6487 lines
826 KiB
JavaScript
6487 lines
826 KiB
JavaScript
(function (global, factory) {
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typeof exports === 'object' && typeof module !== 'undefined' ? factory(exports) :
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typeof define === 'function' && define.amd ? define(['exports'], factory) :
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(global = global || self, factory(global.faceapi = global.faceapi || {}));
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}(this, function (exports) { 'use strict';
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/*! *****************************************************************************
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Copyright (c) Microsoft Corporation. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use
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this file except in compliance with the License. You may obtain a copy of the
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License at http://www.apache.org/licenses/LICENSE-2.0
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THIS CODE IS PROVIDED ON AN *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
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WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
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MERCHANTABLITY OR NON-INFRINGEMENT.
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See the Apache Version 2.0 License for specific language governing permissions
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and limitations under the License.
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***************************************************************************** */
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/* global Reflect, Promise */
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var extendStatics = function(d, b) {
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extendStatics = Object.setPrototypeOf ||
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({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||
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function (d, b) { for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p]; };
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return extendStatics(d, b);
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};
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function __extends(d, b) {
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extendStatics(d, b);
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function __() { this.constructor = d; }
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d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());
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}
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var __assign = function() {
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__assign = Object.assign || function __assign(t) {
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for (var s, i = 1, n = arguments.length; i < n; i++) {
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s = arguments[i];
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for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p)) t[p] = s[p];
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}
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return t;
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};
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return __assign.apply(this, arguments);
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};
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function __awaiter(thisArg, _arguments, P, generator) {
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return new (P || (P = Promise))(function (resolve, reject) {
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function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
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function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
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function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
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step((generator = generator.apply(thisArg, _arguments || [])).next());
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});
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}
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function __generator(thisArg, body) {
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var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
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return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
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function verb(n) { return function (v) { return step([n, v]); }; }
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function step(op) {
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if (f) throw new TypeError("Generator is already executing.");
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while (_) try {
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if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
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if (y = 0, t) op = [op[0] & 2, t.value];
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switch (op[0]) {
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case 0: case 1: t = op; break;
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case 4: _.label++; return { value: op[1], done: false };
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case 5: _.label++; y = op[1]; op = [0]; continue;
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case 7: op = _.ops.pop(); _.trys.pop(); continue;
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default:
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if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
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if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
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if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
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if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
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if (t[2]) _.ops.pop();
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_.trys.pop(); continue;
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}
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op = body.call(thisArg, _);
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} catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
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if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
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}
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}
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/**
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* @license
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* Copyright 2019 Google LLC. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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* =============================================================================
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*/
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var extendStatics$1=function(e,t){return (extendStatics$1=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t;}||function(e,t){for(var n in t)t.hasOwnProperty(n)&&(e[n]=t[n]);})(e,t)};function __extends$1(e,t){function n(){this.constructor=e;}extendStatics$1(e,t),e.prototype=null===t?Object.create(t):(n.prototype=t.prototype,new n);}var __assign$1=function(){return (__assign$1=Object.assign||function(e){for(var t,n=1,r=arguments.length;n<r;n++)for(var o in t=arguments[n])Object.prototype.hasOwnProperty.call(t,o)&&(e[o]=t[o]);return e}).apply(this,arguments)};function __awaiter$1(e,t,n,r){return new(n||(n=Promise))(function(o,a){function i(e){try{u(r.next(e));}catch(e){a(e);}}function s(e){try{u(r.throw(e));}catch(e){a(e);}}function u(e){e.done?o(e.value):new n(function(t){t(e.value);}).then(i,s);}u((r=r.apply(e,t||[])).next());})}function __generator$1(e,t){var n,r,o,a,i={label:0,sent:function(){if(1&o[0])throw o[1];return o[1]},trys:[],ops:[]};return a={next:s(0),throw:s(1),return:s(2)},"function"==typeof Symbol&&(a[Symbol.iterator]=function(){return this}),a;function s(a){return function(s){return function(a){if(n)throw new TypeError("Generator is already executing.");for(;i;)try{if(n=1,r&&(o=2&a[0]?r.return:a[0]?r.throw||((o=r.return)&&o.call(r),0):r.next)&&!(o=o.call(r,a[1])).done)return o;switch(r=0,o&&(a=[2&a[0],o.value]),a[0]){case 0:case 1:o=a;break;case 4:return i.label++,{value:a[1],done:!1};case 5:i.label++,r=a[1],a=[0];continue;case 7:a=i.ops.pop(),i.trys.pop();continue;default:if(!(o=(o=i.trys).length>0&&o[o.length-1])&&(6===a[0]||2===a[0])){i=0;continue}if(3===a[0]&&(!o||a[1]>o[0]&&a[1]<o[3])){i.label=a[1];break}if(6===a[0]&&i.label<o[1]){i.label=o[1],o=a;break}if(o&&i.label<o[2]){i.label=o[2],i.ops.push(a);break}o[2]&&i.ops.pop(),i.trys.pop();continue}a=t.call(e,i);}catch(e){a=[6,e],r=0;}finally{n=o=0;}if(5&a[0])throw a[1];return {value:a[0]?a[1]:void 0,done:!0}}([a,s])}}}var contexts={},WEBGL_ATTRIBUTES={alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!0};function setWebGLContext(e,t){contexts[e]=t;}function getWebGLContext(e){e in contexts||(contexts[e]=getWebGLRenderingContext(e));var t=contexts[e];return t.isContextLost()?(delete contexts[e],getWebGLContext(e)):(t.disable(t.DEPTH_TEST),t.disable(t.STENCIL_TEST),t.disable(t.BLEND),t.disable(t.DITHER),t.disable(t.POLYGON_OFFSET_FILL),t.disable(t.SAMPLE_COVERAGE),t.enable(t.SCISSOR_TEST),t.enable(t.CULL_FACE),t.cullFace(t.BACK),contexts[e])}function getWebGLRenderingContext(e){if(1!==e&&2!==e)throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");var t=document.createElement("canvas");return t.addEventListener("webglcontextlost",function(t){t.preventDefault(),delete contexts[e];},!1),1===e?t.getContext("webgl",WEBGL_ATTRIBUTES)||t.getContext("experimental-webgl",WEBGL_ATTRIBUTES):t.getContext("webgl2",WEBGL_ATTRIBUTES)}function isMobile(){var e=navigator.userAgent||navigator.vendor||window.opera;return /(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(e)||/1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(e.substr(0,4))}function shuffle(e){for(var t=e.length,n=0,r=0;t>0;)r=Math.random()*t|0,n=e[--t],e[t]=e[r],e[r]=n;}function clamp(e,t,n){return Math.max(e,Math.min(t,n))}function nearestLargerEven(e){return e%2==0?e:e+1}function sum(e){for(var t=0,n=0;n<e.length;n++)t+=e[n];return t}function randUniform(e,t){var n=Math.random();return t*n+(1-n)*e}function distSquared(e,t){for(var n=0,r=0;r<e.length;r++){var o=Number(e[r])-Number(t[r]);n+=o*o;}return n}function assert(e,t){if(!e)throw new Error("string"==typeof t?t:t())}function assertShapesMatch(e,t,n){void 0===n&&(n=""),assert(arraysEqual(e,t),function(){return n+" Shapes "+e+" and "+t+" must match"});}function assertNonNull(e){assert(null!=e,function(){return "The input to the tensor constructor must be a non-null value."});}function flatten(e,t){if(void 0===t&&(t=[]),null==t&&(t=[]),Array.isArray(e)||isTypedArray(e))for(var n=0;n<e.length;++n)flatten(e[n],t);else t.push(e);return t}function sizeFromShape(e){if(0===e.length)return 1;for(var t=e[0],n=1;n<e.length;n++)t*=e[n];return t}function isScalarShape(e){return 0===e.length}function arraysEqual(e,t){if(e===t)return !0;if(null==e||null==t)return !1;if(e.length!==t.length)return !1;for(var n=0;n<e.length;n++)if(e[n]!==t[n])return !1;return !0}function isInt(e){return e%1==0}function tanh(e){if(null!=Math.tanh)return Math.tanh(e);if(e===1/0)return 1;if(e===-1/0)return -1;var t=Math.exp(2*e);return (t-1)/(t+1)}function sizeToSquarishShape(e){var t=Math.ceil(Math.sqrt(e));return [t,Math.ceil(e/t)]}function createShuffledIndices(e){for(var t=new Uint32Array(e),n=0;n<e;++n)t[n]=n;return shuffle(t),t}function rightPad(e,t){return t<=e.length?e:e+" ".repeat(t-e.length)}function repeatedTry(e,t,n){return void 0===t&&(t=function(e){return 0}),new Promise(function(r,o){var a=0,i=function(){if(e())r();else{var s=t(++a);null!=n&&a>=n?o():setTimeout(i,s);}};i();})}function inferFromImplicitShape(e,t){for(var n=1,r=-1,o=0;o<e.length;++o)if(e[o]>=0)n*=e[o];else if(-1===e[o]){if(-1!==r)throw Error("Shapes can only have 1 implicit size. Found -1 at dim "+r+" and dim "+o);r=o;}else if(e[o]<0)throw Error("Shapes can not be < 0. Found "+e[o]+" at dim "+o);if(-1===r){if(t>0&&t!==n)throw Error("Size("+t+") must match the product of shape "+e);return e}if(0===n)throw Error("Cannot infer the missing size in ["+e+"] when there are 0 elements");if(t%n!=0)throw Error("The implicit shape can't be a fractional number. Got "+t+" / "+n);var a=e.slice();return a[r]=t/n,a}function parseAxisParam(e,t){var n=t.length;return assert((e=null==e?t.map(function(e,t){return t}):[].concat(e)).every(function(e){return e>=-n&&e<n}),function(){return "All values in axis param must be in range [-"+n+", "+n+") but got axis "+e}),assert(e.every(function(e){return isInt(e)}),function(){return "All values in axis param must be integers but got axis "+e}),e.map(function(e){return e<0?n+e:e})}function squeezeShape(e,t){for(var n=[],r=[],o=null==t?null:parseAxisParam(t,e).sort(),a=0,i=0;i<e.length;++i){if(null!=o){if(o[a]===i&&1!==e[i])throw new Error("Can't squeeze axis "+i+" since its dim '"+e[i]+"' is not 1");(null==o[a]||o[a]>i)&&1===e[i]&&(n.push(e[i]),r.push(i)),o[a]<=i&&a++;}1!==e[i]&&(n.push(e[i]),r.push(i));}return {newShape:n,keptDims:r}}function getTypedArrayFromDType(e,t){var n=null;if(null==e||"float32"===e)n=new Float32Array(t);else if("int32"===e)n=new Int32Array(t);else{if("bool"!==e)throw new Error("Unknown data type "+e);n=new Uint8Array(t);}return n}function getArrayFromDType(e,t){var n=null;if(null==e||"float32"===e)n=new Float32Array(t);else if("int32"===e)n=new Int32Array(t);else if("bool"===e)n=new Uint8Array(t);else{if("string"!==e)throw new Error("Unknown data type "+e);n=new Array(t);}return n}function checkComputationForErrors(e,t,n){if("float32"===t)for(var r=0;r<e.length;r++){var o=e[r];if(isNaN(o)||!isFinite(o))throw Error("The result of the '"+n+"' is "+o+".")}}function checkConversionForErrors(e,t){for(var n=0;n<e.length;n++){var r=e[n];if(isNaN(r)||!isFinite(r))throw Error("A tensor of type "+t+" being uploaded contains "+r+".")}}function hasEncodingLoss(e,t){return "complex64"!==t&&(("float32"!==t||"complex64"===e)&&(("int32"!==t||"float32"===e||"complex64"===e)&&("bool"!==t||"bool"!==e)))}function isTypedArray(e){return e instanceof Float32Array||e instanceof Int32Array||e instanceof Uint8Array}function bytesPerElement(e){if("float32"===e||"int32"===e)return 4;if("complex64"===e)return 8;if("bool"===e)return 1;throw new Error("Unknown dtype "+e)}function bytesFromStringArray(e){if(null==e)return 0;var t=0;return e.forEach(function(e){return t+=2*e.length}),t}function isString(e){return "string"==typeof e||e instanceof String}function isBoolean(e){return "boolean"==typeof e}function isNumber(e){return "number"==typeof e}function inferDtype(e){return Array.isArray(e)?inferDtype(e[0]):e instanceof Float32Array?"float32":e instanceof Int32Array||e instanceof Uint8Array?"int32":isNumber(e)?"float32":isString(e)?"string":isBoolean(e)?"bool":"float32"}function isFunction(e){return !!(e&&e.constructor&&e.call&&e.apply)}function nearestDivisor(e,t){for(var n=t;n<e;++n)if(e%n==0)return n;return e}function computeStrides(e){var t=e.length;if(t<2)return [];var n=new Array(t-1);n[t-2]=e[t-1];for(var r=t-3;r>=0;--r)n[r]=n[r+1]*e[r+1];return n}function toTypedArray(e,t,n){if("string"===t)throw new Error("Cannot convert a string[] to a TypedArray");if(Array.isArray(e)&&(e=flatten(e)),n&&checkConversionForErrors(e,t),noConversionNeeded(e,t))return e;if(null==t||"float32"===t||"complex64"===t)return new Float32Array(e);if("int32"===t)return new Int32Array(e);if("bool"===t){for(var r=new Uint8Array(e.length),o=0;o<r.length;++o)0!==Math.round(e[o])&&(r[o]=1);return r}throw new Error("Unknown data type "+t)}function createNestedArray(e,t,n){var r=new Array;if(1===t.length)for(var o=t[0],a=0;a<o;a++)r[a]=n[e+a];else{o=t[0];var i=t.slice(1),s=i.reduce(function(e,t){return e*t});for(a=0;a<o;a++)r[a]=createNestedArray(e+a*s,i,n);}return r}function toNestedArray(e,t){if(0===e.length)return t[0];var n=e.reduce(function(e,t){return e*t});if(0===n)return [];if(n!==t.length)throw new Error("["+e+"] does not match the input size.");return createNestedArray(0,e,t)}function noConversionNeeded(e,t){return e instanceof Float32Array&&"float32"===t||e instanceof Int32Array&&"int32"===t||e instanceof Uint8Array&&"bool"===t}function makeOnesTypedArray(e,t){for(var n=makeZerosTypedArray(e,t),r=0;r<n.length;r++)n[r]=1;return n}function makeZerosTypedArray(e,t){if(null==t||"float32"===t||"complex64"===t)return new Float32Array(e);if("int32"===t)return new Int32Array(e);if("bool"===t)return new Uint8Array(e);throw new Error("Unknown data type "+t)}function now(){if("undefined"!=typeof performance)return performance.now();if("undefined"!=typeof process){var e=process.hrtime();return 1e3*e[0]+e[1]/1e6}throw new Error("Cannot measure time in this environment. You should run tf.js in the browser or in Node.js")}function assertNonNegativeIntegerDimensions(e){e.forEach(function(t){assert(Number.isInteger(t)&&t>=0,function(){return "Tensor must have a shape comprised of positive integers but got shape ["+e+"]."});});}var util=Object.freeze({shuffle:shuffle,clamp:clamp,nearestLargerEven:nearestLargerEven,sum:sum,randUniform:randUniform,distSquared:distSquared,assert:assert,assertShapesMatch:assertShapesMatch,assertNonNull:assertNonNull,flatten:flatten,sizeFromShape:sizeFromShape,isScalarShape:isScalarShape,arraysEqual:arraysEqual,isInt:isInt,tanh:tanh,sizeToSquarishShape:sizeToSquarishShape,createShuffledIndices:createShuffledIndices,rightPad:rightPad,repeatedTry:repeatedTry,inferFromImplicitShape:inferFromImplicitShape,parseAxisParam:parseAxisParam,squeezeShape:squeezeShape,getTypedArrayFromDType:getTypedArrayFromDType,getArrayFromDType:getArrayFromDType,checkComputationForErrors:checkComputationForErrors,checkConversionForErrors:checkConversionForErrors,hasEncodingLoss:hasEncodingLoss,isTypedArray:isTypedArray,bytesPerElement:bytesPerElement,bytesFromStringArray:bytesFromStringArray,isString:isString,isBoolean:isBoolean,isNumber:isNumber,inferDtype:inferDtype,isFunction:isFunction,nearestDivisor:nearestDivisor,computeStrides:computeStrides,toTypedArray:toTypedArray,toNestedArray:toNestedArray,makeOnesTypedArray:makeOnesTypedArray,makeZerosTypedArray:makeZerosTypedArray,now:now,assertNonNegativeIntegerDimensions:assertNonNegativeIntegerDimensions}),Profiler=function(){function e(e,t){this.backendTimer=e,this.logger=t,null==t&&(this.logger=new Logger);}return e.prototype.profileKernel=function(e,t){var n,r=this,o=this.backendTimer.time(function(){n=t();});return (Array.isArray(n)?n:[n]).forEach(function(t){var n=t.dataSync();checkComputationForErrors(n,t.dtype,e),o.then(function(o){var a="";null!=o.getExtraProfileInfo&&(a=o.getExtraProfileInfo()),r.logger.logKernelProfile(e,t,n,o.kernelMs,a);});}),n},e}(),Logger=function(){function e(){}return e.prototype.logKernelProfile=function(e,t,n,r,o){var a=rightPad(r+"ms",9),i=rightPad(e,25),s=t.rank,u=t.size,c=rightPad(t.shape.toString(),14);console.log("%c"+i+"\t%c"+a+"\t%c"+s+"D "+c+"\t%c"+u+"\t%c"+o,"font-weight:bold","color:red","color:blue","color: orange","color: green");},e}(),FORMAT_LIMIT_NUM_VALS=20,FORMAT_NUM_FIRST_LAST_VALS=3,FORMAT_NUM_SIG_DIGITS=7;function tensorToString(e,t,n,r){var o=computeStrides(t),a=computeMaxSizePerColumn(e,t,n,o),i=t.length,s=subTensorToString(e,t,n,o,a),u=["Tensor"];return r&&(u.push(" dtype: "+n),u.push(" rank: "+i),u.push(" shape: ["+t+"]"),u.push(" values:")),u.push(s.map(function(e){return " "+e}).join("\n")),u.join("\n")}function computeMaxSizePerColumn(e,t,n,r){var o=sizeFromShape(t),a=r[r.length-1],i=new Array(a).fill(0),s=t.length,u="complex64"===n?createComplexTuples(e):e;if(s>1)for(var c=0;c<o/a;c++)for(var l=c*a,p=0;p<a;p++)i[p]=Math.max(i[p],valToString(u[l+p],0,n).length);return i}function valToString(e,t,n){return rightPad(Array.isArray(e)?parseFloat(e[0].toFixed(FORMAT_NUM_SIG_DIGITS))+" + "+parseFloat(e[1].toFixed(FORMAT_NUM_SIG_DIGITS))+"j":isString(e)?"'"+e+"'":"bool"===n?boolNumToString(e):parseFloat(e.toFixed(FORMAT_NUM_SIG_DIGITS)).toString(),t)}function boolNumToString(e){return 0===e?"false":"true"}function subTensorToString(e,t,n,r,o,a){void 0===a&&(a=!0);var i="complex64"===n?2:1,s=t[0],u=t.length;if(0===u)return "complex64"===n?[valToString(createComplexTuples(e)[0],0,n)]:"bool"===n?[boolNumToString(e[0])]:[e[0].toString()];if(1===u){if(s>FORMAT_LIMIT_NUM_VALS){var c=FORMAT_NUM_FIRST_LAST_VALS*i,l=Array.from(e.slice(0,c)),p=Array.from(e.slice(s-FORMAT_NUM_FIRST_LAST_VALS*i,s));return "complex64"===n&&(l=createComplexTuples(l),p=createComplexTuples(p)),["["+l.map(function(e,t){return valToString(e,o[t],n)}).join(", ")+", ..., "+p.map(function(e,t){return valToString(e,o[s-FORMAT_NUM_FIRST_LAST_VALS+t],n)}).join(", ")+"]"]}return ["["+("complex64"===n?createComplexTuples(e):Array.from(e)).map(function(e,t){return valToString(e,o[t],n)}).join(", ")+"]"]}var d=t.slice(1),h=r.slice(1),f=r[0]*i,m=[];if(s>FORMAT_LIMIT_NUM_VALS){for(var g=0;g<FORMAT_NUM_FIRST_LAST_VALS;g++){var v=(y=g*f)+f;m.push.apply(m,subTensorToString(e.slice(y,v),d,n,h,o,!1));}m.push("...");for(g=s-FORMAT_NUM_FIRST_LAST_VALS;g<s;g++){v=(y=g*f)+f;m.push.apply(m,subTensorToString(e.slice(y,v),d,n,h,o,g===s-1));}}else for(g=0;g<s;g++){var y;v=(y=g*f)+f;m.push.apply(m,subTensorToString(e.slice(y,v),d,n,h,o,g===s-1));}var x=2===u?",":"";m[0]="["+m[0]+x;for(g=1;g<m.length-1;g++)m[g]=" "+m[g]+x;var E=",\n";for(g=2;g<u;g++)E+="\n";return m[m.length-1]=" "+m[m.length-1]+"]"+(a?"":E),m}function createComplexTuples(e){for(var t=[],n=0;n<e.length;n+=2)t.push([e[n],e[n+1]]);return t}var TensorBuffer=function(){function e(e,t,n){var r=this;if(this.dtype=t,this.shape=e.slice(),this.size=sizeFromShape(e),null!=n){var o=n.length;assert(o===this.size,function(){return "Length of values '"+o+"' does not match the size inferred by the shape '"+r.size+"'."});}if("complex64"===t)throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=n||getArrayFromDType(t,this.size),this.strides=computeStrides(e);}return e.prototype.set=function(e){for(var t=this,n=[],r=1;r<arguments.length;r++)n[r-1]=arguments[r];0===n.length&&(n=[0]),assert(n.length===this.rank,function(){return "The number of provided coordinates ("+n.length+") must match the rank ("+t.rank+")"});var o=this.locToIndex(n);this.values[o]=e;},e.prototype.get=function(){for(var e=[],t=0;t<arguments.length;t++)e[t]=arguments[t];0===e.length&&(e=[0]);for(var n=0,r=0,o=e;r<o.length;r++){var a=o[r];if(a<0||a>=this.shape[n]){var i="Requested out of range element at "+e+". Buffer shape="+this.shape;throw new Error(i)}n++;}for(var s=e[e.length-1],u=0;u<e.length-1;++u)s+=this.strides[u]*e[u];return this.values[s]},e.prototype.locToIndex=function(e){if(0===this.rank)return 0;if(1===this.rank)return e[0];for(var t=e[e.length-1],n=0;n<e.length-1;++n)t+=this.strides[n]*e[n];return t},e.prototype.indexToLoc=function(e){if(0===this.rank)return [];if(1===this.rank)return [e];for(var t=new Array(this.shape.length),n=0;n<t.length-1;++n)t[n]=Math.floor(e/this.strides[n]),e-=t[n]*this.strides[n];return t[t.length-1]=e,t},Object.defineProperty(e.prototype,"rank",{get:function(){return this.shape.length},enumerable:!0,configurable:!0}),e.prototype.toTensor=function(){return Tensor.make(this.shape,{values:this.values},this.dtype)},e}(),trackerFn=null,opHandler=null,deprecationWarningFn=null;function setTensorTracker(e){trackerFn=e;}function setOpHandler(e){opHandler=e;}function setDeprecationWarningFn(e){deprecationWarningFn=e;}var Tensor=function(){function e(e,t,n,r,o){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=sizeFromShape(e),this.strides=computeStrides(e),this.dataId=null!=r?r:{},this.id=trackerFn().nextTensorId(),this.rankType=this.rank<5?this.rank.toString():"higher",trackerFn().registerTensor(this,o),null!=n&&trackerFn().write(this.dataId,n);}return e.make=function(t,n,r,o){return new e(t,r,n.values,n.dataId,o)},e.prototype.flatten=function(){return this.throwIfDisposed(),this.as1D()},e.prototype.asScalar=function(){return this.throwIfDisposed(),assert(1===this.size,function(){return "The array must have only 1 element."}),this.reshape([])},e.prototype.as1D=function(){return this.throwIfDisposed(),this.reshape([this.size])},e.prototype.as2D=function(e,t){return this.throwIfDisposed(),this.reshape([e,t])},e.prototype.as3D=function(e,t,n){return this.throwIfDisposed(),this.reshape([e,t,n])},e.prototype.as4D=function(e,t,n,r){return this.throwIfDisposed(),this.reshape([e,t,n,r])},e.prototype.as5D=function(e,t,n,r,o){return this.throwIfDisposed(),this.reshape([e,t,n,r,o])},e.prototype.asType=function(e){return this.throwIfDisposed(),opHandler.cast(this,e)},Object.defineProperty(e.prototype,"rank",{get:function(){return this.shape.length},enumerable:!0,configurable:!0}),e.prototype.buffer=function(){return __awaiter$1(this,void 0,void 0,function(){var e;return __generator$1(this,function(t){switch(t.label){case 0:return [4,this.data()];case 1:return e=t.sent(),[2,opHandler.buffer(this.shape,this.dtype,e)]}})})},e.prototype.bufferSync=function(){return opHandler.buffer(this.shape,this.dtype,this.dataSync())},e.prototype.array=function(){return __awaiter$1(this,void 0,void 0,function(){var e;return __generator$1(this,function(t){switch(t.label){case 0:return [4,this.data()];case 1:return e=t.sent(),[2,toNestedArray(this.shape,e)]}})})},e.prototype.arraySync=function(){return toNestedArray(this.shape,this.dataSync())},e.prototype.data=function(){return __awaiter$1(this,void 0,void 0,function(){return __generator$1(this,function(e){return this.throwIfDisposed(),[2,trackerFn().read(this.dataId)]})})},e.prototype.dataSync=function(){return this.throwIfDisposed(),trackerFn().readSync(this.dataId)},e.prototype.dispose=function(){this.isDisposed||(trackerFn().disposeTensor(this),this.isDisposedInternal=!0);},Object.defineProperty(e.prototype,"isDisposed",{get:function(){return this.isDisposedInternal},enumerable:!0,configurable:!0}),e.prototype.throwIfDisposed=function(){if(this.isDisposed)throw new Error("Tensor is disposed.")},e.prototype.toFloat=function(){return this.asType("float32")},e.prototype.toInt=function(){return this.asType("int32")},e.prototype.toBool=function(){return this.asType("bool")},e.prototype.print=function(e){return void 0===e&&(e=!1),opHandler.print(this,e)},e.prototype.reshape=function(e){return this.throwIfDisposed(),opHandler.reshape(this,e)},e.prototype.reshapeAs=function(e){return this.throwIfDisposed(),this.reshape(e.shape)},e.prototype.expandDims=function(e){return void 0===e&&(e=0),opHandler.expandDims(this,e)},e.prototype.cumsum=function(e,t,n){return void 0===e&&(e=0),void 0===t&&(t=!1),void 0===n&&(n=!1),opHandler.cumsum(this,e,t,n)},e.prototype.squeeze=function(e){return this.throwIfDisposed(),opHandler.squeeze(this,e)},e.prototype.clone=function(){return this.throwIfDisposed(),opHandler.clone(this)},e.prototype.oneHot=function(e,t,n){return this.throwIfDisposed(),opHandler.oneHot(this,e,t,n)},e.prototype.toString=function(e){return void 0===e&&(e=!1),tensorToString(this.dataSync(),this.shape,this.dtype,e)},e.prototype.tile=function(e){return this.throwIfDisposed(),opHandler.tile(this,e)},e.prototype.gather=function(e,t){return void 0===t&&(t=0),this.throwIfDisposed(),opHandler.gather(this,e,t)},e.prototype.matMul=function(e,t,n){return void 0===t&&(t=!1),void 0===n&&(n=!1),this.throwIfDisposed(),opHandler.matMul(this,e,t,n)},e.prototype.dot=function(e){return this.throwIfDisposed(),opHandler.dot(this,e)},e.prototype.norm=function(e,t,n){return void 0===e&&(e="euclidean"),void 0===t&&(t=null),void 0===n&&(n=!1),this.throwIfDisposed(),opHandler.norm(this,e,t,n)},e.prototype.slice=function(e,t){return this.throwIfDisposed(),opHandler.slice(this,e,t)},e.prototype.reverse=function(e){return this.throwIfDisposed(),opHandler.reverse(this,e)},e.prototype.concat=function(t,n){return void 0===n&&(n=0),this.throwIfDisposed(),t instanceof e&&(t=[t]),opHandler.concat([this].concat(t),n)},e.prototype.split=function(e,t){return void 0===t&&(t=0),this.throwIfDisposed(),opHandler.split(this,e,t)},e.prototype.stack=function(e,t){return void 0===t&&(t=0),opHandler.stack([this,e],t)},e.prototype.unstack=function(e){return void 0===e&&(e=0),opHandler.unstack(this,e)},e.prototype.pad=function(e,t){return void 0===t&&(t=0),opHandler.pad(this,e,t)},e.prototype.batchNormalization=function(e,t,n,r,o){return void 0===n&&(n=.001),deprecationWarningFn("tf.batchNormalization() is going away. Use tf.batchNorm() instead, and note the positional argument change of scale, offset, and varianceEpsilon"),this.batchNorm(e,t,o,r,n)},e.prototype.batchNorm=function(e,t,n,r,o){return void 0===o&&(o=.001),this.throwIfDisposed(),opHandler.batchNorm(this,e,t,n,r,o)},e.prototype.all=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.all(this,e,t)},e.prototype.any=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.any(this,e,t)},e.prototype.logSumExp=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.logSumExp(this,e,t)},e.prototype.sum=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.sum(this,e,t)},e.prototype.prod=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.prod(this,e,t)},e.prototype.mean=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.mean(this,e,t)},e.prototype.min=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.min(this,e,t)},e.prototype.max=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.max(this,e,t)},e.prototype.argMin=function(e){return void 0===e&&(e=null),this.throwIfDisposed(),opHandler.argMin(this,e)},e.prototype.argMax=function(e){return void 0===e&&(e=null),this.throwIfDisposed(),opHandler.argMax(this,e)},e.prototype.cast=function(e){return this.throwIfDisposed(),opHandler.cast(this,e)},e.prototype.add=function(e){return this.throwIfDisposed(),opHandler.add(this,e)},e.prototype.addStrict=function(e){return this.throwIfDisposed(),opHandler.addStrict(this,e)},e.prototype.atan2=function(e){return this.throwIfDisposed(),opHandler.atan2(this,e)},e.prototype.sub=function(e){return this.throwIfDisposed(),opHandler.sub(this,e)},e.prototype.subStrict=function(e){return this.throwIfDisposed(),opHandler.subStrict(this,e)},e.prototype.pow=function(e){return this.throwIfDisposed(),opHandler.pow(this,e)},e.prototype.powStrict=function(e){return this.throwIfDisposed(),opHandler.powStrict(this,e)},e.prototype.mul=function(e){return this.throwIfDisposed(),opHandler.mul(this,e)},e.prototype.mulStrict=function(e){return this.throwIfDisposed(),opHandler.mulStrict(this,e)},e.prototype.div=function(e){return this.throwIfDisposed(),opHandler.div(this,e)},e.prototype.floorDiv=function(e){return this.throwIfDisposed(),opHandler.floorDiv(this,e)},e.prototype.divStrict=function(e){return this.throwIfDisposed(),opHandler.divStrict(this,e)},e.prototype.minimum=function(e){return this.throwIfDisposed(),opHandler.minimum(this,e)},e.prototype.minimumStrict=function(e){return this.throwIfDisposed(),opHandler.minimumStrict(this,e)},e.prototype.maximum=function(e){return this.throwIfDisposed(),opHandler.maximum(this,e)},e.prototype.maximumStrict=function(e){return this.throwIfDisposed(),opHandler.maximumStrict(this,e)},e.prototype.mod=function(e){return this.throwIfDisposed(),opHandler.mod(this,e)},e.prototype.modStrict=function(e){return this.throwIfDisposed(),opHandler.modStrict(this,e)},e.prototype.squaredDifference=function(e){return this.throwIfDisposed(),opHandler.squaredDifference(this,e)},e.prototype.squaredDifferenceStrict=function(e){return this.throwIfDisposed(),opHandler.squaredDifferenceStrict(this,e)},e.prototype.transpose=function(e){return this.throwIfDisposed(),opHandler.transpose(this,e)},e.prototype.notEqual=function(e){return this.throwIfDisposed(),opHandler.notEqual(this,e)},e.prototype.notEqualStrict=function(e){return this.throwIfDisposed(),opHandler.notEqualStrict(this,e)},e.prototype.less=function(e){return this.throwIfDisposed(),opHandler.less(this,e)},e.prototype.lessStrict=function(e){return this.throwIfDisposed(),opHandler.lessStrict(this,e)},e.prototype.equal=function(e){return this.throwIfDisposed(),opHandler.equal(this,e)},e.prototype.equalStrict=function(e){return this.throwIfDisposed(),opHandler.equalStrict(this,e)},e.prototype.lessEqual=function(e){return this.throwIfDisposed(),opHandler.lessEqual(this,e)},e.prototype.lessEqualStrict=function(e){return this.throwIfDisposed(),opHandler.lessEqualStrict(this,e)},e.prototype.greater=function(e){return this.throwIfDisposed(),opHandler.greater(this,e)},e.prototype.greaterStrict=function(e){return this.throwIfDisposed(),opHandler.greaterStrict(this,e)},e.prototype.greaterEqual=function(e){return this.throwIfDisposed(),opHandler.greaterEqual(this,e)},e.prototype.greaterEqualStrict=function(e){return this.throwIfDisposed(),opHandler.greaterEqualStrict(this,e)},e.prototype.logicalAnd=function(e){return this.throwIfDisposed(),opHandler.logicalAnd(this,e)},e.prototype.logicalOr=function(e){return this.throwIfDisposed(),opHandler.logicalOr(this,e)},e.prototype.logicalNot=function(){return this.throwIfDisposed(),opHandler.logicalNot(this)},e.prototype.logicalXor=function(e){return this.throwIfDisposed(),opHandler.logicalXor(this,e)},e.prototype.where=function(e,t){return this.throwIfDisposed(),opHandler.where(e,this,t)},e.prototype.neg=function(){return this.throwIfDisposed(),opHandler.neg(this)},e.prototype.ceil=function(){return this.throwIfDisposed(),opHandler.ceil(this)},e.prototype.floor=function(){return this.throwIfDisposed(),opHandler.floor(this)},e.prototype.sign=function(){return this.throwIfDisposed(),opHandler.sign(this)},e.prototype.exp=function(){return this.throwIfDisposed(),opHandler.exp(this)},e.prototype.expm1=function(){return this.throwIfDisposed(),opHandler.expm1(this)},e.prototype.log=function(){return this.throwIfDisposed(),opHandler.log(this)},e.prototype.log1p=function(){return this.throwIfDisposed(),opHandler.log1p(this)},e.prototype.sqrt=function(){return this.throwIfDisposed(),opHandler.sqrt(this)},e.prototype.rsqrt=function(){return this.throwIfDisposed(),opHandler.rsqrt(this)},e.prototype.square=function(){return this.throwIfDisposed(),opHandler.square(this)},e.prototype.reciprocal=function(){return this.throwIfDisposed(),opHandler.reciprocal(this)},e.prototype.abs=function(){return this.throwIfDisposed(),opHandler.abs(this)},e.prototype.clipByValue=function(e,t){return this.throwIfDisposed(),opHandler.clipByValue(this,e,t)},e.prototype.relu=function(){return this.throwIfDisposed(),opHandler.relu(this)},e.prototype.elu=function(){return this.throwIfDisposed(),opHandler.elu(this)},e.prototype.selu=function(){return this.throwIfDisposed(),opHandler.selu(this)},e.prototype.leakyRelu=function(e){return void 0===e&&(e=.2),this.throwIfDisposed(),opHandler.leakyRelu(this,e)},e.prototype.prelu=function(e){return this.throwIfDisposed(),opHandler.prelu(this,e)},e.prototype.sigmoid=function(){return this.throwIfDisposed(),opHandler.sigmoid(this)},e.prototype.logSigmoid=function(){return this.throwIfDisposed(),opHandler.logSigmoid(this)},e.prototype.softplus=function(){return this.throwIfDisposed(),opHandler.softplus(this)},e.prototype.zerosLike=function(){return this.throwIfDisposed(),opHandler.zerosLike(this)},e.prototype.onesLike=function(){return this.throwIfDisposed(),opHandler.onesLike(this)},e.prototype.sin=function(){return this.throwIfDisposed(),opHandler.sin(this)},e.prototype.cos=function(){return this.throwIfDisposed(),opHandler.cos(this)},e.prototype.tan=function(){return this.throwIfDisposed(),opHandler.tan(this)},e.prototype.asin=function(){return this.throwIfDisposed(),opHandler.asin(this)},e.prototype.acos=function(){return this.throwIfDisposed(),opHandler.acos(this)},e.prototype.atan=function(){return this.throwIfDisposed(),opHandler.atan(this)},e.prototype.sinh=function(){return this.throwIfDisposed(),opHandler.sinh(this)},e.prototype.cosh=function(){return this.throwIfDisposed(),opHandler.cosh(this)},e.prototype.tanh=function(){return this.throwIfDisposed(),opHandler.tanh(this)},e.prototype.asinh=function(){return this.throwIfDisposed(),opHandler.asinh(this)},e.prototype.acosh=function(){return this.throwIfDisposed(),opHandler.acosh(this)},e.prototype.atanh=function(){return this.throwIfDisposed(),opHandler.atanh(this)},e.prototype.erf=function(){return this.throwIfDisposed(),opHandler.erf(this)},e.prototype.round=function(){return this.throwIfDisposed(),opHandler.round(this)},e.prototype.step=function(e){return void 0===e&&(e=0),this.throwIfDisposed(),opHandler.step(this,e)},e.prototype.softmax=function(e){return void 0===e&&(e=-1),this.throwIfDisposed(),opHandler.softmax(this,e)},e.prototype.logSoftmax=function(e){return void 0===e&&(e=-1),this.throwIfDisposed(),opHandler.logSoftmax(this,e)},e.prototype.resizeBilinear=function(e,t){return void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.image.resizeBilinear(this,e,t)},e.prototype.resizeNearestNeighbor=function(e,t){return void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.image.resizeNearestNeighbor(this,e,t)},e.prototype.conv1d=function(e,t,n,r,o,a){return void 0===r&&(r="NWC"),void 0===o&&(o=1),this.throwIfDisposed(),opHandler.conv1d(this,e,t,n,r,o,a)},e.prototype.conv2d=function(e,t,n,r,o,a){return void 0===r&&(r="NHWC"),void 0===o&&(o=[1,1]),this.throwIfDisposed(),opHandler.conv2d(this,e,t,n,r,o,a)},e.prototype.conv2dTranspose=function(e,t,n,r,o){return this.throwIfDisposed(),opHandler.conv2dTranspose(this,e,t,n,r,o)},e.prototype.depthwiseConv2D=function(e,t,n,r,o,a){return void 0===r&&(r="NHWC"),void 0===o&&(o=[1,1]),this.throwIfDisposed(),opHandler.depthwiseConv2d(this,e,t,n,r,o,a)},e.prototype.separableConv2d=function(e,t,n,r,o,a){return void 0===o&&(o=[1,1]),void 0===a&&(a="NHWC"),this.throwIfDisposed(),opHandler.separableConv2d(this,e,t,n,r,o,a)},e.prototype.avgPool=function(e,t,n,r){return this.throwIfDisposed(),opHandler.avgPool(this,e,t,n,r)},e.prototype.maxPool=function(e,t,n,r){return this.throwIfDisposed(),opHandler.maxPool(this,e,t,n,r)},e.prototype.localResponseNormalization=function(e,t,n,r){return void 0===e&&(e=5),void 0===t&&(t=1),void 0===n&&(n=1),void 0===r&&(r=.5),opHandler.localResponseNormalization(this,e,t,n,r)},e.prototype.pool=function(e,t,n,r,o){return this.throwIfDisposed(),opHandler.pool(this,e,t,n,r,o)},e.prototype.variable=function(e,t,n){return void 0===e&&(e=!0),this.throwIfDisposed(),Variable.variable(this,e,t,n)},e.prototype.unsortedSegmentSum=function(e,t){return this.throwIfDisposed(),opHandler.unsortedSegmentSum(this,e,t)},e.prototype.batchToSpaceND=function(e,t){return this.throwIfDisposed(),opHandler.batchToSpaceND(this,e,t)},e.prototype.spaceToBatchND=function(e,t){return this.throwIfDisposed(),opHandler.spaceToBatchND(this,e,t)},e.prototype.topk=function(e,t){return void 0===e&&(e=1),void 0===t&&(t=!0),this.throwIfDisposed(),opHandler.topk(this,e,t)},e.prototype.stridedSlice=function(e,t,n,r,o,a,i,s){return void 0===r&&(r=0),void 0===o&&(o=0),void 0===a&&(a=0),void 0===i&&(i=0),void 0===s&&(s=0),this.throwIfDisposed(),opHandler.stridedSlice(this,e,t,n,r,o,a,i,s)},e.prototype.depthToSpace=function(e,t){return this.throwIfDisposed(),opHandler.depthToSpace(this,e,t)},e.prototype.fft=function(){return this.throwIfDisposed(),opHandler.spectral.fft(this)},e.prototype.ifft=function(){return this.throwIfDisposed(),opHandler.spectral.ifft(this)},e.prototype.rfft=function(){return this.throwIfDisposed(),opHandler.spectral.rfft(this)},e.prototype.irfft=function(){return this.throwIfDisposed(),opHandler.spectral.irfft(this)},e}();Object.defineProperty(Tensor,Symbol.hasInstance,{value:function(e){return !!e&&null!=e.dataId&&null!=e.shape&&null!=e.dtype}});var Variable=function(e){function t(t,n,r){void 0===n&&(n=!0);var o=e.call(this,t.shape,t.dtype,null,t.dataId)||this;o.trainable=n,o.name=r,null==o.name&&(o.name=trackerFn().nextVariableId().toString());try{trackerFn().registerVariable(o);}catch(e){throw trackerFn().disposeTensor(o),e}return o}return __extends$1(t,e),t.variable=function(e,n,r,o){return void 0===n&&(n=!0),null!=o&&o!==e.dtype&&(e=e.asType(o)),new t(e,n,r)},t.prototype.assign=function(e){if(e.dtype!==this.dtype)throw new Error("dtype of the new value ("+e.dtype+") and previous value ("+this.dtype+") must match");if(!arraysEqual(e.shape,this.shape))throw new Error("shape of the new value ("+e.shape+") and previous value ("+this.shape+") must match");trackerFn().disposeTensor(this),this.dataId=e.dataId,trackerFn().registerTensor(this);},t}(Tensor);Object.defineProperty(Variable,Symbol.hasInstance,{value:function(e){return e instanceof Tensor&&null!=e.assign&&e.assign instanceof Function}});var Rank,UpcastInt32AndMap,UpcastBoolAndMap,UpcastFloat32AndMap,UpcastComplex64AndMap,variable=Variable.variable;function getFilteredNodesXToY(e,t,n){for(var r={},o={},a=0;a<t.length;a++)r[t[a].id]=!0;for(a=0;a<e.length;a++){var i=(f=e[a]).inputs;for(var s in i){for(var u=i[s],c=!1,l=0;l<t.length;l++)if(r[u.id]){f.outputs.forEach(function(e){return r[e.id]=!0}),c=!0,o[f.id]=!0;break}if(c)break}}var p={};p[n.id]=!0;var d={};for(a=e.length-1;a>=0;a--)for(i=(f=e[a]).inputs,l=0;l<f.outputs.length;l++)if(p[f.outputs[l].id]){for(var s in i)p[i[s].id]=!0,d[f.id]=!0;break}var h=[];for(a=0;a<e.length;a++){var f;if(o[(f=e[a]).id]&&d[f.id]){var m={};for(var s in f.inputs){var g=f.inputs[s];r[g.id]&&(m[s]=g);}var v=Object.assign({},f);v.inputs=m,v.outputs=f.outputs,h.push(v);}}return h}function backpropagateGradients(e,t,n){for(var r=function(r){var o=t[r],a=[];if(o.outputs.forEach(function(t){var n=e[t.id];if(null!=n)a.push(n);else{var r=Tensor.make(t.shape,{values:makeZerosTypedArray(t.size,t.dtype)},t.dtype);a.push(r);}}),null==o.gradient)throw new Error("Cannot compute gradient: gradient function not found for "+o.name+".");var i=o.gradient(1===o.outputs.length?a[0]:a),s=function(t){if(!(t in i))throw new Error("Cannot backprop through input "+t+". Available gradients found: "+Object.keys(i)+".");var r=n(function(){return i[t]()});if("float32"!==r.dtype)throw new Error("Error in gradient for op "+o.name+". The gradient of input "+t+" must have 'float32' dtype, but has '"+r.dtype+"'");var a=o.inputs[t];if(!arraysEqual(r.shape,a.shape))throw new Error("Error in gradient for op "+o.name+". The gradient of input '"+t+"' has shape '"+r.shape+"', which does not match the shape of the input '"+a.shape+"'");if(null==e[a.id])e[a.id]=r;else{var s=e[a.id];e[a.id]=s.add(r),s.dispose();}};for(var u in o.inputs)s(u);},o=t.length-1;o>=0;o--)r(o);}!function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6";}(Rank||(Rank={})),function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64";}(UpcastInt32AndMap||(UpcastInt32AndMap={})),function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64";}(UpcastBoolAndMap||(UpcastBoolAndMap={})),function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64";}(UpcastFloat32AndMap||(UpcastFloat32AndMap={})),function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64";}(UpcastComplex64AndMap||(UpcastComplex64AndMap={}));var upcastTypeMap={float32:UpcastFloat32AndMap,int32:UpcastInt32AndMap,bool:UpcastBoolAndMap,complex64:UpcastComplex64AndMap};function upcastType(e,t){if("string"===e||"string"===t){if("string"===e&&"string"===t)return "string";throw new Error("Can not upcast "+e+" with "+t)}return upcastTypeMap[e][t]}function sumOutType(e){return upcastType(e,"int32")}function makeTypesMatch(e,t){if(e.dtype===t.dtype)return [e,t];var n=upcastType(e.dtype,t.dtype);return [e.cast(n),t.cast(n)]}function assertTypesMatch(e,t){assert(e.dtype===t.dtype,function(){return "The dtypes of the first("+e.dtype+") and second("+t.dtype+") input must match"});}function isTensorInList(e,t){for(var n=0;n<t.length;n++)if(t[n].id===e.id)return !0;return !1}function getTensorsInContainer(e){var t=[];return walkTensorContainer(e,t,new Set),t}function walkTensorContainer(e,t,n){if(null!=e)if(e instanceof Tensor)t.push(e);else if(isIterable(e)){var r=e;for(var o in r){var a=r[o];n.has(a)||(n.add(a),walkTensorContainer(a,t,n));}}}function isIterable(e){return Array.isArray(e)||"object"==typeof e}var Type,tensor_util=Object.freeze({makeTypesMatch:makeTypesMatch,assertTypesMatch:assertTypesMatch,isTensorInList:isTensorInList,getTensorsInContainer:getTensorsInContainer}),Engine=function(){function e(e,t,n){this.backend=e,this.safeMode=t,this.debugMode=n,this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.profiling=!1,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiler=new Profiler(e),this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null};}return e.prototype.moveData=function(e){this.write(e,this.readSync(e));},e.prototype.tidy=function(e,t){var n,r=this,o=null;if(null==t){if("function"!=typeof e)throw new Error("Please provide a function to tidy()");t=e;}else{if("string"!=typeof e&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if("function"!=typeof t)throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");o=e;}return this.scopedRun(function(){return r.startScope(o)},function(){return r.endScope(n)},function(){return (n=t())instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),n})},e.prototype.scopedRun=function(e,t,n){e();try{var r=n();return t(),r}catch(e){throw t(),e}},e.prototype.nextTensorId=function(){return e.nextTensorId++},e.prototype.nextVariableId=function(){return e.nextVariableId++},e.prototype.clone=function(e){var t=Tensor.make(e.shape,{dataId:e.dataId},e.dtype);return this.addTapeNode([e],t,function(e){return [e.toFloat()]}),t},e.prototype.runKernel=function(e,t,n){var r,o=this,a=[],i=this.isTapeOn(),s=null!=this.activeScope?this.activeScope.name:"",u=function(e){i&&(a=e.map(function(e){return o.keep(o.clone(e))}));},c=this.numBytes,l=this.numTensors;if(this.scopedRun(function(){return o.kernelDepth++},function(){return o.kernelDepth--},function(){r=o.debugMode()?o.profiler.profileKernel(s,function(){return e(o.backend,u)}):e(o.backend,u);}),i){var p={id:this.nextTapeNodeId++,name:s,inputs:t,outputs:Array.isArray(r)?r:[r],saved:a};null!=n&&(p.gradient=function(e){return n(e,a)}),this.activeTape.push(p);}return this.profiling&&this.activeProfile.kernels.push({name:s,bytesAdded:this.numBytes-c,totalBytesSnapshot:this.numBytes,tensorsAdded:this.numTensors-l,totalTensorsSnapshot:this.numTensors,inputShapes:Object.keys(t).map(function(e){return t[e].shape}),outputShape:Array.isArray(r)?r.map(function(e){return e.shape}):r.shape}),r},e.prototype.registerTensor=function(e,t){var n=this.tensorInfo.has(e.dataId)?this.tensorInfo.get(e.dataId).refCount:0;if(this.numTensors++,"string"===e.dtype&&this.numStringTensors++,0===n){this.numDataBuffers++;var r=0;"complex64"!==e.dtype&&"string"!==e.dtype&&(r=e.size*bytesPerElement(e.dtype)),this.tensorInfo.set(e.dataId,{backend:null!=t?t:this.backend,dtype:e.dtype,shape:e.shape,bytes:r,refCount:0}),this.numBytes+=r,null!=t?t.register(e.dataId,e.shape,e.dtype):this.backend.register(e.dataId,e.shape,e.dtype);}this.tensorInfo.get(e.dataId).refCount++,e instanceof Variable||this.track(e);},e.prototype.registerVariable=function(e){if(null!=this.registeredVariables[e.name])throw new Error("Variable with name "+e.name+" was already registered");this.registeredVariables[e.name]=e;},e.prototype.disposeTensor=function(e){if(this.tensorInfo.has(e.dataId)){this.numTensors--,"string"===e.dtype&&this.numStringTensors--;var t=this.tensorInfo.get(e.dataId);t.refCount<=1?("complex64"!==e.dtype&&(this.numBytes-=t.bytes),this.numDataBuffers--,t.backend.disposeData(e.dataId),this.tensorInfo.delete(e.dataId)):this.tensorInfo.get(e.dataId).refCount--;}},e.prototype.disposeVariables=function(){for(var e in this.registeredVariables){var t=this.registeredVariables[e];this.disposeTensor(t),delete this.registeredVariables[e];}},e.prototype.memory=function(){var e=this.backend.memory();return e.numTensors=this.numTensors,e.numDataBuffers=this.numDataBuffers,e.numBytes=this.numBytes,this.numStringTensors>0&&(e.unreliable=!0,null==e.reasons&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e},e.prototype.profile=function(e){return __awaiter$1(this,void 0,void 0,function(){var t,n;return __generator$1(this,function(r){return this.profiling=!0,t=this.numBytes,n=this.numTensors,this.activeProfile.kernels=[],this.activeProfile.result=e(),this.profiling=!1,this.activeProfile.peakBytes=Math.max.apply(Math,this.activeProfile.kernels.map(function(e){return e.totalBytesSnapshot})),this.activeProfile.newBytes=this.numBytes-t,this.activeProfile.newTensors=this.numTensors-n,[2,this.activeProfile]})})},e.prototype.isTapeOn=function(){return this.gradientDepth>0&&0===this.kernelDepth},e.prototype.addTapeNode=function(e,t,n){var r={};e.forEach(function(e,t){r[t]=e;});var o={id:this.nextTapeNodeId++,name:this.activeScope.name,inputs:r,outputs:[t],gradient:function(e){var t={};return n(e).forEach(function(e,n){t[n]=function(){return e};}),t}};this.activeTape.push(o);},e.prototype.keep=function(e){if(1===this.scopeStack.length&&this.safeMode)throw new Error("Safe mode is ON. Enclose all tensor operations inside tf.tidy(): tf.tidy(() => {...}) to avoid memory leaks.");return e.kept=!0,e},e.prototype.startTape=function(){0===this.gradientDepth&&(this.activeTape=[]),this.gradientDepth++;},e.prototype.endTape=function(){this.gradientDepth--;},e.prototype.startScope=function(e){var t={track:[],name:"unnamed scope",id:this.nextScopeId++};e&&(t.name=e),this.scopeStack.push(t),this.activeScope=t;},e.prototype.endScope=function(e){for(var t=this,n=getTensorsInContainer(e),r=new Set(n.map(function(e){return e.id})),o=0;o<this.activeScope.track.length;o++){var a=this.activeScope.track[o];a.kept||r.has(a.id)||a.dispose();}var i=this.scopeStack.pop();this.activeScope=0===this.scopeStack.length?null:this.scopeStack[this.scopeStack.length-1],n.forEach(function(e){e.kept||e.scopeId!==i.id||t.track(e);});},e.prototype.gradients=function(e,t,n,r){var o=this;if(void 0===r&&(r=!1),assert(t.length>0,function(){return "gradients() received an empty list of xs."}),null!=n&&"float32"!==n.dtype)throw new Error("dy must have 'float32' dtype, but has '"+n.dtype+"'");var a=this.scopedRun(function(){return o.startTape()},function(){return o.endTape()},function(){return o.tidy("forward",e)});assert(a instanceof Tensor,function(){return "The result y returned by f() must be a tensor."});var i=getFilteredNodesXToY(this.activeTape,t,a);if(!r&&0===i.length&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",function(){var e={};e[a.id]=null==n?ones(a.shape):n,backpropagateGradients(e,i,function(e){return o.tidy(e)});var r=t.map(function(t){return e[t.id]});return 0===o.gradientDepth&&(o.activeTape.forEach(function(e){for(var t in e.saved)e.saved[t].dispose();}),o.activeTape=null),{value:a,grads:r}})},e.prototype.customGrad=function(e){var t=this;return assert(isFunction(e),function(){return "The f passed in customGrad(f) must be a function."}),function(){for(var n,r=[],o=0;o<arguments.length;o++)r[o]=arguments[o];assert(r.every(function(e){return e instanceof Tensor}),function(){return "The args passed in customGrad(f)(x1, x2,...) must all be tensors"});var a={};return r.forEach(function(e,t){a[t]=e;}),t.runKernel(function(t,o){return assert((n=e.apply(void 0,r.concat([o]))).value instanceof Tensor,function(){return "The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"}),assert(isFunction(n.gradFunc),function(){return "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."}),n.value},a,function(e,t){var o=n.gradFunc(e,t),a=Array.isArray(o)?o:[o];assert(a.length===r.length,function(){return "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."}),assert(a.every(function(e){return e instanceof Tensor}),function(){return "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors."});var i={};return a.forEach(function(e,t){i[t]=function(){return e};}),i})}},e.prototype.write=function(e,t){var n=this.tensorInfo.get(e);if("string"===n.dtype){var r=bytesFromStringArray(t);this.numBytes+=r-n.bytes,n.bytes=r;}this.backend!==n.backend&&(n.backend.disposeData(e),n.backend=this.backend,this.backend.register(e,n.shape,n.dtype)),this.backend.write(e,t);},e.prototype.readSync=function(e){return this.tensorInfo.get(e).backend.readSync(e)},e.prototype.read=function(e){return this.tensorInfo.get(e).backend.read(e)},e.prototype.fromPixels=function(e,t){return this.backend.fromPixels(e,t)},e.prototype.time=function(e){return __awaiter$1(this,void 0,void 0,function(){var t,n;return __generator$1(this,function(r){switch(r.label){case 0:return t=now(),[4,this.backend.time(e)];case 1:return (n=r.sent()).wallMs=now()-t,[2,n]}})})},e.prototype.track=function(e){if(1===this.scopeStack.length&&this.safeMode)throw new Error("Safe mode is ON. Enclose all tensor operations inside tf.tidy(): tf.tidy(() => {op();...}); to avoid memory leaks.");return null!=this.activeScope&&(e.scopeId=this.activeScope.id,this.activeScope.track.push(e)),e},e.nextTensorId=0,e.nextVariableId=0,e}();function ones(e){var t=makeOnesTypedArray(sizeFromShape(e),"float32");return Tensor.make(e,{values:t})}!function(e){e[e.NUMBER=0]="NUMBER",e[e.BOOLEAN=1]="BOOLEAN",e[e.STRING=2]="STRING";}(Type||(Type={}));var MAX_TEXTURE_SIZE,MAX_TEXTURES_IN_SHADER,URL_PROPERTIES=[{name:"DEBUG",type:Type.BOOLEAN},{name:"IS_BROWSER",type:Type.BOOLEAN},{name:"WEBGL_LAZILY_UNPACK",type:Type.BOOLEAN},{name:"WEBGL_CPU_FORWARD",type:Type.BOOLEAN},{name:"WEBGL_PACK",type:Type.BOOLEAN},{name:"WEBGL_PACK_BATCHNORMALIZATION",type:Type.BOOLEAN},{name:"WEBGL_PACK_CLIP",type:Type.BOOLEAN},{name:"WEBGL_PACK_DEPTHWISECONV",type:Type.BOOLEAN},{name:"WEBGL_PACK_BINARY_OPERATIONS",type:Type.BOOLEAN},{name:"WEBGL_PACK_ARRAY_OPERATIONS",type:Type.BOOLEAN},{name:"WEBGL_PACK_IMAGE_OPERATIONS",type:Type.BOOLEAN},{name:"WEBGL_PACK_REDUCE",type:Type.BOOLEAN},{name:"WEBGL_CONV_IM2COL",type:Type.BOOLEAN},{name:"WEBGL_MAX_TEXTURE_SIZE",type:Type.NUMBER},{name:"WEBGL_NUM_MB_BEFORE_PAGING",type:Type.NUMBER},{name:"WEBGL_MAX_TEXTURES_IN_SHADER",type:Type.NUMBER},{name:"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",type:Type.NUMBER},{name:"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",type:Type.BOOLEAN},{name:"WEBGL_VERSION",type:Type.NUMBER},{name:"WEBGL_RENDER_FLOAT32_ENABLED",type:Type.BOOLEAN},{name:"WEBGL_DOWNLOAD_FLOAT_ENABLED",type:Type.BOOLEAN},{name:"WEBGL_FENCE_API_ENABLED",type:Type.BOOLEAN},{name:"WEBGL_SIZE_UPLOAD_UNIFORM",type:Type.NUMBER},{name:"BACKEND",type:Type.STRING},{name:"EPSILON",type:Type.NUMBER},{name:"PROD",type:Type.BOOLEAN},{name:"TENSORLIKE_CHECK_SHAPE_CONSISTENCY",type:Type.BOOLEAN},{name:"DEPRECATION_WARNINGS_ENABLED",type:Type.BOOLEAN}];function isWebGLVersionEnabled(e){try{if(null!=getWebGLContext(e))return !0}catch(e){return !1}return !1}function getWebGLMaxTextureSize(e){if(null==MAX_TEXTURE_SIZE){var t=getWebGLContext(e);MAX_TEXTURE_SIZE=t.getParameter(t.MAX_TEXTURE_SIZE);}return MAX_TEXTURE_SIZE}function getMaxTexturesInShader(e){if(null==MAX_TEXTURES_IN_SHADER){var t=getWebGLContext(e);MAX_TEXTURES_IN_SHADER=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS);}return Math.min(16,MAX_TEXTURES_IN_SHADER)}function getWebGLDisjointQueryTimerVersion(e){if(0===e)return 0;var t=getWebGLContext(e);return hasExtension(t,"EXT_disjoint_timer_query_webgl2")&&2===e?2:hasExtension(t,"EXT_disjoint_timer_query")?1:0}function isRenderToFloatTextureEnabled(e){if(0===e)return !1;var t=getWebGLContext(e);if(1===e){if(!hasExtension(t,"OES_texture_float"))return !1}else if(!hasExtension(t,"EXT_color_buffer_float"))return !1;return createFloatTextureAndBindToFramebuffer(t,e)}function isDownloadFloatTextureEnabled(e){if(0===e)return !1;var t=getWebGLContext(e);if(1===e){if(!hasExtension(t,"OES_texture_float"))return !1;if(!hasExtension(t,"WEBGL_color_buffer_float"))return !1}else if(!hasExtension(t,"EXT_color_buffer_float"))return !1;return createFloatTextureAndBindToFramebuffer(t,e)}function isWebGLFenceEnabled(e){return 2===e&&null!=getWebGLContext(e).fenceSync}function isChrome(){return "undefined"!=typeof navigator&&null!=navigator&&null!=navigator.userAgent&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor)}var TENSORFLOWJS_FLAGS_PREFIX="tfjsflags";function getFeaturesFromURL(){var e={};if("undefined"==typeof window||void 0===window.location||void 0===window.location.search)return e;var t=getQueryParams(window.location.search);if(TENSORFLOWJS_FLAGS_PREFIX in t){var n={};t[TENSORFLOWJS_FLAGS_PREFIX].split(",").forEach(function(e){var t=e.split(":"),r=t[0],o=t[1];n[r]=o;}),URL_PROPERTIES.forEach(function(t){t.name in n&&(console.log("Setting feature override from URL "+t.name+": "+n[t.name]),t.type===Type.NUMBER?e[t.name]=+n[t.name]:t.type===Type.BOOLEAN?e[t.name]="true"===n[t.name]:t.type===Type.STRING?e[t.name]=n[t.name]:console.warn("Unknown URL param: "+t.name+"."));});}return e}function hasExtension(e,t){return null!=e.getExtension(t)}function createFloatTextureAndBindToFramebuffer(e,t){var n=e.createFramebuffer(),r=e.createTexture();e.bindTexture(e.TEXTURE_2D,r);var o=2===t?e.RGBA32F:e.RGBA;e.texImage2D(e.TEXTURE_2D,0,o,1,1,0,e.RGBA,e.FLOAT,null),e.bindFramebuffer(e.FRAMEBUFFER,n),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,r,0);var a=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(r),e.deleteFramebuffer(n),a}function getQueryParams(e){var t={};return e.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g,function(e){for(var n=[],r=1;r<arguments.length;r++)n[r-1]=arguments[r];return decodeParam(t,n[0],n[1]),n.join("=")}),t}function decodeParam(e,t,n){e[decodeURIComponent(t)]=decodeURIComponent(n||"");}var BEFORE_PAGING_CONSTANT=600;function getNumMBBeforePaging(){return window.screen.height*window.screen.width*window.devicePixelRatio*BEFORE_PAGING_CONSTANT/1024}var _global,EPSILON_FLOAT16=1e-4,TEST_EPSILON_FLOAT16=.1,EPSILON_FLOAT32=1e-7,TEST_EPSILON_FLOAT32=.001,Environment=function(){function e(e){this.features={},this.registry={},this.registryFactory={},null!=e&&(this.features=e),this.get("DEBUG")&&console.warn("Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.");}return e.setBackend=function(e,t){if(void 0===t&&(t=!1),!(e in ENV.registry))throw new Error("Backend name '"+e+"' not found in registry");ENV.engine.backend=ENV.findBackend(e),ENV.backendName=e;},e.getBackend=function(){return ENV.initEngine(),ENV.backendName},e.disposeVariables=function(){ENV.engine.disposeVariables();},e.memory=function(){return ENV.engine.memory()},e.profile=function(e){return ENV.engine.profile(e)},e.tidy=function(e,t){return ENV.engine.tidy(e,t)},e.dispose=function(e){getTensorsInContainer(e).forEach(function(e){return e.dispose()});},e.keep=function(e){return ENV.engine.keep(e)},e.time=function(e){return ENV.engine.time(e)},e.prototype.get=function(e){return e in this.features?this.features[e]:(this.features[e]=this.evaluateFeature(e),this.features[e])},e.prototype.getFeatures=function(){return this.features},e.prototype.set=function(e,t){this.features[e]=t;},e.prototype.getBestBackendName=function(){var e=this;if(0===Object.keys(this.registry).length)throw new Error("No backend found in registry.");return Object.keys(this.registry).map(function(t){return {name:t,entry:e.registry[t]}}).sort(function(e,t){return t.entry.priority-e.entry.priority})[0].name},e.prototype.evaluateFeature=function(e){if("DEBUG"===e)return !1;if("IS_BROWSER"===e)return "undefined"!=typeof window;if("IS_NODE"===e)return "undefined"!=typeof process&&void 0!==process.versions&&void 0!==process.versions.node;if("IS_CHROME"===e)return isChrome();if("WEBGL_CPU_FORWARD"===e)return !0;if("WEBGL_PACK"===e)return 0!==this.get("WEBGL_VERSION");if("WEBGL_PACK_BATCHNORMALIZATION"===e)return this.get("WEBGL_PACK");if("WEBGL_PACK_CLIP"===e)return this.get("WEBGL_PACK");if("WEBGL_PACK_DEPTHWISECONV"===e)return this.get("WEBGL_PACK");if("WEBGL_PACK_BINARY_OPERATIONS"===e)return this.get("WEBGL_PACK");if("WEBGL_PACK_ARRAY_OPERATIONS"===e)return this.get("WEBGL_PACK");if("WEBGL_PACK_IMAGE_OPERATIONS"===e)return this.get("WEBGL_PACK");if("WEBGL_PACK_REDUCE"===e)return this.get("WEBGL_PACK");if("WEBGL_LAZILY_UNPACK"===e)return this.get("WEBGL_PACK");if("WEBGL_CONV_IM2COL"===e)return this.get("WEBGL_PACK");if("WEBGL_NUM_MB_BEFORE_PAGING"===e)return this.get("PROD")||!this.get("IS_BROWSER")?Number.POSITIVE_INFINITY:getNumMBBeforePaging();if("WEBGL_MAX_TEXTURE_SIZE"===e)return getWebGLMaxTextureSize(this.get("WEBGL_VERSION"));if("WEBGL_MAX_TEXTURES_IN_SHADER"===e)return getMaxTexturesInShader(this.get("WEBGL_VERSION"));if("IS_TEST"===e)return !1;if("BACKEND"===e)return this.getBestBackendName();if("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"===e){var t=this.get("WEBGL_VERSION");return 0===t?0:getWebGLDisjointQueryTimerVersion(t)}if("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE"===e)return this.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!isMobile();if("HAS_WEBGL"===e)return this.get("WEBGL_VERSION")>0;if("WEBGL_VERSION"===e)return isWebGLVersionEnabled(2)?2:isWebGLVersionEnabled(1)?1:0;if("WEBGL_RENDER_FLOAT32_ENABLED"===e)return isRenderToFloatTextureEnabled(this.get("WEBGL_VERSION"));if("WEBGL_DOWNLOAD_FLOAT_ENABLED"===e)return isDownloadFloatTextureEnabled(this.get("WEBGL_VERSION"));if("WEBGL_FENCE_API_ENABLED"===e)return isWebGLFenceEnabled(this.get("WEBGL_VERSION"));if("WEBGL_SIZE_UPLOAD_UNIFORM"===e)return this.get("WEBGL_RENDER_FLOAT32_ENABLED")?4:0;if("TEST_EPSILON"===e)return 32===this.backend.floatPrecision()?TEST_EPSILON_FLOAT32:TEST_EPSILON_FLOAT16;if("EPSILON"===e)return 32===this.backend.floatPrecision()?EPSILON_FLOAT32:EPSILON_FLOAT16;if("PROD"===e)return !1;if("TENSORLIKE_CHECK_SHAPE_CONSISTENCY"===e)return !this.get("PROD");if("DEPRECATION_WARNINGS_ENABLED"===e)return !0;throw new Error("Unknown feature "+e+".")},e.prototype.setFeatures=function(e){this.features=Object.assign({},e);},e.prototype.reset=function(){this.features=getFeaturesFromURL(),null!=this.globalEngine&&(this.globalEngine=null);},Object.defineProperty(e.prototype,"backend",{get:function(){return this.engine.backend},enumerable:!0,configurable:!0}),e.prototype.findBackend=function(e){return e in this.registry?this.registry[e].backend:null},e.prototype.findBackendFactory=function(e){return e in this.registryFactory?this.registryFactory[e]:null},e.prototype.registerBackend=function(e,t,n){var r=this;if(void 0===n&&(n=1),e in this.registry)return console.warn(e+" backend was already registered. Reusing existing backend"),!1;try{var o=t();return o.setDataMover({moveData:function(e){return r.engine.moveData(e)}}),this.registry[e]={backend:o,priority:n},this.registryFactory[e]=t,!0}catch(t){return console.warn("Registration of backend "+e+" failed"),console.warn(t.stack||t.message),!1}},e.prototype.removeBackend=function(e){if(!(e in this.registry))throw new Error(e+" backend not found in registry");this.registry[e].backend.dispose(),delete this.registry[e];},Object.defineProperty(e.prototype,"engine",{get:function(){return this.initEngine(),this.globalEngine},enumerable:!0,configurable:!0}),e.prototype.initEngine=function(){var e=this;if(null==this.globalEngine){this.backendName=this.get("BACKEND");var t=this.findBackend(this.backendName);this.globalEngine=new Engine(t,!1,function(){return e.get("DEBUG")});}},Object.defineProperty(e.prototype,"global",{get:function(){return getGlobalNamespace()},enumerable:!0,configurable:!0}),e}();function getGlobalNamespace(){if(null==_global){var e=void 0;if("undefined"!=typeof window)e=window;else if("undefined"!=typeof global)e=global;else{if("undefined"==typeof process)throw new Error("Could not find a global object");e=process;}_global=e;}return _global}function getOrMakeEnvironment(){var e=getGlobalNamespace();return null==e.ENV&&(e.ENV=new Environment(getFeaturesFromURL())),setTensorTracker(function(){return e.ENV.engine}),e.ENV}function enableProdMode(){ENV.set("PROD",!0);}function enableDebugMode(){ENV.set("DEBUG",!0);}function disableDeprecationWarnings(){ENV.set("DEPRECATION_WARNINGS_ENABLED",!1),console.warn("TensorFlow.js deprecation warnings have been disabled.");}function deprecationWarn(e){ENV.get("DEPRECATION_WARNINGS_ENABLED")&&console.warn(e+" You can disable deprecation warnings with tf.disableDeprecationWarnings().");}setDeprecationWarningFn(deprecationWarn);var ENV=getOrMakeEnvironment(),environment=Object.freeze({EPSILON_FLOAT16:EPSILON_FLOAT16,EPSILON_FLOAT32:EPSILON_FLOAT32,Environment:Environment,enableProdMode:enableProdMode,enableDebugMode:enableDebugMode,disableDeprecationWarnings:disableDeprecationWarnings,deprecationWarn:deprecationWarn,ENV:ENV});function inferShape(e){var t=e;if(isTypedArray(e))return [e.length];if(!Array.isArray(e))return [];for(var n=[];Array.isArray(t)||isTypedArray(t);)n.push(t.length),t=t[0];return Array.isArray(e)&&ENV.get("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&deepAssertShapeConsistency(e,n,[]),n}function deepAssertShapeConsistency(e,t,n){if(n=n||[],Array.isArray(e)||isTypedArray(e)){assert(t.length>0,function(){return "Element arr["+n.join("][")+"] should be a primitive, but is an array of "+e.length+" elements"}),assert(e.length===t[0],function(){return "Element arr["+n.join("][")+"] should have "+t[0]+" elements, but has "+e.length+" elements"});for(var r=t.slice(1),o=0;o<e.length;++o)deepAssertShapeConsistency(e[o],r,n.concat(o));}else assert(0===t.length,function(){return "Element arr["+n.join("][")+"] is a primitive, but should be an array/TypedArray of "+t[0]+" elements"});}function assertDtype(e,t,n,r){if(null!=e&&("numeric"!==e&&e!==t||"numeric"===e&&"string"===t))throw new Error("Argument '"+n+"' passed to '"+r+"' must be "+e+" tensor, but got "+t+" tensor")}function convertToTensor(e,t,n,r){if(void 0===r&&(r="numeric"),e instanceof Tensor)return assertDtype(r,e.dtype,t,n),e;var o=inferDtype(e);if("string"!==o&&["bool","int32","float32"].indexOf(r)>=0&&(o=r),assertDtype(r,o,t,n),null==e||!isTypedArray(e)&&!Array.isArray(e)&&"number"!=typeof e&&"boolean"!=typeof e&&"string"!=typeof e){var a=null==e?"null":e.constructor.name;throw new Error("Argument '"+t+"' passed to '"+n+"' must be a Tensor or TensorLike, but got '"+a+"'")}var i=inferShape(e);isTypedArray(e)||Array.isArray(e)||(e=[e]);var s="string"!==o?toTypedArray(e,o,ENV.get("DEBUG")):flatten(e);return Tensor.make(i,{values:s},o)}function convertToTensorArray(e,t,n,r){if(void 0===r&&(r="numeric"),!Array.isArray(e))throw new Error("Argument "+t+" passed to "+n+" must be a `Tensor[]` or `TensorLike[]`");return e.map(function(e,r){return convertToTensor(e,t+"["+r+"]",n)},r)}function grad(e){return assert(isFunction(e),function(){return "The f passed in grad(f) must be a function"}),function(t,n){var r=convertToTensor(t,"x","tf.grad",null),o=null!=n?convertToTensor(n,"dy","tf.grad"):null;return ENV.engine.tidy(function(){var t=ENV.engine.gradients(function(){return e(r)},[r],o),n=t.value,a=t.grads;return null!=o&&assertShapesMatch(n.shape,o.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),checkGrads(a),a[0]})}}function grads(e){return assert(isFunction(e),function(){return "The f passed in grads(f) must be a function"}),function(t,n){assert(Array.isArray(t),function(){return "The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s"});var r=convertToTensorArray(t,"args","tf.grads",null),o=null!=n?convertToTensor(n,"dy","tf.grads"):null;return ENV.engine.tidy(function(){var t=ENV.engine.gradients(function(){return e.apply(void 0,r)},r,o),n=t.value,a=t.grads;return null!=o&&assertShapesMatch(n.shape,o.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),checkGrads(a),a})}}function valueAndGrad(e){return assert(isFunction(e),function(){return "The f passed in valueAndGrad(f) must be a function"}),function(t,n){assert(t instanceof Tensor,function(){return "The x passed in valueAndGrad(f)(x) must be a tensor"}),assert(null==n||n instanceof Tensor,function(){return "The dy passed in valueAndGrad(f)(x, dy) must be a tensor"});var r=ENV.engine.gradients(function(){return e(t)},[t],n),o=r.grads,a=r.value;return checkGrads(o),{grad:o[0],value:a}}}function valueAndGrads(e){return assert(isFunction(e),function(){return "The f passed in valueAndGrads(f) must be a function"}),function(t,n){assert(Array.isArray(t)&&t.every(function(e){return e instanceof Tensor}),function(){return "The args passed in valueAndGrads(f)(args) must be array of tensors"}),assert(null==n||n instanceof Tensor,function(){return "The dy passed in valueAndGrads(f)(args, dy) must be a tensor"});var r=ENV.engine.gradients(function(){return e.apply(void 0,t)},t,n);return null!=n&&assertShapesMatch(r.value.shape,n.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),checkGrads(r.grads),r}}function variableGrads(e,t){if(assert(isFunction(e),function(){return "The f passed in variableGrads(f) must be a function"}),assert(null==t||Array.isArray(t)&&t.every(function(e){return e instanceof Variable}),function(){return "The varList passed in variableGrads(f, varList) must be an array of variables"}),null==t)for(var n in t=[],ENV.engine.registeredVariables)t.push(ENV.engine.registeredVariables[n]);var r=t.length;assert((t=t.filter(function(e){return e.trainable})).length>0,function(){return "variableGrads() expects at least one of the input variables to be trainable, but none of the "+r+" variables is trainable."});var o=ENV.engine.gradients(e,t,null,!0),a=o.value,i=o.grads;assert(i.some(function(e){return null!=e}),function(){return "Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."}),assert(0===a.rank,function(){return "The f passed in variableGrads(f) must return a scalar, but it returned a rank-"+a.rank+" tensor"});var s={};return t.forEach(function(e,t){null!=i[t]&&(s[e.name]=i[t]);}),{value:a,grads:s}}function customGrad(e){return ENV.engine.customGrad(e)}function checkGrads(e){if(e.filter(function(e){return null==e}).length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that\n the f you passed encloses all operations that lead from x to y.")}var tidy=Environment.tidy,keep=Environment.keep,dispose=Environment.dispose,time=Environment.time,profile=Environment.profile;function warn(){for(var e=[],t=0;t<arguments.length;t++)e[t]=arguments[t];ENV.get("IS_TEST")||console.warn.apply(console,e);}function getReshaped(e,t,n,r){void 0===r&&(r=!0);var o=[];if(r)(o=o.concat(t.slice(0))).push(e[0]/n),o=o.concat(e.slice(1));else{o=o.concat(e[0]);for(var a=t.length,i=0;i<a;++i)o=o.concat([e[i+1]/t[i],t[i]]);o=o.concat(e.slice(a+1));}return o}function getPermuted(e,t,n){void 0===n&&(n=!0);var r=[];if(n){r.push(t);for(var o=t+1;o<e;++o)o<=2*t?(r.push(o),r.push(o-(t+1))):r.push(o);}else{var a=[],i=[];for(o=1;o<e;++o)o>=2*t+1||o%2==1?i.push(o):a.push(o);r.push.apply(r,a),r.push(0),r.push.apply(r,i);}return r}function getReshapedPermuted(e,t,n,r){void 0===r&&(r=!0);var o=[];r?o.push(e[0]/n):o.push(e[0]*n);for(var a=1;a<e.length;++a)a<=t.length?r?o.push(t[a-1]*e[a]):o.push(e[a]/t[a-1]):o.push(e[a]);return o}function getSliceBeginCoords(e,t){for(var n=[0],r=0;r<t;++r)n.push(e[r][0]);return n}function getSliceSize(e,t,n){for(var r=e.slice(0,1),o=0;o<n;++o)r.push(e[o+1]-t[o][0]-t[o][1]);return r}function axesAreInnerMostDims(e,t){for(var n=0;n<e.length;++n)if(e[e.length-n-1]!==t-1-n)return !1;return !0}function combineLocations(e,t,n){for(var r=e.length+t.length,o=[],a=0,i=0,s=0;s<r;s++)-1===n.indexOf(s)?o.push(e[a++]):o.push(t[i++]);return o}function computeOutAndReduceShapes(e,t){for(var n=[],r=e.length,o=0;o<r;o++)-1===t.indexOf(o)&&n.push(e[o]);return [n,t.map(function(t){return e[t]})]}function expandShapeToKeepDim(e,t){return combineLocations(e,t.map(function(e){return 1}),t)}function assertAxesAreInnerMostDims(e,t,n){assert(axesAreInnerMostDims(t,n),function(){return e+" supports only inner-most axes for now. Got axes "+t+" and rank-"+n+" input."});}function getAxesPermutation(e,t){if(axesAreInnerMostDims(e,t))return null;for(var n=[],r=0;r<t;++r)-1===e.indexOf(r)&&n.push(r);return e.forEach(function(e){return n.push(e)}),n}function getUndoAxesPermutation(e){return e.map(function(e,t){return [t,e]}).sort(function(e,t){return e[1]-t[1]}).map(function(e){return e[0]})}function getInnerMostAxes(e,t){for(var n=[],r=t-e;r<t;++r)n.push(r);return n}function assertParamsConsistent(e,t){var n=e[0].length;e.forEach(function(e,t){assert(e.length===n,function(){return "Error in concat"+n+"D: rank of tensors["+t+"] must be the same as the rank of the rest ("+n+")"});}),assert(t>=0&&t<n,function(){return "Error in concat"+n+"D: axis must be between 0 and "+(n-1)+"."});var r=e[0];e.forEach(function(e,o){for(var a=0;a<n;a++)assert(a===t||e[a]===r[a],function(){return "Error in concat"+n+"D: Shape of tensors["+o+"] ("+e+") does not match the shape of the rest ("+r+") along the non-concatenated axis "+o+"."});});}function computeOutShape(e,t){for(var n=e[0].slice(),r=1;r<e.length;r++)n[t]+=e[r][t];return n}function prepareAndValidate(e,t){if(e.rank<1)throw new Error("tf.gatherND() expects the input to be rank 1 or higher, but the rank was "+e.rank+".");if(t.rank<1)throw new Error("tf.gatherND() expects the indices to be rank 1 or higher, but the rank was "+t.rank+".");if("int32"!==t.dtype)throw new Error("tf.gatherND() expects the indices to be int32 type, but the dtype was "+t.dtype+".");if(t.shape[t.rank-1]>e.rank)throw new Error("index innermost dimension length must be <= tensor rank; saw: "+t.shape[t.rank-1]+" vs. "+e.rank);if(0===e.size)throw new Error("Requested more than 0 entries, but input is empty. Input shape: "+e.shape+".");for(var n=t.shape,r=n[n.length-1],o=1,a=0;a<n.length-1;++a)o*=n[a];var i=e.shape,s=n.slice();s.pop();var u=1;for(a=r;a<e.rank;++a)u*=i[a],s.push(i[a]);var c=computeStrides(e.shape).map(function(e){return e/u}).concat([1]).slice(0,r);return [s,o,u,c]}var PARALLELIZE_THRESHOLD=30;function computeOptimalWindowSize(e){return e<=PARALLELIZE_THRESHOLD?e:nearestDivisor(e,Math.floor(Math.sqrt(e)))}function validateUpdateShape(e,t,n){var r=t.rank>1?t.shape[t.rank-1]:1,o=t.rank>1?t.rank-1:1,a="Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: "+n.shape+", indices.shape: "+t.shape+", shape: "+e+", sliceDim: "+r+", and batchDim: "+o+".";if(n.rank<o)throw new Error(a+" update.rank < "+o+". ");if(e.length<r+(n.rank-o))throw new Error(a+" Output shape length < "+(r+(n.rank-o)));if(n.rank!==o+e.length-r)throw new Error(a+" update.rank != "+(o+e.length-r));for(var i=0;i<o;++i)if(n.shape[i]!==t.shape[i])throw new Error(a+" updates.shape["+i+"] ("+n.shape[i]+") != indices.shape["+i+"] ("+t.shape[i]+").");for(i=0;i<n.rank-o;++i)if(n.shape[i+o]!==e[i+r])throw new Error(a+" updates.shape["+(i+o)+"] ("+n.shape[i+o]+") != shape["+(i+o)+"] ("+e[i+o]+")")}function validateInput(e,t,n){if(t.rank<1)throw new Error("tf.scatterND() expects the indices to be rank 1 or higher, but the rank was "+t.rank+".");if(e.rank<1)throw new Error("tf.scatterND() expects the updates to be rank 1 or higher, but the rank was "+e.rank+".");if("int32"!==t.dtype)throw new Error("The dtype of 'indices' should be int32, but got dtype: "+t.dtype);if(n.length<1)throw new Error("Output rank must be greater or equal to 1, but got shape: "+n);if(0===n.length){if(0===t.size)throw new Error("Indices specified for empty output. indices shape: "+t.shape);if(0===e.size)throw new Error("Updates specified for empty output. updates shape: "+e.shape)}validateUpdateShape(n,t,e);}function calculateShapes(e,t,n){for(var r=t.rank>1?t.shape[t.rank-1]:1,o=n.length,a=1,i=r;i<o;++i)a*=n[i];var s=r<1?1:r;return {sliceRank:r,numUpdates:t.size/s,sliceSize:a,strides:computeStrides(n.slice(0,r)).concat([1]),outputSize:sizeFromShape(n)}}function segOpComputeOptimalWindowSize(e,t){var n,r=!1;for(e<=PARALLELIZE_THRESHOLD?(n=e,r=!0):n=nearestDivisor(e,Math.floor(Math.sqrt(e)));!r;)n>t||n===e?r=!0:n=nearestDivisor(e,n+1);return n}function computeOutShape$1(e,t,n){for(var r=[],o=e.length,a=0;a<o;a++)a!==t?r.push(e[a]):r.push(n);return r}function collectGatherOpShapeInfo(e,t,n){for(var r=e.shape[n],o=[],a=1,i=1,s=0;s<n;s++)o.push(e.shape[s]),a*=e.shape[s];for(s=0;s<t.rank;s++)o.push(t.shape[s]);for(s=n+1;s<e.rank;s++)o.push(e.shape[s]),i*=e.shape[s];return {batchSize:a,sliceSize:i,dimSize:r,outputShape:o}}function assertParamsValid(e,t,n){assert(e.rank===t.length,function(){return "Error in slice"+e.rank+"D: Length of begin "+t+" must match the rank of the array ("+e.rank+")."}),assert(e.rank===n.length,function(){return "Error in slice"+e.rank+"D: Length of size "+n+" must match the rank of the array ("+e.rank+")."});for(var r=function(r){assert(t[r]+n[r]<=e.shape[r],function(){return "Error in slice"+e.rank+"D: begin["+r+"] + size["+r+"] ("+(t[r]+n[r])+") would overflow input.shape["+r+"] ("+e.shape[r]+")"});},o=0;o<e.rank;++o)r(o);}function getStridedSlicedInfo(e,t,n,r,o,a,i,s,u){if(void 0===o&&(o=0),void 0===a&&(a=0),void 0===i&&(i=0),void 0===s&&(s=0),void 0===u&&(u=0),0!==i)throw new Error("ellipsis mask is not yet supported");if(0!==s)throw new Error("new axis mask is not yet supported");for(var c=[],l=[],p=[],d=0;d<e.length;d++)c[d]=startForAxis(o,t,r,e,d),l[d]=stopForAxis(a,n,r,e,d),u&1<<d&&(l[d]=c[d]+1,p.push(d));var h=new Array(e.length).fill(0);return h=h.map(function(e,t){for(var n=0,o=r[t]||1,a=c[t];!(o>0?a>=l[t]:a<=l[t]);a+=o)n+=1;return n}),[c,h,p]}function startForAxis(e,t,n,r,o){var a=t[o],i=n[o]||1;(e&1<<o||null==a)&&(a=i>0?Number.MIN_SAFE_INTEGER:Number.MAX_SAFE_INTEGER);var s=r[o];return a<0&&(a+=s),a=clamp(0,a,s-1)}function stopForAxis(e,t,n,r,o){var a=t[o],i=n[o]||1;(e&1<<o||null==a)&&(a=i>0?Number.MAX_SAFE_INTEGER:Number.MIN_SAFE_INTEGER);var s=r[o];return a<0&&(a+=s),a=i>0?clamp(0,a,s):clamp(-1,a,s-1)}function isSliceContinous(e,t,n){for(var r=n.length,o=0;o<n.length;o++)if(n[o]>1){r=o;break}for(o=r+1;o<n.length;o++)if(t[o]>0||n[o]!==e[o])return !1;return !0}function computeFlatOffset(e,t){for(var n=e.length>0?e[e.length-1]:1,r=0;r<e.length-1;r++)n+=e[r]*t[r];return n}function op(e){var t=Object.keys(e);if(1!==t.length)throw new Error("Please provide an object with a single key (operation name) mapping to a function. Got an object with "+t.length+" keys.");var n=t[0],r=e[n];n.endsWith("_")&&(n=n.substring(0,n.length-1));var o=function(){for(var e=[],t=0;t<arguments.length;t++)e[t]=arguments[t];ENV.engine.startScope(n);try{var o=r.apply(void 0,e);return o instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),ENV.engine.endScope(o),o}catch(e){throw ENV.engine.endScope(null),e}};return Object.defineProperty(o,"name",{value:n,configurable:!0}),o}function softmax_(e,t){void 0===t&&(t=-1);var n=convertToTensor(e,"logits","softmax");if(-1===t&&(t=n.rank-1),t!==n.rank-1)throw Error("Softmax along a non-last dimension is not yet supported. Logits was rank "+n.rank+" and dim was "+t);return customGrad(function(e,n){var r=e.logSumExp([t],!0),o=e.toFloat().sub(r).exp();n([o]);return {value:o,gradFunc:function(e,n){var r=n[0],o=e.mul(r);return o.sub(o.sum([t],!0).mul(r))}}})(n)}function logSoftmax_(e,t){void 0===t&&(t=-1);var n=convertToTensor(e,"logits","logSoftmax");if(-1===t&&(t=n.rank-1),t!==n.rank-1)throw Error("Log Softmax along a non-last dimension is not yet supported. Logits was rank "+n.rank+" and axis was "+t);return customGrad(function(e,n){var r=e.max(t,!0),o=e.sub(r),a=o.toFloat().sub(o.exp().sum(t,!0).log());n([a]);return {value:a,gradFunc:function(e,n){var r=n[0].exp();return e.sub(e.sum(t,!0).mul(r))}}})(n)}var softmax=op({softmax_:softmax_}),logSoftmax=op({logSoftmax_:logSoftmax_});function complex_(e,t){var n=convertToTensor(e,"real","complex"),r=convertToTensor(t,"imag","complex");return assertShapesMatch(n.shape,r.shape,"real and imag shapes, "+n.shape+" and "+r.shape+", must match in call to tf.complex()."),ENV.engine.runKernel(function(e){return e.complex(n,r)},{$real:n,$imag:r})}function real_(e){var t=convertToTensor(e,"input","real");return ENV.engine.runKernel(function(e){return e.real(t)},{$input:t})}function imag_(e){var t=convertToTensor(e,"input","imag");return ENV.engine.runKernel(function(e){return e.imag(t)},{$input:t})}var complex=op({complex_:complex_}),real=op({real_:real_}),imag=op({imag_:imag_});function tensor(e,t,n){if(null==n&&(n=inferDtype(e)),"complex64"===n)throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!isTypedArray(e)&&!Array.isArray(e)&&"number"!=typeof e&&"boolean"!=typeof e&&"string"!=typeof e)throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");var r=inferShape(e);if(null!=t){assertNonNegativeIntegerDimensions(t);var o=sizeFromShape(t),a=sizeFromShape(r);assert(o===a,function(){return "Based on the provided shape, ["+t+"], the tensor should have "+o+" values but has "+a});for(var i=0;i<r.length;++i){var s=r[i],u=i!==r.length-1||s!==sizeFromShape(t.slice(i));assert(r[i]===t[i]||!u,function(){return "Error creating a new Tensor. Inferred shape ("+r+") does not match the provided shape ("+t+"). "});}}return isTypedArray(e)||Array.isArray(e)||(e=[e]),t=t||r,e="string"!==n?toTypedArray(e,n,ENV.get("DEBUG")):flatten(e),Tensor.make(t,{values:e},n)}function scalar(e,t){if((isTypedArray(e)||Array.isArray(e))&&"complex64"!==t)throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");return tensor(e,[],t)}function tensor1d(e,t){assertNonNull(e);var n=inferShape(e);if(1!==n.length)throw new Error("tensor1d() requires values to be a flat/TypedArray");return tensor(e,n,t)}function tensor2d(e,t,n){if(assertNonNull(e),null!=t&&2!==t.length)throw new Error("tensor2d() requires shape to have two numbers");var r=inferShape(e);if(2!==r.length&&1!==r.length)throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");if(1===r.length&&null==t)throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");return tensor(e,t=t||r,n)}function tensor3d(e,t,n){if(assertNonNull(e),null!=t&&3!==t.length)throw new Error("tensor3d() requires shape to have three numbers");var r=inferShape(e);if(3!==r.length&&1!==r.length)throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");if(1===r.length&&null==t)throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");return tensor(e,t=t||r,n)}function tensor4d(e,t,n){if(assertNonNull(e),null!=t&&4!==t.length)throw new Error("tensor4d() requires shape to have four numbers");var r=inferShape(e);if(4!==r.length&&1!==r.length)throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");if(1===r.length&&null==t)throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");return tensor(e,t=t||r,n)}function tensor5d(e,t,n){if(assertNonNull(e),null!=t&&5!==t.length)throw new Error("tensor5d() requires shape to have five numbers");var r=inferShape(e);if(5!==r.length&&1!==r.length)throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");if(1===r.length&&null==t)throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");return tensor(e,t=t||r,n)}function tensor6d(e,t,n){if(assertNonNull(e),null!=t&&6!==t.length)throw new Error("tensor6d() requires shape to have six numbers");var r=inferShape(e);if(6!==r.length&&1!==r.length)throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");if(1===r.length&&null==t)throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");return tensor(e,t=t||r,n)}function ones$1(e,t){if(void 0===t&&(t="float32"),"complex64"===t){var n=ones$1(e,"float32"),r=ones$1(e,"float32");return complex(n,r)}var o=makeOnesTypedArray(sizeFromShape(e),t);return Tensor.make(e,{values:o},t)}function zeros(e,t){if(void 0===t&&(t="float32"),"complex64"===t){var n=zeros(e,"float32"),r=zeros(e,"float32");return complex(n,r)}var o=makeZerosTypedArray(sizeFromShape(e),t);return Tensor.make(e,{values:o},t)}function fill(e,t,n){return ENV.engine.runKernel(function(r){return r.fill(e,t,n)},{})}function onesLike_(e){var t=convertToTensor(e,"x","onesLike");if("complex64"===t.dtype){var n=onesLike(real(t)),r=zerosLike(imag(t));return complex(n,r)}return ENV.engine.runKernel(function(e){return e.onesLike(t)},{$x:t},null)}function zerosLike_(e){var t=convertToTensor(e,"x","zerosLike");return ENV.engine.runKernel(function(e){return e.zerosLike(t)},{$x:t},null)}function linspace(e,t,n){if(0===n)throw new Error("Cannot request zero samples");var r=(t-e)/(n-1),o=makeZerosTypedArray(n,"float32");o[0]=e;for(var a=1;a<o.length;a++)o[a]=o[a-1]+r;return tensor1d(o,"float32")}function range(e,t,n,r){if(void 0===n&&(n=1),void 0===r&&(r="float32"),0===n)throw new Error("Cannot have a step of zero");if(e===t||e<t&&n<0||t<e&&n>1)return zeros([0],r);var o=makeZerosTypedArray(Math.abs(Math.ceil((t-e)/n)),r);t<e&&1===n&&(n=-1),o[0]=e;for(var a=1;a<o.length;a++)o[a]=o[a-1]+n;return tensor1d(o,r)}var onesLike=op({onesLike_:onesLike_}),zerosLike=op({zerosLike_:zerosLike_}),DataStorage=function(){function e(e){this.dataMover=e,this.data=new WeakMap;}return e.prototype.get=function(e){return this.data.has(e)||this.dataMover.moveData(e),this.data.get(e)},e.prototype.set=function(e,t){this.data.set(e,t);},e.prototype.has=function(e){return this.data.has(e)},e.prototype.delete=function(e){return this.data.delete(e)},e}(),KernelBackend=function(){function e(){}return e.prototype.time=function(e){throw new Error("Not yet implemented.")},e.prototype.read=function(e){throw new Error("Not yet implemented.")},e.prototype.readSync=function(e){throw new Error("Not yet implemented.")},e.prototype.disposeData=function(e){throw new Error("Not yet implemented.")},e.prototype.write=function(e,t){throw new Error("Not yet implemented.")},e.prototype.fromPixels=function(e,t){throw new Error("Not yet implemented.")},e.prototype.register=function(e,t,n){throw new Error("Not yet implemented.")},e.prototype.memory=function(){throw new Error("Not yet implemented.")