tfjs-image-recognition-base - npm Package Compare versions

Comparing version 0.1.3 to 0.2.0

build/commonjs/classes/Dimensions.js

 "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
-var util_1 = require("util");
+var utils_1 = require("../utils");
 var Dimensions = /** @class */ (function () {
     function Dimensions(width, height) {
-        if (!util_1.isNumber(width) || !util_1.isNumber(height)) {
+        if (!utils_1.isValidNumber(width) || !utils_1.isValidNumber(height)) {
             throw new Error("Dimensions.constructor - expected width and height to be valid numbers, instead have " + JSON.stringify({ width: width, height: height }));
@@ -8,0 +8,0 @@ }

build/commonjs/dom/awaitMediaLoaded.js

 "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
+var env_1 = require("../env");
 var isMediaLoaded_1 = require("./isMediaLoaded");
 function awaitMediaLoaded(media) {
     return new Promise(function (resolve, reject) {
-        if (media instanceof HTMLCanvasElement || isMediaLoaded_1.isMediaLoaded(media)) {
+        if (media instanceof env_1.env.getEnv().Canvas || isMediaLoaded_1.isMediaLoaded(media)) {
             return resolve();
@@ -8,0 +9,0 @@ }

build/commonjs/dom/bufferToImage.js

 "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
+var env_1 = require("../env");
 function bufferToImage(buf) {
@@ -13,3 +14,3 @@ return new Promise(function (resolve, reject) {
             }
-            var img = new Image();
+            var img = env_1.env.getEnv().createImageElement();
             img.onload = function () { return resolve(img); };
@@ -16,0 +17,0 @@ img.onerror = reject;

build/commonjs/dom/createCanvas.d.ts

 import { IDimensions } from '../classes/Dimensions';
-export declare function createCanvas({ width, height }: IDimensions): HTMLCanvasElement;
-export declare function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement, dims?: IDimensions): HTMLCanvasElement;
+export declare function createCanvas({width, height}: IDimensions): HTMLCanvasElement;
+export declare function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement;

build/commonjs/dom/createCanvas.js

 "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
+var env_1 = require("../env");
 var getContext2dOrThrow_1 = require("./getContext2dOrThrow");
@@ -8,3 +9,4 @@ var getMediaDimensions_1 = require("./getMediaDimensions");
     var width = _a.width, height = _a.height;
-    var canvas = document.createElement('canvas');
+    var createCanvasElement = env_1.env.getEnv().createCanvasElement;
+    var canvas = createCanvasElement();
     canvas.width = width;
@@ -16,3 +18,4 @@ canvas.height = height;
 function createCanvasFromMedia(media, dims) {
-    if (!isMediaLoaded_1.isMediaLoaded(media)) {
+    var ImageData = env_1.env.getEnv().ImageData;
+    if (!(media instanceof ImageData) && !isMediaLoaded_1.isMediaLoaded(media)) {
         throw new Error('createCanvasFromMedia - media has not finished loading yet');
@@ -22,3 +25,8 @@ }
     var canvas = createCanvas({ width: width, height: height });
-    getContext2dOrThrow_1.getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);
+    if (media instanceof ImageData) {
+        getContext2dOrThrow_1.getContext2dOrThrow(canvas).putImageData(media, 0, 0);
+    }
+    else {
+        getContext2dOrThrow_1.getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);
+    }
     return canvas;
@@ -25,0 +33,0 @@ }

build/commonjs/dom/drawDetection.js

@@ -6,2 +6,3 @@ "use strict";
 var PredictedBox_1 = require("../classes/PredictedBox");
+var env_1 = require("../env");
 var utils_1 = require("../utils");
@@ -14,4 +15,5 @@ var drawBox_1 = require("./drawBox");
 function drawDetection(canvasArg, detection, options) {
+    var Canvas = env_1.env.getEnv().Canvas;
     var canvas = resolveInput_1.resolveInput(canvasArg);
-    if (!(canvas instanceof HTMLCanvasElement)) {
+    if (!(canvas instanceof Canvas)) {
         throw new Error('drawDetection - expected canvas to be of type: HTMLCanvasElement');
@@ -18,0 +20,0 @@ }

build/commonjs/dom/fetchOrThrow.js

 "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
 var tslib_1 = require("tslib");
+var env_1 = require("../env");
 function fetchOrThrow(url, init) {
     return tslib_1.__awaiter(this, void 0, void 0, function () {
-        var res;
+        var fetch, res;
         return tslib_1.__generator(this, function (_a) {
             switch (_a.label) {
-                case 0: return [4 /*yield*/, fetch(url, init)];
+                case 0:
+                    fetch = env_1.env.getEnv().fetch;
+                    return [4 /*yield*/, fetch(url, init)];
                 case 1:
@@ -11,0 +14,0 @@ res = _a.sent();

build/commonjs/dom/getMediaDimensions.js

 "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
 var Dimensions_1 = require("../classes/Dimensions");
+var env_1 = require("../env");
 function getMediaDimensions(input) {
-    if (input instanceof HTMLImageElement) {
+    var _a = env_1.env.getEnv(), Image = _a.Image, Video = _a.Video;
+    if (input instanceof Image) {
         return new Dimensions_1.Dimensions(input.naturalWidth, input.naturalHeight);
     }
-    if (input instanceof HTMLVideoElement) {
+    if (input instanceof Video) {
         return new Dimensions_1.Dimensions(input.videoWidth, input.videoHeight);
@@ -10,0 +12,0 @@ }

build/commonjs/dom/imageTensorToCanvas.js

@@ -5,2 +5,3 @@ "use strict";
 var tf = require("@tensorflow/tfjs-core");
+var env_1 = require("../env");
 var utils_1 = require("../utils");
@@ -13,3 +14,3 @@ function imageTensorToCanvas(imgTensor, canvas) {
                 case 0:
-                    targetCanvas = canvas || document.createElement('canvas');
+                    targetCanvas = canvas || env_1.env.getEnv().createCanvasElement();
                     _a = imgTensor.shape.slice(utils_1.isTensor4D(imgTensor) ? 1 : 0), height = _a[0], width = _a[1], numChannels = _a[2];
@@ -16,0 +17,0 @@ imgTensor3D = tf.tidy(function () { return imgTensor.as3D(height, width, numChannels).toInt(); });

