core';
-import { FaceDetectionNet } from './types';
-export declare function mobileNetV1(x: tf.Tensor4D, params: FaceDetectionNet.MobileNetV1.Params): {
+import { MobileNetV1 } from './types';
+export declare function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params): {
     out: tf.Tensor<tf.Rank.R4>;
     conv11: any;
 };

dist/faceDetectionNet/mobileNetV1.js.map

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\ No newline at end of file
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\ No newline at end of file

dist/faceDetectionNet/outputLayer.d.ts

 import * as tf from '@tensorflow/tfjs-core';
-import { FaceDetectionNet } from './types';
-export declare function outputLayer(boxPredictions: tf.Tensor4D, classPredictions: tf.Tensor4D, params: FaceDetectionNet.OutputLayerParams): {
+import { OutputLayerParams } from './types';
+export declare function outputLayer(boxPredictions: tf.Tensor4D, classPredictions: tf.Tensor4D, params: OutputLayerParams): {
     boxes: tf.Tensor<tf.Rank.R2>[];
     scores: tf.Tensor<tf.Rank.R1>[];
 };

dist/faceDetectionNet/outputLayer.js.map

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\ No newline at end of file
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\ No newline at end of file

dist/faceDetectionNet/pointwiseConvLayer.d.ts

 import * as tf from '@tensorflow/tfjs-core';
-import { FaceDetectionNet } from './types';
-export declare function pointwiseConvLayer(x: tf.Tensor4D, params: FaceDetectionNet.PointwiseConvParams, strides: [number, number]): tf.Tensor<tf.Rank.R4>;
+import { PointwiseConvParams } from './types';
+export declare function pointwiseConvLayer(x: tf.Tensor4D, params: PointwiseConvParams, strides: [number, number]): tf.Tensor<tf.Rank.R4>;

dist/faceDetectionNet/pointwiseConvLayer.js.map

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\ No newline at end of file
+{"version":3,"file":"pointwiseConvLayer.js","sourceRoot":"","sources":["../../src/faceDetectionNet/pointwiseConvLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAI5C,MAAM,6BACJ,CAAc,EACd,MAA2B,EAC3B,OAAyB;IAEzB,OAAO,EAAE,CAAC,IAAI,CAAC;QAEb,IAAI,GAAG,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,EAAE,MAAM,CAAC,OAAO,EAAE,OAAO,EAAE,MAAM,CAAC,CAAA;QACvD,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,MAAM,CAAC,iBAAiB,CAAC,CAAA;QAC3C,OAAO,EAAE,CAAC,WAAW,CAAC,GAAG,EAAE,CAAC,EAAE,CAAC,CAAC,CAAA;IAElC,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file

dist/faceDetectionNet/predictionLayer.d.ts

 import * as tf from '@tensorflow/tfjs-core';
-import { FaceDetectionNet } from './types';
-export declare function predictionLayer(x: tf.Tensor4D, conv11: tf.Tensor4D, params: FaceDetectionNet.PredictionLayerParams): {
+import { PredictionLayerParams } from './types';
+export declare function predictionLayer(x: tf.Tensor4D, conv11: tf.Tensor4D, params: PredictionLayerParams): {
     boxPredictions: tf.Tensor<tf.Rank.R4>;
     classPredictions: tf.Tensor<tf.Rank.R4>;
 };

dist/faceDetectionNet/predictionLayer.js.map

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dist/faceDetectionNet/types.d.ts

 import * as tf from '@tensorflow/tfjs-core';
 import { ConvParams } from '../commons/types';
-export declare namespace FaceDetectionNet {
-    type PointwiseConvParams = {
+export declare type PointwiseConvParams = {
+    filters: tf.Tensor4D;
+    batch_norm_offset: tf.Tensor1D;
+};
+export declare namespace MobileNetV1 {
+    type DepthwiseConvParams = {
         filters: tf.Tensor4D;
+        batch_norm_scale: tf.Tensor1D;
         batch_norm_offset: tf.Tensor1D;
+        batch_norm_mean: tf.Tensor1D;
+        batch_norm_variance: tf.Tensor1D;
     };
-    namespace MobileNetV1 {
-        type DepthwiseConvParams = {
-            filters: tf.Tensor4D;
-            batch_norm_scale: tf.Tensor1D;
-            batch_norm_offset: tf.Tensor1D;
-            batch_norm_mean: tf.Tensor1D;
-            batch_norm_variance: tf.Tensor1D;
-        };
-        type ConvPairParams = {
-            depthwise_conv_params: DepthwiseConvParams;
-            pointwise_conv_params: PointwiseConvParams;
-        };
-        type Params = {
-            conv_0_params: PointwiseConvParams;
-            conv_pair_params: ConvPairParams[];
-        };
-    }
-    type BoxPredictionParams = {
-        box_encoding_predictor_params: ConvParams;
-        class_predictor_params: ConvParams;
+    type ConvPairParams = {
+        depthwise_conv_params: DepthwiseConvParams;
+        pointwise_conv_params: PointwiseConvParams;
     };
-    type PredictionLayerParams = {
+    type Params = {
         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;
-    };
-    type OutputLayerParams = {
-        extra_dim: tf.Tensor3D;
-    };
-    type NetParams = {
-        mobilenetv1_params: MobileNetV1.Params;
-        prediction_layer_params: PredictionLayerParams;
-        output_layer_params: OutputLayerParams;
+        conv_pair_params: ConvPairParams[];
     };
 }
+export declare type BoxPredictionParams = {
+    box_encoding_predictor_params: ConvParams;
+    class_predictor_params: 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;
+};
+export declare type OutputLayerParams = {
+    extra_dim: tf.Tensor3D;
+};
+export declare type NetParams = {
+    mobilenetv1_params: MobileNetV1.Params;
+    prediction_layer_params: PredictionLayerParams;
+    output_layer_params: OutputLayerParams;
+};

dist/faceLandmarkNet/FaceLandmarkNet.d.ts

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

dist/faceLandmarkNet/FaceLandmarkNet.js

+import * as tslib_1 from "tslib";
+import * as tf from '@tensorflow/tfjs-core';
+import { convLayer } from '../commons/convLayer';
+import { getImageTensor } from '../getImageTensor';
+import { Point } from '../Point';
+import { extractParams } from './extractParams';
+import { FaceLandmarks } from './FaceLandmarks';
+import { fullyConnectedLayer } from './fullyConnectedLayer';
+import { loadQuantizedParams } from './loadQuantizedParams';
+function conv(x, params) {
+    return convLayer(x, params, 'valid', true);
+}
+function maxPool(x, strides) {
+    if (strides === void 0) { strides = [2, 2]; }
+    return tf.maxPool(x, [2, 2], strides, 'valid');
+}
+var FaceLandmarkNet = /** @class */ (function () {
+    function FaceLandmarkNet() {
+    }
+    FaceLandmarkNet.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(weightsOrUrl)];
+                    case 1:
+                        _a._params = _b.sent();
+                        return [2 /*return*/];
+                }
+            });
+        });
+    };
+    FaceLandmarkNet.prototype.extractWeights = function (weights) {
+        this._params = extractParams(weights);
+    };
+    FaceLandmarkNet.prototype.detectLandmarks = function (input) {
+        return tslib_1.__awaiter(this, void 0, void 0, function () {
+            var _this = this;
+            var imageDimensions, outTensor, faceLandmarksArray, _a, _b, xCoords, yCoords;
+            return tslib_1.__generator(this, function (_c) {
+                switch (_c.label) {
+                    case 0:
+                        if (!this._params) {
+                            throw new Error('FaceLandmarkNet - load model before inference');
+                        }
+                        outTensor = tf.tidy(function () {
+                            var params = _this._params;
+                            var imgTensor = getImageTensor(input);
+                            var _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];
+                            imageDimensions = { width: width, height: height };
+                            // work with 128 x 128 sized face images
+                            if (imgTensor.shape[1] !== 128 || imgTensor.shape[2] !== 128) {
+                                imgTensor = tf.image.resizeBilinear(imgTensor, [128, 128]);
+                            }
+                            var out = conv(imgTensor, params.conv0_params);
+                            out = maxPool(out);
+                            out = conv(out, params.conv1_params);
+                            out = conv(out, params.conv2_params);
+                            out = maxPool(out);
+                            out = conv(out, params.conv3_params);
+                            out = conv(out, params.conv4_params);
+                            out = maxPool(out);
+                            out = conv(out, params.conv5_params);
+                            out = conv(out, params.conv6_params);
+                            out = maxPool(out, [1, 1]);
+                            out = conv(out, params.conv7_params);
+                            var fc0 = tf.relu(fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc0_params));
+                            var fc1 = fullyConnectedLayer(fc0, params.fc1_params);
+                            return fc1;
+                        });
+                        _b = (_a = Array).from;
+                        return [4 /*yield*/, outTensor.data()];
+                    case 1:
+                        faceLandmarksArray = _b.apply(_a, [_c.sent()]);
+                        outTensor.dispose();
+                        xCoords = faceLandmarksArray.filter(function (c, i) { return (i - 1) % 2; });
+                        yCoords = faceLandmarksArray.filter(function (c, i) { return i % 2; });
+                        return [2 /*return*/, new FaceLandmarks(Array(68).fill(0).map(function (_, i) { return new Point(xCoords[i], yCoords[i]); }), imageDimensions)];
+                }
+            });
+        });
+    };
+    return FaceLandmarkNet;
+}());
+export { FaceLandmarkNet };
+//# sourceMappingURL=FaceLandmarkNet.js.map
\ No newline at end of file

dist/faceLandmarkNet/FaceLandmarkNet.js.map

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\ No newline at end of file

dist/faceLandmarkNet/extractParams.d.ts

-import { FaceLandmarkNet } from './types';
-export declare function extractParams(weights: Float32Array): FaceLandmarkNet.NetParams;
+import { NetParams } from './types';
+export declare function extractParams(weights: Float32Array): NetParams;

dist/faceLandmarkNet/fullyConnectedLayer.d.ts

 import * as tf from '@tensorflow/tfjs-core';
-import { FaceLandmarkNet } from './types';
-export declare function fullyConnectedLayer(x: tf.Tensor2D, params: FaceLandmarkNet.FCParams): tf.Tensor2D;
+import { FCParams } from './types';
+export declare function fullyConnectedLayer(x: tf.Tensor2D, params: FCParams): tf.Tensor2D;

dist/faceLandmarkNet/fullyConnectedLayer.js.map

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dist/faceLandmarkNet/index.d.ts

