check in latest build

build/BoundingBox.d.ts

 import { Rect } from './Rect';
+import { Dimensions } from './types';
 export declare class BoundingBox {
     private _left;
     private _top;
@@ -11,6 +12,7 @@ export declare class BoundingBox {
     readonly bottom: number;
     readonly width: number;
     readonly height: number;
+    readonly area: number;
     toSquare(): BoundingBox;
     round(): BoundingBox;
     padAtBorders(imageHeight: number, imageWidth: number): {
@@ -26,5 +28,6 @@ export declare class BoundingBox {
         h: number;
     };
     calibrate(region: BoundingBox): BoundingBox;
+    rescale(s: Dimensions | number): BoundingBox;
     toRect(): Rect;
 }

build/BoundingBox.js

 "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
 var Rect_1 = require("./Rect");
+var utils_1 = require("./utils");
 var BoundingBox = /** @class */ (function () {
     function BoundingBox(_left, _top, _right, _bottom) {
         this._left = _left;
@@ -50,6 +51,13 @@ var BoundingBox = /** @class */ (function () {
         enumerable: true,
         configurable: true
     });
+    Object.defineProperty(BoundingBox.prototype, "area", {
+        get: function () {
+            return this.width * this.height;
+        },
+        enumerable: true,
+        configurable: true
+    });
     BoundingBox.prototype.toSquare = function () {
         var _a = this, left = _a.left, top = _a.top, right = _a.right, bottom = _a.bottom;
         var off = (Math.abs(this.width - this.height) / 2);
@@ -98,6 +106,11 @@ var BoundingBox = /** @class */ (function () {
     BoundingBox.prototype.calibrate = function (region) {
         return new BoundingBox(this.left + (region.left * this.width), this.top + (region.top * this.height), this.right + (region.right * this.width), this.bottom + (region.bottom * this.height)).toSquare().round();
     };
+    BoundingBox.prototype.rescale = function (s) {
+        var scaleX = utils_1.isDimensions(s) ? s.width : s;
+        var scaleY = utils_1.isDimensions(s) ? s.height : s;
+        return new BoundingBox(this.left * scaleX, this.top * scaleY, this.right * scaleX, this.bottom * scaleY);
+    };
     BoundingBox.prototype.toRect = function () {
         return new Rect_1.Rect(this.left, this.top, this.width, this.height);
     };

build/BoundingBox.js.map

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\ No newline at end of file

build/NetInput.d.ts

 import * as tf from '@tensorflow/tfjs-core';
 import { Point } from './Point';
-import { TResolvedNetInput } from './types';
+import { TResolvedNetInput, Dimensions } from './types';
 export declare class NetInput {
     private _inputs;
     private _canvases;
@@ -8,6 +8,7 @@ export declare class NetInput {
     private _isBatchInput;
     private _inputDimensions;
     private _paddings;
+    private _inputSize;
     constructor(inputs: tf.Tensor4D | Array<TResolvedNetInput>, isBatchInput?: boolean, keepCanvases?: boolean);
     readonly inputs: tf.Tensor3D[];
     readonly canvases: HTMLCanvasElement[];
@@ -16,13 +17,18 @@ export declare class NetInput {
     readonly batchSize: number;
     readonly inputDimensions: number[][];
     readonly paddings: Point[];
+    readonly inputSize: number;
+    readonly relativePaddings: Point[];
+    readonly reshapedInputDimensions: Dimensions[];
     getInputDimensions(batchIdx: number): number[];
     getInputHeight(batchIdx: number): number;
     getInputWidth(batchIdx: number): number;
     getPaddings(batchIdx: number): Point;
+    getRelativePaddings(batchIdx: number): Point;
+    getReshapedInputDimensions(batchIdx: number): Dimensions;
     toBatchTensor(inputSize: number, isCenterInputs?: boolean): tf.Tensor4D;
     /**
-     *  By setting the isManaged flag, all newly created tensors will be automatically
+     *  By setting the isManaged flag, all newly created tensors will be
      *  automatically disposed after the batch tensor has been created
      */
     managed(): this;

build/NetInput.js

@@ -16,6 +16,7 @@ var NetInput = /** @class */ (function () {
         this._isBatchInput = false;
         this._inputDimensions = [];
         this._paddings = [];
+        this._inputSize = 0;
         if (isTensor_1.isTensor4D(inputs)) {
             this._inputs = tf.unstack(inputs);
         }
@@ -92,6 +93,29 @@ var NetInput = /** @class */ (function () {
         enumerable: true,
         configurable: true
     });
+    Object.defineProperty(NetInput.prototype, "inputSize", {
+        get: function () {
+            return this._inputSize;
+        },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(NetInput.prototype, "relativePaddings", {
+        get: function () {
+            var _this = this;
+            return Array(this.inputs.length).fill(0).map(function (_, batchIdx) { return _this.getRelativePaddings(batchIdx); });
+        },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(NetInput.prototype, "reshapedInputDimensions", {
+        get: function () {
+            var _this = this;
+            return Array(this.inputs.length).fill(0).map(function (_, batchIdx) { return _this.getReshapedInputDimensions(batchIdx); });
+        },
+        enumerable: true,
+        configurable: true
+    });
     NetInput.prototype.getInputDimensions = function (batchIdx) {
         return this._inputDimensions[batchIdx];
     };
@@ -104,9 +128,21 @@ var NetInput = /** @class */ (function () {
     NetInput.prototype.getPaddings = function (batchIdx) {
         return this._paddings[batchIdx];
     };
+    NetInput.prototype.getRelativePaddings = function (batchIdx) {
+        return new Point_1.Point((this.getPaddings(batchIdx).x + this.getInputWidth(batchIdx)) / this.getInputWidth(batchIdx), (this.getPaddings(batchIdx).y + this.getInputHeight(batchIdx)) / this.getInputHeight(batchIdx));
+    };
+    NetInput.prototype.getReshapedInputDimensions = function (batchIdx) {
+        var _a = [this.getInputHeight(batchIdx), this.getInputWidth(batchIdx)], h = _a[0], w = _a[1];
+        var f = this.inputSize / Math.max(h, w);
+        return {
+            height: Math.floor(h * f),
+            width: Math.floor(w * f)
+        };
+    };
     NetInput.prototype.toBatchTensor = function (inputSize, isCenterInputs) {
         var _this = this;
         if (isCenterInputs === void 0) { isCenterInputs = true; }
+        this._inputSize = inputSize;
         return tf.tidy(function () {
             var inputTensors = _this._inputs.map(function (inputTensor) {
                 var _a = inputTensor.shape, originalHeight = _a[0], originalWidth = _a[1];
@@ -127,7 +163,7 @@ var NetInput = /** @class */ (function () {
         });
     };
     /**
-     *  By setting the isManaged flag, all newly created tensors will be automatically
+     *  By setting the isManaged flag, all newly created tensors will be
      *  automatically disposed after the batch tensor has been created
      */
     NetInput.prototype.managed = function () {

build/NetInput.js.map

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\ No newline at end of file

build/Rect.d.ts

+import { BoundingBox } from './BoundingBox';
 export interface IRect {
     x: number;
     y: number;
@@ -15,5 +16,6 @@ export declare class Rect implements IRect {
     toSquare(): Rect;
     pad(padX: number, padY: number): Rect;
     floor(): Rect;
+    toBoundingBox(): BoundingBox;
     clipAtImageBorders(imgWidth: number, imgHeight: number): Rect;
 }

build/Rect.js

 "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
+var BoundingBox_1 = require("./BoundingBox");
 var Rect = /** @class */ (function () {
     function Rect(x, y, width, height) {
         this.x = x;
@@ -41,6 +42,9 @@ var Rect = /** @class */ (function () {
     Rect.prototype.floor = function () {
         return new Rect(Math.floor(this.x), Math.floor(this.y), Math.floor(this.width), Math.floor(this.height));
     };
+    Rect.prototype.toBoundingBox = function () {
+        return new BoundingBox_1.BoundingBox(this.x, this.y, this.x + this.width, this.y + this.height);
+    };
     Rect.prototype.clipAtImageBorders = function (imgWidth, imgHeight) {
         var _a = this, x = _a.x, y = _a.y, right = _a.right, bottom = _a.bottom;
         var clippedX = Math.max(x, 0);

build/Rect.js.map

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\ No newline at end of file

build/allFacesFactory.d.ts

+import { TinyYolov2 } from '.';
 import { FaceDetectionNet } from './faceDetectionNet/FaceDetectionNet';
 import { FaceLandmarkNet } from './faceLandmarkNet/FaceLandmarkNet';
 import { FaceRecognitionNet } from './faceRecognitionNet/FaceRecognitionNet';
 import { FullFaceDescription } from './FullFaceDescription';
 import { Mtcnn } from './mtcnn/Mtcnn';
 import { MtcnnForwardParams } from './mtcnn/types';
+import { TinyYolov2ForwardParams } from './tinyYolov2/types';
 import { TNetInput } from './types';
-export declare function allFacesFactory(detectionNet: FaceDetectionNet, landmarkNet: FaceLandmarkNet, recognitionNet: FaceRecognitionNet): (input: TNetInput, minConfidence?: number, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
+export declare function allFacesSsdMobilenetv1Factory(ssdMobilenetv1: FaceDetectionNet, landmarkNet: FaceLandmarkNet, recognitionNet: FaceRecognitionNet): (input: TNetInput, minConfidence?: number, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
+export declare function allFacesTinyYolov2Factory(tinyYolov2: TinyYolov2, landmarkNet: FaceLandmarkNet, recognitionNet: FaceRecognitionNet): (input: TNetInput, forwardParams?: TinyYolov2ForwardParams, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
 export declare function allFacesMtcnnFactory(mtcnn: Mtcnn, recognitionNet: FaceRecognitionNet): (input: TNetInput, mtcnnForwardParams?: MtcnnForwardParams, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;

build/allFacesFactory.js

@@ -30,16 +30,15 @@ function computeDescriptorsFactory(recognitionNet) {
         });
     };
 }
-function allFacesFactory(detectionNet, landmarkNet, recognitionNet) {
+function allFacesFactory(detectFaces, landmarkNet, recognitionNet) {
     var computeDescriptors = computeDescriptorsFactory(recognitionNet);
-    return function (input, minConfidence, useBatchProcessing) {
-        if (minConfidence === void 0) { minConfidence = 0.8; }
+    return function (input, useBatchProcessing) {
         if (useBatchProcessing === void 0) { useBatchProcessing = false; }
         return tslib_1.__awaiter(this, void 0, void 0, function () {
             var detections, faceTensors, faceLandmarksByFace, _a, alignedFaceBoxes, descriptors;
             return tslib_1.__generator(this, function (_b) {
                 switch (_b.label) {
-                    case 0: return [4 /*yield*/, detectionNet.locateFaces(input, minConfidence)];
+                    case 0: return [4 /*yield*/, detectFaces(input)];
                     case 1:
                         detections = _b.sent();
                         return [4 /*yield*/, extractFaceTensors_1.extractFaceTensors(input, detections)];
@@ -69,7 +68,36 @@ function allFacesFactory(detectionNet, landmarkNet, recognitionNet) {
         });
     };
 }
-exports.allFacesFactory = allFacesFactory;
+function allFacesSsdMobilenetv1Factory(ssdMobilenetv1, landmarkNet, recognitionNet) {
+    return function (input, minConfidence, useBatchProcessing) {
+        if (minConfidence === void 0) { minConfidence = 0.8; }
+        if (useBatchProcessing === void 0) { useBatchProcessing = false; }
+        return tslib_1.__awaiter(this, void 0, void 0, function () {
+            var detectFaces, allFaces;
+            return tslib_1.__generator(this, function (_a) {
+                detectFaces = function (input) { return ssdMobilenetv1.locateFaces(input, minConfidence); };
+                allFaces = allFacesFactory(detectFaces, landmarkNet, recognitionNet);
+                return [2 /*return*/, allFaces(input, useBatchProcessing)];
+            });
+        });
+    };
+}
+exports.allFacesSsdMobilenetv1Factory = allFacesSsdMobilenetv1Factory;
+function allFacesTinyYolov2Factory(tinyYolov2, landmarkNet, recognitionNet) {
+    return function (input, forwardParams, useBatchProcessing) {
+        if (forwardParams === void 0) { forwardParams = {}; }
+        if (useBatchProcessing === void 0) { useBatchProcessing = false; }
+        return tslib_1.__awaiter(this, void 0, void 0, function () {
+            var detectFaces, allFaces;
+            return tslib_1.__generator(this, function (_a) {
+                detectFaces = function (input) { return tinyYolov2.locateFaces(input, forwardParams); };
+                allFaces = allFacesFactory(detectFaces, landmarkNet, recognitionNet);
+                return [2 /*return*/, allFaces(input, useBatchProcessing)];
+            });
+        });
+    };
+}
+exports.allFacesTinyYolov2Factory = allFacesTinyYolov2Factory;
 function allFacesMtcnnFactory(mtcnn, recognitionNet) {
     var computeDescriptors = computeDescriptorsFactory(recognitionNet);
     return function (input, mtcnnForwardParams, useBatchProcessing) {

build/allFacesFactory.js.map

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\ No newline at end of file

build/commons/nonMaxSuppression.js

 "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
+var iou_1 = require("../iou");
 function nonMaxSuppression(boxes, scores, iouThreshold, isIOU) {
     if (isIOU === void 0) { isIOU = true; }
-    var areas = boxes.map(function (box) { return (box.width + 1) * (box.height + 1); });
     var indicesSortedByScore = scores
         .map(function (score, boxIndex) { return ({ score: score, boxIndex: boxIndex }); })
         .sort(function (c1, c2) { return c1.score - c2.score; })
@@ -17,13 +17,7 @@ function nonMaxSuppression(boxes, scores, iouThreshold, isIOU) {
             var idx = indices[i];
             var currBox = boxes[curr];
             var idxBox = boxes[idx];
-            var width = Math.max(0.0, Math.min(currBox.right, idxBox.right) - Math.max(currBox.left, idxBox.left) + 1);
-            var height = Math.max(0.0, Math.min(currBox.bottom, idxBox.bottom) - Math.max(currBox.top, idxBox.top) + 1);
-            var interSection = width * height;
-            var out = isIOU
-                ? interSection / (areas[curr] + areas[idx] - interSection)
-                : interSection / Math.min(areas[curr], areas[idx]);
-            outputs.push(out);
+            outputs.push(iou_1.iou(currBox, idxBox, isIOU));
         }
         indicesSortedByScore = indicesSortedByScore.filter(function (_, j) { return outputs[j] <= iouThreshold; });
     };

build/commons/nonMaxSuppression.js.map

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+{"version":3,"file":"nonMaxSuppression.js","sourceRoot":"","sources":["../../src/commons/nonMaxSuppression.ts"],"names":[],"mappings":";;AACA,8BAA6B;AAE7B,2BACE,KAAoB,EACpB,MAAgB,EAChB,YAAoB,EACpB,KAAqB;IAArB,sBAAA,EAAA,YAAqB;IAGrB,IAAI,oBAAoB,GAAG,MAAM;SAC9B,GAAG,CAAC,UAAC,KAAK,EAAE,QAAQ,IAAK,OAAA,CAAC,EAAE,KAAK,OAAA,EAAE,QAAQ,UAAA,EAAE,CAAC,EAArB,CAAqB,CAAC;SAC/C,IAAI,CAAC,UAAC,EAAE,EAAE,EAAE,IAAK,OAAA,EAAE,CAAC,KAAK,GAAG,EAAE,CAAC,KAAK,EAAnB,CAAmB,CAAC;SACrC,GAAG,CAAC,UAAA,CAAC,IAAI,OAAA,CAAC,CAAC,QAAQ,EAAV,CAAU,CAAC,CAAA;IAEvB,IAAM,IAAI,GAAa,EAAE,CAAA;;QAGvB,IAAM,IAAI,GAAG,oBAAoB,CAAC,GAAG,EAAY,CAAA;QACjD,IAAI,CAAC,IAAI,CAAC,IAAI,CAAC,CAAA;QAEf,IAAM,OAAO,GAAG,oBAAoB,CAAA;QAEpC,IAAM,OAAO,GAAa,EAAE,CAAA;QAC5B,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,OAAO,CAAC,MAAM,EAAE,CAAC,EAAE,EAAE;YACvC,IAAM,GAAG,GAAG,OAAO,CAAC,CAAC,CAAC,CAAA;YAEtB,IAAM,OAAO,GAAG,KAAK,CAAC,IAAI,CAAC,CAAA;YAC3B,IAAM,MAAM,GAAG,KAAK,CAAC,GAAG,CAAC,CAAA;YAEzB,OAAO,CAAC,IAAI,CAAC,SAAG,CAAC,OAAO,EAAE,MAAM,EAAE,KAAK,CAAC,CAAC,CAAA;SAC1C;QAED,oBAAoB,GAAG,oBAAoB,CAAC,MAAM,CAChD,UAAC,CAAC,EAAE,CAAC,IAAK,OAAA,OAAO,CAAC,CAAC,CAAC,IAAI,YAAY,EAA1B,CAA0B,CACrC,CAAA;IACH,CAAC;IAnBD,OAAM,oBAAoB,CAAC,MAAM,GAAG,CAAC;;KAmBpC;IAED,OAAO,IAAI,CAAA;AAEb,CAAC;AArCD,8CAqCC"}
\ No newline at end of file