},e.prototype.floatPrecision=function(){throw new Error("Not yet implemented")},e.prototype.batchMatMul=function(e,t,n,r){throw new Error("Not yet implemented")},e.prototype.fusedBatchMatMul=function(e,t,n,r,o,a){throw new Error("Not yet implemented")},e.prototype.slice=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.stridedSlice=function(e,t,n,r,o,a,i,s,u){throw new Error("Not yet implemented")},e.prototype.unstack=function(e,t){throw new Error("Not yet implemented")},e.prototype.reverse=function(e,t){throw new Error("Not yet implemented")},e.prototype.concat=function(e,t){throw new Error("Not yet implemented")},e.prototype.neg=function(e){throw new Error("Not yet implemented")},e.prototype.add=function(e,t){throw new Error("Not yet implemented")},e.prototype.addN=function(e){throw new Error("Not yet implemented")},e.prototype.subtract=function(e,t){throw new Error("Not yet implemented")},e.prototype.multiply=function(e,t){throw new Error("Not yet implemented")},e.prototype.realDivide=function(e,t){throw new Error("Not yet implemented")},e.prototype.floorDiv=function(e,t){throw new Error("Not yet implemented")},e.prototype.sum=function(e,t){throw new Error("Not yet implemented")},e.prototype.prod=function(e,t){throw new Error("Not yet implemented")},e.prototype.unsortedSegmentSum=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.argMin=function(e,t){throw new Error("Not yet implemented")},e.prototype.argMax=function(e,t){throw new Error("Not yet implemented")},e.prototype.equal=function(e,t){throw new Error("Not yet implemented")},e.prototype.notEqual=function(e,t){throw new Error("Not yet implemented")},e.prototype.less=function(e,t){throw new Error("Not yet implemented")},e.prototype.lessEqual=function(e,t){throw new Error("Not yet implemented")},e.prototype.greater=function(e,t){throw new Error("Not yet implemented")},e.prototype.greaterEqual=function(e,t){throw new Error("Not yet implemented")},e.prototype.logicalNot=function(e){throw new Error("Not yet implemented")},e.prototype.logicalAnd=function(e,t){throw new Error("Not yet implemented")},e.prototype.logicalOr=function(e,t){throw new Error("Not yet implemented")},e.prototype.where=function(e){throw new Error("Not yet implemented")},e.prototype.select=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.topk=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.min=function(e,t){throw new Error("Not yet implemented")},e.prototype.minimum=function(e,t){throw new Error("Not yet implemented")},e.prototype.mod=function(e,t){throw new Error("Not yet implemented")},e.prototype.max=function(e,t){throw new Error("Not yet implemented")},e.prototype.maximum=function(e,t){throw new Error("Not yet implemented")},e.prototype.all=function(e,t){throw new Error("Not yet implemented")},e.prototype.any=function(e,t){throw new Error("Not yet implemented")},e.prototype.squaredDifference=function(e,t){throw new Error("Not yet implemented")},e.prototype.ceil=function(e){throw new Error("Not yet implemented")},e.prototype.floor=function(e){throw new Error("Not yet implemented")},e.prototype.round=function(e){throw new Error("Not yet implemented")},e.prototype.sign=function(e){throw new Error("Not yet implemented")},e.prototype.isNaN=function(e){throw new Error("Not yet implemented")},e.prototype.isInf=function(e){throw new Error("Not yet implemented")},e.prototype.isFinite=function(e){throw new Error("Not yet implemented")},e.prototype.pow=function(e,t){throw new Error("Not yet implemented")},e.prototype.exp=function(e){throw new Error("Not yet implemented")},e.prototype.expm1=function(e){throw new Error("Not yet implemented")},e.prototype.log=function(e){throw new Error("Not yet implemented")},e.prototype.log1p=function(e){throw new Error("Not yet implemented")},e.prototype.sqrt=function(e){throw new Error("Not yet implemented")},e.prototype.rsqrt=function(e){throw new Error("Not yet implemented")},e.prototype.square=function(e){throw new Error("Not yet implemented")},e.prototype.reciprocal=function(e){throw new Error("Not yet implemented")},e.prototype.relu=function(e){throw new Error("Not yet implemented")},e.prototype.prelu=function(e,t){throw new Error("Not yet implemented")},e.prototype.elu=function(e){throw new Error("Not yet implemented")},e.prototype.eluDer=function(e,t){throw new Error("Not yet implemented")},e.prototype.selu=function(e){throw new Error("Not yet implemented")},e.prototype.int=function(e){throw new Error("Not yet implemented")},e.prototype.clip=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.abs=function(e){throw new Error("Not yet implemented")},e.prototype.complexAbs=function(e){throw new Error("Not yet implemented")},e.prototype.sigmoid=function(e){throw new Error("Not yet implemented")},e.prototype.softplus=function(e){throw new Error("Not yet implemented")},e.prototype.sin=function(e){throw new Error("Not yet implemented")},e.prototype.cos=function(e){throw new Error("Not yet implemented")},e.prototype.tan=function(e){throw new Error("Not yet implemented")},e.prototype.asin=function(e){throw new Error("Not yet implemented")},e.prototype.acos=function(e){throw new Error("Not yet implemented")},e.prototype.atan=function(e){throw new Error("Not yet implemented")},e.prototype.atan2=function(e,t){throw new Error("Not yet implemented")},e.prototype.sinh=function(e){throw new Error("Not yet implemented")},e.prototype.cosh=function(e){throw new Error("Not yet implemented")},e.prototype.tanh=function(e){throw new Error("Not yet implemented")},e.prototype.asinh=function(e){throw new Error("Not yet implemented")},e.prototype.acosh=function(e){throw new Error("Not yet implemented")},e.prototype.atanh=function(e){throw new Error("Not yet implemented")},e.prototype.erf=function(e){throw new Error("Not yet implemented")},e.prototype.step=function(e,t){throw new Error("Not yet implemented")},e.prototype.conv2d=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.conv2dDerInput=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.conv2dDerFilter=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.depthwiseConv2D=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.depthwiseConv2DDerInput=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.depthwiseConv2DDerFilter=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.conv3d=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.conv3dDerInput=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.conv3dDerFilter=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.maxPool=function(e,t){throw new Error("Not yet implemented")},e.prototype.maxPoolBackprop=function(e,t,n,r){throw new Error("Not yet implemented")},e.prototype.avgPool=function(e,t){throw new Error("Not yet implemented")},e.prototype.avgPoolBackprop=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.reshape=function(e,t){throw new Error("Not yet implemented")},e.prototype.cast=function(e,t){throw new Error("Not yet implemented")},e.prototype.tile=function(e,t){throw new Error("Not yet implemented")},e.prototype.pad=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.transpose=function(e,t){throw new Error("Not yet implemented")},e.prototype.gather=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.gatherND=function(e,t){throw new Error("Not yet implemented")},e.prototype.scatterND=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.batchToSpaceND=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.spaceToBatchND=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.resizeBilinear=function(e,t,n,r){throw new Error("Not yet implemented")},e.prototype.resizeBilinearBackprop=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.resizeNearestNeighbor=function(e,t,n,r){throw new Error("Not yet implemented")},e.prototype.resizeNearestNeighborBackprop=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.batchNormalization=function(e,t,n,r,o,a){throw new Error("Not yet implemented")},e.prototype.localResponseNormalization4D=function(e,t,n,r,o){throw new Error("Not yet implemented")},e.prototype.LRNGrad=function(e,t,n,r,o,a,i){throw new Error("Not yet implemented")},e.prototype.multinomial=function(e,t,n,r){throw new Error("Not yet implemented")},e.prototype.oneHot=function(e,t,n,r){throw new Error("Not yet implemented")},e.prototype.cumsum=function(e,t,n,r){throw new Error("Not yet implemented")},e.prototype.nonMaxSuppression=function(e,t,n,r,o){throw new Error("Not yet implemented")},e.prototype.fft=function(e){throw new Error("Not yet implemented")},e.prototype.ifft=function(e){throw new Error("Not yet implemented")},e.prototype.complex=function(e,t){throw new Error("Not yet implemented")},e.prototype.real=function(e){throw new Error("Not yet implemented")},e.prototype.imag=function(e){throw new Error("Not yet implemented")},e.prototype.cropAndResize=function(e,t,n,r,o,a){throw new Error("Not yet implemented")},e.prototype.depthToSpace=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.split=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.sparseToDense=function(e,t,n,r){throw new Error("Not yet implemented")},e.prototype.fill=function(e,t,n){throw new Error("Not yet implemented.")},e.prototype.onesLike=function(e){throw new Error("Not yet implemented")},e.prototype.zerosLike=function(e){throw new Error("Not yet implemented")},e.prototype.setDataMover=function(e){throw new Error("Not yet implemented")},e.prototype.dispose=function(){throw new Error("Not yet implemented")},e}();function castTensor(e,t,n){if("complex64"===t){if("complex64"===e.dtype)return e.clone();var r=zeros(e.shape),o=e.toFloat(),a=n.complex(o,r);return r.dispose(),o.dispose(),a}if(!hasEncodingLoss(e.dtype,t))return Tensor.make(e.shape,{dataId:e.dataId},t);if("complex64"===e.dtype){var i=n.real(e);a=i.cast(t);return i.dispose(),a}if("int32"===t)return n.int(e);if("bool"===t){var s=scalar(0,e.dtype);a=n.notEqual(e,s);return s.dispose(),a}throw new Error("Error in Cast: unknown dtype argument ("+t+")")}function reshapeTensor(e,t){return Tensor.make(t,{dataId:e.dataId},e.dtype)}function mergeRealAndImagArrays(e,t){if(e.length!==t.length)throw new Error("Cannot merge real and imag arrays of different lengths. real:"+e.length+", imag: "+t.length+".");for(var n=new Float32Array(2*e.length),r=0;r<n.length;r+=2)n[r]=e[r/2],n[r+1]=t[r/2];return n}function splitRealAndImagArrays(e){for(var t=new Float32Array(e.length/2),n=new Float32Array(e.length/2),r=0;r<e.length;r+=2)t[r/2]=e[r],n[r/2]=e[r+1];return {real:t,imag:n}}function complexWithEvenIndex(e){for(var t=Math.ceil(e.length/4),n=new Float32Array(t),r=new Float32Array(t),o=0;o<e.length;o+=4)n[Math.floor(o/4)]=e[o],r[Math.floor(o/4)]=e[o+1];return {real:n,imag:r}}function complexWithOddIndex(e){for(var t=Math.floor(e.length/4),n=new Float32Array(t),r=new Float32Array(t),o=2;o<e.length;o+=4)n[Math.floor(o/4)]=e[o],r[Math.floor(o/4)]=e[o+1];return {real:n,imag:r}}function getComplexWithIndex(e,t){return {real:e[2*t],imag:e[2*t+1]}}function assignToTypedArray(e,t,n,r){e[2*r]=t,e[2*r+1]=n;}function exponents(e,t){for(var n=new Float32Array(e/2),r=new Float32Array(e/2),o=0;o<Math.ceil(e/2);o++){var a=(t?2:-2)*Math.PI*(o/e);n[o]=Math.cos(a),r[o]=Math.sin(a);}return {real:n,imag:r}}function exponent(e,t,n){var r=(n?2:-2)*Math.PI*(e/t);return {real:Math.cos(r),imag:Math.sin(r)}}function nonMaxSuppressionImpl(e,t,n,r,o){for(var a=Array.from(t).map(function(e,t){return {score:e,boxIndex:t}}).filter(function(e){return e.score>o}).sort(function(e,t){return t.score-e.score}),i=[],s=0;s<a.length;s++){var u=a[s],c=u.score,l=u.boxIndex;if(c<o)break;for(var p=!1,d=i.length-1;d>=0;--d){if(intersectionOverUnion(e,l,i[d])>=r){p=!0;break}}if(!p&&(i.push(l),i.length>=n))break}return tensor1d(i,"int32")}function intersectionOverUnion(e,t,n){var r=e.subarray(4*t,4*t+4),o=e.subarray(4*n,4*n+4),a=Math.min(r[0],r[2]),i=Math.min(r[1],r[3]),s=Math.max(r[0],r[2]),u=Math.max(r[1],r[3]),c=Math.min(o[0],o[2]),l=Math.min(o[1],o[3]),p=Math.max(o[0],o[2]),d=Math.max(o[1],o[3]),h=(s-a)*(u-i),f=(p-c)*(d-l);if(h<=0||f<=0)return 0;var m=Math.max(a,c),g=Math.max(i,l),v=Math.min(s,p),y=Math.min(u,d),x=Math.max(v-m,0)*Math.max(y-g,0);return x/(h+f-x)}function split(e,t,n){var r=new Array(e.rank).fill(0),o=e.shape.slice();return t.map(function(t){o[n]=t;var a=e.slice(r,o);return r[n]+=t,a})}function topkImpl(e,t,n,r,o){for(var a=t[t.length-1],i=[e.length/a,a],s=i[0],u=i[1],c=getTypedArrayFromDType(n,s*r),l=getTypedArrayFromDType("int32",s*r),p=0;p<s;p++){for(var d=p*u,h=e.subarray(d,d+u),f=[],m=0;m<h.length;m++)f.push({value:h[m],index:m});f.sort(function(e,t){return t.value-e.value});var g=p*r,v=c.subarray(g,g+r),y=l.subarray(g,g+r);for(m=0;m<r;m++)v[m]=f[m].value,y[m]=f[m].index;}var x=t.slice();return x[x.length-1]=r,[tensor(c,x,n),tensor(l,x,"int32")]}var ArgMinMaxProgram=function(){return function(e,t,n){this.variableNames=["A"];var r=e.windowSize,o=e.batchSize,a=e.inSize,i=Math.ceil(a/r);n||this.variableNames.push("bestIndicesA"),this.outputShape=[o,i];var s="max"===t?">":"<",u=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode="\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * "+r+";\n\n int bestIndex = inOffset;\n float bestValue = getA(batch, bestIndex);\n\n for (int i = 0; i < "+r+"; i++) {\n int inIdx = "+u+";\n float candidate = getA(batch, inIdx);\n if (candidate "+s+" bestValue) {\n bestValue = candidate;\n bestIndex = inIdx;\n }\n }\n setOutput(float(bestIndex));\n }\n ";}}();function getVecChannels(e,t){return ["x","y","z","w","u","v"].slice(0,t).map(function(t){return e+"."+t})}function getChannels(e,t){return 1===t?[e]:getVecChannels(e,t)}function getSourceCoords(e,t){if(1===e)return "rc";for(var n="",r=0;r<e;r++)n+=t[r],r<e-1&&(n+=",");return n}function getBroadcastDims(e,t){for(var n=e.length,r=[],o=0;o<n;o++){var a=n-1-o,i=e[a]||1;(t[t.length-1-o]||1)>1&&1===i&&r.unshift(a);}return r}function getReductionAxes(e,t){for(var n=[],r=0;r<t.length;r++){var o=e[e.length-r-1],a=t.length-r-1,i=t[a];(null==o||1===o&&i>1)&&n.unshift(a);}return n}function assertAndGetBroadcastShape(e,t){for(var n=[],r=Math.max(e.length,t.length),o=0;o<r;o++){var a=e[e.length-o-1];null==a&&(a=1);var i=t[t.length-o-1];if(null==i&&(i=1),1===a)n.unshift(i);else if(1===i)n.unshift(a);else{if(a!==i)throw Error("Operands could not be broadcast together with shapes "+e+" and "+t+".");n.unshift(a);}}return n}function getGlslDifferences(){var e,t,n,r,o,a,i,s,u,c;return 2===ENV.get("WEBGL_VERSION")?(e="#version 300 es",t="in",n="out",r="in",o="texture",a="outputColor",i="out vec4 outputColor;",s="\n const float NAN = uintBitsToFloat(uint(0x7fc00000));\n ",u="\n const float INFINITY = uintBitsToFloat(uint(0x7f800000));\n ",c="\n #define round(value) newRound(value)\n int newRound(float value) {\n return int(floor(value + 0.5));\n }\n\n ivec4 newRound(vec4 value) {\n return ivec4(floor(value + vec4(0.5)));\n }\n "):(e="",t="attribute",n="varying",r="varying",o="texture2D",a="gl_FragColor",i="",s="\n uniform float NAN;\n\n bool isnan(float val) {\n return (val < 1.0 || 0.0 < val || val == 0.0) ? false : true;\n }\n bvec4 isnan(vec4 val) {\n return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));\n }\n ",u="\n uniform float INFINITY;\n\n bool isinf(float val) {\n return abs(val) == INFINITY;\n }\n bvec4 isinf(vec4 val) {\n return equal(abs(val), vec4(INFINITY));\n }\n ",c="\n int round(float value) {\n return int(floor(value + 0.5));\n }\n\n ivec4 round(vec4 value) {\n return ivec4(floor(value + vec4(0.5)));\n }\n "),{version:e,attribute:t,varyingVs:n,varyingFs:r,texture2D:o,output:a,defineOutput:i,defineSpecialNaN:s,defineSpecialInf:u,defineRound:c}}function getLogicalCoordinatesFromFlatIndex(e,t,n){void 0===n&&(n="index");var r=computeStrides(t);return r.map(function(t,o){return "int "+e[o]+" = "+n+" / "+t+"; "+(o===r.length-1?"int "+e[o+1]+" = "+n+" - "+e[o]+" * "+t:"index -= "+e[o]+" * "+t)+";"}).join("")}function buildVec(e){return 1===e.length?""+e[0]:"vec"+e.length+"("+e.join(",")+")"}function dotify(e,t){if(e.length!==t.length)throw new Error("Vectors to be dotted must be of the same length -got "+e.length+" and "+t.length);for(var n=[],r=Math.floor(e.length/4),o=e.length%4,a=0;a<r;a++){var i=e.slice(4*a,4*a+4),s=t.slice(4*a,4*a+4);n.push(buildVec(i)+", "+buildVec(s));}if(0!==o){i=e.slice(4*r),s=t.slice(4*r);1===i.length&&(i=i.map(function(e){return "float("+e+")"}),s=s.map(function(e){return "float("+e+")"})),n.push(buildVec(i)+", "+buildVec(s));}return n.map(function(e,t){return "dot("+e+")"}).join("+")}function makeShader(e,t,n,r){var o=[];e.forEach(function(e){var t=sizeFromShape(e.shapeInfo.logicalShape);e.shapeInfo.isUniform?o.push("uniform float "+e.name+(t>1?"["+t+"]":"")+";"):(o.push("uniform sampler2D "+e.name+";"),o.push("uniform int offset"+e.name+";"));});var a,i,s=o.join("\n"),u=e.map(function(e){return getInputSamplingSnippet(e,t,r)}).join("\n"),c=t.texShape,l=getGlslDifferences(),p=getFloatTextureSampleSnippet(l),d=getShaderPrefix(l);return t.isPacked?(a=getPackedOutputSamplingSnippet(t.logicalShape,c),i=getFloatTextureSetRGBASnippet(l)):(a=getOutputSamplingSnippet(t.logicalShape,c),i=getFloatTextureSetRSnippet(l)),r&&(d+=SHADER_PACKED_PREFIX),[d,p,i,s,a,u,n].join("\n")}function getSamplerFromInInfo(e){var t=e.shapeInfo.logicalShape;switch(t.length){case 0:return getSamplerScalar(e);case 1:return getSampler1D(e);case 2:return getSampler2D(e);case 3:return getSampler3D(e);case 4:return getSampler4D(e);case 5:return getSampler5D(e);case 6:return getSampler6D(e);default:throw new Error(t.length+"-D input sampling is not yet supported")}}function getPackedSamplerFromInInfo(e){switch(e.shapeInfo.logicalShape.length){case 0:return getPackedSamplerScalar(e);case 1:return getPackedSampler1D(e);case 2:return getPackedSampler2D(e);case 3:return getPackedSampler3D(e);default:return getPackedSamplerND(e)}}function getInputSamplingSnippet(e,t,n){void 0===n&&(n=!1);var r="";r+=n?getPackedSamplerFromInInfo(e):getSamplerFromInInfo(e);var o=e.shapeInfo.logicalShape,a=t.logicalShape;return o.length<=a.length&&(r+=n?getPackedSamplerAtOutputCoords(e,t):getSamplerAtOutputCoords(e,t)),r}function getPackedOutputSamplingSnippet(e,t){switch(e.length){case 0:return getOutputScalarCoords();case 1:return getOutputPacked1DCoords(e,t);case 2:return getOutputPacked2DCoords(e,t);case 3:return getOutputPacked3DCoords(e,t);default:return getOutputPackedNDCoords(e,t)}}function getOutputSamplingSnippet(e,t){switch(e.length){case 0:return getOutputScalarCoords();case 1:return getOutput1DCoords(e,t);case 2:return getOutput2DCoords(e,t);case 3:return getOutput3DCoords(e,t);case 4:return getOutput4DCoords(e,t);case 5:return getOutput5DCoords(e,t);case 6:return getOutput6DCoords(e,t);default:throw new Error(e.length+"-D output sampling is not yet supported")}}function getFloatTextureSampleSnippet(e){return "\n float sampleTexture(sampler2D textureSampler, vec2 uv) {\n return "+e.texture2D+"(textureSampler, uv).r;\n }\n "}function getFloatTextureSetRSnippet(e){return "\n void setOutput(float val) {\n "+e.output+" = vec4(val, 0, 0, 0);\n }\n "}function getFloatTextureSetRGBASnippet(e){return "\n void setOutput(vec4 val) {\n "+e.output+" = val;\n }\n "}function getShaderPrefix(e){return e.version+"\n precision highp float;\n precision highp int;\n precision highp sampler2D;\n "+e.varyingFs+" vec2 resultUV;\n "+e.defineOutput+"\n const vec2 halfCR = vec2(0.5, 0.5);\n\n struct ivec5\n {\n int x;\n int y;\n int z;\n int w;\n int u;\n };\n\n struct ivec6\n {\n int x;\n int y;\n int z;\n int w;\n int u;\n int v;\n };\n\n "+e.defineSpecialNaN+"\n "+e.defineSpecialInf+"\n "+e.defineRound+"\n\n int imod(int x, int y) {\n return x - y * (x / y);\n }\n\n //Based on the work of Dave Hoskins\n //https://www.shadertoy.com/view/4djSRW\n #define HASHSCALE1 443.8975\n float random(float seed){\n vec2 p = resultUV * seed;\n vec3 p3 = fract(vec3(p.xyx) * HASHSCALE1);\n p3 += dot(p3, p3.yzx + 19.19);\n return fract((p3.x + p3.y) * p3.z);\n }\n\n "+SAMPLE_1D_SNIPPET+"\n "+SAMPLE_2D_SNIPPET+"\n "+SAMPLE_3D_SNIPPET+"\n "}var SAMPLE_1D_SNIPPET="\nvec2 uvFromFlat(int texNumR, int texNumC, int index) {\n int texR = index / texNumC;\n int texC = index - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n int texelIndex = index / 2;\n int texR = texelIndex / texNumC;\n int texC = texelIndex - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n",SAMPLE_2D_SNIPPET="\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n int texNumC, int row, int col) {\n int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n int texR = texelIndex / texNumC;\n int texC = texelIndex - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n",SAMPLE_3D_SNIPPET="\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n int texelsInBatch, int texelsInLogicalRow, int b,\n int row, int col) {\n int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n int texR = index / texNumC;\n int texC = index - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n",SHADER_PACKED_PREFIX="\n float getChannel(vec4 frag, vec2 innerDims) {\n vec2 modCoord = mod(innerDims, 2.);\n return modCoord.x == 0. ?\n (modCoord.y == 0. ? frag.r : frag.g) :\n (modCoord.y == 0. ? frag.b : frag.a);\n }\n float getChannel(vec4 frag, int dim) {\n float modCoord = mod(float(dim), 2.);\n return modCoord == 0. ? frag.r : frag.g;\n }\n";function getOutputScalarCoords(){return "\n int getOutputCoords() {\n return 0;\n }\n "}function getOutputPacked1DCoords(e,t){var n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return 1===n[0]?"\n int getOutputCoords() {\n return 2 * int(resultUV.x * "+n[1]+".0);\n }\n ":1===n[1]?"\n int getOutputCoords() {\n return 2 * int(resultUV.y * "+n[0]+".0);\n }\n ":"\n int getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2("+n[0]+", "+n[1]+"));\n return resTexRC.x * "+n[1]+" + resTexRC.y;\n }\n "}function getOutput1DCoords(e,t){return 1===t[0]?"\n int getOutputCoords() {\n return int(resultUV.x * "+t[1]+".0);\n }\n ":1===t[1]?"\n int getOutputCoords() {\n return int(resultUV.y * "+t[0]+".0);\n }\n ":"\n int getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2("+t[0]+", "+t[1]+"));\n return resTexRC.x * "+t[1]+" + resTexRC.y;\n }\n "}function getOutputPacked3DCoords(e,t){var n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),o=r*Math.ceil(e[1]/2);return "\n ivec3 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2("+n[0]+", "+n[1]+"));\n int index = resTexRC.x * "+n[1]+" + resTexRC.y;\n\n int b = index / "+o+";\n index -= b * "+o+";\n\n int r = 2 * (index / "+r+");\n int c = imod(index, "+r+") * 2;\n\n return ivec3(b, r, c);\n }\n "}function getOutput3DCoords(e,t){var n=getLogicalCoordinatesFromFlatIndex(["r","c","d"],e);return "\n ivec3 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2("+t[0]+", "+t[1]+"));\n int index = resTexRC.x * "+t[1]+" + resTexRC.y;\n "+n+"\n return ivec3(r, c, d);\n }\n "}function getOutputPackedNDCoords(e,t){for(var n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),o=r*Math.ceil(e[e.length-2]/2),a=o,i="",s="b, r, c",u=2;u<e.length-1;u++)i="\n int b"+u+" = index / "+(a*=e[e.length-u-1])+";\n index -= b"+u+" * "+a+";\n "+i,s="b"+u+", "+s;return "\n ivec"+e.length+" getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2("+n[0]+", "+n[1]+"));\n int index = resTexRC.x * "+n[1]+" + resTexRC.y;\n\n "+i+"\n\n int b = index / "+o+";\n index -= b * "+o+";\n\n int r = 2 * (index / "+r+");\n int c = imod(index, "+r+") * 2;\n\n return ivec"+e.length+"("+s+");\n }\n "}function getOutput4DCoords(e,t){var n=getLogicalCoordinatesFromFlatIndex(["r","c","d","d2"],e);return "\n ivec4 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2("+t[0]+", "+t[1]+"));\n int index = resTexRC.x * "+t[1]+" + resTexRC.y;\n "+n+"\n return ivec4(r, c, d, d2);\n }\n "}function getOutput5DCoords(e,t){var n=getLogicalCoordinatesFromFlatIndex(["r","c","d","d2","d3"],e);return "\n ivec5 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx * vec2("+t[0]+",\n "+t[1]+"));\n\n int index = resTexRC.x * "+t[1]+" + resTexRC.y;\n\n "+n+"\n\n ivec5 outShape = ivec5(r, c, d, d2, d3);\n return outShape;\n }\n "}function getOutput6DCoords(e,t){var n=getLogicalCoordinatesFromFlatIndex(["r","c","d","d2","d3","d4"],e);return "\n ivec6 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2("+t[0]+", "+t[1]+"));\n int index = resTexRC.x * "+t[1]+" + resTexRC.y;\n\n "+n+"\n\n ivec6 result = ivec6(r, c, d, d2, d3, d4);\n return result;\n }\n "}function getOutputPacked2DCoords(e,t){var n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(arraysEqual(e,t))return "\n ivec2 getOutputCoords() {\n return 2 * ivec2(resultUV.yx * vec2("+n[0]+", "+n[1]+"));\n }\n ";var r=Math.ceil(e[1]/2);return "\n ivec2 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2("+n[0]+", "+n[1]+"));\n\n int index = resTexRC.x * "+n[1]+" + resTexRC.y;\n int r = 2 * (index / "+r+");\n int c = imod(index, "+r+") * 2;\n\n return ivec2(r, c);\n }\n "}function getOutput2DCoords(e,t){return arraysEqual(e,t)?"\n ivec2 getOutputCoords() {\n return ivec2(resultUV.yx * vec2("+t[0]+", "+t[1]+"));\n }\n ":1===e[1]?"\n ivec2 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2("+t[0]+", "+t[1]+"));\n int index = resTexRC.x * "+t[1]+" + resTexRC.y;\n return ivec2(index, 0);\n }\n ":1===e[0]?"\n ivec2 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2("+t[0]+", "+t[1]+"));\n int index = resTexRC.x * "+t[1]+" + resTexRC.y;\n return ivec2(0, index);\n }\n ":"\n ivec2 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2("+t[0]+", "+t[1]+"));\n int index = resTexRC.x * "+t[1]+" + resTexRC.y;\n int r = index / "+e[1]+";\n int c = index - r * "+e[1]+";\n return ivec2(r, c);\n }\n "}function getFlatOffsetUniformName(e){return "offset"+e}function getPackedSamplerScalar(e){var t=e.name;return "\n vec4 "+("get"+t.charAt(0).toUpperCase()+t.slice(1))+"() {\n return "+getGlslDifferences().texture2D+"("+t+", halfCR);\n }\n "}function getSamplerScalar(e){var t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return "float "+n+"() {return "+t+";}";var r=e.shapeInfo.texShape,o=r[0],a=r[1];if(1===o&&1===a)return "\n float "+n+"() {\n return sampleTexture("+t+", halfCR);\n }\n ";var i=e.shapeInfo.texShape;return "\n float "+n+"() {\n vec2 uv = uvFromFlat("+i[0]+", "+i[1]+", "+getFlatOffsetUniformName(t)+");\n return sampleTexture("+t+", uv);\n }\n "}function getPackedSampler1D(e){var t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=e.shapeInfo.texShape,o=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)],a=getGlslDifferences();return "\n vec4 "+n+"(int index) {\n vec2 uv = packedUVfrom1D(\n "+o[0]+", "+o[1]+", index);\n return "+a.texture2D+"("+t+", uv);\n }\n "}function getSampler1D(e){var t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return "\n float "+n+"(int index) {\n "+getUniformSampler(e)+"\n }\n ";var r=e.shapeInfo.texShape,o=r[0],a=r[1];if(1===a&&1===o)return "\n float "+n+"(int index) {\n return sampleTexture("+t+", halfCR);\n }\n ";var i=getFlatOffsetUniformName(t);return 1===a?"\n float "+n+"(int index) {\n vec2 uv = vec2(0.5, (float(index + "+i+") + 0.5) / "+o+".0);\n return sampleTexture("+t+", uv);\n }\n ":1===o?"\n float "+n+"(int index) {\n vec2 uv = vec2((float(index + "+i+") + 0.5) / "+a+".0, 0.5);\n return sampleTexture("+t+", uv);\n }\n ":"\n float "+n+"(int index) {\n vec2 uv = uvFromFlat("+o+", "+a+", index + "+i+");\n return sampleTexture("+t+", uv);\n }\n "}function getPackedSampler2D(e){var t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=e.shapeInfo.texShape,a=o[0],i=o[1],s=getGlslDifferences();if(null!=o&&arraysEqual(t,o))return "\n vec4 "+r+"(int row, int col) {\n vec2 uv = (vec2(col, row) + halfCR) / vec2("+i+".0, "+a+".0);\n\n return "+s.texture2D+"("+n+", uv);\n }\n ";var u=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)];return "\n vec4 "+r+"(int row, int col) {\n vec2 uv = packedUVfrom2D("+Math.ceil(t[1]/2)+", "+u[0]+", "+u[1]+", row, col);\n return "+s.texture2D+"("+n+", uv);\n }\n "}function getSampler2D(e){var t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=e.shapeInfo.texShape;if(null!=o&&arraysEqual(t,o)){var a=o[0];return "\n float "+r+"(int row, int col) {\n vec2 uv = (vec2(col, row) + halfCR) / vec2("+o[1]+".0, "+a+".0);\n return sampleTexture("+n+", uv);\n }\n "}var i=squeezeShape(t),s=i.newShape,u=i.keptDims,c=s;if(c.length<t.length){return "\n "+getSamplerFromInInfo(squeezeInputInfo(e,c))+"\n float "+r+"(int row, int col) {\n return "+r+"("+getSqueezedParams(["row","col"],u)+");\n }\n "}if(e.shapeInfo.isUniform)return "\n float "+r+"(int row, int col) {\n int index = round(dot(vec2(row, col), vec2("+t[1]+", 1)));\n "+getUniformSampler(e)+"\n }\n ";var l=o[0],p=o[1],d=getFlatOffsetUniformName(n);return 1===p?"\n float "+r+"(int row, int col) {\n float index = dot(vec3(row, col, "+d+"), vec3("+t[1]+", 1, 1));\n vec2 uv = vec2(0.5, (index + 0.5) / "+l+".0);\n return sampleTexture("+n+", uv);\n }\n ":1===l?"\n float "+r+"(int row, int col) {\n float index = dot(vec3(row, col, "+d+"), vec3("+t[1]+", 1, 1));\n vec2 uv = vec2((index + 0.5) / "+p+".0, 0.5);\n return sampleTexture("+n+", uv);\n }\n ":"\n float "+r+"(int row, int col) {\n // Explicitly use integer operations as dot() only works on floats.\n int index = row * "+t[1]+" + col + "+d+";\n vec2 uv = uvFromFlat("+l+", "+p+", index);\n return sampleTexture("+n+", uv);\n }\n"}function getPackedSampler3D(e){var t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=e.shapeInfo.texShape,a=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)];if(1===t[0]){return "\n "+getPackedSamplerFromInInfo(squeezeInputInfo(e,t.slice(1)))+"\n vec4 "+r+"(int b, int row, int col) {\n return "+r+"("+getSqueezedParams(["b","row","col"],[1,2])+");\n }\n "}var i=a[0],s=a[1],u=Math.ceil(t[2]/2);return "\n vec4 "+r+"(int b, int row, int col) {\n vec2 uv = packedUVfrom3D(\n "+i+", "+s+", "+u*Math.ceil(t[1]/2)+", "+u+", b, row, col);\n return "+getGlslDifferences().texture2D+"("+n+", uv);\n }\n "}function getSampler3D(e){var t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=t[1]*t[2],a=t[2],i=squeezeShape(t),s=i.newShape,u=i.keptDims,c=s;if(c.length<t.length){return "\n "+getSamplerFromInInfo(squeezeInputInfo(e,c))+"\n float "+r+"(int row, int col, int depth) {\n return "+r+"("+getSqueezedParams(["row","col","depth"],u)+");\n }\n "}if(e.shapeInfo.isUniform)return "\n float "+r+"(int row, int col, int depth) {\n int index = round(dot(vec3(row, col, depth),\n vec3("+o+", "+a+", 1)));\n "+getUniformSampler(e)+"\n }\n ";var l=e.shapeInfo.texShape,p=l[0],d=l[1],h=e.shapeInfo.flatOffset;return d===o&&null==h?"\n float "+r+"(int row, int col, int depth) {\n float texR = float(row);\n float texC = dot(vec2(col, depth), vec2("+a+", 1));\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2("+d+".0, "+p+".0);\n return sampleTexture("+n+", uv);\n }\n ":d===a&&null==h?"\n float "+r+"(int row, int col, int depth) {\n float texR = dot(vec2(row, col), vec2("+t[1]+", 1));\n float texC = float(depth);\n vec2 uv = (vec2(texC, texR) + halfCR) / vec2("+d+".0, "+p+".0);\n return sampleTexture("+n+", uv);\n }\n ":"\n float "+r+"(int row, int col, int depth) {\n // Explicitly use integer operations as dot() only works on floats.\n int index = row * "+o+" + col * "+a+" + depth + "+getFlatOffsetUniformName(n)+";\n vec2 uv = uvFromFlat("+p+", "+d+", index);\n return sampleTexture("+n+", uv);\n }\n "}function getPackedSamplerND(e){for(var t=e.shapeInfo.logicalShape,n=t.length,r=e.name,o="get"+r.charAt(0).toUpperCase()+r.slice(1),a=e.shapeInfo.texShape,i=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],s=i[0],u=i[1],c=Math.ceil(t[n-1]/2),l=c*Math.ceil(t[n-2]/2),p="int b, int row, int col",d="b * "+l+" + (row / 2) * "+c+" + (col / 2)",h=2;h<n-1;h++)p="int b"+h+", "+p,d="b"+h+" * "+(l*=t[n-h-1])+" + "+d;return "\n vec4 "+o+"("+p+") {\n int index = "+d+";\n int texR = index / "+u+";\n int texC = index - texR * "+u+";\n vec2 uv = (vec2(texC, texR) + halfCR) / vec2("+u+", "+s+");\n return "+getGlslDifferences().texture2D+"("+r+", uv);\n }\n "}function getSampler4D(e){var t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=t[3],a=t[2]*o,i=t[1]*a,s=squeezeShape(t),u=s.newShape,c=s.keptDims;if(u.length<t.length){return "\n "+getSamplerFromInInfo(squeezeInputInfo(e,u))+"\n float "+r+"(int row, int col, int depth, int depth2) {\n return "+r+"("+getSqueezedParams(["row","col","depth","depth2"],c)+");\n }\n "}if(e.shapeInfo.isUniform)return "\n float "+r+"(int row, int col, int depth, int depth2) {\n int index = round(dot(vec4(row, col, depth, depth2),\n vec4("+i+", "+a+", "+o+", 1)));\n "+getUniformSampler(e)+"\n }\n ";var l=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],h=p[1];return h===i&&null==l?"\n float "+r+"(int row, int col, int depth, int depth2) {\n float texR = float(row);\n float texC =\n dot(vec3(col, depth, depth2),\n vec3("+a+", "+o+", 1));\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2("+h+".0, "+d+".0);\n return sampleTexture("+n+", uv);\n }\n ":h===o&&null==l?"\n float "+r+"(int row, int col, int depth, int depth2) {\n float texR = dot(vec3(row, col, depth),\n vec3("+t[1]*t[2]+", "+t[2]+", 1));\n float texC = float(depth2);\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2("+h+".0, "+d+".0);\n return sampleTexture("+n+", uv);\n }\n ":"\n float "+r+"(int row, int col, int depth, int depth2) {\n // Explicitly use integer operations as dot() only works on floats.\n int index = row * "+i+" + col * "+a+" +\n depth * "+o+" + depth2;\n vec2 uv = uvFromFlat("+d+", "+h+", index + "+getFlatOffsetUniformName(n)+");\n return sampleTexture("+n+", uv);\n }\n "}function getSampler5D(e){var t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=t[4],a=t[3]*o,i=t[2]*a,s=t[1]*i,u=squeezeShape(t),c=u.newShape,l=u.keptDims;if(c.length<t.length){return "\n "+getSamplerFromInInfo(squeezeInputInfo(e,c))+"\n float "+r+"(int row, int col, int depth, int depth2, int depth3) {\n return "+r+"("+getSqueezedParams(["row","col","depth","depth2","depth3"],l)+");\n }\n "}if(e.shapeInfo.isUniform)return "\n float "+r+"(int row, int col, int depth, int depth2, int depth3) {\n float index = dot(\n vec4(row, col, depth, depth2),\n vec4("+s+", "+i+", "+a+", "+o+")) +\n depth3;\n "+getUniformSampler(e)+"\n }\n ";var p=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,h=d[0],f=d[1];return f===s&&null==p?"\n float "+r+"(int row, int col, int depth, int depth2, int depth3) {\n int texR = row;\n float texC = dot(vec4(col, depth, depth2, depth3),\n vec4("+i+", "+a+", "+o+", 1));\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2("+f+".0, "+h+".0);\n return sampleTexture("+n+", uv);\n }\n ":f===o&&null==p?"\n float "+r+"(int row, int col, int depth, int depth2, int depth3) {\n float texR = dot(\n vec4(row, col, depth, depth2),\n vec4("+t[1]*t[2]*t[3]+",\n "+t[2]*t[3]+", "+t[3]+", 1));\n int texC = depth3;\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2("+f+".0, "+h+".0);\n return sampleTexture("+n+", uv);\n }\n ":"\n float "+r+"(int row, int col, int depth, int depth2, int depth3) {\n // Explicitly use integer operations as dot() only works on floats.\n int index = row * "+s+" + col * "+i+" + depth * "+a+" +\n depth2 * "+o+" + depth3 + "+getFlatOffsetUniformName(n)+";\n vec2 uv = uvFromFlat("+h+", "+f+", index);\n return sampleTexture("+n+", uv);\n }\n "}function getSampler6D(e){var t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=squeezeShape(t),a=o.newShape,i=o.keptDims;if(a.length<t.length){return "\n "+getSamplerFromInInfo(squeezeInputInfo(e,a))+"\n float "+r+"(int row, int col, int depth,\n int depth2, int depth3, int depth4) {\n return "+r+"("+getSqueezedParams(["row","col","depth","depth2","depth3","depth4"],i)+");\n }\n "}var s=t[5],u=t[4]*s,c=t[3]*u,l=t[2]*c,p=t[1]*l;if(e.shapeInfo.isUniform)return "\n float "+r+"(int row, int col, int depth,\n int depth2, int depth3, int depth4) {\n int index = round(dot(\n vec4(row, col, depth, depth2),\n vec4("+p+", "+l+", "+c+", "+u+")) +\n dot(\n vec2(depth3, depth4),\n vec2("+s+", 1)));\n "+getUniformSampler(e)+"\n }\n ";var d=e.shapeInfo.flatOffset,h=e.shapeInfo.texShape,f=h[0],m=h[1];return m===p&&null==d?"\n float "+r+"(int row, int col, int depth,\n int depth2, int depth3, int depth4) {\n int texR = row;\n float texC = dot(vec4(col, depth, depth2, depth3),\n vec4("+l+", "+c+", "+u+", "+s+")) +\n float(depth4);\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2("+m+".0, "+f+".0);\n return sampleTexture("+n+", uv);\n }\n ":m===s&&null==d?"\n float "+r+"(int row, int col, int depth,\n int depth2, int depth3, int depth4) {\n float texR = dot(vec4(row, col, depth, depth2),\n vec4("+t[1]*t[2]*t[3]*t[4]+",\n "+t[2]*t[3]*t[4]+",\n "+t[3]*t[4]+",\n "+t[4]+")) + float(depth3);\n int texC = depth4;\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2("+m+".0, "+f+".0);\n return sampleTexture("+n+", uv);\n }\n ":"\n float "+r+"(int row, int col, int depth,\n int depth2, int depth3, int depth4) {\n // Explicitly use integer operations as dot() only works on floats.\n int index = row * "+p+" + col * "+l+" + depth * "+c+" +\n depth2 * "+u+" + depth3 * "+s+" + depth4 + "+getFlatOffsetUniformName(n)+";\n vec2 uv = uvFromFlat("+f+", "+m+", index);\n return sampleTexture("+n+", uv);\n }\n "}function getUniformSampler(e){var t=e.name,n=sizeFromShape(e.shapeInfo.logicalShape);return n<2?"return "+t+";":"\n for (int i = 0; i < "+n+"; i++) {\n if (i == index) {\n return "+t+"[i];\n }\n }\n "}function getPackedSamplerAtOutputCoords(e,t){var n,r=e.name,o=r.charAt(0).toUpperCase()+r.slice(1),a="get"+o+"AtOutCoords",i=e.shapeInfo.logicalShape.length,s=t.logicalShape.length,u=getBroadcastDims(e.shapeInfo.logicalShape,t.logicalShape),c=getCoordsDataType(s),l=s-i,p=["x","y","z","w","u","v"];n=0===i?"":s<2&&u.length>=1?"coords = 0;":u.map(function(e){return "coords."+p[e+l]+" = 0;"}).join("\n");var d="";d=s<2&&i>0?"coords":e.shapeInfo.logicalShape.map(function(e,t){return "coords."+p[t+l]}).join(", ");var h="return outputValue;";if(1===i&&s>1)h="\n return vec4(outputValue.xy, outputValue.xy);\n ";else if(0===i&&s>0)h=1===s?"\n return vec4(outputValue.x, outputValue.x, 0., 0.);\n ":"\n return vec4(outputValue.x);\n ";else if(u.length){var f=i-2,m=i-1;u.indexOf(f)>-1&&u.indexOf(m)>-1?h="return vec4(outputValue.x);":u.indexOf(f)>-1?h="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":u.indexOf(m)>-1&&(h="return vec4(outputValue.xx, outputValue.zz);");}return "\n vec4 "+a+"() {\n "+c+" coords = getOutputCoords();\n "+n+"\n vec4 outputValue = get"+o+"("+d+");\n "+h+"\n }\n "}function getSamplerAtOutputCoords(e,t){var n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),o="get"+r+"AtOutCoords",a=t.texShape,i=e.shapeInfo.texShape,s=e.shapeInfo.logicalShape.length,u=t.logicalShape.length;if(!e.shapeInfo.isUniform&&s===u&&null==e.shapeInfo.flatOffset&&arraysEqual(i,a))return "\n float "+o+"() {\n return sampleTexture("+n+", resultUV);\n }\n ";var c=getCoordsDataType(u),l=getBroadcastDims(e.shapeInfo.logicalShape,t.logicalShape),p=u-s,d=["x","y","z","w","u","v"];return "\n float "+o+"() {\n "+c+" coords = getOutputCoords();\n "+(0===s?"":u<2&&l.length>=1?"coords = 0;":l.map(function(e){return "coords."+d[e+p]+" = 0;"}).join("\n"))+"\n return get"+r+"("+(u<2&&s>0?"coords":e.shapeInfo.logicalShape.map(function(e,t){return "coords."+d[t+p]}).join(", "))+");\n }\n "}function getCoordsDataType(e){if(e<=1)return "int";if(2===e)return "ivec2";if(3===e)return "ivec3";if(4===e)return "ivec4";if(5===e)return "ivec5";if(6===e)return "ivec6";throw Error("GPU for rank "+e+" is not yet supported")}function squeezeInputInfo(e,t){var n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function getSqueezedParams(e,t){return t.map(function(t){return e[t]}).join(", ")}var ArgMinMaxPackedProgram=function(){return function(e,t,n,r){this.variableNames=["A"],this.usesPackedTextures=!0,assert(e.length>2,function(){return "Packed arg"+(n.charAt(0).toUpperCase()+n.slice(1))+" supports only inputs with rank above 2."});var o=e[e.length-1],a=Math.ceil(o/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),r||this.variableNames.push("bestIndicesA");var i,s,u=this.outputShape,c=u.length,l=getCoordsDataType(c),p=getChannels("coords",c);if(1===a){var d=getCoordsDataType(s=c+1);i="\n "+d+" sourceLocR = "+d+"("+p.join()+", 0);\n ++"+p[c-1]+";\n "+d+" sourceLocG = "+d+"("+p.join()+", 0);\n ++"+p[c-2]+";\n "+d+" sourceLocA = "+d+"("+p.join()+", 0);\n --"+p[c-1]+";\n "+d+" sourceLocB = "+d+"("+p.join()+", 0);\n --"+p[c-2]+";";}else s=c,i="\n "+l+" sourceLocR = coords;\n ++"+p[c-1]+";\n "+l+" sourceLocG = coords;\n ++"+p[c-2]+";\n "+l+" sourceLocA = coords;\n --"+p[c-1]+";\n "+l+" sourceLocB = coords;\n --"+p[c-2]+";";var h=["x","y","z","w","u","v"].slice(0,s),f="."+h[s-1],m=h.map(function(e){return "int "+e}),g=getChannels("sourceLocR",s-1).concat("inIdx.r"),v=getChannels("sourceLocG",s-1).concat("inIdx.g"),y=getChannels("sourceLocB",s-1).concat("inIdx.b"),x=getChannels("sourceLocA",s-1).concat("inIdx.a"),E="max"===n?"greaterThan":"lessThan",T=r?"":"\n inIdx = round(vec4(getBestIndicesAChannel("+g.join()+"),\n getBestIndicesAChannel("+v.join()+"),\n getBestIndicesAChannel("+y.join()+"),\n getBestIndicesAChannel("+x.join()+")));",b="vec4(\n getAChannel("+g.join()+"),\n hasNextCol ? getAChannel("+v.join()+") : 0.,\n hasNextRow ? getAChannel("+y.join()+") : 0.,\n hasNextRow && hasNextCol ? getAChannel("+x.join()+") : 0.)",w=r?"":"\n float getBestIndicesAChannel("+m.join()+") {\n return getChannel(getBestIndicesA("+h.join()+"),\n vec2("+h.slice(-2).join()+"));\n }";this.userCode="\n float getAChannel("+m.join()+") {\n return getChannel(getA("+h.join()+"),\n vec2("+h.slice(-2).join()+"));\n }\n "+w+"\n void main() {\n "+l+" coords = getOutputCoords();\n bool hasNextCol = "+p[c-1]+" < "+(u[c-1]-1)+";\n bool hasNextRow = "+p[c-2]+" < "+(u[c-2]-1)+";\n "+i+"\n ivec4 srcIdx = ivec4(sourceLocR"+f+", sourceLocG"+f+",\n sourceLocB"+f+", sourceLocA"+f+") * "+t+";\n ivec4 inIdx = srcIdx;\n vec4 bestIndex = vec4(inIdx);\n vec4 bestValue = "+b+";\n\n for (int i = 0; i < "+t+"; i++) {\n inIdx = srcIdx;\n "+T+"\n vec4 candidate = "+b+";\n bvec4 nan = isnan(candidate);\n bvec4 replace = bvec4(\n vec4("+E+"(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n bestValue = vec4(replace.x ? candidate.x : bestValue.x,\n replace.y ? candidate.y : bestValue.y,\n replace.z ? candidate.z : bestValue.z,\n replace.w ? candidate.w : bestValue.w);\n bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n srcIdx++;\n }\n setOutput(bestIndex);\n }\n ";}}(),AvgPool2DBackpropProgram=function(){return function(e){this.variableNames=["dy"],this.outputShape=e.inShape;var t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,o=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,s=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=s-1-e.padInfo.top,l=u-1-e.padInfo.left,p=1/(t*n);this.userCode="\n const ivec2 pads = ivec2("+c+", "+l+");\n const float avgMultiplier = float("+p+");\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < "+s+";\n wR += "+a+") {\n float dyR = float(dyRCorner + wR) / "+r+".0;\n\n if (dyR < 0.0 || dyR >= "+e.outHeight+".0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < "+u+";\n wC+= "+i+") {\n float dyC = float(dyCCorner + wC) / "+o+".0;\n\n if (dyC < 0.0 || dyC >= "+e.outWidth+".0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n setOutput(dotProd);\n }\n ";}}(),BatchNormProgram=function(){return function(e,t,n,r,o,a){this.outputShape=[],this.variableNames=["x","mean","variance"],assertAndGetBroadcastShape(e,t),assertAndGetBroadcastShape(e,n);var i="0.0";null!=r&&(assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");var s="1.0";null!=o&&(assertAndGetBroadcastShape(e,o),this.variableNames.push("scale"),s="getScaleAtOutCoords()"),this.outputShape=e,this.userCode="\n void main() {\n float x = getXAtOutCoords();\n float mean = getMeanAtOutCoords();\n float variance = getVarianceAtOutCoords();\n float offset = "+i+";\n float scale = "+s+";\n float inv = scale * inversesqrt(variance + float("+a+"));\n setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n }\n ";}}(),BatchNormPackedProgram=function(){return function(e,t,n,r,o,a){this.usesPackedTextures=!0,this.variableNames=["x","mean","variance"],assertAndGetBroadcastShape(e,t),assertAndGetBroadcastShape(e,n);var i="vec4(0.0)";null!=r&&(assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");var s="vec4(1.0)";null!=o&&(assertAndGetBroadcastShape(e,o),this.variableNames.push("scale"),s="getScaleAtOutCoords()"),this.outputShape=e,this.userCode="\n void main() {\n vec4 offset = "+i+";\n vec4 scale = "+s+";\n\n vec4 x = getXAtOutCoords();\n vec4 mean = getMeanAtOutCoords();\n vec4 variance = getVarianceAtOutCoords();\n\n vec4 inv = scale * inversesqrt(variance + vec4("+a+"));\n\n setOutput((x - mean) * inv + offset);\n }\n ";}}(),COMPLEX_MULTIPLY={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},BinaryOpComplexProgram=function(){return function(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=assertAndGetBroadcastShape(t,n),this.userCode="\n float binaryOpComplex(\n float areal, float aimag, float breal, float bimag) {\n "+e+"\n }\n\n void main() {\n float areal = getARealAtOutCoords();\n float aimag = getAImagAtOutCoords();\n float breal = getBRealAtOutCoords();\n float bimag = getBImagAtOutCoords();\n setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n }\n ";}}(),CHECK_NAN_SNIPPET="\n if (isnan(a)) return a;\n if (isnan(b)) return b;\n",ADD="return a + b;",SUB="return a - b;",MUL="return a * b;",DIV="if (a == b) return 1.0;\n return a / b;",INT_DIV="\n float resultSign = sign(a) * sign(b);\n int ia = round(a);\n int ib = round(b);\n int result = ia / ib;\n int amodb = ia - ib * result;\n\n if (resultSign < 0.0 && amodb != 0) {\n result -= 1;\n }\n return float(result);\n",POW="\nif(a < 0.0 && floor(b) < b){\n return NAN;\n}\nreturn (round(mod(b, 2.0)) != 1) ?\n pow(abs(a), b) : sign(a) * pow(abs(a), b);\n",SQUARED_DIFFERENCE="return (a - b) * (a - b);",EQUAL="return float(a == b);",NOT_EQUAL="return float(a != b);",LESS="return float(a < b);",LESS_EQUAL="return float(a <= b);",GREATER="return float(a > b);",GREATER_EQUAL="return float(a >= b);",LOGICAL_AND="return float(a >= 1.0 && b >= 1.0);",LOGICAL_OR="return float(a >= 1.0 || b >= 1.0);",MAX=CHECK_NAN_SNIPPET+"\n return max(a, b);\n",MIN=CHECK_NAN_SNIPPET+"\n return min(a, b);\n",MOD="if (b == 0.0) return NAN;\n return mod(a, b);",ATAN2=CHECK_NAN_SNIPPET+"\n return atan(a, b);\n",ELU_DER="return (b >= 1.0) ? a : a * (b + 1.0);",PRELU="return (a < 0.) ? b * a : a;",BinaryOpProgram=function(){return function(e,t,n){this.variableNames=["A","B"],this.outputShape=assertAndGetBroadcastShape(t,n),this.userCode="\n float binaryOperation(float a, float b) {\n "+e+"\n }\n\n void main() {\n float a = getAAtOutCoords();\n float b = getBAtOutCoords();\n setOutput(binaryOperation(a, b));\n }\n ";}}(),CHECK_NAN_SNIPPET$1="\n result.r = isNaN.r > 0. ? NAN : result.r;\n result.g = isNaN.g > 0. ? NAN : result.g;\n result.b = isNaN.b > 0. ? NAN : result.b;\n result.a = isNaN.a > 0. ? NAN : result.a;\n",INT_DIV$1="\n vec4 resultSign = sign(a) * sign(b);\n ivec4 ia = round(a);\n ivec4 ib = round(b);\n ivec4 result = ia / ib;\n ivec4 amodb = ia - ib * result;\n\n // Vectorize INT_DIV\n // if (resultSign < 0.0 && amodb != 0) result -= 1;\n // return float(result);\n return vec4(result -\n ivec4(lessThan(resultSign, vec4(0.0))) * ivec4(notEqual(amodb, ivec4(0))));\n",POW$1="\n // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n vec4 result = multiplier * pow(abs(a), b);\n\n vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));\n "+CHECK_NAN_SNIPPET$1+"\n return result;\n",PRELU$1="\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n",ELU_DER$1="\n vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n",ATAN2$1="\n vec4 result = atan(a, b);\n vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n "+CHECK_NAN_SNIPPET$1+"\n return result;\n",EQUAL$1="\n return vec4(equal(a, b));\n",NOT_EQUAL$1="\n return vec4(notEqual(a, b));\n",LESS$1="\n return vec4(lessThan(a, b));\n",LESS_EQUAL$1="\n return vec4(lessThanEqual(a, b));\n",GREATER$1="\n return vec4(greaterThan(a, b));\n",GREATER_EQUAL$1="\n return vec4(greaterThanEqual(a, b));\n",LOGICAL_AND$1="\n return vec4(\n vec4(greaterThanEqual(a, vec4(1.0))) *\n vec4(greaterThanEqual(b, vec4(1.0))));\n",LOGICAL_OR$1="\n return min(\n vec4(greaterThanEqual(a, vec4(1.0))) +\n vec4(greaterThanEqual(b, vec4(1.0))),\n vec4(1.0));\n",MAX$1="\n vec4 result = vec4(max(a, b));\n vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n "+CHECK_NAN_SNIPPET$1+"\n return result;\n",MIN$1="\n vec4 result = vec4(min(a, b));\n vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n "+CHECK_NAN_SNIPPET$1+"\n return result;\n",MOD$1="\n vec4 result = mod(a, b);\n vec4 isNaN = vec4(equal(b, vec4(0.0)));\n "+CHECK_NAN_SNIPPET$1+"\n return result;\n",BinaryOpPackedProgram=function(){return function(e,t,n){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.usesPackedTextures=!0,this.outputShape=assertAndGetBroadcastShape(t,n),this.userCode="\n vec4 binaryOperation(vec4 a, vec4 b) {\n "+e+"\n }\n\n void main() {\n vec4 a = getAAtOutCoords();\n vec4 b = getBAtOutCoords();\n setOutput(binaryOperation(a, b));\n }\n ";}}(),ClipProgram=function(){function e(e){this.variableNames=["A"],this.outputShape=e,this.userCode="\n uniform float min;\n uniform float max;\n\n void main() {\n float value = getAAtOutCoords();\n if (isnan(value)) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, min, max));\n }\n ";}return e.prototype.getCustomSetupFunc=function(e,t){var n=this;return function(r,o){null==n.minLoc&&(n.minLoc=r.getUniformLocationNoThrow(o,"min"),n.maxLoc=r.getUniformLocationNoThrow(o,"max")),r.gl.uniform1f(n.minLoc,e),r.gl.uniform1f(n.maxLoc,t);}},e}(),ClipPackedProgram=function(){function e(e){this.variableNames=["A"],this.usesPackedTextures=!0,this.outputShape=e,this.userCode="\n uniform float min;\n uniform float max;\n\n void main() {\n vec4 value = getAAtOutCoords();\n\n if (any(isnan(value))) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, vec4(min), vec4(max)));\n }\n ";}return e.prototype.getCustomSetupFunc=function(e,t){var n=this;return function(r,o){null==n.minLoc&&(n.minLoc=r.getUniformLocationNoThrow(o,"min"),n.maxLoc=r.getUniformLocationNoThrow(o,"max")),r.gl.uniform1f(n.minLoc,e),r.gl.uniform1f(n.maxLoc,t);}},e}(),ComplexAbsProgram=function(){return function(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode="\n void main() {\n float re = abs(getRealAtOutCoords());\n float im = abs(getImagAtOutCoords());\n float mx = max(re, im);\n\n // sadly the length function in glsl is not underflow-safe\n // (at least not on Intel GPUs). So the safe solution is\n // to ensure underflow-safety in all cases.\n setOutput(\n mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n );\n }\n ";}}(),ConcatProgram=function(){return function(e){this.outputShape=[],this.outputShape=computeOutShape(e,1),this.variableNames=e.map(function(e,t){return "T"+t});var t=new Array(e.length-1);t[0]=e[0][1];for(var n=1;n<t.length;n++)t[n]=t[n-1]+e[n][1];var r=["if (yC < "+t[0]+") setOutput(getT0(yR, yC));"];for(n=1;n<t.length;n++){var o=t[n-1];r.push("else if (yC < "+t[n]+") setOutput(getT"+n+"(yR, yC-"+o+"));");}var a=t.length,i=t[t.length-1];r.push("else setOutput(getT"+a+"(yR, yC-"+i+"));"),this.userCode="\n void main() {\n ivec2 coords = getOutputCoords();\n int yR = coords.x;\n int yC = coords.y;\n\n "+r.join("\n ")+"\n }\n ";}}(),ConcatPackedProgram=function(){return function(e,t){this.usesPackedTextures=!0,this.outputShape=[],this.outputShape=computeOutShape(e,t);var n=this.outputShape,r=n.length,o=getCoordsDataType(r),a=getChannels("coords",r),i=["x","y","z","w","u","v"].slice(0,r);this.variableNames=e.map(function(e,t){return "T"+t});var s=new Array(e.length-1);s[0]=e[0][t];for(var u=1;u<s.length;u++)s[u]=s[u-1]+e[u][t];var c=i[t],l="vec2("+i.slice(-2).join()+")",p=i.join(),d="if ("+c+" < "+s[0]+")\n return getChannel(getT0("+p+"), "+l+");";for(u=1;u<s.length;u++){var h=s[u-1];d+="\n else if ("+c+" < "+s[u]+") {\n "+c+" -= "+h+";\n return getChannel(getT"+u+"("+p+"), "+l+");\n }";}var f=s.length;d+="\n else {\n "+c+" -= "+s[s.length-1]+";\n return getChannel(getT"+f+"("+p+"), "+l+");\n }",this.userCode="\n float getValue("+i.map(function(e){return "int "+e})+") {\n "+d+"\n }\n\n void main() {\n "+o+" coords = getOutputCoords();\n vec4 result = vec4(getValue("+a+"), 0., 0., 0.);\n if (++"+a[r-1]+" < "+n[r-1]+") {\n result.g = getValue("+a+");\n }\n if (++"+a[r-2]+" < "+n[r-2]+") {\n result.a = getValue("+a+");\n }\n if ("+a[r-2]+" < "+n[r-2]+" &&\n --"+a[r-1]+" < "+n[r-1]+") {\n result.b = getValue("+a+");\n }\n setOutput(result);\n }\n ";}}(),Conv2DDerFilterProgram=function(){return function(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;var t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,o=e.padInfo.left;this.userCode="\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int d2 = coords.w;\n\n // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int b = 0; b < "+e.batchSize+"; b++) {\n for (int yR = 0; yR < "+e.outHeight+"; yR++) {\n int xR = wR + yR * "+t+" - "+r+";\n\n if (xR < 0 || xR >= "+e.inHeight+") {\n continue;\n }\n\n for (int yC = 0; yC < "+e.outWidth+"; yC++) {\n int xC = wC + yC * "+n+" - "+o+";\n\n if (xC < 0 || xC >= "+e.inWidth+") {\n continue;\n }\n\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n setOutput(dotProd);\n }\n ";}}(),Conv2DDerInputProgram=function(){return function(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;var t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,o=e.strideWidth,a=t-1-e.padInfo.top,i=n-1-e.padInfo.left;this.userCode="\n const ivec2 pads = ivec2("+a+", "+i+");\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n\n ivec2 dyCorner = coords.yz - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < "+t+"; wR++) {\n float dyR = float(dyRCorner + wR) / "+r+".0;\n\n if (dyR < 0.0 || dyR >= "+e.outHeight+".0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = "+t+" - 1 - wR;\n\n for (int wC = 0; wC < "+n+"; wC++) {\n float dyC = float(dyCCorner + wC) / "+o+".0;\n\n if (dyC < 0.0 || dyC >= "+e.outWidth+".0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = "+n+" - 1 - wC;\n\n for (int d2 = 0; d2 < "+e.outChannels+"; d2++) {\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n }\n }\n setOutput(dotProd);\n }\n ";}}(),Conv3DDerFilterProgram=function(){return function(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;var t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,o=e.padInfo.front,a=e.padInfo.top,i=e.padInfo.left;this.userCode="\n void main() {\n ivec5 coords = getOutputCoords();\n int wF = coords.x;\n int wR = coords.y;\n int wC = coords.z;\n int d1 = coords.w;\n int d2 = coords.u;\n\n float dotProd = 0.0;\n\n for (int b = 0; b < "+e.batchSize+"; b++) {\n for (int yF = 0; yF < "+e.outDepth+"; yF++) {\n int xF = wF + yF * "+t+" - "+o+";\n\n if (xF < 0 || xF >= "+e.inDepth+") {\n continue;\n }\n\n for (int yR = 0; yR < "+e.outHeight+"; yR++) {\n int xR = wR + yR * "+n+" - "+a+";\n\n if (xR < 0 || xR >= "+e.inHeight+") {\n continue;\n }\n\n for (int yC = 0; yC < "+e.outWidth+"; yC++) {\n int xC = wC + yC * "+r+" - "+i+";\n\n if (xC < 0 || xC >= "+e.inWidth+") {\n continue;\n }\n\n float dyValue = getDy(b, yF, yR, yC, d2);\n float xValue = getX(b, xF, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n ";}}(),Conv3DDerInputProgram=function(){return function(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;var t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,o=e.strideDepth,a=e.strideHeight,i=e.strideWidth,s=t-1-e.padInfo.front,u=n-1-e.padInfo.top,c=r-1-e.padInfo.left;this.userCode="\n const ivec3 pads = ivec3("+s+", "+u+", "+c+");\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.u;\n\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyFCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n float dotProd = 0.0;\n for (int wF = 0; wF < "+t+"; wF++) {\n float dyF = float(dyFCorner + wF) / "+o+".0;\n\n if (dyF < 0.0 || dyF >= "+e.outDepth+".0 || fract(dyF) > 0.0) {\n continue;\n }\n int idyF = int(dyF);\n\n int wFPerm = "+t+" - 1 - wF;\n\n for (int wR = 0; wR < "+n+"; wR++) {\n float dyR = float(dyRCorner + wR) / "+a+".0;\n\n if (dyR < 0.0 || dyR >= "+e.outHeight+".0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = "+n+" - 1 - wR;\n\n for (int wC = 0; wC < "+r+"; wC++) {\n float dyC = float(dyCCorner + wC) / "+i+".0;\n\n if (dyC < 0.0 || dyC >= "+e.outWidth+".0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = "+r+" - 1 - wC;\n\n for (int d2 = 0; d2 < "+e.outChannels+"; d2++) {\n float xValue = getDy(batch, idyF, idyR, idyC, d2);\n float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n }\n }\n }\n setOutput(dotProd);\n }\n ";}}(),DepthwiseConv2DDerFilterProgram=function(){return function(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;var t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,o=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode="\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int dm = coords.w;\n int d2 = d1 * "+a+" + dm;\n\n float dotProd = 0.0;\n\n // TODO: Vec4 over the batch size\n for (int b = 0; b < "+e.batchSize+"; b++) {\n for (int yR = 0; yR < "+e.outHeight+"; yR++) {\n int xR = wR + yR * "+t+" - "+r+";\n\n if (xR < 0 || xR >= "+e.inHeight+") {\n continue;\n }\n\n for (int yC = 0; yC < "+e.outWidth+"; yC++) {\n int xC = wC + yC * "+n+" - "+o+";\n\n if (xC < 0 || xC >= "+e.inWidth+") {\n continue;\n }\n\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n setOutput(dotProd);\n }\n ";}}(),DepthwiseConv2DDerInputProgram=function(){return function(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;var t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,o=e.strideWidth,a=t-1-e.padInfo.top,i=n-1-e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode="\n const ivec2 pads = ivec2("+a+", "+i+");\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n ivec2 dyCorner = coords.yz - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n float dotProd = 0.0;\n\n for (int wR = 0; wR < "+t+"; wR++) {\n float dyR = float(dyRCorner + wR) / "+r+".0;\n\n if (dyR < 0.0 || dyR >= "+e.outHeight+".0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = "+t+" - 1 - wR;\n\n for (int wC = 0; wC < "+n+"; wC++) {\n float dyC = float(dyCCorner + wC) / "+o+".0;\n\n if (dyC < 0.0 || dyC >= "+e.outWidth+".0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = "+n+" - 1 - wC;\n\n // TODO: Vec4 over the channelMul\n for (int dm = 0; dm < "+s+"; dm++) {\n int d2 = d1 * "+s+" + dm;\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, dm);\n dotProd += xValue * wValue;\n }\n }\n }\n setOutput(dotProd);\n }\n ";}}(),Conv2DProgram=function(){return function(e){this.variableNames=["x","W"],this.outputShape=e.outShape;var t=e.padInfo.top,n=e.padInfo.left,r=e.strideHeight,o=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,s=e.filterHeight,u=e.filterWidth,c=4*Math.floor(e.inChannels/4),l=e.inChannels%4;this.userCode="\n const ivec2 strides = ivec2("+r+", "+o+");\n const ivec2 pads = ivec2("+t+", "+n+");\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d2 = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < "+s+"; wR++) {\n int xR = xRCorner + wR * "+a+";\n\n if (xR < 0 || xR >= "+e.inHeight+") {\n continue;\n }\n\n for (int wC = 0; wC < "+u+"; wC++) {\n int xC = xCCorner + wC * "+i+";\n\n if (xC < 0 || xC >= "+e.inWidth+") {\n continue;\n }\n\n for (int d1 = 0; d1 < "+c+"; d1 += 4) {\n vec4 xValues = vec4(\n getX(batch, xR, xC, d1),\n getX(batch, xR, xC, d1 + 1),\n getX(batch, xR, xC, d1 + 2),\n getX(batch, xR, xC, d1 + 3)\n );\n vec4 wValues = vec4(\n getW(wR, wC, d1, d2),\n getW(wR, wC, d1 + 1, d2),\n getW(wR, wC, d1 + 2, d2),\n getW(wR, wC, d1 + 3, d2)\n );\n\n dotProd += dot(xValues, wValues);\n }\n\n if ("+(1===l)+") {\n dotProd +=\n getX(batch, xR, xC, "+c+") *\n getW(wR, wC, "+c+", d2);\n } else if ("+(2===l)+") {\n vec2 xValues = vec2(\n getX(batch, xR, xC, "+c+"),\n getX(batch, xR, xC, "+c+" + 1)\n );\n vec2 wValues = vec2(\n getW(wR, wC, "+c+", d2),\n getW(wR, wC, "+c+" + 1, d2)\n );\n dotProd += dot(xValues, wValues);\n } else if ("+(3===l)+") {\n vec3 xValues = vec3(\n getX(batch, xR, xC, "+c+"),\n getX(batch, xR, xC, "+c+" + 1),\n getX(batch, xR, xC, "+c+" + 2)\n );\n vec3 wValues = vec3(\n getW(wR, wC, "+c+", d2),\n getW(wR, wC, "+c+" + 1, d2),\n getW(wR, wC, "+c+" + 2, d2)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n }\n setOutput(dotProd);\n }\n ";}}(),Conv3DProgram=function(){return function(e){this.variableNames=["x","W"],this.outputShape=e.outShape;var t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,o=e.strideDepth,a=e.strideHeight,i=e.strideWidth,s=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,l=e.filterDepth,p=e.filterHeight,d=e.filterWidth,h=4*Math.floor(e.inChannels/4),f=e.inChannels%4;this.userCode="\n const ivec3 strides = ivec3("+o+", "+a+", "+i+");\n const ivec3 pads = ivec3("+t+", "+n+", "+r+");\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d2 = coords.u;\n\n ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xFCorner = xFRCCorner.x;\n int xRCorner = xFRCCorner.y;\n int xCCorner = xFRCCorner.z;\n\n // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n // y(yF, yR, yC, d2). ? = to be determined. : = across all\n // values in that axis.\n float dotProd = 0.0;\n for (int wF = 0; wF < "+l+"; wF++) {\n int xF = xFCorner + wF * "+s+";\n\n if (xF < 0 || xF >= "+e.inDepth+") {\n continue;\n }\n\n for (int wR = 0; wR < "+p+"; wR++) {\n int xR = xRCorner + wR * "+u+";\n\n if (xR < 0 || xR >= "+e.inHeight+") {\n continue;\n }\n\n for (int wC = 0; wC < "+d+"; wC++) {\n int xC = xCCorner + wC * "+c+";\n\n if (xC < 0 || xC >= "+e.inWidth+") {\n continue;\n }\n\n for (int d1 = 0; d1 < "+h+"; d1 += 4) {\n vec4 xValues = vec4(\n getX(batch, xF, xR, xC, d1),\n getX(batch, xF, xR, xC, d1 + 1),\n getX(batch, xF, xR, xC, d1 + 2),\n getX(batch, xF, xR, xC, d1 + 3)\n );\n vec4 wValues = vec4(\n getW(wF, wR, wC, d1, d2),\n getW(wF, wR, wC, d1 + 1, d2),\n getW(wF, wR, wC, d1 + 2, d2),\n getW(wF, wR, wC, d1 + 3, d2)\n );\n\n dotProd += dot(xValues, wValues);\n }\n\n if ("+(1===f)+") {\n dotProd +=\n getX(batch, xF, xR, xC, "+h+") *\n getW(wF, wR, wC, "+h+", d2);\n } else if ("+(2===f)+") {\n vec2 xValues = vec2(\n getX(batch, xF, xR, xC, "+h+"),\n getX(batch, xF, xR, xC, "+h+" + 1)\n );\n vec2 wValues = vec2(\n getW(wF, wR, wC, "+h+", d2),\n getW(wF, wR, wC, "+h+" + 1, d2)\n );\n dotProd += dot(xValues, wValues);\n } else if ("+(3===f)+") {\n vec3 xValues = vec3(\n getX(batch, xF, xR, xC, "+h+"),\n getX(batch, xF, xR, xC, "+h+" + 1),\n getX(batch, xF, xR, xC, "+h+" + 2)\n );\n vec3 wValues = vec3(\n getW(wF, wR, wC, "+h+", d2),\n getW(wF, wR, wC, "+h+" + 1, d2),\n getW(wF, wR, wC, "+h+" + 2, d2)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n ";}}(),DepthwiseConv2DProgram=function(){return function(e){this.variableNames=["x","W"],this.outputShape=e.outShape;var t=e.inHeight,n=e.inWidth,r=e.padInfo.top,o=e.padInfo.left,a=e.strideHeight,i=e.strideWidth,s=e.dilationHeight,u=e.dilationWidth,c=e.filterHeight,l=e.filterWidth,p=e.outChannels/e.inChannels;this.userCode="\n const ivec2 strides = ivec2("+a+", "+i+");\n const ivec2 pads = ivec2("+r+", "+o+");\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n ivec2 xRCCorner = coords.yz * strides - pads;\n int d2 = coords.w;\n int d1 = d2 / "+p+";\n int q = d2 - d1 * "+p+";\n\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n // TODO(dsmilkov): Flatten the two for loops and vec4 the operations.\n for (int wR = 0; wR < "+c+"; wR++) {\n int xR = xRCorner + wR * "+s+";\n\n if (xR < 0 || xR >= "+t+") {\n continue;\n }\n\n for (int wC = 0; wC < "+l+"; wC++) {\n int xC = xCCorner + wC * "+u+";\n\n if (xC < 0 || xC >= "+n+") {\n continue;\n }\n\n float xVal = getX(batch, xR, xC, d1);\n float wVal = getW(wR, wC, d1, q);\n dotProd += xVal * wVal;\n }\n }\n setOutput(dotProd);\n }\n ";}}(),DepthwiseConvPacked2DProgram=function(){return function(e){this.variableNames=["x","W"],this.usesPackedTextures=!0,this.outputShape=e.outShape;for(var t=e.inHeight,n=e.inWidth,r=e.padInfo.top,o=e.padInfo.left,a=e.strideHeight,i=e.strideWidth,s=e.dilationHeight,u=e.dilationWidth,c=e.filterHeight,l=e.filterWidth,p=l,d="int xR; int xC; int xCOffset;",h=0;h<c;h++)for(var f=0;f<l;f++)d+="\n vec4 xTexelR"+h+"C"+2*f+" = vec4(0.);\n vec4 wR"+h+"C"+f+" = vec4(0.);\n vec4 xR"+h+"C"+f+" = vec4(0.);";for(h=0;h<c;h++)for(var m=0;m<p;m++){if(d+="\n xR = xRCorner + "+h*s+";\n xC = xCCorner + "+(f=2*m)*u+";\n ",1===i){if(f<l&&(d+=o%2==1?"\n xCOffset = xC + 1;\n if(xR >= 0 && xR < "+t+" && xCOffset >= 0 && xCOffset < "+n+") {\n xTexelR"+h+"C"+f+" = getX(batch, xR, xCOffset, d1);\n } else {\n xTexelR"+h+"C"+f+" = vec4(0.);\n }\n\n xCOffset = xC + 1 - 2;\n if(xR >= 0 && xR < "+t+" && xCOffset >= 0 && xCOffset < "+n+") {\n vec4 previous = getX(batch, xR, xCOffset, d1);\n xR"+h+"C"+f+" = vec4(previous.zw, xTexelR"+h+"C"+f+".xy);\n } else {\n xR"+h+"C"+f+" = vec4(0, 0, xTexelR"+h+"C"+f+".xy);\n }\n ":"\n if(xR >= 0 && xR < "+t+" && xC >= 0 && xC < "+n+") {\n xTexelR"+h+"C"+f+" = getX(batch, xR, xC, d1);\n } else {\n xTexelR"+h+"C"+f+" = vec4(0.);\n }\n\n xR"+h+"C"+f+" = xTexelR"+h+"C"+f+";\n ",f+1<l)){var g=o%2==0?nearestLargerEven(u):u;u%2==0&&o%2==1||u%2!=0&&o%2!=1?(d+="\n xCOffset = xC + "+o%2+" + "+g+";\n\n if(xR >= 0 && xR < "+t+" &&\n xCOffset >= 0 && xCOffset < "+n+") {\n xTexelR"+h+"C"+(f+2)+" = getX(batch, xR, xCOffset, d1);\n }\n ",u>1&&(d+="\n xCOffset -= 2;\n if(xR >= 0 && xR < "+t+" &&\n xCOffset >= 0 && xCOffset < "+n+") {\n xTexelR"+h+"C"+f+" = getX(batch, xR, xCOffset, d1);\n } else {\n xTexelR"+h+"C"+f+" = vec4(0.);\n }\n "),d+="\n xR"+h+"C"+(f+1)+" = vec4(\n xTexelR"+h+"C"+f+".zw, xTexelR"+h+"C"+(f+2)+".xy);\n "):d+="\n xCOffset = xC + "+g+";\n\n if(xR >= 0 && xR < "+t+" &&\n xCOffset >= 0 && xCOffset < "+n+") {\n xTexelR"+h+"C"+(f+2)+" = getX(batch, xR, xCOffset, d1);\n }\n\n xR"+h+"C"+(f+1)+" = xTexelR"+h+"C"+(f+2)+";\n ";}}else f<l&&(d+="\n if(xR >= 0 && xR < "+t+") {\n ",o%2==1?(d+="\n xCOffset = xC + 1 - "+i+";\n if(xCOffset >= 0 && xCOffset < "+n+") {\n xTexelR"+h+"C"+f+" = getX(batch, xR, xCOffset, d1);\n } else {\n xTexelR"+h+"C"+f+" = vec4(0.);\n }\n\n if(xC + 1 >= 0 && xC + 1 < "+n+") {\n xTexelR"+h+"C"+(f+2)+" = getX(batch, xR, xC + 1, d1);\n } else {\n xTexelR"+h+"C"+(f+2)+" = vec4(0.);\n }\n\n xR"+h+"C"+f+" = vec4(\n xTexelR"+h+"C"+f+".zw, xTexelR"+h+"C"+(f+2)+".zw);\n ",f+1<l&&(d+="\n vec4 final = vec4(0.);\n xCOffset = xC + 1 + "+i+";\n if(xCOffset >= 0 && xCOffset < "+n+") {\n final = getX(batch, xR, xCOffset, d1);\n }\n xR"+h+"C"+(f+1)+" = vec4(xTexelR"+h+"C"+(f+2)+".xy, final.xy);\n ")):(d+="\n if(xC >= 0 && xC < "+n+") {\n xTexelR"+h+"C"+f+" = getX(batch, xR, xC, d1);\n } else {\n xTexelR"+h+"C"+f+" = vec4(0.);\n }\n\n xCOffset = xC + "+i+";\n if(xCOffset >= 0 && xCOffset < "+n+") {\n xTexelR"+h+"C"+(f+2)+" = getX(batch, xR, xCOffset, d1);\n } else {\n xTexelR"+h+"C"+(f+2)+" = vec4(0.);\n }\n\n xR"+h+"C"+f+" = vec4(\n xTexelR"+h+"C"+f+".xy, xTexelR"+h+"C"+(f+2)+".xy);\n ",f+1<l&&(d+="\n xR"+h+"C"+(f+1)+" = vec4(\n xTexelR"+h+"C"+f+".zw, xTexelR"+h+"C"+(f+2)+".zw);\n ")),d+="}");f<l&&(d+="\n vec4 wTexelR"+h+"C"+f+" = getW("+h+", "+f+", d1, q);\n wR"+h+"C"+f+" = vec4(wTexelR"+h+"C"+f+".xz, wTexelR"+h+"C"+f+".xz);\n ",f+1<l&&(d+="\n vec4 wTexelR"+h+"C"+(f+1)+" = getW("+h+", "+(f+1)+", d1, q);\n wR"+h+"C"+(f+1)+" =\n vec4(wTexelR"+h+"C"+(f+1)+".xz, wTexelR"+h+"C"+(f+1)+".xz);"));}for(h=0;h<c;h++)for(f=0;f<l;f++)d+="result += xR"+h+"C"+f+" * wR"+h+"C"+f+";";this.userCode="\n const ivec2 strides = ivec2("+a+", "+i+");\n const ivec2 pads = ivec2("+r+", "+o+");\n\n void main() {\n\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n ivec2 xRCCorner = coords.yz * strides - pads;\n int d2 = coords.w;\n int d1 = d2;\n int q = 0;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n vec4 result = vec4(0.);\n\n "+d+"\n\n setOutput(result);\n }\n ";}}(),CropAndResizeProgram=function(){return function(e,t,n,r,o){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];var a=e[0],i=e[1],s=e[2],u=e[3],c=t[0],l=n[0],p=n[1];this.outputShape=[c,l,p,u];var d="bilinear"===r?1:0,h=[i-1+".0",s-1+".0"],f=h[0],m=h[1],g=l>1?[""+(i-1)/(l-1),"(y2-y1) * height_ratio","y1*"+f+" + float(y)*(height_scale)"]:["0.0","0.0","0.5 * (y1+y2) * "+f],v=g[0],y=g[1],x=g[2],E=p>1?[""+(s-1)/(p-1),"(x2-x1) * width_ratio","x1*"+m+" + float(x)*(width_scale)"]:["0.0","0.0","0.5 * (x1+x2) * "+m],T=E[0],b=E[1],w=E[2];this.userCode="\n const float height_ratio = float("+v+");\n const float width_ratio = float("+T+");\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int y = coords[1];\n int x = coords[2];\n int d = coords[3];\n\n // get box vals\n float y1 = getBoxes(b,0);\n float x1 = getBoxes(b,1);\n float y2 = getBoxes(b,2);\n float x2 = getBoxes(b,3);\n\n // get image in batch index\n int bInd = round(getBoxInd(b));\n if(bInd < 0 || bInd >= "+a+") {\n return;\n }\n\n float height_scale = "+y+";\n float width_scale = "+b+";\n\n float in_y = "+x+";\n if( in_y < 0.0 || in_y > "+f+" ) {\n setOutput(float("+o+"));\n return;\n }\n float in_x = "+w+";\n if( in_x < 0.0 || in_x > "+m+" ) {\n setOutput(float("+o+"));\n return;\n }\n\n vec2 sourceFracIndexCR = vec2(in_x,in_y);\n if("+d+" == 1) {\n // Compute the four integer indices.\n ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n float top = topLeft + (topRight - topLeft) * fracCR.x;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n float newValue = top + (bottom - top) * fracCR.y;\n setOutput(newValue);\n } else {\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestCR = ivec2(floor(\n sourceFracIndexCR + vec2(0.5,0.5)));\n float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n setOutput(newValue);\n }\n }\n ";}}(),CumSumProgram=function(){return function(e,t,n){this.variableNames=["x"],this.outputShape=e;var r=e.length,o=e[e.length-1],a=n?"<":">";this.userCode="\n int getIndex(int i) {\n "+(n?"return "+o+" -i - 1;":"return i;")+"\n }\n\n void main() {\n "+getCoordsDataType(r)+" coords = getOutputCoords();\n int end = "+getFinalCoord(r,"coords")+";\n float val = 0.0;\n for (int i = "+o+" - 1; i >= 0; i -= 1) {\n int idx = getIndex(i);\n if (idx "+a+" end) {\n continue;\n }\n if (idx == end && "+t+") {\n continue;\n }\n "+getFinalCoord(r,"coords")+" = idx;\n val += getX("+getCoords(r,"coords")+");\n }\n setOutput(val);\n }\n ";}}();function getCoords(e,t){if(1===e)return ""+t;if(2===e)return t+".x, "+t+".y";if(3===e)return t+".x, "+t+".y, "+t+".z";if(4===e)return t+".x, "+t+".y, "+t+".z, "+t+".w";throw Error("Cumulative sum for rank "+e+" is not yet supported")}function getFinalCoord(e,t){if(1===e)return ""+t;if(2===e)return t+".y";if(3===e)return t+".z";if(4===e)return t+".w";throw Error("Cumulative sum for rank "+e+" is not yet supported")}var DepthToSpaceProgram=function(){function e(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode="\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int h = "+this.getHeightCoordString()+";\n int w = "+this.getWidthCoordString()+";\n int d = "+this.getDepthCoordString()+";\n\n int in_h = h / "+t+";\n int offset_h = imod(h, "+t+");\n int in_w = w / "+t+";\n int offset_w = imod(w, "+t+");\n int offset_d = (offset_h * "+t+" + offset_w) *\n "+this.getOutputDepthSize()+";\n int in_d = d + offset_d;\n\n float result = "+this.getInputSamplingString()+";\n setOutput(result);\n }\n ";}return e.prototype.getHeightCoordString=function(){return "NHWC"===this.dataFormat?"coords[1]":"coords[2]"},e.prototype.getWidthCoordString=function(){return "NHWC"===this.dataFormat?"coords[2]":"coords[3]"},e.prototype.getDepthCoordString=function(){return "NHWC"===this.dataFormat?"coords[3]":"coords[1]"},e.prototype.getOutputDepthSize=function(){return "NHWC"===this.dataFormat?this.outputShape[3]:this.outputShape[1]},e.prototype.getInputSamplingString=function(){return "NHWC"===this.dataFormat?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"},e}(),EncodeFloatProgram=function(){return function(e){this.variableNames=["A"];var t=getGlslDifferences();this.outputShape=e,this.userCode="\n const float FLOAT_MAX = 1.70141184e38;\n const float FLOAT_MIN = 1.17549435e-38;\n\n lowp vec4 encode_float(highp float v) {\n if (isnan(v)) {\n return vec4(255, 255, 255, 255);\n }\n\n highp float av = abs(v);\n\n if(av < FLOAT_MIN) {\n return vec4(0.0, 0.0, 0.0, 0.0);\n } else if(v > FLOAT_MAX) {\n return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n } else if(v < -FLOAT_MAX) {\n return vec4(0.0, 0.0, 128.0, 255.0) / 255.0;\n }\n\n highp vec4 c = vec4(0,0,0,0);\n\n highp float e = floor(log2(av));\n highp float m = exp2(fract(log2(av))) - 1.0;\n\n c[2] = floor(128.0 * m);\n m -= c[2] / 128.0;\n c[1] = floor(32768.0 * m);\n m -= c[1] / 32768.0;\n c[0] = floor(8388608.0 * m);\n\n highp float ebias = e + 127.0;\n c[3] = floor(ebias / 2.0);\n ebias -= c[3] * 2.0;\n c[2] += floor(ebias) * 128.0;\n\n c[3] += 128.0 * step(0.0, -v);\n\n return c / 255.0;\n }\n\n void main() {\n float x = getAAtOutCoords();\n "+t.output+" = encode_float(x);\n }\n ";}}(),COMPLEX_FFT={REAL:"return real * expR - imag * expI;",IMAG:"return real * expI + imag * expR;"},FFTProgram=function(){return function(e,t,n){this.variableNames=["real","imag"];var r=t[1];this.outputShape=t;var o=n?"2.0 * "+Math.PI:"-2.0 * "+Math.PI,a=n?r+".0":"1.0";this.userCode="\n const float exponentMultiplier = "+o+";\n\n float unaryOpComplex(float real, float expR, float imag, float expI) {\n "+e+"\n }\n\n float mulMatDFT(int batch, int index) {\n float indexRatio = float(index) / float("+r+");\n float exponentMultiplierTimesIndexRatio =\n exponentMultiplier * indexRatio;\n\n float result = 0.0;\n\n for (int i = 0; i < "+r+"; i++) {\n // x = (-2|2 * PI / N) * index * i;\n float x = exponentMultiplierTimesIndexRatio * float(i);\n float expR = cos(x);\n float expI = sin(x);\n float real = getReal(batch, i);\n float imag = getImag(batch, i);\n\n result +=\n unaryOpComplex(real, expR, imag, expI) / "+a+";\n }\n\n return result;\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n setOutput(mulMatDFT(coords[0], coords[1]));\n }\n ";}}(),FillProgram=function(){function e(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode="\n uniform float value;\n void main() {\n // Input can be obtained from uniform value.\n setOutput(value);\n }\n ";}return e.prototype.getCustomSetupFunc=function(e){var t=this;return function(n,r){null==t.valueLoc&&(t.valueLoc=n.getUniformLocationNoThrow(r,"value")),n.gl.uniform1f(t.valueLoc,e);}},e}(),FromPixelsProgram=function(){return function(e){this.variableNames=["A"];var t=getGlslDifferences(),n=e[0],r=e[1];this.outputShape=e,this.userCode="\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n vec2 uv = (vec2(texC, texR) + halfCR) / vec2("+r+".0, "+n+".0);\n\n vec4 values = "+t.texture2D+"(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n setOutput(floor(value * 255.0 + 0.5));\n }\n ";}}(),GatherProgram=function(){return function(e,t,n){this.variableNames=["A","indices"];var r=e.slice();r[n]=t,this.outputShape=r,this.rank=r.length;var o=getCoordsDataType(this.rank),a=getSourceCoords$1(e,n);this.userCode="\n void main() {\n "+o+" resRC = getOutputCoords();\n setOutput(getA("+a+"));\n }\n ";}}();function getSourceCoords$1(e,t){var n=e.length;if(n>4)throw Error("Gather for rank "+n+" is not yet supported");if(1===n)return "int(getIndices(resRC))";for(var r=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],a=0;a<e.length;a++)a===t?o.push("int(getIndices("+r[a]+"))"):o.push(""+r[a]);return o.join()}var TextureUsage,PhysicalTextureType,GatherNDProgram=function(){return function(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;var r=getCoordsDataType(t.length),o=getCoordsDataType(n.length),a=this.sliceDim>1?"strides[j]":"strides";this.userCode="\n "+r+" strides = "+r+"("+this.strides+");\n void main() {\n "+o+" coords = getOutputCoords();\n int flattenIndex = 0;\n for (int j = 0; j < "+this.sliceDim+"; j++) {\n int index = round(getIndices(coords[0], j));\n flattenIndex += index * "+a+";\n }\n setOutput(getX(flattenIndex, coords[1]));\n }\n ";}}();function getUnpackedMatrixTextureShapeWidthHeight(e,t){return [t,e]}function getUnpackedArraySizeFromMatrixSize(e,t){return e*t}function getMatrixSizeFromUnpackedArraySize(e,t){if(e%t!=0)throw new Error("unpackedSize ("+e+") must be a multiple of "+t);return e/t}function encodeMatrixToUnpackedArray(e,t,n){var r=getUnpackedArraySizeFromMatrixSize(e.length,n);if(t.length<r)throw new Error("unpackedArray length ("+t.length+") must be >= "+r);for(var o=0,a=0;a<e.length;++a)t[o]=e[a],o+=n;}function decodeMatrixFromUnpackedArray(e,t,n){var r=getMatrixSizeFromUnpackedArraySize(e.length,n);if(t.length<r)throw new Error("matrix length ("+t.length+") must be >= "+r);for(var o=0,a=0;a<e.length;a+=n)t[o++]=e[a];}function getPackedMatrixTextureShapeWidthHeight(e,t){return [Math.max(1,Math.ceil(t/2)),Math.max(1,Math.ceil(e/2))]}function getPackedRGBAArraySizeFromMatrixShape(e,t){var n=getPackedMatrixTextureShapeWidthHeight(e,t);return n[0]*n[1]*4}function encodeMatrixToPackedRGBA(e,t,n,r,o){for(var a=r%2==1,i=n%2==1,s=Math.floor(r/2),u=Math.floor(n/2),c=Math.ceil(r/2),l=c*Math.ceil(n/2),p=nearestLargerEven(n)*nearestLargerEven(r),d=0;d<t;d++){for(var h=d*n*r,f=d*p,m=a?4:0,g=r,v=f,y=0;y<u;++y){for(var x=2*y*r,E=0;E<s;++E){var T=h+x+2*E;o[v]=e[T],o[v+1]=e[T+1],o[v+2]=e[T+g],o[v+3]=e[T+g+1],v+=4;}v+=m;}if(a){T=h+r-1,v=f+4*(c-1);var b=2*r;for(m=4*c,y=0;y<u;++y)o[v]=e[T],o[v+2]=e[T+r],T+=b,v+=m;}if(i){for(T=h+(n-1)*r,v=f+4*(l-c),E=0;E<s;++E)o[v++]=e[T++],o[v++]=e[T++],v+=2;a&&i&&(o[f+p-4]=e[T]);}}return o}function decodeMatrixFromPackedRGBA(e,t,n,r,o){var a=n*r;if(o.length<a)throw new Error("matrix length ("+o.length+") must be >= "+a);for(var i=r%2==1,s=n%2==1,u=Math.floor(r/2),c=Math.floor(n/2),l=Math.ceil(r/2),p=l*Math.ceil(n/2),d=nearestLargerEven(n)*nearestLargerEven(r),h=0;h<t;h++){for(var f=h*n*r,m=h*d,g=i?4:0,v=r+(i?1:0),y=m,x=f,E=f+r,T=0;T<c;++T){for(var b=0;b<u;++b)o[x++]=e[y++],o[x++]=e[y++],o[E++]=e[y++],o[E++]=e[y++];y+=g,x+=v,E+=v;}if(i){y=m+4*(l-1);var w=f+r-1;for(g=4*l,v=2*r,T=0;T<c;++T)o[w]=e[y],o[w+r]=e[y+2],y+=g,w+=v;}if(s){for(y=m+4*(p-l),w=f+(n-1)*r,b=0;b<u;++b)o[w++]=e[y++],o[w++]=e[y++],y+=2;i&&(o[f+n*r-1]=e[y]);}}return o}function callAndCheck(e,t,n){var r=n();return t&&checkWebGLError(e),r}function checkWebGLError(e){var t=e.getError();if(t!==e.NO_ERROR)throw new Error("WebGL Error: "+getWebGLErrorMessage(e,t))}!function(e){e[e.RENDER=0]="RENDER",e[e.UPLOAD=1]="UPLOAD",e[e.PIXELS=2]="PIXELS",e[e.DOWNLOAD=3]="DOWNLOAD";}(TextureUsage||(TextureUsage={})),function(e){e[e.UNPACKED_FLOAT16=0]="UNPACKED_FLOAT16",e[e.UNPACKED_FLOAT32=1]="UNPACKED_FLOAT32",e[e.PACKED_4X1_UNSIGNED_BYTE=2]="PACKED_4X1_UNSIGNED_BYTE",e[e.PACKED_2X2_FLOAT32=3]="PACKED_2X2_FLOAT32",e[e.PACKED_2X2_FLOAT16=4]="PACKED_2X2_FLOAT16";}(PhysicalTextureType||(PhysicalTextureType={}));var MIN_FLOAT16=5.96e-8,MAX_FLOAT16=65504;function canBeRepresented(e){return !!(ENV.get("WEBGL_RENDER_FLOAT32_ENABLED")||0===e||MIN_FLOAT16<Math.abs(e)&&Math.abs(e)<MAX_FLOAT16)}function getWebGLErrorMessage(e,t){switch(t){case e.NO_ERROR:return "NO_ERROR";case e.INVALID_ENUM:return "INVALID_ENUM";case e.INVALID_VALUE:return "INVALID_VALUE";case e.INVALID_OPERATION:return "INVALID_OPERATION";case e.INVALID_FRAMEBUFFER_OPERATION:return "INVALID_FRAMEBUFFER_OPERATION";case e.OUT_OF_MEMORY:return "OUT_OF_MEMORY";case e.CONTEXT_LOST_WEBGL:return "CONTEXT_LOST_WEBGL";default:return "Unknown error code "+t}}function getExtensionOrThrow(e,t,n){return throwIfNull(e,t,function(){return e.getExtension(n)},'Extension "'+n+'" not supported on this browser.')}function createVertexShader(e,t,n){var r=throwIfNull(e,t,function(){return e.createShader(e.VERTEX_SHADER)},"Unable to create vertex WebGLShader.");if(callAndCheck(e,t,function(){return e.shaderSource(r,n)}),callAndCheck(e,t,function(){return e.compileShader(r)}),!1===e.getShaderParameter(r,e.COMPILE_STATUS))throw console.log(e.getShaderInfoLog(r)),new Error("Failed to compile vertex shader.");return r}function createFragmentShader(e,t,n){var r=throwIfNull(e,t,function(){return e.createShader(e.FRAGMENT_SHADER)},"Unable to create fragment WebGLShader.");if(callAndCheck(e,t,function(){return e.shaderSource(r,n)}),callAndCheck(e,t,function(){return e.compileShader(r)}),!1===e.getShaderParameter(r,e.COMPILE_STATUS))throw logShaderSourceAndInfoLog(n,e.getShaderInfoLog(r)),new Error("Failed to compile fragment shader.");return r}var lineNumberRegex=/ERROR: [0-9]+:([0-9]+):/g;function logShaderSourceAndInfoLog(e,t){var n=lineNumberRegex.exec(t);if(null==n)return console.log("Couldn't parse line number in error: "+t),void console.log(e);for(var r=+n[1],o=e.split("\n"),a=o.length.toString().length+2,i=o.map(function(e,t){return rightPad((t+1).toString(),a)+e}),s=0,u=0;u<i.length;u++)s=Math.max(i[u].length,s);var c=i.slice(0,r-1),l=i.slice(r-1,r),p=i.slice(r);console.log(c.join("\n")),console.log(t.split("\n")[0]),console.log("%c "+rightPad(l[0],s),"border:1px solid red; background-color:#e3d2d2; color:#a61717"),console.log(p.join("\n"));}function createProgram(e,t){return throwIfNull(e,t,function(){return e.createProgram()},"Unable to create WebGLProgram.")}function linkProgram(e,t,n){if(callAndCheck(e,t,function(){return e.linkProgram(n)}),!1===e.getProgramParameter(n,e.LINK_STATUS))throw console.log(e.getProgramInfoLog(n)),new Error("Failed to link vertex and fragment shaders.")}function validateProgram(e,t,n){if(callAndCheck(e,t,function(){return e.validateProgram(n)}),!1===e.getProgramParameter(n,e.VALIDATE_STATUS))throw console.log(e.getProgramInfoLog(n)),new Error("Shader program validation failed.")}function createStaticVertexBuffer(e,t,n){var r=throwIfNull(e,t,function(){return e.createBuffer()},"Unable to create WebGLBuffer");return callAndCheck(e,t,function(){return e.bindBuffer(e.ARRAY_BUFFER,r)}),callAndCheck(e,t,function(){return e.bufferData(e.ARRAY_BUFFER,n,e.STATIC_DRAW)}),r}function createStaticIndexBuffer(e,t,n){var r=throwIfNull(e,t,function(){return e.createBuffer()},"Unable to create WebGLBuffer");return callAndCheck(e,t,function(){return e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,r)}),callAndCheck(e,t,function(){return e.bufferData(e.ELEMENT_ARRAY_BUFFER,n,e.STATIC_DRAW)}),r}function getNumChannels(){return 2===ENV.get("WEBGL_VERSION")?1:4}function createTexture(e,t){return throwIfNull(e,t,function(){return e.createTexture()},"Unable to create WebGLTexture.")}function validateTextureSize(e,t){var n=ENV.get("WEBGL_MAX_TEXTURE_SIZE");if(e<=0||t<=0){var r="["+e+"x"+t+"]";throw new Error("Requested texture size "+r+" is invalid.")}if(e>n||t>n){r="["+e+"x"+t+"]";throw new Error("Requested texture size "+r+" greater than WebGL maximum on this browser / GPU "+("["+n+"x"+n+"]")+".")}}function createFramebuffer(e,t){return throwIfNull(e,t,function(){return e.createFramebuffer()},"Unable to create WebGLFramebuffer.")}function bindVertexBufferToProgramAttribute(e,t,n,r,o,a,i,s){var u=e.getAttribLocation(n,r);return -1!==u&&(callAndCheck(e,t,function(){return e.bindBuffer(e.ARRAY_BUFFER,o)}),callAndCheck(e,t,function(){return e.vertexAttribPointer(u,a,e.FLOAT,!1,i,s)}),callAndCheck(e,t,function(){return e.enableVertexAttribArray(u)}),!0)}function bindTextureUnit(e,t,n,r){validateTextureUnit(e,r),callAndCheck(e,t,function(){return e.activeTexture(e.TEXTURE0+r)}),callAndCheck(e,t,function(){return e.bindTexture(e.TEXTURE_2D,n)});}function unbindTextureUnit(e,t,n){validateTextureUnit(e,n),callAndCheck(e,t,function(){return e.activeTexture(e.TEXTURE0+n)}),callAndCheck(e,t,function(){return e.bindTexture(e.TEXTURE_2D,null)});}function getProgramUniformLocationOrThrow(e,t,n,r){return throwIfNull(e,t,function(){return e.getUniformLocation(n,r)},'uniform "'+r+'" not present in program.')}function getProgramUniformLocation(e,t,n){return e.getUniformLocation(t,n)}function bindTextureToProgramUniformSampler(e,t,n,r,o,a){callAndCheck(e,t,function(){return bindTextureUnit(e,t,r,a)}),callAndCheck(e,t,function(){return e.uniform1i(o,a)});}function bindCanvasToFramebuffer(e,t){callAndCheck(e,t,function(){return e.bindFramebuffer(e.FRAMEBUFFER,null)}),callAndCheck(e,t,function(){return e.viewport(0,0,e.canvas.width,e.canvas.height)}),callAndCheck(e,t,function(){return e.scissor(0,0,e.canvas.width,e.canvas.height)});}function bindColorTextureToFramebuffer(e,t,n,r){callAndCheck(e,t,function(){return e.bindFramebuffer(e.FRAMEBUFFER,r)}),callAndCheck(e,t,function(){return e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,n,0)});}function unbindColorTextureFromFramebuffer(e,t,n){callAndCheck(e,t,function(){return e.bindFramebuffer(e.FRAMEBUFFER,n)}),callAndCheck(e,t,function(){return e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,null,0)});}function validateFramebuffer(e){var t=e.checkFramebufferStatus(e.FRAMEBUFFER);if(t!==e.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+getFramebufferErrorMessage(e,t))}function getFramebufferErrorMessage(e,t){switch(t){case e.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:return "FRAMEBUFFER_INCOMPLETE_ATTACHMENT";case e.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:return "FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT";case e.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:return "FRAMEBUFFER_INCOMPLETE_DIMENSIONS";case e.FRAMEBUFFER_UNSUPPORTED:return "FRAMEBUFFER_UNSUPPORTED";default:return "unknown error "+t}}function throwIfNull(e,t,n,r){var o=callAndCheck(e,t,function(){return n()});if(null==o)throw new Error(r);return o}function validateTextureUnit(e,t){var n=e.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,r=t+e.TEXTURE0;if(r<e.TEXTURE0||r>n)throw new Error("textureUnit must be in "+("[gl.TEXTURE0, gl.TEXTURE"+n+"]")+".")}function getBatchDim(e,t){return void 0===t&&(t=2),sizeFromShape(e.slice(0,e.length-t))}function getRowsCols(e){if(0===e.length)throw Error("Cannot get rows and columns of an empty shape array.");return [e.length>1?e[e.length-2]:1,e[e.length-1]]}function getTextureShapeFromLogicalShape(e,t){var n;void 0===t&&(t=!1);var r=ENV.get("WEBGL_MAX_TEXTURE_SIZE");if(t&&(r*=2,1===(e=e.map(function(t,n){return n>=e.length-2?nearestLargerEven(e[n]):e[n]})).length&&(e=[2,e[0]])),2!==e.length){var o=squeezeShape(e);e=o.newShape;}var a=sizeFromShape(e);if(e.length<=1&&a<=r)return [1,a];if(2===e.length&&e[0]<=r&&e[1]<=r)return e;if(3===e.length&&e[0]*e[1]<=r&&e[2]<=r)return [e[0]*e[1],e[2]];if(3===e.length&&e[0]<=r&&e[1]*e[2]<=r)return [e[0],e[1]*e[2]];if(4===e.length&&e[0]*e[1]*e[2]<=r&&e[3]<=r)return [e[0]*e[1]*e[2],e[3]];if(4===e.length&&e[0]<=r&&e[1]*e[2]*e[3]<=r)return [e[0],e[1]*e[2]*e[3]];if(t){var i=getBatchDim(e),s=2,u=2;return e.length&&(s=(n=getRowsCols(e))[0],u=n[1]),sizeToSquarishShape(a=i*(s/2)*(u/2)).map(function(e){return 2*e})}return sizeToSquarishShape(a)}function isEven(e){return e%2==0}function isReshapeFree(e,t){if(arraysEqual(e=e.slice(-2),t=t.slice(-2)))return !0;if(!e.length||!t.length)return !0;if(0===e[0]||0===e[1]||0===t[0]||0===t[1])return !0;if(e.length!==t.length){var n=e.slice(-1)[0],r=t.slice(-1)[0];if(n===r)return !0;if(isEven(n)&&isEven(r)&&(1===e[0]||1===t[0]))return !0}return e[1]===t[1]&&isEven(e[0])&&isEven(t[0])}var webgl_util=Object.freeze({callAndCheck:callAndCheck,canBeRepresented:canBeRepresented,getWebGLErrorMessage:getWebGLErrorMessage,getExtensionOrThrow:getExtensionOrThrow,createVertexShader:createVertexShader,createFragmentShader:createFragmentShader,createProgram:createProgram,linkProgram:linkProgram,validateProgram:validateProgram,createStaticVertexBuffer:createStaticVertexBuffer,createStaticIndexBuffer:createStaticIndexBuffer,getNumChannels:getNumChannels,createTexture:createTexture,validateTextureSize:validateTextureSize,createFramebuffer:createFramebuffer,bindVertexBufferToProgramAttribute:bindVertexBufferToProgramAttribute,bindTextureUnit:bindTextureUnit,unbindTextureUnit:unbindTextureUnit,getProgramUniformLocationOrThrow:getProgramUniformLocationOrThrow,getProgramUniformLocation:getProgramUniformLocation,bindTextureToProgramUniformSampler:bindTextureToProgramUniformSampler,bindCanvasToFramebuffer:bindCanvasToFramebuffer,bindColorTextureToFramebuffer:bindColorTextureToFramebuffer,unbindColorTextureFromFramebuffer:unbindColorTextureFromFramebuffer,validateFramebuffer:validateFramebuffer,getFramebufferErrorMessage:getFramebufferErrorMessage,getBatchDim:getBatchDim,getRowsCols:getRowsCols,getTextureShapeFromLogicalShape:getTextureShapeFromLogicalShape,isReshapeFree:isReshapeFree});function createVertexShader$1(e,t){var n=getGlslDifferences();return createVertexShader(e,t,n.version+"\n precision highp float;\n "+n.attribute+" vec3 clipSpacePos;\n "+n.attribute+" vec2 uv;\n "+n.varyingVs+" vec2 resultUV;\n\n void main() {\n gl_Position = vec4(clipSpacePos, 1);\n resultUV = uv;\n }")}function createVertexBuffer(e,t){return createStaticVertexBuffer(e,t,new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]))}function createIndexBuffer(e,t){return createStaticIndexBuffer(e,t,new Uint16Array([0,1,2,2,1,3]))}function getTextureConfig(e,t){var n,r,o,a,i,s,u,c,l=e;return 2===ENV.get("WEBGL_VERSION")?(n=l.R32F,r=l.R16F,o=l.RGBA16F,a=l.RGBA32F,i=l.RED,s=4,u=1,c=l.HALF_FLOAT):(n=e.RGBA,r=e.RGBA,o=e.RGBA,a=l.RGBA,i=e.RGBA,s=4,u=4,c=null!