-import * as tf from '@tensorflow/tfjs-core';
-import { NetInput } from '../NetInput';
-import { FaceLandmarks } from './FaceLandmarks';
-export declare function faceLandmarkNet(weights: Float32Array): {
-    detectLandmarks: (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | tf.Tensor<tf.Rank> | NetInput) => Promise<FaceLandmarks>;
-};
+import { FaceLandmarkNet } from './FaceLandmarkNet';
+export * from './FaceLandmarkNet';
+export declare function faceLandmarkNet(weights: Float32Array): FaceLandmarkNet;

dist/faceLandmarkNet/index.js

-import * as tslib_1 from "tslib";
-import * as tf from '@tensorflow/tfjs-core';
-import { convLayer } from '../commons/convLayer';
-import { getImageTensor } from '../getImageTensor';
-import { Point } from '../Point';
-import { extractParams } from './extractParams';
-import { FaceLandmarks } from './FaceLandmarks';
-import { fullyConnectedLayer } from './fullyConnectedLayer';
-function conv(x, params) {
-    return convLayer(x, params, 'valid', true);
-}
-function maxPool(x, strides) {
-    if (strides === void 0) { strides = [2, 2]; }
-    return tf.maxPool(x, [2, 2], strides, 'valid');
-}
+import { FaceLandmarkNet } from './FaceLandmarkNet';
+export * from './FaceLandmarkNet';
 export function faceLandmarkNet(weights) {
-    var params = extractParams(weights);
-    function detectLandmarks(input) {
-        return tslib_1.__awaiter(this, void 0, void 0, function () {
-            var imageDimensions, outTensor, faceLandmarksArray, _a, _b, xCoords, yCoords;
-            return tslib_1.__generator(this, function (_c) {
-                switch (_c.label) {
-                    case 0:
-                        outTensor = tf.tidy(function () {
-                            var imgTensor = getImageTensor(input);
-                            var _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];
-                            imageDimensions = { width: width, height: height };
-                            // work with 128 x 128 sized face images
-                            if (imgTensor.shape[1] !== 128 || imgTensor.shape[2] !== 128) {
-                                imgTensor = tf.image.resizeBilinear(imgTensor, [128, 128]);
-                            }
-                            var out = conv(imgTensor, params.conv0_params);
-                            out = maxPool(out);
-                            out = conv(out, params.conv1_params);
-                            out = conv(out, params.conv2_params);
-                            out = maxPool(out);
-                            out = conv(out, params.conv3_params);
-                            out = conv(out, params.conv4_params);
-                            out = maxPool(out);
-                            out = conv(out, params.conv5_params);
-                            out = conv(out, params.conv6_params);
-                            out = maxPool(out, [1, 1]);
-                            out = conv(out, params.conv7_params);
-                            var fc0 = tf.relu(fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc0_params));
-                            var fc1 = fullyConnectedLayer(fc0, params.fc1_params);
-                            return fc1;
-                        });
-                        _b = (_a = Array).from;
-                        return [4 /*yield*/, outTensor.data()];
-                    case 1:
-                        faceLandmarksArray = _b.apply(_a, [_c.sent()]);
-                        outTensor.dispose();
-                        xCoords = faceLandmarksArray.filter(function (c, i) { return (i - 1) % 2; });
-                        yCoords = faceLandmarksArray.filter(function (c, i) { return i % 2; });
-                        return [2 /*return*/, new FaceLandmarks(Array(68).fill(0).map(function (_, i) { return new Point(xCoords[i], yCoords[i]); }), imageDimensions)];
-                }
-            });
-        });
-    }
-    return {
-        detectLandmarks: detectLandmarks
-    };
+    var net = new FaceLandmarkNet();
+    net.extractWeights(weights);
+    return net;
 }
 //# sourceMappingURL=index.js.map
\ No newline at end of file

dist/faceLandmarkNet/index.js.map

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\ No newline at end of file

dist/faceLandmarkNet/loadQuantizedParams.d.ts

+import { NetParams } from './types';
+export declare function loadQuantizedParams(uri: string | undefined): Promise<NetParams>;

dist/faceLandmarkNet/loadQuantizedParams.js

+import * as tslib_1 from "tslib";
+import { loadWeightMap } from '../commons/loadWeightMap';
+import { isTensor4D, isTensor1D, isTensor2D } from '../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"]
+        };
+        if (!isTensor4D(params.filters)) {
+            throw new Error("expected weightMap[" + prefix + "/kernel] to be a Tensor4D, instead have " + params.filters);
+        }
+        if (!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"]
+        };
+        if (!isTensor2D(params.weights)) {
+            throw new Error("expected weightMap[" + prefix + "/kernel] to be a Tensor2D, instead have " + params.weights);
+        }
+        if (!isTensor1D(params.bias)) {
+            throw new Error("expected weightMap[" + prefix + "/bias] to be a Tensor1D, instead have " + params.bias);
+        }
+        return params;
+    }
+    return {
+        extractConvParams: extractConvParams,
+        extractFcParams: extractFcParams
+    };
+}
+export function loadQuantizedParams(uri) {
+    return tslib_1.__awaiter(this, void 0, void 0, function () {
+        var weightMap, _a, extractConvParams, extractFcParams;
+        return tslib_1.__generator(this, function (_b) {
+            switch (_b.label) {
+                case 0: return [4 /*yield*/, 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')
+                        }];
+            }
+        });
+    });
+}
+//# sourceMappingURL=loadQuantizedParams.js.map
\ No newline at end of file

dist/faceLandmarkNet/loadQuantizedParams.js.map

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\ No newline at end of file

dist/faceLandmarkNet/types.d.ts

 import * as tf from '@tensorflow/tfjs-core';
 import { ConvParams } from '../commons/types';
-export declare namespace FaceLandmarkNet {
-    type FCParams = {
-        weights: tf.Tensor2D;
-        bias: tf.Tensor1D;
-    };
-    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;
-    };
-}
+export declare type FCParams = {
+    weights: tf.Tensor2D;
+    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;
+};

dist/faceRecognitionNet/FaceRecognitionNet.d.ts

+import * as tf from '@tensorflow/tfjs-core';
+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;
+    forward(input: tf.Tensor | NetInput | TNetInput): tf.Tensor<tf.Rank.R2>;
+    computeFaceDescriptor(input: tf.Tensor | NetInput | TNetInput): Promise<Int32Array | Uint8Array | Float32Array>;
+    computeFaceDescriptorSync(input: tf.Tensor | NetInput | TNetInput): Promise<Int32Array | Uint8Array | Float32Array>;
+}

dist/faceRecognitionNet/FaceRecognitionNet.js

+import * as tslib_1 from "tslib";
+import * as tf from '@tensorflow/tfjs-core';
+import { getImageTensor } from '../getImageTensor';
+import { padToSquare } from '../padToSquare';
+import { convDown } from './convLayer';
+import { extractParams } from './extractParams';
+import { loadQuantizedParams } from './loadQuantizedParams';
+import { normalize } from './normalize';
+import { residual, residualDown } from './residualLayer';
+var FaceRecognitionNet = /** @class */ (function () {
+    function FaceRecognitionNet() {
+    }
+    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(weightsOrUrl)];
+                    case 1:
+                        _a._params = _b.sent();
+                        return [2 /*return*/];
+                }
+            });
+        });
+    };
+    FaceRecognitionNet.prototype.extractWeights = function (weights) {
+        this._params = extractParams(weights);
+    };
+    FaceRecognitionNet.prototype.forward = function (input) {
+        var _this = this;
+        if (!this._params) {
+            throw new Error('FaceRecognitionNet - load model before inference');
+        }
+        return tf.tidy(function () {
+            var x = padToSquare(getImageTensor(input), true);
+            // work with 150 x 150 sized face images
+            if (x.shape[1] !== 150 || x.shape[2] !== 150) {
+                x = tf.image.resizeBilinear(x, [150, 150]);
+            }
+            x = normalize(x);
+            var out = convDown(x, _this._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 = 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);
+            var globalAvg = out.mean([1, 2]);
+            var fullyConnected = tf.matMul(globalAvg, _this._params.fc);
+            return fullyConnected;
+        });
+    };
+    FaceRecognitionNet.prototype.computeFaceDescriptor = function (input) {
+        return tslib_1.__awaiter(this, void 0, void 0, function () {
+            var result, data;
+            return tslib_1.__generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        result = this.forward(input);
+                        return [4 /*yield*/, result.data()];
+                    case 1:
+                        data = _a.sent();
+                        result.dispose();
+                        return [2 /*return*/, data];
+                }
+            });
+        });
+    };
+    FaceRecognitionNet.prototype.computeFaceDescriptorSync = function (input) {
+        return tslib_1.__awaiter(this, void 0, void 0, function () {
+            var result, data;
+            return tslib_1.__generator(this, function (_a) {
+                result = this.forward(input);
+                data = result.dataSync();
+                result.dispose();
+                return [2 /*return*/, data];
+            });
+        });
+    };
+    return FaceRecognitionNet;
+}());
+export { FaceRecognitionNet };
+//# sourceMappingURL=FaceRecognitionNet.js.map
\ No newline at end of file

dist/faceRecognitionNet/FaceRecognitionNet.js.map

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\ No newline at end of file

dist/faceRecognitionNet/convLayer.d.ts

 import * as tf from '@tensorflow/tfjs-core';
-import { FaceRecognitionNet } from './types';
-export declare function conv(x: tf.Tensor4D, params: FaceRecognitionNet.ConvLayerParams): tf.Tensor<tf.Rank.R4>;
-export declare function convNoRelu(x: tf.Tensor4D, params: FaceRecognitionNet.ConvLayerParams): tf.Tensor<tf.Rank.R4>;
-export declare function convDown(x: tf.Tensor4D, params: FaceRecognitionNet.ConvLayerParams): tf.Tensor<tf.Rank.R4>;
+import { ConvLayerParams } from './types';
+export declare function conv(x: tf.Tensor4D, params: ConvLayerParams): tf.Tensor<tf.Rank.R4>;
+export declare function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams): tf.Tensor<tf.Rank.R4>;
+export declare function convDown(x: tf.Tensor4D, params: ConvLayerParams): tf.Tensor<tf.Rank.R4>;

dist/faceRecognitionNet/convLayer.js.map

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\ No newline at end of file
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\ No newline at end of file

dist/faceRecognitionNet/extractParams.d.ts

-import { FaceRecognitionNet } from './types';
-export declare function extractParams(weights: Float32Array): FaceRecognitionNet.NetParams;
+import { NetParams } from './types';
+export declare function extractParams(weights: Float32Array): NetParams;

dist/faceRecognitionNet/extractParams.js.map

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dist/faceRecognitionNet/index.d.ts