build/commonjs/dom/imageToSquare.js

 "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
+var env_1 = require("../env");
 var createCanvas_1 = require("./createCanvas");
@@ -8,3 +9,4 @@ var getContext2dOrThrow_1 = require("./getContext2dOrThrow");
     if (centerImage === void 0) { centerImage = false; }
-    if (!(input instanceof HTMLImageElement || input instanceof HTMLCanvasElement)) {
+    var _a = env_1.env.getEnv(), Image = _a.Image, Canvas = _a.Canvas;
+    if (!(input instanceof Image || input instanceof Canvas)) {
         throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement');
@@ -17,3 +19,3 @@ }
     var targetCanvas = createCanvas_1.createCanvas({ width: inputSize, height: inputSize });
-    var inputCanvas = input instanceof HTMLCanvasElement ? input : createCanvas_1.createCanvasFromMedia(input);
+    var inputCanvas = input instanceof Canvas ? input : createCanvas_1.createCanvasFromMedia(input);
     var offset = Math.abs(width - height) / 2;
@@ -20,0 +22,0 @@ var dx = centerImage && width < height ? offset : 0;

build/commonjs/dom/isMediaElement.js

 "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
+var env_1 = require("../env");
 function isMediaElement(input) {
-    return input instanceof HTMLImageElement
-        || input instanceof HTMLVideoElement
-        || input instanceof HTMLCanvasElement;
+    var _a = env_1.env.getEnv(), Image = _a.Image, Canvas = _a.Canvas, Video = _a.Video;
+    return input instanceof Image
+        || input instanceof Canvas
+        || input instanceof Video;
 }
 exports.isMediaElement = isMediaElement;
 //# sourceMappingURL=isMediaElement.js.map

build/commonjs/dom/isMediaLoaded.js

 "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
+var env_1 = require("../env");
 function isMediaLoaded(media) {
-    return (media instanceof HTMLImageElement && media.complete)
-        || (media instanceof HTMLVideoElement && media.readyState >= 3);
+    var _a = env_1.env.getEnv(), Image = _a.Image, Video = _a.Video;
+    return (media instanceof Image && media.complete)
+        || (media instanceof Video && media.readyState >= 3);
 }
 exports.isMediaLoaded = isMediaLoaded;
 //# sourceMappingURL=isMediaLoaded.js.map

build/commonjs/dom/loadWeightMap.d.ts

@@ -1,1 +0,2 @@
-export declare function loadWeightMap(uri: string | undefined, defaultModelName: string): Promise<any>;
+import * as tf from '@tensorflow/tfjs-core';
+export declare function loadWeightMap(uri: string | undefined, defaultModelName: string): Promise<tf.NamedTensorMap>;

build/commonjs/dom/NetInput.js

 "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
 var tf = require("@tensorflow/tfjs-core");
+var env_1 = require("../env");
 var padToSquare_1 = require("../ops/padToSquare");
@@ -36,3 +37,3 @@ var utils_1 = require("../utils");
             }
-            var canvas = input instanceof HTMLCanvasElement ? input : createCanvas_1.createCanvasFromMedia(input);
+            var canvas = input instanceof env_1.env.getEnv().Canvas ? input : createCanvas_1.createCanvasFromMedia(input);
             _this._canvases[idx] = canvas;
@@ -136,3 +137,3 @@ _this._inputDimensions[idx] = [canvas.height, canvas.width, 3];
                 }
-                if (input instanceof HTMLCanvasElement) {
+                if (input instanceof env_1.env.getEnv().Canvas) {
                     return tf.fromPixels(imageToSquare_1.imageToSquare(input, inputSize, isCenterInputs));
@@ -139,0 +140,0 @@ }

build/commonjs/dom/resolveInput.js

 "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
+var env_1 = require("../env");
 function resolveInput(arg) {
-    if (typeof arg === 'string') {
+    if (!env_1.env.isNodejs() && typeof arg === 'string') {
         return document.getElementById(arg);
@@ -6,0 +7,0 @@ }

build/commonjs/index.d.ts

 export * from './classes';
 export * from './common';
 export * from './dom';
+export * from './env';
 export * from './ops';
 export * from './utils';
 export * from './NeuralNetwork';

build/commonjs/index.js

@@ -7,2 +7,3 @@ "use strict";
 tslib_1.__exportStar(require("./dom"), exports);
+tslib_1.__exportStar(require("./env"), exports);
 tslib_1.__exportStar(require("./ops"), exports);
@@ -9,0 +10,0 @@ tslib_1.__exportStar(require("./utils"), exports);

build/commonjs/NeuralNetwork.d.ts

 import * as tf from '@tensorflow/tfjs-core';
 import { ParamMapping } from './common';
-export declare class NeuralNetwork<TNetParams> {
+export declare abstract class NeuralNetwork<TNetParams> {
     private _name;
@@ -30,9 +30,13 @@ protected _params: TNetParams | undefined;
     load(weightsOrUrl: Float32Array | string | undefined): Promise<void>;
+    loadFromUri(uri: string | undefined): Promise<void>;
+    loadFromDisk(filePath: string | undefined): Promise<void>;
+    loadFromWeightMap(weightMap: tf.NamedTensorMap): void;
     extractWeights(weights: Float32Array): void;
-    private traversePropertyPath;
-    protected loadQuantizedParams(_: any): Promise<{
+    private traversePropertyPath(paramPath);
+    protected abstract getDefaultModelName(): string;
+    protected abstract extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
         params: TNetParams;
         paramMappings: ParamMapping[];
-    }>;
-    protected extractParams(_: any): {
+    };
+    protected abstract extractParams(weights: Float32Array): {
         params: TNetParams;
@@ -39,0 +43,0 @@ paramMappings: ParamMapping[];

