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human/dist/human.esm.js

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var __create = Object.create;
var __defProp = Object.defineProperty;
var __getProtoOf = Object.getPrototypeOf;
var __hasOwnProp = Object.prototype.hasOwnProperty;
var __getOwnPropNames = Object.getOwnPropertyNames;
var __getOwnPropDesc = Object.getOwnPropertyDescriptor;
var __markAsModule = (target) => __defProp(target, "__esModule", {value: true});
var __commonJS = (callback, module) => () => {
if (!module) {
module = {exports: {}};
callback(module.exports, module);
}
return module.exports;
};
var __export = (target, all3) => {
__markAsModule(target);
for (var name in all3)
__defProp(target, name, {get: all3[name], enumerable: true});
};
var __exportStar = (target, module, desc) => {
__markAsModule(target);
if (typeof module === "object" || typeof module === "function") {
for (let key of __getOwnPropNames(module))
if (!__hasOwnProp.call(target, key) && key !== "default")
__defProp(target, key, {get: () => module[key], enumerable: !(desc = __getOwnPropDesc(module, key)) || desc.enumerable});
}
return target;
};
var __toModule = (module) => {
if (module && module.__esModule)
return module;
return __exportStar(__defProp(__create(__getProtoOf(module)), "default", {value: module, enumerable: true}), module);
};
// node_modules/@tensorflow/tfjs-core/node_modules/seedrandom/lib/alea.js
var require_alea = __commonJS((exports3, module) => {
(function(global2, module2, define2) {
function Alea(seed) {
var me = this, mash = Mash();
me.next = function() {
var t = 2091639 * me.s0 + me.c * 23283064365386963e-26;
me.s0 = me.s1;
me.s1 = me.s2;
return me.s2 = t - (me.c = t | 0);
};
me.c = 1;
me.s0 = mash(" ");
me.s1 = mash(" ");
me.s2 = mash(" ");
me.s0 -= mash(seed);
if (me.s0 < 0) {
me.s0 += 1;
}
me.s1 -= mash(seed);
if (me.s1 < 0) {
me.s1 += 1;
}
me.s2 -= mash(seed);
if (me.s2 < 0) {
me.s2 += 1;
}
mash = null;
}
function copy(f, t) {
t.c = f.c;
t.s0 = f.s0;
t.s1 = f.s1;
t.s2 = f.s2;
return t;
}
function impl(seed, opts) {
var xg = new Alea(seed), state6 = opts && opts.state, prng = xg.next;
prng.int32 = function() {
return xg.next() * 4294967296 | 0;
};
prng.double = function() {
return prng() + (prng() * 2097152 | 0) * 11102230246251565e-32;
};
prng.quick = prng;
if (state6) {
if (typeof state6 == "object")
copy(state6, xg);
prng.state = function() {
return copy(xg, {});
};
}
return prng;
}
function Mash() {
var n = 4022871197;
var mash = function(data2) {
data2 = data2.toString();
for (var i = 0; i < data2.length; i++) {
n += data2.charCodeAt(i);
var h = 0.02519603282416938 * n;
n = h >>> 0;
h -= n;
h *= n;
n = h >>> 0;
h -= n;
n += h * 4294967296;
}
return (n >>> 0) * 23283064365386963e-26;
};
return mash;
}
if (module2 && module2.exports) {
module2.exports = impl;
} else if (define2 && define2.amd) {
define2(function() {
return impl;
});
} else {
this.alea = impl;
}
})(exports3, typeof module == "object" && module, typeof define == "function" && define);
});
// node_modules/@tensorflow/tfjs-core/node_modules/seedrandom/lib/xor128.js
var require_xor128 = __commonJS((exports3, module) => {
(function(global2, module2, define2) {
function XorGen(seed) {
var me = this, strseed = "";
me.x = 0;
me.y = 0;
me.z = 0;
me.w = 0;
me.next = function() {
var t = me.x ^ me.x << 11;
me.x = me.y;
me.y = me.z;
me.z = me.w;
return me.w ^= me.w >>> 19 ^ t ^ t >>> 8;
};
if (seed === (seed | 0)) {
me.x = seed;
} else {
strseed += seed;
}
for (var k = 0; k < strseed.length + 64; k++) {
me.x ^= strseed.charCodeAt(k) | 0;
me.next();
}
}
function copy(f, t) {
t.x = f.x;
t.y = f.y;
t.z = f.z;
t.w = f.w;
return t;
}
function impl(seed, opts) {
var xg = new XorGen(seed), state6 = opts && opts.state, prng = function() {
return (xg.next() >>> 0) / 4294967296;
};
prng.double = function() {
do {
var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
} while (result === 0);
return result;
};
prng.int32 = xg.next;
prng.quick = prng;
if (state6) {
if (typeof state6 == "object")
copy(state6, xg);
prng.state = function() {
return copy(xg, {});
};
}
return prng;
}
if (module2 && module2.exports) {
module2.exports = impl;
} else if (define2 && define2.amd) {
define2(function() {
return impl;
});
} else {
this.xor128 = impl;
}
})(exports3, typeof module == "object" && module, typeof define == "function" && define);
});
// node_modules/@tensorflow/tfjs-core/node_modules/seedrandom/lib/xorwow.js
var require_xorwow = __commonJS((exports3, module) => {
(function(global2, module2, define2) {
function XorGen(seed) {
var me = this, strseed = "";
me.next = function() {
var t = me.x ^ me.x >>> 2;
me.x = me.y;
me.y = me.z;
me.z = me.w;
me.w = me.v;
return (me.d = me.d + 362437 | 0) + (me.v = me.v ^ me.v << 4 ^ (t ^ t << 1)) | 0;
};
me.x = 0;
me.y = 0;
me.z = 0;
me.w = 0;
me.v = 0;
if (seed === (seed | 0)) {
me.x = seed;
} else {
strseed += seed;
}
for (var k = 0; k < strseed.length + 64; k++) {
me.x ^= strseed.charCodeAt(k) | 0;
if (k == strseed.length) {
me.d = me.x << 10 ^ me.x >>> 4;
}
me.next();
}
}
function copy(f, t) {
t.x = f.x;
t.y = f.y;
t.z = f.z;
t.w = f.w;
t.v = f.v;
t.d = f.d;
return t;
}
function impl(seed, opts) {
var xg = new XorGen(seed), state6 = opts && opts.state, prng = function() {
return (xg.next() >>> 0) / 4294967296;
};
prng.double = function() {
do {
var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
} while (result === 0);
return result;
};
prng.int32 = xg.next;
prng.quick = prng;
if (state6) {
if (typeof state6 == "object")
copy(state6, xg);
prng.state = function() {
return copy(xg, {});
};
}
return prng;
}
if (module2 && module2.exports) {
module2.exports = impl;
} else if (define2 && define2.amd) {
define2(function() {
return impl;
});
} else {
this.xorwow = impl;
}
})(exports3, typeof module == "object" && module, typeof define == "function" && define);
});
// node_modules/@tensorflow/tfjs-core/node_modules/seedrandom/lib/xorshift7.js
var require_xorshift7 = __commonJS((exports3, module) => {
(function(global2, module2, define2) {
function XorGen(seed) {
var me = this;
me.next = function() {
var X = me.x, i = me.i, t, v, w;
t = X[i];
t ^= t >>> 7;
v = t ^ t << 24;
t = X[i + 1 & 7];
v ^= t ^ t >>> 10;
t = X[i + 3 & 7];
v ^= t ^ t >>> 3;
t = X[i + 4 & 7];
v ^= t ^ t << 7;
t = X[i + 7 & 7];
t = t ^ t << 13;
v ^= t ^ t << 9;
X[i] = v;
me.i = i + 1 & 7;
return v;
};
function init2(me2, seed2) {
var j, w, X = [];
if (seed2 === (seed2 | 0)) {
w = X[0] = seed2;
} else {
seed2 = "" + seed2;
for (j = 0; j < seed2.length; ++j) {
X[j & 7] = X[j & 7] << 15 ^ seed2.charCodeAt(j) + X[j + 1 & 7] << 13;
}
}
while (X.length < 8)
X.push(0);
for (j = 0; j < 8 && X[j] === 0; ++j)
;
if (j == 8)
w = X[7] = -1;
else
w = X[j];
me2.x = X;
me2.i = 0;
for (j = 256; j > 0; --j) {
me2.next();
}
}
init2(me, seed);
}
function copy(f, t) {
t.x = f.x.slice();
t.i = f.i;
return t;
}
function impl(seed, opts) {
if (seed == null)
seed = +new Date();
var xg = new XorGen(seed), state6 = opts && opts.state, prng = function() {
return (xg.next() >>> 0) / 4294967296;
};
prng.double = function() {
do {
var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
} while (result === 0);
return result;
};
prng.int32 = xg.next;
prng.quick = prng;
if (state6) {
if (state6.x)
copy(state6, xg);
prng.state = function() {
return copy(xg, {});
};
}
return prng;
}
if (module2 && module2.exports) {
module2.exports = impl;
} else if (define2 && define2.amd) {
define2(function() {
return impl;
});
} else {
this.xorshift7 = impl;
}
})(exports3, typeof module == "object" && module, typeof define == "function" && define);
});
// node_modules/@tensorflow/tfjs-core/node_modules/seedrandom/lib/xor4096.js
var require_xor4096 = __commonJS((exports3, module) => {
(function(global2, module2, define2) {
function XorGen(seed) {
var me = this;
me.next = function() {
var w = me.w, X = me.X, i = me.i, t, v;
me.w = w = w + 1640531527 | 0;
v = X[i + 34 & 127];
t = X[i = i + 1 & 127];
v ^= v << 13;
t ^= t << 17;
v ^= v >>> 15;
t ^= t >>> 12;
v = X[i] = v ^ t;
me.i = i;
return v + (w ^ w >>> 16) | 0;
};
function init2(me2, seed2) {
var t, v, i, j, w, X = [], limit = 128;
if (seed2 === (seed2 | 0)) {
v = seed2;
seed2 = null;
} else {
seed2 = seed2 + "\0";
v = 0;
limit = Math.max(limit, seed2.length);
}
for (i = 0, j = -32; j < limit; ++j) {
if (seed2)
v ^= seed2.charCodeAt((j + 32) % seed2.length);
if (j === 0)
w = v;
v ^= v << 10;
v ^= v >>> 15;
v ^= v << 4;
v ^= v >>> 13;
if (j >= 0) {
w = w + 1640531527 | 0;
t = X[j & 127] ^= v + w;
i = t == 0 ? i + 1 : 0;
}
}
if (i >= 128) {
X[(seed2 && seed2.length || 0) & 127] = -1;
}
i = 127;
for (j = 4 * 128; j > 0; --j) {
v = X[i + 34 & 127];
t = X[i = i + 1 & 127];
v ^= v << 13;
t ^= t << 17;
v ^= v >>> 15;
t ^= t >>> 12;
X[i] = v ^ t;
}
me2.w = w;
me2.X = X;
me2.i = i;
}
init2(me, seed);
}
function copy(f, t) {
t.i = f.i;
t.w = f.w;
t.X = f.X.slice();
return t;
}
;
function impl(seed, opts) {
if (seed == null)
seed = +new Date();
var xg = new XorGen(seed), state6 = opts && opts.state, prng = function() {
return (xg.next() >>> 0) / 4294967296;
};
prng.double = function() {
do {
var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
} while (result === 0);
return result;
};
prng.int32 = xg.next;
prng.quick = prng;
if (state6) {
if (state6.X)
copy(state6, xg);
prng.state = function() {
return copy(xg, {});
};
}
return prng;
}
if (module2 && module2.exports) {
module2.exports = impl;
} else if (define2 && define2.amd) {
define2(function() {
return impl;
});
} else {
this.xor4096 = impl;
}
})(exports3, typeof module == "object" && module, typeof define == "function" && define);
});
// node_modules/@tensorflow/tfjs-core/node_modules/seedrandom/lib/tychei.js
var require_tychei = __commonJS((exports3, module) => {
(function(global2, module2, define2) {
function XorGen(seed) {
var me = this, strseed = "";
me.next = function() {
var b = me.b, c = me.c, d = me.d, a = me.a;
b = b << 25 ^ b >>> 7 ^ c;
c = c - d | 0;
d = d << 24 ^ d >>> 8 ^ a;
a = a - b | 0;
me.b = b = b << 20 ^ b >>> 12 ^ c;
me.c = c = c - d | 0;
me.d = d << 16 ^ c >>> 16 ^ a;
return me.a = a - b | 0;
};
me.a = 0;
me.b = 0;
me.c = 2654435769 | 0;
me.d = 1367130551;
if (seed === Math.floor(seed)) {
me.a = seed / 4294967296 | 0;
me.b = seed | 0;
} else {
strseed += seed;
}
for (var k = 0; k < strseed.length + 20; k++) {
me.b ^= strseed.charCodeAt(k) | 0;
me.next();
}
}
function copy(f, t) {
t.a = f.a;
t.b = f.b;
t.c = f.c;
t.d = f.d;
return t;
}
;
function impl(seed, opts) {
var xg = new XorGen(seed), state6 = opts && opts.state, prng = function() {
return (xg.next() >>> 0) / 4294967296;
};
prng.double = function() {
do {
var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
} while (result === 0);
return result;
};
prng.int32 = xg.next;
prng.quick = prng;
if (state6) {
if (typeof state6 == "object")
copy(state6, xg);
prng.state = function() {
return copy(xg, {});
};
}
return prng;
}
if (module2 && module2.exports) {
module2.exports = impl;
} else if (define2 && define2.amd) {
define2(function() {
return impl;
});
} else {
this.tychei = impl;
}
})(exports3, typeof module == "object" && module, typeof define == "function" && define);
});
// empty:crypto
var require_crypto = __commonJS(() => {
});
// node_modules/@tensorflow/tfjs-core/node_modules/seedrandom/seedrandom.js
var require_seedrandom = __commonJS((exports3, module) => {
(function(pool3, math) {
var global2 = this, width = 256, chunks = 6, digits = 52, rngname = "random", startdenom = math.pow(width, chunks), significance = math.pow(2, digits), overflow = significance * 2, mask = width - 1, nodecrypto;
function seedrandom4(seed, options, callback) {
var key = [];
options = options == true ? {entropy: true} : options || {};
var shortseed = mixkey(flatten4(options.entropy ? [seed, tostring(pool3)] : seed == null ? autoseed() : seed, 3), key);
var arc4 = new ARC4(key);
var prng = function() {
var n = arc4.g(chunks), d = startdenom, x = 0;
while (n < significance) {
n = (n + x) * width;
d *= width;
x = arc4.g(1);
}
while (n >= overflow) {
n /= 2;
d /= 2;
x >>>= 1;
}
return (n + x) / d;
};
prng.int32 = function() {
return arc4.g(4) | 0;
};
prng.quick = function() {
return arc4.g(4) / 4294967296;
};
prng.double = prng;
mixkey(tostring(arc4.S), pool3);
return (options.pass || callback || function(prng2, seed2, is_math_call, state6) {
if (state6) {
if (state6.S) {
copy(state6, arc4);
}
prng2.state = function() {
return copy(arc4, {});
};
}
if (is_math_call) {
math[rngname] = prng2;
return seed2;
} else
return prng2;
})(prng, shortseed, "global" in options ? options.global : this == math, options.state);
}
math["seed" + rngname] = seedrandom4;
function ARC4(key) {
var t, keylen = key.length, me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];
if (!keylen) {
key = [keylen++];
}
while (i < width) {
s[i] = i++;
}
for (i = 0; i < width; i++) {
s[i] = s[j = mask & j + key[i % keylen] + (t = s[i])];
s[j] = t;
}
(me.g = function(count2) {
var t2, r = 0, i2 = me.i, j2 = me.j, s2 = me.S;
while (count2--) {
t2 = s2[i2 = mask & i2 + 1];
r = r * width + s2[mask & (s2[i2] = s2[j2 = mask & j2 + t2]) + (s2[j2] = t2)];
}
me.i = i2;
me.j = j2;
return r;
})(width);
}
function copy(f, t) {
t.i = f.i;
t.j = f.j;
t.S = f.S.slice();
return t;
}
;
function flatten4(obj, depth) {
var result = [], typ = typeof obj, prop;
if (depth && typ == "object") {
for (prop in obj) {
try {
result.push(flatten4(obj[prop], depth - 1));
} catch (e) {
}
}
}
return result.length ? result : typ == "string" ? obj : obj + "\0";
}
function mixkey(seed, key) {
var stringseed = seed + "", smear, j = 0;
while (j < stringseed.length) {
key[mask & j] = mask & (smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++);
}
return tostring(key);
}
function autoseed() {
try {
var out;
if (nodecrypto && (out = nodecrypto.randomBytes)) {
out = out(width);
} else {
out = new Uint8Array(width);
(global2.crypto || global2.msCrypto).getRandomValues(out);
}
return tostring(out);
} catch (e) {
var browser = global2.navigator, plugins = browser && browser.plugins;
return [+new Date(), global2, plugins, global2.screen, tostring(pool3)];
}
}
function tostring(a) {
return String.fromCharCode.apply(0, a);
}
mixkey(math.random(), pool3);
if (typeof module == "object" && module.exports) {
module.exports = seedrandom4;
try {
nodecrypto = require_crypto();
} catch (ex) {
}
} else if (typeof define == "function" && define.amd) {
define(function() {
return seedrandom4;
});
}
})([], Math);
});
// node_modules/@tensorflow/tfjs-core/node_modules/seedrandom/index.js
var require_seedrandom2 = __commonJS((exports3, module) => {
var alea4 = require_alea();
var xor128 = require_xor128();
var xorwow = require_xorwow();
var xorshift7 = require_xorshift7();
var xor4096 = require_xor4096();
var tychei = require_tychei();
var sr = require_seedrandom();
sr.alea = alea4;
sr.xor128 = xor128;
sr.xorwow = xorwow;
sr.xorshift7 = xorshift7;
sr.xor4096 = xor4096;
sr.tychei = tychei;
module.exports = sr;
});
// node_modules/seedrandom/lib/alea.js
var require_alea2 = __commonJS((exports3, module) => {
(function(global2, module2, define2) {
function Alea(seed) {
var me = this, mash = Mash();
me.next = function() {
var t = 2091639 * me.s0 + me.c * 23283064365386963e-26;
me.s0 = me.s1;
me.s1 = me.s2;
return me.s2 = t - (me.c = t | 0);
};
me.c = 1;
me.s0 = mash(" ");
me.s1 = mash(" ");
me.s2 = mash(" ");
me.s0 -= mash(seed);
if (me.s0 < 0) {
me.s0 += 1;
}
me.s1 -= mash(seed);
if (me.s1 < 0) {
me.s1 += 1;
}
me.s2 -= mash(seed);
if (me.s2 < 0) {
me.s2 += 1;
}
mash = null;
}
function copy(f, t) {
t.c = f.c;
t.s0 = f.s0;
t.s1 = f.s1;
t.s2 = f.s2;
return t;
}
function impl(seed, opts) {
var xg = new Alea(seed), state6 = opts && opts.state, prng = xg.next;
prng.int32 = function() {
return xg.next() * 4294967296 | 0;
};
prng.double = function() {
return prng() + (prng() * 2097152 | 0) * 11102230246251565e-32;
};
prng.quick = prng;
if (state6) {
if (typeof state6 == "object")
copy(state6, xg);
prng.state = function() {
return copy(xg, {});
};
}
return prng;
}
function Mash() {
var n = 4022871197;
var mash = function(data2) {
data2 = String(data2);
for (var i = 0; i < data2.length; i++) {
n += data2.charCodeAt(i);
var h = 0.02519603282416938 * n;
n = h >>> 0;
h -= n;
h *= n;
n = h >>> 0;
h -= n;
n += h * 4294967296;
}
return (n >>> 0) * 23283064365386963e-26;
};
return mash;
}
if (module2 && module2.exports) {
module2.exports = impl;
} else if (define2 && define2.amd) {
define2(function() {
return impl;
});
} else {
this.alea = impl;
}
})(exports3, typeof module == "object" && module, typeof define == "function" && define);
});
// node_modules/seedrandom/lib/xor128.js
var require_xor1282 = __commonJS((exports3, module) => {
(function(global2, module2, define2) {
function XorGen(seed) {
var me = this, strseed = "";
me.x = 0;
me.y = 0;
me.z = 0;
me.w = 0;
me.next = function() {
var t = me.x ^ me.x << 11;
me.x = me.y;
me.y = me.z;
me.z = me.w;
return me.w ^= me.w >>> 19 ^ t ^ t >>> 8;
};
if (seed === (seed | 0)) {
me.x = seed;
} else {
strseed += seed;
}
for (var k = 0; k < strseed.length + 64; k++) {
me.x ^= strseed.charCodeAt(k) | 0;
me.next();
}
}
function copy(f, t) {
t.x = f.x;
t.y = f.y;
t.z = f.z;
t.w = f.w;
return t;
}
function impl(seed, opts) {
var xg = new XorGen(seed), state6 = opts && opts.state, prng = function() {
return (xg.next() >>> 0) / 4294967296;
};
prng.double = function() {
do {
var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
} while (result === 0);
return result;
};
prng.int32 = xg.next;
prng.quick = prng;
if (state6) {
if (typeof state6 == "object")
copy(state6, xg);
prng.state = function() {
return copy(xg, {});
};
}
return prng;
}
if (module2 && module2.exports) {
module2.exports = impl;
} else if (define2 && define2.amd) {
define2(function() {
return impl;
});
} else {
this.xor128 = impl;
}
})(exports3, typeof module == "object" && module, typeof define == "function" && define);
});
// node_modules/seedrandom/lib/xorwow.js
var require_xorwow2 = __commonJS((exports3, module) => {
(function(global2, module2, define2) {
function XorGen(seed) {
var me = this, strseed = "";
me.next = function() {
var t = me.x ^ me.x >>> 2;
me.x = me.y;
me.y = me.z;
me.z = me.w;
me.w = me.v;
return (me.d = me.d + 362437 | 0) + (me.v = me.v ^ me.v << 4 ^ (t ^ t << 1)) | 0;
};
me.x = 0;
me.y = 0;
me.z = 0;
me.w = 0;
me.v = 0;
if (seed === (seed | 0)) {
me.x = seed;
} else {
strseed += seed;
}
for (var k = 0; k < strseed.length + 64; k++) {
me.x ^= strseed.charCodeAt(k) | 0;
if (k == strseed.length) {
me.d = me.x << 10 ^ me.x >>> 4;
}
me.next();
}
}
function copy(f, t) {
t.x = f.x;
t.y = f.y;
t.z = f.z;
t.w = f.w;
t.v = f.v;
t.d = f.d;
return t;
}
function impl(seed, opts) {
var xg = new XorGen(seed), state6 = opts && opts.state, prng = function() {
return (xg.next() >>> 0) / 4294967296;
};
prng.double = function() {
do {
var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
} while (result === 0);
return result;
};
prng.int32 = xg.next;
prng.quick = prng;
if (state6) {
if (typeof state6 == "object")
copy(state6, xg);
prng.state = function() {
return copy(xg, {});
};
}
return prng;
}
if (module2 && module2.exports) {
module2.exports = impl;
} else if (define2 && define2.amd) {
define2(function() {
return impl;
});
} else {
this.xorwow = impl;
}
})(exports3, typeof module == "object" && module, typeof define == "function" && define);
});
// node_modules/seedrandom/lib/xorshift7.js
var require_xorshift72 = __commonJS((exports3, module) => {
(function(global2, module2, define2) {
function XorGen(seed) {
var me = this;
me.next = function() {
var X = me.x, i = me.i, t, v, w;
t = X[i];
t ^= t >>> 7;
v = t ^ t << 24;
t = X[i + 1 & 7];
v ^= t ^ t >>> 10;
t = X[i + 3 & 7];
v ^= t ^ t >>> 3;
t = X[i + 4 & 7];
v ^= t ^ t << 7;
t = X[i + 7 & 7];
t = t ^ t << 13;
v ^= t ^ t << 9;
X[i] = v;
me.i = i + 1 & 7;
return v;
};
function init2(me2, seed2) {
var j, w, X = [];
if (seed2 === (seed2 | 0)) {
w = X[0] = seed2;
} else {
seed2 = "" + seed2;
for (j = 0; j < seed2.length; ++j) {
X[j & 7] = X[j & 7] << 15 ^ seed2.charCodeAt(j) + X[j + 1 & 7] << 13;
}
}
while (X.length < 8)
X.push(0);
for (j = 0; j < 8 && X[j] === 0; ++j)
;
if (j == 8)
w = X[7] = -1;
else
w = X[j];
me2.x = X;
me2.i = 0;
for (j = 256; j > 0; --j) {
me2.next();
}
}
init2(me, seed);
}
function copy(f, t) {
t.x = f.x.slice();
t.i = f.i;
return t;
}
function impl(seed, opts) {
if (seed == null)
seed = +new Date();
var xg = new XorGen(seed), state6 = opts && opts.state, prng = function() {
return (xg.next() >>> 0) / 4294967296;
};
prng.double = function() {
do {
var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
} while (result === 0);
return result;
};
prng.int32 = xg.next;
prng.quick = prng;
if (state6) {
if (state6.x)
copy(state6, xg);
prng.state = function() {
return copy(xg, {});
};
}
return prng;
}
if (module2 && module2.exports) {
module2.exports = impl;
} else if (define2 && define2.amd) {
define2(function() {
return impl;
});
} else {
this.xorshift7 = impl;
}
})(exports3, typeof module == "object" && module, typeof define == "function" && define);
});
// node_modules/seedrandom/lib/xor4096.js
var require_xor40962 = __commonJS((exports3, module) => {
(function(global2, module2, define2) {
function XorGen(seed) {
var me = this;
me.next = function() {
var w = me.w, X = me.X, i = me.i, t, v;
me.w = w = w + 1640531527 | 0;
v = X[i + 34 & 127];
t = X[i = i + 1 & 127];
v ^= v << 13;
t ^= t << 17;
v ^= v >>> 15;
t ^= t >>> 12;
v = X[i] = v ^ t;
me.i = i;
return v + (w ^ w >>> 16) | 0;
};
function init2(me2, seed2) {
var t, v, i, j, w, X = [], limit = 128;
if (seed2 === (seed2 | 0)) {
v = seed2;
seed2 = null;
} else {
seed2 = seed2 + "\0";
v = 0;
limit = Math.max(limit, seed2.length);
}
for (i = 0, j = -32; j < limit; ++j) {
if (seed2)
v ^= seed2.charCodeAt((j + 32) % seed2.length);
if (j === 0)
w = v;
v ^= v << 10;
v ^= v >>> 15;
v ^= v << 4;
v ^= v >>> 13;
if (j >= 0) {
w = w + 1640531527 | 0;
t = X[j & 127] ^= v + w;
i = t == 0 ? i + 1 : 0;
}
}
if (i >= 128) {
X[(seed2 && seed2.length || 0) & 127] = -1;
}
i = 127;
for (j = 4 * 128; j > 0; --j) {
v = X[i + 34 & 127];
t = X[i = i + 1 & 127];
v ^= v << 13;
t ^= t << 17;
v ^= v >>> 15;
t ^= t >>> 12;
X[i] = v ^ t;
}
me2.w = w;
me2.X = X;
me2.i = i;
}
init2(me, seed);
}
function copy(f, t) {
t.i = f.i;
t.w = f.w;
t.X = f.X.slice();
return t;
}
;
function impl(seed, opts) {
if (seed == null)
seed = +new Date();
var xg = new XorGen(seed), state6 = opts && opts.state, prng = function() {
return (xg.next() >>> 0) / 4294967296;
};
prng.double = function() {
do {
var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
} while (result === 0);
return result;
};
prng.int32 = xg.next;
prng.quick = prng;
if (state6) {
if (state6.X)
copy(state6, xg);
prng.state = function() {
return copy(xg, {});
};
}
return prng;
}
if (module2 && module2.exports) {
module2.exports = impl;
} else if (define2 && define2.amd) {
define2(function() {
return impl;
});
} else {
this.xor4096 = impl;
}
})(exports3, typeof module == "object" && module, typeof define == "function" && define);
});
// node_modules/seedrandom/lib/tychei.js
var require_tychei2 = __commonJS((exports3, module) => {
(function(global2, module2, define2) {
function XorGen(seed) {
var me = this, strseed = "";
me.next = function() {
var b = me.b, c = me.c, d = me.d, a = me.a;
b = b << 25 ^ b >>> 7 ^ c;
c = c - d | 0;
d = d << 24 ^ d >>> 8 ^ a;
a = a - b | 0;
me.b = b = b << 20 ^ b >>> 12 ^ c;
me.c = c = c - d | 0;
me.d = d << 16 ^ c >>> 16 ^ a;
return me.a = a - b | 0;
};
me.a = 0;
me.b = 0;
me.c = 2654435769 | 0;
me.d = 1367130551;
if (seed === Math.floor(seed)) {
me.a = seed / 4294967296 | 0;
me.b = seed | 0;
} else {
strseed += seed;
}
for (var k = 0; k < strseed.length + 20; k++) {
me.b ^= strseed.charCodeAt(k) | 0;
me.next();
}
}
function copy(f, t) {
t.a = f.a;
t.b = f.b;
t.c = f.c;
t.d = f.d;
return t;
}
;
function impl(seed, opts) {
var xg = new XorGen(seed), state6 = opts && opts.state, prng = function() {
return (xg.next() >>> 0) / 4294967296;
};
prng.double = function() {
do {
var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
} while (result === 0);
return result;
};
prng.int32 = xg.next;
prng.quick = prng;
if (state6) {
if (typeof state6 == "object")
copy(state6, xg);
prng.state = function() {
return copy(xg, {});
};
}
return prng;
}
if (module2 && module2.exports) {
module2.exports = impl;
} else if (define2 && define2.amd) {
define2(function() {
return impl;
});
} else {
this.tychei = impl;
}
})(exports3, typeof module == "object" && module, typeof define == "function" && define);
});
// node_modules/seedrandom/seedrandom.js
var require_seedrandom3 = __commonJS((exports3, module) => {
(function(global2, pool3, math) {
var width = 256, chunks = 6, digits = 52, rngname = "random", startdenom = math.pow(width, chunks), significance = math.pow(2, digits), overflow = significance * 2, mask = width - 1, nodecrypto;
function seedrandom4(seed, options, callback) {
var key = [];
options = options == true ? {entropy: true} : options || {};
var shortseed = mixkey(flatten4(options.entropy ? [seed, tostring(pool3)] : seed == null ? autoseed() : seed, 3), key);
var arc4 = new ARC4(key);
var prng = function() {
var n = arc4.g(chunks), d = startdenom, x = 0;
while (n < significance) {
n = (n + x) * width;
d *= width;
x = arc4.g(1);
}
while (n >= overflow) {
n /= 2;
d /= 2;
x >>>= 1;
}
return (n + x) / d;
};
prng.int32 = function() {
return arc4.g(4) | 0;
};
prng.quick = function() {
return arc4.g(4) / 4294967296;
};
prng.double = prng;
mixkey(tostring(arc4.S), pool3);
return (options.pass || callback || function(prng2, seed2, is_math_call, state6) {
if (state6) {
if (state6.S) {
copy(state6, arc4);
}
prng2.state = function() {
return copy(arc4, {});
};
}
if (is_math_call) {
math[rngname] = prng2;
return seed2;
} else
return prng2;
})(prng, shortseed, "global" in options ? options.global : this == math, options.state);
}
function ARC4(key) {
var t, keylen = key.length, me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];
if (!keylen) {
key = [keylen++];
}
while (i < width) {
s[i] = i++;
}
for (i = 0; i < width; i++) {
s[i] = s[j = mask & j + key[i % keylen] + (t = s[i])];
s[j] = t;
}
(me.g = function(count2) {
var t2, r = 0, i2 = me.i, j2 = me.j, s2 = me.S;
while (count2--) {
t2 = s2[i2 = mask & i2 + 1];
r = r * width + s2[mask & (s2[i2] = s2[j2 = mask & j2 + t2]) + (s2[j2] = t2)];
}
me.i = i2;
me.j = j2;
return r;
})(width);
}
function copy(f, t) {
t.i = f.i;
t.j = f.j;
t.S = f.S.slice();
return t;
}
;
function flatten4(obj, depth) {
var result = [], typ = typeof obj, prop;
if (depth && typ == "object") {
for (prop in obj) {
try {
result.push(flatten4(obj[prop], depth - 1));
} catch (e) {
}
}
}
return result.length ? result : typ == "string" ? obj : obj + "\0";
}
function mixkey(seed, key) {
var stringseed = seed + "", smear, j = 0;
while (j < stringseed.length) {
key[mask & j] = mask & (smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++);
}
return tostring(key);
}
function autoseed() {
try {
var out;
if (nodecrypto && (out = nodecrypto.randomBytes)) {
out = out(width);
} else {
out = new Uint8Array(width);
(global2.crypto || global2.msCrypto).getRandomValues(out);
}
return tostring(out);
} catch (e) {
var browser = global2.navigator, plugins = browser && browser.plugins;
return [+new Date(), global2, plugins, global2.screen, tostring(pool3)];
}
}
function tostring(a) {
return String.fromCharCode.apply(0, a);
}
mixkey(math.random(), pool3);
if (typeof module == "object" && module.exports) {
module.exports = seedrandom4;
try {
nodecrypto = require_crypto();
} catch (ex) {
}
} else if (typeof define == "function" && define.amd) {
define(function() {
return seedrandom4;
});
} else {
math["seed" + rngname] = seedrandom4;
}
})(typeof self !== "undefined" ? self : exports3, [], Math);
});
// node_modules/seedrandom/index.js
var require_seedrandom4 = __commonJS((exports3, module) => {
var alea4 = require_alea2();
var xor128 = require_xor1282();
var xorwow = require_xorwow2();
var xorshift7 = require_xorshift72();
var xor4096 = require_xor40962();
var tychei = require_tychei2();
var sr = require_seedrandom3();
sr.alea = alea4;
sr.xor128 = xor128;
sr.xorwow = xorwow;
sr.xorshift7 = xorshift7;
sr.xor4096 = xor4096;
sr.tychei = tychei;
module.exports = sr;
});
// empty:/home/vlado/dev/human/node_modules/string_decoder/lib/string_decoder.js
var require_string_decoder = __commonJS(() => {
});
// empty:path
var require_path = __commonJS(() => {
});
// empty:worker_threads
var require_worker_threads = __commonJS(() => {
});
// empty:perf_hooks
var require_perf_hooks = __commonJS(() => {
});
// node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
var require_tfjs_backend_wasm_threaded_simd = __commonJS((exports3, module) => {
var WasmBackendModuleThreadedSimd = function() {
var _scriptDir = typeof document !== "undefined" && document.currentScript ? document.currentScript.src : void 0;
if (typeof __filename !== "undefined")
_scriptDir = _scriptDir || __filename;
return function(WasmBackendModuleThreadedSimd2) {
WasmBackendModuleThreadedSimd2 = WasmBackendModuleThreadedSimd2 || {};
function GROWABLE_HEAP_I8() {
if (wasmMemory.buffer != buffer10) {
updateGlobalBufferAndViews(wasmMemory.buffer);
}
return HEAP8;
}
function GROWABLE_HEAP_U8() {
if (wasmMemory.buffer != buffer10) {
updateGlobalBufferAndViews(wasmMemory.buffer);
}
return HEAPU8;
}
function GROWABLE_HEAP_I32() {
if (wasmMemory.buffer != buffer10) {
updateGlobalBufferAndViews(wasmMemory.buffer);
}
return HEAP32;
}
function GROWABLE_HEAP_U32() {
if (wasmMemory.buffer != buffer10) {
updateGlobalBufferAndViews(wasmMemory.buffer);
}
return HEAPU32;
}
function GROWABLE_HEAP_F64() {
if (wasmMemory.buffer != buffer10) {
updateGlobalBufferAndViews(wasmMemory.buffer);
}
return HEAPF64;
}
var Module = typeof WasmBackendModuleThreadedSimd2 !== "undefined" ? WasmBackendModuleThreadedSimd2 : {};
var moduleOverrides = {};
var key;
for (key in Module) {
if (Module.hasOwnProperty(key)) {
moduleOverrides[key] = Module[key];
}
}
var arguments_ = [];
var thisProgram = "./this.program";
var quit_ = function(status, toThrow) {
throw toThrow;
};
var ENVIRONMENT_IS_WEB = false;
var ENVIRONMENT_IS_WORKER = false;
var ENVIRONMENT_IS_NODE = false;
var ENVIRONMENT_IS_SHELL = false;
ENVIRONMENT_IS_WEB = typeof window === "object";
ENVIRONMENT_IS_WORKER = typeof importScripts === "function";
ENVIRONMENT_IS_NODE = typeof process === "object" && typeof process.versions === "object" && typeof process.versions.node === "string";
ENVIRONMENT_IS_SHELL = !ENVIRONMENT_IS_WEB && !ENVIRONMENT_IS_NODE && !ENVIRONMENT_IS_WORKER;
var ENVIRONMENT_IS_PTHREAD = Module["ENVIRONMENT_IS_PTHREAD"] || false;
if (ENVIRONMENT_IS_PTHREAD) {
buffer10 = Module["buffer"];
DYNAMIC_BASE = Module["DYNAMIC_BASE"];
DYNAMICTOP_PTR = Module["DYNAMICTOP_PTR"];
}
var scriptDirectory = "";
function locateFile(path) {
if (Module["locateFile"]) {
return Module["locateFile"](path, scriptDirectory);
}
return scriptDirectory + path;
}
var read_, readAsync, readBinary, setWindowTitle;
var nodeFS;
var nodePath;
if (ENVIRONMENT_IS_NODE) {
if (ENVIRONMENT_IS_WORKER) {
scriptDirectory = require_path().dirname(scriptDirectory) + "/";
} else {
scriptDirectory = __dirname + "/";
}
read_ = function shell_read(filename, binary) {
if (!nodeFS)
nodeFS = require("fs");
if (!nodePath)
nodePath = require_path();
filename = nodePath["normalize"](filename);
return nodeFS["readFileSync"](filename, binary ? null : "utf8");
};
readBinary = function readBinary2(filename) {
var ret = read_(filename, true);
if (!ret.buffer) {
ret = new Uint8Array(ret);
}
assert3(ret.buffer);
return ret;
};
if (process["argv"].length > 1) {
thisProgram = process["argv"][1].replace(/\\/g, "/");
}
arguments_ = process["argv"].slice(2);
process["on"]("uncaughtException", function(ex) {
if (!(ex instanceof ExitStatus)) {
throw ex;
}
});
process["on"]("unhandledRejection", abort);
quit_ = function(status) {
process["exit"](status);
};
Module["inspect"] = function() {
return "[Emscripten Module object]";
};
var nodeWorkerThreads;
try {
nodeWorkerThreads = require_worker_threads();
} catch (e) {
console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?');
throw e;
}
Worker = nodeWorkerThreads.Worker;
} else if (ENVIRONMENT_IS_SHELL) {
if (typeof read != "undefined") {
read_ = function shell_read(f) {
return read(f);
};
}
readBinary = function readBinary2(f) {
var data2;
if (typeof readbuffer === "function") {
return new Uint8Array(readbuffer(f));
}
data2 = read(f, "binary");
assert3(typeof data2 === "object");
return data2;
};
if (typeof scriptArgs != "undefined") {
arguments_ = scriptArgs;
} else if (typeof arguments != "undefined") {
arguments_ = arguments;
}
if (typeof quit === "function") {
quit_ = function(status) {
quit(status);
};
}
if (typeof print !== "undefined") {
if (typeof console === "undefined")
console = {};
console.log = print;
console.warn = console.error = typeof printErr !== "undefined" ? printErr : print;
}
} else if (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER) {
if (ENVIRONMENT_IS_WORKER) {
scriptDirectory = self.location.href;
} else if (document.currentScript) {
scriptDirectory = document.currentScript.src;
}
if (_scriptDir) {
scriptDirectory = _scriptDir;
}
if (scriptDirectory.indexOf("blob:") !== 0) {
scriptDirectory = scriptDirectory.substr(0, scriptDirectory.lastIndexOf("/") + 1);
} else {
scriptDirectory = "";
}
if (ENVIRONMENT_IS_NODE) {
read_ = function shell_read(filename, binary) {
if (!nodeFS)
nodeFS = require("fs");
if (!nodePath)
nodePath = require_path();
filename = nodePath["normalize"](filename);
return nodeFS["readFileSync"](filename, binary ? null : "utf8");
};
readBinary = function readBinary2(filename) {
var ret = read_(filename, true);
if (!ret.buffer) {
ret = new Uint8Array(ret);
}
assert3(ret.buffer);
return ret;
};
} else {
read_ = function shell_read(url) {
var xhr = new XMLHttpRequest();
xhr.open("GET", url, false);
xhr.send(null);
return xhr.responseText;
};
if (ENVIRONMENT_IS_WORKER) {
readBinary = function readBinary2(url) {
var xhr = new XMLHttpRequest();
xhr.open("GET", url, false);
xhr.responseType = "arraybuffer";
xhr.send(null);
return new Uint8Array(xhr.response);
};
}
readAsync = function readAsync2(url, onload, onerror) {
var xhr = new XMLHttpRequest();
xhr.open("GET", url, true);
xhr.responseType = "arraybuffer";
xhr.onload = function xhr_onload() {
if (xhr.status == 200 || xhr.status == 0 && xhr.response) {
onload(xhr.response);
return;
}
onerror();
};
xhr.onerror = onerror;
xhr.send(null);
};
}
setWindowTitle = function(title) {
document.title = title;
};
} else {
}
if (ENVIRONMENT_IS_NODE) {
if (typeof performance === "undefined") {
performance = require_perf_hooks().performance;
}
}
var out = Module["print"] || console.log.bind(console);
var err = Module["printErr"] || console.warn.bind(console);
for (key in moduleOverrides) {
if (moduleOverrides.hasOwnProperty(key)) {
Module[key] = moduleOverrides[key];
}
}
moduleOverrides = null;
if (Module["arguments"])
arguments_ = Module["arguments"];
if (Module["thisProgram"])
thisProgram = Module["thisProgram"];
if (Module["quit"])
quit_ = Module["quit"];
var Atomics_load = Atomics.load;
var Atomics_store = Atomics.store;
var Atomics_compareExchange = Atomics.compareExchange;
var wasmBinary;
if (Module["wasmBinary"])
wasmBinary = Module["wasmBinary"];
var noExitRuntime;
if (Module["noExitRuntime"])
noExitRuntime = Module["noExitRuntime"];
if (typeof WebAssembly !== "object") {
err("no native wasm support detected");
}
var wasmMemory;
var wasmTable = new WebAssembly.Table({initial: 165, maximum: 165 + 0, element: "anyfunc"});
var wasmModule;
var threadInfoStruct = 0;
var selfThreadId = 0;
var ABORT = false;
var EXITSTATUS = 0;
function assert3(condition, text) {
if (!condition) {
abort("Assertion failed: " + text);
}
}
function getCFunc(ident) {
var func2 = Module["_" + ident];
assert3(func2, "Cannot call unknown function " + ident + ", make sure it is exported");
return func2;
}
function ccall(ident, returnType, argTypes, args, opts) {
var toC = {string: function(str) {
var ret2 = 0;
if (str !== null && str !== void 0 && str !== 0) {
var len = (str.length << 2) + 1;
ret2 = stackAlloc(len);
stringToUTF8(str, ret2, len);
}
return ret2;
}, array: function(arr) {
var ret2 = stackAlloc(arr.length);
writeArrayToMemory(arr, ret2);
return ret2;
}};
function convertReturnValue(ret2) {
if (returnType === "string")
return UTF8ToString(ret2);
if (returnType === "boolean")
return Boolean(ret2);
return ret2;
}
var func2 = getCFunc(ident);
var cArgs = [];
var stack6 = 0;
if (args) {
for (var i = 0; i < args.length; i++) {
var converter = toC[argTypes[i]];
if (converter) {
if (stack6 === 0)
stack6 = stackSave();
cArgs[i] = converter(args[i]);
} else {
cArgs[i] = args[i];
}
}
}
var ret = func2.apply(null, cArgs);
ret = convertReturnValue(ret);
if (stack6 !== 0)
stackRestore(stack6);
return ret;
}
function cwrap(ident, returnType, argTypes, opts) {
argTypes = argTypes || [];
var numericArgs = argTypes.every(function(type) {
return type === "number";
});
var numericRet = returnType !== "string";
if (numericRet && numericArgs && !opts) {
return getCFunc(ident);
}
return function() {
return ccall(ident, returnType, argTypes, arguments, opts);
};
}
function UTF8ArrayToString(heap, idx, maxBytesToRead) {
var endIdx = idx + maxBytesToRead;
var str = "";
while (!(idx >= endIdx)) {
var u0 = heap[idx++];
if (!u0)
return str;
if (!(u0 & 128)) {
str += String.fromCharCode(u0);
continue;
}
var u1 = heap[idx++] & 63;
if ((u0 & 224) == 192) {
str += String.fromCharCode((u0 & 31) << 6 | u1);
continue;
}
var u2 = heap[idx++] & 63;
if ((u0 & 240) == 224) {
u0 = (u0 & 15) << 12 | u1 << 6 | u2;
} else {
u0 = (u0 & 7) << 18 | u1 << 12 | u2 << 6 | heap[idx++] & 63;
}
if (u0 < 65536) {
str += String.fromCharCode(u0);
} else {
var ch = u0 - 65536;
str += String.fromCharCode(55296 | ch >> 10, 56320 | ch & 1023);
}
}
return str;
}
function UTF8ToString(ptr, maxBytesToRead) {
return ptr ? UTF8ArrayToString(GROWABLE_HEAP_U8(), ptr, maxBytesToRead) : "";
}
function stringToUTF8Array(str, heap, outIdx, maxBytesToWrite) {
if (!(maxBytesToWrite > 0))
return 0;
var startIdx = outIdx;
var endIdx = outIdx + maxBytesToWrite - 1;
for (var i = 0; i < str.length; ++i) {
var u = str.charCodeAt(i);
if (u >= 55296 && u <= 57343) {
var u1 = str.charCodeAt(++i);
u = 65536 + ((u & 1023) << 10) | u1 & 1023;
}
if (u <= 127) {
if (outIdx >= endIdx)
break;
heap[outIdx++] = u;
} else if (u <= 2047) {
if (outIdx + 1 >= endIdx)
break;
heap[outIdx++] = 192 | u >> 6;
heap[outIdx++] = 128 | u & 63;
} else if (u <= 65535) {
if (outIdx + 2 >= endIdx)
break;
heap[outIdx++] = 224 | u >> 12;
heap[outIdx++] = 128 | u >> 6 & 63;
heap[outIdx++] = 128 | u & 63;
} else {
if (outIdx + 3 >= endIdx)
break;
heap[outIdx++] = 240 | u >> 18;
heap[outIdx++] = 128 | u >> 12 & 63;
heap[outIdx++] = 128 | u >> 6 & 63;
heap[outIdx++] = 128 | u & 63;
}
}
heap[outIdx] = 0;
return outIdx - startIdx;
}
function stringToUTF8(str, outPtr, maxBytesToWrite) {
return stringToUTF8Array(str, GROWABLE_HEAP_U8(), outPtr, maxBytesToWrite);
}
function lengthBytesUTF8(str) {
var len = 0;
for (var i = 0; i < str.length; ++i) {
var u = str.charCodeAt(i);
if (u >= 55296 && u <= 57343)
u = 65536 + ((u & 1023) << 10) | str.charCodeAt(++i) & 1023;
if (u <= 127)
++len;
else if (u <= 2047)
len += 2;
else if (u <= 65535)
len += 3;
else
len += 4;
}
return len;
}
function writeArrayToMemory(array2, buffer11) {
GROWABLE_HEAP_I8().set(array2, buffer11);
}
var WASM_PAGE_SIZE = 65536;
function alignUp(x, multiple) {
if (x % multiple > 0) {
x += multiple - x % multiple;
}
return x;
}
var buffer10, HEAP8, HEAPU8, HEAP16, HEAPU16, HEAP32, HEAPU32, HEAPF32, HEAPF64;
function updateGlobalBufferAndViews(buf) {
buffer10 = buf;
Module["HEAP8"] = HEAP8 = new Int8Array(buf);
Module["HEAP16"] = HEAP16 = new Int16Array(buf);
Module["HEAP32"] = HEAP32 = new Int32Array(buf);
Module["HEAPU8"] = HEAPU8 = new Uint8Array(buf);
Module["HEAPU16"] = HEAPU16 = new Uint16Array(buf);
Module["HEAPU32"] = HEAPU32 = new Uint32Array(buf);
Module["HEAPF32"] = HEAPF32 = new Float32Array(buf);
Module["HEAPF64"] = HEAPF64 = new Float64Array(buf);
}
var STACK_BASE = 5256384, STACKTOP = STACK_BASE, STACK_MAX = 13504, DYNAMIC_BASE = 5256384, DYNAMICTOP_PTR = 12576;
if (ENVIRONMENT_IS_PTHREAD) {
}
var INITIAL_INITIAL_MEMORY = Module["INITIAL_MEMORY"] || 16777216;
if (ENVIRONMENT_IS_PTHREAD) {
wasmMemory = Module["wasmMemory"];
buffer10 = Module["buffer"];
} else {
if (Module["wasmMemory"]) {
wasmMemory = Module["wasmMemory"];
} else {
wasmMemory = new WebAssembly.Memory({initial: INITIAL_INITIAL_MEMORY / WASM_PAGE_SIZE, maximum: 2147483648 / WASM_PAGE_SIZE, shared: true});
if (!(wasmMemory.buffer instanceof SharedArrayBuffer)) {
err("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag");
if (ENVIRONMENT_IS_NODE) {
console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)");
}
throw Error("bad memory");
}
}
}
if (wasmMemory) {
buffer10 = wasmMemory.buffer;
}
INITIAL_INITIAL_MEMORY = buffer10.byteLength;
updateGlobalBufferAndViews(buffer10);
if (!ENVIRONMENT_IS_PTHREAD) {
GROWABLE_HEAP_I32()[DYNAMICTOP_PTR >> 2] = DYNAMIC_BASE;
}
function callRuntimeCallbacks(callbacks3) {
while (callbacks3.length > 0) {
var callback = callbacks3.shift();
if (typeof callback == "function") {
callback(Module);
continue;
}
var func2 = callback.func;
if (typeof func2 === "number") {
if (callback.arg === void 0) {
Module["dynCall_v"](func2);
} else {
Module["dynCall_vi"](func2, callback.arg);
}
} else {
func2(callback.arg === void 0 ? null : callback.arg);
}
}
}
var __ATPRERUN__ = [];
var __ATINIT__ = [];
var __ATMAIN__ = [];
var __ATEXIT__ = [];
var __ATPOSTRUN__ = [];
var runtimeInitialized = false;
if (ENVIRONMENT_IS_PTHREAD)
runtimeInitialized = true;
function preRun() {
if (ENVIRONMENT_IS_PTHREAD)
return;
if (Module["preRun"]) {
if (typeof Module["preRun"] == "function")
Module["preRun"] = [Module["preRun"]];
while (Module["preRun"].length) {
addOnPreRun(Module["preRun"].shift());
}
}
callRuntimeCallbacks(__ATPRERUN__);
}
function initRuntime() {
runtimeInitialized = true;
callRuntimeCallbacks(__ATINIT__);
}
function preMain() {
if (ENVIRONMENT_IS_PTHREAD)
return;
callRuntimeCallbacks(__ATMAIN__);
}
function postRun() {
if (ENVIRONMENT_IS_PTHREAD)
return;
if (Module["postRun"]) {
if (typeof Module["postRun"] == "function")
Module["postRun"] = [Module["postRun"]];
while (Module["postRun"].length) {
addOnPostRun(Module["postRun"].shift());
}
}
callRuntimeCallbacks(__ATPOSTRUN__);
}
function addOnPreRun(cb) {
__ATPRERUN__.unshift(cb);
}
function addOnPostRun(cb) {
__ATPOSTRUN__.unshift(cb);
}
var Math_ceil = Math.ceil;
var Math_floor = Math.floor;
var runDependencies = 0;
var runDependencyWatcher = null;
var dependenciesFulfilled = null;
function addRunDependency(id) {
assert3(!ENVIRONMENT_IS_PTHREAD, "addRunDependency cannot be used in a pthread worker");
runDependencies++;
if (Module["monitorRunDependencies"]) {
Module["monitorRunDependencies"](runDependencies);
}
}
function removeRunDependency(id) {
runDependencies--;
if (Module["monitorRunDependencies"]) {
Module["monitorRunDependencies"](runDependencies);
}
if (runDependencies == 0) {
if (runDependencyWatcher !== null) {
clearInterval(runDependencyWatcher);
runDependencyWatcher = null;
}
if (dependenciesFulfilled) {
var callback = dependenciesFulfilled;
dependenciesFulfilled = null;
callback();
}
}
}
Module["preloadedImages"] = {};
Module["preloadedAudios"] = {};
function abort(what) {
if (Module["onAbort"]) {
Module["onAbort"](what);
}
if (ENVIRONMENT_IS_PTHREAD)
console.error("Pthread aborting at " + new Error().stack);
what += "";
out(what);
err(what);
ABORT = true;
EXITSTATUS = 1;
what = "abort(" + what + "). Build with -s ASSERTIONS=1 for more info.";
throw new WebAssembly.RuntimeError(what);
}
function hasPrefix(str, prefix) {
return String.prototype.startsWith ? str.startsWith(prefix) : str.indexOf(prefix) === 0;
}
var dataURIPrefix = "data:application/octet-stream;base64,";
function isDataURI(filename) {
return hasPrefix(filename, dataURIPrefix);
}
var fileURIPrefix = "file://";
function isFileURI(filename) {
return hasPrefix(filename, fileURIPrefix);
}
var wasmBinaryFile = "tfjs-backend-wasm-threaded-simd.wasm";
if (!isDataURI(wasmBinaryFile)) {
wasmBinaryFile = locateFile(wasmBinaryFile);
}
function getBinary() {
try {
if (wasmBinary) {
return new Uint8Array(wasmBinary);
}
if (readBinary) {
return readBinary(wasmBinaryFile);
} else {
throw "both async and sync fetching of the wasm failed";
}
} catch (err2) {
abort(err2);
}
}
function getBinaryPromise() {
if (!wasmBinary && (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER) && typeof fetch === "function" && !isFileURI(wasmBinaryFile)) {
return fetch(wasmBinaryFile, {credentials: "same-origin"}).then(function(response) {
if (!response["ok"]) {
throw "failed to load wasm binary file at '" + wasmBinaryFile + "'";
}
return response["arrayBuffer"]();
}).catch(function() {
return getBinary();
});
}
return new Promise(function(resolve, reject) {
resolve(getBinary());
});
}
function createWasm() {
var info = {a: asmLibraryArg};
function receiveInstance(instance, module2) {
var exports5 = instance.exports;
Module["asm"] = exports5;
wasmModule = module2;
if (!ENVIRONMENT_IS_PTHREAD) {
var numWorkersToLoad = PThread.unusedWorkers.length;
PThread.unusedWorkers.forEach(function(w) {
PThread.loadWasmModuleToWorker(w, function() {
if (!--numWorkersToLoad)
removeRunDependency("wasm-instantiate");
});
});
}
}
if (!ENVIRONMENT_IS_PTHREAD) {
addRunDependency("wasm-instantiate");
}
function receiveInstantiatedSource(output) {
receiveInstance(output["instance"], output["module"]);
}
function instantiateArrayBuffer(receiver) {
return getBinaryPromise().then(function(binary) {
return WebAssembly.instantiate(binary, info);
}).then(receiver, function(reason) {
err("failed to asynchronously prepare wasm: " + reason);
abort(reason);
});
}
function instantiateAsync() {
if (!wasmBinary && typeof WebAssembly.instantiateStreaming === "function" && !isDataURI(wasmBinaryFile) && !isFileURI(wasmBinaryFile) && typeof fetch === "function") {
fetch(wasmBinaryFile, {credentials: "same-origin"}).then(function(response) {
var result = WebAssembly.instantiateStreaming(response, info);
return result.then(receiveInstantiatedSource, function(reason) {
err("wasm streaming compile failed: " + reason);
err("falling back to ArrayBuffer instantiation");
instantiateArrayBuffer(receiveInstantiatedSource);
});
});
} else {
return instantiateArrayBuffer(receiveInstantiatedSource);
}
}
if (Module["instantiateWasm"]) {
try {
var exports4 = Module["instantiateWasm"](info, receiveInstance);
return exports4;
} catch (e) {
err("Module.instantiateWasm callback failed with error: " + e);
return false;
}
}
instantiateAsync();
return {};
}
var ASM_CONSTS = {};
function initPthreadsJS() {
PThread.initRuntime();
}
if (!ENVIRONMENT_IS_PTHREAD)
__ATINIT__.push({func: function() {
___wasm_call_ctors();
}});
var __pthread_ptr = 0;
var __pthread_is_main_runtime_thread = 0;
var __pthread_is_main_browser_thread = 0;
function __register_pthread_ptr(pthreadPtr, isMainBrowserThread, isMainRuntimeThread) {
pthreadPtr = pthreadPtr | 0;
isMainBrowserThread = isMainBrowserThread | 0;
isMainRuntimeThread = isMainRuntimeThread | 0;
__pthread_ptr = pthreadPtr;
__pthread_is_main_browser_thread = isMainBrowserThread;
__pthread_is_main_runtime_thread = isMainRuntimeThread;
}
Module["__register_pthread_ptr"] = __register_pthread_ptr;
var ERRNO_CODES = {EPERM: 63, ENOENT: 44, ESRCH: 71, EINTR: 27, EIO: 29, ENXIO: 60, E2BIG: 1, ENOEXEC: 45, EBADF: 8, ECHILD: 12, EAGAIN: 6, EWOULDBLOCK: 6, ENOMEM: 48, EACCES: 2, EFAULT: 21, ENOTBLK: 105, EBUSY: 10, EEXIST: 20, EXDEV: 75, ENODEV: 43, ENOTDIR: 54, EISDIR: 31, EINVAL: 28, ENFILE: 41, EMFILE: 33, ENOTTY: 59, ETXTBSY: 74, EFBIG: 22, ENOSPC: 51, ESPIPE: 70, EROFS: 69, EMLINK: 34, EPIPE: 64, EDOM: 18, ERANGE: 68, ENOMSG: 49, EIDRM: 24, ECHRNG: 106, EL2NSYNC: 156, EL3HLT: 107, EL3RST: 108, ELNRNG: 109, EUNATCH: 110, ENOCSI: 111, EL2HLT: 112, EDEADLK: 16, ENOLCK: 46, EBADE: 113, EBADR: 114, EXFULL: 115, ENOANO: 104, EBADRQC: 103, EBADSLT: 102, EDEADLOCK: 16, EBFONT: 101, ENOSTR: 100, ENODATA: 116, ETIME: 117, ENOSR: 118, ENONET: 119, ENOPKG: 120, EREMOTE: 121, ENOLINK: 47, EADV: 122, ESRMNT: 123, ECOMM: 124, EPROTO: 65, EMULTIHOP: 36, EDOTDOT: 125, EBADMSG: 9, ENOTUNIQ: 126, EBADFD: 127, EREMCHG: 128, ELIBACC: 129, ELIBBAD: 130, ELIBSCN: 131, ELIBMAX: 132, ELIBEXEC: 133, ENOSYS: 52, ENOTEMPTY: 55, ENAMETOOLONG: 37, ELOOP: 32, EOPNOTSUPP: 138, EPFNOSUPPORT: 139, ECONNRESET: 15, ENOBUFS: 42, EAFNOSUPPORT: 5, EPROTOTYPE: 67, ENOTSOCK: 57, ENOPROTOOPT: 50, ESHUTDOWN: 140, ECONNREFUSED: 14, EADDRINUSE: 3, ECONNABORTED: 13, ENETUNREACH: 40, ENETDOWN: 38, ETIMEDOUT: 73, EHOSTDOWN: 142, EHOSTUNREACH: 23, EINPROGRESS: 26, EALREADY: 7, EDESTADDRREQ: 17, EMSGSIZE: 35, EPROTONOSUPPORT: 66, ESOCKTNOSUPPORT: 137, EADDRNOTAVAIL: 4, ENETRESET: 39, EISCONN: 30, ENOTCONN: 53, ETOOMANYREFS: 141, EUSERS: 136, EDQUOT: 19, ESTALE: 72, ENOTSUP: 138, ENOMEDIUM: 148, EILSEQ: 25, EOVERFLOW: 61, ECANCELED: 11, ENOTRECOVERABLE: 56, EOWNERDEAD: 62, ESTRPIPE: 135};
var __main_thread_futex_wait_address = 13488;
function _emscripten_futex_wake(addr, count2) {
if (addr <= 0 || addr > GROWABLE_HEAP_I8().length || addr & true || count2 < 0)
return -28;
if (count2 == 0)
return 0;
if (count2 >= 2147483647)
count2 = Infinity;
var mainThreadWaitAddress = Atomics.load(GROWABLE_HEAP_I32(), __main_thread_futex_wait_address >> 2);
var mainThreadWoken = 0;
if (mainThreadWaitAddress == addr) {
var loadedAddr = Atomics.compareExchange(GROWABLE_HEAP_I32(), __main_thread_futex_wait_address >> 2, mainThreadWaitAddress, 0);
if (loadedAddr == mainThreadWaitAddress) {
--count2;
mainThreadWoken = 1;
if (count2 <= 0)
return 1;
}
}
var ret = Atomics.notify(GROWABLE_HEAP_I32(), addr >> 2, count2);
if (ret >= 0)
return ret + mainThreadWoken;
throw "Atomics.notify returned an unexpected value " + ret;
}
Module["_emscripten_futex_wake"] = _emscripten_futex_wake;
function __kill_thread(pthread_ptr) {
if (ENVIRONMENT_IS_PTHREAD)
throw "Internal Error! _kill_thread() can only ever be called from main application thread!";
if (!pthread_ptr)
throw "Internal Error! Null pthread_ptr in _kill_thread!";
GROWABLE_HEAP_I32()[pthread_ptr + 12 >> 2] = 0;
var pthread = PThread.pthreads[pthread_ptr];
pthread.worker.terminate();
PThread.freeThreadData(pthread);
PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker), 1);
pthread.worker.pthread = void 0;
}
function __cancel_thread(pthread_ptr) {
if (ENVIRONMENT_IS_PTHREAD)
throw "Internal Error! _cancel_thread() can only ever be called from main application thread!";
if (!pthread_ptr)
throw "Internal Error! Null pthread_ptr in _cancel_thread!";
var pthread = PThread.pthreads[pthread_ptr];
pthread.worker.postMessage({cmd: "cancel"});
}
function __cleanup_thread(pthread_ptr) {
if (ENVIRONMENT_IS_PTHREAD)
throw "Internal Error! _cleanup_thread() can only ever be called from main application thread!";
if (!pthread_ptr)
throw "Internal Error! Null pthread_ptr in _cleanup_thread!";
GROWABLE_HEAP_I32()[pthread_ptr + 12 >> 2] = 0;
var pthread = PThread.pthreads[pthread_ptr];
if (pthread) {
var worker = pthread.worker;
PThread.returnWorkerToPool(worker);
}
}
var PThread = {MAIN_THREAD_ID: 1, mainThreadInfo: {schedPolicy: 0, schedPrio: 0}, unusedWorkers: [], runningWorkers: [], initRuntime: function() {
__register_pthread_ptr(PThread.mainThreadBlock, !ENVIRONMENT_IS_WORKER, 1);
_emscripten_register_main_browser_thread_id(PThread.mainThreadBlock);
}, initMainThreadBlock: function() {
var pthreadPoolSize = 8;
for (var i = 0; i < pthreadPoolSize; ++i) {
PThread.allocateUnusedWorker();
}
PThread.mainThreadBlock = 12736;
for (var i = 0; i < 232 / 4; ++i)
GROWABLE_HEAP_U32()[PThread.mainThreadBlock / 4 + i] = 0;
GROWABLE_HEAP_I32()[PThread.mainThreadBlock + 12 >> 2] = PThread.mainThreadBlock;
var headPtr = PThread.mainThreadBlock + 156;
GROWABLE_HEAP_I32()[headPtr >> 2] = headPtr;
var tlsMemory = 12976;
for (var i = 0; i < 128; ++i)
GROWABLE_HEAP_U32()[tlsMemory / 4 + i] = 0;
Atomics.store(GROWABLE_HEAP_U32(), PThread.mainThreadBlock + 104 >> 2, tlsMemory);
Atomics.store(GROWABLE_HEAP_U32(), PThread.mainThreadBlock + 40 >> 2, PThread.mainThreadBlock);
Atomics.store(GROWABLE_HEAP_U32(), PThread.mainThreadBlock + 44 >> 2, 42);
}, initWorker: function() {
}, pthreads: {}, exitHandlers: null, setThreadStatus: function() {
}, runExitHandlers: function() {
if (PThread.exitHandlers !== null) {
while (PThread.exitHandlers.length > 0) {
PThread.exitHandlers.pop()();
}
PThread.exitHandlers = null;
}
if (ENVIRONMENT_IS_PTHREAD && threadInfoStruct)
___pthread_tsd_run_dtors();
}, threadExit: function(exitCode) {
var tb = _pthread_self();
if (tb) {
Atomics.store(GROWABLE_HEAP_U32(), tb + 4 >> 2, exitCode);
Atomics.store(GROWABLE_HEAP_U32(), tb + 0 >> 2, 1);
Atomics.store(GROWABLE_HEAP_U32(), tb + 60 >> 2, 1);
Atomics.store(GROWABLE_HEAP_U32(), tb + 64 >> 2, 0);
PThread.runExitHandlers();
_emscripten_futex_wake(tb + 0, 2147483647);
__register_pthread_ptr(0, 0, 0);
threadInfoStruct = 0;
if (ENVIRONMENT_IS_PTHREAD) {
postMessage({cmd: "exit"});
}
}
}, threadCancel: function() {
PThread.runExitHandlers();
Atomics.store(GROWABLE_HEAP_U32(), threadInfoStruct + 4 >> 2, -1);
Atomics.store(GROWABLE_HEAP_U32(), threadInfoStruct + 0 >> 2, 1);
_emscripten_futex_wake(threadInfoStruct + 0, 2147483647);
threadInfoStruct = selfThreadId = 0;
__register_pthread_ptr(0, 0, 0);
postMessage({cmd: "cancelDone"});
}, terminateAllThreads: function() {
for (var t in PThread.pthreads) {
var pthread = PThread.pthreads[t];
if (pthread && pthread.worker) {
PThread.returnWorkerToPool(pthread.worker);
}
}
PThread.pthreads = {};
for (var i = 0; i < PThread.unusedWorkers.length; ++i) {
var worker = PThread.unusedWorkers[i];
worker.terminate();
}
PThread.unusedWorkers = [];
for (var i = 0; i < PThread.runningWorkers.length; ++i) {
var worker = PThread.runningWorkers[i];
var pthread = worker.pthread;
PThread.freeThreadData(pthread);
worker.terminate();
}
PThread.runningWorkers = [];
}, freeThreadData: function(pthread) {
if (!pthread)
return;
if (pthread.threadInfoStruct) {
var tlsMemory = GROWABLE_HEAP_I32()[pthread.threadInfoStruct + 104 >> 2];
GROWABLE_HEAP_I32()[pthread.threadInfoStruct + 104 >> 2] = 0;
_free(tlsMemory);
_free(pthread.threadInfoStruct);
}
pthread.threadInfoStruct = 0;
if (pthread.allocatedOwnStack && pthread.stackBase)
_free(pthread.stackBase);
pthread.stackBase = 0;
if (pthread.worker)
pthread.worker.pthread = null;
}, returnWorkerToPool: function(worker) {
delete PThread.pthreads[worker.pthread.thread];
PThread.unusedWorkers.push(worker);
PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker), 1);
PThread.freeThreadData(worker.pthread);
worker.pthread = void 0;
}, receiveObjectTransfer: function(data2) {
}, loadWasmModuleToWorker: function(worker, onFinishedLoading) {
worker.onmessage = function(e) {
var d = e["data"];
var cmd = d["cmd"];
if (worker.pthread)
PThread.currentProxiedOperationCallerThread = worker.pthread.threadInfoStruct;
if (d["targetThread"] && d["targetThread"] != _pthread_self()) {
var thread = PThread.pthreads[d.targetThread];
if (thread) {
thread.worker.postMessage(e.data, d["transferList"]);
} else {
console.error('Internal error! Worker sent a message "' + cmd + '" to target pthread ' + d["targetThread"] + ", but that thread no longer exists!");
}
PThread.currentProxiedOperationCallerThread = void 0;
return;
}
if (cmd === "processQueuedMainThreadWork") {
_emscripten_main_thread_process_queued_calls();
} else if (cmd === "spawnThread") {
__spawn_thread(e.data);
} else if (cmd === "cleanupThread") {
__cleanup_thread(d["thread"]);
} else if (cmd === "killThread") {
__kill_thread(d["thread"]);
} else if (cmd === "cancelThread") {
__cancel_thread(d["thread"]);
} else if (cmd === "loaded") {
worker.loaded = true;
if (onFinishedLoading)
onFinishedLoading(worker);
if (worker.runPthread) {
worker.runPthread();
delete worker.runPthread;
}
} else if (cmd === "print") {
out("Thread " + d["threadId"] + ": " + d["text"]);
} else if (cmd === "printErr") {
err("Thread " + d["threadId"] + ": " + d["text"]);
} else if (cmd === "alert") {
alert("Thread " + d["threadId"] + ": " + d["text"]);
} else if (cmd === "exit") {
var detached = worker.pthread && Atomics.load(GROWABLE_HEAP_U32(), worker.pthread.thread + 68 >> 2);
if (detached) {
PThread.returnWorkerToPool(worker);
}
} else if (cmd === "cancelDone") {
PThread.returnWorkerToPool(worker);
} else if (cmd === "objectTransfer") {
PThread.receiveObjectTransfer(e.data);
} else if (e.data.target === "setimmediate") {
worker.postMessage(e.data);
} else {
err("worker sent an unknown command " + cmd);
}
PThread.currentProxiedOperationCallerThread = void 0;
};
worker.onerror = function(e) {
err("pthread sent an error! " + e.filename + ":" + e.lineno + ": " + e.message);
};
if (ENVIRONMENT_IS_NODE) {
worker.on("message", function(data2) {
worker.onmessage({data: data2});
});
worker.on("error", function(data2) {
worker.onerror(data2);
});
worker.on("exit", function(data2) {
console.log("worker exited - TODO: update the worker queue?");
});
}
worker.postMessage({cmd: "load", urlOrBlob: Module["mainScriptUrlOrBlob"] || _scriptDir, wasmMemory, wasmModule, DYNAMIC_BASE, DYNAMICTOP_PTR});
}, allocateUnusedWorker: function() {
var pthreadMainJs = locateFile("tfjs-backend-wasm-threaded-simd.worker.js");
PThread.unusedWorkers.push(new Worker(pthreadMainJs));
}, getNewWorker: function() {
if (PThread.unusedWorkers.length == 0) {
PThread.allocateUnusedWorker();
PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0]);
}
if (PThread.unusedWorkers.length > 0)
return PThread.unusedWorkers.pop();
else
return null;
}, busySpinWait: function(msecs) {
var t = performance.now() + msecs;
while (performance.now() < t) {
}
}};
function establishStackSpace(stackTop, stackMax) {
STACK_BASE = STACKTOP = stackTop;
STACK_MAX = stackMax;
stackRestore(stackTop);
}
Module["establishStackSpace"] = establishStackSpace;
function getNoExitRuntime() {
return noExitRuntime;
}
Module["getNoExitRuntime"] = getNoExitRuntime;
function ___assert_fail(condition, filename, line, func2) {
abort("Assertion failed: " + UTF8ToString(condition) + ", at: " + [filename ? UTF8ToString(filename) : "unknown filename", line, func2 ? UTF8ToString(func2) : "unknown function"]);
}
function ___call_main(argc, argv) {
var returnCode = _main(argc, argv);
}
var _emscripten_get_now;
if (ENVIRONMENT_IS_NODE) {
_emscripten_get_now = function() {
var t = process["hrtime"]();
return t[0] * 1e3 + t[1] / 1e6;
};
} else if (ENVIRONMENT_IS_PTHREAD) {
_emscripten_get_now = function() {
return performance.now() - Module["__performance_now_clock_drift"];
};
} else if (typeof dateNow !== "undefined") {
_emscripten_get_now = dateNow;
} else
_emscripten_get_now = function() {
return performance.now();
};
function setErrNo(value) {
GROWABLE_HEAP_I32()[___errno_location() >> 2] = value;
return value;
}
function _atexit(func2, arg) {
if (ENVIRONMENT_IS_PTHREAD)
return _emscripten_proxy_to_main_thread_js(1, 1, func2, arg);
__ATEXIT__.unshift({func: func2, arg});
}
function __emscripten_notify_thread_queue(targetThreadId, mainThreadId) {
if (targetThreadId == mainThreadId) {
postMessage({cmd: "processQueuedMainThreadWork"});
} else if (ENVIRONMENT_IS_PTHREAD) {
postMessage({targetThread: targetThreadId, cmd: "processThreadQueue"});
} else {
var pthread = PThread.pthreads[targetThreadId];
var worker = pthread && pthread.worker;
if (!worker) {
return;
}
worker.postMessage({cmd: "processThreadQueue"});
}
return 1;
}
function _abort() {
abort();
}
function _emscripten_conditional_set_current_thread_status(expectedStatus, newStatus) {
expectedStatus = expectedStatus | 0;
newStatus = newStatus | 0;
}
function _emscripten_futex_wait(addr, val, timeout) {
if (addr <= 0 || addr > GROWABLE_HEAP_I8().length || addr & true)
return -28;
if (ENVIRONMENT_IS_WORKER) {
var ret = Atomics.wait(GROWABLE_HEAP_I32(), addr >> 2, val, timeout);
if (ret === "timed-out")
return -73;
if (ret === "not-equal")
return -6;
if (ret === "ok")
return 0;
throw "Atomics.wait returned an unexpected value " + ret;
} else {
var loadedVal = Atomics.load(GROWABLE_HEAP_I32(), addr >> 2);
if (val != loadedVal)
return -6;
var tNow = performance.now();
var tEnd = tNow + timeout;
Atomics.store(GROWABLE_HEAP_I32(), __main_thread_futex_wait_address >> 2, addr);
var ourWaitAddress = addr;
while (addr == ourWaitAddress) {
tNow = performance.now();
if (tNow > tEnd) {
return -73;
}
_emscripten_main_thread_process_queued_calls();
addr = Atomics.load(GROWABLE_HEAP_I32(), __main_thread_futex_wait_address >> 2);
}
return 0;
}
}
function _emscripten_is_main_browser_thread() {
return __pthread_is_main_browser_thread | 0;
}
function _emscripten_is_main_runtime_thread() {
return __pthread_is_main_runtime_thread | 0;
}
function _emscripten_memcpy_big(dest, src, num) {
GROWABLE_HEAP_U8().copyWithin(dest, src, src + num);
}
function _emscripten_num_logical_cores() {
return navigator["hardwareConcurrency"];
}
function _emscripten_proxy_to_main_thread_js(index, sync) {
var numCallArgs = arguments.length - 2;
var stack6 = stackSave();
var args = stackAlloc(numCallArgs * 8);
var b = args >> 3;
for (var i = 0; i < numCallArgs; i++) {
GROWABLE_HEAP_F64()[b + i] = arguments[2 + i];
}
var ret = _emscripten_run_in_main_runtime_thread_js(index, numCallArgs, args, sync);
stackRestore(stack6);
return ret;
}
var _emscripten_receive_on_main_thread_js_callArgs = [];
function readAsmConstArgs(sigPtr, buf) {
if (!readAsmConstArgs.array) {
readAsmConstArgs.array = [];
}
var args = readAsmConstArgs.array;
args.length = 0;
var ch;
while (ch = GROWABLE_HEAP_U8()[sigPtr++]) {
if (ch === 100 || ch === 102) {
buf = buf + 7 & ~7;
args.push(GROWABLE_HEAP_F64()[buf >> 3]);
buf += 8;
} else {
buf = buf + 3 & ~3;
args.push(GROWABLE_HEAP_I32()[buf >> 2]);
buf += 4;
}
}
return args;
}
function _emscripten_receive_on_main_thread_js(index, numCallArgs, args) {
_emscripten_receive_on_main_thread_js_callArgs.length = numCallArgs;
var b = args >> 3;
for (var i = 0; i < numCallArgs; i++) {
_emscripten_receive_on_main_thread_js_callArgs[i] = GROWABLE_HEAP_F64()[b + i];
}
var isEmAsmConst = index < 0;
var func2 = !isEmAsmConst ? proxiedFunctionTable[index] : ASM_CONSTS[-index - 1];
if (isEmAsmConst) {
var sigPtr = _emscripten_receive_on_main_thread_js_callArgs[1];
var varargPtr = _emscripten_receive_on_main_thread_js_callArgs[2];
var constArgs = readAsmConstArgs(sigPtr, varargPtr);
return func2.apply(null, constArgs);
}
return func2.apply(null, _emscripten_receive_on_main_thread_js_callArgs);
}
function _emscripten_get_heap_size() {
return GROWABLE_HEAP_U8().length;
}
function emscripten_realloc_buffer(size) {
try {
wasmMemory.grow(size - buffer10.byteLength + 65535 >>> 16);
updateGlobalBufferAndViews(wasmMemory.buffer);
return 1;
} catch (e) {
}
}
function _emscripten_resize_heap(requestedSize) {
requestedSize = requestedSize >>> 0;
var oldSize = _emscripten_get_heap_size();
if (requestedSize <= oldSize) {
return false;
}
var PAGE_MULTIPLE = 65536;
var maxHeapSize = 2147483648;
if (requestedSize > maxHeapSize) {
return false;
}
var minHeapSize = 16777216;
for (var cutDown = 1; cutDown <= 4; cutDown *= 2) {
var overGrownHeapSize = oldSize * (1 + 0.2 / cutDown);
overGrownHeapSize = Math.min(overGrownHeapSize, requestedSize + 100663296);
var newSize = Math.min(maxHeapSize, alignUp(Math.max(minHeapSize, requestedSize, overGrownHeapSize), PAGE_MULTIPLE));
var replacement = emscripten_realloc_buffer(newSize);
if (replacement) {
return true;
}
}
return false;
}
var JSEvents = {keyEvent: 0, mouseEvent: 0, wheelEvent: 0, uiEvent: 0, focusEvent: 0, deviceOrientationEvent: 0, deviceMotionEvent: 0, fullscreenChangeEvent: 0, pointerlockChangeEvent: 0, visibilityChangeEvent: 0, touchEvent: 0, previousFullscreenElement: null, previousScreenX: null, previousScreenY: null, removeEventListenersRegistered: false, removeAllEventListeners: function() {
for (var i = JSEvents.eventHandlers.length - 1; i >= 0; --i) {
JSEvents._removeHandler(i);
}
JSEvents.eventHandlers = [];
JSEvents.deferredCalls = [];
}, registerRemoveEventListeners: function() {
if (!JSEvents.removeEventListenersRegistered) {
__ATEXIT__.push(JSEvents.removeAllEventListeners);
JSEvents.removeEventListenersRegistered = true;
}
}, deferredCalls: [], deferCall: function(targetFunction, precedence, argsList) {
function arraysHaveEqualContent(arrA, arrB) {
if (arrA.length != arrB.length)
return false;
for (var i2 in arrA) {
if (arrA[i2] != arrB[i2])
return false;
}
return true;
}
for (var i in JSEvents.deferredCalls) {
var call = JSEvents.deferredCalls[i];
if (call.targetFunction == targetFunction && arraysHaveEqualContent(call.argsList, argsList)) {
return;
}
}
JSEvents.deferredCalls.push({targetFunction, precedence, argsList});
JSEvents.deferredCalls.sort(function(x, y) {
return x.precedence < y.precedence;
});
}, removeDeferredCalls: function(targetFunction) {
for (var i = 0; i < JSEvents.deferredCalls.length; ++i) {
if (JSEvents.deferredCalls[i].targetFunction == targetFunction) {
JSEvents.deferredCalls.splice(i, 1);
--i;
}
}
}, canPerformEventHandlerRequests: function() {
return JSEvents.inEventHandler && JSEvents.currentEventHandler.allowsDeferredCalls;
}, runDeferredCalls: function() {
if (!JSEvents.canPerformEventHandlerRequests()) {
return;
}
for (var i = 0; i < JSEvents.deferredCalls.length; ++i) {
var call = JSEvents.deferredCalls[i];
JSEvents.deferredCalls.splice(i, 1);
--i;
call.targetFunction.apply(null, call.argsList);
}
}, inEventHandler: 0, currentEventHandler: null, eventHandlers: [], removeAllHandlersOnTarget: function(target, eventTypeString) {
for (var i = 0; i < JSEvents.eventHandlers.length; ++i) {
if (JSEvents.eventHandlers[i].target == target && (!eventTypeString || eventTypeString == JSEvents.eventHandlers[i].eventTypeString)) {
JSEvents._removeHandler(i--);
}
}
}, _removeHandler: function(i) {
var h = JSEvents.eventHandlers[i];
h.target.removeEventListener(h.eventTypeString, h.eventListenerFunc, h.useCapture);
JSEvents.eventHandlers.splice(i, 1);
}, registerOrRemoveHandler: function(eventHandler) {
var jsEventHandler = function jsEventHandler2(event) {
++JSEvents.inEventHandler;
JSEvents.currentEventHandler = eventHandler;
JSEvents.runDeferredCalls();
eventHandler.handlerFunc(event);
JSEvents.runDeferredCalls();
--JSEvents.inEventHandler;
};
if (eventHandler.callbackfunc) {
eventHandler.eventListenerFunc = jsEventHandler;
eventHandler.target.addEventListener(eventHandler.eventTypeString, jsEventHandler, eventHandler.useCapture);
JSEvents.eventHandlers.push(eventHandler);
JSEvents.registerRemoveEventListeners();
} else {
for (var i = 0; i < JSEvents.eventHandlers.length; ++i) {
if (JSEvents.eventHandlers[i].target == eventHandler.target && JSEvents.eventHandlers[i].eventTypeString == eventHandler.eventTypeString) {
JSEvents._removeHandler(i--);
}
}
}
}, queueEventHandlerOnThread_iiii: function(targetThread, eventHandlerFunc, eventTypeId, eventData, userData) {
var stackTop = stackSave();
var varargs = stackAlloc(12);
GROWABLE_HEAP_I32()[varargs >> 2] = eventTypeId;
GROWABLE_HEAP_I32()[varargs + 4 >> 2] = eventData;
GROWABLE_HEAP_I32()[varargs + 8 >> 2] = userData;
_emscripten_async_queue_on_thread_(targetThread, 637534208, eventHandlerFunc, eventData, varargs);
stackRestore(stackTop);
}, getTargetThreadForEventCallback: function(targetThread) {
switch (targetThread) {
case 1:
return 0;
case 2:
return PThread.currentProxiedOperationCallerThread;
default:
return targetThread;
}
}, getNodeNameForTarget: function(target) {
if (!target)
return "";
if (target == window)
return "#window";
if (target == screen)
return "#screen";
return target && target.nodeName ? target.nodeName : "";
}, fullscreenEnabled: function() {
return document.fullscreenEnabled || document.webkitFullscreenEnabled;
}};
function stringToNewUTF8(jsString) {
var length = lengthBytesUTF8(jsString) + 1;
var cString = _malloc(length);
stringToUTF8(jsString, cString, length);
return cString;
}
function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread, targetCanvas, width, height) {
var stackTop = stackSave();
var varargs = stackAlloc(12);
var targetCanvasPtr = 0;
if (targetCanvas) {
targetCanvasPtr = stringToNewUTF8(targetCanvas);
}
GROWABLE_HEAP_I32()[varargs >> 2] = targetCanvasPtr;
GROWABLE_HEAP_I32()[varargs + 4 >> 2] = width;
GROWABLE_HEAP_I32()[varargs + 8 >> 2] = height;
_emscripten_async_queue_on_thread_(targetThread, 657457152, 0, targetCanvasPtr, varargs);
stackRestore(stackTop);
}
function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread, targetCanvas, width, height) {
targetCanvas = targetCanvas ? UTF8ToString(targetCanvas) : "";
_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread, targetCanvas, width, height);
}
function __maybeCStringToJsString(cString) {
return cString > 2 ? UTF8ToString(cString) : cString;
}
var specialHTMLTargets = [0, typeof document !== "undefined" ? document : 0, typeof window !== "undefined" ? window : 0];
function __findEventTarget(target) {
target = __maybeCStringToJsString(target);
var domElement = specialHTMLTargets[target] || (typeof document !== "undefined" ? document.querySelector(target) : void 0);
return domElement;
}
function __findCanvasEventTarget(target) {
return __findEventTarget(target);
}
function _emscripten_set_canvas_element_size_calling_thread(target, width, height) {
var canvas = __findCanvasEventTarget(target);
if (!canvas)
return -4;
if (canvas.canvasSharedPtr) {
GROWABLE_HEAP_I32()[canvas.canvasSharedPtr >> 2] = width;
GROWABLE_HEAP_I32()[canvas.canvasSharedPtr + 4 >> 2] = height;
}
if (canvas.offscreenCanvas || !canvas.controlTransferredOffscreen) {
if (canvas.offscreenCanvas)
canvas = canvas.offscreenCanvas;
var autoResizeViewport = false;
if (canvas.GLctxObject && canvas.GLctxObject.GLctx) {
var prevViewport = canvas.GLctxObject.GLctx.getParameter(2978);
autoResizeViewport = prevViewport[0] === 0 && prevViewport[1] === 0 && prevViewport[2] === canvas.width && prevViewport[3] === canvas.height;
}
canvas.width = width;
canvas.height = height;
if (autoResizeViewport) {
canvas.GLctxObject.GLctx.viewport(0, 0, width, height);
}
} else if (canvas.canvasSharedPtr) {
var targetThread = GROWABLE_HEAP_I32()[canvas.canvasSharedPtr + 8 >> 2];
_emscripten_set_offscreencanvas_size_on_target_thread(targetThread, target, width, height);
return 1;
} else {
return -4;
}
return 0;
}
function _emscripten_set_canvas_element_size_main_thread(target, width, height) {
if (ENVIRONMENT_IS_PTHREAD)
return _emscripten_proxy_to_main_thread_js(2, 1, target, width, height);
return _emscripten_set_canvas_element_size_calling_thread(target, width, height);
}
function _emscripten_set_canvas_element_size(target, width, height) {
var canvas = __findCanvasEventTarget(target);
if (canvas) {
return _emscripten_set_canvas_element_size_calling_thread(target, width, height);
} else {
return _emscripten_set_canvas_element_size_main_thread(target, width, height);
}
}
function _emscripten_set_current_thread_status(newStatus) {
newStatus = newStatus | 0;
}
function _emscripten_set_thread_name(threadId, name) {
threadId = threadId | 0;
name = name | 0;
}
function __webgl_enable_ANGLE_instanced_arrays(ctx) {
var ext = ctx.getExtension("ANGLE_instanced_arrays");
if (ext) {
ctx["vertexAttribDivisor"] = function(index, divisor) {
ext["vertexAttribDivisorANGLE"](index, divisor);
};
ctx["drawArraysInstanced"] = function(mode, first, count2, primcount) {
ext["drawArraysInstancedANGLE"](mode, first, count2, primcount);
};
ctx["drawElementsInstanced"] = function(mode, count2, type, indices, primcount) {
ext["drawElementsInstancedANGLE"](mode, count2, type, indices, primcount);
};
return 1;
}
}
function __webgl_enable_OES_vertex_array_object(ctx) {
var ext = ctx.getExtension("OES_vertex_array_object");
if (ext) {
ctx["createVertexArray"] = function() {
return ext["createVertexArrayOES"]();
};
ctx["deleteVertexArray"] = function(vao) {
ext["deleteVertexArrayOES"](vao);
};
ctx["bindVertexArray"] = function(vao) {
ext["bindVertexArrayOES"](vao);
};
ctx["isVertexArray"] = function(vao) {
return ext["isVertexArrayOES"](vao);
};
return 1;
}
}
function __webgl_enable_WEBGL_draw_buffers(ctx) {
var ext = ctx.getExtension("WEBGL_draw_buffers");
if (ext) {
ctx["drawBuffers"] = function(n, bufs) {
ext["drawBuffersWEBGL"](n, bufs);
};
return 1;
}
}
var GL = {counter: 1, lastError: 0, buffers: [], mappedBuffers: {}, programs: [], framebuffers: [], renderbuffers: [], textures: [], uniforms: [], shaders: [], vaos: [], contexts: {}, currentContext: null, offscreenCanvases: {}, timerQueriesEXT: [], programInfos: {}, stringCache: {}, unpackAlignment: 4, init: function() {
var miniTempFloatBuffer = new Float32Array(GL.MINI_TEMP_BUFFER_SIZE);
for (var i = 0; i < GL.MINI_TEMP_BUFFER_SIZE; i++) {
GL.miniTempBufferFloatViews[i] = miniTempFloatBuffer.subarray(0, i + 1);
}
var miniTempIntBuffer = new Int32Array(GL.MINI_TEMP_BUFFER_SIZE);
for (var i = 0; i < GL.MINI_TEMP_BUFFER_SIZE; i++) {
GL.miniTempBufferIntViews[i] = miniTempIntBuffer.subarray(0, i + 1);
}
}, recordError: function recordError(errorCode) {
if (!GL.lastError) {
GL.lastError = errorCode;
}
}, getNewId: function(table) {
var ret = GL.counter++;
for (var i = table.length; i < ret; i++) {
table[i] = null;
}
return ret;
}, MINI_TEMP_BUFFER_SIZE: 256, miniTempBufferFloatViews: [0], miniTempBufferIntViews: [0], getSource: function(shader, count2, string, length) {
var source = "";
for (var i = 0; i < count2; ++i) {
var len = length ? GROWABLE_HEAP_I32()[length + i * 4 >> 2] : -1;
source += UTF8ToString(GROWABLE_HEAP_I32()[string + i * 4 >> 2], len < 0 ? void 0 : len);
}
return source;
}, createContext: function(canvas, webGLContextAttributes) {
var ctx = canvas.getContext("webgl", webGLContextAttributes);
if (!ctx)
return 0;
var handle = GL.registerContext(ctx, webGLContextAttributes);
return handle;
}, registerContext: function(ctx, webGLContextAttributes) {
var handle = _malloc(8);
GROWABLE_HEAP_I32()[handle + 4 >> 2] = _pthread_self();
var context = {handle, attributes: webGLContextAttributes, version: webGLContextAttributes.majorVersion, GLctx: ctx};
if (ctx.canvas)
ctx.canvas.GLctxObject = context;
GL.contexts[handle] = context;
if (typeof webGLContextAttributes.enableExtensionsByDefault === "undefined" || webGLContextAttributes.enableExtensionsByDefault) {
GL.initExtensions(context);
}
return handle;
}, makeContextCurrent: function(contextHandle) {
GL.currentContext = GL.contexts[contextHandle];
Module.ctx = GLctx = GL.currentContext && GL.currentContext.GLctx;
return !(contextHandle && !GLctx);
}, getContext: function(contextHandle) {
return GL.contexts[contextHandle];
}, deleteContext: function(contextHandle) {
if (GL.currentContext === GL.contexts[contextHandle])
GL.currentContext = null;
if (typeof JSEvents === "object")
JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);
if (GL.contexts[contextHandle] && GL.contexts[contextHandle].GLctx.canvas)
GL.contexts[contextHandle].GLctx.canvas.GLctxObject = void 0;
_free(GL.contexts[contextHandle].handle);
GL.contexts[contextHandle] = null;
}, initExtensions: function(context) {
if (!context)
context = GL.currentContext;
if (context.initExtensionsDone)
return;
context.initExtensionsDone = true;
var GLctx2 = context.GLctx;
__webgl_enable_ANGLE_instanced_arrays(GLctx2);
__webgl_enable_OES_vertex_array_object(GLctx2);
__webgl_enable_WEBGL_draw_buffers(GLctx2);
GLctx2.disjointTimerQueryExt = GLctx2.getExtension("EXT_disjoint_timer_query");
var automaticallyEnabledExtensions = ["OES_texture_float", "OES_texture_half_float", "OES_standard_derivatives", "OES_vertex_array_object", "WEBGL_compressed_texture_s3tc", "WEBGL_depth_texture", "OES_element_index_uint", "EXT_texture_filter_anisotropic", "EXT_frag_depth", "WEBGL_draw_buffers", "ANGLE_instanced_arrays", "OES_texture_float_linear", "OES_texture_half_float_linear", "EXT_blend_minmax", "EXT_shader_texture_lod", "EXT_texture_norm16", "WEBGL_compressed_texture_pvrtc", "EXT_color_buffer_half_float", "WEBGL_color_buffer_float", "EXT_sRGB", "WEBGL_compressed_texture_etc1", "EXT_disjoint_timer_query", "WEBGL_compressed_texture_etc", "WEBGL_compressed_texture_astc", "EXT_color_buffer_float", "WEBGL_compressed_texture_s3tc_srgb", "EXT_disjoint_timer_query_webgl2", "WEBKIT_WEBGL_compressed_texture_pvrtc"];
var exts = GLctx2.getSupportedExtensions() || [];
exts.forEach(function(ext) {
if (automaticallyEnabledExtensions.indexOf(ext) != -1) {
GLctx2.getExtension(ext);
}
});
}, populateUniformTable: function(program) {
var p = GL.programs[program];
var ptable = GL.programInfos[program] = {uniforms: {}, maxUniformLength: 0, maxAttributeLength: -1, maxUniformBlockNameLength: -1};
var utable = ptable.uniforms;
var numUniforms = GLctx.getProgramParameter(p, 35718);
for (var i = 0; i < numUniforms; ++i) {
var u = GLctx.getActiveUniform(p, i);
var name = u.name;
ptable.maxUniformLength = Math.max(ptable.maxUniformLength, name.length + 1);
if (name.slice(-1) == "]") {
name = name.slice(0, name.lastIndexOf("["));
}
var loc = GLctx.getUniformLocation(p, name);
if (loc) {
var id = GL.getNewId(GL.uniforms);
utable[name] = [u.size, id];
GL.uniforms[id] = loc;
for (var j = 1; j < u.size; ++j) {
var n = name + "[" + j + "]";
loc = GLctx.getUniformLocation(p, n);
id = GL.getNewId(GL.uniforms);
GL.uniforms[id] = loc;
}
}
}
}};
var __emscripten_webgl_power_preferences = ["default", "low-power", "high-performance"];
function _emscripten_webgl_do_create_context(target, attributes) {
var contextAttributes = {};
var a = attributes >> 2;
contextAttributes["alpha"] = !!GROWABLE_HEAP_I32()[a + (0 >> 2)];
contextAttributes["depth"] = !!GROWABLE_HEAP_I32()[a + (4 >> 2)];
contextAttributes["stencil"] = !!GROWABLE_HEAP_I32()[a + (8 >> 2)];
contextAttributes["antialias"] = !!GROWABLE_HEAP_I32()[a + (12 >> 2)];
contextAttributes["premultipliedAlpha"] = !!GROWABLE_HEAP_I32()[a + (16 >> 2)];
contextAttributes["preserveDrawingBuffer"] = !!GROWABLE_HEAP_I32()[a + (20 >> 2)];
var powerPreference = GROWABLE_HEAP_I32()[a + (24 >> 2)];
contextAttributes["powerPreference"] = __emscripten_webgl_power_preferences[powerPreference];
contextAttributes["failIfMajorPerformanceCaveat"] = !!GROWABLE_HEAP_I32()[a + (28 >> 2)];
contextAttributes.majorVersion = GROWABLE_HEAP_I32()[a + (32 >> 2)];
contextAttributes.minorVersion = GROWABLE_HEAP_I32()[a + (36 >> 2)];
contextAttributes.enableExtensionsByDefault = GROWABLE_HEAP_I32()[a + (40 >> 2)];
contextAttributes.explicitSwapControl = GROWABLE_HEAP_I32()[a + (44 >> 2)];
contextAttributes.proxyContextToMainThread = GROWABLE_HEAP_I32()[a + (48 >> 2)];
contextAttributes.renderViaOffscreenBackBuffer = GROWABLE_HEAP_I32()[a + (52 >> 2)];
var canvas = __findCanvasEventTarget(target);
if (!canvas) {
return -4;
}
if (contextAttributes.explicitSwapControl) {
return -1;
}
var contextHandle = GL.createContext(canvas, contextAttributes);
return contextHandle;
}
function _emscripten_webgl_create_context(a0, a1) {
return _emscripten_webgl_do_create_context(a0, a1);
}
var PATH = {splitPath: function(filename) {
var splitPathRe = /^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;
return splitPathRe.exec(filename).slice(1);
}, normalizeArray: function(parts, allowAboveRoot) {
var up = 0;
for (var i = parts.length - 1; i >= 0; i--) {
var last = parts[i];
if (last === ".") {
parts.splice(i, 1);
} else if (last === "..") {
parts.splice(i, 1);
up++;
} else if (up) {
parts.splice(i, 1);
up--;
}
}
if (allowAboveRoot) {
for (; up; up--) {
parts.unshift("..");
}
}
return parts;
}, normalize: function(path) {
var isAbsolute = path.charAt(0) === "/", trailingSlash = path.substr(-1) === "/";
path = PATH.normalizeArray(path.split("/").filter(function(p) {
return !!p;
}), !isAbsolute).join("/");
if (!path && !isAbsolute) {
path = ".";
}
if (path && trailingSlash) {
path += "/";
}
return (isAbsolute ? "/" : "") + path;
}, dirname: function(path) {
var result = PATH.splitPath(path), root = result[0], dir = result[1];
if (!root && !dir) {
return ".";
}
if (dir) {
dir = dir.substr(0, dir.length - 1);
}
return root + dir;
}, basename: function(path) {
if (path === "/")
return "/";
var lastSlash = path.lastIndexOf("/");
if (lastSlash === -1)
return path;
return path.substr(lastSlash + 1);
}, extname: function(path) {
return PATH.splitPath(path)[3];
}, join: function() {
var paths = Array.prototype.slice.call(arguments, 0);
return PATH.normalize(paths.join("/"));
}, join2: function(l, r) {
return PATH.normalize(l + "/" + r);
}};
var SYSCALLS = {mappings: {}, buffers: [null, [], []], printChar: function(stream, curr) {
var buffer11 = SYSCALLS.buffers[stream];
if (curr === 0 || curr === 10) {
(stream === 1 ? out : err)(UTF8ArrayToString(buffer11, 0));
buffer11.length = 0;
} else {
buffer11.push(curr);
}
}, varargs: void 0, get: function() {
SYSCALLS.varargs += 4;
var ret = GROWABLE_HEAP_I32()[SYSCALLS.varargs - 4 >> 2];
return ret;
}, getStr: function(ptr) {
var ret = UTF8ToString(ptr);
return ret;
}, get64: function(low, high) {
return low;
}};
function _fd_close(fd) {
if (ENVIRONMENT_IS_PTHREAD)
return _emscripten_proxy_to_main_thread_js(3, 1, fd);
return 0;
}
function _fd_seek(fd, offset_low, offset_high, whence, newOffset) {
if (ENVIRONMENT_IS_PTHREAD)
return _emscripten_proxy_to_main_thread_js(4, 1, fd, offset_low, offset_high, whence, newOffset);
}
function _fd_write(fd, iov, iovcnt, pnum) {
if (ENVIRONMENT_IS_PTHREAD)
return _emscripten_proxy_to_main_thread_js(5, 1, fd, iov, iovcnt, pnum);
var num = 0;
for (var i = 0; i < iovcnt; i++) {
var ptr = GROWABLE_HEAP_I32()[iov + i * 8 >> 2];
var len = GROWABLE_HEAP_I32()[iov + (i * 8 + 4) >> 2];
for (var j = 0; j < len; j++) {
SYSCALLS.printChar(fd, GROWABLE_HEAP_U8()[ptr + j]);
}
num += len;
}
GROWABLE_HEAP_I32()[pnum >> 2] = num;
return 0;
}
function _pthread_cleanup_pop(execute2) {
var routine = PThread.exitHandlers.pop();
if (execute2)
routine();
}
function _pthread_cleanup_push(routine, arg) {
if (PThread.exitHandlers === null) {
PThread.exitHandlers = [];
}
PThread.exitHandlers.push(function() {
dynCall_vi(routine, arg);
});
}
function __spawn_thread(threadParams) {
if (ENVIRONMENT_IS_PTHREAD)
throw "Internal Error! _spawn_thread() can only ever be called from main application thread!";
var worker = PThread.getNewWorker();
if (worker.pthread !== void 0)
throw "Internal error!";
if (!threadParams.pthread_ptr)
throw "Internal error, no pthread ptr!";
PThread.runningWorkers.push(worker);
var tlsMemory = _malloc(128 * 4);
for (var i = 0; i < 128; ++i) {
GROWABLE_HEAP_I32()[tlsMemory + i * 4 >> 2] = 0;
}
var stackHigh = threadParams.stackBase + threadParams.stackSize;
var pthread = PThread.pthreads[threadParams.pthread_ptr] = {worker, stackBase: threadParams.stackBase, stackSize: threadParams.stackSize, allocatedOwnStack: threadParams.allocatedOwnStack, thread: threadParams.pthread_ptr, threadInfoStruct: threadParams.pthread_ptr};
var tis = pthread.threadInfoStruct >> 2;
Atomics.store(GROWABLE_HEAP_U32(), tis + (0 >> 2), 0);
Atomics.store(GROWABLE_HEAP_U32(), tis + (4 >> 2), 0);
Atomics.store(GROWABLE_HEAP_U32(), tis + (8 >> 2), 0);
Atomics.store(GROWABLE_HEAP_U32(), tis + (68 >> 2), threadParams.detached);
Atomics.store(GROWABLE_HEAP_U32(), tis + (104 >> 2), tlsMemory);
Atomics.store(GROWABLE_HEAP_U32(), tis + (48 >> 2), 0);
Atomics.store(GROWABLE_HEAP_U32(), tis + (40 >> 2), pthread.threadInfoStruct);
Atomics.store(GROWABLE_HEAP_U32(), tis + (44 >> 2), 42);
Atomics.store(GROWABLE_HEAP_U32(), tis + (108 >> 2), threadParams.stackSize);
Atomics.store(GROWABLE_HEAP_U32(), tis + (84 >> 2), threadParams.stackSize);
Atomics.store(GROWABLE_HEAP_U32(), tis + (80 >> 2), stackHigh);
Atomics.store(GROWABLE_HEAP_U32(), tis + (108 + 8 >> 2), stackHigh);
Atomics.store(GROWABLE_HEAP_U32(), tis + (108 + 12 >> 2), threadParams.detached);
Atomics.store(GROWABLE_HEAP_U32(), tis + (108 + 20 >> 2), threadParams.schedPolicy);
Atomics.store(GROWABLE_HEAP_U32(), tis + (108 + 24 >> 2), threadParams.schedPrio);
var global_libc = _emscripten_get_global_libc();
var global_locale = global_libc + 40;
Atomics.store(GROWABLE_HEAP_U32(), tis + (176 >> 2), global_locale);
worker.pthread = pthread;
var msg = {cmd: "run", start_routine: threadParams.startRoutine, arg: threadParams.arg, threadInfoStruct: threadParams.pthread_ptr, selfThreadId: threadParams.pthread_ptr, parentThreadId: threadParams.parent_pthread_ptr, stackBase: threadParams.stackBase, stackSize: threadParams.stackSize};
worker.runPthread = function() {
msg.time = performance.now();
worker.postMessage(msg, threadParams.transferList);
};
if (worker.loaded) {
worker.runPthread();
delete worker.runPthread;
}
}
function _pthread_getschedparam(thread, policy, schedparam) {
if (!policy && !schedparam)
return ERRNO_CODES.EINVAL;
if (!thread) {
err("pthread_getschedparam called with a null thread pointer!");
return ERRNO_CODES.ESRCH;
}
var self2 = GROWABLE_HEAP_I32()[thread + 12 >> 2];
if (self2 !== thread) {
err("pthread_getschedparam attempted on thread " + thread + ", which does not point to a valid thread, or does not exist anymore!");
return ERRNO_CODES.ESRCH;
}
var schedPolicy = Atomics.load(GROWABLE_HEAP_U32(), thread + 108 + 20 >> 2);
var schedPrio = Atomics.load(GROWABLE_HEAP_U32(), thread + 108 + 24 >> 2);
if (policy)
GROWABLE_HEAP_I32()[policy >> 2] = schedPolicy;
if (schedparam)
GROWABLE_HEAP_I32()[schedparam >> 2] = schedPrio;
return 0;
}
function _pthread_self() {
return __pthread_ptr | 0;
}
Module["_pthread_self"] = _pthread_self;
function _pthread_create(pthread_ptr, attr, start_routine, arg) {
if (typeof SharedArrayBuffer === "undefined") {
err("Current environment does not support SharedArrayBuffer, pthreads are not available!");
return 6;
}
if (!pthread_ptr) {
err("pthread_create called with a null thread pointer!");
return 28;
}
var transferList = [];
var error = 0;
if (ENVIRONMENT_IS_PTHREAD && (transferList.length === 0 || error)) {
return _emscripten_sync_run_in_main_thread_4(687865856, pthread_ptr, attr, start_routine, arg);
}
if (error)
return error;
var stackSize = 0;
var stackBase = 0;
var detached = 0;
var schedPolicy = 0;
var schedPrio = 0;
if (attr) {
stackSize = GROWABLE_HEAP_I32()[attr >> 2];
stackSize += 81920;
stackBase = GROWABLE_HEAP_I32()[attr + 8 >> 2];
detached = GROWABLE_HEAP_I32()[attr + 12 >> 2] !== 0;
var inheritSched = GROWABLE_HEAP_I32()[attr + 16 >> 2] === 0;
if (inheritSched) {
var prevSchedPolicy = GROWABLE_HEAP_I32()[attr + 20 >> 2];
var prevSchedPrio = GROWABLE_HEAP_I32()[attr + 24 >> 2];
var parentThreadPtr = PThread.currentProxiedOperationCallerThread ? PThread.currentProxiedOperationCallerThread : _pthread_self();
_pthread_getschedparam(parentThreadPtr, attr + 20, attr + 24);
schedPolicy = GROWABLE_HEAP_I32()[attr + 20 >> 2];
schedPrio = GROWABLE_HEAP_I32()[attr + 24 >> 2];
GROWABLE_HEAP_I32()[attr + 20 >> 2] = prevSchedPolicy;
GROWABLE_HEAP_I32()[attr + 24 >> 2] = prevSchedPrio;
} else {
schedPolicy = GROWABLE_HEAP_I32()[attr + 20 >> 2];
schedPrio = GROWABLE_HEAP_I32()[attr + 24 >> 2];
}
} else {
stackSize = 2097152;
}
var allocatedOwnStack = stackBase == 0;
if (allocatedOwnStack) {
stackBase = _memalign(16, stackSize);
} else {
stackBase -= stackSize;
assert3(stackBase > 0);
}
var threadInfoStruct2 = _malloc(232);
for (var i = 0; i < 232 >> 2; ++i)
GROWABLE_HEAP_U32()[(threadInfoStruct2 >> 2) + i] = 0;
GROWABLE_HEAP_I32()[pthread_ptr >> 2] = threadInfoStruct2;
GROWABLE_HEAP_I32()[threadInfoStruct2 + 12 >> 2] = threadInfoStruct2;
var headPtr = threadInfoStruct2 + 156;
GROWABLE_HEAP_I32()[headPtr >> 2] = headPtr;
var threadParams = {stackBase, stackSize, allocatedOwnStack, schedPolicy, schedPrio, detached, startRoutine: start_routine, pthread_ptr: threadInfoStruct2, parent_pthread_ptr: _pthread_self(), arg, transferList};
if (ENVIRONMENT_IS_PTHREAD) {
threadParams.cmd = "spawnThread";
postMessage(threadParams, transferList);
} else {
__spawn_thread(threadParams);
}
return 0;
}
function _roundf(d) {
d = +d;
return d >= 0 ? +Math_floor(d + 0.5) : +Math_ceil(d - 0.5);
}
function _sysconf(name) {
if (ENVIRONMENT_IS_PTHREAD)
return _emscripten_proxy_to_main_thread_js(6, 1, name);
switch (name) {
case 30:
return 16384;
case 85:
var maxHeapSize = 2147483648;
return maxHeapSize / 16384;
case 132:
case 133:
case 12:
case 137:
case 138:
case 15:
case 235:
case 16:
case 17:
case 18:
case 19:
case 20:
case 149:
case 13:
case 10:
case 236:
case 153:
case 9:
case 21:
case 22:
case 159:
case 154:
case 14:
case 77:
case 78:
case 139:
case 80:
case 81:
case 82:
case 68:
case 67:
case 164:
case 11:
case 29:
case 47:
case 48:
case 95:
case 52:
case 51:
case 46:
case 79:
return 200809;
case 27:
case 246:
case 127:
case 128:
case 23:
case 24:
case 160:
case 161:
case 181:
case 182:
case 242:
case 183:
case 184:
case 243:
case 244:
case 245:
case 165:
case 178:
case 179:
case 49:
case 50:
case 168:
case 169:
case 175:
case 170:
case 171:
case 172:
case 97:
case 76:
case 32:
case 173:
case 35:
return -1;
case 176:
case 177:
case 7:
case 155:
case 8:
case 157:
case 125:
case 126:
case 92:
case 93:
case 129:
case 130:
case 131:
case 94:
case 91:
return 1;
case 74:
case 60:
case 69:
case 70:
case 4:
return 1024;
case 31:
case 42:
case 72:
return 32;
case 87:
case 26:
case 33:
return 2147483647;
case 34:
case 1:
return 47839;
case 38:
case 36:
return 99;
case 43:
case 37:
return 2048;
case 0:
return 2097152;
case 3:
return 65536;
case 28:
return 32768;
case 44:
return 32767;
case 75:
return 16384;
case 39:
return 1e3;
case 89:
return 700;
case 71:
return 256;
case 40:
return 255;
case 2:
return 100;
case 180:
return 64;
case 25:
return 20;
case 5:
return 16;
case 6:
return 6;
case 73:
return 4;
case 84: {
if (typeof navigator === "object")
return navigator["hardwareConcurrency"] || 1;
return 1;
}
}
setErrNo(28);
return -1;
}
if (!ENVIRONMENT_IS_PTHREAD)
PThread.initMainThreadBlock();
else
PThread.initWorker();
var GLctx;
GL.init();
var proxiedFunctionTable = [null, _atexit, _emscripten_set_canvas_element_size_main_thread, _fd_close, _fd_seek, _fd_write, _sysconf];
var asmLibraryArg = {e: ___assert_fail, r: ___call_main, w: __emscripten_notify_thread_queue, a: _abort, l: _emscripten_conditional_set_current_thread_status, d: _emscripten_futex_wait, c: _emscripten_futex_wake, h: _emscripten_get_now, g: _emscripten_is_main_browser_thread, x: _emscripten_is_main_runtime_thread, q: _emscripten_memcpy_big, B: _emscripten_num_logical_cores, t: _emscripten_receive_on_main_thread_js, A: _emscripten_resize_heap, u: _emscripten_set_canvas_element_size, k: _emscripten_set_current_thread_status, s: _emscripten_set_thread_name, v: _emscripten_webgl_create_context, m: _fd_close, o: _fd_seek, i: _fd_write, p: initPthreadsJS, memory: wasmMemory || Module["wasmMemory"], y: _pthread_cleanup_pop, z: _pthread_cleanup_push, j: _pthread_create, b: _pthread_self, f: _roundf, n: _sysconf, table: wasmTable};
var asm = createWasm();
Module["asm"] = asm;
var ___wasm_call_ctors = Module["___wasm_call_ctors"] = function() {
return (___wasm_call_ctors = Module["___wasm_call_ctors"] = Module["asm"]["C"]).apply(null, arguments);
};
var _init = Module["_init"] = function() {
return (_init = Module["_init"] = Module["asm"]["D"]).apply(null, arguments);
};
var _register_tensor = Module["_register_tensor"] = function() {
return (_register_tensor = Module["_register_tensor"] = Module["asm"]["E"]).apply(null, arguments);
};
var _dispose_data = Module["_dispose_data"] = function() {
return (_dispose_data = Module["_dispose_data"] = Module["asm"]["F"]).apply(null, arguments);
};
var _dispose = Module["_dispose"] = function() {
return (_dispose = Module["_dispose"] = Module["asm"]["G"]).apply(null, arguments);
};
var _Abs = Module["_Abs"] = function() {
return (_Abs = Module["_Abs"] = Module["asm"]["H"]).apply(null, arguments);
};
var _Add = Module["_Add"] = function() {
return (_Add = Module["_Add"] = Module["asm"]["I"]).apply(null, arguments);
};
var _AddN = Module["_AddN"] = function() {
return (_AddN = Module["_AddN"] = Module["asm"]["J"]).apply(null, arguments);
};
var _ArgMax = Module["_ArgMax"] = function() {
return (_ArgMax = Module["_ArgMax"] = Module["asm"]["K"]).apply(null, arguments);
};
var _AvgPool = Module["_AvgPool"] = function() {
return (_AvgPool = Module["_AvgPool"] = Module["asm"]["L"]).apply(null, arguments);
};
var _BatchMatMul = Module["_BatchMatMul"] = function() {
return (_BatchMatMul = Module["_BatchMatMul"] = Module["asm"]["M"]).apply(null, arguments);
};
var _ClipByValue = Module["_ClipByValue"] = function() {
return (_ClipByValue = Module["_ClipByValue"] = Module["asm"]["N"]).apply(null, arguments);
};
var _Conv2D = Module["_Conv2D"] = function() {
return (_Conv2D = Module["_Conv2D"] = Module["asm"]["O"]).apply(null, arguments);
};
var _Conv2DBackpropInput = Module["_Conv2DBackpropInput"] = function() {
return (_Conv2DBackpropInput = Module["_Conv2DBackpropInput"] = Module["asm"]["P"]).apply(null, arguments);
};
var _Cos = Module["_Cos"] = function() {
return (_Cos = Module["_Cos"] = Module["asm"]["Q"]).apply(null, arguments);
};
var _CropAndResize = Module["_CropAndResize"] = function() {
return (_CropAndResize = Module["_CropAndResize"] = Module["asm"]["R"]).apply(null, arguments);
};
var _Cumsum = Module["_Cumsum"] = function() {
return (_Cumsum = Module["_Cumsum"] = Module["asm"]["S"]).apply(null, arguments);
};
var _DepthToSpace = Module["_DepthToSpace"] = function() {
return (_DepthToSpace = Module["_DepthToSpace"] = Module["asm"]["T"]).apply(null, arguments);
};
var _DepthwiseConv2dNative = Module["_DepthwiseConv2dNative"] = function() {
return (_DepthwiseConv2dNative = Module["_DepthwiseConv2dNative"] = Module["asm"]["U"]).apply(null, arguments);
};
var _Div = Module["_Div"] = function() {
return (_Div = Module["_Div"] = Module["asm"]["V"]).apply(null, arguments);
};
var _Equal = Module["_Equal"] = function() {
return (_Equal = Module["_Equal"] = Module["asm"]["W"]).apply(null, arguments);
};
var _Exp = Module["_Exp"] = function() {
return (_Exp = Module["_Exp"] = Module["asm"]["X"]).apply(null, arguments);
};
var _FlipLeftRight = Module["_FlipLeftRight"] = function() {
return (_FlipLeftRight = Module["_FlipLeftRight"] = Module["asm"]["Y"]).apply(null, arguments);
};
var _FloorDiv = Module["_FloorDiv"] = function() {
return (_FloorDiv = Module["_FloorDiv"] = Module["asm"]["Z"]).apply(null, arguments);
};
var _FusedBatchNorm = Module["_FusedBatchNorm"] = function() {
return (_FusedBatchNorm = Module["_FusedBatchNorm"] = Module["asm"]["_"]).apply(null, arguments);
};
var _FusedConv2D = Module["_FusedConv2D"] = function() {
return (_FusedConv2D = Module["_FusedConv2D"] = Module["asm"]["$"]).apply(null, arguments);
};
var _FusedDepthwiseConv2D = Module["_FusedDepthwiseConv2D"] = function() {
return (_FusedDepthwiseConv2D = Module["_FusedDepthwiseConv2D"] = Module["asm"]["aa"]).apply(null, arguments);
};
var _Gather = Module["_Gather"] = function() {
return (_Gather = Module["_Gather"] = Module["asm"]["ba"]).apply(null, arguments);
};
var _GatherNd = Module["_GatherNd"] = function() {
return (_GatherNd = Module["_GatherNd"] = Module["asm"]["ca"]).apply(null, arguments);
};
var _Greater = Module["_Greater"] = function() {
return (_Greater = Module["_Greater"] = Module["asm"]["da"]).apply(null, arguments);
};
var _GreaterEqual = Module["_GreaterEqual"] = function() {
return (_GreaterEqual = Module["_GreaterEqual"] = Module["asm"]["ea"]).apply(null, arguments);
};
var _Less = Module["_Less"] = function() {
return (_Less = Module["_Less"] = Module["asm"]["fa"]).apply(null, arguments);
};
var _LessEqual = Module["_LessEqual"] = function() {
return (_LessEqual = Module["_LessEqual"] = Module["asm"]["ga"]).apply(null, arguments);
};
var _Log = Module["_Log"] = function() {
return (_Log = Module["_Log"] = Module["asm"]["ha"]).apply(null, arguments);
};
var _LogicalAnd = Module["_LogicalAnd"] = function() {
return (_LogicalAnd = Module["_LogicalAnd"] = Module["asm"]["ia"]).apply(null, arguments);
};
var _Max = Module["_Max"] = function() {
return (_Max = Module["_Max"] = Module["asm"]["ja"]).apply(null, arguments);
};
var _MaxPool = Module["_MaxPool"] = function() {
return (_MaxPool = Module["_MaxPool"] = Module["asm"]["ka"]).apply(null, arguments);
};
var _Maximum = Module["_Maximum"] = function() {
return (_Maximum = Module["_Maximum"] = Module["asm"]["la"]).apply(null, arguments);
};
var _Min = Module["_Min"] = function() {
return (_Min = Module["_Min"] = Module["asm"]["ma"]).apply(null, arguments);
};
var _Minimum = Module["_Minimum"] = function() {
return (_Minimum = Module["_Minimum"] = Module["asm"]["na"]).apply(null, arguments);
};
var _Multiply = Module["_Multiply"] = function() {
return (_Multiply = Module["_Multiply"] = Module["asm"]["oa"]).apply(null, arguments);
};
var _Negate = Module["_Negate"] = function() {
return (_Negate = Module["_Negate"] = Module["asm"]["pa"]).apply(null, arguments);
};
var _NonMaxSuppressionV3 = Module["_NonMaxSuppressionV3"] = function() {
return (_NonMaxSuppressionV3 = Module["_NonMaxSuppressionV3"] = Module["asm"]["qa"]).apply(null, arguments);
};
var _NonMaxSuppressionV4 = Module["_NonMaxSuppressionV4"] = function() {
return (_NonMaxSuppressionV4 = Module["_NonMaxSuppressionV4"] = Module["asm"]["ra"]).apply(null, arguments);
};
var _NonMaxSuppressionV5 = Module["_NonMaxSuppressionV5"] = function() {
return (_NonMaxSuppressionV5 = Module["_NonMaxSuppressionV5"] = Module["asm"]["sa"]).apply(null, arguments);
};
var _NotEqual = Module["_NotEqual"] = function() {
return (_NotEqual = Module["_NotEqual"] = Module["asm"]["ta"]).apply(null, arguments);
};
var _OneHot = Module["_OneHot"] = function() {
return (_OneHot = Module["_OneHot"] = Module["asm"]["ua"]).apply(null, arguments);
};
var _PadV2 = Module["_PadV2"] = function() {
return (_PadV2 = Module["_PadV2"] = Module["asm"]["va"]).apply(null, arguments);
};
var _Pow = Module["_Pow"] = function() {
return (_Pow = Module["_Pow"] = Module["asm"]["wa"]).apply(null, arguments);
};
var _Prelu = Module["_Prelu"] = function() {
return (_Prelu = Module["_Prelu"] = Module["asm"]["xa"]).apply(null, arguments);
};
var _Relu = Module["_Relu"] = function() {
return (_Relu = Module["_Relu"] = Module["asm"]["ya"]).apply(null, arguments);
};
var _Relu6 = Module["_Relu6"] = function() {
return (_Relu6 = Module["_Relu6"] = Module["asm"]["za"]).apply(null, arguments);
};
var _ResizeBilinear = Module["_ResizeBilinear"] = function() {
return (_ResizeBilinear = Module["_ResizeBilinear"] = Module["asm"]["Aa"]).apply(null, arguments);
};
var _Reverse = Module["_Reverse"] = function() {
return (_Reverse = Module["_Reverse"] = Module["asm"]["Ba"]).apply(null, arguments);
};
var _RotateWithOffset = Module["_RotateWithOffset"] = function() {
return (_RotateWithOffset = Module["_RotateWithOffset"] = Module["asm"]["Ca"]).apply(null, arguments);
};
var _Rsqrt = Module["_Rsqrt"] = function() {
return (_Rsqrt = Module["_Rsqrt"] = Module["asm"]["Da"]).apply(null, arguments);
};
var _ScatterNd = Module["_ScatterNd"] = function() {
return (_ScatterNd = Module["_ScatterNd"] = Module["asm"]["Ea"]).apply(null, arguments);
};
var _SelectV2 = Module["_SelectV2"] = function() {
return (_SelectV2 = Module["_SelectV2"] = Module["asm"]["Fa"]).apply(null, arguments);
};
var _Sigmoid = Module["_Sigmoid"] = function() {
return (_Sigmoid = Module["_Sigmoid"] = Module["asm"]["Ga"]).apply(null, arguments);
};
var _Sin = Module["_Sin"] = function() {
return (_Sin = Module["_Sin"] = Module["asm"]["Ha"]).apply(null, arguments);
};
var _Softmax = Module["_Softmax"] = function() {
return (_Softmax = Module["_Softmax"] = Module["asm"]["Ia"]).apply(null, arguments);
};
var _Sqrt = Module["_Sqrt"] = function() {
return (_Sqrt = Module["_Sqrt"] = Module["asm"]["Ja"]).apply(null, arguments);
};
var _Square = Module["_Square"] = function() {
return (_Square = Module["_Square"] = Module["asm"]["Ka"]).apply(null, arguments);
};
var _SquaredDifference = Module["_SquaredDifference"] = function() {
return (_SquaredDifference = Module["_SquaredDifference"] = Module["asm"]["La"]).apply(null, arguments);
};
var _StridedSlice = Module["_StridedSlice"] = function() {
return (_StridedSlice = Module["_StridedSlice"] = Module["asm"]["Ma"]).apply(null, arguments);
};
var _Sub = Module["_Sub"] = function() {
return (_Sub = Module["_Sub"] = Module["asm"]["Na"]).apply(null, arguments);
};
var _Sum = Module["_Sum"] = function() {
return (_Sum = Module["_Sum"] = Module["asm"]["Oa"]).apply(null, arguments);
};
var _Tanh = Module["_Tanh"] = function() {
return (_Tanh = Module["_Tanh"] = Module["asm"]["Pa"]).apply(null, arguments);
};
var _Tile = Module["_Tile"] = function() {
return (_Tile = Module["_Tile"] = Module["asm"]["Qa"]).apply(null, arguments);
};
var _Transpose = Module["_Transpose"] = function() {
return (_Transpose = Module["_Transpose"] = Module["asm"]["Ra"]).apply(null, arguments);
};
var __FusedMatMul = Module["__FusedMatMul"] = function() {
return (__FusedMatMul = Module["__FusedMatMul"] = Module["asm"]["Sa"]).apply(null, arguments);
};
var _malloc = Module["_malloc"] = function() {
return (_malloc = Module["_malloc"] = Module["asm"]["Ta"]).apply(null, arguments);
};
var _free = Module["_free"] = function() {
return (_free = Module["_free"] = Module["asm"]["Ua"]).apply(null, arguments);
};
var _emscripten_get_global_libc = Module["_emscripten_get_global_libc"] = function() {
return (_emscripten_get_global_libc = Module["_emscripten_get_global_libc"] = Module["asm"]["Va"]).apply(null, arguments);
};
var ___errno_location = Module["___errno_location"] = function() {
return (___errno_location = Module["___errno_location"] = Module["asm"]["Wa"]).apply(null, arguments);
};
var ___em_js__initPthreadsJS = Module["___em_js__initPthreadsJS"] = function() {
return (___em_js__initPthreadsJS = Module["___em_js__initPthreadsJS"] = Module["asm"]["Xa"]).apply(null, arguments);
};
var _memalign = Module["_memalign"] = function() {
return (_memalign = Module["_memalign"] = Module["asm"]["Ya"]).apply(null, arguments);
};
var ___pthread_tsd_run_dtors = Module["___pthread_tsd_run_dtors"] = function() {
return (___pthread_tsd_run_dtors = Module["___pthread_tsd_run_dtors"] = Module["asm"]["Za"]).apply(null, arguments);
};
var _emscripten_main_thread_process_queued_calls = Module["_emscripten_main_thread_process_queued_calls"] = function() {
return (_emscripten_main_thread_process_queued_calls = Module["_emscripten_main_thread_process_queued_calls"] = Module["asm"]["_a"]).apply(null, arguments);
};
var _emscripten_current_thread_process_queued_calls = Module["_emscripten_current_thread_process_queued_calls"] = function() {
return (_emscripten_current_thread_process_queued_calls = Module["_emscripten_current_thread_process_queued_calls"] = Module["asm"]["$a"]).apply(null, arguments);
};
var _emscripten_register_main_browser_thread_id = Module["_emscripten_register_main_browser_thread_id"] = function() {
return (_emscripten_register_main_browser_thread_id = Module["_emscripten_register_main_browser_thread_id"] = Module["asm"]["ab"]).apply(null, arguments);
};
var _emscripten_main_browser_thread_id = Module["_emscripten_main_browser_thread_id"] = function() {
return (_emscripten_main_browser_thread_id = Module["_emscripten_main_browser_thread_id"] = Module["asm"]["bb"]).apply(null, arguments);
};
var _emscripten_async_run_in_main_thread = Module["_emscripten_async_run_in_main_thread"] = function() {
return (_emscripten_async_run_in_main_thread = Module["_emscripten_async_run_in_main_thread"] = Module["asm"]["cb"]).apply(null, arguments);
};
var _emscripten_sync_run_in_main_thread = Module["_emscripten_sync_run_in_main_thread"] = function() {
return (_emscripten_sync_run_in_main_thread = Module["_emscripten_sync_run_in_main_thread"] = Module["asm"]["db"]).apply(null, arguments);
};
var _emscripten_sync_run_in_main_thread_0 = Module["_emscripten_sync_run_in_main_thread_0"] = function() {
return (_emscripten_sync_run_in_main_thread_0 = Module["_emscripten_sync_run_in_main_thread_0"] = Module["asm"]["eb"]).apply(null, arguments);
};
var _emscripten_sync_run_in_main_thread_1 = Module["_emscripten_sync_run_in_main_thread_1"] = function() {
return (_emscripten_sync_run_in_main_thread_1 = Module["_emscripten_sync_run_in_main_thread_1"] = Module["asm"]["fb"]).apply(null, arguments);
};
var _emscripten_sync_run_in_main_thread_2 = Module["_emscripten_sync_run_in_main_thread_2"] = function() {
return (_emscripten_sync_run_in_main_thread_2 = Module["_emscripten_sync_run_in_main_thread_2"] = Module["asm"]["gb"]).apply(null, arguments);
};
var _emscripten_sync_run_in_main_thread_xprintf_varargs = Module["_emscripten_sync_run_in_main_thread_xprintf_varargs"] = function() {
return (_emscripten_sync_run_in_main_thread_xprintf_varargs = Module["_emscripten_sync_run_in_main_thread_xprintf_varargs"] = Module["asm"]["hb"]).apply(null, arguments);
};
var _emscripten_sync_run_in_main_thread_3 = Module["_emscripten_sync_run_in_main_thread_3"] = function() {
return (_emscripten_sync_run_in_main_thread_3 = Module["_emscripten_sync_run_in_main_thread_3"] = Module["asm"]["ib"]).apply(null, arguments);
};
var _emscripten_sync_run_in_main_thread_4 = Module["_emscripten_sync_run_in_main_thread_4"] = function() {
return (_emscripten_sync_run_in_main_thread_4 = Module["_emscripten_sync_run_in_main_thread_4"] = Module["asm"]["jb"]).apply(null, arguments);
};
var _emscripten_sync_run_in_main_thread_5 = Module["_emscripten_sync_run_in_main_thread_5"] = function() {
return (_emscripten_sync_run_in_main_thread_5 = Module["_emscripten_sync_run_in_main_thread_5"] = Module["asm"]["kb"]).apply(null, arguments);
};
var _emscripten_sync_run_in_main_thread_6 = Module["_emscripten_sync_run_in_main_thread_6"] = function() {
return (_emscripten_sync_run_in_main_thread_6 = Module["_emscripten_sync_run_in_main_thread_6"] = Module["asm"]["lb"]).apply(null, arguments);
};
var _emscripten_sync_run_in_main_thread_7 = Module["_emscripten_sync_run_in_main_thread_7"] = function() {
return (_emscripten_sync_run_in_main_thread_7 = Module["_emscripten_sync_run_in_main_thread_7"] = Module["asm"]["mb"]).apply(null, arguments);
};
var _emscripten_run_in_main_runtime_thread_js = Module["_emscripten_run_in_main_runtime_thread_js"] = function() {
return (_emscripten_run_in_main_runtime_thread_js = Module["_emscripten_run_in_main_runtime_thread_js"] = Module["asm"]["nb"]).apply(null, arguments);
};
var _emscripten_async_queue_on_thread_ = Module["_emscripten_async_queue_on_thread_"] = function() {
return (_emscripten_async_queue_on_thread_ = Module["_emscripten_async_queue_on_thread_"] = Module["asm"]["ob"]).apply(null, arguments);
};
var _emscripten_tls_init = Module["_emscripten_tls_init"] = function() {
return (_emscripten_tls_init = Module["_emscripten_tls_init"] = Module["asm"]["pb"]).apply(null, arguments);
};
var stackSave = Module["stackSave"] = function() {
return (stackSave = Module["stackSave"] = Module["asm"]["qb"]).apply(null, arguments);
};
var stackAlloc = Module["stackAlloc"] = function() {
return (stackAlloc = Module["stackAlloc"] = Module["asm"]["rb"]).apply(null, arguments);
};
var stackRestore = Module["stackRestore"] = function() {
return (stackRestore = Module["stackRestore"] = Module["asm"]["sb"]).apply(null, arguments);
};
var dynCall_vi = Module["dynCall_vi"] = function() {
return (dynCall_vi = Module["dynCall_vi"] = Module["asm"]["tb"]).apply(null, arguments);
};
var dynCall_v = Module["dynCall_v"] = function() {
return (dynCall_v = Module["dynCall_v"] = Module["asm"]["ub"]).apply(null, arguments);
};
var dynCall_ii = Module["dynCall_ii"] = function() {
return (dynCall_ii = Module["dynCall_ii"] = Module["asm"]["vb"]).apply(null, arguments);
};
Module["asm"] = asm;
Module["cwrap"] = cwrap;
Module["PThread"] = PThread;
Module["PThread"] = PThread;
Module["_pthread_self"] = _pthread_self;
Module["wasmMemory"] = wasmMemory;
Module["ExitStatus"] = ExitStatus;
var calledRun;
Module["then"] = function(func2) {
if (calledRun) {
func2(Module);
} else {
var old = Module["onRuntimeInitialized"];
Module["onRuntimeInitialized"] = function() {
if (old)
old();
func2(Module);
};
}
return Module;
};
function ExitStatus(status) {
this.name = "ExitStatus";
this.message = "Program terminated with exit(" + status + ")";
this.status = status;
}
dependenciesFulfilled = function runCaller() {
if (!calledRun)
run();
if (!calledRun)
dependenciesFulfilled = runCaller;
};
function run(args) {
args = args || arguments_;
if (runDependencies > 0) {
return;
}
preRun();
if (runDependencies > 0)
return;
function doRun() {
if (calledRun)
return;
calledRun = true;
Module["calledRun"] = true;
if (ABORT)
return;
initRuntime();
preMain();
if (Module["onRuntimeInitialized"])
Module["onRuntimeInitialized"]();
postRun();
}
if (Module["setStatus"]) {
Module["setStatus"]("Running...");
setTimeout(function() {
setTimeout(function() {
Module["setStatus"]("");
}, 1);
doRun();
}, 1);
} else {
doRun();
}
}
Module["run"] = run;
if (Module["preInit"]) {
if (typeof Module["preInit"] == "function")
Module["preInit"] = [Module["preInit"]];
while (Module["preInit"].length > 0) {
Module["preInit"].pop()();
}
}
if (!ENVIRONMENT_IS_PTHREAD)
noExitRuntime = true;
if (!ENVIRONMENT_IS_PTHREAD)
run();
return WasmBackendModuleThreadedSimd2;
};
}();
if (typeof exports3 === "object" && typeof module === "object")
module.exports = WasmBackendModuleThreadedSimd;
else if (typeof define === "function" && define["amd"])
define([], function() {
return WasmBackendModuleThreadedSimd;
});
else if (typeof exports3 === "object")
exports3["WasmBackendModuleThreadedSimd"] = WasmBackendModuleThreadedSimd;
});
// node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js
var require_tfjs_backend_wasm = __commonJS((exports3, module) => {
var WasmBackendModule = function() {
var _scriptDir = typeof document !== "undefined" && document.currentScript ? document.currentScript.src : void 0;
if (typeof __filename !== "undefined")
_scriptDir = _scriptDir || __filename;
return function(WasmBackendModule2) {
WasmBackendModule2 = WasmBackendModule2 || {};
var Module = typeof WasmBackendModule2 !== "undefined" ? WasmBackendModule2 : {};
var moduleOverrides = {};
var key;
for (key in Module) {
if (Module.hasOwnProperty(key)) {
moduleOverrides[key] = Module[key];
}
}
var arguments_ = [];
var thisProgram = "./this.program";
var quit_ = function(status, toThrow) {
throw toThrow;
};
var ENVIRONMENT_IS_WEB = false;
var ENVIRONMENT_IS_WORKER = false;
var ENVIRONMENT_IS_NODE = false;
var ENVIRONMENT_IS_SHELL = false;
ENVIRONMENT_IS_WEB = typeof window === "object";
ENVIRONMENT_IS_WORKER = typeof importScripts === "function";
ENVIRONMENT_IS_NODE = typeof process === "object" && typeof process.versions === "object" && typeof process.versions.node === "string";
ENVIRONMENT_IS_SHELL = !ENVIRONMENT_IS_WEB && !ENVIRONMENT_IS_NODE && !ENVIRONMENT_IS_WORKER;
var scriptDirectory = "";
function locateFile(path) {
if (Module["locateFile"]) {
return Module["locateFile"](path, scriptDirectory);
}
return scriptDirectory + path;
}
var read_, readAsync, readBinary, setWindowTitle;
var nodeFS;
var nodePath;
if (ENVIRONMENT_IS_NODE) {
if (ENVIRONMENT_IS_WORKER) {
scriptDirectory = require_path().dirname(scriptDirectory) + "/";
} else {
scriptDirectory = __dirname + "/";
}
read_ = function shell_read(filename, binary) {
if (!nodeFS)
nodeFS = require("fs");
if (!nodePath)
nodePath = require_path();
filename = nodePath["normalize"](filename);
return nodeFS["readFileSync"](filename, binary ? null : "utf8");
};
readBinary = function readBinary2(filename) {
var ret = read_(filename, true);
if (!ret.buffer) {
ret = new Uint8Array(ret);
}
assert3(ret.buffer);
return ret;
};
if (process["argv"].length > 1) {
thisProgram = process["argv"][1].replace(/\\/g, "/");
}
arguments_ = process["argv"].slice(2);
process["on"]("uncaughtException", function(ex) {
if (!(ex instanceof ExitStatus)) {
throw ex;
}
});
process["on"]("unhandledRejection", abort);
quit_ = function(status) {
process["exit"](status);
};
Module["inspect"] = function() {
return "[Emscripten Module object]";
};
} else if (ENVIRONMENT_IS_SHELL) {
if (typeof read != "undefined") {
read_ = function shell_read(f) {
return read(f);
};
}
readBinary = function readBinary2(f) {
var data2;
if (typeof readbuffer === "function") {
return new Uint8Array(readbuffer(f));
}
data2 = read(f, "binary");
assert3(typeof data2 === "object");
return data2;
};
if (typeof scriptArgs != "undefined") {
arguments_ = scriptArgs;
} else if (typeof arguments != "undefined") {
arguments_ = arguments;
}
if (typeof quit === "function") {
quit_ = function(status) {
quit(status);
};
}
if (typeof print !== "undefined") {
if (typeof console === "undefined")
console = {};
console.log = print;
console.warn = console.error = typeof printErr !== "undefined" ? printErr : print;
}
} else if (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER) {
if (ENVIRONMENT_IS_WORKER) {
scriptDirectory = self.location.href;
} else if (document.currentScript) {
scriptDirectory = document.currentScript.src;
}
if (_scriptDir) {
scriptDirectory = _scriptDir;
}
if (scriptDirectory.indexOf("blob:") !== 0) {
scriptDirectory = scriptDirectory.substr(0, scriptDirectory.lastIndexOf("/") + 1);
} else {
scriptDirectory = "";
}
{
read_ = function shell_read(url) {
var xhr = new XMLHttpRequest();
xhr.open("GET", url, false);
xhr.send(null);
return xhr.responseText;
};
if (ENVIRONMENT_IS_WORKER) {
readBinary = function readBinary2(url) {
var xhr = new XMLHttpRequest();
xhr.open("GET", url, false);
xhr.responseType = "arraybuffer";
xhr.send(null);
return new Uint8Array(xhr.response);
};
}
readAsync = function readAsync2(url, onload, onerror) {
var xhr = new XMLHttpRequest();
xhr.open("GET", url, true);
xhr.responseType = "arraybuffer";
xhr.onload = function xhr_onload() {
if (xhr.status == 200 || xhr.status == 0 && xhr.response) {
onload(xhr.response);
return;
}
onerror();
};
xhr.onerror = onerror;
xhr.send(null);
};
}
setWindowTitle = function(title) {
document.title = title;
};
} else {
}
var out = Module["print"] || console.log.bind(console);
var err = Module["printErr"] || console.warn.bind(console);
for (key in moduleOverrides) {
if (moduleOverrides.hasOwnProperty(key)) {
Module[key] = moduleOverrides[key];
}
}
moduleOverrides = null;
if (Module["arguments"])
arguments_ = Module["arguments"];
if (Module["thisProgram"])
thisProgram = Module["thisProgram"];
if (Module["quit"])
quit_ = Module["quit"];
var wasmBinary;
if (Module["wasmBinary"])
wasmBinary = Module["wasmBinary"];
var noExitRuntime;
if (Module["noExitRuntime"])
noExitRuntime = Module["noExitRuntime"];
if (typeof WebAssembly !== "object") {
err("no native wasm support detected");
}
var wasmMemory;
var wasmTable = new WebAssembly.Table({initial: 147, maximum: 147 + 0, element: "anyfunc"});
var ABORT = false;
var EXITSTATUS = 0;
function assert3(condition, text) {
if (!condition) {
abort("Assertion failed: " + text);
}
}
function getCFunc(ident) {
var func2 = Module["_" + ident];
assert3(func2, "Cannot call unknown function " + ident + ", make sure it is exported");
return func2;
}
function ccall(ident, returnType, argTypes, args, opts) {
var toC = {string: function(str) {
var ret2 = 0;
if (str !== null && str !== void 0 && str !== 0) {
var len = (str.length << 2) + 1;
ret2 = stackAlloc(len);
stringToUTF8(str, ret2, len);
}
return ret2;
}, array: function(arr) {
var ret2 = stackAlloc(arr.length);
writeArrayToMemory(arr, ret2);
return ret2;
}};
function convertReturnValue(ret2) {
if (returnType === "string")
return UTF8ToString(ret2);
if (returnType === "boolean")
return Boolean(ret2);
return ret2;
}
var func2 = getCFunc(ident);
var cArgs = [];
var stack6 = 0;
if (args) {
for (var i = 0; i < args.length; i++) {
var converter = toC[argTypes[i]];
if (converter) {
if (stack6 === 0)
stack6 = stackSave();
cArgs[i] = converter(args[i]);
} else {
cArgs[i] = args[i];
}
}
}
var ret = func2.apply(null, cArgs);
ret = convertReturnValue(ret);
if (stack6 !== 0)
stackRestore(stack6);
return ret;
}
function cwrap(ident, returnType, argTypes, opts) {
argTypes = argTypes || [];
var numericArgs = argTypes.every(function(type) {
return type === "number";
});
var numericRet = returnType !== "string";
if (numericRet && numericArgs && !opts) {
return getCFunc(ident);
}
return function() {
return ccall(ident, returnType, argTypes, arguments, opts);
};
}
var UTF8Decoder = typeof TextDecoder !== "undefined" ? new TextDecoder("utf8") : void 0;
function UTF8ArrayToString(heap, idx, maxBytesToRead) {
var endIdx = idx + maxBytesToRead;
var endPtr = idx;
while (heap[endPtr] && !(endPtr >= endIdx))
++endPtr;
if (endPtr - idx > 16 && heap.subarray && UTF8Decoder) {
return UTF8Decoder.decode(heap.subarray(idx, endPtr));
} else {
var str = "";
while (idx < endPtr) {
var u0 = heap[idx++];
if (!(u0 & 128)) {
str += String.fromCharCode(u0);
continue;
}
var u1 = heap[idx++] & 63;
if ((u0 & 224) == 192) {
str += String.fromCharCode((u0 & 31) << 6 | u1);
continue;
}
var u2 = heap[idx++] & 63;
if ((u0 & 240) == 224) {
u0 = (u0 & 15) << 12 | u1 << 6 | u2;
} else {
u0 = (u0 & 7) << 18 | u1 << 12 | u2 << 6 | heap[idx++] & 63;
}
if (u0 < 65536) {
str += String.fromCharCode(u0);
} else {
var ch = u0 - 65536;
str += String.fromCharCode(55296 | ch >> 10, 56320 | ch & 1023);
}
}
}
return str;
}
function UTF8ToString(ptr, maxBytesToRead) {
return ptr ? UTF8ArrayToString(HEAPU8, ptr, maxBytesToRead) : "";
}
function stringToUTF8Array(str, heap, outIdx, maxBytesToWrite) {
if (!(maxBytesToWrite > 0))
return 0;
var startIdx = outIdx;
var endIdx = outIdx + maxBytesToWrite - 1;
for (var i = 0; i < str.length; ++i) {
var u = str.charCodeAt(i);
if (u >= 55296 && u <= 57343) {
var u1 = str.charCodeAt(++i);
u = 65536 + ((u & 1023) << 10) | u1 & 1023;
}
if (u <= 127) {
if (outIdx >= endIdx)
break;
heap[outIdx++] = u;
} else if (u <= 2047) {
if (outIdx + 1 >= endIdx)
break;
heap[outIdx++] = 192 | u >> 6;
heap[outIdx++] = 128 | u & 63;
} else if (u <= 65535) {
if (outIdx + 2 >= endIdx)
break;
heap[outIdx++] = 224 | u >> 12;
heap[outIdx++] = 128 | u >> 6 & 63;
heap[outIdx++] = 128 | u & 63;
} else {
if (outIdx + 3 >= endIdx)
break;
heap[outIdx++] = 240 | u >> 18;
heap[outIdx++] = 128 | u >> 12 & 63;
heap[outIdx++] = 128 | u >> 6 & 63;
heap[outIdx++] = 128 | u & 63;
}
}
heap[outIdx] = 0;
return outIdx - startIdx;
}
function stringToUTF8(str, outPtr, maxBytesToWrite) {
return stringToUTF8Array(str, HEAPU8, outPtr, maxBytesToWrite);
}
function writeArrayToMemory(array2, buffer11) {
HEAP8.set(array2, buffer11);
}
var buffer10, HEAP8, HEAPU8, HEAP16, HEAPU16, HEAP32, HEAPU32, HEAPF32, HEAPF64;
function updateGlobalBufferAndViews(buf) {
buffer10 = buf;
Module["HEAP8"] = HEAP8 = new Int8Array(buf);
Module["HEAP16"] = HEAP16 = new Int16Array(buf);
Module["HEAP32"] = HEAP32 = new Int32Array(buf);
Module["HEAPU8"] = HEAPU8 = new Uint8Array(buf);
Module["HEAPU16"] = HEAPU16 = new Uint16Array(buf);
Module["HEAPU32"] = HEAPU32 = new Uint32Array(buf);
Module["HEAPF32"] = HEAPF32 = new Float32Array(buf);
Module["HEAPF64"] = HEAPF64 = new Float64Array(buf);
}
var INITIAL_INITIAL_MEMORY = Module["INITIAL_MEMORY"] || 16777216;
function callRuntimeCallbacks(callbacks3) {
while (callbacks3.length > 0) {
var callback = callbacks3.shift();
if (typeof callback == "function") {
callback(Module);
continue;
}
var func2 = callback.func;
if (typeof func2 === "number") {
if (callback.arg === void 0) {
Module["dynCall_v"](func2);
} else {
Module["dynCall_vi"](func2, callback.arg);
}
} else {
func2(callback.arg === void 0 ? null : callback.arg);
}
}
}
var __ATPRERUN__ = [];
var __ATINIT__ = [];
var __ATMAIN__ = [];
var __ATPOSTRUN__ = [];
var runtimeInitialized = false;
var runtimeExited = false;
function preRun() {
if (Module["preRun"]) {
if (typeof Module["preRun"] == "function")
Module["preRun"] = [Module["preRun"]];
while (Module["preRun"].length) {
addOnPreRun(Module["preRun"].shift());
}
}
callRuntimeCallbacks(__ATPRERUN__);
}
function initRuntime() {
runtimeInitialized = true;
callRuntimeCallbacks(__ATINIT__);
}
function preMain() {
callRuntimeCallbacks(__ATMAIN__);
}
function exitRuntime() {
runtimeExited = true;
}
function postRun() {
if (Module["postRun"]) {
if (typeof Module["postRun"] == "function")
Module["postRun"] = [Module["postRun"]];
while (Module["postRun"].length) {
addOnPostRun(Module["postRun"].shift());
}
}
callRuntimeCallbacks(__ATPOSTRUN__);
}
function addOnPreRun(cb) {
__ATPRERUN__.unshift(cb);
}
function addOnPostRun(cb) {
__ATPOSTRUN__.unshift(cb);
}
var Math_ceil = Math.ceil;
var Math_floor = Math.floor;
var runDependencies = 0;
var runDependencyWatcher = null;
var dependenciesFulfilled = null;
function addRunDependency(id) {
runDependencies++;
if (Module["monitorRunDependencies"]) {
Module["monitorRunDependencies"](runDependencies);
}
}
function removeRunDependency(id) {
runDependencies--;
if (Module["monitorRunDependencies"]) {
Module["monitorRunDependencies"](runDependencies);
}
if (runDependencies == 0) {
if (runDependencyWatcher !== null) {
clearInterval(runDependencyWatcher);
runDependencyWatcher = null;
}
if (dependenciesFulfilled) {
var callback = dependenciesFulfilled;
dependenciesFulfilled = null;
callback();
}
}
}
Module["preloadedImages"] = {};
Module["preloadedAudios"] = {};
function abort(what) {
if (Module["onAbort"]) {
Module["onAbort"](what);
}
what += "";
out(what);
err(what);
ABORT = true;
EXITSTATUS = 1;
what = "abort(" + what + "). Build with -s ASSERTIONS=1 for more info.";
throw new WebAssembly.RuntimeError(what);
}
function hasPrefix(str, prefix) {
return String.prototype.startsWith ? str.startsWith(prefix) : str.indexOf(prefix) === 0;
}
var dataURIPrefix = "data:application/octet-stream;base64,";
function isDataURI(filename) {
return hasPrefix(filename, dataURIPrefix);
}
var fileURIPrefix = "file://";
function isFileURI(filename) {
return hasPrefix(filename, fileURIPrefix);
}
var wasmBinaryFile = "tfjs-backend-wasm.wasm";
if (!isDataURI(wasmBinaryFile)) {
wasmBinaryFile = locateFile(wasmBinaryFile);
}
function getBinary() {
try {
if (wasmBinary) {
return new Uint8Array(wasmBinary);
}
if (readBinary) {
return readBinary(wasmBinaryFile);
} else {
throw "both async and sync fetching of the wasm failed";
}
} catch (err2) {
abort(err2);
}
}
function getBinaryPromise() {
if (!wasmBinary && (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER) && typeof fetch === "function" && !isFileURI(wasmBinaryFile)) {
return fetch(wasmBinaryFile, {credentials: "same-origin"}).then(function(response) {
if (!response["ok"]) {
throw "failed to load wasm binary file at '" + wasmBinaryFile + "'";
}
return response["arrayBuffer"]();
}).catch(function() {
return getBinary();
});
}
return new Promise(function(resolve, reject) {
resolve(getBinary());
});
}
function createWasm() {
var info = {env: asmLibraryArg, wasi_snapshot_preview1: asmLibraryArg};
function receiveInstance(instance, module2) {
var exports5 = instance.exports;
Module["asm"] = exports5;
wasmMemory = exports5["memory"];
updateGlobalBufferAndViews(wasmMemory.buffer);
removeRunDependency("wasm-instantiate");
}
addRunDependency("wasm-instantiate");
function receiveInstantiatedSource(output) {
receiveInstance(output["instance"]);
}
function instantiateArrayBuffer(receiver) {
return getBinaryPromise().then(function(binary) {
return WebAssembly.instantiate(binary, info);
}).then(receiver, function(reason) {
err("failed to asynchronously prepare wasm: " + reason);
abort(reason);
});
}
function instantiateAsync() {
if (!wasmBinary && typeof WebAssembly.instantiateStreaming === "function" && !isDataURI(wasmBinaryFile) && !isFileURI(wasmBinaryFile) && typeof fetch === "function") {
fetch(wasmBinaryFile, {credentials: "same-origin"}).then(function(response) {
var result = WebAssembly.instantiateStreaming(response, info);
return result.then(receiveInstantiatedSource, function(reason) {
err("wasm streaming compile failed: " + reason);
err("falling back to ArrayBuffer instantiation");
instantiateArrayBuffer(receiveInstantiatedSource);
});
});
} else {
return instantiateArrayBuffer(receiveInstantiatedSource);
}
}
if (Module["instantiateWasm"]) {
try {
var exports4 = Module["instantiateWasm"](info, receiveInstance);
return exports4;
} catch (e) {
err("Module.instantiateWasm callback failed with error: " + e);
return false;
}
}
instantiateAsync();
return {};
}
__ATINIT__.push();
function _emscripten_notify_memory_growth(memoryIndex) {
updateGlobalBufferAndViews(wasmMemory.buffer);
}
var PATH = {splitPath: function(filename) {
var splitPathRe = /^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;
return splitPathRe.exec(filename).slice(1);
}, normalizeArray: function(parts, allowAboveRoot) {
var up = 0;
for (var i = parts.length - 1; i >= 0; i--) {
var last = parts[i];
if (last === ".") {
parts.splice(i, 1);
} else if (last === "..") {
parts.splice(i, 1);
up++;
} else if (up) {
parts.splice(i, 1);
up--;
}
}
if (allowAboveRoot) {
for (; up; up--) {
parts.unshift("..");
}
}
return parts;
}, normalize: function(path) {
var isAbsolute = path.charAt(0) === "/", trailingSlash = path.substr(-1) === "/";
path = PATH.normalizeArray(path.split("/").filter(function(p) {
return !!p;
}), !isAbsolute).join("/");
if (!path && !isAbsolute) {
path = ".";
}
if (path && trailingSlash) {
path += "/";
}
return (isAbsolute ? "/" : "") + path;
}, dirname: function(path) {
var result = PATH.splitPath(path), root = result[0], dir = result[1];
if (!root && !dir) {
return ".";
}
if (dir) {
dir = dir.substr(0, dir.length - 1);
}
return root + dir;
}, basename: function(path) {
if (path === "/")
return "/";
var lastSlash = path.lastIndexOf("/");
if (lastSlash === -1)
return path;
return path.substr(lastSlash + 1);
}, extname: function(path) {
return PATH.splitPath(path)[3];
}, join: function() {
var paths = Array.prototype.slice.call(arguments, 0);
return PATH.normalize(paths.join("/"));
}, join2: function(l, r) {
return PATH.normalize(l + "/" + r);
}};
var SYSCALLS = {mappings: {}, buffers: [null, [], []], printChar: function(stream, curr) {
var buffer11 = SYSCALLS.buffers[stream];
if (curr === 0 || curr === 10) {
(stream === 1 ? out : err)(UTF8ArrayToString(buffer11, 0));
buffer11.length = 0;
} else {
buffer11.push(curr);
}
}, varargs: void 0, get: function() {
SYSCALLS.varargs += 4;
var ret = HEAP32[SYSCALLS.varargs - 4 >> 2];
return ret;
}, getStr: function(ptr) {
var ret = UTF8ToString(ptr);
return ret;
}, get64: function(low, high) {
return low;
}};
function _fd_close(fd) {
return 0;
}
function _fd_seek(fd, offset_low, offset_high, whence, newOffset) {
}
function _fd_write(fd, iov, iovcnt, pnum) {
var num = 0;
for (var i = 0; i < iovcnt; i++) {
var ptr = HEAP32[iov + i * 8 >> 2];
var len = HEAP32[iov + (i * 8 + 4) >> 2];
for (var j = 0; j < len; j++) {
SYSCALLS.printChar(fd, HEAPU8[ptr + j]);
}
num += len;
}
HEAP32[pnum >> 2] = num;
return 0;
}
function _exit(status) {
exit(status);
}
function _proc_exit(code) {
_exit(code);
}
function _roundf(d) {
d = +d;
return d >= 0 ? +Math_floor(d + 0.5) : +Math_ceil(d - 0.5);
}
var asmLibraryArg = {emscripten_notify_memory_growth: _emscripten_notify_memory_growth, fd_close: _fd_close, fd_seek: _fd_seek, fd_write: _fd_write, proc_exit: _proc_exit, roundf: _roundf};
var asm = createWasm();
Module["asm"] = asm;
var _init = Module["_init"] = function() {
return (_init = Module["_init"] = Module["asm"]["init"]).apply(null, arguments);
};
var _register_tensor = Module["_register_tensor"] = function() {
return (_register_tensor = Module["_register_tensor"] = Module["asm"]["register_tensor"]).apply(null, arguments);
};
var _dispose_data = Module["_dispose_data"] = function() {
return (_dispose_data = Module["_dispose_data"] = Module["asm"]["dispose_data"]).apply(null, arguments);
};
var _dispose = Module["_dispose"] = function() {
return (_dispose = Module["_dispose"] = Module["asm"]["dispose"]).apply(null, arguments);
};
var _Abs = Module["_Abs"] = function() {
return (_Abs = Module["_Abs"] = Module["asm"]["Abs"]).apply(null, arguments);
};
var _Add = Module["_Add"] = function() {
return (_Add = Module["_Add"] = Module["asm"]["Add"]).apply(null, arguments);
};
var _AddN = Module["_AddN"] = function() {
return (_AddN = Module["_AddN"] = Module["asm"]["AddN"]).apply(null, arguments);
};
var _ArgMax = Module["_ArgMax"] = function() {
return (_ArgMax = Module["_ArgMax"] = Module["asm"]["ArgMax"]).apply(null, arguments);
};
var _AvgPool = Module["_AvgPool"] = function() {
return (_AvgPool = Module["_AvgPool"] = Module["asm"]["AvgPool"]).apply(null, arguments);
};
var _BatchMatMul = Module["_BatchMatMul"] = function() {
return (_BatchMatMul = Module["_BatchMatMul"] = Module["asm"]["BatchMatMul"]).apply(null, arguments);
};
var _ClipByValue = Module["_ClipByValue"] = function() {
return (_ClipByValue = Module["_ClipByValue"] = Module["asm"]["ClipByValue"]).apply(null, arguments);
};
var _Conv2D = Module["_Conv2D"] = function() {
return (_Conv2D = Module["_Conv2D"] = Module["asm"]["Conv2D"]).apply(null, arguments);
};
var _Conv2DBackpropInput = Module["_Conv2DBackpropInput"] = function() {
return (_Conv2DBackpropInput = Module["_Conv2DBackpropInput"] = Module["asm"]["Conv2DBackpropInput"]).apply(null, arguments);
};
var _Cos = Module["_Cos"] = function() {
return (_Cos = Module["_Cos"] = Module["asm"]["Cos"]).apply(null, arguments);
};
var _CropAndResize = Module["_CropAndResize"] = function() {
return (_CropAndResize = Module["_CropAndResize"] = Module["asm"]["CropAndResize"]).apply(null, arguments);
};
var _Cumsum = Module["_Cumsum"] = function() {
return (_Cumsum = Module["_Cumsum"] = Module["asm"]["Cumsum"]).apply(null, arguments);
};
var _DepthToSpace = Module["_DepthToSpace"] = function() {
return (_DepthToSpace = Module["_DepthToSpace"] = Module["asm"]["DepthToSpace"]).apply(null, arguments);
};
var _DepthwiseConv2dNative = Module["_DepthwiseConv2dNative"] = function() {
return (_DepthwiseConv2dNative = Module["_DepthwiseConv2dNative"] = Module["asm"]["DepthwiseConv2dNative"]).apply(null, arguments);
};
var _Div = Module["_Div"] = function() {
return (_Div = Module["_Div"] = Module["asm"]["Div"]).apply(null, arguments);
};
var _Equal = Module["_Equal"] = function() {
return (_Equal = Module["_Equal"] = Module["asm"]["Equal"]).apply(null, arguments);
};
var _Exp = Module["_Exp"] = function() {
return (_Exp = Module["_Exp"] = Module["asm"]["Exp"]).apply(null, arguments);
};
var _FlipLeftRight = Module["_FlipLeftRight"] = function() {
return (_FlipLeftRight = Module["_FlipLeftRight"] = Module["asm"]["FlipLeftRight"]).apply(null, arguments);
};
var _FloorDiv = Module["_FloorDiv"] = function() {
return (_FloorDiv = Module["_FloorDiv"] = Module["asm"]["FloorDiv"]).apply(null, arguments);
};
var _FusedBatchNorm = Module["_FusedBatchNorm"] = function() {
return (_FusedBatchNorm = Module["_FusedBatchNorm"] = Module["asm"]["FusedBatchNorm"]).apply(null, arguments);
};
var _FusedConv2D = Module["_FusedConv2D"] = function() {
return (_FusedConv2D = Module["_FusedConv2D"] = Module["asm"]["FusedConv2D"]).apply(null, arguments);
};
var _FusedDepthwiseConv2D = Module["_FusedDepthwiseConv2D"] = function() {
return (_FusedDepthwiseConv2D = Module["_FusedDepthwiseConv2D"] = Module["asm"]["FusedDepthwiseConv2D"]).apply(null, arguments);
};
var _Gather = Module["_Gather"] = function() {
return (_Gather = Module["_Gather"] = Module["asm"]["Gather"]).apply(null, arguments);
};
var _GatherNd = Module["_GatherNd"] = function() {
return (_GatherNd = Module["_GatherNd"] = Module["asm"]["GatherNd"]).apply(null, arguments);
};
var _Greater = Module["_Greater"] = function() {
return (_Greater = Module["_Greater"] = Module["asm"]["Greater"]).apply(null, arguments);
};
var _GreaterEqual = Module["_GreaterEqual"] = function() {
return (_GreaterEqual = Module["_GreaterEqual"] = Module["asm"]["GreaterEqual"]).apply(null, arguments);
};
var _Less = Module["_Less"] = function() {
return (_Less = Module["_Less"] = Module["asm"]["Less"]).apply(null, arguments);
};
var _LessEqual = Module["_LessEqual"] = function() {
return (_LessEqual = Module["_LessEqual"] = Module["asm"]["LessEqual"]).apply(null, arguments);
};
var _Log = Module["_Log"] = function() {
return (_Log = Module["_Log"] = Module["asm"]["Log"]).apply(null, arguments);
};
var _LogicalAnd = Module["_LogicalAnd"] = function() {
return (_LogicalAnd = Module["_LogicalAnd"] = Module["asm"]["LogicalAnd"]).apply(null, arguments);
};
var _Max = Module["_Max"] = function() {
return (_Max = Module["_Max"] = Module["asm"]["Max"]).apply(null, arguments);
};
var _MaxPool = Module["_MaxPool"] = function() {
return (_MaxPool = Module["_MaxPool"] = Module["asm"]["MaxPool"]).apply(null, arguments);
};
var _Maximum = Module["_Maximum"] = function() {
return (_Maximum = Module["_Maximum"] = Module["asm"]["Maximum"]).apply(null, arguments);
};
var _Min = Module["_Min"] = function() {
return (_Min = Module["_Min"] = Module["asm"]["Min"]).apply(null, arguments);
};
var _Minimum = Module["_Minimum"] = function() {
return (_Minimum = Module["_Minimum"] = Module["asm"]["Minimum"]).apply(null, arguments);
};
var _Multiply = Module["_Multiply"] = function() {
return (_Multiply = Module["_Multiply"] = Module["asm"]["Multiply"]).apply(null, arguments);
};
var _Negate = Module["_Negate"] = function() {
return (_Negate = Module["_Negate"] = Module["asm"]["Negate"]).apply(null, arguments);
};
var _NonMaxSuppressionV3 = Module["_NonMaxSuppressionV3"] = function() {
return (_NonMaxSuppressionV3 = Module["_NonMaxSuppressionV3"] = Module["asm"]["NonMaxSuppressionV3"]).apply(null, arguments);
};
var _NonMaxSuppressionV4 = Module["_NonMaxSuppressionV4"] = function() {
return (_NonMaxSuppressionV4 = Module["_NonMaxSuppressionV4"] = Module["asm"]["NonMaxSuppressionV4"]).apply(null, arguments);
};
var _NonMaxSuppressionV5 = Module["_NonMaxSuppressionV5"] = function() {
return (_NonMaxSuppressionV5 = Module["_NonMaxSuppressionV5"] = Module["asm"]["NonMaxSuppressionV5"]).apply(null, arguments);
};
var _NotEqual = Module["_NotEqual"] = function() {
return (_NotEqual = Module["_NotEqual"] = Module["asm"]["NotEqual"]).apply(null, arguments);
};
var _OneHot = Module["_OneHot"] = function() {
return (_OneHot = Module["_OneHot"] = Module["asm"]["OneHot"]).apply(null, arguments);
};
var _PadV2 = Module["_PadV2"] = function() {
return (_PadV2 = Module["_PadV2"] = Module["asm"]["PadV2"]).apply(null, arguments);
};
var _Pow = Module["_Pow"] = function() {
return (_Pow = Module["_Pow"] = Module["asm"]["Pow"]).apply(null, arguments);
};
var _Prelu = Module["_Prelu"] = function() {
return (_Prelu = Module["_Prelu"] = Module["asm"]["Prelu"]).apply(null, arguments);
};
var _Relu = Module["_Relu"] = function() {
return (_Relu = Module["_Relu"] = Module["asm"]["Relu"]).apply(null, arguments);
};
var _Relu6 = Module["_Relu6"] = function() {
return (_Relu6 = Module["_Relu6"] = Module["asm"]["Relu6"]).apply(null, arguments);
};
var _ResizeBilinear = Module["_ResizeBilinear"] = function() {
return (_ResizeBilinear = Module["_ResizeBilinear"] = Module["asm"]["ResizeBilinear"]).apply(null, arguments);
};
var _Reverse = Module["_Reverse"] = function() {
return (_Reverse = Module["_Reverse"] = Module["asm"]["Reverse"]).apply(null, arguments);
};
var _RotateWithOffset = Module["_RotateWithOffset"] = function() {
return (_RotateWithOffset = Module["_RotateWithOffset"] = Module["asm"]["RotateWithOffset"]).apply(null, arguments);
};
var _Rsqrt = Module["_Rsqrt"] = function() {
return (_Rsqrt = Module["_Rsqrt"] = Module["asm"]["Rsqrt"]).apply(null, arguments);
};
var _ScatterNd = Module["_ScatterNd"] = function() {
return (_ScatterNd = Module["_ScatterNd"] = Module["asm"]["ScatterNd"]).apply(null, arguments);
};
var _SelectV2 = Module["_SelectV2"] = function() {
return (_SelectV2 = Module["_SelectV2"] = Module["asm"]["SelectV2"]).apply(null, arguments);
};
var _Sigmoid = Module["_Sigmoid"] = function() {
return (_Sigmoid = Module["_Sigmoid"] = Module["asm"]["Sigmoid"]).apply(null, arguments);
};
var _Sin = Module["_Sin"] = function() {
return (_Sin = Module["_Sin"] = Module["asm"]["Sin"]).apply(null, arguments);
};
var _Softmax = Module["_Softmax"] = function() {
return (_Softmax = Module["_Softmax"] = Module["asm"]["Softmax"]).apply(null, arguments);
};
var _Sqrt = Module["_Sqrt"] = function() {
return (_Sqrt = Module["_Sqrt"] = Module["asm"]["Sqrt"]).apply(null, arguments);
};
var _Square = Module["_Square"] = function() {
return (_Square = Module["_Square"] = Module["asm"]["Square"]).apply(null, arguments);
};
var _SquaredDifference = Module["_SquaredDifference"] = function() {
return (_SquaredDifference = Module["_SquaredDifference"] = Module["asm"]["SquaredDifference"]).apply(null, arguments);
};
var _StridedSlice = Module["_StridedSlice"] = function() {
return (_StridedSlice = Module["_StridedSlice"] = Module["asm"]["StridedSlice"]).apply(null, arguments);
};
var _Sub = Module["_Sub"] = function() {
return (_Sub = Module["_Sub"] = Module["asm"]["Sub"]).apply(null, arguments);
};
var _Sum = Module["_Sum"] = function() {
return (_Sum = Module["_Sum"] = Module["asm"]["Sum"]).apply(null, arguments);
};
var _Tanh = Module["_Tanh"] = function() {
return (_Tanh = Module["_Tanh"] = Module["asm"]["Tanh"]).apply(null, arguments);
};
var _Tile = Module["_Tile"] = function() {
return (_Tile = Module["_Tile"] = Module["asm"]["Tile"]).apply(null, arguments);
};
var _Transpose = Module["_Transpose"] = function() {
return (_Transpose = Module["_Transpose"] = Module["asm"]["Transpose"]).apply(null, arguments);
};
var __FusedMatMul = Module["__FusedMatMul"] = function() {
return (__FusedMatMul = Module["__FusedMatMul"] = Module["asm"]["_FusedMatMul"]).apply(null, arguments);
};
var _malloc = Module["_malloc"] = function() {
return (_malloc = Module["_malloc"] = Module["asm"]["malloc"]).apply(null, arguments);
};
var _free = Module["_free"] = function() {
return (_free = Module["_free"] = Module["asm"]["free"]).apply(null, arguments);
};
var __start = Module["__start"] = function() {
return (__start = Module["__start"] = Module["asm"]["_start"]).apply(null, arguments);
};
var stackSave = Module["stackSave"] = function() {
return (stackSave = Module["stackSave"] = Module["asm"]["stackSave"]).apply(null, arguments);
};
var stackAlloc = Module["stackAlloc"] = function() {
return (stackAlloc = Module["stackAlloc"] = Module["asm"]["stackAlloc"]).apply(null, arguments);
};
var stackRestore = Module["stackRestore"] = function() {
return (stackRestore = Module["stackRestore"] = Module["asm"]["stackRestore"]).apply(null, arguments);
};
Module["asm"] = asm;
Module["cwrap"] = cwrap;
var calledRun;
Module["then"] = function(func2) {
if (calledRun) {
func2(Module);
} else {
var old = Module["onRuntimeInitialized"];
Module["onRuntimeInitialized"] = function() {
if (old)
old();
func2(Module);
};
}
return Module;
};
function ExitStatus(status) {
this.name = "ExitStatus";
this.message = "Program terminated with exit(" + status + ")";
this.status = status;
}
var calledMain = false;
dependenciesFulfilled = function runCaller() {
if (!calledRun)
run();
if (!calledRun)
dependenciesFulfilled = runCaller;
};
function callMain(args) {
var entryFunction = Module["__start"];
try {
entryFunction();
var ret = 0;
exit(ret, true);
} catch (e) {
if (e instanceof ExitStatus) {
return;
} else if (e == "unwind") {
noExitRuntime = true;
return;
} else {
var toLog = e;
if (e && typeof e === "object" && e.stack) {
toLog = [e, e.stack];
}
err("exception thrown: " + toLog);
quit_(1, e);
}
} finally {
calledMain = true;
}
}
function run(args) {
args = args || arguments_;
if (runDependencies > 0) {
return;
}
preRun();
if (runDependencies > 0)
return;
function doRun() {
if (calledRun)
return;
calledRun = true;
Module["calledRun"] = true;
if (ABORT)
return;
initRuntime();
preMain();
if (Module["onRuntimeInitialized"])
Module["onRuntimeInitialized"]();
if (shouldRunNow)
callMain(args);
postRun();
}
if (Module["setStatus"]) {
Module["setStatus"]("Running...");
setTimeout(function() {
setTimeout(function() {
Module["setStatus"]("");
}, 1);
doRun();
}, 1);
} else {
doRun();
}
}
Module["run"] = run;
function exit(status, implicit) {
if (implicit && noExitRuntime && status === 0) {
return;
}
if (noExitRuntime) {
} else {
ABORT = true;
EXITSTATUS = status;
exitRuntime();
if (Module["onExit"])
Module["onExit"](status);
}
quit_(status, new ExitStatus(status));
}
if (Module["preInit"]) {
if (typeof Module["preInit"] == "function")
Module["preInit"] = [Module["preInit"]];
while (Module["preInit"].length > 0) {
Module["preInit"].pop()();
}
}
var shouldRunNow = true;
if (Module["noInitialRun"])
shouldRunNow = false;
noExitRuntime = true;
run();
return WasmBackendModule2;
};
}();
if (typeof exports3 === "object" && typeof module === "object")
module.exports = WasmBackendModule;
else if (typeof define === "function" && define["amd"])
define([], function() {
return WasmBackendModule;
});
else if (typeof exports3 === "object")
exports3["WasmBackendModule"] = WasmBackendModule;
});
// src/face/blazeface.js
var require_blazeface = __commonJS((exports3) => {
const NUM_LANDMARKS = 6;
function generateAnchors(inputSize) {
const spec = {strides: [inputSize / 16, inputSize / 8], anchors: [2, 6]};
const anchors = [];
for (let i = 0; i < spec.strides.length; i++) {
const stride = spec.strides[i];
const gridRows = Math.floor((inputSize + stride - 1) / stride);
const gridCols = Math.floor((inputSize + stride - 1) / stride);
const anchorsNum = spec.anchors[i];
for (let gridY = 0; gridY < gridRows; gridY++) {
const anchorY = stride * (gridY + 0.5);
for (let gridX = 0; gridX < gridCols; gridX++) {
const anchorX = stride * (gridX + 0.5);
for (let n = 0; n < anchorsNum; n++) {
anchors.push([anchorX, anchorY]);
}
}
}
}
return anchors;
}
const disposeBox = (box) => {
box.startEndTensor.dispose();
box.startPoint.dispose();
box.endPoint.dispose();
};
const createBox = (startEndTensor) => ({
startEndTensor,
startPoint: dist_exports2.slice(startEndTensor, [0, 0], [-1, 2]),
endPoint: dist_exports2.slice(startEndTensor, [0, 2], [-1, 2])
});
const scaleBox = (box, factors) => {
const starts = dist_exports2.mul(box.startPoint, factors);
const ends = dist_exports2.mul(box.endPoint, factors);
const newCoordinates = dist_exports2.concat2d([starts, ends], 1);
return createBox(newCoordinates);
};
function decodeBounds(boxOutputs, anchors, inputSize) {
const boxStarts = dist_exports2.slice(boxOutputs, [0, 1], [-1, 2]);
const centers = dist_exports2.add(boxStarts, anchors);
const boxSizes = dist_exports2.slice(boxOutputs, [0, 3], [-1, 2]);
const boxSizesNormalized = dist_exports2.div(boxSizes, inputSize);
const centersNormalized = dist_exports2.div(centers, inputSize);
const halfBoxSize = dist_exports2.div(boxSizesNormalized, 2);
const starts = dist_exports2.sub(centersNormalized, halfBoxSize);
const ends = dist_exports2.add(centersNormalized, halfBoxSize);
const startNormalized = dist_exports2.mul(starts, inputSize);
const endNormalized = dist_exports2.mul(ends, inputSize);
const concatAxis = 1;
return dist_exports2.concat2d([startNormalized, endNormalized], concatAxis);
}
function scaleBoxFromPrediction(face2, scaleFactor) {
return dist_exports2.tidy(() => {
const box = face2["box"] ? face2["box"] : face2;
return scaleBox(box, scaleFactor).startEndTensor.squeeze();
});
}
class BlazeFaceModel {
constructor(model2, config2) {
this.blazeFaceModel = model2;
this.width = config2.detector.inputSize;
this.height = config2.detector.inputSize;
this.anchorsData = generateAnchors(config2.detector.inputSize);
this.anchors = dist_exports2.tensor2d(this.anchorsData);
this.inputSize = dist_exports2.tensor1d([this.width, this.height]);
this.config = config2;
this.scaleFaces = 0.8;
}
async getBoundingBoxes(inputImage) {
if (!inputImage || inputImage.isDisposedInternal || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1)
return null;
const [detectedOutputs, boxes, scores] = dist_exports2.tidy(() => {
const resizedImage = inputImage.resizeBilinear([this.width, this.height]);
const normalizedImage = dist_exports2.sub(resizedImage.div(127.5), 1);
const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);
let prediction;
if (Array.isArray(batchedPrediction)) {
const sorted = batchedPrediction.sort((a, b) => a.size - b.size);
const concat384 = dist_exports2.concat([sorted[0], sorted[2]], 2);
const concat512 = dist_exports2.concat([sorted[1], sorted[3]], 2);
const concat15 = dist_exports2.concat([concat512, concat384], 1);
prediction = concat15.squeeze(0);
} else {
prediction = batchedPrediction.squeeze();
}
const decodedBounds = decodeBounds(prediction, this.anchors, this.inputSize);
const logits = dist_exports2.slice(prediction, [0, 0], [-1, 1]);
const scoresOut = dist_exports2.sigmoid(logits).squeeze();
return [prediction, decodedBounds, scoresOut];
});
const boxIndicesTensor = await dist_exports2.image.nonMaxSuppressionAsync(boxes, scores, this.config.detector.maxFaces, this.config.detector.iouThreshold, this.config.detector.scoreThreshold);
const boxIndices = boxIndicesTensor.arraySync();
boxIndicesTensor.dispose();
const boundingBoxesMap = boxIndices.map((boxIndex) => dist_exports2.slice(boxes, [boxIndex, 0], [1, -1]));
const boundingBoxes = boundingBoxesMap.map((boundingBox) => {
const vals = boundingBox.arraySync();
boundingBox.dispose();
return vals;
});
const scoresVal = scores.dataSync();
const annotatedBoxes = [];
for (const i in boundingBoxes) {
const boxIndex = boxIndices[i];
const confidence = scoresVal[boxIndex];
if (confidence > this.config.detector.minConfidence) {
const box = createBox(boundingBoxes[i]);
const anchor = this.anchorsData[boxIndex];
const landmarks = dist_exports2.tidy(() => dist_exports2.slice(detectedOutputs, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));
annotatedBoxes.push({box, landmarks, anchor, confidence});
}
}
detectedOutputs.dispose();
boxes.dispose();
scores.dispose();
detectedOutputs.dispose();
return {
boxes: annotatedBoxes,
scaleFactor: [inputImage.shape[2] / this.width, inputImage.shape[1] / this.height]
};
}
async estimateFaces(input2) {
const {boxes, scaleFactor} = await this.getBoundingBoxes(input2);
const faces = [];
for (const face2 of boxes) {
const landmarkData = face2.landmarks.arraySync();
const scaledBox = scaleBoxFromPrediction(face2, scaleFactor);
const boxData = scaleBox.arraySync();
const probabilityData = face2.probability.arraySync();
const anchor = face2.anchor;
const [scaleFactorX, scaleFactorY] = scaleFactor;
const scaledLandmarks = landmarkData.map((landmark) => [
(landmark[0] + anchor[0]) * scaleFactorX,
(landmark[1] + anchor[1]) * scaleFactorY
]);
const normalizedFace = {
topLeft: boxData.slice(0, 2),
bottomRight: boxData.slice(2),
landmarks: scaledLandmarks,
probability: probabilityData
};
disposeBox(face2.box);
face2.landmarks.dispose();
face2.probability.dispose();
scaledBox.dispose();
faces.push(normalizedFace);
}
return faces;
}
}
async function load(config2) {
const blazeface = await loadGraphModel2(config2.detector.modelPath, {fromTFHub: config2.detector.modelPath.includes("tfhub.dev")});
const model2 = new BlazeFaceModel(blazeface, config2);
console.log(`Human: load model: ${config2.detector.modelPath.match(/\/(.*)\./)[1]}`);
return model2;
}
exports3.load = load;
exports3.BlazeFaceModel = BlazeFaceModel;
exports3.disposeBox = disposeBox;
});
// src/face/keypoints.js
var require_keypoints = __commonJS((exports3) => {
exports3.MESH_ANNOTATIONS = {
silhouette: [
10,
338,
297,
332,
284,
251,
389,
356,
454,
323,
361,
288,
397,
365,
379,
378,
400,
377,
152,
148,
176,
149,
150,
136,
172,
58,
132,
93,
234,
127,
162,
21,
54,
103,
67,
109
],
lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],
lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],
lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],
lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],
rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],
rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],
rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],
rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],
rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],
rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],
rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],
rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],
rightEyebrowLower: [35, 124, 46, 53, 52, 65],
rightEyeIris: [473, 474, 475, 476, 477],
leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],
leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],
leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],
leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],
leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],
leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],
leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],
leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],
leftEyebrowLower: [265, 353, 276, 283, 282, 295],
leftEyeIris: [468, 469, 470, 471, 472],
midwayBetweenEyes: [168],
noseTip: [1],
noseBottom: [2],
noseRightCorner: [98],
noseLeftCorner: [327],
rightCheek: [205],
leftCheek: [425]
};
exports3.MESH_TO_IRIS_INDICES_MAP = [
{key: "EyeUpper0", indices: [9, 10, 11, 12, 13, 14, 15]},
{key: "EyeUpper1", indices: [25, 26, 27, 28, 29, 30, 31]},
{key: "EyeUpper2", indices: [41, 42, 43, 44, 45, 46, 47]},
{key: "EyeLower0", indices: [0, 1, 2, 3, 4, 5, 6, 7, 8]},
{key: "EyeLower1", indices: [16, 17, 18, 19, 20, 21, 22, 23, 24]},
{key: "EyeLower2", indices: [32, 33, 34, 35, 36, 37, 38, 39, 40]},
{key: "EyeLower3", indices: [54, 55, 56, 57, 58, 59, 60, 61, 62]},
{key: "EyebrowUpper", indices: [63, 64, 65, 66, 67, 68, 69, 70]},
{key: "EyebrowLower", indices: [48, 49, 50, 51, 52, 53]}
];
});
// src/face/box.js
var require_box = __commonJS((exports3) => {
function scaleBoxCoordinates2(box, factor) {
const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];
const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];
return {startPoint, endPoint};
}
exports3.scaleBoxCoordinates = scaleBoxCoordinates2;
function getBoxSize2(box) {
return [
Math.abs(box.endPoint[0] - box.startPoint[0]),
Math.abs(box.endPoint[1] - box.startPoint[1])
];
}
exports3.getBoxSize = getBoxSize2;
function getBoxCenter2(box) {
return [
box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,
box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2
];
}
exports3.getBoxCenter = getBoxCenter2;
function cutBoxFromImageAndResize2(box, image4, cropSize) {
const h = image4.shape[1];
const w = image4.shape[2];
const boxes = [[
box.startPoint[1] / h,
box.startPoint[0] / w,
box.endPoint[1] / h,
box.endPoint[0] / w
]];
return dist_exports2.image.cropAndResize(image4, boxes, [0], cropSize);
}
exports3.cutBoxFromImageAndResize = cutBoxFromImageAndResize2;
function enlargeBox2(box, factor = 1.5) {
const center = getBoxCenter2(box);
const size = getBoxSize2(box);
const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];
const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];
const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];
return {startPoint, endPoint, landmarks: box.landmarks};
}
exports3.enlargeBox = enlargeBox2;
function squarifyBox2(box) {
const centers = getBoxCenter2(box);
const size = getBoxSize2(box);
const maxEdge = Math.max(...size);
const halfSize = maxEdge / 2;
const startPoint = [centers[0] - halfSize, centers[1] - halfSize];
const endPoint = [centers[0] + halfSize, centers[1] + halfSize];
return {startPoint, endPoint, landmarks: box.landmarks};
}
exports3.squarifyBox = squarifyBox2;
});
// src/face/util.js
var require_util = __commonJS((exports3) => {
exports3.IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];
function normalizeRadians2(angle) {
return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));
}
exports3.normalizeRadians = normalizeRadians2;
function computeRotation2(point1, point2) {
const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);
return normalizeRadians2(radians);
}
exports3.computeRotation = computeRotation2;
function radToDegrees(rad) {
return rad * 180 / Math.PI;
}
exports3.radToDegrees = radToDegrees;
function buildTranslationMatrix2(x, y) {
return [[1, 0, x], [0, 1, y], [0, 0, 1]];
}
function dot6(v1, v2) {
let product = 0;
for (let i = 0; i < v1.length; i++) {
product += v1[i] * v2[i];
}
return product;
}
exports3.dot = dot6;
function getColumnFrom2DArr2(arr, columnIndex) {
const column = [];
for (let i = 0; i < arr.length; i++) {
column.push(arr[i][columnIndex]);
}
return column;
}
exports3.getColumnFrom2DArr = getColumnFrom2DArr2;
function multiplyTransformMatrices2(mat1, mat2) {
const product = [];
const size = mat1.length;
for (let row = 0; row < size; row++) {
product.push([]);
for (let col = 0; col < size; col++) {
product[row].push(dot6(mat1[row], getColumnFrom2DArr2(mat2, col)));
}
}
return product;
}
function buildRotationMatrix2(rotation, center) {
const cosA = Math.cos(rotation);
const sinA = Math.sin(rotation);
const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];
const translationMatrix = buildTranslationMatrix2(center[0], center[1]);
const translationTimesRotation = multiplyTransformMatrices2(translationMatrix, rotationMatrix);
const negativeTranslationMatrix = buildTranslationMatrix2(-center[0], -center[1]);
return multiplyTransformMatrices2(translationTimesRotation, negativeTranslationMatrix);
}
exports3.buildRotationMatrix = buildRotationMatrix2;
function invertTransformMatrix2(matrix) {
const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];
const translationComponent = [matrix[0][2], matrix[1][2]];
const invertedTranslation = [
-dot6(rotationComponent[0], translationComponent),
-dot6(rotationComponent[1], translationComponent)
];
return [
rotationComponent[0].concat(invertedTranslation[0]),
rotationComponent[1].concat(invertedTranslation[1]),
[0, 0, 1]
];
}
exports3.invertTransformMatrix = invertTransformMatrix2;
function rotatePoint2(homogeneousCoordinate, rotationMatrix) {
return [
dot6(homogeneousCoordinate, rotationMatrix[0]),
dot6(homogeneousCoordinate, rotationMatrix[1])
];
}
exports3.rotatePoint = rotatePoint2;
function xyDistanceBetweenPoints(a, b) {
return Math.sqrt((a[0] - b[0]) ** 2 + (a[1] - b[1]) ** 2);
}
exports3.xyDistanceBetweenPoints = xyDistanceBetweenPoints;
});
// src/face/facepipeline.js
var require_facepipeline = __commonJS((exports3) => {
const bounding = __toModule(require_box());
const keypoints = __toModule(require_keypoints());
const util145 = __toModule(require_util());
const LANDMARKS_COUNT = 468;
const MESH_MOUTH_INDEX = 13;
const MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [MESH_MOUTH_INDEX, keypoints.MESH_ANNOTATIONS["midwayBetweenEyes"][0]];
const BLAZEFACE_MOUTH_INDEX = 3;
const BLAZEFACE_NOSE_INDEX = 2;
const BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [BLAZEFACE_MOUTH_INDEX, BLAZEFACE_NOSE_INDEX];
const LEFT_EYE_OUTLINE = keypoints.MESH_ANNOTATIONS["leftEyeLower0"];
const LEFT_EYE_BOUNDS = [LEFT_EYE_OUTLINE[0], LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length - 1]];
const RIGHT_EYE_OUTLINE = keypoints.MESH_ANNOTATIONS["rightEyeLower0"];
const RIGHT_EYE_BOUNDS = [RIGHT_EYE_OUTLINE[0], RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length - 1]];
const IRIS_UPPER_CENTER_INDEX = 3;
const IRIS_LOWER_CENTER_INDEX = 4;
const IRIS_IRIS_INDEX = 71;
const IRIS_NUM_COORDINATES = 76;
function replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {
for (let i = 0; i < keypoints.MESH_TO_IRIS_INDICES_MAP.length; i++) {
const {key, indices} = keypoints.MESH_TO_IRIS_INDICES_MAP[i];
const originalIndices = keypoints.MESH_ANNOTATIONS[`${prefix}${key}`];
const shouldReplaceAllKeys = keys == null;
if (shouldReplaceAllKeys || keys.includes(key)) {
for (let j = 0; j < indices.length; j++) {
const index = indices[j];
rawCoords[originalIndices[j]] = [
newCoords[index][0],
newCoords[index][1],
(newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2
];
}
}
}
}
class Pipeline {
constructor(boundingBoxDetector, meshDetector, irisModel, config2) {
this.storedBoxes = [];
this.runsWithoutFaceDetector = 0;
this.boundingBoxDetector = boundingBoxDetector;
this.meshDetector = meshDetector;
this.irisModel = irisModel;
this.meshWidth = config2.mesh.inputSize;
this.meshHeight = config2.mesh.inputSize;
this.irisSize = config2.iris.inputSize;
this.irisEnlarge = 2.3;
this.skipped = 1e3;
this.detectedFaces = 0;
}
transformRawCoords(rawCoords, box, angle, rotationMatrix) {
const boxSize = bounding.getBoxSize({startPoint: box.startPoint, endPoint: box.endPoint});
const scaleFactor = [boxSize[0] / this.meshWidth, boxSize[1] / this.meshHeight];
const coordsScaled = rawCoords.map((coord) => [
scaleFactor[0] * (coord[0] - this.meshWidth / 2),
scaleFactor[1] * (coord[1] - this.meshHeight / 2),
coord[2]
]);
const coordsRotationMatrix = util145.buildRotationMatrix(angle, [0, 0]);
const coordsRotated = coordsScaled.map((coord) => [...util145.rotatePoint(coord, coordsRotationMatrix), coord[2]]);
const inverseRotationMatrix = util145.invertTransformMatrix(rotationMatrix);
const boxCenter = [...bounding.getBoxCenter({startPoint: box.startPoint, endPoint: box.endPoint}), 1];
const originalBoxCenter = [
util145.dot(boxCenter, inverseRotationMatrix[0]),
util145.dot(boxCenter, inverseRotationMatrix[1])
];
return coordsRotated.map((coord) => [
coord[0] + originalBoxCenter[0],
coord[1] + originalBoxCenter[1],
coord[2]
]);
}
getLeftToRightEyeDepthDifference(rawCoords) {
const leftEyeZ = rawCoords[LEFT_EYE_BOUNDS[0]][2];
const rightEyeZ = rawCoords[RIGHT_EYE_BOUNDS[0]][2];
return leftEyeZ - rightEyeZ;
}
getEyeBox(rawCoords, face2, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {
const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));
const boxSize = bounding.getBoxSize(box);
let crop = dist_exports2.image.cropAndResize(face2, [[
box.startPoint[1] / this.meshHeight,
box.startPoint[0] / this.meshWidth,
box.endPoint[1] / this.meshHeight,
box.endPoint[0] / this.meshWidth
]], [0], [this.irisSize, this.irisSize]);
if (flip) {
crop = dist_exports2.image.flipLeftRight(crop);
}
return {box, boxSize, crop};
}
getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {
const eyeRawCoords = [];
for (let i = 0; i < IRIS_NUM_COORDINATES; i++) {
const x = eyeData[i * 3];
const y = eyeData[i * 3 + 1];
const z = eyeData[i * 3 + 2];
eyeRawCoords.push([
(flip ? 1 - x / this.irisSize : x / this.irisSize) * eyeBoxSize[0] + eyeBox.startPoint[0],
y / this.irisSize * eyeBoxSize[1] + eyeBox.startPoint[1],
z
]);
}
return {rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(IRIS_IRIS_INDEX)};
}
getAdjustedIrisCoords(rawCoords, irisCoords, direction) {
const upperCenterZ = rawCoords[keypoints.MESH_ANNOTATIONS[`${direction}EyeUpper0`][IRIS_UPPER_CENTER_INDEX]][2];
const lowerCenterZ = rawCoords[keypoints.MESH_ANNOTATIONS[`${direction}EyeLower0`][IRIS_LOWER_CENTER_INDEX]][2];
const averageZ = (upperCenterZ + lowerCenterZ) / 2;
return irisCoords.map((coord, i) => {
let z = averageZ;
if (i === 2) {
z = upperCenterZ;
} else if (i === 4) {
z = lowerCenterZ;
}
return [coord[0], coord[1], z];
});
}
async predict(input2, config2) {
this.skipped++;
let useFreshBox = false;
let detector;
if (this.skipped > config2.detector.skipFrames || !config2.mesh.enabled) {
detector = await this.boundingBoxDetector.getBoundingBoxes(input2);
if (input2.shape[1] !== 255 && input2.shape[2] !== 255)
this.skipped = 0;
}
if (detector && detector.boxes && detector.boxes.length > 0 && (!config2.mesh.enabled || detector.boxes.length !== this.detectedFaces && this.detectedFaces !== config2.detector.maxFaces)) {
this.storedBoxes = [];
this.detectedFaces = 0;
for (const possible of detector.boxes) {
this.storedBoxes.push({startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence});
}
if (this.storedBoxes.length > 0)
useFreshBox = true;
}
if (useFreshBox) {
if (!detector || !detector.boxes || detector.boxes.length === 0) {
this.storedBoxes = [];
this.detectedFaces = 0;
return null;
}
for (const i in this.storedBoxes) {
const scaledBox = bounding.scaleBoxCoordinates({startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint}, detector.scaleFactor);
const enlargedBox = bounding.enlargeBox(scaledBox);
const landmarks = this.storedBoxes[i].landmarks.arraySync();
const confidence = this.storedBoxes[i].confidence;
this.storedBoxes[i] = {...enlargedBox, confidence, landmarks};
}
this.runsWithoutFaceDetector = 0;
}
if (detector && detector.boxes) {
detector.boxes.forEach((prediction) => {
prediction.box.startPoint.dispose();
prediction.box.endPoint.dispose();
prediction.landmarks.dispose();
});
}
let results = dist_exports2.tidy(() => this.storedBoxes.map((box, i) => {
let angle = 0;
const boxLandmarksFromMeshModel = box.landmarks.length >= LANDMARKS_COUNT;
let [indexOfMouth, indexOfForehead] = MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;
if (boxLandmarksFromMeshModel === false) {
[indexOfMouth, indexOfForehead] = BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;
}
angle = util145.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);
const faceCenter = bounding.getBoxCenter({startPoint: box.startPoint, endPoint: box.endPoint});
const faceCenterNormalized = [faceCenter[0] / input2.shape[2], faceCenter[1] / input2.shape[1]];
let rotatedImage = input2;
let rotationMatrix = util145.IDENTITY_MATRIX;
if (angle !== 0) {
rotatedImage = dist_exports2.image.rotateWithOffset(input2, angle, 0, faceCenterNormalized);
rotationMatrix = util145.buildRotationMatrix(-angle, faceCenter);
}
const boxCPU = {startPoint: box.startPoint, endPoint: box.endPoint};
const face2 = bounding.cutBoxFromImageAndResize(boxCPU, rotatedImage, [this.meshHeight, this.meshWidth]).div(255);
if (!config2.mesh.enabled) {
const prediction2 = {
coords: null,
box,
faceConfidence: null,
confidence: box.confidence,
image: face2
};
return prediction2;
}
const [, confidence, coords] = this.meshDetector.predict(face2);
const confidenceVal = confidence.dataSync()[0];
confidence.dispose();
if (confidenceVal < config2.detector.minConfidence) {
coords.dispose();
return null;
}
const coordsReshaped = dist_exports2.reshape(coords, [-1, 3]);
let rawCoords = coordsReshaped.arraySync();
if (config2.iris.enabled) {
const {box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop} = this.getEyeBox(rawCoords, face2, LEFT_EYE_BOUNDS[0], LEFT_EYE_BOUNDS[1], true);
const {box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop} = this.getEyeBox(rawCoords, face2, RIGHT_EYE_BOUNDS[0], RIGHT_EYE_BOUNDS[1]);
const eyePredictions = this.irisModel.predict(dist_exports2.concat([leftEyeCrop, rightEyeCrop]));
const eyePredictionsData = eyePredictions.dataSync();
eyePredictions.dispose();
const leftEyeData = eyePredictionsData.slice(0, IRIS_NUM_COORDINATES * 3);
const {rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords} = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);
const rightEyeData = eyePredictionsData.slice(IRIS_NUM_COORDINATES * 3);
const {rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords} = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);
const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);
if (Math.abs(leftToRightEyeDepthDifference) < 30) {
replaceRawCoordinates(rawCoords, leftEyeRawCoords, "left");
replaceRawCoordinates(rawCoords, rightEyeRawCoords, "right");
} else if (leftToRightEyeDepthDifference < 1) {
replaceRawCoordinates(rawCoords, leftEyeRawCoords, "left", ["EyeUpper0", "EyeLower0"]);
} else {
replaceRawCoordinates(rawCoords, rightEyeRawCoords, "right", ["EyeUpper0", "EyeLower0"]);
}
const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, "left");
const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, "right");
rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);
}
const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);
dist_exports2.dispose(rawCoords);
const landmarksBox = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData));
const transformedCoords = dist_exports2.tensor2d(transformedCoordsData);
const prediction = {
coords: transformedCoords,
box: landmarksBox,
faceConfidence: confidenceVal,
confidence: box.confidence,
image: face2
};
this.storedBoxes[i] = {...landmarksBox, landmarks: transformedCoords.arraySync(), confidence: box.confidence, faceConfidence: confidenceVal};
return prediction;
}));
results = results.filter((a) => a !== null);
this.detectedFaces = results.length;
return results;
}
calculateLandmarksBoundingBox(landmarks) {
const xs = landmarks.map((d) => d[0]);
const ys = landmarks.map((d) => d[1]);
const startPoint = [Math.min(...xs), Math.min(...ys)];
const endPoint = [Math.max(...xs), Math.max(...ys)];
return {startPoint, endPoint, landmarks};
}
}
exports3.Pipeline = Pipeline;
});
// src/face/uvcoords.js
var require_uvcoords = __commonJS((exports3) => {
exports3.UV_COORDS = [
[0.499976992607117, 0.652534008026123],
[0.500025987625122, 0.547487020492554],
[0.499974012374878, 0.602371990680695],
[0.482113003730774, 0.471979022026062],
[0.500150978565216, 0.527155995368958],
[0.499909996986389, 0.498252987861633],
[0.499523013830185, 0.40106201171875],
[0.289712011814117, 0.380764007568359],
[0.499954998493195, 0.312398016452789],
[0.499987006187439, 0.269918978214264],
[0.500023007392883, 0.107050001621246],
[0.500023007392883, 0.666234016418457],
[0.5000159740448, 0.679224014282227],
[0.500023007392883, 0.692348003387451],
[0.499976992607117, 0.695277988910675],
[0.499976992607117, 0.70593398809433],
[0.499976992607117, 0.719385027885437],
[0.499976992607117, 0.737019002437592],
[0.499967992305756, 0.781370997428894],
[0.499816000461578, 0.562981009483337],
[0.473773002624512, 0.573909997940063],
[0.104906998574734, 0.254140973091125],
[0.365929991006851, 0.409575998783112],
[0.338757991790771, 0.41302502155304],
[0.311120003461838, 0.409460008144379],
[0.274657994508743, 0.389131009578705],
[0.393361985683441, 0.403706014156342],
[0.345234006643295, 0.344011008739471],
[0.370094001293182, 0.346076011657715],