build/faceDetectionNet/FaceDetectionNet.js

@@ -47,7 +47,7 @@ var FaceDetectionNet = /** @class */ (function (_super) {
         if (minConfidence === void 0) { minConfidence = 0.8; }
         if (maxResults === void 0) { maxResults = 100; }
         return tslib_1.__awaiter(this, void 0, void 0, function () {
-            var netInput, _a, _boxes, _scores, boxes, scores, i, scoresData, _b, _c, iouThreshold, indices, paddedHeightRelative, paddedWidthRelative, results;
+            var netInput, _a, _boxes, _scores, boxes, scores, i, scoresData, _b, _c, iouThreshold, indices, paddings, results;
             return tslib_1.__generator(this, function (_d) {
                 switch (_d.label) {
                     case 0: return [4 /*yield*/, toNetInput_1.toNetInput(input, true)];
@@ -66,18 +66,17 @@ var FaceDetectionNet = /** @class */ (function (_super) {
                         scoresData = _c.apply(_b, [_d.sent()]);
                         iouThreshold = 0.5;
                         indices = nonMaxSuppression_1.nonMaxSuppression(boxes, scoresData, maxResults, iouThreshold, minConfidence);
-                        paddedHeightRelative = (netInput.getPaddings(0).y + netInput.getInputHeight(0)) / netInput.getInputHeight(0);
-                        paddedWidthRelative = (netInput.getPaddings(0).x + netInput.getInputWidth(0)) / netInput.getInputWidth(0);
+                        paddings = netInput.getRelativePaddings(0);
                         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];
+                            ].map(function (val) { return val * paddings.y; }), 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];
+                            ].map(function (val) { return val * paddings.x; }), left = _b[0], right = _b[1];
                             return new FaceDetection_1.FaceDetection(scoresData[idx], new Rect_1.Rect(left, top, right - left, bottom - top), {
                                 height: netInput.getInputHeight(0),
                                 width: netInput.getInputWidth(0)

build/faceDetectionNet/FaceDetectionNet.js.map

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\ No newline at end of file

build/faceRecognitionNet/FaceRecognitionNet.js

@@ -3,11 +3,11 @@ Object.defineProperty(exports, "__esModule", { value: true });
 var tslib_1 = require("tslib");
 var tf = require("@tensorflow/tfjs-core");
 var NeuralNetwork_1 = require("../commons/NeuralNetwork");
+var normalize_1 = require("../commons/normalize");
 var toNetInput_1 = require("../toNetInput");
 var convLayer_1 = require("./convLayer");
 var extractParams_1 = require("./extractParams");
 var loadQuantizedParams_1 = require("./loadQuantizedParams");
-var normalize_1 = require("./normalize");
 var residualLayer_1 = require("./residualLayer");
 var FaceRecognitionNet = /** @class */ (function (_super) {
     tslib_1.__extends(FaceRecognitionNet, _super);
@@ -21,7 +21,8 @@ var FaceRecognitionNet = /** @class */ (function (_super) {
         }
         return tf.tidy(function () {
             var batchTensor = input.toBatchTensor(150, true);
-            var normalized = normalize_1.normalize(batchTensor);
+            var meanRgb = [122.782, 117.001, 104.298];
+            var normalized = normalize_1.normalize(batchTensor, meanRgb).div(tf.scalar(256));
             var out = convLayer_1.convDown(normalized, params.conv32_down);
             out = tf.maxPool(out, 3, 2, 'valid');
             out = residualLayer_1.residual(out, params.conv32_1);

build/faceRecognitionNet/FaceRecognitionNet.js.map

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+{"version":3,"file":"FaceRecognitionNet.js","sourceRoot":"","sources":["../../src/faceRecognitionNet/FaceRecognitionNet.ts"],"names":[],"mappings":";;;AAAA,0CAA4C;AAE5C,0DAAyD;AACzD,kDAAiD;AAEjD,4CAA2C;AAE3C,yCAAuC;AACvC,iDAAgD;AAChD,6DAA4D;AAC5D,iDAAyD;AAGzD;IAAwC,8CAAwB;IAE9D;eACE,kBAAM,oBAAoB,CAAC;IAC7B,CAAC;IAEM,yCAAY,GAAnB,UAAoB,KAAe;QAEzB,IAAA,oBAAM,CAAS;QAEvB,IAAI,CAAC,MAAM,EAAE;YACX,MAAM,IAAI,KAAK,CAAC,kDAAkD,CAAC,CAAA;SACpE;QAED,OAAO,EAAE,CAAC,IAAI,CAAC;YACb,IAAM,WAAW,GAAG,KAAK,CAAC,aAAa,CAAC,GAAG,EAAE,IAAI,CAAC,CAAA;YAElD,IAAM,OAAO,GAAG,CAAC,OAAO,EAAE,OAAO,EAAE,OAAO,CAAC,CAAA;YAC3C,IAAM,UAAU,GAAG,qBAAS,CAAC,WAAW,EAAE,OAAO,CAAC,CAAC,GAAG,CAAC,EAAE,CAAC,MAAM,CAAC,GAAG,CAAC,CAAgB,CAAA;YAErF,IAAI,GAAG,GAAG,oBAAQ,CAAC,UAAU,EAAE,MAAM,CAAC,WAAW,CAAC,CAAA;YAClD,GAAG,GAAG,EAAE,CAAC,OAAO,CAAC,GAAG,EAAE,CAAC,EAAE,CAAC,EAAE,OAAO,CAAC,CAAA;YAEpC,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,QAAQ,CAAC,CAAA;YACpC,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,QAAQ,CAAC,CAAA;YACpC,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,QAAQ,CAAC,CAAA;YAEpC,GAAG,GAAG,4BAAY,CAAC,GAAG,EAAE,MAAM,CAAC,WAAW,CAAC,CAAA;YAC3C,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,QAAQ,CAAC,CAAA;YACpC,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,QAAQ,CAAC,CAAA;YACpC,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,QAAQ,CAAC,CAAA;YAEpC,GAAG,GAAG,4BAAY,CAAC,GAAG,EAAE,MAAM,CAAC,YAAY,CAAC,CAAA;YAC5C,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,SAAS,CAAC,CAAA;YACrC,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,SAAS,CAAC,CAAA;YAErC,GAAG,GAAG,4BAAY,CAAC,GAAG,EAAE,MAAM,CAAC,YAAY,CAAC,CAAA;YAC5C,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,SAAS,CAAC,CAAA;YACrC,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,SAAS,CAAC,CAAA;YACrC,GAAG,GAAG,4BAAY,CAAC,GAAG,EAAE,MAAM,CAAC,gBAAgB,CAAC,CAAA;YAEhD,IAAM,SAAS,GAAG,GAAG,CAAC,IAAI,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAgB,CAAA;YACjD,IAAM,cAAc,GAAG,EAAE,CAAC,MAAM,CAAC,SAAS,EAAE,MAAM,CAAC,EAAE,CAAC,CAAA;YAEtD,OAAO,cAAc,CAAA;QACvB,CAAC,CAAC,CAAA;IACJ,CAAC;IAEY,oCAAO,GAApB,UAAqB,KAAgB;;;;;;wBAC5B,KAAA,IAAI,CAAC,YAAY,CAAA;wBAAC,qBAAM,uBAAU,CAAC,KAAK,EAAE,IAAI,CAAC,EAAA;4BAAtD,sBAAO,SAAA,IAAI,GAAc,SAA6B,EAAC,EAAA;;;;KACxD;IAEY,kDAAqB,GAAlC,UAAmC,KAAgB;;;;;;4BAChC,qBAAM,uBAAU,CAAC,KAAK,EAAE,IAAI,CAAC,EAAA;;wBAAxC,QAAQ,GAAG,SAA6B;wBAExC,qBAAqB,GAAG,EAAE,CAAC,IAAI,CACnC,cAAM,OAAA,EAAE,CAAC,OAAO,CAAC,KAAI,CAAC,YAAY,CAAC,QAAQ,CAAC,CAAC,EAAvC,CAAuC,CAC9C,CAAA;wBAE+B,qBAAM,OAAO,CAAC,GAAG,CAAC,qBAAqB,CAAC,GAAG,CACzE,UAAA,CAAC,IAAI,OAAA,CAAC,CAAC,IAAI,EAAE,EAAR,CAAQ,CACd,CAAC,EAAA;;wBAFI,uBAAuB,GAAG,SAEZ;wBAEpB,qBAAqB,CAAC,OAAO,CAAC,UAAA,CAAC,IAAI,OAAA,CAAC,CAAC,OAAO,EAAE,EAAX,CAAW,CAAC,CAAA;wBAE/C,sBAAO,QAAQ,CAAC,YAAY;gCAC1B,CAAC,CAAC,uBAAuB;gCACzB,CAAC,CAAC,uBAAuB,CAAC,CAAC,CAAC,EAAA;;;;KAC/B;IAES,gDAAmB,GAA7B,UAA8B,GAAuB;QACnD,OAAO,yCAAmB,CAAC,GAAG,CAAC,CAAA;IACjC,CAAC;IAES,0CAAa,GAAvB,UAAwB,OAAqB;QAC3C,OAAO,6BAAa,CAAC,OAAO,CAAC,CAAA;IAC/B,CAAC;IACH,yBAAC;AAAD,CAAC,AA7ED,CAAwC,6BAAa,GA6EpD;AA7EY,gDAAkB"}
\ No newline at end of file

build/globalApi.d.ts

@@ -15,24 +15,28 @@ export declare const detectionNet: FaceDetectionNet;
 export declare const landmarkNet: FaceLandmarkNet;
 export declare const recognitionNet: FaceRecognitionNet;
 export declare const nets: {
-    ssdMobilenet: FaceDetectionNet;
+    ssdMobilenetv1: FaceDetectionNet;
     faceLandmark68Net: FaceLandmarkNet;
     faceRecognitionNet: FaceRecognitionNet;
     mtcnn: Mtcnn;
     tinyYolov2: TinyYolov2;
 };
-export declare function loadFaceDetectionModel(url: string): Promise<void>;
+export declare function loadSsdMobilenetv1Model(url: string): Promise<void>;
 export declare function loadFaceLandmarkModel(url: string): Promise<void>;
 export declare function loadFaceRecognitionModel(url: string): Promise<void>;
 export declare function loadMtcnnModel(url: string): Promise<void>;
 export declare function loadTinyYolov2Model(url: string): Promise<void>;
+export declare function loadFaceDetectionModel(url: string): Promise<void>;
 export declare function loadModels(url: string): Promise<[void, void, void, void, void]>;
 export declare function locateFaces(input: TNetInput, minConfidence?: number, maxResults?: number): Promise<FaceDetection[]>;
 export declare function detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]>;
 export declare function computeFaceDescriptor(input: TNetInput): Promise<Float32Array | Float32Array[]>;
 export declare function mtcnn(input: TNetInput, forwardParams: MtcnnForwardParams): Promise<MtcnnResult[]>;
 export declare function tinyYolov2(input: TNetInput, forwardParams: TinyYolov2ForwardParams): Promise<FaceDetection[]>;
-export declare type allFacesFunction = (input: tf.Tensor | NetInput | TNetInput, minConfidence?: number, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
-export declare const allFaces: allFacesFunction;
+export declare type allFacesSsdMobilenetv1Function = (input: tf.Tensor | NetInput | TNetInput, minConfidence?: number, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
+export declare const allFacesSsdMobilenetv1: allFacesSsdMobilenetv1Function;
+export declare type allFacesTinyYolov2Function = (input: tf.Tensor | NetInput | TNetInput, forwardParams?: TinyYolov2ForwardParams, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
+export declare const allFacesTinyYolov2: allFacesTinyYolov2Function;
 export declare type allFacesMtcnnFunction = (input: tf.Tensor | NetInput | TNetInput, mtcnnForwardParams?: MtcnnForwardParams, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
 export declare const allFacesMtcnn: allFacesMtcnnFunction;
+export declare const allFaces: allFacesSsdMobilenetv1Function;

build/globalApi.js

@@ -12,16 +12,16 @@ exports.recognitionNet = new FaceRecognitionNet_1.FaceRecognitionNet();
 // nets need more specific names, to avoid ambiguity in future
 // when alternative net implementations are provided
 exports.nets = {
-    ssdMobilenet: exports.detectionNet,
+    ssdMobilenetv1: exports.detectionNet,
     faceLandmark68Net: exports.landmarkNet,
     faceRecognitionNet: exports.recognitionNet,
     mtcnn: new Mtcnn_1.Mtcnn(),
     tinyYolov2: new TinyYolov2_1.TinyYolov2()
 };
-function loadFaceDetectionModel(url) {
-    return exports.nets.ssdMobilenet.load(url);
+function loadSsdMobilenetv1Model(url) {
+    return exports.nets.ssdMobilenetv1.load(url);
 }
-exports.loadFaceDetectionModel = loadFaceDetectionModel;
+exports.loadSsdMobilenetv1Model = loadSsdMobilenetv1Model;
 function loadFaceLandmarkModel(url) {
     return exports.nets.faceLandmark68Net.load(url);
 }
@@ -38,9 +38,13 @@ function loadTinyYolov2Model(url) {
     return exports.nets.tinyYolov2.load(url);
 }
 exports.loadTinyYolov2Model = loadTinyYolov2Model;
+function loadFaceDetectionModel(url) {
+    return loadSsdMobilenetv1Model(url);
+}
+exports.loadFaceDetectionModel = loadFaceDetectionModel;
 function loadModels(url) {
     return Promise.all([
-        loadFaceDetectionModel(url),
+        loadSsdMobilenetv1Model(url),
         loadFaceLandmarkModel(url),
         loadFaceRecognitionModel(url),
         loadMtcnnModel(url),
@@ -49,7 +53,7 @@ function loadModels(url) {
 }
 exports.loadModels = loadModels;
 function locateFaces(input, minConfidence, maxResults) {
-    return exports.nets.ssdMobilenet.locateFaces(input, minConfidence, maxResults);
+    return exports.nets.ssdMobilenetv1.locateFaces(input, minConfidence, maxResults);
 }
 exports.locateFaces = locateFaces;
 function detectLandmarks(input) {
@@ -68,6 +72,8 @@ function tinyYolov2(input, forwardParams) {
     return exports.nets.tinyYolov2.locateFaces(input, forwardParams);
 }
 exports.tinyYolov2 = tinyYolov2;
-exports.allFaces = allFacesFactory_1.allFacesFactory(exports.nets.ssdMobilenet, exports.nets.faceLandmark68Net, exports.nets.faceRecognitionNet);
+exports.allFacesSsdMobilenetv1 = allFacesFactory_1.allFacesSsdMobilenetv1Factory(exports.nets.ssdMobilenetv1, exports.nets.faceLandmark68Net, exports.nets.faceRecognitionNet);
+exports.allFacesTinyYolov2 = allFacesFactory_1.allFacesTinyYolov2Factory(exports.nets.tinyYolov2, exports.nets.faceLandmark68Net, exports.nets.faceRecognitionNet);
 exports.allFacesMtcnn = allFacesFactory_1.allFacesMtcnnFactory(exports.nets.mtcnn, exports.nets.faceRecognitionNet);
+exports.allFaces = exports.allFacesSsdMobilenetv1;
 //# sourceMappingURL=globalApi.js.map
\ No newline at end of file