build/commonjs/NeuralNetwork.js

@@ -5,2 +5,6 @@ "use strict";
 var tf = require("@tensorflow/tfjs-core");
+var getModelUris_1 = require("./common/getModelUris");
+var dom_1 = require("./dom");
+var env_1 = require("./env");
+var weightsLoaderFactory_1 = require("./weightsLoaderFactory");
 var NeuralNetwork = /** @class */ (function () {
@@ -88,5 +92,4 @@ function NeuralNetwork(_name) {
         return tslib_1.__awaiter(this, void 0, void 0, function () {
-            var _a, paramMappings, params;
-            return tslib_1.__generator(this, function (_b) {
-                switch (_b.label) {
+            return tslib_1.__generator(this, function (_a) {
+                switch (_a.label) {
                     case 0:
@@ -97,10 +100,23 @@ if (weightsOrUrl instanceof Float32Array) {
                         }
-                        if (weightsOrUrl && typeof weightsOrUrl !== 'string') {
-                            throw new Error(this._name + ".load - expected model uri, or weights as Float32Array");
+                        return [4 /*yield*/, this.loadFromUri(weightsOrUrl)];
+                    case 1:
+                        _a.sent();
+                        return [2 /*return*/];
+                }
+            });
+        });
+    };
+    NeuralNetwork.prototype.loadFromUri = function (uri) {
+        return tslib_1.__awaiter(this, void 0, void 0, function () {
+            var weightMap;
+            return tslib_1.__generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        if (uri && typeof uri !== 'string') {
+                            throw new Error(this._name + ".loadFromUri - expected model uri");
                         }
-                        return [4 /*yield*/, this.loadQuantizedParams(weightsOrUrl)];
+                        return [4 /*yield*/, dom_1.loadWeightMap(uri, this.getDefaultModelName())];
                     case 1:
-                        _a = _b.sent(), paramMappings = _a.paramMappings, params = _a.params;
-                        this._paramMappings = paramMappings;
-                        this._params = params;
+                        weightMap = _a.sent();
+                        this.loadFromWeightMap(weightMap);
                         return [2 /*return*/];
@@ -111,2 +127,33 @@ }
     };
+    NeuralNetwork.prototype.loadFromDisk = function (filePath) {
+        return tslib_1.__awaiter(this, void 0, void 0, function () {
+            var readFile, _a, manifestUri, modelBaseUri, fetchWeightsFromDisk, loadWeights, manifest, _b, _c, weightMap;
+            return tslib_1.__generator(this, function (_d) {
+                switch (_d.label) {
+                    case 0:
+                        if (filePath && typeof filePath !== 'string') {
+                            throw new Error(this._name + ".loadFromDisk - expected model file path");
+                        }
+                        readFile = env_1.env.getEnv().readFile;
+                        _a = getModelUris_1.getModelUris(filePath, this.getDefaultModelName()), manifestUri = _a.manifestUri, modelBaseUri = _a.modelBaseUri;
+                        fetchWeightsFromDisk = function (filePaths) { return Promise.all(filePaths.map(function (filePath) { return readFile(filePath).then(function (buf) { return buf.buffer; }); })); };
+                        loadWeights = weightsLoaderFactory_1.weightsLoaderFactory(fetchWeightsFromDisk);
+                        _c = (_b = JSON).parse;
+                        return [4 /*yield*/, readFile(manifestUri)];
+                    case 1:
+                        manifest = _c.apply(_b, [(_d.sent()).toString()]);
+                        return [4 /*yield*/, loadWeights(manifest, modelBaseUri)];
+                    case 2:
+                        weightMap = _d.sent();
+                        this.loadFromWeightMap(weightMap);
+                        return [2 /*return*/];
+                }
+            });
+        });
+    };
+    NeuralNetwork.prototype.loadFromWeightMap = function (weightMap) {
+        var _a = this.extractParamsFromWeigthMap(weightMap), paramMappings = _a.paramMappings, params = _a.params;
+        this._paramMappings = paramMappings;
+        this._params = params;
+    };
     NeuralNetwork.prototype.extractWeights = function (weights) {
@@ -133,8 +180,2 @@ var _a = this.extractParams(weights), paramMappings = _a.paramMappings, params = _a.params;
     };
-    NeuralNetwork.prototype.loadQuantizedParams = function (_) {
-        throw new Error(this._name + ".loadQuantizedParams - not implemented");
-    };
-    NeuralNetwork.prototype.extractParams = function (_) {
-        throw new Error(this._name + ".extractParams - not implemented");
-    };
     return NeuralNetwork;
@@ -141,0 +182,0 @@ }());

build/commonjs/ops/padToSquare.js

@@ -37,3 +37,4 @@ "use strict";
         ]
-            .filter(function (t) { return t !== null; });
+            .filter(function (t) { return !!t; })
+            .map(function (t) { return t.toFloat(); });
         return tf.concat(tensorsToStack, paddingAxis);
@@ -40,0 +41,0 @@ });

build/commonjs/utils/index.d.ts

@@ -13,3 +13,3 @@ import * as tf from '@tensorflow/tfjs-core';
 export declare function isDimensions(obj: any): boolean;
-export declare function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number): Dimensions;
+export declare function computeReshapedDimensions({width, height}: IDimensions, inputSize: number): Dimensions;
 export declare function getCenterPoint(pts: Point[]): Point;
@@ -16,0 +16,0 @@ export declare function range(num: number, start: number, step: number): number[];

build/es6/classes/Dimensions.js

@@ -1,5 +0,5 @@
-import { isNumber } from 'util';
+import { isValidNumber } from '../utils';
 var Dimensions = /** @class */ (function () {
     function Dimensions(width, height) {
-        if (!isNumber(width) || !isNumber(height)) {
+        if (!isValidNumber(width) || !isValidNumber(height)) {
             throw new Error("Dimensions.constructor - expected width and height to be valid numbers, instead have " + JSON.stringify({ width: width, height: height }));
@@ -6,0 +6,0 @@ }