-import * as tf from '@tensorflow/tfjs-core';
-import { NetInput } from '../NetInput';
-export declare function faceRecognitionNet(weights: Float32Array): {
-    computeFaceDescriptor: (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | tf.Tensor<tf.Rank> | NetInput) => Promise<Int32Array | Uint8Array | Float32Array>;
-    computeFaceDescriptorSync: (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | tf.Tensor<tf.Rank> | NetInput) => Int32Array | Uint8Array | Float32Array;
-    forward: (input: string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement | (string | HTMLCanvasElement | HTMLImageElement | HTMLVideoElement)[] | tf.Tensor<tf.Rank> | NetInput) => tf.Tensor<tf.Rank.R2>;
-};
+import { FaceRecognitionNet } from './FaceRecognitionNet';
+export * from './FaceRecognitionNet';
+export declare function faceRecognitionNet(weights: Float32Array): FaceRecognitionNet;

dist/faceRecognitionNet/index.js

-import * as tslib_1 from "tslib";
-import * as tf from '@tensorflow/tfjs-core';
-import { getImageTensor } from '../getImageTensor';
-import { padToSquare } from '../padToSquare';
-import { convDown } from './convLayer';
-import { extractParams } from './extractParams';
-import { normalize } from './normalize';
-import { residual, residualDown } from './residualLayer';
+import { FaceRecognitionNet } from './FaceRecognitionNet';
+export * from './FaceRecognitionNet';
 export function faceRecognitionNet(weights) {
-    var _this = this;
-    var params = extractParams(weights);
-    function forward(input) {
-        return tf.tidy(function () {
-            var x = padToSquare(getImageTensor(input), true);
-            // work with 150 x 150 sized face images
-            if (x.shape[1] !== 150 || x.shape[2] !== 150) {
-                x = tf.image.resizeBilinear(x, [150, 150]);
-            }
-            x = normalize(x);
-            var out = convDown(x, params.conv32_down);
-            out = tf.maxPool(out, 3, 2, 'valid');
-            out = residual(out, params.conv32_1);
-            out = residual(out, params.conv32_2);
-            out = residual(out, params.conv32_3);
-            out = residualDown(out, params.conv64_down);
-            out = residual(out, params.conv64_1);
-            out = residual(out, params.conv64_2);
-            out = residual(out, params.conv64_3);
-            out = residualDown(out, params.conv128_down);
-            out = residual(out, params.conv128_1);
-            out = residual(out, params.conv128_2);
-            out = residualDown(out, params.conv256_down);
-            out = residual(out, params.conv256_1);
-            out = residual(out, params.conv256_2);
-            out = residualDown(out, params.conv256_down_out);
-            var globalAvg = out.mean([1, 2]);
-            var fullyConnected = tf.matMul(globalAvg, params.fc);
-            return fullyConnected;
-        });
-    }
-    var computeFaceDescriptor = function (input) { return tslib_1.__awaiter(_this, void 0, void 0, function () {
-        var result, data;
-        return tslib_1.__generator(this, function (_a) {
-            switch (_a.label) {
-                case 0:
-                    result = forward(input);
-                    return [4 /*yield*/, result.data()];
-                case 1:
-                    data = _a.sent();
-                    result.dispose();
-                    return [2 /*return*/, data];
-            }
-        });
-    }); };
-    var computeFaceDescriptorSync = function (input) {
-        var result = forward(input);
-        var data = result.dataSync();
-        result.dispose();
-        return data;
-    };
-    return {
-        computeFaceDescriptor: computeFaceDescriptor,
-        computeFaceDescriptorSync: computeFaceDescriptorSync,
-        forward: forward
-    };
+    var net = new FaceRecognitionNet();
+    net.extractWeights(weights);
+    return net;
 }
 //# sourceMappingURL=index.js.map
\ No newline at end of file

dist/faceRecognitionNet/index.js.map

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\ No newline at end of file

dist/faceRecognitionNet/loadQuantizedParams.d.ts

+export declare function loadQuantizedParams(uri: string | undefined): Promise<any>;

dist/faceRecognitionNet/loadQuantizedParams.js

+import * as tslib_1 from "tslib";
+import { isTensor1D, isTensor2D, isTensor4D } from '../commons/isTensor';
+import { loadWeightMap } from '../commons/loadWeightMap';
+var DEFAULT_MODEL_NAME = 'face_recognition_model';
+function extractorsFactory(weightMap) {
+    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;
+    }
+    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)
+        };
+    }
+    function extractResidualLayerParams(prefix) {
+        return {
+            conv1: extractConvLayerParams(prefix + "/conv1"),
+            conv2: extractConvLayerParams(prefix + "/conv2")
+        };
+    }
+    return {
+        extractConvLayerParams: extractConvLayerParams,
+        extractResidualLayerParams: extractResidualLayerParams
+    };
+}
+export 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;
+        return tslib_1.__generator(this, function (_b) {
+            switch (_b.label) {
+                case 0: return [4 /*yield*/, loadWeightMap(uri, DEFAULT_MODEL_NAME)];
+                case 1:
+                    weightMap = _b.sent();
+                    _a = extractorsFactory(weightMap), extractConvLayerParams = _a.extractConvLayerParams, extractResidualLayerParams = _a.extractResidualLayerParams;
+                    conv32_down = extractConvLayerParams('conv32_down');
+                    conv32_1 = extractResidualLayerParams('conv32_1');
+                    conv32_2 = extractResidualLayerParams('conv32_2');
+                    conv32_3 = extractResidualLayerParams('conv32_3');
+                    conv64_down = extractResidualLayerParams('conv64_down');
+                    conv64_1 = extractResidualLayerParams('conv64_1');
+                    conv64_2 = extractResidualLayerParams('conv64_2');
+                    conv64_3 = extractResidualLayerParams('conv64_3');
+                    conv128_down = extractResidualLayerParams('conv128_down');
+                    conv128_1 = extractResidualLayerParams('conv128_1');
+                    conv128_2 = extractResidualLayerParams('conv128_2');
+                    conv256_down = extractResidualLayerParams('conv256_down');
+                    conv256_1 = extractResidualLayerParams('conv256_1');
+                    conv256_2 = extractResidualLayerParams('conv256_2');
+                    conv256_down_out = extractResidualLayerParams('conv256_down_out');
+                    fc = weightMap['fc'];
+                    if (!isTensor2D(fc)) {
+                        throw new Error("expected weightMap[fc] to be a Tensor2D, instead have " + fc);
+                    }
+                    return [2 /*return*/, {
+                            conv32_down: conv32_down,
+                            conv32_1: conv32_1,
+                            conv32_2: conv32_2,
+                            conv32_3: conv32_3,
+                            conv64_down: conv64_down,
+                            conv64_1: conv64_1,
+                            conv64_2: conv64_2,
+                            conv64_3: conv64_3,
+                            conv128_down: conv128_down,
+                            conv128_1: conv128_1,
+                            conv128_2: conv128_2,
+                            conv256_down: conv256_down,
+                            conv256_1: conv256_1,
+                            conv256_2: conv256_2,
+                            conv256_down_out: conv256_down_out,
+                            fc: fc
+                        }];
+            }
+        });
+    });
+}
+//# sourceMappingURL=loadQuantizedParams.js.map
\ No newline at end of file

dist/faceRecognitionNet/loadQuantizedParams.js.map

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\ No newline at end of file

dist/faceRecognitionNet/residualLayer.d.ts

 import * as tf from '@tensorflow/tfjs-core';
-import { FaceRecognitionNet } from './types';
-export declare function residual(x: tf.Tensor4D, params: FaceRecognitionNet.ResidualLayerParams): tf.Tensor4D;
-export declare function residualDown(x: tf.Tensor4D, params: FaceRecognitionNet.ResidualLayerParams): tf.Tensor4D;
+import { ResidualLayerParams } from './types';
+export declare function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D;
+export declare function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D;

dist/faceRecognitionNet/residualLayer.js.map

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\ No newline at end of file

dist/faceRecognitionNet/scaleLayer.d.ts

 import * as tf from '@tensorflow/tfjs-core';
-import { FaceRecognitionNet } from './types';
-export declare function scale(x: tf.Tensor4D, params: FaceRecognitionNet.ScaleLayerParams): tf.Tensor4D;
+import { ScaleLayerParams } from './types';
+export declare function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D;

dist/faceRecognitionNet/scaleLayer.js.map

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\ No newline at end of file
+{"version":3,"file":"scaleLayer.js","sourceRoot":"","sources":["../../src/faceRecognitionNet/scaleLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAI5C,MAAM,gBAAgB,CAAc,EAAE,MAAwB;IAC5D,OAAO,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,MAAM,CAAC,OAAO,CAAC,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;AACzD,CAAC"}
\ No newline at end of file

dist/faceRecognitionNet/types.d.ts

 import * as tf from '@tensorflow/tfjs-core';
 import { ConvParams } from '../commons/types';
-export declare namespace FaceRecognitionNet {
-    type ScaleLayerParams = {
-        weights: tf.Tensor1D;
-        biases: tf.Tensor1D;
-    };
-    type ResidualLayerParams = {
-        conv1: ConvLayerParams;
-        conv2: ConvLayerParams;
-    };
-    type ConvLayerParams = {
-        conv: ConvParams;
-        scale: ScaleLayerParams;
-    };
-    type NetParams = {
-        conv32_down: ConvLayerParams;
-        conv32_1: ResidualLayerParams;
-        conv32_2: ResidualLayerParams;
-        conv32_3: ResidualLayerParams;
-        conv64_down: ResidualLayerParams;
-        conv64_1: ResidualLayerParams;
-        conv64_2: ResidualLayerParams;
-        conv64_3: ResidualLayerParams;
-        conv128_down: ResidualLayerParams;
-        conv128_1: ResidualLayerParams;
-        conv128_2: ResidualLayerParams;
-        conv256_down: ResidualLayerParams;
-        conv256_1: ResidualLayerParams;
-        conv256_2: ResidualLayerParams;
-        conv256_down_out: ResidualLayerParams;
-        fc: tf.Tensor2D;
-    };
-}
+export declare type ScaleLayerParams = {
+    weights: tf.Tensor1D;
+    biases: tf.Tensor1D;
+};
+export declare type ResidualLayerParams = {
+    conv1: ConvLayerParams;
+    conv2: ConvLayerParams;
+};
+export declare type ConvLayerParams = {
+    conv: ConvParams;
+    scale: ScaleLayerParams;
+};
+export declare type NetParams = {
+    conv32_down: ConvLayerParams;
+    conv32_1: ResidualLayerParams;
+    conv32_2: ResidualLayerParams;
+    conv32_3: ResidualLayerParams;
+    conv64_down: ResidualLayerParams;
+    conv64_1: ResidualLayerParams;
+    conv64_2: ResidualLayerParams;
+    conv64_3: ResidualLayerParams;
+    conv128_down: ResidualLayerParams;
+    conv128_1: ResidualLayerParams;
+    conv128_2: ResidualLayerParams;
+    conv256_down: ResidualLayerParams;
+    conv256_1: ResidualLayerParams;
+    conv256_2: ResidualLayerParams;
+    conv256_down_out: ResidualLayerParams;
+    fc: tf.Tensor2D;
+};