=t?t.HALF_FLOAT_OES:null),{internalFormatFloat:n,internalFormatHalfFloat:r,internalFormatPackedHalfFloat:o,internalFormatPackedFloat:a,textureFormatFloat:i,downloadTextureFormat:e.RGBA,downloadUnpackNumChannels:s,defaultNumChannels:u,textureTypeHalfFloat:c}}function createAndConfigureTexture(e,t,n,r,o,a,i){validateTextureSize(n,r);var s=createTexture(e,t),u=e.TEXTURE_2D;return callAndCheck(e,t,function(){return e.bindTexture(u,s)}),callAndCheck(e,t,function(){return e.texParameteri(u,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)}),callAndCheck(e,t,function(){return e.texParameteri(u,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)}),callAndCheck(e,t,function(){return e.texParameteri(u,e.TEXTURE_MIN_FILTER,e.NEAREST)}),callAndCheck(e,t,function(){return e.texParameteri(u,e.TEXTURE_MAG_FILTER,e.NEAREST)}),callAndCheck(e,t,function(){return e.texImage2D(u,0,o,n,r,0,a,i,null)}),callAndCheck(e,t,function(){return e.bindTexture(e.TEXTURE_2D,null)}),s}function createFloat32MatrixTexture(e,t,n,r,o){var a=getUnpackedMatrixTextureShapeWidthHeight(n,r);return createAndConfigureTexture(e,t,a[0],a[1],o.internalFormatFloat,o.textureFormatFloat,e.FLOAT)}function createFloat16MatrixTexture(e,t,n,r,o){var a=getUnpackedMatrixTextureShapeWidthHeight(n,r);return createAndConfigureTexture(e,t,a[0],a[1],o.internalFormatHalfFloat,o.textureFormatFloat,o.textureTypeHalfFloat)}function createUnsignedBytesMatrixTexture(e,t,n,r,o){var a=getUnpackedMatrixTextureShapeWidthHeight(n,r);return createAndConfigureTexture(e,t,a[0],a[1],e.RGBA,e.RGBA,e.UNSIGNED_BYTE)}function createPackedMatrixTexture(e,t,n,r,o){var a=getPackedMatrixTextureShapeWidthHeight(n,r);return createAndConfigureTexture(e,t,a[0],a[1],o.internalFormatPackedFloat,e.RGBA,e.FLOAT)}function createFloat16PackedMatrixTexture(e,t,n,r,o){var a=getPackedMatrixTextureShapeWidthHeight(n,r);return createAndConfigureTexture(e,t,a[0],a[1],o.internalFormatPackedHalfFloat,e.RGBA,o.textureTypeHalfFloat)}function bindVertexProgramAttributeStreams(e,t,n,r){return callAndCheck(e,t,function(){return e.bindBuffer(e.ARRAY_BUFFER,r)}),bindVertexBufferToProgramAttribute(e,t,n,"clipSpacePos",r,3,20,0)&&bindVertexBufferToProgramAttribute(e,t,n,"uv",r,2,20,12)}function uploadPixelDataToTexture(e,t,n,r){callAndCheck(e,t,function(){return e.bindTexture(e.TEXTURE_2D,n)}),callAndCheck(e,t,function(){return e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,r)}),callAndCheck(e,t,function(){return e.bindTexture(e.TEXTURE_2D,null)});}function uploadDataToTexture(e,t,n,r,o,a,i){validateTextureSize(r,o),callAndCheck(e,t,function(){return e.bindTexture(e.TEXTURE_2D,n)}),callAndCheck(e,t,function(){return e.texSubImage2D(e.TEXTURE_2D,0,0,0,r,o,i,e.FLOAT,a)}),callAndCheck(e,t,function(){return e.bindTexture(e.TEXTURE_2D,null)});}function uploadMatrixToTexture(e,t,n,r,o,a,i,s){var u,c=getUnpackedMatrixTextureShapeWidthHeight(r,o),l=c[0],p=c[1],d=r*o;1===s.defaultNumChannels&&d===a.length?u=a:encodeMatrixToUnpackedArray(a,u=new Float32Array(d*i),i),uploadDataToTexture(e,t,n,l,p,u,s.textureFormatFloat);}function uploadMatrixToPackedTexture(e,t,n,r,o,a,i,s,u,c){var l=getPackedMatrixTextureShapeWidthHeight(i,s),p=l[0],d=l[1],h=new Float32Array(getPackedRGBAArraySizeFromMatrixShape(i,s));encodeMatrixToPackedRGBA(u,r,o,a,h),uploadDataToTexture(e,t,n,p,d,h,e.RGBA);}function maybeCreateBufferFromOutputTexture(e,t,n,r,o,a){var i=n;if(2===ENV.get("WEBGL_VERSION")){var s=e,u=s.createBuffer();callAndCheck(e,t,function(){return e.bindBuffer(s.PIXEL_PACK_BUFFER,u)});var c=4*getUnpackedArraySizeFromMatrixSize(r*o,a.downloadUnpackNumChannels);callAndCheck(e,t,function(){return e.bufferData(s.PIXEL_PACK_BUFFER,c,s.STREAM_READ)}),callAndCheck(e,t,function(){return s.readPixels(0,0,o,r,e.RGBA,e.FLOAT,0)}),callAndCheck(e,t,function(){return e.bindBuffer(s.PIXEL_PACK_BUFFER,null)}),i=u;}return i}function downloadFloat32MatrixFromBuffer(e,t,n,r,o){var a=e,i=new Float32Array(getUnpackedArraySizeFromMatrixSize(n*r,o.downloadUnpackNumChannels));a.bindBuffer(a.PIXEL_PACK_BUFFER,t),a.getBufferSubData(a.PIXEL_PACK_BUFFER,0,i),a.bindBuffer(a.PIXEL_PACK_BUFFER,null);var s=new Float32Array(n*r);return decodeMatrixFromUnpackedArray(i,s,o.downloadUnpackNumChannels),s}function downloadFloat32MatrixFromOutputTexture(e,t,n,r,o){var a=getUnpackedMatrixTextureShapeWidthHeight(n,r),i=a[0],s=a[1],u=new Float32Array(getUnpackedArraySizeFromMatrixSize(n*r,o.downloadUnpackNumChannels));callAndCheck(e,t,function(){return e.readPixels(0,0,i,s,o.downloadTextureFormat,e.FLOAT,u)});var c=new Float32Array(n*r);return decodeMatrixFromUnpackedArray(u,c,o.downloadUnpackNumChannels),c}function downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n,r,o){var a=getUnpackedMatrixTextureShapeWidthHeight(n,r),i=a[0],s=a[1],u=new Uint8Array(getUnpackedArraySizeFromMatrixSize(n*r,4));return callAndCheck(e,t,function(){return e.readPixels(0,0,i,s,o.downloadTextureFormat,e.UNSIGNED_BYTE,u)}),new Float32Array(u.buffer)}function downloadPackedMatrixFromBuffer(e,t,n,r,o,a,i,s){var u=e,c=new Float32Array(getPackedRGBAArraySizeFromMatrixShape(a,i));u.bindBuffer(u.PIXEL_PACK_BUFFER,t),u.getBufferSubData(u.PIXEL_PACK_BUFFER,0,c),u.bindBuffer(u.PIXEL_PACK_BUFFER,null);var l=new Float32Array(sizeFromShape([n,r,o]));return decodeMatrixFromPackedRGBA(c,n,r,o,l),l}function downloadMatrixFromPackedOutputTexture(e,t,n,r,o,a,i,s){var u=getPackedMatrixTextureShapeWidthHeight(a,i),c=u[0],l=u[1],p=new Float32Array(getPackedRGBAArraySizeFromMatrixShape(a,i));callAndCheck(e,t,function(){return e.readPixels(0,0,c,l,e.RGBA,e.FLOAT,p)});var d=new Float32Array(sizeFromShape([n,r,o]));return decodeMatrixFromPackedRGBA(p,n,r,o,d)}var gpgpu_util=Object.freeze({createVertexShader:createVertexShader$1,createVertexBuffer:createVertexBuffer,createIndexBuffer:createIndexBuffer,getTextureConfig:getTextureConfig,createFloat32MatrixTexture:createFloat32MatrixTexture,createFloat16MatrixTexture:createFloat16MatrixTexture,createUnsignedBytesMatrixTexture:createUnsignedBytesMatrixTexture,createPackedMatrixTexture:createPackedMatrixTexture,createFloat16PackedMatrixTexture:createFloat16PackedMatrixTexture,bindVertexProgramAttributeStreams:bindVertexProgramAttributeStreams,uploadPixelDataToTexture:uploadPixelDataToTexture,uploadMatrixToTexture:uploadMatrixToTexture,uploadMatrixToPackedTexture:uploadMatrixToPackedTexture,maybeCreateBufferFromOutputTexture:maybeCreateBufferFromOutputTexture,downloadFloat32MatrixFromBuffer:downloadFloat32MatrixFromBuffer,downloadFloat32MatrixFromOutputTexture:downloadFloat32MatrixFromOutputTexture,downloadByteEncodedFloatMatrixFromOutputTexture:downloadByteEncodedFloatMatrixFromOutputTexture,downloadPackedMatrixFromBuffer:downloadPackedMatrixFromBuffer,downloadMatrixFromPackedOutputTexture:downloadMatrixFromPackedOutputTexture}),GPGPUContext=function(){function e(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];var t=ENV.get("WEBGL_VERSION");null!=e?(this.gl=e,setWebGLContext(t,e)):this.gl=getWebGLContext(t),1===ENV.get("WEBGL_VERSION")?(this.textureFloatExtension=getExtensionOrThrow(this.gl,this.debug,"OES_texture_float"),this.colorBufferFloatExtension=this.gl.getExtension("WEBGL_color_buffer_float"),ENV.get("WEBGL_RENDER_FLOAT32_ENABLED")||(this.textureHalfFloatExtension=getExtensionOrThrow(this.gl,this.debug,"OES_texture_half_float"),this.colorBufferHalfFloatExtension=this.gl.getExtension("EXT_color_buffer_half_float"))):this.colorBufferFloatExtension=getExtensionOrThrow(this.gl,this.debug,"EXT_color_buffer_float"),this.vertexBuffer=createVertexBuffer(this.gl,this.debug),this.indexBuffer=createIndexBuffer(this.gl,this.debug),this.framebuffer=createFramebuffer(this.gl,this.debug),this.textureConfig=getTextureConfig(this.gl,this.textureHalfFloatExtension);}return Object.defineProperty(e.prototype,"debug",{get:function(){return ENV.get("DEBUG")},enumerable:!0,configurable:!0}),e.prototype.dispose=function(){var e=this;if(!this.disposed){null!=this.program&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),null!=this.outputTexture&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");var t=this.gl;callAndCheck(t,this.debug,function(){return t.finish()}),callAndCheck(t,this.debug,function(){return t.bindFramebuffer(t.FRAMEBUFFER,null)}),callAndCheck(t,this.debug,function(){return t.deleteFramebuffer(e.framebuffer)}),callAndCheck(t,this.debug,function(){return t.bindBuffer(t.ARRAY_BUFFER,null)}),callAndCheck(t,this.debug,function(){return t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,null)}),callAndCheck(t,this.debug,function(){return t.deleteBuffer(e.indexBuffer)}),this.disposed=!0;}},e.prototype.createFloat32MatrixTexture=function(e,t){return this.throwIfDisposed(),createFloat32MatrixTexture(this.gl,this.debug,e,t,this.textureConfig)},e.prototype.createFloat16MatrixTexture=function(e,t){return this.throwIfDisposed(),createFloat16MatrixTexture(this.gl,this.debug,e,t,this.textureConfig)},e.prototype.createUnsignedBytesMatrixTexture=function(e,t){return this.throwIfDisposed(),createUnsignedBytesMatrixTexture(this.gl,this.debug,e,t,this.textureConfig)},e.prototype.uploadPixelDataToTexture=function(e,t){this.throwIfDisposed(),uploadPixelDataToTexture(this.gl,this.debug,e,t);},e.prototype.createFloat16PackedMatrixTexture=function(e,t){return this.throwIfDisposed(),createFloat16PackedMatrixTexture(this.gl,this.debug,e,t,this.textureConfig)},e.prototype.createPackedMatrixTexture=function(e,t){return this.throwIfDisposed(),createPackedMatrixTexture(this.gl,this.debug,e,t,this.textureConfig)},e.prototype.deleteMatrixTexture=function(e){var t=this;this.throwIfDisposed(),this.outputTexture===e&&(unbindColorTextureFromFramebuffer(this.gl,this.debug,this.framebuffer),this.outputTexture=null),callAndCheck(this.gl,this.debug,function(){return t.gl.deleteTexture(e)});},e.prototype.uploadMatrixToTexture=function(e,t,n,r){this.throwIfDisposed();var o=getNumChannels();return uploadMatrixToTexture(this.gl,this.debug,e,t,n,r,o,this.textureConfig)},e.prototype.uploadMatrixToPackedTexture=function(e,t,n,r,o,a,i){return this.throwIfDisposed(),uploadMatrixToPackedTexture(this.gl,this.debug,e,t,n,r,o,a,i,this.textureConfig)},e.prototype.downloadFloat32MatrixFromOutputTexture=function(e,t,n){var r=this;return this.downloadMatrixDriver(e,function(){return downloadFloat32MatrixFromOutputTexture(r.gl,r.debug,t,n,r.textureConfig)})},e.prototype.downloadByteEncodedFloatMatrixFromOutputTexture=function(e,t,n){var r=this;return this.downloadMatrixDriver(e,function(){return downloadByteEncodedFloatMatrixFromOutputTexture(r.gl,r.debug,t,n,r.textureConfig)})},e.prototype.downloadPackedMatrixFromBuffer=function(e,t,n,r,o,a){return downloadPackedMatrixFromBuffer(this.gl,e,t,n,r,o,a,this.textureConfig)},e.prototype.downloadFloat32MatrixFromBuffer=function(e,t,n){return downloadFloat32MatrixFromBuffer(this.gl,e,t,n,this.textureConfig)},e.prototype.maybeCreateBufferFromTexture=function(e,t,n){this.bindTextureToFrameBuffer(e);var r=maybeCreateBufferFromOutputTexture(this.gl,this.debug,e,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),r},e.prototype.createAndWaitForFence=function(){var e=this.createFence(this.gl);return this.pollFence(e)},e.prototype.createFence=function(e){var t,n,r=this;if(ENV.get("WEBGL_FENCE_API_ENABLED")){var o=e,a=o.fenceSync(o.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=function(){var e=o.clientWaitSync(a,0,0);return e===o.ALREADY_SIGNALED||e===o.CONDITION_SATISFIED},t=a;}else ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=function(){return r.isQueryAvailable(t,ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}):n=function(){return !0};return {query:t,isFencePassed:n}},e.prototype.downloadMatrixFromPackedTexture=function(e,t,n,r,o,a){var i=this;return this.downloadMatrixDriver(e,function(){return downloadMatrixFromPackedOutputTexture(i.gl,i.debug,t,n,r,o,a,i.textureConfig)})},e.prototype.createProgram=function(e){this.throwIfDisposed();var t=this.gl,n=createFragmentShader(t,this.debug,e),r=createVertexShader$1(t,this.debug),o=createProgram(t,this.debug);return callAndCheck(t,this.debug,function(){return t.attachShader(o,r)}),callAndCheck(t,this.debug,function(){return t.attachShader(o,n)}),linkProgram(t,this.debug,o),this.debug&&validateProgram(t,this.debug,o),this.vertexAttrsAreBound||(this.setProgram(o),this.vertexAttrsAreBound=bindVertexProgramAttributeStreams(t,this.debug,this.program,this.vertexBuffer)),o},e.prototype.deleteProgram=function(e){var t=this;this.throwIfDisposed(),e===this.program&&(this.program=null),null!=e&&callAndCheck(this.gl,this.debug,function(){return t.gl.deleteProgram(e)});},e.prototype.setProgram=function(e){var t=this;this.throwIfDisposed(),this.program=e,null!=this.program&&this.debug&&validateProgram(this.gl,this.debug,this.program),callAndCheck(this.gl,this.debug,function(){return t.gl.useProgram(e)});},e.prototype.getUniformLocation=function(e,t,n){return void 0===n&&(n=!0),this.throwIfDisposed(),n?getProgramUniformLocationOrThrow(this.gl,this.debug,e,t):getProgramUniformLocation(this.gl,e,t)},e.prototype.getAttributeLocation=function(e,t){var n=this;return this.throwIfDisposed(),callAndCheck(this.gl,this.debug,function(){return n.gl.getAttribLocation(e,t)})},e.prototype.getUniformLocationNoThrow=function(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)},e.prototype.setInputMatrixTexture=function(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),bindTextureToProgramUniformSampler(this.gl,this.debug,this.program,e,t,n);},e.prototype.setOutputMatrixTexture=function(e,t,n){this.setOutputMatrixTextureDriver(e,n,t);},e.prototype.setOutputPackedMatrixTexture=function(e,t,n){this.throwIfDisposed();var r=getPackedMatrixTextureShapeWidthHeight(t,n),o=r[0],a=r[1];this.setOutputMatrixTextureDriver(e,o,a);},e.prototype.setOutputMatrixWriteRegion=function(e,t,n,r){this.setOutputMatrixWriteRegionDriver(n,e,r,t);},e.prototype.setOutputPackedMatrixWriteRegion=function(e,t,n,r){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")},e.prototype.debugValidate=function(){null!=this.program&&validateProgram(this.gl,this.debug,this.program),validateFramebuffer(this.gl);},e.prototype.executeProgram=function(){this.throwIfDisposed(),this.throwIfNoProgram();var e=this.gl;this.debug&&this.debugValidate(),callAndCheck(e,this.debug,function(){return e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0)});},e.prototype.blockUntilAllProgramsCompleted=function(){var e=this;this.throwIfDisposed(),callAndCheck(this.gl,this.debug,function(){return e.gl.finish()});},e.prototype.getQueryTimerExtension=function(){return null==this.disjointQueryTimerExtension&&(this.disjointQueryTimerExtension=getExtensionOrThrow(this.gl,this.debug,2===ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension},e.prototype.getQueryTimerExtensionWebGL2=function(){return this.getQueryTimerExtension()},e.prototype.getQueryTimerExtensionWebGL1=function(){return this.getQueryTimerExtension()},e.prototype.beginQuery=function(){if(2===ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")){var e=this.gl,t=this.getQueryTimerExtensionWebGL2(),n=e.createQuery();return e.beginQuery(t.TIME_ELAPSED_EXT,n),n}var r=this.getQueryTimerExtensionWebGL1(),o=r.createQueryEXT();return r.beginQueryEXT(r.TIME_ELAPSED_EXT,o),o},e.prototype.endQuery=function(){if(2!==ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")){var e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT);}else{var t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);}},e.prototype.waitForQueryAndGetTime=function(e){return __awaiter$1(this,void 0,void 0,function(){var t=this;return __generator$1(this,function(n){switch(n.label){case 0:return [4,repeatedTry(function(){return t.disposed||t.isQueryAvailable(e,ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))})];case 1:return n.sent(),[2,this.getQueryTime(e,ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))]}})})},e.prototype.getQueryTime=function(e,t){if(0===t)return null;if(2===t){var n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}var r=this.getQueryTimerExtensionWebGL1();return r.getQueryObjectEXT(e,r.QUERY_RESULT_EXT)/1e6},e.prototype.isQueryAvailable=function(e,t){if(0===t)return !0;if(2===t){var n=this.gl,r=this.getQueryTimerExtensionWebGL2(),o=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return null==this.disjoint&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),o&&!this.disjoint}o=(r=this.getQueryTimerExtensionWebGL1()).getQueryObjectEXT(e,r.QUERY_RESULT_AVAILABLE_EXT);return null==this.disjoint&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),o&&!this.disjoint},e.prototype.pollFence=function(e){var t=this;return new Promise(function(n){t.addItemToPoll(function(){return e.isFencePassed()},function(){return n()});})},e.prototype.pollItems=function(){for(var e=linearSearchLastTrue(this.itemsToPoll.map(function(e){return e.isDoneFn})),t=0;t<=e;++t){(0, this.itemsToPoll[t].resolveFn)();}this.itemsToPoll=this.itemsToPoll.slice(e+1);},e.prototype.addItemToPoll=function(e,t){var n=this;this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1||repeatedTry(function(){return n.pollItems(),0===n.itemsToPoll.length});},e.prototype.bindTextureToFrameBuffer=function(e){this.throwIfDisposed(),bindColorTextureToFramebuffer(this.gl,this.debug,e,this.framebuffer),this.debug&&validateFramebuffer(this.gl);},e.prototype.unbindTextureToFrameBuffer=function(){null!=this.outputTexture?(bindColorTextureToFramebuffer(this.gl,this.debug,this.outputTexture,this.framebuffer),this.debug&&validateFramebuffer(this.gl)):unbindColorTextureFromFramebuffer(this.gl,this.debug,this.framebuffer);},e.prototype.downloadMatrixDriver=function(e,t){this.bindTextureToFrameBuffer(e);var n=t();return this.unbindTextureToFrameBuffer(),n},e.prototype.setOutputMatrixTextureDriver=function(e,t,n){this.throwIfDisposed();var r=this.gl;bindColorTextureToFramebuffer(r,this.debug,e,this.framebuffer),this.debug&&validateFramebuffer(r),this.outputTexture=e,callAndCheck(r,this.debug,function(){return r.viewport(0,0,t,n)}),callAndCheck(r,this.debug,function(){return r.scissor(0,0,t,n)});},e.prototype.setOutputMatrixWriteRegionDriver=function(e,t,n,r){var o=this;this.throwIfDisposed(),callAndCheck(this.gl,this.debug,function(){return o.gl.scissor(e,t,n,r)});},e.prototype.throwIfDisposed=function(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")},e.prototype.throwIfNoProgram=function(){if(null==this.program)throw new Error("No GPU program is currently set.")},e}();function linearSearchLastTrue(e){for(var t=0;t<e.length;++t){if(!e[t]())break}return t-1}function compileProgram(e,t,n,r){var o=t.userCode,a=n.map(function(e,n){var r={logicalShape:e.shape,texShape:e.isUniform?null:e.texData.texShape,isUniform:e.isUniform,isPacked:!e.isUniform&&e.texData.isPacked,flatOffset:null};return null!=e.texData&&null!=e.texData.slice&&e.texData.slice.flatOffset>0&&(r.flatOffset=e.texData.slice.flatOffset),{name:t.variableNames[n],shapeInfo:r}}),i=a.map(function(e){return e.shapeInfo}),s={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked,flatOffset:null},u=makeShader(a,s,o,t.usesPackedTextures),c=e.createProgram(u),l=null,p=null;1===ENV.get("WEBGL_VERSION")&&(l=e.getUniformLocation(c,"INFINITY",!1),p=e.getUniformLocation(c,"NAN",!1));for(var d={},h=0;h<t.variableNames.length;h++){var f=t.variableNames[h];d[f]=e.getUniformLocation(c,f,!1),d["offset"+f]=e.getUniformLocation(c,"offset"+f,!1);}return {program:t,source:u,webGLProgram:c,uniformLocations:d,inShapeInfos:i,outShapeInfo:s,infLoc:l,nanLoc:p}}function validateBinaryAndProgram(e,t){if(e.length!==t.length)throw Error("Binary was compiled with "+e.length+" inputs, but was executed with "+t.length+" inputs");e.forEach(function(e,n){var r=e.logicalShape,o=t[n],a=o.shape;if(!arraysEqual(r,a))throw Error("Binary was compiled with different shapes than the current args. Shapes "+r+" and "+a+" must match");if(!e.isUniform||!o.isUniform){var i=e.texShape,s=o.isUniform?null:o.texData.texShape;if(!arraysEqual(i,s))throw Error("Binary was compiled with different texture shapes than the current args. Shape "+i+" and "+s+" must match")}});}function runProgram(e,t,n,r,o){validateBinaryAndProgram(t.inShapeInfos,n),validateBinaryAndProgram([t.outShapeInfo],[r]);var a=r.texData.texture,i=r.texData.texShape;r.texData.isPacked?e.setOutputPackedMatrixTexture(a,i[0],i[1]):e.setOutputMatrixTexture(a,i[0],i[1]),e.setProgram(t.webGLProgram),1===ENV.get("WEBGL_VERSION")&&(null!==t.infLoc&&e.gl.uniform1f(t.infLoc,1/0),null!==t.nanLoc&&e.gl.uniform1f(t.nanLoc,NaN)),n.forEach(function(n,r){var o=t.program.variableNames[r],a=t.uniformLocations[o],i=t.uniformLocations["offset"+o];if(null!=a)if(n.isUniform)if(sizeFromShape(n.shape)<2)e.gl.uniform1f(a,n.uniformValues[0]);else{var s=n.uniformValues;s instanceof Float32Array||(s=new Float32Array(s)),e.gl.uniform1fv(a,s);}else null!=n.texData.slice&&null!=i&&e.gl.uniform1i(i,n.texData.slice.flatOffset),e.setInputMatrixTexture(n.texData.texture,a,r);}),null!=o&&o(e,t.webGLProgram),e.executeProgram();}function makeShaderKey(e,t,n){var r="";t.concat(n).forEach(function(e){var t=null!=e.texData&&null!=e.texData.slice&&e.texData.slice.flatOffset>0,n=e.isUniform?"uniform":e.texData.texShape;r+=e.shape+"_"+n+"_"+t;});var o=e.userCode,a=e.constructor.name;return a+="_"+r+"_"+o}var Im2ColPackedProgram=function(){return function(e,t,n){this.variableNames=["A"],this.usesPackedTextures=!0,this.outputShape=e;var r=n.filterWidth,o=n.inChannels,a=n.strideWidth,i=n.strideHeight,s=n.padInfo,u=n.outWidth,c=n.dilationWidth,l=n.dilationHeight,p=s.left,d=s.top,h=o*r,f=getGlslDifferences();this.userCode="\n void main() {\n ivec2 rc = getOutputCoords();\n\n vec4 result = vec4(0);\n\n for(int row=0; row<=1; row++) {\n for(int col=0; col<=1; col++) {\n int blockIndex = rc.y + col;\n int pos = rc.x + row;\n\n if(blockIndex >= "+e[1]+" || pos >= "+e[0]+") continue;\n\n int offsetY = int(blockIndex / ("+u+")) * "+i+" - "+d+";\n int d0 = offsetY + "+l+" * (pos / "+h+");\n\n if(d0 >= "+t[0]+" || d0 < 0) continue;\n\n int offsetX = int(mod(float(blockIndex), "+u+".) * "+a+". - "+p+".);\n int d1 = offsetX + "+c+" * (int(mod(float(pos), "+h+".) / "+o+".));\n\n if(d1 >= "+t[1]+" || d1 < 0) continue;\n\n vec2 innerDims = vec2(d1, int(mod(float(pos), "+o+".)));\n result[row * 2 + col] = getChannel(getA(d0, int(innerDims.x),\n int(innerDims.y)), innerDims);\n }\n }\n\n "+f.output+" = result;\n }\n ";}}(),LRNProgram=function(){return function(e,t,n,r,o){this.variableNames=["x"],this.outputShape=[];var a,i=t,s=e[3]-1;this.outputShape=e;var u="float("+n+") + float("+r+") * sum";a=.5===o?"inversesqrt("+u+")":1===o?"1.0/("+u+")":"exp(log("+u+") * float(-"+o+"));",this.userCode="\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n int d = coords[3];\n float x = getX(b, r, c, d);\n float sum = 0.0;\n for (int j = -"+i+"; j <= "+i+"; j++) {\n int idx = d + j;\n if (idx >= 0 && idx <= "+s+") {\n float z = getX(b, r, c, idx);\n sum += z * z;\n }\n }\n float val = x * "+a+";\n setOutput(val);\n }\n ";}}(),LRNGradProgram=function(){return function(e,t,n,r,o){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=r,this.beta=o,this.userCode="\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n\n float result = 0.0;\n for (int d = 0; d < "+this.depth+"; ++d) {\n int depthBegin = int(max(0.0, float(d - "+t+")));\n int depthEnd = int(min(float("+this.depth+"),\n float(d + "+t+" + 1)));\n\n const int MIN_DEPTH_BEGIN = 0;\n const int MAX_DEPTH_END = "+this.depth+";\n\n float norm = 0.0;\n for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd) {\n norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n }\n else {\n break;\n }\n }\n\n norm = float("+r+") * norm + float("+n+");\n\n for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd){\n float dyi = -2.0 * float("+r+")\n * float("+o+")\n * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n / norm;\n if (k == d) {\n dyi += pow(norm, -1.0 * "+o+");\n }\n if (k == coords[3]) {\n dyi *= getDy(b, r, c, d);\n result += dyi;\n }\n }\n else {\n break;\n }\n }\n }\n setOutput(result);\n }\n ";}}(),MaxPool2DBackpropProgram=function(){return function(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;var t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,o=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=o-1-e.padInfo.top,s=a-1-e.padInfo.left,u=o*a-1;this.userCode="\n const ivec2 pads = ivec2("+i+", "+s+");\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < "+o+";\n wR += "+r+") {\n float dyR = float(dyRCorner + wR) / "+t+".0;\n\n if (dyR < 0.0 || dyR >= "+e.outHeight+".0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < "+a+"; wC++) {\n float dyC = float(dyCCorner + wC) / "+n+".0;\n\n if (dyC < 0.0 || dyC >= "+e.outWidth+".0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n int maxPosValue = "+u+" - int(getMaxPos(b, idyR, idyC, d));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue = wR * "+a+" + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n setOutput(dotProd);\n }\n ";}}(),MatMulPackedProgram=function(){return function(e,t,n,r,o,a){void 0===n&&(n=!1),void 0===r&&(r=!1),void 0===o&&(o=!1),void 0===a&&(a=null),this.variableNames=["matrixA","matrixB"],this.usesPackedTextures=!0,this.outputShape=t;var i=n?e[1]:e[2],s=Math.ceil(i/2),u=n?"i * 2, rc.y":"rc.y, i * 2",c=r?"rc.z, i * 2":"i * 2, rc.z",l=n?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],p=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],d="",h="";a&&(d="vec4 activation(vec4 x) {\n "+a+"\n }",h="result = activation(result);");var f=o?"result += getBiasAtOutCoords();":"";o&&this.variableNames.push("bias"),this.userCode="\n "+d+"\n\n const float sharedDimension = "+s+".0;\n\n vec4 dot2x2ARowBCol(ivec3 rc) {\n vec4 result = vec4(0);\n for (int i = 0; i < "+s+"; i++) {\n vec4 a = getMatrixA(rc.x, "+u+");\n vec4 b = getMatrixB(rc.x, "+c+");\n\n result += ("+l[0]+" * "+p[0]+") + ("+l[1]+" * "+p[1]+");\n }\n return result;\n }\n\n void main() {\n ivec3 rc = getOutputCoords();\n vec4 result = dot2x2ARowBCol(rc);\n\n "+f+"\n\n "+h+"\n\n setOutput(result);\n }\n ";}}(),MultinomialProgram=function(){function e(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode="\n uniform float seed;\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n\n float r = random(seed);\n float cdf = 0.0;\n\n for (int i = 0; i < "+(t-1)+"; i++) {\n cdf += getProbs(batch, i);\n\n if (r < cdf) {\n setOutput(float(i));\n return;\n }\n }\n\n // If no other event happened, last event happened.\n setOutput(float("+(t-1)+"));\n }\n ";}return e.prototype.getCustomSetupFunc=function(e){var t=this;return function(n,r){null==t.seedLoc&&(t.seedLoc=n.getUniformLocation(r,"seed")),n.gl.uniform1f(t.seedLoc,e);}},e}(),OneHotProgram=function(){return function(e,t,n,r){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode="\n void main() {\n ivec2 coords = getOutputCoords();\n int index = round(getIndices(coords.x));\n setOutput(mix(float("+r+"), float("+n+"),\n float(index == coords.y)));\n }\n ";}}(),PackProgram=function(){return function(e){this.variableNames=["A"],this.isPackShader=!0,this.outputShape=e;var t=e.length;if(0===t)this.userCode="\n void main() {\n setOutput(vec4(getA(), 0., 0., 0.));\n }\n ";else{var n=getChannels("rc",t),r=getCoordsDataType(t),o=getOutOfBoundsCondition(t,e,n),a=getSetup(t,e[e.length-1],e[e.length-2],n),i=getOutput(e,n);this.userCode="\n void main() {\n "+r+" rc = getOutputCoords();\n\n if("+o+") {\n setOutput(vec4(0));\n } else {\n "+a+"\n\n setOutput(vec4("+i+"));\n }\n }\n ";}}}();function getSourceCoordsArr(e,t){for(var n=[],r=0;r<=1;r++)for(var o=0;o<=1;o++){for(var a=(0===r?"r":"rp1")+", "+(0===o?"c":"cp1"),i=2;i<e;i++)a=t[t.length-1-i]+","+a;n.push(a);}return n}function getOutOfBoundsCondition(e,t,n){if(1===e)return "rc > "+t[0];for(var r="",o=e-2;o<e;o++)r+=n[o]+" >= "+t[o],o<e-1&&(r+="||");return r}function getSetup(e,t,n,r){if(1===e)return "";var o=r.slice(-2);return "\n int r = "+o[0]+";\n int c = "+o[1]+";\n int rp1 = r + 1;\n int cp1 = c + 1;\n\n bool cEdge = cp1 >= "+t+";\n bool rEdge = rp1 >= "+n+";\n "}function getOutput(e,t){var n=e.length,r=getSourceCoordsArr(n,t);return 1===n?"getA(rc),\n rc + 1 >= "+e[0]+" ? 0. : getA(rc + 1),\n 0, 0":"getA("+r[0]+"),\n cEdge ? 0. : getA("+r[1]+"),\n rEdge ? 0. : getA("+r[2]+"),\n rEdge || cEdge ? 0. : getA("+r[3]+")"}var PadProgram=function(){return function(e,t,n){this.variableNames=["x"],this.outputShape=t.map(function(t,n){return t[0]+e[n]+t[1]});var r=e.length,o=getCoordsDataType(r),a=t.map(function(e){return e[0]}).join(","),i=t.map(function(t,n){return t[0]+e[n]}).join(","),s=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);this.userCode=1!==r?"\n "+o+" start = "+o+"("+a+");\n "+o+" end = "+o+"("+i+");\n\n void main() {\n "+o+" outC = getOutputCoords();\n if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n setOutput(float("+n+"));\n } else {\n "+o+" coords = outC - start;\n setOutput(getX("+s+"));\n }\n }\n ":"\n int start = "+a+";\n int end = "+i+";\n\n void main() {\n int outC = getOutputCoords();\n if (outC < start || outC >= end) {\n setOutput(float("+n+"));\n } else {\n setOutput(getX(outC - start));\n }\n }\n ";}}(),PadPackedProgram=function(){return function(e,t,n){this.variableNames=["x"],this.usesPackedTextures=!0,this.outputShape=t.map(function(t,n){return t[0]+e[n]+t[1]});for(var r=e.length,o=getCoordsDataType(r),a=t.map(function(e){return e[0]}).join(","),i=t.map(function(t,n){return t[0]+e[n]}).join(","),s=getChannels("rc",r),u=getChannels("source",r),c=s[r-1]+" < "+this.outputShape[r-1],l=1===r?"source":"vec2("+u.slice(-2).join()+")",p=[o+" rc = outputLoc;",s[r-1]+" += 1;\n if("+c+") {\n ",1===r?"":"}\n rc = outputLoc;\n "+s[r-2]+" += 1;\n if("+s[r-2]+" < "+this.outputShape[r-2]+") {",1===r?"":" "+s[r-1]+" += 1;\n if("+c+") {"],d=1===r?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="",f=0,m=1===r?2:4;f<m;f++)h+="\n "+p[f]+"\n if ("+d+") {\n result["+f+"] = float("+n+");\n } else {\n "+o+" source = rc - start;\n result["+f+"] = getChannel(getX("+u.join()+"), "+l+");\n }\n ";h+=1===r?"} ":"}}",this.userCode="\n const "+o+" start = "+o+"("+a+");\n const "+o+" end = "+o+"("+i+");\n\n void main() {\n "+o+" outputLoc = getOutputCoords();\n vec4 result = vec4(0.);\n "+h+"\n setOutput(result);\n }\n ";}}(),Pool2DProgram=function(){return function(e,t,n){if(this.variableNames=["x"],"avg"===t&&n)throw new Error("Cannot compute positions for average pool.");var r=e.filterWidth,o=e.strideHeight,a=e.strideWidth,i=e.dilationHeight,s=e.dilationWidth,u=e.effectiveFilterHeight,c=e.effectiveFilterWidth,l=e.padInfo.top,p=e.padInfo.left;this.outputShape=e.outShape;var d="avg"===t,h="0.0";if(d||(h="-1.0 / 1e-20"),n)this.userCode="\n const ivec2 strides = ivec2("+o+", "+a+");\n const ivec2 pads = ivec2("+l+", "+p+");\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n float avgValue = 0.0;\n\n for (int wR = 0; wR < "+u+";\n wR += "+i+") {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= "+e.inHeight+") {\n continue;\n }\n\n for (int wC = 0; wC < "+c+";\n wC += "+s+") {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= "+e.inWidth+") {\n continue;\n }\n\n float value = getX(batch, xR, xC, d);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value >= currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = wR * "+c+" + wC;\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n ";else{var f=t+"("+t+"("+t+"(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])";"avg"===t&&(f="avgValue / count");var m=4*Math.floor(r/4),g=r%4,v="\n if ("+d+") {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = max(values, minMaxValue);\n }\n ";this.userCode="\n const ivec2 strides = ivec2("+o+", "+a+");\n const ivec2 pads = ivec2("+l+", "+p+");\n const float initializationValue = "+h+";\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xR, int xC, int d) {\n if (xC < 0 || xC >= "+e.inWidth+") {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xR, xC, d);\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n vec4 minMaxValue = vec4("+h+");\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wR = 0; wR < "+u+";\n wR += "+i+") {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= "+e.inHeight+") {\n continue;\n }\n\n for (int wC = 0; wC < "+m+"; wC += 4) {\n int xC = xCCorner + wC * "+s+";\n\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + "+s+", d),\n getValue(batch, xR, xC + 2 * "+s+", d),\n getValue(batch, xR, xC + 3 * "+s+", d)\n );\n\n "+v+"\n }\n\n int xC = xCCorner + "+m+";\n if ("+(1===g)+") {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n "+v+"\n } else if ("+(2===g)+") {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + "+s+", d),\n initializationValue,\n initializationValue\n );\n\n "+v+"\n } else if ("+(3===g)+") {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + "+s+", d),\n getValue(batch, xR, xC + 2 * "+s+", d),\n initializationValue\n );\n\n "+v+"\n }\n }\n setOutput("+f+");\n }\n ";}}}(),ReduceProgram=function(){return function(e,t){this.variableNames=["x"];var n=e.windowSize,r=e.batchSize,o=e.inSize,a=Math.ceil(o/n);this.outputShape=[r,a];var i="0.0",s="";"prod"===t?i="1.0":"min"===t?(i="1.0 / 1e-20",s="min"):"max"===t&&(i="-1.0 / 1e-20",s="max");var u=t+"("+t+"("+t+"(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])";"sum"===t?u="sumValue":"prod"===t?u="prodValue":"all"===t?u="allValue":"any"===t&&(u="anyValue");var c=4*Math.floor(n/4),l=n%4,p="\n if ("+("sum"===t)+") {\n sumValue += dot(values, ones);\n } else if ("+("prod"===t)+") {\n vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n prodValue *= tmp[0] * tmp[1];\n } else {\n minMaxValue = "+s+"(values, minMaxValue);\n }\n ",d="vec4";"all"===t?(i="1.0",p="\n bool reducedAllValue = all(values);\n float floatedReducedAllValue = float(reducedAllValue);\n allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n ",d="bvec4"):"any"===t&&(i="0.0",p="\n bool reducedAnyValue = any(values);\n float floatedReducedAnyValue = float(reducedAnyValue);\n anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n ",d="bvec4");var h="";o%n>0&&(h="\n if (inIdx < 0 || inIdx >= "+o+") {\n return initializationValue;\n }\n "),this.userCode="\n const float initializationValue = "+i+";\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n "+h+"\n return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * "+n+";\n\n vec4 minMaxValue = vec4("+i+");\n float prodValue = 1.0;\n float sumValue = 0.0;\n float allValue = 1.0;\n float anyValue = 0.0;\n\n for (int i = 0; i < "+c+"; i += 4) {\n int inIdx = inOffset + i;\n "+d+" values = "+d+"(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n "+p+"\n }\n\n int inIdx = inOffset + "+c+";\n if ("+(1===l)+") {\n "+d+" values = "+d+"(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n "+p+"\n } else if ("+(2===l)+") {\n "+d+" values = "+d+"(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n "+p+"\n } else if ("+(3===l)+") {\n "+d+" values = "+d+"(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n "+p+"\n }\n setOutput("+u+");\n }\n ";}}(),ReshapePackedProgram=function(){return function(e,t){this.variableNames=["A"],this.usesPackedTextures=!0,this.outputShape=e;for(var n="",r=0;r<4;r++){var o="thisRC = rc;";r%2==1&&(o+="thisRC.z += 1;"),r>1&&(o+="thisRC.y += 1;"),n+="\n "+o+"\n "+(r>0?"if(thisRC.y < rows && thisRC.z < cols){":"")+"\n int flatIndex = getFlatIndex(thisRC);\n\n ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n result["+r+"] =\n getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n "+(r>0?"}":"")+"\n ";}this.userCode="\n "+getReshapedInputCoords(t)+"\n "+getFlatIndex(e)+"\n\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0.);\n\n ivec3 thisRC;\n int rows = "+e[1]+";\n int cols = "+e[2]+";\n\n "+n+"\n\n setOutput(result);\n }\n ";}}();function getFlatIndex(e){return "\n int getFlatIndex(ivec3 coords) {\n return round("+dotify(["coords.x","coords.y","coords.z"],computeStrides(e).map(function(e){return e.toString()}).concat(["1."]))+");\n }\n "}function getReshapedInputCoords(e){return "\n ivec3 inputCoordsFromReshapedOutCoords(int index) {\n "+getLogicalCoordinatesFromFlatIndex(["r","c","d"],e)+"\n return ivec3(r, c, d);\n }\n "}var ResizeBilinearBackpropProgram=function(){return function(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t.shape;var r=t.shape,o=r[1],a=r[2],i=e.shape,s=i[1],u=i[2],c=[n&&s>1?o-1:o,n&&u>1?a-1:a],l=[n&&s>1?s-1:s,n&&u>1?u-1:u],p=c[0]/l[0],d=c[1]/l[1],h=1/p,f=1/d,m=2*Math.ceil(h)+2,g=2*Math.ceil(f)+2;this.userCode="\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float("+p+");\n const float widthScale = float("+d+");\n\n const float invHeightScale = float("+h+");\n const float invWidthScale = float("+f+");\n\n const int winHeight = int("+m+");\n const int winWidth = int("+g+");\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(startRLerp - float(winHeight / 2));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(startCLerp - float(winWidth / 2));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= "+s+") {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= "+u+") {\n continue;\n }\n\n float dxR = float(dyR) * heightScale;\n int topDxRIndex = int(floor(dxR));\n int bottomDxRIndex = int(min(ceil(dxR), "+(o-1)+".0));\n float dxRLerp = dxR - float(topDxRIndex);\n float inverseDxRLerp = 1.0 - dxRLerp;\n\n float dxC = float(dyC) * widthScale;\n int leftDxCIndex = int(floor(dxC));\n int rightDxCIndex = int(min(ceil(dxC), "+(a-1)+".0));\n float dxCLerp = dxC - float(leftDxCIndex);\n float inverseDxCLerp = 1.0 - dxCLerp;\n\n if (r == topDxRIndex && c == leftDxCIndex) {\n // topLeft\n accumulator +=\n getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n }\n\n if (r == topDxRIndex && c == rightDxCIndex) {\n // topRight\n accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n }\n\n if (r == bottomDxRIndex && c == leftDxCIndex) {\n // bottomLeft\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n }\n\n if (r == bottomDxRIndex && c == rightDxCIndex) {\n // bottomRight\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n ";}}(),ResizeBilinearProgram=function(){return function(e,t,n,r){this.variableNames=["A"],this.outputShape=[];var o=e[0],a=e[1],i=e[2],s=e[3];this.outputShape=[o,t,n,s];var u=[r&&t>1?a-1:a,r&&n>1?i-1:i],c=[r&&t>1?t-1:t,r&&n>1?n-1:n];this.userCode="\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n "+u[0]/c[0]+",\n "+u[1]/c[1]+");\n const vec2 inputShapeRC = vec2("+a+".0, "+i+".0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n // Compute the four integer indices.\n ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\n ivec2 sourceCeilRC = ivec2(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n float top = topLeft + (topRight - topLeft) * fracRC.y;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n float newValue = top + (bottom - top) * fracRC.x;\n\n setOutput(newValue);\n }\n ";}}(),ResizeBilinearPackedProgram=function(){return function(e,t,n,r){this.variableNames=["A"],this.usesPackedTextures=!0,this.outputShape=[];var o=e[0],a=e[1],i=e[2],s=e[3];this.outputShape=[o,t,n,s];var u=[r&&t>1?a-1:a,r&&n>1?i-1:i],c=[r&&t>1?t-1:t,r&&n>1?n-1:n];this.userCode="\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n "+u[0]/c[0]+",\n "+u[1]/c[1]+",\n "+u[1]/c[1]+");\n const vec3 inputShapeRC = vec3("+a+".0, "+i+".0,\n "+i+".0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = vec3(yRC) * effectiveInputOverOutputRatioRC;\n\n // Compute the four integer indices.\n ivec3 sourceFloorRC = ivec3(sourceFracIndexRC);\n ivec3 sourceCeilRC = ivec3(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n \n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < "+(s-1)+"; \n bool hasNextRow = coords.z < "+(n-1)+";\n\n // In parallel, construct four corners for all four components in\n // packed 2x2 cell.\n vec4 topLeft = vec4(\n getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 bottomLeft = vec4(\n getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 topRight = vec4(\n getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec4 bottomRight = vec4(\n getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n vec4 newValue = mix(top, bottom, fracRC.x);\n\n setOutput(newValue);\n }\n ";}}(),ResizeNearestNeigborBackpropProgram=function(){return function(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t.shape;var r=t.shape,o=r[1],a=r[2],i=e.shape,s=i[1],u=i[2],c=[n&&s>1?o-1:o,n&&u>1?a-1:a],l=[n&&s>1?s-1:s,n&&u>1?u-1:u],p=c[0]/l[0],d=c[1]/l[1],h=1/p,f=1/d,m=2*Math.ceil(h)+2,g=2*Math.ceil(f)+2;this.userCode="\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float("+p+");\n const float widthScale = float("+d+");\n\n const float invHeightScale = float("+h+");\n const float invWidthScale = float("+f+");\n\n const int winHeight = int("+m+");\n const int winWidth = int("+g+");\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= "+s+") {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= "+u+") {\n continue;\n }\n\n float sourceFracRow =\n float("+c[0]+") *\n (float(dyR) / float("+l[0]+"));\n\n float sourceFracCol =\n float("+c[1]+") *\n (float(dyC) / float("+l[1]+"));\n\n int sourceNearestRow = int(min(\n float(int("+o+") - 1),\n "+n+" ? float(round(sourceFracRow)) :\n float(floor(sourceFracRow))));\n\n int sourceNearestCol = int(min(\n float(int("+a+") - 1),\n "+n+" ? float(round(sourceFracCol)) :\n float(floor(sourceFracCol))));\n\n if (r == sourceNearestRow && c == sourceNearestCol) {\n accumulator += getDy(b, dyR, dyC, d);\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n ";}}(),ResizeNearestNeighborProgram=function(){return function(e,t,n,r){this.variableNames=["A"],this.outputShape=[];var o=e[0],a=e[1],i=e[2],s=e[3];this.outputShape=[o,t,n,s];var u=[r&&t>1?a-1:a,r&&n>1?i-1:i],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],l=r?"0.5":"0.0";this.userCode="\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n "+u[0]/c[0]+",\n "+u[1]/c[1]+");\n const vec2 inputShapeRC = vec2("+a+".0, "+i+".0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestRC = ivec2(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + "+l+")));\n\n float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n setOutput(newValue);\n }\n ";}}(),ReverseProgram=function(){return function(e,t){this.variableNames=["x"];var n=e.length;if(n>4)throw new Error("WebGL backend: Reverse of rank-"+n+" tensor is not yet supported");if(this.outputShape=e,1!==n){var r=e.map(function(n,r){return function(n){return -1!==t.indexOf(n)&&1!==e[n]?e[n]+" - coords["+n+"] - 1":"coords["+n+"]"}(r)}).join(","),o=getCoordsDataType(n);this.userCode="\n void main() {\n "+o+" coords = getOutputCoords();\n setOutput(getX("+r+"));\n }\n ";}else this.userCode="\n void main() {\n int coord = getOutputCoords();\n setOutput(getX("+e[0]+" - coord - 1));\n }\n ";}}(),ReversePackedProgram=function(){return function(e,t){this.variableNames=["x"],this.usesPackedTextures=!0;var n=e.length;if(n>4)throw new Error("WebGL backend: Reverse of rank-"+n+" tensor is not yet supported");this.outputShape=e;var r=getChannels("rc",n),o=r[n-1]+" + 1 < "+this.outputShape[n-1],a=r[n-2]+" + 1 < "+this.outputShape[n-2],i=getCoordsDataType(n);function s(n){var r=e.map(function(r,o){return function(n,r){return -1!==t.indexOf(n)&&1!==e[n]?e[n]+" - "+r[n]+" - 1":""+r[n]}(o,n)});return "getChannel(getX("+r.join(",")+"), vec2("+r.slice(-2).join(",")+"))"}this.userCode=1===n?"\n void main(){\n int rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = getChannel(getX("+e[0]+" - rc - 1), rc);\n if("+o+"){\n result.g = getChannel(getX("+e[0]+" - (rc + 1) - 1), rc + 1);\n }\n setOutput(result);\n }\n ":"\n void main() {\n "+i+" rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = "+function(e){return s(e)}(r.slice())+";\n if("+o+"){\n result.g = "+function(e){return e[n-1]="("+e[n-1]+" + 1)",s(e)}(r.slice())+";\n }\n if("+a+") {\n result.b = "+function(e){return e[n-2]="("+e[n-2]+" + 1)",s(e)}(r.slice())+";\n if("+o+") {\n result.a = "+function(e){return e[n-1]="("+e[n-1]+" + 1)",e[n-2]="("+e[n-2]+" + 1)",s(e)}(r.slice())+";\n }\n }\n setOutput(result);\n }\n ";}}(),ScatterProgram=function(){return function(e,t,n,r,o,a,i){void 0===i&&(i=!0),this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;var s=getCoordsDataType(o.length),u=getCoordsDataType(a.length),c="";1===n?c="i":2===n&&(c="i, j");var l="getIndices("+c+")",p="";1===r?p="i":2===r&&(p="i, coords[1]");var d="getUpdates("+p+")",h=t>1?"strides[j]":"strides";this.userCode="\n "+s+" strides = "+s+"("+o+");\n\n void main() {\n "+u+" coords = getOutputCoords();\n float sum = 0.0;\n bool found = false;\n for (int i = 0; i < "+e+"; i++) {\n int flattenedIndex = 0;\n for (int j = 0; j < "+t+"; j++) {\n int index = round("+l+");\n flattenedIndex += index * "+h+";\n }\n if (flattenedIndex == coords[0]) {\n sum += "+d+";\n found = true;\n }\n }\n setOutput(mix(getDefaultValue(), sum, float(found)));\n }\n ";}}(),SegmentOpProgram=function(){return function(e,t){this.variableNames=["x","segmentIds"];var n=e.windowSize,r=e.batchSize,o=e.inSize,a=e.numSegments,i=a*Math.ceil(o/n);this.outputShape=[r,i];var s=4*Math.floor(n/4),u=n%4,c="\n sumValue += dot(values, segFilter);\n ",l="";o%n>0&&(l="\n if (inIdx < 0 || inIdx >= "+o+") {\n return initializationValue;\n }\n ");var p="";o%n>0&&(p="\n if (inIdx < 0 || inIdx >= "+o+") {\n return -1.0;\n }\n "),this.userCode="\n const float initializationValue = 0.0;\n\n float getValue(int batch, int inIdx) {\n "+l+"\n return getX(batch, inIdx);\n }\n\n float getSegmentIdAtIndex(int inIdx) {\n "+p+"\n return getSegmentIds(inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = int(floor(float(outIdx) / float(\n "+a+")) * float("+n+"));\n int currentSeg = int(mod(float(outIdx), float("+a+")));\n\n float sumValue = 0.0;\n\n for (int i = 0; i < "+s+"; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n );\n\n "+c+"\n }\n\n int inIdx = inOffset + "+s+";\n if ("+(1===u)+") {\n vec4 values = vec4(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n 0,\n 0,\n 0\n );\n\n "+c+"\n } else if ("+(2===u)+") {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n 0,\n 0\n );\n\n "+c+"\n } else if ("+(3===u)+") {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n 0\n );\n\n "+c+"\n }\n setOutput(sumValue);\n }\n ";}}(),SelectProgram=function(){return function(e,t,n){var r,o;if(this.variableNames=["c","a","b"],this.outputShape=t,n>4)throw Error("Where for rank "+n+" is not yet supported");if(1===n)o="resRC",r="resRC";else{for(var a=["resRC.x","resRC.y","resRC.z","resRC.w"],i=[],s=[],u=0;u<t.length;u++)s.push(""+a[u]),u<e&&i.push(""+a[u]);r=i.join(),o=s.join();}var c=getCoordsDataType(n);this.userCode="\n void main() {\n "+c+" resRC = getOutputCoords();\n float cVal = getC("+r+");\n if (cVal >= 1.0) {\n setOutput(getA("+o+"));\n } else {\n setOutput(getB("+o+"));\n }\n }\n ";}}(),SliceProgram=function(){function e(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;var t,n=getCoordsDataType(this.rank),r="uniform int start["+this.rank+"];",o=getCoords$1(this.rank);t="\n "+n+" sourceLoc;\n "+n+" coords = getOutputCoords();\n "+e.map(function(e,t){return "sourceLoc."+coords[t]+" = start["+t+"] + coords."+coords[t]+";"}).join("\n")+"\n ",this.userCode="\n "+r+"\n void main() {\n "+t+"\n setOutput(getSource("+o+"));\n }\n ";}return e.prototype.getCustomSetupFunc=function(e){var t=this;if(e.length!==this.rank)throw Error("The rank ("+this.rank+") of the program must match the length of start ("+e.length+")");return function(n,r){null==t.startLoc&&(t.startLoc=n.getUniformLocationNoThrow(r,"start"),null==t.startLoc)||n.gl.uniform1iv(t.startLoc,e);}},e}(),coords=["x","y","z","w","u","v"];function getCoords$1(e){if(1===e)return "sourceLoc";if(e<=6)return coords.slice(0,e).map(function(e){return "sourceLoc."+e}).join(",");throw Error("Slicing for rank "+e+" is not yet supported")}var SlicePackedProgram=function(){function e(e){this.variableNames=["source"],this.usesPackedTextures=!0,this.outputShape=e,this.rank=e.length;var t=getCoordsDataType(this.rank),n=getChannels("coords",this.rank),r=getChannels("sourceLoc",this.rank),o=1===this.rank?"sourceLoc":"vec2("+r.slice(-2).join()+")",a="getChannel(getSource("+r.join()+"), "+o+")",i="\n result.x = "+a+";\n if (++"+n[this.rank-1]+" < "+e[this.rank-1]+") {\n ++"+r[this.rank-1]+";\n result.y = "+a+";\n --"+r[this.rank-1]+";\n }\n ",s=1===this.rank?"":"\n --"+n[this.rank-1]+";\n if (++"+n[this.rank-2]+" < "+e[this.rank-2]+") {\n ++"+r[this.rank-2]+";\n result.z = "+a+";\n if (++"+n[this.rank-1]+" < "+e[this.rank-1]+") {\n ++"+r[this.rank-1]+";\n result.w = "+a+";\n }\n }\n ",u=this.rank<=4?"sourceLoc = coords +\n "+t+"("+e.map(function(e,t){return "start["+t+"]"}).join()+");":e.map(function(e,t){return r[t]+" = "+n[t]+" + start["+t+"];"}).join("\n");this.userCode="\n uniform int start["+this.rank+"];\n void main() {\n "+t+" coords = getOutputCoords();\n "+t+" sourceLoc;\n "+u+" \n vec4 result = vec4(0.);\n "+i+"\n "+s+"\n setOutput(result);\n }\n ";}return e.prototype.getCustomSetupFunc=function(e){var t=this;if(e.length!==this.rank)throw Error("The rank ("+this.rank+") of the program must match the length of start ("+e.length+")");return function(n,r){null==t.startLoc&&(t.startLoc=n.getUniformLocationNoThrow(r,"start"),null==t.startLoc)||n.gl.uniform1iv(t.startLoc,e);}},e}(),StridedSliceProgram=function(){return function(e,t,n,r){this.variableNames=["x"];var o=n.filter(function(e,t){return -1===r.indexOf(t)});this.outputShape=o;var a=n.length,i=getCoordsDataType(n.length),s=getCoordsDataType(o.length),u="";if(1===a)u="coords * strides + begin";else{var c=0;u=n.map(function(e,t){return -1===r.indexOf(t)?(c++,1===o.length?"coords * strides["+t+"] + begin["+t+"]":"coords["+(c-1)+"] * strides["+t+"] + begin["+t+"]"):"begin["+t+"]"}).join(",");}this.userCode="\n "+i+" begin = "+i+"("+e+");\n "+i+" strides = "+i+"("+t+");\n\n void main() {\n "+s+" coords = getOutputCoords();\n setOutput(getX("+u+"));\n }\n ";}}(),TextureManager=function(){function e(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={};}return e.prototype.acquireTexture=function(e,t,n){var r,o=getPhysicalFromLogicalTextureType(t,n),a=getKeyFromTextureShape(e,o,n);if(a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]),this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this.log();var i=this.freeTextures[a].shift();return this.usedTextures[a].push(i),i}return this.numUsedTextures++,this.log(),o===PhysicalTextureType.PACKED_2X2_FLOAT32?r=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):o===PhysicalTextureType.PACKED_2X2_FLOAT16?r=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):o===PhysicalTextureType.UNPACKED_FLOAT32?r=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):o===PhysicalTextureType.UNPACKED_FLOAT16?r=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):o===PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE&&(r=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(r),r},e.prototype.releaseTexture=function(e,t,n,r){if(null!=this.freeTextures){var o=getKeyFromTextureShape(t,getPhysicalFromLogicalTextureType(n,r),r);o in this.freeTextures||(this.freeTextures[o]=[]),this.freeTextures[o].push(e),this.numFreeTextures++,this.numUsedTextures--;var a=this.usedTextures[o],i=a.indexOf(e);if(i<0)throw new Error("Cannot release a texture that was never provided by this texture manager");a.splice(i,1),this.log();}},e.prototype.log=function(){if(this.logEnabled){var e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",this.numFreeTextures+" / "+this.numUsedTextures,"("+e+")");}},e.prototype.getNumUsedTextures=function(){return this.numUsedTextures},e.prototype.getNumFreeTextures=function(){return this.numFreeTextures},e.prototype.dispose=function(){var e=this;if(null!=this.freeTextures){for(var t in this.freeTextures)this.freeTextures[t].forEach(function(t){e.gpgpu.deleteMatrixTexture(t);});for(var t in this.usedTextures)this.usedTextures[t].forEach(function(t){e.gpgpu.deleteMatrixTexture(t);});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0;}},e}();function getPhysicalFromLogicalTextureType(e,t){if(e===TextureUsage.UPLOAD)return t?PhysicalTextureType.PACKED_2X2_FLOAT32:PhysicalTextureType.UNPACKED_FLOAT32;if(e===TextureUsage.RENDER||null==e)return t?ENV.get("WEBGL_RENDER_FLOAT32_ENABLED")?PhysicalTextureType.PACKED_2X2_FLOAT32:PhysicalTextureType.PACKED_2X2_FLOAT16:ENV.get("WEBGL_RENDER_FLOAT32_ENABLED")?PhysicalTextureType.UNPACKED_FLOAT32:PhysicalTextureType.UNPACKED_FLOAT16;if(e===TextureUsage.DOWNLOAD||e===TextureUsage.PIXELS)return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;throw new Error("Unknown logical texture type "+e)}function getKeyFromTextureShape(e,t,n){return e[0]+"_"+e[1]+"_"+t+"_"+n}var TileProgram=function(){return function(e,t){this.variableNames=["A"];for(var n=new Array(e.length),r=0;r<n.length;r++)n[r]=e[r]*t[r];this.outputShape=n,this.rank=n.length;var o=getCoordsDataType(this.rank),a=getSourceCoords$2(e);this.userCode="\n void main() {\n "+o+" resRC = getOutputCoords();\n setOutput(getA("+a+"));\n }\n ";}}();function getSourceCoords$2(e){var t=e.length;if(t>5)throw Error("Tile for rank "+t+" is not yet supported");if(1===t)return "imod(resRC, "+e[0]+")";for(var n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],r=[],o=0;o<e.length;o++)r.push("imod("+n[o]+", "+e[o]+")");return r.join()}var TransposeProgram=function(){return function(e,t){this.variableNames=["A"];for(var n=new Array(e.length),r=0;r<n.length;r++)n[r]=e[t[r]];this.outputShape=n,this.rank=n.length;var o=getCoordsDataType(this.rank),a=getSwitchedCoords(t);this.userCode="\n void main() {\n "+o+" resRC = getOutputCoords();\n setOutput(getA("+a+"));\n }\n ";}}();function getSwitchedCoords(e){var t=e.length;if(t>6)throw Error("Transpose for rank "+t+" is not yet supported");for(var n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],r=new Array(t),o=0;o<e.length;o++)r[e[o]]=n[o];return r.join()}var TransposePackedProgram=function(){return function(e,t){this.variableNames=["A"],this.usesPackedTextures=!0;for(var n=new Array(e.length),r=0;r<n.length;r++)n[r]=e[t[r]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error("Packed transpose for rank "+this.rank+" is not yet supported.");var o=getCoordsDataType(this.rank),a=getVecChannels("rc",this.rank),i=new Array(this.rank);for(r=0;r<t.length;r++)i[t[r]]=a[r];var s="vec2("+i.slice(-2).join()+")",u="++"+a[this.rank-1]+" < "+n[this.rank-1],c="getChannel(getA("+i.join()+"), "+s+")";this.userCode="\n void main() {\n "+o+" rc = getOutputCoords();\n vec4 result = vec4(0.);\n result[0] = "+c+";\n if("+u+") {\n result[1] = "+c+";\n }\n --"+a[this.rank-1]+";\n if(++"+a[this.rank-2]+" < "+n[this.rank-2]+") {\n result[2] = "+c+";\n if("+u+") {\n result[3] = "+c+";\n }\n } \n setOutput(result);\n }\n ";}}(),ERF_P=.3275911,ERF_A1=.254829592,ERF_A2=-.284496736,ERF_A3=1.421413741,ERF_A4=-1.453152027,ERF_A5=1.061405429,SELU_SCALEALPHA=1.7580993408473768,SELU_SCALE=1.0507009873554805,UnaryOpProgram=function(){return function(e,t){this.variableNames=["A"],this.outputShape=e,this.userCode="\n float unaryOperation(float x) {\n "+t+"\n }\n\n void main() {\n float x = getAAtOutCoords();\n float y = unaryOperation(x);\n\n setOutput(y);\n }\n ";}}(),CHECK_NAN_SNIPPET$2="if (isnan(x)) return x;",LINEAR="return x;",ABS="return abs(x);",RELU=CHECK_NAN_SNIPPET$2+"\n return (x < 0.0) ? 0.0 : x;\n",ELU="return (x >= 0.0) ? x : (exp(x) - 1.0);",SELU="\n // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n // see: https://arxiv.org/abs/1706.02515\n float scaleAlpha = "+SELU_SCALEALPHA+";\n float scale = "+SELU_SCALE+";\n return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n";function STEP(e){return void 0===e&&(e=0),CHECK_NAN_SNIPPET$2+"\n return x > 0.0 ? 1.0 : float("+e+");\n "}var NEG="return -x;",CEIL="return ceil(x);",FLOOR="return floor(x);",SIGN="\n if (isnan(x)) { return 0.0; }\n return sign(x);\n",IS_NAN="return float(isnan(x));",IS_INF="return float(isinf(x));",IS_FINITE="return float(!isnan(x) && !isinf(x));",ROUND="\n // OpenGL ES does not support round function.\n // The algorithm is based on banker's rounding.\n float base = floor(x);\n if ((x - base) < 0.5) {\n return floor(x);\n } else if ((x - base) > 0.5) {\n return ceil(x);\n } else {\n if (mod(base, 2.0) == 0.0) {\n return base;\n } else {\n return base + 1.0;\n }\n }\n",EXP="return exp(x);",EXPM1="return exp(x) - 1.0;",LOG="if (x < 0.0) return NAN;\n return log(x);",LOG1P="return log(1.0 + x);",SQRT="return sqrt(x);",RSQRT="return inversesqrt(x);",SIGMOID="return 1.0 / (1.0 + exp(-1.0 * x));",SOFTPLUS="\n float epsilon = 1.1920928955078125e-7;\n float threshold = log(epsilon) + 2.0;\n\n bool too_large = x > -threshold;\n bool too_small = x < threshold;\n\n float result;\n float exp_x = exp(x);\n\n if (too_large){\n result = x;\n }\n else if (too_small){\n result = exp_x;\n }\n else{\n result = log(exp_x + 1.0);\n }\n return result;\n",SIN=CHECK_NAN_SNIPPET$2+"\n return sin(x);\n",COS=CHECK_NAN_SNIPPET$2+"\n return cos(x);\n",TAN="return tan(x);",ASIN="return asin(x);",ACOS="return acos(x);",ATAN=CHECK_NAN_SNIPPET$2+"\n return atan(x);\n",SINH="\n float e2x = exp(x);\n return (e2x - 1.0 / e2x) / 2.0;\n",COSH="\n float e2x = exp(-x);\n return (e2x + 1.0 / e2x) / 2.0;\n",TANH="\n float e2x = exp(-2.0 * abs(x));\n return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n",ASINH="return log(x + sqrt(x * x + 1.0));",ACOSH=CHECK_NAN_SNIPPET$2+"\n if (x < 1.0) return NAN;\n return log(x + sqrt(x * x - 1.0));",ATANH=CHECK_NAN_SNIPPET$2+"\n if ((x < -1.0) || (x > 1.0)) return NAN;\n return (log(1.0 + x) - log(1.0 - x)) / 2.0;",ERF='\n // Error function is calculated approximately with elementary function.\n // See "Handbook of Mathematical Functions with Formulas,\n // Graphs, and Mathematical Tables", Abramowitz and Stegun.\n float p = '+ERF_P+";\n float a1 = "+ERF_A1+";\n float a2 = "+ERF_A2+";\n float a3 = "+ERF_A3+";\n float a4 = "+ERF_A4+";\n float a5 = "+ERF_A5+";\n\n float t = 1.0 / (1.0 + p * x);\n return 1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x);\n",SQUARE="return x * x;",RECIPROCAL="return 1.0 / x;",LOGICAL_NOT="return float(!(x >= 1.0));",TO_INT="return float(int(x));",CLONE="return x;",LINEAR$1="return x;",LOG$1="\n vec4 result = log(x);\n vec4 isNaN = vec4(lessThan(x, vec4(0.0)));\n result.r = isNaN.r == 1.0 ? NAN : result.r;\n result.g = isNaN.g == 1.0 ? NAN : result.g;\n result.b = isNaN.b == 1.0 ? NAN : result.b;\n result.a = isNaN.a == 1.0 ? 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yet.");for(o=0;o<t[0];++o)for(a=0;a<t[1];++a)for(i=0;i<t[2];++i)for(var s=0;s<t[3];++s)n=n.add(e.slice([o*r.shape[0],a*r.shape[1],i*r.shape[2],s*r.shape[3]],[r.shape[0],r.shape[1],r.shape[2],r.shape[3]]));}return n}}})}function pad1d_(e,t,n){return void 0===n&&(n=0),assert(2===t.length,function(){return "Invalid number of paddings. Must be length of 2."}),pad(e,[t],n)}function pad2d_(e,t,n){return void 0===n&&(n=0),assert(2===t.length&&2===t[0].length&&2===t[1].length,function(){return "Invalid number of paddings. Must be length of 2 each."}),pad(e,t,n)}function pad3d_(e,t,n){return void 0===n&&(n=0),assert(3===t.length&&2===t[0].length&&2===t[1].length&&2===t[2].length,function(){return "Invalid number of paddings. Must be length of 2 each."}),pad(e,t,n)}function pad4d_(e,t,n){return void 0===n&&(n=0),assert(4===t.length&&2===t[0].length&&2===t[1].length&&2===t[2].length&&2===t[3].length,function(){return "Invalid number of paddings. Must be length of 2 each."}),pad(e,t,n)}function pad_(e,t,n){void 0===n&&(n=0);var r=convertToTensor(e,"x","pad");if(0===r.rank)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");var o=t.map(function(e){return e[0]});return ENV.engine.runKernel(function(e){return e.pad(r,t,n)},{$x:r},function(e){return {$x:function(){return e.slice(o,r.shape)}}})}function stack_(e,t){void 0===t&&(t=0);var n=convertToTensorArray(e,"tensors","stack");if(assert(n.length>=1,function(){return "Pass at least one tensor to tf.stack"}),1===n.length)return n[0].expandDims(t);var r=n[0].rank,o=n[0].shape,a=n[0].dtype;assert(t<=r,function(){return "Axis must be <= rank of the tensor"}),n.forEach(function(e){assertShapesMatch(o,e.shape,"All tensors passed to stack must have matching shapes");}),n.forEach(function(e){assert(a===e.dtype,function(){return "All tensors passed to stack must have matching dtypes"});});var i=n.map(function(e){return e.expandDims(t)});return concat(i,t)}function batchToSpaceND_(e,t,n){var r=convertToTensor(e,"x","batchToSpaceND"),o=t.reduce(function(e,t){return e*t});assert(r.rank>=1+t.length,function(){return "input rank is "+r.rank+" but should be > than blockShape.length "+t.length}),assert(n.length===t.length,function(){return "crops.length is "+n.length+" but should be equal to blockShape.length "+t.length}),assert(r.shape[0]%o==0,function(){return "input tensor batch is "+r.shape[0]+" but is not divisible by the product of the elements of blockShape "+t.join(" * ")+" === "+o});return ENV.engine.runKernel(function(e){return e.batchToSpaceND(r,t,n)},{$x:r},function(e){return {$x:function(){return e.spaceToBatchND(t,n)}}})}function spaceToBatchND_(e,t,n){var r=convertToTensor(e,"x","spaceToBatchND");assert(r.rank>=1+t.length,function(){return "input rank "+r.rank+" should be > than [blockShape] "+t.length}),assert(n.length===t.length,function(){return "paddings.shape[0] "+n.length+" must be equal to [blockShape] "+t.length}),assert(r.shape.reduce(function(e,r,o){return o>0&&o<=t.length?e&&(r+n[o-1][0]+n[o-1][1])%t[o-1]==0:e},!0),function(){return "input spatial dimensions "+r.shape.slice(1)+" with paddings "+n.toString()+" must be divisible by blockShapes "+t.toString()});return ENV.engine.runKernel(function(e){return e.spaceToBatchND(r,t,n)},{$x:r},function(e){return {$x:function(){return e.batchToSpaceND(t,n)}}})}function unstack_(e,t){void 0===t&&(t=0),t=t||0;var n=convertToTensor(e,"x","unstack");assert(t>=-n.shape.length&&t<n.shape.length,function(){return "Axis = "+t+" is not in [-"+n.shape.length+", "+n.shape.length+")"}),t<0&&(t+=n.shape.length);return ENV.engine.runKernel(function(e){return e.unstack(n,t)},{$x:n},function(e){return {$x:function(){return stack(e,t)}}})}function cumsum_(e,t,n,r){void 0===t&&(t=0),void 0===n&&(n=!1),void 0===r&&(r=!1);var o=convertToTensor(e,"x","cumsum"),a=getAxesPermutation([t|=0],o.rank),i=o;null!=a&&(i=o.transpose(a));var s=getInnerMostAxes(1,o.rank)[0],u=ENV.engine.runKernel(function(e){return e.cumsum(i,s,n,r)},{permutedX:i},function(e){return {permutedX:function(){return e.cumsum(t,n,!r)}}});return null!=a&&(u=u.transpose(a)),u}function expandDims_(e,t){void 0===t&&(t=0);var n=convertToTensor(e,"x","expandDims");assert(t<=n.rank,function(){return "Axis must be <= rank of the tensor"});var r=n.shape.slice();return t<0&&(assert(-(n.rank+1)<=t,function(){return "Axis must be in the interval ["+-(n.rank+1)+", "+n.rank+"]"}),t=n.rank+t+1),r.splice(t,0,1),reshape(n,r)}function depthToSpace_(e,t,n){void 0===n&&(n="NHWC");var r=convertToTensor(e,"x","depthToSpace"),o="NHWC"===n?r.shape[1]:r.shape[2],a="NHWC"===n?r.shape[2]:r.shape[3],i="NHWC"===n?r.shape[3]:r.shape[1];return assert(o*t>=0,function(){return "Negative dimension size caused by overflow when multiplying\n "+o+" and "+t+" for depthToSpace with input shape\n "+r.shape}),assert(a*t>=0,function(){return "Negative dimension size caused by overflow when multiplying\n "+a+" and "+t+" for depthToSpace with input shape\n "+r.shape}),assert(i%(t*t)==0,function(){return "Dimension size must be evenly divisible by "+t*t+" but is "+i+" for depthToSpace with input shape "+r.shape}),ENV.engine.runKernel(function(e){return e.depthToSpace(r,t,n)},{$x:r})}function setdiff1dAsync_(e,t){return __awaiter$1(this,void 0,void 0,function(){var n,r,o,a,i,s,u,c,l,p;return __generator$1(this,function(d){switch(d.label){case 0:return n=convertToTensor(e,"x","setdiff1d"),r=convertToTensor(t,"y","setdiff1d"),assert(n.dtype===r.dtype,function(){return "x and y should have the same dtype, but got x ("+n.dtype+") and y ("+r.dtype+")."}),assert(1===n.rank,function(){return "x should be 1D tensor, but got x ("+n.shape+")."}),assert(1===r.rank,function(){return "y should be 1D tensor, but got y ("+r.shape+")."}),[4,n.data()];case 1:return o=d.sent(),[4,r.data()];case 2:for(a=d.sent(),i=new Set(a),s=0,l=0;l<o.length;l++)i.has(o[l])||s++;for(u=new TensorBuffer([s],n.dtype),c=new TensorBuffer([s],"int32"),l=0,p=0;l<o.length;l++)i.has(o[l])||(u.values[p]=o[l],c.values[p]=l,p++);return [2,[u.toTensor(),c.toTensor()]]}})})}function buffer(e,t,n){return void 0===t&&(t="float32"),t=t||"float32",assertNonNegativeIntegerDimensions(e),new TensorBuffer(e,t,n)}function print(e,t){void 0===t&&(t=!1),console.log(e.toString(t));}var batchToSpaceND=op({batchToSpaceND_:batchToSpaceND_}),cast=op({cast_:cast_}),clone=op({clone_:clone_}),cumsum=op({cumsum_:cumsum_}),depthToSpace=op({depthToSpace_:depthToSpace_}),expandDims=op({expandDims_:expandDims_}),eye=op({eye_:eye_}),multinomial=op({multinomial_:multinomial_}),oneHot=op({oneHot_:oneHot_}),pad=op({pad_:pad_}),pad1d=op({pad1d_:pad1d_}),pad2d=op({pad2d_:pad2d_}),pad3d=op({pad3d_:pad3d_}),pad4d=op({pad4d_:pad4d_}),rand=op({rand_:rand_}),randomNormal=op({randomNormal_:randomNormal_}),randomUniform=op({randomUniform_:randomUniform_}),reshape=op({reshape_:reshape_}),spaceToBatchND=op({spaceToBatchND_:spaceToBatchND_}),squeeze=op({squeeze_:squeeze_}),stack=op({stack_:stack_}),tile=op({tile_:tile_}),truncatedNormal=op({truncatedNormal_:truncatedNormal_}),unstack=op({unstack_:unstack_}),setdiff1dAsync=setdiff1dAsync_;function whereImpl(e,t){for(var n=[],r=0;r<t.length;r++)t[r]&&n.push(r);var o=buffer(e,"int32"),a=buffer([n.length,e.length],"int32");for(r=0;r<n.length;r++){var i=o.indexToLoc(n[r]),s=r*e.length;a.values.set(i,s);}return a.toTensor()}var binaryCaches={};function getBinaryCache(e){return e in binaryCaches?binaryCaches[e]:(binaryCaches[e]={},binaryCaches[e])}function mapActivationToShaderProgram(e,t){if(void 0===t&&(t=!1),"linear"===e)return t?LINEAR$1:LINEAR;if("relu"===e)return t?RELU$1:RELU;throw new Error("Activation "+e+" has not been implemented for the WebGL backend.")}var CPU_HANDOFF_SIZE_THRESHOLD=128,MATMUL_SHARED_DIM_THRESHOLD=1e3,MathBackendWebGL=function(){function e(e){if(this.gpgpu=e,this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.lruDataGPU=[],this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.disposed=!1,ENV.get("WEBGL_VERSION")<1)throw new Error("WebGL is not supported on this device");if(null==e){var t=getWebGLContext(ENV.get("WEBGL_VERSION"));this.binaryCache=getBinaryCache(ENV.get("WEBGL_VERSION")),this.gpgpu=new GPGPUContext(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0;}else this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new TextureManager(this.gpgpu);}return e.prototype.register=function(e,t,n){if(this.texData.has(e))throw new Error("Data buffer is already registered");this.texData.set(e,{shape:t,dtype:n});},e.prototype.setDataMover=function(e){this.texData=new DataStorage(e);},e.prototype.fromPixels=function(e,t){if(null==e)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");var n=[e.height,e.width],r=[e.height,e.width,t];if(ENV.get("IS_BROWSER")){if(!(e instanceof HTMLVideoElement||e instanceof HTMLImageElement||e instanceof HTMLCanvasElement||e instanceof ImageData))throw new Error("pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement or ImageData, but was "+e.constructor.name);if(e instanceof HTMLVideoElement){if(null==this.fromPixels2DContext){if("complete"!==document.readyState)throw new Error("The DOM is not ready yet. Please call tf.browser.fromPixels() once the DOM is ready. One way to do that is to add an event listener for `DOMContentLoaded` on the document object");this.fromPixels2DContext=document.createElement("canvas").getContext("2d");}this.fromPixels2DContext.canvas.width=e.width,this.fromPixels2DContext.canvas.height=e.height,this.fromPixels2DContext.drawImage(e,0,0,e.width,e.height),e=this.fromPixels2DContext.canvas;}}var o=this.makeTensorHandle(n,"int32");this.texData.get(o.dataId).usage=TextureUsage.PIXELS,this.gpgpu.uploadPixelDataToTexture(this.getTexture(o.dataId),e);var a=new FromPixelsProgram(r),i=this.compileAndRun(a,[o]);return this.disposeData(o.dataId),i},e.prototype.makeTensorHandle=function(e,t){var n={};return this.register(n,e,t),{dataId:n,shape:e,dtype:t}},e.prototype.write=function(e,t){if(null==t)throw new Error("MathBackendWebGL.write(): values can not be null");if(ENV.get("DEBUG"))for(var n=0;n<t.length;n++){var r=t[n];if(!canBeRepresented(r))throw Error("The value "+r+" cannot be represented on this device.")}var o=this.texData.get(e),a=o.texture,i=o.texShape,s=o.usage,u=o.dtype,c=o.isPacked;if("complex64"===u)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");null!=a&&(this.releaseTexture(e,a,i,s,u,c),o.texture=null,o.texShape=null),o.usage=TextureUsage.UPLOAD,o.values=t;},e.prototype.readSync=function(e){var t=this.texData.get(e),n=t.values,r=t.dtype,o=t.complexTensors,a=t.slice,i=t.shape;if(null!=a){var s=new UnaryOpProgram(i,CLONE),u=this.compileAndRun(s,[{dataId:e,shape:i,dtype:r}]),c=this.readSync(u.dataId);return u.dispose(),c}if(null!=n)return this.convertAndCacheOnCPU(e);if("string"===r)return n;var l,p,d=null!=this.activeTimers;(d&&(l=performance.now()),"complex64"===r)?p=mergeRealAndImagArrays(o.real.dataSync(),o.imag.dataSync()):p=this.getValuesFromTexture(e);return d&&(this.downloadWaitMs+=performance.now()-l),this.convertAndCacheOnCPU(e,p)},e.prototype.read=function(e){return __awaiter$1(this,void 0,void 0,function(){var t,n,r,o,a,i,s,u,c,l,p,d,h,f,m,g,v,y,x,E,T,b,w,S;return __generator$1(this,function(C){switch(C.label){case 0:if(this.pendingRead.has(e))return r=this.pendingRead.get(e),[2,new Promise(function(e){return r.push(e)})];if(o=this.texData.get(e),a=o.texture,i=o.values,s=o.texShape,u=o.isPacked,c=o.shape,l=o.slice,p=o.dtype,null!=l)return d=new UnaryOpProgram(c,CLONE),h=this.compileAndRun(d,[{dataId:e,shape:c,dtype:p}]),f=this.read(h.dataId),h.dispose(),[2,f];if(null!=i)return [2,this.convertAndCacheOnCPU(e)];if(this.pendingRead.set(e,[]),!ENV.get("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&2===ENV.get("WEBGL_VERSION"))throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");return m=s[1],g=s[0],u&&(t=getPackedMatrixTextureShapeWidthHeight(s[0],s[1]),m=t[0],g=t[1]),v=this.gpgpu.maybeCreateBufferFromTexture(a,g,m),[4,this.gpgpu.createAndWaitForFence()];case 1:return C.sent(),v instanceof WebGLTexture?y=this.getValuesFromTexture(e):(x=sizeFromShape(c),u?(E=getBatchDim(c),T=1,b=1,c.length&&(n=getRowsCols(c),T=n[0],b=n[1]),y=this.gpgpu.downloadPackedMatrixFromBuffer(v,E,T,b,s[0],s[1]).subarray(0,x)):y=this.gpgpu.downloadFloat32MatrixFromBuffer(v,s[0],s[1]).subarray(0,x)),w=this.convertAndCacheOnCPU(e,y),S=this.pendingRead.get(e),this.pendingRead.delete(e),S.forEach(function(e){return e(w)}),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)),[2,w]}})})},e.prototype.getValuesFromTexture=function(e){var t,n=this,r=this.texData.get(e),o=r.shape,a=r.dtype,i=r.texture,s=r.texShape,u=sizeFromShape(o);if(ENV.get("WEBGL_DOWNLOAD_FLOAT_ENABLED")){if(this.texData.get(e).isPacked){var c=getBatchDim(o),l=1,p=1;return o.length&&(l=(t=getRowsCols(o))[0],p=t[1]),this.gpgpu.downloadMatrixFromPackedTexture(i,c,l,p,s[0],s[1]).subarray(0,u)}return this.gpgpu.downloadFloat32MatrixFromOutputTexture(i,s[0],s[1]).subarray(0,u)}var d=this.makeTensorHandle(o,"float32");d.size=sizeFromShape(o),this.texData.get(d.dataId).usage=TextureUsage.DOWNLOAD;var h=tidy(function(){var t=new EncodeFloatProgram(o);return n.compileAndRun(t,[{shape:o,dtype:a,dataId:e}],d,null,!1)}),f=this.texData.get(h.dataId),m=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(f.texture,f.texShape[0],f.texShape[1]).subarray(0,u);return this.disposeData(d.dataId),m},e.prototype.time=function(e){return __awaiter$1(this,void 0,void 0,function(){var t,n,r,o,a,i,s;return __generator$1(this,function(u){switch(u.label){case 0:return t=this.activeTimers,n=[],r=!1,null==this.programTimersStack?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e(),o=flatten(this.activeTimers.map(function(e){return e.query})).filter(function(e){return null!=e}),a=flatten(this.activeTimers.map(function(e){return e.name})).filter(function(e){return null!=e}),this.activeTimers=t,r&&(this.programTimersStack=null),[4,Promise.all(o)];case 1:return i=u.sent(),s={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:sum(i),getExtraProfileInfo:function(){return i.map(function(e,t){return {name:a[t],ms:e}}).map(function(e){return e.name+": "+e.ms}).join(", ")},wallMs:null},this.uploadWaitMs=0,this.downloadWaitMs=0,[2,s]}})})},e.prototype.memory=function(){return {unreliable:!1,numBytesInGPU:this.numBytesInGPU}},e.prototype.startTimer=function(){return ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?this.gpgpu.beginQuery():{startMs:performance.now(),endMs:null}},e.prototype.endTimer=function(e){return ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(this.gpgpu.endQuery(),e):(e.endMs=performance.now(),e)},e.prototype.getQueryTime=function(e){return __awaiter$1(this,void 0,void 0,function(){var t;return __generator$1(this,function(n){return ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?[2,this.gpgpu.waitForQueryAndGetTime(e)]:[2,(t=e).endMs-t.startMs]})})},e.prototype.disposeData=function(e){if(!this.pendingDisposal.has(e))if(this.pendingRead.has(e))this.pendingDisposal.add(e);else if(this.texData.has(e)){var t=this.texData.get(e),n=t.texture,r=t.dtype,o=t.texShape,a=t.usage,i=t.complexTensors,s=t.isPacked,u=t.slice;if(null!=n){var c=u&&u.origDataId||e,l=this.dataRefCount.get(c);l>1?this.dataRefCount.set(c,l-1):(this.dataRefCount.delete(c),this.releaseTexture(e,n,o,a,r,s),this.texData.delete(e));}null!=i&&(i.real.dispose(),i.imag.dispose());}},e.prototype.getTexture=function(e){return this.uploadToGPU(e),this.texData.get(e).texture},e.prototype.getCPUBackend=function(){return ENV.get("WEBGL_CPU_FORWARD")?(null==this.cpuBackend&&(this.cpuBackend=ENV.findBackend("cpu")),this.cpuBackend):null},e.prototype.shouldExecuteOnCPU=function(e,t){var n=this;return void 0===t&&(t=CPU_HANDOFF_SIZE_THRESHOLD),null!=this.getCPUBackend()&&e.every(function(e){return null==n.texData.get(e.dataId).texture&&e.size<t})},e.prototype.getGPGPUContext=function(){return this.gpgpu},e.prototype.getCanvas=function(){return this.canvas},e.prototype.complex=function(e,t){var n=this.makeOutputArray(e.shape,"complex64");return this.texData.get(n.dataId).complexTensors={real:ENV.engine.keep(e.clone()),imag:ENV.engine.keep(t.clone())},n},e.prototype.real=function(e){return this.texData.get(e.dataId).complexTensors.real.clone()},e.prototype.imag=function(e){return this.texData.get(e.dataId).complexTensors.imag.clone()},e.prototype.slice=function(e,t,n){if(this.shouldExecuteOnCPU([e]))return this.cpuBackend.slice(e,t,n);var r=this.texData.get(e.dataId).isPacked,o=isSliceContinous(e.shape,t,n);if(r||!o){var a=ENV.get("WEBGL_PACK_ARRAY_OPERATIONS")?new SlicePackedProgram(n):new SliceProgram(n),i=a.getCustomSetupFunc(t);return this.compileAndRun(a,[e],null,i)}return this.uploadToGPU(e.dataId),this.shallowSlice(e,t,n)},e.prototype.shallowSlice=function(e,t,n){var r=this.texData.get(e.dataId),o=Tensor.make(n,{},e.dtype,this),a=this.texData.get(o.dataId);Object.assign(a,r),a.shape=n,a.dtype=e.dtype;var i=computeFlatOffset(t,e.strides);r.slice&&(i+=r.slice.flatOffset),a.slice={flatOffset:i,origDataId:r.slice&&r.slice.origDataId||e.dataId};var s=this.dataRefCount.get(a.slice.origDataId)||1;return this.dataRefCount.set(a.slice.origDataId,s+1),o},e.prototype.stridedSlice=function(e,t,n,r,o,a,i,s,u){if(this.shouldExecuteOnCPU([e]))return this.cpuBackend.stridedSlice(e,t,n,r,o,a,i,s,u);var c=getStridedSlicedInfo(e.shape,t,n,r,o,a,i,s,u),l=c[0],p=c[1],d=c[2],h=p.filter(function(e,t){return -1===d.indexOf(t)});if(h.some(function(e){return 0===e}))return tensor([],h);var f=new StridedSliceProgram(l,r,p,d);return this.compileAndRun(f,[e])},e.prototype.reverse=function(e,t){var n=ENV.get("WEBGL_PACK_ARRAY_OPERATIONS")?new ReversePackedProgram(e.shape,t):new ReverseProgram(e.shape,t);return this.compileAndRun(n,[e])},e.prototype.concat=function(e,t){if(this.shouldExecuteOnCPU(e))return this.cpuBackend.concat(e,t);if(1===e.length)return e[0];if(e.length>ENV.get("WEBGL_MAX_TEXTURES_IN_SHADER")){var n=Math.floor(e.length/2),r=this.concat(e.slice(0,n),t),o=this.concat(e.slice(n),t);return this.concat([r,o],t)}if(ENV.get("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].rank>1){var a=new ConcatPackedProgram(e.map(function(e){return e.shape}),t);return this.compileAndRun(a,e)}var i=computeOutShape(e.map(function(e){return e.shape}),t),s=e.map(function(e){return e.as2D(-1,sizeFromShape(e.shape.slice(t)))}),u=new ConcatProgram(s.map(function(e){return e.shape}));return this.compileAndRun(u,s).reshape(i)},e.prototype.neg=function(e){var t=new UnaryOpProgram(e.shape,NEG);return this.compileAndRun(t,[e])},e.prototype.batchMatMul=function(e,t,n,r){var o=n?e.shape[2]:e.shape[1],a=r?t.shape[1]:t.shape[2],i=n?e.shape[1]:e.shape[2],s=e.shape[0];if((1===o||1===a)&&i>MATMUL_SHARED_DIM_THRESHOLD){n&&(e=e.transpose([0,2,1])),r&&(t=t.transpose([0,2,1]));var u=1===a?e:e.as3D(s,i,1),c=1===a?2:1,l=1===a?t.as3D(s,1,i):t;return this.multiply(u,l).sum(c,!0)}var p=upcastType(e.dtype,t.dtype),d=new MatMulPackedProgram(e.shape,[s,o,a],n,r),h=this.makePackedTensor(d.outputShape,p);return this.compileAndRun(d,[e,t],h)},e.prototype.fusedBatchMatMul=function(e,t,n,r,o,a){var i=n?e.shape[2]:e.shape[1],s=r?t.shape[1]:t.shape[2],u=e.shape[0],c=upcastType(e.dtype,t.dtype),l=new MatMulPackedProgram(e.shape,[u,i,s],n,r,!!o,a?mapActivationToShaderProgram(a,!0):null),p=this.makePackedTensor(l.outputShape,c),d=[e,t];return o&&d.push(o),this.compileAndRun(l,d,p)},e.prototype.multiply=function(e,t){if("complex64"===e.dtype){var n=this.texData.get(e.dataId),r=this.texData.get(t.dataId),o=new BinaryOpComplexProgram(COMPLEX_MULTIPLY.REAL,e.shape,t.shape),a=new BinaryOpComplexProgram(COMPLEX_MULTIPLY.IMAG,e.shape,t.shape),i=[this.makeComplexComponentTensorHandle(e,n.complexTensors.real),this.makeComplexComponentTensorHandle(e,n.complexTensors.imag),this.makeComplexComponentTensorHandle(t,r.complexTensors.real),this.makeComplexComponentTensorHandle(t,r.complexTensors.imag)],s=this.compileAndRun(o,i),u=this.compileAndRun(a,i),c=this.complex(s,u);return s.dispose(),u.dispose(),c}if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.multiply(e,t);if(ENV.get("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,MUL,e.dtype);var l=new BinaryOpProgram(MUL,e.shape,t.shape),p=this.makeOutputArray(l.outputShape,e.dtype);return this.compileAndRun(l,[e,t],p)},e.prototype.batchNormalization=function(e,t,n,r,o,a){var i=[e,t,n],s=null;null!=a&&(s=a.shape,i.push(a));var u=null;if(null!=o&&(u=o.shape,i.push(o)),ENV.get("WEBGL_PACK_BATCHNORMALIZATION")){var c=new BatchNormPackedProgram(e.shape,t.shape,n.shape,s,u,r);return this.compileAndRun(c,i)}var l=new BatchNormProgram(e.shape,t.shape,n.shape,s,u,r);return this.compileAndRun(l,i)},e.prototype.localResponseNormalization4D=function(e,t,n,r,o){var a=new LRNProgram(e.shape,t,n,r,o);return this.compileAndRun(a,[e])},e.prototype.LRNGrad=function(e,t,n,r,o,a,i){var s=new LRNGradProgram(t.shape,r,o,a,i);return this.compileAndRun(s,[t,n,e])},e.prototype.tile=function(e,t){var n=new TileProgram(e.shape,t);return this.compileAndRun(n,[e])},e.prototype.pad=function(e,t,n){var r=ENV.get("WEBGL_PACK_ARRAY_OPERATIONS")?new PadPackedProgram(e.shape,t,n):new PadProgram(e.shape,t,n);return this.compileAndRun(r,[e])},e.prototype.transpose=function(e,t){if(this.shouldExecuteOnCPU([e]))return this.cpuBackend.transpose(e,t);var n=ENV.get("WEBGL_PACK_ARRAY_OPERATIONS")?new TransposePackedProgram(e.shape,t):new TransposeProgram(e.shape,t);return this.compileAndRun(n,[e])},e.prototype.gather=function(e,t,n){if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.gather(e,t,n);var r=new GatherProgram(e.shape,t.size,n);return this.compileAndRun(r,[e,t])},e.prototype.batchToSpaceND=function(e,t,n){assert(e.rank<=4,function(){return "batchToSpaceND for rank > 4 with a WebGL backend not implemented yet"});var r=t.reduce(function(e,t){return e*t}),o=getReshaped(e.shape,t,r),a=getPermuted(o.length,t.length),i=getReshapedPermuted(e.shape,t,r),s=getSliceBeginCoords(n,t.length),u=getSliceSize(i,n,t.length);return e.reshape(o).transpose(a).reshape(i).slice(s,u)},e.prototype.spaceToBatchND=function(e,t,n){assert(e.rank<=4,function(){return "spaceToBatchND for rank > 4 with a WebGL backend not implemented yet"});var r=t.reduce(function(e,t){return e*t}),o=[[0,0]];o.push.apply(o,n);for(var a=1+t.length;a<e.shape.length;++a)o.push([0,0]);var i=e.pad(o),s=getReshaped(i.shape,t,r,!1),u=getPermuted(s.length,t.length,!1),c=getReshapedPermuted(i.shape,t,r,!1);return i.reshape(s).transpose(u).reshape(c)},e.prototype.reduce=function(e,t,n){var r=e.shape[0],o=e.shape[1],a=computeOptimalWindowSize(o),i=new ReduceProgram({windowSize:a,inSize:o,batchSize:r},t),s=i.outputShape,u=s[0],c=s[1],l=this.makeOutputArray([u,c],n);return this.compileAndRun(i,[e],l),1===l.shape[1]?l:this.reduce(l,t,n)},e.prototype.argReduce=function(e,t,n){void 0===n&&(n=null);var r=e.shape[0],o=e.shape[1];null!=n&&(r=n.shape[0],o=n.shape[1]);var a=computeOptimalWindowSize(o),i=new ArgMinMaxProgram({windowSize:a,inSize:o,batchSize:r},t,null==n),s=i.outputShape,u=s[0],c=s[1],l=this.makeOutputArray([u,c],"int32"),p=[e];return null!=n&&p.push(n),this.compileAndRun(i,p,l),1===l.shape[1]?l:this.argReduce(e,t,l)},e.prototype.argReducePacked=function(e,t,n){void 0===n&&(n=null);var r=null!=n?n.shape:e.shape,o=computeOptimalWindowSize(r[r.length-1]),a=new ArgMinMaxPackedProgram(r,o,t,null==n),i=this.makePackedTensor(a.outputShape,"int32"),s=null==n?[e]:[e,n];return this.compileAndRun(a,s,i),i.rank===e.rank?this.argReducePacked(e,t,i):i},e.prototype.sum=function(e,t){assertAxesAreInnerMostDims("sum",t,e.rank);var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=sizeFromShape(n[1]),a=e.as2D(-1,o),i=sumOutType(e.dtype);return this.reduce(a,"sum",i).reshape(r)},e.prototype.prod=function(e,t){if(this.shouldExecuteOnCPU([e]))return this.cpuBackend.prod(e,t);var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=sizeFromShape(n[1]),a=e.as2D(-1,o),i=sumOutType(e.dtype);return this.reduce(a,"prod",i).reshape(r)},e.prototype.unsortedSegmentSum=function(e,t,n){var r=0,o=getAxesPermutation([r],e.rank),a=e;null!=o&&(a=e.transpose(o),r=getInnerMostAxes(1,e.rank)[0]);var i=computeOutShape$1(a.shape,r,n),s=sizeFromShape([a.shape[r]]),u=a.as2D(-1,s),c=sumOutType(e.dtype),l=this.segOpCompute(u,"unsortedSegmentSum",t,c,n).reshape(i);return null!=o&&(l=l.transpose(getUndoAxesPermutation(o))),l},e.prototype.segOpCompute=function(e,t,n,r,o){var a=e.shape[0],i=e.shape[1],s=segOpComputeOptimalWindowSize(i,o),u=new SegmentOpProgram({windowSize:s,inSize:i,batchSize:a,numSegments:o},t),c=u.outputShape,l=c[0],p=c[1],d=this.makeOutputArray([l,p],r);return this.compileAndRun(u,[e,n],d),d.shape[1]===o?d:(n=range(0,o).tile([i/s]),this.segOpCompute(d,t,n,r,o))},e.prototype.argMinMaxReduce=function(e,t,n){var r=[t];if(assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),r,e.rank),!ENV.get("WEBGL_PACK_REDUCE")||e.rank<=2){var o=computeOutAndReduceShapes(e.shape,r),a=o[0],i=sizeFromShape(o[1]),s=e.as2D(-1,i);return this.argReduce(s,n).reshape(a)}return this.argReducePacked(e,n)},e.prototype.argMin=function(e,t){return this.argMinMaxReduce(e,t,"min")},e.prototype.argMax=function(e,t){return this.argMinMaxReduce(e,t,"max")},e.prototype.cumsum=function(e,t,n,r){if(t!