build/es6/dom/awaitMediaLoaded.js

@@ -0,5 +1,6 @@
+import { env } from '../env';
 import { isMediaLoaded } from './isMediaLoaded';
 export function awaitMediaLoaded(media) {
     return new Promise(function (resolve, reject) {
-        if (media instanceof HTMLCanvasElement || isMediaLoaded(media)) {
+        if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {
             return resolve();
@@ -6,0 +7,0 @@ }

build/es6/dom/bufferToImage.js

@@ -0,1 +1,2 @@
+import { env } from '../env';
 export function bufferToImage(buf) {
@@ -11,3 +12,3 @@ return new Promise(function (resolve, reject) {
             }
-            var img = new Image();
+            var img = env.getEnv().createImageElement();
             img.onload = function () { return resolve(img); };
@@ -14,0 +15,0 @@ img.onerror = reject;

build/es6/dom/createCanvas.d.ts

 import { IDimensions } from '../classes/Dimensions';
-export declare function createCanvas({ width, height }: IDimensions): HTMLCanvasElement;
-export declare function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement, dims?: IDimensions): HTMLCanvasElement;
+export declare function createCanvas({width, height}: IDimensions): HTMLCanvasElement;
+export declare function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement;

build/es6/dom/createCanvas.js

@@ -0,1 +1,2 @@
+import { env } from '../env';
 import { getContext2dOrThrow } from './getContext2dOrThrow';
@@ -6,3 +7,4 @@ import { getMediaDimensions } from './getMediaDimensions';
     var width = _a.width, height = _a.height;
-    var canvas = document.createElement('canvas');
+    var createCanvasElement = env.getEnv().createCanvasElement;
+    var canvas = createCanvasElement();
     canvas.width = width;
@@ -13,3 +15,4 @@ canvas.height = height;
 export function createCanvasFromMedia(media, dims) {
-    if (!isMediaLoaded(media)) {
+    var ImageData = env.getEnv().ImageData;
+    if (!(media instanceof ImageData) && !isMediaLoaded(media)) {
         throw new Error('createCanvasFromMedia - media has not finished loading yet');
@@ -19,5 +22,10 @@ }
     var canvas = createCanvas({ width: width, height: height });
-    getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);
+    if (media instanceof ImageData) {
+        getContext2dOrThrow(canvas).putImageData(media, 0, 0);
+    }
+    else {
+        getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);
+    }
     return canvas;
 }
 //# sourceMappingURL=createCanvas.js.map

build/es6/dom/drawDetection.js

 import { BoxWithText } from '../classes/BoxWithText';
 import { ObjectDetection } from '../classes/ObjectDetection';
 import { PredictedBox } from '../classes/PredictedBox';
+import { env } from '../env';
 import { round } from '../utils';
@@ -11,4 +12,5 @@ import { drawBox } from './drawBox';
 export function drawDetection(canvasArg, detection, options) {
+    var Canvas = env.getEnv().Canvas;
     var canvas = resolveInput(canvasArg);
-    if (!(canvas instanceof HTMLCanvasElement)) {
+    if (!(canvas instanceof Canvas)) {
         throw new Error('drawDetection - expected canvas to be of type: HTMLCanvasElement');
@@ -15,0 +17,0 @@ }

build/es6/dom/fetchOrThrow.js

 import * as tslib_1 from "tslib";
+import { env } from '../env';
 export function fetchOrThrow(url, init) {
     return tslib_1.__awaiter(this, void 0, void 0, function () {
-        var res;
+        var fetch, res;
         return tslib_1.__generator(this, function (_a) {
             switch (_a.label) {
-                case 0: return [4 /*yield*/, fetch(url, init)];
+                case 0:
+                    fetch = env.getEnv().fetch;
+                    return [4 /*yield*/, fetch(url, init)];
                 case 1:
@@ -9,0 +12,0 @@ res = _a.sent();

build/es6/dom/getMediaDimensions.js

 import { Dimensions } from '../classes/Dimensions';
+import { env } from '../env';
 export function getMediaDimensions(input) {
-    if (input instanceof HTMLImageElement) {
+    var _a = env.getEnv(), Image = _a.Image, Video = _a.Video;
+    if (input instanceof Image) {
         return new Dimensions(input.naturalWidth, input.naturalHeight);
     }
-    if (input instanceof HTMLVideoElement) {
+    if (input instanceof Video) {
         return new Dimensions(input.videoWidth, input.videoHeight);
@@ -8,0 +10,0 @@ }

build/es6/dom/imageTensorToCanvas.js

 import * as tslib_1 from "tslib";
 import * as tf from '@tensorflow/tfjs-core';
+import { env } from '../env';
 import { isTensor4D } from '../utils';
@@ -10,3 +11,3 @@ export function imageTensorToCanvas(imgTensor, canvas) {
                 case 0:
-                    targetCanvas = canvas || document.createElement('canvas');
+                    targetCanvas = canvas || env.getEnv().createCanvasElement();
                     _a = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0), height = _a[0], width = _a[1], numChannels = _a[2];
@@ -13,0 +14,0 @@ imgTensor3D = tf.tidy(function () { return imgTensor.as3D(height, width, numChannels).toInt(); });

build/es6/dom/imageToSquare.js

@@ -0,1 +1,2 @@
+import { env } from '../env';
 import { createCanvas, createCanvasFromMedia } from './createCanvas';
@@ -6,3 +7,4 @@ import { getContext2dOrThrow } from './getContext2dOrThrow';
     if (centerImage === void 0) { centerImage = false; }
-    if (!(input instanceof HTMLImageElement || input instanceof HTMLCanvasElement)) {
+    var _a = env.getEnv(), Image = _a.Image, Canvas = _a.Canvas;
+    if (!(input instanceof Image || input instanceof Canvas)) {
         throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement');
@@ -15,3 +17,3 @@ }
     var targetCanvas = createCanvas({ width: inputSize, height: inputSize });
-    var inputCanvas = input instanceof HTMLCanvasElement ? input : createCanvasFromMedia(input);
+    var inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input);
     var offset = Math.abs(width - height) / 2;
@@ -18,0 +20,0 @@ var dx = centerImage && width < height ? offset : 0;