dist/index.d.ts

 import * as tf from '@tensorflow/tfjs-core';
 import { euclideanDistance } from './euclideanDistance';
-import { faceDetectionNet } from './faceDetectionNet';
-import { faceLandmarkNet } from './faceLandmarkNet';
-import { faceRecognitionNet } from './faceRecognitionNet';
 import { NetInput } from './NetInput';
 import { padToSquare } from './padToSquare';
-export { euclideanDistance, faceDetectionNet, faceLandmarkNet, faceRecognitionNet, NetInput, tf, padToSquare };
+export { euclideanDistance, NetInput, tf, padToSquare };
 export * from './extractFaces';
 export * from './extractFaceTensors';
+export * from './faceDetectionNet';
+export * from './faceLandmarkNet';
+export * from './faceRecognitionNet';
 export * from './utils';

dist/index.js

 import * as tf from '@tensorflow/tfjs-core';
 import { euclideanDistance } from './euclideanDistance';
-import { faceDetectionNet } from './faceDetectionNet';
-import { faceLandmarkNet } from './faceLandmarkNet';
-import { faceRecognitionNet } from './faceRecognitionNet';
 import { NetInput } from './NetInput';
 import { padToSquare } from './padToSquare';
-export { euclideanDistance, faceDetectionNet, faceLandmarkNet, faceRecognitionNet, NetInput, tf, padToSquare };
+export { euclideanDistance, NetInput, tf, padToSquare };
 export * from './extractFaces';
 export * from './extractFaceTensors';
+export * from './faceDetectionNet';
+export * from './faceLandmarkNet';
+export * from './faceRecognitionNet';
 export * from './utils';
 //# sourceMappingURL=index.js.map
\ No newline at end of file

dist/index.js.map

-{"version":3,"file":"index.js","sourceRoot":"","sources":["../src/index.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,iBAAiB,EAAE,MAAM,qBAAqB,CAAC;AACxD,OAAO,EAAE,gBAAgB,EAAE,MAAM,oBAAoB,CAAC;AACtD,OAAO,EAAE,eAAe,EAAE,MAAM,mBAAmB,CAAC;AACpD,OAAO,EAAE,kBAAkB,EAAE,MAAM,sBAAsB,CAAC;AAC1D,OAAO,EAAE,QAAQ,EAAE,MAAM,YAAY,CAAC;AACtC,OAAO,EAAE,WAAW,EAAE,MAAM,eAAe,CAAC;AAE5C,OAAO,EACL,iBAAiB,EACjB,gBAAgB,EAChB,eAAe,EACf,kBAAkB,EAClB,QAAQ,EACR,EAAE,EACF,WAAW,EACZ,CAAA;AAED,cAAc,gBAAgB,CAAA;AAC9B,cAAc,sBAAsB,CAAA;AACpC,cAAc,SAAS,CAAA"}
\ No newline at end of file
+{"version":3,"file":"index.js","sourceRoot":"","sources":["../src/index.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,uBAAuB,CAAC;AAE5C,OAAO,EAAE,iBAAiB,EAAE,MAAM,qBAAqB,CAAC;AACxD,OAAO,EAAE,QAAQ,EAAE,MAAM,YAAY,CAAC;AACtC,OAAO,EAAE,WAAW,EAAE,MAAM,eAAe,CAAC;AAE5C,OAAO,EACL,iBAAiB,EACjB,QAAQ,EACR,EAAE,EACF,WAAW,EACZ,CAAA;AAED,cAAc,gBAAgB,CAAA;AAC9B,cAAc,sBAAsB,CAAA;AACpC,cAAc,oBAAoB,CAAC;AACnC,cAAc,mBAAmB,CAAC;AAClC,cAAc,sBAAsB,CAAC;AACrC,cAAc,SAAS,CAAA"}
\ No newline at end of file

examples/package-lock.json

@@ -16,15 +16,6 @@
       "resolved": "https://registry.npmjs.org/array-flatten/-/array-flatten-1.1.1.tgz",
       "integrity": "sha1-ml9pkFGx5wczKPKgCJaLZOopVdI="
     },
-    "axios": {
-      "version": "0.18.0",
-      "resolved": "https://registry.npmjs.org/axios/-/axios-0.18.0.tgz",
-      "integrity": "sha1-MtU+SFHv3AoRmTts0AB4nXDAUQI=",
-      "requires": {
-        "follow-redirects": "1.4.1",
-        "is-buffer": "1.1.6"
-      }
-    },
     "body-parser": {
       "version": "1.18.2",
       "resolved": "https://registry.npmjs.org/body-parser/-/body-parser-1.18.2.tgz",
@@ -156,24 +147,6 @@
         "unpipe": "1.0.0"
       }
     },
-    "follow-redirects": {
-      "version": "1.4.1",
-      "resolved": "https://registry.npmjs.org/follow-redirects/-/follow-redirects-1.4.1.tgz",
-      "integrity": "sha512-uxYePVPogtya1ktGnAAXOacnbIuRMB4dkvqeNz2qTtTQsuzSfbDolV+wMMKxAmCx0bLgAKLbBOkjItMbbkR1vg==",
-      "requires": {
-        "debug": "3.1.0"
-      },
-      "dependencies": {
-        "debug": {
-          "version": "3.1.0",
-          "resolved": "https://registry.npmjs.org/debug/-/debug-3.1.0.tgz",
-          "integrity": "sha512-OX8XqP7/1a9cqkxYw2yXss15f26NKWBpDXQd0/uK/KPqdQhxbPa994hnzjcE2VqQpDslf55723cKPUOGSmMY3g==",
-          "requires": {
-            "ms": "2.0.0"
-          }
-        }
-      }
-    },
     "forwarded": {
       "version": "0.1.2",
       "resolved": "https://registry.npmjs.org/forwarded/-/forwarded-0.1.2.tgz",
@@ -210,11 +183,6 @@
       "resolved": "https://registry.npmjs.org/ipaddr.js/-/ipaddr.js-1.6.0.tgz",
       "integrity": "sha1-4/o1e3c9phnybpXwSdBVxyeW+Gs="
     },
-    "is-buffer": {
-      "version": "1.1.6",
-      "resolved": "https://registry.npmjs.org/is-buffer/-/is-buffer-1.1.6.tgz",
-      "integrity": "sha512-NcdALwpXkTm5Zvvbk7owOUSvVvBKDgKP5/ewfXEznmQFfs4ZRmanOeKBTjRVjka3QFoN6XJ+9F3USqfHqTaU5w=="
-    },
     "media-typer": {
       "version": "0.3.0",
       "resolved": "https://registry.npmjs.org/media-typer/-/media-typer-0.3.0.tgz",

examples/package.json

@@ -5,7 +5,6 @@
   "author": "justadudewhohacks",
   "license": "MIT",
   "dependencies": {
-    "axios": "^0.18.0",
     "express": "^4.16.3"
   }
 }

examples/public/commons.js

@@ -9,25 +9,7 @@ function getFaceImageUri(className, idx) {
 }
 
 async function fetchImage(uri) {
-  return (await axios.get(uri, { responseType: 'blob' })).data
-}
-
-async function initFaceDetectionNet() {
-  const res = await axios.get('face_detection_model.weights', { responseType: 'arraybuffer' })
-  const weights = new Float32Array(res.data)
-  return faceapi.faceDetectionNet(weights)
-}
-
-async function initFaceRecognitionNet() {
-  const res = await axios.get('face_recognition_model.weights', { responseType: 'arraybuffer' })
-  const weights = new Float32Array(res.data)
-  return faceapi.faceRecognitionNet(weights)
-}
-
-async function initFaceLandmarkNet() {
-  const res = await axios.get('face_landmark_68_model.weights', { responseType: 'arraybuffer' })
-  const weights = new Float32Array(res.data)
-  return faceapi.faceLandmarkNet(weights)
+  return (await fetch(uri)).blob()
 }
 
 // fetch first image of each class and compute their descriptors
@@ -111,17 +93,42 @@ function renderNavBar(navbarId, exampleUri) {
   ]
 
   const navbar = $(navbarId).get(0)
-  navbar.classList.add('row')
+  const pageContainer = $('.page-container').get(0)
+
+  const header = document.createElement('h3')
+  header.innerHTML = examples.find(ex => ex.uri === exampleUri).name
+  pageContainer.insertBefore(header, pageContainer.children[0])
+
+  const menuContent = document.createElement('ul')
+  menuContent.id = 'slide-out'
+  menuContent.classList.add('side-nav', 'fixed')
+  navbar.appendChild(menuContent)
+
+  const menuButton = document.createElement('a')
+  menuButton.href='#'
+  menuButton.classList.add('button-collapse', 'show-on-large')
+  menuButton.setAttribute('data-activates', 'slide-out')
+  const menuButtonIcon = document.createElement('img')
+  menuButtonIcon.src = 'menu_icon.png'
+  menuButton.appendChild(menuButtonIcon)
+  navbar.appendChild(menuButton)
+
   examples
     .filter(ex => ex.uri !== exampleUri)
     .forEach(ex => {
+      const li = document.createElement('li')
       const a = document.createElement('a')
-      navbar.appendChild(a)
+      li.appendChild(a)
+      menuContent.appendChild(li)
 
-      a.classList.add('waves-effect', 'waves-light', 'btn', 'margin-sm')
+      a.classList.add('waves-effect', 'waves-light')
       a.href = ex.uri
       a.innerHTML = ex.name
     })
+
+  $('.button-collapse').sideNav({
+    menuWidth: 250
+  })
 }
 
 function renderSelectList(selectListId, onChange, initialValue, renderChildren) {

examples/public/styles.css

@@ -3,6 +3,21 @@
   right: 0;
   margin: auto;
   margin-top: 20px;
+  padding-left: 300px;
+  display: inline-flex !important;
+}
+
+@media only screen and (max-width : 992px) {
+  .page-container {
+    padding-left: 0;
+    display: flex !important;
+  }
+}
+
+#navbar {
+  position: absolute;
+  top: 20px;
+  left: 20px;
 }
 