[0.319321990013123, 0.347265005111694],
[0.297903001308441, 0.353591024875641],
[0.24779200553894, 0.410809993743896],
[0.396889001131058, 0.842755019664764],
[0.280097991228104, 0.375599980354309],
[0.106310002505779, 0.399955987930298],
[0.2099249958992, 0.391353011131287],
[0.355807989835739, 0.534406006336212],
[0.471751004457474, 0.65040397644043],
[0.474155008792877, 0.680191993713379],
[0.439785003662109, 0.657229006290436],
[0.414617002010345, 0.66654098033905],
[0.450374007225037, 0.680860996246338],
[0.428770989179611, 0.682690978050232],
[0.374971002340317, 0.727805018424988],
[0.486716985702515, 0.547628998756409],
[0.485300987958908, 0.527395009994507],
[0.257764995098114, 0.314490020275116],
[0.401223003864288, 0.455172002315521],
[0.429818987846375, 0.548614978790283],
[0.421351999044418, 0.533740997314453],
[0.276895999908447, 0.532056987285614],
[0.483370006084442, 0.499586999416351],
[0.33721199631691, 0.282882988452911],
[0.296391993761063, 0.293242990970612],
[0.169294998049736, 0.193813979625702],
[0.447580009698868, 0.302609980106354],
[0.392390012741089, 0.353887975215912],
[0.354490011930466, 0.696784019470215],
[0.067304998636246, 0.730105042457581],
[0.442739009857178, 0.572826027870178],
[0.457098007202148, 0.584792017936707],
[0.381974011659622, 0.694710969924927],
[0.392388999462128, 0.694203019142151],
[0.277076005935669, 0.271932005882263],
[0.422551989555359, 0.563233017921448],
[0.385919004678726, 0.281364023685455],
[0.383103013038635, 0.255840003490448],
[0.331431001424789, 0.119714021682739],
[0.229923993349075, 0.232002973556519],
[0.364500999450684, 0.189113974571228],
[0.229622006416321, 0.299540996551514],
[0.173287004232407, 0.278747975826263],
[0.472878992557526, 0.666198015213013],
[0.446828007698059, 0.668527007102966],
[0.422762006521225, 0.673889994621277],
[0.445307999849319, 0.580065965652466],
[0.388103008270264, 0.693961024284363],
[0.403039008378983, 0.706539988517761],
[0.403629004955292, 0.693953037261963],
[0.460041999816895, 0.557139039039612],
[0.431158006191254, 0.692366003990173],
[0.452181994915009, 0.692366003990173],
[0.475387006998062, 0.692366003990173],
[0.465828001499176, 0.779190003871918],
[0.472328990697861, 0.736225962638855],
[0.473087012767792, 0.717857003211975],
[0.473122000694275, 0.704625964164734],
[0.473033010959625, 0.695277988910675],
[0.427942007780075, 0.695277988910675],
[0.426479011774063, 0.703539967536926],
[0.423162013292313, 0.711845993995667],
[0.4183090031147, 0.720062971115112],
[0.390094995498657, 0.639572978019714],
[0.013953999616206, 0.560034036636353],
[0.499913990497589, 0.58014702796936],
[0.413199990987778, 0.69539999961853],
[0.409626007080078, 0.701822996139526],
[0.468080013990402, 0.601534962654114],
[0.422728985548019, 0.585985004901886],
[0.463079988956451, 0.593783974647522],
[0.37211999297142, 0.47341400384903],
[0.334562003612518, 0.496073007583618],
[0.411671012639999, 0.546965003013611],
[0.242175996303558, 0.14767599105835],
[0.290776997804642, 0.201445996761322],
[0.327338010072708, 0.256527006626129],
[0.399509996175766, 0.748921036720276],
[0.441727995872498, 0.261676013469696],
[0.429764986038208, 0.187834024429321],
[0.412198007106781, 0.108901023864746],
[0.288955003023148, 0.398952007293701],
[0.218936994671822, 0.435410976409912],
[0.41278201341629, 0.398970007896423],
[0.257135003805161, 0.355440020561218],
[0.427684992551804, 0.437960982322693],
[0.448339998722076, 0.536936044692993],
[0.178560003638268, 0.45755398273468],
[0.247308000922203, 0.457193970680237],
[0.286267012357712, 0.467674970626831],
[0.332827985286713, 0.460712015628815],
[0.368755996227264, 0.447206974029541],
[0.398963987827301, 0.432654976844788],
[0.476410001516342, 0.405806005001068],
[0.189241006970406, 0.523923993110657],
[0.228962004184723, 0.348950982093811],
[0.490725994110107, 0.562400996685028],
[0.404670000076294, 0.485132992267609],
[0.019469000399113, 0.401564002037048],
[0.426243007183075, 0.420431017875671],
[0.396993011236191, 0.548797011375427],
[0.266469985246658, 0.376977026462555],
[0.439121007919312, 0.51895797252655],
[0.032313998788595, 0.644356966018677],
[0.419054001569748, 0.387154996395111],
[0.462783008813858, 0.505746960639954],
[0.238978996872902, 0.779744982719421],
[0.198220998048782, 0.831938028335571],
[0.107550002634525, 0.540755033493042],
[0.183610007166862, 0.740257024765015],
[0.134409993886948, 0.333683013916016],
[0.385764002799988, 0.883153975009918],
[0.490967005491257, 0.579378008842468],
[0.382384985685349, 0.508572995662689],
[0.174399003386497, 0.397670984268188],
[0.318785011768341, 0.39623498916626],
[0.343364000320435, 0.400596976280212],
[0.396100014448166, 0.710216999053955],
[0.187885001301765, 0.588537991046906],
[0.430987000465393, 0.944064974784851],
[0.318993002176285, 0.898285031318665],
[0.266247987747192, 0.869701027870178],
[0.500023007392883, 0.190576016902924],
[0.499976992607117, 0.954452991485596],
[0.366169989109039, 0.398822009563446],
[0.393207013607025, 0.39553701877594],
[0.410373002290726, 0.391080021858215],
[0.194993004202843, 0.342101991176605],
[0.388664990663528, 0.362284004688263],
[0.365961998701096, 0.355970978736877],
[0.343364000320435, 0.355356991291046],
[0.318785011768341, 0.35834002494812],
[0.301414996385574, 0.363156020641327],
[0.058132998645306, 0.319076001644135],
[0.301414996385574, 0.387449026107788],
[0.499987989664078, 0.618434011936188],
[0.415838003158569, 0.624195992946625],
[0.445681989192963, 0.566076993942261],
[0.465844005346298, 0.620640993118286],
[0.49992299079895, 0.351523995399475],
[0.288718998432159, 0.819945991039276],
[0.335278987884521, 0.852819979190826],
[0.440512001514435, 0.902418971061707],
[0.128294005990028, 0.791940987110138],
[0.408771991729736, 0.373893976211548],
[0.455606997013092, 0.451801002025604],
[0.499877005815506, 0.908990025520325],
[0.375436991453171, 0.924192011356354],
[0.11421000212431, 0.615022003650665],
[0.448662012815475, 0.695277988910675],
[0.4480200111866, 0.704632043838501],
[0.447111994028091, 0.715808033943176],
[0.444831997156143, 0.730794012546539],
[0.430011987686157, 0.766808986663818],
[0.406787008047104, 0.685672998428345],
[0.400738000869751, 0.681069016456604],
[0.392399996519089, 0.677703022956848],
[0.367855995893478, 0.663918972015381],
[0.247923001646996, 0.601333022117615],
[0.452769994735718, 0.420849978923798],
[0.43639200925827, 0.359887003898621],
[0.416164010763168, 0.368713974952698],
[0.413385987281799, 0.692366003990173],
[0.228018000721931, 0.683571994304657],
[0.468268007040024, 0.352671027183533],
[0.411361992359161, 0.804327011108398],
[0.499989002943039, 0.469825029373169],
[0.479153990745544, 0.442654013633728],
[0.499974012374878, 0.439637005329132],
[0.432112008333206, 0.493588984012604],
[0.499886006116867, 0.866917014122009],
[0.49991300702095, 0.821729004383087],
[0.456548988819122, 0.819200992584229],
[0.344549000263214, 0.745438992977142],
[0.37890899181366, 0.574010014533997],
[0.374292999505997, 0.780184984207153],
[0.319687992334366, 0.570737957954407],
[0.357154995203018, 0.604269981384277],
[0.295284003019333, 0.621580958366394],
[0.447750002145767, 0.862477004528046],
[0.410986006259918, 0.508723020553589],
[0.31395098567009, 0.775308012962341],
[0.354128003120422, 0.812552988529205],
[0.324548006057739, 0.703992962837219],
[0.189096003770828, 0.646299958229065],
[0.279776990413666, 0.71465802192688],
[0.1338230073452, 0.682700991630554],
[0.336768001317978, 0.644733011722565],
[0.429883986711502, 0.466521978378296],
[0.455527991056442, 0.548622965812683],
[0.437114000320435, 0.558896005153656],
[0.467287987470627, 0.529924988746643],
[0.414712011814117, 0.335219979286194],
[0.37704598903656, 0.322777986526489],
[0.344107985496521, 0.320150971412659],
[0.312875986099243, 0.32233202457428],
[0.283526003360748, 0.333190023899078],
[0.241245999932289, 0.382785975933075],
[0.102986000478268, 0.468762993812561],
[0.267612010240555, 0.424560010433197],
[0.297879010438919, 0.433175981044769],
[0.333433985710144, 0.433878004550934],
[0.366427004337311, 0.426115989685059],
[0.396012008190155, 0.416696012020111],
[0.420121014118195, 0.41022801399231],
[0.007561000064015, 0.480777025222778],
[0.432949006557465, 0.569517970085144],
[0.458638995885849, 0.479089021682739],
[0.473466008901596, 0.545744001865387],
[0.476087987422943, 0.563830018043518],
[0.468472003936768, 0.555056989192963],
[0.433990985155106, 0.582361996173859],
[0.483518004417419, 0.562983989715576],
[0.482482999563217, 0.57784903049469],
[0.42645001411438, 0.389798998832703],
[0.438998997211456, 0.39649498462677],
[0.450067013502121, 0.400434017181396],
[0.289712011814117, 0.368252992630005],
[0.276670008897781, 0.363372981548309],
[0.517862021923065, 0.471948027610779],
[0.710287988185883, 0.380764007568359],
[0.526226997375488, 0.573909997940063],
[0.895093023777008, 0.254140973091125],
[0.634069979190826, 0.409575998783112],
[0.661242008209229, 0.41302502155304],
[0.688880026340485, 0.409460008144379],
[0.725341975688934, 0.389131009578705],
[0.606630027294159, 0.40370500087738],
[0.654766023159027, 0.344011008739471],
[0.629905998706818, 0.346076011657715],
[0.680678009986877, 0.347265005111694],
[0.702096998691559, 0.353591024875641],
[0.75221198797226, 0.410804986953735],
[0.602918028831482, 0.842862963676453],
[0.719901978969574, 0.375599980354309],
[0.893692970275879, 0.399959981441498],
[0.790081977844238, 0.391354024410248],
[0.643998026847839, 0.534487962722778],
[0.528249025344849, 0.65040397644043],
[0.525849997997284, 0.680191040039062],
[0.560214996337891, 0.657229006290436],
[0.585384011268616, 0.66654098033905],
[0.549625992774963, 0.680860996246338],
[0.57122802734375, 0.682691991329193],
[0.624852001667023, 0.72809898853302],
[0.513050019741058, 0.547281980514526],
[0.51509702205658, 0.527251958847046],
[0.742246985435486, 0.314507007598877],
[0.598631024360657, 0.454979002475739],
[0.570338010787964, 0.548575043678284],
[0.578631997108459, 0.533622980117798],
[0.723087012767792, 0.532054007053375],
[0.516445994377136, 0.499638974666595],
[0.662801027297974, 0.282917976379395],
[0.70362401008606, 0.293271005153656],
[0.830704987049103, 0.193813979625702],
[0.552385985851288, 0.302568018436432],
[0.607609987258911, 0.353887975215912],
[0.645429015159607, 0.696707010269165],
[0.932694971561432, 0.730105042457581],
[0.557260990142822, 0.572826027870178],
[0.542901992797852, 0.584792017936707],
[0.6180260181427, 0.694710969924927],
[0.607590973377228, 0.694203019142151],
[0.722943007946014, 0.271963000297546],
[0.577413976192474, 0.563166975975037],
[0.614082992076874, 0.281386971473694],
[0.616907000541687, 0.255886018276215],
[0.668509006500244, 0.119913995265961],
[0.770092010498047, 0.232020974159241],
[0.635536015033722, 0.189248979091644],
[0.77039098739624, 0.299556016921997],
[0.826722025871277, 0.278755009174347],
[0.527121007442474, 0.666198015213013],
[0.553171992301941, 0.668527007102966],
[0.577238023281097, 0.673889994621277],
[0.554691970348358, 0.580065965652466],
[0.611896991729736, 0.693961024284363],
[0.59696102142334, 0.706539988517761],
[0.596370995044708, 0.693953037261963],
[0.539958000183105, 0.557139039039612],
[0.568841993808746, 0.692366003990173],
[0.547818005084991, 0.692366003990173],
[0.52461302280426, 0.692366003990173],
[0.534089982509613, 0.779141008853912],
[0.527670979499817, 0.736225962638855],
[0.526912987232208, 0.717857003211975],
[0.526877999305725, 0.704625964164734],
[0.526966989040375, 0.695277988910675],
[0.572058022022247, 0.695277988910675],
[0.573521018028259, 0.703539967536926],
[0.57683801651001, 0.711845993995667],
[0.581691026687622, 0.720062971115112],
[0.609944999217987, 0.639909982681274],
[0.986046016216278, 0.560034036636353],
[0.5867999792099, 0.69539999961853],
[0.590372025966644, 0.701822996139526],
[0.531915009021759, 0.601536989212036],
[0.577268004417419, 0.585934996604919],
[0.536915004253387, 0.593786001205444],
[0.627542972564697, 0.473352015018463],
[0.665585994720459, 0.495950996875763],
[0.588353991508484, 0.546862006187439],
[0.757824003696442, 0.14767599105835],
[0.709249973297119, 0.201507985591888],
[0.672684013843536, 0.256581008434296],
[0.600408971309662, 0.74900496006012],
[0.55826598405838, 0.261672019958496],
[0.570303976535797, 0.187870979309082],
[0.588165998458862, 0.109044015407562],
[0.711045026779175, 0.398952007293701],
[0.781069993972778, 0.435405015945435],
[0.587247014045715, 0.398931980133057],
[0.742869973182678, 0.355445981025696],
[0.572156012058258, 0.437651991844177],
[0.55186802148819, 0.536570012569427],
[0.821442008018494, 0.457556009292603],
[0.752701997756958, 0.457181990146637],
[0.71375697851181, 0.467626988887787],
[0.66711300611496, 0.460672974586487],
[0.631101012229919, 0.447153985500336],
[0.6008620262146, 0.432473003864288],
[0.523481011390686, 0.405627012252808],
[0.810747981071472, 0.523926019668579],
[0.771045982837677, 0.348959028720856],
[0.509127020835876, 0.562718033790588],
[0.595292985439301, 0.485023975372314],
[0.980530977249146, 0.401564002037048],
[0.573499977588654, 0.420000016689301],
[0.602994978427887, 0.548687994480133],
[0.733529984951019, 0.376977026462555],
[0.560611009597778, 0.519016981124878],
[0.967685997486115, 0.644356966018677],
[0.580985009670258, 0.387160003185272],
[0.537728011608124, 0.505385041236877],
[0.760966002941132, 0.779752969741821],
[0.801778972148895, 0.831938028335571],
[0.892440974712372, 0.54076099395752],
[0.816350996494293, 0.740260004997253],
[0.865594983100891, 0.333687007427216],
[0.614073991775513, 0.883246004581451],
[0.508952975273132, 0.579437971115112],
[0.617941975593567, 0.508316040039062],
[0.825608015060425, 0.397674977779388],
[0.681214988231659, 0.39623498916626],
[0.656635999679565, 0.400596976280212],
[0.603900015354156, 0.710216999053955],
[0.81208598613739, 0.588539004325867],
[0.56801301240921, 0.944564998149872],
[0.681007981300354, 0.898285031318665],
[0.733752012252808, 0.869701027870178],
[0.633830010890961, 0.398822009563446],
[0.606792986392975, 0.39553701877594],
[0.589659988880157, 0.391062021255493],
[0.805015981197357, 0.342108011245728],
[0.611334979534149, 0.362284004688263],
[0.634037971496582, 0.355970978736877],
[0.656635999679565, 0.355356991291046],
[0.681214988231659, 0.35834002494812],
[0.698584973812103, 0.363156020641327],
[0.941866993904114, 0.319076001644135],
[0.698584973812103, 0.387449026107788],
[0.584177017211914, 0.624107003211975],
[0.554318010807037, 0.566076993942261],
[0.534153997898102, 0.62064003944397],
[0.711217999458313, 0.819975018501282],
[0.664629995822906, 0.852871000766754],
[0.559099972248077, 0.902631998062134],
[0.871706008911133, 0.791940987110138],
[0.591234028339386, 0.373893976211548],
[0.544341027736664, 0.451583981513977],
[0.624562978744507, 0.924192011356354],
[0.88577002286911, 0.615028977394104],
[0.551338016986847, 0.695277988910675],
[0.551980018615723, 0.704632043838501],
[0.552887976169586, 0.715808033943176],
[0.555167973041534, 0.730794012546539],
[0.569944024085999, 0.767035007476807],
[0.593203008174896, 0.685675978660583],
[0.599261999130249, 0.681069016456604],
[0.607599973678589, 0.677703022956848],
[0.631937980651855, 0.663500010967255],
[0.752032995223999, 0.601315021514893],
[0.547226011753082, 0.420395016670227],
[0.563543975353241, 0.359827995300293],
[0.583841025829315, 0.368713974952698],
[0.586614012718201, 0.692366003990173],
[0.771915018558502, 0.683578014373779],
[0.531597018241882, 0.352482974529266],
[0.588370978832245, 0.804440975189209],
[0.52079701423645, 0.442565023899078],
[0.567984998226166, 0.493479013442993],
[0.543282985687256, 0.819254994392395],
[0.655317008495331, 0.745514988899231],
[0.621008992195129, 0.574018001556396],
[0.625559985637665, 0.78031200170517],
[0.680198013782501, 0.570719003677368],
[0.64276397228241, 0.604337990283966],
[0.704662978649139, 0.621529996395111],
[0.552012026309967, 0.862591981887817],
[0.589071989059448, 0.508637011051178],
[0.685944974422455, 0.775357007980347],
[0.645735025405884, 0.812640011310577],
[0.675342977046967, 0.703978002071381],
[0.810858011245728, 0.646304965019226],
[0.72012197971344, 0.714666962623596],
[0.866151988506317, 0.682704985141754],
[0.663187026977539, 0.644596993923187],
[0.570082008838654, 0.466325998306274],
[0.544561982154846, 0.548375964164734],
[0.562758982181549, 0.558784961700439],
[0.531987011432648, 0.530140042304993],
[0.585271000862122, 0.335177004337311],
[0.622952997684479, 0.32277899980545],
[0.655896008014679, 0.320163011550903],
[0.687132000923157, 0.322345972061157],
[0.716481983661652, 0.333200991153717],
[0.758756995201111, 0.382786989212036],
[0.897013008594513, 0.468769013881683],
[0.732392013072968, 0.424547016620636],
[0.70211398601532, 0.433162987232208],
[0.66652500629425, 0.433866024017334],
[0.633504986763, 0.426087975502014],
[0.603875994682312, 0.416586995124817],
[0.579657971858978, 0.409945011138916],
[0.992439985275269, 0.480777025222778],
[0.567192018032074, 0.569419980049133],
[0.54136598110199, 0.478899002075195],
[0.526564002037048, 0.546118021011353],
[0.523913025856018, 0.563830018043518],
[0.531529009342194, 0.555056989192963],
[0.566035985946655, 0.582329034805298],
[0.51631098985672, 0.563053965568542],
[0.5174720287323, 0.577877044677734],
[0.573594987392426, 0.389806985855103],
[0.560697972774506, 0.395331978797913],
[0.549755990505219, 0.399751007556915],
[0.710287988185883, 0.368252992630005],
[0.723330020904541, 0.363372981548309]
];
});
// src/face/facemesh.js
var require_facemesh = __commonJS((exports3) => {
const blazeface = __toModule(require_blazeface());
const keypoints = __toModule(require_keypoints());
const pipe = __toModule(require_facepipeline());
const uv_coords = __toModule(require_uvcoords());
class MediaPipeFaceMesh {
constructor(blazeFace, blazeMeshModel, irisModel, config2) {
this.pipeline = new pipe.Pipeline(blazeFace, blazeMeshModel, irisModel, config2);
if (config2)
this.config = config2;
}
async estimateFaces(input2, config2) {
if (config2)
this.config = config2;
const predictions = await this.pipeline.predict(input2, config2);
const results = [];
for (const prediction of predictions || []) {
if (prediction.isDisposedInternal)
continue;
const mesh = prediction.coords ? prediction.coords.arraySync() : null;
const annotations = {};
if (mesh && mesh.length > 0) {
for (const key in keypoints.MESH_ANNOTATIONS) {
if (this.config.iris.enabled || key.includes("Iris") === false) {
annotations[key] = keypoints.MESH_ANNOTATIONS[key].map((index) => mesh[index]);
}
}
}
results.push({
confidence: prediction.confidence || 0,
box: prediction.box ? [prediction.box.startPoint[0], prediction.box.startPoint[1], prediction.box.endPoint[0] - prediction.box.startPoint[0], prediction.box.endPoint[1] - prediction.box.startPoint[1]] : 0,
mesh,
annotations,
image: prediction.image ? dist_exports2.clone(prediction.image) : null
});
if (prediction.coords)
prediction.coords.dispose();
if (prediction.image)
prediction.image.dispose();
}
return results;
}
}
async function load(config2) {
const models4 = await Promise.all([
blazeface.load(config2),
loadGraphModel2(config2.mesh.modelPath, {fromTFHub: config2.mesh.modelPath.includes("tfhub.dev")}),
loadGraphModel2(config2.iris.modelPath, {fromTFHub: config2.iris.modelPath.includes("tfhub.dev")})
]);
const faceMesh = new MediaPipeFaceMesh(models4[0], models4[1], models4[2], config2);
console.log(`Human: load model: ${config2.mesh.modelPath.match(/\/(.*)\./)[1]}`);
console.log(`Human: load model: ${config2.iris.modelPath.match(/\/(.*)\./)[1]}`);
return faceMesh;
}
exports3.load = load;
exports3.MediaPipeFaceMesh = MediaPipeFaceMesh;
exports3.uv_coords = uv_coords;
exports3.triangulation = triangulation_default;
});
// src/profile.js
var require_profile = __commonJS((exports3) => {
const profileData = {};
function profile3(name, data2) {
if (!data2 || !data2.kernels)
return;
const maxResults = 5;
const time2 = data2.kernels.filter((a) => a.kernelTimeMs > 0).reduce((a, b) => a += b.kernelTimeMs, 0);
const slowest = data2.kernels.map((a, i) => {
a.id = i;
return a;
}).filter((a) => a.kernelTimeMs > 0).sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);
const largest = data2.kernels.map((a, i) => {
a.id = i;
return a;
}).filter((a) => a.totalBytesSnapshot > 0).sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);
if (slowest.length > maxResults)
slowest.length = maxResults;
if (largest.length > maxResults)
largest.length = maxResults;
const res = {newBytes: data2.newBytes, newTensors: data2.newTensors, peakBytes: data2.peakBytes, numKernelOps: data2.kernels.length, timeKernelOps: time2, slowestKernelOps: slowest, largestKernelOps: largest};
profileData[name] = res;
console.log("Human profiler", name, res);
}
exports3.run = profile3;
});
// src/age/age.js
var require_age = __commonJS((exports3) => {
const profile3 = __toModule(require_profile());
const models4 = {};
let last = {age: 0};
let frame4 = Number.MAX_SAFE_INTEGER;
const zoom = [0, 0];
async function load(config2) {
if (!models4.age) {
models4.age = await loadGraphModel2(config2.face.age.modelPath);
console.log(`Human: load model: ${config2.face.age.modelPath.match(/\/(.*)\./)[1]}`);
}
return models4.age;
}
async function predict(image4, config2) {
if (frame4 < config2.face.age.skipFrames && last.age && last.age > 0) {
frame4 += 1;
return last;
}
frame4 = 0;
return new Promise(async (resolve) => {
const box = [[
image4.shape[1] * zoom[0] / image4.shape[1],
image4.shape[2] * zoom[1] / image4.shape[2],
(image4.shape[1] - image4.shape[1] * zoom[0]) / image4.shape[1],
(image4.shape[2] - image4.shape[2] * zoom[1]) / image4.shape[2]
]];
const resize = dist_exports2.image.cropAndResize(image4, box, [0], [config2.face.age.inputSize, config2.face.age.inputSize]);
const enhance = dist_exports2.mul(resize, [255]);
dist_exports2.dispose(resize);
let ageT;
const obj = {};
if (!config2.profile) {
if (config2.face.age.enabled)
ageT = await models4.age.predict(enhance);
} else {
const profileAge = config2.face.age.enabled ? await dist_exports2.profile(() => models4.age.predict(enhance)) : {};
ageT = profileAge.result.clone();
profileAge.result.dispose();
profile3.run("age", profileAge);
}
enhance.dispose();
if (ageT) {
const data2 = ageT.dataSync();
obj.age = Math.trunc(10 * data2[0]) / 10;
}
ageT.dispose();
last = obj;
resolve(obj);
});
}
exports3.predict = predict;
exports3.load = load;
});
// src/gender/gender.js
var require_gender = __commonJS((exports3) => {
const profile3 = __toModule(require_profile());
const models4 = {};
let last = {gender: ""};
let frame4 = Number.MAX_SAFE_INTEGER;
let alternative = false;
const zoom = [0, 0];
const rgb = [0.2989, 0.587, 0.114];
async function load(config2) {
if (!models4.gender) {
models4.gender = await loadGraphModel2(config2.face.gender.modelPath);
alternative = models4.gender.inputs[0].shape[3] === 1;
console.log(`Human: load model: ${config2.face.gender.modelPath.match(/\/(.*)\./)[1]}`);
}
return models4.gender;
}
async function predict(image4, config2) {
if (frame4 < config2.face.gender.skipFrames && last.gender !== "") {
frame4 += 1;
return last;
}
frame4 = 0;
return new Promise(async (resolve) => {
const box = [[
image4.shape[1] * zoom[0] / image4.shape[1],
image4.shape[2] * zoom[1] / image4.shape[2],
(image4.shape[1] - image4.shape[1] * zoom[0]) / image4.shape[1],
(image4.shape[2] - image4.shape[2] * zoom[1]) / image4.shape[2]
]];
const resize = dist_exports2.image.cropAndResize(image4, box, [0], [config2.face.gender.inputSize, config2.face.gender.inputSize]);
let enhance;
if (alternative) {
enhance = dist_exports2.tidy(() => {
const [red, green, blue] = dist_exports2.split(resize, 3, 3);
const redNorm = dist_exports2.mul(red, rgb[0]);
const greenNorm = dist_exports2.mul(green, rgb[1]);
const blueNorm = dist_exports2.mul(blue, rgb[2]);
const grayscale = dist_exports2.addN([redNorm, greenNorm, blueNorm]);
return grayscale.sub(0.5).mul(2);
});
} else {
enhance = dist_exports2.mul(resize, [255]);
}
dist_exports2.dispose(resize);
let genderT;
const obj = {};
if (!config2.profile) {
if (config2.face.gender.enabled)
genderT = await models4.gender.predict(enhance);
} else {
const profileGender = config2.face.gender.enabled ? await dist_exports2.profile(() => models4.gender.predict(enhance)) : {};
genderT = profileGender.result.clone();
profileGender.result.dispose();
profile3.run("gender", profileGender);
}
enhance.dispose();
if (genderT) {
const data2 = genderT.dataSync();
if (alternative) {
const confidence = Math.trunc(100 * Math.abs(data2[0] - data2[1])) / 100;
if (confidence > config2.face.gender.minConfidence) {
obj.gender = data2[0] > data2[1] ? "female" : "male";
obj.confidence = confidence;
}
} else {
const confidence = Math.trunc(200 * Math.abs(data2[0] - 0.5)) / 100;
if (confidence > config2.face.gender.minConfidence) {
obj.gender = data2[0] <= 0.5 ? "female" : "male";
obj.confidence = Math.min(0.99, confidence);
}
}
}
genderT.dispose();
last = obj;
resolve(obj);
});
}
exports3.predict = predict;
exports3.load = load;
});
// src/emotion/emotion.js
var require_emotion = __commonJS((exports3) => {
const profile3 = __toModule(require_profile());
const annotations = ["angry", "disgust", "fear", "happy", "sad", "surpise", "neutral"];
const models4 = {};
let last = [];
let frame4 = Number.MAX_SAFE_INTEGER;
const zoom = [0, 0];
const rgb = [0.2989, 0.587, 0.114];
const scale = 1;
async function load(config2) {
if (!models4.emotion) {
models4.emotion = await loadGraphModel2(config2.face.emotion.modelPath);
console.log(`Human: load model: ${config2.face.emotion.modelPath.match(/\/(.*)\./)[1]}`);
}
return models4.emotion;
}
async function predict(image4, config2) {
if (frame4 < config2.face.emotion.skipFrames && last.length > 0) {
frame4 += 1;
return last;
}
frame4 = 0;
return new Promise(async (resolve) => {
const box = [[
image4.shape[1] * zoom[0] / image4.shape[1],
image4.shape[2] * zoom[1] / image4.shape[2],
(image4.shape[1] - image4.shape[1] * zoom[0]) / image4.shape[1],
(image4.shape[2] - image4.shape[2] * zoom[1]) / image4.shape[2]
]];
const resize = dist_exports2.image.cropAndResize(image4, box, [0], [config2.face.emotion.inputSize, config2.face.emotion.inputSize]);
const [red, green, blue] = dist_exports2.split(resize, 3, 3);
resize.dispose();
const redNorm = dist_exports2.mul(red, rgb[0]);
const greenNorm = dist_exports2.mul(green, rgb[1]);
const blueNorm = dist_exports2.mul(blue, rgb[2]);
red.dispose();
green.dispose();
blue.dispose();
const grayscale = dist_exports2.addN([redNorm, greenNorm, blueNorm]);
redNorm.dispose();
greenNorm.dispose();
blueNorm.dispose();
const normalize = dist_exports2.tidy(() => grayscale.sub(0.5).mul(2));
grayscale.dispose();
const obj = [];
if (config2.face.emotion.enabled) {
let data2;
if (!config2.profile) {
const emotionT = await models4.emotion.predict(normalize);
data2 = emotionT.dataSync();
dist_exports2.dispose(emotionT);
} else {
const profileData = await dist_exports2.profile(() => models4.emotion.predict(normalize));
data2 = profileData.result.dataSync();
profileData.result.dispose();
profile3.run("emotion", profileData);
}
for (let i = 0; i < data2.length; i++) {
if (scale * data2[i] > config2.face.emotion.minConfidence)
obj.push({score: Math.min(0.99, Math.trunc(100 * scale * data2[i]) / 100), emotion: annotations[i]});
}
obj.sort((a, b) => b.score - a.score);
}
normalize.dispose();
last = obj;
resolve(obj);
});
}
exports3.predict = predict;
exports3.load = load;
});
// src/body/modelBase.js
var require_modelBase = __commonJS((exports3) => {
class BaseModel {
constructor(model2, outputStride) {
this.model = model2;
this.outputStride = outputStride;
}
predict(input2) {
return dist_exports2.tidy(() => {
const asFloat = this.preprocessInput(input2.toFloat());
const asBatch = asFloat.expandDims(0);
const results = this.model.predict(asBatch);
const results3d = results.map((y) => y.squeeze([0]));
const namedResults = this.nameOutputResults(results3d);
return {
heatmapScores: namedResults.heatmap.sigmoid(),
offsets: namedResults.offsets,
displacementFwd: namedResults.displacementFwd,
displacementBwd: namedResults.displacementBwd
};
});
}
dispose() {
this.model.dispose();
}
}
exports3.BaseModel = BaseModel;
});
// src/body/modelMobileNet.js
var require_modelMobileNet = __commonJS((exports3) => {
const modelBase = __toModule(require_modelBase());
class MobileNet extends modelBase.BaseModel {
preprocessInput(input2) {
return dist_exports2.tidy(() => dist_exports2.div(input2, 127.5).sub(1));
}
nameOutputResults(results) {
const [offsets, heatmap, displacementFwd, displacementBwd] = results;
return {offsets, heatmap, displacementFwd, displacementBwd};
}
}
exports3.MobileNet = MobileNet;
});
// src/body/heapSort.js
var require_heapSort = __commonJS((exports3) => {
function half(k) {
return Math.floor(k / 2);
}
class MaxHeap {
constructor(maxSize, getElementValue) {
this.priorityQueue = new Array(maxSize);
this.numberOfElements = -1;
this.getElementValue = getElementValue;
}
enqueue(x) {
this.priorityQueue[++this.numberOfElements] = x;
this.swim(this.numberOfElements);
}
dequeue() {
const max8 = this.priorityQueue[0];
this.exchange(0, this.numberOfElements--);
this.sink(0);
this.priorityQueue[this.numberOfElements + 1] = null;
return max8;
}
empty() {
return this.numberOfElements === -1;
}
size() {
return this.numberOfElements + 1;
}
all() {
return this.priorityQueue.slice(0, this.numberOfElements + 1);
}
max() {
return this.priorityQueue[0];
}
swim(k) {
while (k > 0 && this.less(half(k), k)) {
this.exchange(k, half(k));
k = half(k);
}
}
sink(k) {
while (2 * k <= this.numberOfElements) {
let j = 2 * k;
if (j < this.numberOfElements && this.less(j, j + 1))
j++;
if (!this.less(k, j))
break;
this.exchange(k, j);
k = j;
}
}
getValueAt(i) {
return this.getElementValue(this.priorityQueue[i]);
}
less(i, j) {
return this.getValueAt(i) < this.getValueAt(j);
}
exchange(i, j) {
const t = this.priorityQueue[i];
this.priorityQueue[i] = this.priorityQueue[j];
this.priorityQueue[j] = t;
}
}
exports3.MaxHeap = MaxHeap;
});
// src/body/buildParts.js
var require_buildParts = __commonJS((exports3) => {
const heapSort = __toModule(require_heapSort());
function scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {
const [height, width] = scores.shape;
let localMaximum = true;
const yStart = Math.max(heatmapY - localMaximumRadius, 0);
const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);
for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {
const xStart = Math.max(heatmapX - localMaximumRadius, 0);
const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);
for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {
if (scores.get(yCurrent, xCurrent, keypointId) > score) {
localMaximum = false;
break;
}
}
if (!localMaximum) {
break;
}
}
return localMaximum;
}
function buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {
const [height, width, numKeypoints] = scores.shape;
const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({score}) => score);
for (let heatmapY = 0; heatmapY < height; ++heatmapY) {
for (let heatmapX = 0; heatmapX < width; ++heatmapX) {
for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {
const score = scores.get(heatmapY, heatmapX, keypointId);
if (score < scoreThreshold)
continue;
if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {
queue.enqueue({score, part: {heatmapY, heatmapX, id: keypointId}});
}
}
}
}
return queue;
}
exports3.buildPartWithScoreQueue = buildPartWithScoreQueue;
});
// src/body/keypoints.js
var require_keypoints2 = __commonJS((exports3) => {
exports3.partNames = [
"nose",
"leftEye",
"rightEye",
"leftEar",
"rightEar",
"leftShoulder",
"rightShoulder",
"leftElbow",
"rightElbow",
"leftWrist",
"rightWrist",
"leftHip",
"rightHip",
"leftKnee",
"rightKnee",
"leftAnkle",
"rightAnkle"
];
exports3.NUM_KEYPOINTS = exports3.partNames.length;
exports3.partIds = exports3.partNames.reduce((result, jointName, i) => {
result[jointName] = i;
return result;
}, {});
const connectedPartNames = [
["leftHip", "leftShoulder"],
["leftElbow", "leftShoulder"],
["leftElbow", "leftWrist"],
["leftHip", "leftKnee"],
["leftKnee", "leftAnkle"],
["rightHip", "rightShoulder"],
["rightElbow", "rightShoulder"],
["rightElbow", "rightWrist"],
["rightHip", "rightKnee"],
["rightKnee", "rightAnkle"],
["leftShoulder", "rightShoulder"],
["leftHip", "rightHip"]
];
exports3.poseChain = [
["nose", "leftEye"],
["leftEye", "leftEar"],
["nose", "rightEye"],
["rightEye", "rightEar"],
["nose", "leftShoulder"],
["leftShoulder", "leftElbow"],
["leftElbow", "leftWrist"],
["leftShoulder", "leftHip"],
["leftHip", "leftKnee"],
["leftKnee", "leftAnkle"],
["nose", "rightShoulder"],
["rightShoulder", "rightElbow"],
["rightElbow", "rightWrist"],
["rightShoulder", "rightHip"],
["rightHip", "rightKnee"],
["rightKnee", "rightAnkle"]
];
exports3.connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => [exports3.partIds[jointNameA], exports3.partIds[jointNameB]]);
exports3.partChannels = [
"left_face",
"right_face",
"right_upper_leg_front",
"right_lower_leg_back",
"right_upper_leg_back",
"left_lower_leg_front",
"left_upper_leg_front",
"left_upper_leg_back",
"left_lower_leg_back",
"right_feet",
"right_lower_leg_front",
"left_feet",
"torso_front",
"torso_back",
"right_upper_arm_front",
"right_upper_arm_back",
"right_lower_arm_back",
"left_lower_arm_front",
"left_upper_arm_front",
"left_upper_arm_back",
"left_lower_arm_back",
"right_hand",
"right_lower_arm_front",
"left_hand"
];
});
// src/body/vectors.js
var require_vectors = __commonJS((exports3) => {
const kpt = __toModule(require_keypoints2());
function getOffsetPoint(y, x, keypoint, offsets) {
return {
y: offsets.get(y, x, keypoint),
x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS)
};
}
exports3.getOffsetPoint = getOffsetPoint;
function getImageCoords(part, outputStride, offsets) {
const {heatmapY, heatmapX, id: keypoint} = part;
const {y, x} = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);
return {
x: part.heatmapX * outputStride + x,
y: part.heatmapY * outputStride + y
};
}
exports3.getImageCoords = getImageCoords;
function fillArray(element, size) {
const result = new Array(size);
for (let i = 0; i < size; i++) {
result[i] = element;
}
return result;
}
exports3.fillArray = fillArray;
function clamp2(a, min6, max8) {
if (a < min6)
return min6;
if (a > max8)
return max8;
return a;
}
exports3.clamp = clamp2;
function squaredDistance(y1, x1, y2, x2) {
const dy = y2 - y1;
const dx = x2 - x1;
return dy * dy + dx * dx;
}
exports3.squaredDistance = squaredDistance;
function addVectors(a, b) {
return {x: a.x + b.x, y: a.y + b.y};
}
exports3.addVectors = addVectors;
function clampVector(a, min6, max8) {
return {y: clamp2(a.y, min6, max8), x: clamp2(a.x, min6, max8)};
}
exports3.clampVector = clampVector;
});
// src/body/decodePose.js
var require_decodePose = __commonJS((exports3) => {
const keypoints = __toModule(require_keypoints2());
const vectors = __toModule(require_vectors());
const parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => [keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]);
const parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);
const childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);
function getDisplacement(edgeId, point, displacements) {
const numEdges = displacements.shape[2] / 2;
return {
y: displacements.get(point.y, point.x, edgeId),
x: displacements.get(point.y, point.x, numEdges + edgeId)
};
}
function getStridedIndexNearPoint(point, outputStride, height, width) {
return {
y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),
x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1)
};
}
function traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {
const [height, width] = scoresBuffer.shape;
const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);
const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);
const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);
let targetKeypoint = displacedPoint;
for (let i = 0; i < offsetRefineStep; i++) {
const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);
const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);
targetKeypoint = vectors.addVectors({
x: targetKeypointIndices.x * outputStride,
y: targetKeypointIndices.y * outputStride
}, {x: offsetPoint.x, y: offsetPoint.y});
}
const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);
const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);
return {position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score};
}
function decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {
const numParts = scores.shape[2];
const numEdges = parentToChildEdges.length;
const instanceKeypoints = new Array(numParts);
const {part: rootPart, score: rootScore} = root;
const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);
instanceKeypoints[rootPart.id] = {
score: rootScore,
part: keypoints.partNames[rootPart.id],
position: rootPoint
};
for (let edge = numEdges - 1; edge >= 0; --edge) {
const sourceKeypointId = parentToChildEdges[edge];
const targetKeypointId = childToParentEdges[edge];
if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {
instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);
}
}
for (let edge = 0; edge < numEdges; ++edge) {
const sourceKeypointId = childToParentEdges[edge];
const targetKeypointId = parentToChildEdges[edge];
if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {
instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);
}
}
return instanceKeypoints;
}
exports3.decodePose = decodePose;
});
// src/body/decodeMultiple.js
var require_decodeMultiple = __commonJS((exports3) => {
const buildParts = __toModule(require_buildParts());
const decodePose = __toModule(require_decodePose());
const vectors = __toModule(require_vectors());
function withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, {x, y}, keypointId) {
return poses.some(({keypoints}) => {
const correspondingKeypoint = keypoints[keypointId].position;
return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;
});
}
function getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {
const notOverlappedKeypointScores = instanceKeypoints.reduce((result, {position, score}, keypointId) => {
if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) {
result += score;
}
return result;
}, 0);
return notOverlappedKeypointScores / instanceKeypoints.length;
}
const kLocalMaximumRadius = 1;
function decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, outputStride, maxPoseDetections, scoreThreshold = 0.5, nmsRadius = 20) {
const poses = [];
const queue = buildParts.buildPartWithScoreQueue(scoreThreshold, kLocalMaximumRadius, scoresBuffer);
const squaredNmsRadius = nmsRadius * nmsRadius;
while (poses.length < maxPoseDetections && !queue.empty()) {
const root = queue.dequeue();
const rootImageCoords = vectors.getImageCoords(root.part, outputStride, offsetsBuffer);
if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id))
continue;
const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, outputStride, displacementsFwdBuffer, displacementsBwdBuffer);
const score = getInstanceScore(poses, squaredNmsRadius, keypoints);
poses.push({keypoints, score});
}
return poses;
}
exports3.decodeMultiplePoses = decodeMultiplePoses;
});
// src/body/util.js
var require_util2 = __commonJS((exports3) => {
const kpt = __toModule(require_keypoints2());
function eitherPointDoesntMeetConfidence(a, b, minConfidence) {
return a < minConfidence || b < minConfidence;
}
function getAdjacentKeyPoints(keypoints, minConfidence) {
return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {
if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {
return result;
}
result.push([keypoints[leftJoint], keypoints[rightJoint]]);
return result;
}, []);
}
exports3.getAdjacentKeyPoints = getAdjacentKeyPoints;
const {NEGATIVE_INFINITY, POSITIVE_INFINITY} = Number;
function getBoundingBox(keypoints) {
return keypoints.reduce(({maxX, maxY, minX, minY}, {position: {x, y}}) => ({
maxX: Math.max(maxX, x),
maxY: Math.max(maxY, y),
minX: Math.min(minX, x),
minY: Math.min(minY, y)
}), {
maxX: NEGATIVE_INFINITY,
maxY: NEGATIVE_INFINITY,
minX: POSITIVE_INFINITY,
minY: POSITIVE_INFINITY
});
}
exports3.getBoundingBox = getBoundingBox;
function getBoundingBoxPoints(keypoints) {
const {minX, minY, maxX, maxY} = getBoundingBox(keypoints);
return [{x: minX, y: minY}, {x: maxX, y: minY}, {x: maxX, y: maxY}, {x: minX, y: maxY}];
}
exports3.getBoundingBoxPoints = getBoundingBoxPoints;
async function toTensorBuffers3D(tensors) {
return Promise.all(tensors.map((tensor16) => tensor16.buffer()));
}
exports3.toTensorBuffers3D = toTensorBuffers3D;
function scalePose(pose, scaleY, scaleX) {
return {
score: pose.score,
keypoints: pose.keypoints.map(({score, part, position}) => ({
score,
part,
position: {x: position.x * scaleX, y: position.y * scaleY}
}))
};
}
exports3.scalePose = scalePose;
function resizeTo(image4, [targetH, targetW]) {
const input2 = image4.squeeze(0);
const resized = input2.resizeBilinear([targetH, targetW]);
input2.dispose();
return resized;
}
exports3.resizeTo = resizeTo;
function scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {
const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));
return scaledPoses;
}
exports3.scaleAndFlipPoses = scaleAndFlipPoses;
});
// src/body/modelPoseNet.js
var require_modelPoseNet = __commonJS((exports3) => {
const modelMobileNet = __toModule(require_modelMobileNet());
const decodeMultiple = __toModule(require_decodeMultiple());
const util145 = __toModule(require_util2());
class PoseNet {
constructor(net) {
this.baseModel = net;
this.outputStride = 16;
}
async estimatePoses(input2, config2) {
return new Promise(async (resolve) => {
const height = input2.shape[1];
const width = input2.shape[2];
const resized = util145.resizeTo(input2, [config2.body.inputSize, config2.body.inputSize]);
const res = this.baseModel.predict(resized);
const allTensorBuffers = await util145.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);
const scoresBuffer = allTensorBuffers[0];
const offsetsBuffer = allTensorBuffers[1];
const displacementsFwdBuffer = allTensorBuffers[2];
const displacementsBwdBuffer = allTensorBuffers[3];
const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, this.outputStride, config2.body.maxDetections, config2.body.scoreThreshold, config2.body.nmsRadius);
const resultPoses = util145.scaleAndFlipPoses(poses, [height, width], [config2.body.inputSize, config2.body.inputSize]);
res.heatmapScores.dispose();
res.offsets.dispose();
res.displacementFwd.dispose();
res.displacementBwd.dispose();
resized.dispose();
resolve(resultPoses);
});
}
dispose() {
this.baseModel.dispose();
}
}
exports3.PoseNet = PoseNet;
async function load(config2) {
const graphModel = await loadGraphModel2(config2.body.modelPath);
const mobilenet = new modelMobileNet.MobileNet(graphModel, this.outputStride);
console.log(`Human: load model: ${config2.body.modelPath.match(/\/(.*)\./)[1]}`);
return new PoseNet(mobilenet);
}
exports3.load = load;
});
// src/body/posenet.js
var require_posenet = __commonJS((exports3) => {
const modelMobileNet = __toModule(require_modelMobileNet());
const modelPoseNet = __toModule(require_modelPoseNet());
const decodeMultiple = __toModule(require_decodeMultiple());
const keypoints = __toModule(require_keypoints2());
const util145 = __toModule(require_util2());
exports3.load = modelPoseNet.load;
exports3.PoseNet = modelPoseNet.PoseNet;
exports3.MobileNet = modelMobileNet.MobileNet;
exports3.decodeMultiplePoses = decodeMultiple.decodeMultiplePoses;
exports3.partChannels = keypoints.partChannels;
exports3.partIds = keypoints.partIds;
exports3.partNames = keypoints.partNames;
exports3.poseChain = keypoints.poseChain;
exports3.getAdjacentKeyPoints = util145.getAdjacentKeyPoints;
exports3.getBoundingBox = util145.getBoundingBox;
exports3.getBoundingBoxPoints = util145.getBoundingBoxPoints;
exports3.scaleAndFlipPoses = util145.scaleAndFlipPoses;
exports3.scalePose = util145.scalePose;
});
// src/hand/handdetector.js
var require_handdetector = __commonJS((exports3) => {
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
class HandDetector {
constructor(model2, inputSize, anchorsAnnotated) {
this.model = model2;
this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);
this.anchorsTensor = dist_exports2.tensor2d(this.anchors);
this.inputSizeTensor = dist_exports2.tensor1d([inputSize, inputSize]);
this.doubleInputSizeTensor = dist_exports2.tensor1d([inputSize * 2, inputSize * 2]);
}
normalizeBoxes(boxes) {
return dist_exports2.tidy(() => {
const boxOffsets = dist_exports2.slice(boxes, [0, 0], [-1, 2]);
const boxSizes = dist_exports2.slice(boxes, [0, 2], [-1, 2]);
const boxCenterPoints = dist_exports2.add(dist_exports2.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);
const halfBoxSizes = dist_exports2.div(boxSizes, this.doubleInputSizeTensor);
const startPoints = dist_exports2.mul(dist_exports2.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);
const endPoints = dist_exports2.mul(dist_exports2.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);
return dist_exports2.concat2d([startPoints, endPoints], 1);
});
}
normalizeLandmarks(rawPalmLandmarks, index) {
return dist_exports2.tidy(() => {
const landmarks = dist_exports2.add(dist_exports2.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);
return dist_exports2.mul(landmarks, this.inputSizeTensor);
});
}
async getBoxes(input2, config2) {
const batched = this.model.predict(input2);
const predictions = batched.squeeze();
batched.dispose();
const scores = dist_exports2.tidy(() => dist_exports2.sigmoid(dist_exports2.slice(predictions, [0, 0], [-1, 1])).squeeze());
const scoresVal = scores.dataSync();
const rawBoxes = dist_exports2.slice(predictions, [0, 1], [-1, 4]);
const boxes = this.normalizeBoxes(rawBoxes);
rawBoxes.dispose();
const filteredT = await dist_exports2.image.nonMaxSuppressionAsync(boxes, scores, config2.maxHands, config2.iouThreshold, config2.scoreThreshold);
const filtered = filteredT.arraySync();
scores.dispose();
filteredT.dispose();
const hands = [];
for (const boxIndex of filtered) {
if (scoresVal[boxIndex] >= config2.minConfidence) {
const matchingBox = dist_exports2.slice(boxes, [boxIndex, 0], [1, -1]);
const rawPalmLandmarks = dist_exports2.slice(predictions, [boxIndex, 5], [1, 14]);
const palmLandmarks = dist_exports2.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, boxIndex).reshape([-1, 2]));
rawPalmLandmarks.dispose();
hands.push({box: matchingBox, palmLandmarks, confidence: scoresVal[boxIndex]});
}
}
predictions.dispose();
boxes.dispose();
return hands;
}
async estimateHandBounds(input2, config2) {
const inputHeight = input2.shape[1];
const inputWidth = input2.shape[2];
const image4 = dist_exports2.tidy(() => input2.resizeBilinear([config2.inputSize, config2.inputSize]).div(127.5).sub(1));
const predictions = await this.getBoxes(image4, config2);
image4.dispose();
if (!predictions || predictions.length === 0)
return null;
const hands = [];
for (const prediction of predictions) {
const boxes = prediction.box.dataSync();
const startPoint = boxes.slice(0, 2);
const endPoint = boxes.slice(2, 4);
const palmLandmarks = prediction.palmLandmarks.arraySync();
prediction.box.dispose();
prediction.palmLandmarks.dispose();
hands.push(scaleBoxCoordinates({startPoint, endPoint, palmLandmarks, confidence: prediction.confidence}, [inputWidth / config2.inputSize, inputHeight / config2.inputSize]));
}
return hands;
}
}
exports3.HandDetector = HandDetector;
});
// src/hand/handpipeline.js
var require_handpipeline = __commonJS((exports3) => {
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const PALM_BOX_SHIFT_VECTOR = [0, -0.4];
const PALM_BOX_ENLARGE_FACTOR = 3;
const HAND_BOX_SHIFT_VECTOR = [0, -0.1];
const HAND_BOX_ENLARGE_FACTOR = 1.65;
const PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];
const PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;
const PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;
class HandPipeline {
constructor(boundingBoxDetector, meshDetector, inputSize) {
this.boxDetector = boundingBoxDetector;
this.meshDetector = meshDetector;
this.inputSize = inputSize;
this.storedBoxes = [];
this.skipped = 1e3;
this.detectedHands = 0;
}
getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {
const rotatedPalmLandmarks = palmLandmarks.map((coord) => {
const homogeneousCoordinate = [...coord, 1];
return rotatePoint(homogeneousCoordinate, rotationMatrix);
});
const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);
return enlargeBox(squarifyBox(shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);
}
getBoxForHandLandmarks(landmarks) {
const boundingBox = this.calculateLandmarksBoundingBox(landmarks);
const boxAroundHand = enlargeBox(squarifyBox(shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);
const palmLandmarks = [];
for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {
palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));
}
boxAroundHand.palmLandmarks = palmLandmarks;
return boxAroundHand;
}
transformRawCoords(rawCoords, box2, angle, rotationMatrix) {
const boxSize = getBoxSize(box2);
const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize];
const coordsScaled = rawCoords.map((coord) => [
scaleFactor[0] * (coord[0] - this.inputSize / 2),
scaleFactor[1] * (coord[1] - this.inputSize / 2),
coord[2]
]);
const coordsRotationMatrix = buildRotationMatrix(angle, [0, 0]);
const coordsRotated = coordsScaled.map((coord) => {
const rotated = rotatePoint(coord, coordsRotationMatrix);
return [...rotated, coord[2]];
});
const inverseRotationMatrix = invertTransformMatrix(rotationMatrix);
const boxCenter = [...getBoxCenter(box2), 1];
const originalBoxCenter = [
dot5(boxCenter, inverseRotationMatrix[0]),
dot5(boxCenter, inverseRotationMatrix[1])
];
return coordsRotated.map((coord) => [
coord[0] + originalBoxCenter[0],
coord[1] + originalBoxCenter[1],
coord[2]
]);
}
async estimateHands(image4, config2) {
this.skipped++;
let useFreshBox = false;
let boxes;
if (this.skipped > config2.skipFrames || !config2.landmarks) {
boxes = await this.boxDetector.estimateHandBounds(image4, config2);
if (image4.shape[1] !== 255 && image4.shape[2] !== 255)
this.skipped = 0;
}
if (boxes && boxes.length > 0 && (boxes.length !== this.detectedHands && this.detectedHands !== config2.maxHands || !config2.landmarks)) {
this.storedBoxes = [];
this.detectedHands = 0;
for (const possible of boxes)
this.storedBoxes.push(possible);
if (this.storedBoxes.length > 0)
useFreshBox = true;
}
const hands = [];
for (const i in this.storedBoxes) {
const currentBox = this.storedBoxes[i];
if (!currentBox)
continue;
if (config2.landmarks) {
const angle = computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]);
const palmCenter = getBoxCenter(currentBox);
const palmCenterNormalized = [palmCenter[0] / image4.shape[2], palmCenter[1] / image4.shape[1]];
const rotatedImage = dist_exports2.image.rotateWithOffset(image4, angle, 0, palmCenterNormalized);
const rotationMatrix = buildRotationMatrix(-angle, palmCenter);
const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;
const croppedInput = cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);
const handImage = croppedInput.div(255);
croppedInput.dispose();
rotatedImage.dispose();
const [confidence, keypoints] = await this.meshDetector.predict(handImage);
handImage.dispose();
const confidenceValue = confidence.dataSync()[0];
confidence.dispose();
if (confidenceValue >= config2.minConfidence) {
const keypointsReshaped = dist_exports2.reshape(keypoints, [-1, 3]);
const rawCoords = keypointsReshaped.arraySync();
keypoints.dispose();
keypointsReshaped.dispose();
const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);
const nextBoundingBox = this.getBoxForHandLandmarks(coords);
this.storedBoxes[i] = nextBoundingBox;
const result = {
landmarks: coords,
confidence: confidenceValue,
box: {
topLeft: nextBoundingBox.startPoint,
bottomRight: nextBoundingBox.endPoint
}
};
hands.push(result);
} else {
this.storedBoxes[i] = null;
}
keypoints.dispose();
} else {
const enlarged = enlargeBox(squarifyBox(shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);
const result = {
confidence: currentBox.confidence,
box: {
topLeft: enlarged.startPoint,
bottomRight: enlarged.endPoint
}
};
hands.push(result);
}
}
this.storedBoxes = this.storedBoxes.filter((a) => a !== null);
this.detectedHands = hands.length;
return hands;
}
calculateLandmarksBoundingBox(landmarks) {
const xs = landmarks.map((d) => d[0]);
const ys = landmarks.map((d) => d[1]);
const startPoint = [Math.min(...xs), Math.min(...ys)];
const endPoint = [Math.max(...xs), Math.max(...ys)];
return {startPoint, endPoint};
}
}
exports3.HandPipeline = HandPipeline;
});
// src/hand/anchors.js
var require_anchors = __commonJS((exports3) => {
exports3.anchors = [
{
w: 1,
h: 1,
x_center: 0.015625,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.015625,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.046875,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.046875,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.078125,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.078125,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.109375,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.109375,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.140625,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.140625,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.171875,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.171875,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.203125,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.203125,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.234375,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.234375,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.265625,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.265625,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.296875,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.296875,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.328125,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.328125,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.359375,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.359375,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.390625,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.390625,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.421875,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.421875,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.453125,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.453125,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.484375,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.484375,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.515625,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.515625,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.546875,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.546875,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.578125,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.578125,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.609375,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.609375,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.640625,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.640625,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.671875,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.671875,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.703125,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.703125,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.734375,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.734375,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.765625,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.765625,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.796875,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.796875,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.828125,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.828125,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.859375,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.859375,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.890625,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.890625,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.921875,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.921875,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.953125,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.953125,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.984375,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.984375,
y_center: 0.015625
},
{
w: 1,
h: 1,
x_center: 0.015625,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.015625,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.046875,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.046875,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.078125,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.078125,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.109375,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.109375,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.140625,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.140625,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.171875,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.171875,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.203125,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.203125,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.234375,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.234375,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.265625,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.265625,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.296875,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.296875,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.328125,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.328125,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.359375,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.359375,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.390625,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.390625,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.421875,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.421875,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.453125,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.453125,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.484375,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.484375,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.515625,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.515625,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.546875,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.546875,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.578125,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.578125,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.609375,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.609375,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.640625,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.640625,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.671875,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.671875,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.703125,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.703125,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.734375,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.734375,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.765625,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.765625,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.796875,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.796875,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.828125,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.828125,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.859375,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.859375,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.890625,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.890625,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.921875,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.921875,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.953125,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.953125,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.984375,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.984375,
y_center: 0.046875
},
{
w: 1,
h: 1,
x_center: 0.015625,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.015625,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.046875,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.046875,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.078125,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.078125,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.109375,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.109375,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.140625,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.140625,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.171875,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.171875,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.203125,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.203125,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.234375,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.234375,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.265625,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.265625,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.296875,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.296875,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.328125,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.328125,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.359375,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.359375,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.390625,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.390625,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.421875,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.421875,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.453125,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.453125,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.484375,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.484375,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.515625,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.515625,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.546875,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.546875,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.578125,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.578125,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.609375,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.609375,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.640625,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.640625,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.671875,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.671875,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.703125,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.703125,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.734375,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.734375,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.765625,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.765625,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.796875,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.796875,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.828125,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.828125,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.859375,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.859375,
y_center: 0.078125
},
{
w: 1,
h: 1,
x_center: 0.890625,
y_center: 0.078125
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},
{
w: 1,
h: 1,
x_center: 0.0625,
y_center: 0.8125
},
{
w: 1,
h: 1,
x_center: 0.0625,
y_center: 0.8125
},
{
w: 1,
h: 1,
x_center: 0.1875,
y_center: 0.8125
},
{
w: 1,
h: 1,
x_center: 0.1875,
y_center: 0.8125
},
{
w: 1,
h: 1,
x_center: 0.1875,
y_center: 0.8125
},
{
w: 1,
h: 1,
x_center: 0.1875,
y_center: 0.8125
},
{
w: 1,
h: 1,
x_center: 0.1875,
y_center: 0.8125
},
{
w: 1,
h: 1,
x_center: 0.1875,
y_center: 0.8125
},
{
w: 1,
h: 1,
x_center: 0.3125,
y_center: 0.8125
},
{
w: 1,
h: 1,
x_center: 0.3125,
y_center: 0.8125
},
{
w: 1,
h: 1,
x_center: 0.3125,
y_center: 0.8125
},
{
w: 1,
h: 1,
x_center: 0.3125,
y_center: 0.8125
},
{
w: 1,
h: 1,
x_center: 0.3125,
y_center: 0.8125
},
{
w: 1,
h: 1,
x_center: 0.3125,
y_center: 0.8125
},
{
w: 1,
h: 1,
x_center: 0.4375,
y_center: 0.8125
},
{
w: 1,
h: 1,
x_center: 0.4375,
y_center: 0.8125
},
{
w: 1,
h: 1,
x_center: 0.4375,
y_center: 0.8125
},
{
w: 1,
h: 1,
x_center: 0.4375,
y_center: 0.8125
},
{
w: 1,
h: 1,
x_center: 0.4375,
y_center: 0.8125
},
{
w: 1,
h: 1,
x_center: 0.4375,
y_center: 0.8125
},
{
w: 1,
h: 1,
x_center: 0.5625,
y_center: 0.8125
},
{
w: 1,
h: 1,
x_center: 0.5625,
y_center: 0.8125
},
{
w: 1,
h: 1,
x_center: 0.5625,
y_center: 0.8125
},
{
w: 1,
h: 1,
x_center: 0.5625,
y_center: 0.8125
},
{
w: 1,
h: 1,
x_center: 0.5625,
y_center: 0.8125
},
{
w: 1,
h: 1,
x_center: 0.5625,
y_center: 0.8125
},
{
w: 1,
h: 1,
x_center: 0.6875,
y_center: 0.8125
},
{
w: 1,
h: 1,
x_center: 0.6875,
y_center: 0.8125
},
{
w: 1,
h: 1,
x_center: 0.6875,
y_center: 0.8125
},
{
w: 1,
h: 1,
x_center: 0.6875,
y_center: 0.8125
},
{
w: 1,
h: 1,
x_center: 0.6875,
y_center: 0.8125
},
{
w: 1,
h: 1,
x_center: 0.6875,
y_center: 0.8125
},
{
w: 1,
h: 1,
x_center: 0.8125,
y_center: 0.8125
},
{
w: 1,
h: 1,
x_center: 0.8125,
y_center: 0.8125
},
{
w: 1,
h: 1,
x_center: 0.8125,
y_center: 0.8125
},
{
w: 1,
h: 1,
x_center: 0.8125,
y_center: 0.8125
},
{
w: 1,
h: 1,
x_center: 0.8125,
y_center: 0.8125
},
{
w: 1,
h: 1,
x_center: 0.8125,
y_center: 0.8125
},
{
w: 1,
h: 1,
x_center: 0.9375,
y_center: 0.8125
},
{
w: 1,
h: 1,
x_center: 0.9375,
y_center: 0.8125
},
{
w: 1,
h: 1,
x_center: 0.9375,
y_center: 0.8125
},
{
w: 1,
h: 1,
x_center: 0.9375,
y_center: 0.8125
},
{
w: 1,
h: 1,
x_center: 0.9375,
y_center: 0.8125
},
{
w: 1,
h: 1,
x_center: 0.9375,
y_center: 0.8125
},
{
w: 1,
h: 1,
x_center: 0.0625,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.0625,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.0625,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.0625,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.0625,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.0625,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.1875,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.1875,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.1875,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.1875,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.1875,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.1875,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.3125,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.3125,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.3125,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.3125,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.3125,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.3125,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.4375,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.4375,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.4375,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.4375,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.4375,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.4375,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.5625,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.5625,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.5625,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.5625,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.5625,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.5625,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.6875,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.6875,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.6875,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.6875,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.6875,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.6875,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.8125,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.8125,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.8125,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.8125,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.8125,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.8125,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.9375,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.9375,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.9375,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.9375,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.9375,
y_center: 0.9375
},
{
w: 1,
h: 1,
x_center: 0.9375,
y_center: 0.9375
}
];
});
// src/hand/handpose.js
var require_handpose = __commonJS((exports3) => {
const handdetector = __toModule(require_handdetector());
const pipeline = __toModule(require_handpipeline());
const anchors = __toModule(require_anchors());
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const MESH_ANNOTATIONS = {
thumb: [1, 2, 3, 4],
indexFinger: [5, 6, 7, 8],
middleFinger: [9, 10, 11, 12],
ringFinger: [13, 14, 15, 16],
pinky: [17, 18, 19, 20],
palmBase: [0]
};
class HandPose {
constructor(pipe) {
this.pipeline = pipe;
}
static getAnnotations() {
return MESH_ANNOTATIONS;
}
async estimateHands(input2, config2) {
const predictions = await this.pipeline.estimateHands(input2, config2);
if (!predictions)
return [];
const hands = [];
for (const prediction of predictions) {
const annotations = {};
if (prediction.landmarks) {
for (const key of Object.keys(MESH_ANNOTATIONS)) {
annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);
}
}
hands.push({
confidence: prediction.confidence,
box: prediction.box ? [
prediction.box.topLeft[0],
prediction.box.topLeft[1],
prediction.box.bottomRight[0] - prediction.box.topLeft[0],
prediction.box.bottomRight[1] - prediction.box.topLeft[1]
] : 0,
landmarks: prediction.landmarks,
annotations
});
}
return hands;
}
}
exports3.HandPose = HandPose;
async function load(config2) {
const [handDetectorModel, handPoseModel] = await Promise.all([
loadGraphModel2(config2.detector.modelPath, {fromTFHub: config2.detector.modelPath.includes("tfhub.dev")}),
loadGraphModel2(config2.skeleton.modelPath, {fromTFHub: config2.skeleton.modelPath.includes("tfhub.dev")})
]);
const detector = new handdetector.HandDetector(handDetectorModel, config2.inputSize, anchors.anchors);
const pipe = new pipeline.HandPipeline(detector, handPoseModel, config2.inputSize);
const handpose2 = new HandPose(pipe);
console.log(`Human: load model: ${config2.detector.modelPath.match(/\/(.*)\./)[1]}`);
console.log(`Human: load model: ${config2.skeleton.modelPath.match(/\/(.*)\./)[1]}`);
return handpose2;
}
exports3.load = load;
});
// src/gesture.js
var require_gesture = __commonJS((exports3) => {
exports3.body = (res) => {
if (!res)
return [];
const gestures = [];
for (const pose of res) {
const leftWrist = pose.keypoints.find((a) => a.part === "leftWrist");
const rightWrist = pose.keypoints.find((a) => a.part === "rightWrist");
const nose = pose.keypoints.find((a) => a.part === "nose");
if (nose && leftWrist && rightWrist && leftWrist.position.y < nose.position.y && rightWrist.position.y < nose.position.y)
gestures.push("i give up");
else if (nose && leftWrist && leftWrist.position.y < nose.position.y)
gestures.push("raise left hand");
else if (nose && rightWrist && rightWrist.position.y < nose.position.y)
gestures.push("raise right hand");
const leftShoulder = pose.keypoints.find((a) => a.part === "leftShoulder");
const rightShoulder = pose.keypoints.find((a) => a.part === "rightShoulder");
if (leftShoulder && rightShoulder)
gestures.push(`leaning ${leftShoulder.position.y > rightShoulder.position.y ? "left" : "right"}`);
}
return gestures;
};
exports3.face = (res) => {
if (!res)
return [];
const gestures = [];
for (const face2 of res) {
if (face2.mesh && face2.mesh.length > 0) {
const eyeFacing = face2.mesh[35][2] - face2.mesh[263][2];
if (Math.abs(eyeFacing) < 10)
gestures.push("facing camera");
else
gestures.push(`facing ${eyeFacing < 0 ? "right" : "left"}`);
const openLeft = Math.abs(face2.mesh[374][1] - face2.mesh[386][1]) / Math.abs(face2.mesh[443][1] - face2.mesh[450][1]);
if (openLeft < 0.2)
gestures.push("blink left eye");
const openRight = Math.abs(face2.mesh[145][1] - face2.mesh[159][1]) / Math.abs(face2.mesh[223][1] - face2.mesh[230][1]);
if (openRight < 0.2)
gestures.push("blink right eye");
const mouthOpen = Math.min(100, 500 * Math.abs(face2.mesh[13][1] - face2.mesh[14][1]) / Math.abs(face2.mesh[10][1] - face2.mesh[152][1]));
if (mouthOpen > 10)
gestures.push(`mouth ${Math.trunc(mouthOpen)}% open`);
const chinDepth = face2.mesh[152][2];
if (Math.abs(chinDepth) > 10)
gestures.push(`head ${chinDepth < 0 ? "up" : "down"}`);
}
}
return gestures;
};
exports3.hand = (res) => {
if (!res)
return [];
const gestures = [];
for (const hand2 of res) {
const fingers = [];
for (const [finger, pos] of Object.entries(hand2["annotations"])) {
if (finger !== "palmBase")
fingers.push({name: finger.toLowerCase(), position: pos[0]});
}
if (fingers && fingers.length > 0) {
const closest = fingers.reduce((best, a) => best.position[2] < a.position[2] ? best : a);
const highest = fingers.reduce((best, a) => best.position[1] < a.position[1] ? best : a);
gestures.push(`${closest.name} forward ${highest.name} up`);
}
}
return gestures;
};
});
// src/imagefx.js
var require_imagefx = __commonJS((exports3) => {
const WebGLProgram = function(gl, vertexSource, fragmentSource) {
const _collect = function(source, prefix, collection) {
const r = new RegExp("\\b" + prefix + " \\w+ (\\w+)", "ig");
source.replace(r, (match, name) => {
collection[name] = 0;
return match;
});
};
const _compile = function(source, type) {
const shader = gl.createShader(type);
gl.shaderSource(shader, source);
gl.compileShader(shader);
if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {
throw new Error("Filter: GL compile failed", gl.getShaderInfoLog(shader));
}
return shader;
};
this.uniform = {};
this.attribute = {};
const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);
const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);
this.id = gl.createProgram();
gl.attachShader(this.id, _vsh);
gl.attachShader(this.id, _fsh);
gl.linkProgram(this.id);
if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) {
throw new Error("Filter: GL link failed", gl.getProgramInfoLog(this.id));
}
gl.useProgram(this.id);
_collect(vertexSource, "attribute", this.attribute);
for (const a in this.attribute) {
this.attribute[a] = gl.getAttribLocation(this.id, a);
}
_collect(vertexSource, "uniform", this.uniform);
_collect(fragmentSource, "uniform", this.uniform);
for (const u in this.uniform) {
this.uniform[u] = gl.getUniformLocation(this.id, u);
}
};
const WebGLImageFilter = function(params) {
if (!params)
params = {};
let _drawCount = 0;
let _sourceTexture = null;
let _lastInChain = false;
let _currentFramebufferIndex = -1;
let _tempFramebuffers = [null, null];
let _filterChain = [];
let _width = -1;
let _height = -1;
let _vertexBuffer = null;
let _currentProgram = null;
const _canvas = params.canvas || document.createElement("canvas");
const _shaderProgramCache = {};
const gl = _canvas.getContext("webgl");
if (!gl)
throw new Error("Filter: getContext() failed");
this.addFilter = function(name) {
const args = Array.prototype.slice.call(arguments, 1);
const filter = _filter[name];
_filterChain.push({func: filter, args});
};
this.reset = function() {
_filterChain = [];
};
this.apply = function(image4) {
_resize(image4.width, image4.height);
_drawCount = 0;
if (!_sourceTexture)
_sourceTexture = gl.createTexture();
gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);
gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);
gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);
gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);
gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);
gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image4);
if (_filterChain.length === 0) {
_draw();
return _canvas;
}
for (let i = 0; i < _filterChain.length; i++) {
_lastInChain = i === _filterChain.length - 1;
const f = _filterChain[i];
f.func.apply(this, f.args || []);
}
return _canvas;
};
const _resize = function(width, height) {
if (width === _width && height === _height) {
return;
}
_canvas.width = width;
_width = width;
_canvas.height = height;
_height = height;
if (!_vertexBuffer) {
const vertices = new Float32Array([
-1,
-1,
0,
1,
1,
-1,
1,
1,
-1,
1,
0,
0,
-1,
1,
0,
0,
1,
-1,
1,
1,
1,
1,
1,
0
]);
_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer);
gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);
gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);
}
gl.viewport(0, 0, _width, _height);
_tempFramebuffers = [null, null];
};
const _getTempFramebuffer = function(index) {
_tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);
return _tempFramebuffers[index];
};
const _createFramebufferTexture = function(width, height) {
const fbo = gl.createFramebuffer();
gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);
const renderbuffer = gl.createRenderbuffer();
gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);
const texture = gl.createTexture();
gl.bindTexture(gl.TEXTURE_2D, texture);
gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);
gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);
gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);
gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);
gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);
gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);
gl.bindTexture(gl.TEXTURE_2D, null);
gl.bindFramebuffer(gl.FRAMEBUFFER, null);
return {fbo, texture};
};
const _draw = function(flags2) {
let source = null;
let target = null;
let flipY = false;
if (_drawCount === 0) {
source = _sourceTexture;
} else {
source = _getTempFramebuffer(_currentFramebufferIndex).texture;
}
_drawCount++;
if (_lastInChain && !(flags2 & DRAW.INTERMEDIATE)) {
target = null;
flipY = _drawCount % 2 === 0;
} else {
_currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;
target = _getTempFramebuffer(_currentFramebufferIndex).fbo;
}
gl.bindTexture(gl.TEXTURE_2D, source);
gl.bindFramebuffer(gl.FRAMEBUFFER, target);
gl.uniform1f(_currentProgram.uniform.flipY, flipY ? -1 : 1);
gl.drawArrays(gl.TRIANGLES, 0, 6);
};
const _compileShader = function(fragmentSource) {
if (_shaderProgramCache[fragmentSource]) {
_currentProgram = _shaderProgramCache[fragmentSource];
gl.useProgram(_currentProgram.id);
return _currentProgram;
}
_currentProgram = new WebGLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);
const floatSize = Float32Array.BYTES_PER_ELEMENT;
const vertSize = 4 * floatSize;
gl.enableVertexAttribArray(_currentProgram.attribute.pos);
gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);
gl.enableVertexAttribArray(_currentProgram.attribute.uv);
gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);
_shaderProgramCache[fragmentSource] = _currentProgram;
return _currentProgram;
};
let DRAW = {INTERMEDIATE: 1};
let SHADER = {};
SHADER.VERTEX_IDENTITY = [
"precision highp float;",
"attribute vec2 pos;",
"attribute vec2 uv;",
"varying vec2 vUv;",
"uniform float flipY;",
"void main(void) {",
"vUv = uv;",
"gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);",
"}"
].join("\n");
SHADER.FRAGMENT_IDENTITY = [
"precision highp float;",
"varying vec2 vUv;",
"uniform sampler2D texture;",
"void main(void) {",
"gl_FragColor = texture2D(texture, vUv);",
"}"
].join("\n");
let _filter = {};
_filter.colorMatrix = function(matrix) {
const m = new Float32Array(matrix);
m[4] /= 255;
m[9] /= 255;
m[14] /= 255;
m[19] /= 255;
const shader = m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0 ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA : _filter.colorMatrix.SHADER.WITH_ALPHA;
const program = _compileShader(shader);
gl.uniform1fv(program.uniform.m, m);
_draw();
};
_filter.colorMatrix.SHADER = {};
_filter.colorMatrix.SHADER.WITH_ALPHA = [
"precision highp float;",
"varying vec2 vUv;",
"uniform sampler2D texture;",
"uniform float m[20];",
"void main(void) {",
"vec4 c = texture2D(texture, vUv);",
"gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];",
"gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];",
"gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];",
"gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];",
"}"
].join("\n");
_filter.colorMatrix.SHADER.WITHOUT_ALPHA = [
"precision highp float;",
"varying vec2 vUv;",
"uniform sampler2D texture;",
"uniform float m[20];",
"void main(void) {",
"vec4 c = texture2D(texture, vUv);",
"gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];",
"gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];",
"gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];",
"gl_FragColor.a = c.a;",
"}"
].join("\n");
_filter.brightness = function(brightness) {
const b = (brightness || 0) + 1;
_filter.colorMatrix([
b,
0,
0,
0,
0,
0,
b,
0,
0,
0,
0,
0,
b,
0,
0,
0,
0,
0,
1,
0
]);
};
_filter.saturation = function(amount) {
const x = (amount || 0) * 2 / 3 + 1;
const y = (x - 1) * -0.5;
_filter.colorMatrix([
x,
y,
y,
0,
0,
y,
x,
y,
0,
0,
y,
y,
x,
0,
0,
0,
0,
0,
1,
0
]);
};
_filter.desaturate = function() {
_filter.saturation(-1);
};
_filter.contrast = function(amount) {
const v = (amount || 0) + 1;
const o = -128 * (v - 1);
_filter.colorMatrix([
v,
0,
0,
0,
o,
0,
v,
0,
0,
o,
0,
0,
v,
0,
o,
0,
0,
0,
1,
0
]);
};
_filter.negative = function() {
_filter.contrast(-2);
};
_filter.hue = function(rotation) {
rotation = (rotation || 0) / 180 * Math.PI;
const cos3 = Math.cos(rotation);
const sin3 = Math.sin(rotation);
const lumR = 0.213;
const lumG = 0.715;
const lumB = 0.072;
_filter.colorMatrix([
lumR + cos3 * (1 - lumR) + sin3 * -lumR,
lumG + cos3 * -lumG + sin3 * -lumG,
lumB + cos3 * -lumB + sin3 * (1 - lumB),
0,
0,
lumR + cos3 * -lumR + sin3 * 0.143,
lumG + cos3 * (1 - lumG) + sin3 * 0.14,
lumB + cos3 * -lumB + sin3 * -0.283,
0,
0,
lumR + cos3 * -lumR + sin3 * -(1 - lumR),
lumG + cos3 * -lumG + sin3 * lumG,
lumB + cos3 * (1 - lumB) + sin3 * lumB,
0,
0,
0,
0,
0,
1,
0
]);
};
_filter.desaturateLuminance = function() {
_filter.colorMatrix([
0.2764723,
0.929708,
0.0938197,
0,
-37.1,
0.2764723,
0.929708,
0.0938197,
0,
-37.1,
0.2764723,
0.929708,
0.0938197,
0,
-37.1,
0,
0,
0,
1,
0
]);
};
_filter.sepia = function() {
_filter.colorMatrix([
0.393,
0.7689999,
0.18899999,
0,
0,
0.349,
0.6859999,
0.16799999,
0,
0,
0.272,
0.5339999,
0.13099999,
0,
0,
0,
0,
0,
1,
0
]);
};
_filter.brownie = function() {
_filter.colorMatrix([
0.5997023498159715,
0.34553243048391263,
-0.2708298674538042,
0,
47.43192855600873,
-0.037703249837783157,
0.8609577587992641,
0.15059552388459913,
0,
-36.96841498319127,
0.24113635128153335,
-0.07441037908422492,
0.44972182064877153,
0,
-7.562075277591283,
0,
0,
0,
1,
0
]);
};
_filter.vintagePinhole = function() {
_filter.colorMatrix([
0.6279345635605994,
0.3202183420819367,
-0.03965408211312453,
0,
9.651285835294123,
0.02578397704808868,
0.6441188644374771,
0.03259127616149294,
0,
7.462829176470591,
0.0466055556782719,
-0.0851232987247891,
0.5241648018700465,
0,
5.159190588235296,
0,
0,
0,
1,
0
]);
};
_filter.kodachrome = function() {
_filter.colorMatrix([
1.1285582396593525,
-0.3967382283601348,
-0.03992559172921793,
0,
63.72958762196502,
-0.16404339962244616,
1.0835251566291304,
-0.05498805115633132,
0,
24.732407896706203,
-0.16786010706155763,
-0.5603416277695248,
1.6014850761964943,
0,
35.62982807460946,
0,
0,
0,
1,
0
]);
};
_filter.technicolor = function() {
_filter.colorMatrix([
1.9125277891456083,
-0.8545344976951645,
-0.09155508482755585,
0,
11.793603434377337,
-0.3087833385928097,
1.7658908555458428,
-0.10601743074722245,
0,
-70.35205161461398,
-0.231103377548616,
-0.7501899197440212,
1.847597816108189,
0,
30.950940869491138,
0,
0,
0,
1,
0
]);
};
_filter.polaroid = function() {
_filter.colorMatrix([
1.438,
-0.062,
-0.062,
0,
0,
-0.122,
1.378,
-0.122,
0,
0,
-0.016,
-0.016,
1.483,
0,
0,
0,
0,
0,
1,
0
]);
};
_filter.shiftToBGR = function() {
_filter.colorMatrix([
0,
0,
1,
0,
0,
0,
1,
0,
0,
0,
1,
0,
0,
0,
0,
0,
0,
0,
1,
0
]);
};
_filter.convolution = function(matrix) {
const m = new Float32Array(matrix);
const pixelSizeX = 1 / _width;
const pixelSizeY = 1 / _height;
const program = _compileShader(_filter.convolution.SHADER);
gl.uniform1fv(program.uniform.m, m);
gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);
_draw();
};
_filter.convolution.SHADER = [
"precision highp float;",
"varying vec2 vUv;",
"uniform sampler2D texture;",
"uniform vec2 px;",
"uniform float m[9];",
"void main(void) {",
"vec4 c11 = texture2D(texture, vUv - px);",
"vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));",
"vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));",
"vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );",
"vec4 c22 = texture2D(texture, vUv);",
"vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );",
"vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );",
"vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );",
"vec4 c33 = texture2D(texture, vUv + px );",
"gl_FragColor = ",
"c11 * m[0] + c12 * m[1] + c22 * m[2] +",
"c21 * m[3] + c22 * m[4] + c23 * m[5] +",
"c31 * m[6] + c32 * m[7] + c33 * m[8];",
"gl_FragColor.a = c22.a;",
"}"
].join("\n");
_filter.detectEdges = function() {
_filter.convolution.call(this, [
0,
1,
0,
1,
-4,
1,
0,
1,
0
]);
};
_filter.sobelX = function() {
_filter.convolution.call(this, [
-1,
0,
1,
-2,
0,
2,
-1,
0,
1
]);
};
_filter.sobelY = function() {
_filter.convolution.call(this, [
-1,
-2,
-1,
0,
0,
0,
1,
2,
1
]);
};
_filter.sharpen = function(amount) {
const a = amount || 1;
_filter.convolution.call(this, [
0,
-1 * a,
0,
-1 * a,
1 + 4 * a,
-1 * a,
0,
-1 * a,
0
]);
};
_filter.emboss = function(size) {
const s = size || 1;
_filter.convolution.call(this, [
-2 * s,
-1 * s,
0,
-1 * s,
1,
1 * s,
0,
1 * s,
2 * s
]);
};
_filter.blur = function(size) {
const blurSizeX = size / 7 / _width;
const blurSizeY = size / 7 / _height;
const program = _compileShader(_filter.blur.SHADER);
gl.uniform2f(program.uniform.px, 0, blurSizeY);
_draw(DRAW.INTERMEDIATE);
gl.uniform2f(program.uniform.px, blurSizeX, 0);
_draw();
};
_filter.blur.SHADER = [
"precision highp float;",
"varying vec2 vUv;",
"uniform sampler2D texture;",
"uniform vec2 px;",
"void main(void) {",
"gl_FragColor = vec4(0.0);",
"gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;",
"gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;",
"gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;",
"gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;",
"gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;",
"gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;",
"gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;",
"gl_FragColor += texture2D(texture, vUv )*0.159576912161;",
"gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;",
"gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;",
"gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;",
"gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;",
"gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;",
"gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;",
"gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;",
"}"
].join("\n");
_filter.pixelate = function(size) {
const blurSizeX = size / _width;
const blurSizeY = size / _height;
const program = _compileShader(_filter.pixelate.SHADER);
gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);
_draw();
};
_filter.pixelate.SHADER = [
"precision highp float;",
"varying vec2 vUv;",
"uniform vec2 size;",
"uniform sampler2D texture;",
"vec2 pixelate(vec2 coord, vec2 size) {",
"return floor( coord / size ) * size;",
"}",
"void main(void) {",
"gl_FragColor = vec4(0.0);",
"vec2 coord = pixelate(vUv, size);",
"gl_FragColor += texture2D(texture, coord);",
"}"
].join("\n");
};
exports3.Canvas = WebGLImageFilter;
});
// src/image.js
var require_image = __commonJS((exports3) => {
const fxImage = __toModule(require_imagefx());
let inCanvas = null;
let outCanvas = null;
function process3(input2, config2) {
let tensor16;
if (input2 instanceof dist_exports2.Tensor) {
tensor16 = dist_exports2.clone(input2);
} else {
const originalWidth = input2.naturalWidth || input2.videoWidth || input2.width || input2.shape && input2.shape[1] > 0;
const originalHeight = input2.naturalHeight || input2.videoHeight || input2.height || input2.shape && input2.shape[2] > 0;
let targetWidth = originalWidth;
let targetHeight = originalHeight;
if (config2.filter.width > 0)
targetWidth = config2.filter.width;
else if (config2.filter.height > 0)
targetWidth = originalWidth * (config2.filter.height / originalHeight);
if (config2.filter.height > 0)
targetHeight = config2.filter.height;
else if (config2.filter.width > 0)
targetHeight = originalHeight * (config2.filter.width / originalWidth);
if (!inCanvas || inCanvas.width !== targetWidth || inCanvas.height !== targetHeight) {
inCanvas = typeof OffscreenCanvas !== "undefined" ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement("canvas");
if (inCanvas.width !== targetWidth)
inCanvas.width = targetWidth;
if (inCanvas.height !== targetHeight)
inCanvas.height = targetHeight;
}
const ctx = inCanvas.getContext("2d");
if (input2 instanceof ImageData)
ctx.putImageData(input2, 0, 0);
else
ctx.drawImage(input2, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);
if (config2.filter.enabled) {
if (!this.fx || !outCanvas || inCanvas.width !== outCanvas.width || inCanvas.height !== outCanvas.height) {
outCanvas = typeof OffscreenCanvas !== "undefined" ? new OffscreenCanvas(inCanvas.width, inCanvas.height) : document.createElement("canvas");
if (outCanvas.width !== inCanvas.width)
outCanvas.width = inCanvas.width;
if (outCanvas.height !== inCanvas.height)
outCanvas.height = inCanvas.height;
this.fx = dist_exports2.ENV.flags.IS_BROWSER ? new fxImage.Canvas({canvas: outCanvas}) : null;
}
this.fx.reset();
this.fx.addFilter("brightness", config2.filter.brightness);
if (config2.filter.contrast !== 0)
this.fx.addFilter("contrast", config2.filter.contrast);
if (config2.filter.sharpness !== 0)
this.fx.addFilter("sharpen", config2.filter.sharpness);
if (config2.filter.blur !== 0)
this.fx.addFilter("blur", config2.filter.blur);
if (config2.filter.saturation !== 0)
this.fx.addFilter("saturation", config2.filter.saturation);
if (config2.filter.hue !== 0)
this.fx.addFilter("hue", config2.filter.hue);
if (config2.filter.negative)
this.fx.addFilter("negative");
if (config2.filter.sepia)
this.fx.addFilter("sepia");
if (config2.filter.vintage)
this.fx.addFilter("brownie");
if (config2.filter.sepia)
this.fx.addFilter("sepia");
if (config2.filter.kodachrome)
this.fx.addFilter("kodachrome");
if (config2.filter.technicolor)
this.fx.addFilter("technicolor");
if (config2.filter.polaroid)
this.fx.addFilter("polaroid");
if (config2.filter.pixelate !== 0)
this.fx.addFilter("pixelate", config2.filter.pixelate);
this.fx.apply(inCanvas);
const gl = false;
if (gl) {
const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);
const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);
gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);
let i = 0;
for (let y = outCanvas.height - 1; y >= 0; y--) {
for (let x = 0; x < outCanvas.width; x++) {
const index = (x + y * outCanvas.width) * 4;
pixBuffer[i++] = glBuffer[index + 0];
pixBuffer[i++] = glBuffer[index + 1];
pixBuffer[i++] = glBuffer[index + 2];
}
}
outCanvas.data = pixBuffer;
}
} else {
outCanvas = inCanvas;
}
let pixels;
if (outCanvas.data) {
const shape = [outCanvas.height, outCanvas.width, 3];
pixels = dist_exports2.tensor3d(outCanvas.data, shape, "int32");
} else if (config2.backend === "webgl" || outCanvas instanceof ImageData) {
pixels = dist_exports2.browser.fromPixels(outCanvas);
} else {
const tempCanvas = typeof OffscreenCanvas !== "undefined" ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement("canvas");
tempCanvas.width = targetWidth;
tempCanvas.height = targetHeight;
const tempCtx = tempCanvas.getContext("2d");
tempCtx.drawImage(outCanvas, 0, 0);
const data2 = tempCtx.getImageData(0, 0, targetWidth, targetHeight);
pixels = dist_exports2.browser.fromPixels(data2);
}
const casted = pixels.toFloat();
tensor16 = casted.expandDims(0);
pixels.dispose();
casted.dispose();
}
return {tensor: tensor16, canvas: config2.filter.return ? outCanvas : null};
}
exports3.process = process3;
});
// node_modules/@tensorflow/tfjs/dist/index.js
const dist_exports2 = {};
__export(dist_exports2, {
Abs: () => Abs,
Acos: () => Acos,
Acosh: () => Acosh,
AdadeltaOptimizer: () => AdadeltaOptimizer,
AdagradOptimizer: () => AdagradOptimizer,
AdamOptimizer: () => AdamOptimizer,
AdamaxOptimizer: () => AdamaxOptimizer,
Add: () => Add,
AddN: () => AddN,
All: () => All,
Any: () => Any,
ArgMax: () => ArgMax,
ArgMin: () => ArgMin,
Asin: () => Asin,
Asinh: () => Asinh,
Atan: () => Atan,
Atan2: () => Atan2,
Atanh: () => Atanh,
AvgPool: () => AvgPool,
AvgPool3D: () => AvgPool3D,
AvgPool3DBackprop: () => AvgPool3DBackprop,
AvgPoolBackprop: () => AvgPoolBackprop,
BatchMatMul: () => BatchMatMul,
BatchToSpaceND: () => BatchToSpaceND,
BroadcastTo: () => BroadcastTo,
Callback: () => Callback,
CallbackList: () => CallbackList,
Cast: () => Cast,
Ceil: () => Ceil,
ClipByValue: () => ClipByValue,
Complex: () => Complex,
Concat: () => Concat,
Conv2D: () => Conv2D,
Conv2DBackpropFilter: () => Conv2DBackpropFilter,
Conv2DBackpropInput: () => Conv2DBackpropInput,
Conv3D: () => Conv3D,
Conv3DBackpropFilterV2: () => Conv3DBackpropFilterV2,
Conv3DBackpropInputV2: () => Conv3DBackpropInputV2,
Cos: () => Cos,
Cosh: () => Cosh,
CropAndResize: () => CropAndResize,
Cumsum: () => Cumsum,
CustomCallback: () => CustomCallback,
DataStorage: () => DataStorage,
DepthToSpace: () => DepthToSpace,
DepthwiseConv2dNative: () => DepthwiseConv2dNative,
DepthwiseConv2dNativeBackpropFilter: () => DepthwiseConv2dNativeBackpropFilter,
DepthwiseConv2dNativeBackpropInput: () => DepthwiseConv2dNativeBackpropInput,
Diag: () => Diag,
Dilation2D: () => Dilation2D,
Dilation2DBackpropFilter: () => Dilation2DBackpropFilter,
Dilation2DBackpropInput: () => Dilation2DBackpropInput,
Div: () => Div,
ENV: () => ENV,
EarlyStopping: () => EarlyStopping,
Elu: () => Elu,
EluGrad: () => EluGrad,
Environment: () => Environment,
Equal: () => Equal,
Erf: () => Erf,
Exp: () => Exp,
Expm1: () => Expm1,
FFT: () => FFT,
Fill: () => Fill,
FlipLeftRight: () => FlipLeftRight,
Floor: () => Floor,
FloorDiv: () => FloorDiv,
FromPixels: () => FromPixels,
FusedBatchNorm: () => FusedBatchNorm,
FusedConv2D: () => FusedConv2D,
FusedDepthwiseConv2D: () => FusedDepthwiseConv2D,
GatherNd: () => GatherNd,
GatherV2: () => GatherV2,
GraphModel: () => GraphModel,
Greater: () => Greater,
GreaterEqual: () => GreaterEqual,
History: () => History,
IFFT: () => IFFT,
Identity: () => Identity,
Imag: () => Imag,
InputSpec: () => InputSpec,
IsFinite: () => IsFinite,
IsInf: () => IsInf,
IsNan: () => IsNan,
KernelBackend: () => KernelBackend,
LRN: () => LRN,
LRNBackprop: () => LRNBackprop,
LayerVariable: () => LayerVariable,
LayersModel: () => LayersModel,
Less: () => Less,
LessEqual: () => LessEqual,
LinSpace: () => LinSpace,
Log: () => Log,
Log1p: () => Log1p,
LogSoftmax: () => LogSoftmax,
LogicalAnd: () => LogicalAnd,
LogicalNot: () => LogicalNot,
LogicalOr: () => LogicalOr,
Max: () => Max,
MaxPool: () => MaxPool,
MaxPool3D: () => MaxPool3D,
MaxPool3DBackprop: () => MaxPool3DBackprop,
MaxPoolBackprop: () => MaxPoolBackprop,
MaxPoolWithArgmax: () => MaxPoolWithArgmax,
Maximum: () => Maximum,
Mean: () => Mean,
Min: () => Min,
Minimum: () => Minimum,
MirrorPad: () => MirrorPad,
Mod: () => Mod,
MomentumOptimizer: () => MomentumOptimizer,
Multiply: () => Multiply,
Negate: () => Negate,
NonMaxSuppressionV3: () => NonMaxSuppressionV3,
NonMaxSuppressionV4: () => NonMaxSuppressionV4,
NonMaxSuppressionV5: () => NonMaxSuppressionV5,
NotEqual: () => NotEqual,
OP_SCOPE_SUFFIX: () => OP_SCOPE_SUFFIX,
OneHot: () => OneHot,
OnesLike: () => OnesLike,
Optimizer: () => Optimizer,
PadV2: () => PadV2,
Pool: () => Pool,
Pow: () => Pow,
Prelu: () => Prelu,
Prod: () => Prod,
RMSPropOptimizer: () => RMSPropOptimizer,
RNN: () => RNN,
Range: () => Range,
Rank: () => Rank,
Real: () => Real,
Reciprocal: () => Reciprocal,
Reduction: () => Reduction,
Relu: () => Relu,
Relu6: () => Relu6,
Reshape: () => Reshape,
ResizeBilinear: () => ResizeBilinear,
ResizeBilinearGrad: () => ResizeBilinearGrad,
ResizeNearestNeighbor: () => ResizeNearestNeighbor,
ResizeNearestNeighborGrad: () => ResizeNearestNeighborGrad,
Reverse: () => Reverse,
RotateWithOffset: () => RotateWithOffset,
Round: () => Round,
Rsqrt: () => Rsqrt,
SGDOptimizer: () => SGDOptimizer,
ScatterNd: () => ScatterNd,
SelectV2: () => SelectV2,
Selu: () => Selu,
Sequential: () => Sequential,
Sigmoid: () => Sigmoid,
Sign: () => Sign,
Sin: () => Sin,
Sinh: () => Sinh,
Slice: () => Slice,
Softmax: () => Softmax,
Softplus: () => Softplus,
SpaceToBatchND: () => SpaceToBatchND,
SparseToDense: () => SparseToDense,
SplitV: () => SplitV,
Sqrt: () => Sqrt,
Square: () => Square,
SquaredDifference: () => SquaredDifference,
Step: () => Step,
StridedSlice: () => StridedSlice,
Sub: () => Sub,
Sum: () => Sum,
SymbolicTensor: () => SymbolicTensor,
Tan: () => Tan,
Tanh: () => Tanh,
Tensor: () => Tensor,
TensorBuffer: () => TensorBuffer,
Tile: () => Tile,
TopK: () => TopK,
Transpose: () => Transpose,
Unique: () => Unique,
Unpack: () => Unpack,
UnsortedSegmentSum: () => UnsortedSegmentSum,
Variable: () => Variable,
ZerosLike: () => ZerosLike,
_FusedMatMul: () => _FusedMatMul,
abs: () => abs,
acos: () => acos,
acosh: () => acosh,
add: () => add2,
addN: () => addN,
addStrict: () => addStrict,
all: () => all,
any: () => any,
argMax: () => argMax,
argMin: () => argMin,
asin: () => asin,
asinh: () => asinh,
atan: () => atan,
atan2: () => atan2,
atanh: () => atanh,
avgPool: () => avgPool,
avgPool3d: () => avgPool3d,
backend: () => backend2,
backend_util: () => backend_util_exports,
basicLSTMCell: () => basicLSTMCell,
batchNorm: () => batchNorm,
batchNorm2d: () => batchNorm2d,
batchNorm3d: () => batchNorm3d,
batchNorm4d: () => batchNorm4d,
batchToSpaceND: () => batchToSpaceND,
booleanMaskAsync: () => booleanMaskAsync,
broadcastTo: () => broadcastTo,
browser: () => browser_exports,
buffer: () => buffer,
callbacks: () => callbacks,
cast: () => cast,
ceil: () => ceil,
clipByValue: () => clipByValue,
clone: () => clone,
complex: () => complex,
concat: () => concat,
concat1d: () => concat1d,
concat2d: () => concat2d,
concat3d: () => concat3d,
concat4d: () => concat4d,
constraints: () => exports_constraints_exports,
conv1d: () => conv1d,
conv2d: () => conv2d,
conv2dTranspose: () => conv2dTranspose,
conv3d: () => conv3d,
conv3dTranspose: () => conv3dTranspose,
copyRegisteredKernels: () => copyRegisteredKernels,
cos: () => cos,
cosh: () => cosh,
cosineWindow: () => cosineWindow,
cumsum: () => cumsum,
customGrad: () => customGrad,
data: () => dist_exports,
deprecationWarn: () => deprecationWarn,
depthToSpace: () => depthToSpace,
depthwiseConv2d: () => depthwiseConv2d,
deregisterOp: () => deregisterOp,
device_util: () => device_util_exports,
diag: () => diag,
dilation2d: () => dilation2d,
disableDeprecationWarnings: () => disableDeprecationWarnings,
dispose: () => dispose,
disposeVariables: () => disposeVariables,
div: () => div,
divNoNan: () => divNoNan,
divStrict: () => divStrict,
dot: () => dot,
dropout: () => dropout,
elu: () => elu,
enableDebugMode: () => enableDebugMode,
enableProdMode: () => enableProdMode,
enclosingPowerOfTwo: () => enclosingPowerOfTwo,
engine: () => engine14,
env: () => env,
equal: () => equal,
equalStrict: () => equalStrict,
erf: () => erf,
exp: () => exp,
expandDims: () => expandDims,
expm1: () => expm1,
eye: () => eye,
fft: () => fft,
fill: () => fill,
findBackend: () => findBackend,
findBackendFactory: () => findBackendFactory,
floor: () => floor,
floorDiv: () => floorDiv,
fused: () => fused_ops_exports,
gather: () => gather,
gatherND: () => gatherND,
gather_util: () => gather_nd_util_exports,
getBackend: () => getBackend,
getGradient: () => getGradient,
getKernel: () => getKernel,
getKernelsForBackend: () => getKernelsForBackend,
grad: () => grad,
grads: () => grads,
greater: () => greater,
greaterEqual: () => greaterEqual,
greaterEqualStrict: () => greaterEqualStrict,
greaterStrict: () => greaterStrict,
ifft: () => ifft,
imag: () => imag,
image: () => image,
inTopKAsync: () => inTopKAsync,
initializers: () => exports_initializers_exports,
input: () => input,
io: () => io_exports,
irfft: () => irfft,
isFinite: () => isFinite2,
isInf: () => isInf,
isNaN: () => isNaN2,
keep: () => keep,
kernel_impls: () => kernel_impls_exports,
layers: () => exports_layers_exports,
leakyRelu: () => leakyRelu,
less: () => less,
lessEqual: () => lessEqual,
lessEqualStrict: () => lessEqualStrict,
lessStrict: () => lessStrict,
linalg: () => linalg,
linspace: () => linspace,
loadGraphModel: () => loadGraphModel,
loadLayersModel: () => loadLayersModel,
localResponseNormalization: () => localResponseNormalization,
log: () => log,
log1p: () => log1p,
logSigmoid: () => logSigmoid,
logSoftmax: () => logSoftmax,
logSumExp: () => logSumExp,
logicalAnd: () => logicalAnd,
logicalNot: () => logicalNot,
logicalOr: () => logicalOr,
logicalXor: () => logicalXor,
losses: () => losses,
matMul: () => matMul,
math: () => math_exports,
max: () => max,
maxPool: () => maxPool,
maxPool3d: () => maxPool3d,
maxPoolWithArgmax: () => maxPoolWithArgmax,
maximum: () => maximum,
maximumStrict: () => maximumStrict,
mean: () => mean,
memory: () => memory,
metrics: () => exports_metrics_exports,
min: () => min,
minimum: () => minimum,
minimumStrict: () => minimumStrict,
mirrorPad: () => mirrorPad,
mod: () => mod,
modStrict: () => modStrict,
model: () => model,
models: () => exports_models_exports,
moments: () => moments,
movingAverage: () => movingAverage,
mul: () => mul,
mulStrict: () => mulStrict,
multiRNNCell: () => multiRNNCell,
multinomial: () => multinomial,
neg: () => neg,
nextFrame: () => nextFrame,
norm: () => norm,
notEqual: () => notEqual,
notEqualStrict: () => notEqualStrict,
oneHot: () => oneHot,
ones: () => ones2,
onesLike: () => onesLike,
op: () => op,
outerProduct: () => outerProduct,
pad: () => pad,
pad1d: () => pad1d,
pad2d: () => pad2d,
pad3d: () => pad3d,
pad4d: () => pad4d,
pool: () => pool,
pow: () => pow,
powStrict: () => powStrict,
prelu: () => prelu,
print: () => print2,
prod: () => prod,
profile: () => profile,
rand: () => rand,
randomGamma: () => randomGamma,
randomNormal: () => randomNormal,
randomUniform: () => randomUniform,
range: () => range,
ready: () => ready,
real: () => real,
reciprocal: () => reciprocal,
registerBackend: () => registerBackend,
registerCallbackConstructor: () => registerCallbackConstructor,
registerGradient: () => registerGradient,
registerKernel: () => registerKernel,
registerOp: () => registerOp,
regularizers: () => exports_regularizers_exports,
relu: () => relu,
relu6: () => relu6,
removeBackend: () => removeBackend,
reshape: () => reshape,
reverse: () => reverse,
reverse1d: () => reverse1d,
reverse2d: () => reverse2d,
reverse3d: () => reverse3d,
reverse4d: () => reverse4d,
rfft: () => rfft,
round: () => round,
rsqrt: () => rsqrt,
scalar: () => scalar,
scatterND: () => scatterND,
scatter_util: () => scatter_nd_util_exports,
selu: () => selu,
separableConv2d: () => separableConv2d,
sequential: () => sequential,
serialization: () => serialization_exports,
setBackend: () => setBackend,
setPlatform: () => setPlatform,
setdiff1dAsync: () => setdiff1dAsync,
sigmoid: () => sigmoid,
sign: () => sign,
signal: () => signal,
sin: () => sin,
sinh: () => sinh,
slice: () => slice,
slice1d: () => slice1d,
slice2d: () => slice2d,
slice3d: () => slice3d,
slice4d: () => slice4d,
slice_util: () => slice_util_exports,
softmax: () => softmax,
softplus: () => softplus,
spaceToBatchND: () => spaceToBatchND,
sparseToDense: () => sparseToDense,
spectral: () => spectral,
split: () => split,
sqrt: () => sqrt,
square: () => square,
squaredDifference: () => squaredDifference,
squaredDifferenceStrict: () => squaredDifferenceStrict,
squeeze: () => squeeze,
stack: () => stack,
step: () => step,
stridedSlice: () => stridedSlice,
sub: () => sub,
subStrict: () => subStrict,
sum: () => sum2,
sumOutType: () => sumOutType,
tan: () => tan,
tanh: () => tanh2,
tensor: () => tensor4,
tensor1d: () => tensor1d,
tensor2d: () => tensor2d,
tensor3d: () => tensor3d,
tensor4d: () => tensor4d,
tensor5d: () => tensor5d,
tensor6d: () => tensor6d,
tensor_util: () => tensor_util_exports,
test_util: () => test_util_exports,
tidy: () => tidy,
tile: () => tile,
time: () => time,
topk: () => topk,
train: () => train,
transpose: () => transpose,
truncatedNormal: () => truncatedNormal,
unique: () => unique,
unregisterGradient: () => unregisterGradient,
unregisterKernel: () => unregisterKernel,
unsortedSegmentSum: () => unsortedSegmentSum,
unstack: () => unstack,
upcastType: () => upcastType,
util: () => util_exports,
valueAndGrad: () => valueAndGrad,
valueAndGrads: () => valueAndGrads,
variable: () => variable,
variableGrads: () => variableGrads,
version: () => version14,
version_converter: () => version6,
version_core: () => version,
version_layers: () => version2,
where: () => where,
whereAsync: () => whereAsync,
zeros: () => zeros,
zerosLike: () => zerosLike
});
// node_modules/@tensorflow/tfjs-core/dist/backends/backend.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const EPSILON_FLOAT32 = 1e-7;
const EPSILON_FLOAT16 = 1e-4;
class DataStorage {
constructor(backend3, dataMover) {
this.backend = backend3;
this.dataMover = dataMover;
this.data = new WeakMap();
this.dataIdsCount = 0;
}
get(dataId) {
if (!this.data.has(dataId)) {
this.dataMover.moveData(this.backend, dataId);
}
return this.data.get(dataId);
}
set(dataId, value) {
this.dataIdsCount++;
this.data.set(dataId, value);
}
has(dataId) {
return this.data.has(dataId);
}
delete(dataId) {
this.dataIdsCount--;
return this.data.delete(dataId);
}
numDataIds() {
return this.dataIdsCount;
}
}
class KernelBackend {
time(f) {
return notYetImplemented("time");
}
read(dataId) {
return notYetImplemented("read");
}
readSync(dataId) {
return notYetImplemented("readSync");
}
numDataIds() {
return notYetImplemented("numDataIds");
}
disposeData(dataId) {
return notYetImplemented("disposeData");
}
write(values, shape, dtype) {
return notYetImplemented("write");
}
move(dataId, values, shape, dtype) {
return notYetImplemented("move");
}
memory() {
return notYetImplemented("memory");
}
floatPrecision() {
return notYetImplemented("floatPrecision");
}
epsilon() {
return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;
}
batchMatMul(a, b, transposeA, transposeB) {
return notYetImplemented("batchMatMul");
}
fusedBatchMatMul({a, b, transposeA, transposeB, bias, activation: activation2, preluActivationWeights}) {
return notYetImplemented("fusedBatchMatMul");
}
slice(x, begin, size) {
return notYetImplemented("slice");
}
stridedSlice(x, begin, end, strides) {
return notYetImplemented("stridedSlice");
}
unstack(x, axis) {
return notYetImplemented("unstack");
}
reverse(a, axis) {
return notYetImplemented("reverse");
}
concat(tensors, axis) {
return notYetImplemented("concat");
}
neg(a) {
return notYetImplemented("neg");
}
add(a, b) {
return notYetImplemented("add");
}
addN(tensors) {
return notYetImplemented("addN");
}
subtract(a, b) {
return notYetImplemented("subtract");
}
multiply(a, b) {
return notYetImplemented("multiply");
}
realDivide(a, b) {
return notYetImplemented("realDivide");
}
floorDiv(a, b) {
return notYetImplemented("floorDiv");
}
sum(x, axes) {
return notYetImplemented("sum");
}
prod(x, axes) {
return notYetImplemented("prod");
}
unsortedSegmentSum(x, segmentIds, numSegments) {
return notYetImplemented("unsortedSegmentSum");
}
argMin(x, axis) {
return notYetImplemented("argMin");
}
argMax(x, axis) {
return notYetImplemented("argMax");
}
equal(a, b) {
return notYetImplemented("equal");
}
notEqual(a, b) {
return notYetImplemented("notEqual");
}
less(a, b) {
return notYetImplemented("less");
}
lessEqual(a, b) {
return notYetImplemented("lessEqual");
}
greater(a, b) {
return notYetImplemented("greater");
}
greaterEqual(a, b) {
return notYetImplemented("greaterEqual");
}
logicalNot(a) {
return notYetImplemented("logicalNot");
}
logicalAnd(a, b) {
return notYetImplemented("logicalAnd");
}
logicalOr(a, b) {
return notYetImplemented("logicalOr");
}
where(condition) {
return notYetImplemented("where");
}
select(condition, a, b) {
return notYetImplemented("select");
}
topk(x, k, sorted) {
return notYetImplemented("topk");
}
min(x, axes) {
return notYetImplemented("min");
}
minimum(a, b) {
return notYetImplemented("minimum");
}
mod(a, b) {
return notYetImplemented("mod");
}
max(x, axes) {
return notYetImplemented("max");
}
maximum(a, b) {
return notYetImplemented("maximum");
}
all(x, axes) {
return notYetImplemented("all");
}
any(x, axes) {
return notYetImplemented("any");
}
squaredDifference(a, b) {
return notYetImplemented("squaredDifference");
}
ceil(x) {
return notYetImplemented("ceil");
}
floor(x) {
return notYetImplemented("floor");
}
round(x) {
return notYetImplemented("round");
}
sign(x) {
return notYetImplemented("sign");
}
isNaN(x) {
return notYetImplemented("isNaN");
}
isInf(x) {
return notYetImplemented("isInf");
}
isFinite(x) {
return notYetImplemented("isFinite");
}
pow(a, b) {
return notYetImplemented("pow");
}
exp(x) {
return notYetImplemented("exp");
}
expm1(x) {
return notYetImplemented("expm1");
}
softmax(x, dim) {
return notYetImplemented("softmax");
}
log(x) {
return notYetImplemented("log");
}
log1p(x) {
return notYetImplemented("log1p");
}
sqrt(x) {
return notYetImplemented("sqrt");
}
rsqrt(x) {
return notYetImplemented("rsqrt");
}
square(x) {
return notYetImplemented("square");
}
reciprocal(x) {
return notYetImplemented("reciprocal");
}
relu(x) {
return notYetImplemented("relu");
}
relu6(x) {
return notYetImplemented("relu6");
}
prelu(x, a) {
return notYetImplemented("prelu");
}
elu(x) {
return notYetImplemented("elu");
}
eluDer(dy, y) {
return notYetImplemented("eluDer");
}
selu(x) {
return notYetImplemented("selu");
}
int(x) {
return notYetImplemented("int");
}
clip(x, min6, max8) {
return notYetImplemented("clip");
}
abs(x) {
return notYetImplemented("abs");
}
complexAbs(x) {
return notYetImplemented("complexAbs");
}
sigmoid(x) {
return notYetImplemented("sigmoid");
}
softplus(x) {
return notYetImplemented("softplus");
}
sin(x) {
return notYetImplemented("sin");
}
cos(x) {
return notYetImplemented("cos");
}
tan(x) {
return notYetImplemented("tan");
}
asin(x) {
return notYetImplemented("asin");
}
acos(x) {
return notYetImplemented("acos");
}
atan(x) {
return notYetImplemented("atan");
}
atan2(a, b) {
return notYetImplemented("atan2");
}
sinh(x) {
return notYetImplemented("sinh");
}
cosh(x) {
return notYetImplemented("cosh");
}
tanh(x) {
return notYetImplemented("tanh");
}
asinh(x) {
return notYetImplemented("asinh");
}
acosh(x) {
return notYetImplemented("acosh");
}
atanh(x) {
return notYetImplemented("atanh");
}
erf(x) {
return notYetImplemented("erf");
}
step(x, alpha) {
return notYetImplemented("step");
}
fusedConv2d({input: input2, filter, convInfo, bias, activation: activation2, preluActivationWeights}) {
return notYetImplemented("fusedConv2d");
}
conv2d(x, filter, convInfo) {
return notYetImplemented("conv2d");
}
conv2dDerInput(dy, filter, convInfo) {
return notYetImplemented("conv2dDerInput");
}
conv2dDerFilter(x, dY, convInfo) {
return notYetImplemented("conv2dDerFilter");
}
fusedDepthwiseConv2D({input: input2, filter, convInfo, bias, activation: activation2, preluActivationWeights}) {
return notYetImplemented("fusedDepthwiseConv2D");
}
depthwiseConv2D(input2, filter, convInfo) {
return notYetImplemented("depthwiseConv2D");
}
depthwiseConv2DDerInput(dy, filter, convInfo) {
return notYetImplemented("depthwiseConv2DDerInput");
}
depthwiseConv2DDerFilter(x, dY, convInfo) {
return notYetImplemented("depthwiseConv2DDerFilter");
}
conv3d(x, filter, convInfo) {
return notYetImplemented("conv3d");
}
conv3dDerInput(dy, filter, convInfo) {
return notYetImplemented("conv3dDerInput");
}
conv3dDerFilter(x, dY, convInfo) {
return notYetImplemented("conv3dDerFilter");
}
maxPool(x, convInfo) {
return notYetImplemented("maxPool");
}
maxPoolBackprop(dy, x, y, convInfo) {
return notYetImplemented("maxPoolBackprop");
}
avgPool(x, convInfo) {
return notYetImplemented("avgPool");
}
avgPoolBackprop(dy, x, convInfo) {
return notYetImplemented("avgPoolBackprop");
}
avgPool3d(x, convInfo) {
return notYetImplemented("avgPool3d");
}
avgPool3dBackprop(dy, x, convInfo) {
return notYetImplemented("avgPool3dBackprop");
}
maxPool3d(x, convInfo) {
return notYetImplemented("maxPool3d");
}
maxPool3dBackprop(dy, x, y, convInfo) {
return notYetImplemented("maxPool3dBackprop");
}
reshape(x, shape) {
return notYetImplemented("reshape");
}
cast(x, dtype) {
return notYetImplemented("cast");
}
tile(x, reps) {
return notYetImplemented("tile");
}
pad(x, paddings, constantValue) {
return notYetImplemented("pad");
}
transpose(x, perm) {
return notYetImplemented("transpose");
}
gather(x, indices, axis) {
return notYetImplemented("gather");
}
gatherND(x, indices) {
return notYetImplemented("gatherND");
}
scatterND(indices, updates, shape) {
return notYetImplemented("scatterND");
}
batchToSpaceND(x, blockShape, crops) {
return notYetImplemented("batchToSpaceND");
}
spaceToBatchND(x, blockShape, paddings) {
return notYetImplemented("spaceToBatchND");
}
resizeBilinear(x, newHeight, newWidth, alignCorners) {
return notYetImplemented("resizeBilinear");
}
resizeBilinearBackprop(dy, x, alignCorners) {
return notYetImplemented("resizeBilinearBackprop");
}
resizeNearestNeighbor(x, newHEight, newWidth, alignCorners) {
return notYetImplemented("resizeNearestNeighbor");
}
resizeNearestNeighborBackprop(dy, x, alignCorners) {
return notYetImplemented("resizeNearestNeighborBackprop");
}
batchNorm(x, mean5, variance, offset, scale, varianceEpsilon) {
return notYetImplemented("batchNorm");
}
localResponseNormalization4D(x, radius, bias, alpha, beta) {
return notYetImplemented("localResponseNormalization4D");
}
LRNGrad(dy, inputImage, outputImage, radius, bias, alpha, beta) {
return notYetImplemented("LRNGrad");
}
multinomial(logits, normalized, numSamples, seed) {
return notYetImplemented("multinomial");
}
oneHot(indices, depth, onValue, offValue) {
return notYetImplemented("oneHot");
}
cumsum(x, axis, exclusive, reverse9) {
return notYetImplemented("cumsum");
}
nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
return notYetImplemented("nonMaxSuppression");
}
fft(x) {
return notYetImplemented("fft");
}
ifft(x) {
return notYetImplemented("ifft");
}
complex(real6, imag6) {
return notYetImplemented("complex");
}
real(input2) {
return notYetImplemented("real");
}
imag(input2) {
return notYetImplemented("imag");
}
cropAndResize(image4, boxes, boxIndex, cropSize, method, extrapolationValue) {
return notYetImplemented("cropAndResize");
}
depthToSpace(x, blockSize, dataFormat) {
return notYetImplemented("depthToSpace");
}
split(value, sizeSplits, axis) {
return notYetImplemented("split");
}
sparseToDense(sparseIndices, sparseValues, outputShape, defaultValue) {
return notYetImplemented("sparseToDense");
}
diag(x) {
return notYetImplemented("diag");
}
fill(shape, value, dtype) {
return notYetImplemented("fill");
}
onesLike(x) {
return notYetImplemented("onesLike");
}
zerosLike(x) {
return notYetImplemented("zerosLike");
}
linspace(start, stop, num) {
return notYetImplemented("linspace");
}
dispose() {
return notYetImplemented("dispose");
}
}
function notYetImplemented(kernelName) {
throw new Error(`'${kernelName}' not yet implemented or not found in the registry. This kernel may not be supported by the tfjs backend you have chosen`);
}
// node_modules/@tensorflow/tfjs-core/dist/util_base.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function shuffle(array2) {
let counter = array2.length;
let temp = 0;
let index = 0;
while (counter > 0) {
index = Math.random() * counter | 0;
counter--;
temp = array2[counter];
array2[counter] = array2[index];
array2[index] = temp;
}
}
function clamp(min6, x, max8) {
return Math.max(min6, Math.min(x, max8));
}
function nearestLargerEven(val) {
return val % 2 === 0 ? val : val + 1;
}
function sum(arr) {
let sum14 = 0;
for (let i = 0; i < arr.length; i++) {
sum14 += arr[i];
}
return sum14;
}
function randUniform(a, b) {
const r = Math.random();
return b * r + (1 - r) * a;
}
function distSquared(a, b) {
let result = 0;
for (let i = 0; i < a.length; i++) {
const diff = Number(a[i]) - Number(b[i]);
result += diff * diff;
}
return result;
}
function assert(expr, msg) {
if (!expr) {
throw new Error(typeof msg === "string" ? msg : msg());
}
}
function assertShapesMatch(shapeA, shapeB, errorMessagePrefix = "") {
assert(arraysEqual(shapeA, shapeB), () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);
}
function assertNonNull(a) {
assert(a != null, () => `The input to the tensor constructor must be a non-null value.`);
}
function flatten(arr, result = [], skipTypedArray = false) {
if (result == null) {
result = [];
}
if (Array.isArray(arr) || isTypedArray(arr) && !skipTypedArray) {
for (let i = 0; i < arr.length; ++i) {
flatten(arr[i], result, skipTypedArray);
}
} else {
result.push(arr);
}
return result;
}
function sizeFromShape(shape) {
if (shape.length === 0) {
return 1;
}
let size = shape[0];
for (let i = 1; i < shape.length; i++) {
size *= shape[i];
}
return size;
}
function isScalarShape(shape) {
return shape.length === 0;
}
function arraysEqual(n1, n2) {
if (n1 === n2) {
return true;
}
if (n1 == null || n2 == null) {
return false;
}
if (n1.length !== n2.length) {
return false;
}
for (let i = 0; i < n1.length; i++) {
if (n1[i] !== n2[i]) {
return false;
}
}
return true;
}
function isInt(a) {
return a % 1 === 0;
}
function tanh(x) {
if (Math.tanh != null) {
return Math.tanh(x);
}
if (x === Infinity) {
return 1;
} else if (x === -Infinity) {
return -1;
} else {
const e2x = Math.exp(2 * x);
return (e2x - 1) / (e2x + 1);
}
}
function sizeToSquarishShape(size) {
const width = Math.ceil(Math.sqrt(size));
return [width, Math.ceil(size / width)];
}
function createShuffledIndices(n) {
const shuffledIndices = new Uint32Array(n);
for (let i = 0; i < n; ++i) {
shuffledIndices[i] = i;
}
shuffle(shuffledIndices);
return shuffledIndices;
}
function rightPad(a, size) {
if (size <= a.length) {
return a;
}
return a + " ".repeat(size - a.length);
}
function repeatedTry(checkFn, delayFn = (counter) => 0, maxCounter) {
return new Promise((resolve, reject) => {
let tryCount = 0;
const tryFn = () => {
if (checkFn()) {
resolve();
return;
}
tryCount++;
const nextBackoff = delayFn(tryCount);
if (maxCounter != null && tryCount >= maxCounter) {
reject();
return;
}
setTimeout(tryFn, nextBackoff);
};
tryFn();
});
}
function inferFromImplicitShape(shape, size) {
let shapeProd = 1;
let implicitIdx = -1;
for (let i = 0; i < shape.length; ++i) {
if (shape[i] >= 0) {
shapeProd *= shape[i];
} else if (shape[i] === -1) {
if (implicitIdx !== -1) {
throw Error(`Shapes can only have 1 implicit size. Found -1 at dim ${implicitIdx} and dim ${i}`);
}
implicitIdx = i;
} else if (shape[i] < 0) {
throw Error(`Shapes can not be < 0. Found ${shape[i]} at dim ${i}`);
}
}
if (implicitIdx === -1) {
if (size > 0 && size !== shapeProd) {
throw Error(`Size(${size}) must match the product of shape ${shape}`);
}
return shape;
}
if (shapeProd === 0) {
throw Error(`Cannot infer the missing size in [${shape}] when there are 0 elements`);
}
if (size % shapeProd !== 0) {
throw Error(`The implicit shape can't be a fractional number. Got ${size} / ${shapeProd}`);
}
const newShape = shape.slice();
newShape[implicitIdx] = size / shapeProd;
return newShape;
}
function parseAxisParam(axis, shape) {
const rank = shape.length;
axis = axis == null ? shape.map((s, i) => i) : [].concat(axis);
assert(axis.every((ax) => ax >= -rank && ax < rank), () => `All values in axis param must be in range [-${rank}, ${rank}) but got axis ${axis}`);
assert(axis.every((ax) => isInt(ax)), () => `All values in axis param must be integers but got axis ${axis}`);
return axis.map((a) => a < 0 ? rank + a : a);
}
function squeezeShape(shape, axis) {
const newShape = [];
const keptDims = [];
const isEmptyArray = axis != null && Array.isArray(axis) && axis.length === 0;
const axes = axis == null || isEmptyArray ? null : parseAxisParam(axis, shape).sort();
let j = 0;
for (let i = 0; i < shape.length; ++i) {
if (axes != null) {
if (axes[j] === i && shape[i] !== 1) {
throw new Error(`Can't squeeze axis ${i} since its dim '${shape[i]}' is not 1`);
}
if ((axes[j] == null || axes[j] > i) && shape[i] === 1) {
newShape.push(shape[i]);
keptDims.push(i);
}
if (axes[j] <= i) {
j++;
}
}
if (shape[i] !== 1) {
newShape.push(shape[i]);
keptDims.push(i);
}
}
return {newShape, keptDims};
}
function getTypedArrayFromDType(dtype, size) {
let values = null;
if (dtype == null || dtype === "float32") {
values = new Float32Array(size);
} else if (dtype === "int32") {
values = new Int32Array(size);
} else if (dtype === "bool") {
values = new Uint8Array(size);
} else {
throw new Error(`Unknown data type ${dtype}`);
}
return values;
}
function getArrayFromDType(dtype, size) {
let values = null;
if (dtype == null || dtype === "float32") {
values = new Float32Array(size);
} else if (dtype === "int32") {
values = new Int32Array(size);
} else if (dtype === "bool") {
values = new Uint8Array(size);
} else if (dtype === "string") {
values = new Array(size);
} else {
throw new Error(`Unknown data type ${dtype}`);
}
return values;
}
function checkConversionForErrors(vals, dtype) {
for (let i = 0; i < vals.length; i++) {
const num = vals[i];
if (isNaN(num) || !isFinite(num)) {
throw Error(`A tensor of type ${dtype} being uploaded contains ${num}.`);
}
}
}
function isValidDtype(dtype) {
return dtype === "bool" || dtype === "complex64" || dtype === "float32" || dtype === "int32" || dtype === "string";
}
function hasEncodingLoss(oldType, newType) {
if (newType === "complex64") {
return false;
}
if (newType === "float32" && oldType !== "complex64") {
return false;
}
if (newType === "int32" && oldType !== "float32" && oldType !== "complex64") {
return false;
}
if (newType === "bool" && oldType === "bool") {
return false;
}
return true;
}
function isTypedArray(a) {
return a instanceof Float32Array || a instanceof Int32Array || a instanceof Uint8Array;
}
function bytesPerElement(dtype) {
if (dtype === "float32" || dtype === "int32") {
return 4;
} else if (dtype === "complex64") {
return 8;
} else if (dtype === "bool") {
return 1;
} else {
throw new Error(`Unknown dtype ${dtype}`);
}
}
function bytesFromStringArray(arr) {
if (arr == null) {
return 0;
}
let bytes = 0;
arr.forEach((x) => bytes += x.length);
return bytes;
}
function isString(value) {
return typeof value === "string" || value instanceof String;
}
function isBoolean(value) {
return typeof value === "boolean";
}
function isNumber(value) {
return typeof value === "number";
}
function inferDtype(values) {
if (Array.isArray(values)) {
return inferDtype(values[0]);
}
if (values instanceof Float32Array) {
return "float32";
} else if (values instanceof Int32Array || values instanceof Uint8Array) {
return "int32";
} else if (isNumber(values)) {
return "float32";
} else if (isString(values)) {
return "string";
} else if (isBoolean(values)) {
return "bool";
}
return "float32";
}
function isFunction(f) {
return !!(f && f.constructor && f.call && f.apply);
}
function nearestDivisor(size, start) {
for (let i = start; i < size; ++i) {
if (size % i === 0) {
return i;
}
}
return size;
}
function computeStrides(shape) {
const rank = shape.length;
if (rank < 2) {
return [];
}
const strides = new Array(rank - 1);
strides[rank - 2] = shape[rank - 1];
for (let i = rank - 3; i >= 0; --i) {
strides[i] = strides[i + 1] * shape[i + 1];
}
return strides;
}
function createNestedArray(offset, shape, a) {
const ret = new Array();
if (shape.length === 1) {
const d = shape[0];
for (let i = 0; i < d; i++) {
ret[i] = a[offset + i];
}
} else {
const d = shape[0];
const rest = shape.slice(1);
const len = rest.reduce((acc, c) => acc * c);
for (let i = 0; i < d; i++) {
ret[i] = createNestedArray(offset + i * len, rest, a);
}
}
return ret;
}
function toNestedArray(shape, a) {
if (shape.length === 0) {
return a[0];
}
const size = shape.reduce((acc, c) => acc * c);
if (size === 0) {
return [];
}
if (size !== a.length) {
throw new Error(`[${shape}] does not match the input size ${a.length}.`);
}
return createNestedArray(0, shape, a);
}
function makeOnesTypedArray(size, dtype) {
const array2 = makeZerosTypedArray(size, dtype);
for (let i = 0; i < array2.length; i++) {
array2[i] = 1;
}
return array2;
}
function makeZerosTypedArray(size, dtype) {
if (dtype == null || dtype === "float32" || dtype === "complex64") {
return new Float32Array(size);
} else if (dtype === "int32") {
return new Int32Array(size);
} else if (dtype === "bool") {
return new Uint8Array(size);
} else {
throw new Error(`Unknown data type ${dtype}`);
}
}
function makeZerosNestedTypedArray(shape, dtype) {
const size = shape.reduce((prev, curr) => prev * curr, 1);
if (dtype == null || dtype === "float32") {
return toNestedArray(shape, new Float32Array(size));
} else if (dtype === "int32") {
return toNestedArray(shape, new Int32Array(size));
} else if (dtype === "bool") {
return toNestedArray(shape, new Uint8Array(size));
} else {
throw new Error(`Unknown data type ${dtype}`);
}
}
function assertNonNegativeIntegerDimensions(shape) {
shape.forEach((dimSize) => {
assert(Number.isInteger(dimSize) && dimSize >= 0, () => `Tensor must have a shape comprised of positive integers but got shape [${shape}].`);
});
}
function locToIndex(locs, rank, strides) {
if (rank === 0) {
return 0;
} else if (rank === 1) {
return locs[0];
}
let index = locs[locs.length - 1];
for (let i = 0; i < locs.length - 1; ++i) {
index += strides[i] * locs[i];
}
return index;
}
function indexToLoc(index, rank, strides) {
if (rank === 0) {
return [];
} else if (rank === 1) {
return [index];
}
const locs = new Array(rank);
for (let i = 0; i < locs.length - 1; ++i) {
locs[i] = Math.floor(index / strides[i]);
index -= locs[i] * strides[i];
}
locs[locs.length - 1] = index;
return locs;
}
function isPromise(object) {
return object && object.then && typeof object.then === "function";
}
// node_modules/@tensorflow/tfjs-core/dist/environment.js
/**
* @license
* Copyright 2017 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const TENSORFLOWJS_FLAGS_PREFIX = "tfjsflags";
class Environment {
constructor(global2) {
this.global = global2;
this.flags = {};
this.flagRegistry = {};
this.urlFlags = {};
this.populateURLFlags();
}
setPlatform(platformName, platform) {
if (this.platform != null) {
console.warn(`Platform ${this.platformName} has already been set. Overwriting the platform with ${platform}.`);
}
this.platformName = platformName;
this.platform = platform;
}
registerFlag(flagName, evaluationFn, setHook) {
this.flagRegistry[flagName] = {evaluationFn, setHook};
if (this.urlFlags[flagName] != null) {
const flagValue = this.urlFlags[flagName];
console.warn(`Setting feature override from URL ${flagName}: ${flagValue}.`);
this.set(flagName, flagValue);
}
}
async getAsync(flagName) {
if (flagName in this.flags) {
return this.flags[flagName];
}
this.flags[flagName] = await this.evaluateFlag(flagName);
return this.flags[flagName];
}
get(flagName) {
if (flagName in this.flags) {
return this.flags[flagName];
}
const flagValue = this.evaluateFlag(flagName);
if (isPromise(flagValue)) {
throw new Error(`Flag ${flagName} cannot be synchronously evaluated. Please use getAsync() instead.`);
}
this.flags[flagName] = flagValue;
return this.flags[flagName];
}
getNumber(flagName) {
return this.get(flagName);
}
getBool(flagName) {
return this.get(flagName);
}
getFlags() {
return this.flags;
}
get features() {
return this.flags;
}
set(flagName, value) {
if (this.flagRegistry[flagName] == null) {
throw new Error(`Cannot set flag ${flagName} as it has not been registered.`);
}
this.flags[flagName] = value;
if (this.flagRegistry[flagName].setHook != null) {
this.flagRegistry[flagName].setHook(value);
}
}
evaluateFlag(flagName) {
if (this.flagRegistry[flagName] == null) {
throw new Error(`Cannot evaluate flag '${flagName}': no evaluation function found.`);
}
return this.flagRegistry[flagName].evaluationFn();
}
setFlags(flags2) {
this.flags = Object.assign({}, flags2);
}
reset() {
this.flags = {};
this.urlFlags = {};
this.populateURLFlags();
}
populateURLFlags() {
if (typeof this.global === "undefined" || typeof this.global.location === "undefined" || typeof this.global.location.search === "undefined") {
return;
}
const urlParams = getQueryParams(this.global.location.search);
if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) {
const keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(",");
keyValues.forEach((keyValue) => {
const [key, value] = keyValue.split(":");
this.urlFlags[key] = parseValue(key, value);
});
}
}
}
function getQueryParams(queryString) {
const params = {};
queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (s, ...t) => {
decodeParam(params, t[0], t[1]);
return t.join("=");
});
return params;
}
function decodeParam(params, name, value) {
params[decodeURIComponent(name)] = decodeURIComponent(value || "");
}
function parseValue(flagName, value) {
value = value.toLowerCase();
if (value === "true" || value === "false") {
return value === "true";
} else if (`${+value}` === value) {
return +value;
}
throw new Error(`Could not parse value flag value ${value} for flag ${flagName}.`);
}
function env() {
return ENV;
}
let ENV = null;
function setEnvironmentGlobal(environment11) {
ENV = environment11;
}
// node_modules/@tensorflow/tfjs-core/dist/global_util.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
let globalNameSpace;
function getGlobalNamespace() {
if (globalNameSpace == null) {
let ns;
if (typeof window !== "undefined") {
ns = window;
} else if (typeof global !== "undefined") {
ns = global;
} else if (typeof process !== "undefined") {
ns = process;
} else if (typeof self !== "undefined") {
ns = self;
} else {
throw new Error("Could not find a global object");
}
globalNameSpace = ns;
}
return globalNameSpace;
}
function getGlobalMap() {
const ns = getGlobalNamespace();
if (ns._tfGlobals == null) {
ns._tfGlobals = new Map();
}
return ns._tfGlobals;
}
function getGlobal(key, init2) {
const globalMap = getGlobalMap();
if (globalMap.has(key)) {
return globalMap.get(key);
} else {
const singleton = init2();
globalMap.set(key, singleton);
return globalMap.get(key);
}
}
// node_modules/@tensorflow/tfjs-core/dist/kernel_names.js
const Abs = "Abs";
const Acos = "Acos";
const Acosh = "Acosh";
const Add = "Add";
const AddN = "AddN";
const All = "All";
const Any = "Any";
const ArgMax = "ArgMax";
const ArgMin = "ArgMin";
const Asin = "Asin";
const Asinh = "Asinh";
const Atan = "Atan";
const Atanh = "Atanh";
const Atan2 = "Atan2";
const AvgPool = "AvgPool";
const AvgPoolBackprop = "AvgPoolBackprop";
const AvgPool3D = "AvgPool3D";
const AvgPool3DBackprop = "AvgPool3DBackprop";
const BatchMatMul = "BatchMatMul";
const BatchToSpaceND = "BatchToSpaceND";
const BroadcastTo = "BroadcastTo";
const Cast = "Cast";
const Ceil = "Ceil";
const ClipByValue = "ClipByValue";
const Complex = "Complex";
const Concat = "Concat";
const Conv2D = "Conv2D";
const Conv2DBackpropFilter = "Conv2DBackpropFilter";
const Conv2DBackpropInput = "Conv2DBackpropInput";
const Conv3D = "Conv3D";
const Conv3DBackpropFilterV2 = "Conv3DBackpropFilterV2";
const Conv3DBackpropInputV2 = "Conv3DBackpropInputV2";
const Cos = "Cos";
const Cosh = "Cosh";
const Cumsum = "Cumsum";
const CropAndResize = "CropAndResize";
const DepthToSpace = "DepthToSpace";
const DepthwiseConv2dNative = "DepthwiseConv2dNative";
const DepthwiseConv2dNativeBackpropFilter = "DepthwiseConv2dNativeBackpropFilter";
const DepthwiseConv2dNativeBackpropInput = "DepthwiseConv2dNativeBackpropInput";
const Diag = "Diag";
const Dilation2D = "Dilation2D";
const Dilation2DBackpropInput = "Dilation2DBackpropInput";
const Dilation2DBackpropFilter = "Dilation2DBackpropFilter";
const Div = "Div";
const Elu = "Elu";
const EluGrad = "EluGrad";
const Erf = "Erf";
const Equal = "Equal";
const Exp = "Exp";
const Expm1 = "Expm1";
const FFT = "FFT";
const Fill = "Fill";
const FlipLeftRight = "FlipLeftRight";
const Floor = "Floor";
const FloorDiv = "FloorDiv";
const FusedBatchNorm = "FusedBatchNorm";
const GatherV2 = "GatherV2";
const GatherNd = "GatherNd";
const Greater = "Greater";
const GreaterEqual = "GreaterEqual";
const Identity = "Identity";
const IFFT = "IFFT";
const Imag = "Imag";
const IsFinite = "IsFinite";
const IsInf = "IsInf";
const IsNan = "IsNan";
const Less = "Less";
const LessEqual = "LessEqual";
const LinSpace = "LinSpace";
const Log = "Log";
const Log1p = "Log1p";
const LogicalAnd = "LogicalAnd";
const LogicalNot = "LogicalNot";
const LogicalOr = "LogicalOr";
const LogSoftmax = "LogSoftmax";
const LRN = "LRN";
const LRNBackprop = "LRNBackprop";
const Max = "Max";
const Maximum = "Maximum";
const MaxPool = "MaxPool";
const MaxPoolBackprop = "MaxPoolBackprop";
const MaxPool3D = "MaxPool3D";
const MaxPool3DBackprop = "MaxPool3DBackprop";
const MaxPoolWithArgmax = "MaxPoolWithArgmax";
const Mean = "Mean";
const Min = "Min";
const Minimum = "Minimum";
const MirrorPad = "MirrorPad";
const Mod = "Mod";
const Multiply = "Multiply";
const Negate = "Negate";
const NotEqual = "NotEqual";
const NonMaxSuppressionV3 = "NonMaxSuppressionV3";
const NonMaxSuppressionV4 = "NonMaxSuppressionV4";
const NonMaxSuppressionV5 = "NonMaxSuppressionV5";
const OnesLike = "OnesLike";
const OneHot = "OneHot";
const PadV2 = "PadV2";
const Pool = "Pool";
const Pow = "Pow";
const Prelu = "Prelu";
const Prod = "Prod";
const Range = "Range";
const Real = "Real";
const Reciprocal = "Reciprocal";
const Relu = "Relu";
const Reshape = "Reshape";
const ResizeNearestNeighbor = "ResizeNearestNeighbor";
const ResizeNearestNeighborGrad = "ResizeNearestNeighborGrad";
const ResizeBilinear = "ResizeBilinear";
const ResizeBilinearGrad = "ResizeBilinearGrad";
const Relu6 = "Relu6";
const Reverse = "Reverse";
const Round = "Round";
const Rsqrt = "Rsqrt";
const ScatterNd = "ScatterNd";
const SelectV2 = "SelectV2";
const Selu = "Selu";
const Slice = "Slice";
const Sin = "Sin";
const Sinh = "Sinh";
const Sign = "Sign";
const Sigmoid = "Sigmoid";
const Softplus = "Softplus";
const Sqrt = "Sqrt";
const Sum = "Sum";
const SpaceToBatchND = "SpaceToBatchND";
const SplitV = "SplitV";
const Softmax = "Softmax";
const SquaredDifference = "SquaredDifference";
const Square = "Square";
const Sub = "Sub";
const SparseToDense = "SparseToDense";
const StridedSlice = "StridedSlice";
const Tan = "Tan";
const Tanh = "Tanh";
const Tile = "Tile";
const TopK = "TopK";
const Transpose = "Transpose";
const Unique = "Unique";
const Unpack = "Unpack";
const UnsortedSegmentSum = "UnsortedSegmentSum";
const ZerosLike = "ZerosLike";
const Step = "Step";
const FromPixels = "FromPixels";
const RotateWithOffset = "RotateWithOffset";
const _FusedMatMul = "_FusedMatMul";
const FusedConv2D = "FusedConv2D";
const FusedDepthwiseConv2D = "FusedDepthwiseConv2D";
// node_modules/@tensorflow/tfjs-core/dist/kernel_registry.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const kernelRegistry = getGlobal("kernelRegistry", () => new Map());
const gradRegistry = getGlobal("gradRegistry", () => new Map());
function getKernel(kernelName, backendName) {
const key = makeKey(kernelName, backendName);
return kernelRegistry.get(key);
}
function getGradient(kernelName) {
return gradRegistry.get(kernelName);
}
function getKernelsForBackend(backendName) {
const it = kernelRegistry.entries();
const result = [];
while (true) {
const {done, value} = it.next();
if (done) {
break;
}
const [key, config2] = value;
const [backend3] = key.split("_");
if (backend3 === backendName) {
result.push(config2);
}
}
return result;
}
function registerKernel(config2) {
const {kernelName, backendName} = config2;
const key = makeKey(kernelName, backendName);
if (kernelRegistry.has(key)) {
console.warn(`The kernel '${kernelName}' for backend '${backendName}' is already registered`);
}
kernelRegistry.set(key, config2);
}
function registerGradient(config2) {
const {kernelName} = config2;
if (gradRegistry.has(kernelName)) {
if (env().getBool("DEBUG")) {
console.warn(`Overriding the gradient for '${kernelName}'`);
}
}
gradRegistry.set(kernelName, config2);
}
function unregisterKernel(kernelName, backendName) {
const key = makeKey(kernelName, backendName);
if (!kernelRegistry.has(key)) {
throw new Error(`The kernel '${kernelName}' for backend '${backendName}' is not registered`);
}
kernelRegistry.delete(key);
}
function unregisterGradient(kernelName) {
if (!gradRegistry.has(kernelName)) {
throw new Error(`The gradient '${kernelName}' for backend is not registered`);
}
gradRegistry.delete(kernelName);
}
function copyRegisteredKernels(registeredBackendName, newBackendName) {
const kernels = getKernelsForBackend(registeredBackendName);
kernels.forEach((kernelConfig) => {
const newKernelConfig = Object.assign({}, kernelConfig, {backendName: newBackendName});
registerKernel(newKernelConfig);
});
}
function makeKey(kernelName, backendName) {
return `${backendName}_${kernelName}`;
}
// node_modules/@tensorflow/tfjs-core/dist/util.js
const util_exports = {};
__export(util_exports, {
arraysEqual: () => arraysEqual,
assert: () => assert,
assertNonNegativeIntegerDimensions: () => assertNonNegativeIntegerDimensions,
assertNonNull: () => assertNonNull,
assertShapesMatch: () => assertShapesMatch,
bytesFromStringArray: () => bytesFromStringArray,
bytesPerElement: () => bytesPerElement,
checkConversionForErrors: () => checkConversionForErrors,
clamp: () => clamp,
computeStrides: () => computeStrides,
createScalarValue: () => createScalarValue,
createShuffledIndices: () => createShuffledIndices,
decodeString: () => decodeString,
distSquared: () => distSquared,
encodeString: () => encodeString,
fetch: () => fetch2,
flatten: () => flatten,
getArrayFromDType: () => getArrayFromDType,
getTypedArrayFromDType: () => getTypedArrayFromDType,
hasEncodingLoss: () => hasEncodingLoss,
indexToLoc: () => indexToLoc,
inferDtype: () => inferDtype,
inferFromImplicitShape: () => inferFromImplicitShape,
isBoolean: () => isBoolean,
isFunction: () => isFunction,
isInt: () => isInt,
isNumber: () => isNumber,
isPromise: () => isPromise,
isScalarShape: () => isScalarShape,
isString: () => isString,
isTypedArray: () => isTypedArray,
isValidDtype: () => isValidDtype,
locToIndex: () => locToIndex,
makeOnesTypedArray: () => makeOnesTypedArray,
makeZerosNestedTypedArray: () => makeZerosNestedTypedArray,
makeZerosTypedArray: () => makeZerosTypedArray,
nearestDivisor: () => nearestDivisor,
nearestLargerEven: () => nearestLargerEven,
now: () => now,
parseAxisParam: () => parseAxisParam,
randUniform: () => randUniform,
repeatedTry: () => repeatedTry,
rightPad: () => rightPad,
shuffle: () => shuffle,
sizeFromShape: () => sizeFromShape,
sizeToSquarishShape: () => sizeToSquarishShape,
squeezeShape: () => squeezeShape,
sum: () => sum,
tanh: () => tanh,
toNestedArray: () => toNestedArray,
toTypedArray: () => toTypedArray
});
/**
* @license
* Copyright 2017 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function createScalarValue(value, dtype) {
if (dtype === "string") {
return encodeString(value);
}
return toTypedArray([value], dtype);
}
function noConversionNeeded(a, dtype) {
return a instanceof Float32Array && dtype === "float32" || a instanceof Int32Array && dtype === "int32" || a instanceof Uint8Array && dtype === "bool";
}
function toTypedArray(a, dtype) {
if (dtype === "string") {
throw new Error("Cannot convert a string[] to a TypedArray");
}
if (Array.isArray(a)) {
a = flatten(a);
}
if (env().getBool("DEBUG")) {
checkConversionForErrors(a, dtype);
}
if (noConversionNeeded(a, dtype)) {
return a;
}
if (dtype == null || dtype === "float32" || dtype === "complex64") {
return new Float32Array(a);
} else if (dtype === "int32") {
return new Int32Array(a);
} else if (dtype === "bool") {
const bool = new Uint8Array(a.length);
for (let i = 0; i < bool.length; ++i) {
if (Math.round(a[i]) !== 0) {
bool[i] = 1;
}
}
return bool;
} else {
throw new Error(`Unknown data type ${dtype}`);
}
}
function now() {
return env().platform.now();
}
function fetch2(path, requestInits) {
return env().platform.fetch(path, requestInits);
}
function encodeString(s, encoding = "utf-8") {
encoding = encoding || "utf-8";
return env().platform.encode(s, encoding);
}
function decodeString(bytes, encoding = "utf-8") {
encoding = encoding || "utf-8";
return env().platform.decode(bytes, encoding);
}
// node_modules/@tensorflow/tfjs-core/dist/profiler.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
class Profiler {
constructor(backendTimer, logger) {
this.backendTimer = backendTimer;
this.logger = logger;
if (logger == null) {
this.logger = new Logger();
}
}
profileKernel(kernelName, inputs, f) {
let outputs;
const holdResultWrapperFn = () => {
outputs = f();
};
const timer = this.backendTimer.time(holdResultWrapperFn);
for (let i = 0; i < outputs.length; i++) {
const output = outputs[i];
output.data().then((tensorVals) => {
checkComputationForErrors(tensorVals, output.dtype, kernelName);
});
}
const kernelProfile = {
kernelName,
outputs,
inputs,
timeMs: timer.then((timing) => timing.kernelMs),
extraInfo: timer.then((timing) => timing.getExtraProfileInfo != null ? timing.getExtraProfileInfo() : "")
};
return kernelProfile;
}
logKernelProfile(kernelProfile) {
const {kernelName, outputs, timeMs, inputs, extraInfo} = kernelProfile;
outputs.forEach((result) => {
Promise.all([result.data(), timeMs, extraInfo]).then((valueContainer) => {
this.logger.logKernelProfile(kernelName, result, valueContainer[0], valueContainer[1], inputs, valueContainer[2]);
});
});
}
}
function checkComputationForErrors(vals, dtype, kernelName) {
if (dtype !== "float32") {
return false;
}
for (let i = 0; i < vals.length; i++) {
const num = vals[i];
if (isNaN(num) || !isFinite(num)) {
console.warn(`Found ${num} in the result of '${kernelName}'`);
return true;
}
}
return false;
}
class Logger {
logKernelProfile(name, result, vals, timeMs, inputs, extraInfo) {
const time2 = typeof timeMs === "number" ? rightPad(`${timeMs}ms`, 9) : timeMs["error"];
const paddedName = rightPad(name, 25);
const rank = result.rank;
const size = result.size;
const shape = rightPad(result.shape.toString(), 14);
let inputShapesDescription = "";
for (const name2 in inputs) {
const input2 = inputs[name2];
if (input2 != null) {
const inputShape = input2.shape || result.shape;
const inputRank = inputShape.length;
inputShapesDescription += `${name2}: ${inputRank}D ${inputRank > 0 ? inputShape : ""} `;
}
}
console.log(`%c${paddedName} %c${time2} %c${rank}D ${shape} %c${size} %c${inputShapesDescription} %c${extraInfo}`, "font-weight:bold", "color:red", "color:blue", "color: orange", "color: green", "color: steelblue");
}
}
// node_modules/@tensorflow/tfjs-core/dist/tape.js
/**
* @license
* Copyright 2017 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function getFilteredNodesXToY(tape2, xs, y) {
const tensorsFromX = {};
const nodesFromX = {};
for (let i = 0; i < xs.length; i++) {
tensorsFromX[xs[i].id] = true;
}
for (let i = 0; i < tape2.length; i++) {
const node = tape2[i];
const nodeInputs = node.inputs;
for (const inputName in nodeInputs) {
const input2 = nodeInputs[inputName];
let anyInputFromX = false;
for (let j = 0; j < xs.length; j++) {
if (tensorsFromX[input2.id]) {
node.outputs.forEach((output) => tensorsFromX[output.id] = true);
anyInputFromX = true;
nodesFromX[node.id] = true;
break;
}
}
if (anyInputFromX) {
break;
}
}
}
const tensorsLeadToY = {};
tensorsLeadToY[y.id] = true;
const nodesToY = {};
for (let i = tape2.length - 1; i >= 0; i--) {
const node = tape2[i];
const nodeInputs = node.inputs;
for (let j = 0; j < node.outputs.length; j++) {
if (tensorsLeadToY[node.outputs[j].id]) {
for (const inputName in nodeInputs) {
tensorsLeadToY[nodeInputs[inputName].id] = true;
nodesToY[node.id] = true;
}
break;
}
}
}
const filteredTape = [];
for (let i = 0; i < tape2.length; i++) {
const node = tape2[i];
if (nodesFromX[node.id] && nodesToY[node.id]) {
const prunedInputs = {};
for (const inputName in node.inputs) {
const nodeInput = node.inputs[inputName];
if (tensorsFromX[nodeInput.id]) {
prunedInputs[inputName] = nodeInput;
}
}
const prunedNode = Object.assign({}, node);
prunedNode.inputs = prunedInputs;
prunedNode.outputs = node.outputs;
filteredTape.push(prunedNode);
}
}
return filteredTape;
}
function backpropagateGradients(tensorAccumulatedGradientMap, filteredTape, tidy2, add24) {
for (let i = filteredTape.length - 1; i >= 0; i--) {
const node = filteredTape[i];
const dys = [];
node.outputs.forEach((o) => {
const gradTensor = tensorAccumulatedGradientMap[o.id];
if (gradTensor != null) {
dys.push(gradTensor);
} else {
dys.push(null);
}
});
if (node.gradient == null) {
throw new Error(`Cannot compute gradient: gradient function not found for ${node.kernelName}.`);
}
const inputGradients = node.gradient(dys);
for (const inputName in node.inputs) {
if (!(inputName in inputGradients)) {
throw new Error(`Cannot backprop through input ${inputName}. Available gradients found: ${Object.keys(inputGradients)}.`);
}
const dx = tidy2(() => inputGradients[inputName]());
if (dx.dtype !== "float32") {
throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);
}
const x = node.inputs[inputName];
if (!arraysEqual(dx.shape, x.shape)) {
throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input '${inputName}' has shape '${dx.shape}', which does not match the shape of the input '${x.shape}'`);
}
if (tensorAccumulatedGradientMap[x.id] == null) {
tensorAccumulatedGradientMap[x.id] = dx;
} else {
const curGradient = tensorAccumulatedGradientMap[x.id];
tensorAccumulatedGradientMap[x.id] = add24(curGradient, dx);
curGradient.dispose();
}
}
}
}
// node_modules/@tensorflow/tfjs-core/dist/tensor_format.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const FORMAT_LIMIT_NUM_VALS = 20;
const FORMAT_NUM_FIRST_LAST_VALS = 3;
const FORMAT_NUM_SIG_DIGITS = 7;
function tensorToString(vals, shape, dtype, verbose) {
const strides = computeStrides(shape);
const padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides);
const rank = shape.length;
const valsLines = subTensorToString(vals, shape, dtype, strides, padPerCol);
const lines = ["Tensor"];
if (verbose) {
lines.push(` dtype: ${dtype}`);
lines.push(` rank: ${rank}`);
lines.push(` shape: [${shape}]`);
lines.push(` values:`);
}
lines.push(valsLines.map((l) => " " + l).join("\n"));
return lines.join("\n");
}
function computeMaxSizePerColumn(vals, shape, dtype, strides) {
const n = sizeFromShape(shape);
const numCols = strides[strides.length - 1];
const padPerCol = new Array(numCols).fill(0);
const rank = shape.length;
const valuesOrTuples = dtype === "complex64" ? createComplexTuples(vals) : vals;
if (rank > 1) {
for (let row = 0; row < n / numCols; row++) {
const offset = row * numCols;
for (let j = 0; j < numCols; j++) {
padPerCol[j] = Math.max(padPerCol[j], valToString(valuesOrTuples[offset + j], 0, dtype).length);
}
}
}
return padPerCol;
}
function valToString(val, pad8, dtype) {
let valStr;
if (Array.isArray(val)) {
valStr = `${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`;
} else if (isString(val)) {
valStr = `'${val}'`;
} else if (dtype === "bool") {
valStr = boolNumToString(val);
} else {
valStr = parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString();
}
return rightPad(valStr, pad8);
}
function boolNumToString(v) {
return v === 0 ? "false" : "true";
}
function subTensorToString(vals, shape, dtype, strides, padPerCol, isLast = true) {
const storagePerElement = dtype === "complex64" ? 2 : 1;
const size = shape[0];
const rank = shape.length;
if (rank === 0) {
if (dtype === "complex64") {
const complexTuple = createComplexTuples(vals);
return [valToString(complexTuple[0], 0, dtype)];
}
if (dtype === "bool") {
return [boolNumToString(vals[0])];
}
return [vals[0].toString()];
}
if (rank === 1) {
if (size > FORMAT_LIMIT_NUM_VALS) {
const firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement;
let firstVals = Array.from(vals.slice(0, firstValsSize));
let lastVals = Array.from(vals.slice((size - FORMAT_NUM_FIRST_LAST_VALS) * storagePerElement, size * storagePerElement));
if (dtype === "complex64") {
firstVals = createComplexTuples(firstVals);
lastVals = createComplexTuples(lastVals);
}
return [
"[" + firstVals.map((x, i) => valToString(x, padPerCol[i], dtype)).join(", ") + ", ..., " + lastVals.map((x, i) => valToString(x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i], dtype)).join(", ") + "]"
];
}
const displayVals = dtype === "complex64" ? createComplexTuples(vals) : Array.from(vals);
return [
"[" + displayVals.map((x, i) => valToString(x, padPerCol[i], dtype)).join(", ") + "]"
];
}
const subshape = shape.slice(1);
const substrides = strides.slice(1);
const stride = strides[0] * storagePerElement;
const lines = [];
if (size > FORMAT_LIMIT_NUM_VALS) {
for (let i = 0; i < FORMAT_NUM_FIRST_LAST_VALS; i++) {
const start = i * stride;
const end = start + stride;
lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, false));
}
lines.push("...");
for (let i = size - FORMAT_NUM_FIRST_LAST_VALS; i < size; i++) {
const start = i * stride;
const end = start + stride;
lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1));
}
} else {
for (let i = 0; i < size; i++) {
const start = i * stride;
const end = start + stride;
lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1));
}
}
const sep = rank === 2 ? "," : "";
lines[0] = "[" + lines[0] + sep;
for (let i = 1; i < lines.length - 1; i++) {
lines[i] = " " + lines[i] + sep;
}
let newLineSep = ",\n";
for (let i = 2; i < rank; i++) {
newLineSep += "\n";
}
lines[lines.length - 1] = " " + lines[lines.length - 1] + "]" + (isLast ? "" : newLineSep);
return lines;
}
function createComplexTuples(vals) {
const complexTuples = [];
for (let i = 0; i < vals.length; i += 2) {
complexTuples.push([vals[i], vals[i + 1]]);
}
return complexTuples;
}
// node_modules/@tensorflow/tfjs-core/dist/tensor.js
/**
* @license
* Copyright 2017 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
class TensorBuffer {
constructor(shape, dtype, values) {
this.dtype = dtype;
this.shape = shape.slice();
this.size = sizeFromShape(shape);
if (values != null) {
const n = values.length;
assert(n === this.size, () => `Length of values '${n}' does not match the size inferred by the shape '${this.size}'.`);
}
if (dtype === "complex64") {
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 = values || getArrayFromDType(dtype, this.size);
this.strides = computeStrides(shape);
}
set(value, ...locs) {
if (locs.length === 0) {
locs = [0];
}
assert(locs.length === this.rank, () => `The number of provided coordinates (${locs.length}) must match the rank (${this.rank})`);
const index = this.locToIndex(locs);
this.values[index] = value;
}
get(...locs) {
if (locs.length === 0) {
locs = [0];
}
let i = 0;
for (const loc of locs) {
if (loc < 0 || loc >= this.shape[i]) {
const msg = `Requested out of range element at ${locs}. Buffer shape=${this.shape}`;
throw new Error(msg);
}
i++;
}
let index = locs[locs.length - 1];
for (let i2 = 0; i2 < locs.length - 1; ++i2) {
index += this.strides[i2] * locs[i2];
}
return this.values[index];
}
locToIndex(locs) {
if (this.rank === 0) {
return 0;
} else if (this.rank === 1) {
return locs[0];
}
let index = locs[locs.length - 1];
for (let i = 0; i < locs.length - 1; ++i) {
index += this.strides[i] * locs[i];
}
return index;
}
indexToLoc(index) {
if (this.rank === 0) {
return [];
} else if (this.rank === 1) {
return [index];
}
const locs = new Array(this.shape.length);
for (let i = 0; i < locs.length - 1; ++i) {
locs[i] = Math.floor(index / this.strides[i]);
index -= locs[i] * this.strides[i];
}
locs[locs.length - 1] = index;
return locs;
}
get rank() {
return this.shape.length;
}
toTensor() {
return trackerFn().makeTensor(this.values, this.shape, this.dtype);
}
}
let trackerFn = null;
let opHandler = null;
let deprecationWarningFn = null;
function setTensorTracker(fn) {
trackerFn = fn;
}
function setDeprecationWarningFn(fn) {
deprecationWarningFn = fn;
}
class Tensor {
constructor(shape, dtype, dataId, id) {
this.kept = false;
this.isDisposedInternal = false;
this.shape = shape.slice();
this.dtype = dtype || "float32";
this.size = sizeFromShape(shape);
this.strides = computeStrides(shape);
this.dataId = dataId;
this.id = id;
this.rankType = this.rank < 5 ? this.rank.toString() : "higher";
}
get rank() {
return this.shape.length;
}
async buffer() {
const vals = await this.data();
return opHandler.buffer(this.shape, this.dtype, vals);
}
bufferSync() {
return opHandler.buffer(this.shape, this.dtype, this.dataSync());
}
async array() {
const vals = await this.data();
return toNestedArray(this.shape, vals);
}
arraySync() {
return toNestedArray(this.shape, this.dataSync());
}
async data() {
this.throwIfDisposed();
const data2 = trackerFn().read(this.dataId);
if (this.dtype === "string") {
const bytes = await data2;
try {
return bytes.map((b) => decodeString(b));
} catch (_a) {
throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().");
}
}
return data2;
}
dataSync() {
this.throwIfDisposed();
const data2 = trackerFn().readSync(this.dataId);
if (this.dtype === "string") {
try {
return data2.map((b) => decodeString(b));
} catch (_a) {
throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().");
}
}
return data2;
}
async bytes() {
this.throwIfDisposed();
const data2 = await trackerFn().read(this.dataId);
if (this.dtype === "string") {
return data2;
} else {
return new Uint8Array(data2.buffer);
}
}
dispose() {
if (this.isDisposed) {
return;
}
trackerFn().disposeTensor(this);
this.isDisposedInternal = true;
}
get isDisposed() {
return this.isDisposedInternal;
}
throwIfDisposed() {
if (this.isDisposed) {
throw new Error(`Tensor is disposed.`);
}
}
print(verbose = false) {
return opHandler.print(this, verbose);
}
clone() {
this.throwIfDisposed();
return opHandler.clone(this);
}
toString(verbose = false) {
const vals = this.dataSync();
return tensorToString(vals, this.shape, this.dtype, verbose);
}
cast(dtype) {
this.throwIfDisposed();
return opHandler.cast(this, dtype);
}
variable(trainable = true, name, dtype) {
this.throwIfDisposed();
return trackerFn().makeVariable(this, trainable, name, dtype);
}
}
Object.defineProperty(Tensor, Symbol.hasInstance, {
value: (instance) => {
return !!instance && instance.data != null && instance.dataSync != null && instance.throwIfDisposed != null;
}
});
class Variable extends Tensor {
constructor(initialValue, trainable, name, tensorId) {
super(initialValue.shape, initialValue.dtype, initialValue.dataId, tensorId);
this.trainable = trainable;
this.name = name;
}
assign(newValue) {
if (newValue.dtype !== this.dtype) {
throw new Error(`dtype of the new value (${newValue.dtype}) and previous value (${this.dtype}) must match`);
}
if (!arraysEqual(newValue.shape, this.shape)) {
throw new Error(`shape of the new value (${newValue.shape}) and previous value (${this.shape}) must match`);
}
trackerFn().disposeTensor(this);
this.dataId = newValue.dataId;
trackerFn().incRef(this, null);
}
dispose() {
trackerFn().disposeVariable(this);
this.isDisposedInternal = true;
}
}
Object.defineProperty(Variable, Symbol.hasInstance, {
value: (instance) => {
return instance instanceof Tensor && instance.assign != null && instance.assign instanceof Function;
}
});
// node_modules/@tensorflow/tfjs-core/dist/tensor_util.js
const tensor_util_exports = {};
__export(tensor_util_exports, {
assertTypesMatch: () => assertTypesMatch,
getTensorsInContainer: () => getTensorsInContainer,
isTensorInList: () => isTensorInList,
makeTypesMatch: () => makeTypesMatch
});
// node_modules/@tensorflow/tfjs-core/dist/types.js
/**
* @license
* Copyright 2017 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
var Rank;
(function(Rank2) {
Rank2["R0"] = "R0";
Rank2["R1"] = "R1";
Rank2["R2"] = "R2";
Rank2["R3"] = "R3";
Rank2["R4"] = "R4";
Rank2["R5"] = "R5";
Rank2["R6"] = "R6";
})(Rank || (Rank = {}));
var UpcastInt32AndMap;
(function(UpcastInt32AndMap2) {
UpcastInt32AndMap2["float32"] = "float32";
UpcastInt32AndMap2["int32"] = "int32";
UpcastInt32AndMap2["bool"] = "int32";
UpcastInt32AndMap2["complex64"] = "complex64";
})(UpcastInt32AndMap || (UpcastInt32AndMap = {}));
var UpcastBoolAndMap;
(function(UpcastBoolAndMap2) {
UpcastBoolAndMap2["float32"] = "float32";
UpcastBoolAndMap2["int32"] = "int32";
UpcastBoolAndMap2["bool"] = "bool";
UpcastBoolAndMap2["complex64"] = "complex64";
})(UpcastBoolAndMap || (UpcastBoolAndMap = {}));
var UpcastFloat32AndMap;
(function(UpcastFloat32AndMap2) {
UpcastFloat32AndMap2["float32"] = "float32";
UpcastFloat32AndMap2["int32"] = "float32";
UpcastFloat32AndMap2["bool"] = "float32";
UpcastFloat32AndMap2["complex64"] = "complex64";
})(UpcastFloat32AndMap || (UpcastFloat32AndMap = {}));
var UpcastComplex64AndMap;
(function(UpcastComplex64AndMap2) {
UpcastComplex64AndMap2["float32"] = "complex64";
UpcastComplex64AndMap2["int32"] = "complex64";
UpcastComplex64AndMap2["bool"] = "complex64";
UpcastComplex64AndMap2["complex64"] = "complex64";
})(UpcastComplex64AndMap || (UpcastComplex64AndMap = {}));
const upcastTypeMap = {
float32: UpcastFloat32AndMap,
int32: UpcastInt32AndMap,
bool: UpcastBoolAndMap,
complex64: UpcastComplex64AndMap
};
function upcastType(typeA, typeB) {
if (typeA === "string" || typeB === "string") {
if (typeA === "string" && typeB === "string") {
return "string";
}
throw new Error(`Can not upcast ${typeA} with ${typeB}`);
}
return upcastTypeMap[typeA][typeB];
}
function sumOutType(type) {
return upcastType(type, "int32");
}
// node_modules/@tensorflow/tfjs-core/dist/tensor_util.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function makeTypesMatch(a, b) {
if (a.dtype === b.dtype) {
return [a, b];
}
const dtype = upcastType(a.dtype, b.dtype);
return [a.cast(dtype), b.cast(dtype)];
}
function assertTypesMatch(a, b) {
assert(a.dtype === b.dtype, () => `The dtypes of the first(${a.dtype}) and second(${b.dtype}) input must match`);
}
function isTensorInList(tensor16, tensorList) {
return tensorList.some((x) => x.id === tensor16.id);
}
function getTensorsInContainer(result) {
const list = [];
const seen = new Set();
walkTensorContainer(result, list, seen);
return list;
}
function walkTensorContainer(container2, list, seen) {
if (container2 == null) {
return;
}
if (container2 instanceof Tensor) {
list.push(container2);
return;
}
if (!isIterable(container2)) {
return;
}
const iterable = container2;
for (const k in iterable) {
const val = iterable[k];
if (!seen.has(val)) {
seen.add(val);
walkTensorContainer(val, list, seen);
}
}
}
function isIterable(obj) {
return Array.isArray(obj) || typeof obj === "object";
}
// node_modules/@tensorflow/tfjs-core/dist/engine.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
class EngineState {
constructor() {
this.registeredVariables = {};
this.nextTapeNodeId = 0;
this.numBytes = 0;
this.numTensors = 0;
this.numStringTensors = 0;
this.numDataBuffers = 0;
this.gradientDepth = 0;
this.kernelDepth = 0;
this.scopeStack = [];
this.numDataMovesStack = [];
this.nextScopeId = 0;
this.tensorInfo = new WeakMap();
this.profiling = false;
this.activeProfile = {newBytes: 0, newTensors: 0, peakBytes: 0, kernels: [], result: null};
}
dispose() {
for (const variableName in this.registeredVariables) {
this.registeredVariables[variableName].dispose();
}
}
}
class Engine {
constructor(ENV4) {
this.ENV = ENV4;
this.registry = {};
this.registryFactory = {};
this.pendingBackendInitId = 0;
this.state = new EngineState();
}
async ready() {
if (this.pendingBackendInit != null) {
return this.pendingBackendInit.then(() => {
});
}
if (this.backendInstance != null) {
return;
}
const sortedBackends = this.getSortedBackends();
for (let i = 0; i < sortedBackends.length; i++) {
const backendName = sortedBackends[i];
const success = await this.initializeBackend(backendName).success;
if (success) {
await this.setBackend(backendName);
return;
}
}
throw new Error(`Could not initialize any backends, all backend initializations failed.`);
}
get backend() {
if (this.pendingBackendInit != null) {
throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);
}
if (this.backendInstance == null) {
const {name, asyncInit} = this.initializeBackendsAndReturnBest();
if (asyncInit) {
throw new Error(`The highest priority backend '${name}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);
}
this.setBackend(name);
}
return this.backendInstance;
}
backendNames() {
return Object.keys(this.registryFactory);
}
findBackend(backendName) {
if (!(backendName in this.registry)) {
if (backendName in this.registryFactory) {
const {asyncInit} = this.initializeBackend(backendName);
if (asyncInit) {
return null;
}
} else {
return null;
}
}
return this.registry[backendName];
}
findBackendFactory(backendName) {
if (!(backendName in this.registryFactory)) {
return null;
}
return this.registryFactory[backendName].factory;
}
registerBackend(backendName, factory, priority = 1) {
if (backendName in this.registryFactory) {
console.warn(`${backendName} backend was already registered. Reusing existing backend factory.`);
return false;
}
this.registryFactory[backendName] = {factory, priority};
return true;
}
async setBackend(backendName) {
if (this.registryFactory[backendName] == null) {
throw new Error(`Backend name '${backendName}' not found in registry`);
}
this.backendName = backendName;
if (this.registry[backendName] == null) {
this.backendInstance = null;
const {success, asyncInit} = this.initializeBackend(backendName);
const result = asyncInit ? await success : success;
if (!result) {
return false;
}
}
this.backendInstance = this.registry[backendName];
this.setupRegisteredKernels();
this.profiler = new Profiler(this.backendInstance);
return true;
}
setupRegisteredKernels() {
const kernels = getKernelsForBackend(this.backendName);
kernels.forEach((kernel) => {
if (kernel.setupFunc != null) {
kernel.setupFunc(this.backendInstance);
}
});
}
disposeRegisteredKernels(backendName) {
const kernels = getKernelsForBackend(backendName);
kernels.forEach((kernel) => {
if (kernel.disposeFunc != null) {
kernel.disposeFunc(this.registry[backendName]);
}
});
}
initializeBackend(backendName) {
const registryFactoryEntry = this.registryFactory[backendName];
if (registryFactoryEntry == null) {
throw new Error(`Cannot initialize backend ${backendName}, no registration found.`);
}
try {
const backend3 = registryFactoryEntry.factory();
if (backend3 && !(backend3 instanceof KernelBackend) && typeof backend3.then === "function") {
const promiseId = ++this.pendingBackendInitId;
const success = backend3.then((backendInstance) => {
if (promiseId < this.pendingBackendInitId) {
return false;
}
this.registry[backendName] = backendInstance;
this.pendingBackendInit = null;
return true;
}).catch((err) => {
if (promiseId < this.pendingBackendInitId) {
return false;
}
this.pendingBackendInit = null;
console.warn(`Initialization of backend ${backendName} failed`);
console.warn(err.stack || err.message);
return false;
});
this.pendingBackendInit = success;
return {success, asyncInit: true};
} else {
this.registry[backendName] = backend3;
return {success: true, asyncInit: false};
}
} catch (err) {
console.warn(`Initialization of backend ${backendName} failed`);
console.warn(err.stack || err.message);
return {success: false, asyncInit: false};
}
}
removeBackend(backendName) {
if (!(backendName in this.registryFactory)) {
throw new Error(`${backendName} backend not found in registry`);
}
if (this.backendName === backendName && this.pendingBackendInit != null) {
this.pendingBackendInitId++;
}
if (backendName in this.registry) {
this.disposeRegisteredKernels(backendName);
this.registry[backendName].dispose();
delete this.registry[backendName];
}
delete this.registryFactory[backendName];
if (this.backendName === backendName) {
this.pendingBackendInit = null;
this.backendName = null;
this.backendInstance = null;
}
}
getSortedBackends() {
if (Object.keys(this.registryFactory).length === 0) {
throw new Error("No backend found in registry.");
}
return Object.keys(this.registryFactory).sort((a, b) => {
return this.registryFactory[b].priority - this.registryFactory[a].priority;
});
}
initializeBackendsAndReturnBest() {
const sortedBackends = this.getSortedBackends();
for (let i = 0; i < sortedBackends.length; i++) {
const backendName = sortedBackends[i];
const {success, asyncInit} = this.initializeBackend(backendName);
if (asyncInit || success) {
return {name: backendName, asyncInit};
}
}
throw new Error(`Could not initialize any backends, all backend initializations failed.`);
}
moveData(backend3, dataId) {
const info = this.state.tensorInfo.get(dataId);
const srcBackend = info.backend;
const values = this.readSync(dataId);
srcBackend.disposeData(dataId);
info.backend = backend3;
backend3.move(dataId, values, info.shape, info.dtype);
if (this.shouldCheckForMemLeaks()) {
this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++;
}
}
tidy(nameOrFn, fn) {
let name = null;
if (fn == null) {
if (typeof nameOrFn !== "function") {
throw new Error("Please provide a function to tidy()");