build/es6/dom/isMediaElement.js

@@ -0,6 +1,8 @@
+import { env } from '../env';
 export function isMediaElement(input) {
-    return input instanceof HTMLImageElement
-        || input instanceof HTMLVideoElement
-        || input instanceof HTMLCanvasElement;
+    var _a = env.getEnv(), Image = _a.Image, Canvas = _a.Canvas, Video = _a.Video;
+    return input instanceof Image
+        || input instanceof Canvas
+        || input instanceof Video;
 }
 //# sourceMappingURL=isMediaElement.js.map

build/es6/dom/isMediaLoaded.js

@@ -0,5 +1,7 @@
+import { env } from '../env';
 export function isMediaLoaded(media) {
-    return (media instanceof HTMLImageElement && media.complete)
-        || (media instanceof HTMLVideoElement && media.readyState >= 3);
+    var _a = env.getEnv(), Image = _a.Image, Video = _a.Video;
+    return (media instanceof Image && media.complete)
+        || (media instanceof Video && media.readyState >= 3);
 }
 //# sourceMappingURL=isMediaLoaded.js.map

build/es6/dom/loadWeightMap.d.ts

@@ -1,1 +0,2 @@
-export declare function loadWeightMap(uri: string | undefined, defaultModelName: string): Promise<any>;
+import * as tf from '@tensorflow/tfjs-core';
+export declare function loadWeightMap(uri: string | undefined, defaultModelName: string): Promise<tf.NamedTensorMap>;

build/es6/dom/NetInput.js

 import * as tf from '@tensorflow/tfjs-core';
+import { env } from '../env';
 import { padToSquare } from '../ops/padToSquare';
@@ -34,3 +35,3 @@ import { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils';
             }
-            var canvas = input instanceof HTMLCanvasElement ? input : createCanvasFromMedia(input);
+            var canvas = input instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input);
             _this._canvases[idx] = canvas;
@@ -134,3 +135,3 @@ _this._inputDimensions[idx] = [canvas.height, canvas.width, 3];
                 }
-                if (input instanceof HTMLCanvasElement) {
+                if (input instanceof env.getEnv().Canvas) {
                     return tf.fromPixels(imageToSquare(input, inputSize, isCenterInputs));
@@ -137,0 +138,0 @@ }

build/es6/dom/resolveInput.js

@@ -0,3 +1,4 @@
+import { env } from '../env';
 export function resolveInput(arg) {
-    if (typeof arg === 'string') {
+    if (!env.isNodejs() && typeof arg === 'string') {
         return document.getElementById(arg);
@@ -4,0 +5,0 @@ }

build/es6/index.d.ts

 export * from './classes';
 export * from './common';
 export * from './dom';
+export * from './env';
 export * from './ops';
 export * from './utils';
 export * from './NeuralNetwork';

build/es6/index.js

 export * from './classes';
 export * from './common';
 export * from './dom';
+export * from './env';
 export * from './ops';
@@ -5,0 +6,0 @@ export * from './utils';

build/es6/NeuralNetwork.d.ts

 import * as tf from '@tensorflow/tfjs-core';
 import { ParamMapping } from './common';
-export declare class NeuralNetwork<TNetParams> {
+export declare abstract class NeuralNetwork<TNetParams> {
     private _name;
@@ -30,9 +30,13 @@ protected _params: TNetParams | undefined;
     load(weightsOrUrl: Float32Array | string | undefined): Promise<void>;
+    loadFromUri(uri: string | undefined): Promise<void>;
+    loadFromDisk(filePath: string | undefined): Promise<void>;
+    loadFromWeightMap(weightMap: tf.NamedTensorMap): void;
     extractWeights(weights: Float32Array): void;
-    private traversePropertyPath;
-    protected loadQuantizedParams(_: any): Promise<{
+    private traversePropertyPath(paramPath);
+    protected abstract getDefaultModelName(): string;
+    protected abstract extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
         params: TNetParams;
         paramMappings: ParamMapping[];
-    }>;
-    protected extractParams(_: any): {
+    };
+    protected abstract extractParams(weights: Float32Array): {
         params: TNetParams;
@@ -39,0 +43,0 @@ paramMappings: ParamMapping[];