build/globalApi.js.map

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\ No newline at end of file

build/index.d.ts

 import * as tf from '@tensorflow/tfjs-core';
 export { tf };
+export * from './BoundingBox';
 export * from './FaceDetection';
 export * from './FullFaceDescription';
 export * from './NetInput';
@@ -13,8 +14,10 @@ export * from './faceDetectionNet';
 export * from './faceLandmarkNet';
 export * from './faceRecognitionNet';
 export * from './globalApi';
+export * from './iou';
 export * from './mtcnn';
 export * from './padToSquare';
 export * from './tinyYolov2';
 export * from './toNetInput';
 export * from './utils';
+export * from './types';

build/index.js

@@ -3,6 +3,7 @@ Object.defineProperty(exports, "__esModule", { value: true });
 var tslib_1 = require("tslib");
 var tf = require("@tensorflow/tfjs-core");
 exports.tf = tf;
+tslib_1.__exportStar(require("./BoundingBox"), exports);
 tslib_1.__exportStar(require("./FaceDetection"), exports);
 tslib_1.__exportStar(require("./FullFaceDescription"), exports);
 tslib_1.__exportStar(require("./NetInput"), exports);
@@ -16,6 +17,7 @@ tslib_1.__exportStar(require("./faceDetectionNet"), exports);
 tslib_1.__exportStar(require("./faceLandmarkNet"), exports);
 tslib_1.__exportStar(require("./faceRecognitionNet"), exports);
 tslib_1.__exportStar(require("./globalApi"), exports);
+tslib_1.__exportStar(require("./iou"), exports);
 tslib_1.__exportStar(require("./mtcnn"), exports);
 tslib_1.__exportStar(require("./padToSquare"), exports);
 tslib_1.__exportStar(require("./tinyYolov2"), exports);

build/index.js.map

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\ No newline at end of file

build/tinyYolov2/TinyYolov2.d.ts

 import * as tf from '@tensorflow/tfjs-core';
+import { BoundingBox } from '../BoundingBox';
 import { NeuralNetwork } from '../commons/NeuralNetwork';
 import { FaceDetection } from '../FaceDetection';
 import { NetInput } from '../NetInput';
+import { Point } from '../Point';
 import { TNetInput } from '../types';
-import { NetParams, TinyYolov2ForwardParams } from './types';
+import { NetParams, PostProcessingParams, TinyYolov2ForwardParams } from './types';
 export declare class TinyYolov2 extends NeuralNetwork<NetParams> {
-    constructor();
+    private _withSeparableConvs;
+    private _anchors;
+    constructor(withSeparableConvs?: boolean);
+    readonly withSeparableConvs: boolean;
+    readonly anchors: Point[];
     forwardInput(input: NetInput, inputSize: number): tf.Tensor4D;
     forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D>;
     locateFaces(input: TNetInput, forwardParams?: TinyYolov2ForwardParams): Promise<FaceDetection[]>;
+    postProcess(outputTensor: tf.Tensor4D, {scoreThreshold, paddings}: PostProcessingParams): {
+        box: BoundingBox;
+        score: number;
+        row: number;
+        col: number;
+        anchor: number;
+    }[];
     protected loadQuantizedParams(uri: string | undefined): Promise<{
         params: NetParams;
         paramMappings: {

build/tinyYolov2/TinyYolov2.js

@@ -6,8 +6,10 @@ var BoundingBox_1 = require("../BoundingBox");
 var convLayer_1 = require("../commons/convLayer");
 var NeuralNetwork_1 = require("../commons/NeuralNetwork");
 var nonMaxSuppression_1 = require("../commons/nonMaxSuppression");
+var normalize_1 = require("../commons/normalize");
 var FaceDetection_1 = require("../FaceDetection");
 var toNetInput_1 = require("../toNetInput");
+var utils_1 = require("../utils");
 var config_1 = require("./config");
 var convWithBatchNorm_1 = require("./convWithBatchNorm");
 var extractParams_1 = require("./extractParams");
@@ -15,16 +17,39 @@ var getDefaultParams_1 = require("./getDefaultParams");
 var loadQuantizedParams_1 = require("./loadQuantizedParams");
 var TinyYolov2 = /** @class */ (function (_super) {
     tslib_1.__extends(TinyYolov2, _super);
-    function TinyYolov2() {
-        return _super.call(this, 'TinyYolov2') || this;
+    function TinyYolov2(withSeparableConvs) {
+        if (withSeparableConvs === void 0) { withSeparableConvs = true; }
+        var _this = _super.call(this, 'TinyYolov2') || this;
+        _this._withSeparableConvs = withSeparableConvs;
+        _this._anchors = withSeparableConvs ? config_1.BOX_ANCHORS_SEPARABLE : config_1.BOX_ANCHORS;
+        return _this;
     }
+    Object.defineProperty(TinyYolov2.prototype, "withSeparableConvs", {
+        get: function () {
+            return this._withSeparableConvs;
+        },
+        enumerable: true,
+        configurable: true
+    });
+    Object.defineProperty(TinyYolov2.prototype, "anchors", {
+        get: function () {
+            return this._anchors;
+        },
+        enumerable: true,
+        configurable: true
+    });
     TinyYolov2.prototype.forwardInput = function (input, inputSize) {
+        var _this = this;
         var params = this.params;
         if (!params) {
             throw new Error('TinyYolov2 - load model before inference');
         }
         var out = tf.tidy(function () {
-            var batchTensor = input.toBatchTensor(inputSize, false).div(tf.scalar(255)).toFloat();
+            var batchTensor = input.toBatchTensor(inputSize, false);
+            batchTensor = _this.withSeparableConvs
+                ? normalize_1.normalize(batchTensor, config_1.MEAN_RGB)
+                : batchTensor;
+            batchTensor = batchTensor.div(tf.scalar(256));
             var out = convWithBatchNorm_1.convWithBatchNorm(batchTensor, params.conv0);
             out = tf.maxPool(out, [2, 2], [2, 2], 'same');
             out = convWithBatchNorm_1.convWithBatchNorm(out, params.conv1);
@@ -61,69 +86,85 @@ var TinyYolov2 = /** @class */ (function (_super) {
     TinyYolov2.prototype.locateFaces = function (input, forwardParams) {
         if (forwardParams === void 0) { forwardParams = {}; }
         return tslib_1.__awaiter(this, void 0, void 0, function () {
-            var _a, _inputSize, scoreThreshold, inputSize, netInput, out, numCells, _b, boxesTensor, scoresTensor, expit, paddedHeightRelative, paddedWidthRelative, boxes, scores, row, col, box, score, ctX, ctY, width, height, x, y, indices, detections;
-            return tslib_1.__generator(this, function (_c) {
-                switch (_c.label) {
+            var _a, _inputSize, scoreThreshold, inputSize, netInput, out, out0, inputDimensions, paddings, results, boxes, scores, indices, detections;
+            return tslib_1.__generator(this, function (_b) {
+                switch (_b.label) {
                     case 0:
                         _a = getDefaultParams_1.getDefaultParams(forwardParams), _inputSize = _a.inputSize, scoreThreshold = _a.scoreThreshold;
                         inputSize = typeof _inputSize === 'string'
                             ? config_1.INPUT_SIZES[_inputSize]
                             : _inputSize;
                         if (typeof inputSize !== 'number') {
-                            throw new Error("TinyYolov2 - unkown inputSize: " + inputSize + ", expected number or one of xs | sm | md | lg");
+                            throw new Error("TinyYolov2 - unknown inputSize: " + inputSize + ", expected number or one of xs | sm | md | lg");
                         }
                         return [4 /*yield*/, toNetInput_1.toNetInput(input, true)];
                     case 1:
-                        netInput = _c.sent();
+                        netInput = _b.sent();
                         return [4 /*yield*/, this.forwardInput(netInput, inputSize)];
                     case 2:
-                        out = _c.sent();
-                        numCells = out.shape[1];
-                        _b = tf.tidy(function () {
-                            var reshaped = out.reshape([numCells, numCells, config_1.NUM_BOXES, 6]);
+                        out = _b.sent();
+                        out0 = tf.tidy(function () { return tf.unstack(out)[0].expandDims(); });
+                        inputDimensions = {
+                            width: netInput.getInputWidth(0),
+                            height: netInput.getInputHeight(0)
+                        };
+                        paddings = netInput.getRelativePaddings(0);
+                        results = this.postProcess(out0, { scoreThreshold: scoreThreshold, paddings: paddings });
+                        boxes = results.map(function (res) { return res.box; });
+                        scores = results.map(function (res) { return res.score; });
                         out.dispose();
+                        out0.dispose();
+                        indices = nonMaxSuppression_1.nonMaxSuppression(boxes.map(function (box) { return box.rescale(inputSize); }), scores, config_1.IOU_THRESHOLD, true);
+                        detections = indices.map(function (idx) {
+                            return new FaceDetection_1.FaceDetection(scores[idx], boxes[idx].toRect(), inputDimensions);
+                        });
+                        return [2 /*return*/, detections];
+                }
+            });
+        });
+    };
+    TinyYolov2.prototype.postProcess = function (outputTensor, _a) {
+        var _this = this;
+        var scoreThreshold = _a.scoreThreshold, paddings = _a.paddings;
+        var numCells = outputTensor.shape[1];
+        var _b = tf.tidy(function () {
+            var reshaped = outputTensor.reshape([numCells, numCells, config_1.NUM_BOXES, _this.withSeparableConvs ? 5 : 6]);
             var boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, config_1.NUM_BOXES, 4]);
             var scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, config_1.NUM_BOXES, 1]);
             return [boxes, scores];
         }), boxesTensor = _b[0], scoresTensor = _b[1];
-                        expit = function (x) { return 1 / (1 + Math.exp(-x)); };
-                        paddedHeightRelative = (netInput.getPaddings(0).y + netInput.getInputHeight(0)) / netInput.getInputHeight(0);
-                        paddedWidthRelative = (netInput.getPaddings(0).x + netInput.getInputWidth(0)) / netInput.getInputWidth(0);
-                        boxes = [];
-                        scores = [];
-                        for (row = 0; row < numCells; row++) {
-                            for (col = 0; col < numCells; col++) {
-                                for (box = 0; box < config_1.NUM_BOXES; box++) {
-                                    score = expit(scoresTensor.get(row, col, box, 0));
-                                    if (score > scoreThreshold) {
-                                        ctX = ((col + expit(boxesTensor.get(row, col, box, 0))) / numCells) * paddedWidthRelative;
-                                        ctY = ((row + expit(boxesTensor.get(row, col, box, 1))) / numCells) * paddedHeightRelative;
-                                        width = ((Math.exp(boxesTensor.get(row, col, box, 2)) * config_1.BOX_ANCHORS[box].x) / numCells) * paddedWidthRelative;
-                                        height = ((Math.exp(boxesTensor.get(row, col, box, 3)) * config_1.BOX_ANCHORS[box].y) / numCells) * paddedHeightRelative;
-                                        x = (ctX - (width / 2));
-                                        y = (ctY - (height / 2));
-                                        boxes.push(new BoundingBox_1.BoundingBox(x, y, x + width, y + height));
-                                        scores.push(score);
+        var results = [];
+        for (var row = 0; row < numCells; row++) {
+            for (var col = 0; col < numCells; col++) {
+                for (var anchor = 0; anchor < config_1.NUM_BOXES; anchor++) {
+                    var score = utils_1.sigmoid(scoresTensor.get(row, col, anchor, 0));
+                    if (!scoreThreshold || score > scoreThreshold) {
+                        var ctX = ((col + utils_1.sigmoid(boxesTensor.get(row, col, anchor, 0))) / numCells) * paddings.x;
+                        var ctY = ((row + utils_1.sigmoid(boxesTensor.get(row, col, anchor, 1))) / numCells) * paddings.y;
+                        var width = ((Math.exp(boxesTensor.get(row, col, anchor, 2)) * this.anchors[anchor].x) / numCells) * paddings.x;
+                        var height = ((Math.exp(boxesTensor.get(row, col, anchor, 3)) * this.anchors[anchor].y) / numCells) * paddings.y;
+                        var x = (ctX - (width / 2));
+                        var y = (ctY - (height / 2));
+                        results.push({
+                            box: new BoundingBox_1.BoundingBox(x, y, x + width, y + height),
+                            score: score,
+                            row: row,
+                            col: col,
+                            anchor: anchor
+                        });
                     }
                 }
             }
         }
         boxesTensor.dispose();
         scoresTensor.dispose();
-                        indices = nonMaxSuppression_1.nonMaxSuppression(boxes.map(function (box) { return new BoundingBox_1.BoundingBox(box.left * inputSize, box.top * inputSize, box.right * inputSize, box.bottom * inputSize); }), scores, config_1.IOU_THRESHOLD, true);
-                        detections = indices.map(function (idx) {
-                            return new FaceDetection_1.FaceDetection(scores[idx], boxes[idx].toRect(), { width: netInput.getInputWidth(0), height: netInput.getInputHeight(0) });
-                        });
-                        return [2 /*return*/, detections];
-                }
-            });
-        });
+        return results;
     };
     TinyYolov2.prototype.loadQuantizedParams = function (uri) {
-        return loadQuantizedParams_1.loadQuantizedParams(uri);
+        return loadQuantizedParams_1.loadQuantizedParams(uri, this.withSeparableConvs);
     };
     TinyYolov2.prototype.extractParams = function (weights) {
-        return extractParams_1.extractParams(weights);
+        return extractParams_1.extractParams(weights, this.withSeparableConvs);
     };
     return TinyYolov2;
 }(NeuralNetwork_1.NeuralNetwork));

build/tinyYolov2/TinyYolov2.js.map

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\ No newline at end of file

build/tinyYolov2/config.d.ts

@@ -8,3 +8,5 @@ export declare const INPUT_SIZES: {
 export declare const NUM_BOXES = 5;
 export declare const IOU_THRESHOLD = 0.4;
 export declare const BOX_ANCHORS: Point[];
+export declare const BOX_ANCHORS_SEPARABLE: Point[];
+export declare const MEAN_RGB: number[];

build/tinyYolov2/config.js

@@ -11,4 +11,12 @@ exports.BOX_ANCHORS = [
     new Point_1.Point(10.246, 4.59428),
     new Point_1.Point(12.6868, 11.8741)
 ];
+exports.BOX_ANCHORS_SEPARABLE = [
+    new Point_1.Point(1.603231, 2.094468),
+    new Point_1.Point(6.041143, 7.080126),
+    new Point_1.Point(2.882459, 3.518061),
+    new Point_1.Point(4.266906, 5.178857),
+    new Point_1.Point(9.041765, 10.66308)
+];
+exports.MEAN_RGB = [117.001, 114.697, 97.404];
 //# sourceMappingURL=config.js.map
\ No newline at end of file

build/tinyYolov2/config.js.map

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\ No newline at end of file

build/tinyYolov2/convWithBatchNorm.d.ts

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

build/tinyYolov2/convWithBatchNorm.js

@@ -2,13 +2,20 @@
 Object.defineProperty(exports, "__esModule", { value: true });
 var tf = require("@tensorflow/tfjs-core");
 var leaky_1 = require("./leaky");
+var types_1 = require("./types");
 function convWithBatchNorm(x, params) {
     return tf.tidy(function () {
         var out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);
+        if (params instanceof types_1.SeparableConvParams) {
+            out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid');
+            out = tf.add(out, params.bias);
+        }
+        else {
             out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid');
             out = tf.sub(out, params.bn.sub);
             out = tf.mul(out, params.bn.truediv);
             out = tf.add(out, params.conv.bias);
+        }
         return leaky_1.leaky(out);
     });
 }