}
fn = nameOrFn;
} else {
if (typeof nameOrFn !== "string" && !(nameOrFn instanceof String)) {
throw new Error("When calling with two arguments, the first argument to tidy() must be a string");
}
if (typeof fn !== "function") {
throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");
}
name = nameOrFn;
}
let result;
return this.scopedRun(() => this.startScope(name), () => this.endScope(result), () => {
result = fn();
if (result instanceof Promise) {
console.error("Cannot return a Promise inside of tidy.");
}
return result;
});
}
scopedRun(start, end, f) {
start();
try {
const res = f();
end();
return res;
} catch (ex) {
end();
throw ex;
}
}
nextTensorId() {
return Engine.nextTensorId++;
}
nextVariableId() {
return Engine.nextVariableId++;
}
clone(x) {
const y = this.makeTensorFromDataId(x.dataId, x.shape, x.dtype);
const inputs = {x};
const grad2 = (dy) => ({
x: () => {
const dtype = "float32";
const gradInputs = {x: dy};
const attrs = {dtype};
return ENGINE.runKernelFunc((backend3) => backend3.cast(dy, dtype), gradInputs, null, Cast, attrs);
}
});
const saved = [];
this.addTapeNode(this.state.activeScope.name, inputs, [y], grad2, saved, {});
return y;
}
runKernel(kernelName, inputs, attrs, inputsToSave, outputsToSave) {
const forwardFunc = null;
const backwardsFunc = null;
return this.runKernelFunc(forwardFunc, inputs, backwardsFunc, kernelName, attrs, inputsToSave, outputsToSave);
}
shouldCheckForMemLeaks() {
return this.ENV.getBool("IS_TEST");
}
checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos) {
const numDataIdsAfter = this.backend.numDataIds();
let numOutputDataIds = 0;
outInfos.forEach((info) => {
numOutputDataIds += info.dtype === "complex64" ? 3 : 1;
});
const numMoves = this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1];
const dataIdsLeaked = numDataIdsAfter - numDataIdsBefore - numOutputDataIds - numMoves;
if (dataIdsLeaked > 0) {
throw new Error(`Backend '${this.backendName}' has an internal memory leak (${dataIdsLeaked} data ids) after running '${kernelName}'`);
}
}
runKernelFunc(forwardFunc, inputs, backwardsFunc, kernelName, attrs, inputsToSave, outputsToSave) {
let outputs;
let saved = [];
const isTapeOn = this.isTapeOn();
if (kernelName == null) {
kernelName = this.state.activeScope != null ? this.state.activeScope.name : "";
}
const startingBytecount = this.state.numBytes;
const startingNumTensors = this.state.numTensors;
if (this.shouldCheckForMemLeaks()) {
this.state.numDataMovesStack.push(0);
}
let kernelFunc3;
const kernel = getKernel(kernelName, this.backendName);
let out;
if (kernel != null) {
kernelFunc3 = () => {
const numDataIdsBefore = this.backend.numDataIds();
out = kernel.kernelFunc({inputs, attrs, backend: this.backend});
const outInfos = Array.isArray(out) ? out : [out];
if (this.shouldCheckForMemLeaks()) {
this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos);
}
const outTensors = outInfos.map(({dataId, shape, dtype}) => this.makeTensorFromDataId(dataId, shape, dtype));
if (isTapeOn) {
let tensorsToSave = this.getTensorsForGradient(kernelName, inputs, outTensors);
if (tensorsToSave == null) {
if (outputsToSave == null) {
outputsToSave = [];
}
const outsToSave = outTensors.filter((_, i) => outputsToSave[i]);
tensorsToSave = (inputsToSave || []).slice().concat(outsToSave);
}
saved = this.saveTensorsForBackwardMode(tensorsToSave);
}
return outTensors;
};
} else {
const saveFunc = (tensors) => {
if (!isTapeOn) {
return;
}
saved = tensors.map((tensor16) => this.keep(this.clone(tensor16)));
};
kernelFunc3 = () => {
const numDataIdsBefore = this.backend.numDataIds();
out = this.tidy(() => forwardFunc(this.backend, saveFunc));
const outs = Array.isArray(out) ? out : [out];
if (this.shouldCheckForMemLeaks()) {
this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outs);
}
return outs;
};
}
let kernelProfile;
this.scopedRun(() => this.state.kernelDepth++, () => this.state.kernelDepth--, () => {
if (!this.ENV.getBool("DEBUG") && !this.state.profiling) {
outputs = kernelFunc3();
} else {
kernelProfile = this.profiler.profileKernel(kernelName, inputs, () => kernelFunc3());
if (this.ENV.getBool("DEBUG")) {
this.profiler.logKernelProfile(kernelProfile);
}
outputs = kernelProfile.outputs;
}
});
if (isTapeOn) {
this.addTapeNode(kernelName, inputs, outputs, backwardsFunc, saved, attrs);
}
if (this.state.profiling) {
this.state.activeProfile.kernels.push({
name: kernelName,
bytesAdded: this.state.numBytes - startingBytecount,
totalBytesSnapshot: this.state.numBytes,
tensorsAdded: this.state.numTensors - startingNumTensors,
totalTensorsSnapshot: this.state.numTensors,
inputShapes: Object.keys(inputs).map((key) => inputs[key] != null ? inputs[key].shape : null),
outputShapes: outputs.map((item) => item.shape),
kernelTimeMs: kernelProfile.timeMs,
extraInfo: kernelProfile.extraInfo
});
}
return Array.isArray(out) ? outputs : outputs[0];
}
saveTensorsForBackwardMode(tensors) {
const saved = tensors.map((tensor16) => this.keep(this.clone(tensor16)));
return saved;
}
getTensorsForGradient(kernelName, inputs, outputs) {
const gradConfig = getGradient(kernelName);
if (gradConfig != null) {
const inputsToSave = gradConfig.inputsToSave || [];
const outputsToSave = gradConfig.outputsToSave || [];
let inputTensorsToSave;
if (gradConfig.saveAllInputs) {
assert(Array.isArray(inputs), () => "saveAllInputs is true, expected inputs to be an array.");
inputTensorsToSave = Object.keys(inputs).map((key) => inputs[key]);
} else {
inputTensorsToSave = inputsToSave.map((inputName) => inputs[inputName]);
}
const outputTensorsToSave = outputs.filter((_, i) => outputsToSave[i]);
return inputTensorsToSave.concat(outputTensorsToSave);
}
return null;
}
makeTensor(values, shape, dtype, backend3) {
if (values == null) {
throw new Error("Values passed to engine.makeTensor() are null");
}
dtype = dtype || "float32";
backend3 = backend3 || this.backend;
let backendVals = values;
if (dtype === "string" && isString(values[0])) {
backendVals = values.map((d) => encodeString(d));
}
const dataId = backend3.write(backendVals, shape, dtype);
const t = new Tensor(shape, dtype, dataId, this.nextTensorId());
this.incRef(t, backend3);
if (dtype === "string") {
const info = this.state.tensorInfo.get(dataId);
const newBytes = bytesFromStringArray(backendVals);
this.state.numBytes += newBytes - info.bytes;
info.bytes = newBytes;
}
return t;
}
makeTensorFromDataId(dataId, shape, dtype, backend3) {
dtype = dtype || "float32";
const t = new Tensor(shape, dtype, dataId, this.nextTensorId());
this.incRef(t, backend3);
return t;
}
makeVariable(initialValue, trainable = true, name, dtype) {
name = name || this.nextVariableId().toString();
if (dtype != null && dtype !== initialValue.dtype) {
initialValue = initialValue.cast(dtype);
}
const v = new Variable(initialValue, trainable, name, this.nextTensorId());
if (this.state.registeredVariables[v.name] != null) {
throw new Error(`Variable with name ${v.name} was already registered`);
}
this.state.registeredVariables[v.name] = v;
this.incRef(v, this.backend);
return v;
}
incRef(a, backend3) {
const refCount = this.state.tensorInfo.has(a.dataId) ? this.state.tensorInfo.get(a.dataId).refCount : 0;
this.state.numTensors++;
if (a.dtype === "string") {
this.state.numStringTensors++;
}
if (refCount === 0) {
this.state.numDataBuffers++;
let bytes = 0;
if (a.dtype !== "complex64" && a.dtype !== "string") {
bytes = a.size * bytesPerElement(a.dtype);
}
this.state.tensorInfo.set(a.dataId, {
backend: backend3 || this.backend,
dtype: a.dtype,
shape: a.shape,
bytes,
refCount: 0
});
this.state.numBytes += bytes;
}
this.state.tensorInfo.get(a.dataId).refCount++;
if (!(a instanceof Variable)) {
this.track(a);
}
}
disposeTensor(a) {
if (!this.state.tensorInfo.has(a.dataId)) {
return;
}
this.state.numTensors--;
if (a.dtype === "string") {
this.state.numStringTensors--;
}
const info = this.state.tensorInfo.get(a.dataId);
const refCount = info.refCount;
if (refCount <= 1) {
if (a.dtype !== "complex64") {
this.state.numBytes -= info.bytes;
}
this.state.numDataBuffers--;
info.backend.disposeData(a.dataId);
this.state.tensorInfo.delete(a.dataId);
} else {
this.state.tensorInfo.get(a.dataId).refCount--;
}
}
disposeVariables() {
for (const varName in this.state.registeredVariables) {
const v = this.state.registeredVariables[varName];
this.disposeVariable(v);
}
}
disposeVariable(v) {
this.disposeTensor(v);
if (this.state.registeredVariables[v.name] != null) {
delete this.state.registeredVariables[v.name];
}
}
memory() {
const info = this.backend.memory();
info.numTensors = this.state.numTensors;
info.numDataBuffers = this.state.numDataBuffers;
info.numBytes = this.state.numBytes;
if (this.state.numStringTensors > 0) {
info.unreliable = true;
if (info.reasons == null) {
info.reasons = [];
}
info.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)");
}
return info;
}
async profile(query) {
this.state.profiling = true;
const startBytes = this.state.numBytes;
const startNumTensors = this.state.numTensors;
this.state.activeProfile.kernels = [];
this.state.activeProfile.result = await query();
this.state.profiling = false;
this.state.activeProfile.peakBytes = Math.max(...this.state.activeProfile.kernels.map((d) => d.totalBytesSnapshot));
this.state.activeProfile.newBytes = this.state.numBytes - startBytes;
this.state.activeProfile.newTensors = this.state.numTensors - startNumTensors;
for (const kernel of this.state.activeProfile.kernels) {
kernel.kernelTimeMs = await kernel.kernelTimeMs;
kernel.extraInfo = await kernel.extraInfo;
}
return this.state.activeProfile;
}
isTapeOn() {
return this.state.gradientDepth > 0 && this.state.kernelDepth === 0;
}
addTapeNode(kernelName, inputs, outputs, gradientsFunc, saved, attrs) {
const tapeNode = {id: this.state.nextTapeNodeId++, kernelName, inputs, outputs, saved};
const gradConfig = getGradient(kernelName);
if (gradConfig != null) {
gradientsFunc = gradConfig.gradFunc;
}
if (gradientsFunc != null) {
tapeNode.gradient = (dys) => {
dys = dys.map((dy, i) => {
if (dy == null) {
const output = outputs[i];
const vals = makeZerosTypedArray(output.size, output.dtype);
return this.makeTensor(vals, output.shape, output.dtype);
}
return dy;
});
return gradientsFunc(dys.length > 1 ? dys : dys[0], saved, attrs);
};
}
this.state.activeTape.push(tapeNode);
}
keep(result) {
result.kept = true;
return result;
}
startTape() {
if (this.state.gradientDepth === 0) {
this.state.activeTape = [];
}
this.state.gradientDepth++;
}
endTape() {
this.state.gradientDepth--;
}
startScope(name) {
const scopeInfo = {
track: [],
name: "unnamed scope",
id: this.state.nextScopeId++
};
if (name) {
scopeInfo.name = name;
}
this.state.scopeStack.push(scopeInfo);
this.state.activeScope = scopeInfo;
}
endScope(result) {
const tensorsToTrackInParent = getTensorsInContainer(result);
const tensorsToTrackInParentSet = new Set(tensorsToTrackInParent.map((t) => t.id));
for (let i = 0; i < this.state.activeScope.track.length; i++) {
const tensor16 = this.state.activeScope.track[i];
if (!tensor16.kept && !tensorsToTrackInParentSet.has(tensor16.id)) {
tensor16.dispose();
}
}
const oldScope = this.state.scopeStack.pop();
this.state.activeScope = this.state.scopeStack.length === 0 ? null : this.state.scopeStack[this.state.scopeStack.length - 1];
tensorsToTrackInParent.forEach((tensor16) => {
if (!tensor16.kept && tensor16.scopeId === oldScope.id) {
this.track(tensor16);
}
});
}
gradients(f, xs, dy, allowNoGradients = false) {
assert(xs.length > 0, () => "gradients() received an empty list of xs.");
if (dy != null && dy.dtype !== "float32") {
throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);
}
const y = this.scopedRun(() => this.startTape(), () => this.endTape(), () => this.tidy("forward", f));
assert(y instanceof Tensor, () => "The result y returned by f() must be a tensor.");
const filteredTape = getFilteredNodesXToY(this.state.activeTape, xs, y);
if (!allowNoGradients && filteredTape.length === 0 && xs.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", () => {
const accumulatedGradientMap = {};
accumulatedGradientMap[y.id] = dy == null ? ones(y.shape) : dy;
backpropagateGradients(accumulatedGradientMap, filteredTape, (f2) => this.tidy(f2), add);
const grads2 = xs.map((x) => accumulatedGradientMap[x.id]);
if (this.state.gradientDepth === 0) {
this.state.activeTape.forEach((node) => {
for (const tensor16 of node.saved) {
tensor16.dispose();
}
});
this.state.activeTape = null;
}
return {value: y, grads: grads2};
});
}
customGrad(f) {
assert(isFunction(f), () => "The f passed in customGrad(f) must be a function.");
return (...inputs) => {
assert(inputs.every((t) => t instanceof Tensor), () => "The args passed in customGrad(f)(x1, x2,...) must all be tensors");
let res;
const inputMap = {};
inputs.forEach((input2, i) => {
inputMap[i] = input2;
});
return this.runKernelFunc((_, save) => {
res = f(...[...inputs, save]);
assert(res.value instanceof Tensor, () => "The function f passed in customGrad(f) must return an object where `obj.value` is a tensor");
assert(isFunction(res.gradFunc), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function.");
return res.value;
}, inputMap, (dy, saved) => {
const gradRes = res.gradFunc(dy, saved);
const grads2 = Array.isArray(gradRes) ? gradRes : [gradRes];
assert(grads2.length === inputs.length, () => "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(grads2.every((t) => t instanceof Tensor), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");
const gradMap = {};
grads2.forEach((grad2, i) => {
gradMap[i] = () => grad2;
});
return gradMap;
});
};
}
readSync(dataId) {
const info = this.state.tensorInfo.get(dataId);
return info.backend.readSync(dataId);
}
read(dataId) {
const info = this.state.tensorInfo.get(dataId);
return info.backend.read(dataId);
}
async time(query) {
const start = now();
const timingInfo = await this.backend.time(query);
timingInfo.wallMs = now() - start;
return timingInfo;
}
track(result) {
if (this.state.activeScope != null) {
result.scopeId = this.state.activeScope.id;
this.state.activeScope.track.push(result);
}
return result;
}
get registeredVariables() {
return this.state.registeredVariables;
}
reset() {
this.pendingBackendInitId++;
this.state.dispose();
this.ENV.reset();
this.state = new EngineState();
for (const backendName in this.registry) {
this.disposeRegisteredKernels(backendName);
this.registry[backendName].dispose();
delete this.registry[backendName];
}
this.backendName = null;
this.backendInstance = null;
this.pendingBackendInit = null;
}
}
Engine.nextTensorId = 0;
Engine.nextVariableId = 0;
function ones(shape) {
const values = makeOnesTypedArray(sizeFromShape(shape), "float32");
return ENGINE.makeTensor(values, shape, "float32");
}
function getOrMakeEngine() {
const ns = getGlobalNamespace();
if (ns._tfengine == null) {
const environment11 = new Environment(ns);
ns._tfengine = new Engine(environment11);
}
setEnvironmentGlobal(ns._tfengine.ENV);
setTensorTracker(() => ns._tfengine);
return ns._tfengine;
}
const ENGINE = getOrMakeEngine();
function add(a, b) {
const inputs = {a, b};
return ENGINE.runKernelFunc((backend3, save) => {
const res = backend3.add(a, b);
save([a, b]);
return res;
}, inputs, null, Add);
}
// node_modules/@tensorflow/tfjs-core/dist/device_util.js
const device_util_exports = {};
__export(device_util_exports, {
isBrowser: () => isBrowser,
isMobile: () => isMobile
});
/**
* @license
* Copyright 2017 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function _isNavigatorDefined() {
return typeof navigator !== "undefined" && navigator != null;
}
function isMobile() {
if (_isNavigatorDefined()) {
const a = 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(a) || /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(a.substr(0, 4));
}
return false;
}
function isBrowser() {
return typeof window !== "undefined" && window.document != null || typeof WorkerGlobalScope !== "undefined";
}
// node_modules/@tensorflow/tfjs-core/dist/flags.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const ENV2 = env();
ENV2.registerFlag("DEBUG", () => false, (debugValue) => {
if (debugValue) {
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.");
}
});
ENV2.registerFlag("IS_BROWSER", () => isBrowser());
ENV2.registerFlag("IS_NODE", () => typeof process !== "undefined" && typeof process.versions !== "undefined" && typeof process.versions.node !== "undefined");
ENV2.registerFlag("IS_CHROME", () => typeof navigator !== "undefined" && navigator != null && navigator.userAgent != null && /Chrome/.test(navigator.userAgent) && /Google Inc/.test(navigator.vendor));
ENV2.registerFlag("PROD", () => false);
ENV2.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY", () => ENV2.getBool("DEBUG"));
ENV2.registerFlag("DEPRECATION_WARNINGS_ENABLED", () => true);
ENV2.registerFlag("IS_TEST", () => false);
// node_modules/@tensorflow/tfjs-core/dist/tensor_util_env.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function inferShape(val, dtype) {
let firstElem = val;
if (isTypedArray(val)) {
return dtype === "string" ? [] : [val.length];
}
if (!Array.isArray(val)) {
return [];
}
const shape = [];
while (Array.isArray(firstElem) || isTypedArray(firstElem) && dtype !== "string") {
shape.push(firstElem.length);
firstElem = firstElem[0];
}
if (Array.isArray(val) && env().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")) {
deepAssertShapeConsistency(val, shape, []);
}
return shape;
}
function deepAssertShapeConsistency(val, shape, indices) {
indices = indices || [];
if (!Array.isArray(val) && !isTypedArray(val)) {
assert(shape.length === 0, () => `Element arr[${indices.join("][")}] is a primitive, but should be an array/TypedArray of ${shape[0]} elements`);
return;
}
assert(shape.length > 0, () => `Element arr[${indices.join("][")}] should be a primitive, but is an array of ${val.length} elements`);
assert(val.length === shape[0], () => `Element arr[${indices.join("][")}] should have ${shape[0]} elements, but has ${val.length} elements`);
const subShape = shape.slice(1);
for (let i = 0; i < val.length; ++i) {
deepAssertShapeConsistency(val[i], subShape, indices.concat(i));
}
}
function assertDtype(expectedDtype, actualDType, argName, functionName) {
if (expectedDtype == null) {
return;
}
if (expectedDtype !== "numeric" && expectedDtype !== actualDType || expectedDtype === "numeric" && actualDType === "string") {
throw new Error(`Argument '${argName}' passed to '${functionName}' must be ${expectedDtype} tensor, but got ${actualDType} tensor`);
}
}
function convertToTensor(x, argName, functionName, parseAsDtype = "numeric") {
if (x instanceof Tensor) {
assertDtype(parseAsDtype, x.dtype, argName, functionName);
return x;
}
let inferredDtype = inferDtype(x);
if (inferredDtype !== "string" && ["bool", "int32", "float32"].indexOf(parseAsDtype) >= 0) {
inferredDtype = parseAsDtype;
}
assertDtype(parseAsDtype, inferredDtype, argName, functionName);
if (x == null || !isTypedArray(x) && !Array.isArray(x) && typeof x !== "number" && typeof x !== "boolean" && typeof x !== "string") {
const type = x == null ? "null" : x.constructor.name;
throw new Error(`Argument '${argName}' passed to '${functionName}' must be a Tensor or TensorLike, but got '${type}'`);
}
const inferredShape = inferShape(x, inferredDtype);
if (!isTypedArray(x) && !Array.isArray(x)) {
x = [x];
}
const skipTypedArray = true;
const values = inferredDtype !== "string" ? toTypedArray(x, inferredDtype) : flatten(x, [], skipTypedArray);
return ENGINE.makeTensor(values, inferredShape, inferredDtype);
}
function convertToTensorArray(arg, argName, functionName, parseAsDtype = "numeric") {
if (!Array.isArray(arg)) {
throw new Error(`Argument ${argName} passed to ${functionName} must be a \`Tensor[]\` or \`TensorLike[]\``);
}
const tensors = arg;
return tensors.map((t, i) => convertToTensor(t, `${argName}[${i}]`, functionName), parseAsDtype);
}
// node_modules/@tensorflow/tfjs-core/dist/ops/operation.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const OP_SCOPE_SUFFIX = "__op";
function op(f) {
const keys = Object.keys(f);
if (keys.length !== 1) {
throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${keys.length} keys.`);
}
let opName = keys[0];
const fn = f[opName];
if (opName.endsWith("_")) {
opName = opName.substring(0, opName.length - 1);
}
opName = opName + OP_SCOPE_SUFFIX;
const f2 = (...args) => {
ENGINE.startScope(opName);
try {
const result = fn(...args);
if (isPromise(result)) {
console.error("Cannot return a Promise inside of tidy.");
}
ENGINE.endScope(result);
return result;
} catch (ex) {
ENGINE.endScope(null);
throw ex;
}
};
Object.defineProperty(f2, "name", {value: opName, configurable: true});
return f2;
}
// node_modules/@tensorflow/tfjs-core/dist/ops/complex.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function complex_(real6, imag6) {
const $real = convertToTensor(real6, "real", "complex");
const $imag = convertToTensor(imag6, "imag", "complex");
assertShapesMatch($real.shape, $imag.shape, `real and imag shapes, ${$real.shape} and ${$imag.shape}, must match in call to tf.complex().`);
const forward = (backend3) => {
return backend3.complex($real, $imag);
};
const inputs = {real: $real, imag: $imag};
return ENGINE.runKernelFunc(forward, inputs, null, Complex);
}
const complex = op({complex_});
// node_modules/@tensorflow/tfjs-core/dist/ops/tensor_ops_util.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function makeTensor(values, shape, inferredShape, dtype) {
if (dtype == null) {
dtype = inferDtype(values);
}
if (dtype === "complex64") {
throw new Error(`Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).`);
}
if (!isTypedArray(values) && !Array.isArray(values) && typeof values !== "number" && typeof values !== "boolean" && typeof values !== "string") {
throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");
}
if (shape != null) {
assertNonNegativeIntegerDimensions(shape);
const providedSize = sizeFromShape(shape);
const inferredSize = sizeFromShape(inferredShape);
assert(providedSize === inferredSize, () => `Based on the provided shape, [${shape}], the tensor should have ${providedSize} values but has ${inferredSize}`);
for (let i = 0; i < inferredShape.length; ++i) {
const inferred = inferredShape[i];
const flatDimsDontMatch = i === inferredShape.length - 1 ? inferred !== sizeFromShape(shape.slice(i)) : true;
assert(inferredShape[i] === shape[i] || !flatDimsDontMatch, () => `Error creating a new Tensor. Inferred shape (${inferredShape}) does not match the provided shape (${shape}). `);
}
}
if (!isTypedArray(values) && !Array.isArray(values)) {
values = [values];
}
shape = shape || inferredShape;
values = dtype !== "string" ? toTypedArray(values, dtype) : flatten(values, [], true);
return ENGINE.makeTensor(values, shape, dtype);
}
// node_modules/@tensorflow/tfjs-core/dist/ops/tensor.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function tensor4(values, shape, dtype) {
const inferredShape = inferShape(values, dtype);
return makeTensor(values, shape, inferredShape, dtype);
}
// node_modules/@tensorflow/tfjs-core/dist/io/types.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const DTYPE_VALUE_SIZE_MAP = {
float32: 4,
float16: 2,
int32: 4,
uint16: 2,
uint8: 1,
bool: 1,
complex64: 8
};
// node_modules/@tensorflow/tfjs-core/dist/io/io_utils.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const NUM_BYTES_STRING_LENGTH = 4;
async function encodeWeights(tensors, group) {
const specs = [];
const dataPromises = [];
const names = Array.isArray(tensors) ? tensors.map((tensor16) => tensor16.name) : Object.keys(tensors);
for (let i = 0; i < names.length; ++i) {
const name = names[i];
const t = Array.isArray(tensors) ? tensors[i].tensor : tensors[name];
if (t.dtype !== "float32" && t.dtype !== "int32" && t.dtype !== "bool" && t.dtype !== "string" && t.dtype !== "complex64") {
throw new Error(`Unsupported dtype in weight '${name}': ${t.dtype}`);
}
const spec = {name, shape: t.shape, dtype: t.dtype};
if (t.dtype === "string") {
const utf8bytes = new Promise(async (resolve) => {
const vals = await t.bytes();
const totalNumBytes = vals.reduce((p, c) => p + c.length, 0) + NUM_BYTES_STRING_LENGTH * vals.length;
const bytes = new Uint8Array(totalNumBytes);
let offset = 0;
for (let i2 = 0; i2 < vals.length; i2++) {
const val = vals[i2];
const bytesOfLength = new Uint8Array(new Uint32Array([val.length]).buffer);
bytes.set(bytesOfLength, offset);
offset += NUM_BYTES_STRING_LENGTH;
bytes.set(val, offset);
offset += val.length;
}
resolve(bytes);
});
dataPromises.push(utf8bytes);
} else {
dataPromises.push(t.data());
}
if (group != null) {
spec.group = group;
}
specs.push(spec);
}
const tensorValues = await Promise.all(dataPromises);
return {data: concatenateTypedArrays(tensorValues), specs};
}
function decodeWeights(buffer10, specs) {
const out = {};
let float16Decode;
let offset = 0;
for (const spec of specs) {
const name = spec.name;
const dtype = spec.dtype;
const shape = spec.shape;
const size = sizeFromShape(shape);
let values;
if ("quantization" in spec) {
const quantization = spec.quantization;
if (quantization.dtype === "uint8" || quantization.dtype === "uint16") {
if (!("min" in quantization && "scale" in quantization)) {
throw new Error(`Weight ${spec.name} with quantization ${quantization.dtype} doesn't have corresponding metadata min and scale.`);
}
} else if (quantization.dtype === "float16") {
if (dtype !== "float32") {
throw new Error(`Weight ${spec.name} is quantized with ${quantization.dtype} which only supports weights of type float32 not ${dtype}.`);
}
} else {
throw new Error(`Weight ${spec.name} has unknown quantization dtype ${quantization.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);
}
const quantizationSizeFactor = DTYPE_VALUE_SIZE_MAP[quantization.dtype];
const byteBuffer = buffer10.slice(offset, offset + size * quantizationSizeFactor);
const quantizedArray = quantization.dtype === "uint8" ? new Uint8Array(byteBuffer) : new Uint16Array(byteBuffer);
if (dtype === "float32") {
if (quantization.dtype === "uint8" || quantization.dtype === "uint16") {
values = new Float32Array(quantizedArray.length);
for (let i = 0; i < quantizedArray.length; i++) {
const v = quantizedArray[i];
values[i] = v * quantization.scale + quantization.min;
}
} else if (quantization.dtype === "float16") {
if (float16Decode === void 0) {
float16Decode = getFloat16Decoder();
}
values = float16Decode(quantizedArray);
} else {
throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type float32.`);
}
} else if (dtype === "int32") {
if (quantization.dtype !== "uint8" && quantization.dtype !== "uint16") {
throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type int32.`);
}
values = new Int32Array(quantizedArray.length);
for (let i = 0; i < quantizedArray.length; i++) {
const v = quantizedArray[i];
values[i] = Math.round(v * quantization.scale + quantization.min);
}
} else {
throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);
}
offset += size * quantizationSizeFactor;
} else if (dtype === "string") {
const size2 = sizeFromShape(spec.shape);
values = [];
for (let i = 0; i < size2; i++) {
const byteLength = new Uint32Array(buffer10.slice(offset, offset + NUM_BYTES_STRING_LENGTH))[0];
offset += NUM_BYTES_STRING_LENGTH;
const bytes = new Uint8Array(buffer10.slice(offset, offset + byteLength));
values.push(bytes);
offset += byteLength;
}
} else {
const dtypeFactor = DTYPE_VALUE_SIZE_MAP[dtype];
const byteBuffer = buffer10.slice(offset, offset + size * dtypeFactor);
if (dtype === "float32") {
values = new Float32Array(byteBuffer);
} else if (dtype === "int32") {
values = new Int32Array(byteBuffer);
} else if (dtype === "bool") {
values = new Uint8Array(byteBuffer);
} else if (dtype === "complex64") {
values = new Float32Array(byteBuffer);
const real6 = new Float32Array(values.length / 2);
const image4 = new Float32Array(values.length / 2);
for (let i = 0; i < real6.length; i++) {
real6[i] = values[i * 2];
image4[i] = values[i * 2 + 1];
}
const realTensor = tensor4(real6, shape, "float32");
const imageTensor = tensor4(image4, shape, "float32");
out[name] = complex(realTensor, imageTensor);
realTensor.dispose();
imageTensor.dispose();
} else {
throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);
}
offset += size * dtypeFactor;
}
if (dtype !== "complex64") {
out[name] = tensor4(values, shape, dtype);
}
}
return out;
}
function concatenateTypedArrays(xs) {
if (xs === null) {
throw new Error(`Invalid input value: ${JSON.stringify(xs)}`);
}
let totalByteLength = 0;
const normalizedXs = [];
xs.forEach((x) => {
totalByteLength += x.byteLength;
normalizedXs.push(x.byteLength === x.buffer.byteLength ? x : new x.constructor(x));
if (!(x instanceof Float32Array || x instanceof Int32Array || x instanceof Uint8Array)) {
throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`);
}
});
const y = new Uint8Array(totalByteLength);
let offset = 0;
normalizedXs.forEach((x) => {
y.set(new Uint8Array(x.buffer), offset);
offset += x.byteLength;
});
return y.buffer;
}
const useNodeBuffer = typeof Buffer !== "undefined" && (typeof Blob === "undefined" || typeof atob === "undefined" || typeof btoa === "undefined");
function stringByteLength(str) {
if (useNodeBuffer) {
return Buffer.byteLength(str);
}
return new Blob([str]).size;
}
function concatenateArrayBuffers(buffers) {
if (buffers.length === 1) {
return buffers[0];
}
let totalByteLength = 0;
buffers.forEach((buffer10) => {
totalByteLength += buffer10.byteLength;
});
const temp = new Uint8Array(totalByteLength);
let offset = 0;
buffers.forEach((buffer10) => {
temp.set(new Uint8Array(buffer10), offset);
offset += buffer10.byteLength;
});
return temp.buffer;
}
function basename(path) {
const SEPARATOR = "/";
path = path.trim();
while (path.endsWith(SEPARATOR)) {
path = path.slice(0, path.length - 1);
}
const items = path.split(SEPARATOR);
return items[items.length - 1];
}
function getModelArtifactsInfoForJSON(modelArtifacts) {
if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
throw new Error("Expected JSON model topology, received ArrayBuffer.");
}
return {
dateSaved: new Date(),
modelTopologyType: "JSON",
modelTopologyBytes: modelArtifacts.modelTopology == null ? 0 : stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),
weightSpecsBytes: modelArtifacts.weightSpecs == null ? 0 : stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),
weightDataBytes: modelArtifacts.weightData == null ? 0 : modelArtifacts.weightData.byteLength
};
}
function computeFloat16MantisaTable() {
const convertMantissa = (i) => {
let m = i << 13;
let e = 0;
while ((m & 8388608) === 0) {
e -= 8388608;
m <<= 1;
}
m &= ~8388608;
e += 947912704;
return m | e;
};
const mantisaTable = new Uint32Array(2048);
mantisaTable[0] = 0;
for (let i = 1; i < 1024; i++) {
mantisaTable[i] = convertMantissa(i);
}
for (let i = 1024; i < 2048; i++) {
mantisaTable[i] = 939524096 + (i - 1024 << 13);
}
return mantisaTable;
}
function computeFloat16ExponentTable() {
const exponentTable = new Uint32Array(64);
exponentTable[0] = 0;
exponentTable[31] = 1199570944;
exponentTable[32] = 2147483648;
exponentTable[63] = 3347054592;
for (let i = 1; i < 31; i++) {
exponentTable[i] = i << 23;
}
for (let i = 33; i < 63; i++) {
exponentTable[i] = 2147483648 + (i - 32 << 23);
}
return exponentTable;
}
function computeFloat16OffsetTable() {
const offsetTable = new Uint32Array(64);
for (let i = 0; i < 64; i++) {
offsetTable[i] = 1024;
}
offsetTable[0] = offsetTable[32] = 0;
return offsetTable;
}
function getFloat16Decoder() {
const mantisaTable = computeFloat16MantisaTable();
const exponentTable = computeFloat16ExponentTable();
const offsetTable = computeFloat16OffsetTable();
return (quantizedArray) => {
const buffer10 = new ArrayBuffer(4 * quantizedArray.length);
const bufferUint32View = new Uint32Array(buffer10);
for (let index = 0; index < quantizedArray.length; index++) {
const float16Bits = quantizedArray[index];
const float32Bits = mantisaTable[offsetTable[float16Bits >> 10] + (float16Bits & 1023)] + exponentTable[float16Bits >> 10];
bufferUint32View[index] = float32Bits;
}
return new Float32Array(buffer10);
};
}
// node_modules/@tensorflow/tfjs-core/dist/io/router_registry.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
class IORouterRegistry {
constructor() {
this.saveRouters = [];
this.loadRouters = [];
}
static getInstance() {
if (IORouterRegistry.instance == null) {
IORouterRegistry.instance = new IORouterRegistry();
}
return IORouterRegistry.instance;
}
static registerSaveRouter(saveRouter) {
IORouterRegistry.getInstance().saveRouters.push(saveRouter);
}
static registerLoadRouter(loadRouter) {
IORouterRegistry.getInstance().loadRouters.push(loadRouter);
}
static getSaveHandlers(url) {
return IORouterRegistry.getHandlers(url, "save");
}
static getLoadHandlers(url, loadOptions) {
return IORouterRegistry.getHandlers(url, "load", loadOptions);
}
static getHandlers(url, handlerType, loadOptions) {
const validHandlers = [];
const routers = handlerType === "load" ? IORouterRegistry.getInstance().loadRouters : IORouterRegistry.getInstance().saveRouters;
routers.forEach((router) => {
const handler = router(url, loadOptions);
if (handler !== null) {
validHandlers.push(handler);
}
});
return validHandlers;
}
}
const registerSaveRouter = (loudRouter) => IORouterRegistry.registerSaveRouter(loudRouter);
const registerLoadRouter = (loudRouter) => IORouterRegistry.registerLoadRouter(loudRouter);
const getSaveHandlers = (url) => IORouterRegistry.getSaveHandlers(url);
const getLoadHandlers = (url, loadOptions) => IORouterRegistry.getLoadHandlers(url, loadOptions);
// node_modules/@tensorflow/tfjs-core/dist/io/model_management.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const URL_SCHEME_SUFFIX = "://";
class ModelStoreManagerRegistry {
constructor() {
this.managers = {};
}
static getInstance() {
if (ModelStoreManagerRegistry.instance == null) {
ModelStoreManagerRegistry.instance = new ModelStoreManagerRegistry();
}
return ModelStoreManagerRegistry.instance;
}
static registerManager(scheme, manager) {
assert(scheme != null, () => "scheme must not be undefined or null.");
if (scheme.endsWith(URL_SCHEME_SUFFIX)) {
scheme = scheme.slice(0, scheme.indexOf(URL_SCHEME_SUFFIX));
}
assert(scheme.length > 0, () => "scheme must not be an empty string.");
const registry = ModelStoreManagerRegistry.getInstance();
assert(registry.managers[scheme] == null, () => `A model store manager is already registered for scheme '${scheme}'.`);
registry.managers[scheme] = manager;
}
static getManager(scheme) {
const manager = this.getInstance().managers[scheme];
if (manager == null) {
throw new Error(`Cannot find model manager for scheme '${scheme}'`);
}
return manager;
}
static getSchemes() {
return Object.keys(this.getInstance().managers);
}
}
function parseURL(url) {
if (url.indexOf(URL_SCHEME_SUFFIX) === -1) {
throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${ModelStoreManagerRegistry.getSchemes().join(",")}`);
}
return {
scheme: url.split(URL_SCHEME_SUFFIX)[0],
path: url.split(URL_SCHEME_SUFFIX)[1]
};
}
async function cloneModelInternal(sourceURL, destURL, deleteSource = false) {
assert(sourceURL !== destURL, () => `Old path and new path are the same: '${sourceURL}'`);
const loadHandlers = IORouterRegistry.getLoadHandlers(sourceURL);
assert(loadHandlers.length > 0, () => `Copying failed because no load handler is found for source URL ${sourceURL}.`);
assert(loadHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) load handlers for source URL ${sourceURL}.`);
const loadHandler = loadHandlers[0];
const saveHandlers = IORouterRegistry.getSaveHandlers(destURL);
assert(saveHandlers.length > 0, () => `Copying failed because no save handler is found for destination URL ${destURL}.`);
assert(saveHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) save handlers for destination URL ${destURL}.`);
const saveHandler = saveHandlers[0];
const sourceScheme = parseURL(sourceURL).scheme;
const sourcePath = parseURL(sourceURL).path;
const sameMedium = sourceScheme === parseURL(sourceURL).scheme;
const modelArtifacts = await loadHandler.load();
if (deleteSource && sameMedium) {
await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath);
}
const saveResult = await saveHandler.save(modelArtifacts);
if (deleteSource && !sameMedium) {
await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath);
}
return saveResult.modelArtifactsInfo;
}
async function listModels() {
const schemes = ModelStoreManagerRegistry.getSchemes();
const out = {};
for (const scheme of schemes) {
const schemeOut = await ModelStoreManagerRegistry.getManager(scheme).listModels();
for (const path in schemeOut) {
const url = scheme + URL_SCHEME_SUFFIX + path;
out[url] = schemeOut[path];
}
}
return out;
}
async function removeModel(url) {
const schemeAndPath = parseURL(url);
const manager = ModelStoreManagerRegistry.getManager(schemeAndPath.scheme);
return manager.removeModel(schemeAndPath.path);
}
async function copyModel(sourceURL, destURL) {
const deleteSource = false;
return cloneModelInternal(sourceURL, destURL, deleteSource);
}
async function moveModel(sourceURL, destURL) {
const deleteSource = true;
return cloneModelInternal(sourceURL, destURL, deleteSource);
}
// node_modules/@tensorflow/tfjs-core/dist/ops/buffer.js
/**
* @license
* Copyright 2020 Google Inc. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function buffer(shape, dtype = "float32", values) {
dtype = dtype || "float32";
assertNonNegativeIntegerDimensions(shape);
return new TensorBuffer(shape, dtype, values);
}
// node_modules/@tensorflow/tfjs-core/dist/ops/cast.js
/**
* @license
* Copyright 2020 Google Inc. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function cast_(x, dtype) {
const $x = convertToTensor(x, "x", "cast");
if (!isValidDtype(dtype)) {
throw new Error(`Failed to cast to unknown dtype ${dtype}`);
}
if (dtype === "string" && $x.dtype !== "string" || dtype !== "string" && $x.dtype === "string") {
throw new Error("Only strings can be casted to strings");
}
const inputs = {x: $x};
const attrs = {dtype};
return ENGINE.runKernelFunc((backend3) => backend3.cast($x, dtype), inputs, null, Cast, attrs);
}
const cast = op({cast_});
// node_modules/@tensorflow/tfjs-core/dist/ops/clone.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function clone_(x) {
const $x = convertToTensor(x, "x", "clone", null);
const forward = () => ENGINE.makeTensorFromDataId($x.dataId, $x.shape, $x.dtype);
const inputs = {x: $x};
return ENGINE.runKernelFunc(forward, inputs, null, Identity);
}
const clone = op({clone_});
// node_modules/@tensorflow/tfjs-core/dist/ops/print.js
/**
* @license
* Copyright 2020 Google Inc. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function print2(x, verbose = false) {
console.log(x.toString(verbose));
}
// node_modules/@tensorflow/tfjs-core/dist/io/io.js
const io_exports = {};
__export(io_exports, {
browserFiles: () => browserFiles,
browserHTTPRequest: () => browserHTTPRequest,
concatenateArrayBuffers: () => concatenateArrayBuffers,
copyModel: () => copyModel,
decodeWeights: () => decodeWeights,
encodeWeights: () => encodeWeights,
fromMemory: () => fromMemory,
getLoadHandlers: () => getLoadHandlers,
getModelArtifactsInfoForJSON: () => getModelArtifactsInfoForJSON,
getSaveHandlers: () => getSaveHandlers,
http: () => http,
isHTTPScheme: () => isHTTPScheme,
listModels: () => listModels,
loadWeights: () => loadWeights,
moveModel: () => moveModel,
registerLoadRouter: () => registerLoadRouter,
registerSaveRouter: () => registerSaveRouter,
removeModel: () => removeModel,
weightsLoaderFactory: () => weightsLoaderFactory,
withSaveHandler: () => withSaveHandler
});
// node_modules/@tensorflow/tfjs-core/dist/io/browser_files.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const DEFAULT_FILE_NAME_PREFIX = "model";
const DEFAULT_JSON_EXTENSION_NAME = ".json";
const DEFAULT_WEIGHT_DATA_EXTENSION_NAME = ".weights.bin";
function defer(f) {
return new Promise((resolve) => setTimeout(resolve)).then(f);
}
class BrowserDownloads {
constructor(fileNamePrefix) {
if (!env().getBool("IS_BROWSER")) {
throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");
}
if (fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)) {
fileNamePrefix = fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length);
}
if (fileNamePrefix == null || fileNamePrefix.length === 0) {
fileNamePrefix = DEFAULT_FILE_NAME_PREFIX;
}
this.modelTopologyFileName = fileNamePrefix + DEFAULT_JSON_EXTENSION_NAME;
this.weightDataFileName = fileNamePrefix + DEFAULT_WEIGHT_DATA_EXTENSION_NAME;
}
async save(modelArtifacts) {
if (typeof document === "undefined") {
throw new Error("Browser downloads are not supported in this environment since `document` is not present");
}
const weightsURL = window.URL.createObjectURL(new Blob([modelArtifacts.weightData], {type: "application/octet-stream"}));
if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");
} else {
const weightsManifest = [{
paths: ["./" + this.weightDataFileName],
weights: modelArtifacts.weightSpecs
}];
const modelTopologyAndWeightManifest = {
modelTopology: modelArtifacts.modelTopology,
format: modelArtifacts.format,
generatedBy: modelArtifacts.generatedBy,
convertedBy: modelArtifacts.convertedBy,
weightsManifest
};
const modelTopologyAndWeightManifestURL = window.URL.createObjectURL(new Blob([JSON.stringify(modelTopologyAndWeightManifest)], {type: "application/json"}));
const jsonAnchor = this.jsonAnchor == null ? document.createElement("a") : this.jsonAnchor;
jsonAnchor.download = this.modelTopologyFileName;
jsonAnchor.href = modelTopologyAndWeightManifestURL;
await defer(() => jsonAnchor.dispatchEvent(new MouseEvent("click")));
if (modelArtifacts.weightData != null) {
const weightDataAnchor = this.weightDataAnchor == null ? document.createElement("a") : this.weightDataAnchor;
weightDataAnchor.download = this.weightDataFileName;
weightDataAnchor.href = weightsURL;
await defer(() => weightDataAnchor.dispatchEvent(new MouseEvent("click")));
}
return {modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts)};
}
}
}
BrowserDownloads.URL_SCHEME = "downloads://";
class BrowserFiles {
constructor(files) {
if (files == null || files.length < 1) {
throw new Error(`When calling browserFiles, at least 1 file is required, but received ${files}`);
}
this.files = files;
}
async load() {
const jsonFile = this.files[0];
const weightFiles = this.files.slice(1);
return new Promise((resolve, reject) => {
const jsonReader = new FileReader();
jsonReader.onload = (event) => {
const modelJSON = JSON.parse(event.target.result);
const modelTopology = modelJSON.modelTopology;
if (modelTopology == null) {
reject(new Error(`modelTopology field is missing from file ${jsonFile.name}`));
return;
}
if (weightFiles.length === 0) {
resolve({modelTopology});
}
const weightsManifest = modelJSON.weightsManifest;
if (weightsManifest == null) {
reject(new Error(`weightManifest field is missing from file ${jsonFile.name}`));
return;
}
let pathToFile;
try {
pathToFile = this.checkManifestAndWeightFiles(weightsManifest, weightFiles);
} catch (err) {
reject(err);
return;
}
const weightSpecs = [];
const paths = [];
const perFileBuffers = [];
weightsManifest.forEach((weightsGroup) => {
weightsGroup.paths.forEach((path) => {
paths.push(path);
perFileBuffers.push(null);
});
weightSpecs.push(...weightsGroup.weights);
});
weightsManifest.forEach((weightsGroup) => {
weightsGroup.paths.forEach((path) => {
const weightFileReader = new FileReader();
weightFileReader.onload = (event2) => {
const weightData = event2.target.result;
const index = paths.indexOf(path);
perFileBuffers[index] = weightData;
if (perFileBuffers.indexOf(null) === -1) {
resolve({
modelTopology,
weightSpecs,
weightData: concatenateArrayBuffers(perFileBuffers),
format: modelJSON.format,
generatedBy: modelJSON.generatedBy,
convertedBy: modelJSON.convertedBy,
userDefinedMetadata: modelJSON.userDefinedMetadata
});
}
};
weightFileReader.onerror = (error) => reject(`Failed to weights data from file of path '${path}'.`);
weightFileReader.readAsArrayBuffer(pathToFile[path]);
});
});
};
jsonReader.onerror = (error) => reject(`Failed to read model topology and weights manifest JSON from file '${jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`);
jsonReader.readAsText(jsonFile);
});
}
checkManifestAndWeightFiles(manifest, files) {
const basenames = [];
const fileNames = files.map((file) => basename(file.name));
const pathToFile = {};
for (const group of manifest) {
group.paths.forEach((path) => {
const pathBasename = basename(path);
if (basenames.indexOf(pathBasename) !== -1) {
throw new Error(`Duplicate file basename found in weights manifest: '${pathBasename}'`);
}
basenames.push(pathBasename);
if (fileNames.indexOf(pathBasename) === -1) {
throw new Error(`Weight file with basename '${pathBasename}' is not provided.`);
} else {
pathToFile[path] = files[fileNames.indexOf(pathBasename)];
}
});
}
if (basenames.length !== files.length) {
throw new Error(`Mismatch in the number of files in weights manifest (${basenames.length}) and the number of weight files provided (${files.length}).`);
}
return pathToFile;
}
}
const browserDownloadsRouter = (url) => {
if (!env().getBool("IS_BROWSER")) {
return null;
} else {
if (!Array.isArray(url) && url.startsWith(BrowserDownloads.URL_SCHEME)) {
return browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length));
} else {
return null;
}
}
};
IORouterRegistry.registerSaveRouter(browserDownloadsRouter);
function browserDownloads(fileNamePrefix = "model") {
return new BrowserDownloads(fileNamePrefix);
}
function browserFiles(files) {
return new BrowserFiles(files);
}
// node_modules/@tensorflow/tfjs-core/dist/io/progress.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function monitorPromisesProgress(promises, onProgress, startFraction, endFraction) {
checkPromises(promises);
startFraction = startFraction == null ? 0 : startFraction;
endFraction = endFraction == null ? 1 : endFraction;
checkFraction(startFraction, endFraction);
let resolvedPromise = 0;
const registerMonitor = (promise) => {
promise.then((value) => {
const fraction = startFraction + ++resolvedPromise / promises.length * (endFraction - startFraction);
onProgress(fraction);
return value;
});
return promise;
};
function checkPromises(promises2) {
assert(promises2 != null && Array.isArray(promises2) && promises2.length > 0, () => "promises must be a none empty array");
}
function checkFraction(startFraction2, endFraction2) {
assert(startFraction2 >= 0 && startFraction2 <= 1, () => `Progress fraction must be in range [0, 1], but got startFraction ${startFraction2}`);
assert(endFraction2 >= 0 && endFraction2 <= 1, () => `Progress fraction must be in range [0, 1], but got endFraction ${endFraction2}`);
assert(endFraction2 >= startFraction2, () => `startFraction must be no more than endFraction, but got startFraction ${startFraction2} and endFraction ${endFraction2}`);
}
return Promise.all(promises.map(registerMonitor));
}
// node_modules/@tensorflow/tfjs-core/dist/io/weights_loader.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
async function loadWeightsAsArrayBuffer(fetchURLs, loadOptions) {
if (loadOptions == null) {
loadOptions = {};
}
const fetchFunc = loadOptions.fetchFunc == null ? env().platform.fetch : loadOptions.fetchFunc;
const requests = fetchURLs.map((fetchURL) => fetchFunc(fetchURL, loadOptions.requestInit, {isBinary: true}));
const fetchStartFraction = 0;
const fetchEndFraction = 0.5;
const responses = loadOptions.onProgress == null ? await Promise.all(requests) : await monitorPromisesProgress(requests, loadOptions.onProgress, fetchStartFraction, fetchEndFraction);
const bufferPromises = responses.map((response) => response.arrayBuffer());
const bufferStartFraction = 0.5;
const bufferEndFraction = 1;
const buffers = loadOptions.onProgress == null ? await Promise.all(bufferPromises) : await monitorPromisesProgress(bufferPromises, loadOptions.onProgress, bufferStartFraction, bufferEndFraction);
return buffers;
}
async function loadWeights(manifest, filePathPrefix = "", weightNames, requestInit) {
const fetchWeights = (fetchUrls) => loadWeightsAsArrayBuffer(fetchUrls, {requestInit});
const loadWeights2 = weightsLoaderFactory(fetchWeights);
return loadWeights2(manifest, filePathPrefix, weightNames);
}
function weightsLoaderFactory(fetchWeightsFunction) {
return async (manifest, filePathPrefix = "", weightNames) => {
const groupIndicesToFetchMap = manifest.map(() => false);
const groupWeightsToFetch = {};
const weightsFound = weightNames != null ? weightNames.map(() => false) : [];
const allManifestWeightNames = [];
manifest.forEach((manifestGroupConfig, groupIndex) => {
let groupOffset = 0;
manifestGroupConfig.weights.forEach((weightsEntry) => {
const rawDtype = "quantization" in weightsEntry ? weightsEntry.quantization.dtype : weightsEntry.dtype;
const weightsBytes = DTYPE_VALUE_SIZE_MAP[rawDtype] * sizeFromShape(weightsEntry.shape);
const enqueueWeightsForFetchingFn = () => {
groupIndicesToFetchMap[groupIndex] = true;
if (groupWeightsToFetch[groupIndex] == null) {
groupWeightsToFetch[groupIndex] = [];
}
groupWeightsToFetch[groupIndex].push({
manifestEntry: weightsEntry,
groupOffset,
sizeBytes: weightsBytes
});
};
if (weightNames != null) {
weightNames.forEach((weightName, weightIndex) => {
if (weightName === weightsEntry.name) {
enqueueWeightsForFetchingFn();
weightsFound[weightIndex] = true;
}
});
} else {
enqueueWeightsForFetchingFn();
}
allManifestWeightNames.push(weightsEntry.name);
groupOffset += weightsBytes;
});
});
if (!weightsFound.every((found) => found)) {
const weightsNotFound = weightNames.filter((_, i) => !weightsFound[i]);
throw new Error(`Could not find weights in manifest with names: ${weightsNotFound.join(", ")}.
Manifest JSON has weights with names: ${allManifestWeightNames.join(", ")}.`);
}
const groupIndicesToFetch = groupIndicesToFetchMap.reduce((accumulator, shouldFetch, i) => {
if (shouldFetch) {
accumulator.push(i);
}
return accumulator;
}, []);
const fetchUrls = [];
groupIndicesToFetch.forEach((i) => {
manifest[i].paths.forEach((filepath) => {
const fetchUrl = filePathPrefix + (!filePathPrefix.endsWith("/") ? "/" : "") + filepath;
fetchUrls.push(fetchUrl);
});
});
const buffers = await fetchWeightsFunction(fetchUrls);
const weightsTensorMap = {};
let bufferIndexOffset = 0;
groupIndicesToFetch.forEach((i) => {
const numBuffers = manifest[i].paths.length;
let groupBytes = 0;
for (let i2 = 0; i2 < numBuffers; i2++) {
groupBytes += buffers[bufferIndexOffset + i2].byteLength;
}
const groupBuffer = new ArrayBuffer(groupBytes);
const groupByteBuffer = new Uint8Array(groupBuffer);
let groupBufferOffset = 0;
for (let i2 = 0; i2 < numBuffers; i2++) {
const buffer10 = new Uint8Array(buffers[bufferIndexOffset + i2]);
groupByteBuffer.set(buffer10, groupBufferOffset);
groupBufferOffset += buffer10.byteLength;
}
const weightsEntries = groupWeightsToFetch[i];
weightsEntries.forEach((weightsEntry) => {
const byteBuffer = groupBuffer.slice(weightsEntry.groupOffset, weightsEntry.groupOffset + weightsEntry.sizeBytes);
const nameToTensorMap = decodeWeights(byteBuffer, [weightsEntry.manifestEntry]);
for (const name in nameToTensorMap) {
weightsTensorMap[name] = nameToTensorMap[name];
}
});
bufferIndexOffset += numBuffers;
});
return weightsTensorMap;
};
}
// node_modules/@tensorflow/tfjs-core/dist/io/http.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const OCTET_STREAM_MIME_TYPE = "application/octet-stream";
const JSON_TYPE = "application/json";
class HTTPRequest {
constructor(path, loadOptions) {
this.DEFAULT_METHOD = "POST";
if (loadOptions == null) {
loadOptions = {};
}
this.weightPathPrefix = loadOptions.weightPathPrefix;
this.onProgress = loadOptions.onProgress;
this.weightUrlConverter = loadOptions.weightUrlConverter;
if (loadOptions.fetchFunc != null) {
assert(typeof loadOptions.fetchFunc === "function", () => "Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)");
this.fetch = loadOptions.fetchFunc;
} else {
this.fetch = env().platform.fetch;
}
assert(path != null && path.length > 0, () => "URL path for http must not be null, undefined or empty.");
if (Array.isArray(path)) {
assert(path.length === 2, () => `URL paths for http must have a length of 2, (actual length is ${path.length}).`);
}
this.path = path;
if (loadOptions.requestInit != null && loadOptions.requestInit.body != null) {
throw new Error("requestInit is expected to have no pre-existing body, but has one.");
}
this.requestInit = loadOptions.requestInit || {};
}
async save(modelArtifacts) {
if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");
}
const init2 = Object.assign({method: this.DEFAULT_METHOD}, this.requestInit);
init2.body = new FormData();
const weightsManifest = [{
paths: ["./model.weights.bin"],
weights: modelArtifacts.weightSpecs
}];
const modelTopologyAndWeightManifest = {
modelTopology: modelArtifacts.modelTopology,
format: modelArtifacts.format,
generatedBy: modelArtifacts.generatedBy,
convertedBy: modelArtifacts.convertedBy,
userDefinedMetadata: modelArtifacts.userDefinedMetadata,
weightsManifest
};
init2.body.append("model.json", new Blob([JSON.stringify(modelTopologyAndWeightManifest)], {type: JSON_TYPE}), "model.json");
if (modelArtifacts.weightData != null) {
init2.body.append("model.weights.bin", new Blob([modelArtifacts.weightData], {type: OCTET_STREAM_MIME_TYPE}), "model.weights.bin");
}
const response = await this.fetch(this.path, init2);
if (response.ok) {
return {
modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts),
responses: [response]
};
} else {
throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${response.status}.`);
}
}
async load() {
const modelConfigRequest = await this.fetch(this.path, this.requestInit);
if (!modelConfigRequest.ok) {
throw new Error(`Request to ${this.path} failed with status code ${modelConfigRequest.status}. Please verify this URL points to the model JSON of the model to load.`);
}
let modelConfig;
try {
modelConfig = await modelConfigRequest.json();
} catch (e) {
let message = `Failed to parse model JSON of response from ${this.path}.`;
if (this.path.endsWith(".pb")) {
message += " 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.";
} else {
message += " Please make sure the server is serving valid JSON for this request.";
}
throw new Error(message);
}
const modelTopology = modelConfig.modelTopology;
const weightsManifest = modelConfig.weightsManifest;
const generatedBy = modelConfig.generatedBy;
const convertedBy = modelConfig.convertedBy;
const format = modelConfig.format;
const userDefinedMetadata = modelConfig.userDefinedMetadata;
if (modelTopology == null && weightsManifest == null) {
throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);
}
let weightSpecs;
let weightData;
if (weightsManifest != null) {
const results = await this.loadWeights(weightsManifest);
[weightSpecs, weightData] = results;
}
const artifacts = {
modelTopology,
weightSpecs,
weightData,
userDefinedMetadata,
generatedBy,
convertedBy,
format
};
const initializer = modelConfig.modelInitializer;
if (initializer) {
artifacts.modelInitializer = initializer;
}
return artifacts;
}
async loadWeights(weightsManifest) {
const weightPath = Array.isArray(this.path) ? this.path[1] : this.path;
const [prefix, suffix] = parseUrl(weightPath);
const pathPrefix = this.weightPathPrefix || prefix;
const weightSpecs = [];
for (const entry of weightsManifest) {
weightSpecs.push(...entry.weights);
}
const fetchURLs = [];
const urlPromises = [];
for (const weightsGroup of weightsManifest) {
for (const path of weightsGroup.paths) {
if (this.weightUrlConverter != null) {
urlPromises.push(this.weightUrlConverter(path));
} else {
fetchURLs.push(pathPrefix + path + suffix);
}
}
}
if (this.weightUrlConverter) {
fetchURLs.push(...await Promise.all(urlPromises));
}
const buffers = await loadWeightsAsArrayBuffer(fetchURLs, {
requestInit: this.requestInit,
fetchFunc: this.fetch,
onProgress: this.onProgress
});
return [weightSpecs, concatenateArrayBuffers(buffers)];
}
}
HTTPRequest.URL_SCHEME_REGEX = /^https?:\/\//;
function parseUrl(url) {
const lastSlash = url.lastIndexOf("/");
const lastSearchParam = url.lastIndexOf("?");
const prefix = url.substring(0, lastSlash);
const suffix = lastSearchParam > lastSlash ? url.substring(lastSearchParam) : "";
return [prefix + "/", suffix];
}
function isHTTPScheme(url) {
return url.match(HTTPRequest.URL_SCHEME_REGEX) != null;
}
const httpRouter = (url, loadOptions) => {
if (typeof fetch === "undefined" && (loadOptions == null || loadOptions.fetchFunc == null)) {
return null;
} else {
let isHTTP = true;
if (Array.isArray(url)) {
isHTTP = url.every((urlItem) => isHTTPScheme(urlItem));
} else {
isHTTP = isHTTPScheme(url);
}
if (isHTTP) {
return http(url, loadOptions);
}
}
return null;
};
IORouterRegistry.registerSaveRouter(httpRouter);
IORouterRegistry.registerLoadRouter(httpRouter);
function http(path, loadOptions) {
return new HTTPRequest(path, loadOptions);
}
function browserHTTPRequest(path, loadOptions) {
return http(path, loadOptions);
}
// node_modules/@tensorflow/tfjs-core/dist/io/passthrough.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
class PassthroughLoader {
constructor(modelArtifacts) {
this.modelArtifacts = modelArtifacts;
}
async load() {
return this.modelArtifacts;
}
}
class PassthroughSaver {
constructor(saveHandler) {
this.saveHandler = saveHandler;
}
async save(modelArtifacts) {
return this.saveHandler(modelArtifacts);
}
}
function fromMemory(modelArtifacts, weightSpecs, weightData, trainingConfig) {
if (arguments.length === 1) {
const isModelArtifacts = modelArtifacts.modelTopology != null || modelArtifacts.weightSpecs != null;
if (isModelArtifacts) {
return new PassthroughLoader(modelArtifacts);
} else {
console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release.");
return new PassthroughLoader({modelTopology: modelArtifacts});
}
} else {
console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release.");
return new PassthroughLoader({
modelTopology: modelArtifacts,
weightSpecs,
weightData,
trainingConfig
});
}
}
function withSaveHandler(saveHandler) {
return new PassthroughSaver(saveHandler);
}
// node_modules/@tensorflow/tfjs-core/dist/io/io.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
// node_modules/@tensorflow/tfjs-core/dist/math.js
const math_exports = {};
__export(math_exports, {
confusionMatrix: () => confusionMatrix
});
// node_modules/@tensorflow/tfjs-core/dist/ops/reshape.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function reshape_(x, shape) {
const $x = convertToTensor(x, "x", "reshape", null);
const inputs = {x: $x};
const attrs = {shape};
const forward = (backend3, save) => {
shape = inferFromImplicitShape(shape, $x.size);
assert($x.size === sizeFromShape(shape), () => "new shape and old shape must have the same number of elements.");
save([$x]);
return backend3.reshape($x, shape);
};
return ENGINE.runKernelFunc(forward, inputs, null, Reshape, attrs);
}
const reshape = op({reshape_});
// node_modules/@tensorflow/tfjs-core/dist/ops/mat_mul.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function matMul_(a, b, transposeA = false, transposeB = false) {
let $a = convertToTensor(a, "a", "matMul");
let $b = convertToTensor(b, "b", "matMul");
[$a, $b] = makeTypesMatch($a, $b);
const forward = (backend3, save) => {
save([$a, $b]);
const innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];
const innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];
const outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];
const outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];
const outerDimsA = $a.shape.slice(0, -2);
const outerDimsB = $b.shape.slice(0, -2);
const batchDimA = sizeFromShape(outerDimsA);
const batchDimB = sizeFromShape(outerDimsB);
const batchDimsCompatible = batchDimA === batchDimB || batchDimA === 1 || batchDimB === 1;
assert($a.rank >= 2 && $b.rank >= 2 && batchDimsCompatible, () => `Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${outerDimsA}) and (${outerDimsB}).`);
assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${$a.shape} and ${$b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);
const outShapeOuterDims = batchDimA > batchDimB ? outerDimsA : outerDimsB;
const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);
const a3D = transposeA ? reshape($a, [batchDimA, innerShapeA, outerShapeA]) : reshape($a, [batchDimA, outerShapeA, innerShapeA]);
const b3D = transposeB ? reshape($b, [batchDimB, outerShapeB, innerShapeB]) : reshape($b, [batchDimB, innerShapeB, outerShapeB]);
const res3d = backend3.batchMatMul(a3D, b3D, transposeA, transposeB);
return reshape(res3d, outShape);
};
const inputs = {a: $a, b: $b};
const attrs = {transposeA, transposeB};
return ENGINE.runKernelFunc(forward, inputs, null, BatchMatMul, attrs);
}
const matMul = op({matMul_});
// node_modules/@tensorflow/tfjs-core/dist/ops/one_hot.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function oneHot_(indices, depth, onValue = 1, offValue = 0) {
if (depth < 2) {
throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);
}
const $indices = convertToTensor(indices, "indices", "oneHot", "int32");
const outShape = [...$indices.shape, depth];
const forward = (backend3, save) => {
save([$indices]);
return reshape(backend3.oneHot(reshape($indices, [$indices.size]), depth, onValue, offValue), outShape);
};
const inputs = {indices: $indices};
const attrs = {depth, onValue, offValue};
return ENGINE.runKernelFunc(forward, inputs, null, OneHot, attrs);
}
const oneHot = op({oneHot_});
// node_modules/@tensorflow/tfjs-core/dist/ops/transpose.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function transpose_(x, perm) {
const $x = convertToTensor(x, "x", "transpose");
if (perm == null) {
perm = $x.shape.map((s, i) => i).reverse();
}
assert($x.rank === perm.length, () => `Error in transpose: rank of input ${$x.rank} must match length of perm ${perm}.`);
perm.forEach((axis) => {
assert(axis >= 0 && axis < $x.rank, () => `All entries in 'perm' must be between 0 and ${$x.rank - 1} but got ${perm}`);
});
if ($x.rank <= 1) {
return $x.clone();
}
const inputs = {x: $x};
const attrs = {perm};
return ENGINE.runKernelFunc((backend3) => backend3.transpose($x, perm), inputs, null, Transpose, attrs);
}
const transpose = op({transpose_});
// node_modules/@tensorflow/tfjs-core/dist/ops/confusion_matrix.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function confusionMatrix_(labels, predictions, numClasses) {
const $labels = convertToTensor(labels, "labels", "confusionMatrix");
const $predictions = convertToTensor(predictions, "predictions", "confusionMatrix");
assert(numClasses == null || numClasses > 0 && Number.isInteger(numClasses), () => `If provided, numClasses must be a positive integer, but got ${numClasses}`);
assert($labels.rank === 1, () => `Expected the rank of labels to be 1, but got ${$labels.rank}`);
assert($predictions.rank === 1, () => `Expected the rank of predictions to be 1, but got ${$predictions.rank}`);
assert($labels.shape[0] === $predictions.shape[0], () => `Mismatch in the number of examples: ${$labels.shape[0]} vs. ${$predictions.shape[0]}. Labels and predictions should have the same number of elements.`);
assert(numClasses > 0 && Number.isInteger(numClasses), () => `numClasses is required to be a positive integer, but got ${numClasses}`);
const oneHotLabels = oneHot(cast($labels, "int32"), numClasses);
const oneHotPredictions = oneHot(cast($predictions, "int32"), numClasses);
const oneHotLabelsT = transpose(oneHotLabels);
const product = matMul(oneHotLabelsT, oneHotPredictions);
return cast(product, "int32");
}
const confusionMatrix = op({confusionMatrix_});
// node_modules/@tensorflow/tfjs-core/dist/math.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
// node_modules/@tensorflow/tfjs-core/dist/ops/browser.js
const browser_exports = {};
__export(browser_exports, {
fromPixels: () => fromPixels,
toPixels: () => toPixels
});
// node_modules/@tensorflow/tfjs-core/dist/ops/tensor3d.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function tensor3d(values, shape, dtype) {
assertNonNull(values);
if (shape != null && shape.length !== 3) {
throw new Error("tensor3d() requires shape to have three numbers");
}
const inferredShape = inferShape(values, dtype);
if (inferredShape.length !== 3 && inferredShape.length !== 1) {
throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");
}
if (inferredShape.length === 1 && shape == null) {
throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");
}
return makeTensor(values, shape, inferredShape, dtype);
}
// node_modules/@tensorflow/tfjs-core/dist/ops/browser.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
let fromPixels2DContext;
function fromPixels_(pixels, numChannels = 3) {
if (numChannels > 4) {
throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");
}
if (pixels == null) {
throw new Error("pixels passed to tf.browser.fromPixels() can not be null");
}
let isPixelData = false;
let isImageData = false;
let isVideo = false;
let isImage = false;
let isCanvasLike = false;
if (pixels.data instanceof Uint8Array) {
isPixelData = true;
} else if (typeof ImageData !== "undefined" && pixels instanceof ImageData) {
isImageData = true;
} else if (typeof HTMLVideoElement !== "undefined" && pixels instanceof HTMLVideoElement) {
isVideo = true;
} else if (typeof HTMLImageElement !== "undefined" && pixels instanceof HTMLImageElement) {
isImage = true;
} else if (pixels.getContext != null) {
isCanvasLike = true;
} else {
throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${pixels.constructor.name}`);
}
if (isVideo) {
const HAVE_CURRENT_DATA_READY_STATE = 2;
if (isVideo && pixels.readyState < HAVE_CURRENT_DATA_READY_STATE) {
throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the <video> element.");
}
}
const kernel = getKernel(FromPixels, ENGINE.backendName);
if (kernel != null) {
const inputs = {pixels};
const attrs = {numChannels};
return ENGINE.runKernel(FromPixels, inputs, attrs);
}
const [width, height] = isVideo ? [
pixels.videoWidth,
pixels.videoHeight
] : [pixels.width, pixels.height];
let vals;
if (isCanvasLike) {
vals = pixels.getContext("2d").getImageData(0, 0, width, height).data;
} else if (isImageData || isPixelData) {
vals = pixels.data;
} else if (isImage || isVideo) {
if (fromPixels2DContext == null) {
fromPixels2DContext = document.createElement("canvas").getContext("2d");
}
fromPixels2DContext.canvas.width = width;
fromPixels2DContext.canvas.height = height;
fromPixels2DContext.drawImage(pixels, 0, 0, width, height);
vals = fromPixels2DContext.getImageData(0, 0, width, height).data;
}
let values;
if (numChannels === 4) {
values = new Int32Array(vals);
} else {
const numPixels = width * height;
values = new Int32Array(numPixels * numChannels);
for (let i = 0; i < numPixels; i++) {
for (let channel = 0; channel < numChannels; ++channel) {
values[i * numChannels + channel] = vals[i * 4 + channel];
}
}
}
const outShape = [height, width, numChannels];
return tensor3d(values, outShape, "int32");
}
async function toPixels(img, canvas) {
let $img = convertToTensor(img, "img", "toPixels");
if (!(img instanceof Tensor)) {
const originalImgTensor = $img;
$img = cast(originalImgTensor, "int32");
originalImgTensor.dispose();
}
if ($img.rank !== 2 && $img.rank !== 3) {
throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${$img.rank}.`);
}
const [height, width] = $img.shape.slice(0, 2);
const depth = $img.rank === 2 ? 1 : $img.shape[2];
if (depth > 4 || depth === 2) {
throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${depth}`);
}
if ($img.dtype !== "float32" && $img.dtype !== "int32") {
throw new Error(`Unsupported type for toPixels: ${$img.dtype}. Please use float32 or int32 tensors.`);
}
const data2 = await $img.data();
const multiplier = $img.dtype === "float32" ? 255 : 1;
const bytes = new Uint8ClampedArray(width * height * 4);
for (let i = 0; i < height * width; ++i) {
const rgba = [0, 0, 0, 255];
for (let d = 0; d < depth; d++) {
const value = data2[i * depth + d];
if ($img.dtype === "float32") {
if (value < 0 || value > 1) {
throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${value}.`);
}
} else if ($img.dtype === "int32") {
if (value < 0 || value > 255) {
throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${value}.`);
}
}
if (depth === 1) {
rgba[0] = value * multiplier;
rgba[1] = value * multiplier;
rgba[2] = value * multiplier;
} else {
rgba[d] = value * multiplier;
}
}
const j = i * 4;
bytes[j + 0] = Math.round(rgba[0]);
bytes[j + 1] = Math.round(rgba[1]);
bytes[j + 2] = Math.round(rgba[2]);
bytes[j + 3] = Math.round(rgba[3]);
}
if (canvas != null) {
canvas.width = width;
canvas.height = height;
const ctx = canvas.getContext("2d");
const imageData = new ImageData(bytes, width, height);
ctx.putImageData(imageData, 0, 0);
}
if ($img !== img) {
$img.dispose();
}
return bytes;
}
const fromPixels = op({fromPixels_});
// node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd_util.js
const gather_nd_util_exports = {};
__export(gather_nd_util_exports, {
prepareAndValidate: () => prepareAndValidate
});
function prepareAndValidate(tensor16, indices) {
if (tensor16.rank < 1) {
throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${tensor16.rank}.`);
}
if (indices.rank < 1) {
throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${indices.rank}.`);
}
if (indices.dtype !== "int32") {
throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${indices.dtype}.`);
}
if (indices.shape[indices.rank - 1] > tensor16.rank) {
throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${indices.shape[indices.rank - 1]} vs. ${tensor16.rank}`);
}
if (tensor16.size === 0) {
throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${tensor16.shape}.`);
}
const indicesShape = indices.shape;
const sliceRank = indicesShape[indicesShape.length - 1];
let nResult = 1;
for (let i = 0; i < indicesShape.length - 1; ++i) {
nResult *= indicesShape[i];
}
const inputShape = tensor16.shape;
const resultShape = indicesShape.slice();
resultShape.pop();
let sliceSize = 1;
for (let i = sliceRank; i < tensor16.rank; ++i) {
sliceSize *= inputShape[i];
resultShape.push(inputShape[i]);
}
const strides = [
...computeStrides(tensor16.shape).map((stride) => stride / sliceSize),
1
].slice(0, sliceRank);
return [resultShape, nResult, sliceSize, strides];
}
// node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd_util.js
const scatter_nd_util_exports = {};
__export(scatter_nd_util_exports, {
calculateShapes: () => calculateShapes,
validateInput: () => validateInput,
validateUpdateShape: () => validateUpdateShape
});
function validateUpdateShape(shape, indices, updates) {
const sliceDim = indices.rank > 1 ? indices.shape[indices.rank - 1] : 1;
const batchDim = indices.rank > 1 ? indices.rank - 1 : 1;
const shapeError = `Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${updates.shape}, indices.shape: ${indices.shape}, shape: ${shape}, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;
if (updates.rank < batchDim) {
throw new Error(shapeError + ` update.rank < ${batchDim}. `);
}
if (shape.length < sliceDim + (updates.rank - batchDim)) {
throw new Error(shapeError + ` Output shape length < ${sliceDim + (updates.rank - batchDim)}`);
}
if (updates.rank !== batchDim + shape.length - sliceDim) {
throw new Error(shapeError + ` update.rank != ${batchDim + shape.length - sliceDim}`);
}
for (let d = 0; d < batchDim; ++d) {
if (updates.shape[d] !== indices.shape[d]) {
throw new Error(shapeError + ` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${indices.shape[d]}).`);
}
}
for (let d = 0; d < updates.rank - batchDim; ++d) {
if (updates.shape[d + batchDim] !== shape[d + sliceDim]) {
throw new Error(shapeError + ` updates.shape[${d + batchDim}] (${updates.shape[d + batchDim]}) != shape[${d + batchDim}] (${shape[d + batchDim]})`);
}
}
}
function validateInput(updates, indices, shape) {
if (indices.rank < 1) {
throw new Error(`tf.scatterND() expects the indices to be rank 1 or higher, but the rank was ${indices.rank}.`);
}
if (updates.rank < 1) {
throw new Error(`tf.scatterND() expects the updates to be rank 1 or higher, but the rank was ${updates.rank}.`);
}
if (indices.dtype !== "int32") {
throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${indices.dtype}`);
}
if (shape.length < 1) {
throw new Error(`Output rank must be greater or equal to 1, but got shape: ${shape}`);
}
if (shape.length === 0) {
if (indices.size === 0) {
throw new Error(`Indices specified for empty output. indices shape: ${indices.shape}`);
}
if (updates.size === 0) {
throw new Error(`Updates specified for empty output. updates shape: ${updates.shape}`);
}
}
validateUpdateShape(shape, indices, updates);
}
function calculateShapes(updates, indices, shape) {
const indicesRank = indices.shape.length;
const sliceRank = indicesRank > 1 ? indices.shape[indicesRank - 1] : 1;
const totalNd = shape.length;
let sliceSize = 1;
for (let i = sliceRank; i < totalNd; ++i) {
sliceSize *= shape[i];
}
const safeSliceDim = sliceRank < 1 ? 1 : sliceRank;
const numUpdates = sizeFromShape(indices.shape) / safeSliceDim;
const strides = [...computeStrides(shape.slice(0, sliceRank)), 1];
const outputSize = sizeFromShape(shape);
return {sliceRank, numUpdates, sliceSize, strides, outputSize};
}
// node_modules/@tensorflow/tfjs-core/dist/ops/slice_util.js
const slice_util_exports = {};
__export(slice_util_exports, {
assertParamsValid: () => assertParamsValid,
computeFlatOffset: () => computeFlatOffset,
computeOutShape: () => computeOutShape,
getNormalizedAxes: () => getNormalizedAxes,
isSliceContinous: () => isSliceContinous,
maskToAxes: () => maskToAxes,
parseSliceParams: () => parseSliceParams,
startForAxis: () => startForAxis,
startIndicesWithElidedDims: () => startIndicesWithElidedDims,
stopForAxis: () => stopForAxis,
stopIndicesWithElidedDims: () => stopIndicesWithElidedDims,
stridesForAxis: () => stridesForAxis,
stridesWithElidedDims: () => stridesWithElidedDims
});
/**
* @license
* Copyright 2017 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function assertParamsValid(input2, begin, size) {
const inputRank = input2.shape.length;
assert(inputRank === begin.length, () => `Error in slice${inputRank}D: Length of begin ${begin} must match the rank of the array (${inputRank}).`);
assert(inputRank === size.length, () => `Error in slice${inputRank}D: Length of size ${size} must match the rank of the array (${inputRank}).`);
for (let i = 0; i < inputRank; ++i) {
assert(begin[i] + size[i] <= input2.shape[i], () => `Error in slice${inputRank}D: begin[${i}] + size[${i}] (${begin[i] + size[i]}) would overflow input.shape[${i}] (${input2.shape[i]})`);
}
}
function maskToAxes(mask) {
const axes = [];
let axis = 0;
while (mask > 0) {
if (mask & 1) {
axes.push(axis);
}
mask /= 2;
axis++;
}
return axes;
}
function computeOutShape(begin, end, strides) {
const size = [];
for (let axis = 0; axis < begin.length; axis++) {
size[axis] = Math.ceil((end[axis] - begin[axis]) / strides[axis]);
}
return size;
}
function stridesWithElidedDims(strides, ellipsisInsertionIndex, numElidedAxes, inputShape) {
const newStrides = [...strides];
for (let i = newStrides.length; i < inputShape.length; i++) {
newStrides.push(1);
}
for (let i = 0; i < numElidedAxes; i++) {
if (i === 0) {
newStrides[ellipsisInsertionIndex] = 1;
} else {
newStrides.splice(ellipsisInsertionIndex, 0, 1);
newStrides.pop();
}
}
return newStrides;
}
function unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, normalizedAxis) {
if (normalizedAxis <= ellipsisInsertionIndex) {
return normalizedAxis;
}
return normalizedAxis - (numElidedAxes - 1);
}
function getElidedAxes(numElidedAxes, ellipsisInsertionIndex) {
const elidedAxes = [];
for (let i = 0; i < numElidedAxes; i++) {
elidedAxes.push(ellipsisInsertionIndex + i);
}
return elidedAxes;
}
function getNormalizedAxes(inputShape, ellipsisAxes, numInterpolatedAxes, begin, end, strides, beginMask, endMask, ellipsisMask) {
const inputRank = inputShape.length;
let normalizedBegin = new Array(inputRank), normalizedEnd = new Array(inputRank), normalizedStrides = new Array(inputRank);
if (ellipsisAxes.length && numInterpolatedAxes > 0) {
const fullIndex = ellipsisAxes[0];
const numElidedAxes = numInterpolatedAxes + 1;
normalizedBegin = startIndicesWithElidedDims(beginMask, fullIndex, numElidedAxes, begin, inputShape);
normalizedEnd = stopIndicesWithElidedDims(endMask, fullIndex, numElidedAxes, end, inputShape);
normalizedStrides = stridesWithElidedDims(strides, fullIndex, numElidedAxes, inputShape);
} else {
for (let axis = 0; axis < inputRank; axis++) {
normalizedBegin[axis] = startForAxis(beginMask, begin, strides, inputShape, axis, ellipsisMask);
normalizedEnd[axis] = stopForAxis(endMask, end, strides, inputShape, axis, ellipsisMask);
normalizedStrides[axis] = stridesForAxis(strides, axis, ellipsisMask);
}
}
return {
begin: normalizedBegin,
end: normalizedEnd,
strides: normalizedStrides
};
}
function startIndicesWithElidedDims(beginMask, ellipsisInsertionIndex, numElidedAxes, originalBegin, inputShape) {
const newIndices = [...inputShape];
const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);
for (let axis = 0; axis < newIndices.length; axis++) {
if (elidedAxes.indexOf(axis) > -1) {
newIndices[axis] = 0;
} else {
const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);
let originalValue = originalBegin[originalAxis];
if (beginMask & 1 << originalAxis) {
originalValue = 0;
}
newIndices[axis] = originalValue;
}
}
return newIndices;
}
function stopIndicesWithElidedDims(endMask, ellipsisInsertionIndex, numElidedAxes, originalEnd, inputShape) {
const newIndices = [...inputShape];
const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);
for (let axis = 0; axis < newIndices.length; axis++) {
if (elidedAxes.indexOf(axis) > -1) {
newIndices[axis] = Number.MAX_SAFE_INTEGER;
} else {
const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);
let originalValue = originalEnd[originalAxis];
if (endMask & 1 << originalAxis) {
originalValue = Number.MAX_SAFE_INTEGER;
}
newIndices[axis] = originalValue;
}
}
for (let i = 0; i < newIndices.length; i++) {
const axisSize = inputShape[i];
if (newIndices[i] < 0) {
newIndices[i] += axisSize;
}
newIndices[i] = clamp(0, newIndices[i], inputShape[i]);
}
return newIndices;
}
function stridesForAxis(strides, axis, ellipsisMask) {
let stride = strides[axis];
if (ellipsisMask & 1 << axis || stride == null) {
stride = 1;
}
return stride;
}
function startForAxis(beginMask, startIndices, strides, inputShape, axis, ellipsisMask) {
let start = startIndices[axis];
const stride = strides[axis] || 1;
if (beginMask & 1 << axis || ellipsisMask & 1 << axis || start == null) {
if (stride > 0) {
start = Number.MIN_SAFE_INTEGER;
} else {
start = Number.MAX_SAFE_INTEGER;
}
}
const axisSize = inputShape[axis];
if (start < 0) {
start += axisSize;
}
start = clamp(0, start, axisSize - 1);
return start;
}
function stopForAxis(endMask, stopIndices, strides, inputShape, axis, ellipsisMask) {
let stop = stopIndices[axis];
const stride = strides[axis] || 1;
if (endMask & 1 << axis || ellipsisMask & 1 << axis || stop == null) {
if (stride > 0) {
stop = Number.MAX_SAFE_INTEGER;
} else {
stop = Number.MIN_SAFE_INTEGER;
}
}
const axisSize = inputShape[axis];
if (stop < 0) {
stop += axisSize;
}
if (stride > 0) {
stop = clamp(0, stop, axisSize);
} else {
stop = clamp(-1, stop, axisSize - 1);
}
return stop;
}
function isSliceContinous(shape, begin, size) {
let firstNonOneAxis = size.length;
for (let i = 0; i < size.length; i++) {
if (size[i] > 1) {
firstNonOneAxis = i;
break;
}
}
for (let i = firstNonOneAxis + 1; i < size.length; i++) {
if (begin[i] > 0 || size[i] !== shape[i]) {
return false;
}
}
return true;
}
function computeFlatOffset(begin, strides) {
let flatOffset = begin.length > 0 ? begin[begin.length - 1] : 1;
for (let i = 0; i < begin.length - 1; i++) {
flatOffset += begin[i] * strides[i];
}
return flatOffset;
}
function parseSliceParams(x, begin, size) {
let begin_;
const xRank = x.shape.length;
if (typeof begin === "number") {
begin_ = [begin, ...new Array(xRank - 1).fill(0)];
} else if (begin.length < xRank) {
begin_ = begin.concat(new Array(xRank - begin.length).fill(0));
} else {
begin_ = begin.slice();
}
begin_.forEach((d) => {
assert(d !== -1, () => "slice() does not support negative begin indexing.");
});
let size_;
if (size == null) {
size_ = new Array(xRank).fill(-1);
} else if (typeof size === "number") {
size_ = [size, ...new Array(xRank - 1).fill(-1)];
} else if (size.length < xRank) {
size_ = size.concat(new Array(xRank - size.length).fill(-1));
} else {
size_ = size;
}
size_ = size_.map((d, i) => {
if (d >= 0) {
return d;
} else {
assert(d === -1, () => `Negative size values should be exactly -1 but got ${d} for the slice() size at index ${i}.`);
return x.shape[i] - begin_[i];
}
});
return [begin_, size_];
}
// node_modules/@tensorflow/tfjs-core/dist/serialization.js
const serialization_exports = {};
__export(serialization_exports, {
Serializable: () => Serializable,
SerializationMap: () => SerializationMap,
registerClass: () => registerClass
});
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
class Serializable {
getClassName() {
return this.constructor.className;
}
static fromConfig(cls, config2) {
return new cls(config2);
}
}
class SerializationMap {
constructor() {
this.classNameMap = {};
}
static getMap() {
if (SerializationMap.instance == null) {
SerializationMap.instance = new SerializationMap();
}
return SerializationMap.instance;
}
static register(cls) {
SerializationMap.getMap().classNameMap[cls.className] = [cls, cls.fromConfig];
}
}
function registerClass(cls) {
assert(cls.className != null, () => `Class being registered does not have the static className property defined.`);
assert(typeof cls.className === "string", () => `className is required to be a string, but got type ` + typeof cls.className);
assert(cls.className.length > 0, () => `Class being registered has an empty-string as its className, which is disallowed.`);
SerializationMap.register(cls);
}
// node_modules/@tensorflow/tfjs-core/dist/test_util.js
const test_util_exports = {};
__export(test_util_exports, {
TEST_EPSILON_FLOAT16: () => TEST_EPSILON_FLOAT16,
expectArrayBuffersEqual: () => expectArrayBuffersEqual,
expectArraysClose: () => expectArraysClose,
expectArraysEqual: () => expectArraysEqual,
expectNumbersClose: () => expectNumbersClose,
expectPromiseToFail: () => expectPromiseToFail,
expectValuesInRange: () => expectValuesInRange,
testEpsilon: () => testEpsilon
});
/**
* @license
* Copyright 2017 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const TEST_EPSILON_FLOAT32 = 1e-3;
const TEST_EPSILON_FLOAT16 = 0.1;
function expectArraysClose(actual, expected, epsilon2) {
if (epsilon2 == null) {
epsilon2 = testEpsilon();
}
return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, epsilon2));
}
function testEpsilon() {
return ENGINE.backend.floatPrecision() === 32 ? TEST_EPSILON_FLOAT32 : TEST_EPSILON_FLOAT16;
}
function expectArraysPredicate(actual, expected, predicate) {
let checkClassType = true;
if (isTypedArray(actual) || isTypedArray(expected)) {
checkClassType = false;
}
if (isTypedArray(actual) && isTypedArray(expected)) {
checkClassType = true;
}
if (checkClassType) {
const aType = actual.constructor.name;
const bType = expected.constructor.name;
if (aType !== bType) {
throw new Error(`Arrays are of different type. Actual: ${aType}. Expected: ${bType}`);
}
}
if (Array.isArray(actual) && Array.isArray(expected)) {
const actualShape = inferShape(actual);
const expectedShape = inferShape(expected);
if (!arraysEqual(actualShape, expectedShape)) {
throw new Error(`Arrays have different shapes. Actual: [${actualShape}]. Expected: [${expectedShape}]`);
}
}
const actualFlat = isTypedArray(actual) ? actual : flatten(actual);
const expectedFlat = isTypedArray(expected) ? expected : flatten(expected);
if (actualFlat.length !== expectedFlat.length) {
throw new Error(`Arrays have different lengths actual: ${actualFlat.length} vs expected: ${expectedFlat.length}.
Actual: ${actualFlat}.
Expected: ${expectedFlat}.`);
}
for (let i = 0; i < expectedFlat.length; ++i) {
const a = actualFlat[i];
const e = expectedFlat[i];
if (!predicate(a, e)) {
throw new Error(`Arrays differ: actual[${i}] = ${a}, expected[${i}] = ${e}.
Actual: ${actualFlat}.
Expected: ${expectedFlat}.`);
}
}
}
function expectPromiseToFail(fn, done) {
fn().then(() => done.fail(), () => done());
}
function expectArraysEqual(actual, expected) {
const exp7 = typeof expected === "string" || typeof expected === "number" || typeof expected === "boolean" ? [expected] : expected;
if (isString(actual) || isString(actual[0]) || isString(expected) || isString(expected[0])) {
return expectArraysPredicate(actual, exp7, (a, b) => a == b);
}
return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, 0));
}
function expectNumbersClose(a, e, epsilon2) {
if (epsilon2 == null) {
epsilon2 = testEpsilon();
}
if (!areClose(a, e, epsilon2)) {
throw new Error(`Numbers differ: actual === ${a}, expected === ${e}`);
}
}
function areClose(a, e, epsilon2) {
if (!isFinite(a) && !isFinite(e)) {
return true;
}
if (isNaN(a) || isNaN(e) || Math.abs(a - e) > epsilon2) {
return false;
}
return true;
}
function expectValuesInRange(actual, low, high) {
for (let i = 0; i < actual.length; i++) {
if (actual[i] < low || actual[i] > high) {
throw new Error(`Value out of range:${actual[i]} low: ${low}, high: ${high}`);
}
}
}
function expectArrayBuffersEqual(actual, expected) {
expect(new Float32Array(actual)).toEqual(new Float32Array(expected));
}
// node_modules/@tensorflow/tfjs-core/dist/version.js
/** @license See the LICENSE file. */
const version = "2.7.0";
// node_modules/@tensorflow/tfjs-core/dist/globals.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function enableProdMode() {
env().set("PROD", true);
}
function enableDebugMode() {
env().set("DEBUG", true);
}
function disableDeprecationWarnings() {
env().set("DEPRECATION_WARNINGS_ENABLED", false);
console.warn(`TensorFlow.js deprecation warnings have been disabled.`);
}
function deprecationWarn(msg) {
if (env().getBool("DEPRECATION_WARNINGS_ENABLED")) {
console.warn(msg + " You can disable deprecation warnings with tf.disableDeprecationWarnings().");
}
}
setDeprecationWarningFn(deprecationWarn);
function disposeVariables() {
ENGINE.disposeVariables();
}
function engine14() {
return ENGINE;
}
function memory() {
return ENGINE.memory();
}
function profile(f) {
return ENGINE.profile(f);
}
function tidy(nameOrFn, fn) {
return ENGINE.tidy(nameOrFn, fn);
}
function dispose(container2) {
const tensors = getTensorsInContainer(container2);
tensors.forEach((tensor16) => tensor16.dispose());
}
function keep(result) {
return ENGINE.keep(result);
}
function time(f) {
return ENGINE.time(f);
}
function setBackend(backendName) {
return ENGINE.setBackend(backendName);
}
function ready() {
return ENGINE.ready();
}
function getBackend() {
return ENGINE.backendName;
}
function removeBackend(name) {
ENGINE.removeBackend(name);
}
function findBackend(name) {
return ENGINE.findBackend(name);
}
function findBackendFactory(name) {
return ENGINE.findBackendFactory(name);
}
function registerBackend(name, factory, priority = 1) {
return ENGINE.registerBackend(name, factory, priority);
}
function backend2() {
return ENGINE.backend;
}
function setPlatform(platformName, platform) {
env().setPlatform(platformName, platform);
}
// node_modules/@tensorflow/tfjs-core/dist/ops/add.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function add_(a, b) {
let $a = convertToTensor(a, "a", "add");
let $b = convertToTensor(b, "b", "add");
[$a, $b] = makeTypesMatch($a, $b);
const forward = (backend3, save) => {
const res = backend3.add($a, $b);
save([$a, $b]);
return res;
};
const inputs = {a: $a, b: $b};
return ENGINE.runKernelFunc(forward, inputs, null, Add);
}
const add2 = op({add_});
// node_modules/@tensorflow/tfjs-core/dist/ops/floorDiv.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function floorDiv_(a, b) {
let $a = convertToTensor(a, "a", "floorDiv");
let $b = convertToTensor(b, "b", "floorDiv");
[$a, $b] = makeTypesMatch($a, $b);
const forward = (backend3, save) => {
const res = backend3.floorDiv($a, $b);
save([$a, $b]);
return res;
};
const inputs = {a: $a, b: $b};
return ENGINE.runKernelFunc(forward, inputs, null, FloorDiv);
}
const floorDiv = op({floorDiv_});
// node_modules/@tensorflow/tfjs-core/dist/ops/div.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function div_(a, b) {
let $a = convertToTensor(a, "a", "div");
let $b = convertToTensor(b, "b", "div");
[$a, $b] = makeTypesMatch($a, $b);
if ($a.dtype === "int32" && $b.dtype === "int32") {
return floorDiv($a, $b);
}
const forward = (backend3, save) => {
const res = backend3.realDivide($a, $b);
save([$a, $b]);
return res;
};
const inputs = {a: $a, b: $b};
const attrs = {};
return ENGINE.runKernelFunc(forward, inputs, null, Div, attrs);
}
const div = op({div_});
// node_modules/@tensorflow/tfjs-core/dist/ops/mul.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function mul_(a, b) {
let $a = convertToTensor(a, "a", "mul");
let $b = convertToTensor(b, "b", "mul");
[$a, $b] = makeTypesMatch($a, $b);
const forward = (backend3, save) => {
const res = backend3.multiply($a, $b);
save([$a, $b]);
return res;
};
const inputs = {a: $a, b: $b};
return ENGINE.runKernelFunc(forward, inputs, null, Multiply);
}
const mul = op({mul_});
// node_modules/@tensorflow/tfjs-core/dist/ops/abs.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function abs_(x) {
const $x = convertToTensor(x, "x", "abs");
const inputs = {x: $x};
return ENGINE.runKernelFunc((backend3, save) => {
save([$x]);
if ($x.dtype === "complex64") {
return backend3.complexAbs($x);
}
return backend3.abs($x);
}, inputs, null, Abs);
}
const abs = op({abs_});
// node_modules/@tensorflow/tfjs-core/dist/ops/acos.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function acos_(x) {
const $x = convertToTensor(x, "x", "acos");
const inputs = {x: $x};
return ENGINE.runKernelFunc((backend3, save) => {
const res = backend3.acos($x);
save([$x]);
return res;
}, inputs, null, Acos);
}
const acos = op({acos_});
// node_modules/@tensorflow/tfjs-core/dist/ops/acosh.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function acosh_(x) {
const $x = convertToTensor(x, "x", "acosh");
const inputs = {x: $x};
return ENGINE.runKernelFunc((backend3, save) => {
const res = backend3.acosh($x);
save([$x]);
return res;
}, inputs, null, Acosh);
}
const acosh = op({acosh_});
// node_modules/@tensorflow/tfjs-core/dist/ops/add_n.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function addN_(tensors) {
assert(Array.isArray(tensors), () => "The argument passed to tf.addN() must be a list of tensors");
assert(tensors.length >= 1, () => `Must pass at least one tensor to tf.addN(), but got ${tensors.length}`);
const $tensors = tensors.map((t, i) => convertToTensor(t, `tensors${i}`, "addN"));
const firstTensor = $tensors[0];
$tensors.forEach((t) => {
if (t.dtype !== firstTensor.dtype) {
throw new Error("All tensors passed to tf.addN() must have the same dtype");
}
});
$tensors.forEach((t) => {
if (!arraysEqual(t.shape, firstTensor.shape)) {
throw new Error("All tensors passed to tf.addN() must have the same shape");
}
});
const forward = (backend3, save) => {
const res = backend3.addN($tensors);
save($tensors);
return res;
};
const inputs = $tensors;
return ENGINE.runKernelFunc(forward, inputs, null, AddN);
}
const addN = op({addN_});
// node_modules/@tensorflow/tfjs-core/dist/ops/axis_util.js
/**
* @license
* Copyright 2017 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function axesAreInnerMostDims(axes, rank) {
for (let i = 0; i < axes.length; ++i) {
if (axes[axes.length - i - 1] !== rank - 1 - i) {
return false;
}
}
return true;
}
function combineLocations(outputLoc, reduceLoc, axes) {
const rank = outputLoc.length + reduceLoc.length;
const loc = [];
let outIdx = 0;
let reduceIdx = 0;
for (let dim = 0; dim < rank; dim++) {
if (axes.indexOf(dim) === -1) {
loc.push(outputLoc[outIdx++]);
} else {
loc.push(reduceLoc[reduceIdx++]);
}
}
return loc;
}
function computeOutAndReduceShapes(aShape, axes) {
const outShape = [];
const rank = aShape.length;
for (let dim = 0; dim < rank; dim++) {
if (axes.indexOf(dim) === -1) {
outShape.push(aShape[dim]);
}
}
const reduceShape = axes.map((dim) => aShape[dim]);
return [outShape, reduceShape];
}
function expandShapeToKeepDim(shape, axes) {
const reduceSubShape = axes.map((x) => 1);
return combineLocations(shape, reduceSubShape, axes);
}
function assertAxesAreInnerMostDims(msg, axes, rank) {
assert(axesAreInnerMostDims(axes, rank), () => `${msg} supports only inner-most axes for now. Got axes ${axes} and rank-${rank} input.`);
}
function getAxesPermutation(axes, rank) {
if (axesAreInnerMostDims(axes, rank)) {
return null;
}
const result = [];
for (let i = 0; i < rank; ++i) {
if (axes.indexOf(i) === -1) {
result.push(i);
}
}
axes.forEach((axis) => result.push(axis));
return result;
}
function getUndoAxesPermutation(axes) {
return axes.map((axis, i) => [i, axis]).sort((a, b) => a[1] - b[1]).map((x) => x[0]);
}
function getInnerMostAxes(numAxes, rank) {
const res = [];
for (let i = rank - numAxes; i < rank; ++i) {
res.push(i);
}
return res;
}
// node_modules/@tensorflow/tfjs-core/dist/ops/all.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function all_(x, axis = null, keepDims = false) {
let $x = convertToTensor(x, "x", "all", "bool");
const forward = (backend3) => {
const origAxes = parseAxisParam(axis, $x.shape);
let axes = origAxes;
const permutedAxes = getAxesPermutation(axes, $x.rank);
if (permutedAxes != null) {
$x = transpose($x, permutedAxes);
axes = getInnerMostAxes(axes.length, $x.rank);
}
const res = backend3.all($x, axes);
if (keepDims) {
const newShape = expandShapeToKeepDim(res.shape, origAxes);
return reshape(res, newShape);
}
return res;
};
const inputs = {x: $x};
const attrs = {axis, keepDims};
return ENGINE.runKernelFunc(forward, inputs, null, All, attrs);
}
const all = op({all_});
// node_modules/@tensorflow/tfjs-core/dist/ops/any.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function any_(x, axis = null, keepDims = false) {
let $x = convertToTensor(x, "x", "any", "bool");
const forward = (backend3) => {
const origAxes = parseAxisParam(axis, $x.shape);
let axes = origAxes;
const permutedAxes = getAxesPermutation(axes, $x.rank);
if (permutedAxes != null) {
$x = transpose($x, permutedAxes);
axes = getInnerMostAxes(axes.length, $x.rank);
}
const res = backend3.any($x, axes);
if (keepDims) {
const newShape = expandShapeToKeepDim(res.shape, origAxes);
return reshape(res, newShape);
}
return res;
};
const inputs = {x: $x};
const attrs = {axis, keepDims};
return ENGINE.runKernelFunc(forward, inputs, null, Any, attrs);
}
const any = op({any_});
// node_modules/@tensorflow/tfjs-core/dist/ops/arg_max.js
/**
* @license
* Copyright 2020 Google Inc. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function argMax_(x, axis = 0) {
let $x = convertToTensor(x, "x", "argMax");
const forward = (backend3, save) => {
save([$x]);
let axes = parseAxisParam(axis, $x.shape);
const permutedAxes = getAxesPermutation(axes, $x.rank);
if (permutedAxes != null) {
$x = transpose($x, permutedAxes);
axes = getInnerMostAxes(axes.length, $x.rank);
}
return backend3.argMax($x, axes[0]);
};
const inputs = {x: $x};
const attrs = {axis};
return ENGINE.runKernelFunc(forward, inputs, null, ArgMax, attrs);
}
const argMax = op({argMax_});
// node_modules/@tensorflow/tfjs-core/dist/ops/arg_min.js
/**
* @license
* Copyright 2020 Google Inc. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function argMin_(x, axis = 0) {
let $x = convertToTensor(x, "x", "argMin");
const forward = (backend3, save) => {
save([$x]);
if (axis == null) {
axis = 0;
}
let axes = parseAxisParam(axis, $x.shape);
const permutedAxes = getAxesPermutation(axes, $x.rank);
if (permutedAxes != null) {
$x = transpose($x, permutedAxes);
axes = getInnerMostAxes(axes.length, $x.rank);
}
return backend3.argMin($x, axes[0]);
};
const inputs = {x: $x};
const attrs = {axis};
return ENGINE.runKernelFunc(forward, inputs, null, ArgMin, attrs);
}
const argMin = op({argMin_});
// node_modules/@tensorflow/tfjs-core/dist/ops/asin.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function asin_(x) {
const $x = convertToTensor(x, "x", "asin");
const inputs = {x: $x};
return ENGINE.runKernelFunc((backend3, save) => {
const res = backend3.asin($x);
save([$x]);
return res;
}, inputs, null, Asin);
}
const asin = op({asin_});
// node_modules/@tensorflow/tfjs-core/dist/ops/asinh.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function asinh_(x) {
const $x = convertToTensor(x, "x", "asinh");
const inputs = {x: $x};
return ENGINE.runKernelFunc((backend3, save) => {
const res = backend3.asinh($x);
save([$x]);
return res;
}, inputs, null, Asinh);
}
const asinh = op({asinh_});
// node_modules/@tensorflow/tfjs-core/dist/ops/atan.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function atan_(x) {
const $x = convertToTensor(x, "x", "atan");
const inputs = {x: $x};
return ENGINE.runKernelFunc((backend3, save) => {
const res = backend3.atan($x);
save([$x]);
return res;
}, inputs, null, Atan);
}
const atan = op({atan_});
// node_modules/@tensorflow/tfjs-core/dist/ops/atan2.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function atan2_(a, b) {
let $a = convertToTensor(a, "a", "atan2");
let $b = convertToTensor(b, "b", "atan2");
[$a, $b] = makeTypesMatch($a, $b);
const forward = (backend3, save) => {
const res = backend3.atan2($a, $b);
save([$a, $b]);
return res;
};
const inputs = {a: $a, b: $b};
return ENGINE.runKernelFunc(forward, inputs, null, Atan2);
}
const atan2 = op({atan2_});
// node_modules/@tensorflow/tfjs-core/dist/ops/atanh.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function atanh_(x) {
const $x = convertToTensor(x, "x", "atanh");
const inputs = {x: $x};
return ENGINE.runKernelFunc((backend3, save) => {
const res = backend3.atanh($x);
save([$x]);
return res;
}, inputs, null, Atanh);
}
const atanh = op({atanh_});
// node_modules/@tensorflow/tfjs-core/dist/ops/conv_util.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function computeDilation2DInfo(inputShape, filterShape, strides, pad8, dataFormat = "NHWC", dilations) {
const inputChannels = inputShape[3];
const $filterShape = [...filterShape, inputChannels];
const $dataFormat = convertConv2DDataFormat(dataFormat);
return computeConv2DInfo(inputShape, $filterShape, strides, dilations, pad8, null, null, $dataFormat);
}
function computePool2DInfo(inShape, filterSize, strides, dilations, pad8, roundingMode, dataFormat = "channelsLast") {
const [filterHeight, filterWidth] = parseTupleParam(filterSize);
let filterShape;
if (dataFormat === "channelsLast") {
filterShape = [filterHeight, filterWidth, inShape[3], inShape[3]];
} else if (dataFormat === "channelsFirst") {
filterShape = [filterHeight, filterWidth, inShape[1], inShape[1]];
} else {
throw new Error(`Unknown dataFormat ${dataFormat}`);
}
return computeConv2DInfo(inShape, filterShape, strides, dilations, pad8, roundingMode, false, dataFormat);
}
function computePool3DInfo(inShape, filterSize, strides, dilations, pad8, roundingMode, dataFormat = "NDHWC") {
const [filterDepth, filterHeight, filterWidth] = parse3TupleParam(filterSize);
let filterShape;
let $dataFormat;
if (dataFormat === "NDHWC") {
$dataFormat = "channelsLast";
filterShape = [filterDepth, filterHeight, filterWidth, inShape[4], inShape[4]];
} else if (dataFormat === "NCDHW") {
$dataFormat = "channelsFirst";
filterShape = [filterDepth, filterHeight, filterWidth, inShape[1], inShape[1]];
} else {
throw new Error(`Unknown dataFormat ${dataFormat}`);
}
return computeConv3DInfo(inShape, filterShape, strides, dilations, pad8, false, $dataFormat, roundingMode);
}
function computeConv2DInfo(inShape, filterShape, strides, dilations, pad8, roundingMode, depthwise = false, dataFormat = "channelsLast") {
let [batchSize, inHeight, inWidth, inChannels] = [-1, -1, -1, -1];
if (dataFormat === "channelsLast") {
[batchSize, inHeight, inWidth, inChannels] = inShape;
} else if (dataFormat === "channelsFirst") {
[batchSize, inChannels, inHeight, inWidth] = inShape;
} else {
throw new Error(`Unknown dataFormat ${dataFormat}`);
}
const [filterHeight, filterWidth, , filterChannels] = filterShape;
const [strideHeight, strideWidth] = parseTupleParam(strides);
const [dilationHeight, dilationWidth] = parseTupleParam(dilations);
const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);
const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);
const {padInfo, outHeight, outWidth} = getPadAndOutInfo(pad8, inHeight, inWidth, strideHeight, strideWidth, effectiveFilterHeight, effectiveFilterWidth, roundingMode, dataFormat);
const outChannels = depthwise ? filterChannels * inChannels : filterChannels;
let outShape;
if (dataFormat === "channelsFirst") {
outShape = [batchSize, outChannels, outHeight, outWidth];
} else if (dataFormat === "channelsLast") {
outShape = [batchSize, outHeight, outWidth, outChannels];
}
return {
batchSize,
dataFormat,
inHeight,
inWidth,
inChannels,
outHeight,
outWidth,
outChannels,
padInfo,
strideHeight,
strideWidth,
filterHeight,
filterWidth,
effectiveFilterHeight,
effectiveFilterWidth,
dilationHeight,
dilationWidth,
inShape,
outShape,
filterShape
};
}
function computeConv3DInfo(inShape, filterShape, strides, dilations, pad8, depthwise = false, dataFormat = "channelsLast", roundingMode) {
let [batchSize, inDepth, inHeight, inWidth, inChannels] = [-1, -1, -1, -1, -1];
if (dataFormat === "channelsLast") {
[batchSize, inDepth, inHeight, inWidth, inChannels] = inShape;
} else if (dataFormat === "channelsFirst") {
[batchSize, inChannels, inDepth, inHeight, inWidth] = inShape;
} else {
throw new Error(`Unknown dataFormat ${dataFormat}`);
}
const [filterDepth, filterHeight, filterWidth, , filterChannels] = filterShape;
const [strideDepth, strideHeight, strideWidth] = parse3TupleParam(strides);
const [dilationDepth, dilationHeight, dilationWidth] = parse3TupleParam(dilations);
const effectiveFilterDepth = getEffectiveFilterSize(filterDepth, dilationDepth);
const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);
const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);
const {padInfo, outDepth, outHeight, outWidth} = get3DPadAndOutInfo(pad8, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, effectiveFilterDepth, effectiveFilterHeight, effectiveFilterWidth, roundingMode);
const outChannels = depthwise ? filterChannels * inChannels : filterChannels;
let outShape;
if (dataFormat === "channelsFirst") {
outShape = [batchSize, outChannels, outDepth, outHeight, outWidth];
} else if (dataFormat === "channelsLast") {
outShape = [batchSize, outDepth, outHeight, outWidth, outChannels];
}
return {
batchSize,
dataFormat,
inDepth,
inHeight,
inWidth,
inChannels,
outDepth,
outHeight,
outWidth,
outChannels,
padInfo,
strideDepth,
strideHeight,
strideWidth,
filterDepth,
filterHeight,
filterWidth,
effectiveFilterDepth,
effectiveFilterHeight,
effectiveFilterWidth,
dilationDepth,
dilationHeight,
dilationWidth,
inShape,
outShape,
filterShape
};
}
function computeOutputShape2D(inShape, fieldSize, stride, zeroPad, roundingMode) {
if (zeroPad == null) {
zeroPad = computeDefaultPad(inShape, fieldSize, stride);
}
const inputRows = inShape[0];
const inputCols = inShape[1];
const outputRows = conditionalRound((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
assert(isInt(outputRows), () => `The output # of rows (${outputRows}) must be an integer. Change the stride and/or zero pad parameters`);
const outputCols = conditionalRound((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
assert(isInt(outputCols), () => `The output # of columns (${outputCols}) must be an integer. Change the stride and/or zero pad parameters`);
return [outputRows, outputCols];
}
function computeOutputShape4D(inShape, fieldSize, outChannels, stride, zeroPad, roundingMode) {
if (zeroPad == null) {
zeroPad = computeDefaultPad(inShape, fieldSize, stride);
}
const inputDepth = inShape[0];
const inputRows = inShape[1];
const inputCols = inShape[2];
const outputDepths = conditionalRound((inputDepth - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
assert(isInt(outputDepths), () => `The output # of depths (${outputDepths}) must be an integer. Change the stride and/or zero pad parameters`);
const outputRows = conditionalRound((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
assert(isInt(outputRows), () => `The output # of rows (${outputRows}) must be an integer. Change the stride and/or zero pad parameters`);
const outputCols = conditionalRound((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
assert(isInt(outputCols), () => `The output # of columns (${outputCols}) must be an integer. Change the stride and/or zero pad parameters`);
return [outputDepths, outputRows, outputCols, outChannels];
}
function computeDefaultPad(inputShape, fieldSize, stride, dilation = 1) {
const effectiveFieldSize = getEffectiveFilterSize(fieldSize, dilation);
return Math.floor((inputShape[0] * (stride - 1) - stride + effectiveFieldSize) / 2);
}
function parseTupleParam(param) {
if (typeof param === "number") {
return [param, param, param];
}
if (param.length === 2) {
return [param[0], param[1], 1];
}
return param;
}
function parse3TupleParam(param) {
return typeof param === "number" ? [param, param, param] : param;
}
function getEffectiveFilterSize(filterSize, dilation) {
if (dilation <= 1) {
return filterSize;
}
return filterSize + (filterSize - 1) * (dilation - 1);
}
function getPadAndOutInfo(pad8, inHeight, inWidth, strideHeight, strideWidth, filterHeight, filterWidth, roundingMode, dataFormat) {
let padInfo;
let outHeight;
let outWidth;
if (typeof pad8 === "number") {
const padType = pad8 === 0 ? "VALID" : "NUMBER";
padInfo = {top: pad8, bottom: pad8, left: pad8, right: pad8, type: padType};
const outShape = computeOutputShape2D([inHeight, inWidth], filterHeight, strideHeight, pad8, roundingMode);
outHeight = outShape[0];
outWidth = outShape[1];
} else if (pad8 === "same") {
outHeight = Math.ceil(inHeight / strideHeight);
outWidth = Math.ceil(inWidth / strideWidth);
const padAlongHeight = Math.max(0, (outHeight - 1) * strideHeight + filterHeight - inHeight);
const padAlongWidth = Math.max(0, (outWidth - 1) * strideWidth + filterWidth - inWidth);
const top = Math.floor(padAlongHeight / 2);
const bottom = padAlongHeight - top;
const left = Math.floor(padAlongWidth / 2);
const right = padAlongWidth - left;
padInfo = {top, bottom, left, right, type: "SAME"};
} else if (pad8 === "valid") {
padInfo = {top: 0, bottom: 0, left: 0, right: 0, type: "VALID"};
outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);
outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);
} else if (typeof pad8 === "object") {
const top = dataFormat === "channelsLast" ? pad8[1][0] : pad8[2][0];
const bottom = dataFormat === "channelsLast" ? pad8[1][1] : pad8[2][1];
const left = dataFormat === "channelsLast" ? pad8[2][0] : pad8[3][0];
const right = dataFormat === "channelsLast" ? pad8[2][1] : pad8[3][1];
const padType = top === 0 && bottom === 0 && left === 0 && right === 0 ? "VALID" : "EXPLICIT";
padInfo = {top, bottom, left, right, type: padType};
outHeight = conditionalRound((inHeight - filterHeight + top + bottom) / strideHeight + 1, roundingMode);
outWidth = conditionalRound((inWidth - filterWidth + left + right) / strideWidth + 1, roundingMode);
} else {
throw Error(`Unknown padding parameter: ${pad8}`);
}
return {padInfo, outHeight, outWidth};
}
function get3DPadAndOutInfo(pad8, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, filterDepth, filterHeight, filterWidth, roundingMode) {
let padInfo;
let outDepth;
let outHeight;
let outWidth;
if (typeof pad8 === "number") {
const padType = pad8 === 0 ? "VALID" : "NUMBER";
padInfo = {
top: pad8,
bottom: pad8,
left: pad8,
right: pad8,
front: pad8,
back: pad8,
type: padType
};
const outShape = computeOutputShape4D([inDepth, inHeight, inWidth, 1], filterDepth, 1, strideDepth, pad8, roundingMode);
outDepth = outShape[0];
outHeight = outShape[1];
outWidth = outShape[2];
} else if (pad8 === "same") {
outDepth = Math.ceil(inDepth / strideDepth);
outHeight = Math.ceil(inHeight / strideHeight);
outWidth = Math.ceil(inWidth / strideWidth);
const padAlongDepth = (outDepth - 1) * strideDepth + filterDepth - inDepth;
const padAlongHeight = (outHeight - 1) * strideHeight + filterHeight - inHeight;
const padAlongWidth = (outWidth - 1) * strideWidth + filterWidth - inWidth;
const front = Math.floor(padAlongDepth / 2);
const back = padAlongDepth - front;
const top = Math.floor(padAlongHeight / 2);
const bottom = padAlongHeight - top;
const left = Math.floor(padAlongWidth / 2);
const right = padAlongWidth - left;
padInfo = {top, bottom, left, right, front, back, type: "SAME"};
} else if (pad8 === "valid") {
padInfo = {
top: 0,
bottom: 0,
left: 0,
right: 0,
front: 0,
back: 0,
type: "VALID"
};
outDepth = Math.ceil((inDepth - filterDepth + 1) / strideDepth);
outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);
outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);
} else {
throw Error(`Unknown padding parameter: ${pad8}`);
}
return {padInfo, outDepth, outHeight, outWidth};
}
function conditionalRound(value, roundingMode) {
if (!roundingMode) {
return value;
}
switch (roundingMode) {
case "round":
return Math.round(value);
case "ceil":
return Math.ceil(value);
case "floor":
return Math.floor(value);
default:
throw new Error(`Unknown roundingMode ${roundingMode}`);
}
}
function tupleValuesAreOne(param) {
const [dimA, dimB, dimC] = parseTupleParam(param);
return dimA === 1 && dimB === 1 && dimC === 1;
}
function eitherStridesOrDilationsAreOne(strides, dilations) {
return tupleValuesAreOne(strides) || tupleValuesAreOne(dilations);
}
function convertConv2DDataFormat(dataFormat) {
if (dataFormat === "NHWC") {
return "channelsLast";
} else if (dataFormat === "NCHW") {
return "channelsFirst";
} else {
throw new Error(`Unknown dataFormat ${dataFormat}`);
}
}
// node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function avgPool_(x, filterSize, strides, pad8, dimRoundingMode) {
const $x = convertToTensor(x, "x", "avgPool", "float32");
const dilations = 1;
assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
let x4D = $x;
let reshapedTo4D = false;
if ($x.rank === 3) {
reshapedTo4D = true;
x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
}
assert(x4D.rank === 4, () => `Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`);
if (dimRoundingMode != null) {
assert(isInt(pad8), () => `Error in avgPool: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
}
const forward = (backend3, save) => {
const convInfo = computePool2DInfo(x4D.shape, filterSize, strides, 1, pad8, dimRoundingMode);
save([x4D]);
if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && arraysEqual(convInfo.inShape, convInfo.outShape)) {
return x4D.clone();
}
return backend3.avgPool(x4D, convInfo);
};
const inputs = {x: x4D};
const attrs = {filterSize, strides, pad: pad8, dimRoundingMode};
let res = ENGINE.runKernelFunc(forward, inputs, null, AvgPool, attrs);
res = cast(res, $x.dtype);
if (reshapedTo4D) {
return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
}
return res;
}
const avgPool = op({avgPool_});
// node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool_3d.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function avgPool3d_(x, filterSize, strides, pad8, dimRoundingMode, dataFormat = "NDHWC", dilations) {
if (dilations == null) {
dilations = [1, 1, 1];
} else {
deprecationWarn("dilations is deprecated, this field will be gone in v3.0.0.");
}
const $x = convertToTensor(x, "x", "avgPool3d", "float32");
let x5D = $x;
let reshapedTo5D = false;
if ($x.rank === 4) {
reshapedTo5D = true;
x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);
}
assert(x5D.rank === 5, () => `Error in avgPool3d: x must be rank 5 but got rank ${x5D.rank}.`);
assert(dataFormat === "NDHWC", () => `Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`);
assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in avgPool3d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
if (dimRoundingMode != null) {
assert(isInt(pad8), () => `Error in avgPool3d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
}
const forward = (backend3, save) => {
if (dilations == null) {
dilations = [1, 1, 1];
}
const convInfo = computePool3DInfo(x5D.shape, filterSize, strides, dilations, pad8, dimRoundingMode, dataFormat);
save([x5D]);
return backend3.avgPool3d(x5D, convInfo);
};
const inputs = {x: x5D};
const attrs = {filterSize, strides, pad: pad8, dimRoundingMode, dataFormat, dilations};
let res = ENGINE.runKernelFunc(forward, inputs, null, AvgPool3D, attrs);
res = cast(res, x5D.dtype);
if (reshapedTo5D) {
return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
}
return res;
}
const avgPool3d = op({avgPool3d_});
// node_modules/@tensorflow/tfjs-core/dist/ops/concat_util.js
/**
* @license
* Copyright 2017 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function assertParamsConsistent(shapes, axis) {
const rank = shapes[0].length;
shapes.forEach((shape, i) => {
assert(shape.length === rank, () => `Error in concat${rank}D: rank of tensors[${i}] must be the same as the rank of the rest (${rank})`);
});
assert(axis >= 0 && axis < rank, () => `Error in concat${rank}D: axis must be between 0 and ${rank - 1}.`);
const firstShape = shapes[0];
shapes.forEach((shape, i) => {
for (let r = 0; r < rank; r++) {
assert(r === axis || shape[r] === firstShape[r], () => `Error in concat${rank}D: Shape of tensors[${i}] (${shape}) does not match the shape of the rest (${firstShape}) along the non-concatenated axis ${i}.`);
}
});
}
function computeOutShape2(shapes, axis) {
const outputShape = shapes[0].slice();
for (let i = 1; i < shapes.length; i++) {
outputShape[axis] += shapes[i][axis];
}
return outputShape;
}
// node_modules/@tensorflow/tfjs-core/dist/ops/concat.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function concat_(tensors, axis = 0) {
assert(tensors.length >= 1, () => "Pass at least one tensor to concat");
let $tensors = convertToTensorArray(tensors, "tensors", "concat");
if ($tensors[0].dtype === "complex64") {
$tensors.forEach((tensor16) => {
if (tensor16.dtype !== "complex64") {
throw new Error(`Cannot concatenate complex64 tensors with a tensor
with dtype ${tensor16.dtype}. `);
}
});
}
const forward = (backend3, save) => {
const $axis = parseAxisParam(axis, $tensors[0].shape)[0];
const outShape = computeOutShape2($tensors.map((t) => t.shape), $axis);
if (sizeFromShape(outShape) === 0) {
return tensor4([], outShape);
}
$tensors = $tensors.filter((t) => t.size > 0);
if ($tensors.length === 1) {
return $tensors[0];
}
const shapes = $tensors.map((t) => t.shape);
assertParamsConsistent(shapes, $axis);
const res = backend3.concat($tensors, $axis);
save($tensors);
return res;
};
const inputs = $tensors;
const attr = {axis};
return ENGINE.runKernelFunc(forward, inputs, null, Concat, attr);
}
const concat = op({concat_});
// node_modules/@tensorflow/tfjs-core/dist/ops/sigmoid.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function sigmoid_(x) {
const $x = convertToTensor(x, "x", "sigmoid");
const inputs = {x: $x};
return ENGINE.runKernelFunc((backend3, save) => {
const res = backend3.sigmoid($x);
save([res]);
return res;
}, inputs, null, Sigmoid);
}
const sigmoid = op({sigmoid_});
// node_modules/@tensorflow/tfjs-core/dist/ops/slice.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function slice_(x, begin, size) {
const $x = convertToTensor(x, "x", "slice");
if ($x.rank === 0) {
throw new Error("Slicing scalar is not possible");
}
const forward = (backend3, save) => {
const [begin_, size_] = parseSliceParams($x, begin, size);
assertParamsValid($x, begin_, size_);
save([$x]);
return backend3.slice($x, begin_, size_);
};
const inputs = {x: $x};
const attrs = {begin, size};
return ENGINE.runKernelFunc(forward, inputs, null, Slice, attrs);
}
const slice = op({slice_});
// node_modules/@tensorflow/tfjs-core/dist/ops/tanh.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function tanh_(x) {
const $x = convertToTensor(x, "x", "tanh");
const inputs = {x: $x};
return ENGINE.runKernelFunc((backend3, save) => {
const y = backend3.tanh($x);
save([y]);
return y;
}, inputs, null, Tanh);
}
const tanh2 = op({tanh_});
// node_modules/@tensorflow/tfjs-core/dist/ops/basic_lstm_cell.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function basicLSTMCell_(forgetBias, lstmKernel, lstmBias, data2, c, h) {
const $forgetBias = convertToTensor(forgetBias, "forgetBias", "basicLSTMCell");
const $lstmKernel = convertToTensor(lstmKernel, "lstmKernel", "basicLSTMCell");
const $lstmBias = convertToTensor(lstmBias, "lstmBias", "basicLSTMCell");
const $data = convertToTensor(data2, "data", "basicLSTMCell");
const $c = convertToTensor(c, "c", "basicLSTMCell");
const $h = convertToTensor(h, "h", "basicLSTMCell");
const combined = concat([$data, $h], 1);
const weighted = matMul(combined, $lstmKernel);
const res = add2(weighted, $lstmBias);
const batchSize = res.shape[0];
const sliceCols = res.shape[1] / 4;
const sliceSize = [batchSize, sliceCols];
const i = slice(res, [0, 0], sliceSize);
const j = slice(res, [0, sliceCols], sliceSize);
const f = slice(res, [0, sliceCols * 2], sliceSize);
const o = slice(res, [0, sliceCols * 3], sliceSize);
const newC = add2(mul(sigmoid(i), tanh2(j)), mul($c, sigmoid(add2($forgetBias, f))));
const newH = mul(tanh2(newC), sigmoid(o));
return [newC, newH];
}
const basicLSTMCell = op({basicLSTMCell_});
// node_modules/@tensorflow/tfjs-core/dist/ops/batch_to_space_nd.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function batchToSpaceND_(x, blockShape, crops) {
const $x = convertToTensor(x, "x", "batchToSpaceND");
const prod3 = blockShape.reduce((a, b) => a * b);
assert($x.rank >= 1 + blockShape.length, () => `input rank is ${$x.rank} but should be > than blockShape.length ${blockShape.length}`);
assert(crops.length === blockShape.length, () => `crops.length is ${crops.length} but should be equal to blockShape.length ${blockShape.length}`);
assert($x.shape[0] % prod3 === 0, () => `input tensor batch is ${$x.shape[0]} but is not divisible by the product of the elements of blockShape ${blockShape.join(" * ")} === ${prod3}`);
const forward = (backend3) => {
return backend3.batchToSpaceND($x, blockShape, crops);
};
const inputs = {x: $x};
const attrs = {blockShape, crops};
return ENGINE.runKernelFunc(forward, inputs, null, BatchToSpaceND, attrs);
}
const batchToSpaceND = op({batchToSpaceND_});
// node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm_util.js
function xAs4D(x) {
let x4D;
if (x.rank === 0 || x.rank === 1) {
x4D = reshape(x, [1, 1, 1, x.size]);
} else if (x.rank === 2) {
x4D = reshape(x, [1, 1, x.shape[0], x.shape[1]]);
} else if (x.rank === 3) {
x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);
} else {
x4D = x;
}
return x4D;
}
// node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function batchNorm_(x, mean5, variance, offset, scale, varianceEpsilon) {
if (varianceEpsilon == null) {
varianceEpsilon = 1e-3;
}
const $x = convertToTensor(x, "x", "batchNorm");
const $mean = convertToTensor(mean5, "mean", "batchNorm");
const $variance = convertToTensor(variance, "variance", "batchNorm");
let $scale;
if (scale != null) {
$scale = convertToTensor(scale, "scale", "batchNorm");
}
let $offset;
if (offset != null) {
$offset = convertToTensor(offset, "offset", "batchNorm");
}
assert($mean.rank === $variance.rank, () => "Batch normalization gradient requires mean and variance to have equal ranks.");
assert($offset == null || $mean.rank === $offset.rank, () => "Batch normalization gradient requires mean and offset to have equal ranks.");
assert($scale == null || $mean.rank === $scale.rank, () => "Batch normalization gradient requires mean and scale to have equal ranks.");
const x4D = xAs4D($x);
const forward = (backend3, save) => {
save([x4D, $mean, $variance, $scale]);
return backend3.batchNorm(x4D, as1DOr4D($mean), as1DOr4D($variance), as1DOr4D($offset), as1DOr4D($scale), varianceEpsilon);
};
const inputs = {
x: x4D,
scale: $scale,
offset: $offset,
mean: $mean,
variance: $variance
};
const attrs = {varianceEpsilon};
const res = ENGINE.runKernelFunc(forward, inputs, null, FusedBatchNorm, attrs);
return reshape(res, $x.shape);
}
function as1DOr4D(x) {
if (x == null) {
return null;
}
if (x.rank === 0) {
return reshape(x, [x.size]);
} else if (x.rank === 1) {
return x;
} else if (x.rank === 2) {
return reshape(x, [1, 1, x.shape[0], x.shape[1]]);
} else if (x.rank === 3) {
return reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);
}
return x;
}
const batchNorm = op({batchNorm_});
// node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm2d.js
function batchNorm2d_(x, mean5, variance, offset, scale, varianceEpsilon) {
const $x = convertToTensor(x, "x", "batchNorm");
const $mean = convertToTensor(mean5, "mean", "batchNorm");
const $variance = convertToTensor(variance, "variance", "batchNorm");
let $scale;
if (scale != null) {
$scale = convertToTensor(scale, "scale", "batchNorm");
}
let $offset;
if (offset != null) {
$offset = convertToTensor(offset, "offset", "batchNorm");
}
assert($x.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ${$x.rank}.`);
assert($mean.rank === 2 || $mean.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${$mean.rank}.`);
assert($variance.rank === 2 || $variance.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${$variance.rank}.`);
if ($scale != null) {
assert($scale.rank === 2 || $scale.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${$scale.rank}.`);
}
if ($offset != null) {
assert($offset.rank === 2 || $offset.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${$offset.rank}.`);
}
return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);
}
const batchNorm2d = op({batchNorm2d_});
// node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm3d.js
function batchNorm3d_(x, mean5, variance, offset, scale, varianceEpsilon) {
const $x = convertToTensor(x, "x", "batchNorm");
const $mean = convertToTensor(mean5, "mean", "batchNorm");
const $variance = convertToTensor(variance, "variance", "batchNorm");
let $scale;
if (scale != null) {
$scale = convertToTensor(scale, "scale", "batchNorm");
}
let $offset;
if (offset != null) {
$offset = convertToTensor(offset, "offset", "batchNorm");
}
assert($x.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ${$x.rank}.`);
assert($mean.rank === 3 || $mean.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${$mean.rank}.`);
assert($variance.rank === 3 || $variance.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${$variance.rank}.`);
if ($scale != null) {
assert($scale.rank === 3 || $scale.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${$scale.rank}.`);
}
if ($offset != null) {
assert($offset.rank === 3 || $offset.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${$offset.rank}.`);
}
return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);
}
const batchNorm3d = op({batchNorm3d_});
// node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm4d.js
function batchNorm4d_(x, mean5, variance, offset, scale, varianceEpsilon) {
const $x = convertToTensor(x, "x", "batchNorm");
const $mean = convertToTensor(mean5, "mean", "batchNorm");
const $variance = convertToTensor(variance, "variance", "batchNorm");
let $scale;
if (scale != null) {
$scale = convertToTensor(scale, "scale", "batchNorm");
}
let $offset;
if (offset != null) {
$offset = convertToTensor(offset, "offset", "batchNorm");
}
assert($x.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ${$x.rank}.`);
assert($mean.rank === 4 || $mean.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${$mean.rank}.`);
assert($variance.rank === 4 || $variance.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${$variance.rank}.`);
if ($scale != null) {
assert($scale.rank === 4 || $scale.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${$scale.rank}.`);
}
if ($offset != null) {
assert($offset.rank === 4 || $offset.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${$offset.rank}.`);
}
return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);
}
const batchNorm4d = op({batchNorm4d_});
// node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_to.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function broadcastTo_(x, shape) {
let input2 = convertToTensor(x, "broadcastTo", "x");
const xShape = input2.shape;
if (shape.some((d) => !(d > 0) || d % 1 !== 0)) {
throw new Error(`broadcastTo(): Invalid broadcast shape [${shape}].`);
}
if (shape.length < input2.rank) {
throw new Error(`broadcastTo(): shape.length=${shape.length} < input.rank=${input2.rank}.`);
}
if (shape.length > input2.rank) {
const newShape = input2.shape.slice();
while (newShape.length < shape.length) {
newShape.unshift(1);
}
input2 = reshape(input2, newShape);
}
const inputShape = input2.shape;
const reps = Array.from(shape);
for (let i = shape.length - 1; i >= 0; i--) {
if (inputShape[i] === shape[i]) {
reps[i] = 1;
} else if (input2.shape[i] !== 1) {
throw new Error(`broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`);
}
}
const axes = reps.map((n, i) => n > 1 ? i : -1).filter((i) => i >= 0);
if (axes.length === 0) {
return clone(input2);
}
const forward = (backend3) => backend3.tile(input2, reps);
const inputs = {x: input2};
const attrs = {shape, inputShape};
return ENGINE.runKernelFunc(forward, inputs, null, BroadcastTo, attrs);
}
const broadcastTo = op({broadcastTo_});
// node_modules/@tensorflow/tfjs-core/dist/ops/ceil.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function ceil_(x) {
const $x = convertToTensor(x, "x", "ceil");
const inputs = {x: $x};
return ENGINE.runKernelFunc((backend3) => backend3.ceil($x), inputs, null, Ceil);
}
const ceil = op({ceil_});
// node_modules/@tensorflow/tfjs-core/dist/ops/clip_by_value.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function clipByValue_(x, clipValueMin, clipValueMax) {
const $x = convertToTensor(x, "x", "clipByValue");
assert(clipValueMin <= clipValueMax, () => `Error in clip: min (${clipValueMin}) must be less than or equal to max (${clipValueMax}).`);
const inputs = {x: $x};
const attrs = {clipValueMin, clipValueMax};
return ENGINE.runKernelFunc((backend3, save) => {
const res = backend3.clip($x, clipValueMin, clipValueMax);
save([$x]);
return res;
}, inputs, null, ClipByValue, attrs);
}
const clipByValue = op({clipByValue_});
// node_modules/@tensorflow/tfjs-core/dist/ops/concat_1d.js
function concat1d_(tensors) {
return concat(tensors, 0);
}
const concat1d = op({concat1d_});
// node_modules/@tensorflow/tfjs-core/dist/ops/concat_2d.js
function concat2d_(tensors, axis) {
return concat(tensors, axis);
}
const concat2d = op({concat2d_});
// node_modules/@tensorflow/tfjs-core/dist/ops/concat_3d.js
function concat3d_(tensors, axis) {
return concat(tensors, axis);
}
const concat3d = op({concat3d_});
// node_modules/@tensorflow/tfjs-core/dist/ops/concat_4d.js
function concat4d_(tensors, axis) {
return concat(tensors, axis);
}
const concat4d = op({concat4d_});
// node_modules/@tensorflow/tfjs-core/dist/ops/conv2d.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function conv2d_(x, filter, strides, pad8, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode) {
const $x = convertToTensor(x, "x", "conv2d");
const $filter = convertToTensor(filter, "filter", "conv2d");
let x4D = $x;
let reshapedTo4D = false;
if ($x.rank === 3) {
reshapedTo4D = true;
x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
}
assert(x4D.rank === 4, () => `Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`);
assert($filter.rank === 4, () => `Error in conv2d: filter must be rank 4, but got rank ${$filter.rank}.`);
if (dimRoundingMode != null) {
assert(isInt(pad8), () => `Error in conv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
}
const inDepth = dataFormat === "NHWC" ? x4D.shape[3] : x4D.shape[1];
assert(inDepth === $filter.shape[2], () => `Error in conv2d: depth of input (${inDepth}) must match input depth for filter ${$filter.shape[2]}.`);
assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
const forward = (backend3, save) => {
const $dataFormat = convertConv2DDataFormat(dataFormat);
const convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad8, dimRoundingMode, false, $dataFormat);
const res2 = backend3.conv2d(x4D, $filter, convInfo);
save([x4D, $filter]);
return res2;
};
const inputs = {x: x4D, filter: $filter};
const attrs = {strides, pad: pad8, dataFormat, dilations, dimRoundingMode};
const res = ENGINE.runKernelFunc(forward, inputs, null, Conv2D, attrs);
if (reshapedTo4D) {
return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
}
return res;
}
const conv2d = op({conv2d_});
// node_modules/@tensorflow/tfjs-core/dist/ops/conv1d.js
function conv1d_(x, filter, stride, pad8, dataFormat = "NWC", dilation = 1, dimRoundingMode) {
const $x = convertToTensor(x, "x", "conv1d");
const $filter = convertToTensor(filter, "filter", "conv1d");
let x3D = $x;
let reshapedTo3D = false;
if ($x.rank === 2) {
reshapedTo3D = true;
x3D = reshape($x, [1, $x.shape[0], $x.shape[1]]);
}
assert(x3D.rank === 3, () => `Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`);
assert($filter.rank === 3, () => `Error in conv1d: filter must be rank 3, but got rank ${$filter.rank}.`);
if (dimRoundingMode != null) {
assert(isInt(pad8), () => `Error in conv1d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
}
assert(x3D.shape[2] === $filter.shape[1], () => `Error in conv1d: depth of input (${x3D.shape[2]}) must match input depth for filter ${$filter.shape[1]}.`);
assert(eitherStridesOrDilationsAreOne(stride, dilation), () => `Error in conv1D: Either stride or dilation must be 1. Got stride ${stride} and dilation '${dilation}'`);
assert(dataFormat === "NWC", () => `Error in conv1d: got dataFormat of ${dataFormat} but only NWC is currently supported.`);
const filter4D = reshape($filter, [1, $filter.shape[0], $filter.shape[1], $filter.shape[2]]);
const input4D = reshape(x3D, [x3D.shape[0], 1, x3D.shape[1], x3D.shape[2]]);
const strides = [1, stride];
const dilations = [1, dilation];
const conv2dDataFormat = "NHWC";
const res = conv2d(input4D, filter4D, strides, pad8, conv2dDataFormat, dilations, dimRoundingMode);
if (reshapedTo3D) {
return reshape(res, [res.shape[2], res.shape[3]]);
}
return reshape(res, [res.shape[0], res.shape[2], res.shape[3]]);
}
const conv1d = op({conv1d_});
// node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_backprop_input.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function conv2DBackpropInput_(xShape, dy, filter, strides, pad8, dataFormat = "NHWC", dimRoundingMode) {
assert(xShape.length === dy.rank, () => `Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);
let xShape4D = xShape;
let dy4D = dy;
let reshapedTo4D = false;
if (dy.rank === 3) {
reshapedTo4D = true;
dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);
xShape4D = [1, xShape[0], xShape[1], xShape[2]];
}
assert(xShape4D.length === 4, () => `Error in conv2dDerInput: inShape must be length 4, but got length ${xShape4D.length}.`);
assert(dy4D.rank === 4, () => `Error in conv2dDerInput: dy must be rank 4, but got rank ${dy4D.rank}`);
assert(filter.rank === 4, () => `Error in conv2dDerInput: filter must be rank 4, but got rank ${filter.rank}`);
const inDepth = dataFormat === "NHWC" ? xShape4D[3] : xShape4D[1];
const outDepth = dataFormat === "NHWC" ? dy4D.shape[3] : dy4D.shape[1];
assert(inDepth === filter.shape[2], () => `Error in conv2dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[2]}.`);
assert(outDepth === filter.shape[3], () => `Error in conv2dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[3]}.`);
if (dimRoundingMode != null) {
assert(isInt(pad8), () => `Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
}
const forward = (backend3, save) => {
const dilations = 1;
const $dataFormat = convertConv2DDataFormat(dataFormat);
const convInfo = computeConv2DInfo(xShape4D, filter.shape, strides, dilations, pad8, dimRoundingMode, false, $dataFormat);
const res2 = backend3.conv2dDerInput(dy4D, filter, convInfo);
save([dy4D, filter]);
return res2;
};
const inputs = {dy: dy4D, filter};
const attrs = {strides, pad: pad8, dataFormat, dimRoundingMode, inputShape: xShape4D};
const res = ENGINE.runKernelFunc(forward, inputs, null, Conv2DBackpropInput, attrs);
if (reshapedTo4D) {
return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
}
return res;
}
const conv2DBackpropInput = op({conv2DBackpropInput_});
// node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_transpose.js
function conv2dTranspose_(x, filter, outputShape, strides, pad8, dimRoundingMode) {
const $x = convertToTensor(x, "x", "conv2dTranspose");
const $filter = convertToTensor(filter, "filter", "conv2dTranspose");
return conv2DBackpropInput(outputShape, $x, $filter, strides, pad8, "NHWC", dimRoundingMode);
}
const conv2dTranspose = op({conv2dTranspose_});
// node_modules/@tensorflow/tfjs-core/dist/ops/conv3d.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function conv3d_(x, filter, strides, pad8, dataFormat = "NDHWC", dilations = [1, 1, 1]) {
const $x = convertToTensor(x, "x", "conv3d");
const $filter = convertToTensor(filter, "filter", "conv3d");
let x5D = $x;
let reshapedTo5D = false;
if ($x.rank === 4) {
reshapedTo5D = true;
x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);
}
assert(x5D.rank === 5, () => `Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`);
assert($filter.rank === 5, () => `Error in conv3d: filter must be rank 5, but got rank ${$filter.rank}.`);
assert(x5D.shape[4] === $filter.shape[3], () => `Error in conv3d: depth of input (${x5D.shape[4]}) must match input depth for filter ${$filter.shape[3]}.`);
assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in conv3D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
assert(dataFormat === "NDHWC", () => `Error in conv3d: got dataFormat of ${dataFormat} but only NDHWC is currently supported.`);
const forward = (backend3, save) => {
const convInfo = computeConv3DInfo(x5D.shape, $filter.shape, strides, dilations, pad8);
const res2 = backend3.conv3d(x5D, $filter, convInfo);
save([x5D, $filter]);
return res2;
};
const inputs = {x: x5D, filter: $filter};
const attrs = {strides, pad: pad8, dataFormat, dilations};
const res = ENGINE.runKernelFunc(forward, inputs, null, Conv3D, attrs);
if (reshapedTo5D) {
return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
}
return res;
}
const conv3d = op({conv3d_});
// node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_backprop_input.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function conv3DBackpropInput_(xShape, dy, filter, strides, pad8) {
assert(xShape.length === dy.rank, () => `Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);
let xShape5D = xShape;
let dy5D = dy;
let reshapedTo5D = false;
if (dy.rank === 4) {
reshapedTo5D = true;
dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);
xShape5D = [1, xShape[0], xShape[1], xShape[2], xShape[3]];
}
const inDepth = xShape5D[4];
const outDepth = dy5D.shape[4];
assert(xShape5D.length === 5, () => `Error in conv3dDerInput: inShape must be length 5, but got length ${xShape5D.length}.`);
assert(dy5D.rank === 5, () => `Error in conv3dDerInput: dy must be rank 5, but got rank ${dy5D.rank}`);
assert(filter.rank === 5, () => `Error in conv3dDerInput: filter must be rank 5, but got rank ${filter.rank}`);
assert(inDepth === filter.shape[3], () => `Error in conv3dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[3]}.`);
assert(outDepth === filter.shape[4], () => `Error in conv3dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[4]}.`);
const forward = (backend3) => {
const dilations = 1;
const convInfo = computeConv3DInfo(xShape5D, filter.shape, strides, dilations, pad8);
return backend3.conv3dDerInput(dy5D, filter, convInfo);
};
const inputs = {dy: dy5D, filter};
const attrs = {pad: pad8, strides, inputShape: xShape5D};
const res = ENGINE.runKernelFunc(forward, inputs, null, Conv3DBackpropInputV2, attrs);
if (reshapedTo5D) {
return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
}
return res;
}
const conv3DBackpropInput = op({conv3DBackpropInput_});
// node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_transpose.js
function conv3dTranspose_(x, filter, outputShape, strides, pad8) {
const $x = convertToTensor(x, "x", "conv3dTranspose");
const $filter = convertToTensor(filter, "filter", "conv3dTranspose");
return conv3DBackpropInput(outputShape, $x, $filter, strides, pad8);
}
const conv3dTranspose = op({conv3dTranspose_});
// node_modules/@tensorflow/tfjs-core/dist/ops/cos.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function cos_(x) {
const $x = convertToTensor(x, "x", "cos");
const inputs = {x: $x};
return ENGINE.runKernelFunc((backend3, save) => {
const res = backend3.cos($x);
save([$x]);
return res;
}, inputs, null, Cos);
}
const cos = op({cos_});
// node_modules/@tensorflow/tfjs-core/dist/ops/cosh.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function cosh_(x) {
const $x = convertToTensor(x, "x", "cosh");
const inputs = {x: $x};
return ENGINE.runKernelFunc((backend3, save) => {
const res = backend3.cosh($x);
save([$x]);
return res;
}, inputs, null, Cosh);
}
const cosh = op({cosh_});
// node_modules/@tensorflow/tfjs-core/dist/ops/cumsum.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function cumsum_(x, axis = 0, exclusive = false, reverse9 = false) {
const $x = convertToTensor(x, "x", "cumsum");
const forward = (backend3, save) => {
const permutation = getAxesPermutation([axis], $x.rank);
let permutedX = $x;
if (permutation != null) {
permutedX = transpose($x, permutation);
}
const permutedAxis = getInnerMostAxes(1, $x.rank)[0];
let value = backend3.cumsum(permutedX, permutedAxis, exclusive, reverse9);
save([$x]);
if (permutation != null) {
const reversePermutation = getUndoAxesPermutation(permutation);
value = transpose(value, reversePermutation);
}
return value;
};
const inputs = {x: $x};
const attrs = {axis, exclusive, reverse: reverse9};
return ENGINE.runKernelFunc(forward, inputs, null, Cumsum, attrs);
}
const cumsum = op({cumsum_});
// node_modules/@tensorflow/tfjs-core/dist/ops/depth_to_space.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function depthToSpace_(x, blockSize, dataFormat = "NHWC") {
const $x = convertToTensor(x, "x", "depthToSpace");
const inputHeight = dataFormat === "NHWC" ? $x.shape[1] : $x.shape[2];
const inputWidth = dataFormat === "NHWC" ? $x.shape[2] : $x.shape[3];
const inputDepth = dataFormat === "NHWC" ? $x.shape[3] : $x.shape[1];
assert(inputHeight * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying
${inputHeight} and ${blockSize} for depthToSpace with input shape
${$x.shape}`);
assert(inputWidth * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying
${inputWidth} and ${blockSize} for depthToSpace with input shape
${$x.shape}`);
assert(inputDepth % (blockSize * blockSize) === 0, () => `Dimension size must be evenly divisible by ${blockSize * blockSize} but is ${inputDepth} for depthToSpace with input shape ${$x.shape}`);
const forward = (backend3) => backend3.depthToSpace($x, blockSize, dataFormat);
const inputs = {x: $x};
const attrs = {blockSize, dataFormat};
return ENGINE.runKernelFunc(forward, inputs, null, DepthToSpace, attrs);
}
const depthToSpace = op({depthToSpace_});
// node_modules/@tensorflow/tfjs-core/dist/ops/depthwise_conv2d.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function depthwiseConv2d_(x, filter, strides, pad8, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode) {
const $x = convertToTensor(x, "x", "depthwiseConv2d");
const $filter = convertToTensor(filter, "filter", "depthwiseConv2d");
let x4D = $x;
let reshapedTo4D = false;
if ($x.rank === 3) {
reshapedTo4D = true;
x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
}
assert(x4D.rank === 4, () => `Error in depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`);
assert($filter.rank === 4, () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`);
assert(x4D.shape[3] === $filter.shape[2], () => `Error in depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`);
if (dimRoundingMode != null) {
assert(isInt(pad8), () => `Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
}
const forward = (backend3, save) => {
if (dilations == null) {
dilations = [1, 1];
}
assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
const convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad8, dimRoundingMode, true);
const res2 = backend3.depthwiseConv2D(x4D, $filter, convInfo);
save([x4D, $filter]);
return res2;
};
const inputs = {x: x4D, filter: $filter};
const attrs = {strides, pad: pad8, dataFormat, dilations, dimRoundingMode};
const res = ENGINE.runKernelFunc(forward, inputs, null, DepthwiseConv2dNative, attrs);
if (reshapedTo4D) {
return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
}
return res;
}
const depthwiseConv2d = op({depthwiseConv2d_});
// node_modules/@tensorflow/tfjs-core/dist/ops/diag.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function diag_(x) {
const $x = convertToTensor(x, "x", "diag");
const forward = (backend3) => {
const flat = reshape($x, [$x.size]);
const result = backend3.diag(flat);
const outShape = [...x.shape, ...x.shape];
return reshape(result, outShape);
};
const inputs = {x: $x};
return ENGINE.runKernelFunc(forward, inputs, null, Diag);
}
const diag = op({diag_});
// node_modules/@tensorflow/tfjs-core/dist/ops/dilation2d.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function dilation2d_(x, filter, strides, pad8, dilations = [1, 1], dataFormat = "NHWC") {
const $x = convertToTensor(x, "x", "dilation2d");
const $filter = convertToTensor(filter, "filter", "dilation2d");
assert($x.rank === 3 || $x.rank === 4, () => `Error in dilation2d: input must be rank 3 or 4, but got rank ${$x.rank}.`);
assert($filter.rank === 3, () => `Error in dilation2d: filter must be rank 3, but got rank ${$filter.rank}.`);
assert(dataFormat === "NHWC", () => `Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${dataFormat}`);
let x4D = $x;
let reshapedTo4D = false;
if ($x.rank === 3) {
x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
reshapedTo4D = true;
}
const inputs = {x: x4D, filter: $filter};
const attrs = {strides, pad: pad8, dilations};
const res = ENGINE.runKernel(Dilation2D, inputs, attrs);
if (reshapedTo4D) {
return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
}
return res;
}
const dilation2d = op({dilation2d_});
// node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_util.js
/**
* @license
* Copyright 2017 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function getBroadcastDims(inShape, outShape) {
const inRank = inShape.length;
const dims = [];
for (let i = 0; i < inRank; i++) {
const dim = inRank - 1 - i;
const a = inShape[dim] || 1;
const b = outShape[outShape.length - 1 - i] || 1;
if (b > 1 && a === 1) {
dims.unshift(dim);
}
}
return dims;
}
function getReductionAxes(inShape, outShape) {
const result = [];
for (let i = 0; i < outShape.length; i++) {
const inDim = inShape[inShape.length - i - 1];
const outAxis = outShape.length - i - 1;
const outDim = outShape[outAxis];
if (inDim == null || inDim === 1 && outDim > 1) {
result.unshift(outAxis);
}
}
return result;
}
function assertAndGetBroadcastShape(shapeA, shapeB) {
const result = [];
const l = Math.max(shapeA.length, shapeB.length);
for (let i = 0; i < l; i++) {
let a = shapeA[shapeA.length - i - 1];
if (a == null) {
a = 1;
}
let b = shapeB[shapeB.length - i - 1];
if (b == null) {
b = 1;
}
if (a === 1) {
result.unshift(b);
} else if (b === 1) {
result.unshift(a);
} else if (a !== b) {
const errMsg = `Operands could not be broadcast together with shapes ${shapeA} and ${shapeB}.`;
throw Error(errMsg);
} else {
result.unshift(a);
}
}
return result;
}
// node_modules/@tensorflow/tfjs-core/dist/ops/equal.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function equal_(a, b) {
let $a = convertToTensor(a, "a", "equal");
let $b = convertToTensor(b, "b", "equal");
[$a, $b] = makeTypesMatch($a, $b);
assertAndGetBroadcastShape($a.shape, $b.shape);
const forward = (backend3) => backend3.equal($a, $b);
const inputs = {a: $a, b: $b};
return ENGINE.runKernelFunc(forward, inputs, null, Equal);
}
const equal = op({equal_});
// node_modules/@tensorflow/tfjs-core/dist/ops/where.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function where_(condition, a, b) {
const $a = convertToTensor(a, "a", "where");
const $b = convertToTensor(b, "b", "where");
const $condition = convertToTensor(condition, "condition", "where", "bool");
const broadcastShape = assertAndGetBroadcastShape($a.shape, $b.shape);
const $broadcastedA = broadcastTo($a, broadcastShape);
const $broadcastedB = broadcastTo($b, broadcastShape);
if ($condition.rank === 1) {
assert($condition.shape[0] === $a.shape[0], () => "The first dimension of `a` must match the size of `condition`.");
}
if ($condition.rank !== 1) {
assertShapesMatch($condition.shape, $broadcastedB.shape, "Error in where: ");
}
const forward = (backend3, save) => {
const res = backend3.select($condition, $broadcastedA, $broadcastedB);
save([$condition]);
return res;
};
const inputs = {
condition: $condition,
t: $broadcastedA,
e: $broadcastedB
};
return ENGINE.runKernelFunc(forward, inputs, null, SelectV2);
}
const where = op({where_});
// node_modules/@tensorflow/tfjs-core/dist/ops/zeros_like.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function zerosLike_(x) {
const $x = convertToTensor(x, "x", "zerosLike");
const inputs = {x: $x};
return ENGINE.runKernelFunc((backend3) => backend3.zerosLike($x), inputs, null, ZerosLike);
}
const zerosLike = op({zerosLike_});
// node_modules/@tensorflow/tfjs-core/dist/ops/div_no_nan.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function divNoNan_(a, b) {
let $a = convertToTensor(a, "a", "div");
let $b = convertToTensor(b, "b", "div");
[$a, $b] = makeTypesMatch($a, $b);
const divResult = div($a, $b);
const zeros9 = zerosLike(divResult);
const bEqualsZero = equal($b, zeros9);
return where(bEqualsZero, zeros9, divResult);
}
const divNoNan = op({divNoNan_});
// node_modules/@tensorflow/tfjs-core/dist/ops/dot.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function dot_(t1, t2) {
const $t1 = convertToTensor(t1, "t1", "dot");
const $t2 = convertToTensor(t2, "t2", "dot");
assert(($t1.rank === 1 || $t1.rank === 2) && ($t2.rank === 1 || $t2.rank === 2), () => `Error in dot: inputs must all be rank 1 or 2, but got ranks ${$t1.rank} and ${$t2.rank}.`);
const t1Inner = $t1.rank === 1 ? $t1.size : $t1.shape[1];
const t2Inner = $t2.rank === 1 ? $t2.size : $t2.shape[0];
assert(t1Inner === t2Inner, () => `Error in dot: inner dimensions of inputs must match, but got ${t1Inner} and ${t2Inner}.`);
if ($t1.rank === 1 && $t2.rank === 1) {
const t12D = reshape($t1, [1, -1]);
const t22D = reshape($t2, [-1, 1]);
const t1t2 = matMul(t12D, t22D);
return reshape(t1t2, []);
} else if ($t1.rank === 1 && $t2.rank === 2) {
const t12D = reshape($t1, [1, -1]);
const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);
const t1t2 = matMul(t12D, t22D);
return reshape(t1t2, [t1t2.size]);
} else if ($t1.rank === 2 && $t2.rank === 1) {
const t22D = reshape($t2, [-1, 1]);
const t1t2 = matMul($t1, t22D);
return reshape(t1t2, [t1t2.size]);
} else {
const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);
const t1t2 = matMul($t1, t22D);
return t1t2;
}
}
const dot = op({dot_});
// node_modules/@tensorflow/tfjs-core/dist/ops/elu.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function elu_(x) {
const $x = convertToTensor(x, "x", "elu");
const forward = (backend3, save) => {
const y = backend3.elu($x);
save([y]);
return y;
};
const inputs = {x: $x};
return ENGINE.runKernelFunc(forward, inputs, null, Elu);
}
const elu = op({elu_});
// node_modules/@tensorflow/tfjs-core/dist/ops/erf.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function erf_(x) {
let $x = convertToTensor(x, "x", "erf");
assert($x.dtype === "int32" || $x.dtype === "float32", () => "Input dtype must be `int32` or `float32`.");
if ($x.dtype === "int32") {
$x = cast($x, "float32");
}
const inputs = {x: $x};
return ENGINE.runKernelFunc((backend3, save) => {
const res = backend3.erf($x);
save([$x]);
return res;
}, inputs, null, Erf);
}
const erf = op({erf_});
// node_modules/@tensorflow/tfjs-core/dist/ops/exp.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function exp_(x) {
const $x = convertToTensor(x, "x", "exp");
const inputs = {x: $x};
return ENGINE.runKernelFunc((backend3, save) => {
const res = backend3.exp($x);
save([res]);
return res;
}, inputs, null, Exp);
}
const exp = op({exp_});
// node_modules/@tensorflow/tfjs-core/dist/ops/expand_dims.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function expandDims_(x, axis = 0) {
const parseAs = null;
const $x = convertToTensor(x, "x", "expandDims", parseAs);
assert(axis <= $x.rank, () => "Axis must be <= rank of the tensor");
const newShape = $x.shape.slice();
if (axis < 0) {
assert(-($x.rank + 1) <= axis, () => `Axis must be in the interval [${-($x.rank + 1)}, ${$x.rank}]`);
axis = $x.rank + axis + 1;
}
newShape.splice(axis, 0, 1);
return reshape($x, newShape);
}
const expandDims = op({expandDims_});
// node_modules/@tensorflow/tfjs-core/dist/ops/expm1.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function expm1_(x) {
const $x = convertToTensor(x, "x", "expm1");
const inputs = {x: $x};
return ENGINE.runKernelFunc((backend3, save) => {
const res = backend3.expm1($x);
save([$x]);
return res;
}, inputs, null, Expm1);
}
const expm1 = op({expm1_});
// node_modules/@tensorflow/tfjs-core/dist/ops/tile.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function tile_(x, reps) {
const parseAs = null;
const $x = convertToTensor(x, "x", "tile", parseAs);
assert($x.rank === reps.length, () => `Error in transpose: rank of input ${$x.rank} must match length of reps ${reps}.`);
const forward = (backend3, save) => {
const res = backend3.tile($x, reps);
save([$x]);
return res;
};
const inputsToSave = [$x];
const inputs = {x: $x};
const attrs = {reps};
return ENGINE.runKernelFunc(forward, inputs, null, Tile, attrs, inputsToSave);
}
const tile = op({tile_});
// node_modules/@tensorflow/tfjs-core/dist/ops/eye.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function eye_(numRows, numColumns, batchShape, dtype = "float32") {
if (numColumns == null) {
numColumns = numRows;
}
const buff = buffer([numRows, numColumns], dtype);
const n = numRows <= numColumns ? numRows : numColumns;
for (let i = 0; i < n; ++i) {
buff.set(1, i, i);
}
const out = reshape(buff.toTensor(), [numRows, numColumns]);
if (batchShape == null) {
return out;
} else {
if (batchShape.length === 1) {
return tile(expandDims(out, 0), [batchShape[0], 1, 1]);
} else if (batchShape.length === 2) {
return tile(expandDims(expandDims(out, 0), 0), [batchShape[0], batchShape[1], 1, 1]);
} else if (batchShape.length === 3) {
return tile(expandDims(expandDims(expandDims(out, 0), 0), 0), [
batchShape[0],
batchShape[1],
batchShape[2],
1,
1
]);
} else {
throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${batchShape.length}D.`);
}
}
}
const eye = op({eye_});
// node_modules/@tensorflow/tfjs-core/dist/ops/fill.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function fill(shape, value, dtype) {
const attrs = {shape, value, dtype};
return ENGINE.runKernelFunc((backend3) => backend3.fill(shape, value, dtype), {}, null, Fill, attrs);
}
// node_modules/@tensorflow/tfjs-core/dist/ops/floor.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function floor_(x) {
const $x = convertToTensor(x, "x", "floor");
const inputs = {x: $x};
return ENGINE.runKernelFunc((backend3) => backend3.floor($x), inputs, null, Floor);
}
const floor = op({floor_});
// node_modules/@tensorflow/tfjs-core/dist/ops/segment_util.js
const segment_util_exports = {};
__export(segment_util_exports, {
collectGatherOpShapeInfo: () => collectGatherOpShapeInfo,
computeOutShape: () => computeOutShape3,
segOpComputeOptimalWindowSize: () => segOpComputeOptimalWindowSize
});
// node_modules/@tensorflow/tfjs-core/dist/ops/reduce_util.js
/**
* @license
* Copyright 2017 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const PARALLELIZE_THRESHOLD = 30;
function computeOptimalWindowSize(inSize) {
if (inSize <= PARALLELIZE_THRESHOLD) {
return inSize;
}
return nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));
}
// node_modules/@tensorflow/tfjs-core/dist/ops/segment_util.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function segOpComputeOptimalWindowSize(inSize, numSegments) {
let done = false;
let res;
if (inSize <= PARALLELIZE_THRESHOLD) {
res = inSize;
done = true;
} else {
res = nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));
}
while (!done) {
if (res > numSegments || res === inSize) {
done = true;
} else {
res = nearestDivisor(inSize, res + 1);
}
}
return res;
}
function computeOutShape3(aShape, axis, numSegments) {
const outShape = [];
const rank = aShape.length;
for (let dim = 0; dim < rank; dim++) {
if (dim !== axis) {
outShape.push(aShape[dim]);
} else {
outShape.push(numSegments);
}
}
return outShape;
}
function collectGatherOpShapeInfo(x, indices, axis) {
const dimSize = x.shape[axis];
const outputShape = [];
let batchSize = 1;
let sliceSize = 1;
for (let i = 0; i < axis; i++) {
outputShape.push(x.shape[i]);
batchSize *= x.shape[i];
}
for (let i = 0; i < indices.rank; i++) {
outputShape.push(indices.shape[i]);
}
for (let i = axis + 1; i < x.rank; i++) {
outputShape.push(x.shape[i]);
sliceSize *= x.shape[i];
}
return {batchSize, sliceSize, dimSize, outputShape};
}
// node_modules/@tensorflow/tfjs-core/dist/ops/gather.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function gather_(x, indices, axis = 0) {
const $x = convertToTensor(x, "x", "gather");
const $indices = convertToTensor(indices, "indices", "gather", "int32");
const inputs = {x: $x, indices: $indices};
const attrs = {axis};
const forward = (backend3, save) => {
const parsedAxis = parseAxisParam(axis, $x.shape)[0];
const shapeInfo = collectGatherOpShapeInfo($x, $indices, parsedAxis);
const res = backend3.gather($x, reshape($indices, [$indices.size]), parsedAxis);
save([$x, $indices]);
return reshape(res, shapeInfo.outputShape);
};
return ENGINE.runKernelFunc(forward, inputs, null, GatherV2, attrs);
}
const gather = op({gather_});
// node_modules/@tensorflow/tfjs-core/dist/ops/greater.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function greater_(a, b) {
let $a = convertToTensor(a, "a", "greater");
let $b = convertToTensor(b, "b", "greater");
[$a, $b] = makeTypesMatch($a, $b);
assertAndGetBroadcastShape($a.shape, $b.shape);
const forward = (backend3) => backend3.greater($a, $b);
const inputs = {a: $a, b: $b};
return ENGINE.runKernelFunc(forward, inputs, null, Greater);
}
const greater = op({greater_});
// node_modules/@tensorflow/tfjs-core/dist/ops/greater_equal.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function greaterEqual_(a, b) {
let $a = convertToTensor(a, "a", "greaterEqual");
let $b = convertToTensor(b, "b", "greaterEqual");
[$a, $b] = makeTypesMatch($a, $b);
assertAndGetBroadcastShape($a.shape, $b.shape);
const forward = (backend3, save) => {
const res = backend3.greaterEqual($a, $b);
save([$a, $b]);
return res;
};
const inputs = {a: $a, b: $b};
return ENGINE.runKernelFunc(forward, inputs, null, GreaterEqual);
}
const greaterEqual = op({greaterEqual_});
// node_modules/@tensorflow/tfjs-core/dist/ops/imag.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function imag_(input2) {
const $input = convertToTensor(input2, "input", "imag");
const forward = (backend3) => {
return backend3.imag($input);
};
const inputs = {input: $input};
return ENGINE.runKernelFunc(forward, inputs, null, Imag);
}
const imag = op({imag_});
// node_modules/@tensorflow/tfjs-core/dist/ops/is_finite.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function isFinite_(x) {
const $x = convertToTensor(x, "x", "isFinite");
const inputs = {x: $x};
return ENGINE.runKernelFunc((backend3) => backend3.isFinite($x), inputs, null, IsFinite);
}
const isFinite2 = op({isFinite_});
// node_modules/@tensorflow/tfjs-core/dist/ops/is_inf.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function isInf_(x) {
const $x = convertToTensor(x, "x", "isInf");
const inputs = {x: $x};
return ENGINE.runKernelFunc((backend3) => backend3.isInf($x), inputs, null, IsInf);
}
const isInf = op({isInf_});
// node_modules/@tensorflow/tfjs-core/dist/ops/is_nan.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function isNaN_(x) {
const $x = convertToTensor(x, "x", "isNaN");
const inputs = {x: $x};
return ENGINE.runKernelFunc((backend3) => backend3.isNaN($x), inputs, null, IsNan);
}
const isNaN2 = op({isNaN_});
// node_modules/@tensorflow/tfjs-core/dist/ops/maximum.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function maximum_(a, b) {
let $a = convertToTensor(a, "a", "maximum");
let $b = convertToTensor(b, "b", "maximum");
[$a, $b] = makeTypesMatch($a, $b);
if ($a.dtype === "bool") {
$a = cast($a, "int32");
$b = cast($b, "int32");
}
assertAndGetBroadcastShape($a.shape, $b.shape);
const forward = (backend3, save) => {
const res = backend3.maximum($a, $b);
save([$a, $b]);
return res;
};
const inputs = {a: $a, b: $b};
return ENGINE.runKernelFunc(forward, inputs, null, Maximum);
}
const maximum = op({maximum_});
// node_modules/@tensorflow/tfjs-core/dist/ops/scalar.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function scalar(value, dtype) {
if ((isTypedArray(value) && dtype !== "string" || Array.isArray(value)) && dtype !== "complex64") {
throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");
}
if (dtype === "string" && isTypedArray(value) && !(value instanceof Uint8Array)) {
throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");
}
const shape = [];
const inferredShape = [];
return makeTensor(value, shape, inferredShape, dtype);
}
// node_modules/@tensorflow/tfjs-core/dist/ops/leaky_relu.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function leakyRelu_(x, alpha = 0.2) {
const $x = convertToTensor(x, "x", "leakyRelu");
return maximum(mul(scalar(alpha), $x), $x);
}
const leakyRelu = op({leakyRelu_});
// node_modules/@tensorflow/tfjs-core/dist/ops/less.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function less_(a, b) {
let $a = convertToTensor(a, "a", "less");
let $b = convertToTensor(b, "b", "less");
[$a, $b] = makeTypesMatch($a, $b);
assertAndGetBroadcastShape($a.shape, $b.shape);
const forward = (backend3) => backend3.less($a, $b);
const inputs = {a: $a, b: $b};
return ENGINE.runKernelFunc(forward, inputs, null, Less);
}
const less = op({less_});
// node_modules/@tensorflow/tfjs-core/dist/ops/less_equal.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function lessEqual_(a, b) {
let $a = convertToTensor(a, "a", "lessEqual");
let $b = convertToTensor(b, "b", "lessEqual");
[$a, $b] = makeTypesMatch($a, $b);
assertAndGetBroadcastShape($a.shape, $b.shape);
const forward = (backend3, save) => {
const res = backend3.lessEqual($a, $b);
save([$a, $b]);
return res;
};
const inputs = {a: $a, b: $b};
return ENGINE.runKernelFunc(forward, inputs, null, LessEqual);
}
const lessEqual = op({lessEqual_});
// node_modules/@tensorflow/tfjs-core/dist/ops/linspace.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function linspace(start, stop, num) {
if (num <= 0) {
throw new Error("The number of values should be positive.");
}
const attrs = {start, stop, num};
return ENGINE.runKernelFunc((backend3) => backend3.linspace(start, stop, num), {}, null, LinSpace, attrs);
}
// node_modules/@tensorflow/tfjs-core/dist/ops/local_response_normalization.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function localResponseNormalization_(x, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {
const $x = convertToTensor(x, "x", "localResponseNormalization");
assert($x.rank === 4 || $x.rank === 3, () => `Error in localResponseNormalization: x must be rank 3 or 4 but got
rank ${$x.rank}.`);
assert(isInt(depthRadius), () => `Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${depthRadius}.`);
let x4D = $x;
let reshapedTo4D = false;
if ($x.rank === 3) {
reshapedTo4D = true;
x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
}
const forward = (backend3, save) => {
const y = backend3.localResponseNormalization4D(x4D, depthRadius, bias, alpha, beta);
save([x4D, y]);
return y;
};
const inputs = {x: x4D};
const attrs = {depthRadius, bias, alpha, beta};
const res = ENGINE.runKernelFunc(forward, inputs, null, LRN, attrs);
if (reshapedTo4D) {
return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
} else {
return res;
}
}
const localResponseNormalization = op({localResponseNormalization_});
// node_modules/@tensorflow/tfjs-core/dist/ops/log.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function log_(x) {
const $x = convertToTensor(x, "x", "log");
const inputs = {x: $x};
return ENGINE.runKernelFunc((backend3, save) => {
const res = backend3.log($x);
save([$x]);
return res;
}, inputs, null, Log);
}
const log = op({log_});
// node_modules/@tensorflow/tfjs-core/dist/ops/log1p.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function log1p_(x) {
const $x = convertToTensor(x, "x", "log1p");
const inputs = {x: $x};
return ENGINE.runKernelFunc((backend3, save) => {
const res = backend3.log1p($x);
save([$x]);
return res;
}, inputs, null, Log1p);
}
const log1p = op({log1p_});
// node_modules/@tensorflow/tfjs-core/dist/gradients.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function grad(f) {
assert(isFunction(f), () => "The f passed in grad(f) must be a function");
return (x, dy) => {
const $x = convertToTensor(x, "x", "tf.grad", null);
const $dy = dy != null ? convertToTensor(dy, "dy", "tf.grad") : null;
return ENGINE.tidy(() => {
const {value, grads: grads2} = ENGINE.gradients(() => f($x), [$x], $dy);
if ($dy != null) {
assertShapesMatch(value.shape, $dy.shape, "The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)");
}
checkGrads(grads2);
return grads2[0];
});
};
}
function grads(f) {
assert(isFunction(f), () => "The f passed in grads(f) must be a function");
return (args, dy) => {
assert(Array.isArray(args), () => "The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");
const $args = convertToTensorArray(args, "args", "tf.grads", null);
const $dy = dy != null ? convertToTensor(dy, "dy", "tf.grads") : null;
return ENGINE.tidy(() => {
const {value, grads: grads2} = ENGINE.gradients(() => f(...$args), $args, $dy);
if ($dy != null) {
assertShapesMatch(value.shape, $dy.shape, "The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])");
}
checkGrads(grads2);
return grads2;
});
};
}
function valueAndGrad(f) {
assert(isFunction(f), () => "The f passed in valueAndGrad(f) must be a function");
return (x, dy) => {
assert(x instanceof Tensor, () => "The x passed in valueAndGrad(f)(x) must be a tensor");
assert(dy == null || dy instanceof Tensor, () => "The dy passed in valueAndGrad(f)(x, dy) must be a tensor");
const {grads: grads2, value} = ENGINE.gradients(() => f(x), [x], dy);
checkGrads(grads2);
return {grad: grads2[0], value};
};
}
function valueAndGrads(f) {
assert(isFunction(f), () => "The f passed in valueAndGrads(f) must be a function");
return (args, dy) => {
assert(Array.isArray(args) && args.every((arg) => arg instanceof Tensor), () => "The args passed in valueAndGrads(f)(args) must be array of tensors");
assert(dy == null || dy instanceof Tensor, () => "The dy passed in valueAndGrads(f)(args, dy) must be a tensor");
const res = ENGINE.gradients(() => f(...args), args, dy);
if (dy != null) {
assertShapesMatch(res.value.shape, dy.shape, "The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])");
}
checkGrads(res.grads);
return res;
};
}
function variableGrads(f, varList) {
assert(isFunction(f), () => "The f passed in variableGrads(f) must be a function");
assert(varList == null || Array.isArray(varList) && varList.every((v) => v instanceof Variable), () => "The varList passed in variableGrads(f, varList) must be an array of variables");
const specifiedVarList = varList != null;
if (!specifiedVarList) {
varList = [];
for (const varName in ENGINE.registeredVariables) {
varList.push(ENGINE.registeredVariables[varName]);
}
}
const specifiedNonTrainable = specifiedVarList ? varList.filter((variable3) => !variable3.trainable) : null;
const originalVarCount = varList.length;
varList = varList.filter((variable3) => variable3.trainable);
assert(varList.length > 0, () => `variableGrads() expects at least one of the input variables to be trainable, but none of the ${originalVarCount} variables is trainable.`);
const allowNoGradients = true;
const {value, grads: grads2} = ENGINE.gradients(f, varList, null, allowNoGradients);
assert(grads2.some((g) => g != null), () => "Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize().");
assert(value.rank === 0, () => `The f passed in variableGrads(f) must return a scalar, but it returned a rank-${value.rank} tensor`);
const namedGrads = {};
varList.forEach((v, i) => {
if (grads2[i] != null) {
namedGrads[v.name] = grads2[i];
}
});
if (specifiedNonTrainable != null) {
specifiedNonTrainable.forEach((v) => namedGrads[v.name] = null);
}
return {value, grads: namedGrads};
}
function customGrad(f) {
return ENGINE.customGrad(f);
}
function checkGrads(grads2) {
const numNullGradients = grads2.filter((g) => g == null).length;
if (numNullGradients > 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.`);
}
}
// node_modules/@tensorflow/tfjs-core/dist/ops/neg.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function neg_(x) {
const $x = convertToTensor(x, "x", "neg");
const inputs = {x: $x};
return ENGINE.runKernelFunc((backend3) => backend3.neg($x), inputs, null, Negate);
}
const neg = op({neg_});
// node_modules/@tensorflow/tfjs-core/dist/ops/softplus.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function softplus_(x) {