==e.rank-1)throw new Error("WebGL cumsum shader expects an inner-most axis="+(e.rank-1)+" but got axis="+t);var o=new CumSumProgram(e.shape,n,r);return this.compileAndRun(o,[e])},e.prototype.equal=function(e,t){if(ENV.get("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,EQUAL$1,"bool");var n=new BinaryOpProgram(EQUAL,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[e,t],r)},e.prototype.notEqual=function(e,t){if(ENV.get("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,NOT_EQUAL$1,"bool");var n=new BinaryOpProgram(NOT_EQUAL,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[e,t],r)},e.prototype.less=function(e,t){if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.less(e,t);if(ENV.get("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,LESS$1,"bool");var n=new BinaryOpProgram(LESS,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[e,t],r)},e.prototype.lessEqual=function(e,t){if(ENV.get("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,LESS_EQUAL$1,"bool");var n=new BinaryOpProgram(LESS_EQUAL,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[e,t],r)},e.prototype.greater=function(e,t){if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.greater(e,t);if(ENV.get("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,GREATER$1,"bool");var n=new BinaryOpProgram(GREATER,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[e,t],r)},e.prototype.greaterEqual=function(e,t){if(ENV.get("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,GREATER_EQUAL$1,"bool");var n=new BinaryOpProgram(GREATER_EQUAL,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[e,t],r)},e.prototype.logicalNot=function(e){var t=new UnaryOpProgram(e.shape,LOGICAL_NOT);return this.compileAndRun(t,[e])},e.prototype.logicalAnd=function(e,t){if(ENV.get("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,LOGICAL_AND$1,"bool");var n=new BinaryOpProgram(LOGICAL_AND,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[e,t],r)},e.prototype.logicalOr=function(e,t){if(ENV.get("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,LOGICAL_OR$1,"bool");var n=new BinaryOpProgram(LOGICAL_OR,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[e,t],r)},e.prototype.select=function(e,t,n){var r=new SelectProgram(e.rank,t.shape,t.rank),o=this.makeOutputArray(r.outputShape,upcastType(t.dtype,n.dtype));return this.compileAndRun(r,[e,t,n],o)},e.prototype.where=function(e){warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");var t=e.dataSync();return whereImpl(e.shape,t)},e.prototype.topk=function(e,t,n){return topkImpl(e.dataSync(),e.shape,e.dtype,t,n)},e.prototype.min=function(e,t){assertAxesAreInnerMostDims("min",t,e.rank);var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=sizeFromShape(n[1]),a=e.as2D(-1,o);return this.reduce(a,"min",a.dtype).reshape(r)},e.prototype.minimum=function(e,t){if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.minimum(e,t);var n=ENV.get("WEBGL_PACK_BINARY_OPERATIONS")?new BinaryOpPackedProgram(MIN$1,e.shape,t.shape):new BinaryOpProgram(MIN,e.shape,t.shape);return this.compileAndRun(n,[e,t])},e.prototype.mod=function(e,t){var n=ENV.get("WEBGL_PACK_BINARY_OPERATIONS")?new BinaryOpPackedProgram(MOD$1,e.shape,t.shape):new BinaryOpProgram(MOD,e.shape,t.shape);return this.compileAndRun(n,[e,t])},e.prototype.max=function(e,t){if(this.shouldExecuteOnCPU([e]))return this.cpuBackend.max(e,t);assertAxesAreInnerMostDims("max",t,e.rank);var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=sizeFromShape(n[1]),a=e.as2D(-1,o);return this.reduce(a,"max",a.dtype).reshape(r)},e.prototype.maximum=function(e,t){if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.maximum(e,t);var n=ENV.get("WEBGL_PACK_BINARY_OPERATIONS")?new BinaryOpPackedProgram(MAX$1,e.shape,t.shape):new BinaryOpProgram(MAX,e.shape,t.shape);return this.compileAndRun(n,[e,t])},e.prototype.all=function(e,t){assertAxesAreInnerMostDims("all",t,e.rank);var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=sizeFromShape(n[1]),a=e.as2D(-1,o);return this.reduce(a,"all",a.dtype).reshape(r)},e.prototype.any=function(e,t){assertAxesAreInnerMostDims("any",t,e.rank);var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=sizeFromShape(n[1]),a=e.as2D(-1,o);return this.reduce(a,"any",a.dtype).reshape(r)},e.prototype.squaredDifference=function(e,t){var n=ENV.get("WEBGL_PACK_BINARY_OPERATIONS")?new BinaryOpPackedProgram(SQUARED_DIFFERENCE,e.shape,t.shape):new BinaryOpProgram(SQUARED_DIFFERENCE,e.shape,t.shape);return this.compileAndRun(n,[e,t])},e.prototype.realDivide=function(e,t){var n=new BinaryOpProgram(DIV,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,"float32");return this.compileAndRun(n,[e,t],r)},e.prototype.floorDiv=function(e,t){var n=INT_DIV;if(ENV.get("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,INT_DIV$1,"int32");var r=new BinaryOpProgram(n,e.shape,t.shape),o=this.makeOutputArray(r.outputShape,"int32");return this.compileAndRun(r,[e,t],o)},e.prototype.add=function(e,t){if("complex64"===e.dtype&&"complex64"===t.dtype)return this.complexSeparableBinaryOp(e,t,ADD);if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.add(e,t);var n=upcastType(e.dtype,t.dtype);if(ENV.get("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,ADD,n);var r=new BinaryOpProgram(ADD,e.shape,t.shape),o=this.makeOutputArray(r.outputShape,n);return this.compileAndRun(r,[e,t],o)},e.prototype.packedBinaryOp=function(e,t,n,r){var o=new BinaryOpPackedProgram(n,e.shape,t.shape),a=this.makePackedTensor(o.outputShape,r);return this.compileAndRun(o,[e,t],a)},e.prototype.complexSeparableBinaryOp=function(e,t,n){var r=this,o=this.texData.get(e.dataId),a=this.texData.get(t.dataId),i=[[o.complexTensors.real,a.complexTensors.real],[o.complexTensors.imag,a.complexTensors.imag]].map(function(o){var a=o[0],i=o[1],s=r.makeComplexComponentTensorHandle(e,a),u=r.makeComplexComponentTensorHandle(t,i),c=new BinaryOpProgram(n,e.shape,t.shape),l=r.makeOutputArray(c.outputShape,upcastType(a.dtype,i.dtype));return r.compileAndRun(c,[s,u],l)}),s=i[0],u=i[1],c=this.complex(s,u);return s.dispose(),u.dispose(),c},e.prototype.makeComplexComponentTensorHandle=function(e,t){return {dataId:t.dataId,dtype:t.dtype,shape:e.shape}},e.prototype.addN=function(e){for(var t=e[0],n=1;n<e.length;n++)t=this.add(t,e[n]);return t},e.prototype.subtract=function(e,t){if("complex64"===e.dtype&&"complex64"===t.dtype)return this.complexSeparableBinaryOp(e,t,SUB);if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.subtract(e,t);var n=upcastType(e.dtype,t.dtype);if(ENV.get("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,SUB,e.dtype);var r=new BinaryOpProgram(SUB,e.shape,t.shape),o=this.makeOutputArray(r.outputShape,n);return this.compileAndRun(r,[e,t],o)},e.prototype.pow=function(e,t){var n=ENV.get("WEBGL_PACK_BINARY_OPERATIONS"),r=n?new BinaryOpPackedProgram(POW$1,e.shape,t.shape):new BinaryOpProgram(POW,e.shape,t.shape),o=upcastType(e.dtype,t.dtype),a=n?this.makePackedTensor(r.outputShape,o):this.makeOutputArray(r.outputShape,o);return this.compileAndRun(r,[e,t],a)},e.prototype.ceil=function(e){var t=new UnaryOpProgram(e.shape,CEIL);return this.compileAndRun(t,[e])},e.prototype.floor=function(e){var t=new UnaryOpProgram(e.shape,FLOOR);return this.compileAndRun(t,[e])},e.prototype.sign=function(e){var t=new UnaryOpProgram(e.shape,SIGN);return this.compileAndRun(t,[e])},e.prototype.isNaN=function(e){var t=new UnaryOpProgram(e.shape,IS_NAN),n=this.makeOutputArray(t.outputShape,"bool");return this.compileAndRun(t,[e],n)},e.prototype.isInf=function(e){var t=new UnaryOpProgram(e.shape,IS_INF),n=this.makeOutputArray(t.outputShape,"bool");return this.compileAndRun(t,[e],n)},e.prototype.isFinite=function(e){var t=new UnaryOpProgram(e.shape,IS_FINITE),n=this.makeOutputArray(t.outputShape,"bool");return this.compileAndRun(t,[e],n)},e.prototype.round=function(e){var t=new UnaryOpProgram(e.shape,ROUND);return this.compileAndRun(t,[e])},e.prototype.exp=function(e){var t;return t=ENV.get("WEBGL_PACK")?new UnaryOpPackedProgram(e.shape,EXP):new UnaryOpProgram(e.shape,EXP),this.compileAndRun(t,[e])},e.prototype.expm1=function(e){var t=new UnaryOpProgram(e.shape,EXPM1);return this.compileAndRun(t,[e])},e.prototype.log=function(e){var t;return t=ENV.get("WEBGL_PACK")?new UnaryOpPackedProgram(e.shape,LOG$1):new UnaryOpProgram(e.shape,LOG),this.compileAndRun(t,[e])},e.prototype.log1p=function(e){var t=new UnaryOpProgram(e.shape,LOG1P);return this.compileAndRun(t,[e])},e.prototype.sqrt=function(e){var t=new UnaryOpProgram(e.shape,SQRT);return this.compileAndRun(t,[e])},e.prototype.rsqrt=function(e){if(this.shouldExecuteOnCPU([e]))return this.cpuBackend.rsqrt(e);var t=new UnaryOpProgram(e.shape,RSQRT);return this.compileAndRun(t,[e])},e.prototype.square=function(e){var t=new UnaryOpProgram(e.shape,SQUARE);return this.compileAndRun(t,[e])},e.prototype.reciprocal=function(e){var t=new UnaryOpProgram(e.shape,RECIPROCAL);return this.compileAndRun(t,[e])},e.prototype.relu=function(e){var t;return t=ENV.get("WEBGL_PACK")?new UnaryOpPackedProgram(e.shape,RELU$1):new UnaryOpProgram(e.shape,RELU),this.compileAndRun(t,[e])},e.prototype.prelu=function(e,t){var n=ENV.get("WEBGL_PACK_BINARY_OPERATIONS")?new BinaryOpPackedProgram(PRELU$1,e.shape,t.shape):new BinaryOpProgram(PRELU,e.shape,t.shape);return this.compileAndRun(n,[e,t])},e.prototype.elu=function(e){var t=new UnaryOpProgram(e.shape,ELU);return this.compileAndRun(t,[e])},e.prototype.eluDer=function(e,t){var n=ENV.get("WEBGL_PACK_BINARY_OPERATIONS")?new BinaryOpPackedProgram(ELU_DER$1,e.shape,t.shape):new BinaryOpProgram(ELU_DER,e.shape,t.shape);return this.compileAndRun(n,[e,t])},e.prototype.selu=function(e){var t=new UnaryOpProgram(e.shape,SELU);return this.compileAndRun(t,[e])},e.prototype.int=function(e){var t=new UnaryOpProgram(e.shape,TO_INT),n=this.makeOutputArray(t.outputShape,"int32");return this.compileAndRun(t,[e],n)},e.prototype.clip=function(e,t,n){var r,o=(r=ENV.get("WEBGL_PACK_CLIP")?new ClipPackedProgram(e.shape):new ClipProgram(e.shape)).getCustomSetupFunc(t,n);return this.compileAndRun(r,[e],null,o)},e.prototype.abs=function(e){var t=new UnaryOpProgram(e.shape,ABS);return this.compileAndRun(t,[e])},e.prototype.complexAbs=function(e){var t=this.texData.get(e.dataId),n=new ComplexAbsProgram(e.shape),r=[this.makeComplexComponentTensorHandle(e,t.complexTensors.real),this.makeComplexComponentTensorHandle(e,t.complexTensors.imag)];return this.compileAndRun(n,r)},e.prototype.sigmoid=function(e){var t=new UnaryOpProgram(e.shape,SIGMOID);return this.compileAndRun(t,[e])},e.prototype.softplus=function(e){var t=new UnaryOpProgram(e.shape,SOFTPLUS);return this.compileAndRun(t,[e])},e.prototype.sin=function(e){var t=new UnaryOpProgram(e.shape,SIN);return this.compileAndRun(t,[e])},e.prototype.cos=function(e){var t=new UnaryOpProgram(e.shape,COS);return this.compileAndRun(t,[e])},e.prototype.tan=function(e){var t=new UnaryOpProgram(e.shape,TAN);return this.compileAndRun(t,[e])},e.prototype.asin=function(e){var t=new UnaryOpProgram(e.shape,ASIN);return this.compileAndRun(t,[e])},e.prototype.acos=function(e){var t=new UnaryOpProgram(e.shape,ACOS);return this.compileAndRun(t,[e])},e.prototype.atan=function(e){var t=new UnaryOpProgram(e.shape,ATAN);return this.compileAndRun(t,[e])},e.prototype.atan2=function(e,t){var n=ENV.get("WEBGL_PACK_BINARY_OPERATIONS")?new BinaryOpPackedProgram(ATAN2$1,e.shape,t.shape):new BinaryOpProgram(ATAN2,e.shape,t.shape);return this.compileAndRun(n,[e,t])},e.prototype.sinh=function(e){var t=new UnaryOpProgram(e.shape,SINH);return this.compileAndRun(t,[e])},e.prototype.cosh=function(e){var t=new UnaryOpProgram(e.shape,COSH);return this.compileAndRun(t,[e])},e.prototype.tanh=function(e){var t=new UnaryOpProgram(e.shape,TANH);return this.compileAndRun(t,[e])},e.prototype.asinh=function(e){var t=new UnaryOpProgram(e.shape,ASINH);return this.compileAndRun(t,[e])},e.prototype.acosh=function(e){var t=new UnaryOpProgram(e.shape,ACOSH);return this.compileAndRun(t,[e])},e.prototype.atanh=function(e){var t=new UnaryOpProgram(e.shape,ATANH);return this.compileAndRun(t,[e])},e.prototype.erf=function(e){var t=new UnaryOpProgram(e.shape,ERF);return this.compileAndRun(t,[e])},e.prototype.step=function(e,t){var n=new UnaryOpProgram(e.shape,STEP(t));return this.compileAndRun(n,[e])},e.prototype.conv2dByMatMul=function(e,t,n){var r=e.shape,o=this.texData.get(e.dataId),a=n.inChannels,i=r[0]*r[1]*r[2],s=n.outChannels,u=(1===i||1===s)&&a>MATMUL_SHARED_DIM_THRESHOLD,c=r[2]%2!=0&&!!o.isPacked;if(u||!ENV.get("WEBGL_LAZILY_UNPACK")||!ENV.get("WEBGL_PACK_BINARY_OPERATIONS")||!c){var l=this.reshape(e,[1,r[0]*r[1]*r[2],n.inChannels]),p=this.reshape(t,[1,n.inChannels,n.outChannels]);return this.reshape(this.batchMatMul(l,p,!1,!1),n.outShape)}var d=Tensor.make([1,r[0]*r[1]*(r[2]+1),n.inChannels],{dataId:e.dataId},e.dtype,this),h=o.shape;o.shape=o.shape.slice(),o.shape[o.shape.length-2]++,assert(isReshapeFree(o.shape,d.shape),function(){return "packed reshape "+o.shape+" to "+d.shape+" isn't free"});var f=this.reshape(t,[1,n.inChannels,n.outChannels]),m=this.batchMatMul(d,f,!1,!1),g=this.texData.get(m.dataId);return assert(g.isPacked,function(){return "batchMatMul result is expected to be packed"}),o.shape=h,g.shape=n.outShape,Tensor.make(n.outShape,{dataId:m.dataId},m.dtype,this)},e.prototype.conv2dWithIm2Row=function(e,t,n){var r=n.filterWidth,o=n.filterHeight,a=n.inChannels,i=n.outWidth,s=n.outHeight,u=r*o*a,c=s*i,l=[u,c],p=e.squeeze([0]),d=t.reshape([1,u,-1]),h=new Im2ColPackedProgram(l,p.shape,n),f=this.compileAndRun(h,[p]).reshape([1,l[0],l[1]]),m=new MatMulPackedProgram(f.shape,[1,c,n.outChannels],!0,!1);return this.compileAndRun(m,[f,d]).reshape([1,s,i,n.outChannels])},e.prototype.conv2d=function(e,t,n){if(1===n.filterHeight&&1===n.filterWidth&&1===n.dilationHeight&&1===n.dilationWidth&&1===n.strideHeight&&1===n.strideWidth&&("SAME"===n.padInfo.type||"VALID"===n.padInfo.type))return this.conv2dByMatMul(e,t,n);if(ENV.get("WEBGL_CONV_IM2COL")&&1===e.shape[0])return this.conv2dWithIm2Row(e,t,n);var r=new Conv2DProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.conv2dDerInput=function(e,t,n){var r=new Conv2DDerInputProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.conv2dDerFilter=function(e,t,n){var r=new Conv2DDerFilterProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.depthwiseConv2D=function(e,t,n){var r;return ENV.get("WEBGL_PACK_DEPTHWISECONV")&&n.strideWidth<=2&&n.outChannels/n.inChannels==1?(r=new DepthwiseConvPacked2DProgram(n),this.compileAndRun(r,[e,t],this.makePackedTensor(n.outShape,e.dtype))):(r=new DepthwiseConv2DProgram(n),this.compileAndRun(r,[e,t]))},e.prototype.depthwiseConv2DDerInput=function(e,t,n){var r=new DepthwiseConv2DDerInputProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.depthwiseConv2DDerFilter=function(e,t,n){var r=new DepthwiseConv2DDerFilterProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.conv3d=function(e,t,n){var r=new Conv3DProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.conv3dDerInput=function(e,t,n){var r=new Conv3DDerInputProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.conv3dDerFilter=function(e,t,n){var r=new Conv3DDerFilterProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.maxPool=function(e,t){var n=new Pool2DProgram(t,"max",!1),r=this.makeOutputArray(n.outputShape,e.dtype);return this.compileAndRun(n,[e],r)},e.prototype.avgPool=function(e,t){var n=new Pool2DProgram(t,"avg",!1),r=this.makeOutputArray(n.outputShape,"float32");return this.compileAndRun(n,[e],r)},e.prototype.maxPoolBackprop=function(e,t,n,r){var o=new Pool2DProgram(r,"max",!0),a=this.compileAndRun(o,[t]),i=new MaxPool2DBackpropProgram(r),s=this.makeOutputArray(i.outputShape,t.dtype),u=this.compileAndRun(i,[e,a],s);return a.dispose(),u},e.prototype.avgPoolBackprop=function(e,t,n){var r=new AvgPool2DBackpropProgram(n),o=this.makeOutputArray(r.outputShape,t.dtype);return this.compileAndRun(r,[e],o)},e.prototype.cast=function(e,t){return castTensor(e,t,this)},e.prototype.unstack=function(e,t){for(var n=e.shape[t],r=new Array(e.rank-1),o=0,a=0;a<e.rank;a++)a!==t&&(r[o++]=e.shape[a]);var i=new Array(e.rank).fill(0),s=e.shape.slice();s[t]=1;var u=new Array(n);for(a=0;a<u.length;a++)i[t]=a,u[a]=this.slice(e,i,s).reshape(r);return u},e.prototype.reshape=function(e,t){var n=this.texData.get(e.dataId);return !n.isPacked||isReshapeFree(e.shape,t)||null!==n.texture&&isReshapeFree(n.shape,t)?reshapeTensor(e,t):this.packedReshape(e,t)},e.prototype.resizeBilinear=function(e,t,n,r){var o=ENV.get("WEBGL_PACK_IMAGE_OPERATIONS")?new ResizeBilinearPackedProgram(e.shape,t,n,r):new ResizeBilinearProgram(e.shape,t,n,r);return this.compileAndRun(o,[e])},e.prototype.resizeBilinearBackprop=function(e,t,n){var r=new ResizeBilinearBackpropProgram(e,t,n);return this.compileAndRun(r,[e])},e.prototype.resizeNearestNeighbor=function(e,t,n,r){var o=new ResizeNearestNeighborProgram(e.shape,t,n,r);return this.compileAndRun(o,[e])},e.prototype.resizeNearestNeighborBackprop=function(e,t,n){var r=new ResizeNearestNeigborBackpropProgram(e,t,n);return this.compileAndRun(r,[e])},e.prototype.multinomial=function(e,t,n,r){var o=t?e:softmax(e),a=o.shape[0],i=o.shape[1],s=new MultinomialProgram(a,i,n),u=this.makeOutputArray(s.outputShape,"int32"),c=s.getCustomSetupFunc(r);return this.compileAndRun(s,[o],u,c)},e.prototype.oneHot=function(e,t,n,r){var o=new OneHotProgram(e.size,t,n,r);return this.compileAndRun(o,[e])},e.prototype.nonMaxSuppression=function(e,t,n,r,o){return warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead"),nonMaxSuppressionImpl(e.dataSync(),t.dataSync(),n,r,o)},e.prototype.cropAndResize=function(e,t,n,r,o,a){var i=new CropAndResizeProgram(e.shape,t.shape,r,o,a);return this.compileAndRun(i,[e,t,n])},e.prototype.depthToSpace=function(e,t,n){assert(t>1,function(){return "blockSize should be > 1 for depthToSpace, but was: "+t});var r=e.shape[0],o="NHWC"===n?e.shape[1]:e.shape[2],a="NHWC"===n?e.shape[2]:e.shape[3],i="NHWC"===n?e.shape[3]:e.shape[1],s=o*t,u=a*t,c=i/(t*t),l=new DepthToSpaceProgram("NHWC"===n?[r,s,u,c]:[r,c,s,u],t,n);return this.compileAndRun(l,[e])},e.prototype.split=function(e,t,n){return split(e,t,n)},e.prototype.scatterND=function(e,t,n){var r=calculateShapes(t,e,n),o=r.sliceRank,a=r.numUpdates,i=r.sliceSize,s=r.strides,u=r.outputSize,c=[u/i,i],l=e.reshape([a,o]),p=t.reshape([a,i]);if(0===u)return reshapeTensor(tensor([]),n);var d=scalar(0),h=new ScatterProgram(a,o,l.rank,p.rank,s,c);return this.compileAndRun(h,[p,l,d]).reshape(n)},e.prototype.sparseToDense=function(e,t,n,r){var o=calculateShapes(t,e,n),a=o.sliceRank,i=o.numUpdates,s=o.strides,u=o.outputSize,c=new ScatterProgram(i,a,e.rank,t.rank,s,[u,1],!1);return this.compileAndRun(c,[t,e,r]).reshape(n)},e.prototype.fft=function(e){return this.fftImpl(e,!1)},e.prototype.ifft=function(e){return this.fftImpl(e,!0)},e.prototype.fftImpl=function(e,t){var n=this.texData.get(e.dataId),r=new FFTProgram(COMPLEX_FFT.REAL,e.shape,t),o=new FFTProgram(COMPLEX_FFT.IMAG,e.shape,t),a=[this.makeComplexComponentTensorHandle(e,n.complexTensors.real),this.makeComplexComponentTensorHandle(e,n.complexTensors.imag)],i=this.compileAndRun(r,a),s=this.compileAndRun(o,a),u=this.complex(i,s).as2D(e.shape[0],e.shape[1]);return i.dispose(),s.dispose(),u},e.prototype.gatherND=function(e,t){var n=t.shape,r=n[n.length-1],o=prepareAndValidate(e,t),a=o[0],i=o[1],s=o[2],u=o[3],c=t.reshape([i,r]),l=e.reshape([e.size/s,s]),p=new GatherNDProgram(r,u,[i,s]);return this.compileAndRun(p,[l,c]).reshape(a)},e.prototype.fill=function(e,t,n){if("string"===(n=n||inferDtype(t))){var r=getArrayFromDType(n,sizeFromShape(e));return r.fill(t),Tensor.make(e,{values:r},n)}var o=new FillProgram(e,t),a=o.getCustomSetupFunc(t),i=this.makeOutputArray(e,n);return this.compileAndRun(o,[],i,a)},e.prototype.onesLike=function(e){if("string"===e.dtype)throw new Error("onesLike is not supported under string dtype");return this.fill(e.shape,1,e.dtype)},e.prototype.zerosLike=function(e){return this.fill(e.shape,"string"===e.dtype?"":0,e.dtype)},e.prototype.makeOutputArray=function(e,t){return Tensor.make(e,{},t,this)},e.prototype.makePackedTensor=function(e,t){var n=Tensor.make(e,{},t,this);return this.texData.get(n.dataId).isPacked=!0,n},e.prototype.unpackTensor=function(e){var t=new UnpackProgram(e.shape);return this.compileAndRun(t,[e],Tensor.make(t.outputShape,{},e.dtype,this))},e.prototype.packTensor=function(e){var t=new PackProgram(e.shape);return this.compileAndRun(t,[e],this.makePackedTensor(e.shape,e.dtype))},e.prototype.packedReshape=function(e,t){var n=e.reshape([getBatchDim(e.shape)].concat(getRowsCols(e.shape))),r=[getBatchDim(t)].concat(getRowsCols(t)),o=new ReshapePackedProgram(r,n.shape);return this.compileAndRun(o,[n]).reshape(t)},e.prototype.compileAndRun=function(e,t,n,r,o){var a=this;if(void 0===o&&(o=!0),null==n&&(n=e.usesPackedTextures?this.makePackedTensor(e.outputShape,t[0].dtype):this.makeOutputArray(e.outputShape,t[0].dtype)),0===n.size)return this.texData.get(n.dataId).values=getTypedArrayFromDType(n.dtype,0),n;var i=t.map(function(t){if("complex64"===t.dtype)throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");var n=a.texData.get(t.dataId);if(null==n.texture){if(!e.usesPackedTextures&&sizeFromShape(t.shape)<=ENV.get("WEBGL_SIZE_UPLOAD_UNIFORM"))return {shape:t.shape,texData:null,isUniform:!0,uniformValues:a.readSync(t.dataId)};e.usesPackedTextures&&(n.isPacked=!0,n.shape=t.shape);}else if(!!n.isPacked!=!!e.usesPackedTextures)t=n.isPacked?a.unpackTensor(t):a.packTensor(t),n=a.texData.get(t.dataId);else if(n.isPacked&&!isReshapeFree(n.shape,t.shape)){var r=t,o=t.shape;t.shape=n.shape,t=a.packedReshape(t,o),n=a.texData.get(t.dataId),r.shape=o;}return a.uploadToGPU(t.dataId),{shape:t.shape,texData:n,isUniform:!1}});this.uploadToGPU(n.dataId);var s,u={shape:n.shape,texData:this.texData.get(n.dataId),isUniform:!1},c=makeShaderKey(e,i,u),l=this.getAndSaveBinary(c,function(){return compileProgram(a.gpgpu,e,i,u)}),p=null!=this.activeTimers;p&&(s=this.startTimer()),runProgram(this.gpgpu,l,i,u,r);var d=1024*ENV.get("WEBGL_NUM_MB_BEFORE_PAGING");if(o&&this.numBytesInGPU>d)for(var h=this.numBytesInGPU-d;h>0&&this.lruDataGPU.length>0;){var f=this.lruDataGPU.shift(),m=this.texData.get(f),g=m.texShape,v=m.dtype;h-=this.computeBytes(g,v),this.read(f);}return p&&(s=this.endTimer(s),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(s)})),ENV.get("WEBGL_LAZILY_UNPACK")||!this.texData.get(n.dataId).isPacked||e.isPackShader?n:this.unpackTensor(n)},e.prototype.getAndSaveBinary=function(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]},e.prototype.getTextureManager=function(){return this.textureManager},e.prototype.dispose=function(){this.disposed||(this.textureManager.dispose(),this.canvas.remove(),null!=this.fromPixels2DContext&&this.fromPixels2DContext.canvas.remove(),this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0);},e.prototype.floatPrecision=function(){var e=this;return tidy(function(){var t=ENV.get("DEBUG");ENV.set("DEBUG",!1);var n=e.abs(scalar(1e-8)).dataSync()[0];return ENV.set("DEBUG",t),n>0?32:16})},e.prototype.uploadToGPU=function(e){var t,n=this.texData.get(e),r=n.shape,o=n.dtype,a=n.values,i=n.texture,s=n.usage,u=n.isPacked;if(null==i){var c,l=null!=this.activeTimers;l&&(c=performance.now());var p=getTextureShapeFromLogicalShape(r,u);n.texShape=p;var d=this.acquireTexture(e,p,s,o,u);if(n.texture=d,null!=a){if(u){var h=getBatchDim(r),f=1,m=1;r.length&&(f=(t=getRowsCols(r))[0],m=t[1]),this.gpgpu.uploadMatrixToPackedTexture(d,h,f,m,p[0],p[1],typedArrayToFloat32(a));}else this.gpgpu.uploadMatrixToTexture(d,p[0],p[1],typedArrayToFloat32(a));n.values=null,l&&(this.uploadWaitMs+=performance.now()-c);}}else{ENV.get("WEBGL_NUM_MB_BEFORE_PAGING")<Number.POSITIVE_INFINITY&&this.lruDataGPU.indexOf(e)>=0&&(this.lruDataGPU.splice(this.lruDataGPU.indexOf(e),1),this.lruDataGPU.push(e));}},e.prototype.convertAndCacheOnCPU=function(e,t){var n=this.texData.get(e),r=n.texture,o=n.texShape,a=n.dtype,i=n.usage,s=n.isPacked;return null!=r&&(this.releaseTexture(e,r,o,i,a,s),n.texture=null,n.texShape=null,n.isPacked=!1),n.usage=TextureUsage.UPLOAD,null!=t&&(n.values=float32ToTypedArray(t,a)),n.values},e.prototype.releaseTexture=function(e,t,n,r,o,a){if(ENV.get("WEBGL_NUM_MB_BEFORE_PAGING")<Number.POSITIVE_INFINITY){var i=this.lruDataGPU.indexOf(e);i>=0&&this.lruDataGPU.splice(i,1);}this.numBytesInGPU-=this.computeBytes(n,o),this.textureManager.releaseTexture(t,n,r,a);},e.prototype.acquireTexture=function(e,t,n,r,o){return ENV.get("WEBGL_NUM_MB_BEFORE_PAGING")<Number.POSITIVE_INFINITY&&this.lruDataGPU.push(e),this.numBytesInGPU+=this.computeBytes(t,r),this.textureManager.acquireTexture(t,n,o)},e.prototype.computeBytes=function(e,t){return e[0]*e[1]*bytesPerElement(t)},e}();function float32ToTypedArray(e,t){if("float32"===t||"complex64"===t)return e;if("int32"===t||"bool"===t){for(var n="int32"===t?new Int32Array(e.length):new Uint8Array(e.length),r=0;r<n.length;++r)n[r]=Math.round(e[r]);return n}throw new Error("Unknown dtype "+t)}function typedArrayToFloat32(e){return e instanceof Float32Array?e:new Float32Array(e)}function neg_(e){var t=convertToTensor(e,"x","neg");return ENV.engine.runKernel(function(e){return e.neg(t)},{$x:t},function(e){return {$x:function(){return e.neg()}}})}function ceil_(e){var t=convertToTensor(e,"x","ceil");return ENV.engine.runKernel(function(e){return e.ceil(t)},{$x:t},function(e){return {$x:function(){return zerosLike(e)}}})}function floor_(e){var t=convertToTensor(e,"x","floor");return ENV.engine.runKernel(function(e){return e.floor(t)},{$x:t},function(e){return {$x:function(){return zerosLike(e)}}})}function sign_(e){var t=convertToTensor(e,"x","sign");return ENV.engine.runKernel(function(e){return e.sign(t)},{$x:t},function(e){return {$x:function(){return zerosLike(e)}}})}function isNaN_(e){var t=convertToTensor(e,"x","isNaN");return ENV.engine.runKernel(function(e){return e.isNaN(t)},{$x:t},function(e){return {$x:function(){return zerosLike(e)}}})}function isInf_(e){var t=convertToTensor(e,"x","isInf");return ENV.engine.runKernel(function(e){return e.isInf(t)},{$x:t},function(e){return {$x:function(){return zerosLike(e)}}})}function isFinite_(e){var t=convertToTensor(e,"x","isFinite");return ENV.engine.runKernel(function(e){return e.isFinite(t)},{$x:t},function(e){return {$x:function(){return zerosLike(e)}}})}function round_(e){var t=convertToTensor(e,"x","round");return ENV.engine.runKernel(function(e){return e.round(t)},{$x:t},function(e){return {$x:function(){return zerosLike(e)}}})}function exp_(e){var t=convertToTensor(e,"x","exp");return ENV.engine.runKernel(function(e,n){var r=e.exp(t);return n([r]),r},{$x:t},function(e,t){return {$x:function(){return e.mulStrict(t[0])}}})}function expm1_(e){var t=convertToTensor(e,"x","expm1");return ENV.engine.runKernel(function(e,n){var r=e.expm1(t);return n([t]),r},{$x:t},function(e,t){var n=t[0];return {$x:function(){return e.mul(n.exp())}}})}function log_(e){var t=convertToTensor(e,"x","log");return ENV.engine.runKernel(function(e,n){var r=e.log(t);return n([t]),r},{$x:t},function(e,t){var n=t[0];return {$x:function(){return e.div(n.toFloat())}}})}function log1p_(e){var t=convertToTensor(e,"x","log1p");return ENV.engine.runKernel(function(e,n){var r=e.log1p(t);return n([t]),r},{$x:t},function(e,t){var n=t[0];return {$x:function(){return e.div(n.add(1))}}})}function sqrt_(e){var t=convertToTensor(e,"x","sqrt");return ENV.engine.runKernel(function(e,n){var r=e.sqrt(t);return n([t]),r},{$x:t},function(e,t){var n=t[0];return {$x:function(){return e.div(n.toFloat().sqrt().mul(2))}}})}function rsqrt_(e){var t=convertToTensor(e,"x","rsqrt");return ENV.engine.runKernel(function(e,n){var r=e.rsqrt(t);return n([t]),r},{$x:t},function(e,t){var n=t[0];return {$x:function(){return e.div(n.pow(1.5).mul(2)).neg()}}})}function square_(e){var t=convertToTensor(e,"x","square");return ENV.engine.runKernel(function(e,n){return n([t]),e.square(t)},{$x:t},function(e,t){var n=t[0];return {$x:function(){return e.mul(n.toFloat().mul(2))}}})}function reciprocal_(e){var t=convertToTensor(e,"x","reciprocal");return ENV.engine.runKernel(function(e,n){var r=e.reciprocal(t);return n([t]),r},{$x:t},function(e,t){var n=t[0];return {$x:function(){return e.div(n.square().neg())}}})}function abs_(e){var t=convertToTensor(e,"x","abs");if("complex64"===t.dtype)return ENV.engine.runKernel(function(e){return e.complexAbs(t)},{$x:t});return ENV.engine.runKernel(function(e,n){var r=e.abs(t);return n([t]),r},{$x:t},function(e,t){var n=t[0];return {$x:function(){return e.mul(n.toFloat().step(-1))}}})}function clipByValue_(e,t,n){var r=convertToTensor(e,"x","clipByValue");assert(t<=n,function(){return "Error in clip: min ("+t+") must be less than or equal to max ("+n+")."});return ENV.engine.runKernel(function(e,o){var a=e.clip(r,t,n);return o([r]),a},{$x:r},function(e,r){var o=r[0];return {$x:function(){return e.where(o.greaterEqual(t).logicalAnd(o.lessEqual(n)),zerosLike(e))}}})}function sigmoid_(e){var t=convertToTensor(e,"x","sigmoid");return ENV.engine.runKernel(function(e,n){var r=e.sigmoid(t);return n([r]),r},{$x:t},function(e,t){var n=t[0];return {$x:function(){return e.mul(n.mul(scalar(1).sub(n)))}}})}function logSigmoid_(e){var t=convertToTensor(e,"x","logSigmoid");return ENV.engine.runKernel(function(e,n){var r=e.softplus(t.neg()).neg();return n([t]),r},{$x:t},function(e,t){var n=t[0];return {$x:function(){return e.mul(n.neg().sigmoid())}}})}function softplus_(e){var t=convertToTensor(e,"x","softplus");return ENV.engine.runKernel(function(e,n){var r=e.softplus(t);return n([t]),r},{$x:t},function(e,t){var n=t[0];return {$x:function(){return e.mul(n.sigmoid())}}})}function sin_(e){var t=convertToTensor(e,"x","sin");return ENV.engine.runKernel(function(e,n){var r=e.sin(t);return n([t]),r},{$x:t},function(e,t){var n=t[0];return {$x:function(){return n.toFloat().cos().mul(e)}}})}function cos_(e){var t=convertToTensor(e,"x","cos");return ENV.engine.runKernel(function(e,n){var r=e.cos(t);return n([t]),r},{$x:t},function(e,t){var n=t[0];return {$x:function(){return n.toFloat().sin().neg().mul(e)}}})}function tan_(e){var t=convertToTensor(e,"x","tan");return ENV.engine.runKernel(function(e,n){var r=e.tan(t);return n([t]),r},{$x:t},function(e,t){var n=t[0];return {$x:function(){return e.div(n.cos().square())}}})}function asin_(e){var t=convertToTensor(e,"x","asin");return ENV.engine.runKernel(function(e,n){var r=e.asin(t);return n([t]),r},{$x:t},function(e,t){var n=t[0];return {$x:function(){return e.divStrict(scalar(1).sub(n.toFloat().square()).sqrt())}}})}function acos_(e){var t=convertToTensor(e,"x","acos");return ENV.engine.runKernel(function(e,n){var r=e.acos(t);return n([t]),r},{$x:t},function(e,t){var n=t[0];return {$x:function(){return e.divStrict(scalar(1).sub(n.toFloat().square()).sqrt()).neg()}}})}function atan_(e){var t=convertToTensor(e,"x","atan");return ENV.engine.runKernel(function(e,n){var r=e.atan(t);return n([t]),r},{$x:t},function(e,t){var n=t[0];return {$x:function(){return e.div(n.toFloat().square().add(1))}}})}function sinh_(e){var t=convertToTensor(e,"x","sinh");return ENV.engine.runKernel(function(e,n){var r=e.sinh(t);return n([t]),r},{$x:t},function(e,t){var n=t[0];return {$x:function(){return n.toFloat().cosh().mulStrict(e)}}})}function cosh_(e){var t=convertToTensor(e,"x","cosh");return ENV.engine.runKernel(function(e,n){var r=e.cosh(t);return n([t]),r},{$x:t},function(e,t){var n=t[0];return {$x:function(){return n.toFloat().sinh().mulStrict(e)}}})}function tanh_(e){var t=convertToTensor(e,"x","tanh");return ENV.engine.runKernel(function(e,n){var r=e.tanh(t);return n([r]),r},{$x:t},function(e,t){var n=t[0];return {$x:function(){return scalar(1).sub(n.square()).mulStrict(e)}}})}function asinh_(e){var t=convertToTensor(e,"x","asinh");return ENV.engine.runKernel(function(e,n){var r=e.asinh(t);return n([t]),r},{$x:t},function(e,t){var n=t[0];return {$x:function(){return e.divStrict(scalar(1).add(n.toFloat().square()).sqrt())}}})}function acosh_(e){var t=convertToTensor(e,"x","acosh");return ENV.engine.runKernel(function(e,n){var r=e.acosh(t);return n([t]),r},{$x:t},function(e,t){var n=t[0];return {$x:function(){return e.divStrict(n.toFloat().square().sub(1).sqrt())}}})}function atanh_(e){var t=convertToTensor(e,"x","atanh");return ENV.engine.runKernel(function(e,n){var r=e.atanh(t);return n([t]),r},{$x:t},function(e,t){var n=t[0];return {$x:function(){return e.div(scalar(1).sub(n.toFloat().square()))}}})}function erf_(e){var t=convertToTensor(e,"x","erf");assert("int32"===t.dtype||"float32"===t.dtype,function(){return "Input dtype must be `int32` or `float32`."}),"int32"===t.dtype&&(t=t.toFloat());return ENV.engine.runKernel(function(e,n){var r=e.erf(t);return n([t]),r},{$x:t},function(e,t){var n=t[0];return {$x:function(){return e.mul(n.square().neg().exp().mul(2/Math.sqrt(Math.PI)))}}})}function step_(e,t){void 0===t&&(t=0);var n=convertToTensor(e,"x","step");return ENV.engine.runKernel(function(e){return e.step(n,t)},{$x:n},function(e){return {$x:function(){return zerosLike(e)}}})}ENV.get("IS_BROWSER")&&ENV.registerBackend("webgl",function(){return new MathBackendWebGL},2);var abs=op({abs_:abs_}),acos=op({acos_:acos_}),acosh=op({acosh_:acosh_}),asin=op({asin_:asin_}),asinh=op({asinh_:asinh_}),atan=op({atan_:atan_}),atanh=op({atanh_:atanh_}),ceil=op({ceil_:ceil_}),clipByValue=op({clipByValue_:clipByValue_}),cos=op({cos_:cos_}),cosh=op({cosh_:cosh_}),erf=op({erf_:erf_}),exp=op({exp_:exp_}),expm1=op({expm1_:expm1_}),floor=op({floor_:floor_}),log$1=op({log_:log_}),log1p=op({log1p_:log1p_}),logSigmoid=op({logSigmoid_:logSigmoid_}),neg=op({neg_:neg_}),reciprocal=op({reciprocal_:reciprocal_}),round=op({round_:round_}),rsqrt=op({rsqrt_:rsqrt_}),sigmoid=op({sigmoid_:sigmoid_}),sign=op({sign_:sign_}),isNaN$1=op({isNaN_:isNaN_}),isInf=op({isInf_:isInf_}),isFinite$1=op({isFinite_:isFinite_}),sin=op({sin_:sin_}),sinh=op({sinh_:sinh_}),softplus=op({softplus_:softplus_}),sqrt=op({sqrt_:sqrt_}),square=op({square_:square_}),step=op({step_:step_}),tan=op({tan_:tan_}),tanh$1=op({tanh_:tanh_});function batchNorm2d_(e,t,n,r,o,a){var i,s,u=convertToTensor(e,"x","batchNorm"),c=convertToTensor(t,"mean","batchNorm"),l=convertToTensor(n,"variance","batchNorm");return null!=o&&(i=convertToTensor(o,"scale","batchNorm")),null!=r&&(s=convertToTensor(r,"offset","batchNorm")),assert(2===u.rank,function(){return "Error in batchNorm3D: x must be rank 3 but got rank "+u.rank+"."}),assert(2===c.rank||1===c.rank,function(){return "Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank "+c.rank+"."}),assert(2===l.rank||1===l.rank,function(){return "Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank "+l.rank+"."}),null!=i&&assert(2===i.rank||1===i.rank,function(){return "Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank "+i.rank+"."}),null!=s&&assert(2===s.rank||1===s.rank,function(){return "Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank "+s.rank+"."}),batchNorm_(u,c,l,s,i,a)}function batchNorm3d_(e,t,n,r,o,a){var i,s,u=convertToTensor(e,"x","batchNorm"),c=convertToTensor(t,"mean","batchNorm"),l=convertToTensor(n,"variance","batchNorm");return null!=o&&(i=convertToTensor(o,"scale","batchNorm")),null!=r&&(s=convertToTensor(r,"offset","batchNorm")),assert(3===u.rank,function(){return "Error in batchNorm3D: x must be rank 3 but got rank "+u.rank+"."}),assert(3===c.rank||1===c.rank,function(){return "Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank "+c.rank+"."}),assert(3===l.rank||1===l.rank,function(){return "Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank "+l.rank+"."}),null!=i&&assert(3===i.rank||1===i.rank,function(){return "Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank "+i.rank+"."}),null!=s&&assert(3===s.rank||1===s.rank,function(){return "Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank "+s.rank+"."}),batchNorm_(u,c,l,s,i,a)}function batchNorm4d_(e,t,n,r,o,a){var i,s,u=convertToTensor(e,"x","batchNorm"),c=convertToTensor(t,"mean","batchNorm"),l=convertToTensor(n,"variance","batchNorm");return null!=o&&(i=convertToTensor(o,"scale","batchNorm")),null!=r&&(s=convertToTensor(r,"offset","batchNorm")),assert(4===u.rank,function(){return "Error in batchNorm4D: x must be rank 4 but got rank "+u.rank+"."}),assert(4===c.rank||1===c.rank,function(){return "Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank "+c.rank+"."}),assert(4===l.rank||1===l.rank,function(){return "Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank "+l.rank+"."}),null!=i&&assert(4===i.rank||1===i.rank,function(){return "Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank "+i.rank+"."}),null!=s&&assert(4===s.rank||1===s.rank,function(){return "Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank "+s.rank+"."}),batchNorm_(u,c,l,s,i,a)}function batchNormalization_(e,t,n,r,o,a){return void 0===r&&(r=.001),warnDeprecation(),batchNorm_(e,t,n,a,o,r)}function batchNorm_(e,t,n,r,o,a){null==a&&(a=.001);var i,s,u,c=convertToTensor(e,"x","batchNorm"),l=convertToTensor(t,"mean","batchNorm"),p=convertToTensor(n,"variance","batchNorm");null!=o&&(i=convertToTensor(o,"scale","batchNorm")),null!=r&&(s=convertToTensor(r,"offset","batchNorm")),assert(l.rank===p.rank,function(){return "Batch normalization gradient requires mean and variance to have equal ranks."}),assert(null==s||l.rank===s.rank,function(){return "Batch normalization gradient requires mean and offset to have equal ranks."}),assert(null==i||l.rank===i.rank,function(){return "Batch normalization gradient requires mean and scale to have equal ranks."}),u=0===c.rank||1===c.rank?c.as4D(1,1,1,c.size):2===c.rank?c.as4D(1,1,c.shape[0],c.shape[1]):3===c.rank?c.as4D(1,c.shape[0],c.shape[1],c.shape[2]):c;return ENV.engine.runKernel(function(e,t){var n=e.batchNormalization(u,batchnormReshape4D(l),batchnormReshape4D(p),a,batchnormReshape4D(i),batchnormReshape4D(s));return t([c,l,p,i]),n},{$x:c,$mean:l,$variance:p,$scale:i,$offset:s},function(e,t){var n=t[0],r=t[1],o=t[2],i=t[3],s=null==i?scalar(1):i,c=getReductionAxes(r.shape,u.shape),l=[];if(1===r.rank){for(var p=0;p<u.shape.length-1;++p)l.push(u.shape[p]);l.push(1);}var d=n.sub(r),h=e.mul(s),f=rsqrt(o.add(scalar(a))),m=f.mul(f).mul(f).mul(scalar(-.5));return {$x:function(){return 1===r.rank?e.mul(tile(f.as4D(1,1,1,r.shape[0]),l)).mul(s).reshape(n.shape):e.mul(f).mul(s).reshape(n.shape)},$mean:function(){var e=f.mul(scalar(-1)).mul(h);return 1===r.rank&&(e=e.sum(c)),e.reshape(r.shape)},$variance:function(){var e=m.mul(d).mul(h);return 1===r.rank&&(e=e.sum(c)),e.reshape(r.shape)},$scale:function(){var t=d.mul(f),n=e.mul(t);return 1===r.rank&&(n=n.sum(c)),n.reshape(r.shape)},$offset:function(){var t=e;return 1===r.rank&&(t=t.sum(c)),t.reshape(r.shape)}}}).reshape(c.shape)}function batchnormReshape4D(e){return null==e?null:0===e.rank?e.as1D():1===e.rank?e:2===e.rank?e.as4D(1,1,e.shape[0],e.shape[1]):3===e.rank?e.as4D(1,e.shape[0],e.shape[1],e.shape[2]):e}function batchNormalization2d_(e,t,n,r,o,a){return void 0===r&&(r=.001),warnDeprecation(),batchNorm2d_(e,t,n,a,o,r)}function batchNormalization3d_(e,t,n,r,o,a){return void 0===r&&(r=.001),warnDeprecation(),batchNorm3d_(e,t,n,a,o,r)}function batchNormalization4d_(e,t,n,r,o,a){return void 0===r&&(r=.001),warnDeprecation(),batchNorm4d_(e,t,n,a,o,r)}function warnDeprecation(){deprecationWarn("tf.batchNormalization() is going away. Use tf.batchNorm() instead, and note the positional argument change of scale, offset, and varianceEpsilon");}var batchNormalization2d=op({batchNormalization2d_:batchNormalization2d_}),batchNormalization3d=op({batchNormalization3d_:batchNormalization3d_}),batchNormalization4d=op({batchNormalization4d_:batchNormalization4d_}),batchNormalization=op({batchNormalization_:batchNormalization_}),batchNorm=op({batchNorm_:batchNorm_}),batchNorm2d=op({batchNorm2d_:batchNorm2d_}),batchNorm3d=op({batchNorm3d_:batchNorm3d_}),batchNorm4d=op({batchNorm4d_:batchNorm4d_});function computePool2DInfo(e,t,n,r,o,a,i){void 0===i&&(i="channelsLast");var s,u=parseTupleParam(t),c=u[0],l=u[1];if("channelsLast"===i)s=[c,l,e[3],e[3]];else{if("channelsFirst"!==i)throw new Error("Unknown dataFormat "+i);s=[c,l,e[1],e[1]];}return computeConv2DInfo(e,s,n,r,o,a,!1,i)}function computeConv2DInfo(e,t,n,r,o,a,i,s){void 0===i&&(i=!1),void 0===s&&(s="channelsLast");var u=[-1,-1,-1,-1],c=u[0],l=u[1],p=u[2],d=u[3];if("channelsLast"===s)c=e[0],l=e[1],p=e[2],d=e[3];else{if("channelsFirst"!==s)throw new Error("Unknown dataFormat "+s);c=e[0],d=e[1],l=e[2],p=e[3];}var h,f=t[0],m=t[1],g=t[3],v=parseTupleParam(n),y=v[0],x=v[1],E=parseTupleParam(r),T=E[0],b=E[1],w=getEffectiveFilterSize(f,T),S=getEffectiveFilterSize(m,b),C=getPadAndOutInfo(o,l,p,y,x,w,S,a),A=C.padInfo,N=C.outHeight,_=C.outWidth,R=i?g*d:g;return "channelsFirst"===s?h=[c,R,N,_]:"channelsLast"===s&&(h=[c,N,_,R]),{batchSize:c,dataFormat:s,inHeight:l,inWidth:p,inChannels:d,outHeight:N,outWidth:_,outChannels:R,padInfo:A,strideHeight:y,strideWidth:x,filterHeight:f,filterWidth:m,effectiveFilterHeight:w,effectiveFilterWidth:S,dilationHeight:T,dilationWidth:b,inShape:e,outShape:h,filterShape:t}}function computeConv3DInfo(e,t,n,r,o,a,i){void 0===a&&(a=!1),void 0===i&&(i="channelsLast");var s=[-1,-1,-1,-1,-1],u=s[0],c=s[1],l=s[2],p=s[3],d=s[4];if("channelsLast"===i)u=e[0],c=e[1],l=e[2],p=e[3],d=e[4];else{if("channelsFirst"!==i)throw new Error("Unknown dataFormat "+i);u=e[0],d=e[1],c=e[2],l=e[3],p=e[4];}var h,f=t[0],m=t[1],g=t[2],v=t[4],y=parse3TupleParam(n),x=y[0],E=y[1],T=y[2],b=parse3TupleParam(r),w=b[0],S=b[1],C=b[2],A=get3DPadAndOutInfo(o,c,l,p,x,E,T,getEffectiveFilterSize(f,w),getEffectiveFilterSize(m,S),getEffectiveFilterSize(g,C)),N=A.padInfo,_=A.outDepth,R=A.outHeight,I=A.outWidth,k=a?v*d:v;return "channelsFirst"===i?h=[u,k,_,R,I]:"channelsLast"===i&&(h=[u,_,R,I,k]),{batchSize:u,dataFormat:i,inDepth:c,inHeight:l,inWidth:p,inChannels:d,outDepth:_,outHeight:R,outWidth:I,outChannels:k,padInfo:N,strideDepth:x,strideHeight:E,strideWidth:T,filterDepth:f,filterHeight:m,filterWidth:g,dilationDepth:w,dilationHeight:S,dilationWidth:C,inShape:e,outShape:h,filterShape:t}}function computeOutputShape3D(e,t,n,r,o,a){null==o&&(o=computeDefaultPad(e,t,r));var i=e[0],s=e[1],u=conditionalRound((i-t+2*o)/r+1,a);assert(isInt(u),function(){return "The output # of rows ("+u+") must be an integer. Change the stride and/or zero pad parameters"});var c=conditionalRound((s-t+2*o)/r+1,a);return assert(isInt(c),function(){return "The output # of columns ("+c+") must be an integer. Change the stride and/or zero pad parameters"}),[u,c,n]}function computeDefaultPad(e,t,n,r){void 0===r&&(r=1);var o=getEffectiveFilterSize(t,r);return Math.floor((e[0]*(n-1)-n+o)/2)}function parseTupleParam(e){return "number"==typeof e?[e,e]:e}function parse3TupleParam(e){return "number"==typeof e?[e,e,e]:e}function getEffectiveFilterSize(e,t){return t<=1?e:e+(e-1)*(t-1)}function getPadAndOutInfo(e,t,n,r,o,a,i,s){var u,c,l;if("number"==typeof e){u={top:e,bottom:e,left:e,right:e,type:0===e?"VALID":"NUMBER"};var p=computeOutputShape3D([t,n,1],a,1,r,e,s);c=p[0],l=p[1];}else if("same"===e){c=Math.ceil(t/r),l=Math.ceil(n/o);var d=Math.max(0,(c-1)*r+a-t),h=Math.max(0,(l-1)*o+i-n),f=Math.floor(d/2),m=d-f,g=Math.floor(h/2);u={top:f,bottom:m,left:g,right:h-g,type:"SAME"};}else{if("valid"!==e)throw Error("Unknown padding parameter: "+e);u={top:0,bottom:0,left:0,right:0,type:"VALID"},c=Math.ceil((t-a+1)/r),l=Math.ceil((n-i+1)/o);}return {padInfo:u,outHeight:c,outWidth:l}}function get3DPadAndOutInfo(e,t,n,r,o,a,i,s,u,c){var l,p,d,h;if("same"===e){var f=((p=Math.ceil(t/o))-1)*o+s-t,m=((d=Math.ceil(n/a))-1)*a+u-n,g=((h=Math.ceil(r/i))-1)*i+c-r,v=Math.floor(f/2),y=f-v,x=Math.floor(m/2),E=m-x,T=Math.floor(g/2);l={top:x,bottom:E,left:T,right:g-T,front:v,back:y,type:"SAME"};}else{if("valid"!==e)throw Error("Unknown padding parameter: "+e);l={top:0,bottom:0,left:0,right:0,front:0,back:0,type:"VALID"},p=Math.ceil((t-s+1)/o),d=Math.ceil((n-u+1)/a),h=Math.ceil((r-c+1)/i);}return {padInfo:l,outDepth:p,outHeight:d,outWidth:h}}function conditionalRound(e,t){if(!t)return e;switch(t){case"round":return Math.round(e);case"ceil":return Math.ceil(e);case"floor":return Math.floor(e);default:throw new Error("Unknown roundingMode "+t)}}function tupleValuesAreOne(e){var t=parseTupleParam(e),n=t[0],r=t[1];return 1===n&&1===r}function eitherStridesOrDilationsAreOne(e,t){return tupleValuesAreOne(e)||tupleValuesAreOne(t)}function conv1d_(e,t,n,r,o,a,i){void 0===o&&(o="NWC"),void 0===a&&(a=1);var s=convertToTensor(e,"x","conv1d"),u=convertToTensor(t,"filter","conv1d"),c=s,l=!1;2===s.rank&&(l=!0,c=s.as3D(1,s.shape[0],s.shape[1])),assert(3===c.rank,function(){return "Error in conv1d: input must be rank 3, but got rank "+c.rank+"."}),assert(3===u.rank,function(){return "Error in conv1d: filter must be rank 3, but got rank "+u.rank+"."}),null!=i&&assert(isInt(r),function(){return "Error in conv1d: pad must be an integer when using, dimRoundingMode "+i+" but got pad "+r+"."}),assert(c.shape[2]===u.shape[1],function(){return "Error in conv1d: depth of input ("+c.shape[2]+") must match input depth for filter "+u.shape[1]+"."}),assert(eitherStridesOrDilationsAreOne(n,a),function(){return "Error in conv1D: Either stride or dilation must be 1. Got stride "+n+" and dilation '"+a+"'"}),assert("NWC"===o,function(){return "Error in conv1d: got dataFormat of "+o+" but only NWC is currently supported."});var p=u.as4D(1,u.shape[0],u.shape[1],u.shape[2]),d=c.as4D(c.shape[0],1,c.shape[1],c.shape[2]),h=conv2d(d,p,[1,n],r,"NHWC",[1,a],i);return l?h.as2D(h.shape[2],h.shape[3]):h.as3D(h.shape[0],h.shape[2],h.shape[3])}function conv2d_(e,t,n,r,o,a,i){void 0===o&&(o="NHWC"),void 0===a&&(a=[1,1]);var s=convertToTensor(e,"x","conv2d"),u=convertToTensor(t,"filter","conv2d"),c=s,l=!1;3===s.rank&&(l=!0,c=s.as4D(1,s.shape[0],s.shape[1],s.shape[2])),assert(4===c.rank,function(){return "Error in conv2d: input must be rank 4, but got rank "+c.rank+"."}),assert(4===u.rank,function(){return "Error in conv2d: filter must be rank 4, but got rank "+u.rank+"."}),null!=i&&assert(isInt(r),function(){return "Error in conv2d: pad must be an integer when using, dimRoundingMode "+i+" but got pad "+r+"."}),assert(c.shape[3]===u.shape[2],function(){return "Error in conv2d: depth of input ("+c.shape[3]+") must match input depth for filter "+u.shape[2]+"."}),assert(eitherStridesOrDilationsAreOne(n,a),function(){return "Error in conv2D: Either strides or dilations must be 1. Got strides "+n+" and dilations '"+a+"'"}),assert("NHWC"===o,function(){return "Error in conv2d: got dataFormat of "+o+" but only NHWC is currently supported."});var p=computeConv2DInfo(c.shape,u.shape,n,a,r,i),d=ENV.engine.runKernel(function(e,t){var n=e.conv2d(c,u,p);return t([u,c]),n},{x:c,$filter:u},function(e,t){var o=t,i=o[0],s=o[1];return assert(tupleValuesAreOne(a),function(){return "Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '"+a+"'"}),{x:function(){return conv2dDerInput_(s.shape,e,i,n,r)},$filter:function(){return conv2dDerFilter_(s,e,i.shape,n,r)}}});return l?d.as3D(d.shape[1],d.shape[2],d.shape[3]):d}function conv2dDerInput_(e,t,n,r,o,a){assert(e.length===t.rank,function(){return "Length of inShape ("+e.length+") and rank of dy ("+t.rank+") must match"});var i=e,s=t,u=!1;3===t.rank&&(u=!0,s=t.as4D(1,t.shape[0],t.shape[1],t.shape[2]),i=[1,e[0],e[1],e[2]]);var c=i[3],l=s.shape[3];assert(4===i.length,function(){return "Error in conv2dDerInput: inShape must be length 4, but got length "+i.length+"."}),assert(4===s.rank,function(){return "Error in conv2dDerInput: dy must be rank 4, but got rank "+s.rank}),assert(4===n.rank,function(){return "Error in conv2dDerInput: filter must be rank 4, but got rank "+n.rank}),assert(c===n.shape[2],function(){return "Error in conv2dDerInput: depth of input ("+c+") must match input depth for filter "+n.shape[2]+"."}),assert(l===n.shape[3],function(){return "Error in conv2dDerInput: depth of output ("+l+") must match output depth for filter "+n.shape[3]+"."}),null!=a&&assert(isInt(o),function(){return "Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode "+a+" but got pad "+o+"."});var p=computeConv2DInfo(i,n.shape,r,1,o,a),d=ENV.engine.runKernel(function(e,t){var r=e.conv2dDerInput(s,n,p);return t([n,s]),r},{dy4D:s,filter:n},function(e,t){var n=t[0],i=t[1];return {dy4D:function(){return conv2d(e,n,r,o,"NHWC",1,a)},filter:function(){return conv2dDerFilter(e,i,n.shape,r,o,a)}}});return u?d.as3D(d.shape[1],d.shape[2],d.shape[3]):d}function conv2dDerFilter_(e,t,n,r,o,a){var i=e;3===e.rank&&(i=e.as4D(1,e.shape[0],e.shape[1],e.shape[2]));var s=t;3===s.rank&&(s=t.as4D(1,t.shape[0],t.shape[1],t.shape[2])),assert(4===i.rank,function(){return "Error in conv2dDerFilter: input must be rank 4, but got shape "+i.shape+"."}),assert(4===s.rank,function(){return "Error in conv2dDerFilter: dy must be rank 4, but got shape "+s.shape+"."}),assert(4===n.length,function(){return "Error in conv2dDerFilter: filterShape must be length 4, but got "+n+"."}),assert(i.shape[3]===n[2],function(){return "Error in conv2dDerFilter: depth of input "+i.shape[3]+") must match input depth in filter ("+n[2]+"."}),assert(s.shape[3]===n[3],function(){return "Error in conv2dDerFilter: depth of dy ("+s.shape[3]+") must match output depth for filter ("+n[3]+")."}),null!=a&&assert(isInt(o),function(){return "Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode "+a+" but got pad "+o+"."});var u=computeConv2DInfo(i.shape,n,r,1,o,a);return ENV.engine.runKernel(function(e){return e.conv2dDerFilter(i,s,u)},{x4D:i,dy4D:s})}function conv2dTranspose_(e,t,n,r,o,a){return conv2dDerInput_(n,convertToTensor(e,"x","conv2dTranspose"),convertToTensor(t,"filter","conv2dTranspose"),r,o,a)}function depthwiseConv2d_(e,t,n,r,o,a,i){void 0===o&&(o="NHWC"),void 0===a&&(a=[1,1]);var s=convertToTensor(e,"x","depthwiseConv2d"),u=convertToTensor(t,"filter","depthwiseConv2d"),c=s,l=!1;3===s.rank&&(l=!0,c=s.as4D(1,s.shape[0],s.shape[1],s.shape[2])),assert(4===c.rank,function(){return "Error in depthwiseConv2d: input must be rank 4, but got rank "+c.rank+"."}),assert(4===u.rank,function(){return "Error in depthwiseConv2d: filter must be rank 4, but got rank "+u.rank+"."}),assert(c.shape[3]===u.shape[2],function(){return "Error in depthwiseConv2d: number of input channels ("+c.shape[3]+") must match the inChannels dimension in filter "+u.shape[2]+"."}),null==a&&(a=[1,1]),assert(eitherStridesOrDilationsAreOne(n,a),function(){return "Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides "+n+" and dilations '"+a+"'"}),null!=i&&assert(isInt(r),function(){return "Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode "+i+" but got pad "+r+"."});var p=computeConv2DInfo(c.shape,u.shape,n,a,r,i,!0),d=ENV.engine.runKernel(function(e,t){var n=e.depthwiseConv2D(c,u,p);return t([c,u]),n},{x:c,$filter:u},function(e,t){assert(tupleValuesAreOne(a),function(){return "Error in gradient of depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '"+a+"'"});var n=t[0],r=t[1];return {x:function(){return depthwiseConv2dDerInput(n.shape,e,r,p)},$filter:function(){return depthwiseConv2dDerFilter(n,e,r.shape,p)}}});return l?d.as3D(d.shape[1],d.shape[2],d.shape[3]):d}function separableConv2d_(e,t,n,r,o,a,i){void 0===a&&(a=[1,1]),void 0===i&&(i="NHWC");var s=convertToTensor(e,"x","separableConv2d"),u=convertToTensor(t,"depthwiseFilter","separableConv2d"),c=convertToTensor(n,"pointwiseFilter","separableConv2d"),l=s,p=!1;if(3===s.rank&&(p=!0,l=s.as4D(1,s.shape[0],s.shape[1],s.shape[2])),"NCHW"===i)throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");assert(4===l.rank,function(){return "Error in separableConv2d: input must be rank 4, but got rank "+l.rank+"."}),assert(4===u.rank,function(){return "Error in separableConv2d: depthwise filter must be rank 4, but got rank "+u.rank+"."}),assert(4===c.rank,function(){return "Error in separableConv2d: pointwise filter must be rank 4, but got rank "+u.rank+"."}),assert(1===c.shape[0],function(){return "Error in separableConv2d: the first dimension of pointwise filter must be 1, but got "+c.shape[0]+"."}),assert(1===c.shape[1],function(){return "Error in separableConv2d: the second dimension of pointwise filter must be 1, but got "+c.shape[1]+"."});var d=u.shape[2],h=u.shape[3];assert(c.shape[2]===d*h,function(){return "Error in separableConv2d: the third dimension of pointwise filter must be "+d*h+", but got "+c.shape[2]+"."});var f=depthwiseConv2d(l,u,r,o,i,a),m=conv2d(f,c,1,"valid",i);return p?m.as3D(m.shape[1],m.shape[2],m.shape[3]):m}function parseTupleParam$1(e){return "number"==typeof e?[e,e,e]:2===e.length?[e[0],e[1],1]:e}function tupleValuesAreOne$1(e){var t=parseTupleParam$1(e),n=t[0],r=t[1],o=t[2];return 1===n&&1===r&&1===o}function eitherStridesOrDilationsAreOne$1(e,t){return tupleValuesAreOne$1(e)||tupleValuesAreOne$1(t)}function depthwiseConv2dDerInput(e,t,n,r){var o=t,a=!1;3===t.rank&&(a=!0,o=t.as4D(1,t.shape[0],t.shape[1],t.shape[2]));var i=ENV.engine.runKernel(function(e){return e.depthwiseConv2DDerInput(o,n,r)},{dy4D:o});return a?i.as3D(i.shape[1],i.shape[2],i.shape[3]):i}function depthwiseConv2dDerFilter(e,t,n,r){var o=e;3===e.rank&&(o=e.as4D(1,e.shape[0],e.shape[1],e.shape[2]));var a=t;return 3===a.rank&&(a=t.as4D(1,t.shape[0],t.shape[1],t.shape[2])),ENV.engine.runKernel(function(e){return e.depthwiseConv2DDerFilter(o,a,r)},{x4D:o,dy4D:a})}function conv3d_(e,t,n,r,o,a){void 0===o&&(o="NHWC"),void 0===a&&(a=[1,1,1]);var i=convertToTensor(e,"x","conv3d"),s=convertToTensor(t,"filter","conv3d"),u=i,c=!1;4===i.rank&&(c=!0,u=i.as5D(1,i.shape[0],i.shape[1],i.shape[2],i.shape[3])),assert(5===u.rank,function(){return "Error in conv3d: input must be rank 5, but got rank "+u.rank+"."}),assert(5===s.rank,function(){return "Error in conv3d: filter must be rank 5, but got rank "+s.rank+"."}),assert(u.shape[4]===s.shape[3],function(){return "Error in conv3d: depth of input ("+u.shape[4]+") must match input depth for filter "+s.shape[3]+"."}),assert(eitherStridesOrDilationsAreOne$1(n,a),function(){return "Error in conv3D: Either strides or dilations must be 1. Got strides "+n+" and dilations '"+a+"'"}),assert("NHWC"===o,function(){return "Error in conv3d: got dataFormat of "+o+" but only NHWC is currently supported."});var l=computeConv3DInfo(u.shape,s.shape,n,a,r),p=ENV.engine.runKernel(function(e,t){var n=e.conv3d(u,s,l);return t([u,s]),n},{x:u,$filter:s},function(e,t){assert(tupleValuesAreOne$1(a),function(){return "Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '"+a+"'"});var o=t[0],i=t[1];return {x:function(){return conv3dDerInput_(o.shape,e,i,n,r)},$filter:function(){return conv3dDerFilter_(o,e,i.shape,n,r)}}});return c?p.as4D(p.shape[1],p.shape[2],p.shape[3],p.shape[4]):p}function conv3dDerInput_(e,t,n,r,o){assert(e.length===t.rank,function(){return "Length of inShape ("+e.length+") and rank of dy ("+t.rank+") must match"});var a=e,i=t,s=!1;4===t.rank&&(s=!0,i=t.as5D(1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]),a=[1,e[0],e[1],e[2],e[3]]);var u=a[4],c=i.shape[4];assert(5===a.length,function(){return "Error in conv3dDerInput: inShape must be length 5, but got length "+a.length+"."}),assert(5===i.rank,function(){return "Error in conv3dDerInput: dy must be rank 5, but got rank "+i.rank}),assert(5===n.rank,function(){return "Error in conv3dDerInput: filter must be rank 5, but got rank "+n.rank}),assert(u===n.shape[3],function(){return "Error in conv3dDerInput: depth of input ("+u+") must match input depth for filter "+n.shape[3]+"."}),assert(c===n.shape[4],function(){return "Error in conv3dDerInput: depth of output ("+c+") must match output depth for filter "+n.shape[4]+"."});var l=computeConv3DInfo(a,n.shape,r,1,o),p=ENV.engine.runKernel(function(e){return e.conv3dDerInput(i,n,l)},{dy5D:i});return s?p.as4D(p.shape[1],p.shape[2],p.shape[3],p.shape[4]):p}function conv3dDerFilter_(e,t,n,r,o){var a=e;4===e.rank&&(a=e.as5D(1,e.shape[0],e.shape[1],e.shape[2],e.shape[3]));var i=t;4===i.rank&&(i=t.as5D(1,t.shape[0],t.shape[1],t.shape[2],t.shape[3])),assert(5===a.rank,function(){return "Error in conv3dDerFilter: input must be rank 5, but got shape "+a.shape+"."}),assert(5===i.rank,function(){return "Error in conv3dDerFilter: dy must be rank 5, but got shape "+i.shape+"."}),assert(5===n.length,function(){return "Error in conv3dDerFilter: filterShape must be length 5, but got "+n+"."}),assert(a.shape[4]===n[3],function(){return "Error in conv3dDerFilter: depth of input "+a.shape[4]+") must match input depth in filter ("+n[3]+"."}),assert(i.shape[4]===n[4],function(){return "Error in conv3dDerFilter: depth of dy ("+i.shape[4]+") must match output depth for filter ("+n[4]+")."});var s=computeConv3DInfo(a.shape,n,r,1,o);return ENV.engine.runKernel(function(e){return e.conv3dDerFilter(a,i,s)},{x5D:a,dy5D:i})}var conv1d=op({conv1d_:conv1d_}),conv2d=op({conv2d_:conv2d_}),conv3d=op({conv3d_:conv3d_}),conv2dDerFilter=op({conv2dDerFilter_:conv2dDerFilter_}),depthwiseConv2d=op({depthwiseConv2d_:depthwiseConv2d_}),separableConv2d=op({separableConv2d_:separableConv2d_}),conv2dTranspose=op({conv2dTranspose_:conv2dTranspose_});function matMul_(e,t,n,r){var o;void 0===n&&(n=!1),void 0===r&&(r=!1);var a=convertToTensor(e,"a","matMul"),i=convertToTensor(t,"b","matMul");o=makeTypesMatch(a,i),a=o[0],i=o[1];var s=n?a.shape[a.rank-2]:a.shape[a.rank-1],u=r?i.shape[i.rank-1]:i.shape[i.rank-2],c=n?a.shape[a.rank-1]:a.shape[a.rank-2],l=r?i.shape[i.rank-2]:i.shape[i.rank-1],p=a.shape.slice(0,-2),d=i.shape.slice(0,-2),h=sizeFromShape(p),f=sizeFromShape(d);assert(a.rank>=2&&i.rank>=2&&a.rank===i.rank,function(){return "Error in matMul: inputs must have the same rank of at least 2, got ranks "+a.rank+" and "+i.rank+"."}),assert(arraysEqual(p,d),function(){return "Error in matMul: outer dimensions ("+p+") and ("+d+") of Tensors with shapes "+a.shape+" and "+i.shape+" must match."}),assert(s===u,function(){return "Error in matMul: inner shapes ("+s+") and ("+u+") of Tensors with shapes "+a.shape+" and "+i.shape+" and transposeA="+n+" and transposeB="+r+" must match."});var m=a.shape.slice(0,-2).concat([c,l]),g=n?a.as3D(h,s,c):a.as3D(h,c,s),v=r?i.as3D(f,l,u):i.as3D(f,u,l);return ENV.engine.runKernel(function(e,t){var o=e.batchMatMul(g,v,n,r);return t([g,v]),o},{$a:g,$b:v},function(e,t){var o=t,a=o[0],i=o[1];return n||r?!n&&r?{$a:function(){return e.matMul(i,!1,!1)},$b:function(){return e.matMul(a,!0,!1)}}:n&&!r?{$a:function(){return i.matMul(e,!1,!0)},$b:function(){return a.matMul(e,!1,!1)}}:{$a:function(){return i.matMul(e,!0,!0)},$b:function(){return e.matMul(a,!0,!0)}}:{$a:function(){return e.matMul(i,!1,!0)},$b:function(){return a.matMul(e,!0,!1)}}}).reshape(m)}function outerProduct_(e,t){var n=convertToTensor(e,"v1","outerProduct"),r=convertToTensor(t,"v2","outerProduct");return assert(1===n.rank&&1===r.rank,function(){return "Error in outerProduct: inputs must be rank 1, but got ranks "+n.rank+" and "+r.rank+"."}),n.as2D(-1,1).matMul(r.as2D(1,-1))}function dot_(e,t){var n=convertToTensor(e,"t1","dot"),r=convertToTensor(t,"t2","dot");assert(!(1!==n.rank&&2!==n.rank||1!==r.rank&&2!==r.rank),function(){return "Error in dot: inputs must all be rank 1 or 2, but got ranks "+n.rank+" and "+r.rank+"."});var o=1===n.rank?n.size:n.shape[1],a=1===r.rank?r.size:r.shape[0];return assert(o===a,function(){return "Error in dot: inner dimensions of inputs must match, but got "+o+" and "+a+"."}),1===n.rank&&1===r.rank?n.as2D(1,-1).matMul(r.as2D(-1,1)).asScalar():1===n.rank&&2===r.rank?n.as2D(1,-1).matMul(r.as2D(r.shape[0],r.shape[1])).as1D():2===n.rank&&1===r.rank?n.matMul(r.as2D(-1,1)).as1D():n.matMul(r.as2D(r.shape[0],r.shape[1]))}var matMul=op({matMul_:matMul_}),dot=op({dot_:dot_}),outerProduct=op({outerProduct_:outerProduct_});function reverse1d_(e){var t=convertToTensor(e,"x","reverse");return assert(1===t.rank,function(){return "Error in reverse1D: x must be rank 1 but got rank "+t.rank+"."}),reverse(t,0)}function reverse2d_(e,t){var n=convertToTensor(e,"x","reverse");return assert(2===n.rank,function(){return "Error in reverse2D: x must be rank 2 but got rank "+n.rank+"."}),reverse(n,t)}function reverse3d_(e,t){var n=convertToTensor(e,"x","reverse");return assert(3===n.rank,function(){return "Error in reverse3D: x must be rank 3 but got rank "+n.rank+"."}),reverse(n,t)}function reverse4d_(e,t){var n=convertToTensor(e,"x","reverse");return assert(4===n.rank,function(){return "Error in reverse4D: x must be rank 4 but got rank "+n.rank+"."}),reverse(n,t)}function reverse_(e,t){var n=convertToTensor(e,"x","reverse");if(0===n.rank)return n.clone();var r=parseAxisParam(t,n.shape);return ENV.engine.runKernel(function(e){return e.reverse(n,r)},{$x:n},function(e){return {$x:function(){return e.reverse(r)}}}).reshapeAs(n)}var reverse=op({reverse_:reverse_}),reverse1d=op({reverse1d_:reverse1d_}),reverse2d=op({reverse2d_:reverse2d_}),reverse3d=op({reverse3d_:reverse3d_}),reverse4d=op({reverse4d_:reverse4d_});function maxPoolImpl_(e,t,n,r,o,a){var i=convertToTensor(e,"x","maxPool"),s=i,u=!1;3===i.rank&&(u=!0,s=i.as4D(1,i.shape[0],i.shape[1],i.shape[2])),null==r&&(r=[1,1]),assert(4===s.rank,function(){return "Error in maxPool: input must be rank 4 but got rank "+s.rank+"."}),assert(eitherStridesOrDilationsAreOne(n,r),function(){return "Error in maxPool: Either strides or dilations must be 1. Got strides "+n+" and dilations '"+r+"'"}),null!=a&&assert(isInt(o),function(){return "Error in maxPool: pad must be an integer when using, dimRoundingMode "+a+" but got pad "+o+"."});var c=computePool2DInfo(s.shape,t,n,r,o,a),l=ENV.engine.runKernel(function(e,t){var n=e.maxPool(s,c);return t([s,n]),n},{x:s},function(e,a){var i=a[0],s=a[1];return {x:function(){return maxPoolBackprop(e,i,s,t,n,r,o)}}});return u?l.as3D(l.shape[1],l.shape[2],l.shape[3]):l}function maxPool_(e,t,n,r,o){return maxPoolImpl_(e,t,n,1,r,o)}function avgPoolImpl_(e,t,n,r,o,a){var i=convertToTensor(e,"x","avgPool","float32");null==r&&(r=[1,1]),assert(eitherStridesOrDilationsAreOne(n,r),function(){return "Error in avgPool: Either strides or dilations must be 1. Got strides "+n+" and dilations '"+r+"'"});var s=i,u=!1;3===i.rank&&(u=!0,s=i.as4D(1,i.shape[0],i.shape[1],i.shape[2])),assert(4===s.rank,function(){return "Error in avgPool: x must be rank 4 but got rank "+s.rank+"."}),null!=a&&assert(isInt(o),function(){return "Error in avgPool: pad must be an integer when using, dimRoundingMode "+a+" but got pad "+o+"."});var c=computePool2DInfo(s.shape,t,n,r,o,a),l=ENV.engine.runKernel(function(e){return e.avgPool(s,c)},{x:s},function(e){return {x:function(){return avgPoolBackprop(e,s,t,n,r,o)}}});return l=l.cast(i.dtype),u?l.as3D(l.shape[1],l.shape[2],l.shape[3]):l}function avgPool_(e,t,n,r,o){return avgPoolImpl_(e,t,n,1,r,o)}function pool_(e,t,n,r,o,a){null==o&&(o=[1,1]),null==a&&(a=1),0===r&&(r="valid");var i=convertToTensor(e,"x","maxPool"),s=i,u=!1;3===i.rank&&(u=!0,s=i.as4D(1,i.shape[0],i.shape[1],i.shape[2])),assert(eitherStridesOrDilationsAreOne(a,o),function(){return "Error in pool: Either strides or dilations must be 1. Got strides "+a+" and dilations '"+o+"'"});var c,l=computePool2DInfo(s.shape,t,a,o,r),p=[l.dilationHeight,l.dilationWidth];c="same"===r?withSpaceToBatchBasePaddings([l.filterHeight,l.filterWidth],p):[[0,0],[0,0]];var d=1===p[0]&&1===p[1],h=requiredSpaceToBatchPaddings([l.inHeight,l.inWidth],p,c),f=h[0],m=h[1],g=d?r:"valid",v=d?s:spaceToBatchND(s,p,f),y=("avg"===n?function(){return avgPoolImpl_(v,t,a,1,g)}:function(){return maxPoolImpl_(v,t,a,1,g)})(),x=d?y:batchToSpaceND(y,p,m);return u?x.as3D(x.shape[1],x.shape[2],x.shape[3]):x}function maxPoolBackprop(e,t,n,r,o,a,i,s){var u=convertToTensor(e,"dy","maxPoolBackprop"),c=convertToTensor(t,"input","maxPoolBackprop"),l=convertToTensor(n,"output","maxPoolBackprop");assert(c.rank===u.rank,function(){return "Rank of input ("+c.rank+") does not match rank of dy ("+u.rank+")"}),null==a&&(a=[1,1]),assert(eitherStridesOrDilationsAreOne(o,a),function(){return "Error in maxPoolBackProp: Either strides or dilations must be 1. Got strides "+o+" and dilations '"+a+"'"}),assert(4===u.rank,function(){return "Error in maxPoolBackprop: dy must be rank 4 but got rank "+u.rank+"."}),assert(4===c.rank,function(){return "Error in maxPoolBackprop: input must be rank 4 but got rank "+c.rank+"."}),null!=s&&assert(isInt(i),function(){return "Error in maxPoolBackprop: pad must be an integer when using, dimRoundingMode "+s+" but got pad "+i+"."});var p=computePool2DInfo(c.shape,r,o,a,i,s);return ENV.engine.runKernel(function(e){return e.maxPoolBackprop(u,c,l,p)},{$dy:u,$input:c})}function avgPoolBackprop(e,t,n,r,o,a){var i=convertToTensor(e,"dy","avgPoolBackprop"),s=convertToTensor(t,"input","avgPoolBackprop");assert(s.rank===i.rank,function(){return "Rank of input ("+s.rank+") does not match rank of dy ("+i.rank+")"}),null==o&&(o=[1,1]),assert(eitherStridesOrDilationsAreOne(r,o),function(){return "Error in avgPoolBackprop: Either strides or dilations must be 1. Got strides "+r+" and dilations '"+o+"'"});var u=s,c=i,l=!1;3===s.rank&&(l=!0,u=s.as4D(1,s.shape[0],s.shape[1],s.shape[2]),c=i.as4D(1,i.shape[0],i.shape[1],i.shape[2])),assert(4===c.rank,function(){return "Error in avgPoolBackprop: dy must be rank 4 but got rank "+c.rank+"."}),assert(4===u.rank,function(){return "Error in avgPoolBackprop: input must be rank 4 but got rank "+u.rank+"."});var p=computePool2DInfo(u.shape,n,r,o,a),d=ENV.engine.runKernel(function(e){return e.avgPoolBackprop(c,u,p)},{dy4D:c,input4D:u});return l?d.as3D(d.shape[1],d.shape[2],d.shape[3]):d}function requiredSpaceToBatchPaddings(e,t,n){var r=n.map(function(e){return e[0]}),o=n.map(function(e){return e[1]}),a=e.concat(r,o),i=t.map(function(e,t){return (e-a[t]%e)%e}),s=o.map(function(e,t){return e+i[t]});return [t.map(function(e,t){return [r[t],s[t]]}),t.map(function(e,t){return [0,i[t]]})]}function withSpaceToBatchBasePaddings(e,t){var n=e.map(function(e,n){return e+(e-1)*(t[n]-1)}).map(function(e){return e-1}),r=n.map(function(e){return Math.floor(e/2)}),o=n.map(function(e,t){return e-r[t]});return n.map(function(e,t){return [r[t],o[t]]})}var maxPool=op({maxPool_:maxPool_}),avgPool=op({avgPool_:avgPool_}),pool=op({pool_:pool_});function slice1d_(e,t,n){var r=convertToTensor(e,"x","slice1d");return assert(1===r.rank,function(){return "slice1d expects a rank-1 tensor, but got a rank-"+r.rank+" tensor"}),slice(r,[t],[n])}function slice2d_(e,t,n){var r=convertToTensor(e,"x","slice2d");return assert(2===r.rank,function(){return "slice2d expects a rank-2 tensor, but got a rank-"+r.rank+" tensor"}),slice(r,t,n)}function slice3d_(e,t,n){var r=convertToTensor(e,"x","slice3d");return assert(3===r.rank,function(){return "slice3d expects a rank-3 tensor, but got a rank-"+r.rank+" tensor"}),slice(r,t,n)}function slice4d_(e,t,n){var r=convertToTensor(e,"x","slice4d");return assert(4===r.rank,function(){return "slice4d expects a rank-4 tensor, but got a rank-"+r.rank+" tensor"}),slice(r,t,n)}function slice_(e,t,n){var r,o,a=convertToTensor(e,"x","slice");if(0===a.rank)throw new Error("Slicing scalar is not possible");r="number"==typeof t?[t].concat(new Array(a.rank-1).fill(0)):t.length<a.rank?t.concat(new Array(a.rank-t.length).fill(0)):t.slice(),o=(o=null==n?new Array(a.rank).fill(-1):"number"==typeof n?[n].concat(new Array(a.rank-1).fill(-1)):n.length<a.rank?n.concat(new Array(a.rank-n.length).fill(-1)):n).map(function(e,t){return e>=0?e:(assert(-1===e,function(){return "Bad value in size"}),a.shape[t]-r[t])}),assertParamsValid(a,r,o);var i=a.shape;return ENV.engine.runKernel(function(e){return e.slice(a,r,o)},{$x:a},function(e){for(var t=[],n=0;n<e.rank;n++)t.push([r[n],i[n]-r[n]-o[n]]);return {$x:function(){return e.pad(t)}}})}var slice=op({slice_:slice_}),slice1d=op({slice1d_:slice1d_}),slice2d=op({slice2d_:slice2d_}),slice3d=op({slice3d_:slice3d_}),slice4d=op({slice4d_:slice4d_});function logSumExp_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,"x","logSumExp"),o=parseAxisParam(t,r.shape),a=r.max(o,!0),i=r.sub(a).exp().sum(o).log(),s=a.reshape(i.shape).add(i);if(n){var u=expandShapeToKeepDim(s.shape,o);return s.reshape(u)}return s}function sum_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,"x","sum");"bool"===r.dtype&&(r=r.toInt());var o=parseAxisParam(t,r.shape);return customGrad(function(e){var t=getAxesPermutation(o,e.rank),r=o,a=e;null!=t&&(a=e.transpose(t),r=getInnerMostAxes(r.length,e.rank));var i=ENV.engine.runKernel(function(e){return e.sum(a,r)},{permutedX:a});if(n){var s=expandShapeToKeepDim(i.shape,o);i=i.reshape(s);}return {value:i,gradFunc:function(t){var n=e.shape.slice();return o.forEach(function(e){n[e]=1;}),t.reshape(n).mul(ones$1(e.shape,"float32"))}}})(r)}function prod_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,"x","prod");"bool"===r.dtype&&(r=r.toInt());var o=parseAxisParam(t,r.shape),a=getAxesPermutation(o,r.rank),i=o,s=r;null!=a&&(s=r.transpose(a),i=getInnerMostAxes(i.length,r.rank));var u=ENV.engine.runKernel(function(e){return e.prod(s,i)},{permutedX:s});if(n){var c=expandShapeToKeepDim(u.shape,o);u=u.reshape(c);}return u}function mean_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,"x","mean"),o=parseAxisParam(t,r.shape),a=sizeFromShape(computeOutAndReduceShapes(r.shape,o)[1]);return customGrad(function(e){var r=scalar(a);return {value:(r.dtype===e.dtype?e:e.cast(r.dtype)).div(r).sum(t,n),gradFunc:function(t){var n=e.shape.slice();return o.forEach(function(e){n[e]=1;}),t.reshape(n).mul(ones$1(e.shape,"float32")).div(a)}}})(r)}function gradForMinAndMax(e,t,n,r,o){return t.rank<n.rank&&(t=t.reshape(expandShapeToKeepDim(t.shape,r))),e.rank<n.rank&&(e=e.reshape(expandShapeToKeepDim(e.shape,r))),{$x:function(){var r=e.mul(n.equal(t).cast(e.dtype));return null==o?r:r.transpose(o)}}}function min_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,"x","min"),o=r,a=parseAxisParam(t,r.shape),i=a,s=getAxesPermutation(i,r.rank);null!=s&&(r=r.transpose(s),i=getInnerMostAxes(i.length,r.rank));var u=ENV.engine.runKernel(function(e,t){var n=e.min(r,i);return t([o,n]),n},{$x:r},function(e,t){return gradForMinAndMax(e,t[1],t[0],a,s)});if(n){var c=expandShapeToKeepDim(u.shape,a);u=u.reshape(c);}return u}function max_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,"x","max"),o=r,a=parseAxisParam(t,r.shape),i=a,s=getAxesPermutation(i,r.rank);null!=s&&(r=r.transpose(s),i=getInnerMostAxes(i.length,r.rank));var u=ENV.engine.runKernel(function(e,t){var n=e.max(r,i);return t([o,n]),n},{$x:r},function(e,t){return gradForMinAndMax(e,t[1],t[0],a,s)});if(n){var c=expandShapeToKeepDim(u.shape,a);u=u.reshape(c);}return u}function argMin_(e,t){void 0===t&&(t=0);var n=convertToTensor(e,"x","argMin");null==t&&(t=0);var r=parseAxisParam(t,n.shape),o=getAxesPermutation(r,n.rank);null!=o&&(n=n.transpose(o),r=getInnerMostAxes(r.length,n.rank));return ENV.engine.runKernel(function(e,t){var o=e.argMin(n,r[0]);return t([n]),o},{$x:n},function(e,t){var n=t[0];return {$x:function(){return zerosLike(n)}}})}function argMax_(e,t){void 0===t&&(t=0);var n=convertToTensor(e,"x","argMax");null==t&&(t=0);var r=parseAxisParam(t,n.shape),o=getAxesPermutation(r,n.rank);null!=o&&(n=n.transpose(o),r=getInnerMostAxes(r.length,n.rank));return ENV.engine.runKernel(function(e,t){var o=e.argMax(n,r[0]);return t([n]),o},{$x:n},function(e,t){var n=t[0];return {$x:function(){return zerosLike(n)}}})}function all_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,"x","all","bool"),o=parseAxisParam(t,r.shape),a=o,i=getAxesPermutation(a,r.rank);null!=i&&(r=r.transpose(i),a=getInnerMostAxes(a.length,r.rank));var s=ENV.engine.runKernel(function(e){return e.all(r,a)},{$x:r});if(n){var u=expandShapeToKeepDim(s.shape,o);return s.reshape(u)}return s}function any_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,"x","any","bool"),o=parseAxisParam(t,r.shape),a=o,i=getAxesPermutation(a,r.rank);null!=i&&(r=r.transpose(i),a=getInnerMostAxes(a.length,r.rank));var s=ENV.engine.runKernel(function(e){return e.any(r,a)},{$x:r});if(n){var u=expandShapeToKeepDim(s.shape,o);return s.reshape(u)}return s}function moments_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=parseAxisParam(t,(e=convertToTensor(e,"x","moments")).shape),o=e.mean(r,n),a=o.shape;return n||(a=expandShapeToKeepDim(o.shape,r)),{mean:o,variance:e.toFloat().sub(o.reshape(a)).square().mean(r,n)}}var all=op({all_:all_}),any=op({any_:any_}),argMax=op({argMax_:argMax_}),argMin=op({argMin_:argMin_}),logSumExp=op({logSumExp_:logSumExp_}),max=op({max_:max_}),mean=op({mean_:mean_}),min=op({min_:min_}),moments=op({moments_:moments_}),sum$1=op({sum_:sum_}),prod=op({prod_:prod_});function notEqual_(e,t){var n,r=convertToTensor(e,"a","notEqual"),o=convertToTensor(t,"b","notEqual");return n=makeTypesMatch(r,o),r=n[0],o=n[1],assertAndGetBroadcastShape(r.shape,o.shape),ENV.engine.runKernel(function(e){return e.notEqual(r,o)},{$a:r,$b:o})}function notEqualStrict_(e,t){var n=convertToTensor(e,"a","notEqualStrict"),r=convertToTensor(t,"b","notEqualStrict");return assertShapesMatch(n.shape,r.shape,"Error in notEqualStrict: "),n.notEqual(r)}function less_(e,t){var n,r=convertToTensor(e,"a","less"),o=convertToTensor(t,"b","less");return n=makeTypesMatch(r,o),r=n[0],o=n[1],assertAndGetBroadcastShape(r.shape,o.shape),ENV.engine.runKernel(function(e){return e.less(r,o)},{$a:r,$b:o})}function lessStrict_(e,t){var n=convertToTensor(e,"a","lessStrict"),r=convertToTensor(t,"b","lessStrict");return assertShapesMatch(n.shape,r.shape,"Error in lessStrict: "),n.less(r)}function equal_(e,t){var n,r=convertToTensor(e,"a","equal"),o=convertToTensor(t,"b","equal");return n=makeTypesMatch(r,o),r=n[0],o=n[1],assertAndGetBroadcastShape(r.shape,o.shape),ENV.engine.runKernel(function(e){return e.equal(r,o)},{$a:r,$b:o})}function equalStrict_(e,t){var n=convertToTensor(e,"a","equalStrict"),r=convertToTensor(t,"b","equalStrict");return assertShapesMatch(n.shape,r.shape,"Error in equalStrict: "),n.equal(r)}function lessEqual_(e,t){var n,r=convertToTensor(e,"a","lessEqual"),o=convertToTensor(t,"b","lessEqual");return n=makeTypesMatch(r,o),r=n[0],o=n[1],assertAndGetBroadcastShape(r.shape,o.shape),ENV.engine.runKernel(function(e){return e.lessEqual(r,o)},{$a:r,$b:o})}function lessEqualStrict_(e,t){var n=convertToTensor(e,"a","lessEqualStrict"),r=convertToTensor(t,"b","lessEqualStrict");return assertShapesMatch(n.shape,r.shape,"Error in lessEqualStrict: "),n.lessEqual(r)}function greater_(e,t){var n,r=convertToTensor(e,"a","greater"),o=convertToTensor(t,"b","greater");return n=makeTypesMatch(r,o),r=n[0],o=n[1],assertAndGetBroadcastShape(r.shape,o.shape),ENV.engine.runKernel(function(e){return e.greater(r,o)},{$a:r,$b:o})}function greaterStrict_(e,t){var n=convertToTensor(e,"a","greaterStrict"),r=convertToTensor(t,"b","greaterStrict");return assertShapesMatch(n.shape,r.shape,"Error in greaterStrict: "),n.greater(r)}function greaterEqual_(e,t){var n,r=convertToTensor(e,"a","greaterEqual"),o=convertToTensor(t,"b","greaterEqual");n=makeTypesMatch(r,o),r=n[0],o=n[1],assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e,t){var n=e.greaterEqual(r,o);return t([r,o]),n},{$a:r,$b:o},function(e,t){var n=t[0],r=t[1];return {$a:function(){return zerosLike(n)},$b:function(){return zerosLike(r)}}})}function greaterEqualStrict_(e,t){var n=convertToTensor(e,"a","greaterEqualStrict"),r=convertToTensor(t,"b","greaterEqualStrict");return assertShapesMatch(n.shape,r.shape,"Error in greaterEqualStrict: "),n.greaterEqual(r)}var equal=op({equal_:equal_}),equalStrict=op({equalStrict_:equalStrict_}),greater=op({greater_:greater_}),greaterEqual=op({greaterEqual_:greaterEqual_}),greaterEqualStrict=op({greaterEqualStrict_:greaterEqualStrict_}),greaterStrict=op({greaterStrict_:greaterStrict_}),less=op({less_:less_}),lessEqual=op({lessEqual_:lessEqual_}),lessEqualStrict=op({lessEqualStrict_:lessEqualStrict_}),lessStrict=op({lessStrict_:lessStrict_}),notEqual=op({notEqual_:notEqual_}),notEqualStrict=op({notEqualStrict_:notEqualStrict_});function add_(e,t){var n,r=convertToTensor(e,"a","add"),o=convertToTensor(t,"b","add");n=makeTypesMatch(r,o),r=n[0],o=n[1];var a=assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.add(r,o)},{$a:r,$b:o},function(e){return {$a:function(){var t=e,n=getReductionAxes(r.shape,a);return n.length>0&&(t=t.sum(n)),t.reshape(r.shape)},$b:function(){var t=e,n=getReductionAxes(o.shape,a);return n.length>0&&(t=t.sum(n)),t.reshape(o.shape)}}})}function addN_(e){assert(Array.isArray(e),function(){return "The argument passed to tf.addN() must be a list of tensors"}),assert(e.length>=1,function(){return "Must pass at least one tensor to tf.addN(), but got "+e.length});var t=e.map(function(e,t){return convertToTensor(e,"tensors"+t,"addN")}),n=t[0];t.forEach(function(e){if(e.dtype!==n.dtype)throw new Error("All tensors passed to tf.addN() must have the same dtype")}),t.forEach(function(e){if(!arraysEqual(e.shape,n.shape))throw new Error("All tensors passed to tf.addN() must have the same shape")});var r=t;return ENV.engine.runKernel(function(e){return e.addN(t)},r,function(e){var n={};return t.forEach(function(t,r){n[r]=function(){return e.clone()};}),n})}function addStrict_(e,t){var n=convertToTensor(e,"a","addStrict"),r=convertToTensor(t,"b","addStrict");return assertShapesMatch(n.shape,r.shape,"Error in addStrict: "),n.add(r)}function sub_(e,t){var n,r=convertToTensor(e,"a","sub"),o=convertToTensor(t,"b","sub");n=makeTypesMatch(r,o),r=n[0],o=n[1];var a=assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.subtract(r,o)},{$a:r,$b:o},function(e){return {$a:function(){var t=e,n=getReductionAxes(r.shape,a);return n.length>0&&(t=t.sum(n)),t.reshape(r.shape)},$b:function(){var t=e,n=getReductionAxes(o.shape,a);return n.length>0&&(t=t.sum(n)),t.neg().reshape(o.shape)}}})}function subStrict_(e,t){var n=convertToTensor(e,"a","subStrict"),r=convertToTensor(t,"b","subStrict");return assertShapesMatch(n.shape,r.shape,"Error in subStrict: "),n.sub(r)}function pow_(e,t){var n=convertToTensor(e,"base","pow"),r=convertToTensor(t,"exp","pow"),o=assertAndGetBroadcastShape(n.shape,r.shape);e=n.cast(upcastType(n.dtype,r.dtype)),t=r.cast(upcastType(n.dtype,r.dtype));return ENV.engine.runKernel(function(e,t){var o=e.pow(n,r);return t([n,r,o]),o},{$base:n,$exp:r},function(e,t){var n=t[0],r=t[1],a=t[2];return {$base:function(){var t=r.toFloat(),a=e.mul(t.mul(n.pow(t.sub(scalar(1))))),i=getReductionAxes(n.shape,o);return i.length>0&&(a=a.sum(i)),a.reshape(n.shape)},$exp:function(){var t=n.greater(0),i=n.log().where(t,zerosLike(n)),s=e.mul(a.mul(i)),u=getReductionAxes(r.shape,o);return u.length>0&&(s=s.sum(u)),s.reshape(r.shape)}}})}function powStrict_(e,t){return assertShapesMatch(e.shape,t.shape,"Error in powStrict: "),e.pow(t)}function mul_(e,t){var n,r=convertToTensor(e,"a","mul"),o=convertToTensor(t,"b","mul");n=makeTypesMatch(r,o),r=n[0],o=n[1];var a=assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e,t){var n=e.multiply(r,o);return t([r,o]),n},{$a:r,$b:o},function(e,t){var n=t[0],r=t[1];return {$a:function(){var t=e.mul(r.toFloat()),o=getReductionAxes(n.shape,a);return o.length>0?t.sum(o).reshape(n.shape):t},$b:function(){var t=e.mul(n.toFloat()),o=getReductionAxes(r.shape,a);return o.length>0?t.sum(o).reshape(r.shape):t}}})}function mulStrict_(e,t){var n=convertToTensor(e,"a","mul"),r=convertToTensor(t,"b","mul");return assertShapesMatch(n.shape,r.shape,"Error in multiplyStrict: "),n.mul(r)}function div_(e,t){var n,r=convertToTensor(e,"a","div"),o=convertToTensor(t,"b","div");if(n=makeTypesMatch(r,o),r=n[0],o=n[1],"int32"===r.dtype&&"int32"===o.dtype)return floorDiv(r,o);var a=assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e,t){var n=e.realDivide(r,o);return t([r,o]),n},{$a:r,$b:o},function(e,t){var n=t[0],r=t[1];return {$a:function(){var t=e.div(r.toFloat()),o=getReductionAxes(n.shape,a);return o.length>0?t.sum(o).reshape(n.shape):t},$b:function(){var t=e.mul(n.toFloat()),o=getReductionAxes(r.shape,a);o.length>0&&(t=t.sum(o).reshape(r.shape));var i=r.square();return t.div(i.toFloat()).neg()}}})}function floorDiv_(e,t){var n,r=convertToTensor(e,"a","floorDiv"),o=convertToTensor(t,"b","floorDiv");n=makeTypesMatch(r,o),r=n[0],o=n[1];var a=assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e,t){var n=e.floorDiv(r,o);return t([r,o]),n},{$a:r,$b:o},function(e,t){var n=t[0],r=t[1];return {$a:function(){var t=e.div(r.toFloat()),o=getReductionAxes(n.shape,a);return o.length>0?t.sum(o).reshape(n.shape):t},$b:function(){var t=e.mul(n.toFloat()),o=getReductionAxes(r.shape,a);o.length>0&&(t=t.sum(o).reshape(r.shape));var i=r.square();return t.div(i.toFloat()).neg()}}})}function divStrict_(e,t){var n=convertToTensor(e,"a","div"),r=convertToTensor(t,"b","div");return assertShapesMatch(n.shape,r.shape,"Error in divideStrict: "),n.div(r)}function mod_(e,t){var n,r=convertToTensor(e,"a","mod"),o=convertToTensor(t,"b","mod");n=makeTypesMatch(r,o),r=n[0],o=n[1];var a=assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e,t){var n=e.mod(r,o);return t([r,o]),n},{$a:r,$b:o},function(e,t){var n=t[0],r=t[1];return {$a:function(){var t=getReductionAxes(n.shape,a);return t.length>0?e.sum(t).reshape(n.shape):e},$b:function(){var t=e.mul(n.div(r).floor().neg()),o=getReductionAxes(r.shape,a);return o.length>0?t.sum(o).reshape(r.shape):t}}})}function modStrict_(e,t){var n=convertToTensor(e,"a","modStrict"),r=convertToTensor(t,"b","modStrict");return assertShapesMatch(n.shape,r.shape,"Error in modStrict: "),n.mod(r)}function minimum_(e,t){var n,r=convertToTensor(e,"a","minimum"),o=convertToTensor(t,"b","minimum");n=makeTypesMatch(r,o),r=n[0],o=n[1],"bool"===r.dtype&&(r=r.toInt(),o=o.toInt()),assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e,t){var n=e.minimum(r,o);return t([r,o]),n},{$a:r,$b:o},function(e,t){var n=t[0],r=t[1];return {$a:function(){return e.mul(n.lessEqual(r).toFloat())},$b:function(){return e.mul(n.greater(r).toFloat())}}})}function minimumStrict_(e,t){var n=convertToTensor(e,"a","minimumStrict"),r=convertToTensor(t,"b","minimumStrict");return assertShapesMatch(n.shape,r.shape,"Error in minimumStrict: "),n.minimum(r)}function maximum_(e,t){var n,r=convertToTensor(e,"a","maximum"),o=convertToTensor(t,"b","maximum");n=makeTypesMatch(r,o),r=n[0],o=n[1],"bool"===r.dtype&&(r=r.toInt(),o=o.toInt()),assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e,t){var n=e.maximum(r,o);return t([r,o]),n},{$a:r,$b:o},function(e,t){var n=t[0],r=t[1];return {$a:function(){return e.mul(n.greaterEqual(r).toFloat())},$b:function(){return e.mul(n.less(r).toFloat())}}})}function maximumStrict_(e,t){var n=convertToTensor(e,"a","maximumStrict"),r=convertToTensor(t,"b","maximumStrict");return assertShapesMatch(n.shape,r.shape,"Error in maximumStrict: "),n.maximum(r)}function squaredDifference_(e,t){var n,r=convertToTensor(e,"a","squaredDifference"),o=convertToTensor(t,"b","squaredDifference");n=makeTypesMatch(r,o),r=n[0],o=n[1],assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e,t){var n=e.squaredDifference(r,o);return t([r,o]),n},{$a:r,$b:o},function(e,t){var n=t[0],r=t[1],o=scalar(2);return {$a:function(){return e.mul(n.sub(r).mul(o))},$b:function(){return e.mul(r.sub(n).mul(o))}}})}function squaredDifferenceStrict_(e,t){var n=convertToTensor(e,"a","squaredDifferenceStrict"),r=convertToTensor(t,"b","squaredDifferenceStrict");return assertShapesMatch(n.shape,r.shape,"Error in squaredDifferenceStrict: "),n.squaredDifference(r)}function atan2_(e,t){var n,r=convertToTensor(e,"a","atan2"),o=convertToTensor(t,"b","atan2");n=makeTypesMatch(r,o),r=n[0],o=n[1];var a=assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e,t){var n=e.atan2(r,o);return t([r,o]),n},{$a:r,$b:o},function(e,t){var n=t[0],r=t[1];return {$a:function(){var t=add(n.square(),r.square()),o=e.mul(r.div(t)),i=getReductionAxes(n.shape,a);return i.length>0&&(o=o.sum(i)),o.reshape(n.shape)},$b:function(){var t=add(n.square(),r.square()),o=neg(e.mul(n.div(t))),i=getReductionAxes(r.shape,a);return i.length>0&&(o=o.sum(i)),o.reshape(r.shape)}}})}var add=op({add_:add_}),addN=op({addN_:addN_}),addStrict=op({addStrict_:addStrict_}),atan2=op({atan2_:atan2_}),div=op({div_:div_}),divStrict=op({divStrict_:divStrict_}),floorDiv=op({floorDiv_:floorDiv_}),maximum=op({maximum_:maximum_}),maximumStrict=op({maximumStrict_:maximumStrict_}),minimum=op({minimum_:minimum_}),minimumStrict=op({minimumStrict_:minimumStrict_}),mod=op({mod_:mod_}),modStrict=op({modStrict_:modStrict_}),mul=op({mul_:mul_}),mulStrict=op({mulStrict_:mulStrict_}),pow=op({pow_:pow_}),powStrict=op({powStrict_:powStrict_}),squaredDifference=op({squaredDifference_:squaredDifference_}),squaredDifferenceStrict=op({squaredDifferenceStrict_:squaredDifferenceStrict_}),sub=op({sub_:sub_}),subStrict=op({subStrict_:subStrict_});function logicalNot_(e){var t=convertToTensor(e,"x","logicalNot","bool");return ENV.engine.runKernel(function(e){return e.logicalNot(t)},{$x:t})}function logicalAnd_(e,t){var n=convertToTensor(e,"a","logicalAnd","bool"),r=convertToTensor(t,"b","logicalAnd","bool");return assertAndGetBroadcastShape(n.shape,r.shape),ENV.engine.runKernel(function(e){return e.logicalAnd(n,r)},{$a:n,$b:r})}function logicalOr_(e,t){var n=convertToTensor(e,"a","logicalOr","bool"),r=convertToTensor(t,"b","logicalOr","bool");return assertAndGetBroadcastShape(n.shape,r.shape),ENV.engine.runKernel(function(e){return e.logicalOr(n,r)},{$a:n,$b:r})}function logicalXor_(e,t){var n=convertToTensor(e,"a","logicalXor","bool"),r=convertToTensor(t,"b","logicalXor","bool");return assertAndGetBroadcastShape(n.shape,r.shape),logicalOr(e,t).logicalAnd(logicalAnd(e,t).logicalNot())}function where_(e,t,n){var r=convertToTensor(t,"a","where"),o=convertToTensor(n,"b","where"),a=convertToTensor(e,"condition","where","bool");assertShapesMatch(r.shape,o.shape,"Error in where: "),1===a.rank?assert(a.shape[0]===r.shape[0],function(){return "The first dimension of `a` must match the size of `condition`."}):assertShapesMatch(a.shape,o.shape,"Error in where: ");return ENV.engine.runKernel(function(e,t){var n=e.select(a,r,o);return t([a]),n},{$condition:a,$a:r,$b:o},function(e,t){var n=t[0];return {$condition:function(){return zerosLike(n).toFloat()},$a:function(){return e.mul(n.cast(e.dtype))},$b:function(){return e.mul(n.logicalNot().cast(e.dtype))}}})}function whereAsync_(e){return __awaiter$1(this,void 0,void 0,function(){var t,n,r;return __generator$1(this,function(o){switch(o.label){case 0:return [4,(t=convertToTensor(e,"condition","whereAsync","bool")).data()];case 1:return n=o.sent(),r=whereImpl(t.shape,n),e!==t&&t.dispose(),[2,r]}})})}var logicalAnd=op({logicalAnd_:logicalAnd_}),logicalNot=op({logicalNot_:logicalNot_}),logicalOr=op({logicalOr_:logicalOr_}),logicalXor=op({logicalXor_:logicalXor_}),where=op({where_:where_}),whereAsync=whereAsync_;function relu_(e){var t=convertToTensor(e,"x","relu");if("bool"===t.dtype)return t.toInt();return ENV.engine.runKernel(function(e,n){var r=e.relu(t);return n([t]),r},{$x:t},function(e,t){var n=t[0];return {$x:function(){return e.mulStrict(n.step().toFloat())}}})}function elu_(e){var t=convertToTensor(e,"x","elu");return ENV.engine.runKernel(function(e,n){var r=e.elu(t);return n([r]),r},{$x:t},function(e,t){var n=t[0];return {$x:function(){return ENV.engine.runKernel(function(t){return t.eluDer(e,n)},{dy:e,y:n})}}})}function selu_(e){var t=convertToTensor(e,"x","selu");return ENV.engine.runKernel(function(e,n){var r=e.selu(t);return n([t]),r},{$x:t},function(e,t){var n=t[0];return {$x:function(){var t=n.greater(scalar(0)),r=scalar(SELU_SCALEALPHA),o=scalar(SELU_SCALE),a=e.mul(o),i=e.mul(r).mul(n.toFloat().exp());return where(t,a,i)}}})}function leakyRelu_(e,t){void 0===t&&(t=.2);var n=convertToTensor(e,"x","leakyRelu");return maximum(scalar(t).mul(n),n)}function prelu_(e,t){var n=convertToTensor(e,"x","prelu"),r=convertToTensor(t,"alpha","prelu");return ENV.engine.runKernel(function(e,t){var o=e.prelu(n,r);return t([n,r]),o},{$x:n,$alpha:r},function(e,t){var n=t[0],r=t[1],o=n.greater(0);return {$x:function(){return where(o,e,e.mul(r))},$alpha:function(){var t=where(o,zerosLike(e),e.mul(n)),a=getReductionAxes(r.shape,e.shape);return a.length>0&&(t=t.sum(a)),t.reshape(r.shape)}}})}var elu=op({elu_:elu_}),leakyRelu=op({leakyRelu_:leakyRelu_}),prelu=op({prelu_:prelu_}),relu=op({relu_:relu_}),selu=op({selu_:selu_});function transpose_(e,t){var n=convertToTensor(e,"x","transpose");if(null==t&&(t=n.shape.map(function(e,t){return t}).reverse()),assert(n.rank===t.length,function(){return "Error in transpose: rank of input "+n.rank+" must match length of perm "+t+"."}),t.forEach(function(e){assert(e>=0&&e<n.rank,function(){return "All entries in 'perm' must be between 0 and "+(n.rank-1)+" but got "+t});}),n.rank<=1)return n.clone();return ENV.engine.runKernel(function(e){return e.transpose(n,t)},{$x:n},function(e){var n=getUndoAxesPermutation(t);return {$x:function(){return e.transpose(n)}}})}var transpose=op({transpose_:transpose_});function localResponseNormalization_(e,t,n,r,o){void 0===t&&(t=5),void 0===n&&(n=1),void 0===r&&(r=1),void 0===o&&(o=.5);var a=convertToTensor(e,"x","localResponseNormalization");assert(4===a.rank||3===a.rank,function(){return "Error in localResponseNormalization: x must be rank 3 or 4 but got\n rank "+a.rank+"."}),assert(isInt(t),function(){return "Error in localResponseNormalization: depthRadius must be an integer but got depthRadius "+t+"."});var i=a,s=!1;3===a.rank&&(s=!0,i=a.as4D(1,a.shape[0],a.shape[1],a.shape[2]));var u=ENV.engine.runKernel(function(e,a){var s=e.localResponseNormalization4D(i,t,n,r,o);return a([i,s]),s},{x4D:i},function(e,a){var i=a[0],s=a[1];return {x4D:function(){return ENV.engine.runKernel(function(a){return a.LRNGrad(e,i,s,t,n,r,o)},{})}}});return s?u.as3D(u.shape[1],u.shape[2],u.shape[3]):u}var localResponseNormalization=op({localResponseNormalization_:localResponseNormalization_});function norm_(e,t,n,r){void 0===t&&(t="euclidean"),void 0===n&&(n=null),void 0===r&&(r=!1);var o=normImpl(e=convertToTensor(e,"x","norm"),t,n),a=o.shape;if(r){var i=parseAxisParam(n,e.shape);a=expandShapeToKeepDim(o.shape,i);}return o.reshape(a)}function normImpl(e,t,n){if(void 0===n&&(n=null),0===e.rank)return e.abs();if(1!==e.rank&&null===n)return normImpl(e.reshape([-1]),t,n);if(1===e.rank||"number"==typeof n||Array.isArray(n)&&1===n.length){if(1===t)return e.abs().sum(n);if(t===1/0)return e.abs().max(n);if(t===-1/0)return e.abs().min(n);if("euclidean"===t||2===t)return e.abs().pow(scalar(2,"int32")).sum(n).sqrt();throw new Error("Error in norm: invalid ord value: "+t)}if(Array.isArray(n)&&2===n.length){if(1===t)return e.abs().sum(n[0]).max(n[1]-1);if(t===1/0)return e.abs().sum(n[1]).max(n[0]);if(t===-1/0)return e.abs().sum(n[1]).min(n[0]);if("fro"===t||"euclidean"===t)return e.square().sum(n).sqrt();throw new Error("Error in norm: invalid ord value: "+t)}throw new Error("Error in norm: invalid axis: "+n)}var norm=op({norm_:norm_});function unsortedSegmentSum_(e,t,n){var r=convertToTensor(e,"x","unsortedSegmentSum"),o=convertToTensor(t,"segmentIds","unsortedSegmentSum","int32");assert(isInt(n),function(){return "numSegments must be of dtype int"});return ENV.engine.runKernel(function(e,t){var a=e.unsortedSegmentSum(r,o,n);return t([o]),a},{$x:r},function(e,t){var n=t[0];return {$x:function(){return gatherDropNegatives(e,n)}}})}function gather_(e,t,n){void 0===n&&(n=0);var r=convertToTensor(e,"x","gather"),o=convertToTensor(t,"indices","gather","int32");n=parseAxisParam(n,r.shape)[0];var a=collectGatherOpShapeInfo(r,o,n);return ENV.engine.runKernel(function(e,t){var a=e.gather(r,o.flatten(),n);return t([o]),a},{$x:r},function(e,t){var o=t[0];return {$x:function(){var t=r.shape,a=o.size,i=t.slice(0,n),s=i.length,u=t.slice(n,t.length).slice(1),c=u.length,l=arrayRange(0,s),p=arrayRange(s+1,s+1+c),d=arrayConcat([i,[a],u]),h=e.reshape(d),f=o.reshape([a]),m=arrayConcat([[s],l,p]),g=h.transpose(m),v=unsortedSegmentSum(g,f,r.shape[n]),y=getUndoAxesPermutation(m);return v=v.transpose(y)}}}).reshape(a.outputShape)}function arrayRange(e,t){for(var n=[],r=e;r<t;++r)n.push(r);return n}function arrayConcat(e){for(var t=[],n=0;n<e.length;++n)for(var r=0;r<e[n].length;++r)t.push(e[n][r]);return t}function gatherDropNegatives(e,t){for(var n=maximum(t,zerosLike(t)),r=gather(e,n),o=greaterEqual(t,scalar(0,"int32")),a=r.rank-o.rank,i=0;i<a;++i)o=expandDims(o,i+1);o=logicalAnd(o,ones$1(r.shape,"bool"));var s=zerosLike(r);return where(o,r,s)}var gather=op({gather_:gather_}),unsortedSegmentSum=op({unsortedSegmentSum_:unsortedSegmentSum_});function multiRNNCell_(e,t,n,r){for(var o=convertToTensor(t,"data","multiRNNCell"),a=convertToTensorArray(n,"c","multiRNNCell"),i=convertToTensorArray(r,"h","multiRNNCell"),s=o,u=[],c=0;c<e.length;c++){var l=e[c](s,a[c],i[c]);u.push(l[0]),u.push(l[1]),s=l[1];}var p=[],d=[];for(c=0;c<u.length;c+=2)p.push(u[c]),d.push(u[c+1]);return [p,d]}function basicLSTMCell_(e,t,n,r,o,a){var i=convertToTensor(e,"forgetBias","basicLSTMCell"),s=convertToTensor(t,"lstmKernel","basicLSTMCell"),u=convertToTensor(n,"lstmBias","basicLSTMCell"),c=convertToTensor(r,"data","basicLSTMCell"),l=convertToTensor(o,"c","basicLSTMCell"),p=convertToTensor(a,"h","basicLSTMCell"),d=c.concat(p,1).matMul(s).add(u),h=d.shape[0],f=d.shape[1]/4,m=[h,f],g=d.slice([0,0],m),v=d.slice([0,f],m),y=d.slice([0,2*f],m),x=d.slice([0,3*f],m),E=g.sigmoid().mulStrict(v.tanh()).addStrict(l.mulStrict(i.add(y).sigmoid()));return [E,E.tanh().mulStrict(x.sigmoid())]}var basicLSTMCell=op({basicLSTMCell_:basicLSTMCell_}),multiRNNCell=op({multiRNNCell_:multiRNNCell_});function movingAverage_(e,t,n,r,o){void 0===o&&(o=!0);var a=convertToTensor(e,"v","movingAverage"),i=convertToTensor(t,"x","movingAverage"),s=convertToTensor(n,"decay","movingAverage");assertTypesMatch(a,i),assert(arraysEqual(a.shape,i.shape),function(){return "Shape mismatch in v and x"});var u=scalar(1),c=u.sub(s),l=i.sub(a).mul(c);if(o){assert(null!=r,function(){return "When using zeroDebias: true, step is required."});var p=convertToTensor(r,"step","movingAverage");l=l.div(u.sub(pow(s,p)));}return a.add(l)}var movingAverage=op({movingAverage_:movingAverage_});function stridedSlice_(e,t,n,r,o,a,i,s,u){if(void 0===o&&(o=0),void 0===a&&(a=0),void 0===i&&(i=0),void 0===s&&(s=0),void 0===u&&(u=0),0!==i)throw new Error("ellipsis mask is not yet supported");if(0!