build/tinyYolov2/convWithBatchNorm.js.map

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+{"version":3,"file":"convWithBatchNorm.js","sourceRoot":"","sources":["../../src/tinyYolov2/convWithBatchNorm.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAE5C,iCAAgC;AAChC,iCAAiE;AAEjE,2BAAkC,CAAc,EAAE,MAA+C;IAC/F,OAAO,EAAE,CAAC,IAAI,CAAC;QACb,IAAI,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAgB,CAAA;QAEpE,IAAI,MAAM,YAAY,2BAAmB,EAAE;YACzC,GAAG,GAAG,EAAE,CAAC,eAAe,CAAC,GAAG,EAAE,MAAM,CAAC,gBAAgB,EAAE,MAAM,CAAC,gBAAgB,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAA;YAChG,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,MAAM,CAAC,IAAI,CAAC,CAAA;SAC/B;aAAM;YACL,GAAG,GAAG,EAAE,CAAC,MAAM,CAAC,GAAG,EAAE,MAAM,CAAC,IAAI,CAAC,OAAO,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAA;YAC1D,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,MAAM,CAAC,EAAE,CAAC,GAAG,CAAC,CAAA;YAChC,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,MAAM,CAAC,EAAE,CAAC,OAAO,CAAC,CAAA;YACpC,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,MAAM,CAAC,IAAI,CAAC,IAAI,CAAC,CAAA;SACpC;QAED,OAAO,aAAK,CAAC,GAAG,CAAC,CAAA;IACnB,CAAC,CAAC,CAAA;AACJ,CAAC;AAhBD,8CAgBC"}
\ No newline at end of file

build/tinyYolov2/extractParams.d.ts

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

build/tinyYolov2/extractParams.js

@@ -3,6 +3,7 @@ Object.defineProperty(exports, "__esModule", { value: true });
 var tf = require("@tensorflow/tfjs-core");
 var extractConvParamsFactory_1 = require("../commons/extractConvParamsFactory");
 var extractWeightsFactory_1 = require("../commons/extractWeightsFactory");
+var types_1 = require("./types");
 function extractorsFactory(extractWeights, paramMappings) {
     var extractConvParams = extractConvParamsFactory_1.extractConvParamsFactory(extractWeights, paramMappings);
     function extractBatchNormParams(size, mappedPrefix) {
@@ -16,24 +17,34 @@ function extractorsFactory(extractWeights, paramMappings) {
         var bn = extractBatchNormParams(channelsOut, mappedPrefix + "/bn");
         return { conv: conv, bn: bn };
     }
+    function extractSeparableConvParams(channelsIn, channelsOut, mappedPrefix) {
+        var depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);
+        var pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);
+        var bias = tf.tensor1d(extractWeights(channelsOut));
+        paramMappings.push({ paramPath: mappedPrefix + "/depthwise_filter" }, { paramPath: mappedPrefix + "/pointwise_filter" }, { paramPath: mappedPrefix + "/bias" });
+        return new types_1.SeparableConvParams(depthwise_filter, pointwise_filter, bias);
+    }
     return {
         extractConvParams: extractConvParams,
-        extractConvWithBatchNormParams: extractConvWithBatchNormParams
+        extractConvWithBatchNormParams: extractConvWithBatchNormParams,
+        extractSeparableConvParams: extractSeparableConvParams
     };
 }
-function extractParams(weights) {
+function extractParams(weights, withSeparableConvs) {
     var _a = extractWeightsFactory_1.extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
     var paramMappings = [];
-    var _b = extractorsFactory(extractWeights, paramMappings), extractConvParams = _b.extractConvParams, extractConvWithBatchNormParams = _b.extractConvWithBatchNormParams;
-    var conv0 = extractConvWithBatchNormParams(3, 16, 'conv0');
-    var conv1 = extractConvWithBatchNormParams(16, 32, 'conv1');
-    var conv2 = extractConvWithBatchNormParams(32, 64, 'conv2');
-    var conv3 = extractConvWithBatchNormParams(64, 128, 'conv3');
-    var conv4 = extractConvWithBatchNormParams(128, 256, 'conv4');
-    var conv5 = extractConvWithBatchNormParams(256, 512, 'conv5');
-    var conv6 = extractConvWithBatchNormParams(512, 1024, 'conv6');
-    var conv7 = extractConvWithBatchNormParams(1024, 1024, 'conv7');
-    var conv8 = extractConvParams(1024, 30, 1, 'conv8');
+    var _b = extractorsFactory(extractWeights, paramMappings), extractConvParams = _b.extractConvParams, extractConvWithBatchNormParams = _b.extractConvWithBatchNormParams, extractSeparableConvParams = _b.extractSeparableConvParams;
+    var extractConvFn = withSeparableConvs ? extractSeparableConvParams : extractConvWithBatchNormParams;
+    var numAnchorEncodings = withSeparableConvs ? 5 : 6;
+    var conv0 = extractConvFn(3, 16, 'conv0');
+    var conv1 = extractConvFn(16, 32, 'conv1');
+    var conv2 = extractConvFn(32, 64, 'conv2');
+    var conv3 = extractConvFn(64, 128, 'conv3');
+    var conv4 = extractConvFn(128, 256, 'conv4');
+    var conv5 = extractConvFn(256, 512, 'conv5');
+    var conv6 = extractConvFn(512, 1024, 'conv6');
+    var conv7 = extractConvFn(1024, 1024, 'conv7');
+    var conv8 = extractConvParams(1024, 5 * numAnchorEncodings, 1, 'conv8');
     if (getRemainingWeights().length !== 0) {
         throw new Error("weights remaing after extract: " + getRemainingWeights().length);
     }

build/tinyYolov2/extractParams.js.map

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+{"version":3,"file":"extractParams.js","sourceRoot":"","sources":["../../src/tinyYolov2/extractParams.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAE5C,gFAA+E;AAC/E,0EAAyE;AAEzE,iCAAuF;AAEvF,2BAA2B,cAAsC,EAAE,aAA6B;IAE9F,IAAM,iBAAiB,GAAG,mDAAwB,CAAC,cAAc,EAAE,aAAa,CAAC,CAAA;IAEjF,gCAAgC,IAAY,EAAE,YAAoB;QAEhE,IAAM,GAAG,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,IAAI,CAAC,CAAC,CAAA;QAC7C,IAAM,OAAO,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,IAAI,CAAC,CAAC,CAAA;QAEjD,aAAa,CAAC,IAAI,CAChB,EAAE,SAAS,EAAK,YAAY,SAAM,EAAE,EACpC,EAAE,SAAS,EAAK,YAAY,aAAU,EAAE,CACzC,CAAA;QAED,OAAO,EAAE,GAAG,KAAA,EAAE,OAAO,SAAA,EAAE,CAAA;IACzB,CAAC;IAED,wCAAwC,UAAkB,EAAE,WAAmB,EAAE,YAAoB;QAEnG,IAAM,IAAI,GAAG,iBAAiB,CAAC,UAAU,EAAE,WAAW,EAAE,CAAC,EAAK,YAAY,UAAO,CAAC,CAAA;QAClF,IAAM,EAAE,GAAG,sBAAsB,CAAC,WAAW,EAAK,YAAY,QAAK,CAAC,CAAA;QAEpE,OAAO,EAAE,IAAI,MAAA,EAAE,EAAE,IAAA,EAAE,CAAA;IACrB,CAAC;IAED,oCAAoC,UAAkB,EAAE,WAAmB,EAAE,YAAoB;QAC/F,IAAM,gBAAgB,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,CAAC,GAAG,CAAC,GAAG,UAAU,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,UAAU,EAAE,CAAC,CAAC,CAAC,CAAA;QAC/F,IAAM,gBAAgB,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,UAAU,GAAG,WAAW,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,UAAU,EAAE,WAAW,CAAC,CAAC,CAAA;QAC/G,IAAM,IAAI,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,WAAW,CAAC,CAAC,CAAA;QAErD,aAAa,CAAC,IAAI,CAChB,EAAE,SAAS,EAAK,YAAY,sBAAmB,EAAE,EACjD,EAAE,SAAS,EAAK,YAAY,sBAAmB,EAAE,EACjD,EAAE,SAAS,EAAK,YAAY,UAAO,EAAE,CACtC,CAAA;QAED,OAAO,IAAI,2BAAmB,CAC5B,gBAAgB,EAChB,gBAAgB,EAChB,IAAI,CACL,CAAA;IACH,CAAC;IAED,OAAO;QACL,iBAAiB,mBAAA;QACjB,8BAA8B,gCAAA;QAC9B,0BAA0B,4BAAA;KAC3B,CAAA;AAEH,CAAC;AAED,uBAA8B,OAAqB,EAAE,kBAA2B;IAExE,IAAA,2DAG4B,EAFhC,kCAAc,EACd,4CAAmB,CACa;IAElC,IAAM,aAAa,GAAmB,EAAE,CAAA;IAElC,IAAA,qDAI8C,EAHlD,wCAAiB,EACjB,kEAA8B,EAC9B,0DAA0B,CACwB;IAEpD,IAAM,aAAa,GAAG,kBAAkB,CAAC,CAAC,CAAC,0BAA0B,CAAC,CAAC,CAAC,8BAA8B,CAAA;IACtG,IAAM,kBAAkB,GAAG,kBAAkB,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA;IAErD,IAAM,KAAK,GAAG,aAAa,CAAC,CAAC,EAAE,EAAE,EAAE,OAAO,CAAE,CAAA;IAC5C,IAAM,KAAK,GAAG,aAAa,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAA;IAC5C,IAAM,KAAK,GAAG,aAAa,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAA;IAC5C,IAAM,KAAK,GAAG,aAAa,CAAC,EAAE,EAAE,GAAG,EAAE,OAAO,CAAC,CAAA;IAC7C,IAAM,KAAK,GAAG,aAAa,CAAC,GAAG,EAAE,GAAG,EAAE,OAAO,CAAC,CAAA;IAC9C,IAAM,KAAK,GAAG,aAAa,CAAC,GAAG,EAAE,GAAG,EAAE,OAAO,CAAC,CAAA;IAC9C,IAAM,KAAK,GAAG,aAAa,CAAC,GAAG,EAAE,IAAI,EAAE,OAAO,CAAC,CAAA;IAC/C,IAAM,KAAK,GAAG,aAAa,CAAC,IAAI,EAAE,IAAI,EAAE,OAAO,CAAC,CAAA;IAChD,IAAM,KAAK,GAAG,iBAAiB,CAAC,IAAI,EAAE,CAAC,GAAG,kBAAkB,EAAE,CAAC,EAAE,OAAO,CAAC,CAAA;IAEzE,IAAI,mBAAmB,EAAE,CAAC,MAAM,KAAK,CAAC,EAAE;QACtC,MAAM,IAAI,KAAK,CAAC,oCAAkC,mBAAmB,EAAE,CAAC,MAAQ,CAAC,CAAA;KAClF;IAED,IAAM,MAAM,GAAG,EAAE,KAAK,OAAA,EAAE,KAAK,OAAA,EAAE,KAAK,OAAA,EAAE,KAAK,OAAA,EAAE,KAAK,OAAA,EAAE,KAAK,OAAA,EAAE,KAAK,OAAA,EAAE,KAAK,OAAA,EAAE,KAAK,OAAA,EAAE,CAAA;IAEhF,OAAO,EAAE,MAAM,QAAA,EAAE,aAAa,eAAA,EAAE,CAAA;AAClC,CAAC;AAnCD,sCAmCC"}
\ No newline at end of file

build/tinyYolov2/getDefaultParams.d.ts

 import { SizeType, TinyYolov2ForwardParams } from './types';
 export declare function getDefaultParams(params: TinyYolov2ForwardParams): {
-    sizeType: SizeType;
+    inputSize: SizeType;
     scoreThreshold: number;
 } & TinyYolov2ForwardParams;

build/tinyYolov2/getDefaultParams.js

@@ -3,7 +3,7 @@ Object.defineProperty(exports, "__esModule", { value: true });
 var types_1 = require("./types");
 function getDefaultParams(params) {
     return Object.assign({}, {
-        sizeType: types_1.SizeType.MD,
+        inputSize: types_1.SizeType.MD,
         scoreThreshold: 0.5
     }, params);
 }

build/tinyYolov2/getDefaultParams.js.map

-{"version":3,"file":"getDefaultParams.js","sourceRoot":"","sources":["../../src/tinyYolov2/getDefaultParams.ts"],"names":[],"mappings":";;AAAA,iCAA4D;AAE5D,0BAAiC,MAA+B;IAC9D,OAAO,MAAM,CAAC,MAAM,CAClB,EAAE,EACF;QACE,QAAQ,EAAE,gBAAQ,CAAC,EAAE;QACrB,cAAc,EAAE,GAAG;KACpB,EACD,MAAM,CACP,CAAA;AACH,CAAC;AATD,4CASC"}
+{"version":3,"file":"getDefaultParams.js","sourceRoot":"","sources":["../../src/tinyYolov2/getDefaultParams.ts"],"names":[],"mappings":";;AAAA,iCAA4D;AAE5D,0BAAiC,MAA+B;IAC9D,OAAO,MAAM,CAAC,MAAM,CAClB,EAAE,EACF;QACE,SAAS,EAAE,gBAAQ,CAAC,EAAE;QACtB,cAAc,EAAE,GAAG;KACpB,EACD,MAAM,CACP,CAAA;AACH,CAAC;AATD,4CASC"}
\ No newline at end of file

build/tinyYolov2/index.d.ts

 import { TinyYolov2 } from './TinyYolov2';
 export * from './TinyYolov2';
-export declare function createTinyYolov2(weights: Float32Array): TinyYolov2;
+export declare function createTinyYolov2(weights: Float32Array, withSeparableConvs?: boolean): TinyYolov2;

build/tinyYolov2/index.js

@@ -3,8 +3,9 @@ Object.defineProperty(exports, "__esModule", { value: true });
 var tslib_1 = require("tslib");
 var TinyYolov2_1 = require("./TinyYolov2");
 tslib_1.__exportStar(require("./TinyYolov2"), exports);
-function createTinyYolov2(weights) {
-    var net = new TinyYolov2_1.TinyYolov2();
+function createTinyYolov2(weights, withSeparableConvs) {
+    if (withSeparableConvs === void 0) { withSeparableConvs = true; }
+    var net = new TinyYolov2_1.TinyYolov2(withSeparableConvs);
     net.extractWeights(weights);
     return net;
 }

build/tinyYolov2/index.js.map

-{"version":3,"file":"index.js","sourceRoot":"","sources":["../../src/tinyYolov2/index.ts"],"names":[],"mappings":";;;AAAA,2CAA0C;AAE1C,uDAA6B;AAE7B,0BAAiC,OAAqB;IACpD,IAAM,GAAG,GAAG,IAAI,uBAAU,EAAE,CAAA;IAC5B,GAAG,CAAC,cAAc,CAAC,OAAO,CAAC,CAAA;IAC3B,OAAO,GAAG,CAAA;AACZ,CAAC;AAJD,4CAIC"}
+{"version":3,"file":"index.js","sourceRoot":"","sources":["../../src/tinyYolov2/index.ts"],"names":[],"mappings":";;;AAAA,2CAA0C;AAE1C,uDAA6B;AAE7B,0BAAiC,OAAqB,EAAE,kBAAkC;IAAlC,mCAAA,EAAA,yBAAkC;IACxF,IAAM,GAAG,GAAG,IAAI,uBAAU,CAAC,kBAAkB,CAAC,CAAA;IAC9C,GAAG,CAAC,cAAc,CAAC,OAAO,CAAC,CAAA;IAC3B,OAAO,GAAG,CAAA;AACZ,CAAC;AAJD,4CAIC"}
\ No newline at end of file

build/tinyYolov2/leaky.js

@@ -3,7 +3,9 @@ Object.defineProperty(exports, "__esModule", { value: true });
 var tf = require("@tensorflow/tfjs-core");
 function leaky(x) {
     return tf.tidy(function () {
-        return tf.maximum(x, tf.mul(x, tf.scalar(0.10000000149011612)));
+        var min = tf.mul(x, tf.scalar(0.10000000149011612));
+        return tf.add(tf.relu(tf.sub(x, min)), min);
+        //return tf.maximum(x, min)
     });
 }
 exports.leaky = leaky;