build/es6/NeuralNetwork.js

 import * as tslib_1 from "tslib";
 import * as tf from '@tensorflow/tfjs-core';
+import { getModelUris } from './common/getModelUris';
+import { loadWeightMap } from './dom';
+import { env } from './env';
+import { weightsLoaderFactory } from './weightsLoaderFactory';
 var NeuralNetwork = /** @class */ (function () {
@@ -85,5 +89,4 @@ function NeuralNetwork(_name) {
         return tslib_1.__awaiter(this, void 0, void 0, function () {
-            var _a, paramMappings, params;
-            return tslib_1.__generator(this, function (_b) {
-                switch (_b.label) {
+            return tslib_1.__generator(this, function (_a) {
+                switch (_a.label) {
                     case 0:
@@ -94,10 +97,23 @@ if (weightsOrUrl instanceof Float32Array) {
                         }
-                        if (weightsOrUrl && typeof weightsOrUrl !== 'string') {
-                            throw new Error(this._name + ".load - expected model uri, or weights as Float32Array");
+                        return [4 /*yield*/, this.loadFromUri(weightsOrUrl)];
+                    case 1:
+                        _a.sent();
+                        return [2 /*return*/];
+                }
+            });
+        });
+    };
+    NeuralNetwork.prototype.loadFromUri = function (uri) {
+        return tslib_1.__awaiter(this, void 0, void 0, function () {
+            var weightMap;
+            return tslib_1.__generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        if (uri && typeof uri !== 'string') {
+                            throw new Error(this._name + ".loadFromUri - expected model uri");
                         }
-                        return [4 /*yield*/, this.loadQuantizedParams(weightsOrUrl)];
+                        return [4 /*yield*/, loadWeightMap(uri, this.getDefaultModelName())];
                     case 1:
-                        _a = _b.sent(), paramMappings = _a.paramMappings, params = _a.params;
-                        this._paramMappings = paramMappings;
-                        this._params = params;
+                        weightMap = _a.sent();
+                        this.loadFromWeightMap(weightMap);
                         return [2 /*return*/];
@@ -108,2 +124,33 @@ }
     };
+    NeuralNetwork.prototype.loadFromDisk = function (filePath) {
+        return tslib_1.__awaiter(this, void 0, void 0, function () {
+            var readFile, _a, manifestUri, modelBaseUri, fetchWeightsFromDisk, loadWeights, manifest, _b, _c, weightMap;
+            return tslib_1.__generator(this, function (_d) {
+                switch (_d.label) {
+                    case 0:
+                        if (filePath && typeof filePath !== 'string') {
+                            throw new Error(this._name + ".loadFromDisk - expected model file path");
+                        }
+                        readFile = env.getEnv().readFile;
+                        _a = getModelUris(filePath, this.getDefaultModelName()), manifestUri = _a.manifestUri, modelBaseUri = _a.modelBaseUri;
+                        fetchWeightsFromDisk = function (filePaths) { return Promise.all(filePaths.map(function (filePath) { return readFile(filePath).then(function (buf) { return buf.buffer; }); })); };
+                        loadWeights = weightsLoaderFactory(fetchWeightsFromDisk);
+                        _c = (_b = JSON).parse;
+                        return [4 /*yield*/, readFile(manifestUri)];
+                    case 1:
+                        manifest = _c.apply(_b, [(_d.sent()).toString()]);
+                        return [4 /*yield*/, loadWeights(manifest, modelBaseUri)];
+                    case 2:
+                        weightMap = _d.sent();
+                        this.loadFromWeightMap(weightMap);
+                        return [2 /*return*/];
+                }
+            });
+        });
+    };
+    NeuralNetwork.prototype.loadFromWeightMap = function (weightMap) {
+        var _a = this.extractParamsFromWeigthMap(weightMap), paramMappings = _a.paramMappings, params = _a.params;
+        this._paramMappings = paramMappings;
+        this._params = params;
+    };
     NeuralNetwork.prototype.extractWeights = function (weights) {
@@ -130,8 +177,2 @@ var _a = this.extractParams(weights), paramMappings = _a.paramMappings, params = _a.params;
     };
-    NeuralNetwork.prototype.loadQuantizedParams = function (_) {
-        throw new Error(this._name + ".loadQuantizedParams - not implemented");
-    };
-    NeuralNetwork.prototype.extractParams = function (_) {
-        throw new Error(this._name + ".extractParams - not implemented");
-    };
     return NeuralNetwork;
@@ -138,0 +179,0 @@ }());

build/es6/ops/padToSquare.js

@@ -35,3 +35,4 @@ import * as tf from '@tensorflow/tfjs-core';
         ]
-            .filter(function (t) { return t !== null; });
+            .filter(function (t) { return !!t; })
+            .map(function (t) { return t.toFloat(); });
         return tf.concat(tensorsToStack, paddingAxis);
@@ -38,0 +39,0 @@ });

build/es6/utils/index.d.ts

@@ -13,3 +13,3 @@ import * as tf from '@tensorflow/tfjs-core';
 export declare function isDimensions(obj: any): boolean;
-export declare function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number): Dimensions;
+export declare function computeReshapedDimensions({width, height}: IDimensions, inputSize: number): Dimensions;
 export declare function getCenterPoint(pts: Point[]): Point;
@@ -16,0 +16,0 @@ export declare function range(num: number, start: number, step: number): number[];

karma.conf.js

@@ -26,5 +26,3 @@ const dataFiles = [
     },
-    browsers: process.env.KARMA_BROWSERS
-      ? process.env.KARMA_BROWSERS.split(',')
-      : ['Chrome'],
+    browsers: ['Chrome'],
     browserNoActivityTimeout: 120000,
@@ -36,10 +34,4 @@ captureTimeout: 60000,
       }
-    },
-    customLaunchers: {
-      ChromeNoSandbox: {
-        base: 'Chrome',
-        flags: ['--no-sandbox']
-      }
     }
   })
 }

package.json

 {
   "name": "tfjs-image-recognition-base",
-  "version": "0.1.3",
+  "version": "0.2.0",
   "description": "A shared codebase for face-api.js and tfjs-tiny-yolo-v2.",
@@ -11,5 +11,7 @@ "module": "./build/es6/index.js",
     "tsc-es6": "tsc --p tsconfig.es6.json",
-    "build": "npm run tsc && npm run tsc-es6",
+    "build": "rm -rf ./build && npm run tsc && npm run tsc-es6",
     "test": "karma start",
-    "test-travis": "set KARMA_BROWSERS=ChromeHeadlessNoSandbox&& karma start --single-run"
+    "test-browser": "karma start --single-run",
+    "test-node": "ts-node node_modules/jasmine/bin/jasmine --config=jasmine-node.json",
+    "test-all": "npm run test-browser && npm run test-node"
   },
@@ -19,8 +21,11 @@ "author": "justadudewhohacks",
   "dependencies": {
-    "@tensorflow/tfjs-core": "^0.13.2",
+    "@tensorflow/tfjs-core": "0.13.8",
     "tslib": "^1.9.3"
   },
   "devDependencies": {
+    "@tensorflow/tfjs-node": "^0.1.19",
     "@types/jasmine": "^2.8.8",
     "@types/node": "^10.9.2",
+    "canvas": "^2.0.1",
+    "jasmine": "^3.3.0",
     "jasmine-core": "^3.2.1",
@@ -31,4 +36,5 @@ "karma": "^3.0.0",
     "karma-typescript": "^3.0.12",
-    "typescript": "^2.9.2"
+    "ts-node": "^7.0.1",
+    "typescript": "2.8.4"
   }
 }

src/classes/Dimensions.ts

@@ -1,2 +0,2 @@
-import { isNumber } from 'util';
+import { isValidNumber } from '../utils';
 