==s)throw new Error("new axis mask is not yet supported");var c=convertToTensor(e,"x","stridedSlice");if(r.every(function(e){return 1===e})){var l=getStridedSlicedInfo(c.shape,t,n,r,o,a,i,s,u),p=l[0],d=l[1],h=l[2],f=d.filter(function(e,t){return -1===h.indexOf(t)});return slice(c,p,d).reshape(f)}return ENV.engine.runKernel(function(e){return e.stridedSlice(c,t,n,r,o,a,i,s,u)},{$x:c})}var stridedSlice=op({stridedSlice_:stridedSlice_});function topk_(e,t,n){void 0===t&&(t=1),void 0===n&&(n=!0);var r=convertToTensor(e,"x","topk");if(0===r.rank)throw new Error("topk() expects the input to be of rank 1 or higher");var o=r.shape[r.shape.length-1];if(t>o)throw new Error("'k' passed to topk() must be <= the last dimension ("+o+") but got "+t);var a=ENV.engine.runKernel(function(e){return e.topk(r,t,n)},{$x:r});return {values:a[0],indices:a[1]}}var topk=op({topk_:topk_});function scatterND_(e,t,n){var r=convertToTensor(e,"indices","scatterND","int32"),o=convertToTensor(t,"updates","scatterND");return validateInput(o,r,n),ENV.engine.runKernel(function(e){return e.scatterND(r,o,n)},{$indices:r,$updates:o})}var scatterND=op({scatterND_:scatterND_});function fft_(e){assert("complex64"===e.dtype,function(){return "The dtype for tf.spectral.fft() must be complex64 but got "+e.dtype+"."});var t=e.shape[e.shape.length-1],n=e.size/t,r=e.as2D(n,t);return ENV.engine.runKernel(function(e){return e.fft(r)},{input:e}).reshape(e.shape)}function ifft_(e){assert("complex64"===e.dtype,function(){return "The dtype for tf.spectral.ifft() must be complex64 but got "+e.dtype+"."});var t=e.shape[e.shape.length-1],n=e.size/t,r=e.as2D(n,t);return ENV.engine.runKernel(function(e){return e.ifft(r)},{input:e}).reshape(e.shape)}function rfft_(e){assert("float32"===e.dtype,function(){return "The dtype for rfft() must be real value but got "+e.dtype});var t=e.shape[e.shape.length-1],n=e.size/t,r=e.zerosLike(),o=complex(e,r).as2D(n,t),a=fft(o),i=Math.floor(t/2)+1,s=real(a),u=imag(a),c=s.split([i,t-i],s.shape.length-1),l=u.split([i,t-i],u.shape.length-1),p=e.shape.slice();return p[e.shape.length-1]=i,complex(c[0],l[0]).reshape(p)}function irfft_(e){var t=e.shape[e.shape.length-1],n=e.size/t;if(t<=2){var r=e.as2D(n,t),o=ifft(r);return real(o)}var a=[n,2*(t-1)],i=real(e).as2D(n,t),s=imag(e).as2D(n,t),u=i.slice([0,1],[n,t-2]).reverse(1),c=s.slice([0,1],[n,t-2]).reverse(1).mul(scalar(-1)),l=i.concat(u,1),p=s.concat(c,1);r=complex(l,p).as2D(a[0],a[1]),o=ifft(r);return real(o)}var fft=op({fft_:fft_}),ifft=op({ifft_:ifft_}),rfft=op({rfft_:rfft_}),irfft=op({irfft_:irfft_}),spectral_ops=Object.freeze({fft:fft,ifft:ifft,rfft:rfft,irfft:irfft});function validateInput$1(e,t,n,r){if("int32"!==e.dtype)throw new Error("tf.sparseToDense() expects the indices to be int32 type, but the dtype was "+e.dtype+".");if(e.rank>2)throw new Error("sparseIndices should be a scalar, vector, or matrix, but got shape "+e.shape+".");var o=e.rank>0?e.shape[0]:1,a=e.rank>1?e.shape[1]:1;if(n.length!==a)throw new Error("outputShape has incorrect number of elements:, "+n.length+", should be: "+a+".");var i=t.size;if(0!==t.rank&&(1!==t.rank||i!==o))throw new Error("sparseValues has incorrect shape "+t.shape+", should be [] or ["+o+"]");if(t.dtype!==r.dtype)throw new Error("sparseValues.dtype must match defaultValues.dtype")}function sparseToDense_(e,t,n,r){void 0===r&&(r=0);var o=convertToTensor(e,"sparseIndices","sparseToDense","int32"),a=convertToTensor(t,"sparseValues","sparseToDense"),i=convertToTensor(r,"defaultValue","sparseToDense",a.dtype);return validateInput$1(o,a,n,i),ENV.engine.runKernel(function(e){return e.sparseToDense(o,a,n,i)},{$sparseIndices:o,$sparseValues:a,$defaultValue:i})}var sparseToDense=op({sparseToDense_:sparseToDense_});function gatherND_(e,t){var n=convertToTensor(t,"indices","gatherND","int32"),r=convertToTensor(e,"x","gatherND");return ENV.engine.runKernel(function(e){return e.gatherND(r,n)},{$x:r,$indices:n})}var Reduction,gatherND=op({gatherND_:gatherND_});function computeWeightedLoss_(e,t,n){void 0===n&&(n=Reduction.SUM_BY_NONZERO_WEIGHTS);var r=convertToTensor(e,"losses","computeWeightedLoss"),o=null;null!=t&&(o=convertToTensor(t,"weights","computeWeightedLoss"));var a=null==o?r:r.mul(o);if(n===Reduction.NONE)return a;if(n===Reduction.SUM)return a.sum();if(n===Reduction.MEAN){if(null==o)return a.mean();var i=r.size/o.size,s=a.sum().div(o.sum());return i>1?s.div(scalar(i)):s}if(n===Reduction.SUM_BY_NONZERO_WEIGHTS){if(null==o)return a.sum().div(scalar(r.size));var u=o.mul(ones$1(r.shape)).notEqual(scalar(0)).sum().toFloat();return a.sum().div(u)}throw Error("Unknown reduction: "+n)}function absoluteDifference_(e,t,n,r){void 0===r&&(r=Reduction.SUM_BY_NONZERO_WEIGHTS);var o=convertToTensor(e,"labels","absoluteDifference"),a=convertToTensor(t,"predictions","absoluteDifference"),i=null;null!=n&&(i=convertToTensor(n,"weights","absoluteDifference")),assertShapesMatch(o.shape,a.shape,"Error in absoluteDifference: ");var s=o.sub(a).abs();return computeWeightedLoss(s,i,r)}function meanSquaredError_(e,t,n,r){void 0===r&&(r=Reduction.SUM_BY_NONZERO_WEIGHTS);var o=convertToTensor(e,"labels","meanSquaredError"),a=convertToTensor(t,"predictions","meanSquaredError"),i=null;null!=n&&(i=convertToTensor(n,"weights","meanSquaredError")),assertShapesMatch(o.shape,a.shape,"Error in meanSquaredError: ");var s=o.squaredDifference(a);return computeWeightedLoss(s,i,r)}function cosineDistance_(e,t,n,r,o){void 0===o&&(o=Reduction.SUM_BY_NONZERO_WEIGHTS);var a=convertToTensor(e,"labels","cosineDistance"),i=convertToTensor(t,"predictions","cosineDistance"),s=null;null!=r&&(s=convertToTensor(r,"weights","cosineDistance")),assertShapesMatch(a.shape,i.shape,"Error in cosineDistance: ");var u=scalar(1).sub(a.mul(i).sum(n,!0));return computeWeightedLoss(u,s,o)}function hingeLoss_(e,t,n,r){void 0===r&&(r=Reduction.SUM_BY_NONZERO_WEIGHTS);var o=convertToTensor(e,"labels","hingeLoss"),a=convertToTensor(t,"predictions","hingeLoss"),i=null;null!=n&&(i=convertToTensor(n,"weights","hingeLoss")),assertShapesMatch(o.shape,a.shape,"Error in hingeLoss: ");var s=scalar(1);o=scalar(2).mul(o).sub(s);var u=s.sub(o.mul(a)).relu();return computeWeightedLoss(u,i,r)}function logLoss_(e,t,n,r,o){void 0===r&&(r=1e-7),void 0===o&&(o=Reduction.SUM_BY_NONZERO_WEIGHTS);var a=convertToTensor(e,"labels","logLoss"),i=convertToTensor(t,"predictions","logLoss"),s=null;null!=n&&(s=convertToTensor(n,"weights","logLoss")),assertShapesMatch(a.shape,i.shape,"Error in logLoss: ");var u=scalar(1),c=scalar(r),l=a.mul(i.add(c).log()).neg().sub(u.sub(a).mul(u.sub(i).add(c).log()));return computeWeightedLoss(l,s,o)}function sigmoidCrossEntropyWithLogits_(e,t){var n=convertToTensor(e,"labels","sigmoidCrossEntropyWithLogits"),r=convertToTensor(t,"logits","sigmoidCrossEntropyWithLogits");assertShapesMatch(n.shape,r.shape,"Error in sigmoidCrossEntropyWithLogits: ");var o=r.relu(),a=r.mul(n),i=r.abs().neg().exp().log1p();return o.sub(a).add(i)}function sigmoidCrossEntropy_(e,t,n,r,o){void 0===r&&(r=0),void 0===o&&(o=Reduction.SUM_BY_NONZERO_WEIGHTS);var a=convertToTensor(e,"multiClassLabels","sigmoidCrossEntropy"),i=convertToTensor(t,"logits","sigmoidCrossEntropy"),s=null;if(null!=n&&(s=convertToTensor(n,"weights","sigmoidCrossEntropy")),assertShapesMatch(a.shape,i.shape,"Error in sigmoidCrossEntropy: "),r>0){var u=scalar(r),c=scalar(1),l=scalar(.5);a=a.mul(c.sub(u)).add(l.mul(u));}var p=sigmoidCrossEntropyWithLogits_(a,i);return computeWeightedLoss(p,s,o)}function huberLoss_(e,t,n,r,o){void 0===r&&(r=1),void 0===o&&(o=Reduction.SUM_BY_NONZERO_WEIGHTS);var a=convertToTensor(e,"labels","huberLoss"),i=convertToTensor(t,"predictions","huberLoss"),s=null;null!=n&&(s=convertToTensor(n,"weights","huberLoss")),assertShapesMatch(a.shape,i.shape,"Error in huberLoss: ");var u=scalar(r),c=i.sub(a).abs(),l=minimum(c,u),p=c.sub(l),d=scalar(.5).mul(l.square()).add(u.mul(p));return computeWeightedLoss(d,s,o)}function softmaxCrossEntropyWithLogits_(e,t,n){if(void 0===n&&(n=-1),-1===n&&(n=t.rank-1),n!==t.rank-1)throw Error("Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank "+t.rank+" and dim was "+n);return customGrad(function(e,t,r){var o=t.logSumExp([n],!0),a=t.toFloat().sub(o);r([e,a]);return {value:a.mul(e).neg().sum([n]),gradFunc:function(e,t){var r=t[0],o=t[1],a=expandShapeToKeepDim(e.shape,[n]);return [e.reshape(a).mul(r.toFloat().sub(o.exp())),e.reshape(a).mul(o.exp().sub(r.toFloat()))]}}})(e,t)}function softmaxCrossEntropy_(e,t,n,r,o){void 0===r&&(r=0),void 0===o&&(o=Reduction.SUM_BY_NONZERO_WEIGHTS);var a=convertToTensor(e,"onehotLabels","softmaxCrossEntropy"),i=convertToTensor(t,"logits","softmaxCrossEntropy"),s=null;if(null!=n&&(s=convertToTensor(n,"weights","softmaxCrossEntropy")),assertShapesMatch(a.shape,i.shape,"Error in softmaxCrossEntropy: "),r>0){var u=scalar(r),c=scalar(1),l=scalar(a.shape[1]);a=a.mul(c.sub(u)).add(u.div(l));}var p=softmaxCrossEntropyWithLogits_(a,i);return computeWeightedLoss(p,s,o)}!function(e){e[e.NONE=0]="NONE",e[e.MEAN=1]="MEAN",e[e.SUM=2]="SUM",e[e.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS";}(Reduction||(Reduction={}));var absoluteDifference=op({absoluteDifference_:absoluteDifference_}),computeWeightedLoss=op({computeWeightedLoss_:computeWeightedLoss_}),cosineDistance=op({cosineDistance_:cosineDistance_}),hingeLoss=op({hingeLoss_:hingeLoss_}),huberLoss=op({huberLoss_:huberLoss_}),logLoss=op({logLoss_:logLoss_}),meanSquaredError=op({meanSquaredError_:meanSquaredError_}),sigmoidCrossEntropy=op({sigmoidCrossEntropy_:sigmoidCrossEntropy_}),softmaxCrossEntropy=op({softmaxCrossEntropy_:softmaxCrossEntropy_}),loss_ops=Object.freeze({get Reduction(){return Reduction},absoluteDifference:absoluteDifference,computeWeightedLoss:computeWeightedLoss,cosineDistance:cosineDistance,hingeLoss:hingeLoss,huberLoss:huberLoss,logLoss:logLoss,meanSquaredError:meanSquaredError,sigmoidCrossEntropy:sigmoidCrossEntropy,softmaxCrossEntropy:softmaxCrossEntropy});function gramSchmidt_(e){var t;if(Array.isArray(e)){t=!1,assert(null!=e&&e.length>0,function(){return "Gram-Schmidt process: input must not be null, undefined, or empty"});for(var n=e[0].shape[0],r=function(t){assert(e[t].shape[0]===n,function(){return "Gram-Schmidt: Non-unique lengths found in the input vectors: ("+e[t].shape[0]+" vs. "+n+")"});},o=1;o<e.length;++o)r(o);}else t=!0,e=split$1(e,e.shape[0],0).map(function(e){return squeeze(e,[0])});assert(e.length<=e[0].shape[0],function(){return "Gram-Schmidt: Number of vectors ("+e.length+") exceeds number of dimensions ("+e[0].shape[0]+")."});var a=[],i=e,s=function(e){a.push(ENV.engine.tidy(function(){var t=i[e];if(e>0)for(var n=0;n<e;++n){var r=sum$1(a[n].mulStrict(t)).mul(a[n]);t=t.sub(r);}return t.div(norm(t,"euclidean"))}));};for(o=0;o<e.length;++o)s(o);return t?stack(a,0):a}function qr_(e,t){if(void 0===t&&(t=!1),e.rank<2)throw new Error("qr() requires input tensor to have a rank >= 2, but got rank "+e.rank);if(2===e.rank)return qr2d(e,t);var n=e.shape.slice(0,e.shape.length-2).reduce(function(e,t){return e*t}),r=[],o=[];return unstack(e.reshape([n,e.shape[e.shape.length-2],e.shape[e.shape.length-1]]),0).forEach(function(e){var n=qr2d(e,t),a=n[0],i=n[1];r.push(a),o.push(i);}),[stack(r,0).reshape(e.shape),stack(o,0).reshape(e.shape)]}function qr2d(e,t){return void 0===t&&(t=!1),ENV.engine.tidy(function(){if(2!==e.shape.length)throw new Error("qr2d() requires a 2D Tensor, but got a "+e.shape.length+"D Tensor.");for(var n=e.shape[0],r=e.shape[1],o=eye(n),a=e.clone(),i=tensor2d([[1]],[1,1]),s=i.clone(),u=n>=r?r:n,c=function(e){var t,u=a,c=s,l=o;t=ENV.engine.tidy(function(){var t=a.slice([e,e],[n-e,1]),u=t.norm(),c=a.slice([e,e],[1,1]),l=c.sign().neg(),p=c.sub(l.mul(u)),d=t.div(p);s=1===d.shape[0]?i.clone():i.concat(d.slice([1,0],[d.shape[0]-1,d.shape[1]]),0);var h=l.matMul(p).div(u).neg(),f=a.slice([e,0],[n-e,r]),m=h.mul(s);a=0===e?f.sub(m.matMul(s.transpose().matMul(f))):a.slice([0,0],[e,r]).concat(f.sub(m.matMul(s.transpose().matMul(f))),0);var g=o.slice([0,e],[n,o.shape[1]-e]);return o=0===e?g.sub(g.matMul(s).matMul(m.transpose())):o.slice([0,0],[n,e]).concat(g.sub(g.matMul(s).matMul(m.transpose())),1),[s,a,o]}),s=t[0],a=t[1],o=t[2],dispose([u,c,l]);},l=0;l<u;++l)c(l);return !t&&n>r&&(o=o.slice([0,0],[n,r]),a=a.slice([0,0],[r,r])),[o,a]})}var gramSchmidt=op({gramSchmidt_:gramSchmidt_}),qr=op({qr_:qr_}),linalg_ops=Object.freeze({gramSchmidt:gramSchmidt,qr:qr});function resizeBilinear_(e,t,n){void 0===n&&(n=!1);var r=convertToTensor(e,"images","resizeBilinear");assert(3===r.rank||4===r.rank,function(){return "Error in resizeBilinear: x must be rank 3 or 4, but got rank "+r.rank+"."}),assert(2===t.length,function(){return "Error in resizeBilinear: new shape must 2D, but got shape "+t+"."});var o=r,a=!1;3===r.rank&&(a=!0,o=r.as4D(1,r.shape[0],r.shape[1],r.shape[2]));var i=t[0],s=t[1],u=ENV.engine.runKernel(function(e,t){return t([o]),e.resizeBilinear(o,i,s,n)},{batchImages:o},function(e,t){return {batchImages:function(){return ENV.engine.runKernel(function(r){return r.resizeBilinearBackprop(e,t[0],n)},{})}}});return a?u.as3D(u.shape[1],u.shape[2],u.shape[3]):u}function resizeNearestNeighbor_(e,t,n){void 0===n&&(n=!1);var r=convertToTensor(e,"images","resizeNearestNeighbor");assert(3===r.rank||4===r.rank,function(){return "Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank "+r.rank+"."}),assert(2===t.length,function(){return "Error in resizeNearestNeighbor: new shape must 2D, but got shape "+t+"."}),assert("float32"===r.dtype||"int32"===r.dtype,function(){return "`images` must have `int32` or `float32` as dtype"});var o=r,a=!1;3===r.rank&&(a=!0,o=r.as4D(1,r.shape[0],r.shape[1],r.shape[2]));var i=t[0],s=t[1],u=ENV.engine.runKernel(function(e,t){return t([o]),e.resizeNearestNeighbor(o,i,s,n)},{batchImages:o},function(e,t){return {batchImages:function(){return ENV.engine.runKernel(function(r){return r.resizeNearestNeighborBackprop(e,t[0],n)},{})}}});return a?u.as3D(u.shape[1],u.shape[2],u.shape[3]):u}function nonMaxSuppression_(e,t,n,r,o){void 0===r&&(r=.5),void 0===o&&(o=Number.NEGATIVE_INFINITY);var a=convertToTensor(e,"boxes","nonMaxSuppression"),i=convertToTensor(t,"scores","nonMaxSuppression"),s=nonMaxSuppSanityCheck(a,i,n,r,o);return n=s.maxOutputSize,r=s.iouThreshold,o=s.scoreThreshold,ENV.engine.runKernel(function(e){return e.nonMaxSuppression(a,i,n,r,o)},{$boxes:a})}function nonMaxSuppressionAsync_(e,t,n,r,o){return void 0===r&&(r=.5),void 0===o&&(o=Number.NEGATIVE_INFINITY),__awaiter$1(this,void 0,void 0,function(){var a,i,s,u,c,l;return __generator$1(this,function(p){switch(p.label){case 0:return a=convertToTensor(e,"boxes","nonMaxSuppressionAsync"),i=convertToTensor(t,"scores","nonMaxSuppressionAsync"),s=nonMaxSuppSanityCheck(a,i,n,r,o),n=s.maxOutputSize,r=s.iouThreshold,o=s.scoreThreshold,[4,a.data()];case 1:return u=p.sent(),[4,i.data()];case 2:return c=p.sent(),l=nonMaxSuppressionImpl(u,c,n,r,o),a!==e&&a.dispose(),i!==t&&i.dispose(),[2,l]}})})}function nonMaxSuppSanityCheck(e,t,n,r,o){null==r&&(r=.5),null==o&&(o=Number.NEGATIVE_INFINITY);var a=e.shape[0];return n=Math.min(n,a),assert(0<=r&&r<=1,function(){return "iouThreshold must be in [0, 1], but was '"+r+"'"}),assert(2===e.rank,function(){return "boxes must be a 2D tensor, but was of rank '"+e.rank+"'"}),assert(4===e.shape[1],function(){return "boxes must have 4 columns, but 2nd dimension was "+e.shape[1]}),assert(1===t.rank,function(){return "scores must be a 1D tensor"}),assert(t.shape[0]===a,function(){return "scores has incompatible shape with boxes. Expected "+a+", but was "+t.shape[0]}),{maxOutputSize:n,iouThreshold:r,scoreThreshold:o}}function cropAndResize_(e,t,n,r,o,a){var i=convertToTensor(e,"image","cropAndResize","float32"),s=convertToTensor(t,"boxes","cropAndResize","float32"),u=convertToTensor(n,"boxInd","cropAndResize","int32");o=o||"bilinear",a=a||0;var c=s.shape[0];assert(4===i.rank,function(){return "Error in cropAndResize: image must be rank 4,but got rank "+i.rank+"."}),assert(2===s.rank&&4===s.shape[1],function(){return "Error in cropAndResize: boxes must be have size ["+c+",4] but had shape "+s.shape+"."}),assert(1===u.rank&&u.shape[0]===c,function(){return "Error in cropAndResize: boxInd must be have size ["+c+"] but had shape "+s.shape+"."}),assert(2===r.length,function(){return "Error in cropAndResize: cropSize must be of length 2, but got length "+r.length+"."}),assert(r[0]>=1&&r[1]>=1,function(){return "cropSize must be atleast [1,1], but was "+r}),assert("bilinear"===o||"nearest"===o,function(){return "method must be bilinear or nearest, but was "+o});return ENV.engine.runKernel(function(e,t){return e.cropAndResize(i,s,u,r,o,a)},{$image:i,$boxes:s})}var resizeBilinear=op({resizeBilinear_:resizeBilinear_}),resizeNearestNeighbor=op({resizeNearestNeighbor_:resizeNearestNeighbor_}),nonMaxSuppression=op({nonMaxSuppression_:nonMaxSuppression_}),nonMaxSuppressionAsync=nonMaxSuppressionAsync_,cropAndResize=op({cropAndResize_:cropAndResize_}),image_ops=Object.freeze({resizeBilinear:resizeBilinear,resizeNearestNeighbor:resizeNearestNeighbor,nonMaxSuppression:nonMaxSuppression,nonMaxSuppressionAsync:nonMaxSuppressionAsync,cropAndResize:cropAndResize});function matMul_$1(e,t,n,r,o,a){var i;void 0===n&&(n=!1),void 0===r&&(r=!1),void 0===a&&(a="linear");var s=convertToTensor(e,"a","fused matMul"),u=convertToTensor(t,"b","fused matMul");i=makeTypesMatch(s,u),s=i[0],u=i[1];var c=n?s.shape[s.rank-2]:s.shape[s.rank-1],l=r?u.shape[u.rank-1]:u.shape[u.rank-2],p=n?s.shape[s.rank-1]:s.shape[s.rank-2],d=r?u.shape[u.rank-2]:u.shape[u.rank-1],h=s.shape.slice(0,-2),f=u.shape.slice(0,-2),m=sizeFromShape(h),g=sizeFromShape(f);assert(s.rank>=2&&u.rank>=2&&s.rank===u.rank,function(){return "Error in fused matMul: inputs must have the same rank of at least 2, got ranks "+s.rank+" and "+u.rank+"."}),assert(arraysEqual(h,f),function(){return "Error in fused matMul: outer dimensions ("+h+") and ("+f+") of Tensors with shapes "+s.shape+" and "+u.shape+" must match."}),assert(c===l,function(){return "Error in fused matMul: inner shapes ("+c+") and ("+l+") of Tensors with shapes "+s.shape+" and "+u.shape+" and transposeA="+n+" and transposeB="+r+" must match."});var v,y=s.shape.slice(0,-2).concat([p,d]),x=n?s.as3D(m,c,p):s.as3D(m,p,c),E=r?u.as3D(g,d,l):u.as3D(g,l,d);null!=o&&assertAndGetBroadcastShape(y,(v=makeTypesMatch(v=convertToTensor(o,"bias","fused matMul"),s)[0]).shape);var T={$a:x,$b:E};return null!=o&&(T.$bias=v),ENV.engine.runKernel(function(e,t){var o=e.fusedBatchMatMul(x,E,n,r,v,a);return t([x,E,o]),o},T,function(e,t){var i,s=t[0],u=t[1],c=t[2];if(null==a||"linear"===a)i=e;else{if("relu"!==a)throw new Error("Gradient for activation "+a+" has not been implemented yet.");i=e.mul(c.step());}var l={};return null!=o&&(l={$bias:function(){var e=i,t=getReductionAxes(v.shape,i.shape);return t.length>0&&(e=e.sum(t)),e.reshape(v.shape)}}),n||r?!n&&r?Object.assign({$a:function(){return i.matMul(u,!1,!1)},$b:function(){return i.matMul(s,!0,!1)}},l):n&&!r?Object.assign({$a:function(){return u.matMul(i,!1,!0)},$b:function(){return s.matMul(i,!1,!1)}},l):Object.assign({$a:function(){return u.matMul(i,!0,!0)},$b:function(){return i.matMul(s,!0,!0)}},l):Object.assign({$a:function(){return i.matMul(u,!1,!0)},$b:function(){return s.matMul(i,!0,!1)}},l)}).reshape(y)}var matMul$1=op({matMul_:matMul_$1}),fused_ops=Object.freeze({matMul:matMul$1}),ops=Object.freeze({image:image_ops,linalg:linalg_ops,losses:loss_ops,spectral:spectral_ops,fused:fused_ops,op:op,batchNormalization2d:batchNormalization2d,batchNormalization3d:batchNormalization3d,batchNormalization4d:batchNormalization4d,batchNormalization:batchNormalization,batchNorm:batchNorm,batchNorm2d:batchNorm2d,batchNorm3d:batchNorm3d,batchNorm4d:batchNorm4d,complex:complex,real:real,imag:imag,concat:concat,concat1d:concat1d,concat2d:concat2d,concat3d:concat3d,concat4d:concat4d,split:split$1,conv1d:conv1d,conv2d:conv2d,conv3d:conv3d,conv2dDerFilter:conv2dDerFilter,depthwiseConv2d:depthwiseConv2d,separableConv2d:separableConv2d,conv2dTranspose:conv2dTranspose,matMul:matMul,dot:dot,outerProduct:outerProduct,reverse:reverse,reverse1d:reverse1d,reverse2d:reverse2d,reverse3d:reverse3d,reverse4d:reverse4d,maxPool:maxPool,avgPool:avgPool,pool:pool,slice:slice,slice1d:slice1d,slice2d:slice2d,slice3d:slice3d,slice4d:slice4d,abs:abs,acos:acos,acosh:acosh,asin:asin,asinh:asinh,atan:atan,atanh:atanh,ceil:ceil,clipByValue:clipByValue,cos:cos,cosh:cosh,erf:erf,exp:exp,expm1:expm1,floor:floor,log:log$1,log1p:log1p,logSigmoid:logSigmoid,neg:neg,reciprocal:reciprocal,round:round,rsqrt:rsqrt,sigmoid:sigmoid,sign:sign,isNaN:isNaN$1,isInf:isInf,isFinite:isFinite$1,sin:sin,sinh:sinh,softplus:softplus,sqrt:sqrt,square:square,step:step,tan:tan,tanh:tanh$1,all:all,any:any,argMax:argMax,argMin:argMin,logSumExp:logSumExp,max:max,mean:mean,min:min,moments:moments,sum:sum$1,prod:prod,equal:equal,equalStrict:equalStrict,greater:greater,greaterEqual:greaterEqual,greaterEqualStrict:greaterEqualStrict,greaterStrict:greaterStrict,less:less,lessEqual:lessEqual,lessEqualStrict:lessEqualStrict,lessStrict:lessStrict,notEqual:notEqual,notEqualStrict:notEqualStrict,add:add,addN:addN,addStrict:addStrict,atan2:atan2,div:div,divStrict:divStrict,floorDiv:floorDiv,maximum:maximum,maximumStrict:maximumStrict,minimum:minimum,minimumStrict:minimumStrict,mod:mod,modStrict:modStrict,mul:mul,mulStrict:mulStrict,pow:pow,powStrict:powStrict,squaredDifference:squaredDifference,squaredDifferenceStrict:squaredDifferenceStrict,sub:sub,subStrict:subStrict,elu:elu,leakyRelu:leakyRelu,prelu:prelu,relu:relu,selu:selu,logicalAnd:logicalAnd,logicalNot:logicalNot,logicalOr:logicalOr,logicalXor:logicalXor,where:where,whereAsync:whereAsync,buffer:buffer,print:print,batchToSpaceND:batchToSpaceND,cast:cast,clone:clone,cumsum:cumsum,depthToSpace:depthToSpace,expandDims:expandDims,eye:eye,multinomial:multinomial,oneHot:oneHot,pad:pad,pad1d:pad1d,pad2d:pad2d,pad3d:pad3d,pad4d:pad4d,rand:rand,randomNormal:randomNormal,randomUniform:randomUniform,reshape:reshape,spaceToBatchND:spaceToBatchND,squeeze:squeeze,stack:stack,tile:tile,truncatedNormal:truncatedNormal,unstack:unstack,setdiff1dAsync:setdiff1dAsync,fill:fill,linspace:linspace,ones:ones$1,range:range,scalar:scalar,tensor:tensor,tensor1d:tensor1d,tensor2d:tensor2d,tensor3d:tensor3d,tensor4d:tensor4d,tensor5d:tensor5d,tensor6d:tensor6d,zeros:zeros,onesLike:onesLike,zerosLike:zerosLike,transpose:transpose,softmax:softmax,logSoftmax:logSoftmax,localResponseNormalization:localResponseNormalization,norm:norm,gather:gather,unsortedSegmentSum:unsortedSegmentSum,basicLSTMCell:basicLSTMCell,multiRNNCell:multiRNNCell,movingAverage:movingAverage,stridedSlice:stridedSlice,topk:topk,scatterND:scatterND,fft:fft,ifft:ifft,rfft:rfft,irfft:irfft,sparseToDense:sparseToDense,gatherND:gatherND});function mapActivation(e,t,n){if("linear"===t)return e.linear(n);if("relu"===t)return e.relu(n);throw new Error("Activation "+t+" has not been implemented for the CPU backend.")}var MathBackendCPU=function(){function e(){this.blockSize=48,this.firstUse=!0,ENV.get("IS_BROWSER")&&(this.fromPixels2DContext=document.createElement("canvas").getContext("2d"));}return e.prototype.setDataMover=function(e){this.data=new DataStorage(e);},e.prototype.register=function(e,t,n){if(this.firstUse&&(this.firstUse=!1,ENV.get("IS_NODE")&&warn("\n============================\nHi there 👋. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.\n============================\n")),this.data.has(e))throw new Error("Data buffer is already registered");this.data.set(e,{dtype:n});},e.prototype.write=function(e,t){if(null==t)throw new Error("MathBackendCPU.write(): values can not be null");this.data.get(e).values=t;},e.prototype.fromPixels=function(e,t){if(null==e)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");var n,r;if(ENV.get("IS_NODE")&&null==e.getContext)throw new Error("When running in node, pixels must be an HTMLCanvasElement like the one returned by the `canvas` npm package");if(null!=e.getContext)n=e.getContext("2d").getImageData(0,0,e.width,e.height).data;else if(e instanceof ImageData)n=e.data;else{if(!(e instanceof HTMLImageElement||e instanceof HTMLVideoElement))throw new Error("pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement or ImageData, but was "+e.constructor.name);if(null==this.fromPixels2DContext)throw new Error("Can't read pixels from HTMLImageElement outside the browser.");this.fromPixels2DContext.canvas.width=e.width,this.fromPixels2DContext.canvas.height=e.height,this.fromPixels2DContext.drawImage(e,0,0,e.width,e.height),n=this.fromPixels2DContext.getImageData(0,0,e.width,e.height).data;}if(4===t)r=new Int32Array(n);else{var o=e.width*e.height;r=new Int32Array(o*t);for(var a=0;a<o;a++)for(var i=0;i<t;++i)r[a*t+i]=n[4*a+i];}return tensor3d(r,[e.height,e.width,t],"int32")},e.prototype.read=function(e){return __awaiter$1(this,void 0,void 0,function(){return __generator$1(this,function(t){return [2,this.readSync(e)]})})},e.prototype.readSync=function(e){var t=this.data.get(e),n=t.dtype,r=t.complexTensors;return "complex64"===n?mergeRealAndImagArrays(r.real.dataSync(),r.imag.dataSync()):this.data.get(e).values},e.prototype.disposeData=function(e){if(this.data.has(e)){var t=this.data.get(e).complexTensors;null!=t&&(t.real.dispose(),t.imag.dispose()),this.data.delete(e);}},e.prototype.time=function(e){return __awaiter$1(this,void 0,void 0,function(){var t;return __generator$1(this,function(n){return t=now(),e(),[2,{kernelMs:now()-t}]})})},e.prototype.memory=function(){return {unreliable:!0,reasons:["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."]}},e.prototype.complex=function(e,t){var n=Tensor.make(e.shape,{},"complex64");return this.data.get(n.dataId).complexTensors={real:ENV.engine.keep(e.clone()),imag:ENV.engine.keep(t.clone())},n},e.prototype.real=function(e){return this.data.get(e.dataId).complexTensors.real.clone()},e.prototype.imag=function(e){return this.data.get(e.dataId).complexTensors.imag.clone()},e.prototype.assertNotComplex=function(e,t){Array.isArray(e)||(e=[e]),e.forEach(function(e){null!=e&&assert("complex64"!==e.dtype,function(){return t+" does not support complex64 tensors."});});},e.prototype.slice=function(e,t,n){if(this.assertNotComplex(e,"slice"),isSliceContinous(e.shape,t,n)){var r=computeFlatOffset(t,e.strides),o=sizeFromShape(n);return tensor(e.dataSync().subarray(r,r+o),n,e.dtype)}for(var a=buffer(n,e.dtype),i=e.bufferSync(),s=0;s<a.size;++s){var u=a.indexToLoc(s).map(function(e,n){return e+t[n]});a.values[s]=i.get.apply(i,u);}return a.toTensor()},e.prototype.stridedSlice=function(e,t,n,r,o,a,i,s,u){this.assertNotComplex(e,"stridedSlice");var c=getStridedSlicedInfo(e.shape,t,n,r,o,a,i,s,u),l=c[0],p=c[1],d=c[2],h=p.filter(function(e,t){return -1===d.indexOf(t)});if(h.some(function(e){return 0===e}))return tensor([],h);for(var f=buffer(p,e.dtype),m=e.bufferSync(),g=0;g<f.size;g++){for(var v=f.indexToLoc(g),y=new Array(v.length),x=0;x<y.length;x++)y[x]=v[x]*r[x]+l[x];f.set.apply(f,[m.get.apply(m,y)].concat(v));}return f.toTensor().reshape(h)},e.prototype.unstack=function(e,t){for(var n=e.shape[t],r=new Array(e.rank-1),o=0,a=0;a<e.rank;a++)a!==t&&(r[o++]=e.shape[a]);var i=new Array(e.rank).fill(0),s=e.shape.slice();s[t]=1;var u=new Array(n);for(a=0;a<u.length;a++)i[t]=a,u[a]=this.slice(e,i,s).reshape(r);return u},e.prototype.reverse=function(e,t){this.assertNotComplex(e,"reverse");for(var n=buffer(e.shape,e.dtype),r=e.bufferSync(),o=function(o){var a=n.indexToLoc(o),i=a.slice();t.forEach(function(t){return i[t]=e.shape[t]-1-i[t]}),n.set.apply(n,[r.get.apply(r,i)].concat(a));},a=0;a<n.size;a++)o(a);return n.toTensor()},e.prototype.concat=function(e,t){this.assertNotComplex(e,"concat");var n=e.map(function(e){var n=sizeFromShape(e.shape.slice(t));return e.as2D(-1,n)}),r=computeOutShape(n.map(function(e){return e.shape}),1),o=buffer(r,e[0].dtype).values;if(1===n[0].shape[0]){var a=0;n.forEach(function(e){o.set(e.dataSync(),a),a+=e.size;});}else{var i=0;n.forEach(function(e){for(var t=e.dataSync(),n=0,a=0;a<e.shape[0];++a)for(var s=a*r[1]+i,u=0;u<e.shape[1];++u)o[s+u]=t[n++];i+=e.shape[1];});}var s=computeOutShape(e.map(function(e){return e.shape}),t);return tensor(o,s,e[0].dtype)},e.prototype.neg=function(e){return this.assertNotComplex(e,"neg"),this.multiply(scalar(-1),e)},e.prototype.add=function(e,t){return "complex64"===e.dtype||"complex64"===t.dtype?this.broadcastedBinaryComplexOp(e.cast("complex64"),t.cast("complex64"),function(e,t,n,r){return {real:e+n,imag:t+r}}):this.broadcastedBinaryOp(e,t,upcastType(e.dtype,t.dtype),function(e,t){return e+t})},e.prototype.addN=function(e){this.assertNotComplex(e,"addN");for(var t=e.map(function(e){return e.dataSync()}),n=buffer(e[0].shape,e[0].dtype),r=n.values,o=0;o<e.length;o++)for(var a=t[o],i=0;i<r.length;i++)r[i]+=a[i];return n.toTensor()},e.prototype.subtract=function(e,t){return "complex64"===e.dtype||"complex64"===t.dtype?this.broadcastedBinaryComplexOp(e.cast("complex64"),t.cast("complex64"),function(e,t,n,r){return {real:e-n,imag:t-r}}):this.broadcastedBinaryOp(e,t,upcastType(e.dtype,t.dtype),function(e,t){return e-t})},e.prototype.pow=function(e,t){return this.assertNotComplex([e,t],"pow"),this.broadcastedBinaryOp(e,t,e.dtype,function(e,t){return Math.pow(e,t)})},e.prototype.batchMatMul=function(e,t,n,r){this.assertNotComplex([e,t],"matMul");for(var o=n?e.shape[1]:e.shape[2],a=n?e.shape[2]:e.shape[1],i=r?t.shape[1]:t.shape[2],s=e.shape[0],u=e.dataSync(),c=t.dataSync(),l=n?[e.strides[0],1,e.strides[1]]:[e.strides[0],e.strides[1],1],p=l[0],d=l[1],h=l[2],f=r?[1,t.strides[1],t.strides[0]]:[t.strides[1],1,t.strides[0]],m=f[0],g=f[1],v=f[2],y=a*i,x=buffer([s,a,i],e.dtype),E=x.values,T=this.blockSize,b=0;b<s;b++)for(var w=0;w<a;w+=T)for(var S=0;S<i;S+=T)for(var C=0;C<o;C+=T)for(var A=Math.min(w+T,a),N=Math.min(S+T,i),_=Math.min(C+T,o),R=w;R<A;R++)for(var I=S;I<N;I++){for(var k=0,D=C;D<_;D++)k+=u[b*p+R*d+D*h]*c[D*m+I*g+b*v];E[b*y+(R*i+I)]+=k;}return x.toTensor()},e.prototype.fusedBatchMatMul=function(e,t,n,r,o,a){var i=this.batchMatMul(e,t,n,r);return o&&(i=this.add(i,o)),a&&(i=mapActivation(this,a,i)),i},e.prototype.multiply=function(e,t){return "complex64"===e.dtype||"complex64"===t.dtype?this.broadcastedBinaryComplexOp(e.cast("complex64"),t.cast("complex64"),function(e,t,n,r){return {real:e*n-t*r,imag:e*r+t*n}}):this.broadcastedBinaryOp(e,t,upcastType(e.dtype,t.dtype),function(e,t){return e*t})},e.prototype.realDivide=function(e,t){this.assertNotComplex([e,t],"realDivide");return this.broadcastedBinaryOp(e,t,"float32",function(e,t){return e/t})},e.prototype.floorDiv=function(e,t){this.assertNotComplex([e,t],"floorDiv");return this.broadcastedBinaryOp(e,t,"int32",function(e,t){return Math.floor(e/t)})},e.prototype.sum=function(e,t){this.assertNotComplex(e,"sum"),assertAxesAreInnerMostDims("sum",t,e.rank);for(var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=n[1],a=zeros(r,upcastType(e.dtype,"int32")),i=sizeFromShape(o),s=a.dataSync(),u=e.dataSync(),c=0;c<s.length;++c){for(var l=c*i,p=0,d=0;d<i;++d)p+=u[l+d];s[c]=p;}return a},e.prototype.prod=function(e,t){this.assertNotComplex(e,"sum");for(var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=n[1],a=zeros(r,upcastType(e.dtype,"int32")),i=sizeFromShape(o),s=a.dataSync(),u=e.dataSync(),c=0;c<s.length;++c){for(var l=c*i,p=1,d=0;d<i;++d)p*=u[l+d];s[c]=p;}return a},e.prototype.unsortedSegmentSum=function(e,t,n){this.assertNotComplex(e,"unsortedSegmentSum");for(var r=[],o=e.rank-t.rank,a=0;a<o;++a)t=t.expandDims(a+1);for(a=0;a<n;++a){var i=scalar(a,"int32"),s=equal(i,t).asType("float32").mul(e).sum(0);r.push(s);}return stack(r)},e.prototype.argMin=function(e,t){this.assertNotComplex(e,"argMin");var n=[t];assertAxesAreInnerMostDims("argMin",n,e.rank);for(var r=computeOutAndReduceShapes(e.shape,n),o=r[0],a=r[1],i=zeros(o,"int32"),s=sizeFromShape(a),u=i.dataSync(),c=e.dataSync(),l=0;l<u.length;++l){for(var p=l*s,d=c[p],h=0,f=0;f<s;++f){var m=c[p+f];m<d&&(d=m,h=f);}u[l]=h;}return i},e.prototype.argMax=function(e,t){this.assertNotComplex(e,"argMax");var n=[t];assertAxesAreInnerMostDims("argMax",n,e.rank);for(var r=computeOutAndReduceShapes(e.shape,n),o=r[0],a=r[1],i=zeros(o,"int32"),s=sizeFromShape(a),u=i.dataSync(),c=e.dataSync(),l=0;l<u.length;++l){for(var p=l*s,d=c[p],h=0,f=0;f<s;++f){var m=c[p+f];m>d&&(d=m,h=f);}u[l]=h;}return i},e.prototype.cumsum=function(e,t,n,r){if(this.assertNotComplex(e,"cumsum"),t!==e.rank-1)throw new Error("backend.cumsum in CPU expects an inner-most axis="+(e.rank-1)+" but got axis="+t);for(var o=upcastType(e.dtype,"int32"),a=zeros(e.shape,o),i=a.dataSync(),s=e.dataSync(),u=e.shape[e.rank-1],c=r?function(e,t){return e+u-t-1}:function(e,t){return e+t},l=0;l<s.length;l+=u)for(var p=0;p<u;p++){var d=c(l,p);if(0===p)i[d]=n?0:s[d];else{var h=c(l,p-1);i[d]=n?s[h]+i[h]:s[d]+i[h];}}return a},e.prototype.equal=function(e,t){return this.assertNotComplex([e,t],"equal"),this.broadcastedBinaryOp(e,t,"bool",function(e,t){return e===t?1:0})},e.prototype.notEqual=function(e,t){return this.assertNotComplex([e,t],"notEqual"),this.broadcastedBinaryOp(e,t,"bool",function(e,t){return e!==t?1:0})},e.prototype.less=function(e,t){return this.assertNotComplex([e,t],"less"),this.broadcastedBinaryOp(e,t,"bool",function(e,t){return e<t?1:0})},e.prototype.lessEqual=function(e,t){return this.assertNotComplex([e,t],"lessEqual"),this.broadcastedBinaryOp(e,t,"bool",function(e,t){return e<=t?1:0})},e.prototype.greater=function(e,t){return this.assertNotComplex([e,t],"greater"),this.broadcastedBinaryOp(e,t,"bool",function(e,t){return e>t?1:0})},e.prototype.greaterEqual=function(e,t){return this.assertNotComplex([e,t],"greaterEqual"),this.broadcastedBinaryOp(e,t,"bool",function(e,t){return e>=t?1:0})},e.prototype.logicalNot=function(e){this.assertNotComplex(e,"logicalNot");for(var t=e.dataSync(),n=new Uint8Array(t.length),r=0;r<t.length;++r)n[r]=t[r]?0:1;return Tensor.make(e.shape,{values:n},"bool")},e.prototype.logicalAnd=function(e,t){return this.assertNotComplex([e,t],"logicalAnd"),this.broadcastedBinaryOp(e,t,"bool",function(e,t){return e&&t})},e.prototype.logicalOr=function(e,t){return this.assertNotComplex([e,t],"logicalOr"),this.broadcastedBinaryOp(e,t,"bool",function(e,t){return e||t})},e.prototype.select=function(e,t,n){this.assertNotComplex([e,t,n],"select");for(var r=e.dataSync(),o=t.dataSync(),a=n.dataSync(),i=zeros(t.shape,upcastType(t.dtype,n.dtype)),s=i.dataSync(),u=0,c=0===e.rank||e.rank>1||1===t.rank?1:t.shape[1],l=0;l<r.length;l++)for(var p=0;p<c;p++)1===r[l]?s[u++]=o[l]:s[u++]=a[l];return i},e.prototype.where=function(e){this.assertNotComplex([e],"where");var t=e.dataSync();return whereImpl(e.shape,t)},e.prototype.topk=function(e,t,n){return this.assertNotComplex(e,"topk"),topkImpl(e.dataSync(),e.shape,e.dtype,t,n)},e.prototype.min=function(e,t){this.assertNotComplex(e,"min"),assertAxesAreInnerMostDims("min",t,e.rank);for(var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=n[1],a=zeros(r,e.dtype),i=sizeFromShape(o),s=a.dataSync(),u=e.dataSync(),c=0;c<s.length;++c){for(var l=c*i,p=u[l],d=0;d<i;++d){var h=u[l+d];h<p&&(p=h);}s[c]=p;}return a},e.prototype.minimum=function(e,t){return this.assertNotComplex([e,t],"minimum"),this.broadcastedBinaryOp(e,t,e.dtype,function(e,t){return Math.min(e,t)})},e.prototype.mod=function(e,t){return this.assertNotComplex([e,t],"mod"),this.broadcastedBinaryOp(e,t,e.dtype,function(e,t){var n=e%t;return e<0&&t<0||e>=0&&t>=0?n:(n+t)%t})},e.prototype.max=function(e,t){this.assertNotComplex(e,"max"),assertAxesAreInnerMostDims("max",t,e.rank);for(var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=n[1],a=zeros(r,e.dtype),i=sizeFromShape(o),s=a.dataSync(),u=e.dataSync(),c=0;c<s.length;++c){for(var l=c*i,p=u[l],d=0;d<i;++d){var h=u[l+d];h>p&&(p=h);}s[c]=p;}return a},e.prototype.maximum=function(e,t){return this.assertNotComplex([e,t],"maximum"),this.broadcastedBinaryOp(e,t,e.dtype,function(e,t){return Math.max(e,t)})},e.prototype.all=function(e,t){this.assertNotComplex(e,"all"),assertAxesAreInnerMostDims("all",t,e.rank);for(var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=n[1],a=zeros(r,e.dtype),i=sizeFromShape(o),s=a.dataSync(),u=e.dataSync(),c=0;c<s.length;++c){for(var l=c*i,p=u[l],d=0;d<i;++d){var h=u[l+d];p=p&&h;}s[c]=p;}return a},e.prototype.any=function(e,t){this.assertNotComplex(e,"any"),assertAxesAreInnerMostDims("any",t,e.rank);for(var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=n[1],a=zeros(r,e.dtype),i=sizeFromShape(o),s=a.dataSync(),u=e.dataSync(),c=0;c<s.length;++c){for(var l=c*i,p=u[l],d=0;d<i;++d){var h=u[l+d];p=p||h;}s[c]=p;}return a},e.prototype.squaredDifference=function(e,t){return this.assertNotComplex([e,t],"squaredDifference"),this.broadcastedBinaryOp(e,t,e.dtype,function(e,t){var n=e-t;return n*n})},e.prototype.ceil=function(e){this.assertNotComplex(e,"ceil");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r)n[r]=Math.ceil(t[r]);return Tensor.make(e.shape,{values:n})},e.prototype.floor=function(e){this.assertNotComplex(e,"floor");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r)n[r]=Math.floor(t[r]);return Tensor.make(e.shape,{values:n})},e.prototype.sign=function(e){this.assertNotComplex(e,"x");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r)t[r]<0?n[r]=-1:t[r]>0?n[r]=1:n[r]=0;return Tensor.make(e.shape,{values:n})},e.prototype.isNaN=function(e){this.assertNotComplex(e,"x");for(var t=e.dataSync(),n=new Uint8Array(t.length),r=0;r<t.length;++r)Number.isNaN(t[r])&&(n[r]=1);return Tensor.make(e.shape,{values:n},"bool")},e.prototype.isInf=function(e){this.assertNotComplex(e,"x");for(var t=e.dataSync(),n=new Uint8Array(t.length),r=0;r<t.length;++r)Math.abs(t[r])===1/0&&(n[r]=1);return Tensor.make(e.shape,{values:n},"bool")},e.prototype.isFinite=function(e){this.assertNotComplex(e,"x");for(var t=e.dataSync(),n=new Uint8Array(t.length),r=0;r<t.length;++r)Number.isFinite(t[r])&&(n[r]=1);return Tensor.make(e.shape,{values:n},"bool")},e.prototype.round=function(e){this.assertNotComplex(e,"round");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r){var o=Math.floor(t[r]);t[r]-o<.5?n[r]=Math.floor(t[r]):t[r]-o>.5?n[r]=Math.ceil(t[r]):n[r]=o%2==0?o:o+1;}return Tensor.make(e.shape,{values:n})},e.prototype.exp=function(e){this.assertNotComplex(e,"exp");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r)n[r]=Math.exp(t[r]);return Tensor.make(e.shape,{values:n})},e.prototype.expm1=function(e){this.assertNotComplex(e,"expm1");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r)n[r]=Math.expm1(t[r]);return Tensor.make(e.shape,{values:n})},e.prototype.log=function(e){this.assertNotComplex(e,"log");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r){var o=t[r];n[r]=Math.log(o);}return Tensor.make(e.shape,{values:n})},e.prototype.log1p=function(e){this.assertNotComplex(e,"log1p");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r){var o=t[r];n[r]=Math.log1p(o);}return Tensor.make(e.shape,{values:n})},e.prototype.sqrt=function(e){this.assertNotComplex(e,"sqrt");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r){var o=t[r];n[r]=Math.sqrt(o);}return Tensor.make(e.shape,{values:n})},e.prototype.rsqrt=function(e){this.assertNotComplex(e,"rsqrt");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r){var o=t[r];n[r]=1/Math.sqrt(o);}return Tensor.make(e.shape,{values:n})},e.prototype.square=function(e){this.assertNotComplex(e,"square");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r){var o=t[r];n[r]=o*o;}return Tensor.make(e.shape,{values:n})},e.prototype.reciprocal=function(e){this.assertNotComplex(e,"reciprocal");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r)n[r]=1/t[r];return Tensor.make(e.shape,{values:n})},e.prototype.linear=function(e){return e},e.prototype.relu=function(e){this.assertNotComplex(e,"relu");for(var t=zeros(e.shape,e.dtype),n=t.dataSync(),r=e.dataSync(),o=0;o<r.length;++o)n[o]=Math.max(0,r[o]);return t},e.prototype.prelu=function(e,t){return this.assertNotComplex([e,t],"prelu"),this.broadcastedBinaryOp(e,t,e.dtype,function(e,t){return e<0?t*e:e})},e.prototype.elu=function(e){this.assertNotComplex(e,"elu");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r){var o=n[r];t[r]=o>=0?o:Math.exp(o)-1;}return Tensor.make(e.shape,{values:t})},e.prototype.eluDer=function(e,t){this.assertNotComplex([e,t],"eluDer");for(var n=new Float32Array(t.size),r=t.dataSync(),o=e.dataSync(),a=0;a<r.length;++a){var i=r[a];n[a]=i>=1?o[a]:o[a]*(i+1);}return Tensor.make(t.shape,{values:n})},e.prototype.selu=function(e){this.assertNotComplex(e,"selu");for(var t=SELU_SCALEALPHA,n=SELU_SCALE,r=new Float32Array(e.size),o=e.dataSync(),a=0;a<o.length;++a){var i=o[a];r[a]=i>=0?n*i:t*(Math.exp(i)-1);}return Tensor.make(e.shape,{values:r})},e.prototype.clip=function(e,t,n){this.assertNotComplex(e,"clip");for(var r=new Float32Array(e.size),o=e.dataSync(),a=0;a<o.length;++a){var i=o[a];r[a]=i>n?n:i<t?t:i;}return Tensor.make(e.shape,{values:r})},e.prototype.abs=function(e){for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.abs(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.complexAbs=function(e){for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<e.size;++r){var o=n[2*r],a=n[2*r+1];t[r]=Math.hypot(o,a);}return Tensor.make(e.shape,{values:t})},e.prototype.int=function(e){this.assertNotComplex(e,"int");for(var t=new Int32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=n[r];return Tensor.make(e.shape,{values:t},"int32")},e.prototype.sigmoid=function(e){this.assertNotComplex(e,"sigmoid");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=1/(1+Math.exp(-n[r]));return Tensor.make(e.shape,{values:t})},e.prototype.softplus=function(e){this.assertNotComplex(e,"softplus");for(var t=Math.log(1.1920928955078125e-7)+2,n=new Float32Array(e.size),r=e.dataSync(),o=0;o<r.length;++o){var a=r[o]>-t,i=r[o]<t,s=Math.exp(r[o]),u=void 0;u=i?s:a?r[o]:Math.log(1+s),n[o]=u;}return Tensor.make(e.shape,{values:n})},e.prototype.sin=function(e){this.assertNotComplex(e,"sin");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.sin(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.cos=function(e){this.assertNotComplex(e,"cos");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.cos(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.tan=function(e){this.assertNotComplex(e,"tan");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.tan(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.asin=function(e){this.assertNotComplex(e,"asin");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.asin(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.acos=function(e){this.assertNotComplex(e,"acos");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.acos(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.atan=function(e){this.assertNotComplex(e,"atan");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.atan(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.atan2=function(e,t){return this.assertNotComplex([e,t],"atan2"),this.broadcastedBinaryOp(e,t,e.dtype,function(e,t){return Math.atan2(e,t)})},e.prototype.sinh=function(e){this.assertNotComplex(e,"sinh");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.sinh(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.cosh=function(e){this.assertNotComplex(e,"cosh");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.cosh(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.tanh=function(e){this.assertNotComplex(e,"tanh");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=tanh(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.asinh=function(e){this.assertNotComplex(e,"asinh");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.asinh(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.acosh=function(e){this.assertNotComplex(e,"acosh");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.acosh(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.atanh=function(e){this.assertNotComplex(e,"atanh");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.atanh(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.erf=function(e){this.assertNotComplex(e,"erf");for(var t=new Float32Array(e.size),n=e.dataSync(),r=ERF_P,o=ERF_A1,a=ERF_A2,i=ERF_A3,s=ERF_A4,u=ERF_A5,c=0;c<n.length;++c){var l=n[c],p=1/(1+r*l);t[c]=1-((((u*p+s)*p+i)*p+a)*p+o)*p*Math.exp(-l*l);}return Tensor.make(e.shape,{values:t})},e.prototype.step=function(e,t){void 0===t&&(t=0),this.assertNotComplex(e,"step");for(var n=new Float32Array(e.size),r=e.dataSync(),o=0;o<r.length;++o){var a=r[o];isNaN(a)?n[o]=NaN:n[o]=a>0?1:t;}return Tensor.make(e.shape,{values:n})},e.prototype.conv2d=function(e,t,n){this.assertNotComplex([e,t],"conv2d");for(var r=n.filterHeight,o=n.filterWidth,a=n.dilationHeight,i=n.dilationWidth,s=n.padInfo.left,u=n.padInfo.top,c=buffer(n.outShape,e.dtype),l=e.dataSync(),p=t.dataSync(),d=c.values,h=0;h<n.batchSize;++h)for(var f=h*e.strides[0],m=h*c.strides[0],g=0;g<n.outHeight;++g)for(var v=m+g*c.strides[1],y=g*n.strideHeight-s,x=0;x<r;x++){var E=y+x*a;if(!(E<0||E>=n.inHeight))for(var T=x*t.strides[0],b=f+E*e.strides[1],w=0;w<n.outWidth;++w)for(var S=v+w*n.outChannels,C=w*n.strideWidth-u,A=0;A<o;A++){var N=C+A*i;if(!(N<0||N>=n.inWidth))for(var _=T+A*t.strides[1],R=b+N*n.inChannels,I=_,k=0;k<n.inChannels;++k){for(var D=l[R+k],O=0;O<n.outChannels;++O)d[S+O]+=D*p[I+O];I+=n.outChannels;}}}return c.toTensor()},e.prototype.conv3d=function(e,t,n){for(var r=n.filterDepth,o=n.filterHeight,a=n.filterWidth,i=n.dilationDepth,s=n.dilationHeight,u=n.dilationWidth,c=n.padInfo.front,l=n.padInfo.left,p=n.padInfo.top,d=buffer(n.outShape,e.dtype),h=e.dataSync(),f=t.dataSync(),m=d.values,g=0;g<n.batchSize;++g)for(var v=g*e.strides[0],y=g*d.strides[0],x=0;x<n.outDepth;++x)for(var E=y+x*d.strides[1],T=x*n.strideDepth-c,b=0;b<r;b++){var w=T+b*i;if(!(w<0||w>=n.inDepth))for(var S=b*t.strides[0],C=v+w*e.strides[1],A=0;A<n.outHeight;++A)for(var N=E+A*d.strides[2],_=A*n.strideHeight-p,R=0;R<o;R++){var I=_+R*s;if(!(I<0||I>=n.inHeight))for(var k=S+R*t.strides[1],D=C+I*e.strides[2],O=0;O<n.outWidth;++O)for(var P=N+O*n.outChannels,M=O*n.strideWidth-l,F=0;F<a;F++){var L=M+F*u;if(!(L<0||L>=n.inWidth))for(var B=k+F*t.strides[2],V=D+L*n.inChannels,U=B,z=0;z<n.inChannels;++z){for(var W=h[V+z],G=0;G<n.outChannels;++G)m[P+G]+=W*f[U+G];U+=n.outChannels;}}}}return d.toTensor()},e.prototype.conv2dDerInput=function(e,t,n){this.assertNotComplex([e,t],"conv2dDerInput");for(var r=buffer(n.inShape,"float32"),o=r.values,a=r.strides,i=a[0],s=a[1],u=a[2],c=e.dataSync(),l=e.strides,p=l[0],d=l[1],h=l[2],f=t.dataSync(),m=t.strides,g=m[0],v=m[1],y=m[2],x=n.batchSize,E=n.filterHeight,T=n.filterWidth,b=n.inChannels,w=n.inHeight,S=n.inWidth,C=n.outChannels,A=n.outHeight,N=n.outWidth,_=n.strideHeight,R=n.strideWidth,I=E-1-n.padInfo.top,k=T-1-n.padInfo.left,D=0;D<x;++D)for(var O=0;O<b;++O)for(var P=0;P<w;++P)for(var M=P-I,F=Math.max(0,Math.ceil(M/_)),L=Math.min(A,(E+M)/_),B=0;B<S;++B){for(var V=B-k,U=Math.max(0,Math.ceil(V/R)),z=Math.min(N,(T+V)/R),W=0,G=F;G<L;++G)for(var H=G*_-M,q=U;q<z;++q)for(var $=p*D+d*G+h*q,K=g*(E-1-H)+v*(T-1-(q*R-V))+y*O,X=0;X<C;++X){W+=c[$+X]*f[K+X];}o[i*D+s*P+u*B+O]=W;}return r.toTensor()},e.prototype.conv3dDerInput=function(e,t,n){for(var r=buffer(n.inShape,"float32"),o=r.values,a=r.strides,i=a[0],s=a[1],u=a[2],c=a[3],l=e.dataSync(),p=e.strides,d=p[0],h=p[1],f=p[2],m=p[3],g=t.dataSync(),v=t.strides,y=v[0],x=v[1],E=v[2],T=v[3],b=n.batchSize,w=n.filterDepth,S=n.filterHeight,C=n.filterWidth,A=n.inChannels,N=n.inDepth,_=n.inHeight,R=n.inWidth,I=n.outChannels,k=n.outDepth,D=n.outHeight,O=n.outWidth,P=n.strideDepth,M=n.strideHeight,F=n.strideWidth,L=w-1-n.padInfo.front,B=S-1-n.padInfo.top,V=C-1-n.padInfo.left,U=0;U<b;++U)for(var z=0;z<A;++z)for(var W=0;W<N;++W)for(var G=W-L,H=Math.max(0,Math.ceil(G/P)),q=Math.min(k,(w+G)/P),$=0;$<_;++$)for(var K=$-B,X=Math.max(0,Math.ceil(K/M)),j=Math.min(D,(S+K)/M),Y=0;Y<R;++Y){for(var Q=Y-V,J=Math.max(0,Math.ceil(Q/F)),Z=Math.min(O,(C+Q)/F),ee=0,te=H;te<q;++te)for(var ne=te*P-G,re=X;re<j;++re)for(var oe=re*M-K,ae=J;ae<Z;++ae)for(var ie=d*U+h*te+f*re+m*ae,se=y*(w-1-ne)+x*(S-1-oe)+E*(C-1-(ae*F-Q))+T*z,ue=0;ue<I;++ue){ee+=l[ie+ue]*g[se+ue];}o[i*U+s*W+u*$+c*Y+z]=ee;}return r.toTensor()},e.prototype.conv2dDerFilter=function(e,t,n){this.assertNotComplex([e,t],"conv2dDerFilter");for(var r=n.strideHeight,o=n.strideWidth,a=n.filterHeight,i=n.filterWidth,s=buffer(n.filterShape,"float32"),u=n.padInfo.left,c=n.padInfo.top,l=e.bufferSync(),p=t.bufferSync(),d=0;d<a;++d)for(var h=Math.max(0,Math.ceil((c-d)/r)),f=Math.min(n.outHeight,(n.inHeight+c-d)/r),m=0;m<i;++m)for(var g=Math.max(0,Math.ceil((u-m)/o)),v=Math.min(n.outWidth,(n.inWidth+u-m)/o),y=0;y<n.inChannels;++y)for(var x=0;x<n.outChannels;++x){for(var E=0,T=0;T<n.batchSize;++T)for(var b=h;b<f;++b)for(var w=d+b*r-c,S=g;S<v;++S){var C=m+S*o-u;E+=l.get(T,w,C,y)*p.get(T,b,S,x);}s.set(E,d,m,y,x);}return s.toTensor()},e.prototype.conv3dDerFilter=function(e,t,n){for(var r=n.strideDepth,o=n.strideHeight,a=n.strideWidth,i=n.filterDepth,s=n.filterHeight,u=n.filterWidth,c=buffer(n.filterShape,"float32"),l=c.values,p=c.strides,d=p[0],h=p[1],f=p[2],m=p[3],g=t.dataSync(),v=t.strides,y=v[0],x=v[1],E=v[2],T=v[3],b=e.dataSync(),w=e.strides,S=w[0],C=w[1],A=w[2],N=w[3],_=n.padInfo.front,R=n.padInfo.left,I=n.padInfo.top,k=0;k<i;++k)for(var D=Math.max(0,Math.ceil((_-k)/r)),O=Math.min(n.outDepth,(n.inDepth+_-k)/r),P=k*d,M=0;M<s;++M)for(var F=Math.max(0,Math.ceil((I-M)/o)),L=Math.min(n.outHeight,(n.inHeight+I-M)/o),B=M*h+P,V=0;V<u;++V)for(var U=Math.max(0,Math.ceil((R-V)/a)),z=Math.min(n.outWidth,(n.inWidth+R-V)/a),W=V*f+B,G=0;G<n.inChannels;++G)for(var H=G*m+W,q=0;q<n.outChannels;++q){for(var $=0,K=0;K<n.batchSize;++K)for(var X=K*S,j=K*y,Y=D;Y<O;++Y)for(var Q=(k+Y*r-_)*C+X,J=Y*x+j,Z=F;Z<L;++Z)for(var ee=(M+Z*o-I)*A+Q,te=Z*E+J,ne=U;ne<z;++ne){var re=ne*T+te;$+=b[(V+ne*a-R)*N+ee+G]*g[re+q];}l[H+q]=$;}return c.toTensor()},e.prototype.depthwiseConv2D=function(e,t,n){this.assertNotComplex([e,t],"depthwiseConv2D");for(var r=n.filterHeight,o=n.filterWidth,a=n.dilationHeight,i=n.dilationWidth,s=n.padInfo.left,u=n.padInfo.top,c=n.outChannels/n.inChannels,l=buffer(n.outShape,e.dtype),p=e.dataSync(),d=t.dataSync(),h=l.values,f=0;f<n.batchSize;++f)for(var m=f*e.strides[0],g=f*l.strides[0],v=0;v<n.outHeight;++v)for(var y=g+v*l.strides[1],x=v*n.strideHeight-s,E=0;E<r;++E){var T=x+E*a;if(!(T<0||T>=n.inHeight))for(var b=E*t.strides[0],w=m+T*e.strides[1],S=0;S<n.outWidth;++S)for(var C=y+S*l.strides[2],A=S*n.strideWidth-u,N=0;N<o;++N){var _=A+N*i;if(!(_<0||_>=n.inWidth))for(var R=b+N*t.strides[1],I=w+_*n.inChannels,k=C,D=R,O=0;O<n.inChannels;++O){for(var P=p[I+O],M=0;M<c;++M)h[k+M]+=P*d[D+M];k+=c,D+=c;}}}return l.toTensor()},e.prototype.depthwiseConv2DDerInput=function(e,t,n){this.assertNotComplex([e,t],"depthwiseConv2DDerInput");for(var r=buffer(n.inShape,"float32"),o=r.values,a=r.strides,i=a[0],s=a[1],u=a[2],c=e.dataSync(),l=e.strides,p=l[0],d=l[1],h=l[2],f=t.dataSync(),m=t.strides,g=m[0],v=m[1],y=m[2],x=n.batchSize,E=n.filterHeight,T=n.filterWidth,b=n.inChannels,w=n.inHeight,S=n.inWidth,C=n.outChannels,A=n.outHeight,N=n.outWidth,_=n.strideHeight,R=n.strideWidth,I=E-1-n.padInfo.top,k=T-1-n.padInfo.left,D=C/b,O=0;O<x;++O)for(var P=0;P<b;++P)for(var M=0;M<w;++M)for(var F=M-I,L=Math.max(0,Math.ceil(F/_)),B=Math.min(A,(E+F)/_),V=0;V<S;++V){for(var U=V-k,z=Math.max(0,Math.ceil(U/R)),W=Math.min(N,(T+U)/R),G=0,H=L;H<B;++H)for(var q=H*_-F,$=z;$<W;++$)for(var K=p*O+d*H+h*$,X=g*(E-1-q)+v*(T-1-($*R-U))+y*P,j=0;j<D;++j){G+=c[K+(P*D+j)]*f[X+j];}o[i*O+s*M+u*V+P]=G;}return r.toTensor()},e.prototype.depthwiseConv2DDerFilter=function(e,t,n){this.assertNotComplex([e,t],"depthwiseConv2DDerFilter");for(var r=n.strideHeight,o=n.strideWidth,a=n.filterHeight,i=n.filterWidth,s=buffer(n.filterShape,"float32"),u=n.padInfo.left,c=n.padInfo.top,l=n.outChannels/n.inChannels,p=e.bufferSync(),d=t.bufferSync(),h=0;h<a;++h)for(var f=Math.max(0,Math.ceil((c-h)/r)),m=Math.min(n.outHeight,(n.inHeight+c-h)/r),g=0;g<i;++g)for(var v=Math.max(0,Math.ceil((u-g)/o)),y=Math.min(n.outWidth,(n.inWidth+u-g)/o),x=0;x<n.outChannels;++x){for(var E=Math.trunc(x/l),T=x%l,b=0,w=0;w<n.batchSize;++w)for(var S=f;S<m;++S)for(var C=h+S*r-c,A=v;A<y;++A){var N=g+A*o-u;b+=p.get(w,C,N,E)*d.get(w,S,A,x);}s.set(b,h,g,E,T);}return s.toTensor()},e.prototype.tile=function(e,t){this.assertNotComplex(e,"tile");for(var n=new Array(e.rank),r=0;r<n.length;r++)n[r]=e.shape[r]*t[r];var o=buffer(n,e.dtype),a=e.bufferSync();for(r=0;r<o.values.length;++r){for(var i=o.indexToLoc(r),s=new Array(e.rank),u=0;u<s.length;u++)s[u]=i[u]%e.shape[u];var c=a.locToIndex(s);o.values[r]=a.values[c];}return o.toTensor()},e.prototype.pad=function(e,t,n){this.assertNotComplex(e,"pad");var r=t.map(function(t,n){return t[0]+e.shape[n]+t[1]}),o=t.map(function(e){return e[0]}),a=e.bufferSync(),i=buffer(r,e.dtype);0!==n&&i.values.fill(n);for(var s=0;s<e.size;s++){var u=a.indexToLoc(s),c=u.map(function(e,t){return e+o[t]});i.set.apply(i,[a.get.apply(a,u)].concat(c));}return i.toTensor()},e.prototype.transpose=function(e,t){this.assertNotComplex(e,"transpose");for(var n=new Array(e.rank),r=0;r<n.length;r++)n[r]=e.shape[t[r]];var o=e.dataSync(),a=buffer(n,e.dtype),i=e.bufferSync();for(r=0;r<e.size;++r){for(var s=i.indexToLoc(r),u=new Array(s.length),c=0;c<u.length;c++)u[c]=s[t[c]];var l=a.locToIndex(u);a.values[l]=o[r];}return a.toTensor()},e.prototype.gather=function(e,t,n){this.assertNotComplex([e,t],"gather");var r=e.shape.slice(),o=t.dataSync();r[n]=o.length;for(var a=buffer(r,e.dtype),i=e.bufferSync(),s=0;s<a.size;++s){var u=a.indexToLoc(s),c=u.slice();c[n]=o[u[n]];var l=i.locToIndex(c);a.values[s]=i.values[l];}return a.toTensor()},e.prototype.batchToSpaceND=function(e,t,n){this.assertNotComplex([e],"batchToSpaceND");var r=t.reduce(function(e,t){return e*t}),o=getReshaped(e.shape,t,r),a=getPermuted(o.length,t.length),i=getReshapedPermuted(e.shape,t,r),s=getSliceBeginCoords(n,t.length),u=getSliceSize(i,n,t.length);return e.reshape(o).transpose(a).reshape(i).slice(s,u)},e.prototype.spaceToBatchND=function(e,t,n){this.assertNotComplex([e],"spaceToBatchND");var r=t.reduce(function(e,t){return e*t}),o=[[0,0]];o.push.apply(o,n);for(var a=1+t.length;a<e.shape.length;++a)o.push([0,0]);var i=e.pad(o),s=getReshaped(i.shape,t,r,!1),u=getPermuted(s.length,t.length,!1),c=getReshapedPermuted(i.shape,t,r,!1);return i.reshape(s).transpose(u).reshape(c)},e.prototype.pool=function(e,t,n){this.assertNotComplex(e,"pool");for(var r=t.strideHeight,o=t.strideWidth,a=t.dilationHeight,i=t.dilationWidth,s=t.effectiveFilterHeight,u=t.effectiveFilterWidth,c=t.padInfo.top,l=t.padInfo.left,p="max"===n?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,d=e.dataSync(),h=buffer(t.outShape,e.dtype),f=h.values,m=t.outShape[1]*t.outShape[2]*t.outShape[3],g=t.outShape[2]*t.outShape[3],v=t.outShape[3],y=0;y<t.batchSize;++y)for(var x=y*m,E=y*e.strides[0],T=0;T<t.inChannels;++T)for(var b=0;b<t.outHeight;++b)for(var w=b*r-c,S=Math.max(0,w),C=Math.min(t.inHeight,s+w),A=x+b*g,N=0;N<t.outWidth;++N){for(var _=N*o-l,R=Math.max(0,_),I=Math.min(t.inWidth,u+_),k=p,D=0,O=0,P=S;P<C;P+=a){for(var M=E+P*e.strides[1],F=R;F<I;F+=i){var L=d[M+F*e.strides[2]+T];"max"===n&&L>k?k=L:"avg"===n&&(D+=L,O++);}if(isNaN(k))break}f[A+N*v+T]="avg"===n?D/O:k;}return h.toTensor()},e.prototype.maxPool=function(e,t){return this.pool(e,t,"max")},e.prototype.maxPoolPositions=function(e,t){for(var n=buffer(t.outShape,"int32"),r=t.strideHeight,o=t.strideWidth,a=t.dilationHeight,i=t.dilationWidth,s=t.effectiveFilterHeight,u=t.effectiveFilterWidth,c=t.padInfo.top,l=t.padInfo.left,p=e.bufferSync(),d=0;d<t.batchSize;++d)for(var h=0;h<t.inChannels;++h)for(var f=0;f<t.outHeight;++f){for(var m=f*r-c,g=m;g<0;)g+=a;for(var v=Math.min(t.inHeight,s+m),y=0;y<t.outWidth;++y){for(var x=y*o-l,E=x;E<0;)E+=i;for(var T=Math.min(t.inWidth,u+x),b=Number.NEGATIVE_INFINITY,w=-1,S=g;S<v;S+=a)for(var C=S-m,A=E;A<T;A+=i){var N=A-x,_=p.get(d,S,A,h);_>b&&(b=_,w=C*u+N);}n.set(w,d,f,y,h);}}return n.toTensor()},e.prototype.maxPoolBackprop=function(e,t,n,r){this.assertNotComplex([t,n],"maxPoolBackprop");for(var o=this.maxPoolPositions(t,r),a=r.strideHeight,i=r.strideWidth,s=r.dilationHeight,u=r.dilationWidth,c=r.effectiveFilterHeight,l=r.effectiveFilterWidth,p=l-1-r.padInfo.left,d=c-1-r.padInfo.top,h=buffer(t.shape,"float32"),f=o.bufferSync(),m=e.bufferSync(),g=0;g<r.batchSize;++g)for(var v=0;v<r.inChannels;++v)for(var y=0;y<r.inHeight;++y)for(var x=0;x<r.inWidth;++x){for(var E=y-d,T=x-p,b=0,w=0;w<c;w+=s){var S=(E+w)/a;if(!(S<0||S>=r.outHeight||Math.floor(S)!==S))for(var C=0;C<l;C+=u){var A=(T+C)/i;if(!(A<0||A>=r.outWidth||Math.floor(A)!==A)){var N=c*l-1-f.get(g,S,A,v)===w*l+C?1:0;if(0!==N)b+=m.get(g,S,A,v)*N;}}}h.set(b,g,y,x,v);}return h.toTensor()},e.prototype.avgPoolBackprop=function(e,t,n){this.assertNotComplex([e,t],"avgPoolBackprop");for(var r=n.strideHeight,o=n.strideWidth,a=n.filterHeight,i=n.filterWidth,s=n.dilationHeight,u=n.dilationWidth,c=n.effectiveFilterHeight,l=n.effectiveFilterWidth,p=l-1-n.padInfo.left,d=c-1-n.padInfo.top,h=buffer(t.shape,"float32"),f=1/(a*i),m=e.bufferSync(),g=0;g<n.batchSize;++g)for(var v=0;v<n.inChannels;++v)for(var y=0;y<n.inHeight;++y)for(var x=0;x<n.inWidth;++x){for(var E=y-d,T=x-p,b=0,w=0;w<c;w+=s){var S=(E+w)/r;if(!(S<0||S>=n.outHeight||Math.floor(S)!==S))for(var C=0;C<l;C+=u){var A=(T+C)/o;if(!(A<0||A>=n.outWidth||Math.floor(A)!==A))b+=m.get(g,S,A,v);}}h.set(b*f,g,y,x,v);}return h.toTensor()},e.prototype.cast=function(e,t){return castTensor(e,t,this)},e.prototype.reshape=function(e,t){return reshapeTensor(e,t)},e.prototype.avgPool=function(e,t){return this.assertNotComplex(e,"avgPool"),this.pool(e,t,"avg").toFloat()},e.prototype.resizeBilinear=function(e,t,n,r){this.assertNotComplex(e,"resizeBilinear");for(var o=e.shape,a=o[0],i=o[1],s=o[2],u=o[3],c=e.dataSync(),l=new Float32Array(sizeFromShape([a,t,n,u])),p=[r&&t>1?i-1:i,r&&n>1?s-1:s],d=[r&&t>1?t-1:t,r&&n>1?n-1:n],h=0,f=p[0]/d[0],m=p[1]/d[1],g=0;g<a;g++)for(var v=0;v<t;v++)for(var y=f*v,x=Math.floor(y),E=y-x,T=Math.min(i-1,Math.ceil(y)),b=g*e.strides[0]+x*e.strides[1],w=g*e.strides[0]+T*e.strides[1],S=0;S<n;S++)for(var C=m*S,A=Math.floor(C),N=C-A,_=Math.min(s-1,Math.ceil(C)),R=b+A*e.strides[2],I=w+A*e.strides[2],k=b+ +_*e.strides[2],D=w+_*e.strides[2],O=0;O<u;O++){var P=c[R+O],M=c[I+O],F=P+(c[k+O]-P)*N,L=F+(M+(c[D+O]-M)*N-F)*E;l[h++]=L;}return tensor(l,[a,t,n,u])},e.prototype.resizeBilinearBackprop=function(e,t,n){this.assertNotComplex([e,t],"resizeBilinearBackprop");for(var r=t.shape,o=r[0],a=r[1],i=r[2],s=r[3],u=e.shape,c=u[1],l=u[2],p=new Float32Array(o*a*i*s),d=[n&&c>1?a-1:a,n&&l>1?i-1:i],h=[n&&c>1?c-1:c,n&&l>1?l-1:l],f=d[0]/h[0],m=d[1]/h[1],g=e.dataSync(),v=0,y=0;y<o;y++)for(var x=y*t.strides[0],E=0;E<c;E++)for(var T=E*f,b=Math.floor(T),w=Math.min(Math.ceil(T),a-1),S=x+b*t.strides[1],C=x+w*t.strides[1],A=T-b,N=1-A,_=0;_<l;_++)for(var R=_*m,I=Math.floor(R),k=Math.min(Math.ceil(R),i-1),D=R-I,O=1-D,P=S+I*t.strides[2],M=S+k*t.strides[2],F=C+I*t.strides[2],L=C+k*t.strides[2],B=N*O,V=N*D,U=A*O,z=A*D,W=0;W<s;W++){var G=g[v++];p[P+W]+=G*B,p[M+W]+=G*V,p[F+W]+=G*U,p[L+W]+=G*z;}return tensor4d(p,[o,i,a,s],t.dtype)},e.prototype.resizeNearestNeighbor=function(e,t,n,r){this.assertNotComplex(e,"resizeNearestNeighbor");for(var o=e.shape,a=o[0],i=o[1],s=o[2],u=o[3],c=e.dataSync(),l=new Float32Array(a*t*n*u),p=[r&&t>1?i-1:i,r&&n>1?s-1:s],d=[r&&t>1?t-1:t,r&&n>1?n-1:n],h=p[0]/d[0],f=p[1]/d[1],m=0,g=0;g<a;g++)for(var v=g*e.strides[0],y=0;y<t;y++)for(var x=h*y,E=v+Math.min(i-1,r?Math.round(x):Math.floor(x))*e.strides[1],T=0;T<n;T++)for(var b=f*T,w=E+Math.min(s-1,r?Math.round(b):Math.floor(b))*e.strides[2],S=0;S<u;S++){var C=c[w+S];l[m++]=C;}return tensor(l,[a,t,n,u],e.dtype)},e.prototype.resizeNearestNeighborBackprop=function(e,t,n){this.assertNotComplex([e,t],"resizeNearestNeighborBackprop");for(var r=t.shape,o=r[0],a=r[1],i=r[2],s=r[3],u=e.shape,c=u[1],l=u[2],p=new Float32Array(o*a*i*s),d=e.dataSync(),h=[n&&c>1?a-1:a,n&&l>1?i-1:i],f=[n&&c>1?c-1:c,n&&l>1?l-1:l],m=h[0]/f[0],g=h[1]/f[1],v=1/m,y=1/g,x=2*Math.ceil(v)+2,E=2*Math.ceil(y)+2,T=0;T<o;T++)for(var b=T*t.strides[0],w=0;w<a;w++)for(var S=b+w*t.strides[1],C=Math.floor(w*v),A=Math.floor(C-x/2),N=0;N<i;N++)for(var _=S+N*t.strides[2],R=Math.floor(N*y),I=Math.floor(R-E/2),k=0;k<s;k++){for(var D=0,O=0;O<x;O++){var P=O+A;if(!(P<0||P>=c)){var M=b+P*e.strides[1],F=P*m;if(w===Math.min(a-1,n?Math.round(F):Math.floor(F)))for(var L=0;L<E;L++){var B=L+I;if(!(B<0||B>=l)){var V=M+B*e.strides[2],U=B*g;N===Math.min(i-1,n?Math.round(U):Math.floor(U))&&(D+=d[V+k]);}}}}p[_+k]=D;}return tensor4d(p,t.shape,t.dtype)},e.prototype.batchNormalization=function(e,t,n,r,o,a){this.assertNotComplex([e,t,n,o,a],"batchNorm");for(var i=e.dataSync(),s=t.dataSync(),u=n.dataSync(),c=o?o.dataSync():new Float32Array([1]),l=a?a.dataSync():new Float32Array([0]),p=new Float32Array(i.length),d=l.length,h=c.length,f=u.length,m=s.length,g=0,v=0,y=0,x=0,E=0;E<i.length;++E)p[E]=l[g++]+(i[E]-s[v++])*c[y++]/Math.sqrt(u[x++]+r),g>=d&&(g=0),v>=m&&(v=0),y>=h&&(y=0),x>=f&&(x=0);return tensor4d(p,e.shape)},e.prototype.localResponseNormalization4D=function(e,t,n,r,o){this.assertNotComplex(e,"localResponseNormalization4D");var a=e.shape[3],i=a-1,s=e.dataSync(),u=e.size,c=new Float32Array(u);function l(e){for(var n=e%a,r=e-n+Math.max(0,n-t),o=e-n+Math.min(n+t,i),u=0;r<=o;r++){var c=s[r];u+=c*c;}return u}for(var p=0;p<u;p++){var d=l(p),h=s[p]*Math.pow(n+r*d,-o);c[p]=h;}return tensor4d(c,e.shape)},e.prototype.LRNGrad=function(e,t,n,r,o,a,i){this.assertNotComplex(e,"LRNGrad");for(var s=e.shape[3],u=e.dataSync(),c=t.dataSync(),l=n.dataSync(),p=new Float32Array(e.size),d=e.size,h=0;h<d;h++){for(var f=h%s,m=h-f+Math.max(0,f-r),g=h-f+Math.min(s,f+r+1),v=0,y=m;y<g;y++)v+=Math.pow(c[y],2);v=a*v+o;for(y=m;y<g;y++){var x=-2*a*i*c[y]*l[h]/v;h===y&&(x+=Math.pow(v,-i)),x*=u[h],p[y]+=x;}}return tensor4d(p,e.shape)},e.prototype.multinomial=function(e,t,n,r){this.assertNotComplex(e,"multinomial");for(var o=t?e:softmax(e),a=o.shape[0],i=o.shape[1],s=zeros([a,n],"int32"),u=s.dataSync(),c=o.dataSync(),l=0;l<a;++l){var p=l*i,d=new Float32Array(i-1);d[0]=c[p];for(var h=1;h<d.length;++h)d[h]=d[h-1]+c[p+h];for(var f=seedrandom_1(r.toString()),m=l*n,g=0;g<n;++g){var v=f();u[m+g]=d.length;for(var y=0;y<d.length;y++)if(v<d[y]){u[m+g]=y;break}}}return s},e.prototype.oneHot=function(e,t,n,r){this.assertNotComplex(e,"oneHot");var o=new Float32Array(e.size*t);o.fill(r);for(var a=e.dataSync(),i=0;i<e.size;++i)a[i]>=0&&a[i]<t&&(o[i*t+a[i]]=n);return tensor2d(o,[e.size,t],"int32")},e.prototype.nonMaxSuppression=function(e,t,n,r,o){return this.assertNotComplex(e,"nonMaxSuppression"),nonMaxSuppressionImpl(e.dataSync(),t.dataSync(),n,r,o)},e.prototype.fft=function(e){return this.fftBatch(e,!1)},e.prototype.ifft=function(e){return this.fftBatch(e,!0)},e.prototype.fftBatch=function(e,t){for(var n=e.shape[0],r=e.shape[1],o=buffer(e.shape,"float32"),a=buffer(e.shape,"float32"),i=real(e).as2D(n,r),s=imag(e).as2D(n,r),u=0;u<n;u++)for(var c=i.slice([u,0],[1,r]),l=s.slice([u,0],[1,r]),p=complex(c,l),d=this.fftImpl(p,t).dataSync(),h=0;h<r;h++){var f=getComplexWithIndex(d,h);o.values[u*r+h]=f.real,a.values[u*r+h]=f.imag;}return complex(o.toTensor(),a.toTensor()).as2D(n,r)},e.prototype.fftImpl=function(e,t){var n=e.as1D(),r=n.size;if(this.isExponentOf2(r)){var o=this.fftRadix2(n,r,t).as2D(e.shape[0],e.shape[1]);return t&&(o=complex(real(o).div(scalar(r)),imag(o).div(scalar(r)))),o}var a=e.dataSync(),i=splitRealAndImagArrays(this.fourierTransformByMatmul(a,r,t));return complex(i.real,i.imag).as2D(e.shape[0],e.shape[1])},e.prototype.isExponentOf2=function(e){return 0==(e&e-1)},e.prototype.fftRadix2=function(e,t,n){if(1===t)return e;var r=e.dataSync(),o=t/2,a=complexWithEvenIndex(r),i=complex(a.real,a.imag).as1D(),s=complexWithOddIndex(r),u=complex(s.real,s.imag).as1D();i=this.fftRadix2(i,o,n),u=this.fftRadix2(u,o,n);var c=exponents(t,n),l=complex(c.real,c.imag).mul(u),p=i.add(l),d=i.sub(l),h=real(p).concat(real(d)),f=imag(p).concat(imag(d));return complex(h,f).as1D()},e.prototype.fourierTransformByMatmul=function(e,t,n){for(var r=new Float32Array(2*t),o=0;o<t;o++){for(var a=0,i=0,s=0;s<t;s++){var u=exponent(o*s,t,n),c=getComplexWithIndex(e,s);a+=c.real*u.real-c.imag*u.imag,i+=c.real*u.imag+c.imag*u.real;}n&&(a/=t,i/=t),assignToTypedArray(r,a,i,o);}return r},e.prototype.depthToSpace=function(e,t,n){assert("NHWC"===n,function(){return "Only NHWC dataFormat supported on CPU for depthToSpace. Got "+n}),assert(t>1,function(){return "blockSize should be > 1 for depthToSpace, but was: "+t});for(var r=e.shape[0],o=e.shape[1],a=e.shape[2],i=e.shape[3],s=o*t,u=a*t,c=i/(t*t),l=e.dataSync(),p=new Float32Array(r*s*u*c),d=0,h=0;h<r;++h)for(var f=0;f<s;++f)for(var m=Math.floor(f/t),g=f%t,v=0;v<u;++v)for(var y=Math.floor(v/t),x=(g*t+v%t)*c,E=0;E<c;++E){var T=E+x+i*(y+a*(m+o*h));p[d++]=l[T];}return tensor4d(p,[r,s,u,c])},e.prototype.broadcastedBinaryOp=function(e,t,n,r){var o=assertAndGetBroadcastShape(e.shape,t.shape),a=buffer(o,n),i=e.dataSync(),s=t.dataSync(),u=getBroadcastDims(e.shape,o),c=getBroadcastDims(t.shape,o),l=a.values;if(u.length+c.length===0)for(var p=0;p<l.length;++p)l[p]=r(i[p%i.length],s[p%s.length]);else{var d=e.bufferSync(),h=t.bufferSync(),f=function(n){var o=a.indexToLoc(n),p=o.slice(-e.rank);u.forEach(function(e){return p[e]=0});var f=d.locToIndex(p),m=o.slice(-t.rank);c.forEach(function(e){return m[e]=0});var g=h.locToIndex(m);l[n]=r(i[f],s[g]);};for(p=0;p<l.length;++p)f(p);}return a.toTensor()},e.prototype.broadcastedBinaryComplexOp=function(e,t,n){var r=assertAndGetBroadcastShape(e.shape,t.shape),o=buffer(r,"float32"),a=buffer(r,"float32"),i=e.dataSync(),s=t.dataSync(),u=getBroadcastDims(e.shape,r),c=getBroadcastDims(t.shape,r),l=o.values,p=a.values;if(u.length+c.length===0)for(var d=0;d<l.length;d++){var h=d%i.length,f=d%s.length,m=n(i[2*h],i[2*h+1],s[2*f],s[2*f+1]);l[d]=m.real,p[d]=m.imag;}else{var g=this.data.get(e.dataId).complexTensors.real.bufferSync(),v=this.data.get(t.dataId).complexTensors.real.bufferSync(),y=function(r){var a=o.indexToLoc(r),d=a.slice(-e.rank);u.forEach(function(e){return d[e]=0});var h=g.locToIndex(d),f=a.slice(-t.rank);c.forEach(function(e){return f[e]=0});var m=v.locToIndex(f),y=n(i[2*h],i[2*h+1],s[2*m],s[2*m+1]);l[r]=y.real,p[r]=y.imag;};for(d=0;d<l.length;d++)y(d);}return this.complex(o.toTensor(),a.toTensor())},e.prototype.split=function(e,t,n){return split(e,t,n)},e.prototype.dispose=function(){},e.prototype.floatPrecision=function(){return 32},e.prototype.cropAndResize=function(e,t,n,r,o,a){for(var i=e.shape,s=i[0],u=i[1],c=i[2],l=i[3],p=t.shape[0],d=r[0],h=r[1],f=buffer([p,d,h,l],e.dtype),m=t.dataSync(),g=n.dataSync(),v=e.dataSync(),y=e.strides,x=f.strides,E=0;E<p;E++){var T=4*E,b=m[T],w=m[T+1],S=m[T+2],C=m[T+3],A=g[E];if(!(A>=s))for(var N=d>1?(S-b)*(u-1)/(d-1):0,_=h>1?(C-w)*(c-1)/(h-1):0,R=0;R<d;R++){var I=d>1?b*(u-1)+R*N:.5*(b+S)*(u-1);if(I<0||I>u-1)for(var k=0;k<h;k++)for(var D=0;D<l;D++){var O=D+k*x[2]+R*x[1]+E*x[0];f.values[O]=a;}else if("bilinear"===o){var P=Math.floor(I),M=Math.ceil(I),F=I-P;for(k=0;k<h;k++){if((q=h>1?w*(c-1)+k*_:.5*(w+C)*(c-1))<0||q>c-1)for(D=0;D<l;D++){O=D+k*x[2]+R*x[1]+E*x[0];f.values[O]=a;}else{var L=Math.floor(q),B=Math.ceil(q),V=q-L;for(D=0;D<l;D++){var U=v[O=D+L*y[2]+P*y[1]+A*y[0]],z=v[O=D+B*y[2]+P*y[1]+A*y[0]],W=v[O=D+L*y[2]+M*y[1]+A*y[0]],G=U+(z-U)*V,H=W+(v[O=D+B*y[2]+M*y[1]+A*y[0]]-W)*V;O=D+k*x[2]+R*x[1]+E*x[0],f.values[O]=G+(H-G)*F;}}}}else for(k=0;k<h;++k){var q;if((q=h>1?w*(c-1)+k*_:.5*(w+C)*(c-1))<0||q>c-1)for(D=0;D<l;D++){O=D+k*x[2]+R*x[1]+E*x[0];f.values[O]=a;}else{var $=Math.round(q),K=Math.round(I);for(D=0;D<l;D++){var X=D+$*y[2]+K*y[1]+A*y[0],j=D+k*x[2]+R*x[1]+E*x[0];f.values[j]=v[X];}}}}}return f.toTensor()},e.prototype.sparseToDense=function(e,t,n,r){var o=calculateShapes(t,e,n),a=o.sliceRank,i=o.numUpdates,s=o.sliceSize,u=o.strides,c=o.outputSize;return this.scatter(e,t,n,c,s,i,a,u,r,!1)},e.prototype.gatherND=function(e,t){var n=t.shape,r=n[n.length-1],o=prepareAndValidate(e,t),a=o[0],i=o[1],s=o[2],u=o[3];if(0===i)return tensor([],a,e.dtype);for(var c=new TensorBuffer([i,s],e.dtype),l=t.dataSync(),p=e.dataSync(),d=0;d<i;d++){for(var h=[],f=0,m=0;m<r;m++){var g=l[d*r+m];f+=g*u[m],h.push(g);}if(f<0||f>=e.size/s)throw new Error("Invalid indices: "+h+" does not index into "+e.shape);for(var v=0;v<s;v++)c.values[d*s+v]=p[f*s+v];}return c.toTensor().reshape(a)},e.prototype.scatterND=function(e,t,n){var r=calculateShapes(t,e,n),o=r.sliceRank,a=r.numUpdates,i=r.sliceSize,s=r.strides,u=r.outputSize,c=scalar(0);return this.scatter(e,t,n,u,i,a,o,s,c,!0)},e.prototype.fill=function(e,t,n){var r=getArrayFromDType(n=n||inferDtype(t),sizeFromShape(e));return r.fill(t),Tensor.make(e,{values:r},n)},e.prototype.onesLike=function(e){if("string"===e.dtype)throw new Error("onesLike is not supported for string tensors");return this.fill(e.shape,1,e.dtype)},e.prototype.zerosLike=function(e){var t=getArrayFromDType(e.dtype,sizeFromShape(e.shape));return Tensor.make(e.shape,{values:t},e.dtype)},e.prototype.scatter=function(e,t,n,r,o,a,i,s,u,c){var l=[r/o,o],p=e.dataSync(),d=t.dataSync();if(0===r)return tensor([],n,t.dtype);var h=new TensorBuffer(l,t.dtype);h.values.fill(u.dataSync()[0]);for(var f=0;f<a;f++){for(var m=[],g=0,v=0;v<i;v++){var y=p[f*i+v];m.push(y),g+=y*s[v];}if(g<0||g>=r/o)throw new Error("Invalid indices: "+m+" does not index into "+n);for(var x=0;x<o;x++)c?h.values[g*o+x]+=d[f*o+x]:h.values[g*o+x]=0===t.rank?d[0]:d[f*o+x];}return h.toTensor().reshape(n)},e}();ENV.registerBackend("cpu",function(){return new MathBackendCPU},1);var delayCallback="undefined"!=typeof requestAnimationFrame?requestAnimationFrame:"undefined"!=typeof setImmediate?setImmediate:function(e){return e()};function nextFrame(){return new Promise(function(e){return delayCallback(function(){return e()})})}var DTYPE_VALUE_SIZE_MAP={float32:4,int32:4,uint16:2,uint8:1,bool:1};function encodeWeights(e){return __awaiter$1(this,void 0,void 0,function(){var t,n,r,o;return __generator$1(this,function(a){switch(a.label){case 0:for(r in t=[],n=[],e){if("float32"!==(o=e[r]).dtype&&"int32"!==o.dtype&&"bool"!==o.dtype)throw new Error("Unsupported dtype in weight '"+r+"': "+o.dtype);t.push({name:r,shape:o.shape,dtype:o.dtype}),n.push(o.data());}return [4,Promise.all(n)];case 1:return [2,{data:concatenateTypedArrays(a.sent()),specs:t}]}})})}function decodeWeights(e,t){for(var n={},r=0,o=function(t){var o=t.name,a=t.dtype,i=t.shape,s=sizeFromShape(i),u=void 0;if("quantization"in t){var c=t.quantization;if("uint8"!==c.dtype&&"uint16"!==c.dtype)throw new Error("Weight "+t.name+" has unknown quantization dtype "+c.dtype+". Supported quantization dtypes are: 'uint8' and 'uint16'.");var l=DTYPE_VALUE_SIZE_MAP[c.dtype],p=e.slice(r,r+s*l),d="uint8"===c.dtype?new Uint8Array(p):new Uint16Array(p);if("float32"===a)u=Float32Array.from(d,function(e){return e*c.scale+c.min});else{if("int32"!==a)throw new Error("Unsupported dtype in weight '"+o+"': "+a);u=Int32Array.from(d,function(e){return Math.round(e*c.scale+c.min)});}r+=s*l;}else{var h=DTYPE_VALUE_SIZE_MAP[a];p=e.slice(r,r+s*h);if("float32"===a)u=new Float32Array(p);else if("int32"===a)u=new Int32Array(p);else{if("bool"!==a)throw new Error("Unsupported dtype in weight '"+o+"': "+a);u=new Uint8Array(p);}r+=s*h;}var f=void 0;if("float32"===a)f=tensor(u,i,"float32");else if("int32"===a)f=tensor(u,i,"int32");else{if("bool"!==a)throw new Error("Unsupported dtype in weight '"+o+"': "+a);f=tensor(u,i,"bool");}n[o]=f;},a=0,i=t;a<i.length;a++){o(i[a]);}return n}function concatenateTypedArrays(e){if(null===e)throw new Error("Invalid input value: "+JSON.stringify(e));var t=0,n=[];e.forEach(function(e){if(t+=e.byteLength,n.push(e.byteLength===e.buffer.byteLength?e:new e.constructor(e)),!(e instanceof Float32Array||e instanceof Int32Array||e instanceof Uint8Array))throw new Error("Unsupported TypedArray subtype: "+e.constructor.name)});var r=new Uint8Array(t),o=0;return n.forEach(function(e){r.set(new Uint8Array(e.buffer),o),o+=e.byteLength;}),r.buffer}var useNodeBuffer="undefined"!=typeof Buffer&&("undefined"==typeof Blob||"undefined"==typeof atob||"undefined"==typeof btoa);function stringByteLength(e){return useNodeBuffer?Buffer.byteLength(e):new Blob([e]).size}function arrayBufferToBase64String(e){return useNodeBuffer?Buffer.from(e).toString("base64"):btoa(String.fromCharCode.apply(null,new Uint8Array(e)))}function base64StringToArrayBuffer(e){if(useNodeBuffer){var t=Buffer.from(e,"base64");return t.buffer.slice(t.byteOffset,t.byteOffset+t.byteLength)}for(var n=atob(e),r=new Uint8Array(n.length),o=0;o<n.length;++o)r.set([n.charCodeAt(o)],o);return r.buffer}function concatenateArrayBuffers(e){var t=0;e.forEach(function(e){t+=e.byteLength;});var n=new Uint8Array(t),r=0;return e.forEach(function(e){n.set(new Uint8Array(e),r),r+=e.byteLength;}),n.buffer}function basename(e){for(e=e.trim();e.endsWith("/");)e=e.slice(0,e.length-1);var t=e.split("/");return t[t.length-1]}function getModelArtifactsInfoForJSON(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("Expected JSON model topology, received ArrayBuffer.");return {dateSaved:new Date,modelTopologyType:"JSON",modelTopologyBytes:null==e.modelTopology?0:stringByteLength(JSON.stringify(e.modelTopology)),weightSpecsBytes:null==e.weightSpecs?0:stringByteLength(JSON.stringify(e.weightSpecs)),weightDataBytes:null==e.weightData?0:e.weightData.byteLength}}var IORouterRegistry=function(){function e(){this.saveRouters=[],this.loadRouters=[];}return e.getInstance=function(){return null==e.instance&&(e.instance=new e),e.instance},e.registerSaveRouter=function(t){e.getInstance().saveRouters.push(t);},e.registerLoadRouter=function(t){e.getInstance().loadRouters.push(t);},e.getSaveHandlers=function(t){return e.getHandlers(t,"save")},e.getLoadHandlers=function(t,n){return e.getHandlers(t,"load",n)},e.getHandlers=function(t,n,r){var o=[];return ("load"===n?e.getInstance().loadRouters:e.getInstance().saveRouters).forEach(function(e){var n=e(t,r);null!==n&&o.push(n);}),o},e}(),registerSaveRouter=function(e){return IORouterRegistry.registerSaveRouter(e)},registerLoadRouter=function(e){return IORouterRegistry.registerLoadRouter(e)},getSaveHandlers=function(e){return IORouterRegistry.getSaveHandlers(e)},getLoadHandlers=function(e,t){return IORouterRegistry.getLoadHandlers(e)},URL_SCHEME_SUFFIX="://",ModelStoreManagerRegistry=function(){function e(){this.managers={};}return e.getInstance=function(){return null==e.instance&&(e.instance=new e),e.instance},e.registerManager=function(t,n){assert(null!=t,function(){return "scheme must not be undefined or null."}),t.endsWith(URL_SCHEME_SUFFIX)&&(t=t.slice(0,t.indexOf(URL_SCHEME_SUFFIX))),assert(t.length>0,function(){return "scheme must not be an empty string."});var r=e.getInstance();assert(null==r.managers[t],function(){return "A model store manager is already registered for scheme '"+t+"'."}),r.managers[t]=n;},e.getManager=function(e){var t=this.getInstance().managers[e];if(null==t)throw new Error("Cannot find model manager for scheme '"+e+"'");return t},e.getSchemes=function(){return Object.keys(this.getInstance().managers)},e}();function parseURL(e){if(-1===e.indexOf(URL_SCHEME_SUFFIX))throw new Error("The url string provided does not contain a scheme. Supported schemes are: "+ModelStoreManagerRegistry.getSchemes().join(","));return {scheme:e.split(URL_SCHEME_SUFFIX)[0],path:e.split(URL_SCHEME_SUFFIX)[1]}}function cloneModelInternal(e,t,n){return void 0===n&&(n=!1),__awaiter$1(this,void 0,void 0,function(){var r,o,a,i,s,u,c,l,p;return __generator$1(this,function(d){switch(d.label){case 0:return assert(e!==t,function(){return "Old path and new path are the same: '"+e+"'"}),assert((r=IORouterRegistry.getLoadHandlers(e)).length>0,function(){return "Copying failed because no load handler is found for source URL "+e+"."}),assert(r.length<2,function(){return "Copying failed because more than one ("+r.length+") load handlers for source URL "+e+"."}),o=r[0],assert((a=IORouterRegistry.getSaveHandlers(t)).length>0,function(){return "Copying failed because no save handler is found for destination URL "+t+"."}),assert(a.length<2,function(){return "Copying failed because more than one ("+r.length+") save handlers for destination URL "+t+"."}),i=a[0],s=parseURL(e).scheme,u=parseURL(e).path,c=s===parseURL(e).scheme,[4,o.load()];case 1:return l=d.sent(),n&&c?[4,ModelStoreManagerRegistry.getManager(s).removeModel(u)]:[3,3];case 2:d.sent(),d.label=3;case 3:return [4,i.save(l)];case 4:return p=d.sent(),!n||c?[3,6]:[4,ModelStoreManagerRegistry.getManager(s).removeModel(u)];case 5:d.sent(),d.label=6;case 6:return [2,p.modelArtifactsInfo]}})})}function listModels(){return __awaiter$1(this,void 0,void 0,function(){var e,t,n,r,o,a,i;return __generator$1(this,function(s){switch(s.label){case 0:e=ModelStoreManagerRegistry.getSchemes(),t={},n=0,r=e,s.label=1;case 1:return n<r.length?(o=r[n],[4,ModelStoreManagerRegistry.getManager(o).listModels()]):[3,4];case 2:for(i in a=s.sent())t[o+URL_SCHEME_SUFFIX+i]=a[i];s.label=3;case 3:return n++,[3,1];case 4:return [2,t]}})})}function removeModel(e){return __awaiter$1(this,void 0,void 0,function(){var t;return __generator$1(this,function(n){switch(n.label){case 0:return t=parseURL(e),[4,ModelStoreManagerRegistry.getManager(t.scheme).removeModel(t.path)];case 1:return [2,n.sent()]}})})}function copyModel(e,t){return __awaiter$1(this,void 0,void 0,function(){return __generator$1(this,function(n){switch(n.label){case 0:return [4,cloneModelInternal(e,t,!1)];case 1:return [2,n.sent()]}})})}function moveModel(e,t){return __awaiter$1(this,void 0,void 0,function(){return __generator$1(this,function(n){switch(n.label){case 0:return [4,cloneModelInternal(e,t,!0)];case 1:return [2,n.sent()]}})})}var DATABASE_NAME="tensorflowjs",DATABASE_VERSION=1,MODEL_STORE_NAME="models_store",INFO_STORE_NAME="model_info_store";function getIndexedDBFactory(){if(!ENV.get("IS_BROWSER"))throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");var e=window,t=e.indexedDB||e.mozIndexedDB||e.webkitIndexedDB||e.msIndexedDB||e.shimIndexedDB;if(null==t)throw new Error("The current browser does not appear to support IndexedDB.");return t}function setUpDatabase(e){var t=e.result;t.createObjectStore(MODEL_STORE_NAME,{keyPath:"modelPath"}),t.createObjectStore(INFO_STORE_NAME,{keyPath:"modelPath"});}var BrowserIndexedDB=function(){function e(e){if(this.indexedDB=getIndexedDBFactory(),null==e||!e)throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");this.modelPath=e;}return e.prototype.save=function(e){return __awaiter$1(this,void 0,void 0,function(){return __generator$1(this,function(t){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");return [2,this.databaseAction(this.modelPath,e)]})})},e.prototype.load=function(){return __awaiter$1(this,void 0,void 0,function(){return __generator$1(this,function(e){return [2,this.databaseAction(this.modelPath)]})})},e.prototype.databaseAction=function(e,t){var n=this;return new Promise(function(e,r){var o=n.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);o.onupgradeneeded=function(){return setUpDatabase(o)},o.onsuccess=function(){var a=o.result;if(null==t){var i=a.transaction(MODEL_STORE_NAME,"readonly"),s=i.objectStore(MODEL_STORE_NAME).get(n.modelPath);s.onsuccess=function(){if(null==s.result)return a.close(),r(new Error("Cannot find model with path '"+n.modelPath+"' in IndexedDB."));e(s.result.modelArtifacts);},s.onerror=function(e){return a.close(),r(s.error)},i.oncomplete=function(){return a.close()};}else{var u,c=getModelArtifactsInfoForJSON(t),l=a.transaction(INFO_STORE_NAME,"readwrite"),p=l.objectStore(INFO_STORE_NAME),d=p.put({modelPath:n.modelPath,modelArtifactsInfo:c});d.onsuccess=function(){var o=(u=a.transaction(MODEL_STORE_NAME,"readwrite")).objectStore(MODEL_STORE_NAME).put({modelPath:n.modelPath,modelArtifacts:t,modelArtifactsInfo:c});o.onsuccess=function(){return e({modelArtifactsInfo:c})},o.onerror=function(e){var t=(p=l.objectStore(INFO_STORE_NAME)).delete(n.modelPath);t.onsuccess=function(){return a.close(),r(o.error)},t.onerror=function(e){return a.close(),r(o.error)};};},d.onerror=function(e){return a.close(),r(d.error)},l.oncomplete=function(){null==u?a.close():u.oncomplete=function(){return a.close()};};}},o.onerror=function(e){return r(o.error)};})},e.URL_SCHEME="indexeddb://",e}(),indexedDBRouter=function(e){return ENV.get("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(BrowserIndexedDB.URL_SCHEME)?browserIndexedDB(e.slice(BrowserIndexedDB.URL_SCHEME.length)):null};function browserIndexedDB(e){return new BrowserIndexedDB(e)}function maybeStripScheme(e){return e.startsWith(BrowserIndexedDB.URL_SCHEME)?e.slice(BrowserIndexedDB.URL_SCHEME.length):e}IORouterRegistry.registerSaveRouter(indexedDBRouter),IORouterRegistry.registerLoadRouter(indexedDBRouter);var BrowserIndexedDBManager=function(){function e(){this.indexedDB=getIndexedDBFactory();}return e.prototype.listModels=function(){return __awaiter$1(this,void 0,void 0,function(){var e=this;return __generator$1(this,function(t){return [2,new Promise(function(t,n){var r=e.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);r.onupgradeneeded=function(){return setUpDatabase(r)},r.onsuccess=function(){var e=r.result,o=e.transaction(INFO_STORE_NAME,"readonly"),a=o.objectStore(INFO_STORE_NAME).getAll();a.onsuccess=function(){for(var e={},n=0,r=a.result;n<r.length;n++){var o=r[n];e[o.modelPath]=o.modelArtifactsInfo;}t(e);},a.onerror=function(t){return e.close(),n(a.error)},o.oncomplete=function(){return e.close()};},r.onerror=function(e){return n(r.error)};})]})})},e.prototype.removeModel=function(e){return __awaiter$1(this,void 0,void 0,function(){var t=this;return __generator$1(this,function(n){return e=maybeStripScheme(e),[2,new Promise(function(n,r){var o=t.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);o.onupgradeneeded=function(){return setUpDatabase(o)},o.onsuccess=function(){var t,a=o.result,i=a.transaction(INFO_STORE_NAME,"readwrite"),s=i.objectStore(INFO_STORE_NAME),u=s.get(e);u.onsuccess=function(){if(null==u.result)return a.close(),r(new Error("Cannot find model with path '"+e+"' in IndexedDB."));var o=s.delete(e),i=function(){var o=(t=a.transaction(MODEL_STORE_NAME,"readwrite")).objectStore(MODEL_STORE_NAME).delete(e);o.onsuccess=function(){return n(u.result.modelArtifactsInfo)},o.onerror=function(e){return r(u.error)};};o.onsuccess=i,o.onerror=function(e){return i(),a.close(),r(u.error)};},u.onerror=function(e){return a.close(),r(u.error)},i.oncomplete=function(){null==t?a.close():t.oncomplete=function(){return a.close()};};},o.onerror=function(e){return r(o.error)};})]})})},e}();if(ENV.get("IS_BROWSER"))try{ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME,new BrowserIndexedDBManager);}catch(e){}var PATH_SEPARATOR="/",PATH_PREFIX="tensorflowjs_models",INFO_SUFFIX="info",MODEL_TOPOLOGY_SUFFIX="model_topology",WEIGHT_SPECS_SUFFIX="weight_specs",WEIGHT_DATA_SUFFIX="weight_data",MODEL_METADATA_SUFFIX="model_metadata";function getModelKeys(e){return {info:[PATH_PREFIX,e,INFO_SUFFIX].join(PATH_SEPARATOR),topology:[PATH_PREFIX,e,MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),weightSpecs:[PATH_PREFIX,e,WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),weightData:[PATH_PREFIX,e,WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR),modelMetadata:[PATH_PREFIX,e,MODEL_METADATA_SUFFIX].join(PATH_SEPARATOR)}}function getModelPathFromKey(e){var t=e.split(PATH_SEPARATOR);if(t.length<3)throw new Error("Invalid key format: "+e);return t.slice(1,t.length-1).join(PATH_SEPARATOR)}function maybeStripScheme$1(e){return e.startsWith(BrowserLocalStorage.URL_SCHEME)?e.slice(BrowserLocalStorage.URL_SCHEME.length):e}var BrowserLocalStorage=function(){function e(e){if(!ENV.get("IS_BROWSER")||void 0===window.localStorage)throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,null==e||!e)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=e,this.keys=getModelKeys(this.modelPath);}return e.prototype.save=function(e){return __awaiter$1(this,void 0,void 0,function(){var t,n,r;return __generator$1(this,function(o){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");t=JSON.stringify(e.modelTopology),n=JSON.stringify(e.weightSpecs),r=getModelArtifactsInfoForJSON(e);try{return this.LS.setItem(this.keys.info,JSON.stringify(r)),this.LS.setItem(this.keys.topology,t),this.LS.setItem(this.keys.weightSpecs,n),this.LS.setItem(this.keys.weightData,arrayBufferToBase64String(e.weightData)),this.LS.setItem(this.keys.modelMetadata,JSON.stringify({format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy})),[2,{modelArtifactsInfo:r}]}catch(e){throw this.LS.removeItem(this.keys.info),this.LS.removeItem(this.keys.topology),this.LS.removeItem(this.keys.weightSpecs),this.LS.removeItem(this.keys.weightData),this.LS.removeItem(this.keys.modelMetadata),new Error("Failed to save model '"+this.modelPath+"' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes="+r.modelTopologyBytes+", weightSpecsBytes="+r.weightSpecsBytes+", weightDataBytes="+r.weightDataBytes+".")}return [2]})})},e.prototype.load=function(){return __awaiter$1(this,void 0,void 0,function(){var e,t,n,r,o,a,i;return __generator$1(this,function(s){if(null==(e=JSON.parse(this.LS.getItem(this.keys.info))))throw new Error("In local storage, there is no model with name '"+this.modelPath+"'");if("JSON"!==e.modelTopologyType)throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");if(t={},null==(n=JSON.parse(this.LS.getItem(this.keys.topology))))throw new Error("In local storage, the topology of model '"+this.modelPath+"' is missing.");if(t.modelTopology=n,null==(r=JSON.parse(this.LS.getItem(this.keys.weightSpecs))))throw new Error("In local storage, the weight specs of model '"+this.modelPath+"' are missing.");if(t.weightSpecs=r,null!=(o=this.LS.getItem(this.keys.modelMetadata))&&(a=JSON.parse(o),t.format=a.format,t.generatedBy=a.generatedBy,t.convertedBy=a.convertedBy),null==(i=this.LS.getItem(this.keys.weightData)))throw new Error("In local storage, the binary weight values of model '"+this.modelPath+"' are missing.");return t.weightData=base64StringToArrayBuffer(i),[2,t]})})},e.URL_SCHEME="localstorage://",e}(),localStorageRouter=function(e){return ENV.get("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(BrowserLocalStorage.URL_SCHEME)?browserLocalStorage(e.slice(BrowserLocalStorage.URL_SCHEME.length)):null};function browserLocalStorage(e){return new BrowserLocalStorage(e)}IORouterRegistry.registerSaveRouter(localStorageRouter),IORouterRegistry.registerLoadRouter(localStorageRouter);var BrowserLocalStorageManager=function(){function e(){assert(ENV.get("IS_BROWSER"),function(){return "Current environment is not a web browser"}),assert(void 0!==window.localStorage,function(){return "Current browser does not appear to support localStorage"}),this.LS=window.localStorage;}return e.prototype.listModels=function(){return __awaiter$1(this,void 0,void 0,function(){var e,t,n,r,o,a;return __generator$1(this,function(i){for(e={},t=PATH_PREFIX+PATH_SEPARATOR,n=PATH_SEPARATOR+INFO_SUFFIX,r=0;r<this.LS.length;++r)(o=this.LS.key(r)).startsWith(t)&&o.endsWith(n)&&(a=getModelPathFromKey(o),e[a]=JSON.parse(this.LS.getItem(o)));return [2,e]})})},e.prototype.removeModel=function(e){return __awaiter$1(this,void 0,void 0,function(){var t,n;return __generator$1(this,function(r){if(e=maybeStripScheme$1(e),t=getModelKeys(e),null==this.LS.getItem(t.info))throw new Error("Cannot find model at path '"+e+"'");return n=JSON.parse(this.LS.getItem(t.info)),this.LS.removeItem(t.info),this.LS.removeItem(t.topology),this.LS.removeItem(t.weightSpecs),this.LS.removeItem(t.weightData),[2,n]})})},e}();if(ENV.get("IS_BROWSER"))try{ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME,new BrowserLocalStorageManager);}catch(e){}var DEFAULT_FILE_NAME_PREFIX="model",DEFAULT_JSON_EXTENSION_NAME=".json",DEFAULT_WEIGHT_DATA_EXTENSION_NAME=".weights.bin",BrowserDownloads=function(){function e(t){if(!ENV.get("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");t.startsWith(e.URL_SCHEME)&&(t=t.slice(e.URL_SCHEME.length)),null!=t&&0!==t.length||(t=DEFAULT_FILE_NAME_PREFIX),this.modelTopologyFileName=t+DEFAULT_JSON_EXTENSION_NAME,this.weightDataFileName=t+DEFAULT_WEIGHT_DATA_EXTENSION_NAME;}return e.prototype.save=function(e){return __awaiter$1(this,void 0,void 0,function(){var t,n,r,o,a,i;return __generator$1(this,function(s){if(t=window.URL.createObjectURL(new Blob([e.weightData],{type:"application/octet-stream"})),e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");return n=[{paths:["./"+this.weightDataFileName],weights:e.weightSpecs}],r={modelTopology:e.modelTopology,format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,weightsManifest:n},o=window.URL.createObjectURL(new Blob([JSON.stringify(r)],{type:"application/json"})),(a=null==this.jsonAnchor?document.createElement("a"):this.jsonAnchor).download=this.modelTopologyFileName,a.href=o,a.click(),null!