build/tinyYolov2/leaky.js.map

-{"version":3,"file":"leaky.js","sourceRoot":"","sources":["../../src/tinyYolov2/leaky.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAE5C,eAAsB,CAAc;IAClC,OAAO,EAAE,CAAC,IAAI,CAAC;QACb,OAAO,EAAE,CAAC,OAAO,CAAC,CAAC,EAAE,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,mBAAmB,CAAC,CAAC,CAAC,CAAA;IACjE,CAAC,CAAC,CAAA;AACJ,CAAC;AAJD,sBAIC"}
+{"version":3,"file":"leaky.js","sourceRoot":"","sources":["../../src/tinyYolov2/leaky.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAE5C,eAAsB,CAAc;IAClC,OAAO,EAAE,CAAC,IAAI,CAAC;QACb,IAAM,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,mBAAmB,CAAC,CAAC,CAAA;QACrD,OAAO,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,IAAI,CAAC,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,GAAG,CAAC,CAAC,EAAE,GAAG,CAAC,CAAA;QAC3C,2BAA2B;IAC7B,CAAC,CAAC,CAAA;AACJ,CAAC;AAND,sBAMC"}
\ No newline at end of file

build/tinyYolov2/loadQuantizedParams.d.ts

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

build/tinyYolov2/loadQuantizedParams.js

@@ -4,7 +4,9 @@ var tslib_1 = require("tslib");
 var disposeUnusedWeightTensors_1 = require("../commons/disposeUnusedWeightTensors");
 var extractWeightEntryFactory_1 = require("../commons/extractWeightEntryFactory");
 var loadWeightMap_1 = require("../commons/loadWeightMap");
+var types_1 = require("./types");
 var DEFAULT_MODEL_NAME = 'tiny_yolov2_model';
+var DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model';
 function extractorsFactory(weightMap, paramMappings) {
     var extractWeightEntry = extractWeightEntryFactory_1.extractWeightEntryFactory(weightMap, paramMappings);
     function extractBatchNormParams(prefix) {
@@ -22,30 +24,38 @@ function extractorsFactory(weightMap, paramMappings) {
         var bn = extractBatchNormParams(prefix + "/bn");
         return { conv: conv, bn: bn };
     }
+    function extractSeparableConvParams(prefix) {
+        var depthwise_filter = extractWeightEntry(prefix + "/depthwise_filter", 4);
+        var pointwise_filter = extractWeightEntry(prefix + "/pointwise_filter", 4);
+        var bias = extractWeightEntry(prefix + "/bias", 1);
+        return new types_1.SeparableConvParams(depthwise_filter, pointwise_filter, bias);
+    }
     return {
         extractConvParams: extractConvParams,
-        extractConvWithBatchNormParams: extractConvWithBatchNormParams
+        extractConvWithBatchNormParams: extractConvWithBatchNormParams,
+        extractSeparableConvParams: extractSeparableConvParams
     };
 }
-function loadQuantizedParams(uri) {
+function loadQuantizedParams(uri, withSeparableConvs) {
     return tslib_1.__awaiter(this, void 0, void 0, function () {
-        var weightMap, paramMappings, _a, extractConvParams, extractConvWithBatchNormParams, params;
+        var weightMap, paramMappings, _a, extractConvParams, extractConvWithBatchNormParams, extractSeparableConvParams, extractConvFn, params;
         return tslib_1.__generator(this, function (_b) {
             switch (_b.label) {
-                case 0: return [4 /*yield*/, loadWeightMap_1.loadWeightMap(uri, DEFAULT_MODEL_NAME)];
+                case 0: return [4 /*yield*/, loadWeightMap_1.loadWeightMap(uri, withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME)];
                 case 1:
                     weightMap = _b.sent();
                     paramMappings = [];
-                    _a = extractorsFactory(weightMap, paramMappings), extractConvParams = _a.extractConvParams, extractConvWithBatchNormParams = _a.extractConvWithBatchNormParams;
+                    _a = extractorsFactory(weightMap, paramMappings), extractConvParams = _a.extractConvParams, extractConvWithBatchNormParams = _a.extractConvWithBatchNormParams, extractSeparableConvParams = _a.extractSeparableConvParams;
+                    extractConvFn = withSeparableConvs ? extractSeparableConvParams : extractConvWithBatchNormParams;
                     params = {
-                        conv0: extractConvWithBatchNormParams('conv0'),
-                        conv1: extractConvWithBatchNormParams('conv1'),
-                        conv2: extractConvWithBatchNormParams('conv2'),
-                        conv3: extractConvWithBatchNormParams('conv3'),
-                        conv4: extractConvWithBatchNormParams('conv4'),
-                        conv5: extractConvWithBatchNormParams('conv5'),
-                        conv6: extractConvWithBatchNormParams('conv6'),
-                        conv7: extractConvWithBatchNormParams('conv7'),
+                        conv0: extractConvFn('conv0'),
+                        conv1: extractConvFn('conv1'),
+                        conv2: extractConvFn('conv2'),
+                        conv3: extractConvFn('conv3'),
+                        conv4: extractConvFn('conv4'),
+                        conv5: extractConvFn('conv5'),
+                        conv6: extractConvFn('conv6'),
+                        conv7: extractConvFn('conv7'),
                         conv8: extractConvParams('conv8')
                     };
                     disposeUnusedWeightTensors_1.disposeUnusedWeightTensors(weightMap, paramMappings);

build/tinyYolov2/loadQuantizedParams.js.map

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\ No newline at end of file

build/tinyYolov2/types.d.ts

 import * as tf from '@tensorflow/tfjs-core';
 import { ConvParams } from '../commons/types';
+import { Point } from '../Point';
 export declare type BatchNorm = {
     sub: tf.Tensor1D;
     truediv: tf.Tensor1D;
 };
+export declare class SeparableConvParams {
+    depthwise_filter: tf.Tensor4D;
+    pointwise_filter: tf.Tensor4D;
+    bias: tf.Tensor1D;
+    constructor(depthwise_filter: tf.Tensor4D, pointwise_filter: tf.Tensor4D, bias: tf.Tensor1D);
+}
 export declare type ConvWithBatchNorm = {
     conv: ConvParams;
     bn: BatchNorm;
 };
 export declare type NetParams = {
-    conv0: ConvWithBatchNorm;
-    conv1: ConvWithBatchNorm;
-    conv2: ConvWithBatchNorm;
-    conv3: ConvWithBatchNorm;
-    conv4: ConvWithBatchNorm;
-    conv5: ConvWithBatchNorm;
-    conv6: ConvWithBatchNorm;
-    conv7: ConvWithBatchNorm;
+    conv0: ConvWithBatchNorm | SeparableConvParams;
+    conv1: ConvWithBatchNorm | SeparableConvParams;
+    conv2: ConvWithBatchNorm | SeparableConvParams;
+    conv3: ConvWithBatchNorm | SeparableConvParams;
+    conv4: ConvWithBatchNorm | SeparableConvParams;
+    conv5: ConvWithBatchNorm | SeparableConvParams;
+    conv6: ConvWithBatchNorm | SeparableConvParams;
+    conv7: ConvWithBatchNorm | SeparableConvParams;
     conv8: ConvParams;
 };
 export declare enum SizeType {
@@ -29,3 +36,7 @@ export declare type TinyYolov2ForwardParams = {
     inputSize?: SizeType | number;
     scoreThreshold?: number;
 };
+export declare type PostProcessingParams = {
+    scoreThreshold?: number;
+    paddings: Point;
+};

build/tinyYolov2/types.js

 "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
+var SeparableConvParams = /** @class */ (function () {
+    function SeparableConvParams(depthwise_filter, pointwise_filter, bias) {
+        this.depthwise_filter = depthwise_filter;
+        this.pointwise_filter = pointwise_filter;
+        this.bias = bias;
+    }
+    return SeparableConvParams;
+}());
+exports.SeparableConvParams = SeparableConvParams;
 var SizeType;
 (function (SizeType) {
     SizeType["XS"] = "xs";

build/tinyYolov2/types.js.map

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+{"version":3,"file":"types.js","sourceRoot":"","sources":["../../src/tinyYolov2/types.ts"],"names":[],"mappings":";;AAUA;IACE,6BACS,gBAA6B,EAC7B,gBAA6B,EAC7B,IAAiB;QAFjB,qBAAgB,GAAhB,gBAAgB,CAAa;QAC7B,qBAAgB,GAAhB,gBAAgB,CAAa;QAC7B,SAAI,GAAJ,IAAI,CAAa;IACvB,CAAC;IACN,0BAAC;AAAD,CAAC,AAND,IAMC;AANY,kDAAmB;AAyBhC,IAAY,QAKX;AALD,WAAY,QAAQ;IAClB,qBAAS,CAAA;IACT,qBAAS,CAAA;IACT,qBAAS,CAAA;IACT,qBAAS,CAAA;AACX,CAAC,EALW,QAAQ,GAAR,gBAAQ,KAAR,gBAAQ,QAKnB"}
\ No newline at end of file

build/utils.d.ts

@@ -3,6 +3,8 @@ import { Dimensions } from './types';
 export declare function isFloat(num: number): boolean;
 export declare function isEven(num: number): boolean;
 export declare function round(num: number): number;
+export declare function sigmoid(x: number): number;
+export declare function isDimensions(obj: any): boolean;
 export declare function resolveInput(arg: string | any): any;
 export declare function isLoaded(media: HTMLImageElement | HTMLVideoElement): boolean;
 export declare function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement): Promise<{}>;

build/utils.js

@@ -15,6 +15,14 @@ function round(num) {
     return Math.floor(num * 100) / 100;
 }
 exports.round = round;
+function sigmoid(x) {
+    return 1 / (1 + Math.exp(-x));
+}
+exports.sigmoid = sigmoid;
+function isDimensions(obj) {
+    return obj && obj.width && obj.height;
+}
+exports.isDimensions = isDimensions;
 function resolveInput(arg) {
     if (typeof arg === 'string') {
         return document.getElementById(arg);
@@ -37,14 +45,14 @@ function awaitMediaLoaded(media) {
                 return;
             e.currentTarget.removeEventListener('load', onLoad);
             e.currentTarget.removeEventListener('error', onError);
-            resolve();
+            resolve(e);
         }
         function onError(e) {
             if (!e.currentTarget)
                 return;
             e.currentTarget.removeEventListener('load', onLoad);
             e.currentTarget.removeEventListener('error', onError);
-            reject();
+            reject(e);
         }
         media.addEventListener('load', onLoad);
         media.addEventListener('error', onError);

build/utils.js.map

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\ No newline at end of file

dist/face-api.js

@@ -276,6 +276,9 @@
       Rect.prototype.floor = function () {
           return new Rect(Math.floor(this.x), Math.floor(this.y), Math.floor(this.width), Math.floor(this.height));
       };
+      Rect.prototype.toBoundingBox = function () {
+          return new BoundingBox(this.x, this.y, this.x + this.width, this.y + this.height);
+      };
       Rect.prototype.clipAtImageBorders = function (imgWidth, imgHeight) {
           var _a = this, x = _a.x, y = _a.y, right = _a.right, bottom = _a.bottom;
           var clippedX = Math.max(x, 0);
@@ -289,182 +292,6 @@
       return Rect;
   }());
 
-  var FaceDetection = /** @class */ (function () {
-      function FaceDetection(score, relativeBox, imageDims) {
-          var width = imageDims.width, height = imageDims.height;
-          this._imageWidth = width;
-          this._imageHeight = height;
-          this._score = score;
-          this._box = new Rect(relativeBox.x * width, relativeBox.y * height, relativeBox.width * width, relativeBox.height * height);
-      }
-      Object.defineProperty(FaceDetection.prototype, "score", {
-          get: function () {
-              return this._score;
-          },
-          enumerable: true,
-          configurable: true
-      });
-      Object.defineProperty(FaceDetection.prototype, "box", {
-          get: function () {
-              return this._box;
-          },
-          enumerable: true,
-          configurable: true
-      });
-      Object.defineProperty(FaceDetection.prototype, "imageWidth", {
-          get: function () {
-              return this._imageWidth;
-          },
-          enumerable: true,
-          configurable: true
-      });
-      Object.defineProperty(FaceDetection.prototype, "imageHeight", {
-          get: function () {
-              return this._imageHeight;
-          },
-          enumerable: true,
-          configurable: true
-      });
-      Object.defineProperty(FaceDetection.prototype, "relativeBox", {
-          get: function () {
-              return new Rect(this._box.x / this._imageWidth, this._box.y / this._imageHeight, this._box.width / this._imageWidth, this._box.height / this._imageHeight);
-          },
-          enumerable: true,
-          configurable: true
-      });
-      FaceDetection.prototype.getScore = function () {
-          return this.score;
-      };
-      FaceDetection.prototype.getBox = function () {
-          return this.box;
-      };
-      FaceDetection.prototype.getImageWidth = function () {
-          return this.imageWidth;
-      };
-      FaceDetection.prototype.getImageHeight = function () {
-          return this.imageHeight;
-      };
-      FaceDetection.prototype.getRelativeBox = function () {
-          return this.relativeBox;
-      };
-      FaceDetection.prototype.forSize = function (width, height) {
-          return new FaceDetection(this._score, this.getRelativeBox(), { width: width, height: height });
-      };
-      return FaceDetection;
-  }());
-
-  var FullFaceDescription = /** @class */ (function () {
-      function FullFaceDescription(_detection, _landmarks, _descriptor) {
-          this._detection = _detection;
-          this._landmarks = _landmarks;
-          this._descriptor = _descriptor;
-      }
-      Object.defineProperty(FullFaceDescription.prototype, "detection", {
-          get: function () {
-              return this._detection;
-          },
-          enumerable: true,
-          configurable: true
-      });
-      Object.defineProperty(FullFaceDescription.prototype, "landmarks", {
-          get: function () {
-              return this._landmarks;
-          },
-          enumerable: true,
-          configurable: true
-      });
-      Object.defineProperty(FullFaceDescription.prototype, "descriptor", {
-          get: function () {
-              return this._descriptor;
-          },
-          enumerable: true,
-          configurable: true
-      });
-      FullFaceDescription.prototype.forSize = function (width, height) {
-          return new FullFaceDescription(this._detection.forSize(width, height), this._landmarks.forSize(width, height), this._descriptor);
-      };
-      return FullFaceDescription;
-  }());
-
-  function isTensor(tensor$$1, dim) {
-      return tensor$$1 instanceof Tensor && tensor$$1.shape.length === dim;
-  }
-  function isTensor2D(tensor$$1) {
-      return isTensor(tensor$$1, 2);
-  }
-  function isTensor3D(tensor$$1) {
-      return isTensor(tensor$$1, 3);
-  }
-  function isTensor4D(tensor$$1) {
-      return isTensor(tensor$$1, 4);
-  }
-
-  /**
-   * Pads the smaller dimension of an image tensor with zeros, such that width === height.
-   *
-   * @param imgTensor The image tensor.
-   * @param isCenterImage (optional, default: false) If true, add padding on both sides of the image, such that the image.
-   * @returns The padded tensor with width === height.
-   */
-  function padToSquare(imgTensor, isCenterImage) {
-      if (isCenterImage === void 0) { isCenterImage = false; }
-      return tidy(function () {
-          var _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];
-          if (height === width) {
-              return imgTensor;
-          }
-          var dimDiff = Math.abs(height - width);
-          var paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1));
-          var paddingAxis = height > width ? 2 : 1;
-          var createPaddingTensor = function (paddingAmount) {
-              var paddingTensorShape = imgTensor.shape.slice();
-              paddingTensorShape[paddingAxis] = paddingAmount;
-              return fill(paddingTensorShape, 0);
-          };
-          var paddingTensorAppend = createPaddingTensor(paddingAmount);
-          var remainingPaddingAmount = dimDiff - paddingTensorAppend.shape[paddingAxis];
-          var paddingTensorPrepend = isCenterImage && remainingPaddingAmount
-              ? createPaddingTensor(remainingPaddingAmount)
-              : null;
-          var tensorsToStack = [
-              paddingTensorPrepend,
-              imgTensor,
-              paddingTensorAppend
-          ]
-              .filter(function (t) { return t !== null; });
-          return concat(tensorsToStack, paddingAxis);
-      });
-  }
-
-  var Point = /** @class */ (function () {
-      function Point(x, y) {
-          this.x = x;
-          this.y = y;
-      }
-      Point.prototype.add = function (pt) {
-          return new Point(this.x + pt.x, this.y + pt.y);
-      };
-      Point.prototype.sub = function (pt) {
-          return new Point(this.x - pt.x, this.y - pt.y);
-      };
-      Point.prototype.mul = function (pt) {
-          return new Point(this.x * pt.x, this.y * pt.y);
-      };
-      Point.prototype.div = function (pt) {
-          return new Point(this.x / pt.x, this.y / pt.y);
-      };
-      Point.prototype.abs = function () {
-          return new Point(Math.abs(this.x), Math.abs(this.y));
-      };
-      Point.prototype.magnitude = function () {
-          return Math.sqrt(Math.pow(this.x, 2) + Math.pow(this.y, 2));
-      };
-      Point.prototype.floor = function () {
-          return new Point(Math.floor(this.x), Math.floor(this.y));
-      };
-      return Point;
-  }());
-
   /*! *****************************************************************************
   Copyright (c) Microsoft Corporation. All rights reserved.
   Licensed under the Apache License, Version 2.0 (the "License"); you may not use
@@ -536,6 +363,19 @@
       }
   }
 