 .center-content {

examples/views/detectAndDrawFaces.html

@@ -2,7 +2,6 @@
 <html>
 <head>
   <script src="face-api.js"></script>
-  <script src="axios.min.js"></script>
   <script src="commons.js"></script>
   <link rel="stylesheet" href="styles.css">
   <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/materialize/0.100.2/css/materialize.css">
@@ -10,8 +9,8 @@
   <script src="https://cdnjs.cloudflare.com/ajax/libs/materialize/0.100.2/js/materialize.min.js"></script>
 </head>
 <body>
+  <div id="navbar"></div>
   <div class="center-content page-container">
-    <div id="navbar"></div>
     <div class="progress" id="loader">
       <div class="indeterminate"></div>
     </div>
@@ -80,7 +79,8 @@
     }
 
     async function run() {
-      net = await initFaceDetectionNet()
+      net = new faceapi.FaceDetectionNet()
+      await net.load('/')
       $('#loader').hide()
       onSelectionChanged($('#selectList select').val())
     }

examples/views/detectAndDrawLandmarks.html

@@ -2,7 +2,6 @@
 <html>
 <head>
   <script src="face-api.js"></script>
-  <script src="axios.min.js"></script>
   <script src="commons.js"></script>
   <link rel="stylesheet" href="styles.css">
   <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/materialize/0.100.2/css/materialize.css">
@@ -10,8 +9,8 @@
   <script src="https://cdnjs.cloudflare.com/ajax/libs/materialize/0.100.2/js/materialize.min.js"></script>
 </head>
 <body>
+  <div id="navbar"></div>
   <div class="center-content page-container">
-    <div id="navbar"></div>
     <div class="progress" id="loader">
       <div class="indeterminate"></div>
     </div>
@@ -89,8 +88,10 @@
     }
 
     async function run() {
-      detectionNet = await initFaceDetectionNet()
-      landmarkNet = await initFaceLandmarkNet()
+      detectionNet = new faceapi.FaceDetectionNet()
+      await detectionNet.load('/')
+      landmarkNet = new faceapi.FaceLandmarkNet()
+      await landmarkNet.load('/')
       $('#loader').hide()
       onSelectionChanged($('#selectList select').val())
     }

examples/views/detectAndRecognizeFaces.html

@@ -2,7 +2,6 @@
 <html>
 <head>
   <script src="face-api.js"></script>
-  <script src="axios.min.js"></script>
   <script src="commons.js"></script>
   <link rel="stylesheet" href="styles.css">
   <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/materialize/0.100.2/css/materialize.css">
@@ -10,8 +9,8 @@
   <script src="https://cdnjs.cloudflare.com/ajax/libs/materialize/0.100.2/js/materialize.min.js"></script>
 </head>
 <body>
+  <div id="navbar"></div>
   <div class="center-content page-container">
-    <div id="navbar"></div>
     <div class="progress" id="loader">
       <div class="indeterminate"></div>
     </div>
@@ -143,9 +142,12 @@
     }
 
     async function run() {
-      detectionNet = await initFaceDetectionNet()
-      recognitionNet = await initFaceRecognitionNet()
-      landmarkNet = await initFaceLandmarkNet()
+      detectionNet = new faceapi.FaceDetectionNet()
+      await detectionNet.load('/')
+      landmarkNet = new faceapi.FaceLandmarkNet()
+      await landmarkNet.load('/')
+      recognitionNet = new faceapi.FaceRecognitionNet()
+      await recognitionNet.load('/')
       trainDescriptorsByClass = await initTrainDescriptorsByClass(recognitionNet, 1)
       $('#loader').hide()
       onSelectionChanged($('#selectList select').val())

examples/views/faceAlignment.html

@@ -2,7 +2,6 @@
 <html>
 <head>
   <script src="face-api.js"></script>
-  <script src="axios.min.js"></script>
   <script src="commons.js"></script>
   <link rel="stylesheet" href="styles.css">
   <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/materialize/0.100.2/css/materialize.css">
@@ -10,8 +9,8 @@
   <script src="https://cdnjs.cloudflare.com/ajax/libs/materialize/0.100.2/js/materialize.min.js"></script>
 </head>
 <body>
+  <div id="navbar"></div>
   <div class="center-content page-container">
-    <div id="navbar"></div>
     <div class="progress" id="loader">
       <div class="indeterminate"></div>
     </div>
@@ -93,8 +92,10 @@
     }
 
     async function run() {
-      detectionNet = await initFaceDetectionNet()
-      landmarkNet = await initFaceLandmarkNet()
+      detectionNet = new faceapi.FaceDetectionNet()
+      await detectionNet.load('/')
+      landmarkNet = new faceapi.FaceLandmarkNet()
+      await landmarkNet.load('/')
       $('#loader').hide()
       onSelectionChanged($('#selectList select').val())
     }

examples/views/faceDetection.html

@@ -2,7 +2,6 @@
 <html>
 <head>
   <script src="face-api.js"></script>
-  <script src="axios.min.js"></script>
   <script src="commons.js"></script>
   <link rel="stylesheet" href="styles.css">
   <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/materialize/0.100.2/css/materialize.css">
@@ -10,8 +9,8 @@
   <script src="https://cdnjs.cloudflare.com/ajax/libs/materialize/0.100.2/js/materialize.min.js"></script>
 </head>
 <body>
+  <div id="navbar"></div>
   <div class="center-content page-container">
-    <div id="navbar"></div>
     <div class="progress" id="loader">
       <div class="indeterminate"></div>
     </div>
@@ -75,7 +74,8 @@
     }
 
     async function run() {
-      net = await initFaceDetectionNet()
+      net = new faceapi.FaceDetectionNet()
+      await net.load('/')
       $('#loader').hide()
       onSelectionChanged($('#selectList select').val())
     }

examples/views/faceDetectionVideo.html

@@ -2,7 +2,6 @@
 <html>
 <head>
   <script src="face-api.js"></script>
-  <script src="axios.min.js"></script>
   <script src="commons.js"></script>
   <link rel="stylesheet" href="styles.css">
   <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/materialize/0.100.2/css/materialize.css">
@@ -10,8 +9,8 @@
   <script src="https://cdnjs.cloudflare.com/ajax/libs/materialize/0.100.2/js/materialize.min.js"></script>
 </head>
 <body>
+  <div id="navbar"></div>
   <div class="center-content page-container">
-    <div id="navbar"></div>
     <div class="progress" id="loader">
       <div class="indeterminate"></div>
     </div>
@@ -87,7 +86,8 @@
     }
 
     async function run() {
-      net = await initFaceDetectionNet()
+      net = new faceapi.FaceDetectionNet()
+      await net.load('/')
       $('#loader').hide()
     }
 

examples/views/faceLandmarks.html

@@ -2,7 +2,6 @@
 <html>
 <head>
   <script src="face-api.js"></script>
-  <script src="axios.min.js"></script>
   <script src="commons.js"></script>
   <link rel="stylesheet" href="styles.css">
   <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/materialize/0.100.2/css/materialize.css">
@@ -10,9 +9,8 @@
   <script src="https://cdnjs.cloudflare.com/ajax/libs/materialize/0.100.2/js/materialize.min.js"></script>
 </head>
 <body>
+  <div id="navbar"></div>
   <div class="center-content page-container">
-    <div id="navbar"></div>
-
     <div>
       <div class="progress" id="loader">
         <div class="indeterminate"></div>
@@ -56,7 +54,8 @@
     }
 
     async function run() {
-      net = await initFaceLandmarkNet()
+      net = new faceapi.FaceLandmarkNet()
+      await net.load('/')
       $('#loader').hide()
       await onSelectionChanged($('#selectList select').val())
     }

examples/views/faceRecognition.html

@@ -2,7 +2,6 @@
 <html>
 <head>
   <script src="face-api.js"></script>
-  <script src="axios.min.js"></script>
   <script src="commons.js"></script>
   <link rel="stylesheet" href="styles.css">
   <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/materialize/0.100.2/css/materialize.css">
@@ -10,9 +9,8 @@
   <script src="https://cdnjs.cloudflare.com/ajax/libs/materialize/0.100.2/js/materialize.min.js"></script>
 </head>
 <body>
+  <div id="navbar"></div>
   <div class="center-content page-container">
-    <div id="navbar"></div>
-
     <div>
       <div class="row center-content" id="loader">
         <input disabled value="" id="status" type="text" class="bold">
@@ -137,7 +135,9 @@
       try {
         setStatusText('loading model file...')
 
-        net = await initFaceRecognitionNet()
+        net = new faceapi.FaceRecognitionNet()
+        await net.load('/')
+
         setStatusText('computing initial descriptors...')
 
         trainDescriptorsByClass = await initTrainDescriptorsByClass(net)

examples/views/faceSimilarity.html

@@ -2,7 +2,6 @@
 <html>
 <head>
   <script src="face-api.js"></script>
-  <script src="axios.min.js"></script>
   <script src="commons.js"></script>
   <link rel="stylesheet" href="styles.css">
   <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/materialize/0.100.2/css/materialize.css">
@@ -10,9 +9,8 @@
   <script src="https://cdnjs.cloudflare.com/ajax/libs/materialize/0.100.2/js/materialize.min.js"></script>
 </head>
 <body>
+  <div id="navbar"></div>
   <div class="center-content page-container">
-    <div id="navbar"></div>
-
     <div>
       <div class="progress" id="loader">
         <div class="indeterminate"></div>
@@ -62,7 +60,8 @@
     }
 
     async function run() {
-      net = await initFaceRecognitionNet()
+      net = new faceapi.FaceRecognitionNet()
+      await net.load('/')
       $('#loader').hide()
       await onSelectionChanged(1, $('#selectList1 select').val())
       await onSelectionChanged(2, $('#selectList2 select').val())

karma.conf.js

@@ -2,7 +2,7 @@ const dataFiles = [
   'test/images/*.jpg',
   'test/images/*.png',
   'test/data/*.json',
-  'weights/*.weights'
+  'weights/**/*'
 ].map(pattern => ({
   pattern,
   watched: false,

package.json

@@ -23,10 +23,8 @@
     "@tensorflow/tfjs-core": "^0.11.0"
   },
   "devDependencies": {
-    "@types/axios": "^0.14.0",
     "@types/jasmine": "^2.8.8",
     "@types/node": "^10.1.1",
-    "axios": "^0.18.0",
     "jasmine-core": "^3.1.0",
     "karma": "^2.0.3",
     "karma-chrome-launcher": "^2.2.0",

src/commons/isTensor.ts

+import * as tf from '@tensorflow/tfjs-core';
+
+export function isTensor(tensor: tf.Tensor, dim: number) {
+  return tensor instanceof tf.Tensor && tensor.shape.length === dim
+}
+
+export function isTensor1D(tensor: tf.Tensor) {
+  return isTensor(tensor, 1)
+}
+
+export function isTensor2D(tensor: tf.Tensor) {
+  return isTensor(tensor, 2)
+}
+
+export function isTensor3D(tensor: tf.Tensor) {
+  return isTensor(tensor, 3)
+}
+
+export function isTensor4D(tensor: tf.Tensor) {
+  return isTensor(tensor, 4)
+}
\ No newline at end of file