=e.weightData&&((i=null==this.weightDataAnchor?document.createElement("a"):this.weightDataAnchor).download=this.weightDataFileName,i.href=t,i.click()),[2,{modelArtifactsInfo:getModelArtifactsInfoForJSON(e)}]})})},e.URL_SCHEME="downloads://",e}(),BrowserFiles=function(){function e(e){if(null==e||e.length<1)throw new Error("When calling browserFiles, at least 1 file is required, but received "+e);this.files=e;}return e.prototype.load=function(){return __awaiter$1(this,void 0,void 0,function(){var e,t,n=this;return __generator$1(this,function(r){return e=this.files[0],t=this.files.slice(1),[2,new Promise(function(r,o){var a=new FileReader;a.onload=function(a){var i=JSON.parse(a.target.result),s=i.modelTopology;if(null!=s){0===t.length&&r({modelTopology:s});var u=i.weightsManifest;if(null!=u){var c;try{c=n.checkManifestAndWeightFiles(u,t);}catch(e){return void o(e)}var l=[],p=[],d=[];u.forEach(function(e){e.paths.forEach(function(e){p.push(e),d.push(null);}),l.push.apply(l,e.weights);}),u.forEach(function(e){e.paths.forEach(function(e){var t=new FileReader;t.onload=function(t){var n=t.target.result,o=p.indexOf(e);d[o]=n,-1===d.indexOf(null)&&r({modelTopology:s,weightSpecs:l,weightData:concatenateArrayBuffers(d)});},t.onerror=function(t){return o("Failed to weights data from file of path '"+e+"'.")},t.readAsArrayBuffer(c[e]);});});}else o(new Error("weightManifest field is missing from file "+e.name));}else o(new Error("modelTopology field is missing from file "+e.name));},a.onerror=function(t){return o("Failed to read model topology and weights manifest JSON from file '"+e.name+"'. BrowserFiles supports loading Keras-style tf.Model artifacts only.")},a.readAsText(e);})]})})},e.prototype.checkManifestAndWeightFiles=function(e,t){for(var n=[],r=t.map(function(e){return basename(e.name)}),o={},a=0,i=e;a<i.length;a++){i[a].paths.forEach(function(e){var a=basename(e);if(-1!==n.indexOf(a))throw new Error("Duplicate file basename found in weights manifest: '"+a+"'");if(n.push(a),-1===r.indexOf(a))throw new Error("Weight file with basename '"+a+"' is not provided.");o[e]=t[r.indexOf(a)];});}if(n.length!==t.length)throw new Error("Mismatch in the number of files in weights manifest ("+n.length+") and the number of weight files provided ("+t.length+").");return o},e}(),browserDownloadsRouter=function(e){return ENV.get("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(BrowserDownloads.URL_SCHEME)?browserDownloads(e.slice(BrowserDownloads.URL_SCHEME.length)):null};function browserDownloads(e){return void 0===e&&(e="model"),new BrowserDownloads(e)}function browserFiles(e){return new BrowserFiles(e)}function monitorPromisesProgress(e,t,n,r){!function(e){assert(null!=e&&Array.isArray(e)&&e.length>0,function(){return "promises must be a none empty array"});}(e),function(e,t){assert(e>=0&&e<=1,function(){return "Progress fraction must be in range [0, 1], but got startFraction "+e}),assert(t>=0&&t<=1,function(){return "Progress fraction must be in range [0, 1], but got endFraction "+t}),assert(t>=e,function(){return "startFraction must be no more than endFraction, but got startFraction "+e+" and endFraction "+t});}(n=null==n?0:n,r=null==r?1:r);var o=0;return Promise.all(e.map(function(a){return a.then(function(a){var i=n+ ++o/e.length*(r-n);return t(i),a}),a}))}function loadWeightsAsArrayBuffer(e,t){return __awaiter$1(this,void 0,void 0,function(){var n,r,o,a,i,s,u,c,l;return __generator$1(this,function(p){switch(p.label){case 0:return null==t&&(t={}),n=null==t.fetchFunc?fetch:t.fetchFunc,r=e.map(function(e){return n(e,t.requestInit)}),o=0,a=.5,null!=t.onProgress?[3,2]:[4,Promise.all(r)];case 1:return i=p.sent(),[3,4];case 2:return [4,monitorPromisesProgress(r,t.onProgress,o,a)];case 3:i=p.sent(),p.label=4;case 4:return s=i.map(function(e){return e.arrayBuffer()}),u=.5,c=1,null!=t.onProgress?[3,6]:[4,Promise.all(s)];case 5:return l=p.sent(),[3,8];case 6:return [4,monitorPromisesProgress(s,t.onProgress,u,c)];case 7:l=p.sent(),p.label=8;case 8:return [2,l]}})})}function loadWeights(e,t,n,r){return void 0===t&&(t=""),__awaiter$1(this,void 0,void 0,function(){return __generator$1(this,function(o){return [2,weightsLoaderFactory(function(e){return loadWeightsAsArrayBuffer(e,{requestInit:r})})(e,t,n)]})})}function weightsLoaderFactory(e){var t=this;return function(n,r,o){return void 0===r&&(r=""),__awaiter$1(t,void 0,void 0,function(){var t,a,i,s,u,c,l,p,d,h;return __generator$1(this,function(f){switch(f.label){case 0:if(t=n.map(function(){return !1}),a={},i=null!=o?o.map(function(){return !1}):[],s=[],n.forEach(function(e,n){var r=0;e.weights.forEach(function(e){var u="quantization"in e?e.quantization.dtype:e.dtype,c=DTYPE_VALUE_SIZE_MAP[u]*sizeFromShape(e.shape),l=function(){t[n]=!0,null==a[n]&&(a[n]=[]),a[n].push({manifestEntry:e,groupOffset:r,sizeBytes:c});};null!=o?o.forEach(function(t,n){t===e.name&&(l(),i[n]=!0);}):l(),s.push(e.name),r+=c;});}),!i.every(function(e){return e}))throw u=o.filter(function(e,t){return !i[t]}),new Error("Could not find weights in manifest with names: "+u.join(", ")+". \nManifest JSON has weights with names: "+s.join(", ")+".");return c=t.reduce(function(e,t,n){return t&&e.push(n),e},[]),l=[],c.forEach(function(e){n[e].paths.forEach(function(e){var t=r+(r.endsWith("/")?"":"/")+e;l.push(t);});}),[4,e(l)];case 1:return p=f.sent(),d={},h=0,c.forEach(function(e){for(var t=n[e].paths.length,r=0,o=0;o<t;o++)r+=p[h+o].byteLength;for(var i=new ArrayBuffer(r),s=new Uint8Array(i),u=0,c=0;c<t;c++){var l=new Uint8Array(p[h+c]);s.set(l,u),u+=l.byteLength;}a[e].forEach(function(e){var t=decodeWeights(i.slice(e.groupOffset,e.groupOffset+e.sizeBytes),[e.manifestEntry]);for(var n in t)d[n]=t[n];}),h+=t;}),[2,d]}})})}}IORouterRegistry.registerSaveRouter(browserDownloadsRouter);var OCTET_STREAM_MIME_TYPE="application/octet-stream",JSON_TYPE="application/json",BrowserHTTPRequest=function(){function e(e,t){if(this.DEFAULT_METHOD="POST",null==t&&(t={}),this.weightPathPrefix=t.weightPathPrefix,this.onProgress=t.onProgress,null==t.fetchFunc){var n=ENV.global.fetch;if(void 0===n)throw new Error("browserHTTPRequest is not supported outside the web browser without a fetch polyfill.");t.fetchFunc=n.bind(ENV.global);}else assert("function"==typeof t.fetchFunc,function(){return "Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"});if(this.fetchFunc=function(e,n){return t.fetchFunc(e,n).catch(function(t){throw new Error("Request for "+e+" failed due to error: "+t)})},assert(null!=e&&e.length>0,function(){return "URL path for browserHTTPRequest must not be null, undefined or empty."}),Array.isArray(e)&&assert(2===e.length,function(){return "URL paths for browserHTTPRequest must have a length of 2, (actual length is "+e.length+")."}),this.path=e,null!=t.requestInit&&null!=t.requestInit.body)throw new Error("requestInit is expected to have no pre-existing body, but has one.");this.requestInit=t.requestInit||{};}return e.prototype.save=function(e){return __awaiter$1(this,void 0,void 0,function(){var t,n,r,o;return __generator$1(this,function(a){switch(a.label){case 0:if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");return (t=Object.assign({method:this.DEFAULT_METHOD},this.requestInit)).body=new FormData,n=[{paths:["./model.weights.bin"],weights:e.weightSpecs}],r={modelTopology:e.modelTopology,format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,weightsManifest:n},t.body.append("model.json",new Blob([JSON.stringify(r)],{type:JSON_TYPE}),"model.json"),null!=e.weightData&&t.body.append("model.weights.bin",new Blob([e.weightData],{type:OCTET_STREAM_MIME_TYPE}),"model.weights.bin"),[4,this.getFetchFunc()(this.path,t)];case 1:if((o=a.sent()).ok)return [2,{modelArtifactsInfo:getModelArtifactsInfoForJSON(e),responses:[o]}];throw new Error("BrowserHTTPRequest.save() failed due to HTTP response status "+o.status+".")}})})},e.prototype.load=function(){return __awaiter$1(this,void 0,void 0,function(){var e,t,n,r,o,a,i,s;return __generator$1(this,function(u){switch(u.label){case 0:return [4,this.getFetchFunc()(this.path,this.requestInit)];case 1:if(!(e=u.sent()).ok)throw new Error("Request to "+this.path+" failed with status code "+e.status+". Please verify this URL points to the model JSON of the model to load.");u.label=2;case 2:return u.trys.push([2,4,,5]),[4,e.json()];case 3:return t=u.sent(),[3,5];case 4:throw u.sent(),n="Failed to parse model JSON of response from "+this.path+".",this.path.endsWith(".pb")?n+=" Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository.":n+=" Please make sure the server is serving valid JSON for this request.",new Error(n);case 5:if(r=t.modelTopology,o=t.weightsManifest,null==r&&null==o)throw new Error("The JSON from HTTP path "+this.path+" contains neither model topology or manifest for weights.");return null==o?[3,7]:[4,this.loadWeights(o)];case 6:s=u.sent(),a=s[0],i=s[1],u.label=7;case 7:return [2,{modelTopology:r,weightSpecs:a,weightData:i}]}})})},e.prototype.loadWeights=function(e){return __awaiter$1(this,void 0,void 0,function(){var t,n,r,o,a,i,s,u,c,l,p;return __generator$1(this,function(d){switch(d.label){case 0:for(t=Array.isArray(this.path)?this.path[1]:this.path,n=parseUrl(t),r=n[0],o=n[1],a=this.weightPathPrefix||r,i=[],s=0,u=e;s<u.length;s++)c=u[s],i.push.apply(i,c.weights);return l=[],e.forEach(function(e){e.paths.forEach(function(e){l.push(a+e+o);});}),[4,loadWeightsAsArrayBuffer(l,{requestInit:this.requestInit,fetchFunc:this.getFetchFunc(),onProgress:this.onProgress})];case 1:return p=d.sent(),[2,[i,concatenateArrayBuffers(p)]]}})})},e.prototype.getFetchFunc=function(){return this.fetchFunc},e.URL_SCHEME_REGEX=/^https?:\/\//,e}();function parseUrl(e){var t=e.lastIndexOf("/"),n=e.lastIndexOf("?");return [e.substring(0,t)+"/",n>t?e.substring(n):""]}function isHTTPScheme(e){return null!=e.match(BrowserHTTPRequest.URL_SCHEME_REGEX)}var httpRequestRouter=function(e,t){if("undefined"==typeof fetch)return null;return (Array.isArray(e)?e.every(function(e){return isHTTPScheme(e)}):isHTTPScheme(e))?browserHTTPRequest(e,{onProgress:t}):null};function browserHTTPRequest(e,t){return new BrowserHTTPRequest(e,t)}IORouterRegistry.registerSaveRouter(httpRequestRouter),IORouterRegistry.registerLoadRouter(httpRequestRouter);var PassthroughLoader=function(){function e(e,t,n){this.modelTopology=e,this.weightSpecs=t,this.weightData=n;}return e.prototype.load=function(){return __awaiter$1(this,void 0,void 0,function(){var e;return __generator$1(this,function(t){return e={},null!=this.modelTopology&&(e=__assign$1({modelTopology:this.modelTopology},e)),null!=this.weightSpecs&&this.weightSpecs.length>0&&(e=__assign$1({weightSpecs:this.weightSpecs},e)),null!=this.weightData&&this.weightData.byteLength>0&&(e=__assign$1({weightData:this.weightData},e)),[2,e]})})},e}(),PassthroughSaver=function(){function e(e){this.saveHandler=e;}return e.prototype.save=function(e){return __awaiter$1(this,void 0,void 0,function(){return __generator$1(this,function(t){return [2,this.saveHandler(e)]})})},e}();function fromMemory(e,t,n){return new PassthroughLoader(e,t,n)}function withSaveHandler(e){return new PassthroughSaver(e)}var io=Object.freeze({browserFiles:browserFiles,browserHTTPRequest:browserHTTPRequest,concatenateArrayBuffers:concatenateArrayBuffers,decodeWeights:decodeWeights,encodeWeights:encodeWeights,fromMemory:fromMemory,getLoadHandlers:getLoadHandlers,getModelArtifactsInfoForJSON:getModelArtifactsInfoForJSON,getSaveHandlers:getSaveHandlers,isHTTPScheme:isHTTPScheme,loadWeights:loadWeights,registerLoadRouter:registerLoadRouter,registerSaveRouter:registerSaveRouter,weightsLoaderFactory:weightsLoaderFactory,withSaveHandler:withSaveHandler,copyModel:copyModel,listModels:listModels,moveModel:moveModel,removeModel:removeModel});function confusionMatrix_(e,t,n){var r=convertToTensor(e,"labels","confusionMatrix"),o=convertToTensor(t,"predictions","confusionMatrix");assert(null==n||n>0&&Number.isInteger(n),function(){return "If provided, numClasses must be a positive integer, but got "+n}),assert(1===r.rank,function(){return "Expected the rank of labels to be 1, but got "+r.rank}),assert(1===o.rank,function(){return "Expected the rank of predictions to be 1, but got "+o.rank}),assert(r.shape[0]===o.shape[0],function(){return "Mismatch in the number of examples: "+r.shape[0]+" vs. "+o.shape[0]+". Labels and predictions should have the same number of elements."}),assert(n>0&&Number.isInteger(n),function(){return "numClasses is required to be a positive integer, but got "+n});var a=oneHot(r.asType("int32"),n),i=oneHot(o.asType("int32"),n);return a.transpose().matMul(i).asType("int32")}var confusionMatrix=op({confusionMatrix_:confusionMatrix_}),math=Object.freeze({confusionMatrix:confusionMatrix});function fromPixels_(e,t){if(void 0===t&&(t=3),t>4)throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");return ENV.engine.fromPixels(e,t)}function toPixels(e,t){return __awaiter$1(this,void 0,void 0,function(){var n,r,o,a,i,s,u,c,l,p,d,h,f,m,g,v,y,x,E,T;return __generator$1(this,function(b){switch(b.label){case 0:if(n=convertToTensor(e,"img","toPixels"),e instanceof Tensor||(n=n.toInt()),2!==n.rank&&3!==n.rank)throw new Error("toPixels only supports rank 2 or 3 tensors, got rank "+n.rank+".");if(r=n.shape.slice(0,2),o=r[0],a=r[1],(i=2===n.rank?1:n.shape[2])>4||2===i)throw new Error("toPixels only supports depth of size 1, 3 or 4 but got "+i);return s=n.min(),u=n.max(),[4,s.data()];case 1:return c=b.sent()[0],[4,u.data()];case 2:if(l=b.sent()[0],s.dispose(),u.dispose(),"float32"===n.dtype){if(c<0||l>1)throw new Error("Tensor values for a float32 Tensor must be in the range [0 - 1] but got range ["+c+" - "+l+"].")}else{if("int32"!==n.dtype)throw new Error("Unsupported type for toPixels: "+n.dtype+". Please use float32 or int32 tensors.");if(c<0||l>255)throw new Error("Tensor values for a int32 Tensor must be in the range [0 - 255] but got range ["+c+" - "+l+"].")}return [4,n.data()];case 3:for(p=b.sent(),d="float32"===n.dtype?255:1,h=new Uint8ClampedArray(a*o*4),f=0;f<o*a;++f)m=void 0,g=void 0,v=void 0,y=void 0,1===i?(m=p[f]*d,g=p[f]*d,v=p[f]*d,y=255):3===i?(m=p[3*f]*d,g=p[3*f+1]*d,v=p[3*f+2]*d,y=255):4===i&&(m=p[4*f]*d,g=p[4*f+1]*d,v=p[4*f+2]*d,y=p[4*f+3]*d),h[(x=4*f)+0]=Math.round(m),h[x+1]=Math.round(g),h[x+2]=Math.round(v),h[x+3]=Math.round(y);return null!=t&&(t.width=a,t.height=o,E=t.getContext("2d"),T=new ImageData(h,a,o),E.putImageData(T,0,0)),n!==e&&n.dispose(),[2,h]}})})}var fromPixels=op({fromPixels_:fromPixels_}),browser=Object.freeze({toPixels:toPixels,fromPixels:fromPixels}),Serializable=function(){function e(){}return e.prototype.getClassName=function(){return this.constructor.className},e.fromConfig=function(e,t){return new e(t)},e}(),SerializationMap=function(){function e(){this.classNameMap={};}return e.getMap=function(){return null==e.instance&&(e.instance=new e),e.instance},e.register=function(t){e.getMap().classNameMap[t.className]=[t,t.fromConfig];},e}();function registerClass(e){assert(null!=e.className,function(){return "Class being registered does not have the static className property defined."}),assert("string"==typeof e.className,function(){return "className is required to be a string, but got type "+typeof e.className}),assert(e.className.length>0,function(){return "Class being registered has an empty-string as its className, which is disallowed."}),SerializationMap.register(e);}var serialization=Object.freeze({Serializable:Serializable,SerializationMap:SerializationMap,registerClass:registerClass}),WEBGL_ENVS={HAS_WEBGL:!0},PACKED_ENVS={WEBGL_PACK:!0},NODE_ENVS={IS_NODE:!0},CHROME_ENVS={IS_CHROME:!0},BROWSER_ENVS={IS_BROWSER:!0},CPU_ENVS={HAS_WEBGL:!1},ALL_ENVS={};function expectArraysClose(e,t,n){return null==n&&(n=ENV.get("TEST_EPSILON")),expectArraysPredicate(e,"number"==typeof t||"boolean"==typeof t?[t]:t,function(e,t){return areClose(e,Number(t),n)})}function expectArraysPredicate(e,t,n){if(e instanceof Tensor||t instanceof Tensor){if(e instanceof Tensor&&t instanceof Tensor){if(e.dtype!==t.dtype)throw new Error("Arrays are of different type actual: "+e.dtype+" vs expected: "+t.dtype+".");if(!arraysEqual(e.shape,t.shape))throw new Error("Arrays are of different shape actual: "+e.shape+" vs expected: "+t.shape+".")}}else{var r=e.constructor.name,o=t.constructor.name;if(r!==o)throw new Error("Arrays are of different type actual: "+r+" vs expected: "+o)}var a,i;if(a=e instanceof Tensor?e.dataSync():e,i=t instanceof Tensor?t.dataSync():t,a.length!==i.length)throw new Error("Arrays have different lengths actual: "+a.length+" vs expected: "+i.length+".\nActual: "+a+".\nExpected: "+i+".");for(var s=0;s<i.length;++s){var u=a[s],c=i[s];if(!n(u,c))throw new Error("Arrays differ: actual["+s+"] = "+u+", expected["+s+"] = "+c+".\nActual: "+a+".\nExpected: "+i+".")}}function expectPromiseToFail(e,t){e().then(function(){return t.fail()},function(){return t()});}function expectArraysEqual(e,t){var n="string"==typeof t||"number"==typeof t||"boolean"==typeof t?[t]:t;return e instanceof Tensor&&"string"===e.dtype||t instanceof Tensor&&"string"===t.dtype||Array.isArray(e)&&isString(e[0])||Array.isArray(t)&&isString(t[0])?expectArraysPredicate(e,n,function(e,t){return e==t}):expectArraysClose(e,t,0)}function expectNumbersClose(e,t,n){if(null==n&&(n=ENV.get("TEST_EPSILON")),!areClose(e,t,n))throw new Error("Numbers differ: actual === "+e+", expected === "+t)}function areClose(e,t,n){return !(!isNaN(e)||!isNaN(t))||!(isNaN(e)||isNaN(t)||Math.abs(e-t)>n)}function expectValuesInRange(e,t,n){var r;r=e instanceof Tensor?e.dataSync():e;for(var o=0;o<r.length;o++)if(r[o]<t||r[o]>n)throw new Error("Value out of range:"+r[o]+" low: "+t+", high: "+n)}function expectArrayBuffersEqual(e,t){expect(new Float32Array(e)).toEqual(new Float32Array(t));}var test_util=Object.freeze({WEBGL_ENVS:WEBGL_ENVS,PACKED_ENVS:PACKED_ENVS,NODE_ENVS:NODE_ENVS,CHROME_ENVS:CHROME_ENVS,BROWSER_ENVS:BROWSER_ENVS,CPU_ENVS:CPU_ENVS,ALL_ENVS:ALL_ENVS,expectArraysClose:expectArraysClose,expectPromiseToFail:expectPromiseToFail,expectArraysEqual:expectArraysEqual,expectNumbersClose:expectNumbersClose,expectValuesInRange:expectValuesInRange,expectArrayBuffersEqual:expectArrayBuffersEqual}),version="1.0.3",webgl=Object.freeze({gpgpu_util:gpgpu_util,webgl_util:webgl_util,MathBackendWebGL:MathBackendWebGL,GPGPUContext:GPGPUContext}),Optimizer=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return __extends$1(t,e),t.prototype.minimize=function(e,t,n){void 0===t&&(t=!1);var r=this.computeGradients(e,n),o=r.value,a=r.grads;return this.applyGradients(a),Object.keys(a).forEach(function(e){return a[e].dispose()}),t?o:(o.dispose(),null)},t.prototype.computeGradients=function(e,t){return variableGrads(e,t)},t.prototype.dispose=function(){},t}(Serializable);Object.defineProperty(Optimizer,Symbol.hasInstance,{value:function(e){return null!=e.minimize&&null!=e.computeGradients&&null!=e.applyGradients}});var AdadeltaOptimizer=function(e){function t(t,n,r){void 0===r&&(r=null);var o=e.call(this)||this;return o.learningRate=t,o.rho=n,o.epsilon=r,o.accumulatedGrads={},o.accumulatedUpdates={},o.c=keep(scalar(-t)),o.rhoScalar=keep(scalar(n)),o.oneMinusRho=keep(scalar(1-n)),null===r&&(r=ENV.get("EPSILON")),o.epsilonScalar=keep(scalar(r)),o}return __extends$1(t,e),t.prototype.applyGradients=function(e){var t=this,n=function(n){var o=ENV.engine.registeredVariables[n];if(null==r.accumulatedGrads[n]){tidy(function(){t.accumulatedGrads[n]=zerosLike(o).variable(!1);});}if(null==r.accumulatedUpdates[n]){tidy(function(){t.accumulatedUpdates[n]=zerosLike(o).variable(!1);});}var a=e[n],i=r.accumulatedGrads[n],s=r.accumulatedUpdates[n];tidy(function(){var e=t.rhoScalar.mul(i).add(t.oneMinusRho.mul(a.square())),r=s.add(t.epsilonScalar).sqrt().div(i.add(t.epsilonScalar).sqrt()).mul(a),u=t.rhoScalar.mul(s).add(t.oneMinusRho.mul(r.square()));t.accumulatedGrads[n].assign(e),t.accumulatedUpdates[n].assign(u);var c=t.c.mul(r).add(o);o.assign(c);});},r=this;for(var o in e)n(o);},t.prototype.dispose=function(){var e=this;this.c.dispose(),this.epsilonScalar.dispose(),this.rhoScalar.dispose(),this.oneMinusRho.dispose(),null!=this.accumulatedUpdates&&(Object.keys(this.accumulatedUpdates).forEach(function(t){return e.accumulatedUpdates[t].dispose()}),Object.keys(this.accumulatedGrads).forEach(function(t){return e.accumulatedGrads[t].dispose()}));},t.prototype.getConfig=function(){return {learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}},t.fromConfig=function(e,t){return new e(t.learningRate,t.rho,t.epsilon)},t.className="AdadeltaOptimizer",t}(Optimizer);registerClass(AdadeltaOptimizer);var AdagradOptimizer=function(e){function t(t,n){void 0===n&&(n=.1);var r=e.call(this)||this;return r.learningRate=t,r.initialAccumulatorValue=n,r.accumulatedGrads={},r.c=keep(scalar(-t)),r.epsilon=keep(scalar(ENV.get("EPSILON"))),r}return __extends$1(t,e),t.prototype.applyGradients=function(e){var t=this,n=function(n){var o=ENV.engine.registeredVariables[n];if(null==r.accumulatedGrads[n]){tidy(function(){t.accumulatedGrads[n]=fill(o.shape,t.initialAccumulatorValue).variable(!1);});}var a=e[n],i=r.accumulatedGrads[n];tidy(function(){var e=i.add(a.square());t.accumulatedGrads[n].assign(e);var r=t.c.mul(a.div(e.add(t.epsilon).sqrt())).add(o);o.assign(r);});},r=this;for(var o in e)n(o);},t.prototype.dispose=function(){var e=this;this.epsilon.dispose(),this.c.dispose(),null!=this.accumulatedGrads&&Object.keys(this.accumulatedGrads).forEach(function(t){return e.accumulatedGrads[t].dispose()});},t.prototype.getConfig=function(){return {learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}},t.fromConfig=function(e,t){return new e(t.learningRate,t.initialAccumulatorValue)},t.className="AdagradOptimizer",t}(Optimizer);registerClass(AdagradOptimizer);var AdamOptimizer=function(e){function t(t,n,r,o){void 0===o&&(o=null);var a=e.call(this)||this;return a.learningRate=t,a.beta1=n,a.beta2=r,a.epsilon=o,a.accumulatedFirstMoment={},a.accumulatedSecondMoment={},a.c=keep(scalar(-t)),a.beta1Scalar=keep(scalar(n)),a.beta2Scalar=keep(scalar(r)),tidy(function(){a.accBeta1=scalar(n).variable(),a.accBeta2=scalar(r).variable();}),a.oneMinusBeta1=keep(scalar(1-n)),a.oneMinusBeta2=keep(scalar(1-r)),a.one=keep(scalar(1)),null===o&&(o=ENV.get("EPSILON")),a.epsScalar=keep(scalar(o)),a}return __extends$1(t,e),t.prototype.applyGradients=function(e){var t=this;tidy(function(){var n=t.one.sub(t.accBeta1),r=t.one.sub(t.accBeta2);for(var o in e){var a=ENV.engine.registeredVariables[o];if(null==t.accumulatedFirstMoment[o]){var i=!1;t.accumulatedFirstMoment[o]=zerosLike(a).variable(i);}if(null==t.accumulatedSecondMoment[o]){i=!1;t.accumulatedSecondMoment[o]=zerosLike(a).variable(i);}var s=e[o],u=t.accumulatedFirstMoment[o],c=t.accumulatedSecondMoment[o],l=t.beta1Scalar.mul(u).add(t.oneMinusBeta1.mul(s)),p=t.beta2Scalar.mul(c).add(t.oneMinusBeta2.mul(s.square())),d=l.div(n),h=p.div(r);t.accumulatedFirstMoment[o].assign(l),t.accumulatedSecondMoment[o].assign(p);var f=t.c.mul(d.div(t.epsScalar.add(h.sqrt()))).add(a);a.assign(f);}t.accBeta1.assign(t.accBeta1.mul(t.beta1Scalar)),t.accBeta2.assign(t.accBeta2.mul(t.beta2Scalar));});},t.prototype.dispose=function(){var e=this;this.c.dispose(),this.epsScalar.dispose(),this.beta1Scalar.dispose(),this.beta2Scalar.dispose(),this.accBeta1.dispose(),this.accBeta2.dispose(),this.oneMinusBeta1.dispose(),this.oneMinusBeta2.dispose(),this.one.dispose(),null!=this.accumulatedFirstMoment&&Object.keys(this.accumulatedFirstMoment).forEach(function(t){return e.accumulatedFirstMoment[t].dispose()}),null!=this.accumulatedSecondMoment&&Object.keys(this.accumulatedSecondMoment).forEach(function(t){return e.accumulatedSecondMoment[t].dispose()});},t.prototype.getConfig=function(){return {learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}},t.fromConfig=function(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)},t.className="AdamOptimizer",t}(Optimizer);registerClass(AdamOptimizer);var AdamaxOptimizer=function(e){function t(t,n,r,o,a){void 0===o&&(o=null),void 0===a&&(a=0);var i=e.call(this)||this;return i.learningRate=t,i.beta1=n,i.beta2=r,i.epsilon=o,i.decay=a,i.accumulatedFirstMoment={},i.accumulatedWeightedInfNorm={},i.c=keep(scalar(-t)),i.beta1Scalar=keep(scalar(n)),i.beta2Scalar=keep(scalar(r)),i.decayScalar=keep(scalar(a)),tidy(function(){i.iteration=scalar(0).variable(),i.accBeta1=scalar(n).variable();}),i.oneMinusBeta1=keep(scalar(1-n)),i.one=keep(scalar(1)),null===o&&(o=ENV.get("EPSILON")),i.epsScalar=keep(scalar(o)),i}return __extends$1(t,e),t.prototype.applyGradients=function(e){var t=this;tidy(function(){var n=t.one.sub(t.accBeta1),r=t.c.div(t.one.add(t.decayScalar.mul(t.iteration)));for(var o in e){var a=ENV.engine.registeredVariables[o];if(null==t.accumulatedFirstMoment[o]){var i=!1;t.accumulatedFirstMoment[o]=zerosLike(a).variable(i);}if(null==t.accumulatedWeightedInfNorm[o]){i=!1;t.accumulatedWeightedInfNorm[o]=zerosLike(a).variable(i);}var s=e[o],u=t.accumulatedFirstMoment[o],c=t.accumulatedWeightedInfNorm[o],l=t.beta1Scalar.mul(u).add(t.oneMinusBeta1.mul(s)),p=t.beta2Scalar.mul(c),d=s.abs(),h=p.maximum(d);t.accumulatedFirstMoment[o].assign(l),t.accumulatedWeightedInfNorm[o].assign(h);var f=r.div(n).mul(l.div(t.epsScalar.add(h))).add(a);a.assign(f);}t.iteration.assign(t.iteration.add(t.one)),t.accBeta1.assign(t.accBeta1.mul(t.beta1Scalar));});},t.prototype.dispose=function(){var e=this;this.c.dispose(),this.epsScalar.dispose(),this.accBeta1.dispose(),this.beta1Scalar.dispose(),this.beta2Scalar.dispose(),this.oneMinusBeta1.dispose(),this.decayScalar.dispose(),this.iteration.dispose(),this.one.dispose(),null!=this.accumulatedFirstMoment&&Object.keys(this.accumulatedFirstMoment).forEach(function(t){return e.accumulatedFirstMoment[t].dispose()}),null!=this.accumulatedWeightedInfNorm&&Object.keys(this.accumulatedWeightedInfNorm).forEach(function(t){return e.accumulatedWeightedInfNorm[t].dispose()});},t.prototype.getConfig=function(){return {learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}},t.fromConfig=function(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)},t.className="AdamaxOptimizer",t}(Optimizer);registerClass(AdamaxOptimizer);var SGDOptimizer=function(e){function t(t){var n=e.call(this)||this;return n.learningRate=t,n.setLearningRate(t),n}return __extends$1(t,e),t.prototype.applyGradients=function(e){var t=this;Object.keys(e).forEach(function(n){var r=e[n],o=ENV.engine.registeredVariables[n];tidy(function(){var e=t.c.mul(r).add(o);o.assign(e);});});},t.prototype.setLearningRate=function(e){this.learningRate=e,null!=this.c&&this.c.dispose(),this.c=keep(scalar(-e));},t.prototype.dispose=function(){this.c.dispose();},t.prototype.getConfig=function(){return {learningRate:this.learningRate}},t.fromConfig=function(e,t){return new e(t.learningRate)},t.className="SGDOptimizer",t}(Optimizer);registerClass(SGDOptimizer);var MomentumOptimizer=function(e){function t(t,n,r){void 0===r&&(r=!1);var o=e.call(this,t)||this;return o.learningRate=t,o.momentum=n,o.useNesterov=r,o.m=scalar(o.momentum),o.accumulations={},o}return __extends$1(t,e),t.prototype.applyGradients=function(e){var t=this,n=function(n){var o=ENV.engine.registeredVariables[n];if(null==r.accumulations[n]){tidy(function(){t.accumulations[n]=zerosLike(o).variable(!1);});}var a=r.accumulations[n],i=e[n];tidy(function(){var e,r=t.m.mul(a).add(i);e=t.useNesterov?t.c.mul(i.add(r.mul(t.m))).add(o):t.c.mul(r).add(o),t.accumulations[n].assign(r),o.assign(e);});},r=this;for(var o in e)n(o);},t.prototype.dispose=function(){if(e.prototype.dispose.call(this),this.m.dispose(),null!=this.accumulations)for(var t in this.accumulations)this.accumulations[t].dispose();},t.prototype.setMomentum=function(e){this.momentum=e;},t.prototype.getConfig=function(){return {learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}},t.fromConfig=function(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)},t.className="MomentumOptimizer",t}(SGDOptimizer);registerClass(MomentumOptimizer);var RMSPropOptimizer=function(e){function t(t,n,r,o,a){void 0===n&&(n=.9),void 0===r&&(r=0),void 0===o&&(o=null),void 0===a&&(a=!1);var i=e.call(this)||this;return i.learningRate=t,i.decay=n,i.momentum=r,i.epsilon=o,i.accumulatedMeanSquares={},i.accumulatedMeanGrads={},i.accumulatedMoments={},i.c=keep(scalar(t)),i.decayScalar=keep(scalar(n)),i.momentumScalar=keep(scalar(r)),i.oneMinusDecay=keep(scalar(1-n)),i.centered=a,null===o&&(o=ENV.get("EPSILON")),i.epsilonScalar=keep(scalar(o)),i}return __extends$1(t,e),t.prototype.applyGradients=function(e){var t=this,n=function(n){var o=ENV.engine.registeredVariables[n];if(null==r.accumulatedMeanSquares[n]){tidy(function(){t.accumulatedMeanSquares[n]=zerosLike(o).variable(!1);});}if(null==r.accumulatedMeanGrads[n]&&r.centered){tidy(function(){t.accumulatedMeanGrads[n]=zerosLike(o).variable(!1);});}if(null==r.accumulatedMoments[n]){tidy(function(){t.accumulatedMoments[n]=zerosLike(o).variable(!1);});}var a=r.accumulatedMeanSquares[n],i=r.accumulatedMeanGrads[n],s=r.accumulatedMoments[n],u=e[n];tidy(function(){var e=t.decayScalar.mul(a).add(t.oneMinusDecay.mul(u.square()));if(t.centered){var r=t.decayScalar.mul(i).add(t.oneMinusDecay.mul(u)),c=t.momentumScalar.mul(s).add(t.c.mul(u).div(e.sub(r.square().add(t.epsilonScalar)).sqrt()));t.accumulatedMeanSquares[n].assign(e),t.accumulatedMeanGrads[n].assign(r),t.accumulatedMoments[n].assign(c);var l=o.sub(c);o.assign(l);}else{var p=t.decayScalar.mul(a).add(t.oneMinusDecay.mul(u.square()));c=t.momentumScalar.mul(s).add(t.c.mul(u).div(p.add(t.epsilonScalar).sqrt()));t.accumulatedMeanSquares[n].assign(p),t.accumulatedMoments[n].assign(c);l=o.sub(c);o.assign(l);}});},r=this;for(var o in e)n(o);},t.prototype.dispose=function(){var e=this;this.c.dispose(),this.epsilonScalar.dispose(),this.decayScalar.dispose(),this.momentumScalar.dispose(),this.oneMinusDecay.dispose(),null!=this.accumulatedMeanSquares&&Object.keys(this.accumulatedMeanSquares).forEach(function(t){return e.accumulatedMeanSquares[t].dispose()}),null!=this.accumulatedMeanGrads&&this.centered&&Object.keys(this.accumulatedMeanGrads).forEach(function(t){return e.accumulatedMeanGrads[t].dispose()}),null!=this.accumulatedMoments&&Object.keys(this.accumulatedMoments).forEach(function(t){return e.accumulatedMoments[t].dispose()});},t.prototype.getConfig=function(){return {learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}},t.fromConfig=function(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)},t.className="RMSPropOptimizer",t}(Optimizer);registerClass(RMSPropOptimizer);var OptimizerConstructors=function(){function e(){}return e.sgd=function(e){return new SGDOptimizer(e)},e.momentum=function(e,t,n){return void 0===n&&(n=!1),new MomentumOptimizer(e,t,n)},e.rmsprop=function(e,t,n,r,o){return void 0===t&&(t=.9),void 0===n&&(n=0),void 0===r&&(r=null),void 0===o&&(o=!1),new RMSPropOptimizer(e,t,n,r,o)},e.adam=function(e,t,n,r){return void 0===e&&(e=.001),void 0===t&&(t=.9),void 0===n&&(n=.999),void 0===r&&(r=null),new AdamOptimizer(e,t,n,r)},e.adadelta=function(e,t,n){return void 0===e&&(e=.001),void 0===t&&(t=.95),void 0===n&&(n=null),new AdadeltaOptimizer(e,t,n)},e.adamax=function(e,t,n,r,o){return void 0===e&&(e=.002),void 0===t&&(t=.9),void 0===n&&(n=.999),void 0===r&&(r=null),void 0===o&&(o=0),new AdamaxOptimizer(e,t,n,r,o)},e.adagrad=function(e,t){return void 0===t&&(t=.1),new AdagradOptimizer(e,t)},e}(),train={sgd:OptimizerConstructors.sgd,momentum:OptimizerConstructors.momentum,adadelta:OptimizerConstructors.adadelta,adagrad:OptimizerConstructors.adagrad,rmsprop:OptimizerConstructors.rmsprop,adamax:OptimizerConstructors.adamax,adam:OptimizerConstructors.adam},setBackend=Environment.setBackend,getBackend=Environment.getBackend,disposeVariables=Environment.disposeVariables,memory=Environment.memory;setOpHandler(ops);
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var tfCore_esm = /*#__PURE__*/Object.freeze({
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setBackend: setBackend,
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getBackend: getBackend,
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disposeVariables: disposeVariables,
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memory: memory,
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version_core: version,
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nextFrame: nextFrame,
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enableProdMode: enableProdMode,
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enableDebugMode: enableDebugMode,
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disableDeprecationWarnings: disableDeprecationWarnings,
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deprecationWarn: deprecationWarn,
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browser: browser,
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environment: environment,
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io: io,
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math: math,
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serialization: serialization,
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test_util: test_util,
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util: util,
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webgl: webgl,
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tensor_util: tensor_util,
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AdadeltaOptimizer: AdadeltaOptimizer,
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AdagradOptimizer: AdagradOptimizer,
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AdamOptimizer: AdamOptimizer,
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AdamaxOptimizer: AdamaxOptimizer,
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MomentumOptimizer: MomentumOptimizer,
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Optimizer: Optimizer,
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RMSPropOptimizer: RMSPropOptimizer,
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SGDOptimizer: SGDOptimizer,
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Tensor: Tensor,
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TensorBuffer: TensorBuffer,
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variable: variable,
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Variable: Variable,
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get Rank () { return Rank; },
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get Reduction () { return Reduction; },
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ENV: ENV,
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Environment: Environment,
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KernelBackend: KernelBackend,
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DataStorage: DataStorage,
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image: image_ops,
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linalg: linalg_ops,
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losses: loss_ops,
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spectral: spectral_ops,
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fused: fused_ops,
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op: op,
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batchNormalization2d: batchNormalization2d,
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batchNormalization3d: batchNormalization3d,
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batchNormalization4d: batchNormalization4d,
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batchNormalization: batchNormalization,
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batchNorm: batchNorm,
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batchNorm2d: batchNorm2d,
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batchNorm3d: batchNorm3d,
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batchNorm4d: batchNorm4d,
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complex: complex,
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real: real,
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imag: imag,
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concat: concat,
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concat1d: concat1d,
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concat2d: concat2d,
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concat3d: concat3d,
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concat4d: concat4d,
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split: split$1,
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conv1d: conv1d,
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conv2d: conv2d,
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conv3d: conv3d,
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conv2dDerFilter: conv2dDerFilter,
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depthwiseConv2d: depthwiseConv2d,
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separableConv2d: separableConv2d,
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conv2dTranspose: conv2dTranspose,
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matMul: matMul,
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dot: dot,
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outerProduct: outerProduct,
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reverse: reverse,
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reverse1d: reverse1d,
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reverse2d: reverse2d,
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reverse3d: reverse3d,
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reverse4d: reverse4d,
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maxPool: maxPool,
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avgPool: avgPool,
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pool: pool,
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slice: slice,
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slice1d: slice1d,
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slice2d: slice2d,
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slice3d: slice3d,
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slice4d: slice4d,
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abs: abs,
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acos: acos,
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acosh: acosh,
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asin: asin,
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asinh: asinh,
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atan: atan,
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atanh: atanh,
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ceil: ceil,
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clipByValue: clipByValue,
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cos: cos,
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cosh: cosh,
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erf: erf,
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exp: exp,
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expm1: expm1,
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floor: floor,
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log: log$1,
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log1p: log1p,
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logSigmoid: logSigmoid,
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neg: neg,
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reciprocal: reciprocal,
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round: round,
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rsqrt: rsqrt,
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sigmoid: sigmoid,
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sign: sign,
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isNaN: isNaN$1,
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isInf: isInf,
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isFinite: isFinite$1,
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sin: sin,
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sinh: sinh,
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softplus: softplus,
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sqrt: sqrt,
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square: square,
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step: step,
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tan: tan,
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tanh: tanh$1,
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all: all,
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any: any,
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argMax: argMax,
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argMin: argMin,
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logSumExp: logSumExp,
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max: max,
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mean: mean,
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min: min,
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moments: moments,
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sum: sum$1,
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prod: prod,
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equal: equal,
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equalStrict: equalStrict,
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greater: greater,
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greaterEqual: greaterEqual,
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greaterEqualStrict: greaterEqualStrict,
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greaterStrict: greaterStrict,
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less: less,
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lessEqual: lessEqual,
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lessEqualStrict: lessEqualStrict,
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lessStrict: lessStrict,
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notEqual: notEqual,
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notEqualStrict: notEqualStrict,
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add: add,
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addN: addN,
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addStrict: addStrict,
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atan2: atan2,
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div: div,
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divStrict: divStrict,
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floorDiv: floorDiv,
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maximum: maximum,
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maximumStrict: maximumStrict,
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minimum: minimum,
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minimumStrict: minimumStrict,
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mod: mod,
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modStrict: modStrict,
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mul: mul,
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mulStrict: mulStrict,
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pow: pow,
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powStrict: powStrict,
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squaredDifference: squaredDifference,
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squaredDifferenceStrict: squaredDifferenceStrict,
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sub: sub,
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subStrict: subStrict,
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elu: elu,
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leakyRelu: leakyRelu,
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prelu: prelu,
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relu: relu,
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selu: selu,
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logicalAnd: logicalAnd,
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logicalNot: logicalNot,
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logicalOr: logicalOr,
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logicalXor: logicalXor,
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where: where,
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whereAsync: whereAsync,
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buffer: buffer,
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print: print,
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batchToSpaceND: batchToSpaceND,
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cast: cast,
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clone: clone,
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cumsum: cumsum,
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depthToSpace: depthToSpace,
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expandDims: expandDims,
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eye: eye,
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multinomial: multinomial,
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oneHot: oneHot,
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pad: pad,
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pad1d: pad1d,
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pad2d: pad2d,
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pad3d: pad3d,
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pad4d: pad4d,
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rand: rand,
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randomNormal: randomNormal,
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randomUniform: randomUniform,
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reshape: reshape,
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spaceToBatchND: spaceToBatchND,
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squeeze: squeeze,
|
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stack: stack,
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|
tile: tile,
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|
truncatedNormal: truncatedNormal,
|
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unstack: unstack,
|
|
setdiff1dAsync: setdiff1dAsync,
|
|
fill: fill,
|
|
linspace: linspace,
|
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ones: ones$1,
|
|
range: range,
|
|
scalar: scalar,
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tensor: tensor,
|
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tensor1d: tensor1d,
|
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tensor2d: tensor2d,
|
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tensor3d: tensor3d,
|
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tensor4d: tensor4d,
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tensor5d: tensor5d,
|
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tensor6d: tensor6d,
|
|
zeros: zeros,
|
|
onesLike: onesLike,
|
|
zerosLike: zerosLike,
|
|
transpose: transpose,
|
|
softmax: softmax,
|
|
logSoftmax: logSoftmax,
|
|
localResponseNormalization: localResponseNormalization,
|
|
norm: norm,
|
|
gather: gather,
|
|
unsortedSegmentSum: unsortedSegmentSum,
|
|
basicLSTMCell: basicLSTMCell,
|
|
multiRNNCell: multiRNNCell,
|
|
movingAverage: movingAverage,
|
|
stridedSlice: stridedSlice,
|
|
topk: topk,
|
|
scatterND: scatterND,
|
|
fft: fft,
|
|
ifft: ifft,
|
|
rfft: rfft,
|
|
irfft: irfft,
|
|
sparseToDense: sparseToDense,
|
|
gatherND: gatherND,
|
|
train: train,
|
|
tidy: tidy,
|
|
keep: keep,
|
|
dispose: dispose,
|
|
time: time,
|
|
profile: profile,
|
|
customGrad: customGrad,
|
|
grad: grad,
|
|
grads: grads,
|
|
valueAndGrad: valueAndGrad,
|
|
valueAndGrads: valueAndGrads,
|
|
variableGrads: variableGrads
|
|
});
|
|
|
|
var Dimensions = /** @class */ (function () {
|
|
function Dimensions(width, 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 }));
|
|
}
|
|
this._width = width;
|
|
this._height = height;
|
|
}
|
|
Object.defineProperty(Dimensions.prototype, "width", {
|
|
get: function () { return this._width; },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(Dimensions.prototype, "height", {
|
|
get: function () { return this._height; },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Dimensions.prototype.reverse = function () {
|
|
return new Dimensions(1 / this.width, 1 / this.height);
|
|
};
|
|
return Dimensions;
|
|
}());
|
|
|
|
var Point = /** @class */ (function () {
|
|
function Point(x, y) {
|
|
this._x = x;
|
|
this._y = y;
|
|
}
|
|
Object.defineProperty(Point.prototype, "x", {
|
|
get: function () { return this._x; },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(Point.prototype, "y", {
|
|
get: function () { return this._y; },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
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;
|
|
}());
|
|
|
|
function isTensor(tensor, dim) {
|
|
return tensor instanceof Tensor && tensor.shape.length === dim;
|
|
}
|
|
function isTensor1D(tensor) {
|
|
return isTensor(tensor, 1);
|
|
}
|
|
function isTensor2D(tensor) {
|
|
return isTensor(tensor, 2);
|
|
}
|
|
function isTensor3D(tensor) {
|
|
return isTensor(tensor, 3);
|
|
}
|
|
function isTensor4D(tensor) {
|
|
return isTensor(tensor, 4);
|
|
}
|
|
function isFloat(num) {
|
|
return num % 1 !== 0;
|
|
}
|
|
function isEven$1(num) {
|
|
return num % 2 === 0;
|
|
}
|
|
function round$1(num, prec) {
|
|
if (prec === void 0) { prec = 2; }
|
|
var f = Math.pow(10, prec);
|
|
return Math.floor(num * f) / f;
|
|
}
|
|
function isDimensions(obj) {
|
|
return obj && obj.width && obj.height;
|
|
}
|
|
function computeReshapedDimensions(_a, inputSize) {
|
|
var width = _a.width, height = _a.height;
|
|
var scale = inputSize / Math.max(height, width);
|
|
return new Dimensions(Math.round(width * scale), Math.round(height * scale));
|
|
}
|
|
function getCenterPoint(pts) {
|
|
return pts.reduce(function (sum, pt) { return sum.add(pt); }, new Point(0, 0))
|
|
.div(new Point(pts.length, pts.length));
|
|
}
|
|
function range$1(num, start, step) {
|
|
return Array(num).fill(0).map(function (_, i) { return start + (i * step); });
|
|
}
|
|
function isValidNumber(num) {
|
|
return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0;
|
|
}
|
|
function isValidProbablitiy(num) {
|
|
return isValidNumber(num) && 0 <= num && num <= 1.0;
|
|
}
|
|
|
|
var Box = /** @class */ (function () {
|
|
function Box(_box, allowNegativeDimensions) {
|
|
if (allowNegativeDimensions === void 0) { allowNegativeDimensions = true; }
|
|
var box = (_box || {});
|
|
var isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber);
|
|
var isRect = [box.x, box.y, box.width, box.height].every(isValidNumber);
|
|
if (!isRect && !isBbox) {
|
|
throw new Error("Box.constructor - expected box to be IBoundingBox | IRect, instead have " + JSON.stringify(box));
|
|
}
|
|
var _a = isRect
|
|
? [box.x, box.y, box.width, box.height]
|
|
: [box.left, box.top, box.right - box.left, box.bottom - box.top], x = _a[0], y = _a[1], width = _a[2], height = _a[3];
|
|
Box.assertIsValidBox({ x: x, y: y, width: width, height: height }, 'Box.constructor', allowNegativeDimensions);
|
|
this._x = x;
|
|
this._y = y;
|
|
this._width = width;
|
|
this._height = height;
|
|
}
|
|
Box.isRect = function (rect) {
|
|
return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber);
|
|
};
|
|
Box.assertIsValidBox = function (box, callee, allowNegativeDimensions) {
|
|
if (allowNegativeDimensions === void 0) { allowNegativeDimensions = false; }
|
|
if (!Box.isRect(box)) {
|
|
throw new Error(callee + " - invalid box: " + JSON.stringify(box) + ", expected object with properties x, y, width, height");
|
|
}
|
|
if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {
|
|
throw new Error(callee + " - width (" + box.width + ") and height (" + box.height + ") must be positive numbers");
|
|
}
|
|
};
|
|
Object.defineProperty(Box.prototype, "x", {
|
|
get: function () { return this._x; },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(Box.prototype, "y", {
|
|
get: function () { return this._y; },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(Box.prototype, "width", {
|
|
get: function () { return this._width; },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(Box.prototype, "height", {
|
|
get: function () { return this._height; },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(Box.prototype, "left", {
|
|
get: function () { return this.x; },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(Box.prototype, "top", {
|
|
get: function () { return this.y; },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(Box.prototype, "right", {
|
|
get: function () { return this.x + this.width; },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(Box.prototype, "bottom", {
|
|
get: function () { return this.y + this.height; },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(Box.prototype, "area", {
|
|
get: function () { return this.width * this.height; },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(Box.prototype, "topLeft", {
|
|
get: function () { return new Point(this.left, this.top); },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(Box.prototype, "topRight", {
|
|
get: function () { return new Point(this.right, this.top); },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(Box.prototype, "bottomLeft", {
|
|
get: function () { return new Point(this.left, this.bottom); },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(Box.prototype, "bottomRight", {
|
|
get: function () { return new Point(this.right, this.bottom); },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Box.prototype.round = function () {
|
|
var _a = [this.x, this.y, this.width, this.height]
|
|
.map(function (val) { return Math.round(val); }), x = _a[0], y = _a[1], width = _a[2], height = _a[3];
|
|
return new Box({ x: x, y: y, width: width, height: height });
|
|
};
|
|
Box.prototype.floor = function () {
|
|
var _a = [this.x, this.y, this.width, this.height]
|
|
.map(function (val) { return Math.floor(val); }), x = _a[0], y = _a[1], width = _a[2], height = _a[3];
|
|
return new Box({ x: x, y: y, width: width, height: height });
|
|
};
|
|
Box.prototype.toSquare = function () {
|
|
var _a = this, x = _a.x, y = _a.y, width = _a.width, height = _a.height;
|
|
var diff = Math.abs(width - height);
|
|
if (width < height) {
|
|
x -= (diff / 2);
|
|
width += diff;
|
|
}
|
|
if (height < width) {
|
|
y -= (diff / 2);
|
|
height += diff;
|
|
}
|
|
return new Box({ x: x, y: y, width: width, height: height });
|
|
};
|
|
Box.prototype.rescale = function (s) {
|
|
var scaleX = isDimensions(s) ? s.width : s;
|
|
var scaleY = isDimensions(s) ? s.height : s;
|
|
return new Box({
|
|
x: this.x * scaleX,
|
|
y: this.y * scaleY,
|
|
width: this.width * scaleX,
|
|
height: this.height * scaleY
|
|
});
|
|
};
|
|
Box.prototype.pad = function (padX, padY) {
|
|
var _a = [
|
|
this.x - (padX / 2),
|
|
this.y - (padY / 2),
|
|
this.width + padX,
|
|
this.height + padY
|
|
], x = _a[0], y = _a[1], width = _a[2], height = _a[3];
|
|
return new Box({ x: x, y: y, width: width, height: height });
|
|
};
|
|
Box.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);
|
|
var clippedY = Math.max(y, 0);
|
|
var newWidth = right - clippedX;
|
|
var newHeight = bottom - clippedY;
|
|
var clippedWidth = Math.min(newWidth, imgWidth - clippedX);
|
|
var clippedHeight = Math.min(newHeight, imgHeight - clippedY);
|
|
return (new Box({ x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight })).floor();
|
|
};
|
|
Box.prototype.shift = function (sx, sy) {
|
|
var _a = this, width = _a.width, height = _a.height;
|
|
var x = this.x + sx;
|
|
var y = this.y + sy;
|
|
return new Box({ x: x, y: y, width: width, height: height });
|
|
};
|
|
Box.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 };
|
|
};
|
|
Box.prototype.calibrate = function (region) {
|
|
return new Box({
|
|
left: this.left + (region.left * this.width),
|
|
top: this.top + (region.top * this.height),
|
|
right: this.right + (region.right * this.width),
|
|
bottom: this.bottom + (region.bottom * this.height)
|
|
}).toSquare().round();
|
|
};
|
|
return Box;
|
|
}());
|
|
|
|
var BoundingBox = /** @class */ (function (_super) {
|
|
__extends(BoundingBox, _super);
|
|
function BoundingBox(left, top, right, bottom, allowNegativeDimensions) {
|
|
if (allowNegativeDimensions === void 0) { allowNegativeDimensions = false; }
|
|
return _super.call(this, { left: left, top: top, right: right, bottom: bottom }, allowNegativeDimensions) || this;
|
|
}
|
|
return BoundingBox;
|
|
}(Box));
|
|
|
|
var LabeledBox = /** @class */ (function (_super) {
|
|
__extends(LabeledBox, _super);
|
|
function LabeledBox(box, label) {
|
|
var _this = _super.call(this, box) || this;
|
|
_this._label = label;
|
|
return _this;
|
|
}
|
|
LabeledBox.assertIsValidLabeledBox = function (box, callee) {
|
|
Box.assertIsValidBox(box, callee);
|
|
if (!isValidNumber(box.label)) {
|
|
throw new Error(callee + " - expected property label (" + box.label + ") to be a number");
|
|
}
|
|
};
|
|
Object.defineProperty(LabeledBox.prototype, "label", {
|
|
get: function () { return this._label; },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
return LabeledBox;
|
|
}(Box));
|
|
|
|
var ObjectDetection = /** @class */ (function () {
|
|
function ObjectDetection(score, classScore, className, relativeBox, imageDims) {
|
|
this._imageDims = new Dimensions(imageDims.width, imageDims.height);
|
|
this._score = score;
|
|
this._classScore = classScore;
|
|
this._className = className;
|
|
this._box = new Box(relativeBox).rescale(this._imageDims);
|
|
}
|
|
Object.defineProperty(ObjectDetection.prototype, "score", {
|
|
get: function () { return this._score; },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(ObjectDetection.prototype, "classScore", {
|
|
get: function () { return this._classScore; },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(ObjectDetection.prototype, "className", {
|
|
get: function () { return this._className; },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(ObjectDetection.prototype, "box", {
|
|
get: function () { return this._box; },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(ObjectDetection.prototype, "imageDims", {
|
|
get: function () { return this._imageDims; },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(ObjectDetection.prototype, "imageWidth", {
|
|
get: function () { return this.imageDims.width; },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(ObjectDetection.prototype, "imageHeight", {
|
|
get: function () { return this.imageDims.height; },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(ObjectDetection.prototype, "relativeBox", {
|
|
get: function () { return new Box(this._box).rescale(this.imageDims.reverse()); },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
ObjectDetection.prototype.forSize = function (width, height) {
|
|
return new ObjectDetection(this.score, this.classScore, this.className, this.relativeBox, { width: width, height: height });
|
|
};
|
|
return ObjectDetection;
|
|
}());
|
|
|
|
var PredictedBox = /** @class */ (function (_super) {
|
|
__extends(PredictedBox, _super);
|
|
function PredictedBox(box, label, score, classScore) {
|
|
var _this = _super.call(this, box, label) || this;
|
|
_this._score = score;
|
|
_this._classScore = classScore;
|
|
return _this;
|
|
}
|
|
PredictedBox.assertIsValidPredictedBox = function (box, callee) {
|
|
LabeledBox.assertIsValidLabeledBox(box, callee);
|
|
if (!isValidProbablitiy(box.score)
|
|
|| !isValidProbablitiy(box.classScore)) {
|
|
throw new Error(callee + " - expected properties score (" + box.score + ") and (" + box.classScore + ") to be a number between [0, 1]");
|
|
}
|
|
};
|
|
Object.defineProperty(PredictedBox.prototype, "score", {
|
|
get: function () { return this._score; },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(PredictedBox.prototype, "classScore", {
|
|
get: function () { return this._classScore; },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
return PredictedBox;
|
|
}(LabeledBox));
|
|
|
|
var Rect = /** @class */ (function (_super) {
|
|
__extends(Rect, _super);
|
|
function Rect(x, y, width, height, allowNegativeDimensions) {
|
|
if (allowNegativeDimensions === void 0) { allowNegativeDimensions = false; }
|
|
return _super.call(this, { x: x, y: y, width: width, height: height }, allowNegativeDimensions) || this;
|
|
}
|
|
return Rect;
|
|
}(Box));
|
|
|
|
function createBrowserEnv() {
|
|
var fetch = window['fetch'] || function () {
|
|
throw new Error('fetch - missing fetch implementation for browser environment');
|
|
};
|
|
var readFile = function () {
|
|
throw new Error('readFile - filesystem not available for browser environment');
|
|
};
|
|
return {
|
|
Canvas: HTMLCanvasElement,
|
|
CanvasRenderingContext2D: CanvasRenderingContext2D,
|
|
Image: HTMLImageElement,
|
|
ImageData: ImageData,
|
|
Video: HTMLVideoElement,
|
|
createCanvasElement: function () { return document.createElement('canvas'); },
|
|
createImageElement: function () { return document.createElement('img'); },
|
|
fetch: fetch,
|
|
readFile: readFile
|
|
};
|
|
}
|
|
|
|
function createFileSystem(fs) {
|
|
var requireFsError = '';
|
|
if (!fs) {
|
|
try {
|
|
fs = require('fs');
|
|
}
|
|
catch (err) {
|
|
requireFsError = err.toString();
|
|
}
|
|
}
|
|
var readFile = fs
|
|
? function (filePath) {
|
|
return new Promise(function (res, rej) {
|
|
fs.readFile(filePath, function (err, buffer) {
|
|
return err ? rej(err) : res(buffer);
|
|
});
|
|
});
|
|
}
|
|
: function () {
|
|
throw new Error("readFile - failed to require fs in nodejs environment with error: " + requireFsError);
|
|
};
|
|
return {
|
|
readFile: readFile
|
|
};
|
|
}
|
|
|
|
function createNodejsEnv() {
|
|
var Canvas = global['Canvas'] || global['HTMLCanvasElement'];
|
|
var Image = global['Image'] || global['HTMLImageElement'];
|
|
var createCanvasElement = function () {
|
|
if (Canvas) {
|
|
return new Canvas();
|
|
}
|
|
throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment');
|
|
};
|
|
var createImageElement = function () {
|
|
if (Image) {
|
|
return new Image();
|
|
}
|
|
throw new Error('createImageElement - missing Image implementation for nodejs environment');
|
|
};
|
|
var fetch = global['fetch'] || function () {
|
|
throw new Error('fetch - missing fetch implementation for nodejs environment');
|
|
};
|
|
var fileSystem = createFileSystem();
|
|
return __assign({ Canvas: Canvas || /** @class */ (function () {
|
|
function Canvas() {
|
|
}
|
|
return Canvas;
|
|
}()), CanvasRenderingContext2D: global['CanvasRenderingContext2D'] || /** @class */ (function () {
|
|
function class_1() {
|
|
}
|
|
return class_1;
|
|
}()), Image: Image || /** @class */ (function () {
|
|
function Image() {
|
|
}
|
|
return Image;
|
|
}()), ImageData: global['ImageData'] || /** @class */ (function () {
|
|
function class_2() {
|
|
}
|
|
return class_2;
|
|
}()), Video: global['HTMLVideoElement'] || /** @class */ (function () {
|
|
function class_3() {
|
|
}
|
|
return class_3;
|
|
}()), createCanvasElement: createCanvasElement,
|
|
createImageElement: createImageElement,
|
|
fetch: fetch }, fileSystem);
|
|
}
|
|
|
|
function isBrowser() {
|
|
return typeof window === 'object'
|
|
&& typeof document !== 'undefined'
|
|
&& typeof HTMLImageElement !== 'undefined'
|
|
&& typeof HTMLCanvasElement !== 'undefined'
|
|
&& typeof HTMLVideoElement !== 'undefined'
|
|
&& typeof ImageData !== 'undefined'
|
|
&& typeof CanvasRenderingContext2D !== 'undefined';
|
|
}
|
|
|
|
function isNodejs() {
|
|
return typeof global === 'object'
|
|
&& typeof require === 'function'
|
|
&& typeof module !== 'undefined'
|
|
// issues with gatsby.js: module.exports is undefined
|
|
// && !!module.exports
|
|
&& typeof process !== 'undefined' && !!process.version;
|
|
}
|
|
|
|
var environment$1;
|
|
function getEnv() {
|
|
if (!environment$1) {
|
|
throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()');
|
|
}
|
|
return environment$1;
|
|
}
|
|
function setEnv(env) {
|
|
environment$1 = env;
|
|
}
|
|
function initialize() {
|
|
// check for isBrowser() first to prevent electron renderer process
|
|
// to be initialized with wrong environment due to isNodejs() returning true
|
|
if (isBrowser()) {
|
|
setEnv(createBrowserEnv());
|
|
}
|
|
if (isNodejs()) {
|
|
setEnv(createNodejsEnv());
|
|
}
|
|
}
|
|
function monkeyPatch(env) {
|
|
if (!environment$1) {
|
|
initialize();
|
|
}
|
|
if (!environment$1) {
|
|
throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()');
|
|
}
|
|
var _a = env.Canvas, Canvas = _a === void 0 ? environment$1.Canvas : _a, _b = env.Image, Image = _b === void 0 ? environment$1.Image : _b;
|
|
environment$1.Canvas = Canvas;
|
|
environment$1.Image = Image;
|
|
environment$1.createCanvasElement = env.createCanvasElement || (function () { return new Canvas(); });
|
|
environment$1.createImageElement = env.createImageElement || (function () { return new Image(); });
|
|
environment$1.ImageData = env.ImageData || environment$1.ImageData;
|
|
environment$1.Video = env.Video || environment$1.Video;
|
|
environment$1.fetch = env.fetch || environment$1.fetch;
|
|
environment$1.readFile = env.readFile || environment$1.readFile;
|
|
}
|
|
var env = {
|
|
getEnv: getEnv,
|
|
setEnv: setEnv,
|
|
initialize: initialize,
|
|
createBrowserEnv: createBrowserEnv,
|
|
createFileSystem: createFileSystem,
|
|
createNodejsEnv: createNodejsEnv,
|
|
monkeyPatch: monkeyPatch,
|
|
isBrowser: isBrowser,
|
|
isNodejs: isNodejs
|
|
};
|
|
initialize();
|
|
|
|
function resolveInput(arg) {
|
|
if (!env.isNodejs() && typeof arg === 'string') {
|
|
return document.getElementById(arg);
|
|
}
|
|
return arg;
|
|
}
|
|
|
|
function getContext2dOrThrow(canvasArg) {
|
|
var _a = env.getEnv(), Canvas = _a.Canvas, CanvasRenderingContext2D = _a.CanvasRenderingContext2D;
|
|
if (canvasArg instanceof CanvasRenderingContext2D) {
|
|
return canvasArg;
|
|
}
|
|
var canvas = resolveInput(canvasArg);
|
|
if (!(canvas instanceof Canvas)) {
|
|
throw new Error('resolveContext2d - expected canvas to be of instance of Canvas');
|
|
}
|
|
var ctx = canvas.getContext('2d');
|
|
if (!ctx) {
|
|
throw new Error('resolveContext2d - canvas 2d context is null');
|
|
}
|
|
return ctx;
|
|
}
|
|
|
|
var AnchorPosition;
|
|
(function (AnchorPosition) {
|
|
AnchorPosition["TOP_LEFT"] = "TOP_LEFT";
|
|
AnchorPosition["TOP_RIGHT"] = "TOP_RIGHT";
|
|
AnchorPosition["BOTTOM_LEFT"] = "BOTTOM_LEFT";
|
|
AnchorPosition["BOTTOM_RIGHT"] = "BOTTOM_RIGHT";
|
|
})(AnchorPosition || (AnchorPosition = {}));
|
|
var DrawTextFieldOptions = /** @class */ (function () {
|
|
function DrawTextFieldOptions(options) {
|
|
if (options === void 0) { options = {}; }
|
|
var anchorPosition = options.anchorPosition, backgroundColor = options.backgroundColor, fontColor = options.fontColor, fontSize = options.fontSize, fontStyle = options.fontStyle, padding = options.padding;
|
|
this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT;
|
|
this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)';
|
|
this.fontColor = fontColor || 'rgba(255, 255, 255, 1)';
|
|
this.fontSize = fontSize || 14;
|
|
this.fontStyle = fontStyle || 'Georgia';
|
|
this.padding = padding || 4;
|
|
}
|
|
return DrawTextFieldOptions;
|
|
}());
|
|
var DrawTextField = /** @class */ (function () {
|
|
function DrawTextField(text, anchor, options) {
|
|
if (options === void 0) { options = {}; }
|
|
this.text = typeof text === 'string'
|
|
? [text]
|
|
: (text instanceof DrawTextField ? text.text : text);
|
|
this.anchor = anchor;
|
|
this.options = new DrawTextFieldOptions(options);
|
|
}
|
|
DrawTextField.prototype.measureWidth = function (ctx) {
|
|
var padding = this.options.padding;
|
|
return this.text.map(function (l) { return ctx.measureText(l).width; }).reduce(function (w0, w1) { return w0 < w1 ? w1 : w0; }, 0) + (2 * padding);
|
|
};
|
|
DrawTextField.prototype.measureHeight = function () {
|
|
var _a = this.options, fontSize = _a.fontSize, padding = _a.padding;
|
|
return this.text.length * fontSize + (2 * padding);
|
|
};
|
|
DrawTextField.prototype.getUpperLeft = function (ctx, canvasDims) {
|
|
var anchorPosition = this.options.anchorPosition;
|
|
var isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT;
|
|
var isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT;
|
|
var textFieldWidth = this.measureWidth(ctx);
|
|
var textFieldHeight = this.measureHeight();
|
|
var x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x);
|
|
var y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y;
|
|
// adjust anchor if text box exceeds canvas borders
|
|
if (canvasDims) {
|
|
var width = canvasDims.width, height = canvasDims.height;
|
|
var newX = Math.max(Math.min(x, width - textFieldWidth), 0);
|
|
var newY = Math.max(Math.min(y, height - textFieldHeight), 0);
|
|
return { x: newX, y: newY };
|
|
}
|
|
return { x: x, y: y };
|
|
};
|
|
DrawTextField.prototype.draw = function (canvasArg) {
|
|
var canvas = resolveInput(canvasArg);
|
|
var ctx = getContext2dOrThrow(canvas);
|
|
var _a = this.options, backgroundColor = _a.backgroundColor, fontColor = _a.fontColor, fontSize = _a.fontSize, fontStyle = _a.fontStyle, padding = _a.padding;
|
|
ctx.font = fontSize + "px " + fontStyle;
|
|
var maxTextWidth = this.measureWidth(ctx);
|
|
var textHeight = this.measureHeight();
|
|
ctx.fillStyle = backgroundColor;
|
|
var upperLeft = this.getUpperLeft(ctx, canvas);
|
|
ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight);
|
|
ctx.fillStyle = fontColor;
|
|
this.text.forEach(function (textLine, i) {
|
|
var x = padding + upperLeft.x;
|
|
var y = padding + upperLeft.y + ((i + 1) * fontSize);
|
|
ctx.fillText(textLine, x, y);
|
|
});
|
|
};
|
|
return DrawTextField;
|
|
}());
|
|
|
|
var DrawBoxOptions = /** @class */ (function () {
|
|
function DrawBoxOptions(options) {
|
|
if (options === void 0) { options = {}; }
|
|
var boxColor = options.boxColor, lineWidth = options.lineWidth, label = options.label, drawLabelOptions = options.drawLabelOptions;
|
|
this.boxColor = boxColor || 'rgba(0, 0, 255, 1)';
|
|
this.lineWidth = lineWidth || 2;
|
|
this.label = label;
|
|
var defaultDrawLabelOptions = {
|
|
anchorPosition: AnchorPosition.BOTTOM_LEFT,
|
|
backgroundColor: this.boxColor
|
|
};
|
|
this.drawLabelOptions = new DrawTextFieldOptions(Object.assign({}, defaultDrawLabelOptions, drawLabelOptions));
|
|
}
|
|
return DrawBoxOptions;
|
|
}());
|
|
var DrawBox = /** @class */ (function () {
|
|
function DrawBox(box, options) {
|
|
if (options === void 0) { options = {}; }
|
|
this.box = new Box(box);
|
|
this.options = new DrawBoxOptions(options);
|
|
}
|
|
DrawBox.prototype.draw = function (canvasArg) {
|
|
var ctx = getContext2dOrThrow(canvasArg);
|
|
var _a = this.options, boxColor = _a.boxColor, lineWidth = _a.lineWidth;
|
|
var _b = this.box, x = _b.x, y = _b.y, width = _b.width, height = _b.height;
|
|
ctx.strokeStyle = boxColor;
|
|
ctx.lineWidth = lineWidth;
|
|
ctx.strokeRect(x, y, width, height);
|
|
var label = this.options.label;
|
|
if (label) {
|
|
new DrawTextField([label], { x: x - (lineWidth / 2), y: y }, this.options.drawLabelOptions).draw(canvasArg);
|
|
}
|
|
};
|
|
return DrawBox;
|
|
}());
|
|
|
|
|
|
|
|
var drawBase = /*#__PURE__*/Object.freeze({
|
|
DrawBoxOptions: DrawBoxOptions,
|
|
DrawBox: DrawBox,
|
|
get AnchorPosition () { return AnchorPosition; },
|
|
DrawTextFieldOptions: DrawTextFieldOptions,
|
|
DrawTextField: DrawTextField
|
|
});
|
|
|
|
function convLayer(x, params, padding, withRelu) {
|
|
if (padding === void 0) { padding = 'same'; }
|
|
if (withRelu === void 0) { withRelu = false; }
|
|
return tidy(function () {
|
|
var out = add(conv2d(x, params.filters, [1, 1], padding), params.bias);
|
|
return withRelu ? relu(out) : out;
|
|
});
|
|
}
|
|
|
|
function disposeUnusedWeightTensors(weightMap, paramMappings) {
|
|
Object.keys(weightMap).forEach(function (path) {
|
|
if (!paramMappings.some(function (pm) { return pm.originalPath === path; })) {
|
|
weightMap[path].dispose();
|
|
}
|
|
});
|
|
}
|
|
|
|
function extractConvParamsFactory(extractWeights, paramMappings) {
|
|
return function (channelsIn, channelsOut, filterSize, mappedPrefix) {
|
|
var filters = tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
|
|
var bias = tensor1d(extractWeights(channelsOut));
|
|
paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/bias" });
|
|
return { filters: filters, bias: bias };
|
|
};
|
|
}
|
|
|
|
function extractFCParamsFactory(extractWeights, paramMappings) {
|
|
return function (channelsIn, channelsOut, mappedPrefix) {
|
|
var fc_weights = tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);
|
|
var fc_bias = tensor1d(extractWeights(channelsOut));
|
|
paramMappings.push({ paramPath: mappedPrefix + "/weights" }, { paramPath: mappedPrefix + "/bias" });
|
|
return {
|
|
weights: fc_weights,
|
|
bias: fc_bias
|
|
};
|
|
};
|
|
}
|
|
|
|
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;
|
|
}());
|
|
|
|
function extractSeparableConvParamsFactory(extractWeights, paramMappings) {
|
|
return function (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);
|
|
};
|
|
}
|
|
function loadSeparableConvParamsFactory(extractWeightEntry) {
|
|
return function (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);
|
|
};
|
|
}
|
|
|
|
function extractWeightEntryFactory(weightMap, paramMappings) {
|
|
return function (originalPath, paramRank, mappedPath) {
|
|
var tensor = weightMap[originalPath];
|
|
if (!isTensor(tensor, paramRank)) {
|
|
throw new Error("expected weightMap[" + originalPath + "] to be a Tensor" + paramRank + "D, instead have " + tensor);
|
|
}
|
|
paramMappings.push({ originalPath: originalPath, paramPath: mappedPath || originalPath });
|
|
return tensor;
|
|
};
|
|
}
|
|
|
|
function extractWeightsFactory(weights) {
|
|
var remainingWeights = weights;
|
|
function extractWeights(numWeights) {
|
|
var ret = remainingWeights.slice(0, numWeights);
|
|
remainingWeights = remainingWeights.slice(numWeights);
|
|
return ret;
|
|
}
|
|
function getRemainingWeights() {
|
|
return remainingWeights;
|
|
}
|
|
return {
|
|
extractWeights: extractWeights,
|
|
getRemainingWeights: getRemainingWeights
|
|
};
|
|
}
|
|
|
|
function getModelUris(uri, defaultModelName) {
|
|
var defaultManifestFilename = defaultModelName + "-weights_manifest.json";
|
|
if (!uri) {
|
|
return {
|
|
modelBaseUri: '',
|
|
manifestUri: defaultManifestFilename
|
|
};
|
|
}
|
|
if (uri === '/') {
|
|
return {
|
|
modelBaseUri: '/',
|
|
manifestUri: "/" + defaultManifestFilename
|
|
};
|
|
}
|
|
var protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';
|
|
uri = uri.replace(protocol, '');
|
|
var parts = uri.split('/').filter(function (s) { return s; });
|
|
var manifestFile = uri.endsWith('.json')
|
|
? parts[parts.length - 1]
|
|
: defaultManifestFilename;
|
|
var modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/');
|
|
modelBaseUri = uri.startsWith('/') ? "/" + modelBaseUri : modelBaseUri;
|
|
return {
|
|
modelBaseUri: modelBaseUri,
|
|
manifestUri: modelBaseUri === '/' ? "/" + manifestFile : modelBaseUri + "/" + manifestFile
|
|
};
|
|
}
|
|
|
|
function isMediaLoaded(media) {
|
|
var _a = env.getEnv(), Image = _a.Image, Video = _a.Video;
|
|
return (media instanceof Image && media.complete)
|
|
|| (media instanceof Video && media.readyState >= 3);
|
|
}
|
|
|
|
function awaitMediaLoaded(media) {
|
|
return new Promise(function (resolve, reject) {
|
|
if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {
|
|
return resolve();
|
|
}
|
|
function onLoad(e) {
|
|
if (!e.currentTarget)
|
|
return;
|
|
e.currentTarget.removeEventListener('load', onLoad);
|
|
e.currentTarget.removeEventListener('error', onError);
|
|
resolve(e);
|
|
}
|
|
function onError(e) {
|
|
if (!e.currentTarget)
|
|
return;
|
|
e.currentTarget.removeEventListener('load', onLoad);
|
|
e.currentTarget.removeEventListener('error', onError);
|
|
reject(e);
|
|
}
|
|
media.addEventListener('load', onLoad);
|
|
media.addEventListener('error', onError);
|
|
});
|
|
}
|
|
|
|
function bufferToImage(buf) {
|
|
return new Promise(function (resolve, reject) {
|
|
if (!(buf instanceof Blob)) {
|
|
return reject('bufferToImage - expected buf to be of type: Blob');
|
|
}
|
|
var reader = new FileReader();
|
|
reader.onload = function () {
|
|
if (typeof reader.result !== 'string') {
|
|
return reject('bufferToImage - expected reader.result to be a string, in onload');
|
|
}
|
|
var img = env.getEnv().createImageElement();
|
|
img.onload = function () { return resolve(img); };
|
|
img.onerror = reject;
|
|
img.src = reader.result;
|
|
};
|
|
reader.onerror = reject;
|
|
reader.readAsDataURL(buf);
|
|
});
|
|
}
|
|
|
|
function getMediaDimensions(input) {
|
|
var _a = env.getEnv(), Image = _a.Image, Video = _a.Video;
|
|
if (input instanceof Image) {
|
|
return new Dimensions(input.naturalWidth, input.naturalHeight);
|
|
}
|
|
if (input instanceof Video) {
|
|
return new Dimensions(input.videoWidth, input.videoHeight);
|
|
}
|
|
return new Dimensions(input.width, input.height);
|
|
}
|
|
|
|
function createCanvas(_a) {
|
|
var width = _a.width, height = _a.height;
|
|
var createCanvasElement = env.getEnv().createCanvasElement;
|
|
var canvas = createCanvasElement();
|
|
canvas.width = width;
|
|
canvas.height = height;
|
|
return canvas;
|
|
}
|
|
function createCanvasFromMedia(media, dims) {
|
|
var ImageData = env.getEnv().ImageData;
|
|
if (!(media instanceof ImageData) && !isMediaLoaded(media)) {
|
|
throw new Error('createCanvasFromMedia - media has not finished loading yet');
|
|
}
|
|
var _a = dims || getMediaDimensions(media), width = _a.width, height = _a.height;
|
|
var canvas = createCanvas({ width: width, height: height });
|
|
if (media instanceof ImageData) {
|
|
getContext2dOrThrow(canvas).putImageData(media, 0, 0);
|
|
}
|
|
else {
|
|
getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);
|
|
}
|
|
return canvas;
|
|
}
|
|
|
|
function fetchOrThrow(url, init) {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var fetch, res;
|
|
return __generator(this, function (_a) {
|
|
switch (_a.label) {
|
|
case 0:
|
|
fetch = env.getEnv().fetch;
|
|
return [4 /*yield*/, fetch(url, init)];
|
|
case 1:
|
|
res = _a.sent();
|
|
if (!(res.status < 400)) {
|
|
throw new Error("failed to fetch: (" + res.status + ") " + res.statusText + ", from url: " + res.url);
|
|
}
|
|
return [2 /*return*/, res];
|
|
}
|
|
});
|
|
});
|
|
}
|
|
|
|
function fetchImage(uri) {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var res, blob;
|
|
return __generator(this, function (_a) {
|
|
switch (_a.label) {
|
|
case 0: return [4 /*yield*/, fetchOrThrow(uri)];
|
|
case 1:
|
|
res = _a.sent();
|
|
return [4 /*yield*/, (res).blob()];
|
|
case 2:
|
|
blob = _a.sent();
|
|
if (!blob.type.startsWith('image/')) {
|
|
throw new Error("fetchImage - expected blob type to be of type image/*, instead have: " + blob.type + ", for url: " + res.url);
|
|
}
|
|
return [2 /*return*/, bufferToImage(blob)];
|
|
}
|
|
});
|
|
});
|
|
}
|
|
|
|
function fetchJson(uri) {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
return __generator(this, function (_a) {
|
|
switch (_a.label) {
|
|
case 0: return [4 /*yield*/, fetchOrThrow(uri)];
|
|
case 1: return [2 /*return*/, (_a.sent()).json()];
|
|
}
|
|
});
|
|
});
|
|
}
|
|
|
|
function fetchNetWeights(uri) {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var _a;
|
|
return __generator(this, function (_b) {
|
|
switch (_b.label) {
|
|
case 0:
|
|
_a = Float32Array.bind;
|
|
return [4 /*yield*/, fetchOrThrow(uri)];
|
|
case 1: return [4 /*yield*/, (_b.sent()).arrayBuffer()];
|
|
case 2: return [2 /*return*/, new (_a.apply(Float32Array, [void 0, _b.sent()]))()];
|
|
}
|
|
});
|
|
});
|
|
}
|
|
|
|
function imageTensorToCanvas(imgTensor, canvas) {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var targetCanvas, _a, height, width, numChannels, imgTensor3D;
|
|
return __generator(this, function (_b) {
|
|
switch (_b.label) {
|
|
case 0:
|
|
targetCanvas = canvas || env.getEnv().createCanvasElement();
|
|
_a = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0), height = _a[0], width = _a[1], numChannels = _a[2];
|
|
imgTensor3D = tidy(function () { return imgTensor.as3D(height, width, numChannels).toInt(); });
|
|
return [4 /*yield*/, browser.toPixels(imgTensor3D, targetCanvas)];
|
|
case 1:
|
|
_b.sent();
|
|
imgTensor3D.dispose();
|
|
return [2 /*return*/, targetCanvas];
|
|
}
|
|
});
|
|
});
|
|
}
|
|
|
|
function imageToSquare(input, inputSize, centerImage) {
|
|
if (centerImage === void 0) { centerImage = false; }
|
|
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');
|
|
}
|
|
var dims = getMediaDimensions(input);
|
|
var scale = inputSize / Math.max(dims.height, dims.width);
|
|
var width = scale * dims.width;
|
|
var height = scale * dims.height;
|
|
var targetCanvas = createCanvas({ width: inputSize, height: inputSize });
|
|
var inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input);
|
|
var offset = Math.abs(width - height) / 2;
|
|
var dx = centerImage && width < height ? offset : 0;
|
|
var dy = centerImage && height < width ? offset : 0;
|
|
getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height);
|
|
return targetCanvas;
|
|
}
|
|
|
|
function isMediaElement(input) {
|
|
var _a = env.getEnv(), Image = _a.Image, Canvas = _a.Canvas, Video = _a.Video;
|
|
return input instanceof Image
|
|
|| input instanceof Canvas
|
|
|| input instanceof Video;
|
|
}
|
|
|
|
function loadWeightMap(uri, defaultModelName) {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var _a, manifestUri, modelBaseUri, manifest;
|
|
return __generator(this, function (_b) {
|
|
switch (_b.label) {
|
|
case 0:
|
|
_a = getModelUris(uri, defaultModelName), manifestUri = _a.manifestUri, modelBaseUri = _a.modelBaseUri;
|
|
return [4 /*yield*/, fetchJson(manifestUri)];
|
|
case 1:
|
|
manifest = _b.sent();
|
|
return [2 /*return*/, io.loadWeights(manifest, modelBaseUri)];
|
|
}
|
|
});
|
|
});
|
|
}
|
|
|
|
function matchDimensions(input, reference, useMediaDimensions) {
|
|
if (useMediaDimensions === void 0) { useMediaDimensions = false; }
|
|
var _a = useMediaDimensions
|
|
? getMediaDimensions(reference)
|
|
: reference, width = _a.width, height = _a.height;
|
|
input.width = width;
|
|
input.height = height;
|
|
return { width: width, height: height };
|
|
}
|
|
|
|
/**
|
|
* 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 an equal amount of padding on
|
|
* both sides of the minor dimension oof 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; })
|
|
.map(function (t) { return t.toFloat(); });
|
|
return concat(tensorsToStack, paddingAxis);
|
|
});
|
|
}
|
|
|
|
var NetInput = /** @class */ (function () {
|
|
function NetInput(inputs, treatAsBatchInput) {
|
|
if (treatAsBatchInput === void 0) { treatAsBatchInput = false; }
|
|
var _this = this;
|
|
this._imageTensors = [];
|
|
this._canvases = [];
|
|
this._treatAsBatchInput = false;
|
|
this._inputDimensions = [];
|
|
if (!Array.isArray(inputs)) {
|
|
throw new Error("NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have " + inputs);
|
|
}
|
|
this._treatAsBatchInput = treatAsBatchInput;
|
|
this._batchSize = inputs.length;
|
|
inputs.forEach(function (input, idx) {
|
|
if (isTensor3D(input)) {
|
|
_this._imageTensors[idx] = input;
|
|
_this._inputDimensions[idx] = input.shape;
|
|
return;
|
|
}
|
|
if (isTensor4D(input)) {
|
|
var batchSize = input.shape[0];
|
|
if (batchSize !== 1) {
|
|
throw new Error("NetInput - tf.Tensor4D with batchSize " + batchSize + " passed, but not supported in input array");
|
|
}
|
|
_this._imageTensors[idx] = input;
|
|
_this._inputDimensions[idx] = input.shape.slice(1);
|
|
return;
|
|
}
|
|
var canvas = input instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input);
|
|
_this._canvases[idx] = canvas;
|
|
_this._inputDimensions[idx] = [canvas.height, canvas.width, 3];
|
|
});
|
|
}
|
|
Object.defineProperty(NetInput.prototype, "imageTensors", {
|
|
get: function () {
|
|
return this._imageTensors;
|
|
},
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(NetInput.prototype, "canvases", {
|
|
get: function () {
|
|
return this._canvases;
|
|
},
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(NetInput.prototype, "isBatchInput", {
|
|
get: function () {
|
|
return this.batchSize > 1 || this._treatAsBatchInput;
|
|
},
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(NetInput.prototype, "batchSize", {
|
|
get: function () {
|
|
return this._batchSize;
|
|
},
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(NetInput.prototype, "inputDimensions", {
|
|
get: function () {
|
|
return this._inputDimensions;
|
|
},
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(NetInput.prototype, "inputSize", {
|
|
get: function () {
|
|
return this._inputSize;
|
|
},
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(NetInput.prototype, "reshapedInputDimensions", {
|
|
get: function () {
|
|
var _this = this;
|
|
return range$1(this.batchSize, 0, 1).map(function (_, batchIdx) { return _this.getReshapedInputDimensions(batchIdx); });
|
|
},
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
NetInput.prototype.getInput = function (batchIdx) {
|
|
return this.canvases[batchIdx] || this.imageTensors[batchIdx];
|
|
};
|
|
NetInput.prototype.getInputDimensions = function (batchIdx) {
|
|
return this._inputDimensions[batchIdx];
|
|
};
|
|
NetInput.prototype.getInputHeight = function (batchIdx) {
|
|
return this._inputDimensions[batchIdx][0];
|
|
};
|
|
NetInput.prototype.getInputWidth = function (batchIdx) {
|
|
return this._inputDimensions[batchIdx][1];
|
|
};
|
|
NetInput.prototype.getReshapedInputDimensions = function (batchIdx) {
|
|
if (typeof this.inputSize !== 'number') {
|
|
throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet');
|
|
}
|
|
var width = this.getInputWidth(batchIdx);
|
|
var height = this.getInputHeight(batchIdx);
|
|
return computeReshapedDimensions({ width: width, height: height }, this.inputSize);
|
|
};
|
|
/**
|
|
* Create a batch tensor from all input canvases and tensors
|
|
* with size [batchSize, inputSize, inputSize, 3].
|
|
*
|
|
* @param inputSize Height and width of the tensor.
|
|
* @param isCenterImage (optional, default: false) If true, add an equal amount of padding on
|
|
* both sides of the minor dimension oof the image.
|
|
* @returns The batch tensor.
|
|
*/
|
|
NetInput.prototype.toBatchTensor = function (inputSize, isCenterInputs) {
|
|
var _this = this;
|
|
if (isCenterInputs === void 0) { isCenterInputs = true; }
|
|
this._inputSize = inputSize;
|
|
return tidy(function () {
|
|
var inputTensors = range$1(_this.batchSize, 0, 1).map(function (batchIdx) {
|
|
var input = _this.getInput(batchIdx);
|
|
if (input instanceof Tensor) {
|
|
var imgTensor = isTensor4D(input) ? input : input.expandDims();
|
|
imgTensor = padToSquare(imgTensor, isCenterInputs);
|
|
if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {
|
|
imgTensor = image_ops.resizeBilinear(imgTensor, [inputSize, inputSize]);
|
|
}
|
|
return imgTensor.as3D(inputSize, inputSize, 3);
|
|
}
|
|
if (input instanceof env.getEnv().Canvas) {
|
|
return browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs));
|
|
}
|
|
throw new Error("toBatchTensor - at batchIdx " + batchIdx + ", expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have " + input);
|
|
});
|
|
var batchTensor = stack(inputTensors.map(function (t) { return t.toFloat(); })).as4D(_this.batchSize, inputSize, inputSize, 3);
|
|
return batchTensor;
|
|
});
|
|
};
|
|
return NetInput;
|
|
}());
|
|
|
|
/**
|
|
* Validates the input to make sure, they are valid net inputs and awaits all media elements
|
|
* to be finished loading.
|
|
*
|
|
* @param input The input, which can be a media element or an array of different media elements.
|
|
* @returns A NetInput instance, which can be passed into one of the neural networks.