+  function isTensor(tensor$$1, dim) {
+      return tensor$$1 instanceof Tensor && tensor$$1.shape.length === dim;
+  }
+  function isTensor2D(tensor$$1) {
+      return isTensor(tensor$$1, 2);
+  }
+  function isTensor3D(tensor$$1) {
+      return isTensor(tensor$$1, 3);
+  }
+  function isTensor4D(tensor$$1) {
+      return isTensor(tensor$$1, 4);
+  }
+
   function isFloat(num) {
       return num % 1 !== 0;
   }
@@ -545,6 +385,12 @@
   function round$1(num) {
       return Math.floor(num * 100) / 100;
   }
+  function sigmoid$1(x) {
+      return 1 / (1 + Math.exp(-x));
+  }
+  function isDimensions(obj) {
+      return obj && obj.width && obj.height;
+  }
   function resolveInput(arg) {
       if (typeof arg === 'string') {
           return document.getElementById(arg);
@@ -565,14 +411,14 @@
                   return;
               e.currentTarget.removeEventListener('load', onLoad);
               e.currentTarget.removeEventListener('error', onError);
-              resolve();
+              resolve(e);
           }
           function onError(e) {
               if (!e.currentTarget)
                   return;
               e.currentTarget.removeEventListener('load', onLoad);
               e.currentTarget.removeEventListener('error', onError);
-              reject();
+              reject(e);
           }
           media.addEventListener('load', onLoad);
           media.addEventListener('error', onError);
@@ -645,6 +491,284 @@
       });
   }
 
+  var BoundingBox = /** @class */ (function () {
+      function BoundingBox(_left, _top, _right, _bottom) {
+          this._left = _left;
+          this._top = _top;
+          this._right = _right;
+          this._bottom = _bottom;
+      }
+      Object.defineProperty(BoundingBox.prototype, "left", {
+          get: function () {
+              return this._left;
+          },
+          enumerable: true,
+          configurable: true
+      });
+      Object.defineProperty(BoundingBox.prototype, "top", {
+          get: function () {
+              return this._top;
+          },
+          enumerable: true,
+          configurable: true
+      });
+      Object.defineProperty(BoundingBox.prototype, "right", {
+          get: function () {
+              return this._right;
+          },
+          enumerable: true,
+          configurable: true
+      });
+      Object.defineProperty(BoundingBox.prototype, "bottom", {
+          get: function () {
+              return this._bottom;
+          },
+          enumerable: true,
+          configurable: true
+      });
+      Object.defineProperty(BoundingBox.prototype, "width", {
+          get: function () {
+              return this.right - this.left;
+          },
+          enumerable: true,
+          configurable: true
+      });
+      Object.defineProperty(BoundingBox.prototype, "height", {
+          get: function () {
+              return this.bottom - this.top;
+          },
+          enumerable: true,
+          configurable: true
+      });
+      Object.defineProperty(BoundingBox.prototype, "area", {
+          get: function () {
+              return this.width * this.height;
+          },
+          enumerable: true,
+          configurable: true
+      });
+      BoundingBox.prototype.toSquare = function () {
+          var _a = this, left = _a.left, top = _a.top, right = _a.right, bottom = _a.bottom;
+          var off = (Math.abs(this.width - this.height) / 2);
+          if (this.width < this.height) {
+              left -= off;
+              right += off;
+          }
+          if (this.height < this.width) {
+              top -= off;
+              bottom += off;
+          }
+          return new BoundingBox(left, top, right, bottom);
+      };
+      BoundingBox.prototype.round = function () {
+          return new BoundingBox(Math.round(this.left), Math.round(this.top), Math.round(this.right), Math.round(this.bottom));
+      };
+      BoundingBox.prototype.padAtBorders = function (imageHeight, imageWidth) {
+          var w = this.width + 1;
+          var h = this.height + 1;
+          var dx = 1;
+          var dy = 1;
+          var edx = w;
+          var edy = h;
+          var x = this.left;
+          var y = this.top;
+          var ex = this.right;
+          var ey = this.bottom;
+          if (ex > imageWidth) {
+              edx = -ex + imageWidth + w;
+              ex = imageWidth;
+          }
+          if (ey > imageHeight) {
+              edy = -ey + imageHeight + h;
+              ey = imageHeight;
+          }
+          if (x < 1) {
+              edy = 2 - x;
+              x = 1;
+          }
+          if (y < 1) {
+              edy = 2 - y;
+              y = 1;
+          }
+          return { dy: dy, edy: edy, dx: dx, edx: edx, y: y, ey: ey, x: x, ex: ex, w: w, h: h };
+      };
+      BoundingBox.prototype.calibrate = function (region) {
+          return new BoundingBox(this.left + (region.left * this.width), this.top + (region.top * this.height), this.right + (region.right * this.width), this.bottom + (region.bottom * this.height)).toSquare().round();
+      };
+      BoundingBox.prototype.rescale = function (s) {
+          var scaleX = isDimensions(s) ? s.width : s;
+          var scaleY = isDimensions(s) ? s.height : s;
+          return new BoundingBox(this.left * scaleX, this.top * scaleY, this.right * scaleX, this.bottom * scaleY);
+      };
+      BoundingBox.prototype.toRect = function () {
+          return new Rect(this.left, this.top, this.width, this.height);
+      };
+      return BoundingBox;
+  }());
+
+  var FaceDetection = /** @class */ (function () {
+      function FaceDetection(score, relativeBox, imageDims) {
+          var width = imageDims.width, height = imageDims.height;
+          this._imageWidth = width;
+          this._imageHeight = height;
+          this._score = score;
+          this._box = new Rect(relativeBox.x * width, relativeBox.y * height, relativeBox.width * width, relativeBox.height * height);
+      }
+      Object.defineProperty(FaceDetection.prototype, "score", {
+          get: function () {
+              return this._score;
+          },
+          enumerable: true,
+          configurable: true
+      });
+      Object.defineProperty(FaceDetection.prototype, "box", {
+          get: function () {
+              return this._box;
+          },
+          enumerable: true,
+          configurable: true
+      });
+      Object.defineProperty(FaceDetection.prototype, "imageWidth", {
+          get: function () {
+              return this._imageWidth;
+          },
+          enumerable: true,
+          configurable: true
+      });
+      Object.defineProperty(FaceDetection.prototype, "imageHeight", {
+          get: function () {
+              return this._imageHeight;
+          },
+          enumerable: true,
+          configurable: true
+      });
+      Object.defineProperty(FaceDetection.prototype, "relativeBox", {
+          get: function () {
+              return new Rect(this._box.x / this._imageWidth, this._box.y / this._imageHeight, this._box.width / this._imageWidth, this._box.height / this._imageHeight);
+          },
+          enumerable: true,
+          configurable: true
+      });
+      FaceDetection.prototype.getScore = function () {
+          return this.score;
+      };
+      FaceDetection.prototype.getBox = function () {
+          return this.box;
+      };
+      FaceDetection.prototype.getImageWidth = function () {
+          return this.imageWidth;
+      };
+      FaceDetection.prototype.getImageHeight = function () {
+          return this.imageHeight;
+      };
+      FaceDetection.prototype.getRelativeBox = function () {
+          return this.relativeBox;
+      };
+      FaceDetection.prototype.forSize = function (width, height) {
+          return new FaceDetection(this._score, this.getRelativeBox(), { width: width, height: height });
+      };
+      return FaceDetection;
+  }());
+
+  var FullFaceDescription = /** @class */ (function () {
+      function FullFaceDescription(_detection, _landmarks, _descriptor) {
+          this._detection = _detection;
+          this._landmarks = _landmarks;
+          this._descriptor = _descriptor;
+      }
+      Object.defineProperty(FullFaceDescription.prototype, "detection", {
+          get: function () {
+              return this._detection;
+          },
+          enumerable: true,
+          configurable: true
+      });
+      Object.defineProperty(FullFaceDescription.prototype, "landmarks", {
+          get: function () {
+              return this._landmarks;
+          },
+          enumerable: true,
+          configurable: true
+      });
+      Object.defineProperty(FullFaceDescription.prototype, "descriptor", {
+          get: function () {
+              return this._descriptor;
+          },
+          enumerable: true,
+          configurable: true
+      });
+      FullFaceDescription.prototype.forSize = function (width, height) {
+          return new FullFaceDescription(this._detection.forSize(width, height), this._landmarks.forSize(width, height), this._descriptor);
+      };
+      return FullFaceDescription;
+  }());
+
+  /**
+   * Pads the smaller dimension of an image tensor with zeros, such that width === height.
+   *
+   * @param imgTensor The image tensor.
+   * @param isCenterImage (optional, default: false) If true, add padding on both sides of the image, such that the image.
+   * @returns The padded tensor with width === height.
+   */
+  function padToSquare(imgTensor, isCenterImage) {
+      if (isCenterImage === void 0) { isCenterImage = false; }
+      return tidy(function () {
+          var _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];
+          if (height === width) {
+              return imgTensor;
+          }
+          var dimDiff = Math.abs(height - width);
+          var paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1));
+          var paddingAxis = height > width ? 2 : 1;
+          var createPaddingTensor = function (paddingAmount) {
+              var paddingTensorShape = imgTensor.shape.slice();
+              paddingTensorShape[paddingAxis] = paddingAmount;
+              return fill(paddingTensorShape, 0);
+          };
+          var paddingTensorAppend = createPaddingTensor(paddingAmount);
+          var remainingPaddingAmount = dimDiff - paddingTensorAppend.shape[paddingAxis];
+          var paddingTensorPrepend = isCenterImage && remainingPaddingAmount
+              ? createPaddingTensor(remainingPaddingAmount)
+              : null;
+          var tensorsToStack = [
+              paddingTensorPrepend,
+              imgTensor,
+              paddingTensorAppend
+          ]
+              .filter(function (t) { return t !== null; });
+          return concat(tensorsToStack, paddingAxis);
+      });
+  }
+
+  var Point = /** @class */ (function () {
+      function Point(x, y) {
+          this.x = x;
+          this.y = y;
+      }
+      Point.prototype.add = function (pt) {
+          return new Point(this.x + pt.x, this.y + pt.y);
+      };
+      Point.prototype.sub = function (pt) {
+          return new Point(this.x - pt.x, this.y - pt.y);
+      };
+      Point.prototype.mul = function (pt) {
+          return new Point(this.x * pt.x, this.y * pt.y);
+      };
+      Point.prototype.div = function (pt) {
+          return new Point(this.x / pt.x, this.y / pt.y);
+      };
+      Point.prototype.abs = function () {
+          return new Point(Math.abs(this.x), Math.abs(this.y));
+      };
+      Point.prototype.magnitude = function () {
+          return Math.sqrt(Math.pow(this.x, 2) + Math.pow(this.y, 2));
+      };
+      Point.prototype.floor = function () {
+          return new Point(Math.floor(this.x), Math.floor(this.y));
+      };
+      return Point;
+  }());
+
   var NetInput = /** @class */ (function () {
       function NetInput(inputs, isBatchInput, keepCanvases) {
           if (isBatchInput === void 0) { isBatchInput = false; }
@@ -656,6 +780,7 @@
           this._isBatchInput = false;
           this._inputDimensions = [];
           this._paddings = [];
+          this._inputSize = 0;
           if (isTensor4D(inputs)) {
               this._inputs = unstack(inputs);
           }
@@ -732,6 +857,29 @@
           enumerable: true,
           configurable: true
       });
+      Object.defineProperty(NetInput.prototype, "inputSize", {
+          get: function () {
+              return this._inputSize;
+          },
+          enumerable: true,
+          configurable: true
+      });
+      Object.defineProperty(NetInput.prototype, "relativePaddings", {
+          get: function () {
+              var _this = this;
+              return Array(this.inputs.length).fill(0).map(function (_, batchIdx) { return _this.getRelativePaddings(batchIdx); });
+          },
+          enumerable: true,
+          configurable: true
+      });
+      Object.defineProperty(NetInput.prototype, "reshapedInputDimensions", {
+          get: function () {
+              var _this = this;
+              return Array(this.inputs.length).fill(0).map(function (_, batchIdx) { return _this.getReshapedInputDimensions(batchIdx); });
+          },
+          enumerable: true,
+          configurable: true
+      });
       NetInput.prototype.getInputDimensions = function (batchIdx) {
           return this._inputDimensions[batchIdx];
       };
@@ -744,9 +892,21 @@
       NetInput.prototype.getPaddings = function (batchIdx) {
           return this._paddings[batchIdx];
       };
+      NetInput.prototype.getRelativePaddings = function (batchIdx) {
+          return new Point((this.getPaddings(batchIdx).x + this.getInputWidth(batchIdx)) / this.getInputWidth(batchIdx), (this.getPaddings(batchIdx).y + this.getInputHeight(batchIdx)) / this.getInputHeight(batchIdx));
+      };
+      NetInput.prototype.getReshapedInputDimensions = function (batchIdx) {
+          var _a = [this.getInputHeight(batchIdx), this.getInputWidth(batchIdx)], h = _a[0], w = _a[1];
+          var f = this.inputSize / Math.max(h, w);
+          return {
+              height: Math.floor(h * f),
+              width: Math.floor(w * f)
+          };
+      };
       NetInput.prototype.toBatchTensor = function (inputSize, isCenterInputs) {
           var _this = this;
           if (isCenterInputs === void 0) { isCenterInputs = true; }
+          this._inputSize = inputSize;
           return tidy(function () {
               var inputTensors = _this._inputs.map(function (inputTensor) {
                   var _a = inputTensor.shape, originalHeight = _a[0], originalWidth = _a[1];
@@ -767,7 +927,7 @@
           });
       };
       /**
-       *  By setting the isManaged flag, all newly created tensors will be automatically
+       *  By setting the isManaged flag, all newly created tensors will be
        *  automatically disposed after the batch tensor has been created
        */
       NetInput.prototype.managed = function () {
@@ -2107,7 +2267,7 @@
           if (minConfidence === void 0) { minConfidence = 0.8; }
           if (maxResults === void 0) { maxResults = 100; }
           return __awaiter$1(this, void 0, void 0, function () {
-              var netInput, _a, _boxes, _scores, boxes, scores, i, scoresData, _b, _c, iouThreshold, indices, paddedHeightRelative, paddedWidthRelative, results;
+              var netInput, _a, _boxes, _scores, boxes, scores, i, scoresData, _b, _c, iouThreshold, indices, paddings, results;
               return __generator$1(this, function (_d) {
                   switch (_d.label) {
                       case 0: return [4 /*yield*/, toNetInput(input, true)];
@@ -2126,18 +2286,17 @@
                           scoresData = _c.apply(_b, [_d.sent()]);
                           iouThreshold = 0.5;
                           indices = nonMaxSuppression(boxes, scoresData, maxResults, iouThreshold, minConfidence);
-                          paddedHeightRelative = (netInput.getPaddings(0).y + netInput.getInputHeight(0)) / netInput.getInputHeight(0);
-                          paddedWidthRelative = (netInput.getPaddings(0).x + netInput.getInputWidth(0)) / netInput.getInputWidth(0);
+                          paddings = netInput.getRelativePaddings(0);
                           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];
+                              ].map(function (val) { return val * paddings.y; }), 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];
+                              ].map(function (val) { return val * paddings.x; }), left = _b[0], right = _b[1];
                               return new FaceDetection(scoresData[idx], new Rect(left, top, right - left, bottom - top), {
                                   height: netInput.getInputHeight(0),
                                   width: netInput.getInputWidth(0)
@@ -2169,6 +2328,17 @@
       return createFaceDetectionNet(weights);
   }
 