src/commons/loadWeightMap.ts

+import * as tf from '@tensorflow/tfjs-core';
+
+export function getModelUris(uri: string | undefined, defaultModelName: string) {
+  const parts = (uri || '').split('/')
+
+  const modelBaseUri = (
+    (uri || '').endsWith('.json')
+      ? parts.slice(0, parts.length - 1)
+      : parts
+  ).filter(s => s).join('/')
+
+  const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`
+  const manifestUri = !uri || !modelBaseUri
+    ? defaultManifestFilename
+    : (
+      uri.endsWith('.json')
+        ? uri
+        : `${modelBaseUri}/${defaultManifestFilename}`
+    )
+
+  return { manifestUri, modelBaseUri }
+}
+
+export async function loadWeightMap(
+  uri: string | undefined,
+  defaultModelName: string
+): Promise<any> {
+
+  const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName)
+
+  const manifest = await (await fetch(manifestUri)).json()
+
+  return tf.io.loadWeights(manifest, modelBaseUri)
+}
\ No newline at end of file

src/faceDetectionNet/FaceDetectionNet.ts

+import * as tf from '@tensorflow/tfjs-core';
+
+import { getImageTensor } from '../getImageTensor';
+import { NetInput } from '../NetInput';
+import { padToSquare } from '../padToSquare';
+import { Rect } from '../Rect';
+import { Dimensions, TNetInput } from '../types';
+import { extractParams } from './extractParams';
+import { FaceDetection } from './FaceDetection';
+import { loadQuantizedParams } from './loadQuantizedParams';
+import { mobileNetV1 } from './mobileNetV1';
+import { nonMaxSuppression } from './nonMaxSuppression';
+import { outputLayer } from './outputLayer';
+import { predictionLayer } from './predictionLayer';
+import { resizeLayer } from './resizeLayer';
+import { NetParams } from './types';
+
+export class FaceDetectionNet {
+
+  private _params: NetParams
+
+  public async load(weightsOrUrl?: Float32Array | string): Promise<void> {
+    if (weightsOrUrl instanceof Float32Array) {
+      this.extractWeights(weightsOrUrl)
+      return
+    }
+
+    if (weightsOrUrl && typeof weightsOrUrl !== 'string') {
+      throw new Error('FaceDetectionNet.load - expected model uri, or weights as Float32Array')
+    }
+    this._params = await loadQuantizedParams(weightsOrUrl)
+  }
+
+  public extractWeights(weights: Float32Array) {
+    this._params = extractParams(weights)
+  }
+
+  private forwardTensor(imgTensor: tf.Tensor4D) {
+    if (!this._params) {
+      throw new Error('FaceDetectionNet - load model before inference')
+    }
+
+    return tf.tidy(() => {
+
+      const resized = resizeLayer(imgTensor) as tf.Tensor4D
+      const features = mobileNetV1(resized, this._params.mobilenetv1_params)
+
+      const {
+        boxPredictions,
+        classPredictions
+      } = predictionLayer(features.out, features.conv11, this._params.prediction_layer_params)
+
+      return outputLayer(boxPredictions, classPredictions, this._params.output_layer_params)
+    })
+  }
+
+  public forward(input: tf.Tensor | NetInput | TNetInput) {
+    return tf.tidy(
+      () => this.forwardTensor(padToSquare(getImageTensor(input)))
+    )
+  }
+
+  public async locateFaces(
+    input: tf.Tensor | NetInput | TNetInput,
+    minConfidence: number = 0.8,
+    maxResults: number = 100,
+  ): Promise<FaceDetection[]> {
+
+    let paddedHeightRelative = 1, paddedWidthRelative = 1
+    let imageDimensions: Dimensions | undefined
+
+    const {
+      boxes: _boxes,
+      scores: _scores
+    } = tf.tidy(() => {
+
+      let imgTensor = getImageTensor(input)
+      const [height, width] = imgTensor.shape.slice(1)
+      imageDimensions = { width, height }
+
+      imgTensor = padToSquare(imgTensor)
+      paddedHeightRelative = imgTensor.shape[1] / height
+      paddedWidthRelative = imgTensor.shape[2] / width
+
+      return this.forwardTensor(imgTensor)
+    })
+
+    // TODO batches
+    const boxes = _boxes[0]
+    const scores = _scores[0]
+    for (let i = 1; i < _boxes.length; i++) {
+      _boxes[i].dispose()
+      _scores[i].dispose()
+    }
+
+    // TODO find a better way to filter by minConfidence
+    const scoresData = Array.from(await scores.data())
+
+    const iouThreshold = 0.5
+    const indices = nonMaxSuppression(
+      boxes,
+      scoresData,
+      maxResults,
+      iouThreshold,
+      minConfidence
+    )
+
+    const results = indices
+      .map(idx => {
+        const [top, bottom] = [
+          Math.max(0, boxes.get(idx, 0)),
+          Math.min(1.0, boxes.get(idx, 2))
+        ].map(val => val * paddedHeightRelative)
+        const [left, right] = [
+          Math.max(0, boxes.get(idx, 1)),
+          Math.min(1.0, boxes.get(idx, 3))
+        ].map(val => val * paddedWidthRelative)
+        return new FaceDetection(
+          scoresData[idx],
+          new Rect(
+            left,
+            top,
+            right - left,
+            bottom - top
+          ),
+          imageDimensions as Dimensions
+        )
+      })
+
+    boxes.dispose()
+    scores.dispose()
+
+    return results
+  }
+}
\ No newline at end of file

src/faceDetectionNet/boxPredictionLayer.ts

 import * as tf from '@tensorflow/tfjs-core';
 
 import { convLayer } from '../commons/convLayer';
-import { FaceDetectionNet } from './types';
+import { BoxPredictionParams } from './types';
 
 
 export function boxPredictionLayer(
   x: tf.Tensor4D,
-  params: FaceDetectionNet.BoxPredictionParams
+  params: BoxPredictionParams
 ) {
   return tf.tidy(() => {
 

src/faceDetectionNet/extractParams.ts

@@ -2,11 +2,11 @@ import * as tf from '@tensorflow/tfjs-core';
 
 import { extractWeightsFactory } from '../commons/extractWeightsFactory';
 import { ConvParams } from '../commons/types';
-import { FaceDetectionNet } from './types';
+import { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';
 
 function extractorsFactory(extractWeights: (numWeights: number) => Float32Array) {
 
-  function extractDepthwiseConvParams(numChannels: number): FaceDetectionNet.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))
@@ -43,7 +43,7 @@ function extractorsFactory(extractWeights: (numWeights: number) => Float32Array)
     channelsIn: number,
     channelsOut: number,
     filterSize: number
-  ): FaceDetectionNet.PointwiseConvParams {
+  ): PointwiseConvParams {
     const {
       filters,
       bias
@@ -55,7 +55,10 @@ function extractorsFactory(extractWeights: (numWeights: number) => Float32Array)
     }
   }
 
-  function extractConvPairParams(channelsIn: number, channelsOut: number): FaceDetectionNet.MobileNetV1.ConvPairParams {
+  function extractConvPairParams(
+    channelsIn: number,
+    channelsOut: number
+  ): MobileNetV1.ConvPairParams {
     const depthwise_conv_params = extractDepthwiseConvParams(channelsIn)
     const pointwise_conv_params = extractPointwiseConvParams(channelsIn, channelsOut, 1)
 
@@ -65,7 +68,7 @@ function extractorsFactory(extractWeights: (numWeights: number) => Float32Array)
     }
   }
 
-  function extractMobilenetV1Params(): FaceDetectionNet.MobileNetV1.Params {
+  function extractMobilenetV1Params(): MobileNetV1.Params {
 
     const conv_0_params = extractPointwiseConvParams(3, 32, 3)
 
@@ -96,7 +99,7 @@ function extractorsFactory(extractWeights: (numWeights: number) => Float32Array)
 
   }
 
-  function extractPredictionLayerParams(): FaceDetectionNet.PredictionLayerParams {
+  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)
@@ -170,7 +173,7 @@ function extractorsFactory(extractWeights: (numWeights: number) => Float32Array)
 
 }
 
-export function extractParams(weights: Float32Array): FaceDetectionNet.NetParams {
+export function extractParams(weights: Float32Array): NetParams {
   const {
     extractWeights,
     getRemainingWeights

src/faceDetectionNet/index.ts

-import * as tf from '@tensorflow/tfjs-core';
+import { FaceDetectionNet } from './FaceDetectionNet';
 
-import { getImageTensor } from '../getImageTensor';
-import { NetInput } from '../NetInput';
-import { padToSquare } from '../padToSquare';
-import { TNetInput, Dimensions } from '../types';
-import { extractParams } from './extractParams';
-import { FaceDetection } from './FaceDetection';
-import { mobileNetV1 } from './mobileNetV1';
-import { nonMaxSuppression } from './nonMaxSuppression';
-import { outputLayer } from './outputLayer';
-import { predictionLayer } from './predictionLayer';
-import { resizeLayer } from './resizeLayer';
-import { Rect } from '../Rect';
+export * from './FaceDetectionNet';
 