const $x = convertToTensor(x, "x", "softplus");
const inputs = {x: $x};
return ENGINE.runKernelFunc((backend3, save) => {
const res = backend3.softplus($x);
save([$x]);
return res;
}, inputs, null, Softplus);
}
const softplus = op({softplus_});
// node_modules/@tensorflow/tfjs-core/dist/ops/log_sigmoid.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function logSigmoid_(x) {
const $x = convertToTensor(x, "x", "logSigmoid");
const customOp = customGrad((x2) => {
const value = neg(softplus(neg(x2)));
const gradFunc = (dy) => {
const derX = mul(dy, sigmoid(neg(x2)));
return derX;
};
return {value, gradFunc};
});
return customOp($x);
}
const logSigmoid = op({logSigmoid_});
// node_modules/@tensorflow/tfjs-core/dist/ops/max.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function max_(x, axis = null, keepDims = false) {
const $x = convertToTensor(x, "x", "max");
const forward = (backend3, save) => {
const origAxes = parseAxisParam(axis, $x.shape);
let axes = origAxes;
const permutedAxes = getAxesPermutation(axes, $x.rank);
let maxInput = $x;
if (permutedAxes != null) {
maxInput = transpose($x, permutedAxes);
axes = getInnerMostAxes(axes.length, maxInput.rank);
}
const y = backend3.max(maxInput, axes);
if (permutedAxes != null) {
maxInput.dispose();
}
let res = y;
if (keepDims) {
const expandedShape = expandShapeToKeepDim(res.shape, parseAxisParam(axis, $x.shape));
res = reshape(res, expandedShape);
y.dispose();
}
save([$x, res]);
return res;
};
const inputs = {x: $x};
const attrs = {reductionIndices: axis, keepDims};
return ENGINE.runKernelFunc(forward, inputs, null, Max, attrs);
}
const max = op({max_});
// node_modules/@tensorflow/tfjs-core/dist/ops/sub.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function sub_(a, b) {
let $a = convertToTensor(a, "a", "sub");
let $b = convertToTensor(b, "b", "sub");
[$a, $b] = makeTypesMatch($a, $b);
const forward = (backend3, save) => {
const res = backend3.subtract($a, $b);
save([$a, $b]);
return res;
};
const inputs = {a: $a, b: $b};
return ENGINE.runKernelFunc(forward, inputs, null, Sub);
}
const sub = op({sub_});
// node_modules/@tensorflow/tfjs-core/dist/ops/sum.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function sum_(x, axis = null, keepDims = false) {
let $x = convertToTensor(x, "x", "sum");
if ($x.dtype === "bool") {
$x = cast($x, "int32");
}
const forward = (backend3, save) => {
save([$x]);
const axes = parseAxisParam(axis, $x.shape);
const permutation = getAxesPermutation(axes, $x.rank);
let reductionAxes = axes;
let permutedX = $x;
if (permutation != null) {
permutedX = transpose($x, permutation);
reductionAxes = getInnerMostAxes(reductionAxes.length, $x.rank);
}
let value = backend3.sum(permutedX, reductionAxes);
if (keepDims) {
const newShape = expandShapeToKeepDim(value.shape, axes);
value = reshape(value, newShape);
}
return value;
};
const inputs = {x: $x};
const attrs = {axis, keepDims};
return ENGINE.runKernelFunc(forward, inputs, null, Sum, attrs);
}
const sum2 = op({sum_});
// node_modules/@tensorflow/tfjs-core/dist/ops/log_softmax.js
/**
* @license
* Copyright 2020 Google Inc. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function logSoftmax_(logits, axis = -1) {
const $logits = convertToTensor(logits, "logits", "logSoftmax");
if (axis === -1) {
axis = $logits.rank - 1;
}
if (axis !== $logits.rank - 1) {
throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and axis was ${axis}`);
}
const forward = (backend3, save) => {
const keepDims = true;
const xMax = max(logits, axis, true);
const shifted = sub(logits, xMax);
const value = sub(cast(shifted, "float32"), log(sum2(exp(shifted), axis, keepDims)));
save([value]);
return value;
};
const inputs = {logits: $logits};
const attrs = {axis};
return ENGINE.runKernelFunc(forward, inputs, null, LogSoftmax, attrs);
}
const logSoftmax = op({logSoftmax_});
// node_modules/@tensorflow/tfjs-core/dist/ops/log_sum_exp.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function logSumExp_(x, axis = null, keepDims = false) {
const $x = convertToTensor(x, "x", "logSumExp");
const axes = parseAxisParam(axis, $x.shape);
const xMax = max($x, axes, true);
const a = sub($x, xMax);
const b = exp(a);
const c = sum2(b, axes);
const d = log(c);
const res = add2(reshape(xMax, d.shape), d);
if (keepDims) {
const newShape = expandShapeToKeepDim(res.shape, axes);
return reshape(res, newShape);
}
return res;
}
const logSumExp = op({logSumExp_});
// node_modules/@tensorflow/tfjs-core/dist/ops/logical_and.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function logicalAnd_(a, b) {
const $a = convertToTensor(a, "a", "logicalAnd", "bool");
const $b = convertToTensor(b, "b", "logicalAnd", "bool");
assertAndGetBroadcastShape($a.shape, $b.shape);
const inputs = {a: $a, b: $b};
return ENGINE.runKernelFunc((backend3) => backend3.logicalAnd($a, $b), inputs, null, LogicalAnd);
}
const logicalAnd = op({logicalAnd_});
// node_modules/@tensorflow/tfjs-core/dist/ops/logical_not.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function logicalNot_(x) {
const $x = convertToTensor(x, "x", "logicalNot", "bool");
const inputs = {x: $x};
return ENGINE.runKernelFunc((backend3) => backend3.logicalNot($x), inputs, null, LogicalNot);
}
const logicalNot = op({logicalNot_});
// node_modules/@tensorflow/tfjs-core/dist/ops/logical_or.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function logicalOr_(a, b) {
const $a = convertToTensor(a, "a", "logicalOr", "bool");
const $b = convertToTensor(b, "b", "logicalOr", "bool");
assertAndGetBroadcastShape($a.shape, $b.shape);
const inputs = {a: $a, b: $b};
return ENGINE.runKernelFunc((backend3) => backend3.logicalOr($a, $b), inputs, null, LogicalOr);
}
const logicalOr = op({logicalOr_});
// node_modules/@tensorflow/tfjs-core/dist/ops/logical_xor.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function logicalXor_(a, b) {
const $a = convertToTensor(a, "a", "logicalXor", "bool");
const $b = convertToTensor(b, "b", "logicalXor", "bool");
assertAndGetBroadcastShape($a.shape, $b.shape);
return logicalAnd(logicalOr(a, b), logicalNot(logicalAnd(a, b)));
}
const logicalXor = op({logicalXor_});
// node_modules/@tensorflow/tfjs-core/dist/ops/max_pool.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function maxPool_(x, filterSize, strides, pad8, dimRoundingMode) {
const $x = convertToTensor(x, "x", "maxPool");
const dilations = 1;
let x4D = $x;
let reshapedTo4D = false;
if ($x.rank === 3) {
reshapedTo4D = true;
x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
}
assert(x4D.rank === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`);
assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
if (dimRoundingMode != null) {
assert(isInt(pad8), () => `Error in maxPool: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
}
const forward = (backend3, save) => {
const convInfo = computePool2DInfo(x4D.shape, filterSize, strides, 1, pad8, dimRoundingMode);
let y;
if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && arraysEqual(convInfo.inShape, convInfo.outShape)) {
y = x4D.clone();
} else {
y = backend3.maxPool(x4D, convInfo);
}
save([x4D, y]);
return y;
};
const inputs = {x: x4D};
const attrs = {filterSize, strides, pad: pad8, dimRoundingMode};
const res = ENGINE.runKernelFunc(forward, inputs, null, MaxPool, attrs);
if (reshapedTo4D) {
return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
}
return res;
}
const maxPool = op({maxPool_});
// node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_3d.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function maxPool3d_(x, filterSize = [1, 1, 1], strides, pad8, dimRoundingMode, dataFormat = "NDHWC", dilations) {
if (dilations == null) {
dilations = [1, 1, 1];
} else {
deprecationWarn("dilations is deprecated, this field will be gone in v3.0.0.");
}
const $x = convertToTensor(x, "x", "maxPool3d");
let x5D = $x;
let reshapedTo5D = false;
if ($x.rank === 4) {
reshapedTo5D = true;
x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);
}
assert(x5D.rank === 5, () => `Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`);
assert(dataFormat === "NDHWC", () => `Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`);
assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool3d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
if (dimRoundingMode != null) {
assert(isInt(pad8), () => `Error in maxPool3d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
}
const forward = (backend3, save) => {
if (dilations == null) {
dilations = [1, 1, 1];
}
const convInfo = computePool3DInfo(x5D.shape, filterSize, strides, dilations, pad8, dimRoundingMode, dataFormat);
const y = backend3.maxPool3d(x5D, convInfo);
save([x5D, y]);
return y;
};
const inputs = {x: x5D};
const attrs = {filterSize, strides, pad: pad8, dimRoundingMode, dataFormat, dilations};
const res = ENGINE.runKernelFunc(forward, inputs, null, MaxPool3D, attrs);
if (reshapedTo5D) {
return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
}
return res;
}
const maxPool3d = op({maxPool3d_});
// node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_with_argmax.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function maxPoolWithArgmax_(x, filterSize, strides, pad8, includeBatchInIndex = false) {
const $x = convertToTensor(x, "x", "maxPoolWithArgmax");
const inputs = {x: $x};
const attrs = {filterSize, strides, pad: pad8, includeBatchInIndex};
const result = ENGINE.runKernel(MaxPoolWithArgmax, inputs, attrs);
return {result: result[0], indexes: result[1]};
}
const maxPoolWithArgmax = op({maxPoolWithArgmax_});
// node_modules/@tensorflow/tfjs-core/dist/ops/zeros.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function zeros(shape, dtype = "float32") {
if (dtype === "complex64") {
const real6 = zeros(shape, "float32");
const imag6 = zeros(shape, "float32");
return complex(real6, imag6);
}
const values = makeZerosTypedArray(sizeFromShape(shape), dtype);
return ENGINE.makeTensor(values, shape, dtype);
}
// node_modules/@tensorflow/tfjs-core/dist/ops/ones.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function ones2(shape, dtype = "float32") {
if (dtype === "complex64") {
const real6 = ones2(shape, "float32");
const imag6 = zeros(shape, "float32");
return complex(real6, imag6);
}
const values = makeOnesTypedArray(sizeFromShape(shape), dtype);
return ENGINE.makeTensor(values, shape, dtype);
}
// node_modules/@tensorflow/tfjs-core/dist/ops/mean.js
/**
* @license
* Copyright 2020 Google Inc. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function mean_(x, axis = null, keepDims = false) {
const $x = convertToTensor(x, "x", "mean");
const axes = parseAxisParam(axis, $x.shape);
const shapes = computeOutAndReduceShapes($x.shape, axes);
const reduceShape = shapes[1];
const reduceSize = sizeFromShape(reduceShape);
const inputs = {x: $x};
const attrs = {axis, keepDims};
const forward = () => {
const reduceSizeScalar = scalar(reduceSize);
const xReduce = reduceSizeScalar.dtype === $x.dtype ? $x : cast($x, reduceSizeScalar.dtype);
const res = div(xReduce, reduceSizeScalar);
return sum2(res, axis, keepDims);
};
const customOp = customGrad((x2) => {
const value = ENGINE.runKernelFunc(forward, inputs, null, Mean, attrs);
const gradFunc = (dy) => {
const expandedDyShape = x2.shape.slice();
axes.forEach((axis2) => {
expandedDyShape[axis2] = 1;
});
const expandedDy = reshape(dy, expandedDyShape);
const derX = div(mul(expandedDy, ones2(x2.shape, "float32")), reduceSize);
return derX;
};
return {value, gradFunc};
});
return customOp($x);
}
const mean = op({mean_});
// node_modules/@tensorflow/tfjs-core/dist/ops/min.js
function min_(x, axis = null, keepDims = false) {
const $x = convertToTensor(x, "x", "min");
const forward = (backend3, save) => {
const origAxes = parseAxisParam(axis, $x.shape);
let axes = origAxes;
const permutedAxes = getAxesPermutation(axes, $x.rank);
let minInput = $x;
if (permutedAxes != null) {
minInput = transpose($x, permutedAxes);
axes = getInnerMostAxes(axes.length, $x.rank);
}
const y = backend3.min(minInput, axes);
if (permutedAxes != null) {
minInput.dispose();
}
let res = y;
if (keepDims) {
const expandedShape = expandShapeToKeepDim(res.shape, origAxes);
res = reshape(y, expandedShape);
y.dispose();
}
save([$x, res]);
return res;
};
const inputs = {x: $x};
const attrs = {axis, keepDims};
return ENGINE.runKernelFunc(forward, inputs, null, Min, attrs);
}
const min = op({min_});
// node_modules/@tensorflow/tfjs-core/dist/ops/minimum.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function minimum_(a, b) {
let $a = convertToTensor(a, "a", "minimum");
let $b = convertToTensor(b, "b", "minimum");
[$a, $b] = makeTypesMatch($a, $b);
if ($a.dtype === "bool") {
$a = cast($a, "int32");
$b = cast($b, "int32");
}
assertAndGetBroadcastShape($a.shape, $b.shape);
const forward = (backend3, save) => {
const res = backend3.minimum($a, $b);
save([$a, $b]);
return res;
};
const inputs = {a: $a, b: $b};
return ENGINE.runKernelFunc(forward, inputs, null, Minimum);
}
const minimum = op({minimum_});
// node_modules/@tensorflow/tfjs-core/dist/ops/mirror_pad.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function mirrorPad_(x, paddings, mode) {
assert(mode === "reflect" || mode === "symmetric", () => `Invalid mode. Mode must be either reflect or symmetric. Got ${mode}.`);
const $x = convertToTensor(x, "x", "mirrorPad");
if ($x.rank === 0) {
throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");
}
assert(paddings.length === $x.rank, () => `Padding doesn't match input. Must be ${$x.rank}. Got ${paddings.length}.`);
const shapeOffset = mode === "reflect" ? 1 : 0;
for (let i = 0; i < $x.rank; i++) {
assert(paddings[i].length === 2, () => `Invalid number of paddings. Must be length of 2 each.`);
assert(paddings[i][0] >= 0 && paddings[i][0] <= $x.shape[i] - shapeOffset && paddings[i][1] >= 0 && paddings[i][1] <= $x.shape[i] - shapeOffset, () => `Padding in dimension ${i} cannot be greater than or equal to ${$x.shape[i] - shapeOffset} or less than 0 for input of shape ${$x.shape}`);
}
const attrs = {paddings, mode};
const inputs = {x: $x};
return ENGINE.runKernel(MirrorPad, inputs, attrs);
}
const mirrorPad = op({mirrorPad_});
// node_modules/@tensorflow/tfjs-core/dist/ops/mod.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function mod_(a, b) {
let $a = convertToTensor(a, "a", "mod");
let $b = convertToTensor(b, "b", "mod");
[$a, $b] = makeTypesMatch($a, $b);
const forward = (backend3, save) => {
const res = backend3.mod($a, $b);
save([$a, $b]);
return res;
};
const inputs = {a: $a, b: $b};
return ENGINE.runKernelFunc(forward, inputs, null, Mod);
}
const mod = op({mod_});
// node_modules/@tensorflow/tfjs-core/dist/ops/square.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function square_(x) {
const $x = convertToTensor(x, "x", "square");
const attrs = {};
const inputsToSave = [$x];
const outputsToSave = [];
return ENGINE.runKernelFunc((backend3, save) => {
save([$x]);
return backend3.square($x);
}, {x: $x}, null, "Square", attrs, inputsToSave, outputsToSave);
}
const square = op({square_});
// node_modules/@tensorflow/tfjs-core/dist/ops/moments.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function moments_(x, axis = null, keepDims = false) {
x = convertToTensor(x, "x", "moments");
const axes = parseAxisParam(axis, x.shape);
const xMean = mean(x, axes, keepDims);
let keepDimsShape = xMean.shape;
if (!keepDims) {
keepDimsShape = expandShapeToKeepDim(xMean.shape, axes);
}
const devSquared = square(sub(cast(x, "float32"), reshape(xMean, keepDimsShape)));
const variance = mean(devSquared, axes, keepDims);
return {mean: xMean, variance};
}
const moments = op({moments_});
// node_modules/@tensorflow/tfjs-core/dist/ops/multi_rnn_cell.js
function multiRNNCell_(lstmCells, data2, c, h) {
const $data = convertToTensor(data2, "data", "multiRNNCell");
const $c = convertToTensorArray(c, "c", "multiRNNCell");
const $h = convertToTensorArray(h, "h", "multiRNNCell");
let input2 = $data;
const newStates = [];
for (let i = 0; i < lstmCells.length; i++) {
const output = lstmCells[i](input2, $c[i], $h[i]);
newStates.push(output[0]);
newStates.push(output[1]);
input2 = output[1];
}
const newC = [];
const newH = [];
for (let i = 0; i < newStates.length; i += 2) {
newC.push(newStates[i]);
newH.push(newStates[i + 1]);
}
return [newC, newH];
}
const multiRNNCell = op({multiRNNCell_});
// node_modules/@tensorflow/tfjs-core/dist/ops/multinomial.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function multinomial_(logits, numSamples, seed, normalized = false) {
const $logits = convertToTensor(logits, "logits", "multinomial");
const numOutcomes = $logits.size;
const origRank = $logits.rank;
if (numOutcomes < 2) {
throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${numOutcomes}.`);
}
if (origRank > 2) {
throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);
}
seed = seed || Math.random();
const logits2D = origRank === 1 ? reshape($logits, [1, -1]) : $logits;
const res = ENGINE.runKernelFunc((backend3) => backend3.multinomial(logits2D, normalized, numSamples, seed), {logits2D});
return origRank === 1 ? reshape(res, [res.size]) : res;
}
const multinomial = op({multinomial_});
// node_modules/@tensorflow/tfjs-core/dist/ops/not_equal.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function notEqual_(a, b) {
let $a = convertToTensor(a, "a", "notEqual");
let $b = convertToTensor(b, "b", "notEqual");
[$a, $b] = makeTypesMatch($a, $b);
assertAndGetBroadcastShape($a.shape, $b.shape);
const forward = (backend3) => backend3.notEqual($a, $b);
const inputs = {a: $a, b: $b};
return ENGINE.runKernelFunc(forward, inputs, null, NotEqual);
}
const notEqual = op({notEqual_});
// node_modules/@tensorflow/tfjs-core/dist/ops/real.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function real_(input2) {
const $input = convertToTensor(input2, "input", "real");
const forward = (backend3) => {
return backend3.real($input);
};
const inputs = {input: $input};
return ENGINE.runKernelFunc(forward, inputs, null, Real);
}
const real = op({real_});
// node_modules/@tensorflow/tfjs-core/dist/ops/ones_like.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function onesLike_(x) {
const $x = convertToTensor(x, "x", "onesLike");
const forward = (backend3, save) => {
if ($x.dtype === "complex64") {
const r = onesLike(real($x));
const i = zerosLike(imag($x));
return complex(r, i);
}
return backend3.onesLike($x);
};
const inputs = {x: $x};
return ENGINE.runKernelFunc(forward, inputs, null, OnesLike);
}
const onesLike = op({onesLike_});
// node_modules/@tensorflow/tfjs-core/dist/ops/outer_product.js
function outerProduct_(v1, v2) {
const $v1 = convertToTensor(v1, "v1", "outerProduct");
const $v2 = convertToTensor(v2, "v2", "outerProduct");
assert($v1.rank === 1 && $v2.rank === 1, () => `Error in outerProduct: inputs must be rank 1, but got ranks ${$v1.rank} and ${$v2.rank}.`);
const v12D = reshape($v1, [-1, 1]);
const v22D = reshape($v2, [1, -1]);
return matMul(v12D, v22D);
}
const outerProduct = op({outerProduct_});
// node_modules/@tensorflow/tfjs-core/dist/ops/pad.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function pad_(x, paddings, constantValue = 0) {
const $x = convertToTensor(x, "x", "pad");
if ($x.rank === 0) {
throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");
}
const forward = (backend3, save) => {
save([$x]);
return backend3.pad($x, paddings, constantValue);
};
const attrs = {paddings, constantValue};
const inputs = {x: $x};
return ENGINE.runKernelFunc(forward, inputs, null, PadV2, attrs);
}
const pad = op({pad_});
// node_modules/@tensorflow/tfjs-core/dist/ops/pad1d.js
function pad1d_(x, paddings, constantValue = 0) {
assert(paddings.length === 2, () => "Invalid number of paddings. Must be length of 2.");
return pad(x, [paddings], constantValue);
}
const pad1d = op({pad1d_});
// node_modules/@tensorflow/tfjs-core/dist/ops/pad2d.js
function pad2d_(x, paddings, constantValue = 0) {
assert(paddings.length === 2 && paddings[0].length === 2 && paddings[1].length === 2, () => "Invalid number of paddings. Must be length of 2 each.");
return pad(x, paddings, constantValue);
}
const pad2d = op({pad2d_});
// node_modules/@tensorflow/tfjs-core/dist/ops/pad3d.js
function pad3d_(x, paddings, constantValue = 0) {
assert(paddings.length === 3 && paddings[0].length === 2 && paddings[1].length === 2 && paddings[2].length === 2, () => "Invalid number of paddings. Must be length of 2 each.");
return pad(x, paddings, constantValue);
}
const pad3d = op({pad3d_});
// node_modules/@tensorflow/tfjs-core/dist/ops/pad4d.js
function pad4d_(x, paddings, constantValue = 0) {
assert(paddings.length === 4 && paddings[0].length === 2 && paddings[1].length === 2 && paddings[2].length === 2 && paddings[3].length === 2, () => "Invalid number of paddings. Must be length of 2 each.");
return pad(x, paddings, constantValue);
}
const pad4d = op({pad4d_});
// node_modules/@tensorflow/tfjs-core/dist/ops/space_to_batch_nd.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function spaceToBatchND_(x, blockShape, paddings) {
const $x = convertToTensor(x, "x", "spaceToBatchND");
assert($x.rank >= 1 + blockShape.length, () => `input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`);
assert(paddings.length === blockShape.length, () => `paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`);
assert($x.shape.reduce((a, b, i) => {
if (i > 0 && i <= blockShape.length) {
return a && (b + paddings[i - 1][0] + paddings[i - 1][1]) % blockShape[i - 1] === 0;
}
return a;
}, true), () => `input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`);
const forward = (backend3) => backend3.spaceToBatchND($x, blockShape, paddings);
const inputs = {x: $x};
const attrs = {blockShape, paddings};
return ENGINE.runKernelFunc(forward, inputs, null, SpaceToBatchND, attrs);
}
const spaceToBatchND = op({spaceToBatchND_});
// node_modules/@tensorflow/tfjs-core/dist/ops/pool.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function pool_(input2, windowShape, poolingType, pad8, dilations, strides) {
if (dilations == null) {
dilations = [1, 1];
}
if (strides == null) {
strides = 1;
}
if (pad8 === 0) {
pad8 = "valid";
}
const $x = convertToTensor(input2, "x", "maxPool");
let x4D = $x;
let reshapedTo4D = false;
if ($x.rank === 3) {
reshapedTo4D = true;
x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
}
assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in pool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
const convInfo = computePool2DInfo(x4D.shape, windowShape, strides, dilations, pad8);
const dilation = [convInfo.dilationHeight, convInfo.dilationWidth];
let basePadding;
if (pad8 === "same") {
basePadding = withSpaceToBatchBasePaddings([convInfo.filterHeight, convInfo.filterWidth], dilation);
} else {
basePadding = [[0, 0], [0, 0]];
}
const isDilationOne = dilation[0] === 1 && dilation[1] === 1;
const [adjustedPadding, adjustedCrops] = requiredSpaceToBatchPaddings([convInfo.inHeight, convInfo.inWidth], dilation, basePadding);
const convertedPad = isDilationOne ? pad8 : "valid";
const convertedX = isDilationOne ? x4D : spaceToBatchND(x4D, dilation, adjustedPadding);
const forwardOp = poolingType === "avg" ? () => avgPool(convertedX, windowShape, strides, convertedPad) : () => maxPool(convertedX, windowShape, strides, convertedPad);
const y = forwardOp();
const res = isDilationOne ? y : batchToSpaceND(y, dilation, adjustedCrops);
if (reshapedTo4D) {
return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
}
return res;
}
function requiredSpaceToBatchPaddings(inputShape, blockShape, basePadding) {
const padStart = basePadding.map((b) => b[0]);
const origPadEnd = basePadding.map((b) => b[1]);
const fullInputShape = inputShape.concat(padStart, origPadEnd);
const padEndExtra = blockShape.map((b, i) => (b - fullInputShape[i] % b) % b);
const padEnd = origPadEnd.map((s, i) => s + padEndExtra[i]);
const paddings = blockShape.map((_, i) => [padStart[i], padEnd[i]]);
const crops = blockShape.map((_, i) => [0, padEndExtra[i]]);
return [paddings, crops];
}
function withSpaceToBatchBasePaddings(filterShape, dilation) {
const dilatedFilterShape = filterShape.map((s, i) => {
return s + (s - 1) * (dilation[i] - 1);
});
const padExtraShape = dilatedFilterShape.map((s) => s - 1);
const padExtraStart = padExtraShape.map((s) => Math.floor(s / 2));
const padExtraEnd = padExtraShape.map((s, i) => s - padExtraStart[i]);
return padExtraShape.map((_, i) => {
return [padExtraStart[i], padExtraEnd[i]];
});
}
const pool = op({pool_});
// node_modules/@tensorflow/tfjs-core/dist/ops/pow.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function pow_(base2, exp7) {
let $base = convertToTensor(base2, "base", "pow");
let $exp = convertToTensor(exp7, "exp", "pow");
[$base, $exp] = makeTypesMatch($base, $exp);
const inputs = {a: $base, b: $exp};
const forward = (backend3, save) => {
const y = backend3.pow($base, $exp);
save([$base, $exp, y]);
return y;
};
return ENGINE.runKernelFunc(forward, inputs, null, Pow);
}
const pow = op({pow_});
// node_modules/@tensorflow/tfjs-core/dist/ops/prelu.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function prelu_(x, alpha) {
const $x = convertToTensor(x, "x", "prelu");
const $alpha = convertToTensor(alpha, "alpha", "prelu");
const forward = (backend3, save) => {
const res = backend3.prelu($x, $alpha);
save([$x, $alpha]);
return res;
};
const inputs = {x: $x, alpha: $alpha};
return ENGINE.runKernelFunc(forward, inputs, null, Prelu);
}
const prelu = op({prelu_});
// node_modules/@tensorflow/tfjs-core/dist/ops/prod.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function prod_(x, axis = null, keepDims = false) {
let $x = convertToTensor(x, "x", "prod");
if ($x.dtype === "bool") {
$x = cast($x, "int32");
}
const forward = (backend3) => {
const axes = parseAxisParam(axis, $x.shape);
const permutation = getAxesPermutation(axes, $x.rank);
let reductionAxes = axes;
let permutedX = $x;
if (permutation != null) {
permutedX = transpose($x, permutation);
reductionAxes = getInnerMostAxes(reductionAxes.length, $x.rank);
}
let value = backend3.prod(permutedX, reductionAxes);
if (keepDims) {
const newShape = expandShapeToKeepDim(value.shape, axes);
value = reshape(value, newShape);
}
return value;
};
const inputs = {x: $x};
const attrs = {axis, keepDims};
return ENGINE.runKernelFunc(forward, inputs, null, Prod, attrs);
}
const prod = op({prod_});
// node_modules/@tensorflow/tfjs-core/dist/ops/rand.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function rand_(shape, randFunction, dtype) {
const size = sizeFromShape(shape);
let values = null;
if (dtype == null || dtype === "float32") {
values = new Float32Array(size);
} else if (dtype === "int32") {
values = new Int32Array(size);
} else if (dtype === "bool") {
values = new Uint8Array(size);
} else {
throw new Error(`Unknown data type ${dtype}`);
}
for (let i = 0; i < size; i++) {
values[i] = randFunction();
}
return ENGINE.makeTensor(values, shape, dtype);
}
const rand = op({rand_});
// node_modules/@tensorflow/tfjs-core/dist/ops/rand_util.js
const seedrandom = __toModule(require_seedrandom2());
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
class MPRandGauss {
constructor(mean5, stdDeviation, dtype, truncated, seed) {
this.mean = mean5;
this.stdDev = stdDeviation;
this.dtype = dtype;
this.nextVal = NaN;
this.truncated = truncated;
if (this.truncated) {
this.upper = this.mean + this.stdDev * 2;
this.lower = this.mean - this.stdDev * 2;
}
const seedValue = seed ? seed : Math.random();
this.random = seedrandom.alea(seedValue.toString());
}
nextValue() {
if (!isNaN(this.nextVal)) {
const value = this.nextVal;
this.nextVal = NaN;
return value;
}
let resultX, resultY;
let isValid = false;
while (!isValid) {
let v1, v2, s;
do {
v1 = 2 * this.random() - 1;
v2 = 2 * this.random() - 1;
s = v1 * v1 + v2 * v2;
} while (s >= 1 || s === 0);
const mul29 = Math.sqrt(-2 * Math.log(s) / s);
resultX = this.mean + this.stdDev * v1 * mul29;
resultY = this.mean + this.stdDev * v2 * mul29;
if (!this.truncated || this.isValidTruncated(resultX)) {
isValid = true;
}
}
if (!this.truncated || this.isValidTruncated(resultY)) {
this.nextVal = this.convertValue(resultY);
}
return this.convertValue(resultX);
}
convertValue(value) {
if (this.dtype == null || this.dtype === "float32") {
return value;
}
return Math.round(value);
}
isValidTruncated(value) {
return value <= this.upper && value >= this.lower;
}
}
class RandGamma {
constructor(alpha, beta, dtype, seed) {
this.alpha = alpha;
this.beta = 1 / beta;
this.dtype = dtype;
const seedValue = seed ? seed : Math.random();
this.randu = seedrandom.alea(seedValue.toString());
this.randn = new MPRandGauss(0, 1, dtype, false, this.randu());
if (alpha < 1) {
this.d = alpha + 2 / 3;
} else {
this.d = alpha - 1 / 3;
}
this.c = 1 / Math.sqrt(9 * this.d);
}
nextValue() {
let x2, v0, v1, x, u, v;
while (true) {
do {
x = this.randn.nextValue();
v = 1 + this.c * x;
} while (v <= 0);
v *= v * v;
x2 = x * x;
v0 = 1 - 0.331 * x2 * x2;
v1 = 0.5 * x2 + this.d * (1 - v + Math.log(v));
u = this.randu();
if (u < v0 || Math.log(u) < v1) {
break;
}
}
v = 1 / this.beta * this.d * v;
if (this.alpha < 1) {
v *= Math.pow(this.randu(), 1 / this.alpha);
}
return this.convertValue(v);
}
convertValue(value) {
if (this.dtype === "float32") {
return value;
}
return Math.round(value);
}
}
class UniformRandom {
constructor(min6 = 0, max8 = 1, dtype, seed) {
this.canReturnFloat = () => this.dtype == null || this.dtype === "float32";
this.min = min6;
this.range = max8 - min6;
this.dtype = dtype;
if (seed == null) {
seed = Math.random();
}
if (typeof seed === "number") {
seed = seed.toString();
}
if (!this.canReturnFloat() && this.range <= 1) {
throw new Error(`The difference between ${min6} - ${max8} <= 1 and dtype is not float`);
}
this.random = seedrandom.alea(seed);
}
convertValue(value) {
if (this.canReturnFloat()) {
return value;
}
return Math.round(value);
}
nextValue() {
return this.convertValue(this.min + this.range * this.random());
}
}
// node_modules/@tensorflow/tfjs-core/dist/ops/random_gamma.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function randomGamma_(shape, alpha, beta = 1, dtype = "float32", seed) {
if (beta == null) {
beta = 1;
}
if (dtype == null) {
dtype = "float32";
}
if (dtype !== "float32" && dtype !== "int32") {
throw new Error(`Unsupported data type ${dtype}`);
}
const rgamma = new RandGamma(alpha, beta, dtype, seed);
const res = buffer(shape, dtype);
for (let i = 0; i < res.values.length; i++) {
res.values[i] = rgamma.nextValue();
}
return res.toTensor();
}
const randomGamma = op({randomGamma_});
// node_modules/@tensorflow/tfjs-core/dist/ops/random_normal.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function randomNormal_(shape, mean5 = 0, stdDev = 1, dtype, seed) {
if (dtype != null && dtype === "bool") {
throw new Error(`Unsupported data type ${dtype}`);
}
const randGauss = new MPRandGauss(mean5, stdDev, dtype, false, seed);
const res = buffer(shape, dtype);
for (let i = 0; i < res.values.length; i++) {
res.values[i] = randGauss.nextValue();
}
return res.toTensor();
}
const randomNormal = op({randomNormal_});
// node_modules/@tensorflow/tfjs-core/dist/ops/random_uniform.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function randomUniform_(shape, minval = 0, maxval = 1, dtype = "float32", seed) {
const res = buffer(shape, dtype);
const random = new UniformRandom(minval, maxval, null, seed);
for (let i = 0; i < res.values.length; i++) {
res.values[i] = random.nextValue();
}
return res.toTensor();
}
const randomUniform = op({randomUniform_});
// node_modules/@tensorflow/tfjs-core/dist/ops/tensor1d.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function tensor1d(values, dtype) {
assertNonNull(values);
const inferredShape = inferShape(values, dtype);
if (inferredShape.length !== 1) {
throw new Error("tensor1d() requires values to be a flat/TypedArray");
}
const shape = null;
return makeTensor(values, shape, inferredShape, dtype);
}
// node_modules/@tensorflow/tfjs-core/dist/ops/range.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function range(start, stop, step4 = 1, dtype = "float32") {
if (step4 === 0) {
throw new Error("Cannot have a step of zero");
}
const forward = () => {
const sameStartStop = start === stop;
const increasingRangeNegativeStep = start < stop && step4 < 0;
const decreasingRangePositiveStep = stop < start && step4 > 1;
if (sameStartStop || increasingRangeNegativeStep || decreasingRangePositiveStep) {
return zeros([0], dtype);
}
const numElements = Math.abs(Math.ceil((stop - start) / step4));
const values = makeZerosTypedArray(numElements, dtype);
if (stop < start && step4 === 1) {
step4 = -1;
}
values[0] = start;
for (let i = 1; i < values.length; i++) {
values[i] = values[i - 1] + step4;
}
return tensor1d(values, dtype);
};
const attrs = {start, stop, step: step4, dtype};
return ENGINE.runKernelFunc(forward, {}, null, Range, attrs);
}
// node_modules/@tensorflow/tfjs-core/dist/ops/reciprocal.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function reciprocal_(x) {
const $x = convertToTensor(x, "x", "reciprocal");
const inputs = {x: $x};
return ENGINE.runKernelFunc((backend3, save) => {
const res = backend3.reciprocal($x);
save([$x]);
return res;
}, inputs, null, Reciprocal);
}
const reciprocal = op({reciprocal_});
// node_modules/@tensorflow/tfjs-core/dist/ops/relu.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function relu_(x) {
const $x = convertToTensor(x, "x", "relu");
const forward = (backend3, save) => {
save([$x]);
if ($x.dtype === "bool") {
return cast($x, "int32");
}
return backend3.relu($x);
};
const inputs = {x: $x};
return ENGINE.runKernelFunc(forward, inputs, null, Relu);
}
const relu = op({relu_});
// node_modules/@tensorflow/tfjs-core/dist/ops/relu6.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function relu6_(x) {
const $x = convertToTensor(x, "x", "relu6");
const forward = (backend3, save) => {
save([$x]);
if ($x.dtype === "bool") {
return cast($x, "int32");
}
return backend3.relu6($x);
};
const inputs = {x: $x};
return ENGINE.runKernelFunc(forward, inputs, null, Relu6);
}
const relu6 = op({relu6_});
// node_modules/@tensorflow/tfjs-core/dist/ops/reverse.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function reverse_(x, axis) {
const $x = convertToTensor(x, "x", "reverse");
const forward = (backend3) => {
const axes = parseAxisParam(axis, $x.shape);
if ($x.rank === 0) {
return clone($x);
}
const res = backend3.reverse($x, axes);
return reshape(res, $x.shape);
};
const inputs = {x: $x};
const attrs = {dims: axis};
return ENGINE.runKernelFunc(forward, inputs, null, Reverse, attrs);
}
const reverse = op({reverse_});
// node_modules/@tensorflow/tfjs-core/dist/ops/reverse_1d.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function reverse1d_(x) {
const $x = convertToTensor(x, "x", "reverse");
assert($x.rank === 1, () => `Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`);
return reverse($x, 0);
}
const reverse1d = op({reverse1d_});
// node_modules/@tensorflow/tfjs-core/dist/ops/reverse_2d.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function reverse2d_(x, axis) {
const $x = convertToTensor(x, "x", "reverse");
assert($x.rank === 2, () => `Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`);
return reverse($x, axis);
}
const reverse2d = op({reverse2d_});
// node_modules/@tensorflow/tfjs-core/dist/ops/reverse_3d.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function reverse3d_(x, axis) {
const $x = convertToTensor(x, "x", "reverse");
assert($x.rank === 3, () => `Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`);
return reverse($x, axis);
}
const reverse3d = op({reverse3d_});
// node_modules/@tensorflow/tfjs-core/dist/ops/reverse_4d.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function reverse4d_(x, axis) {
const $x = convertToTensor(x, "x", "reverse");
assert($x.rank === 4, () => `Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`);
return reverse($x, axis);
}
const reverse4d = op({reverse4d_});
// node_modules/@tensorflow/tfjs-core/dist/ops/round.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function round_(x) {
const $x = convertToTensor(x, "x", "round");
const inputs = {x: $x};
return ENGINE.runKernelFunc((backend3) => backend3.round($x), inputs, null, Round);
}
const round = op({round_});
// node_modules/@tensorflow/tfjs-core/dist/ops/rsqrt.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function rsqrt_(x) {
const $x = convertToTensor(x, "x", "rsqrt");
const inputs = {x: $x};
return ENGINE.runKernelFunc((backend3, save) => {
const res = backend3.rsqrt($x);
save([$x]);
return res;
}, inputs, null, Rsqrt);
}
const rsqrt = op({rsqrt_});
// node_modules/@tensorflow/tfjs-core/dist/ops/selu.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function selu_(x) {
const $x = convertToTensor(x, "x", "selu");
const forward = (backend3, save) => {
const res = backend3.selu($x);
save([$x]);
return res;
};
const inputs = {x: $x};
return ENGINE.runKernelFunc(forward, inputs, null, Selu);
}
const selu = op({selu_});
// node_modules/@tensorflow/tfjs-core/dist/ops/separable_conv2d.js
function separableConv2d_(x, depthwiseFilter, pointwiseFilter, strides, pad8, dilation = [1, 1], dataFormat = "NHWC") {
const $x = convertToTensor(x, "x", "separableConv2d");
const $depthwiseFilter = convertToTensor(depthwiseFilter, "depthwiseFilter", "separableConv2d");
const $pointwiseFilter = convertToTensor(pointwiseFilter, "pointwiseFilter", "separableConv2d");
let x4D = $x;
let reshapedTo4D = false;
if ($x.rank === 3) {
reshapedTo4D = true;
x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
}
if (dataFormat === "NCHW") {
throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");
}
assert(x4D.rank === 4, () => `Error in separableConv2d: input must be rank 4, but got rank ${x4D.rank}.`);
assert($depthwiseFilter.rank === 4, () => `Error in separableConv2d: depthwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`);
assert($pointwiseFilter.rank === 4, () => `Error in separableConv2d: pointwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`);
assert($pointwiseFilter.shape[0] === 1, () => `Error in separableConv2d: the first dimension of pointwise filter must be 1, but got ${$pointwiseFilter.shape[0]}.`);
assert($pointwiseFilter.shape[1] === 1, () => `Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);
const inChannels = $depthwiseFilter.shape[2];
const channelMultiplier = $depthwiseFilter.shape[3];
assert($pointwiseFilter.shape[2] === inChannels * channelMultiplier, () => `Error in separableConv2d: the third dimension of pointwise filter must be ${inChannels * channelMultiplier}, but got ${$pointwiseFilter.shape[2]}.`);
const depthwise = depthwiseConv2d(x4D, $depthwiseFilter, strides, pad8, dataFormat, dilation);
const pointwiseStride = 1;
const res = conv2d(depthwise, $pointwiseFilter, pointwiseStride, "valid", dataFormat);
if (reshapedTo4D) {
return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
}
return res;
}
const separableConv2d = op({separableConv2d_});
// node_modules/@tensorflow/tfjs-core/dist/ops/setdiff1d_async.js
/**
* @license
* Copyright 2020 Google Inc. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
async function setdiff1dAsync_(x, y) {
const $x = convertToTensor(x, "x", "setdiff1d");
const $y = convertToTensor(y, "y", "setdiff1d");
assert($x.dtype === $y.dtype, () => `x and y should have the same dtype, but got x (${$x.dtype}) and y (${$y.dtype}).`);
assert($x.rank === 1, () => `x should be 1D tensor, but got x (${$x.shape}).`);
assert($y.rank === 1, () => `y should be 1D tensor, but got y (${$y.shape}).`);
const xVals = await $x.data();
const yVals = await $y.data();
const ySet = new Set(yVals);
let outputSize = 0;
for (let i = 0; i < xVals.length; i++) {
if (!ySet.has(xVals[i])) {
outputSize++;
}
}
const buffer10 = new TensorBuffer([outputSize], $x.dtype);
const indices = new TensorBuffer([outputSize], "int32");
for (let i = 0, p = 0; i < xVals.length; i++) {
if (!ySet.has(xVals[i])) {
buffer10.values[p] = xVals[i];
indices.values[p] = i;
p++;
}
}
return [buffer10.toTensor(), indices.toTensor()];
}
const setdiff1dAsync = setdiff1dAsync_;
// node_modules/@tensorflow/tfjs-core/dist/ops/sign.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function sign_(x) {
const $x = convertToTensor(x, "x", "sign");
const inputs = {x: $x};
return ENGINE.runKernelFunc((backend3) => backend3.sign($x), inputs, null, Sign);
}
const sign = op({sign_});
// node_modules/@tensorflow/tfjs-core/dist/ops/sin.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function sin_(x) {
const $x = convertToTensor(x, "x", "sin");
const inputs = {x: $x};
return ENGINE.runKernelFunc((backend3, save) => {
const res = backend3.sin($x);
save([$x]);
return res;
}, inputs, null, Sin);
}
const sin = op({sin_});
// node_modules/@tensorflow/tfjs-core/dist/ops/sinh.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function sinh_(x) {
const $x = convertToTensor(x, "x", "sinh");
const inputs = {x: $x};
return ENGINE.runKernelFunc((backend3, save) => {
const res = backend3.sinh($x);
save([$x]);
return res;
}, inputs, null, Sinh);
}
const sinh = op({sinh_});
// node_modules/@tensorflow/tfjs-core/dist/ops/slice1d.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function slice1d_(x, begin, size) {
const $x = convertToTensor(x, "x", "slice1d");
assert($x.rank === 1, () => `slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`);
return slice($x, [begin], [size]);
}
const slice1d = op({slice1d_});
// node_modules/@tensorflow/tfjs-core/dist/ops/slice2d.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function slice2d_(x, begin, size) {
const $x = convertToTensor(x, "x", "slice2d");
assert($x.rank === 2, () => `slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`);
return slice($x, begin, size);
}
const slice2d = op({slice2d_});
// node_modules/@tensorflow/tfjs-core/dist/ops/slice3d.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function slice3d_(x, begin, size) {
const $x = convertToTensor(x, "x", "slice3d");
assert($x.rank === 3, () => `slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`);
return slice($x, begin, size);
}
const slice3d = op({slice3d_});
// node_modules/@tensorflow/tfjs-core/dist/ops/slice4d.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function slice4d_(x, begin, size) {
const $x = convertToTensor(x, "x", "slice4d");
assert($x.rank === 4, () => `slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`);
return slice($x, begin, size);
}
const slice4d = op({slice4d_});
// node_modules/@tensorflow/tfjs-core/dist/ops/softmax.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function softmax_(logits, dim = -1) {
const $logits = convertToTensor(logits, "logits", "softmax", "float32");
if (dim === -1) {
dim = $logits.rank - 1;
}
if (dim !== $logits.rank - 1) {
throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and dim was ${dim}`);
}
const inputs = {logits: $logits};
const attrs = {dim};
return ENGINE.runKernelFunc((backend3, save) => {
const y = backend3.softmax($logits, dim);
save([y]);
return y;
}, inputs, null, Softmax, attrs);
}
const softmax = op({softmax_});
// node_modules/@tensorflow/tfjs-core/dist/ops/spectral/fft.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function fft_(input2) {
assert(input2.dtype === "complex64", () => `The dtype for tf.spectral.fft() must be complex64 but got ${input2.dtype}.`);
const inputs = {input: input2};
return ENGINE.runKernelFunc((backend3) => {
const innerDimensionSize = input2.shape[input2.shape.length - 1];
const batch = input2.size / innerDimensionSize;
const input2D = input2.as2D(batch, innerDimensionSize);
const result = backend3.fft(input2D);
return result.reshape(input2.shape);
}, inputs, null, FFT);
}
const fft = op({fft_});
// node_modules/@tensorflow/tfjs-core/dist/ops/spectral/ifft.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function ifft_(input2) {
assert(input2.dtype === "complex64", () => `The dtype for tf.spectral.ifft() must be complex64 but got ${input2.dtype}.`);
const inputs = {input: input2};
return ENGINE.runKernelFunc((backend3) => {
const innerDimensionSize = input2.shape[input2.shape.length - 1];
const batch = input2.size / innerDimensionSize;
const input2D = reshape(input2, [batch, innerDimensionSize]);
const result = backend3.ifft(input2D);
return reshape(result, input2.shape);
}, inputs, null, IFFT);
}
const ifft = op({ifft_});
// node_modules/@tensorflow/tfjs-core/dist/ops/spectral/irfft.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function irfft_(input2) {
const innerDimensionSize = input2.shape[input2.shape.length - 1];
const batch = input2.size / innerDimensionSize;
let ret;
if (innerDimensionSize <= 2) {
const complexInput = reshape(input2, [batch, innerDimensionSize]);
ret = ifft(complexInput);
} else {
const outputShape = [batch, 2 * (innerDimensionSize - 1)];
const realInput = reshape(real(input2), [batch, innerDimensionSize]);
const imagInput = reshape(imag(input2), [batch, innerDimensionSize]);
const realConjugate = reverse(slice(realInput, [0, 1], [batch, innerDimensionSize - 2]), 1);
const imagConjugate = mul(reverse(slice(imagInput, [0, 1], [batch, innerDimensionSize - 2]), 1), scalar(-1));
const r = concat([realInput, realConjugate], 1);
const i = concat([imagInput, imagConjugate], 1);
const complexInput = reshape(complex(r, i), [outputShape[0], outputShape[1]]);
ret = ifft(complexInput);
}
ret = real(ret);
if (input2.rank === 3 && input2.shape[0] !== 0) {
const temp = ret;
const batch2 = input2.shape[0];
ret = reshape(ret, [batch2, ret.shape[0] / batch2, ret.shape[1]]);
temp.dispose();
}
return ret;
}
const irfft = op({irfft_});
// node_modules/@tensorflow/tfjs-core/dist/ops/split_util.js
function prepareSplitSize(x, numOrSizeSplits, axis = 0) {
let splitSizes = [];
if (typeof numOrSizeSplits === "number") {
assert(x.shape[axis] % numOrSizeSplits === 0, () => "Number of splits must evenly divide the axis.");
splitSizes = new Array(numOrSizeSplits).fill(x.shape[axis] / numOrSizeSplits);
} else {
const numOfNegs = numOrSizeSplits.reduce((count2, value) => {
if (value === -1) {
count2 += 1;
}
return count2;
}, 0);
assert(numOfNegs <= 1, () => "There should be only one negative value in split array.");
const negIndex = numOrSizeSplits.indexOf(-1);
if (negIndex !== -1) {
const total = numOrSizeSplits.reduce((a, b) => b > 0 ? a + b : a);
numOrSizeSplits[negIndex] = x.shape[axis] - total;
}
assert(x.shape[axis] === numOrSizeSplits.reduce((a, b) => a + b), () => "The sum of sizes must match the size of the axis dimension.");
splitSizes = numOrSizeSplits;
}
return splitSizes;
}
// node_modules/@tensorflow/tfjs-core/dist/ops/split.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function split_(x, numOrSizeSplits, axis = 0) {
const $x = convertToTensor(x, "x", "split");
const forward = (backend3, _) => {
const $axis = parseAxisParam(axis, $x.shape)[0];
const splitSizes = prepareSplitSize($x, numOrSizeSplits, $axis);
return backend3.split($x, splitSizes, $axis);
};
const inputs = {x: $x};
const attr = {numOrSizeSplits, axis};
return ENGINE.runKernelFunc(forward, inputs, null, SplitV, attr);
}
const split = op({split_});
// node_modules/@tensorflow/tfjs-core/dist/ops/spectral/rfft.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function rfft_(input2, fftLength) {
assert(input2.dtype === "float32", () => `The dtype for rfft() must be real value but got ${input2.dtype}`);
let innerDimensionSize = input2.shape[input2.shape.length - 1];
const batch = input2.size / innerDimensionSize;
let adjustedInput;
if (fftLength != null && fftLength < innerDimensionSize) {
const begin = input2.shape.map((v) => 0);
const size = input2.shape.map((v) => v);
size[input2.shape.length - 1] = fftLength;
adjustedInput = slice(input2, begin, size);
innerDimensionSize = fftLength;
} else if (fftLength != null && fftLength > innerDimensionSize) {
const zerosShape = input2.shape.map((v) => v);
zerosShape[input2.shape.length - 1] = fftLength - innerDimensionSize;
adjustedInput = concat([input2, zeros(zerosShape)], input2.shape.length - 1);
innerDimensionSize = fftLength;
} else {
adjustedInput = input2;
}
const zerosInput = zerosLike(adjustedInput);
const complexInput = reshape(complex(adjustedInput, zerosInput), [batch, innerDimensionSize]);
const ret = fft(complexInput);
const half = Math.floor(innerDimensionSize / 2) + 1;
const realValues = real(ret);
const imagValues = imag(ret);
const realComplexConjugate = split(realValues, [half, innerDimensionSize - half], realValues.shape.length - 1);
const imagComplexConjugate = split(imagValues, [half, innerDimensionSize - half], imagValues.shape.length - 1);
const outputShape = adjustedInput.shape.slice();
outputShape[adjustedInput.shape.length - 1] = half;
return reshape(complex(realComplexConjugate[0], imagComplexConjugate[0]), outputShape);
}
const rfft = op({rfft_});
// node_modules/@tensorflow/tfjs-core/dist/ops/sqrt.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function sqrt_(x) {
const $x = convertToTensor(x, "x", "sqrt");
const inputs = {x: $x};
return ENGINE.runKernelFunc((backend3, save) => {
const res = backend3.sqrt($x);
save([$x]);
return res;
}, inputs, null, Sqrt);
}
const sqrt = op({sqrt_});
// node_modules/@tensorflow/tfjs-core/dist/ops/squared_difference.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function squaredDifference_(a, b) {
let $a = convertToTensor(a, "a", "squaredDifference");
let $b = convertToTensor(b, "b", "squaredDifference");
[$a, $b] = makeTypesMatch($a, $b);
assertAndGetBroadcastShape($a.shape, $b.shape);
const forward = (backend3, save) => {
const res = backend3.squaredDifference($a, $b);
save([$a, $b]);
return res;
};
const inputs = {a: $a, b: $b};
const attrs = {};
return ENGINE.runKernelFunc(forward, inputs, null, SquaredDifference, attrs);
}
const squaredDifference = op({squaredDifference_});
// node_modules/@tensorflow/tfjs-core/dist/ops/squeeze.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function squeeze_(x, axis) {
const $x = convertToTensor(x, "x", "squeeze");
return reshape($x, squeezeShape($x.shape, axis).newShape);
}
const squeeze = op({squeeze_});
// node_modules/@tensorflow/tfjs-core/dist/ops/stack.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function stack_(tensors, axis = 0) {
const $tensors = convertToTensorArray(tensors, "tensors", "stack");
assert($tensors.length >= 1, () => "Pass at least one tensor to tf.stack");
if ($tensors.length === 1) {
return expandDims($tensors[0], axis);
}
const rank = $tensors[0].rank;
const shape = $tensors[0].shape;
const dtype = $tensors[0].dtype;
assert(axis <= rank, () => "Axis must be <= rank of the tensor");
$tensors.forEach((t) => {
assertShapesMatch(shape, t.shape, "All tensors passed to stack must have matching shapes");
assert(dtype === t.dtype, () => "All tensors passed to stack must have matching dtypes");
});
const expandedTensors = $tensors.map((t) => expandDims(t, axis));
return concat(expandedTensors, axis);
}
const stack = op({stack_});
// node_modules/@tensorflow/tfjs-core/dist/ops/step.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function step_(x, alpha = 0) {
const $x = convertToTensor(x, "x", "step");
const inputs = {x: $x};
const attrs = {alpha};
return ENGINE.runKernelFunc((backend3) => backend3.step($x, alpha), inputs, null, Step, attrs);
}
const step = op({step_});
// node_modules/@tensorflow/tfjs-core/dist/ops/strided_slice.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function stridedSlice_(x, begin, end, strides, beginMask = 0, endMask = 0, ellipsisMask = 0, newAxisMask = 0, shrinkAxisMask = 0) {
let $x = convertToTensor(x, "x", "stridedSlice");
const forward = (backend3) => {
if (strides == null) {
strides = new Array(begin.length);
}
const ellipsisAxes = maskToAxes(ellipsisMask);
if (ellipsisAxes.length > 1) {
throw new Error("Multiple ellipses in slice is not allowed.");
}
if (ellipsisMask !== 0 && newAxisMask !== 0) {
throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");
}
if (ellipsisMask !== 0 && shrinkAxisMask !== 0) {
throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");
}
const numInterpolatedAxes = $x.rank - begin.length;
const expandAxes = maskToAxes(newAxisMask);
const newShape = $x.shape.slice();
expandAxes.forEach((axis) => {
begin[axis] = 0;
end[axis] = 1;
newShape.splice(axis, 0, 1);
});
$x = reshape($x, newShape);
const {begin: normalizedBegin, end: normalizedEnd, strides: normalizedStrides} = getNormalizedAxes($x.shape, ellipsisAxes, numInterpolatedAxes, begin, end, strides, beginMask, endMask, ellipsisMask);
begin = normalizedBegin;
end = normalizedEnd;
strides = normalizedStrides;
const shrinkAxes = maskToAxes(shrinkAxisMask);
shrinkAxes.forEach((axis) => {
end[axis] = begin[axis] + 1;
strides[axis] = 1;
});
const size = computeOutShape(begin, end, strides);
const outShape = size.filter((_, axis) => shrinkAxes.indexOf(axis) === -1);
const nonStrided = strides.every((v) => v === 1);
if (nonStrided) {
return reshape(slice($x, begin, size), outShape);
}
const res = backend3.stridedSlice($x, begin, end, strides);
return reshape(res, outShape);
};
const inputs = {x: $x};
const attrs = {
begin,
end,
strides,
beginMask,
endMask,
ellipsisMask,
newAxisMask,
shrinkAxisMask
};
return ENGINE.runKernelFunc(forward, inputs, null, StridedSlice, attrs);
}
const stridedSlice = op({stridedSlice_});
// node_modules/@tensorflow/tfjs-core/dist/ops/tan.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function tan_(x) {
const $x = convertToTensor(x, "x", "tan");
const inputs = {x: $x};
return ENGINE.runKernelFunc((backend3, save) => {
const res = backend3.tan($x);
save([$x]);
return res;
}, inputs, null, Tan);
}
const tan = op({tan_});
// node_modules/@tensorflow/tfjs-core/dist/ops/tensor2d.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function tensor2d(values, shape, dtype) {
assertNonNull(values);
if (shape != null && shape.length !== 2) {
throw new Error("tensor2d() requires shape to have two numbers");
}
const inferredShape = inferShape(values, dtype);
if (inferredShape.length !== 2 && inferredShape.length !== 1) {
throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");
}
if (inferredShape.length === 1 && shape == null) {
throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");
}
return makeTensor(values, shape, inferredShape, dtype);
}
// node_modules/@tensorflow/tfjs-core/dist/ops/tensor4d.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function tensor4d(values, shape, dtype) {
assertNonNull(values);
if (shape != null && shape.length !== 4) {
throw new Error("tensor4d() requires shape to have four numbers");
}
const inferredShape = inferShape(values, dtype);
if (inferredShape.length !== 4 && inferredShape.length !== 1) {
throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");
}
if (inferredShape.length === 1 && shape == null) {
throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");
}
return makeTensor(values, shape, inferredShape, dtype);
}
// node_modules/@tensorflow/tfjs-core/dist/ops/tensor5d.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function tensor5d(values, shape, dtype) {
assertNonNull(values);
if (shape != null && shape.length !== 5) {
throw new Error("tensor5d() requires shape to have five numbers");
}
const inferredShape = inferShape(values, dtype);
if (inferredShape.length !== 5 && inferredShape.length !== 1) {
throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");
}
if (inferredShape.length === 1 && shape == null) {
throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");
}
return makeTensor(values, shape, inferredShape, dtype);
}
// node_modules/@tensorflow/tfjs-core/dist/ops/tensor6d.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function tensor6d(values, shape, dtype) {
assertNonNull(values);
if (shape != null && shape.length !== 6) {
throw new Error("tensor6d() requires shape to have six numbers");
}
const inferredShape = inferShape(values, dtype);
if (inferredShape.length !== 6 && inferredShape.length !== 1) {
throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");
}
if (inferredShape.length === 1 && shape == null) {
throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");
}
shape = shape || inferredShape;
return makeTensor(values, shape, inferredShape, dtype);
}
// node_modules/@tensorflow/tfjs-core/dist/ops/topk.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function topk_(x, k = 1, sorted = true) {
const $x = convertToTensor(x, "x", "topk");
if ($x.rank === 0) {
throw new Error("topk() expects the input to be of rank 1 or higher");
}
const lastDim = $x.shape[$x.shape.length - 1];
if (k > lastDim) {
throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) but got ${k}`);
}
const inputs = {x: $x};
const attrs = {k, sorted};
const [values, indices] = ENGINE.runKernelFunc((b) => b.topk($x, k, sorted), inputs, null, TopK, attrs);
return {values, indices};
}
const topk = op({topk_});
// node_modules/@tensorflow/tfjs-core/dist/ops/truncated_normal.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function truncatedNormal_(shape, mean5 = 0, stdDev = 1, dtype, seed) {
if (dtype != null && dtype === "bool") {
throw new Error(`Unsupported data type $ { dtype }`);
}
const randGauss = new MPRandGauss(mean5, stdDev, dtype, true, seed);
const res = buffer(shape, dtype);
for (let i = 0; i < res.values.length; i++) {
res.values[i] = randGauss.nextValue();
}
return res.toTensor();
}
const truncatedNormal = op({truncatedNormal_});
// node_modules/@tensorflow/tfjs-core/dist/ops/unique.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function unique_(x, axis = 0) {
const $x = convertToTensor(x, "x", "unique", null);
assert($x.rank > 0, () => "The input tensor must be at least 1D");
const inputs = {x: $x};
const attrs = {axis};
const [values, indices] = ENGINE.runKernel(Unique, inputs, attrs);
return {values, indices};
}
const unique = op({unique_});
// node_modules/@tensorflow/tfjs-core/dist/ops/unsorted_segment_sum.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function unsortedSegmentSum_(x, segmentIds, numSegments) {
const $x = convertToTensor(x, "x", "unsortedSegmentSum");
const $segmentIds = convertToTensor(segmentIds, "segmentIds", "unsortedSegmentSum", "int32");
assert(isInt(numSegments), () => "numSegments must be of dtype int");
const inputs = {x: $x, segmentIds: $segmentIds};
const attrs = {numSegments};
const forward = (backend3, save) => {
const res = backend3.unsortedSegmentSum($x, $segmentIds, numSegments);
save([$segmentIds]);
return res;
};
return ENGINE.runKernelFunc(forward, inputs, null, UnsortedSegmentSum, attrs);
}
const unsortedSegmentSum = op({unsortedSegmentSum_});
// node_modules/@tensorflow/tfjs-core/dist/ops/unstack.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function unstack_(x, axis = 0) {
const $x = convertToTensor(x, "x", "unstack");
assert(axis >= -$x.shape.length && axis < $x.shape.length, () => `Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`);
if (axis < 0) {
axis += $x.shape.length;
}
const inputs = {value: $x};
const attrs = {axis};
const forward = (backend3) => backend3.unstack($x, axis);
return ENGINE.runKernelFunc(forward, inputs, null, Unpack, attrs);
}
const unstack = op({unstack_});
// node_modules/@tensorflow/tfjs-core/dist/ops/variable.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function variable(initialValue, trainable = true, name, dtype) {
return ENGINE.makeVariable(initialValue, trainable, name, dtype);
}
// node_modules/@tensorflow/tfjs-core/dist/backends/where_impl.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function whereImpl(condShape, condVals) {
const indices = [];
for (let i = 0; i < condVals.length; i++) {
if (condVals[i]) {
indices.push(i);
}
}
const inBuffer = buffer(condShape, "int32");
const out = buffer([indices.length, condShape.length], "int32");
for (let i = 0; i < indices.length; i++) {
const loc = inBuffer.indexToLoc(indices[i]);
const offset = i * condShape.length;
out.values.set(loc, offset);
}
return out.toTensor();
}
// node_modules/@tensorflow/tfjs-core/dist/ops/where_async.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
async function whereAsync_(condition) {
const $condition = convertToTensor(condition, "condition", "whereAsync", "bool");
const vals = await $condition.data();
const res = whereImpl($condition.shape, vals);
if (condition !== $condition) {
$condition.dispose();
}
return res;
}
const whereAsync = whereAsync_;
// node_modules/@tensorflow/tfjs-core/dist/ops/boolean_mask.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
async function booleanMaskAsync_(tensor16, mask, axis) {
const $tensor = convertToTensor(tensor16, "tensor", "boolMask");
const $mask = convertToTensor(mask, "mask", "boolMask", "bool");
const axisFrom = axis == null ? 0 : axis;
const maskDim = $mask.rank;
const tensorShape = $tensor.shape;
assert(maskDim > 0, () => "mask cannot be scalar");
assertShapesMatch(tensorShape.slice(axisFrom, axisFrom + maskDim), $mask.shape, `mask's shape must match the first K dimensions of tensor's shape,`);
let leadingSize = 1;
for (let i = axisFrom; i < axisFrom + maskDim; i++) {
leadingSize *= tensorShape[i];
}
const targetTensorShape = tensorShape.slice(0, axisFrom).concat([leadingSize], tensorShape.slice(axisFrom + maskDim));
const reshapedTensor = reshape($tensor, targetTensorShape);
const reshapedMask = reshape($mask, [-1]);
const positivePositions = await whereAsync(reshapedMask);
const indices = squeeze(positivePositions, [1]);
const res = gather(reshapedTensor, indices, axisFrom);
if (tensor16 !== $tensor) {
$tensor.dispose();
}
if (mask !== $mask) {
$mask.dispose();
}
indices.dispose();
reshapedTensor.dispose();
reshapedMask.dispose();
positivePositions.dispose();
return res;
}
const booleanMaskAsync = booleanMaskAsync_;
// node_modules/@tensorflow/tfjs-core/dist/ops/compare.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function notEqualStrict_(a, b) {
deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
const $a = convertToTensor(a, "a", "notEqualStrict");
const $b = convertToTensor(b, "b", "notEqualStrict");
assertShapesMatch($a.shape, $b.shape, "Error in notEqualStrict: ");
return notEqual($a, $b);
}
function lessStrict_(a, b) {
deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
const $a = convertToTensor(a, "a", "lessStrict");
const $b = convertToTensor(b, "b", "lessStrict");
assertShapesMatch($a.shape, $b.shape, "Error in lessStrict: ");
return less($a, $b);
}
function equalStrict_(a, b) {
deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
const $a = convertToTensor(a, "a", "equalStrict");
const $b = convertToTensor(b, "b", "equalStrict");
assertShapesMatch($a.shape, $b.shape, "Error in equalStrict: ");
return equal($a, $b);
}
function lessEqualStrict_(a, b) {
deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
const $a = convertToTensor(a, "a", "lessEqualStrict");
const $b = convertToTensor(b, "b", "lessEqualStrict");
assertShapesMatch($a.shape, $b.shape, "Error in lessEqualStrict: ");
return lessEqual($a, $b);
}
function greaterStrict_(a, b) {
deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
const $a = convertToTensor(a, "a", "greaterStrict");
const $b = convertToTensor(b, "b", "greaterStrict");
assertShapesMatch($a.shape, $b.shape, "Error in greaterStrict: ");
return greater($a, $b);
}
function greaterEqualStrict_(a, b) {
deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
const $a = convertToTensor(a, "a", "greaterEqualStrict");
const $b = convertToTensor(b, "b", "greaterEqualStrict");
assertShapesMatch($a.shape, $b.shape, "Error in greaterEqualStrict: ");
return greaterEqual($a, $b);
}
const equalStrict = op({equalStrict_});
const greaterEqualStrict = op({greaterEqualStrict_});
const greaterStrict = op({greaterStrict_});
const lessEqualStrict = op({lessEqualStrict_});
const lessStrict = op({lessStrict_});
const notEqualStrict = op({notEqualStrict_});
// node_modules/@tensorflow/tfjs-core/dist/ops/binary_ops.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function addStrict_(a, b) {
deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
const $a = convertToTensor(a, "a", "addStrict");
const $b = convertToTensor(b, "b", "addStrict");
assertShapesMatch($a.shape, $b.shape, "Error in addStrict: ");
return add2($a, $b);
}
function subStrict_(a, b) {
deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
const $a = convertToTensor(a, "a", "subStrict");
const $b = convertToTensor(b, "b", "subStrict");
assertShapesMatch($a.shape, $b.shape, "Error in subStrict: ");
return sub($a, $b);
}
function powStrict_(base2, exp7) {
deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
assertShapesMatch(base2.shape, exp7.shape, "Error in powStrict: ");
return pow(base2, exp7);
}
function mulStrict_(a, b) {
deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
const $a = convertToTensor(a, "a", "mul");
const $b = convertToTensor(b, "b", "mul");
assertShapesMatch($a.shape, $b.shape, "Error in multiplyStrict: ");
return mul($a, $b);
}
function divStrict_(a, b) {
deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
const $a = convertToTensor(a, "a", "div");
const $b = convertToTensor(b, "b", "div");
assertShapesMatch($a.shape, $b.shape, "Error in divideStrict: ");
return div($a, $b);
}
function modStrict_(a, b) {
deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
const $a = convertToTensor(a, "a", "modStrict");
const $b = convertToTensor(b, "b", "modStrict");
assertShapesMatch($a.shape, $b.shape, "Error in modStrict: ");
return mod($a, $b);
}
function minimumStrict_(a, b) {
deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
const $a = convertToTensor(a, "a", "minimumStrict");
const $b = convertToTensor(b, "b", "minimumStrict");
assertShapesMatch($a.shape, $b.shape, "Error in minimumStrict: ");
return minimum($a, $b);
}
function maximumStrict_(a, b) {
deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
const $a = convertToTensor(a, "a", "maximumStrict");
const $b = convertToTensor(b, "b", "maximumStrict");
assertShapesMatch($a.shape, $b.shape, "Error in maximumStrict: ");
return maximum($a, $b);
}
function squaredDifferenceStrict_(a, b) {
deprecationWarn("strict variants of ops have been deprecated and will be removed in future");
const $a = convertToTensor(a, "a", "squaredDifferenceStrict");
const $b = convertToTensor(b, "b", "squaredDifferenceStrict");
assertShapesMatch($a.shape, $b.shape, "Error in squaredDifferenceStrict: ");
return squaredDifference($a, $b);
}
const addStrict = op({addStrict_});
const divStrict = op({divStrict_});
const maximumStrict = op({maximumStrict_});
const minimumStrict = op({minimumStrict_});
const modStrict = op({modStrict_});
const mulStrict = op({mulStrict_});
const powStrict = op({powStrict_});
const squaredDifferenceStrict = op({squaredDifferenceStrict_});
const subStrict = op({subStrict_});
// node_modules/@tensorflow/tfjs-core/dist/ops/norm.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function norm_(x, ord = "euclidean", axis = null, keepDims = false) {
x = convertToTensor(x, "x", "norm");
const norm4 = normImpl(x, ord, axis);
let keepDimsShape = norm4.shape;
if (keepDims) {
const axes = parseAxisParam(axis, x.shape);
keepDimsShape = expandShapeToKeepDim(norm4.shape, axes);
}
return reshape(norm4, keepDimsShape);
}
function normImpl(x, p, axis = null) {
if (x.rank === 0) {
return abs(x);
}
if (x.rank !== 1 && axis === null) {
return normImpl(reshape(x, [-1]), p, axis);
}
if (x.rank === 1 || typeof axis === "number" || Array.isArray(axis) && axis.length === 1) {
if (p === 1) {
return sum2(abs(x), axis);
}
if (p === Infinity) {
return max(abs(x), axis);
}
if (p === -Infinity) {
return min(abs(x), axis);
}
if (p === "euclidean" || p === 2) {
return sqrt(sum2(pow(abs(x), scalar(2, "int32")), axis));
}
throw new Error(`Error in norm: invalid ord value: ${p}`);
}
if (Array.isArray(axis) && axis.length === 2) {
if (p === 1) {
return max(sum2(abs(x), axis[0]), axis[1] - 1);
}
if (p === Infinity) {
return max(sum2(abs(x), axis[1]), axis[0]);
}
if (p === -Infinity) {
return min(sum2(abs(x), axis[1]), axis[0]);
}
if (p === "fro" || p === "euclidean") {
return sqrt(sum2(square(x), axis));
}
throw new Error(`Error in norm: invalid ord value: ${p}`);
}
throw new Error(`Error in norm: invalid axis: ${axis}`);
}
const norm = op({norm_});
// node_modules/@tensorflow/tfjs-core/dist/ops/moving_average.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function movingAverage_(v, x, decay, step4, zeroDebias = true) {
const $v = convertToTensor(v, "v", "movingAverage");
const $x = convertToTensor(x, "x", "movingAverage");
const $decay = convertToTensor(decay, "decay", "movingAverage");
assertTypesMatch($v, $x);
assert(arraysEqual($v.shape, $x.shape), () => "Shape mismatch in v and x");
const one = scalar(1);
const oneMinusDecay = sub(one, $decay);
let update = mul(sub($x, $v), oneMinusDecay);
if (zeroDebias) {
assert(step4 != null, () => "When using zeroDebias: true, step is required.");
const $step = convertToTensor(step4, "step", "movingAverage");
update = div(update, sub(one, pow($decay, $step)));
}
return add2($v, update);
}
const movingAverage = op({movingAverage_});
// node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function scatterND_(indices, updates, shape) {
const $indices = convertToTensor(indices, "indices", "scatterND", "int32");
const $updates = convertToTensor(updates, "updates", "scatterND");
validateInput($updates, $indices, shape);
const forward = (backend3) => {
return backend3.scatterND($indices, $updates, shape);
};
const inputs = {indices: $indices, updates: $updates};
const attrs = {shape};
return ENGINE.runKernelFunc(forward, inputs, null, ScatterNd, attrs);
}
const scatterND = op({scatterND_});
// node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense_util.js
function validateInput2(sparseIndices, sparseValues, outputShape, defaultValues) {
if (sparseIndices.dtype !== "int32") {
throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${sparseIndices.dtype}.`);
}
if (sparseIndices.rank > 2) {
throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${sparseIndices.shape}.`);
}
const numElems = sparseIndices.rank > 0 ? sparseIndices.shape[0] : 1;
const numDims = sparseIndices.rank > 1 ? sparseIndices.shape[1] : 1;
if (outputShape.length !== numDims) {
throw new Error(`outputShape has incorrect number of elements:, ${outputShape.length}, should be: ${numDims}.`);
}
const numValues = sparseValues.size;
if (!(sparseValues.rank === 0 || sparseValues.rank === 1 && numValues === numElems)) {
throw new Error(`sparseValues has incorrect shape ${sparseValues.shape}, should be [] or [${numElems}]`);
}
if (sparseValues.dtype !== defaultValues.dtype) {
throw new Error("sparseValues.dtype must match defaultValues.dtype");
}
}
// node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function sparseToDense_(sparseIndices, sparseValues, outputShape, defaultValue = 0) {
const $sparseIndices = convertToTensor(sparseIndices, "sparseIndices", "sparseToDense", "int32");
const $sparseValues = convertToTensor(sparseValues, "sparseValues", "sparseToDense");
const $defaultValue = convertToTensor(defaultValue, "defaultValue", "sparseToDense", $sparseValues.dtype);
validateInput2($sparseIndices, $sparseValues, outputShape, $defaultValue);
const inputs = {
sparseIndices: $sparseIndices,
sparseValues: $sparseValues,
defaultValue: $defaultValue
};
const attrs = {outputShape};
return ENGINE.runKernelFunc((backend3) => backend3.sparseToDense($sparseIndices, $sparseValues, outputShape, $defaultValue), inputs, null, SparseToDense, attrs);
}
const sparseToDense = op({sparseToDense_});
// node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function gatherND_(x, indices) {
const $indices = convertToTensor(indices, "indices", "gatherND", "int32");
const $x = convertToTensor(x, "x", "gatherND");
const forward = (backend3) => {
return backend3.gatherND($x, $indices);
};
const inputs = {params: $x, indices: $indices};
return ENGINE.runKernelFunc(forward, inputs, null, GatherNd);
}
const gatherND = op({gatherND_});
// node_modules/@tensorflow/tfjs-core/dist/ops/dropout_util.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function getNoiseShape(x, noiseShape) {
if (noiseShape == null) {
return x.shape.slice();
}
if (arraysEqual(x.shape, noiseShape)) {
return noiseShape;
}
if (x.shape.length === noiseShape.length) {
const newDimension = [];
for (let i = 0; i < x.shape.length; i++) {
if (noiseShape[i] == null && x.shape[i] != null) {
newDimension.push(x.shape[i]);
} else {
newDimension.push(noiseShape[i]);
}
}
return newDimension;
}
return noiseShape;
}
// node_modules/@tensorflow/tfjs-core/dist/ops/dropout.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function dropout_(x, rate, noiseShape, seed) {
const $x = convertToTensor(x, "x", "dropout");
assert($x.dtype === "float32", () => `x has to be a floating point tensor since it's going to be scaled, but got a ${$x.dtype} tensor instead.`);
assert(rate >= 0 && rate < 1, () => `rate must be a float in the range [0, 1), but got ${rate}.`);
if (rate === 0) {
return x instanceof Tensor ? $x.clone() : $x;
}
const $noiseShape = getNoiseShape($x, noiseShape);
const keepProb = 1 - rate;
const multiplier = div(floor(add2(randomUniform($noiseShape, 0, 1, "float32", seed), keepProb)), keepProb);
return mul($x, multiplier);
}
const dropout = op({dropout_});
// node_modules/@tensorflow/tfjs-core/dist/ops/signal_ops_util.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function enclosingPowerOfTwo(value) {
return Math.floor(Math.pow(2, Math.ceil(Math.log(value) / Math.log(2))));
}
function cosineWindow(windowLength, a, b) {
const even = 1 - windowLength % 2;
const newValues = new Float32Array(windowLength);
for (let i = 0; i < windowLength; ++i) {
const cosArg = 2 * Math.PI * i / (windowLength + even - 1);
newValues[i] = a - b * Math.cos(cosArg);
}
return tensor1d(newValues, "float32");
}
// node_modules/@tensorflow/tfjs-core/dist/ops/in_top_k.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
async function inTopKAsync_(predictions, targets, k = 1) {
const $predictions = convertToTensor(predictions, "predictions", "inTopK");
const $targets = convertToTensor(targets, "targets", "inTopK");
assert($predictions.rank > 1, () => `inTopK() expects the predictions to be of rank 2 or higher, but got ${$predictions.rank}`);
assert($predictions.rank - 1 === $targets.rank, () => `predictions rank should be 1 larger than targets rank, but got predictions rank ${$predictions.rank} and targets rank ${$targets.rank}`);
assertShapesMatch($predictions.shape.slice(0, $predictions.shape.length - 1), $targets.shape, `predictions's shape should be align with the targets' shape, except the last dimension.`);
const lastDim = $predictions.shape[$predictions.shape.length - 1];
assert(k > 0 && k <= lastDim, () => `'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${lastDim}), but got ${k}`);
const predictionsVals = await $predictions.data();
const targetsVals = await $targets.data();
const [batch, size] = [predictionsVals.length / lastDim, lastDim];
const precision3 = getTypedArrayFromDType("bool", batch);
for (let b = 0; b < batch; b++) {
const offset = b * size;
const vals = predictionsVals.subarray(offset, offset + size);
const valAndInd = [];
for (let i = 0; i < vals.length; i++) {
valAndInd.push({value: vals[i], index: i});
}
valAndInd.sort((a, b2) => b2.value - a.value);
precision3[b] = 0;
for (let i = 0; i < k; i++) {
if (valAndInd[i].index === targetsVals[b]) {
precision3[b] = 1;
break;
}
}
}
if (predictions !== $predictions) {
$predictions.dispose();
}
if (targets !== $targets) {
$targets.dispose();
}
return tensor4(precision3, $targets.shape, "bool");
}
const inTopKAsync = inTopKAsync_;
// node_modules/@tensorflow/tfjs-core/dist/ops/fused_ops.js
const fused_ops_exports = {};
__export(fused_ops_exports, {
conv2d: () => conv2d5,
depthwiseConv2d: () => depthwiseConv2d2,
matMul: () => matMul2
});
// node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_backprop_filter.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function conv2DBackpropFilter_(x, dy, filterShape, strides, pad8, dataFormat = "NHWC", dimRoundingMode) {
let x4D = x;
if (x.rank === 3) {
x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);
}
let dy4D = dy;
if (dy4D.rank === 3) {
dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);
}
assert(x4D.rank === 4, () => `Error in conv2dDerFilter: input must be rank 4, but got shape ${x4D.shape}.`);
assert(dy4D.rank === 4, () => `Error in conv2dDerFilter: dy must be rank 4, but got shape ${dy4D.shape}.`);
assert(filterShape.length === 4, () => `Error in conv2dDerFilter: filterShape must be length 4, but got ${filterShape}.`);
const inDepth = dataFormat === "NHWC" ? x4D.shape[3] : x4D.shape[1];
const outDepth = dataFormat === "NHWC" ? dy4D.shape[3] : dy4D.shape[1];
assert(inDepth === filterShape[2], () => `Error in conv2dDerFilter: depth of input ${inDepth}) must match input depth in filter (${filterShape[2]}.`);
assert(outDepth === filterShape[3], () => `Error in conv2dDerFilter: depth of dy (${outDepth}) must match output depth for filter (${filterShape[3]}).`);
if (dimRoundingMode != null) {
assert(isInt(pad8), () => `Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
}
const forward = (backend3) => {
const dilations = 1;
const $dataFormat = convertConv2DDataFormat(dataFormat);
const convInfo = computeConv2DInfo(x4D.shape, filterShape, strides, dilations, pad8, dimRoundingMode, false, $dataFormat);
return backend3.conv2dDerFilter(x4D, dy4D, convInfo);
};
const inputs = {x: x4D, dy: dy4D};
const attrs = {strides, pad: pad8, dataFormat, dimRoundingMode, filterShape};
return ENGINE.runKernelFunc(forward, inputs, null, Conv2DBackpropFilter, attrs);
}
const conv2DBackpropFilter = op({conv2DBackpropFilter_});
// node_modules/@tensorflow/tfjs-core/dist/ops/fused_util.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function getFusedDyActivation(dy, y, activation2) {
if (activation2 == null || activation2 === "linear") {
return dy;
}
if (activation2 === "relu") {
return mul(dy, step(y));
}
throw new Error(`Cannot compute gradient for fused activation ${activation2}.`);
}
function getFusedBiasGradient(bias, dyActivation) {
let res = dyActivation;
const reduceAxes = getReductionAxes(bias.shape, dyActivation.shape);
if (reduceAxes.length > 0) {
res = sum2(res, reduceAxes);
}
return reshape(res, bias.shape);
}
function applyActivation(x, activation2, preluActivationWeights) {
if (activation2 === "linear") {
return x;
} else if (activation2 === "relu") {
return relu(x);
} else if (activation2 === "elu") {
return elu(x);
} else if (activation2 === "relu6") {
return relu6(x);
} else if (activation2 === "prelu") {
return prelu(x, preluActivationWeights);
}
throw new Error(`Unknown fused activation ${activation2}.`);
}
const shouldFuse = (gradientDepth, activation2) => {
const gradientMode = gradientDepth > 0;
return !gradientMode || activation2 === "linear";
};
// node_modules/@tensorflow/tfjs-core/dist/ops/fused/conv2d.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function fusedConv2d_({x, filter, strides, pad: pad8, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode, bias, activation: activation2 = "linear", preluActivationWeights}) {
activation2 = activation2 || "linear";
if (shouldFuse(ENGINE.state.gradientDepth, activation2) === false) {
let result = conv2d(x, filter, strides, pad8, dataFormat, dilations, dimRoundingMode);
if (bias != null) {
result = add2(result, bias);
}
return applyActivation(result, activation2, preluActivationWeights);
}
const $x = convertToTensor(x, "x", "conv2d");
const $filter = convertToTensor(filter, "filter", "conv2d");
let x4D = $x;
let reshapedTo4D = false;
if ($x.rank === 3) {
reshapedTo4D = true;
x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
}
assert(x4D.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ${x4D.rank}.`);
assert($filter.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ${$filter.rank}.`);
if (dimRoundingMode != null) {
assert(isInt(pad8), () => `Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
}
assert(x4D.shape[3] === $filter.shape[2], () => `Error in conv2d: depth of input (${x4D.shape[3]}) must match input depth for filter ${$filter.shape[2]}.`);
assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
assert(dataFormat === "NHWC", () => `Error in conv2d: got dataFormat of ${dataFormat} but only NHWC is currently supported.`);
const convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad8, dimRoundingMode);
let $bias;
if (bias != null) {
$bias = convertToTensor(bias, "bias", "fused conv2d");
[$bias] = makeTypesMatch($bias, $x);
assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);
}
let $preluActivationWeights;
if (preluActivationWeights != null) {
$preluActivationWeights = convertToTensor(preluActivationWeights, "prelu weights", "fused conv2d");
}
const grad2 = (dy, saved) => {
const [$filter2, x4D2, y, $bias2] = saved;
const dyActivation = getFusedDyActivation(dy, y, activation2);
assert(tupleValuesAreOne(dilations), () => `Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);
const xDer = conv2DBackpropInput(x4D2.shape, dyActivation, $filter2, strides, pad8);
const filterDer = conv2DBackpropFilter(x4D2, dyActivation, $filter2.shape, strides, pad8);
const der = [xDer, filterDer];
if ($bias2 != null) {
const biasDer = getFusedBiasGradient($bias2, dyActivation);
der.push(biasDer);
}
return der;
};
const forward = (backend3) => {
const res = backend3.fusedConv2d({
input: x4D,
filter: $filter,
convInfo,
bias: $bias,
activation: activation2,
preluActivationWeights: $preluActivationWeights
});
return res;
};
const inputs = {
x: x4D,
filter: $filter,
bias: $bias,
preluActivationWeights: $preluActivationWeights
};
const attrs = {strides, pad: pad8, dataFormat, dilations, dimRoundingMode, activation: activation2};
if (bias == null) {
const customOp = customGrad((x4D2, filter2, save) => {
let res = ENGINE.runKernelFunc(forward, inputs, null, FusedConv2D, attrs);
save([filter2, x4D2, res]);
if (reshapedTo4D) {
res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
}
return {value: res, gradFunc: grad2};
});
return customOp(x4D, $filter);
} else {
const customOpWithBias = customGrad((x4D2, filter2, bias2, save) => {
let res = ENGINE.runKernelFunc(forward, inputs, null, FusedConv2D, attrs);
save([filter2, x4D2, res, bias2]);
if (reshapedTo4D) {
res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
}
return {value: res, gradFunc: grad2};
});
return customOpWithBias(x4D, $filter, $bias);
}
}
const conv2d5 = op({fusedConv2d_});
// node_modules/@tensorflow/tfjs-core/dist/ops/depthwise_conv2d_native_backprop_filter.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function depthwiseConv2dNativeBackpropFilter_(x, dy, filterShape, strides, pad8, dilations = [1, 1], dimRoundingMode) {
let x4D = x;
if (x.rank === 3) {
x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);
}
let dy4D = dy;
if (dy4D.rank === 3) {
dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);
}
const forward = (backend3) => {
const convInfo = computeConv2DInfo(x.shape, filterShape, strides, dilations, pad8, dimRoundingMode, true);
return backend3.depthwiseConv2DDerFilter(x4D, dy4D, convInfo);
};
const inputs = {x: x4D, dy: dy4D};
const attrs = {strides, pad: pad8, dimRoundingMode, dilations, filterShape};
return ENGINE.runKernelFunc(forward, inputs, null, DepthwiseConv2dNativeBackpropFilter, attrs);
}
const depthwiseConv2dNativeBackpropFilter = op({depthwiseConv2dNativeBackpropFilter_});
// node_modules/@tensorflow/tfjs-core/dist/ops/depthwise_conv2d_native_backprop_input.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function depthwiseConv2dNativeBackpropInput_(xShape, dy, filter, strides, pad8, dilations = [1, 1], dimRoundingMode) {
let dy4D = dy;
let reshapedTo4D = false;
if (dy.rank === 3) {
reshapedTo4D = true;
dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);
}
const forward = (backend3) => {
const convInfo = computeConv2DInfo(xShape, filter.shape, strides, dilations, pad8, dimRoundingMode, true);
return backend3.depthwiseConv2DDerInput(dy4D, filter, convInfo);
};
const inputs = {dy: dy4D, filter};
const attrs = {strides, pad: pad8, dimRoundingMode, dilations, inputShape: xShape};
const res = ENGINE.runKernelFunc(forward, inputs, null, DepthwiseConv2dNativeBackpropInput, attrs);
if (reshapedTo4D) {
return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
}
return res;
}
const depthwiseConv2dNativeBackpropInput = op({depthwiseConv2dNativeBackpropInput_});
// node_modules/@tensorflow/tfjs-core/dist/ops/fused/depthwise_conv2d.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function fusedDepthwiseConv2d_({x, filter, strides, pad: pad8, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode, bias, activation: activation2 = "linear", preluActivationWeights}) {
if (shouldFuse(ENGINE.state.gradientDepth, activation2) === false) {
let result = depthwiseConv2d(x, filter, strides, pad8, dataFormat, dilations, dimRoundingMode);
if (bias != null) {
result = add2(result, bias);
}
return applyActivation(result, activation2, preluActivationWeights);
}
const $x = convertToTensor(x, "x", "depthwiseConv2d");
const $filter = convertToTensor(filter, "filter", "depthwiseConv2d");
let x4D = $x;
let reshapedTo4D = false;
if ($x.rank === 3) {
reshapedTo4D = true;
x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
}
assert(x4D.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`);
assert($filter.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`);
assert(x4D.shape[3] === $filter.shape[2], () => `Error in fused depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`);
if (dilations == null) {
dilations = [1, 1];
}
assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
if (dimRoundingMode != null) {
assert(isInt(pad8), () => `Error in fused depthwiseConv2d: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${pad8}.`);
}
const convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad8, dimRoundingMode, true);
let $bias;
if (bias != null) {
$bias = convertToTensor(bias, "bias", "fused conv2d");
[$bias] = makeTypesMatch($bias, $x);
assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);
}
let $preluActivationWeights;
if (preluActivationWeights != null) {
$preluActivationWeights = convertToTensor(preluActivationWeights, "prelu weights", "fused depthwiseConv2d");
}
const grad2 = (dy, saved) => {
assert(tupleValuesAreOne(dilations), () => `Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${dilations}'`);
const [$filter2, x4D2, y, bias2] = saved;
const dyActivation = getFusedDyActivation(dy, y, activation2);
const xDer = depthwiseConv2dNativeBackpropInput(x4D2.shape, dyActivation, $filter2, strides, pad8, dilations, dimRoundingMode);
const filterDer = depthwiseConv2dNativeBackpropFilter(x4D2, dyActivation, $filter2.shape, strides, pad8, dilations, dimRoundingMode);
if (bias2 != null) {
const biasDer = getFusedBiasGradient($bias, dyActivation);
return [xDer, filterDer, biasDer];
}
return [xDer, filterDer];
};
const forward = (backend3) => {
const res = backend3.fusedDepthwiseConv2D({
input: x4D,
filter: $filter,
convInfo,
bias: $bias,
activation: activation2,
preluActivationWeights: $preluActivationWeights
});
return res;
};
const inputs = {
x: x4D,
filter: $filter,
bias: $bias,
preluActivationWeights: $preluActivationWeights
};
const attrs = {strides, pad: pad8, dataFormat, dilations, dimRoundingMode, activation: activation2};
if (bias == null) {
const customOp = customGrad((x4D2, filter2, save) => {
let res = ENGINE.runKernelFunc(forward, inputs, null, FusedDepthwiseConv2D, attrs);
save([filter2, x4D2, res]);
if (reshapedTo4D) {
res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
}
return {value: res, gradFunc: grad2};
});
return customOp(x4D, $filter);
} else {
const customOpWithBias = customGrad((x4D2, filter2, bias2, save) => {
let res = ENGINE.runKernelFunc(forward, inputs, null, FusedDepthwiseConv2D, attrs);
save([filter2, x4D2, res, bias2]);
if (reshapedTo4D) {
res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
}
return {value: res, gradFunc: grad2};
});
return customOpWithBias(x4D, $filter, $bias);
}
}
const depthwiseConv2d2 = op({fusedDepthwiseConv2d_});
// node_modules/@tensorflow/tfjs-core/dist/ops/fused/mat_mul.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function fusedMatMul_({a, b, transposeA = false, transposeB = false, bias, activation: activation2 = "linear", preluActivationWeights}) {
if (shouldFuse(ENGINE.state.gradientDepth, activation2) === false) {
let result = matMul(a, b, transposeA, transposeB);
if (bias != null) {
result = add2(result, bias);
}
return applyActivation(result, activation2, preluActivationWeights);
}
let $a = convertToTensor(a, "a", "fused matMul");
let $b = convertToTensor(b, "b", "fused matMul");
[$a, $b] = makeTypesMatch($a, $b);
const innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];
const innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];
const outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];
const outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];
const outerDimsA = $a.shape.slice(0, -2);
const outerDimsB = $b.shape.slice(0, -2);
const batchDimA = sizeFromShape(outerDimsA);
const batchDimB = sizeFromShape(outerDimsB);
assert($a.rank >= 2 && $b.rank >= 2 && $a.rank === $b.rank, () => `Error in fused matMul: inputs must have the same rank of at least 2, got ranks ${$a.rank} and ${$b.rank}.`);
assert(arraysEqual(outerDimsA, outerDimsB), () => `Error in fused matMul: outer dimensions (${outerDimsA}) and (${outerDimsB}) of Tensors with shapes ${$a.shape} and ${$b.shape} must match.`);
assert(innerShapeA === innerShapeB, () => `Error in fused matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${$a.shape} and ${$b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);
const outShape = $a.shape.slice(0, -2).concat([outerShapeA, outerShapeB]);
const a3D = transposeA ? reshape($a, [batchDimA, innerShapeA, outerShapeA]) : reshape($a, [batchDimA, outerShapeA, innerShapeA]);
const b3D = transposeB ? reshape($b, [batchDimB, outerShapeB, innerShapeB]) : reshape($b, [batchDimB, innerShapeB, outerShapeB]);
let $bias;
if (bias != null) {
$bias = convertToTensor(bias, "bias", "fused matMul");
[$bias] = makeTypesMatch($bias, $a);
assertAndGetBroadcastShape(outShape, $bias.shape);
}
let $preluActivationWeights;
if (preluActivationWeights != null) {
$preluActivationWeights = convertToTensor(preluActivationWeights, "prelu weights", "fused matMul");
}
const grad2 = (dy, saved) => {
const [a3D2, b3D2, y, $bias2] = saved;
const dyActivation = getFusedDyActivation(reshape(dy, y.shape), y, activation2);
let aDer;
let bDer;
if (!transposeA && !transposeB) {
aDer = matMul(dyActivation, b3D2, false, true);
bDer = matMul(a3D2, dyActivation, true, false);
} else if (!transposeA && transposeB) {
aDer = matMul(dyActivation, b3D2, false, false);
bDer = matMul(dyActivation, a3D2, true, false);
} else if (transposeA && !transposeB) {
aDer = matMul(b3D2, dyActivation, false, true);
bDer = matMul(a3D2, dyActivation, false, false);
} else {
aDer = matMul(b3D2, dyActivation, true, true);
bDer = matMul(dyActivation, a3D2, true, true);
}
if (bias != null) {
const biasDer = getFusedBiasGradient($bias2, dyActivation);
return [aDer, bDer, biasDer];
} else {
return [aDer, bDer];
}
};
const forward = (backend3) => {
const y = backend3.fusedBatchMatMul({
a: a3D,
b: b3D,
transposeA,
transposeB,
bias: $bias,
activation: activation2,
preluActivationWeights: $preluActivationWeights
});
return y;
};
const inputs = {
a: a3D,
b: b3D,
bias: $bias,
preluActivationWeights: $preluActivationWeights
};
const attrs = {transposeA, transposeB, activation: activation2};
if (bias == null) {
const customOp = customGrad((a3D2, b3D2, save) => {
const res = ENGINE.runKernelFunc(forward, inputs, null, _FusedMatMul, attrs);
save([a3D2, b3D2, res]);
return {value: reshape(res, outShape), gradFunc: grad2};
});
return customOp(a3D, b3D);
} else {
const customOpWithBias = customGrad((a3D2, b3D2, $bias2, save) => {
const res = ENGINE.runKernelFunc(forward, inputs, null, _FusedMatMul, attrs);
save([a3D2, b3D2, res, $bias2]);
return {value: reshape(res, outShape), gradFunc: grad2};
});
return customOpWithBias(a3D, b3D, $bias);
}
}
const matMul2 = op({fusedMatMul_});
// node_modules/@tensorflow/tfjs-core/dist/ops/fused_ops.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
// node_modules/@tensorflow/tfjs-core/dist/ops/signal/hamming_window.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function hammingWindow_(windowLength) {
return cosineWindow(windowLength, 0.54, 0.46);
}
const hammingWindow = op({hammingWindow_});
// node_modules/@tensorflow/tfjs-core/dist/ops/signal/hann_window.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function hannWindow_(windowLength) {
return cosineWindow(windowLength, 0.5, 0.5);
}
const hannWindow = op({hannWindow_});
// node_modules/@tensorflow/tfjs-core/dist/ops/signal/frame.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function frame_(signal2, frameLength, frameStep, padEnd = false, padValue = 0) {
let start = 0;
const output = [];
while (start + frameLength <= signal2.size) {
output.push(slice(signal2, start, frameLength));
start += frameStep;
}
if (padEnd) {
while (start < signal2.size) {
const padLen = start + frameLength - signal2.size;
const pad8 = concat([
slice(signal2, start, frameLength - padLen),
fill([padLen], padValue)
]);
output.push(pad8);
start += frameStep;
}
}
if (output.length === 0) {
return tensor2d([], [0, frameLength]);
}
return reshape(concat(output), [output.length, frameLength]);
}
const frame = op({frame_});
// node_modules/@tensorflow/tfjs-core/dist/ops/signal/stft.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function stft_(signal2, frameLength, frameStep, fftLength, windowFn = hannWindow) {
if (fftLength == null) {
fftLength = enclosingPowerOfTwo(frameLength);
}
const framedSignal = frame(signal2, frameLength, frameStep);
const windowedSignal = mul(framedSignal, windowFn(frameLength));
const output = [];
for (let i = 0; i < framedSignal.shape[0]; i++) {
output.push(rfft(slice(windowedSignal, [i, 0], [1, frameLength]), fftLength));
}
return concat(output);
}
const stft = op({stft_});
// node_modules/@tensorflow/tfjs-core/dist/ops/image/crop_and_resize.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function cropAndResize_(image4, boxes, boxInd, cropSize, method, extrapolationValue) {
const $image = convertToTensor(image4, "image", "cropAndResize");
const $boxes = convertToTensor(boxes, "boxes", "cropAndResize", "float32");
const $boxInd = convertToTensor(boxInd, "boxInd", "cropAndResize", "int32");
method = method || "bilinear";
extrapolationValue = extrapolationValue || 0;
const numBoxes = $boxes.shape[0];
assert($image.rank === 4, () => `Error in cropAndResize: image must be rank 4,but got rank ${$image.rank}.`);
assert($boxes.rank === 2 && $boxes.shape[1] === 4, () => `Error in cropAndResize: boxes must be have size [${numBoxes},4] but had shape ${$boxes.shape}.`);
assert($boxInd.rank === 1 && $boxInd.shape[0] === numBoxes, () => `Error in cropAndResize: boxInd must be have size [${numBoxes}] but had shape ${$boxes.shape}.`);
assert(cropSize.length === 2, () => `Error in cropAndResize: cropSize must be of length 2, but got length ${cropSize.length}.`);
assert(cropSize[0] >= 1 && cropSize[1] >= 1, () => `cropSize must be atleast [1,1], but was ${cropSize}`);
assert(method === "bilinear" || method === "nearest", () => `method must be bilinear or nearest, but was ${method}`);
const forward = (backend3) => backend3.cropAndResize($image, $boxes, $boxInd, cropSize, method, extrapolationValue);
const inputs = {image: $image, boxes: $boxes, boxInd: $boxInd};
const attrs = {method, extrapolationValue, cropSize};
const res = ENGINE.runKernelFunc(forward, inputs, null, CropAndResize, attrs);
return res;
}
const cropAndResize = op({cropAndResize_});
// node_modules/@tensorflow/tfjs-core/dist/ops/image/flip_left_right.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function flipLeftRight_(image4) {
const $image = convertToTensor(image4, "image", "flipLeftRight", "float32");
assert($image.rank === 4, () => `Error in flipLeftRight: image must be rank 4,but got rank ${$image.rank}.`);
const inputs = {image: $image};
const res = ENGINE.runKernel(FlipLeftRight, inputs, {});
return res;
}
const flipLeftRight = op({flipLeftRight_});
// node_modules/@tensorflow/tfjs-core/dist/ops/image/rotate_with_offset.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function rotateWithOffset_(image4, radians, fillValue = 0, center = 0.5) {
const $image = convertToTensor(image4, "image", "rotateWithOffset", "float32");
assert($image.rank === 4, () => `Error in rotateWithOffset: image must be rank 4,but got rank ${$image.rank}.`);
const inputs = {image: $image};
const attrs = {radians, fillValue, center};
const res = ENGINE.runKernel(RotateWithOffset, inputs, attrs);
return res;
}
const rotateWithOffset = op({rotateWithOffset_});
// node_modules/@tensorflow/tfjs-core/dist/ops/nonmax_util.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function nonMaxSuppSanityCheck(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {
if (iouThreshold == null) {
iouThreshold = 0.5;
}
if (scoreThreshold == null) {
scoreThreshold = Number.NEGATIVE_INFINITY;
}
if (softNmsSigma == null) {
softNmsSigma = 0;
}
const numBoxes = boxes.shape[0];
maxOutputSize = Math.min(maxOutputSize, numBoxes);
assert(0 <= iouThreshold && iouThreshold <= 1, () => `iouThreshold must be in [0, 1], but was '${iouThreshold}'`);
assert(boxes.rank === 2, () => `boxes must be a 2D tensor, but was of rank '${boxes.rank}'`);
assert(boxes.shape[1] === 4, () => `boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`);
assert(scores.rank === 1, () => "scores must be a 1D tensor");
assert(scores.shape[0] === numBoxes, () => `scores has incompatible shape with boxes. Expected ${numBoxes}, but was ${scores.shape[0]}`);
assert(0 <= softNmsSigma && softNmsSigma <= 1, () => `softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`);
return {maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma};
}
// node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function nonMaxSuppression_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {
const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppression");
const $scores = convertToTensor(scores, "scores", "nonMaxSuppression");
const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);
maxOutputSize = inputs.maxOutputSize;
iouThreshold = inputs.iouThreshold;
scoreThreshold = inputs.scoreThreshold;
const attrs = {maxOutputSize, iouThreshold, scoreThreshold};
return ENGINE.runKernelFunc((b) => b.nonMaxSuppression($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold), {boxes: $boxes, scores: $scores}, null, NonMaxSuppressionV3, attrs);
}
const nonMaxSuppression = op({nonMaxSuppression_});
// node_modules/@tensorflow/tfjs-core/dist/backends/array_util.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function binaryInsert(arr, element, comparator) {
const index = binarySearch(arr, element, comparator);
const insertionPoint = index < 0 ? -(index + 1) : index;
arr.splice(insertionPoint, 0, element);
}
function binarySearch(arr, target, comparator) {
return binarySearch_(arr, target, comparator || defaultComparator);
}
function defaultComparator(a, b) {
return a > b ? 1 : a < b ? -1 : 0;
}
function binarySearch_(arr, target, comparator) {
let left = 0;
let right = arr.length;
let middle = 0;
let found = false;
while (left < right) {
middle = left + (right - left >>> 1);
const compareResult = comparator(target, arr[middle]);
if (compareResult > 0) {
left = middle + 1;
} else {
right = middle;
found = !compareResult;
}
}
return found ? left : -left - 1;
}
// node_modules/@tensorflow/tfjs-core/dist/backends/non_max_suppression_impl.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function nonMaxSuppressionV3Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0).selectedIndices;
}
function nonMaxSuppressionV4Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize) {
return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0, false, padToMaxOutputSize, true);
}
function nonMaxSuppressionV5Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {
return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, true);
}
function nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, returnScoresTensor = false, padToMaxOutputSize = false, returnValidOutputs = false) {
const candidates = [];
for (let i = 0; i < scores.length; i++) {
if (scores[i] > scoreThreshold) {
candidates.push({score: scores[i], boxIndex: i, suppressBeginIndex: 0});
}
}
candidates.sort(ascendingComparator);
const scale = softNmsSigma > 0 ? -0.5 / softNmsSigma : 0;
const selectedIndices = [];
const selectedScores = [];
while (selectedIndices.length < maxOutputSize && candidates.length > 0) {
const candidate = candidates.pop();
const {score: originalScore, boxIndex, suppressBeginIndex} = candidate;
if (originalScore < scoreThreshold) {
break;
}
let ignoreCandidate = false;
for (let j = selectedIndices.length - 1; j >= suppressBeginIndex; --j) {
const iou = intersectionOverUnion(boxes, boxIndex, selectedIndices[j]);
if (iou >= iouThreshold) {
ignoreCandidate = true;
break;
}
candidate.score = candidate.score * suppressWeight(iouThreshold, scale, iou);
if (candidate.score <= scoreThreshold) {
break;
}
}
candidate.suppressBeginIndex = selectedIndices.length;
if (!ignoreCandidate) {
if (candidate.score === originalScore) {
selectedIndices.push(boxIndex);
selectedScores.push(candidate.score);
} else if (candidate.score > scoreThreshold) {
binaryInsert(candidates, candidate, ascendingComparator);
}
}
}
const validOutputs = selectedIndices.length;
const elemsToPad = maxOutputSize - validOutputs;
if (padToMaxOutputSize && elemsToPad > 0) {
selectedIndices.push(...new Array(elemsToPad).fill(0));
selectedScores.push(...new Array(elemsToPad).fill(0));
}
const result = {selectedIndices: tensor1d(selectedIndices, "int32")};
if (returnScoresTensor) {
result["selectedScores"] = tensor1d(selectedScores, "float32");
}
if (returnValidOutputs) {
result["validOutputs"] = scalar(validOutputs, "int32");
}
return result;
}
function intersectionOverUnion(boxes, i, j) {
const iCoord = boxes.subarray(i * 4, i * 4 + 4);
const jCoord = boxes.subarray(j * 4, j * 4 + 4);
const yminI = Math.min(iCoord[0], iCoord[2]);
const xminI = Math.min(iCoord[1], iCoord[3]);
const ymaxI = Math.max(iCoord[0], iCoord[2]);
const xmaxI = Math.max(iCoord[1], iCoord[3]);
const yminJ = Math.min(jCoord[0], jCoord[2]);
const xminJ = Math.min(jCoord[1], jCoord[3]);
const ymaxJ = Math.max(jCoord[0], jCoord[2]);
const xmaxJ = Math.max(jCoord[1], jCoord[3]);
const areaI = (ymaxI - yminI) * (xmaxI - xminI);
const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);
if (areaI <= 0 || areaJ <= 0) {
return 0;
}
const intersectionYmin = Math.max(yminI, yminJ);
const intersectionXmin = Math.max(xminI, xminJ);
const intersectionYmax = Math.min(ymaxI, ymaxJ);
const intersectionXmax = Math.min(xmaxI, xmaxJ);
const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0) * Math.max(intersectionXmax - intersectionXmin, 0);
return intersectionArea / (areaI + areaJ - intersectionArea);
}
function suppressWeight(iouThreshold, scale, iou) {
const weight = Math.exp(scale * iou * iou);
return iou <= iouThreshold ? weight : 0;
}
function ascendingComparator(c1, c2) {
return c1.score - c2.score || c1.score === c2.score && c2.boxIndex - c1.boxIndex;
}
// node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_async.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
async function nonMaxSuppressionAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {
const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppressionAsync");
const $scores = convertToTensor(scores, "scores", "nonMaxSuppressionAsync");
const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);
maxOutputSize = inputs.maxOutputSize;
iouThreshold = inputs.iouThreshold;
scoreThreshold = inputs.scoreThreshold;
const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);
const boxesVals = boxesAndScores[0];
const scoresVals = boxesAndScores[1];
const res = nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);
if ($boxes !== boxes) {
$boxes.dispose();
}
if ($scores !== scores) {
$scores.dispose();
}
return res;
}
const nonMaxSuppressionAsync = nonMaxSuppressionAsync_;
// node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_with_score.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function nonMaxSuppressionWithScore_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0) {
const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppression");
const $scores = convertToTensor(scores, "scores", "nonMaxSuppression");
const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
maxOutputSize = params.maxOutputSize;
iouThreshold = params.iouThreshold;
scoreThreshold = params.scoreThreshold;
softNmsSigma = params.softNmsSigma;
const inputs = {boxes: $boxes, scores: $scores};
const attrs = {maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma};
const result = ENGINE.runKernel(NonMaxSuppressionV5, inputs, attrs);
return {selectedIndices: result[0], selectedScores: result[1]};
}
const nonMaxSuppressionWithScore = op({nonMaxSuppressionWithScore_});
// node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_with_score_async.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
async function nonMaxSuppressionWithScoreAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0) {
const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppressionAsync");
const $scores = convertToTensor(scores, "scores", "nonMaxSuppressionAsync");
const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
maxOutputSize = params.maxOutputSize;
iouThreshold = params.iouThreshold;
scoreThreshold = params.scoreThreshold;
softNmsSigma = params.softNmsSigma;
const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);
const boxesVals = boxesAndScores[0];
const scoresVals = boxesAndScores[1];
const res = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
if ($boxes !== boxes) {
$boxes.dispose();
}
if ($scores !== scores) {
$scores.dispose();
}
return res;
}
const nonMaxSuppressionWithScoreAsync = nonMaxSuppressionWithScoreAsync_;
// node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_padded.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function nonMaxSuppressionPadded_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {
const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppression");
const $scores = convertToTensor(scores, "scores", "nonMaxSuppression");
const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null);
const $maxOutputSize = params.maxOutputSize;
const $iouThreshold = params.iouThreshold;
const $scoreThreshold = params.scoreThreshold;
const inputs = {boxes: $boxes, scores: $scores};
const attrs = {
maxOutputSize: $maxOutputSize,
iouThreshold: $iouThreshold,
scoreThreshold: $scoreThreshold,
padToMaxOutputSize
};
const result = ENGINE.runKernel(NonMaxSuppressionV4, inputs, attrs);
return {selectedIndices: result[0], validOutputs: result[1]};
}
const nonMaxSuppressionPadded = op({nonMaxSuppressionPadded_});
// node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_padded_async.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
async function nonMaxSuppressionPaddedAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {
const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppressionAsync");
const $scores = convertToTensor(scores, "scores", "nonMaxSuppressionAsync");
const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null);
const $maxOutputSize = params.maxOutputSize;
const $iouThreshold = params.iouThreshold;
const $scoreThreshold = params.scoreThreshold;
const [boxesVals, scoresVals] = await Promise.all([$boxes.data(), $scores.data()]);
const res = nonMaxSuppressionV4Impl(boxesVals, scoresVals, $maxOutputSize, $iouThreshold, $scoreThreshold, padToMaxOutputSize);
if ($boxes !== boxes) {
$boxes.dispose();
}
if ($scores !== scores) {
$scores.dispose();
}
return res;
}
const nonMaxSuppressionPaddedAsync = nonMaxSuppressionPaddedAsync_;
// node_modules/@tensorflow/tfjs-core/dist/ops/image/resize_bilinear.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function resizeBilinear_(images, size, alignCorners = false) {
const $images = convertToTensor(images, "images", "resizeBilinear");
assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeBilinear: x must be rank 3 or 4, but got rank ${$images.rank}.`);
assert(size.length === 2, () => `Error in resizeBilinear: new shape must 2D, but got shape ${size}.`);
let batchImages = $images;
let reshapedTo4D = false;
if ($images.rank === 3) {
reshapedTo4D = true;
batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);
}
const [newHeight, newWidth] = size;
const forward = (backend3, save) => {
save([batchImages]);
return backend3.resizeBilinear(batchImages, newHeight, newWidth, alignCorners);
};
const inputs = {images: batchImages};
const attrs = {alignCorners, size};
const res = ENGINE.runKernelFunc(forward, inputs, null, ResizeBilinear, attrs);
if (reshapedTo4D) {
return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
}
return res;
}
const resizeBilinear = op({resizeBilinear_});
// node_modules/@tensorflow/tfjs-core/dist/ops/image/resize_nearest_neighbor.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function resizeNearestNeighbor_(images, size, alignCorners = false) {
const $images = convertToTensor(images, "images", "resizeNearestNeighbor");
assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${$images.rank}.`);
assert(size.length === 2, () => `Error in resizeNearestNeighbor: new shape must 2D, but got shape ${size}.`);
assert($images.dtype === "float32" || $images.dtype === "int32", () => "`images` must have `int32` or `float32` as dtype");
let batchImages = $images;
let reshapedTo4D = false;
if ($images.rank === 3) {
reshapedTo4D = true;
batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);
}
const [newHeight, newWidth] = size;
const inputs = {images: batchImages};
const attrs = {alignCorners, size};
const forward = (backend3, save) => {
save([batchImages]);
return backend3.resizeNearestNeighbor(batchImages, newHeight, newWidth, alignCorners);
};
const res = ENGINE.runKernelFunc(forward, inputs, null, ResizeNearestNeighbor, attrs);
if (reshapedTo4D) {
return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
}
return res;
}
const resizeNearestNeighbor = op({resizeNearestNeighbor_});
// node_modules/@tensorflow/tfjs-core/dist/ops/linalg/band_part.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function bandPart_(a, numLower, numUpper) {
assert(numLower % 1 === 0, () => `bandPart(): numLower must be an integer, got ${numLower}.`);
assert(numUpper % 1 === 0, () => `bandPart(): numUpper must be an integer, got ${numUpper}.`);
const $a = convertToTensor(a, "a", "bandPart");
assert($a.rank >= 2, () => `bandPart(): Rank must be at least 2, got ${$a.rank}.`);
const shape = $a.shape;
const [M, N] = $a.shape.slice(-2);
if (!(numLower <= M)) {
throw new Error(`bandPart(): numLower (${numLower}) must not be greater than the number of rows (${M}).`);
}
if (!(numUpper <= N)) {
throw new Error(`bandPart(): numUpper (${numUpper}) must not be greater than the number of columns (${N}).`);
}
if (numLower < 0) {
numLower = M;
}
if (numUpper < 0) {
numUpper = N;
}
const i = reshape(range(0, M, 1, "int32"), [-1, 1]);
const j = range(0, N, 1, "int32");
const ij = sub(i, j);
const inBand = logicalAnd(lessEqual(ij, scalar(+numLower, "int32")), greaterEqual(ij, scalar(-numUpper, "int32")));
const zero = zeros([M, N], $a.dtype);
return reshape(stack(unstack(reshape($a, [-1, M, N])).map((mat) => where(inBand, mat, zero))), shape);
}
const bandPart = op({bandPart_});
// node_modules/@tensorflow/tfjs-core/dist/ops/linalg/gram_schmidt.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function gramSchmidt_(xs) {
let inputIsTensor2D;
if (Array.isArray(xs)) {
inputIsTensor2D = false;
assert(xs != null && xs.length > 0, () => "Gram-Schmidt process: input must not be null, undefined, or empty");
const dim = xs[0].shape[0];
for (let i = 1; i < xs.length; ++i) {
assert(xs[i].shape[0] === dim, () => `Gram-Schmidt: Non-unique lengths found in the input vectors: (${xs[i].shape[0]} vs. ${dim})`);
}
} else {
inputIsTensor2D = true;
xs = split(xs, xs.shape[0], 0).map((x) => squeeze(x, [0]));
}
assert(xs.length <= xs[0].shape[0], () => `Gram-Schmidt: Number of vectors (${xs.length}) exceeds number of dimensions (${xs[0].shape[0]}).`);
const ys = [];
const xs1d = xs;
for (let i = 0; i < xs.length; ++i) {
ys.push(ENGINE.tidy(() => {
let x = xs1d[i];
if (i > 0) {
for (let j = 0; j < i; ++j) {
const proj = mul(sum2(mul(ys[j], x)), ys[j]);
x = sub(x, proj);
}
}
return div(x, norm(x, "euclidean"));
}));
}
if (inputIsTensor2D) {
return stack(ys, 0);
} else {
return ys;
}
}
const gramSchmidt = op({gramSchmidt_});
// node_modules/@tensorflow/tfjs-core/dist/ops/linalg/qr.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function qr_(x, fullMatrices = false) {
assert(x.rank >= 2, () => `qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`);
if (x.rank === 2) {
return qr2d(x, fullMatrices);
} else {
const outerDimsProd = x.shape.slice(0, x.shape.length - 2).reduce((value, prev) => value * prev);
const x2ds = unstack(reshape(x, [
outerDimsProd,
x.shape[x.shape.length - 2],
x.shape[x.shape.length - 1]
]), 0);
const q2ds = [];
const r2ds = [];
x2ds.forEach((x2d) => {
const [q2d, r2d] = qr2d(x2d, fullMatrices);
q2ds.push(q2d);
r2ds.push(r2d);
});
const q = reshape(stack(q2ds, 0), x.shape);
const r = reshape(stack(r2ds, 0), x.shape);
return [q, r];
}
}
function qr2d(x, fullMatrices = false) {
return ENGINE.tidy(() => {
assert(x.shape.length === 2, () => `qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);
const m = x.shape[0];
const n = x.shape[1];
let q = eye(m);
let r = clone(x);
const one2D = tensor2d([[1]], [1, 1]);
let w = clone(one2D);
const iters = m >= n ? n : m;
for (let j = 0; j < iters; ++j) {
const rTemp = r;
const wTemp = w;
const qTemp = q;
[w, r, q] = ENGINE.tidy(() => {
const rjEnd1 = slice(r, [j, j], [m - j, 1]);
const normX = norm(rjEnd1);
const rjj = slice(r, [j, j], [1, 1]);
const s = where(greater(rjj, 0), tensor2d([[-1]]), tensor2d([[1]]));
const u1 = sub(rjj, mul(s, normX));
const wPre = div(rjEnd1, u1);
if (wPre.shape[0] === 1) {
w = clone(one2D);
} else {
w = concat([
one2D,
slice(wPre, [1, 0], [wPre.shape[0] - 1, wPre.shape[1]])
], 0);
}
const tau = neg(div(matMul(s, u1), normX));
const rjEndAll = slice(r, [j, 0], [m - j, n]);
const tauTimesW = mul(tau, w);
const wT = transpose(w);
if (j === 0) {
r = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));
} else {
const rTimesTau = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));
r = concat([slice(r, [0, 0], [j, n]), rTimesTau], 0);
}
const tawTimesWT = transpose(tauTimesW);
const qAllJEnd = slice(q, [0, j], [m, q.shape[1] - j]);
if (j === 0) {
q = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));
} else {
const qTimesTau = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));
q = concat([slice(q, [0, 0], [m, j]), qTimesTau], 1);
}
return [w, r, q];
});
dispose([rTemp, wTemp, qTemp]);
}
if (!fullMatrices && m > n) {
q = slice(q, [0, 0], [m, n]);
r = slice(r, [0, 0], [n, n]);
}
return [q, r];
});
}
const qr = op({qr_});
// node_modules/@tensorflow/tfjs-core/dist/ops/loss_ops_utils.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
var Reduction;
(function(Reduction2) {
Reduction2[Reduction2["NONE"] = 0] = "NONE";
Reduction2[Reduction2["MEAN"] = 1] = "MEAN";
Reduction2[Reduction2["SUM"] = 2] = "SUM";
Reduction2[Reduction2["SUM_BY_NONZERO_WEIGHTS"] = 3] = "SUM_BY_NONZERO_WEIGHTS";
})(Reduction || (Reduction = {}));
// node_modules/@tensorflow/tfjs-core/dist/ops/losses/compute_weighted_loss.js
function computeWeightedLoss_(losses8, weights, reduction2 = Reduction.SUM_BY_NONZERO_WEIGHTS) {
const $losses = convertToTensor(losses8, "losses", "computeWeightedLoss");
let $weights = null;
if (weights != null) {
$weights = convertToTensor(weights, "weights", "computeWeightedLoss");
}
const weightedLoss = $weights == null ? $losses : mul($losses, $weights);
if (reduction2 === Reduction.NONE) {
return weightedLoss;
}
if (reduction2 === Reduction.SUM) {
return sum2(weightedLoss);
}
if (reduction2 === Reduction.MEAN) {
if ($weights == null) {
return mean(weightedLoss);
} else {
const broadcastFactor = $losses.size / $weights.size;
const result = div(sum2(weightedLoss), sum2($weights));
return broadcastFactor > 1 ? div(result, scalar(broadcastFactor)) : result;
}
}
if (reduction2 === Reduction.SUM_BY_NONZERO_WEIGHTS) {
if ($weights == null) {
return div(sum2(weightedLoss), scalar($losses.size));
} else {
const broadcastedWeights = mul($weights, ones2($losses.shape));
const numNonZeros = cast(sum2(notEqual(broadcastedWeights, scalar(0))), "float32");
return div(sum2(weightedLoss), numNonZeros);
}
}
throw Error(`Unknown reduction: ${reduction2}`);
}
const computeWeightedLoss = op({computeWeightedLoss_});
// node_modules/@tensorflow/tfjs-core/dist/ops/losses/absolute_difference.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function absoluteDifference_(labels, predictions, weights, reduction2 = Reduction.SUM_BY_NONZERO_WEIGHTS) {
const $labels = convertToTensor(labels, "labels", "absoluteDifference");
const $predictions = convertToTensor(predictions, "predictions", "absoluteDifference");
let $weights = null;
if (weights != null) {
$weights = convertToTensor(weights, "weights", "absoluteDifference");
}
assertShapesMatch($labels.shape, $predictions.shape, "Error in absoluteDifference: ");
const losses8 = abs(sub($labels, $predictions));
return computeWeightedLoss(losses8, $weights, reduction2);
}
const absoluteDifference = op({absoluteDifference_});
// node_modules/@tensorflow/tfjs-core/dist/ops/losses/cosine_distance.js
function cosineDistance_(labels, predictions, axis, weights, reduction2 = Reduction.SUM_BY_NONZERO_WEIGHTS) {
const $labels = convertToTensor(labels, "labels", "cosineDistance");
const $predictions = convertToTensor(predictions, "predictions", "cosineDistance");
let $weights = null;
if (weights != null) {
$weights = convertToTensor(weights, "weights", "cosineDistance");
}
assertShapesMatch($labels.shape, $predictions.shape, "Error in cosineDistance: ");
const one = scalar(1);
const losses8 = sub(one, sum2(mul($labels, $predictions), axis, true));
return computeWeightedLoss(losses8, $weights, reduction2);
}
const cosineDistance = op({cosineDistance_});
// node_modules/@tensorflow/tfjs-core/dist/ops/losses/hinge_loss.js
function hingeLoss_(labels, predictions, weights, reduction2 = Reduction.SUM_BY_NONZERO_WEIGHTS) {
let $labels = convertToTensor(labels, "labels", "hingeLoss");
const $predictions = convertToTensor(predictions, "predictions", "hingeLoss");
let $weights = null;
if (weights != null) {
$weights = convertToTensor(weights, "weights", "hingeLoss");
}
assertShapesMatch($labels.shape, $predictions.shape, "Error in hingeLoss: ");
const one = scalar(1);
$labels = sub(mul(scalar(2), $labels), one);
const losses8 = relu(sub(one, mul($labels, $predictions)));
return computeWeightedLoss(losses8, $weights, reduction2);
}
const hingeLoss = op({hingeLoss_});
// node_modules/@tensorflow/tfjs-core/dist/ops/losses/huber_loss.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function huberLoss_(labels, predictions, weights, delta = 1, reduction2 = Reduction.SUM_BY_NONZERO_WEIGHTS) {
const $labels = convertToTensor(labels, "labels", "huberLoss");
const $predictions = convertToTensor(predictions, "predictions", "huberLoss");
let $weights = null;
if (weights != null) {
$weights = convertToTensor(weights, "weights", "huberLoss");
}
assertShapesMatch($labels.shape, $predictions.shape, "Error in huberLoss: ");
const deltaScalar = scalar(delta);
const error = abs(sub($predictions, $labels));
const quadratic = minimum(error, deltaScalar);
const linear = sub(error, quadratic);
const losses8 = add2(mul(scalar(0.5), square(quadratic)), mul(deltaScalar, linear));
return computeWeightedLoss(losses8, $weights, reduction2);
}
const huberLoss = op({huberLoss_});
// node_modules/@tensorflow/tfjs-core/dist/ops/losses/log_loss.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function logLoss_(labels, predictions, weights, epsilon2 = 1e-7, reduction2 = Reduction.SUM_BY_NONZERO_WEIGHTS) {
const $labels = convertToTensor(labels, "labels", "logLoss");
const $predictions = convertToTensor(predictions, "predictions", "logLoss");
let $weights = null;
if (weights != null) {
$weights = convertToTensor(weights, "weights", "logLoss");
}
assertShapesMatch($labels.shape, $predictions.shape, "Error in logLoss: ");
const one = scalar(1);
const epsilonScalar = scalar(epsilon2);
const l13 = neg(mul($labels, log(add2($predictions, epsilonScalar))));
const l23 = mul(sub(one, $labels), log(add2(sub(one, $predictions), epsilonScalar)));
const losses8 = sub(l13, l23);
return computeWeightedLoss(losses8, $weights, reduction2);
}
const logLoss = op({logLoss_});
// node_modules/@tensorflow/tfjs-core/dist/ops/losses/mean_squared_error.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function meanSquaredError_(labels, predictions, weights, reduction2 = Reduction.SUM_BY_NONZERO_WEIGHTS) {
const $labels = convertToTensor(labels, "labels", "meanSquaredError");
const $predictions = convertToTensor(predictions, "predictions", "meanSquaredError");
let $weights = null;
if (weights != null) {
$weights = convertToTensor(weights, "weights", "meanSquaredError");
}
assertShapesMatch($labels.shape, $predictions.shape, "Error in meanSquaredError: ");
const losses8 = squaredDifference($labels, $predictions);
return computeWeightedLoss(losses8, $weights, reduction2);
}
const meanSquaredError = op({meanSquaredError_});
// node_modules/@tensorflow/tfjs-core/dist/ops/losses/sigmoid_cross_entropy.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function sigmoidCrossEntropyWithLogits_(labels, logits) {
const $labels = convertToTensor(labels, "labels", "sigmoidCrossEntropyWithLogits");
const $logits = convertToTensor(logits, "logits", "sigmoidCrossEntropyWithLogits");
assertShapesMatch($labels.shape, $logits.shape, "Error in sigmoidCrossEntropyWithLogits: ");
const maxOutput = relu($logits);
const outputXTarget = mul($logits, $labels);
const sigmoidOutput = log1p(exp(neg(abs($logits))));
return add2(sub(maxOutput, outputXTarget), sigmoidOutput);
}
function sigmoidCrossEntropy_(multiClassLabels, logits, weights, labelSmoothing = 0, reduction2 = Reduction.SUM_BY_NONZERO_WEIGHTS) {
let $multiClassLabels = convertToTensor(multiClassLabels, "multiClassLabels", "sigmoidCrossEntropy");
const $logits = convertToTensor(logits, "logits", "sigmoidCrossEntropy");
let $weights = null;
if (weights != null) {
$weights = convertToTensor(weights, "weights", "sigmoidCrossEntropy");
}
assertShapesMatch($multiClassLabels.shape, $logits.shape, "Error in sigmoidCrossEntropy: ");
if (labelSmoothing > 0) {
const labelSmoothingScalar = scalar(labelSmoothing);
const one = scalar(1);
const half = scalar(0.5);
$multiClassLabels = add2(mul($multiClassLabels, sub(one, labelSmoothingScalar)), mul(half, labelSmoothingScalar));
}
const losses8 = sigmoidCrossEntropyWithLogits_($multiClassLabels, $logits);
return computeWeightedLoss(losses8, $weights, reduction2);
}
const sigmoidCrossEntropy = op({sigmoidCrossEntropy_});
// node_modules/@tensorflow/tfjs-core/dist/ops/losses/softmax_cross_entropy.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function softmaxCrossEntropyWithLogits_(labels, logits, dim = -1) {
if (dim === -1) {
dim = logits.rank - 1;
}
if (dim !== logits.rank - 1) {
throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${logits.rank} and dim was ${dim}`);
}
const customOp = customGrad((labels2, logits2, save) => {
const keepDims = true;
const lse = logSumExp(logits2, [dim], keepDims);
const logResult = sub(cast(logits2, "float32"), lse);
save([labels2, logResult]);
const costVector = neg(mul(logResult, labels2));
const value = sum2(costVector, [dim]);
const gradFunc = (dy, saved) => {
const [labels3, logResult2] = saved;
const dyShape = expandShapeToKeepDim(dy.shape, [dim]);
return [
mul(reshape(dy, dyShape), sub(cast(labels3, "float32"), exp(logResult2))),
mul(reshape(dy, dyShape), sub(exp(logResult2), cast(labels3, "float32")))
];
};
return {value, gradFunc};
});
return customOp(labels, logits);
}
function softmaxCrossEntropy_(onehotLabels, logits, weights, labelSmoothing = 0, reduction2 = Reduction.SUM_BY_NONZERO_WEIGHTS) {
let $onehotLabels = convertToTensor(onehotLabels, "onehotLabels", "softmaxCrossEntropy");
const $logits = convertToTensor(logits, "logits", "softmaxCrossEntropy");
let $weights = null;
if (weights != null) {
$weights = convertToTensor(weights, "weights", "softmaxCrossEntropy");
}
assertShapesMatch($onehotLabels.shape, $logits.shape, "Error in softmaxCrossEntropy: ");
if (labelSmoothing > 0) {
const labelSmoothingScalar = scalar(labelSmoothing);
const one = scalar(1);
const numClasses = scalar($onehotLabels.shape[1]);
$onehotLabels = add2(mul($onehotLabels, sub(one, labelSmoothingScalar)), div(labelSmoothingScalar, numClasses));
}
const losses8 = softmaxCrossEntropyWithLogits_($onehotLabels, $logits);
return computeWeightedLoss(losses8, $weights, reduction2);
}
const softmaxCrossEntropy = op({softmaxCrossEntropy_});
// node_modules/@tensorflow/tfjs-core/dist/ops/ops.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const spectral = {
fft,
ifft,
rfft,
irfft
};
const signal = {
hammingWindow,
hannWindow,
frame,
stft
};
const image = {
flipLeftRight,
resizeNearestNeighbor,
resizeBilinear,
rotateWithOffset,
cropAndResize,
nonMaxSuppression,
nonMaxSuppressionAsync,
nonMaxSuppressionWithScore,
nonMaxSuppressionWithScoreAsync,
nonMaxSuppressionPadded,
nonMaxSuppressionPaddedAsync
};
const linalg = {
bandPart,
gramSchmidt,
qr
};
const losses = {
absoluteDifference,
computeWeightedLoss,
cosineDistance,
hingeLoss,
huberLoss,
logLoss,
meanSquaredError,
sigmoidCrossEntropy,
softmaxCrossEntropy
};
// node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
class Optimizer extends Serializable {
minimize(f, returnCost = false, varList) {
const {value, grads: grads2} = this.computeGradients(f, varList);
if (varList != null) {
const gradArray = varList.map((v) => ({name: v.name, tensor: grads2[v.name]}));
this.applyGradients(gradArray);
} else {
this.applyGradients(grads2);
}
dispose(grads2);
if (returnCost) {
return value;
} else {
value.dispose();
return null;
}
}
get iterations() {
if (this.iterations_ == null) {
this.iterations_ = 0;
}
return this.iterations_;
}
incrementIterations() {
this.iterations_ = this.iterations + 1;
}
computeGradients(f, varList) {
return variableGrads(f, varList);
}
dispose() {
if (this.iterations_ != null) {
dispose(this.iterations_);
}
}
async saveIterations() {
if (this.iterations_ == null) {
this.iterations_ = 0;
}
return {
name: "iter",
tensor: scalar(this.iterations_, "int32")
};
}
async getWeights() {
throw new Error("getWeights() is not implemented for this optimizer yet.");
}
async setWeights(weightValues) {
throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`);
}
async extractIterations(weightValues) {
this.iterations_ = (await weightValues[0].tensor.data())[0];
return weightValues.slice(1);
}
}
Object.defineProperty(Optimizer, Symbol.hasInstance, {
value: (instance) => {
return instance.minimize != null && instance.computeGradients != null && instance.applyGradients != null;
}
});
// node_modules/@tensorflow/tfjs-core/dist/optimizers/adadelta_optimizer.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
class AdadeltaOptimizer extends Optimizer {
constructor(learningRate, rho, epsilon2 = null) {
super();
this.learningRate = learningRate;
this.rho = rho;
this.epsilon = epsilon2;
this.accumulatedGrads = [];
this.accumulatedUpdates = [];
if (epsilon2 == null) {
this.epsilon = ENGINE.backend.epsilon();
}
}
applyGradients(variableGradients) {
const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
variableNames.forEach((name, i) => {
const value = ENGINE.registeredVariables[name];
const trainable = false;
if (this.accumulatedGrads[i] == null) {
this.accumulatedGrads[i] = {
originalName: `${name}/accum_grad`,
variable: tidy(() => zerosLike(value).variable(trainable))
};
}
if (this.accumulatedUpdates[i] == null) {
this.accumulatedUpdates[i] = {
originalName: `${name}/accum_var`,
variable: tidy(() => zerosLike(value).variable(trainable))
};
}
const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
if (gradient == null) {
return;
}
const accumulatedGrad = this.accumulatedGrads[i].variable;
const accumulatedUpdate = this.accumulatedUpdates[i].variable;
tidy(() => {
const newAccumulatedGrad = add2(mul(accumulatedGrad, this.rho), mul(square(gradient), 1 - this.rho));
const updates = mul(div(sqrt(add2(accumulatedUpdate, this.epsilon)), sqrt(add2(accumulatedGrad, this.epsilon))), gradient);
const newAccumulatedUpdate = add2(mul(accumulatedUpdate, this.rho), mul(square(updates), 1 - this.rho));
accumulatedGrad.assign(newAccumulatedGrad);
accumulatedUpdate.assign(newAccumulatedUpdate);
const newValue = add2(mul(updates, -this.learningRate), value);
value.assign(newValue);
});
});
this.incrementIterations();
}
dispose() {
if (this.accumulatedUpdates != null) {
dispose(this.accumulatedGrads.map((v) => v.variable));
dispose(this.accumulatedUpdates.map((v) => v.variable));
}
}
async getWeights() {
const variables5 = [...this.accumulatedGrads, ...this.accumulatedUpdates];
return [await this.saveIterations()].concat(variables5.map((v) => ({name: v.originalName, tensor: v.variable})));
}
async setWeights(weightValues) {
weightValues = await this.extractIterations(weightValues);
const variableCount = weightValues.length / 2;
const trainable = false;
this.accumulatedGrads = weightValues.slice(0, variableCount).map((v) => ({
originalName: v.name,
variable: v.tensor.variable(trainable)
}));
this.accumulatedUpdates = weightValues.slice(variableCount, variableCount * 2).map((v) => ({
originalName: v.name,
variable: v.tensor.variable(trainable)
}));
}
getConfig() {
return {
learningRate: this.learningRate,
rho: this.rho,
epsilon: this.epsilon
};
}
static fromConfig(cls, config2) {
return new cls(config2["learningRate"], config2["rho"], config2["epsilon"]);
}
}
AdadeltaOptimizer.className = "Adadelta";
registerClass(AdadeltaOptimizer);
// node_modules/@tensorflow/tfjs-core/dist/optimizers/adagrad_optimizer.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
class AdagradOptimizer extends Optimizer {
constructor(learningRate, initialAccumulatorValue = 0.1) {
super();
this.learningRate = learningRate;
this.initialAccumulatorValue = initialAccumulatorValue;
this.accumulatedGrads = [];
}
applyGradients(variableGradients) {
const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
variableNames.forEach((name, i) => {
const value = ENGINE.registeredVariables[name];
if (this.accumulatedGrads[i] == null) {
const trainable = false;
this.accumulatedGrads[i] = {
originalName: `${name}/accumulator`,
variable: tidy(() => fill(value.shape, this.initialAccumulatorValue).variable(trainable))
};
}
const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
if (gradient == null) {
return;
}
const accumulatedGrad = this.accumulatedGrads[i].variable;
tidy(() => {
const newAccumulatedGrad = add2(accumulatedGrad, square(gradient));
accumulatedGrad.assign(newAccumulatedGrad);
const newValue = add2(mul(div(gradient, sqrt(add2(newAccumulatedGrad, ENGINE.backend.epsilon()))), -this.learningRate), value);
value.assign(newValue);
});
});
this.incrementIterations();
}
dispose() {
if (this.accumulatedGrads != null) {
dispose(this.accumulatedGrads.map((v) => v.variable));
}
}
async getWeights() {
return [await this.saveIterations()].concat(this.accumulatedGrads.map((v) => ({name: v.originalName, tensor: v.variable})));
}
async setWeights(weightValues) {
weightValues = await this.extractIterations(weightValues);
const trainable = false;
this.accumulatedGrads = weightValues.map((v) => ({originalName: v.name, variable: v.tensor.variable(trainable)}));
}
getConfig() {
return {
learningRate: this.learningRate,
initialAccumulatorValue: this.initialAccumulatorValue
};
}
static fromConfig(cls, config2) {
return new cls(config2["learningRate"], config2["initialAccumulatorValue"]);
}
}
AdagradOptimizer.className = "Adagrad";
registerClass(AdagradOptimizer);
// node_modules/@tensorflow/tfjs-core/dist/optimizers/adam_optimizer.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
class AdamOptimizer extends Optimizer {
constructor(learningRate, beta1, beta2, epsilon2 = null) {
super();
this.learningRate = learningRate;
this.beta1 = beta1;
this.beta2 = beta2;
this.epsilon = epsilon2;
this.accumulatedFirstMoment = [];
this.accumulatedSecondMoment = [];
tidy(() => {
this.accBeta1 = scalar(beta1).variable();
this.accBeta2 = scalar(beta2).variable();
});
if (epsilon2 == null) {
this.epsilon = ENGINE.backend.epsilon();
}
}
applyGradients(variableGradients) {
const varNames = Array.isArray(variableGradients) ? variableGradients.map((v) => v.name) : Object.keys(variableGradients);
tidy(() => {
const oneMinusAccBeta1 = sub(1, this.accBeta1);
const oneMinusAccBeta2 = sub(1, this.accBeta2);
varNames.forEach((name, i) => {
const value = ENGINE.registeredVariables[name];
const trainable = false;
if (this.accumulatedFirstMoment[i] == null) {
this.accumulatedFirstMoment[i] = {
originalName: `${name}/m`,
variable: tidy(() => zerosLike(value).variable(trainable))
};
}
if (this.accumulatedSecondMoment[i] == null) {
this.accumulatedSecondMoment[i] = {
originalName: `${name}/v`,
variable: tidy(() => zerosLike(value).variable(trainable))
};
}
const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
if (gradient == null) {
return;
}
const firstMoment = this.accumulatedFirstMoment[i].variable;
const secondMoment = this.accumulatedSecondMoment[i].variable;
const newFirstMoment = add2(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));
const newSecondMoment = add2(mul(secondMoment, this.beta2), mul(square(gradient), 1 - this.beta2));
const biasCorrectedFirstMoment = div(newFirstMoment, oneMinusAccBeta1);
const biasCorrectedSecondMoment = div(newSecondMoment, oneMinusAccBeta2);
firstMoment.assign(newFirstMoment);
secondMoment.assign(newSecondMoment);
const newValue = add2(mul(div(biasCorrectedFirstMoment, add2(sqrt(biasCorrectedSecondMoment), this.epsilon)), -this.learningRate), value);
value.assign(newValue);
});
this.accBeta1.assign(mul(this.accBeta1, this.beta1));
this.accBeta2.assign(mul(this.accBeta2, this.beta2));
});
this.incrementIterations();
}
dispose() {
this.accBeta1.dispose();
this.accBeta2.dispose();
if (this.accumulatedFirstMoment != null) {
dispose(this.accumulatedFirstMoment.map((v) => v.variable));
}
if (this.accumulatedSecondMoment != null) {
dispose(this.accumulatedSecondMoment.map((v) => v.variable));
}
}
async getWeights() {
const variables5 = [...this.accumulatedFirstMoment, ...this.accumulatedSecondMoment];
return [await this.saveIterations()].concat(variables5.map((v) => ({name: v.originalName, tensor: v.variable})));
}
async setWeights(weightValues) {
weightValues = await this.extractIterations(weightValues);
tidy(() => {
this.accBeta1.assign(pow(this.beta1, this.iterations_ + 1));
this.accBeta2.assign(pow(this.beta2, this.iterations_ + 1));
});
const variableCount = weightValues.length / 2;
const trainable = false;
this.accumulatedFirstMoment = weightValues.slice(0, variableCount).map((v) => ({
originalName: v.name,
variable: v.tensor.variable(trainable)
}));
this.accumulatedSecondMoment = weightValues.slice(variableCount, variableCount * 2).map((v) => ({
originalName: v.name,
variable: v.tensor.variable(trainable)
}));
}
getConfig() {
return {
learningRate: this.learningRate,
beta1: this.beta1,
beta2: this.beta2,
epsilon: this.epsilon
};
}
static fromConfig(cls, config2) {
return new cls(config2["learningRate"], config2["beta1"], config2["beta2"], config2["epsilon"]);
}
}
AdamOptimizer.className = "Adam";
registerClass(AdamOptimizer);
// node_modules/@tensorflow/tfjs-core/dist/optimizers/adamax_optimizer.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
class AdamaxOptimizer extends Optimizer {
constructor(learningRate, beta1, beta2, epsilon2 = null, decay = 0) {
super();
this.learningRate = learningRate;
this.beta1 = beta1;
this.beta2 = beta2;
this.epsilon = epsilon2;
this.decay = decay;
this.accumulatedFirstMoment = [];
this.accumulatedWeightedInfNorm = [];
tidy(() => {
this.iteration = scalar(0).variable();
this.accBeta1 = scalar(beta1).variable();
});
if (epsilon2 == null) {
this.epsilon = ENGINE.backend.epsilon();
}
}
applyGradients(variableGradients) {
const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
tidy(() => {
const oneMinusAccBeta1 = sub(1, this.accBeta1);
const lr = div(-this.learningRate, add2(mul(this.iteration, this.decay), 1));
variableNames.forEach((name, i) => {
const value = ENGINE.registeredVariables[name];
const trainable = false;
if (this.accumulatedFirstMoment[i] == null) {
this.accumulatedFirstMoment[i] = {
originalName: `${name}/m`,
variable: zerosLike(value).variable(trainable)
};
}
if (this.accumulatedWeightedInfNorm[i] == null) {
this.accumulatedWeightedInfNorm[i] = {
originalName: `${name}/v`,
variable: zerosLike(value).variable(trainable)
};
}
const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
if (gradient == null) {
return;
}
const firstMoment = this.accumulatedFirstMoment[i].variable;
const weightedInfNorm = this.accumulatedWeightedInfNorm[i].variable;
const newFirstMoment = add2(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));
const ut0 = mul(weightedInfNorm, this.beta2);
const ut1 = abs(gradient);
const newWeightedInfNorm = maximum(ut0, ut1);
firstMoment.assign(newFirstMoment);
weightedInfNorm.assign(newWeightedInfNorm);
const newValue = add2(mul(div(lr, oneMinusAccBeta1), div(newFirstMoment, add2(newWeightedInfNorm, this.epsilon))), value);
value.assign(newValue);
});
this.iteration.assign(add2(this.iteration, 1));
this.accBeta1.assign(mul(this.accBeta1, this.beta1));
});
this.incrementIterations();
}
dispose() {
this.accBeta1.dispose();
this.iteration.dispose();
if (this.accumulatedFirstMoment != null) {
dispose(this.accumulatedFirstMoment.map((v) => v.variable));
}
if (this.accumulatedWeightedInfNorm != null) {
dispose(this.accumulatedWeightedInfNorm.map((v) => v.variable));
}
}
async getWeights() {
throw new Error("getWeights() is not implemented for Adamax yet.");
}
async setWeights(weightValues) {
throw new Error("setWeights() is not implemented for Adamax yet.");
}
getConfig() {
return {
learningRate: this.learningRate,
beta1: this.beta1,
beta2: this.beta2,
epsilon: this.epsilon,
decay: this.decay
};
}
static fromConfig(cls, config2) {
return new cls(config2["learningRate"], config2["beta1"], config2["beta2"], config2["epsilon"], config2["decay"]);
}
}
AdamaxOptimizer.className = "Adamax";
registerClass(AdamaxOptimizer);
// node_modules/@tensorflow/tfjs-core/dist/optimizers/sgd_optimizer.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
class SGDOptimizer extends Optimizer {
constructor(learningRate) {
super();
this.learningRate = learningRate;
this.setLearningRate(learningRate);
}
applyGradients(variableGradients) {
const varNames = Array.isArray(variableGradients) ? variableGradients.map((v) => v.name) : Object.keys(variableGradients);
varNames.forEach((name, i) => {
const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
if (gradient == null) {
return;
}
const value = ENGINE.registeredVariables[name];
tidy(() => {
const newValue = add2(mul(this.c, gradient), value);
value.assign(newValue);
});
});
this.incrementIterations();
}
setLearningRate(learningRate) {
this.learningRate = learningRate;
if (this.c != null) {
this.c.dispose();
}
this.c = keep(scalar(-learningRate));
}
dispose() {
this.c.dispose();
}
async getWeights() {
return [await this.saveIterations()];
}
async setWeights(weightValues) {
weightValues = await this.extractIterations(weightValues);
if (weightValues.length !== 0) {
throw new Error("SGD optimizer does not have settable weights.");
}
}
getConfig() {
return {learningRate: this.learningRate};
}
static fromConfig(cls, config2) {
return new cls(config2["learningRate"]);
}
}
SGDOptimizer.className = "SGD";
registerClass(SGDOptimizer);
// node_modules/@tensorflow/tfjs-core/dist/optimizers/momentum_optimizer.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
class MomentumOptimizer extends SGDOptimizer {
constructor(learningRate, momentum, useNesterov = false) {
super(learningRate);
this.learningRate = learningRate;
this.momentum = momentum;
this.useNesterov = useNesterov;
this.accumulations = [];
this.m = scalar(this.momentum);
}
applyGradients(variableGradients) {
const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
variableNames.forEach((name, i) => {
const value = ENGINE.registeredVariables[name];
if (this.accumulations[i] == null) {
const trainable = false;
this.accumulations[i] = {
originalName: `${name}/momentum`,
variable: tidy(() => zerosLike(value).variable(trainable))
};
}
const accumulation = this.accumulations[i].variable;
const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
if (gradient == null) {
return;
}
tidy(() => {
let newValue;
const newAccumulation = add2(mul(this.m, accumulation), gradient);
if (this.useNesterov) {
newValue = add2(mul(this.c, add2(gradient, mul(newAccumulation, this.m))), value);
} else {
newValue = add2(mul(this.c, newAccumulation), value);
}
accumulation.assign(newAccumulation);
value.assign(newValue);
});
});
this.incrementIterations();
}
dispose() {
this.m.dispose();
if (this.accumulations != null) {
dispose(this.accumulations.map((v) => v.variable));
}
}
setMomentum(momentum) {
this.momentum = momentum;
}
async getWeights() {
return [await this.saveIterations()].concat(this.accumulations.map((v) => ({name: v.originalName, tensor: v.variable})));
}
async setWeights(weightValues) {
weightValues = await this.extractIterations(weightValues);
const trainable = false;
this.accumulations = weightValues.map((v) => ({originalName: v.name, variable: v.tensor.variable(trainable)}));
}
getConfig() {
return {
learningRate: this.learningRate,
momentum: this.momentum,
useNesterov: this.useNesterov
};
}
static fromConfig(cls, config2) {
return new cls(config2["learningRate"], config2["momentum"], config2["useNesterov"]);
}
}
MomentumOptimizer.className = "Momentum";
registerClass(MomentumOptimizer);
// node_modules/@tensorflow/tfjs-core/dist/optimizers/rmsprop_optimizer.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
class RMSPropOptimizer extends Optimizer {
constructor(learningRate, decay = 0.9, momentum = 0, epsilon2 = null, centered = false) {
super();
this.learningRate = learningRate;
this.decay = decay;
this.momentum = momentum;
this.epsilon = epsilon2;
this.accumulatedMeanSquares = [];
this.accumulatedMoments = [];
this.accumulatedMeanGrads = [];
this.centered = centered;
if (epsilon2 == null) {
this.epsilon = ENGINE.backend.epsilon();
}
if (learningRate == null) {
throw new Error(`learningRate for RMSPropOptimizer must be defined.`);
}
}
applyGradients(variableGradients) {
const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
variableNames.forEach((name, i) => {
const value = ENGINE.registeredVariables[name];
const trainable = false;
if (this.accumulatedMeanSquares[i] == null) {
this.accumulatedMeanSquares[i] = {
originalName: `${name}/rms`,
variable: tidy(() => zerosLike(value).variable(trainable))
};
}
if (this.accumulatedMoments[i] == null) {
this.accumulatedMoments[i] = {
originalName: `${name}/momentum`,
variable: tidy(() => zerosLike(value).variable(trainable))
};
}
if (this.accumulatedMeanGrads[i] == null && this.centered) {
this.accumulatedMeanGrads[i] = {
originalName: `${name}/mg`,
variable: tidy(() => zerosLike(value).variable(trainable))
};
}
const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
if (gradient == null) {
return;
}
const accumulatedMeanSquare = this.accumulatedMeanSquares[i].variable;
const accumulatedMoments = this.accumulatedMoments[i].variable;
tidy(() => {
const newAccumulatedMeanSquare = add2(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));
if (this.centered) {
const accumulatedMeanGrad = this.accumulatedMeanGrads[i].variable;
const newAccumulatedMeanGrad = add2(mul(accumulatedMeanGrad, this.decay), mul(gradient, 1 - this.decay));
const gradContribution = div(mul(gradient, this.learningRate), sqrt(sub(newAccumulatedMeanSquare, add2(square(newAccumulatedMeanGrad), this.epsilon))));
const newAccumulatedMoments = add2(mul(accumulatedMoments, this.momentum), gradContribution);
accumulatedMeanSquare.assign(newAccumulatedMeanSquare);
accumulatedMeanGrad.assign(newAccumulatedMeanGrad);
accumulatedMoments.assign(newAccumulatedMoments);
const newValue = sub(value, newAccumulatedMoments);
value.assign(newValue);
} else {
const newAccumulatedMeanSquare2 = add2(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));
const newAccumulatedMoments = add2(mul(accumulatedMoments, this.momentum), div(mul(gradient, this.learningRate), sqrt(add2(newAccumulatedMeanSquare2, this.epsilon))));
accumulatedMeanSquare.assign(newAccumulatedMeanSquare2);
accumulatedMoments.assign(newAccumulatedMoments);
const newValue = sub(value, newAccumulatedMoments);
value.assign(newValue);
}
});
});
this.incrementIterations();
}
dispose() {
if (this.accumulatedMeanSquares != null) {
dispose(this.accumulatedMeanSquares.map((v) => v.variable));
}
if (this.accumulatedMeanGrads != null && this.centered) {
dispose(this.accumulatedMeanGrads.map((v) => v.variable));
}
if (this.accumulatedMoments != null) {
dispose(this.accumulatedMoments.map((v) => v.variable));
}
}
async getWeights() {
const variables5 = [...this.accumulatedMeanSquares, ...this.accumulatedMoments];
if (this.centered) {
variables5.push(...this.accumulatedMeanGrads);
}
return [await this.saveIterations()].concat(variables5.map((v) => ({name: v.originalName, tensor: v.variable})));
}
async setWeights(weightValues) {
weightValues = await this.extractIterations(weightValues);
const variableCount = this.centered ? weightValues.length / 3 : weightValues.length / 2;
const trainable = false;
this.accumulatedMeanSquares = weightValues.slice(0, variableCount).map((v) => ({
originalName: v.name,
variable: v.tensor.variable(trainable)
}));
this.accumulatedMoments = weightValues.slice(variableCount, variableCount * 2).map((v) => ({
originalName: v.name,
variable: v.tensor.variable(trainable)
}));
if (this.centered) {
this.accumulatedMeanGrads = weightValues.slice(variableCount * 2, variableCount * 3).map((v) => ({
originalName: v.name,
variable: v.tensor.variable(trainable)
}));
}
}
getConfig() {
return {
learningRate: this.learningRate,
decay: this.decay,
momentum: this.momentum,
epsilon: this.epsilon,
centered: this.centered
};
}
static fromConfig(cls, config2) {
return new cls(config2["learningRate"], config2["decay"], config2["momentum"], config2["epsilon"], config2["centered"]);
}
}
RMSPropOptimizer.className = "RMSProp";
registerClass(RMSPropOptimizer);
// node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer_constructors.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
class OptimizerConstructors {
static sgd(learningRate) {
return new SGDOptimizer(learningRate);
}
static momentum(learningRate, momentum, useNesterov = false) {
return new MomentumOptimizer(learningRate, momentum, useNesterov);
}
static rmsprop(learningRate, decay = 0.9, momentum = 0, epsilon2 = null, centered = false) {
return new RMSPropOptimizer(learningRate, decay, momentum, epsilon2, centered);
}
static adam(learningRate = 1e-3, beta1 = 0.9, beta2 = 0.999, epsilon2 = null) {
return new AdamOptimizer(learningRate, beta1, beta2, epsilon2);
}
static adadelta(learningRate = 1e-3, rho = 0.95, epsilon2 = null) {
return new AdadeltaOptimizer(learningRate, rho, epsilon2);
}
static adamax(learningRate = 2e-3, beta1 = 0.9, beta2 = 0.999, epsilon2 = null, decay = 0) {
return new AdamaxOptimizer(learningRate, beta1, beta2, epsilon2, decay);
}
static adagrad(learningRate, initialAccumulatorValue = 0.1) {
return new AdagradOptimizer(learningRate, initialAccumulatorValue);
}
}
// node_modules/@tensorflow/tfjs-core/dist/train.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
[
MomentumOptimizer,
SGDOptimizer,
AdadeltaOptimizer,
AdagradOptimizer,
RMSPropOptimizer,
AdamaxOptimizer,
AdamOptimizer
];
const train = {
sgd: OptimizerConstructors.sgd,
momentum: OptimizerConstructors.momentum,
adadelta: OptimizerConstructors.adadelta,
adagrad: OptimizerConstructors.adagrad,
rmsprop: OptimizerConstructors.rmsprop,
adamax: OptimizerConstructors.adamax,
adam: OptimizerConstructors.adam
};
// node_modules/@tensorflow/tfjs-core/dist/browser_util.js
/**
* @license
* Copyright 2017 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const delayCallback = (() => {
if (typeof requestAnimationFrame !== "undefined") {
return requestAnimationFrame;
} else if (typeof setImmediate !== "undefined") {
return setImmediate;
}
return (f) => f();
})();
function nextFrame() {
return new Promise((resolve) => delayCallback(() => resolve()));
}
// node_modules/@tensorflow/tfjs-core/dist/backends/backend_util.js
const backend_util_exports = {};
__export(backend_util_exports, {
ERF_A1: () => ERF_A1,
ERF_A2: () => ERF_A2,
ERF_A3: () => ERF_A3,
ERF_A4: () => ERF_A4,
ERF_A5: () => ERF_A5,
ERF_P: () => ERF_P,
PARALLELIZE_THRESHOLD: () => PARALLELIZE_THRESHOLD,
SELU_SCALE: () => SELU_SCALE,
SELU_SCALEALPHA: () => SELU_SCALEALPHA,
applyActivation: () => applyActivation,
assertAndGetBroadcastShape: () => assertAndGetBroadcastShape,
assertAxesAreInnerMostDims: () => assertAxesAreInnerMostDims,
assertParamsConsistent: () => assertParamsConsistent,
assignToTypedArray: () => assignToTypedArray,
axesAreInnerMostDims: () => axesAreInnerMostDims,
calculateShapes: () => calculateShapes,
castTensor: () => castTensor,
combineLocations: () => combineLocations,
complexWithEvenIndex: () => complexWithEvenIndex,
complexWithOddIndex: () => complexWithOddIndex,
computeConv2DInfo: () => computeConv2DInfo,
computeConv3DInfo: () => computeConv3DInfo,
computeDefaultPad: () => computeDefaultPad,
computeDilation2DInfo: () => computeDilation2DInfo,
computeOptimalWindowSize: () => computeOptimalWindowSize,
computeOutAndReduceShapes: () => computeOutAndReduceShapes,
computeOutShape: () => computeOutShape2,
computePool2DInfo: () => computePool2DInfo,
computePool3DInfo: () => computePool3DInfo,
convertConv2DDataFormat: () => convertConv2DDataFormat,
eitherStridesOrDilationsAreOne: () => eitherStridesOrDilationsAreOne,
expandShapeToKeepDim: () => expandShapeToKeepDim,
exponent: () => exponent,
exponents: () => exponents,
getAxesPermutation: () => getAxesPermutation,
getBroadcastDims: () => getBroadcastDims,
getComplexWithIndex: () => getComplexWithIndex,
getFusedBiasGradient: () => getFusedBiasGradient,
getFusedDyActivation: () => getFusedDyActivation,
getImageCenter: () => getImageCenter,
getInnerMostAxes: () => getInnerMostAxes,
getPermuted: () => getPermuted,
getReductionAxes: () => getReductionAxes,
getReshaped: () => getReshaped,
getReshapedPermuted: () => getReshapedPermuted,
getSliceBeginCoords: () => getSliceBeginCoords,
getSliceSize: () => getSliceSize,
getUndoAxesPermutation: () => getUndoAxesPermutation,
linspaceImpl: () => linspaceImpl,
log: () => log6,
mergeRealAndImagArrays: () => mergeRealAndImagArrays,
prepareAndValidate: () => prepareAndValidate,
prepareSplitSize: () => prepareSplitSize,
reshapeTensor: () => reshapeTensor,
segment_util: () => segment_util_exports,
shouldFuse: () => shouldFuse,
slice_util: () => slice_util_exports,
splitRealAndImagArrays: () => splitRealAndImagArrays,
tupleValuesAreOne: () => tupleValuesAreOne,
upcastType: () => upcastType,
validateInput: () => validateInput,
validateUpdateShape: () => validateUpdateShape,
warn: () => warn
});
// node_modules/@tensorflow/tfjs-core/dist/ops/rotate_util.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function getImageCenter(center, imageHeight, imageWidth) {
const centerX = imageWidth * (typeof center === "number" ? center : center[0]);
const centerY = imageHeight * (typeof center === "number" ? center : center[1]);
return [centerX, centerY];
}
// node_modules/@tensorflow/tfjs-core/dist/ops/array_ops_util.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function getReshaped(inputShape, blockShape, prod3, batchToSpace = true) {
let reshaped = [];
if (batchToSpace) {
reshaped = reshaped.concat(blockShape.slice(0));
reshaped.push(inputShape[0] / prod3);
reshaped = reshaped.concat(inputShape.slice(1));
} else {
reshaped = reshaped.concat(inputShape[0]);
const spatialLength = blockShape.length;
for (let i = 0; i < spatialLength; ++i) {
reshaped = reshaped.concat([inputShape[i + 1] / blockShape[i], blockShape[i]]);
}
reshaped = reshaped.concat(inputShape.slice(spatialLength + 1));
}
return reshaped;
}
function getPermuted(reshapedRank, blockShapeRank, batchToSpace = true) {
const permuted = [];
if (batchToSpace) {
permuted.push(blockShapeRank);
for (let i = blockShapeRank + 1; i < reshapedRank; ++i) {
if (i <= 2 * blockShapeRank) {
permuted.push(i);
permuted.push(i - (blockShapeRank + 1));
} else {
permuted.push(i);
}
}
} else {
const permutedBeforeBatch = [];
const permutedAfterBatch = [];
for (let i = 1; i < reshapedRank; ++i) {
if (i >= blockShapeRank * 2 + 1 || i % 2 === 1) {
permutedAfterBatch.push(i);
} else {
permutedBeforeBatch.push(i);
}
}
permuted.push(...permutedBeforeBatch);
permuted.push(0);
permuted.push(...permutedAfterBatch);
}
return permuted;
}
function getReshapedPermuted(inputShape, blockShape, prod3, batchToSpace = true) {
const reshapedPermuted = [];
if (batchToSpace) {
reshapedPermuted.push(inputShape[0] / prod3);
} else {
reshapedPermuted.push(inputShape[0] * prod3);
}
for (let i = 1; i < inputShape.length; ++i) {
if (i <= blockShape.length) {
if (batchToSpace) {
reshapedPermuted.push(blockShape[i - 1] * inputShape[i]);
} else {
reshapedPermuted.push(inputShape[i] / blockShape[i - 1]);
}
} else {
reshapedPermuted.push(inputShape[i]);
}
}
return reshapedPermuted;
}
function getSliceBeginCoords(crops, blockShape) {
const sliceBeginCoords = [0];
for (let i = 0; i < blockShape; ++i) {
sliceBeginCoords.push(crops[i][0]);
}
return sliceBeginCoords;
}
function getSliceSize(uncroppedShape, crops, blockShape) {
const sliceSize = uncroppedShape.slice(0, 1);
for (let i = 0; i < blockShape; ++i) {
sliceSize.push(uncroppedShape[i + 1] - crops[i][0] - crops[i][1]);
}
return sliceSize;
}
// node_modules/@tensorflow/tfjs-core/dist/ops/selu_util.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const SELU_SCALEALPHA = 1.7580993408473768;
const SELU_SCALE = 1.0507009873554805;
// node_modules/@tensorflow/tfjs-core/dist/ops/erf_util.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const ERF_P = 0.3275911;
const ERF_A1 = 0.254829592;
const ERF_A2 = -0.284496736;
const ERF_A3 = 1.421413741;
const ERF_A4 = -1.453152027;
const ERF_A5 = 1.061405429;
// node_modules/@tensorflow/tfjs-core/dist/log.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function warn(...msg) {
if (!env().getBool("IS_TEST")) {
console.warn(...msg);
}
}
function log6(...msg) {
if (!env().getBool("IS_TEST")) {
console.log(...msg);
}
}
// node_modules/@tensorflow/tfjs-core/dist/backends/complex_util.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function mergeRealAndImagArrays(real6, imag6) {
if (real6.length !== imag6.length) {
throw new Error(`Cannot merge real and imag arrays of different lengths. real:${real6.length}, imag: ${imag6.length}.`);
}
const result = new Float32Array(real6.length * 2);
for (let i = 0; i < result.length; i += 2) {
result[i] = real6[i / 2];
result[i + 1] = imag6[i / 2];
}
return result;
}
function splitRealAndImagArrays(complex9) {
const real6 = new Float32Array(complex9.length / 2);
const imag6 = new Float32Array(complex9.length / 2);
for (let i = 0; i < complex9.length; i += 2) {
real6[i / 2] = complex9[i];
imag6[i / 2] = complex9[i + 1];
}
return {real: real6, imag: imag6};
}
function complexWithEvenIndex(complex9) {
const len = Math.ceil(complex9.length / 4);
const real6 = new Float32Array(len);
const imag6 = new Float32Array(len);
for (let i = 0; i < complex9.length; i += 4) {
real6[Math.floor(i / 4)] = complex9[i];
imag6[Math.floor(i / 4)] = complex9[i + 1];
}
return {real: real6, imag: imag6};
}
function complexWithOddIndex(complex9) {
const len = Math.floor(complex9.length / 4);
const real6 = new Float32Array(len);
const imag6 = new Float32Array(len);
for (let i = 2; i < complex9.length; i += 4) {
real6[Math.floor(i / 4)] = complex9[i];
imag6[Math.floor(i / 4)] = complex9[i + 1];
}
return {real: real6, imag: imag6};
}
function getComplexWithIndex(complex9, index) {
const real6 = complex9[index * 2];
const imag6 = complex9[index * 2 + 1];
return {real: real6, imag: imag6};
}
function assignToTypedArray(data2, real6, imag6, index) {
data2[index * 2] = real6;
data2[index * 2 + 1] = imag6;
}
function exponents(n, inverse) {
const real6 = new Float32Array(n / 2);
const imag6 = new Float32Array(n / 2);
for (let i = 0; i < Math.ceil(n / 2); i++) {
const x = (inverse ? 2 : -2) * Math.PI * (i / n);
real6[i] = Math.cos(x);
imag6[i] = Math.sin(x);
}
return {real: real6, imag: imag6};
}
function exponent(k, n, inverse) {
const x = (inverse ? 2 : -2) * Math.PI * (k / n);
const real6 = Math.cos(x);
const imag6 = Math.sin(x);
return {real: real6, imag: imag6};
}
// node_modules/@tensorflow/tfjs-core/dist/backends/backend_util.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function castTensor(x, dtype, backend3) {
if (dtype === "complex64") {
if (x.dtype === "complex64") {
return x.clone();
}
const zerosTensor = zeros(x.shape);
const floatX = cast(x, "float32");
const result = backend3.complex(floatX, zerosTensor);
zerosTensor.dispose();
floatX.dispose();
return result;
}
if (!hasEncodingLoss(x.dtype, dtype)) {
return ENGINE.makeTensorFromDataId(x.dataId, x.shape, dtype);
}
if (x.dtype === "complex64") {
const real6 = backend3.real(x);
const result = cast(real6, dtype);
real6.dispose();
return result;
}
if (dtype === "int32") {
return backend3.int(x);
} else if (dtype === "bool") {
const zero = scalar(0, x.dtype);
const result = backend3.notEqual(x, zero);
zero.dispose();
return result;
} else {
throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);
}
}
function reshapeTensor(x, shape) {
return ENGINE.makeTensorFromDataId(x.dataId, shape, x.dtype);
}
function linspaceImpl(start, stop, num) {
const step4 = (stop - start) / (num - 1);
const values = makeZerosTypedArray(num, "float32");
values[0] = start;
for (let i = 1; i < values.length; i++) {
values[i] = values[i - 1] + step4;
}
return tensor1d(values, "float32");
}
// node_modules/@tensorflow/tfjs-core/dist/backends/kernel_impls.js
const kernel_impls_exports = {};
__export(kernel_impls_exports, {
nonMaxSuppressionV3Impl: () => nonMaxSuppressionV3Impl,
nonMaxSuppressionV4Impl: () => nonMaxSuppressionV4Impl,
nonMaxSuppressionV5Impl: () => nonMaxSuppressionV5Impl,
split: () => split5,
tile: () => tile4,
topkImpl: () => topkImpl,
whereImpl: () => whereImpl
});
// node_modules/@tensorflow/tfjs-core/dist/backends/split_shared.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function split5(x, sizeSplits, axis) {
const begin = new Array(x.rank).fill(0);
const size = x.shape.slice();
return sizeSplits.map((s) => {
const sliceSize = [...size];
sliceSize[axis] = s;
const sliceT = slice(x, begin, sliceSize);
begin[axis] += s;
return sliceT;
});
}
// node_modules/@tensorflow/tfjs-core/dist/backends/tile_impl.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function tile4(xBuf, reps) {
const newShape = new Array(xBuf.rank);
for (let i = 0; i < newShape.length; i++) {
newShape[i] = xBuf.shape[i] * reps[i];
}
const result = buffer(newShape, xBuf.dtype);
for (let i = 0; i < result.values.length; ++i) {
const newLoc = result.indexToLoc(i);
const originalLoc = new Array(xBuf.rank);
for (let j = 0; j < originalLoc.length; j++) {
originalLoc[j] = newLoc[j] % xBuf.shape[j];
}
const originalIndex = xBuf.locToIndex(originalLoc);
result.values[i] = xBuf.values[originalIndex];
}
return result.toTensor();
}
// node_modules/@tensorflow/tfjs-core/dist/backends/topk_impl.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function topkImpl(x, xShape, xDtype, k, sorted) {
const lastDim = xShape[xShape.length - 1];
const [batch, size] = [x.length / lastDim, lastDim];
const allTopKVals = getTypedArrayFromDType(xDtype, batch * k);
const allTopKIndices = getTypedArrayFromDType("int32", batch * k);
for (let b = 0; b < batch; b++) {
const offset = b * size;
const vals = x.subarray(offset, offset + size);
const valAndInd = [];
for (let i = 0; i < vals.length; i++) {
valAndInd.push({value: vals[i], index: i});
}
valAndInd.sort((a, b2) => b2.value - a.value);
const outOffset = b * k;
const topKVals = allTopKVals.subarray(outOffset, outOffset + k);
const topKIndices = allTopKIndices.subarray(outOffset, outOffset + k);
for (let i = 0; i < k; i++) {
topKVals[i] = valAndInd[i].value;
topKIndices[i] = valAndInd[i].index;
}
}
const outputShape = xShape.slice();
outputShape[outputShape.length - 1] = k;
return [
tensor4(allTopKVals, outputShape, xDtype),
tensor4(allTopKIndices, outputShape, "int32")
];
}
// node_modules/@tensorflow/tfjs-core/dist/backends/kernel_impls.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
// node_modules/@tensorflow/tfjs-layers/dist/exports_constraints.js
const exports_constraints_exports = {};
__export(exports_constraints_exports, {
maxNorm: () => maxNorm,
minMaxNorm: () => minMaxNorm,
nonNeg: () => nonNeg,
unitNorm: () => unitNorm
});
// node_modules/@tensorflow/tfjs-layers/dist/backend/common.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
let _epsilon;
function epsilon() {
if (_epsilon == null) {
_epsilon = backend2().epsilon();
}
return _epsilon;
}
function imageDataFormat() {
return "channelsLast";
}
// node_modules/@tensorflow/tfjs-layers/dist/errors.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
class AttributeError extends Error {
constructor(message) {
super(message);
Object.setPrototypeOf(this, AttributeError.prototype);
}
}
class RuntimeError extends Error {
constructor(message) {
super(message);
Object.setPrototypeOf(this, RuntimeError.prototype);
}
}
class ValueError extends Error {
constructor(message) {
super(message);
Object.setPrototypeOf(this, ValueError.prototype);
}
}
class NotImplementedError extends Error {
constructor(message) {
super(message);
Object.setPrototypeOf(this, NotImplementedError.prototype);
}
}
class AssertionError extends Error {
constructor(message) {
super(message);
Object.setPrototypeOf(this, AssertionError.prototype);
}
}
class IndexError extends Error {
constructor(message) {
super(message);
Object.setPrototypeOf(this, IndexError.prototype);
}
}
// node_modules/@tensorflow/tfjs-layers/dist/utils/generic_utils.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
function pyListRepeat(value, numValues) {
if (Array.isArray(value)) {
let newArray = [];
for (let i = 0; i < numValues; i++) {
newArray = newArray.concat(value);
}
return newArray;
} else {
const newArray = new Array(numValues);
newArray.fill(value);
return newArray;
}
}
function assert2(val, message) {
if (!val) {
throw new AssertionError(message);
}
}
function count(array2, refernce) {
let counter = 0;
for (const item of array2) {
if (item === refernce) {
counter++;
}
}
return counter;
}
function singletonOrArray(xs) {
if (xs.length === 1) {
return xs[0];
}
return xs;
}
function toList(x) {
if (Array.isArray(x)) {
return x;
}
return [x];
}
function toSnakeCase(name) {
const intermediate = name.replace(/(.)([A-Z][a-z0-9]+)/g, "$1_$2");
const insecure = intermediate.replace(/([a-z])([A-Z])/g, "$1_$2").toLowerCase();
if (insecure[0] !== "_") {
return insecure;
}
return "private" + insecure;
}
function toCamelCase(identifier) {
if (identifier.length <= 1) {
return identifier;
}
if (identifier.indexOf("_") === -1) {
return identifier;
}
return identifier.replace(/[_]+(\w|$)/g, (m, p1) => p1.toUpperCase());
}
let _GLOBAL_CUSTOM_OBJECTS = {};
function serializeKerasObject(instance) {
if (instance === null || instance === void 0) {
return null;
}
const dict = {};
dict["className"] = instance.getClassName();
dict["config"] = instance.getConfig();
return dict;
}
function convertNDArrayScalarsInConfig(config2) {
if (config2 == null || typeof config2 !== "object") {
return;
} else if (Array.isArray(config2)) {
config2.forEach((configItem) => convertNDArrayScalarsInConfig(configItem));
} else {
const fields = Object.keys(config2);
for (const field of fields) {
const value = config2[field];
if (value != null && typeof value === "object") {
if (!Array.isArray(value) && value["type"] === "ndarray" && typeof value["value"] === "number") {
config2[field] = value["value"];
} else {
convertNDArrayScalarsInConfig(value);
}
}
}
}
}
function deserializeKerasObject(identifier, moduleObjects = {}, customObjects = {}, printableModuleName = "object", fastWeightInit = false) {
if (typeof identifier === "string") {
const functionName = identifier;
let fn;
if (functionName in customObjects) {
fn = customObjects[functionName];
} else if (functionName in _GLOBAL_CUSTOM_OBJECTS) {
fn = _GLOBAL_CUSTOM_OBJECTS[functionName];
} else {
fn = moduleObjects[functionName];
if (fn == null) {
throw new ValueError(`Unknown ${printableModuleName}: ${identifier}. This may be due to one of the following reasons:
1. The ${printableModuleName} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);
}
}
return fn;
} else {
const config2 = identifier;
if (config2["className"] == null || config2["config"] == null) {
throw new ValueError(`${printableModuleName}: Improper config format: ${JSON.stringify(config2)}.