@@ -14,3 +14,3 @@ export interface IDimensions {
   constructor(width: number, height: number) {
-    if (!isNumber(width) || !isNumber(height)) {
+    if (!isValidNumber(width) || !isValidNumber(height)) {
       throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`)
@@ -17,0 +17,0 @@ }

src/dom/awaitMediaLoaded.ts

@@ -0,6 +1,8 @@
+import { env } from '../env';
 import { isMediaLoaded } from './isMediaLoaded';
 
 export function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {
+
   return new Promise((resolve, reject) => {
-    if (media instanceof HTMLCanvasElement || isMediaLoaded(media)) {
+    if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {
       return resolve()
@@ -7,0 +9,0 @@ }

src/dom/bufferToImage.ts

@@ -0,1 +1,3 @@
+import { env } from '../env';
+
 export function bufferToImage(buf: Blob): Promise<HTMLImageElement> {
@@ -13,3 +15,3 @@ return new Promise((resolve, reject) => {
 
-      const img = new Image()
+      const img = env.getEnv().createImageElement()
       img.onload = () => resolve(img)
@@ -16,0 +18,0 @@ img.onerror = reject

src/dom/createCanvas.ts

 import { IDimensions } from '../classes/Dimensions';
+import { env } from '../env';
 import { getContext2dOrThrow } from './getContext2dOrThrow';
@@ -7,3 +8,5 @@ import { getMediaDimensions } from './getMediaDimensions';
 export function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {
-  const canvas = document.createElement('canvas')
+
+  const { createCanvasElement } = env.getEnv()
+  const canvas = createCanvasElement()
   canvas.width = width
@@ -14,4 +17,7 @@ canvas.height = height
 
-export function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement, dims?: IDimensions): HTMLCanvasElement {
-  if (!isMediaLoaded(media)) {
+export function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {
+
+  const { ImageData } = env.getEnv()
+
+  if (!(media instanceof ImageData) && !isMediaLoaded(media)) {
     throw new Error('createCanvasFromMedia - media has not finished loading yet')
@@ -22,4 +28,9 @@ }
   const canvas = createCanvas({ width, height })
-  getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height)
+
+  if (media instanceof ImageData) {
+    getContext2dOrThrow(canvas).putImageData(media, 0, 0)
+  } else {
+    getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height)
+  }
   return canvas
 }

src/dom/drawDetection.ts

@@ -5,2 +5,3 @@ import { BoxWithText } from '../classes/BoxWithText';
 import { Rect } from '../classes/Rect';
+import { env } from '../env';
 import { round } from '../utils';
@@ -19,4 +20,7 @@ import { drawBox } from './drawBox';
 ) {
+
+  const { Canvas } = env.getEnv()
+
   const canvas = resolveInput(canvasArg)
-  if (!(canvas instanceof HTMLCanvasElement)) {
+  if (!(canvas instanceof Canvas)) {
     throw new Error('drawDetection - expected canvas to be of type: HTMLCanvasElement')
@@ -23,0 +27,0 @@ }

src/dom/fetchOrThrow.ts

@@ -0,1 +1,3 @@
+import { env } from '../env';
+
 export async function fetchOrThrow(
@@ -5,2 +7,4 @@ url: string,
 ): Promise<Response> {
+
+  const fetch = env.getEnv().fetch
   const res = await fetch(url, init)
@@ -7,0 +11,0 @@ if (!(res.status < 400)) {

src/dom/getMediaDimensions.ts

 import { Dimensions, IDimensions } from '../classes/Dimensions';
+import { env } from '../env';
 
 export function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {
-  if (input instanceof HTMLImageElement) {
+
+  const { Image, Video } = env.getEnv()
+
+  if (input instanceof Image) {
     return new Dimensions(input.naturalWidth, input.naturalHeight)
   }
-  if (input instanceof HTMLVideoElement) {
+  if (input instanceof Video) {
     return new Dimensions(input.videoWidth, input.videoHeight)
@@ -9,0 +13,0 @@ }

src/dom/imageTensorToCanvas.ts

 import * as tf from '@tensorflow/tfjs-core';
 
+import { env } from '../env';
 import { isTensor4D } from '../utils';
@@ -9,4 +10,5 @@
 ): Promise<HTMLCanvasElement> {
-  const targetCanvas = canvas ||  document.createElement('canvas')
 
+  const targetCanvas = canvas || env.getEnv().createCanvasElement()
+
   const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0)
@@ -13,0 +15,0 @@ const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt())

src/dom/imageToSquare.ts

@@ -0,1 +1,2 @@
+import { env } from '../env';
 import { createCanvas, createCanvasFromMedia } from './createCanvas';
@@ -7,3 +8,5 @@ import { getContext2dOrThrow } from './getContext2dOrThrow';
 
-  if (!(input instanceof HTMLImageElement || input instanceof HTMLCanvasElement)) {
+  const { Image, Canvas } = env.getEnv()
+
+  if (!(input instanceof Image || input instanceof Canvas)) {
     throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement')
@@ -18,3 +21,3 @@ }
   const targetCanvas = createCanvas({ width: inputSize, height: inputSize })
-  const inputCanvas = input instanceof HTMLCanvasElement ? input : createCanvasFromMedia(input)
+  const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input)
 