 export function faceDetectionNet(weights: Float32Array) {
-  const params = extractParams(weights)
-
-  function forwardTensor(imgTensor: tf.Tensor4D) {
-    return tf.tidy(() => {
-
-      const resized = resizeLayer(imgTensor) as tf.Tensor4D
-      const features = mobileNetV1(resized, params.mobilenetv1_params)
-
-      const {
-        boxPredictions,
-        classPredictions
-      } = predictionLayer(features.out, features.conv11, params.prediction_layer_params)
-
-      return outputLayer(boxPredictions, classPredictions, params.output_layer_params)
-    })
-  }
-
-  function forward(input: tf.Tensor | NetInput | TNetInput) {
-    return tf.tidy(
-      () => forwardTensor(padToSquare(getImageTensor(input)))
-    )
-  }
-
-  async function locateFaces(
-    input: tf.Tensor | NetInput | TNetInput,
-    minConfidence: number = 0.8,
-    maxResults: number = 100,
-  ): Promise<FaceDetection[]> {
-
-    let paddedHeightRelative = 1, paddedWidthRelative = 1
-    let imageDimensions: Dimensions | undefined
-
-    const {
-      boxes: _boxes,
-      scores: _scores
-    } = tf.tidy(() => {
-
-      let imgTensor = getImageTensor(input)
-      const [height, width] = imgTensor.shape.slice(1)
-      imageDimensions = { width, height }
-
-      imgTensor = padToSquare(imgTensor)
-      paddedHeightRelative = imgTensor.shape[1] / height
-      paddedWidthRelative = imgTensor.shape[2] / width
-
-      return forwardTensor(imgTensor)
-    })
-
-    // TODO batches
-    const boxes = _boxes[0]
-    const scores = _scores[0]
-    for (let i = 1; i < _boxes.length; i++) {
-      _boxes[i].dispose()
-      _scores[i].dispose()
-    }
-
-    // TODO find a better way to filter by minConfidence
-    const scoresData = Array.from(await scores.data())
-
-    const iouThreshold = 0.5
-    const indices = nonMaxSuppression(
-      boxes,
-      scoresData,
-      maxResults,
-      iouThreshold,
-      minConfidence
-    )
-
-    const results = indices
-      .map(idx => {
-        const [top, bottom] = [
-          Math.max(0, boxes.get(idx, 0)),
-          Math.min(1.0, boxes.get(idx, 2))
-        ].map(val => val * paddedHeightRelative)
-        const [left, right] = [
-          Math.max(0, boxes.get(idx, 1)),
-          Math.min(1.0, boxes.get(idx, 3))
-        ].map(val => val * paddedWidthRelative)
-        return new FaceDetection(
-          scoresData[idx],
-          new Rect(
-            left,
-            top,
-            right - left,
-            bottom - top
-          ),
-          imageDimensions as Dimensions
-        )
-      })
-
-    boxes.dispose()
-    scores.dispose()
-
-    return results
-  }
-
-  return {
-    forward,
-    locateFaces
-  }
+  const net = new FaceDetectionNet()
+  net.extractWeights(weights)
+  return net
 }
\ No newline at end of file

src/faceDetectionNet/loadQuantizedParams.ts

+import { isTensor1D, isTensor4D, isTensor3D } from '../commons/isTensor';
+import { loadWeightMap } from '../commons/loadWeightMap';
+import { BoxPredictionParams, MobileNetV1, PointwiseConvParams, PredictionLayerParams } from './types';
+
+const DEFAULT_MODEL_NAME = 'face_detection_model'
+
+function extractorsFactory(weightMap: any) {
+
+  function extractPointwiseConvParams(prefix: string, idx: number): PointwiseConvParams {
+
+    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 ${pointwise_conv_params.filters}`)
+    }
+
+    if (!isTensor1D(pointwise_conv_params.batch_norm_offset)) {
+      throw new Error(`expected weightMap[${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset] to be a Tensor1D, instead have ${pointwise_conv_params.batch_norm_offset}`)
+    }
+
+    return pointwise_conv_params
+  }
+
+  function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {
+
+    const depthwise_conv_params = {
+      filters: weightMap[`MobilenetV1/Conv2d_${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 ${depthwise_conv_params.batch_norm_mean}`)
+    }
+
+    if (!isTensor1D(depthwise_conv_params.batch_norm_variance)) {
+      throw new Error(`expected weightMap[MobilenetV1/Conv2d_${idx}_depthwise/BatchNorm/moving_variance] to be a Tensor1D, instead have ${depthwise_conv_params.batch_norm_variance}`)
+    }
+
+    return {
+      depthwise_conv_params,
+      pointwise_conv_params: extractPointwiseConvParams('MobilenetV1', idx)
+    }
+  }
+
+  function extractMobilenetV1Params(): MobileNetV1.Params {
+    return {
+      conv_0_params: extractPointwiseConvParams('MobilenetV1', 0),
+      conv_pair_params: Array(13).fill(0).map((_, i) => extractConvPairParams(i + 1))
+    }
+  }
+
+  function extractBoxPredictorParams(idx: number): BoxPredictionParams {
+
+    const params = {
+      box_encoding_predictor_params: {
+        filters: weightMap[`Prediction/BoxPredictor_${idx}/BoxEncodingPredictor/weights`],
+        bias: weightMap[`Prediction/BoxPredictor_${idx}/BoxEncodingPredictor/biases`]
+      },
+      class_predictor_params: {
+        filters: weightMap[`Prediction/BoxPredictor_${idx}/ClassPredictor/weights`],
+        bias: weightMap[`Prediction/BoxPredictor_${idx}/ClassPredictor/biases`]
+      }
+    }
+
+    if (!isTensor4D(params.box_encoding_predictor_params.filters)) {
+      throw new Error(`expected weightMap[Prediction/BoxPredictor_${idx}/BoxEncodingPredictor/weights] to be a Tensor4D, instead have ${params.box_encoding_predictor_params.filters}`)
+    }
+
+    if (!isTensor1D(params.box_encoding_predictor_params.bias)) {
+      throw new Error(`expected weightMap[Prediction/BoxPredictor_${idx}/BoxEncodingPredictor/biases] to be a Tensor1D, instead have ${params.box_encoding_predictor_params.bias}`)
+    }
+
+    if (!isTensor4D(params.class_predictor_params.filters)) {
+      throw new Error(`expected weightMap[Prediction/BoxPredictor_${idx}/ClassPredictor/weights] to be a Tensor4D, instead have ${params.class_predictor_params.filters}`)
+    }
+
+    if (!isTensor1D(params.class_predictor_params.bias)) {
+      throw new Error(`expected weightMap[Prediction/BoxPredictor_${idx}/ClassPredictor/biases] to be a Tensor1D, instead have ${params.class_predictor_params.bias}`)
+    }
+
+    return params
+  }
+
+  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)
+    }
+  }
+
+  return {
+    extractMobilenetV1Params,
+    extractPredictionLayerParams
+  }
+}
+
+export async function loadQuantizedParams(uri: string | undefined): Promise<any> {//Promise<NetParams> {
+  const weightMap = await loadWeightMap(uri, DEFAULT_MODEL_NAME)
+
+  const {
+    extractMobilenetV1Params,
+    extractPredictionLayerParams
+  } = extractorsFactory(weightMap)
+
+  const extra_dim = weightMap['Output/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: {
+      extra_dim
+    }
+  }
+}
\ No newline at end of file

src/faceDetectionNet/mobileNetV1.ts

 import * as tf from '@tensorflow/tfjs-core';
 
 import { pointwiseConvLayer } from './pointwiseConvLayer';
-import { FaceDetectionNet } from './types';
+import { MobileNetV1 } from './types';
 
 const epsilon = 0.0010000000474974513
 
 function depthwiseConvLayer(
   x: tf.Tensor4D,
-  params: FaceDetectionNet.MobileNetV1.DepthwiseConvParams,
+  params: MobileNetV1.DepthwiseConvParams,
   strides: [number, number]
 ) {
   return tf.tidy(() => {
@@ -30,7 +30,7 @@ function getStridesForLayerIdx(layerIdx: number): [number, number] {
   return [2, 4, 6, 12].some(idx => idx === layerIdx) ? [2, 2] : [1, 1]
 }
 
-export function mobileNetV1(x: tf.Tensor4D, params: FaceDetectionNet.MobileNetV1.Params) {
+export function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {
   return tf.tidy(() => {
 
     let conv11 = null

src/faceDetectionNet/outputLayer.ts

 import * as tf from '@tensorflow/tfjs-core';
 
-import { FaceDetectionNet } from './types';
+import { OutputLayerParams } from './types';
+
 
 function getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {
   const vec = tf.unstack(tf.transpose(x, [1, 0]))
@@ -49,7 +50,7 @@ function decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {
 export function outputLayer(
   boxPredictions: tf.Tensor4D,
   classPredictions: tf.Tensor4D,
-  params: FaceDetectionNet.OutputLayerParams
+  params: OutputLayerParams
 ) {
   return tf.tidy(() => {
 

src/faceDetectionNet/pointwiseConvLayer.ts

 import * as tf from '@tensorflow/tfjs-core';
 
-import { FaceDetectionNet } from './types';
+import { PointwiseConvParams } from './types';
 
 export function pointwiseConvLayer(
   x: tf.Tensor4D,
-  params: FaceDetectionNet.PointwiseConvParams,
+  params: PointwiseConvParams,
   strides: [number, number]
 ) {
   return tf.tidy(() => {

src/faceDetectionNet/predictionLayer.ts

@@ -2,12 +2,12 @@ import * as tf from '@tensorflow/tfjs-core';
 
 import { boxPredictionLayer } from './boxPredictionLayer';
 import { pointwiseConvLayer } from './pointwiseConvLayer';
-import { FaceDetectionNet } from './types';
+import { PredictionLayerParams } from './types';
 
 export function predictionLayer(
   x: tf.Tensor4D,
   conv11: tf.Tensor4D,
-  params: FaceDetectionNet.PredictionLayerParams
+  params: PredictionLayerParams
 ) {
   return tf.tidy(() => {
 

src/faceDetectionNet/types.ts

@@ -2,64 +2,61 @@ import * as tf from '@tensorflow/tfjs-core';
 
 import { ConvParams } from '../commons/types';
 
-export namespace FaceDetectionNet {
+export type PointwiseConvParams = {
+  filters: tf.Tensor4D
+  batch_norm_offset: tf.Tensor1D
+}
+
+export namespace MobileNetV1 {
 
-  export type PointwiseConvParams = {
+  export type DepthwiseConvParams = {
     filters: tf.Tensor4D
+    batch_norm_scale: tf.Tensor1D
     batch_norm_offset: tf.Tensor1D
+    batch_norm_mean: tf.Tensor1D
+    batch_norm_variance: tf.Tensor1D
   }
 
-  export namespace MobileNetV1 {
-
-    export type DepthwiseConvParams = {
-      filters: tf.Tensor4D
-      batch_norm_scale: tf.Tensor1D
-      batch_norm_offset: tf.Tensor1D
-      batch_norm_mean: tf.Tensor1D
-      batch_norm_variance: tf.Tensor1D
-    }
-
-    export type ConvPairParams = {
-      depthwise_conv_params: DepthwiseConvParams
-      pointwise_conv_params: PointwiseConvParams
-    }
-
-    export type Params = {
-      conv_0_params: PointwiseConvParams
-      conv_pair_params: ConvPairParams[]
-    }
-
-  }
-
-  export type BoxPredictionParams = {
-    box_encoding_predictor_params: ConvParams
-    class_predictor_params: ConvParams
+  export type ConvPairParams = {
+    depthwise_conv_params: DepthwiseConvParams
+    pointwise_conv_params: PointwiseConvParams
   }
 
-  export type PredictionLayerParams = {
+  export type Params = {
     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_pair_params: ConvPairParams[]
   }
 