'className' and 'config' must set.`);
}
const className = config2["className"];
let cls, fromConfig;
if (className in customObjects) {
[cls, fromConfig] = customObjects[className];
} else if (className in _GLOBAL_CUSTOM_OBJECTS) {
[cls, fromConfig] = _GLOBAL_CUSTOM_OBJECTS["className"];
} else if (className in moduleObjects) {
[cls, fromConfig] = moduleObjects[className];
}
if (cls == null) {
throw new ValueError(`Unknown ${printableModuleName}: ${className}. This may be due to one of the following reasons:
1. The ${printableModuleName} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);
}
if (fromConfig != null) {
const customObjectsCombined = {};
for (const key of Object.keys(_GLOBAL_CUSTOM_OBJECTS)) {
customObjectsCombined[key] = _GLOBAL_CUSTOM_OBJECTS[key];
}
for (const key of Object.keys(customObjects)) {
customObjectsCombined[key] = customObjects[key];
}
const nestedConfig = config2["config"];
nestedConfig["customObjects"] = customObjectsCombined;
const backupCustomObjects = Object.assign({}, _GLOBAL_CUSTOM_OBJECTS);
for (const key of Object.keys(customObjects)) {
_GLOBAL_CUSTOM_OBJECTS[key] = customObjects[key];
}
convertNDArrayScalarsInConfig(config2["config"]);
const returnObj = fromConfig(cls, config2["config"], customObjects, fastWeightInit);
_GLOBAL_CUSTOM_OBJECTS = Object.assign({}, backupCustomObjects);
return returnObj;
} else {
const backupCustomObjects = Object.assign({}, _GLOBAL_CUSTOM_OBJECTS);
for (const key of Object.keys(customObjects)) {
_GLOBAL_CUSTOM_OBJECTS[key] = customObjects[key];
}
const returnObj = new cls(config2["config"]);
_GLOBAL_CUSTOM_OBJECTS = Object.assign({}, backupCustomObjects);
return returnObj;
}
}
}
function numberCompare(a, b) {
return a < b ? -1 : a > b ? 1 : 0;
}
function reverseNumberCompare(a, b) {
return -1 * numberCompare(a, b);
}
function unique3(xs) {
if (xs == null) {
return xs;
}
const out = [];
for (const x of xs) {
if (out.indexOf(x) === -1) {
out.push(x);
}
}
return out;
}
function isObjectEmpty(obj) {
if (obj == null) {
throw new ValueError(`Invalid value in obj: ${JSON.stringify(obj)}`);
}
for (const key in obj) {
if (obj.hasOwnProperty(key)) {
return false;
}
}
return true;
}
function checkStringTypeUnionValue(values, label, value) {
if (value == null) {
return;
}
if (values.indexOf(value) < 0) {
throw new ValueError(`${value} is not a valid ${label}. Valid values are ${values} or null/undefined.`);
}
}
function checkArrayTypeAndLength(x, expectedType, minLength = 0, maxLength = Infinity) {
assert2(minLength >= 0);
assert2(maxLength >= minLength);
return Array.isArray(x) && x.length >= minLength && x.length <= maxLength && x.every((e) => typeof e === expectedType);
}
function assertPositiveInteger(value, name) {
if (Array.isArray(value)) {
util_exports.assert(value.length > 0, () => `${name} is unexpectedly an empty array.`);
value.forEach((v, i) => assertPositiveInteger(v, `element ${i + 1} of ${name}`));
} else {
util_exports.assert(Number.isInteger(value) && value > 0, () => `Expected ${name} to be a positive integer, but got ${formatAsFriendlyString(value)}.`);
}
}
function formatAsFriendlyString(value) {
if (value === null) {
return "null";
} else if (Array.isArray(value)) {
return "[" + value.map((v) => formatAsFriendlyString(v)).join(",") + "]";
} else if (typeof value === "string") {
return `"${value}"`;
} else {
return `${value}`;
}
}
function debounce(f, waitMs) {
let lastTime = util_exports.now();
let lastResult;
const f2 = (...args) => {
const now3 = util_exports.now();
if (now3 - lastTime < waitMs) {
return lastResult;
}
lastTime = now3;
lastResult = f(...args);
return lastResult;
};
return f2;
}
function mapActivationToFusedKernel(activationName) {
if (activationName === "relu") {
return "relu";
}
if (activationName === "linear") {
return "linear";
}
if (activationName === "elu") {
return "elu";
}
return null;
}
// node_modules/@tensorflow/tfjs-layers/dist/constraints.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
function calcL2Norms(w, axis) {
return tidy(() => sqrt(sum2(mul(w, w), axis, true)));
}
class Constraint extends serialization_exports.Serializable {
getConfig() {
return {};
}
}
class MaxNorm extends Constraint {
constructor(args) {
super();
this.defaultMaxValue = 2;
this.defaultAxis = 0;
this.maxValue = args.maxValue != null ? args.maxValue : this.defaultMaxValue;
this.axis = args.axis != null ? args.axis : this.defaultAxis;
}
apply(w) {
return tidy(() => {
const norms = calcL2Norms(w, this.axis);
const desired = clipByValue(norms, 0, this.maxValue);
return mul(w, div(desired, add2(epsilon(), norms)));
});
}
getConfig() {
return {maxValue: this.maxValue, axis: this.axis};
}
}
MaxNorm.className = "MaxNorm";
serialization_exports.registerClass(MaxNorm);
class UnitNorm extends Constraint {
constructor(args) {
super();
this.defaultAxis = 0;
this.axis = args.axis != null ? args.axis : this.defaultAxis;
}
apply(w) {
return tidy(() => div(w, add2(epsilon(), calcL2Norms(w, this.axis))));
}
getConfig() {
return {axis: this.axis};
}
}
UnitNorm.className = "UnitNorm";
serialization_exports.registerClass(UnitNorm);
class NonNeg extends Constraint {
apply(w) {
return relu(w);
}
}
NonNeg.className = "NonNeg";
serialization_exports.registerClass(NonNeg);
class MinMaxNorm extends Constraint {
constructor(args) {
super();
this.defaultMinValue = 0;
this.defaultMaxValue = 1;
this.defaultRate = 1;
this.defaultAxis = 0;
this.minValue = args.minValue != null ? args.minValue : this.defaultMinValue;
this.maxValue = args.maxValue != null ? args.maxValue : this.defaultMaxValue;
this.rate = args.rate != null ? args.rate : this.defaultRate;
this.axis = args.axis != null ? args.axis : this.defaultAxis;
}
apply(w) {
return tidy(() => {
const norms = calcL2Norms(w, this.axis);
const desired = add2(mul(this.rate, clipByValue(norms, this.minValue, this.maxValue)), mul(1 - this.rate, norms));
return mul(w, div(desired, add2(epsilon(), norms)));
});
}
getConfig() {
return {
minValue: this.minValue,
maxValue: this.maxValue,
rate: this.rate,
axis: this.axis
};
}
}
MinMaxNorm.className = "MinMaxNorm";
serialization_exports.registerClass(MinMaxNorm);
const CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP = {
maxNorm: "MaxNorm",
minMaxNorm: "MinMaxNorm",
nonNeg: "NonNeg",
unitNorm: "UnitNorm"
};
function serializeConstraint(constraint) {
return serializeKerasObject(constraint);
}
function deserializeConstraint(config2, customObjects = {}) {
return deserializeKerasObject(config2, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, "constraint");
}
function getConstraint(identifier) {
if (identifier == null) {
return null;
}
if (typeof identifier === "string") {
const className = identifier in CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP ? CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] : identifier;
const config2 = {className, config: {}};
return deserializeConstraint(config2);
} else if (identifier instanceof Constraint) {
return identifier;
} else {
return deserializeConstraint(identifier);
}
}
// node_modules/@tensorflow/tfjs-layers/dist/exports_constraints.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
function maxNorm(args) {
return new MaxNorm(args);
}
function unitNorm(args) {
return new UnitNorm(args);
}
function nonNeg() {
return new NonNeg();
}
function minMaxNorm(config2) {
return new MinMaxNorm(config2);
}
// node_modules/@tensorflow/tfjs-layers/dist/exports_initializers.js
const exports_initializers_exports = {};
__export(exports_initializers_exports, {
constant: () => constant,
glorotNormal: () => glorotNormal,
glorotUniform: () => glorotUniform,
heNormal: () => heNormal,
heUniform: () => heUniform,
identity: () => identity,
leCunNormal: () => leCunNormal,
leCunUniform: () => leCunUniform,
ones: () => ones6,
orthogonal: () => orthogonal,
randomNormal: () => randomNormal3,
randomUniform: () => randomUniform2,
truncatedNormal: () => truncatedNormal2,
varianceScaling: () => varianceScaling,
zeros: () => zeros8
});
// node_modules/@tensorflow/tfjs-layers/dist/keras_format/common.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
const VALID_DATA_FORMAT_VALUES = ["channelsFirst", "channelsLast"];
const VALID_PADDING_MODE_VALUES = ["valid", "same", "causal"];
const VALID_POOL_MODE_VALUES = ["max", "avg"];
const VALID_BIDIRECTIONAL_MERGE_MODES = ["sum", "mul", "concat", "ave"];
// node_modules/@tensorflow/tfjs-layers/dist/common.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
const nameMap = new Map();
function checkDataFormat(value) {
checkStringTypeUnionValue(VALID_DATA_FORMAT_VALUES, "DataFormat", value);
}
function checkPaddingMode(value) {
checkStringTypeUnionValue(VALID_PADDING_MODE_VALUES, "PaddingMode", value);
}
function checkPoolMode(value) {
checkStringTypeUnionValue(VALID_POOL_MODE_VALUES, "PoolMode", value);
}
const _nameScopeStack = [];
const _nameScopeDivider = "/";
function nameScope(name, fn) {
_nameScopeStack.push(name);
try {
const val = fn();
_nameScopeStack.pop();
return val;
} catch (e) {
_nameScopeStack.pop();
throw e;
}
}
function currentNameScopePrefix() {
if (_nameScopeStack.length === 0) {
return "";
} else {
return _nameScopeStack.join(_nameScopeDivider) + _nameScopeDivider;
}
}
function getScopedTensorName(tensorName) {
if (!isValidTensorName(tensorName)) {
throw new Error("Not a valid tensor name: '" + tensorName + "'");
}
return currentNameScopePrefix() + tensorName;
}
function getUniqueTensorName(scopedName) {
if (!isValidTensorName(scopedName)) {
throw new Error("Not a valid tensor name: '" + scopedName + "'");
}
if (!nameMap.has(scopedName)) {
nameMap.set(scopedName, 0);
}
const index = nameMap.get(scopedName);
nameMap.set(scopedName, nameMap.get(scopedName) + 1);
if (index > 0) {
const result = `${scopedName}_${index}`;
nameMap.set(result, 1);
return result;
} else {
return scopedName;
}
}
const tensorNameRegex = new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);
function isValidTensorName(name) {
return !!name.match(tensorNameRegex);
}
// node_modules/@tensorflow/tfjs-layers/dist/utils/math_utils.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
function isInteger(x) {
return x === parseInt(x.toString(), 10);
}
function arrayProd(array2, begin, end) {
if (begin == null) {
begin = 0;
}
if (end == null) {
end = array2.length;
}
let prod3 = 1;
for (let i = begin; i < end; ++i) {
prod3 *= array2[i];
}
return prod3;
}
function toArray1D(array2) {
array2 = Array.isArray(array2) ? new Float32Array(array2) : array2;
return tensor1d(array2);
}
function min4(array2) {
return min(toArray1D(array2)).dataSync()[0];
}
function max6(array2) {
return max(toArray1D(array2)).dataSync()[0];
}
function range4(begin, end) {
if (end < begin) {
throw new ValueError(`end (${end}) < begin (${begin}) is forbidden.`);
}
const out = [];
for (let i = begin; i < end; ++i) {
out.push(i);
}
return out;
}
// node_modules/@tensorflow/tfjs-layers/dist/backend/tfjs_backend.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
function cast20(x, dtype) {
return x.asType(dtype);
}
function expandDims2(x, axis = -1) {
const outShape = x.shape.slice();
if (axis < 0) {
axis = outShape.length + axis + 1;
}
outShape.splice(axis, 0, 1);
return x.reshape(outShape);
}
function repeat(x, n) {
return tidy(() => {
if (x.shape.length !== 2) {
throw new ValueError(`repeat() expects a rank-2 tensor, but received a rank-${x.shape.length} tensor.`);
}
const y = expandDims2(x, 1);
return tile5(y, [1, n, 1]);
});
}
function flatten2(x) {
const newShape = [arrayProd(x.shape)];
return x.reshape(newShape);
}
function batchFlatten(x) {
if (x.rank <= 1) {
throw new ValueError(`batchFlatten requires a minimum rank of 2. Got rank: ${x.rank}.`);
}
const newShape = [x.shape[0], arrayProd(x.shape, 1)];
return x.reshape(newShape);
}
function sliceAlongFirstAxis(array2, start, size) {
return tidy(() => {
switch (array2.rank) {
case 1:
return slice1d(array2, start, size);
case 2:
return slice2d(array2, [start, 0], [size, array2.shape[1]]);
case 3:
return slice3d(array2, [start, 0, 0], [size, array2.shape[1], array2.shape[2]]);
case 4:
return slice4d(array2, [start, 0, 0, 0], [size, array2.shape[1], array2.shape[2], array2.shape[3]]);
case 5:
return slice(array2, [start, 0, 0, 0, 0], [
size,
array2.shape[1],
array2.shape[2],
array2.shape[3],
array2.shape[4]
]);
case 6:
return slice(array2, [start, 0, 0, 0, 0, 0], [
size,
array2.shape[1],
array2.shape[2],
array2.shape[3],
array2.shape[4],
array2.shape[5]
]);
default:
throw new ValueError(`sliceAlongFirstAxis() received an unsupported tensor rank: ${array2.rank}`);
}
});
}
function sliceAlongLastAxis(array2, start, size) {
return tidy(() => {
switch (array2.rank) {
case 1:
return slice1d(array2, start, size);
case 2:
return slice2d(array2, [0, start], [array2.shape[0], size]);
case 3:
return slice3d(array2, [0, 0, start], [array2.shape[0], array2.shape[1], size]);
case 4:
return slice4d(array2, [0, 0, 0, start], [array2.shape[0], array2.shape[1], array2.shape[2], size]);
default:
throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`);
}
});
}
function sliceAlongAxis(array2, start, size, axis) {
return tidy(() => {
switch (array2.rank) {
case 1:
return slice1d(array2, start, size);
case 2:
switch (axis) {
case 1:
return sliceAlongFirstAxis(array2, start, size);
case 2:
return sliceAlongLastAxis(array2, start, size);
default:
throw new ValueError(`The axis is not within the rank of the tensor ${axis}`);
}
case 3:
switch (axis) {
case 1:
return sliceAlongFirstAxis(array2, start, size);
case 2:
return slice3d(array2, [0, start, 0], [array2.shape[0], size, array2.shape[2]]);
case 3:
return sliceAlongLastAxis(array2, start, size);
default:
throw new ValueError(`The axis is not within the rank of the tensor ${axis}`);
}
case 4:
switch (axis) {
case 1:
return sliceAlongFirstAxis(array2, start, size);
case 2:
return slice4d(array2, [0, start, 0, 0], [array2.shape[0], size, array2.shape[2], array2.shape[3]]);
case 3:
return slice4d(array2, [0, 0, start, 0], [array2.shape[0], array2.shape[1], size, array2.shape[3]]);
case 4:
return sliceAlongLastAxis(array2, start, size);
default:
throw new ValueError(`The axis is not within the rank of the tensor ${axis}`);
}
default:
throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`);
}
});
}
function concatenate(tensors, axis = -1) {
let rank;
if (axis < 0) {
rank = tensors[0].rank;
if (rank !== 0) {
axis = rank;
} else {
axis = 0;
}
}
if (axis === tensors[0].rank) {
axis = -1;
}
return concat(tensors, axis);
}
function concatAlongFirstAxis(a, b) {
switch (a.rank) {
case 1:
return concat1d([a, b]);
case 2:
return concat2d([a, b], 0);
case 3:
return concat3d([a, b], 0);
case 4:
return concat4d([a, b], 0);
default:
throw new ValueError(`concatAlongFirstAxis() received an unsupported tensor rank: ${a.rank}`);
}
}
function tile5(x, n) {
if (!Array.isArray(n)) {
n = [n];
}
if (x.rank !== n.length) {
throw new ValueError(`The length of input n (${n.length}) does not match the number of dimensions in input x (${x.rank})`);
}
return tile(x, n);
}
function randomNormal2(shape, mean5 = 0, stddev = 1, dtype, seed) {
return randomNormal(shape, mean5, stddev, dtype, seed);
}
function dot3(a, b, activation2, bias) {
if (a.rank < 2 || b.rank < 2) {
throw new NotImplementedError(`dot requires both inputs to be rank >= 2 but got x shape = ${a.shape} and y shape = ${b.shape}`);
}
if (b.rank >= 3) {
const xLastDim = a.shape.slice(-1)[0];
const ySecondLastDim = b.shape.slice(-2)[0];
if (xLastDim !== ySecondLastDim) {
throw new NotImplementedError(`If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = ${a.shape} and y shape = ${b.shape}`);
}
}
if (a.rank === 2 && b.rank === 2) {
const transposeA = false;
const transposeB = false;
return fused_ops_exports.matMul({
a,
b,
transposeA,
transposeB,
bias: bias ? reshapeBias(a.rank, bias, imageDataFormat()) : null,
activation: activation2
});
} else {
const aFirstDims = a.shape.slice();
const aLastDim = aFirstDims.pop();
a = a.reshape([-1, aLastDim]);
const bShape = b.shape.slice();
const bLastDim = bShape.pop();
const ySecondLastDim = bShape.pop();
const yOtherDims = [...bShape, bLastDim];
const perm = Array.from({length: b.rank}, (_, i) => {
if (i === 0) {
return b.rank - 2;
} else if (i <= b.rank - 2) {
return i - 1;
}
return i;
});
b = b.transpose(perm).reshape([ySecondLastDim, -1]);
const outputShape = [...aFirstDims, ...yOtherDims];
const transposeA = false;
const transposeB = false;
return fused_ops_exports.matMul({
a,
b,
transposeA,
transposeB,
bias: bias ? reshapeBias(a.rank, bias, imageDataFormat()) : null,
activation: activation2
}).reshape(outputShape);
}
}
function gather4(reference, indices, axis) {
return tidy(() => {
if (Array.isArray(indices)) {
indices = tensor1d(indices, "int32");
} else {
indices = indices.toInt();
}
return gather(reference, indices, axis);
});
}
function square10(x) {
return mul(x, x);
}
function reshapeBias(xRank, bias, dataFormat) {
const biasShape = bias.shape;
if (bias.rank !== 1 && bias.rank !== xRank) {
throw new ValueError(`Unexpected bias dimensions: ${bias.rank}; expected it to be 1 or ${xRank}`);
}
if (xRank === 5) {
if (dataFormat === "channelsFirst") {
if (biasShape.length === 1) {
return bias.reshape([1, biasShape[0], 1, 1, 1]);
} else {
return bias.reshape([1, biasShape[3], biasShape[0], biasShape[1], biasShape[2]]);
}
} else if (dataFormat === "channelsLast") {
if (biasShape.length === 1) {
return bias.reshape([1, 1, 1, 1, biasShape[0]]);
} else {
return bias.reshape([1].concat(biasShape));
}
}
} else if (xRank === 4) {
if (dataFormat === "channelsFirst") {
if (biasShape.length === 1) {
return bias.reshape([1, biasShape[0], 1, 1]);
} else {
return bias.reshape([1, biasShape[2], biasShape[0], biasShape[1]]);
}
} else if (dataFormat === "channelsLast") {
if (biasShape.length === 1) {
return bias.reshape([1, 1, 1, biasShape[0]]);
} else {
return bias.reshape([1].concat(biasShape));
}
}
} else if (xRank === 3) {
if (dataFormat === "channelsFirst") {
if (biasShape.length === 1) {
return bias.reshape([1, biasShape[0], 1]);
} else {
return bias.reshape([1, biasShape[1], biasShape[0]]);
}
} else if (dataFormat === "channelsLast") {
if (biasShape.length === 1) {
return bias.reshape([1, 1, biasShape[0]]);
} else {
return bias.reshape([1].concat(biasShape));
}
}
} else if (xRank < 3) {
return bias;
}
throw new ValueError(`Unsupported input rank by biasAdd: ${bias.rank}`);
}
function biasAdd(x, bias, dataFormat) {
return tidy(() => {
if (dataFormat == null) {
dataFormat = imageDataFormat();
}
checkDataFormat(dataFormat);
return x.add(reshapeBias(x.rank, bias, dataFormat));
});
}
function elu4(x, alpha = 1) {
if (alpha !== 1) {
throw new NotImplementedError(`Support for alpha values other than 1 (${alpha}) is not implemented yet.`);
}
return elu(x);
}
function softsign(x) {
return tidy(() => div(x, abs(x).add(1)));
}
function dropout2(x, level, noiseShape, seed) {
return tidy(() => dropout(x, level, noiseShape, seed));
}
function hardSigmoid(x) {
return tidy(() => {
const y = add2(0.5, mul(0.2, x));
return clipByValue(y, 0, 1);
});
}
function inTrainPhase(x, alt, training5 = false) {
return training5 ? x() : alt();
}
// node_modules/@tensorflow/tfjs-layers/dist/keras_format/initializer_config.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
const VALID_FAN_MODE_VALUES = ["fanIn", "fanOut", "fanAvg"];
const VALID_DISTRIBUTION_VALUES = ["normal", "uniform", "truncatedNormal"];
// node_modules/@tensorflow/tfjs-layers/dist/initializers.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
function checkFanMode(value) {
checkStringTypeUnionValue(VALID_FAN_MODE_VALUES, "FanMode", value);
}
function checkDistribution(value) {
checkStringTypeUnionValue(VALID_DISTRIBUTION_VALUES, "Distribution", value);
}
class Initializer extends serialization_exports.Serializable {
fromConfigUsesCustomObjects() {
return false;
}
getConfig() {
return {};
}
}
class Zeros extends Initializer {
apply(shape, dtype) {
return zeros(shape, dtype);
}
}
Zeros.className = "Zeros";
serialization_exports.registerClass(Zeros);
class Ones extends Initializer {
apply(shape, dtype) {
return ones2(shape, dtype);
}
}
Ones.className = "Ones";
serialization_exports.registerClass(Ones);
class Constant extends Initializer {
constructor(args) {
super();
if (typeof args !== "object") {
throw new ValueError(`Expected argument of type ConstantConfig but got ${args}`);
}
if (args.value === void 0) {
throw new ValueError(`config must have value set but got ${args}`);
}
this.value = args.value;
}
apply(shape, dtype) {
return tidy(() => mul(scalar(this.value), ones2(shape, dtype)));
}
getConfig() {
return {
value: this.value
};
}
}
Constant.className = "Constant";
serialization_exports.registerClass(Constant);
class RandomUniform extends Initializer {
constructor(args) {
super();
this.DEFAULT_MINVAL = -0.05;
this.DEFAULT_MAXVAL = 0.05;
this.minval = args.minval || this.DEFAULT_MINVAL;
this.maxval = args.maxval || this.DEFAULT_MAXVAL;
this.seed = args.seed;
}
apply(shape, dtype) {
return randomUniform(shape, this.minval, this.maxval, dtype);
}
getConfig() {
return {minval: this.minval, maxval: this.maxval, seed: this.seed};
}
}
RandomUniform.className = "RandomUniform";
serialization_exports.registerClass(RandomUniform);
class RandomNormal extends Initializer {
constructor(args) {
super();
this.DEFAULT_MEAN = 0;
this.DEFAULT_STDDEV = 0.05;
this.mean = args.mean || this.DEFAULT_MEAN;
this.stddev = args.stddev || this.DEFAULT_STDDEV;
this.seed = args.seed;
}
apply(shape, dtype) {
dtype = dtype || "float32";
if (dtype !== "float32" && dtype !== "int32") {
throw new NotImplementedError(`randomNormal does not support dType ${dtype}.`);
}
return randomNormal2(shape, this.mean, this.stddev, dtype, this.seed);
}
getConfig() {
return {mean: this.mean, stddev: this.stddev, seed: this.seed};
}
}
RandomNormal.className = "RandomNormal";
serialization_exports.registerClass(RandomNormal);
class TruncatedNormal extends Initializer {
constructor(args) {
super();
this.DEFAULT_MEAN = 0;
this.DEFAULT_STDDEV = 0.05;
this.mean = args.mean || this.DEFAULT_MEAN;
this.stddev = args.stddev || this.DEFAULT_STDDEV;
this.seed = args.seed;
}
apply(shape, dtype) {
dtype = dtype || "float32";
if (dtype !== "float32" && dtype !== "int32") {
throw new NotImplementedError(`truncatedNormal does not support dType ${dtype}.`);
}
return truncatedNormal(shape, this.mean, this.stddev, dtype, this.seed);
}
getConfig() {
return {mean: this.mean, stddev: this.stddev, seed: this.seed};
}
}
TruncatedNormal.className = "TruncatedNormal";
serialization_exports.registerClass(TruncatedNormal);
class Identity2 extends Initializer {
constructor(args) {
super();
this.gain = args.gain != null ? args.gain : 1;
}
apply(shape, dtype) {
return tidy(() => {
if (shape.length !== 2 || shape[0] !== shape[1]) {
throw new ValueError("Identity matrix initializer can only be used for 2D square matrices.");
} else {
return mul(this.gain, eye(shape[0]));
}
});
}
getConfig() {
return {gain: this.gain};
}
}
Identity2.className = "Identity";
serialization_exports.registerClass(Identity2);
function computeFans(shape, dataFormat = "channelsLast") {
let fanIn;
let fanOut;
checkDataFormat(dataFormat);
if (shape.length === 2) {
fanIn = shape[0];
fanOut = shape[1];
} else if ([3, 4, 5].indexOf(shape.length) !== -1) {
if (dataFormat === "channelsFirst") {
const receptiveFieldSize = arrayProd(shape, 2);
fanIn = shape[1] * receptiveFieldSize;
fanOut = shape[0] * receptiveFieldSize;
} else if (dataFormat === "channelsLast") {
const receptiveFieldSize = arrayProd(shape, 0, shape.length - 2);
fanIn = shape[shape.length - 2] * receptiveFieldSize;
fanOut = shape[shape.length - 1] * receptiveFieldSize;
}
} else {
const shapeProd = arrayProd(shape);
fanIn = Math.sqrt(shapeProd);
fanOut = Math.sqrt(shapeProd);
}
return [fanIn, fanOut];
}
class VarianceScaling extends Initializer {
constructor(args) {
super();
if (args.scale < 0) {
throw new ValueError(`scale must be a positive float. Got: ${args.scale}`);
}
this.scale = args.scale == null ? 1 : args.scale;
this.mode = args.mode == null ? "fanIn" : args.mode;
checkFanMode(this.mode);
this.distribution = args.distribution == null ? "normal" : args.distribution;
checkDistribution(this.distribution);
this.seed = args.seed;
}
apply(shape, dtype) {
const fans = computeFans(shape);
const fanIn = fans[0];
const fanOut = fans[1];
let scale = this.scale;
if (this.mode === "fanIn") {
scale /= Math.max(1, fanIn);
} else if (this.mode === "fanOut") {
scale /= Math.max(1, fanOut);
} else {
scale /= Math.max(1, (fanIn + fanOut) / 2);
}
if (this.distribution === "normal") {
const stddev = Math.sqrt(scale);
dtype = dtype || "float32";
if (dtype !== "float32" && dtype !== "int32") {
throw new NotImplementedError(`${this.getClassName()} does not support dType ${dtype}.`);
}
return truncatedNormal(shape, 0, stddev, dtype, this.seed);
} else {
const limit = Math.sqrt(3 * scale);
return randomUniform(shape, -limit, limit, dtype);
}
}
getConfig() {
return {
scale: this.scale,
mode: this.mode,
distribution: this.distribution,
seed: this.seed
};
}
}
VarianceScaling.className = "VarianceScaling";
serialization_exports.registerClass(VarianceScaling);
class GlorotUniform extends VarianceScaling {
constructor(args) {
super({
scale: 1,
mode: "fanAvg",
distribution: "uniform",
seed: args == null ? null : args.seed
});
}
getClassName() {
return VarianceScaling.className;
}
}
GlorotUniform.className = "GlorotUniform";
serialization_exports.registerClass(GlorotUniform);
class GlorotNormal extends VarianceScaling {
constructor(args) {
super({
scale: 1,
mode: "fanAvg",
distribution: "normal",
seed: args == null ? null : args.seed
});
}
getClassName() {
return VarianceScaling.className;
}
}
GlorotNormal.className = "GlorotNormal";
serialization_exports.registerClass(GlorotNormal);
class HeNormal extends VarianceScaling {
constructor(args) {
super({
scale: 2,
mode: "fanIn",
distribution: "normal",
seed: args == null ? null : args.seed
});
}
getClassName() {
return VarianceScaling.className;
}
}
HeNormal.className = "HeNormal";
serialization_exports.registerClass(HeNormal);
class HeUniform extends VarianceScaling {
constructor(args) {
super({
scale: 2,
mode: "fanIn",
distribution: "uniform",
seed: args == null ? null : args.seed
});
}
getClassName() {
return VarianceScaling.className;
}
}
HeUniform.className = "HeUniform";
serialization_exports.registerClass(HeUniform);
class LeCunNormal extends VarianceScaling {
constructor(args) {
super({
scale: 1,
mode: "fanIn",
distribution: "normal",
seed: args == null ? null : args.seed
});
}
getClassName() {
return VarianceScaling.className;
}
}
LeCunNormal.className = "LeCunNormal";
serialization_exports.registerClass(LeCunNormal);
class LeCunUniform extends VarianceScaling {
constructor(args) {
super({
scale: 1,
mode: "fanIn",
distribution: "uniform",
seed: args == null ? null : args.seed
});
}
getClassName() {
return VarianceScaling.className;
}
}
LeCunUniform.className = "LeCunNormal";
serialization_exports.registerClass(LeCunUniform);
class Orthogonal extends Initializer {
constructor(args) {
super();
this.DEFAULT_GAIN = 1;
this.gain = args.gain == null ? this.DEFAULT_GAIN : args.gain;
this.seed = args.seed;
if (this.seed != null) {
throw new NotImplementedError("Random seed is not implemented for Orthogonal Initializer yet.");
}
}
apply(shape, dtype) {
return tidy(() => {
if (shape.length < 2) {
throw new NotImplementedError("Shape must be at least 2D.");
}
if (shape[0] * shape[1] > 2e3) {
console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${shape[0] * shape[1]}) elements: Slowness may result.`);
}
const normalizedShape = shape[0] > shape[1] ? [shape[1], shape[0]] : shape;
const a = randomNormal2(normalizedShape, 0, 1, "float32");
let q = linalg.gramSchmidt(a);
if (shape[0] > shape[1]) {
q = q.transpose();
}
return mul(this.gain, q);
});
}
getConfig() {
return {
gain: this.gain,
seed: this.seed
};
}
}
Orthogonal.className = "Orthogonal";
serialization_exports.registerClass(Orthogonal);
const INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP = {
constant: "Constant",
glorotNormal: "GlorotNormal",
glorotUniform: "GlorotUniform",
heNormal: "HeNormal",
heUniform: "HeUniform",
identity: "Identity",
leCunNormal: "LeCunNormal",
leCunUniform: "LeCunUniform",
ones: "Ones",
orthogonal: "Orthogonal",
randomNormal: "RandomNormal",
randomUniform: "RandomUniform",
truncatedNormal: "TruncatedNormal",
varianceScaling: "VarianceScaling",
zeros: "Zeros"
};
function deserializeInitializer(config2, customObjects = {}) {
return deserializeKerasObject(config2, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, "initializer");
}
function serializeInitializer(initializer) {
return serializeKerasObject(initializer);
}
function getInitializer(identifier) {
if (typeof identifier === "string") {
const className = identifier in INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP ? INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] : identifier;
if (className === "GlorotNormal") {
return new GlorotNormal();
} else if (className === "GlorotUniform") {
return new GlorotUniform();
} else if (className === "HeNormal") {
return new HeNormal();
} else if (className === "HeUniform") {
return new HeUniform();
} else if (className === "LeCunNormal") {
return new LeCunNormal();
} else if (className === "LeCunUniform") {
return new LeCunUniform();
} else {
const config2 = {};
config2["className"] = className;
config2["config"] = {};
return deserializeInitializer(config2);
}
} else if (identifier instanceof Initializer) {
return identifier;
} else {
return deserializeInitializer(identifier);
}
}
// node_modules/@tensorflow/tfjs-layers/dist/exports_initializers.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
function zeros8() {
return new Zeros();
}
function ones6() {
return new Ones();
}
function constant(args) {
return new Constant(args);
}
function randomUniform2(args) {
return new RandomUniform(args);
}
function randomNormal3(args) {
return new RandomNormal(args);
}
function truncatedNormal2(args) {
return new TruncatedNormal(args);
}
function identity(args) {
return new Identity2(args);
}
function varianceScaling(config2) {
return new VarianceScaling(config2);
}
function glorotUniform(args) {
return new GlorotUniform(args);
}
function glorotNormal(args) {
return new GlorotNormal(args);
}
function heNormal(args) {
return new HeNormal(args);
}
function heUniform(args) {
return new HeUniform(args);
}
function leCunNormal(args) {
return new LeCunNormal(args);
}
function leCunUniform(args) {
return new LeCunUniform(args);
}
function orthogonal(args) {
return new Orthogonal(args);
}
// node_modules/@tensorflow/tfjs-layers/dist/exports_layers.js
const exports_layers_exports = {};
__export(exports_layers_exports, {
Layer: () => Layer,
RNN: () => RNN,
RNNCell: () => RNNCell,
activation: () => activation,
add: () => add23,
alphaDropout: () => alphaDropout,
average: () => average,
averagePooling1d: () => averagePooling1d,
averagePooling2d: () => averagePooling2d,
averagePooling3d: () => averagePooling3d,
avgPool1d: () => avgPool1d,
avgPool2d: () => avgPool2d,
avgPool3d: () => avgPool3d2,
avgPooling1d: () => avgPooling1d,
avgPooling2d: () => avgPooling2d,
avgPooling3d: () => avgPooling3d,
batchNormalization: () => batchNormalization2,
bidirectional: () => bidirectional,
concatenate: () => concatenate2,
conv1d: () => conv1d3,
conv2d: () => conv2d7,
conv2dTranspose: () => conv2dTranspose2,
conv3d: () => conv3d3,
convLstm2d: () => convLstm2d,
convLstm2dCell: () => convLstm2dCell,
cropping2D: () => cropping2D,
dense: () => dense,
depthwiseConv2d: () => depthwiseConv2d4,
dot: () => dot4,
dropout: () => dropout3,
elu: () => elu5,
embedding: () => embedding,
flatten: () => flatten3,
gaussianDropout: () => gaussianDropout,
gaussianNoise: () => gaussianNoise,
globalAveragePooling1d: () => globalAveragePooling1d,
globalAveragePooling2d: () => globalAveragePooling2d,
globalMaxPool1d: () => globalMaxPool1d,
globalMaxPool2d: () => globalMaxPool2d,
globalMaxPooling1d: () => globalMaxPooling1d,
globalMaxPooling2d: () => globalMaxPooling2d,
gru: () => gru,
gruCell: () => gruCell,
input: () => input,
inputLayer: () => inputLayer,
layerNormalization: () => layerNormalization,
leakyReLU: () => leakyReLU,
lstm: () => lstm,
lstmCell: () => lstmCell,
masking: () => masking,
maxPool1d: () => maxPool1d,
maxPool2d: () => maxPool2d,
maxPooling1d: () => maxPooling1d,
maxPooling2d: () => maxPooling2d,
maxPooling3d: () => maxPooling3d,
maximum: () => maximum6,
minimum: () => minimum5,
multiply: () => multiply,
permute: () => permute,
prelu: () => prelu4,
reLU: () => reLU,
repeatVector: () => repeatVector,
reshape: () => reshape59,
rnn: () => rnn2,
separableConv2d: () => separableConv2d2,
simpleRNN: () => simpleRNN,
simpleRNNCell: () => simpleRNNCell,
softmax: () => softmax3,
spatialDropout1d: () => spatialDropout1d,
stackedRNNCells: () => stackedRNNCells,
thresholdedReLU: () => thresholdedReLU,
timeDistributed: () => timeDistributed,
upSampling2d: () => upSampling2d,
zeroPadding2d: () => zeroPadding2d
});
// node_modules/@tensorflow/tfjs-layers/dist/backend/state.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
let _nextUniqueTensorId = 0;
function getNextUniqueTensorId() {
return _nextUniqueTensorId++;
}
const _uidPrefixes = {};
function getUid(prefix = "") {
if (!(prefix in _uidPrefixes)) {
_uidPrefixes[prefix] = 0;
}
_uidPrefixes[prefix] += 1;
return prefix + _uidPrefixes[prefix].toString();
}
// node_modules/@tensorflow/tfjs-layers/dist/utils/types_utils.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
function isArrayOfShapes(x) {
return Array.isArray(x) && Array.isArray(x[0]);
}
function normalizeShapeList(x) {
if (x.length === 0) {
return [];
}
if (!Array.isArray(x[0])) {
return [x];
}
return x;
}
function getExactlyOneTensor(xs) {
let x;
if (Array.isArray(xs)) {
if (xs.length !== 1) {
throw new ValueError(`Expected Tensor length to be 1; got ${xs.length}`);
}
x = xs[0];
} else {
x = xs;
}
return x;
}
function getExactlyOneShape(shapes) {
if (Array.isArray(shapes) && Array.isArray(shapes[0])) {
if (shapes.length === 1) {
shapes = shapes;
return shapes[0];
} else {
throw new ValueError(`Expected exactly 1 Shape; got ${shapes.length}`);
}
} else {
return shapes;
}
}
// node_modules/@tensorflow/tfjs-layers/dist/utils/variable_utils.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
function countParamsInWeights(weights) {
let count2 = 0;
for (const weight of weights) {
if (weight.shape.length === 0) {
count2 += 1;
} else {
count2 += weight.shape.reduce((a, b) => a * b);
}
}
return count2;
}
// node_modules/@tensorflow/tfjs-layers/dist/variables.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
const DEFAULT_VARIABLE_NAME_PREFIX = "Variable";
class LayerVariable {
constructor(val, dtype = "float32", name = DEFAULT_VARIABLE_NAME_PREFIX, trainable = true, constraint = null) {
this.dtype = dtype == null ? "float32" : dtype;
this.shape = val.shape;
this.id = getNextUniqueTensorId();
name = name == null ? DEFAULT_VARIABLE_NAME_PREFIX : name;
this.originalName = getScopedTensorName(name);
this.name = getUniqueTensorName(this.originalName);
this.trainable_ = trainable;
this.constraint = constraint;
this.val = variable(val, this.trainable_, this.name, this.dtype);
}
read() {
this.assertNotDisposed();
return this.val;
}
write(newVal) {
this.assertNotDisposed();
checkShapesMatch(this.val, newVal);
if (this.val.id !== newVal.id) {
this.val.assign(newVal);
if (this.constraint != null) {
this.val.assign(this.constraint.apply(this.val));
}
}
return this;
}
dispose() {
this.assertNotDisposed();
this.val.dispose();
}
assertNotDisposed() {
if (this.val.isDisposed) {
throw new Error(`LayersVariable ${this.name} is already disposed.`);
}
}
get trainable() {
return this.trainable_;
}
set trainable(trainable) {
this.trainable_ = trainable;
this.val.trainable = trainable;
}
}
function checkShapesMatch(x, y) {
if (x.shape.toString() !== y.shape.toString()) {
throw new Error("Shape mismatch: " + JSON.stringify(x.shape) + " vs. " + JSON.stringify(y.shape));
}
}
function batchGetValue(xs) {
return xs.map((x) => x.read());
}
function batchSetValue(variablesAndValues) {
variablesAndValues.forEach((variableAndValue) => {
const variable3 = variableAndValue[0];
variable3.write(variableAndValue[1]);
});
}
// node_modules/@tensorflow/tfjs-layers/dist/engine/topology.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
class InputSpec {
constructor(args) {
this.dtype = args.dtype;
this.shape = args.shape;
if (args.shape != null) {
this.ndim = args.shape.length;
} else {
this.ndim = args.ndim;
}
this.maxNDim = args.maxNDim;
this.minNDim = args.minNDim;
this.axes = args.axes || {};
}
}
class SymbolicTensor {
constructor(dtype, shape, sourceLayer, inputs, callArgs, name, outputTensorIndex) {
this.dtype = dtype;
this.shape = shape;
this.sourceLayer = sourceLayer;
this.inputs = inputs;
this.callArgs = callArgs;
this.outputTensorIndex = outputTensorIndex;
this.id = getNextUniqueTensorId();
if (name != null) {
this.originalName = getScopedTensorName(name);
this.name = getUniqueTensorName(this.originalName);
}
this.rank = shape.length;
}
}
let _nextNodeID = 0;
class Node {
constructor(args, callArgs) {
this.callArgs = callArgs;
this.id = _nextNodeID++;
this.outboundLayer = args.outboundLayer;
this.inboundLayers = args.inboundLayers;
this.nodeIndices = args.nodeIndices;
this.tensorIndices = args.tensorIndices;
this.inputTensors = args.inputTensors;
this.outputTensors = args.outputTensors;
this.inputMasks = args.inputMasks;
this.outputMasks = args.outputMasks;
this.inputShapes = args.inputShapes;
this.outputShapes = args.outputShapes;
for (const layer of args.inboundLayers) {
if (layer != null) {
layer.outboundNodes.push(this);
}
}
args.outboundLayer.inboundNodes.push(this);
}
getConfig() {
const inboundNames = [];
for (const layer of this.inboundLayers) {
if (layer != null) {
inboundNames.push(layer.name);
} else {
inboundNames.push(null);
}
}
return {
outboundLayer: this.outboundLayer ? this.outboundLayer.name : null,
inboundLayers: inboundNames,
nodeIndices: this.nodeIndices,
tensorIndices: this.tensorIndices
};
}
}
let _nextLayerID = 0;
class Layer extends serialization_exports.Serializable {
constructor(args = {}) {
super();
this._callHook = null;
this._addedWeightNames = [];
this._stateful = false;
this.id = _nextLayerID++;
this.activityRegularizer = null;
this.inputSpec = null;
this.supportsMasking = false;
this._trainableWeights = [];
this._nonTrainableWeights = [];
this._losses = [];
this._updates = [];
this._built = false;
this.inboundNodes = [];
this.outboundNodes = [];
let name = args.name;
if (!name) {
const prefix = this.getClassName();
name = toSnakeCase(prefix) + "_" + getUid(prefix);
}
this.name = name;
this.trainable_ = args.trainable == null ? true : args.trainable;
if (args.inputShape != null || args.batchInputShape != null) {
let batchInputShape;
if (args.batchInputShape != null) {
batchInputShape = args.batchInputShape;
} else if (args.inputShape != null) {
let batchSize = null;
if (args.batchSize != null) {
batchSize = args.batchSize;
}
batchInputShape = [batchSize].concat(args.inputShape);
}
this.batchInputShape = batchInputShape;
let dtype = args.dtype;
if (dtype == null) {
dtype = args.inputDType;
}
if (dtype == null) {
dtype = "float32";
}
this.dtype = dtype;
}
if (args.weights != null) {
this.initialWeights = args.weights;
} else {
this.initialWeights = null;
}
this._refCount = null;
this.fastWeightInitDuringBuild = false;
}
static nodeKey(layer, nodeIndex) {
return layer.name + "_ib-" + nodeIndex.toString();
}
getNodeAtIndex(nodeIndex, attrName) {
if (this.inboundNodes.length === 0) {
throw new RuntimeError(`The layer has never been called and thus has no defined ${attrName}.`);
}
if (this.inboundNodes.length <= nodeIndex) {
throw new ValueError(`Asked to get ${attrName} at node ${nodeIndex}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);
}
return this.inboundNodes[nodeIndex];
}
getInputAt(nodeIndex) {
return singletonOrArray(this.getNodeAtIndex(nodeIndex, "input").inputTensors);
}
getOutputAt(nodeIndex) {
return singletonOrArray(this.getNodeAtIndex(nodeIndex, "output").outputTensors);
}
get input() {
if (this.inboundNodes.length > 1) {
throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. Use \`getInputAt(nodeIndex)\` instead.`);
} else if (this.inboundNodes.length === 0) {
throw new AttributeError(`Layer ${this.name} is not connected, no input to return.`);
}
return singletonOrArray(this.getNodeAtIndex(0, "input").inputTensors);
}
get output() {
if (this.inboundNodes.length === 0) {
throw new AttributeError(`Layer ${this.name} has no inbound nodes.`);
}
if (this.inboundNodes.length > 1) {
throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);
}
return singletonOrArray(this.getNodeAtIndex(0, "output").outputTensors);
}
get losses() {
return this._losses;
}
calculateLosses() {
return this.losses.map((lossFn) => lossFn());
}
get updates() {
return this._updates;
}
get built() {
return this._built;
}
set built(built) {
this._built = built;
}
get trainable() {
return this.trainable_;
}
set trainable(trainable) {
this._trainableWeights.forEach((w) => w.trainable = trainable);
this.trainable_ = trainable;
}
get trainableWeights() {
if (this.trainable_) {
return this._trainableWeights.filter((w) => w.trainable);
} else {
return [];
}
}
set trainableWeights(weights) {
this._trainableWeights = weights;
}
get nonTrainableWeights() {
if (this.trainable) {
return this._trainableWeights.filter((w) => !w.trainable).concat(this._nonTrainableWeights);
} else {
return this._trainableWeights.concat(this._nonTrainableWeights);
}
}
set nonTrainableWeights(weights) {
this._nonTrainableWeights = weights;
}
get weights() {
return this.trainableWeights.concat(this.nonTrainableWeights);
}
get stateful() {
return this._stateful;
}
resetStates() {
if (!this.stateful) {
throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.");
}
}
assertInputCompatibility(inputs) {
inputs = toList(inputs);
if (this.inputSpec == null || this.inputSpec.length === 0) {
return;
}
const inputSpec = toList(this.inputSpec);
if (inputs.length !== inputSpec.length) {
throw new ValueError(`Layer ${this.name} expects ${inputSpec.length} inputs, but it received ${inputs.length} input tensors. Input received: ${inputs}`);
}
for (let inputIndex = 0; inputIndex < inputs.length; inputIndex++) {
const x = inputs[inputIndex];
const spec = inputSpec[inputIndex];
if (spec == null) {
continue;
}
const ndim = x.rank;
if (spec.ndim != null) {
if (ndim !== spec.ndim) {
throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected ndim=${spec.ndim}, found ndim=${ndim}`);
}
}
if (spec.maxNDim != null) {
if (ndim > spec.maxNDim) {
throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected max_ndim=${spec.maxNDim}, found ndim=${ndim}`);
}
}
if (spec.minNDim != null) {
if (ndim < spec.minNDim) {
throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected min_ndim=${spec.minNDim}, found ndim=${ndim}.`);
}
}
if (spec.dtype != null) {
if (x.dtype !== spec.dtype) {
throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name} : expected dtype=${spec.dtype}, found dtype=${x.dtype}.`);
}
}
if (spec.axes) {
const xShape = x.shape;
for (const key in spec.axes) {
const axis = Number(key);
const value = spec.axes[key];
const xShapeAtAxis = axis >= 0 ? xShape[axis] : xShape[xShape.length + axis];
if (value != null && [value, null].indexOf(xShapeAtAxis) === -1) {
throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected axis ${axis} of input shape to have value ${value} but got shape ${xShape}.`);
}
}
}
if (spec.shape != null) {
for (let i = 0; i < spec.shape.length; ++i) {
const specDim = spec.shape[i];
const dim = x.shape[i];
if (specDim != null && dim != null) {
if (specDim !== dim) {
throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected shape=${spec.shape}, found shape=${x.shape}.`);
}
}
}
}
}
}
call(inputs, kwargs) {
return inputs;
}
invokeCallHook(inputs, kwargs) {
if (this._callHook != null) {
this._callHook(inputs, kwargs);
}
}
setCallHook(callHook) {
this._callHook = callHook;
}
clearCallHook() {
this._callHook = null;
}
apply(inputs, kwargs) {
kwargs = kwargs || {};
this.assertNotDisposed();
const inputsList = toList(inputs);
let allAreSymbolic = true;
for (const input2 of inputsList) {
if (!(input2 instanceof SymbolicTensor)) {
allAreSymbolic = false;
break;
}
}
let noneAreSymbolic = true;
for (const input2 of inputsList) {
if (input2 instanceof SymbolicTensor) {
noneAreSymbolic = false;
break;
}
}
if (allAreSymbolic === noneAreSymbolic) {
throw new ValueError("Arguments to apply() must be all SymbolicTensors or all Tensors");
}
return nameScope(this.name, () => {
if (!this.built) {
this.assertInputCompatibility(inputs);
const inputShapes = [];
for (const xElem of toList(inputs)) {
inputShapes.push(xElem.shape);
}
this.build(singletonOrArray(inputShapes));
this.built = true;
if (this.initialWeights) {
this.setWeights(this.initialWeights);
}
if (this._refCount === null && noneAreSymbolic) {
this._refCount = 1;
}
}
this.assertInputCompatibility(inputs);
if (noneAreSymbolic) {
let output = this.call(inputs, kwargs);
const outputList = toList(output);
const outputListCopy = [];
for (let x of outputList) {
if (inputsList.indexOf(x) !== -1) {
x = x.clone();
}
outputListCopy.push(x);
}
output = singletonOrArray(outputListCopy);
if (this.activityRegularizer != null) {
throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet.");
}
return output;
} else {
const inputShape = collectInputShape(inputs);
const outputShape = this.computeOutputShape(inputShape);
let output;
const outputDType = guessOutputDType(inputs);
this.warnOnIncompatibleInputShape(Array.isArray(inputs) ? inputShape[0] : inputShape);
if (outputShape != null && outputShape.length > 0 && Array.isArray(outputShape[0])) {
output = outputShape.map((shape, index) => new SymbolicTensor(outputDType, shape, this, toList(inputs), kwargs, this.name, index));
} else {
output = new SymbolicTensor(outputDType, outputShape, this, toList(inputs), kwargs, this.name);
}
this.addInboundNode(inputs, output, null, null, inputShape, outputShape, kwargs);
this._refCount++;
if (this.activityRegularizer != null) {
throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet.");
}
return output;
}
});
}
warnOnIncompatibleInputShape(inputShape) {
if (this.batchInputShape == null) {
return;
} else if (inputShape.length !== this.batchInputShape.length) {
console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(inputShape)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);
} else {
let dimMismatch = false;
this.batchInputShape.forEach((dimension, i) => {
if (dimension != null && inputShape[i] != null && inputShape[i] !== dimension) {
dimMismatch = true;
}
});
if (dimMismatch) {
console.warn(`The shape of the input tensor (${JSON.stringify(inputShape)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`);
}
}
}
get outputShape() {
if (this.inboundNodes == null || this.inboundNodes.length === 0) {
throw new AttributeError(`The layer ${this.name} has never been called and thus has no defined output shape.`);
}
const allOutputShapes = [];
for (const node of this.inboundNodes) {
const shapeString = JSON.stringify(node.outputShapes);
if (allOutputShapes.indexOf(shapeString) === -1) {
allOutputShapes.push(shapeString);
}
}
if (allOutputShapes.length === 1) {
const outputShapes = this.inboundNodes[0].outputShapes;
if (Array.isArray(outputShapes) && Array.isArray(outputShapes[0]) && outputShapes.length === 1) {
return outputShapes[0];
} else {
return outputShapes;
}
} else {
throw new AttributeError(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`);
}
}
countParams() {
if (!this.built) {
throw new RuntimeError(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);
}
return countParamsInWeights(this.weights);
}
build(inputShape) {
this.built = true;
}
getWeights(trainableOnly = false) {
return batchGetValue(trainableOnly ? this.trainableWeights : this.weights);
}
setWeights(weights) {
tidy(() => {
const params = this.weights;
if (params.length !== weights.length) {
throw new ValueError(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${weights.length}, but the layer was expecting ${params.length} weights. Provided weights: ${weights}...`);
}
if (params.length === 0) {
return;
}
const weightValueTuples = [];
const paramValues = batchGetValue(params);
for (let i = 0; i < paramValues.length; ++i) {
const pv = paramValues[i];
const p = params[i];
const w = weights[i];
if (!util_exports.arraysEqual(pv.shape, w.shape)) {
throw new ValueError(`Layer weight shape ${pv.shape} not compatible with provided weight shape ${w.shape}`);
}
weightValueTuples.push([p, w]);
}
batchSetValue(weightValueTuples);
});
}
addWeight(name, shape, dtype, initializer, regularizer, trainable, constraint) {
if (this._addedWeightNames.indexOf(name) !== -1) {
throw new ValueError(`Duplicate weight name ${name} for layer ${this.name}`);
}
this._addedWeightNames.push(name);
if (dtype == null) {
dtype = "float32";
}
if (this.fastWeightInitDuringBuild) {
initializer = getInitializer("zeros");
}
const initValue = initializer.apply(shape, dtype);
const weight = new LayerVariable(initValue, dtype, name, trainable, constraint);
initValue.dispose();
if (regularizer != null) {
this.addLoss(() => regularizer.apply(weight.read()));
}
if (trainable == null) {
trainable = true;
}
if (trainable) {
this._trainableWeights.push(weight);
} else {
this._nonTrainableWeights.push(weight);
}
return weight;
}
setFastWeightInitDuringBuild(value) {
this.fastWeightInitDuringBuild = value;
}
addLoss(losses8) {
if (losses8 == null || Array.isArray(losses8) && losses8.length === 0) {
return;
}
losses8 = toList(losses8);
if (this._losses !== void 0 && this._losses !== null) {
this.losses.push(...losses8);
}
}
computeOutputShape(inputShape) {
return inputShape;
}
computeMask(inputs, mask) {
if (!this.supportsMasking) {
if (mask != null) {
if (Array.isArray(mask)) {
mask.forEach((maskElement) => {
if (maskElement != null) {
throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);
}
});
} else {
throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);
}
}
return null;
}
return mask;
}
addInboundNode(inputTensors, outputTensors, inputMasks, outputMasks, inputShapes, outputShapes, kwargs = null) {
const inputTensorList = toList(inputTensors);
outputTensors = toList(outputTensors);
inputMasks = toList(inputMasks);
outputMasks = toList(outputMasks);
inputShapes = normalizeShapeList(inputShapes);
outputShapes = normalizeShapeList(outputShapes);
const inboundLayers = [];
const nodeIndices = [];
const tensorIndices = [];
for (const x of inputTensorList) {
inboundLayers.push(x.sourceLayer);
nodeIndices.push(x.nodeIndex);
tensorIndices.push(x.tensorIndex);
}
new Node({
outboundLayer: this,
inboundLayers,
nodeIndices,
tensorIndices,
inputTensors: inputTensorList,
outputTensors,
inputMasks,
outputMasks,
inputShapes,
outputShapes
}, kwargs);
for (let i = 0; i < outputTensors.length; i++) {
outputTensors[i].sourceLayer = this;
outputTensors[i].nodeIndex = this.inboundNodes.length - 1;
outputTensors[i].tensorIndex = i;
}
}
getConfig() {
const config2 = {name: this.name, trainable: this.trainable};
if (this.batchInputShape != null) {
config2["batchInputShape"] = this.batchInputShape;
}
if (this.dtype != null) {
config2["dtype"] = this.dtype;
}
return config2;
}
disposeWeights() {
this.weights.forEach((weight) => weight.dispose());
return this.weights.length;
}
assertNotDisposed() {
if (this._refCount === 0) {
throw new Error(`Layer '${this.name}' is already disposed.`);
}
}
dispose() {
if (!this.built) {
throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);
}
if (this._refCount === null) {
throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);
}
this.assertNotDisposed();
let numDisposedVariables = 0;
if (--this._refCount === 0) {
numDisposedVariables = this.disposeWeights();
}
return {refCountAfterDispose: this._refCount, numDisposedVariables};
}
}
function collectInputShape(inputTensors) {
inputTensors = toList(inputTensors);
const shapes = [];
for (const x of inputTensors) {
shapes.push(x.shape);
}
return singletonOrArray(shapes);
}
function guessOutputDType(inputTensors) {
return "float32";
}
function getSourceInputs(tensor16, layer, nodeIndex) {
if (layer == null || nodeIndex != null && nodeIndex > 0) {
layer = tensor16.sourceLayer;
nodeIndex = tensor16.nodeIndex;
}
if (layer.inboundNodes.length === 0) {
return [tensor16];
} else {
const node = layer.inboundNodes[nodeIndex];
if (node.inboundLayers.length === 0) {
return node.inputTensors;
} else {
const sourceTensors = [];
for (let i = 0; i < node.inboundLayers.length; i++) {
const x = node.inputTensors[i];
const layer2 = node.inboundLayers[i];
const nodeIndex2 = node.nodeIndices[i];
const previousSources = getSourceInputs(x, layer2, nodeIndex2);
for (const x2 of previousSources) {
if (sourceTensors.indexOf(x2) === -1) {
sourceTensors.push(x2);
}
}
}
return sourceTensors;
}
}
}
// node_modules/@tensorflow/tfjs-layers/dist/engine/input_layer.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
class InputLayer extends Layer {
constructor(args) {
super({
dtype: args.dtype,
name: args.name != null ? args.name : getUid("input").toString()
});
if (args.batchSize == null) {
args.batchSize = null;
}
if (args.sparse == null) {
args.sparse = false;
}
this.trainable = false;
this.built = true;
this.sparse = args.sparse;
if (args.inputShape != null && args.batchInputShape != null) {
throw new ValueError("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");
}
let batchInputShape = args.batchInputShape;
if (batchInputShape == null) {
if (args.inputShape == null) {
throw new ValueError("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");
} else {
batchInputShape = [args.batchSize].concat(args.inputShape);
}
} else {
if (args.batchSize != null) {
throw new ValueError("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");
}
}
const dtype = args.dtype || "float32";
this.batchInputShape = batchInputShape;
this.dtype = dtype;
this.inputSpec = [{shape: batchInputShape}];
const inputTensor = new SymbolicTensor(this.dtype, this.batchInputShape, this, [], {}, this.name);
inputTensor.nodeIndex = 0;
inputTensor.tensorIndex = 0;
new Node({
outboundLayer: this,
inboundLayers: [],
nodeIndices: [],
tensorIndices: [],
inputTensors: [inputTensor],
outputTensors: [inputTensor],
inputMasks: [null],
outputMasks: [null],
inputShapes: [batchInputShape],
outputShapes: [batchInputShape]
});
}
apply(inputs, kwargs) {
throw new ValueError(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`);
}
dispose() {
return {refCountAfterDispose: this._refCount, numDisposedVariables: 0};
}
getConfig() {
return {
batchInputShape: this.batchInputShape,
dtype: this.dtype,
sparse: this.sparse,
name: this.name
};
}
}
InputLayer.className = "InputLayer";
serialization_exports.registerClass(InputLayer);
function Input(config2) {
if (config2.batchShape == null && config2.shape == null) {
throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.");
}
if (config2.batchShape != null && config2.shape != null) {
throw new ValueError("Please provide either a `shape` or `batchShape` argument to Input, but not both.");
}
let batchShape = config2.batchShape;
if (config2.shape != null && batchShape == null) {
batchShape = [null].concat(config2.shape);
}
let dtype = config2.dtype;
if (dtype == null) {
dtype = "float32";
}
const inputLayer2 = new InputLayer({
batchInputShape: batchShape,
name: config2.name,
dtype,
sparse: config2.sparse
});
const outputs = inputLayer2.inboundNodes[0].outputTensors;
return outputs[0];
}
// node_modules/@tensorflow/tfjs-layers/dist/logs.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
async function resolveScalarsInLogs(logs5) {
if (logs5 == null) {
return;
}
const promises = [];
const keys = [];
const scalarsToDispose = [];
for (const key in logs5) {
const value = logs5[key];
if (typeof value !== "number") {
const valueScalar = value;
promises.push(valueScalar.data());
keys.push(key);
scalarsToDispose.push(valueScalar);
}
}
if (promises.length > 0) {
const values = await Promise.all(promises);
for (let i = 0; i < values.length; ++i) {
logs5[keys[i]] = values[i][0];
}
dispose(scalarsToDispose);
}
}
function disposeTensorsInLogs(logs5) {
if (logs5 == null) {
return;
}
for (const key in logs5) {
const value = logs5[key];
if (typeof value !== "number") {
value.dispose();
}
}
}
// node_modules/@tensorflow/tfjs-layers/dist/base_callbacks.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
var ModelLoggingVerbosity;
(function(ModelLoggingVerbosity2) {
ModelLoggingVerbosity2[ModelLoggingVerbosity2["SILENT"] = 0] = "SILENT";
ModelLoggingVerbosity2[ModelLoggingVerbosity2["VERBOSE"] = 1] = "VERBOSE";
})(ModelLoggingVerbosity || (ModelLoggingVerbosity = {}));
const DEFAULT_YIELD_EVERY_MS = 125;
class BaseCallback {
constructor() {
this.validationData = null;
}
setParams(params) {
this.params = params;
}
async onEpochBegin(epoch, logs5) {
}
async onEpochEnd(epoch, logs5) {
}
async onBatchBegin(batch, logs5) {
}
async onBatchEnd(batch, logs5) {
}
async onTrainBegin(logs5) {
}
async onTrainEnd(logs5) {
}
setModel(model2) {
}
}
class CallbackList {
constructor(callbacks3, queueLength = 10) {
if (callbacks3 == null) {
callbacks3 = [];
}
this.callbacks = callbacks3;
this.queueLength = queueLength;
}
append(callback) {
this.callbacks.push(callback);
}
setParams(params) {
for (const callback of this.callbacks) {
callback.setParams(params);
}
}
setModel(model2) {
for (const callback of this.callbacks) {
callback.setModel(model2);
}
}
async onEpochBegin(epoch, logs5) {
if (logs5 == null) {
logs5 = {};
}
for (const callback of this.callbacks) {
await callback.onEpochBegin(epoch, logs5);
}
}
async onEpochEnd(epoch, logs5) {
if (logs5 == null) {
logs5 = {};
}
for (const callback of this.callbacks) {
await callback.onEpochEnd(epoch, logs5);
}
}
async onBatchBegin(batch, logs5) {
if (logs5 == null) {
logs5 = {};
}
for (const callback of this.callbacks) {
await callback.onBatchBegin(batch, logs5);
}
}
async onBatchEnd(batch, logs5) {
if (logs5 == null) {
logs5 = {};
}
for (const callback of this.callbacks) {
await callback.onBatchEnd(batch, logs5);
}
}
async onTrainBegin(logs5) {
if (logs5 == null) {
logs5 = {};
}
for (const callback of this.callbacks) {
await callback.onTrainBegin(logs5);
}
}
async onTrainEnd(logs5) {
if (logs5 == null) {
logs5 = {};
}
for (const callback of this.callbacks) {
await callback.onTrainEnd(logs5);
}
}
}
class BaseLogger extends BaseCallback {
constructor() {
super();
}
async onEpochBegin(epoch) {
this.seen = 0;
this.totals = {};
}
async onBatchEnd(batch, logs5) {
if (logs5 == null) {
logs5 = {};
}
const batchSize = logs5["size"] == null ? 0 : logs5["size"];
this.seen += batchSize;
for (const key in logs5) {
const value = logs5[key];
if (typeof value === "number") {
if (!this.totals.hasOwnProperty(key)) {
this.totals[key] = 0;
}
this.totals[key] = this.totals[key] + value * batchSize;
} else {
let oldTotalsToDispose;
if (key in this.totals) {
oldTotalsToDispose = this.totals[key];
} else {
this.totals[key] = 0;
}
const total = tidy(() => add2(this.totals[key], mul(value, batchSize)));
this.totals[key] = total;
if (oldTotalsToDispose != null) {
oldTotalsToDispose.dispose();
}
}
}
}
async onEpochEnd(epoch, logs5) {
if (logs5 != null) {
for (const key of this.params["metrics"]) {
if (this.totals[key] == null) {
continue;
}
if (typeof this.totals[key] === "number") {
logs5[key] = this.totals[key] / this.seen;
} else {
tidy(() => {
const log7 = mul(div(1, this.seen), this.totals[key]);
logs5[key] = log7;
this.totals[key].dispose();
keep(logs5[key]);
});
}
}
}
}
}
class History extends BaseCallback {
async onTrainBegin(logs5) {
this.epoch = [];
this.history = {};
}
async onEpochEnd(epoch, logs5) {
if (logs5 == null) {
logs5 = {};
}
this.epoch.push(epoch);
for (const key in logs5) {
if (this.history[key] == null) {
this.history[key] = [];
}
this.history[key].push(logs5[key]);
}
}
async syncData() {
const promises = [];
const keys = [];
const indices = [];
for (const key in this.history) {
const valueArray = this.history[key];
for (let i = 0; i < valueArray.length; ++i) {
if (typeof valueArray[i] !== "number") {
const valueScalar = valueArray[i];
promises.push(valueScalar.data());
keys.push(key);
indices.push(i);
}
}
}
const values = await Promise.all(promises);
for (let n = 0; n < values.length; ++n) {
const tensorToDispose = this.history[keys[n]][indices[n]];
tensorToDispose.dispose();
this.history[keys[n]][indices[n]] = values[n][0];
}
}
}
class CustomCallback extends BaseCallback {
constructor(args, yieldEvery) {
super();
this.currentEpoch = 0;
this.yieldEvery = yieldEvery || "auto";
if (this.yieldEvery === "auto") {
this.yieldEvery = DEFAULT_YIELD_EVERY_MS;
}
if (this.yieldEvery === "never" && args.onYield != null) {
throw new Error("yieldEvery is `never` but you provided an `onYield` callback. Either change `yieldEvery` or remove the callback");
}
if (util_exports.isNumber(this.yieldEvery)) {
this.maybeWait = debounce(this.maybeWait.bind(this), this.yieldEvery);
}
this.trainBegin = args.onTrainBegin;
this.trainEnd = args.onTrainEnd;
this.epochBegin = args.onEpochBegin;
this.epochEnd = args.onEpochEnd;
this.batchBegin = args.onBatchBegin;
this.batchEnd = args.onBatchEnd;
this.yield = args.onYield;
}
async maybeWait(epoch, batch, logs5) {
const ps = [];
if (this.yield != null) {
await resolveScalarsInLogs(logs5);
ps.push(this.yield(epoch, batch, logs5));
}
ps.push(nextFrame());
await Promise.all(ps);
}
async onEpochBegin(epoch, logs5) {
this.currentEpoch = epoch;
if (this.epochBegin != null) {
await resolveScalarsInLogs(logs5);
await this.epochBegin(epoch, logs5);
}
}
async onEpochEnd(epoch, logs5) {
const ps = [];
if (this.epochEnd != null) {
await resolveScalarsInLogs(logs5);
ps.push(this.epochEnd(epoch, logs5));
}
if (this.yieldEvery === "epoch") {
ps.push(nextFrame());
}
await Promise.all(ps);
}
async onBatchBegin(batch, logs5) {
if (this.batchBegin != null) {
await resolveScalarsInLogs(logs5);
await this.batchBegin(batch, logs5);
}
}
async onBatchEnd(batch, logs5) {
const ps = [];
if (this.batchEnd != null) {
await resolveScalarsInLogs(logs5);
ps.push(this.batchEnd(batch, logs5));
}
if (this.yieldEvery === "batch") {
ps.push(nextFrame());
} else if (util_exports.isNumber(this.yieldEvery)) {
ps.push(this.maybeWait(this.currentEpoch, batch, logs5));
}
await Promise.all(ps);
}
async onTrainBegin(logs5) {
if (this.trainBegin != null) {
await resolveScalarsInLogs(logs5);
await this.trainBegin(logs5);
}
}
async onTrainEnd(logs5) {
if (this.trainEnd != null) {
await resolveScalarsInLogs(logs5);
await this.trainEnd(logs5);
}
}
}
function standardizeCallbacks(callbacks3, yieldEvery) {
if (callbacks3 == null) {
callbacks3 = {};
}
if (callbacks3 instanceof BaseCallback) {
return [callbacks3];
}
if (Array.isArray(callbacks3) && callbacks3[0] instanceof BaseCallback) {
return callbacks3;
}
const callbackConfigs = toList(callbacks3);
return callbackConfigs.map((callbackConfig) => new CustomCallback(callbackConfig, yieldEvery));
}
class CallbackConstructorRegistry {
constructor() {
}
static registerCallbackConstructor(verbosityLevel, callbackConstructor) {
util_exports.assert(verbosityLevel >= 0 && Number.isInteger(verbosityLevel), () => `Verbosity level is expected to be an integer >= 0, but got ${verbosityLevel}`);
CallbackConstructorRegistry.checkForDuplicate(callbackConstructor);
if (CallbackConstructorRegistry.constructors[verbosityLevel] == null) {
CallbackConstructorRegistry.constructors[verbosityLevel] = [];
}
CallbackConstructorRegistry.constructors[verbosityLevel].push(callbackConstructor);
}
static checkForDuplicate(callbackConstructor) {
for (const levelName in CallbackConstructorRegistry.constructors) {
const constructors = CallbackConstructorRegistry.constructors[+levelName];
constructors.forEach((ctor) => {
if (ctor === callbackConstructor) {
throw new ValueError("Duplicate callback constructor.");
}
});
}
}
static clear() {
CallbackConstructorRegistry.constructors = {};
}
static createCallbacks(verbosityLevel) {
const constructors = [];
for (const levelName in CallbackConstructorRegistry.constructors) {
const level = +levelName;
if (verbosityLevel >= level) {
constructors.push(...CallbackConstructorRegistry.constructors[level]);
}
}
return constructors.map((ctor) => new ctor());
}
}
CallbackConstructorRegistry.constructors = {};
function configureCallbacks(callbacks3, verbose, epochs, initialEpoch, numTrainSamples, stepsPerEpoch, batchSize, doValidation, callbackMetrics) {
const history = new History();
const actualCallbacks = [
new BaseLogger(),
...CallbackConstructorRegistry.createCallbacks(verbose)
];
if (callbacks3 != null) {
actualCallbacks.push(...callbacks3);
}
actualCallbacks.push(history);
const callbackList = new CallbackList(actualCallbacks);
callbackList.setParams({
epochs,
initialEpoch,
samples: numTrainSamples,
steps: stepsPerEpoch,
batchSize,
verbose,
doValidation,
metrics: callbackMetrics
});
return {callbackList, history};
}
// node_modules/@tensorflow/tfjs-layers/dist/layers/serialization.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
function deserialize(config2, customObjects = {}, fastWeightInit = false) {
return deserializeKerasObject(config2, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, "layer", fastWeightInit);
}
// node_modules/@tensorflow/tfjs-layers/dist/losses.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
function l2Normalize(x, axis) {
return tidy(() => {
if (x.dtype !== "float32") {
x = x.asType("float32");
}
const squareSum = sum2(square10(x), axis, true);
const epsilonTensor = fill(squareSum.shape, epsilon());
const norm4 = sqrt(maximum(squareSum, epsilonTensor));
return div(x, norm4);
});
}
function meanSquaredError2(yTrue, yPred) {
return tidy(() => mean(square10(sub(yPred, yTrue)), -1));
}
function meanAbsoluteError(yTrue, yPred) {
return tidy(() => mean(abs(sub(yPred, yTrue)), -1));
}
function meanAbsolutePercentageError(yTrue, yPred) {
return tidy(() => {
const diff = sub(yTrue, yPred);
const clippedTrue = clipByValue(abs(yTrue), epsilon(), Number.MAX_VALUE);
const absResult = abs(div(diff, clippedTrue));
return mul(100, mean(absResult, -1));
});
}
function meanSquaredLogarithmicError(yTrue, yPred) {
return tidy(() => {
const clippedPred = clipByValue(yPred, epsilon(), Number.MAX_VALUE);
const firstLog = log(add2(1, clippedPred));
const clippedTrue = clipByValue(yTrue, epsilon(), Number.MAX_VALUE);
const secondLog = log(add2(1, clippedTrue));
return mean(square10(sub(firstLog, secondLog)), -1);
});
}
function squaredHinge(yTrue, yPred) {
return tidy(() => {
const maxResult = maximum(0, sub(1, mul(yTrue, yPred)));
return mean(square10(maxResult), -1);
});
}
function hinge(yTrue, yPred) {
return tidy(() => {
const maxResult = maximum(0, sub(1, mul(yTrue, yPred)));
return mean(maxResult, -1);
});
}
function categoricalHinge(yTrue, yPred) {
return tidy(() => {
const pos = sum2(mul(yTrue, yPred), -1);
const neg8 = max(mul(sub(1, yTrue), yPred), -1);
return maximum(0, add2(1, sub(neg8, pos)));
});
}
function logcosh(yTrue, yPred) {
return tidy(() => {
const log22 = Math.log(2);
const predictionDiff = sub(yPred, yTrue);
const logcoshResult = sub(add2(predictionDiff, softplus(mul(-2, predictionDiff))), log22);
return mean(logcoshResult, -1);
});
}
function categoricalCrossentropy(target, output, fromLogits = false) {
return tidy(() => {
if (fromLogits) {
output = softmax(output);
} else {
const outputSum = sum2(output, output.shape.length - 1, true);
output = div(output, outputSum);
}
output = clipByValue(output, epsilon(), 1 - epsilon());
return neg(sum2(mul(target.toFloat(), log(output)), output.shape.length - 1));
});
}
function sparseCategoricalCrossentropy(target, output, fromLogits = false) {
return tidy(() => {
const flatTarget = floor(flatten2(target)).toInt();
output = clipByValue(output, epsilon(), 1 - epsilon());
const outputShape = output.shape;
const oneHotTarget = oneHot(flatTarget, outputShape[outputShape.length - 1]).reshape(outputShape);
return categoricalCrossentropy(oneHotTarget, output, fromLogits);
});
}
function sigmoidCrossEntropyWithLogits(labels, logits) {
if (!util_exports.arraysEqual(labels.shape, logits.shape)) {
throw new ValueError(`logits and labels must have the same shape, but got shapes ${JSON.stringify(labels.shape)} and ${JSON.stringify(logits.shape)}`);
}
return tidy(() => {
const reluLogits = logits.relu();
const negAbsLogits = logits.abs().neg();
return reluLogits.sub(logits.mul(labels)).add(negAbsLogits.exp().log1p());
});
}
function binaryCrossentropy(yTrue, yPred) {
return tidy(() => {
let y;
y = clipByValue(yPred, epsilon(), 1 - epsilon());
y = log(div(y, sub(1, y)));
return mean(sigmoidCrossEntropyWithLogits(yTrue, y), -1);
});
}
function kullbackLeiblerDivergence(yTrue, yPred) {
return tidy(() => {
const clippedTrue = clipByValue(yTrue, epsilon(), 1);
const clippedPred = clipByValue(yPred, epsilon(), 1);
return sum2(mul(yTrue, log(div(clippedTrue, clippedPred))), -1);
});
}
function poisson(yTrue, yPred) {
return tidy(() => {
const logPred = log(add2(epsilon(), yPred));
return mean(sub(yPred, mul(yTrue, logPred)), -1);
});
}
function cosineProximity(yTrue, yPred) {
return tidy(() => {
const trueNormalized = l2Normalize(yTrue, -1);
const predNormalized = l2Normalize(yPred, -1);
const trueXPred = mul(trueNormalized, predNormalized);
return neg(sum2(trueXPred, -1));
});
}
const lossesMap = {
meanSquaredError: meanSquaredError2,
meanAbsoluteError,
meanAbsolutePercentageError,
meanSquaredLogarithmicError,
squaredHinge,
hinge,
categoricalHinge,
logcosh,
categoricalCrossentropy,
sparseCategoricalCrossentropy,
binaryCrossentropy,
kullbackLeiblerDivergence,
poisson,
cosineProximity
};
function get(identifierOrFn) {
if (typeof identifierOrFn === "string") {
if (identifierOrFn in lossesMap) {
return lossesMap[identifierOrFn];
}
let errMsg = `Unknown loss ${identifierOrFn}`;
if (identifierOrFn.toLowerCase().includes("softmaxcrossentropy")) {
errMsg = `Unknown loss ${identifierOrFn}. Use "categoricalCrossentropy" as the string name for tf.losses.softmaxCrossEntropy`;
}
throw new ValueError(errMsg);
} else {
return identifierOrFn;
}
}
// node_modules/@tensorflow/tfjs-layers/dist/metrics.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
function binaryAccuracy(yTrue, yPred) {
return tidy(() => {
const threshold = mul(0.5, onesLike(yPred));
const yPredThresholded = cast20(greater(yPred, threshold), yTrue.dtype);
return mean(equal(yTrue, yPredThresholded), -1);
});
}
function categoricalAccuracy(yTrue, yPred) {
return tidy(() => cast20(equal(argMax(yTrue, -1), argMax(yPred, -1)), "float32"));
}
function truePositives(yTrue, yPred) {
return tidy(() => {
return logicalAnd(yTrue.equal(1), yPred.equal(1)).sum().cast("float32");
});
}
function falseNegatives(yTrue, yPred) {
return tidy(() => {
return logicalAnd(yTrue.equal(1), yPred.equal(0)).sum().cast("float32");
});
}
function falsePositives(yTrue, yPred) {
return tidy(() => {
return logicalAnd(yTrue.equal(0), yPred.equal(1)).sum().cast("float32");
});
}
function precision(yTrue, yPred) {
return tidy(() => {
const tp = truePositives(yTrue, yPred);
const fp = falsePositives(yTrue, yPred);
const denominator = tp.add(fp);
return where(greater(denominator, 0), tp.div(denominator), 0).cast("float32");
});
}
function recall(yTrue, yPred) {
return tidy(() => {
const tp = truePositives(yTrue, yPred);
const fn = falseNegatives(yTrue, yPred);
const denominator = tp.add(fn);
return where(greater(denominator, 0), tp.div(denominator), 0).cast("float32");
});
}
function binaryCrossentropy2(yTrue, yPred) {
return binaryCrossentropy(yTrue, yPred);
}
function sparseCategoricalAccuracy(yTrue, yPred) {
if (yTrue.rank === yPred.rank) {
yTrue = yTrue.squeeze([yTrue.rank - 1]);
}
yPred = yPred.argMax(-1);
if (yPred.dtype !== yTrue.dtype) {
yPred = yPred.asType(yTrue.dtype);
}
return equal(yTrue, yPred).asType("float32");
}
const mse = meanSquaredError2;
const MSE = meanSquaredError2;
const mae = meanAbsoluteError;
const MAE = meanAbsoluteError;
const mape = meanAbsolutePercentageError;
const MAPE = meanAbsolutePercentageError;
const categoricalCrossentropy2 = categoricalCrossentropy;
const cosine = cosineProximity;
const sparseCategoricalCrossentropy2 = sparseCategoricalCrossentropy;
const metricsMap = {
binaryAccuracy,
categoricalAccuracy,
precision,
categoricalCrossentropy: categoricalCrossentropy2,
sparseCategoricalCrossentropy: sparseCategoricalCrossentropy2,
mse,
MSE,
mae,
MAE,
mape,
MAPE,
cosine
};
function get2(identifier) {
if (typeof identifier === "string" && identifier in metricsMap) {
return metricsMap[identifier];
} else if (typeof identifier !== "string" && identifier != null) {
return identifier;
} else {
throw new ValueError(`Unknown metric ${identifier}`);
}
}
function getLossOrMetricName(fn) {
assert2(fn !== null, `Unknown LossOrMetricFn ${fn}`);
if (typeof fn === "string") {
return fn;
} else {
let fnName;
for (const key of Object.keys(lossesMap)) {
if (lossesMap[key] === fn) {
fnName = key;
break;
}
}
if (fnName !== void 0) {
return fnName;
}
for (const key of Object.keys(metricsMap)) {
if (metricsMap[key] === fn) {
fnName = key;
break;
}
}
if (fnName !== void 0) {
return fnName;
}
return fn.name;
}
}
// node_modules/@tensorflow/tfjs-layers/dist/optimizers.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
function getOptimizer(identifier) {
const optimizerMap = {
Adagrad: () => train.adagrad(0.01),
Adadelta: () => train.adadelta(1, 0.95, epsilon()),
Adam: () => train.adam(1e-3, 0.9, 0.999, epsilon()),
Adamax: () => train.adamax(2e-3, 0.9, 0.999, epsilon(), 0),
RMSProp: () => train.rmsprop(1e-3, 0.9, 0, epsilon()),
SGD: () => train.sgd(0.01)
};
optimizerMap["adagrad"] = optimizerMap["Adagrad"];
optimizerMap["adadelta"] = optimizerMap["Adadelta"];
optimizerMap["adam"] = optimizerMap["Adam"];
optimizerMap["adamax"] = optimizerMap["Adamax"];
optimizerMap["rmsprop"] = optimizerMap["RMSProp"];
optimizerMap["sgd"] = optimizerMap["SGD"];
if (identifier in optimizerMap) {
return optimizerMap[identifier]();
}
throw new ValueError(`Unknown Optimizer ${identifier}`);
}
// node_modules/@tensorflow/tfjs-layers/dist/user_defined_metadata.js
/**
* @license
* Copyright 2019 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
const MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH = 1 * 1024 * 1024;
function checkUserDefinedMetadata(userDefinedMetadata, modelName, checkSize = false) {
if (userDefinedMetadata == null || typeof userDefinedMetadata !== "object" || Object.getPrototypeOf(userDefinedMetadata) !== Object.prototype || !plainObjectCheck(userDefinedMetadata)) {
throw new Error("User-defined metadata is expected to be a JSON object, but is not.");
}
if (checkSize) {
const out = JSON.stringify(userDefinedMetadata);
if (out.length > MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH) {
console.warn(`User-defined metadata of model "${modelName}" is too large in size (length=${out.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH}.`);
}
}
}
function plainObjectCheck(x) {
if (x === null) {
return true;
} else if (typeof x === "object") {
if (Object.getPrototypeOf(x) === Object.prototype) {
const keys = Object.keys(x);
for (const key of keys) {
if (typeof key !== "string") {
return false;
}
if (!plainObjectCheck(x[key])) {
return false;
}
}
return true;
} else {
if (Array.isArray(x)) {
for (const item of x) {
if (!plainObjectCheck(item)) {
return false;
}
}
return true;
} else {
return false;
}
}
} else {
const xType = typeof x;
return xType === "string" || xType === "number" || xType === "boolean";
}
}
// node_modules/@tensorflow/tfjs-layers/dist/utils/layer_utils.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
function printSummary(model2, lineLength, positions, printFn = console.log) {
const sequentialLike = isModelSequentialLike(model2);
const toDisplay = ["Layer (type)", "Output shape", "Param #"];
if (sequentialLike) {
lineLength = lineLength || 65;
positions = positions || [0.45, 0.85, 1];
} else {
lineLength = lineLength || 98;
positions = positions || [0.33, 0.55, 0.67, 1];
}
if (positions[positions.length - 1] <= 1) {
positions = positions.map((p) => Math.floor(lineLength * p));
}
let relevantNodes;
if (!sequentialLike) {
toDisplay.push("Receives inputs");
relevantNodes = [];
for (const depth in model2.nodesByDepth) {
relevantNodes.push(...model2.nodesByDepth[depth]);
}
}
printFn("_".repeat(lineLength));
printRow(toDisplay, positions, printFn);
printFn("=".repeat(lineLength));
const layers = model2.layers;
for (let i = 0; i < layers.length; ++i) {
if (sequentialLike) {
printLayerSummary(layers[i], positions, printFn);
} else {
printLayerSummaryWithConnections(layers[i], positions, relevantNodes, printFn);
}
printFn((i === layers.length - 1 ? "=" : "_").repeat(lineLength));
}
model2.checkTrainableWeightsConsistency();
const trainableCount = countTrainableParams(model2);
const nonTrainableCount = countParamsInWeights(model2.nonTrainableWeights);
printFn(`Total params: ${trainableCount + nonTrainableCount}`);
printFn(`Trainable params: ${trainableCount}`);
printFn(`Non-trainable params: ${nonTrainableCount}`);
printFn("_".repeat(lineLength));
}
function countTrainableParams(model2) {
let trainableCount;
if (model2.collectedTrainableWeights != null) {
trainableCount = countParamsInWeights(model2.collectedTrainableWeights);
} else {
trainableCount = countParamsInWeights(model2.trainableWeights);
}
return trainableCount;
}
function isModelSequentialLike(model2) {
let sequentialLike = true;
const nodesByDepth = [];
const nodes = [];
for (const depth in model2.nodesByDepth) {
nodesByDepth.push(model2.nodesByDepth[depth]);
}
for (const depthNodes of nodesByDepth) {
if (depthNodes.length > 1 || depthNodes.length === 1 && depthNodes[0].inboundLayers.length > 1) {
sequentialLike = false;
break;
}
nodes.push(...depthNodes);
}
if (sequentialLike) {
for (const layer of model2.layers) {
let flag = false;
for (const node of layer.inboundNodes) {
if (nodes.indexOf(node) !== -1) {
if (flag) {
sequentialLike = false;
break;
} else {
flag = true;
}
}
}
if (!sequentialLike) {
break;
}
}
}
return sequentialLike;
}
function printRow(fields, positions, printFn = console.log) {
let line = "";
for (let i = 0; i < fields.length; ++i) {
if (i > 0) {
line = line.slice(0, line.length - 1) + " ";
}
line += fields[i];
line = line.slice(0, positions[i]);
line += " ".repeat(positions[i] - line.length);
}
printFn(line);
}
function printLayerSummary(layer, positions, printFn) {
let outputShape;
try {
outputShape = JSON.stringify(layer.outputShape);
} catch (err) {
outputShape = "multiple";
}
const name = layer.name;
const className = layer.getClassName();
const fields = [`${name} (${className})`, outputShape, layer.countParams().toString()];
printRow(fields, positions, printFn);
}
function printLayerSummaryWithConnections(layer, positions, relevantNodes, printFn) {
let outputShape;
try {
outputShape = JSON.stringify(layer.outputShape);
} catch (err) {
outputShape = "multiple";
}
const connections = [];
for (const node of layer.inboundNodes) {
if (relevantNodes != null && relevantNodes.length > 0 && relevantNodes.indexOf(node) === -1) {
continue;
}
for (let i = 0; i < node.inboundLayers.length; ++i) {
const inboundLayer = node.inboundLayers[i].name;
const inboundLayerIndex = node.nodeIndices[i];
const inboundTensorIndex = node.tensorIndices[i];
connections.push(`${inboundLayer}[${inboundLayerIndex}][${inboundTensorIndex}]`);
}
}
const name = layer.name;
const className = layer.getClassName();
const firstConnection = connections.length === 0 ? "" : connections[0];
const fields = [
`${name} (${className})`,
outputShape,
layer.countParams().toString(),
firstConnection
];
printRow(fields, positions, printFn);
for (let i = 1; i < connections.length; ++i) {
printRow(["", "", "", connections[i]], positions, printFn);
}
}
// node_modules/@tensorflow/tfjs-layers/dist/utils/serialization_utils.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
function isArrayItemInputOrOutputName(key, index, value) {
return (key === "inboundNodes" || key === "outputLayers" || key === "inputLayers") && index === 0 && typeof value === "string";
}
function convertPythonicToTs(pythonicConfig, key) {
if (pythonicConfig === null) {
return null;
} else if (typeof pythonicConfig === "string") {
return toCamelCase(pythonicConfig);
} else if (typeof pythonicConfig === "number" || typeof pythonicConfig === "boolean") {
return pythonicConfig;
} else if (pythonicConfig instanceof Array) {
const tsArray = [];
const arrayLength = pythonicConfig.length;
for (let i = 0; i < arrayLength; ++i) {
const item = pythonicConfig[i];
if (isArrayItemInputOrOutputName(key, i, item)) {
tsArray.push(item);
} else {
tsArray.push(convertPythonicToTs(item, key));
}
}
return tsArray;
} else {
const tsDict = {};
for (const pythonicKey of Object.keys(pythonicConfig)) {
const pythonicValue = pythonicConfig[pythonicKey];
if (pythonicKey === "name" && typeof pythonicValue === "string") {
tsDict[pythonicKey] = pythonicValue;
} else {
const tsKey = toCamelCase(pythonicKey);
tsDict[tsKey] = convertPythonicToTs(pythonicValue, tsKey);
}
}
return tsDict;
}
}
function convertTsToPythonic(tsConfig, key) {
if (tsConfig === null || tsConfig === void 0) {
return null;
} else if (typeof tsConfig === "string") {
return toSnakeCase(tsConfig);
} else if (typeof tsConfig === "number" || typeof tsConfig === "boolean") {
return tsConfig;
} else if (tsConfig instanceof Array) {
const pyArray = [];
const arrayLength = tsConfig.length;
for (let i = 0; i < arrayLength; ++i) {
const item = tsConfig[i];
if (isArrayItemInputOrOutputName(key, i, item)) {
pyArray.push(item);
} else {
pyArray.push(convertTsToPythonic(item, key));
}
}
return pyArray;
} else {
const pyDict = {};
for (const tsKey of Object.keys(tsConfig)) {
const tsValue = tsConfig[tsKey];
const pyKey = toSnakeCase(tsKey);
if ((tsKey === "name" || tsKey === "className") && typeof tsValue === "string") {
pyDict[pyKey] = tsValue;
} else {
pyDict[pyKey] = convertTsToPythonic(tsValue, tsKey);
}
}
return pyDict;
}
}
// node_modules/@tensorflow/tfjs-layers/dist/version.js
/** @license See the LICENSE file. */
const version2 = "2.7.0";
// node_modules/@tensorflow/tfjs-layers/dist/engine/executor.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
function assertFeedCompatibility(key, val) {
if (key.dtype == null || key.dtype === val.dtype) {
return val;
}
try {
return cast(val, key.dtype);
} catch (err) {
throw new ValueError(`The dtype of the feed (${val.dtype}) can not be cast to the dtype of the key '${key.name}' (${key.dtype}).`);
}
}
class FeedDict {
constructor(feeds) {
this.id2Value = {};
this.id2Mask = {};
this.name2Id = {};
if (feeds instanceof FeedDict) {
for (const id in feeds.id2Value) {
this.id2Value[id] = feeds.id2Value[id];
if (id in feeds.id2Mask) {
this.id2Mask[id] = feeds.id2Mask[id];
}
}
} else {
if (feeds == null) {
return;
}
for (const feed of feeds) {
this.add(feed.key, feed.value);
}
}
}
add(key, value, mask) {
if (this.id2Value[key.id] == null) {
this.id2Value[key.id] = assertFeedCompatibility(key, value);
this.name2Id[key.name] = key.id;
if (mask != null) {
this.id2Mask[key.id] = mask;
}
} else {
throw new ValueError(`Duplicate key: name=${key.name}, id=${key.id}`);
}
return this;
}
addFeed(feed) {
this.add(feed.key, feed.value);
}
hasKey(key) {
return this.id2Value[key.id] != null;
}
names() {
return Object.keys(this.name2Id);
}
getValue(key) {
if (key instanceof SymbolicTensor) {
if (this.id2Value[key.id] == null) {
throw new ValueError(`Nonexistent key: ${key.name}`);
} else {
return this.id2Value[key.id];
}
} else {
const id = this.name2Id[key];
if (id == null) {
throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);
}
return this.id2Value[id];
}
}
getMask(key) {
if (key instanceof SymbolicTensor) {
if (this.id2Value[key.id] == null) {
throw new ValueError(`Nonexistent key: ${key.name}`);
} else {
return this.id2Mask[key.id];
}
} else {
const id = this.name2Id[key];
if (id == null) {
throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);
}
return this.id2Mask[id];
}
}
disposeMasks() {
if (this.id2Mask != null) {
dispose(this.id2Mask);
}
}
}
const cachedSorted = {};
const cachedRecipientCounts = {};
function execute(fetches, feedDict, kwargs, probe) {
const training5 = kwargs == null ? false : kwargs["training"];
const arrayFetches = Array.isArray(fetches);
const fetchArray = arrayFetches ? fetches : [fetches];
const outputNames = fetchArray.map((t) => t.name);
const finalOutputs = [];
const feedNames = feedDict.names();
for (const outputName of outputNames) {
if (feedNames.indexOf(outputName) !== -1) {
finalOutputs.push(feedDict.getValue(outputName));
} else {
finalOutputs.push(null);
}
}
if (probe != null) {
probe.maxNumTensors = -Infinity;
probe.minNumTensors = Infinity;
}
const fetchAndFeedKey = outputNames.join(",") + "|" + feedDict.names().join(",");
let sorted;
let recipientCounts;
if (cachedSorted[fetchAndFeedKey] == null) {
const out = getTopologicalSortAndRecipientCounts(fetchArray, feedDict);
sorted = out.sorted;
recipientCounts = out.recipientCounts;
cachedSorted[fetchAndFeedKey] = sorted;
cachedRecipientCounts[fetchAndFeedKey] = recipientCounts;
}
sorted = cachedSorted[fetchAndFeedKey];
recipientCounts = {};
if (!training5) {
Object.assign(recipientCounts, cachedRecipientCounts[fetchAndFeedKey]);
}
const internalFeedDict = new FeedDict(feedDict);
for (let i = 0; i < sorted.length; ++i) {
if (probe != null) {
const numTensors = memory().numTensors;
if (numTensors > probe.maxNumTensors) {
probe.maxNumTensors = numTensors;
}
if (numTensors < probe.minNumTensors) {
probe.minNumTensors = numTensors;
}
}
const symbolic = sorted[i];
const srcLayer = symbolic.sourceLayer;
if (srcLayer instanceof InputLayer) {
continue;
}
const inputValues = [];
const inputMasks = [];
const tensorsToDispose = [];
let maskExists = false;
for (const input2 of symbolic.inputs) {
const value = internalFeedDict.getValue(input2);
const mask = internalFeedDict.getMask(input2);
inputValues.push(value);
inputMasks.push(mask);
if (mask != null) {
maskExists = true;
}
if (!training5) {
recipientCounts[input2.name]--;
if (recipientCounts[input2.name] === 0 && !feedDict.hasKey(input2) && outputNames.indexOf(input2.name) === -1 && !value.isDisposed && input2.sourceLayer.stateful !== true) {
tensorsToDispose.push(value);
}
}
}
if (maskExists) {
kwargs = kwargs || {};
kwargs["mask"] = inputMasks[0];
}
const outputTensors = toList(srcLayer.apply(inputValues, kwargs));
let outputMask = null;
if (srcLayer.supportsMasking) {
outputMask = srcLayer.computeMask(inputValues, inputMasks);
}
const layerOutputs = getNodeOutputs(symbolic);
const outputSymbolicTensors = Array.isArray(layerOutputs) ? layerOutputs : [layerOutputs];
for (let i2 = 0; i2 < outputSymbolicTensors.length; ++i2) {
if (!internalFeedDict.hasKey(outputSymbolicTensors[i2])) {
internalFeedDict.add(outputSymbolicTensors[i2], outputTensors[i2], Array.isArray(outputMask) ? outputMask[0] : outputMask);
}
const index = outputNames.indexOf(outputSymbolicTensors[i2].name);
if (index !== -1) {
finalOutputs[index] = outputTensors[i2];
}
}
if (!training5) {
dispose(tensorsToDispose);
}
}
internalFeedDict.disposeMasks();
return arrayFetches ? finalOutputs : finalOutputs[0];
}
function getTopologicalSortAndRecipientCounts(fetches, feedDict) {
util_exports.assert(fetches != null && fetches.length > 0, () => `Expected at least one fetch, got none`);
let finalSorted = [];
let finalRecipientMap = {};
if (fetches.length === 1) {
const out = getTopologicalSortAndRecipientCountsForOneFetch(fetches[0], feedDict);
finalSorted = out.sorted;
finalRecipientMap = out.recipientMap;
} else {
const visited = new Set();
for (const fetch3 of fetches) {
const {sorted, recipientMap} = getTopologicalSortAndRecipientCountsForOneFetch(fetch3, feedDict);
for (const symbolicTensor of sorted) {
if (!visited.has(symbolicTensor.name)) {
finalSorted.push(symbolicTensor);
visited.add(symbolicTensor.name);
}
}
for (const name in recipientMap) {
if (finalRecipientMap[name] == null) {
finalRecipientMap[name] = new Set();
}
recipientMap[name].forEach((recipient) => finalRecipientMap[name].add(recipient));
}
}
}
return {
sorted: finalSorted,
recipientCounts: recipientMap2Counts(finalRecipientMap)
};
}
function recipientMap2Counts(recipientMap) {
const recipientCounts = {};
for (const name in recipientMap) {
recipientCounts[name] = recipientMap[name].size;
}
return recipientCounts;
}
function getTopologicalSortAndRecipientCountsForOneFetch(fetch3, feedDict) {
const visited = new Set();
const sorted = [];
const recipientMap = {};
for (const key of feedDict.names()) {
visited.add(key);
}
const stack6 = [];
const marks = [];
stack6.push(fetch3);
while (stack6.length > 0) {
const top = stack6[stack6.length - 1];
if (visited.has(top.name)) {
stack6.pop();
continue;
}
const topIsMarked = marks[marks.length - 1] === stack6.length - 1;
if (top.inputs.length === 0 || topIsMarked) {
stack6.pop();
sorted.push(top);
visited.add(top.name);
if (topIsMarked) {
marks.pop();
}
} else {
marks.push(stack6.length - 1);
for (const input2 of top.inputs) {
if (recipientMap[input2.name] == null) {
recipientMap[input2.name] = new Set();
}
recipientMap[input2.name].add(top.name);
if (visited.has(input2.name)) {
continue;
}
stack6.push(input2);
}
}
}
return {sorted, recipientMap};
}
function getNodeOutputs(fetch3) {
let layerOutputs;
if (fetch3.sourceLayer.inboundNodes.length === 1) {
layerOutputs = fetch3.sourceLayer.output;
} else {
let nodeIndex = null;
for (let i = 0; i < fetch3.sourceLayer.inboundNodes.length; ++i) {
for (const outputTensor of fetch3.sourceLayer.inboundNodes[i].outputTensors) {
if (outputTensor.id === fetch3.id) {
nodeIndex = i;
break;
}
}
}
layerOutputs = fetch3.sourceLayer.getOutputAt(nodeIndex);
}
return layerOutputs;
}
// node_modules/@tensorflow/tfjs-layers/dist/engine/container.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
class Container extends Layer {
constructor(args) {
super({});
this.containerNodes = new Set();
this.name = args.name;
if (this.name == null) {
const prefix = this.getClassName().toLowerCase();
this.name = getUid(prefix);
}
this.supportsMasking = false;
this.trainable_ = true;
if (Array.isArray(args.inputs)) {
this.inputs = args.inputs.slice();
} else {
this.inputs = [args.inputs];
}
if (Array.isArray(args.outputs)) {
this.outputs = args.outputs.slice();
} else {
this.outputs = [args.outputs];
}
if (unique3(this.inputs).length !== this.inputs.length) {
throw new ValueError(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map((x) => x.name)}`);
}
if (unique3(this.outputs).length !== this.outputs.length) {
console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map((x) => x.name)}`);
}
this.inputLayers = [];
this.inputLayersNodeIndices = [];
this.inputLayersTensorIndices = [];
this.outputLayers = [];
this.outputLayersNodeIndices = [];
this.outputLayersTensorIndices = [];
this.layers = [];
this.internalContainerRefs = [];
for (const x of this.outputs) {
const layer = x.sourceLayer;
const nodeIndex = x.nodeIndex;
const tensorIndex = x.tensorIndex;
this.outputLayers.push(layer);
this.outputLayersNodeIndices.push(nodeIndex);
this.outputLayersTensorIndices.push(tensorIndex);
}
for (const x of this.inputs) {
const layer = x.sourceLayer;
const nodeIndex = x.nodeIndex;
const tensorIndex = x.tensorIndex;
assert2(nodeIndex === 0, "input layer has >1 nodes");
assert2(tensorIndex === 0, "input layer has >1 tensors");
this.inputLayers.push(layer);
this.inputLayersNodeIndices.push(nodeIndex);
this.inputLayersTensorIndices.push(tensorIndex);
}
this.inputNames = [];
this.outputNames = [];
this.feedInputShapes = [];
this.feedInputNames = [];
this.feedOutputNames = [];
for (let i = 0; i < this.inputLayers.length; i++) {
const layer = this.inputLayers[i];
if (!(layer instanceof InputLayer)) {
throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${args.inputs}. Input ${i} (0-based) originates from layer type ${layer.getClassName()}.`);
}
this.inputNames.push(layer.name);
this.feedInputShapes.push(layer.batchInputShape);
this.feedInputNames.push(layer.name);
}
for (const layer of this.outputLayers) {
this.outputNames.push(layer.name);
}
this.internalInputShapes = this.inputs.map((x) => x.shape);
this.internalOutputShapes = this.outputs.map((x) => x.shape);
const nodesDepths = {};
const nodeIDToNode = {};
const layersDepths = {};
const layerIDToLayer = {};
const layerIndices = {};
const nodesInDecreasingDepth = [];
const buildMapOfGraph = (tensor16, finishedNodes2, nodesInProgress2, layer, nodeIndex, tensorIndex) => {
if (layer == null || nodeIndex == null || tensorIndex == null) {
layer = tensor16.sourceLayer;
nodeIndex = tensor16.nodeIndex;
tensorIndex = tensor16.tensorIndex;
}
const node = layer.inboundNodes[nodeIndex];
if (nodesInProgress2.indexOf(node) !== -1) {
throw new RuntimeError(`The tensor ${tensor16.name} at layer "${layer.name}" is part of a cycle.`);
}
if (finishedNodes2.indexOf(node) !== -1) {
return;
}
this.containerNodes.add(Container.nodeKey(layer, nodeIndex));
if (!(layer.id in layerIndices)) {
layerIndices[layer.id] = Object.keys(layerIndices).length;
}
if (nodesInProgress2.indexOf(node) === -1) {
nodesInProgress2.push(node);
}
const numInboundLayers = node.inboundLayers.length;
for (let i = 0; i < numInboundLayers; i++) {
const x = node.inputTensors[i];
const layer2 = node.inboundLayers[i];
const nodeIndex2 = node.nodeIndices[i];
const tensorIndex2 = node.tensorIndices[i];
buildMapOfGraph(x, finishedNodes2, nodesInProgress2, layer2, nodeIndex2, tensorIndex2);
}
finishedNodes2.push(node);
while (nodesInProgress2.indexOf(node) >= 0) {
nodesInProgress2.splice(nodesInProgress2.indexOf(node), 1);
}
nodesInDecreasingDepth.push(node);
};
const finishedNodes = [];
const nodesInProgress = [];
for (const x of this.outputs) {
buildMapOfGraph(x, finishedNodes, nodesInProgress);
}
const reversedNodesInDecreasingDepth = nodesInDecreasingDepth.slice().reverse();
for (const node of reversedNodesInDecreasingDepth) {
nodeIDToNode[node.id] = node;
if (!(node.id in nodesDepths)) {
nodesDepths[node.id] = 0;
}
let depth = nodesDepths[node.id];
const previousDepth = layersDepths[node.outboundLayer.id] == null ? 0 : layersDepths[node.outboundLayer.id];
depth = Math.max(depth, previousDepth);
layersDepths[node.outboundLayer.id] = depth;
layerIDToLayer[node.outboundLayer.id] = node.outboundLayer;
nodesDepths[node.id] = depth;
for (let i = 0; i < node.inboundLayers.length; i++) {
const inboundLayer = node.inboundLayers[i];
const nodeIndex = node.nodeIndices[i];
const inboundNode = inboundLayer.inboundNodes[nodeIndex];
const previousDepth2 = nodesDepths[inboundNode.id] == null ? 0 : nodesDepths[inboundNode.id];
nodesDepths[inboundNode.id] = Math.max(depth + 1, previousDepth2);
nodeIDToNode[inboundNode.id] = inboundNode;
}
}
const nodesByDepth = {};
for (const nodeID in nodesDepths) {
const depth = nodesDepths[nodeID];
if (!(depth in nodesByDepth)) {
nodesByDepth[depth] = [];
}
nodesByDepth[depth].push(nodeIDToNode[nodeID]);
}
const layersByDepth = {};
for (const layerID in layersDepths) {
const depth = layersDepths[layerID];
if (!(depth in layersByDepth)) {
layersByDepth[depth] = [];
}
layersByDepth[depth].push(layerIDToLayer[layerID]);
}
let depthKeys = Object.keys(layersByDepth).map((x) => parseInt(x, 10)).sort(reverseNumberCompare);
this.layers = [];
for (const depth of depthKeys) {
const layersForDepth = layersByDepth[depth];
layersForDepth.sort((a, b) => {
const aIndex = layerIndices[a.id];
const bIndex = layerIndices[b.id];
if (aIndex < bIndex) {
return -1;
}
if (aIndex > bIndex) {
return 1;
}
return 0;
});
for (const layer of layersForDepth) {
if (layer instanceof Container) {
this.internalContainerRefs.push(layer);
}
this.layers.push(layer);
}
}
this.layersByDepth = layersByDepth;
depthKeys = Object.keys(nodesByDepth).map((x) => parseInt(x, 10)).sort(reverseNumberCompare);
const computableTensors = this.inputs.slice();
const layersWithCompleteInput = [];
for (const depth of depthKeys) {
for (const node of nodesByDepth[depth]) {
const layer = node.outboundLayer;
if (layer != null) {
for (const x of node.inputTensors) {
if (computableTensors.indexOf(x) === -1) {
throw new RuntimeError(`Graph disconnected: cannot obtain value for tensor ${x} at layer "${layer.name}". The following previous layers were accessed without issue: ${layersWithCompleteInput}`);
}
}
for (const x of node.outputTensors) {
computableTensors.push(x);
}
layersWithCompleteInput.push(layer.name);
}
}
}
this.nodesByDepth = nodesByDepth;
const allNames = this.layers.map((x) => x.name);
for (const name of allNames) {
const numOccurrences = allNames.filter((x) => x === name).length;
if (numOccurrences !== 1) {
throw new RuntimeError(`The name "${name}" is used ${numOccurrences} times in the model. All layer names should be unique. Layer names: ` + JSON.stringify(allNames));
}
}
this.outboundNodes = [];
this.inboundNodes = [];
new Node({
outboundLayer: this,
inboundLayers: [],
nodeIndices: [],
tensorIndices: [],
inputTensors: this.inputs,
outputTensors: this.outputs,
inputMasks: this.inputs.map((x) => null),
outputMasks: this.outputs.map((x) => null),
inputShapes: this.inputs.map((x) => x.shape),
outputShapes: this.outputs.map((x) => x.shape)
});
this.built = true;
this._refCount = 1;
}
assertNotDisposed() {
if (this._refCount === 0) {
throw new Error(`Container '${this.name}' is already disposed.`);
}
}
dispose() {
this.assertNotDisposed();
const result = {refCountAfterDispose: null, numDisposedVariables: 0};
if (--this._refCount === 0) {
for (const layer of this.layers) {
result.numDisposedVariables += layer.dispose().numDisposedVariables;
}
for (const container2 of this.internalContainerRefs) {
result.numDisposedVariables += container2.dispose().numDisposedVariables;
}
}
result.refCountAfterDispose = this._refCount;
return result;
}
get trainable() {
return this.trainable_;
}
set trainable(trainable) {
this.layers.forEach((layer) => {
layer._trainableWeights.forEach((w) => w.trainable = trainable);
});
this.trainable_ = trainable;
}
get trainableWeights() {
if (this._trainableWeights.length > 0) {
throw new ValueError("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");
}
if (!this.trainable) {
return [];
}
let weights = [];
for (const layer of this.layers) {
weights = weights.concat(layer.trainableWeights);
}
return weights;
}
get nonTrainableWeights() {
const weights = [];
for (const layer of this.layers) {
weights.push(...layer.nonTrainableWeights);
}
if (!this.trainable) {
const trainableWeights = [];
for (const layer of this.layers) {
trainableWeights.push(...layer.trainableWeights);
}
return trainableWeights.concat(weights);
}
return weights;
}
get weights() {
return this.trainableWeights.concat(this.nonTrainableWeights);
}
loadWeights(weights, strict = true) {
const nameToWeight = {};
let totalWeightsCount = 0;
for (const layer of this.layers) {
for (const weight of layer.weights) {
if (nameToWeight[weight.originalName] != null) {
throw new ValueError(`Duplicate weight name: ${weight.originalName}`);
}
nameToWeight[weight.originalName] = weight;
totalWeightsCount++;
}
}
const weightValueTuples = [];
for (const name in weights) {
let validatedName = name;
if (nameToWeight[name] == null) {
const tokens = name.split("/");
const shortenNameArray = tokens.slice(0, -2).concat([tokens[tokens.length - 1]]);
validatedName = shortenNameArray.join("/");
}
if (nameToWeight[validatedName] != null) {
weightValueTuples.push([nameToWeight[validatedName], weights[name]]);
} else if (strict) {
throw new ValueError(`Provided weight data has no target variable: ${name}`);
}
delete nameToWeight[validatedName];
}
if (strict) {
const unsetNames = [];
for (const name in nameToWeight) {
unsetNames.push(name);
}
if (unsetNames.length > 0) {
throw new ValueError(`${unsetNames.length} of ${totalWeightsCount} weights are not set: ${unsetNames}`);
}
}
batchSetValue(weightValueTuples);
}
updatedConfig() {
const theConfig = this.getConfig();
const modelConfig = {};
modelConfig["className"] = this.getClassName();
modelConfig["config"] = theConfig;
modelConfig["kerasVersion"] = `tfjs-layers ${version2}`;
modelConfig["backend"] = "TensorFlow.js";
return modelConfig;
}
toJSON(unused, returnString = true) {
const modelConfig = convertTsToPythonic(this.updatedConfig());
return returnString ? JSON.stringify(modelConfig) : modelConfig;
}
call(inputs, kwargs) {
return tidy(() => {
inputs = toList(inputs);
const feedDict = new FeedDict();
for (let i = 0; i < this.inputs.length; ++i) {
feedDict.add(this.inputs[i], inputs[i]);
}
return execute(this.outputs, feedDict, kwargs);
});
}
computeMask(inputs, mask) {
return tidy(() => {
inputs = toList(inputs);
let masks;
if (mask == null) {
masks = pyListRepeat(null, inputs.length);
} else {
masks = toList(mask);
}
return this.runInternalGraph(inputs, masks)[1];
});
}
computeOutputShape(inputShape) {
const inputShapes = normalizeShapeList(inputShape);
if (inputShapes.length !== this.inputLayers.length) {
throw new ValueError(`Invalid inputShape argument ${inputShape}: model has ${this.inputLayers.length} tensor inputs.`);
}
const layersToOutputShapes = {};
for (let i = 0; i < inputShapes.length; i++) {
const layer = this.inputLayers[i];
const inputShape2 = inputShapes[i];
const shapeKey = layer.name + "_0_0";
layersToOutputShapes[shapeKey] = inputShape2;
}
const depthKeys = Object.keys(this.nodesByDepth).map((x) => parseInt(x, 10)).sort(reverseNumberCompare);
if (depthKeys.length > 1) {
for (const depth of depthKeys) {
const nodes = this.nodesByDepth[depth];
for (const node of nodes) {
const layer = node.outboundLayer;
if (this.inputLayers.map((x) => x.id).indexOf(layer.id) !== -1) {
continue;
}
const inputShapes2 = [];
for (let j = 0; j < node.inboundLayers.length; j++) {
const inboundLayer = node.inboundLayers[j];
const nodeIndex2 = node.nodeIndices[j];
const tensorIndex = node.tensorIndices[j];
const shapeKey = `${inboundLayer.name}_${nodeIndex2}_${tensorIndex}`;
const inputShape2 = layersToOutputShapes[shapeKey];
inputShapes2.push(inputShape2);
}
const outputShape = layer.computeOutputShape(singletonOrArray(inputShapes2));
const outputShapes2 = normalizeShapeList(outputShape);
const nodeIndex = layer.inboundNodes.indexOf(node);
for (let j = 0; j < outputShapes2.length; j++) {
const shapeKey = `${layer.name}_${nodeIndex}_${j}`;
layersToOutputShapes[shapeKey] = outputShapes2[j];
}
}
}
}
const outputShapes = [];
const outputShapeKeys = [];
for (let i = 0; i < this.outputLayers.length; i++) {
const layer = this.outputLayers[i];
const nodeIndex = this.outputLayersNodeIndices[i];
const tensorIndex = this.outputLayersTensorIndices[i];
const shapeKey = `${layer.name}_${nodeIndex}_${tensorIndex}`;
outputShapeKeys.push(shapeKey);
}
for (let i = 0; i < outputShapeKeys.length; i++) {
const key = outputShapeKeys[i];
assert2(key in layersToOutputShapes);
outputShapes.push(layersToOutputShapes[key]);
}
return singletonOrArray(outputShapes);
}
runInternalGraph(inputs, masks) {
if (masks == null) {
masks = pyListRepeat(null, inputs.length);
}
const tensorMap = {};
for (let i = 0; i < this.inputs.length; ++i) {
const x = this.inputs[i];
const y = inputs[i];
const mask = masks[i];
tensorMap[x.id] = [y, mask];
}
const depthKeys = Object.keys(this.nodesByDepth).map((x) => parseInt(x, 10)).sort(reverseNumberCompare);
for (const depth of depthKeys) {
const nodes = this.nodesByDepth[depth];
for (const node of nodes) {
const layer = node.outboundLayer;
const referenceInputTensors = node.inputTensors;
const referenceOutputTensors = node.outputTensors;
const computedData = new Array();
for (const x of referenceInputTensors) {
if (x.id in tensorMap) {
computedData.push(tensorMap[x.id]);
}
}
if (computedData.length === referenceInputTensors.length) {
let kwargs = {};
let computedTensors;
let computedMasks;
let outputTensors2;
let outputMasks2;
if (node.callArgs != null) {
kwargs = node.callArgs;
}
if (computedData.length === 1) {
const [computedTensor, computedMask] = computedData[0];
if (kwargs["mask"] == null) {
kwargs["mask"] = computedMask;
}
outputTensors2 = toList(layer.call(computedTensor, kwargs));
outputMasks2 = toList(layer.computeMask(computedTensor, computedMask));
computedTensors = [computedTensor];
computedMasks = [computedMask];
} else {
computedTensors = computedData.map((x) => x[0]);
computedMasks = computedData.map((x) => x[1]);
if (kwargs["mask"] == null) {
kwargs["mask"] = computedMasks;
}
outputTensors2 = toList(layer.call(computedTensors, kwargs));
outputMasks2 = toList(layer.computeMask(computedTensors, computedMasks));
}
if (layer.activityRegularizer) {
throw new NotImplementedError("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");
}
for (let i = 0; i < referenceOutputTensors.length; ++i) {
const x = referenceOutputTensors[i];
const y = outputTensors2[i];
const mask = outputMasks2[i];
tensorMap[x.id] = [y, mask];
}
}
}
}
const outputTensors = [];
const outputMasks = [];
const outputShapes = [];
for (const x of this.outputs) {
assert2(x.id in tensorMap, `Could not compute output ${x.name} : ${x.id}`);
const [tensor16, mask] = tensorMap[x.id];
outputShapes.push(tensor16.shape);
outputTensors.push(tensor16);
outputMasks.push(mask);
}
return [outputTensors, outputMasks, outputShapes];
}
buildNodeConversionMap(layers) {
const nodeConversionMap = {};
let keptNodes;
for (const layer of this.layers) {
keptNodes = layer instanceof Container ? 1 : 0;
for (let originalNodeIndex = 0; originalNodeIndex < layer.inboundNodes.length; originalNodeIndex++) {
const nodeKey = Container.nodeKey(layer, originalNodeIndex);
if (this.containerNodes.has(nodeKey)) {
nodeConversionMap[nodeKey] = keptNodes;
keptNodes += 1;
}
}
}
return nodeConversionMap;
}
getLayer(name, index) {
if (index != null) {
if (this.layers.length <= index) {
throw new ValueError(`Was asked to retrieve layer at index ${index}, but model only has ${this.layers.length} layer(s).`);
} else {
return this.layers[index];
}
} else {
if (name == null) {
throw new ValueError("Provide either a layer name or layer index");
}
}
for (const layer of this.layers) {
if (layer.name === name) {
return layer;
}
}
throw new ValueError(`No such layer: ${name}`);
}
calculateLosses() {
return tidy(() => {
const losses8 = [];
for (const layer of this.layers) {
for (let nodeIndex = 0; nodeIndex < layer.inboundNodes.length; ++nodeIndex) {
const nodeKey = Container.nodeKey(layer, nodeIndex);
if (this.containerNodes.has(nodeKey)) {
losses8.push(...layer.calculateLosses());
}
}
}
return losses8;
});
}
getConfig() {
const config2 = {name: this.name};
const nodeConversionMap = this.buildNodeConversionMap(this.layers);
const layerConfigs = [];
for (const layer of this.layers) {
const layerClassName = layer.getClassName();
const layerConfig = layer.getConfig();
const filteredInboundNodes = [];
for (let originalNodeIndex = 0; originalNodeIndex < layer.inboundNodes.length; originalNodeIndex++) {
const node = layer.inboundNodes[originalNodeIndex];
const nodeKey = Container.nodeKey(layer, originalNodeIndex);
let kwargs = {};
if (this.containerNodes.has(nodeKey)) {
if (node.callArgs) {
try {
JSON.stringify(node.callArgs);
kwargs = node.callArgs;
} catch (err) {
console.warn(`Layer ${layer.name} was passed non-serializable keyword arguments: ${node.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`);
kwargs = {};
}
}
if (node.inboundLayers.length > 0) {
const nodeData = [];
for (let i = 0; i < node.inboundLayers.length; i++) {
const inboundLayer = node.inboundLayers[i];
const nodeIndex = node.nodeIndices[i];
const tensorIndex = node.tensorIndices[i];
const nodeKey2 = Container.nodeKey(inboundLayer, nodeIndex);
let newNodeIndex = nodeConversionMap[nodeKey2];
if (newNodeIndex == null) {
newNodeIndex = 0;
}
nodeData.push([inboundLayer.name, newNodeIndex, tensorIndex, kwargs]);
}
filteredInboundNodes.push(nodeData);
}
}
}
const dict = {};
dict["name"] = layer.name;
dict["className"] = layerClassName;
dict["config"] = layerConfig;
dict["inboundNodes"] = filteredInboundNodes;
layerConfigs.push(dict);
}
config2["layers"] = layerConfigs;
const modelInputs = [];
for (let i = 0; i < this.inputLayers.length; i++) {
const layer = this.inputLayers[i];
const nodeIndex = this.inputLayersNodeIndices[i];
const nodeKey = Container.nodeKey(layer, nodeIndex);
if (!this.containerNodes.has(nodeKey)) {
continue;
}
let newNodeIndex = nodeConversionMap[nodeKey];
if (newNodeIndex === null || newNodeIndex === void 0) {
newNodeIndex = 0;
}
const tensorIndex = this.inputLayersTensorIndices[i];
modelInputs.push([layer.name, newNodeIndex, tensorIndex]);
}
config2["inputLayers"] = modelInputs;
const modelOutputs = [];
for (let i = 0; i < this.outputLayers.length; i++) {
const layer = this.outputLayers[i];
const nodeIndex = this.outputLayersNodeIndices[i];
const nodeKey = Container.nodeKey(layer, nodeIndex);
if (!this.containerNodes.has(nodeKey)) {
continue;
}
let newNodeIndex = nodeConversionMap[nodeKey];
if (newNodeIndex === null || newNodeIndex === void 0) {
newNodeIndex = 0;
}
const tensorIndex = this.outputLayersTensorIndices[i];
modelOutputs.push([layer.name, newNodeIndex, tensorIndex]);
}
config2["outputLayers"] = modelOutputs;
return config2;
}
static fromConfig(cls, config2, customObjects = {}, fastWeightInit = false) {
const createdLayers = {};
const unprocessedNodes = {};
function addUnprocessedNode(layer, nodeData) {
if (!(layer.name in unprocessedNodes)) {
unprocessedNodes[layer.name] = [nodeData];
} else {
unprocessedNodes[layer.name].push(nodeData);
}
}
function processNode(layer, nodeData) {
const inputTensors2 = [];
let kwargs;
for (const inputData of nodeData) {
const inboundLayerName = inputData[0];
const inboundNodeIndex = inputData[1];
const inboundTensorIndex = inputData[2];
kwargs = inputData[3] == null ? {} : inputData[3];
if (!(inboundLayerName in createdLayers)) {
addUnprocessedNode(layer, nodeData);
return;
}
const inboundLayer = createdLayers[inboundLayerName];
if (inboundLayer.inboundNodes.length <= inboundNodeIndex) {
addUnprocessedNode(layer, nodeData);
return;
}
const inboundNode = inboundLayer.inboundNodes[inboundNodeIndex];
inputTensors2.push(inboundNode.outputTensors[inboundTensorIndex]);
}
if (inputTensors2.length > 0) {
layer.apply(singletonOrArray(inputTensors2), kwargs);
}
}
function processLayer(layerData) {
const layerName = layerData["name"];
const layer = deserialize(layerData, config2["customObjects"] != null ? config2["customObjects"] : {});
layer.setFastWeightInitDuringBuild(fastWeightInit);
createdLayers[layerName] = layer;
const inboundNodesData = layerData["inboundNodes"];
inboundNodesData.forEach((nodeData) => {
if (!(nodeData instanceof Array)) {
throw new ValueError(`Corrupted configuration, expected array for nodeData: ${nodeData}`);
}
addUnprocessedNode(layer, nodeData);
});
}
const name = config2["name"];
const layersFromConfig = config2["layers"];
for (const layerData of layersFromConfig) {
processLayer(layerData);
}
while (!isObjectEmpty(unprocessedNodes)) {
for (const layerData of layersFromConfig) {
const layer = createdLayers[layerData["name"]];
if (layer.name in unprocessedNodes) {
const currentUnprocessedNodesForLayer = unprocessedNodes[layer.name];
delete unprocessedNodes[layer.name];
for (const nodeData of currentUnprocessedNodesForLayer) {
processNode(layer, nodeData);
}
}
}
}
const inputTensors = [];
const outputTensors = [];
const inputLayersFromConfig = config2["inputLayers"];
for (const layerData of inputLayersFromConfig) {
const layerName = layerData[0];
const nodeIndex = layerData[1];
const tensorIndex = layerData[2];
assert2(layerName in createdLayers);
const layer = createdLayers[layerName];
const layerOutputTensors = layer.inboundNodes[nodeIndex].outputTensors;
inputTensors.push(layerOutputTensors[tensorIndex]);
}
const outputLayersFromConfig = config2["outputLayers"];
for (const layerData of outputLayersFromConfig) {
const layerName = layerData[0];
const nodeIndex = layerData[1];
const tensorIndex = layerData[2];
assert2(layerName in createdLayers);
const layer = createdLayers[layerName];
const layerOutputTensors = layer.inboundNodes[nodeIndex].outputTensors;
outputTensors.push(layerOutputTensors[tensorIndex]);
}
return new cls({inputs: inputTensors, outputs: outputTensors, name});
}
get stateful() {
if (this._stateful) {
throw new ValueError("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");
}
for (const layer of this.layers) {
if (layer.stateful) {
return true;
}
}
return false;
}
resetStates() {
tidy(() => {
this.layers.forEach((layer) => {
if (layer.stateful) {
layer.resetStates();
}
});
});
}
}
// node_modules/@tensorflow/tfjs-layers/dist/engine/training_utils.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
function standardizeSampleOrClassWeights(xWeight, outputNames, weightType) {
const numOutputs = outputNames.length;
if (xWeight == null || Array.isArray(xWeight) && xWeight.length === 0) {
return outputNames.map((name) => null);
}
if (numOutputs === 1) {
if (Array.isArray(xWeight) && xWeight.length === 1) {
return xWeight;
} else if (typeof xWeight === "object" && outputNames[0] in xWeight) {
return [xWeight[outputNames[0]]];
} else {
return [xWeight];
}
}
if (Array.isArray(xWeight)) {
if (xWeight.length !== numOutputs) {
throw new Error(`Provided ${weightType} is an array of ${xWeight.length} element(s), but the model has ${numOutputs} outputs. Make sure a set of weights is provided for each model output.`);
}
return xWeight;
} else if (typeof xWeight === "object" && Object.keys(xWeight).length > 0 && typeof xWeight[Object.keys(xWeight)[0]] === "object") {
const output = [];
outputNames.forEach((outputName) => {
if (outputName in xWeight) {
output.push(xWeight[outputName]);
} else {
output.push(null);
}
});
return output;
} else {
throw new Error(`The model has multiple (${numOutputs}) outputs, so ${weightType} must be either an array with ${numOutputs} elements or an object with ${outputNames} keys. Provided ${weightType} not understood: ${JSON.stringify(xWeight)}`);
}
}
function standardizeClassWeights(classWeight, outputNames) {
return standardizeSampleOrClassWeights(classWeight, outputNames, "classWeight");
}
async function standardizeWeights(y, sampleWeight, classWeight, sampleWeightMode) {
if (sampleWeight != null || sampleWeightMode != null) {
throw new Error("Support sampleWeight is not implemented yet");
}
if (classWeight != null) {
const yClasses = tidy(() => {
if (y.shape.length === 1) {
return y.clone();
} else if (y.shape.length === 2) {
if (y.shape[1] > 1) {
const axis = 1;
return y.argMax(axis);
} else if (y.shape[1] === 1) {
return y.reshape([y.shape[0]]);
} else {
throw new Error(`Encountered unexpected last-dimension size (${y.shape[1]}) during handling of class weights. The size is expected to be >= 1.`);
}
} else {
throw new Error(`Unexpected rank of target (y) tensor (${y.rank}) during handling of class weights. The rank is expected to be 1 or 2.`);
}
});
const yClassIndices = Array.from(await yClasses.data());
dispose(yClasses);
const classSampleWeight = [];
yClassIndices.forEach((classIndex) => {
if (classWeight[classIndex] == null) {
throw new Error(`classWeight must contain all classes in the training data. The class ${classIndex} exists in the data but not in classWeight`);
} else {
classSampleWeight.push(classWeight[classIndex]);
}
});
return tensor1d(classSampleWeight, "float32");
} else {
return null;
}
}
function computeWeightedLoss2(losses8, sampleWeights) {
return mul(losses8, sampleWeights);
}
// node_modules/@tensorflow/tfjs-layers/dist/engine/training_dataset.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
const DEFAULT_VALIDATION_BATCH_SIZE = 32;
function standardizeDataIteratorOutput(model2, iteratorOut) {
let xs;
let ys;
const iteratorOutObj = iteratorOut;
xs = iteratorOutObj["xs"];
ys = iteratorOutObj["ys"];
util_exports.assert(xs != null && ys != null, () => `A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor. The provided Dataset instead generates ${iteratorOut}`);
const flattenedXs = flattenTensorOrArrayOrMap("input", model2.inputNames, xs);
const flattenedYs = flattenTensorOrArrayOrMap("output", model2.outputNames, ys);
const batchSize = flattenedXs[0].shape[0];
util_exports.assert(flattenedXs.length === model2.inputs.length, () => `LayersModel has ${model2.inputs.length} inputs, but the dataset provides ${flattenedXs.length} inputs. (Expected input keys: ${JSON.stringify(model2.inputNames)})`);
util_exports.assert(flattenedYs.length === model2.outputs.length, () => `LayersModel has ${model2.outputs.length} outputs, but the dataset provides ${flattenedYs.length} outputs. (Expected output keys: ${JSON.stringify(model2.outputNames)})`);
for (let xIndex = 0; xIndex < flattenedXs.length; xIndex++) {
util_exports.assert(flattenedXs[xIndex].shape[0] === batchSize, () => `Batch size mismatch: input ${model2.inputNames[xIndex]} has ${flattenedXs[xIndex].shape[0]}; expected ${batchSize} based on input ${model2.inputNames[0]}.`);
}
for (let yIndex = 0; yIndex < flattenedYs.length; yIndex++) {
util_exports.assert(flattenedYs[yIndex].shape[0] === batchSize, () => `Batch size mismatch: output ${model2.outputNames[yIndex]} has ${flattenedYs[yIndex].shape[0]}; expected ${batchSize} based on input ${model2.inputNames[0]}.`);
}
return {xs: flattenedXs, ys: flattenedYs};
}
function flattenTensorOrArrayOrMap(inputOrOutput, names, values) {
if (values instanceof Tensor) {
return [values];
} else if (Array.isArray(values)) {
util_exports.assert(values.length === names.length, () => `Received an array of ${values.length} Tensors, but expected ${names.length} to match the ${inputOrOutput} keys ${names}.`);
return values;
} else {
const result = [];
for (const name of names) {
if (values[name] == null) {
throw new ValueError(`The feature data generated by the dataset lacks the required ${inputOrOutput} key '${name}'.`);
}
result.push(values[name]);
}
return result;
}
}
function standardizeTensorValidationData(data2) {
if (data2.length === 3) {
throw new NotImplementedError("Validation with sample weights is not implemented yet.");
}
return {xs: data2[0], ys: data2[1]};
}
async function fitDataset(model2, dataset5, args) {
const hasBatchesPerEpoch = args.batchesPerEpoch != null;
util_exports.assert(model2.optimizer != null, () => "You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig).");
util_exports.assert(args != null, () => `For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call.`);
util_exports.assert(args.epochs != null && args.epochs > 0 && Number.isInteger(args.epochs), () => `For fitDataset(), config.epochs is expected to be a positive integer, but got ${args.epochs}`);
util_exports.assert(!hasBatchesPerEpoch || args.batchesPerEpoch > 0 && Number.isInteger(args.batchesPerEpoch), () => `For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${args.batchesPerEpoch}`);
util_exports.assert(args["validationSplit"] == null, () => "`validationSplit` is not supported by `fitDataset()`. Use validationData instead.");
if (model2.isTraining) {
throw new Error("Cannot start training because another fit() call is ongoing.");
}
model2.isTraining = true;
try {
const doValidation = args.validationData != null;
let valXs;
let valYs;
if (doValidation) {
if (isDatasetObject(args.validationData)) {
util_exports.assert(args.validationBatches == null || args.validationBatches > 0 && Number.isInteger(args.validationBatches), () => `For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${args.validationBatches}`);
} else {
const validationData = standardizeTensorValidationData(args.validationData);
valXs = validationData.xs;
valYs = validationData.ys;
}
}
const trainFunction = model2.makeTrainFunction();
const outLabels = model2.getDedupedMetricsNames();
let callbackMetrics;
if (doValidation) {
callbackMetrics = outLabels.slice().concat(outLabels.map((n) => "val_" + n));
} else {
callbackMetrics = outLabels.slice();
}
const callbacks3 = standardizeCallbacks(args.callbacks, args.yieldEvery);
const verbose = args.verbose == null ? 1 : args.verbose;
const {callbackList, history} = configureCallbacks(callbacks3, verbose, args.epochs, null, null, getStepsPerEpoch(dataset5, args), null, doValidation, callbackMetrics);
callbackList.setModel(model2);
model2.history = history;
await callbackList.onTrainBegin();
model2.stopTraining_ = false;
let epoch = args.initialEpoch == null ? 0 : args.initialEpoch;
let dataIterator = await dataset5.iterator();
while (epoch < args.epochs) {
const epochLogs = {};
await callbackList.onEpochBegin(epoch);
let stepsDone = 0;
let batchIndex = 0;
if (!hasBatchesPerEpoch) {
dataIterator = await dataset5.iterator();
}
while (hasBatchesPerEpoch ? stepsDone < args.batchesPerEpoch : true) {
const iteratorOut = await dataIterator.next();
if (hasBatchesPerEpoch && iteratorOut.done) {
console.warn(`You provided \`batchesPerEpoch\` as ${args.batchesPerEpoch}, but your dataset iterator ran out of data after ${stepsDone} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${args.batchesPerEpoch * args.epochs} batches). You may need to use the repeat() function when building your dataset.`);
break;
}
if (iteratorOut.value != null) {
const {xs, ys} = standardizeDataIteratorOutput(model2, iteratorOut.value);
const batchLogs = {};
batchLogs["batch"] = batchIndex;
batchLogs["size"] = xs[0].shape[0];
await callbackList.onBatchBegin(batchIndex, batchLogs);
const sampleWeights = [];
if (args.classWeight != null) {
const standardClassWeights = standardizeClassWeights(args.classWeight, model2.outputNames);
for (let i = 0; i < standardClassWeights.length; ++i) {
sampleWeights.push(await standardizeWeights(ys[i], null, standardClassWeights[i]));
}
}
const ins = xs.concat(ys).concat(sampleWeights);
const outs = trainFunction(ins);
dispose(ins);
for (let i = 0; i < outLabels.length; ++i) {
const label = outLabels[i];
const out = outs[i];
batchLogs[label] = out;
keep(out);
}
await callbackList.onBatchEnd(batchIndex, batchLogs);
disposeTensorsInLogs(batchLogs);
batchIndex++;
stepsDone++;
}
if (hasBatchesPerEpoch ? stepsDone >= args.batchesPerEpoch : iteratorOut.done) {
if (doValidation) {
let valOuts;
if (isDatasetObject(args.validationData)) {
valOuts = toList(await model2.evaluateDataset(args.validationData, {batches: args.validationBatches}));
} else {
valOuts = toList(model2.evaluate(valXs, valYs, {
batchSize: args.validationBatchSize == null ? DEFAULT_VALIDATION_BATCH_SIZE : args.validationBatchSize,
verbose: 0
}));
}
for (let i = 0; i < model2.metricsNames.length; ++i) {
epochLogs[`val_${model2.metricsNames[i]}`] = valOuts[i];
}
}
break;
}
if (model2.stopTraining_) {
break;
}
}
await callbackList.onEpochEnd(epoch, epochLogs);
epoch++;
if (model2.stopTraining_) {
break;
}
}
await callbackList.onTrainEnd();
await model2.history.syncData();
return model2.history;
} finally {
model2.isTraining = false;
}
}
function getStepsPerEpoch(dataset5, args) {
let stepsPerEpoch = null;
if (args.batchesPerEpoch != null) {
stepsPerEpoch = args.batchesPerEpoch;
} else if (Number.isFinite(dataset5.size)) {
stepsPerEpoch = dataset5.size;
}
return stepsPerEpoch;
}
function isDatasetObject(dataset5) {
return typeof dataset5.iterator === "function";
}
function isLazyIteratorObject(iterator) {
return typeof iterator.next === "function";
}
async function evaluateDataset(model2, dataset5, args) {
args = args || {};
const hasBatches = args.batches != null;
const f = model2.testFunction;
let outs = [];
if (args.verbose > 0) {
throw new NotImplementedError("Verbose mode is not implemented yet.");
}
util_exports.assert(!hasBatches || args.batches > 0 && Number.isInteger(args.batches), () => `Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(args.batches)}`);
const dataIterator = isLazyIteratorObject(dataset5) ? dataset5 : await dataset5.iterator();
let numExamples = 0;
let batch = 0;
while (hasBatches ? batch < args.batches : true) {
const iteratorOut = await dataIterator.next();
outs = tidy(() => {
if (iteratorOut.value) {
const {xs, ys} = standardizeDataIteratorOutput(model2, iteratorOut.value);
const xsAndYs = xs.concat(ys);
const batchOuts = tidy(() => f(xsAndYs));
dispose(xsAndYs);
if (batch === 0) {
for (let i = 0; i < batchOuts.length; ++i) {
outs.push(scalar(0));
}
}
const batchSize = xsAndYs[0].shape[0];
for (let i = 0; i < batchOuts.length; ++i) {
const batchOut = batchOuts[i];
const oldScalar = outs[i];
outs[i] = tidy(() => add2(outs[i], mul(batchSize, batchOut)));
if (batch > 0) {
dispose(oldScalar);
}
}
dispose(batchOuts);
numExamples += batchSize;
++batch;
}
return outs;
});
if (iteratorOut.done) {
if (hasBatches) {
console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${args.batches} batches). You may need to use the repeat() function when building your dataset.`);
}
break;
}
}
for (let i = 0; i < outs.length; ++i) {
const oldScalar = outs[i];
outs[i] = div(outs[i], numExamples);
dispose(oldScalar);
}
return singletonOrArray(outs);
}
// node_modules/@tensorflow/tfjs-layers/dist/engine/training_tensors.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
function checkBatchSize(batchSize) {
util_exports.assert(batchSize > 0 && Number.isInteger(batchSize), () => `batchSize is required to be a positive integer, but got ${batchSize}`);
}
function sliceArrays(arrays, start, stop) {
if (arrays == null) {
return [null];
} else if (Array.isArray(arrays)) {
return arrays.map((array2) => sliceAlongFirstAxis(array2, start, stop - start));
} else {
return sliceAlongFirstAxis(arrays, start, stop - start);
}
}
function sliceArraysByIndices(arrays, indices) {
return tidy(() => {
if (arrays == null) {
return null;
} else if (Array.isArray(arrays)) {
return arrays.map((array2) => sliceArraysByIndices(array2, indices));
} else {
return gather4(arrays, indices.dtype === "int32" ? indices : indices.toInt());
}
});
}
function makeBatches(size, batchSize) {
const output = [];
let batchStart = 0;
let batchEnd = null;
while (batchStart < size) {
batchEnd = batchStart + batchSize;
if (batchEnd >= size) {
batchEnd = size;
}
output.push([batchStart, batchEnd]);
batchStart = batchEnd;
}
return output;
}
async function fitLoop(model2, f, ins, outLabels, batchSize, epochs, verbose, callbacks3, valF, valIns, shuffle2, callbackMetrics, initialEpoch, stepsPerEpoch, validationSteps) {
if (batchSize == null) {
batchSize = 32;
}
if (epochs == null) {
epochs = 1;
}
if (shuffle2 == null) {
shuffle2 = true;
}
if (initialEpoch == null) {
initialEpoch = 0;
}
let doValidation = false;
if (valF != null && valIns != null) {
doValidation = true;
}
if (validationSteps != null) {
doValidation = true;
if (stepsPerEpoch == null) {
throw new ValueError("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");
}
}
const numTrainSamples = model2.checkNumSamples(ins, batchSize, stepsPerEpoch, "steps_per_epoch");
let indexArray;
if (numTrainSamples != null) {
indexArray = range4(0, numTrainSamples);
}
if (verbose == null) {
verbose = 1;
}
const {callbackList, history} = configureCallbacks(callbacks3, verbose, epochs, initialEpoch, numTrainSamples, stepsPerEpoch, batchSize, doValidation, callbackMetrics);
callbackList.setModel(model2);
model2.history = history;
await callbackList.onTrainBegin();
model2.stopTraining_ = false;
for (let epoch = initialEpoch; epoch < epochs; ++epoch) {
await callbackList.onEpochBegin(epoch);
const epochLogs = {};
if (stepsPerEpoch != null) {
throw new NotImplementedError("stepsPerEpoch mode is not implemented yet.");
} else {
if (shuffle2 === "batch") {
throw new NotImplementedError("batch shuffling is not implemneted yet");
} else if (shuffle2) {
util_exports.shuffle(indexArray);
}
const epochIndexArray1D = tensor1d(indexArray);
const batches = makeBatches(numTrainSamples, batchSize);
for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {
const batchLogs = {};
await callbackList.onBatchBegin(batchIndex, batchLogs);
tidy(() => {
const batchStart = batches[batchIndex][0];
const batchEnd = batches[batchIndex][1];
const batchIds = sliceAlongFirstAxis(epochIndexArray1D, batchStart, batchEnd - batchStart);
batchLogs["batch"] = batchIndex;
batchLogs["size"] = batchEnd - batchStart;
const insBatch = sliceArraysByIndices(ins, batchIds);
const outs = f(insBatch);
for (let i = 0; i < outLabels.length; ++i) {
const label = outLabels[i];
const out = outs[i];
batchLogs[label] = out;
keep(out);
}
if (batchIndex === batches.length - 1) {
if (doValidation) {
const valOuts = model2.testLoop(valF, valIns, batchSize);
for (let i = 0; i < outLabels.length; ++i) {
const label = outLabels[i];
const out = valOuts[i];
keep(out);
epochLogs["val_" + label] = out;
}
}
}
});
await callbackList.onBatchEnd(batchIndex, batchLogs);
disposeTensorsInLogs(batchLogs);
if (model2.stopTraining_) {
break;
}
}
epochIndexArray1D.dispose();
}
await callbackList.onEpochEnd(epoch, epochLogs);
if (model2.stopTraining_) {
break;
}
}
await callbackList.onTrainEnd();
await model2.history.syncData();
return model2.history;
}
async function fitTensors(model2, x, y, args = {}) {
if (model2.isTraining) {
throw new Error("Cannot start training because another fit() call is ongoing.");
}
model2.isTraining = true;
let inputs;
let targets;
let inputValX;
let inputValY;
let valX;
let valY;
let sampleWeights;
try {
const batchSize = args.batchSize == null ? 32 : args.batchSize;
checkBatchSize(batchSize);
const checkBatchAxis = false;
const standardizedOuts = await model2.standardizeUserData(x, y, args.sampleWeight, args.classWeight, checkBatchAxis, batchSize);
inputs = standardizedOuts[0];
targets = standardizedOuts[1];
sampleWeights = standardizedOuts[2];
let doValidation = false;
let valIns;
if (args.validationData != null && args.validationData.length > 0) {
doValidation = true;
if (args.validationData.length === 2) {
inputValX = args.validationData[0];
inputValY = args.validationData[1];
} else if (args.validationData.length === 3) {
throw new NotImplementedError("validationData including sample weights is not supported yet.");
} else {
throw new ValueError(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${args.validationData} is invalid.`);
}
const checkBatchAxis2 = true;
const valStandardized = await model2.standardizeUserData(inputValX, inputValY, null, null, checkBatchAxis2, batchSize);
valX = valStandardized[0];
valY = valStandardized[1];
valIns = valX.concat(valY);
} else if (args.validationSplit != null && args.validationSplit > 0 && args.validationSplit < 1) {
doValidation = true;
const splitAt = Math.floor(inputs[0].shape[0] * (1 - args.validationSplit));
const originalBatchSize = inputs[0].shape[0];
valX = sliceArrays(inputs, splitAt, originalBatchSize);
inputs = sliceArrays(inputs, 0, splitAt);
valY = sliceArrays(targets, splitAt, originalBatchSize);
targets = sliceArrays(targets, 0, splitAt);
valIns = valX.concat(valY);
} else if (args.validationSteps != null) {
doValidation = true;
}
const ins = inputs.concat(targets).concat(sampleWeights);
model2.checkTrainableWeightsConsistency();
const trainFunction = model2.makeTrainFunction();
const outLabels = model2.getDedupedMetricsNames();
let valFunction;
let callbackMetrics;
if (doValidation) {
model2.makeTestFunction();
valFunction = model2.testFunction;
callbackMetrics = outLabels.slice().concat(outLabels.map((n) => "val_" + n));
} else {
valFunction = null;
valIns = [];
callbackMetrics = outLabels.slice();
}
const callbacks3 = standardizeCallbacks(args.callbacks, args.yieldEvery);
const out = await fitLoop(model2, trainFunction, ins, outLabels, batchSize, args.epochs, args.verbose, callbacks3, valFunction, valIns, args.shuffle, callbackMetrics, args.initialEpoch, null, null);
return out;
} finally {
model2.isTraining = false;
disposeNewTensors(inputs, x);
disposeNewTensors(targets, y);
disposeNewTensors(valX, inputValX);
disposeNewTensors(valY, inputValY);
if (sampleWeights != null) {
dispose(sampleWeights);
}
}
}
function ensureTensorsRank2OrHigher(tensors) {
const outs = [];
if (tensors instanceof Tensor) {
tensors = [tensors];
}
for (let i = 0; i < tensors.length; ++i) {
const tensor16 = tensors[i];
if (tensor16.rank === 1) {
outs.push(expandDims2(tensor16, 1));
} else if (tensor16.rank === 0) {
throw new Error("Expected tensor to be at least 1D, but received a 0D tensor (scalar).");
} else {
outs.push(tensor16);
}
}
return outs;
}
function disposeNewTensors(tensors, refTensors) {
if (tensors == null) {
return;
}
const oldTensorIds = [];
if (refTensors instanceof Tensor) {
oldTensorIds.push(refTensors.id);
} else if (Array.isArray(refTensors)) {
refTensors.forEach((t) => oldTensorIds.push(t.id));
} else if (refTensors != null) {
for (const name in refTensors) {
const oldTensor = refTensors[name];
oldTensorIds.push(oldTensor.id);
}
}
const tensorsToDispose = [];
if (tensors instanceof Tensor) {
if (oldTensorIds.indexOf(tensors.id) === -1) {
tensorsToDispose.push(tensors);
}
} else if (Array.isArray(tensors)) {
tensors.forEach((t) => {
if (oldTensorIds.indexOf(t.id) === -1) {
tensorsToDispose.push(t);
}
});
} else if (tensors != null) {
for (const name in tensors) {
const tensor16 = tensors[name];
if (oldTensorIds.indexOf(tensor16.id) === -1) {
tensorsToDispose.push(tensor16);
}
}
}
tensorsToDispose.forEach((t) => {
if (!t.isDisposed) {
t.dispose();
}
});
}
// node_modules/@tensorflow/tfjs-layers/dist/engine/training.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
function isDataTensor(x) {
return x instanceof Tensor;
}
function isDataArray(x) {
return Array.isArray(x);
}
function isDataDict(x) {
return !isDataTensor(x) && !isDataArray(x);
}
function standardizeInputData(data2, names, shapes, checkBatchAxis = true, exceptionPrefix = "") {
if (names == null || names.length === 0) {
if (data2 != null) {
let gotUnexpectedData = false;
if (isDataArray(data2) && data2.length > 0) {
gotUnexpectedData = true;
} else if (isDataDict(data2)) {
for (const key in data2) {
if (data2.hasOwnProperty(key)) {
gotUnexpectedData = true;
break;
}
}
} else {
gotUnexpectedData = true;
}
if (gotUnexpectedData) {
throw new ValueError(`Error when checking model ${exceptionPrefix} expected no data, but got ${data2}`);
}
}
return [];
}
if (data2 == null) {
return names.map((name) => null);
}
let arrays;
if (isDataDict(data2)) {
data2 = data2;
arrays = [];
for (const name of names) {
if (data2[name] == null) {
throw new ValueError(`No data provided for "${name}". Need data for each key in: ${names}`);
}
arrays.push(data2[name]);
}
} else if (isDataArray(data2)) {
data2 = data2;
if (data2.length !== names.length) {
throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${names.length} Tensor(s), but instead got the following list of Tensor(s): ${data2}`);
}
arrays = data2;
} else {
data2 = data2;
if (names.length > 1) {
throw new ValueError(`The model ${exceptionPrefix} expects ${names.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${data2.shape}`);
}
arrays = [data2];
}
arrays = ensureTensorsRank2OrHigher(arrays);
if (shapes != null) {
for (let i = 0; i < names.length; ++i) {
if (shapes[i] == null) {
continue;
}
const array2 = arrays[i];
if (array2.shape.length !== shapes[i].length) {
throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have ${shapes[i].length} dimension(s). but got array with shape ${array2.shape}`);
}
for (let j = 0; j < shapes[i].length; ++j) {
if (j === 0 && !checkBatchAxis) {
continue;
}
const dim = array2.shape[j];
const refDim = shapes[i][j];
if (refDim != null && refDim >= 0 && dim !== refDim) {
throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have shape [${shapes[i]}], but got array with shape [${array2.shape}].`);
}
}
}
}
return arrays;
}
function checkArrayLengths(inputs, targets, weights) {
const setX = unique3(inputs.map((input2) => input2.shape[0]));
setX.sort();
const setY = unique3(targets.map((target) => target.shape[0]));
setY.sort();
if (setX.length > 1) {
throw new ValueError(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(inputs.map((input2) => input2.shape))}`);
}
if (setY.length > 1) {
throw new ValueError(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(targets.map((target) => target.shape))}`);
}
if (setX.length > 0 && setY.length > 0 && !util_exports.arraysEqual(setX, setY)) {
throw new ValueError(`Input Tensors should have the same number of samples as target Tensors. Found ${setX[0]} input sample(s) and ${setY[0]} target sample(s).`);
}
}
function checkLossAndTargetCompatibility(targets, lossFns, outputShapes) {
const keyLosses = [
meanSquaredError2,
binaryCrossentropy,
categoricalCrossentropy
];
for (let i = 0; i < targets.length; ++i) {
const y = targets[i];
const loss = lossFns[i];
const shape = outputShapes[i];
if (loss == null) {
continue;
}
if (loss === categoricalCrossentropy) {
if (y.shape[y.shape.length - 1] === 1) {
throw new ValueError(`You are passing a target array of shape ${y.shape} while using a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].`);
}
}
if (keyLosses.indexOf(loss) !== -1) {
const slicedYShape = y.shape.slice(1);
const slicedShape = shape.slice(1);
for (let j = 0; j < slicedYShape.length; ++j) {
const targetDim = slicedYShape[j];
const outDim = slicedShape[j];
if (outDim != null && targetDim !== outDim) {
throw new ValueError(`A target Tensor with shape ${y.shape} was passed for an output of shape ${shape}, while using a loss function that expects targets to have the same shape as the output.`);
}
}
}
}
}
function checkInputData(data2, names, shapes, checkBatchAxis = true, exceptionPrefix = "") {
let arrays;
if (Array.isArray(data2)) {
if (data2.length !== names.length) {
throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the the model expected. Expected to see ${names.length} Tensor(s), but instead got ${data2.length} Tensors(s).`);
}
arrays = data2;
} else {
if (names.length > 1) {
throw new ValueError(`The model expects ${names.length} ${exceptionPrefix} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(data2.shape)}.`);
}
arrays = [data2];
}
if (shapes != null) {
for (let i = 0; i < names.length; ++i) {
if (shapes[i] == null) {
continue;
}
const array2 = arrays[i];
if (array2.shape.length !== shapes[i].length) {
throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have ${shapes[i].length} dimension(s), but got array with shape ${JSON.stringify(array2.shape)}`);
}
for (let j = 0; j < shapes[i].length; ++j) {
if (j === 0 && !checkBatchAxis) {
continue;
}
const dim = array2.shape[j];
const refDim = shapes[i][j];
if (refDim != null) {
if (refDim !== dim) {
throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have shape ${JSON.stringify(shapes[i])} but got array with shape ${JSON.stringify(array2.shape)}.`);
}
}
}
}
}
}
function collectMetrics(metrics2, outputNames) {
if (metrics2 == null || Array.isArray(metrics2) && metrics2.length === 0) {
return outputNames.map((name) => []);
}
let wrappedMetrics;
if (typeof metrics2 === "string" || typeof metrics2 === "function") {
wrappedMetrics = [metrics2];
} else if (Array.isArray(metrics2) || typeof metrics2 === "object") {
wrappedMetrics = metrics2;
} else {
throw new TypeError(`Type of metrics argument not understood. Expected an string,function, Array, or Object, found: ${metrics2}`);
}
if (Array.isArray(wrappedMetrics)) {
return outputNames.map((name) => wrappedMetrics);
} else {
const nestedMetrics = [];
for (const name of outputNames) {
let outputMetrics = wrappedMetrics.hasOwnProperty(name) ? wrappedMetrics[name] : [];
if (!Array.isArray(outputMetrics)) {
outputMetrics = [outputMetrics];
}
nestedMetrics.push(outputMetrics);
}
return nestedMetrics;
}
}
const LAYERS_MODEL_FORMAT_NAME = "layers-model";
class LayersModel extends Container {
constructor(args) {
super(args);
this.isTraining = false;
}
summary(lineLength, positions, printFn = console.log) {
if (!this.built) {
throw new ValueError(`This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).`);
}
printSummary(this, lineLength, positions, printFn);
}
compile(args) {
if (args.loss == null) {
args.loss = [];
}
this.loss = args.loss;
if (typeof args.optimizer === "string") {
this.optimizer_ = getOptimizer(args.optimizer);
this.isOptimizerOwned = true;
} else {
if (!(args.optimizer instanceof Optimizer)) {
throw new ValueError(`User-defined optimizer must be an instance of tf.Optimizer.`);
}
this.optimizer_ = args.optimizer;
this.isOptimizerOwned = false;
}
let lossFunctions = [];
if (!Array.isArray(args.loss) && typeof args.loss !== "string" && typeof args.loss !== "function") {
args.loss = args.loss;
for (const name in args.loss) {
if (this.outputNames.indexOf(name) === -1) {
throw new ValueError(`Unknown entry in loss dictionary: "${name}". Only expected the following keys: ${this.outputNames}`);
}
}
for (const name of this.outputNames) {
if (args.loss[name] == null) {
console.warn(`Output "${name}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${name} during training`);
}
lossFunctions.push(get(args.loss[name]));
}
} else if (Array.isArray(args.loss)) {
if (args.loss.length !== this.outputs.length) {
throw new ValueError(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${args.loss}.`);
}
const theLosses = args.loss;
lossFunctions = theLosses.map((l) => get(l));
} else {
const lossFunction = get(args.loss);
this.outputs.forEach((_) => {
lossFunctions.push(lossFunction);
});
}
this.lossFunctions = lossFunctions;
this.feedOutputNames = [];
this.feedOutputShapes = [];
this.feedLossFns = [];
for (let i = 0; i < this.outputs.length; ++i) {
const shape = this.internalOutputShapes[i];
const name = this.outputNames[i];
this.feedOutputNames.push(name);
this.feedOutputShapes.push(shape);
this.feedLossFns.push(this.lossFunctions[i]);
}
const skipTargetIndices = [];
this.metrics = args.metrics;
this.metricsNames = ["loss"];
this.metricsTensors = [];
nameScope("loss", () => {
for (let i = 0; i < this.outputs.length; ++i) {
if (skipTargetIndices.indexOf(i) !== -1) {
continue;
}
const weightedLoss = this.lossFunctions[i];
if (this.outputs.length > 1) {
this.metricsTensors.push([weightedLoss, i]);
this.metricsNames.push(this.outputNames[i] + "_loss");
}
}
});
const nestedMetrics = collectMetrics(args.metrics, this.outputNames);
const appendMetric = (outputIndex, metricName, metricTensor) => {
if (this.outputNames.length > 1) {
metricName = this.outputNames[outputIndex] + "_" + metricName;
}
this.metricsNames.push(metricName);
this.metricsTensors.push([metricTensor, outputIndex]);
};
nameScope("metric", () => {
for (let i = 0; i < this.outputs.length; ++i) {
if (skipTargetIndices.indexOf(i) !== -1) {
continue;
}
const outputMetrics = nestedMetrics[i];
const handleMetrics = (metrics2) => {
const metricNamePrefix = "";
let metricName;
let accFn;
let weightedMetricFn;
for (const metric of metrics2) {
if (typeof metric === "string" && ["accuracy", "acc", "crossentropy", "ce"].indexOf(metric) !== -1) {
const outputShape = this.internalOutputShapes[i];
if (outputShape[outputShape.length - 1] === 1 || this.lossFunctions[i] === binaryCrossentropy) {
if (["accuracy", "acc"].indexOf(metric) !== -1) {
accFn = binaryAccuracy;
} else if (["crossentropy", "ce"].indexOf(metric) !== -1) {
accFn = binaryCrossentropy2;
}
} else if (this.lossFunctions[i] === sparseCategoricalCrossentropy) {
if (["accuracy", "acc"].indexOf(metric) !== -1) {
accFn = sparseCategoricalAccuracy;
} else if (["crossentropy", "ce"].indexOf(metric) !== -1) {
accFn = sparseCategoricalCrossentropy2;
}
} else {
if (["accuracy", "acc"].indexOf(metric) !== -1) {
accFn = categoricalAccuracy;
} else if (["crossentropy", "ce"].indexOf(metric) !== -1) {
accFn = categoricalCrossentropy2;
}
}
let suffix;
if (["accuracy", "acc"].indexOf(metric) !== -1) {
suffix = "acc";
} else if (["crossentropy", "ce"].indexOf(metric) !== -1) {
suffix = "ce";
}
weightedMetricFn = accFn;
metricName = metricNamePrefix + suffix;
} else {
const metricFn = get2(metric);
weightedMetricFn = metricFn;
metricName = metricNamePrefix + getLossOrMetricName(metric);
}
let metricResult;
nameScope(metricName, () => {
metricResult = weightedMetricFn;
});
appendMetric(i, metricName, metricResult);
}
};
handleMetrics(outputMetrics);
}
});
this.collectedTrainableWeights = this.trainableWeights;
}
checkTrainableWeightsConsistency() {
if (this.collectedTrainableWeights == null) {
return;
}
if (this.trainableWeights.length !== this.collectedTrainableWeights.length) {
console.warn("Discrepancy between trainableweights and collected trainable weights. Did you set `model.trainable` without calling `model.compile()` afterwards?");
}
}
evaluate(x, y, args = {}) {
const batchSize = args.batchSize == null ? 32 : args.batchSize;
checkBatchSize(batchSize);
const checkBatchAxis = true;
const standardizedOuts = this.standardizeUserDataXY(x, y, checkBatchAxis, batchSize);
try {
const ins = standardizedOuts[0].concat(standardizedOuts[1]);
this.makeTestFunction();
const f = this.testFunction;
const testOuts = this.testLoop(f, ins, batchSize, args.verbose, args.steps);
return singletonOrArray(testOuts);
} finally {
disposeNewTensors(standardizedOuts[0], x);
disposeNewTensors(standardizedOuts[1], y);
}
}
async evaluateDataset(dataset5, args) {
this.makeTestFunction();
return evaluateDataset(this, dataset5, args);
}
checkNumSamples(ins, batchSize, steps, stepsName = "steps") {
let numSamples;
if (steps != null) {
numSamples = null;
if (batchSize != null) {
throw new ValueError(`If ${stepsName} is set, batchSize must be null or undefined.Got batchSize = ${batchSize}`);
}
} else if (ins != null) {
if (Array.isArray(ins)) {
numSamples = ins[0].shape[0];
} else {
numSamples = ins.shape[0];
}
} else {
throw new ValueError(`Either the input data should have a defined shape, or ${stepsName} shoud be specified.`);
}
return numSamples;
}
execute(inputs, outputs) {
if (Array.isArray(outputs) && outputs.length === 0) {
throw new ValueError("`outputs` is an empty Array, which is not allowed.");
}
const outputsIsArray = Array.isArray(outputs);
const outputNames = outputsIsArray ? outputs : [outputs];
const outputSymbolicTensors = this.retrieveSymbolicTensors(outputNames);
const feedDict = new FeedDict();
if (inputs instanceof Tensor) {
inputs = [inputs];
}
if (Array.isArray(inputs)) {
if (inputs.length !== this.inputs.length) {
throw new ValueError(`The number of inputs provided (${inputs.length}) does not match the number of inputs of this model (${this.inputs.length}).`);
}
for (let i = 0; i < this.inputs.length; ++i) {
feedDict.add(this.inputs[i], inputs[i]);
}
} else {
for (const input2 of this.inputs) {
const tensorValue = inputs[input2.name];
if (tensorValue == null) {
throw new ValueError(`No value is provided for the model's input ${input2.name}`);
}
feedDict.add(input2, tensorValue);
}
}
const executeOutputs = execute(outputSymbolicTensors, feedDict);
return outputsIsArray ? executeOutputs : executeOutputs[0];
}
retrieveSymbolicTensors(symbolicTensorNames) {
const outputSymbolicTensors = pyListRepeat(null, symbolicTensorNames.length);
let outputsRemaining = symbolicTensorNames.length;
for (const layer of this.layers) {
const layerOutputs = Array.isArray(layer.output) ? layer.output : [layer.output];
const layerOutputNames = layerOutputs.map((output) => output.name);
for (let i = 0; i < symbolicTensorNames.length; ++i) {
const index = layerOutputNames.indexOf(symbolicTensorNames[i]);
if (index !== -1) {
outputSymbolicTensors[i] = layerOutputs[index];
outputsRemaining--;
}
if (outputsRemaining === 0) {
break;
}
}
if (outputsRemaining === 0) {
break;
}
}
if (outputsRemaining > 0) {
const remainingNames = [];
outputSymbolicTensors.forEach((tensor16, i) => {
if (tensor16 == null) {
remainingNames.push(symbolicTensorNames[i]);
}
});
throw new ValueError(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(remainingNames)}`);
}
return outputSymbolicTensors;
}
predictLoop(ins, batchSize = 32, verbose = false) {
return tidy(() => {
const numSamples = this.checkNumSamples(ins);
if (verbose) {
throw new NotImplementedError("Verbose predictLoop() is not implemented yet.");
}
const batches = makeBatches(numSamples, batchSize);
const outsBatches = this.outputs.map((output) => []);
for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {
const batchOuts = tidy(() => {
const batchStart = batches[batchIndex][0];
const batchEnd = batches[batchIndex][1];
const insBatch = sliceArrays(ins, batchStart, batchEnd);
const feeds = [];
if (Array.isArray(insBatch)) {
for (let i = 0; i < insBatch.length; ++i) {
feeds.push({key: this.inputs[i], value: insBatch[i]});
}
} else {
feeds.push({key: this.inputs[0], value: insBatch});
}
const feedDict = new FeedDict(feeds);
return execute(this.outputs, feedDict);
});
batchOuts.forEach((batchOut, i) => outsBatches[i].push(batchOut));
}
return singletonOrArray(outsBatches.map((batches2) => concat(batches2, 0)));
});
}
predict(x, args = {}) {
const xsRank2OrHigher = ensureTensorsRank2OrHigher(x);
checkInputData(xsRank2OrHigher, this.inputNames, this.feedInputShapes, false);
try {
const batchSize = args.batchSize == null ? 32 : args.batchSize;
checkBatchSize(batchSize);
return this.predictLoop(xsRank2OrHigher, batchSize);
} finally {
disposeNewTensors(xsRank2OrHigher, x);
}
}
predictOnBatch(x) {
checkInputData(x, this.inputNames, this.feedInputShapes, true);
const batchSize = (Array.isArray(x) ? x[0] : x).shape[0];
return this.predictLoop(x, batchSize);
}
standardizeUserDataXY(x, y, checkBatchAxis = true, batchSize) {
if (this.optimizer_ == null) {
throw new RuntimeError("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");
}
const outputShapes = [];
for (let i = 0; i < this.feedOutputShapes.length; ++i) {
const outputShape = this.feedOutputShapes[i];
const lossFn = this.feedLossFns[i];
if (lossFn === sparseCategoricalCrossentropy) {
outputShapes.push(outputShape.slice(0, outputShape.length - 1).concat([1]));
} else {
outputShapes.push(outputShape);
}
}
x = standardizeInputData(x, this.feedInputNames, this.feedInputShapes, false, "input");
y = standardizeInputData(y, this.feedOutputNames, outputShapes, false, "target");
checkArrayLengths(x, y, null);
checkLossAndTargetCompatibility(y, this.feedLossFns, this.feedOutputShapes);
if (this.stateful && batchSize != null && batchSize > 0) {
if (x[0].shape[0] % batchSize !== 0) {
throw new ValueError(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${batchSize}. Found: ${x[0].shape[0]} sample(s).`);
}
}
return [x, y];
}
async standardizeUserData(x, y, sampleWeight, classWeight, checkBatchAxis = true, batchSize) {
const [standardXs, standardYs] = this.standardizeUserDataXY(x, y, checkBatchAxis, batchSize);
if (sampleWeight != null) {
throw new Error("sample weight is not supported yet.");
}
let standardSampleWeights = null;
if (classWeight != null) {
const classWeights = standardizeClassWeights(classWeight, this.outputNames);
standardSampleWeights = [];
for (let i = 0; i < classWeights.length; ++i) {
standardSampleWeights.push(await standardizeWeights(standardYs[i], null, classWeights[i]));
}
}
return [standardXs, standardYs, standardSampleWeights];
}
testLoop(f, ins, batchSize, verbose = 0, steps) {
return tidy(() => {
const numSamples = this.checkNumSamples(ins, batchSize, steps, "steps");
const outs = [];
if (verbose > 0) {
throw new NotImplementedError("Verbose mode is not implemented yet.");
}
if (steps != null) {
throw new NotImplementedError("steps mode in testLoop() is not implemented yet");
} else {
const batches = makeBatches(numSamples, batchSize);
const indexArray = tensor1d(range4(0, numSamples));
for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {
const batchStart = batches[batchIndex][0];
const batchEnd = batches[batchIndex][1];
const batchIds = sliceAlongFirstAxis(indexArray, batchStart, batchEnd - batchStart);
const insBatch = sliceArraysByIndices(ins, batchIds);
const batchOuts = f(insBatch);
if (batchIndex === 0) {
for (let i = 0; i < batchOuts.length; ++i) {
outs.push(scalar(0));
}
}
for (let i = 0; i < batchOuts.length; ++i) {
const batchOut = batchOuts[i];
outs[i] = add2(outs[i], mul(batchEnd - batchStart, batchOut));
}
}
for (let i = 0; i < outs.length; ++i) {
outs[i] = div(outs[i], numSamples);
}
}
return outs;
});
}
getDedupedMetricsNames() {
const outLabels = this.metricsNames;
const dedupedOutLabels = [];
for (let i = 0; i < outLabels.length; ++i) {
const label = outLabels[i];
let newLabel = label;
if (count(outLabels, label) > 1) {
const dupIndex = count(outLabels.slice(0, i), label);
newLabel += `_${dupIndex}`;
}
dedupedOutLabels.push(newLabel);
}
return dedupedOutLabels;
}
makeTrainFunction() {
return (data2) => {
const lossValues = [];
const inputs = data2.slice(0, this.inputs.length);
const targets = data2.slice(this.inputs.length, this.inputs.length + this.outputs.length);
const sampleWeights = data2.slice(this.inputs.length + this.outputs.length, this.inputs.length + this.outputs.length * 2);
const metricsValues = [];
const totalLossFunction = () => {
const feeds = [];
for (let i = 0; i < this.inputs.length; ++i) {
feeds.push({key: this.inputs[i], value: inputs[i]});
}
const feedDict = new FeedDict(feeds);
const outputs = execute(this.outputs, feedDict, {training: true});
let totalLoss;
for (let i = 0; i < this.lossFunctions.length; ++i) {
const lossFunction = this.lossFunctions[i];
let loss = lossFunction(targets[i], outputs[i]);
if (sampleWeights[i] != null) {
loss = computeWeightedLoss2(loss, sampleWeights[i]);
}
const meanLoss = mean(loss);
lossValues.push(meanLoss);
if (i === 0) {
totalLoss = loss;
} else {
totalLoss = add2(totalLoss, loss);
}
}
for (let i = 0; i < this.metricsTensors.length; ++i) {
let weightedMetric;
if (this.outputs.length > 1 && i < this.outputs.length) {
weightedMetric = lossValues[i];
} else {
const metric = this.metricsTensors[i][0];
const outputIndex = this.metricsTensors[i][1];
weightedMetric = mean(metric(targets[outputIndex], outputs[outputIndex]));
}
keep(weightedMetric);
metricsValues.push(weightedMetric);
}
totalLoss = mean(totalLoss);
this.calculateLosses().forEach((regularizerLoss) => {
totalLoss = add2(totalLoss, regularizerLoss);
});
return totalLoss;
};
const variables5 = this.collectedTrainableWeights.map((param) => param.read());
const returnCost = true;
const totalLossValue = this.optimizer_.minimize(totalLossFunction, returnCost, variables5);
return [totalLossValue].concat(metricsValues);
};
}
makeTestFunction() {
this.testFunction = (data2) => {
return tidy(() => {
const valOutputs = [];
let totalLoss;
const inputs = data2.slice(0, this.inputs.length);
const targets = data2.slice(this.inputs.length, this.inputs.length + this.outputs.length);
const feeds = [];
for (let i = 0; i < this.inputs.length; ++i) {
feeds.push({key: this.inputs[i], value: inputs[i]});
}
const feedDict = new FeedDict(feeds);
const outputs = execute(this.outputs, feedDict);
for (let i = 0; i < this.lossFunctions.length; ++i) {
const lossFunction = this.lossFunctions[i];
const loss = mean(lossFunction(targets[i], outputs[i]));
if (i === 0) {
totalLoss = loss;
} else {
totalLoss = add2(totalLoss, loss);
}
valOutputs.push(totalLoss);
}
for (let i = 0; i < this.metricsTensors.length; ++i) {
const metric = this.metricsTensors[i][0];
const outputIndex = this.metricsTensors[i][1];
const meanMetric = mean(metric(targets[outputIndex], outputs[outputIndex]));
valOutputs.push(meanMetric);
}
return valOutputs;
});
};
}
async fit(x, y, args = {}) {
return fitTensors(this, x, y, args);
}
async fitDataset(dataset5, args) {
return fitDataset(this, dataset5, args);
}
async trainOnBatch(x, y) {
const standardizeOut = await this.standardizeUserData(x, y);
const inputs = standardizeOut[0];
const targets = standardizeOut[1];
const trainFunction = this.makeTrainFunction();
const losses8 = trainFunction(inputs.concat(targets));
const lossValues = [];
for (const loss of losses8) {
const v = await loss.data();
lossValues.push(v[0]);
}
dispose(losses8);
return singletonOrArray(lossValues);
}
getNamedWeights(config2) {
const namedWeights = [];
const trainableOnly = config2 != null && config2.trainableOnly;
const weights = trainableOnly ? this.trainableWeights : this.weights;
const weightValues = this.getWeights(trainableOnly);
for (let i = 0; i < weights.length; ++i) {
if (trainableOnly && !weights[i].trainable) {
continue;
}
namedWeights.push({name: weights[i].originalName, tensor: weightValues[i]});
}
return namedWeights;
}
set stopTraining(stop) {
this.stopTraining_ = stop;
}
get stopTraining() {
return this.stopTraining_;
}
get optimizer() {
return this.optimizer_;
}
set optimizer(optimizer7) {
if (this.optimizer_ !== optimizer7) {
this.optimizer_ = optimizer7;
this.isOptimizerOwned = false;
}
}
dispose() {
const result = super.dispose();
if (result.refCountAfterDispose === 0 && this.optimizer != null && this.isOptimizerOwned) {
const numTensorsBeforeOptmizerDisposal = memory().numTensors;
this.optimizer_.dispose();
result.numDisposedVariables += numTensorsBeforeOptmizerDisposal - memory().numTensors;
}
return result;
}
getLossIdentifiers() {
let lossNames;
if (typeof this.loss === "string") {
lossNames = toSnakeCase(this.loss);
} else if (Array.isArray(this.loss)) {
for (const loss of this.loss) {
if (typeof loss !== "string") {
throw new Error("Serialization of non-string loss is not supported.");
}
}
lossNames = this.loss.map((name) => toSnakeCase(name));
} else {
const outputNames = Object.keys(this.loss);
lossNames = {};
const losses8 = this.loss;
for (const outputName of outputNames) {
if (typeof losses8[outputName] === "string") {
lossNames[outputName] = toSnakeCase(losses8[outputName]);
} else {
throw new Error("Serialization of non-string loss is not supported.");
}
}
}
return lossNames;
}
getMetricIdentifiers() {
if (typeof this.metrics === "string" || typeof this.metrics === "function") {
return [toSnakeCase(getLossOrMetricName(this.metrics))];
} else if (Array.isArray(this.metrics)) {
return this.metrics.map((metric) => toSnakeCase(getLossOrMetricName(metric)));
} else {
const metricsIdentifiers = {};
for (const key in this.metrics) {
metricsIdentifiers[key] = toSnakeCase(getLossOrMetricName(this.metrics[key]));
}
return metricsIdentifiers;
}
}
getTrainingConfig() {
return {
loss: this.getLossIdentifiers(),
metrics: this.getMetricIdentifiers(),
optimizer_config: {
class_name: this.optimizer.getClassName(),
config: this.optimizer.getConfig()
}
};
}
loadTrainingConfig(trainingConfig) {
if (trainingConfig.weighted_metrics != null) {
throw new Error("Loading weight_metrics is not supported yet.");
}
if (trainingConfig.loss_weights != null) {
throw new Error("Loading loss_weights is not supported yet.");
}
if (trainingConfig.sample_weight_mode != null) {
throw new Error("Loading sample_weight_mode is not supported yet.");
}
const tsConfig = convertPythonicToTs(trainingConfig.optimizer_config);
const optimizer7 = deserialize(tsConfig);
let loss;
if (typeof trainingConfig.loss === "string") {
loss = toCamelCase(trainingConfig.loss);
} else if (Array.isArray(trainingConfig.loss)) {
loss = trainingConfig.loss.map((lossEntry) => toCamelCase(lossEntry));
} else if (trainingConfig.loss != null) {
loss = {};
for (const key in trainingConfig.loss) {
loss[key] = toCamelCase(trainingConfig.loss[key]);
}
}
let metrics2;
if (Array.isArray(trainingConfig.metrics)) {
metrics2 = trainingConfig.metrics.map((metric) => toCamelCase(metric));
} else if (trainingConfig.metrics != null) {
metrics2 = {};
for (const key in trainingConfig.metrics) {
metrics2[key] = toCamelCase(trainingConfig.metrics[key]);
}
}
this.compile({loss, metrics: metrics2, optimizer: optimizer7});
}
async save(handlerOrURL, config2) {
if (typeof handlerOrURL === "string") {
const handlers = io_exports.getSaveHandlers(handlerOrURL);
if (handlers.length === 0) {
throw new ValueError(`Cannot find any save handlers for URL '${handlerOrURL}'`);
} else if (handlers.length > 1) {
throw new ValueError(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);
}
handlerOrURL = handlers[0];
}
if (handlerOrURL.save == null) {
throw new ValueError("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");
}
const weightDataAndSpecs = await io_exports.encodeWeights(this.getNamedWeights(config2));
const returnString = false;
const unusedArg = null;
const modelConfig = this.toJSON(unusedArg, returnString);
const modelArtifacts = {
modelTopology: modelConfig,
format: LAYERS_MODEL_FORMAT_NAME,
generatedBy: `TensorFlow.js tfjs-layers v${version2}`,
convertedBy: null
};
const includeOptimizer = config2 == null ? false : config2.includeOptimizer;
if (includeOptimizer && this.optimizer != null) {
modelArtifacts.trainingConfig = this.getTrainingConfig();
const weightType = "optimizer";
const {data: optimizerWeightData, specs: optimizerWeightSpecs} = await io_exports.encodeWeights(await this.optimizer.getWeights(), weightType);
weightDataAndSpecs.specs.push(...optimizerWeightSpecs);
weightDataAndSpecs.data = io_exports.concatenateArrayBuffers([weightDataAndSpecs.data, optimizerWeightData]);