+  function normalize(x, meanRgb) {
+      return tidy(function () {
+          var r = meanRgb[0], g = meanRgb[1], b = meanRgb[2];
+          var avg_r = fill(x.shape.slice(0, 3).concat([1]), r);
+          var avg_g = fill(x.shape.slice(0, 3).concat([1]), g);
+          var avg_b = fill(x.shape.slice(0, 3).concat([1]), b);
+          var avg_rgb = concat([avg_r, avg_g, avg_b], 3);
+          return sub(x, avg_rgb);
+      });
+  }
+
   function scale(x, params) {
       return add(mul(x, params.weights), params.biases);
   }
@@ -2356,16 +2526,6 @@
       });
   }
 
-  function normalize(x) {
-      return tidy(function () {
-          var avg_r = fill(x.shape.slice(0, 3).concat([1]), 122.782);
-          var avg_g = fill(x.shape.slice(0, 3).concat([1]), 117.001);
-          var avg_b = fill(x.shape.slice(0, 3).concat([1]), 104.298);
-          var avg_rgb = concat([avg_r, avg_g, avg_b], 3);
-          return div(sub(x, avg_rgb), scalar(256));
-      });
-  }
-
   function residual(x, params) {
       var out = conv$1(x, params.conv1);
       out = convNoRelu(out, params.conv2);
@@ -2408,7 +2568,8 @@
           }
           return tidy(function () {
               var batchTensor = input.toBatchTensor(150, true);
-              var normalized = normalize(batchTensor);
+              var meanRgb = [122.782, 117.001, 104.298];
+              var normalized = normalize(batchTensor, meanRgb).div(scalar(256));
               var out = convDown(normalized, params.conv32_down);
               out = maxPool(out, 3, 2, 'valid');
               out = residual(out, params.conv32_1);
@@ -2510,16 +2671,15 @@
           });
       };
   }
-  function allFacesFactory(detectionNet, landmarkNet, recognitionNet) {
+  function allFacesFactory(detectFaces, landmarkNet, recognitionNet) {
       var computeDescriptors = computeDescriptorsFactory(recognitionNet);
-      return function (input, minConfidence, useBatchProcessing) {
-          if (minConfidence === void 0) { minConfidence = 0.8; }
+      return function (input, useBatchProcessing) {
           if (useBatchProcessing === void 0) { useBatchProcessing = false; }
           return __awaiter$1(this, void 0, void 0, function () {
               var detections, faceTensors, faceLandmarksByFace, _a, alignedFaceBoxes, descriptors;
               return __generator$1(this, function (_b) {
                   switch (_b.label) {
-                      case 0: return [4 /*yield*/, detectionNet.locateFaces(input, minConfidence)];
+                      case 0: return [4 /*yield*/, detectFaces(input)];
                       case 1:
                           detections = _b.sent();
                           return [4 /*yield*/, extractFaceTensors(input, detections)];
@@ -2549,6 +2709,34 @@
           });
       };
   }
+  function allFacesSsdMobilenetv1Factory(ssdMobilenetv1, landmarkNet, recognitionNet) {
+      return function (input, minConfidence, useBatchProcessing) {
+          if (minConfidence === void 0) { minConfidence = 0.8; }
+          if (useBatchProcessing === void 0) { useBatchProcessing = false; }
+          return __awaiter$1(this, void 0, void 0, function () {
+              var detectFaces, allFaces;
+              return __generator$1(this, function (_a) {
+                  detectFaces = function (input) { return ssdMobilenetv1.locateFaces(input, minConfidence); };
+                  allFaces = allFacesFactory(detectFaces, landmarkNet, recognitionNet);
+                  return [2 /*return*/, allFaces(input, useBatchProcessing)];
+              });
+          });
+      };
+  }
+  function allFacesTinyYolov2Factory(tinyYolov2, landmarkNet, recognitionNet) {
+      return function (input, forwardParams, useBatchProcessing) {
+          if (forwardParams === void 0) { forwardParams = {}; }
+          if (useBatchProcessing === void 0) { useBatchProcessing = false; }
+          return __awaiter$1(this, void 0, void 0, function () {
+              var detectFaces, allFaces;
+              return __generator$1(this, function (_a) {
+                  detectFaces = function (input) { return tinyYolov2.locateFaces(input, forwardParams); };
+                  allFaces = allFacesFactory(detectFaces, landmarkNet, recognitionNet);
+                  return [2 /*return*/, allFaces(input, useBatchProcessing)];
+              });
+          });
+      };
+  }
   function allFacesMtcnnFactory(mtcnn, recognitionNet) {
       var computeDescriptors = computeDescriptorsFactory(recognitionNet);
       return function (input, mtcnnForwardParams, useBatchProcessing) {
@@ -2770,112 +2958,18 @@
       return scales;
   }
 
-  var BoundingBox = /** @class */ (function () {
-      function BoundingBox(_left, _top, _right, _bottom) {
-          this._left = _left;
-          this._top = _top;
-          this._right = _right;
-          this._bottom = _bottom;
-      }
-      Object.defineProperty(BoundingBox.prototype, "left", {
-          get: function () {
-              return this._left;
-          },
-          enumerable: true,
-          configurable: true
-      });
-      Object.defineProperty(BoundingBox.prototype, "top", {
-          get: function () {
-              return this._top;
-          },
-          enumerable: true,
-          configurable: true
-      });
-      Object.defineProperty(BoundingBox.prototype, "right", {
-          get: function () {
-              return this._right;
-          },
-          enumerable: true,
-          configurable: true
-      });
-      Object.defineProperty(BoundingBox.prototype, "bottom", {
-          get: function () {
-              return this._bottom;
-          },
-          enumerable: true,
-          configurable: true
-      });
-      Object.defineProperty(BoundingBox.prototype, "width", {
-          get: function () {
-              return this.right - this.left;
-          },
-          enumerable: true,
-          configurable: true
-      });
-      Object.defineProperty(BoundingBox.prototype, "height", {
-          get: function () {
-              return this.bottom - this.top;
-          },
-          enumerable: true,
-          configurable: true
-      });
-      BoundingBox.prototype.toSquare = function () {
-          var _a = this, left = _a.left, top = _a.top, right = _a.right, bottom = _a.bottom;
-          var off = (Math.abs(this.width - this.height) / 2);
-          if (this.width < this.height) {
-              left -= off;
-              right += off;
-          }
-          if (this.height < this.width) {
-              top -= off;
-              bottom += off;
-          }
-          return new BoundingBox(left, top, right, bottom);
-      };
-      BoundingBox.prototype.round = function () {
-          return new BoundingBox(Math.round(this.left), Math.round(this.top), Math.round(this.right), Math.round(this.bottom));
-      };
-      BoundingBox.prototype.padAtBorders = function (imageHeight, imageWidth) {
-          var w = this.width + 1;
-          var h = this.height + 1;
-          var dx = 1;
-          var dy = 1;
-          var edx = w;
-          var edy = h;
-          var x = this.left;
-          var y = this.top;
-          var ex = this.right;
-          var ey = this.bottom;
-          if (ex > imageWidth) {
-              edx = -ex + imageWidth + w;
-              ex = imageWidth;
-          }
-          if (ey > imageHeight) {
-              edy = -ey + imageHeight + h;
-              ey = imageHeight;
-          }
-          if (x < 1) {
-              edy = 2 - x;
-              x = 1;
-          }
-          if (y < 1) {
-              edy = 2 - y;
-              y = 1;
+  function iou(box1, box2, isIOU) {
+      if (isIOU === void 0) { isIOU = true; }
+      var width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left) + 1);
+      var height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top) + 1);
+      var interSection = width * height;
+      return isIOU
+          ? interSection / (box1.area + box2.area - interSection)
+          : interSection / Math.min(box1.area, box2.area);
   }
-          return { dy: dy, edy: edy, dx: dx, edx: edx, y: y, ey: ey, x: x, ex: ex, w: w, h: h };
-      };
-      BoundingBox.prototype.calibrate = function (region) {
-          return new BoundingBox(this.left + (region.left * this.width), this.top + (region.top * this.height), this.right + (region.right * this.width), this.bottom + (region.bottom * this.height)).toSquare().round();
-      };
-      BoundingBox.prototype.toRect = function () {
-          return new Rect(this.left, this.top, this.width, this.height);
-      };
-      return BoundingBox;
-  }());
 
   function nonMaxSuppression$1(boxes, scores, iouThreshold, isIOU) {
       if (isIOU === void 0) { isIOU = true; }
-      var areas = boxes.map(function (box) { return (box.width + 1) * (box.height + 1); });
       var indicesSortedByScore = scores
           .map(function (score, boxIndex) { return ({ score: score, boxIndex: boxIndex }); })
           .sort(function (c1, c2) { return c1.score - c2.score; })
@@ -2890,13 +2984,7 @@
               var idx = indices[i];
               var currBox = boxes[curr];
               var idxBox = boxes[idx];
-              var width = Math.max(0.0, Math.min(currBox.right, idxBox.right) - Math.max(currBox.left, idxBox.left) + 1);
-              var height = Math.max(0.0, Math.min(currBox.bottom, idxBox.bottom) - Math.max(currBox.top, idxBox.top) + 1);
-              var interSection = width * height;
-              var out = isIOU
-                  ? interSection / (areas[curr] + areas[idx] - interSection)
-                  : interSection / Math.min(areas[curr], areas[idx]);
-              outputs.push(out);
+              outputs.push(iou(currBox, idxBox, isIOU));
           }
           indicesSortedByScore = indicesSortedByScore.filter(function (_, j) { return outputs[j] <= iouThreshold; });
       };
@@ -3366,20 +3454,52 @@
       new Point(10.246, 4.59428),
       new Point(12.6868, 11.8741)
   ];
+  var BOX_ANCHORS_SEPARABLE = [
+      new Point(1.603231, 2.094468),
+      new Point(6.041143, 7.080126),
+      new Point(2.882459, 3.518061),
+      new Point(4.266906, 5.178857),
+      new Point(9.041765, 10.66308)
+  ];
+  var MEAN_RGB = [117.001, 114.697, 97.404];
 
   function leaky(x) {
       return tidy(function () {
-          return maximum(x, mul(x, scalar(0.10000000149011612)));
+          var min$$1 = mul(x, scalar(0.10000000149011612));
+          return add(relu(sub(x, min$$1)), min$$1);
+          //return tf.maximum(x, min)
       });
   }
 
+  var SeparableConvParams = /** @class */ (function () {
+      function SeparableConvParams(depthwise_filter, pointwise_filter, bias) {
+          this.depthwise_filter = depthwise_filter;
+          this.pointwise_filter = pointwise_filter;
+          this.bias = bias;
+      }
+      return SeparableConvParams;
+  }());
+  var SizeType;
+  (function (SizeType) {
+      SizeType["XS"] = "xs";
+      SizeType["SM"] = "sm";
+      SizeType["MD"] = "md";
+      SizeType["LG"] = "lg";
+  })(SizeType || (SizeType = {}));
+
   function convWithBatchNorm(x, params) {
       return tidy(function () {
           var out = pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);
+          if (params instanceof SeparableConvParams) {
+              out = separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid');
+              out = add(out, params.bias);
+          }
+          else {
               out = conv2d(out, params.conv.filters, [1, 1], 'valid');
               out = sub(out, params.bn.sub);
               out = mul(out, params.bn.truediv);
               out = add(out, params.conv.bias);
+          }
           return leaky(out);
       });
   }
@@ -3397,24 +3517,34 @@
           var bn = extractBatchNormParams(channelsOut, mappedPrefix + "/bn");
           return { conv: conv, bn: bn };
       }
+      function extractSeparableConvParams(channelsIn, channelsOut, mappedPrefix) {
+          var depthwise_filter = tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);
+          var pointwise_filter = tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);
+          var bias = tensor1d(extractWeights(channelsOut));
+          paramMappings.push({ paramPath: mappedPrefix + "/depthwise_filter" }, { paramPath: mappedPrefix + "/pointwise_filter" }, { paramPath: mappedPrefix + "/bias" });
+          return new SeparableConvParams(depthwise_filter, pointwise_filter, bias);
+      }
       return {
           extractConvParams: extractConvParams,
-          extractConvWithBatchNormParams: extractConvWithBatchNormParams
+          extractConvWithBatchNormParams: extractConvWithBatchNormParams,
+          extractSeparableConvParams: extractSeparableConvParams
       };
   }
-  function extractParams$4(weights) {
+  function extractParams$4(weights, withSeparableConvs) {
       var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
       var paramMappings = [];
-      var _b = extractorsFactory$7(extractWeights, paramMappings), extractConvParams = _b.extractConvParams, extractConvWithBatchNormParams = _b.extractConvWithBatchNormParams;
-      var conv0 = extractConvWithBatchNormParams(3, 16, 'conv0');
-      var conv1 = extractConvWithBatchNormParams(16, 32, 'conv1');
-      var conv2 = extractConvWithBatchNormParams(32, 64, 'conv2');
-      var conv3 = extractConvWithBatchNormParams(64, 128, 'conv3');
-      var conv4 = extractConvWithBatchNormParams(128, 256, 'conv4');
-      var conv5 = extractConvWithBatchNormParams(256, 512, 'conv5');
-      var conv6 = extractConvWithBatchNormParams(512, 1024, 'conv6');
-      var conv7 = extractConvWithBatchNormParams(1024, 1024, 'conv7');
-      var conv8 = extractConvParams(1024, 30, 1, 'conv8');
+      var _b = extractorsFactory$7(extractWeights, paramMappings), extractConvParams = _b.extractConvParams, extractConvWithBatchNormParams = _b.extractConvWithBatchNormParams, extractSeparableConvParams = _b.extractSeparableConvParams;
+      var extractConvFn = withSeparableConvs ? extractSeparableConvParams : extractConvWithBatchNormParams;
+      var numAnchorEncodings = withSeparableConvs ? 5 : 6;
+      var conv0 = extractConvFn(3, 16, 'conv0');
+      var conv1 = extractConvFn(16, 32, 'conv1');
+      var conv2 = extractConvFn(32, 64, 'conv2');
+      var conv3 = extractConvFn(64, 128, 'conv3');
+      var conv4 = extractConvFn(128, 256, 'conv4');
+      var conv5 = extractConvFn(256, 512, 'conv5');
+      var conv6 = extractConvFn(512, 1024, 'conv6');
+      var conv7 = extractConvFn(1024, 1024, 'conv7');
+      var conv8 = extractConvParams(1024, 5 * numAnchorEncodings, 1, 'conv8');
       if (getRemainingWeights().length !== 0) {
           throw new Error("weights remaing after extract: " + getRemainingWeights().length);
       }
@@ -3422,22 +3552,15 @@
       return { params: params, paramMappings: paramMappings };
   }
 