-  export type OutputLayerParams = {
-    extra_dim: tf.Tensor3D
-  }
+}
 
-  export type NetParams = {
-    mobilenetv1_params: MobileNetV1.Params,
-    prediction_layer_params: PredictionLayerParams,
-    output_layer_params: OutputLayerParams
-  }
+export type BoxPredictionParams = {
+  box_encoding_predictor_params: ConvParams
+  class_predictor_params: 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
+}
+
+export type OutputLayerParams = {
+  extra_dim: tf.Tensor3D
+}
+
+export type NetParams = {
+  mobilenetv1_params: MobileNetV1.Params,
+  prediction_layer_params: PredictionLayerParams,
+  output_layer_params: OutputLayerParams
 }

src/faceLandmarkNet/FaceLandmarkNet.ts

+import * as tf from '@tensorflow/tfjs-core';
+
+import { convLayer } from '../commons/convLayer';
+import { ConvParams } from '../commons/types';
+import { getImageTensor } from '../getImageTensor';
+import { NetInput } from '../NetInput';
+import { Point } from '../Point';
+import { Dimensions, TNetInput } from '../types';
+import { extractParams } from './extractParams';
+import { FaceLandmarks } from './FaceLandmarks';
+import { fullyConnectedLayer } from './fullyConnectedLayer';
+import { loadQuantizedParams } from './loadQuantizedParams';
+import { NetParams } from './types';
+
+function conv(x: tf.Tensor4D, params: ConvParams): tf.Tensor4D {
+  return convLayer(x, params, 'valid', true)
+}
+
+function maxPool(x: tf.Tensor4D, strides: [number, number] = [2, 2]): tf.Tensor4D {
+  return tf.maxPool(x, [2, 2], strides, 'valid')
+}
+
+export class FaceLandmarkNet {
+
+  private _params: 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')
+    }
+    this._params = await loadQuantizedParams(weightsOrUrl)
+  }
+
+  public extractWeights(weights: Float32Array) {
+    this._params = extractParams(weights)
+  }
+
+  public async detectLandmarks(input: tf.Tensor | NetInput | TNetInput) {
+    if (!this._params) {
+      throw new Error('FaceLandmarkNet - load model before inference')
+    }
+
+    let imageDimensions: Dimensions | undefined
+
+    const outTensor = tf.tidy(() => {
+      const params = this._params
+
+      let imgTensor = getImageTensor(input)
+      const [height, width] = imgTensor.shape.slice(1)
+      imageDimensions = { width, height }
+
+
+      // work with 128 x 128 sized face images
+      if (imgTensor.shape[1] !== 128 || imgTensor.shape[2] !== 128) {
+        imgTensor = tf.image.resizeBilinear(imgTensor, [128, 128])
+      }
+
+      let out = conv(imgTensor, params.conv0_params)
+      out = maxPool(out)
+      out = conv(out, params.conv1_params)
+      out = conv(out, params.conv2_params)
+      out = maxPool(out)
+      out = conv(out, params.conv3_params)
+      out = conv(out, params.conv4_params)
+      out = maxPool(out)
+      out = conv(out, params.conv5_params)
+      out = conv(out, params.conv6_params)
+      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)
+
+      return fc1
+    })
+
+    const faceLandmarksArray = Array.from(await outTensor.data())
+    outTensor.dispose()
+
+    const xCoords = faceLandmarksArray.filter((c, i) => (i - 1) % 2)
+    const yCoords = faceLandmarksArray.filter((c, i) => i % 2)
+
+    return new FaceLandmarks(
+      Array(68).fill(0).map((_, i) => new Point(xCoords[i], yCoords[i])),
+      imageDimensions as Dimensions
+    )
+  }
+}
\ No newline at end of file

src/faceLandmarkNet/extractParams.ts

@@ -2,9 +2,9 @@ import * as tf from '@tensorflow/tfjs-core';
 
 import { extractConvParamsFactory } from '../commons/extractConvParamsFactory';
 import { extractWeightsFactory } from '../commons/extractWeightsFactory';
-import { FaceLandmarkNet } from './types';
+import { FCParams, NetParams } from './types';
 
-export function extractParams(weights: Float32Array): FaceLandmarkNet.NetParams {
+export function extractParams(weights: Float32Array): NetParams {
   const {
     extractWeights,
     getRemainingWeights
@@ -12,7 +12,7 @@ export function extractParams(weights: Float32Array): FaceLandmarkNet.NetParams
 
   const extractConvParams = extractConvParamsFactory(extractWeights)
 
-  function extractFcParams(channelsIn: number, channelsOut: number,): FaceLandmarkNet.FCParams {
+  function extractFcParams(channelsIn: number, channelsOut: number,): FCParams {
     const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut])
     const fc_bias = tf.tensor1d(extractWeights(channelsOut))
     return {

src/faceLandmarkNet/fullyConnectedLayer.ts

 import * as tf from '@tensorflow/tfjs-core';
 
-import { FaceLandmarkNet } from './types';
+import { FCParams } from './types';
 
 export function fullyConnectedLayer(
   x: tf.Tensor2D,
-  params: FaceLandmarkNet.FCParams
+  params: FCParams
 ): tf.Tensor2D {
   return tf.tidy(() =>
     tf.add(

src/faceLandmarkNet/index.ts

-import * as tf from '@tensorflow/tfjs-core';
+import { FaceLandmarkNet } from './FaceLandmarkNet';
 
-import { convLayer } from '../commons/convLayer';
-import { ConvParams } from '../commons/types';
-import { getImageTensor } from '../getImageTensor';
-import { NetInput } from '../NetInput';
-import { Point } from '../Point';
-import { Dimensions, TNetInput } from '../types';
-import { extractParams } from './extractParams';
-import { FaceLandmarks } from './FaceLandmarks';
-import { fullyConnectedLayer } from './fullyConnectedLayer';
-
-function conv(x: tf.Tensor4D, params: ConvParams): tf.Tensor4D {
-  return convLayer(x, params, 'valid', true)
-}
-
-function maxPool(x: tf.Tensor4D, strides: [number, number] = [2, 2]): tf.Tensor4D {
-  return tf.maxPool(x, [2, 2], strides, 'valid')
-}
+export * from './FaceLandmarkNet';
 
 export function faceLandmarkNet(weights: Float32Array) {
-  const params = extractParams(weights)
-
-  async function detectLandmarks(input: tf.Tensor | NetInput | TNetInput) {
-    let imageDimensions: Dimensions | undefined
-
-    const outTensor = tf.tidy(() => {
-      let imgTensor = getImageTensor(input)
-      const [height, width] = imgTensor.shape.slice(1)
-      imageDimensions = { width, height }
-
-
-      // work with 128 x 128 sized face images
-      if (imgTensor.shape[1] !== 128 || imgTensor.shape[2] !== 128) {
-        imgTensor = tf.image.resizeBilinear(imgTensor, [128, 128])
-      }
-
-      let out = conv(imgTensor, params.conv0_params)
-      out = maxPool(out)
-      out = conv(out, params.conv1_params)
-      out = conv(out, params.conv2_params)
-      out = maxPool(out)
-      out = conv(out, params.conv3_params)
-      out = conv(out, params.conv4_params)
-      out = maxPool(out)
-      out = conv(out, params.conv5_params)
-      out = conv(out, params.conv6_params)
-      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)
-
-      return fc1
-    })
-
-    const faceLandmarksArray = Array.from(await outTensor.data())
-    outTensor.dispose()
-
-    const xCoords = faceLandmarksArray.filter((c, i) => (i - 1) % 2)
-    const yCoords = faceLandmarksArray.filter((c, i) => i % 2)
-
-    return new FaceLandmarks(
-      Array(68).fill(0).map((_, i) => new Point(xCoords[i], yCoords[i])),
-      imageDimensions as Dimensions
-    )
-  }
-
-  return {
-    detectLandmarks
-  }
+  const net = new FaceLandmarkNet()
+  net.extractWeights(weights)
+  return net
 }
\ No newline at end of file

src/faceLandmarkNet/loadQuantizedParams.ts

+import * as tf from '@tensorflow/tfjs-core';
+
+import { loadWeightMap } from '../commons/loadWeightMap';
+import { ConvParams } from '../commons/types';
+import { FCParams, NetParams } from './types';
+import { isTensor4D, isTensor1D, isTensor2D } from '../commons/isTensor';
+
+const DEFAULT_MODEL_NAME = 'face_landmark_68_model'
+
+function extractorsFactory(weightMap: any) {
+
+  function extractConvParams(prefix: string): ConvParams {
+    const params = {
+      filters: weightMap[`${prefix}/kernel`] as tf.Tensor4D,
+      bias: weightMap[`${prefix}/bias`] as tf.Tensor1D
+    }
+
+    if (!isTensor4D(params.filters)) {
+      throw new Error(`expected weightMap[${prefix}/kernel] to be a Tensor4D, instead have ${params.filters}`)
+    }
+
+    if (!isTensor1D(params.bias)) {
+      throw new Error(`expected weightMap[${prefix}/bias] to be a Tensor1D, instead have ${params.bias}`)
+    }
+
+    return params
+  }
+
+  function extractFcParams(prefix: string): FCParams {
+    const params = {
+      weights: weightMap[`${prefix}/kernel`] as tf.Tensor2D,
+      bias: weightMap[`${prefix}/bias`] as tf.Tensor1D
+    }
+
+    if (!isTensor2D(params.weights)) {
+      throw new Error(`expected weightMap[${prefix}/kernel] to be a Tensor2D, instead have ${params.weights}`)
+    }
+
+    if (!isTensor1D(params.bias)) {
+      throw new Error(`expected weightMap[${prefix}/bias] to be a Tensor1D, instead have ${params.bias}`)
+    }
+
+    return params
+  }
+
+  return {
+    extractConvParams,
+    extractFcParams
+  }
+}
+
+export async function loadQuantizedParams(uri: string | undefined): Promise<NetParams> {
+  const weightMap = await loadWeightMap(uri, DEFAULT_MODEL_NAME)
+
+  const {
+    extractConvParams,
+    extractFcParams
+  } = extractorsFactory(weightMap)
+
+  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')
+  }
+}
\ No newline at end of file

src/faceLandmarkNet/types.ts

@@ -2,24 +2,20 @@ import * as tf from '@tensorflow/tfjs-core';
 
 import { ConvParams } from '../commons/types';
 
-export namespace FaceLandmarkNet {
-
-  export type FCParams = {
-    weights: tf.Tensor2D
-    bias: tf.Tensor1D
-  }
-
-  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
-  }
+export type FCParams = {
+  weights: tf.Tensor2D
+  bias: tf.Tensor1D
+}
 
+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
 }
\ No newline at end of file