}
if (this.userDefinedMetadata != null) {
const checkSize = true;
checkUserDefinedMetadata(this.userDefinedMetadata, this.name, checkSize);
modelArtifacts.userDefinedMetadata = this.userDefinedMetadata;
}
modelArtifacts.weightData = weightDataAndSpecs.data;
modelArtifacts.weightSpecs = weightDataAndSpecs.specs;
return handlerOrURL.save(modelArtifacts);
}
setUserDefinedMetadata(userDefinedMetadata) {
checkUserDefinedMetadata(userDefinedMetadata, this.name);
this.userDefinedMetadata = userDefinedMetadata;
}
getUserDefinedMetadata() {
return this.userDefinedMetadata;
}
}
LayersModel.className = "Model";
serialization_exports.registerClass(LayersModel);
class Functional extends LayersModel {
}
Functional.className = "Functional";
serialization_exports.registerClass(Functional);
// node_modules/@tensorflow/tfjs-layers/dist/models.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
async function modelFromJSON(modelAndWeightsConfig, customObjects) {
if (!("modelTopology" in modelAndWeightsConfig)) {
modelAndWeightsConfig = {modelTopology: modelAndWeightsConfig};
}
modelAndWeightsConfig = modelAndWeightsConfig;
let modelTopology = modelAndWeightsConfig.modelTopology;
if (modelTopology["model_config"] != null) {
modelTopology = modelTopology["model_config"];
}
const tsConfig = convertPythonicToTs(modelTopology);
const model2 = deserialize(tsConfig, customObjects);
if (modelAndWeightsConfig.weightsManifest != null) {
const weightValues = await io_exports.loadWeights(modelAndWeightsConfig.weightsManifest, modelAndWeightsConfig.pathPrefix, model2.weights.map((weight) => weight.originalName));
const uniqueWeightValues = {};
for (const weight of model2.weights) {
uniqueWeightValues[weight.originalName] = weightValues[weight.originalName];
}
model2.loadWeights(uniqueWeightValues);
dispose(weightValues);
}
return model2;
}
async function loadLayersModelInternal(pathOrIOHandler, options) {
if (options == null) {
options = {};
}
if (typeof pathOrIOHandler === "string") {
const handlers = io_exports.getLoadHandlers(pathOrIOHandler, options);
if (handlers.length === 0) {
handlers.push(io_exports.browserHTTPRequest(pathOrIOHandler, options));
} else if (handlers.length > 1) {
throw new ValueError(`Found more than one (${handlers.length}) load handlers for URL '${pathOrIOHandler}'`);
}
pathOrIOHandler = handlers[0];
}
return loadLayersModelFromIOHandler(pathOrIOHandler, void 0, options);
}
async function loadLayersModelFromIOHandler(handler, customObjects, options) {
if (options == null) {
options = {};
}
if (handler.load == null) {
throw new ValueError("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");
}
const artifacts = await handler.load();
let modelTopology = artifacts.modelTopology;
if (modelTopology["model_config"] != null) {
modelTopology = modelTopology["model_config"];
}
const strict = options.strict == null ? true : options.strict;
const fastWeightInit = artifacts.weightData != null && artifacts.weightSpecs != null && strict;
const model2 = deserialize(convertPythonicToTs(modelTopology), customObjects, fastWeightInit);
const trainingConfig = artifacts.trainingConfig;
if (trainingConfig != null) {
model2.loadTrainingConfig(trainingConfig);
}
if (artifacts.userDefinedMetadata != null) {
model2.setUserDefinedMetadata(artifacts.userDefinedMetadata);
}
if (artifacts.weightData != null) {
if (artifacts.weightSpecs == null) {
throw new ValueError("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");
}
const {modelWeights, optimizerWeights} = decodeModelAndOptimizerWeights(artifacts.weightData, artifacts.weightSpecs);
model2.loadWeights(modelWeights, strict);
if (model2.optimizer != null && optimizerWeights.length > 0) {
await model2.optimizer.setWeights(optimizerWeights);
}
dispose(modelWeights);
dispose(optimizerWeights.map((w) => w.tensor));
}
return model2;
}
function decodeModelAndOptimizerWeights(buffer10, specs) {
const name2Tensor = io_exports.decodeWeights(buffer10, specs);
const modelWeights = {};
const optimizerWeights = [];
specs.forEach((spec) => {
if (spec.group === "optimizer") {
optimizerWeights.push({name: spec.name, tensor: name2Tensor[spec.name]});
} else {
modelWeights[spec.name] = name2Tensor[spec.name];
}
});
return {modelWeights, optimizerWeights};
}
class Sequential extends LayersModel {
constructor(args) {
super({inputs: [], outputs: []});
args = args || {};
this.trainable = true;
this.built = false;
this.name = args.name != null ? args.name : getUid("sequential_");
if (args.layers != null) {
for (const layer of args.layers) {
this.add(layer);
}
}
}
checkShape(layer) {
const shape = layer.inboundNodes[0].outputTensors[0].shape;
if (shape.some((x) => x < 0)) {
throw new ValueError(`Negative dimension size caused by adding layer ${layer.name} with input shape [${layer.inboundNodes[0].inputTensors[0].shape}]`);
}
}
add(layer) {
const isLayerModelInstance = layer instanceof Sequential || layer instanceof LayersModel;
let modelLayer;
if (isLayerModelInstance) {
modelLayer = layer;
if (modelLayer.outputs.length !== 1) {
throw new ValueError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");
}
if (modelLayer.inputs.length !== 1) {
throw new ValueError("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.");
}
}
if (this.outputs.length === 0) {
if (layer.inboundNodes.length === 0) {
if (layer.batchInputShape == null) {
throw new ValueError("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");
}
const x = Input({
batchShape: layer.batchInputShape,
dtype: layer.dtype,
name: layer.name + "_input"
});
layer.apply(x);
}
if (isLayerModelInstance) {
this.outputs = modelLayer.outputs;
this.inputs = modelLayer.inputs;
} else {
if (layer.inboundNodes.length !== 1) {
throw new ValueError(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${layer.name} which has ${layer.inboundNodes.length} pre-existing inbound connections.`);
}
if (layer.inboundNodes[0].outputTensors.length !== 1) {
throw new ValueError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");
}
this.checkShape(layer);
this.outputs = [layer.inboundNodes[0].outputTensors[0]];
this.inputs = getSourceInputs(this.outputs[0]);
}
this.inboundNodes = [];
new Node({
outboundLayer: this,
inboundLayers: [],
nodeIndices: [],
tensorIndices: [],
inputTensors: this.inputs,
outputTensors: this.outputs,
inputMasks: pyListRepeat(null, this.inputs.length),
outputMasks: [null],
inputShapes: this.inputs.map((x) => x.shape),
outputShapes: this.outputs[0].shape
});
} else {
const outputTensor = layer.apply(this.outputs[0]);
if (Array.isArray(outputTensor)) {
throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");
}
this.checkShape(layer);
this.outputs = [outputTensor];
this.inboundNodes[0].outputTensors = this.outputs;
this.inboundNodes[0].outputShapes = [this.outputs[0].shape];
}
this.layers.push(layer);
this.built = false;
}
pop() {
if (this.layers.length === 0) {
throw new TypeError("There are no layers in the model.");
}
this.layers.pop();
if (this.layers.length === 0) {
this.outputs = [];
this.inboundNodes = [];
this.outboundNodes = [];
} else {
const lastLayerIndex = this.layers.length - 1;
this.layers[lastLayerIndex].outboundNodes = [];
this.outputs = [this.layers[lastLayerIndex].output];
this.inboundNodes[0].outputTensors = this.outputs;
this.inboundNodes[0].outputShapes = [this.outputs[0].shape];
}
}
call(inputs, kwargs) {
if (this.model == null) {
this.build();
}
return this.model.call(inputs, kwargs);
}
build(inputShape) {
getExactlyOneShape(inputShape);
if (this.inputs.length === 0 || this.outputs.length === 0) {
throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");
}
this.model = new LayersModel({
inputs: this.inputs,
outputs: this.outputs[0],
name: this.name + "_model"
});
this.model.trainable = this.trainable;
this.supportsMasking = this.model.supportsMasking;
this.inputLayers = this.model.inputLayers;
this.inputLayersNodeIndices = this.model.inputLayersNodeIndices;
this.inputLayersTensorIndices = this.model.inputLayersTensorIndices;
this.outputLayers = this.model.outputLayers;
this.outputLayersNodeIndices = this.model.outputLayersNodeIndices;
this.outputLayersTensorIndices = this.model.outputLayersTensorIndices;
this.nodesByDepth = this.model.nodesByDepth;
this.containerNodes = this.model.containerNodes;
this.outputNames = this.model.outputNames;
this.inputNames = this.model.inputNames;
this.built = true;
}
countParams() {
if (!this.built) {
this.build();
}
return super.countParams();
}
summary(lineLength, positions, printFn = console.log) {
if (!this.built) {
this.build();
}
super.summary(lineLength, positions, printFn);
}
setWeights(weights) {
if (this.model == null) {
this.build();
}
this.model.setWeights(weights);
}
evaluate(x, y, args = {}) {
if (!this.built) {
throw new RuntimeError("The model needs to be compiled before being used.");
}
return this.model.evaluate(x, y, args);
}
async evaluateDataset(dataset5, args) {
if (!this.built) {
throw new RuntimeError("The model needs to be compiled before being used.");
}
return this.model.evaluateDataset(dataset5, args);
}
predict(x, args = {}) {
if (this.model == null) {
this.build();
}
return this.model.predict(x, args);
}
predictOnBatch(x) {
if (this.model == null) {
this.build();
}
return this.model.predictOnBatch(x);
}
compile(args) {
this.build();
this.model.compile(args);
this.optimizer_ = this.model.optimizer;
this.isOptimizerOwned = this.model.isOptimizerOwned;
this.loss = this.model.loss;
this.metrics = this.model.metrics;
this.metricsTensors = this.model.metricsTensors;
this.metricsNames = this.model.metricsNames;
}
get optimizer() {
return this.model == null ? void 0 : this.model.optimizer;
}
set optimizer(optimizer7) {
this.model.optimizer = optimizer7;
}
async fit(x, y, args = {}) {
if (!this.built) {
throw new RuntimeError("The model needs to be compiled before being used.");
}
return this.model.fit(x, y, args);
}
async fitDataset(dataset5, args) {
if (!this.built) {
throw new RuntimeError("The model needs to be compiled before being used.");
}
return this.model.fitDataset(dataset5, args);
}
async trainOnBatch(x, y) {
return this.model.trainOnBatch(x, y);
}
static fromConfig(cls, config2, customObjects = {}, fastWeightInit = false) {
let configArray;
let extraModelConfig = {};
if (config2 instanceof Array) {
if (!(config2[0].className != null) || config2[0]["className"] === "Merge") {
throw new ValueError("Legacy serialization format not supported yet.");
}
configArray = config2;
} else {
util_exports.assert(config2["layers"] != null, () => `When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field.`);
configArray = config2["layers"];
delete config2["layers"];
extraModelConfig = config2;
}
const model2 = new cls(extraModelConfig);
if (!(model2 instanceof Sequential)) {
throw new NotImplementedError(`Sequential.fromConfig called on non-Sequential input: ${model2}`);
}
for (const conf of configArray) {
const customObjects2 = void 0;
const layer = deserialize(conf, customObjects2, fastWeightInit);
if (fastWeightInit) {
layer.setFastWeightInitDuringBuild(true);
}
model2.add(layer);
}
return model2;
}
set stopTraining(stop) {
if (this.model == null) {
throw new ValueError("Cannot set the stopTraining property of a sequential model before it is compiled.");
}
this.model.stopTraining = stop;
}
get stopTraining() {
if (this.model == null) {
throw new ValueError("Cannot get the stopTraining property of a sequential model before it is compiled.");
}
return this.model.stopTraining;
}
getConfig() {
const layers = [];
for (const layer of this.layers) {
const dict = {};
dict["className"] = layer.getClassName();
dict["config"] = layer.getConfig();
layers.push(dict);
}
return {name: this.name, layers};
}
}
Sequential.className = "Sequential";
serialization_exports.registerClass(Sequential);
// node_modules/@tensorflow/tfjs-layers/dist/exports.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
function model(args) {
return new LayersModel(args);
}
function sequential(config2) {
return new Sequential(config2);
}
function loadLayersModel(pathOrIOHandler, options) {
if (options == null) {
options = {};
}
return loadLayersModelInternal(pathOrIOHandler, options);
}
function input(config2) {
return Input(config2);
}
function registerCallbackConstructor(verbosityLevel, callbackConstructor) {
CallbackConstructorRegistry.registerCallbackConstructor(verbosityLevel, callbackConstructor);
}
// node_modules/@tensorflow/tfjs-layers/dist/activations.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
class Activation extends serialization_exports.Serializable {
getConfig() {
return {};
}
}
class Elu2 extends Activation {
apply(x, alpha = 1) {
return elu4(x, alpha);
}
}
Elu2.className = "elu";
serialization_exports.registerClass(Elu2);
class Selu2 extends Activation {
apply(x) {
return selu(x);
}
}
Selu2.className = "selu";
serialization_exports.registerClass(Selu2);
class Relu2 extends Activation {
apply(x) {
return relu(x);
}
}
Relu2.className = "relu";
serialization_exports.registerClass(Relu2);
class Relu62 extends Activation {
apply(x) {
return tidy(() => minimum(6, relu(x)));
}
}
Relu62.className = "relu6";
serialization_exports.registerClass(Relu62);
class Linear extends Activation {
apply(x) {
return x;
}
}
Linear.className = "linear";
serialization_exports.registerClass(Linear);
class Sigmoid2 extends Activation {
apply(x) {
return sigmoid(x);
}
}
Sigmoid2.className = "sigmoid";
serialization_exports.registerClass(Sigmoid2);
class HardSigmoid extends Activation {
apply(x) {
return hardSigmoid(x);
}
}
HardSigmoid.className = "hardSigmoid";
serialization_exports.registerClass(HardSigmoid);
class Softplus2 extends Activation {
apply(x) {
return softplus(x);
}
}
Softplus2.className = "softplus";
serialization_exports.registerClass(Softplus2);
class Softsign extends Activation {
apply(x) {
return softsign(x);
}
}
Softsign.className = "softsign";
serialization_exports.registerClass(Softsign);
class Tanh2 extends Activation {
apply(x) {
return tanh2(x);
}
}
Tanh2.className = "tanh";
serialization_exports.registerClass(Tanh2);
class Softmax2 extends Activation {
apply(x, axis = -1) {
return softmax(x, axis);
}
}
Softmax2.className = "softmax";
serialization_exports.registerClass(Softmax2);
class LogSoftmax2 extends Activation {
apply(x, axis = -1) {
return logSoftmax(x, axis);
}
}
LogSoftmax2.className = "logSoftmax";
serialization_exports.registerClass(LogSoftmax2);
class Swish extends Activation {
apply(x, alpha = 1) {
return tidy(() => sigmoid(x.mul(alpha)).mul(x));
}
}
Swish.className = "swish";
serialization_exports.registerClass(Swish);
function serializeActivation(activation2) {
return activation2.getClassName();
}
function deserializeActivation(config2, customObjects = {}) {
return deserializeKerasObject(config2, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, "activation");
}
function getActivation(identifier) {
if (identifier == null) {
const config2 = {};
config2["className"] = "linear";
config2["config"] = {};
return deserializeActivation(config2);
}
if (typeof identifier === "string") {
const config2 = {};
config2["className"] = identifier;
config2["config"] = {};
return deserializeActivation(config2);
} else if (identifier instanceof Activation) {
return identifier;
} else {
return deserializeActivation(identifier);
}
}
// node_modules/@tensorflow/tfjs-layers/dist/regularizers.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
function assertObjectArgs(args) {
if (args != null && typeof args !== "object") {
throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${args}`);
}
}
class Regularizer extends serialization_exports.Serializable {
}
class L1L2 extends Regularizer {
constructor(args) {
super();
assertObjectArgs(args);
this.l1 = args == null || args.l1 == null ? 0.01 : args.l1;
this.l2 = args == null || args.l2 == null ? 0.01 : args.l2;
this.hasL1 = this.l1 !== 0;
this.hasL2 = this.l2 !== 0;
}
apply(x) {
return tidy(() => {
let regularization = zeros([1]);
if (this.hasL1) {
regularization = add2(regularization, sum2(mul(this.l1, abs(x))));
}
if (this.hasL2) {
regularization = add2(regularization, sum2(mul(this.l2, square10(x))));
}
return regularization.asScalar();
});
}
getConfig() {
return {l1: this.l1, l2: this.l2};
}
static fromConfig(cls, config2) {
return new cls({l1: config2["l1"], l2: config2["l2"]});
}
}
L1L2.className = "L1L2";
serialization_exports.registerClass(L1L2);
function l1(args) {
assertObjectArgs(args);
return new L1L2({l1: args != null ? args.l1 : null, l2: 0});
}
function l2(args) {
assertObjectArgs(args);
return new L1L2({l2: args != null ? args.l2 : null, l1: 0});
}
const REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP = {
l1l2: "L1L2"
};
function serializeRegularizer(constraint) {
return serializeKerasObject(constraint);
}
function deserializeRegularizer(config2, customObjects = {}) {
return deserializeKerasObject(config2, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, "regularizer");
}
function getRegularizer(identifier) {
if (identifier == null) {
return null;
}
if (typeof identifier === "string") {
const className = identifier in REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP ? REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] : identifier;
const config2 = {className, config: {}};
return deserializeRegularizer(config2);
} else if (identifier instanceof Regularizer) {
return identifier;
} else {
return deserializeRegularizer(identifier);
}
}
// node_modules/@tensorflow/tfjs-layers/dist/layers/advanced_activations.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
class ReLU extends Layer {
constructor(args) {
super(args == null ? {} : args);
this.supportsMasking = true;
if (args != null) {
this.maxValue = args.maxValue;
}
}
call(inputs, kwargs) {
inputs = getExactlyOneTensor(inputs);
let output = relu(inputs);
if (this.maxValue != null) {
output = clipByValue(output, 0, this.maxValue);
}
return output;
}
computeOutputShape(inputShape) {
return inputShape;
}
getConfig() {
const config2 = {maxValue: this.maxValue};
const baseConfig = super.getConfig();
Object.assign(config2, baseConfig);
return config2;
}
}
ReLU.className = "ReLU";
serialization_exports.registerClass(ReLU);
class LeakyReLU extends Layer {
constructor(args) {
super(args == null ? {} : args);
this.DEFAULT_ALPHA = 0.3;
if (args == null) {
args = {};
}
this.alpha = args.alpha == null ? this.DEFAULT_ALPHA : args.alpha;
}
call(inputs, kwargs) {
const x = getExactlyOneTensor(inputs);
return leakyRelu(x, this.alpha);
}
computeOutputShape(inputShape) {
return inputShape;
}
getConfig() {
const config2 = {alpha: this.alpha};
const baseConfig = super.getConfig();
Object.assign(config2, baseConfig);
return config2;
}
}
LeakyReLU.className = "LeakyReLU";
serialization_exports.registerClass(LeakyReLU);
class PReLU extends Layer {
constructor(args) {
super(args == null ? {} : args);
this.DEFAULT_ALPHA_INITIALIZER = "zeros";
if (args == null) {
args = {};
}
this.supportsMasking = true;
this.alphaInitializer = getInitializer(args.alphaInitializer || this.DEFAULT_ALPHA_INITIALIZER);
this.alphaRegularizer = getRegularizer(args.alphaRegularizer);
this.alphaConstraint = getConstraint(args.alphaConstraint);
if (args.sharedAxes == null) {
this.sharedAxes = null;
} else if (Array.isArray(args.sharedAxes)) {
this.sharedAxes = args.sharedAxes;
} else if (typeof args.sharedAxes === "number") {
this.sharedAxes = [args.sharedAxes];
} else {
throw new ValueError(`Expected sharedAxes to be a number or an array of numbers, but got ${args.sharedAxes}`);
}
}
build(inputShape) {
inputShape = getExactlyOneShape(inputShape);
const paramShape = inputShape.slice(1);
if (this.sharedAxes != null) {
for (const i of this.sharedAxes) {
paramShape[i - 1] = 1;
}
}
this.alpha = this.addWeight("alpha", paramShape, "float32", this.alphaInitializer, this.alphaRegularizer, true, this.alphaConstraint);
const axes = {};
if (this.sharedAxes != null) {
for (let i = 1; i < inputShape.length; ++i) {
axes[i] = inputShape[i];
}
}
this.inputSpec = [new InputSpec({
ndim: inputShape.length,
axes
})];
this.built = true;
}
call(inputs, kwargs) {
inputs = getExactlyOneTensor(inputs);
return prelu(inputs, this.alpha.read());
}
getConfig() {
const config2 = {
alphaInitializer: serializeInitializer(this.alphaInitializer),
alphaRegularizer: serializeRegularizer(this.alphaRegularizer),
alphaConstraint: serializeConstraint(this.alphaConstraint),
sharedAxes: this.sharedAxes
};
const baseConfig = super.getConfig();
Object.assign(config2, baseConfig);
return config2;
}
}
PReLU.className = "PReLU";
serialization_exports.registerClass(PReLU);
class ELU extends Layer {
constructor(args) {
super(args == null ? {} : args);
this.DEFAULT_ALPHA = 1;
if (args == null) {
args = {};
}
if (args.alpha != null && args.alpha !== this.DEFAULT_ALPHA) {
throw new NotImplementedError(`Non-default alpha value (${args.alpha}) is not supported by the ELU layer yet.`);
}
this.alpha = args.alpha == null ? this.DEFAULT_ALPHA : args.alpha;
}
call(inputs, kwargs) {
const x = getExactlyOneTensor(inputs);
return elu(x);
}
computeOutputShape(inputShape) {
return inputShape;
}
getConfig() {
const config2 = {alpha: this.alpha};
const baseConfig = super.getConfig();
Object.assign(config2, baseConfig);
return config2;
}
}
ELU.className = "ELU";
serialization_exports.registerClass(ELU);
class ThresholdedReLU extends Layer {
constructor(args) {
super(args == null ? {} : args);
this.DEFAULT_THETA = 1;
if (args == null) {
args = {};
}
this.theta = args.theta == null ? this.DEFAULT_THETA : args.theta;
}
call(inputs, kwargs) {
const x = getExactlyOneTensor(inputs);
return x.mul(cast20(x.greater(this.theta), "float32"));
}
computeOutputShape(inputShape) {
return inputShape;
}
getConfig() {
const config2 = {theta: this.theta};
const baseConfig = super.getConfig();
Object.assign(config2, baseConfig);
return config2;
}
}
ThresholdedReLU.className = "ThresholdedReLU";
serialization_exports.registerClass(ThresholdedReLU);
class Softmax3 extends Layer {
constructor(args) {
super(args == null ? {} : args);
this.DEFAULT_AXIS = 1;
if (args == null) {
args = {};
}
this.softmax = new Softmax2().apply;
this.axis = args.axis == null ? this.DEFAULT_AXIS : args.axis;
}
call(inputs, kwargs) {
const x = getExactlyOneTensor(inputs);
return this.softmax(x, this.axis);
}
computeOutputShape(inputShape) {
return inputShape;
}
getConfig() {
const config2 = {axis: this.axis};
const baseConfig = super.getConfig();
Object.assign(config2, baseConfig);
return config2;
}
}
Softmax3.className = "Softmax";
serialization_exports.registerClass(Softmax3);
// node_modules/@tensorflow/tfjs-layers/dist/utils/conv_utils.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
function normalizeArray(value, n, name) {
if (typeof value === "number") {
return pyListRepeat(value, n);
} else {
if (value.length !== n) {
throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers. Received: ${value.length} elements.`);
}
for (let i = 0; i < n; ++i) {
const singleValue = value[i];
if (!isInteger(singleValue)) {
throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers. Received: ${JSON.stringify(value)} including a non-integer number ${singleValue}`);
}
}
return value;
}
}
function convOutputLength(inputLength, filterSize, padding2, stride, dilation = 1) {
if (inputLength == null) {
return inputLength;
}
const dilatedFilterSize = filterSize + (filterSize - 1) * (dilation - 1);
let outputLength;
if (padding2 === "same") {
outputLength = inputLength;
} else {
outputLength = inputLength - dilatedFilterSize + 1;
}
return Math.floor((outputLength + stride - 1) / stride);
}
function deconvLength(dimSize, strideSize, kernelSize, padding2) {
if (dimSize == null) {
return null;
}
if (padding2 === "valid") {
dimSize = dimSize * strideSize + max6([kernelSize - strideSize, 0]);
} else if (padding2 === "same") {
dimSize = dimSize * strideSize;
} else {
throw new ValueError(`Unsupport padding mode: ${padding2}.`);
}
return dimSize;
}
// node_modules/@tensorflow/tfjs-layers/dist/layers/convolutional.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
function preprocessConv2DInput(x, dataFormat) {
return tidy(() => {
checkDataFormat(dataFormat);
if (dataFormat === "channelsFirst") {
return transpose(x, [0, 2, 3, 1]);
} else {
return x;
}
});
}
function preprocessConv3DInput(x, dataFormat) {
return tidy(() => {
checkDataFormat(dataFormat);
if (dataFormat === "channelsFirst") {
return transpose(x, [0, 2, 3, 4, 1]);
} else {
return x;
}
});
}
function conv1dWithBias(x, kernel, bias, strides = 1, padding2 = "valid", dataFormat, dilationRate = 1) {
return tidy(() => {
if (dataFormat == null) {
dataFormat = imageDataFormat();
}
checkDataFormat(dataFormat);
if (x.shape.length !== 3) {
throw new ValueError(`The input of a conv1dWithBias operation should be 3, but is ${x.shape.length} instead.`);
}
if (kernel.shape.length !== 3) {
throw new ValueError(`The kernel for a conv1dWithBias operation should be 3, but is ${kernel.shape.length} instead`);
}
if (bias != null && bias.shape.length !== 1) {
throw new ValueError(`The bias for a conv1dWithBias operation should be 1, but is ${kernel.shape.length} instead`);
}
if (dataFormat === "channelsFirst") {
x = transpose(x, [0, 2, 1]);
}
if (padding2 === "causal") {
throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");
}
let y = conv1d(x, kernel, strides, padding2 === "same" ? "same" : "valid", "NWC", dilationRate);
if (bias != null) {
y = biasAdd(y, bias);
}
return y;
});
}
function conv2dWithBiasActivation(x, kernel, bias, strides = [1, 1], padding2 = "valid", dataFormat, dilationRate, activation2 = null) {
return tidy(() => {
if (dataFormat == null) {
dataFormat = imageDataFormat();
}
checkDataFormat(dataFormat);
if (x.rank !== 3 && x.rank !== 4) {
throw new ValueError(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${x.rank}.`);
}
if (kernel.rank !== 3 && kernel.rank !== 4) {
throw new ValueError(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${x.rank}.`);
}
let y = preprocessConv2DInput(x, dataFormat);
if (padding2 === "causal") {
throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");
}
y = fused_ops_exports.conv2d({
x: y,
filter: kernel,
strides,
pad: padding2 === "same" ? "same" : "valid",
dilations: dilationRate,
dataFormat: "NHWC",
bias,
activation: activation2
});
if (dataFormat === "channelsFirst") {
y = transpose(y, [0, 3, 1, 2]);
}
return y;
});
}
function conv3dWithBias(x, kernel, bias, strides = [1, 1, 1], padding2 = "valid", dataFormat, dilationRate) {
return tidy(() => {
if (dataFormat == null) {
dataFormat = imageDataFormat();
}
checkDataFormat(dataFormat);
if (x.rank !== 4 && x.rank !== 5) {
throw new ValueError(`conv3dWithBias expects input to be of rank 4 or 5, but received ${x.rank}.`);
}
if (kernel.rank !== 4 && kernel.rank !== 5) {
throw new ValueError(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${x.rank}.`);
}
let y = preprocessConv3DInput(x, dataFormat);
if (padding2 === "causal") {
throw new NotImplementedError("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");
}
y = conv3d(y, kernel, strides, padding2 === "same" ? "same" : "valid", "NDHWC", dilationRate);
if (bias != null) {
y = biasAdd(y, bias);
}
if (dataFormat === "channelsFirst") {
y = transpose(y, [0, 4, 1, 2, 3]);
}
return y;
});
}
class BaseConv extends Layer {
constructor(rank, args) {
super(args);
this.bias = null;
this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal";
this.DEFAULT_BIAS_INITIALIZER = "zeros";
BaseConv.verifyArgs(args);
this.rank = rank;
assertPositiveInteger(this.rank, "rank");
if (this.rank !== 1 && this.rank !== 2 && this.rank !== 3) {
throw new NotImplementedError(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);
}
this.kernelSize = normalizeArray(args.kernelSize, rank, "kernelSize");
this.strides = normalizeArray(args.strides == null ? 1 : args.strides, rank, "strides");
this.padding = args.padding == null ? "valid" : args.padding;
checkPaddingMode(this.padding);
this.dataFormat = args.dataFormat == null ? "channelsLast" : args.dataFormat;
checkDataFormat(this.dataFormat);
this.activation = getActivation(args.activation);
this.useBias = args.useBias == null ? true : args.useBias;
this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);
this.biasConstraint = getConstraint(args.biasConstraint);
this.biasRegularizer = getRegularizer(args.biasRegularizer);
this.activityRegularizer = getRegularizer(args.activityRegularizer);
this.dilationRate = normalizeArray(args.dilationRate == null ? 1 : args.dilationRate, rank, "dilationRate");
if (this.rank === 1 && (Array.isArray(this.dilationRate) && this.dilationRate.length !== 1)) {
throw new ValueError(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);
} else if (this.rank === 2) {
if (typeof this.dilationRate === "number") {
this.dilationRate = [this.dilationRate, this.dilationRate];
} else if (this.dilationRate.length !== 2) {
throw new ValueError(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`);
}
} else if (this.rank === 3) {
if (typeof this.dilationRate === "number") {
this.dilationRate = [this.dilationRate, this.dilationRate, this.dilationRate];
} else if (this.dilationRate.length !== 3) {
throw new ValueError(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`);
}
}
}
static verifyArgs(args) {
assert2("kernelSize" in args, `required key 'kernelSize' not in config`);
if (typeof args.kernelSize !== "number" && !checkArrayTypeAndLength(args.kernelSize, "number", 1, 3)) {
throw new ValueError(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(args.kernelSize)}.`);
}
}
getConfig() {
const config2 = {
kernelSize: this.kernelSize,
strides: this.strides,
padding: this.padding,
dataFormat: this.dataFormat,
dilationRate: this.dilationRate,
activation: serializeActivation(this.activation),
useBias: this.useBias,
biasInitializer: serializeInitializer(this.biasInitializer),
biasRegularizer: serializeRegularizer(this.biasRegularizer),
activityRegularizer: serializeRegularizer(this.activityRegularizer),
biasConstraint: serializeConstraint(this.biasConstraint)
};
const baseConfig = super.getConfig();
Object.assign(config2, baseConfig);
return config2;
}
}
class Conv extends BaseConv {
constructor(rank, args) {
super(rank, args);
this.kernel = null;
Conv.verifyArgs(args);
this.filters = args.filters;
assertPositiveInteger(this.filters, "filters");
this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);
this.kernelConstraint = getConstraint(args.kernelConstraint);
this.kernelRegularizer = getRegularizer(args.kernelRegularizer);
}
build(inputShape) {
inputShape = getExactlyOneShape(inputShape);
const channelAxis = this.dataFormat === "channelsFirst" ? 1 : inputShape.length - 1;
if (inputShape[channelAxis] == null) {
throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);
}
const inputDim = inputShape[channelAxis];
const kernelShape = this.kernelSize.concat([inputDim, this.filters]);
this.kernel = this.addWeight("kernel", kernelShape, null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);
if (this.useBias) {
this.bias = this.addWeight("bias", [this.filters], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);
}
this.inputSpec = [{ndim: this.rank + 2, axes: {[channelAxis]: inputDim}}];
this.built = true;
}
call(inputs, kwargs) {
return tidy(() => {
inputs = getExactlyOneTensor(inputs);
let outputs;
const biasValue = this.bias == null ? null : this.bias.read();
const fusedActivationName = mapActivationToFusedKernel(this.activation.getClassName());
if (fusedActivationName != null && this.rank === 2) {
outputs = conv2dWithBiasActivation(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate, fusedActivationName);
} else {
if (this.rank === 1) {
outputs = conv1dWithBias(inputs, this.kernel.read(), biasValue, this.strides[0], this.padding, this.dataFormat, this.dilationRate[0]);
} else if (this.rank === 2) {
outputs = conv2dWithBiasActivation(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate);
} else if (this.rank === 3) {
outputs = conv3dWithBias(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate);
} else {
throw new NotImplementedError("convolutions greater than 3D are not implemented yet.");
}
if (this.activation != null) {
outputs = this.activation.apply(outputs);
}
}
return outputs;
});
}
computeOutputShape(inputShape) {
inputShape = getExactlyOneShape(inputShape);
const newSpace = [];
const space = this.dataFormat === "channelsLast" ? inputShape.slice(1, inputShape.length - 1) : inputShape.slice(2);
for (let i = 0; i < space.length; ++i) {
const newDim = convOutputLength(space[i], this.kernelSize[i], this.padding, this.strides[i], typeof this.dilationRate === "number" ? this.dilationRate : this.dilationRate[i]);
newSpace.push(newDim);
}
let outputShape = [inputShape[0]];
if (this.dataFormat === "channelsLast") {
outputShape = outputShape.concat(newSpace);
outputShape.push(this.filters);
} else {
outputShape.push(this.filters);
outputShape = outputShape.concat(newSpace);
}
return outputShape;
}
getConfig() {
const config2 = {
filters: this.filters,
kernelInitializer: serializeInitializer(this.kernelInitializer),
kernelRegularizer: serializeRegularizer(this.kernelRegularizer),
kernelConstraint: serializeConstraint(this.kernelConstraint)
};
const baseConfig = super.getConfig();
Object.assign(config2, baseConfig);
return config2;
}
static verifyArgs(args) {
if (!("filters" in args) || typeof args.filters !== "number" || args.filters < 1) {
throw new ValueError(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(args.filters)}`);
}
}
}
class Conv2D2 extends Conv {
constructor(args) {
super(2, args);
Conv2D2.verifyArgs(args);
}
getConfig() {
const config2 = super.getConfig();
delete config2["rank"];
return config2;
}
static verifyArgs(args) {
if (typeof args.kernelSize !== "number" && !checkArrayTypeAndLength(args.kernelSize, "number", 1, 2)) {
throw new ValueError(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(args.kernelSize)}.`);
}
}
}
Conv2D2.className = "Conv2D";
serialization_exports.registerClass(Conv2D2);
class Conv3D2 extends Conv {
constructor(args) {
super(3, args);
Conv3D2.verifyArgs(args);
}
getConfig() {
const config2 = super.getConfig();
delete config2["rank"];
return config2;
}
static verifyArgs(args) {
if (typeof args.kernelSize !== "number") {
if (!(Array.isArray(args.kernelSize) && (args.kernelSize.length === 1 || args.kernelSize.length === 3))) {
throw new ValueError(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(args.kernelSize)}.`);
}
}
}
}
Conv3D2.className = "Conv3D";
serialization_exports.registerClass(Conv3D2);
class Conv2DTranspose extends Conv2D2 {
constructor(args) {
super(args);
this.inputSpec = [new InputSpec({ndim: 4})];
if (this.padding !== "same" && this.padding !== "valid") {
throw new ValueError(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`);
}
}
build(inputShape) {
inputShape = getExactlyOneShape(inputShape);
if (inputShape.length !== 4) {
throw new ValueError("Input should have rank 4; Received input shape: " + JSON.stringify(inputShape));
}
const channelAxis = this.dataFormat === "channelsFirst" ? 1 : inputShape.length - 1;
if (inputShape[channelAxis] == null) {
throw new ValueError("The channel dimension of the inputs should be defined. Found `None`.");
}
const inputDim = inputShape[channelAxis];
const kernelShape = this.kernelSize.concat([this.filters, inputDim]);
this.kernel = this.addWeight("kernel", kernelShape, "float32", this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);
if (this.useBias) {
this.bias = this.addWeight("bias", [this.filters], "float32", this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);
}
this.inputSpec = [new InputSpec({ndim: 4, axes: {[channelAxis]: inputDim}})];
this.built = true;
}
call(inputs, kwargs) {
return tidy(() => {
let input2 = getExactlyOneTensor(inputs);
if (input2.shape.length !== 4) {
throw new ValueError(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${input2.shape.length}`);
}
const inputShape = input2.shape;
const batchSize = inputShape[0];
let hAxis;
let wAxis;
if (this.dataFormat === "channelsFirst") {
hAxis = 2;
wAxis = 3;
} else {
hAxis = 1;
wAxis = 2;
}
const height = inputShape[hAxis];
const width = inputShape[wAxis];
const kernelH = this.kernelSize[0];
const kernelW = this.kernelSize[1];
const strideH = this.strides[0];
const strideW = this.strides[1];
const outHeight = deconvLength(height, strideH, kernelH, this.padding);
const outWidth = deconvLength(width, strideW, kernelW, this.padding);
const outputShape = [batchSize, outHeight, outWidth, this.filters];
if (this.dataFormat !== "channelsLast") {
input2 = transpose(input2, [0, 2, 3, 1]);
}
let outputs = conv2dTranspose(input2, this.kernel.read(), outputShape, this.strides, this.padding);
if (this.dataFormat !== "channelsLast") {
outputs = transpose(outputs, [0, 3, 1, 2]);
}
if (this.bias != null) {
outputs = biasAdd(outputs, this.bias.read(), this.dataFormat);
}
if (this.activation != null) {
outputs = this.activation.apply(outputs);
}
return outputs;
});
}
computeOutputShape(inputShape) {
inputShape = getExactlyOneShape(inputShape);
const outputShape = inputShape.slice();
let channelAxis;
let heightAxis;
let widthAxis;
if (this.dataFormat === "channelsFirst") {
channelAxis = 1;
heightAxis = 2;
widthAxis = 3;
} else {
channelAxis = 3;
heightAxis = 1;
widthAxis = 2;
}
const kernelH = this.kernelSize[0];
const kernelW = this.kernelSize[1];
const strideH = this.strides[0];
const strideW = this.strides[1];
outputShape[channelAxis] = this.filters;
outputShape[heightAxis] = deconvLength(outputShape[heightAxis], strideH, kernelH, this.padding);
outputShape[widthAxis] = deconvLength(outputShape[widthAxis], strideW, kernelW, this.padding);
return outputShape;
}
getConfig() {
const config2 = super.getConfig();
delete config2["dilationRate"];
return config2;
}
}
Conv2DTranspose.className = "Conv2DTranspose";
serialization_exports.registerClass(Conv2DTranspose);
class SeparableConv extends Conv {
constructor(rank, config2) {
super(rank, config2);
this.DEFAULT_DEPTHWISE_INITIALIZER = "glorotUniform";
this.DEFAULT_POINTWISE_INITIALIZER = "glorotUniform";
this.depthwiseKernel = null;
this.pointwiseKernel = null;
if (config2.filters == null) {
throw new ValueError("The `filters` configuration field is required by SeparableConv, but is unspecified.");
}
if (config2.kernelInitializer != null || config2.kernelRegularizer != null || config2.kernelConstraint != null) {
throw new ValueError("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");
}
if (config2.padding != null && config2.padding !== "same" && config2.padding !== "valid") {
throw new ValueError(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(config2.padding)}`);
}
this.depthMultiplier = config2.depthMultiplier == null ? 1 : config2.depthMultiplier;
this.depthwiseInitializer = getInitializer(config2.depthwiseInitializer || this.DEFAULT_DEPTHWISE_INITIALIZER);
this.depthwiseRegularizer = getRegularizer(config2.depthwiseRegularizer);
this.depthwiseConstraint = getConstraint(config2.depthwiseConstraint);
this.pointwiseInitializer = getInitializer(config2.depthwiseInitializer || this.DEFAULT_POINTWISE_INITIALIZER);
this.pointwiseRegularizer = getRegularizer(config2.pointwiseRegularizer);
this.pointwiseConstraint = getConstraint(config2.pointwiseConstraint);
}
build(inputShape) {
inputShape = getExactlyOneShape(inputShape);
if (inputShape.length < this.rank + 2) {
throw new ValueError(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank + 2}, but received input shape: ${JSON.stringify(inputShape)}`);
}
const channelAxis = this.dataFormat === "channelsFirst" ? 1 : inputShape.length - 1;
if (inputShape[channelAxis] == null || inputShape[channelAxis] < 0) {
throw new ValueError(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(inputShape[channelAxis])}`);
}
const inputDim = inputShape[channelAxis];
const depthwiseKernelShape = this.kernelSize.concat([inputDim, this.depthMultiplier]);
const pointwiseKernelShape = [];
for (let i = 0; i < this.rank; ++i) {
pointwiseKernelShape.push(1);
}
pointwiseKernelShape.push(inputDim * this.depthMultiplier, this.filters);
const trainable = true;
this.depthwiseKernel = this.addWeight("depthwise_kernel", depthwiseKernelShape, "float32", this.depthwiseInitializer, this.depthwiseRegularizer, trainable, this.depthwiseConstraint);
this.pointwiseKernel = this.addWeight("pointwise_kernel", pointwiseKernelShape, "float32", this.pointwiseInitializer, this.pointwiseRegularizer, trainable, this.pointwiseConstraint);
if (this.useBias) {
this.bias = this.addWeight("bias", [this.filters], "float32", this.biasInitializer, this.biasRegularizer, trainable, this.biasConstraint);
} else {
this.bias = null;
}
this.inputSpec = [new InputSpec({ndim: this.rank + 2, axes: {[channelAxis]: inputDim}})];
this.built = true;
}
call(inputs, kwargs) {
return tidy(() => {
inputs = getExactlyOneTensor(inputs);
let output;
if (this.rank === 1) {
throw new NotImplementedError("1D separable convolution is not implemented yet.");
} else if (this.rank === 2) {
if (this.dataFormat === "channelsFirst") {
inputs = transpose(inputs, [0, 2, 3, 1]);
}
output = separableConv2d(inputs, this.depthwiseKernel.read(), this.pointwiseKernel.read(), this.strides, this.padding, this.dilationRate, "NHWC");
}
if (this.useBias) {
output = biasAdd(output, this.bias.read(), this.dataFormat);
}
if (this.activation != null) {
output = this.activation.apply(output);
}
if (this.dataFormat === "channelsFirst") {
output = transpose(output, [0, 3, 1, 2]);
}
return output;
});
}
getConfig() {
const config2 = super.getConfig();
delete config2["rank"];
delete config2["kernelInitializer"];
delete config2["kernelRegularizer"];
delete config2["kernelConstraint"];
config2["depthwiseInitializer"] = serializeInitializer(this.depthwiseInitializer);
config2["pointwiseInitializer"] = serializeInitializer(this.pointwiseInitializer);
config2["depthwiseRegularizer"] = serializeRegularizer(this.depthwiseRegularizer);
config2["pointwiseRegularizer"] = serializeRegularizer(this.pointwiseRegularizer);
config2["depthwiseConstraint"] = serializeConstraint(this.depthwiseConstraint);
config2["pointwiseConstraint"] = serializeConstraint(this.pointwiseConstraint);
return config2;
}
}
SeparableConv.className = "SeparableConv";
class SeparableConv2D extends SeparableConv {
constructor(args) {
super(2, args);
}
}
SeparableConv2D.className = "SeparableConv2D";
serialization_exports.registerClass(SeparableConv2D);
class Conv1D extends Conv {
constructor(args) {
super(1, args);
Conv1D.verifyArgs(args);
this.inputSpec = [{ndim: 3}];
}
getConfig() {
const config2 = super.getConfig();
delete config2["rank"];
delete config2["dataFormat"];
return config2;
}
static verifyArgs(args) {
if (typeof args.kernelSize !== "number" && !checkArrayTypeAndLength(args.kernelSize, "number", 1, 1)) {
throw new ValueError(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(args.kernelSize)}.`);
}
}
}
Conv1D.className = "Conv1D";
serialization_exports.registerClass(Conv1D);
class Cropping2D extends Layer {
constructor(args) {
super(args);
if (typeof args.cropping === "number") {
this.cropping = [[args.cropping, args.cropping], [args.cropping, args.cropping]];
} else if (typeof args.cropping[0] === "number") {
this.cropping = [
[args.cropping[0], args.cropping[0]],
[args.cropping[1], args.cropping[1]]
];
} else {
this.cropping = args.cropping;
}
this.dataFormat = args.dataFormat === void 0 ? "channelsLast" : args.dataFormat;
this.inputSpec = [{ndim: 4}];
}
computeOutputShape(inputShape) {
if (this.dataFormat === "channelsFirst") {
return [
inputShape[0],
inputShape[1],
inputShape[2] - this.cropping[0][0] - this.cropping[0][1],
inputShape[3] - this.cropping[1][0] - this.cropping[1][1]
];
} else {
return [
inputShape[0],
inputShape[1] - this.cropping[0][0] - this.cropping[0][1],
inputShape[2] - this.cropping[1][0] - this.cropping[1][1],
inputShape[3]
];
}
}
call(inputs, kwargs) {
return tidy(() => {
inputs = getExactlyOneTensor(inputs);
if (this.dataFormat === "channelsLast") {
const hSliced = sliceAlongAxis(inputs, this.cropping[0][0], inputs.shape[1] - this.cropping[0][0] - this.cropping[0][1], 2);
return sliceAlongAxis(hSliced, this.cropping[1][0], inputs.shape[2] - this.cropping[1][1] - this.cropping[1][0], 3);
} else {
const hSliced = sliceAlongAxis(inputs, this.cropping[0][0], inputs.shape[2] - this.cropping[0][0] - this.cropping[0][1], 3);
return sliceAlongAxis(hSliced, this.cropping[1][0], inputs.shape[3] - this.cropping[1][1] - this.cropping[1][0], 4);
}
});
}
getConfig() {
const config2 = {cropping: this.cropping, dataFormat: this.dataFormat};
const baseConfig = super.getConfig();
Object.assign(config2, baseConfig);
return config2;
}
}
Cropping2D.className = "Cropping2D";
serialization_exports.registerClass(Cropping2D);
class UpSampling2D extends Layer {
constructor(args) {
super(args);
this.DEFAULT_SIZE = [2, 2];
this.inputSpec = [{ndim: 4}];
this.size = args.size == null ? this.DEFAULT_SIZE : args.size;
this.dataFormat = args.dataFormat == null ? "channelsLast" : args.dataFormat;
}
computeOutputShape(inputShape) {
if (this.dataFormat === "channelsFirst") {
const height = inputShape[2] == null ? null : this.size[0] * inputShape[2];
const width = inputShape[3] == null ? null : this.size[1] * inputShape[3];
return [inputShape[0], inputShape[1], height, width];
} else {
const height = inputShape[1] == null ? null : this.size[0] * inputShape[1];
const width = inputShape[2] == null ? null : this.size[1] * inputShape[2];
return [inputShape[0], height, width, inputShape[3]];
}
}
call(inputs, kwargs) {
return tidy(() => {
let input2 = getExactlyOneTensor(inputs);
const inputShape = input2.shape;
if (this.dataFormat === "channelsFirst") {
input2 = transpose(input2, [0, 2, 3, 1]);
const height = this.size[0] * inputShape[2];
const width = this.size[1] * inputShape[3];
const resized = input2.resizeNearestNeighbor([height, width]);
return transpose(resized, [0, 3, 1, 2]);
} else {
const height = this.size[0] * inputShape[1];
const width = this.size[1] * inputShape[2];
return input2.resizeNearestNeighbor([height, width]);
}
});
}
getConfig() {
const config2 = {size: this.size, dataFormat: this.dataFormat};
const baseConfig = super.getConfig();
Object.assign(config2, baseConfig);
return config2;
}
}
UpSampling2D.className = "UpSampling2D";
serialization_exports.registerClass(UpSampling2D);
// node_modules/@tensorflow/tfjs-layers/dist/layers/convolutional_depthwise.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
function depthwiseConv2d3(x, depthwiseKernel, strides = [1, 1], padding2 = "valid", dataFormat, dilationRate) {
return tidy(() => {
if (dataFormat == null) {
dataFormat = imageDataFormat();
}
checkDataFormat(dataFormat);
let y = preprocessConv2DInput(x, dataFormat);
if (x.rank !== 4) {
throw new ValueError(`Input for depthwiseConv2d is required to be 4-D, but is instead ${x.rank}-D`);
}
if (depthwiseKernel.rank !== 4) {
throw new ValueError(`depthwiseKernel is required to be 4-D, but is instead ${depthwiseKernel.rank}-D`);
}
y = depthwiseConv2d(y, depthwiseKernel, strides, padding2 === "same" ? "same" : "valid", "NHWC", dilationRate);
if (dataFormat === "channelsFirst") {
y = transpose(y, [0, 3, 1, 2]);
}
return y;
});
}
class DepthwiseConv2D extends BaseConv {
constructor(args) {
super(2, args);
this.depthwiseKernel = null;
this.depthMultiplier = args.depthMultiplier == null ? 1 : args.depthMultiplier;
this.depthwiseInitializer = getInitializer(args.depthwiseInitializer || this.DEFAULT_KERNEL_INITIALIZER);
this.depthwiseConstraint = getConstraint(args.depthwiseConstraint);
this.depthwiseRegularizer = getRegularizer(args.depthwiseRegularizer);
}
build(inputShape) {
inputShape = getExactlyOneShape(inputShape);
if (inputShape.length < 4) {
throw new ValueError(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(inputShape)}.`);
}
const channelAxis = this.dataFormat === "channelsFirst" ? 1 : 3;
if (inputShape[channelAxis] == null || inputShape[channelAxis] < 0) {
throw new ValueError(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${inputShape[channelAxis]}).`);
}
const inputDim = inputShape[channelAxis];
const depthwiseKernelShape = [
this.kernelSize[0],
this.kernelSize[1],
inputDim,
this.depthMultiplier
];
this.depthwiseKernel = this.addWeight("depthwise_kernel", depthwiseKernelShape, null, this.depthwiseInitializer, this.depthwiseRegularizer, true, this.depthwiseConstraint);
if (this.useBias) {
this.bias = this.addWeight("bias", [inputDim * this.depthMultiplier], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);
} else {
this.bias = null;
}
this.built = true;
}
call(inputs, kwargs) {
return tidy(() => {
inputs = getExactlyOneTensor(inputs);
let outputs = depthwiseConv2d3(inputs, this.depthwiseKernel.read(), this.strides, this.padding, this.dataFormat, null);
if (this.useBias) {
outputs = biasAdd(outputs, this.bias.read(), this.dataFormat);
}
if (this.activation != null) {
outputs = this.activation.apply(outputs);
}
return outputs;
});
}
computeOutputShape(inputShape) {
inputShape = getExactlyOneShape(inputShape);
const rows = this.dataFormat === "channelsFirst" ? inputShape[2] : inputShape[1];
const cols = this.dataFormat === "channelsFirst" ? inputShape[3] : inputShape[2];
const outFilters = this.dataFormat === "channelsFirst" ? inputShape[1] * this.depthMultiplier : inputShape[3] * this.depthMultiplier;
const outRows = convOutputLength(rows, this.kernelSize[0], this.padding, this.strides[0]);
const outCols = convOutputLength(cols, this.kernelSize[1], this.padding, this.strides[1]);
if (this.dataFormat === "channelsFirst") {
return [inputShape[0], outFilters, outRows, outCols];
} else {
return [inputShape[0], outRows, outCols, outFilters];
}
}
getConfig() {
const config2 = super.getConfig();
config2["depthMultiplier"] = this.depthMultiplier;
config2["depthwiseInitializer"] = serializeInitializer(this.depthwiseInitializer);
config2["depthwiseRegularizer"] = serializeRegularizer(this.depthwiseRegularizer);
config2["depthwiseConstraint"] = serializeConstraint(this.depthwiseRegularizer);
return config2;
}
}
DepthwiseConv2D.className = "DepthwiseConv2D";
serialization_exports.registerClass(DepthwiseConv2D);
// node_modules/@tensorflow/tfjs-layers/dist/layers/recurrent.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
function standardizeArgs(inputs, initialState, constants, numConstants) {
if (Array.isArray(inputs)) {
if (initialState != null || constants != null) {
throw new ValueError("When inputs is an array, neither initialState or constants should be provided");
}
if (numConstants != null) {
constants = inputs.slice(inputs.length - numConstants, inputs.length);
inputs = inputs.slice(0, inputs.length - numConstants);
}
if (inputs.length > 1) {
initialState = inputs.slice(1, inputs.length);
}
inputs = inputs[0];
}
function toListOrNull(x) {
if (x == null || Array.isArray(x)) {
return x;
} else {
return [x];
}
}
initialState = toListOrNull(initialState);
constants = toListOrNull(constants);
return {inputs, initialState, constants};
}
function rnn(stepFunction, inputs, initialStates, goBackwards = false, mask, constants, unroll = false, needPerStepOutputs = false) {
return tidy(() => {
const ndim = inputs.shape.length;
if (ndim < 3) {
throw new ValueError(`Input should be at least 3D, but is ${ndim}D.`);
}
const axes = [1, 0].concat(range4(2, ndim));
inputs = transpose(inputs, axes);
if (constants != null) {
throw new NotImplementedError("The rnn() functoin of the deeplearn.js backend does not support constants yet.");
}
if (unroll) {
console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend.");
}
if (mask != null) {
mask = mask.asType("bool").asType("float32");
if (mask.rank === ndim - 1) {
mask = expandDims(mask, -1);
}
mask = transpose(mask, axes);
}
if (goBackwards) {
inputs = reverse(inputs, 0);
if (mask != null) {
mask = reverse(mask, 0);
}
}
const perStepOutputs = [];
let lastOutput;
let states = initialStates;
const timeSteps = inputs.shape[0];
const perStepInputs = unstack(inputs);
let perStepMasks;
if (mask != null) {
perStepMasks = unstack(mask);
}
for (let t = 0; t < timeSteps; ++t) {
const currentInput = perStepInputs[t];
const stepOutputs = tidy(() => stepFunction(currentInput, states));
if (mask == null) {
lastOutput = stepOutputs[0];
states = stepOutputs[1];
} else {
const maskedOutputs = tidy(() => {
const stepMask = perStepMasks[t];
const negStepMask = onesLike(stepMask).sub(stepMask);
const output = stepOutputs[0].mul(stepMask).add(states[0].mul(negStepMask));
const newStates = states.map((state6, i) => {
return stepOutputs[1][i].mul(stepMask).add(state6.mul(negStepMask));
});
return {output, newStates};
});
lastOutput = maskedOutputs.output;
states = maskedOutputs.newStates;
}
if (needPerStepOutputs) {
perStepOutputs.push(lastOutput);
}
}
let outputs;
if (needPerStepOutputs) {
const axis = 1;
outputs = stack(perStepOutputs, axis);
}
return [lastOutput, outputs, states];
});
}
class RNN extends Layer {
constructor(args) {
super(args);
let cell;
if (args.cell == null) {
throw new ValueError("cell property is missing for the constructor of RNN.");
} else if (Array.isArray(args.cell)) {
cell = new StackedRNNCells({cells: args.cell});
} else {
cell = args.cell;
}
if (cell.stateSize == null) {
throw new ValueError("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");
}
this.cell = cell;
this.returnSequences = args.returnSequences == null ? false : args.returnSequences;
this.returnState = args.returnState == null ? false : args.returnState;
this.goBackwards = args.goBackwards == null ? false : args.goBackwards;
this._stateful = args.stateful == null ? false : args.stateful;
this.unroll = args.unroll == null ? false : args.unroll;
this.supportsMasking = true;
this.inputSpec = [new InputSpec({ndim: 3})];
this.stateSpec = null;
this.states_ = null;
this.numConstants = null;
this.keptStates = [];
}
getStates() {
if (this.states_ == null) {
const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;
return range4(0, numStates).map((x) => null);
} else {
return this.states_;
}
}
setStates(states) {
this.states_ = states;
}
computeOutputShape(inputShape) {
if (isArrayOfShapes(inputShape)) {
inputShape = inputShape[0];
}
inputShape = inputShape;
let stateSize = this.cell.stateSize;
if (!Array.isArray(stateSize)) {
stateSize = [stateSize];
}
const outputDim = stateSize[0];
let outputShape;
if (this.returnSequences) {
outputShape = [inputShape[0], inputShape[1], outputDim];
} else {
outputShape = [inputShape[0], outputDim];
}
if (this.returnState) {
const stateShape = [];
for (const dim of stateSize) {
stateShape.push([inputShape[0], dim]);
}
return [outputShape].concat(stateShape);
} else {
return outputShape;
}
}
computeMask(inputs, mask) {
return tidy(() => {
if (Array.isArray(mask)) {
mask = mask[0];
}
const outputMask = this.returnSequences ? mask : null;
if (this.returnState) {
const stateMask = this.states.map((s) => null);
return [outputMask].concat(stateMask);
} else {
return outputMask;
}
});
}
get states() {
if (this.states_ == null) {
const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;
const output = [];
for (let i = 0; i < numStates; ++i) {
output.push(null);
}
return output;
} else {
return this.states_;
}
}
set states(s) {
this.states_ = s;
}
build(inputShape) {
const constantShape = null;
if (this.numConstants != null) {
throw new NotImplementedError("Constants support is not implemented in RNN yet.");
}
if (isArrayOfShapes(inputShape)) {
inputShape = inputShape[0];
}
inputShape = inputShape;
const batchSize = this.stateful ? inputShape[0] : null;
const inputDim = inputShape.slice(2);
this.inputSpec[0] = new InputSpec({shape: [batchSize, null, ...inputDim]});
const stepInputShape = [inputShape[0]].concat(inputShape.slice(2));
if (constantShape != null) {
throw new NotImplementedError("Constants support is not implemented in RNN yet.");
} else {
this.cell.build(stepInputShape);
}
let stateSize;
if (Array.isArray(this.cell.stateSize)) {
stateSize = this.cell.stateSize;
} else {
stateSize = [this.cell.stateSize];
}
if (this.stateSpec != null) {
if (!util_exports.arraysEqual(this.stateSpec.map((spec) => spec.shape[spec.shape.length - 1]), stateSize)) {
throw new ValueError(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`);
}
} else {
this.stateSpec = stateSize.map((dim) => new InputSpec({shape: [null, dim]}));
}
if (this.stateful) {
this.resetStates();
}
}
resetStates(states, training5 = false) {
tidy(() => {
if (!this.stateful) {
throw new AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful.");
}
const batchSize = this.inputSpec[0].shape[0];
if (batchSize == null) {
throw new ValueError("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");
}
if (this.states_ == null) {
if (Array.isArray(this.cell.stateSize)) {
this.states_ = this.cell.stateSize.map((dim) => zeros([batchSize, dim]));
} else {
this.states_ = [zeros([batchSize, this.cell.stateSize])];
}
} else if (states == null) {
dispose(this.states_);
if (this.keptStates != null) {
dispose(this.keptStates);
this.keptStates = [];
}
if (Array.isArray(this.cell.stateSize)) {
this.states_ = this.cell.stateSize.map((dim) => zeros([batchSize, dim]));
} else {
this.states_[0] = zeros([batchSize, this.cell.stateSize]);
}
} else {
if (!Array.isArray(states)) {
states = [states];
}
if (states.length !== this.states_.length) {
throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);
}
if (training5 === true) {
this.keptStates.push(this.states_.slice());
} else {
dispose(this.states_);
}
for (let index = 0; index < this.states_.length; ++index) {
const value = states[index];
const dim = Array.isArray(this.cell.stateSize) ? this.cell.stateSize[index] : this.cell.stateSize;
const expectedShape = [batchSize, dim];
if (!util_exports.arraysEqual(value.shape, expectedShape)) {
throw new ValueError(`State ${index} is incompatible with layer ${this.name}: expected shape=${expectedShape}, received shape=${value.shape}`);
}
this.states_[index] = value;
}
}
this.states_ = this.states_.map((state6) => keep(state6.clone()));
});
}
apply(inputs, kwargs) {
let initialState = kwargs == null ? null : kwargs["initialState"];
let constants = kwargs == null ? null : kwargs["constants"];
if (kwargs == null) {
kwargs = {};
}
const standardized = standardizeArgs(inputs, initialState, constants, this.numConstants);
inputs = standardized.inputs;
initialState = standardized.initialState;
constants = standardized.constants;
let additionalInputs = [];
let additionalSpecs = [];
if (initialState != null) {
kwargs["initialState"] = initialState;
additionalInputs = additionalInputs.concat(initialState);
this.stateSpec = [];
for (const state6 of initialState) {
this.stateSpec.push(new InputSpec({shape: state6.shape}));
}
additionalSpecs = additionalSpecs.concat(this.stateSpec);
}
if (constants != null) {
kwargs["constants"] = constants;
additionalInputs = additionalInputs.concat(constants);
this.numConstants = constants.length;
}
const isTensor = additionalInputs[0] instanceof SymbolicTensor;
if (isTensor) {
const fullInput = [inputs].concat(additionalInputs);
const fullInputSpec = this.inputSpec.concat(additionalSpecs);
const originalInputSpec = this.inputSpec;
this.inputSpec = fullInputSpec;
const output = super.apply(fullInput, kwargs);
this.inputSpec = originalInputSpec;
return output;
} else {
return super.apply(inputs, kwargs);
}
}
call(inputs, kwargs) {
return tidy(() => {
const mask = kwargs == null ? null : kwargs["mask"];
const training5 = kwargs == null ? null : kwargs["training"];
let initialState = kwargs == null ? null : kwargs["initialState"];
inputs = getExactlyOneTensor(inputs);
if (initialState == null) {
if (this.stateful) {
initialState = this.states_;
} else {
initialState = this.getInitialState(inputs);
}
}
const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;
if (initialState.length !== numStates) {
throw new ValueError(`RNN Layer has ${numStates} state(s) but was passed ${initialState.length} initial state(s).`);
}
if (this.unroll) {
console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");
}
const cellCallKwargs = {training: training5};
const step4 = (inputs2, states2) => {
const outputs2 = this.cell.call([inputs2].concat(states2), cellCallKwargs);
return [outputs2[0], outputs2.slice(1)];
};
const rnnOutputs = rnn(step4, inputs, initialState, this.goBackwards, mask, null, this.unroll, this.returnSequences);
const lastOutput = rnnOutputs[0];
const outputs = rnnOutputs[1];
const states = rnnOutputs[2];
if (this.stateful) {
this.resetStates(states, training5);
}
const output = this.returnSequences ? outputs : lastOutput;
if (this.returnState) {
return [output].concat(states);
} else {
return output;
}
});
}
getInitialState(inputs) {
return tidy(() => {
let initialState = zeros(inputs.shape);
initialState = sum2(initialState, [1, 2]);
initialState = expandDims2(initialState);
if (Array.isArray(this.cell.stateSize)) {
return this.cell.stateSize.map((dim) => dim > 1 ? tile5(initialState, [1, dim]) : initialState);
} else {
return this.cell.stateSize > 1 ? [tile5(initialState, [1, this.cell.stateSize])] : [initialState];
}
});
}
get trainableWeights() {
if (!this.trainable) {
return [];
}
return this.cell.trainableWeights;
}
get nonTrainableWeights() {
if (!this.trainable) {
return this.cell.weights;
}
return this.cell.nonTrainableWeights;
}
setFastWeightInitDuringBuild(value) {
super.setFastWeightInitDuringBuild(value);
if (this.cell != null) {
this.cell.setFastWeightInitDuringBuild(value);
}
}
getConfig() {
const baseConfig = super.getConfig();
const config2 = {
returnSequences: this.returnSequences,
returnState: this.returnState,
goBackwards: this.goBackwards,
stateful: this.stateful,
unroll: this.unroll
};
if (this.numConstants != null) {
config2["numConstants"] = this.numConstants;
}
const cellConfig = this.cell.getConfig();
if (this.getClassName() === RNN.className) {
config2["cell"] = {
className: this.cell.getClassName(),
config: cellConfig
};
}
return Object.assign({}, cellConfig, baseConfig, config2);
}
static fromConfig(cls, config2, customObjects = {}) {
const cellConfig = config2["cell"];
const cell = deserialize(cellConfig, customObjects);
return new cls(Object.assign(config2, {cell}));
}
}
RNN.className = "RNN";
serialization_exports.registerClass(RNN);
class RNNCell extends Layer {
}
class SimpleRNNCell extends RNNCell {
constructor(args) {
super(args);
this.DEFAULT_ACTIVATION = "tanh";
this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal";
this.DEFAULT_RECURRENT_INITIALIZER = "orthogonal";
this.DEFAULT_BIAS_INITIALIZER = "zeros";
this.units = args.units;
assertPositiveInteger(this.units, `units`);
this.activation = getActivation(args.activation == null ? this.DEFAULT_ACTIVATION : args.activation);
this.useBias = args.useBias == null ? true : args.useBias;
this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);
this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);
this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);
this.kernelRegularizer = getRegularizer(args.kernelRegularizer);
this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);
this.biasRegularizer = getRegularizer(args.biasRegularizer);
this.kernelConstraint = getConstraint(args.kernelConstraint);
this.recurrentConstraint = getConstraint(args.recurrentConstraint);
this.biasConstraint = getConstraint(args.biasConstraint);
this.dropout = min4([1, max6([0, args.dropout == null ? 0 : args.dropout])]);
this.recurrentDropout = min4([
1,
max6([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])
]);
this.stateSize = this.units;
this.dropoutMask = null;
this.recurrentDropoutMask = null;
}
build(inputShape) {
inputShape = getExactlyOneShape(inputShape);
this.kernel = this.addWeight("kernel", [inputShape[inputShape.length - 1], this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);
this.recurrentKernel = this.addWeight("recurrent_kernel", [this.units, this.units], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);
if (this.useBias) {
this.bias = this.addWeight("bias", [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);
} else {
this.bias = null;
}
this.built = true;
}
call(inputs, kwargs) {
return tidy(() => {
inputs = inputs;
if (inputs.length !== 2) {
throw new ValueError(`SimpleRNNCell expects 2 input Tensors, got ${inputs.length}.`);
}
let prevOutput = inputs[1];
inputs = inputs[0];
const training5 = kwargs["training"] == null ? false : kwargs["training"];
if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {
this.dropoutMask = generateDropoutMask({
ones: () => onesLike(inputs),
rate: this.dropout,
training: training5
});
}
if (0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null) {
this.recurrentDropoutMask = generateDropoutMask({
ones: () => onesLike(prevOutput),
rate: this.recurrentDropout,
training: training5
});
}
let h;
const dpMask = this.dropoutMask;
const recDpMask = this.recurrentDropoutMask;
if (dpMask != null) {
h = dot3(mul(inputs, dpMask), this.kernel.read());
} else {
h = dot3(inputs, this.kernel.read());
}
if (this.bias != null) {
h = biasAdd(h, this.bias.read());
}
if (recDpMask != null) {
prevOutput = mul(prevOutput, recDpMask);
}
let output = add2(h, dot3(prevOutput, this.recurrentKernel.read()));
if (this.activation != null) {
output = this.activation.apply(output);
}
return [output, output];
});
}
getConfig() {
const baseConfig = super.getConfig();
const config2 = {
units: this.units,
activation: serializeActivation(this.activation),
useBias: this.useBias,
kernelInitializer: serializeInitializer(this.kernelInitializer),
recurrentInitializer: serializeInitializer(this.recurrentInitializer),
biasInitializer: serializeInitializer(this.biasInitializer),
kernelRegularizer: serializeRegularizer(this.kernelRegularizer),
recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),
biasRegularizer: serializeRegularizer(this.biasRegularizer),
activityRegularizer: serializeRegularizer(this.activityRegularizer),
kernelConstraint: serializeConstraint(this.kernelConstraint),
recurrentConstraint: serializeConstraint(this.recurrentConstraint),
biasConstraint: serializeConstraint(this.biasConstraint),
dropout: this.dropout,
recurrentDropout: this.recurrentDropout
};
return Object.assign({}, baseConfig, config2);
}
}
SimpleRNNCell.className = "SimpleRNNCell";
serialization_exports.registerClass(SimpleRNNCell);
class SimpleRNN extends RNN {
constructor(args) {
args.cell = new SimpleRNNCell(args);
super(args);
}
call(inputs, kwargs) {
return tidy(() => {
if (this.cell.dropoutMask != null) {
dispose(this.cell.dropoutMask);
this.cell.dropoutMask = null;
}
if (this.cell.recurrentDropoutMask != null) {
dispose(this.cell.recurrentDropoutMask);
this.cell.recurrentDropoutMask = null;
}
const mask = kwargs == null ? null : kwargs["mask"];
const training5 = kwargs == null ? null : kwargs["training"];
const initialState = kwargs == null ? null : kwargs["initialState"];
return super.call(inputs, {mask, training: training5, initialState});
});
}
static fromConfig(cls, config2) {
return new cls(config2);
}
}
SimpleRNN.className = "SimpleRNN";
serialization_exports.registerClass(SimpleRNN);
class GRUCell extends RNNCell {
constructor(args) {
super(args);
this.DEFAULT_ACTIVATION = "tanh";
this.DEFAULT_RECURRENT_ACTIVATION = "hardSigmoid";
this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal";
this.DEFAULT_RECURRENT_INITIALIZER = "orthogonal";
this.DEFAULT_BIAS_INITIALIZER = "zeros";
if (args.resetAfter) {
throw new ValueError(`GRUCell does not support reset_after parameter set to true.`);
}
this.units = args.units;
assertPositiveInteger(this.units, "units");
this.activation = getActivation(args.activation === void 0 ? this.DEFAULT_ACTIVATION : args.activation);
this.recurrentActivation = getActivation(args.recurrentActivation === void 0 ? this.DEFAULT_RECURRENT_ACTIVATION : args.recurrentActivation);
this.useBias = args.useBias == null ? true : args.useBias;
this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);
this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);
this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);
this.kernelRegularizer = getRegularizer(args.kernelRegularizer);
this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);
this.biasRegularizer = getRegularizer(args.biasRegularizer);
this.kernelConstraint = getConstraint(args.kernelConstraint);
this.recurrentConstraint = getConstraint(args.recurrentConstraint);
this.biasConstraint = getConstraint(args.biasConstraint);
this.dropout = min4([1, max6([0, args.dropout == null ? 0 : args.dropout])]);
this.recurrentDropout = min4([
1,
max6([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])
]);
this.implementation = args.implementation;
this.stateSize = this.units;
this.dropoutMask = null;
this.recurrentDropoutMask = null;
}
build(inputShape) {
inputShape = getExactlyOneShape(inputShape);
const inputDim = inputShape[inputShape.length - 1];
this.kernel = this.addWeight("kernel", [inputDim, this.units * 3], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);
this.recurrentKernel = this.addWeight("recurrent_kernel", [this.units, this.units * 3], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);
if (this.useBias) {
this.bias = this.addWeight("bias", [this.units * 3], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);
} else {
this.bias = null;
}
this.built = true;
}
call(inputs, kwargs) {
return tidy(() => {
inputs = inputs;
if (inputs.length !== 2) {
throw new ValueError(`GRUCell expects 2 input Tensors (inputs, h, c), got ${inputs.length}.`);
}
const training5 = kwargs["training"] == null ? false : kwargs["training"];
let hTMinus1 = inputs[1];
inputs = inputs[0];
if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {
this.dropoutMask = generateDropoutMask({
ones: () => onesLike(inputs),
rate: this.dropout,
training: training5,
count: 3
});
}
if (0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null) {
this.recurrentDropoutMask = generateDropoutMask({
ones: () => onesLike(hTMinus1),
rate: this.recurrentDropout,
training: training5,
count: 3
});
}
const dpMask = this.dropoutMask;
const recDpMask = this.recurrentDropoutMask;
let z;
let r;
let hh;
if (0 < this.dropout && this.dropout < 1) {
inputs = mul(inputs, dpMask[0]);
}
let matrixX = dot3(inputs, this.kernel.read());
if (this.useBias) {
matrixX = biasAdd(matrixX, this.bias.read());
}
if (0 < this.recurrentDropout && this.recurrentDropout < 1) {
hTMinus1 = mul(hTMinus1, recDpMask[0]);
}
const recurrentKernelValue = this.recurrentKernel.read();
const [rk1, rk2] = split(recurrentKernelValue, [2 * this.units, this.units], recurrentKernelValue.rank - 1);
const matrixInner = dot3(hTMinus1, rk1);
const [xZ, xR, xH] = split(matrixX, 3, matrixX.rank - 1);
const [recurrentZ, recurrentR] = split(matrixInner, 2, matrixInner.rank - 1);
z = this.recurrentActivation.apply(add2(xZ, recurrentZ));
r = this.recurrentActivation.apply(add2(xR, recurrentR));
const recurrentH = dot3(mul(r, hTMinus1), rk2);
hh = this.activation.apply(add2(xH, recurrentH));
const h = add2(mul(z, hTMinus1), mul(add2(1, neg(z)), hh));
return [h, h];
});
}
getConfig() {
const baseConfig = super.getConfig();
const config2 = {
units: this.units,
activation: serializeActivation(this.activation),
recurrentActivation: serializeActivation(this.recurrentActivation),
useBias: this.useBias,
kernelInitializer: serializeInitializer(this.kernelInitializer),
recurrentInitializer: serializeInitializer(this.recurrentInitializer),
biasInitializer: serializeInitializer(this.biasInitializer),
kernelRegularizer: serializeRegularizer(this.kernelRegularizer),
recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),
biasRegularizer: serializeRegularizer(this.biasRegularizer),
activityRegularizer: serializeRegularizer(this.activityRegularizer),
kernelConstraint: serializeConstraint(this.kernelConstraint),
recurrentConstraint: serializeConstraint(this.recurrentConstraint),
biasConstraint: serializeConstraint(this.biasConstraint),
dropout: this.dropout,
recurrentDropout: this.recurrentDropout,
implementation: this.implementation,
resetAfter: false
};
return Object.assign({}, baseConfig, config2);
}
}
GRUCell.className = "GRUCell";
serialization_exports.registerClass(GRUCell);
class GRU extends RNN {
constructor(args) {
if (args.implementation === 0) {
console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call.");
}
args.cell = new GRUCell(args);
super(args);
}
call(inputs, kwargs) {
return tidy(() => {
if (this.cell.dropoutMask != null) {
dispose(this.cell.dropoutMask);
this.cell.dropoutMask = null;
}
if (this.cell.recurrentDropoutMask != null) {
dispose(this.cell.recurrentDropoutMask);
this.cell.recurrentDropoutMask = null;
}
const mask = kwargs == null ? null : kwargs["mask"];
const training5 = kwargs == null ? null : kwargs["training"];
const initialState = kwargs == null ? null : kwargs["initialState"];
return super.call(inputs, {mask, training: training5, initialState});
});
}
static fromConfig(cls, config2) {
if (config2["implmentation"] === 0) {
config2["implementation"] = 1;
}
return new cls(config2);
}
}
GRU.className = "GRU";
serialization_exports.registerClass(GRU);
class LSTMCell extends RNNCell {
constructor(args) {
super(args);
this.DEFAULT_ACTIVATION = "tanh";
this.DEFAULT_RECURRENT_ACTIVATION = "hardSigmoid";
this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal";
this.DEFAULT_RECURRENT_INITIALIZER = "orthogonal";
this.DEFAULT_BIAS_INITIALIZER = "zeros";
this.units = args.units;
assertPositiveInteger(this.units, "units");
this.activation = getActivation(args.activation === void 0 ? this.DEFAULT_ACTIVATION : args.activation);
this.recurrentActivation = getActivation(args.recurrentActivation === void 0 ? this.DEFAULT_RECURRENT_ACTIVATION : args.recurrentActivation);
this.useBias = args.useBias == null ? true : args.useBias;
this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);
this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);
this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);
this.unitForgetBias = args.unitForgetBias;
this.kernelRegularizer = getRegularizer(args.kernelRegularizer);
this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);
this.biasRegularizer = getRegularizer(args.biasRegularizer);
this.kernelConstraint = getConstraint(args.kernelConstraint);
this.recurrentConstraint = getConstraint(args.recurrentConstraint);
this.biasConstraint = getConstraint(args.biasConstraint);
this.dropout = min4([1, max6([0, args.dropout == null ? 0 : args.dropout])]);
this.recurrentDropout = min4([
1,
max6([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])
]);
this.implementation = args.implementation;
this.stateSize = [this.units, this.units];
this.dropoutMask = null;
this.recurrentDropoutMask = null;
}
build(inputShape) {
var _a;
inputShape = getExactlyOneShape(inputShape);
const inputDim = inputShape[inputShape.length - 1];
this.kernel = this.addWeight("kernel", [inputDim, this.units * 4], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);
this.recurrentKernel = this.addWeight("recurrent_kernel", [this.units, this.units * 4], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);
let biasInitializer;
if (this.useBias) {
if (this.unitForgetBias) {
const capturedBiasInit = this.biasInitializer;
const capturedUnits = this.units;
biasInitializer = new (_a = class CustomInit extends Initializer {
apply(shape, dtype) {
const bI = capturedBiasInit.apply([capturedUnits]);
const bF = new Ones().apply([capturedUnits]);
const bCAndH = capturedBiasInit.apply([capturedUnits * 2]);
return concatAlongFirstAxis(concatAlongFirstAxis(bI, bF), bCAndH);
}
}, _a.className = "CustomInit", _a)();
} else {
biasInitializer = this.biasInitializer;
}
this.bias = this.addWeight("bias", [this.units * 4], null, biasInitializer, this.biasRegularizer, true, this.biasConstraint);
} else {
this.bias = null;
}
this.built = true;
}
call(inputs, kwargs) {
return tidy(() => {
const training5 = kwargs["training"] == null ? false : kwargs["training"];
inputs = inputs;
if (inputs.length !== 3) {
throw new ValueError(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);
}
let hTMinus1 = inputs[1];
const cTMinus1 = inputs[2];
inputs = inputs[0];
if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {
this.dropoutMask = generateDropoutMask({
ones: () => onesLike(inputs),
rate: this.dropout,
training: training5,
count: 4
});
}
if (0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null) {
this.recurrentDropoutMask = generateDropoutMask({
ones: () => onesLike(hTMinus1),
rate: this.recurrentDropout,
training: training5,
count: 4
});
}
const dpMask = this.dropoutMask;
const recDpMask = this.recurrentDropoutMask;
let i;
let f;
let c;
let o;
if (0 < this.dropout && this.dropout < 1) {
inputs = mul(inputs, dpMask[0]);
}
let z = dot3(inputs, this.kernel.read());
if (0 < this.recurrentDropout && this.recurrentDropout < 1) {
hTMinus1 = mul(hTMinus1, recDpMask[0]);
}
z = add2(z, dot3(hTMinus1, this.recurrentKernel.read()));
if (this.useBias) {
z = biasAdd(z, this.bias.read());
}
const [z0, z1, z2, z3] = split(z, 4, z.rank - 1);
i = this.recurrentActivation.apply(z0);
f = this.recurrentActivation.apply(z1);
c = add2(mul(f, cTMinus1), mul(i, this.activation.apply(z2)));
o = this.recurrentActivation.apply(z3);
const h = mul(o, this.activation.apply(c));
return [h, h, c];
});
}
getConfig() {
const baseConfig = super.getConfig();
const config2 = {
units: this.units,
activation: serializeActivation(this.activation),
recurrentActivation: serializeActivation(this.recurrentActivation),
useBias: this.useBias,
kernelInitializer: serializeInitializer(this.kernelInitializer),
recurrentInitializer: serializeInitializer(this.recurrentInitializer),
biasInitializer: serializeInitializer(this.biasInitializer),
unitForgetBias: this.unitForgetBias,
kernelRegularizer: serializeRegularizer(this.kernelRegularizer),
recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),
biasRegularizer: serializeRegularizer(this.biasRegularizer),
activityRegularizer: serializeRegularizer(this.activityRegularizer),
kernelConstraint: serializeConstraint(this.kernelConstraint),
recurrentConstraint: serializeConstraint(this.recurrentConstraint),
biasConstraint: serializeConstraint(this.biasConstraint),
dropout: this.dropout,
recurrentDropout: this.recurrentDropout,
implementation: this.implementation
};
return Object.assign({}, baseConfig, config2);
}
}
LSTMCell.className = "LSTMCell";
serialization_exports.registerClass(LSTMCell);
class LSTM extends RNN {
constructor(args) {
if (args.implementation === 0) {
console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call.");
}
args.cell = new LSTMCell(args);
super(args);
}
call(inputs, kwargs) {
return tidy(() => {
if (this.cell.dropoutMask != null) {
dispose(this.cell.dropoutMask);
this.cell.dropoutMask = null;
}
if (this.cell.recurrentDropoutMask != null) {
dispose(this.cell.recurrentDropoutMask);
this.cell.recurrentDropoutMask = null;
}
const mask = kwargs == null ? null : kwargs["mask"];
const training5 = kwargs == null ? null : kwargs["training"];
const initialState = kwargs == null ? null : kwargs["initialState"];
return super.call(inputs, {mask, training: training5, initialState});
});
}
static fromConfig(cls, config2) {
if (config2["implmentation"] === 0) {
config2["implementation"] = 1;
}
return new cls(config2);
}
}
LSTM.className = "LSTM";
serialization_exports.registerClass(LSTM);
class StackedRNNCells extends RNNCell {
constructor(args) {
super(args);
this.cells = args.cells;
}
get stateSize() {
const stateSize = [];
for (const cell of this.cells.slice().reverse()) {
if (Array.isArray(cell.stateSize)) {
stateSize.push(...cell.stateSize);
} else {
stateSize.push(cell.stateSize);
}
}
return stateSize;
}
call(inputs, kwargs) {
return tidy(() => {
inputs = inputs;
let states = inputs.slice(1);
const nestedStates = [];
for (const cell of this.cells.slice().reverse()) {
if (Array.isArray(cell.stateSize)) {
nestedStates.push(states.splice(0, cell.stateSize.length));
} else {
nestedStates.push(states.splice(0, 1));
}
}
nestedStates.reverse();
const newNestedStates = [];
let callInputs;
for (let i = 0; i < this.cells.length; ++i) {
const cell = this.cells[i];
states = nestedStates[i];
if (i === 0) {
callInputs = [inputs[0]].concat(states);
} else {
callInputs = [callInputs[0]].concat(states);
}
callInputs = cell.call(callInputs, kwargs);
newNestedStates.push(callInputs.slice(1));
}
states = [];
for (const cellStates of newNestedStates.slice().reverse()) {
states.push(...cellStates);
}
return [callInputs[0]].concat(states);
});
}
build(inputShape) {
if (isArrayOfShapes(inputShape)) {
inputShape = inputShape[0];
}
inputShape = inputShape;
let outputDim;
this.cells.forEach((cell, i) => {
nameScope(`RNNCell_${i}`, () => {
cell.build(inputShape);
if (Array.isArray(cell.stateSize)) {
outputDim = cell.stateSize[0];
} else {
outputDim = cell.stateSize;
}
inputShape = [inputShape[0], outputDim];
});
});
this.built = true;
}
getConfig() {
const baseConfig = super.getConfig();
const getCellConfig = (cell) => {
return {
className: cell.getClassName(),
config: cell.getConfig()
};
};
const cellConfigs = this.cells.map(getCellConfig);
const config2 = {cells: cellConfigs};
return Object.assign({}, baseConfig, config2);
}
static fromConfig(cls, config2, customObjects = {}) {
const cells = [];
for (const cellConfig of config2["cells"]) {
cells.push(deserialize(cellConfig, customObjects));
}
return new cls({cells});
}
get trainableWeights() {
if (!this.trainable) {
return [];
}
const weights = [];
for (const cell of this.cells) {
weights.push(...cell.trainableWeights);
}
return weights;
}
get nonTrainableWeights() {
const weights = [];
for (const cell of this.cells) {
weights.push(...cell.nonTrainableWeights);
}
if (!this.trainable) {
const trainableWeights = [];
for (const cell of this.cells) {
trainableWeights.push(...cell.trainableWeights);
}
return trainableWeights.concat(weights);
}
return weights;
}
getWeights() {
const weights = [];
for (const cell of this.cells) {
weights.push(...cell.weights);
}
return batchGetValue(weights);
}
setWeights(weights) {
const tuples = [];
for (const cell of this.cells) {
const numParams = cell.weights.length;
const inputWeights = weights.splice(numParams);
for (let i = 0; i < cell.weights.length; ++i) {
tuples.push([cell.weights[i], inputWeights[i]]);
}
}
batchSetValue(tuples);
}
}
StackedRNNCells.className = "StackedRNNCells";
serialization_exports.registerClass(StackedRNNCells);
function generateDropoutMask(args) {
const {ones: ones7, rate, training: training5 = false, count: count2 = 1} = args;
const droppedInputs = () => dropout2(ones7(), rate);
const createMask = () => inTrainPhase(droppedInputs, ones7, training5);
if (!count2 || count2 <= 1) {
return keep(createMask().clone());
}
const masks = Array(count2).fill(void 0).map(createMask);
return masks.map((m) => keep(m.clone()));
}
// node_modules/@tensorflow/tfjs-layers/dist/layers/convolutional_recurrent.js
/**
* @license
* Copyright 2020 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
var __rest = function(s, e) {
var t = {};
for (var p in s)
if (Object.prototype.hasOwnProperty.call(s, p) && e.indexOf(p) < 0)
t[p] = s[p];
if (s != null && typeof Object.getOwnPropertySymbols === "function")
for (var i = 0, p = Object.getOwnPropertySymbols(s); i < p.length; i++) {
if (e.indexOf(p[i]) < 0 && Object.prototype.propertyIsEnumerable.call(s, p[i]))
t[p[i]] = s[p[i]];
}
return t;
};
class ConvRNN2DCell extends RNNCell {
}
class ConvRNN2D extends RNN {
constructor(args) {
if (args.unroll) {
throw new NotImplementedError("Unrolling is not possible with convolutional RNNs.");
}
if (Array.isArray(args.cell)) {
throw new NotImplementedError("It is not possible at the moment to stack convolutional cells.");
}
super(args);
this.inputSpec = [new InputSpec({ndim: 5})];
}
call(inputs, kwargs) {
return tidy(() => {
if (this.cell.dropoutMask != null) {
dispose(this.cell.dropoutMask);
this.cell.dropoutMask = null;
}
if (this.cell.recurrentDropoutMask != null) {
dispose(this.cell.recurrentDropoutMask);
this.cell.recurrentDropoutMask = null;
}
if (kwargs && kwargs["constants"]) {
throw new ValueError("ConvRNN2D cell does not support constants");
}
const mask = kwargs == null ? null : kwargs["mask"];
const training5 = kwargs == null ? null : kwargs["training"];
const initialState = kwargs == null ? null : kwargs["initialState"];
return super.call(inputs, {mask, training: training5, initialState});
});
}
computeOutputShape(inputShape) {
let outShape = this.computeSingleOutputShape(inputShape);
if (!this.returnSequences) {
outShape = [outShape[0], ...outShape.slice(2)];
}
if (this.returnState) {
outShape = [outShape, ...Array(2).fill([inputShape[0], ...outShape.slice(-3)])];
}
return outShape;
}
getInitialState(inputs) {
return tidy(() => {
const {stateSize} = this.cell;
const inputShape = inputs.shape;
const outputShape = this.computeSingleOutputShape(inputShape);
const stateShape = [outputShape[0], ...outputShape.slice(2)];
const initialState = zeros(stateShape);
if (Array.isArray(stateSize)) {
return Array(stateSize.length).fill(initialState);
}
return [initialState];
});
}
resetStates(states, training5 = false) {
tidy(() => {
if (!this.stateful) {
throw new AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful.");
}
const inputShape = this.inputSpec[0].shape;
const outputShape = this.computeSingleOutputShape(inputShape);
const stateShape = [outputShape[0], ...outputShape.slice(2)];
const batchSize = inputShape[0];
if (batchSize == null) {
throw new ValueError("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");
}
if (this.getStates() == null) {
if (Array.isArray(this.cell.stateSize)) {
this.states_ = this.cell.stateSize.map(() => zeros(stateShape));
} else {
this.states_ = [zeros(stateShape)];
}
} else if (states == null) {
dispose(this.states_);
if (this.keptStates != null) {
dispose(this.keptStates);
this.keptStates = [];
}
if (Array.isArray(this.cell.stateSize)) {
this.states_ = this.cell.stateSize.map(() => zeros(stateShape));
} else {
this.states_[0] = zeros(stateShape);
}
} else {
if (!Array.isArray(states)) {
states = [states];
}
if (states.length !== this.states_.length) {
throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);
}
if (training5) {
this.keptStates.push(this.states_.slice());
} else {
dispose(this.states_);
}
for (let index = 0; index < this.states_.length; ++index) {
const value = states[index];
const expectedShape = stateShape;
if (!util_exports.arraysEqual(value.shape, expectedShape)) {
throw new ValueError(`State ${index} is incompatible with layer ${this.name}: expected shape=${expectedShape}, received shape=${value.shape}`);
}
this.states_[index] = value;
}
}
this.states_ = this.states_.map((state6) => keep(state6.clone()));
});
}
computeSingleOutputShape(inputShape) {
const {dataFormat, filters, kernelSize, padding: padding2, strides, dilationRate} = this.cell;
const isChannelsFirst = dataFormat === "channelsFirst";
const h = inputShape[isChannelsFirst ? 3 : 2];
const w = inputShape[isChannelsFirst ? 4 : 3];
const hOut = convOutputLength(h, kernelSize[0], padding2, strides[0], dilationRate[0]);
const wOut = convOutputLength(w, kernelSize[1], padding2, strides[1], dilationRate[1]);
const outShape = [
...inputShape.slice(0, 2),
...isChannelsFirst ? [filters, hOut, wOut] : [hOut, wOut, filters]
];
return outShape;
}
}
ConvRNN2D.className = "ConvRNN2D";
class ConvLSTM2DCell extends LSTMCell {
constructor(args) {
const {filters, kernelSize, strides, padding: padding2, dataFormat, dilationRate} = args;
super(Object.assign({}, args, {units: filters}));
this.filters = filters;
assertPositiveInteger(this.filters, "filters");
this.kernelSize = normalizeArray(kernelSize, 2, "kernelSize");
this.kernelSize.forEach((size) => assertPositiveInteger(size, "kernelSize"));
this.strides = normalizeArray(strides || 1, 2, "strides");
this.strides.forEach((stride) => assertPositiveInteger(stride, "strides"));
this.padding = padding2 || "valid";
checkPaddingMode(this.padding);
this.dataFormat = dataFormat || "channelsLast";
checkDataFormat(this.dataFormat);
this.dilationRate = normalizeArray(dilationRate || 1, 2, "dilationRate");
this.dilationRate.forEach((rate) => assertPositiveInteger(rate, "dilationRate"));
}
build(inputShape) {
var _a;
inputShape = getExactlyOneShape(inputShape);
const channelAxis = this.dataFormat === "channelsFirst" ? 1 : inputShape.length - 1;
if (inputShape[channelAxis] == null) {
throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);
}
const inputDim = inputShape[channelAxis];
const numOfKernels = 4;
const kernelShape = this.kernelSize.concat([inputDim, this.filters * numOfKernels]);
this.kernel = this.addWeight("kernel", kernelShape, null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);
const recurrentKernelShape = this.kernelSize.concat([this.filters, this.filters * numOfKernels]);
this.recurrentKernel = this.addWeight("recurrent_kernel", recurrentKernelShape, null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);
if (this.useBias) {
let biasInitializer;
if (this.unitForgetBias) {
const init2 = this.biasInitializer;
const filters = this.filters;
biasInitializer = new (_a = class CustomInit extends Initializer {
apply(shape, dtype) {
const biasI = init2.apply([filters]);
const biasF = ones2([filters]);
const biasCAndO = init2.apply([filters * 2]);
return concatenate([biasI, biasF, biasCAndO]);
}
}, _a.className = "CustomInit", _a)();
} else {
biasInitializer = this.biasInitializer;
}
this.bias = this.addWeight("bias", [this.filters * numOfKernels], null, biasInitializer, this.biasRegularizer, true, this.biasConstraint);
}
this.built = true;
}
call(inputs, kwargs) {
return tidy(() => {
if (inputs.length !== 3) {
throw new ValueError(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);
}
const training5 = kwargs["training"] || false;
const x = inputs[0];
const hTMinus1 = inputs[1];
const cTMinus1 = inputs[2];
const numOfKernels = 4;
if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {
this.dropoutMask = generateDropoutMask({
ones: () => onesLike(x),
rate: this.dropout,
training: training5,
count: numOfKernels
});
}
const dropoutMask = this.dropoutMask;
const applyDropout = (x2, mask, index) => {
if (!mask || !mask[index]) {
return x2;
}
return mul(mask[index], x2);
};
let xI = applyDropout(x, dropoutMask, 0);
let xF = applyDropout(x, dropoutMask, 1);
let xC = applyDropout(x, dropoutMask, 2);
let xO = applyDropout(x, dropoutMask, 3);
if (0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null) {
this.recurrentDropoutMask = generateDropoutMask({
ones: () => onesLike(hTMinus1),
rate: this.recurrentDropout,
training: training5,
count: numOfKernels
});
}
const recDropoutMask = this.recurrentDropoutMask;
let hI = applyDropout(hTMinus1, recDropoutMask, 0);
let hF = applyDropout(hTMinus1, recDropoutMask, 1);
let hC = applyDropout(hTMinus1, recDropoutMask, 2);
let hO = applyDropout(hTMinus1, recDropoutMask, 3);
const kernelChannelAxis = 3;
const [kernelI, kernelF, kernelC, kernelO] = split(this.kernel.read(), numOfKernels, kernelChannelAxis);
const [biasI, biasF, biasC, biasO] = this.useBias ? split(this.bias.read(), numOfKernels) : [null, null, null, null];
xI = this.inputConv(xI, kernelI, biasI, this.padding);
xF = this.inputConv(xF, kernelF, biasF, this.padding);
xC = this.inputConv(xC, kernelC, biasC, this.padding);
xO = this.inputConv(xO, kernelO, biasO, this.padding);
const [recKernelI, recKernelF, recKernelC, recKernelO] = split(this.recurrentKernel.read(), numOfKernels, kernelChannelAxis);
hI = this.recurrentConv(hI, recKernelI);
hF = this.recurrentConv(hF, recKernelF);
hC = this.recurrentConv(hC, recKernelC);
hO = this.recurrentConv(hO, recKernelO);
const i = this.recurrentActivation.apply(add2(xI, hI));
const f = this.recurrentActivation.apply(add2(xF, hF));
const c = add2(mul(f, cTMinus1), mul(i, this.activation.apply(add2(xC, hC))));
const h = mul(this.recurrentActivation.apply(add2(xO, hO)), this.activation.apply(c));
return [h, h, c];
});
}
getConfig() {
const _a = super.getConfig(), {units: _} = _a, baseConfig = __rest(_a, ["units"]);
const config2 = {
filters: this.filters,
kernelSize: this.kernelSize,
padding: this.padding,
dataFormat: this.dataFormat,
dilationRate: this.dilationRate,
strides: this.strides
};
return Object.assign({}, baseConfig, config2);
}
inputConv(x, w, b, padding2) {
const out = conv2d(x, w, this.strides, padding2 || "valid", this.dataFormat === "channelsFirst" ? "NCHW" : "NHWC", this.dilationRate);
if (b) {
return biasAdd(out, b, this.dataFormat);
}
return out;
}
recurrentConv(x, w) {
const strides = 1;
return conv2d(x, w, strides, "same", this.dataFormat === "channelsFirst" ? "NCHW" : "NHWC");
}
}
ConvLSTM2DCell.className = "ConvLSTM2DCell";
serialization_exports.registerClass(ConvLSTM2DCell);
class ConvLSTM2D extends ConvRNN2D {
constructor(args) {
const cell = new ConvLSTM2DCell(args);
super(Object.assign({}, args, {cell}));
}
static fromConfig(cls, config2) {
return new cls(config2);
}
}
ConvLSTM2D.className = "ConvLSTM2D";
serialization_exports.registerClass(ConvLSTM2D);
// node_modules/@tensorflow/tfjs-layers/dist/layers/core.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
class Dropout extends Layer {
constructor(args) {
super(args);
this.rate = Math.max(Math.min(args.rate, 1), 0);
this.noiseShape = args.noiseShape;
this.seed = args.seed;
this.supportsMasking = true;
}
getNoiseShape(input2) {
if (this.noiseShape == null) {
return this.noiseShape;
}
const inputShape = input2.shape;
const noiseShape = [];
for (let i = 0; i < this.noiseShape.length; ++i) {
noiseShape.push(this.noiseShape[i] == null ? inputShape[i] : this.noiseShape[i]);
}
return noiseShape;
}
call(inputs, kwargs) {
return tidy(() => {
this.invokeCallHook(inputs, kwargs);
const input2 = getExactlyOneTensor(inputs);
if (0 < this.rate && this.rate < 1) {
const training5 = kwargs["training"] == null ? false : kwargs["training"];
const noiseShape = this.getNoiseShape(input2);
const output = inTrainPhase(() => dropout2(input2, this.rate, noiseShape, this.seed), () => input2, training5);
return output;
}
return inputs;
});
}
getConfig() {
const config2 = {
rate: this.rate,
noiseShape: this.noiseShape,
seed: this.seed
};
const baseConfig = super.getConfig();
Object.assign(config2, baseConfig);
return config2;
}
dispose() {
return super.dispose();
}
}
Dropout.className = "Dropout";
serialization_exports.registerClass(Dropout);
class SpatialDropout1D extends Dropout {
constructor(args) {
super(args);
this.inputSpec = [{ndim: 3}];
}
getNoiseShape(input2) {
const inputShape = input2.shape;
return [inputShape[0], 1, inputShape[2]];
}
}
SpatialDropout1D.className = "SpatialDropout1D";
serialization_exports.registerClass(SpatialDropout1D);
class Dense extends Layer {
constructor(args) {
super(args);
this.activation = null;
this.useBias = true;
this.kernel = null;
this.bias = null;
this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal";
this.DEFAULT_BIAS_INITIALIZER = "zeros";
if (args.batchInputShape == null && args.inputShape == null && args.inputDim != null) {
let batchSize = null;
if (args.batchSize != null) {
batchSize = args.batchSize;
}
this.batchInputShape = [batchSize, args.inputDim];
}
this.units = args.units;
assertPositiveInteger(this.units, "units");
this.activation = getActivation(args.activation);
if (args.useBias != null) {
this.useBias = args.useBias;
}
this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);
this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);
this.kernelConstraint = getConstraint(args.kernelConstraint);
this.biasConstraint = getConstraint(args.biasConstraint);
this.kernelRegularizer = getRegularizer(args.kernelRegularizer);
this.biasRegularizer = getRegularizer(args.biasRegularizer);
this.activityRegularizer = getRegularizer(args.activityRegularizer);
this.supportsMasking = true;
this.inputSpec = [{minNDim: 2}];
}
build(inputShape) {
inputShape = getExactlyOneShape(inputShape);
const inputLastDim = inputShape[inputShape.length - 1];
if (this.kernel == null) {
this.kernel = this.addWeight("kernel", [inputLastDim, this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);
if (this.useBias) {
this.bias = this.addWeight("bias", [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);
}
}
this.inputSpec = [{minNDim: 2, axes: {[-1]: inputLastDim}}];
this.built = true;
}
computeOutputShape(inputShape) {
inputShape = getExactlyOneShape(inputShape);
const outputShape = inputShape.slice();
outputShape[outputShape.length - 1] = this.units;
return outputShape;
}
call(inputs, kwargs) {
return tidy(() => {
this.invokeCallHook(inputs, kwargs);
const input2 = getExactlyOneTensor(inputs);
const fusedActivationName = mapActivationToFusedKernel(this.activation.getClassName());
let output;
if (fusedActivationName != null) {
output = dot3(input2, this.kernel.read(), fusedActivationName, this.bias ? this.bias.read() : null);
} else {
output = dot3(input2, this.kernel.read());
if (this.bias != null) {
output = biasAdd(output, this.bias.read());
}
if (this.activation != null) {
output = this.activation.apply(output);
}
}
return output;
});
}
getConfig() {
const config2 = {
units: this.units,
activation: serializeActivation(this.activation),
useBias: this.useBias,
kernelInitializer: serializeInitializer(this.kernelInitializer),
biasInitializer: serializeInitializer(this.biasInitializer),
kernelRegularizer: serializeRegularizer(this.kernelRegularizer),
biasRegularizer: serializeRegularizer(this.biasRegularizer),
activityRegularizer: serializeRegularizer(this.activityRegularizer),
kernelConstraint: serializeConstraint(this.kernelConstraint),
biasConstraint: serializeConstraint(this.biasConstraint)
};
const baseConfig = super.getConfig();
Object.assign(config2, baseConfig);
return config2;
}
}
Dense.className = "Dense";
serialization_exports.registerClass(Dense);
class Flatten extends Layer {
constructor(args) {
args = args || {};
super(args);
this.inputSpec = [{minNDim: 3}];
this.dataFormat = args.dataFormat;
}
computeOutputShape(inputShape) {
inputShape = getExactlyOneShape(inputShape);
for (const dim of inputShape.slice(1)) {
if (dim == null) {
throw new ValueError(`The shape of the input to "Flatten" is not fully defined (got ${inputShape.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);
}
}
return [inputShape[0], arrayProd(inputShape, 1)];
}
call(inputs, kwargs) {
return tidy(() => {
this.invokeCallHook(inputs, kwargs);
let input2 = getExactlyOneTensor(inputs);
if (this.dataFormat === "channelsFirst" && input2.rank > 1) {
const permutation = [0];
for (let i = 2; i < input2.rank; ++i) {
permutation.push(i);
}
permutation.push(1);
input2 = input2.transpose(permutation);
}
return batchFlatten(input2);
});
}
getConfig() {
const config2 = {};
if (this.dataFormat != null) {
config2["dataFormat"] = this.dataFormat;
}
const baseConfig = super.getConfig();
Object.assign(config2, baseConfig);
return config2;
}
}
Flatten.className = "Flatten";
serialization_exports.registerClass(Flatten);
class Activation2 extends Layer {
constructor(args) {
super(args);
this.supportsMasking = true;
this.activation = getActivation(args.activation);
}
call(inputs, kwargs) {
return tidy(() => {
this.invokeCallHook(inputs, kwargs);
const input2 = getExactlyOneTensor(inputs);
return this.activation.apply(input2);
});
}
getConfig() {
const config2 = {activation: serializeActivation(this.activation)};
const baseConfig = super.getConfig();
Object.assign(config2, baseConfig);
return config2;
}
}
Activation2.className = "Activation";
serialization_exports.registerClass(Activation2);
class RepeatVector extends Layer {
constructor(args) {
super(args);
this.n = args.n;
this.inputSpec = [{ndim: 2}];
}
computeOutputShape(inputShape) {
return [inputShape[0], this.n, inputShape[1]];
}
call(inputs, kwargs) {
return tidy(() => {
inputs = getExactlyOneTensor(inputs);
return repeat(inputs, this.n);
});
}
getConfig() {
const config2 = {
n: this.n
};
const baseConfig = super.getConfig();
Object.assign(config2, baseConfig);
return config2;
}
}
RepeatVector.className = "RepeatVector";
serialization_exports.registerClass(RepeatVector);
class Reshape2 extends Layer {
constructor(args) {
super(args);
this.targetShape = args.targetShape;
for (let i = 0; i < this.targetShape.length; ++i) {
if (this.isUnknown(this.targetShape[i])) {
this.targetShape[i] = null;
}
}
}
isUnknown(dim) {
return dim < 0 || dim == null;
}
fixUnknownDimension(inputShape, outputShape) {
const errorMsg = "Total size of new array must be unchanged.";
const finalShape = outputShape.slice();
let known = 1;
let unknown = null;
for (let i = 0; i < finalShape.length; ++i) {
const dim = finalShape[i];
if (this.isUnknown(dim)) {
if (unknown === null) {
unknown = i;
} else {
throw new ValueError("Can only specifiy one unknown dimension.");
}
} else {
known *= dim;
}
}
const originalSize = arrayProd(inputShape);
if (unknown !== null) {
if (known === 0 || originalSize % known !== 0) {
throw new ValueError(errorMsg);
}
finalShape[unknown] = originalSize / known;
} else if (originalSize !== known) {
throw new ValueError(errorMsg);
}
return finalShape;
}
computeOutputShape(inputShape) {
let anyUnknownDims = false;
for (let i = 0; i < inputShape.length; ++i) {
if (this.isUnknown(inputShape[i])) {
anyUnknownDims = true;
break;
}
}
if (anyUnknownDims) {
return inputShape.slice(0, 1).concat(this.targetShape);
} else {
return inputShape.slice(0, 1).concat(this.fixUnknownDimension(inputShape.slice(1), this.targetShape));
}
}
call(inputs, kwargs) {
return tidy(() => {
this.invokeCallHook(inputs, kwargs);
const input2 = getExactlyOneTensor(inputs);
const inputShape = input2.shape;
const outputShape = inputShape.slice(0, 1).concat(this.fixUnknownDimension(inputShape.slice(1), this.targetShape));
return input2.reshape(outputShape);
});
}
getConfig() {
const config2 = {
targetShape: this.targetShape
};
const baseConfig = super.getConfig();
Object.assign(config2, baseConfig);
return config2;
}
}
Reshape2.className = "Reshape";
serialization_exports.registerClass(Reshape2);
class Permute extends Layer {
constructor(args) {
super(args);
if (args.dims == null) {
throw new Error("Required configuration field `dims` is missing during Permute constructor call.");
}
if (!Array.isArray(args.dims)) {
throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${args.dims} instead.`);
}
const expectedSortedIndices = range4(1, args.dims.length + 1);
if (!util_exports.arraysEqual(args.dims.slice().sort(), expectedSortedIndices)) {
throw new Error("Invalid permutation `dims`: " + JSON.stringify(args.dims) + " `dims` must contain consecutive integers starting from 1.");
}
this.dims = args.dims;
this.dimsIncludingBatch = [0].concat(this.dims);
this.inputSpec = [new InputSpec({ndim: this.dims.length + 1})];
}
computeOutputShape(inputShape) {
inputShape = getExactlyOneShape(inputShape);
const outputShape = inputShape.slice();
this.dims.forEach((dim, i) => {
outputShape[i + 1] = inputShape[dim];
});
return outputShape;
}
call(inputs, kwargs) {
return transpose(getExactlyOneTensor(inputs), this.dimsIncludingBatch);
}
getConfig() {
const config2 = {
dims: this.dims
};
const baseConfig = super.getConfig();
Object.assign(config2, baseConfig);
return config2;
}
}
Permute.className = "Permute";
serialization_exports.registerClass(Permute);
class Masking extends Layer {
constructor(args) {
super(args == null ? {} : args);
this.supportsMasking = true;
if (args != null) {
this.maskValue = args.maskValue == null ? 0 : args.maskValue;
} else {
this.maskValue = 0;
}
}
computeOutputShape(inputShape) {
return inputShape;
}
getConfig() {
const baseConfig = super.getConfig();
const config2 = {maskValue: this.maskValue};
Object.assign(config2, baseConfig);
return config2;
}
computeMask(inputs, mask) {
const input2 = getExactlyOneTensor(inputs);
const axis = -1;
return any(notEqual(input2, this.maskValue), axis);
}
call(inputs, kwargs) {
return tidy(() => {
this.invokeCallHook(inputs, kwargs);
const input2 = getExactlyOneTensor(inputs);
const axis = -1;
const keepDims = true;
const booleanMask = any(notEqual(input2, this.maskValue), axis, keepDims);
const output = input2.mul(booleanMask.asType(input2.dtype));
return output;
});
}
}
Masking.className = "Masking";
serialization_exports.registerClass(Masking);
// node_modules/@tensorflow/tfjs-layers/dist/layers/embeddings.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
class Embedding extends Layer {
constructor(args) {
super(args);
this.embeddings = null;
this.DEFAULT_EMBEDDINGS_INITIALIZER = "randomUniform";
if (args.batchInputShape == null && args.inputShape == null) {
let batchSize = null;
if (args.batchSize != null) {
batchSize = args.batchSize;
}
if (args.inputLength == null) {
this.batchInputShape = [batchSize, null];
} else {
this.batchInputShape = [batchSize].concat(toList(args.inputLength));
}
}
this.inputDim = args.inputDim;
assertPositiveInteger(this.inputDim, "inputDim");
this.outputDim = args.outputDim;
assertPositiveInteger(this.outputDim, "outputDim");
this.embeddingsInitializer = getInitializer(args.embeddingsInitializer || this.DEFAULT_EMBEDDINGS_INITIALIZER);
this.embeddingsRegularizer = getRegularizer(args.embeddingsRegularizer);
this.activityRegularizer = getRegularizer(args.activityRegularizer);
this.embeddingsConstraint = getConstraint(args.embeddingsConstraint);
this.maskZero = args.maskZero;
this.supportsMasking = args.maskZero;
this.inputLength = args.inputLength;
}
build(inputShape) {
this.embeddings = this.addWeight("embeddings", [this.inputDim, this.outputDim], this.dtype, this.embeddingsInitializer, this.embeddingsRegularizer, true, this.embeddingsConstraint);
this.built = true;
}
warnOnIncompatibleInputShape(inputShape) {
}
computeMask(inputs, mask) {
return tidy(() => {
if (!this.maskZero) {
return null;
} else {
inputs = getExactlyOneTensor(inputs);
return notEqual(inputs, zerosLike(inputs));
}
});
}
computeOutputShape(inputShape) {
inputShape = getExactlyOneShape(inputShape);
if (this.inputLength == null) {
return [...inputShape, this.outputDim];
}
const inLens = toList(this.inputLength);
if (inLens.length !== inputShape.length - 1) {
throw new ValueError(`"inputLength" is ${this.inputLength}, but received input shape has shape ${inputShape}`);
} else {
let i = 0;
for (let k = 0; k < inLens.length; ++k) {
const s1 = inLens[k];
const s2 = inputShape[k + 1];
if (s1 != null && s2 != null && s1 !== s2) {
throw new ValueError(`"inputLength" is ${this.inputLength}, but received input shape has shape ${inputShape}`);
} else if (s1 == null) {
inLens[i] = s2;
}
i++;
}
}
return [inputShape[0], ...inLens, this.outputDim];
}
call(inputs, kwargs) {
return tidy(() => {
this.invokeCallHook(inputs, kwargs);
let input2 = getExactlyOneTensor(inputs);
if (input2.dtype !== "int32") {
input2 = cast20(input2, "int32");
}
const output = gather4(this.embeddings.read(), input2.as1D());
return output.reshape(getExactlyOneShape(this.computeOutputShape(input2.shape)));
});
}
getConfig() {
const config2 = {
inputDim: this.inputDim,
outputDim: this.outputDim,
embeddingsInitializer: serializeInitializer(this.embeddingsInitializer),
embeddingsRegularizer: serializeRegularizer(this.embeddingsRegularizer),
activityRegularizer: serializeRegularizer(this.activityRegularizer),
embeddingsConstraint: serializeConstraint(this.embeddingsConstraint),
maskZero: this.maskZero,
inputLength: this.inputLength
};
const baseConfig = super.getConfig();
Object.assign(config2, baseConfig);
return config2;
}
}
Embedding.className = "Embedding";
serialization_exports.registerClass(Embedding);
// node_modules/@tensorflow/tfjs-layers/dist/layers/merge.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
class Merge extends Layer {
constructor(args) {
super(args || {});
this.supportsMasking = true;
}
mergeFunction(inputs) {
throw new NotImplementedError();
}
computeElementwiseOpOutputShape(shape1, shape2) {
if (shape1 == null || shape2 == null) {
return null;
} else if (shape1.length < shape2.length) {
return this.computeElementwiseOpOutputShape(shape2, shape1);
} else if (shape2.length === 0) {
return shape1;
}
const outputShape = shape1.slice(0, shape1.length - shape2.length);
for (let k = 0; k < shape2.length; ++k) {
const i = shape1[shape1.length - shape2.length + k];
const j = shape2[k];
if (i == null || j == null || i < 0 || j < 0) {
outputShape.push(null);
} else if (i === 1) {
outputShape.push(j);
} else if (j === 1) {
outputShape.push(i);
} else {
if (i !== j) {
throw new ValueError("Operands could not be broadcast together with shapes " + JSON.stringify(shape1) + " " + JSON.stringify(shape2));
}
outputShape.push(i);
}
}
return outputShape;
}
build(inputShape) {
if (Array.isArray(inputShape) && !Array.isArray(inputShape[0])) {
inputShape = [getExactlyOneShape(inputShape)];
}
inputShape = inputShape;
if (inputShape.length < 2) {
throw new ValueError(`A merge layer should be called on an Array of at least 2 inputs. Got ${inputShape.length} input(s).`);
}
let batchSizes = [];
for (const shape of inputShape) {
if (shape != null && shape[0] !== null) {
batchSizes.push(shape[0]);
}
}
batchSizes = unique3(batchSizes);
if (batchSizes.length > 1) {
throw new ValueError(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(inputShape)}.`);
}
let outputShape = inputShape[0] == null ? null : inputShape[0].slice(1);
for (let i = 1; i < inputShape.length; ++i) {
const shape = inputShape[i] == null ? null : inputShape[i].slice(1);
outputShape = this.computeElementwiseOpOutputShape(outputShape, shape);
}
const allRanks = inputShape.map((shape) => shape.length);
if (inputShape.indexOf(null) === -1 && unique3(allRanks).length === 1) {
this.reshapeRequired = false;
} else {
this.reshapeRequired = true;
}
}
call(inputs, kwargs) {
return tidy(() => {
inputs = inputs;
if (this.reshapeRequired) {
const reshapedInputs = [];
const inputDims = inputs.map((input2) => input2.rank);
if (inputDims.indexOf(null) === -1) {
const maxNDim = max6(inputDims);
for (let x of inputs) {
const xNDim = x.rank;
for (let k = 0; k < maxNDim - xNDim; ++k) {
x = expandDims2(x, 1);
}
reshapedInputs.push(x);
}
return this.mergeFunction(reshapedInputs);
} else {
let transposed = false;
for (const x of inputs) {
const xNDim = x.rank;
if (xNDim == null) {
const xShape = x.shape;
const batchSize = xShape[0];
const newShape = xShape.slice(1).concat([batchSize]);
let xTransposed = x.reshape([batchSize].concat(arrayProd(xShape.slice(1))));
xTransposed = transpose(xTransposed, [1, 0]);
xTransposed = xTransposed.reshape(newShape);
reshapedInputs.push(xTransposed);
transposed = true;
} else if (xNDim > 1) {
const dims = range4(1, xNDim).concat([0]);
reshapedInputs.push(transpose(x, dims));
transposed = true;
} else {
reshapedInputs.push(x);
}
}
let y = this.mergeFunction(reshapedInputs);
const yNDim = y.rank;
if (transposed) {
if (yNDim == null) {
const yShape = y.shape;
const yNDim2 = yShape.length;
const batchSize = yShape[yNDim2 - 1];
const newShape = [batchSize].concat(yShape.slice(0, yShape.length - 1));
y = transpose(y.reshape([-1, batchSize]), [1, 0]).reshape(newShape);
} else if (yNDim > 1) {
const dims = [yNDim - 1].concat(range4(0, yNDim - 1));
y = transpose(y, dims);
}
}
return y;
}
} else {
return this.mergeFunction(inputs);
}
});
}
computeOutputShape(inputShape) {
inputShape = inputShape;
let outputShape;
if (inputShape[0] == null) {
outputShape = null;
} else {
outputShape = inputShape[0].slice(1);
}
for (let i = 1; i < inputShape.length; ++i) {
const shape = inputShape[i] == null ? null : inputShape[i].slice(1);
outputShape = this.computeElementwiseOpOutputShape(outputShape, shape);
}
let batchSizes = [];
for (const shape of inputShape) {
if (shape != null && shape[0] !== null) {
batchSizes.push(shape[0]);
}
}
batchSizes = unique3(batchSizes);
if (batchSizes.length === 1) {
outputShape = batchSizes.concat(outputShape);
} else {
outputShape = [null].concat(outputShape);
}
return outputShape;
}
computeMask(inputs, mask) {
return tidy(() => {
if (mask == null) {
return null;
}
if (!Array.isArray(mask)) {
throw new ValueError("`mask` should be an Array");
}
if (!Array.isArray(inputs)) {
throw new ValueError("`inputs` should be an Array");
}
if (mask.length !== inputs.length) {
throw new ValueError(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${inputs.length} vs ${mask.length})`);
}
if (mask.every((m) => m == null)) {
return null;
}
mask = mask.map((m) => m == null ? m : expandDims(m, 0));
let output = mask[0];
for (let i = 1; i < mask.length - 1; ++i) {
output = logicalAnd(output, mask[i]);
}
return output;
});
}
}
class Add2 extends Merge {
constructor(args) {
super(args);
}
mergeFunction(inputs) {
return tidy(() => {
let output = inputs[0].clone();
for (let i = 1; i < inputs.length; ++i) {
output = add2(output, inputs[i]);
}
return output;
});
}
}
Add2.className = "Add";
serialization_exports.registerClass(Add2);
class Multiply2 extends Merge {
constructor(args) {
super(args);
}
mergeFunction(inputs) {
return tidy(() => {
let output = inputs[0].clone();
for (let i = 1; i < inputs.length; ++i) {
output = mul(output, inputs[i]);
}
return output;
});
}
}
Multiply2.className = "Multiply";
serialization_exports.registerClass(Multiply2);
class Average extends Merge {
constructor(args) {
super(args);
}
mergeFunction(inputs) {
return tidy(() => {
let output = inputs[0].clone();
for (let i = 1; i < inputs.length; ++i) {
output = add2(output, inputs[i]);
}
return mul(1 / inputs.length, output);
});
}
}
Average.className = "Average";
serialization_exports.registerClass(Average);
class Maximum2 extends Merge {
constructor(args) {
super(args);
}
mergeFunction(inputs) {
return tidy(() => {
let output = inputs[0];
for (let i = 1; i < inputs.length; ++i) {
output = maximum(output, inputs[i]);
}
return output;
});
}
}
Maximum2.className = "Maximum";
serialization_exports.registerClass(Maximum2);
class Minimum2 extends Merge {
constructor(args) {
super(args);
}
mergeFunction(inputs) {
return tidy(() => {
let output = inputs[0];
for (let i = 1; i < inputs.length; ++i) {
output = minimum(output, inputs[i]);
}
return output;
});
}
}
Minimum2.className = "Minimum";
serialization_exports.registerClass(Minimum2);
class Concatenate extends Merge {
constructor(args) {
super(args);
this.DEFAULT_AXIS = -1;
if (args == null) {
args = {};
}
this.axis = args.axis == null ? this.DEFAULT_AXIS : args.axis;
this.supportsMasking = true;
this.reshapeRequired = false;
}
build(inputShape) {
if (!(Array.isArray(inputShape) && Array.isArray(inputShape[0])) || inputShape.length === 1) {
throw new ValueError("A `Concatenate` layer should be called on a list of at least 2 inputs");
}
inputShape = inputShape;
let allNoneShape = true;
for (const shape of inputShape) {
if (shape != null) {
allNoneShape = false;
break;
}
}
if (allNoneShape) {
return;
}
const shapeSet = [];
for (let i = 0; i < inputShape.length; ++i) {
const shapeWithoutConcatAxis = inputShape[i].slice();
shapeWithoutConcatAxis.splice(this.axis, 1);
let exists = false;
for (const shape of shapeSet) {
if (util_exports.arraysEqual(shape, shapeWithoutConcatAxis)) {
exists = true;
break;
}
}
if (!exists) {
shapeSet.push(shapeWithoutConcatAxis);
}
}
if (shapeSet.length > 1) {
throw new ValueError("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: " + JSON.stringify(inputShape));
}
}
mergeFunction(inputs) {
return tidy(() => {
return concatenate(inputs, this.axis);
});
}
computeOutputShape(inputShape) {
if (!(Array.isArray(inputShape) && Array.isArray(inputShape[0]))) {
throw new ValueError("A `Concatenate` layer should be called on a list of inputs.");
}
const inputShapes = inputShape;
const outputShape = inputShapes[0].slice();
const axis = this.axis < 0 ? outputShape.length + this.axis : this.axis;
for (const shape of inputShapes.slice(1)) {
if (outputShape[axis] == null || shape[axis] == null) {
outputShape[axis] = null;
break;
}
outputShape[axis] += shape[axis];
}
return outputShape;
}
computeMask(inputs, mask) {
if (mask == null) {
return null;
}
if (!Array.isArray(mask)) {
throw new ValueError("`mask` should be an array for Concatenate");
}
if (!Array.isArray(inputs)) {
throw new ValueError("`inputs` should be an array for Concatenate");
}
if (mask.length !== inputs.length) {
throw new ValueError(`Mismatch in the length of mask (${mask.length}) and the legnth of inputs (${inputs.length})`);
}
return tidy(() => {
let allNullMasks = true;
mask.forEach((m) => {
if (m != null) {
allNullMasks = false;
return;
}
});
if (allNullMasks) {
return null;
}
const outputMasks = [];
for (let i = 0; i < inputs.length; ++i) {
if (mask[i] == null) {
outputMasks.push(onesLike(inputs[i]).asType("bool"));
} else if (mask[i].rank < inputs[i].rank) {
outputMasks.push(expandDims(mask[i], -1));
} else {
outputMasks.push(mask[i]);
}
}
const concatenatedMasks = concat(outputMasks, this.axis);
return all(concatenatedMasks, -1, false);
});
}
getConfig() {
const config2 = {
axis: this.axis
};
const baseConfig = super.getConfig();
Object.assign(config2, baseConfig);
return config2;
}
}
Concatenate.className = "Concatenate";
serialization_exports.registerClass(Concatenate);
function interpretAxis(axis, dim) {
while (axis < 0) {
axis += dim;
}
return axis;
}
function batchDot(x, y, axes) {
if (x.shape.length > 3 || y.shape.length > 3) {
throw new NotImplementedError("batchDot is not implemented for tensors of 4D or higher rank yet");
}
util_exports.assert(x.shape.length >= 2, () => `batchDot requires the rank of x to be >= 2, but got ${x.shape.length}`);
util_exports.assert(x.shape.length >= 2, () => `batchDot requires the rank of y to be >= 2, but got ${y.shape.length}`);
if (typeof axes === "number") {
axes = [axes, axes];
}
if (x.dtype === "complex64" || y.dtype === "complex64") {
throw new NotImplementedError("batchDot is not implemented for complex64-type Tensors yet.");
}
const xNDim = x.shape.length;
const yNDim = y.shape.length;
if (axes == null) {
axes = [xNDim - 1, yNDim - 2];
}
const axesArray = axes;
return tidy(() => {
let diff;
if (xNDim > yNDim) {
diff = xNDim - yNDim;
const diffShape = [];
for (let i = 0; i < diff; ++i) {
diffShape.push(1);
}
y = y.reshape(y.shape.concat(diffShape));
} else if (yNDim > xNDim) {
diff = yNDim - xNDim;
const diffShape = [];
for (let i = 0; i < diff; ++i) {
diffShape.push(1);
}
x = x.reshape(x.shape.concat(diffShape));
} else {
diff = 0;
}
let out;
if (x.shape.length === 2 && y.shape.length === 2) {
if (axesArray[0] === axesArray[1]) {
out = x.mul(y).sum(axesArray[0]);
} else {
out = x.transpose([1, 0]).mul(y).sum(axesArray[1]);
}
} else {
const adjX = axesArray[0] !== x.shape.length - 1;
const adjY = axesArray[1] === y.shape.length - 1;
out = x.matMul(y, adjX, adjY);
}
if (diff > 0) {
let idx;
if (xNDim > yNDim) {
idx = xNDim + yNDim - 3;
} else {
idx = xNDim - 1;
}
const squeezeAxes = [];
for (let i = idx; i < idx + diff; ++i) {
squeezeAxes.push(i);
}
out = out.squeeze(squeezeAxes);
}
if (out.shape.length === 1) {
out = out.expandDims(1);
}
return out;
});
}
class Dot extends Merge {
constructor(args) {
super(args);
this.axes = args.axes;
this.normalize = args.normalize == null ? false : args.normalize;
this.supportsMasking = true;
this.reshapeRequired = false;
}
build(inputShape) {
util_exports.assert(Array.isArray(inputShape) && inputShape.length === 2 && Array.isArray(inputShape[0]) && Array.isArray(inputShape[1]), () => "A `Dot` layer should be called on a list of exactly 2 inputs.");
const shape1 = inputShape[0];
const shape2 = inputShape[1];
if (shape1.length > 3 || shape2.length > 3) {
throw new NotImplementedError("Dot layer does not support tensors of 4D or higher rank yet.");
}
const axes = this.interpretAxes(shape1, shape2);
if (shape1[axes[0]] !== shape2[axes[1]]) {
throw new ValueError(`Dimension incompatibility: ${shape1[axes[0]]} !== ${shape2[axes[1]]}`);
}
}
mergeFunction(inputs) {
if (inputs.length !== 2) {
throw new ValueError(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${inputs.length} input(s).`);
}
let x1 = inputs[0];
let x2 = inputs[1];
let axes;
if (!Array.isArray(this.axes)) {
axes = [
interpretAxis(this.axes, x1.shape.length),
interpretAxis(this.axes, x2.shape.length)
];
} else {
axes = this.axes.map((axis, i) => interpretAxis(axis, inputs[i].shape.length));
}
if (this.normalize) {
x1 = l2Normalize(x1, axes[0]);
x2 = l2Normalize(x2, axes[1]);
}
return batchDot(x1, x2, axes);
}
interpretAxes(shape1, shape2) {
let axes;
if (!Array.isArray(this.axes)) {
axes = [
interpretAxis(this.axes, shape1.length),
interpretAxis(this.axes, shape2.length)
];
} else {
axes = this.axes;
}
return axes;
}
computeOutputShape(inputShape) {
util_exports.assert(Array.isArray(inputShape) && inputShape.length === 2 && Array.isArray(inputShape[0]) && Array.isArray(inputShape[1]), () => "A `Dot` layer should be called on a list of exactly 2 inputs.");
const shape1 = inputShape[0].slice();
const shape2 = inputShape[1].slice();
if (shape1.length > 3 || shape2.length > 3) {
throw new NotImplementedError("Dot layer does not support tensors of 4D or higher rank yet.");
}
const axes = this.interpretAxes(shape1, shape2);
shape1.splice(axes[0], 1);
shape2.splice(axes[1], 1);
shape2.splice(0, 1);
const outputShape = shape1.concat(shape2);
if (outputShape.length === 1) {
outputShape.push(1);
}
return outputShape;
}
computeMask(inputs, mask) {
return null;
}
getConfig() {
const config2 = {
axes: this.axes,
normalize: this.normalize
};
const baseConfig = super.getConfig();
Object.assign(config2, baseConfig);
return config2;
}
}
Dot.className = "Dot";
serialization_exports.registerClass(Dot);
// node_modules/@tensorflow/tfjs-layers/dist/layers/noise.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
class GaussianNoise extends Layer {
constructor(args) {
super(args);
this.supportsMasking = true;
this.stddev = args.stddev;
}
computeOutputShape(inputShape) {
return inputShape;
}
getConfig() {
const baseConfig = super.getConfig();
const config2 = {stddev: this.stddev};
Object.assign(config2, baseConfig);
return config2;
}
call(inputs, kwargs) {
return tidy(() => {
this.invokeCallHook(inputs, kwargs);
const input2 = getExactlyOneTensor(inputs);
const noised = () => randomNormal2(input2.shape, 0, this.stddev).add(input2);
const output = inTrainPhase(noised, () => input2, kwargs["training"] || false);
return output;
});
}
}
GaussianNoise.className = "GaussianNoise";
serialization_exports.registerClass(GaussianNoise);
class GaussianDropout extends Layer {
constructor(args) {
super(args);
this.supportsMasking = true;
this.rate = args.rate;
}
computeOutputShape(inputShape) {
return inputShape;
}
getConfig() {
const baseConfig = super.getConfig();
const config2 = {rate: this.rate};
Object.assign(config2, baseConfig);
return config2;
}
call(inputs, kwargs) {
return tidy(() => {
this.invokeCallHook(inputs, kwargs);
const input2 = getExactlyOneTensor(inputs);
if (this.rate > 0 && this.rate < 1) {
const noised = () => {
const stddev = Math.sqrt(this.rate / (1 - this.rate));
return input2.mul(randomNormal2(input2.shape, 1, stddev));
};
return inTrainPhase(noised, () => input2, kwargs["training"] || false);
}
return input2;
});
}
}
GaussianDropout.className = "GaussianDropout";
serialization_exports.registerClass(GaussianDropout);
class AlphaDropout extends Layer {
constructor(args) {
super(args);
this.supportsMasking = true;
this.rate = args.rate;
this.noiseShape = args.noiseShape;
}
_getNoiseShape(inputs) {
return this.noiseShape || getExactlyOneTensor(inputs).shape;
}
computeOutputShape(inputShape) {
return inputShape;
}
getConfig() {
const baseConfig = super.getConfig();
const config2 = {rate: this.rate};
Object.assign(config2, baseConfig);
return config2;
}
call(inputs, kwargs) {
return tidy(() => {
if (this.rate < 1 && this.rate > 0) {
const noiseShape = this._getNoiseShape(inputs);
const droppedInputs = () => {
const input2 = getExactlyOneTensor(inputs);
const alpha = 1.6732632423543772;
const scale = 1.0507009873554805;
const alphaP = -alpha * scale;
let keptIdx = greaterEqual(randomUniform(noiseShape), this.rate);
keptIdx = cast20(keptIdx, "float32");
const a = ((1 - this.rate) * (1 + this.rate * alphaP ** 2)) ** -0.5;
const b = -a * alphaP * this.rate;
const x = input2.mul(keptIdx).add(keptIdx.add(-1).mul(alphaP));
return x.mul(a).add(b);
};
return inTrainPhase(droppedInputs, () => getExactlyOneTensor(inputs), kwargs["training"] || false);
}
return inputs;
});
}
}
AlphaDropout.className = "AlphaDropout";
serialization_exports.registerClass(AlphaDropout);
// node_modules/@tensorflow/tfjs-layers/dist/layers/normalization.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
function batchNormalization(x, mean5, variance, beta, gamma, epsilon2 = 1e-3) {
let out;
if (x.rank === 2) {
out = batchNorm2d(x, mean5, variance, beta, gamma, epsilon2);
} else if (x.rank === 3) {
out = batchNorm3d(x, mean5, variance, beta, gamma, epsilon2);
} else if (x.rank === 4) {
out = batchNorm4d(x, mean5, variance, beta, gamma, epsilon2);
} else {
throw new NotImplementedError(`batchNormalization is not implemented for array of rank ${x.rank} yet`);
}
return out;
}
function regularNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon2 = 1e-3) {
return tidy(() => {
const meanAndVariance = moments(x, reductionAxes);
const mean5 = meanAndVariance.mean;
const variance = meanAndVariance.variance;
const normed = batchNormalization(x, mean5, variance, beta, gamma, epsilon2);
return [normed, mean5, variance];
});
}
function broadcastNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon2 = 1e-3) {
return tidy(() => {
const meanAndVariance = moments(x, reductionAxes);
const mean5 = meanAndVariance.mean;
const variance = meanAndVariance.variance;
const targetShape = [];
for (const axis of range4(0, x.rank)) {
if (reductionAxes.indexOf(axis) !== -1) {
targetShape.push(1);
} else {
targetShape.push(x.shape[axis]);
}
}
const broadcastMean = mean5.reshape(targetShape);
const broadcastVariance = variance.reshape(targetShape);
const broadcastGamma = gamma == null ? null : gamma.reshape(targetShape);
const broadcastBeta = beta == null ? null : beta.reshape(targetShape);
const normed = batchNormalization(x, broadcastMean, broadcastVariance, broadcastBeta, broadcastGamma, epsilon2);
return [normed, mean5, variance];
});
}
function normalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon2 = 1e-3) {
if (util_exports.arraysEqual(reductionAxes.slice().sort(), range4(0, x.rank - 1))) {
return regularNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon2);
} else {
return broadcastNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon2);
}
}
class BatchNormalization extends Layer {
constructor(args) {
if (args == null) {
args = {};
}
super(args);
this.supportsMasking = true;
this.axis = args.axis == null ? -1 : args.axis;
this.momentum = args.momentum == null ? 0.99 : args.momentum;
this.epsilon = args.epsilon == null ? 1e-3 : args.epsilon;
this.center = args.center == null ? true : args.center;
this.scale = args.scale == null ? true : args.scale;
this.betaInitializer = getInitializer(args.betaInitializer || "zeros");
this.gammaInitializer = getInitializer(args.gammaInitializer || "ones");
this.movingMeanInitializer = getInitializer(args.movingMeanInitializer || "zeros");
this.movingVarianceInitializer = getInitializer(args.movingVarianceInitializer || "ones");
this.betaConstraint = getConstraint(args.betaConstraint);
this.gammaConstraint = getConstraint(args.gammaConstraint);
this.betaRegularizer = getRegularizer(args.betaRegularizer);
this.gammaRegularizer = getRegularizer(args.gammaRegularizer);
}
build(inputShape) {
inputShape = getExactlyOneShape(inputShape);
const axis = this.axis >= 0 ? this.axis : this.axis + inputShape.length;
const dim = inputShape[axis];
if (dim == null) {
throw new ValueError(`Axis ${axis} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(inputShape)}.`);
}
this.inputSpec = [new InputSpec({ndim: inputShape.length, axes: {[axis]: dim}})];
const shape = [dim];
if (this.scale) {
this.gamma = this.addWeight("gamma", shape, null, this.gammaInitializer, this.gammaRegularizer, true, this.gammaConstraint);
}
if (this.center) {
this.beta = this.addWeight("beta", shape, null, this.betaInitializer, this.betaRegularizer, true, this.betaConstraint);
}
this.movingMean = this.addWeight("moving_mean", shape, null, this.movingMeanInitializer, null, false);
this.movingVariance = this.addWeight("moving_variance", shape, null, this.movingVarianceInitializer, null, false);
this.built = true;
}
call(inputs, kwargs) {
return tidy(() => {
const training5 = kwargs["training"] == null ? false : kwargs["training"];
const input2 = getExactlyOneTensor(inputs);
const inputShape = input2.shape;
const ndim = inputShape.length;
const reductionAxes = range4(0, ndim);
const axis = this.axis >= 0 ? this.axis : this.axis + ndim;
reductionAxes.splice(axis, 1);
const broadcastShape = pyListRepeat(1, ndim);
broadcastShape[axis] = inputShape[axis];
const sortedReductionAxes = reductionAxes.slice();
sortedReductionAxes.sort();
const needsBroadcasting = !util_exports.arraysEqual(sortedReductionAxes, range4(0, ndim).slice(0, ndim - 1));
const normalizeInference = () => {
if (needsBroadcasting) {
const broadcastMovingMean = this.movingMean.read().reshape(broadcastShape);
const broadcastMovingVariance = this.movingVariance.read().reshape(broadcastShape);
const broadcastBeta = this.center ? this.beta.read().reshape(broadcastShape) : null;
const broadcastGamma = this.scale ? this.gamma.read().reshape(broadcastShape) : null;
return batchNormalization(input2, broadcastMovingMean, broadcastMovingVariance, broadcastBeta, broadcastGamma, this.epsilon);
} else {
return batchNormalization(input2, this.movingMean.read(), this.movingVariance.read(), this.beta == null ? null : this.beta.read(), this.gamma == null ? null : this.gamma.read(), this.epsilon);
}
};
if (!training5) {
return normalizeInference();
}
const [normedTraining, mean5, variance] = normalizeBatchInTraining(input2, this.gamma.read(), this.beta.read(), reductionAxes, this.epsilon);
const doMovingAverage = (variable3, value, momentum) => {
tidy(() => {
const decay = 1 - momentum;
const origValue = variable3.read();
const updateDelta = origValue.sub(value).mul(decay);
variable3.write(origValue.sub(updateDelta));
});
};
const updateMovingMeanAndVariance = () => {
doMovingAverage(this.movingMean, mean5, this.momentum);
doMovingAverage(this.movingVariance, variance, this.momentum);
};
updateMovingMeanAndVariance();
return normedTraining;
});
}
getConfig() {
const config2 = {
axis: this.axis,
momentum: this.momentum,
epsilon: this.epsilon,
center: this.center,
scale: this.scale,
betaInitializer: serializeInitializer(this.betaInitializer),
gammaInitializer: serializeInitializer(this.gammaInitializer),
movingMeanInitializer: serializeInitializer(this.movingMeanInitializer),
movingVarianceInitializer: serializeInitializer(this.movingVarianceInitializer),
betaRegularizer: serializeRegularizer(this.betaRegularizer),
gammaRegularizer: serializeRegularizer(this.gammaRegularizer),
betaConstraint: serializeConstraint(this.betaConstraint),
gammaConstraint: serializeConstraint(this.gammaConstraint)
};
const baseConfig = super.getConfig();
Object.assign(config2, baseConfig);
return config2;
}
}
BatchNormalization.className = "BatchNormalization";
serialization_exports.registerClass(BatchNormalization);
class LayerNormalization extends Layer {
constructor(args) {
if (args == null) {
args = {};
}
super(args);
this.axis = args.axis == null ? -1 : args.axis;
if (typeof this.axis === "number") {
if (!Number.isInteger(this.axis)) {
throw new Error(`Expected axis to be an integer, but received ${this.axis}`);
}
} else if (Array.isArray(this.axis)) {
for (const axis of this.axis) {
if (!Number.isInteger(axis)) {
throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`);
}
}
} else {
throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);
}
this.epsilon = args.epsilon == null ? 1e-3 : args.epsilon;
this.center = args.center == null ? true : args.center;
this.scale = args.scale == null ? true : args.scale;
this.betaInitializer = getInitializer(args.betaInitializer || "zeros");
this.gammaInitializer = getInitializer(args.gammaInitializer || "ones");
this.betaRegularizer = getRegularizer(args.betaRegularizer);
this.gammaRegularizer = getRegularizer(args.gammaRegularizer);
this.supportsMasking = true;
}
build(inputShape) {
inputShape = getExactlyOneShape(inputShape);
const nDims = inputShape.length;
if (typeof this.axis === "number") {
this.axis = [this.axis];
}
for (let i = 0; i < this.axis.length; ++i) {
if (this.axis[i] < 0) {
this.axis[i] += nDims;
}
}
for (const axis of this.axis) {
if (axis < 0 || axis >= nDims) {
throw new Error(`Invalid axis: ${axis}`);
}
}
if (this.axis.length !== unique3(this.axis).length) {
throw new Error(`Found duplicate axes in: ${this.axis}`);
}
const paramShape = this.axis.map((axis) => inputShape[axis]);
const trainable = true;
if (this.scale) {
this.gamma = this.addWeight("gamma", paramShape, "float32", this.gammaInitializer, this.gammaRegularizer, trainable);
} else {
this.gamma = null;
}
if (this.center) {
this.beta = this.addWeight("beta", paramShape, "float32", this.betaInitializer, this.betaRegularizer, trainable);
} else {
this.beta = null;
}
this.built = true;
}
call(inputs, kwargs) {
const input2 = getExactlyOneTensor(inputs);
const inputShape = input2.shape;
const nDims = inputShape.length;
return tidy(() => {
const keepDims = true;
let {mean: mean5, variance} = moments(input2, this.axis, keepDims);
const broadcastShape = pyListRepeat(1, nDims);
for (const dim of this.axis) {
broadcastShape[dim] = inputShape[dim];
}
const broadcast = (v) => {
if (v != null && v.shape.length !== nDims && this.axis !== [nDims - 1]) {
return v.reshape(broadcastShape);
} else {
return v;
}
};
let scale = broadcast(this.gamma.read());
let offset = broadcast(this.beta.read());
const momentsTiling = [];
const scaleOffsetTiling = [];
for (let i = 0; i < nDims; ++i) {
if (this.axis.indexOf(i) !== -1) {
momentsTiling.push(inputShape[i]);
scaleOffsetTiling.push(1);
} else {
momentsTiling.push(1);
scaleOffsetTiling.push(inputShape[i]);
}
}
mean5 = mean5.tile(momentsTiling);
variance = variance.tile(momentsTiling);
scale = scale.tile(scaleOffsetTiling);
offset = offset.tile(scaleOffsetTiling);
return batchNormalization(input2, mean5, variance, offset, scale, this.epsilon);
});
}
getConfig() {
const config2 = {
axis: this.axis,
epsilon: this.epsilon,
center: this.center,
scale: this.scale,
betaInitializer: serializeInitializer(this.betaInitializer),
gammaInitializer: serializeInitializer(this.gammaInitializer),
betaRegularizer: serializeRegularizer(this.betaRegularizer),
gammaRegularizer: serializeRegularizer(this.gammaRegularizer)
};
const baseConfig = super.getConfig();
Object.assign(config2, baseConfig);
return config2;
}
}
LayerNormalization.className = "LayerNormalization";
serialization_exports.registerClass(LayerNormalization);
// node_modules/@tensorflow/tfjs-layers/dist/layers/padding.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
function spatial2dPadding(x, padding2, dataFormat) {
return tidy(() => {
if (x.rank !== 4) {
throw new ValueError(`temporalPadding expects input tensor to be 4-D, but received a ${x.rank}-D tensor.`);
}
if (padding2 == null) {
padding2 = [[1, 1], [1, 1]];
}
if (padding2.length !== 2 || padding2[0].length !== 2 || padding2[1].length !== 2) {
throw new ValueError("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");
}
if (dataFormat == null) {
dataFormat = imageDataFormat();
}
if (dataFormat !== "channelsLast" && dataFormat !== "channelsFirst") {
throw new ValueError(`Unknown data format: ${dataFormat}. Supported data formats are 'channelsLast' and 'channelsFirst.`);
}
let pattern;
if (dataFormat === "channelsFirst") {
pattern = [[0, 0], [0, 0], padding2[0], padding2[1]];
} else {
pattern = [[0, 0], padding2[0], padding2[1], [0, 0]];
}
return pad(x, pattern);
});
}
class ZeroPadding2D extends Layer {
constructor(args) {
if (args == null) {
args = {};
}
super(args);
this.dataFormat = args.dataFormat == null ? imageDataFormat() : args.dataFormat;
if (args.padding == null) {
this.padding = [[1, 1], [1, 1]];
} else if (typeof args.padding === "number") {
this.padding = [[args.padding, args.padding], [args.padding, args.padding]];
} else {
args.padding = args.padding;
if (args.padding.length !== 2) {
throw new ValueError(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${args.padding.length} array.`);
}
let heightPadding;
let widthPadding;
if (typeof args.padding[0] === "number") {
heightPadding = [args.padding[0], args.padding[0]];
widthPadding = [args.padding[1], args.padding[1]];
} else {
args.padding = args.padding;
if (args.padding[0].length !== 2) {
throw new ValueError(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${args.padding[0].length} array.`);
}
heightPadding = args.padding[0];
if (args.padding[1].length !== 2) {
throw new ValueError(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${args.padding[1].length} array.`);
}
widthPadding = args.padding[1];
}
this.padding = [heightPadding, widthPadding];
}
this.inputSpec = [new InputSpec({ndim: 4})];
}
computeOutputShape(inputShape) {
inputShape = getExactlyOneShape(inputShape);
let rows;
let cols;
if (this.dataFormat === "channelsFirst") {
if (inputShape[2] != null && inputShape[2] >= 0) {
rows = inputShape[2] + this.padding[0][0] + this.padding[0][1];
} else {
rows = null;
}
if (inputShape[3] != null && inputShape[3] >= 0) {
cols = inputShape[3] + this.padding[1][0] + this.padding[1][1];
} else {
cols = null;
}
return [inputShape[0], inputShape[1], rows, cols];
} else {
if (inputShape[1] != null && inputShape[1] >= 0) {
rows = inputShape[1] + this.padding[0][0] + this.padding[0][1];
} else {
rows = null;
}
if (inputShape[2] != null && inputShape[2] >= 0) {
cols = inputShape[2] + this.padding[1][0] + this.padding[1][1];
} else {
cols = null;
}
return [inputShape[0], rows, cols, inputShape[3]];
}
}
call(inputs, kwargs) {
return tidy(() => spatial2dPadding(getExactlyOneTensor(inputs), this.padding, this.dataFormat));
}
getConfig() {
const config2 = {
padding: this.padding,
dataFormat: this.dataFormat
};
const baseConfig = super.getConfig();
Object.assign(config2, baseConfig);
return config2;
}
}
ZeroPadding2D.className = "ZeroPadding2D";
serialization_exports.registerClass(ZeroPadding2D);
// node_modules/@tensorflow/tfjs-layers/dist/layers/pooling.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
function pool2d(x, poolSize, strides, padding2, dataFormat, poolMode) {
return tidy(() => {
checkDataFormat(dataFormat);
checkPoolMode(poolMode);
checkPaddingMode(padding2);
if (strides == null) {
strides = [1, 1];
}
if (padding2 == null) {
padding2 = "valid";
}
if (dataFormat == null) {
dataFormat = imageDataFormat();
}
if (poolMode == null) {
poolMode = "max";
}
x = preprocessConv2DInput(x, dataFormat);
let y;
const paddingString = padding2 === "same" ? "same" : "valid";
if (poolMode === "max") {
y = maxPool(x, poolSize, strides, paddingString);
} else {
y = avgPool(x, poolSize, strides, paddingString);
}
if (dataFormat === "channelsFirst") {
y = transpose(y, [0, 3, 1, 2]);
}
return y;
});
}
function pool3d(x, poolSize, strides, padding2, dataFormat, poolMode) {
return tidy(() => {
checkDataFormat(dataFormat);
checkPoolMode(poolMode);
checkPaddingMode(padding2);
if (strides == null) {
strides = [1, 1, 1];
}
if (padding2 == null) {
padding2 = "valid";
}
if (dataFormat == null) {
dataFormat = imageDataFormat();
}
if (poolMode == null) {
poolMode = "max";
}
x = preprocessConv3DInput(x, dataFormat);
let y;
const paddingString = padding2 === "same" ? "same" : "valid";
if (poolMode === "max") {
y = maxPool3d(x, poolSize, strides, paddingString);
} else {
y = avgPool3d(x, poolSize, strides, paddingString);
}
if (dataFormat === "channelsFirst") {
y = transpose(y, [0, 4, 1, 2, 3]);
}
return y;
});
}
class Pooling1D extends Layer {
constructor(args) {
if (args.poolSize == null) {
args.poolSize = 2;
}
super(args);
if (typeof args.poolSize === "number") {
this.poolSize = [args.poolSize];
} else if (Array.isArray(args.poolSize) && args.poolSize.length === 1 && typeof args.poolSize[0] === "number") {
this.poolSize = args.poolSize;
} else {
throw new ValueError(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.poolSize)}`);
}
assertPositiveInteger(this.poolSize, "poolSize");
if (args.strides == null) {
this.strides = this.poolSize;
} else {
if (typeof args.strides === "number") {
this.strides = [args.strides];
} else if (Array.isArray(args.strides) && args.strides.length === 1 && typeof args.strides[0] === "number") {
this.strides = args.strides;
} else {
throw new ValueError(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.strides)}`);
}
}
assertPositiveInteger(this.strides, "strides");
this.padding = args.padding == null ? "valid" : args.padding;
checkPaddingMode(this.padding);
this.inputSpec = [new InputSpec({ndim: 3})];
}
computeOutputShape(inputShape) {
inputShape = getExactlyOneShape(inputShape);
const length = convOutputLength(inputShape[1], this.poolSize[0], this.padding, this.strides[0]);
return [inputShape[0], length, inputShape[2]];
}
call(inputs, kwargs) {
return tidy(() => {
this.invokeCallHook(inputs, kwargs);
inputs = expandDims2(getExactlyOneTensor(inputs), 2);
const output = this.poolingFunction(getExactlyOneTensor(inputs), [this.poolSize[0], 1], [this.strides[0], 1], this.padding, "channelsLast");
return squeeze(output, [2]);
});
}
getConfig() {
const config2 = {
poolSize: this.poolSize,
padding: this.padding,
strides: this.strides
};
const baseConfig = super.getConfig();
Object.assign(config2, baseConfig);
return config2;
}
}
class MaxPooling1D extends Pooling1D {
constructor(args) {
super(args);
}
poolingFunction(inputs, poolSize, strides, padding2, dataFormat) {
checkDataFormat(dataFormat);
checkPaddingMode(padding2);
return pool2d(inputs, poolSize, strides, padding2, dataFormat, "max");
}
}
MaxPooling1D.className = "MaxPooling1D";
serialization_exports.registerClass(MaxPooling1D);
class AveragePooling1D extends Pooling1D {
constructor(args) {
super(args);
}
poolingFunction(inputs, poolSize, strides, padding2, dataFormat) {
checkDataFormat(dataFormat);
checkPaddingMode(padding2);
return pool2d(inputs, poolSize, strides, padding2, dataFormat, "avg");
}
}
AveragePooling1D.className = "AveragePooling1D";
serialization_exports.registerClass(AveragePooling1D);
class Pooling2D extends Layer {
constructor(args) {
if (args.poolSize == null) {
args.poolSize = [2, 2];
}
super(args);
this.poolSize = Array.isArray(args.poolSize) ? args.poolSize : [args.poolSize, args.poolSize];
if (args.strides == null) {
this.strides = this.poolSize;
} else if (Array.isArray(args.strides)) {
if (args.strides.length !== 2) {
throw new ValueError(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${args.strides.length}.`);
}
this.strides = args.strides;
} else {
this.strides = [args.strides, args.strides];
}
assertPositiveInteger(this.poolSize, "poolSize");
assertPositiveInteger(this.strides, "strides");
this.padding = args.padding == null ? "valid" : args.padding;
this.dataFormat = args.dataFormat == null ? "channelsLast" : args.dataFormat;
checkDataFormat(this.dataFormat);
checkPaddingMode(this.padding);
this.inputSpec = [new InputSpec({ndim: 4})];
}
computeOutputShape(inputShape) {
inputShape = getExactlyOneShape(inputShape);
let rows = this.dataFormat === "channelsFirst" ? inputShape[2] : inputShape[1];
let cols = this.dataFormat === "channelsFirst" ? inputShape[3] : inputShape[2];
rows = convOutputLength(rows, this.poolSize[0], this.padding, this.strides[0]);
cols = convOutputLength(cols, this.poolSize[1], this.padding, this.strides[1]);
if (this.dataFormat === "channelsFirst") {
return [inputShape[0], inputShape[1], rows, cols];
} else {
return [inputShape[0], rows, cols, inputShape[3]];
}
}
call(inputs, kwargs) {
return tidy(() => {
this.invokeCallHook(inputs, kwargs);
return this.poolingFunction(getExactlyOneTensor(inputs), this.poolSize, this.strides, this.padding, this.dataFormat);
});
}
getConfig() {
const config2 = {
poolSize: this.poolSize,
padding: this.padding,
strides: this.strides,
dataFormat: this.dataFormat
};
const baseConfig = super.getConfig();
Object.assign(config2, baseConfig);
return config2;
}
}
class MaxPooling2D extends Pooling2D {
constructor(args) {
super(args);
}
poolingFunction(inputs, poolSize, strides, padding2, dataFormat) {
checkDataFormat(dataFormat);
checkPaddingMode(padding2);
return pool2d(inputs, poolSize, strides, padding2, dataFormat, "max");
}
}
MaxPooling2D.className = "MaxPooling2D";
serialization_exports.registerClass(MaxPooling2D);
class AveragePooling2D extends Pooling2D {
constructor(args) {
super(args);
}
poolingFunction(inputs, poolSize, strides, padding2, dataFormat) {
checkDataFormat(dataFormat);
checkPaddingMode(padding2);
return pool2d(inputs, poolSize, strides, padding2, dataFormat, "avg");
}
}
AveragePooling2D.className = "AveragePooling2D";
serialization_exports.registerClass(AveragePooling2D);
class Pooling3D extends Layer {
constructor(args) {
if (args.poolSize == null) {
args.poolSize = [2, 2, 2];
}
super(args);
this.poolSize = Array.isArray(args.poolSize) ? args.poolSize : [args.poolSize, args.poolSize, args.poolSize];
if (args.strides == null) {
this.strides = this.poolSize;
} else if (Array.isArray(args.strides)) {
if (args.strides.length !== 3) {
throw new ValueError(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${args.strides.length}.`);
}
this.strides = args.strides;
} else {
this.strides = [args.strides, args.strides, args.strides];
}
assertPositiveInteger(this.poolSize, "poolSize");
assertPositiveInteger(this.strides, "strides");
this.padding = args.padding == null ? "valid" : args.padding;
this.dataFormat = args.dataFormat == null ? "channelsLast" : args.dataFormat;
checkDataFormat(this.dataFormat);
checkPaddingMode(this.padding);
this.inputSpec = [new InputSpec({ndim: 5})];
}
computeOutputShape(inputShape) {
inputShape = getExactlyOneShape(inputShape);
let depths = this.dataFormat === "channelsFirst" ? inputShape[2] : inputShape[1];
let rows = this.dataFormat === "channelsFirst" ? inputShape[3] : inputShape[2];
let cols = this.dataFormat === "channelsFirst" ? inputShape[4] : inputShape[3];
depths = convOutputLength(depths, this.poolSize[0], this.padding, this.strides[0]);
rows = convOutputLength(rows, this.poolSize[1], this.padding, this.strides[1]);
cols = convOutputLength(cols, this.poolSize[2], this.padding, this.strides[2]);
if (this.dataFormat === "channelsFirst") {
return [inputShape[0], inputShape[1], depths, rows, cols];
} else {
return [inputShape[0], depths, rows, cols, inputShape[4]];
}
}
call(inputs, kwargs) {
return tidy(() => {
this.invokeCallHook(inputs, kwargs);
return this.poolingFunction(getExactlyOneTensor(inputs), this.poolSize, this.strides, this.padding, this.dataFormat);
});
}
getConfig() {
const config2 = {
poolSize: this.poolSize,
padding: this.padding,
strides: this.strides,
dataFormat: this.dataFormat
};
const baseConfig = super.getConfig();
Object.assign(config2, baseConfig);
return config2;
}
}
class MaxPooling3D extends Pooling3D {
constructor(args) {
super(args);
}
poolingFunction(inputs, poolSize, strides, padding2, dataFormat) {
checkDataFormat(dataFormat);
checkPaddingMode(padding2);
return pool3d(inputs, poolSize, strides, padding2, dataFormat, "max");
}
}
MaxPooling3D.className = "MaxPooling3D";
serialization_exports.registerClass(MaxPooling3D);
class AveragePooling3D extends Pooling3D {
constructor(args) {
super(args);
}
poolingFunction(inputs, poolSize, strides, padding2, dataFormat) {
checkDataFormat(dataFormat);
checkPaddingMode(padding2);
return pool3d(inputs, poolSize, strides, padding2, dataFormat, "avg");
}
}
AveragePooling3D.className = "AveragePooling3D";
serialization_exports.registerClass(AveragePooling3D);
class GlobalPooling1D extends Layer {
constructor(args) {
super(args);
this.inputSpec = [new InputSpec({ndim: 3})];
}
computeOutputShape(inputShape) {
return [inputShape[0], inputShape[2]];
}
call(inputs, kwargs) {
throw new NotImplementedError();
}
}
class GlobalAveragePooling1D extends GlobalPooling1D {
constructor(args) {
super(args || {});
}
call(inputs, kwargs) {
return tidy(() => {
const input2 = getExactlyOneTensor(inputs);
return mean(input2, 1);
});
}
}
GlobalAveragePooling1D.className = "GlobalAveragePooling1D";
serialization_exports.registerClass(GlobalAveragePooling1D);
class GlobalMaxPooling1D extends GlobalPooling1D {
constructor(args) {
super(args || {});
}
call(inputs, kwargs) {
return tidy(() => {
const input2 = getExactlyOneTensor(inputs);
return max(input2, 1);
});
}
}
GlobalMaxPooling1D.className = "GlobalMaxPooling1D";
serialization_exports.registerClass(GlobalMaxPooling1D);
class GlobalPooling2D extends Layer {
constructor(args) {
super(args);
this.dataFormat = args.dataFormat == null ? "channelsLast" : args.dataFormat;
checkDataFormat(this.dataFormat);
this.inputSpec = [new InputSpec({ndim: 4})];
}
computeOutputShape(inputShape) {
inputShape = inputShape;
if (this.dataFormat === "channelsLast") {
return [inputShape[0], inputShape[3]];
} else {
return [inputShape[0], inputShape[1]];
}
}
call(inputs, kwargs) {
throw new NotImplementedError();
}
getConfig() {
const config2 = {dataFormat: this.dataFormat};
const baseConfig = super.getConfig();
Object.assign(config2, baseConfig);
return config2;
}
}
class GlobalAveragePooling2D extends GlobalPooling2D {
call(inputs, kwargs) {
return tidy(() => {
const input2 = getExactlyOneTensor(inputs);
if (this.dataFormat === "channelsLast") {
return mean(input2, [1, 2]);
} else {
return mean(input2, [2, 3]);
}
});
}
}
GlobalAveragePooling2D.className = "GlobalAveragePooling2D";
serialization_exports.registerClass(GlobalAveragePooling2D);
class GlobalMaxPooling2D extends GlobalPooling2D {
call(inputs, kwargs) {
return tidy(() => {
const input2 = getExactlyOneTensor(inputs);
if (this.dataFormat === "channelsLast") {
return max(input2, [1, 2]);
} else {
return max(input2, [2, 3]);
}
});
}
}
GlobalMaxPooling2D.className = "GlobalMaxPooling2D";
serialization_exports.registerClass(GlobalMaxPooling2D);
// node_modules/@tensorflow/tfjs-layers/dist/layers/wrappers.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
class Wrapper extends Layer {
constructor(args) {
super(args);
this.layer = args.layer;
}
build(inputShape) {
this.built = true;
}
get trainable() {
if (this.layer != null) {
return this.layer.trainable;
} else {
return false;
}
}
set trainable(value) {
if (this.layer != null) {
this.layer.trainable = value;
}
}
get trainableWeights() {
return this.layer.trainableWeights;
}
get nonTrainableWeights() {
return this.layer.nonTrainableWeights;
}
get updates() {
return this.layer._updates;
}
get losses() {
return this.layer.losses;
}
getWeights() {
return this.layer.getWeights();
}
setWeights(weights) {
this.layer.setWeights(weights);
}
getConfig() {
const config2 = {
layer: {
className: this.layer.getClassName(),
config: this.layer.getConfig()
}
};
const baseConfig = super.getConfig();
Object.assign(config2, baseConfig);
return config2;
}
setFastWeightInitDuringBuild(value) {
super.setFastWeightInitDuringBuild(value);
if (this.layer != null) {
this.layer.setFastWeightInitDuringBuild(value);
}
}
static fromConfig(cls, config2, customObjects = {}) {
const layerConfig = config2["layer"];
const layer = deserialize(layerConfig, customObjects);
delete config2["layer"];
const newConfig = {layer};
Object.assign(newConfig, config2);
return new cls(newConfig);
}
}
class TimeDistributed extends Wrapper {
constructor(args) {
super(args);
this.supportsMasking = true;
}
build(inputShape) {
inputShape = getExactlyOneShape(inputShape);
if (inputShape.length < 3) {
throw new ValueError(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(inputShape)}`);
}
this.inputSpec = [{shape: inputShape}];
const childInputShape = [inputShape[0]].concat(inputShape.slice(2));
if (!this.layer.built) {
this.layer.build(childInputShape);
this.layer.built = true;
}
super.build(inputShape);
}
computeOutputShape(inputShape) {
inputShape = getExactlyOneShape(inputShape);
const childInputShape = [inputShape[0]].concat(inputShape.slice(2));
const childOutputShape = this.layer.computeOutputShape(childInputShape);
const timesteps = inputShape[1];
return [childOutputShape[0], timesteps].concat(childOutputShape.slice(1));
}
call(inputs, kwargs) {
return tidy(() => {
inputs = getExactlyOneTensor(inputs);
const step4 = (inputs2, states) => {
const output = getExactlyOneTensor(this.layer.call(inputs2, kwargs));
return [output, []];
};
const rnnOutputs = rnn(step4, inputs, [], false, null, null, false, true);
const y = rnnOutputs[1];
return y;
});
}
}
TimeDistributed.className = "TimeDistributed";
serialization_exports.registerClass(TimeDistributed);
function checkBidirectionalMergeMode(value) {
checkStringTypeUnionValue(VALID_BIDIRECTIONAL_MERGE_MODES, "BidirectionalMergeMode", value);
}
const DEFAULT_BIDIRECTIONAL_MERGE_MODE = "concat";
class Bidirectional extends Wrapper {
constructor(args) {
super(args);
const layerConfig = args.layer.getConfig();
const forwDict = {};
forwDict["className"] = args.layer.getClassName();
forwDict["config"] = layerConfig;
this.forwardLayer = deserialize(forwDict);
layerConfig["goBackwards"] = layerConfig["goBackwards"] === true ? false : true;
const backDict = {};
backDict["className"] = args.layer.getClassName();
backDict["config"] = layerConfig;
this.backwardLayer = deserialize(backDict);
this.forwardLayer.name = "forward_" + this.forwardLayer.name;
this.backwardLayer.name = "backward_" + this.backwardLayer.name;
this.mergeMode = args.mergeMode === void 0 ? DEFAULT_BIDIRECTIONAL_MERGE_MODE : args.mergeMode;
checkBidirectionalMergeMode(this.mergeMode);
if (args.weights) {
throw new NotImplementedError("weights support is not implemented for Bidirectional layer yet.");
}
this._stateful = args.layer.stateful;
this.returnSequences = args.layer.returnSequences;
this.returnState = args.layer.returnState;
this.supportsMasking = true;
this._trainable = true;
this.inputSpec = args.layer.inputSpec;
this.numConstants = null;
}
get trainable() {
return this._trainable;
}
set trainable(value) {
this._trainable = value;
if (this.forwardLayer != null) {
this.forwardLayer.trainable = value;
}
if (this.backwardLayer != null) {
this.backwardLayer.trainable = value;
}
}
getWeights() {
return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights());
}
setWeights(weights) {
const numWeights = weights.length;
const numeightsOver2 = Math.floor(numWeights / 2);
this.forwardLayer.setWeights(weights.slice(0, numeightsOver2));
this.backwardLayer.setWeights(weights.slice(numeightsOver2));
}
computeOutputShape(inputShape) {
let layerShapes = this.forwardLayer.computeOutputShape(inputShape);
if (!(Array.isArray(layerShapes) && Array.isArray(layerShapes[0]))) {
layerShapes = [layerShapes];
}
layerShapes = layerShapes;
let outputShape;
let outputShapes;
let stateShape;
if (this.returnState) {
stateShape = layerShapes.slice(1);
outputShape = layerShapes[0];
} else {
outputShape = layerShapes[0];
}
outputShape = outputShape;
if (this.mergeMode === "concat") {
outputShape[outputShape.length - 1] *= 2;
outputShapes = [outputShape];
} else if (this.mergeMode == null) {
outputShapes = [outputShape, outputShape.slice()];
} else {
outputShapes = [outputShape];
}
if (this.returnState) {
if (this.mergeMode == null) {
return outputShapes.concat(stateShape).concat(stateShape.slice());
}
return [outputShape].concat(stateShape).concat(stateShape.slice());
}
return singletonOrArray(outputShapes);
}
apply(inputs, kwargs) {
let initialState = kwargs == null ? null : kwargs["initialState"];
let constants = kwargs == null ? null : kwargs["constants"];
if (kwargs == null) {
kwargs = {};
}
const standardized = standardizeArgs(inputs, initialState, constants, this.numConstants);
inputs = standardized.inputs;
initialState = standardized.initialState;
constants = standardized.constants;
if (Array.isArray(inputs)) {
initialState = inputs.slice(1);
inputs = inputs[0];
}
if ((initialState == null || initialState.length === 0) && constants == null) {
return super.apply(inputs, kwargs);
}
const additionalInputs = [];
const additionalSpecs = [];
if (initialState != null) {
const numStates = initialState.length;
if (numStates % 2 > 0) {
throw new ValueError("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");
}
kwargs["initialState"] = initialState;
additionalInputs.push(...initialState);
const stateSpecs = initialState.map((state6) => new InputSpec({shape: state6.shape}));
this.forwardLayer.stateSpec = stateSpecs.slice(0, numStates / 2);
this.backwardLayer.stateSpec = stateSpecs.slice(numStates / 2);
additionalSpecs.push(...stateSpecs);
}
if (constants != null) {
throw new NotImplementedError("Support for constants in Bidirectional layers is not implemented yet.");
}
const isSymbolicTensor = additionalInputs[0] instanceof SymbolicTensor;
for (const tensor16 of additionalInputs) {
if (tensor16 instanceof SymbolicTensor !== isSymbolicTensor) {
throw new ValueError("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");
}
}
if (isSymbolicTensor) {
const fullInput = [inputs].concat(additionalInputs);
const fullInputSpec = this.inputSpec.concat(additionalSpecs);
const originalInputSpec = this.inputSpec;
this.inputSpec = fullInputSpec;
const output = super.apply(fullInput, kwargs);
this.inputSpec = originalInputSpec;
return output;
} else {
return super.apply(inputs, kwargs);
}
}
call(inputs, kwargs) {
return tidy(() => {
const initialState = kwargs["initialState"];
let y;
let yRev;
if (initialState == null) {
y = this.forwardLayer.call(inputs, kwargs);
yRev = this.backwardLayer.call(inputs, kwargs);
} else {
const forwardState = initialState.slice(0, initialState.length / 2);
const backwardState = initialState.slice(initialState.length / 2);
y = this.forwardLayer.call(inputs, Object.assign(kwargs, {initialState: forwardState}));
yRev = this.backwardLayer.call(inputs, Object.assign(kwargs, {initialState: backwardState}));
}
let states;
if (this.returnState) {
if (Array.isArray(y)) {
states = y.slice(1).concat(yRev.slice(1));
} else {
}
y = y[0];
yRev = yRev[0];
}
if (this.returnSequences) {
yRev = reverse(yRev, 1);
}
let output;
if (this.mergeMode === "concat") {
output = concatenate([y, yRev]);
} else if (this.mergeMode === "sum") {
output = add2(y, yRev);
} else if (this.mergeMode === "ave") {
output = mul(0.5, add2(y, yRev));
} else if (this.mergeMode === "mul") {
output = mul(y, yRev);
} else if (this.mergeMode == null) {
output = [y, yRev];
}
if (this.returnState) {
if (this.mergeMode == null) {
return output.concat(states);
}
return [output].concat(states);
}
return output;
});
}
resetStates(states) {
this.forwardLayer.resetStates();
this.backwardLayer.resetStates();
}
build(inputShape) {
nameScope(this.forwardLayer.name, () => {
this.forwardLayer.build(inputShape);
});
nameScope(this.backwardLayer.name, () => {
this.backwardLayer.build(inputShape);
});
this.built = true;
}
computeMask(inputs, mask) {
if (Array.isArray(mask)) {
mask = mask[0];
}
let outputMask;
if (this.returnSequences) {
if (this.mergeMode == null) {
outputMask = [mask, mask];
} else {
outputMask = mask;
}
} else {
if (this.mergeMode == null) {
outputMask = [null, null];
} else {
outputMask = null;
}
}
if (this.returnState) {
const states = this.forwardLayer.states;
const stateMask = states.map((state6) => null);
if (Array.isArray(outputMask)) {
return outputMask.concat(stateMask).concat(stateMask);
} else {
return [outputMask].concat(stateMask).concat(stateMask);
}
} else {
return outputMask;
}
}
get trainableWeights() {
return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights);
}
get nonTrainableWeights() {
return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights);
}
setFastWeightInitDuringBuild(value) {
super.setFastWeightInitDuringBuild(value);
if (this.forwardLayer != null) {
this.forwardLayer.setFastWeightInitDuringBuild(value);
}
if (this.backwardLayer != null) {
this.backwardLayer.setFastWeightInitDuringBuild(value);
}
}
getConfig() {
const config2 = {
mergeMode: this.mergeMode
};
const baseConfig = super.getConfig();
Object.assign(config2, baseConfig);
return config2;
}
static fromConfig(cls, config2) {
const rnnLayer = deserialize(config2["layer"]);
delete config2["layer"];
if (config2["numConstants"] != null) {
throw new NotImplementedError(`Deserialization of a Bidirectional layer with numConstants present is not supported yet.`);
}
const newConfig = config2;
newConfig["layer"] = rnnLayer;
return new cls(newConfig);
}
}
Bidirectional.className = "Bidirectional";
serialization_exports.registerClass(Bidirectional);
// node_modules/@tensorflow/tfjs-layers/dist/exports_layers.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
function inputLayer(args) {
return new InputLayer(args);
}
function elu5(args) {
return new ELU(args);
}
function reLU(args) {