-  var SizeType;
-  (function (SizeType) {
-      SizeType["XS"] = "xs";
-      SizeType["SM"] = "sm";
-      SizeType["MD"] = "md";
-      SizeType["LG"] = "lg";
-  })(SizeType || (SizeType = {}));
-
   function getDefaultParams(params) {
       return Object.assign({}, {
-          sizeType: SizeType.MD,
+          inputSize: SizeType.MD,
           scoreThreshold: 0.5
       }, params);
   }
 
   var DEFAULT_MODEL_NAME$4 = 'tiny_yolov2_model';
+  var DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model';
   function extractorsFactory$8(weightMap, paramMappings) {
       var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
       function extractBatchNormParams(prefix) {
@@ -3455,30 +3578,38 @@
           var bn = extractBatchNormParams(prefix + "/bn");
           return { conv: conv, bn: bn };
       }
+      function extractSeparableConvParams(prefix) {
+          var depthwise_filter = extractWeightEntry(prefix + "/depthwise_filter", 4);
+          var pointwise_filter = extractWeightEntry(prefix + "/pointwise_filter", 4);
+          var bias = extractWeightEntry(prefix + "/bias", 1);
+          return new SeparableConvParams(depthwise_filter, pointwise_filter, bias);
+      }
       return {
           extractConvParams: extractConvParams,
-          extractConvWithBatchNormParams: extractConvWithBatchNormParams
+          extractConvWithBatchNormParams: extractConvWithBatchNormParams,
+          extractSeparableConvParams: extractSeparableConvParams
       };
   }
-  function loadQuantizedParams$4(uri) {
+  function loadQuantizedParams$4(uri, withSeparableConvs) {
       return __awaiter$1(this, void 0, void 0, function () {
-          var weightMap, paramMappings, _a, extractConvParams, extractConvWithBatchNormParams, params;
+          var weightMap, paramMappings, _a, extractConvParams, extractConvWithBatchNormParams, extractSeparableConvParams, extractConvFn, params;
           return __generator$1(this, function (_b) {
               switch (_b.label) {
-                  case 0: return [4 /*yield*/, loadWeightMap(uri, DEFAULT_MODEL_NAME$4)];
+                  case 0: return [4 /*yield*/, loadWeightMap(uri, withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME$4)];
                   case 1:
                       weightMap = _b.sent();
                       paramMappings = [];
-                      _a = extractorsFactory$8(weightMap, paramMappings), extractConvParams = _a.extractConvParams, extractConvWithBatchNormParams = _a.extractConvWithBatchNormParams;
+                      _a = extractorsFactory$8(weightMap, paramMappings), extractConvParams = _a.extractConvParams, extractConvWithBatchNormParams = _a.extractConvWithBatchNormParams, extractSeparableConvParams = _a.extractSeparableConvParams;
+                      extractConvFn = withSeparableConvs ? extractSeparableConvParams : extractConvWithBatchNormParams;
                       params = {
-                          conv0: extractConvWithBatchNormParams('conv0'),
-                          conv1: extractConvWithBatchNormParams('conv1'),
-                          conv2: extractConvWithBatchNormParams('conv2'),
-                          conv3: extractConvWithBatchNormParams('conv3'),
-                          conv4: extractConvWithBatchNormParams('conv4'),
-                          conv5: extractConvWithBatchNormParams('conv5'),
-                          conv6: extractConvWithBatchNormParams('conv6'),
-                          conv7: extractConvWithBatchNormParams('conv7'),
+                          conv0: extractConvFn('conv0'),
+                          conv1: extractConvFn('conv1'),
+                          conv2: extractConvFn('conv2'),
+                          conv3: extractConvFn('conv3'),
+                          conv4: extractConvFn('conv4'),
+                          conv5: extractConvFn('conv5'),
+                          conv6: extractConvFn('conv6'),
+                          conv7: extractConvFn('conv7'),
                           conv8: extractConvParams('conv8')
                       };
                       disposeUnusedWeightTensors(weightMap, paramMappings);
@@ -3490,16 +3621,39 @@
 
   var TinyYolov2 = /** @class */ (function (_super) {
       __extends$1(TinyYolov2, _super);
-      function TinyYolov2() {
-          return _super.call(this, 'TinyYolov2') || this;
-      }
+      function TinyYolov2(withSeparableConvs) {
+          if (withSeparableConvs === void 0) { withSeparableConvs = true; }
+          var _this = _super.call(this, 'TinyYolov2') || this;
+          _this._withSeparableConvs = withSeparableConvs;
+          _this._anchors = withSeparableConvs ? BOX_ANCHORS_SEPARABLE : BOX_ANCHORS;
+          return _this;
+      }
+      Object.defineProperty(TinyYolov2.prototype, "withSeparableConvs", {
+          get: function () {
+              return this._withSeparableConvs;
+          },
+          enumerable: true,
+          configurable: true
+      });
+      Object.defineProperty(TinyYolov2.prototype, "anchors", {
+          get: function () {
+              return this._anchors;
+          },
+          enumerable: true,
+          configurable: true
+      });
       TinyYolov2.prototype.forwardInput = function (input, inputSize) {
+          var _this = this;
           var params = this.params;
           if (!params) {
               throw new Error('TinyYolov2 - load model before inference');
           }
           var out = tidy(function () {
-              var batchTensor = input.toBatchTensor(inputSize, false).div(scalar(255)).toFloat();
+              var batchTensor = input.toBatchTensor(inputSize, false);
+              batchTensor = _this.withSeparableConvs
+                  ? normalize(batchTensor, MEAN_RGB)
+                  : batchTensor;
+              batchTensor = batchTensor.div(scalar(256));
               var out = convWithBatchNorm(batchTensor, params.conv0);
               out = maxPool(out, [2, 2], [2, 2], 'same');
               out = convWithBatchNorm(out, params.conv1);
@@ -3536,69 +3690,85 @@
       TinyYolov2.prototype.locateFaces = function (input, forwardParams) {
           if (forwardParams === void 0) { forwardParams = {}; }
           return __awaiter$1(this, void 0, void 0, function () {
-              var _a, _inputSize, scoreThreshold, inputSize, netInput, out, numCells, _b, boxesTensor, scoresTensor, expit, paddedHeightRelative, paddedWidthRelative, boxes, scores, row, col, box, score, ctX, ctY, width, height, x, y, indices, detections;
-              return __generator$1(this, function (_c) {
-                  switch (_c.label) {
+              var _a, _inputSize, scoreThreshold, inputSize, netInput, out, out0, inputDimensions, paddings, results, boxes, scores, indices, detections;
+              return __generator$1(this, function (_b) {
+                  switch (_b.label) {
                       case 0:
                           _a = getDefaultParams(forwardParams), _inputSize = _a.inputSize, scoreThreshold = _a.scoreThreshold;
                           inputSize = typeof _inputSize === 'string'
                               ? INPUT_SIZES[_inputSize]
                               : _inputSize;
                           if (typeof inputSize !== 'number') {
-                              throw new Error("TinyYolov2 - unkown inputSize: " + inputSize + ", expected number or one of xs | sm | md | lg");
+                              throw new Error("TinyYolov2 - unknown inputSize: " + inputSize + ", expected number or one of xs | sm | md | lg");
                           }
                           return [4 /*yield*/, toNetInput(input, true)];
                       case 1:
-                          netInput = _c.sent();
+                          netInput = _b.sent();
                           return [4 /*yield*/, this.forwardInput(netInput, inputSize)];
                       case 2:
-                          out = _c.sent();
-                          numCells = out.shape[1];
-                          _b = tidy(function () {
-                              var reshaped = out.reshape([numCells, numCells, NUM_BOXES, 6]);
+                          out = _b.sent();
+                          out0 = tidy(function () { return unstack(out)[0].expandDims(); });
+                          inputDimensions = {
+                              width: netInput.getInputWidth(0),
+                              height: netInput.getInputHeight(0)
+                          };
+                          paddings = netInput.getRelativePaddings(0);
+                          results = this.postProcess(out0, { scoreThreshold: scoreThreshold, paddings: paddings });
+                          boxes = results.map(function (res) { return res.box; });
+                          scores = results.map(function (res) { return res.score; });
                           out.dispose();
+                          out0.dispose();
+                          indices = nonMaxSuppression$1(boxes.map(function (box) { return box.rescale(inputSize); }), scores, IOU_THRESHOLD, true);
+                          detections = indices.map(function (idx) {
+                              return new FaceDetection(scores[idx], boxes[idx].toRect(), inputDimensions);
+                          });
+                          return [2 /*return*/, detections];
+                  }
+              });
+          });
+      };
+      TinyYolov2.prototype.postProcess = function (outputTensor, _a) {
+          var _this = this;
+          var scoreThreshold = _a.scoreThreshold, paddings = _a.paddings;
+          var numCells = outputTensor.shape[1];
+          var _b = tidy(function () {
+              var reshaped = outputTensor.reshape([numCells, numCells, NUM_BOXES, _this.withSeparableConvs ? 5 : 6]);
               var boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, NUM_BOXES, 4]);
               var scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, NUM_BOXES, 1]);
               return [boxes, scores];
           }), boxesTensor = _b[0], scoresTensor = _b[1];
-                          expit = function (x) { return 1 / (1 + Math.exp(-x)); };
-                          paddedHeightRelative = (netInput.getPaddings(0).y + netInput.getInputHeight(0)) / netInput.getInputHeight(0);
-                          paddedWidthRelative = (netInput.getPaddings(0).x + netInput.getInputWidth(0)) / netInput.getInputWidth(0);
-                          boxes = [];
-                          scores = [];
-                          for (row = 0; row < numCells; row++) {
-                              for (col = 0; col < numCells; col++) {
-                                  for (box = 0; box < NUM_BOXES; box++) {
-                                      score = expit(scoresTensor.get(row, col, box, 0));
-                                      if (score > scoreThreshold) {
-                                          ctX = ((col + expit(boxesTensor.get(row, col, box, 0))) / numCells) * paddedWidthRelative;
-                                          ctY = ((row + expit(boxesTensor.get(row, col, box, 1))) / numCells) * paddedHeightRelative;
-                                          width = ((Math.exp(boxesTensor.get(row, col, box, 2)) * BOX_ANCHORS[box].x) / numCells) * paddedWidthRelative;
-                                          height = ((Math.exp(boxesTensor.get(row, col, box, 3)) * BOX_ANCHORS[box].y) / numCells) * paddedHeightRelative;
-                                          x = (ctX - (width / 2));
-                                          y = (ctY - (height / 2));
-                                          boxes.push(new BoundingBox(x, y, x + width, y + height));
-                                          scores.push(score);
+          var results = [];
+          for (var row = 0; row < numCells; row++) {
+              for (var col = 0; col < numCells; col++) {
+                  for (var anchor = 0; anchor < NUM_BOXES; anchor++) {
+                      var score = sigmoid$1(scoresTensor.get(row, col, anchor, 0));
+                      if (!scoreThreshold || score > scoreThreshold) {
+                          var ctX = ((col + sigmoid$1(boxesTensor.get(row, col, anchor, 0))) / numCells) * paddings.x;
+                          var ctY = ((row + sigmoid$1(boxesTensor.get(row, col, anchor, 1))) / numCells) * paddings.y;
+                          var width = ((Math.exp(boxesTensor.get(row, col, anchor, 2)) * this.anchors[anchor].x) / numCells) * paddings.x;
+                          var height = ((Math.exp(boxesTensor.get(row, col, anchor, 3)) * this.anchors[anchor].y) / numCells) * paddings.y;
+                          var x = (ctX - (width / 2));
+                          var y = (ctY - (height / 2));
+                          results.push({
+                              box: new BoundingBox(x, y, x + width, y + height),
+                              score: score,
+                              row: row,
+                              col: col,
+                              anchor: anchor
+                          });
                       }
                   }
               }
           }
           boxesTensor.dispose();
           scoresTensor.dispose();
-                          indices = nonMaxSuppression$1(boxes.map(function (box) { return new BoundingBox(box.left * inputSize, box.top * inputSize, box.right * inputSize, box.bottom * inputSize); }), scores, IOU_THRESHOLD, true);
-                          detections = indices.map(function (idx) {
-                              return new FaceDetection(scores[idx], boxes[idx].toRect(), { width: netInput.getInputWidth(0), height: netInput.getInputHeight(0) });
-                          });
-                          return [2 /*return*/, detections];
-                  }
-              });
-          });
+          return results;
       };
       TinyYolov2.prototype.loadQuantizedParams = function (uri) {
-          return loadQuantizedParams$4(uri);
+          return loadQuantizedParams$4(uri, this.withSeparableConvs);
       };
       TinyYolov2.prototype.extractParams = function (weights) {
-          return extractParams$4(weights);
+          return extractParams$4(weights, this.withSeparableConvs);
       };
       return TinyYolov2;
   }(NeuralNetwork));
@@ -3609,14 +3779,14 @@
   // nets need more specific names, to avoid ambiguity in future
   // when alternative net implementations are provided
   var nets = {
-      ssdMobilenet: detectionNet,
+      ssdMobilenetv1: detectionNet,
       faceLandmark68Net: landmarkNet,
       faceRecognitionNet: recognitionNet,
       mtcnn: new Mtcnn(),
       tinyYolov2: new TinyYolov2()
   };
-  function loadFaceDetectionModel(url) {
-      return nets.ssdMobilenet.load(url);
+  function loadSsdMobilenetv1Model(url) {
+      return nets.ssdMobilenetv1.load(url);
   }
   function loadFaceLandmarkModel(url) {
       return nets.faceLandmark68Net.load(url);
@@ -3630,9 +3800,12 @@
   function loadTinyYolov2Model(url) {
       return nets.tinyYolov2.load(url);
   }
+  function loadFaceDetectionModel(url) {
+      return loadSsdMobilenetv1Model(url);
+  }
   function loadModels(url) {
       return Promise.all([
-          loadFaceDetectionModel(url),
+          loadSsdMobilenetv1Model(url),
           loadFaceLandmarkModel(url),
           loadFaceRecognitionModel(url),
           loadMtcnnModel(url),
@@ -3640,7 +3813,7 @@
       ]);
   }
   function locateFaces(input, minConfidence, maxResults) {
-      return nets.ssdMobilenet.locateFaces(input, minConfidence, maxResults);
+      return nets.ssdMobilenetv1.locateFaces(input, minConfidence, maxResults);
   }
   function detectLandmarks(input) {
       return nets.faceLandmark68Net.detectLandmarks(input);
@@ -3654,8 +3827,10 @@
   function tinyYolov2(input, forwardParams) {
       return nets.tinyYolov2.locateFaces(input, forwardParams);
   }
-  var allFaces = allFacesFactory(nets.ssdMobilenet, nets.faceLandmark68Net, nets.faceRecognitionNet);
+  var allFacesSsdMobilenetv1 = allFacesSsdMobilenetv1Factory(nets.ssdMobilenetv1, nets.faceLandmark68Net, nets.faceRecognitionNet);
+  var allFacesTinyYolov2 = allFacesTinyYolov2Factory(nets.tinyYolov2, nets.faceLandmark68Net, nets.faceRecognitionNet);
   var allFacesMtcnn = allFacesMtcnnFactory(nets.mtcnn, nets.faceRecognitionNet);
+  var allFaces = allFacesSsdMobilenetv1;
 
   function createMtcnn(weights) {
       var net = new Mtcnn();
@@ -3663,13 +3838,15 @@
       return net;
   }
 
-  function createTinyYolov2(weights) {
-      var net = new TinyYolov2();
+  function createTinyYolov2(weights, withSeparableConvs) {
+      if (withSeparableConvs === void 0) { withSeparableConvs = true; }
+      var net = new TinyYolov2(withSeparableConvs);
       net.extractWeights(weights);
       return net;
   }
 
   exports.tf = tfCore_esm;
+  exports.BoundingBox = BoundingBox;
   exports.FaceDetection = FaceDetection;
   exports.FullFaceDescription = FullFaceDescription;
   exports.NetInput = NetInput;
@@ -3697,19 +3874,23 @@
   exports.landmarkNet = landmarkNet;
   exports.recognitionNet = recognitionNet;
   exports.nets = nets;
-  exports.loadFaceDetectionModel = loadFaceDetectionModel;
+  exports.loadSsdMobilenetv1Model = loadSsdMobilenetv1Model;
   exports.loadFaceLandmarkModel = loadFaceLandmarkModel;
   exports.loadFaceRecognitionModel = loadFaceRecognitionModel;
   exports.loadMtcnnModel = loadMtcnnModel;
   exports.loadTinyYolov2Model = loadTinyYolov2Model;
+  exports.loadFaceDetectionModel = loadFaceDetectionModel;
   exports.loadModels = loadModels;
   exports.locateFaces = locateFaces;
   exports.detectLandmarks = detectLandmarks;
   exports.computeFaceDescriptor = computeFaceDescriptor;
   exports.mtcnn = mtcnn;
   exports.tinyYolov2 = tinyYolov2;
-  exports.allFaces = allFaces;
+  exports.allFacesSsdMobilenetv1 = allFacesSsdMobilenetv1;
+  exports.allFacesTinyYolov2 = allFacesTinyYolov2;
   exports.allFacesMtcnn = allFacesMtcnn;
+  exports.allFaces = allFaces;
+  exports.iou = iou;
   exports.createMtcnn = createMtcnn;
   exports.Mtcnn = Mtcnn;
   exports.FaceLandmarks5 = FaceLandmarks5;
@@ -3720,6 +3901,8 @@
   exports.isFloat = isFloat;
   exports.isEven = isEven;
   exports.round = round$1;
+  exports.sigmoid = sigmoid$1;
+  exports.isDimensions = isDimensions;
   exports.resolveInput = resolveInput;
   exports.isLoaded = isLoaded;
   exports.awaitMediaLoaded = awaitMediaLoaded;