|
|
*/
|
|
function toNetInput(inputs) {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var inputArgArray, getIdxHint, inputArray;
|
|
return __generator(this, function (_a) {
|
|
switch (_a.label) {
|
|
case 0:
|
|
if (inputs instanceof NetInput) {
|
|
return [2 /*return*/, inputs];
|
|
}
|
|
inputArgArray = Array.isArray(inputs)
|
|
? inputs
|
|
: [inputs];
|
|
if (!inputArgArray.length) {
|
|
throw new Error('toNetInput - empty array passed as input');
|
|
}
|
|
getIdxHint = function (idx) { return Array.isArray(inputs) ? " at input index " + idx + ":" : ''; };
|
|
inputArray = inputArgArray.map(resolveInput);
|
|
inputArray.forEach(function (input, i) {
|
|
if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {
|
|
if (typeof inputArgArray[i] === 'string') {
|
|
throw new Error("toNetInput -" + getIdxHint(i) + " string passed, but could not resolve HTMLElement for element id " + inputArgArray[i]);
|
|
}
|
|
throw new Error("toNetInput -" + getIdxHint(i) + " expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id");
|
|
}
|
|
if (isTensor4D(input)) {
|
|
// if tf.Tensor4D is passed in the input array, the batch size has to be 1
|
|
var batchSize = input.shape[0];
|
|
if (batchSize !== 1) {
|
|
throw new Error("toNetInput -" + getIdxHint(i) + " tf.Tensor4D with batchSize " + batchSize + " passed, but not supported in input array");
|
|
}
|
|
}
|
|
});
|
|
// wait for all media elements being loaded
|
|
return [4 /*yield*/, Promise.all(inputArray.map(function (input) { return isMediaElement(input) && awaitMediaLoaded(input); }))];
|
|
case 1:
|
|
// wait for all media elements being loaded
|
|
_a.sent();
|
|
return [2 /*return*/, new NetInput(inputArray, Array.isArray(inputs))];
|
|
}
|
|
});
|
|
});
|
|
}
|
|
|
|
var NeuralNetwork = /** @class */ (function () {
|
|
function NeuralNetwork(_name) {
|
|
this._name = _name;
|
|
this._params = undefined;
|
|
this._paramMappings = [];
|
|
}
|
|
Object.defineProperty(NeuralNetwork.prototype, "params", {
|
|
get: function () { return this._params; },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(NeuralNetwork.prototype, "paramMappings", {
|
|
get: function () { return this._paramMappings; },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(NeuralNetwork.prototype, "isLoaded", {
|
|
get: function () { return !!this.params; },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
NeuralNetwork.prototype.getParamFromPath = function (paramPath) {
|
|
var _a = this.traversePropertyPath(paramPath), obj = _a.obj, objProp = _a.objProp;
|
|
return obj[objProp];
|
|
};
|
|
NeuralNetwork.prototype.reassignParamFromPath = function (paramPath, tensor) {
|
|
var _a = this.traversePropertyPath(paramPath), obj = _a.obj, objProp = _a.objProp;
|
|
obj[objProp].dispose();
|
|
obj[objProp] = tensor;
|
|
};
|
|
NeuralNetwork.prototype.getParamList = function () {
|
|
var _this = this;
|
|
return this._paramMappings.map(function (_a) {
|
|
var paramPath = _a.paramPath;
|
|
return ({
|
|
path: paramPath,
|
|
tensor: _this.getParamFromPath(paramPath)
|
|
});
|
|
});
|
|
};
|
|
NeuralNetwork.prototype.getTrainableParams = function () {
|
|
return this.getParamList().filter(function (param) { return param.tensor instanceof Variable; });
|
|
};
|
|
NeuralNetwork.prototype.getFrozenParams = function () {
|
|
return this.getParamList().filter(function (param) { return !(param.tensor instanceof Variable); });
|
|
};
|
|
NeuralNetwork.prototype.variable = function () {
|
|
var _this = this;
|
|
this.getFrozenParams().forEach(function (_a) {
|
|
var path = _a.path, tensor = _a.tensor;
|
|
_this.reassignParamFromPath(path, tensor.variable());
|
|
});
|
|
};
|
|
NeuralNetwork.prototype.freeze = function () {
|
|
var _this = this;
|
|
this.getTrainableParams().forEach(function (_a) {
|
|
var path = _a.path, variable = _a.tensor;
|
|
var tensor$1 = tensor(variable.dataSync());
|
|
variable.dispose();
|
|
_this.reassignParamFromPath(path, tensor$1);
|
|
});
|
|
};
|
|
NeuralNetwork.prototype.dispose = function (throwOnRedispose) {
|
|
if (throwOnRedispose === void 0) { throwOnRedispose = true; }
|
|
this.getParamList().forEach(function (param) {
|
|
if (throwOnRedispose && param.tensor.isDisposed) {
|
|
throw new Error("param tensor has already been disposed for path " + param.path);
|
|
}
|
|
param.tensor.dispose();
|
|
});
|
|
this._params = undefined;
|
|
};
|
|
NeuralNetwork.prototype.serializeParams = function () {
|
|
return new Float32Array(this.getParamList()
|
|
.map(function (_a) {
|
|
var tensor = _a.tensor;
|
|
return Array.from(tensor.dataSync());
|
|
})
|
|
.reduce(function (flat, arr) { return flat.concat(arr); }));
|
|
};
|
|
NeuralNetwork.prototype.load = function (weightsOrUrl) {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
return __generator(this, function (_a) {
|
|
switch (_a.label) {
|
|
case 0:
|
|
if (weightsOrUrl instanceof Float32Array) {
|
|
this.extractWeights(weightsOrUrl);
|
|
return [2 /*return*/];
|
|
}
|
|
return [4 /*yield*/, this.loadFromUri(weightsOrUrl)];
|
|
case 1:
|
|
_a.sent();
|
|
return [2 /*return*/];
|
|
}
|
|
});
|
|
});
|
|
};
|
|
NeuralNetwork.prototype.loadFromUri = function (uri) {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var weightMap;
|
|
return __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*/, loadWeightMap(uri, this.getDefaultModelName())];
|
|
case 1:
|
|
weightMap = _a.sent();
|
|
this.loadFromWeightMap(weightMap);
|
|
return [2 /*return*/];
|
|
}
|
|
});
|
|
});
|
|
};
|
|
NeuralNetwork.prototype.loadFromDisk = function (filePath) {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var readFile, _a, manifestUri, modelBaseUri, fetchWeightsFromDisk, loadWeights, manifest, _b, _c, weightMap;
|
|
return __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 = io.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) {
|
|
var _a = this.extractParams(weights), paramMappings = _a.paramMappings, params = _a.params;
|
|
this._paramMappings = paramMappings;
|
|
this._params = params;
|
|
};
|
|
NeuralNetwork.prototype.traversePropertyPath = function (paramPath) {
|
|
if (!this.params) {
|
|
throw new Error("traversePropertyPath - model has no loaded params");
|
|
}
|
|
var result = paramPath.split('/').reduce(function (res, objProp) {
|
|
if (!res.nextObj.hasOwnProperty(objProp)) {
|
|
throw new Error("traversePropertyPath - object does not have property " + objProp + ", for path " + paramPath);
|
|
}
|
|
return { obj: res.nextObj, objProp: objProp, nextObj: res.nextObj[objProp] };
|
|
}, { nextObj: this.params });
|
|
var obj = result.obj, objProp = result.objProp;
|
|
if (!obj || !objProp || !(obj[objProp] instanceof Tensor)) {
|
|
throw new Error("traversePropertyPath - parameter is not a tensor, for path " + paramPath);
|
|
}
|
|
return { obj: obj, objProp: objProp };
|
|
};
|
|
return NeuralNetwork;
|
|
}());
|
|
|
|
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));
|
|
var height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top));
|
|
var interSection = width * height;
|
|
return isIOU
|
|
? interSection / (box1.area + box2.area - interSection)
|
|
: interSection / Math.min(box1.area, box2.area);
|
|
}
|
|
|
|
function nonMaxSuppression$1(boxes, scores, iouThreshold, isIOU) {
|
|
if (isIOU === void 0) { isIOU = true; }
|
|
var indicesSortedByScore = scores
|
|
.map(function (score, boxIndex) { return ({ score: score, boxIndex: boxIndex }); })
|
|
.sort(function (c1, c2) { return c1.score - c2.score; })
|
|
.map(function (c) { return c.boxIndex; });
|
|
var pick = [];
|
|
var _loop_1 = function () {
|
|
var curr = indicesSortedByScore.pop();
|
|
pick.push(curr);
|
|
var indices = indicesSortedByScore;
|
|
var outputs = [];
|
|
for (var i = 0; i < indices.length; i++) {
|
|
var idx = indices[i];
|
|
var currBox = boxes[curr];
|
|
var idxBox = boxes[idx];
|
|
outputs.push(iou(currBox, idxBox, isIOU));
|
|
}
|
|
indicesSortedByScore = indicesSortedByScore.filter(function (_, j) { return outputs[j] <= iouThreshold; });
|
|
};
|
|
while (indicesSortedByScore.length > 0) {
|
|
_loop_1();
|
|
}
|
|
return pick;
|
|
}
|
|
|
|
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 shuffleArray(inputArray) {
|
|
var array = inputArray.slice();
|
|
for (var i = array.length - 1; i > 0; i--) {
|
|
var j = Math.floor(Math.random() * (i + 1));
|
|
var x = array[i];
|
|
array[i] = array[j];
|
|
array[j] = x;
|
|
}
|
|
return array;
|
|
}
|
|
|
|
function sigmoid$1(x) {
|
|
return 1 / (1 + Math.exp(-x));
|
|
}
|
|
function inverseSigmoid(x) {
|
|
return Math.log(x / (1 - x));
|
|
}
|
|
|
|
var isNumber$1 = function (arg) { return typeof arg === 'number'; };
|
|
function validateConfig(config) {
|
|
if (!config) {
|
|
throw new Error("invalid config: " + config);
|
|
}
|
|
if (typeof config.withSeparableConvs !== 'boolean') {
|
|
throw new Error("config.withSeparableConvs has to be a boolean, have: " + config.withSeparableConvs);
|
|
}
|
|
if (!isNumber$1(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {
|
|
throw new Error("config.iouThreshold has to be a number between [0, 1], have: " + config.iouThreshold);
|
|
}
|
|
if (!Array.isArray(config.classes)
|
|
|| !config.classes.length
|
|
|| !config.classes.every(function (c) { return typeof c === 'string'; })) {
|
|
throw new Error("config.classes has to be an array class names: string[], have: " + JSON.stringify(config.classes));
|
|
}
|
|
if (!Array.isArray(config.anchors)
|
|
|| !config.anchors.length
|
|
|| !config.anchors.map(function (a) { return a || {}; }).every(function (a) { return isNumber$1(a.x) && isNumber$1(a.y); })) {
|
|
throw new Error("config.anchors has to be an array of { x: number, y: number }, have: " + JSON.stringify(config.anchors));
|
|
}
|
|
if (config.meanRgb && (!Array.isArray(config.meanRgb)
|
|
|| config.meanRgb.length !== 3
|
|
|| !config.meanRgb.every(isNumber$1))) {
|
|
throw new Error("config.meanRgb has to be an array of shape [number, number, number], have: " + JSON.stringify(config.meanRgb));
|
|
}
|
|
}
|
|
|
|
function leaky(x) {
|
|
return tidy(function () {
|
|
var min = mul(x, scalar(0.10000000149011612));
|
|
return add(relu(sub(x, min)), min);
|
|
//return tf.maximum(x, min)
|
|
});
|
|
}
|
|
|
|
function convWithBatchNorm(x, params) {
|
|
return tidy(function () {
|
|
var out = pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);
|
|
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);
|
|
});
|
|
}
|
|
|
|
function depthwiseSeparableConv(x, params) {
|
|
return tidy(function () {
|
|
var out = pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);
|
|
out = separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid');
|
|
out = add(out, params.bias);
|
|
return leaky(out);
|
|
});
|
|
}
|
|
|
|
function extractorsFactory(extractWeights, paramMappings) {
|
|
var extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);
|
|
function extractBatchNormParams(size, mappedPrefix) {
|
|
var sub = tensor1d(extractWeights(size));
|
|
var truediv = tensor1d(extractWeights(size));
|
|
paramMappings.push({ paramPath: mappedPrefix + "/sub" }, { paramPath: mappedPrefix + "/truediv" });
|
|
return { sub: sub, truediv: truediv };
|
|
}
|
|
function extractConvWithBatchNormParams(channelsIn, channelsOut, mappedPrefix) {
|
|
var conv = extractConvParams(channelsIn, channelsOut, 3, mappedPrefix + "/conv");
|
|
var bn = extractBatchNormParams(channelsOut, mappedPrefix + "/bn");
|
|
return { conv: conv, bn: bn };
|
|
}
|
|
var extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);
|
|
return {
|
|
extractConvParams: extractConvParams,
|
|
extractConvWithBatchNormParams: extractConvWithBatchNormParams,
|
|
extractSeparableConvParams: extractSeparableConvParams
|
|
};
|
|
}
|
|
function extractParams(weights, config, boxEncodingSize, filterSizes) {
|
|
var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
|
|
var paramMappings = [];
|
|
var _b = extractorsFactory(extractWeights, paramMappings), extractConvParams = _b.extractConvParams, extractConvWithBatchNormParams = _b.extractConvWithBatchNormParams, extractSeparableConvParams = _b.extractSeparableConvParams;
|
|
var params;
|
|
if (config.withSeparableConvs) {
|
|
var s0 = filterSizes[0], s1 = filterSizes[1], s2 = filterSizes[2], s3 = filterSizes[3], s4 = filterSizes[4], s5 = filterSizes[5], s6 = filterSizes[6], s7 = filterSizes[7], s8 = filterSizes[8];
|
|
var conv0 = config.isFirstLayerConv2d
|
|
? extractConvParams(s0, s1, 3, 'conv0')
|
|
: extractSeparableConvParams(s0, s1, 'conv0');
|
|
var conv1 = extractSeparableConvParams(s1, s2, 'conv1');
|
|
var conv2 = extractSeparableConvParams(s2, s3, 'conv2');
|
|
var conv3 = extractSeparableConvParams(s3, s4, 'conv3');
|
|
var conv4 = extractSeparableConvParams(s4, s5, 'conv4');
|
|
var conv5 = extractSeparableConvParams(s5, s6, 'conv5');
|
|
var conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined;
|
|
var conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined;
|
|
var conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8');
|
|
params = { conv0: conv0, conv1: conv1, conv2: conv2, conv3: conv3, conv4: conv4, conv5: conv5, conv6: conv6, conv7: conv7, conv8: conv8 };
|
|
}
|
|
else {
|
|
var s0 = filterSizes[0], s1 = filterSizes[1], s2 = filterSizes[2], s3 = filterSizes[3], s4 = filterSizes[4], s5 = filterSizes[5], s6 = filterSizes[6], s7 = filterSizes[7], s8 = filterSizes[8];
|
|
var conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0');
|
|
var conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1');
|
|
var conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2');
|
|
var conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3');
|
|
var conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4');
|
|
var conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5');
|
|
var conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6');
|
|
var conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7');
|
|
var conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8');
|
|
params = { conv0: conv0, conv1: conv1, conv2: conv2, conv3: conv3, conv4: conv4, conv5: conv5, conv6: conv6, conv7: conv7, conv8: conv8 };
|
|
}
|
|
if (getRemainingWeights().length !== 0) {
|
|
throw new Error("weights remaing after extract: " + getRemainingWeights().length);
|
|
}
|
|
return { params: params, paramMappings: paramMappings };
|
|
}
|
|
|
|
function extractorsFactory$1(weightMap, paramMappings) {
|
|
var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
|
|
function extractBatchNormParams(prefix) {
|
|
var sub = extractWeightEntry(prefix + "/sub", 1);
|
|
var truediv = extractWeightEntry(prefix + "/truediv", 1);
|
|
return { sub: sub, truediv: truediv };
|
|
}
|
|
function extractConvParams(prefix) {
|
|
var filters = extractWeightEntry(prefix + "/filters", 4);
|
|
var bias = extractWeightEntry(prefix + "/bias", 1);
|
|
return { filters: filters, bias: bias };
|
|
}
|
|
function extractConvWithBatchNormParams(prefix) {
|
|
var conv = extractConvParams(prefix + "/conv");
|
|
var bn = extractBatchNormParams(prefix + "/bn");
|
|
return { conv: conv, bn: bn };
|
|
}
|
|
var extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);
|
|
return {
|
|
extractConvParams: extractConvParams,
|
|
extractConvWithBatchNormParams: extractConvWithBatchNormParams,
|
|
extractSeparableConvParams: extractSeparableConvParams
|
|
};
|
|
}
|
|
function extractParamsFromWeigthMap(weightMap, config) {
|
|
var paramMappings = [];
|
|
var _a = extractorsFactory$1(weightMap, paramMappings), extractConvParams = _a.extractConvParams, extractConvWithBatchNormParams = _a.extractConvWithBatchNormParams, extractSeparableConvParams = _a.extractSeparableConvParams;
|
|
var params;
|
|
if (config.withSeparableConvs) {
|
|
var numFilters = (config.filterSizes && config.filterSizes.length || 9);
|
|
params = {
|
|
conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),
|
|
conv1: extractSeparableConvParams('conv1'),
|
|
conv2: extractSeparableConvParams('conv2'),
|
|
conv3: extractSeparableConvParams('conv3'),
|
|
conv4: extractSeparableConvParams('conv4'),
|
|
conv5: extractSeparableConvParams('conv5'),
|
|
conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,
|
|
conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,
|
|
conv8: extractConvParams('conv8')
|
|
};
|
|
}
|
|
else {
|
|
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'),
|
|
conv8: extractConvParams('conv8')
|
|
};
|
|
}
|
|
disposeUnusedWeightTensors(weightMap, paramMappings);
|
|
return { params: params, paramMappings: paramMappings };
|
|
}
|
|
|
|
var TinyYolov2SizeType;
|
|
(function (TinyYolov2SizeType) {
|
|
TinyYolov2SizeType[TinyYolov2SizeType["XS"] = 224] = "XS";
|
|
TinyYolov2SizeType[TinyYolov2SizeType["SM"] = 320] = "SM";
|
|
TinyYolov2SizeType[TinyYolov2SizeType["MD"] = 416] = "MD";
|
|
TinyYolov2SizeType[TinyYolov2SizeType["LG"] = 608] = "LG";
|
|
})(TinyYolov2SizeType || (TinyYolov2SizeType = {}));
|
|
var TinyYolov2Options = /** @class */ (function () {
|
|
function TinyYolov2Options(_a) {
|
|
var _b = _a === void 0 ? {} : _a, inputSize = _b.inputSize, scoreThreshold = _b.scoreThreshold;
|
|
this._name = 'TinyYolov2Options';
|
|
this._inputSize = inputSize || 416;
|
|
this._scoreThreshold = scoreThreshold || 0.5;
|
|
if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {
|
|
throw new Error(this._name + " - expected inputSize to be a number divisible by 32");
|
|
}
|
|
if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {
|
|
throw new Error(this._name + " - expected scoreThreshold to be a number between 0 and 1");
|
|
}
|
|
}
|
|
Object.defineProperty(TinyYolov2Options.prototype, "inputSize", {
|
|
get: function () { return this._inputSize; },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(TinyYolov2Options.prototype, "scoreThreshold", {
|
|
get: function () { return this._scoreThreshold; },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
return TinyYolov2Options;
|
|
}());
|
|
|
|
var TinyYolov2 = /** @class */ (function (_super) {
|
|
__extends(TinyYolov2, _super);
|
|
function TinyYolov2(config) {
|
|
var _this = _super.call(this, 'TinyYolov2') || this;
|
|
validateConfig(config);
|
|
_this._config = config;
|
|
return _this;
|
|
}
|
|
Object.defineProperty(TinyYolov2.prototype, "config", {
|
|
get: function () {
|
|
return this._config;
|
|
},
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(TinyYolov2.prototype, "withClassScores", {
|
|
get: function () {
|
|
return this.config.withClassScores || this.config.classes.length > 1;
|
|
},
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(TinyYolov2.prototype, "boxEncodingSize", {
|
|
get: function () {
|
|
return 5 + (this.withClassScores ? this.config.classes.length : 0);
|
|
},
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
TinyYolov2.prototype.runTinyYolov2 = function (x, params) {
|
|
var out = convWithBatchNorm(x, params.conv0);
|
|
out = maxPool(out, [2, 2], [2, 2], 'same');
|
|
out = convWithBatchNorm(out, params.conv1);
|
|
out = maxPool(out, [2, 2], [2, 2], 'same');
|
|
out = convWithBatchNorm(out, params.conv2);
|
|
out = maxPool(out, [2, 2], [2, 2], 'same');
|
|
out = convWithBatchNorm(out, params.conv3);
|
|
out = maxPool(out, [2, 2], [2, 2], 'same');
|
|
out = convWithBatchNorm(out, params.conv4);
|
|
out = maxPool(out, [2, 2], [2, 2], 'same');
|
|
out = convWithBatchNorm(out, params.conv5);
|
|
out = maxPool(out, [2, 2], [1, 1], 'same');
|
|
out = convWithBatchNorm(out, params.conv6);
|
|
out = convWithBatchNorm(out, params.conv7);
|
|
return convLayer(out, params.conv8, 'valid', false);
|
|
};
|
|
TinyYolov2.prototype.runMobilenet = function (x, params) {
|
|
var out = this.config.isFirstLayerConv2d
|
|
? leaky(convLayer(x, params.conv0, 'valid', false))
|
|
: depthwiseSeparableConv(x, params.conv0);
|
|
out = maxPool(out, [2, 2], [2, 2], 'same');
|
|
out = depthwiseSeparableConv(out, params.conv1);
|
|
out = maxPool(out, [2, 2], [2, 2], 'same');
|
|
out = depthwiseSeparableConv(out, params.conv2);
|
|
out = maxPool(out, [2, 2], [2, 2], 'same');
|
|
out = depthwiseSeparableConv(out, params.conv3);
|
|
out = maxPool(out, [2, 2], [2, 2], 'same');
|
|
out = depthwiseSeparableConv(out, params.conv4);
|
|
out = maxPool(out, [2, 2], [2, 2], 'same');
|
|
out = depthwiseSeparableConv(out, params.conv5);
|
|
out = maxPool(out, [2, 2], [1, 1], 'same');
|
|
out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out;
|
|
out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out;
|
|
return convLayer(out, params.conv8, 'valid', false);
|
|
};
|
|
TinyYolov2.prototype.forwardInput = function (input, inputSize) {
|
|
var _this = this;
|
|
var params = this.params;
|
|
if (!params) {
|
|
throw new Error('TinyYolov2 - load model before inference');
|
|
}
|
|
return tidy(function () {
|
|
var batchTensor = input.toBatchTensor(inputSize, false).toFloat();
|
|
batchTensor = _this.config.meanRgb
|
|
? normalize(batchTensor, _this.config.meanRgb)
|
|
: batchTensor;
|
|
batchTensor = batchTensor.div(scalar(256));
|
|
return _this.config.withSeparableConvs
|
|
? _this.runMobilenet(batchTensor, params)
|
|
: _this.runTinyYolov2(batchTensor, params);
|
|
});
|
|
};
|
|
TinyYolov2.prototype.forward = function (input, inputSize) {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var _a;
|
|
return __generator(this, function (_b) {
|
|
switch (_b.label) {
|
|
case 0:
|
|
_a = this.forwardInput;
|
|
return [4 /*yield*/, toNetInput(input)];
|
|
case 1: return [4 /*yield*/, _a.apply(this, [_b.sent(), inputSize])];
|
|
case 2: return [2 /*return*/, _b.sent()];
|
|
}
|
|
});
|
|
});
|
|
};
|
|
TinyYolov2.prototype.detect = function (input, forwardParams) {
|
|
if (forwardParams === void 0) { forwardParams = {}; }
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var _a, inputSize, scoreThreshold, netInput, out, out0, inputDimensions, results, boxes, scores, classScores, classNames, indices, detections;
|
|
var _this = this;
|
|
return __generator(this, function (_b) {
|
|
switch (_b.label) {
|
|
case 0:
|
|
_a = new TinyYolov2Options(forwardParams), inputSize = _a.inputSize, scoreThreshold = _a.scoreThreshold;
|
|
return [4 /*yield*/, toNetInput(input)];
|
|
case 1:
|
|
netInput = _b.sent();
|
|
return [4 /*yield*/, this.forwardInput(netInput, inputSize)];
|
|
case 2:
|
|
out = _b.sent();
|
|
out0 = tidy(function () { return unstack(out)[0].expandDims(); });
|
|
inputDimensions = {
|
|
width: netInput.getInputWidth(0),
|
|
height: netInput.getInputHeight(0)
|
|
};
|
|
return [4 /*yield*/, this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold)];
|
|
case 3:
|
|
results = _b.sent();
|
|
out.dispose();
|
|
out0.dispose();
|
|
boxes = results.map(function (res) { return res.box; });
|
|
scores = results.map(function (res) { return res.score; });
|
|
classScores = results.map(function (res) { return res.classScore; });
|
|
classNames = results.map(function (res) { return _this.config.classes[res.label]; });
|
|
indices = nonMaxSuppression$1(boxes.map(function (box) { return box.rescale(inputSize); }), scores, this.config.iouThreshold, true);
|
|
detections = indices.map(function (idx) {
|
|
return new ObjectDetection(scores[idx], classScores[idx], classNames[idx], boxes[idx], inputDimensions);
|
|
});
|
|
return [2 /*return*/, detections];
|
|
}
|
|
});
|
|
});
|
|
};
|
|
TinyYolov2.prototype.getDefaultModelName = function () {
|
|
return '';
|
|
};
|
|
TinyYolov2.prototype.extractParamsFromWeigthMap = function (weightMap) {
|
|
return extractParamsFromWeigthMap(weightMap, this.config);
|
|
};
|
|
TinyYolov2.prototype.extractParams = function (weights) {
|
|
var filterSizes = this.config.filterSizes || TinyYolov2.DEFAULT_FILTER_SIZES;
|
|
var numFilters = filterSizes ? filterSizes.length : undefined;
|
|
if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {
|
|
throw new Error("TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found " + numFilters + " filterSizes in config");
|
|
}
|
|
return extractParams(weights, this.config, this.boxEncodingSize, filterSizes);
|
|
};
|
|
TinyYolov2.prototype.extractBoxes = function (outputTensor, inputBlobDimensions, scoreThreshold) {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var width, height, inputSize, correctionFactorX, correctionFactorY, numCells, numBoxes, _a, boxesTensor, scoresTensor, classScoresTensor, results, scoresData, boxesData, row, col, anchor, score, ctX, ctY, width_1, height_1, x, y, pos, _b, classScore, label, _c;
|
|
var _this = this;
|
|
return __generator(this, function (_d) {
|
|
switch (_d.label) {
|
|
case 0:
|
|
width = inputBlobDimensions.width, height = inputBlobDimensions.height;
|
|
inputSize = Math.max(width, height);
|
|
correctionFactorX = inputSize / width;
|
|
correctionFactorY = inputSize / height;
|
|
numCells = outputTensor.shape[1];
|
|
numBoxes = this.config.anchors.length;
|
|
_a = tidy(function () {
|
|
var reshaped = outputTensor.reshape([numCells, numCells, numBoxes, _this.boxEncodingSize]);
|
|
var boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4]);
|
|
var scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1]);
|
|
var classScores = _this.withClassScores
|
|
? softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, _this.config.classes.length]), 3)
|
|
: scalar(0);
|
|
return [boxes, scores, classScores];
|
|
}), boxesTensor = _a[0], scoresTensor = _a[1], classScoresTensor = _a[2];
|
|
results = [];
|
|
return [4 /*yield*/, scoresTensor.array()];
|
|
case 1:
|
|
scoresData = _d.sent();
|
|
return [4 /*yield*/, boxesTensor.array()];
|
|
case 2:
|
|
boxesData = _d.sent();
|
|
row = 0;
|
|
_d.label = 3;
|
|
case 3:
|
|
if (!(row < numCells)) return [3 /*break*/, 12];
|
|
col = 0;
|
|
_d.label = 4;
|
|
case 4:
|
|
if (!(col < numCells)) return [3 /*break*/, 11];
|
|
anchor = 0;
|
|
_d.label = 5;
|
|
case 5:
|
|
if (!(anchor < numBoxes)) return [3 /*break*/, 10];
|
|
score = sigmoid$1(scoresData[row][col][anchor][0]);
|
|
if (!(!scoreThreshold || score > scoreThreshold)) return [3 /*break*/, 9];
|
|
ctX = ((col + sigmoid$1(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX;
|
|
ctY = ((row + sigmoid$1(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY;
|
|
width_1 = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX;
|
|
height_1 = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY;
|
|
x = (ctX - (width_1 / 2));
|
|
y = (ctY - (height_1 / 2));
|
|
pos = { row: row, col: col, anchor: anchor };
|
|
if (!this.withClassScores) return [3 /*break*/, 7];
|
|
return [4 /*yield*/, this.extractPredictedClass(classScoresTensor, pos)];
|
|
case 6:
|
|
_c = _d.sent();
|
|
return [3 /*break*/, 8];
|
|
case 7:
|
|
_c = { classScore: 1, label: 0 };
|
|
_d.label = 8;
|
|
case 8:
|
|
_b = _c, classScore = _b.classScore, label = _b.label;
|
|
results.push(__assign({ box: new BoundingBox(x, y, x + width_1, y + height_1), score: score, classScore: score * classScore, label: label }, pos));
|
|
_d.label = 9;
|
|
case 9:
|
|
anchor++;
|
|
return [3 /*break*/, 5];
|
|
case 10:
|
|
col++;
|
|
return [3 /*break*/, 4];
|
|
case 11:
|
|
row++;
|
|
return [3 /*break*/, 3];
|
|
case 12:
|
|
boxesTensor.dispose();
|
|
scoresTensor.dispose();
|
|
classScoresTensor.dispose();
|
|
return [2 /*return*/, results];
|
|
}
|
|
});
|
|
});
|
|
};
|
|
TinyYolov2.prototype.extractPredictedClass = function (classesTensor, pos) {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var row, col, anchor, classesData;
|
|
return __generator(this, function (_a) {
|
|
switch (_a.label) {
|
|
case 0:
|
|
row = pos.row, col = pos.col, anchor = pos.anchor;
|
|
return [4 /*yield*/, classesTensor.array()];
|
|
case 1:
|
|
classesData = _a.sent();
|
|
return [2 /*return*/, Array(this.config.classes.length).fill(0)
|
|
.map(function (_, i) { return classesData[row][col][anchor][i]; })
|
|
.map(function (classScore, label) { return ({
|
|
classScore: classScore,
|
|
label: label
|
|
}); })
|
|
.reduce(function (max, curr) { return max.classScore > curr.classScore ? max : curr; })];
|
|
}
|
|
});
|
|
});
|
|
};
|
|
TinyYolov2.DEFAULT_FILTER_SIZES = [
|
|
3, 16, 32, 64, 128, 256, 512, 1024, 1024
|
|
];
|
|
return TinyYolov2;
|
|
}(NeuralNetwork));
|
|
|
|
|
|
|
|
var tfjsImageRecognitionBase = /*#__PURE__*/Object.freeze({
|
|
convLayer: convLayer,
|
|
disposeUnusedWeightTensors: disposeUnusedWeightTensors,
|
|
extractConvParamsFactory: extractConvParamsFactory,
|
|
extractFCParamsFactory: extractFCParamsFactory,
|
|
extractSeparableConvParamsFactory: extractSeparableConvParamsFactory,
|
|
loadSeparableConvParamsFactory: loadSeparableConvParamsFactory,
|
|
extractWeightEntryFactory: extractWeightEntryFactory,
|
|
extractWeightsFactory: extractWeightsFactory,
|
|
getModelUris: getModelUris,
|
|
SeparableConvParams: SeparableConvParams,
|
|
TinyYolov2: TinyYolov2,
|
|
get TinyYolov2SizeType () { return TinyYolov2SizeType; },
|
|
TinyYolov2Options: TinyYolov2Options,
|
|
validateConfig: validateConfig
|
|
});
|
|
|
|
function drawContour(ctx, points, isClosed) {
|
|
if (isClosed === void 0) { isClosed = false; }
|
|
ctx.beginPath();
|
|
points.slice(1).forEach(function (_a, prevIdx) {
|
|
var x = _a.x, y = _a.y;
|
|
var from = points[prevIdx];
|
|
ctx.moveTo(from.x, from.y);
|
|
ctx.lineTo(x, y);
|
|
});
|
|
if (isClosed) {
|
|
var from = points[points.length - 1];
|
|
var to = points[0];
|
|
if (!from || !to) {
|
|
return;
|
|
}
|
|
ctx.moveTo(from.x, from.y);
|
|
ctx.lineTo(to.x, to.y);
|
|
}
|
|
ctx.stroke();
|
|
}
|
|
|
|
var FaceDetection = /** @class */ (function (_super) {
|
|
__extends(FaceDetection, _super);
|
|
function FaceDetection(score, relativeBox, imageDims) {
|
|
return _super.call(this, score, score, '', relativeBox, imageDims) || this;
|
|
}
|
|
FaceDetection.prototype.forSize = function (width, height) {
|
|
var _a = _super.prototype.forSize.call(this, width, height), score = _a.score, relativeBox = _a.relativeBox, imageDims = _a.imageDims;
|
|
return new FaceDetection(score, relativeBox, imageDims);
|
|
};
|
|
return FaceDetection;
|
|
}(ObjectDetection));
|
|
|
|
function isWithFaceDetection(obj) {
|
|
return obj['detection'] instanceof FaceDetection;
|
|
}
|
|
function extendWithFaceDetection(sourceObj, detection) {
|
|
var extension = { detection: detection };
|
|
return Object.assign({}, sourceObj, extension);
|
|
}
|
|
|
|
function drawDetections(canvasArg, detections) {
|
|
var detectionsArray = Array.isArray(detections) ? detections : [detections];
|
|
detectionsArray.forEach(function (det) {
|
|
var score = det instanceof FaceDetection
|
|
? det.score
|
|
: (isWithFaceDetection(det) ? det.detection.score : undefined);
|
|
var box = det instanceof FaceDetection
|
|
? det.box
|
|
: (isWithFaceDetection(det) ? det.detection.box : new Box(det));
|
|
var label = score ? "" + round$1(score) : undefined;
|
|
new DrawBox(box, { label: label }).draw(canvasArg);
|
|
});
|
|
}
|
|
|
|
function depthwiseSeparableConv$1(x, params, stride) {
|
|
return tidy(function () {
|
|
var out = separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same');
|
|
out = add(out, params.bias);
|
|
return out;
|
|
});
|
|
}
|
|
|
|
function denseBlock3(x, denseBlockParams, isFirstLayer) {
|
|
if (isFirstLayer === void 0) { isFirstLayer = false; }
|
|
return tidy(function () {
|
|
var out1 = relu(isFirstLayer
|
|
? add(conv2d(x, denseBlockParams.conv0.filters, [2, 2], 'same'), denseBlockParams.conv0.bias)
|
|
: depthwiseSeparableConv$1(x, denseBlockParams.conv0, [2, 2]));
|
|
var out2 = depthwiseSeparableConv$1(out1, denseBlockParams.conv1, [1, 1]);
|
|
var in3 = relu(add(out1, out2));
|
|
var out3 = depthwiseSeparableConv$1(in3, denseBlockParams.conv2, [1, 1]);
|
|
return relu(add(out1, add(out2, out3)));
|
|
});
|
|
}
|
|
function denseBlock4(x, denseBlockParams, isFirstLayer, isScaleDown) {
|
|
if (isFirstLayer === void 0) { isFirstLayer = false; }
|
|
if (isScaleDown === void 0) { isScaleDown = true; }
|
|
return tidy(function () {
|
|
var out1 = relu(isFirstLayer
|
|
? add(conv2d(x, denseBlockParams.conv0.filters, isScaleDown ? [2, 2] : [1, 1], 'same'), denseBlockParams.conv0.bias)
|
|
: depthwiseSeparableConv$1(x, denseBlockParams.conv0, isScaleDown ? [2, 2] : [1, 1]));
|
|
var out2 = depthwiseSeparableConv$1(out1, denseBlockParams.conv1, [1, 1]);
|
|
var in3 = relu(add(out1, out2));
|
|
var out3 = depthwiseSeparableConv$1(in3, denseBlockParams.conv2, [1, 1]);
|
|
var in4 = relu(add(out1, add(out2, out3)));
|
|
var out4 = depthwiseSeparableConv$1(in4, denseBlockParams.conv3, [1, 1]);
|
|
return relu(add(out1, add(out2, add(out3, out4))));
|
|
});
|
|
}
|
|
|
|
function extractorsFactory$2(extractWeights, paramMappings) {
|
|
var extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);
|
|
var extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);
|
|
function extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer) {
|
|
if (isFirstLayer === void 0) { isFirstLayer = false; }
|
|
var conv0 = isFirstLayer
|
|
? extractConvParams(channelsIn, channelsOut, 3, mappedPrefix + "/conv0")
|
|
: extractSeparableConvParams(channelsIn, channelsOut, mappedPrefix + "/conv0");
|
|
var conv1 = extractSeparableConvParams(channelsOut, channelsOut, mappedPrefix + "/conv1");
|
|
var conv2 = extractSeparableConvParams(channelsOut, channelsOut, mappedPrefix + "/conv2");
|
|
return { conv0: conv0, conv1: conv1, conv2: conv2 };
|
|
}
|
|
function extractDenseBlock4Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer) {
|
|
if (isFirstLayer === void 0) { isFirstLayer = false; }
|
|
var _a = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer), conv0 = _a.conv0, conv1 = _a.conv1, conv2 = _a.conv2;
|
|
var conv3 = extractSeparableConvParams(channelsOut, channelsOut, mappedPrefix + "/conv3");
|
|
return { conv0: conv0, conv1: conv1, conv2: conv2, conv3: conv3 };
|
|
}
|
|
return {
|
|
extractDenseBlock3Params: extractDenseBlock3Params,
|
|
extractDenseBlock4Params: extractDenseBlock4Params
|
|
};
|
|
}
|
|
|
|
function extractParams$1(weights) {
|
|
var paramMappings = [];
|
|
var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
|
|
var extractDenseBlock4Params = extractorsFactory$2(extractWeights, paramMappings).extractDenseBlock4Params;
|
|
var dense0 = extractDenseBlock4Params(3, 32, 'dense0', true);
|
|
var dense1 = extractDenseBlock4Params(32, 64, 'dense1');
|
|
var dense2 = extractDenseBlock4Params(64, 128, 'dense2');
|
|
var dense3 = extractDenseBlock4Params(128, 256, 'dense3');
|
|
if (getRemainingWeights().length !== 0) {
|
|
throw new Error("weights remaing after extract: " + getRemainingWeights().length);
|
|
}
|
|
return {
|
|
paramMappings: paramMappings,
|
|
params: { dense0: dense0, dense1: dense1, dense2: dense2, dense3: dense3 }
|
|
};
|
|
}
|
|
|
|
function loadConvParamsFactory(extractWeightEntry) {
|
|
return function (prefix) {
|
|
var filters = extractWeightEntry(prefix + "/filters", 4);
|
|
var bias = extractWeightEntry(prefix + "/bias", 1);
|
|
return { filters: filters, bias: bias };
|
|
};
|
|
}
|
|
|
|
function loadParamsFactory(weightMap, paramMappings) {
|
|
var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
|
|
var extractConvParams = loadConvParamsFactory(extractWeightEntry);
|
|
var extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);
|
|
function extractDenseBlock3Params(prefix, isFirstLayer) {
|
|
if (isFirstLayer === void 0) { isFirstLayer = false; }
|
|
var conv0 = isFirstLayer
|
|
? extractConvParams(prefix + "/conv0")
|
|
: extractSeparableConvParams(prefix + "/conv0");
|
|
var conv1 = extractSeparableConvParams(prefix + "/conv1");
|
|
var conv2 = extractSeparableConvParams(prefix + "/conv2");
|
|
return { conv0: conv0, conv1: conv1, conv2: conv2 };
|
|
}
|
|
function extractDenseBlock4Params(prefix, isFirstLayer) {
|
|
if (isFirstLayer === void 0) { isFirstLayer = false; }
|
|
var conv0 = isFirstLayer
|
|
? extractConvParams(prefix + "/conv0")
|
|
: extractSeparableConvParams(prefix + "/conv0");
|
|
var conv1 = extractSeparableConvParams(prefix + "/conv1");
|
|
var conv2 = extractSeparableConvParams(prefix + "/conv2");
|
|
var conv3 = extractSeparableConvParams(prefix + "/conv3");
|
|
return { conv0: conv0, conv1: conv1, conv2: conv2, conv3: conv3 };
|
|
}
|
|
return {
|
|
extractDenseBlock3Params: extractDenseBlock3Params,
|
|
extractDenseBlock4Params: extractDenseBlock4Params
|
|
};
|
|
}
|
|
|
|
function extractParamsFromWeigthMap$1(weightMap) {
|
|
var paramMappings = [];
|
|
var extractDenseBlock4Params = loadParamsFactory(weightMap, paramMappings).extractDenseBlock4Params;
|
|
var params = {
|
|
dense0: extractDenseBlock4Params('dense0', true),
|
|
dense1: extractDenseBlock4Params('dense1'),
|
|
dense2: extractDenseBlock4Params('dense2'),
|
|
dense3: extractDenseBlock4Params('dense3')
|
|
};
|
|
disposeUnusedWeightTensors(weightMap, paramMappings);
|
|
return { params: params, paramMappings: paramMappings };
|
|
}
|
|
|
|
var FaceFeatureExtractor = /** @class */ (function (_super) {
|
|
__extends(FaceFeatureExtractor, _super);
|
|
function FaceFeatureExtractor() {
|
|
return _super.call(this, 'FaceFeatureExtractor') || this;
|
|
}
|
|
FaceFeatureExtractor.prototype.forwardInput = function (input) {
|
|
var params = this.params;
|
|
if (!params) {
|
|
throw new Error('FaceFeatureExtractor - load model before inference');
|
|
}
|
|
return tidy(function () {
|
|
var batchTensor = input.toBatchTensor(112, true);
|
|
var meanRgb = [122.782, 117.001, 104.298];
|
|
var normalized = normalize(batchTensor, meanRgb).div(scalar(255));
|
|
var out = denseBlock4(normalized, params.dense0, true);
|
|
out = denseBlock4(out, params.dense1);
|
|
out = denseBlock4(out, params.dense2);
|
|
out = denseBlock4(out, params.dense3);
|
|
out = avgPool(out, [7, 7], [2, 2], 'valid');
|
|
return out;
|
|
});
|
|
};
|
|
FaceFeatureExtractor.prototype.forward = function (input) {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var _a;
|
|
return __generator(this, function (_b) {
|
|
switch (_b.label) {
|
|
case 0:
|
|
_a = this.forwardInput;
|
|
return [4 /*yield*/, toNetInput(input)];
|
|
case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
|
|
}
|
|
});
|
|
});
|
|
};
|
|
FaceFeatureExtractor.prototype.getDefaultModelName = function () {
|
|
return 'face_feature_extractor_model';
|
|
};
|
|
FaceFeatureExtractor.prototype.extractParamsFromWeigthMap = function (weightMap) {
|
|
return extractParamsFromWeigthMap$1(weightMap);
|
|
};
|
|
FaceFeatureExtractor.prototype.extractParams = function (weights) {
|
|
return extractParams$1(weights);
|
|
};
|
|
return FaceFeatureExtractor;
|
|
}(NeuralNetwork));
|
|
|
|
function fullyConnectedLayer(x, params) {
|
|
return tidy(function () {
|
|
return add(matMul(x, params.weights), params.bias);
|
|
});
|
|
}
|
|
|
|
function extractParams$2(weights, channelsIn, channelsOut) {
|
|
var paramMappings = [];
|
|
var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
|
|
var extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);
|
|
var fc = extractFCParams(channelsIn, channelsOut, 'fc');
|
|
if (getRemainingWeights().length !== 0) {
|
|
throw new Error("weights remaing after extract: " + getRemainingWeights().length);
|
|
}
|
|
return {
|
|
paramMappings: paramMappings,
|
|
params: { fc: fc }
|
|
};
|
|
}
|
|
|
|
function extractParamsFromWeigthMap$2(weightMap) {
|
|
var paramMappings = [];
|
|
var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
|
|
function extractFcParams(prefix) {
|
|
var weights = extractWeightEntry(prefix + "/weights", 2);
|
|
var bias = extractWeightEntry(prefix + "/bias", 1);
|
|
return { weights: weights, bias: bias };
|
|
}
|
|
var params = {
|
|
fc: extractFcParams('fc')
|
|
};
|
|
disposeUnusedWeightTensors(weightMap, paramMappings);
|
|
return { params: params, paramMappings: paramMappings };
|
|
}
|
|
|
|
function seperateWeightMaps(weightMap) {
|
|
var featureExtractorMap = {};
|
|
var classifierMap = {};
|
|
Object.keys(weightMap).forEach(function (key) {
|
|
var map = key.startsWith('fc') ? classifierMap : featureExtractorMap;
|
|
map[key] = weightMap[key];
|
|
});
|
|
return { featureExtractorMap: featureExtractorMap, classifierMap: classifierMap };
|
|
}
|
|
|
|
var FaceProcessor = /** @class */ (function (_super) {
|
|
__extends(FaceProcessor, _super);
|
|
function FaceProcessor(_name, faceFeatureExtractor) {
|
|
var _this = _super.call(this, _name) || this;
|
|
_this._faceFeatureExtractor = faceFeatureExtractor;
|
|
return _this;
|
|
}
|
|
Object.defineProperty(FaceProcessor.prototype, "faceFeatureExtractor", {
|
|
get: function () {
|
|
return this._faceFeatureExtractor;
|
|
},
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
FaceProcessor.prototype.runNet = function (input) {
|
|
var _this = this;
|
|
var params = this.params;
|
|
if (!params) {
|
|
throw new Error(this._name + " - load model before inference");
|
|
}
|
|
return tidy(function () {
|
|
var bottleneckFeatures = input instanceof NetInput
|
|
? _this.faceFeatureExtractor.forwardInput(input)
|
|
: input;
|
|
return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc);
|
|
});
|
|
};
|
|
FaceProcessor.prototype.dispose = function (throwOnRedispose) {
|
|
if (throwOnRedispose === void 0) { throwOnRedispose = true; }
|
|
this.faceFeatureExtractor.dispose(throwOnRedispose);
|
|
_super.prototype.dispose.call(this, throwOnRedispose);
|
|
};
|
|
FaceProcessor.prototype.loadClassifierParams = function (weights) {
|
|
var _a = this.extractClassifierParams(weights), params = _a.params, paramMappings = _a.paramMappings;
|
|
this._params = params;
|
|
this._paramMappings = paramMappings;
|
|
};
|
|
FaceProcessor.prototype.extractClassifierParams = function (weights) {
|
|
return extractParams$2(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut());
|
|
};
|
|
FaceProcessor.prototype.extractParamsFromWeigthMap = function (weightMap) {
|
|
var _a = seperateWeightMaps(weightMap), featureExtractorMap = _a.featureExtractorMap, classifierMap = _a.classifierMap;
|
|
this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);
|
|
return extractParamsFromWeigthMap$2(classifierMap);
|
|
};
|
|
FaceProcessor.prototype.extractParams = function (weights) {
|
|
var cIn = this.getClassifierChannelsIn();
|
|
var cOut = this.getClassifierChannelsOut();
|
|
var classifierWeightSize = (cOut * cIn) + cOut;
|
|
var featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);
|
|
var classifierWeights = weights.slice(weights.length - classifierWeightSize);
|
|
this.faceFeatureExtractor.extractWeights(featureExtractorWeights);
|
|
return this.extractClassifierParams(classifierWeights);
|
|
};
|
|
return FaceProcessor;
|
|
}(NeuralNetwork));
|
|
|
|
var FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised'];
|
|
var FaceExpressions = /** @class */ (function () {
|
|
function FaceExpressions(probabilities) {
|
|
var _this = this;
|
|
if (probabilities.length !== 7) {
|
|
throw new Error("FaceExpressions.constructor - expected probabilities.length to be 7, have: " + probabilities.length);
|
|
}
|
|
FACE_EXPRESSION_LABELS.forEach(function (expression, idx) {
|
|
_this[expression] = probabilities[idx];
|
|
});
|
|
}
|
|
FaceExpressions.prototype.asSortedArray = function () {
|
|
var _this = this;
|
|
return FACE_EXPRESSION_LABELS
|
|
.map(function (expression) { return ({ expression: expression, probability: _this[expression] }); })
|
|
.sort(function (e0, e1) { return e1.probability - e0.probability; });
|
|
};
|
|
return FaceExpressions;
|
|
}());
|
|
|
|
var FaceExpressionNet = /** @class */ (function (_super) {
|
|
__extends(FaceExpressionNet, _super);
|
|
function FaceExpressionNet(faceFeatureExtractor) {
|
|
if (faceFeatureExtractor === void 0) { faceFeatureExtractor = new FaceFeatureExtractor(); }
|
|
return _super.call(this, 'FaceExpressionNet', faceFeatureExtractor) || this;
|
|
}
|
|
FaceExpressionNet.prototype.forwardInput = function (input) {
|
|
var _this = this;
|
|
return tidy(function () { return softmax(_this.runNet(input)); });
|
|
};
|
|
FaceExpressionNet.prototype.forward = function (input) {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var _a;
|
|
return __generator(this, function (_b) {
|
|
switch (_b.label) {
|
|
case 0:
|
|
_a = this.forwardInput;
|
|
return [4 /*yield*/, toNetInput(input)];
|
|
case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
|
|
}
|
|
});
|
|
});
|
|
};
|
|
FaceExpressionNet.prototype.predictExpressions = function (input) {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var netInput, out, probabilitesByBatch, predictionsByBatch;
|
|
var _this = this;
|
|
return __generator(this, function (_a) {
|
|
switch (_a.label) {
|
|
case 0: return [4 /*yield*/, toNetInput(input)];
|
|
case 1:
|
|
netInput = _a.sent();
|
|
return [4 /*yield*/, this.forwardInput(netInput)];
|
|
case 2:
|
|
out = _a.sent();
|
|
return [4 /*yield*/, Promise.all(unstack(out).map(function (t) { return __awaiter(_this, void 0, void 0, function () {
|
|
var data;
|
|
return __generator(this, function (_a) {
|
|
switch (_a.label) {
|
|
case 0: return [4 /*yield*/, t.data()];
|
|
case 1:
|
|
data = _a.sent();
|
|
t.dispose();
|
|
return [2 /*return*/, data];
|
|
}
|
|
});
|
|
}); }))];
|
|
case 3:
|
|
probabilitesByBatch = _a.sent();
|
|
out.dispose();
|
|
predictionsByBatch = probabilitesByBatch
|
|
.map(function (probabilites) { return new FaceExpressions(probabilites); });
|
|
return [2 /*return*/, netInput.isBatchInput
|
|
? predictionsByBatch
|
|
: predictionsByBatch[0]];
|
|
}
|
|
});
|
|
});
|
|
};
|
|
FaceExpressionNet.prototype.getDefaultModelName = function () {
|
|
return 'face_expression_model';
|
|
};
|
|
FaceExpressionNet.prototype.getClassifierChannelsIn = function () {
|
|
return 256;
|
|
};
|
|
FaceExpressionNet.prototype.getClassifierChannelsOut = function () {
|
|
return 7;
|
|
};
|
|
return FaceExpressionNet;
|
|
}(FaceProcessor));
|
|
|
|
function isWithFaceExpressions(obj) {
|
|
return obj['expressions'] instanceof FaceExpressions;
|
|
}
|
|
function extendWithFaceExpressions(sourceObj, expressions) {
|
|
var extension = { expressions: expressions };
|
|
return Object.assign({}, sourceObj, extension);
|
|
}
|
|
|
|
function drawFaceExpressions(canvasArg, faceExpressions, minConfidence, textFieldAnchor) {
|
|
if (minConfidence === void 0) { minConfidence = 0.1; }
|
|
var faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions];
|
|
faceExpressionsArray.forEach(function (e) {
|
|
var expr = e instanceof FaceExpressions
|
|
? e
|
|
: (isWithFaceExpressions(e) ? e.expressions : undefined);
|
|
if (!expr) {
|
|
throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof');
|
|
}
|
|
var sorted = expr.asSortedArray();
|
|
var resultsToDisplay = sorted.filter(function (expr) { return expr.probability > minConfidence; });
|
|
var anchor = isWithFaceDetection(e)
|
|
? e.detection.box.bottomLeft
|
|
: (textFieldAnchor || new Point(0, 0));
|
|
var drawTextField = new DrawTextField(resultsToDisplay.map(function (expr) { return expr.expression + " (" + round$1(expr.probability) + ")"; }), anchor);
|
|
drawTextField.draw(canvasArg);
|
|
});
|
|
}
|
|
|
|
function minBbox(pts) {
|
|
var xs = pts.map(function (pt) { return pt.x; });
|
|
var ys = pts.map(function (pt) { return pt.y; });
|
|
var minX = xs.reduce(function (min, x) { return x < min ? x : min; }, Infinity);
|
|
var minY = ys.reduce(function (min, y) { return y < min ? y : min; }, Infinity);
|
|
var maxX = xs.reduce(function (max, x) { return max < x ? x : max; }, 0);
|
|
var maxY = ys.reduce(function (max, y) { return max < y ? y : max; }, 0);
|
|
return new BoundingBox(minX, minY, maxX, maxY);
|
|
}
|
|
|
|
// face alignment constants
|
|
var relX = 0.5;
|
|
var relY = 0.43;
|
|
var relScale = 0.45;
|
|
var FaceLandmarks = /** @class */ (function () {
|
|
function FaceLandmarks(relativeFaceLandmarkPositions, imgDims, shift) {
|
|
if (shift === void 0) { shift = new Point(0, 0); }
|
|
var width = imgDims.width, height = imgDims.height;
|
|
this._imgDims = new Dimensions(width, height);
|
|
this._shift = shift;
|
|
this._positions = relativeFaceLandmarkPositions.map(function (pt) { return pt.mul(new Point(width, height)).add(shift); });
|
|
}
|
|
Object.defineProperty(FaceLandmarks.prototype, "shift", {
|
|
get: function () { return new Point(this._shift.x, this._shift.y); },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(FaceLandmarks.prototype, "imageWidth", {
|
|
get: function () { return this._imgDims.width; },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(FaceLandmarks.prototype, "imageHeight", {
|
|
get: function () { return this._imgDims.height; },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(FaceLandmarks.prototype, "positions", {
|
|
get: function () { return this._positions; },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(FaceLandmarks.prototype, "relativePositions", {
|
|
get: function () {
|
|
var _this = this;
|
|
return this._positions.map(function (pt) { return pt.sub(_this._shift).div(new Point(_this.imageWidth, _this.imageHeight)); });
|
|
},
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
FaceLandmarks.prototype.forSize = function (width, height) {
|
|
return new this.constructor(this.relativePositions, { width: width, height: height });
|
|
};
|
|
FaceLandmarks.prototype.shiftBy = function (x, y) {
|
|
return new this.constructor(this.relativePositions, this._imgDims, new Point(x, y));
|
|
};
|
|
FaceLandmarks.prototype.shiftByPoint = function (pt) {
|
|
return this.shiftBy(pt.x, pt.y);
|
|
};
|
|
/**
|
|
* Aligns the face landmarks after face detection from the relative positions of the faces
|
|
* bounding box, or it's current shift. This function should be used to align the face images
|
|
* after face detection has been performed, before they are passed to the face recognition net.
|
|
* This will make the computed face descriptor more accurate.
|
|
*
|
|
* @param detection (optional) The bounding box of the face or the face detection result. If
|
|
* no argument was passed the position of the face landmarks are assumed to be relative to
|
|
* it's current shift.
|
|
* @returns The bounding box of the aligned face.
|
|
*/
|
|
FaceLandmarks.prototype.align = function (detection, options) {
|
|
if (options === void 0) { options = {}; }
|
|
if (detection) {
|
|
var box = detection instanceof FaceDetection
|
|
? detection.box.floor()
|
|
: new Box(detection);
|
|
return this.shiftBy(box.x, box.y).align(null, options);
|
|
}
|
|
var _a = Object.assign({}, { useDlibAlignment: false, minBoxPadding: 0.2 }, options), useDlibAlignment = _a.useDlibAlignment, minBoxPadding = _a.minBoxPadding;
|
|
if (useDlibAlignment) {
|
|
return this.alignDlib();
|
|
}
|
|
return this.alignMinBbox(minBoxPadding);
|
|
};
|
|
FaceLandmarks.prototype.alignDlib = function () {
|
|
var centers = this.getRefPointsForAlignment();
|
|
var leftEyeCenter = centers[0], rightEyeCenter = centers[1], mouthCenter = centers[2];
|
|
var distToMouth = function (pt) { return mouthCenter.sub(pt).magnitude(); };
|
|
var eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2;
|
|
var size = Math.floor(eyeToMouthDist / relScale);
|
|
var refPoint = getCenterPoint(centers);
|
|
// TODO: pad in case rectangle is out of image bounds
|
|
var x = Math.floor(Math.max(0, refPoint.x - (relX * size)));
|
|
var y = Math.floor(Math.max(0, refPoint.y - (relY * size)));
|
|
return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y));
|
|
};
|
|
FaceLandmarks.prototype.alignMinBbox = function (padding) {
|
|
var box = minBbox(this.positions);
|
|
return box.pad(box.width * padding, box.height * padding);
|
|
};
|
|
FaceLandmarks.prototype.getRefPointsForAlignment = function () {
|
|
throw new Error('getRefPointsForAlignment not implemented by base class');
|
|
};
|
|
return FaceLandmarks;
|
|
}());
|
|
|
|
var FaceLandmarks68 = /** @class */ (function (_super) {
|
|
__extends(FaceLandmarks68, _super);
|
|
function FaceLandmarks68() {
|
|
return _super !== null && _super.apply(this, arguments) || this;
|
|
}
|
|
FaceLandmarks68.prototype.getJawOutline = function () {
|
|
return this.positions.slice(0, 17);
|
|
};
|
|
FaceLandmarks68.prototype.getLeftEyeBrow = function () {
|
|
return this.positions.slice(17, 22);
|
|
};
|
|
FaceLandmarks68.prototype.getRightEyeBrow = function () {
|
|
return this.positions.slice(22, 27);
|
|
};
|
|
FaceLandmarks68.prototype.getNose = function () {
|
|
return this.positions.slice(27, 36);
|
|
};
|
|
FaceLandmarks68.prototype.getLeftEye = function () {
|
|
return this.positions.slice(36, 42);
|
|
};
|
|
FaceLandmarks68.prototype.getRightEye = function () {
|
|
return this.positions.slice(42, 48);
|
|
};
|
|
FaceLandmarks68.prototype.getMouth = function () {
|
|
return this.positions.slice(48, 68);
|
|
};
|
|
FaceLandmarks68.prototype.getRefPointsForAlignment = function () {
|
|
return [
|
|
this.getLeftEye(),
|
|
this.getRightEye(),
|
|
this.getMouth()
|
|
].map(getCenterPoint);
|
|
};
|
|
return FaceLandmarks68;
|
|
}(FaceLandmarks));
|
|
|
|
function isWithFaceLandmarks(obj) {
|
|
return isWithFaceDetection(obj)
|
|
&& obj['landmarks'] instanceof FaceLandmarks
|
|
&& obj['unshiftedLandmarks'] instanceof FaceLandmarks
|
|
&& obj['alignedRect'] instanceof FaceDetection;
|
|
}
|
|
function extendWithFaceLandmarks(sourceObj, unshiftedLandmarks) {
|
|
var shift = sourceObj.detection.box;
|
|
var landmarks = unshiftedLandmarks.shiftBy(shift.x, shift.y);
|
|
var rect = landmarks.align();
|
|
var imageDims = sourceObj.detection.imageDims;
|
|
var alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims);
|
|
var extension = {
|
|
landmarks: landmarks,
|
|
unshiftedLandmarks: unshiftedLandmarks,
|
|
alignedRect: alignedRect
|
|
};
|
|
return Object.assign({}, sourceObj, extension);
|
|
}
|
|
|
|
var DrawFaceLandmarksOptions = /** @class */ (function () {
|
|
function DrawFaceLandmarksOptions(options) {
|
|
if (options === void 0) { options = {}; }
|
|
var _a = options.drawLines, drawLines = _a === void 0 ? true : _a, _b = options.drawPoints, drawPoints = _b === void 0 ? true : _b, lineWidth = options.lineWidth, lineColor = options.lineColor, pointSize = options.pointSize, pointColor = options.pointColor;
|
|
this.drawLines = drawLines;
|
|
this.drawPoints = drawPoints;
|
|
this.lineWidth = lineWidth || 1;
|
|
this.pointSize = pointSize || 2;
|
|
this.lineColor = lineColor || 'rgba(0, 255, 255, 1)';
|
|
this.pointColor = pointColor || 'rgba(255, 0, 255, 1)';
|
|
}
|
|
return DrawFaceLandmarksOptions;
|
|
}());
|
|
var DrawFaceLandmarks = /** @class */ (function () {
|
|
function DrawFaceLandmarks(faceLandmarks, options) {
|
|
if (options === void 0) { options = {}; }
|
|
this.faceLandmarks = faceLandmarks;
|
|
this.options = new DrawFaceLandmarksOptions(options);
|
|
}
|
|
DrawFaceLandmarks.prototype.draw = function (canvasArg) {
|
|
var ctx = getContext2dOrThrow(canvasArg);
|
|
var _a = this.options, drawLines = _a.drawLines, drawPoints = _a.drawPoints, lineWidth = _a.lineWidth, lineColor = _a.lineColor, pointSize = _a.pointSize, pointColor = _a.pointColor;
|
|
if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {
|
|
ctx.strokeStyle = lineColor;
|
|
ctx.lineWidth = lineWidth;
|
|
drawContour(ctx, this.faceLandmarks.getJawOutline());
|
|
drawContour(ctx, this.faceLandmarks.getLeftEyeBrow());
|
|
drawContour(ctx, this.faceLandmarks.getRightEyeBrow());
|
|
drawContour(ctx, this.faceLandmarks.getNose());
|
|
drawContour(ctx, this.faceLandmarks.getLeftEye(), true);
|
|
drawContour(ctx, this.faceLandmarks.getRightEye(), true);
|
|
drawContour(ctx, this.faceLandmarks.getMouth(), true);
|
|
}
|
|
if (drawPoints) {
|
|
ctx.strokeStyle = pointColor;
|
|
ctx.fillStyle = pointColor;
|
|
var drawPoint = function (pt) {
|
|
ctx.beginPath();
|
|
ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI);
|
|
ctx.fill();
|
|
};
|
|
this.faceLandmarks.positions.forEach(drawPoint);
|
|
}
|
|
};
|
|
return DrawFaceLandmarks;
|
|
}());
|
|
function drawFaceLandmarks(canvasArg, faceLandmarks) {
|
|
var faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks];
|
|
faceLandmarksArray.forEach(function (f) {
|
|
var landmarks = f instanceof FaceLandmarks
|
|
? f
|
|
: (isWithFaceLandmarks(f) ? f.landmarks : undefined);
|
|
if (!landmarks) {
|
|
throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof');
|
|
}
|
|
new DrawFaceLandmarks(landmarks).draw(canvasArg);
|
|
});
|
|
}
|
|
|
|
|
|
|
|
var drawExtended = /*#__PURE__*/Object.freeze({
|
|
drawContour: drawContour,
|
|
drawDetections: drawDetections,
|
|
drawFaceExpressions: drawFaceExpressions,
|
|
DrawFaceLandmarksOptions: DrawFaceLandmarksOptions,
|
|
DrawFaceLandmarks: DrawFaceLandmarks,
|
|
drawFaceLandmarks: drawFaceLandmarks
|
|
});
|
|
|
|
function extractorsFactory$3(extractWeights, paramMappings) {
|
|
var extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);
|
|
var extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);
|
|
function extractReductionBlockParams(channelsIn, channelsOut, mappedPrefix) {
|
|
var separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, mappedPrefix + "/separable_conv0");
|
|
var separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, mappedPrefix + "/separable_conv1");
|
|
var expansion_conv = extractConvParams(channelsIn, channelsOut, 1, mappedPrefix + "/expansion_conv");
|
|
return { separable_conv0: separable_conv0, separable_conv1: separable_conv1, expansion_conv: expansion_conv };
|
|
}
|
|
function extractMainBlockParams(channels, mappedPrefix) {
|
|
var separable_conv0 = extractSeparableConvParams(channels, channels, mappedPrefix + "/separable_conv0");
|
|
var separable_conv1 = extractSeparableConvParams(channels, channels, mappedPrefix + "/separable_conv1");
|
|
var separable_conv2 = extractSeparableConvParams(channels, channels, mappedPrefix + "/separable_conv2");
|
|
return { separable_conv0: separable_conv0, separable_conv1: separable_conv1, separable_conv2: separable_conv2 };
|
|
}
|
|
return {
|
|
extractConvParams: extractConvParams,
|
|
extractSeparableConvParams: extractSeparableConvParams,
|
|
extractReductionBlockParams: extractReductionBlockParams,
|
|
extractMainBlockParams: extractMainBlockParams
|
|
};
|
|
}
|
|
function extractParams$3(weights, numMainBlocks) {
|
|
var paramMappings = [];
|
|
var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
|
|
var _b = extractorsFactory$3(extractWeights, paramMappings), extractConvParams = _b.extractConvParams, extractSeparableConvParams = _b.extractSeparableConvParams, extractReductionBlockParams = _b.extractReductionBlockParams, extractMainBlockParams = _b.extractMainBlockParams;
|
|
var entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in');
|
|
var entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0');
|
|
var entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1');
|
|
var entry_flow = {
|
|
conv_in: entry_flow_conv_in,
|
|
reduction_block_0: entry_flow_reduction_block_0,
|
|
reduction_block_1: entry_flow_reduction_block_1
|
|
};
|
|
var middle_flow = {};
|
|
range$1(numMainBlocks, 0, 1).forEach(function (idx) {
|
|
middle_flow["main_block_" + idx] = extractMainBlockParams(128, "middle_flow/main_block_" + idx);
|
|
});
|
|
var exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block');
|
|
var exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv');
|
|
var exit_flow = {
|
|
reduction_block: exit_flow_reduction_block,
|
|
separable_conv: exit_flow_separable_conv
|
|
};
|
|
if (getRemainingWeights().length !== 0) {
|
|
throw new Error("weights remaing after extract: " + getRemainingWeights().length);
|
|
}
|
|
return {
|
|
paramMappings: paramMappings,
|
|
params: { entry_flow: entry_flow, middle_flow: middle_flow, exit_flow: exit_flow }
|
|
};
|
|
}
|
|
|
|
function loadParamsFactory$1(weightMap, paramMappings) {
|
|
var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
|
|
var extractConvParams = loadConvParamsFactory(extractWeightEntry);
|
|
var extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);
|
|
function extractReductionBlockParams(mappedPrefix) {
|
|
var separable_conv0 = extractSeparableConvParams(mappedPrefix + "/separable_conv0");
|
|
var separable_conv1 = extractSeparableConvParams(mappedPrefix + "/separable_conv1");
|
|
var expansion_conv = extractConvParams(mappedPrefix + "/expansion_conv");
|
|
return { separable_conv0: separable_conv0, separable_conv1: separable_conv1, expansion_conv: expansion_conv };
|
|
}
|
|
function extractMainBlockParams(mappedPrefix) {
|
|
var separable_conv0 = extractSeparableConvParams(mappedPrefix + "/separable_conv0");
|
|
var separable_conv1 = extractSeparableConvParams(mappedPrefix + "/separable_conv1");
|
|
var separable_conv2 = extractSeparableConvParams(mappedPrefix + "/separable_conv2");
|
|
return { separable_conv0: separable_conv0, separable_conv1: separable_conv1, separable_conv2: separable_conv2 };
|
|
}
|
|
return {
|
|
extractConvParams: extractConvParams,
|
|
extractSeparableConvParams: extractSeparableConvParams,
|
|
extractReductionBlockParams: extractReductionBlockParams,
|
|
extractMainBlockParams: extractMainBlockParams
|
|
};
|
|
}
|
|
function extractParamsFromWeigthMap$3(weightMap, numMainBlocks) {
|
|
var paramMappings = [];
|
|
var _a = loadParamsFactory$1(weightMap, paramMappings), extractConvParams = _a.extractConvParams, extractSeparableConvParams = _a.extractSeparableConvParams, extractReductionBlockParams = _a.extractReductionBlockParams, extractMainBlockParams = _a.extractMainBlockParams;
|
|
var entry_flow_conv_in = extractConvParams('entry_flow/conv_in');
|
|
var entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0');
|
|
var entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1');
|
|
var entry_flow = {
|
|
conv_in: entry_flow_conv_in,
|
|
reduction_block_0: entry_flow_reduction_block_0,
|
|
reduction_block_1: entry_flow_reduction_block_1
|
|
};
|
|
var middle_flow = {};
|
|
range$1(numMainBlocks, 0, 1).forEach(function (idx) {
|
|
middle_flow["main_block_" + idx] = extractMainBlockParams("middle_flow/main_block_" + idx);
|
|
});
|
|
var exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block');
|
|
var exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv');
|
|
var exit_flow = {
|
|
reduction_block: exit_flow_reduction_block,
|
|
separable_conv: exit_flow_separable_conv
|
|
};
|
|
disposeUnusedWeightTensors(weightMap, paramMappings);
|
|
return { params: { entry_flow: entry_flow, middle_flow: middle_flow, exit_flow: exit_flow }, paramMappings: paramMappings };
|
|
}
|
|
|
|
function conv(x, params, stride) {
|
|
return add(conv2d(x, params.filters, stride, 'same'), params.bias);
|
|
}
|
|
function reductionBlock(x, params, isActivateInput) {
|
|
if (isActivateInput === void 0) { isActivateInput = true; }
|
|
var out = isActivateInput ? relu(x) : x;
|
|
out = depthwiseSeparableConv$1(out, params.separable_conv0, [1, 1]);
|
|
out = depthwiseSeparableConv$1(relu(out), params.separable_conv1, [1, 1]);
|
|
out = maxPool(out, [3, 3], [2, 2], 'same');
|
|
out = add(out, conv(x, params.expansion_conv, [2, 2]));
|
|
return out;
|
|
}
|
|
function mainBlock(x, params) {
|
|
var out = depthwiseSeparableConv$1(relu(x), params.separable_conv0, [1, 1]);
|
|
out = depthwiseSeparableConv$1(relu(out), params.separable_conv1, [1, 1]);
|
|
out = depthwiseSeparableConv$1(relu(out), params.separable_conv2, [1, 1]);
|
|
out = add(out, x);
|
|
return out;
|
|
}
|
|
var TinyXception = /** @class */ (function (_super) {
|
|
__extends(TinyXception, _super);
|
|
function TinyXception(numMainBlocks) {
|
|
var _this = _super.call(this, 'TinyXception') || this;
|
|
_this._numMainBlocks = numMainBlocks;
|
|
return _this;
|
|
}
|
|
TinyXception.prototype.forwardInput = function (input) {
|
|
var _this = this;
|
|
var params = this.params;
|
|
if (!params) {
|
|
throw new Error('TinyXception - load model before inference');
|
|
}
|
|
return tidy(function () {
|
|
var batchTensor = input.toBatchTensor(112, true);
|
|
var meanRgb = [122.782, 117.001, 104.298];
|
|
var normalized = normalize(batchTensor, meanRgb).div(scalar(256));
|
|
var out = relu(conv(normalized, params.entry_flow.conv_in, [2, 2]));
|
|
out = reductionBlock(out, params.entry_flow.reduction_block_0, false);
|
|
out = reductionBlock(out, params.entry_flow.reduction_block_1);
|
|
range$1(_this._numMainBlocks, 0, 1).forEach(function (idx) {
|
|
out = mainBlock(out, params.middle_flow["main_block_" + idx]);
|
|
});
|
|
out = reductionBlock(out, params.exit_flow.reduction_block);
|
|
out = relu(depthwiseSeparableConv$1(out, params.exit_flow.separable_conv, [1, 1]));
|
|
return out;
|
|
});
|
|
};
|
|
TinyXception.prototype.forward = function (input) {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var _a;
|
|
return __generator(this, function (_b) {
|
|
switch (_b.label) {
|
|
case 0:
|
|
_a = this.forwardInput;
|
|
return [4 /*yield*/, toNetInput(input)];
|
|
case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
|
|
}
|
|
});
|
|
});
|
|
};
|
|
TinyXception.prototype.getDefaultModelName = function () {
|
|
return 'tiny_xception_model';
|
|
};
|
|
TinyXception.prototype.extractParamsFromWeigthMap = function (weightMap) {
|
|
return extractParamsFromWeigthMap$3(weightMap, this._numMainBlocks);
|
|
};
|
|
TinyXception.prototype.extractParams = function (weights) {
|
|
return extractParams$3(weights, this._numMainBlocks);
|
|
};
|
|
return TinyXception;
|
|
}(NeuralNetwork));
|
|
|
|
function extractParams$4(weights) {
|
|
var paramMappings = [];
|
|
var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
|
|
var extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);
|
|
var age = extractFCParams(512, 1, 'fc/age');
|
|
var gender = extractFCParams(512, 2, 'fc/gender');
|
|
if (getRemainingWeights().length !== 0) {
|
|
throw new Error("weights remaing after extract: " + getRemainingWeights().length);
|
|
}
|
|
return {
|
|
paramMappings: paramMappings,
|
|
params: { fc: { age: age, gender: gender } }
|
|
};
|
|
}
|
|
|
|
function extractParamsFromWeigthMap$4(weightMap) {
|
|
var paramMappings = [];
|
|
var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
|
|
function extractFcParams(prefix) {
|
|
var weights = extractWeightEntry(prefix + "/weights", 2);
|
|
var bias = extractWeightEntry(prefix + "/bias", 1);
|
|
return { weights: weights, bias: bias };
|
|
}
|
|
var params = {
|
|
fc: {
|
|
age: extractFcParams('fc/age'),
|
|
gender: extractFcParams('fc/gender')
|
|
}
|
|
};
|
|
disposeUnusedWeightTensors(weightMap, paramMappings);
|
|
return { params: params, paramMappings: paramMappings };
|
|
}
|
|
|
|
(function (Gender) {
|
|
Gender["FEMALE"] = "female";
|
|
Gender["MALE"] = "male";
|
|
})(exports.Gender || (exports.Gender = {}));
|
|
|
|
var AgeGenderNet = /** @class */ (function (_super) {
|
|
__extends(AgeGenderNet, _super);
|
|
function AgeGenderNet(faceFeatureExtractor) {
|
|
if (faceFeatureExtractor === void 0) { faceFeatureExtractor = new TinyXception(2); }
|
|
var _this = _super.call(this, 'AgeGenderNet') || this;
|
|
_this._faceFeatureExtractor = faceFeatureExtractor;
|
|
return _this;
|
|
}
|
|
Object.defineProperty(AgeGenderNet.prototype, "faceFeatureExtractor", {
|
|
get: function () {
|
|
return this._faceFeatureExtractor;
|
|
},
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
AgeGenderNet.prototype.runNet = function (input) {
|
|
var _this = this;
|
|
var params = this.params;
|
|
if (!params) {
|
|
throw new Error(this._name + " - load model before inference");
|
|
}
|
|
return tidy(function () {
|
|
var bottleneckFeatures = input instanceof NetInput
|
|
? _this.faceFeatureExtractor.forwardInput(input)
|
|
: input;
|
|
var pooled = avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1);
|
|
var age = fullyConnectedLayer(pooled, params.fc.age).as1D();
|
|
var gender = fullyConnectedLayer(pooled, params.fc.gender);
|
|
return { age: age, gender: gender };
|
|
});
|
|
};
|
|
AgeGenderNet.prototype.forwardInput = function (input) {
|
|
var _this = this;
|
|
return tidy(function () {
|
|
var _a = _this.runNet(input), age = _a.age, gender = _a.gender;
|
|
return { age: age, gender: softmax(gender) };
|
|
});
|
|
};
|
|
AgeGenderNet.prototype.forward = function (input) {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var _a;
|
|
return __generator(this, function (_b) {
|
|
switch (_b.label) {
|
|
case 0:
|
|
_a = this.forwardInput;
|
|
return [4 /*yield*/, toNetInput(input)];
|
|
case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
|
|
}
|
|
});
|
|
});
|
|
};
|
|
AgeGenderNet.prototype.predictAgeAndGender = function (input) {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var netInput, out, ages, genders, ageAndGenderTensors, predictionsByBatch;
|
|
var _this = this;
|
|
return __generator(this, function (_a) {
|
|
switch (_a.label) {
|
|
case 0: return [4 /*yield*/, toNetInput(input)];
|
|
case 1:
|
|
netInput = _a.sent();
|
|
return [4 /*yield*/, this.forwardInput(netInput)];
|
|
case 2:
|
|
out = _a.sent();
|
|
ages = unstack(out.age);
|
|
genders = unstack(out.gender);
|
|
ageAndGenderTensors = ages.map(function (ageTensor, i) { return ({
|
|
ageTensor: ageTensor,
|
|
genderTensor: genders[i]
|
|
}); });
|
|
return [4 /*yield*/, Promise.all(ageAndGenderTensors.map(function (_a) {
|
|
var ageTensor = _a.ageTensor, genderTensor = _a.genderTensor;
|
|
return __awaiter(_this, void 0, void 0, function () {
|
|
var age, probMale, isMale, gender, genderProbability;
|
|
return __generator(this, function (_b) {
|
|
switch (_b.label) {
|
|
case 0: return [4 /*yield*/, ageTensor.data()];
|
|
case 1:
|
|
age = (_b.sent())[0];
|
|
return [4 /*yield*/, genderTensor.data()];
|
|
case 2:
|
|
probMale = (_b.sent())[0];
|
|
isMale = probMale > 0.5;
|
|
gender = isMale ? exports.Gender.MALE : exports.Gender.FEMALE;
|
|
genderProbability = isMale ? probMale : (1 - probMale);
|
|
ageTensor.dispose();
|
|
genderTensor.dispose();
|
|
return [2 /*return*/, { age: age, gender: gender, genderProbability: genderProbability }];
|
|
}
|
|
});
|
|
});
|
|
}))];
|
|
case 3:
|
|
predictionsByBatch = _a.sent();
|
|
out.age.dispose();
|
|
out.gender.dispose();
|
|
return [2 /*return*/, netInput.isBatchInput
|
|
? predictionsByBatch
|
|
: predictionsByBatch[0]];
|
|
}
|
|
});
|
|
});
|
|
};
|
|
AgeGenderNet.prototype.getDefaultModelName = function () {
|
|
return 'age_gender_model';
|
|
};
|
|
AgeGenderNet.prototype.dispose = function (throwOnRedispose) {
|
|
if (throwOnRedispose === void 0) { throwOnRedispose = true; }
|
|
this.faceFeatureExtractor.dispose(throwOnRedispose);
|
|
_super.prototype.dispose.call(this, throwOnRedispose);
|
|
};
|
|
AgeGenderNet.prototype.loadClassifierParams = function (weights) {
|
|
var _a = this.extractClassifierParams(weights), params = _a.params, paramMappings = _a.paramMappings;
|
|
this._params = params;
|
|
this._paramMappings = paramMappings;
|
|
};
|
|
AgeGenderNet.prototype.extractClassifierParams = function (weights) {
|
|
return extractParams$4(weights);
|
|
};
|
|
AgeGenderNet.prototype.extractParamsFromWeigthMap = function (weightMap) {
|
|
var _a = seperateWeightMaps(weightMap), featureExtractorMap = _a.featureExtractorMap, classifierMap = _a.classifierMap;
|
|
this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);
|
|
return extractParamsFromWeigthMap$4(classifierMap);
|
|
};
|
|
AgeGenderNet.prototype.extractParams = function (weights) {
|
|
var classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2);
|
|
var featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);
|
|
var classifierWeights = weights.slice(weights.length - classifierWeightSize);
|
|
this.faceFeatureExtractor.extractWeights(featureExtractorWeights);
|
|
return this.extractClassifierParams(classifierWeights);
|
|
};
|
|
return AgeGenderNet;
|
|
}(NeuralNetwork));
|
|
|
|
var FaceLandmarks5 = /** @class */ (function (_super) {
|
|
__extends(FaceLandmarks5, _super);
|
|
function FaceLandmarks5() {
|
|
return _super !== null && _super.apply(this, arguments) || this;
|
|
}
|
|
FaceLandmarks5.prototype.getRefPointsForAlignment = function () {
|
|
var pts = this.positions;
|
|
return [
|
|
pts[0],
|
|
pts[1],
|
|
getCenterPoint([pts[3], pts[4]])
|
|
];
|
|
};
|
|
return FaceLandmarks5;
|
|
}(FaceLandmarks));
|
|
|
|
var FaceMatch = /** @class */ (function () {
|
|
function FaceMatch(label, distance) {
|
|
this._label = label;
|
|
this._distance = distance;
|
|
}
|
|
Object.defineProperty(FaceMatch.prototype, "label", {
|
|
get: function () { return this._label; },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(FaceMatch.prototype, "distance", {
|
|
get: function () { return this._distance; },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
FaceMatch.prototype.toString = function (withDistance) {
|
|
if (withDistance === void 0) { withDistance = true; }
|
|
return "" + this.label + (withDistance ? " (" + round$1(this.distance) + ")" : '');
|
|
};
|
|
return FaceMatch;
|
|
}());
|
|
|
|
var LabeledFaceDescriptors = /** @class */ (function () {
|
|
function LabeledFaceDescriptors(label, descriptors) {
|
|
if (!(typeof label === 'string')) {
|
|
throw new Error('LabeledFaceDescriptors - constructor expected label to be a string');
|
|
}
|
|
if (!Array.isArray(descriptors) || descriptors.some(function (desc) { return !(desc instanceof Float32Array); })) {
|
|
throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array');
|
|
}
|
|
this._label = label;
|
|
this._descriptors = descriptors;
|
|
}
|
|
Object.defineProperty(LabeledFaceDescriptors.prototype, "label", {
|
|
get: function () { return this._label; },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(LabeledFaceDescriptors.prototype, "descriptors", {
|
|
get: function () { return this._descriptors; },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
return LabeledFaceDescriptors;
|
|
}());
|
|
|
|
/**
|
|
* Extracts the image regions containing the detected faces.
|
|
*
|
|
* @param input The image that face detection has been performed on.
|
|
* @param detections The face detection results or face bounding boxes for that image.
|
|
* @returns The Canvases of the corresponding image region for each detected face.
|
|
*/
|
|
function extractFaces(input, detections) {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var Canvas, canvas, netInput, tensorOrCanvas, _a, ctx, boxes;
|
|
return __generator(this, function (_b) {
|
|
switch (_b.label) {
|
|
case 0:
|
|
Canvas = env.getEnv().Canvas;
|
|
canvas = input;
|
|
if (!!(input instanceof Canvas)) return [3 /*break*/, 5];
|
|
return [4 /*yield*/, toNetInput(input)];
|
|
case 1:
|
|
netInput = _b.sent();
|
|
if (netInput.batchSize > 1) {
|
|
throw new Error('extractFaces - batchSize > 1 not supported');
|
|
}
|
|
tensorOrCanvas = netInput.getInput(0);
|
|
if (!(tensorOrCanvas instanceof Canvas)) return [3 /*break*/, 2];
|
|
_a = tensorOrCanvas;
|
|
return [3 /*break*/, 4];
|
|
case 2: return [4 /*yield*/, imageTensorToCanvas(tensorOrCanvas)];
|
|
case 3:
|
|
_a = _b.sent();
|
|
_b.label = 4;
|
|
case 4:
|
|
canvas = _a;
|
|
_b.label = 5;
|
|
case 5:
|
|
ctx = getContext2dOrThrow(canvas);
|
|
boxes = detections.map(function (det) { return det instanceof FaceDetection
|
|
? det.forSize(canvas.width, canvas.height).box.floor()
|
|
: det; })
|
|
.map(function (box) { return box.clipAtImageBorders(canvas.width, canvas.height); });
|
|
return [2 /*return*/, boxes.map(function (_a) {
|
|
var x = _a.x, y = _a.y, width = _a.width, height = _a.height;
|
|
var faceImg = createCanvas({ width: width, height: height });
|
|
getContext2dOrThrow(faceImg)
|
|
.putImageData(ctx.getImageData(x, y, width, height), 0, 0);
|
|
return faceImg;
|
|
})];
|
|
}
|
|
});
|
|
});
|
|
}
|
|
|
|
/**
|
|
* Extracts the tensors of the image regions containing the detected faces.
|
|
* Useful if you want to compute the face descriptors for the face images.
|
|
* Using this method is faster then extracting a canvas for each face and
|
|
* converting them to tensors individually.
|
|
*
|
|
* @param imageTensor The image tensor that face detection has been performed on.
|
|
* @param detections The face detection results or face bounding boxes for that image.
|
|
* @returns Tensors of the corresponding image region for each detected face.
|
|
*/
|
|
function extractFaceTensors(imageTensor, detections) {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
return __generator(this, function (_a) {
|
|
if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {
|
|
throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D');
|
|
}
|
|
if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {
|
|
throw new Error('extractFaceTensors - batchSize > 1 not supported');
|
|
}
|
|
return [2 /*return*/, tidy(function () {
|
|
var _a = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0), imgHeight = _a[0], imgWidth = _a[1], numChannels = _a[2];
|
|
var boxes = detections.map(function (det) { return det instanceof FaceDetection
|
|
? det.forSize(imgWidth, imgHeight).box
|
|
: det; })
|
|
.map(function (box) { return box.clipAtImageBorders(imgWidth, imgHeight); });
|
|
var faceTensors = boxes.map(function (_a) {
|
|
var x = _a.x, y = _a.y, width = _a.width, height = _a.height;
|
|
return slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]);
|
|
});
|
|
return faceTensors;
|
|
})];
|
|
});
|
|
});
|
|
}
|
|
|
|
var FaceLandmark68NetBase = /** @class */ (function (_super) {
|
|
__extends(FaceLandmark68NetBase, _super);
|
|
function FaceLandmark68NetBase() {
|
|
return _super !== null && _super.apply(this, arguments) || this;
|
|
}
|
|
FaceLandmark68NetBase.prototype.postProcess = function (output, inputSize, originalDimensions) {
|
|
var inputDimensions = originalDimensions.map(function (_a) {
|
|
var width = _a.width, height = _a.height;
|
|
var scale = inputSize / Math.max(height, width);
|
|
return {
|
|
width: width * scale,
|
|
height: height * scale
|
|
};
|
|
});
|
|
var batchSize = inputDimensions.length;
|
|
return tidy(function () {
|
|
var createInterleavedTensor = function (fillX, fillY) {
|
|
return stack([
|
|
fill([68], fillX),
|
|
fill([68], fillY)
|
|
], 1).as2D(1, 136).as1D();
|
|
};
|
|
var getPadding = function (batchIdx, cond) {
|
|
var _a = inputDimensions[batchIdx], width = _a.width, height = _a.height;
|
|
return cond(width, height) ? Math.abs(width - height) / 2 : 0;
|
|
};
|
|
var getPaddingX = function (batchIdx) { return getPadding(batchIdx, function (w, h) { return w < h; }); };
|
|
var getPaddingY = function (batchIdx) { return getPadding(batchIdx, function (w, h) { return h < w; }); };
|
|
var landmarkTensors = output
|
|
.mul(fill([batchSize, 136], inputSize))
|
|
.sub(stack(Array.from(Array(batchSize), function (_, batchIdx) {
|
|
return createInterleavedTensor(getPaddingX(batchIdx), getPaddingY(batchIdx));
|
|
})))
|
|
.div(stack(Array.from(Array(batchSize), function (_, batchIdx) {
|
|
return createInterleavedTensor(inputDimensions[batchIdx].width, inputDimensions[batchIdx].height);
|
|
})));
|
|
return landmarkTensors;
|
|
});
|
|
};
|
|
FaceLandmark68NetBase.prototype.forwardInput = function (input) {
|
|
var _this = this;
|
|
return tidy(function () {
|
|
var out = _this.runNet(input);
|
|
return _this.postProcess(out, input.inputSize, input.inputDimensions.map(function (_a) {
|
|
var height = _a[0], width = _a[1];
|
|
return ({ height: height, width: width });
|
|
}));
|
|
});
|
|
};
|
|
FaceLandmark68NetBase.prototype.forward = function (input) {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var _a;
|
|
return __generator(this, function (_b) {
|
|
switch (_b.label) {
|
|
case 0:
|
|
_a = this.forwardInput;
|
|
return [4 /*yield*/, toNetInput(input)];
|
|
case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
|
|
}
|
|
});
|
|
});
|
|
};
|
|
FaceLandmark68NetBase.prototype.detectLandmarks = function (input) {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var netInput, landmarkTensors, landmarksForBatch;
|
|
var _this = this;
|
|
return __generator(this, function (_a) {
|
|
switch (_a.label) {
|
|
case 0: return [4 /*yield*/, toNetInput(input)];
|
|
case 1:
|
|
netInput = _a.sent();
|
|
landmarkTensors = tidy(function () { return unstack(_this.forwardInput(netInput)); });
|
|
return [4 /*yield*/, Promise.all(landmarkTensors.map(function (landmarkTensor, batchIdx) { return __awaiter(_this, void 0, void 0, function () {
|
|
var landmarksArray, _a, _b, xCoords, yCoords;
|
|
return __generator(this, function (_c) {
|
|
switch (_c.label) {
|
|
case 0:
|
|
_b = (_a = Array).from;
|
|
return [4 /*yield*/, landmarkTensor.data()];
|
|
case 1:
|
|
landmarksArray = _b.apply(_a, [_c.sent()]);
|
|
xCoords = landmarksArray.filter(function (_, i) { return isEven$1(i); });
|
|
yCoords = landmarksArray.filter(function (_, i) { return !isEven$1(i); });
|
|
return [2 /*return*/, new FaceLandmarks68(Array(68).fill(0).map(function (_, i) { return new Point(xCoords[i], yCoords[i]); }), {
|
|
height: netInput.getInputHeight(batchIdx),
|
|
width: netInput.getInputWidth(batchIdx),
|
|
})];
|
|
}
|
|
});
|
|
}); }))];
|
|
case 2:
|
|
landmarksForBatch = _a.sent();
|
|
landmarkTensors.forEach(function (t) { return t.dispose(); });
|
|
return [2 /*return*/, netInput.isBatchInput
|
|
? landmarksForBatch
|
|
: landmarksForBatch[0]];
|
|
}
|
|
});
|
|
});
|
|
};
|
|
FaceLandmark68NetBase.prototype.getClassifierChannelsOut = function () {
|
|
return 136;
|
|
};
|
|
return FaceLandmark68NetBase;
|
|
}(FaceProcessor));
|
|
|
|
var FaceLandmark68Net = /** @class */ (function (_super) {
|
|
__extends(FaceLandmark68Net, _super);
|
|
function FaceLandmark68Net(faceFeatureExtractor) {
|
|
if (faceFeatureExtractor === void 0) { faceFeatureExtractor = new FaceFeatureExtractor(); }
|
|
return _super.call(this, 'FaceLandmark68Net', faceFeatureExtractor) || this;
|
|
}
|
|
FaceLandmark68Net.prototype.getDefaultModelName = function () {
|
|
return 'face_landmark_68_model';
|
|
};
|
|
FaceLandmark68Net.prototype.getClassifierChannelsIn = function () {
|
|
return 256;
|
|
};
|
|
return FaceLandmark68Net;
|
|
}(FaceLandmark68NetBase));
|
|
|
|
function extractParamsFromWeigthMapTiny(weightMap) {
|
|
var paramMappings = [];
|
|
var extractDenseBlock3Params = loadParamsFactory(weightMap, paramMappings).extractDenseBlock3Params;
|
|
var params = {
|
|
dense0: extractDenseBlock3Params('dense0', true),
|
|
dense1: extractDenseBlock3Params('dense1'),
|
|
dense2: extractDenseBlock3Params('dense2')
|
|
};
|
|
disposeUnusedWeightTensors(weightMap, paramMappings);
|
|
return { params: params, paramMappings: paramMappings };
|
|
}
|
|
|
|
function extractParamsTiny(weights) {
|
|
var paramMappings = [];
|
|
var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
|
|
var extractDenseBlock3Params = extractorsFactory$2(extractWeights, paramMappings).extractDenseBlock3Params;
|
|
var dense0 = extractDenseBlock3Params(3, 32, 'dense0', true);
|
|
var dense1 = extractDenseBlock3Params(32, 64, 'dense1');
|
|
var dense2 = extractDenseBlock3Params(64, 128, 'dense2');
|
|
if (getRemainingWeights().length !== 0) {
|
|
throw new Error("weights remaing after extract: " + getRemainingWeights().length);
|
|
}
|
|
return {
|
|
paramMappings: paramMappings,
|
|
params: { dense0: dense0, dense1: dense1, dense2: dense2 }
|
|
};
|
|
}
|
|
|
|
var TinyFaceFeatureExtractor = /** @class */ (function (_super) {
|
|
__extends(TinyFaceFeatureExtractor, _super);
|
|
function TinyFaceFeatureExtractor() {
|
|
return _super.call(this, 'TinyFaceFeatureExtractor') || this;
|
|
}
|
|
TinyFaceFeatureExtractor.prototype.forwardInput = function (input) {
|
|
var params = this.params;
|
|
if (!params) {
|
|
throw new Error('TinyFaceFeatureExtractor - load model before inference');
|
|
}
|
|
return tidy(function () {
|
|
var batchTensor = input.toBatchTensor(112, true);
|
|
var meanRgb = [122.782, 117.001, 104.298];
|
|
var normalized = normalize(batchTensor, meanRgb).div(scalar(255));
|
|
var out = denseBlock3(normalized, params.dense0, true);
|
|
out = denseBlock3(out, params.dense1);
|
|
out = denseBlock3(out, params.dense2);
|
|
out = avgPool(out, [14, 14], [2, 2], 'valid');
|
|
return out;
|
|
});
|
|
};
|
|
TinyFaceFeatureExtractor.prototype.forward = function (input) {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var _a;
|
|
return __generator(this, function (_b) {
|
|
switch (_b.label) {
|
|
case 0:
|
|
_a = this.forwardInput;
|
|
return [4 /*yield*/, toNetInput(input)];
|
|
case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
|
|
}
|
|
});
|
|
});
|
|
};
|
|
TinyFaceFeatureExtractor.prototype.getDefaultModelName = function () {
|
|
return 'face_feature_extractor_tiny_model';
|
|
};
|
|
TinyFaceFeatureExtractor.prototype.extractParamsFromWeigthMap = function (weightMap) {
|
|
return extractParamsFromWeigthMapTiny(weightMap);
|
|
};
|
|
TinyFaceFeatureExtractor.prototype.extractParams = function (weights) {
|
|
return extractParamsTiny(weights);
|
|
};
|
|
return TinyFaceFeatureExtractor;
|
|
}(NeuralNetwork));
|
|
|
|
var FaceLandmark68TinyNet = /** @class */ (function (_super) {
|
|
__extends(FaceLandmark68TinyNet, _super);
|
|
function FaceLandmark68TinyNet(faceFeatureExtractor) {
|
|
if (faceFeatureExtractor === void 0) { faceFeatureExtractor = new TinyFaceFeatureExtractor(); }
|
|
return _super.call(this, 'FaceLandmark68TinyNet', faceFeatureExtractor) || this;
|
|
}
|
|
FaceLandmark68TinyNet.prototype.getDefaultModelName = function () {
|
|
return 'face_landmark_68_tiny_model';
|
|
};
|
|
FaceLandmark68TinyNet.prototype.getClassifierChannelsIn = function () {
|
|
return 128;
|
|
};
|
|
return FaceLandmark68TinyNet;
|
|
}(FaceLandmark68NetBase));
|
|
|
|
var FaceLandmarkNet = /** @class */ (function (_super) {
|
|
__extends(FaceLandmarkNet, _super);
|
|
function FaceLandmarkNet() {
|
|
return _super !== null && _super.apply(this, arguments) || this;
|
|
}
|
|
return FaceLandmarkNet;
|
|
}(FaceLandmark68Net));
|
|
|
|
function scale(x, params) {
|
|
return add(mul(x, params.weights), params.biases);
|
|
}
|
|
|
|
function convLayer$1(x, params, strides, withRelu, padding) {
|
|
if (padding === void 0) { padding = 'same'; }
|
|
var _a = params.conv, filters = _a.filters, bias = _a.bias;
|
|
var out = conv2d(x, filters, strides, padding);
|
|
out = add(out, bias);
|
|
out = scale(out, params.scale);
|
|
return withRelu ? relu(out) : out;
|
|
}
|
|
function conv$1(x, params) {
|
|
return convLayer$1(x, params, [1, 1], true);
|
|
}
|
|
function convNoRelu(x, params) {
|
|
return convLayer$1(x, params, [1, 1], false);
|
|
}
|
|
function convDown(x, params) {
|
|
return convLayer$1(x, params, [2, 2], true, 'valid');
|
|
}
|
|
|
|
function extractorsFactory$4(extractWeights, paramMappings) {
|
|
function extractFilterValues(numFilterValues, numFilters, filterSize) {
|
|
var weights = extractWeights(numFilterValues);
|
|
var depth = weights.length / (numFilters * filterSize * filterSize);
|
|
if (isFloat(depth)) {
|
|
throw new Error("depth has to be an integer: " + depth + ", weights.length: " + weights.length + ", numFilters: " + numFilters + ", filterSize: " + filterSize);
|
|
}
|
|
return tidy(function () { return transpose(tensor4d(weights, [numFilters, depth, filterSize, filterSize]), [2, 3, 1, 0]); });
|
|
}
|
|
function extractConvParams(numFilterValues, numFilters, filterSize, mappedPrefix) {
|
|
var filters = extractFilterValues(numFilterValues, numFilters, filterSize);
|
|
var bias = tensor1d(extractWeights(numFilters));
|
|
paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/bias" });
|
|
return { filters: filters, bias: bias };
|
|
}
|
|
function extractScaleLayerParams(numWeights, mappedPrefix) {
|
|
var weights = tensor1d(extractWeights(numWeights));
|
|
var biases = tensor1d(extractWeights(numWeights));
|
|
paramMappings.push({ paramPath: mappedPrefix + "/weights" }, { paramPath: mappedPrefix + "/biases" });
|
|
return {
|
|
weights: weights,
|
|
biases: biases
|
|
};
|
|
}
|
|
function extractConvLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix) {
|
|
var conv = extractConvParams(numFilterValues, numFilters, filterSize, mappedPrefix + "/conv");
|
|
var scale = extractScaleLayerParams(numFilters, mappedPrefix + "/scale");
|
|
return { conv: conv, scale: scale };
|
|
}
|
|
function extractResidualLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix, isDown) {
|
|
if (isDown === void 0) { isDown = false; }
|
|
var conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, mappedPrefix + "/conv1");
|
|
var conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix + "/conv2");
|
|
return { conv1: conv1, conv2: conv2 };
|
|
}
|
|
return {
|
|
extractConvLayerParams: extractConvLayerParams,
|
|
extractResidualLayerParams: extractResidualLayerParams
|
|
};
|
|
}
|
|
function extractParams$5(weights) {
|
|
var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
|
|
var paramMappings = [];
|
|
var _b = extractorsFactory$4(extractWeights, paramMappings), extractConvLayerParams = _b.extractConvLayerParams, extractResidualLayerParams = _b.extractResidualLayerParams;
|
|
var conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down');
|
|
var conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1');
|
|
var conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2');
|
|
var conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3');
|
|
var conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true);
|
|
var conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1');
|
|
var conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2');
|
|
var conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3');
|
|
var conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true);
|
|
var conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1');
|
|
var conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2');
|
|
var conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true);
|
|
var conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1');
|
|
var conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2');
|
|
var conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out');
|
|
var fc = tidy(function () { return transpose(tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]); });
|
|
paramMappings.push({ paramPath: "fc" });
|
|
if (getRemainingWeights().length !== 0) {
|
|
throw new Error("weights remaing after extract: " + getRemainingWeights().length);
|
|
}
|
|
var params = {
|
|
conv32_down: conv32_down,
|
|
conv32_1: conv32_1,
|
|
conv32_2: conv32_2,
|
|
conv32_3: conv32_3,
|
|
conv64_down: conv64_down,
|
|
conv64_1: conv64_1,
|
|
conv64_2: conv64_2,
|
|
conv64_3: conv64_3,
|
|
conv128_down: conv128_down,
|
|
conv128_1: conv128_1,
|
|
conv128_2: conv128_2,
|
|
conv256_down: conv256_down,
|
|
conv256_1: conv256_1,
|
|
conv256_2: conv256_2,
|
|
conv256_down_out: conv256_down_out,
|
|
fc: fc
|
|
};
|
|
return { params: params, paramMappings: paramMappings };
|
|
}
|
|
|
|
function extractorsFactory$5(weightMap, paramMappings) {
|
|
var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
|
|
function extractScaleLayerParams(prefix) {
|
|
var weights = extractWeightEntry(prefix + "/scale/weights", 1);
|
|
var biases = extractWeightEntry(prefix + "/scale/biases", 1);
|
|
return { weights: weights, biases: biases };
|
|
}
|
|
function extractConvLayerParams(prefix) {
|
|
var filters = extractWeightEntry(prefix + "/conv/filters", 4);
|
|
var bias = extractWeightEntry(prefix + "/conv/bias", 1);
|
|
var scale = extractScaleLayerParams(prefix);
|
|
return { conv: { filters: filters, bias: bias }, scale: scale };
|
|
}
|
|
function extractResidualLayerParams(prefix) {
|
|
return {
|
|
conv1: extractConvLayerParams(prefix + "/conv1"),
|
|
conv2: extractConvLayerParams(prefix + "/conv2")
|
|
};
|
|
}
|
|
return {
|
|
extractConvLayerParams: extractConvLayerParams,
|
|
extractResidualLayerParams: extractResidualLayerParams
|
|
};
|
|
}
|
|
function extractParamsFromWeigthMap$5(weightMap) {
|
|
var paramMappings = [];
|
|
var _a = extractorsFactory$5(weightMap, paramMappings), extractConvLayerParams = _a.extractConvLayerParams, extractResidualLayerParams = _a.extractResidualLayerParams;
|
|
var conv32_down = extractConvLayerParams('conv32_down');
|
|
var conv32_1 = extractResidualLayerParams('conv32_1');
|
|
var conv32_2 = extractResidualLayerParams('conv32_2');
|
|
var conv32_3 = extractResidualLayerParams('conv32_3');
|
|
var conv64_down = extractResidualLayerParams('conv64_down');
|
|
var conv64_1 = extractResidualLayerParams('conv64_1');
|
|
var conv64_2 = extractResidualLayerParams('conv64_2');
|
|
var conv64_3 = extractResidualLayerParams('conv64_3');
|
|
var conv128_down = extractResidualLayerParams('conv128_down');
|
|
var conv128_1 = extractResidualLayerParams('conv128_1');
|
|
var conv128_2 = extractResidualLayerParams('conv128_2');
|
|
var conv256_down = extractResidualLayerParams('conv256_down');
|
|
var conv256_1 = extractResidualLayerParams('conv256_1');
|
|
var conv256_2 = extractResidualLayerParams('conv256_2');
|
|
var conv256_down_out = extractResidualLayerParams('conv256_down_out');
|
|
var fc = weightMap['fc'];
|
|
paramMappings.push({ originalPath: 'fc', paramPath: 'fc' });
|
|
if (!isTensor2D(fc)) {
|
|
throw new Error("expected weightMap[fc] to be a Tensor2D, instead have " + fc);
|
|
}
|
|
var params = {
|
|
conv32_down: conv32_down,
|
|
conv32_1: conv32_1,
|
|
conv32_2: conv32_2,
|
|
conv32_3: conv32_3,
|
|
conv64_down: conv64_down,
|
|
conv64_1: conv64_1,
|
|
conv64_2: conv64_2,
|
|
conv64_3: conv64_3,
|
|
conv128_down: conv128_down,
|
|
conv128_1: conv128_1,
|
|
conv128_2: conv128_2,
|
|
conv256_down: conv256_down,
|
|
conv256_1: conv256_1,
|
|
conv256_2: conv256_2,
|
|
conv256_down_out: conv256_down_out,
|
|
fc: fc
|
|
};
|
|
disposeUnusedWeightTensors(weightMap, paramMappings);
|
|
return { params: params, paramMappings: paramMappings };
|
|
}
|
|
|
|
function residual(x, params) {
|
|
var out = conv$1(x, params.conv1);
|
|
out = convNoRelu(out, params.conv2);
|
|
out = add(out, x);
|
|
out = relu(out);
|
|
return out;
|
|
}
|
|
function residualDown(x, params) {
|
|
var out = convDown(x, params.conv1);
|
|
out = convNoRelu(out, params.conv2);
|
|
var pooled = avgPool(x, 2, 2, 'valid');
|
|
var zeros$1 = zeros(pooled.shape);
|
|
var isPad = pooled.shape[3] !== out.shape[3];
|
|
var isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2];
|
|
if (isAdjustShape) {
|
|
var padShapeX = out.shape.slice();
|
|
padShapeX[1] = 1;
|
|
var zerosW = zeros(padShapeX);
|
|
out = concat([out, zerosW], 1);
|
|
var padShapeY = out.shape.slice();
|
|
padShapeY[2] = 1;
|
|
var zerosH = zeros(padShapeY);
|
|
out = concat([out, zerosH], 2);
|
|
}
|
|
pooled = isPad ? concat([pooled, zeros$1], 3) : pooled;
|
|
out = add(pooled, out);
|
|
out = relu(out);
|
|
return out;
|
|
}
|
|
|
|
var FaceRecognitionNet = /** @class */ (function (_super) {
|
|
__extends(FaceRecognitionNet, _super);
|
|
function FaceRecognitionNet() {
|
|
return _super.call(this, 'FaceRecognitionNet') || this;
|
|
}
|
|
FaceRecognitionNet.prototype.forwardInput = function (input) {
|
|
var params = this.params;
|
|
if (!params) {
|
|
throw new Error('FaceRecognitionNet - load model before inference');
|
|
}
|
|
return tidy(function () {
|
|
var batchTensor = input.toBatchTensor(150, true).toFloat();
|
|
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);
|
|
out = residual(out, params.conv32_2);
|
|
out = residual(out, params.conv32_3);
|
|
out = residualDown(out, params.conv64_down);
|
|
out = residual(out, params.conv64_1);
|
|
out = residual(out, params.conv64_2);
|
|
out = residual(out, params.conv64_3);
|
|
out = residualDown(out, params.conv128_down);
|
|
out = residual(out, params.conv128_1);
|
|
out = residual(out, params.conv128_2);
|
|
out = residualDown(out, params.conv256_down);
|
|
out = residual(out, params.conv256_1);
|
|
out = residual(out, params.conv256_2);
|
|
out = residualDown(out, params.conv256_down_out);
|
|
var globalAvg = out.mean([1, 2]);
|
|
var fullyConnected = matMul(globalAvg, params.fc);
|
|
return fullyConnected;
|
|
});
|
|
};
|
|
FaceRecognitionNet.prototype.forward = function (input) {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var _a;
|
|
return __generator(this, function (_b) {
|
|
switch (_b.label) {
|
|
case 0:
|
|
_a = this.forwardInput;
|
|
return [4 /*yield*/, toNetInput(input)];
|
|
case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
|
|
}
|
|
});
|
|
});
|
|
};
|
|
FaceRecognitionNet.prototype.computeFaceDescriptor = function (input) {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var netInput, faceDescriptorTensors, faceDescriptorsForBatch;
|
|
var _this = this;
|
|
return __generator(this, function (_a) {
|
|
switch (_a.label) {
|
|
case 0: return [4 /*yield*/, toNetInput(input)];
|
|
case 1:
|
|
netInput = _a.sent();
|
|
faceDescriptorTensors = tidy(function () { return unstack(_this.forwardInput(netInput)); });
|
|
return [4 /*yield*/, Promise.all(faceDescriptorTensors.map(function (t) { return t.data(); }))];
|
|
case 2:
|
|
faceDescriptorsForBatch = _a.sent();
|
|
faceDescriptorTensors.forEach(function (t) { return t.dispose(); });
|
|
return [2 /*return*/, netInput.isBatchInput
|
|
? faceDescriptorsForBatch
|
|
: faceDescriptorsForBatch[0]];
|
|
}
|
|
});
|
|
});
|
|
};
|
|
FaceRecognitionNet.prototype.getDefaultModelName = function () {
|
|
return 'face_recognition_model';
|
|
};
|
|
FaceRecognitionNet.prototype.extractParamsFromWeigthMap = function (weightMap) {
|
|
return extractParamsFromWeigthMap$5(weightMap);
|
|
};
|
|
FaceRecognitionNet.prototype.extractParams = function (weights) {
|
|
return extractParams$5(weights);
|
|
};
|
|
return FaceRecognitionNet;
|
|
}(NeuralNetwork));
|
|
|
|
function createFaceRecognitionNet(weights) {
|
|
var net = new FaceRecognitionNet();
|
|
net.extractWeights(weights);
|
|
return net;
|
|
}
|
|
|
|
function extendWithFaceDescriptor(sourceObj, descriptor) {
|
|
var extension = { descriptor: descriptor };
|
|
return Object.assign({}, sourceObj, extension);
|
|
}
|
|
|
|
var MtcnnOptions = /** @class */ (function () {
|
|
function MtcnnOptions(_a) {
|
|
var _b = _a === void 0 ? {} : _a, minFaceSize = _b.minFaceSize, scaleFactor = _b.scaleFactor, maxNumScales = _b.maxNumScales, scoreThresholds = _b.scoreThresholds, scaleSteps = _b.scaleSteps;
|
|
this._name = 'MtcnnOptions';
|
|
this._minFaceSize = minFaceSize || 20;
|
|
this._scaleFactor = scaleFactor || 0.709;
|
|
this._maxNumScales = maxNumScales || 10;
|
|
this._scoreThresholds = scoreThresholds || [0.6, 0.7, 0.7];
|
|
this._scaleSteps = scaleSteps;
|
|
if (typeof this._minFaceSize !== 'number' || this._minFaceSize < 0) {
|
|
throw new Error(this._name + " - expected minFaceSize to be a number > 0");
|
|
}
|
|
if (typeof this._scaleFactor !== 'number' || this._scaleFactor <= 0 || this._scaleFactor >= 1) {
|
|
throw new Error(this._name + " - expected scaleFactor to be a number between 0 and 1");
|
|
}
|
|
if (typeof this._maxNumScales !== 'number' || this._maxNumScales < 0) {
|
|
throw new Error(this._name + " - expected maxNumScales to be a number > 0");
|
|
}
|
|
if (!Array.isArray(this._scoreThresholds)
|
|
|| this._scoreThresholds.length !== 3
|
|
|| this._scoreThresholds.some(function (th) { return typeof th !== 'number'; })) {
|
|
throw new Error(this._name + " - expected scoreThresholds to be an array of numbers of length 3");
|
|
}
|
|
if (this._scaleSteps
|
|
&& (!Array.isArray(this._scaleSteps) || this._scaleSteps.some(function (th) { return typeof th !== 'number'; }))) {
|
|
throw new Error(this._name + " - expected scaleSteps to be an array of numbers");
|
|
}
|
|
}
|
|
Object.defineProperty(MtcnnOptions.prototype, "minFaceSize", {
|
|
get: function () { return this._minFaceSize; },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(MtcnnOptions.prototype, "scaleFactor", {
|
|
get: function () { return this._scaleFactor; },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(MtcnnOptions.prototype, "maxNumScales", {
|
|
get: function () { return this._maxNumScales; },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(MtcnnOptions.prototype, "scoreThresholds", {
|
|
get: function () { return this._scoreThresholds; },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(MtcnnOptions.prototype, "scaleSteps", {
|
|
get: function () { return this._scaleSteps; },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
return MtcnnOptions;
|
|
}());
|
|
|
|
function extractorsFactory$6(extractWeights, paramMappings) {
|
|
function extractDepthwiseConvParams(numChannels, mappedPrefix) {
|
|
var filters = tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);
|
|
var batch_norm_scale = tensor1d(extractWeights(numChannels));
|
|
var batch_norm_offset = tensor1d(extractWeights(numChannels));
|
|
var batch_norm_mean = tensor1d(extractWeights(numChannels));
|
|
var batch_norm_variance = tensor1d(extractWeights(numChannels));
|
|
paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/batch_norm_scale" }, { paramPath: mappedPrefix + "/batch_norm_offset" }, { paramPath: mappedPrefix + "/batch_norm_mean" }, { paramPath: mappedPrefix + "/batch_norm_variance" });