return new ReLU(args);
}
function leakyReLU(args) {
return new LeakyReLU(args);
}
function prelu4(args) {
return new PReLU(args);
}
function softmax3(args) {
return new Softmax3(args);
}
function thresholdedReLU(args) {
return new ThresholdedReLU(args);
}
function conv1d3(args) {
return new Conv1D(args);
}
function conv2d7(args) {
return new Conv2D2(args);
}
function conv2dTranspose2(args) {
return new Conv2DTranspose(args);
}
function conv3d3(args) {
return new Conv3D2(args);
}
function separableConv2d2(args) {
return new SeparableConv2D(args);
}
function cropping2D(args) {
return new Cropping2D(args);
}
function upSampling2d(args) {
return new UpSampling2D(args);
}
function depthwiseConv2d4(args) {
return new DepthwiseConv2D(args);
}
function activation(args) {
return new Activation2(args);
}
function dense(args) {
return new Dense(args);
}
function dropout3(args) {
return new Dropout(args);
}
function spatialDropout1d(args) {
return new SpatialDropout1D(args);
}
function flatten3(args) {
return new Flatten(args);
}
function repeatVector(args) {
return new RepeatVector(args);
}
function reshape59(args) {
return new Reshape2(args);
}
function permute(args) {
return new Permute(args);
}
function embedding(args) {
return new Embedding(args);
}
function add23(args) {
return new Add2(args);
}
function average(args) {
return new Average(args);
}
function concatenate2(args) {
return new Concatenate(args);
}
function maximum6(args) {
return new Maximum2(args);
}
function minimum5(args) {
return new Minimum2(args);
}
function multiply(args) {
return new Multiply2(args);
}
function dot4(args) {
return new Dot(args);
}
function batchNormalization2(args) {
return new BatchNormalization(args);
}
function layerNormalization(args) {
return new LayerNormalization(args);
}
function zeroPadding2d(args) {
return new ZeroPadding2D(args);
}
function averagePooling1d(args) {
return new AveragePooling1D(args);
}
function avgPool1d(args) {
return averagePooling1d(args);
}
function avgPooling1d(args) {
return averagePooling1d(args);
}
function averagePooling2d(args) {
return new AveragePooling2D(args);
}
function avgPool2d(args) {
return averagePooling2d(args);
}
function avgPooling2d(args) {
return averagePooling2d(args);
}
function averagePooling3d(args) {
return new AveragePooling3D(args);
}
function avgPool3d2(args) {
return averagePooling3d(args);
}
function avgPooling3d(args) {
return averagePooling3d(args);
}
function globalAveragePooling1d(args) {
return new GlobalAveragePooling1D(args);
}
function globalAveragePooling2d(args) {
return new GlobalAveragePooling2D(args);
}
function globalMaxPooling1d(args) {
return new GlobalMaxPooling1D(args);
}
function globalMaxPooling2d(args) {
return new GlobalMaxPooling2D(args);
}
function maxPooling1d(args) {
return new MaxPooling1D(args);
}
function maxPooling2d(args) {
return new MaxPooling2D(args);
}
function maxPooling3d(args) {
return new MaxPooling3D(args);
}
function gru(args) {
return new GRU(args);
}
function gruCell(args) {
return new GRUCell(args);
}
function lstm(args) {
return new LSTM(args);
}
function lstmCell(args) {
return new LSTMCell(args);
}
function simpleRNN(args) {
return new SimpleRNN(args);
}
function simpleRNNCell(args) {
return new SimpleRNNCell(args);
}
function convLstm2d(args) {
return new ConvLSTM2D(args);
}
function convLstm2dCell(args) {
return new ConvLSTM2DCell(args);
}
function rnn2(args) {
return new RNN(args);
}
function stackedRNNCells(args) {
return new StackedRNNCells(args);
}
function bidirectional(args) {
return new Bidirectional(args);
}
function timeDistributed(args) {
return new TimeDistributed(args);
}
const globalMaxPool1d = globalMaxPooling1d;
const globalMaxPool2d = globalMaxPooling2d;
const maxPool1d = maxPooling1d;
const maxPool2d = maxPooling2d;
function gaussianNoise(args) {
return new GaussianNoise(args);
}
function gaussianDropout(args) {
return new GaussianDropout(args);
}
function alphaDropout(args) {
return new AlphaDropout(args);
}
function masking(args) {
return new Masking(args);
}
// node_modules/@tensorflow/tfjs-layers/dist/exports_metrics.js
const exports_metrics_exports = {};
__export(exports_metrics_exports, {
MAPE: () => MAPE2,
MSE: () => MSE2,
binaryAccuracy: () => binaryAccuracy2,
binaryCrossentropy: () => binaryCrossentropy3,
categoricalAccuracy: () => categoricalAccuracy2,
categoricalCrossentropy: () => categoricalCrossentropy3,
cosineProximity: () => cosineProximity2,
mape: () => mape2,
meanAbsoluteError: () => meanAbsoluteError2,
meanAbsolutePercentageError: () => meanAbsolutePercentageError2,
meanSquaredError: () => meanSquaredError3,
mse: () => mse2,
precision: () => precision2,
recall: () => recall2,
sparseCategoricalAccuracy: () => sparseCategoricalAccuracy2
});
function binaryAccuracy2(yTrue, yPred) {
return binaryAccuracy(yTrue, yPred);
}
function binaryCrossentropy3(yTrue, yPred) {
return binaryCrossentropy2(yTrue, yPred);
}
function sparseCategoricalAccuracy2(yTrue, yPred) {
return sparseCategoricalAccuracy(yTrue, yPred);
}
function categoricalAccuracy2(yTrue, yPred) {
return categoricalAccuracy(yTrue, yPred);
}
function categoricalCrossentropy3(yTrue, yPred) {
return categoricalCrossentropy2(yTrue, yPred);
}
function precision2(yTrue, yPred) {
return precision(yTrue, yPred);
}
function recall2(yTrue, yPred) {
return recall(yTrue, yPred);
}
function cosineProximity2(yTrue, yPred) {
return cosineProximity(yTrue, yPred);
}
function meanAbsoluteError2(yTrue, yPred) {
return meanAbsoluteError(yTrue, yPred);
}
function meanAbsolutePercentageError2(yTrue, yPred) {
return meanAbsolutePercentageError(yTrue, yPred);
}
function MAPE2(yTrue, yPred) {
return meanAbsolutePercentageError(yTrue, yPred);
}
function mape2(yTrue, yPred) {
return meanAbsolutePercentageError(yTrue, yPred);
}
function meanSquaredError3(yTrue, yPred) {
return meanSquaredError2(yTrue, yPred);
}
function MSE2(yTrue, yPred) {
return meanSquaredError2(yTrue, yPred);
}
function mse2(yTrue, yPred) {
return meanSquaredError2(yTrue, yPred);
}
// node_modules/@tensorflow/tfjs-layers/dist/exports_models.js
const exports_models_exports = {};
__export(exports_models_exports, {
modelFromJSON: () => modelFromJSON
});
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
// node_modules/@tensorflow/tfjs-layers/dist/exports_regularizers.js
const exports_regularizers_exports = {};
__export(exports_regularizers_exports, {
l1: () => l12,
l1l2: () => l1l2,
l2: () => l22
});
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
function l1l2(config2) {
return new L1L2(config2);
}
function l12(config2) {
return l1(config2);
}
function l22(config2) {
return l2(config2);
}
// node_modules/@tensorflow/tfjs-layers/dist/callbacks.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
class Callback extends BaseCallback {
constructor() {
super(...arguments);
this.model = null;
}
setModel(model2) {
if (!(model2 instanceof LayersModel)) {
throw new Error("model must be a LayersModel, not some other Container");
}
this.model = model2;
}
}
function less4(currVal, prevVal) {
return currVal < prevVal;
}
function greater5(currVal, prevVal) {
return currVal > prevVal;
}
class EarlyStopping extends Callback {
constructor(args) {
super();
if (args == null) {
args = {};
}
if (args.restoreBestWeights) {
throw new NotImplementedError("restoreBestWeights = True is not implemented in EarlyStopping yet.");
}
this.monitor = args.monitor || "val_loss";
this.minDelta = Math.abs(args.minDelta || 0);
this.patience = args.patience || 0;
this.verbose = args.verbose || 0;
this.mode = args.mode || "auto";
this.baseline = args.baseline;
if (["auto", "min", "max"].indexOf(this.mode) === -1) {
console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`);
this.mode = "auto";
}
if (this.mode === "min") {
this.monitorFunc = less4;
} else if (this.mode === "max") {
this.monitorFunc = greater5;
} else {
if (this.monitor.indexOf("acc") !== -1) {
this.monitorFunc = greater5;
} else {
this.monitorFunc = less4;
}
}
if (this.monitorFunc === less4) {
this.minDelta *= -1;
}
}
async onTrainBegin(logs5) {
this.wait = 0;
this.stoppedEpoch = 0;
if (this.baseline != null) {
this.best = this.baseline;
} else {
this.best = this.monitorFunc === less4 ? Infinity : -Infinity;
}
}
async onEpochEnd(epoch, logs5) {
await resolveScalarsInLogs(logs5);
const current = this.getMonitorValue(logs5);
if (current == null) {
return;
}
if (this.monitorFunc(current - this.minDelta, this.best)) {
this.best = current;
this.wait = 0;
} else {
this.wait++;
if (this.wait >= this.patience) {
this.stoppedEpoch = epoch;
this.model.stopTraining = true;
}
}
}
async onTrainEnd(logs5) {
if (this.stoppedEpoch > 0 && this.verbose) {
console.log(`Epoch ${this.stoppedEpoch}: early stopping.`);
}
}
getMonitorValue(logs5) {
if (logs5 == null) {
logs5 = {};
}
const monitorValue = logs5[this.monitor];
if (monitorValue == null) {
console.warn(`Metric for EarlyStopping ${this.monitor} is not available. Available metrics are: ${Object.keys(logs5)}`);
}
return monitorValue;
}
}
function earlyStopping(args) {
return new EarlyStopping(args);
}
const callbacks = {earlyStopping};
// node_modules/@tensorflow/tfjs-layers/dist/index.js
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
// node_modules/@tensorflow/tfjs-converter/dist/data/compiled_api.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
var DataType;
(function(DataType2) {
DataType2[DataType2["DT_INVALID"] = 0] = "DT_INVALID";
DataType2[DataType2["DT_FLOAT"] = 1] = "DT_FLOAT";
DataType2[DataType2["DT_DOUBLE"] = 2] = "DT_DOUBLE";
DataType2[DataType2["DT_INT32"] = 3] = "DT_INT32";
DataType2[DataType2["DT_UINT8"] = 4] = "DT_UINT8";
DataType2[DataType2["DT_INT16"] = 5] = "DT_INT16";
DataType2[DataType2["DT_INT8"] = 6] = "DT_INT8";
DataType2[DataType2["DT_STRING"] = 7] = "DT_STRING";
DataType2[DataType2["DT_COMPLEX64"] = 8] = "DT_COMPLEX64";
DataType2[DataType2["DT_INT64"] = 9] = "DT_INT64";
DataType2[DataType2["DT_BOOL"] = 10] = "DT_BOOL";
DataType2[DataType2["DT_QINT8"] = 11] = "DT_QINT8";
DataType2[DataType2["DT_QUINT8"] = 12] = "DT_QUINT8";
DataType2[DataType2["DT_QINT32"] = 13] = "DT_QINT32";
DataType2[DataType2["DT_BFLOAT16"] = 14] = "DT_BFLOAT16";
DataType2[DataType2["DT_FLOAT_REF"] = 101] = "DT_FLOAT_REF";
DataType2[DataType2["DT_DOUBLE_REF"] = 102] = "DT_DOUBLE_REF";
DataType2[DataType2["DT_INT32_REF"] = 103] = "DT_INT32_REF";
DataType2[DataType2["DT_UINT8_REF"] = 104] = "DT_UINT8_REF";
DataType2[DataType2["DT_INT16_REF"] = 105] = "DT_INT16_REF";
DataType2[DataType2["DT_INT8_REF"] = 106] = "DT_INT8_REF";
DataType2[DataType2["DT_STRING_REF"] = 107] = "DT_STRING_REF";
DataType2[DataType2["DT_COMPLEX64_REF"] = 108] = "DT_COMPLEX64_REF";
DataType2[DataType2["DT_INT64_REF"] = 109] = "DT_INT64_REF";
DataType2[DataType2["DT_BOOL_REF"] = 110] = "DT_BOOL_REF";
DataType2[DataType2["DT_QINT8_REF"] = 111] = "DT_QINT8_REF";
DataType2[DataType2["DT_QUINT8_REF"] = 112] = "DT_QUINT8_REF";
DataType2[DataType2["DT_QINT32_REF"] = 113] = "DT_QINT32_REF";
DataType2[DataType2["DT_BFLOAT16_REF"] = 114] = "DT_BFLOAT16_REF";
})(DataType || (DataType = {}));
var SaverDef;
(function(SaverDef2) {
let CheckpointFormatVersion;
(function(CheckpointFormatVersion2) {
CheckpointFormatVersion2[CheckpointFormatVersion2["LEGACY"] = 0] = "LEGACY";
CheckpointFormatVersion2[CheckpointFormatVersion2["V1"] = 1] = "V1";
CheckpointFormatVersion2[CheckpointFormatVersion2["V2"] = 2] = "V2";
})(CheckpointFormatVersion = SaverDef2.CheckpointFormatVersion || (SaverDef2.CheckpointFormatVersion = {}));
})(SaverDef || (SaverDef = {}));
// node_modules/@tensorflow/tfjs-converter/dist/operations/custom_op/register.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const CUSTOM_OPS = {};
function registerOp(name, opFunc) {
const opMapper = {
tfOpName: name,
category: "custom",
inputs: [],
attrs: [],
customExecutor: opFunc
};
CUSTOM_OPS[name] = opMapper;
}
function getRegisteredOp(name) {
return CUSTOM_OPS[name];
}
function deregisterOp(name) {
delete CUSTOM_OPS[name];
}
// node_modules/@tensorflow/tfjs-converter/dist/operations/executors/utils.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function getParamValue(paramName, node, tensorMap, context, resourceManager) {
const inputParam = node.inputParams[paramName];
if (inputParam && inputParam.inputIndexStart !== void 0) {
const start = inputParam.inputIndexStart;
const end = inputParam.inputIndexEnd === 0 ? void 0 : inputParam.inputIndexEnd === void 0 ? start + 1 : inputParam.inputIndexEnd;
if (inputParam.type === "tensor") {
return getTensor(node.inputNames[inputParam.inputIndexStart], tensorMap, context, resourceManager);
}
if (inputParam.type === "tensors") {
const inputs = node.inputNames.slice(start, end);
return inputs.map((name) => getTensor(name, tensorMap, context, resourceManager));
}
const tensor16 = getTensor(node.inputNames.slice(start)[0], tensorMap, context, resourceManager);
const data2 = tensor16.dataSync();
return inputParam.type === "number" ? data2[0] : util_exports.toNestedArray(tensor16.shape, data2);
}
const attrParam = node.attrParams[paramName];
return attrParam && attrParam.value;
}
function getTensor(name, tensorsMap, context, resourceManager) {
const [nodeName, index] = parseNodeName(name);
if (resourceManager != null) {
const tensor16 = resourceManager.getHashTableHandleByName(nodeName);
if (tensor16 != null) {
return tensor16;
}
}
const contextId = context.currentContextIds.find((contextId2) => {
return !!tensorsMap[getNodeNameWithContextId(nodeName, contextId2)];
});
return contextId !== void 0 ? tensorsMap[getNodeNameWithContextId(nodeName, contextId)][index] : void 0;
}
function getTensorsForCurrentContenxt(name, tensorsMap, context) {
return tensorsMap[getNodeNameWithContextId(name, context.currentContextId)];
}
function getNodeNameAndIndex(inputName, context) {
const [nodeName, index] = parseNodeName(inputName);
return [
getNodeNameWithContextId(nodeName, context && context.currentContextId),
index
];
}
function getNodeNameWithContextId(name, contextId) {
return !!contextId ? `${name}-${contextId}` : name;
}
function parseNodeName(name) {
const parts = name.split(":");
if (parts.length === 1) {
return [name, 0];
}
const nodeName = parts[0];
return [nodeName, Number(parts[parts.length - 1])];
}
function getPadding(node, tensorMap, context) {
let pad8 = getParamValue("pad", node, tensorMap, context);
if (pad8 === "explicit") {
pad8 = getParamValue("explicitPaddings", node, tensorMap, context);
const explicitPadding = [[0, 0], [0, 0], [0, 0], [0, 0]];
for (let i = 0; i < 4; i++) {
explicitPadding[i][0] = pad8[i * 2];
explicitPadding[i][1] = pad8[i * 2 + 1];
}
return explicitPadding;
}
return pad8;
}
function cloneTensor(tensor16) {
return tensor16.kept ? tensor16 : clone(tensor16);
}
// node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/arithmetic.js
const arithmetic_exports = {};
__export(arithmetic_exports, {
json: () => json
});
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const json = [
{
tfOpName: "Add",
category: "arithmetic",
inputs: [
{start: 0, name: "a", type: "tensor"},
{start: 1, name: "b", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "AddV2",
category: "arithmetic",
inputs: [
{start: 0, name: "a", type: "tensor"},
{start: 1, name: "b", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "AddN",
category: "arithmetic",
inputs: [{start: 0, end: 0, name: "tensors", type: "tensors"}]
},
{
tfOpName: "BiasAdd",
category: "arithmetic",
inputs: [
{start: 0, name: "a", type: "tensor"},
{start: 1, name: "b", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Sub",
category: "arithmetic",
inputs: [
{start: 0, name: "a", type: "tensor"},
{start: 1, name: "b", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "RealDiv",
category: "arithmetic",
inputs: [
{start: 0, name: "a", type: "tensor"},
{start: 1, name: "b", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Div",
category: "arithmetic",
inputs: [
{start: 0, name: "a", type: "tensor"},
{start: 1, name: "b", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "DivNoNan",
category: "arithmetic",
inputs: [
{start: 0, name: "a", type: "tensor"},
{start: 1, name: "b", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "FloorDiv",
category: "arithmetic",
inputs: [
{start: 0, name: "a", type: "tensor"},
{start: 1, name: "b", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Mul",
category: "arithmetic",
inputs: [
{start: 0, name: "a", type: "tensor"},
{start: 1, name: "b", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Maximum",
category: "arithmetic",
inputs: [
{start: 0, name: "a", type: "tensor"},
{start: 1, name: "b", type: "tensor"}
]
},
{
tfOpName: "Minimum",
category: "arithmetic",
inputs: [
{start: 0, name: "a", type: "tensor"},
{start: 1, name: "b", type: "tensor"}
]
},
{
tfOpName: "Pow",
category: "arithmetic",
inputs: [
{start: 0, name: "a", type: "tensor"},
{start: 1, name: "b", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "SquaredDifference",
category: "arithmetic",
inputs: [
{start: 0, name: "a", type: "tensor"},
{start: 1, name: "b", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Mod",
category: "arithmetic",
inputs: [
{start: 0, name: "a", type: "tensor"},
{start: 1, name: "b", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "FloorMod",
category: "arithmetic",
inputs: [
{start: 0, name: "a", type: "tensor"},
{start: 1, name: "b", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
}
];
// node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/basic_math.js
const basic_math_exports = {};
__export(basic_math_exports, {
json: () => json2
});
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const json2 = [
{
tfOpName: "Abs",
category: "basic_math",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Acos",
category: "basic_math",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Asin",
category: "basic_math",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Atan",
category: "basic_math",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Atan2",
category: "basic_math",
inputs: [
{start: 0, name: "x", type: "tensor"},
{start: 1, name: "y", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Ceil",
category: "basic_math",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "ClipByValue",
category: "basic_math",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{tfName: "clip_value_min", name: "clipValueMin", type: "number"},
{tfName: "clip_value_max", name: "clipValueMax", type: "number"}
]
},
{
tfOpName: "Complex",
category: "basic_math",
inputs: [
{start: 0, name: "real", type: "tensor"},
{start: 1, name: "imag", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "ComplexAbs",
category: "basic_math",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Cos",
category: "basic_math",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Cosh",
category: "basic_math",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Elu",
category: "basic_math",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Exp",
category: "basic_math",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Floor",
category: "basic_math",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Log",
category: "basic_math",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Imag",
category: "basic_math",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true},
{
tfName: "Tout",
name: "outputType",
type: "dtype",
notSupported: true
}
]
},
{
tfOpName: "Neg",
category: "basic_math",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Real",
category: "basic_math",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true},
{
tfName: "Tout",
name: "outputType",
type: "dtype",
notSupported: true
}
]
},
{
tfOpName: "Prelu",
category: "basic_math",
inputs: [
{start: 0, name: "x", type: "tensor"},
{start: 1, name: "alpha", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Relu",
category: "basic_math",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Relu6",
category: "basic_math",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true},
{
tfName: "clipValueMin",
name: "clipValueMin",
type: "number",
defaultValue: 0
},
{
tfName: "clipValueMax",
name: "clipValueMax",
type: "number",
defaultValue: 6
}
]
},
{
tfOpName: "Selu",
category: "basic_math",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Sigmoid",
category: "basic_math",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Sin",
category: "basic_math",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Sinh",
category: "basic_math",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Sqrt",
category: "basic_math",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Rsqrt",
category: "basic_math",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Square",
category: "basic_math",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Tan",
category: "basic_math",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Tanh",
category: "basic_math",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Sign",
category: "basic_math",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Round",
category: "basic_math",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Expm1",
category: "basic_math",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Log1p",
category: "basic_math",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Reciprocal",
category: "basic_math",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Softplus",
category: "basic_math",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Asinh",
category: "basic_math",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Acosh",
category: "basic_math",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Atanh",
category: "basic_math",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Erf",
category: "basic_math",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Prod",
category: "basic_math",
inputs: [
{start: 0, name: "x", type: "tensor"},
{start: 1, name: "axes", type: "number[]"}
],
attrs: [
{
tfName: "keep_dims",
name: "keepDims",
type: "bool",
notSupported: true
},
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "LeakyRelu",
category: "basic_math",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{
tfName: "alpha",
name: "alpha",
type: "number",
defaultValue: 0.2
},
{
tfName: "T",
name: "dtype",
type: "dtype",
notSupported: true
}
]
}
];
// node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/control.js
const control_exports = {};
__export(control_exports, {
json: () => json3
});
const json3 = [
{
tfOpName: "LoopCond",
category: "control",
inputs: [{start: 0, name: "pred", type: "tensor"}]
},
{
tfOpName: "Switch",
category: "control",
inputs: [
{start: 0, name: "data", type: "tensor"},
{start: 1, name: "pred", type: "tensor"}
]
},
{
tfOpName: "Merge",
category: "control",
inputs: [{start: 0, end: 0, name: "tensors", type: "tensors"}]
},
{
tfOpName: "Enter",
category: "control",
inputs: [
{start: 0, name: "tensor", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true},
{tfName: "frame_name", name: "frameName", type: "string"},
{tfName: "is_constant", name: "isConstant", type: "bool"}
]
},
{
tfOpName: "Exit",
category: "control",
inputs: [
{start: 0, name: "tensor", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "NextIteration",
category: "control",
inputs: [
{start: 0, name: "tensor", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "TensorArrayV3",
category: "control",
inputs: [
{start: 0, name: "size", type: "number"}
],
attrs: [
{tfName: "dtype", name: "dtype", type: "dtype"},
{tfName: "element_shape", name: "elementShape", type: "shape"},
{tfName: "dynamic_size", name: "dynamicSize", type: "bool"},
{tfName: "clear_after_read", name: "clearAfterRead", type: "bool"},
{
tfName: "identical_element_shapes",
name: "identicalElementShapes",
type: "bool"
},
{tfName: "tensor_array_name", name: "name", type: "string"}
]
},
{
tfOpName: "TensorArrayWriteV3",
category: "control",
inputs: [
{start: 0, name: "tensorArrayId", type: "tensor"},
{start: 1, name: "index", type: "number"},
{start: 2, name: "tensor", type: "tensor"},
{start: 3, name: "flowIn", type: "number"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "TensorArrayReadV3",
category: "control",
inputs: [
{start: 0, name: "tensorArrayId", type: "tensor"},
{start: 1, name: "index", type: "number"},
{start: 2, name: "flowIn", type: "number"}
],
attrs: [{
tfName: "dtype",
name: "dtype",
type: "dtype",
notSupported: true
}]
},
{
tfOpName: "TensorArrayGatherV3",
category: "control",
inputs: [
{start: 0, name: "tensorArrayId", type: "tensor"},
{start: 1, name: "indices", type: "number[]"},
{start: 2, name: "flowIn", type: "number"}
],
attrs: [
{tfName: "dtype", name: "dtype", type: "dtype"},
{tfName: "element_shape", name: "elementShape", type: "shape"}
]
},
{
tfOpName: "TensorArrayScatterV3",
category: "control",
inputs: [
{start: 0, name: "tensorArrayId", type: "tensor"},
{start: 1, name: "indices", type: "number[]"},
{start: 2, name: "tensor", type: "tensor"},
{start: 3, name: "flowIn", type: "number"}
],
attrs: [{tfName: "T", name: "dtype", type: "dtype"}]
},
{
tfOpName: "TensorArrayConcatV3",
category: "control",
inputs: [
{start: 0, name: "tensorArrayId", type: "tensor"},
{start: 1, name: "flowIn", type: "number"}
],
attrs: [
{tfName: "dtype", name: "dtype", type: "dtype"},
{
tfName: "element_shape_except0",
name: "elementShapeExcept0",
type: "shape",
notSupported: true
}
]
},
{
tfOpName: "TensorArraySplitV3",
category: "control",
inputs: [
{start: 0, name: "tensorArrayId", type: "tensor"},
{start: 1, name: "tensor", type: "tensor"},
{start: 2, name: "lengths", type: "number[]"},
{start: 3, name: "flowIn", type: "number"}
],
attrs: [{tfName: "T", name: "dtype", type: "dtype"}]
},
{
tfOpName: "TensorArraySizeV3",
category: "control",
inputs: [
{start: 0, name: "tensorArrayId", type: "tensor"},
{start: 1, name: "flowIn", type: "number"}
]
},
{
tfOpName: "TensorArrayCloseV3",
category: "control",
inputs: [{start: 0, name: "tensorArrayId", type: "tensor"}]
},
{
tfOpName: "StatelessIf",
category: "control",
inputs: [
{start: 0, name: "cond", type: "tensor"},
{start: 1, end: 0, name: "args", type: "tensors"}
],
attrs: [
{tfName: "then_branch", name: "thenBranch", type: "func"},
{tfName: "else_branch", name: "elseBranch", type: "func"}
]
},
{
tfOpName: "If",
category: "control",
inputs: [
{start: 0, name: "cond", type: "tensor"},
{start: 1, end: 0, name: "args", type: "tensors"}
],
attrs: [
{tfName: "then_branch", name: "thenBranch", type: "func"},
{tfName: "else_branch", name: "elseBranch", type: "func"}
]
},
{
tfOpName: "StatelessWhile",
category: "control",
inputs: [
{start: 0, end: 0, name: "args", type: "tensors"}
],
attrs: [
{tfName: "cond", name: "cond", type: "func"},
{tfName: "body", name: "body", type: "func"}
]
},
{
tfOpName: "While",
category: "control",
inputs: [
{start: 0, end: 0, name: "args", type: "tensors"}
],
attrs: [
{tfName: "cond", name: "cond", type: "func"},
{tfName: "body", name: "body", type: "func"}
]
},
{
tfOpName: "TensorListScatter",
category: "control",
inputs: [
{start: 0, name: "tensor", type: "tensor"},
{start: 1, name: "indices", type: "number[]"},
{start: 2, name: "elementShape", type: "shape"}
],
attrs: [{tfName: "element_dtype", name: "elementDType", type: "dtype"}]
},
{
tfOpName: "TensorListScatterV2",
category: "control",
inputs: [
{start: 0, name: "tensor", type: "tensor"},
{start: 1, name: "indices", type: "number[]"},
{start: 2, name: "elementShape", type: "shape"},
{start: 3, name: "numElements", type: "number"}
],
attrs: [{tfName: "element_dtype", name: "elementDType", type: "dtype"}]
},
{
tfOpName: "TensorListGather",
category: "control",
inputs: [
{start: 0, name: "tensorListId", type: "tensor"},
{start: 1, name: "indices", type: "number[]"},
{start: 2, name: "elementShape", type: "shape"}
],
attrs: [{tfName: "element_dtype", name: "elementDType", type: "dtype"}]
},
{
tfOpName: "TensorListGetItem",
category: "control",
inputs: [
{start: 0, name: "tensorListId", type: "tensor"},
{start: 1, name: "index", type: "number"},
{start: 2, name: "elementShape", type: "shape"}
],
attrs: [{tfName: "element_dtype", name: "elementDType", type: "dtype"}]
},
{
tfOpName: "TensorListSetItem",
category: "control",
inputs: [
{start: 0, name: "tensorListId", type: "tensor"},
{start: 1, name: "index", type: "number"},
{start: 2, name: "tensor", type: "tensor"}
],
attrs: [{tfName: "element_dtype", name: "elementDType", type: "dtype"}]
},
{
tfOpName: "TensorListReserve",
category: "control",
inputs: [
{start: 0, name: "elementShape", type: "shape"},
{start: 1, name: "numElements", type: "number"}
],
attrs: [{tfName: "element_dtype", name: "elementDType", type: "dtype"}]
},
{
tfOpName: "TensorListFromTensor",
category: "control",
inputs: [
{start: 0, name: "tensor", type: "tensor"},
{start: 1, name: "elementShape", type: "shape"}
],
attrs: [{tfName: "element_dtype", name: "elementDType", type: "dtype"}]
},
{
tfOpName: "TensorListStack",
category: "control",
inputs: [
{start: 0, name: "tensorListId", type: "tensor"},
{start: 1, name: "elementShape", type: "shape"}
],
attrs: [
{tfName: "element_dtype", name: "elementDType", type: "dtype"},
{tfName: "num_elements", name: "numElements", type: "dtype"}
]
},
{
tfOpName: "TensorListSplit",
category: "control",
inputs: [
{start: 0, name: "tensor", type: "tensor"},
{start: 1, name: "elementShape", type: "shape"},
{start: 2, name: "lengths", type: "number[]"}
],
attrs: [{tfName: "element_dtype", name: "elementDType", type: "dtype"}]
},
{
tfOpName: "TensorListConcat",
category: "control",
inputs: [
{start: 0, name: "tensorListId", type: "tensor"}
],
attrs: [
{tfName: "element_shape", name: "elementShape", type: "shape"},
{tfName: "element_dtype", name: "elementDType", type: "dtype"}
]
},
{
tfOpName: "TensorListPopBack",
category: "control",
inputs: [
{start: 0, name: "tensorListId", type: "tensor"},
{start: 1, name: "elementShape", type: "shape"}
],
attrs: [{tfName: "element_dtype", name: "elementDType", type: "dtype"}]
},
{
tfOpName: "TensorListPushBack",
category: "control",
inputs: [
{start: 0, name: "tensorListId", type: "tensor"},
{start: 1, name: "tensor", type: "tensor"}
],
attrs: [
{tfName: "element_dtype", name: "elementDType", type: "dtype"}
]
}
];
// node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/convolution.js
const convolution_exports = {};
__export(convolution_exports, {
json: () => json4
});
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const json4 = [
{
tfOpName: "AvgPool",
category: "convolution",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{tfName: "strides", name: "strides", type: "number[]"},
{tfName: "padding", name: "pad", type: "string"},
{
tfName: "data_format",
name: "dataFormat",
type: "string",
notSupported: true
},
{tfName: "ksize", name: "kernelSize", type: "number[]"},
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "MaxPool",
category: "convolution",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{tfName: "strides", name: "strides", type: "number[]"},
{tfName: "padding", name: "pad", type: "string"},
{
tfName: "data_format",
name: "dataFormat",
type: "string",
notSupported: true
},
{tfName: "ksize", name: "kernelSize", type: "number[]"},
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "MaxPoolWithArgmax",
category: "convolution",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{tfName: "strides", name: "strides", type: "number[]"},
{tfName: "padding", name: "pad", type: "string"},
{tfName: "ksize", name: "kernelSize", type: "number[]"},
{
tfName: "include_batch_in_index",
name: "includeBatchInIndex",
type: "bool"
},
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "AvgPool3D",
category: "convolution",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{tfName: "strides", name: "strides", type: "number[]"},
{tfName: "padding", name: "pad", type: "string"},
{
tfName: "data_format",
name: "dataFormat",
type: "string",
notSupported: true
},
{tfName: "ksize", name: "kernelSize", type: "number[]"},
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "MaxPool3D",
category: "convolution",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{tfName: "strides", name: "strides", type: "number[]"},
{tfName: "padding", name: "pad", type: "string"},
{
tfName: "data_format",
name: "dataFormat",
type: "string",
notSupported: true
},
{tfName: "ksize", name: "kernelSize", type: "number[]"},
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Conv1D",
category: "convolution",
inputs: [
{start: 0, name: "x", type: "tensor"},
{start: 1, name: "filter", type: "tensor"}
],
attrs: [
{tfName: "stride", name: "stride", type: "number"},
{tfName: "padding", name: "pad", type: "string"},
{
tfName: "data_format",
name: "dataFormat",
type: "string",
defaultValue: "NWC"
},
{tfName: "T", name: "dtype", type: "dtype", notSupported: true},
{
tfName: "dilation",
name: "dilation",
type: "number",
defaultValue: 1
}
]
},
{
tfOpName: "Conv2D",
category: "convolution",
inputs: [
{start: 0, name: "x", type: "tensor"},
{start: 1, name: "filter", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true},
{tfName: "strides", name: "strides", type: "number[]"},
{tfName: "padding", name: "pad", type: "string"},
{tfName: "useCudnnOnGpu", name: "useCudnnOnGpu", type: "bool"},
{
tfName: "data_format",
name: "dataFormat",
type: "string",
defaultValue: "NHWC"
},
{
tfName: "explicit_paddings",
name: "explicitPaddings",
type: "number[]",
defaultValue: []
},
{tfName: "dilations", name: "dilations", type: "number[]"}
]
},
{
tfOpName: "_FusedConv2D",
category: "convolution",
inputs: [
{start: 0, name: "x", type: "tensor"},
{start: 1, name: "filter", type: "tensor"},
{start: 2, end: 0, name: "args", type: "tensors"}
],
attrs: [
{tfName: "num_args", name: "numArgs", type: "number"},
{tfName: "T", name: "dtype", type: "dtype", notSupported: true},
{tfName: "strides", name: "strides", type: "number[]"},
{tfName: "padding", name: "pad", type: "string"},
{
tfName: "explicit_paddings",
name: "explicitPaddings",
type: "number[]",
defaultValue: []
},
{
tfName: "use_cudnn_on_gpu",
name: "useCudnnOnGpu",
type: "bool",
defaultValue: true
},
{
tfName: "data_format",
name: "dataFormat",
type: "string",
defaultValue: "NHWC"
},
{
tfName: "dilations",
name: "dilations",
type: "number[]",
defaultValue: [1, 1, 1, 1]
},
{
tfName: "fused_ops",
name: "fusedOps",
type: "string[]",
defaultValue: []
},
{
tfName: "epsilon",
name: "epsilon",
type: "number",
defaultValue: 1e-4
}
]
},
{
tfOpName: "Conv2DBackpropInput",
category: "convolution",
inputs: [
{start: 2, name: "x", type: "tensor"},
{start: 1, name: "filter", type: "tensor"},
{start: 0, name: "outputShape", type: "number[]"}
],
attrs: [
{tfName: "strides", name: "strides", type: "number[]"},
{tfName: "padding", name: "pad", type: "string"},
{
tfName: "data_format",
name: "dataFormat",
type: "string",
notSupported: true
},
{
tfName: "explicit_paddings",
name: "explicitPaddings",
type: "number[]",
defaultValue: []
}
]
},
{
tfOpName: "DepthwiseConv2d",
category: "convolution",
inputs: [
{start: 0, name: "input", type: "tensor"},
{start: 1, name: "filter", type: "tensor"}
],
attrs: [
{tfName: "strides", name: "strides", type: "number[]"},
{tfName: "padding", name: "pad", type: "string"},
{
tfName: "data_format",
name: "dataFormat",
type: "string",
defaultValue: "NHWC"
},
{
tfName: "explicit_paddings",
name: "explicitPaddings",
type: "number[]",
defaultValue: []
},
{tfName: "dilations", name: "dilations", type: "number[]"}
]
},
{
tfOpName: "DepthwiseConv2dNative",
category: "convolution",
inputs: [
{start: 0, name: "input", type: "tensor"},
{start: 1, name: "filter", type: "tensor"}
],
attrs: [
{tfName: "strides", name: "strides", type: "number[]"},
{tfName: "padding", name: "pad", type: "string"},
{
tfName: "data_format",
name: "dataFormat",
type: "string",
defaultValue: "NHWC"
},
{
tfName: "explicit_paddings",
name: "explicitPaddings",
type: "number[]",
defaultValue: []
},
{tfName: "dilations", name: "dilations", type: "number[]"}
]
},
{
tfOpName: "FusedDepthwiseConv2dNative",
category: "convolution",
inputs: [
{start: 0, name: "x", type: "tensor"},
{start: 1, name: "filter", type: "tensor"},
{start: 2, end: 0, name: "args", type: "tensors"}
],
attrs: [
{tfName: "num_args", name: "numArgs", type: "number"},
{tfName: "T", name: "dtype", type: "dtype", notSupported: true},
{tfName: "strides", name: "strides", type: "number[]"},
{tfName: "padding", name: "pad", type: "string"},
{
tfName: "data_format",
name: "dataFormat",
type: "string",
defaultValue: "NHWC"
},
{
tfName: "dilations",
name: "dilations",
type: "number[]",
defaultValue: [1, 1, 1, 1]
},
{
tfName: "fused_ops",
name: "fusedOps",
type: "string[]",
defaultValue: []
}
]
},
{
tfOpName: "Conv3D",
category: "convolution",
inputs: [
{start: 0, name: "x", type: "tensor"},
{start: 1, name: "filter", type: "tensor"}
],
attrs: [
{tfName: "strides", name: "strides", type: "number[]"},
{tfName: "padding", name: "pad", type: "string"},
{
tfName: "data_format",
name: "dataFormat",
type: "string",
defaultValue: "NHWC"
},
{tfName: "dilations", name: "dilations", type: "number[]"}
]
},
{
tfOpName: "Dilation2D",
category: "convolution",
inputs: [
{start: 0, name: "x", type: "tensor"},
{start: 1, name: "filter", type: "tensor"}
],
attrs: [
{tfName: "strides", name: "strides", type: "number[]"},
{tfName: "rates", name: "dilations", type: "number[]"},
{tfName: "padding", name: "pad", type: "string"}
]
}
];
// node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/creation.js
const creation_exports = {};
__export(creation_exports, {
json: () => json5
});
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const json5 = [
{
tfOpName: "Fill",
category: "creation",
inputs: [
{start: 0, name: "shape", type: "number[]"},
{start: 1, name: "value", type: "number"}
],
attrs: [{tfName: "T", name: "dtype", type: "dtype"}]
},
{
tfOpName: "LinSpace",
category: "creation",
inputs: [
{start: 0, name: "start", type: "number"},
{start: 1, name: "stop", type: "number"},
{start: 2, name: "num", type: "number"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "OneHot",
category: "creation",
inputs: [
{start: 0, name: "indices", type: "tensor"},
{start: 1, name: "depth", type: "number"},
{start: 2, name: "onValue", type: "number", defaultValue: 1},
{start: 3, name: "offValue", type: "number", defaultValue: 0}
],
attrs: [
{
tfName: "axis",
name: "axis",
type: "number",
notSupported: true
},
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Ones",
category: "creation",
inputs: [
{start: 0, name: "shape", type: "number[]"}
],
attrs: [{tfName: "T", name: "dtype", type: "dtype"}]
},
{
tfOpName: "OnesLike",
category: "creation",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [{tfName: "dtype", name: "dtype", type: "dtype"}]
},
{
tfOpName: "RandomUniform",
category: "creation",
inputs: [
{start: 0, name: "shape", type: "number[]"}
],
attrs: [
{
tfName: "minval",
name: "minval",
type: "number",
defaultValue: 0
},
{
tfName: "maxval",
name: "maxval",
type: "number",
defaultValue: 1
},
{tfName: "dtype", name: "dtype", type: "dtype"},
{tfName: "seed", name: "seed", type: "number", defaultValue: 0},
{
tfName: "seed2",
name: "seed2",
type: "number",
defaultValue: 0,
notSupported: true
},
{tfName: "T", name: "T", type: "number", notSupported: true}
]
},
{
tfOpName: "Range",
category: "creation",
inputs: [
{start: 0, name: "start", type: "number"},
{start: 1, name: "stop", type: "number"},
{start: 2, name: "step", type: "number", defaultValue: 0}
],
attrs: [{tfName: "Tidx", name: "dtype", type: "dtype"}]
},
{
tfOpName: "TruncatedNormal",
category: "creation",
inputs: [
{start: 0, name: "shape", type: "number[]"}
],
attrs: [
{
tfName: "means",
name: "mean",
type: "number",
defaultValue: 0
},
{
tfName: "stddev",
name: "stdDev",
type: "number",
defaultValue: 1
},
{tfName: "seed", name: "seed", type: "number"},
{
tfName: "seed2",
name: "seed2",
type: "number",
defaultValue: 0,
notSupported: true
},
{tfName: "dtype", name: "dtype", type: "dtype"},
{tfName: "T", name: "T", type: "number", notSupported: true}
]
},
{
tfOpName: "Zeros",
category: "creation",
inputs: [
{start: 0, name: "shape", type: "number[]"}
],
attrs: [{tfName: "T", name: "dtype", type: "dtype"}]
},
{
tfOpName: "ZerosLike",
category: "creation",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [{tfName: "T", name: "dtype", type: "dtype"}]
},
{
tfOpName: "Multinomial",
category: "creation",
inputs: [
{start: 0, name: "logits", type: "tensor"},
{start: 1, name: "numSamples", type: "number"}
],
attrs: [
{tfName: "seed", name: "seed", type: "number"},
{tfName: "seed2", name: "seed2", type: "number"},
{tfName: "T", name: "dtype", type: "dtype"},
{tfName: "output_dtype", name: "output_dtype", type: "dtype"}
]
}
];
// node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/dynamic.js
const dynamic_exports = {};
__export(dynamic_exports, {
json: () => json6
});
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const json6 = [
{
tfOpName: "NonMaxSuppressionV2",
category: "dynamic",
inputs: [
{start: 0, name: "boxes", type: "tensor"},
{start: 1, name: "scores", type: "tensor"},
{start: 2, name: "maxOutputSize", type: "number"},
{start: 3, name: "iouThreshold", type: "number"}
]
},
{
tfOpName: "NonMaxSuppressionV3",
category: "dynamic",
inputs: [
{start: 0, name: "boxes", type: "tensor"},
{start: 1, name: "scores", type: "tensor"},
{start: 2, name: "maxOutputSize", type: "number"},
{start: 3, name: "iouThreshold", type: "number"},
{start: 4, name: "scoreThreshold", type: "number"}
]
},
{
tfOpName: "NonMaxSuppressionV4",
category: "dynamic",
inputs: [
{start: 0, name: "boxes", type: "tensor"},
{start: 1, name: "scores", type: "tensor"},
{start: 2, name: "maxOutputSize", type: "number"},
{start: 3, name: "iouThreshold", type: "number"},
{start: 4, name: "scoreThreshold", type: "number"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true},
{
tfName: "T_threshold",
name: "threshold",
type: "dtype",
notSupported: true
},
{
tfName: "pad_to_max_output_size",
name: "padToMaxOutputSize",
type: "bool"
}
]
},
{
tfOpName: "NonMaxSuppressionV5",
category: "dynamic",
inputs: [
{start: 0, name: "boxes", type: "tensor"},
{start: 1, name: "scores", type: "tensor"},
{start: 2, name: "maxOutputSize", type: "number"},
{start: 3, name: "iouThreshold", type: "number"},
{start: 4, name: "scoreThreshold", type: "number"},
{start: 5, name: "softNmsSigma", type: "number"}
]
},
{
tfOpName: "Where",
category: "dynamic",
inputs: [
{start: 0, name: "condition", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "ListDiff",
category: "dynamic",
inputs: [
{start: 0, name: "x", type: "tensor"},
{start: 1, name: "y", type: "tensor"}
],
attrs: [{
tfName: "T",
name: "dtype",
type: "dtype",
notSupported: true
}]
}
];
// node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/evaluation.js
const evaluation_exports = {};
__export(evaluation_exports, {
json: () => json7
});
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const json7 = [
{
tfOpName: "TopKV2",
category: "evaluation",
inputs: [
{start: 0, name: "x", type: "tensor"},
{start: 1, name: "k", type: "number"}
],
attrs: [{tfName: "sorted", name: "sorted", type: "bool"}]
},
{
tfOpName: "Unique",
category: "evaluation",
inputs: [
{start: 0, name: "x", type: "tensor"}
]
},
{
tfOpName: "UniqueV2",
category: "evaluation",
inputs: [
{start: 0, name: "x", type: "tensor"},
{start: 1, name: "axis", type: "number"}
]
}
];
// node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/graph.js
const graph_exports = {};
__export(graph_exports, {
json: () => json8
});
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const json8 = [
{
tfOpName: "PlaceholderWithDefault",
category: "graph",
inputs: [
{start: 0, name: "default", type: "tensor"}
],
attrs: [
{tfName: "shape", name: "shape", type: "shape"},
{tfName: "dtype", name: "dtype", type: "dtype"}
]
},
{
tfOpName: "Placeholder",
category: "graph",
attrs: [
{tfName: "shape", name: "shape", type: "shape"},
{tfName: "dtype", name: "dtype", type: "dtype"}
]
},
{tfOpName: "Const", category: "graph"},
{
tfOpName: "Identity",
category: "graph",
inputs: [{start: 0, name: "x", type: "tensor"}]
},
{
tfOpName: "IdentityN",
category: "graph",
inputs: [{start: 0, end: 0, name: "x", type: "tensors"}]
},
{
tfOpName: "Snapshot",
category: "graph",
inputs: [{start: 0, name: "x", type: "tensor"}]
},
{
tfOpName: "Rank",
category: "graph",
inputs: [{start: 0, name: "x", type: "tensor"}]
},
{
tfOpName: "Size",
category: "graph",
inputs: [{start: 0, name: "x", type: "tensor"}]
},
{
tfOpName: "Shape",
category: "graph",
inputs: [{start: 0, name: "x", type: "tensor"}]
},
{
tfOpName: "ShapeN",
category: "graph",
inputs: [{start: 0, end: 0, name: "x", type: "tensors"}]
},
{
tfOpName: "Print",
category: "graph",
inputs: [
{start: 0, name: "x", type: "tensor"},
{start: 1, name: "data", type: "tensors"}
],
attrs: [
{tfName: "message", name: "message", type: "string"},
{
tfName: "first_n",
name: "firstN",
type: "number",
notSupported: true
},
{
tfName: "summarize",
name: "summarize",
type: "number",
defaultValue: 3
}
]
},
{tfOpName: "NoOp", category: "graph", inputs: []},
{
tfOpName: "StopGradient",
category: "graph",
inputs: [{start: 0, name: "x", type: "tensor"}]
},
{
tfOpName: "FakeQuantWithMinMaxVars",
category: "graph",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{tfName: "min", name: "min", type: "number"},
{tfName: "max", name: "max", type: "number"}
]
}
];
// node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/hash_table.js
const hash_table_exports = {};
__export(hash_table_exports, {
json: () => json9
});
const json9 = [
{
tfOpName: "HashTable",
category: "hash_table",
inputs: [],
attrs: [
{tfName: "shared_name", name: "sharedName", type: "string"},
{
tfName: "use_node_name_sharing",
name: "useNodeNameSharing",
type: "bool"
},
{tfName: "key_dtype", name: "keyDType", type: "dtype"},
{tfName: "value_dtype", name: "valueDType", type: "dtype"}
]
},
{
tfOpName: "HashTableV2",
category: "hash_table",
inputs: [],
attrs: [
{tfName: "shared_name", name: "sharedName", type: "string"},
{
tfName: "use_node_name_sharing",
name: "useNodeNameSharing",
type: "bool"
},
{tfName: "key_dtype", name: "keyDType", type: "dtype"},
{tfName: "value_dtype", name: "valueDType", type: "dtype"}
]
},
{
tfOpName: "LookupTableImport",
category: "hash_table",
inputs: [
{start: 0, name: "tableHandle", type: "tensor"},
{start: 1, name: "keys", type: "tensor"},
{start: 2, name: "values", type: "tensor"}
],
attrs: [
{tfName: "Tin", name: "tIn", type: "dtype", notSupported: true},
{
tfName: "Tout",
name: "tOut",
type: "dtype",
notSupported: true
}
]
},
{
tfOpName: "LookupTableImportV2",
category: "hash_table",
inputs: [
{start: 0, name: "tableHandle", type: "tensor"},
{start: 1, name: "keys", type: "tensor"},
{start: 2, name: "values", type: "tensor"}
],
attrs: [
{tfName: "Tin", name: "tIn", type: "dtype", notSupported: true},
{
tfName: "Tout",
name: "tOut",
type: "dtype",
notSupported: true
}
]
},
{
tfOpName: "LookupTableFind",
category: "hash_table",
inputs: [
{start: 0, name: "tableHandle", type: "tensor"},
{start: 1, name: "keys", type: "tensor"},
{start: 2, name: "defaultValue", type: "tensor"}
],
attrs: [
{tfName: "Tin", name: "tIn", type: "dtype", notSupported: true},
{
tfName: "Tout",
name: "tOut",
type: "dtype",
notSupported: true
}
]
},
{
tfOpName: "LookupTableFindV2",
category: "hash_table",
inputs: [
{start: 0, name: "tableHandle", type: "tensor"},
{start: 1, name: "keys", type: "tensor"},
{start: 2, name: "defaultValue", type: "tensor"}
],
attrs: [
{tfName: "Tin", name: "tIn", type: "dtype", notSupported: true},
{
tfName: "Tout",
name: "tOut",
type: "dtype",
notSupported: true
}
]
}
];
// node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/image.js
const image_exports = {};
__export(image_exports, {
json: () => json10
});
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const json10 = [
{
tfOpName: "ResizeBilinear",
category: "image",
inputs: [
{start: 0, name: "images", type: "tensor"},
{start: 1, name: "size", type: "number[]"}
],
attrs: [
{tfName: "align_corners", name: "alignCorners", type: "bool"},
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "ResizeNearestNeighbor",
category: "image",
inputs: [
{start: 0, name: "images", type: "tensor"},
{start: 1, name: "size", type: "number[]"}
],
attrs: [
{tfName: "align_corners", name: "alignCorners", type: "bool"},
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "CropAndResize",
category: "image",
inputs: [
{start: 0, name: "image", type: "tensor"},
{start: 1, name: "boxes", type: "tensor"},
{start: 2, name: "boxInd", type: "tensor"},
{start: 3, name: "cropSize", type: "number[]"}
],
attrs: [
{tfName: "method", name: "method", type: "string"},
{
tfName: "extrapolation_value",
name: "extrapolationValue",
type: "number"
}
]
}
];
// node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/logical.js
const logical_exports = {};
__export(logical_exports, {
json: () => json11
});
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const json11 = [
{
tfOpName: "Equal",
category: "logical",
inputs: [
{start: 0, name: "a", type: "tensor"},
{start: 1, name: "b", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "NotEqual",
category: "logical",
inputs: [
{start: 0, name: "a", type: "tensor"},
{start: 1, name: "b", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Greater",
category: "logical",
inputs: [
{start: 0, name: "a", type: "tensor"},
{start: 1, name: "b", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "GreaterEqual",
category: "logical",
inputs: [
{start: 0, name: "a", type: "tensor"},
{start: 1, name: "b", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Less",
category: "logical",
inputs: [
{start: 0, name: "a", type: "tensor"},
{start: 1, name: "b", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "LessEqual",
category: "logical",
inputs: [
{start: 0, name: "a", type: "tensor"},
{start: 1, name: "b", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "LogicalAnd",
category: "logical",
inputs: [
{start: 0, name: "a", type: "tensor"},
{start: 1, name: "b", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "LogicalNot",
category: "logical",
inputs: [
{start: 0, name: "a", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "LogicalOr",
category: "logical",
inputs: [
{start: 0, name: "a", type: "tensor"},
{start: 1, name: "b", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Select",
category: "logical",
inputs: [
{start: 0, name: "condition", type: "tensor"},
{start: 1, name: "a", type: "tensor"},
{start: 2, name: "b", type: "tensor"}
],
attrs: [
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "SelectV2",
category: "logical",
inputs: [
{start: 0, name: "condition", type: "tensor"},
{start: 1, name: "a", type: "tensor"},
{start: 2, name: "b", type: "tensor"}
],
attrs: [{
tfName: "T",
name: "dtype",
type: "dtype",
notSupported: true
}]
}
];
// node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/matrices.js
const matrices_exports = {};
__export(matrices_exports, {
json: () => json12
});
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const json12 = [
{
tfOpName: "_FusedMatMul",
category: "matrices",
inputs: [
{start: 0, name: "a", type: "tensor"},
{start: 1, name: "b", type: "tensor"},
{start: 2, end: 0, name: "args", type: "tensors"}
],
attrs: [
{tfName: "num_args", name: "numArgs", type: "number"},
{
tfName: "fused_ops",
name: "fusedOps",
type: "string[]",
defaultValue: []
},
{
tfName: "epsilon",
name: "epsilon",
type: "number",
defaultValue: 1e-4
},
{
tfName: "transpose_a",
name: "transposeA",
type: "bool",
defaultValue: false
},
{
tfName: "transpose_b",
name: "transposeB",
type: "bool",
defaultValue: false
},
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "MatMul",
category: "matrices",
inputs: [
{start: 0, name: "a", type: "tensor"},
{start: 1, name: "b", type: "tensor"}
],
attrs: [
{
tfName: "transpose_a",
name: "transposeA",
type: "bool",
defaultValue: false
},
{
tfName: "transpose_b",
name: "transposeB",
type: "bool",
defaultValue: false
},
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "BatchMatMul",
category: "matrices",
inputs: [
{start: 0, name: "a", type: "tensor"},
{start: 1, name: "b", type: "tensor"}
],
attrs: [
{
tfName: "adj_x",
name: "transposeA",
type: "bool",
defaultValue: false
},
{
tfName: "adj_y",
name: "transposeB",
type: "bool",
defaultValue: false
},
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "BatchMatMulV2",
category: "matrices",
inputs: [
{start: 0, name: "a", type: "tensor"},
{start: 1, name: "b", type: "tensor"}
],
attrs: [
{
tfName: "adj_x",
name: "transposeA",
type: "bool",
defaultValue: false
},
{
tfName: "adj_y",
name: "transposeB",
type: "bool",
defaultValue: false
},
{tfName: "T", name: "dtype", type: "dtype", notSupported: true}
]
},
{
tfOpName: "Transpose",
category: "matrices",
inputs: [
{start: 0, name: "x", type: "tensor"},
{start: 1, name: "perm", type: "number[]"}
],
attrs: [{
tfName: "T",
name: "dtype",
type: "dtype",
notSupported: true
}]
}
];
// node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/normalization.js
const normalization_exports = {};
__export(normalization_exports, {
json: () => json13
});
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const json13 = [
{
tfOpName: "FusedBatchNorm",
category: "normalization",
inputs: [
{start: 0, name: "x", type: "tensor"},
{start: 1, name: "scale", type: "tensor"},
{start: 2, name: "offset", type: "tensor"},
{start: 3, name: "mean", type: "tensor"},
{start: 4, name: "variance", type: "tensor"}
],
attrs: [
{
tfName: "epsilon",
name: "epsilon",
type: "number",
defaultValue: 1e-3
},
{
tfName: "data_format",
name: "dataFormat",
type: "string",
notSupported: true
}
]
},
{
tfOpName: "FusedBatchNormV2",
category: "normalization",
inputs: [
{start: 0, name: "x", type: "tensor"},
{start: 1, name: "scale", type: "tensor"},
{start: 2, name: "offset", type: "tensor"},
{start: 3, name: "mean", type: "tensor"},
{start: 4, name: "variance", type: "tensor"}
],
attrs: [
{
tfName: "epsilon",
name: "epsilon",
type: "number",
defaultValue: 1e-3
},
{
tfName: "data_format",
name: "dataFormat",
type: "string",
notSupported: true
}
]
},
{
tfOpName: "FusedBatchNormV3",
category: "normalization",
inputs: [
{start: 0, name: "x", type: "tensor"},
{start: 1, name: "scale", type: "tensor"},
{start: 2, name: "offset", type: "tensor"},
{start: 3, name: "mean", type: "tensor"},
{start: 4, name: "variance", type: "tensor"}
],
attrs: [
{
tfName: "epsilon",
name: "epsilon",
type: "number",
defaultValue: 1e-3
},
{
tfName: "data_format",
name: "dataFormat",
type: "string",
notSupported: true
}
]
},
{
tfOpName: "LRN",
category: "normalization",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{
tfName: "depth_radius",
name: "radius",
type: "number",
defaultValue: 5
},
{tfName: "bias", name: "bias", type: "number", defaultValue: 1},
{
tfName: "alpha",
name: "alpha",
type: "number",
defaultValue: 1
},
{
tfName: "beta",
name: "beta",
type: "number",
defaultValue: 0.5
}
]
},
{
tfOpName: "Softmax",
category: "normalization",
inputs: [{start: 0, name: "x", type: "tensor"}]
},
{
tfOpName: "LogSoftmax",
category: "normalization",
inputs: [{start: 0, name: "x", type: "tensor"}]
},
{
tfOpName: "SparseToDense",
category: "normalization",
inputs: [
{start: 0, name: "sparseIndices", type: "tensor"},
{start: 1, name: "outputShape", type: "number[]"},
{start: 2, name: "sparseValues", type: "tensor"},
{start: 3, name: "defaultValue", type: "tensor"}
],
attrs: [{
tfName: "validate_indices",
name: "validateIndices",
type: "bool",
defaultValue: true,
notSupported: true
}]
}
];
// node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/reduction.js
const reduction_exports = {};
__export(reduction_exports, {
json: () => json14
});
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const json14 = [
{
tfOpName: "Max",
category: "reduction",
inputs: [
{start: 0, name: "x", type: "tensor"},
{start: 1, name: "axis", type: "number[]"}
],
attrs: [{tfName: "keep_dims", name: "keepDims", type: "bool"}]
},
{
tfOpName: "Mean",
category: "reduction",
inputs: [
{start: 0, name: "x", type: "tensor"},
{start: 1, name: "axis", type: "number[]"}
],
attrs: [{tfName: "keep_dims", name: "keepDims", type: "bool"}]
},
{
tfOpName: "Min",
category: "reduction",
inputs: [
{start: 0, name: "x", type: "tensor"},
{start: 1, name: "axis", type: "number[]"}
],
attrs: [{tfName: "keep_dims", name: "keepDims", type: "bool"}]
},
{
tfOpName: "Sum",
category: "reduction",
inputs: [
{start: 0, name: "x", type: "tensor"},
{start: 1, name: "axis", type: "number[]"}
],
attrs: [{tfName: "keep_dims", name: "keepDims", type: "bool"}]
},
{
tfOpName: "All",
category: "reduction",
inputs: [
{start: 0, name: "x", type: "tensor"},
{start: 1, name: "axis", type: "number[]"}
],
attrs: [{tfName: "keep_dims", name: "keepDims", type: "bool"}]
},
{
tfOpName: "Any",
category: "reduction",
inputs: [
{start: 0, name: "x", type: "tensor"},
{start: 1, name: "axis", type: "number[]"}
],
attrs: [{tfName: "keep_dims", name: "keepDims", type: "bool"}]
},
{
tfOpName: "ArgMax",
category: "reduction",
inputs: [
{start: 0, name: "x", type: "tensor"},
{start: 1, name: "axis", type: "number"}
]
},
{
tfOpName: "ArgMin",
category: "reduction",
inputs: [
{start: 0, name: "x", type: "tensor"},
{start: 1, name: "axis", type: "number"}
]
},
{
tfOpName: "Prod",
category: "reduction",
inputs: [
{start: 0, name: "x", type: "tensor"},
{start: 1, name: "axis", type: "number[]"}
],
attrs: [{tfName: "keep_dims", name: "keepDims", type: "bool"}]
},
{
tfOpName: "Cumsum",
category: "reduction",
inputs: [
{start: 0, name: "x", type: "tensor"},
{start: 1, name: "axis", type: "number"}
],
attrs: [
{tfName: "exclusive", name: "exclusive", type: "bool"},
{tfName: "reverse", name: "reverse", type: "bool"}
]
}
];
// node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/slice_join.js
const slice_join_exports = {};
__export(slice_join_exports, {
json: () => json15
});
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const json15 = [
{
tfOpName: "ConcatV2",
category: "slice_join",
inputs: [
{start: 0, end: -1, name: "tensors", type: "tensors"},
{start: -1, name: "axis", type: "number"}
],
attrs: [{tfName: "N", name: "n", type: "number", defaultValue: 2}]
},
{
tfOpName: "Concat",
category: "slice_join",
inputs: [
{start: 1, end: 0, name: "tensors", type: "tensors"},
{start: 0, name: "axis", type: "number"}
],
attrs: [{tfName: "N", name: "n", type: "number", defaultValue: 2}]
},
{
tfOpName: "GatherV2",
category: "slice_join",
inputs: [
{start: 0, name: "x", type: "tensor"},
{start: 1, name: "indices", type: "tensor"},
{start: 2, name: "axis", type: "number", defaultValue: 0}
]
},
{
tfOpName: "Gather",
category: "slice_join",
inputs: [
{start: 0, name: "x", type: "tensor"},
{start: 1, name: "indices", type: "tensor"}
],
attrs: [
{tfName: "axis", name: "axis", type: "number", defaultValue: 0},
{
tfName: "validate_indices",
name: "validateIndices",
type: "bool",
notSupported: true
}
]
},
{
tfOpName: "Reverse",
category: "slice_join",
inputs: [
{start: 0, name: "x", type: "tensor"},
{start: 1, name: "dims", type: "bool", notSupported: true}
]
},
{
tfOpName: "ReverseV2",
category: "slice_join",
inputs: [
{start: 0, name: "x", type: "tensor"},
{start: 1, name: "axis", type: "number[]"}
]
},
{
tfOpName: "Slice",
category: "slice_join",
inputs: [
{start: 0, name: "x", type: "tensor"},
{start: 1, name: "begin", type: "number[]"},
{start: 2, name: "size", type: "number[]"}
]
},
{
tfOpName: "StridedSlice",
category: "slice_join",
inputs: [
{start: 0, name: "x", type: "tensor"},
{start: 1, name: "begin", type: "number[]"},
{start: 2, name: "end", type: "number[]"},
{start: 3, name: "strides", type: "number[]"}
],
attrs: [
{
tfName: "begin_mask",
name: "beginMask",
type: "number",
defaultValue: 0
},
{
tfName: "end_mask",
name: "endMask",
type: "number",
defaultValue: 0
},
{
tfName: "new_axis_mask",
name: "newAxisMask",
type: "number",
defaultValue: 0
},
{
tfName: "ellipsis_mask",
name: "ellipsisMask",
type: "number",
defaultValue: 0
},
{
tfName: "shrink_axis_mask",
name: "shrinkAxisMask",
type: "number",
defaultValue: 0
}
]
},
{
tfOpName: "Pack",
category: "slice_join",
inputs: [
{start: 0, end: 0, name: "tensors", type: "tensors"}
],
attrs: [
{tfName: "axis", name: "axis", type: "number", defaultValue: 0}
]
},
{
tfOpName: "Unpack",
category: "slice_join",
inputs: [
{start: 0, name: "tensor", type: "tensor"}
],
attrs: [
{tfName: "axis", name: "axis", type: "number", defaultValue: 0},
{
tfName: "num",
name: "num",
type: "number",
defaultValue: 0,
notSupported: true
}
]
},
{
tfOpName: "Tile",
category: "slice_join",
inputs: [
{start: 0, name: "x", type: "tensor"},
{start: 1, name: "reps", type: "number[]"}
]
},
{
tfOpName: "Split",
category: "slice_join",
inputs: [
{start: 0, name: "axis", type: "number", defaultValue: 0},
{start: 1, name: "x", type: "tensor"}
],
attrs: [{
tfName: "num_split",
name: "numOrSizeSplits",
type: "number",
defaultValue: 1
}]
},
{
tfOpName: "SplitV",
category: "slice_join",
inputs: [
{start: 0, name: "x", type: "tensor"},
{start: 1, name: "numOrSizeSplits", type: "number[]"},
{start: 2, name: "axis", type: "number", defaultValue: 0}
]
},
{
tfOpName: "ScatterNd",
category: "slice_join",
inputs: [
{start: 0, name: "indices", type: "tensor"},
{start: 1, name: "values", type: "tensor"},
{start: 2, name: "shape", type: "number[]"}
]
},
{
tfOpName: "GatherNd",
category: "slice_join",
inputs: [
{start: 0, name: "x", type: "tensor"},
{start: 1, name: "indices", type: "tensor"}
]
},
{
tfOpName: "SparseToDense",
category: "slice_join",
inputs: [
{start: 0, name: "sparseIndices", type: "tensor"},
{start: 1, name: "outputShape", type: "number[]"},
{start: 2, name: "sparseValues", type: "tensor"},
{start: 3, name: "defaultValue", type: "tensor"}
],
attrs: [{
tfName: "validate_indices",
name: "validateIndices",
type: "bool",
defaultValue: false,
notSupported: true
}]
}
];
// node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/spectral.js
const spectral_exports = {};
__export(spectral_exports, {
json: () => json16
});
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const json16 = [
{
tfOpName: "FFT",
category: "spectral",
inputs: [{start: 0, name: "x", type: "tensor"}]
},
{
tfOpName: "IFFT",
category: "spectral",
inputs: [{start: 0, name: "x", type: "tensor"}]
},
{
tfOpName: "RFFT",
category: "spectral",
inputs: [
{start: 0, name: "x", type: "tensor"},
{
start: 1,
name: "fft_length",
type: "number",
notSupported: true
}
]
},
{
tfOpName: "IRFFT",
category: "spectral",
inputs: [
{start: 0, name: "x", type: "tensor"},
{
start: 1,
name: "fft_length",
type: "number",
notSupported: true
}
]
}
];
// node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/transformation.js
const transformation_exports = {};
__export(transformation_exports, {
json: () => json17
});
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const json17 = [
{
tfOpName: "Cast",
category: "transformation",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{
tfName: "SrcT",
name: "sdtype",
type: "dtype",
notSupported: true
},
{tfName: "DstT", name: "dtype", type: "dtype"}
]
},
{
tfOpName: "ExpandDims",
category: "transformation",
inputs: [
{start: 0, name: "x", type: "tensor"},
{start: 1, name: "axis", type: "number"}
]
},
{
tfOpName: "MirrorPad",
category: "transformation",
inputs: [
{start: 0, name: "x", type: "tensor"},
{start: 1, name: "padding", type: "number[]"}
],
attrs: [{tfName: "mode", name: "mode", type: "string"}]
},
{
tfOpName: "Pad",
category: "transformation",
inputs: [
{start: 0, name: "x", type: "tensor"},
{start: 1, name: "padding", type: "number[]"}
],
attrs: [{
tfName: "constant_value",
name: "constantValue",
type: "number",
defaultValue: 0
}]
},
{
tfOpName: "PadV2",
category: "transformation",
inputs: [
{start: 0, name: "x", type: "tensor"},
{start: 1, name: "padding", type: "number[]"},
{
start: 2,
name: "constantValue",
type: "number",
defaultValue: 0
}
]
},
{
tfOpName: "Reshape",
category: "transformation",
inputs: [
{start: 0, name: "x", type: "tensor"},
{start: 1, name: "shape", type: "number[]"}
]
},
{
tfOpName: "Squeeze",
category: "transformation",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [{
tfName: "axis",
tfDeprecatedName: "squeeze_dims",
name: "axis",
type: "number[]"
}]
},
{
tfOpName: "SpaceToBatchND",
category: "transformation",
inputs: [
{start: 0, name: "x", type: "tensor"},
{start: 1, name: "blockShape", type: "number[]"},
{start: 2, name: "paddings", type: "number[]"}
]
},
{
tfOpName: "BatchToSpaceND",
category: "transformation",
inputs: [
{start: 0, name: "x", type: "tensor"},
{start: 1, name: "blockShape", type: "number[]"},
{start: 2, name: "crops", type: "number[]"}
]
},
{
tfOpName: "DepthToSpace",
category: "transformation",
inputs: [
{start: 0, name: "x", type: "tensor"}
],
attrs: [
{tfName: "block_size", name: "blockSize", type: "number"},
{tfName: "data_format", name: "dataFormat", type: "string"}
]
},
{
tfOpName: "BroadcastTo",
category: "transformation",
inputs: [
{start: 0, name: "x", type: "tensor"},
{start: 1, name: "shape", type: "number[]"}
],
attrs: []
}
];
// node_modules/@tensorflow/tfjs-converter/dist/operations/operation_mapper.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
class OperationMapper {
static get Instance() {
return this._instance || (this._instance = new this());
}
constructor() {
const ops3 = [
arithmetic_exports,
basic_math_exports,
control_exports,
convolution_exports,
creation_exports,
dynamic_exports,
evaluation_exports,
logical_exports,
image_exports,
graph_exports,
matrices_exports,
normalization_exports,
reduction_exports,
slice_join_exports,
spectral_exports,
transformation_exports,
hash_table_exports
];
const mappersJson = [].concat(...ops3.map((op2) => op2.json));
this.opMappers = mappersJson.reduce((map, mapper) => {
map[mapper.tfOpName] = mapper;
return map;
}, {});
}
transformGraph(graph2, signature = {}) {
const tfNodes = graph2.node;
const placeholders = [];
const weights = [];
const initNodes = [];
const nodes = tfNodes.reduce((map, node) => {
map[node.name] = this.mapNode(node);
if (node.op.startsWith("Placeholder")) {
placeholders.push(map[node.name]);
} else if (node.op === "Const") {
weights.push(map[node.name]);
} else if (node.input == null || node.input.length === 0) {
initNodes.push(map[node.name]);
}
return map;
}, {});
let inputs = [];
const outputs = [];
let inputNodeNameToKey = {};
let outputNodeNameToKey = {};
if (signature != null) {
inputNodeNameToKey = this.mapSignatureEntries(signature.inputs);
outputNodeNameToKey = this.mapSignatureEntries(signature.outputs);
}
const allNodes = Object.keys(nodes);
allNodes.forEach((key) => {
const node = nodes[key];
node.inputNames.forEach((name) => {
const [nodeName] = getNodeNameAndIndex(name);
node.inputs.push(nodes[nodeName]);
nodes[nodeName].children.push(node);
});
});
if (Object.keys(outputNodeNameToKey).length === 0) {
allNodes.forEach((key) => {
const node = nodes[key];
if (node.children.length === 0) {
outputs.push(node);
}
});
} else {
Object.keys(outputNodeNameToKey).forEach((name) => {
const [nodeName] = getNodeNameAndIndex(name);
const node = nodes[nodeName];
if (node != null) {
node.signatureKey = outputNodeNameToKey[name];
outputs.push(node);
}
});
}
if (Object.keys(inputNodeNameToKey).length > 0) {
Object.keys(inputNodeNameToKey).forEach((name) => {
const [nodeName] = getNodeNameAndIndex(name);
const node = nodes[nodeName];
if (node) {
node.signatureKey = inputNodeNameToKey[name];
inputs.push(node);
}
});
} else {
inputs = placeholders;
}
let functions = {};
if (graph2.library != null && graph2.library.function != null) {
functions = graph2.library.function.reduce((functions2, func2) => {
functions2[func2.signature.name] = this.mapFunction(func2);
return functions2;
}, {});
}
const result = {nodes, inputs, outputs, weights, placeholders, signature, functions};
if (initNodes.length > 0) {
result.initNodes = initNodes;
}
return result;
}
mapSignatureEntries(entries) {
return Object.keys(entries || {}).reduce((prev, curr) => {
prev[entries[curr].name] = curr;
return prev;
}, {});
}
mapNode(node) {
const mapper = getRegisteredOp(node.op) || this.opMappers[node.op] || {};
if (node.attr == null) {
node.attr = {};
}
const newNode = {
name: node.name,
op: node.op,
category: mapper.category,
inputNames: (node.input || []).map((input2) => input2.startsWith("^") ? input2.substr(1) : input2),
inputs: [],
children: [],
inputParams: {},
attrParams: {},
rawAttrs: node.attr
};
if (mapper.inputs != null) {
newNode.inputParams = mapper.inputs.reduce((map, param) => {
map[param.name] = {
type: param.type,
inputIndexStart: param.start,
inputIndexEnd: param.end
};
return map;
}, {});
}
if (mapper.attrs != null) {
newNode.attrParams = mapper.attrs.reduce((map, param) => {
const type = param.type;
let value = void 0;
switch (param.type) {
case "string":
value = getStringParam(node.attr, param.tfName, param.defaultValue);
if (value === void 0 && !!param.tfDeprecatedName) {
value = getStringParam(node.attr, param.tfDeprecatedName, param.defaultValue);
}
break;
case "string[]":
value = getStringArrayParam(node.attr, param.tfName, param.defaultValue);
if (value === void 0 && !!param.tfDeprecatedName) {
value = getStringArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);
}
break;
case "number":
value = getNumberParam(node.attr, param.tfName, param.defaultValue || 0);
if (value === void 0 && !!param.tfDeprecatedName) {
value = getNumberParam(node.attr, param.tfDeprecatedName, param.defaultValue);
}
break;
case "number[]":
value = getNumericArrayParam(node.attr, param.tfName, param.defaultValue);
if (value === void 0 && !!param.tfDeprecatedName) {
value = getNumericArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);
}
break;
case "bool":
value = getBoolParam(node.attr, param.tfName, param.defaultValue);
if (value === void 0 && !!param.tfDeprecatedName) {
value = getBoolParam(node.attr, param.tfDeprecatedName, param.defaultValue);
}
break;
case "bool[]":
value = getBoolArrayParam(node.attr, param.tfName, param.defaultValue);
if (value === void 0 && !!param.tfDeprecatedName) {
value = getBoolArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);
}
break;
case "shape":
value = getTensorShapeParam(node.attr, param.tfName, param.defaultValue);
if (value === void 0 && !!param.tfDeprecatedName) {
value = getTensorShapeParam(node.attr, param.tfDeprecatedName, param.defaultValue);
}
break;
case "shape[]":
value = getTensorShapeArrayParam(node.attr, param.tfName, param.defaultValue);
if (value === void 0 && !!param.tfDeprecatedName) {
value = getTensorShapeArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);
}
break;
case "dtype":
value = getDtypeParam(node.attr, param.tfName, param.defaultValue);
if (value === void 0 && !!param.tfDeprecatedName) {
value = getDtypeParam(node.attr, param.tfDeprecatedName, param.defaultValue);
}
break;
case "dtype[]":
value = getDtypeArrayParam(node.attr, param.tfName, param.defaultValue);
if (value === void 0 && !!param.tfDeprecatedName) {
value = getDtypeArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);
}
break;
case "func":
value = getFuncParam(node.attr, param.tfName, param.defaultValue);
if (value === void 0 && !!param.tfDeprecatedName) {
value = getFuncParam(node.attr, param.tfDeprecatedName, param.defaultValue);
}
break;
case "tensor":
case "tensors":
break;
default:
throw new Error(`Unsupported param type: ${param.type} for op: ${node.op}`);
}
map[param.name] = {value, type};
return map;
}, {});
}
return newNode;
}
mapFunction(functionDef) {
const tfNodes = functionDef.nodeDef;
const placeholders = [];
const weights = [];
let nodes = {};
if (tfNodes != null) {
nodes = tfNodes.reduce((map, node) => {
map[node.name] = this.mapNode(node);
if (node.op === "Const") {
weights.push(map[node.name]);
}
return map;
}, {});
}
const inputs = [];
const outputs = [];
functionDef.signature.inputArg.forEach((arg) => {
const [nodeName] = getNodeNameAndIndex(arg.name);
const node = {
name: nodeName,
op: "Placeholder",
inputs: [],
inputNames: [],
category: "graph",
inputParams: {},
attrParams: {dtype: {value: parseDtypeParam(arg.type), type: "dtype"}},
children: []
};
node.signatureKey = arg.name;
inputs.push(node);
nodes[nodeName] = node;
});
const allNodes = Object.keys(nodes);
allNodes.forEach((key) => {
const node = nodes[key];
node.inputNames.forEach((name) => {
const [nodeName] = getNodeNameAndIndex(name);
node.inputs.push(nodes[nodeName]);
nodes[nodeName].children.push(node);
});
});
const returnNodeMap = functionDef.ret;
functionDef.signature.outputArg.forEach((output) => {
const [nodeName, index] = getNodeNameAndIndex(returnNodeMap[output.name]);
const node = nodes[nodeName];
if (node != null) {
node.defaultOutput = index;
outputs.push(node);
}
});
const signature = this.mapArgsToSignature(functionDef);
return {nodes, inputs, outputs, weights, placeholders, signature};
}
mapArgsToSignature(functionDef) {
return {
methodName: functionDef.signature.name,
inputs: functionDef.signature.inputArg.reduce((map, arg) => {
map[arg.name] = this.mapArgToTensorInfo(arg);
return map;
}, {}),
outputs: functionDef.signature.outputArg.reduce((map, arg) => {
map[arg.name] = this.mapArgToTensorInfo(arg, functionDef.ret);
return map;
}, {})
};
}
mapArgToTensorInfo(arg, nameMap2) {
let name = arg.name;
if (nameMap2 != null) {
name = nameMap2[name];
}
return {name, dtype: arg.type};
}
}
function decodeBase64(text) {
const global2 = env().global;
if (typeof global2.atob !== "undefined") {
return global2.atob(text);
} else if (typeof Buffer !== "undefined") {
return new Buffer(text, "base64").toString();
} else {
throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()");
}
}
function parseStringParam(s, keepCase) {
const value = Array.isArray(s) ? String.fromCharCode.apply(null, s) : decodeBase64(s);
return keepCase ? value : value.toLowerCase();
}
function getStringParam(attrs, name, def, keepCase = false) {
const param = attrs[name];
if (param != null) {
return parseStringParam(param.s, keepCase);
}
return def;
}
function getBoolParam(attrs, name, def) {
const param = attrs[name];
return param ? param.b : def;
}
function getNumberParam(attrs, name, def) {
const param = attrs[name] || {};
const value = param["i"] != null ? param["i"] : param["f"] != null ? param["f"] : def;
return typeof value === "number" ? value : parseInt(value, 10);
}
function parseDtypeParam(value) {
if (typeof value === "string") {
value = DataType[value];
}
switch (value) {
case DataType.DT_FLOAT:
return "float32";
case DataType.DT_INT32:
case DataType.DT_INT64:
case DataType.DT_INT8:
case DataType.DT_UINT8:
return "int32";
case DataType.DT_BOOL:
return "bool";
case DataType.DT_DOUBLE:
return "float32";
case DataType.DT_STRING:
return "string";
default:
return null;
}
}
function getFuncParam(attrs, name, def) {
const param = attrs[name];
if (param && param.func) {
return param.func.name;
}
return def;
}
function getDtypeParam(attrs, name, def) {
const param = attrs[name];
if (param && param.type) {
return parseDtypeParam(param.type);
}
return def;
}
function getDtypeArrayParam(attrs, name, def) {
const param = attrs[name];
if (param && param.list && param.list.type) {
return param.list.type.map((v) => parseDtypeParam(v));
}
return def;
}
function parseTensorShapeParam(shape) {
if (shape.unknownRank) {
return void 0;
}
if (shape.dim != null) {
return shape.dim.map((dim) => typeof dim.size === "number" ? dim.size : parseInt(dim.size, 10));
}
return [];
}
function getTensorShapeParam(attrs, name, def) {
const param = attrs[name];
if (param && param.shape) {
return parseTensorShapeParam(param.shape);
}
return def;
}
function getNumericArrayParam(attrs, name, def) {
const param = attrs[name];
if (param) {
return ((param.list.f && param.list.f.length ? param.list.f : param.list.i) || []).map((v) => typeof v === "number" ? v : parseInt(v, 10));
}
return def;
}
function getStringArrayParam(attrs, name, def, keepCase = false) {
const param = attrs[name];
if (param && param.list && param.list.s) {
return param.list.s.map((v) => {
return parseStringParam(v, keepCase);
});
}
return def;
}
function getTensorShapeArrayParam(attrs, name, def) {
const param = attrs[name];
if (param && param.list && param.list.shape) {
return param.list.shape.map((v) => {
return parseTensorShapeParam(v);
});
}
return def;
}
function getBoolArrayParam(attrs, name, def) {
const param = attrs[name];
if (param && param.list && param.list.b) {
return param.list.b;
}
return def;
}
// node_modules/@tensorflow/tfjs-converter/dist/operations/custom_op/node_value_impl.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
class NodeValueImpl {
constructor(node, tensorMap, context) {
this.node = node;
this.tensorMap = tensorMap;
this.context = context;
this.inputs = [];
this.attrs = {};
this.inputs = node.inputNames.map((name) => this.getInput(name));
if (node.rawAttrs != null) {
this.attrs = Object.keys(node.rawAttrs).reduce((attrs, key) => {
attrs[key] = this.getAttr(key);
return attrs;
}, {});
}
}
getInput(name) {
return getTensor(name, this.tensorMap, this.context);
}
getAttr(name, defaultValue) {
const value = this.node.rawAttrs[name];
if (value.tensor != null) {
return getTensor(name, this.tensorMap, this.context);
}
if (value.i != null || value.f != null) {
return getNumberParam(this.node.rawAttrs, name, defaultValue);
}
if (value.s != null) {
return getStringParam(this.node.rawAttrs, name, defaultValue);
}
if (value.b != null) {
return getBoolParam(this.node.rawAttrs, name, defaultValue);
}
if (value.shape != null) {
return getTensorShapeParam(this.node.rawAttrs, name, defaultValue);
}
if (value.type != null) {
return getDtypeParam(this.node.rawAttrs, name, defaultValue);
}
if (value.list != null) {
if (value.list.i != null || value.list.f != null) {
return getNumericArrayParam(this.node.rawAttrs, name, defaultValue);
}
if (value.list.s != null) {
return getStringArrayParam(this.node.rawAttrs, name, defaultValue);
}
if (value.list.shape != null) {
return getTensorShapeArrayParam(this.node.rawAttrs, name, defaultValue);
}
if (value.list.b != null) {
return getBoolArrayParam(this.node.rawAttrs, name, defaultValue);
}
if (value.list.type != null) {
return getDtypeArrayParam(this.node.rawAttrs, name, defaultValue);
}
}
return defaultValue;
}
}
// node_modules/@tensorflow/tfjs-converter/dist/operations/executors/arithmetic_executor.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const executeOp = (node, tensorMap, context) => {
switch (node.op) {
case "BiasAdd":
case "AddV2":
case "Add": {
return [add2(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))];
}
case "AddN": {
return [addN(getParamValue("tensors", node, tensorMap, context))];
}
case "FloorMod":
case "Mod":
return [mod(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))];
case "Mul":
return [mul(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))];
case "RealDiv":
case "Div": {
return [div(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))];
}
case "DivNoNan": {
return [divNoNan(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))];
}
case "FloorDiv": {
return [floorDiv(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))];
}
case "Sub": {
return [sub(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))];
}
case "Minimum": {
return [minimum(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))];
}
case "Maximum": {
return [maximum(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))];
}
case "Pow": {
return [pow(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))];
}
case "SquaredDifference": {
return [squaredDifference(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))];
}
default:
throw TypeError(`Node type ${node.op} is not implemented`);
}
};
// node_modules/@tensorflow/tfjs-converter/dist/operations/executors/basic_math_executor.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const executeOp2 = (node, tensorMap, context) => {
switch (node.op) {
case "Abs":
case "ComplexAbs":
return [abs(getParamValue("x", node, tensorMap, context))];
case "Acos":
return [acos(getParamValue("x", node, tensorMap, context))];
case "Acosh":
return [acosh(getParamValue("x", node, tensorMap, context))];
case "Asin":
return [asin(getParamValue("x", node, tensorMap, context))];
case "Asinh":
return [asinh(getParamValue("x", node, tensorMap, context))];
case "Atan":
return [atan(getParamValue("x", node, tensorMap, context))];
case "Atan2":
return [atan2(getParamValue("x", node, tensorMap, context), getParamValue("y", node, tensorMap, context))];
case "Atanh":
return [atanh(getParamValue("x", node, tensorMap, context))];
case "Ceil":
return [ceil(getParamValue("x", node, tensorMap, context))];
case "Complex":
return [complex(getParamValue("real", node, tensorMap, context), getParamValue("imag", node, tensorMap, context))];
case "Cos":
return [cos(getParamValue("x", node, tensorMap, context))];
case "Cosh":
return [cosh(getParamValue("x", node, tensorMap, context))];
case "Elu":
return [elu(getParamValue("x", node, tensorMap, context))];
case "Erf":
return [erf(getParamValue("x", node, tensorMap, context))];
case "Exp":
return [exp(getParamValue("x", node, tensorMap, context))];
case "Expm1": {
return [expm1(getParamValue("x", node, tensorMap, context))];
}
case "Floor":
return [floor(getParamValue("x", node, tensorMap, context))];
case "Log":
return [log(getParamValue("x", node, tensorMap, context))];
case "Log1p": {
return [log1p(getParamValue("x", node, tensorMap, context))];
}
case "Imag":
return [imag(getParamValue("x", node, tensorMap, context))];
case "Neg":
return [neg(getParamValue("x", node, tensorMap, context))];
case "Reciprocal": {
return [reciprocal(getParamValue("x", node, tensorMap, context))];
}
case "Real":
return [real(getParamValue("x", node, tensorMap, context))];
case "Relu":
return [relu(getParamValue("x", node, tensorMap, context))];
case "Round": {
return [round(getParamValue("x", node, tensorMap, context))];
}
case "Selu":
return [selu(getParamValue("x", node, tensorMap, context))];
case "Sigmoid":
return [sigmoid(getParamValue("x", node, tensorMap, context))];
case "Sin":
return [sin(getParamValue("x", node, tensorMap, context))];
case "Sign": {
return [sign(getParamValue("x", node, tensorMap, context))];
}
case "Sinh": {
return [sinh(getParamValue("x", node, tensorMap, context))];
}
case "Softplus": {
return [softplus(getParamValue("x", node, tensorMap, context))];
}
case "Sqrt": {
return [sqrt(getParamValue("x", node, tensorMap, context))];
}
case "Square": {
return [square(getParamValue("x", node, tensorMap, context))];
}
case "Tanh": {
return [tanh2(getParamValue("x", node, tensorMap, context))];
}
case "Tan":
return [tan(getParamValue("x", node, tensorMap, context))];
case "Relu6":
case "ClipByValue":
return [clipByValue(getParamValue("x", node, tensorMap, context), getParamValue("clipValueMin", node, tensorMap, context), getParamValue("clipValueMax", node, tensorMap, context))];
case "Rsqrt":
return [rsqrt(getTensor(node.inputNames[0], tensorMap, context))];
case "Prod":
return [prod(getParamValue("x", node, tensorMap, context), getParamValue("axes", node, tensorMap, context))];
case "LeakyRelu":
return [leakyRelu(getParamValue("x", node, tensorMap, context), getParamValue("alpha", node, tensorMap, context))];
case "Prelu":
return [prelu(getParamValue("x", node, tensorMap, context), getParamValue("alpha", node, tensorMap, context))];
default:
throw TypeError(`Node type ${node.op} is not implemented`);
}
};
// node_modules/@tensorflow/tfjs-converter/dist/executor/tensor_utils.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function assertShapesMatchAllowUndefinedSize(shapeA, shapeB, errorMessagePrefix = "") {
util_exports.assert(shapesEqualAllowUndefinedSize(shapeA, shapeB), () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);
}
function shapesEqualAllowUndefinedSize(n1, n2) {
if (n1.length !== n2.length) {
return false;
}
for (let i = 0; i < n1.length; i++) {
if (n1[i] !== -1 && n2[i] !== -1 && n1[i] !== n2[i]) {
return false;
}
}
return true;
}
// node_modules/@tensorflow/tfjs-converter/dist/executor/tensor_array.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
class TensorArray {
constructor(name, dtype, maxSize, elementShape, identicalElementShapes, dynamicSize, clearAfterRead) {
this.name = name;
this.dtype = dtype;
this.maxSize = maxSize;
this.elementShape = elementShape;
this.identicalElementShapes = identicalElementShapes;
this.dynamicSize = dynamicSize;
this.clearAfterRead = clearAfterRead;
this.tensors = [];
this.closed_ = false;
this.idTensor = scalar(0);
keep(this.idTensor);
}
get id() {
return this.idTensor.id;
}
get closed() {
return this.closed_;
}
clearAndClose(keepIds) {
this.tensors.forEach((tensor16) => {
if (keepIds == null || !keepIds.has(tensor16.tensor.id)) {
tensor16.tensor.dispose();
}
});
this.tensors = [];
this.closed_ = true;
this.idTensor.dispose();
}
size() {
return this.tensors.length;
}
read(index) {
if (this.closed_) {
throw new Error(`TensorArray ${this.name} has already been closed.`);
}
if (index < 0 || index >= this.size()) {
throw new Error(`Tried to read from index ${index}, but array size is: ${this.size()}`);
}
const tensorWithState = this.tensors[index];
if (tensorWithState.cleared) {
throw new Error(`TensorArray ${this.name}: Could not read index ${index} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);
}
if (this.clearAfterRead) {
tensorWithState.cleared = true;
}
tensorWithState.read = true;
return tensorWithState.tensor;
}
readMany(indices) {
return indices.map((index) => this.read(index));
}
write(index, tensor16) {
if (this.closed_) {
throw new Error(`TensorArray ${this.name} has already been closed.`);
}
if (index < 0 || !this.dynamicSize && index >= this.maxSize) {
throw new Error(`Tried to write to index ${index}, but array is not resizeable and size is: ${this.maxSize}`);
}
const t = this.tensors[index] || {};
if (tensor16.dtype !== this.dtype) {
throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index},
because the value dtype is ${tensor16.dtype}, but TensorArray dtype is ${this.dtype}.`);
}
if (this.size() === 0 && (this.elementShape == null || this.elementShape.length === 0)) {
this.elementShape = tensor16.shape;
}
assertShapesMatchAllowUndefinedSize(this.elementShape, tensor16.shape, `TensorArray ${this.name}: Could not write to TensorArray index ${index}.`);
if (t.read) {
throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been read.`);
}
if (t.written) {
throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been written.`);
}
t.tensor = tensor16;
keep(tensor16);
t.written = true;
this.tensors[index] = t;
}
writeMany(indices, tensors) {
if (indices.length !== tensors.length) {
throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${indices.length} is not the same as tensors size: ${tensors.length}.`);
}
indices.forEach((i, index) => this.write(i, tensors[index]));
}
gather(indices, dtype) {
if (!!dtype && dtype !== this.dtype) {
throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${dtype}`);
}
if (!indices) {
indices = [];
for (let i = 0; i < this.size(); i++) {
indices.push(i);
}
} else {
indices = indices.slice(0, this.size());
}
if (indices.length === 0) {
return tensor4([], [0].concat(this.elementShape));
}
const tensors = this.readMany(indices);
assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, "TensorArray shape mismatch: ");
return stack(tensors, 0);
}
concat(dtype) {
if (!!dtype && dtype !== this.dtype) {
throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${dtype}`);
}
if (this.size() === 0) {
return tensor4([], [0].concat(this.elementShape));
}
const indices = [];
for (let i = 0; i < this.size(); i++) {
indices.push(i);
}
const tensors = this.readMany(indices);
assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, `TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${tensors[0].shape})`);
return concat(tensors, 0);
}
scatter(indices, tensor16) {
if (tensor16.dtype !== this.dtype) {
throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor16.dtype}`);
}
if (indices.length !== tensor16.shape[0]) {
throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor16.shape[0]}`);
}
const maxIndex = Math.max(...indices);
if (!this.dynamicSize && maxIndex >= this.maxSize) {
throw new Error(`Max index must be < array size (${maxIndex} vs. ${this.maxSize})`);
}
this.writeMany(indices, unstack(tensor16, 0));
}
split(length, tensor16) {
if (tensor16.dtype !== this.dtype) {
throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor16.dtype}`);
}
let totalLength = 0;
const cumulativeLengths = length.map((len) => {
totalLength += len;
return totalLength;
});
if (totalLength !== tensor16.shape[0]) {
throw new Error(`Expected sum of lengths to be equal to
tensor.shape[0], but sum of lengths is
${totalLength}, and tensor's shape is: ${tensor16.shape}`);
}
if (!this.dynamicSize && length.length !== this.maxSize) {
throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${length.length}), and the TensorArray is not marked as dynamically resizeable`);
}
const elementPerRow = totalLength === 0 ? 0 : tensor16.size / totalLength;
const tensors = [];
tidy(() => {
tensor16 = reshape(tensor16, [1, totalLength, elementPerRow]);
for (let i = 0; i < length.length; ++i) {
const previousLength = i === 0 ? 0 : cumulativeLengths[i - 1];
const indices2 = [0, previousLength, 0];
const sizes = [1, length[i], elementPerRow];
tensors[i] = reshape(slice(tensor16, indices2, sizes), this.elementShape);
}
return tensors;
});
const indices = [];
for (let i = 0; i < length.length; i++) {
indices[i] = i;
}
this.writeMany(indices, tensors);
}
}
// node_modules/@tensorflow/tfjs-converter/dist/executor/tensor_list.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
class TensorList {
constructor(tensors, elementShape, elementDtype, maxNumElements = -1) {
this.tensors = tensors;
this.elementShape = elementShape;
this.elementDtype = elementDtype;
if (tensors != null) {
tensors.forEach((tensor16) => {
if (elementDtype !== tensor16.dtype) {
throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${tensor16.dtype}`);
}
assertShapesMatchAllowUndefinedSize(elementShape, tensor16.shape, "TensorList shape mismatch: ");
keep(tensor16);
});
}
this.idTensor = scalar(0);
this.maxNumElements = maxNumElements;
keep(this.idTensor);
}
get id() {
return this.idTensor.id;
}
copy() {
return new TensorList([...this.tensors], this.elementShape, this.elementDtype);
}
clearAndClose(keepIds) {
this.tensors.forEach((tensor16) => {
if (keepIds == null || !keepIds.has(tensor16.id)) {
tensor16.dispose();
}
});
this.tensors.length = 0;
this.idTensor.dispose();
}
size() {
return this.tensors.length;
}
stack(elementShape, elementDtype, numElements = -1) {
if (elementDtype !== this.elementDtype) {
throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);
}
if (numElements !== -1 && this.tensors.length !== numElements) {
throw new Error(`Operation expected a list with ${numElements} elements but got a list with ${this.tensors.length} elements.`);
}
assertShapesMatchAllowUndefinedSize(elementShape, this.elementShape, "TensorList shape mismatch: ");
return tidy(() => {
const reshapedTensors = this.tensors.map((tensor16) => reshape(tensor16, elementShape));
return stack(reshapedTensors, 0);
});
}
popBack(elementShape, elementDtype) {
if (elementDtype !== this.elementDtype) {
throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);
}
if (this.size() === 0) {
throw new Error("Trying to pop from an empty list.");
}
const tensor16 = this.tensors.pop();
assertShapesMatchAllowUndefinedSize(tensor16.shape, elementShape, "TensorList shape mismatch: ");
return reshape(tensor16, elementShape);
}
pushBack(tensor16) {
if (tensor16.dtype !== this.elementDtype) {
throw new Error(`Invalid data types; op elements ${tensor16.dtype}, but list elements ${this.elementDtype}`);
}
assertShapesMatchAllowUndefinedSize(tensor16.shape, this.elementShape, "TensorList shape mismatch: ");
if (this.maxNumElements === this.size()) {
throw new Error(`Trying to push element into a full list.`);
}
keep(tensor16);
this.tensors.push(tensor16);
}
resize(size) {
if (size < 0) {
throw new Error(`TensorListResize expects size to be non-negative. Got: ${size}`);
}
if (this.maxNumElements !== -1 && size > this.maxNumElements) {
throw new Error(`TensorListResize input size ${size} is greater maxNumElement ${this.maxNumElements}.`);
}
this.tensors.length = size;
}
getItem(elementIndex, elementShape, elementDtype) {
if (elementDtype !== this.elementDtype) {
throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);
}
if (elementIndex < 0 || elementIndex > this.tensors.length) {
throw new Error(`Trying to access element ${elementIndex} in a list with ${this.tensors.length} elements.`);
}
if (this.tensors[elementIndex] == null) {
throw new Error(`element at index ${elementIndex} is null.`);
}
assertShapesMatchAllowUndefinedSize(this.tensors[elementIndex].shape, elementShape, "TensorList shape mismatch: ");
return this.tensors[elementIndex];
}
setItem(elementIndex, tensor16) {
if (tensor16.dtype !== this.elementDtype) {
throw new Error(`Invalid data types; op elements ${tensor16.dtype}, but list elements ${this.elementDtype}`);
}
if (elementIndex < 0 || this.maxNumElements !== -1 && elementIndex >= this.maxNumElements) {
throw new Error(`Trying to set element ${elementIndex} in a list with max ${this.maxNumElements} elements.`);
}
assertShapesMatchAllowUndefinedSize(this.elementShape, tensor16.shape, "TensorList shape mismatch: ");
keep(tensor16);
this.tensors[elementIndex] = tensor16;
}
gather(indices, elementDtype, elementShape) {
if (elementDtype !== this.elementDtype) {
throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);
}
assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, "TensorList shape mismatch: ");
indices = indices.slice(0, this.size());
if (indices.length === 0) {
return tensor4([], [0].concat(this.elementShape));
}
return tidy(() => {
const tensors = indices.map((i) => reshape(this.tensors[i], elementShape));
return stack(tensors, 0);
});
}
concat(elementDtype, elementShape) {
if (!!elementDtype && elementDtype !== this.elementDtype) {
throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${elementDtype}`);
}
assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, "TensorList shape mismatch: ");
if (this.size() === 0) {
return tensor4([], [0].concat(this.elementShape));
}
return tidy(() => {
const tensors = this.tensors.map((t) => reshape(t, elementShape));
return concat(tensors, 0);
});
}
}
function fromTensor(tensor16, elementShape, elementDtype) {
const dtype = tensor16.dtype;
if (tensor16.shape.length < 1) {
throw new Error(`Tensor must be at least a vector, but saw shape: ${tensor16.shape}`);
}
if (tensor16.dtype !== elementDtype) {
throw new Error(`Invalid data types; op elements ${tensor16.dtype}, but list elements ${elementDtype}`);
}
const outputShape = tensor16.shape.slice(1);
assertShapesMatchAllowUndefinedSize(outputShape, elementShape, "TensorList shape mismatch: ");
const tensorList = unstack(tensor16);
return new TensorList(tensorList, elementShape, dtype);
}
function reserve(elementShape, elementDtype, numElements) {
return new TensorList([], elementShape, elementDtype, numElements);
}
function scatter(tensor16, indices, elementShape, numElements) {
if (indices.length !== tensor16.shape[0]) {
throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor16.shape[0]}`);
}
const maxIndex = Math.max(...indices);
if (numElements != null && numElements !== -1 && maxIndex >= numElements) {
throw new Error(`Max index must be < array size (${maxIndex} vs. ${numElements})`);
}
const list = new TensorList([], elementShape, tensor16.dtype, numElements);
const tensors = unstack(tensor16, 0);
indices.forEach((value, index) => {
list.setItem(value, tensors[index]);
});
return list;
}
function split6(tensor16, length, elementShape) {
let totalLength = 0;
const cumulativeLengths = length.map((len) => {
totalLength += len;
return totalLength;
});
if (totalLength !== tensor16.shape[0]) {
throw new Error(`Expected sum of lengths to be equal to
tensor.shape[0], but sum of lengths is
${totalLength}, and tensor's shape is: ${tensor16.shape}`);
}
const elementPerRow = totalLength === 0 ? 0 : tensor16.size / totalLength;
const tensors = tidy(() => {
const tensors2 = [];
tensor16 = reshape(tensor16, [1, totalLength, elementPerRow]);
for (let i = 0; i < length.length; ++i) {
const previousLength = i === 0 ? 0 : cumulativeLengths[i - 1];
const indices = [0, previousLength, 0];
const sizes = [1, length[i], elementPerRow];
tensors2[i] = reshape(slice(tensor16, indices, sizes), elementShape);
}
tensor16.dispose();
return tensors2;
});
const list = new TensorList([], elementShape, tensor16.dtype, length.length);
for (let i = 0; i < tensors.length; i++) {
list.setItem(i, tensors[i]);
}
return list;
}
// node_modules/@tensorflow/tfjs-converter/dist/operations/executors/control_executor.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const executeOp3 = async (node, tensorMap, context) => {
switch (node.op) {
case "If":
case "StatelessIf": {
const thenFunc = getParamValue("thenBranch", node, tensorMap, context);
const elseFunc = getParamValue("elseBranch", node, tensorMap, context);
const cond = getParamValue("cond", node, tensorMap, context);
const args = getParamValue("args", node, tensorMap, context);
const condValue = await cond.data();
if (condValue[0]) {
return context.functionMap[thenFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);
} else {
return context.functionMap[elseFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);
}
}
case "While":
case "StatelessWhile": {
const bodyFunc = getParamValue("body", node, tensorMap, context);
const condFunc = getParamValue("cond", node, tensorMap, context);
const args = getParamValue("args", node, tensorMap, context);
const condResult = await context.functionMap[condFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);
const argIds = args.map((tensor16) => tensor16.id);
let condValue = await condResult[0].data();
condResult.forEach((tensor16) => {
if (!tensor16.kept && argIds.indexOf(tensor16.id) === -1) {
tensor16.dispose();
}
});
let result = args;
while (condValue[0]) {
const origResult = result;
result = await context.functionMap[bodyFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap);
const resultIds = result.map((tensor16) => tensor16.id);
origResult.forEach((tensor16) => {
if (!tensor16.kept && argIds.indexOf(tensor16.id) === -1 && resultIds.indexOf(tensor16.id) === -1) {
tensor16.dispose();
}
});
const condResult2 = await context.functionMap[condFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap);
condValue = await condResult2[0].data();
condResult2.forEach((tensor16) => {
if (!tensor16.kept && argIds.indexOf(tensor16.id) === -1 && resultIds.indexOf(tensor16.id) === -1) {
tensor16.dispose();
}
});
}
return result;
}
case "LoopCond": {
const pred = getParamValue("pred", node, tensorMap, context);
return [cloneTensor(pred)];
}
case "Switch": {
const pred = getParamValue("pred", node, tensorMap, context);
let data2 = getParamValue("data", node, tensorMap, context);
if (!data2.kept) {
data2 = cloneTensor(data2);
}
return (await pred.data())[0] ? [void 0, data2] : [data2, void 0];
}
case "Merge": {
const inputName = node.inputNames.find((name) => getTensor(name, tensorMap, context) !== void 0);
if (inputName) {
const data2 = getTensor(inputName, tensorMap, context);
return [cloneTensor(data2)];
}
return void 0;
}
case "Enter": {
const frameId = getParamValue("frameName", node, tensorMap, context);
const data2 = getParamValue("tensor", node, tensorMap, context);
context.enterFrame(frameId);
return [cloneTensor(data2)];
}
case "Exit": {
const data2 = getParamValue("tensor", node, tensorMap, context);
context.exitFrame();
return [cloneTensor(data2)];
}
case "NextIteration": {
const data2 = getParamValue("tensor", node, tensorMap, context);
context.nextIteration();
return [cloneTensor(data2)];
}
case "TensorArrayV3": {
const size = getParamValue("size", node, tensorMap, context);
const dtype = getParamValue("dtype", node, tensorMap, context);
const elementShape = getParamValue("elementShape", node, tensorMap, context);
const dynamicSize = getParamValue("dynamicSize", node, tensorMap, context);
const clearAfterRead = getParamValue("clearAfterRead", node, tensorMap, context);
const identicalElementShapes = getParamValue("identicalElementShapes", node, tensorMap, context);
const name = getParamValue("name", node, tensorMap, context);
const tensorArray = new TensorArray(name, dtype, size, elementShape, identicalElementShapes, dynamicSize, clearAfterRead);
context.addTensorArray(tensorArray);
return [tensorArray.idTensor, scalar(1)];
}
case "TensorArrayWriteV3": {
const id = getParamValue("tensorArrayId", node, tensorMap, context);
const index = getParamValue("index", node, tensorMap, context);
const writeTensor = getParamValue("tensor", node, tensorMap, context);
const writeTensorArray = context.getTensorArray(id.id);
writeTensorArray.write(index, writeTensor);
return [writeTensorArray.idTensor];
}
case "TensorArrayReadV3": {
const readId = getParamValue("tensorArrayId", node, tensorMap, context);
const readIndex = getParamValue("index", node, tensorMap, context);
const readTensorArray = context.getTensorArray(readId.id);
return [readTensorArray.read(readIndex)];
}
case "TensorArrayGatherV3": {
const gatherId = getParamValue("tensorArrayId", node, tensorMap, context);
const gatherIndices = getParamValue("indices", node, tensorMap, context);
const gatherDtype = getParamValue("dtype", node, tensorMap, context);
const gatherTensorArray = context.getTensorArray(gatherId.id);
return [gatherTensorArray.gather(gatherIndices, gatherDtype)];
}
case "TensorArrayScatterV3": {
const scatterId = getParamValue("tensorArrayId", node, tensorMap, context);
const scatterIndices = getParamValue("indices", node, tensorMap, context);
const scatterTensor = getParamValue("tensor", node, tensorMap, context);
const scatterTensorArray = context.getTensorArray(scatterId.id);
scatterTensorArray.scatter(scatterIndices, scatterTensor);
return [scatterTensorArray.idTensor];
}
case "TensorArrayConcatV3": {
const concatId = getParamValue("tensorArrayId", node, tensorMap, context);
const concatTensorArray = context.getTensorArray(concatId.id);
const concatDtype = getParamValue("dtype", node, tensorMap, context);
return [concatTensorArray.concat(concatDtype)];
}
case "TensorArraySplitV3": {
const splitId = getParamValue("tensorArrayId", node, tensorMap, context);
const splitTensor = getParamValue("tensor", node, tensorMap, context);
const lengths = getParamValue("lengths", node, tensorMap, context);
const splitTensorArray = context.getTensorArray(splitId.id);
splitTensorArray.split(lengths, splitTensor);
return [splitTensorArray.idTensor];
}
case "TensorArraySizeV3": {
const sizeId = getParamValue("tensorArrayId", node, tensorMap, context);
const sizeTensorArray = context.getTensorArray(sizeId.id);
return [scalar(sizeTensorArray.size(), "int32")];
}
case "TensorArrayCloseV3": {
const closeId = getParamValue("tensorArrayId", node, tensorMap, context);
const closeTensorArray = context.getTensorArray(closeId.id);
closeTensorArray.clearAndClose();
return [closeTensorArray.idTensor];
}
case "TensorListSetItem": {
const idTensor = getParamValue("tensorListId", node, tensorMap, context);
const index = getParamValue("index", node, tensorMap, context);
const writeTensor = getParamValue("tensor", node, tensorMap, context);
const tensorList = context.getTensorList(idTensor.id);
tensorList.setItem(index, writeTensor);
return [tensorList.idTensor];
}
case "TensorListGetItem": {
const idTensor = getParamValue("tensorListId", node, tensorMap, context);
const readIndex = getParamValue("index", node, tensorMap, context);
const elementShape = getParamValue("elementShape", node, tensorMap, context);
const elementDType = getParamValue("elementDType", node, tensorMap, context);
const tensorList = context.getTensorList(idTensor.id);
return [tensorList.getItem(readIndex, elementShape, elementDType)];
}
case "TensorListScatterV2":
case "TensorListScatter": {
const scatterIndices = getParamValue("indices", node, tensorMap, context);
const scatterTensor = getParamValue("tensor", node, tensorMap, context);
const elementShape = getParamValue("elementShape", node, tensorMap, context);
const numElements = getParamValue("numElements", node, tensorMap, context);
const tensorList = scatter(scatterTensor, scatterIndices, elementShape, numElements);
context.addTensorList(tensorList);
return [tensorList.idTensor];
}
case "TensorListReserve": {
const elementShape = getParamValue("elementShape", node, tensorMap, context);
const elementDtype = getParamValue("elementDType", node, tensorMap, context);
const numElements = getParamValue("numElements", node, tensorMap, context);
const tensorList = reserve(elementShape, elementDtype, numElements);
context.addTensorList(tensorList);
return [tensorList.idTensor];
}
case "TensorListGather": {
const gatherId = getParamValue("tensorListId", node, tensorMap, context);
const gatherIndices = getParamValue("indices", node, tensorMap, context);
const elementShape = getParamValue("elementShape", node, tensorMap, context);
const elementDtype = getParamValue("elementDType", node, tensorMap, context);
const tensorList = context.getTensorList(gatherId.id);
return [tensorList.gather(gatherIndices, elementDtype, elementShape)];
}
case "TensorListStack": {
const idTensor = getParamValue("tensorListId", node, tensorMap, context);
const elementShape = getParamValue("elementShape", node, tensorMap, context);
const elementDtype = getParamValue("elementDType", node, tensorMap, context);
const numElements = getParamValue("numElements", node, tensorMap, context);
const tensorList = context.getTensorList(idTensor.id);
return [tensorList.stack(elementShape, elementDtype, numElements)];
}
case "TensorListFromTensor": {
const tensor16 = getParamValue("tensor", node, tensorMap, context);
const elementShape = getParamValue("elementShape", node, tensorMap, context);
const elementDtype = getParamValue("elementDType", node, tensorMap, context);
const tensorList = fromTensor(tensor16, elementShape, elementDtype);
context.addTensorList(tensorList);
return [tensorList.idTensor];
}
case "TensorListConcat": {
const concatId = getParamValue("tensorListId", node, tensorMap, context);
const tensorList = context.getTensorList(concatId.id);
const concatDtype = getParamValue("dtype", node, tensorMap, context);
const elementShape = getParamValue("elementShape", node, tensorMap, context);
return [tensorList.concat(concatDtype, elementShape)];
}
case "TensorListPushBack": {
const idTensor = getParamValue("tensorListId", node, tensorMap, context);
const writeTensor = getParamValue("tensor", node, tensorMap, context);
const tensorList = context.getTensorList(idTensor.id);
tensorList.pushBack(writeTensor);
return [tensorList.idTensor];
}
case "TensorListPopBack": {
const idTensor = getParamValue("tensorListId", node, tensorMap, context);
const elementShape = getParamValue("elementShape", node, tensorMap, context);
const elementDType = getParamValue("elementDType", node, tensorMap, context);
const tensorList = context.getTensorList(idTensor.id);
return [tensorList.popBack(elementShape, elementDType)];
}
case "TensorListSplit": {
const splitTensor = getParamValue("tensor", node, tensorMap, context);
const elementShape = getParamValue("elementShape", node, tensorMap, context);
const lengths = getParamValue("lengths", node, tensorMap, context);
const tensorList = split6(splitTensor, lengths, elementShape);
context.addTensorList(tensorList);
return [tensorList.idTensor];
}
default:
throw TypeError(`Node type ${node.op} is not implemented`);
}
};
// node_modules/@tensorflow/tfjs-converter/dist/operations/executors/convolution_executor.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function fusedConvAndDepthWiseParams(node, tensorMap, context) {
const [extraOp, activationFunc] = getParamValue("fusedOps", node, tensorMap, context);
const isBiasAdd = extraOp === "biasadd";
const isPrelu = activationFunc === "prelu";
const isBatchNorm = extraOp === "fusedbatchnorm";
const numArgs = getParamValue("numArgs", node, tensorMap, context);
if (isBiasAdd) {
if (isPrelu && numArgs !== 2) {
throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");
}
if (!isPrelu && numArgs !== 1) {
throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.");
}
}
if (isBatchNorm) {
throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.");
}
const stride = getParamValue("strides", node, tensorMap, context);
const pad8 = getPadding(node, tensorMap, context);
const dataFormat = getParamValue("dataFormat", node, tensorMap, context).toUpperCase();
const dilations = getParamValue("dilations", node, tensorMap, context);
const [biasArg, preluArg] = getParamValue("args", node, tensorMap, context);
return {
stride,
pad: pad8,
dataFormat,
dilations,
biasArg,
preluArg,
activationFunc
};
}
const executeOp4 = (node, tensorMap, context) => {
switch (node.op) {
case "Conv1D": {
const stride = getParamValue("stride", node, tensorMap, context);
const pad8 = getParamValue("pad", node, tensorMap, context);
const dataFormat = getParamValue("dataFormat", node, tensorMap, context).toUpperCase();
const dilation = getParamValue("dilation", node, tensorMap, context);
return [conv1d(getParamValue("x", node, tensorMap, context), getParamValue("filter", node, tensorMap, context), stride, pad8, dataFormat, dilation)];
}
case "Conv2D": {
const stride = getParamValue("strides", node, tensorMap, context);
const pad8 = getPadding(node, tensorMap, context);
const dataFormat = getParamValue("dataFormat", node, tensorMap, context).toUpperCase();
const dilations = getParamValue("dilations", node, tensorMap, context);
return [conv2d(getParamValue("x", node, tensorMap, context), getParamValue("filter", node, tensorMap, context), [stride[1], stride[2]], pad8, dataFormat, [dilations[1], dilations[2]])];
}
case "_FusedConv2D": {
const {stride, pad: pad8, dataFormat, dilations, biasArg, preluArg, activationFunc} = fusedConvAndDepthWiseParams(node, tensorMap, context);
return [fused_ops_exports.conv2d({
x: getParamValue("x", node, tensorMap, context),
filter: getParamValue("filter", node, tensorMap, context),
strides: [stride[1], stride[2]],
pad: pad8,
dataFormat,
dilations: [dilations[1], dilations[2]],
bias: biasArg,
activation: activationFunc,
preluActivationWeights: preluArg
})];
}
case "FusedDepthwiseConv2dNative": {
const {stride, pad: pad8, dataFormat, dilations, biasArg, preluArg, activationFunc} = fusedConvAndDepthWiseParams(node, tensorMap, context);
return [fused_ops_exports.depthwiseConv2d({
x: getParamValue("x", node, tensorMap, context),
filter: getParamValue("filter", node, tensorMap, context),
strides: [stride[1], stride[2]],
pad: pad8,
dataFormat,
dilations: [dilations[1], dilations[2]],
bias: biasArg,
activation: activationFunc,
preluActivationWeights: preluArg
})];
}
case "Conv2DBackpropInput":
case "Conv2dTranspose": {
const shape = getParamValue("outputShape", node, tensorMap, context);
const stride = getParamValue("strides", node, tensorMap, context);
const pad8 = getPadding(node, tensorMap, context);
return [conv2dTranspose(getParamValue("x", node, tensorMap, context), getParamValue("filter", node, tensorMap, context), shape, [stride[1], stride[2]], pad8)];
}
case "DepthwiseConv2dNative":
case "DepthwiseConv2d": {
const stride = getParamValue("strides", node, tensorMap, context);
const pad8 = getPadding(node, tensorMap, context);
const dilations = getParamValue("dilations", node, tensorMap, context);
const dataFormat = getParamValue("dataFormat", node, tensorMap, context).toUpperCase();
return [depthwiseConv2d(getParamValue("input", node, tensorMap, context), getParamValue("filter", node, tensorMap, context), [stride[1], stride[2]], pad8, dataFormat, [dilations[1], dilations[2]])];
}
case "Conv3D": {
const stride = getParamValue("strides", node, tensorMap, context);
const pad8 = getParamValue("pad", node, tensorMap, context);
const dataFormat = getParamValue("dataFormat", node, tensorMap, context).toUpperCase();
const dilations = getParamValue("dilations", node, tensorMap, context);
return [conv3d(getParamValue("x", node, tensorMap, context), getParamValue("filter", node, tensorMap, context), [stride[1], stride[2], stride[3]], pad8, dataFormat, [dilations[1], dilations[2], dilations[3]])];
}
case "AvgPool": {
const stride = getParamValue("strides", node, tensorMap, context);
const pad8 = getParamValue("pad", node, tensorMap, context);
const kernelSize = getParamValue("kernelSize", node, tensorMap, context);
return [avgPool(getParamValue("x", node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad8)];
}
case "MaxPool": {
const stride = getParamValue("strides", node, tensorMap, context);
const pad8 = getParamValue("pad", node, tensorMap, context);
const kernelSize = getParamValue("kernelSize", node, tensorMap, context);
return [maxPool(getParamValue("x", node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad8)];
}
case "MaxPoolWithArgmax": {
const stride = getParamValue("strides", node, tensorMap, context);
const pad8 = getParamValue("pad", node, tensorMap, context);
const kernelSize = getParamValue("kernelSize", node, tensorMap, context);
const includeBatchInIndex = getParamValue("includeBatchInIndex", node, tensorMap, context);
const {result, indexes} = maxPoolWithArgmax(getParamValue("x", node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad8, includeBatchInIndex);
return [result, indexes];
}
case "AvgPool3D": {
const stride = getParamValue("strides", node, tensorMap, context);
const pad8 = getParamValue("pad", node, tensorMap, context);
const kernelSize = getParamValue("kernelSize", node, tensorMap, context);
return [avgPool3d(getParamValue("x", node, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad8)];
}
case "MaxPool3D": {
const stride = getParamValue("strides", node, tensorMap, context);
const pad8 = getParamValue("pad", node, tensorMap, context);
const kernelSize = getParamValue("kernelSize", node, tensorMap, context);
return [maxPool3d(getParamValue("x", node, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad8)];
}
case "Dilation2D": {
const strides = getParamValue("strides", node, tensorMap, context);
const pad8 = getParamValue("pad", node, tensorMap, context);
const dilations = getParamValue("dilations", node, tensorMap, context);
const strideHeight = strides[1];
const strideWidth = strides[2];
const dilationHeight = dilations[1];
const dilationWidth = dilations[2];
return [dilation2d(getParamValue("x", node, tensorMap, context), getParamValue("filter", node, tensorMap, context), [strideHeight, strideWidth], pad8, [dilationHeight, dilationWidth], "NHWC")];
}
default:
throw TypeError(`Node type ${node.op} is not implemented`);
}
};
// node_modules/@tensorflow/tfjs-converter/dist/operations/executors/creation_executor.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const executeOp5 = (node, tensorMap, context) => {
switch (node.op) {
case "Fill": {
const shape = getParamValue("shape", node, tensorMap, context);
const dtype = getParamValue("dtype", node, tensorMap, context);
const value = getParamValue("value", node, tensorMap, context);
return [fill(shape, value, dtype)];
}
case "LinSpace": {
const start = getParamValue("start", node, tensorMap, context);
const stop = getParamValue("stop", node, tensorMap, context);
const num = getParamValue("num", node, tensorMap, context);
return [linspace(start, stop, num)];
}
case "Multinomial": {
const logits = getParamValue("logits", node, tensorMap, context);
const numSamples = getParamValue("numSamples", node, tensorMap, context);
const seed = getParamValue("seed", node, tensorMap, context);
return [multinomial(logits, numSamples, seed)];
}
case "OneHot": {
const indices = getParamValue("indices", node, tensorMap, context);
const depth = getParamValue("depth", node, tensorMap, context);
const onValue = getParamValue("onValue", node, tensorMap, context);
const offValue = getParamValue("offValue", node, tensorMap, context);
return [oneHot(indices, depth, onValue, offValue)];
}
case "Ones": {
return [ones2(getParamValue("shape", node, tensorMap, context), getParamValue("dtype", node, tensorMap, context))];
}
case "OnesLike": {
return [onesLike(getParamValue("x", node, tensorMap, context))];
}
case "RandomUniform": {
return [randomUniform(getParamValue("shape", node, tensorMap, context), getParamValue("minval", node, tensorMap, context), getParamValue("maxval", node, tensorMap, context), getParamValue("dtype", node, tensorMap, context))];
}
case "Range": {
const start = getParamValue("start", node, tensorMap, context);
const stop = getParamValue("stop", node, tensorMap, context);
const step4 = getParamValue("step", node, tensorMap, context);
return [range(start, stop, step4, getParamValue("dtype", node, tensorMap, context))];
}
case "TruncatedNormal": {
const shape = getParamValue("shape", node, tensorMap, context);
const mean5 = getParamValue("mean", node, tensorMap, context);
const stdDev = getParamValue("stdDev", node, tensorMap, context);
const seed = getParamValue("seed", node, tensorMap, context);
return [truncatedNormal(shape, mean5, stdDev, getParamValue("dtype", node, tensorMap, context), seed)];
}
case "Zeros": {
return [zeros(getParamValue("shape", node, tensorMap, context), getParamValue("dtype", node, tensorMap, context))];
}
case "ZerosLike": {
return [zerosLike(getParamValue("x", node, tensorMap, context))];
}
default:
throw TypeError(`Node type ${node.op} is not implemented`);
}
};
// node_modules/@tensorflow/tfjs-converter/dist/operations/executors/dynamic_executor.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function nmsParams(node, tensorMap, context) {
const boxes = getParamValue("boxes", node, tensorMap, context);
const scores = getParamValue("scores", node, tensorMap, context);
const maxOutputSize = getParamValue("maxOutputSize", node, tensorMap, context);
const iouThreshold = getParamValue("iouThreshold", node, tensorMap, context);
const scoreThreshold = getParamValue("scoreThreshold", node, tensorMap, context);
const softNmsSigma = getParamValue("softNmsSigma", node, tensorMap, context);
return {
boxes,
scores,
maxOutputSize,
iouThreshold,
scoreThreshold,
softNmsSigma
};
}
const executeOp6 = async (node, tensorMap, context) => {
switch (node.op) {
case "NonMaxSuppressionV5": {
const {boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma} = nmsParams(node, tensorMap, context);
const result = await image.nonMaxSuppressionWithScoreAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
return [result.selectedIndices, result.selectedScores];
}
case "NonMaxSuppressionV4": {
const {boxes, scores, maxOutputSize, iouThreshold, scoreThreshold} = nmsParams(node, tensorMap, context);
const padToMaxOutputSize = getParamValue("padToMaxOutputSize", node, tensorMap, context);
const result = await image.nonMaxSuppressionPaddedAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);
return [result.selectedIndices, result.validOutputs];
}
case "NonMaxSuppressionV3":
case "NonMaxSuppressionV2": {
const {boxes, scores, maxOutputSize, iouThreshold, scoreThreshold} = nmsParams(node, tensorMap, context);
return [await image.nonMaxSuppressionAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold)];
}
case "Where": {
const condition = cast(getParamValue("condition", node, tensorMap, context), "bool");
const result = [await whereAsync(condition)];
condition.dispose();
return result;
}
case "ListDiff": {
return setdiff1dAsync(getParamValue("x", node, tensorMap, context), getParamValue("y", node, tensorMap, context));
}
default:
throw TypeError(`Node type ${node.op} is not implemented`);
}
};
// node_modules/@tensorflow/tfjs-converter/dist/operations/executors/evaluation_executor.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const executeOp7 = (node, tensorMap, context) => {
switch (node.op) {
case "TopKV2": {
const x = getParamValue("x", node, tensorMap, context);
const k = getParamValue("k", node, tensorMap, context);
const sorted = getParamValue("sorted", node, tensorMap, context);
const result = topk(x, k, sorted);
return [result.values, result.indices];
}
case "Unique": {
const x = getParamValue("x", node, tensorMap, context);
const result = unique(x);
return [result.values, result.indices];
}
case "UniqueV2": {
const x = getParamValue("x", node, tensorMap, context);
const axis = getParamValue("axis", node, tensorMap, context);
const result = unique(x, axis);
return [result.values, result.indices];
}
default:
throw TypeError(`Node type ${node.op} is not implemented`);
}
};
// node_modules/@tensorflow/tfjs-converter/dist/operations/executors/graph_executor.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const executeOp8 = (node, tensorMap, context) => {
switch (node.op) {
case "Const": {
return tensorMap[node.name];
}
case "PlaceholderWithDefault":
const def = getParamValue("default", node, tensorMap, context);
return [getTensor(node.name, tensorMap, context) || def];
case "Placeholder":
return [getTensor(node.name, tensorMap, context)];
case "Identity":
case "StopGradient":
case "FakeQuantWithMinMaxVars": {
const data3 = getParamValue("x", node, tensorMap, context);
return [cloneTensor(data3)];
}
case "IdentityN":
return getParamValue("x", node, tensorMap, context).map((t) => cloneTensor(t));
case "Snapshot":
const snapshot = getParamValue("x", node, tensorMap, context);
return [cloneTensor(snapshot)];
case "Shape":
return [tensor1d(getParamValue("x", node, tensorMap, context).shape, "int32")];
case "ShapeN":
return getParamValue("x", node, tensorMap, context).map((t) => tensor1d(t.shape));
case "Size":
return [scalar(getParamValue("x", node, tensorMap, context).size, "int32")];
case "Rank":
return [scalar(getParamValue("x", node, tensorMap, context).rank, "int32")];
case "NoOp":
return [scalar(1)];
case "Print":
const input2 = getParamValue("x", node, tensorMap, context);
const data2 = getParamValue("data", node, tensorMap, context);
const message = getParamValue("message", node, tensorMap, context);
const summarize = getParamValue("summarize", node, tensorMap, context);
console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance.");
console.log(message);
for (let i = 0; i < data2.length; i++) {
console.log(Array.prototype.slice.call(data2[i].dataSync()).slice(0, summarize));
}
return [input2];
default:
throw TypeError(`Node type ${node.op} is not implemented`);
}
};
// node_modules/@tensorflow/tfjs-converter/dist/executor/hash_table.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
class HashTable {
constructor(keyDType, valueDType) {
this.keyDType = keyDType;
this.valueDType = valueDType;
this.handle = scalar(0);
this.tensorMap = new Map();
keep(this.handle);
}
get id() {
return this.handle.id;
}
clearAndClose() {
this.tensorMap.forEach((value) => value.dispose());
this.tensorMap.clear();
this.handle.dispose();
}
size() {
return this.tensorMap.size;
}
async import(keys, values) {
this.checkKeyAndValueTensor(keys, values);
const $keys = await keys.data();
this.tensorMap.forEach((value) => value.dispose());
this.tensorMap.clear();
return tidy(() => {
const $values = unstack(values);
const keysLength = $keys.length;
const valuesLength = $values.length;
util_exports.assert(keysLength === valuesLength, () => `The number of elements doesn't match, keys has ${keysLength} elements, the values has ${valuesLength} elements.`);
for (let i = 0; i < keysLength; i++) {
const key = $keys[i];
const value = $values[i];
keep(value);
this.tensorMap.set(key, value);
}
return this.handle;
});
}
async find(keys, defaultValue) {
this.checkKeyAndValueTensor(keys, defaultValue);
const $keys = await keys.data();
return tidy(() => {
const result = [];
for (let i = 0; i < $keys.length; i++) {
const key = $keys[i];
const value = this.findWithDefault(key, defaultValue);
result.push(value);
}
return stack(result);
});
}
findWithDefault(key, defaultValue) {
const result = this.tensorMap.get(key);
return result != null ? result : defaultValue;
}
checkKeyAndValueTensor(key, value) {
if (key.dtype !== this.keyDType) {
throw new Error(`Expect key dtype ${this.keyDType}, but got ${key.dtype}`);
}
if (value.dtype !== this.valueDType) {
throw new Error(`Expect value dtype ${this.valueDType}, but got ${value.dtype}`);
}
}
}
// node_modules/@tensorflow/tfjs-converter/dist/operations/executors/hash_table_executor.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const executeOp9 = async (node, tensorMap, context, resourceManager) => {
switch (node.op) {
case "HashTable":
case "HashTableV2": {
const keyDType = getParamValue("keyDType", node, tensorMap, context);
const valueDType = getParamValue("valueDType", node, tensorMap, context);
const hashTable2 = new HashTable(keyDType, valueDType);
resourceManager.addHashTable(node.name, hashTable2);
return [hashTable2.handle];
}
case "LookupTableImport":
case "LookupTableImportV2": {
const handle = getParamValue("tableHandle", node, tensorMap, context, resourceManager);
const keys = getParamValue("keys", node, tensorMap, context);
const values = getParamValue("values", node, tensorMap, context);
const hashTable2 = resourceManager.getHashTableById(handle.id);
return [await hashTable2.import(keys, values)];
}
case "LookupTableFind":
case "LookupTableFindV2": {
const handle = getParamValue("tableHandle", node, tensorMap, context, resourceManager);
const keys = getParamValue("keys", node, tensorMap, context);
const defaultValue = getParamValue("defaultValue", node, tensorMap, context);
const hashTable2 = resourceManager.getHashTableById(handle.id);
return [await hashTable2.find(keys, defaultValue)];
}
default:
throw TypeError(`Node type ${node.op} is not implemented`);
}
};
// node_modules/@tensorflow/tfjs-converter/dist/operations/executors/image_executor.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const executeOp10 = (node, tensorMap, context) => {
switch (node.op) {
case "ResizeBilinear": {
const images = getParamValue("images", node, tensorMap, context);
const size = getParamValue("size", node, tensorMap, context);
const alignCorners = getParamValue("alignCorners", node, tensorMap, context);
return [image.resizeBilinear(images, [size[0], size[1]], alignCorners)];
}
case "ResizeNearestNeighbor": {
const images = getParamValue("images", node, tensorMap, context);
const size = getParamValue("size", node, tensorMap, context);
const alignCorners = getParamValue("alignCorners", node, tensorMap, context);
return [image.resizeNearestNeighbor(images, [size[0], size[1]], alignCorners)];
}
case "CropAndResize": {
const image4 = getParamValue("image", node, tensorMap, context);
const boxes = getParamValue("boxes", node, tensorMap, context);
const boxInd = getParamValue("boxInd", node, tensorMap, context);
const cropSize = getParamValue("cropSize", node, tensorMap, context);
const method = getParamValue("method", node, tensorMap, context);
const extrapolationValue = getParamValue("extrapolationValue", node, tensorMap, context);
return [image.cropAndResize(image4, boxes, boxInd, cropSize, method, extrapolationValue)];
}
default:
throw TypeError(`Node type ${node.op} is not implemented`);
}
};
// node_modules/@tensorflow/tfjs-converter/dist/operations/executors/logical_executor.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const executeOp11 = (node, tensorMap, context) => {
switch (node.op) {
case "Equal": {
return [equal(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))];
}
case "NotEqual": {
return [notEqual(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))];
}
case "Greater": {
return [greater(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))];
}
case "GreaterEqual": {
return [greaterEqual(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))];
}
case "Less": {
return [less(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))];
}
case "LessEqual": {
return [lessEqual(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))];
}
case "LogicalAnd": {
return [logicalAnd(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))];
}
case "LogicalNot": {
return [logicalNot(getParamValue("a", node, tensorMap, context))];
}
case "LogicalOr": {
return [logicalOr(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))];
}
case "Select":
case "SelectV2": {
return [where(getParamValue("condition", node, tensorMap, context), getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))];
}
default:
throw TypeError(`Node type ${node.op} is not implemented`);
}
};
// node_modules/@tensorflow/tfjs-converter/dist/operations/executors/matrices_executor.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const executeOp12 = (node, tensorMap, context) => {
switch (node.op) {
case "BatchMatMul":
case "BatchMatMulV2":
case "MatMul":
return [matMul(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context), getParamValue("transposeA", node, tensorMap, context), getParamValue("transposeB", node, tensorMap, context))];
case "Transpose":
return [transpose(getParamValue("x", node, tensorMap, context), getParamValue("perm", node, tensorMap, context))];
case "_FusedMatMul":
const [extraOp, activationFunc] = getParamValue("fusedOps", node, tensorMap, context);
const isBiasAdd = extraOp === "biasadd";
const isPrelu = activationFunc === "prelu";
const numArgs = getParamValue("numArgs", node, tensorMap, context);
if (isBiasAdd) {
if (isPrelu && numArgs !== 2) {
throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");
}
if (!isPrelu && numArgs !== 1) {
throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.");
}
}
const [biasArg, preluArg] = getParamValue("args", node, tensorMap, context);
return [fused_ops_exports.matMul({
a: getParamValue("a", node, tensorMap, context),
b: getParamValue("b", node, tensorMap, context),
transposeA: getParamValue("transposeA", node, tensorMap, context),
transposeB: getParamValue("transposeB", node, tensorMap, context),
bias: biasArg,
activation: activationFunc,
preluActivationWeights: preluArg
})];
default:
throw TypeError(`Node type ${node.op} is not implemented`);
}
};
// node_modules/@tensorflow/tfjs-converter/dist/operations/executors/normalization_executor.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const executeOp13 = (node, tensorMap, context) => {
switch (node.op) {
case "FusedBatchNorm":
case "FusedBatchNormV2": {
return [batchNorm(getParamValue("x", node, tensorMap, context), getParamValue("mean", node, tensorMap, context), getParamValue("variance", node, tensorMap, context), getParamValue("offset", node, tensorMap, context), getParamValue("scale", node, tensorMap, context), getParamValue("epsilon", node, tensorMap, context))];
}
case "FusedBatchNormV3": {
return [batchNorm(getParamValue("x", node, tensorMap, context), getParamValue("mean", node, tensorMap, context), getParamValue("variance", node, tensorMap, context), getParamValue("offset", node, tensorMap, context), getParamValue("scale", node, tensorMap, context), getParamValue("epsilon", node, tensorMap, context))];
}
case "LRN": {
return [localResponseNormalization(getParamValue("x", node, tensorMap, context), getParamValue("radius", node, tensorMap, context), getParamValue("bias", node, tensorMap, context), getParamValue("alpha", node, tensorMap, context), getParamValue("beta", node, tensorMap, context))];
}
case "Softmax": {
return [softmax(getParamValue("x", node, tensorMap, context))];
}
case "LogSoftmax": {
return [logSoftmax(getParamValue("x", node, tensorMap, context))];
}
case "SparseToDense": {
return [sparseToDense(getParamValue("sparseIndices", node, tensorMap, context), getParamValue("outputShape", node, tensorMap, context), getParamValue("sparseValues", node, tensorMap, context), getParamValue("defaultValue", node, tensorMap, context))];
}
default:
throw TypeError(`Node type ${node.op} is not implemented`);
}
};
// node_modules/@tensorflow/tfjs-converter/dist/operations/executors/reduction_executor.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const executeOp14 = (node, tensorMap, context) => {
switch (node.op) {
case "Max": {
const axis = getParamValue("axis", node, tensorMap, context);
const keepDims = getParamValue("keepDims", node, tensorMap, context);
return [max(getParamValue("x", node, tensorMap, context), axis, keepDims)];
}
case "Mean": {
const axis = getParamValue("axis", node, tensorMap, context);
const keepDims = getParamValue("keepDims", node, tensorMap, context);
return [mean(getParamValue("x", node, tensorMap, context), axis, keepDims)];
}
case "Min": {
const axis = getParamValue("axis", node, tensorMap, context);
const keepDims = getParamValue("keepDims", node, tensorMap, context);
return [min(getParamValue("x", node, tensorMap, context), axis, keepDims)];
}
case "Sum": {
const axis = getParamValue("axis", node, tensorMap, context);
const keepDims = getParamValue("keepDims", node, tensorMap, context);
return [sum2(getParamValue("x", node, tensorMap, context), axis, keepDims)];
}
case "All": {
const axis = getParamValue("axis", node, tensorMap, context);
const keepDims = getParamValue("keepDims", node, tensorMap, context);
return [all(getParamValue("x", node, tensorMap, context), axis, keepDims)];
}
case "Any": {
const axis = getParamValue("axis", node, tensorMap, context);
const keepDims = getParamValue("keepDims", node, tensorMap, context);
return [any(getParamValue("x", node, tensorMap, context), axis, keepDims)];
}
case "ArgMax": {
const axis = getParamValue("axis", node, tensorMap, context);
return [argMax(getParamValue("x", node, tensorMap, context), axis)];
}
case "ArgMin": {
const axis = getParamValue("axis", node, tensorMap, context);
return [argMin(getParamValue("x", node, tensorMap, context), axis)];
}
case "Prod": {
const axis = getParamValue("axis", node, tensorMap, context);
const keepDims = getParamValue("keepDims", node, tensorMap, context);
return [prod(getParamValue("x", node, tensorMap, context), axis, keepDims)];
}
case "Cumsum": {
const axis = getParamValue("axis", node, tensorMap, context);
const exclusive = getParamValue("exclusive", node, tensorMap, context);
const reverse9 = getParamValue("reverse", node, tensorMap, context);
return [cumsum(getParamValue("x", node, tensorMap, context), axis, exclusive, reverse9)];
}
default:
throw TypeError(`Node type ${node.op} is not implemented`);
}
};
// node_modules/@tensorflow/tfjs-converter/dist/operations/executors/slice_join_executor.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const executeOp15 = (node, tensorMap, context) => {
switch (node.op) {
case "ConcatV2":
case "Concat": {
const n = getParamValue("n", node, tensorMap, context);
const axis = getParamValue("axis", node, tensorMap, context);
let inputs = getParamValue("tensors", node, tensorMap, context);
inputs = inputs.slice(0, n);
return [concat(inputs, axis)];
}
case "GatherV2":
case "Gather": {
const axis = getParamValue("axis", node, tensorMap, context);
const input2 = getParamValue("x", node, tensorMap, context);
const indices = getParamValue("indices", node, tensorMap, context);
return [gather(input2, cast(indices, "int32"), axis)];
}
case "ReverseV2":
case "Reverse": {
const axis = getParamValue("axis", node, tensorMap, context);
const input2 = getParamValue("x", node, tensorMap, context);
return [reverse(input2, axis)];
}
case "Slice": {
const begin = getParamValue("begin", node, tensorMap, context);
const size = getParamValue("size", node, tensorMap, context);
return [slice(getParamValue("x", node, tensorMap, context), begin, size)];
}
case "StridedSlice": {
const begin = getParamValue("begin", node, tensorMap, context);
const end = getParamValue("end", node, tensorMap, context);
const strides = getParamValue("strides", node, tensorMap, context);
const beginMask = getParamValue("beginMask", node, tensorMap, context);
const endMask = getParamValue("endMask", node, tensorMap, context);
const ellipsisMask = getParamValue("ellipsisMask", node, tensorMap, context);
const newAxisMask = getParamValue("newAxisMask", node, tensorMap, context);
const shrinkAxisMask = getParamValue("shrinkAxisMask", node, tensorMap, context);
const tensor16 = getParamValue("x", node, tensorMap, context);
return [stridedSlice(tensor16, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask)];
}
case "Pack": {
return tidy(() => {
const axis = getParamValue("axis", node, tensorMap, context);
const tensors = getParamValue("tensors", node, tensorMap, context);
const shape = tensors[0].shape;
const squeezedShape = squeeze(tensors[0]).shape;
const mapped = tensors.map((tensor16) => {
const sameShape = util_exports.arraysEqual(tensor16.shape, shape);
if (!sameShape && !util_exports.arraysEqual(squeeze(tensor16).shape, squeezedShape)) {
throw new Error("the input tensors shape does not match");
}
return sameShape ? tensor16 : reshape(tensor16, shape);
});
return [stack(mapped, axis)];
});
}
case "Unpack": {
const axis = getParamValue("axis", node, tensorMap, context);
const tensor16 = getParamValue("tensor", node, tensorMap, context);
return unstack(tensor16, axis);
}
case "Tile": {
const reps = getParamValue("reps", node, tensorMap, context);
return [tile(getParamValue("x", node, tensorMap, context), reps)];
}
case "Split":
case "SplitV": {
const axis = getParamValue("axis", node, tensorMap, context);
const numOrSizeSplits = getParamValue("numOrSizeSplits", node, tensorMap, context);
const tensor16 = getParamValue("x", node, tensorMap, context);
return split(tensor16, numOrSizeSplits, axis);
}
case "ScatterNd": {
const indices = getParamValue("indices", node, tensorMap, context);
const values = getParamValue("values", node, tensorMap, context);
const shape = getParamValue("shape", node, tensorMap, context);
return [scatterND(indices, values, shape)];
}
case "GatherNd": {
const x = getParamValue("x", node, tensorMap, context);
const indices = getParamValue("indices", node, tensorMap, context);
return [gatherND(x, indices)];
}
case "SparseToDense": {
const indices = getParamValue("sparseIndices", node, tensorMap, context);
const shape = getParamValue("outputShape", node, tensorMap, context);
const sparseValues = getParamValue("sparseValues", node, tensorMap, context);
const defaultValue = getParamValue("defaultValue", node, tensorMap, context);
return [sparseToDense(indices, sparseValues, shape, sparseValues.dtype === defaultValue.dtype ? defaultValue : cast(defaultValue, sparseValues.dtype))];
}
default:
throw TypeError(`Node type ${node.op} is not implemented`);
}
};
// node_modules/@tensorflow/tfjs-converter/dist/operations/executors/spectral_executor.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const executeOp16 = (node, tensorMap, context) => {
switch (node.op) {
case "FFT": {
return [fft(getParamValue("x", node, tensorMap, context))];
}
case "IFFT": {
return [ifft(getParamValue("x", node, tensorMap, context))];
}
case "RFFT": {
return [rfft(getParamValue("x", node, tensorMap, context))];
}
case "IRFFT": {
return [irfft(getParamValue("x", node, tensorMap, context))];
}
default:
throw TypeError(`Node type ${node.op} is not implemented`);
}
};
// node_modules/@tensorflow/tfjs-converter/dist/operations/executors/transformation_executor.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const executeOp17 = (node, tensorMap, context) => {
switch (node.op) {
case "Cast": {
return [cast(getParamValue("x", node, tensorMap, context), getParamValue("dtype", node, tensorMap, context))];
}
case "ExpandDims": {
const axis = getParamValue("axis", node, tensorMap, context);
return [expandDims(getParamValue("x", node, tensorMap, context), axis)];
}
case "Squeeze": {
const axis = getParamValue("axis", node, tensorMap, context);
return [squeeze(getParamValue("x", node, tensorMap, context), axis)];
}
case "Reshape": {
return [reshape(getParamValue("x", node, tensorMap, context), getParamValue("shape", node, tensorMap, context))];
}
case "MirrorPad": {
return [mirrorPad(getParamValue("x", node, tensorMap, context), getParamValue("padding", node, tensorMap, context), getParamValue("mode", node, tensorMap, context))];
}
case "PadV2":
case "Pad": {
return [pad(getParamValue("x", node, tensorMap, context), getParamValue("padding", node, tensorMap, context), getParamValue("constantValue", node, tensorMap, context))];
}
case "SpaceToBatchND": {
const blockShape = getParamValue("blockShape", node, tensorMap, context);
const paddings = getParamValue("paddings", node, tensorMap, context);
return [spaceToBatchND(getParamValue("x", node, tensorMap, context), blockShape, paddings)];
}
case "BatchToSpaceND": {
const blockShape = getParamValue("blockShape", node, tensorMap, context);
const crops = getParamValue("crops", node, tensorMap, context);
return [batchToSpaceND(getParamValue("x", node, tensorMap, context), blockShape, crops)];
}
case "DepthToSpace": {
const blockSize = getParamValue("blockSize", node, tensorMap, context);
const dataFormat = getParamValue("dataFormat", node, tensorMap, context).toUpperCase();
return [depthToSpace(getParamValue("x", node, tensorMap, context), blockSize, dataFormat)];
}
case "BroadcastTo": {
return [broadcastTo(getParamValue("x", node, tensorMap, context), getParamValue("shape", node, tensorMap, context))];
}
default:
throw TypeError(`Node type ${node.op} is not implemented`);
}
};
// node_modules/@tensorflow/tfjs-converter/dist/operations/operation_executor.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function executeOp18(node, tensorMap, context, resourceManager) {
const value = ((node2, tensorMap2, context2) => {
switch (node2.category) {
case "arithmetic":
return tidy(() => executeOp(node2, tensorMap2, context2));
case "basic_math":
return tidy(() => executeOp2(node2, tensorMap2, context2));
case "control":
return executeOp3(node2, tensorMap2, context2);
case "convolution":
return tidy(() => executeOp4(node2, tensorMap2, context2));
case "creation":
return tidy(() => executeOp5(node2, tensorMap2, context2));
case "dynamic":
return executeOp6(node2, tensorMap2, context2);
case "evaluation":
return tidy(() => executeOp7(node2, tensorMap2, context2));
case "image":
return tidy(() => executeOp10(node2, tensorMap2, context2));
case "graph":
return tidy(() => executeOp8(node2, tensorMap2, context2));
case "logical":
return tidy(() => executeOp11(node2, tensorMap2, context2));
case "matrices":
return tidy(() => executeOp12(node2, tensorMap2, context2));
case "normalization":
return tidy(() => executeOp13(node2, tensorMap2, context2));
case "reduction":
return tidy(() => executeOp14(node2, tensorMap2, context2));
case "slice_join":
return tidy(() => executeOp15(node2, tensorMap2, context2));
case "spectral":
return tidy(() => executeOp16(node2, tensorMap2, context2));
case "transformation":
return tidy(() => executeOp17(node2, tensorMap2, context2));
case "hash_table":
return executeOp9(node2, tensorMap2, context2, resourceManager);
case "custom":
const opMapper = getRegisteredOp(node2.op);
if (opMapper && opMapper.customExecutor) {
return opMapper.customExecutor(new NodeValueImpl(node2, tensorMap2, context2));
} else {
throw TypeError(`Custom op ${node2.op} is not registered.`);
}
default:
throw TypeError(`Unknown op '${node2.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`);
}
})(node, tensorMap, context);
if (util_exports.isPromise(value)) {
return value.then((data2) => [].concat(data2));
}
return [].concat(value);
}
// node_modules/@tensorflow/tfjs-converter/dist/executor/execution_context.js
class ExecutionContext {
constructor(weightMap = {}, tensorArrayMap = {}, tensorListMap = {}, functionMap = {}) {
this.weightMap = weightMap;
this.tensorArrayMap = tensorArrayMap;
this.tensorListMap = tensorListMap;
this.functionMap = functionMap;
this.rootContext = {id: 0, frameName: "", iterationId: 0};
this.contexts = [this.rootContext];
this.lastId = 0;
this.generateCurrentContextIds();
}
newFrame(id, frameName) {
return {id, frameName, iterationId: 0};
}
set currentContext(contexts) {
if (this.contexts !== contexts) {
this.contexts = contexts;
this.generateCurrentContextIds();
}
}
get currentContext() {
return this.contexts;
}
get currentContextId() {
return this._currentContextIds[0];
}
get currentContextIds() {
return this._currentContextIds;
}
generateCurrentContextIds() {
const names = [];
for (let i = 0; i < this.contexts.length - 1; i++) {
const contexts = this.contexts.slice(0, this.contexts.length - i);
names.push(this.contextIdforContexts(contexts));
}
names.push("");
this._currentContextIds = names;
}
contextIdforContexts(contexts) {
return contexts ? contexts.map((context) => context.id === 0 && context.iterationId === 0 ? "" : `${context.frameName}-${context.iterationId}`).join("/") : "";
}
enterFrame(frameId) {
if (this.contexts) {
this.lastId++;
this.contexts = this.contexts.slice();
this.contexts.push(this.newFrame(this.lastId, frameId));
this._currentContextIds.unshift(this.contextIdforContexts(this.contexts));
}
}
exitFrame() {
if (this.contexts && this.contexts.length > 1) {
this.contexts = this.contexts.slice();
this.contexts.splice(-1);
this.currentContextIds.shift();
} else {
throw new Error("Cannot exit frame, the context is empty");
}
}
nextIteration() {
if (this.contexts && this.contexts.length > 0) {
this.contexts = this.contexts.slice();
this.lastId++;
const context = Object.assign({}, this.contexts[this.contexts.length - 1]);
context.iterationId += 1;
context.id = this.lastId;
this.contexts.splice(-1, 1, context);
this._currentContextIds.splice(0, 1, this.contextIdforContexts(this.contexts));
} else {
throw new Error("Cannot increase frame iteration, the context is empty");
}
}
getWeight(name) {
return this.weightMap[name];
}
addTensorArray(tensorArray) {
this.tensorArrayMap[tensorArray.id] = tensorArray;
}
getTensorArray(id) {
return this.tensorArrayMap[id];
}
addTensorList(tensorList) {
this.tensorListMap[tensorList.id] = tensorList;
}
getTensorList(id) {
return this.tensorListMap[id];
}
dispose(keepIds) {
for (const key in this.tensorArrayMap) {
this.tensorArrayMap[key].clearAndClose(keepIds);
}
for (const key in this.tensorListMap) {
this.tensorListMap[key].clearAndClose(keepIds);
}
}
}
// node_modules/@tensorflow/tfjs-converter/dist/executor/model_analysis.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function getExecutionSubgraph(inputs, outputs, weightMap, initNodes) {
const usedNodes = new Set();
const missingInputs = [];
let dynamicNode = null;
let syncInputs = null;
const seen = new Set();
const inputNodeNames = Object.keys(inputs).map((name) => parseNodeName(name)[0]);
let initNodeNames = [];
if (initNodes != null) {
initNodeNames = initNodes.map((node) => parseNodeName(node.name)[0]);
}
const frontier = [...outputs];
while (frontier.length > 0) {
const node = frontier.pop();
if (isControlFlow(node) || isDynamicShape(node) || isHashTable(node)) {
if (dynamicNode == null) {
dynamicNode = node;
syncInputs = dynamicNode.children.map((child) => child.name).filter((name) => usedNodes.has(name));
}
}
usedNodes.add(node.name);
if (weightMap[node.name] != null) {
continue;
}
if (inputNodeNames.indexOf(node.name) !== -1) {
continue;
}
if (initNodeNames.indexOf(node.name) !== -1) {
continue;
}
if (node.inputs.length === 0) {
missingInputs.push(node.name);
continue;
}
node.inputs.forEach((input2) => {
if (seen.has(input2.name)) {
return;
}
seen.add(input2.name);
frontier.push(input2);
});
}
return {inputs, outputs, usedNodes, missingInputs, dynamicNode, syncInputs};
}
function getNodesInTopologicalOrder(graph2, weightMap, executionInfo) {
const {usedNodes, inputs} = executionInfo;
const frontier = [];
const inputNodes = Object.keys(inputs).map((name) => parseNodeName(name)[0]).map((name) => graph2.nodes[name]);
const initNodes = graph2.initNodes;
inputNodes.forEach((input2) => {
if (usedNodes.has(input2.name)) {
frontier.push(input2);
}
});
graph2.weights.forEach((weight) => {
if (usedNodes.has(weight.name)) {
frontier.push(weight);
}
});
if (initNodes != null) {
initNodes.forEach((node) => {
if (usedNodes.has(node.name)) {
frontier.push(node);
}
});
}
const seen = new Set();
const orderedNodes = [];
while (frontier.length > 0) {
const node = frontier.pop();
seen.add(node.name);
if (!weightMap[node.name]) {
orderedNodes.push(node);
}
node.children.forEach((child) => {
if (!seen.has(child.name) && usedNodes.has(child.name) && child.inputs.every((input2) => seen.has(input2.name))) {
frontier.push(child);
}
});
}
return orderedNodes;
}
const CONTROL_FLOW_OPS = [
"Switch",
"Merge",
"Enter",
"Exit",
"NextIteration",
"StatelessIf",
"StatelessWhile",
"if",
"While"
];
const DYNAMIC_SHAPE_OPS = [
"NonMaxSuppressionV2",
"NonMaxSuppressionV3",
"NonMaxSuppressionV5",
"Where"
];
const HASH_TABLE_OPS = [
"HashTable",
"HashTableV2",
"LookupTableImport",
"LookupTableImportV2",
"LookupTableFind",
"LookupTableFindV2"
];
function isControlFlow(node) {
return CONTROL_FLOW_OPS.indexOf(node.op) >= 0;
}
function isDynamicShape(node) {
return DYNAMIC_SHAPE_OPS.indexOf(node.op) >= 0;
}
function isHashTable(node) {
return HASH_TABLE_OPS.indexOf(node.op) >= 0;
}
// node_modules/@tensorflow/tfjs-converter/dist/executor/graph_executor.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
class GraphExecutor {
constructor(graph2, parent) {
this.graph = graph2;
this.parent = parent;
this.compiledMap = new Map();
this._weightMap = {};
this.SEPERATOR = ",";
this._functions = {};
this._functionExecutorMap = {};
this._outputs = graph2.outputs;
this._inputs = graph2.inputs;
this._initNodes = graph2.initNodes;
this._signature = graph2.signature;
this._functions = graph2.functions;
if (graph2.functions != null) {
Object.keys(graph2.functions).forEach((name) => {
this._functionExecutorMap[name] = new GraphExecutor(graph2.functions[name], this);
});
}
}
get weightIds() {
return this.parent ? this.parent.weightIds : this._weightIds;
}
get functionExecutorMap() {
return this.parent ? this.parent.functionExecutorMap : this._functionExecutorMap;
}
get weightMap() {
return this.parent ? this.parent.weightMap : this._weightMap;
}
set weightMap(weightMap) {
const weightIds = Object.keys(weightMap).map((key) => weightMap[key].map((tensor16) => tensor16.id));
this._weightIds = [].concat(...weightIds);
this._weightMap = weightMap;
}
set resourceManager(resourceManager) {
this._resourceManager = resourceManager;
}
get inputs() {
return this._inputs.map((node) => {
return {
name: node.name,
shape: node.attrParams["shape"] ? node.attrParams["shape"].value : void 0,
dtype: node.attrParams["dtype"] ? node.attrParams["dtype"].value : void 0
};
});
}
get outputs() {
return this._outputs.map((node) => {
return {
name: node.name,
shape: node.attrParams["shape"] ? node.attrParams["shape"].value : void 0,
dtype: node.attrParams["dtype"] ? node.attrParams["dtype"].value : void 0
};
});
}
get inputNodes() {
return this._inputs.map((node) => node.signatureKey || node.name);
}
get outputNodes() {
return this._outputs.map((node) => {
const name = node.signatureKey || node.name;
return node.defaultOutput ? `${name}:${node.defaultOutput}` : name;
});
}
get functions() {
return Object.keys(this._functions).reduce((map, key) => {
map[key] = this._functions[key].signature;
return map;
}, {});
}
getCompilationKey(inputs, outputs) {
const sortedInputs = inputs.map((node) => node.name).sort();
const sortedOutputs = outputs.map((node) => node.name).sort();
return sortedInputs.join(this.SEPERATOR) + "--" + sortedOutputs.join(this.SEPERATOR);
}
compile(inputs, outputs) {
const executionInfo = getExecutionSubgraph(inputs, outputs, this.weightMap, this._initNodes);
const {missingInputs, dynamicNode, syncInputs} = executionInfo;
if (dynamicNode != null) {
throw new Error(`This execution contains the node '${dynamicNode.name}', which has the dynamic op '${dynamicNode.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${syncInputs}]`);
}
if (missingInputs.length > 0) {
const outNames = outputs.map((n) => n.name);
const inNames = Object.keys(inputs);
throw new Error(`Cannot compute the outputs [${outNames}] from the provided inputs [${inNames}]. Missing the following inputs: [${missingInputs}]`);
}
return getNodesInTopologicalOrder(this.graph, this.weightMap, executionInfo);
}
execute(inputs, outputs) {
inputs = this.mapInputs(inputs);
const names = Object.keys(inputs).sort();
this.checkInputs(inputs);
this.checkInputShapeAndType(inputs);
outputs = this.mapOutputs(outputs);
this.checkOutputs(outputs);
const inputNodes = names.map((name) => this.graph.nodes[parseNodeName(name)[0]]);
const outputNodeNames = outputs.map((name) => parseNodeName(name)[0]);
let outputNodes = outputNodeNames.map((name) => this.graph.nodes[name]);
if (outputNodes.length === 0) {
outputNodes = this._outputs;
}
const compilationKey = this.getCompilationKey(inputNodes, outputNodes);
let orderedNodes = this.compiledMap.get(compilationKey);
if (orderedNodes == null) {
orderedNodes = this.compile(inputs, outputNodes);
this.compiledMap.set(compilationKey, orderedNodes);
}
const tensorArrayMap = {};
const tensorListMap = {};
return tidy(() => {
const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap);
const tensorsMap = Object.assign({}, this.weightMap);
Object.keys(inputs).forEach((name) => {
const [nodeName, index] = parseNodeName(name);
const tensors = [];
tensors[index] = inputs[name];
tensorsMap[nodeName] = tensors;
});
const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);
const intermediateTensorConsumerCount = {};
for (let i = 0; i < orderedNodes.length; i++) {
const node = orderedNodes[i];
if (!tensorsMap[node.name]) {
const tensors = executeOp18(node, tensorsMap, context, this._resourceManager);
if (util_exports.isPromise(tensors)) {
throw new Error(`The execution of the op '${node.op}' returned a promise. Please use model.executeAsync() instead.`);
}
tensorsMap[node.name] = tensors;
this.checkTensorForDisposal(node.name, node, tensorsMap, context, tensorsToKeep, outputNodeNames, intermediateTensorConsumerCount);
}
}
if (this.parent == null) {
context.dispose(tensorsToKeep);
}
return outputs.map((name) => getTensor(name, tensorsMap, context));
});
}
getFrozenTensorIds(tensorMap) {
const ids = [].concat.apply([], Object.keys(tensorMap).map((key) => tensorMap[key]).map((tensors) => tensors.map((tensor16) => tensor16.id)));
return new Set(ids);
}
checkTensorForDisposal(nodeName, node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount) {
if (node.category === "control" || outputNames.indexOf(nodeName) !== -1) {
return;
}
tensorMap[nodeName].forEach((tensor16) => {
if (tensor16 != null) {
intermediateTensorConsumerCount[tensor16.id] = (intermediateTensorConsumerCount[tensor16.id] || 0) + node.children.length;
}
});
node.inputs.forEach((input2) => {
if (input2.category !== "control") {
const tensors = getTensorsForCurrentContenxt(input2.name, tensorMap, context);
if (tensors != null) {
tensors.forEach((tensor16) => {
if (tensor16 && !tensorsToKeep.has(tensor16.id)) {
const count2 = intermediateTensorConsumerCount[tensor16.id];
if (count2 === 1) {
tensor16.dispose();
delete intermediateTensorConsumerCount[tensor16.id];
} else if (count2 != null) {
intermediateTensorConsumerCount[tensor16.id]--;
}
}
});
}
}
});
}
async executeAsync(inputs, outputs) {
return this._executeAsync(inputs, outputs);
}
async _executeAsync(inputs, outputs, isFunctionExecution = false, tensorArrayMap = {}, tensorListMap = {}) {
if (!isFunctionExecution) {
inputs = this.mapInputs(inputs);
this.checkInputs(inputs);
this.checkInputShapeAndType(inputs);
outputs = this.mapOutputs(outputs);
this.checkOutputs(outputs);
}
const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap);
const tensorMap = await this.executeWithControlFlow(inputs, context, outputs, isFunctionExecution);
const results = outputs.map((name) => getTensor(name, tensorMap, context));
const outputIds = results.map((t) => t.id);
const inputIds = Object.keys(inputs).map((name) => inputs[name].id);
const keepIds = new Set([...outputIds, ...inputIds, ...this.weightIds]);
Object.keys(tensorMap).forEach((key) => {
const tensorArray = tensorMap[key];
tensorArray.forEach((tensor16) => {
if (tensor16 && !tensor16.isDisposed && !keepIds.has(tensor16.id)) {
tensor16.dispose();
}
});
});
if (this.parent == null) {
context.dispose(keepIds);
}
return results;
}
async executeFunctionAsync(inputs, tensorArrayMap, tensorListMap) {
const mappedInputs = inputs.reduce((map, tensor16, index) => {
map[this.inputs[index].name] = tensor16;
return map;
}, {});
return this._executeAsync(mappedInputs, this.outputNodes, true, tensorArrayMap, tensorListMap);
}
async executeWithControlFlow(inputs, context, outputNames, isFunctionExecution) {
const names = Object.keys(inputs);
const inputNodes = names.map((name) => this.graph.nodes[parseNodeName(name)[0]]);
const outputNodeNames = outputNames.map((name) => parseNodeName(name)[0]);
let outputNodes = outputNodeNames.map((name) => this.graph.nodes[name]);
if (outputNodes.length === 0) {
outputNodes = this._outputs;
}
const {usedNodes, missingInputs, dynamicNode, syncInputs} = getExecutionSubgraph(inputs, outputNodes, this.weightMap, this._initNodes);
const stack6 = [
...inputNodes,
...this.graph.weights,
...this._initNodes || []
].map((node) => {
return {node, contexts: context.currentContext};
});
const tensorsMap = Object.assign({}, this.weightMap);
Object.keys(inputs).forEach((name) => {
const [nodeName, index] = parseNodeName(name);
const tensors = [];
tensors[index] = inputs[name];
tensorsMap[nodeName] = tensors;
});
const intermediateTensorConsumerCount = {};
const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);
const added = {};
while (stack6.length > 0) {
const promises = this.processStack(inputNodes, stack6, context, tensorsMap, added, tensorsToKeep, outputNodeNames, intermediateTensorConsumerCount, usedNodes);
await Promise.all(promises);
}
if (dynamicNode == null && !isFunctionExecution) {
console.warn(`This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.`);
}
const missingOutputs = outputNodes.filter((node) => !isControlFlow(node) && !getTensor(node.name, tensorsMap, context)).map((node) => node.name);
if (missingOutputs.length > 0) {
let alternativeMsg = "";
if (dynamicNode != null) {
alternativeMsg = `Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${syncInputs}]`;
}
throw new Error(`Cannot compute the outputs [${missingOutputs}] from the provided inputs [${names}]. Consider providing the following inputs: [${missingInputs}]. ${alternativeMsg}`);
}
return tensorsMap;
}
processStack(inputNodes, stack6, context, tensorMap, added, tensorsToKeep, outputNames, intermediateTensorConsumerCount, usedNodes) {
const promises = [];
while (stack6.length > 0) {
const item = stack6.pop();
context.currentContext = item.contexts;
let nodeName = "";
if (item.node.op === "Enter" && getParamValue("isConstant", item.node, tensorMap, context)) {
[nodeName] = getNodeNameAndIndex(item.node.name, context);
}
if (tensorMap[item.node.name] == null) {
const tensors = executeOp18(item.node, tensorMap, context, this._resourceManager);
if (!nodeName) {
[nodeName] = getNodeNameAndIndex(item.node.name, context);
}
const currentContext = context.currentContext;
if (util_exports.isPromise(tensors)) {
promises.push(tensors.then((t) => {
tensorMap[nodeName] = t;
context.currentContext = currentContext;
this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount);
this.processChildNodes(item.node, stack6, context, tensorMap, added, usedNodes);
return t;
}));
} else {
tensorMap[nodeName] = tensors;
this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount);
this.processChildNodes(item.node, stack6, context, tensorMap, added, usedNodes);
}
} else {
this.processChildNodes(item.node, stack6, context, tensorMap, added, usedNodes);
}
}
return promises;
}
processChildNodes(node, stack6, context, tensorMap, added, usedNodes) {
node.children.forEach((childNode) => {
const [nodeName] = getNodeNameAndIndex(childNode.name, context);
if (added[nodeName] || !usedNodes.has(childNode.name)) {
return;
}
if (childNode.op === "Merge") {
if (childNode.inputNames.some((name) => {
return !!getTensor(name, tensorMap, context);
})) {
added[nodeName] = true;
stack6.push({contexts: context.currentContext, node: childNode});
}
} else if (childNode.inputNames.every((name) => {
return !!getTensor(name, tensorMap, context);
})) {
added[nodeName] = true;
stack6.push({contexts: context.currentContext, node: childNode});
}
});
}
dispose() {
Object.keys(this.weightMap).forEach((key) => this.weightMap[key].forEach((tensor16) => tensor16.dispose()));
}
checkInputShapeAndType(inputs) {
Object.keys(inputs).forEach((name) => {
const input2 = inputs[name];
const [nodeName] = parseNodeName(name);
const node = this.graph.nodes[nodeName];
if (node.attrParams["shape"] && node.attrParams["shape"].value) {
const shape = node.attrParams["shape"].value;
const match = shape.length === input2.shape.length && input2.shape.every((dim, index) => shape[index] === -1 || shape[index] === dim);