@@ -21,0 +24,0 @@ const offset = Math.abs(width - height) / 2

src/dom/isMediaElement.ts

@@ -0,5 +1,10 @@
+import { env } from '../env';
+
 export function isMediaElement(input: any) {
-  return input instanceof HTMLImageElement
-    || input instanceof HTMLVideoElement
-    || input instanceof HTMLCanvasElement
+
+  const { Image, Canvas, Video } = env.getEnv()
+
+  return input instanceof Image
+    || input instanceof Canvas
+    || input instanceof Video
 }

src/dom/isMediaLoaded.ts

@@ -0,4 +1,9 @@
+import { env } from '../env';
+
 export function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {
-  return (media instanceof HTMLImageElement && media.complete)
-    || (media instanceof HTMLVideoElement && media.readyState >= 3)
+
+  const { Image, Video } = env.getEnv()
+
+  return (media instanceof Image && media.complete)
+    || (media instanceof Video && media.readyState >= 3)
 }

src/dom/loadWeightMap.ts

@@ -8,10 +8,9 @@ import * as tf from '@tensorflow/tfjs-core';
   uri: string | undefined,
-  defaultModelName: string
-): Promise<any> {
-
+  defaultModelName: string,
+): Promise<tf.NamedTensorMap> {
   const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName)
 
-  const manifest = await fetchJson<any>(manifestUri)
+  const manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri)
 
   return tf.io.loadWeights(manifest, modelBaseUri)
 }

src/dom/NetInput.ts

 import * as tf from '@tensorflow/tfjs-core';
 
 import { Dimensions } from '../classes/Dimensions';
+import { env } from '../env';
 import { padToSquare } from '../ops/padToSquare';
@@ -49,3 +50,3 @@ import { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils';
 
-      const canvas = input instanceof HTMLCanvasElement ? input : createCanvasFromMedia(input as HTMLImageElement | HTMLVideoElement)
+      const canvas = input instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input)
       this._canvases[idx] = canvas
@@ -141,3 +142,3 @@ this._inputDimensions[idx] = [canvas.height, canvas.width, 3]
 
-        if (input instanceof HTMLCanvasElement) {
+        if (input instanceof env.getEnv().Canvas) {
           return tf.fromPixels(imageToSquare(input, inputSize, isCenterInputs))
@@ -144,0 +145,0 @@ }

src/dom/resolveInput.ts

@@ -0,3 +1,5 @@
+import { env } from '../env';
+
 export function resolveInput(arg: string | any) {
-  if (typeof arg === 'string') {
+  if (!env.isNodejs() && typeof arg === 'string') {
     return document.getElementById(arg)
@@ -4,0 +6,0 @@ }

src/index.ts

 export * from './classes';
 export * from './common';
 export * from './dom';
+export * from './env';
 export * from './ops';
 export * from './utils';
 export * from './NeuralNetwork';

src/NeuralNetwork.ts

 import * as tf from '@tensorflow/tfjs-core';
 
 import { ParamMapping } from './common';
+import { getModelUris } from './common/getModelUris';
+import { loadWeightMap } from './dom';
+import { env } from './env';
+import { weightsLoaderFactory } from './weightsLoaderFactory';
 
-export class NeuralNetwork<TNetParams> {
+export abstract class NeuralNetwork<TNetParams> {
 
@@ -80,9 +84,39 @@ protected _params: TNetParams | undefined = undefined
 
-    if (weightsOrUrl && typeof weightsOrUrl !== 'string') {
-      throw new Error(`${this._name}.load - expected model uri, or weights as Float32Array`)
+    await this.loadFromUri(weightsOrUrl)
+  }
+
+  public async loadFromUri(uri: string | undefined) {
+    if (uri && typeof uri !== 'string') {
+      throw new Error(`${this._name}.loadFromUri - expected model uri`)
     }
+
+    const weightMap = await loadWeightMap(uri, this.getDefaultModelName())
+    this.loadFromWeightMap(weightMap)
+  }
+
+  public async loadFromDisk(filePath: string | undefined) {
+    if (filePath && typeof filePath !== 'string') {
+      throw new Error(`${this._name}.loadFromDisk - expected model file path`)
+    }
+
+    const { readFile } = env.getEnv()
+
+    const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName())
+
+    const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(
+      filePaths.map(filePath => readFile(filePath).then(buf => buf.buffer))
+    )
+    const loadWeights = weightsLoaderFactory(fetchWeightsFromDisk)
+
+    const manifest = JSON.parse((await readFile(manifestUri)).toString())
+    const weightMap = await loadWeights(manifest, modelBaseUri)
+
+    this.loadFromWeightMap(weightMap)
+  }
+
+  public loadFromWeightMap(weightMap: tf.NamedTensorMap) {
     const {
       paramMappings,
       params
-    } = await this.loadQuantizedParams(weightsOrUrl)
+    } = this.extractParamsFromWeigthMap(weightMap)
 
@@ -124,9 +158,5 @@ this._paramMappings = paramMappings
 
-  protected loadQuantizedParams(_: any): Promise<{ params: TNetParams, paramMappings: ParamMapping[] }> {
-    throw new Error(`${this._name}.loadQuantizedParams - not implemented`)
-  }
-
-  protected extractParams(_: any): { params: TNetParams, paramMappings: ParamMapping[] } {
-    throw new Error(`${this._name}.extractParams - not implemented`)
-  }
+  protected abstract getDefaultModelName(): string
+  protected abstract extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }
+  protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }
 }

src/ops/padToSquare.ts

@@ -44,5 +44,6 @@ import * as tf from '@tensorflow/tfjs-core';
     ]
-      .filter(t => t !== null) as tf.Tensor4D[]
+      .filter(t => !!t)
+      .map((t: tf.Tensor) => t.toFloat()) as tf.Tensor4D[]
     return tf.concat(tensorsToStack, paddingAxis)
   })
 }

New alerts

Filesystem access

Supply chain risk

Accesses the file system, and could potentially read sensitive data.

Found 1 instance in 1 package

Improved metrics

Total package byte prevSize

387992

increased by19.48%

Number of package files

408

increased by9.68%

Lines of code

6194

increased by18.5%

Worsened metrics

Dev dependency count

12

increased by50%

Number of low supply chain risk alerts

3

increased by200%

Number of medium supply chain risk alerts

6

increased by200%

Dependency changes

• + Added@tensorflow/tfjs-core@0.13.8(transitive)

• - Removed@tensorflow/tfjs-core@0.13.12(transitive)

• Updated@tensorflow/tfjs-core@0.13.8