|
|
return {
|
|
filters: filters,
|
|
batch_norm_scale: batch_norm_scale,
|
|
batch_norm_offset: batch_norm_offset,
|
|
batch_norm_mean: batch_norm_mean,
|
|
batch_norm_variance: batch_norm_variance
|
|
};
|
|
}
|
|
function extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, isPointwiseConv) {
|
|
var filters = tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
|
|
var bias = tensor1d(extractWeights(channelsOut));
|
|
paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/" + (isPointwiseConv ? 'batch_norm_offset' : 'bias') });
|
|
return { filters: filters, bias: bias };
|
|
}
|
|
function extractPointwiseConvParams(channelsIn, channelsOut, filterSize, mappedPrefix) {
|
|
var _a = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true), filters = _a.filters, bias = _a.bias;
|
|
return {
|
|
filters: filters,
|
|
batch_norm_offset: bias
|
|
};
|
|
}
|
|
function extractConvPairParams(channelsIn, channelsOut, mappedPrefix) {
|
|
var depthwise_conv = extractDepthwiseConvParams(channelsIn, mappedPrefix + "/depthwise_conv");
|
|
var pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, mappedPrefix + "/pointwise_conv");
|
|
return { depthwise_conv: depthwise_conv, pointwise_conv: pointwise_conv };
|
|
}
|
|
function extractMobilenetV1Params() {
|
|
var conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0');
|
|
var conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1');
|
|
var conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2');
|
|
var conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3');
|
|
var conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4');
|
|
var conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5');
|
|
var conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6');
|
|
var conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7');
|
|
var conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8');
|
|
var conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9');
|
|
var conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10');
|
|
var conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11');
|
|
var conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12');
|
|
var conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13');
|
|
return {
|
|
conv_0: conv_0,
|
|
conv_1: conv_1,
|
|
conv_2: conv_2,
|
|
conv_3: conv_3,
|
|
conv_4: conv_4,
|
|
conv_5: conv_5,
|
|
conv_6: conv_6,
|
|
conv_7: conv_7,
|
|
conv_8: conv_8,
|
|
conv_9: conv_9,
|
|
conv_10: conv_10,
|
|
conv_11: conv_11,
|
|
conv_12: conv_12,
|
|
conv_13: conv_13
|
|
};
|
|
}
|
|
function extractPredictionLayerParams() {
|
|
var conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0');
|
|
var conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1');
|
|
var conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2');
|
|
var conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3');
|
|
var conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4');
|
|
var conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5');
|
|
var conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6');
|
|
var conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7');
|
|
var box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor');
|
|
var class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor');
|
|
var box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor');
|
|
var class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor');
|
|
var box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor');
|
|
var class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor');
|
|
var box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor');
|
|
var class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor');
|
|
var box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor');
|
|
var class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor');
|
|
var box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor');
|
|
var class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor');
|
|
var box_predictor_0 = {
|
|
box_encoding_predictor: box_encoding_0_predictor,
|
|
class_predictor: class_predictor_0
|
|
};
|
|
var box_predictor_1 = {
|
|
box_encoding_predictor: box_encoding_1_predictor,
|
|
class_predictor: class_predictor_1
|
|
};
|
|
var box_predictor_2 = {
|
|
box_encoding_predictor: box_encoding_2_predictor,
|
|
class_predictor: class_predictor_2
|
|
};
|
|
var box_predictor_3 = {
|
|
box_encoding_predictor: box_encoding_3_predictor,
|
|
class_predictor: class_predictor_3
|
|
};
|
|
var box_predictor_4 = {
|
|
box_encoding_predictor: box_encoding_4_predictor,
|
|
class_predictor: class_predictor_4
|
|
};
|
|
var box_predictor_5 = {
|
|
box_encoding_predictor: box_encoding_5_predictor,
|
|
class_predictor: class_predictor_5
|
|
};
|
|
return {
|
|
conv_0: conv_0,
|
|
conv_1: conv_1,
|
|
conv_2: conv_2,
|
|
conv_3: conv_3,
|
|
conv_4: conv_4,
|
|
conv_5: conv_5,
|
|
conv_6: conv_6,
|
|
conv_7: conv_7,
|
|
box_predictor_0: box_predictor_0,
|
|
box_predictor_1: box_predictor_1,
|
|
box_predictor_2: box_predictor_2,
|
|
box_predictor_3: box_predictor_3,
|
|
box_predictor_4: box_predictor_4,
|
|
box_predictor_5: box_predictor_5
|
|
};
|
|
}
|
|
return {
|
|
extractMobilenetV1Params: extractMobilenetV1Params,
|
|
extractPredictionLayerParams: extractPredictionLayerParams
|
|
};
|
|
}
|
|
function extractParams$6(weights) {
|
|
var paramMappings = [];
|
|
var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
|
|
var _b = extractorsFactory$6(extractWeights, paramMappings), extractMobilenetV1Params = _b.extractMobilenetV1Params, extractPredictionLayerParams = _b.extractPredictionLayerParams;
|
|
var mobilenetv1 = extractMobilenetV1Params();
|
|
var prediction_layer = extractPredictionLayerParams();
|
|
var extra_dim = tensor3d(extractWeights(5118 * 4), [1, 5118, 4]);
|
|
var output_layer = {
|
|
extra_dim: extra_dim
|
|
};
|
|
paramMappings.push({ paramPath: 'output_layer/extra_dim' });
|
|
if (getRemainingWeights().length !== 0) {
|
|
throw new Error("weights remaing after extract: " + getRemainingWeights().length);
|
|
}
|
|
return {
|
|
params: {
|
|
mobilenetv1: mobilenetv1,
|
|
prediction_layer: prediction_layer,
|
|
output_layer: output_layer
|
|
},
|
|
paramMappings: paramMappings
|
|
};
|
|
}
|
|
|
|
function extractorsFactory$7(weightMap, paramMappings) {
|
|
var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
|
|
function extractPointwiseConvParams(prefix, idx, mappedPrefix) {
|
|
var filters = extractWeightEntry(prefix + "/Conv2d_" + idx + "_pointwise/weights", 4, mappedPrefix + "/filters");
|
|
var batch_norm_offset = extractWeightEntry(prefix + "/Conv2d_" + idx + "_pointwise/convolution_bn_offset", 1, mappedPrefix + "/batch_norm_offset");
|
|
return { filters: filters, batch_norm_offset: batch_norm_offset };
|
|
}
|
|
function extractConvPairParams(idx) {
|
|
var mappedPrefix = "mobilenetv1/conv_" + idx;
|
|
var prefixDepthwiseConv = "MobilenetV1/Conv2d_" + idx + "_depthwise";
|
|
var mappedPrefixDepthwiseConv = mappedPrefix + "/depthwise_conv";
|
|
var mappedPrefixPointwiseConv = mappedPrefix + "/pointwise_conv";
|
|
var filters = extractWeightEntry(prefixDepthwiseConv + "/depthwise_weights", 4, mappedPrefixDepthwiseConv + "/filters");
|
|
var batch_norm_scale = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/gamma", 1, mappedPrefixDepthwiseConv + "/batch_norm_scale");
|
|
var batch_norm_offset = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/beta", 1, mappedPrefixDepthwiseConv + "/batch_norm_offset");
|
|
var batch_norm_mean = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/moving_mean", 1, mappedPrefixDepthwiseConv + "/batch_norm_mean");
|
|
var batch_norm_variance = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/moving_variance", 1, mappedPrefixDepthwiseConv + "/batch_norm_variance");
|
|
return {
|
|
depthwise_conv: {
|
|
filters: filters,
|
|
batch_norm_scale: batch_norm_scale,
|
|
batch_norm_offset: batch_norm_offset,
|
|
batch_norm_mean: batch_norm_mean,
|
|
batch_norm_variance: batch_norm_variance
|
|
},
|
|
pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv)
|
|
};
|
|
}
|
|
function extractMobilenetV1Params() {
|
|
return {
|
|
conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),
|
|
conv_1: extractConvPairParams(1),
|
|
conv_2: extractConvPairParams(2),
|
|
conv_3: extractConvPairParams(3),
|
|
conv_4: extractConvPairParams(4),
|
|
conv_5: extractConvPairParams(5),
|
|
conv_6: extractConvPairParams(6),
|
|
conv_7: extractConvPairParams(7),
|
|
conv_8: extractConvPairParams(8),
|
|
conv_9: extractConvPairParams(9),
|
|
conv_10: extractConvPairParams(10),
|
|
conv_11: extractConvPairParams(11),
|
|
conv_12: extractConvPairParams(12),
|
|
conv_13: extractConvPairParams(13)
|
|
};
|
|
}
|
|
function extractConvParams(prefix, mappedPrefix) {
|
|
var filters = extractWeightEntry(prefix + "/weights", 4, mappedPrefix + "/filters");
|
|
var bias = extractWeightEntry(prefix + "/biases", 1, mappedPrefix + "/bias");
|
|
return { filters: filters, bias: bias };
|
|
}
|
|
function extractBoxPredictorParams(idx) {
|
|
var box_encoding_predictor = extractConvParams("Prediction/BoxPredictor_" + idx + "/BoxEncodingPredictor", "prediction_layer/box_predictor_" + idx + "/box_encoding_predictor");
|
|
var class_predictor = extractConvParams("Prediction/BoxPredictor_" + idx + "/ClassPredictor", "prediction_layer/box_predictor_" + idx + "/class_predictor");
|
|
return { box_encoding_predictor: box_encoding_predictor, class_predictor: class_predictor };
|
|
}
|
|
function extractPredictionLayerParams() {
|
|
return {
|
|
conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),
|
|
conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),
|
|
conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),
|
|
conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),
|
|
conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),
|
|
conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),
|
|
conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),
|
|
conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),
|
|
box_predictor_0: extractBoxPredictorParams(0),
|
|
box_predictor_1: extractBoxPredictorParams(1),
|
|
box_predictor_2: extractBoxPredictorParams(2),
|
|
box_predictor_3: extractBoxPredictorParams(3),
|
|
box_predictor_4: extractBoxPredictorParams(4),
|
|
box_predictor_5: extractBoxPredictorParams(5)
|
|
};
|
|
}
|
|
return {
|
|
extractMobilenetV1Params: extractMobilenetV1Params,
|
|
extractPredictionLayerParams: extractPredictionLayerParams
|
|
};
|
|
}
|
|
function extractParamsFromWeigthMap$6(weightMap) {
|
|
var paramMappings = [];
|
|
var _a = extractorsFactory$7(weightMap, paramMappings), extractMobilenetV1Params = _a.extractMobilenetV1Params, extractPredictionLayerParams = _a.extractPredictionLayerParams;
|
|
var extra_dim = weightMap['Output/extra_dim'];
|
|
paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' });
|
|
if (!isTensor3D(extra_dim)) {
|
|
throw new Error("expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have " + extra_dim);
|
|
}
|
|
var params = {
|
|
mobilenetv1: extractMobilenetV1Params(),
|
|
prediction_layer: extractPredictionLayerParams(),
|
|
output_layer: {
|
|
extra_dim: extra_dim
|
|
}
|
|
};
|
|
disposeUnusedWeightTensors(weightMap, paramMappings);
|
|
return { params: params, paramMappings: paramMappings };
|
|
}
|
|
|
|
function pointwiseConvLayer(x, params, strides) {
|
|
return tidy(function () {
|
|
var out = conv2d(x, params.filters, strides, 'same');
|
|
out = add(out, params.batch_norm_offset);
|
|
return clipByValue(out, 0, 6);
|
|
});
|
|
}
|
|
|
|
var epsilon = 0.0010000000474974513;
|
|
function depthwiseConvLayer(x, params, strides) {
|
|
return tidy(function () {
|
|
var out = depthwiseConv2d(x, params.filters, strides, 'same');
|
|
out = batchNorm(out, params.batch_norm_mean, params.batch_norm_variance, params.batch_norm_offset, params.batch_norm_scale, epsilon);
|
|
return clipByValue(out, 0, 6);
|
|
});
|
|
}
|
|
function getStridesForLayerIdx(layerIdx) {
|
|
return [2, 4, 6, 12].some(function (idx) { return idx === layerIdx; }) ? [2, 2] : [1, 1];
|
|
}
|
|
function mobileNetV1(x, params) {
|
|
return tidy(function () {
|
|
var conv11 = null;
|
|
var out = pointwiseConvLayer(x, params.conv_0, [2, 2]);
|
|
var convPairParams = [
|
|
params.conv_1,
|
|
params.conv_2,
|
|
params.conv_3,
|
|
params.conv_4,
|
|
params.conv_5,
|
|
params.conv_6,
|
|
params.conv_7,
|
|
params.conv_8,
|
|
params.conv_9,
|
|
params.conv_10,
|
|
params.conv_11,
|
|
params.conv_12,
|
|
params.conv_13
|
|
];
|
|
convPairParams.forEach(function (param, i) {
|
|
var layerIdx = i + 1;
|
|
var depthwiseConvStrides = getStridesForLayerIdx(layerIdx);
|
|
out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides);
|
|
out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1]);
|
|
if (layerIdx === 11) {
|
|
conv11 = out;
|
|
}
|
|
});
|
|
if (conv11 === null) {
|
|
throw new Error('mobileNetV1 - output of conv layer 11 is null');
|
|
}
|
|
return {
|
|
out: out,
|
|
conv11: conv11
|
|
};
|
|
});
|
|
}
|
|
|
|
function nonMaxSuppression$2(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
|
|
var numBoxes = boxes.shape[0];
|
|
var outputSize = Math.min(maxOutputSize, numBoxes);
|
|
var candidates = scores
|
|
.map(function (score, boxIndex) { return ({ score: score, boxIndex: boxIndex }); })
|
|
.filter(function (c) { return c.score > scoreThreshold; })
|
|
.sort(function (c1, c2) { return c2.score - c1.score; });
|
|
var suppressFunc = function (x) { return x <= iouThreshold ? 1 : 0; };
|
|
var selected = [];
|
|
candidates.forEach(function (c) {
|
|
if (selected.length >= outputSize) {
|
|
return;
|
|
}
|
|
var originalScore = c.score;
|
|
for (var j = selected.length - 1; j >= 0; --j) {
|
|
var iou = IOU(boxes, c.boxIndex, selected[j]);
|
|
if (iou === 0.0) {
|
|
continue;
|
|
}
|
|
c.score *= suppressFunc(iou);
|
|
if (c.score <= scoreThreshold) {
|
|
break;
|
|
}
|
|
}
|
|
if (originalScore === c.score) {
|
|
selected.push(c.boxIndex);
|
|
}
|
|
});
|
|
return selected;
|
|
}
|
|
function IOU(boxes, i, j) {
|
|
var boxesData = boxes.arraySync();
|
|
var yminI = Math.min(boxesData[i][0], boxesData[i][2]);
|
|
var xminI = Math.min(boxesData[i][1], boxesData[i][3]);
|
|
var ymaxI = Math.max(boxesData[i][0], boxesData[i][2]);
|
|
var xmaxI = Math.max(boxesData[i][1], boxesData[i][3]);
|
|
var yminJ = Math.min(boxesData[j][0], boxesData[j][2]);
|
|
var xminJ = Math.min(boxesData[j][1], boxesData[j][3]);
|
|
var ymaxJ = Math.max(boxesData[j][0], boxesData[j][2]);
|
|
var xmaxJ = Math.max(boxesData[j][1], boxesData[j][3]);
|
|
var areaI = (ymaxI - yminI) * (xmaxI - xminI);
|
|
var areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);
|
|
if (areaI <= 0 || areaJ <= 0) {
|
|
return 0.0;
|
|
}
|
|
var intersectionYmin = Math.max(yminI, yminJ);
|
|
var intersectionXmin = Math.max(xminI, xminJ);
|
|
var intersectionYmax = Math.min(ymaxI, ymaxJ);
|
|
var intersectionXmax = Math.min(xmaxI, xmaxJ);
|
|
var intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) *
|
|
Math.max(intersectionXmax - intersectionXmin, 0.0);
|
|
return intersectionArea / (areaI + areaJ - intersectionArea);
|
|
}
|
|
|
|
function getCenterCoordinatesAndSizesLayer(x) {
|
|
var vec = unstack(transpose(x, [1, 0]));
|
|
var sizes = [
|
|
sub(vec[2], vec[0]),
|
|
sub(vec[3], vec[1])
|
|
];
|
|
var centers = [
|
|
add(vec[0], div(sizes[0], scalar(2))),
|
|
add(vec[1], div(sizes[1], scalar(2)))
|
|
];
|
|
return {
|
|
sizes: sizes,
|
|
centers: centers
|
|
};
|
|
}
|
|
function decodeBoxesLayer(x0, x1) {
|
|
var _a = getCenterCoordinatesAndSizesLayer(x0), sizes = _a.sizes, centers = _a.centers;
|
|
var vec = unstack(transpose(x1, [1, 0]));
|
|
var div0_out = div(mul(exp(div(vec[2], scalar(5))), sizes[0]), scalar(2));
|
|
var add0_out = add(mul(div(vec[0], scalar(10)), sizes[0]), centers[0]);
|
|
var div1_out = div(mul(exp(div(vec[3], scalar(5))), sizes[1]), scalar(2));
|
|
var add1_out = add(mul(div(vec[1], scalar(10)), sizes[1]), centers[1]);
|
|
return transpose(stack([
|
|
sub(add0_out, div0_out),
|
|
sub(add1_out, div1_out),
|
|
add(add0_out, div0_out),
|
|
add(add1_out, div1_out)
|
|
]), [1, 0]);
|
|
}
|
|
function outputLayer(boxPredictions, classPredictions, params) {
|
|
return tidy(function () {
|
|
var batchSize = boxPredictions.shape[0];
|
|
var boxes = decodeBoxesLayer(reshape(tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]), reshape(boxPredictions, [-1, 4]));
|
|
boxes = reshape(boxes, [batchSize, (boxes.shape[0] / batchSize), 4]);
|
|
var scoresAndClasses = sigmoid(slice(classPredictions, [0, 0, 1], [-1, -1, -1]));
|
|
var scores = slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]);
|
|
scores = reshape(scores, [batchSize, scores.shape[1]]);
|
|
var boxesByBatch = unstack(boxes);
|
|
var scoresByBatch = unstack(scores);
|
|
return {
|
|
boxes: boxesByBatch,
|
|
scores: scoresByBatch
|
|
};
|
|
});
|
|
}
|
|
|
|
function boxPredictionLayer(x, params) {
|
|
return tidy(function () {
|
|
var batchSize = x.shape[0];
|
|
var boxPredictionEncoding = reshape(convLayer(x, params.box_encoding_predictor), [batchSize, -1, 1, 4]);
|
|
var classPrediction = reshape(convLayer(x, params.class_predictor), [batchSize, -1, 3]);
|
|
return {
|
|
boxPredictionEncoding: boxPredictionEncoding,
|
|
classPrediction: classPrediction
|
|
};
|
|
});
|
|
}
|
|
|
|
function predictionLayer(x, conv11, params) {
|
|
return tidy(function () {
|
|
var conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]);
|
|
var conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]);
|
|
var conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]);
|
|
var conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2]);
|
|
var conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1]);
|
|
var conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2]);
|
|
var conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1]);
|
|
var conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2]);
|
|
var boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0);
|
|
var boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1);
|
|
var boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2);
|
|
var boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3);
|
|
var boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4);
|
|
var boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5);
|
|
var boxPredictions = concat([
|
|
boxPrediction0.boxPredictionEncoding,
|
|
boxPrediction1.boxPredictionEncoding,
|
|
boxPrediction2.boxPredictionEncoding,
|
|
boxPrediction3.boxPredictionEncoding,
|
|
boxPrediction4.boxPredictionEncoding,
|
|
boxPrediction5.boxPredictionEncoding
|
|
], 1);
|
|
var classPredictions = concat([
|
|
boxPrediction0.classPrediction,
|
|
boxPrediction1.classPrediction,
|
|
boxPrediction2.classPrediction,
|
|
boxPrediction3.classPrediction,
|
|
boxPrediction4.classPrediction,
|
|
boxPrediction5.classPrediction
|
|
], 1);
|
|
return {
|
|
boxPredictions: boxPredictions,
|
|
classPredictions: classPredictions
|
|
};
|
|
});
|
|
}
|
|
|
|
var SsdMobilenetv1Options = /** @class */ (function () {
|
|
function SsdMobilenetv1Options(_a) {
|
|
var _b = _a === void 0 ? {} : _a, minConfidence = _b.minConfidence, maxResults = _b.maxResults;
|
|
this._name = 'SsdMobilenetv1Options';
|
|
this._minConfidence = minConfidence || 0.5;
|
|
this._maxResults = maxResults || 100;
|
|
if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {
|
|
throw new Error(this._name + " - expected minConfidence to be a number between 0 and 1");
|
|
}
|
|
if (typeof this._maxResults !== 'number') {
|
|
throw new Error(this._name + " - expected maxResults to be a number");
|
|
}
|
|
}
|
|
Object.defineProperty(SsdMobilenetv1Options.prototype, "minConfidence", {
|
|
get: function () { return this._minConfidence; },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(SsdMobilenetv1Options.prototype, "maxResults", {
|
|
get: function () { return this._maxResults; },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
return SsdMobilenetv1Options;
|
|
}());
|
|
|
|
var SsdMobilenetv1 = /** @class */ (function (_super) {
|
|
__extends(SsdMobilenetv1, _super);
|
|
function SsdMobilenetv1() {
|
|
return _super.call(this, 'SsdMobilenetv1') || this;
|
|
}
|
|
SsdMobilenetv1.prototype.forwardInput = function (input) {
|
|
var params = this.params;
|
|
if (!params) {
|
|
throw new Error('SsdMobilenetv1 - load model before inference');
|
|
}
|
|
return tidy(function () {
|
|
var batchTensor = input.toBatchTensor(512, false).toFloat();
|
|
var x = sub(mul(batchTensor, scalar(0.007843137718737125)), scalar(1));
|
|
var features = mobileNetV1(x, params.mobilenetv1);
|
|
var _a = predictionLayer(features.out, features.conv11, params.prediction_layer), boxPredictions = _a.boxPredictions, classPredictions = _a.classPredictions;
|
|
return outputLayer(boxPredictions, classPredictions, params.output_layer);
|
|
});
|
|
};
|
|
SsdMobilenetv1.prototype.forward = function (input) {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var _a;
|
|
return __generator(this, function (_b) {
|
|
switch (_b.label) {
|
|
case 0:
|
|
_a = this.forwardInput;
|
|
return [4 /*yield*/, toNetInput(input)];
|
|
case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
|
|
}
|
|
});
|
|
});
|
|
};
|
|
SsdMobilenetv1.prototype.locateFaces = function (input, options) {
|
|
if (options === void 0) { options = {}; }
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var _a, maxResults, minConfidence, netInput, _b, _boxes, _scores, boxes, scores, i, scoresData, _c, _d, iouThreshold, indices, reshapedDims, inputSize, padX, padY, boxesData, results;
|
|
return __generator(this, function (_e) {
|
|
switch (_e.label) {
|
|
case 0:
|
|
_a = new SsdMobilenetv1Options(options), maxResults = _a.maxResults, minConfidence = _a.minConfidence;
|
|
return [4 /*yield*/, toNetInput(input)];
|
|
case 1:
|
|
netInput = _e.sent();
|
|
_b = this.forwardInput(netInput), _boxes = _b.boxes, _scores = _b.scores;
|
|
boxes = _boxes[0];
|
|
scores = _scores[0];
|
|
for (i = 1; i < _boxes.length; i++) {
|
|
_boxes[i].dispose();
|
|
_scores[i].dispose();
|
|
}
|
|
_d = (_c = Array).from;
|
|
return [4 /*yield*/, scores.data()];
|
|
case 2:
|
|
scoresData = _d.apply(_c, [_e.sent()]);
|
|
iouThreshold = 0.5;
|
|
indices = nonMaxSuppression$2(boxes, scoresData, maxResults, iouThreshold, minConfidence);
|
|
reshapedDims = netInput.getReshapedInputDimensions(0);
|
|
inputSize = netInput.inputSize;
|
|
padX = inputSize / reshapedDims.width;
|
|
padY = inputSize / reshapedDims.height;
|
|
boxesData = boxes.arraySync();
|
|
results = indices
|
|
.map(function (idx) {
|
|
var _a = [
|
|
Math.max(0, boxesData[idx][0]),
|
|
Math.min(1.0, boxesData[idx][2])
|
|
].map(function (val) { return val * padY; }), top = _a[0], bottom = _a[1];
|
|
var _b = [
|
|
Math.max(0, boxesData[idx][1]),
|
|
Math.min(1.0, boxesData[idx][3])
|
|
].map(function (val) { return val * padX; }), 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)
|
|
});
|
|
});
|
|
boxes.dispose();
|
|
scores.dispose();
|
|
return [2 /*return*/, results];
|
|
}
|
|
});
|
|
});
|
|
};
|
|
SsdMobilenetv1.prototype.getDefaultModelName = function () {
|
|
return 'ssd_mobilenetv1_model';
|
|
};
|
|
SsdMobilenetv1.prototype.extractParamsFromWeigthMap = function (weightMap) {
|
|
return extractParamsFromWeigthMap$6(weightMap);
|
|
};
|
|
SsdMobilenetv1.prototype.extractParams = function (weights) {
|
|
return extractParams$6(weights);
|
|
};
|
|
return SsdMobilenetv1;
|
|
}(NeuralNetwork));
|
|
|
|
function createSsdMobilenetv1(weights) {
|
|
var net = new SsdMobilenetv1();
|
|
net.extractWeights(weights);
|
|
return net;
|
|
}
|
|
function createFaceDetectionNet(weights) {
|
|
return createSsdMobilenetv1(weights);
|
|
}
|
|
// alias for backward compatibily
|
|
var FaceDetectionNet = /** @class */ (function (_super) {
|
|
__extends(FaceDetectionNet, _super);
|
|
function FaceDetectionNet() {
|
|
return _super !== null && _super.apply(this, arguments) || this;
|
|
}
|
|
return FaceDetectionNet;
|
|
}(SsdMobilenetv1));
|
|
|
|
var TinyFaceDetectorOptions = /** @class */ (function (_super) {
|
|
__extends(TinyFaceDetectorOptions, _super);
|
|
function TinyFaceDetectorOptions() {
|
|
var _this = _super !== null && _super.apply(this, arguments) || this;
|
|
_this._name = 'TinyFaceDetectorOptions';
|
|
return _this;
|
|
}
|
|
return TinyFaceDetectorOptions;
|
|
}(TinyYolov2Options));
|
|
|
|
var ComposableTask = /** @class */ (function () {
|
|
function ComposableTask() {
|
|
}
|
|
ComposableTask.prototype.then = function (onfulfilled) {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var _a;
|
|
return __generator(this, function (_b) {
|
|
switch (_b.label) {
|
|
case 0:
|
|
_a = onfulfilled;
|
|
return [4 /*yield*/, this.run()];
|
|
case 1: return [2 /*return*/, _a.apply(void 0, [_b.sent()])];
|
|
}
|
|
});
|
|
});
|
|
};
|
|
ComposableTask.prototype.run = function () {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
return __generator(this, function (_a) {
|
|
throw new Error('ComposableTask - run is not implemented');
|
|
});
|
|
});
|
|
};
|
|
return ComposableTask;
|
|
}());
|
|
|
|
function extractAllFacesAndComputeResults(parentResults, input, computeResults, extractedFaces, getRectForAlignment) {
|
|
if (getRectForAlignment === void 0) { getRectForAlignment = function (_a) {
|
|
var alignedRect = _a.alignedRect;
|
|
return alignedRect;
|
|
}; }
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var faceBoxes, faces, _a, _b, results;
|
|
return __generator(this, function (_c) {
|
|
switch (_c.label) {
|
|
case 0:
|
|
faceBoxes = parentResults.map(function (parentResult) {
|
|
return isWithFaceLandmarks(parentResult)
|
|
? getRectForAlignment(parentResult)
|
|
: parentResult.detection;
|
|
});
|
|
_a = extractedFaces;
|
|
if (_a) return [3 /*break*/, 5];
|
|
if (!(input instanceof Tensor)) return [3 /*break*/, 2];
|
|
return [4 /*yield*/, extractFaceTensors(input, faceBoxes)];
|
|
case 1:
|
|
_b = _c.sent();
|
|
return [3 /*break*/, 4];
|
|
case 2: return [4 /*yield*/, extractFaces(input, faceBoxes)];
|
|
case 3:
|
|
_b = _c.sent();
|
|
_c.label = 4;
|
|
case 4:
|
|
_a = (_b);
|
|
_c.label = 5;
|
|
case 5:
|
|
faces = _a;
|
|
return [4 /*yield*/, computeResults(faces)];
|
|
case 6:
|
|
results = _c.sent();
|
|
faces.forEach(function (f) { return f instanceof Tensor && f.dispose(); });
|
|
return [2 /*return*/, results];
|
|
}
|
|
});
|
|
});
|
|
}
|
|
function extractSingleFaceAndComputeResult(parentResult, input, computeResult, extractedFaces, getRectForAlignment) {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var _this = this;
|
|
return __generator(this, function (_a) {
|
|
return [2 /*return*/, extractAllFacesAndComputeResults([parentResult], input, function (faces) { return __awaiter(_this, void 0, void 0, function () { return __generator(this, function (_a) {
|
|
return [2 /*return*/, computeResult(faces[0])];
|
|
}); }); }, extractedFaces, getRectForAlignment)];
|
|
});
|
|
});
|
|
}
|
|
|
|
function bgrToRgbTensor(tensor) {
|
|
return tidy(function () { return stack(unstack(tensor, 3).reverse(), 3); });
|
|
}
|
|
|
|
var CELL_STRIDE = 2;
|
|
var CELL_SIZE = 12;
|
|
|
|
function extractorsFactory$8(extractWeights, paramMappings) {
|
|
var extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);
|
|
var extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);
|
|
function extractPReluParams(size, paramPath) {
|
|
var alpha = tensor1d(extractWeights(size));
|
|
paramMappings.push({ paramPath: paramPath });
|
|
return alpha;
|
|
}
|
|
function extractSharedParams(numFilters, mappedPrefix, isRnet) {
|
|
if (isRnet === void 0) { isRnet = false; }
|
|
var conv1 = extractConvParams(numFilters[0], numFilters[1], 3, mappedPrefix + "/conv1");
|
|
var prelu1_alpha = extractPReluParams(numFilters[1], mappedPrefix + "/prelu1_alpha");
|
|
var conv2 = extractConvParams(numFilters[1], numFilters[2], 3, mappedPrefix + "/conv2");
|
|
var prelu2_alpha = extractPReluParams(numFilters[2], mappedPrefix + "/prelu2_alpha");
|
|
var conv3 = extractConvParams(numFilters[2], numFilters[3], isRnet ? 2 : 3, mappedPrefix + "/conv3");
|
|
var prelu3_alpha = extractPReluParams(numFilters[3], mappedPrefix + "/prelu3_alpha");
|
|
return { conv1: conv1, prelu1_alpha: prelu1_alpha, conv2: conv2, prelu2_alpha: prelu2_alpha, conv3: conv3, prelu3_alpha: prelu3_alpha };
|
|
}
|
|
function extractPNetParams() {
|
|
var sharedParams = extractSharedParams([3, 10, 16, 32], 'pnet');
|
|
var conv4_1 = extractConvParams(32, 2, 1, 'pnet/conv4_1');
|
|
var conv4_2 = extractConvParams(32, 4, 1, 'pnet/conv4_2');
|
|
return __assign({}, sharedParams, { conv4_1: conv4_1, conv4_2: conv4_2 });
|
|
}
|
|
function extractRNetParams() {
|
|
var sharedParams = extractSharedParams([3, 28, 48, 64], 'rnet', true);
|
|
var fc1 = extractFCParams(576, 128, 'rnet/fc1');
|
|
var prelu4_alpha = extractPReluParams(128, 'rnet/prelu4_alpha');
|
|
var fc2_1 = extractFCParams(128, 2, 'rnet/fc2_1');
|
|
var fc2_2 = extractFCParams(128, 4, 'rnet/fc2_2');
|
|
return __assign({}, sharedParams, { fc1: fc1, prelu4_alpha: prelu4_alpha, fc2_1: fc2_1, fc2_2: fc2_2 });
|
|
}
|
|
function extractONetParams() {
|
|
var sharedParams = extractSharedParams([3, 32, 64, 64], 'onet');
|
|
var conv4 = extractConvParams(64, 128, 2, 'onet/conv4');
|
|
var prelu4_alpha = extractPReluParams(128, 'onet/prelu4_alpha');
|
|
var fc1 = extractFCParams(1152, 256, 'onet/fc1');
|
|
var prelu5_alpha = extractPReluParams(256, 'onet/prelu5_alpha');
|
|
var fc2_1 = extractFCParams(256, 2, 'onet/fc2_1');
|
|
var fc2_2 = extractFCParams(256, 4, 'onet/fc2_2');
|
|
var fc2_3 = extractFCParams(256, 10, 'onet/fc2_3');
|
|
return __assign({}, sharedParams, { conv4: conv4, prelu4_alpha: prelu4_alpha, fc1: fc1, prelu5_alpha: prelu5_alpha, fc2_1: fc2_1, fc2_2: fc2_2, fc2_3: fc2_3 });
|
|
}
|
|
return {
|
|
extractPNetParams: extractPNetParams,
|
|
extractRNetParams: extractRNetParams,
|
|
extractONetParams: extractONetParams
|
|
};
|
|
}
|
|
function extractParams$7(weights) {
|
|
var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
|
|
var paramMappings = [];
|
|
var _b = extractorsFactory$8(extractWeights, paramMappings), extractPNetParams = _b.extractPNetParams, extractRNetParams = _b.extractRNetParams, extractONetParams = _b.extractONetParams;
|
|
var pnet = extractPNetParams();
|
|
var rnet = extractRNetParams();
|
|
var onet = extractONetParams();
|
|
if (getRemainingWeights().length !== 0) {
|
|
throw new Error("weights remaing after extract: " + getRemainingWeights().length);
|
|
}
|
|
return { params: { pnet: pnet, rnet: rnet, onet: onet }, paramMappings: paramMappings };
|
|
}
|
|
|
|
function extractorsFactory$9(weightMap, paramMappings) {
|
|
var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
|
|
function extractConvParams(prefix) {
|
|
var filters = extractWeightEntry(prefix + "/weights", 4, prefix + "/filters");
|
|
var bias = extractWeightEntry(prefix + "/bias", 1);
|
|
return { filters: filters, bias: bias };
|
|
}
|
|
function extractFCParams(prefix) {
|
|
var weights = extractWeightEntry(prefix + "/weights", 2);
|
|
var bias = extractWeightEntry(prefix + "/bias", 1);
|
|
return { weights: weights, bias: bias };
|
|
}
|
|
function extractPReluParams(paramPath) {
|
|
return extractWeightEntry(paramPath, 1);
|
|
}
|
|
function extractSharedParams(prefix) {
|
|
var conv1 = extractConvParams(prefix + "/conv1");
|
|
var prelu1_alpha = extractPReluParams(prefix + "/prelu1_alpha");
|
|
var conv2 = extractConvParams(prefix + "/conv2");
|
|
var prelu2_alpha = extractPReluParams(prefix + "/prelu2_alpha");
|
|
var conv3 = extractConvParams(prefix + "/conv3");
|
|
var prelu3_alpha = extractPReluParams(prefix + "/prelu3_alpha");
|
|
return { conv1: conv1, prelu1_alpha: prelu1_alpha, conv2: conv2, prelu2_alpha: prelu2_alpha, conv3: conv3, prelu3_alpha: prelu3_alpha };
|
|
}
|
|
function extractPNetParams() {
|
|
var sharedParams = extractSharedParams('pnet');
|
|
var conv4_1 = extractConvParams('pnet/conv4_1');
|
|
var conv4_2 = extractConvParams('pnet/conv4_2');
|
|
return __assign({}, sharedParams, { conv4_1: conv4_1, conv4_2: conv4_2 });
|
|
}
|
|
function extractRNetParams() {
|
|
var sharedParams = extractSharedParams('rnet');
|
|
var fc1 = extractFCParams('rnet/fc1');
|
|
var prelu4_alpha = extractPReluParams('rnet/prelu4_alpha');
|
|
var fc2_1 = extractFCParams('rnet/fc2_1');
|
|
var fc2_2 = extractFCParams('rnet/fc2_2');
|
|
return __assign({}, sharedParams, { fc1: fc1, prelu4_alpha: prelu4_alpha, fc2_1: fc2_1, fc2_2: fc2_2 });
|
|
}
|
|
function extractONetParams() {
|
|
var sharedParams = extractSharedParams('onet');
|
|
var conv4 = extractConvParams('onet/conv4');
|
|
var prelu4_alpha = extractPReluParams('onet/prelu4_alpha');
|
|
var fc1 = extractFCParams('onet/fc1');
|
|
var prelu5_alpha = extractPReluParams('onet/prelu5_alpha');
|
|
var fc2_1 = extractFCParams('onet/fc2_1');
|
|
var fc2_2 = extractFCParams('onet/fc2_2');
|
|
var fc2_3 = extractFCParams('onet/fc2_3');
|
|
return __assign({}, sharedParams, { conv4: conv4, prelu4_alpha: prelu4_alpha, fc1: fc1, prelu5_alpha: prelu5_alpha, fc2_1: fc2_1, fc2_2: fc2_2, fc2_3: fc2_3 });
|
|
}
|
|
return {
|
|
extractPNetParams: extractPNetParams,
|
|
extractRNetParams: extractRNetParams,
|
|
extractONetParams: extractONetParams
|
|
};
|
|
}
|
|
function extractParamsFromWeigthMap$7(weightMap) {
|
|
var paramMappings = [];
|
|
var _a = extractorsFactory$9(weightMap, paramMappings), extractPNetParams = _a.extractPNetParams, extractRNetParams = _a.extractRNetParams, extractONetParams = _a.extractONetParams;
|
|
var pnet = extractPNetParams();
|
|
var rnet = extractRNetParams();
|
|
var onet = extractONetParams();
|
|
disposeUnusedWeightTensors(weightMap, paramMappings);
|
|
return { params: { pnet: pnet, rnet: rnet, onet: onet }, paramMappings: paramMappings };
|
|
}
|
|
|
|
function getSizesForScale(scale, _a) {
|
|
var height = _a[0], width = _a[1];
|
|
return {
|
|
height: Math.floor(height * scale),
|
|
width: Math.floor(width * scale)
|
|
};
|
|
}
|
|
|
|
function pyramidDown(minFaceSize, scaleFactor, dims) {
|
|
var height = dims[0], width = dims[1];
|
|
var m = CELL_SIZE / minFaceSize;
|
|
var scales = [];
|
|
var minLayer = Math.min(height, width) * m;
|
|
var exp = 0;
|
|
while (minLayer >= 12) {
|
|
scales.push(m * Math.pow(scaleFactor, exp));
|
|
minLayer = minLayer * scaleFactor;
|
|
exp += 1;
|
|
}
|
|
return scales;
|
|
}
|
|
|
|
var MtcnnBox = /** @class */ (function (_super) {
|
|
__extends(MtcnnBox, _super);
|
|
function MtcnnBox(left, top, right, bottom) {
|
|
return _super.call(this, { left: left, top: top, right: right, bottom: bottom }, true) || this;
|
|
}
|
|
return MtcnnBox;
|
|
}(Box));
|
|
|
|
function normalize$1(x) {
|
|
return tidy(function () { return mul(sub(x, scalar(127.5)), scalar(0.0078125)); });
|
|
}
|
|
|
|
function prelu$1(x, alpha) {
|
|
return tidy(function () {
|
|
return add(relu(x), mul(alpha, neg(relu(neg(x)))));
|
|
});
|
|
}
|
|
|
|
function sharedLayer(x, params, isPnet) {
|
|
if (isPnet === void 0) { isPnet = false; }
|
|
return tidy(function () {
|
|
var out = convLayer(x, params.conv1, 'valid');
|
|
out = prelu$1(out, params.prelu1_alpha);
|
|
out = maxPool(out, isPnet ? [2, 2] : [3, 3], [2, 2], 'same');
|
|
out = convLayer(out, params.conv2, 'valid');
|
|
out = prelu$1(out, params.prelu2_alpha);
|
|
out = isPnet ? out : maxPool(out, [3, 3], [2, 2], 'valid');
|
|
out = convLayer(out, params.conv3, 'valid');
|
|
out = prelu$1(out, params.prelu3_alpha);
|
|
return out;
|
|
});
|
|
}
|
|
|
|
function PNet(x, params) {
|
|
return tidy(function () {
|
|
var out = sharedLayer(x, params, true);
|
|
var conv = convLayer(out, params.conv4_1, 'valid');
|
|
var max$1 = expandDims(max(conv, 3), 3);
|
|
var prob = softmax(sub(conv, max$1), 3);
|
|
var regions = convLayer(out, params.conv4_2, 'valid');
|
|
return { prob: prob, regions: regions };
|
|
});
|
|
}
|
|
|
|
function rescaleAndNormalize(x, scale) {
|
|
return tidy(function () {
|
|
var _a = getSizesForScale(scale, x.shape.slice(1)), height = _a.height, width = _a.width;
|
|
var resized = image_ops.resizeBilinear(x, [height, width]);
|
|
var normalized = normalize$1(resized);
|
|
return transpose(normalized, [0, 2, 1, 3]);
|
|
});
|
|
}
|
|
function extractBoundingBoxes(scoresTensor, regionsTensor, scale, scoreThreshold) {
|
|
// TODO: fix this!, maybe better to use tf.gather here
|
|
var indices = [];
|
|
var scoresData = scoresTensor.arraySync();
|
|
for (var y = 0; y < scoresTensor.shape[0]; y++) {
|
|
for (var x = 0; x < scoresTensor.shape[1]; x++) {
|
|
if (scoresData[y][x] >= scoreThreshold) {
|
|
indices.push(new Point(x, y));
|
|
}
|
|
}
|
|
}
|
|
var boundingBoxes = indices.map(function (idx) {
|
|
var cell = new BoundingBox(Math.round((idx.y * CELL_STRIDE + 1) / scale), Math.round((idx.x * CELL_STRIDE + 1) / scale), Math.round((idx.y * CELL_STRIDE + CELL_SIZE) / scale), Math.round((idx.x * CELL_STRIDE + CELL_SIZE) / scale));
|
|
var score = scoresData[idx.y][idx.x];
|
|
var regionsData = regionsTensor.arraySync();
|
|
var region = new MtcnnBox(regionsData[idx.y][idx.x][0], regionsData[idx.y][idx.x][1], regionsData[idx.y][idx.x][2], regionsData[idx.y][idx.x][3]);
|
|
return {
|
|
cell: cell,
|
|
score: score,
|
|
region: region
|
|
};
|
|
});
|
|
return boundingBoxes;
|
|
}
|
|
function stage1(imgTensor, scales, scoreThreshold, params, stats) {
|
|
stats.stage1 = [];
|
|
var pnetOutputs = scales.map(function (scale) { return tidy(function () {
|
|
var statsForScale = { scale: scale };
|
|
var resized = rescaleAndNormalize(imgTensor, scale);
|
|
var ts = Date.now();
|
|
var _a = PNet(resized, params), prob = _a.prob, regions = _a.regions;
|
|
statsForScale.pnet = Date.now() - ts;
|
|
var scoresTensor = unstack(unstack(prob, 3)[1])[0];
|
|
var regionsTensor = unstack(regions)[0];
|
|
return {
|
|
scoresTensor: scoresTensor,
|
|
regionsTensor: regionsTensor,
|
|
scale: scale,
|
|
statsForScale: statsForScale
|
|
};
|
|
}); });
|
|
var boxesForScale = pnetOutputs.map(function (_a) {
|
|
var scoresTensor = _a.scoresTensor, regionsTensor = _a.regionsTensor, scale = _a.scale, statsForScale = _a.statsForScale;
|
|
var boundingBoxes = extractBoundingBoxes(scoresTensor, regionsTensor, scale, scoreThreshold);
|
|
scoresTensor.dispose();
|
|
regionsTensor.dispose();
|
|
if (!boundingBoxes.length) {
|
|
stats.stage1.push(statsForScale);
|
|
return [];
|
|
}
|
|
var ts = Date.now();
|
|
var indices = nonMaxSuppression$1(boundingBoxes.map(function (bbox) { return bbox.cell; }), boundingBoxes.map(function (bbox) { return bbox.score; }), 0.5);
|
|
statsForScale.nms = Date.now() - ts;
|
|
statsForScale.numBoxes = indices.length;
|
|
stats.stage1.push(statsForScale);
|
|
return indices.map(function (boxIdx) { return boundingBoxes[boxIdx]; });
|
|
});
|
|
var allBoxes = boxesForScale.reduce(function (all, boxes) { return all.concat(boxes); }, []);
|
|
var finalBoxes = [];
|
|
var finalScores = [];
|
|
if (allBoxes.length > 0) {
|
|
var ts = Date.now();
|
|
var indices = nonMaxSuppression$1(allBoxes.map(function (bbox) { return bbox.cell; }), allBoxes.map(function (bbox) { return bbox.score; }), 0.7);
|
|
stats.stage1_nms = Date.now() - ts;
|
|
finalScores = indices.map(function (idx) { return allBoxes[idx].score; });
|
|
finalBoxes = indices
|
|
.map(function (idx) { return allBoxes[idx]; })
|
|
.map(function (_a) {
|
|
var cell = _a.cell, region = _a.region;
|
|
return new BoundingBox(cell.left + (region.left * cell.width), cell.top + (region.top * cell.height), cell.right + (region.right * cell.width), cell.bottom + (region.bottom * cell.height)).toSquare().round();
|
|
});
|
|
}
|
|
return {
|
|
boxes: finalBoxes,
|
|
scores: finalScores
|
|
};
|
|
}
|
|
|
|
function extractImagePatches(img, boxes, _a) {
|
|
var width = _a.width, height = _a.height;
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var imgCtx, bitmaps, imagePatchesDatas;
|
|
var _this = this;
|
|
return __generator(this, function (_b) {
|
|
switch (_b.label) {
|
|
case 0:
|
|
imgCtx = getContext2dOrThrow(img);
|
|
return [4 /*yield*/, Promise.all(boxes.map(function (box) { return __awaiter(_this, void 0, void 0, function () {
|
|
var _a, y, ey, x, ex, fromX, fromY, imgData;
|
|
return __generator(this, function (_b) {
|
|
_a = box.padAtBorders(img.height, img.width), y = _a.y, ey = _a.ey, x = _a.x, ex = _a.ex;
|
|
fromX = x - 1;
|
|
fromY = y - 1;
|
|
imgData = imgCtx.getImageData(fromX, fromY, (ex - fromX), (ey - fromY));
|
|
return [2 /*return*/, env.isNodejs() ? createCanvasFromMedia(imgData) : createImageBitmap(imgData)];
|
|
});
|
|
}); }))];
|
|
case 1:
|
|
bitmaps = _b.sent();
|
|
imagePatchesDatas = [];
|
|
bitmaps.forEach(function (bmp) {
|
|
var patch = createCanvas({ width: width, height: height });
|
|
var patchCtx = getContext2dOrThrow(patch);
|
|
patchCtx.drawImage(bmp, 0, 0, width, height);
|
|
var data = patchCtx.getImageData(0, 0, width, height).data;
|
|
var currData = [];
|
|
// RGBA -> BGR
|
|
for (var i = 0; i < data.length; i += 4) {
|
|
currData.push(data[i + 2]);
|
|
currData.push(data[i + 1]);
|
|
currData.push(data[i]);
|
|
}
|
|
imagePatchesDatas.push(currData);
|
|
});
|
|
return [2 /*return*/, imagePatchesDatas.map(function (data) {
|
|
var t = tidy(function () {
|
|
var imagePatchTensor = transpose(tensor4d(data, [1, width, height, 3]), [0, 2, 1, 3]).toFloat();
|
|
return normalize$1(imagePatchTensor);
|
|
});
|
|
return t;
|
|
})];
|
|
}
|
|
});
|
|
});
|
|
}
|
|
|
|
function RNet(x, params) {
|
|
return tidy(function () {
|
|
var convOut = sharedLayer(x, params);
|
|
var vectorized = reshape(convOut, [convOut.shape[0], params.fc1.weights.shape[0]]);
|
|
var fc1 = fullyConnectedLayer(vectorized, params.fc1);
|
|
var prelu4 = prelu$1(fc1, params.prelu4_alpha);
|
|
var fc2_1 = fullyConnectedLayer(prelu4, params.fc2_1);
|
|
var max$1 = expandDims(max(fc2_1, 1), 1);
|
|
var prob = softmax(sub(fc2_1, max$1), 1);
|
|
var regions = fullyConnectedLayer(prelu4, params.fc2_2);
|
|
var scores = unstack(prob, 1)[1];
|
|
return { scores: scores, regions: regions };
|
|
});
|
|
}
|
|
|
|
function stage2(img, inputBoxes, scoreThreshold, params, stats) {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var ts, rnetInputs, rnetOuts, scoresTensor, scores, _a, _b, indices, filteredBoxes, filteredScores, finalBoxes, finalScores, indicesNms, regions_1;
|
|
return __generator(this, function (_c) {
|
|
switch (_c.label) {
|
|
case 0:
|
|
ts = Date.now();
|
|
return [4 /*yield*/, extractImagePatches(img, inputBoxes, { width: 24, height: 24 })];
|
|
case 1:
|
|
rnetInputs = _c.sent();
|
|
stats.stage2_extractImagePatches = Date.now() - ts;
|
|
ts = Date.now();
|
|
rnetOuts = rnetInputs.map(function (rnetInput) {
|
|
var out = RNet(rnetInput, params);
|
|
rnetInput.dispose();
|
|
return out;
|
|
});
|
|
stats.stage2_rnet = Date.now() - ts;
|
|
scoresTensor = rnetOuts.length > 1
|
|
? concat(rnetOuts.map(function (out) { return out.scores; }))
|
|
: rnetOuts[0].scores;
|
|
_b = (_a = Array).from;
|
|
return [4 /*yield*/, scoresTensor.data()];
|
|
case 2:
|
|
scores = _b.apply(_a, [_c.sent()]);
|
|
scoresTensor.dispose();
|
|
indices = scores
|
|
.map(function (score, idx) { return ({ score: score, idx: idx }); })
|
|
.filter(function (c) { return c.score > scoreThreshold; })
|
|
.map(function (_a) {
|
|
var idx = _a.idx;
|
|
return idx;
|
|
});
|
|
filteredBoxes = indices.map(function (idx) { return inputBoxes[idx]; });
|
|
filteredScores = indices.map(function (idx) { return scores[idx]; });
|
|
finalBoxes = [];
|
|
finalScores = [];
|
|
if (filteredBoxes.length > 0) {
|
|
ts = Date.now();
|
|
indicesNms = nonMaxSuppression$1(filteredBoxes, filteredScores, 0.7);
|
|
stats.stage2_nms = Date.now() - ts;
|
|
regions_1 = indicesNms.map(function (idx) {
|
|
var regionsData = rnetOuts[indices[idx]].regions.arraySync();
|
|
return new MtcnnBox(regionsData[0][0], regionsData[0][1], regionsData[0][2], regionsData[0][3]);
|
|
});
|
|
finalScores = indicesNms.map(function (idx) { return filteredScores[idx]; });
|
|
finalBoxes = indicesNms.map(function (idx, i) { return filteredBoxes[idx].calibrate(regions_1[i]); });
|
|
}
|
|
rnetOuts.forEach(function (t) {
|
|
t.regions.dispose();
|
|
t.scores.dispose();
|
|
});
|
|
return [2 /*return*/, {
|
|
boxes: finalBoxes,
|
|
scores: finalScores
|
|
}];
|
|
}
|
|
});
|
|
});
|
|
}
|
|
|
|
function ONet(x, params) {
|
|
return tidy(function () {
|
|
var out = sharedLayer(x, params);
|
|
out = maxPool(out, [2, 2], [2, 2], 'same');
|
|
out = convLayer(out, params.conv4, 'valid');
|
|
out = prelu$1(out, params.prelu4_alpha);
|
|
var vectorized = reshape(out, [out.shape[0], params.fc1.weights.shape[0]]);
|
|
var fc1 = fullyConnectedLayer(vectorized, params.fc1);
|
|
var prelu5 = prelu$1(fc1, params.prelu5_alpha);
|
|
var fc2_1 = fullyConnectedLayer(prelu5, params.fc2_1);
|
|
var max$1 = expandDims(max(fc2_1, 1), 1);
|
|
var prob = softmax(sub(fc2_1, max$1), 1);
|
|
var regions = fullyConnectedLayer(prelu5, params.fc2_2);
|
|
var points = fullyConnectedLayer(prelu5, params.fc2_3);
|
|
var scores = unstack(prob, 1)[1];
|
|
return { scores: scores, regions: regions, points: points };
|
|
});
|
|
}
|
|
|
|
function stage3(img, inputBoxes, scoreThreshold, params, stats) {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var ts, onetInputs, onetOuts, scoresTensor, scores, _a, _b, indices, filteredRegions, filteredBoxes, filteredScores, finalBoxes, finalScores, points, indicesNms;
|
|
return __generator(this, function (_c) {
|
|
switch (_c.label) {
|
|
case 0:
|
|
ts = Date.now();
|
|
return [4 /*yield*/, extractImagePatches(img, inputBoxes, { width: 48, height: 48 })];
|
|
case 1:
|
|
onetInputs = _c.sent();
|
|
stats.stage3_extractImagePatches = Date.now() - ts;
|
|
ts = Date.now();
|
|
onetOuts = onetInputs.map(function (onetInput) {
|
|
var out = ONet(onetInput, params);
|
|
onetInput.dispose();
|
|
return out;
|
|
});
|
|
stats.stage3_onet = Date.now() - ts;
|
|
scoresTensor = onetOuts.length > 1
|
|
? concat(onetOuts.map(function (out) { return out.scores; }))
|
|
: onetOuts[0].scores;
|
|
_b = (_a = Array).from;
|
|
return [4 /*yield*/, scoresTensor.data()];
|
|
case 2:
|
|
scores = _b.apply(_a, [_c.sent()]);
|
|
scoresTensor.dispose();
|
|
indices = scores
|
|
.map(function (score, idx) { return ({ score: score, idx: idx }); })
|
|
.filter(function (c) { return c.score > scoreThreshold; })
|
|
.map(function (_a) {
|
|
var idx = _a.idx;
|
|
return idx;
|
|
});
|
|
filteredRegions = indices.map(function (idx) {
|
|
var regionsData = onetOuts[idx].regions.arraySync();
|
|
return new MtcnnBox(regionsData[0][0], regionsData[0][1], regionsData[0][2], regionsData[0][3]);
|
|
});
|
|
filteredBoxes = indices
|
|
.map(function (idx, i) { return inputBoxes[idx].calibrate(filteredRegions[i]); });
|
|
filteredScores = indices.map(function (idx) { return scores[idx]; });
|
|
finalBoxes = [];
|
|
finalScores = [];
|
|
points = [];
|
|
if (filteredBoxes.length > 0) {
|
|
ts = Date.now();
|
|
indicesNms = nonMaxSuppression$1(filteredBoxes, filteredScores, 0.7, false);
|
|
stats.stage3_nms = Date.now() - ts;
|
|
finalBoxes = indicesNms.map(function (idx) { return filteredBoxes[idx]; });
|
|
finalScores = indicesNms.map(function (idx) { return filteredScores[idx]; });
|
|
points = indicesNms.map(function (idx, i) {
|
|
return Array(5).fill(0).map(function (_, ptIdx) {
|
|
var pointsData = onetOuts[idx].points.arraySync();
|
|
return new Point(((pointsData[0][ptIdx] * (finalBoxes[i].width + 1)) + finalBoxes[i].left), ((pointsData[0][ptIdx + 5] * (finalBoxes[i].height + 1)) + finalBoxes[i].top));
|
|
});
|
|
});
|
|
}
|
|
onetOuts.forEach(function (t) {
|
|
t.regions.dispose();
|
|
t.scores.dispose();
|
|
t.points.dispose();
|
|
});
|
|
return [2 /*return*/, {
|
|
boxes: finalBoxes,
|
|
scores: finalScores,
|
|
points: points
|
|
}];
|
|
}
|
|
});
|
|
});
|
|
}
|
|
|
|
var Mtcnn = /** @class */ (function (_super) {
|
|
__extends(Mtcnn, _super);
|
|
function Mtcnn() {
|
|
return _super.call(this, 'Mtcnn') || this;
|
|
}
|
|
Mtcnn.prototype.forwardInput = function (input, forwardParams) {
|
|
if (forwardParams === void 0) { forwardParams = {}; }
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var params, inputCanvas, stats, tsTotal, imgTensor, onReturn, _a, height, width, _b, minFaceSize, scaleFactor, maxNumScales, scoreThresholds, scaleSteps, scales, ts, out1, out2, out3, results;
|
|
return __generator(this, function (_c) {
|
|
switch (_c.label) {
|
|
case 0:
|
|
params = this.params;
|
|
if (!params) {
|
|
throw new Error('Mtcnn - load model before inference');
|
|
}
|
|
inputCanvas = input.canvases[0];
|
|
if (!inputCanvas) {
|
|
throw new Error('Mtcnn - inputCanvas is not defined, note that passing tensors into Mtcnn.forwardInput is not supported yet.');
|
|
}
|
|
stats = {};
|
|
tsTotal = Date.now();
|
|
imgTensor = tidy(function () {
|
|
return bgrToRgbTensor(expandDims(browser.fromPixels(inputCanvas)).toFloat());
|
|
});
|
|
onReturn = function (results) {
|
|
// dispose tensors on return
|
|
imgTensor.dispose();
|
|
stats.total = Date.now() - tsTotal;
|
|
return results;
|
|
};
|
|
_a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];
|
|
_b = new MtcnnOptions(forwardParams), minFaceSize = _b.minFaceSize, scaleFactor = _b.scaleFactor, maxNumScales = _b.maxNumScales, scoreThresholds = _b.scoreThresholds, scaleSteps = _b.scaleSteps;
|
|
scales = (scaleSteps || pyramidDown(minFaceSize, scaleFactor, [height, width]))
|
|
.filter(function (scale) {
|
|
var sizes = getSizesForScale(scale, [height, width]);
|
|
return Math.min(sizes.width, sizes.height) > CELL_SIZE;
|
|
})
|
|
.slice(0, maxNumScales);
|
|
stats.scales = scales;
|
|
stats.pyramid = scales.map(function (scale) { return getSizesForScale(scale, [height, width]); });
|
|
ts = Date.now();
|
|
return [4 /*yield*/, stage1(imgTensor, scales, scoreThresholds[0], params.pnet, stats)];
|
|
case 1:
|
|
out1 = _c.sent();
|
|
stats.total_stage1 = Date.now() - ts;
|
|
if (!out1.boxes.length) {
|
|
return [2 /*return*/, onReturn({ results: [], stats: stats })];
|
|
}
|
|
stats.stage2_numInputBoxes = out1.boxes.length;
|
|
// using the inputCanvas to extract and resize the image patches, since it is faster
|
|
// than doing this on the gpu
|
|
ts = Date.now();
|
|
return [4 /*yield*/, stage2(inputCanvas, out1.boxes, scoreThresholds[1], params.rnet, stats)];
|
|
case 2:
|
|
out2 = _c.sent();
|
|
stats.total_stage2 = Date.now() - ts;
|
|
if (!out2.boxes.length) {
|
|
return [2 /*return*/, onReturn({ results: [], stats: stats })];
|
|
}
|
|
stats.stage3_numInputBoxes = out2.boxes.length;
|
|
ts = Date.now();
|
|
return [4 /*yield*/, stage3(inputCanvas, out2.boxes, scoreThresholds[2], params.onet, stats)];
|
|
case 3:
|
|
out3 = _c.sent();
|
|
stats.total_stage3 = Date.now() - ts;
|
|
results = out3.boxes.map(function (box, idx) { return extendWithFaceLandmarks(extendWithFaceDetection({}, new FaceDetection(out3.scores[idx], new Rect(box.left / width, box.top / height, box.width / width, box.height / height), {
|
|
height: height,
|
|
width: width
|
|
})), new FaceLandmarks5(out3.points[idx].map(function (pt) { return pt.sub(new Point(box.left, box.top)).div(new Point(box.width, box.height)); }), { width: box.width, height: box.height })); });
|
|
return [2 /*return*/, onReturn({ results: results, stats: stats })];
|
|
}
|
|
});
|
|
});
|
|
};
|
|
Mtcnn.prototype.forward = function (input, forwardParams) {
|
|
if (forwardParams === void 0) { forwardParams = {}; }
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var _a;
|
|
return __generator(this, function (_b) {
|
|
switch (_b.label) {
|
|
case 0:
|
|
_a = this.forwardInput;
|
|
return [4 /*yield*/, toNetInput(input)];
|
|
case 1: return [4 /*yield*/, _a.apply(this, [_b.sent(),
|
|
forwardParams])];
|
|
case 2: return [2 /*return*/, (_b.sent()).results];
|
|
}
|
|
});
|
|
});
|
|
};
|
|
Mtcnn.prototype.forwardWithStats = function (input, forwardParams) {
|
|
if (forwardParams === void 0) { forwardParams = {}; }
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var _a;
|
|
return __generator(this, function (_b) {
|
|
switch (_b.label) {
|
|
case 0:
|
|
_a = this.forwardInput;
|
|
return [4 /*yield*/, toNetInput(input)];
|
|
case 1: return [2 /*return*/, _a.apply(this, [_b.sent(),
|
|
forwardParams])];
|
|
}
|
|
});
|
|
});
|
|
};
|
|
Mtcnn.prototype.getDefaultModelName = function () {
|
|
return 'mtcnn_model';
|
|
};
|
|
Mtcnn.prototype.extractParamsFromWeigthMap = function (weightMap) {
|
|
return extractParamsFromWeigthMap$7(weightMap);
|
|
};
|
|
Mtcnn.prototype.extractParams = function (weights) {
|
|
return extractParams$7(weights);
|
|
};
|
|
return Mtcnn;
|
|
}(NeuralNetwork));
|
|
|
|
var IOU_THRESHOLD = 0.4;
|
|
var BOX_ANCHORS = [
|
|
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];
|
|
|
|
var TinyFaceDetector = /** @class */ (function (_super) {
|
|
__extends(TinyFaceDetector, _super);
|
|
function TinyFaceDetector() {
|
|
var _this = this;
|
|
var config = {
|
|
withSeparableConvs: true,
|
|
iouThreshold: IOU_THRESHOLD,
|
|
classes: ['face'],
|
|
anchors: BOX_ANCHORS,
|
|
meanRgb: MEAN_RGB,
|
|
isFirstLayerConv2d: true,
|
|
filterSizes: [3, 16, 32, 64, 128, 256, 512]
|
|
};
|
|
_this = _super.call(this, config) || this;
|
|
return _this;
|
|
}
|
|
Object.defineProperty(TinyFaceDetector.prototype, "anchors", {
|
|
get: function () {
|
|
return this.config.anchors;
|
|
},
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
TinyFaceDetector.prototype.locateFaces = function (input, forwardParams) {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var objectDetections;
|
|
return __generator(this, function (_a) {
|
|
switch (_a.label) {
|
|
case 0: return [4 /*yield*/, this.detect(input, forwardParams)];
|
|
case 1:
|
|
objectDetections = _a.sent();
|
|
return [2 /*return*/, objectDetections.map(function (det) { return new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }); })];
|
|
}
|
|
});
|
|
});
|
|
};
|
|
TinyFaceDetector.prototype.getDefaultModelName = function () {
|
|
return 'tiny_face_detector_model';
|
|
};
|
|
TinyFaceDetector.prototype.extractParamsFromWeigthMap = function (weightMap) {
|
|
return _super.prototype.extractParamsFromWeigthMap.call(this, weightMap);
|
|
};
|
|
return TinyFaceDetector;
|
|
}(TinyYolov2));
|
|
|
|
var IOU_THRESHOLD$1 = 0.4;
|
|
var BOX_ANCHORS$1 = [
|
|
new Point(0.738768, 0.874946),
|
|
new Point(2.42204, 2.65704),
|
|
new Point(4.30971, 7.04493),
|
|
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_SEPARABLE = [117.001, 114.697, 97.404];
|
|
var DEFAULT_MODEL_NAME = 'tiny_yolov2_model';
|
|
var DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model';
|
|
|
|
var TinyYolov2$1 = /** @class */ (function (_super) {
|
|
__extends(TinyYolov2, _super);
|
|
function TinyYolov2(withSeparableConvs) {
|
|
if (withSeparableConvs === void 0) { withSeparableConvs = true; }
|
|
var _this = this;
|
|
var config = Object.assign({}, {
|
|
withSeparableConvs: withSeparableConvs,
|
|
iouThreshold: IOU_THRESHOLD$1,
|
|
classes: ['face']
|
|
}, withSeparableConvs
|
|
? {
|
|
anchors: BOX_ANCHORS_SEPARABLE,
|
|
meanRgb: MEAN_RGB_SEPARABLE
|
|
}
|
|
: {
|
|
anchors: BOX_ANCHORS$1,
|
|
withClassScores: true
|
|
});
|
|
_this = _super.call(this, config) || this;
|
|
return _this;
|
|
}
|
|
Object.defineProperty(TinyYolov2.prototype, "withSeparableConvs", {
|
|
get: function () {
|
|
return this.config.withSeparableConvs;
|
|
},
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(TinyYolov2.prototype, "anchors", {
|
|
get: function () {
|
|
return this.config.anchors;
|
|
},
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
TinyYolov2.prototype.locateFaces = function (input, forwardParams) {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var objectDetections;
|
|
return __generator(this, function (_a) {
|
|
switch (_a.label) {
|
|
case 0: return [4 /*yield*/, this.detect(input, forwardParams)];
|
|
case 1:
|
|
objectDetections = _a.sent();
|
|
return [2 /*return*/, objectDetections.map(function (det) { return new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }); })];
|
|
}
|
|
});
|
|
});
|
|
};
|
|
TinyYolov2.prototype.getDefaultModelName = function () {
|
|
return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME;
|
|
};
|
|
TinyYolov2.prototype.extractParamsFromWeigthMap = function (weightMap) {
|
|
return _super.prototype.extractParamsFromWeigthMap.call(this, weightMap);
|
|
};
|
|
return TinyYolov2;
|
|
}(TinyYolov2));
|
|
|
|
function createTinyYolov2(weights, withSeparableConvs) {
|
|
if (withSeparableConvs === void 0) { withSeparableConvs = true; }
|
|
var net = new TinyYolov2$1(withSeparableConvs);
|
|
net.extractWeights(weights);
|
|
return net;
|
|
}
|
|
|
|
var nets = {
|
|
ssdMobilenetv1: new SsdMobilenetv1(),
|
|
tinyFaceDetector: new TinyFaceDetector(),
|
|
tinyYolov2: new TinyYolov2$1(),
|
|
mtcnn: new Mtcnn(),
|
|
faceLandmark68Net: new FaceLandmark68Net(),
|
|
faceLandmark68TinyNet: new FaceLandmark68TinyNet(),
|
|
faceRecognitionNet: new FaceRecognitionNet(),
|
|
faceExpressionNet: new FaceExpressionNet(),
|
|
ageGenderNet: new AgeGenderNet()
|
|
};
|
|
/**
|
|
* Attempts to detect all faces in an image using SSD Mobilenetv1 Network.
|
|
*
|
|
* @param input The input image.
|
|
* @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).
|
|
* @returns Bounding box of each face with score.
|
|
*/
|
|
var ssdMobilenetv1 = function (input, options) {
|
|
return nets.ssdMobilenetv1.locateFaces(input, options);
|
|
};
|
|
/**
|
|
* Attempts to detect all faces in an image using the Tiny Face Detector.
|
|
*
|
|
* @param input The input image.
|
|
* @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).
|
|
* @returns Bounding box of each face with score.
|
|
*/
|
|
var tinyFaceDetector = function (input, options) {
|
|
return nets.tinyFaceDetector.locateFaces(input, options);
|
|
};
|
|
/**
|
|
* Attempts to detect all faces in an image using the Tiny Yolov2 Network.
|
|
*
|
|
* @param input The input image.
|
|
* @param options (optional, default: see TinyYolov2Options constructor for default parameters).
|
|
* @returns Bounding box of each face with score.
|
|
*/
|
|
var tinyYolov2 = function (input, options) {
|
|
return nets.tinyYolov2.locateFaces(input, options);
|
|
};
|
|
/**
|
|
* Attempts to detect all faces in an image and the 5 point face landmarks
|
|
* of each detected face using the MTCNN Network.
|
|
*
|
|
* @param input The input image.
|
|
* @param options (optional, default: see MtcnnOptions constructor for default parameters).
|
|
* @returns Bounding box of each face with score and 5 point face landmarks.
|
|
*/
|
|
var mtcnn = function (input, options) {
|
|
return nets.mtcnn.forward(input, options);
|
|
};
|
|
/**
|
|
* Detects the 68 point face landmark positions of the face shown in an image.
|
|
*
|
|
* @param inputs The face image extracted from the bounding box of a face. Can
|
|
* also be an array of input images, which will be batch processed.
|
|
* @returns 68 point face landmarks or array thereof in case of batch input.
|
|
*/
|
|
var detectFaceLandmarks = function (input) {
|
|
return nets.faceLandmark68Net.detectLandmarks(input);
|
|
};
|
|
/**
|
|
* Detects the 68 point face landmark positions of the face shown in an image
|
|
* using a tinier version of the 68 point face landmark model, which is slightly
|
|
* faster at inference, but also slightly less accurate.
|
|
*
|
|
* @param inputs The face image extracted from the bounding box of a face. Can
|
|
* also be an array of input images, which will be batch processed.
|
|
* @returns 68 point face landmarks or array thereof in case of batch input.
|
|
*/
|
|
var detectFaceLandmarksTiny = function (input) {
|
|
return nets.faceLandmark68TinyNet.detectLandmarks(input);
|
|
};
|
|
/**
|
|
* Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,
|
|
* which uniquely represents the features of that persons face. The computed face descriptor can
|
|
* be used to measure the similarity between faces, by computing the euclidean distance of two
|
|
* face descriptors.
|
|
*
|
|
* @param inputs The face image extracted from the aligned bounding box of a face. Can
|
|
* also be an array of input images, which will be batch processed.
|
|
* @returns Face descriptor with 128 entries or array thereof in case of batch input.
|
|
*/
|
|
var computeFaceDescriptor = function (input) {
|
|
return nets.faceRecognitionNet.computeFaceDescriptor(input);
|
|
};
|
|
/**
|
|
* Recognizes the facial expressions from a face image.
|
|
*
|
|
* @param inputs The face image extracted from the bounding box of a face. Can
|
|
* also be an array of input images, which will be batch processed.
|
|
* @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.
|
|
*/
|
|
var recognizeFaceExpressions = function (input) {
|
|
return nets.faceExpressionNet.predictExpressions(input);
|
|
};
|
|
/**
|
|
* Predicts age and gender from a face image.
|
|
*
|
|
* @param inputs The face image extracted from the bounding box of a face. Can
|
|
* also be an array of input images, which will be batch processed.
|
|
* @returns Predictions with age, gender and gender probability or array thereof in case of batch input.
|
|
*/
|
|
var predictAgeAndGender = function (input) {
|
|
return nets.ageGenderNet.predictAgeAndGender(input);
|
|
};
|
|
var loadSsdMobilenetv1Model = function (url) { return nets.ssdMobilenetv1.load(url); };
|
|
var loadTinyFaceDetectorModel = function (url) { return nets.tinyFaceDetector.load(url); };
|
|
var loadMtcnnModel = function (url) { return nets.mtcnn.load(url); };
|
|
var loadTinyYolov2Model = function (url) { return nets.tinyYolov2.load(url); };
|
|
var loadFaceLandmarkModel = function (url) { return nets.faceLandmark68Net.load(url); };
|
|
var loadFaceLandmarkTinyModel = function (url) { return nets.faceLandmark68TinyNet.load(url); };
|
|
var loadFaceRecognitionModel = function (url) { return nets.faceRecognitionNet.load(url); };
|
|
var loadFaceExpressionModel = function (url) { return nets.faceExpressionNet.load(url); };
|
|
var loadAgeGenderModel = function (url) { return nets.ageGenderNet.load(url); };
|
|
// backward compatibility
|
|
var loadFaceDetectionModel = loadSsdMobilenetv1Model;
|
|
var locateFaces = ssdMobilenetv1;
|
|
var detectLandmarks = detectFaceLandmarks;
|
|
|
|
function extendWithAge(sourceObj, age) {
|
|
var extension = { age: age };
|
|
return Object.assign({}, sourceObj, extension);
|
|
}
|
|
|
|
function extendWithGender(sourceObj, gender, genderProbability) {
|
|
var extension = { gender: gender, genderProbability: genderProbability };
|
|
return Object.assign({}, sourceObj, extension);
|
|
}
|
|
|
|
var PredictFaceExpressionsTaskBase = /** @class */ (function (_super) {
|
|
__extends(PredictFaceExpressionsTaskBase, _super);
|
|
function PredictFaceExpressionsTaskBase(parentTask, input, extractedFaces) {
|
|
var _this = _super.call(this) || this;
|
|
_this.parentTask = parentTask;
|
|
_this.input = input;
|
|
_this.extractedFaces = extractedFaces;
|
|
return _this;
|
|
}
|
|
return PredictFaceExpressionsTaskBase;
|
|
}(ComposableTask));
|
|
var PredictAllFaceExpressionsTask = /** @class */ (function (_super) {
|
|
__extends(PredictAllFaceExpressionsTask, _super);
|
|
function PredictAllFaceExpressionsTask() {
|
|
return _super !== null && _super.apply(this, arguments) || this;
|
|
}
|
|
PredictAllFaceExpressionsTask.prototype.run = function () {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var parentResults, faceExpressionsByFace;
|
|
var _this = this;
|
|
return __generator(this, function (_a) {
|
|
switch (_a.label) {
|
|
case 0: return [4 /*yield*/, this.parentTask];
|
|
case 1:
|
|
parentResults = _a.sent();
|
|
return [4 /*yield*/, extractAllFacesAndComputeResults(parentResults, this.input, function (faces) { return __awaiter(_this, void 0, void 0, function () {
|
|
return __generator(this, function (_a) {
|
|
switch (_a.label) {
|
|
case 0: return [4 /*yield*/, Promise.all(faces.map(function (face) { return nets.faceExpressionNet.predictExpressions(face); }))];
|
|
case 1: return [2 /*return*/, _a.sent()];
|
|
}
|
|
});
|
|
}); }, this.extractedFaces)];
|
|
case 2:
|
|
faceExpressionsByFace = _a.sent();
|
|
return [2 /*return*/, parentResults.map(function (parentResult, i) { return extendWithFaceExpressions(parentResult, faceExpressionsByFace[i]); })];
|
|
}
|
|
});
|
|
});
|
|
};
|
|
PredictAllFaceExpressionsTask.prototype.withAgeAndGender = function () {
|
|
return new PredictAllAgeAndGenderTask(this, this.input);
|
|
};
|
|
return PredictAllFaceExpressionsTask;
|
|
}(PredictFaceExpressionsTaskBase));
|
|
var PredictSingleFaceExpressionsTask = /** @class */ (function (_super) {
|
|
__extends(PredictSingleFaceExpressionsTask, _super);
|
|
function PredictSingleFaceExpressionsTask() {
|
|
return _super !== null && _super.apply(this, arguments) || this;
|
|
}
|
|
PredictSingleFaceExpressionsTask.prototype.run = function () {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var parentResult, faceExpressions;
|
|
return __generator(this, function (_a) {
|
|
switch (_a.label) {
|
|
case 0: return [4 /*yield*/, this.parentTask];
|
|
case 1:
|
|
parentResult = _a.sent();
|
|
if (!parentResult) {
|
|
return [2 /*return*/];
|
|
}
|
|
return [4 /*yield*/, extractSingleFaceAndComputeResult(parentResult, this.input, function (face) { return nets.faceExpressionNet.predictExpressions(face); }, this.extractedFaces)];
|
|
case 2:
|
|
faceExpressions = _a.sent();
|
|
return [2 /*return*/, extendWithFaceExpressions(parentResult, faceExpressions)];
|
|
}
|
|
});
|
|
});
|
|
};
|
|
PredictSingleFaceExpressionsTask.prototype.withAgeAndGender = function () {
|
|
return new PredictSingleAgeAndGenderTask(this, this.input);
|
|
};
|
|
return PredictSingleFaceExpressionsTask;
|
|
}(PredictFaceExpressionsTaskBase));
|
|
var PredictAllFaceExpressionsWithFaceAlignmentTask = /** @class */ (function (_super) {
|
|
__extends(PredictAllFaceExpressionsWithFaceAlignmentTask, _super);
|
|
function PredictAllFaceExpressionsWithFaceAlignmentTask() {
|
|
return _super !== null && _super.apply(this, arguments) || this;
|
|
}
|
|
PredictAllFaceExpressionsWithFaceAlignmentTask.prototype.withAgeAndGender = function () {
|
|
return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);
|
|
};
|
|
PredictAllFaceExpressionsWithFaceAlignmentTask.prototype.withFaceDescriptors = function () {
|
|
return new ComputeAllFaceDescriptorsTask(this, this.input);
|
|
};
|
|
return PredictAllFaceExpressionsWithFaceAlignmentTask;
|
|
}(PredictAllFaceExpressionsTask));
|
|
var PredictSingleFaceExpressionsWithFaceAlignmentTask = /** @class */ (function (_super) {
|
|
__extends(PredictSingleFaceExpressionsWithFaceAlignmentTask, _super);
|
|
function PredictSingleFaceExpressionsWithFaceAlignmentTask() {
|
|
return _super !== null && _super.apply(this, arguments) || this;
|
|
}
|
|
PredictSingleFaceExpressionsWithFaceAlignmentTask.prototype.withAgeAndGender = function () {
|
|
return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);
|
|
};
|
|
PredictSingleFaceExpressionsWithFaceAlignmentTask.prototype.withFaceDescriptor = function () {
|
|
return new ComputeSingleFaceDescriptorTask(this, this.input);
|
|
};
|
|
return PredictSingleFaceExpressionsWithFaceAlignmentTask;
|
|
}(PredictSingleFaceExpressionsTask));
|
|
|
|
var PredictAgeAndGenderTaskBase = /** @class */ (function (_super) {
|
|
__extends(PredictAgeAndGenderTaskBase, _super);
|
|
function PredictAgeAndGenderTaskBase(parentTask, input, extractedFaces) {
|
|
var _this = _super.call(this) || this;
|
|
_this.parentTask = parentTask;
|
|
_this.input = input;
|
|
_this.extractedFaces = extractedFaces;
|
|
return _this;
|
|
}
|
|
return PredictAgeAndGenderTaskBase;
|
|
}(ComposableTask));
|
|
var PredictAllAgeAndGenderTask = /** @class */ (function (_super) {
|
|
__extends(PredictAllAgeAndGenderTask, _super);
|
|
function PredictAllAgeAndGenderTask() {
|
|
return _super !== null && _super.apply(this, arguments) || this;
|
|
}
|
|
PredictAllAgeAndGenderTask.prototype.run = function () {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var parentResults, ageAndGenderByFace;
|
|
var _this = this;
|
|
return __generator(this, function (_a) {
|
|
switch (_a.label) {
|
|
case 0: return [4 /*yield*/, this.parentTask];
|
|
case 1:
|
|
parentResults = _a.sent();
|
|
return [4 /*yield*/, extractAllFacesAndComputeResults(parentResults, this.input, function (faces) { return __awaiter(_this, void 0, void 0, function () {
|
|
return __generator(this, function (_a) {
|
|
switch (_a.label) {
|
|
case 0: return [4 /*yield*/, Promise.all(faces.map(function (face) { return nets.ageGenderNet.predictAgeAndGender(face); }))];
|
|
case 1: return [2 /*return*/, _a.sent()];
|
|
}
|
|
});
|
|
}); }, this.extractedFaces)];
|
|
case 2:
|
|
ageAndGenderByFace = _a.sent();
|
|
return [2 /*return*/, parentResults.map(function (parentResult, i) {
|
|
var _a = ageAndGenderByFace[i], age = _a.age, gender = _a.gender, genderProbability = _a.genderProbability;
|
|
return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);
|
|
})];
|
|
}
|
|
});
|
|
});
|
|
};
|
|
PredictAllAgeAndGenderTask.prototype.withFaceExpressions = function () {
|
|
return new PredictAllFaceExpressionsTask(this, this.input);
|
|
};
|
|
return PredictAllAgeAndGenderTask;
|
|
}(PredictAgeAndGenderTaskBase));
|
|
var PredictSingleAgeAndGenderTask = /** @class */ (function (_super) {
|
|
__extends(PredictSingleAgeAndGenderTask, _super);
|
|
function PredictSingleAgeAndGenderTask() {
|
|
return _super !== null && _super.apply(this, arguments) || this;
|
|
}
|
|
PredictSingleAgeAndGenderTask.prototype.run = function () {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var parentResult, _a, age, gender, genderProbability;
|
|
return __generator(this, function (_b) {
|
|
switch (_b.label) {
|
|
case 0: return [4 /*yield*/, this.parentTask];
|
|
case 1:
|
|
parentResult = _b.sent();
|
|
if (!parentResult) {
|
|
return [2 /*return*/];
|
|
}
|
|
return [4 /*yield*/, extractSingleFaceAndComputeResult(parentResult, this.input, function (face) { return nets.ageGenderNet.predictAgeAndGender(face); }, this.extractedFaces)];
|
|
case 2:
|
|
_a = _b.sent(), age = _a.age, gender = _a.gender, genderProbability = _a.genderProbability;
|
|
return [2 /*return*/, extendWithAge(extendWithGender(parentResult, gender, genderProbability), age)];
|
|
}
|
|
});
|
|
});
|
|
};
|
|
PredictSingleAgeAndGenderTask.prototype.withFaceExpressions = function () {
|
|
return new PredictSingleFaceExpressionsTask(this, this.input);
|
|
};
|
|
return PredictSingleAgeAndGenderTask;
|
|
}(PredictAgeAndGenderTaskBase));
|
|
var PredictAllAgeAndGenderWithFaceAlignmentTask = /** @class */ (function (_super) {
|
|
__extends(PredictAllAgeAndGenderWithFaceAlignmentTask, _super);
|
|
function PredictAllAgeAndGenderWithFaceAlignmentTask() {
|
|
return _super !== null && _super.apply(this, arguments) || this;
|
|
}
|
|
PredictAllAgeAndGenderWithFaceAlignmentTask.prototype.withFaceExpressions = function () {
|
|
return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);
|
|
};
|
|
PredictAllAgeAndGenderWithFaceAlignmentTask.prototype.withFaceDescriptors = function () {
|
|
return new ComputeAllFaceDescriptorsTask(this, this.input);
|
|
};
|
|
return PredictAllAgeAndGenderWithFaceAlignmentTask;
|
|
}(PredictAllAgeAndGenderTask));
|
|
var PredictSingleAgeAndGenderWithFaceAlignmentTask = /** @class */ (function (_super) {
|
|
__extends(PredictSingleAgeAndGenderWithFaceAlignmentTask, _super);
|
|
function PredictSingleAgeAndGenderWithFaceAlignmentTask() {
|
|
return _super !== null && _super.apply(this, arguments) || this;
|
|
}
|
|
PredictSingleAgeAndGenderWithFaceAlignmentTask.prototype.withFaceExpressions = function () {
|
|
return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);
|
|
};
|
|
PredictSingleAgeAndGenderWithFaceAlignmentTask.prototype.withFaceDescriptor = function () {
|
|
return new ComputeSingleFaceDescriptorTask(this, this.input);
|
|
};
|
|
return PredictSingleAgeAndGenderWithFaceAlignmentTask;
|
|
}(PredictSingleAgeAndGenderTask));
|
|
|
|
var ComputeFaceDescriptorsTaskBase = /** @class */ (function (_super) {
|
|
__extends(ComputeFaceDescriptorsTaskBase, _super);
|
|
function ComputeFaceDescriptorsTaskBase(parentTask, input) {
|
|
var _this = _super.call(this) || this;
|
|
_this.parentTask = parentTask;
|
|
_this.input = input;
|
|
return _this;
|
|
}
|
|
return ComputeFaceDescriptorsTaskBase;
|
|
}(ComposableTask));
|
|
var ComputeAllFaceDescriptorsTask = /** @class */ (function (_super) {
|
|
__extends(ComputeAllFaceDescriptorsTask, _super);
|
|
function ComputeAllFaceDescriptorsTask() {
|
|
return _super !== null && _super.apply(this, arguments) || this;
|
|
}
|
|
ComputeAllFaceDescriptorsTask.prototype.run = function () {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var parentResults, descriptors;
|
|
return __generator(this, function (_a) {
|
|
switch (_a.label) {
|
|
case 0: return [4 /*yield*/, this.parentTask];
|
|
case 1:
|
|
parentResults = _a.sent();
|
|
return [4 /*yield*/, extractAllFacesAndComputeResults(parentResults, this.input, function (faces) { return Promise.all(faces.map(function (face) {
|
|
return nets.faceRecognitionNet.computeFaceDescriptor(face);
|
|
})); }, null, function (parentResult) { return parentResult.landmarks.align(null, { useDlibAlignment: true }); })];
|
|
case 2:
|
|
descriptors = _a.sent();
|
|
return [2 /*return*/, descriptors.map(function (descriptor, i) { return extendWithFaceDescriptor(parentResults[i], descriptor); })];
|
|
}
|
|
});
|
|
});
|
|
};
|
|
ComputeAllFaceDescriptorsTask.prototype.withFaceExpressions = function () {
|
|
return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);
|
|
};
|
|
ComputeAllFaceDescriptorsTask.prototype.withAgeAndGender = function () {
|
|
return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);
|
|
};
|
|
return ComputeAllFaceDescriptorsTask;
|
|
}(ComputeFaceDescriptorsTaskBase));
|
|
var ComputeSingleFaceDescriptorTask = /** @class */ (function (_super) {
|
|
__extends(ComputeSingleFaceDescriptorTask, _super);
|
|
function ComputeSingleFaceDescriptorTask() {
|
|
return _super !== null && _super.apply(this, arguments) || this;
|
|
}
|
|
ComputeSingleFaceDescriptorTask.prototype.run = function () {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var parentResult, descriptor;
|
|
return __generator(this, function (_a) {
|
|
switch (_a.label) {
|
|
case 0: return [4 /*yield*/, this.parentTask];
|
|
case 1:
|
|
parentResult = _a.sent();
|
|
if (!parentResult) {
|
|
return [2 /*return*/];
|
|
}
|
|
return [4 /*yield*/, extractSingleFaceAndComputeResult(parentResult, this.input, function (face) { return nets.faceRecognitionNet.computeFaceDescriptor(face); }, null, function (parentResult) { return parentResult.landmarks.align(null, { useDlibAlignment: true }); })];
|
|
case 2:
|
|
descriptor = _a.sent();
|
|
return [2 /*return*/, extendWithFaceDescriptor(parentResult, descriptor)];
|
|
}
|
|
});
|
|
});
|
|
};
|
|
ComputeSingleFaceDescriptorTask.prototype.withFaceExpressions = function () {
|
|
return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);
|
|
};
|
|
ComputeSingleFaceDescriptorTask.prototype.withAgeAndGender = function () {
|
|
return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);
|
|
};
|
|
return ComputeSingleFaceDescriptorTask;
|
|
}(ComputeFaceDescriptorsTaskBase));
|
|
|
|
var DetectFaceLandmarksTaskBase = /** @class */ (function (_super) {
|
|
__extends(DetectFaceLandmarksTaskBase, _super);
|
|
function DetectFaceLandmarksTaskBase(parentTask, input, useTinyLandmarkNet) {
|
|
var _this = _super.call(this) || this;
|
|
_this.parentTask = parentTask;
|
|
_this.input = input;
|
|
_this.useTinyLandmarkNet = useTinyLandmarkNet;
|
|
return _this;
|
|
}
|
|
Object.defineProperty(DetectFaceLandmarksTaskBase.prototype, "landmarkNet", {
|
|
get: function () {
|
|
return this.useTinyLandmarkNet
|
|
? nets.faceLandmark68TinyNet
|
|
: nets.faceLandmark68Net;
|
|
},
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
return DetectFaceLandmarksTaskBase;
|
|
}(ComposableTask));
|
|
var DetectAllFaceLandmarksTask = /** @class */ (function (_super) {
|
|
__extends(DetectAllFaceLandmarksTask, _super);
|
|
function DetectAllFaceLandmarksTask() {
|
|
return _super !== null && _super.apply(this, arguments) || this;
|
|
}
|
|
DetectAllFaceLandmarksTask.prototype.run = function () {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var parentResults, detections, faces, _a, faceLandmarksByFace;
|
|
var _this = this;
|
|
return __generator(this, function (_b) {
|
|
switch (_b.label) {
|
|
case 0: return [4 /*yield*/, this.parentTask];
|
|
case 1:
|
|
parentResults = _b.sent();
|
|
detections = parentResults.map(function (res) { return res.detection; });
|
|
if (!(this.input instanceof Tensor)) return [3 /*break*/, 3];
|
|
return [4 /*yield*/, extractFaceTensors(this.input, detections)];
|
|
case 2:
|
|
_a = _b.sent();
|
|
return [3 /*break*/, 5];
|
|
case 3: return [4 /*yield*/, extractFaces(this.input, detections)];
|
|
case 4:
|
|
_a = _b.sent();
|
|
_b.label = 5;
|
|
case 5:
|
|
faces = _a;
|
|
return [4 /*yield*/, Promise.all(faces.map(function (face) { return _this.landmarkNet.detectLandmarks(face); }))];
|
|
case 6:
|
|
faceLandmarksByFace = _b.sent();
|
|
faces.forEach(function (f) { return f instanceof Tensor && f.dispose(); });
|
|
return [2 /*return*/, parentResults.map(function (parentResult, i) {
|
|
return extendWithFaceLandmarks(parentResult, faceLandmarksByFace[i]);
|
|
})];
|
|
}
|
|
});
|
|
});
|
|
};
|
|
DetectAllFaceLandmarksTask.prototype.withFaceExpressions = function () {
|
|
return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);
|
|
};
|
|
DetectAllFaceLandmarksTask.prototype.withAgeAndGender = function () {
|
|
return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);
|
|
};
|
|
DetectAllFaceLandmarksTask.prototype.withFaceDescriptors = function () {
|
|
return new ComputeAllFaceDescriptorsTask(this, this.input);
|
|
};
|
|
return DetectAllFaceLandmarksTask;
|
|
}(DetectFaceLandmarksTaskBase));
|
|
var DetectSingleFaceLandmarksTask = /** @class */ (function (_super) {
|
|
__extends(DetectSingleFaceLandmarksTask, _super);
|
|
function DetectSingleFaceLandmarksTask() {
|
|
return _super !== null && _super.apply(this, arguments) || this;
|
|
}
|
|
DetectSingleFaceLandmarksTask.prototype.run = function () {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var parentResult, detection, faces, _a, landmarks;
|
|
return __generator(this, function (_b) {
|
|
switch (_b.label) {
|
|
case 0: return [4 /*yield*/, this.parentTask];
|
|
case 1:
|
|
parentResult = _b.sent();
|
|
if (!parentResult) {
|
|
return [2 /*return*/];
|
|
}
|
|
detection = parentResult.detection;
|
|
if (!(this.input instanceof Tensor)) return [3 /*break*/, 3];
|
|
return [4 /*yield*/, extractFaceTensors(this.input, [detection])];
|
|
case 2:
|
|
_a = _b.sent();
|
|
return [3 /*break*/, 5];
|
|
case 3: return [4 /*yield*/, extractFaces(this.input, [detection])];
|
|
case 4:
|
|
_a = _b.sent();
|
|
_b.label = 5;
|
|
case 5:
|
|
faces = _a;
|
|
return [4 /*yield*/, this.landmarkNet.detectLandmarks(faces[0])];
|
|
case 6:
|
|
landmarks = _b.sent();
|
|
faces.forEach(function (f) { return f instanceof Tensor && f.dispose(); });
|
|
return [2 /*return*/, extendWithFaceLandmarks(parentResult, landmarks)];
|
|
}
|
|
});
|
|
});
|
|
};
|
|
DetectSingleFaceLandmarksTask.prototype.withFaceExpressions = function () {
|
|
return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);
|
|
};
|
|
DetectSingleFaceLandmarksTask.prototype.withAgeAndGender = function () {
|
|
return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);
|
|
};
|
|
DetectSingleFaceLandmarksTask.prototype.withFaceDescriptor = function () {
|
|
return new ComputeSingleFaceDescriptorTask(this, this.input);
|
|
};
|
|
return DetectSingleFaceLandmarksTask;
|
|
}(DetectFaceLandmarksTaskBase));
|
|
|
|
var DetectFacesTaskBase = /** @class */ (function (_super) {
|
|
__extends(DetectFacesTaskBase, _super);
|
|
function DetectFacesTaskBase(input, options) {
|
|
if (options === void 0) { options = new SsdMobilenetv1Options(); }
|
|
var _this = _super.call(this) || this;
|
|
_this.input = input;
|
|
_this.options = options;
|
|
return _this;
|
|
}
|
|
return DetectFacesTaskBase;
|
|
}(ComposableTask));
|
|
var DetectAllFacesTask = /** @class */ (function (_super) {
|
|
__extends(DetectAllFacesTask, _super);
|
|
function DetectAllFacesTask() {
|
|
return _super !== null && _super.apply(this, arguments) || this;
|
|
}
|
|
DetectAllFacesTask.prototype.run = function () {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var _a, input, options, faceDetectionFunction;
|
|
return __generator(this, function (_b) {
|
|
switch (_b.label) {
|
|
case 0:
|
|
_a = this, input = _a.input, options = _a.options;
|
|
if (!(options instanceof MtcnnOptions)) return [3 /*break*/, 2];
|
|
return [4 /*yield*/, nets.mtcnn.forward(input, options)];
|
|
case 1: return [2 /*return*/, (_b.sent())
|
|
.map(function (result) { return result.detection; })];
|
|
case 2:
|
|
faceDetectionFunction = options instanceof TinyFaceDetectorOptions
|
|
? function (input) { return nets.tinyFaceDetector.locateFaces(input, options); }
|
|
: (options instanceof SsdMobilenetv1Options
|
|
? function (input) { return nets.ssdMobilenetv1.locateFaces(input, options); }
|
|
: (options instanceof TinyYolov2Options
|
|
? function (input) { return nets.tinyYolov2.locateFaces(input, options); }
|
|
: null));
|
|
if (!faceDetectionFunction) {
|
|
throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options');
|
|
}
|
|
return [2 /*return*/, faceDetectionFunction(input)];
|
|
}
|
|
});
|
|
});
|
|
};
|
|
DetectAllFacesTask.prototype.runAndExtendWithFaceDetections = function () {
|
|
var _this = this;
|
|
return new Promise(function (res) { return __awaiter(_this, void 0, void 0, function () {
|
|
var detections;
|
|
return __generator(this, function (_a) {
|
|
switch (_a.label) {
|
|
case 0: return [4 /*yield*/, this.run()];
|
|
case 1:
|
|
detections = _a.sent();
|
|
return [2 /*return*/, res(detections.map(function (detection) { return extendWithFaceDetection({}, detection); }))];
|
|
}
|
|
});
|
|
}); });
|
|
};
|
|
DetectAllFacesTask.prototype.withFaceLandmarks = function (useTinyLandmarkNet) {
|
|
if (useTinyLandmarkNet === void 0) { useTinyLandmarkNet = false; }
|
|
return new DetectAllFaceLandmarksTask(this.runAndExtendWithFaceDetections(), this.input, useTinyLandmarkNet);
|
|
};
|
|
DetectAllFacesTask.prototype.withFaceExpressions = function () {
|
|
return new PredictAllFaceExpressionsTask(this.runAndExtendWithFaceDetections(), this.input);
|
|
};
|
|
DetectAllFacesTask.prototype.withAgeAndGender = function () {
|
|
return new PredictAllAgeAndGenderTask(this.runAndExtendWithFaceDetections(), this.input);
|
|
};
|
|
return DetectAllFacesTask;
|
|
}(DetectFacesTaskBase));
|
|
var DetectSingleFaceTask = /** @class */ (function (_super) {
|
|
__extends(DetectSingleFaceTask, _super);
|
|
function DetectSingleFaceTask() {
|
|
return _super !== null && _super.apply(this, arguments) || this;
|
|
}
|
|
DetectSingleFaceTask.prototype.run = function () {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
var faceDetections, faceDetectionWithHighestScore;
|
|
return __generator(this, function (_a) {
|
|
switch (_a.label) {
|
|
case 0: return [4 /*yield*/, new DetectAllFacesTask(this.input, this.options)];
|
|
case 1:
|
|
faceDetections = _a.sent();
|
|
faceDetectionWithHighestScore = faceDetections[0];
|
|
faceDetections.forEach(function (faceDetection) {
|
|
if (faceDetection.score > faceDetectionWithHighestScore.score) {
|
|
faceDetectionWithHighestScore = faceDetection;
|
|
}
|
|
});
|
|
return [2 /*return*/, faceDetectionWithHighestScore];
|
|
}
|
|
});
|
|
});
|
|
};
|
|
DetectSingleFaceTask.prototype.runAndExtendWithFaceDetection = function () {
|
|
var _this = this;
|
|
return new Promise(function (res) { return __awaiter(_this, void 0, void 0, function () {
|
|
var detection;
|
|
return __generator(this, function (_a) {
|
|
switch (_a.label) {
|
|
case 0: return [4 /*yield*/, this.run()];
|
|
case 1:
|
|
detection = _a.sent();
|
|
return [2 /*return*/, res(detection ? extendWithFaceDetection({}, detection) : undefined)];
|
|
}
|
|
});
|
|
}); });
|
|
};
|
|
DetectSingleFaceTask.prototype.withFaceLandmarks = function (useTinyLandmarkNet) {
|
|
if (useTinyLandmarkNet === void 0) { useTinyLandmarkNet = false; }
|
|
return new DetectSingleFaceLandmarksTask(this.runAndExtendWithFaceDetection(), this.input, useTinyLandmarkNet);
|
|
};
|
|
DetectSingleFaceTask.prototype.withFaceExpressions = function () {
|
|
return new PredictSingleFaceExpressionsTask(this.runAndExtendWithFaceDetection(), this.input);
|
|
};
|
|
DetectSingleFaceTask.prototype.withAgeAndGender = function () {
|
|
return new PredictSingleAgeAndGenderTask(this.runAndExtendWithFaceDetection(), this.input);
|
|
};
|
|
return DetectSingleFaceTask;
|
|
}(DetectFacesTaskBase));
|
|
|
|
function detectSingleFace(input, options) {
|
|
if (options === void 0) { options = new SsdMobilenetv1Options(); }
|
|
return new DetectSingleFaceTask(input, options);
|
|
}
|
|
function detectAllFaces(input, options) {
|
|
if (options === void 0) { options = new SsdMobilenetv1Options(); }
|
|
return new DetectAllFacesTask(input, options);
|
|
}
|
|
|
|
// export allFaces API for backward compatibility
|
|
function allFacesSsdMobilenetv1(input, minConfidence) {
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
return __generator(this, function (_a) {
|
|
switch (_a.label) {
|
|
case 0: return [4 /*yield*/, detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence: minConfidence } : {}))
|
|
.withFaceLandmarks()
|
|
.withFaceDescriptors()];
|
|
case 1: return [2 /*return*/, _a.sent()];
|
|
}
|
|
});
|
|
});
|
|
}
|
|
function allFacesTinyYolov2(input, forwardParams) {
|
|
if (forwardParams === void 0) { forwardParams = {}; }
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
return __generator(this, function (_a) {
|
|
switch (_a.label) {
|
|
case 0: return [4 /*yield*/, detectAllFaces(input, new TinyYolov2Options(forwardParams))
|
|
.withFaceLandmarks()
|
|
.withFaceDescriptors()];
|
|
case 1: return [2 /*return*/, _a.sent()];
|
|
}
|
|
});
|
|
});
|
|
}
|
|
function allFacesMtcnn(input, forwardParams) {
|
|
if (forwardParams === void 0) { forwardParams = {}; }
|
|
return __awaiter(this, void 0, void 0, function () {
|
|
return __generator(this, function (_a) {
|
|
switch (_a.label) {
|
|
case 0: return [4 /*yield*/, detectAllFaces(input, new MtcnnOptions(forwardParams))
|
|
.withFaceLandmarks()
|
|
.withFaceDescriptors()];
|
|
case 1: return [2 /*return*/, _a.sent()];
|
|
}
|
|
});
|
|
});
|
|
}
|
|
var allFaces = allFacesSsdMobilenetv1;
|
|
|
|
function euclideanDistance(arr1, arr2) {
|
|
if (arr1.length !== arr2.length)
|
|
throw new Error('euclideanDistance: arr1.length !== arr2.length');
|
|
var desc1 = Array.from(arr1);
|
|
var desc2 = Array.from(arr2);
|
|
return Math.sqrt(desc1
|
|
.map(function (val, i) { return val - desc2[i]; })
|
|
.reduce(function (res, diff) { return res + Math.pow(diff, 2); }, 0));
|
|
}
|
|
|
|
var FaceMatcher = /** @class */ (function () {
|
|
function FaceMatcher(inputs, distanceThreshold) {
|
|
if (distanceThreshold === void 0) { distanceThreshold = 0.6; }
|
|
this._distanceThreshold = distanceThreshold;
|
|
var inputArray = Array.isArray(inputs) ? inputs : [inputs];
|
|
if (!inputArray.length) {
|
|
throw new Error("FaceRecognizer.constructor - expected atleast one input");
|
|
}
|
|
var count = 1;
|
|
var createUniqueLabel = function () { return "person " + count++; };
|
|
this._labeledDescriptors = inputArray.map(function (desc) {
|
|
if (desc instanceof LabeledFaceDescriptors) {
|
|
return desc;
|
|
}
|
|
if (desc instanceof Float32Array) {
|
|
return new LabeledFaceDescriptors(createUniqueLabel(), [desc]);
|
|
}
|
|
if (desc.descriptor && desc.descriptor instanceof Float32Array) {
|
|
return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor]);
|
|
}
|
|
throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>");
|
|
});
|
|
}
|
|
Object.defineProperty(FaceMatcher.prototype, "labeledDescriptors", {
|
|
get: function () { return this._labeledDescriptors; },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
Object.defineProperty(FaceMatcher.prototype, "distanceThreshold", {
|
|
get: function () { return this._distanceThreshold; },
|
|
enumerable: true,
|
|
configurable: true
|
|
});
|
|
FaceMatcher.prototype.computeMeanDistance = function (queryDescriptor, descriptors) {
|
|
return descriptors
|
|
.map(function (d) { return euclideanDistance(d, queryDescriptor); })
|
|
.reduce(function (d1, d2) { return d1 + d2; }, 0)
|
|
/ (descriptors.length || 1);
|
|
};
|
|
FaceMatcher.prototype.matchDescriptor = function (queryDescriptor) {
|
|
var _this = this;
|
|
return this.labeledDescriptors
|
|
.map(function (_a) {
|
|
var descriptors = _a.descriptors, label = _a.label;
|
|
return new FaceMatch(label, _this.computeMeanDistance(queryDescriptor, descriptors));
|
|
})
|
|
.reduce(function (best, curr) { return best.distance < curr.distance ? best : curr; });
|
|
};
|
|
FaceMatcher.prototype.findBestMatch = function (queryDescriptor) {
|
|
var bestMatch = this.matchDescriptor(queryDescriptor);
|
|
return bestMatch.distance < this.distanceThreshold
|
|
? bestMatch
|
|
: new FaceMatch('unknown', bestMatch.distance);
|
|
};
|
|
return FaceMatcher;
|
|
}());
|
|
|
|
function createMtcnn(weights) {
|
|
var net = new Mtcnn();
|
|
net.extractWeights(weights);
|
|
return net;
|
|
}
|
|
|
|
function createTinyFaceDetector(weights) {
|
|
var net = new TinyFaceDetector();
|
|
net.extractWeights(weights);
|
|
return net;
|
|
}
|
|
|
|
function resizeResults(results, dimensions) {
|
|
var _a = new Dimensions(dimensions.width, dimensions.height), width = _a.width, height = _a.height;
|
|
if (width <= 0 || height <= 0) {
|
|
throw new Error("resizeResults - invalid dimensions: " + JSON.stringify({ width: width, height: height }));
|
|
}
|
|
if (Array.isArray(results)) {
|
|
return results.map(function (obj) { return resizeResults(obj, { width: width, height: height }); });
|
|
}
|
|
if (isWithFaceLandmarks(results)) {
|
|
var resizedDetection = results.detection.forSize(width, height);
|
|
var resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height);
|
|
return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks);
|
|
}
|
|
if (isWithFaceDetection(results)) {
|
|
return extendWithFaceDetection(results, results.detection.forSize(width, height));
|
|
}
|
|
if (results instanceof FaceLandmarks || results instanceof FaceDetection) {
|
|
return results.forSize(width, height);
|
|
}
|
|
return results;
|
|
}
|
|
|
|
var draw = __assign({}, drawBase, drawExtended);
|
|
|
|
exports.AgeGenderNet = AgeGenderNet;
|
|
exports.BoundingBox = BoundingBox;
|
|
exports.Box = Box;
|
|
exports.ComposableTask = ComposableTask;
|
|
exports.ComputeAllFaceDescriptorsTask = ComputeAllFaceDescriptorsTask;
|
|
exports.ComputeFaceDescriptorsTaskBase = ComputeFaceDescriptorsTaskBase;
|
|
exports.ComputeSingleFaceDescriptorTask = ComputeSingleFaceDescriptorTask;
|
|
exports.DetectAllFaceLandmarksTask = DetectAllFaceLandmarksTask;
|
|
exports.DetectAllFacesTask = DetectAllFacesTask;
|
|
exports.DetectFaceLandmarksTaskBase = DetectFaceLandmarksTaskBase;
|
|
exports.DetectFacesTaskBase = DetectFacesTaskBase;
|
|
exports.DetectSingleFaceLandmarksTask = DetectSingleFaceLandmarksTask;
|
|
exports.DetectSingleFaceTask = DetectSingleFaceTask;
|
|
exports.Dimensions = Dimensions;
|
|
exports.FACE_EXPRESSION_LABELS = FACE_EXPRESSION_LABELS;
|
|
exports.FaceDetection = FaceDetection;
|
|
exports.FaceDetectionNet = FaceDetectionNet;
|
|
exports.FaceExpressionNet = FaceExpressionNet;
|
|
exports.FaceExpressions = FaceExpressions;
|
|
exports.FaceLandmark68Net = FaceLandmark68Net;
|
|
exports.FaceLandmark68TinyNet = FaceLandmark68TinyNet;
|
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exports.FaceLandmarkNet = FaceLandmarkNet;
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exports.FaceLandmarks = FaceLandmarks;
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exports.FaceLandmarks5 = FaceLandmarks5;
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exports.FaceLandmarks68 = FaceLandmarks68;
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exports.FaceMatch = FaceMatch;
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exports.FaceMatcher = FaceMatcher;
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exports.FaceRecognitionNet = FaceRecognitionNet;
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exports.LabeledBox = LabeledBox;
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exports.LabeledFaceDescriptors = LabeledFaceDescriptors;
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exports.Mtcnn = Mtcnn;
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exports.MtcnnOptions = MtcnnOptions;
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exports.NetInput = NetInput;
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exports.NeuralNetwork = NeuralNetwork;
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exports.ObjectDetection = ObjectDetection;
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exports.Point = Point;
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exports.PredictedBox = PredictedBox;
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exports.Rect = Rect;
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exports.SsdMobilenetv1 = SsdMobilenetv1;
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exports.SsdMobilenetv1Options = SsdMobilenetv1Options;
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exports.TfjsImageRecognitionBase = tfjsImageRecognitionBase;
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exports.TinyFaceDetector = TinyFaceDetector;
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exports.TinyFaceDetectorOptions = TinyFaceDetectorOptions;
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exports.TinyYolov2 = TinyYolov2$1;
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exports.allFaces = allFaces;
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exports.allFacesMtcnn = allFacesMtcnn;
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exports.allFacesSsdMobilenetv1 = allFacesSsdMobilenetv1;
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exports.allFacesTinyYolov2 = allFacesTinyYolov2;
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exports.awaitMediaLoaded = awaitMediaLoaded;
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exports.bufferToImage = bufferToImage;
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exports.computeFaceDescriptor = computeFaceDescriptor;
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exports.computeReshapedDimensions = computeReshapedDimensions;
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exports.createCanvas = createCanvas;
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exports.createCanvasFromMedia = createCanvasFromMedia;
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exports.createFaceDetectionNet = createFaceDetectionNet;
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exports.createFaceRecognitionNet = createFaceRecognitionNet;
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exports.createMtcnn = createMtcnn;
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exports.createSsdMobilenetv1 = createSsdMobilenetv1;
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exports.createTinyFaceDetector = createTinyFaceDetector;
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exports.createTinyYolov2 = createTinyYolov2;
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exports.detectAllFaces = detectAllFaces;
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|
exports.detectFaceLandmarks = detectFaceLandmarks;
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|
exports.detectFaceLandmarksTiny = detectFaceLandmarksTiny;
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|
exports.detectLandmarks = detectLandmarks;
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|
exports.detectSingleFace = detectSingleFace;
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|
exports.draw = draw;
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|
exports.env = env;
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|
exports.euclideanDistance = euclideanDistance;
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|
exports.extendWithFaceDescriptor = extendWithFaceDescriptor;
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|
exports.extendWithFaceDetection = extendWithFaceDetection;
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|
exports.extendWithFaceExpressions = extendWithFaceExpressions;
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|
exports.extendWithFaceLandmarks = extendWithFaceLandmarks;
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|
exports.extractFaceTensors = extractFaceTensors;
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|
exports.extractFaces = extractFaces;
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|
exports.fetchImage = fetchImage;
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|
exports.fetchJson = fetchJson;
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|
exports.fetchNetWeights = fetchNetWeights;
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|
exports.fetchOrThrow = fetchOrThrow;
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|
exports.getCenterPoint = getCenterPoint;
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|
exports.getContext2dOrThrow = getContext2dOrThrow;
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|
exports.getMediaDimensions = getMediaDimensions;
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|
exports.imageTensorToCanvas = imageTensorToCanvas;
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|
exports.imageToSquare = imageToSquare;
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|
exports.inverseSigmoid = inverseSigmoid;
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|
exports.iou = iou;
|
|
exports.isDimensions = isDimensions;
|
|
exports.isEven = isEven$1;
|
|
exports.isFloat = isFloat;
|
|
exports.isMediaElement = isMediaElement;
|
|
exports.isMediaLoaded = isMediaLoaded;
|
|
exports.isTensor = isTensor;
|
|
exports.isTensor1D = isTensor1D;
|
|
exports.isTensor2D = isTensor2D;
|
|
exports.isTensor3D = isTensor3D;
|
|
exports.isTensor4D = isTensor4D;
|
|
exports.isValidNumber = isValidNumber;
|
|
exports.isValidProbablitiy = isValidProbablitiy;
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|
exports.isWithFaceDetection = isWithFaceDetection;
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|
exports.isWithFaceExpressions = isWithFaceExpressions;
|
|
exports.isWithFaceLandmarks = isWithFaceLandmarks;
|
|
exports.loadAgeGenderModel = loadAgeGenderModel;
|
|
exports.loadFaceDetectionModel = loadFaceDetectionModel;
|
|
exports.loadFaceExpressionModel = loadFaceExpressionModel;
|
|
exports.loadFaceLandmarkModel = loadFaceLandmarkModel;
|
|
exports.loadFaceLandmarkTinyModel = loadFaceLandmarkTinyModel;
|
|
exports.loadFaceRecognitionModel = loadFaceRecognitionModel;
|
|
exports.loadMtcnnModel = loadMtcnnModel;
|
|
exports.loadSsdMobilenetv1Model = loadSsdMobilenetv1Model;
|
|
exports.loadTinyFaceDetectorModel = loadTinyFaceDetectorModel;
|
|
exports.loadTinyYolov2Model = loadTinyYolov2Model;
|
|
exports.loadWeightMap = loadWeightMap;
|
|
exports.locateFaces = locateFaces;
|
|
exports.matchDimensions = matchDimensions;
|
|
exports.mtcnn = mtcnn;
|
|
exports.nets = nets;
|
|
exports.nonMaxSuppression = nonMaxSuppression$1;
|
|
exports.normalize = normalize;
|
|
exports.padToSquare = padToSquare;
|
|
exports.predictAgeAndGender = predictAgeAndGender;
|
|
exports.range = range$1;
|
|
exports.recognizeFaceExpressions = recognizeFaceExpressions;
|
|
exports.resizeResults = resizeResults;
|
|
exports.resolveInput = resolveInput;
|
|
exports.round = round$1;
|
|
exports.shuffleArray = shuffleArray;
|
|
exports.sigmoid = sigmoid$1;
|
|
exports.ssdMobilenetv1 = ssdMobilenetv1;
|
|
exports.tf = tfCore_esm;
|
|
exports.tinyFaceDetector = tinyFaceDetector;
|
|
exports.tinyYolov2 = tinyYolov2;
|
|
exports.toNetInput = toNetInput;
|
|
|
|
Object.defineProperty(exports, '__esModule', { value: true });
|
|
|
|
}));
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//# sourceMappingURL=face-api.js.map
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