util_exports.assert(match, () => `The shape of dict['${node.name}'] provided in model.execute(dict) must be [${shape}], but was [${input2.shape}]`);
}
if (node.attrParams["dtype"] && node.attrParams["dtype"].value) {
util_exports.assert(input2.dtype === node.attrParams["dtype"].value, () => `The dtype of dict['${node.name}'] provided in model.execute(dict) must be ${node.attrParams["dtype"].value}, but was ${input2.dtype}`);
}
});
}
mapInputs(inputs) {
const result = {};
for (const inputName in inputs) {
if (this._signature != null && this._signature.inputs != null && this._signature.inputs[inputName] != null) {
const tensor16 = this._signature.inputs[inputName];
result[tensor16.name] = inputs[inputName];
} else {
result[inputName] = inputs[inputName];
}
}
return result;
}
checkInputs(inputs) {
const notInGraph = Object.keys(inputs).filter((name) => {
const [nodeName] = parseNodeName(name);
return this.graph.nodes[nodeName] == null;
});
if (notInGraph.length > 0) {
throw new Error(`The dict provided in model.execute(dict) has keys: [${notInGraph}] that are not part of graph`);
}
}
mapOutputs(outputs) {
return outputs.map((name) => {
if (this._signature != null && this._signature.outputs != null && this._signature.outputs[name] != null) {
const tensor16 = this._signature.outputs[name];
return tensor16.name;
}
return name;
}, {});
}
checkOutputs(outputs) {
outputs.forEach((name) => {
const [normalizedName] = parseNodeName(name);
if (!this.graph.nodes[normalizedName]) {
throw new Error(`The output '${name}' is not found in the graph`);
}
});
}
}
// node_modules/@tensorflow/tfjs-converter/dist/executor/resource_manager.js
class ResourceManager {
constructor(hashTableNameToHandle = {}, hashTableMap = {}) {
this.hashTableNameToHandle = hashTableNameToHandle;
this.hashTableMap = hashTableMap;
}
addHashTable(name, hashTable2) {
this.hashTableNameToHandle[name] = hashTable2.handle;
this.hashTableMap[hashTable2.id] = hashTable2;
}
getHashTableHandleByName(name) {
return this.hashTableNameToHandle[name];
}
getHashTableById(id) {
return this.hashTableMap[id];
}
dispose() {
for (const key in this.hashTableMap) {
this.hashTableMap[key].clearAndClose();
delete this.hashTableMap[key];
}
for (const name in this.hashTableNameToHandle) {
this.hashTableNameToHandle[name].dispose();
delete this.hashTableNameToHandle[name];
}
}
}
// node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const TFHUB_SEARCH_PARAM = "?tfjs-format=file";
const DEFAULT_MODEL_NAME = "model.json";
class GraphModel {
constructor(modelUrl, loadOptions = {}) {
this.modelUrl = modelUrl;
this.loadOptions = loadOptions;
this.version = "n/a";
if (loadOptions == null) {
this.loadOptions = {};
}
this.resourceManager = new ResourceManager();
}
get modelVersion() {
return this.version;
}
get inputNodes() {
return this.executor.inputNodes;
}
get outputNodes() {
return this.executor.outputNodes;
}
get inputs() {
return this.executor.inputs;
}
get outputs() {
return this.executor.outputs;
}
get weights() {
return this.executor.weightMap;
}
findIOHandler() {
const path = this.modelUrl;
if (path.load != null) {
this.handler = path;
} else if (this.loadOptions.requestInit != null) {
this.handler = io_exports.browserHTTPRequest(path, this.loadOptions);
} else {
const handlers = io_exports.getLoadHandlers(path, this.loadOptions);
if (handlers.length === 0) {
handlers.push(io_exports.browserHTTPRequest(path, this.loadOptions));
} else if (handlers.length > 1) {
throw new Error(`Found more than one (${handlers.length}) load handlers for URL '${[path]}'`);
}
this.handler = handlers[0];
}
}
async load() {
this.findIOHandler();
if (this.handler.load == null) {
throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");
}
const artifacts = await this.handler.load();
return this.loadSync(artifacts);
}
loadSync(artifacts) {
this.artifacts = artifacts;
const graph2 = this.artifacts.modelTopology;
let signature = {};
if (this.artifacts.userDefinedMetadata != null) {
signature = this.artifacts.userDefinedMetadata.signature;
}
this.version = `${graph2.versions.producer}.${graph2.versions.minConsumer}`;
const weightMap = io_exports.decodeWeights(this.artifacts.weightData, this.artifacts.weightSpecs);
this.executor = new GraphExecutor(OperationMapper.Instance.transformGraph(graph2, signature));
this.executor.weightMap = this.convertTensorMapToTensorsMap(weightMap);
this.executor.resourceManager = this.resourceManager;
if (artifacts.modelInitializer != null) {
const initializer = OperationMapper.Instance.transformGraph(artifacts.modelInitializer);
this.initializer = new GraphExecutor(initializer);
this.initializer.weightMap = this.executor.weightMap;
this.initializer.resourceManager = this.resourceManager;
this.initializer.executeAsync({}, []);
}
return true;
}
async save(handlerOrURL, config2) {
if (typeof handlerOrURL === "string") {
const handlers = io_exports.getSaveHandlers(handlerOrURL);
if (handlers.length === 0) {
throw new Error(`Cannot find any save handlers for URL '${handlerOrURL}'`);
} else if (handlers.length > 1) {
throw new Error(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);
}
handlerOrURL = handlers[0];
}
if (handlerOrURL.save == null) {
throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");
}
return handlerOrURL.save(this.artifacts);
}
predict(inputs, config2) {
return this.execute(inputs, this.outputNodes);
}
normalizeInputs(inputs) {
if (!(inputs instanceof Tensor) && !Array.isArray(inputs)) {
return inputs;
}
inputs = Array.isArray(inputs) ? inputs : [inputs];
if (inputs.length !== this.inputNodes.length) {
throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${inputs.length} input tensors.`);
}
return this.inputNodes.reduce((map, inputName, i) => {
map[inputName] = inputs[i];
return map;
}, {});
}
normalizeOutputs(outputs) {
outputs = outputs || this.outputNodes;
return !Array.isArray(outputs) ? [outputs] : outputs;
}
execute(inputs, outputs) {
inputs = this.normalizeInputs(inputs);
outputs = this.normalizeOutputs(outputs);
const result = this.executor.execute(inputs, outputs);
return result.length > 1 ? result : result[0];
}
async executeAsync(inputs, outputs) {
inputs = this.normalizeInputs(inputs);
outputs = this.normalizeOutputs(outputs);
const result = await this.executor.executeAsync(inputs, outputs);
return result.length > 1 ? result : result[0];
}
convertTensorMapToTensorsMap(map) {
return Object.keys(map).reduce((newMap, key) => {
newMap[key] = [map[key]];
return newMap;
}, {});
}
dispose() {
this.executor.dispose();
if (this.initializer) {
this.initializer.dispose();
}
this.resourceManager.dispose();
}
}
async function loadGraphModel(modelUrl, options = {}) {
if (modelUrl == null) {
throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");
}
if (options == null) {
options = {};
}
if (options.fromTFHub) {
if (modelUrl.load == null) {
if (!modelUrl.endsWith("/")) {
modelUrl = modelUrl + "/";
}
modelUrl = `${modelUrl}${DEFAULT_MODEL_NAME}${TFHUB_SEARCH_PARAM}`;
}
}
const model2 = new GraphModel(modelUrl, options);
await model2.load();
return model2;
}
// node_modules/@tensorflow/tfjs-converter/dist/version.js
/** @license See the LICENSE file. */
const version6 = "2.7.0";
// node_modules/@tensorflow/tfjs-converter/dist/index.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
// node_modules/@tensorflow/tfjs-data/dist/index.js
const dist_exports = {};
__export(dist_exports, {
CSVDataset: () => CSVDataset,
Dataset: () => Dataset,
FileDataSource: () => FileDataSource,
TextLineDataset: () => TextLineDataset,
URLDataSource: () => URLDataSource,
array: () => array,
csv: () => csv,
func: () => func,
generator: () => generator,
microphone: () => microphone,
version_data: () => version8,
webcam: () => webcam,
zip: () => zip
});
// node_modules/@tensorflow/tfjs-data/dist/dataset.js
const seedrandom3 = __toModule(require_seedrandom4());
// node_modules/@tensorflow/tfjs-data/dist/iterators/lazy_iterator.js
const seedrandom2 = __toModule(require_seedrandom4());
// node_modules/@tensorflow/tfjs-data/dist/util/deep_map.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
function deepMap(input2, mapFn) {
return deepMapInternal(input2, mapFn);
}
function deepMapInternal(input2, mapFn, seen = new Map(), containedIn = new Set()) {
if (input2 == null) {
return null;
}
if (containedIn.has(input2)) {
throw new Error("Circular references are not supported.");
}
if (seen.has(input2)) {
return seen.get(input2);
}
const result = mapFn(input2);
if (result.recurse && result.value !== null) {
throw new Error("A deep map function may not return both a value and recurse=true.");
}
if (!result.recurse) {
seen.set(input2, result.value);
return result.value;
} else if (isIterable2(input2)) {
const mappedIterable = Array.isArray(input2) ? [] : {};
containedIn.add(input2);
for (const k in input2) {
const child = input2[k];
const childResult = deepMapInternal(child, mapFn, seen, containedIn);
mappedIterable[k] = childResult;
}
containedIn.delete(input2);
return mappedIterable;
} else {
throw new Error(`Can't recurse into non-iterable type: ${input2}`);
}
}
function deepZip(inputs, zipFn = zipToList) {
return deepZipInternal(inputs, zipFn);
}
function deepZipInternal(inputs, zipFn, containedIn = new Set()) {
const input2 = inputs[0];
if (containedIn.has(input2)) {
throw new Error("Circular references are not supported.");
}
const result = zipFn(inputs);
if (result.recurse && result.value !== null) {
throw new Error("A deep zip function may not return both a value and recurse=true.");
}
if (!result.recurse) {
return result.value;
} else if (isIterable2(input2)) {
const mappedIterable = Array.isArray(input2) ? [] : {};
containedIn.add(input2);
for (const k in input2) {
const children = inputs.map((x) => x[k]);
const childResult = deepZipInternal(children, zipFn, containedIn);
mappedIterable[k] = childResult;
}
containedIn.delete(input2);
return mappedIterable;
} else {
throw new Error(`Can't recurse into non-iterable type: ${input2}`);
}
}
function zipToList(x) {
if (x === null) {
return null;
}
if (isIterable2(x[0])) {
return {value: null, recurse: true};
} else {
return {value: x, recurse: false};
}
}
async function deepMapAndAwaitAll(input2, mapFn) {
const seen = new Map();
deepMapInternal(input2, mapFn, seen);
for (const key of Array.from(seen.keys())) {
const value = seen.get(key);
if (util_exports.isPromise(value)) {
const mappedValue = await value;
seen.set(key, mappedValue);
}
}
const result = deepMapInternal(input2, mapFn, seen);
return result;
}
function isIterable2(obj) {
return obj != null && !ArrayBuffer.isView(obj) && (Array.isArray(obj) || typeof obj === "object" && !(obj instanceof Tensor));
}
function canTensorify(obj) {
return obj == null || isPrimitive(obj) || Array.isArray(obj) || typeof obj === "object" && obj instanceof Tensor || util_exports.isTypedArray(obj);
}
function isPrimitive(value) {
return value === null || typeof value !== "object" && typeof value !== "function";
}
// node_modules/@tensorflow/tfjs-data/dist/util/deep_clone.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
function deepClone(container2) {
return deepMap(container2, cloneIfTensor);
}
function cloneIfTensor(item) {
if (item instanceof Tensor) {
return {value: item.clone(), recurse: false};
} else if (isIterable2(item)) {
return {value: null, recurse: true};
} else {
return {value: item, recurse: false};
}
}
// node_modules/@tensorflow/tfjs-data/dist/util/ring_buffer.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
class RingBuffer {
constructor(capacity) {
this.capacity = capacity;
this.begin = 0;
this.end = 0;
if (capacity == null) {
throw new RangeError("Can't create a ring buffer of unknown capacity.");
}
if (capacity < 1) {
throw new RangeError("Can't create ring buffer of capacity < 1.");
}
this.data = new Array(capacity);
this.doubledCapacity = 2 * capacity;
}
wrap(index) {
while (index < 0) {
index += this.doubledCapacity;
}
return index % this.doubledCapacity;
}
get(index) {
if (index < 0) {
throw new RangeError("Can't get item at a negative index.");
}
return this.data[index % this.capacity];
}
set(index, value) {
if (index < 0) {
throw new RangeError("Can't set item at a negative index.");
}
this.data[index % this.capacity] = value;
}
length() {
let length = this.end - this.begin;
if (length < 0) {
length = this.doubledCapacity + length;
}
return length;
}
isFull() {
return this.length() === this.capacity;
}
isEmpty() {
return this.length() === 0;
}
push(value) {
if (this.isFull()) {
throw new RangeError("Ring buffer is full.");
}
this.set(this.end, value);
this.end = this.wrap(this.end + 1);
}
pushAll(values) {
for (const value of values) {
this.push(value);
}
}
pop() {
if (this.isEmpty()) {
throw new RangeError("Ring buffer is empty.");
}
this.end = this.wrap(this.end - 1);
const result = this.get(this.end);
this.set(this.end, void 0);
return result;
}
unshift(value) {
if (this.isFull()) {
throw new RangeError("Ring buffer is full.");
}
this.begin = this.wrap(this.begin - 1);
this.set(this.begin, value);
}
shift() {
if (this.isEmpty()) {
throw new RangeError("Ring buffer is empty.");
}
const result = this.get(this.begin);
this.set(this.begin, void 0);
this.begin = this.wrap(this.begin + 1);
return result;
}
shuffleExcise(relativeIndex) {
if (this.isEmpty()) {
throw new RangeError("Ring buffer is empty.");
}
const index = this.wrap(this.begin + relativeIndex);
const result = this.get(index);
this.set(index, this.pop());
return result;
}
}
// node_modules/@tensorflow/tfjs-data/dist/util/growing_ring_buffer.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
class GrowingRingBuffer extends RingBuffer {
constructor() {
super(GrowingRingBuffer.INITIAL_CAPACITY);
}
isFull() {
return false;
}
push(value) {
if (super.isFull()) {
this.expand();
}
super.push(value);
}
unshift(value) {
if (super.isFull()) {
this.expand();
}
super.unshift(value);
}
expand() {
const newCapacity = this.capacity * 2;
const newData = new Array(newCapacity);
const len = this.length();
for (let i = 0; i < len; i++) {
newData[i] = this.get(this.wrap(this.begin + i));
}
this.data = newData;
this.capacity = newCapacity;
this.doubledCapacity = 2 * this.capacity;
this.begin = 0;
this.end = len;
}
}
GrowingRingBuffer.INITIAL_CAPACITY = 32;
// node_modules/@tensorflow/tfjs-data/dist/iterators/lazy_iterator.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
function iteratorFromItems(items) {
return new ArrayIterator(items);
}
function iteratorFromFunction(func2) {
return new FunctionCallIterator(func2);
}
function iteratorFromConcatenated(baseIterators, baseErrorHandler) {
return new ChainedIterator(baseIterators, baseErrorHandler);
}
function iteratorFromZipped(iterators, mismatchMode = ZipMismatchMode.FAIL) {
return new ZipIterator(iterators, mismatchMode);
}
class LazyIterator {
async toArray() {
const result = [];
let x = await this.next();
while (!x.done) {
result.push(x.value);
x = await this.next();
}
return result;
}
async toArrayForTest() {
const stream = this.prefetch(100);
const result = [];
let x = await stream.next();
while (!x.done) {
result.push(x.value);
x = await stream.next();
}
return result;
}
async resolveFully() {
let x = await this.next();
while (!x.done) {
x = await this.next();
}
}
async resolveWhile(predicate) {
let x = await this.next();
let shouldContinue = predicate(x.value);
while (!x.done && shouldContinue) {
x = await this.next();
shouldContinue = predicate(x.value);
}
}
handleErrors(handler) {
return new ErrorHandlingLazyIterator(this, handler);
}
filter(predicate) {
return new FilterIterator(this, predicate);
}
map(transform) {
return new MapIterator(this, transform);
}
mapAsync(transform) {
return new AsyncMapIterator(this, transform);
}
serialMapAsync(transform) {
return new AsyncMapIterator(this, transform).serial();
}
flatmap(transform) {
return new FlatmapIterator(this, transform);
}
async forEachAsync(f) {
return this.map(f).resolveFully();
}
async serialForEach(f) {
return this.serialMapAsync(f).resolveWhile((x) => x === true);
}
rowMajorBatch(batchSize, smallLastBatch = true) {
return new RowMajorBatchIterator(this, batchSize, smallLastBatch);
}
columnMajorBatch(batchSize, smallLastBatch = true, zipFn = zipToList) {
const rowBatches = this.rowMajorBatch(batchSize, smallLastBatch);
return rowBatches.map((x) => deepZip(x, zipFn));
}
concatenate(iterator, baseErrorHandler) {
return new ChainedIterator(iteratorFromItems([this, iterator]), baseErrorHandler);
}
take(count2) {
if (count2 < 0 || count2 == null) {
return this;
}
return new TakeIterator(this, count2);
}
skip(count2) {
if (count2 < 0 || count2 == null) {
return this;
}
return new SkipIterator(this, count2);
}
prefetch(bufferSize) {
return new PrefetchIterator(this, bufferSize);
}
shuffle(windowSize, seed) {
return new ShuffleIterator(this, windowSize, seed);
}
serial() {
return new SerialIterator(this);
}
}
class ArrayIterator extends LazyIterator {
constructor(items) {
super();
this.items = items;
this.trav = 0;
}
summary() {
return `Array of ${this.items.length} items`;
}
async next() {
if (this.trav >= this.items.length) {
return {value: null, done: true};
}
const item = this.items[this.trav];
this.trav++;
return {value: deepClone(item), done: false};
}
}
class FunctionCallIterator extends LazyIterator {
constructor(nextFn) {
super();
this.nextFn = nextFn;
}
summary() {
return `Function call`;
}
async next() {
try {
return this.nextFn();
} catch (e) {
e.message = `Error thrown while iterating through a dataset: ${e.message}`;
throw e;
}
}
}
class SerialIterator extends LazyIterator {
constructor(upstream) {
super();
this.upstream = upstream;
this.lastRead = Promise.resolve({value: null, done: false});
}
summary() {
return `${this.upstream.summary()} -> Serial`;
}
async next() {
this.lastRead = this.lastRead.then(() => this.serialNext());
return this.lastRead;
}
async serialNext() {
return this.upstream.next();
}
}
class SkipIterator extends LazyIterator {
constructor(upstream, maxCount) {
super();
this.upstream = upstream;
this.maxCount = maxCount;
this.count = 0;
this.lastRead = Promise.resolve({value: null, done: false});
}
summary() {
return `${this.upstream.summary()} -> Skip`;
}
async next() {
this.lastRead = this.lastRead.then(() => this.serialNext());
return this.lastRead;
}
async serialNext() {
while (this.count++ < this.maxCount) {
const skipped = await this.upstream.next();
if (skipped.done) {
return skipped;
}
dispose(skipped.value);
}
return this.upstream.next();
}
}
class TakeIterator extends LazyIterator {
constructor(upstream, maxCount) {
super();
this.upstream = upstream;
this.maxCount = maxCount;
this.count = 0;
}
summary() {
return `${this.upstream.summary()} -> Take`;
}
async next() {
if (this.count++ >= this.maxCount) {
return {value: null, done: true};
}
return this.upstream.next();
}
}
class RowMajorBatchIterator extends LazyIterator {
constructor(upstream, batchSize, enableSmallLastBatch = true) {
super();
this.upstream = upstream;
this.batchSize = batchSize;
this.enableSmallLastBatch = enableSmallLastBatch;
this.lastRead = Promise.resolve({value: null, done: false});
}
summary() {
return `${this.upstream.summary()} -> RowMajorBatch`;
}
async next() {
this.lastRead = this.lastRead.then(() => this.serialNext());
return this.lastRead;
}
async serialNext() {
const batch = [];
while (batch.length < this.batchSize) {
const item = await this.upstream.next();
if (item.done) {
if (this.enableSmallLastBatch && batch.length > 0) {
return {value: batch, done: false};
}
return {value: null, done: true};
}
batch.push(item.value);
}
return {value: batch, done: false};
}
}
class FilterIterator extends LazyIterator {
constructor(upstream, predicate) {
super();
this.upstream = upstream;
this.predicate = predicate;
this.lastRead = Promise.resolve({value: null, done: false});
}
summary() {
return `${this.upstream.summary()} -> Filter`;
}
async next() {
this.lastRead = this.lastRead.then(() => this.serialNext());
return this.lastRead;
}
async serialNext() {
while (true) {
const item = await this.upstream.next();
if (item.done || this.predicate(item.value)) {
return item;
}
dispose(item.value);
}
}
}
class MapIterator extends LazyIterator {
constructor(upstream, transform) {
super();
this.upstream = upstream;
this.transform = transform;
}
summary() {
return `${this.upstream.summary()} -> Map`;
}
async next() {
const item = await this.upstream.next();
if (item.done) {
return {value: null, done: true};
}
const inputTensors = tensor_util_exports.getTensorsInContainer(item.value);
const mapped = this.transform(item.value);
const outputTensors = tensor_util_exports.getTensorsInContainer(mapped);
for (const t of inputTensors) {
if (!tensor_util_exports.isTensorInList(t, outputTensors)) {
t.dispose();
}
}
return {value: mapped, done: false};
}
}
class ErrorHandlingLazyIterator extends LazyIterator {
constructor(upstream, handler) {
super();
this.upstream = upstream;
this.handler = handler;
this.count = 0;
this.lastRead = Promise.resolve({value: null, done: false});
}
summary() {
return `${this.upstream.summary()} -> handleErrors`;
}
async next() {
this.lastRead = this.lastRead.then(() => this.serialNext());
return this.lastRead;
}
async serialNext() {
while (true) {
try {
return await this.upstream.next();
} catch (e) {
if (!this.handler(e)) {
return {value: null, done: true};
}
}
}
}
}
class AsyncMapIterator extends LazyIterator {
constructor(upstream, transform) {
super();
this.upstream = upstream;
this.transform = transform;
}
summary() {
return `${this.upstream.summary()} -> AsyncMap`;
}
async next() {
const item = await this.upstream.next();
if (item.done) {
return {value: null, done: true};
}
const inputTensors = tensor_util_exports.getTensorsInContainer(item.value);
const mapped = await this.transform(item.value);
const outputTensors = tensor_util_exports.getTensorsInContainer(mapped);
for (const t of inputTensors) {
if (!tensor_util_exports.isTensorInList(t, outputTensors)) {
t.dispose();
}
}
return {value: mapped, done: false};
}
}
class OneToManyIterator extends LazyIterator {
constructor() {
super();
this.outputQueue = new GrowingRingBuffer();
this.lastRead = Promise.resolve({value: null, done: false});
}
async next() {
this.lastRead = this.lastRead.then(() => this.serialNext());
return this.lastRead;
}
async serialNext() {
while (this.outputQueue.length() === 0) {
if (!await this.pump()) {
return {value: null, done: true};
}
}
return {value: this.outputQueue.shift(), done: false};
}
}
class FlatmapIterator extends OneToManyIterator {
constructor(upstream, transform) {
super();
this.upstream = upstream;
this.transform = transform;
}
summary() {
return `${this.upstream.summary()} -> Flatmap`;
}
async pump() {
const item = await this.upstream.next();
if (item.done) {
return false;
}
const inputTensors = tensor_util_exports.getTensorsInContainer(item.value);
const mappedArray = this.transform(item.value);
const outputTensors = tensor_util_exports.getTensorsInContainer(mappedArray);
this.outputQueue.pushAll(mappedArray);
for (const t of inputTensors) {
if (!tensor_util_exports.isTensorInList(t, outputTensors)) {
t.dispose();
}
}
return true;
}
}
class ChainedIterator extends LazyIterator {
constructor(iterators, baseErrorHandler) {
super();
this.baseErrorHandler = baseErrorHandler;
this.lastRead = null;
this.iterator = null;
this.moreIterators = iterators;
}
summary() {
const upstreamSummaries = "TODO: fill in upstream of chained summaries";
return `${upstreamSummaries} -> Chained`;
}
async next() {
this.lastRead = this.readFromChain(this.lastRead);
return this.lastRead;
}
async readFromChain(lastRead) {
await lastRead;
if (this.iterator == null) {
const iteratorResult = await this.moreIterators.next();
if (iteratorResult.done) {
return {value: null, done: true};
}
this.iterator = iteratorResult.value;
if (this.baseErrorHandler != null) {
this.iterator = this.iterator.handleErrors(this.baseErrorHandler);
}
}
const itemResult = await this.iterator.next();
if (itemResult.done) {
this.iterator = null;
return this.readFromChain(lastRead);
}
return itemResult;
}
}
var ZipMismatchMode;
(function(ZipMismatchMode2) {
ZipMismatchMode2[ZipMismatchMode2["FAIL"] = 0] = "FAIL";
ZipMismatchMode2[ZipMismatchMode2["SHORTEST"] = 1] = "SHORTEST";
ZipMismatchMode2[ZipMismatchMode2["LONGEST"] = 2] = "LONGEST";
})(ZipMismatchMode || (ZipMismatchMode = {}));
class ZipIterator extends LazyIterator {
constructor(iterators, mismatchMode = ZipMismatchMode.FAIL) {
super();
this.iterators = iterators;
this.mismatchMode = mismatchMode;
this.count = 0;
this.currentPromise = null;
}
summary() {
const upstreamSummaries = "TODO: fill in upstream of zip summaries";
return `{${upstreamSummaries}} -> Zip`;
}
async nextState(afterState) {
await afterState;
let numIterators = 0;
let iteratorsDone = 0;
function getNext(container2) {
if (container2 instanceof LazyIterator) {
const result = container2.next();
return {
value: result.then((x) => {
numIterators++;
if (x.done) {
iteratorsDone++;
}
return x.value;
}),
recurse: false
};
} else {
return {value: null, recurse: true};
}
}
const mapped = await deepMapAndAwaitAll(this.iterators, getNext);
if (numIterators === iteratorsDone) {
return {value: null, done: true};
}
if (iteratorsDone > 0) {
switch (this.mismatchMode) {
case ZipMismatchMode.FAIL:
throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);
case ZipMismatchMode.SHORTEST:
return {value: null, done: true};
case ZipMismatchMode.LONGEST:
default:
}
}
this.count++;
return {value: mapped, done: false};
}
async next() {
this.currentPromise = this.nextState(this.currentPromise);
return this.currentPromise;
}
}
class PrefetchIterator extends LazyIterator {
constructor(upstream, bufferSize) {
super();
this.upstream = upstream;
this.bufferSize = bufferSize;
this.buffer = new RingBuffer(bufferSize);
}
summary() {
return `${this.upstream.summary()} -> Prefetch`;
}
refill() {
while (!this.buffer.isFull()) {
const v = this.upstream.next();
this.buffer.push(v);
}
}
next() {
this.refill();
return this.buffer.shift();
}
}
class ShuffleIterator extends PrefetchIterator {
constructor(upstream, windowSize, seed) {
super(upstream, windowSize);
this.upstream = upstream;
this.windowSize = windowSize;
this.upstreamExhausted = false;
this.random = seedrandom2.alea(seed || util_exports.now().toString());
this.lastRead = Promise.resolve({value: null, done: false});
}
async next() {
this.lastRead = this.lastRead.then(() => this.serialNext());
return this.lastRead;
}
randomInt(max8) {
return Math.floor(this.random() * max8);
}
chooseIndex() {
return this.randomInt(this.buffer.length());
}
async serialNext() {
if (!this.upstreamExhausted) {
this.refill();
}
while (!this.buffer.isEmpty()) {
const chosenIndex = this.chooseIndex();
const result = await this.buffer.shuffleExcise(chosenIndex);
if (result.done) {
this.upstreamExhausted = true;
} else {
this.refill();
return result;
}
}
return {value: null, done: true};
}
}
// node_modules/@tensorflow/tfjs-data/dist/dataset.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
class Dataset {
constructor() {
this.size = null;
}
batch(batchSize, smallLastBatch = true) {
const base2 = this;
util_exports.assert(batchSize > 0, () => `batchSize needs to be positive, but it is
${batchSize}`);
let size;
if (this.size === Infinity || this.size == null) {
size = this.size;
} else if (smallLastBatch) {
size = Math.ceil(this.size / batchSize);
} else {
size = Math.floor(this.size / batchSize);
}
return datasetFromIteratorFn(async () => {
return (await base2.iterator()).columnMajorBatch(batchSize, smallLastBatch, deepBatchConcat);
}, size);
}
concatenate(dataset5) {
const base2 = this;
let size;
if (this.size === Infinity || dataset5.size === Infinity) {
size = Infinity;
} else if (this.size != null && dataset5.size != null) {
size = this.size + dataset5.size;
} else {
size = null;
}
return datasetFromIteratorFn(async () => (await base2.iterator()).concatenate(await dataset5.iterator()), size);
}
filter(predicate) {
const base2 = this;
let size;
if (this.size === Infinity) {
size = Infinity;
} else {
size = null;
}
return datasetFromIteratorFn(async () => {
return (await base2.iterator()).filter((x) => tidy(() => predicate(x)));
}, size);
}
async forEachAsync(f) {
return (await this.iterator()).forEachAsync(f);
}
map(transform) {
const base2 = this;
return datasetFromIteratorFn(async () => {
return (await base2.iterator()).map((x) => tidy(() => transform(x)));
}, this.size);
}
mapAsync(transform) {
const base2 = this;
return datasetFromIteratorFn(async () => {
return (await base2.iterator()).mapAsync(transform);
}, this.size);
}
prefetch(bufferSize) {
if (bufferSize == null) {
throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");
}
const base2 = this;
return datasetFromIteratorFn(async () => (await base2.iterator()).prefetch(bufferSize), this.size);
}
repeat(count2) {
const base2 = this;
let size;
if (this.size != null && count2 > 0) {
size = this.size * count2;
} else if (count2 === 0) {
size = 0;
} else if (this.size != null && (count2 === void 0 || count2 < 0)) {
size = Infinity;
} else {
size = null;
}
return datasetFromIteratorFn(async () => {
const iteratorIterator = iteratorFromFunction(async () => ({value: await base2.iterator(), done: false}));
return iteratorFromConcatenated(iteratorIterator.take(count2));
}, size);
}
skip(count2) {
const base2 = this;
let size;
if (this.size != null && count2 >= 0 && this.size >= count2) {
size = this.size - count2;
} else if (this.size != null && (this.size < count2 || count2 === void 0 || count2 < 0)) {
size = 0;
} else {
size = null;
}
return datasetFromIteratorFn(async () => (await base2.iterator()).skip(count2), size);
}
shuffle(bufferSize, seed, reshuffleEachIteration = true) {
if (bufferSize == null || bufferSize < 0) {
if (this.size == null) {
throw new RangeError("`Dataset.shuffle()` requires bufferSize to be specified.");
} else {
throw new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);
}
}
const base2 = this;
const random = seedrandom3.alea(seed || util_exports.now().toString());
return datasetFromIteratorFn(async () => {
let seed2 = random.int32();
if (reshuffleEachIteration) {
seed2 += random.int32();
}
return (await base2.iterator()).shuffle(bufferSize, seed2.toString());
}, this.size);
}
take(count2) {
const base2 = this;
let size;
if (this.size != null && this.size > count2) {
size = count2;
} else if (this.size != null && this.size <= count2) {
size = this.size;
} else {
size = null;
}
return datasetFromIteratorFn(async () => (await base2.iterator()).take(count2), size);
}
async toArray() {
if (this.size === Infinity) {
throw new Error("Can not convert infinite data stream to array.");
}
return (await this.iterator()).toArray();
}
async toArrayForTest() {
if (this.size === Infinity) {
throw new Error("Can not convert infinite data stream to array.");
}
return (await this.iterator()).toArrayForTest();
}
}
Dataset.MAX_BUFFER_SIZE = 1e4;
function datasetFromIteratorFn(iteratorFn, size = null) {
return new class extends Dataset {
constructor() {
super(...arguments);
this.size = size;
}
async iterator() {
return iteratorFn();
}
}();
}
function array(items) {
return datasetFromIteratorFn(async () => iteratorFromItems(items), items.length);
}
function zip(datasets) {
if (!isIterable2(datasets)) {
throw new Error("The argument to zip() must be an object or array.");
}
let size;
if (Array.isArray(datasets)) {
for (let i = 0; i < datasets.length; i++) {
size = size == null ? datasets[i].size : Math.min(size, datasets[i].size);
}
} else if (datasets instanceof Object) {
for (const ds in datasets) {
size = size == null ? datasets[ds].size : Math.min(size, datasets[ds].size);
}
}
return datasetFromIteratorFn(async () => {
const streams = await deepMapAndAwaitAll(datasets, (d) => {
if (d instanceof Dataset) {
return {value: d.iterator(), recurse: false};
} else if (isIterable2(d)) {
return {value: null, recurse: true};
} else {
throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.");
}
});
return iteratorFromZipped(streams, ZipMismatchMode.SHORTEST);
}, size);
}
function deepBatchConcat(rows) {
if (rows === null) {
return null;
}
const exampleRow = rows[0];
if (canTensorify(exampleRow)) {
const value = batchConcat(rows);
return {value, recurse: false};
}
return {value: null, recurse: true};
}
function batchConcat(arrays) {
if (arrays.length === 0) {
throw new Error("Can't make a batch of zero elements.");
}
if (arrays[0] instanceof Tensor) {
return stack(arrays);
} else {
return tensor4(arrays);
}
}
// node_modules/@tensorflow/tfjs-data/dist/datasets/text_line_dataset.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
class TextLineDataset extends Dataset {
constructor(input2) {
super();
this.input = input2;
}
async iterator() {
const inputIterator = await this.input.iterator();
const utf8Iterator = inputIterator.decodeUTF8();
const lineIterator = utf8Iterator.split("\n").map((line) => {
if (line.endsWith("\r")) {
line = line.slice(0, -1);
}
return line;
});
return lineIterator;
}
}
// node_modules/@tensorflow/tfjs-data/dist/datasets/csv_dataset.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
const CODE_QUOTE = '"';
const STATE_OUT = Symbol("out");
const STATE_FIELD = Symbol("field");
const STATE_QUOTE = Symbol("quote");
const STATE_QUOTE_AFTER_QUOTE = Symbol("quoteafterquote");
const STATE_WITHIN_QUOTE_IN_QUOTE = Symbol("quoteinquote");
class CSVDataset extends Dataset {
constructor(input2, csvConfig) {
super();
this.input = input2;
this.hasHeader = true;
this.fullColumnNames = null;
this.columnNamesValidated = false;
this.columnConfigs = null;
this.configuredColumnsOnly = false;
this.delimiter = ",";
this.delimWhitespace = false;
this.base = new TextLineDataset(input2);
if (!csvConfig) {
csvConfig = {};
}
this.hasHeader = csvConfig.hasHeader === false ? false : true;
this.fullColumnNames = csvConfig.columnNames;
this.columnConfigs = csvConfig.columnConfigs;
this.configuredColumnsOnly = csvConfig.configuredColumnsOnly;
if (csvConfig.delimWhitespace) {
util_exports.assert(csvConfig.delimiter == null, () => "Delimiter should not be provided when delimWhitespace is true.");
this.delimWhitespace = true;
this.delimiter = " ";
} else {
this.delimiter = csvConfig.delimiter ? csvConfig.delimiter : ",";
}
}
async columnNames() {
if (!this.columnNamesValidated) {
await this.setColumnNames();
}
return this.configuredColumnsOnly ? Object.keys(this.columnConfigs) : this.fullColumnNames;
}
async setColumnNames() {
const columnNamesFromFile = await this.maybeReadHeaderLine();
if (!this.fullColumnNames && !columnNamesFromFile) {
throw new Error("Column names must be provided if there is no header line.");
} else if (this.fullColumnNames && columnNamesFromFile) {
util_exports.assert(columnNamesFromFile.length === this.fullColumnNames.length, () => "The length of provided columnNames (" + this.fullColumnNames.length.toString() + ") does not match the length of the header line read from file (" + columnNamesFromFile.length.toString() + ").");
}
if (!this.fullColumnNames) {
this.fullColumnNames = columnNamesFromFile;
}
const counts = this.fullColumnNames.reduce((countAcc, name) => {
countAcc[name] = countAcc[name] + 1 || 1;
return countAcc;
}, {});
const duplicateNames = Object.keys(counts).filter((name) => counts[name] > 1);
util_exports.assert(duplicateNames.length === 0, () => "Duplicate column names found: " + duplicateNames.toString());
if (this.columnConfigs) {
for (const key of Object.keys(this.columnConfigs)) {
const index = this.fullColumnNames.indexOf(key);
if (index === -1) {
throw new Error('The key "' + key + '" provided in columnConfigs does not match any of the column names (' + this.fullColumnNames.toString() + ").");
}
}
}
this.columnNamesValidated = true;
}
async maybeReadHeaderLine() {
if (this.hasHeader) {
const iter = await this.base.iterator();
const firstElement = await iter.next();
if (firstElement.done) {
throw new Error("No data was found for CSV parsing.");
}
const firstLine = firstElement.value;
const headers = this.parseRow(firstLine, false);
return headers;
} else {
return null;
}
}
async iterator() {
if (!this.columnNamesValidated) {
await this.setColumnNames();
}
let lines = await this.base.iterator();
if (this.hasHeader) {
lines = lines.skip(1);
}
return lines.map((x) => this.makeDataElement(x));
}
makeDataElement(line) {
const values = this.parseRow(line);
const features = {};
const labels = {};
for (let i = 0; i < this.fullColumnNames.length; i++) {
const key = this.fullColumnNames[i];
const config2 = this.columnConfigs ? this.columnConfigs[key] : null;
if (this.configuredColumnsOnly && !config2) {
continue;
} else {
const value = values[i];
let parsedValue = null;
if (value === "") {
if (config2 && config2.default !== void 0) {
parsedValue = config2.default;
} else if (config2 && (config2.required || config2.isLabel)) {
throw new Error(`Required column ${key} is empty in this line: ${line}`);
} else {
parsedValue = void 0;
}
} else {
const valueAsNum = Number(value);
if (isNaN(valueAsNum)) {
if (config2 && config2.dtype === "bool") {
parsedValue = this.getBoolean(value);
} else {
parsedValue = value;
}
} else if (!config2 || !config2.dtype) {
parsedValue = valueAsNum;
} else {
switch (config2.dtype) {
case "float32":
parsedValue = valueAsNum;
break;
case "int32":
parsedValue = Math.floor(valueAsNum);
break;
case "bool":
parsedValue = this.getBoolean(value);
break;
default:
parsedValue = valueAsNum;
}
}
}
config2 && config2.isLabel ? labels[key] = parsedValue : features[key] = parsedValue;
}
}
if (Object.keys(labels).length === 0) {
return features;
} else {
return {xs: features, ys: labels};
}
}
getBoolean(value) {
if (value === "1" || value.toLowerCase() === "true") {
return 1;
} else {
return 0;
}
}
parseRow(line, validateElementCount = true) {
const result = [];
let readOffset = 0;
const readLength = line.length;
let currentState = STATE_OUT;
for (let i = 0; i < readLength; i++) {
switch (currentState) {
case STATE_OUT:
switch (line.charAt(i)) {
case CODE_QUOTE:
readOffset = i + 1;
currentState = STATE_QUOTE;
break;
case this.delimiter:
readOffset = i + 1;
if (this.delimiter === " " && this.delimWhitespace) {
break;
}
result.push("");
currentState = STATE_OUT;
break;
default:
currentState = STATE_FIELD;
readOffset = i;
break;
}
break;
case STATE_FIELD:
switch (line.charAt(i)) {
case this.delimiter:
result.push(line.substring(readOffset, i));
currentState = STATE_OUT;
readOffset = i + 1;
break;
default:
}
break;
case STATE_QUOTE:
switch (line.charAt(i)) {
case CODE_QUOTE:
currentState = STATE_QUOTE_AFTER_QUOTE;
break;
default:
}
break;
case STATE_QUOTE_AFTER_QUOTE:
switch (line.charAt(i)) {
case this.delimiter:
result.push(line.substring(readOffset, i - 1));
currentState = STATE_OUT;
readOffset = i + 1;
break;
case CODE_QUOTE:
currentState = STATE_QUOTE;
break;
default:
currentState = STATE_WITHIN_QUOTE_IN_QUOTE;
break;
}
break;
case STATE_WITHIN_QUOTE_IN_QUOTE:
switch (line.charAt(i)) {
case CODE_QUOTE:
currentState = STATE_QUOTE;
break;
default:
}
break;
default:
}
}
if (currentState === STATE_QUOTE_AFTER_QUOTE) {
result.push(line.substring(readOffset, readLength - 1));
} else {
result.push(line.substring(readOffset));
}
if (validateElementCount && result.length !== this.fullColumnNames.length) {
throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${result}`);
}
return result;
}
}
// node_modules/@tensorflow/tfjs-data/dist/iterators/microphone_iterator.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
class MicrophoneIterator extends LazyIterator {
constructor(microphoneConfig) {
super();
this.microphoneConfig = microphoneConfig;
this.isClosed = false;
this.fftSize = microphoneConfig.fftSize || 1024;
const fftSizeLog2 = Math.log2(this.fftSize);
if (this.fftSize < 0 || fftSizeLog2 < 4 || fftSizeLog2 > 14 || !Number.isInteger(fftSizeLog2)) {
throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);
}
this.numFrames = microphoneConfig.numFramesPerSpectrogram || 43;
this.sampleRateHz = microphoneConfig.sampleRateHz;
this.columnTruncateLength = microphoneConfig.columnTruncateLength || this.fftSize;
this.audioTrackConstraints = microphoneConfig.audioTrackConstraints;
this.smoothingTimeConstant = microphoneConfig.smoothingTimeConstant || 0;
this.includeSpectrogram = microphoneConfig.includeSpectrogram === false ? false : true;
this.includeWaveform = microphoneConfig.includeWaveform === true ? true : false;
if (!this.includeSpectrogram && !this.includeWaveform) {
throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.");
}
}
summary() {
return `microphone`;
}
static async create(microphoneConfig = {}) {
if (env().get("IS_NODE")) {
throw new Error("microphone API is only supported in browser environment.");
}
const microphoneIterator = new MicrophoneIterator(microphoneConfig);
await microphoneIterator.start();
return microphoneIterator;
}
async start() {
try {
this.stream = await navigator.mediaDevices.getUserMedia({
audio: this.audioTrackConstraints == null ? true : this.audioTrackConstraints,
video: false
});
} catch (e) {
throw new Error(`Error thrown while initializing video stream: ${e.message}`);
}
if (!this.stream) {
throw new Error("Could not obtain audio from microphone.");
}
const ctxConstructor = window.AudioContext || window.webkitAudioContext;
this.audioContext = new ctxConstructor();
if (!this.sampleRateHz) {
this.sampleRateHz = this.audioContext.sampleRate;
} else if (this.audioContext.sampleRate !== this.sampleRateHz) {
throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);
}
const streamSource = this.audioContext.createMediaStreamSource(this.stream);
this.analyser = this.audioContext.createAnalyser();
this.analyser.fftSize = this.fftSize * 2;
this.analyser.smoothingTimeConstant = this.smoothingTimeConstant;
streamSource.connect(this.analyser);
this.freqData = new Float32Array(this.fftSize);
this.timeData = new Float32Array(this.fftSize);
return;
}
async next() {
if (this.isClosed) {
return {value: null, done: true};
}
let spectrogramTensor;
let waveformTensor;
const audioDataQueue = await this.getAudioData();
if (this.includeSpectrogram) {
const freqData = this.flattenQueue(audioDataQueue.freqDataQueue);
spectrogramTensor = this.getTensorFromAudioDataArray(freqData, [this.numFrames, this.columnTruncateLength, 1]);
}
if (this.includeWaveform) {
const timeData = this.flattenQueue(audioDataQueue.timeDataQueue);
waveformTensor = this.getTensorFromAudioDataArray(timeData, [this.numFrames * this.fftSize, 1]);
}
return {
value: {spectrogram: spectrogramTensor, waveform: waveformTensor},
done: false
};
}
async capture() {
return (await this.next()).value;
}
async getAudioData() {
const freqDataQueue = [];
const timeDataQueue = [];
let currentFrames = 0;
return new Promise((resolve) => {
const intervalID = setInterval(() => {
if (this.includeSpectrogram) {
this.analyser.getFloatFrequencyData(this.freqData);
if (this.freqData[0] === -Infinity) {
resolve({freqDataQueue, timeDataQueue});
}
freqDataQueue.push(this.freqData.slice(0, this.columnTruncateLength));
}
if (this.includeWaveform) {
this.analyser.getFloatTimeDomainData(this.timeData);
timeDataQueue.push(this.timeData.slice());
}
if (++currentFrames === this.numFrames) {
clearInterval(intervalID);
resolve({freqDataQueue, timeDataQueue});
}
}, this.fftSize / this.sampleRateHz * 1e3);
});
}
stop() {
if (!this.isClosed) {
this.isClosed = true;
this.analyser.disconnect();
this.audioContext.close();
if (this.stream != null && this.stream.getTracks().length > 0) {
this.stream.getTracks()[0].stop();
}
}
}
toArray() {
throw new Error("Can not convert infinite audio stream to array.");
}
getSampleRate() {
return this.sampleRateHz;
}
flattenQueue(queue) {
const frameSize = queue[0].length;
const freqData = new Float32Array(queue.length * frameSize);
queue.forEach((data2, i) => freqData.set(data2, i * frameSize));
return freqData;
}
getTensorFromAudioDataArray(freqData, shape) {
const vals = new Float32Array(util_exports.sizeFromShape(shape));
vals.set(freqData, vals.length - freqData.length);
return tensor4(vals, shape);
}
}
// node_modules/@tensorflow/tfjs-data/dist/iterators/webcam_iterator.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
class WebcamIterator extends LazyIterator {
constructor(webcamVideoElement, webcamConfig) {
super();
this.webcamVideoElement = webcamVideoElement;
this.webcamConfig = webcamConfig;
this.isClosed = true;
this.resize = false;
if (this.needToResize()) {
this.resize = true;
this.cropSize = [this.webcamConfig.resizeHeight, this.webcamConfig.resizeWidth];
this.cropBoxInd = tensor1d([0], "int32");
if (this.webcamConfig.centerCrop) {
const widthCroppingRatio = this.webcamConfig.resizeWidth * 1 / this.webcamVideoElement.width;
const heightCroppingRatio = this.webcamConfig.resizeHeight * 1 / this.webcamVideoElement.height;
const widthCropStart = (1 - widthCroppingRatio) / 2;
const heightCropStart = (1 - heightCroppingRatio) / 2;
const widthCropEnd = widthCropStart + widthCroppingRatio;
const heightCropEnd = heightCroppingRatio + heightCropStart;
this.cropBox = tensor2d([heightCropStart, widthCropStart, heightCropEnd, widthCropEnd], [1, 4]);
} else {
this.cropBox = tensor2d([0, 0, 1, 1], [1, 4]);
}
}
}
summary() {
return `webcam`;
}
static async create(webcamVideoElement, webcamConfig = {}) {
if (env().get("IS_NODE")) {
throw new Error("tf.data.webcam is only supported in browser environment.");
}
if (!webcamVideoElement) {
webcamVideoElement = document.createElement("video");
if (!webcamConfig.resizeWidth || !webcamConfig.resizeHeight) {
throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");
}
webcamVideoElement.width = webcamConfig.resizeWidth;
webcamVideoElement.height = webcamConfig.resizeHeight;
}
const webcamIterator = new WebcamIterator(webcamVideoElement, webcamConfig);
await webcamIterator.start();
return webcamIterator;
}
async start() {
if (this.webcamConfig.facingMode) {
util_exports.assert(this.webcamConfig.facingMode === "user" || this.webcamConfig.facingMode === "environment", () => `Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);
}
try {
this.stream = await navigator.mediaDevices.getUserMedia({
video: {
deviceId: this.webcamConfig.deviceId,
facingMode: this.webcamConfig.facingMode ? this.webcamConfig.facingMode : "user",
width: this.webcamVideoElement.width,
height: this.webcamVideoElement.height
}
});
} catch (e) {
e.message = `Error thrown while initializing video stream: ${e.message}`;
throw e;
}
if (!this.stream) {
throw new Error("Could not obtain video from webcam.");
}
try {
this.webcamVideoElement.srcObject = this.stream;
} catch (error) {
console.log(error);
this.webcamVideoElement.src = window.URL.createObjectURL(this.stream);
}
this.webcamVideoElement.play();
this.isClosed = false;
return new Promise((resolve) => {
this.webcamVideoElement.onloadedmetadata = () => {
resolve();
};
});
}
async next() {
if (this.isClosed) {
return {value: null, done: true};
}
let img;
try {
img = browser_exports.fromPixels(this.webcamVideoElement);
} catch (e) {
throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`);
}
if (this.resize) {
try {
return {value: this.cropAndResizeFrame(img), done: false};
} catch (e) {
throw new Error(`Error thrown cropping the video: ${e.message}`);
} finally {
img.dispose();
}
} else {
return {value: img, done: false};
}
}
needToResize() {
if (this.webcamConfig.resizeWidth && this.webcamConfig.resizeHeight && (this.webcamVideoElement.width !== this.webcamConfig.resizeWidth || this.webcamVideoElement.height !== this.webcamConfig.resizeHeight)) {
return true;
}
return false;
}
cropAndResizeFrame(img) {
return tidy(() => {
const expandedImage = img.toFloat().expandDims(0);
let resizedImage;
resizedImage = image.cropAndResize(expandedImage, this.cropBox, this.cropBoxInd, this.cropSize, "bilinear");
const shape = resizedImage.shape;
return resizedImage.reshape(shape.slice(1));
});
}
async capture() {
return (await this.next()).value;
}
stop() {
const tracks = this.stream.getTracks();
tracks.forEach((track) => track.stop());
try {
this.webcamVideoElement.srcObject = null;
} catch (error) {
console.log(error);
this.webcamVideoElement.src = null;
}
this.isClosed = true;
}
toArray() {
throw new Error("Can not convert infinite video stream to array.");
}
}
// node_modules/@tensorflow/tfjs-data/dist/datasource.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
class DataSource {
}
// node_modules/@tensorflow/tfjs-data/dist/iterators/string_iterator.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
class StringIterator extends LazyIterator {
split(separator) {
return new SplitIterator(this, separator);
}
}
class SplitIterator extends StringIterator {
constructor(upstream, separator) {
super();
this.upstream = upstream;
this.impl = new SplitIteratorImpl(upstream, separator);
}
summary() {
return this.impl.summary();
}
async next() {
return this.impl.next();
}
}
class SplitIteratorImpl extends OneToManyIterator {
constructor(upstream, separator) {
super();
this.upstream = upstream;
this.separator = separator;
this.carryover = "";
}
summary() {
return `${this.upstream.summary()} -> Split('${this.separator}')`;
}
async pump() {
const chunkResult = await this.upstream.next();
if (chunkResult.done) {
if (this.carryover === "") {
return false;
}
this.outputQueue.push(this.carryover);
this.carryover = "";
return true;
}
const lines = chunkResult.value.split(this.separator);
lines[0] = this.carryover + lines[0];
for (const line of lines.slice(0, -1)) {
this.outputQueue.push(line);
}
this.carryover = lines[lines.length - 1];
return true;
}
}
// node_modules/@tensorflow/tfjs-data/dist/iterators/byte_chunk_iterator.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
class ByteChunkIterator extends LazyIterator {
decodeUTF8() {
return new Utf8Iterator(this);
}
}
class Utf8Iterator extends StringIterator {
constructor(upstream) {
super();
this.upstream = upstream;
this.impl = new Utf8IteratorImpl(upstream);
}
summary() {
return this.impl.summary();
}
async next() {
return this.impl.next();
}
}
class Utf8IteratorImpl extends OneToManyIterator {
constructor(upstream) {
super();
this.upstream = upstream;
if (env().get("IS_BROWSER")) {
this.decoder = new TextDecoder("utf-8");
} else {
const {StringDecoder} = require_string_decoder();
this.decoder = new StringDecoder("utf8");
}
}
summary() {
return `${this.upstream.summary()} -> Utf8`;
}
async pump() {
const chunkResult = await this.upstream.next();
let chunk;
if (chunkResult.done) {
return false;
} else {
chunk = chunkResult.value;
}
let text;
if (env().get("IS_BROWSER")) {
text = this.decoder.decode(chunk, {stream: true});
} else {
text = this.decoder.write(Buffer.from(chunk.buffer));
}
this.outputQueue.push(text);
return true;
}
}
// node_modules/@tensorflow/tfjs-data/dist/iterators/file_chunk_iterator.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
class FileChunkIterator extends ByteChunkIterator {
constructor(file, options = {}) {
super();
this.file = file;
this.options = options;
util_exports.assert(file instanceof Uint8Array || (env().get("IS_BROWSER") ? file instanceof File || file instanceof Blob : false), () => "FileChunkIterator only supports File, Blob and Uint8Array right now.");
this.offset = options.offset || 0;
this.chunkSize = options.chunkSize || 1024 * 1024;
}
summary() {
return `FileChunks ${this.file}`;
}
async next() {
if (this.offset >= (this.file instanceof Uint8Array ? this.file.byteLength : this.file.size)) {
return {value: null, done: true};
}
const chunk = new Promise((resolve, reject) => {
const end = this.offset + this.chunkSize;
if (this.file instanceof Uint8Array) {
resolve(new Uint8Array(this.file.slice(this.offset, end)));
} else {
const fileReader = new FileReader();
fileReader.onload = (event) => {
let data2 = fileReader.result;
if (data2 instanceof ArrayBuffer) {
data2 = new Uint8Array(data2);
}
if (!(data2 instanceof Uint8Array)) {
return reject(new TypeError("FileReader returned unknown type."));
}
resolve(data2);
};
fileReader.onabort = (event) => {
return reject(new Error("Aborted"));
};
fileReader.onerror = (event) => {
return reject(new Error(event.type));
};
const slice16 = this.file.slice(this.offset, end);
fileReader.readAsArrayBuffer(slice16);
}
this.offset = end;
});
return {value: await chunk, done: false};
}
}
// node_modules/@tensorflow/tfjs-data/dist/iterators/url_chunk_iterator.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
async function urlChunkIterator(url, options = {}) {
let urlString;
let requestInit;
if (typeof url === "string") {
urlString = url;
} else {
urlString = url.url;
requestInit = getRequestInitFromRequest(url);
}
const response = await util_exports.fetch(urlString, requestInit);
if (response.ok) {
const uint8Array = new Uint8Array(await response.arrayBuffer());
return new FileChunkIterator(uint8Array, options);
} else {
throw new Error(response.statusText);
}
}
const getRequestInitFromRequest = (request) => {
const init2 = {
method: request.method,
headers: request.headers,
body: request.body,
mode: request.mode,
credentials: request.credentials,
cache: request.cache,
redirect: request.redirect,
referrer: request.referrer,
integrity: request.integrity
};
return init2;
};
// node_modules/@tensorflow/tfjs-data/dist/util/source_util.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
function isLocalPath(source) {
return typeof source === "string" && source.substr(0, 7) === "file://";
}
// node_modules/@tensorflow/tfjs-data/dist/sources/file_data_source.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
class FileDataSource extends DataSource {
constructor(input2, options = {}) {
super();
this.input = input2;
this.options = options;
}
async iterator() {
if (isLocalPath(this.input) && env().get("IS_NODE")) {
const fs = require("fs");
this.input = fs.readFileSync(this.input.substr(7));
}
return new FileChunkIterator(this.input, this.options);
}
}
// node_modules/@tensorflow/tfjs-data/dist/sources/url_data_source.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
class URLDataSource extends DataSource {
constructor(url, fileOptions = {}) {
super();
this.url = url;
this.fileOptions = fileOptions;
}
async iterator() {
if (isLocalPath(this.url)) {
return new FileDataSource(this.url, this.fileOptions).iterator();
} else {
return urlChunkIterator(this.url, this.fileOptions);
}
}
}
// node_modules/@tensorflow/tfjs-data/dist/readers.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
function csv(source, csvConfig = {}) {
return new CSVDataset(new URLDataSource(source), csvConfig);
}
function func(f) {
const iter = iteratorFromFunction(f);
return datasetFromIteratorFn(async () => iter);
}
function generator(generator2) {
return datasetFromIteratorFn(async () => {
const gen = await generator2();
return iteratorFromFunction(() => gen.next());
});
}
async function webcam(webcamVideoElement, webcamConfig) {
return WebcamIterator.create(webcamVideoElement, webcamConfig);
}
async function microphone(microphoneConfig) {
return MicrophoneIterator.create(microphoneConfig);
}
// node_modules/@tensorflow/tfjs-data/dist/version.js
/** @license See the LICENSE file. */
const version8 = "2.7.0";
// node_modules/@tensorflow/tfjs-data/dist/index.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
// node_modules/@tensorflow/tfjs-backend-cpu/dist/version.js
/** @license See the LICENSE file. */
const version10 = "2.7.0";
// node_modules/@tensorflow/tfjs-backend-webgl/dist/version.js
/** @license See the LICENSE file. */
const version11 = "2.7.0";
// node_modules/@tensorflow/tfjs/dist/version.js
/** @license See the LICENSE file. */
const version12 = "2.7.0";
// node_modules/@tensorflow/tfjs/dist/index.js
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const version14 = {
"tfjs-core": version,
"tfjs-backend-cpu": version10,
"tfjs-backend-webgl": version11,
"tfjs-data": version8,
"tfjs-layers": version2,
"tfjs-converter": version6,
tfjs: version12
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/types.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
var CppDType;
(function(CppDType2) {
CppDType2[CppDType2["float32"] = 0] = "float32";
CppDType2[CppDType2["int32"] = 1] = "int32";
CppDType2[CppDType2["bool"] = 2] = "bool";
CppDType2[CppDType2["string"] = 3] = "string";
CppDType2[CppDType2["complex64"] = 4] = "complex64";
})(CppDType || (CppDType = {}));
var FusableActivation;
(function(FusableActivation2) {
FusableActivation2[FusableActivation2["linear"] = 0] = "linear";
FusableActivation2[FusableActivation2["relu"] = 1] = "relu";
FusableActivation2[FusableActivation2["relu6"] = 2] = "relu6";
FusableActivation2[FusableActivation2["prelu"] = 3] = "prelu";
})(FusableActivation || (FusableActivation = {}));
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/_FusedMatMul.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
let wasmFusedMatMul;
function setup(backend3) {
wasmFusedMatMul = backend3.wasm.cwrap(_FusedMatMul, null, [
"number",
"array",
"number",
"number",
"array",
"number",
"number",
"number",
"number",
"number",
"number",
"number"
]);
}
function fusedBatchMatMul(args) {
const {inputs, backend: backend3, attrs} = args;
const {a, b, bias, preluActivationWeights} = inputs;
if (a.dtype !== "float32" || b.dtype !== "float32") {
throw new Error(`_FusedMatMul for non non-float32 tensors not yet supported.`);
}
const {transposeA, transposeB, activation: activation2} = attrs;
const aId = backend3.dataIdMap.get(a.dataId).id;
const bId = backend3.dataIdMap.get(b.dataId).id;
let biasId = 0;
if (bias != null) {
const biasData = backend3.dataIdMap.get(bias.dataId);
if (biasData.shape.length !== 1) {
throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${biasData.shape.length}.`);
}
biasId = biasData.id;
}
const preluActivationWeightsId = preluActivationWeights == null ? 0 : backend3.dataIdMap.get(preluActivationWeights.dataId).id;
const fusedActivation = FusableActivation[activation2];
if (fusedActivation == null) {
throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);
}
const leftDim = transposeA ? a.shape[2] : a.shape[1];
const rightDim = transposeB ? b.shape[1] : b.shape[2];
const batchDim = a.shape[0];
const out = backend3.makeOutput([batchDim, leftDim, rightDim], a.dtype);
const outId = backend3.dataIdMap.get(out.dataId).id;
const aShapeBytes = new Uint8Array(new Int32Array(a.shape).buffer);
const bShapeBytes = new Uint8Array(new Int32Array(b.shape).buffer);
wasmFusedMatMul(aId, aShapeBytes, a.shape.length, bId, bShapeBytes, b.shape.length, transposeA, transposeB, fusedActivation, biasId, preluActivationWeightsId, outId);
return out;
}
const fusedMatMulConfig = {
kernelName: _FusedMatMul,
backendName: "wasm",
setupFunc: setup,
kernelFunc: fusedBatchMatMul
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/unary_kernel.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function createUnaryKernelConfig(kernelName) {
let wasmFunc8;
function setupFunc2(backend3) {
wasmFunc8 = backend3.wasm.cwrap(kernelName, null, ["number", "number"]);
}
function kernelFunc3(args) {
const {backend: backend3, inputs: {x}} = args;
const xId = backend3.dataIdMap.get(x.dataId).id;
const out = backend3.makeOutput(x.shape, x.dtype);
const outId = backend3.dataIdMap.get(out.dataId).id;
if (util_exports.sizeFromShape(out.shape) === 0) {
return out;
}
wasmFunc8(xId, outId);
return out;
}
return {kernelName, backendName: "wasm", setupFunc: setupFunc2, kernelFunc: kernelFunc3};
}
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Abs.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const absConfig = createUnaryKernelConfig(Abs);
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/binary_kernel.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function createBinaryKernelConfig(kernelName, supportsFullBroadcast17, dtype) {
let wasmFunc8;
function setupFunc2(backend3) {
wasmFunc8 = backend3.wasm.cwrap(kernelName, null, [
"number",
"array",
"number",
"number",
"array",
"number",
"number",
"number"
]);
}
function kernelFunc3(args) {
const {backend: backend3, inputs} = args;
const {a, b} = inputs;
const aId = backend3.dataIdMap.get(a.dataId).id;
const bId = backend3.dataIdMap.get(b.dataId).id;
const outputType = dtype != null ? dtype : a.dtype;
const newShape = backend_util_exports.assertAndGetBroadcastShape(a.shape, b.shape);
const out = backend3.makeOutput(newShape, outputType);
if (util_exports.sizeFromShape(newShape) === 0) {
return out;
}
const aShapeBytes = new Uint8Array(new Int32Array(a.shape).buffer);
const bShapeBytes = new Uint8Array(new Int32Array(b.shape).buffer);
const outId = backend3.dataIdMap.get(out.dataId).id;
const kernelFunc4 = () => wasmFunc8(aId, aShapeBytes, a.shape.length, bId, bShapeBytes, b.shape.length, CppDType[a.dtype], outId);
if (supportsFullBroadcast17 && a.dtype === "float32") {
kernelFunc4();
return out;
}
const aBroadcastDims = backend_util_exports.getBroadcastDims(a.shape, newShape);
const bBroadcastDims = backend_util_exports.getBroadcastDims(b.shape, newShape);
const loopsOverAllOfA = aBroadcastDims.every((v, i) => v === i);
const loopsOverAllOfB = bBroadcastDims.every((v, i) => v === i);
if (loopsOverAllOfA && loopsOverAllOfB) {
kernelFunc4();
return out;
} else {
throw new Error(`Broadcasting along outer dims is not yet supported for ${a.dtype} ${kernelName}.`);
}
}
return {kernelName, backendName: "wasm", setupFunc: setupFunc2, kernelFunc: kernelFunc3};
}
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Add.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const supportsFullBroadcast = true;
const addConfig = createBinaryKernelConfig(Add, supportsFullBroadcast);
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/AddN.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
let wasmFunc;
function setupFunc(backend3) {
wasmFunc = backend3.wasm.cwrap(AddN, null, [
"array",
"number",
"number",
"number"
]);
}
function addn(args) {
const {inputs, backend: backend3} = args;
const out = backend3.makeOutput(inputs[0].shape, inputs[0].dtype);
if (util_exports.sizeFromShape(out.shape) === 0) {
return out;
}
const inputIds = inputs.map((x) => backend3.dataIdMap.get(x.dataId).id);
const inputIdsBytes = new Uint8Array(new Int32Array(inputIds).buffer);
const outId = backend3.dataIdMap.get(out.dataId).id;
wasmFunc(inputIdsBytes, inputIds.length, CppDType[out.dtype], outId);
return out;
}
const addNConfig = {
kernelName: AddN,
backendName: "wasm",
setupFunc,
kernelFunc: addn
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Identity.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function identity2(args) {
const {inputs: {x}, backend: backend3} = args;
const out = backend3.makeOutput(x.shape, x.dtype);
const inVals = backend3.typedArrayFromHeap(x);
const outVals = backend3.typedArrayFromHeap(out);
outVals.set(inVals);
return out;
}
const identityConfig = {
kernelName: Identity,
backendName: "wasm",
kernelFunc: identity2
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Transpose.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
let wasmTranspose;
function setup2(backend3) {
wasmTranspose = backend3.wasm.cwrap(Transpose, null, [
"number",
"array",
"number",
"number",
"number",
"array",
"number"
]);
}
function transpose13(args) {
const {inputs, backend: backend3, attrs} = args;
const [reducedShape, perm] = removeOneSizeDims(inputs.x.shape, attrs.perm);
let permIsNoOp = true;
for (let i = 0; i < perm.length; i++) {
if (perm[i] !== i) {
permIsNoOp = false;
}
}
const outShape = computeOutShape4(inputs.x.shape, attrs.perm);
const x = {
dataId: inputs.x.dataId,
shape: reducedShape,
dtype: inputs.x.dtype
};
if (permIsNoOp) {
const cloned = identity2({inputs, backend: backend3});
cloned.shape = outShape;
return cloned;
}
const out = backend3.makeOutput(outShape, x.dtype);
const xId = backend3.dataIdMap.get(x.dataId).id;
const outId = backend3.dataIdMap.get(out.dataId).id;
const permBytes = new Uint8Array(new Int32Array(perm).buffer);
const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);
wasmTranspose(xId, xShapeBytes, x.shape.length, CppDType[x.dtype], outId, permBytes, perm.length);
return out;
}
function computeOutShape4(inShape, perm) {
const outShape = new Array(inShape.length);
for (let i = 0; i < outShape.length; i++) {
outShape[i] = inShape[perm[i]];
}
return outShape;
}
function removeOneSizeDims(shape, perm) {
const newShape = [];
const newPerm = [];
for (let i = 0; i < shape.length; ++i) {
if (shape[i] !== 1) {
newShape.push(shape[i]);
}
if (shape[perm[i]] !== 1) {
newPerm.push(perm[i]);
}
}
for (let i = 0; i < newPerm.length; ++i) {
let minValIdx = -1;
for (let j = 0; j < newPerm.length; ++j) {
if (newPerm[j] >= i && (minValIdx === -1 || newPerm[minValIdx] > newPerm[j])) {
minValIdx = j;
}
}
newPerm[minValIdx] = i;
}
return [newShape, newPerm];
}
const transposeConfig = {
kernelName: Transpose,
backendName: "wasm",
kernelFunc: transpose13,
setupFunc: setup2
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/kernel_utils.js
/**
* @license
* Copyright 2020 Google Inc. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function permuteAxesAndTranspose(x, axis, backend3) {
const xShape = x.shape;
const xRank = x.shape.length;
const originalAxes = util_exports.parseAxisParam(axis, xShape);
let axes = originalAxes;
const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);
let xTransposed = null;
let inputWasTransposed = false;
if (permutedAxes != null) {
const newShape = new Array(xRank);
for (let i = 0; i < newShape.length; i++) {
newShape[i] = xShape[permutedAxes[i]];
}
axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);
xTransposed = transpose13({inputs: {x}, attrs: {perm: permutedAxes}, backend: backend3});
const xId = backend3.dataIdMap.get(x.dataId).id;
const transposedId = backend3.dataIdMap.get(xTransposed.dataId).id;
if (transposedId !== xId) {
inputWasTransposed = true;
}
}
return {transposed: xTransposed, originalAxes, axes, inputWasTransposed};
}
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ArgMax.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
let wasmFunc2;
function setup3(backend3) {
wasmFunc2 = backend3.wasm.cwrap(ArgMax, null, [
"number",
"number",
"number",
"number",
"number"
]);
}
function argmax(args) {
const {backend: backend3, inputs, attrs} = args;
const {axis} = attrs;
const {x} = inputs;
const xId = backend3.dataIdMap.get(x.dataId).id;
let inputId = xId;
let input2 = x;
const {transposed, axes, inputWasTransposed} = permuteAxesAndTranspose(x, axis, backend3);
if (inputWasTransposed) {
const transposedId = backend3.dataIdMap.get(transposed.dataId).id;
if (transposedId !== xId) {
input2 = transposed;
inputId = transposedId;
}
}
const outShape = input2.shape.slice(0, -1);
const out = backend3.makeOutput(outShape, "int32");
const outId = backend3.dataIdMap.get(out.dataId).id;
const outerSize = util_exports.sizeFromShape(out.shape);
const innerSize = input2.shape[axes[0]];
wasmFunc2(inputId, CppDType[input2.dtype], outerSize, innerSize, outId);
if (inputWasTransposed) {
backend3.disposeData(transposed.dataId);
}
return out;
}
const argMaxConfig = {
kernelName: ArgMax,
backendName: "wasm",
kernelFunc: argmax,
setupFunc: setup3
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/AvgPool.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
let wasmAvgPool;
function setup4(backend3) {
wasmAvgPool = backend3.wasm.cwrap(AvgPool, null, [
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number"
]);
}
function avgPool2(args) {
const {inputs, attrs, backend: backend3} = args;
const x = inputs.x;
const xId = backend3.dataIdMap.get(x.dataId).id;
const {filterSize, strides, pad: pad8, dimRoundingMode} = attrs;
const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad8, dimRoundingMode);
const filterHeight = convInfo.filterHeight;
const filterWidth = convInfo.filterWidth;
const padTop = convInfo.padInfo.top;
const padRight = convInfo.padInfo.right;
const padBottom = convInfo.padInfo.bottom;
const padLeft = convInfo.padInfo.left;
const strideHeight = convInfo.strideHeight;
const strideWidth = convInfo.strideWidth;
const channels = convInfo.inChannels;
if (convInfo.dataFormat !== "channelsLast") {
throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);
}
if (convInfo.dilationWidth !== 1 || convInfo.dilationHeight !== 1) {
throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${convInfo.dilationHeight}, ${convInfo.dilationWidth}].`);
}
const out = backend3.makeOutput(convInfo.outShape, "float32");
const outId = backend3.dataIdMap.get(out.dataId).id;
wasmAvgPool(xId, x.shape[0], x.shape[1], x.shape[2], filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, strideHeight, strideWidth, channels, outId);
return out;
}
const avgPoolConfig = {
kernelName: AvgPool,
backendName: "wasm",
setupFunc: setup4,
kernelFunc: avgPool2
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Reshape.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function reshape60(args) {
const {inputs, attrs} = args;
const {x} = inputs;
const {shape} = attrs;
const xSize = util_exports.sizeFromShape(x.shape);
const $shape = util_exports.inferFromImplicitShape(shape, xSize);
util_exports.assert(xSize === util_exports.sizeFromShape($shape), () => `new shape: ${$shape}, old shape: ${x.shape}. New shape and old shape must have the same number of elements.`);
return {dataId: x.dataId, shape: $shape, dtype: x.dtype};
}
const reshapeConfig = {
kernelName: Reshape,
backendName: "wasm",
kernelFunc: reshape60
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/BatchMatMul.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
let wasmBatchMatMul;
function setup5(backend3) {
wasmBatchMatMul = backend3.wasm.cwrap(BatchMatMul, null, [
"number",
"array",
"number",
"number",
"array",
"number",
"number",
"number",
"number"
]);
}
function batchMatMul(args) {
const {inputs, backend: backend3, attrs} = args;
const {a, b} = inputs;
const {transposeA, transposeB} = attrs;
if (a.dtype !== "float32" || b.dtype !== "float32") {
throw new Error(`BatchMatMul for non non-float32 tensors not yet supported.`);
}
const aRank = a.shape.length;
const bRank = b.shape.length;
const innerShapeA = transposeA ? a.shape[aRank - 2] : a.shape[aRank - 1];
const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2];
const outerShapeA = transposeA ? a.shape[aRank - 1] : a.shape[aRank - 2];
const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1];
const outerDimsA = a.shape.slice(0, -2);
const outerDimsB = b.shape.slice(0, -2);
const batchDimA = util_exports.sizeFromShape(outerDimsA);
const batchDimB = util_exports.sizeFromShape(outerDimsB);
const batchDimsCompatible = batchDimA === batchDimB || batchDimA === 1 || batchDimB === 1;
util_exports.assert(aRank >= 2 && bRank >= 2 && batchDimsCompatible, () => `Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${outerDimsA}) and (${outerDimsB}).`);
const outShapeOuterDims = batchDimA > batchDimB ? a.shape.slice(0, -2) : b.shape.slice(0, -2);
const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);
util_exports.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);
const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] : [batchDimA, outerShapeA, innerShapeA];
const b3dShape = transposeB ? [batchDimB, outerShapeB, innerShapeB] : [batchDimB, innerShapeB, outerShapeB];
const a3d = reshape60({inputs: {x: a}, backend: backend3, attrs: {shape: a3dShape}});
const b3d = reshape60({inputs: {x: b}, backend: backend3, attrs: {shape: b3dShape}});
const a3dId = backend3.dataIdMap.get(a3d.dataId).id;
const b3dId = backend3.dataIdMap.get(b3d.dataId).id;
const leftDim = transposeA ? a3d.shape[2] : a3d.shape[1];
const rightDim = transposeB ? b3d.shape[1] : b3d.shape[2];
const batchDim = Math.max(batchDimA, batchDimB);
const out = backend3.makeOutput([batchDim, leftDim, rightDim], a3d.dtype);
const outId = backend3.dataIdMap.get(out.dataId).id;
const aShapeBytes = new Uint8Array(new Int32Array(a3d.shape).buffer);
const bShapeBytes = new Uint8Array(new Int32Array(b3d.shape).buffer);
wasmBatchMatMul(a3dId, aShapeBytes, a3d.shape.length, b3dId, bShapeBytes, b3d.shape.length, transposeA, transposeB, outId);
out.shape = outShape;
return out;
}
const batchMatMulConfig = {
kernelName: BatchMatMul,
backendName: "wasm",
setupFunc: setup5,
kernelFunc: batchMatMul
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Cast.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function cast21(args) {
const {inputs: {x}, attrs: {dtype}, backend: backend3} = args;
const out = backend3.makeOutput(x.shape, dtype);
const inVals = backend3.typedArrayFromHeap(x);
const outVals = backend3.typedArrayFromHeap(out);
outVals.set(inVals);
return out;
}
const castConfig = {
kernelName: Cast,
backendName: "wasm",
kernelFunc: cast21
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ClipByValue.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
let wasmClip;
function setup6(backend3) {
wasmClip = backend3.wasm.cwrap(ClipByValue, null, [
"number",
"number",
"number",
"number"
]);
}
function clip(args) {
const {inputs, backend: backend3, attrs} = args;
const {x} = inputs;
const {clipValueMin, clipValueMax} = attrs;
const xId = backend3.dataIdMap.get(x.dataId).id;
const out = backend3.makeOutput(x.shape, x.dtype);
const outId = backend3.dataIdMap.get(out.dataId).id;
wasmClip(xId, clipValueMin, clipValueMax, outId);
return out;
}
const clipByValueConfig = {
kernelName: ClipByValue,
backendName: "wasm",
setupFunc: setup6,
kernelFunc: clip
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Concat.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function concat14(args) {
const {inputs, backend: backend3} = args;
const axis = util_exports.parseAxisParam(args.attrs.axis, inputs[0].shape)[0];
const outShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), axis);
const out = backend3.makeOutput(outShape, inputs[0].dtype);
if (util_exports.sizeFromShape(outShape) === 0) {
return out;
}
const $inputs = inputs.filter((t) => util_exports.sizeFromShape(t.shape) > 0);
if ($inputs.length === 1) {
return $inputs[0];
}
const shapes = $inputs.map((t) => t.shape);
backend_util_exports.assertParamsConsistent(shapes, axis);
const batchDim = util_exports.sizeFromShape($inputs[0].shape.slice(0, axis));
let sumInnerDims = 0;
const innerDims = $inputs.map((input2) => {
const innerDim = util_exports.sizeFromShape(input2.shape.slice(axis));
sumInnerDims += innerDim;
return innerDim;
});
const inVals = $inputs.map((input2) => backend3.typedArrayFromHeap(input2));
const outVals = backend3.typedArrayFromHeap(out);
for (let b = 0; b < batchDim; b++) {
let outOffset = b * sumInnerDims;
for (let i = 0; i < inVals.length; i++) {
const innerDim = innerDims[i];
const inOffset = b * innerDim;
const vals = inVals[i].subarray(inOffset, inOffset + innerDim);
outVals.set(vals, outOffset);
outOffset += innerDim;
}
}
return out;
}
const concatConfig = {
kernelName: Concat,
backendName: "wasm",
kernelFunc: concat14
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Conv2D.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
let wasmConv2d;
function setup7(backend3) {
wasmConv2d = backend3.wasm.cwrap(Conv2D, null, [
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number"
]);
}
function conv2d8(args) {
const {inputs, attrs, backend: backend3} = args;
const {x, filter} = inputs;
const xId = backend3.dataIdMap.get(x.dataId).id;
const filterId = backend3.dataIdMap.get(filter.dataId).id;
const {strides, dilations, pad: pad8, dimRoundingMode, dataFormat} = attrs;
const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);
const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad8, dimRoundingMode, false, $dataFormat);
const filterHeight = convInfo.filterHeight;
const filterWidth = convInfo.filterWidth;
const padTop = convInfo.padInfo.top;
const padRight = convInfo.padInfo.right;
const padBottom = convInfo.padInfo.bottom;
const padLeft = convInfo.padInfo.left;
const dilationHeight = convInfo.dilationHeight;
const dilationWidth = convInfo.dilationWidth;
const strideHeight = convInfo.strideHeight;
const strideWidth = convInfo.strideWidth;
const inputChannels = convInfo.inChannels;
const outputChannels = convInfo.outChannels;
const isSamePad = convInfo.padInfo.type === "SAME" ? 1 : 0;
if (convInfo.dataFormat !== "channelsLast") {
throw new Error(`wasm backend Conv2D does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);
}
const out = backend3.makeOutput(convInfo.outShape, "float32");
const outId = backend3.dataIdMap.get(out.dataId).id;
wasmConv2d(xId, x.shape[0], x.shape[1], x.shape[2], filterId, filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, outId);
return out;
}
const conv2DConfig = {
kernelName: Conv2D,
backendName: "wasm",
setupFunc: setup7,
kernelFunc: conv2d8
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Conv2DBackpropInput.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
let wasmConv2DBackpropInput;
function setup8(backend3) {
wasmConv2DBackpropInput = backend3.wasm.cwrap(Conv2DBackpropInput, null, [
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number"
]);
}
function conv2DBackpropInput2(args) {
const {backend: backend3, inputs, attrs} = args;
const {dy, filter} = inputs;
const {strides, pad: pad8, dataFormat, dimRoundingMode, inputShape} = attrs;
const dilations = 1;
const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);
const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, dilations, pad8, dimRoundingMode, false, $dataFormat);
const {batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth} = convInfo;
const topPad = filterHeight - 1 - convInfo.padInfo.top;
const leftPad = filterWidth - 1 - convInfo.padInfo.left;
const isChannelsLast = convInfo.dataFormat === "channelsLast";
const dxStrides = util_exports.computeStrides(convInfo.inShape);
const dyStrides = util_exports.computeStrides(dy.shape);
const [fltS0, fltS1, fltS2] = util_exports.computeStrides(filter.shape);
const xBatchStride = dxStrides[0];
const xRowStride = isChannelsLast ? dxStrides[1] : dxStrides[2];
const xColStride = isChannelsLast ? dxStrides[2] : 1;
const xChannelStride = isChannelsLast ? 1 : dxStrides[1];
const yBatchStride = dyStrides[0];
const yRowStride = isChannelsLast ? dyStrides[1] : dyStrides[2];
const yColStride = isChannelsLast ? dyStrides[2] : 1;
const yChannelStride = isChannelsLast ? 1 : dyStrides[1];
const out = backend3.makeOutput(convInfo.inShape, "float32");
const outId = backend3.dataIdMap.get(out.dataId).id;
const dyId = backend3.dataIdMap.get(dy.dataId).id;
const filterId = backend3.dataIdMap.get(filter.dataId).id;
wasmConv2DBackpropInput(dyId, filterId, batchSize, filterHeight, filterWidth, inHeight, inWidth, inChannels, outHeight, outWidth, outChannels, strideHeight, strideWidth, topPad, leftPad, fltS0, fltS1, fltS2, xBatchStride, xRowStride, xColStride, xChannelStride, yBatchStride, yRowStride, yColStride, yChannelStride, outId);
return out;
}
const conv2DBackpropInputConfig = {
kernelName: Conv2DBackpropInput,
backendName: "wasm",
setupFunc: setup8,
kernelFunc: conv2DBackpropInput2
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Cos.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const cosConfig = createUnaryKernelConfig(Cos);
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/CropAndResize.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
var InterpolationMethod;
(function(InterpolationMethod2) {
InterpolationMethod2[InterpolationMethod2["bilinear"] = 0] = "bilinear";
InterpolationMethod2[InterpolationMethod2["nearest"] = 1] = "nearest";
})(InterpolationMethod || (InterpolationMethod = {}));
let wasmCropAndResize;
function setup9(backend3) {
wasmCropAndResize = backend3.wasm.cwrap(CropAndResize, null, [
"number",
"number",
"number",
"number",
"array",
"number",
"number",
"number",
"number",
"number"
]);
}
function cropAndResize2(args) {
const {backend: backend3, inputs, attrs} = args;
const {method, extrapolationValue, cropSize} = attrs;
const {image: image4, boxes, boxInd} = inputs;
const numBoxes = boxes.shape[0];
const [cropHeight, cropWidth] = cropSize;
const outShape = [numBoxes, cropHeight, cropWidth, image4.shape[3]];
let imagesData = backend3.dataIdMap.get(image4.dataId);
let castedData;
if (image4.dtype !== "float32") {
castedData = cast21({backend: backend3, inputs: {x: image4}, attrs: {dtype: "float32"}});
imagesData = backend3.dataIdMap.get(castedData.dataId);
}
const imagesId = imagesData.id;
const boxesId = backend3.dataIdMap.get(boxes.dataId).id;
const boxIndId = backend3.dataIdMap.get(boxInd.dataId).id;
const out = backend3.makeOutput(outShape, "float32");
const outId = backend3.dataIdMap.get(out.dataId).id;
const imagesShapeBytes = new Uint8Array(new Int32Array(image4.shape).buffer);
wasmCropAndResize(imagesId, boxesId, boxIndId, numBoxes, imagesShapeBytes, cropHeight, cropWidth, InterpolationMethod[method], extrapolationValue, outId);
if (castedData != null) {
backend3.disposeData(castedData.dataId);
}
return out;
}
const cropAndResizeConfig = {
kernelName: CropAndResize,
backendName: "wasm",
setupFunc: setup9,
kernelFunc: cropAndResize2
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Cumsum.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
let wasmCumsum;
function setup10(backend3) {
wasmCumsum = backend3.wasm.cwrap(Cumsum, null, [
"number",
"number",
"number",
"number",
"number",
"number"
]);
}
function cumsum3(args) {
const {inputs, backend: backend3, attrs} = args;
const {x} = inputs;
const {axis, exclusive, reverse: reverse9} = attrs;
const xRank = x.shape.length;
util_exports.assert(x.dtype === "float32" || x.dtype === "int32", () => `cumsum does not support ${x.dtype} tensors in the WASM backend`);
const permutation = backend_util_exports.getAxesPermutation([axis], xRank);
let permutedX = x;
if (permutation !== null) {
permutedX = transpose13({inputs: {x}, attrs: {perm: permutation}, backend: backend3});
}
const permutedAxis = backend_util_exports.getInnerMostAxes(1, xRank)[0];
backend_util_exports.assertAxesAreInnerMostDims("cumsum", [permutedAxis], xRank);
const permutedOut = backend3.makeOutput(permutedX.shape, permutedX.dtype);
const finalDim = permutedX.shape[permutedAxis];
const permutedXId = backend3.dataIdMap.get(permutedX.dataId).id;
const permutedOutId = backend3.dataIdMap.get(permutedOut.dataId).id;
wasmCumsum(permutedXId, exclusive ? 1 : 0, reverse9 ? 1 : 0, finalDim, permutedOutId, CppDType[x.dtype]);
let out = permutedOut;
if (permutation !== null) {
const undoPermutation = backend_util_exports.getUndoAxesPermutation(permutation);
out = transpose13({inputs: {x: permutedOut}, attrs: {perm: undoPermutation}, backend: backend3});
backend3.disposeData(permutedX.dataId);
backend3.disposeData(permutedOut.dataId);
}
return out;
}
const cumsumConfig = {
kernelName: Cumsum,
backendName: "wasm",
setupFunc: setup10,
kernelFunc: cumsum3
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/DepthToSpace.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
let wasmDepthToSpace;
function setup11(backend3) {
wasmDepthToSpace = backend3.wasm.cwrap(DepthToSpace, null, [
"number",
"number",
"number",
"array",
"number",
"array",
"array",
"number",
"number"
]);
}
function depthToSpace2(args) {
const {backend: backend3, inputs, attrs} = args;
const {x} = inputs;
const {blockSize, dataFormat} = attrs;
util_exports.assert(blockSize > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);
const batchSize = x.shape[0];
const inputHeight = dataFormat === "NHWC" ? x.shape[1] : x.shape[2];
const inputWidth = dataFormat === "NHWC" ? x.shape[2] : x.shape[3];
const inputDepth = dataFormat === "NHWC" ? x.shape[3] : x.shape[1];
const outputHeight = inputHeight * blockSize;
const outputWidth = inputWidth * blockSize;
const outputDepth = inputDepth / (blockSize * blockSize);
const outputShape = dataFormat === "NHWC" ? [batchSize, outputHeight, outputWidth, outputDepth] : [batchSize, outputDepth, outputHeight, outputWidth];
const out = backend3.makeOutput(outputShape, "float32");
const xData = backend3.dataIdMap.get(x.dataId);
const xId = xData.id;
const xStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(x.shape)).buffer);
const outputShapeBytes = new Uint8Array(new Int32Array(outputShape).buffer);
const outStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(outputShape)).buffer);
const outId = backend3.dataIdMap.get(out.dataId).id;
const channelsLast = dataFormat === "NHWC" ? 1 : 0;
wasmDepthToSpace(xId, blockSize, channelsLast, xStridesBytes, x.shape.length - 1, outputShapeBytes, outStridesBytes, outputShape.length, outId);
return out;
}
const depthToSpaceConfig = {
kernelName: DepthToSpace,
backendName: "wasm",
setupFunc: setup11,
kernelFunc: depthToSpace2
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/DepthwiseConv2dNative.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
let wasmDepthwiseConv2d;
function setup12(backend3) {
wasmDepthwiseConv2d = backend3.wasm.cwrap(DepthwiseConv2dNative, null, [
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number"
]);
}
function depthwiseConv2d5(args) {
const {inputs, attrs, backend: backend3} = args;
const {x, filter} = inputs;
const xId = backend3.dataIdMap.get(x.dataId).id;
const filterId = backend3.dataIdMap.get(filter.dataId).id;
const {strides, dilations, pad: pad8, dimRoundingMode} = attrs;
const $dilations = dilations == null ? [1, 1] : dilations;
const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad8, dimRoundingMode, true);
const filterHeight = convInfo.filterHeight;
const filterWidth = convInfo.filterWidth;
const padTop = convInfo.padInfo.top;
const padRight = convInfo.padInfo.right;
const padBottom = convInfo.padInfo.bottom;
const padLeft = convInfo.padInfo.left;
const dilationHeight = convInfo.dilationHeight;
const dilationWidth = convInfo.dilationWidth;
const strideHeight = convInfo.strideHeight;
const strideWidth = convInfo.strideWidth;
const inputChannels = convInfo.inChannels;
const outputChannels = convInfo.outChannels;
const isSamePad = convInfo.padInfo.type === "SAME" ? 1 : 0;
if (convInfo.dataFormat !== "channelsLast") {
throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);
}
const out = backend3.makeOutput(convInfo.outShape, "float32");
const outId = backend3.dataIdMap.get(out.dataId).id;
wasmDepthwiseConv2d(xId, x.shape[0], x.shape[1], x.shape[2], filterId, filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, outId);
return out;
}
const depthwiseConv2dNativeConfig = {
kernelName: DepthwiseConv2dNative,
backendName: "wasm",
setupFunc: setup12,
kernelFunc: depthwiseConv2d5
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Div.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const supportsFullBroadcast2 = true;
const divConfig = createBinaryKernelConfig(Div, supportsFullBroadcast2);
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Equal.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const supportsFullBroadcast3 = false;
const equalConfig = createBinaryKernelConfig(Equal, supportsFullBroadcast3, "bool");
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Exp.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const expConfig = createUnaryKernelConfig(Exp);
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Fill.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function fill5(args) {
const {attrs: {shape, value, dtype}, backend: backend3} = args;
const out = backend3.makeOutput(shape, dtype);
const outVals = backend3.typedArrayFromHeap(out);
outVals.fill(value);
return out;
}
const fillConfig = {
kernelName: Fill,
backendName: "wasm",
kernelFunc: fill5
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FlipLeftRight.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
let wasmFlipLeftRight;
function setup13(backend3) {
wasmFlipLeftRight = backend3.wasm.cwrap(FlipLeftRight, null, [
"number",
"number",
"number",
"number",
"number",
"number"
]);
}
function flipLeftRight2(args) {
const {inputs, backend: backend3} = args;
const {image: image4} = inputs;
const out = backend3.makeOutput(image4.shape, image4.dtype);
const imageId = backend3.dataIdMap.get(image4.dataId).id;
const outId = backend3.dataIdMap.get(out.dataId).id;
const [batch, imageHeight, imageWidth, numChannels] = image4.shape;
wasmFlipLeftRight(imageId, batch, imageHeight, imageWidth, numChannels, outId);
return out;
}
const flipLeftRightConfig = {
kernelName: FlipLeftRight,
backendName: "wasm",
kernelFunc: flipLeftRight2,
setupFunc: setup13
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FloorDiv.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const supportsFullBroadcast4 = false;
const floorDivConfig = createBinaryKernelConfig(FloorDiv, supportsFullBroadcast4);
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FusedBatchNorm.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
let wasmBatchNorm;
function setup14(backend3) {
wasmBatchNorm = backend3.wasm.cwrap(FusedBatchNorm, null, ["number", "number", "number", "number", "number", "number", "number"]);
}
function fusedBatchNorm(args) {
const {backend: backend3, inputs, attrs} = args;
const {varianceEpsilon} = attrs;
const {x, mean: mean5, variance, offset, scale} = inputs;
const xId = backend3.dataIdMap.get(x.dataId).id;
const meanId = backend3.dataIdMap.get(mean5.dataId).id;
const varianceId = backend3.dataIdMap.get(variance.dataId).id;
const offsetId = offset != null ? backend3.dataIdMap.get(offset.dataId).id : 0;
const scaleId = scale != null ? backend3.dataIdMap.get(scale.dataId).id : 0;
const out = backend3.makeOutput(x.shape, x.dtype);
if (util_exports.sizeFromShape(x.shape) === 0) {
return out;
}
const outId = backend3.dataIdMap.get(out.dataId).id;
wasmBatchNorm(xId, meanId, varianceId, offsetId, scaleId, varianceEpsilon, outId);
return out;
}
const fusedBatchNormConfig = {
kernelName: FusedBatchNorm,
backendName: "wasm",
setupFunc: setup14,
kernelFunc: fusedBatchNorm
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FusedConv2D.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
let wasmFusedConv2d;
function setup15(backend3) {
wasmFusedConv2d = backend3.wasm.cwrap(FusedConv2D, null, [
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number"
]);
}
function fusedConv2d(args) {
const {inputs, attrs, backend: backend3} = args;
const {x, filter, bias, preluActivationWeights} = inputs;
const {strides, pad: pad8, dilations, dataFormat, dimRoundingMode, activation: activation2} = attrs;
const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad8, dimRoundingMode);
const fusedActivation = FusableActivation[activation2];
if (fusedActivation == null) {
throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);
}
const xId = backend3.dataIdMap.get(x.dataId).id;
const filterId = backend3.dataIdMap.get(filter.dataId).id;
const outputChannels = convInfo.outChannels;
let biasId = 0;
if (bias != null) {
const biasData = backend3.dataIdMap.get(bias.dataId);
if (biasData.shape.length !== 1) {
throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);
}
if (biasData.shape[0] !== outputChannels) {
throw new Error(`FusedConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);
}
biasId = biasData.id;
}
const filterHeight = convInfo.filterHeight;
const filterWidth = convInfo.filterWidth;
const padTop = convInfo.padInfo.top;
const padRight = convInfo.padInfo.right;
const padBottom = convInfo.padInfo.bottom;
const padLeft = convInfo.padInfo.left;
const dilationHeight = convInfo.dilationHeight;
const dilationWidth = convInfo.dilationWidth;
const strideHeight = convInfo.strideHeight;
const strideWidth = convInfo.strideWidth;
const inputChannels = convInfo.inChannels;
const isSamePad = convInfo.padInfo.type === "SAME" ? 1 : 0;
const batchSize = convInfo.batchSize;
const inHeight = convInfo.inHeight;
const inWidth = convInfo.inWidth;
if (dataFormat !== "NHWC") {
throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);
}
const out = backend3.makeOutput(convInfo.outShape, "float32");
const outId = backend3.dataIdMap.get(out.dataId).id;
const preluActivationWeightsId = preluActivationWeights == null ? 0 : backend3.dataIdMap.get(preluActivationWeights.dataId).id;
wasmFusedConv2d(xId, batchSize, inHeight, inWidth, filterId, filterHeight, filterWidth, biasId, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, fusedActivation, preluActivationWeightsId, outId);
return out;
}
const fusedConv2DConfig = {
kernelName: FusedConv2D,
backendName: "wasm",
setupFunc: setup15,
kernelFunc: fusedConv2d
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FusedDepthwiseConv2D.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
let wasmFusedDepthwiseConv2d;
function setup16(backend3) {
wasmFusedDepthwiseConv2d = backend3.wasm.cwrap(FusedDepthwiseConv2D, null, [
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number"
]);
}
function fusedDepthwiseConv2d(args) {
const {inputs, attrs, backend: backend3} = args;
const {x, filter, bias, preluActivationWeights} = inputs;
const {strides, pad: pad8, dilations, dataFormat, dimRoundingMode, activation: activation2} = attrs;
const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad8, dimRoundingMode, true);
const fusedActivation = FusableActivation[activation2];
if (fusedActivation == null) {
throw new Error(`${activation2} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);
}
const xId = backend3.dataIdMap.get(x.dataId).id;
const filterId = backend3.dataIdMap.get(filter.dataId).id;
const outputChannels = convInfo.outChannels;
let biasId = 0;
if (bias != null) {
const biasData = backend3.dataIdMap.get(bias.dataId);
if (biasData.shape.length !== 1) {
throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);
}
if (biasData.shape[0] !== outputChannels) {
throw new Error(`FusedDepthwiseConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);
}
biasId = biasData.id;
}
const filterHeight = convInfo.filterHeight;
const filterWidth = convInfo.filterWidth;
const padTop = convInfo.padInfo.top;
const padRight = convInfo.padInfo.right;
const padBottom = convInfo.padInfo.bottom;
const padLeft = convInfo.padInfo.left;
const dilationHeight = convInfo.dilationHeight;
const dilationWidth = convInfo.dilationWidth;
const strideHeight = convInfo.strideHeight;
const strideWidth = convInfo.strideWidth;
const inputChannels = convInfo.inChannels;
const isSamePad = convInfo.padInfo.type === "SAME" ? 1 : 0;
const batchSize = convInfo.batchSize;
const inHeight = convInfo.inHeight;
const inWidth = convInfo.inWidth;
if (dataFormat !== "NHWC") {
throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);
}
const out = backend3.makeOutput(convInfo.outShape, "float32");
const outId = backend3.dataIdMap.get(out.dataId).id;
const preluActivationWeightsId = preluActivationWeights == null ? 0 : backend3.dataIdMap.get(preluActivationWeights.dataId).id;
wasmFusedDepthwiseConv2d(xId, batchSize, inHeight, inWidth, filterId, filterHeight, filterWidth, biasId, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, fusedActivation, preluActivationWeightsId, outId);
return out;
}
const fusedDepthwiseConv2DConfig = {
kernelName: FusedDepthwiseConv2D,
backendName: "wasm",
setupFunc: setup16,
kernelFunc: fusedDepthwiseConv2d
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/GatherNd.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
let wasmGatherNd;
function setup17(backend3) {
wasmGatherNd = backend3.wasm.cwrap(GatherNd, null, [
"number",
"number",
"number",
"number",
"number",
"number",
"array",
"number"
]);
}
function gatherNd(args) {
const {backend: backend3, inputs} = args;
const {params, indices} = inputs;
const [resultShape, numSlices, sliceSize, strides] = gather_nd_util_exports.prepareAndValidate(params, indices);
const out = backend3.makeOutput(resultShape, params.dtype);
if (numSlices === 0) {
return out;
}
const indicesShape = indices.shape;
const sliceRank = indicesShape[indicesShape.length - 1];
const xData = backend3.dataIdMap.get(params.dataId);
const xId = xData.id;
const indicesData = backend3.dataIdMap.get(indices.dataId);
const indicesId = indicesData.id;
const stridesBytes = new Uint8Array(new Int32Array(strides).buffer);
const outId = backend3.dataIdMap.get(out.dataId).id;
wasmGatherNd(xId, CppDType[params.dtype], indicesId, numSlices, sliceRank, sliceSize, stridesBytes, outId);
return out;
}
const gatherNdConfig = {
kernelName: GatherNd,
backendName: "wasm",
setupFunc: setup17,
kernelFunc: gatherNd
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/GatherV2.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
let wasmGather;
function setup18(backend3) {
wasmGather = backend3.wasm.cwrap("Gather", null, [
"number",
"number",
"array",
"number",
"number",
"number",
"array",
"number"
]);
}
function gatherV2(args) {
const {backend: backend3, inputs, attrs} = args;
const {x, indices} = inputs;
const {axis} = attrs;
const newShape = x.shape.slice();
newShape[axis] = util_exports.sizeFromShape(indices.shape);
const stridesSize = x.shape.length - 1;
const out = backend3.makeOutput(newShape, x.dtype);
if (util_exports.sizeFromShape(x.shape) === 0) {
return out;
}
const xData = backend3.dataIdMap.get(x.dataId);
const xId = xData.id;
const indicesData = backend3.dataIdMap.get(indices.dataId);
const indicesId = indicesData.id;
const outId = backend3.dataIdMap.get(out.dataId).id;
const xStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(x.shape)).buffer);
const outStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(newShape)).buffer);
wasmGather(xId, CppDType[x.dtype], xStridesBytes, stridesSize, indicesId, axis, outStridesBytes, outId);
const parsedAxis = util_exports.parseAxisParam(axis, x.shape)[0];
const shapeInfo = backend_util_exports.segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis);
out.shape = shapeInfo.outputShape;
return out;
}
const gatherV2Config = {
kernelName: GatherV2,
backendName: "wasm",
setupFunc: setup18,
kernelFunc: gatherV2
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Greater.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const supportsFullBroadcast5 = false;
const greaterConfig = createBinaryKernelConfig(Greater, supportsFullBroadcast5, "bool");
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/GreaterEqual.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const supportsFullBroadcast6 = false;
const greaterEqualConfig = createBinaryKernelConfig(GreaterEqual, supportsFullBroadcast6, "bool");
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Less.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const supportsFullBroadcast7 = false;
const lessConfig = createBinaryKernelConfig(Less, supportsFullBroadcast7, "bool");
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/LessEqual.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const supportsFullBroadcast8 = false;
const lessEqualConfig = createBinaryKernelConfig(LessEqual, supportsFullBroadcast8, "bool");
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Log.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const logConfig = createUnaryKernelConfig(Log);
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/LogicalAnd.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const supportsFullBroadcast9 = false;
const logicalAndConfig = createBinaryKernelConfig(LogicalAnd, supportsFullBroadcast9, "bool");
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Max.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
let wasmMax;
function setup19(backend3) {
wasmMax = backend3.wasm.cwrap(Max, null, ["number, number, number"]);
}
function max7(args) {
const {backend: backend3, inputs, attrs} = args;
const {reductionIndices: axis, keepDims} = attrs;
const {x} = inputs;
const xId = backend3.dataIdMap.get(x.dataId).id;
let inputId = xId;
let input2 = x;
const {transposed, axes, originalAxes, inputWasTransposed} = permuteAxesAndTranspose(x, axis, backend3);
if (inputWasTransposed) {
const transposedId = backend3.dataIdMap.get(transposed.dataId).id;
input2 = transposed;
inputId = transposedId;
}
const inputRank = input2.shape.length;
backend_util_exports.assertAxesAreInnerMostDims("max", axes, inputRank);
const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, axes);
const reduceSize = util_exports.sizeFromShape(reduceShape);
const out = backend3.makeOutput(outShape, x.dtype);
if (util_exports.sizeFromShape(input2.shape) !== 0) {
const outId = backend3.dataIdMap.get(out.dataId).id;
wasmMax(inputId, reduceSize, outId);
}
if (inputWasTransposed) {
backend3.disposeData(transposed.dataId);
}
if (keepDims) {
const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);
out.shape = newShape;
}
return out;
}
const maxConfig = {
kernelName: Max,
backendName: "wasm",
setupFunc: setup19,
kernelFunc: max7
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Maximum.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const supportsFullBroadcast10 = false;
const maximumConfig = createBinaryKernelConfig(Maximum, supportsFullBroadcast10);
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/MaxPool.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
let wasmMaxPool;
function setup20(backend3) {
wasmMaxPool = backend3.wasm.cwrap(MaxPool, null, [
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number"
]);
}
function maxPool2(args) {
const {inputs, attrs, backend: backend3} = args;
const x = inputs.x;
const xId = backend3.dataIdMap.get(x.dataId).id;
const {filterSize, strides, pad: pad8, dimRoundingMode} = attrs;
const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad8, dimRoundingMode);
const filterHeight = convInfo.filterHeight;
const filterWidth = convInfo.filterWidth;
const padTop = convInfo.padInfo.top;
const padRight = convInfo.padInfo.right;
const padBottom = convInfo.padInfo.bottom;
const padLeft = convInfo.padInfo.left;
const dilationHeight = convInfo.dilationHeight;
const dilationWidth = convInfo.dilationWidth;
const strideHeight = convInfo.strideHeight;
const strideWidth = convInfo.strideWidth;
const inputChannels = convInfo.inChannels;
const outputChannels = convInfo.outChannels;
if (convInfo.dataFormat !== "channelsLast") {
throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);
}
const out = backend3.makeOutput(convInfo.outShape, "float32");
const outId = backend3.dataIdMap.get(out.dataId).id;
wasmMaxPool(xId, x.shape[0], x.shape[1], x.shape[2], filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, outId);
return out;
}
const maxPoolConfig = {
kernelName: MaxPool,
backendName: "wasm",
setupFunc: setup20,
kernelFunc: maxPool2
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Min.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
let wasmMin;
function setup21(backend3) {
wasmMin = backend3.wasm.cwrap(Min, null, ["number, number, number"]);
}
function min5(args) {
const {backend: backend3, inputs, attrs} = args;
const {axis, keepDims} = attrs;
const {x} = inputs;
const xId = backend3.dataIdMap.get(x.dataId).id;
let inputId = xId;
let input2 = x;
const {transposed, axes, originalAxes, inputWasTransposed} = permuteAxesAndTranspose(x, axis, backend3);
if (inputWasTransposed) {
const transposedId = backend3.dataIdMap.get(transposed.dataId).id;
if (transposedId !== xId) {
input2 = transposed;
inputId = transposedId;
}
}
const inputRank = input2.shape.length;
backend_util_exports.assertAxesAreInnerMostDims("min", axes, inputRank);
const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, axes);
const reduceSize = util_exports.sizeFromShape(reduceShape);
const out = backend3.makeOutput(outShape, input2.dtype);
if (util_exports.sizeFromShape(input2.shape) !== 0) {
const outId = backend3.dataIdMap.get(out.dataId).id;
wasmMin(inputId, reduceSize, outId);
}
if (inputWasTransposed) {
backend3.disposeData(transposed.dataId);
}
if (keepDims) {
const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);
out.shape = newShape;
}
return out;
}
const minConfig = {
kernelName: Min,
backendName: "wasm",
setupFunc: setup21,
kernelFunc: min5
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Minimum.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const supportsFullBroadcast11 = false;
const minimumConfig = createBinaryKernelConfig(Minimum, supportsFullBroadcast11);
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Multiply.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const supportsFullBroadcast12 = true;
const multiplyConfig = createBinaryKernelConfig(Multiply, supportsFullBroadcast12);
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Negate.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const negateConfig = createUnaryKernelConfig(Negate);
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NonMaxSuppression_util.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function parseResultStruct(backend3, resOffset) {
const result = new Int32Array(backend3.wasm.HEAPU8.buffer, resOffset, 4);
const pSelectedIndices = result[0];
const selectedSize = result[1];
const pSelectedScores = result[2];
const pValidOutputs = result[3];
backend3.wasm._free(resOffset);
return {pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs};
}
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NonMaxSuppressionV3.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
let wasmFunc3;
function setup22(backend3) {
wasmFunc3 = backend3.wasm.cwrap(NonMaxSuppressionV3, "number", [
"number",
"number",
"number",
"number",
"number"
]);
}
function kernelFunc(args) {
const {backend: backend3, inputs, attrs} = args;
const {iouThreshold, maxOutputSize, scoreThreshold} = attrs;
const {boxes, scores} = inputs;
const boxesId = backend3.dataIdMap.get(boxes.dataId).id;
const scoresId = backend3.dataIdMap.get(scores.dataId).id;
const resOffset = wasmFunc3(boxesId, scoresId, maxOutputSize, iouThreshold, scoreThreshold);
const {pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs} = parseResultStruct(backend3, resOffset);
backend3.wasm._free(pSelectedScores);
backend3.wasm._free(pValidOutputs);
const selectedIndicesTensor = backend3.makeOutput([selectedSize], "int32", pSelectedIndices);
return selectedIndicesTensor;
}
const nonMaxSuppressionV3Config = {
kernelName: NonMaxSuppressionV3,
backendName: "wasm",
setupFunc: setup22,
kernelFunc
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NonMaxSuppressionV4.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
let wasmFunc4;
function setup23(backend3) {
wasmFunc4 = backend3.wasm.cwrap(NonMaxSuppressionV4, "number", [
"number",
"number",
"number",
"number",
"number",
"bool"
]);
}
function nonMaxSuppressionV4(args) {
const {backend: backend3, inputs, attrs} = args;
const {iouThreshold, maxOutputSize, scoreThreshold, padToMaxOutputSize} = attrs;
const {boxes, scores} = inputs;
const boxesId = backend3.dataIdMap.get(boxes.dataId).id;
const scoresId = backend3.dataIdMap.get(scores.dataId).id;
const resOffset = wasmFunc4(boxesId, scoresId, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);
const {pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs} = parseResultStruct(backend3, resOffset);
backend3.wasm._free(pSelectedScores);
const selectedIndicesTensor = backend3.makeOutput([selectedSize], "int32", pSelectedIndices);
const validOutputsTensor = backend3.makeOutput([], "int32", pValidOutputs);
return [selectedIndicesTensor, validOutputsTensor];
}
const nonMaxSuppressionV4Config = {
kernelName: NonMaxSuppressionV4,
backendName: "wasm",
setupFunc: setup23,
kernelFunc: nonMaxSuppressionV4
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NonMaxSuppressionV5.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
let wasmFunc5;
function setup24(backend3) {
wasmFunc5 = backend3.wasm.cwrap(NonMaxSuppressionV5, "number", [
"number",
"number",
"number",
"number",
"number",
"number"
]);
}
function kernelFunc2(args) {
const {backend: backend3, inputs, attrs} = args;
const {iouThreshold, maxOutputSize, scoreThreshold, softNmsSigma} = attrs;
const {boxes, scores} = inputs;
const boxesId = backend3.dataIdMap.get(boxes.dataId).id;
const scoresId = backend3.dataIdMap.get(scores.dataId).id;
const resOffset = wasmFunc5(boxesId, scoresId, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
const {pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs} = parseResultStruct(backend3, resOffset);
backend3.wasm._free(pValidOutputs);
const selectedIndicesTensor = backend3.makeOutput([selectedSize], "int32", pSelectedIndices);
const selectedScoresTensor = backend3.makeOutput([selectedSize], "float32", pSelectedScores);
return [selectedIndicesTensor, selectedScoresTensor];
}
const nonMaxSuppressionV5Config = {
kernelName: NonMaxSuppressionV5,
backendName: "wasm",
setupFunc: setup24,
kernelFunc: kernelFunc2
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NotEqual.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const supportsFullBroadcast13 = false;
const notEqualConfig = createBinaryKernelConfig(NotEqual, supportsFullBroadcast13, "bool");
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/OneHot.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
let wasmOneHot;
function setup25(backend3) {
wasmOneHot = backend3.wasm.cwrap(OneHot, null, [
"number",
"number",
"number",
"number",
"number"
]);
}
function oneHot2(args) {
const {inputs, backend: backend3, attrs} = args;
const {indices} = inputs;
const {depth, onValue, offValue} = attrs;
const out = backend3.makeOutput([...indices.shape, depth], "int32");
const outId = backend3.dataIdMap.get(out.dataId).id;
const indicesData = backend3.dataIdMap.get(indices.dataId);
const indicesId = indicesData.id;
wasmOneHot(indicesId, depth, onValue, offValue, outId);
return out;
}
const oneHotConfig = {
kernelName: OneHot,
backendName: "wasm",
setupFunc: setup25,
kernelFunc: oneHot2
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/OnesLike.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function onesLike2(args) {
const {inputs: {x}, backend: backend3} = args;
const out = backend3.makeOutput(x.shape, x.dtype);
const outVals = backend3.typedArrayFromHeap(out);
outVals.fill(1);
return out;
}
const onesLikeConfig = {
kernelName: OnesLike,
backendName: "wasm",
kernelFunc: onesLike2
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/PadV2.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
let wasmPadV2;
function setup26(backend3) {
wasmPadV2 = backend3.wasm.cwrap(PadV2, null, [
"number",
"array",
"number",
"number",
"array",
"array",
"number",
"number"
]);
}
function pad7(args) {
const {inputs: {x}, backend: backend3, attrs: {paddings, constantValue}} = args;
const outShape = paddings.map((p, i) => p[0] + x.shape[i] + p[1]);
const xId = backend3.dataIdMap.get(x.dataId).id;
const out = backend3.makeOutput(outShape, x.dtype);
const outId = backend3.dataIdMap.get(out.dataId).id;
const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);
const prePaddingsFlat = paddings.map((padTuple) => padTuple[0]);
const postPaddingsFlat = paddings.map((padTuple) => padTuple[1]);
const prePaddingsBytes = new Uint8Array(new Int32Array(prePaddingsFlat).buffer);
const postPaddingsBytes = new Uint8Array(new Int32Array(postPaddingsFlat).buffer);
wasmPadV2(xId, xShapeBytes, x.shape.length, CppDType[x.dtype], prePaddingsBytes, postPaddingsBytes, constantValue, outId);
return out;
}
const padV2Config = {
kernelName: PadV2,
backendName: "wasm",
kernelFunc: pad7,
setupFunc: setup26
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Pow.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const supportsFullBroadcast14 = false;
const powConfig = createBinaryKernelConfig(Pow, supportsFullBroadcast14);
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Prelu.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
let wasmPrelu;
function setup27(backend3) {
wasmPrelu = backend3.wasm.cwrap(Prelu, null, [
"number",
"number",
"number"
]);
}
function prelu5(args) {
const {inputs, backend: backend3} = args;
const {x, alpha} = inputs;
const xId = backend3.dataIdMap.get(x.dataId).id;
const weightsId = backend3.dataIdMap.get(alpha.dataId).id;
const out = backend3.makeOutput(x.shape, "float32");
const outId = backend3.dataIdMap.get(out.dataId).id;
wasmPrelu(xId, weightsId, outId);
return out;
}
const preluConfig = {
kernelName: Prelu,
backendName: "wasm",
setupFunc: setup27,
kernelFunc: prelu5
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Relu.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const reluConfig = createUnaryKernelConfig(Relu);
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Relu6.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const relu6Config = createUnaryKernelConfig(Relu6);
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ResizeBilinear.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
let wasmResizeBilinear;
function setup28(backend3) {
wasmResizeBilinear = backend3.wasm.cwrap(ResizeBilinear, null, [
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number"
]);
}
function resizeBilinear2(args) {
const {backend: backend3, inputs, attrs} = args;
const {images} = inputs;
const {alignCorners, size} = attrs;
const [newHeight, newWidth] = size;
const [batch, oldHeight, oldWidth, numChannels] = images.shape;
const outShape = [batch, newHeight, newWidth, numChannels];
let xData = backend3.dataIdMap.get(images.dataId);
let castedData;
if (xData.dtype !== "float32") {
castedData = cast21({backend: backend3, inputs: {x: images}, attrs: {dtype: "float32"}});
xData = backend3.dataIdMap.get(castedData.dataId);
}
const xId = xData.id;
const out = backend3.makeOutput(outShape, "float32");
if (util_exports.sizeFromShape(images.shape) === 0) {
return out;
}
const outId = backend3.dataIdMap.get(out.dataId).id;
wasmResizeBilinear(xId, batch, oldHeight, oldWidth, numChannels, newHeight, newWidth, alignCorners ? 1 : 0, outId);
if (castedData != null) {
backend3.disposeData(castedData.dataId);
}
return out;
}
const resizeBilinearConfig = {
kernelName: ResizeBilinear,
backendName: "wasm",
setupFunc: setup28,
kernelFunc: resizeBilinear2
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Reverse.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
let wasmReverse;
function setup29(backend3) {
wasmReverse = backend3.wasm.cwrap(Reverse, null, [
"number",
"array",
"number",
"array",
"number",
"number"
]);
}
function reverse8(args) {
const {inputs, backend: backend3, attrs} = args;
const {x} = inputs;
const {dims} = attrs;
const axes = util_exports.parseAxisParam(dims, x.shape);
if (x.shape.length === 0) {
return identity2({inputs: {x}, backend: backend3});
}
const out = backend3.makeOutput(x.shape, x.dtype);
const xId = backend3.dataIdMap.get(x.dataId).id;
const outId = backend3.dataIdMap.get(out.dataId).id;
const axesBytes = new Uint8Array(new Int32Array(axes).buffer);
const outShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);
wasmReverse(xId, axesBytes, axes.length, outShapeBytes, x.shape.length, outId);
return reshape60({inputs: {x: out}, attrs: {shape: x.shape}, backend: backend3});
}
const reverseConfig = {
kernelName: Reverse,
backendName: "wasm",
kernelFunc: reverse8,
setupFunc: setup29
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/RotateWithOffset.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
let wasmRotate;
function setup30(backend3) {
wasmRotate = backend3.wasm.cwrap(RotateWithOffset, null, [
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"array",
"number",
"number"
]);
}
function rotateWithOffset2(args) {
const {inputs, backend: backend3, attrs} = args;
const {image: image4} = inputs;
const {radians, fillValue, center} = attrs;
const out = backend3.makeOutput(image4.shape, image4.dtype);
const imageId = backend3.dataIdMap.get(image4.dataId).id;
const outId = backend3.dataIdMap.get(out.dataId).id;
const [batch, imageHeight, imageWidth, numChannels] = image4.shape;
const [centerX, centerY] = backend_util_exports.getImageCenter(center, imageHeight, imageWidth);
const fillIsBlack = fillValue === 0;
const fullOpacityValue = 255;
const fillValues = typeof fillValue === "number" ? [fillValue, fillValue, fillValue, fillIsBlack ? 0 : fullOpacityValue] : [...fillValue, fullOpacityValue];
const fillBytes = new Uint8Array(new Int32Array(fillValues).buffer);
wasmRotate(imageId, batch, imageHeight, imageWidth, numChannels, radians, centerX, centerY, fillBytes, fillValues.length, outId);
return out;
}
const rotateWithOffsetConfig = {
kernelName: RotateWithOffset,
backendName: "wasm",
kernelFunc: rotateWithOffset2,
setupFunc: setup30
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Rsqrt.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const rsqrtConfig = createUnaryKernelConfig(Rsqrt);
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ScatterNd.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
let wasmScatterNd;
function setup31(backend3) {
wasmScatterNd = backend3.wasm.cwrap(ScatterNd, null, [
"number",
"number",
"number",
"number",
"number",
"number",
"array",
"number",
"number"
]);
}
function scatterNd(args) {
const {backend: backend3, inputs, attrs} = args;
const {indices, updates} = inputs;
const {shape} = attrs;
const out = backend3.makeOutput(shape, updates.dtype);
if (util_exports.sizeFromShape(shape) === 0) {
return out;
}
const {sliceRank, numUpdates, sliceSize, strides, outputSize} = scatter_nd_util_exports.calculateShapes(updates, indices, shape);
const indicesData = backend3.dataIdMap.get(indices.dataId);
const indicesId = indicesData.id;
const updatesData = backend3.dataIdMap.get(updates.dataId);
const updatesId = updatesData.id;
const stridesBytes = new Uint8Array(new Int32Array(strides).buffer);
const outId = backend3.dataIdMap.get(out.dataId).id;
wasmScatterNd(indicesId, updatesId, CppDType[updates.dtype], sliceRank, numUpdates, sliceSize, stridesBytes, outputSize, outId);
return out;
}
const scatterNdConfig = {
kernelName: ScatterNd,
backendName: "wasm",
setupFunc: setup31,
kernelFunc: scatterNd
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SelectV2.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
let wasmSelect;
function setup32(backend3) {
wasmSelect = backend3.wasm.cwrap(SelectV2, null, [
"number",
"number",
"number",
"number",
"number"
]);
}
function select(args) {
const {inputs, backend: backend3} = args;
const {condition, t, e} = inputs;
const conditionId = backend3.dataIdMap.get(condition.dataId).id;
const tId = backend3.dataIdMap.get(t.dataId).id;
const eId = backend3.dataIdMap.get(e.dataId).id;
const out = backend3.makeOutput(t.shape, t.dtype);
const outId = backend3.dataIdMap.get(out.dataId).id;
const cRank = condition.shape.length;
const tRank = t.shape.length;
const offset = cRank === 0 || cRank > 1 || tRank === 1 ? 1 : util_exports.sizeFromShape(t.shape.slice(1));
wasmSelect(conditionId, tId, eId, offset, outId);
return out;
}
const selectV2Config = {
kernelName: SelectV2,
backendName: "wasm",
kernelFunc: select,
setupFunc: setup32
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sigmoid.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
let wasmFunc6;
function setup33(backend3) {
wasmFunc6 = backend3.wasm.cwrap(Sigmoid, null, ["number", "number"]);
}
function sigmoid5(args) {
const {backend: backend3, inputs: {x}} = args;
const xId = backend3.dataIdMap.get(x.dataId).id;
const out = backend3.makeOutput(x.shape, x.dtype);
const outId = backend3.dataIdMap.get(out.dataId).id;
if (util_exports.sizeFromShape(out.shape) === 0) {
return out;
}
wasmFunc6(xId, outId);
return out;
}
const sigmoidConfig = {
kernelName: "Sigmoid",
backendName: "wasm",
setupFunc: setup33,
kernelFunc: sigmoid5
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sin.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const sinConfig = createUnaryKernelConfig(Sin);
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Slice.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function slice15(args) {
const {inputs: {x}, attrs: {begin, size}, backend: backend3} = args;
const [begin_, size_] = slice_util_exports.parseSliceParams(x, begin, size);
const isContinous = slice_util_exports.isSliceContinous(x.shape, begin_, size_);
const xVals = backend3.typedArrayFromHeap(x);
const out = backend3.makeOutput(size_, x.dtype);
const outVals = backend3.typedArrayFromHeap(out);
const xStrides = util_exports.computeStrides(x.shape);
if (isContinous) {
const flatOffset = slice_util_exports.computeFlatOffset(begin_, xStrides);
outVals.set(xVals.subarray(flatOffset, flatOffset + util_exports.sizeFromShape(size_)));
return out;
}
const rank = x.shape.length;
if (rank === 2) {
slice2d3(xVals, xStrides[0], outVals, begin_, size_);
} else if (rank === 3) {
slice3d3(xVals, xStrides[0], xStrides[1], outVals, begin_, size_);
} else if (rank === 4) {
slice4d3(xVals, xStrides[0], xStrides[1], xStrides[2], outVals, begin_, size_);
} else {
genericSliceSlow(xVals, x, outVals, begin_, size_);
}
return out;
}
function slice2d3(xVals, xStride, outVals, begin, size) {
let outOffset = 0;
const beginI = begin[0];
const beginJ = begin[1];
const endI = beginI + size[0];
for (let i = beginI; i < endI; i++) {
const xOffset = i * xStride + beginJ;
outVals.set(xVals.subarray(xOffset, xOffset + size[1]), outOffset);
outOffset += size[1];
}
}
function slice3d3(xVals, xStride1, xStride2, outVals, begin, size) {
let outOffset = 0;
const beginI = begin[0];
const beginJ = begin[1];
const beginK = begin[2];
const endI = beginI + size[0];
const endJ = beginJ + size[1];
for (let i = beginI; i < endI; i++) {
for (let j = beginJ; j < endJ; j++) {
const xOffset = i * xStride1 + j * xStride2 + beginK;
outVals.set(xVals.subarray(xOffset, xOffset + size[2]), outOffset);
outOffset += size[2];
}
}
}
function slice4d3(xVals, xStride1, xStride2, xStride3, outVals, begin, size) {
let outOffset = 0;
const beginI = begin[0];
const beginJ = begin[1];
const beginK = begin[2];
const endI = beginI + size[0];
const endJ = beginJ + size[1];
const endK = beginK + size[2];
const beginL = begin[3];
for (let i = beginI; i < endI; i++) {
for (let j = beginJ; j < endJ; j++) {
for (let k = beginK; k < endK; k++) {
const xOffset = i * xStride1 + j * xStride2 + k * xStride3 + beginL;
outVals.set(xVals.subarray(xOffset, xOffset + size[3]), outOffset);
outOffset += size[3];
}
}
}
}
function genericSliceSlow(xVals, xInfo, outVals, begin, size) {
const outBuf = buffer(size, xInfo.dtype, outVals);
const xBuf = buffer(xInfo.shape, xInfo.dtype, xVals);
for (let i = 0; i < outBuf.size; ++i) {
const loc = outBuf.indexToLoc(i);
const xLoc = loc.map((idx, j) => idx + begin[j]);
outVals[i] = xBuf.get(...xLoc);
}
}
const sliceConfig = {
kernelName: Slice,
backendName: "wasm",
kernelFunc: slice15
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Softmax.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
let wasmFunc7;
function setup34(backend3) {
wasmFunc7 = backend3.wasm.cwrap(Softmax, null, [
"number",
"number",
"number",
"number"
]);
}
function softmax4(args) {
const {backend: backend3, inputs: {logits}, attrs: {dim}} = args;
const xId = backend3.dataIdMap.get(logits.dataId).id;
const out = backend3.makeOutput(logits.shape, logits.dtype);
const outId = backend3.dataIdMap.get(out.dataId).id;
const channels = logits.shape[dim];
const batch = util_exports.sizeFromShape(logits.shape) / channels;
if (util_exports.sizeFromShape(out.shape) === 0) {
return out;
}
wasmFunc7(xId, outId, channels, batch);
return out;
}
const softmaxConfig = {
kernelName: Softmax,
backendName: "wasm",
setupFunc: setup34,
kernelFunc: softmax4
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Split.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function split7(args) {
const {inputs, attrs, backend: backend3} = args;
const {x} = inputs;
const {numOrSizeSplits, axis} = attrs;
const $axis = util_exports.parseAxisParam(axis, x.shape)[0];
const splitSizes = backend_util_exports.prepareSplitSize(x, numOrSizeSplits, axis);
const begin = new Array(x.shape.length).fill(0);
const size = x.shape.slice();
return splitSizes.map((s) => {
const xSliceSize = [...size];
xSliceSize[$axis] = s;
const xSlice = slice15({inputs: {x}, attrs: {begin, size: xSliceSize}, backend: backend3});
begin[$axis] += s;
return xSlice;
});
}
const splitVConfig = {
kernelName: SplitV,
backendName: "wasm",
kernelFunc: split7
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sqrt.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const sqrtConfig = createUnaryKernelConfig(Sqrt);
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Square.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const squareConfig = createUnaryKernelConfig(Square);
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SquaredDifference.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const supportsFullBroadcast15 = true;
const squaredDifferenceConfig = createBinaryKernelConfig(SquaredDifference, supportsFullBroadcast15);
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/StridedSlice.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
let wasmStridedSlice;
function setup35(backend3) {
wasmStridedSlice = backend3.wasm.cwrap(StridedSlice, null, [
"number",
"array",
"number",
"array",
"array",
"array",
"array",
"array",
"number",
"number"
]);
}
function stridedSlice2(args) {
const {backend: backend3, inputs, attrs} = args;
const {x} = inputs;
let {begin, end, strides} = attrs;
if (strides == null) {
strides = new Array(begin.length);
}
const {beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask} = attrs;
const ellipsisAxes = backend_util_exports.slice_util.maskToAxes(ellipsisMask);
if (ellipsisAxes.length > 1) {
throw new Error("Multiple ellipses in slice is not allowed.");
}
if (ellipsisMask !== 0 && newAxisMask !== 0) {
throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");
}
if (ellipsisMask !== 0 && shrinkAxisMask !== 0) {
throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");
}
const numInterpolatedAxes = x.shape.length - begin.length;
const expandAxes = backend_util_exports.slice_util.maskToAxes(newAxisMask);
const newShape = x.shape.slice();
expandAxes.forEach((axis) => {
begin[axis] = 0;
end[axis] = 1;
newShape.splice(axis, 0, 1);
});
const xReshaped = reshape60({inputs: {x}, attrs: {shape: newShape}, backend: backend3});
const {begin: normalizedBegin, end: normalizedEnd, strides: normalizedStrides} = backend_util_exports.slice_util.getNormalizedAxes(xReshaped.shape, ellipsisAxes, numInterpolatedAxes, begin, end, strides, beginMask, endMask, ellipsisMask);
begin = normalizedBegin;
end = normalizedEnd;
strides = normalizedStrides;
const shrinkAxes = backend_util_exports.slice_util.maskToAxes(shrinkAxisMask);
shrinkAxes.forEach((axis) => {
end[axis] = begin[axis] + 1;
strides[axis] = 1;
});
const size = backend_util_exports.slice_util.computeOutShape(begin, end, strides);
const outShape = size.filter((_, axis) => shrinkAxes.indexOf(axis) === -1);
const nonStrided = strides.every((v) => v === 1);
if (nonStrided) {
const xSliced = slice15({inputs: {x}, attrs: {begin, size}, backend: backend3});
return reshape60({inputs: {x: xSliced}, attrs: {shape: outShape}, backend: backend3});
}
const out = backend3.makeOutput(outShape, "float32");
if (!outShape.some((axis) => axis === 0)) {
const xId = backend3.dataIdMap.get(xReshaped.dataId).id;
const xStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(xReshaped.shape)).buffer);
const beginBytes = new Uint8Array(new Int32Array(begin).buffer);
const endBytes = new Uint8Array(new Int32Array(end).buffer);
const stridesBytes = new Uint8Array(new Int32Array(strides).buffer);
const outputShapeBytes = new Uint8Array(new Int32Array(outShape).buffer);
const outStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(outShape)).buffer);
const outId = backend3.dataIdMap.get(out.dataId).id;
wasmStridedSlice(xId, xStridesBytes, xReshaped.shape.length, beginBytes, endBytes, stridesBytes, outputShapeBytes, outStridesBytes, outShape.length, outId);
}
return reshape60({inputs: {x: out}, attrs: {shape: outShape}, backend: backend3});
}
const stridedSliceConfig = {
kernelName: StridedSlice,
backendName: "wasm",
setupFunc: setup35,
kernelFunc: stridedSlice2
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sub.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const supportsFullBroadcast16 = true;
const subConfig = createBinaryKernelConfig(Sub, supportsFullBroadcast16);
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sum.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
let wasmSum;
function setup36(backend3) {
wasmSum = backend3.wasm.cwrap(Sum, null, ["number, number, number"]);
}
function sum13(args) {
const {backend: backend3, inputs, attrs} = args;
const {axis, keepDims} = attrs;
const {x} = inputs;
const xId = backend3.dataIdMap.get(x.dataId).id;
let inputId = xId;
let input2 = x;
const {transposed, axes, originalAxes, inputWasTransposed} = permuteAxesAndTranspose(x, axis, backend3);
let reductionAxes = axes;
if (inputWasTransposed) {
const transposedId = backend3.dataIdMap.get(transposed.dataId).id;
if (transposedId !== xId) {
input2 = transposed;
inputId = transposedId;
reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, input2.shape.length);
}
}
backend_util_exports.assertAxesAreInnerMostDims("sum", reductionAxes, input2.shape.length);
const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, reductionAxes);
const reduceSize = util_exports.sizeFromShape(reduceShape);
const out = backend3.makeOutput(outShape, input2.dtype);
if (util_exports.sizeFromShape(input2.shape) !== 0) {
const outId = backend3.dataIdMap.get(out.dataId).id;
wasmSum(inputId, reduceSize, outId);
}
if (inputWasTransposed) {
backend3.disposeData(transposed.dataId);
}
if (keepDims) {
const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);
out.shape = newShape;
}
return out;
}
const sumConfig = {
kernelName: Sum,
backendName: "wasm",
setupFunc: setup36,
kernelFunc: sum13
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Tanh.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const tanhConfig = createUnaryKernelConfig(Tanh);
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Tile.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
let wasmTile;
function setup37(backend3) {
wasmTile = backend3.wasm.cwrap(Tile, null, [
"number",
"array",
"number",
"array",
"number",
"number"
]);
}
function tile6(args) {
const {inputs, backend: backend3, attrs} = args;
const {x} = inputs;
const xId = backend3.dataIdMap.get(x.dataId).id;
const {reps} = attrs;
const newShape = new Array(x.shape.length);
for (let i = 0; i < newShape.length; i++) {
newShape[i] = x.shape[i] * reps[i];
}
const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);
const newShapeBytes = new Uint8Array(new Int32Array(newShape).buffer);
const out = backend3.makeOutput(newShape, x.dtype);
const outId = backend3.dataIdMap.get(out.dataId).id;
wasmTile(xId, xShapeBytes, x.shape.length, newShapeBytes, newShape.length, CppDType[out.dtype], outId);
return out;
}
const tileConfig = {
kernelName: Tile,
backendName: "wasm",
setupFunc: setup37,
kernelFunc: tile6
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Unpack.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function unpack(args) {
const {inputs, backend: backend3, attrs} = args;
const {value} = inputs;
const {axis} = attrs;
const numOutputs = value.shape[axis];
const rank = value.shape.length;
const outShape = new Array(rank - 1);
let outIndex = 0;
for (let i = 0; i < rank; i++) {
if (i !== axis) {
outShape[outIndex++] = value.shape[i];
}
}
const outs = new Array(numOutputs);
const begin = new Array(rank).fill(0);
const size = value.shape.slice();
size[axis] = 1;
for (let i = 0; i < outs.length; i++) {
begin[axis] = i;
outs[i] = slice15({inputs: {x: value}, attrs: {begin, size}, backend: backend3});
}
return outs.map(({dataId, dtype}) => ({dataId, dtype, shape: outShape}));
}
const unpackConfig = {
kernelName: Unpack,
backendName: "wasm",
kernelFunc: unpack
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ZerosLike.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function zerosLike2(args) {
const {inputs: {x}, backend: backend3} = args;
const out = backend3.makeOutput(x.shape, x.dtype);
const outVals = backend3.typedArrayFromHeap(out);
outVals.fill(0);
return out;
}
const zerosLikeConfig = {
kernelName: ZerosLike,
backendName: "wasm",
kernelFunc: zerosLike2
};
// node_modules/@tensorflow/tfjs-backend-wasm/dist/register_all_kernels.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const kernelConfigs = [
absConfig,
addConfig,
addNConfig,
argMaxConfig,
avgPoolConfig,
batchMatMulConfig,
castConfig,
clipByValueConfig,
concatConfig,
conv2DConfig,
conv2DBackpropInputConfig,
cosConfig,
cropAndResizeConfig,
cumsumConfig,
depthToSpaceConfig,
depthwiseConv2dNativeConfig,
divConfig,
equalConfig,
expConfig,
fillConfig,
flipLeftRightConfig,
floorDivConfig,
fusedMatMulConfig,
fusedBatchNormConfig,
fusedConv2DConfig,
fusedDepthwiseConv2DConfig,
gatherNdConfig,
gatherV2Config,
greaterConfig,
greaterEqualConfig,
identityConfig,
lessConfig,
lessEqualConfig,
logConfig,
logicalAndConfig,
maxConfig,
maximumConfig,
maxPoolConfig,
minConfig,
minimumConfig,
multiplyConfig,
negateConfig,
nonMaxSuppressionV3Config,
nonMaxSuppressionV4Config,
nonMaxSuppressionV5Config,
notEqualConfig,
oneHotConfig,
onesLikeConfig,
padV2Config,
powConfig,
preluConfig,
reluConfig,
relu6Config,
reshapeConfig,
resizeBilinearConfig,
reverseConfig,
rotateWithOffsetConfig,
rsqrtConfig,
scatterNdConfig,
selectV2Config,
sigmoidConfig,
sinConfig,
sliceConfig,
softmaxConfig,
splitVConfig,
sqrtConfig,
squareConfig,
squaredDifferenceConfig,
stridedSliceConfig,
subConfig,
sumConfig,
tanhConfig,
tileConfig,
transposeConfig,
unpackConfig,
zerosLikeConfig
];
for (const kernelConfig of kernelConfigs) {
registerKernel(kernelConfig);
}
// node_modules/@tensorflow/tfjs-backend-wasm/dist/flags_wasm.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const ENV3 = env();
ENV3.registerFlag("WASM_HAS_SIMD_SUPPORT", async () => WebAssembly.validate(new Uint8Array([
0,
97,
115,
109,
1,
0,
0,
0,
1,
4,
1,
96,
0,
0,
3,
2,
1,
0,
10,
9,
1,
7,
0,
65,
0,
253,
15,
26,
11
])));
ENV3.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT", async () => {
if (ENV3.get("IS_NODE")) {
return false;
}
try {
new MessageChannel().port1.postMessage(new SharedArrayBuffer(1));
return WebAssembly.validate(new Uint8Array([
0,
97,
115,
109,
1,
0,
0,
0,
1,
4,
1,
96,
0,
0,
3,
2,
1,
0,
5,
4,
1,
3,
1,
1,
10,
11,
1,
9,
0,
65,
0,
254,
16,
2,
0,
26,
11
]));
} catch (e) {
return false;
}
});
// node_modules/@tensorflow/tfjs-backend-wasm/dist/backend_wasm.js
const tfjs_backend_wasm_threaded_simd = __toModule(require_tfjs_backend_wasm_threaded_simd());
// node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.worker.js
const wasmWorkerContents = 'var threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:selfThreadId})}var err=threadPrintErr;this.alert=threadAlert;Module["instantiateWasm"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module["wasmModule"],info);Module["wasmModule"]=null;receiveInstance(instance);return instance.exports};this.onmessage=function(e){try{if(e.data.cmd==="load"){Module["DYNAMIC_BASE"]=e.data.DYNAMIC_BASE;Module["DYNAMICTOP_PTR"]=e.data.DYNAMICTOP_PTR;Module["wasmModule"]=e.data.wasmModule;Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob==="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({"cmd":"loaded"})}else if(e.data.cmd==="objectTransfer"){Module["PThread"].receiveObjectTransfer(e.data)}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;threadInfoStruct=e.data.threadInfoStruct;Module["__register_pthread_ptr"](threadInfoStruct,0,0);selfThreadId=e.data.selfThreadId;parentThreadId=e.data.parentThreadId;var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module["establishStackSpace"](top,max);Module["_emscripten_tls_init"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].setThreadStatus(Module["_pthread_self"](),1);try{var result=Module["dynCall_ii"](e.data.start_routine,e.data.arg);if(!Module["getNoExitRuntime"]())Module["PThread"].threadExit(result)}catch(ex){if(ex==="Canceled!"){Module["PThread"].threadCancel()}else if(ex!="unwind"){Atomics.store(Module["HEAPU32"],threadInfoStruct+4>>2,ex instanceof Module["ExitStatus"]?ex.status:-2);Atomics.store(Module["HEAPU32"],threadInfoStruct+0>>2,1);Module["_emscripten_futex_wake"](threadInfoStruct+0,2147483647);if(!(ex instanceof Module["ExitStatus"]))throw ex}}}else if(e.data.cmd==="cancel"){if(threadInfoStruct){Module["PThread"].threadCancel()}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processThreadQueue"){if(threadInfoStruct){Module["_emscripten_current_thread_process_queued_calls"]()}}else{err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){err("worker.js onmessage() captured an uncaught exception: "+ex);if(ex.stack)err(ex.stack);throw ex}};if(typeof process==="object"&&typeof process.versions==="object"&&typeof process.versions.node==="string"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require("worker_threads");Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",function(data){onmessage({data:data})});var nodeFS=require("fs");var nodeRead=function(filename){return nodeFS.readFileSync(filename,"utf8")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance==="undefined"){performance={now:function(){return Date.now()}}}}';
// node_modules/@tensorflow/tfjs-backend-wasm/dist/backend_wasm.js
const tfjs_backend_wasm = __toModule(require_tfjs_backend_wasm());
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
const WASM_PRIORITY = 2;
class BackendWasm extends KernelBackend {
constructor(wasm) {
super();
this.wasm = wasm;
this.dataIdNextNumber = 1;
this.wasm.tfjs.init();
this.dataIdMap = new DataStorage(this, engine14());
}
write(values, shape, dtype) {
const dataId = {};
this.move(dataId, values, shape, dtype);
return dataId;
}
numDataIds() {
return this.dataIdMap.numDataIds();
}
async time(f) {
const start = util_exports.now();
f();
const kernelMs = util_exports.now() - start;
return {kernelMs};
}
move(dataId, values, shape, dtype) {
const id = this.dataIdNextNumber++;
if (dtype === "string") {
const stringBytes = values;
this.dataIdMap.set(dataId, {id, stringBytes, shape, dtype, memoryOffset: null});
return;
}
const size = util_exports.sizeFromShape(shape);
const numBytes = size * util_exports.bytesPerElement(dtype);
const memoryOffset = this.wasm._malloc(numBytes);
this.dataIdMap.set(dataId, {id, memoryOffset, shape, dtype});
this.wasm.tfjs.registerTensor(id, size, memoryOffset);
if (values != null) {
this.wasm.HEAPU8.set(new Uint8Array(values.buffer, values.byteOffset, numBytes), memoryOffset);
}
}
async read(dataId) {
return this.readSync(dataId);
}
readSync(dataId) {
const {memoryOffset, dtype, shape, stringBytes} = this.dataIdMap.get(dataId);
if (dtype === "string") {
return stringBytes;
}
const bytes = this.wasm.HEAPU8.slice(memoryOffset, memoryOffset + util_exports.sizeFromShape(shape) * util_exports.bytesPerElement(dtype));
return typedArrayFromBuffer(bytes.buffer, dtype);
}
disposeData(dataId) {
const data2 = this.dataIdMap.get(dataId);
this.wasm._free(data2.memoryOffset);
this.wasm.tfjs.disposeData(data2.id);
this.dataIdMap.delete(dataId);
}
floatPrecision() {
return 32;
}
getMemoryOffset(dataId) {
return this.dataIdMap.get(dataId).memoryOffset;
}
dispose() {
this.wasm.tfjs.dispose();
this.wasm = null;
}
memory() {
return {unreliable: false};
}
makeOutput(shape, dtype, memoryOffset) {
let dataId;
if (memoryOffset == null) {
dataId = this.write(null, shape, dtype);
} else {
dataId = {};
const id = this.dataIdNextNumber++;
this.dataIdMap.set(dataId, {id, memoryOffset, shape, dtype});
const size = util_exports.sizeFromShape(shape);
this.wasm.tfjs.registerTensor(id, size, memoryOffset);
}
return {dataId, shape, dtype};
}
typedArrayFromHeap({shape, dtype, dataId}) {
const buffer10 = this.wasm.HEAPU8.buffer;
const {memoryOffset} = this.dataIdMap.get(dataId);
const size = util_exports.sizeFromShape(shape);
switch (dtype) {
case "float32":
return new Float32Array(buffer10, memoryOffset, size);
case "int32":
return new Int32Array(buffer10, memoryOffset, size);
case "bool":
return new Uint8Array(buffer10, memoryOffset, size);
default:
throw new Error(`Unknown dtype ${dtype}`);
}
}
}
registerBackend("wasm", async () => {
const {wasm} = await init();
return new BackendWasm(wasm);
}, WASM_PRIORITY);
function createInstantiateWasmFunc(path) {
return (imports, callback) => {
util_exports.fetch(path, {credentials: "same-origin"}).then((response) => {
if (!response["ok"]) {
imports.env.a(`failed to load wasm binary file at '${path}'`);
}
response.arrayBuffer().then((binary) => {
WebAssembly.instantiate(binary, imports).then((output) => {
callback(output.instance);
});
});
});
return {};
};
}
function getPathToWasmBinary(simdSupported, threadsSupported, wasmModuleFolder) {
if (wasmPath != null) {
return wasmPath;
}
let path = "tfjs-backend-wasm.wasm";
if (simdSupported && threadsSupported) {
path = "tfjs-backend-wasm-threaded-simd.wasm";
} else if (simdSupported) {
path = "tfjs-backend-wasm-simd.wasm";
}
if (wasmFileMap != null) {
if (wasmFileMap[path] != null) {
return wasmFileMap[path];
}
}
return wasmModuleFolder + path;
}
async function init() {
const [simdSupported, threadsSupported] = await Promise.all([
env().getAsync("WASM_HAS_SIMD_SUPPORT"),
env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")
]);
return new Promise((resolve, reject) => {
const factoryConfig = {};
factoryConfig.locateFile = (path, prefix) => {
if (path.endsWith(".worker.js")) {
const response = wasmWorkerContents;
const blob = new Blob([response], {type: "application/javascript"});
return URL.createObjectURL(blob);
}
if (path.endsWith(".wasm")) {
return getPathToWasmBinary(simdSupported, threadsSupported, wasmPathPrefix != null ? wasmPathPrefix : prefix);
}
return prefix + path;
};
if (customFetch) {
factoryConfig.instantiateWasm = createInstantiateWasmFunc(getPathToWasmBinary(simdSupported, threadsSupported, wasmPathPrefix != null ? wasmPathPrefix : ""));
}
let wasm;
if (threadsSupported && simdSupported && wasmPath == null) {
wasm = tfjs_backend_wasm_threaded_simd.default(factoryConfig);
wasm.mainScriptUrlOrBlob = new Blob([`var _scriptDir = undefined; var WasmBackendModuleThreadedSimd = ` + tfjs_backend_wasm_threaded_simd.default.toString()], {type: "text/javascript"});
} else {
wasm = tfjs_backend_wasm.default(factoryConfig);
}
const voidReturnType = null;
wasm.tfjs = {
init: wasm.cwrap("init", null, []),
registerTensor: wasm.cwrap("register_tensor", null, [
"number",
"number",
"number"
]),
disposeData: wasm.cwrap("dispose_data", voidReturnType, ["number"]),
dispose: wasm.cwrap("dispose", voidReturnType, [])
};
let initialized = false;
wasm.onRuntimeInitialized = () => {
initialized = true;
initAborted = false;
resolve({wasm});
};
wasm.onAbort = () => {
if (initialized) {
return;
}
if (initAborted) {
return;
}
initAborted = true;
const rejectMsg = "Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers";
reject({message: rejectMsg});
};
});
}
function typedArrayFromBuffer(buffer10, dtype) {
switch (dtype) {
case "float32":
return new Float32Array(buffer10);
case "int32":
return new Int32Array(buffer10);
case "bool":
return new Uint8Array(buffer10);
default:
throw new Error(`Unknown dtype ${dtype}`);
}
}
const wasmBinaryNames = [
"tfjs-backend-wasm.wasm",
"tfjs-backend-wasm-simd.wasm",
"tfjs-backend-wasm-threaded-simd.wasm"
];
let wasmPath = null;
let wasmPathPrefix = null;
let wasmFileMap = {};
let initAborted = false;
let customFetch = false;
function setWasmPaths(prefixOrFileMap, usePlatformFetch = false) {
if (initAborted) {
throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");
}
if (typeof prefixOrFileMap === "string") {
wasmPathPrefix = prefixOrFileMap;
} else {
wasmFileMap = prefixOrFileMap;
const missingPaths = wasmBinaryNames.filter((name) => wasmFileMap[name] == null);
if (missingPaths.length > 0) {
throw new Error(`There were no entries found for the following binaries: ${missingPaths.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`);
}
}
customFetch = usePlatformFetch;
}
// node_modules/@tensorflow/tfjs-backend-wasm/dist/base.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
// node_modules/@tensorflow/tfjs-backend-wasm/dist/index.js
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
// src/tf.js
const loadGraphModel2 = loadGraphModel;
// src/face/triangulation.js
var triangulation_default = [
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// src/human.js
const facemesh = __toModule(require_facemesh());
const age = __toModule(require_age());
const gender = __toModule(require_gender());
const emotion = __toModule(require_emotion());
const posenet = __toModule(require_posenet());
// src/hand/box.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function getBoxSize(box) {
return [
Math.abs(box.endPoint[0] - box.startPoint[0]),
Math.abs(box.endPoint[1] - box.startPoint[1])
];
}
function getBoxCenter(box) {
return [
box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,
box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2
];
}
function cutBoxFromImageAndResize(box, image4, cropSize) {
const h = image4.shape[1];
const w = image4.shape[2];
const boxes = [[
box.startPoint[1] / h,
box.startPoint[0] / w,
box.endPoint[1] / h,
box.endPoint[0] / w
]];
return dist_exports2.image.cropAndResize(image4, boxes, [0], cropSize);
}
function scaleBoxCoordinates(box, factor) {
const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];
const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];
const palmLandmarks = box.palmLandmarks.map((coord) => {
const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];
return scaledCoord;
});
return {startPoint, endPoint, palmLandmarks, confidence: box.confidence};
}
function enlargeBox(box, factor = 1.5) {
const center = getBoxCenter(box);
const size = getBoxSize(box);
const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];
const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];
const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];
return {startPoint, endPoint, palmLandmarks: box.palmLandmarks};
}
function squarifyBox(box) {
const centers = getBoxCenter(box);
const size = getBoxSize(box);
const maxEdge = Math.max(...size);
const halfSize = maxEdge / 2;
const startPoint = [centers[0] - halfSize, centers[1] - halfSize];
const endPoint = [centers[0] + halfSize, centers[1] + halfSize];
return {startPoint, endPoint, palmLandmarks: box.palmLandmarks};
}
function shiftBox(box, shiftFactor) {
const boxSize = [
box.endPoint[0] - box.startPoint[0],
box.endPoint[1] - box.startPoint[1]
];
const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];
const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];
const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];
return {startPoint, endPoint, palmLandmarks: box.palmLandmarks};
}
// src/hand/util.js
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
function normalizeRadians(angle) {
return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));
}
function computeRotation(point1, point2) {
const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);
return normalizeRadians(radians);
}
const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];
function dot5(v1, v2) {
let product = 0;
for (let i = 0; i < v1.length; i++) {
product += v1[i] * v2[i];
}
return product;
}
function getColumnFrom2DArr(arr, columnIndex) {
const column = [];
for (let i = 0; i < arr.length; i++) {
column.push(arr[i][columnIndex]);
}
return column;
}
function multiplyTransformMatrices(mat1, mat2) {
const product = [];
const size = mat1.length;
for (let row = 0; row < size; row++) {
product.push([]);
for (let col = 0; col < size; col++) {
product[row].push(dot5(mat1[row], getColumnFrom2DArr(mat2, col)));
}
}
return product;
}
function buildRotationMatrix(rotation, center) {
const cosA = Math.cos(rotation);
const sinA = Math.sin(rotation);
const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];
const translationMatrix = buildTranslationMatrix(center[0], center[1]);
const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);
const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);
return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);
}
function invertTransformMatrix(matrix) {
const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];
const translationComponent = [matrix[0][2], matrix[1][2]];
const invertedTranslation = [
-dot5(rotationComponent[0], translationComponent),
-dot5(rotationComponent[1], translationComponent)
];
return [
rotationComponent[0].concat(invertedTranslation[0]),
rotationComponent[1].concat(invertedTranslation[1]),
[0, 0, 1]
];
}
function rotatePoint(homogeneousCoordinate, rotationMatrix) {
return [
dot5(homogeneousCoordinate, rotationMatrix[0]),
dot5(homogeneousCoordinate, rotationMatrix[1])
];
}
// src/human.js
const handpose = __toModule(require_handpose());
const gesture = __toModule(require_gesture());
const image3 = __toModule(require_image());
const profile2 = __toModule(require_profile());
// config.js
var config_default = {
backend: "webgl",
wasmPath: "../assets/",
console: true,
async: true,
profile: false,
deallocate: false,
scoped: false,
videoOptimized: true,
filter: {
enabled: true,
width: 0,
height: 0,
return: true,
brightness: 0,
contrast: 0,
sharpness: 0,
blur: 0,
saturation: 0,
hue: 0,
negative: false,
sepia: false,
vintage: false,
kodachrome: false,
technicolor: false,
polaroid: false,
pixelate: 0
},
gesture: {
enabled: true
},
face: {
enabled: true,
detector: {
modelPath: "../models/blazeface-back.json",
inputSize: 256,
maxFaces: 10,
skipFrames: 15,
minConfidence: 0.5,
iouThreshold: 0.2,
scoreThreshold: 0.5
},
mesh: {
enabled: true,
modelPath: "../models/facemesh.json",
inputSize: 192
},
iris: {
enabled: true,
modelPath: "../models/iris.json",
inputSize: 64
},
age: {
enabled: true,
modelPath: "../models/age-ssrnet-imdb.json",
inputSize: 64,
skipFrames: 15
},
gender: {
enabled: true,
minConfidence: 0.1,
modelPath: "../models/gender-ssrnet-imdb.json",
inputSize: 64,
skipFrames: 15
},
emotion: {
enabled: true,
inputSize: 64,
minConfidence: 0.2,
skipFrames: 15,
modelPath: "../models/emotion-large.json"
}
},
body: {
enabled: true,
modelPath: "../models/posenet.json",
inputSize: 257,
maxDetections: 10,
scoreThreshold: 0.8,
nmsRadius: 20
},
hand: {
enabled: true,
inputSize: 256,
skipFrames: 15,
minConfidence: 0.5,
iouThreshold: 0.1,
scoreThreshold: 0.8,
maxHands: 1,
landmarks: true,
detector: {
modelPath: "../models/handdetect.json"
},
skeleton: {
modelPath: "../models/handskeleton.json"
}
}
};
// package.json
var version17 = "0.8.7";
// src/human.js
const disableSkipFrames = {
face: {detector: {skipFrames: 0}, age: {skipFrames: 0}, gender: {skipFrames: 0}, emotion: {skipFrames: 0}},
hand: {skipFrames: 0}
};
const now2 = () => {
if (typeof performance !== "undefined")
return performance.now();
return parseInt(Number(process.hrtime.bigint()) / 1e3 / 1e3);
};
function mergeDeep(...objects) {
const isObject = (obj) => obj && typeof obj === "object";
return objects.reduce((prev, obj) => {
Object.keys(obj || {}).forEach((key) => {
const pVal = prev[key];
const oVal = obj[key];
if (Array.isArray(pVal) && Array.isArray(oVal)) {
prev[key] = pVal.concat(...oVal);
} else if (isObject(pVal) && isObject(oVal)) {
prev[key] = mergeDeep(pVal, oVal);
} else {
prev[key] = oVal;
}
});
return prev;
}, {});
}
class Human {
constructor(userConfig = {}) {
this.tf = dist_exports2;
this.version = version17;
this.config = mergeDeep(config_default, userConfig);
this.fx = null;
this.state = "idle";
this.numTensors = 0;
this.analyzeMemoryLeaks = false;
this.checkSanity = false;
this.firstRun = true;
this.perf = {};
this.models = {
facemesh: null,
posenet: null,
handpose: null,
iris: null,
age: null,
gender: null,
emotion: null
};
this.facemesh = facemesh;
this.age = age;
this.gender = gender;
this.emotion = emotion;
this.body = posenet;
this.hand = handpose;
}
log(...msg) {
if (msg && this.config.console)
console.log("Human:", ...msg);
}
profile() {
if (this.config.profile)
return profile2.data;
return {};
}
analyze(...msg) {
if (!this.analyzeMemoryLeaks)
return;
const current = dist_exports2.engine().state.numTensors;
const previous = this.numTensors;
this.numTensors = current;
const leaked = current - previous;
if (leaked !== 0)
this.log(...msg, leaked);
}
sanity(input2) {
if (!this.checkSanity)
return null;
if (!input2)
return "input is not defined";
if (dist_exports2.ENV.flags.IS_NODE && !(input2 instanceof dist_exports2.Tensor)) {
return "input must be a tensor";
}
try {
dist_exports2.getBackend();
} catch (e) {
return "backend not loaded";
}
return null;
}
async load(userConfig) {
this.state = "load";
const timeStamp = now2();
if (userConfig)
this.config = mergeDeep(this.config, userConfig);
if (this.firstRun) {
this.checkBackend(true);
this.log(`version: ${this.version} TensorFlow/JS version: ${dist_exports2.version_core}`);
this.log("configuration:", this.config);
this.log("flags:", dist_exports2.ENV.flags);
this.firstRun = false;
}
if (this.config.async) {
[
this.models.facemesh,
this.models.age,
this.models.gender,
this.models.emotion,
this.models.posenet,
this.models.handpose
] = await Promise.all([
this.models.facemesh || (this.config.face.enabled ? facemesh.load(this.config.face) : null),
this.models.age || (this.config.face.enabled && this.config.face.age.enabled ? age.load(this.config) : null),
this.models.gender || (this.config.face.enabled && this.config.face.gender.enabled ? gender.load(this.config) : null),
this.models.emotion || (this.config.face.enabled && this.config.face.emotion.enabled ? emotion.load(this.config) : null),
this.models.posenet || (this.config.body.enabled ? posenet.load(this.config) : null),
this.models.handpose || (this.config.hand.enabled ? handpose.load(this.config.hand) : null)
]);
} else {
if (this.config.face.enabled && !this.models.facemesh)
this.models.facemesh = await facemesh.load(this.config.face);
if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age)
this.models.age = await age.load(this.config);
if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender)
this.models.gender = await gender.load(this.config);
if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion)
this.models.emotion = await emotion.load(this.config);
if (this.config.body.enabled && !this.models.posenet)
this.models.posenet = await posenet.load(this.config);
if (this.config.hand.enabled && !this.models.handpose)
this.models.handpose = await handpose.load(this.config.hand);
}
const current = Math.trunc(now2() - timeStamp);
if (current > (this.perf.load || 0))
this.perf.load = current;
}
async checkBackend(force) {
const timeStamp = now2();
if (this.config.backend && this.config.backend !== "" && force || dist_exports2.getBackend() !== this.config.backend) {
this.state = "backend";
this.log("setting backend:", this.config.backend);
if (this.config.backend === "wasm") {
this.log("settings wasm path:", this.config.wasmPath);
setWasmPaths(this.config.wasmPath);
const simd = await dist_exports2.env().getAsync("WASM_HAS_SIMD_SUPPORT");
if (!simd)
this.log("warning: wasm simd support is not enabled");
}
await dist_exports2.setBackend(this.config.backend);
dist_exports2.enableProdMode();
if (this.config.backend === "webgl") {
if (this.config.deallocate) {
this.log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:", this.config.deallocate);
dist_exports2.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD", this.config.deallocate ? 0 : -1);
}
dist_exports2.ENV.set("WEBGL_PACK_DEPTHWISECONV", true);
}
await dist_exports2.ready();
}
const current = Math.trunc(now2() - timeStamp);
if (current > (this.perf.backend || 0))
this.perf.backend = current;
}
async detectFace(input2) {
let timeStamp;
let ageRes;
let genderRes;
let emotionRes;
const faceRes = [];
this.state = "run:face";
timeStamp = now2();
const faces = await this.models.facemesh.estimateFaces(input2, this.config.face);
this.perf.face = Math.trunc(now2() - timeStamp);
for (const face2 of faces) {
this.analyze("Get Face");
if (!face2.image || face2.image.isDisposedInternal) {
this.log("Face object is disposed:", face2.image);
continue;
}
this.analyze("Start Age:");
if (this.config.async) {
ageRes = this.config.face.age.enabled ? age.predict(face2.image, this.config) : {};
} else {
this.state = "run:age";
timeStamp = now2();
ageRes = this.config.face.age.enabled ? await age.predict(face2.image, this.config) : {};
this.perf.age = Math.trunc(now2() - timeStamp);
}
this.analyze("Start Gender:");
if (this.config.async) {
genderRes = this.config.face.gender.enabled ? gender.predict(face2.image, this.config) : {};
} else {
this.state = "run:gender";
timeStamp = now2();
genderRes = this.config.face.gender.enabled ? await gender.predict(face2.image, this.config) : {};
this.perf.gender = Math.trunc(now2() - timeStamp);
}
this.analyze("Start Emotion:");
if (this.config.async) {
emotionRes = this.config.face.emotion.enabled ? emotion.predict(face2.image, this.config) : {};
} else {
this.state = "run:emotion";
timeStamp = now2();
emotionRes = this.config.face.emotion.enabled ? await emotion.predict(face2.image, this.config) : {};
this.perf.emotion = Math.trunc(now2() - timeStamp);
}
this.analyze("End Emotion:");
if (this.config.async) {
[ageRes, genderRes, emotionRes] = await Promise.all([ageRes, genderRes, emotionRes]);
}
this.analyze("Finish Face:");
face2.image.dispose();
const irisSize = face2.annotations.leftEyeIris && face2.annotations.rightEyeIris ? 11.7 * Math.max(Math.abs(face2.annotations.leftEyeIris[3][0] - face2.annotations.leftEyeIris[1][0]), Math.abs(face2.annotations.rightEyeIris[4][1] - face2.annotations.rightEyeIris[2][1])) : 0;
faceRes.push({
confidence: face2.confidence,
box: face2.box,
mesh: face2.mesh,
annotations: face2.annotations,
age: ageRes.age,
gender: genderRes.gender,
genderConfidence: genderRes.confidence,
emotion: emotionRes,
iris: irisSize !== 0 ? Math.trunc(irisSize) / 100 : 0
});
this.analyze("End Face");
}
this.analyze("End FaceMesh:");
if (this.config.async) {
if (this.perf.face)
delete this.perf.face;
if (this.perf.age)
delete this.perf.age;
if (this.perf.gender)
delete this.perf.gender;
if (this.perf.emotion)
delete this.perf.emotion;
}
return faceRes;
}
async image(input2, userConfig = {}) {
this.state = "image";
this.config = mergeDeep(this.config, userConfig);
const process3 = image3.process(input2, this.config);
process3.tensor.dispose();
return process3.canvas;
}
async detect(input2, userConfig = {}) {
this.state = "config";
let timeStamp;
this.config = mergeDeep(this.config, userConfig);
if (!this.config.videoOptimized)
this.config = mergeDeep(this.config, disableSkipFrames);
this.state = "check";
const error = this.sanity(input2);
if (error) {
this.log(error, input2);
return {error};
}
return new Promise(async (resolve) => {
let poseRes;
let handRes;
let faceRes;
const timeStart = now2();
await this.checkBackend();
await this.load();
if (this.config.scoped)
dist_exports2.engine().startScope();
this.analyze("Start Scope:");
timeStamp = now2();
const process3 = image3.process(input2, this.config);
this.perf.image = Math.trunc(now2() - timeStamp);
this.analyze("Get Image:");
if (this.config.async) {
faceRes = this.config.face.enabled ? this.detectFace(process3.tensor) : [];
if (this.perf.face)
delete this.perf.face;
} else {
this.state = "run:face";
timeStamp = now2();
faceRes = this.config.face.enabled ? await this.detectFace(process3.tensor) : [];
this.perf.face = Math.trunc(now2() - timeStamp);
}
this.analyze("Start Body:");
if (this.config.async) {
poseRes = this.config.body.enabled ? this.models.posenet.estimatePoses(process3.tensor, this.config) : [];
if (this.perf.body)
delete this.perf.body;
} else {
this.state = "run:body";
timeStamp = now2();
poseRes = this.config.body.enabled ? await this.models.posenet.estimatePoses(process3.tensor, this.config) : [];
this.perf.body = Math.trunc(now2() - timeStamp);
}
this.analyze("End Body:");
this.analyze("Start Hand:");
if (this.config.async) {
handRes = this.config.hand.enabled ? this.models.handpose.estimateHands(process3.tensor, this.config.hand) : [];
if (this.perf.hand)
delete this.perf.hand;
} else {
this.state = "run:hand";
timeStamp = now2();
handRes = this.config.hand.enabled ? await this.models.handpose.estimateHands(process3.tensor, this.config.hand) : [];
this.perf.hand = Math.trunc(now2() - timeStamp);
}
if (this.config.async) {
[faceRes, poseRes, handRes] = await Promise.all([faceRes, poseRes, handRes]);
}
process3.tensor.dispose();
if (this.config.scoped)
dist_exports2.engine().endScope();
this.analyze("End Scope:");
let gestureRes = [];
if (this.config.gesture.enabled) {
timeStamp = now2();
gestureRes = {face: gesture.face(faceRes), body: gesture.body(poseRes), hand: gesture.hand(handRes)};
if (!this.config.async)
this.perf.gesture = Math.trunc(now2() - timeStamp);
else if (this.perf.gesture)
delete this.perf.gesture;
}
this.perf.total = Math.trunc(now2() - timeStart);
this.state = "idle";
resolve({face: faceRes, body: poseRes, hand: handRes, gesture: gestureRes, performance: this.perf, canvas: process3.canvas});
});
}
async warmup(userConfig) {
const warmup = new ImageData(255, 255);
await this.detect(warmup, userConfig);
this.log("warmed up");
}
}
export {
Human as default
};
//# sourceMappingURL=human.esm.js.map
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